def plot_evolution():
N = ff.current_generation
M = ff.generation_size
fig, ax = plt.subplots(figsize=(20, 20))
grid = np.mgrid[0.1:0.9:complex(0, N), 0.1:0.9:complex(0, M)].T
patches = []
colors = []
line_color = {'replace_by_other': 'green', 'replace_by_random': 'cyan', 'promoted': 'black', 'duplicate': 'orange'}
dx = 0.01
for j in range(N):
dup_dx = 0.5 * dx
for i in range(M):
entry_id = ff.lineage[str(i)][j]
nres2 = ff.population.get_entry(entry_id, {'properties.nres2': 1})['properties']['nres2']
if nres2 < 1E14:
lw = 1
ls = 'solid'
else:
lw = 1
ls = 'dotted'
if ff.population.is_evaluated(entry_id):
colors.append(ff.population.value(entry_id))
circle = mpatches.Circle(grid[i, j], 0.4 / float(M), ec="black", linewidth=lw, linestyle=ls)
patches.append(circle)
label(grid[i, j], "%7.2f" % ff.population.value(entry_id), 0.0)
for ichange in ff.population.pcdb.db.generation_changes.find({'from': entry_id, 'generation': j}):
if ichange['change'] == 'duplicate':
orig = ichange['from']
dest = ichange['to']
newi = int(ff.lineage_inv[dest])
dup_dx += dx/10.0
x, y = np.array([[grid[i, j][0] - 1.5 * dup_dx, grid[i, j][0] - 2 * dup_dx,
grid[newi, j][0] - 2 * dup_dx, grid[newi, j][0] - dx],
[grid[i, j][1], grid[i, j][1], grid[newi, j][1], grid[newi, j][1]]])
line = mlines.Line2D(x, y, lw=1., alpha=0.8, color=line_color[ichange['change']],
marker='>', markersize=5, markeredgecolor='none')
line.set_markevery([3])
ax.add_line(line)
elif j < N - 1:
x, y = np.array(
[[grid[i, j][0] + dx, grid[i, j + 1][0] - 2 * dx], [grid[i, j][1], grid[i, j + 1][1]]])
line = mlines.Line2D(x, y, lw=5., alpha=0.3, color=line_color[ichange['change']])
# label(0.5*(grid[i, j]+grid[i, j+1]), ichange['change'], 0.0)
ax.add_line(line)
collection = PatchCollection(patches, cmap=plt.cm.hsv, alpha=0.3)
collection.set_array(np.array(colors))
ax.add_collection(collection)
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
plt.axis('equal')
plt.axis('off')
plt.savefig(figname+'_evo.pdf')
def get_circles_for_scatter(x, y, color='black', edgecolor='none', colormap='jet', radius=0.01, colornorm=None, alpha=1, radiusnorm=None, maxradius=1, minradius=0):
cmap = plt.get_cmap(colormap)
if colornorm is not None:
colornorm = plt.Normalize(colornorm[0], colornorm[1], clip=True)
# setup normalizing for radius scale factor (if used)
if type(radius) is list or type(radius) is np.array or type(radius) is np.ndarray:
if radiusnorm is None:
radiusnorm = matplotlib.colors.Normalize(np.min(radius), np.max(radius), clip=True)
else:
radiusnorm = matplotlib.colors.Normalize(radiusnorm[0], radiusnorm[1], clip=True)
# make circles
points = np.array([x, y]).T
circles = [None for i in range(len(x))]
for i, pt in enumerate(points):
if type(radius) is list or type(radius) is np.array or type(radius) is np.ndarray:
r = radiusnorm(radius[i])*(maxradius-minradius) + minradius
else:
r = radius
circles[i] = patches.Circle( pt, radius=r )
# make a collection of those circles
cc = PatchCollection(circles, cmap=cmap, norm=colornorm) # potentially useful option: match_original=True
# set properties for collection
cc.set_edgecolors(edgecolor)
if type(color) is list or type(color) is np.array or type(color) is np.ndarray:
cc.set_array(color)
else:
cc.set_facecolors(color)
cc.set_alpha(alpha)
return cc
def plot_(pnts):
"""plot a circle, arc sector etc
"""
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
#x_min = pnts[:,0].min()
#x_max = pnts[:,0].max()
#y_min = pnts[:,1].min()
#y_max = pnts[:,1].max()
fig, ax = plt.subplots()
patches = []
# Points need to form a closed loopset closed to True if your 1st and
# last pnt aren't equal.
for i in pnts:
polygon = Polygon(i, closed=False)
patches.append(polygon)
p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=1.0)
colors = 100*np.random.rand(len(patches))
p.set_array(np.array(colors))
#ax.set_xlim(x_min-0.5, x_max+0.5) # (x_min, x_max)
#ax.set_ylim(y_min-0.5, y_max+0.5) # y_min, y_max)
ax.add_collection(p)
plt.axis('equal')
plt.show()
def circles(x, y, s, c='b', vmin=None, vmax=None, **kwargs):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
if np.isscalar(c):
kwargs.setdefault('color', c)
c = None
if 'fc' in kwargs: kwargs.setdefault('facecolor', kwargs.pop('fc'))
if 'ec' in kwargs: kwargs.setdefault('edgecolor', kwargs.pop('ec'))
if 'ls' in kwargs: kwargs.setdefault('linestyle', kwargs.pop('ls'))
if 'lw' in kwargs: kwargs.setdefault('linewidth', kwargs.pop('lw'))
patches = [Circle((x_, y_), s_) for x_, y_, s_ in np.broadcast(x, y, s)]
collection = PatchCollection(patches, **kwargs)
if c is not None:
collection.set_array(np.asarray(c))
collection.set_clim(vmin, vmax)
ax = plt.gca()
ax.add_collection(collection)
ax.autoscale_view()
if c is not None:
plt.sci(collection)
return collection
def draw(self, colorbars=True, **kwargs):
self.cbars = []
for coll, cmap, label in zip(self.collections, self.cmaps, self.cbar_labels):
pc = PatchCollection(coll, cmap=cmap)
pc.set_array(np.array([ p.value for p in coll ]))
self._ax.add_collection(pc)
if colorbars:
options = {
'orientation':'horizontal',
'pad':0.05, 'aspect':60
}
options.update(kwargs.get('colorbar-options', {}))
cbar = plt.colorbar(pc, **options)
cbar.set_label(label)
self.cbars.append(cbar)
fontdict = kwargs.get('font', {'color':'white'})
for s in self.squares:
if not s.label:
continue
x = s.x + s.dx/2
y = s.y + s.dy/2
self._ax.text(x, y, s.label, ha='center',
va='center',
fontdict=fontdict)
if self.guide_square:
self.guide_square.set_labels(self.labels)
pc = PatchCollection(self.guide_square.patches, match_original=True)
self._ax.add_collection(pc)
self._ax.autoscale_view()
def plot_single_circle_grid(centroids, radiuses, ax, intensities, grid=True, alpha=0.75):
# intensities = np.ma.masked_equal(abs(np.array(intensities)), .0)
patches = []
count = 0
if grid:
for n, x in enumerate(centroids):
for y, r in zip(centroids, radiuses):
# ax.text(x, y, count)
count += 1
circle = Circle((x, y), r)
patches.append(circle)
else:
for xy, r in zip(centroids, radiuses):
count += 1
circle = Circle(xy, r)
patches.append(circle)
sorted_index = [idx for (intensity, idx) in sorted(zip(intensities, range(len(intensities))))]
patches = [patches[idx] for idx in sorted_index]
intensities = [intensities[idx] for idx in sorted_index]
norm = mpl.colors.Normalize(vmin=0.0, vmax=max(intensities))
cm.jet.set_bad(color='white', alpha=0.0)
colors = [('white')] + [(cm.jet(i)) for i in xrange(1, 256)]
new_map = mpl.colors.LinearSegmentedColormap.from_list('new_map', colors, N=256)
p = PatchCollection(patches, cmap=new_map, alpha=alpha, norm=norm, linewidth=0)
p.set_array(np.array(intensities))
ax.add_collection(p)
ax.annotate(int(np.sqrt(count)), xy=(2, 90), fontsize=30,
path_effects=[PathEffects.withStroke(linewidth=3, foreground="w")])
def drawboxes(breaks, axis, boxcolor=1):
'''Draws boxes on the current plot.'''
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
ax = plt.gca()
x1, x2 = plt.xlim()
y1, y2 = plt.ylim()
patches = []
if axis == 0:
for i in range(len(breaks) - 1):
y1, y2 = (breaks[i + 1], breaks[i])
patches.append(
Polygon([[x1, y2], [x1, y1], [x2, y1], [x2, y2]], True))
else:
for i in range(len(breaks) - 1):
x1, x2 = (breaks[i + 1], breaks[i])
patches.append(
Polygon([[x1, y2], [x1, y1], [x2, y1], [x2, y2]], True))
if boxcolor == 1:
p = PatchCollection(patches, cmap=plt.cm.jet, alpha=0.4)
else:
p = PatchCollection(patches, cmap=plt.cm.Greys, alpha=0.2)
p.set_array(np.array([0, 0.2, 0.4, 0.5, 0.7, 0.9, 1]))
ax.add_collection(p)
def circles(ax, x, y, r, c="b", **kwargs):
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
if isinstance(c, basestring):
color = c
else:
color = None
kwargs.update(color=color)
if np.isscalar(x):
patches = [Circle((x, y), r)]
elif np.isscalar(r):
patches = [Circle((x_, y_), r) for x_, y_ in zip(x, y)]
else:
patches = [Circle((x_, y_), s_) for x_, y_, s_ in zip(x, y, r)]
collection = PatchCollection(patches, **kwargs)
if color is None:
collection.set_array(np.asarray(c))
ax.add_collection(collection)
return collection
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 circles(x, y, s, c='b', ax=None, vmin=None, vmax=None, **kwargs): from matplotlib.patches import Circle from matplotlib.collections import PatchCollection import pylab as plt if ax is None: ax = plt.gca() if isinstance(c,basestring): color = c # ie. use colors.colorConverter.to_rgba_array(c) else: color = None # use cmap, norm after collection is created kwargs.update(color=color) if np.isscalar(x): patches = [Circle((x, y), s),] elif np.isscalar(s): patches = [Circle((x_,y_), s) for x_,y_ in zip(x,y)] else: patches = [Circle((x_,y_), s_) for x_,y_,s_ in zip(x,y,s)] collection = PatchCollection(patches, **kwargs) if color is None: collection.set_array(np.asarray(c)) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) ax.add_collection(collection) ax.autoscale_view() return collection
def scatter_withcirclesize(ax,x,y,s,alpha=1.0,c='k',cmap=plt.cm.PRGn,colorbar=False,**kwargs):
if c != 'k':
if type(c)==str:
c = [c for dump in range(len(s))]
cmap=None
if type(c)==list:
if len(c) == len(s):
c = c
else:
print 'incorrect number of colors specified.';return None
else:
c = [c for dump in range(len(s))]
points=[]
for (x_i,y_i,r_i,c_i) in zip(x,y,s,c):
points.append(patches.Circle((x_i,y_i),radius=r_i))
if cmap is not None:
p = PatchCollection(points,cmap=cmap,alpha=alpha,clim=(-1,1))
p.set_array(np.array(c))
ax.add_collection(p)
else:
p = PatchCollection(points,color=c,alpha=alpha)
ax.add_collection(p)
if colorbar:
plt.colorbar(p)
def animate_path(self, path, key_xy):
fig, ax = plt.subplots()
colors = 100*np.random.rand(len(self.plot_obstacles_polygon))
p = PatchCollection(self.plot_obstacles_polygon, cmap=matplotlib.cm.jet, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)
plt.colorbar(p)
plt.plot([self.initial_state[0]], [self.initial_state[1]], 'bs', self.goal_state[0], self.goal_state[1], 'g^')
plt.axis([0, self.resolution, 0, self.resolution])
x_0, y_0 = key_xy(path[0])[0], key_xy(path[0])[1]
x_1, y_1 = key_xy(path[0 + 1])[0], key_xy(path[0 + 1])[1]
dx, dy = x_1 - x_0, y_0 - y_1
qv = ax.quiver(x_0, y_0, dx, dy, angles='xy',scale_units='xy',scale=1)
def animate(i):
x_init, y_init =key_xy(path[i])[0], key_xy(path[i])[1]
x_f, y_f = key_xy(path[i + 1])[0], key_xy(path[i + 1])[1]
dx, dy = x_f - x_init, y_f - y_init
qv.set_UVC(np.array(dx), np.array(dy))
qv.set_offsets((x_init, y_init))
return qv
anim = animation.FuncAnimation(fig, animate, frames=range(0, len(path)-1), interval=500)
plt.show()
def plot_refexons(self, fig, ax):
x=1
width=5
patches=[]
y=5.0
cnt=0
for p in self.exons:
x= p[0]-xstart
width= p[1] - p[0]
print x, width
rect= Rectangle( (x,y), width, self.height )
patches.append(rect)
epsilon=-0.25
ax.annotate(self.exonLabels[cnt], (x+(width)/2., y-(self.height/2.+epsilon)), fontsize=10, ha='center', va='center')
cnt+=1
colors = 100*np.random.rand(len(patches))
q = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.6)
q.set_array(np.array(colors))
ax.add_collection(q)
def _setup(self):
# "Cheat" to see 2D map of collision data
patches = []
if isinstance(self.environment, Environment_Simulator):
for obj in self.environment.get_objects():
patch = self._create_patch(obj)
if patch is not None:
patches.append(patch)
p = None
if len(patches) > 0:
p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)
patch_colors = 50*np.ones(len(patches))
p.set_array(np.array(patch_colors))
self.plot_polygons = p
self.arrow_options = {
"arrowstyle": "->",
"linewidth": 2,
"alpha": 0.5
}
self.plt = plt
self.fig, self.ax = self.plt.subplots()
# Set up interactive drawing of the memory map. This makes the
# dronekit/mavproxy fairly annoyed since it creates additional
# threads/windows. One might have to press Ctrl-C and normal keys to
# make the program stop.
self.plt.gca().set_aspect("equal", adjustable="box")
if self.interactive:
self.plt.ion()
self.plt.show()
def plotshapefile(shpfile, colorlist):
fig, ax = plt.subplots()
#shpfile = 'C:/_DATA/CancerData/SatScan/NorthEasternUS.shp'
sf = shapefile.Reader(shpfile)
shapes = sf.shapes()
#records = np.array(sf.records())
[x1, y1, x2, y2] = find_bounding_box(shpfile)
patches = []
for shape in shapes:
lons,lats = zip(*shape.points)
data = np.array([lons, lats]).T
polygon = Polygon(data, True)
patches.append(polygon)
#colors = 100*np.random.rand(len(patches))
#colors = normalize(records[:,-3].astype(np.int32))
c#olors = records[:,-3].astype(np.int32)
colors = colorlist
p = PatchCollection(patches, cmap=cm.OrRd, alpha=0.8)
p.set_array(np.array(colors))
ax.add_collection(p)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
plt.colorbar(p)
#plt.savefig('tutorial10.png',dpi=300)
plt.show()
def matrix_figure(N1=160, N2=32):
r=0.4
fsx=20.
fsy=fsx*N2/N1
f=Figure(figsize=(fsx,fsy),frameon=False)
#f=plt.figure(figsize=(fsx,fsy),frameon=False)
ax=f.add_subplot(111,axisbg='k')
ax.set_xlim([-2*r,N1-1+2*r])
ax.set_ylim([-2*r,N2-1+2*r])
ax.set_axis_bgcolor('k')
ax.set_yticks([])
ax.set_xticks([])
ax.set_frame_on(False)
x=np.arange(N1)
y=np.arange(N2)
xx,yy=np.meshgrid(x,y)
cmap = col.ListedColormap([ '#6E6E6E','#FE2E2E', '#64FE2E', '#FF8000'])
colors=np.random.randint(0,4,(N1,N2))
patches = []
for x1,y1 in zip(xx.flatten(), yy.flatten()):
circle = Circle((x1,y1), r)
patches.append(circle)
p = PatchCollection(patches, cmap=cmap)
p.set_array(colors.flatten())
ax.add_collection(p)
f.subplots_adjust(0,0,1,1)
return ax, colors
def plot_patches(self, x_range, y_range, file_name="voronoi_p1.png"):
t0=time.time()
self.run()
print((time.time()-t0))
pts = self.sites
pts_dict = defaultdict(list)
patches = []
colors = []
for edge in self.edges:
pts_dict[edge.pl].append((edge.start, edge.end))
pts_dict[edge.pr].append((edge.start, edge.end))
for center, v_raw in pts_dict.items():
starts, ends = zip(*v_raw)
vertices = set(starts + ends)
vertices = sorted(vertices, key=lambda p: np.arctan2(p.y-center.y,p.x-center.x))
vertices = [(v.x, v.y) for v in vertices]
patches.append(Polygon(vertices, True))
colors.append(center.dist_to_point(Point(0,0)))
fig, ax = plt.subplots()
colors = 100*np.random.rand(len(patches))
pc = PatchCollection(patches, cmap=jet, alpha=0.2)
pc.set_array(np.array(colors))
ax.axis([*x_range, *y_range])
ax.add_collection(pc)
ax.margins(0.1)
xs, ys = zip(*[(p.x, p.y) for p in pts])
ax.plot(xs, ys, 'ro', markersize=1)
fig.savefig(file_name)
def plotRectilinearTriangles(verbose=True, save=False, show=True, dpi=300, resolution='c'):
lonlat_islands, dymax_islands = getIslands(resolution)
plt.figure(figsize=(20,12))
plt.title('The dymax face polygons look super-f****d on a rectilinear projection')
patches = []
faces = []
for island in lonlat_islands:
polygon = Polygon(np.array(island),closed=False, fill=True)
patches.append(polygon)
for face in range(constants.facecount):
derp = np.zeros((3,2))
for vtex in range(3):
derp[vtex] = constants.lon_lat_verts[constants.vert_indices[face,vtex]]
polygon = Polygon(derp,closed=False,fill=True)
faces.append(polygon)
colors = 100*np.random.random(len(patches))
p = PatchCollection(patches, cmap=plt.cm.jet, alpha=0.7,linewidths=0.)
f = PatchCollection(faces, cmap=plt.cm.jet, alpha=0.3,linewidths=1.)
p.set_array(np.array(colors))
f.set_array(np.array(colors))
plt.gca().add_collection(p)
plt.gca().add_collection(f)
if verbose: print(':: plotted',len(patches),'coastlines')
plt.xlim(-180,180)
plt.ylim(-90,90)
if save: plt.savefig('dymax_rectilineartriangles.png',bbox_inches='tight',dpi=dpi,transparent=True,pad_inches=0)
if show:
plt.tight_layout()
plt.show()
else: plt.close()
def plotGrid(verbose=True, save=False, show=True, dpi=300):
'''Show Dymaxion Grid'''
plt.figure(figsize=(20,12))
patches = []
for zdx, vertset in enumerate(constants.vert_indices):
if zdx==8 or zdx==15: continue # Edge Triangles
x,y = [],[]
for i,vert in enumerate(vertset):
xt,yt = convert.vert2dymax(vert,vertset)
#print(xt,yt)
x += [xt]
y += [yt]
#print(xt,yt,i,vert)
#plt.plot(x,y,'k',lw=.1)
patches.append(Polygon(np.array([x,y]).T,closed=False, fill=True))
colors = 100*np.random.random(len(patches))
p = PatchCollection(patches, cmap=plt.cm.jet, alpha=1,linewidths=0.)
p.set_array(np.array(colors))
plt.gca().add_collection(p)
if verbose: print(':: plotted',len(patches),'coastlines')
plt.gca().set_aspect('equal')
plt.gca().set_xlim([0,5.5])
plt.gca().set_ylim([0,2.6])
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.gca().axis('off')
if save: plt.savefig('dymax_grid.png',bbox_inches='tight',dpi=dpi,transparent=True,pad_inches=0)
if show:
plt.tight_layout()
plt.show()
else: plt.close()
def plotLandmasses(verbose=True, save=False, show=True, dpi=300, resolution='c'):
'''Draw Landmasses Only, no Background'''
lonlat_islands, dymax_islands = getIslands(resolution)
patches = []
for island in dymax_islands:
#if np.all(island==islands[4]): print (island)
try: polygon = Polygon(np.array(island),closed=True, fill=True)
except: continue
#plt.plot(island[:,0],island[:,1])
patches.append(polygon)
plt.figure(figsize=(20,12),frameon=False)
colors = 100*np.random.random(len(patches))
p = PatchCollection(patches, cmap=plt.cm.jet, alpha=1,linewidths=0.)
