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Dev test02 #180

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merged 5 commits into from
Nov 12, 2024
Merged

Dev test02 #180

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c59d58e
dash fix
sanapolsky Nov 8, 2024
d138967
correcciones en el dash
sanapolsky Nov 11, 2024
007e851
Update lineas_deseo.py
sanapolsky Nov 11, 2024
b2da1a4
Create __init__.py
sanapolsky Nov 12, 2024
c6f534e
Restore configs
alephcero Nov 12, 2024
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245 changes: 162 additions & 83 deletions urbantrips/dashboard/dash_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,60 +354,124 @@ def create_data_folium(etapas,
agg_hora=False,
agg_distancia=False,
agg_cols_etapas=[],
agg_cols_viajes=[]):

etapas = calculate_weighted_means_ods(etapas,
agg_cols_etapas,
['distance_osm_drive', 'lat1_norm', 'lon1_norm', 'lat2_norm',
'lon2_norm', 'lat3_norm', 'lon3_norm', 'lat4_norm', 'lon4_norm'],

'factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
zero_to_nan=['lat1_norm', 'lon1_norm', 'lat2_norm', 'lon2_norm', 'lat3_norm', 'lon3_norm', 'lat4_norm', 'lon4_norm'])

etapas[['lat1_norm',
'lon1_norm',
'lat2_norm',
'lon2_norm',
'lat3_norm',
'lon3_norm',
'lat4_norm',
'lon4_norm']] = etapas[['lat1_norm',
'lon1_norm',
'lat2_norm',
'lon2_norm',
'lat3_norm',
'lon3_norm',
'lat4_norm',
'lon4_norm']].fillna(0)

viajes = calculate_weighted_means_ods(etapas,
agg_cols_viajes,
['distance_osm_drive',
'lat1_norm',
'lon1_norm',
'lat4_norm',
'lon4_norm'],
'factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
zero_to_nan=['lat1_norm', 'lon1_norm', 'lat4_norm', 'lon4_norm'])
viajes[['lat1_norm',
'lon1_norm',
'lat4_norm',
'lon4_norm']] = viajes[['lat1_norm',
'lon1_norm',
'lat4_norm',
'lon4_norm']].fillna(0)

if 'id_polygon' not in viajes_matrices.columns:
viajes_matrices['id_polygon'] = 'NONE'
agg_cols_viajes=[],
desc_etapas = True,
desc_viajes = True,
desc_origenes = True,
desc_destinos = True,
desc_transferencias = False):


if desc_transferencias:

t1 = etapas.loc[etapas.transfer1_norm!='', ['zona',
'transfer1_norm',
'lat2_norm',
'lon2_norm',
'transferencia',
'modo_agregado',
'rango_hora',
'distancia',
'factor_expansion_linea',]].rename(columns={'transfer1_norm':'transfer', 'lat2_norm':'lat_norm', 'lon2_norm':'lon_norm'})
t2 = etapas.loc[etapas.transfer2_norm!='', ['zona',
'transfer2_norm',
'lat3_norm',
'lon3_norm',
'transferencia',
'modo_agregado',
'rango_hora',
'distancia',
'factor_expansion_linea',]].rename(columns={'transfer2_norm':'transfer', 'lat3_norm':'lat_norm', 'lon3_norm':'lon_norm'})
transferencias = pd.concat([t1,t2], ignore_index=True)
transferencias['id_polygon'] = 'NONE'

trans_cols_o = ['id_polygon',
'zona',
'transfer',
'transferencia',
'modo_agregado',
'rango_hora',
'distancia']

transferencias = creo_bubble_od(transferencias,
aggregate_cols=trans_cols_o,
weighted_mean_cols=['lat_norm', 'lon_norm'],
weight_col='factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
od='transfer',
lat='lat_norm',
lon='lon_norm')
else:
transferencias = pd.DataFrame([])

if desc_etapas | desc_transferencias:
etapas = calculate_weighted_means_ods(etapas,
agg_cols_etapas,
['distance_osm_drive', 'lat1_norm', 'lon1_norm', 'lat2_norm',
'lon2_norm', 'lat3_norm', 'lon3_norm', 'lat4_norm', 'lon4_norm'],
'factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
zero_to_nan=['lat1_norm', 'lon1_norm', 'lat2_norm', 'lon2_norm', 'lat3_norm', 'lon3_norm', 'lat4_norm', 'lon4_norm'])

