def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
self.underlays = []
self.add_pan_tool = False
self.add_zoom_tool = False
self.add_dragzoom = False
self.show_grid = False
class HistogramPlotHandler(HasTraits):
"""
Class for handling the histograms.
"""
# Index for the histogram plot
index = Array
# The selection handler object for the selected data
selection_handler = Instance(SelectionHandler)
# OVerlayPlotContainer for the histogram plot
container = Instance(OverlayPlotContainer)
# Number of bins of the histogram
nbins = Int(10)
# Whether the data is a pandas dataframe or a numpy array
AS_PANDAS_DATAFRAME = Bool
def __init__(self):
self.index = range(self.nbins)
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def draw_histogram(self):
"""
Default function called when drawing the histogram.
"""
for component in self.container.components:
self.container.remove(component)
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
tuple_list = self.selection_handler.selected_indices[0]
if self.AS_PANDAS_DATAFRAME:
column_name = self.data.columns[tuple_list[1]]
y = self.data[column_name]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="bar", bar_width=0.5)
self.container.add(plot)
else:
column = tuple_list[1]
y = self.data[:, column]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="bar", bar_width=0.5)
self.container.add(plot)
self.container.request_redraw()
self.selection_handler.flush()
class HistogramPlotHandler(HasTraits):
'''
Class for handling the histograms.
'''
# Index for the histogram plot
index = Array
# The selection handler object for the selected data
selection_handler = Instance(SelectionHandler)
# OVerlayPlotContainer for the histogram plot
container = Instance(OverlayPlotContainer)
# Number of bins of the histogram
nbins = Int(10)
# Whether the data is a pandas dataframe or a numpy array
AS_PANDAS_DATAFRAME = Bool
def __init__(self):
self.index = range(self.nbins)
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def draw_histogram(self):
'''
Default function called when drawing the histogram.
'''
for component in self.container.components:
self.container.remove(component)
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
tuple_list = self.selection_handler.selected_indices[0]
if self.AS_PANDAS_DATAFRAME:
column_name = self.data.columns[tuple_list[1]]
y = self.data[column_name]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type='bar', bar_width=0.5)
self.container.add(plot)
else:
column = tuple_list[1]
y = self.data[:, column]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type='bar', bar_width=0.5)
self.container.add(plot)
self.container.request_redraw()
self.selection_handler.flush()
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
self.underlays = []
self.add_pan_tool = False
self.add_zoom_tool = False
self.add_dragzoom = False
self.show_grid = False
class PCPlotHandler(HasTraits):
'''
Class for handling principal component plots.
'''
# The container for the plot.
container = OverlayPlotContainer()
# the sklearn.decmoposition.PCA object
pca = PCA
# Whether or not to normalize the data, one of the parameters of the PCA
# object
whiten = Bool
# The input data.
table = Array
# The selection_handler instance for the tableview
selection_handler = Instance(SelectionHandler)
def __init__(self):
self.pca = PCA(n_components=2)
self.pca.whiten = True
self.container = OverlayPlotContainer()
self.selection_handler = SelectionHandler()
def draw_pc_plot(self):
'''
Called to draw the PCA plot.
'''
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
top_left = self.selection_handler.selected_indices[0][0:2]
bot_right = self.selection_handler.selected_indices[0][2:4]
data = self.table[top_left[0]:bot_right[0],
top_left[1]:bot_right[1]]
pc_red = self.pca.fit_transform(data)
plotdata = ArrayPlotData(x=pc_red[:, 0], y=pc_red[:, 1])
plot = Plot(plotdata)
plot.plot(("x", "y"), type='scatter')
self.container.add(plot)
self.container.request_redraw()
def __init__(self, vectorizer=None, data_frame=None):
if vectorizer is not None:
self.vectorizer = vectorizer
else:
self.vectorizer = TfidfVectorizer()
if data_frame is not None:
self.data_frame = data_frame
self.selection_handler = SelectionHandler()
self.classifier_dict = {
'LinearSVC':LinearSVC(),
'Perceptron':Perceptron(),
'RidgeClassifier':RidgeClassifier(),
'SGDClassifier':SGDClassifier(),
'BernoulliNB':BernoulliNB(),
'MultinomialNB':MultinomialNB(),
'KneighborsClassifier':KNeighborsClassifier(),
'NearestCentroid':NearestCentroid()
}
class PCPlotHandler(HasTraits):
"""
Class for handling principal component plots.