p.set_array(np.array(colors))
plt.gca().add_collection(p)
if verbose: print(':: plotted',len(patches),'coastlines')
plt.gca().set_aspect('equal')
plt.gca().set_xlim([0,5.5])
plt.gca().set_ylim([0,2.6])
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.gca().axis('off')
if save: plt.savefig('dymax_landmasses.png',bbox_inches='tight',dpi=dpi,transparent=True,pad_inches=0)
if show:
plt.tight_layout()
plt.show()
else: plt.close()
def plot_Vexons(self, ybar, Vbar, fig, ax):
x=1
width=5
patches=[]
y=ybar
cnt=0
for p in Vbar:
x= p[0]-xstart
width= p[1] - p[0]
print x, width
rect= Rectangle( (x,y), width, self.height )
patches.append(rect)
epsilon=-0.35
"""
ax.annotate(self.exonLabels[cnt], (x+(width)/2.,y-epsilon),
fontsize=10, ha='center', va='center')
"""
cnt+=1
colors = 100*np.random.rand(len(patches))
q = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.6)
q.set_array(np.array(colors))
ax.add_collection(q)
ax.set_xlim([self.xLower, self.xUpper])
ax.set_ylim([4, 6])
def quantile_map(coords,y,k, title='Quantile'):
"""
Quantile choropleth map
Arguments
=========
coords: Map_Projection instance
y: array
variable to map
k: int
number of classes
title: string
map title
"""
classification = ps.Quantiles(y,k)
fig = plt.figure()
ax = fig.add_subplot(111)
patches = []
colors = []
i = 0
shape_colors = classification.bins[classification.yb]
shape_colors = y
#classification.bins[classification.yb]
for shp in coords.projected:
for ring in shp:
x,y = ring
x = x / coords.bounding_box[2]
y = y / coords.bounding_box[3]
n = len(x)
x.shape = (n,1)
y.shape = (n,1)
xy = np.hstack((x,y))
polygon = Polygon(xy, True)
patches.append(polygon)
colors.append(shape_colors[i])
i += 1
cmap = cm.get_cmap('hot_r', k+1)
boundaries = classification.bins.tolist()
boundaries.insert(0,0)
norm = clrs.BoundaryNorm(boundaries, cmap.N)
p = PatchCollection(patches, cmap=cmap, alpha=0.4, norm=norm)
colors = np.array(colors)
p.set_array(colors)
ax.add_collection(p)
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
ax.set_title(title)
plt.colorbar(p, cmap=cmap, norm = norm, boundaries = boundaries, ticks=
boundaries)
plt.show()
return classification
def plot_exons(self, strand, cand_exons, best_exons, fig, ax):
patches=[]
patchesBest = []
y=4.5
ymax = 4.5
height = 0.2
xlast=0
epsilon=-0.35
cnt=0
for p in cand_exons:
if strand<0:
x= (self.xend - self.xstart) - p[0]
else:
x= p[0]
width= strand * (p[1] - p[0])
print x, width
if cnt in best_exons:
rect= Rectangle( (x,ymax), width, height )
patchesBest.append(rect)
else:
rect= Rectangle( (x,y), width, height )
patches.append(rect)
"""
if strand<0:
exNum = len(cand_exons)-cnt
else:
exNum = cnt
"""
exNum = cnt
if cnt in best_exons:
ax.annotate(str(exNum), (x+(width)/2., ymax-(self.height/2.+epsilon)), fontsize=8, ha='center', va='center')
else:
ax.annotate(str(exNum), (x+(width)/2., y-(self.height/2.+epsilon)), fontsize=8, ha='center', va='center')
if np.abs(x-xlast)>100:
y = y+0.075
else:
y = y-0.075
xlast = x
cnt+=1
colors = 100*np.random.rand(len(patches))
q = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.2)
q.set_array(np.array(colors))
ax.add_collection(q)
qB = PatchCollection(patchesBest, cmap=matplotlib.cm.jet, alpha=0.9)
qB.set_array(np.array(colors))
ax.add_collection(qB)
ax.set_xlim([3500, 7000])
ax.set_ylim([4, 6])
def plot():
from matplotlib.patches import Circle, Ellipse, Polygon, Rectangle, Wedge
from matplotlib.collections import PatchCollection
from matplotlib import pyplot as plt
import numpy as np
import matplotlib as mpl
np.random.seed(123)
fig = plt.figure()
ax = fig.add_subplot(111)
N = 3
x = np.random.rand(N)
y = np.random.rand(N)
radii = 0.1 * np.random.rand(N)
patches = []
for x1, y1, r in zip(x, y, radii):
circle = Circle((x1, y1), r)
patches.append(circle)
rect = Rectangle(xy=[0.0, 0.25], width=1.0, height=0.5, angle=-45.0)
patches.append(rect)
x = np.random.rand(N)
y = np.random.rand(N)
radii = 0.1 * np.random.rand(N)
theta1 = 360.0 * np.random.rand(N)
theta2 = 360.0 * np.random.rand(N)
for x1, y1, r, t1, t2 in zip(x, y, radii, theta1, theta2):
wedge = Wedge((x1, y1), r, t1, t2)
patches.append(wedge)
# Some limiting conditions on Wedge
patches += [
Wedge((0.3, 0.7), 0.1, 0, 360), # Full circle
Wedge((0.7, 0.8), 0.2, 0, 360, width=0.05), # Full ring
Wedge((0.8, 0.3), 0.2, 0, 45), # Full sector
Wedge((0.8, 0.3), 0.2, 45, 90, width=0.10), # Ring sector
]
for _ in range(N):
polygon = Polygon(np.random.rand(N, 2), True)
patches.append(polygon)
colors = 100 * np.random.rand(len(patches))
p = PatchCollection(patches, cmap=mpl.cm.jet, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)
ellipse = Ellipse(xy=[1.0, 0.5], width=1.0, height=0.5, angle=45.0, alpha=0.4)
ax.add_patch(ellipse)
circle = Circle(xy=[0.0, 1.0], radius=0.5, color="r", alpha=0.4)
ax.add_patch(circle)
plt.colorbar(p)
return fig
def plotseedsandctg(shpfile, cur_scale):
import matplotlib.colors as colors
import numpy as np
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
import shapefile
import matplotlib.pyplot as plt
from matplotlib import cm
cur_amg_weight = scale_amg_weight[cur_scale]
fig, ax = plt.subplots()
#shpfile = 'C:/_DATA/CancerData/SatScan/NorthEasternUS.shp'
sf = shapefile.Reader(shpfile)
shapes = sf.shapes()
#shapes = shapefile.Reader(shpfile).shapes()
#records = np.array(sf.records())
[x1, y1, x2, y2] = find_bounding_box(shapes)
points = []
patches = []
for shape in shapes:
lons,lats = zip(*shape.points)
data = np.array([lons, lats]).T
points.append([np.mean(lons), np.mean(lats)])
polygon = Polygon(data, True)
patches.append(polygon)
colorlist = np.zeros(len(patches))
for idx in scale_amg_weight[cur_scale].seeds:
colorlist[idx] = 1.0
#tcmap = cm.get_cmap(cmap_color)
print colorlist
#p = PatchCollection(patches, cmap=tcmap, alpha=0.8, edgecolors='grey')
p = PatchCollection(patches, facecolors=colorlist, alpha=0.8, edgecolors='grey')
p.set_array(np.array(colorlist))
ax.add_collection(p)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
'''
## plot association between pixels and seeds
for idx in cur_amg_weight.seeds:
#tempimagedata[idx] = 0
temp_set = cur_amg_weight.seeds[idx] ## pixels associated with the seed sid
for pid in temp_set:
#if pid <> idx:
plt.plot([points[idx][0], points[pid][0]], [points[idx][1], points[pid][1]], color = 'r', marker='o')
'''
## plot neigbhors of seeds
for idx in cur_amg_weight.seedsweight:
temp_set = cur_amg_weight.seedsweight[idx]
for pid in temp_set:
plt.plot([points[idx][0], points[pid][0]], [points[idx][1], points[pid][1]], color = 'y', marker='o')
plt.colorbar(p)
#plt.savefig('tutorial10.png',dpi=300)
plt.show()
def plotEarthMeridiansTriangles(verbose=True, save=False, show=True, dpi=300, resolution='c'):
'''Draw Dymax Triangles, All countries, and Meridians'''
lonlat_islands, dymax_islands = getIslands(resolution)
n = 1000
plt.figure(figsize=(20,12))
plt.title('Dymaxion Map Projection')
### Dymaxion Latitude Meridians
lons = np.linspace(-180,180,n)
latgrid = np.linspace(-85,85,35)
points = []
start = time.time()
for lat in latgrid:
for lon in lons:
points += [convert.lonlat2dymax(lon,lat)]
if verbose: print(':: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]'.format(len(points),len(points)/(time.time()-start),time.time()-start))
points = np.array(points)
plt.plot(points[:,0],points[:,1],',',c='k',alpha=.3)#,'.',lw=0)#,c=range(n))
### Dymaxion Longitude Meridians
lats = np.linspace(-85,85,n)
longrid = np.linspace(-180,175,72)
points = []
start = time.time()
for lon in longrid:
for lat in lats:
points += [convert.lonlat2dymax(lon,lat)]
if verbose: print(':: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]'.format(len(points),len(points)/(time.time()-start),time.time()-start))
points = np.array(points)
plt.plot(points[:,0],points[:,1],',',c='k',alpha=.3)#,'.',lw=0)#,c=range(n))
### Dymaxion Face Tiles
for jdx in range(constants.facecount):
if jdx == 8 or jdx == 15: continue
points = convert.face2dymax(jdx,push=.95)
xcenter,ycenter = convert.dymax_centers[jdx]
plt.text(xcenter,ycenter,str(jdx),size='x-large')
plt.plot(points[:,0],points[:,1],lw=5,alpha=.7)
### Draw Landmasses
patches = []
for island in dymax_islands:
polygon = Polygon(np.array(island))#, closed=False, fill=False)
patches.append(polygon)
p = PatchCollection(patches, alpha=.3, linewidths=1.,facecolors=None)
colors = 100*np.random.random(len(patches))
p.set_array(np.array(colors))
plt.gca().add_collection(p)
if verbose: print(':: plotted',len(patches),'coastlines')
plt.gca().set_xlim([0,5.5])
plt.gca().set_ylim([0,2.6])
plt.gca().set_aspect('equal')
if save: plt.savefig('dymax_earthmeridianstriangles.png',bbox_inches='tight',dpi=dpi,transparent=True,pad_inches=0)
if show:
plt.tight_layout()
plt.show()
else: plt.close()
def plot2D(patches, values, vmin, vmax):
color_map = plt.cm.get_cmap('plasma_r')
p = PatchCollection(patches, cmap=color_map, edgecolor="#ffffff", linewidth=0)
colors = values
p.set_array(np.array(colors))
ax = plt.axes()
ax.add_collection(p)
#plt.colorbar(p)
p.set_clim([vmin, vmax])
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 scatterRectangles(x, y, z, norm=None, cmap=None):
patches = [Rectangle(numerix.array([X - 0.5, Y - 0.5]), 1., 1.,
edgecolor='none') for X, Y in zip(x, y)]
collection = PatchCollection(patches, norm=norm, cmap=cmap,
edgecolors='none')
collection.set_array(z)
return collection
def show_voronoibin(self, datain=None, shownode=1, mycmap=None) :
"""
Display the voronoi bins on a map
datain: if None (Default), will use random colors to display the bins
if provided, will display that with a jet (or specified mycmap) cmap
(should be either the length of the voronoi nodes array or the size of the initial pixels)
shownode: default is 1 -> show the voronoi nodes, otherwise ignore (0)
mycmap: in case datain is provide, will use that cmpa to display the bins
"""
from distutils import version
try:
import matplotlib
except ImportError:
raise Exception("matplotlib 0.99.0 or later is required for this routine")
if version.LooseVersion(matplotlib.__version__) < version.LooseVersion('0.99.0'):
raise Exception("matplotlib 0.99.0 or later is required for this routine")
from matplotlib.collections import PatchCollection
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
fig = plt.figure(1,figsize=(7,7))
plt.clf()
ax = plt.gca()
patches = []
binsize = self.pixelsize
for i in range(len(self.xin)) :
patches.append(mpatches.Rectangle((self.xin[i],self.yin[i]), binsize*10, binsize*10))
if datain is None :
dataout = self.status
mycmap = 'prism'
else :
if len(datain) == self.xnode.size :
dataout = np.zeros(self.xin.size, Nfloat)
for i in range(self.xnode.size) :
listbins = self.listbins[i]
dataout[listbins] = [datain[i]]*len(listbins)
elif len(datain) == self.xin.size :
dataout = datain
if mycmap is None : mycmap = 'jet'
colors = dataout * 100.0 / max(dataout)
collection = PatchCollection(patches, cmap=mycmap)
collection.set_array(np.array(colors))
ax.add_collection(collection)
if shownode :
plt.scatter(self.xnode,self.ynode, marker='o', edgecolors='k', facecolors='none', s=100)
for i in range(self.xnode.size):
ax.annotate(i, (self.xnode[i], self.ynode[i]))
listbins = self.listbins[i]
plt.axis('image')
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.title("Voronoi Map")
def circles(x, y, s, c='b', ax=None, vmin=None, vmax=None, **kwargs):
"""
Make a scatter of circles plot of x vs y, where x and y are sequence
like objects of the same lengths. The size of circles are in data scale.
Parameters
----------
x,y : scalar or array_like, shape (n, )
Input data
s : scalar or array_like, shape (n, )
Radius of circle in data scale (ie. in data unit)
c : color or sequence of color, optional, default : 'b'
`c` can be a single color format string, or a sequence of color
specifications of length `N`, or a sequence of `N` numbers to be
mapped to colors using the `cmap` and `norm` specified via kwargs.
Note that `c` should not be a single numeric RGB or
RGBA sequence because that is indistinguishable from an array of
values to be colormapped. `c` can be a 2-D array in which the
rows are RGB or RGBA, however.
ax : Axes object, optional, default: None
Parent axes of the plot. It uses gca() if not specified.
vmin, vmax : scalar, optional, default: None
`vmin` and `vmax` are used in conjunction with `norm` to normalize
luminance data. If either are `None`, the min and max of the
color array is used. (Note if you pass a `norm` instance, your
settings for `vmin` and `vmax` will be ignored.)
Returns
-------
paths : `~matplotlib.collections.PathCollection`
Other parameters
----------------
kwargs : `~matplotlib.collections.Collection` properties
eg. alpha, edgecolors, facecolors, linewidths, linestyles, norm, cmap
Examples
--------
a = np.arange(11)
circles(a, a, a*0.2, c=a, alpha=0.5, edgecolor='none')
License
--------
This code is under [The BSD 3-Clause License]
(http://opensource.org/licenses/BSD-3-Clause)
"""
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
#import matplotlib.colors as colors
if ax is None:
ax = plt.gca()
if isinstance(c, basestring):
color = c # ie. use colors.colorConverter.to_rgba_array(c)
else:
color = None # use cmap, norm after collection is created
kwargs.update(color=color)
if isinstance(x, (int, long, float)):
patches = [
Circle((x, y), s),
]
elif isinstance(s, (int, long, float)):
patches = [Circle((x_, y_), s) for x_, y_ in zip(x, y)]
else:
patches = [Circle((x_, y_), s_) for x_, y_, s_ in zip(x, y, s)]
collection = PatchCollection(patches, **kwargs)
if color is None:
collection.set_array(np.asarray(c))
if vmin is not None or vmax is not None:
collection.set_clim(vmin, vmax)
ax.add_collection(collection)
return collection
class ArrayDisplay:
"""
Display a top-town view of a telescope array
"""
def __init__(self,
telx,
tely,
tel_type=None,
radius=20,
axes=None,
title="Array",
autoupdate=True):
if tel_type is None:
tel_type = np.ones(len(telx))
patches = [
Rectangle(xy=(x - radius / 2, y - radius / 2),
width=radius,
height=radius,
fill=False) for x, y in zip(telx, tely)
]
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_clim(1, 9)
rgb = matplotlib.cm.Set1((tel_type - 1) / 9)
self.telescopes.set_edgecolor(rgb)
self.telescopes.set_linewidth(2.0)
self.axes = axes if axes is not None else plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self.axes.set_xlim(-1000, 1000)
self.axes.set_ylim(-1000, 1000)
self.axes_hillas = axes if axes is not None else plt.gca()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def add_ellipse(self, centroid, length, width, angle, **kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid,
width=length,
height=width,
angle=np.degrees(angle),
fill=True,
**kwargs)
self.axes.add_patch(ellipse)
return ellipse
def add_polygon(self, centroid, radius, nsides=3, **kwargs):
"""
plot a polygon on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
radius: float
radius
nsides: int
Number of points on polygon
kwargs:
any MatPlotLib style arguments to pass to the RegularPolygon patch
"""
polygon = RegularPolygon(xy=centroid,
radius=radius,
numVertices=nsides,
**kwargs)
self.axes.add_patch(polygon)
return polygon
def overlay_moments(self, momparams, tel_position, scale_fac, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
scale_fac: float
scaling factor to apply to width and length when overlaying moments
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
ellipse_list = list()
size_list = list()
i = 0
for h in momparams:
length = u.Quantity(momparams[h].length).value * scale_fac
width = u.Quantity(momparams[h].width).value * scale_fac
size_list.append(u.Quantity(momparams[h].size).value)
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
i += 1
ellipse = Ellipse(xy=(tel_x, tel_y),
width=length,
height=width,
angle=np.degrees(momparams[h].psi.rad))
ellipse_list.append(ellipse)
patches = PatchCollection(ellipse_list, **kwargs)
patches.set_clim(0, 1000) # Set ellipse colour based on image size
patches.set_array(np.asarray(size_list))
self.axes_hillas.add_collection(patches)
def overlay_axis(self, momparams, tel_position, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
line_list = list()
size_list = list()
i = 0
for h in momparams:
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
psi = u.Quantity(momparams[h].psi).value
x_sc = [tel_x - np.cos(psi) * 10000, tel_x + np.cos(psi) * 10000]
y_sc = [tel_y - np.sin(psi) * 10000, tel_y + np.sin(psi) * 10000]
i += 1
self.axes_hillas.add_line(
Line2D(x_sc, y_sc, linestyle='dashed', color='black'))
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 plot(values, binheader = None, plotbins = False,
ax = None, figsize = (8,8), figure = None, geometry = (1,1,1),
fitsfile = None, imrot = False, wcsax = True, invert=True,
pa = 0, center = [0,0], reffiber = 105, alpha=1.0,
clabel = '', cmap = 'gnuplot2', minval = None, maxval = None,
labelfibers = True, sky = False, exclude = []):
"""Generate a spatial plot of the GradPack IFU fibers with each fiber colored
based on user-supplied values. This is one of the main user-level
functions in this module, and returns an Axes object for integration into
whatever higher-level plotting the user is doing.
It is of the utmost importance that the values input variable is of length
109 and is ordered by fiber number.
A quick example of how to use this function:
>>> GradPak_plot.plot(np.arange(109)).figure.show()
This will show you a simple plot with the fibers colored by their fiber
number and presented on a relative arcsec scale. More advanced usage can
be achieved with the following options:
Parameters
----------
values : numpy.ndarray
The data values associated with each fiber. The length of this array
should be equal to either 109 or the number of bins specified in **binheader**.
binheader : pyfits.header.Header
A FITS header that contains keywords BINFXXX and BINPXXX. This header will be used
to group fibers together during plotting.
plotbins : bool
An extremely poorly named parameter. If True, plot every *fiber* (not aperture) with
a value given by the value of its aperture assignment as specified by **binheader**.
If False the apertures specified by **binheader** will be plotted as contiguous regions.
alpha : float
The desired opacity of the plotted patches.
ax : matplotlib.axes.Axes
If supplied, the GradPack patches will be plotted
in this axis. This is very useful for plotting multiple pointings on
the same plot. Setting this option causes **fitsfile**, **imrot**, **invert**,
and **wcsax** to be ignored.
figure : matplotlib.pyplot.Figure instance
If not None, the axis object created by :func:`plot` will be
placed into this figure with the geometry specified by
**geometry**.
figsize : tup
The size of the figure, in inches. Passed directly to
plt.figure(). This option is ignored if **figure** is not None
geometry : int or tup
A pyplot-style geometry argument for placing the axis in its
parent figure. Can be a 3 digit integer (e.g., 111) or a tuple
(e.g., (1,1,1)
fitsfile : str -
The name of a FITS image to draw on the plot. The
FITS header must contain WCS parameters in the CDELT, CRVAL, CRPIX
format.
imrot : float
Rotation of fits image in relation to the axes. This
is useful for, e.g., aligning a galaxy's major axis along the x
axis. This option is ignored if **fitsfile** = None or **ax** != None
wcsax : bool
If True the axis labels will be in Fk5 WCS
coordinates. This option is ignored if **fitsfile** = None or **ax** != None
invert : bool
If True, the colormap of the fits image will be
inverted. This option is ignored if **fitsfile** = None or **ax** != None
pa : float
Position angle of GradPak in decimal degrees. This
angle is measured East of North and should be whatever you told the
telescope operator.
center : tup or list
Length 2 tuple or list containing the coordinates of the GradPak array.
