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()
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 render(self,ax,alpha=.7,usePyLeaflet=False,
minValueThreshold=-float('Inf'),logScale=True,colormapname='BlueRed'):
if not usePyLeaflet or colormapname=='nothingRed':
alpha=1.0
patches = []
values = []
colorvalues = []
for d in self.countPerGeohash.keys():
try:
if (self.countPerGeohash[d]>minValueThreshold):
bbox = geohash.bbox(d)
rect = mpatches.Rectangle([ bbox['w'],
bbox['s']],
bbox['e'] - bbox['w'],
bbox['n'] - bbox['s'],
ec='none', lw=.1, fc='red', alpha=alpha)
patches.append(rect)
# values.append(self.countPerGeohash[d] \
# if self.countPerGeohash[d]<3 else self.countPerGeohash[d]+10)
# colorvalues.append(self.countPerGeohash[d] \
# if self.countPerGeohash[d]<3 else self.countPerGeohash[d]+10)
values.append(self.countPerGeohash[d])
colorvalues.append(self.countPerGeohash[d])
except KeyError:
print("'"+d +"' is not a valid geohash.")
try:
maxval = max(values)
minval = min(values)
except ValueError:
print('heatmap appears to be empty...')
maxval = 1
minval = 0
p = PatchCollection(patches,cmap=plt.get_cmap(colormapname),alpha=alpha)
# if usePyLeaflet:
if (len(values)<100):
p.set_edgecolors(np.array(['black' for x in values]))
else:
p.set_edgecolors(np.array(['#333333' if x<=2 \
else ('#666666' if x<=10 else 'black') for x in values]))
# else:
# p.set_edgecolors(np.array(['white' for x in values]))
p.set_array(np.array(colorvalues))
if logScale:
p.set_norm(colors.LogNorm(vmin=.01, vmax=maxval))
else:
p.set_norm(colors.Normalize(vmin=0, vmax=maxval))
ax.add_collection(p)
ax.set_xlim(self.bbox['w'], self.bbox['e'])
ax.set_ylim(self.bbox['s'], self.bbox['n'])
divider = make_axes_locatable(ax)
cbar = plt.colorbar(p)
cbar.set_clim(vmin=max(0,minval),vmax=maxval)
cbar.update_normal(p)
return
def draw1DColumn(ax,
x,
val,
thk,
width=30,
ztopo=0,
cmin=1,
cmax=1000,
cmap=None,
name=None,
textoffset=0.0):
"""Draw a 1D column (e.g., from a 1D inversion) on a given ax.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pygimli.mplviewer import draw1DColumn
>>> thk = [1, 2, 3, 4]
>>> val = thk
>>> fig, ax = plt.subplots()
>>> draw1DColumn(ax, 0.5, val, thk, width=0.1, cmin=1, cmax=4, name="VES")
<matplotlib.collections.PatchCollection object at ...>
>>> ax.set_ylim(-np.sum(thk), 0)
(-10, 0)
"""
z = -np.hstack((0., np.cumsum(thk), np.sum(thk) * 1.5)) + ztopo
recs = []
for i in range(len(val)):
recs.append(Rectangle((x - width / 2., z[i]), width, z[i + 1] - z[i]))
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if cmap is not None:
if isinstance(cmap, str):
pp.set_cmap(pg.mplviewer.cmapFromName(cmap))
else:
pp.set_cmap(cmap)
pp.set_norm(colors.LogNorm(cmin, cmax))
pp.set_array(np.array(val))
pp.set_clim(cmin, cmax)
if name:
ax.text(x + textoffset, ztopo, name, ha='center', va='bottom')
updateAxes_(ax)
return col
def plot_polygons(geoms,
ax,
values=None,
colormap='Set1',
facecolor=None,
edgecolor=None,
alpha=1.0,
linewidth=1.0,
**kwargs):
"""Makes a MatPlotLib PatchCollection out of Polygon and/or MultiPolygon geometries
Thanks to http://stackoverflow.com/a/33753927 and David Sullivan"""
# init list to store
patches = []
newvals = []
for polynum in range(len(geoms)): # for polygon # i
poly = geoms.iloc[polynum] # find data.geometry[i]
if type(
poly
) != shapely.geometry.polygon.Polygon: # if that is not a shapely Polygon object
for currpoly in poly.geoms: # then for data.geometry[i].geoms
a = np.asarray(
currpoly.exterior
) # make a an array of those exterior values and
patches.append(Polygon(a)) # append ato patches
if values is not None: # if values, add value to newvals
newvals.append(values.iloc[polynum])
else:
a = np.asarray(poly.exterior)
patches.append(Polygon(a))
if values is not None:
newvals.append(values.iloc[polynum])
patches = PatchCollection(patches,
facecolor=facecolor,
linewidth=linewidth,
edgecolor=edgecolor,
alpha=alpha,
**kwargs)
if values is not None:
patches.set_array(np.asarray(newvals))
patches.set_cmap(colormap)
norm = matplotlib.colors.Normalize()
norm.autoscale(newvals)
patches.set_norm(norm)
ax.add_collection(patches, autolim=True)
ax.autoscale_view()
return patches
def draw1DColumn(ax, x, val, thk, width=30, ztopo=0, cmin=1, cmax=1000,
cmap=None, name=None, textoffset=0.0):
"""Draw a 1D column (e.g., from a 1D inversion) on a given ax.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pygimli.mplviewer import draw1DColumn
>>> thk = [1, 2, 3, 4]
>>> val = thk
>>> fig, ax = plt.subplots()
>>> draw1DColumn(ax, 0.5, val, thk, width=0.1, cmin=1, cmax=4, name="VES")
<matplotlib.collections.PatchCollection object at ...>
>>> ax.set_ylim(-np.sum(thk), 0)
(-10, 0)
"""
z = -np.hstack((0., np.cumsum(thk), np.sum(thk) * 1.5)) + ztopo
recs = []
for i in range(len(val)):
recs.append(Rectangle((x - width / 2., z[i]), width, z[i + 1] - z[i]))
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if cmap is not None:
if isinstance(cmap, str):
pp.set_cmap(pg.mplviewer.cmapFromName(cmap))
else:
pp.set_cmap(cmap)
pp.set_norm(colors.LogNorm(cmin, cmax))
pp.set_array(np.array(val))
pp.set_clim(cmin, cmax)
if name:
ax.text(x+textoffset, ztopo, name, ha='center', va='bottom')
updateAxes_(ax)
return col
def draw_patches(list_of_polygons,
intensity_list=None,
cm='inferno',
empty_face=False,
norm=None,
alpha=None):
patches = [Polygon(polygon, lw=0.1) for polygon in list_of_polygons]
pc = PatchCollection(patches)
if not empty_face:
if intensity_list is None:
raise ValueError('values not provided for Voronoi plot.')
pc.set_array(np.array(intensity_list))
pc.set_edgecolor('face')
pc.set_cmap(cm)
pc.set_norm(norm)
else:
pc.set_edgecolor([0.3, 0.3, 0.5])
pc.set_facecolor([0, 0, 0, 0])
return pc
def draw_map(map: GeographicalMap, mode='random', data=None):
fig, ax = plt.subplots(figsize=(18, 10))
ax.set_xlim(map.xmin, map.xmax)
ax.set_ylim(map.ymin, map.ymax)
patches = PatchCollection(
[matplotlib.patches.Polygon(p, fill=False) for p in map.polygons])
to_line = lambda e: map.centroids[(e.v1, e.v2), :]
land_edges = [to_line(e) for e in map.edges.values() if e.type == 'land']
air_edges = [to_line(e) for e in map.edges.values() if e.type == 'air']
land_edges = LineCollection(land_edges, linewidths=0.3, colors='red')
air_edges = LineCollection(air_edges, linewidths=0.1, colors='green')
ax.add_collection(patches)
if mode == 'random':
patches.set_array(np.random.randint(0, 20, size=map.N))
patches.set_cmap(matplotlib.cm.jet)
elif mode == 'population':
patches.set_array(map.population)
patches.set_cmap(matplotlib.cm.jet)
patches.set_norm(matplotlib.colors.LogNorm())
plt.colorbar(patches, ax=ax)
elif mode == 'graph':
patches.set_color('black')
patches.set_facecolor('white')
patches.set_linewidth(0.1)
ax.scatter(map.centroids[:, 0], map.centroids[:, 1], s=5)
# Plot edges.
ax.add_collection(land_edges)
ax.add_collection(air_edges)
elif mode == 'data':
patches.set_array(data)
patches.set_cmap(matplotlib.cm.jet)
plt.colorbar(patches, ax=ax)
plt.show()
def patchValMap(vals, xvec=None, yvec=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, dy=None, **kwargs):
""" plot previously generated (generateVecMatrix) y map (category)
Parameters
----------
A : iterable
to show
xvec : dict {i:num}
dict (must match A.shape[0])
ymap : iterable
vector for x axis (must match A.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(A)>0]
label : string
colorbar label
"""
if cMin is None:
cMin = np.min(vals)
if cMax is None:
cMax = np.max(vals)
if logScale is None:
logScale = (cMin > 0.0)
if logScale:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if 'ax' is None:
fig, ax = plt.subplots()
recs = []
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
for i in range(len(vals)):
recs.append(Rectangle((xvec[i]-dx/2, ymap[yvec[i]]-0.5), dx, 1))
else:
for i in range(len(vals)):
recs.append(Rectangle((xvec[i]-dx/2, yvec[i]-dy/2), dx, dy))
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if 'cmap' in kwargs:
pp.set_cmap(kwargs.pop('cmap'))
pp.set_norm(norm)
pp.set_array(np.array(vals))
pp.set_clim(cMin, cMax)
ax.set_xlim(min(xvec)-dx/2, max(xvec)+dx/2)
ax.set_ylim(len(ymap)-0.5, -0.5)
updateAxes_(ax)
cbar = None
if kwargs.pop('colorBar', True):
cbar = pg.mplviewer.createColorbar(col, cMin=cMin, cMax=cMax, nLevs=5,
label=label)
return ax, cbar, ymap
def patchMatrix(A, xmap=None, ymap=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, **kwargs):
""" plot previously generated (generateVecMatrix) matrix
Parameters
----------
A : numpy.array2d
matrix to show
xmap : dict {i:num}
dict (must match A.shape[0])
ymap : iterable
vector for x axis (must match A.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(A)>0]
label : string
colorbar label
"""
mat = np.ma.masked_where(A == 0.0, A, False)
if cMin is None:
cMin = np.min(mat)
if cMax is None:
cMax = np.max(mat)
if logScale is None:
logScale = (cMin > 0.0)
if logScale:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if 'ax' is None:
fig, ax = plt.subplots()
iy, ix = np.nonzero(A) # != 0)
recs = []
vals = []
for i in range(len(ix)):
recs.append(Rectangle((ix[i]-dx/2, iy[i]-0.5), dx, 1))
vals.append(A[iy[i], ix[i]])
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if 'cmap' in kwargs:
pp.set_cmap(kwargs.pop('cmap'))
pp.set_norm(norm)
pp.set_array(np.array(vals))
pp.set_clim(cMin, cMax)
xval = [k for k in xmap.keys()]
ax.set_xlim(min(xval)-dx/2, max(xval)+dx/2)
ax.set_ylim(len(ymap)+0.5, -0.5)
updateAxes_(ax)
cbar = None
if kwargs.pop('colorBar', True):
cbar = pg.mplviewer.createColorbar(col, cMin=cMin, cMax=cMax, nLevs=5,
label=label)
return ax, cbar
# and finally we use the dipping constraint matrix
fop.setConstraints(Cdip)
res = inv.run(vals, error, lam=15)
print(('Cg-9/2m: ' + '{:.1f} ' * 6).format(*fop(res), inv.chi2()))
pg.show(mesh, res, ax=ax[1, 1], **kw)
ax[1, 1].set_title("I=[10/2], dip=25")
np.testing.assert_array_less(inv.chi2(), 1.2)
# plot the position of the priors
patches = [CirclePolygon(po, 0.2) for po in pos]
for ai in ax.flat:
p = PatchCollection(patches, cmap=kw['cMap'])
p.set_facecolor(None)
p.set_array(np.array(vals))
p.set_norm(LogNorm(kw['cMin'], kw['cMax']))
ai.add_collection(p)
