# %% [markdown]
# # Visualization of trajectories
# If we work with trajectory data, we often want to visualize them from a
# so called `birds eye view`. The following example demonstrates how to achieve
# this with ``TASI`` using the [DLR Urban Traffic Dataset](https://doi.org/10.5281/zenodo.11396371).
#
# ## Load trajectories
# At first, let's load trajectories from the DLR dataset.
# %%
from tasi.dlr import DLRTrajectoryDataset, DLRUTDatasetManager, DLRUTVersion

dataset = DLRUTDatasetManager(DLRUTVersion.latest)
dataset.load()
ut = DLRTrajectoryDataset.from_csv(dataset.trajectory()[0])
ut

# %% [markdown]
# ## Plot trajectories
# We now utilize the `TrajectoryPlotter` to visualize the trajectories of the
# traffic participants that we've loaded.
# %%
import matplotlib.pyplot as plt

from tasi.plotting import TrajectoryPlotter

f, ax = plt.subplots()

plotter = TrajectoryPlotter()
plotter.plot(ut, ax=ax)
# %% [markdown]
# ### Change trajectory color
# Note that the trajectories are colorized with the default color which we can
# change so any arbitrary color, such as `lightgray`.
# %%
f, ax = plt.subplots()
plotter.plot(ut, ax=ax, color="lightgray")
# %% [markdown]
# ### Change color of a specific trajectory
# You can also define the color of a specific trajectory.
# %%
f, ax = plt.subplots()
plotter.plot(
    ut, ax=ax, color="black", trajectory_kwargs={ut.ids[-20]: {"color": "red"}}
)
# %% [markdown]
# ### Change trajectory opacity
# or even change the opacity of each trajectory to quickly get an overview of
# the traffic density. Let's change the opacity to 20% via the `alpha` argument.
# %%
f, ax = plt.subplots()
plotter.plot(ut, ax=ax, alpha=0.2)
# %% [markdown]
# The plot already indicates the various traffic volumes on the different routes
# at the intersection.
# %% [markdown]
# ## Plot DLR UT trajectories on orthophoto
# We can also combine the `TrajectoryPlotter` with the `BoundingboxPlotter` to
# visualize trajectories on an orthophoto.
# %%
from tasi.plotting.wms import BoundingboxPlotter, LowerSaxonyOrthophotoTile

f, ax = plt.subplots()

# plot the orthophoto first
bbox_plotter = BoundingboxPlotter(ut.roi, LowerSaxonyOrthophotoTile())
bbox_plotter.plot(ax)

# and the trajectories on top
tj_plotter = TrajectoryPlotter()
tj_plotter.plot(ut, ax=ax)

# hide the axis
_ = ax.axis("off")
# %% [markdown]
# Note that it may become hard to see the trajectories on the background.
# To increase the contrast, we can adapt the opacity of the ortophoto via the
# `alpha` argument. Let's set is to 0.5 (50%) to highlight the trajectories.
# %%
f, ax = plt.subplots()

bbox_plotter.plot(ax, alpha=0.5)
tj_plotter.plot(ut, ax=ax, alpha=0.25)

_ = ax.axis("off")

# %% [markdown]
# ## Plot DLR HT trajectories on orthophoto
# We can also use the `TrajectoryPlotter` with the `BoundingboxPlotter` to
# visualize trajectories of the DLR HT dataset on an orthophoto. Let's load
# and plot the trajectories from the DLR HT dataset. This time, let's use the
# DLRTrajectoryPlotter to color the trajectories in default DLR-colors.
# %%
from tasi.dlr.dataset import DLRHTDatasetManager, DLRHTVersion
from tasi.dlr.plotting import DLRTrajectoryPlotter

# load dataset
dataset = DLRHTDatasetManager(DLRHTVersion.v1_1_0)
dataset.load()
ht = DLRTrajectoryDataset.from_csv(dataset.trajectory()[0])

f, ax = plt.subplots()

# plot the orthophoto first
bbox_plotter = BoundingboxPlotter(ht.roi, LowerSaxonyOrthophotoTile())
bbox_plotter.plot(ax)

# and the trajectories on top
tj_plotter = DLRTrajectoryPlotter()
tj_plotter.plot(ht, ax=ax)

# hide the axis
_ = ax.axis("off")
# %% [markdown]
# ## Visualizing geospatial trajectories
# The ``TASI`` model for representing trajectory data using geospatial objects opens the world to utilize tools that
# support `geopandas`. For instance, if you want an interactive view on trajectory data, we can utilize
# [folium](https://python-visualization.github.io/folium/latest/) via `geopandas`. For this purpose, we need two
# conversion steps.
#
# At first, we need to convert the dataset to a native ``TASI`` representation.
# %%
ds = ut.to_tasi()
ds.head()
# %% [markdown]
# Note that the `boundingbox` attribute was added in this step. Since  `ds` is a `tasi.TrajectoryDataset` it
# also uses `pandas`. Hence, we will now convert to use `geopandas` while the `position` attribute should be encoded as
# a `GeoObject`.
# %%
gds = ds.as_geo("position")
# %% [markdown]
# We need to set the position attribute as the
# [*active*](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) geometry.
# %%
gds.set_geometry("position", inplace=True)
# %% [markdown]
# Now, we are ready to visualize the trajectories in a dynamic window, while we use the "CartoDB positron" background
# layer for reference.
# %%
gds.explore(crs="EPSG:32632", tiles="CartoDB positron")
# %% [markdown]
# Note that for each traffic participant (or trajectory), additional information is available when hovering over its
# representation in the map. You can customize the representaton of the trajectories and add or remove attributes to you liking.