"""
# The container for the plot.
container = OverlayPlotContainer()
# the sklearn.decmoposition.PCA object
pca = PCA
# Whether or not to normalize the data, one of the parameters of the PCA
# object
whiten = Bool
# The input data.
table = Array
# The selection_handler instance for the tableview
selection_handler = Instance(SelectionHandler)
def __init__(self):
self.pca = PCA(n_components=2)
self.pca.whiten = True
self.container = OverlayPlotContainer()
self.selection_handler = SelectionHandler()
def draw_pc_plot(self):
"""
Called to draw the PCA plot.
"""
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
top_left = self.selection_handler.selected_indices[0][0:2]
bot_right = self.selection_handler.selected_indices[0][2:4]
data = self.table[top_left[0] : bot_right[0], top_left[1] : bot_right[1]]
pc_red = self.pca.fit_transform(data)
plotdata = ArrayPlotData(x=pc_red[:, 0], y=pc_red[:, 1])
plot = Plot(plotdata)
plot.plot(("x", "y"), type="scatter")
self.container.add(plot)
self.container.request_redraw()
def __init__(self):
self.index = range(self.nbins)
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
class RegressionPlotHandler(HasTraits):
"""
Class for handling regression plots
"""
# The input data from the csv file
data = Array
# The input, or the selected column / row
Y = Array
# OLS fitted values of the current selection
selection_olsfit = Array
# the index used to plot the output
index = Array
# the container for the plots
container = Instance(OverlayPlotContainer)
# The selection handler object for the tableview
selection_handler = Instance(SelectionHandler)
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def fit_selection(self):
"""
Function that computes the curve to fit.
"""
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
tuple_list = self.selection_handler.selected_indices[0]
if tuple_list[1] == tuple_list[3]:
L = tuple_list[2] - tuple_list[0]
self.index = np.arange(L + 1)
self.Y = self.data[:, tuple_list[1]]
results = OLS(self.Y, self.index).fit()
self.selection_olsfit = results.fittedvalues
self.selection_handler.flush()
def plot_fits(self):
"""
Function called to plot the regression fits.
"""
components = []
for component in self.container.components:
components.append(component)
for component in components:
self.container.components.remove(component)
plotdata = ArrayPlotData(x=self.index, y=self.Y)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="line", color="red")
plot.line_style = "dash"
self.container.add(plot)
plotdata = ArrayPlotData(x=self.index, y=self.selection_olsfit)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="line", color="blue")
self.container.add(plot)
self.container.request_redraw()
def __init__(self):
self.pca = PCA(n_components=2)
self.pca.whiten = True
self.container = OverlayPlotContainer()
self.selection_handler = SelectionHandler()
class XYPlotHandler(HasTraits):
'''
Class for handling XY plots
'''
# Whether the data is a pandas dataframe
AS_PANDAS_DATAFRAME = Bool
# The container for all current plots. Gets updated everytime a plot is
# added.
container = OverlayPlotContainer
# This can be removed.
plotdata = ArrayPlotData
# The current Plot object.
plot = Plot
# ColorTrait, mainly required for the TraitsUIItem view.
color = ColorTrait("blue")
# Marker trait for the view
marker = marker_trait
# Marker size trait
marker_size = Int(4)
