class MatplotlibPlot:
""" Class encapsulating a matplotlib plot"""
def __init__(self, parent = None, dpi = 100, size = (5,5)):
""" Class initialiser """
self.dpi = dpi
self.figure = Figure(size, dpi = self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
# Create the navigation toolbar, tied to the canvas
self.toolbar = NavigationToolbar(self.canvas, parent)
self.canvas.show()
self.toolbar.show()
# Reset the plot landscape
self.figure.clear()
def plotMultiPixel(self, info, data):
""" Generate multi-pixel plot """
# Tabula Rasa
self.figure.clear()
rows = math.ceil(math.sqrt(info['nbeams']))
# Display a subplot per beam (randomly for now)
for i in range(info['nbeams']):
ax = self.figure.add_subplot(rows, rows, i)
ax.plot(data[:,512,i])
def updatePlot(self):
self.canvas.draw()
class MatplotlibPlot:
""" Class encapsulating an matplotlib plot"""
def __init__(self, parent=None, dpi=100, size=(5, 4)):
""" Class initialiser """
self.dpi = dpi
self.figure = Figure(size, dpi=self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
# Create the navigation toolbar, tied to the canvas
self.toolbar = NavigationToolbar(self.canvas, parent)
self.canvas.show()
self.toolbar.show()
def plotCurve(self,
data,
xAxisRange=None,
yAxisRange=None,
xLabel="",
yLabel=""):
""" Plot the data as a curve"""
# clear the axes and redraw the plot anew
self.figure.clear()
self.axes = self.figure.add_subplot(111)
self.axes.grid(True)
self.axes.plot(range(np.size(data)), data)
if xAxisRange is not None:
self.xAxisRange = xAxisRange
self.axes.xaxis.set_major_formatter(
ticker.FuncFormatter(
lambda x, pos=None: '%.2f' % self.xAxisRange[x]
if 0 <= x < len(xAxisRange) else ''))
for tick in self.axes.xaxis.get_ticklabels():
tick.set_rotation(15)
if yAxisRange is not None:
self.yAxisRange = yAxisRange
self.axes.xaxis.set_major_formatter(
ticker.FuncFormatter(
lambda x, pos=None: '%.1f' % self.yAxisRange[y]
if 0 <= y < len(yAxisRange) else ''))
for tick in self.axes.yaxis.get_ticklabels():
tick.set_rotation(15)
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
self.canvas.draw()
class MatplotlibPlot:
""" Class encapsulating an matplotlib plot"""
def __init__(self, parent = None, dpi = 100, size = (5,4)):
""" Class initialiser """
self.dpi = dpi
self.figure = Figure(size, dpi = self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
# Create the navigation toolbar, tied to the canvas
self.toolbar = NavigationToolbar(self.canvas, parent)
self.canvas.show()
self.toolbar.show()
def plotCurve(self, data, xAxisRange = None, yAxisRange = None, xLabel = "", yLabel = ""):
""" Plot the data as a curve"""
# clear the axes and redraw the plot anew
self.figure.clear()
self.axes = self.figure.add_subplot(111)
self.axes.grid(True)
self.axes.plot(range(np.size(data)), data)
if xAxisRange is not None:
self.xAxisRange = xAxisRange
self.axes.xaxis.set_major_formatter(ticker.FuncFormatter(
lambda x, pos=None: '%.2f' % self.xAxisRange[x] if 0 <= x < len(xAxisRange) else ''))
for tick in self.axes.xaxis.get_ticklabels():
tick.set_rotation(15)
if yAxisRange is not None:
self.yAxisRange = yAxisRange
self.axes.xaxis.set_major_formatter(ticker.FuncFormatter(
lambda x, pos=None: '%.1f' % self.yAxisRange[y] if 0 <= y < len(yAxisRange) else ''))
for tick in self.axes.yaxis.get_ticklabels():
tick.set_rotation(15)
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
self.canvas.draw()
class MatplotlibPlot:
""" Class encapsulating an matplotlib plot """
def __init__(self, parent=None, dpi=100, size=(5, 4)):
""" Class initialiser """
self.dpi = dpi
self.figure = Figure(dpi=self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
# Create the navigation toolbar, tied to the canvas
self.toolbar = NavigationToolbar(self.canvas, parent)
self.canvas.show()
self.toolbar.show()
def plotCurve(self,
data,
numCurves=1,
labels=None,
xAxisRange=None,
yAxisRange=None,
xLabel="",
yLabel=""):
""" Normal plots """
self.figure.clear()
self.axes = self.figure.add_subplot(111)
self.axes.grid(True)
# Set empty labels if non defined
if labels is None:
labels = ['' for i in range(numImages)]
# Place all plots in axes
for i in range(numCurves):
self.axes.plot(data[:, i], label=labels[i])
self.axes.set_xlim((0, len(data[:, i])))
# Final touches
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
self.canvas.draw()
def subplotCurve(self,
data,
numPlots=1,
labels=None,
xAxisRange=None,
yAxisRange=None,
xLabel="",
yLabel=""):
""" Subplot mode """
self.figure.clear()
# Set empty labels if non defined
if labels is None:
labels = ['' for i in range(numImages)]
if numPlots == 1:
ax = self.figure.add_subplot(111)
ax.plot(data[:])
ax.set_xlabel(xLabel)
ax.set_ylabel(yLabel)
ax.set_title(labels[0])
ax.grid(True)
else:
# Plot each image
num_rows = ceil(sqrt(numPlots))
for i in range(numPlots):
ax = self.figure.add_subplot(num_rows, num_rows, i + 1)
ax.plot(data[:, i])
ax.set_xlim((0, len(data[:, i])))
ax.set_xlabel(xLabel)
ax.set_ylabel(yLabel)
ax.set_title(labels[i])
ax.grid(True)
# Final touches
self.figure.tight_layout()
self.canvas.draw()
def plotImage(self,
data,
numImages=1,
labels=None,
xAxisRange=None,
yAxisRange=None,
xLabel="",
yLabel=""):
""" Image plot """
self.figure.clear()
self.axes = self.figure.add_subplot(111)
# Show image
im = self.axes.imshow(data, origin='lower', aspect='auto')
# Final touches
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
self.figure.colorbar(im)
self.canvas.draw()
def subplotImage(self,
data,
numImages=1,
labels=None,
xAxisRange=None,
yAxisRange=None,
xLabel="",
yLabel=""):
""" Image subplot """
# Clear figure
self.figure.clear()
# Set empty labels if non defined
if labels is None:
labels = ['' for i in range(numImages)]
# Plot each image
num_rows = ceil(sqrt(numImages))
for i in range(numImages):
ax = self.figure.add_subplot(num_rows, num_rows, i + 1)
im = ax.imshow(data[:, :, i], origin='lower', aspect='auto')
ax.set_xlabel(xLabel)
ax.set_ylabel(yLabel)
ax.set_title(labels[i])
# Final touches
self.figure.tight_layout()
self.canvas.draw()
class plothistogram(QFrame):
def __init__(self, parent):
print "__init__()" # DEBUG
QFrame.__init__(self)
self.parent=parent
#self.setModal(False)
self.data1=self.parent.y1 # get plotted y values from solutions plot
self.data2=self.parent.y2 # get plotted y values from parameter plot
self.nbins=50 # default=50 bins
self.parameter=self.parent.parent.parametersComboBox.currentText()
self.data=self.parent.y2 # set histogram to solver parameter data
#print "self.data = ", self.data # DEBUG
# Create canvas for plotting
self.rim=0.1
self.fig = Figure((5, 4), dpi=100)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=self.rim, right=1.0-self.rim, top=1.0-self.rim, bottom=self.rim) # set a small rim
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show() # first hide the toolbar
self.ax=self.fig.add_subplot(111)
self.createWidgets()
self.createLayout()
self.connectSignals()
self.setLabels()
self.plot()
def createWidgets(self):
#print "createWidgets()" # DEBUG
self.closeButton=QPushButton("Close")
self.closeButton.setToolTip("close this plotwindow")
self.closeButton.show()
self.histogramBinSpin=QSpinBox() # spinbox for histogram binsize
self.histogramBinSpin.setToolTip("Number of bins to histogram into")
self.histogramBinSpin.setMinimum(5)
#self.histogramBinSpin.setMaximum(150) # set a maximum of 150 (reasonable?)
self.histogramBinSpin.setSingleStep(10) # allow only stepping by 10
self.histogramBinSpin.setMaximumWidth(120)
self.histogramBinSpin.setMinimumHeight(25)
self.histogramBinSpin.setValue(self.nbins)
self.histogramBinSpin.show()
self.histogramBinLabel=QLabel("Bins")
self.normedCheckBox=QCheckBox()
self.normedCheckLabel=QLabel("Normalize")
self.normedCheckBox.setToolTip("Normalize histogram")
self.normedCheckBox.show()
#self.dpi = 100
self.dataComboBox=QComboBox()
self.dataComboBox.addItem(self.parent.parent.parmValueComboBox.currentText())
self.dataComboBox.addItem(self.parent.parent.parametersComboBox.currentText())
self.dataComboBox.setMaximumWidth(120)
self.dataComboBox.setMinimumHeight(25)
self.dataComboBox.setCurrentIndex(1)
def createLayout(self):
#print "createLayout()" # DEBUG
self.normedLayout=QHBoxLayout()
self.normedLayout.addWidget(self.normedCheckBox)
self.normedLayout.addWidget(self.normedCheckLabel)
self.buttonLayout=QVBoxLayout()
self.plotLayout=QVBoxLayout()
self.mainLayout=QHBoxLayout()
self.buttonLayout.addLayout(self.normedLayout)
#self.buttonLayout.addWidget(self.normedCheckBox)
#self.buttonLayout.addWidget(self.normedCheckLabel)
self.buttonLayout.addWidget(self.histogramBinSpin)
self.buttonLayout.addWidget(self.dataComboBox)
self.buttonLayout.insertStretch(-1)
self.buttonLayout.addWidget(self.closeButton)
self.plotLayout.addWidget(self.canvas)
self.plotLayout.addWidget(self.mpl_toolbar)
self.mainLayout.addLayout(self.buttonLayout)
self.mainLayout.addLayout(self.plotLayout)
self.setLayout(self.mainLayout)
def setLabels(self):
self.ax.xlabel="Bin"
self.ax.ylabel="N"
def setTitle(self):
#=self.parent.parametersComboBox.currentText()
#self.ax.xlabel=self.parmValueComboBox.currentText()
#self.ax.xlabel="Bin No."
#self.ax.ylabel="n"
self.ax.title=self.parent.parent.parametersComboBox.currentText()
def connectSignals(self):
self.connect(self.closeButton, SIGNAL('clicked()'), SLOT('close()'))
self.connect(self.histogramBinSpin, SIGNAL('valueChanged(int)'), self.on_changeBinSpin)
self.connect(self.normedCheckBox, SIGNAL('stateChanged(int)'), self.on_normedCheckBox)
self.connect(self.dataComboBox, SIGNAL('currentIndexChanged(int)'), self.on_data)
def on_changeBinSpin(self):
self.nbins=self.histogramBinSpin.value()
# create histogram
#n, bins = np.histogram(self.data, self.histogramBinSpin.value(), normed=self.normedCheckBox.isChecked())
#hist, bins=np.histogram(self.data, self.nbins, normed=self.normedCheckBox.isChecked())
#print "len(bins) = ", len(bins), " len(n) = ", len(n)
#self.ax.plot(bins[1:], n, color='red')
#self.canvas.draw()
#self.ax.legend()
#self.fig.draw_idle() # redraw (preferably when idle)
self.plot()
def on_normedCheckBox(self):
self.plot()
def on_data(self):
print "on_data()" # DEBUG
if self.dataComboBox.currentText()==self.parent.parent.parametersComboBox.currentText():
self.data=self.data2
else:
self.data=self.data1
self.plot()
def plot(self):
self.fig.delaxes(self.ax) # delete all axes first
self.ax=self.fig.add_subplot(111)
n, bins = np.histogram(self.data, self.histogramBinSpin.value(), normed=self.normedCheckBox.isChecked())
self.xmin=bins[1]
self.xmax=bins[len(bins)-1]
self.ax.bar(bins[1:], n, color='blue', width=(self.xmax-self.xmin)/len(bins))
self.canvas.draw()
class MatplotlibPlot:
""" Class encapsulating a matplotlib plot"""
def __init__(self, parent = None, dpi = 100, size = (5,5)):
""" Class initialiser """
self.dpi = dpi
self.figure = Figure(size, dpi = self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
# Create the navigation toolbar, tied to the canvas
self.toolbar = NavigationToolbar(self.canvas, parent)
self.canvas.show()
self.toolbar.show()
# Reset the plot landscape
self.figure.clear()
self.nSubplot=0
def resetSubplots(self):
self.nSubplot=0
def plotCurve(self, data, xAxisRange = None, yAxisRange = None, xLabel = "", yLabel = "", title="", label="", plotLog=False, nSubplots=1, hold=False):
""" Plot the data as a curve"""
if len(data) != 0:
# Plan what dimensions the grid will have if there are to be subplots
# Attempt to be roughly square but give preference to vertical stacking
nSubplots_v = np.ceil(np.sqrt(nSubplots))
nSubplots_h = np.ceil(float(nSubplots)/nSubplots_v)
yAxisRange=np.array(yAxisRange)
if yAxisRange is not None:
auto_scale_y = False
if plotLog:
data[data==0] = 1
yAxisRange[yAxisRange==0] = 1
data= 10*np.log(data)
yAxisRange = 10*np.log(yAxisRange)
else:
auto_scale_y = True
# draw the new plot
if self.nSubplot < nSubplots:
self.axes = self.figure.add_subplot(nSubplots_v, nSubplots_h, self.nSubplot+1, label=label, title=title, ylim=yAxisRange) #subplots start from 1
self.axes.grid(True)
#if plotLog:
# self.axes.semilogy(range(np.size(data)), data, scaley=auto_scale_y)
#else:
# self.axes.plot(range(np.size(data)), data, scaley=auto_scale_y)
self.axes.plot(range(np.size(data)), data, scaley=auto_scale_y)
#if xAxisRange is not None:
# self.xAxisRange = xAxisRange
# self.axes.xaxis.set_major_formatter(ticker.FuncFormatter(
# lambda x, pos=None: '%.2f' % self.xAxisRange[x] if 0 <= x < len(xAxisRange) else ''))
# for tick in self.axes.xaxis.get_ticklabels():
# tick.set_rotation(15)
#if yAxisRange is not None:
