def speed_on_the_ground(graph: nx.DiGraph, filename: str):
mpl.use("Agg")
with commons.Section("Getting the background OSM map"):
nodes = pd.DataFrame(data=nx.get_node_attributes(graph, name="pos"),
index=["lon", "lat"]).T
extent = maps.ax4(nodes.lat, nodes.lon)
osmap = maps.get_map_by_bbox(maps.ax2mb(*extent))
with mpl.rc_context(PARAM['mpl_style']), commons.Axes() as ax1:
velocity = pd.Series(nx.get_edge_attributes(
graph, name="len")) / pd.Series(
nx.get_edge_attributes(graph, name=PARAM['edge_time_attr']))
cmap = LinearSegmentedColormap.from_list(
name="noname", colors=["brown", "r", "orange", "g"])
ax1.imshow(osmap, extent=extent, interpolation='quadric', zorder=-100)
nx.draw(graph,
ax=ax1,
pos=nx.get_node_attributes(graph, name="pos"),
edgelist=list(velocity.index),
edge_color=list(velocity),
edge_cmap=cmap,
edge_vmin=0,
edge_vmax=7,
with_labels=False,
arrows=False,
node_size=0,
alpha=0.8,
width=0.3)
fn = PARAM['out_images_path'] / commons.myname() / F"{filename}.png"
ax1.figure.savefig(commons.makedirs(fn))
def nx_draw_met_by_len(graph, edges_met=None, mpl_backend="Agg", printer=None):
mpl.use(mpl_backend)
with Section("Preparing to draw graph", out=printer):
nodes = pd.DataFrame(data=nx.get_node_attributes(graph, name="loc"),
index=["lat", "lon"]).T
edges_len = pd.Series(data=nx.get_edge_attributes(graph, name="len"),
name="len")
if edges_met is not None:
edges_met = pd.Series(name="met", data=edges_met)
else:
edges_met = pd.Series(name="met",
data=nx.get_edge_attributes(graph,
name="met"))
cmap = LinearSegmentedColormap.from_list(
name="noname", colors=["g", "y", "r", "brown"])
with Section("Getting the background OSM map", out=printer):
extent = maps.ax4(nodes.lat, nodes.lon)
osmap = maps.get_map_by_bbox(maps.ax2mb(*extent))
with Section("Drawing image", out=printer):
fig: plt.Figure
ax1: plt.Axes
(fig, ax1) = plt.subplots()
# The background map
ax1.imshow(osmap, extent=extent, interpolation='quadric', zorder=-100)
ax1.axis("off")
edge_colors = (edges_met / edges_len).clip(lower=0.9, upper=1.5)
nx.draw(graph,
ax=ax1,
pos=nx.get_node_attributes(graph, name="pos"),
edge_list=list(edge_colors.index),
edge_color=list(edge_colors),
edge_cmap=cmap,
with_labels=False,
arrows=False,
node_size=0,
alpha=1,
width=0.4)
ax1.set_xlim(extent[0:2])
ax1.set_ylim(extent[2:4])
try:
# Note:
# "yield (fig, ax1)" does not work with the "retry" context manager
return iter([(fig, ax1)])
finally:
plt.close(fig)
def compare_multiple_trajectories(table_name):
mpl.use("Agg")
# Number of trips to plot
N = 10
# Number of trajectories per trip
M = 12
graph = get_road_graph()
nodes = pd.DataFrame(data=nx.get_node_attributes(graph, "loc"), index=["lat", "lon"]).T
edges_len = nx.get_edge_attributes(graph, name="len")
where = "('2016-05-02 08:00' <= pickup_datetime) and (pickup_datetime <= '2016-05-02 09:00')"
trips = get_trip_data(table_name, graph, order="", where=where)
trips = trips.sample(min(N, len(trips)), random_state=1)
logger.debug(F"{len(trips)} trips")
with Section("Getting the background OSM map", out=logger.debug):
extent = maps.ax4(nodes.lat, nodes.lon)
osmap = maps.get_map_by_bbox(maps.ax2mb(*extent))
with plt.style.context({**PARAM['mpl_style'], 'font.size': 5}), Axes() as ax1:
# The background map
ax1.imshow(osmap, extent=extent, interpolation='quadric', zorder=-100)
ax1.axis("off")
ax1.set_xlim(extent[0:2])
ax1.set_ylim(extent[2:4])
for (__, trip) in trips.iterrows():
with Section("Computing candidate trajectories", out=logger.debug):
trajectories = pd.DataFrame(data={'path': [
path
for (__, path) in
zip(range(M), nx.shortest_simple_paths(graph, source=trip.u, target=trip.v))
]})
trajectories['dist'] = [sum(edges_len[e] for e in pairwise(path)) for path in trajectories.path]
trajectories = trajectories.sort_values(by='dist', ascending=False)
marker = dict(markersize=2, markeredgewidth=0.2, markerfacecolor="None")
ax1.plot(trip['pickup_longitude'], trip['pickup_latitude'], 'og', **marker)
ax1.plot(trip['dropoff_longitude'], trip['dropoff_latitude'], 'xr', **marker)
cmap = LinearSegmentedColormap.from_list(name="noname", colors=["g", "orange", "r", "brown"])
colors = cmap(pd.Series(trajectories['dist'] / trip['distance']).rank(pct=True))
