def create_line_collection(net,
lines=None,
use_line_geodata=True,
infofunc=None,
cmap=None,
norm=None,
picker=False,
z=None,
cbar_title="Line Loading [%]",
**kwargs):
"""
Creates a matplotlib line collection of pandapower lines.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**lines** (list, None) - The lines for which the collections are created. If None, all lines in the network are considered.
*use_line_geodata** (bool, True) - defines if lines patches are based on net.line_geodata of the lines (True) or on net.bus_geodata of the connected buses (False)
**infofunc** (function, None) - infofunction for the patch element
**kwargs - key word arguments are passed to the patch function
"""
lines = net.line_geodata.index.tolist() if lines is None and use_line_geodata else \
net.line.index.tolist() if lines is None and not use_line_geodata else list(lines)
if len(lines) == 0:
return None
if use_line_geodata:
data = [(net.line_geodata.coords.loc[line],
infofunc(line) if infofunc else []) for line in lines
if line in net.line_geodata.index]
else:
data = [([(net.bus_geodata.x.at[a], net.bus_geodata.y.at[a]),
(net.bus_geodata.x.at[b], net.bus_geodata.y.at[b])],
infofunc(line) if infofunc else [])
for line, (a,
b) in net.line[["from_bus", "to_bus"]].iterrows()
if line in lines and a in net.bus_geodata.index
and b in net.bus_geodata.index]
data, info = list(zip(*data))
# This would be done anyways by matplotlib - doing it explicitly makes it a) clear and
# b) prevents unexpected behavior when observing colors being "none"
lc = LineCollection(data, picker=picker, **kwargs)
lc.line_indices = np.array(lines)
if cmap:
if z is None:
z = net.res_line.loading_percent.loc[lines]
lc.set_cmap(cmap)
lc.set_norm(norm)
lc.set_array(z)
lc.has_colormap = True
lc.cbar_title = "Line Loading [%]"
lc.info = info
return lc
def create_line_collection(net,
lines=None,
line_geodata=None,
bus_geodata=None,
use_bus_geodata=False,
infofunc=None,
cmap=None,
norm=None,
picker=False,
z=None,
cbar_title="Line Loading [%]",
clim=None,
**kwargs):
"""
Creates a matplotlib line collection of pandapower lines.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**lines** (list, None) - The lines for which the collections are created. If None, all lines
in the network are considered.
**line_geodata** (DataFrame, None) - coordinates to use for plotting If None,
net["line_geodata"] is used
**infofunc** (function, None) - infofunction for the patch element
**kwargs - key word arguments are passed to the patch function
OUTPUT:
**lc** - line collection
"""
lines = net.line.index.tolist() if lines is None else list(lines)
if len(lines) == 0:
return None
if line_geodata is None:
line_geodata = net["line_geodata"]
if bus_geodata is None:
bus_geodata = net["bus_geodata"]
if len(lines) == 0:
return None
if use_bus_geodata:
data = [
([(bus_geodata.at[a, "x"], bus_geodata.at[a, "y"]),
(bus_geodata.at[b, "x"], bus_geodata.at[b, "y"])],
infofunc(line) if infofunc else [])
for line, (a,
b) in net.line.loc[lines,
["from_bus", "to_bus"]].iterrows()
if a in bus_geodata.index.values and b in bus_geodata.index.values
]
else:
data = [(line_geodata.loc[line, "coords"],
infofunc(line) if infofunc else []) for line in lines
if line in line_geodata.index.values]
if len(data) == 0:
return None
data, info = list(zip(*data))
# This would be done anyways by matplotlib - doing it explicitly makes it a) clear and
# b) prevents unexpected behavior when observing colors being "none"
lc = LineCollection(data, picker=picker, **kwargs)
lc.line_indices = np.array(lines)
if cmap:
if z is None:
z = net.res_line.loading_percent.loc[lines]
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.array(z))
lc.has_colormap = True
lc.cbar_title = cbar_title
lc.info = info
return lc
def create_line_collection(net, lines=None, line_geodata=None, bus_geodata=None,
use_bus_geodata=False, infofunc=None,
cmap=None, norm=None, picker=False, z=None,
cbar_title="Line Loading [%]", clim=None, **kwargs):
"""
Creates a matplotlib line collection of pandapower lines.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**lines** (list, None) - The lines for which the collections are created. If None, all lines
in the network are considered.
**line_geodata** (DataFrame, None) - coordinates to use for plotting. If None,
net["line_geodata"] is used
**bus_geodata** (DataFrame, None) - coordinates to use for plotting
If None, net["bus_geodata"] is used
**use_bus_geodata** (bool, False) - Defines whether bus or line geodata are used.
**infofunc** (function, None) - infofunction for the patch element
**cmap** - colormap for the patch colors
**norm** (matplotlib norm object, None) - matplotlib norm object
**picker** (bool, False) - picker argument passed to the patch collection
**z** (array, None) - array of bus voltage magnitudes for colormap. Used in case of given
cmap. If None net.res_bus.vm_pu is used.
**cbar_title** (str, "Bus Voltage [pu]") - colormap bar title in case of given cmap
**clim** (tuple of floats, None) - setting the norm limits for image scaling
**kwargs - key word arguments are passed to the patch function
OUTPUT:
**lc** - line collection
"""
if use_bus_geodata:
linetab = net.line if lines is None else net.line.loc[lines]
lines = net.line.index.tolist() if lines is None else list(lines)
if len(lines) == 0:
return None
if line_geodata is None:
line_geodata = net["line_geodata"]
if bus_geodata is None:
bus_geodata = net["bus_geodata"]
if len(lines) == 0:
return None
lines_with_geo = []
if use_bus_geodata:
data = []
buses_with_geodata = bus_geodata.index.values
bg_dict = bus_geodata.to_dict() #transforming to dict to make lookup faster
for line, fb, tb in zip(linetab.index, linetab.from_bus.values, linetab.to_bus.values):
if fb in buses_with_geodata and tb in buses_with_geodata:
lines_with_geo.append(line)
data.append(([(bg_dict["x"][fb], bg_dict["y"][fb]),
(bg_dict["x"][tb], bg_dict["y"][tb])],
infofunc(line) if infofunc else[]))
lines_without_geo = set(lines)-set(lines_with_geo)
if lines_without_geo:
logger.warning("Could not plot lines %s. Bus geodata is missing for those lines!"
% lines_without_geo)
else:
data = []
for line in lines:
if line in line_geodata.index.values:
lines_with_geo.append(line)
data.append((line_geodata.loc[line, "coords"], infofunc(line) if infofunc else []))
lines_without_geo = set(lines)-set(lines_with_geo)
if len(lines_without_geo) > 0:
logger.warning("Could not plot lines %s. Line geodata is missing for those lines!"
% lines_without_geo)
if len(data) == 0:
return None
data, info = list(zip(*data))
# This would be done anyways by matplotlib - doing it explicitly makes it a) clear and
# b) prevents unexpected behavior when observing colors being "none"
lc = LineCollection(data, picker=picker, **kwargs)
lc.line_indices = np.array(lines_with_geo)
if cmap is not None:
if z is None:
z = net.res_line.loading_percent.loc[lines_with_geo]
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.array(z))
lc.has_colormap = True
lc.cbar_title = cbar_title
lc.info = info
return lc