# An instance of SelectionHandler for adding plots from the current
# selection.
selection_handler = Instance(SelectionHandler)
# Bool traits for checking the type of the plot (discrete / continuous)
plot_type_disc = Bool
plot_type_cont = Bool
# The data from which to draw the plots, same as the table attribute of
# CsvModel
table = Array
# The pandas data frame if AS_PANDAS_DATAFRAME
data_frame = Instance(DataFrame)
# Contains the grid underlays of all the current plots
grid_underlays = List
# Used for viewing the list of the plots and the legend
plot_list_view = Dict
# TraitsUI view for plot properties, yet to find an enaml implementation
view = View(Item('color'), Item('marker'), Item('marker_size'))
# Trait that defines whether tools are present.
add_pan_tool = Bool
add_zoom_tool = Bool
add_dragzoom = Bool
# Whether grids and axes are visible
show_grid = Bool
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
self.underlays = []
self.add_pan_tool = False
self.add_zoom_tool = False
self.add_dragzoom = False
self.show_grid = False
def add_xyplot_selection(self, plot_name):
'''
Called when the 'add plot from selection button is clicked.'
'''
self.selection_handler.create_selection()
if self.selection_handler.xyplot_check():
if self.AS_PANDAS_DATAFRAME:
x_column = self.data_frame.columns[
self.selection_handler.selected_indices[0][1]]
y_column = self.data_frame.columns[
self.selection_handler.selected_indices[1][1]]
x = np.array(self.data_frame[x_column])
y = np.array(self.data_frame[y_column])
self.plotdata = ArrayPlotData(x=x, y=y)
else:
first_column = self.selection_handler.selected_indices[0]
second_column = self.selection_handler.selected_indices[1]
self.plotdata = ArrayPlotData(x=self.table[:, first_column[1]],
y=self.table[:,
second_column[1]])
plot = Plot(self.plotdata)
if self.plot_type_disc:
plot_type = 'scatter'
else:
plot_type = 'line'
plot.plot(("x", "y"),
type=plot_type,
color=self.color,
marker=self.marker,
marker_size=self.marker_size)
self.plot = plot
for underlay in self.plot.underlays:
if isinstance(underlay, PlotGrid):
if underlay not in self.grid_underlays:
self.grid_underlays.append(underlay)
for underlay in self.grid_underlays:
if underlay in self.plot.underlays:
self.plot.underlays.remove(underlay)
if plot_name == '':
self.plot_list_view['plot' +
str(len(self.plot_list_view))] = self.plot
else:
self.plot_list_view[plot_name] = self.plot
self.container.add(self.plot)
self.container.request_redraw()
self.selection_handler.flush()
def grid_toggle(self, checked):
'''
Called when the 'Show Grid' checkbox ins toggled
'''
if not checked:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay in plot.underlays:
plot.underlays.remove(underlay)
else:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay not in plot.underlays:
plot.underlays.append(underlay)
self.container.request_redraw()
def remove_selected_plots(self, selection):
'''
Called when the 'Remove Selected Plots' button is clicked
'''
remove_indices = []
for model_index in selection:
remove_indices.append(model_index[0].row)
remove_plots = []
for index in remove_indices:
remove_plots.append(self.plot_list_view.keys()[index])
removed_plots = []
for plot in remove_plots:
removed_plots.append(self.plot_list_view.pop(plot))
for plot in self.container.components:
self.container.remove(plot)
for plot in self.plot_list_view.keys():
self.container.add(self.plot_list_view[plot])
self.container.request_redraw()
def edit_selection(self, show_grid, plot_visible, plot_type_disc):
'''
Called to start editing the selected plot. Should accompany the 'Edit
Plot' dialog.
'''
#self.selection_handler.create_selection()
#index = self.selection_handler.selected_indices[0][0]
#plot_name = self.plot_list_view.keys()[index]
#plot = self.plot_list_view[plot_name]
self.container.remove(self.plot)
self.plot_type_disc = plot_type_disc
if self.plot_type_disc:
plot_type = 'scatter'
else:
plot_type = 'line'
plot = Plot(self.plot.data)
plot.plot(("x", "y"),
color=self.color,
type=plot_type,
marker=self.marker,
marker_size=self.marker_size)
self.plot = plot
self.plot.visible = plot_visible
grid_underlays = []
if not show_grid:
for underlay in self.plot.underlays:
if isinstance(underlay, PlotGrid):
grid_underlays.append(underlay)
for underlay in grid_underlays:
self.plot.underlays.remove(underlay)
self.container.add(self.plot)
self.container.request_redraw()
self.selection_handler.flush()
def _add_pan_tool_changed(self):
'''
Method called when the Pan Tool checkbox is checked or unchecked.