for (c, path) in zip(colors, trajectories.path):
(y, x) = nodes.loc[list(path)].values.T
ax1.plot(x, y, c=c, alpha=0.5, lw=0.3)
# Save to file
fn = os.path.join(PARAM['out_images_path'], F"{myname()}/{table_name}.png")
ax1.figure.savefig(makedirs(fn))
def trip_trajectories_ingraph(table_name):
mpl.use("Agg")
# Max number of trajectories to plot
N = 1000
graph = get_road_graph()
nodes = pd.DataFrame(data=nx.get_node_attributes(graph, "loc"), index=["lat", "lon"]).T
trips = get_trip_data(table_name, graph)
trips = trips.sample(min(N, len(trips)))
logger.debug(F"{len(trips)} trips")
logger.debug("Computing trajectories")
trajectories = parallel_map(GraphPathDist(graph).path_only, zip(trips.u, trips.v))
with Section("Getting the background OSM map", out=logger.debug):
extent = maps.ax4(nodes.lat, nodes.lon)
osmap = maps.get_map_by_bbox(maps.ax2mb(*extent))
with plt.style.context({**PARAM['mpl_style'], 'font.size': 5}):
with Axes() as ax1:
# The background map
ax1.imshow(osmap, extent=extent, interpolation='quadric', zorder=-100)
ax1.axis("off")
ax1.set_xlim(extent[0:2])
ax1.set_ylim(extent[2:4])
c = 'b'
if ("green" in table_name): c = "green"
if ("yello" in table_name): c = "orange"
logger.debug("Plotting trajectories")
for traj in trajectories:
(y, x) = nodes.loc[list(traj)].values.T
ax1.plot(x, y, c=c, alpha=0.1, lw=0.3)
# Save to file
fn = os.path.join(PARAM['out_images_path'], F"{myname()}/{table_name}.png")
ax1.figure.savefig(makedirs(fn))
# Meta info
json.dump({'number_of_trajectories': len(trips)}, open((fn + ".txt"), 'w'))
def trip_trajectories_velocity(table_name):
mpl.use("Agg")
# Max number of trajectories to use
N = 10000
graph = get_road_graph()
nodes = pd.DataFrame(data=nx.get_node_attributes(graph, "loc"), index=["lat", "lon"]).T
edge_name = pd.Series(nx.get_edge_attributes(graph, name="name"))
where = "('2016-05-02 08:00' <= pickup_datetime) and (pickup_datetime <= '2016-05-02 09:00')"
trips = get_trip_data(table_name, graph, order="", limit=N, where=where)
trips['velocity'] = trips['distance'] / trips['duration/s']
trips = trips.sort_values(by='velocity', ascending=True)
logger.debug(F"{len(trips)} trips")
with Section("Computing estimated trajectories", out=logger.debug):
trips['traj'] = parallel_map(GraphPathDist(graph).path_only, zip(trips.u, trips.v))
with Section("Getting the background OSM map", out=logger.debug):
extent = maps.ax4(nodes.lat, nodes.lon)
osmap = maps.get_map_by_bbox(maps.ax2mb(*extent))
with Section("Computing edge velocities", out=logger.debug):
edge_vel = defaultdict(list)
for (traj, v) in zip(trips.traj, trips.velocity):
for e in pairwise(traj):
edge_vel[e].append(v)
edge_vel = pd.Series({e: np.mean(v or np.nan) for (e, v) in edge_vel.items()}, index=graph.edges)
edge_vel = edge_vel.dropna()
with plt.style.context({**PARAM['mpl_style'], 'font.size': 5}), Axes() as ax1:
# The background map
ax1.imshow(osmap, extent=extent, interpolation='quadric', zorder=-100)
ax1.axis("off")
ax1.set_xlim(extent[0:2])
ax1.set_ylim(extent[2:4])
cmap_velocity = LinearSegmentedColormap.from_list(name="noname", colors=["brown", "r", "orange", "g"])
# marker = dict(markersize=0.5, markeredgewidth=0.1, markerfacecolor="None")
# ax1.plot(trips['pickup_longitude'], trips['pickup_latitude'], 'og', **marker)
# ax1.plot(trips['dropoff_longitude'], trips['dropoff_latitude'], 'xr', **marker)
# for e in edge_name[edge_name == "65th Street Transverse"].index:
# print(e, edge_vel[e])
edge_vel: pd.Series
# edge_vel = edge_vel.rank(pct=True)
edge_vel = edge_vel.clip(lower=2, upper=6).round()
edge_vel = (edge_vel - edge_vel.min()) / (edge_vel.max() - edge_vel.min())
edge_vel = edge_vel.apply(cmap_velocity)
nx.draw_networkx_edges(
graph.edge_subgraph(edge_vel.index),
ax=ax1,
pos=nx.get_node_attributes(graph, name="pos"),
edge_list=list(edge_vel.index),
edge_color=list(edge_vel),
# edge_cmap=cmap_velocity,
# vmin=0, vmax=1,
with_labels=False, arrows=False, node_size=0, alpha=0.8, width=0.3,
)
# Save to file
fn = os.path.join(PARAM['out_images_path'], F"{myname()}/{table_name}.png")
ax1.figure.savefig(makedirs(fn))
# Meta info
json.dump({'number_of_trajectories': len(trips)}, open((fn + ".txt"), 'w'))