topotoolbox.StreamObject

class topotoolbox.StreamObject(flow: FlowObject, units: str = 'pixels', threshold: int | float | GridObject | ndarray = 0, stream_pixels: GridObject | ndarray | None = None, channelheads: ndarray | None = None)

Bases: object

A class to represent stream flow accumulation based on a FlowObject.

__init__(flow: FlowObject, units: str = 'pixels', threshold: int | float | GridObject | ndarray = 0, stream_pixels: GridObject | ndarray | None = None, channelheads: ndarray | None = None) → None

Initializes the StreamObject by processing flow accumulation.

Parameters:

• flow (FlowObject) – The input flow object containing source, target, direction, and other properties related to flow data.

• units (str, optional) – Units of measurement for the flow data. Can be ‘pixels’, ‘mapunits’, ‘m2’, or ‘km2’. Default is ‘pixels’.

• threshold (int | float | GridObject | np.ndarray, optional) – The upslope area threshold for flow accumulation. This can be an integer, float, GridObject, or a NumPy array. If more water than in the threshold has accumulated in a cell, it is part of the stream. Default is 0, which will result in the threshold being generated based on this formula: threshold = (avg^2)*0.01 where shape = (n,m).

• stream_pixels (GridObject | np.ndarray, optional) – A GridObject or np.ndarray made up of zeros and ones to denote where the stream is located. Using this will overwrite any use of the threshold argument.

• channelheads ((rows, cols), optional) – A tuple of two array-like objects containing the row and column indices of the channel heads. All streams downstream of the indicated channel heads will be returned in the StreamObject.

Raises:

• ValueError – If the units parameter is not ‘pixels’, ‘mapunits’, ‘m2’, or ‘km2’.

• ValueError – If the shape of the threshold does not match the flow object shape.

Example

>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> plt.subplots()
>>> dem.plot(cmap="terrain")
>>> s.plot(color='r')

Methods

__init__(flow[, units, threshold, ...])	Initializes the StreamObject by processing flow accumulation.
chitransform(upstream_area[, a0, mn, k, ...])	Coordinate transformation using the integral approach
clean()	Remove disconnected nodes in stream networks
conncomps()	Label connected components in a stream network
crs(dem[, tau, k, mingradient, fixedoutlet])	Elevation values along stream networks are frequently affected by large scatter, often as a result of data artifacts or errors.
crslin(dem[, k, mingradient, attachheads, ...])	Elevation values along stream networks are frequently affected by large scatter, often as a result of data artifacts or errors.
distance()	Compute the pixel-to-pixel distance for each edge.
downstream_distance()	Compute the maximum distance between a node in the stream network and the channel head.
downstreamto(nodes)	Extract the portion of the stream network downstream of the given nodes
ezgetnal(k[, dtype])	Retrieve a node attribute list from k
gradient(dem[, impose])	Calculates the stream slope for each node in the stream network S based on the associated digital elevation model DEM.
klargestconncomps([k])	Extract the k largest connected components of the stream network
knickpointfinder(dem[, knickpoints, ...])	Find knickpoints in river profiles
ksn(dem, a[, impose, theta])	Returns the normalized steepness index using a default concavity index of 0.45.
lowerenv(dem, kn)	Compute the lower convex envelope of a stream profile
node_indices_where(nal)	The row and column indices of the nodes of the stream network where a condition is satisfied.
plot([ax])	Plot the StreamObject
plotdz(z[, ax, dunit, doffset])	Plot a node attribute list against upstream distance
quantcarve(dem[, tau, mingradient, fixedoutlet])	Elevation values along stream networks are frequently affected by large scatter, often as a result of data artifacts or errors.
streamorder([method])	Calculates stream order from the StreamObject using the Strahler or Shreve method
streampoi(point_type)	Extract points of interest from the stream network
subgraph(nal)	Extract a subset of the stream network
to_geodataframe()	Convert the stream network to a GeoDataFrame.
to_shapefile(path)	Convert the stream network to a georeferenced shapefile.
trunk([downstream_distance, flow_accumulation])	Reduces a stream network to the longest streams in each stream network tree (e.g. connected component).
upstream_distance()	Compute the upstream distance for a node in the stream network
upstreamto(nodes)	Extract the portion of the stream network upstream of the given nodes
xy([data])	Compute the x and y coordinates of continuous stream segments

