import transbigdata as tbd
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

# Read data
data = pd.read_csv('data/TaxiData-Sample.csv', header=None)
data.columns = ['VehicleNum', 'Time', 'Lng', 'Lat', 'OpenStatus', 'Speed']
data.head()

# Read the GeoDataFrame of the study area
sz = gpd.read_file(r'data/sz.json')
sz.crs = None
sz.head()

fig = plt.figure(1, (8, 3), dpi=150) 
ax1 = plt.subplot(111)
sz.plot(ax=ax1)
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10);

# Data gridding
# Define the bounds and generate gridding parameters
bounds = [113.6, 22.4, 114.8, 22.9]
params = tbd.area_to_params(bounds, accuracy=500)
print(params)

# Mapping GPS data to grids
data['LONCOL'], data['LATCOL'] = tbd.GPS_to_grid(data['Lng'], data['Lat'], params)
data.head()

# Aggregate data into grids
datatest = data.groupby(['LONCOL', 'LATCOL'])['VehicleNum'].count().reset_index()
datatest.head()

# Generate the geometry for grids
datatest['geometry'] = tbd.grid_to_polygon([datatest['LONCOL'], datatest['LATCOL']], params)

# Change it into GeoDataFrame
# import geopandas as gpd
datatest = gpd.GeoDataFrame(datatest)
datatest.head()

# Plot the grids
fig = plt.figure(1, (16, 6), dpi=300) 
ax1 = plt.subplot(111)

# tbd.plot_map(plt, bounds, zoom=10, style=4)
datatest.plot(ax=ax1, column='VehicleNum', legend=True)
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10)
plt.title('Counting of Taxi GPS Trajectory Points', fontsize=12);

# Plot the grids
fig = plt.figure(1, (16, 6), dpi=300) # 确定图形高为6，宽为8；图形清晰度
ax1 = plt.subplot(111)
datatest.plot(ax=ax1, column='VehicleNum', legend=True, scheme='quantiles')
# plt.legend(fontsize=10)
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10)
plt.title('Counting of Taxi GPS Trajectory Points', fontsize=12);

# Plot the grids
fig = plt.figure(1, (16, 6), dpi=150) # 确定图形高为6，宽为8；图形清晰度
ax1 = plt.subplot(111)
datatest.plot(ax=ax1, column='VehicleNum', legend=True, cmap='OrRd', scheme='quantiles')
# plt.legend(fontsize=10)
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10)
plt.title('Counting of Taxi GPS Trajectory Points', fontsize=12);

# Extract taxi OD from GPS data
oddata = tbd.taxigps_to_od(data,col = ['VehicleNum', 'Time', 'Lng', 'Lat', 'OpenStatus'])
oddata

# Gridding and aggragate data
od_gdf = tbd.odagg_grid(oddata, params)
od_gdf.head()

# Plot the grids
fig = plt.figure(1, (16, 6), dpi=150) # 确定图形高为6，宽为8；图形清晰度
ax1 = plt.subplot(111)
# data_grid_count.plot(ax=ax1, column='VehicleNum', legend=True, cmap='OrRd', scheme='quantiles')

od_gdf.plot(ax=ax1, column='count', legend=True, scheme='quantiles')
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10)
plt.title('OD Trips', fontsize=12);

# Aggragate OD data to polygons 
# without passing gridding parameters, the algorithm will map the data 
# to polygons directly using their coordinates
od_gdf = tbd.odagg_shape(oddata, sz, round_accuracy=6)
fig = plt.figure(1, (16, 6), dpi=150) # 确定图形高为6，宽为8；图形清晰度
ax1 = plt.subplot(111)
od_gdf.plot(ax=ax1, column='count')
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10)
plt.title('OD Trips', fontsize=12);

# Create figure
fig = plt.figure(1, (10, 10), dpi=300)
ax = plt.subplot(111)
plt.sca(ax)

# Load basemap
tbd.plot_map(plt, bounds, zoom=12, style=4)

# Define an ax for colorbar
cax = plt.axes([0.05, 0.33, 0.02, 0.3])
plt.title('OD\nMatrix')
plt.sca(ax)

# Plot the OD
od_gdf.plot(ax=ax, vmax=100, column='count', cax=cax, legend=True)

# Plot the polygons
sz.plot(ax=ax, edgecolor=(0, 0, 0, 1), facecolor=(0, 0, 0, 0.2), linewidths=0.5)

# Add compass and scale
tbd.plotscale(ax, bounds=bounds, textsize=10, compasssize=1, accuracy=2000, rect=[0.06, 0.03], zorder=10)
plt.axis('off')
plt.xlim(bounds[0], bounds[2])
plt.ylim(bounds[1], bounds[3])
plt.show()

data_deliver.head()

data_idle.head()

traj_deliver = tbd.points_to_traj(data_deliver)
traj_deliver.plot();

traj_idle = tbd.points_to_traj(data_idle[data_idle['OpenStatus'] == 0])
traj_idle.plot()

tbd.visualization_trip(data_deliver)