def load_bounds():
'''
Load town boundary data
'''
# db connection params
db_config = {
'host': 'localhost',
'port': 5433,
'user': '******',
'database': 'gareth',
'password': ''
}
# load boundaries data
bound_data = util_funcs.load_data_as_pd_df(db_config,
['pop_id',
'city_name',
'city_type',
'city_population'],
'analysis.city_boundaries_150',
'WHERE pop_id IS NOT NULL ORDER BY pop_id')
# add indices for city-wide data
bound_data.set_index('pop_id', inplace=True)
bound_data.sort_index(inplace=True)
# label dataset according to classifications
town_classifications = ['New Town',
'New Town Like',
'Expanded Town',
'Expanded Town Like']
selected_data = bound_data.copy()
new_towns = selected_data[selected_data.city_type.isin(town_classifications)]
largest = new_towns.city_population.max()
smallest = new_towns.city_population.min()
"""
# 298701.0 - largest pop
# 8186.0 - smallest pop
# 650 - largest city id
# 19 - smallest city id (largest by size)
"""
selected_data = selected_data[np.logical_and(selected_data.city_population >= smallest,
selected_data.city_population <= largest)]
# prepare targets
selected_data['targets'] = 0
for pop_id, city_row in selected_data.iterrows():
if (city_row.city_type in town_classifications):
selected_data.loc[pop_id, 'targets'] = 1
return selected_data
# %%
# db connection params
db_config = {
'host': 'localhost',
'port': 5433,
'user': '******',
'database': 'gareth',
'password': ''
}
# load boundaries data
bound_data = util_funcs.load_data_as_pd_df(
db_config, [
'pop_id', 'city_name', 'city_type', 'city_area', 'city_area_petite',
'city_population', 'city_species_count', 'city_species_unique',
'city_streets_len', 'city_intersections_count'
], 'analysis.city_boundaries_150',
'WHERE pop_id IS NOT NULL ORDER BY pop_id')
#%%
def pop_corr_plot(city_data, theme_1, theme_2, towns_data, xlabel, sup_title):
new_towns = []
other_towns = []
for i, d in bound_data.iterrows():
if d['city_type'] in ['New Town']:
new_towns.append(d['pop_id'])
else:
other_towns.append(d['pop_id'])
'ac_cultural',
'ac_sports',
'ac_total'
]
col_template = '{col}[{i}] as {col}_{dist}'
distances = [50, 100, 200, 300, 400, 600, 800, 1200, 1600]
for col_base in columns_base:
for i, d in enumerate(distances):
columns.append(col_template.format(col=col_base, i=i + 1, dist=d))
# bandwise distances do not include 50m
distances_bandwise = distances[1:]
# %%
# load data
df_full = util_funcs.load_data_as_pd_df(
db_config, columns, 'analysis.nodes_full',
'WHERE city_pop_id = 1 and within = true')
df_full = df_full.set_index('id')
df_full = util_funcs.clean_pd(df_full, drop_na='all', fill_inf=np.nan)
df_100 = util_funcs.load_data_as_pd_df(
db_config, columns, 'analysis.nodes_100',
'WHERE city_pop_id = 1 and within = true')
df_100 = df_100.set_index('id')
df_100 = util_funcs.clean_pd(df_100, drop_na='all', fill_inf=np.nan)
df_50 = util_funcs.load_data_as_pd_df(
db_config, columns, 'analysis.nodes_50',
'WHERE city_pop_id = 1 and within = true')
df_50 = df_50.set_index('id')
df_50 = util_funcs.clean_pd(df_50, drop_na='all', fill_inf=np.nan)
]
columns_base = [
'c_node_density', 'c_node_harmonic', 'c_node_beta', 'c_node_betweenness',
'c_node_betweenness_beta', 'c_node_harmonic_angular',
'c_node_betweenness_angular'
]
col_template = '{col}[{i}] as {col}_{dist}'
distances = [50, 100, 200, 400, 800, 1600]
for col_base in columns_base:
for i, d in enumerate(distances):
columns.append(col_template.format(col=col_base, i=i + 1, dist=d))
# %% load nodes data
print('loading columns')
df_full = util_funcs.load_data_as_pd_df(
db_config, columns, 'analysis.nodes_full_dual',
'WHERE city_pop_id = 1 and within = true')
df_full = df_full.set_index('id')
df_full = util_funcs.clean_pd(df_full, drop_na='all', fill_inf=np.nan)
# %%
'''
correlation matrix plot
'''
themes = [
'c_node_density_{dist}', 'c_node_harmonic_{dist}',
'c_node_harmonic_angular_{dist}', 'c_node_beta_{dist}',
'c_node_betweenness_{dist}', 'c_node_betweenness_angular_{dist}',
'c_node_betweenness_beta_{dist}'
]