# %%
# Note that all four regularization operators fit the data equivalently but
# the images (i.e. how the gaps between the data points are filled) are quite
# different. This is something we should have in mind using regularization.
# %%
# Generating geostatistical media
# -------------------------------
# For generating geostatistical media, one can use the function
# generateGeostatisticalModel. It computes a correlation matrix and multiplies
# it with a pseudo-random (randn) series. The arguments are the same as for the
# correlation or constraint matrices.
model = pg.utils.generateGeostatisticalModel(mesh, I=[20, 4])
def plot_stack(npixels, hp, cr, snr, filename=None, scale=None):
lon, lat = hp.healpix_to_lonlat(npixels)
boundaries = hp.boundaries_lonlat(npixels, 1)
patches = []
for blon, blat in zip(*boundaries):
patches.append(
Polygon(np.array([blon.value, blat.value]).T, closed=True))
if not scale:
vmin_cr, vmax_cr = cr.flatten().min(), cr.flatten().max()
vmin_snr, vmax_snr = snr.flatten().min(), snr.flatten().max()
scale = [vmin_cr, vmax_cr, vmin_snr, vmax_snr]
else:
vmin_cr, vmax_cr = scale[0], scale[1]
vmin_snr, vmax_snr = scale[2], scale[3]
norm_cr = Normalize(vmin=vmin_cr, vmax=vmax_cr)
norm_snr = Normalize(vmin=vmin_snr, vmax=vmax_snr)
fig, axs = plt.subplots(2, 3, constrained_layout=False, figsize=(5.5, 4))
for i, eband in enumerate(["6", "7", "8"]):
# Count-rate "images"
pcm_cr = axs[0, i].scatter(lon,
lat,
c=cr[:, i],
s=1,
vmin=vmin_cr,
vmax=vmax_cr)
p = PatchCollection(patches, alpha=1)
p.set_array(cr[:, i])
p.set_norm(norm_cr)
axs[0, i].add_collection(p)
axs[0, i].set_title(f"Energy band {eband}")
axs[0, i].set_xticks([])
axs[0, i].set_yticks([])
# signal-to-noise ratio "images"
pcm_snr = axs[1, i].scatter(lon,
lat,
c=snr[:, i],
s=1,
vmin=vmin_snr,
vmax=vmax_snr)
p = PatchCollection(patches, alpha=1)
p.set_array(snr[:, i])
p.set_norm(norm_snr)
axs[1, i].add_collection(p)
axs[1, i].set_xticks([])
axs[1, i].set_yticks([])
if i == 0:
axs[0, i].set_ylabel("Stack net CR (median)")
axs[1, i].set_ylabel("Stack SNR (median)")
plt.tight_layout()
fig.colorbar(pcm_cr, ax=axs[0, :], shrink=0.6, location='bottom', pad=0.02)
fig.colorbar(pcm_snr,
ax=axs[1, :],
shrink=0.6,
location='bottom',
pad=0.02)
if filename:
fig.savefig(filename, bbox_inches='tight', pad_inches=0)
plt.close()
else:
plt.show()
return scale
def patchValMap(vals,
xvec=None,
yvec=None,
ax=None,
cMin=None,
cMax=None,
logScale=None,
label=None,
dx=1,
dy=None,
**kwargs):
"""Plot previously generated (generateVecMatrix) y map (category).
Parameters
----------
vals : iterable
Data values to show.
xvec : dict {i:num}
dict (must match vals.shape[0])
ymap : iterable
vector for x axis (must match vals.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(vals)>0]
label : string
colorbar label
** kwargs:
* circular : bool
Plot in polar coordinates.
"""
if cMin is None:
cMin = np.min(vals)
if cMax is None:
cMax = np.max(vals)
if logScale is None:
logScale = (cMin > 0.0)
norm = None
if logScale and cMin > 0:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if ax is None:
ax = plt.subplots()[1]
recs = []
circular = kwargs.pop('circular', False)
if circular:
recs = [None] * len(xvec)
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
xyMap = {}
for i, y in enumerate(yvec):
if y not in xyMap:
xyMap[y] = []
xyMap[y].append(i)
maxR = max(ymap.values()) # what's that for? not used
dR = 1 / (len(ymap.values()) + 1)
dOff = np.pi / 2 # what's that for? not used
for y, xIds in xyMap.items():
r = 1. - dR * (ymap[y] + 1)
# ax.plot(r * np.cos(xvec[xIds]),
# r * np.sin(xvec[xIds]), 'o')
# print(y, ymap[y])
for i in xIds:
phi = xvec[i]
x = r * np.cos(phi) # what's that for? not used
y = r * np.sin(phi)
dPhi = (xvec[1] - xvec[0])
recs[i] = Wedge((0., 0.),
r + dR / 1.5,
(phi - dPhi) * 360 / (2 * np.pi),
(phi + dPhi) * 360 / (2 * np.pi),
width=dR,
zorder=1 + r)
# if i < 5:
# ax.text(x, y, str(i))
# pg.wait()
else:
raise ("Implementme")
else:
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
for i in range(len(vals)):
recs.append(
Rectangle((xvec[i] - dx / 2, ymap[yvec[i]] - 0.5), dx, 1))
else:
for i in range(len(vals)):
recs.append(
Rectangle((xvec[i] - dx / 2, yvec[i] - dy / 2), dx, dy))
ax.set_xlim(min(xvec) - dx / 2, max(xvec) + dx / 2)
ax.set_ylim(len(ymap) - 0.5, -0.5)
pp = PatchCollection(recs)
# ax.clear()
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if circular:
pp.set_edgecolor('black')
pp.set_linewidths(0.1)
cmap = pg.mplviewer.cmapFromName(**kwargs)
if kwargs.pop('markOutside', False):
cmap.set_bad('grey')
cmap.set_under('darkgrey')
cmap.set_over('lightgrey')
cmap.set_bad('black')
pp.set_cmap(cmap)
pp.set_norm(norm)
pp.set_array(vals)
pp.set_clim(cMin, cMax)
updateAxes_(ax)
cbar = kwargs.pop('colorBar', True)
ori = kwargs.pop('orientation', 'horizontal')
if cbar in ['horizontal', 'vertical']:
ori = cbar
cbar = True
if cbar is True: # not for cbar=1, which is really confusing!
cbar = pg.mplviewer.createColorBar(col,
cMin=cMin,
cMax=cMax,
nLevs=5,
label=label,
orientation=ori)
elif cbar is not False:
# .. cbar is an already existing cbar .. so we update its values
pg.mplviewer.updateColorBar(cbar,
cMin=cMin,
cMax=cMax,
nLevs=5,
label=label)
updateAxes_(ax)
return ax, cbar, ymap
def create_bus_symbol_collection(coords,
buses=None,
size=5,
marker="o",
patch_type="circle",
colors=None,
z=None,
cmap=None,
norm=None,
infofunc=None,
picker=False,
net=None,
**kwargs):
infos = []
if 'height' in kwargs and 'width' in kwargs:
height, width = kwargs['height'], kwargs['width']
else:
height, width = size, size
def figmaker(x, y, i):
if patch_type == "circle":
if colors:
fig = Circle((x, y), size, color=colors[i], **kwargs)
else:
fig = Circle((x, y), size, **kwargs)
elif patch_type == 'ellipse':
angle = kwargs['angle'] if 'angle' in kwargs else 0
if colors:
fig = Ellipse((x, y),
width=width,
height=height,
color=colors[i],
**kwargs)
else:
fig = Ellipse((x, y),
width=width,
height=height,
angle=angle,
**kwargs)
elif patch_type == "rect":
if colors:
fig = Rectangle([x - width, y - height],
2 * width,
2 * height,
color=colors[i],
**kwargs)
else:
fig = Rectangle([x - width, y - height], 2 * width, 2 * height,
**kwargs)
elif patch_type.startswith("poly"):
edges = int(patch_type[4:])
if colors:
fig = RegularPolygon([x, y],
numVertices=edges,
radius=size,
color=colors[i],
**kwargs)
else:
fig = RegularPolygon([x, y],
numVertices=edges,
radius=size,
**kwargs)
else:
logger.error(
"Wrong patchtype. Please choose a correct patch type.")
if infofunc:
infos.append(infofunc(buses[i]))
return fig
patches = [
figmaker(x, y, i) for i, (x, y) in enumerate(coords) if x != np.nan
]
pc = PatchCollection(patches, match_original=True, picker=picker)
pc.bus_indices = np.array(buses)
if cmap:
pc.set_cmap(cmap)
pc.set_norm(norm)
if z is None and net:
z = net.res_bus.vm_pu.loc[buses]
else:
logger.warning("z is None and no net is provided")
pc.set_array(np.array(z))
pc.has_colormap = True
pc.cbar_title = "Bus Voltage [pu]"
pc.patch_type = patch_type
pc.size = size
if 'orientation' in kwargs:
pc.orientation = kwargs['orientation']
if "zorder" in kwargs:
pc.set_zorder(kwargs["zorder"])
pc.info = infos
return pc
def showStitchedModels(mods, axes=None, cmin=None, cmax=None, **kwargs):
"""
Show several 1d block models as (stitched) section.
