def __plot_all(self, spectrum):
total = len(spectrum)
count = 0.0
for timeStamp in spectrum:
if self.settings.fadeScans:
alpha = (count + 1) / total
else:
alpha = 1
data = list(spectrum[timeStamp].items())
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.__create_segments(data)
if segments is not None:
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.settings.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
return peakF, peakL
class Visualize:
def __init__(self, v, t, e, fig, win, axesLimit=[-3,3.5,-2,2]):
self.e = e.copy()
self.p = [Polygon(v[ti]) for ti in t]
self.p = PatchCollection(self.p, edgecolors='none')
self.l = LineCollection(v[e[:,:2]])
win = win or fig.canvas.manager.window
if fig is None: fig = gcf()
fig.clf()
ax = fig.add_axes([0.02,0.02,.98,.98])
ax.axis('scaled')
ax.axis(axesLimit)
ax.set_autoscale_on(False)
self.axis, self.fig, self.win = ax, fig, win
ax.add_collection(self.p)
ax.add_collection(self.l)
# ax.add_collection(self.l1)
# ax.add_collection(self.l2)
def update(self, title, phi):
norm = Normalize(phi.min(), phi.max())
self.p.set_norm(norm)
self.l.set_norm(norm)
self.p.set_array(phi)
self.l.set_array(phi[self.e[:,2:]].mean(1))
if not self.__dict__.has_key('colorbar'):
self.colorbar = self.fig.colorbar(self.p)
self.win.set_title(title)
#self.fig.canvas.set_window_title(title)
self.fig.canvas.draw()
def __plot_all(self, spectrum):
total = len(spectrum)
count = 0.0
for timeStamp in spectrum:
if self.settings.fadeScans:
alpha = (total - count) / total
else:
alpha = 1
data = spectrum[timeStamp].items()
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.__create_segments(data)
if segments is not None:
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.settings.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
return peakF, peakL
def __plot_all(self):
total = len(self.data)
count = 0.0
for timeStamp in self.data:
if len(self.data[timeStamp]) < 2:
self.parent.threadPlot = None
return None, None
if self.fade:
alpha = (total - count) / total
else:
alpha = 1
data = self.data[timeStamp].items()
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.__create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
return peakF, peakL
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_trafo_connection_collection(net, trafos=None, bus_geodata=None, infofunc=None,
cmap=None, clim=None, norm=None, z=None,
cbar_title="Transformer Loading", **kwargs):
"""
Creates a matplotlib line collection of pandapower transformers.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**trafos** (list, None) - The transformers for which the collections are created.
If None, all transformers in the network are considered.
**bus_geodata** (DataFrame, None) - coordinates to use for plotting
If None, net["bus_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
"""
trafos = net.trafo if trafos is None else net.trafo.loc[trafos]
if bus_geodata is None:
bus_geodata = net["bus_geodata"]
hv_geo = list(zip(bus_geodata.loc[trafos["hv_bus"], "x"].values,
bus_geodata.loc[trafos["hv_bus"], "y"].values))
lv_geo = list(zip(bus_geodata.loc[trafos["lv_bus"], "x"].values,
bus_geodata.loc[trafos["lv_bus"], "y"].values))
tg = list(zip(hv_geo, lv_geo))
info = [infofunc(tr) if infofunc is not None else [] for tr in trafos.index.values]
lc = LineCollection([(tgd[0], tgd[1]) for tgd in tg], **kwargs)
lc.info = info
if cmap is not None:
if z is None:
z = net.res_trafo.loading_percent.loc[trafos.index]
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.ma.masked_invalid(z))
lc.has_colormap = True
lc.cbar_title = cbar_title
return lc
def __plot_single(self, points):
data = points.items()
peakF, peakL = max(data, key=lambda item: item[1])
segments, levels = self.__create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.settings.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
self.axes.add_collection(lc)
return peakF, peakL
def __plot_single(self, points):
data = points.items()
peakF, peakL = max(data, key=lambda item: item[1])
segments, levels = self.__create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.__get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
self.axes.add_collection(lc)
return peakF, peakL
def plot(self, time, beam=None, maxground=2000, maxalt=500,
iscat=True, gscat=True, title=False, weighted=False, cmap='hot_r',
fig=None, rect=111, ax=None, aax=None, zorder=4):