Adds the Pan Tool to the plot container if it isn't there and vice versa.
'''
broadcaster = BroadcasterTool()
for plot in self.container.components:
if self.add_pan_tool:
pan = PanTool(plot)
broadcaster.tools.append(pan)
self.container.tools.append(broadcaster)
else:
for tool in self.container.tools:
if isinstance(tool, BroadcasterTool):
self.container.tools.remove(tool)
def _add_zoom_tool_changed(self):
'''
Method called when the Zoom Tool checkbox is checked or unchecked.
Adds the Zoom Tool to the plot container if it isn't there and vice versa.
'''
broadcaster = BroadcasterTool()
for plot in self.container.components:
if self.add_zoom_tool:
pan = ZoomTool(plot)
broadcaster.tools.append(pan)
self.container.tools.append(broadcaster)
else:
for tool in self.container.tools:
if isinstance(tool, BroadcasterTool):
self.container.tools.remove(tool)
def _add_dragzoom_changed(self):
'''
Method called when the Pan Tool checkbox is checked or unchecked.
Adds the Pan Tool to the plot container if it isn't there and vice versa.
'''
broadcaster = BroadcasterTool()
for plot in self.container.components:
if self.add_dragzoom:
pan = BetterSelectingZoom(plot,
always_on=True,
tool_mode='box',
drag_button='left',
color='lightskyblue',
alpha=0.4,
border_color='dodgerblue')
broadcaster.tools.append(pan)
self.container.tools.append(broadcaster)
else:
for tool in self.container.tools:
if isinstance(tool, BroadcasterTool):
self.container.tools.remove(tool)
def _show_grid_changed(self):
'''
Called when the Show grid checkbox is checked or unchecked. Adds a grid
if one is not present and removes if present.
'''
if not self.show_grid:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay in plot.underlays:
plot.underlays.remove(underlay)
else:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay not in plot.underlays:
plot.underlays.append(underlay)
self.container.request_redraw()
def reassign_current_plot(self):
'''
Reassigns the currently selected plot.
'''
self.selection_handler.create_selection()
plot_index = self.selection_handler.selected_indices[0][0]
plot_name = self.plot_list_view.keys()[plot_index]
self.plot = self.plot_list_view[plot_name]
self.selection_handler.flush()
class TextClassifier(HasTraits):
'''
Class for implementing sklean text classifiers in the editor.
'''
# The text vectorizer from sklearn.feature_extraction.text
vectorizer = Instance(TfidfVectorizer)
# The training data
x_train = Instance(csr_matrix)
# The testing data
x_test = Instance(csr_matrix)
# The average performance score of the classifier
classifier_score = Float
# Length of the selected column that should be considered as training data
train_length = CInt(0)
# The column index that should be considered as containing the targets
target_col_no = CInt(0)
# The DataFrame instance containing the data
data_frame = Instance(DataFrame)
# The selection_handler object for the tableview
selection_handler = Instance(SelectionHandler)
# The string representation of the selected classifier
classifier_select = String
# A dictionary that maps classifier_string into sklearn classifier
# instances
classifier_dict = Dict
def __init__(self, vectorizer=None, data_frame=None):
if vectorizer is not None:
self.vectorizer = vectorizer
else:
self.vectorizer = TfidfVectorizer()
if data_frame is not None:
self.data_frame = data_frame
self.selection_handler = SelectionHandler()
self.classifier_dict = {
'LinearSVC':LinearSVC(),
'Perceptron':Perceptron(),
'RidgeClassifier':RidgeClassifier(),
'SGDClassifier':SGDClassifier(),
'BernoulliNB':BernoulliNB(),
'MultinomialNB':MultinomialNB(),
'KneighborsClassifier':KNeighborsClassifier(),
'NearestCentroid':NearestCentroid()
}
def select_classifier(self):
'''
Sets the classifier as per the selection from the user.