"""
x = kwargs.pop('x', np.arange(len(mods)))
topo = kwargs.pop('topo', x*0)
nlay = int(np.floor((len(mods[0]) - 1) / 2.)) + 1
if cmin is None or cmax is None:
cmin = 1e9
cmax = 1e-9
for model in mods:
res = np.asarray(model)[nlay - 1:nlay * 2 - 1]
cmin = min(cmin, min(res))
cmax = max(cmax, max(res))
if kwargs.pop('sameSize', True): # all having the same width
dx = np.ones_like(x)*np.median(np.diff(x))
else:
dx = np.diff(x) * 1.05
dx = np.hstack((dx, dx[-1]))
x1 = x - dx / 2
if axes is None:
fig, ax = plt.subplots()
else:
ax = axes
fig = ax.figure
# ax.plot(x, x * 0., 'k.')
zm = kwargs.pop('zm', None)
maxz = 0.
if zm is not None:
maxz = zm
recs = []
RES = []
for i, mod in enumerate(mods):
mod1 = np.asarray(mod)
res = mod1[nlay - 1:]
RES.extend(res)
thk = mod1[:nlay - 1]
thk = np.hstack((thk, thk[-1]))
z = np.hstack((0., np.cumsum(thk)))
if zm is not None:
thk[-1] = zm - z[-2]
z[-1] = zm
else:
maxz = max(maxz, z[-1])
for j in range(len(thk)):
recs.append(Rectangle((x1[i], topo[i]-z[j]), dx[i], -thk[j]))
pp = PatchCollection(recs, edgecolors=kwargs.pop('edgecolors', 'none'))
pp.set_edgecolor(kwargs.pop('edgecolors', 'none'))
pp.set_linewidths(0.0)
ax.add_collection(pp)
if 'cmap' in kwargs:
pp.set_cmap(kwargs['cmap'])
print(cmin, cmax)
norm = LogNorm(cmin, cmax)
pp.set_norm(norm)
pp.set_array(np.array(RES))
# pp.set_clim(cmin, cmax)
ax.set_ylim((-maxz, max(topo)))
ax.set_xlim((x1[0], x1[-1] + dx[-1]))
cbar = None
if kwargs.pop('colorBar', True):
cbar = plt.colorbar(pp, ax=ax, norm=norm, orientation='horizontal',
aspect=60) # , ticks=[1, 3, 10, 30, 100, 300])
if 'ticks' in kwargs:
cbar.set_ticks(kwargs['ticks'])
# cbar.autoscale_None()
if axes is None: # newly created fig+ax
return fig, ax
else: # already given, better give back color bar
return cbar
class InstrumentView:
def __init__(self,
scipp_obj=None,
bins=None,
masks=None,
cmap=None,
log=None,
vmin=None,
vmax=None,
aspect=None,
size=1,
projection=None,
nan_color=None,
filename=None,
continuous_update=None,
dim=None):
self.fig2d = None
self.fig3d = None
self.scatter2d = None
self.scatter3d = None
self.outline = None
self.size = size
self.aspect = aspect
self.do_update = None
self.figurewidget = widgets.Output()
self.figure2d = False
self.figure3d = False
self.image = None
self.nan_color = nan_color
self.log = log
self.current_projection = None
self.dim = dim
self.data_arrays = {}
tp = type(scipp_obj)
if tp is sc.Dataset or tp is sc.DatasetProxy:
for key in sorted(scipp_obj.keys()):
var = scipp_obj[key]
if self.dim in var.dims:
self.data_arrays[key] = var
elif tp is sc.DataArray or tp is sc.DataProxy:
self.data_arrays[scipp_obj.name] = scipp_obj
else:
raise RuntimeError("Unknown input type: {}. Allowed inputs "
"are a Dataset or a DataArray (and their "
"respective proxies).".format(tp))
self.globs = {"cmap": cmap, "log": log, "vmin": vmin, "vmax": vmax}
self.params = {}
self.hist_data_array = {}
self.scalar_map = {}
self.minmax = {}
# Find the min/max time-of-flight limits and store them
self.minmax["tof"] = [np.Inf, np.NINF, 1]
for key, data_array in self.data_arrays.items():
bins_here = bins
if data_array.sparse_dim is not None and bins_here is None:
bins_here = True
if bins_here is not None:
dim = None if data_array.sparse_dim is not None else self.dim
spdim = None if data_array.sparse_dim is None else self.dim
var = make_bins(data_array=data_array,
sparse_dim=spdim,
dim=dim,
bins=bins_here,
padding=(data_array.sparse_dim is not None))
else:
var = data_array.coords[self.dim]
self.minmax["tof"][0] = min(self.minmax["tof"][0], var.values[0])
self.minmax["tof"][1] = max(self.minmax["tof"][1], var.values[-1])
self.minmax["tof"][2] = var.shape[0]
# Rebin all DataArrays to common Tof axis
self.rebin_data(np.linspace(*self.minmax["tof"]))
# Create dropdown menu to select the DataArray
keys = list(self.hist_data_array.keys())
self.dropdown = widgets.Dropdown(options=keys,
description="Select entry:",
layout={"width": "initial"})
self.dropdown.observe(self.change_data_array, names="value")
# Store current active data entry (DataArray)
self.key = keys[0]
# Create a Tof slider and its label
self.tof_dim_indx = self.hist_data_array[self.key].dims.index(self.dim)
self.slider = widgets.IntSlider(
value=0,
min=0,
step=1,
description=str(self.dim).replace("Dim.", ""),
max=self.hist_data_array[self.key].shape[self.tof_dim_indx] - 1,
continuous_update=continuous_update,
readout=False)
self.slider.observe(self.update_colors, names="value")
self.label = widgets.Label()
# Add text boxes to change number of bins/bin size
self.nbins = widgets.Text(value=str(
self.hist_data_array[self.key].shape[self.tof_dim_indx]),
description="Number of bins:",
style={"description_width": "initial"})
self.nbins.on_submit(self.update_nbins)
tof_values = self.hist_data_array[self.key].coords[self.dim].values
self.bin_size = widgets.Text(value=str(tof_values[1] - tof_values[0]),
description="Bin size:")
self.bin_size.on_submit(self.update_bin_size)
projections = [
"3D", "Cylindrical X", "Cylindrical Y", "Cylindrical Z",
"Spherical X", "Spherical Y", "Spherical Z"
]
# Create toggle buttons to change projection
self.buttons = {}
for p in projections:
self.buttons[p] = widgets.Button(
description=p,
disabled=False,
button_style=("info" if (p == projection) else ""))
self.buttons[p].on_click(self.change_projection)
items = [self.buttons["3D"]]
for x in "XYZ":
items.append(self.buttons["Cylindrical {}".format(x)])
items.append(self.buttons["Spherical {}".format(x)])
if x != "Z":
items.append(widgets.Label())
self.togglebuttons = widgets.GridBox(
items,
layout=widgets.Layout(grid_template_columns="repeat(3, 150px)"))
# Place widgets in boxes
self.vbox = widgets.VBox([
widgets.HBox([self.dropdown, self.slider, self.label]),
widgets.HBox([self.nbins, self.bin_size]), self.togglebuttons
])
self.box = widgets.VBox([self.figurewidget, self.vbox])
self.box.layout.align_items = "center"
# Protect against uninstalled ipyvolume
if ipv is None and projection == "3D":
print("Warning: 3D projection requires ipyvolume to be "
"installed. Use conda/pip install ipyvolume. Reverting to "
"2D projection.")
self.buttons[projections[1]].button_style = "info"
self.buttons["3D"].button_style = ""
self.buttons["3D"].disabled = True
# Render the plot here instead of at the top level because to capture
# the matplotlib output (if a 2D projection is requested to begin with,
# the output widget needs to be displayed first, before any mpl figure
# is displayed.
render_plot(widgets=self.box, filename=filename, ipv=ipv)
# Get detector positions
self.det_pos = np.array(
sn.position(self.hist_data_array[self.key]).values)
# Find extents of the detectors
for i, x in enumerate("xyz"):
self.minmax[x] = [
np.amin(self.det_pos[:, i]),
np.amax(self.det_pos[:, i])
]
# Update the figure
self.change_projection(self.buttons[projection])
# Create members object
self.members = {
"widgets": {
"sliders": self.slider,
"buttons": self.buttons,
"text": {
"nbins": self.nbins,
"bin_size": self.bin_size
},
"dropdown": self.dropdown
},
"fig2d": self.fig2d,
"fig3d": self.fig3d,
"scatter2d": self.scatter2d,
"scatter3d": self.scatter3d,
"outline": self.outline
}
return
def rebin_data(self, bins):
"""
Rebin the original data to Tof given some bins. This is executed both
on first instrument display and when either the number of bins or the
bin width is changed.
"""
for key, data_array in self.data_arrays.items():
# Histogram the data in the Tof dimension
if data_array.sparse_dim is not None:
self.hist_data_array[key] = histogram_sparse_data(
data_array, data_array.sparse_dim, bins)
else:
self.hist_data_array[key] = sc.rebin(
data_array, self.dim,
make_bins(data_array=data_array,
dim=self.dim,
bins=bins,
padding=False))
# Parse input parameters for colorbar
self.params[key] = parse_params(
globs=self.globs, array=self.hist_data_array[key].values)
cmap = cm.get_cmap(self.params[key]["cmap"])
cmap.set_bad(color=self.nan_color)
self.scalar_map[key] = cm.ScalarMappable(
cmap=cmap, norm=self.params[key]["norm"])
return
def update_colors(self, change):
self.do_update(change)
self.label.value = name_with_unit(
var=self.hist_data_array[self.key].coords[self.dim],
name=value_to_string(self.hist_data_array[self.key].coords[
self.dim].values[change["new"]]))
return
def change_projection(self, owner):
if owner.description == self.current_projection:
owner.button_style = "info"
return
if self.current_projection is not None:
self.buttons[self.current_projection].button_style = ""
# Temporarily disable automatic plotting in notebook
if plt.isinteractive():
plt.ioff()
re_enable_interactive = True
else:
re_enable_interactive = False
update_children = False
if owner.description == "3D":
self.projection_3d()
self.do_update = self.update_colors_3d
else:
if self.current_projection == "3D" or \
self.current_projection is None:
update_children = True
self.projection_2d(owner.description, update_children)
self.do_update = self.update_colors_2d
self.update_colors({"new": self.slider.value})
self.current_projection = owner.description
self.buttons[owner.description].button_style = "info"
# Re-enable automatic plotting in notebook
if re_enable_interactive:
plt.ion()
return
def projection_3d(self):
# Initialise Figure
if not self.figure3d:
self.fig3d = ipv.figure(width=config.plot.width,
height=config.plot.height,
animation=0,
lighting=False)
max_size = 0.0
dx = {"x": 0, "y": 0, "z": 0}
for ax in dx.keys():
dx[ax] = np.ediff1d(self.minmax[ax])
max_size = np.amax(list(dx.values()))
# Make plot outline if aspect ratio is to be conserved
if self.aspect == "equal":
arrays = dict()
for ax, s in dx.items():
diff = max_size - s
arrays[ax] = [
self.minmax[ax][0] - 0.5 * diff,
self.minmax[ax][1] + 0.5 * diff
]
outl_x, outl_y, outl_z = np.meshgrid(arrays["x"],
arrays["y"],
arrays["z"],
indexing="ij")
self.outline = ipv.plot_wireframe(outl_x,
outl_y,
outl_z,
color="black")
# Try to guess marker size
perc_size = 100.0 * self.size / max_size
self.scatter3d = ipv.scatter(x=self.det_pos[:, 0],
y=self.det_pos[:, 1],
z=self.det_pos[:, 2],
marker="square_2d",
size=perc_size)
self.figure3d = True
self.figurewidget.clear_output()
self.box.children = tuple([self.figurewidget, ipv.gcc(), self.vbox])
return
def update_colors_3d(self, change):
arr = self.hist_data_array[self.key][self.dim, change["new"]].values
if self.log:
arr = np.ma.masked_where(arr <= 0, arr)
self.scatter3d.color = self.scalar_map[self.key].to_rgba(arr)
return
def projection_2d(self, projection, update_children):