Attributes

node_indices	The row and column indices of the nodes of the stream network.
source_indices	The row and column indices of the sources of each edge in the stream network.
target_indices	The row and column indices of the targets of each edge in the stream network.

chitransform(upstream_area: GridObject | ndarray, a0: float = 1000000.0, mn: float = 0.45, k: GridObject | ndarray | None = None, correctcellsize: bool = True)

Coordinate transformation using the integral approach

Transforms the horizontal spatial coordinates of a river longitudinal profile using an integration in upstream direction of drainage area (chi, see Perron and Royden, 2013).

Parameters:

• upstream_area (GridObject | np.ndarray) – Raster with the upstream areas. Must be the same size and projection as the GridObject used to create the StreamObject.

• a0 (float, optional) – Reference area in the same units as the upstream_area raster. Defaults to 100_000.

• mn (float, optional) – mn-ratio. Defaults to 0.45.

• k (GridObject | np.ndarray | None, optional) – Erosional efficiency, which may vary spatially. If k is supplied, then chitransform returns the time needed for a signal (knickpoint) propagating upstream from the outlet of the stream network. If k has units of m^(1 - 2m) / y, then time will have units of y. Note that calculating the response time requires the assumption that n=1. Defaults to None, which does not use the erosional efficiency.

• correctcellsize (bool, optional) – If true, multiplies the upstream_area raster by self.cellsize**2. Use if a0 has the same units of self.cellsize**2 and upstream_area has units of pixels, such as the default output from flow_accumulation. If the units of upstream_area are already m^2, then set correctcellsize to False. Defaults to True.

Raises:

• ValueError – If upstream_area or k does not have the right shape to be indexed by the StreamObject.

• TypeError – If upstream_area or k does not represent a type of data that can be extracted into a node attribute list.

• TypeError – If the modified upstream area is not a supported floating point type.

clean() → StreamObject

Remove disconnected nodes in stream networks

Returns:

A stream network where all isolated nodes have been removed

Return type:

StreamObject

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('bigtujunga')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000)
>>> sc = s.clean()
>>> assert sc.stream.shape <= s.stream.shape

conncomps() → ndarray

Label connected components in a stream network

Returns:

A node attribute list containing the labels of each connected component. The labels range from 1 to the number of connected components in the network.

Return type:

np.ndarray

crs(dem, tau=0.5, k=1, mingradient=0.0, fixedoutlet=False) → ndarray

Elevation values along stream networks are frequently affected by large scatter, often as a result of data artifacts or errors. This function returns a node attribute list of smoothed elevation values calculated by nonparametric quantile regression with monotonicity constraints.

The algorithm written in this function follows Appendix A4 in the Schwanghart and Scherler 2017 paper.

Parameters:

s: StreamObject dem: GridObject | np.ndarray

DEM

tau: float

Quantile. Default is 0.5

k: float

positive scalar that dictates the degree of stiffness. Default is 1

mingradient: double

Minimum downward gradient. Choose carefully, because length profile may dip to steeply. Set this parameter to nan if you do not wish to have a monotonous dowstream elevation decrease.

fixedoutlet: bool

If true, elevations of outlets are fixed.

returns:

node attribute list with smoothed elevation values

rtype:

zs

crslin(dem, k=1, mingradient=0.0, attachheads=False, attachtomin=False)

Elevation values along stream networks are frequently affected by large scatter, often as a result of data artifacts or errors. This function returns a node attribute list of elevations calculated by regularized smoothing.

The algorithm written in this function follows Appendix A2 in the Schwanghart and Scherler 2017 paper.

