def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
namefieldname = self.parameterAsString(parameters, self.NAME_FIELD,
context) # NOQA FIXME unused?
valuefieldname = self.parameterAsString(parameters, self.VALUE_FIELD,
context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, valuefieldname)
data = [
go.Area(r=values[valuefieldname],
t=np.degrees(
np.arange(0.0, 2 * np.pi,
2 * np.pi / len(values[valuefieldname]))))
]
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}
def wind_rose_plot(df_wind_rose):
data = []
wind_rose = pd.DataFrame({
'wind_dir_cat':
np.array(
'N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW'.split()),
'wind_dir': [0] * 16
})
i = 0
bfs = df_wind_rose.groupby('wind_speed_bfs')
color = cl.scales[str(df_wind_rose.wind_speed_bfs.unique().shape[0] +
1)]['qual']['Set1']
for bfs_wind, group_bfs in bfs:
df1 = pd.DataFrame({
'wind_dir_cat':
group_bfs.wind_dir_cat.value_counts().index,
'wind_dir':
group_bfs.wind_dir_cat.value_counts().values
})
df2 = pd.merge(wind_rose, df1, how='left', on='wind_dir_cat').fillna(0)
data.append(
go.Area(t=df2.wind_dir_cat,
r=df2.wind_dir_y.fillna(0),
marker=dict(color=color[i]),
name=bfs_wind))
i += 1
layout_windrose = dict(
title='Rosa de los vientos de ' +
df_wind_rose.model_name.iloc[0].title() + 'en ' +
df_wind_rose.name.iloc[0].title(),
orientation=270,
barmode='stack',
)
plot(dict(data=data, layout=layout_windrose),
filename=df_wind_rose.model_name.iloc[0].title() + '_' +
df_wind_rose.name.iloc[0].title() + 'windrose.html',
auto_open=False,
image='png',
image_filename=df_wind_rose.model_name.iloc[0].title() + '_' +
df_wind_rose.name.iloc[0].title())
cardinal_pops = pd.DataFrame({'direction': dirs})
sums = pop12.groupby(['income', 'card'])['population'].sum().reset_index()
sums = sums.drop(22)
min_max = sklearn.preprocessing.MinMaxScaler()
X = sums['population']
X = X.values.reshape(-1, 1)
sums['popscaled'] = min_max.fit_transform(X)
sums['poppercent'] = sums['population']/(7128176935)
trace1 = go.Area(
r=list(sums[sums['income'] == 'High income']['poppercent']),
t=list(sums[sums['income'] == 'High income']['card']),
name='High Income',
marker=dict(
color='#F79D84'
)
)
trace2 = go.Area(
r=list(sums[sums['income'] == 'Upper middle income']['poppercent']),
t=list(sums[sums['income'] == 'Upper middle income']['card']),
name='Upper Middle Income',
marker=dict(
color='#6184D8'
)
)
trace3 = go.Area(
r=list(sums[sums['income'] == 'Lower middle income']['poppercent']),
t=list(sums[sums['income'] == 'Lower middle income']['card']),
name='Lower Middle Income',
def windrose(self, column, color=None):
color, _ = self.__get_base_args(color, 1)
args = dict(t=self.df.index, r=list(self.df[column]), marker={'color': color})
self.__add_series(go.Area(args))