The units should be decimal Ra and Dec. This is probably the
coordinates you had listed in your cache.
reffiber : int
The IFU fiber placed at the coordinate given in
center. Default is fiber 105.
clabel : str
The label of the colorbar. This is typically a
description of the values being plotted.
cmap : str
The name of a matplotlib colormap that will be applied
to the data values. This is passed directly to plt.get_cmap()
Don't use 'jet'.
minval/maxval : float
The lower and upper limits of the colorbar,
respectively. These are passed directly to
matplotlib.colors.Normalize()
labelfibers : bool
If True, each fiber will be labeled with its
fiber number.
sky : bool
If True, sky fibers will be plotted and the axes limits
expanded to view the sky fibers.
exclude : list
A list of fiber numbers to be excluded from
plotting. These patches are simply deleted.
Returns
-------
ax : matplotlib.axes.Axes
The Axes containing all the plotting requested.
"""
tmpax, hdu = prep_axis(fitsfile = fitsfile,
invert = invert,
sky = sky,
imrot = imrot,
wcsax = wcsax,
figsize = figsize,
geometry = geometry,
figure = figure)
if not ax:
ax = tmpax
patches, pval, refcenter = prep_patches(values, binheader=binheader,
plotbins = plotbins,
hdu = hdu, pa = pa,
center = center,
reffiber = reffiber,
sky = sky, exclude = exclude)
if hdu is not None:
xdelt = 1.5/(60. * hdu.header['CDELT1'])
ydelt = 1.5/(60. * hdu.header['CDELT2'])
ax.set_xlim(refcenter[0] + xdelt, refcenter[0] - xdelt)
ax.set_ylim(refcenter[1] - ydelt, refcenter[1] + ydelt)
else:
xmin, xmax = ax.get_xlim()
ax.set_xlim(xmax, xmin)
if labelfibers:
for c in patches:
ax.text(c[1].center[0],c[1].center[1],c[0],fontsize=7,
ha='center',va='center')
if minval is None:
minval = pval.min()
if maxval is None:
maxval = pval.max()
collection = PatchCollection(patches[:,1],
cmap=plt.get_cmap(cmap),
norm=matplotlib.colors.Normalize(
vmin=minval,vmax=maxval),
edgecolor = 'none',
alpha=alpha)
collection.set_array(pval)
ax.add_collection(collection)
cbar = plt.colorbar(collection,cax=ax.cax,orientation='horizontal')
cbar.set_label(clabel)
cbar.ax.xaxis.set_ticks_position('top')
cbar.ax.xaxis.set_label_position('top')
if not plotbins and binheader is not None:
boxes, bval = get_bin_boxes(binheader, patches, pval)
boxColl = PatchCollection(boxes,
cmap=plt.get_cmap(cmap),
norm=matplotlib.colors.Normalize(
vmin=minval,vmax=maxval),
edgecolor = 'none',
alpha=alpha)
boxColl.set_array(bval)
ax.add_collection(boxColl)
return ax
def plot_params_map(header=None,
av_image=None,
df=None,
limits=None,
filename=None,
vlimits=(None, None),
contours=None,
parameter='phi_cnm'):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.6, 5))
nrows_ncols = (3, 1)
ngrids = 3
axesgrid = AxesGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
axes_class=(wcs.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=False)
# ------------------
# Av image
# ------------------
parameters = ['phi_cnm', 'alphaG', 'hi_transition']
for i in xrange(ngrids):
# create axes
ax = axesgrid[i]
# show the image
X, Y = np.meshgrid(np.arange(av_image.shape[1]),
np.arange(av_image.shape[0]))
im = ax.contour(
X,
Y,
av_image,
origin='lower',
levels=contours,
cmap=myplt.truncate_colormap(
plt.cm.binary,
minval=0.3,
)
#vmin=vlimits[0],
#vmax=vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension [J2000]', )
ax.set_ylabel('Declination [J2000]', )
ax.locator_params(nbins=4)
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header)
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
# Plot cores for each cloud
# -------------------------
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
parameter = parameters[i]
patches = get_patches(df, header)
cmap = myplt.truncate_colormap(plt.cm.copper, minval=0.1, maxval=1.0)
collection = PatchCollection(
patches,
cmap=cmap,
edgecolors='none',
zorder=1000,
)
collection.set_array(df[parameter])
ax.add_collection(collection, )
# colorbar
#cbar = axesgrid.cbar_axes[i].colorbar(collection)
cbar = ax.cax.colorbar(collection)
if parameter == 'phi_cnm':
cbar.set_label_text(r'$\phi_{\rm CNM}$', )
elif parameter == 'alphaG':
cbar.set_label_text(r'$\alpha G$', )
elif parameter == 'hi_transition':
cbar.set_label_text(r'$\Sigma_{\rm HI,trans}$ ' + \
r'$[M_\odot\,{\rm pc}^{-2}]$',)
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=100)
def plot_flow(G, P, F, colorbar=True):
plt.figure()
gs = mgridspec.GridSpec(4,
4,
width_ratios=[3, 18, 3, 1],
height_ratios=[1, 14, 1, 1])
ax = plt.subplot(gs[0:3, 0:3])
if colorbar:
cax1 = plt.subplot(gs[1, 3])
cax2 = plt.subplot(gs[3, 1])
ax.set_aspect('equal')
# Nodes
vmax = abs(P).max()
pos = {c: np.asarray(sh.centroid) for c, sh in iteritems(shapes)}
x, y = np.asarray(itemgetter(*nodelist)(pos)).T
mappable = ax.scatter(x,
y,
s=45.,
c=P,
cmap='coolwarm',
vmin=-vmax,
vmax=+vmax)
draw_countries([np.NaN] * 30, facecolors='None', ax=ax, zorder=-3)
if colorbar:
plt.colorbar(mappable, cax=cax1).set_label(r'$P_n$ / GW')
# Edges
cc = mcolors.ColorConverter()
cmap = mcolors.LinearSegmentedColormap.from_list(
'darkblue-alpha',
[cc.to_rgba('darkblue', alpha=a) for a in (0., 1.)])
norm = mcolors.Normalize()
lines = []
arrows = []
for (u, v), f in zip(edgelist, F):
if f < 0: u, v = v, u
x1, y1 = pos[u]
x2, y2 = pos[v]
lines.append([(x1, y1), (x2, y2)])
arrows.append(
FancyArrow(x1,
y1,
0.5 * (x2 - x1),
0.5 * (y2 - y1),
head_width=1.5))
linecol = LineCollection(lines, lw=3., cmap=cmap, zorder=-2, norm=norm)
linecol.set_array(abs(F))
ax.add_collection(linecol)
arrowcol = PatchCollection(arrows,
cmap=cmap,
zorder=-1,
norm=norm,
edgecolors='none')
arrowcol.set_array(abs(F))
ax.add_collection(arrowcol)
#plt.plot((x1, x2), (y1, y2), color='darkblue', alpha=norm(abs(f)), lw=3., zorder=-2)
plt.colorbar(linecol, cax=cax2,
orientation='horizontal').set_label(r'$|F_l|$ / GW')
# Function for polygon ploting
import matplotlib
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
def plot_polys(plist, scale=500.0):
fig, ax = plt.subplots()
patches = []
for p in plist:
poly = Polygon(np.array(p) / scale, True)
patches.append(poly)
pc = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.5)
colors = 100 * np.random.rand(len(patches))
pc.set_array(np.array(colors))
ax.add_collection(pc)
plt.show()
# Demo on ConvexHull
points = np.random.rand(30, 2) # 30 random points in 2-D
hull = ConvexHull(points)
# **In 2D "volume" is is area, "area" is perimeter
print(('Hull area: ', hull.volume))
for simplex in hull.simplices:
print(simplex)
# Demo on convex hull overlaps
sub_poly = [(0, 0), (300, 0), (300, 300), (0, 300)]
clip_poly = [(150, 150), (300, 300), (150, 450), (0, 300)]
inter_poly = polygon_clip(sub_poly, clip_poly)
def zonal_ts_rcp_lon(lon0, lat_min, lat_max, save=False, fig_name=None):
# File paths
mesh_path = '/short/y99/kaa561/FESOM/mesh/meshB/'
directory_beg = '/short/y99/kaa561/FESOM/highres_spinup/'
directories = [
'/short/y99/kaa561/FESOM/rcp45_M/', '/short/y99/kaa561/FESOM/rcp45_A/',
'/short/y99/kaa561/FESOM/rcp85_M/', '/short/y99/kaa561/FESOM/rcp85_A/',
'/short/y99/kaa561/FESOM/highres_spinup/'
]
file_beg = 'annual_avg.oce.mean.1996.2005.nc'
file_end = 'annual_avg.oce.mean.2091.2100.nc'
# Titles for plotting
expt_names = [
'RCP 4.5 M', 'RCP 4.5 A', 'RCP 8.5 M', 'RCP 8.5 A', 'CONTROL'
]
num_expts = len(directories)
# Start and end years for each period
beg_years = [1996, 2005]
end_years = [2091, 2100]
# Figure out what to write on the title about longitude
if lon0 < 0:
lon_string = str(-lon0) + r'$^{\circ}$W'
else:
lon_string = str(lon0) + r'$^{\circ}$E'
print 'Building FESOM mesh'
elm2D = fesom_grid(mesh_path)
print 'Reading temperature and salinity data'
id = Dataset(directory_beg + file_beg, 'r')
temp_nodes_beg = id.variables['temp'][0, :]
salt_nodes_beg = id.variables['salt'][0, :]
id.close()
n3d = size(temp_nodes_beg)
# Get anomalies for each experiment
temp_nodes_diff = zeros([num_expts, n3d])
salt_nodes_diff = zeros([num_expts, n3d])
for expt in range(num_expts):
id = Dataset(directories[expt] + file_end, 'r')
temp_nodes_diff[expt, :] = id.variables['temp'][0, :] - temp_nodes_beg
salt_nodes_diff[expt, :] = id.variables['salt'][0, :] - salt_nodes_beg
print 'Interpolating to ' + lon_string
# Build arrays of SideElements making up zonal slices
# Start with beginning
print '...' + str(beg_years[0]) + '-' + str(beg_years[1])
selements_temp_beg = fesom_sidegrid(elm2D, temp_nodes_beg, lon0, lat_max)
selements_salt_beg = fesom_sidegrid(elm2D, salt_nodes_beg, lon0, lat_max)
# Build array of quadrilateral patches for the plots, and data values
# corresponding to each SideElement
patches = []
temp_beg = []
for selm in selements_temp_beg:
# Make patch
coord = transpose(vstack((selm.y, selm.z)))
patches.append(Polygon(coord, True, linewidth=0.))
# Save data value
temp_beg.append(selm.var)
temp_beg = array(temp_beg)
# Salinity has same patches but different values
salt_beg = []
for selm in selements_salt_beg:
salt_beg.append(selm.var)
salt_beg = array(salt_beg)
# Repeat for anomalies for each experiment
temp_diff = zeros([num_expts, len(patches)])
salt_diff = zeros([num_expts, len(patches)])
for expt in range(num_expts):
print '...' + expt_names[expt]
selements_temp_diff = fesom_sidegrid(elm2D, temp_nodes_diff[expt, :],
lon0, lat_max)
selements_salt_diff = fesom_sidegrid(elm2D, salt_nodes_diff[expt, :],
lon0, lat_max)
i = 0
for selm in selements_temp_diff:
temp_diff[expt, i] = selm.var
i += 1
i = 0
for selm in selements_salt_diff:
salt_diff[expt, i] = selm.var
i += 1
# Find bounds on each variable
temp_min = amin(temp_beg)
temp_max = amax(temp_beg)
salt_min = amin(salt_beg)
salt_max = amax(salt_beg)
temp_max_diff = 2.0
salt_max_diff = 0.5
# Find deepest depth
# Start with 0
depth_min = 0
# Modify with patches
for selm in selements_temp_beg:
depth_min = min(depth_min, amin(selm.z))
# Round down to nearest 50 metres
depth_min = floor(depth_min / 50) * 50
print 'Plotting'
fig = figure(figsize=(20, 16))
# Temperature
gs_temp = GridSpec(2, 3)
gs_temp.update(left=0.11,
right=0.9,
bottom=0.51,
top=0.9,
wspace=0.05,
hspace=0.5)
# Beginning
ax = subplot(gs_temp[0, 0])
img = PatchCollection(patches, cmap='jet')
img.set_array(temp_beg)
img.set_edgecolor('face')
img.set_clim(vmin=temp_min, vmax=temp_max)
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title(str(beg_years[0]) + '-' + str(beg_years[1]), fontsize=20)
xlabel('Latitude', fontsize=16)
ylabel('Depth (m)', fontsize=16)
# Add a colorbar on the left
cbaxes = fig.add_axes([0.02, 0.75, 0.015, 0.15])
cbar = colorbar(img, cax=cbaxes, extend='both')
# Anomalies for each experiment
for expt in range(num_expts):
if expt < 2:
ax = subplot(gs_temp[0, expt + 1])
else:
ax = subplot(gs_temp[1, expt - 2])
img = PatchCollection(patches, cmap='RdBu_r')
img.set_array(temp_diff[expt, :])
img.set_edgecolor('face')
img.set_clim(vmin=-temp_max_diff, vmax=temp_max_diff)
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title(expt_names[expt], fontsize=20)
ax.set_xticklabels([])
ax.set_yticklabels([])
if expt == 0:
xlabel(str(end_years[0]) + '-' + str(end_years[1]) + ' anomalies',
fontsize=20)
text(-69.5,
1200,
r'Temperature ($^{\circ}$C) at ' + lon_string,
fontsize=28)
if expt == num_expts - 1:
# Add a colorbar on the right
cbaxes = fig.add_axes([0.92, 0.75, 0.015, 0.15])
cbar = colorbar(img, cax=cbaxes, extend='both')
# Salinity
gs_salt = GridSpec(2, 3)
gs_salt.update(left=0.11,
right=0.9,
bottom=0.02,
top=0.41,
wspace=0.05,
hspace=0.5)
# Beginning
ax = subplot(gs_salt[0, 0])
img = PatchCollection(patches, cmap='jet')
img.set_array(salt_beg)
img.set_edgecolor('face')
img.set_clim(vmin=salt_min, vmax=salt_max)
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title(str(beg_years[0]) + '-' + str(beg_years[1]), fontsize=20)
xlabel('Latitude', fontsize=16)
ylabel('Depth (m)', fontsize=16)
# Add a colorbar on the left
cbaxes = fig.add_axes([0.02, 0.26, 0.015, 0.15])
cbar = colorbar(img, cax=cbaxes, extend='both')
# Anomalies for each experiment
for expt in range(num_expts):
if expt < 2:
ax = subplot(gs_salt[0, expt + 1])
else:
ax = subplot(gs_salt[1, expt - 2])
img = PatchCollection(patches, cmap='RdBu_r')
img.set_array(salt_diff[expt, :])
img.set_edgecolor('face')
img.set_clim(vmin=-salt_max_diff, vmax=salt_max_diff)
ax.add_collection(img)
xlim([lat_min, lat_max])
ylim([depth_min, 0])
title(expt_names[expt], fontsize=20)
ax.set_xticklabels([])
ax.set_yticklabels([])
if expt == 0:
xlabel(str(end_years[0]) + '-' + str(end_years[1]) + ' anomalies',
fontsize=20)
text(-68, 1200, 'Salinity (psu) at ' + lon_string, fontsize=28)
if expt == num_expts - 1:
# Add a colorbar on the right
cbaxes = fig.add_axes([0.92, 0.26, 0.015, 0.15])
cbar = colorbar(img, cax=cbaxes, extend='both')
if save:
fig.savefig(fig_name)
else:
fig.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
----------
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
"""
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', False):
cmap.set_under('darkgrey')
cmap.set_over('lightgrey')
cmap.set_bad('black')
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)
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 circles(x, y, s, c='b', vmin=None, vmax=None, **kwargs):
"""
See https://gist.github.com/syrte/592a062c562cd2a98a83
Make a scatter plot of circles.
Similar to plt.scatter, but the size of circles are in data scale.
Parameters
----------
x, y : scalar or array_like, shape (n, )
Input data
s : scalar or array_like, shape (n, )
Radius of circles.
c : color or sequence of color, optional, default : 'b'
`c` can be a single color format string, or a sequence of color
specifications of length `N`, or a sequence of `N` numbers to be
mapped to colors using the `cmap` and `norm` specified via kwargs.
Note that `c` should not be a single numeric RGB or RGBA sequence
because that is indistinguishable from an array of values
to be colormapped. (If you insist, use `color` instead.)
`c` can be a 2-D array in which the rows are RGB or RGBA, however.
vmin, vmax : scalar, optional, default: None
`vmin` and `vmax` are used in conjunction with `norm` to normalize
luminance data. If either are `None`, the min and max of the
color array is used.
kwargs : `~matplotlib.collections.Collection` properties
Eg. alpha, edgecolor(ec), facecolor(fc), linewidth(lw), linestyle(ls),
norm, cmap, transform, etc.
Returns
-------
paths : `~matplotlib.collections.PathCollection`
Examples
--------
a = np.arange(11)
circles(a, a, s=a*0.2, c=a, alpha=0.5, ec='none')
plt.colorbar()
License
--------
This code is under [The BSD 3-Clause License]
(http://opensource.org/licenses/BSD-3-Clause)
"""
if np.isscalar(c):
kwargs.setdefault('color', c)
c = None
if 'fc' in kwargs:
kwargs.setdefault('facecolor', kwargs.pop('fc'))
if 'ec' in kwargs:
kwargs.setdefault('edgecolor', kwargs.pop('ec'))
if 'ls' in kwargs:
kwargs.setdefault('linestyle', kwargs.pop('ls'))
if 'lw' in kwargs:
kwargs.setdefault('linewidth', kwargs.pop('lw'))
# You can set `facecolor` with an array for each patch,
# while you can only set `facecolors` with a value for all.
zipped = np.broadcast(x, y, s)
patches = [Circle((x_, y_), s_) for x_, y_, s_ in zipped]
collection = PatchCollection(patches, **kwargs)
if c is not None:
c = np.broadcast_to(c, zipped.shape).ravel()
collection.set_array(c)
collection.set_clim(vmin, vmax)
ax = plt.gca()
ax.add_collection(collection)
ax.autoscale_view()
plt.draw_if_interactive()
if c is not None:
plt.sci(collection)
return collection
def plot():
from matplotlib.patches import Circle, Ellipse, Polygon, Rectangle, Wedge
from matplotlib.collections import PatchCollection
from matplotlib import pyplot as plt
import numpy as np
import matplotlib as mpl
np.random.seed(123)
fig = plt.figure()
ax = fig.add_subplot(111)
N = 3
x = np.random.rand(N)
y = np.random.rand(N)
radii = 0.1 * np.random.rand(N)
patches = []
for x1, y1, r in zip(x, y, radii):
circle = Circle((x1, y1), r)
patches.append(circle)
rect = Rectangle(xy=[0.0, 0.25], width=1.0, height=0.5, angle=-45.0)
patches.append(rect)
x = np.random.rand(N)
y = np.random.rand(N)
radii = 0.1 * np.random.rand(N)
theta1 = 360.0 * np.random.rand(N)
theta2 = 360.0 * np.random.rand(N)
for x1, y1, r, t1, t2 in zip(x, y, radii, theta1, theta2):
wedge = Wedge((x1, y1), r, t1, t2)
patches.append(wedge)
# Some limiting conditions on Wedge
patches += [
Wedge((0.3, 0.7), .1, 0, 360), # Full circle
Wedge((0.7, 0.8), .2, 0, 360, width=0.05), # Full ring
Wedge((0.8, 0.3), .2, 0, 45), # Full sector
Wedge((0.8, 0.3), .2, 45, 90, width=0.10), # Ring sector
]
for i in range(N):
polygon = Polygon(np.random.rand(N, 2), True)
patches.append(polygon)
colors = 100 * np.random.rand(len(patches))
p = PatchCollection(patches, cmap=mpl.cm.jet, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)
ellipse = Ellipse(xy=[1.0, 0.5],
width=1.0,
height=0.5,
angle=45.0,
alpha=0.4)
ax.add_patch(ellipse)
circle = Circle(xy=[0.0, 1.0], radius=0.5, color='r', alpha=0.4)
ax.add_patch(circle)
plt.colorbar(p)
return fig
def _plot_polygon_collection(ax,
geoms,
values=None,
color=None,
cmap=None,
vmin=None,
vmax=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)
values : a sequence of `N` values, optional
Values will be mapped to colors using vmin/vmax/cmap. They should
have 1:1 correspondence with the geometries (not their components).