etapas[['lat1_norm',
'lon1_norm',
'lat2_norm',
'lon2_norm',
'lat3_norm',
'lon3_norm',
'lat4_norm',
'lon4_norm']] = etapas[['lat1_norm',
'lon1_norm',
'lat2_norm',
'lon2_norm',
'lat3_norm',
'lon3_norm',
'lat4_norm',
'lon4_norm']].fillna(0)

etapas = df_to_linestrings(etapas,
lat_cols=['lat1_norm', 'lat2_norm', 'lat3_norm', 'lat4_norm'], lon_cols=['lon1_norm', 'lon2_norm', 'lon3_norm', 'lon4_norm'])

etapas = etapas[etapas.inicio_norm != etapas.fin_norm].copy()

if desc_viajes:
viajes = calculate_weighted_means_ods(etapas,
agg_cols_viajes,
['distance_osm_drive',
'lat1_norm',
'lon1_norm',
'lat4_norm',
'lon4_norm'],
'factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
zero_to_nan=['lat1_norm', 'lon1_norm', 'lat4_norm', 'lon4_norm'])
viajes[['lat1_norm',
'lon1_norm',
'lat4_norm',
'lon4_norm']] = viajes[['lat1_norm',
'lon1_norm',
'lat4_norm',
'lon4_norm']].fillna(0)

if 'id_polygon' not in viajes_matrices.columns:
viajes_matrices['id_polygon'] = 'NONE'

viajes = df_to_linestrings(viajes,
lat_cols=['lat1_norm', 'lat4_norm'], lon_cols=['lon1_norm', 'lon4_norm'])

viajes = viajes[viajes.inicio_norm != viajes.fin_norm].copy()
else:
viajes = pd.DataFrame([])

matriz = agg_matriz(viajes_matrices,
aggregate_cols=['id_polygon', 'zona', 'Origen', 'Destino',
'transferencia', 'modo_agregado', 'rango_hora', 'distancia'],
Expand All @@ -429,37 +493,41 @@ def create_data_folium(etapas,
bubble_cols_d = ['id_polygon', 'zona', 'fin',
'transferencia', 'modo_agregado', 'rango_hora', 'distancia']

origen = creo_bubble_od(viajes_matrices,
aggregate_cols=bubble_cols_o,
weighted_mean_cols=['lat1', 'lon1'],
weight_col='factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
od='inicio',
lat='lat1',
lon='lon1')

destino = creo_bubble_od(viajes_matrices,
aggregate_cols=bubble_cols_d,
weighted_mean_cols=['lat4', 'lon4'],
weight_col='factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
od='fin',
lat='lat4',
lon='lon4')

etapas = df_to_linestrings(etapas,
lat_cols=['lat1_norm', 'lat2_norm', 'lat3_norm', 'lat4_norm'], lon_cols=['lon1_norm', 'lon2_norm', 'lon3_norm', 'lon4_norm'])
if desc_origenes:
origen = creo_bubble_od(viajes_matrices,
aggregate_cols=bubble_cols_o,
weighted_mean_cols=['lat1', 'lon1'],
weight_col='factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
od='inicio',
lat='lat1',
lon='lon1')
else:
origen = pd.DataFrame([])

if desc_destinos:
destino = creo_bubble_od(viajes_matrices,
aggregate_cols=bubble_cols_d,
weighted_mean_cols=['lat4', 'lon4'],
weight_col='factor_expansion_linea',
agg_transferencias=agg_transferencias,
agg_modo=agg_modo,
agg_hora=agg_hora,
agg_distancia=agg_distancia,
od='fin',
lat='lat4',
lon='lon4')
else:
destino = pd.DataFrame([])

if not desc_etapas:
etapas = pd.DataFrame([])

viajes = df_to_linestrings(viajes,
lat_cols=['lat1_norm', 'lat4_norm'], lon_cols=['lon1_norm', 'lon4_norm'])

return etapas, viajes, matriz, origen, destino
return etapas, viajes, matriz, origen, destino, transferencias


@st.cache_data
Expand Down Expand Up @@ -518,4 +586,15 @@ def extract_hex_colors_from_cmap(cmap, n=5):
# Convert the colors to hex format
hex_colors = [mcolors.rgb2hex(color) for color in colors]

return hex_colors
return hex_colors

@st.cache_data
def bring_latlon():
try:
latlon = levanto_tabla_sql('agg_etapas', 'dash', 'SELECT lat1_norm, lon1_norm FROM agg_etapas ORDER BY RANDOM() LIMIT 100;')
lat = latlon['lat1_norm'].mean()
lon = latlon['lon1_norm'].mean()
latlon = [lat, lon]
except:
latlon = [-34.593, -58.451]
return latlon
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