"""Plot scatter on ground/altitude profile
Parameters
----------
time : datetime.datetime
time of profile
beam : Optional[ ]
beam number
maxground : Optional[int]
maximum ground range [km]
maxalt : Optional[int]
highest altitude limit [km]
iscat : Optional[bool]
show ionospheric scatter
gscat : Optional[bool]
show ground scatter
title : Optional[bool]
Show default title
weighted : Optional[bool]
plot ionospheric scatter relative strength (based on background density and range)
cmap : Optional[str]
colormap used for weighted ionospheric scatter
fig : Optional[pylab.figure]
object (default to gcf)
rect : Optional[int]
subplot spcification
ax : Optional[ ]
Existing main axes
aax : Optional[ ]
Existing auxialary axes
zorder : Optional[int]
Returns
-------
ax : matplotlib.axes
object containing formatting
aax : matplotlib.axes
object containing data
cbax : matplotlib.axes
object containing colorbar
Example
-------
# Show ionospheric scatter
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, title=True)
rto.readScatter() # read scatter into memory
rto.scatter.plot(sTime, ax=ax, aax=aax)
ax.grid()
written by Sebastien, 2013-04
"""
from davitpy.utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.isc.keys() or time in self.gsc.keys()), logging.error('Unkown time %s' % time)
if beam:
assert (beam in self.isc[time].keys()), logging.error('Unkown beam %s' % beam)
else:
beam = self.isc[time].keys()[0]
if gscat and time in self.gsc.keys():
for ir, (el, rays) in enumerate( sorted(self.gsc[time][beam].items()) ):
if len(rays['r']) == 0: continue
_ = aax.scatter(rays['th'], ax.Re*np.ones(rays['th'].shape),
color='0', zorder=zorder)
if iscat and time in self.isc.keys():
if weighted:
wmin = np.min( [ r['w'].min() for r in self.isc[time][beam].values() if r['nstp'] > 0] )
wmax = np.max( [ r['w'].max() for r in self.isc[time][beam].values() if r['nstp'] > 0] )
for ir, (el, rays) in enumerate( sorted(self.isc[time][beam].items()) ):
if rays['nstp'] == 0: continue
t = rays['th']
r = rays['r']*1e-3
spts = np.array([t, r]).T.reshape(-1, 1, 2)
h = rays['h']*1e-3
rel = np.radians( rays['rel'] )
r = np.sqrt( r**2 + h**2 + 2*r*h*np.sin( rel ) )
t = t + np.arcsin( h/r * np.cos( rel ) )
epts = np.array([t, r]).T.reshape(-1, 1, 2)
segments = np.concatenate([spts, epts], axis=1)
lcol = LineCollection( segments, zorder=zorder )
if weighted:
_ = lcol.set_cmap( cmap )
_ = lcol.set_norm( plt.Normalize(0, 1) )
_ = lcol.set_array( ( rays['w'] - wmin ) / wmax )
else:
_ = lcol.set_color('0')
_ = aax.add_collection( lcol )
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, None)
ax.set_title( stitle )
# If weighted, plot ionospheric scatter with colormap
if weighted:
# Add a colorbar
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("Ionospheric Scatter")
else: cbax = None
ax.beam = beam
return ax, aax, cbax
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
def plot(self, time, beam=None,
maxground=2000, maxalt=500, step=1,
showrefract=False, nr_cmap='jet_r', nr_lim=[0.8, 1.],
raycolor='0.3', title=False, zorder=2, alpha=1,
fig=None, rect=111, ax=None, aax=None):
"""Plot ray paths
**Args**:
* **time** (datetime.datetime): time of rays
* [**beam**]: beam number
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**step**]: step between each plotted ray (in number of ray steps)
* [**showrefract**]: show refractive index along ray paths (supersedes raycolor)
* [**nr_cmap**]: color map name for refractive index coloring
* [**nr_lim**]: refractive index plotting limits
* [**raycolor**]: color of ray paths
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
* [**title**]: Show default title
* [**ax**]: Existing main axes
* [**aax**]: Existing auxialary axes
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ray paths with colored refractive index along path
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12, title=True)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=10, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
from utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
from types import MethodType
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'time'):
time = ax.time
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.paths.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.paths[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.paths[time].keys()[0]