Parameters:

s: StreamObject dem: GridObject | np.ndarray

DEM

k: float

positive scalar that dictates the degree of stiffness. Default is 1

mingradient: double

Minimum downward gradient. Choose carefully, because length profile may dip to steeply. Set this parameter to nan if you do not wish to have a monotonous dowstream elevation decrease.

attachtomin: bool

Smoothed elevations will not exceed local minima along the downstream path. (only applicable if ‘mingradient’ is not nan)

attachheads: bool

If true, elevations of channelheads are fixed. (only applicable if ‘mingradient’ is not nan). Note that for large K, setting attachheads to true can result in excessive smoothing and underestimation of elevation values directly downstream to channelheads.

returns:

node attribute list with smoothed elevation values

rtype:

zs

distance() → ndarray

Compute the pixel-to-pixel distance for each edge.

Returns:

An edge attribute list with the distance between pixels

Return type:

np.ndarray, float32

Example

>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> print(s.distance())

downstream_distance() → ndarray

Compute the maximum distance between a node in the stream network and the channel head.

Returns:

A node attribute list with the downstream distances

Return type:

np.ndarray, float32

downstreamto(nodes) → StreamObject

Extract the portion of the stream network downstream of the given nodes

Parameters:

nodes (GridObject or np.ndarray) – A logical node attribute list or grid that is True for the desired nodes.

Returns:

A stream network containing those nodes of the original one that are downstream of the given nodes.

Return type:

StreamObject

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> confluences = s.streampoi('confluences')
>>> s2 = s.downstream(confluences)
>>> fig,ax = plt.subplots()
>>> dem.plot(ax=ax,cmap="terrain")
>>> s2.plot(ax=ax,color='k')

ezgetnal(k, dtype=None)

Retrieve a node attribute list from k

Parameters:

k (GridObject | np.ndarray | float) – The object from which node values will be extracted. If k is a GridObject or an np.ndarray with the same shape as the underlying DEM of this StreamObject, the node values will be extracted from the grid by indexing. If k is an array with the same shape as the node attribute list, ezgetnal returns a copy of k. If k is a scalar value, ezgetnal returns an array of the right shape filled with k.

Returns:

The resulting array will always be a copy of the input array.

Return type:

np.ndarray

Raises:

ValueError – If k does not have the right shape to be indexed by the StreamObject.

gradient(dem, impose=False) → ndarray

Calculates the stream slope for each node in the stream network S based on the associated digital elevation model DEM.

Parameters:

• dem (GridObject or np.ndarray) – A node attribute list or grid that provides the elevation that we take the gradient of.

• impose (bool) – Minima imposition to avoid negative slopes (see imposemin)

Returns:

stream gradient as node-attribute list

Return type:

s

klargestconncomps(k=1) → StreamObject

Extract the k largest connected components of the stream network

Components are ordered by the number of stream network pixels.

Parameters:

k (integer, optional) – The number of components to keep. The default is 1

Returns:

A new StreamObject containing only the k largest connected components

Return type:

StreamObject

Example

>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> s2 = s.klargestconncomps(1)
>>> fig, ax = plt.subplots()
>>> dem.plot(ax=ax,cmap="terrain")
>>> s2.plot(ax=ax,color='k')
>>> plt.show()

knickpointfinder(dem: GridObject | ndarray, knickpoints: ndarray | None = None, tolerance: float = 100.0, iterations: int | None = None) → ndarray

Find knickpoints in river profiles

Rivers that adjust to changing base levels or have diverse lithologies often feature knickzones, i.e. pronounced convex sections that separate the otherwise concave equilibrium profile. The profile should be monotoneously decreasing (see imposemin, quantcarve, crs).

This function extracts knickpoints, i.e. sharp convex sections in the river profile. This is accomplished by an algorithm that adjusts a strictly concave upward profile to an actual profile in the DEM or node-attribute list. The algorithm iteratively relaxes the concavity constraint at those nodes in the river profile that have the largest elevation offsets between the strictly concave and actual profile until the offset falls below a user-defined tolerance.

