Xns11 - Basic reading and plotting of cross sections¶

import mikeio1d

xns = mikeio1d.open("../tests/testdata/mikep_cs_demo.xns11")
xns

# How many cross sections in the file?
len(xns)

# You can index specific cross sections using their location id, chainage, and topo id.
# For example, to access the cross section at location 'basin_left1', chainage '122.042', and topo id '1':
xns["basin_left1", "122.042", "1"]

# Alternatively, you can use 'sel()' method to be more explicit (however without autocompletion):
xns.sel(location_id="basin_left1", chainage="122.042", topo_id="1")

# Use ':' or '...' as a wildcard to get a list of CrossSection objects matching the specified criteria.
xns["basin_left1", :, "1"]

# You can also provide fewer arguments and the remaining ones will automatically use '...'.
# For example, xns['basin_left1', ..., ...] can be more concisely expressed as:
xns["basin_left1"]

# Or with the 'sel()' method:
xns.sel(location_id="basin_left1")

# Get a nice overview of the collection as a pandas DataFrame.
xns.to_dataframe()

# You can access some basic properties of the collection:
print(xns.location_ids)
print(xns.chainages)
print(xns.topo_ids)

# Access the raw data of a cross section using the 'raw' attribute, which returns a Pandas DataFrame.
df = xns["basin_left1", "481.451", "1"].raw
df.head()

# Plot the cross section.
ax = xns["basin_left1", "122.042", "1"].plot()

# Plot another cross section on the same axis.
ax = xns["basin_left1", "166.107", "1"].plot(ax=ax, linestyle="--")
ax.figure

# Turn off marker labels if it gets too crowded.
ax = xns["basin_left1", "166.107", "1"].plot(with_marker_labels=False)

# Or turn off markers completely.
ax = xns["basin_left1", "166.107", "1"].plot(with_markers=False)

# You can create a new Xns11 object from a subset of the original collection.
from mikeio1d import Xns11

new_xns11 = Xns11(xns.sel(location_id="basin_left1"))
new_xns11

# Plot all cross sections part of the new collection.
ax = new_xns11.plot(with_marker_labels=False)

# You can convert xsections into a GeoDataframe. Note that this requires installing GeoPandas seperately.
gdf = xns.to_geopandas()
gdf.plot()

# You can use convert any subset to a GeoDataFrame.
subset = Xns11(xns.sel(location_id="basin_left1"))
subset.to_geopandas().plot()

# This xns11 file has three topo ids, let's get an overview of where they are located.
gdf = xns.to_geopandas()
gdf.plot(column="topo_id", legend=True, legend_kwds={"title": "Topo"})

# You can check for overlapping cross sections by seeing if there's duplicate geometries.
# Here there's no duplicates, however, we notice a number of cross sections have no coordinates.
# This can be the case where cross section coordinates are not defined in the xns11 file. For example,
# sometimes coordinates are excluded, then MIKE+ will estimate the coordinates based on the river geometry,
# cross section chainage, and the river's centerline.
gdf[gdf.geometry.duplicated()]

# Plot all of the markers.
xns.to_geopandas(mode='markers').plot(legend=True)

# Plot markers on top of cross sections.

gdf1 = xns.to_geopandas(mode='sections')
gdf2 = xns.to_geopandas(mode='markers')

ax = gdf1.plot(figsize=(15, 15))

gdf2.plot(ax=ax, column="marker_label", markersize=10, zorder=10, legend=True)