Otherwise follows `color` / `facecolor` kwargs.
edgecolor : single color or sequence of `N` colors
Color for the edge of the polygons
facecolor : single color or sequence of `N` colors
Color to fill the polygons. Cannot be used together with `values`.
color : single color or sequence of `N` colors
Sets both `edgecolor` and `facecolor`
**kwargs
Additional keyword arguments passed to the collection
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
try:
from descartes.patch import PolygonPatch
except ImportError:
raise ImportError(
"The descartes package is required for plotting polygons in geopandas. "
"You can install it using 'conda install -c conda-forge descartes' or "
"'pip install descartes'.")
from matplotlib.collections import PatchCollection
geoms, multiindex = _flatten_multi_geoms(geoms)
if values is not None:
values = np.take(values, multiindex, axis=0)
# PatchCollection does not accept some kwargs.
kwargs = {
att: value
for att, value in kwargs.items()
if att not in ["markersize", "marker"]
}
# Add to kwargs for easier checking below.
if color is not None:
kwargs["color"] = color
_expand_kwargs(kwargs, multiindex)
collection = PatchCollection([PolygonPatch(poly) for poly in geoms],
**kwargs)
if values is not None:
collection.set_array(np.asarray(values))
collection.set_cmap(cmap)
if "norm" not in kwargs:
collection.set_clim(vmin, vmax)
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
def get_grid_patch_collection(self, zpts, plotarray, **kwargs):
"""
Get a PatchCollection of plotarray in unmasked cells
Parameters
----------
zpts : numpy.ndarray
array of z elevations that correspond to the x, y, and horizontal
distance along the cross-section (self.xpts). Constructed using
plotutil.cell_value_points().
plotarray : numpy.ndarray
Three-dimensional array to attach to the Patch Collection.
**kwargs : dictionary
keyword arguments passed to matplotlib.collections.PatchCollection
Returns
-------
patches : matplotlib.collections.PatchCollection
"""
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
rectcol = []
if 'vmin' in kwargs:
vmin = kwargs.pop('vmin')
else:
vmin = None
if 'vmax' in kwargs:
vmax = kwargs.pop('vmax')
else:
vmax = None
colors = []
if self.geographic_coords:
xpts = self.geographic_xpts
else:
xpts = self.xpts
for k in range(zpts.shape[0] - 1):
for idx in range(0, len(xpts) - 1, 2):
try:
ll = ((xpts[idx][2], zpts[k + 1, idx]))
try:
dx = xpts[idx + 2][2] - xpts[idx][2]
except:
dx = xpts[idx + 1][2] - xpts[idx][2]
dz = zpts[k, idx] - zpts[k + 1, idx]
pts = (ll, (ll[0], ll[1] + dz), (ll[0] + dx, ll[1] + dz),
(ll[0] + dx, ll[1])) # , ll)
if np.isnan(plotarray[k, idx]):
continue
if plotarray[k, idx] is np.ma.masked:
continue
rectcol.append(Polygon(pts, closed=True))
colors.append(plotarray[k, idx])
except:
pass
if len(rectcol) > 0:
patches = PatchCollection(rectcol, **kwargs)
patches.set_array(np.array(colors))
patches.set_clim(vmin, vmax)
else:
patches = None
return patches
def cavity_warming_depth(rcp, model):
# File paths
mesh_path = '/short/y99/kaa561/FESOM/mesh/meshB/'
directory_beg = '/short/y99/kaa561/FESOM/highres_spinup/'
directory_end = '/short/y99/kaa561/FESOM/rcp' + rcp + '_' + model + '/'
annual_file_beg = 'annual_avg.oce.mean.1996.2005.nc'
annual_file_end = 'annual_avg.oce.mean.2091.2100.nc'
monthly_file_beg = 'monthly_climatology_temp_1996_2005.nc'
monthly_file_end = 'monthly_climatology_temp_2091_2100.nc'
# Bound on plot
lat_max = -64 + 90
if model == 'M':
model_title = 'MMM'
elif model == 'A':
model_title = 'ACCESS'
print 'Building mesh'
# Mask open ocean
elements, mask_patches = make_patches(mesh_path,
circumpolar=True,
mask_cavities=True)
# Unmask ice shelf patches
patches = iceshelf_mask(elements)
# Build ice shelf front contours
contour_lines = []
for elm in elements:
# Select elements where exactly 2 of the 3 nodes are in a cavity
if count_nonzero(elm.cavity_nodes) == 2:
# Save the coastal flags and x- and y- coordinates of these 2
coast_tmp = []
x_tmp = []
y_tmp = []
for i in range(3):
if elm.cavity_nodes[i]:
coast_tmp.append(elm.coast_nodes[i])
x_tmp.append(elm.x[i])
y_tmp.append(elm.y[i])
# Select elements where at most 1 of these 2 nodes are coastal
if count_nonzero(coast_tmp) < 2:
# Draw a line between the 2 nodes
contour_lines.append([(x_tmp[0], y_tmp[0]),
(x_tmp[1], y_tmp[1])])
# Set up a grey square covering the domain, anything that isn't covered
# up later is land
x_reg, y_reg = meshgrid(linspace(-lat_max, lat_max, num=100),
linspace(-lat_max, lat_max, num=100))
land_square = zeros(shape(x_reg))
# Read the cavity flag for each 2D surface node
node_cavity = []
f = open(mesh_path + 'cavity_flag_nod2d.out', 'r')
for line in f:
tmp = int(line)
if tmp == 1:
node_cavity.append(True)
elif tmp == 0:
node_cavity.append(False)
else:
print 'Problem'
f.close()
# Save the number of 2D nodes
n2d = len(node_cavity)
# Read the depth of each 3D node
f = open(mesh_path + 'nod3d.out', 'r')
f.readline()
node_depth = []
for line in f:
tmp = line.split()
node_depth.append(-1 * float(tmp[3]))
f.close()
node_depth = array(node_depth)
# Read lists of which nodes are directly below which
f = open(mesh_path + 'aux3d.out', 'r')
max_num_layers = int(f.readline())
node_columns = zeros([n2d, max_num_layers])
for n in range(n2d):
for k in range(max_num_layers):
node_columns[n, k] = int(f.readline())
node_columns = node_columns.astype(int)
f.close()
print 'Reading data'
# Annual average
id = Dataset(directory_beg + annual_file_beg, 'r')
temp_beg = id.variables['temp'][0, :]
id.close()
id = Dataset(directory_end + annual_file_end, 'r')
temp_end = id.variables['temp'][0, :]
id.close()
# Monthly climatology
id = Dataset(directory_beg + monthly_file_beg, 'r')
monthly_temp_beg = id.variables['temp'][:, :]
id.close()
id = Dataset(directory_end + monthly_file_end, 'r')
monthly_temp_end = id.variables['temp'][:, :]
id.close()
print 'Processing nodes'
max_warming_nodes = zeros(n2d)
fractional_depth_nodes = zeros(n2d)
seasonality_nodes = zeros(n2d)
for n in range(n2d):
if node_cavity[n]:
# Calculate the warming at each depth down the water column
# Also save the depths
warming_column = []
depth_column = []
for k in range(max_num_layers):
if node_columns[n, k] == -999:
# Reached the bottom
break
node_id = node_columns[n, k] - 1
warming_column.append(temp_end[node_id] - temp_beg[node_id])
depth_column.append(node_depth[node_id])
warming_column = array(warming_column)
depth_column = array(depth_column)
# Save surface depth and water column thickness
sfc_depth = depth_column[0]
wct = depth_column[-1] - depth_column[0]
# Select index of maximum warming
k0 = argmax(warming_column)
node_id = node_columns[n, k0] - 1
# Save maximum warming
max_warming_nodes[n] = warming_column[k0]
# Save fractional depth in cavity
# 0 is ice shelf base, 1 is bottom
fractional_depth_nodes[n] = (depth_column[k0] - sfc_depth) / wct
# For this node, get warming for every month in monthly climatology
monthly_warming = monthly_temp_end[:,
node_id] - monthly_temp_beg[:,
node_id]
# Calculate seasonality metric
# Difference between max and min warming over months, divided
# by annual warming. 0 means no seasonality.
seasonality_nodes[n] = (
amax(monthly_warming) -
amin(monthly_warming)) / max_warming_nodes[n]
print 'Calculating element averages'
max_warming = []
fractional_depth = []
seasonality = []
cooling_patches = []
i = 0
for elm in elements:
if elm.cavity:
if any(
array([
max_warming_nodes[elm.nodes[0].id], max_warming_nodes[
elm.nodes[1].id], max_warming_nodes[
elm.nodes[2].id]
]) < 0):
max_warming.append(NaN)
fractional_depth.append(NaN)
seasonality.append(NaN)
cooling_patches.append(patches[i])
else:
max_warming.append(
mean(
array([
max_warming_nodes[elm.nodes[0].id],
max_warming_nodes[elm.nodes[1].id],
max_warming_nodes[elm.nodes[2].id]
])))
fractional_depth.append(
mean(
array([
fractional_depth_nodes[elm.nodes[0].id],
fractional_depth_nodes[elm.nodes[1].id],
fractional_depth_nodes[elm.nodes[2].id]
])))
seasonality.append(
mean(
array([
seasonality_nodes[elm.nodes[0].id],
seasonality_nodes[elm.nodes[1].id],
seasonality_nodes[elm.nodes[2].id]
])))
i += 1
max_warming = array(max_warming)
fractional_depth = array(fractional_depth)
seasonality = array(seasonality)
# Mask out regions which cool
max_warming = ma.masked_where(isnan(max_warming), max_warming)
fractional_depth = ma.masked_where(isnan(fractional_depth),
fractional_depth)
seasonality = ma.masked_where(isnan(seasonality), seasonality)
# Make a nonlinear colour scale for warming
max_warming_plot = 1.65
bounds = linspace(0, max_warming_plot**(1.0 / 2), num=100)**2
norm = BoundaryNorm(boundaries=bounds, ncolors=256)
print 'Plotting'
fig = figure(figsize=(8, 14))
fig.patch.set_facecolor('white')
gs = GridSpec(3, 1)
gs.update(left=0, right=1, bottom=0, top=0.9, hspace=0.07)
# Maximum warming
ax = subplot(gs[0, 0], aspect='equal')
# Start with grey square background for land
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
# Mask out cooling patches in white
cooling = PatchCollection(cooling_patches, facecolor=(1, 1, 1))
cooling.set_edgecolor('face')
ax.add_collection(cooling)
img = PatchCollection(patches, cmap='jet', norm=norm)
img.set_array(max_warming)
img.set_clim(vmin=0, vmax=max_warming_plot)
img.set_edgecolor('face')
ax.add_collection(img)
# Mask out the open ocean in white
overlay = PatchCollection(mask_patches, facecolor=(1, 1, 1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
# Contour ice shelf fronts
contours = LineCollection(contour_lines, edgecolor='black', linewidth=1)
ax.add_collection(contours)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
axis('off')
title(r'a) Maximum warming over depth ($^{\circ}$C)', fontsize=22)
cbaxes_a = fig.add_axes([0.8, 0.67, 0.03, 0.16])
cbar = colorbar(img,
cax=cbaxes_a,
extend='max',
ticks=arange(0, 1.5 + 0.5, 0.5))
cbar.ax.tick_params(labelsize=14)
# Fractional depth
ax = subplot(gs[1, 0], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
cooling = PatchCollection(cooling_patches, facecolor=(1, 1, 1))
cooling.set_edgecolor('face')
ax.add_collection(cooling)
img = PatchCollection(patches, cmap='jet')
img.set_array(fractional_depth)
img.set_edgecolor('face')
ax.add_collection(img)
overlay = PatchCollection(mask_patches, facecolor=(1, 1, 1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
contours = LineCollection(contour_lines, edgecolor='black', linewidth=1)
ax.add_collection(contours)
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
axis('off')
title('b) Fractional depth below ice shelf base\nof maximum warming',
fontsize=22)
cbaxes_b = fig.add_axes([0.8, 0.37, 0.03, 0.16])
cbar = colorbar(img, cax=cbaxes_b, ticks=arange(0, 1 + 0.25, 0.25))
cbar.ax.tick_params(labelsize=14)
# Seasonality
ax = subplot(gs[2, 0], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches, cmap='jet')
cooling = PatchCollection(cooling_patches, facecolor=(1, 1, 1))
cooling.set_edgecolor('face')
ax.add_collection(cooling)
img.set_array(seasonality)
img.set_edgecolor('face')
img.set_clim(vmin=0, vmax=3)
ax.add_collection(img)
overlay = PatchCollection(mask_patches, facecolor=(1, 1, 1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
contours = LineCollection(contour_lines, edgecolor='black', linewidth=1)
ax.add_collection(contours)
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
axis('off')
title('c) Seasonality metric', fontsize=22)
cbaxes_c = fig.add_axes([0.8, 0.07, 0.03, 0.16])
cbar = colorbar(img, cax=cbaxes_c, extend='max', ticks=arange(0, 3 + 1, 1))
cbar.ax.tick_params(labelsize=14)
suptitle('RCP ' + rcp[0] + '.' + rcp[1] + ' ' + model_title +
', 2091-2100 minus 1996-2005',
fontsize=24)
fig.show()
fig.savefig('warming_depth_rcp' + rcp + '_' + model + '.png')
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'))
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 polygons(latitudes, longitudes, clusters, maptype=MAPTYPE, alpha=0.25):
"""Plot clusters of points on map, including them in a polygon defining their convex hull.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param pandas.Series clusters: marker clusters
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:param float alpha: transparency for polygons overlay, between 0 (transparent) and 1 (opaque)
:return: None
"""
width = SCALE * MAX_SIZE
img, pixels = background_and_pixels(latitudes, longitudes, MAX_SIZE,
maptype)
# Building collection of polygons
polygon_list = []
unique_clusters = clusters.unique()
cmap = pd.Series(np.arange(unique_clusters.shape[0] - 1, -1, -1),
index=unique_clusters)
for c in unique_clusters:
in_polygon = clusters == c
if in_polygon.sum() < 3:
print(
'[WARN] Cannot draw polygon for cluster {} - only {} samples.'.
format(c, in_polygon.sum()))
continue
cluster_pixels = pixels.loc[clusters == c]
polygon_list.append(
Polygon(cluster_pixels.iloc[ConvexHull(cluster_pixels).vertices],
closed=True))
# Background map
plt.figure(figsize=(10, 10))
ax = plt.subplot(111)
plt.imshow(np.array(img))
# Collection of polygons
p = PatchCollection(polygon_list, cmap='jet', alpha=alpha)
p.set_array(cmap.values)
ax.add_collection(p)
# Scatter plot
plt.scatter(
pixels['x_pixel'],
pixels['y_pixel'],
c=cmap.loc[clusters],
cmap='jet',
s=width / 40,
linewidth=0,
alpha=alpha,
)
# Axis options
plt.gca().invert_yaxis() # Origin of map is upper left
plt.axis([0, width, width, 0]) # Remove margin
plt.axis('off')
plt.tight_layout()
# Building legend box
jet_cmap = cm.get_cmap('jet')
plt.legend(
[
Rectangle((0, 0),
1,
1,
fc=jet_cmap(i / max(cmap.shape[0] - 1, 1)),
alpha=alpha) for i in cmap.values
],
cmap.index,
loc=4,
bbox_to_anchor=(1.1, 0),
)
plt.show()
def oe_pfig(self):
"""Make an omega-eta polefigure"""
# some constants
deg2rad = numpy.pi / 180.0
radius = numpy.sqrt(2)
offset = 2.5 * radius
nocolor = 'none'
pdir_cho = 'X' # to come from choice interactor
# parent window and data
exp = wx.GetApp().ws
p = self.GetParent()
ome_eta = p.data
hkldata = ome_eta.getData(self.idata)
# axes/figure
p.figure.delaxes(p.axes)
p.axes = p.figure.gca()
p.axes.set_autoscale_on(True)
p.axes.set_aspect('equal')
p.axes.axis([-2.0, offset + 2.0, -1.5, 1.5])
# outlines for pole figure
C1 = Circle((0, 0), radius)
C2 = Circle((offset, 0), radius)
outline_circles = [C1, C2]
pc_outl = PatchCollection(outline_circles, facecolors=nocolor)
p.axes.add_collection(pc_outl)
# build the rectangles
pf_rects = []
tTh = exp.activeMaterial.planeData.getTTh()[self.idata]
etas = ome_eta.etaEdges
netas = len(etas) - 1
deta = abs(etas[1] - etas[0])
omes = ome_eta.omeEdges
nomes = len(omes) - 1
dome = abs(omes[1] - omes[0])
if pdir_cho == 'X':
pdir = numpy.c_[1, 0, 0].T # X
elif pdir_cho == 'Y':
pdir = numpy.c_[0, 1, 0].T # X
elif pdir_cho == 'Z':
pdir = numpy.c_[0, 0, 1].T # X
pass
ii = 0
for i in range(nomes):
for j in range(netas):
qc = makeMSV(tTh, etas[j] + 0.5 * deta, omes[i] + 0.5 * dome)
qll = makeMSV(tTh, etas[j], omes[i])
qlr = makeMSV(tTh, etas[j] + deta, omes[i])
qur = makeMSV(tTh, etas[j] + deta, omes[i] + dome)
qul = makeMSV(tTh, etas[j], omes[i] + dome)
pdot_p = numpy.dot(qll.T, pdir) >= 0 \
and numpy.dot(qlr.T, pdir) >= 0 \
and numpy.dot(qur.T, pdir) >= 0 \
and numpy.dot(qul.T, pdir) >= 0
pdot_m = numpy.dot(qll.T, pdir) < 0 \
and numpy.dot(qlr.T, pdir) < 0 \
and numpy.dot(qur.T, pdir) < 0 \
and numpy.dot(qul.T, pdir) < 0
if pdot_p:
sgn = 1.0
ii += 1
elif pdot_m:
sgn = -1.0
ii += 1
elif not pdot_p and not pdot_m:
continue
# the vertex chords
qll = makeMSV(tTh, etas[j], omes[i]) - sgn * pdir
qlr = makeMSV(tTh, etas[j] + deta, omes[i]) - sgn * pdir
qur = makeMSV(tTh, etas[j] + deta, omes[i] + dome) - sgn * pdir
qul = makeMSV(tTh, etas[j], omes[i] + dome) - sgn * pdir
nll = columnNorm(qll)
nlr = columnNorm(qlr)
nur = columnNorm(qur)
nul = columnNorm(qul)
if pdir_cho == 'X':
pqll = nll * unitVector(qll[[1, 2]].reshape(2, 1))
pqlr = nlr * unitVector(qlr[[1, 2]].reshape(2, 1))
pqur = nur * unitVector(qur[[1, 2]].reshape(2, 1))
pqul = nul * unitVector(qul[[1, 2]].reshape(2, 1))
elif pdir_cho == 'Y':
pqll = nll * unitVector(qll[[0, 2]].reshape(2, 1))
pqlr = nlr * unitVector(qlr[[0, 2]].reshape(2, 1))
pqur = nur * unitVector(qur[[0, 2]].reshape(2, 1))
pqul = nul * unitVector(qul[[0, 2]].reshape(2, 1))
elif pdir_cho == 'Z':
pqll = nll * unitVector(qll[[0, 1]].reshape(2, 1))
pqlr = nlr * unitVector(qlr[[0, 1]].reshape(2, 1))
pqur = nur * unitVector(qur[[0, 1]].reshape(2, 1))
pqul = nul * unitVector(qul[[0, 1]].reshape(2, 1))
xy = numpy.hstack([pqll, pqlr, pqur, pqul]).T
if sgn == -1:
xy[:, 0] = xy[:, 0] + offset
pf_rects.append(Polygon(xy, aa=False))
pass
pass
cmap = matplotlib.cm.jet
pf_coll = PatchCollection(pf_rects, cmap=cmap, edgecolors='None')
pf_coll.set_array(numpy.array(hkldata.T.flatten()))
p.axes.add_collection(pf_coll)
p.canvas.draw()
p.axes.axis('off')
return
class CameraDisplay:
"""
Camera Display using matplotlib.
Parameters
----------
geometry : `~ctapipe.instrument.CameraGeometry`
Definition of the Camera/Image
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
ax : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one
title : str (default "Camera")
Title to put on camera plot
norm : str or `matplotlib.colors.Normalize` instance (default 'lin')
Normalization for the color scale.
Supported str arguments are
- 'lin': linear scale
- 'log': logarithmic scale (base 10)
cmap : str or `matplotlib.colors.Colormap` (default 'hot')
Color map to use (see `matplotlib.cm`)
allow_pick : bool (default False)
if True, allow user to click and select a pixel
autoupdate : bool (default True)
redraw automatically (otherwise need to call plt.draw())
autoscale : bool (default True)
rescale the vmin/vmax values when the image changes.
This is set to False if ``set_limits_*`` is called to explicity
set data limits.
Notes
-----
Speed:
CameraDisplay is not intended to be very fast (matplotlib
is not a very speed performant graphics library, it is
intended for nice output plots). However, most of the
slowness of CameraDisplay is in the constructor. Once one is
displayed, changing the image that is displayed is relatively
fast and efficient. Therefore it is best to initialize an
instance, and change the data, rather than generating new
CameraDisplays.
Pixel Implementation:
Pixels are rendered as a
`matplotlib.collections.PatchCollection` of Polygons (either 6
or 4 sided). You can access the PatchCollection directly (to
e.g. change low-level style parameters) via
``CameraDisplay.pixels``
Output:
Since CameraDisplay uses matplotlib, any display can be
saved to any output file supported via
plt.savefig(filename). This includes ``.pdf`` and ``.png``.