Parameters:

• dem (GridObject or np.ndarray) – The elevation data of the stream network provided either as a GridObject compatible with this StreamObject or as a node attribute list. The elevation data is automatically processed using imposemin to ensure that it is decreasing downstream. If additional smoothing of the stream profile is needed, use a function like quantcarve or crslin and pass the resulting node attribute list to knickpointfinder.

• knickpoints (np.ndarray) – A logical node attribute list that is True for any stream network nodes that should be considered knickpoints a priori

• tolerance (float) –

  The maximum difference between the DEM and a modeled convex profile that will be considered a knickpoint

  Setting this higher will identify fewer knickpoints while setting it lower finds more knickpoints. It is set by default to 100 m, but users should choose a tolerance based on their data and needs.

• iterations (int) –

  The maximum number of iterations that will be run

  knickpointfinder will identify more than one knickpoint per iteration, so this argument does not completely control the number of knickpoints identified. The default is the number of nodes in the stream network, and the iteration count is always limited to the number of nodes in the stream network.

Returns:

A logical node attribute list that is True for stream network nodes that are identified as knickpoints

Return type:

np.ndarray

Example

>>> import topotoolbox
>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('bigtujunga')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd)
>>> s = s.klargestconncomps(1)
>>> z = topotoolbox.imposemin(s, dem)
>>> kp = s.knickpointfinder(z, tolerance=50.0)
>>> d = s.upstream_distance()
>>> fig,ax = plt.subplots()
>>> s.plotdz(dem, ax=ax, color='k')
>>> ax.scatter(d[kp], z[kp])
>>> ax.autoscale_view()

ksn(dem, a, impose=False, theta=0.45) → ndarray

Returns the normalized steepness index using a default concavity index of 0.45.

Parameters:

• dem (GridObject or np.ndarray) – Digital elevation model

• a (GridObject) – Flow accumulation as returned by flowacc (GridObject)

• impose (bool) – Minima imposition to avoid negative slopes (see imposemin

• theta (float) – Concavity (default 0.45)

Returns:

Normalized steepness index

Return type:

k

lowerenv(dem: GridObject | ndarray, kn: ndarray) → ndarray

Compute the lower convex envelope of a stream profile

Parameters:

• dem (GridObject or np.ndarray) – The elevation data of the stream network provided either as a GridObject compatible with this StreamObject or as a node attribute list. The elevation data should be decreasing downstream. Preprocess with imposemin or quantcarve if necessary.

• kn (np.ndarray) – A logical node attribute list that is true for nodes that should be considered knickpoints, where the stream profile need not be convex.

Returns:

A node attribute list containing the elevation of the smoothed profile.

Return type:

np.ndarray

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('bigtujunga')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd)
>>> s = s.klargestconncomps(1)
>>> z = topotoolbox.imposemin(s, dem)
>>> kn = np.zeros(len(z), dtype=bool)
>>> ze = s.lowerenv(z, kn)
>>> fig,ax = plt.subplots()
>>> s.plotdz(dem, ax=ax, color='gray')
>>> s.plotdz(ze, ax=ax, color='black')
>>> ax.autoscale_view()

property node_indices: tuple[ndarray, ...]

The row and column indices of the nodes of the stream network.

Returns:

• tuple of ndarray – A tuple of arrays containing the row indices and column

• indices for the nodes in the stream network. Each of these

• arrays is a node attribute lists and have a length equal to

• the number of nodes in the stream network. This tuple of

• arrays is suitable for indexing GridObjects or arrays shaped

• like the GridObject from which this StreamObject was derived.

node_indices_where(nal: ndarray) → tuple[ndarray, ...]

The row and column indices of the nodes of the stream network where a condition is satisfied.