# Initialise figure if we switched from 3D view, if not re-use current
# figure.
if update_children:
self.box.children = tuple([self.figurewidget, self.vbox])
if not self.figure2d:
self.fig2d, self.ax = plt.subplots(
1,
1,
figsize=(config.plot.width / config.plot.dpi,
config.plot.height / config.plot.dpi))
if update_children:
with self.figurewidget:
disp.display(self.fig2d)
# Compute cylindrical or spherical projections
permutations = {"X": [0, 2, 1], "Y": [1, 0, 2], "Z": [2, 1, 0]}
axis = projection[-1]
theta = np.arctan2(self.det_pos[:, permutations[axis][2]],
self.det_pos[:, permutations[axis][1]])
if projection.startswith("Cylindrical"):
z_or_phi = self.det_pos[:, permutations[axis][0]]
elif projection.startswith("Spherical"):
z_or_phi = np.arcsin(
self.det_pos[:, permutations[axis][0]] /
np.sqrt(self.det_pos[:, 0]**2 + self.det_pos[:, 1]**2 +
self.det_pos[:, 2]**2))
# Create the scatter
if not self.figure2d:
patches = []
for x, y in zip(theta, z_or_phi):
patches.append(
Rectangle((x - 0.5 * self.size, y - 0.5 * self.size),
self.size, self.size))
self.scatter2d = PatchCollection(
patches,
cmap=self.params[self.key]["cmap"],
norm=self.params[self.key]["norm"],
array=self.hist_data_array[self.key][self.dim,
self.slider.value].values)
self.ax.add_collection(self.scatter2d)
self.save_xy = np.array([theta, z_or_phi]).T
if self.params[self.key]["cbar"]:
self.cbar = plt.colorbar(self.scatter2d, ax=self.ax)
self.cbar.ax.set_ylabel(
name_with_unit(var=self.hist_data_array[self.key],
name=""))
self.cbar.ax.yaxis.set_label_coords(-1.1, 0.5)
self.figure2d = True
else:
self.scatter2d.set_offset_position("data")
self.scatter2d.set_offsets(
np.array([theta, z_or_phi]).T - self.save_xy)
self.ax.set_xlim(
[np.amin(theta) - self.size,
np.amax(theta) + self.size])
self.ax.set_ylim(
[np.amin(z_or_phi) - self.size,
np.amax(z_or_phi) + self.size])
return
def update_colors_2d(self, change):
self.scatter2d.set_array(
self.hist_data_array[self.key][self.dim, change["new"]].values)
self.fig2d.canvas.draw_idle()
return
def update_nbins(self, owner):
try:
nbins = int(owner.value)
except ValueError:
print("Warning: could not convert value: {} to an "
"integer.".format(owner.value))
return
# self.rebin_data(nbins, from_nbins_text)
self.rebin_data(
np.linspace(self.minmax["tof"][0], self.minmax["tof"][1],
nbins + 1))
x = self.hist_data_array[self.key].coords[self.dim].values
self.bin_size.value = str(x[1] - x[0])
self.update_slider()
return
def update_bin_size(self, owner):
try:
binw = float(owner.value)
except ValueError:
print("Warning: could not convert value: {} to a "
"float.".format(owner.value))
return
self.rebin_data(
np.arange(self.minmax["tof"][0], self.minmax["tof"][1], binw))
self.nbins.value = str(
self.hist_data_array[self.key].shape[self.tof_dim_indx])
self.update_slider()
return
def update_slider(self):
"""
Try to replace the slider around the same position
"""
# Compute percentage position
perc_pos = self.slider.value / self.slider.max
# Compute new position
nbins = int(self.nbins.value)
new_pos = int(perc_pos * nbins)
# Either place new upper boundary first, or change slider value first
if new_pos > self.slider.max:
self.slider.max = nbins
self.slider.value = new_pos
else:
self.slider.value = new_pos
self.slider.max = nbins
return
def change_data_array(self, change):
self.key = change["new"]
if self.scatter2d is not None:
# Apparently, you have to set norm, clim on PatchCollection and
# clim on the colorbar to get this working. Only setting norm
# seems to work only on the first change.
self.scatter2d.set_norm(self.params[self.key]["norm"])
self.scatter2d.set_clim(vmin=self.params[self.key]["vmin"],
vmax=self.params[self.key]["vmax"])
self.cbar.set_clim(vmin=self.params[self.key]["vmin"],
vmax=self.params[self.key]["vmax"])
self.update_colors({"new": self.slider.value})
def create_bus_collection(net, buses=None, size=5, marker="o", patch_type="circle", colors=None,
z=None, cmap=None, norm=None, infofunc=None, picker=False,
bus_geodata=None, cbar_title="Bus Voltage [pu]", **kwargs):
"""
Creates a matplotlib patch collection of pandapower buses.
Input:
**net** (pandapowerNet) - The pandapower network
OPTIONAL:
**buses** (list, None) - The buses for which the collections are created.
If None, all buses in the network are considered.
**size** (int, 5) - patch size
**marker** (str, "o") - patch marker
**patch_type** (str, "circle") - patch type, can be
- "circle" for a circle
- "rect" for a rectangle
- "poly<n>" for a polygon with n edges
**infofunc** (function, None) - infofunction for the patch element
**colors** (list, None) - list of colors for every element
**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.
**cmap** (ListedColormap, None) - colormap for the patch colors
**norm** (matplotlib norm object, None) - matplotlib norm object
**picker** (bool, False) - picker argument passed to the patch collection
**bus_geodata** (DataFrame, None) - coordinates to use for plotting
If None, net["bus_geodata"] is used
**cbar_title** (str, "Bus Voltage [pu]") - colormap bar title in case of given cmap
**kwargs - key word arguments are passed to the patch function
OUTPUT:
**pc** - patch collection
"""
buses = net.bus.index.tolist() if buses is None else list(buses)
if len(buses) == 0:
return None
if bus_geodata is None:
bus_geodata = net["bus_geodata"]
coords = zip(bus_geodata.loc[buses, "x"].values, bus_geodata.loc[buses, "y"].values)
infos = []
# RegularPolygon has no param width/height, everything else might use defaults
if not patch_type.startswith("poly"):
if 'height' not in kwargs and 'width' not in kwargs:
kwargs['height'] = kwargs['width'] = 2 * size
if patch_type == "rect":
kwargs['height'] *= 2
kwargs['width'] *= 2
def figmaker(x, y, i):
if colors is not None:
kwargs["color"] = colors[i]
if patch_type == 'ellipse' or patch_type == 'circle': # circles are just ellipses
angle = kwargs['angle'] if 'angle' in kwargs else 0
fig = Ellipse((x, y), angle=angle, **kwargs)
elif patch_type == "rect":
fig = Rectangle([x - kwargs['width'] / 2, y - kwargs['height'] / 2], **kwargs)
elif patch_type.startswith("poly"):
edges = int(patch_type[4:])
fig = RegularPolygon([x, y], numVertices=edges, radius=size, **kwargs)
else:
logger.error("Wrong patchtype. Please choose a correct patch type.")
if infofunc:
infos.append(infofunc(buses[i]))
return fig
patches = [figmaker(x, y, i)
for i, (x, y) in enumerate(coords)
if x != np.nan]
pc = PatchCollection(patches, match_original=True, picker=picker)
pc.bus_indices = np.array(buses)
if cmap is not None:
pc.set_cmap(cmap)
pc.set_norm(norm)
if z is None and net is not None:
z = net.res_bus.vm_pu.loc[buses]
else:
logger.warning("z is None and no net is provided")
pc.set_array(np.array(z))
pc.has_colormap = True
pc.cbar_title = cbar_title
pc.patch_type = patch_type
pc.size = size
if 'orientation' in kwargs:
pc.orientation = kwargs['orientation']
if "zorder" in kwargs:
pc.set_zorder(kwargs["zorder"])
pc.info = infos
return pc
def hexPlot(self,
what='tallies',
fixed=None,
ax=None,
cmap=None,
logColor=False,
xlabel=None,
ylabel=None,
logx=False,
logy=False,
loglog=False,
title=None,
normalizer=None,
cbarLabel=None,
borderpad=2.5,
**kwargs):
"""
Create and return a hexagonal mesh plot.
Parameters
----------
what: {'tallies', 'errors', 'scores'}
Quantity to plot
fixed: None or dict
Dictionary of slicing arguments to pass to :meth:`slice`
{ax}
{cmap}
{logColor}
{xlabel}
{ylabel}
{logx}
{logy}
{loglog}
{title}
borderpad: int or float
Percentage of total plot to apply as a border. A value of
zero means that the extreme edges of the hexagons will touch
the x and y axis.
{kwargs} :class:`matplotlib.patches.RegularPolygon`
Raises
------
AttributeError
If :attr:`pitch` and :attr:`hexType` are not set.