for ir, (el, rays) in enumerate( sorted(self.paths[time][beam].items()) ):
if not ir % step:
if not showrefract:
aax.plot(rays['th'], rays['r']*1e-3, c=raycolor,
zorder=zorder, alpha=alpha)
else:
points = np.array([rays['th'], rays['r']*1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lcol = LineCollection( segments, zorder=zorder, alpha=alpha)
_ = lcol.set_cmap( nr_cmap )
_ = lcol.set_norm( plt.Normalize(*nr_lim) )
_ = lcol.set_array( rays['nr'] )
_ = aax.add_collection( lcol )
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, self.name)
ax.set_title( stitle )
# Add a colorbar when plotting refractive index
if showrefract:
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("refractive index")
else: cbax = None
# Declare a new method to show range markers
# This method is only available after rays have been plotted
# This ensures that the markers match the plotted rays
def showRange(self, markers=None,
color='.8', s=2, zorder=3,
**kwargs):
"""Plot ray paths
**Args**:
* [**markers**]: range markers. Defaults to every 250 km
* All other keywords are borrowed from :func:`matplotlib.pyplot.scatter`
**Returns**:
* **coll**: a collection of range markers
**Example**:
::
# Add range markers to an existing ray plot
ax, aax, cbax = rto.rays.plot(sTime, step=10)
rto.rays.showRange()
written by Sebastien, 2013-04
"""
if not markers:
markers = np.arange(0, 5000, 250)
x, y = [], []
for el, rays in self.paths[time][beam].items():
for rm in markers:
inds = (rays['gran']*1e-3 >= rm)
if inds.any():
x.append( rays['th'][inds][0] )
y.append( rays['r'][inds][0]*1e-3 )
coll = aax.scatter(x, y,
color=color, s=s, zorder=zorder, **kwargs)
return coll
# End of new method
# Assign new method
self.showRange = MethodType(showRange, self)
ax.beam = beam
return ax, aax, cbax
def plot(self,
time,
beam=None,
maxground=2000,
maxalt=500,
iscat=True,
gscat=True,
title=False,
weighted=False,
cmap='hot_r',
fig=None,
rect=111,
ax=None,
aax=None,
zorder=4):
"""Plot scatter on ground/altitude profile
**Args**:
* **time** (datetime.datetime): time of profile
* [**beam**]: beam number
* [**iscat**] (bool): show ionospheric scatter
* [**gscat**] (bool): show ground scatter
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
* [**ax**]: Existing main axes
* [**aax**]: Existing auxialary axes
* [**title**]: Show default title
* [**weighted**] (bool): plot ionospheric scatter relative strength (based on background density and range)
* [**cmap**]: colormap used for weighted ionospheric scatter
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ionospheric scatter
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, title=True)
rto.readScatter() # read scatter into memory
rto.scatter.plot(sTime, ax=ax, aax=aax)
ax.grid()
written by Sebastien, 2013-04
"""
from davitpy.utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig,
rect=rect,
maxground=maxground,
maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
assert (time in self.isc.keys()
or time in self.gsc.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.isc[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.isc[time].keys()[0]
if gscat and time in self.gsc.keys():
for ir, (el,
rays) in enumerate(sorted(self.gsc[time][beam].items())):
if len(rays['r']) == 0: continue
_ = aax.scatter(rays['th'],
ax.Re * np.ones(rays['th'].shape),
color='0',
zorder=zorder)
if iscat and time in self.isc.keys():
if weighted:
wmin = np.min([
r['w'].min() for r in self.isc[time][beam].values()
if r['nstp'] > 0
])
wmax = np.max([
r['w'].max() for r in self.isc[time][beam].values()
if r['nstp'] > 0
])
for ir, (el,
rays) in enumerate(sorted(self.isc[time][beam].items())):
if rays['nstp'] == 0: continue
t = rays['th']
r = rays['r'] * 1e-3
spts = np.array([t, r]).T.reshape(-1, 1, 2)
h = rays['h'] * 1e-3
rel = np.radians(rays['rel'])
r = np.sqrt(r**2 + h**2 + 2 * r * h * np.sin(rel))
t = t + np.arcsin(h / r * np.cos(rel))
epts = np.array([t, r]).T.reshape(-1, 1, 2)
segments = np.concatenate([spts, epts], axis=1)
lcol = LineCollection(segments, zorder=zorder)
if weighted:
_ = lcol.set_cmap(cmap)
_ = lcol.set_norm(plt.Normalize(0, 1))
_ = lcol.set_array((rays['w'] - wmin) / wmax)
else:
_ = lcol.set_color('0')
_ = aax.add_collection(lcol)
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, None)
ax.set_title(stitle)
# If weighted, plot ionospheric scatter with colormap
if weighted:
# Add a colorbar
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("Ionospheric Scatter")
else:
cbax = None
ax.beam = beam
return ax, aax, cbax
def plot(self,
time,
beam=None,
maxground=2000,
maxalt=500,
step=1,
showrefract=False,
nr_cmap='jet_r',
nr_lim=[0.8, 1.],
raycolor='0.3',
title=False,
zorder=2,
alpha=1,
fig=None,
rect=111,
ax=None,
aax=None):
"""Plot ray paths
**Args**:
* **time** (datetime.datetime): time of rays
* [**beam**]: beam number
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**step**]: step between each plotted ray (in number of ray steps)
* [**showrefract**]: show refractive index along ray paths (supersedes raycolor)
* [**nr_cmap**]: color map name for refractive index coloring
* [**nr_lim**]: refractive index plotting limits
* [**raycolor**]: color of ray paths
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
* [**title**]: Show default title
* [**ax**]: Existing main axes
* [**aax**]: Existing auxialary axes
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ray paths with colored refractive index along path
import datetime as dt
from davitpy.models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12, title=True)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=10, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
import datetime as dt
from davitpy.utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
from types import MethodType
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig,
rect=rect,
maxground=maxground,
maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'time'):
time = ax.time
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
# Allow a 60 second difference between the requested time and the time
# available.
keys = np.array(self.paths.keys())
diffs = np.abs(keys - time)
if diffs.min() < dt.timedelta(minutes=1):
time = keys[diffs.argmin()]
assert (time in self.paths.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.paths[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.paths[time].keys()[0]
for ir, (el,
rays) in enumerate(sorted(self.paths[time][beam].items())):
if not ir % step:
if not showrefract:
aax.plot(rays['th'],
rays['r'] * 1e-3,
c=raycolor,
zorder=zorder,
alpha=alpha)
else:
points = np.array([rays['th'],
rays['r'] * 1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]],
axis=1)
lcol = LineCollection(segments, zorder=zorder, alpha=alpha)
_ = lcol.set_cmap(nr_cmap)
_ = lcol.set_norm(plt.Normalize(*nr_lim))
_ = lcol.set_array(rays['nr'])
_ = aax.add_collection(lcol)
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, self.name)
ax.set_title(stitle)
# Add a colorbar when plotting refractive index
if showrefract:
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("refractive index")
else:
cbax = None
# Declare a new method to show range markers
# This method is only available after rays have been plotted
# This ensures that the markers match the plotted rays
def showRange(self, markers=None, color='.8', s=2, zorder=3, **kwargs):
"""Plot ray paths
**Args**:
* [**markers**]: range markers. Defaults to every 250 km
* All other keywords are borrowed from :func:`matplotlib.pyplot.scatter`
**Returns**:
* **coll**: a collection of range markers
**Example**:
::
# Add range markers to an existing ray plot
ax, aax, cbax = rto.rays.plot(sTime, step=10)
rto.rays.showRange()
written by Sebastien, 2013-04
"""
if not markers:
markers = np.arange(0, 5000, 250)
x, y = [], []
for el, rays in self.paths[time][beam].items():
for rm in markers:
inds = (rays['gran'] * 1e-3 >= rm)
if inds.any():
x.append(rays['th'][inds][0])
y.append(rays['r'][inds][0] * 1e-3)
coll = aax.scatter(x, y, color=color, s=s, zorder=zorder, **kwargs)
return coll
# End of new method
# Assign new method
self.showRange = MethodType(showRange, self)
ax.beam = beam
return ax, aax, cbax
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_trafo3w_collection(net, trafo3ws=None, picker=False, infofunc=None, cmap=None, norm=None,
z=None, clim=None, cbar_title="3W-Transformer Loading",
plot_colormap=True, **kwargs):
"""
Creates a matplotlib line collection of pandapower transformers.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**trafo3ws** (list, None) - The three winding transformers for which the collections are
created. If None, all three winding transformers in the network are considered.