Returns:

• tuple of ndarray – A tuple of arrays containing the row indices and column

• indices for the nodes in the stream network where the input

• Boolean node attribute list nal is true. Each of these

• arrays is a node attribute lists and have a length equal to

• the number of nodes in the stream network. This tuple of

• arrays is suitable for indexing GridObjects or arrays shaped

• like the GridObject from which this StreamObject was derived.

plot(ax=None, **kwargs)

Plot the StreamObject

Stream segments as computed by StreamObject.xy are plotted using a LineCollection. Note that collections are not used in autoscaling the provided axis. If the axis limits are not already set, by another underlying plot, for example, call ax.autoscale_view() on the returned axes to show the plot.

Parameters:

• ax (matplotlib.axes.Axes, optional) – The axes in which to plot the StreamObject. If no axes are given, the current axes are used.

• **kwargs – Additional keyword arguments are forwarded to LineCollection

Returns:

The axes into which the StreamObject has been plotted.

Return type:

matplotlib.axes.Axes

Example

>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> plt.subplots()
>>> dem.plot(cmap="terrain")
>>> s.plot(color='r')

plotdz(z, ax=None, dunit: str = 'm', doffset: float = 0, **kwargs)

Plot a node attribute list against upstream distance

Note that collections are not used in autoscaling the provided axis. If the axis limits are not already set, by another underlying plot, for example, call ax.autoscale_view() on the returned axes to show the plot.

Parameters:

• z (GridObject, np.ndarray) – The node attribute list that will be plotted

• ax (matplotlib.axes.Axes, optional) – The axes in which to plot the StreamObject. If no axes are given, the current axes are used.

• dunit (str, optional) – The unit to plot the upstream distance. Should be either ‘m’ for meters or ‘km’ for kilometers.

• doffset (float, optional) – An offset to be applied to the upstream distance. doffset should be in the units specified by dunit.

• **kwargs – Additional keyword arguments are forwarded to LineCollection

Returns:

The axes into which the plot as been added

Return type:

matplotlib.axes.Axes

Raises:

ValueError – If dunit is not one of ‘m’ or ‘km’.

quantcarve(dem, tau=0.5, mingradient=0.0, fixedoutlet=False) → ndarray

Elevation values along stream networks are frequently affected by large scatter, often as a result of data artifacts or errors. This function returns a node attribute list of elevations calculated by carving the DEM. Conversely to conventional carving, quantcarve will not run along minimas of the DEM. Instead, quantcarve returns a profile that runs along the tau’s quantile of elevation conditional horizontal distance of the river profile.

The algorithm written in this function follows Appendix A3 in the Schwanghart and Scherler 2017 paper.

Parameters:

• s (StreamObject)

• dem (GridObject | np.ndarray) – Digital Elevation Model

• tau (float) – Quantile. Default is 0.5

• mingradient (float) – Minimum downward gradient.

• fixedoutlet (bool) – If true, elevations of outlets are fixed.

• mingradient – Minimum downward gradient.

Returns:

Node attribute list with with smoothed elevation values

Return type:

zs

property source_indices: tuple[ndarray, ...]

The row and column indices of the sources of each edge in the stream network.

Returns:

• tuple of ndarray – A tuple of arrays containing the row indices and column

• indices of the sources of each edge in the stream

• network. Each of these arrays is an edge attribute lists and

• have a length equal to the number of edges in the stream

• network. This tuple of arrays is suitable for indexing

• GridObjects or arrays shaped like the GridObject from which

• this StreamObject was derived.

streamorder(method='strahler') → ndarray

Calculates stream order from the StreamObject using the Strahler or Shreve method

Parameters:

• s (StreamObject)

• method (string) – Strahler (default) or Shreve method

Returns:

Stream order for each node in StreamObject

Return type:

s_order

streampoi(point_type: str) → ndarray

Extract points of interest from the stream network

Currently supported points of interest are ‘channelheads’, ‘outlets’ and ‘confluences’

Parameters:

point_type ('channelheads' or 'outlets' or 'confluences') – The type of points to select from the stream network

Returns:

A logical node attribute list indicating the locations of points.

Return type:

np.ndarray

Raises:

ValueError – If an unknown point type is requested.

subgraph(nal)

Extract a subset of the stream network

Parameters:

nal (GridObject or np.ndarray) – A logical node attribute list indicating the desired nodes of the new stream network

Returns:

A StreamObject representing the desired subset of the stream network.