"""
borderpad = max(0, float(borderpad))
if fixed and ('xcoord' in fixed or 'ycoord' in fixed):
raise KeyError("Refusing to restrict along one of the hexagonal "
"dimensions {x/y}coord")
for attr in {'pitch', 'hexType'}:
if getattr(self, attr) is None:
raise AttributeError("{} is not set.".format(attr))
for key in {'color', 'fc', 'facecolor', 'orientation'}:
checkClearKwargs(key, 'hexPlot', **kwargs)
ec = kwargs.get('ec', None) or kwargs.get('edgecolor', None)
if ec is None:
ec = 'k'
kwargs['ec'] = kwargs['edgecolor'] = ec
if 'figure' in kwargs and kwargs['figure'] is not None:
fig = kwargs['figure']
if not isinstance(fig, Figure):
raise TypeError(
"Expected 'figure' to be of type Figure, is {}".format(
type(fig)))
if len(fig.axes) != 1 and not ax:
raise TypeError("Don't know where to place the figure since"
"'figure' argument has multiple axes.")
if ax and fig.axes and ax not in fig.axes:
raise IndexError("Passed argument for 'figure' and 'ax', "
"but ax is not attached to figure.")
ax = ax or (fig.axes[0] if fig.axes else axes())
alpha = kwargs.get('alpha', None)
ny = len(self.indexes['ycoord'])
nx = len(self.indexes['xcoord'])
data = self.slice(fixed, what)
if data.shape != (ny, nx):
raise IndexError("Constrained data does not agree with hexagonal "
"grid structure. Coordinate grid: {}. "
"Constrained shape: {}".format((ny, nx),
data.shape))
nItems = ny * nx
patches = empty(nItems, dtype=object)
values = empty(nItems)
coords = self.grids['COORD']
ax = ax or axes()
pos = 0
xmax, ymax = [
-inf,
] * 2
xmin, ymin = [
inf,
] * 2
radius = self.pitch / sqrt(3)
for xy, val in zip(coords, data.flat):
values[pos] = val
h = RegularPolygon(xy, 6, radius, self.__hexRot, **kwargs)
verts = h.get_verts()
vmins = verts.min(0)
vmaxs = verts.max(0)
xmax = max(xmax, vmaxs[0])
xmin = min(xmin, vmins[0])
ymax = max(ymax, vmaxs[1])
ymin = min(ymin, vmins[1])
patches[pos] = h
pos += 1
normalizer = normalizerFactory(values, normalizer, logColor,
coords[:, 0], coords[:, 1])
pc = PatchCollection(patches, cmap=cmap, alpha=alpha)
pc.set_array(values)
pc.set_norm(normalizer)
ax.add_collection(pc)
addColorbar(ax, pc, None, cbarLabel)
formatPlot(
ax,
loglog=loglog,
logx=logx,
logy=logy,
xlabel=xlabel or "X [cm]",
ylabel=ylabel or "Y [cm]",
title=title,
)
setAx_xlims(ax, xmin, xmax, pad=borderpad)
setAx_ylims(ax, ymin, ymax, pad=borderpad)
return ax
def plot_polygon_collection(ax, geoms, colors_or_values, plot_values=False,
vmin=None, vmax=None, cmap=None,
edgecolor='black', alpha=0.5, linewidth=1.0,label="Area",norm=None, **kwargs):
"""
Plots a collection of Polygon and MultiPolygon geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` Polygons and/or MultiPolygons (can be mixed)
colors_or_values : a sequence of `N` values or RGBA tuples
It should have 1:1 correspondence with the geometries (not their components).
plot_values : bool
If True, `colors_or_values` is interpreted as a list of values, and will
be mapped to colors using vmin/vmax/cmap (which become required).
Otherwise `colors_or_values` is interpreted as a list of colors.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
components, component_colors_or_values = _flatten_multi_geoms(
geoms, colors_or_values)
# PatchCollection does not accept some kwargs.
collection = PatchCollection([PolygonPatch(poly) for poly in components],
linewidth=linewidth, edgecolor=edgecolor,
alpha=alpha,label=label, **kwargs)
if plot_values:
collection.set_array(np.array(component_colors_or_values))
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_clim(vmin, vmax)
else:
# set_color magically sets the correct combination of facecolor and
# edgecolor, based on collection type.
collection.set_color(component_colors_or_values)
# If the user set facecolor and/or edgecolor explicitly, the previous
# call to set_color might have overridden it (remember, the 'color' may
# have come from plot_series, not from the user). The user should be
# able to override matplotlib's default behavior, by setting them again
# after set_color.
if 'facecolor' in kwargs:
collection.set_facecolor(kwargs['facecolor'])
if edgecolor:
collection.set_edgecolor(edgecolor)
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
def colorflood(gridobject, arr, **kwargs):
"""
Method for color flooding an array for a Modflow grid.
"""
#imports
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
#set defaults from kwargs
ax = matplotlib.pyplot.gca()
arrexclude = []
arrmin = arr.min()
arrmax = arr.max()
alpha = 1.0
cb = False
if kwargs.has_key('ax'): ax = kwargs['ax']
if kwargs.has_key('arrexclude'): arrexclude = kwargs['arrexclude']
if kwargs.has_key('arrmin'): arrmin = kwargs['arrmin']
if kwargs.has_key('arrmax'): arrmax = kwargs['arrmax']
if kwargs.has_key('alpha'): alpha = kwargs['alpha']
if kwargs.has_key('cb'): cb = kwargs['cb'] #plot colorbar?
norm = colors.normalize(arrmin, arrmax)
patches = []
for nodenumber in range(gridobject.nodes):
nodeobj = gridobject.get_nodeobj(nodenumber)
(k, i, j) = gridobject.get_indices(nodenumber)
x, y = nodeobj.position[0], nodeobj.position[1]
dx, dy = nodeobj.dxdydz[0], nodeobj.dxdydz[1]
xmin = x - dx / 2.
xmax = x + dx / 2.
ymin = y - dy / 2.
ymax = y + dy / 2.
vertices = [ (xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)]
if arr[k, i, j] in arrexclude:
#cannot get visible and alpha to work, so this is the hack.
vertices = [(0,0), (0,0), (0,0), (0,0) ]
poly = Polygon(vertices, linewidth=0., fc = None,
ec='none')
patches.append(poly)
#set up the patchcollection for faster coloring
p = PatchCollection(patches, match_original=True)
p.set_array(arr.reshape(-1))
p.set_norm(norm)
p.set_edgecolor(None)
p.set_alpha(alpha)
ax.add_collection(p)
#create the colorbar
if cb:
pc = []
color = cm.jet(norm(arrmin))
poly = Polygon( ((0,0), (1,0), (1,1), (0,1)), linewidth=0.,
fc = color, ec='none', alpha=0.4)
pc.append(poly)
color = cm.jet(norm(arrmax))
poly = Polygon( ((0,0), (1,0), (1,1), (0,1)), linewidth=0.,
fc = color, ec='none', alpha=0.4)
pc.append(poly)
p = PatchCollection(pc, match_original=True)
p.set_array(numpy.array([arrmin, arrmax]))
matplotlib.pyplot.colorbar(p, shrink=0.5)
return
def showStitchedModels(models,
ax=None,
x=None,
cMin=None,
cMax=None,
thk=None,
logScale=True,
title=None,
zMin=0,
zMax=0,
zLog=False,
**kwargs):
"""Show several 1d block models as (stitched) section.
Parameters
----------
model : iterable of iterable (np.ndarray or list of np.array)
1D models (consisting of thicknesses and values) to plot
ax : matplotlib axes [None - create new]
axes object to plot in
x : iterable
positions of individual models
cMin/cMax : float [None - autodetection from range]
minimum and maximum colorscale range
logScale : bool [True]
use logarithmic color scaling
zMin/zMax : float [0 - automatic]
range for z (y axis) limits
zLog : bool
use logarithmic z (y axis) instead of linear
topo : iterable
vector of elevation for shifting
thk : iterable
vector of layer thicknesses for all models
Returns
-------
ax : matplotlib axes [None - create new]
axes object to plot in
"""
if x is None:
x = np.arange(len(models))
topo = kwargs.pop('topo', np.zeros_like(x))
fig = None
if ax is None:
fig, ax = plt.subplots()
dxmed2 = np.median(np.diff(x)) / 2.
patches = []
zMinLimit = 9e99
zMaxLimit = 0
if thk is not None:
nlay = len(models[0])
else:
nlay = int(np.floor((len(models[0]) + 1) / 2.))
vals = np.zeros((len(models), nlay))
for i, imod in enumerate(models):
if thk is not None: # take only resistivity from model
vals[i, :] = imod
thki = thk
else: # extract thickness from model vector
if isinstance(imod, pg.Vector):
vals[i, :] = imod[nlay - 1:2 * nlay - 1]
thki = np.asarray(imod[:nlay - 1])
else:
vals[i, :] = imod[nlay - 1:2 * nlay - 1]
thki = imod[:nlay - 1]
if zMax > 0:
z = np.hstack((0., np.cumsum(thki), zMax))
else:
thki = np.hstack((thki, thki[-1] * 3))
z = np.hstack((0., np.cumsum(thki)))
z = topo[i] - z
zMinLimit = min(zMinLimit, z[-1])
zMaxLimit = max(zMaxLimit, z[0])
for j in range(nlay):
rect = Rectangle((x[i] - dxmed2, z[j]), dxmed2 * 2,
z[j + 1] - z[j])
patches.append(rect)
p = PatchCollection(patches) # , cmap=cmap, linewidths=0)
if cMin is not None:
p.set_clim(cMin, cMax)
setMappableData(p, vals.ravel(), logScale=logScale)
ax.add_collection(p)
if logScale:
norm = colors.LogNorm(cMin, cMax)
p.set_norm(norm)
if 'cMap' in kwargs:
p.set_cmap(kwargs['cMap'])
# ax.set_ylim((zMaxLimit, zMin))
ax.set_ylim((zMinLimit, zMaxLimit))
if zLog:
ax.set_yscale("log", nonposy='clip')
ax.set_xlim((min(x) - dxmed2, max(x) + dxmed2))
if title is not None:
ax.set_title(title)
if kwargs.pop('colorBar', True):
cb = pg.viewer.mpl.createColorBar(p, cMin=cMin, cMax=cMax, nLevs=5)
# cb = plt.colorbar(p, orientation='horizontal',aspect=50,pad=0.1)
if 'cticks' in kwargs:
xt = np.unique(np.clip(kwargs['cticks'], cMin, cMax))
cb.set_ticks(xt)
cb.set_ticklabels([str(xti) for xti in xt])
if 'label' in kwargs:
cb.set_label(kwargs['label'])
plt.draw()
return ax # maybe return cb as well?
def patchValMap(vals, xvec=None, yvec=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, dy=None, **kwargs):
"""Plot previously generated (generateVecMatrix) y map (category).