**picker** (bool, False) - picker argument passed to the patch collection
**infofunc** (function, None) - infofunction for the patch element
**kwargs - key word arguments are passed to the patch function
OUTPUT:
**lc** - line collection
**pc** - patch collection
"""
trafo3ws = get_index_array(trafo3ws, net.trafo3w.index)
trafo3w_table = net.trafo3w.loc[trafo3ws]
lines = []
circles = []
infos = []
color = kwargs.pop("color", "k")
linewidth = kwargs.pop("linewidths", 2.)
if cmap is not None and z is None:
z = net.res_trafo3w.loading_percent
for i, idx in enumerate(trafo3w_table.index):
# get bus geodata
p1 = net.bus_geodata[["x", "y"]].loc[net.trafo3w.at[idx, "hv_bus"]].values
p2 = net.bus_geodata[["x", "y"]].loc[net.trafo3w.at[idx, "mv_bus"]].values
p3 = net.bus_geodata[["x", "y"]].loc[net.trafo3w.at[idx, "lv_bus"]].values
if np.all(p1 == p2) and np.all(p1 == p3):
continue
p = np.array([p1, p2, p3])
# determine center of buses and minimum distance center-buses
center = sum(p) / 3
d = np.linalg.norm(p - center, axis=1)
r = d.min() / 3
# determine closest bus to center and vector from center to circle midpoint in closest
# direction
closest = d.argmin()
to_closest = (p[closest] - center) / d[closest] * 2 * r / 3
# determine vectors from center to circle midpoint
order = list(range(closest, 3)) + list(range(closest))
cm = np.empty((3, 2))
cm[order.pop(0)] = to_closest
ang = 2 * np.pi / 3 # 120 degree
cm[order.pop(0)] = _rotate_dim2(to_closest, ang)
cm[order.pop(0)] = _rotate_dim2(to_closest, -ang)
# determine midpoints of circles
m = center + cm
# determine endpoints of circles
e = (center - p) * (1 - 5 * r / 3 / d).reshape(3, 1) + p
# save circle and line collection data
ec = color if cmap is None else cmap(norm(z.at[idx]))
for j in range(3):
circles.append(Circle(m[j], r, fc=(1, 0, 0, 0), ec=ec))
lines.append([p[j], e[j]])
if infofunc is not None:
infos.append(infofunc(i))
infos.append(infofunc(i))
if len(circles) == 0:
return None, None
lc = LineCollection(lines, color=color, picker=picker, linewidths=linewidth, **kwargs)
lc.info = infos
pc = PatchCollection(circles, match_original=True, picker=picker, linewidth=linewidth, **kwargs)
pc.info = infos
if cmap is not None:
z_duplicated = np.repeat(z.values, 3)
lc.set_cmap(cmap)
lc.set_norm(norm)
if clim is not None:
lc.set_clim(clim)
lc.set_array(np.ma.masked_invalid(z_duplicated))
lc.has_colormap = plot_colormap
lc.cbar_title = cbar_title
return lc, pc
def run(self):
if self.data is None:
self.parent.threadPlot = None
return
peakF = None
peakL = None
total = len(self.data)
if total > 0:
self.parent.clear_plots()
lc = None
if self.average:
avg = OrderedDict()
count = len(self.data)
for timeStamp in self.data:
if len(self.data[timeStamp]) < 2:
return
for x, y in self.data[timeStamp].items():
if x in avg:
avg[x] = (avg[x] + y) / 2
else:
avg[x] = y
data = avg.items()
peakF, peakL = max(data, key=lambda item: item[1])
segments, levels = self.create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
self.axes.add_collection(lc)
self.parent.lc = lc
else:
count = 0.0
for timeStamp in self.data:
if len(self.data[timeStamp]) < 2:
self.parent.threadPlot = None
return
if self.fade:
alpha = (total - count) / total
else:
alpha = 1
data = self.data[timeStamp].items()
peakF, peakL = self.extent.get_peak_fl()
segments, levels = self.create_segments(data)
lc = LineCollection(segments)
lc.set_array(numpy.array(levels))
lc.set_norm(self.get_norm(self.autoL, self.extent))
lc.set_cmap(self.colourMap)
lc.set_linewidth(self.lineWidth)
lc.set_gid('plot')
lc.set_alpha(alpha)
self.axes.add_collection(lc)
count += 1
if self.annotate:
self.annotate_plot(peakF, peakL)
