class Plot:
"""
Class for matplotlib plots
This class is used to store the matplotlib figure and axes instances,
as well as the points plotted and linear regression useful data.
Attributes
----------
fig : matplotlib.figure.Figure
The top level container for the plot (according to matplotlib documentation)
This is basically the window which contains the plot.
ax : matplotlib.axes.Axes
The Axes contains most of the figure elements, and sets the coordinate system.
This is basically the plot, which contains the coordinate system, the points, etc.
parent : MainApplication
A reference to the MainApplication object which created the plot
This is used to enable access to the PlotData object inside the main app.
check_state : bool
This tells the plot whether to add a new point if you click on it or not.
cid : int
A connection id that can be used to disconnect the on_click callback from the figure (self.fig)
points : list
A list which stores all the Point objects displayed on the plot
lin_reg : LinearRegression
An object used to compute the alpha and beta coefficients of the best fitting line
The best fitting line's equation is: y = alpha + beta * x.
Methods
-------
get_alpha()
Returns the alpha coefficient of the best fitting line.
get_beta()
Returns the beta coefficient of the best fitting line.
add_point(x, y)
Adds a new point on the plot.
update_best_fitting_line()
Updates the best fitting line coefficients and makes it fill the axes.
delete_existing_lines()
Deletes the previous drawn line(s).
update_check_state(new_val)
Updates the check_state attribute to new_val.
on_click(event)
Callback which handles mouse clicks on the figure
disconnect_on_click_clb()
Disconnects on_click callback from the figure.
Note: I added docstrings only to the most important class methods for the sake of code simplicity.
"""
def __init__(self, parent):
"""
Parameters
----------
parent : MainApplication
A reference to the MainApplication object which created the plot
This is used to enable access to the PlotData object inside the main app.
"""
self.fig, self.ax = plt.subplots()
plt.show(block=False)
self.parent = parent
self.check_state = False
self.cid = self.fig.canvas.mpl_connect("button_press_event",
self.on_click)
self.points = []
self.lin_reg = LinearRegression()
self.ax.set(xlim=(-10, 10), ylim=(-10, 10))
def get_alpha(self):
return self.lin_reg.get_alpha()
def get_beta(self):
return self.lin_reg.get_beta()
def add_point(self, x, y):
self.points.append(Point(x, y))
self.lin_reg.add_point(x, y)
plt.scatter(x, y, color="red")
if len(self.points) > 1:
self.update_best_fitting_line()
def update_best_fitting_line(self):
a = self.lin_reg.get_alpha()
b = self.lin_reg.get_beta()
xmin, xmax, ymin, ymax = plt.axis()
x = np.arange(xmin - 1, xmax + 1, (xmax - xmin) / 50)
y = a + b * x
self.delete_existing_lines()
plt.plot(x, y, color="blue")
def delete_existing_lines(self):
while len(self.ax.lines):
self.ax.lines.pop()
def update_check_state(self, new_val):
self.check_state = new_val
def on_click(self, event):
""" Callback which handles mouse clicks on the figure
If the "Add multiple points" check is activated you can add points on the plot by clicking inside the axes.
This adds a point on the plot only if the left mouse button was clicked inside the axes.
"""
if len(self.points) > 1:
self.update_best_fitting_line()
if self.check_state is True and event.button == 1 and event.xdata is not None and event.ydata is not None:
self.add_point(event.xdata, event.ydata)
self.parent.plot_data.update_data(event.xdata, event.ydata,
self.lin_reg.get_alpha(),
self.lin_reg.get_beta())
def disconnect_on_click_clb(self):
""" Disconnects on_click callback from the figure.
The callback should be disconnected from the figure when the plot is closed.
"""
self.fig.canvas.mpl_disconnect(self.cid)