# self.yAxisRange = yAxisRange
# self.axes.xaxis.set_major_formatter(ticker.FuncFormatter(
# lambda x, pos=None: '%.1f' % self.yAxisRange[y] if 0 <= y < len(yAxisRange) else ''))
# for tick in self.axes.yaxis.get_ticklabels():
# tick.set_rotation(15)
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
# Increment the subplot number ready for the plot method to be called again.
# Count modulo number of subplots so that nSubplots=1 is the same as having a
# single shared axis.
# Skip this step if hold is set, in which case the next plot will be overlaid with the current one
if not hold:
self.nSubplot = (self.nSubplot + 1)
def plotNewCurve(self, data, xAxisRange=None, yAxisRange=None, xLabel="", yLabel="", plotLog=False):
# Clear the plot
self.figure.clear()
# Start from a new set of subplots
self.nSubplot = 0
self.plotCurve(self, data, xAxisRange=xAxisRange, yAxisRange=yAxisRange, xLabel=xLabel, yLabel=yLabel, plotLog=plotLog)
def updatePlot(self):
self.canvas.draw()
class MatplotlibPlot:
""" Class encapsulating an matplotlib plot """
def __init__(self, parent = None, dpi = 100, size = (5,4)):
""" Class initialiser """
self.dpi = dpi
self.figure = Figure(dpi = self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
# Create the navigation toolbar, tied to the canvas
self.toolbar = NavigationToolbar(self.canvas, parent)
self.canvas.show()
self.toolbar.show()
def plotCurve(self, data, numCurves = 1, labels = None, xAxisRange = None, yAxisRange = None, xLabel = "", yLabel = ""):
""" Normal plots """
self.figure.clear()
self.axes = self.figure.add_subplot(111)
self.axes.grid(True)
# Set empty labels if non defined
if labels is None:
labels = ['' for i in range(numImages)]
# Place all plots in axes
for i in range(numCurves):
self.axes.plot(data[:,i], label=labels[i])
self.axes.set_xlim((0, len(data[:,i])))
# Final touches
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
self.canvas.draw()
def subplotCurve(self, data, numPlots = 1, labels = None, xAxisRange = None, yAxisRange = None, xLabel = "", yLabel = ""):
""" Subplot mode """
self.figure.clear()
# Set empty labels if non defined
if labels is None:
labels = ['' for i in range(numImages)]
if numPlots == 1:
ax = self.figure.add_subplot(111)
ax.plot(data[:])
ax.set_xlabel(xLabel)
ax.set_ylabel(yLabel)
ax.set_title(labels[0])
ax.grid(True)
else:
# Plot each image
num_rows = ceil(sqrt(numPlots))
for i in range(numPlots):
ax = self.figure.add_subplot(num_rows, num_rows, i + 1)
ax.plot(data[:,i])
ax.set_xlim((0, len(data[:,i])))
ax.set_xlabel(xLabel)
ax.set_ylabel(yLabel)
ax.set_title(labels[i])
ax.grid(True)
# Final touches
self.figure.tight_layout()
self.canvas.draw()
def plotImage(self, data, numImages = 1, labels = None, xAxisRange = None, yAxisRange = None, xLabel = "", yLabel = ""):
""" Image plot """
self.figure.clear()
self.axes = self.figure.add_subplot(111)
# Show image
im = self.axes.imshow(data, origin='lower', aspect='auto')
# Final touches
self.axes.xaxis.set_label_text(xLabel)
self.axes.yaxis.set_label_text(yLabel)
self.figure.colorbar(im)
self.canvas.draw()
def subplotImage(self, data, numImages = 1, labels = None, xAxisRange = None, yAxisRange = None, xLabel = "", yLabel = ""):
""" Image subplot """
# Clear figure
self.figure.clear()
# Set empty labels if non defined
if labels is None:
labels = ['' for i in range(numImages)]
# Plot each image
num_rows = ceil(sqrt(numImages))
for i in range(numImages):
ax = self.figure.add_subplot(num_rows, num_rows, i + 1)
im = ax.imshow(data[:,:,i], origin='lower', aspect='auto')
ax.set_xlabel(xLabel)
ax.set_ylabel(yLabel)
ax.set_title(labels[i])
# Final touches
self.figure.tight_layout()
self.canvas.draw()
class MatplotlibWidget(QtGui.QWidget):
""" This subclass of QtWidget will manage the widget drawing; name matches the class in the *_ui.py file"""
# Global colors dictionary
colors_dict = {
"Discharge": "b",
"Subsurface Flow": "g",
"Impervious Flow": "SteelBlue",
"Infiltration Excess": "SeaGreen",
"Initial Abstracted Flow": "MediumBlue",
"Overland Flow": "RoyalBlue",
"PET": "orange",
"AET": "DarkOrange",
"Average Soil Root zone": "Gray",
"Average Soil Unsaturated Zone": "DarkGray",
"Snow Pack": "PowderBlue",
"Precipitation": "SkyBlue",
"Storage Deficit": "Brown",
"Return Flow": "Aqua",
"Water Use": "DarkCyan",
"Discharge + Water Use": "DarkBlue"
}
def __init__(self, parent=None):
super(MatplotlibWidget, self).__init__(parent)
# create object scope variables for watertxt data plot; used by radio buttons and span selector
self.watertxt_data = None
self.parameter = None
self.color_str = None
self.axes_text = None
self.axes_radio = None
self.parent = parent
# create figure
self.figure = Figure()
# create canvas and set some of its properties
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
self.canvas.setSizePolicy(QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.canvas.setFocusPolicy(QtCore.Qt.StrongFocus)
self.canvas.setFocus()
# set up axes and its properties
self.ax = self.figure.add_subplot(111)
self.ax.grid(True)
# create toolbar
self.matplotlib_toolbar = NavigationToolbar(
self.canvas, parent) # the matplotlib toolbar object
# create the layout
self.layout = QtGui.QVBoxLayout()
# add the widgets to the layout
self.layout.addWidget(self.matplotlib_toolbar)
self.layout.addWidget(self.canvas)
# set the layout
self.setLayout(self.layout)
#-------------------------------- WATER.txt Parameter Plot ------------------------------------
def setup_watertxt_plot(self):
""" Setup the watertxt plot """
self.clear_watertxt_plot()
# set up axes and its properties
self.axes = self.figure.add_subplot(111)
self.axes.grid(True)
# create radio buttons
self.axes_radio = self.figure.add_axes(
[0.01, 0.02, 0.10, 0.15]
) # [left, bottom, width, height] = fractions of figure width and height
self.figure.subplots_adjust(bottom=0.2)
self.radio_buttons = RadioButtons(ax=self.axes_radio,
labels=("Span On", "Span Off"),
active=1,
activecolor="r")
self.radio_buttons.on_clicked(self.toggle_selector)
# create SpanSelector; invisble at first unless activated with toggle selector
self.span_selector = SpanSelector(self.axes,
self.on_select_axes,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5,
facecolor='red'))
self.span_selector.visible = False
def plot_watertxt_parameter(self, watertxt_data, name):
""" Plot a parameter from a WATER.txt file """
self.reset_watertxt_plot()
self.dates = watertxt_data["dates"]
self.watertxt_data = watertxt_data
self.parameter = watertxt.get_parameter(watertxt_data, name=name)
assert self.parameter is not None, "Parameter name {} is not in watertxt_data".format(
name)
self.axes.set_title("Parameter: {}".format(self.parameter["name"]))
self.axes.set_xlabel("Date")
ylabel = "\n".join(wrap(self.parameter["name"], 60))
self.axes.set_ylabel(ylabel)
# get proper color that corresponds to parameter name
self.color_str = self.colors_dict[name.split('(')[0].strip()]
# plot parameter
self.axes.plot(self.dates,
self.parameter["data"],
color=self.color_str,
label=self.parameter["name"],
linewidth=2)
# legend; make it transparent
handles, labels = self.axes.get_legend_handles_labels()
legend = self.axes.legend(handles, labels, fancybox=True)
legend.get_frame().set_alpha(0.5)
legend.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(
self.parameter["mean"], self.parameter["max"],
self.parameter["min"])
patch_properties = {
"boxstyle": "round",
"facecolor": "wheat",
"alpha": 0.5
}
self.axes_text = self.axes.text(0.05,
0.95,
text,
transform=self.axes.transAxes,
fontsize=14,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties)
# use a more precise date string for the x axis locations in the toolbar and rotate labels
self.axes.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# rotate and align the tick labels so they look better; note that self.figure.autofmt_xdate() does not work because of the radio button axes
for label in self.axes.get_xticklabels():
label.set_ha("right")
label.set_rotation(30)
# draw the plot
self.canvas.draw()
def on_select_helper(self, xmin, xmax):
""" Helper for on_select methods """
# convert matplotlib float dates to a datetime format
date_min = mdates.num2date(xmin)
date_max = mdates.num2date(xmax)
# put the xmin and xmax in datetime format to compare
date_min = datetime.datetime(date_min.year, date_min.month,
date_min.day, date_min.hour,
date_min.minute)
date_max = datetime.datetime(date_max.year, date_max.month,
date_max.day, date_max.hour,
date_max.minute)
# find the indices that were selected
indices = np.where((self.dates >= date_min) & (self.dates <= date_max))
indices = indices[0]
# get the selected dates and values
selected_dates = self.dates[indices]
selected_values = self.parameter["data"][indices]
# compute simple stats on selected values
selected_values_mean = nanmean(selected_values)
selected_value_max = np.nanmax(selected_values)
selected_value_min = np.nanmin(selected_values)
return selected_dates, selected_values, selected_values_mean, selected_value_max, selected_value_min
def on_select_axes(self, xmin, xmax):
""" A select handler for SpanSelector that updates axes with the new x and y limits selected by user """
selected_dates, selected_values, selected_values_mean, selected_value_max, selected_value_min = self.on_select_helper(
xmin, xmax)
# plot the selected values and update plots limits and text
self.axes.plot(selected_dates, selected_values, self.color_str)
self.axes.set_xlim(selected_dates[0], selected_dates[-1])
self.axes.set_ylim(selected_values.min(), selected_values.max())
text = 'mean = %.2f\nmax = %.2f\nmin = %.2f' % (
selected_values_mean, selected_value_max, selected_value_min)
self.axes_text.set_text(text)
# draw the updated plot
self.canvas.draw()
def toggle_selector(self, radio_button_label):
"""
A toggle radio buttons for the matplotlib SpanSelector widget.
"""
if radio_button_label == "Span On":
self.span_selector.visible = True
self.matplotlib_toolbar.hide()
elif radio_button_label == "Span Off":
self.span_selector.visible = False
self.matplotlib_toolbar.show()
self.plot_watertxt_parameter(watertxt_data=self.watertxt_data,
name=self.parameter["name"])
def clear_watertxt_plot(self):
""" Clear the plot axes """
self.figure.clear()
self.canvas.draw()
def reset_watertxt_plot(self):
""" Clear the plot axes """
self.axes.clear()
self.canvas.draw()
self.axes.grid(True)
#-------------------------------- WATER.txt Parameter Comparison Plot ------------------------------------
def setup_watertxtcmp_plot(self):
""" Setup the watertxt plot """
self.clear_watertxtcmp_plot()
# set up axes and its properties
self.axes1 = self.figure.add_subplot(211)
self.axes2 = self.figure.add_subplot(212, sharex=self.axes1)
self.axes1.grid(True)
self.axes2.grid(True)
def plot_watertxtcmp_parameter(self, watertxt_data1, watertxt_data2,
filename1, filename2, name):
""" Plot a parameter from a WATER.txt file """
self.reset_watertxtcmp_plot()
dates = watertxt_data1["dates"]
parameter1 = watertxt.get_parameter(watertxt_data=watertxt_data1,
name=name)
parameter2 = watertxt.get_parameter(watertxt_data=watertxt_data2,
name=name)
assert parameter1 is not None, "Parameter name {} is not in watertxt_data".format(
name)
assert parameter2 is not None, "Parameter name {} is not in watertxt_data".format(
name)
# calculate the difference
diff = parameter2["data"] - parameter1["data"]
# plot parameters on axes1
self.axes1.plot(dates,
parameter1["data"],
color="b",
label=filename1 + ": " + parameter1["name"],
linewidth=2)
self.axes1.hold(True)
self.axes1.plot(dates,
parameter2["data"],
color="r",
label=filename2 + ": " + parameter2["name"],
linewidth=2)
# plot the difference on axes2
self.axes2.plot(dates, diff, color="k", linewidth=2)
# add title, labels, legend
self.axes1.set_title(parameter1["name"])
self.axes2.set_xlabel("Date")
self.axes2.set_ylabel("Difference")
handles1, labels1 = self.axes1.get_legend_handles_labels()
legend1 = self.axes1.legend(handles1, labels1, fancybox=True)
legend1.get_frame().set_alpha(0.5)
legend1.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text1 = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(
parameter1["mean"], parameter1["max"], parameter1["min"])
text2 = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(
parameter2["mean"], parameter2["max"], parameter2["min"])
text_diff = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(
nanmean(diff), np.max(diff), np.min(diff))
patch_properties1 = {
"boxstyle": "round",
"facecolor": "b",
"alpha": 0.5
}
patch_properties2 = {
"boxstyle": "round",
"facecolor": "r",
"alpha": 0.5
}
patch_properties_diff = {
"boxstyle": "round",
"facecolor": "wheat",
"alpha": 0.5
}
self.axes1.text(0.02,
0.95,
text1,
transform=self.axes1.transAxes,
fontsize=12,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties1)
self.axes1.text(0.02,
0.45,
text2,
transform=self.axes1.transAxes,
fontsize=12,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties2)
self.axes2.text(0.02,
0.95,
text_diff,
transform=self.axes2.transAxes,
fontsize=12,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties_diff)
# use a more precise date string for the x axis locations in the toolbar and rotate labels
self.axes2.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# rotate and align the tick labels so they look better
self.figure.autofmt_xdate()
# draw the plot
self.canvas.draw()
def clear_watertxtcmp_plot(self):
""" Clear the plot axes """
self.figure.clear()
self.canvas.draw()
def reset_watertxtcmp_plot(self):
""" Clear the plot axes """
self.axes1.clear()
self.axes2.clear()
self.canvas.draw()
self.axes1.grid(True)
self.axes2.grid(True)
#-------------------------------- Basemap Plot ------------------------------------
def setup_basemap_plot(self):
""" Setup the watertxt plot """
self.clear_basemap_plot()
# set up axes and its properties
self.basemap_axes = self.figure.add_subplot(111)
self.basemap_axes.grid(True)
def get_map_extents(self, shapefiles, shp_name=None):
"""
Get max and min extent coordinates from a list of shapefiles to use as the
extents on the map. Use the map extents to calculate the map center and the
standard parallels.
Parameters
----------
shapefiles : list
List of shapefile_data dictionaries
shp_name : string
String name of shapefile to use for getting map extents
Returns
-------
extent_coords : dictionary
Dictionary containing "lon_min", "lon_max", "lat_max", "lat_min" keys with respective calculated values
center_coords : dictionary
Dictionary containing "lon", "lat" keys with respective calculated values
standard_parallels : dictionary
Dictionary containing "first", "second" keys with respective calculated values (first = min(lat), second = max(lat))
"""
extent_coords = {}
center_coords = {}
standard_parallels = {}
lons = []
lats = []
if shp_name:
for shapefile_data in shapefiles:
if shp_name in shapefile_data["name"].split("_")[0]:
lons.append(shapefile_data["extents"][0:2])
lats.append(shapefile_data["extents"][2:])
else:
for shapefile_data in shapefiles:
lons.append(shapefile_data["extents"][0:2])
lats.append(shapefile_data["extents"][2:])
extent_coords["lon_min"] = np.min(lons)
extent_coords["lon_max"] = np.max(lons)
extent_coords["lat_min"] = np.min(lats)
extent_coords["lat_max"] = np.max(lats)
center_coords["lon"] = np.mean(
[extent_coords["lon_min"], extent_coords["lon_max"]])
center_coords["lat"] = np.mean(
[extent_coords["lat_min"], extent_coords["lat_max"]])
standard_parallels["first"] = extent_coords["lat_min"]
standard_parallels["second"] = extent_coords["lat_max"]
return extent_coords, center_coords, standard_parallels
def plot_shapefiles_map(self,
shapefiles,
display_fields=[],
colors=[],
title=None,
shp_name=None,
buff=1.0):
"""
Generate a map showing all the shapefiles in the shapefile_list.
Shapefiles should be in a Geographic Coordinate System (longitude and
latitude coordinates) such as World WGS84; Matplotlib"s basemap library
does the proper transformation to a projected coordinate system. The projected
coordinate system used is Albers Equal Area.
Parameters
----------
shapefiles : list
List of dictionaries containing shapefile information
title : string
String title for plot
display_fields : list
List of strings that correspond to a shapefile field where the corresponding value(s) will be displayed.
colors : list
List of strings that correspond to colors to be displayed
shp_name : string
String name of shapefile to use for getting map extents
buff : float
Float value in coordinate degrees to buffer the map with
"""
self.setup_basemap_plot()
extent_coords, center_coords, standard_parallels = self.get_map_extents(
shapefiles, shp_name=shp_name)
# create the basemap object with Albers Equal Area Conic Projection
bmap = Basemap(projection="aea",
llcrnrlon=extent_coords["lon_min"] - buff,
llcrnrlat=extent_coords["lat_min"] - buff,
urcrnrlon=extent_coords["lon_max"] + buff,
urcrnrlat=extent_coords["lat_max"] + buff,
lat_1=standard_parallels["first"],
lat_2=standard_parallels["second"],
lon_0=center_coords["lon"],
lat_0=center_coords["lat"],
resolution="h",
area_thresh=10000,
ax=self.basemap_axes)
# have basemap object plot background stuff
bmap.drawcoastlines()
bmap.drawcountries()
bmap.drawrivers(linewidth=1, color="blue")
bmap.drawstates()
bmap.drawmapboundary(fill_color="aqua")
bmap.fillcontinents(color="coral", lake_color="aqua")
bmap.drawparallels(np.arange(-80., 81., 1.),
labels=[1, 0, 0, 0],
linewidth=0.5)
bmap.drawmeridians(np.arange(-180., 181., 1.),
labels=[0, 0, 0, 1],
linewidth=0.5)
# plot each shapefile on the basemap
legend_handles = []
legend_labels = []
colors_index = 0
colors_list = [
"b", "g", "y", "r", "c", "y", "m", "orange", "aqua", "darksalmon",
"gold", "k"
]
for shapefile_data in shapefiles:
# set up colors to use
if colors:
color = colors[colors_index]
elif colors_index > len(colors_list) - 1:
color = np.random.rand(3, )
else:
color = colors_list[colors_index]
full_path = os.path.join(shapefile_data["path"],
shapefile_data["name"].split(".")[0])
shp_tuple = bmap.readshapefile(
full_path, "shp", drawbounds=False
) # use basemap shapefile reader for ease of plotting
for shape_dict, shape in zip(
bmap.shp_info, bmap.shp
): # zip the shapefile information and its shape as defined by basemap
if shapefile_data["type"] == "POLYGON":
p1 = Polygon(shape,
facecolor=color,
edgecolor="k",
linewidth=1,
alpha=0.7,
label=shapefile_data["name"])
self.basemap_axes.add_patch(p1)
xx, yy = zip(*shape)
txt_x = str(np.mean(xx))
txt_y = str(np.mean(yy))
elif shapefile_data["type"] == "POINT":
x, y = shape
if "usgsgages" in shapefile_data["name"].split("_")[0]:
p1 = bmap.plot(x,
y,
color=color,
marker="^",
markersize=10,
label=shapefile_data["name"])
elif "wateruse" in shapefile_data["name"].split("_")[0]:
p1 = bmap.plot(x,
y,
color=color,
marker="o",
markersize=5,
label=shapefile_data["name"])
else:
print("what!!")