'''
self.classifier = self.classifier_dict[self.classifier_select]
def create_dataset(self):
'''
Splits the data frame into training data, testing data and targets.
'''
self.selection_handler.create_selection()
column = self.selection_handler.selected_indices[0][1]
column_name = self.data_frame.columns[column]
data = self.data_frame[column_name]
self.training_data = data[:self.train_length]
self.testing_data = data[self.train_length:]
column_name = self.data_frame.columns[self.target_col_no]
target_data = self.data_frame[column_name]
self.train_targets = np.array(target_data[:self.train_length]).astype(float)
self.test_targets = target_data[self.train_length:]
self.selection_handler.flush()
def text_vectorize(self):
'''
Vectorizes the text input.
'''
self.x_train = self.vectorizer.fit_transform(self.training_data)
#self.x_test = self.vectorizer.transform(self.testing_data)
def train_classifier(self):
'''
Trains the selected classifier.
'''
self.classifier.fit(self.x_train, self.train_targets)
def test_classifier(self):
'''
Tests the selected classifier to generate the prediction score.
'''
self.x_test = self.vectorizer.transform(self.testing_data)
self.prediction = self.classifier.predict(self.x_test)
self.classifier_score = self.classifier.score(self.x_test,
self.test_targets)
def save_classifier(self, pickle_filename):
'''
Saves the classifier and vectorizer objects as a pickle file.
'''
op = open(pickle_filename, 'w')
pickle.dump(self.classifier, op)
pickle.dump(self.vectorizer, op)
op.close()
def load_classifier(self, pickle_filename):
'''
Loads a classifier and a vectorizer from a pickle file.
'''
op = open(pickle_filename, 'r')
self.classifier = pickle.load(op)
self.vectorizer = pickle.load(op)
op.close()
def make_prediction(self):
'''
Makes prediction considering the selected column as test input.
'''
self.selection_handler.create_selection()
pred_data_index = self.selection_handler.selected_indices[0][1]
pred_column_name = self.data_frame.columns[pred_data_index]
self.testing_data = self.data_frame[pred_column_name]
self.x_test = self.vectorizer.transform(self.testing_data)
self.prediction = self.classifier.predict(self.x_test)
def __init__(self):
self.index = range(self.nbins)
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def __init__(self):
self.pca = PCA(n_components=2)
self.pca.whiten = True
self.container = OverlayPlotContainer()
self.selection_handler = SelectionHandler()
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
class RegressionPlotHandler(HasTraits):
'''
Class for handling regression plots
'''
# The input data from the csv file
data = Array
# The input, or the selected column / row
Y = Array
# OLS fitted values of the current selection
selection_olsfit = Array
# the index used to plot the output
index = Array
# the container for the plots
container = Instance(OverlayPlotContainer)
# The selection handler object for the tableview
selection_handler = Instance(SelectionHandler)
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def fit_selection(self):
'''
Function that computes the curve to fit.
'''
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
tuple_list = self.selection_handler.selected_indices[0]
if tuple_list[1] == tuple_list[3]:
L = tuple_list[2] - tuple_list[0]
self.index = np.arange(L + 1)
self.Y = self.data[:, tuple_list[1]]
results = OLS(self.Y, self.index).fit()
self.selection_olsfit = results.fittedvalues
self.selection_handler.flush()
def plot_fits(self):
'''
Function called to plot the regression fits.