"""
def __init__(
self,
geometry,
image=None,
ax=None,
title=None,
norm="lin",
cmap=None,
allow_pick=False,
autoupdate=True,
autoscale=True,
show_frame=True,
):
self.axes = ax if ax is not None else plt.gca()
self.pixels = None
self.colorbar = None
self.autoupdate = autoupdate
self.autoscale = autoscale
self._active_pixel = None
self._active_pixel_label = None
self._axes_overlays = []
self.geom = geometry
if title is None:
title = f"{geometry.camera_name}"
# initialize the plot and generate the pixels as a
# RegularPolyCollection
patches = []
if hasattr(self.geom, "mask"):
self.mask = self.geom.mask
else:
self.mask = np.ones_like(self.geom.pix_x.value, dtype=bool)
pix_x = self.geom.pix_x.value[self.mask]
pix_y = self.geom.pix_y.value[self.mask]
pix_width = self.geom.pixel_width.value[self.mask]
for x, y, w in zip(pix_x, pix_y, pix_width):
if self.geom.pix_type == PixelShape.HEXAGON:
r = w / np.sqrt(3)
patch = RegularPolygon(
(x, y),
6,
radius=r,
orientation=self.geom.pix_rotation.to_value(u.rad),
fill=True,
)
elif self.geom.pix_type == PixelShape.CIRCLE:
patch = Circle((x, y), radius=w / 2, fill=True)
elif self.geom.pix_type == PixelShape.SQUARE:
patch = Rectangle(
(x - w / 2, y - w / 2),
width=w,
height=w,
angle=self.geom.pix_rotation.to_value(u.deg),
fill=True,
)
else:
raise ValueError(
f"Unsupported pixel_shape {self.geom.pix_type}")
patches.append(patch)
self.pixels = PatchCollection(patches, cmap=cmap, linewidth=0)
self.axes.add_collection(self.pixels)
self.pixel_highlighting = copy.copy(self.pixels)
self.pixel_highlighting.set_facecolor("none")
self.pixel_highlighting.set_linewidth(0)
self.axes.add_collection(self.pixel_highlighting)
# Set up some nice plot defaults
self.axes.set_aspect("equal", "datalim")
self.axes.set_title(title)
self.axes.autoscale_view()
if show_frame:
self.add_frame_name()
# set up a patch to display when a pixel is clicked (and
# pixel_picker is enabled):
self._active_pixel = copy.copy(patches[0])
self._active_pixel.set_facecolor("r")
self._active_pixel.set_alpha(0.5)
self._active_pixel.set_linewidth(2.0)
self._active_pixel.set_visible(False)
self.axes.add_patch(self._active_pixel)
if hasattr(self._active_pixel, "xy"):
center = self._active_pixel.xy
else:
center = self._active_pixel.center
self._active_pixel_label = self.axes.text(*center,
"0",
horizontalalignment="center",
verticalalignment="center")
self._active_pixel_label.set_visible(False)
# enable ability to click on pixel and do something (can be
# enabled on-the-fly later as well:
if allow_pick:
self.enable_pixel_picker()
if image is not None:
self.image = image
else:
self.image = np.zeros_like(self.geom.pix_id, dtype=np.float64)
self.norm = norm
self.auto_set_axes_labels()
def highlight_pixels(self, pixels, color="g", linewidth=1, alpha=0.75):
"""
Highlight the given pixels with a colored line around them
Parameters
----------
pixels : index-like
The pixels to highlight.
Can either be a list or array of integers or a
boolean mask of length number of pixels
color: a matplotlib conform color
the color for the pixel highlighting
linewidth: float
linewidth of the highlighting in points
alpha: 0 <= alpha <= 1
The transparency
"""
l = np.zeros_like(self.image)
l[pixels] = linewidth
self.pixel_highlighting.set_linewidth(l)
self.pixel_highlighting.set_alpha(alpha)
self.pixel_highlighting.set_edgecolor(color)
self._update()
def enable_pixel_picker(self):
""" enable ability to click on pixels """
self.pixels.set_picker(True)
self.pixels.set_pickradius(self.geom.pixel_width.value[0] / 2)
self.axes.figure.canvas.mpl_connect("pick_event", self._on_pick)
def set_limits_minmax(self, zmin, zmax):
""" set the color scale limits from min to max """
self.pixels.set_clim(zmin, zmax)
self.autoscale = False
self._update()
def set_limits_percent(self, percent=95):
""" auto-scale the color range to percent of maximum """
zmin = np.nanmin(self.pixels.get_array())
zmax = np.nanmax(self.pixels.get_array())
dz = zmax - zmin
frac = percent / 100.0
self.autoscale = False
self.set_limits_minmax(zmin, zmax - (1.0 - frac) * dz)
@property
def norm(self):
"""
The norm instance of the Display
Possible values:
- "lin": linear scale
- "log": log scale (cannot have negative values)
- "symlog": symmetric log scale (negative values are ok)
- any matplotlib.colors.Normalize instance, e. g. PowerNorm(gamma=-2)
"""
return self.pixels.norm
@norm.setter
def norm(self, norm):
if norm == "lin":
self.pixels.norm = Normalize()
elif norm == "log":
self.pixels.norm = LogNorm()
self.pixels.autoscale() # this is to handle matplotlib bug #5424
elif norm == "symlog":
self.pixels.norm = SymLogNorm(linthresh=1.0, base=10)
self.pixels.autoscale()
elif isinstance(norm, Normalize):
self.pixels.norm = norm
else:
raise ValueError(
"Unsupported norm: '{}', options are 'lin',"
"'log','symlog', or a matplotlib Normalize object".format(
norm))
self.update(force=True)
self.pixels.autoscale()
@property
def cmap(self):
"""
Color map to use. Either name or `matplotlib.colors.Colormap`
"""
return self.pixels.get_cmap()
@cmap.setter
def cmap(self, cmap):
self.pixels.set_cmap(cmap)
self._update()
@property
def image(self):
"""The image displayed on the camera (1D array of pixel values)"""
return self.pixels.get_array()
@image.setter
def image(self, image):
"""
Change the image displayed on the Camera.
Parameters
----------
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
"""
image = np.asanyarray(image)
if image.shape != self.geom.pix_x.shape:
raise ValueError(
("Image has a different shape {} than the "
"given CameraGeometry {}").format(image.shape,
self.geom.pix_x.shape))
self.pixels.set_array(np.ma.masked_invalid(image[self.mask]))
self.pixels.changed()
if self.autoscale:
self.pixels.autoscale()
self._update()
def _update(self, force=False):
""" signal a redraw if autoupdate is turned on """
if self.autoupdate:
self.update(force)
def update(self, force=False):
""" redraw the display now """
self.axes.figure.canvas.draw()
if self.colorbar is not None:
if force is True:
self.colorbar.update_bruteforce(self.pixels)
else:
self.colorbar.update_normal(self.pixels)
self.colorbar.draw_all()
def add_colorbar(self, **kwargs):
"""
add a colorbar to the camera plot
kwargs are passed to ``figure.colorbar(self.pixels, **kwargs)``
See matplotlib documentation for the supported kwargs:
http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
"""
if self.colorbar is not None:
raise ValueError(
"There is already a colorbar attached to this CameraDisplay")
else:
if "ax" not in kwargs:
kwargs["ax"] = self.axes
self.colorbar = self.axes.figure.colorbar(self.pixels, **kwargs)
self.update()
def add_ellipse(self,
centroid,
length,
width,
angle,
asymmetry=0.0,
**kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(
xy=centroid,
width=length,
height=width,
angle=np.degrees(angle),
fill=False,
**kwargs,
)
self.axes.add_patch(ellipse)
self.update()
return ellipse
def overlay_moments(self,
hillas_parameters,
with_label=True,
keep_old=False,
**kwargs):
"""helper to overlay ellipse from a `~ctapipe.containers.HillasParametersContainer` structure
Parameters
----------
hillas_parameters: `HillasParametersContainer`
structuring containing Hillas-style parameterization
with_label: bool
If True, show coordinates of centroid and width and length
keep_old: bool
If True, to not remove old overlays
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
if not keep_old:
self.clear_overlays()
# strip off any units
cen_x = u.Quantity(hillas_parameters.x).value
cen_y = u.Quantity(hillas_parameters.y).value
length = u.Quantity(hillas_parameters.length).value
width = u.Quantity(hillas_parameters.width).value
el = self.add_ellipse(
centroid=(cen_x, cen_y),
length=length * 2,
width=width * 2,
angle=hillas_parameters.psi.to_value("rad"),
**kwargs,
)
self._axes_overlays.append(el)
if with_label:
text = self.axes.text(
cen_x,
cen_y,
"({:.02f},{:.02f})\n[w={:.02f},l={:.02f}]".format(
hillas_parameters.x,
hillas_parameters.y,
hillas_parameters.width,
hillas_parameters.length,
),
color=el.get_edgecolor(),
)
self._axes_overlays.append(text)
def clear_overlays(self):
""" Remove added overlays from the axes """
while self._axes_overlays:
overlay = self._axes_overlays.pop()
overlay.remove()
def _on_pick(self, event):
""" handler for when a pixel is clicked """
pix_id = event.ind[-1]
x = self.geom.pix_x[pix_id].value
y = self.geom.pix_y[pix_id].value
if self.geom.pix_type in (PixelShape.HEXAGON, PixelShape.CIRCLE):
self._active_pixel.xy = (x, y)
else:
w = self.geom.pixel_width.value[0]
self._active_pixel.xy = (x - w / 2.0, y - w / 2.0)
self._active_pixel.set_visible(True)
self._active_pixel_label.set_x(x)
self._active_pixel_label.set_y(y)
self._active_pixel_label.set_text(f"{pix_id:003d}")
self._active_pixel_label.set_visible(True)
self._update()
self.on_pixel_clicked(pix_id) # call user-function
def on_pixel_clicked(self, pix_id):
"""virtual function to overide in sub-classes to do something special
when a pixel is clicked
"""
print(f"Clicked pixel_id {pix_id}")
def show(self):
self.axes.figure.show()
def auto_set_axes_labels(self):
""" set the axes labels based on the Frame attribute"""
axes_labels = ("X", "Y")
if self.geom.frame is not None:
axes_labels = list(
self.geom.frame.get_representation_component_names().keys())
self.axes.set_xlabel(f"{axes_labels[0]} ({self.geom.pix_x.unit})")
self.axes.set_ylabel(f"{axes_labels[1]} ({self.geom.pix_y.unit})")
def add_frame_name(self, color="grey"):
""" label the frame type of the display (e.g. CameraFrame) """
frame_name = (self.geom.frame.__class__.__name__
if self.geom.frame is not None else "Unknown Frame")
self.axes.text( # position text relative to Axes
1.0,
0.0,
frame_name,
ha="right",
va="bottom",
transform=self.axes.transAxes,
color=color,
fontsize="smaller",
)
def Main():
args = ParseArg()
pair_dist = args.pair_dist
step = args.step
print "\nChecking if linkedPair file is tabixed..."
if not os.path.isfile(args.linkedPair):
print "LinkedPair file is not exist, please check!!"
sys.exit(0)
if not os.path.isfile(args.linkedPair + ".tbi"):
print " tabix-ing..."
os.system("sort -k1,1 -k2,2n " + args.linkedPair +
" > temp_linkedPair.txt")
os.system("bgzip temp_linkedPair.txt")
os.system("tabix -p bed temp_linkedPair.txt.gz")
linkedPair = 'temp_linkedPair.txt.gz'
else:
linkedPair = args.linkedPair
print " linkedPair file is tabixed."
print "\nTabixing the interaction file..."
os.system("sort -k1,1 -k2,2n " + args.interaction +
" > temp_interaction.txt")
os.system("bgzip temp_interaction.txt")
os.system("tabix -p bed temp_interaction.txt.gz")
print " interaction file is tabixed."
# start column number for second regions
# s1 for interaction file and s2 for linkedPair file
(s1, s2) = args.start
print "\nGet region information."
if args.r:
Region = read_region(args.r)
elif args.name:
os.system('grep "%s" %s > temp2.txt' % (args.name, args.genebed))
g = open("temp2.txt").read().split('\t')
if len(g) < 2:
print >> sys.stderr, "Error: the gene name is not found in database"
sys.exit(0)
s = int(g[1])
e = int(g[2])
Region = Bed(
[g[0], s - (e - s) / 10, e + (e - s) / 10, "region", ".", "."])
else:
print >> sys.stderr, "Error: Need to specify the region by '-r' or specify the gene name by '-n'"
sys.exit(0)
print "\n Start plot heatmaps on region: " + Region.str_region()
fig = plt.figure(figsize=(8, 6))
ax = plt.subplot(111, frameon=False, yticks=[])
start = Region.start
end = Region.stop
ax.set_xlim(start, end)
#set x ticks withour offset
locs = ax.get_xticks()
ax.set_xticklabels(map(lambda x: "%i" % x, locs), fontsize=6)
print "\nStart draw gene track"
gene_dbi = DBI.init(args.genebed, "bed")
print " genebed indexed!"
print " Plot gene track"
gene_top = Genetrack(Region, gene_dbi, ax, 0.08)
h = 1.5 * step / (end - start
) # unit height for triangles or polycons in heatmap
print "\nQuery linkedPairs within specified region"
os.system("tabix " + linkedPair + " %s:%i-%i > temp2.txt" %
(Region.chr, Region.start, Region.stop))
Count = {}
for b in read_interaction("temp2.txt", s2):
col = 'k'
if args.Slim and SingleFragment(b[0], b[1], pair_dist): continue
if Region.overlap(b[0], 0) and Region.overlap(b[1], 0):
if b[0].strand == '-':
i = b[0].start
else:
i = b[0].stop
if b[1].strand == '-':
j = b[1].start
else:
j = b[1].stop
i = (i / step +
1) * step # approximate to the nearest central point
j = (j / step + 1) * step
if i > j:
temp = j
j = i
i = temp
if (i, j) not in Count:
Count[(i, j)] = 1
else:
Count[(i, j)] += 1
print Count
patches = []
colors = []
for i in range(start, end + 1):
if i % step != 0: continue
for j in range(i, end + 1):
if j % step != 0 or (i, j) not in Count: continue
patches.append(PatchGen(i, j, h, step, gene_top + 0.01))
colors.append(np.log(Count[(i, j)] + 1))
p = PatchCollection(patches,
cmap=matplotlib.cm.Reds,
alpha=0.7,
edgecolor='k',
linewidths=0.1)
p.set_array(np.array(colors))
ax.add_collection(p)
ax.set_ylim(0, ((end - start) / step + 2) * h + gene_top + 0.01)
plt.colorbar(p)
if not args.SI:
plt.savefig(args.output)
plt.show()
os.system("rm temp_interaction.txt.gz*")
if not os.path.isfile(args.linkedPair + ".tbi"):
os.system("rm temp_linkedPair.txt.gz*")
os.system("rm temp2.txt")
sys.exit(0)
print "\nQuery interactions"
os.system("tabix temp_interaction.txt.gz %s:%i-%i > temp2.txt" %
(Region.chr, Region.start, Region.stop))
print "\nList of interactions plotted: "
k = 1
cmap = cm.get_cmap('Paired', 10)
cmap = cmap(range(10))
bottom = gene_top + 0.01
for b in read_interaction("temp2.txt", s1):
if b[0].overlap(b[1], 0): continue
if Region.overlap(b[1], 0):
k += 1
if b[1].stop > b[0].stop:
start1 = b[0].start
end1 = b[0].stop
start2 = b[1].start
end2 = b[1].stop
else:
start1 = b[1].start
end1 = b[1].stop
start2 = b[0].start
end2 = b[0].stop
P1 = Polygon([[start1, bottom], [end1, bottom],
[(end1 + end2) * 0.5,
(end2 - end1) * h / step + bottom],
[(start1 + end2) * 0.5,
(end2 - start1) * h / step + bottom]],
"True",
facecolor='none',
edgecolor=cmap[k % 10],
alpha=0.4,
lw=0.5)
P2 = Polygon([[start2, bottom], [end2, bottom],
[(start1 + end2) * 0.5,
(end2 - start1) * h / step + bottom],
[(start1 + start2) * 0.5,
(start2 - start1) * h / step + bottom]],
"True",
facecolor='none',
edgecolor=cmap[k % 10],
alpha=0.4,
lw=0.5)
ax.add_patch(P1)
ax.add_patch(P2)
print " " + b[0].str_region() + " <-> " + b[1].str_region()
plt.savefig(args.output)
plt.show()
# remove temp file
os.system("rm temp_interaction.txt.gz*")
if not os.path.isfile(args.linkedPair + ".tbi"):
os.system("rm temp_linkedPair.txt.gz*")
os.system("rm temp2.txt")
def impactParam(self, basis=None, dx=0, dy=0):
x = self.detections[..., 0]
y = self.detections[..., 1]
c = self.detections[..., 3]
fig, ax = plt.subplots(figsize=(8.0, 6.0))
patches = []
colours = []
maxX = dx
maxY = dy
if dx == 0 and dy == 2:
maxX = np.max(x)
maxY = np.max(y)
ax.set_xlim(right=maxX)
ax.set_ylim(top=maxY)
if basis is not None:
minz = 1e6
for site in basis:
if site[2] < minz:
minz = site[2]
for i in range(2):
for j in range(2):
colours.append(site[2])
circle = Circle((site[0] + i * dx, site[1] + j * dy),
1)
patches.append(circle)
p = PatchCollection(patches, alpha=0.4)
p.set_array(np.array(colours))
#Draw the basis
ax.add_collection(p)
#Draw the points
scat = ax.scatter(x, y, c=c)
fig.colorbar(scat, ax=ax)
#Add a heightmap
fig.colorbar(p, ax=ax)
#Add selected point label
text = fig.text(0.1, 0.95, 'None Selected', fontsize=9)
ax.set_title("Detections: " + str(len(x)))
ax.set_xlabel('X Target (Angstroms)')
ax.set_ylabel('Y Target (Angstroms)')
fig.text(
0.6,
0.9,
"Left Click: View Point\nDouble Left Click: Open Normal-Colored VMD\nDouble Right Click: Open Nearest-Colored VMD\nShift + Left Click: Open Velocity-Colored VMD",
fontsize=9)
self.p, = ax.plot(0, 0, 'r+')
def onclick(event):
if event.xdata is None:
return
close = [1e20, 1e20]
distsq = close[0]**2 + close[1]**2
index = -1
for i in range(len(x)):
dxsq = (x[i] - event.xdata)**2
dysq = (y[i] - event.ydata)**2
if distsq > dxsq + dysq:
distsq = dxsq + dysq
close[0] = x[i]
close[1] = y[i]
index = i
if event.dblclick and event.button == 1 and not shift_is_held:
