def prev(self, event):
ax = linear_curve_fitting_and_plotting(ind_filename2, dep_filename)
plt.subplots_adjust(bottom=0.25)
x_vals = df2[df2.columns[0]].values.tolist()
y_vals = df2['Global Mean Sea Levels'].values.tolist()
line_equation = stats_analysis.best_fit_regression_equation(x_vals, y_vals)
correlation = str(stats_analysis.correlation(x_vals, y_vals))
r_squared = str(stats_analysis.r_squared(x_vals, y_vals))
ax.set_xlabel(df2.columns[0] + '\nCorrelation of Data: ' + correlation +
'\nEquation of line fit: ' + line_equation +
'\nR Squared Value: ' + r_squared)
plt.draw()
def line1(self, _: Event) -> None:
"""Plot the scatter plot and line of best fit for the data in df1."""
ax = linear_curve_fitting_and_plotting(self._ind_filename1,
self._dep_filename)
plt.subplots_adjust(bottom=0.25)
x_vals = self._df1[self._df1.columns[0]].values.tolist()
y_vals = self._df1['Global Mean Sea Levels'].values.tolist()
line_equation = stats_analysis.best_fit_regression_equation(
x_vals, y_vals)
correlation = str(stats_analysis.correlation(x_vals, y_vals))
r_squared = str(stats_analysis.r_squared(x_vals, y_vals))
ax.set_xlabel(self._df1.columns[0] + '\nCorrelation of Data: ' +
correlation + '\nEquation of line fit: ' +
line_equation + '\nR Squared Value: ' + r_squared)
plt.draw()
def plot_graphs(df1: DataFrame, df2: DataFrame, df3: DataFrame) -> None:
"""Plotting the graphs using Plotly."""
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2, right=0.8)
ax.set_title(df1.columns[0] + '\nClick on legend line to toggle line on/off')
line1, = ax.plot(df1[df1.columns[0]], df1['Global Mean Sea Levels'],
lw=2, color='blue', label=df1.columns[0])
######
# stats analysis
######
y_vals = df1[df1.columns[0]].values.tolist()
x_vals = df1['Global Mean Sea Levels'].values.tolist()
line_equation = stats_analysis.best_fit_regression_equation(x_vals, y_vals)
correlation = str(stats_analysis.correlation(x_vals, y_vals))
r_squared = str(stats_analysis.r_squared(x_vals, y_vals))
ax.set_xlabel('Correlation of Data: ' + correlation + '\nEquation of line fit: ' + line_equation +
'\nR Squared Value: ' + r_squared)
# equation = stats_analysis.best_fit_regression_equation(x_vals, y_vals)[3:]
# y_int = stats_analysis.y_intercept_of_best_fit(x_vals, y_vals)
# slope = stats_analysis.slope_of_best_fit(x_vals, y_vals)
# y_vals_act = [slope * x + y_int for x in x_vals]
# line_reg = ax.plot(df1[df1.columns[0]], y_vals_act, lw=2, color='red')
# line2, = ax.plot(df2[df2.columns[0]], df2['Global Mean Sea Levels'],
# lw=2, color='red', label=df2.columns[0])
# line3, = ax.plot(df3[df3.columns[0]], df3['Global Mean Sea Levels'],
# lw=2, color='green', label=df3.columns[0])
# positioning the legend
leg = ax.legend(loc='lower right', fancybox=True, shadow=True, title='Legend')
leg.get_frame().set_alpha(0.5) # sets opacity of the legend -- todo: could perhaps make
# it so that it goes transparent when you're not hovering over it?
# higher value in the brackets means less transparent
# TODO: there's a better way to do this
line1.set_marker('8')
line1.set_linestyle('None')
leg_line = leg.get_lines()[0]
leg_line.set_picker(True)
leg_line.set_pickradius(5)
leg_line = line1
def onpick(event) -> None: # TODO: move this outside the function
"""
It's getting mad at me for not having a docstring but i honestly don't understand
why the tutorials are saying to have this function in here
"""
# on the pick event, find the orig line corresponding to the
# legend proxy line, and toggle the visibility
legend_line = event.artist
# original_line = lined[legend_line]
original_line = line1
vis = not original_line.get_visible()
original_line.set_visible(vis)
# Change the alpha on the line in the legend so we can see what lines
# have been toggled
if vis:
legend_line.set_alpha(1.0)
else:
legend_line.set_alpha(0.2)
fig.canvas.draw()
fig.canvas.mpl_connect('pick_event', onpick)
plt.draw()