p1 = bmap.plot(x,
y,
color=color,
marker="o",
markersize=10,
label=shapefile_data["name"])
txt_x = str(x)
txt_y = str(y)
else:
xx, yy = zip(*shape)
p1 = bmap.plot(xx,
yy,
linewidth=1,
color=color,
label=shapefile_data["name"])
txt_x = str(np.mean(xx))
txt_y = str(np.mean(yy))
if isinstance(p1, list):
p1 = p1[0]
# control text display of shapefile fields
for display_field in display_fields:
if display_field in shape_dict.keys():
self.basemap_axes.text(txt_x,
txt_y,
shape_dict[display_field],
color="k",
fontsize=12,
fontweight="bold")
colors_index += 1
legend_handles.append(p1)
legend_labels.append(shapefile_data["name"].split("_")[0])
handles, labels = self.basemap_axes.get_legend_handles_labels()
# edit the contents of handles and labels to only show 1 legend per shape
handles = legend_handles
labels = legend_labels
legend = self.basemap_axes.legend(handles,
labels,
fancybox=True,
numpoints=1)
legend.get_frame().set_alpha(0.5)
legend.draggable(state=True)
# draw the plot
self.canvas.draw()
def clear_basemap_plot(self):
""" Clear the plot axes """
self.figure.clear()
self.canvas.draw()
def reset_basemap_plot(self):
""" Clear the plot axes """
self.basemap_axes.clear()
self.canvas.draw()
class PlotWindow(QFrame):
# Init the class: create plot window and canvas layout (and corresponding buttons)
#
def __init__(self, parent):
QDialog.__init__(self)
self.rim=0.05 # rim around plot
# The plot class holds its data now, so that it can be exported after a different
# PlotWindow has been created
self.parent=parent # parent object/class
self.fig=None
self.showCursor=False
self.cursorId=None
self.markerId=None
self.marker1=None # position marker in the plot
self.marker2=None
self.x = parent.x # plotted x axis data
self.x0 = self.parent.solverQuery.getTimeSlots()[0]['STARTTIME']
self.y1 = parent.y1 # plotted y axis data
self.y2 = parent.y2 # plotted y2 axis data
self.xdata = None # cursor click x coordinate in data value
self.ydata = None # cursor click y coordinate in data value
self.messages = parent.messages # solver messages
# Create canvas for plotting
self.fig = Figure((5, 4), dpi=100)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=self.rim+0.05, right=1.0-self.rim, top=1.0-self.rim, bottom=self.rim) # set a small rim
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show() # first hide the toolbar
self.setMinimumWidth(900)
self.setMinimumHeight(600)
self.setWindowTitle = self.parent.tableName + ": " + str(self.parent.solverQuery.getRank()) + " Parameters"
self.createWidgets()
self.createConnections()
self.createLayouts()
def createWidgets(self):
# Create Buttons for data export
#
self.exportButton=QPushButton("&Export Data")
self.exportButton.setToolTip("Export the currently plotted data")
self.exportButton.setMaximumWidth(100)
self.exportComboBox=QComboBox()
self.exportComboBox.addItem("ASCII")
# export in Matlab format is only possible if scipy.io module has been imported
if self.parent.haveModule('scipy') == True or self.parent.haveModule('scipy.io') == True:
self.exportComboBox.addItem("Matlab")
self.exportComboBox.setToolTip('File format for exporting data')
self.exportComboBox.setMaximumWidth(100)
self.exportComboBox.setMinimumHeight(25)
self.showCursorCheckBox=QCheckBox("Show cursor")
self.showCursorCheckBox.setToolTip("Show a marker line in plot")
self.showCursorCheckBox.setCheckState(Qt.Unchecked) # default off
self.histogramButton=QPushButton("&Histogram") # button to create a histogram
self.histogramButton.setToolTip("Create a histogram of the current parameter")
self.histogramButton.setMaximumWidth(150)
self.histogramButton.setToolTip('Create a histogram of current data')
self.onClickLabel=QLabel("On click display:")
self.onClickComboBox=QComboBox()
self.onClickComboBox.setMaximumWidth(150)
self.onClickComboBox.setToolTip('What to display when data point in graph is clicked')
self.onClickComboBox.addItem("CorrMatrix") # first, default = correlation matrix
self.onClickComboBox.addItem("Zoom")
#self.onClickComboBox.addItem("Per iteration")
#self.onClickComboBox.addItem("CorrMatrix and Iterations")
self.solverMessageLabel=QLabel("Solver Message:")
self.solverMessageText=QLineEdit()
self.solverMessageText.setText('Undefined')
self.solverMessageText.setToolTip('Message returned by LSQFit after iteration')
self.solverMessageText.setReadOnly(True)
self.solverMessageText.setMaximumWidth(125)
self.closeButton=QPushButton()
self.closeButton.setText('Close')
self.closeButton.setToolTip('close this plot window')
self.closeButton.setMaximumWidth(120)
def createConnections(self):
# Set connections
self.connect(self.exportButton, SIGNAL('clicked()'), self.on_export)
self.connect(self.histogramButton, SIGNAL('clicked()'), self.on_histogram)
self.connect(self.closeButton, SIGNAL('clicked()'), SLOT('close()'))
self.connect(self.showCursorCheckBox, SIGNAL('stateChanged(int)'), self.on_cursor)
self.connect(self.onClickComboBox, SIGNAL('valueChanged(int)'), self.on_onClickComboBox)
# Layouts for canvas and buttons
#
def createLayouts(self):
buttonLayout = QVBoxLayout()
buttonLayout.addWidget(self.exportButton)
buttonLayout.addWidget(self.exportComboBox)
buttonLayout.addWidget(self.showCursorCheckBox)
buttonLayout.addWidget(self.histogramButton)
buttonLayout.addWidget(self.onClickComboBox)
buttonLayout.addWidget(self.solverMessageLabel)
buttonLayout.addWidget(self.solverMessageText)
buttonLayout.insertStretch(-1)
buttonLayout.addWidget(self.closeButton)
#buttonLayout.insertStretch(-1)
#buttonLayout.setMaximumWidth(160)
# Canvas layout
canvasLayout = QVBoxLayout()
canvasLayout.addWidget(self.canvas, 1)
canvasLayout.addWidget(self.mpl_toolbar)
mainLayout = QHBoxLayout()
mainLayout.addLayout(buttonLayout)
mainLayout.addLayout(canvasLayout)
self.setLayout(mainLayout)
self.show() # show the plotWindow widget
self.parent.setXLabel()
self.parent.setYLabel()
# Matplotlib event connections
#if self.showMarker==True:
# cid = self.fig.canvas.mpl_connect('motion_notify_event', self.update_marker)
# cid = self.fig.canvas.mpl_connect('button_press_event', onclick)
cid = self.fig.canvas.mpl_connect('motion_notify_event', self.on_solverMessage) # TODO: this doesn work
self.cursorId = self.fig.canvas.mpl_connect('button_press_event', self.on_click)
self.plot()
# React on showMarkerCheckBox signal
#
"""
def on_marker(self):
#print "on_marker checkState = ", self.showMarkerCheckBox.checkState() # DEBUG
if self.showMarkerCheckBox.isChecked()==True:
self.showMarker=True
self.markerId = self.fig.canvas.mpl_connect('motion_notify_event', self.update_marker)
else:
print "on_marker() disconnecting marker event"
self.showMarker=False
self.fig.canvas.mpl_disconnect(self.markerId)
# Plot a vertical line marker on the solutions plot showing the solution
# of the currently plotted solver parameters
# TODO: EXPERIMENTAL
#
def update_marker(self, event):
#print "plotMarker() pos = ", event.xdata # DEBUG
#print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(
# event.button, event.x, event.y, event.xdata, event.ydata) # DEBUG
# Create markers (vertical lines) in both plots
self.marker1=self.ax1.axvline(x=event.xdata, linewidth=1.5, color='r')
self.marker2=self.ax2.axvline(x=event.xdata, linewidth=1.5, color='r')
# We need to remove all unnecessary marker in plot 1 and plot 2
# TODO: find better method, keeps double marker sometimes
while len(self.ax1.lines)-1 > 1:
self.ax1.lines[len(self.ax1.lines)-1].remove()
while len(self.ax2.lines)-1 > 1:
self.ax2.lines[len(self.ax1.lines)-1].remove()
self.marker1=self.ax1.axvline(x=event.xdata, linewidth=1.5, color='r')
self.marker2=self.ax1.axvline(x=event.xdata, linewidth=1.5, color='r')
# self.canvas.draw_idle()
self.canvas.draw()
"""
def plotcorrmatrix(self):
print "plotcorrmatrix()" # DEBUG
#print "self.parent.xAxisType = ", self.parent.xAxisType # DEBUG
indx = np.searchsorted(self.x, [self.xdata])[0]
if self.parent.xAxisType=="Time":
start_time=self.parent.solverQuery.timeSlots[indx]['STARTTIME']
end_time=self.parent.solverQuery.timeSlots[indx]['ENDTIME']
start_freq=self.parent.solverQuery.frequencies[self.parent.frequencyStartSlider.value()]['STARTFREQ']
end_freq=self.parent.solverQuery.frequencies[self.parent.frequencyEndSlider.value()]['ENDFREQ']
elif self.parent.xAxisType=="Freq":
start_freq=self.parent.solverQuery.frequencySlots[indx]['STARTFREQ']
end_freq=self.parent.solverQuery.frequencylots[indx]['ENDFREQ']
start_time=self.parent.solverQuery.frequencies[self.parent.timeStartSlider.value()]['STARTTIME']
end_time=self.parent.solverQuery.frequencies[self.parent.timeEndSlider.value()]['ENDTIME']
else: # Iteration
start_time=self.parent.solverQuery.timeSlots[self.parent.timeStartSlider.value()]['STARTTIME']
end_time=self.parent.solverQuery.timeSlots[self.parent.timeEndSlider.value()]['ENDTIME']
start_freq=self.parent.solverQuery.frequencies[self.parent.frequencyStartSlider.value()]['STARTFREQ']
end_freq=self.parent.solverQuery.frequencies[self.parent.frequencyEndSlider.value()]['ENDFREQ']
#print "plotwindow::plotcorrmatri() start_time = ", start_time # DEBUG
#print "plotwindow::plotcorrmatri() end_time = ", end_time # DEBUG
#print "plotwindow::plotcorrmatri() start_freq = ", start_freq # DEBUG
#print "plotwindow::plotcorrmatri() end_freq = ", end_freq # DEBUG
corrMatrix=self.parent.solverQuery.getCorrMatrix(start_time, end_time, start_freq, end_freq)
self.corrmatrixDialog=pc.plotCorrmatrix(self, corrMatrix)
self.corrmatrixDialog.show()
# Activate / Deactivate Matplotlib demo cursor
#
def on_cursor(self):
print "on_cursor()" # DEBUG
self.showCursor=self.showCursorCheckBox.isChecked()
print "on_cursor() self.showCursor = ", self.showCursor
if self.showCursor==True:
self.cursor = Cursor(self.ax1, self)
#self.cursor = SnaptoCursor(self.ax1, self.x, self.y1, self)
self.fig.canvas.mpl_connect('motion_notify_event', self.cursor.mouse_move)
else:
self.cursor.lx.remove()
self.cursor.ly.remove()
self.fig.canvas.mpl_disconnect(self.cursorId)
# Handle export button clicked()
#
def on_export(self):
fileformat=self.exportComboBox.currentText()
self.parent.on_export(fileformat)
# TODO!
def on_logarithmic(self):
self.yscale('log')
# Functio to execute on a click event (experimental)
#
def on_click(self, event):
print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(
event.button, event.x, event.y, event.xdata, event.ydata) # DEBUG
print "event.key = ", event.key # DEBUG
self.xdata=event.xdata
self.ydata=event.ydata
# Depending on the setting in the onClickComboBox launch appropriate dialog
#
if self.onClickComboBox.currentText()=="CorrMatrix": # if CorrMatrix should be displayed
# Pick per iteration details if possible
if self.parent.tableType == "PERSOLUTION_CORRMATRIX" or self.parent.tableType == "PERITERATION_CORRMATRIX":
self.plotcorrmatrix()
else:
print "on_clickMarker() table is not of correct type"
elif self.onClickComboBox.currentText()=="Per iteration":
print "plotwindow::on_click() trying to launch per iteration plotwindow"
# Display a histogram of the converged solutions (i.e. LASTITER=TRUE)
# for the currently selected parameter
#
def on_histogram(self):
self.histoDialog=ph.plothistogram(self)
self.histoDialog.show()
def on_onClickComboBox(self):
print "on_onClickComboBox()" # DEBUG
if self.onClickComboBox.currentText()=="Zoom":
self.fig.canvas.mpl_disconnect(self.cursorId)
elif self.onClickComboBox.currentText()=="CorrMatrix":
self.cursorId = self.fig.canvas.mpl_connect('button_press_event', self.on_click)
elif self.onClickComboBox.currentText()=="Per iteration":
self.cursorId = self.fig.canvas.mpl_connect('button_press_event', self.on_click)
def on_solverMessage(self, event):
if self.messages!=None: # only if we indeed inherited messages from parent
self.xdata = event.xdata
self.ydata = event.ydata # get cursor position from figure
index = np.searchsorted(self.x, [self.xdata])[0] # get Message for the index of cursor position
resultType=self.messages['result']
#print "resultType = ", resultType
#print "len(self.messages) = ", len(self.messages['last'])
#print "self.xdata = ", self.xdata
#print "on_solverMessage() index = ", index
#print "self.messages[index] = ", self.messages[resultType][index]
#print "self.parent.messages = ", self.parent.messages
#self.solverMessageText.setText(self.messages[resultType][index])
self.solverMessageText.setReadOnly(False) # make it writable
#print "resultType =", resultType # DEBUG
if resultType=="last":
#self.solverMessageText.setText(self.messages[resultType][index])
self.solverMessageText.setText(self.messages[index])
elif resultType=="all":
#self.solverMessageText.setText(self.messages[resultType][index])
self.solverMessageText.setText(self.messages[index])
elif resultType==None:
print "on_solverMessage() None messages"
return
self.solverMessageText.setReadOnly(True) # make it readonly again that user can't mess with it
# Plot data that has been read
#
def plot(self):
print "PlotWindow::plot()" # DEBUG
parm=self.parent.parmsComboBox.currentText() # Solution parameter, e.g. Gain:1:1:LBA001
parameter=str(self.parent.parametersComboBox.currentText()) # Get solver parameter from drop down
# Give for Time axis only time relative to start time
# TODO this does not work
if self.parent.xAxisType=="Time":
self.x=self.parent.computeRelativeX()
self.ax1=self.fig.add_subplot(211) # create solutions subplot
# Set title and labels
self.ax1.set_xlabel(self.parent.xLabel)
self.ax1.set_ylabel(parm + ":" + self.parent.parmValueComboBox.currentText())
np.set_printoptions(precision=1)
# self.ax1.get_xaxis().set_visible(False) # TODO: How to get correct x-axis ticks?
np.set_printoptions(precision=2) # does this work?