Parameters
----------
vals : iterable
to show
xvec : dict {i:num}
dict (must match vals.shape[0])
ymap : iterable
vector for x axis (must match vals.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(vals)>0]
label : string
colorbar label
"""
if cMin is None:
cMin = np.min(vals)
if cMax is None:
cMax = np.max(vals)
if logScale is None:
logScale = (cMin > 0.0)
norm = None
if logScale and cMin > 0:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if 'ax' is None:
ax = plt.subplots()[1]
recs = []
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
for i in range(len(vals)):
recs.append(Rectangle((xvec[i] - dx / 2, ymap[yvec[i]] - 0.5),
dx, 1))
else:
for i in range(len(vals)):
recs.append(Rectangle((xvec[i] - dx / 2, yvec[i] - dy / 2),
dx, dy))
pp = PatchCollection(recs)
# ax.clear()
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
cmap = pg.mplviewer.cmapFromName(**kwargs)
cmap.set_bad('grey')
if kwargs.pop('markOutside', True):
cmap.set_under('darkgrey')
cmap.set_bad('lightgrey')
pp.set_cmap(cmap)
pp.set_norm(norm)
pp.set_array(np.array(vals))
pp.set_clim(cMin, cMax)
ax.set_xlim(min(xvec) - dx / 2, max(xvec) + dx / 2)
ax.set_ylim(len(ymap) - 0.5, -0.5)
updateAxes_(ax)
cbar = kwargs.pop('colorBar', True)
if cbar is True: # not for cbar=1, which is really confusing!
cbar = pg.mplviewer.createColorBar(col, cMin=cMin, cMax=cMax,
nLevs=5, label=label)
elif cbar is not False: # what the hell is this?
pg.mplviewer.updateColorBar(cbar, cMin=cMin, cMax=cMax,
nLevs=5, label=label)
return ax, cbar, ymap
def create_bus_collection(net, buses=None, size=5, marker="o", patch_type="circle", colors=None,
cmap=None, norm=None, infofunc=None, **kwargs):
"""
Creates a matplotlib patch collection of pandapower buses.
Input:
**net** (PandapowerNet) - The pandapower network
Optional:
**buses** (list, None) - The buses for which the collections are created. If None, all buses in the network are considered.
**size** (int, 5) - patch size
**marker** (str, "o") - patch marker
**patch_type** (str, "circle") - patch type, can be
- "circle" for a circle
- "rect" for a rectanlge
- "poly<n>" for a polygon with n edges
**infofunc** (function, None) - infofunction for the patch element
**colors** (list, None) - list of colors for every element
**cmap** - colormap for the patch colors
**picker** - picker argument passed to the patch collection
**kwargs - key word arguments are passed to the patch function
"""
buses = net.bus.index.tolist() if buses is None else list(buses)
patches = []
infos = []
def figmaker(x, y, i):
if patch_type=="circle":
if colors:
fig = Circle((x, y), size, color=colors[i], **kwargs)
else:
fig = Circle((x, y), size, **kwargs)
elif patch_type=="rect":
if colors:
fig = Rectangle([x - size, y - size], 2*size, 2*size, color=colors[i], **kwargs)
else:
fig = Rectangle([x - size, y - size], 2*size, 2*size, **kwargs)
elif patch_type.startswith("poly"):
edges = int(patch_type[4:])
if colors:
fig = RegularPolygon([x, y], numVertices=edges, radius=size, color=colors[i],
**kwargs)
else:
fig = RegularPolygon([x, y], numVertices=edges, radius=size, **kwargs)
if infofunc:
infos.append(infofunc(buses[i]))
return fig
patches = [figmaker(x, y, i)
for i, (x, y) in enumerate(zip(net.bus_geodata.loc[buses].x.values,
net.bus_geodata.loc[buses].y.values))
if x != -1 and x != np.nan]
pc = PatchCollection(patches, match_original=True)
if cmap:
pc.set_cmap(cmap)
pc.set_norm(norm)
pc.set_array(net.res_bus.vm_pu.loc[buses])
pc.has_colormap = True
pc.cbar_title = "Bus Voltage [pu]"
pc.patch_type = patch_type
pc.size = size
if "zorder" in kwargs:
pc.set_zorder(kwargs["zorder"])
pc.info = infos
return pc
def showStitchedModels_Redundant(mods,
ax=None,
cmin=None,
cmax=None,
**kwargs):
"""Show several 1d block models as (stitched) section."""
x = kwargs.pop('x', np.arange(len(mods)))
topo = kwargs.pop('topo', x * 0)
nlay = int(np.floor((len(mods[0]) - 1) / 2.)) + 1
if cmin is None or cmax is None:
cmin = 1e9
cmax = 1e-9
for model in mods:
res = np.asarray(model)[nlay - 1:nlay * 2 - 1]
cmin = min(cmin, min(res))
cmax = max(cmax, max(res))
if kwargs.pop('sameSize', True): # all having the same width
dx = np.ones_like(x) * np.median(np.diff(x))
else:
dx = np.diff(x) * 1.05
dx = np.hstack((dx, dx[-1]))
x1 = x - dx / 2
if ax is None:
fig, ax = plt.subplots()
else:
ax = ax
fig = ax.figure
# ax.plot(x, x * 0., 'k.')
zm = kwargs.pop('zm', None)
maxz = 0.
if zm is not None:
maxz = zm
recs = []
RES = []
for i, mod in enumerate(mods):
mod1 = np.asarray(mod)
res = mod1[nlay - 1:]
RES.extend(res)
thk = mod1[:nlay - 1]
thk = np.hstack((thk, thk[-1]))
z = np.hstack((0., np.cumsum(thk)))
if zm is not None:
thk[-1] = zm - z[-2]
z[-1] = zm
else:
maxz = max(maxz, z[-1])
for j, _ in enumerate(thk):
recs.append(Rectangle((x1[i], topo[i] - z[j]), dx[i], -thk[j]))
pp = PatchCollection(recs, edgecolors=kwargs.pop('edgecolors', 'none'))
pp.set_edgecolor(kwargs.pop('edgecolors', 'none'))
pp.set_linewidths(0.0)
ax.add_collection(pp)
if 'cmap' in kwargs:
pp.set_cmap(kwargs['cmap'])
print(cmin, cmax)
norm = colors.LogNorm(cmin, cmax)
pp.set_norm(norm)
pp.set_array(np.array(RES))
# pp.set_clim(cmin, cmax)
ax.set_ylim((-maxz, max(topo)))
ax.set_xlim((x1[0], x1[-1] + dx[-1]))
cbar = None
if kwargs.pop('colorBar', True):
cbar = plt.colorbar(pp,
ax=ax,
norm=norm,
orientation='horizontal',
aspect=60) # , ticks=[1, 3, 10, 30, 100, 300])
if 'ticks' in kwargs:
cbar.set_ticks(kwargs['ticks'])
# cbar.autoscale_None()
if ax is None: # newly created fig+ax
return fig, ax
else: # already given, better give back color bar
return cbar
def patchValMap(vals, xvec=None, yvec=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, dy=None, **kwargs):
"""Plot previously generated (generateVecMatrix) y map (category).
Parameters
----------
vals : iterable
Data values to show.
xvec : dict {i:num}
dict (must match vals.shape[0])
ymap : iterable
vector for x axis (must match vals.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(vals)>0]
label : string
colorbar label
** kwargs:
* circular : bool
Plot in polar coordinates.
"""
if cMin is None:
cMin = np.min(vals)
if cMax is None:
cMax = np.max(vals)
if logScale is None:
logScale = (cMin > 0.0)
norm = None
if logScale and cMin > 0:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if ax is None:
ax = plt.subplots()[1]
recs = []
circular = kwargs.pop('circular', False)
if circular:
recs = [None] * len(xvec)
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
xyMap = {}
for i, y in enumerate(yvec):
if y not in xyMap:
xyMap[y] = []
xyMap[y].append(i)
# maxR = max(ymap.values()) # what's that for? not used
dR = 1 / (len(ymap.values())+1)
# dOff = np.pi / 2 # what's that for? not used
for y, xIds in xyMap.items():
r = 1. - dR*(ymap[y]+1)
# ax.plot(r * np.cos(xvec[xIds]),
# r * np.sin(xvec[xIds]), 'o')
# print(y, ymap[y])
for i in xIds:
phi = xvec[i]
# x = r * np.cos(phi) # what's that for? not used
y = r * np.sin(phi)
dPhi = (xvec[1] - xvec[0])
recs[i] = Wedge((0., 0.), r + dR/1.5,
(phi - dPhi)*360/(2*np.pi),
(phi + dPhi)*360/(2*np.pi),
width=dR,
zorder=1+r)
# if i < 5:
# ax.text(x, y, str(i))
# pg.wait()
else:
raise("Implementme")
else:
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
for i in range(len(vals)):
recs.append(Rectangle((xvec[i] - dx / 2, ymap[yvec[i]] - 0.5),
dx, 1))
else:
for i in range(len(vals)):
recs.append(Rectangle((xvec[i] - dx / 2, yvec[i] - dy / 2),
dx, dy))
ax.set_xlim(min(xvec) - dx / 2, max(xvec) + dx / 2)
ax.set_ylim(len(ymap) - 0.5, -0.5)
pp = PatchCollection(recs)
# ax.clear()
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if circular:
pp.set_edgecolor('black')
pp.set_linewidths(0.1)
cmap = pg.mplviewer.cmapFromName(**kwargs)
if kwargs.pop('markOutside', False):
cmap.set_bad('grey')
cmap.set_under('darkgrey')
cmap.set_over('lightgrey')
cmap.set_bad('black')
pp.set_cmap(cmap)
pp.set_norm(norm)
pp.set_array(vals)
pp.set_clim(cMin, cMax)
updateAxes_(ax)
cbar = kwargs.pop('colorBar', True)
ori = kwargs.pop('orientation', 'horizontal')
if cbar in ['horizontal', 'vertical']:
ori = cbar
cbar = True
if cbar is True: # not for cbar=1, which is really confusing!
cbar = pg.mplviewer.createColorBar(col, cMin=cMin, cMax=cMax,
nLevs=5, label=label,
orientation=ori)
elif cbar is not False:
# .. cbar is an already existing cbar .. so we update its values
pg.mplviewer.updateColorBar(cbar, cMin=cMin, cMax=cMax,
nLevs=5, label=label)
updateAxes_(ax)
return ax, cbar, ymap
def patchMatrix(mat,
xmap=None,
ymap=None,
ax=None,
cMin=None,
cMax=None,
logScale=None,
label=None,
dx=1,
**kwargs):
"""Plot previously generated (generateVecMatrix) matrix.