if total > 0:
self.parent.scale_plot()
self.parent.redraw_plot()
self.parent.threadPlot = None
def plot(self, time, beam=None,
maxground=2000, maxalt=500, step=1,
showrefract=False, nr_cmap='jet_r', nr_lim=[0.8, 1.],
raycolor='0.3', title=False, zorder=2, alpha=1,
fig=None, rect=111, ax=None, aax=None):
"""Plot ray paths
Parameters
----------
time : datetime.datetime
time of rays
beam: Optional[ ]
beam number
maxground : Optional[int]
maximum ground range [km]
maxalt : Optional[int]
highest altitude limit [km]
step : Optional[int]
step between each plotted ray (in number of ray steps)
showrefract : Optional[bool]
show refractive index along ray paths (supersedes raycolor)
nr_cmap : Optional[str]
color map name for refractive index coloring
nr_lim : Optional[list, float]
refractive index plotting limits
raycolor : Optional[float]
color of ray paths
title : Optional[bool]
Show default title
zorder : Optional[int]
alpha : Optional[int]
fig : Optional[pylab.figure]
object (default to gcf)
rect : Optional[int]
subplot spcification
ax : Optional[ ]
Existing main axes
aax : Optional[ ]
Existing auxialary axes
Returns
-------
ax : matplotlib.axes
object containing formatting
aax : matplotlib.axes
object containing data
cbax : matplotlib.axes
object containing colorbar
Example
-------
# Show ray paths with colored refractive index along path
import datetime as dt
from davitpy.models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.RtRun(sTime, rCode='bks', beam=12, title=True)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=10, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
import datetime as dt
from davitpy.utils import plotUtils
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
from types import MethodType
# Set up axes
if not ax and not aax:
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
else:
ax = ax
aax = aax
if hasattr(ax, 'time'):
time = ax.time
if hasattr(ax, 'beam'):
beam = ax.beam
# make sure that the required time and beam are present
# Allow a 60 second difference between the requested time and the time
# available.
keys = np.array(self.paths.keys())
diffs = np.abs(keys-time)
if diffs.min() < dt.timedelta(minutes=1):
time = keys[diffs.argmin()]
assert (time in self.paths.keys()), logging.error('Unkown time %s' % time)
if beam:
assert (beam in self.paths[time].keys()), logging.error('Unkown beam %s' % beam)
else:
beam = self.paths[time].keys()[0]
for ir, (el, rays) in enumerate( sorted(self.paths[time][beam].items()) ):
if not ir % step:
if not showrefract:
aax.plot(rays['th'], rays['r']*1e-3, c=raycolor,
zorder=zorder, alpha=alpha)
else:
points = np.array([rays['th'], rays['r']*1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lcol = LineCollection( segments, zorder=zorder, alpha=alpha)
_ = lcol.set_cmap( nr_cmap )
_ = lcol.set_norm( plt.Normalize(*nr_lim) )
_ = lcol.set_array( rays['nr'] )
_ = aax.add_collection( lcol )
# Plot title with date ut time and local time
if title:
stitle = _getTitle(time, beam, self.header, self.name)
ax.set_title( stitle )
# Add a colorbar when plotting refractive index
if showrefract:
cbax = plotUtils.addColorbar(lcol, ax)
_ = cbax.set_ylabel("refractive index")
else: cbax = None
# Declare a new method to show range markers
# This method is only available after rays have been plotted
# This ensures that the markers match the plotted rays
def showRange(self, markers=None,
color='.8', s=2, zorder=3,
**kwargs):
"""Plot ray paths
Parameters
----------
markers : Optional[ ]
range markers. Defaults to every 250 km
color : Optional[float]
s : Optional[int]
zorder : Optional[int]
**kwargs :
Returns
-------
coll :
a collection of range markers