# Setup a single run safari for this.
self.safio.fileIn = self.safio.fileIn.replace(
'_mod.input', '_ss.input')
self.safio.setGridScat(True)
self.safio.NUMCHA = 1
self.safio.XSTART = close[0]
self.safio.YSTART = close[1]
self.safio.genInputFile(fileIn=self.safio.fileIn)
command = 'Safari.exe'
if platform.system() == 'Linux':
command = './Safari'
sub = subprocess.run(command, shell=True)
close[0] = round(close[0], 2)
close[1] = round(close[1], 2)
name = self.safio.fileIn.replace('.input', '')
xyz_p.process_file(name + '.xyz',
name + str(close[0]) + ',' + str(close[1]) +
'.xyz',
load_vmd=True)
print(sub)
if event.dblclick and event.button == 3:
# Setup a single run safari using nearness colored data
print("Setting up a safari run for a nearness colored dataset")
self.safio.fileIn = self.safio.fileIn.replace(
'_mod.input', '_ss.input')
self.safio.setGridScat(True)
self.safio.NUMCHA = 1
self.safio.XSTART = close[0]
self.safio.YSTART = close[1]
self.safio.genInputFile(fileIn=self.safio.fileIn)
command = 'Safari.exe'
if platform.system() == 'Linux':
command = './Safari'
sub = subprocess.run(command, shell=True)
close[0] = round(close[0], 2)
close[1] = round(close[1], 2)
name = self.safio.fileIn.replace('.input', '')
xyz_p.process_file(name + '.xyz',
name + str(close[0]) + ',' + str(close[1]) +
'.xyz',
color="nearest",
load_vmd=True)
print(sub)
if event.button == 1 and shift_is_held:
# Setup a single run safari using velocity colored data
print("Setting up a safari run for a velocity colored dataset")
self.safio.fileIn = self.safio.fileIn.replace(
'_mod.input', '_ss.input')
self.safio.setGridScat(True)
self.safio.NUMCHA = 1
self.safio.XSTART = close[0]
self.safio.YSTART = close[1]
self.safio.genInputFile(fileIn=self.safio.fileIn)
command = 'Safari.exe'
if platform.system() == 'Linux':
command = './Safari'
sub = subprocess.run(command, shell=True)
close[0] = round(close[0], 2)
close[1] = round(close[1], 2)
name = self.safio.fileIn.replace('.input', '')
xyz_p.process_file(name + '.xyz',
name + str(close[0]) + ',' + str(close[1]) +
'.xyz',
color="velocity",
load_vmd=True)
print(sub)
close[0] = round(close[0], 5)
close[1] = round(close[1], 5)
energy = round(self.detections[index][3], 2)
if self.E_over_E0:
text.set_text(str(close) + ', ' + str(energy))
else:
text.set_text(str(close) + ', ' + str(energy) + 'eV')
self.p.set_xdata([close[0]])
self.p.set_ydata([close[1]])
fig.canvas.draw()
def on_key_press(event):
if event.key == 'shift':
global shift_is_held
shift_is_held = True
def on_key_release(event):
if event.key == 'shift':
global shift_is_held
shift_is_held = False
fig.canvas.mpl_connect('key_press_event', on_key_press)
fig.canvas.mpl_connect('key_release_event', on_key_release)
fig.canvas.mpl_connect('button_press_event', onclick)
fig.show()
def plot_diffuseLOS_map(header=None,
av_image=None,
df=None,
core_dict=None,
limits=None,
filename=None,
vlimits=(None, None),
contours=None,
parameter='phi_cnm',
models=['krumholz', 'sternberg']):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.6, 10))
if 0:
parameters = []
#if 'krumholz' in models:
# parameters.append('phi_cnm', 'alphaG', 'hi_transition', 'n_H', 'T_H']
parameters = [
'fraction_diffuse',
]
ngrids = len(parameters)
nrows_ncols = (ngrids, 1)
axesgrid = AxesGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='top',
cbar_pad="2%",
cbar_size='6%',
axes_pad=0.1,
axes_class=(wcs.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=False)
# ------------------
# Av image
# ------------------
for i in xrange(ngrids):
# create axes
ax = axesgrid[i]
# show the image
X, Y = np.meshgrid(np.arange(av_image.shape[1]),
np.arange(av_image.shape[0]))
im = ax.contour(
X,
Y,
av_image,
origin='lower',
levels=contours,
cmap=myplt.truncate_colormap(
plt.cm.binary,
minval=0.3,
)
#vmin=vlimits[0],
#vmax=vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension [J2000]', )
ax.set_ylabel('Declination [J2000]', )
ax.locator_params(nbins=4)
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header)
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
# Plot cores for each cloud
# -------------------------
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
parameter = parameters[i]
patches = get_patches(df, header)
cmap = myplt.truncate_colormap(plt.cm.copper, minval=0.1, maxval=1.0)
collection = PatchCollection(
patches,
cmap=cmap,
edgecolors='k',
linewidth=0.75,
zorder=1000,
)
# set values in collection
collection_values = []
if parameter in [
'fraction_diffuse',
]:
collection.set_array(df[parameter])
else:
for core in df['core']:
collection_values.append(core_dict[core][parameter])
collection.set_array(np.array(collection_values))
ax.add_collection(collection, )
# colorbar
#cbar = axesgrid.cbar_axes[i].colorbar(collection)
cbar = ax.cax.colorbar(collection)
if parameter == 'fraction_diffuse':
cbar.set_label_text(r'Fraction of Diffuse LOS', )
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=600)
def draw(self):
xa = self.dxfprocessor.xa
xi = self.dxfprocessor.xi
ya = self.dxfprocessor.ya
yi = self.dxfprocessor.yi
line = self.dxfprocessor.line
arc = self.dxfprocessor.arc
ellipse = self.dxfprocessor.ellipse
lwpline = self.dxfprocessor.lwpline
plt.clf()
plt.close('all')
figure, ax = plt.subplots(figsize=((xa - xi) / 10000,
(ya - yi) / 10000),
dpi=100)
# 设置x，y值域
ax.set_xlim(left=xi, right=xa, auto=False)
ax.set_ylim(bottom=yi, top=ya, auto=False)
# 底图绘制
line_xs = [0] * len(line)
line_ys = [0] * len(line)
for i in range(len(line)):
(line_xs[i], line_ys[i]) = zip(*line[i])
for i in range(len(line_xs)):
ax.add_line(
Line2D(line_xs[i], line_ys[i], linewidth=1, color='black'))
print('底图 1/4')
for i in arc:
ax.add_patch(Arc(i[0], i[1], i[1], theta1=i[2], theta2=i[3]))
print('底图 2/4')
for i in ellipse:
ax.add_patch(
Arc(i[0], i[1], i[2], angle=i[3], theta1=i[4], theta2=i[5]))
print('底图 3/4')
lwpl_xs = [0] * len(lwpline)
lwpl_ys = [0] * len(lwpline)
for i in range(len(lwpline)):
(lwpl_xs[i], lwpl_ys[i]) = zip(*lwpline[i])
for i in range(len(lwpl_xs)):
ax.add_line(
Line2D(lwpl_xs[i], lwpl_ys[i], linewidth=1, color='black'))
print('底图 4/4')
print('完成:底图绘制， 开始填充热力……')
#热度填充
self.df_store_heat = self.get_df_store_heat()
'''begin,end = self.duration
begin = pd.to_datetime(begin)
end = pd.to_datetime(end)
days =int((end-begin)//datetime.timedelta(1))
dates = [begin + datetime.timedelta(1)*i for i in range(days+1)]'''
dates = util.get_dates_within_duration(self.duration)
self.heat = self.df_store_heat[dates].sum(axis=1)
boundaries = list(self.df_store_heat['boundary'])
names = list(self.df_store_heat['name'])
patches = []
for boundary, name, heat in zip(boundaries, names, self.heat):
text = name + '\n' + str(int(heat))
center = util.get_center(boundary)
polygon = Polygon(np.array(boundary), True)
patches.append(polygon)
plt.text(center[0], center[1], text, ha='center', va='center')
p = PatchCollection(patches, cmap=plt.get_cmap('rainbow'), alpha=0.4)
p.set_array(np.array(self.heat))
p.set_clim([0, max(list(self.heat))])
ax.add_collection(p)
ax.set_aspect(1)
figure.colorbar(p, ax=ax, orientation='horizontal')
print('完成:热力填充，正在保存……')
# 展示
plt.plot()
plt.savefig(self.path + '/heat' + str(self.duration[0]) + '-' +
str(self.duration[-1]) + '.jpg',
dpi=100)
#plt.show()
print('保存为 ' + self.path + '/heat' + str(self.duration[0]) + '-' +
str(self.duration[-1]) + '.jpg')
return self.path + '/heat' + str(self.duration[0]) + '-' + str(
self.duration[-1]) + '.jpg'
def plot_mesh(mesh,
ax,
faces=True,
edges=True,
halfedges=True,
vertices=True,
boundaries=True,
v2h=True,
v2f=False,
count_from_1=True):
import numpy
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Wedge, Polygon
from matplotlib.collections import PatchCollection
if count_from_1:
offset = 1
else:
offset = 0
# vertex labels
if vertices:
for v in mesh.verts:
txt = ax.text(v.co[0], v.co[1], str(v.index + offset), zorder=10)
txt.set_bbox(dict(facecolor='y', alpha=0.5, edgecolor='y'))
# face centroids
fctr = numpy.zeros((len(mesh.f2v), 2))
for i in range(len(mesh.f2v)):
f = mesh.f2v[i]
for v in f:
fctr[i] = fctr[i] + mesh.verts[v].co[0:2]
fctr[i] = fctr[i] / float(len(f))
# plot faces
if faces:
patches = []
for i, f in enumerate(mesh.f2v):
ax.text(fctr[i, 0], fctr[i, 1], str(i + offset))
polygon = numpy.array([mesh.verts[v].co[0:2] for v in f])
patches.append(Polygon(polygon, True))
colors = 100 * numpy.arange(len(mesh.f2v))
p = PatchCollection(patches, alpha=0.2, edgecolors=None)
p.set_array(numpy.array(colors))
ax.add_collection(p)
# plot edges
for i in range(len(mesh.f2v)):
fi = mesh.f2v[i]
for j in range(len(fi)):
v1 = mesh.verts[get_orig([i, j], mesh)]
v2 = mesh.verts[get_dest([i, j], mesh)]
if is_boundary_edge([i, j], mesh):
if halfedges:
xym = 0.5 * (v1.co + v2.co)
x = [fctr[i, 0], xym[0]]
y = [fctr[i, 1], xym[1]]
ax.plot(x, y, 'b-', lw=0.5)
if edges:
ax.plot([v1.co[0], v2.co[0]], [v1.co[1], v2.co[1]],
'k',
lw=0.8)
else:
ej = get_twin([i, j], mesh)
if edges:
if v1.index < v2.index:
ax.plot([v1.co[0], v2.co[0]], [v1.co[1], v2.co[1]],
'k',
lw=0.8)
if halfedges:
ax.plot([fctr[i, 0], fctr[ej[0], 0]],
[fctr[i, 1], fctr[ej[0], 1]],
'g-',
lw=0.5)
# boundaries
if boundaries:
for loop in get_boundary_loops(mesh):
lenloop = len(loop)
for i in range(lenloop):
j = (i + 1) % lenloop
x = [mesh.verts[k].co[0] for k in [i, j]]
y = [mesh.verts[k].co[1] for k in [i, j]]
ax.plot(x, y, 'b-', lw=1.0)
# vertex to incident halfedge
if v2h:
for v in mesh.verts:
if not is_boundary_edge(v.edge, mesh): continue
w = mesh.verts[get_dest(v.edge, mesh)]
vec = 0.5 * (w.co - v.co)
ax.quiver(v.co[0],
v.co[1],
vec[0],
vec[1],
color='r',
lw=1.5,
edgecolor='r',
scale_units='xy',
scale=1,
zorder=2)
if v2f:
for v in mesh.verts:
xy = []
e = v.edge
n = 0
if is_boundary_edge(e, mesh):
w = mesh.verts[get_dest(e, mesh)]
xy.append(0.5 * (w.co[0:2] + v.co[0:2]))
n += 1
while True:
xy.append(fctr[e[0]])
n += 1
e = get_prev(e, mesh)
if is_boundary_edge(e, mesh):
w = mesh.verts[get_orig(e, mesh)]
xy.append(0.5 * (w.co[0:2] + v.co[0:2]))
break
e = get_twin(e, mesh)
if e[0] == v.edge[0]:
break
#
for i in range(n):
xyi = 0.5 * (xy[i] + v.co[0:2])
xyj = 0.5 * (xy[(i + 1) % len(xy)] + v.co[0:2])
ax.quiver(xyi[0],
xyi[1],
xyj[0] - xyi[0],
xyj[1] - xyi[1],
color='c',
lw=0.1,
edgecolor='c',
scale_units='xy',
scale=1,
zorder=3)
return
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
class CameraDisplay:
"""
Camera Display using matplotlib.
Parameters
----------
geometry : `~ctapipe.instrument.CameraGeometry`
Definition of the Camera/Image
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
ax : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one
title : str (default "Camera")
Title to put on camera plot
norm : str or `matplotlib.color.Normalize` instance (default 'lin')
Normalization for the color scale.
Supported str arguments are
- 'lin': linear scale
- 'log': logarithmic scale (base 10)
cmap : str or `matplotlib.colors.Colormap` (default 'hot')
Color map to use (see `matplotlib.cm`)
allow_pick : bool (default False)
if True, allow user to click and select a pixel
autoupdate : bool (default True)
redraw automatically (otherwise need to call plt.draw())
autoscale : bool (default True)
rescale the vmin/vmax values when the image changes.
This is set to False if `set_limits_*` is called to explicity
set data limits.
antialiased : bool (default True)
whether to draw in antialiased mode or not.
Notes
-----
Speed:
CameraDisplay is not intended to be very fast (matplotlib
is not a very speed performant graphics library, it is
intended for nice output plots). However, most of the
slowness of CameraDisplay is in the constructor. Once one is
displayed, changing the image that is displayed is relatively
fast and efficient. Therefore it is best to initialize an
instance, and change the data, rather than generating new
CameraDisplays.
Pixel Implementation:
Pixels are rendered as a
`matplotlib.collections.PatchCollection` of Polygons (either 6
or 4 sided). You can access the PatchCollection directly (to
e.g. change low-level style parameters) via
`CameraDisplay.pixels`
Output:
Since CameraDisplay uses matplotlib, any display can be
saved to any output file supported via
plt.savefig(filename). This includes `.pdf` and `.png`.
"""
def __init__(
self,
geometry,
image=None,
ax=None,
title=None,
norm="lin",
cmap=None,
allow_pick=False,
autoupdate=True,
autoscale=True,
antialiased=True,
):
self.axes = ax if ax is not None else plt.gca()
self.geom = geometry
self.pixels = None
self.colorbar = None
self.autoupdate = autoupdate
self.autoscale = autoscale
self._active_pixel = None
self._active_pixel_label = None
if title is None:
title = geometry.cam_id
# initialize the plot and generate the pixels as a
# RegularPolyCollection
patches = []
if not hasattr(self.geom, "mask"):
self.geom.mask = np.ones_like(self.geom.pix_x.value, dtype=bool)
for xx, yy, aa in zip(
u.Quantity(self.geom.pix_x[self.geom.mask]).value,
u.Quantity(self.geom.pix_y[self.geom.mask]).value,
u.Quantity(np.array(
self.geom.pix_area)[self.geom.mask]).value):
if self.geom.pix_type.startswith("hex"):
rr = sqrt(aa * 2 / 3 / sqrt(3)) + 2 * PIXEL_EPSILON
poly = RegularPolygon(
(xx, yy),
6,
radius=rr,
orientation=self.geom.pix_rotation.rad,
fill=True,
)
else:
rr = sqrt(aa) + PIXEL_EPSILON
poly = Rectangle(
(xx - rr / 2., yy - rr / 2.),
width=rr,
height=rr,
angle=self.geom.pix_rotation.deg,
fill=True,
)
patches.append(poly)
self.pixels = PatchCollection(patches, cmap=cmap, linewidth=0)
self.axes.add_collection(self.pixels)
self.pixel_highlighting = copy.copy(self.pixels)
self.pixel_highlighting.set_facecolor('none')
self.pixel_highlighting.set_linewidth(0)
self.axes.add_collection(self.pixel_highlighting)
# Set up some nice plot defaults
self.axes.set_aspect('equal', 'datalim')
self.axes.set_title(title)
self.axes.set_xlabel("X position ({})".format(self.geom.pix_x.unit))
self.axes.set_ylabel("Y position ({})".format(self.geom.pix_y.unit))
self.axes.autoscale_view()
# set up a patch to display when a pixel is clicked (and
# pixel_picker is enabled):
self._active_pixel = copy.copy(patches[0])
self._active_pixel.set_facecolor('r')
self._active_pixel.set_alpha(0.5)
self._active_pixel.set_linewidth(2.0)
self._active_pixel.set_visible(False)
self.axes.add_patch(self._active_pixel)
self._active_pixel_label = self.axes.text(self._active_pixel.xy[0],
self._active_pixel.xy[1],
"0",
horizontalalignment='center',
verticalalignment='center')
self._active_pixel_label.set_visible(False)
# enable ability to click on pixel and do something (can be
# enabled on-the-fly later as well:
if allow_pick:
self.enable_pixel_picker()
if image is not None:
self.image = image
else:
self.image = np.zeros_like(self.geom.pix_id, dtype=np.float)
self.norm = norm
def highlight_pixels(self, pixels, color='g', linewidth=1, alpha=0.75):
'''
Highlight the given pixels with a colored line around them
Parameters
----------
pixels : index-like
The pixels to highlight.
Can either be a list or array of integers or a
boolean mask of length number of pixels
color: a matplotlib conform color
the color for the pixel highlighting
linewidth: float
linewidth of the highlighting in points
alpha: 0 <= alpha <= 1
The transparency
'''
l = np.zeros_like(self.image)
l[pixels] = linewidth
self.pixel_highlighting.set_linewidth(l)
self.pixel_highlighting.set_alpha(alpha)
self.pixel_highlighting.set_edgecolor(color)
self._update()
def enable_pixel_picker(self):
""" enable ability to click on pixels """
self.pixels.set_picker(True) # enable click
self.pixels.set_pickradius(
sqrt(u.Quantity(self.geom.pix_area[0]).value) / np.pi)
self.pixels.set_snap(True) # snap cursor to pixel center
self.axes.figure.canvas.mpl_connect('pick_event', self._on_pick)
def set_limits_minmax(self, zmin, zmax):
""" set the color scale limits from min to max """
self.pixels.set_clim(zmin, zmax)
self.autoscale = False
self._update()
def set_limits_percent(self, percent=95):
""" auto-scale the color range to percent of maximum """
zmin = self.pixels.get_array().min()
zmax = self.pixels.get_array().max()
dz = zmax - zmin
frac = percent / 100.0
self.autoscale = False
self.set_limits_minmax(zmin, zmax - (1.0 - frac) * dz)
@property
def norm(self):
'''
The norm instance of the Display
Possible values:
- "lin": linear scale
- "log": log scale (cannot have negative values)
- "symlog": symmetric log scale (negative values are ok)
- any matplotlib.colors.Normalize instance, e. g. PowerNorm(gamma=-2)
'''
return self.pixels.norm
@norm.setter
def norm(self, norm):
if norm == 'lin':
self.pixels.norm = Normalize()
elif norm == 'log':
self.pixels.norm = LogNorm()
self.pixels.autoscale() # this is to handle matplotlib bug #5424
elif norm == 'symlog':
self.pixels.norm = SymLogNorm(linthresh=1.0)
self.pixels.autoscale()
elif isinstance(norm, Normalize):
self.pixels.norm = norm
else:
raise ValueError(
"Unsupported norm: '{}', options are 'lin',"
"'log','symlog', or a matplotlib Normalize object".format(
norm))
self.update(force=True)
self.pixels.autoscale()
@property
def cmap(self):
"""
Color map to use. Either a name or `matplotlib.colors.ColorMap`
instance, e.g. from `matplotlib.pyplot.cm`
"""
return self.pixels.get_cmap()
@cmap.setter
def cmap(self, cmap):
self.pixels.set_cmap(cmap)
self._update()
@property
def image(self):
"""The image displayed on the camera (1D array of pixel values)"""
return self.pixels.get_array()
@image.setter
def image(self, image):
"""
Change the image displayed on the Camera.