if self.parent.perIteration==True:
x=range(1, len(self.y1)+1) # we want the first iteration to be called "1"
if self.parent.scatterCheckBox.isChecked()==True:
self.ax1.scatter(x, self.y1)
else:
self.ax1.plot(x, self.y1)
else:
if self.parent.scatterCheckBox.isChecked()==True:
self.ax1.scatter(self.x, self.y1)
else:
if len(self.y1)==1 or isinstance(self.y1, float):
self.ax1.scatter(self.x, self.y1)
else:
self.ax1.plot(self.x, self.y1)
# Solver log plot
self.ax2=self.fig.add_subplot(212, sharex=self.ax1) # sharex for common zoom
# Set labels
#self.ax2.set_xticklabels(self.ax1.get_xticklabels(), visible=True)
self.ax2.set_ylabel(self.parent.parametersComboBox.currentText())
if self.parent.perIteration==True:
x=range(1, len(self.y2)+1)
if self.parent.scatterCheckBox.isChecked()==True:
self.ax2.scatter(x, self.y2)
else:
self.ax2.plot(x, self.y2) # have to increase lower y limit (for unknown reason)
else:
if self.parent.scatterCheckBox.isChecked()==True:
self.ax2.scatter(self.x, self.y2)
else:
if len(self.y1)==1 or isinstance(self.y2, float):
self.ax2.scatter(self.x, self.y2)
else:
self.ax2.plot(self.x, self.y2)
self.fig.canvas.draw()
class plotCorrmatrix(QDialog):
def __init__(self, parent, corrmatrix):
QFrame.__init__(self)
self.setWindowTitle("Correlation Matrix")
self.corrmatrix = [[]]
self.rank = None
self.rim = 0.05
self.parent = parent # parent object/class
self.fig = None
# Create canvas for plotting
self.fig = Figure((7, 7), dpi=100)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=self.rim,
right=1.0 - self.rim,
top=1.0 - self.rim,
bottom=self.rim) # set a small rim
self.ax = self.fig.add_subplot(111)
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show()
self.setMinimumWidth(700)
self.setMinimumHeight(700)
self.corrmatrix = corrmatrix
self.xLabel = "Parameter index"
self.yLabel = "Parameter index"
self.createWidgets()
self.createLayouts()
self.connectSignals()
self.plot(self.corrmatrix)
def createWidgets(self):
# Get time indices (and frequency range) from solverdialog class according to plotwindow index
self.index = np.searchsorted(self.parent.x, [self.parent.xdata])[0]
print("self.index = ", self.index) # DEBUG
self.start_time = self.parent.parent.solverQuery.timeSlots[
self.index]['STARTTIME']
self.end_time = self.parent.parent.solverQuery.timeSlots[
self.index]['ENDTIME']
self.start_freq = self.parent.parent.solverQuery.frequencies[
self.parent.parent.frequencyStartSlider.value()]['STARTFREQ']
self.end_freq = self.parent.parent.solverQuery.frequencies[
self.parent.parent.frequencyEndSlider.value()]['ENDFREQ']
self.timeLabel = QLabel("Time cell")
self.startTimeLabel = QLabel(
"S: " + str(self.parent.parent.convertDate(self.start_time)))
self.endTimeLabel = QLabel(
"E: " + str(self.parent.parent.convertDate(self.end_time)))
self.startTimeLabel.show()
self.endTimeLabel.show()
self.freqLabel = QLabel("Freq cell")
self.startFreqLabel = QLabel("S: " + str(self.start_freq) + " Hz")
self.endFreqLabel = QLabel("E: " + str(self.end_freq) + " Hz")
self.startFreqLabel.show()
self.endFreqLabel.show()
self.closeButton = QPushButton()
self.closeButton = QPushButton()
self.closeButton.setText('Close')
self.closeButton.setToolTip('close this plotcorrmatrix window')
self.closeButton.setMaximumWidth(120)
self.closeButton.show()
self.prevButton = QPushButton("Prev")
self.nextButton = QPushButton("Next")
def createLayouts(self):
self.buttonLayout = QVBoxLayout() # layout containing widgets
self.matrixLayout = QVBoxLayout(
) # layout containing canvas and toolbar
self.mainLayout = QHBoxLayout()
self.buttonLayout.addWidget(self.timeLabel)
self.buttonLayout.addWidget(self.startTimeLabel)
self.buttonLayout.addWidget(self.endTimeLabel)
self.buttonLayout.addWidget(self.freqLabel)
self.buttonLayout.addWidget(self.startFreqLabel)
self.buttonLayout.addWidget(self.endFreqLabel)
self.prevNextLayout = QHBoxLayout()
if self.parent.parent.xAxisType != "Iteration": # we don't support previous and next in iteration mode
self.prevNextLayout.addWidget(self.prevButton)
self.prevNextLayout.addWidget(self.nextButton)
self.buttonLayout.addLayout(self.prevNextLayout)
self.buttonLayout.insertStretch(-1)
self.buttonLayout.addWidget(self.closeButton)
self.matrixLayout.addWidget(self.canvas)
self.matrixLayout.addWidget(self.mpl_toolbar)
self.mainLayout.addLayout(self.buttonLayout)
self.mainLayout.addLayout(self.matrixLayout)
self.setLayout(self.mainLayout)
def connectSignals(self):
self.connect(self.closeButton, SIGNAL('clicked()'), SLOT('close()'))
self.connect(self.prevButton, SIGNAL('clicked()'), self.on_prevButton)
self.connect(self.nextButton, SIGNAL('clicked()'), self.on_nextButton)
self.connect(self.prevButton, SIGNAL('clicked()'),
self.retrieveCorrMatrix)
self.connect(self.nextButton, SIGNAL('clicked()'),
self.retrieveCorrMatrix)
#*********************************************
#
# Handler functions for events
#
#*********************************************
def on_prevButton(self):
self.index = self.index - 1
if self.index == 0:
self.prevButton.setDisabled(True)
def on_nextButton(self):
self.index = self.index + 1
if self.index > 0 and self.index < len(self.parent.x) - 1:
self.prevButton.setEnabled(True)
if self.index == len(self.parent.x) - 1:
self.nextButton.setDisabled(True)
def retrieveCorrMatrix(self):
if self.parent.parent.xAxisType == "Time":
self.start_time = self.parent.parent.solverQuery.timeSlots[
self.index]['STARTTIME']
self.end_time = self.parent.parent.solverQuery.timeSlots[
self.index]['ENDTIME']
self.start_freq = self.parent.parent.solverQuery.frequencies[
self.parent.parent.frequencyStartSlider.value()]['STARTFREQ']
self.end_freq = self.parent.parent.solverQuery.frequencies[
self.parent.parent.frequencyEndSlider.value()]['ENDFREQ']
elif self.parent.parent.xAxisType == "Freq":
self.start_freq = self.parent.parent.solverQuery.frequencySlots[
self.index]['STARTFREQ']
self.end_freq = self.parent.parent.solverQuery.frequencylots[
self.index]['ENDFREQ']
self.start_time = self.parent.parent.solverQuery.frequencies[
self.parent.parent.timeStartSlider.value()]['STARTTIME']
self.end_time = self.parent.solverQuery.frequencies[
self.parent.parent.timeEndSlider.value()]['ENDTIME']
else: # Iteration
# Do nothing? Because there is only one Corrmatrix per solution but not per iteration!?
print(
"plotcorrmatrix::retrieveCorrMatrix() can't step forward or backward in per iteration mode"
)
return
self.start_time = self.parent.parent.solverQuery.timeSlots[
self.parent.parent.timeStartSlider.value()]['STARTTIME']
self.end_time = self.parent.parent.solverQuery.timeSlots[
self.parent.parent.timeEndSlider.value()]['ENDTIME']
self.start_freq = self.parent.parent.solverQuery.frequencies[
self.parent.parent.frequencyStartSlider.value()]['STARTFREQ']
self.end_freq = self.parent.parent.solverQuery.frequencies[
self.parent.parent.frequencyEndSlider.value()]['ENDFREQ']
print("plotcorrmatrix::retrieveCorrMatrix()") # DEBUG
print("plotwindow::plotcorrmatri() start_time = ",
self.start_time) # DEBUG
print("plotwindow::plotcorrmatri() end_time = ",
self.end_time) # DEBUG
print("plotwindow::plotcorrmatri() start_freq = ",
self.start_freq) # DEBUG
print("plotwindow::plotcorrmatri() end_freq = ",
self.end_freq) # DEBUG
self.corrmatrix = self.parent.parent.solverQuery.getCorrMatrix(
self.start_time, self.end_time, self.start_freq, self.end_freq)
self.updateLabels()
self.plot(self.corrmatrix)
def updateLabels(self):
self.startTimeLabel.setText(
"S: " + str(self.parent.parent.convertDate(self.start_time)))
self.endTimeLabel.setText(
"E: " + str(self.parent.parent.convertDate(self.end_time)))
self.startFreqLabel.setText("S: " + str(self.start_freq) + " Hz")
self.endFreqLabel.setText("E: " + str(self.end_freq) + " Hz")
#*********************************************
#
# Plot a correlation matrix in a plotWindow
#
#*********************************************
#
# corrmatrix - numpy.ndarray holding (linearized) correlation Matrix
#
def plot(self, corrmatrix):
print("plotCorrmatrix::plotCorrMatrix()") # DEBUG
# We plot into the existing canvas object of the PlotWindow class
rank = self.parent.parent.solverQuery.getRank()
if rank != math.sqrt(len(corrmatrix)):
raise ValueError
shape = corrmatrix.shape # get shape of array (might be 1-D)
corrmatrix = np.reshape(corrmatrix, (rank, rank))
if len(shape) == 1: # if we got only one dimension...
if shape[0] != rank * rank: # if the length of the array is not rank^2
raise ValueError
else:
corrmatrix = np.reshape(corrmatrix, (rank, rank))
elif len(shape) == 2: # we already have a two-dimensional array
print("shape[0] = ", shape[0], " shape[1] = ", shape[1]) # DEBUG
if shape[0] != rank or shape[1] != rank:
raise ValueError
# Clear all axes, and clear figure (needed to remove colorbar)
self.ax.cla()
self.fig.clf()
self.ax = self.fig.add_subplot(111) # create axes again in figure
# plot CorrMatrix as a figure image
self.img = self.ax.imshow(corrmatrix,
cmap=cm.jet,
aspect='equal',
interpolation=None)
self.colorbar = self.fig.colorbar(self.img)
self.canvas.draw()
def rebuild_widget(self, number_of_plots, plot_stretching):
self.__number_of_plots = number_of_plots
self.__plot_stretching = plot_stretching
ids = []
if self.__top_vbox.count() < self.__number_of_plots:
ids = range(self.__top_vbox.count(), self.__number_of_plots)
for i in ids:
label = QLabel()
label.setFont(self.__font)
label.setAlignment(Qt.AlignCenter)
# Create the mpl Figure and FigCanvas objects.
# 5x4 inches, 100 dots-per-inch
#
#dpi = 100
#self.fig = Figure((5.0, 4.0), dpi=self.dpi)
fig = pyplot.figure()
canvas = FigureCanvas(fig)
canvas.setParent(self)
# Since we have only one plot, we can use add_axes
# instead of add_subplot, but then the subplot
# configuration tool in the navigation toolbar wouldn't
# work.
#
axes = fig.add_subplot(111)
# Create the navigation toolbar, tied to the canvas
#
mpl_toolbar = NavigationToolbar(canvas, self, False)
if self.__show_toolbar:
mpl_toolbar.show()
else:
mpl_toolbar.hide()
# Other GUI controls
#
tmp_vbox = QVBoxLayout()
tmp_vbox.addWidget(label)
tmp_vbox.addWidget(canvas, 1)
tmp_vbox.addWidget(mpl_toolbar)
widget = QWidget()
widget.setLayout(tmp_vbox)
self.__plots.append((label, canvas, fig, axes, mpl_toolbar, widget))
self.__plot_infos.append((self.PLOT_TYPE_NONE, '', None, {}))
self.__top_vbox.addWidget(widget)
for i in xrange(self.__number_of_plots):
stretch = 0
if plot_stretching != None:
stretch = plot_stretching[i]
self.__top_vbox.setStretch(i, stretch)
else:
self.__top_vbox.setStretch(i, 1)
plot = self.__plots[i]
label, canvas, fig, axes, mpl_toolbar, widget = plot
widget.show()
for i in xrange(self.__number_of_plots, self.__top_vbox.count()):
plot = self.__plots[i]
label, canvas, fig, axes, mpl_toolbar, widget = plot
widget.hide()
if self.__show_menu:
menubar = QMenuBar()
save_menu = menubar.addMenu("&Save")
menu_items = []
for i,plot_info in enumerate(self.__plot_infos):
plot_caption = plot_info[1]
menu_name = "Save &plot '%s' [%d]" % (plot_caption, i+1)
if len(plot_caption) == 0:
menu_name = "Save &plot #%d" % (i+1)
save_plot_action = self.__make_action(menu_name,
shortcut="Ctrl+P",
slot=functools.partial(self.on_save_plot, i))
menu_items.append(save_plot_action)
menu_items.append(None)
save_all_action = self.__make_action("Save &all plots",
shortcut="Ctrl+A", slot=self.on_save_all_plots)
menu_items.append(save_all_action)
self.__add_actions(save_menu, menu_items)
self.layout().setMenuBar(menubar)
class PlotWindow(QFrame):
# Init the class: create plot window and canvas layout (and corresponding buttons)
#
def __init__(self, parent):
QDialog.__init__(self)
self.rim = 0.05 # rim around plot
# The plot class holds its data now, so that it can be exported after a different
# PlotWindow has been created
self.parent = parent # parent object/class
self.fig = None
self.showCursor = False
self.cursorId = None
self.markerId = None
self.marker1 = None # position marker in the plot
self.marker2 = None
self.x = parent.x # plotted x axis data
self.x0 = self.parent.solverQuery.getTimeSlots()[0]['STARTTIME']
self.y1 = parent.y1 # plotted y axis data
self.y2 = parent.y2 # plotted y2 axis data
self.xdata = None # cursor click x coordinate in data value
self.ydata = None # cursor click y coordinate in data value
self.messages = parent.messages # solver messages
# Create canvas for plotting
self.fig = Figure((5, 4), dpi=100)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=self.rim + 0.05,
right=1.0 - self.rim,
top=1.0 - self.rim,
bottom=self.rim) # set a small rim
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show() # first hide the toolbar
self.setMinimumWidth(900)
self.setMinimumHeight(600)
self.setWindowTitle = self.parent.tableName + ": " + str(
self.parent.solverQuery.getRank()) + " Parameters"
self.createWidgets()
self.createConnections()
self.createLayouts()
def createWidgets(self):
# Create Buttons for data export
#
self.exportButton = QPushButton("&Export Data")
self.exportButton.setToolTip("Export the currently plotted data")
self.exportButton.setMaximumWidth(100)
self.exportComboBox = QComboBox()
self.exportComboBox.addItem("ASCII")
# export in Matlab format is only possible if scipy.io module has been imported
if self.parent.haveModule('scipy') == True or self.parent.haveModule(
'scipy.io') == True:
self.exportComboBox.addItem("Matlab")
self.exportComboBox.setToolTip('File format for exporting data')
self.exportComboBox.setMaximumWidth(100)
self.exportComboBox.setMinimumHeight(25)
self.showCursorCheckBox = QCheckBox("Show cursor")
self.showCursorCheckBox.setToolTip("Show a marker line in plot")
self.showCursorCheckBox.setCheckState(Qt.Unchecked) # default off
self.histogramButton = QPushButton(
"&Histogram") # button to create a histogram
self.histogramButton.setToolTip(
"Create a histogram of the current parameter")
self.histogramButton.setMaximumWidth(150)
self.histogramButton.setToolTip('Create a histogram of current data')
self.onClickLabel = QLabel("On click display:")
self.onClickComboBox = QComboBox()
self.onClickComboBox.setMaximumWidth(150)
self.onClickComboBox.setToolTip(
'What to display when data point in graph is clicked')
self.onClickComboBox.addItem(
"CorrMatrix") # first, default = correlation matrix
self.onClickComboBox.addItem("Zoom")
#self.onClickComboBox.addItem("Per iteration")
#self.onClickComboBox.addItem("CorrMatrix and Iterations")
self.solverMessageLabel = QLabel("Solver Message:")
self.solverMessageText = QLineEdit()
self.solverMessageText.setText('Undefined')
self.solverMessageText.setToolTip(
'Message returned by LSQFit after iteration')
self.solverMessageText.setReadOnly(True)
self.solverMessageText.setMaximumWidth(125)
self.closeButton = QPushButton()
self.closeButton.setText('Close')
self.closeButton.setToolTip('close this plot window')
self.closeButton.setMaximumWidth(120)
def createConnections(self):
# Set connections
self.connect(self.exportButton, SIGNAL('clicked()'), self.on_export)
self.connect(self.histogramButton, SIGNAL('clicked()'),
self.on_histogram)
self.connect(self.closeButton, SIGNAL('clicked()'), SLOT('close()'))
self.connect(self.showCursorCheckBox, SIGNAL('stateChanged(int)'),
self.on_cursor)
self.connect(self.onClickComboBox, SIGNAL('valueChanged(int)'),
self.on_onClickComboBox)
# Layouts for canvas and buttons
#
def createLayouts(self):
buttonLayout = QVBoxLayout()
buttonLayout.addWidget(self.exportButton)
buttonLayout.addWidget(self.exportComboBox)
buttonLayout.addWidget(self.showCursorCheckBox)
buttonLayout.addWidget(self.histogramButton)
buttonLayout.addWidget(self.onClickComboBox)
buttonLayout.addWidget(self.solverMessageLabel)
buttonLayout.addWidget(self.solverMessageText)
buttonLayout.insertStretch(-1)
buttonLayout.addWidget(self.closeButton)
#buttonLayout.insertStretch(-1)
#buttonLayout.setMaximumWidth(160)
# Canvas layout
canvasLayout = QVBoxLayout()
canvasLayout.addWidget(self.canvas, 1)
canvasLayout.addWidget(self.mpl_toolbar)
mainLayout = QHBoxLayout()
mainLayout.addLayout(buttonLayout)
mainLayout.addLayout(canvasLayout)
self.setLayout(mainLayout)
self.show() # show the plotWindow widget
self.parent.setXLabel()
self.parent.setYLabel()
# Matplotlib event connections
#if self.showMarker==True:
# cid = self.fig.canvas.mpl_connect('motion_notify_event', self.update_marker)
# cid = self.fig.canvas.mpl_connect('button_press_event', onclick)
cid = self.fig.canvas.mpl_connect(
'motion_notify_event',
self.on_solverMessage) # TODO: this doesn work
self.cursorId = self.fig.canvas.mpl_connect('button_press_event',
self.on_click)
self.plot()
# React on showMarkerCheckBox signal
#
"""
def on_marker(self):
#print "on_marker checkState = ", self.showMarkerCheckBox.checkState() # DEBUG
if self.showMarkerCheckBox.isChecked()==True:
self.showMarker=True
self.markerId = self.fig.canvas.mpl_connect('motion_notify_event', self.update_marker)
else:
print "on_marker() disconnecting marker event"
self.showMarker=False
self.fig.canvas.mpl_disconnect(self.markerId)
# Plot a vertical line marker on the solutions plot showing the solution
# of the currently plotted solver parameters
# TODO: EXPERIMENTAL
#
def update_marker(self, event):
#print "plotMarker() pos = ", event.xdata # DEBUG
#print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(
# event.button, event.x, event.y, event.xdata, event.ydata) # DEBUG
# Create markers (vertical lines) in both plots
self.marker1=self.ax1.axvline(x=event.xdata, linewidth=1.5, color='r')
self.marker2=self.ax2.axvline(x=event.xdata, linewidth=1.5, color='r')
# We need to remove all unnecessary marker in plot 1 and plot 2
# TODO: find better method, keeps double marker sometimes
while len(self.ax1.lines)-1 > 1:
self.ax1.lines[len(self.ax1.lines)-1].remove()
while len(self.ax2.lines)-1 > 1:
self.ax2.lines[len(self.ax1.lines)-1].remove()
self.marker1=self.ax1.axvline(x=event.xdata, linewidth=1.5, color='r')
self.marker2=self.ax1.axvline(x=event.xdata, linewidth=1.5, color='r')
# self.canvas.draw_idle()
self.canvas.draw()
"""
def plotcorrmatrix(self):
print("plotcorrmatrix()") # DEBUG
#print "self.parent.xAxisType = ", self.parent.xAxisType # DEBUG
indx = np.searchsorted(self.x, [self.xdata])[0]
if self.parent.xAxisType == "Time":
start_time = self.parent.solverQuery.timeSlots[indx]['STARTTIME']
end_time = self.parent.solverQuery.timeSlots[indx]['ENDTIME']
start_freq = self.parent.solverQuery.frequencies[
self.parent.frequencyStartSlider.value()]['STARTFREQ']
end_freq = self.parent.solverQuery.frequencies[
self.parent.frequencyEndSlider.value()]['ENDFREQ']
elif self.parent.xAxisType == "Freq":
start_freq = self.parent.solverQuery.frequencySlots[indx][
'STARTFREQ']
end_freq = self.parent.solverQuery.frequencylots[indx]['ENDFREQ']
start_time = self.parent.solverQuery.frequencies[
self.parent.timeStartSlider.value()]['STARTTIME']
end_time = self.parent.solverQuery.frequencies[
self.parent.timeEndSlider.value()]['ENDTIME']
else: # Iteration
start_time = self.parent.solverQuery.timeSlots[
self.parent.timeStartSlider.value()]['STARTTIME']
end_time = self.parent.solverQuery.timeSlots[
self.parent.timeEndSlider.value()]['ENDTIME']
start_freq = self.parent.solverQuery.frequencies[
self.parent.frequencyStartSlider.value()]['STARTFREQ']
end_freq = self.parent.solverQuery.frequencies[
self.parent.frequencyEndSlider.value()]['ENDFREQ']
#print "plotwindow::plotcorrmatri() start_time = ", start_time # DEBUG
#print "plotwindow::plotcorrmatri() end_time = ", end_time # DEBUG
#print "plotwindow::plotcorrmatri() start_freq = ", start_freq # DEBUG
#print "plotwindow::plotcorrmatri() end_freq = ", end_freq # DEBUG
corrMatrix = self.parent.solverQuery.getCorrMatrix(
start_time, end_time, start_freq, end_freq)
self.corrmatrixDialog = pc.plotCorrmatrix(self, corrMatrix)
self.corrmatrixDialog.show()
# Activate / Deactivate Matplotlib demo cursor
#
def on_cursor(self):
print("on_cursor()") # DEBUG
self.showCursor = self.showCursorCheckBox.isChecked()
print("on_cursor() self.showCursor = ", self.showCursor)
if self.showCursor == True:
self.cursor = Cursor(self.ax1, self)
#self.cursor = SnaptoCursor(self.ax1, self.x, self.y1, self)
self.fig.canvas.mpl_connect('motion_notify_event',
self.cursor.mouse_move)
else:
self.cursor.lx.remove()
self.cursor.ly.remove()
self.fig.canvas.mpl_disconnect(self.cursorId)
# Handle export button clicked()
#
def on_export(self):
fileformat = self.exportComboBox.currentText()
self.parent.on_export(fileformat)