Return type:

StreamObject

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('perfectworld')
>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> shape = dem.shape
>>> arr = (np.arange(np.prod(shape))<np.prod(shape)//4).reshape(shape)
>>> s2 = s.subgraph(arr)
>>> fig,ax = plt.subplots()
>>> dem.plot(ax=ax,cmap="terrain")
>>> s2.plot(ax=ax,color='k')

property target_indices: tuple[ndarray, ...]

The row and column indices of the targets of each edge in the stream network.

Returns:

• tuple of ndarray – A tuple of arrays containing the row indices and column

• indices of the sources of each edge in the stream

• network. Each of these arrays is an edge attribute lists and

• have a length equal to the number of edges in the stream

• network. This tuple of arrays is suitable for indexing

• GridObjects or arrays shaped like the GridObject from which

• this StreamObject was derived.

to_geodataframe()

Convert the stream network to a GeoDataFrame.

Returns:

The GeoDataFrame.

Return type:

geopandas.GeoDataFrame

Example

dem = tt3.load_dem(‘bigtujunga’) fd = tt3.FlowObject(dem) s = tt3.StreamObject(fd) s_gdf = s.to_geodataframe()

to_shapefile(path: str) → None

Convert the stream network to a georeferenced shapefile.

Parameters:

path (str) – path where the shapefile will be saved.

Example

dem = tt3.load_dem(‘bigtujunga’) fd = tt3.FlowObject(dem) s = tt3.StreamObject(fd) s.to_shapefile(‘stream_network.shp’)

trunk(downstream_distance: ndarray | None = None, flow_accumulation: GridObject | None = None) → StreamObject

Reduces a stream network to the longest streams in each stream network tree (e.g. connected component). The algorithm identifies the main trunk by sequently tracing the maximum downstream distance in upstream direction.

Parameters:

• flow_accumulation (Gridobject, optional) – A GridObject filled with flow accumulation values (as returned by the function FlowObject.flow_accumulation). Defaults to None.

• downstream_distance (np.ndarray, optional) – A numpy ndarray node-attribute list as generated by ezgetnal(). This argument overwrites the flow_accumulation if used. Defaults to None.

Returns:

StreamObject with truncated streams.

Return type:

StreamObject

Example

>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> s2 = s.klargestconncomps(1)
>>> st = s2.trunk()
>>> fig,ax = plt.subplots()
>>> dem.plot(ax=ax,cmap="terrain")
>>> s.plot(ax=ax, color='r')
>>> s2.plot(ax=ax,color='k')
>>> st.plot(ax=ax, color='b')

upstream_distance() → ndarray

Compute the upstream distance for a node in the stream network

Returns:

A node attribute list with the upstream distances

Return type:

np.ndarray, float32

upstreamto(nodes) → StreamObject

Extract the portion of the stream network upstream of the given nodes

Parameters:

nodes (GridObject or np.ndarray) – A logical node attribute list or grid that is True for the desired nodes.

Returns:

A stream network containing those nodes of the original one that are upstream of the given nodes.

Return type:

StreamObject

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> dem = topotoolbox.load_dem('perfectworld')
>>> fd = topotoolbox.FlowObject(dem)
>>> s = topotoolbox.StreamObject(fd,threshold=1000,units='pixels')
>>> confluences = s.streampoi('confluences')
>>> s2 = s.upstreamto(confluences)
>>> fig,ax = plt.subplots()
>>> dem.plot(ax=ax,cmap="terrain")
>>> s2.plot(ax=ax,color='k')

xy(data=None)

Compute the x and y coordinates of continuous stream segments

Parameters:

data (tuple, optional) – A tuple of two node attribute lists representing the desired x and y values for each pixel in the stream network. If this argument is not supplied, the returned x and y values are the geographic coordinates of the node.

Returns:

A list of lists of (x,y) pairs.

Return type:

list