Parameters
----------
mat : numpy.array2d
matrix to show
xmap : dict {i:num}
dict (must match A.shape[0])
ymap : iterable
vector for x axis (must match A.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(A)>0]
label : string
colorbar label
dx : float
width of the matrix elements (by default 1)
"""
mat = np.ma.masked_where(mat == 0.0, mat, False)
if cMin is None:
cMin = np.min(mat)
if cMax is None:
cMax = np.max(mat)
if logScale is None:
logScale = (cMin > 0.0)
if logScale:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if 'ax' is None:
ax = plt.subplots()[1]
iy, ix = np.nonzero(mat) # != 0)
recs = []
vals = []
for i, _ in enumerate(ix):
recs.append(Rectangle((ix[i] - dx / 2, iy[i] - 0.5), dx, 1))
vals.append(mat[iy[i], ix[i]])
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if 'cmap' in kwargs:
pp.set_cmap(kwargs.pop('cmap'))
if 'cMap' in kwargs:
pp.set_cmap(kwargs.pop('cMap'))
pp.set_norm(norm)
pp.set_array(np.array(vals))
pp.set_clim(cMin, cMax)
xval = [k for k in xmap.keys()]
ax.set_xlim(min(xval) - dx / 2, max(xval) + dx / 2)
ax.set_ylim(len(ymap) + 0.5, -0.5)
updateAxes_(ax)
cbar = None
if kwargs.pop('colorBar', True):
ori = kwargs.pop('orientation', 'horizontal')
cbar = pg.mplviewer.createColorBar(col,
cMin=cMin,
cMax=cMax,
nLevs=5,
label=label,
orientation=ori)
return ax, cbar
def plot_filled_pixels(
map, save_filename=None, title='', ra_range=[], dec_range=[],
colorbar_range=[], log=False, colorbar_label='Surface Brightness (Jy/sr)',
ra_cut=None,
overplot_points=False, point_ras=None, point_decs=None, point_values=None,
overplot_points_vmin=-np.pi, overplot_points_vmax=np.pi,
overplot_points_colormap='seismic',
overplot_mwa_beam_contours=False,
mwa_beam_center_ras=[0, 60], mwa_beam_center_decs=[-27, -27],
overplot_hera_band=False,
overplot_bright_sources=False,
overplot_sgp=False,
big=False,
galactic_coord_contours=False
):
if map.coords == '':
print('WARNING: No map coordinate scheme supplied.')
print('Assuming equatorial coordinates.')
coords = 'equatorial'
else:
coords = map.coords
# Set branch cut location
if ra_cut is None:
if len(ra_range) == 2:
ra_cut = (ra_range[1]+ra_range[0])/2.+12.
else:
ra_cut = 18.
if overplot_points:
point_ras[np.where(point_ras > ra_cut)] -= 24.
point_ras[np.where(point_ras < ra_cut-24.)] += 24.
map.get_ra_dec(ra_cut=ra_cut*15.)
# Limit pixel calculation when axis ranges are set
if len(ra_range) == 2 or len(dec_range) == 2:
if len(ra_range) != 2:
ra_range = np.array([np.min(map.ra_arr), np.max(map.ra_arr)])/15.
if len(dec_range) != 2:
dec_range = [np.min(map.dec_arr), np.max(map.dec_arr)]
use_indices = np.arange(len(map.signal_arr))[
(map.ra_arr/15.>ra_range[0]) & (map.ra_arr/15.<ra_range[1])
& (map.dec_arr>dec_range[0]) & (map.dec_arr<dec_range[1])
]
use_map = healpix_utils.HealpixMap(
map.signal_arr[use_indices], map.pix_arr[use_indices], map.nside,
nest=map.nest, coords=coords
)
else:
use_map = map
use_map.get_pixel_corners(ra_cut=ra_cut*15.)
patches = []
for ind in range(len(use_map.signal_arr)):
polygon = Polygon(np.stack([
use_map.pix_corner_ras_arr[ind]/15.,
use_map.pix_corner_decs_arr[ind]
], axis=1))
patches.append(polygon)
colors = use_map.signal_arr
# Establish axis ranges
if len(ra_range) != 2:
ra_range = np.array([
np.amin(use_map.pix_corner_ras_arr),
np.amax(use_map.pix_corner_ras_arr)
])/15.
if len(dec_range) != 2:
dec_range = np.array([
np.amin(use_map.pix_corner_decs_arr),
np.amax(use_map.pix_corner_decs_arr)
])
cm_use = 'Greys_r'
#cm_use = 'viridis'
collection = PatchCollection(patches, cmap=cm_use, lw=0.05)
collection.set_array(np.array(colors)) # set the data colors
collection.set_edgecolor('face') # make the face and edge colors match
if log: # set the color bar to a log scale
collection.set_norm(LogNorm())
if len(colorbar_range) == 2: # set the colorbar min and max
collection.set_clim(vmin=colorbar_range[0], vmax=colorbar_range[1])
else:
signal_mean = np.mean(colors)
signal_std = np.std(colors)
collection.set_clim(
vmin=max([min(colors), signal_mean-5*signal_std]),
vmax=min([max(colors), signal_mean+5*signal_std])
)
plt.rcParams.update({'font.size': 9})
plt.rcParams['axes.facecolor']='gray'
if big:
fig, ax = plt.subplots(figsize=(10, 4), dpi=600)
else:
fig, ax = plt.subplots(figsize=(6, 0.6*4), dpi=600)
ax.add_collection(collection) # plot data
plt.xlabel('RA (hours)')
plt.ylabel('Dec (degrees)')
#plt.axis('equal')
ax.set_aspect(1./15.)
#ax.set_facecolor('gray') # make plot background gray
if galactic_coord_contours:
npoints_ra = 200
npoints_dec = 100
coord_ra_vals = np.linspace(ra_range[0], ra_range[1], num=npoints_ra)
coord_dec_vals = np.linspace(dec_range[0], dec_range[1], num=npoints_dec)
phi_eq = np.radians(coord_ra_vals*15.)
for phi_ind, phi_val in enumerate(phi_eq):
if phi_val < 0:
phi_eq[phi_ind] += 2*np.pi
theta_eq = np.radians(90. - coord_dec_vals)
rot = hp.rotator.Rotator(coord=['C', 'G'])
theta_eq_grid, phi_eq_grid = np.meshgrid(theta_eq, phi_eq)
theta_gal, phi_gal = rot(theta_eq_grid.flatten(), phi_eq_grid.flatten())
theta_gal = 90. - np.degrees(theta_gal.reshape((npoints_ra, npoints_dec)))
phi_gal = np.degrees(phi_gal.reshape((npoints_ra, npoints_dec))) + 180.
theta_cont = plt.contour(
coord_ra_vals, coord_dec_vals,
theta_gal.T, levels=np.arange(-90, 90, 15),
colors='white', linestyles=['solid'], linewidths=0.2
)
plt.clabel(theta_cont, inline=True, fontsize=5, fmt="%.0f$^\circ$")
# Plot nonzero contours to aviod branch cut
phi_gal_nonzero = np.copy(phi_gal)
phi_gal_nonzero[np.where(phi_gal_nonzero < 20.)] = np.nan
phi_gal_nonzero[np.where(phi_gal_nonzero > 340.)] = np.nan
phi_cont_nonzero = plt.contour(
coord_ra_vals, coord_dec_vals,
phi_gal_nonzero.T, levels=np.arange(45, 360, 45),
colors='white', linestyles=['solid'], linewidths=0.2
)
plt.clabel(phi_cont_nonzero, inline=True, fontsize=5, fmt="%.0f$^\circ$")
# Plot zero contour
phi_gal_zero = np.copy(phi_gal)
phi_gal_zero[np.where(phi_gal_zero > 180.)] = phi_gal_zero[np.where(phi_gal_zero > 180.)] - 360.
phi_gal_zero[np.where(phi_gal_zero > 20.)] = np.nan
phi_gal_zero[np.where(phi_gal_zero < -20.)] = np.nan
phi_cont_zero = plt.contour(
coord_ra_vals, coord_dec_vals,
phi_gal_zero.T, levels=[0],
colors='white', linestyles=['solid'], linewidths=0.2
)
plt.clabel(phi_cont_zero, inline=True, fontsize=5, fmt="%.0f$^\circ$")
if overplot_points:
cm_overplot_points = plt.cm.get_cmap(overplot_points_colormap)
plt.scatter(
point_ras, point_decs, c=point_values,
vmin=overplot_points_vmin, vmax=overplot_points_vmax, s=30,
cmap=cm_overplot_points, edgecolor='black', linewidth=.5
)
if overplot_mwa_beam_contours:
beam_ras, beam_decs, beam_val = get_mwa_beam()
for beam_ind, use_beam_center_ra in enumerate(mwa_beam_center_ras):
use_beam_center_dec = mwa_beam_center_decs[beam_ind]
plt.contour(
(beam_ras+use_beam_center_ra)/15., beam_decs+use_beam_center_dec,
beam_val, levels=[.5],
colors='cyan', linestyles=['solid'], linewidths=0.7
)
if overplot_hera_band:
hera_band_center = -30.
hera_band_width = 11.
plt.plot(
ra_range, np.full(2, hera_band_center+hera_band_width/2),
'--', color='cyan', linewidth=0.7
)
plt.plot(
ra_range, np.full(2, hera_band_center-hera_band_width/2),
'--', color='cyan', linewidth=0.7
)
if overplot_bright_sources:
source_names = [
'Pictor A', 'Fornax A'
]
named_source_ras = np.array([
79.9572, 50.6738
])/15.
named_source_decs = np.array([
-45.7788, -37.2083
])
plt.plot(
named_source_ras, named_source_decs, 'x',
color='yellow', markersize=3
)
for source_ind, name in enumerate(source_names):
plt.annotate(
name, (named_source_ras[source_ind]-2/15., named_source_decs[source_ind]),
fontsize=8., color='black',
path_effects=[patheffects.withStroke(linewidth=0.5, foreground="white")]
)
if overplot_sgp:
sgp_ra = 0.857222
sgp_dec = -27.1283
plt.plot(
[sgp_ra], [sgp_dec], '+', color='red', markersize=6
)
plt.annotate(
'SGP', (sgp_ra, sgp_dec-8), fontsize=8,
horizontalalignment='center', color='black',
path_effects=[patheffects.withStroke(linewidth=0.5, foreground="white")]
)
plt.axis([ra_range[1], ra_range[0], dec_range[0], dec_range[1]])
plt.title(title)
cbar = fig.colorbar(collection, ax=ax, extend='both') # add colorbar
# label colorbar
cbar.ax.set_ylabel(colorbar_label, rotation=270, labelpad=15)
if save_filename is not None:
print('Saving plot to {}'.format(save_filename))
plt.savefig(save_filename, format='png', dpi=600)
plt.close()
else:
plt.show()
def GridStrain(pos, disp, k, par, plotpar, plotst):
'''
GridStrain computes the infinitesimal strain of a network
of stations with displacements in x (east) and y (north).
Strain in z is assumed to be zero (plane strain)
USE: cent,eps,ome,pstrain,rotc = GridStrain(pos,disp,k,par,plotpar,plotst)
pos = nstations x 2 matrix with x (east) and y (north)
positions of stations in meters
disp = nstations x 2 matrix with x (east) and y (north)
displacements of stations in meters
k = Type of computation: Delaunay (k = 0), nearest
neighbor (k = 1), or distance weighted (k = 2)
par = Parameters for nearest neighbor or distance
weighted computation. If Delaunay (k = 0), enter
a scalar corresponding to the minimum internal
angle of a triangle valid for computation.