Parameters
----------
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
"""
image = np.asanyarray(image)
if image.shape != self.geom.pix_x.shape:
raise ValueError("Image has a different shape {} than the "
"given CameraGeometry {}".format(
image.shape, self.geom.pix_x.shape))
self.pixels.set_array(image[self.geom.mask])
self.pixels.changed()
if self.autoscale:
self.pixels.autoscale()
self._update()
def _update(self, force=False):
""" signal a redraw if autoupdate is turned on """
if self.autoupdate:
self.update(force)
def update(self, force=False):
""" redraw the display now """
self.axes.figure.canvas.draw()
if self.colorbar is not None:
if force is True:
self.colorbar.update_bruteforce(self.pixels)
else:
self.colorbar.update_normal(self.pixels)
self.colorbar.draw_all()
def add_colorbar(self, **kwargs):
"""
add a colobar to the camera plot
kwargs are passed to `figure.colorbar(self.pixels, **kwargs)`
See matplotlib documentation for the supported kwargs:
http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
"""
if self.colorbar is not None:
raise ValueError(
'There is already a colorbar attached to this CameraDisplay')
else:
self.colorbar = self.axes.figure.colorbar(self.pixels, **kwargs)
self.update()
def add_ellipse(self,
centroid,
length,
width,
angle,
asymmetry=0.0,
**kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid,
width=length,
height=width,
angle=np.degrees(angle),
fill=False,
**kwargs)
self.axes.add_patch(ellipse)
self.update()
return ellipse
def overlay_moments(self, momparams, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
cen_x = u.Quantity(momparams.cen_x).value
cen_y = u.Quantity(momparams.cen_y).value
length = u.Quantity(momparams.length).value
width = u.Quantity(momparams.width).value
el = self.add_ellipse(centroid=(cen_x, cen_y),
length=length * 2,
width=width * 2,
angle=momparams.psi.rad,
**kwargs)
self.axes.text(cen_x,
cen_y, ("({:.02f},{:.02f})\n"
"[w={:.02f},l={:.02f}]").format(
momparams.cen_x, momparams.cen_y,
momparams.width, momparams.length),
color=el.get_edgecolor())
def _on_pick(self, event):
""" handler for when a pixel is clicked """
pix_id = event.ind[-1]
xx, yy, aa = u.Quantity(self.geom.pix_x[pix_id]).value, \
u.Quantity(self.geom.pix_y[pix_id]).value, \
u.Quantity(np.array(self.geom.pix_area)[pix_id])
if self.geom.pix_type.startswith("hex"):
self._active_pixel.xy = (xx, yy)
else:
rr = sqrt(aa)
self._active_pixel.xy = (xx - rr / 2., yy - rr / 2.)
self._active_pixel.set_visible(True)
self._active_pixel_label.set_x(xx)
self._active_pixel_label.set_y(yy)
self._active_pixel_label.set_text("{:003d}".format(pix_id))
self._active_pixel_label.set_visible(True)
self._update()
self.on_pixel_clicked(pix_id) # call user-function
def on_pixel_clicked(self, pix_id):
"""virtual function to overide in sub-classes to do something special
when a pixel is clicked
"""
print("Clicked pixel_id {}".format(pix_id))
def show(self):
self.axes.figure.show()
#virtual buoys
cx.plot(x2, y2, 'o', linewidth=2, color='purple')
#triangles
print len(gtri2)
patches = []
for i in range(len(gtri2)):
patch = Polygon(gtri2[i], edgecolor='orchid', alpha=1, fill=False)
patches.append(patch)
#label individual triangles
#centroid = np.mean(gtri2[i],axis=0)
#cx.text(centroid[0], centroid[1],trinum[i])
p = PatchCollection(patches, cmap=cmap, alpha=0.4)
p.set_array(np.array(deform))
cx.add_collection(p)
p.set_clim(interval)
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(cx)
cax = divider.append_axes("bottom", size="5%", pad=0.1)
cbar = plt.colorbar(p, cax=cax, orientation='horizontal')
cbar.set_label(label, size=16)
fig3.savefig(outpath + outname4, bbox_inches='tight')
##############################################################3
#outname5= 'map_diff_f'
#label = r'Freeboard difference (m)'
#interval = [-.3, .3]
#cmap=plt.cm.bwr
def plot_evolution():
N = ff.current_generation
M = ff.generation_size
fig, ax = plt.subplots(figsize=(20, 20))
grid = np.mgrid[0.1:0.9:complex(0, N), 0.1:0.9:complex(0, M)].T
patches = []
colors = []
line_color = {
'replace_by_other': 'green',
'replace_by_random': 'cyan',
'promoted': 'black',
'duplicate': 'orange'
}
dx = 0.01
for j in range(N):
dup_dx = 0.5 * dx
for i in range(M):
entry_id = ff.lineage[str(i)][j]
nres2 = ff.population.get_entry(
entry_id, {'properties.nres2': 1})['properties']['nres2']
if nres2 < 1E14:
lw = 1
ls = 'solid'
else:
lw = 1
ls = 'dotted'
if ff.population.is_evaluated(entry_id):
colors.append(ff.population.value(entry_id))
circle = mpatches.Circle(grid[i, j],
0.4 / float(M),
ec="black",
linewidth=lw,
linestyle=ls)
patches.append(circle)
label(grid[i, j], "%7.2f" % ff.population.value(entry_id), 0.0)
for ichange in ff.population.pcdb.db.generation_changes.find({
'from':
entry_id,
'generation':
j
}):
if ichange['change'] == 'duplicate':
orig = ichange['from']
dest = ichange['to']
newi = int(ff.lineage_inv[dest])
dup_dx += dx / 10.0
x, y = np.array([[
grid[i, j][0] - 1.5 * dup_dx,
grid[i, j][0] - 2 * dup_dx,
grid[newi, j][0] - 2 * dup_dx, grid[newi, j][0] - dx
],
[
grid[i, j][1], grid[i, j][1],
grid[newi, j][1], grid[newi, j][1]
]])
line = mlines.Line2D(x,
y,
lw=1.,
alpha=0.8,
color=line_color[ichange['change']],
marker='>',
markersize=5,
markeredgecolor='none')
line.set_markevery([3])
ax.add_line(line)
elif j < N - 1:
x, y = np.array(
[[grid[i, j][0] + dx, grid[i, j + 1][0] - 2 * dx],
[grid[i, j][1], grid[i, j + 1][1]]])
line = mlines.Line2D(x,
y,
lw=5.,
alpha=0.3,
color=line_color[ichange['change']])
# label(0.5*(grid[i, j]+grid[i, j+1]), ichange['change'], 0.0)
ax.add_line(line)
collection = PatchCollection(patches, cmap=plt.cm.hsv, alpha=0.3)
collection.set_array(np.array(colors))
ax.add_collection(collection)
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
plt.axis('equal')
plt.axis('off')
plt.savefig(figname + '_evo.pdf')
class SceneVisualizer:
"""Context for social nav vidualization"""
def __init__(self,
scene,
output=None,
writer="imagemagick",
cmap="viridis",
agent_colors=None,
**kwargs):
self.scene = scene
self.states, self.group_states = self.scene.get_states()
self.cmap = cmap
self.agent_colors = agent_colors
self.frames = self.scene.get_length()
self.output = output
self.writer = writer
self.fig, self.ax = plt.subplots(**kwargs)
self.ani = None
self.group_actors = None
self.group_collection = PatchCollection([])
self.group_collection.set(
animated=True,
alpha=0.2,
cmap=self.cmap,
facecolors="none",
edgecolors="purple",
linewidth=2,
clip_on=True,
)
self.human_actors = None
self.human_collection = PatchCollection([])
self.human_collection.set(animated=True,
alpha=0.6,
cmap=self.cmap,
clip_on=True)
def plot(self):
"""Main method for create plot"""
self.plot_obstacles()
groups = self.group_states[0] # static group for now
if not groups:
for ped in range(self.scene.peds.size()):
x = self.states[:, ped, 0]
y = self.states[:, ped, 1]
self.ax.plot(x, y, "-o", label=f"ped {ped}", markersize=2.5)
else:
colors = plt.cm.rainbow(np.linspace(0, 1, len(groups)))
for i, group in enumerate(groups):
for ped in group:
x = self.states[:, ped, 0]
y = self.states[:, ped, 1]
self.ax.plot(x,
y,
"-o",
label=f"ped {ped}",
markersize=2.5,
color=colors[i])
self.ax.legend()
return self.fig
def animate(self):
"""Main method to create animation"""
self.ani = mpl_animation.FuncAnimation(
self.fig,
init_func=self.animation_init,
func=self.animation_update,
frames=self.frames,
blit=True,
)
return self.ani
def __enter__(self):
logger.info("Start plotting.")
self.fig.set_tight_layout(True)
self.ax.grid(linestyle="dotted")
self.ax.set_aspect("equal")
self.ax.margins(2.0)
self.ax.set_axisbelow(True)
self.ax.set_xlabel("x [m]")
self.ax.set_ylabel("y [m]")
plt.rcParams["animation.html"] = "jshtml"
# x, y limit from states, only for animation
margin = 2.0
xy_limits = np.array([minmax(state) for state in self.states
]) # (x_min, y_min, x_max, y_max)
xy_min = np.min(xy_limits[:, :2], axis=0) - margin
xy_max = np.max(xy_limits[:, 2:4], axis=0) + margin
self.ax.set(xlim=(xy_min[0], xy_max[0]), ylim=(xy_min[1], xy_max[1]))
# # recompute the ax.dataLim
# self.ax.relim()
# # update ax.viewLim using the new dataLim
# self.ax.autoscale_view()
return self
def __exit__(self, exception_type, exception_value, traceback):
if exception_type:
logger.error(
f"Exception type: {exception_type}; Exception value: {exception_value}; Traceback: {traceback}"
)
logger.info("Plotting ends.")
if self.output:
if self.ani:
output = self.output + ".gif"
logger.info(f"Saving animation as {output}")
self.ani.save(output, writer=self.writer)
else:
output = self.output + ".png"
logger.info(f"Saving plot as {output}")
self.fig.savefig(output, dpi=300)
plt.close(self.fig)
def plot_human(self, step=-1):
"""Generate patches for human
:param step: index of state, default is the latest
:return: list of patches
"""
states, _ = self.scene.get_states()
current_state = states[step]
# radius = 0.2 + np.linalg.norm(current_state[:, 2:4], axis=-1) / 2.0 * 0.3
radius = [0.2] * current_state.shape[0]
if self.human_actors:
for i, human in enumerate(self.human_actors):
human.center = current_state[i, :2]
human.set_radius(0.2)
# human.set_radius(radius[i])
else:
self.human_actors = [
Circle(pos, radius=r)
for pos, r in zip(current_state[:, :2], radius)
]
self.human_collection.set_paths(self.human_actors)
if not self.agent_colors:
self.human_collection.set_array(np.arange(current_state.shape[0]))
else:
# set colors for each agent
assert len(self.human_actors) == len(
self.agent_colors
), "agent_colors must be the same length as the agents"
self.human_collection.set_facecolor(self.agent_colors)
def plot_groups(self, step=-1):
"""Generate patches for groups
:param step: index of state, default is the latest
:return: list of patches
"""
states, group_states = self.scene.get_states()
current_state = states[step]
current_groups = group_states[step]
if self.group_actors: # update patches, else create
points = [current_state[g, :2] for g in current_groups]
for i, p in enumerate(points):
self.group_actors[i].set_xy(p)
else:
self.group_actors = [
Polygon(current_state[g, :2]) for g in current_groups
]
self.group_collection.set_paths(self.group_actors)
def plot_obstacles(self):
for s in self.scene.get_obstacles():
self.ax.plot(s[:, 0], s[:, 1], "-o", color="black", markersize=2.5)
def animation_init(self):
self.plot_obstacles()
self.ax.add_collection(self.group_collection)
self.ax.add_collection(self.human_collection)
return (self.group_collection, self.human_collection)
def animation_update(self, i):
self.plot_groups(i)
self.plot_human(i)
return (self.group_collection, self.human_collection)
class FlowCollectionPlot(object):
'''
arguments:
- ax - matplotlib.Axes() object
- flowcollection - FlowCollection object
kwargs:
- flow_range: 2
- normalized: True
- norm: LogNorm(1e-flow_range, 1) (*MaxFlow if not normalized)
- cmap: 'jet'
- scale_arrows: True
- kwargs for FancyArrow
'''
def __init__(self, ax, flowcollection, frange=2, normalized=True, scale_arrows=True, **kwargs):
self.ax = ax
self.flowcollection = flowcollection
self.normalized = normalized
self.frange = frange
self.scale_arrows = scale_arrows
self.MaxFlow = flowcollection.getMaxFlow()
self.MinFlow = flowcollection.getMinFlow()
if frange == 'all':
if normalized:
self.norm = kwargs.pop('norm', LogNorm(self.MinFlow/self.MaxFlow, 1, clip=True))
else:
self.norm = kwargs.pop('norm', LogNorm(self.MinFlow, self.MaxFlow, clip=True))
else:
if normalized:
self.norm = kwargs.pop('norm', LogNorm(10**(-frange), 1, clip=True))
else:
self.norm = kwargs.pop('norm', LogNorm(self.MaxFlow*10**(-frange), self.MaxFlow, clip=True))
self.cmap = cm.get_cmap(kwargs.pop('cmap', 'jet'))
self.ax.axis(self.flowcollection.getBounds())
kwargs.setdefault('lw', .3)
Arrows = []
Acol = []
if normalized:
flows = np.vectorize(lambda f: f.flow / self.MaxFlow)(self.flowcollection.flows)
else:
flows = np.vectorize(lambda f: f.flow)(self.flowcollection.flows)
for fl, flow in zip(flows, self.flowcollection.flows):
if frange != 'all' and self.norm(fl) <= 0:
continue
if scale_arrows:
width = .2*self.norm(fl) + 0.01
else:
width = .15
st = {"ec": 'k',
"width": width,
"head_width": 2.5*width,
"head_length": .5}
nkwargs = kwargs.copy()
for kv in st.items():
nkwargs.setdefault(*kv)
ar = FancyArrow(flow.N0, flow.Z0, flow.dN, flow.dZ,
length_includes_head=True,
**nkwargs)
Arrows.append(ar)
Acol.append(fl)
Arrows = np.array(Arrows)
Acol = np.array(Acol)
self.Patches = PatchCollection(Arrows, norm=self.norm, cmap=self.cmap, match_original=True)
self.Patches.set_array(Acol)
self.ax.add_collection(self.Patches, )
def addColorBar(self, xshift=-.06, yshift=.01, loc='lower right', **kwargs):
'''
adds a colorbar as inset at location loc
xshift and yshift are bbox offset to anchor
kwargs are passed to colorbar
returns colorbar object
'''
if loc.split(' ')[1] == 'left':
xshift += .035
if loc.split(' ')[0] == 'upper':
yshift -= .06
self.cax = inset_axes(self.ax, width="3%", height="50%", loc=loc,
bbox_transform=self.ax.transAxes,
bbox_to_anchor=(xshift, yshift, 1, 1))
self.cbar = plt.colorbar(self.Patches, cax=self.cax, **kwargs)
if self.normalized:
self.cax.set_title(r'$\frac{\Delta Y}{\rm{max}(\Delta Y)}$')
else:
self.cax.set_title(r'$\Delta Y$')
return self.cbar
class HexModelGrid(VoronoiDelaunayGrid):
"""A grid of hexagonal cells.
This inherited class implements a regular 2D grid with hexagonal cells and
triangular patches. It is a special type of VoronoiDelaunay grid in which
the initial set of points is arranged in a triangular/hexagonal lattice.
Parameters
----------
base_num_rows : int
Number of rows of nodes in the left column.
base_num_cols : int
Number of nodes on the first row.
dx : float, optional
Node spacing.
orientation : string, optional
One of the 3 cardinal directions in the grid, either 'horizontal'
(default) or 'vertical'
shape : string, optional
Controls the shape of the bounding hull, i.e., are the nodes arranged
in a hexagon, or a rectangle? Either 'hex' (default) or 'rect'.
Returns
-------
HexModelGrid
A newly-created grid.
Examples
--------
Create a hex grid with 2 rows of nodes. The first and third rows will
have 2 nodes, and the second nodes.
>>> from landlab import HexModelGrid
>>> hmg = HexModelGrid(3, 2, 1.0)
>>> hmg.number_of_nodes
7
"""
def __init__(self,
base_num_rows=0,
base_num_cols=0,
dx=1.0,
orientation='horizontal',
shape='hex',
reorient_links=True,
**kwds):
"""Create a grid of hexagonal cells.
Create a regular 2D grid with hexagonal cells and triangular patches.
It is a special type of VoronoiDelaunay grid in which the initial set
of points is arranged in a triangular/hexagonal lattice.
Parameters
----------
base_num_rows : int
Number of rows of nodes in the left column.
base_num_cols : int
Number of nodes on the first row.
dx : float, optional
Node spacing.
orientation : string, optional
One of the 3 cardinal directions in the grid, either 'horizontal'
(default) or 'vertical'
Returns
-------
HexModelGrid
A newly-created grid.
Examples
--------
Create a hex grid with 2 rows of nodes. The first and third rows will
have 2 nodes, and the second nodes.
>>> from landlab import HexModelGrid
>>> hmg = HexModelGrid(3, 2, 1.0)
>>> hmg.number_of_nodes
7
"""
# Set number of nodes, and initialize if caller has given dimensions
if base_num_rows * base_num_cols > 0:
self._initialize(base_num_rows, base_num_cols, dx, orientation,
shape, reorient_links)
super(HexModelGrid, self).__init__(**kwds)
@classmethod
def from_dict(cls, params):
"""
LLCATS: GINF
"""
shape = params['shape']
spacing = params.get('spacing', 1.)
return cls(shape[0], shape[1], spacing)
def _initialize(self,
base_num_rows,
base_num_cols,
dx,
orientation,
shape,
reorient_links=True):
r"""Set up a hexagonal grid.
Sets up a hexagonal grid with cell spacing dx and
(by default) regular boundaries (that is, all perimeter cells are
boundaries and all interior cells are active).
Parameters
----------
base_num_rows : int
Number of rows along left side of grid
base_num_cols : int
Number of columns along bottom side of grid
dx : float
Distance between nodes
orientation : string
Either 'horizontal' (default in __init__) or 'vertical'
shape : string
Either 'hex' (default in __init__) or 'rect'
reorient_links : bool
Whether or not to re-orient all links to point between -45 deg
and +135 deg clockwise from "north" (i.e., along y axis)
Returns
-------
(none)
Creates/modifies
----------------
Creates and initializes and self._dx
Notes
-----
To be consistent with unstructured grids, the hex grid is
managed not as a 2D array but rather as a set of arrays that
describe connectivity information between nodes, links, cells, faces,
patches, corners, and junctions.
'Horizontal' orientation means that one of the 3 axes of the grid is
horizontal, whereas the other two are at 30 degree angles to the
horizontal, like:
\ /
-----
/ \
'Vertical' means that one axis is vertical, with the other
two at 30 degree angles to the vertical, more like:
\ | /
\ | /
/ | \
/ | \
(of course, these keyboard characters don't represent the angles quite
right)
Numbers of rows and columns: a hex grid with a rectangular shape will
have a fixed number of rows and columns, and so for rectangular shaped
grids we record this information in self._nrows and self._ncols. With
a hex-shaped grid, either the number of columns (if 'horizontal') or
the number of rows (if 'vertical') will vary across the grid.
Therefore, for hex-shaped grids we record only self._nrows for
'horizontal' grids, and only self._ncols for 'vertical' grids.
"""
if self._DEBUG_TRACK_METHODS:
six.print_('HexModelGrid._initialize(' + str(base_num_rows) +
', ' + str(base_num_cols) + ', ' + str(dx) + ')')
# Make sure the parameter *orientation* is correct
assert (orientation[0].lower() == 'h' or
orientation[0].lower() == 'v'), \
'orientation must be either "horizontal" (default) or "vertical"'
# Make sure the parameter *shape* is correct
assert (shape[0].lower() == 'h' or shape[0].lower() == 'r'), \
'shape must be either "hex" (default) or "rect"'
# Create a set of hexagonally arranged points. These will be our nodes.
if orientation[0].lower() == 'h' and shape[0].lower() == 'h':
pts = HexModelGrid._hex_points_with_horizontal_hex(
base_num_rows, base_num_cols, dx)
self.orientation = 'horizontal'
self._nrows = base_num_rows
elif orientation[0].lower() == 'h' and shape[0].lower() == 'r':
pts = HexModelGrid._hex_points_with_horizontal_rect(
base_num_rows, base_num_cols, dx)
self.orientation = 'horizontal'
self._nrows = base_num_rows
self._ncols = base_num_cols
self._shape = (self._nrows, self._ncols)
self._nodes = numpy.arange(self._nrows * self._ncols,
dtype=int).reshape(self._shape)
elif orientation[0].lower() == 'v' and shape[0].lower() == 'h':
pts = HexModelGrid._hex_points_with_vertical_hex(
base_num_rows, base_num_cols, dx)
self.orientation = 'vertical'
self._ncols = base_num_cols
else:
pts = HexModelGrid._hex_points_with_vertical_rect(
base_num_rows, base_num_cols, dx)
self.orientation = 'vertical'
self._nrows = base_num_rows
self._ncols = base_num_cols
self._shape = (self._nrows, self._ncols)
self._nodes = numpy.arange(self._nrows * self._ncols,
dtype=int).reshape(self._shape)
for col in range(self._ncols):
base_node = (col // 2) + (col % 2) * ((self._ncols + 1) // 2)
self._nodes[:, col] = numpy.arange(base_node,
self._nrows * self._ncols,
self._ncols)
# Call the VoronoiDelaunayGrid constructor to triangulate/Voronoi
# the nodes into a grid.
super(HexModelGrid, self)._initialize(pts[:, 0], pts[:, 1],
reorient_links)
# Handle special case of boundary nodes in rectangular grid shape.
# One pair of edges will have the nodes staggered. By default, only the
# outer nodes will be assigned boundary status; we need the inner edge
# nodes on these "ragged" edges also to be flagged as boundary nodes.
if shape[0].lower() == 'r':
self._set_boundary_stat_at_rect_grid_ragged_edges(orientation, dx)
# Remember grid spacing
self._dx = dx
def _set_boundary_stat_at_rect_grid_ragged_edges(self, orientation, dx):
"""Assign boundary status to all edge nodes along the 'ragged' edges.
Handle special case of boundary nodes in rectangular grid shape.
One pair of edges will have the nodes staggered. By default, only the
outer nodes will be assigned boundary status; we need the inner edge
nodes on these "ragged" edges also to be flagged as boundary nodes.
Examples
--------
>>> from landlab import HexModelGrid
>>> hg = HexModelGrid(3, 3, shape='rect', dx=2.0)
>>> hg.status_at_node
array([1, 1, 1, 1, 0, 1, 1, 1, 1], dtype=uint8)
>>> hg = HexModelGrid(3, 3, shape='rect', orientation='vert')
>>> hg.status_at_node
array([1, 1, 1, 1, 1, 0, 1, 1, 1], dtype=uint8)
>>> hg = HexModelGrid(4, 4, shape='rect', orientation='vert')
>>> hg.status_at_node
array([1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1], dtype=uint8)
>>> hg = HexModelGrid(3, 4, shape='rect')
>>> hg.status_at_node
array([1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1], dtype=uint8)
"""
if orientation[0].lower() == 'v': # vert; top & bottom staggered
bot_row = numpy.where(self.y_of_node <= 0.5 * dx)[0]
self.status_at_node[bot_row] = self.status_at_node[0]
top_row = numpy.where(self.y_of_node >= (self._nrows - 1) * dx)[0]
self.status_at_node[top_row] = self.status_at_node[0]
else: # horizontal orientation; left & right staggered
left_row = numpy.where(self.x_of_node <= 0.5 * dx)[0]
self.status_at_node[left_row] = self.status_at_node[0]
right_row = numpy.where(
self.x_of_node >= (self._ncols - 1) * dx)[0]
self.status_at_node[right_row] = self.status_at_node[0]
def _create_cell_areas_array(self):
r"""Create an array of surface areas of hexagonal cells.