'''
components = []
for component in self.container.components:
components.append(component)
for component in components:
self.container.components.remove(component)
plotdata = ArrayPlotData(x=self.index, y=self.Y)
plot = Plot(plotdata)
plot.plot(("x", "y"), type='line', color='red')
plot.line_style = 'dash'
self.container.add(plot)
plotdata = ArrayPlotData(x=self.index, y=self.selection_olsfit)
plot = Plot(plotdata)
plot.plot(("x", "y"), type='line', color='blue')
self.container.add(plot)
self.container.request_redraw()
class XYPlotHandler(HasTraits):
"""
Class for handling XY plots
"""
# Whether the data is a pandas dataframe
AS_PANDAS_DATAFRAME = Bool
# The container for all current plots. Gets updated everytime a plot is
# added.
container = OverlayPlotContainer
# This can be removed.
plotdata = ArrayPlotData
# The current Plot object.
plot = Plot
# ColorTrait, mainly required for the TraitsUIItem view.
color = ColorTrait("blue")
# Marker trait for the view
marker = marker_trait
# Marker size trait
marker_size = Int(4)
# An instance of SelectionHandler for adding plots from the current
# selection.
selection_handler = Instance(SelectionHandler)
# Bool traits for checking the type of the plot (discrete / continuous)
plot_type_disc = Bool
plot_type_cont = Bool
# The data from which to draw the plots, same as the table attribute of
# CsvModel
table = Array
# The pandas data frame if AS_PANDAS_DATAFRAME
data_frame = Instance(DataFrame)
# Contains the grid underlays of all the current plots
grid_underlays = List
# Used for viewing the list of the plots and the legend
plot_list_view = Dict
# TraitsUI view for plot properties, yet to find an enaml implementation
view = View(Item("color"), Item("marker"), Item("marker_size"))
# Trait that defines whether tools are present.
add_pan_tool = Bool
add_zoom_tool = Bool
add_dragzoom = Bool
# Whether grids and axes are visible
show_grid = Bool
def __init__(self):
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
self.underlays = []
self.add_pan_tool = False
self.add_zoom_tool = False
self.add_dragzoom = False
self.show_grid = False
def add_xyplot_selection(self, plot_name):
"""
Called when the 'add plot from selection button is clicked.'
"""
self.selection_handler.create_selection()
if self.selection_handler.xyplot_check():
if self.AS_PANDAS_DATAFRAME:
x_column = self.data_frame.columns[self.selection_handler.selected_indices[0][1]]
y_column = self.data_frame.columns[self.selection_handler.selected_indices[1][1]]
x = np.array(self.data_frame[x_column])
y = np.array(self.data_frame[y_column])
self.plotdata = ArrayPlotData(x=x, y=y)
else:
first_column = self.selection_handler.selected_indices[0]
second_column = self.selection_handler.selected_indices[1]
self.plotdata = ArrayPlotData(x=self.table[:, first_column[1]], y=self.table[:, second_column[1]])
plot = Plot(self.plotdata)
if self.plot_type_disc:
plot_type = "scatter"
else:
plot_type = "line"
plot.plot(("x", "y"), type=plot_type, color=self.color, marker=self.marker, marker_size=self.marker_size)
self.plot = plot
for underlay in self.plot.underlays:
if isinstance(underlay, PlotGrid):
if underlay not in self.grid_underlays:
self.grid_underlays.append(underlay)
for underlay in self.grid_underlays:
if underlay in self.plot.underlays:
self.plot.underlays.remove(underlay)
if plot_name == "":
self.plot_list_view["plot" + str(len(self.plot_list_view))] = self.plot
else:
self.plot_list_view[plot_name] = self.plot
self.container.add(self.plot)
self.container.request_redraw()
self.selection_handler.flush()
def grid_toggle(self, checked):
"""
Called when the 'Show Grid' checkbox ins toggled
"""
if not checked:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay in plot.underlays:
plot.underlays.remove(underlay)
else:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay not in plot.underlays:
plot.underlays.append(underlay)
self.container.request_redraw()
def remove_selected_plots(self, selection):
"""
Called when the 'Remove Selected Plots' button is clicked
"""
remove_indices = []
for model_index in selection:
remove_indices.append(model_index[0].row)
remove_plots = []
for index in remove_indices:
remove_plots.append(self.plot_list_view.keys()[index])
removed_plots = []
for plot in remove_plots:
removed_plots.append(self.plot_list_view.pop(plot))
for plot in self.container.components:
self.container.remove(plot)
for plot in self.plot_list_view.keys():
self.container.add(self.plot_list_view[plot])
self.container.request_redraw()
def edit_selection(self, show_grid, plot_visible, plot_type_disc):
"""
Called to start editing the selected plot. Should accompany the 'Edit
Plot' dialog.