If nearest neighbor (k = 1), input a 1 x 3 vector
with grid spacing, number of nearest neighbors,
and maximum distance to neighbors. If distance
weighted (k = 2), input a 1 x 2 vector with grid
spacing and distance weighting factor alpha
plotpar = Parameter to color the cells: Max elongation
(plotpar = 0), minimum elongation
(plotpar = 1), rotation (plotpar = 2),
or dilatation (plotpar = 3)
plotst = A flag to plot the stations (1) or not (0)
cent = ncells x 2 matrix with x and y positions of cells
centroids
eps = 3 x 3 x ncells array with strain tensors of
the cells
ome = 3 x 3 x ncells array with rotation tensors of
the cells
pstrain = 3 x 3 x ncells array with magnitude and
orientation of principal strains of
the cells
rotc = ncells x 3 matrix with rotation components
of cells
NOTE: Input/Output angles are in radians. Output
azimuths are given with respect to North
pos, disp, grid spacing, max. distance to
neighbors, and alpha should be in meters
GridStrain uses functions lscov and InfStrain
Python function translated from the Matlab function
GridStrain in Allmendinger et al. (2012)
'''
pi = np.pi
# If Delaunay
if k == 0:
# Indexes of triangles vertices
# Use function Delaunay
tri = Delaunay(pos)
inds = tri.simplices
# Number of cells
ncells = np.size(inds, 0)
# Number of stations per cell = 3
nstat = 3
# Centers of cells
cent = np.zeros((ncells, 2))
for i in range(0, ncells):
# Triangle vertices
v1x = pos[inds[i, 0], 0]
v2x = pos[inds[i, 1], 0]
v3x = pos[inds[i, 2], 0]
v1y = pos[inds[i, 0], 1]
v2y = pos[inds[i, 1], 1]
v3y = pos[inds[i, 2], 1]
# Center of cell
cent[i, 0] = (v1x + v2x + v3x) / 3.0
cent[i, 1] = (v1y + v2y + v3y) / 3.0
# Triangle internal angles
s1 = np.sqrt((v3x - v2x)**2 + (v3y - v2y)**2)
s2 = np.sqrt((v1x - v3x)**2 + (v1y - v3y)**2)
s3 = np.sqrt((v2x - v1x)**2 + (v2y - v1y)**2)
a1 = np.arccos((v2x-v1x)*(v3x-v1x)/(s3*s2)+\
(v2y-v1y)*(v3y-v1y)/(s3*s2))
a2 = np.arccos((v3x-v2x)*(v1x-v2x)/(s1*s3)+\
(v3y-v2y)*(v1y-v2y)/(s1*s3))
a3 = np.arccos((v2x-v3x)*(v1x-v3x)/(s1*s2)+\
(v2y-v3y)*(v1y-v3y)/(s1*s2))
# If any of the internal angles is less than
# specified minimum, invalidate triangle
if a1 < par or a2 < par or a3 < par:
inds[i, :] = np.zeros(3)
# If nearest neighbor or distance weighted
else:
# Construct grid
xmin = min(pos[:, 0])
xmax = max(pos[:, 0])
ymin = min(pos[:, 1])
ymax = max(pos[:, 1])
cellsx = int(np.ceil((xmax - xmin) / par[0]))
cellsy = int(np.ceil((ymax - ymin) / par[0]))
xgrid = np.arange(xmin, (xmin + (cellsx + 1) * par[0]), par[0])
ygrid = np.arange(ymin, (ymin + (cellsy + 1) * par[0]), par[0])
XX, YY = np.meshgrid(xgrid, ygrid)
# Number of cells
ncells = cellsx * cellsy
# Number of stations per cell (nstat) and
# other parameters
# If nearest neighbor
if k == 1:
nstat = par[1] # max neighbors
sqmd = par[2]**2 # max squared distance
# If distance weighted
elif k == 2:
nstat = np.size(pos, 0) # all stations
dalpha = 2.0 * par[1] * par[1] # 2*alpha*alpha
# Cells' centers
cent = np.zeros((ncells, 2))
count = 0
for i in range(0, cellsy):
for j in range(0, cellsx):
cent[count, 0] = (XX[i, j] + XX[i, j + 1]) / 2.0
cent[count, 1] = (YY[i, j] + YY[i + 1, j]) / 2.0
count += 1
# Initialize stations indexes for cells to -1
inds = np.ones((ncells, nstat), dtype=int) * -1
# Initialize weight matrix for distance weighted
wv = np.zeros((ncells, nstat * 2))
# For all cells set stations indexes
for i in range(0, ncells):
# Initialize sq distances to -1.0
sds = np.ones(nstat) * -1.0
# For all stations
for j in range(0, np.size(pos, 0)):
# Sq distance from cell center to station
dx = cent[i, 0] - pos[j, 0]
dy = cent[i, 1] - pos[j, 1]
sd = dx**2 + dy**2
# If nearest neighbor
if k == 1:
# If within the max sq distance
if sd <= sqmd:
minsd = min(sds)
mini = np.argmin(sds)
# If less than max neighbors
if minsd == -1.0:
sds[mini] = sd
inds[i, mini] = j
# If max neighbors
else:
# If sq distance is less
# than neighbors max sq distance
maxsd = max(sds)
maxi = np.argmax(sds)
if sd < maxsd:
sds[maxi] = sd
inds[i, maxi] = j
# If distance weighted
elif k == 2:
# All stations indexes
inds[i, :] = np.arange(nstat)
# Eq. 8.27: Weight factor
weight = np.exp(-sd / dalpha)
wv[i, j * 2] = weight
wv[i, j * 2 + 1] = weight
# Initialize arrays
y = np.zeros(nstat * 2)
M = np.zeros((nstat * 2, 6))
e = np.zeros((3, 3))
eps = np.zeros((3, 3, ncells))
ome = np.zeros((3, 3, ncells))
pstrain = np.zeros((3, 3, ncells))
rotc = np.zeros((ncells, 3))
# For each cell
for i in range(0, ncells):
# If required minimum number of stations
if min(inds[i, :]) >= 0:
# Eq. 8.24: Displacements column vector y
# and design matrix M. X1 = North, X2 = East
for j in range(0, nstat):
ic = inds[i, j]
y[j * 2] = disp[ic, 1]
y[j * 2 + 1] = disp[ic, 0]
M[j * 2, :] = [1., 0., pos[ic, 1], pos[ic, 0], 0., 0.]
M[j * 2 + 1, :] = [0., 1., 0., 0., pos[ic, 1], pos[ic, 0]]
# Eqs. 8.25-8.26: Find x using function lscov
# If Delaunay or nearest neighbor
if k == 0 or k == 1:
x = lscov(M, y)
# If distance weighted
elif k == 2:
x = lscov(M, y, wv[i, :])
# Displacement gradient tensor
for j in range(0, 2):
e[j, 0] = x[j * 2 + 2]
e[j, 1] = x[j * 2 + 3]
# Compute strain
eps[:,:,i],ome[:,:,i],pstrain[:,:,i],\
rotc[i,:],_ = InfStrain(e)
# Variable to plot
# If maximum principal strain
if plotpar == 0:
vp = pstrain[0, 0, :]
lcb = "emax"
# If minimum principal strain
elif plotpar == 1:
vp = pstrain[2, 0, :]
lcb = "emin"
# If rotation:
# For plane strain, rotation = rotc(3)
elif plotpar == 2:
vp = rotc[:, 2] * 180 / pi
lcb = "Rotation (deg)"
# If dilatation
elif plotpar == 3:
vp = pstrain[0, 0, :] + pstrain[1, 0, :] + pstrain[2, 0, :]
lcb = "dilatation"
# Make a figure
fig, ax = plt.subplots()
fig.set_size_inches(15.0, 7.5)
# Patches and colors for cells
patches = []
colors = []
# Fill cells patches and colors
# If Delaunay
if k == 0:
for i in range(0, ncells):
# If minimum number of stations
if min(inds[i, :]) >= 0:
xpyp = [[pos[inds[i,0],0],pos[inds[i,0],1]],\
[pos[inds[i,1],0],pos[inds[i,1],1]],\
[pos[inds[i,2],0],pos[inds[i,2],1]]]
# length in km
xpyp = np.divide(xpyp, 1e3)
polygon = Polygon(xpyp, True)
patches.append(polygon)
colors.append(vp[i])
# If nearest neighbor or distance weighted
if k == 1 or k == 2:
count = 0
for i in range(0, cellsy):
for j in range(0, cellsx):
# If minimum number of stations
if min(inds[count, :]) >= 0:
xpyp = [[XX[i,j],YY[i,j]],[XX[i,j+1],YY[i,j+1]],\
[XX[i+1,j+1],YY[i+1,j+1]],[XX[i+1,j],YY[i+1,j]]]
# length in km
xpyp = np.divide(xpyp, 1e3)
polygon = Polygon(xpyp, True)
patches.append(polygon)
colors.append(vp[count])
count += 1
# Collect cells patches
pcoll = PatchCollection(patches)
# Cells colors
pcoll.set_array(np.array(colors))
# Color map is blue to red
pcoll.set_cmap('bwr')
# Positive values are red, negative are
# blue and zero is white
vmin = min(vp)
vmax = max(vp)
norm = mcolors.TwoSlopeNorm(vmin=vmin, vcenter=0.0, vmax=vmax)
pcoll.set_norm(norm)
# Draw cells
ax.add_collection(pcoll)
# Plot stations
if plotst == 1:
plt.plot(pos[:, 0] * 1e-3, pos[:, 1] * 1e-3, 'k.', markersize=2)
# Axes
plt.axis('equal')
plt.xlabel('x (km)')
plt.ylabel('y (km)')
# Color bar with nice ticks
intv = (vmax - vmin) * 0.25
ticks = [vmin, vmin + intv, vmin + 2 * intv, vmin + 3 * intv, vmax]
lticks = ['{:.2e}'.format(ticks[0]),\
'{:.2e}'.format(ticks[1]),'{:.2e}'.format(ticks[2]),\
'{:.2e}'.format(ticks[3]),'{:.2e}'.format(ticks[4])]
cbar = fig.colorbar(pcoll, label=lcb, ticks=ticks)
cbar.ax.set_yticklabels(lticks)
# Show plot
plt.show()
return cent, eps, ome, pstrain, rotc
cmap = matplotlib.cm.get_cmap('Spectral')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim(-100, 100)
ax.set_ylim(-100, 100)
norm = matplotlib.colors.LogNorm(vmin=min(values), vmax=3000)
center = (0, 0)
patches = [Circle(center, r) for r in reversed(radius)]
p = PatchCollection(patches)
p.set_norm(norm)
p.set_linewidth(0)
p.set_array(np.array(values[::-1]))
ax.add_collection(p)
ax.set_xlabel('Distance in [AU]')
ax.set_ylabel('Distance in [AU]')
cbar = fig.colorbar(p, ax=ax)
cbar.set_label('Surface density [kg/m^2]')
'''
fig, ax = plt.subplots()
ax.set_xlim(-100, 100)
ax.set_ylim(-100, 100)
ax.set_xlabel('Radius in [AU]')
ax.set_ylabel('Radius in [AU]')
