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timespace diagram merge bug fix #974

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Jun 19, 2020
Merged

timespace diagram merge bug fix #974

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25e623a
timespace diagram merge bug fix
akashvelu Jun 19, 2020
71dee84
docstring fix
akashvelu Jun 19, 2020
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3 changes: 2 additions & 1 deletion flow/utils/rllib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,7 +95,8 @@ def get_flow_params(config):
if type(config) == dict:
flow_params = json.loads(config['env_config']['flow_params'])
else:
flow_params = json.load(open(config, 'r'))
config = json.load(open(config, 'r'))
flow_params = json.loads(config['env_config']['flow_params'])

# reinitialize the vehicles class from stored data
veh = VehicleParams()
Expand Down
122 changes: 7 additions & 115 deletions flow/visualize/time_space_diagram.py
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Original file line number Diff line number Diff line change
Expand Up @@ -169,31 +169,7 @@ def _merge(data):

return segs, data

# compute the absolute position
for veh_id in data.keys():
data[veh_id]['abs_pos'] = _get_abs_pos(data[veh_id]['edge'],
data[veh_id]['pos'], edgestarts)

# prepare the speed and absolute position in a way that is compatible with
# the space-time diagram, and compute the number of vehicles at each step
pos = np.zeros((all_time.shape[0], len(data.keys())))
speed = np.zeros((all_time.shape[0], len(data.keys())))
for i, veh_id in enumerate(sorted(data.keys())):
for spd, abs_pos, ti, edge in zip(data[veh_id]['vel'],
data[veh_id]['abs_pos'],
data[veh_id]['time'],
data[veh_id]['edge']):
# avoid vehicles outside the main highway
if edge in ['inflow_merge', 'bottom', ':bottom_0']:
continue
ind = np.where(ti == all_time)[0]
pos[ind, i] = abs_pos
speed[ind, i] = spd

return pos, speed, all_time


def _highway(data, params, all_time):
def _highway(data):
r"""Generate position and speed data for the highway subnetwork.
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update docstring


Parameters
Expand All @@ -220,61 +196,11 @@ def _highway(data, params, all_time):
time step. Set to zero if the vehicle is not present in the network at
that time step.
"""
junction_length = 0.1
length = params['net'].additional_params["length"]
num_edges = params['net'].additional_params.get("num_edges", 1)
edge_starts = {}
# Add the main edges.
edge_starts.update({
"highway_{}".format(i): i * (length / num_edges + junction_length)
for i in range(num_edges)
})

if params['net'].additional_params["use_ghost_edge"]:
edge_starts.update({"highway_end": length + num_edges * junction_length})

edge_starts.update({
":edge_{}".format(i + 1): (i + 1) * length / num_edges + i * junction_length
for i in range(num_edges - 1)
})

if params['net'].additional_params["use_ghost_edge"]:
edge_starts.update({
":edge_{}".format(num_edges): length + (num_edges - 1) * junction_length
})

# compute the absolute position
for veh_id in data.keys():
data[veh_id]['abs_pos'] = _get_abs_pos_1_edge(data[veh_id]['edge'],
data[veh_id]['pos'],
edge_starts)

# track only vehicles that were around during this time period
# create the output variables
pos = np.zeros((all_time.shape[0], len(data.keys())))
speed = np.zeros((all_time.shape[0], len(data.keys())))
observed_row_list = []
for i, veh_id in enumerate(sorted(data.keys())):
for spd, abs_pos, ti, edge, lane in zip(data[veh_id]['vel'],
data[veh_id]['abs_pos'],
data[veh_id]['time'],
data[veh_id]['edge'],
data[veh_id]['lane']):
# avoid vehicles not on the relevant edges. Also only check the
# second to last lane
if edge not in edge_starts.keys() or ti not in all_time:
continue
else:
if i not in observed_row_list:
observed_row_list.append(i)
ind = np.where(ti == all_time)[0]
pos[ind, i] = abs_pos
speed[ind, i] = spd

pos = pos[:, observed_row_list]
speed = speed[:, observed_row_list]

return pos, speed, all_time
data.loc[:, :] = data[(data['distance'] > 500)]
data.loc[:, :] = data[(data['distance'] < 2300)]
segs = data[['time_step', 'distance', 'next_time', 'next_pos']].values.reshape((len(data), 2, 2))

return segs, data


def _ring_road(data, params, all_time):
Expand Down Expand Up @@ -566,6 +492,7 @@ def plot_tsd(ax, df, segs, args, lane=None):

for lane, df in traj_df.groupby('lane_id'):
ax = plt.subplot(nlanes, 1, lane+1)

plot_tsd(ax, df, segs[lane], args, lane)
else:
# perform plotting operation
Expand All @@ -574,41 +501,6 @@ def plot_tsd(ax, df, segs, args, lane=None):

plot_tsd(ax, traj_df, segs, args)

for indx_car in range(pos.shape[1]):
unique_car_pos = pos[:, indx_car]

if flow_params['network'] == I210SubNetwork or flow_params['network'] == HighwayNetwork:
indices = np.where(pos[:, indx_car] != 0)[0]
unique_car_speed = speed[indices, indx_car]
points = np.array([time[indices], pos[indices, indx_car]]).T.reshape(-1, 1, 2)
else:

# discontinuity from wraparound
disc = np.where(np.abs(np.diff(unique_car_pos)) >= 10)[0] + 1
unique_car_time = np.insert(time, disc, np.nan)
unique_car_pos = np.insert(unique_car_pos, disc, np.nan)
unique_car_speed = np.insert(speed[:, indx_car], disc, np.nan)
#
points = np.array(
[unique_car_time, unique_car_pos]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments, cmap=my_cmap, norm=norm)

# Set the values used for color mapping
lc.set_array(unique_car_speed)
lc.set_linewidth(1.75)
cols.append(lc)

plt.title(args.title, fontsize=25)
plt.ylabel('Position (m)', fontsize=20)
plt.xlabel('Time (s)', fontsize=20)

for col in cols:
line = ax.add_collection(col)
cbar = plt.colorbar(line, ax=ax, norm=norm)
cbar.set_label('Velocity (m/s)', fontsize=20)
cbar.ax.tick_params(labelsize=18)

###########################################################################
# Note: For MergeNetwork only #
if flow_params['network'] == 'MergeNetwork': #
Expand Down