"""
# self.selection_handler.create_selection()
# index = self.selection_handler.selected_indices[0][0]
# plot_name = self.plot_list_view.keys()[index]
# plot = self.plot_list_view[plot_name]
self.container.remove(self.plot)
self.plot_type_disc = plot_type_disc
if self.plot_type_disc:
plot_type = "scatter"
else:
plot_type = "line"
plot = Plot(self.plot.data)
plot.plot(("x", "y"), color=self.color, type=plot_type, marker=self.marker, marker_size=self.marker_size)
self.plot = plot
self.plot.visible = plot_visible
grid_underlays = []
if not show_grid:
for underlay in self.plot.underlays:
if isinstance(underlay, PlotGrid):
grid_underlays.append(underlay)
for underlay in grid_underlays:
self.plot.underlays.remove(underlay)
self.container.add(self.plot)
self.container.request_redraw()
self.selection_handler.flush()
def _add_pan_tool_changed(self):
"""
Method called when the Pan Tool checkbox is checked or unchecked.
Adds the Pan Tool to the plot container if it isn't there and vice versa.
"""
broadcaster = BroadcasterTool()
for plot in self.container.components:
if self.add_pan_tool:
pan = PanTool(plot)
broadcaster.tools.append(pan)
self.container.tools.append(broadcaster)
else:
for tool in self.container.tools:
if isinstance(tool, BroadcasterTool):
self.container.tools.remove(tool)
def _add_zoom_tool_changed(self):
"""
Method called when the Zoom Tool checkbox is checked or unchecked.
Adds the Zoom Tool to the plot container if it isn't there and vice versa.
"""
broadcaster = BroadcasterTool()
for plot in self.container.components:
if self.add_zoom_tool:
pan = ZoomTool(plot)
broadcaster.tools.append(pan)
self.container.tools.append(broadcaster)
else:
for tool in self.container.tools:
if isinstance(tool, BroadcasterTool):
self.container.tools.remove(tool)
def _add_dragzoom_changed(self):
"""
Method called when the Pan Tool checkbox is checked or unchecked.
Adds the Pan Tool to the plot container if it isn't there and vice versa.
"""
broadcaster = BroadcasterTool()
for plot in self.container.components:
if self.add_dragzoom:
pan = BetterSelectingZoom(
plot,
always_on=True,
tool_mode="box",
drag_button="left",
color="lightskyblue",
alpha=0.4,
border_color="dodgerblue",
)
broadcaster.tools.append(pan)
self.container.tools.append(broadcaster)
else:
for tool in self.container.tools:
if isinstance(tool, BroadcasterTool):
self.container.tools.remove(tool)
def _show_grid_changed(self):
"""
Called when the Show grid checkbox is checked or unchecked. Adds a grid
if one is not present and removes if present.
"""
if not self.show_grid:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay in plot.underlays:
plot.underlays.remove(underlay)
else:
for plot in self.container.components:
for underlay in self.grid_underlays:
if underlay not in plot.underlays:
plot.underlays.append(underlay)
self.container.request_redraw()
def reassign_current_plot(self):
"""
Reassigns the currently selected plot.
"""
self.selection_handler.create_selection()
plot_index = self.selection_handler.selected_indices[0][0]
plot_name = self.plot_list_view.keys()[plot_index]
self.plot = self.plot_list_view[plot_name]
self.selection_handler.flush()
def _selection_handler_default(self):
return SelectionHandler()