# TODO!
def on_logarithmic(self):
self.yscale('log')
# Functio to execute on a click event (experimental)
#
def on_click(self, event):
print('button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %
(event.button, event.x, event.y, event.xdata,
event.ydata)) # DEBUG
print("event.key = ", event.key) # DEBUG
self.xdata = event.xdata
self.ydata = event.ydata
# Depending on the setting in the onClickComboBox launch appropriate dialog
#
if self.onClickComboBox.currentText(
) == "CorrMatrix": # if CorrMatrix should be displayed
# Pick per iteration details if possible
if self.parent.tableType == "PERSOLUTION_CORRMATRIX" or self.parent.tableType == "PERITERATION_CORRMATRIX":
self.plotcorrmatrix()
else:
print("on_clickMarker() table is not of correct type")
elif self.onClickComboBox.currentText() == "Per iteration":
print(
"plotwindow::on_click() trying to launch per iteration plotwindow"
)
# Display a histogram of the converged solutions (i.e. LASTITER=TRUE)
# for the currently selected parameter
#
def on_histogram(self):
self.histoDialog = ph.plothistogram(self)
self.histoDialog.show()
def on_onClickComboBox(self):
print("on_onClickComboBox()") # DEBUG
if self.onClickComboBox.currentText() == "Zoom":
self.fig.canvas.mpl_disconnect(self.cursorId)
elif self.onClickComboBox.currentText() == "CorrMatrix":
self.cursorId = self.fig.canvas.mpl_connect(
'button_press_event', self.on_click)
elif self.onClickComboBox.currentText() == "Per iteration":
self.cursorId = self.fig.canvas.mpl_connect(
'button_press_event', self.on_click)
def on_solverMessage(self, event):
if self.messages != None: # only if we indeed inherited messages from parent
self.xdata = event.xdata
self.ydata = event.ydata # get cursor position from figure
index = np.searchsorted(
self.x, [self.xdata
])[0] # get Message for the index of cursor position
resultType = self.messages['result']
#print "resultType = ", resultType
#print "len(self.messages) = ", len(self.messages['last'])
#print "self.xdata = ", self.xdata
#print "on_solverMessage() index = ", index
#print "self.messages[index] = ", self.messages[resultType][index]
#print "self.parent.messages = ", self.parent.messages
#self.solverMessageText.setText(self.messages[resultType][index])
self.solverMessageText.setReadOnly(False) # make it writable
#print "resultType =", resultType # DEBUG
if resultType == "last":
#self.solverMessageText.setText(self.messages[resultType][index])
self.solverMessageText.setText(self.messages[index])
elif resultType == "all":
#self.solverMessageText.setText(self.messages[resultType][index])
self.solverMessageText.setText(self.messages[index])
elif resultType == None:
print("on_solverMessage() None messages")
return
self.solverMessageText.setReadOnly(
True) # make it readonly again that user can't mess with it
# Plot data that has been read
#
def plot(self):
print("PlotWindow::plot()") # DEBUG
parm = self.parent.parmsComboBox.currentText(
) # Solution parameter, e.g. Gain:1:1:LBA001
parameter = str(self.parent.parametersComboBox.currentText()
) # Get solver parameter from drop down
# Give for Time axis only time relative to start time
# TODO this does not work
if self.parent.xAxisType == "Time":
self.x = self.parent.computeRelativeX()
self.ax1 = self.fig.add_subplot(211) # create solutions subplot
# Set title and labels
self.ax1.set_xlabel(self.parent.xLabel)
self.ax1.set_ylabel(parm + ":" +
self.parent.parmValueComboBox.currentText())
np.set_printoptions(precision=1)
# self.ax1.get_xaxis().set_visible(False) # TODO: How to get correct x-axis ticks?
np.set_printoptions(precision=2) # does this work?
if self.parent.perIteration == True:
x = list(range(1,
len(self.y1) +
1)) # we want the first iteration to be called "1"
if self.parent.scatterCheckBox.isChecked() == True:
self.ax1.scatter(x, self.y1)
else:
self.ax1.plot(x, self.y1)
else:
if self.parent.scatterCheckBox.isChecked() == True:
self.ax1.scatter(self.x, self.y1)
else:
if len(self.y1) == 1 or isinstance(self.y1, float):
self.ax1.scatter(self.x, self.y1)
else:
self.ax1.plot(self.x, self.y1)
# Solver log plot
self.ax2 = self.fig.add_subplot(
212, sharex=self.ax1) # sharex for common zoom
# Set labels
#self.ax2.set_xticklabels(self.ax1.get_xticklabels(), visible=True)
self.ax2.set_ylabel(self.parent.parametersComboBox.currentText())
if self.parent.perIteration == True:
x = list(range(1, len(self.y2) + 1))
if self.parent.scatterCheckBox.isChecked() == True:
self.ax2.scatter(x, self.y2)
else:
self.ax2.plot(
x, self.y2
) # have to increase lower y limit (for unknown reason)
else:
if self.parent.scatterCheckBox.isChecked() == True:
self.ax2.scatter(self.x, self.y2)
else:
if len(self.y1) == 1 or isinstance(self.y2, float):
self.ax2.scatter(self.x, self.y2)
else:
self.ax2.plot(self.x, self.y2)
self.fig.canvas.draw()
class plotCorrmatrix(QDialog):
def __init__(self, parent, corrmatrix):
QFrame.__init__(self)
self.setWindowTitle("Correlation Matrix")
self.corrmatrix=[[]]
self.rank=None
self.rim=0.05
self.parent=parent # parent object/class
self.fig=None
# Create canvas for plotting
self.fig = Figure((7, 7), dpi=100)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=self.rim, right=1.0-self.rim, top=1.0-self.rim, bottom=self.rim) # set a small rim
self.ax=self.fig.add_subplot(111)
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show()
self.setMinimumWidth(700)
self.setMinimumHeight(700)
self.corrmatrix=corrmatrix
self.xLabel="Parameter index"
self.yLabel="Parameter index"
self.createWidgets()
self.createLayouts()
self.connectSignals()
self.plot(self.corrmatrix)
def createWidgets(self):
# Get time indices (and frequency range) from solverdialog class according to plotwindow index
self.index = np.searchsorted(self.parent.x, [self.parent.xdata])[0]
print "self.index = ", self.index # DEBUG
self.start_time=self.parent.parent.solverQuery.timeSlots[self.index]['STARTTIME']
self.end_time=self.parent.parent.solverQuery.timeSlots[self.index]['ENDTIME']
self.start_freq=self.parent.parent.solverQuery.frequencies[self.parent.parent.frequencyStartSlider.value()]['STARTFREQ']
self.end_freq=self.parent.parent.solverQuery.frequencies[self.parent.parent.frequencyEndSlider.value()]['ENDFREQ']
self.timeLabel=QLabel("Time cell")
self.startTimeLabel=QLabel("S: " + str(self.parent.parent.convertDate(self.start_time)))
self.endTimeLabel=QLabel("E: " + str(self.parent.parent.convertDate(self.end_time)))
self.startTimeLabel.show()
self.endTimeLabel.show()
self.freqLabel=QLabel("Freq cell")
self.startFreqLabel=QLabel("S: " + str(self.start_freq) + " Hz")
self.endFreqLabel=QLabel("E: " + str(self.end_freq) + " Hz")
self.startFreqLabel.show()
self.endFreqLabel.show()
self.closeButton=QPushButton()
self.closeButton=QPushButton()
self.closeButton.setText('Close')
self.closeButton.setToolTip('close this plotcorrmatrix window')
self.closeButton.setMaximumWidth(120)
self.closeButton.show()
self.prevButton=QPushButton("Prev")
self.nextButton=QPushButton("Next")
def createLayouts(self):
self.buttonLayout=QVBoxLayout() # layout containing widgets
self.matrixLayout=QVBoxLayout() # layout containing canvas and toolbar
self.mainLayout=QHBoxLayout()
self.buttonLayout.addWidget(self.timeLabel)
self.buttonLayout.addWidget(self.startTimeLabel)
self.buttonLayout.addWidget(self.endTimeLabel)
self.buttonLayout.addWidget(self.freqLabel)
self.buttonLayout.addWidget(self.startFreqLabel)
self.buttonLayout.addWidget(self.endFreqLabel)
self.prevNextLayout=QHBoxLayout()
if self.parent.parent.xAxisType!="Iteration": # we don't support previous and next in iteration mode
self.prevNextLayout.addWidget(self.prevButton)
self.prevNextLayout.addWidget(self.nextButton)
self.buttonLayout.addLayout(self.prevNextLayout)
self.buttonLayout.insertStretch(-1)
self.buttonLayout.addWidget(self.closeButton)
self.matrixLayout.addWidget(self.canvas)
self.matrixLayout.addWidget(self.mpl_toolbar)
self.mainLayout.addLayout(self.buttonLayout)
self.mainLayout.addLayout(self.matrixLayout)
self.setLayout(self.mainLayout)
def connectSignals(self):
self.connect(self.closeButton, SIGNAL('clicked()'), SLOT('close()'))
self.connect(self.prevButton, SIGNAL('clicked()'), self.on_prevButton)
self.connect(self.nextButton, SIGNAL('clicked()'), self.on_nextButton)
self.connect(self.prevButton, SIGNAL('clicked()'), self.retrieveCorrMatrix)
self.connect(self.nextButton, SIGNAL('clicked()'), self.retrieveCorrMatrix)
#*********************************************
#
# Handler functions for events
#
#*********************************************
def on_prevButton(self):
self.index = self.index - 1
if self.index == 0:
self.prevButton.setDisabled(True)
def on_nextButton(self):
self.index = self.index + 1
if self.index > 0 and self.index < len(self.parent.x)-1:
self.prevButton.setEnabled(True)
if self.index == len(self.parent.x)-1:
self.nextButton.setDisabled(True)
def retrieveCorrMatrix(self):
if self.parent.parent.xAxisType=="Time":
self.start_time=self.parent.parent.solverQuery.timeSlots[self.index]['STARTTIME']
self.end_time=self.parent.parent.solverQuery.timeSlots[self.index]['ENDTIME']
self.start_freq=self.parent.parent.solverQuery.frequencies[self.parent.parent.frequencyStartSlider.value()]['STARTFREQ']
self.end_freq=self.parent.parent.solverQuery.frequencies[self.parent.parent.frequencyEndSlider.value()]['ENDFREQ']
elif self.parent.parent.xAxisType=="Freq":
self.start_freq=self.parent.parent.solverQuery.frequencySlots[self.index]['STARTFREQ']
self.end_freq=self.parent.parent.solverQuery.frequencylots[self.index]['ENDFREQ']
self.start_time=self.parent.parent.solverQuery.frequencies[self.parent.parent.timeStartSlider.value()]['STARTTIME']
self.end_time=self.parent.solverQuery.frequencies[self.parent.parent.timeEndSlider.value()]['ENDTIME']
else: # Iteration
# Do nothing? Because there is only one Corrmatrix per solution but not per iteration!?
print "plotcorrmatrix::retrieveCorrMatrix() can't step forward or backward in per iteration mode"
return
self.start_time=self.parent.parent.solverQuery.timeSlots[self.parent.parent.timeStartSlider.value()]['STARTTIME']
self.end_time=self.parent.parent.solverQuery.timeSlots[self.parent.parent.timeEndSlider.value()]['ENDTIME']
self.start_freq=self.parent.parent.solverQuery.frequencies[self.parent.parent.frequencyStartSlider.value()]['STARTFREQ']
self.end_freq=self.parent.parent.solverQuery.frequencies[self.parent.parent.frequencyEndSlider.value()]['ENDFREQ']
print "plotcorrmatrix::retrieveCorrMatrix()" # DEBUG
print "plotwindow::plotcorrmatri() start_time = ", self.start_time # DEBUG
print "plotwindow::plotcorrmatri() end_time = ", self.end_time # DEBUG
print "plotwindow::plotcorrmatri() start_freq = ", self.start_freq # DEBUG
print "plotwindow::plotcorrmatri() end_freq = ", self.end_freq # DEBUG
self.corrmatrix=self.parent.parent.solverQuery.getCorrMatrix(self.start_time, self.end_time, self.start_freq, self.end_freq)
self.updateLabels()
self.plot(self.corrmatrix)
def updateLabels(self):
self.startTimeLabel.setText("S: " + str(self.parent.parent.convertDate(self.start_time)))
self.endTimeLabel.setText("E: " + str(self.parent.parent.convertDate(self.end_time)))
self.startFreqLabel.setText("S: " + str(self.start_freq) + " Hz")
self.endFreqLabel.setText("E: " + str(self.end_freq) + " Hz")
#*********************************************
#
# Plot a correlation matrix in a plotWindow
#
#*********************************************
#
# corrmatrix - numpy.ndarray holding (linearized) correlation Matrix
#
def plot(self, corrmatrix):
print "plotCorrmatrix::plotCorrMatrix()" # DEBUG
# We plot into the existing canvas object of the PlotWindow class
rank=self.parent.parent.solverQuery.getRank()
if rank != math.sqrt(len(corrmatrix)):
raise ValueError
shape=corrmatrix.shape # get shape of array (might be 1-D)
corrmatrix=np.reshape(corrmatrix, (rank, rank))
if len(shape)==1: # if we got only one dimension...