Notes
-----
Parameters other than markers are borrowed from matplotlib.pyplot.scatter
Example
-------
# Add range markers to an existing ray plot
ax, aax, cbax = rto.rays.plot(sTime, step=10)
rto.rays.showRange()
written by Sebastien, 2013-04
"""
if not markers:
markers = np.arange(0, 5000, 250)
x, y = [], []
for el, rays in self.paths[time][beam].items():
for rm in markers:
inds = (rays['gran']*1e-3 >= rm)
if inds.any():
x.append( rays['th'][inds][0] )
y.append( rays['r'][inds][0]*1e-3 )
coll = aax.scatter(x, y,
color=color, s=s, zorder=zorder, **kwargs)
return coll
# End of new method
# Assign new method
self.showRange = MethodType(showRange, self)
ax.beam = beam
return ax, aax, cbax
def plot(self, time, beam=None, maxground=2000, maxalt=500, step=1,
showrefract=False, nr_cmap='jet_r', nr_lim=[0.8, 1.],
raycolor='0.4',
fig=None, rect=111):
"""Plot ray paths
**Args**:
* **time** (datetime.datetime): time of rays
* [**beam**]: beam number
* [**maxground**]: maximum ground range [km]
* [**maxalt**]: highest altitude limit [km]
* [**step**]: step between each plotted ray (in number of ray steps)
* [**showrefract**]: show refractive index along ray paths (supersedes raycolor)
* [**nr_cmap**]: color map name for refractive index coloring
* [**nr_lim**]: refractive index plotting limits
* [**raycolor**]: color of ray paths
* [**rect**]: subplot spcification
* [**fig**]: A pylab.figure object (default to gcf)
**Returns**:
* **ax**: matplotlib.axes object containing formatting
* **aax**: matplotlib.axes object containing data
* **cbax**: matplotlib.axes object containing colorbar
**Example**:
::
# Show ray paths with colored refractive index along path
import datetime as dt
from models import raydarn
sTime = dt.datetime(2012, 11, 18, 5)
rto = raydarn.rtRun(sTime, rCode='bks', beam=12)
rto.readRays() # read rays into memory
ax, aax, cbax = rto.rays.plot(sTime, step=2, showrefract=True, nr_lim=[.85,1])
ax.grid()
written by Sebastien, 2013-04
"""
from utils import plotUtils
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
import numpy as np
ax, aax = plotUtils.curvedEarthAxes(fig=fig, rect=rect,
maxground=maxground, maxalt=maxalt)
# make sure that the required time and beam are present
assert (time in self.paths.keys()), 'Unkown time %s' % time
if beam:
assert (beam in self.paths[time].keys()), 'Unkown beam %s' % beam
else:
beam = self.paths[time].keys()[0]
for ir, (el, rays) in enumerate( sorted(self.paths[time][beam].items()) ):
if not ir % step:
if not showrefract:
aax.plot(rays['th'], rays['r']*1e-3, c=raycolor, zorder=2)
else:
points = np.array([rays['th'], rays['r']*1e-3]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lcol = LineCollection( segments )
lcol.set_cmap( nr_cmap )
lcol.set_norm( plt.Normalize(*nr_lim) )
lcol.set_array( rays['nr'] )
aax.add_collection( lcol )
# Add a colorbar when plotting refractive index
if showrefract:
from mpl_toolkits.axes_grid1 import SubplotDivider, LocatableAxes, Size
fig1 = ax.get_figure()
divider = SubplotDivider(fig1, *ax.get_geometry(), aspect=True)
# axes for colorbar
cbax = LocatableAxes(fig1, divider.get_position())
h = [Size.AxesX(ax), # main axes
Size.Fixed(0.1), # padding
Size.Fixed(0.2)] # colorbar
v = [Size.AxesY(ax)]
divider.set_horizontal(h)
divider.set_vertical(v)
ax.set_axes_locator(divider.new_locator(nx=0, ny=0))
cbax.set_axes_locator(divider.new_locator(nx=2, ny=0))
fig1.add_axes(cbax)
cbax.axis["left"].toggle(all=False)
cbax.axis["top"].toggle(all=False)
cbax.axis["bottom"].toggle(all=False)
cbax.axis["right"].toggle(ticklabels=True, label=True)
plt.colorbar(lcol, cax=cbax)
cbax.set_ylabel("refractive index")
return ax, aax, cbax
