def denial_and_emissions(self) -> None:
"""Display plots showing the relationship between denial and emissions"""
x0_values = []
x1_values = []
y0_values = []
y1_values = []
for region in self.regions:
concerned = self.regions[region].percentage_concerned
if concerned is not None:
emissions = self.regions[region].co2_emissions[2016]
emissions_per_capita = self.regions[
region].co2_emissions_per_capita[2016]
if emissions is not None:
x0_values.append(concerned)
y0_values.append(emissions)
if emissions_per_capita is not None:
x1_values.append(concerned)
y1_values.append(emissions_per_capita)
slope0, intercept0 = Regression.linear_regression(x0_values, y0_values)
slope1, intercept1 = Regression.linear_regression(x1_values, y1_values)
r_squared0 = Regression.strength_of_correlation(
slope0, intercept0, x0_values, y0_values)
r_squared1 = Regression.strength_of_correlation(
slope1, intercept1, x1_values, y1_values)
t = np.linspace(0, 100, 100)
title0 = "slope = " + str(round(slope0, 2)) + \
", intercept = " + str(round(intercept0, 2)) + \
", R^2 = " + str(round(r_squared0, 2))
title1 = "slope = " + str(round(slope1, 2)) + \
", intercept = " + str(round(intercept1, 2)) + \
", R^2 = " + str(round(r_squared1, 2))
fig0 = px.line(x=t,
y=slope0 * t + intercept0,
labels={
'x': 'Percent Concerened',
'y': 'CO_2 Emissions'
},
title=title0)
fig0.add_trace(go.Scatter(x=x0_values, y=y0_values, mode='markers'),
row=1,
col=1)
fig1 = px.line(x=t,
y=slope1 * t + intercept1,
labels={
'x': 'Percent Concerened',
'y': 'CO_2 Emissions per Capita'
},
title=title1)
fig1.add_trace(go.Scatter(x=x1_values, y=y1_values, mode='markers'),
row=1,
col=1)
fig0.show()
fig1.show()
def denial_and_energy_production(self) -> None:
"""Display plots showing the relationship between denial and energy production"""
x_values = []
y_values = []
for region in self.regions:
concerned = self.regions[region].percentage_concerned
if concerned is not None:
renewable_energy = self.regions[region].renewable_energy[2016]
if renewable_energy is not None:
x_values.append(concerned)
y_values.append(renewable_energy)
slope, intercept = Regression.linear_regression(x_values, y_values)
r_squared = Regression.strength_of_correlation(slope, intercept,
x_values, y_values)
t = np.linspace(0, 100, 100)
title = "slope = " + str(round(slope, 2)) + \
", intercept = " + str(round(intercept, 2)) + ", R^2 = " + str(round(r_squared, 2))
fig = px.line(x=t,
y=slope * t + intercept,
labels={
'x': 'Percent Concerened',
'y': 'Renewable Energy Production'
},
title=title)
fig.add_trace(go.Scatter(x=x_values, y=y_values, mode='markers'),
row=1,
col=1)
fig.show()
def denial_and_carbon_tax(self) -> None:
"""Display plots showing the relationship between denial
and the implementation of a carbon tax
"""
x_values = []
y_values = []
for region in self.regions:
concerned = self.regions[region].percentage_concerned
if concerned is not None:
carbon_tax = self.regions[region].has_carbon_tax
if carbon_tax:
x_values.append(concerned)
y_values.append(1)
elif not carbon_tax:
x_values.append(concerned)
y_values.append(0)
slope, intercept = Regression.linear_regression(x_values, y_values)
r_squared = Regression.strength_of_correlation(slope, intercept,
x_values, y_values)
t = np.linspace(0, 100, 100)
title = "slope = " + str(round(slope, 2)) + \
", intercept = " + str(round(intercept, 2)) + ", R^2 = " + str(round(r_squared, 2))
fig = px.line(x=t,
y=slope * t + intercept,
labels={
'x': 'Percent Concerened',
'y': 'Has Carbon Tax'
},
title=title)
fig.add_trace(go.Scatter(x=x_values, y=y_values, mode='markers'),
row=1,
col=1)
fig.show()
def gdp_per_capita_and_carbon_taxes(self) -> None:
"""Display plots showing the relationship between GDP per capita and carbon tax implementation"""
x_values = []
y_values = []
for region in self.regions:
gdp_per_capita = self.regions[region].gdp_per_capita[2016]
if gdp_per_capita is not None:
carbon_tax = self.regions[region].has_carbon_tax
if carbon_tax:
x_values.append(gdp_per_capita)
y_values.append(1)
elif not carbon_tax:
x_values.append(gdp_per_capita)
y_values.append(0)
slope, intercept = Regression.linear_regression(x_values, y_values)
r_squared = Regression.strength_of_correlation(slope, intercept,
x_values, y_values)
t = np.linspace(0, max(x_values), 100)
title = "slope = " + str(round(slope, 2)) + \
", intercept = " + str(round(intercept, 2)) + ", R^2 = " + str(round(r_squared, 2))
fig = px.line(x=t,
y=slope * t + intercept,
labels={
'x': 'GDP per Capita',
'y': 'Has Carbon Tax'
},
title=title)
fig.add_trace(go.Scatter(x=x_values, y=y_values, mode='markers'),
row=1,
col=1)
fig.show()
