def test_get_included_area(get_sample_geodata):
df = gp.GeoDataFrame(get_sample_geodata)
df[['geometry']] = df.geometry.apply(shapely.wkt.loads)
area, included = map_fi_plot.get_included_area(df,
how="intersection",
orig_name="Vattuniemi",
range_km=0,
target="Vattuniemi")
included.drop_duplicates(subset="pono", inplace=True)
included.loc[:, "pono"] = [str(x).rjust(5, '0') for x in included.pono]
assert included.pono.values == "00210"
area, included = map_fi_plot.get_included_area(df,
how="difference",
orig_name="Vattuniemi",
range_km=10,
target="Vattuniemi")
included.drop_duplicates(subset="pono", inplace=True)
included.loc[:, "pono"] = [str(x).rjust(5, '0') for x in included.pono]
array_res = np.array([
'00690', '00390', '00410', '00420', '00430', '00560', '00680', '00700',
'00710', '00720', '00730', '00740', '00800', '00750', '00760', '00770',
'00780', '00790', '00910', '00820', '00920', '00930', '00830', '00940',
'00950', '00960', '00840', '00850', '00860', '00880', '00970', '00980',
'00890', '00900', '00990', '00670', '00640', '00650', '00660', '00210'
],
dtype=object)
assert (included.pono.values == array_res).all()
def update_table(origin_name, move_type,
range_km, n_most,
max_price, target):
df_filtered = similarity.filter_w_price(df, max_price, [origin_name, target])
# transform values to ranks for easy understanding
df_filtered_ranks = similarity.full_df_to_ranks(df_filtered, bins=10)
df_origin = df.loc[df['nimi'] == origin_name, :]
if move_type == 'difference':
target = origin_name
area, included = map_fi_plot.get_included_area(df_filtered, move_type, origin_name, range_km, target)
NA, included_ranks = map_fi_plot.get_included_area(df_filtered_ranks, move_type, origin_name, range_km, target)
X, y, target_names = viz.get_pca_data(included, 2018, 5)
target_names.index = range(len(target_names))
X_pca = pipe.transform(X)
d = similarity.pairwise_distances(X_pca, X_pca, 'euclidean')
similar = similarity.get_similar_in_geo_area(included, origin_name, d,
target_names, n_most)
tb = viz.table_similar_with_names(included_ranks, origin_name, similar, target_names, X_pca,
['pono', 'nimi', 'he_kika',
'ra_asunn', 'te_laps',
'te_as_valj', 'tp_tyopy',
'tr_mtu', 'yliopistot', 'amk'],
tail=False)
tb = tb.drop_duplicates()
tb = format_numeric_table_cols(tb, numcols=['dist'])
cols = [x for x in tb.columns.values if
x not in ['geometry', 'kunta', 'kuntanro', 'pono', 'pono.level', 'nimi', 'nimi_x', 'vuosi',
'dist', 'rakennukset_bin']]
# tb.loc[:, cols] = tb.loc[:, cols].applymap(lambda x: similarity.value_to_plusses(x))
trace = go.Table(
header=dict(values=list(
['Pono', 'Nimi', 'Keski-ikä', 'Asunnot', 'Lapsitaloudet', 'Työpaikat', 'Mediaanitulo', 'Yliopistot', 'AMK',
'Dist']),
fill=dict(color='#C2D4FF'),
align=['left'] * 5,
height=40),
cells=dict(values=[tb.pono, tb.nimi, tb.he_kika, tb.ra_asunn, tb.te_laps, tb.tp_tyopy, tb.tr_mtu, tb.yliopistot,
tb.amk, tb.dist],
fill=dict(color='#F5F8FF'),
align=['left'] * 5,
height=30)
)
# py.plot([trace], 'test.html')
return {'data': [trace],
'layout': dict(autosize=True, margin=dict(
t=0,
b=0,
r=0,
l=0
)
)
}
def update_graph(origin_name, move_type,
range_km, n_most,
max_price, target):
df_filtered = similarity.filter_w_price(df, max_price, [origin_name, target])
df_origin = df.loc[df['nimi'] == origin_name, :]
if move_type == 'difference':
target = origin_name
area, included = map_fi_plot.get_included_area(df_filtered, move_type, origin_name, range_km, target)
X, y, target_names = viz.get_pca_data(included, 2018, 5)
target_names.index = range(len(target_names))
X_pca = pipe.transform(X)
d = similarity.pairwise_distances(X_pca, X_pca, 'euclidean')
similar = similarity.get_similar_in_geo_area(included, origin_name, d,
target_names, n_most)
df_comparison = df.loc[df['nimi'].isin(similar), :]
coords_max = {
'miny': df.bounds.miny.min(),
'minx': df.bounds.minx.min(),
'maxy': df.bounds.miny.max(),
'maxx': df.bounds.miny.max()
}
layout = define_layout()
return {
# 'data': set_fill_colors_for_origin_and_comp(plot_data4, origin_name, similar, target),
'data': set_fill_colors_for_origin_and_comp(make_graph_data(included), origin_name, similar, target),
'layout': layout
}