def plot_violin_pollutant(pollutant: POLLUTANT, save=False):
df = create_pollutant_df(pollutant, "../../data/main/cleaned/mean/")
plt.subplots(figsize=(14, 6))
sns.set_style("whitegrid", {'grid.linestyle': '-'})
axes = sns.violinplot(x=CITY_NAME,
y=f'mean {pollutant.name} (µg/m3)',
data=df,
palette='terrain')
finalize_plot(axes, f'{pollutant.name} emissions', save,
f'violin_plot_{pollutant.name}.png')
def plot_pollutant_last_years(pollutant: POLLUTANT, save=False):
df = create_pollutant_df(pollutant, "../../data/main/cleaned/mean/")
df = df[df['Date'] > pd.Timestamp(2019, 1, 1, 0).tz_localize(
df['Date'].iloc[0].tz)]
plt.subplots(figsize=(14, 6))
sns.set_style("whitegrid", {'grid.linestyle': '-'})
axes = sns.lineplot(x=DATE_NAME,
y=f'mean {pollutant.name} (µg/m3)',
data=df,
hue=CITY_NAME)
finalize_plot(axes, f'{pollutant.name} emissions', save,
f'plot_{pollutant.name}_last_year.png')
def compare_19_20(pollutant: POLLUTANT, save=False):
df = create_pollutant_df(pollutant, "../../data/main/cleaned/mean/")
df = df[df[DATE_NAME] >= pd.Timestamp(2019, 1, 1, 0).tz_localize(
df['Date'].iloc[0].tz)]
df = df[df[DATE_NAME].dt.month.isin([2, 3, 4])]
df = compare_year_to_year(df)
df = df.round(2)
df.drop(['AirPollutant', 'UnitOfMeasurement'], axis=1, inplace=True)
fig = go.Figure(data=[
go.Table(header=dict(
values=list(df.columns), fill_color='grey', align='center'),
cells=dict(values=[
df['Countrycode'], df['City'], df['Date'],
df['Previous year value'], df['Year value'], df['diff'],
df['diff %']
],
fill_color='lightgrey',
align='center'))
])
fig.update_layout(
title=f'Difference for {pollutant.name} emissions between 2019 and 2020'
)
fig.show()
def test_compare_year_to_year():
df = create_pollutant_df(POLLUTANT.o3, "../../data/test/mean/")
df = compare_year_to_year(df)
assert len(df.columns) == 9
assert len(df) == 24
def test_create_pollutant_df():
df = create_pollutant_df(POLLUTANT.o3, "../../data/test/mean/")
assert len(df.columns) == 6
assert len(df) == 48