if shape[0] != rank*rank: # if the length of the array is not rank^2
raise ValueError
else:
corrmatrix=np.reshape(corrmatrix, (rank, rank))
elif len(shape)==2: # we already have a two-dimensional array
print "shape[0] = ", shape[0], " shape[1] = ", shape[1] # DEBUG
if shape[0] != rank or shape[1] != rank:
raise ValueError
# Clear all axes, and clear figure (needed to remove colorbar)
self.ax.cla()
self.fig.clf()
self.ax=self.fig.add_subplot(111) # create axes again in figure
# plot CorrMatrix as a figure image
self.img=self.ax.imshow(corrmatrix, cmap=cm.jet , aspect='equal', interpolation=None)
self.colorbar = self.fig.colorbar(self.img)
self.canvas.draw()
class MatplotlibWidget(QtGui.QWidget):
""" This subclass of QtWidget will manage the widget drawing; name matches the class in the *_ui.py file"""
# Global colors dictionary
colors_dict = {
"Discharge": "b",
"Subsurface Flow": "g",
"Impervious Flow": "SteelBlue",
"Infiltration Excess": "SeaGreen",
"Initial Abstracted Flow": "MediumBlue",
"Overland Flow": "RoyalBlue",
"PET": "orange",
"AET": "DarkOrange",
"Average Soil Root zone": "Gray",
"Average Soil Unsaturated Zone": "DarkGray",
"Snow Pack": "PowderBlue",
"Precipitation": "SkyBlue",
"Storage Deficit": "Brown",
"Return Flow": "Aqua",
"Water Use": "DarkCyan",
"Discharge + Water Use": "DarkBlue"
}
def __init__(self, parent = None):
super(MatplotlibWidget, self).__init__(parent)
# create object scope variables for watertxt data plot; used by radio buttons and span selector
self.watertxt_data = None
self.parameter = None
self.color_str = None
self.axes_text = None
self.axes_radio = None
self.parent = parent
# create figure
self.figure = Figure()
# create canvas and set some of its properties
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(parent)
self.canvas.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.canvas.setFocusPolicy(QtCore.Qt.StrongFocus)
self.canvas.setFocus()
# set up axes and its properties
self.ax = self.figure.add_subplot(111)
self.ax.grid(True)
# create toolbar
self.matplotlib_toolbar = NavigationToolbar(self.canvas, parent) # the matplotlib toolbar object
# create the layout
self.layout = QtGui.QVBoxLayout()
# add the widgets to the layout
self.layout.addWidget(self.matplotlib_toolbar)
self.layout.addWidget(self.canvas)
# set the layout
self.setLayout(self.layout)
#-------------------------------- WATER.txt Parameter Plot ------------------------------------
def setup_watertxt_plot(self):
""" Setup the watertxt plot """
self.clear_watertxt_plot()
# set up axes and its properties
self.axes = self.figure.add_subplot(111)
self.axes.grid(True)
# create radio buttons
self.axes_radio = self.figure.add_axes([0.01, 0.02, 0.10, 0.15]) # [left, bottom, width, height] = fractions of figure width and height
self.figure.subplots_adjust(bottom=0.2)
self.radio_buttons = RadioButtons(ax = self.axes_radio, labels = ("Span On", "Span Off"), active = 1, activecolor= "r")
self.radio_buttons.on_clicked(self.toggle_selector)
# create SpanSelector; invisble at first unless activated with toggle selector
self.span_selector = SpanSelector(self.axes, self.on_select_axes, 'horizontal', useblit = True, rectprops = dict(alpha=0.5, facecolor='red'))
self.span_selector.visible = False
def plot_watertxt_parameter(self, watertxt_data, name):
""" Plot a parameter from a WATER.txt file """
self.reset_watertxt_plot()
self.dates = watertxt_data["dates"]
self.watertxt_data = watertxt_data
self.parameter = watertxt.get_parameter(watertxt_data, name = name)
assert self.parameter is not None, "Parameter name {} is not in watertxt_data".format(name)
self.axes.set_title("Parameter: {}".format(self.parameter["name"]))
self.axes.set_xlabel("Date")
ylabel = "\n".join(wrap(self.parameter["name"], 60))
self.axes.set_ylabel(ylabel)
# get proper color that corresponds to parameter name
self.color_str = self.colors_dict[name.split('(')[0].strip()]
# plot parameter
self.axes.plot(self.dates, self.parameter["data"], color = self.color_str, label = self.parameter["name"], linewidth = 2)
# legend; make it transparent
handles, labels = self.axes.get_legend_handles_labels()
legend = self.axes.legend(handles, labels, fancybox = True)
legend.get_frame().set_alpha(0.5)
legend.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(self.parameter["mean"], self.parameter["max"], self.parameter["min"])
patch_properties = {"boxstyle": "round", "facecolor": "wheat", "alpha": 0.5}
self.axes_text = self.axes.text(0.05, 0.95, text, transform = self.axes.transAxes, fontsize = 14,
verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties)
# use a more precise date string for the x axis locations in the toolbar and rotate labels
self.axes.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# rotate and align the tick labels so they look better; note that self.figure.autofmt_xdate() does not work because of the radio button axes
for label in self.axes.get_xticklabels():
label.set_ha("right")
label.set_rotation(30)
# draw the plot
self.canvas.draw()
def on_select_helper(self, xmin, xmax):
""" Helper for on_select methods """
# convert matplotlib float dates to a datetime format
date_min = mdates.num2date(xmin)
date_max = mdates.num2date(xmax)
# put the xmin and xmax in datetime format to compare
date_min = datetime.datetime(date_min.year, date_min.month, date_min.day, date_min.hour, date_min.minute)
date_max = datetime.datetime(date_max.year, date_max.month, date_max.day, date_max.hour, date_max.minute)
# find the indices that were selected
indices = np.where((self.dates >= date_min) & (self.dates <= date_max))
indices = indices[0]
# get the selected dates and values
selected_dates = self.dates[indices]
selected_values = self.parameter["data"][indices]
# compute simple stats on selected values
selected_values_mean = nanmean(selected_values)
selected_value_max = np.nanmax(selected_values)
selected_value_min = np.nanmin(selected_values)
return selected_dates, selected_values, selected_values_mean, selected_value_max, selected_value_min
def on_select_axes(self, xmin, xmax):
""" A select handler for SpanSelector that updates axes with the new x and y limits selected by user """
selected_dates, selected_values, selected_values_mean, selected_value_max, selected_value_min = self.on_select_helper(xmin, xmax)
# plot the selected values and update plots limits and text
self.axes.plot(selected_dates, selected_values, self.color_str)
self.axes.set_xlim(selected_dates[0], selected_dates[-1])
self.axes.set_ylim(selected_values.min(), selected_values.max())
text = 'mean = %.2f\nmax = %.2f\nmin = %.2f' % (selected_values_mean, selected_value_max, selected_value_min)
self.axes_text.set_text(text)
# draw the updated plot
self.canvas.draw()
def toggle_selector(self, radio_button_label):
"""
A toggle radio buttons for the matplotlib SpanSelector widget.
"""
if radio_button_label == "Span On":
self.span_selector.visible = True
self.matplotlib_toolbar.hide()
elif radio_button_label == "Span Off":
self.span_selector.visible = False
self.matplotlib_toolbar.show()
self.plot_watertxt_parameter(watertxt_data = self.watertxt_data, name = self.parameter["name"])
def clear_watertxt_plot(self):
""" Clear the plot axes """
self.figure.clear()
self.canvas.draw()
def reset_watertxt_plot(self):
""" Clear the plot axes """
self.axes.clear()
self.canvas.draw()
self.axes.grid(True)
#-------------------------------- WATER.txt Parameter Comparison Plot ------------------------------------
def setup_watertxtcmp_plot(self):
""" Setup the watertxt plot """
self.clear_watertxtcmp_plot()
# set up axes and its properties
self.axes1 = self.figure.add_subplot(211)
self.axes2 = self.figure.add_subplot(212, sharex = self.axes1)
self.axes1.grid(True)
self.axes2.grid(True)
def plot_watertxtcmp_parameter(self, watertxt_data1, watertxt_data2, filename1, filename2, name):
""" Plot a parameter from a WATER.txt file """
self.reset_watertxtcmp_plot()
dates = watertxt_data1["dates"]
parameter1 = watertxt.get_parameter(watertxt_data = watertxt_data1, name = name)
parameter2 = watertxt.get_parameter(watertxt_data = watertxt_data2, name = name)
assert parameter1 is not None, "Parameter name {} is not in watertxt_data".format(name)
assert parameter2 is not None, "Parameter name {} is not in watertxt_data".format(name)
# calculate the difference
diff = parameter2["data"] - parameter1["data"]
# plot parameters on axes1
self.axes1.plot(dates, parameter1["data"], color = "b", label = filename1 + ": " + parameter1["name"], linewidth = 2)
self.axes1.hold(True)
self.axes1.plot(dates, parameter2["data"], color = "r", label = filename2 + ": " + parameter2["name"], linewidth = 2)
# plot the difference on axes2
self.axes2.plot(dates, diff, color = "k", linewidth = 2)
# add title, labels, legend
self.axes1.set_title(parameter1["name"])
self.axes2.set_xlabel("Date")
self.axes2.set_ylabel("Difference")
handles1, labels1 = self.axes1.get_legend_handles_labels()
legend1 = self.axes1.legend(handles1, labels1, fancybox = True)
legend1.get_frame().set_alpha(0.5)
legend1.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text1 = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(parameter1["mean"], parameter1["max"], parameter1["min"])
text2 = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(parameter2["mean"], parameter2["max"], parameter2["min"])
text_diff = "mean = {:.2f}\nmax = {:.2f}\nmin = {:.2f}".format(nanmean(diff), np.max(diff), np.min(diff))
patch_properties1 = {"boxstyle": "round", "facecolor": "b", "alpha": 0.5}
patch_properties2 = {"boxstyle": "round", "facecolor": "r", "alpha": 0.5}
patch_properties_diff = {"boxstyle": "round", "facecolor": "wheat", "alpha": 0.5}
self.axes1.text(0.02, 0.95, text1, transform = self.axes1.transAxes, fontsize = 12, verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties1)
self.axes1.text(0.02, 0.45, text2, transform = self.axes1.transAxes, fontsize = 12, verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties2)
self.axes2.text(0.02, 0.95, text_diff, transform = self.axes2.transAxes, fontsize = 12, verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties_diff)
# use a more precise date string for the x axis locations in the toolbar and rotate labels
self.axes2.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# rotate and align the tick labels so they look better
self.figure.autofmt_xdate()
# draw the plot
self.canvas.draw()
def clear_watertxtcmp_plot(self):
""" Clear the plot axes """
self.figure.clear()
self.canvas.draw()
def reset_watertxtcmp_plot(self):
""" Clear the plot axes """
self.axes1.clear()
self.axes2.clear()
self.canvas.draw()
self.axes1.grid(True)
self.axes2.grid(True)
#-------------------------------- Basemap Plot ------------------------------------
def setup_basemap_plot(self):
""" Setup the watertxt plot """
self.clear_basemap_plot()
# set up axes and its properties
self.basemap_axes = self.figure.add_subplot(111)
self.basemap_axes.grid(True)
def get_map_extents(self, shapefiles, shp_name = None):
"""
Get max and min extent coordinates from a list of shapefiles to use as the
extents on the map. Use the map extents to calculate the map center and the
standard parallels.
Parameters
----------
shapefiles : list
List of shapefile_data dictionaries
shp_name : string
String name of shapefile to use for getting map extents
Returns
-------
extent_coords : dictionary
Dictionary containing "lon_min", "lon_max", "lat_max", "lat_min" keys with respective calculated values
center_coords : dictionary
Dictionary containing "lon", "lat" keys with respective calculated values
standard_parallels : dictionary
Dictionary containing "first", "second" keys with respective calculated values (first = min(lat), second = max(lat))
"""
extent_coords = {}
center_coords = {}
standard_parallels = {}
lons = []
lats = []
if shp_name:
for shapefile_data in shapefiles:
if shp_name in shapefile_data["name"].split("_")[0]:
lons.append(shapefile_data["extents"][0:2])
lats.append(shapefile_data["extents"][2:])
else:
for shapefile_data in shapefiles:
lons.append(shapefile_data["extents"][0:2])
lats.append(shapefile_data["extents"][2:])
extent_coords["lon_min"] = np.min(lons)
extent_coords["lon_max"] = np.max(lons)
extent_coords["lat_min"] = np.min(lats)
extent_coords["lat_max"] = np.max(lats)
center_coords["lon"] = np.mean([extent_coords["lon_min"], extent_coords["lon_max"]])
center_coords["lat"] = np.mean([extent_coords["lat_min"], extent_coords["lat_max"]])
standard_parallels["first"] = extent_coords["lat_min"]
standard_parallels["second"] = extent_coords["lat_max"]
return extent_coords, center_coords, standard_parallels
def plot_shapefiles_map(self, shapefiles, display_fields = [], colors = [], title = None, shp_name = None, buff = 1.0):
"""
Generate a map showing all the shapefiles in the shapefile_list.
Shapefiles should be in a Geographic Coordinate System (longitude and
latitude coordinates) such as World WGS84; Matplotlib"s basemap library
does the proper transformation to a projected coordinate system. The projected
coordinate system used is Albers Equal Area.
Parameters
----------
shapefiles : list
List of dictionaries containing shapefile information
title : string
String title for plot
display_fields : list
List of strings that correspond to a shapefile field where the corresponding value(s) will be displayed.
colors : list
List of strings that correspond to colors to be displayed
shp_name : string
String name of shapefile to use for getting map extents
buff : float
Float value in coordinate degrees to buffer the map with
"""
self.setup_basemap_plot()
extent_coords, center_coords, standard_parallels = self.get_map_extents(shapefiles, shp_name = shp_name)
# create the basemap object with Albers Equal Area Conic Projection
bmap = Basemap(projection = "aea",
llcrnrlon = extent_coords["lon_min"] - buff, llcrnrlat = extent_coords["lat_min"] - buff,
urcrnrlon = extent_coords["lon_max"] + buff, urcrnrlat = extent_coords["lat_max"] + buff,
lat_1 = standard_parallels["first"], lat_2 = standard_parallels["second"],
lon_0 = center_coords["lon"], lat_0 = center_coords["lat"],
resolution = "h", area_thresh = 10000, ax = self.basemap_axes)
# have basemap object plot background stuff
bmap.drawcoastlines()
bmap.drawcountries()
bmap.drawrivers(linewidth = 1, color = "blue")
bmap.drawstates()
bmap.drawmapboundary(fill_color = "aqua")
bmap.fillcontinents(color = "coral", lake_color = "aqua")
bmap.drawparallels(np.arange(-80., 81., 1.), labels = [1, 0, 0, 0], linewidth = 0.5)
bmap.drawmeridians(np.arange(-180., 181., 1.), labels = [0, 0, 0, 1], linewidth = 0.5)
# plot each shapefile on the basemap
legend_handles = []
legend_labels = []
colors_index = 0
colors_list = ["b", "g", "y", "r", "c", "y", "m", "orange", "aqua", "darksalmon", "gold", "k"]
for shapefile_data in shapefiles:
# set up colors to use
if colors:
color = colors[colors_index]
elif colors_index > len(colors_list) - 1:
color = np.random.rand(3,)
else:
color = colors_list[colors_index]
full_path = os.path.join(shapefile_data["path"], shapefile_data["name"].split(".")[0])
shp_tuple = bmap.readshapefile(full_path, "shp", drawbounds = False) # use basemap shapefile reader for ease of plotting
for shape_dict, shape in zip(bmap.shp_info, bmap.shp): # zip the shapefile information and its shape as defined by basemap
if shapefile_data["type"] == "POLYGON":
p1 = Polygon(shape, facecolor = color, edgecolor = "k",
linewidth = 1, alpha = 0.7, label = shapefile_data["name"])
self.basemap_axes.add_patch(p1)
xx, yy = zip(*shape)
txt_x = str(np.mean(xx))
txt_y = str(np.mean(yy))
elif shapefile_data["type"] == "POINT":
x, y = shape
if "usgsgages" in shapefile_data["name"].split("_")[0]:
p1 = bmap.plot(x, y, color = color, marker = "^", markersize = 10, label = shapefile_data["name"])
elif "wateruse" in shapefile_data["name"].split("_")[0]:
p1 = bmap.plot(x, y, color = color, marker = "o", markersize = 5, label = shapefile_data["name"])
else:
print("what!!")