Creates and returns an array containing the surface areas of the
hexagonal (Voronoi) cells.
These cells are perfect hexagons in which the apothem is dx/2. The
formula for area is:
.. math::
A = 3 dx^2 / 2 \sqrt{3} \approx 0.866 dx^2
"""
self._area_of_cell = (0.8660254 * self._dx**2 +
numpy.zeros(self.number_of_cells))
return self._area_of_cell
@staticmethod
def _hex_points_with_horizontal_hex(num_rows, base_num_cols, dxh):
"""Create a set of points on a staggered grid.
Creates and returns a set of (x,y) points in a staggered grid in which
the points represent the centers of regular hexagonal cells, and the
points could be connected to form equilateral triangles. The overall
shape of the lattice is hexagonal, and one of the 3 axes is horizontal.
Parameters
----------
num_rows : int
Number of rows in lattice
base_num_cols : int
Number of columns in the bottom and top rows (middle rows have
more)
dxh : float
Horizontal and diagonal spacing between points
Returns
-------
poinst : ndarray
A 2D numpy array containing point (x,y) coordinates, and total
number of points.
Examples
--------
>>> from landlab import HexModelGrid
>>> points = HexModelGrid._hex_points_with_horizontal_hex(3, 2, 1.0)
>>> len(points)
7
>>> points[1, :]
array([ 1., 0.])
>>> points[:3, 0]
array([ 0. , 1. , -0.5])
"""
dxv = dxh * numpy.sqrt(3.) / 2.
half_dxh = dxh / 2.
if numpy.mod(num_rows, 2) == 0: # even number of rows
npts = num_rows * base_num_cols + (num_rows * num_rows) // 4
else: # odd number of rows
npts = num_rows * base_num_cols + \
((num_rows - 1) // 2) * ((num_rows - 1) // 2)
pts = numpy.zeros((npts, 2))
middle_row = num_rows // 2
extra_cols = 0
xshift = 0.
i = 0
for r in range(num_rows):
for c in range(base_num_cols + extra_cols):
pts[i, 0] = c * dxh + xshift
pts[i, 1] = r * dxv
i += 1
if r < middle_row:
extra_cols += 1
else:
extra_cols -= 1
xshift = -half_dxh * extra_cols
return pts
@staticmethod
def _hex_points_with_horizontal_rect(num_rows, num_cols, dxh):
"""Create a set of points in a taggered grid.
Creates and returns a set of (x,y) points in a staggered grid in which
the points represent the centers of regular hexagonal cells, and the
points could be connected to form equilateral triangles. The overall
shape of the lattice is rectangular, and one of the 3 axes is
horizontal.
Parameters
----------
num_rows : int
Number of rows in lattice
num_cols : int
Number of columns in lattice
dxh : float
Horizontal and diagonal spacing between points
Returns
-------
points : ndarray of shape `(n_points, 2)`
A 2D numpy array containing point (x, y) coordinates, and total
number of points.
Examples
--------
>>> from landlab import HexModelGrid
>>> points = HexModelGrid._hex_points_with_horizontal_rect(3, 3, 1.0)
>>> len(points)
9
>>> points[1, :]
array([ 1., 0.])
>>> points[:3, 0]
array([ 0., 1., 2.])
"""
dxv = dxh * numpy.sqrt(3.) / 2.
half_dxh = dxh / 2.
npts = num_rows * num_cols
pts = numpy.zeros((npts, 2))
xshift = 0.
i = 0
for r in range(num_rows):
for c in range(num_cols):
xshift = half_dxh * (r % 2)
pts[i, 0] = c * dxh + xshift
pts[i, 1] = r * dxv
i += 1
return pts
@staticmethod
def _hex_points_with_vertical_hex(base_num_rows, num_cols, dxv):
"""
Creates and returns a set of (x,y) points in a staggered grid in which
the points represent the centers of regular hexagonal cells, and the
points could be connected to form equilateral triangles. The overall
shape of the lattice is hexagonal.
Parameters
----------
base_num_rows : int
Number of columns in the left and right columns (middle columns
have more)
num_cols : int
Number of columns in lattice
dxv : float
Vertical and diagonal spacing between points
Returns
-------
points : ndarray of shape `(n_points, 2)`
2D numpy array containing point (x,y) coordinates, and total
number of points.
Examples
--------
>>> from landlab import HexModelGrid
>>> points = HexModelGrid._hex_points_with_vertical_hex(2, 3, 1.0)
>>> len(points)
7
>>> points[1, :]
array([ 0., 1.])
>>> points[:3, 1]
array([ 0. , 1. , -0.5])
"""
dxh = dxv * numpy.sqrt(3.) / 2.
half_dxv = dxv / 2.
if numpy.mod(num_cols, 2) == 0: # even number of columns
npts = base_num_rows * num_cols + (num_cols * num_cols) // 4
else: # odd number of columns
npts = base_num_rows * num_cols + \
((num_cols - 1) // 2) * ((num_cols - 1) // 2)
pts = numpy.zeros((npts, 2))
middle_col = num_cols // 2
extra_rows = 0
yshift = 0.
i = 0
for c in range(num_cols):
for r in range(base_num_rows + extra_rows):
pts[i, 1] = r * dxv + yshift
pts[i, 0] = c * dxh
i += 1
if c < middle_col:
extra_rows += 1
else:
extra_rows -= 1
yshift = -half_dxv * extra_rows
return pts
@staticmethod
def _hex_points_with_vertical_rect(num_rows, num_cols, dxv):
"""
Creates and returns a set of (x,y) points in a staggered grid in which
the points represent the centers of regular hexagonal cells, and the
points could be connected to form equilateral triangles. The overall
shape of the lattice is rectangular.
Parameters
----------
num_rows : int
Number of columns in lattice
num_cols : int
Number of columns in lattice
dxv : float
Vertical and diagonal spacing between points
Returns
-------
points : ndarray of shape `(n_points, 2)`
2D numpy array containing point (x,y) coordinates, and total
number of points.
Examples
--------
>>> from landlab import HexModelGrid
>>> points = HexModelGrid._hex_points_with_vertical_rect(3, 3, 1.0)
>>> len(points)
9
>>> points[1, :]
array([ 0., 1.])
>>> points[:3, 1]
array([ 0., 1., 2.])
"""
dxh = dxv * numpy.sqrt(3.) / 2.
half_dxv = dxv / 2.
npts = num_rows * num_cols
pts = numpy.zeros((npts, 2))
yshift = 0.
i = 0
for c in range(num_cols):
for r in range(num_rows):
yshift = half_dxv * (c % 2)
pts[i, 1] = r * dxv + yshift
pts[i, 0] = c * dxh
i += 1
return pts
@property
def number_of_node_columns(self):
"""Number of node columns hex grid.
Number of node columns in a rectangular-shaped and/or
vertically oriented hex grid.
Returns the number of columns, including boundaries.
Notes
-----
Will generate an error if called with a hex-shaped, horizontally
aligned grid.
Examples
--------
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(5, 5, shape='rect')
>>> grid.number_of_node_columns
5
LLCATS: GINF NINF
"""
return self._ncols
@property
def number_of_node_rows(self):
"""Number of node rows in a rectangular-shaped and/or
horizontally oriented hex grid.
Returns the number of rows, including boundaries.
Notes
-----
Will generate an error if called with a hex-shaped, vertically
aligned grid.
Examples
--------
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(5, 5, shape='rect')
>>> grid.number_of_node_rows
5
LLCATS: GINF NINF
"""
return self._nrows
@property
def nodes_at_left_edge(self):
"""Get nodes along the left edge of a grid, if grid is rectangular.
Examples
--------
>>> import numpy as np
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(3, 4, shape='rect')
>>> grid.nodes_at_left_edge
array([0, 4, 8])
LLCATS: NINF BC SUBSET
"""
try:
return self._nodes[:, 0]
except AttributeError:
raise AttributeError(
'Only rectangular Hex grids have defined edges.')
@property
def nodes_at_right_edge(self):
"""Get nodes along the right edge of a grid, if grid is rectangular.
Examples
--------
>>> import numpy as np
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(3, 4, shape='rect')
>>> grid.nodes_at_right_edge
array([ 3, 7, 11])
LLCATS: NINF BC SUBSET
"""
try:
return self._nodes[:, -1]
except AttributeError:
raise AttributeError(
'Only rectangular Hex grids have defined edges.')
@property
def nodes_at_top_edge(self):
"""Get nodes along the top edge of a grid, if grid is rectangular.
Examples
--------
>>> import numpy as np
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(3, 4, shape='rect')
>>> grid.nodes_at_top_edge
array([ 8, 9, 10, 11])
LLCATS: NINF BC SUBSET
"""
try:
return self._nodes[-1, :]
except AttributeError:
raise AttributeError(
'Only rectangular Hex grids have defined edges.')
@property
def nodes_at_bottom_edge(self):
"""Get nodes along the bottom edge of a grid, if grid is rectangular.
Examples
--------
>>> import numpy as np
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(3, 4, shape='rect')
>>> grid.nodes_at_bottom_edge
array([0, 1, 2, 3])
LLCATS: NINF BC SUBSET
"""
try:
return self._nodes[0, :]
except AttributeError:
raise AttributeError(
'Only rectangular Hex grids have defined edges.')
def node_row_and_column(self, node_id):
"""Row and column from node ID, FOR VERT RECT CONFIGURATION ONLY.
Examples
--------
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid(3, 4, shape='rect', orientation='vert')
>>> grid.node_row_and_column(5)
(1, 2)
>>> grid = HexModelGrid(3, 5, shape='rect', orientation='vert')
>>> grid.node_row_and_column(13)
(2, 1)
"""
assert self.orientation[0] == 'v', 'grid orientation must be vertical'
try:
(nr, nc) = self._shape
except:
raise AttributeError(
'Only rectangular Hex grids have defined rows and columns.')
row = node_id // nc
n_mod_nc = node_id % nc
half_nc = (nc + 1) // 2
col = 2 * (n_mod_nc % half_nc) + n_mod_nc // half_nc
return (row, col)
def _configure_hexplot(self, data, data_label=None, color_map=None):
"""
Sets up necessary information for making plots of the hexagonal grid
colored by a given data element.
Parameters
----------
data : str OR node array (1d numpy array with number_of_nodes entries)
Data field to be colored
data_label : str, optional
Label for colorbar
color_map : matplotlib colormap object, None
Color map to apply (defaults to "jet")
Returns
-------
(none)
Notes
-----
Creates and stores a PatchCollection representing the hexagons. Also
stores a handle to the current plotting axis. Both of these are then
used by hexplot().
"""
from numpy import array, sqrt, zeros
import matplotlib
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
# color
if color_map is None:
color_map = matplotlib.cm.jet
# geometry
apothem = self._dx / 2.0
# distance from node to each hexagon cell vertex
radius = 2.0 * apothem / sqrt(3.0)
# offsets from node x,y position
offsets = zeros((6, 2))
poly_verts = zeros((6, 2))
# Figure out whether the orientation is horizontal or vertical
if self.orientation[0] == 'h': # horizontal
offsets[:,
0] = array([0., apothem, apothem, 0., -apothem, -apothem])
offsets[:, 1] = array([
radius, radius / 2.0, -radius / 2.0, -radius, -radius / 2.0,
radius / 2.0
])
else: # vertical
offsets[:, 0] = array([
radius / 2.0, radius, radius / 2.0, -radius / 2.0, -radius,
-radius / 2.0
])
offsets[:,
1] = array([apothem, 0., -apothem, -apothem, 0., apothem])
patches = []
for i in range(self.number_of_nodes):
poly_verts[:, 0] = self.node_x[i] + offsets[:, 0]
poly_verts[:, 1] = self.node_y[i] + offsets[:, 1]
p = Polygon(poly_verts, True)
patches.append(p)
self._hexplot_pc = PatchCollection(patches,
cmap=color_map,
edgecolor='none',
linewidth=0.0)
self._hexplot_configured = True
def hexplot(self, data, data_label=None, color_map=None):
"""Create a plot of the grid elements.
Creates a plot of the grid and one node-data field, showing hexagonal
cells colored by values in the field.
Parameters
----------
data : str or node array (1d numpy array with number_of_nodes entries)
Data field to be colored.
data_label : str, optional
Label for colorbar.
color_map : matplotlib colormap object, None
Color map to apply (defaults to "jet")
See also
--------
plot.imshow_grid
Another Landlab function capable of producing hexplots, with a
fuller-featured set of options.
LLCATS: GINF
"""
from numpy import array, amin, amax
import matplotlib.pyplot as plt
import copy
try:
self._hexplot_configured
except:
self._configure_hexplot(data, data_label, color_map)
else:
if self._hexplot_pc.cmap != color_map:
self._configure_hexplot(data, data_label, color_map)
# Handle *data*: if it's a numpy array, then we consider it the
# data to be plotted. If it's a string, we consider it the name of the
# node-field to plot, and we fetch it.
if type(data) is str:
data_label = data
data = self.at_node[data]
ax = plt.gca()
self._hexplot_pc.set_array(array(data))
copy_of_pc = copy.copy(self._hexplot_pc)
ax.add_collection(copy_of_pc)
plt.xlim([amin(self.node_x) - self._dx, amax(self.node_x) + self._dx])
plt.ylim([amin(self.node_y) - self._dx, amax(self.node_y) + self._dx])
return ax
def node_has_boundary_neighbor(self, ids):
"""Check if HexModelGrid nodes have neighbors that are boundary nodes.
Parameters
----------
mg : HexModelGrid
Source grid
node_id : int
ID of node to test.
Returns
-------
boolean
``True`` if node has a neighbor with a boundary ID,
``False`` otherwise.
Checks to see if one of the eight neighbor nodes of node(s) with
*id* has a boundary node. Returns True if a node has a boundary node,
False if all neighbors are interior.
0, 1, 2, 3,
4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19,
20, 21, 22, 23
Examples
--------
>>> from landlab import HexModelGrid
>>> hmg = HexModelGrid(5, 4)
>>> hmg.node_has_boundary_neighbor(6)
True
>>> hmg.node_has_boundary_neighbor(12)
False
>>> hmg.node_has_boundary_neighbor([12, 0])
[False, True]
LLCATS: NINF CONN BC
"""
ans = []
for i in numpy.atleast_1d(numpy.asarray(ids)):
neighbors = self.adjacent_nodes_at_node[i]
real_neighbors = neighbors[neighbors != BAD_INDEX_VALUE]
if real_neighbors.size == 0:
ans.append(True)
else:
neighbor_status = self.status_at_node[real_neighbors].astype(
bool)
if numpy.any(neighbor_status != CORE_NODE):
ans.append(True)
else:
ans.append(False)
if len(ans) == 1:
return ans[0]
else:
return ans
def set_watershed_boundary_condition_outlet_id(self,
outlet_id,
node_data,
nodata_value=-9999.):
"""Set the boundary conditions for a watershed on a HexModelGrid.
All nodes with nodata_value are set to CLOSED_BOUNDARY (4).
All nodes with data values are set to CORE_NODES (0), with the
exception that the outlet node is set to a FIXED_VALUE_BOUNDARY (1).
Note that the outer ring of the HexModelGrid is set to CLOSED_BOUNDARY, even
if there are nodes that have values. The only exception to this would
be if the outlet node is on the boundary, which is acceptable.
Assumes that the id of the outlet is already known.
This assumes that the grid has a single watershed. If this is not
the case this will not work.
Parameters
----------
outlet_id : integer
id of the outlet node
node_data : ndarray
Data values.
nodata_value : float, optional
Value that indicates an invalid value.
Examples
--------
The example will use a HexModelGrid with node data values
as illustrated:
1. , 2. , 3. , 4. ,
0.5, 1.5, 2.5, 3.5, 4.5,
0. , 1. , 2. , 3. , 4. , 5.,
0.5, 1.5, 2.5, 3.5, 4.5,
1. , 2. , 3. , 4.
>>> from landlab import HexModelGrid
>>> hmg = HexModelGrid(5, 4)
>>> z = hmg.add_zeros('node', 'topographic__elevation')
>>> z += hmg.x_of_node + 1.0
>>> hmg.status_at_node
array([1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1,
1], dtype=uint8)
>>> outlet = hmg.set_watershed_boundary_condition_outlet_id(
... 9, z, -9999.)
>>> hmg.status_at_node
array([4, 4, 4, 4, 4, 0, 0, 0, 4, 1, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 4, 4,
4], dtype=uint8)
LLCATS: BC
"""
# make ring of no data nodes
self.status_at_node[self.boundary_nodes] = CLOSED_BOUNDARY
# set no data nodes to inactive boundaries
self.set_nodata_nodes_to_closed(node_data, nodata_value)
# set the boundary condition (fixed value) at the outlet_node
self.status_at_node[outlet_id] = FIXED_VALUE_BOUNDARY
def set_watershed_boundary_condition(self,
node_data,
nodata_value=-9999.,
return_outlet_id=False):
"""
Finds the node adjacent to a boundary node with the smallest value.
This node is set as the outlet. The outlet node must have a data
value. Can return the outlet id as a one element numpy array if
return_outlet_id is set to True.
All nodes with nodata_value are set to CLOSED_BOUNDARY
(grid.status_at_node == 4). All nodes with data values are set to
CORE_NODES (grid.status_at_node == 0), with the exception that the
outlet node is set to a FIXED_VALUE_BOUNDARY (grid.status_at_node == 1).
Note that the outer ring (perimeter) of the grid is set to
CLOSED_BOUNDARY, even if there are nodes that have values. The only
exception to this would be if the outlet node is on the perimeter, which
is acceptable.
This routine assumes that all of the nodata_values are on the outside of
the data values. In other words, there are no islands of nodata_values
surrounded by nodes with data.
This also assumes that the grid has a single watershed (that is a single
outlet node).
Parameters
----------
node_data : ndarray
Data values.
nodata_value : float, optional
Value that indicates an invalid value.
return_outlet_id : boolean, optional
Indicates whether or not to return the id of the found outlet
Examples
--------
The example will use a HexModelGrid with node data values
as illustrated:
1. , 2. , 3. , 4. ,
0.5, 1.5, 2.5, 3.5, 4.5,
0. , 1. , 2. , 3. , 4. , 5.,
0.5, 1.5, 2.5, 3.5, 4.5,
1. , 2. , 3. , 4.
>>> from landlab import HexModelGrid
>>> hmg = HexModelGrid(5, 4)
>>> z = hmg.add_zeros('node', 'topographic__elevation')
>>> z += hmg.x_of_node + 1.0
>>> out_id = hmg.set_watershed_boundary_condition(z, -9999.,
... True)
>>> out_id
array([9])
>>> hmg.status_at_node
array([4, 4, 4, 4, 4, 0, 0, 0, 4, 1, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 4, 4,
4], dtype=uint8)
LLCATS: BC
"""
# make ring of no data nodes
self.status_at_node[self.boundary_nodes] = CLOSED_BOUNDARY
# set no data nodes to inactive boundaries
self.set_nodata_nodes_to_closed(node_data, nodata_value)
#locs is a list that contains locations where
#node data is not equal to the nodata value
locs = numpy.where(node_data != nodata_value)
if len(locs) < 1:
raise ValueError('All data values are no_data values')
# now find minimum of the data values
min_val = numpy.min(node_data[locs])
# now find where minimum values are
min_locs = numpy.where(node_data == min_val)[0]
# check all the locations with the minimum value to see if one
not_found = True
while not_found:
# now check the min locations to see if any are next to
# a boundary node
local_not_found = True
next_to_boundary = []
# check all nodes rather than selecting the first node that meets
# the criteria
for i in range(len(min_locs)):
next_to_boundary.append(
self.node_has_boundary_neighbor(min_locs[i]))
# if any of those nodes were adjacent to the boundary, check
#that there is only one. If only one, set as outlet loc, else,
# raise a value error
if any(next_to_boundary):
local_not_found = False
if sum(next_to_boundary) > 1:
potential_locs = min_locs[numpy.where(
numpy.asarray(next_to_boundary))[0]]
raise ValueError((
'Grid has two potential outlet nodes.'
'They have the following node IDs: \n' +
str(potential_locs) +
'\nUse the method set_watershed_boundary_condition_outlet_id '
'to explicitly select one of these '
'IDs as the outlet node.'))
else:
outlet_loc = min_locs[numpy.where(next_to_boundary)[0][0]]
# checked all of the min vals, (so done with inner while)
# and none of the min values were outlet candidates
if local_not_found:
# need to find the next largest minimum value
# first find the locations of all values greater
# than the old minimum
# not done with outer while
locs = numpy.where((node_data > min_val)
& (node_data != nodata_value))
# now find new minimum of these values
min_val = numpy.min(node_data[locs])
min_locs = numpy.where(node_data == min_val)[0]
else:
# if locally found, it is also globally found
# so done with outer while
not_found = False
# set outlet boundary condition
self.status_at_node[outlet_loc] = FIXED_VALUE_BOUNDARY
if return_outlet_id:
return as_id_array(numpy.array([outlet_loc]))