p1 = bmap.plot(x, y, color = color, marker = "o", markersize = 10, label = shapefile_data["name"])
txt_x = str(x)
txt_y = str(y)
else:
xx, yy = zip(*shape)
p1 = bmap.plot(xx, yy, linewidth = 1, color = color, label = shapefile_data["name"])
txt_x = str(np.mean(xx))
txt_y = str(np.mean(yy))
if isinstance(p1, list):
p1 = p1[0]
# control text display of shapefile fields
for display_field in display_fields:
if display_field in shape_dict.keys():
self.basemap_axes.text(txt_x, txt_y, shape_dict[display_field], color = "k", fontsize = 12, fontweight = "bold")
colors_index += 1
legend_handles.append(p1)
legend_labels.append(shapefile_data["name"].split("_")[0])
handles, labels = self.basemap_axes.get_legend_handles_labels()
# edit the contents of handles and labels to only show 1 legend per shape
handles = legend_handles
labels = legend_labels
legend = self.basemap_axes.legend(handles, labels, fancybox = True, numpoints = 1)
legend.get_frame().set_alpha(0.5)
legend.draggable(state = True)
# draw the plot
self.canvas.draw()
def clear_basemap_plot(self):
""" Clear the plot axes """
self.figure.clear()
self.canvas.draw()
def reset_basemap_plot(self):
""" Clear the plot axes """
self.basemap_axes.clear()
self.canvas.draw()
class LMatplotlibWidget(LWidget):
def __init__(self, parent=None):
super(LMatplotlibWidget, self).__init__(parent)
self.setName(WIDGET_NAME)
self.buildUi()
self.dataContainer = LMyDataContainer(self)
self.actions = LMyActions(self.dataContainer,self)
self.setDebugLevel(5)
self.updateUi()
@LInAndOut(DEBUG & WIDGETS)
def buildUi(self):
self.verticalLayout = QVBoxLayout(self)
self.figure = Figure()
self.canvas = Canvas(self.figure) # <-- figure required
self.navigationToolbar = NavigationToolbar(self.canvas, self)
self.verticalLayout.addWidget(self.canvas)
self.verticalLayout.addWidget(self.navigationToolbar)
#Canvas.setSizePolicy(self.canvas, QSizePolicy.Expanding, QSizePolicy.Expanding)
#Canvas.updateGeometry(self.canvas)
@LInAndOut(DEBUG & WIDGETS)
def updateUi(self):
left = self.dataContainer.leftMargin
bottom = self.dataContainer.bottomMargin
right = self.dataContainer.rightMargin
top = self.dataContainer.topMargin
wspace = self.dataContainer.verticalSeparation
hspace = self.dataContainer.horizontalSeparation
self.figure.subplots_adjust(left, bottom, right, top, wspace, hspace)
if self.dataContainer.showNavigationToolbar:
self.navigationToolbar.show()
else:
self.navigationToolbar.hide()
#----------------------------------------------------------------------
# Interface (set)
@LInAndOut(DEBUG & WIDGETS)
def setOptions(self, options):
super(LMatplotlibWidget, self).setOptions(options)
if options.verboseLevel is not None:
self.setVerboseLevel(options.verboseLevel)
if options.navigationToolbar is not None:
self.showNavigationToolbar(options.navigationToolbar)
self.updateUi()
@LInAndOut(DEBUG & WIDGETS)
def setVerboseLevel(self, level):
self.dataContainer.verboseLevel = level
#----------------------------------------------------------------------
# Interface (get)
def getFigure(self):
return self.figure
#----------------------------------------------------------------------
# Interface (misc)
@LInAndOut(DEBUG & WIDGETS)
def showNavigationToolbar(self, flag=True):
self.dataContainer.showNavigationToolbar = flag
self.updateUi()
@LInAndOut(DEBUG & WIDGETS)
def snapshot(self, fileName = None):
if fileName is None:
caption = "%s - Save File" % self.name
filter = "PNG Image (*.png);;All files (*.*)"
fileName = QFileDialog.getSaveFileName(self.parent(), caption=caption,filter=filter )
fileName = "%s" % fileName
if len(fileName) > 0:
self.figure.savefig(fileName, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1)
@LInAndOut(DEBUG & WIDGETS)
def draw(self):
self.canvas.draw()
class PlotWindow(QFrame):
# Init the class: create plot window and canvas layout (and corresponding buttons)
# Steps contains a dictionary of dictionaries with the identified steps
#
def __init__(self, parent):
#QFrame.__init__(self)
QMainWindow.__init__(self, None)
self.setWindowTitle('BBS timing statistics')
self.parent = parent # Parent: BBSTiming class
self.currentPlotStyle = "bar" # current style of plot: bar, colorbar, lines
self.plotStyles = ["bar", "colorbar",
"line"] # supported plot styles #
self.summation = False # show "individual" execution times or "summation"
self.fig = None # figure
self.axes = None # plot axes
self.create_main_frame()
self.createWidgets()
self.createLayouts()
self.createConnections()
self.setMinimumWidth(700)
self.setMinimumHeight(400)
# Create a main frame
#
def create_main_frame(self):
self.main_frame = QWidget()
self.dpi = 75
self.setMinimumWidth(300)
self.setMinimumHeight(300)
# We want matplotlib export functionality, so include toolbar
# MPL Canvas and Toolbar
# Create canvas for plotting
self.fig = Figure((5, 4), dpi=75)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=0.1, right=0.96, top=0.94,
bottom=0.06) # set a small rim
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show() # first hide the toolbar
# Create the layouts for the widgets
#
def createLayouts(self):
# Layouts
self.buttonLayout = QVBoxLayout()
self.canvasLayout = QVBoxLayout()
self.mainLayout = QHBoxLayout()
# Add Widgets to the layout
self.buttonLayout.addWidget(self.loadButton)
self.buttonLayout.addWidget(self.quitButton)
self.buttonLayout.insertStretch(-1)
self.subStepsLayout = QHBoxLayout()
self.subStepsLayout.addWidget(self.showSubstepsCheckBox)
self.subStepsLayout.addWidget(self.showSubstepsLabel)
self.buttonLayout.addLayout(self.subStepsLayout)
self.showIndividualStepsLayout = QHBoxLayout()
self.showIndividualStepsLayout.addWidget(self.showIndividualCheckBox)
self.showIndividualStepsLayout.addWidget(self.showIndividualLabel)
self.buttonLayout.addLayout(self.showIndividualStepsLayout)
self.buttonLayout.addWidget(self.stepComboBox)
#self.buttonLayout.addWidget(self.stepListView)
self.buttonLayout.addWidget(self.substepComboBox)
self.buttonLayout.addWidget(self.keywordComboBox)
self.buttonLayout.addWidget(self.plotStyleComboBox)
self.buttonLayout.addWidget(self.stepComboBox)
self.buttonLayout.addWidget(self.plotButton)
self.buttonLayout.insertStretch(-1)
self.canvasLayout.addWidget(self.canvas)
self.canvasLayout.addWidget(self.mpl_toolbar)
self.mainLayout.addLayout(self.buttonLayout)
self.mainLayout.addLayout(self.canvasLayout)
self.setLayout(self.mainLayout)
# Create GUI widgets
#
def createWidgets(self):
#print "createWidgets()" # DEBUG
self.loadButton = QPushButton("Load logfile")
self.loadButton.setToolTip('load a BBS kernellog file')
self.loadButton.setMaximumWidth(200)
self.quitButton = QPushButton("Quit")
self.quitButton.setMaximumWidth(200)
self.quitButton.setToolTip('Quit the application')
self.quitButton.setMaximumWidth(200)
self.plotButton = QPushButton("Plot")
self.plotButton.setToolTip('force a replot')
self.showSubstepsCheckBox = QCheckBox()
self.showSubstepsLabel = QLabel("Show substeps")
self.showSubstepsCheckBox.setToolTip("Show also substeps in timing")
self.showSubstepsCheckBox.setCheckState(Qt.Unchecked) # Default: False
self.showSubstepsCheckBox.show()
self.showSubstepsLabel.show()
self.showIndividualCheckBox = QCheckBox()
self.showIndividualLabel = QLabel("Numbered steps")
self.showIndividualCheckBox.setToolTip(
"Show individually numbered steps")
self.showIndividualCheckBox.setCheckState(
Qt.Unchecked) # Default: False
self.showIndividualCheckBox.show()
self.showIndividualLabel.show()
# GUI Widgets depending on log file
self.createStepComboBox() # Selector for Step
self.createKeywordComboBox() # Selector for: total, count, avg
# Selector for plot type: bar, line, multibar
self.createPlotstyleComboBox()
self.createStepComboBox()
#self.createStepListView()
# This is now done in a function, too
# self.substepComboBox=QComboBox() # we create this here once
# self.substepComboBox.hide()
# self.substepComboBox.setToolTip('Substeps of step')
# self.substepComboBox.setMaximumWidth(200)
self.createSubbandComboBox()
# Create the step combobox containing the timed steps
# This should be a multi-selection box to plot multiple
# steps
#
def createStepComboBox(self):
#print "createStepComboBox()" # DEBUG
self.stepComboBox = QComboBox()
for step in self.parent.timedSteps:
self.stepComboBox.addItem(step)
self.stepComboBox.addItem("all")
self.stepComboBox.show()
self.stepComboBox.setMaximumWidth(200)
def createSubstepComboBox(self):
print("createSubstepComboBox()") # DEBUG
self.substepComboBox = QComboBox() # we create this here once
self.substepComboBox.hide()
self.substepComboBox.setToolTip('Substeps of step')
self.substepComboBox.setMaximumWidth(200)
# Fill step Checkbox with step names, either these are
# grouped by their common name, or shown as individually
# numbered steps
#
def fillSteps(self):
print("fillSteps()") # DEBUG
self.stepComboBox.clear()
# Decide if we want the steps grouped or individually numbered
if self.showIndividualCheckBox.isChecked() == True:
steps = self.parent.timedStepsCount
else:
steps = self.parent.timedSteps
#print "fillSteps() steps = ", steps
for step in steps:
# Check if that substep is already in there, avoid duplicates
if self.stepComboBox.findData(step) == -1:
self.stepComboBox.addItem(step)
self.stepComboBox.addItem(
"all") # we again have to add our "all" option
# Get the corresponding substeps for a step
#
def fillSubsteps(self):
print("fillSubsteps()") # DEBUG
# Get substeps for currently selected step (or all if "all")
step = str(self.stepComboBox.currentText())
self.substepComboBox.clear() # first clear substeps comboBox
if step != "all" and step != "ALL":
substeps = self.parent.getSubsteps(step)
#print "fillSubsteps() substeps = ", substeps # DEBUG
for substep in substeps:
# Check if that substep is already in there, avoid duplicates
if self.substepComboBox.findData(substep) == -1:
self.substepComboBox.addItem(substep)
else:
self.substepComboBox.clear() # First remove existing substeps
index = 0
while index < self.stepComboBox.count(): # loop over all steps
# TODO: Might not be supported to show substeps for ALL steps...
#substeps=str(self.stepComboBox.itemText(index))
#print "fillSubsteps() substeps = ", substeps
#print "fillSubsteps() index = ", index, "maxCount = ", self.stepComboBox.count()
step = str(self.stepComboBox.itemText(index))
print("fillSubsteps() step = ", step)
if step != "all" and step != "ALL":
substeps = self.parent.getSubsteps(step)
keys = list(substeps.keys())
#print "keys = ", keys
for j in range(len(substeps) - 1):
#print "fillSubsteps() substeps = ", substeps
substep = keys[j]
if substep != "all" and substep != "ALL":
self.substepComboBox.addItem(substep)
j = j + 1
index = index + 1
self.substepComboBox.addItem("ALL") # we need to add an "ALL"
"""
# Alternative view, displaying all the steps
# in a list view that then can be selected for
# plotting
#
def createStepListView(self):
print "createStepListBox()" # DEBUG
self.stepListView=QListView()
for step in self.parent.timedSteps:
self.stepListView.insertStringList(step)
self.stepListView.show()
step.stepListView.setMaximumWidth(200)
"""
# On timing combobox event
#
def createKeywordComboBox(self):
print("createTimingComboBox()") # DEBUG
self.keywordComboBox = QComboBox()
for key in self.parent.keywords:
self.keywordComboBox.addItem(key)
self.keywordComboBox.show()
self.keywordComboBox.setMaximumWidth(200)
# Create a comboBox offering different Matplotlib styles
#
def createPlotstyleComboBox(self):
print("createPlotstyleComboBox()") # DEBUG
self.plotStyleComboBox = QComboBox()
for style in self.plotStyles:
self.plotStyleComboBox.addItem(style)
self.plotStyleComboBox.show()
self.plotStyleComboBox.setMaximumWidth(200)
# Create subbands comboBox which allows selection of an individual subband
# from a pipeline.log
def createSubbandComboBox(self):
print("createSubbandComboBox()") # DEBUG
self.subbandComboBox = QComboBox()
for sub in self.parent.subbands:
self.subbandComboBox.addItem(sub)
self.subbandComboBox.show()
self.subbandComboBox.setMaximumWidth(200)
#**************************************
#
# Create connections
#
#**************************************
def createConnections(self):
print("createConnections()") # DEBUG
self.connect(self.loadButton, SIGNAL('clicked()'), self.on_loadfile)
self.connect(self.quitButton, SIGNAL('clicked()'), self,
SLOT('close()'))
self.connect(self.stepComboBox, SIGNAL('currentIndexChanged(int)'),
self.on_step)
self.connect(self.stepComboBox, SIGNAL('currentIndexChanged(int)'),
self.on_showSubsteps)
#self.connect(self.substepComboBox, SIGNAL('currentIndexChanged(int)'), self.on_step)
self.connect(self.substepComboBox, SIGNAL('currentIndexChanged(int)'),
self.on_plot)
self.connect(self.keywordComboBox, SIGNAL('currentIndexChanged(int)'),
self.on_keyword)
self.connect(self.plotStyleComboBox,
SIGNAL('currentIndexChanged(int)'), self.on_plotStyle)
self.connect(self.showSubstepsCheckBox, SIGNAL('stateChanged(int)'),
self.on_showSubsteps)
self.connect(self.showIndividualCheckBox, SIGNAL('stateChanged(int)'),
self.on_individualSteps)
self.connect(self.subbandComboBox, SIGNAL('currentIndexChanged(int)'),
self.on_subband)
self.connect(self.plotButton, SIGNAL('clicked()'), self.on_plot)
#**************************************
#
# Event handlers
#
#**************************************
# Load Kernellog file dialog
#
def on_loadfile(self):
#print "on_loadfile()" # DEBUG
# Customization: check if ~/Cluster/SolutionTests exists
if os.path.exists('/Users/duscha/Desktop/'):
setDir = QString('/Users/duscha/Desktop/')
else:
setDir = QString('')
path = str(QFileDialog.getOpenFileName(self, 'Load Kernellog', setDir))
path = str(
path) # Convert to string so that it can be used by load table
if path:
self.parent.readLogfile(path)
else:
print("load_table: invalid path")
# On step combobox event
#
def on_step(self):
print("on_step()") # DEBUG
#step=str(self.stepComboBox.currentText())
self.on_plot()
# On substep combobox event
#
def on_substep(self):
print("on_substep()") # DEBUG
#step=str(self.stepComboBox.currentText())
self.on_plot()
# On toggle individual treatment of counted steps
#
def on_individualSteps(self):
print("on_individualSteps()") # DEBUG
self.fillSteps() # update steps combobox
self.on_showSubsteps() # also update substeps combobox
#step=str(self.stepComboBox.currentText())
self.on_plot()
# On selection of different subbands
#
def on_subband(self):
print("on_subband()") # DEBUG
self.fillSteps() # update steps combobox
self.on_showSubsteps() # also update substeps combobox
self.on_plot()
# On toggling of show substeps
#
def on_showSubsteps(self):
print("on_substeps()") # DEBUG
if self.showSubstepsCheckBox.isChecked() == True:
self.showSubSteps = True
self.substepComboBox.show()
self.fillSubsteps()
else:
self.showSubSteps = False
self.substepComboBox.hide()
self.substepComboBox.clear()
# On keyword combobox event
#
def on_keyword(self):
print("on_keyword()") # DEBUG
self.on_plot()
# On change of plot style
#
def on_plotStyle(self):
print("on_plotStyle()") # DEBUG
self.plotStyle = self.plotStyleComboBox.currentText(
) # change class attribute
# On plot button action
#
def on_plot(self):
self.fig.clf() # clear the figure
print("on_plot()")
step = str(self.stepComboBox.currentText())
self.plot(step)
"""
step=str(self.stepComboBox.currentText())
# If we have all steps selected:
if step=="ALL" or step=="all":
for i in range(0, self.stepComboBox.count()): # loop over all steps
currentStep=self.stepComboBox.itemText(i) # get the step name from QComboBox
print "on_plot() currentStep = ", currentStep
self.plot(currentStep) # plot it
else:
#print "foo"
self.plot(step) # replot with new plotstyle
"""
# Replot diagram
#
def plot(self, step):
print("plot()") # DEBUG
width = 0.25 # width of bar plots
# Get the data according to GUI settings
step = str(step)
substep = str(self.substepComboBox.currentText())
keyword = str(self.keywordComboBox.currentText())
style = str(self.plotStyleComboBox.currentText())
print("plot() step = ", step, "substep = ", substep, "keyword = ",
keyword, "style = ", style) # DEBUG
self.axes = self.fig.add_subplot(111) # add 1 subplot to the canvas
result = []
# TODO: plot all substeps if we get multiple in results
if step == "all" or step == "ALL":
if self.showIndividualCheckBox.isChecked():
steps = self.parent.timedStepsCount
else:
steps = self.parent.timedSteps
print("plot() steps = ", steps) # DEBUG
for i in range(0, len(steps)):
#print "plot()", self.parent.getSubStepValue(str(steps[i]), substep, keyword)
result.append(self.parent.getStepFinal(str(steps[i]), keyword))
#print "len(result) = ", len(result)
print("plot() result[" + str(i) + "] = " + str(result[i]))
#self.axes.bar(i, result[i], width)
# If we want to see all steps, but not for individual substeps
elif step == "all" or step == "ALL" and (substep == None
or substep == ""):
if self.showIndividualCheckBox.isChecked():
steps = self.parent.getSubsteps(step, keyword)
else:
steps = self.parent.timedSteps
print("steps = ", steps)
#steps.append(self.parent.timedSteps)
newsteps = []
if isinstance(steps, list):
for i in range(0, len(steps)): # overplot them in one plot
newsteps.append(steps[i])
result = newsteps
elif substep == "all" or substep == "ALL":
print("plot(): substep=all")
result = (self.parent.getSubStepValue(step, keyword))
elif substep == None or substep == "":
result = (self.parent.getStepFinal(step, keyword))
else:
result = (self.parent.getSubStepValue(step, substep, keyword))
#if isinstance(result[0], list):
# result=self.linearizeList(result)
if isinstance(result, list):
print("plot() len(result) = ", len(result)) # DEBUG
print("plot() result = ", result) # DEBUG
#print "plot() result[1] = ", result[1] # DEBUG
#
# Plot on canvas
#
ind = []
if isinstance(result, float) or isinstance(result, int):
ind = 0
elif isinstance(result, bool):
print("plot() invalid result returned")
else:
maxInd = len(result)
for i in range(0, maxInd):
ind.append(width * 1.1 * i)
# Decide on plotstyle which plotting to do
if self.currentPlotStyle == "bar":
print("ind = ", ind) # DEBUG
print("result = ", result) # DEBUG
rects1 = self.axes.bar(ind, result, width, color='r')
elif self.currentPlotStyle == "colorbar":
print("plot() colorbar")
else:
print("plot() lines")
self.axes.scatter(0, result)
#
# Set axes labels according to selected keyword on y-axis and
# for each plotted step/substep identifier on the x-axis
#
ylabel = str(self.keywordComboBox.currentText())
if self.keywordComboBox.currentText(
) == "total" or self.keywordComboBox.currentText() == "avg":
ylabel = ylabel + " s"
self.axes.set_ylabel(ylabel)
self.canvas.draw()
#******************************************************
#
# Helper functions
#
#*******************************************************
def linearizeList(self, reorderlist):
print("linearizeList()") # DEBUG
newlist = []
if isinstance(reorderlist, list):
Nlists = len(reorderlist)
for i in range(Nlists):
if isinstance(reorderlist[i], list):
for j in len(reorderlist[i]):
newlist.append(reorderlist[i][j])
else:
newlist.append(reorderlist[i])
return newlist
#******************************************************
#
# Update GUI Widgets on loading a new Kernellog file
#
#*******************************************************
# Update the Kernel log dependent widgets, i.e. deleting
# and recreating them
#
def updateWidgets(self):
print("updateWidgets()") # DEBUG
self.deleteWidgets()
self.createWidgets()
# Delete GUI Widgets that are created dynamically from the log
#
def deleteWidgets(self): # DEBUG
print("deleteWidgets()")
self.stepComboBox.deleteLater()
self.keywordComboBox.deleteLater()
self.plotstyleComboBox.deleteLater()
self.stepComboBox.deleteLater()
class PlotWindow(QFrame):
# Init the class: create plot window and canvas layout (and corresponding buttons)
# Steps contains a dictionary of dictionaries with the identified steps
#
def __init__(self, parent):
#QFrame.__init__(self)
QMainWindow.__init__(self, None)
self.setWindowTitle('BBS timing statistics')
self.parent=parent # Parent: BBSTiming class
self.currentPlotStyle="bar" # current style of plot: bar, colorbar, lines
self.plotStyles=["bar", "colorbar", "line"] # supported plot styles #
self.summation=False # show "individual" execution times or "summation"
self.fig=None # figure
self.axes=None # plot axes
self.create_main_frame()
self.createWidgets()
self.createLayouts()
self.createConnections()
self.setMinimumWidth(700)
self.setMinimumHeight(400)
# Create a main frame
#
def create_main_frame(self):
self.main_frame = QWidget()
self.dpi = 75
self.setMinimumWidth(300)
self.setMinimumHeight(300)
# We want matplotlib export functionality, so include toolbar
# MPL Canvas and Toolbar
# Create canvas for plotting
self.fig = Figure((5, 4), dpi=75)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
self.fig.subplots_adjust(left=0.1, right=0.96, top=0.94, bottom=0.06) # set a small rim
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
self.mpl_toolbar.show() # first hide the toolbar
# Create the layouts for the widgets
#
def createLayouts(self):
# Layouts
self.buttonLayout=QVBoxLayout()
self.canvasLayout=QVBoxLayout()
self.mainLayout=QHBoxLayout()
# Add Widgets to the layout
self.buttonLayout.addWidget(self.loadButton)
self.buttonLayout.addWidget(self.quitButton)
self.buttonLayout.insertStretch(-1)
self.subStepsLayout=QHBoxLayout()
self.subStepsLayout.addWidget(self.showSubstepsCheckBox)
self.subStepsLayout.addWidget(self.showSubstepsLabel)
self.buttonLayout.addLayout(self.subStepsLayout)
self.showIndividualStepsLayout=QHBoxLayout()
self.showIndividualStepsLayout.addWidget(self.showIndividualCheckBox)
self.showIndividualStepsLayout.addWidget(self.showIndividualLabel)
self.buttonLayout.addLayout(self.showIndividualStepsLayout)
self.buttonLayout.addWidget(self.stepComboBox)
#self.buttonLayout.addWidget(self.stepListView)
self.buttonLayout.addWidget(self.substepComboBox)
self.buttonLayout.addWidget(self.keywordComboBox)
self.buttonLayout.addWidget(self.plotStyleComboBox)
self.buttonLayout.addWidget(self.stepComboBox)
self.buttonLayout.addWidget(self.plotButton)
self.buttonLayout.insertStretch(-1)
self.canvasLayout.addWidget(self.canvas)
self.canvasLayout.addWidget(self.mpl_toolbar)
self.mainLayout.addLayout(self.buttonLayout)
self.mainLayout.addLayout(self.canvasLayout)
self.setLayout(self.mainLayout)
# Create GUI widgets
#
def createWidgets(self):
#print "createWidgets()" # DEBUG
self.loadButton=QPushButton("Load logfile")
self.loadButton.setToolTip('load a BBS kernellog file')
self.loadButton.setMaximumWidth(200)
self.quitButton=QPushButton("Quit")
self.quitButton.setMaximumWidth(200)
self.quitButton.setToolTip('Quit the application')
self.quitButton.setMaximumWidth(200)
self.plotButton=QPushButton("Plot")
self.plotButton.setToolTip('force a replot')
self.showSubstepsCheckBox=QCheckBox()
self.showSubstepsLabel=QLabel("Show substeps")
self.showSubstepsCheckBox.setToolTip("Show also substeps in timing")
self.showSubstepsCheckBox.setCheckState(Qt.Unchecked) # Default: False
self.showSubstepsCheckBox.show()
self.showSubstepsLabel.show()
self.showIndividualCheckBox=QCheckBox()
self.showIndividualLabel=QLabel("Numbered steps")
self.showIndividualCheckBox.setToolTip("Show individually numbered steps")
self.showIndividualCheckBox.setCheckState(Qt.Unchecked) # Default: False
self.showIndividualCheckBox.show()
self.showIndividualLabel.show()
# GUI Widgets depending on log file
self.createStepComboBox() # Selector for Step
self.createKeywordComboBox() # Selector for: total, count, avg
# Selector for plot type: bar, line, multibar
self.createPlotstyleComboBox()
self.createStepComboBox()
#self.createStepListView()
# This is now done in a function, too
# self.substepComboBox=QComboBox() # we create this here once
# self.substepComboBox.hide()
# self.substepComboBox.setToolTip('Substeps of step')
# self.substepComboBox.setMaximumWidth(200)
self.createSubbandComboBox()
# Create the step combobox containing the timed steps
# This should be a multi-selection box to plot multiple
# steps
#
def createStepComboBox(self):
#print "createStepComboBox()" # DEBUG
self.stepComboBox=QComboBox()
for step in self.parent.timedSteps:
self.stepComboBox.addItem(step)
self.stepComboBox.addItem("all")
self.stepComboBox.show()
self.stepComboBox.setMaximumWidth(200)
def createSubstepComboBox(self):
print "createSubstepComboBox()" # DEBUG
self.substepComboBox=QComboBox() # we create this here once
self.substepComboBox.hide()
self.substepComboBox.setToolTip('Substeps of step')
self.substepComboBox.setMaximumWidth(200)
# Fill step Checkbox with step names, either these are
# grouped by their common name, or shown as individually
# numbered steps
#
def fillSteps(self):
print "fillSteps()" # DEBUG
self.stepComboBox.clear()
# Decide if we want the steps grouped or individually numbered
if self.showIndividualCheckBox.isChecked()==True:
steps=self.parent.timedStepsCount
else:
steps=self.parent.timedSteps
#print "fillSteps() steps = ", steps
for step in steps:
# Check if that substep is already in there, avoid duplicates
if self.stepComboBox.findData(step) == -1:
self.stepComboBox.addItem(step)
self.stepComboBox.addItem("all") # we again have to add our "all" option
# Get the corresponding substeps for a step
#
def fillSubsteps(self):
print "fillSubsteps()" # DEBUG
# Get substeps for currently selected step (or all if "all")
step=str(self.stepComboBox.currentText())
self.substepComboBox.clear() # first clear substeps comboBox
if step != "all" and step!="ALL":
substeps=self.parent.getSubsteps(step)
#print "fillSubsteps() substeps = ", substeps # DEBUG
for substep in substeps:
# Check if that substep is already in there, avoid duplicates
if self.substepComboBox.findData(substep) == -1:
self.substepComboBox.addItem(substep)
else:
self.substepComboBox.clear() # First remove existing substeps
index=0
while index < self.stepComboBox.count(): # loop over all steps
# TODO: Might not be supported to show substeps for ALL steps...
#substeps=str(self.stepComboBox.itemText(index))
#print "fillSubsteps() substeps = ", substeps
#print "fillSubsteps() index = ", index, "maxCount = ", self.stepComboBox.count()
step=str(self.stepComboBox.itemText(index))
print "fillSubsteps() step = ", step
if step != "all" and step != "ALL":
substeps=self.parent.getSubsteps(step)
keys=substeps.keys()
#print "keys = ", keys
for j in range(len(substeps)-1):
#print "fillSubsteps() substeps = ", substeps
substep=keys[j]
if substep != "all" and substep != "ALL":
self.substepComboBox.addItem(substep)
j=j+1
index=index+1
self.substepComboBox.addItem("ALL") # we need to add an "ALL"
"""
# Alternative view, displaying all the steps
# in a list view that then can be selected for
# plotting
#
def createStepListView(self):
print "createStepListBox()" # DEBUG
self.stepListView=QListView()
for step in self.parent.timedSteps:
self.stepListView.insertStringList(step)
self.stepListView.show()
step.stepListView.setMaximumWidth(200)
"""
# On timing combobox event
#
def createKeywordComboBox(self):
print "createTimingComboBox()" # DEBUG
self.keywordComboBox=QComboBox()
for key in self.parent.keywords:
self.keywordComboBox.addItem(key)
self.keywordComboBox.show()
self.keywordComboBox.setMaximumWidth(200)
# Create a comboBox offering different Matplotlib styles
#
def createPlotstyleComboBox(self):
print "createPlotstyleComboBox()" # DEBUG
self.plotStyleComboBox=QComboBox()
for style in self.plotStyles:
self.plotStyleComboBox.addItem(style)
self.plotStyleComboBox.show()
self.plotStyleComboBox.setMaximumWidth(200)
# Create subbands comboBox which allows selection of an individual subband
# from a pipeline.log
def createSubbandComboBox(self):
print "createSubbandComboBox()" # DEBUG
self.subbandComboBox=QComboBox()
for sub in self.parent.subbands:
self.subbandComboBox.addItem(sub)
self.subbandComboBox.show()
self.subbandComboBox.setMaximumWidth(200)
#**************************************
#
# Create connections
#
#**************************************
def createConnections(self):
print "createConnections()" # DEBUG
self.connect(self.loadButton, SIGNAL('clicked()'), self.on_loadfile)
self.connect(self.quitButton, SIGNAL('clicked()'), self, SLOT('close()'))
self.connect(self.stepComboBox, SIGNAL('currentIndexChanged(int)'), self.on_step)
self.connect(self.stepComboBox, SIGNAL('currentIndexChanged(int)'), self.on_showSubsteps)
#self.connect(self.substepComboBox, SIGNAL('currentIndexChanged(int)'), self.on_step)
self.connect(self.substepComboBox, SIGNAL('currentIndexChanged(int)'), self.on_plot)
self.connect(self.keywordComboBox, SIGNAL('currentIndexChanged(int)'), self.on_keyword)
self.connect(self.plotStyleComboBox, SIGNAL('currentIndexChanged(int)'), self.on_plotStyle)
self.connect(self.showSubstepsCheckBox, SIGNAL('stateChanged(int)'), self.on_showSubsteps)
self.connect(self.showIndividualCheckBox, SIGNAL('stateChanged(int)'), self.on_individualSteps)
self.connect(self.subbandComboBox, SIGNAL('currentIndexChanged(int)'), self.on_subband)
self.connect(self.plotButton, SIGNAL('clicked()'), self.on_plot)
#**************************************
#
# Event handlers
#
#**************************************
# Load Kernellog file dialog
#
def on_loadfile(self):
#print "on_loadfile()" # DEBUG
# Customization: check if ~/Cluster/SolutionTests exists
if os.path.exists('/Users/duscha/Desktop/'):
setDir=QString('/Users/duscha/Desktop/')
else:
setDir=QString('')
path = unicode(QFileDialog.getOpenFileName(self, 'Load Kernellog', setDir))
path=str(path) # Convert to string so that it can be used by load table
if path:
self.parent.readLogfile(path)
else:
print "load_table: invalid path"
# On step combobox event
#
def on_step(self):
print "on_step()" # DEBUG
#step=str(self.stepComboBox.currentText())
self.on_plot()
# On substep combobox event
#
def on_substep(self):
print "on_substep()" # DEBUG
#step=str(self.stepComboBox.currentText())
self.on_plot()
# On toggle individual treatment of counted steps
#
def on_individualSteps(self):
print "on_individualSteps()" # DEBUG
self.fillSteps() # update steps combobox
self.on_showSubsteps() # also update substeps combobox
#step=str(self.stepComboBox.currentText())
self.on_plot()
# On selection of different subbands
#
def on_subband(self):
print "on_subband()" # DEBUG
self.fillSteps() # update steps combobox
self.on_showSubsteps() # also update substeps combobox
self.on_plot()
# On toggling of show substeps
#
def on_showSubsteps(self):
print "on_substeps()" # DEBUG
if self.showSubstepsCheckBox.isChecked()==True:
self.showSubSteps=True
self.substepComboBox.show()
self.fillSubsteps()
else:
self.showSubSteps=False
self.substepComboBox.hide()
self.substepComboBox.clear()
# On keyword combobox event
#
def on_keyword(self):
print "on_keyword()" # DEBUG
self.on_plot()
# On change of plot style
#
def on_plotStyle(self):
print "on_plotStyle()" # DEBUG
self.plotStyle=self.plotStyleComboBox.currentText() # change class attribute
# On plot button action
#
def on_plot(self):
self.fig.clf() # clear the figure
print "on_plot()"
step=str(self.stepComboBox.currentText())
self.plot(step)
"""
step=str(self.stepComboBox.currentText())
# If we have all steps selected:
if step=="ALL" or step=="all":
for i in range(0, self.stepComboBox.count()): # loop over all steps
currentStep=self.stepComboBox.itemText(i) # get the step name from QComboBox
print "on_plot() currentStep = ", currentStep
self.plot(currentStep) # plot it
else:
#print "foo"
self.plot(step) # replot with new plotstyle
"""
# Replot diagram
#
def plot(self, step):
print "plot()" # DEBUG
width=0.25 # width of bar plots
# Get the data according to GUI settings
step=str(step)
substep=str(self.substepComboBox.currentText())
keyword=str(self.keywordComboBox.currentText())
style=str(self.plotStyleComboBox.currentText())
print "plot() step = ", step, "substep = ", substep, "keyword = ", keyword, "style = ", style # DEBUG
self.axes=self.fig.add_subplot(111) # add 1 subplot to the canvas
result=[]
# TODO: plot all substeps if we get multiple in results
if step=="all" or step=="ALL":
if self.showIndividualCheckBox.isChecked():
steps=self.parent.timedStepsCount
else:
steps=self.parent.timedSteps
print "plot() steps = ", steps # DEBUG
for i in range(0, len(steps)):
#print "plot()", self.parent.getSubStepValue(str(steps[i]), substep, keyword)
result.append(self.parent.getStepFinal(str(steps[i]), keyword))
#print "len(result) = ", len(result)
print "plot() result[" + str(i) + "] = " + str(result[i])
#self.axes.bar(i, result[i], width)
# If we want to see all steps, but not for individual substeps
elif step=="all" or step=="ALL" and (substep==None or substep==""):
if self.showIndividualCheckBox.isChecked():
steps=self.parent.getSubsteps(step, keyword)
else:
steps=self.parent.timedSteps
print "steps = ", steps
#steps.append(self.parent.timedSteps)
newsteps=[]
if isinstance(steps, list):
for i in range(0, len(steps)): # overplot them in one plot
newsteps.append(steps[i])
result=newsteps
elif substep=="all" or substep=="ALL":
print "plot(): substep=all"
result=(self.parent.getSubStepValue(step, keyword))
elif substep==None or substep=="":
result=(self.parent.getStepFinal(step, keyword))
else:
result=(self.parent.getSubStepValue(step, substep, keyword))
#if isinstance(result[0], list):
# result=self.linearizeList(result)
if isinstance(result, list):
print "plot() len(result) = ", len(result) # DEBUG
print "plot() result = ", result # DEBUG
#print "plot() result[1] = ", result[1] # DEBUG
#
# Plot on canvas
#
ind=[]
if isinstance(result, float) or isinstance(result, int):
ind=0
elif isinstance(result, bool):
print "plot() invalid result returned"
else:
maxInd=len(result)
for i in range(0, maxInd):
ind.append(width*1.1*i)
# Decide on plotstyle which plotting to do
if self.currentPlotStyle=="bar":
print "ind = ", ind # DEBUG
print "result = ", result # DEBUG
rects1 = self.axes.bar(ind, result, width, color='r')
elif self.currentPlotStyle=="colorbar":
print "plot() colorbar"
else:
print "plot() lines"
self.axes.scatter(0, result)
#
# Set axes labels according to selected keyword on y-axis and
# for each plotted step/substep identifier on the x-axis
#
ylabel=str(self.keywordComboBox.currentText())
if self.keywordComboBox.currentText() == "total" or self.keywordComboBox.currentText() == "avg":
ylabel=ylabel + " s"
self.axes.set_ylabel(ylabel)
self.canvas.draw()
#******************************************************
#
# Helper functions
#
#*******************************************************
def linearizeList(self, reorderlist):
print "linearizeList()" # DEBUG
newlist=[]
if isinstance(reorderlist, list):
Nlists=len(reorderlist)
for i in range(Nlists):
if isinstance(reorderlist[i], list):
for j in len(reorderlist[i]):
newlist.append(reorderlist[i][j])
else:
newlist.append(reorderlist[i])
return newlist
#******************************************************
#
# Update GUI Widgets on loading a new Kernellog file
#
#*******************************************************
# Update the Kernel log dependent widgets, i.e. deleting
# and recreating them
#
def updateWidgets(self):
print "updateWidgets()" # DEBUG
self.deleteWidgets()
self.createWidgets()
# Delete GUI Widgets that are created dynamically from the log
#
def deleteWidgets(self): # DEBUG
print "deleteWidgets()"
self.stepComboBox.deleteLater()
self.keywordComboBox.deleteLater()
self.plotstyleComboBox.deleteLater()
self.stepComboBox.deleteLater()
