def __init__(self, x, y, dx, dy, width=0.001, length_includes_head=False, \
head_width=None, head_length=None, shape='full', overhang=0, \
head_starts_at_zero=False,**kwargs):
"""Returns a new Arrow.
length_includes_head: True if head is counted in calculating the length.
shape: ['full', 'left', 'right']
overhang: distance that the arrow is swept back (0 overhang means
triangular shape).
head_starts_at_zero: if True, the head starts being drawn at coordinate
0 instead of ending at coordinate 0.
"""
if head_width is None:
head_width = 3 * width
if head_length is None:
head_length = 1.5 * head_width
distance = sqrt(dx**2 + dy**2)
if length_includes_head:
length=distance
else:
length=distance+head_length
if not length:
verts = [] #display nothing if empty
else:
#start by drawing horizontal arrow, point at (0,0)
hw, hl, hs, lw = head_width, head_length, overhang, width
left_half_arrow = array([
[0.0,0.0], #tip
[-hl, -hw/2.0], #leftmost
[-hl*(1-hs), -lw/2.0], #meets stem
[-length, -lw/2.0], #bottom left
[-length, 0],
])
#if we're not including the head, shift up by head length
if not length_includes_head:
left_half_arrow += [head_length, 0]
#if the head starts at 0, shift up by another head length
if head_starts_at_zero:
left_half_arrow += [head_length/2.0, 0]
#figure out the shape, and complete accordingly
if shape == 'left':
coords = left_half_arrow
else:
right_half_arrow = left_half_arrow*[1,-1]
if shape == 'right':
coords = right_half_arrow
elif shape == 'full':
coords=concatenate([left_half_arrow,right_half_arrow[::-1]])
else:
raise ValueError, "Got unknown shape: %s" % shape
cx = float(dx)/distance
sx = float(dy)/distance
M = array([[cx, sx],[-sx,cx]])
verts = dot(coords, M) + (x+dx, y+dy)
Polygon.__init__(self, map(tuple, verts), **kwargs)
def __init__(self, xyz, **kargs):
self._gl_3dpath = kargs.pop('gl_3dpath', None)
self._gl_lighting = kargs.pop('gl_lighting', True)
xy = xyz[:,0:2]
Polygon.__init__(self, xy, **kargs)
self.do_stencil_test = True
self.set_3d_properties(zs = xyz[:,2])
def write_body(self):
try:
from matplotlib.path import Path
except ImportError:
Path = None
from matplotlib.patches import Circle, Polygon
else:
from matplotlib.patches import Circle, PathPatch
indices = self.X[:, 2].argsort()
for a in indices:
xy = self.X[a, :2]
if a < self.natoms:
r = self.d[a] / 2
if ((xy[1] + r > 0) and (xy[1] - r < self.h) and
(xy[0] + r > 0) and (xy[0] - r < self.w)):
circle = Circle(xy, r, facecolor=self.colors[a])
circle.draw(self.renderer)
else:
a -= self.natoms
c = self.T[a]
if c != -1:
hxy = self.D[c]
if Path is None:
line = Polygon((xy + hxy, xy - hxy))
else:
line = PathPatch(Path((xy + hxy, xy - hxy)))
line.draw(self.renderer)
def __init__(self, xy, closed=True, plotview=None, **opts):
shape = Polygon(xy, closed, **opts)
if 'linewidth' not in opts:
shape.set_linewidth(1.0)
if 'facecolor' not in opts:
shape.set_facecolor('r')
super(NXpolygon, self).__init__(shape, resize=False, plotview=plotview)
self.shape.set_label('Polygon')
self.polygon = self.shape
def getGuards(polygon_no):
positions = []
data = triangulate(polygons[str(polygon_no)])
vertices = data['tri']['vertices']
colours = get_colour(data)
min_c = get_min_colour(colours)
stars = get_stars(data)
star_polygons = star_poly_array(stars, vertices)
# print(stars)
# print(star_polygons)
# print(vertices)
# print(min_c)
adj = adj_matrix(data)
# print(adj[24])
# print(adj[33])
# print(adj[60])
# pdb.set_trace()
def compare(plt, A, B):
ax1 = plt.subplot(121, aspect='equal')
triangle.plot.plot(ax1, **A)
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
triangle.plot.plot(ax2, **B)
return (ax1, ax2)
ax1, ax2 = compare(plt, data['original'], data['tri'])
# Colour polygons
patches = []
for polygon in star_polygons:
p = Polygon(polygon, True)
patches.append(p)
p = PatchCollection(patches, cmap=matplotlib.cm.jet)
plt.gca().add_collection(p)
# Label vertex indices
for i, v in enumerate(vertices):
ax1.text(v[0], v[1], str(i))
for c, v in zip(colours, vertices):
if c == min_c:
positions.append(v.tolist())
plt.annotate(c, xy=tuple(v), color='darkblue')
# positions = starise.kernels(stars, vertices)
# Plot kernel points
ax1.plot(*zip(*positions), marker='^', color='g', ls='')
# Set colours and config for plot
colors = range(0,1000, int(1000/len(patches)))
p.set_array(np.array(colors))
plt.subplots_adjust(left=0, bottom=0, right=1, top=1,
wspace=0, hspace=0)
plt.show()
# pdb.set_trace()
return positions
def plotReachSet_norm1(self, NUM, figname):
fig = p.figure()
for j in range(n):
ax = fig.add_subplot(2,2,j+1 , aspect='equal')
ax.set_xlim(0, 4)
ax.set_ylim(0, 1)
ax.set_xlabel('$x_'+str(j+1)+'$')
ax.set_ylabel('$y_'+str(j+1)+'$')
for trace in self:
for i in [int(floor(k*len(trace.T)/NUM)) for k in range(NUM)]:
verts = [(trace.x[i][j] + 1/trace.d_norm1[i][2*j], trace.y[i][j] ),
(trace.x[i][j] , trace.y[i][j] - 1/trace.d_norm1[i][2*j+1]),
(trace.x[i][j] - 1/trace.d_norm1[i][2*j], trace.y[i][j] ),
(trace.x[i][j] , trace.y[i][j] + 1/trace.d_norm1[i][2*j+1])]
# poly = Ellipse((trace.x[i][j],trace.y[i][j]), width=trace.d1[i], height=trace.d2[i], angle=trace.theta[i])
poly = Polygon(verts, facecolor='0.8', edgecolor='k')
ax.add_artist(poly)
poly.set_clip_box(ax.bbox)
poly.set_alpha(1)
if i==0:
poly.set_facecolor('r')
else:
poly.set_facecolor(p.rand(3))
#for trace in self:
#e = Ellipse((trace.x[0][j],trace.y[0][j]), width=trace.d1[0], height=trace.d2[0], angle=trace.theta[0])
#ax.add_artist(e)
#e.set_clip_box(ax.bbox)
#e.set_alpha(1)
#e.set_facecolor('r')
#e.set_edgecolor('r')
p.savefig(figname)
def add_to_axes(self, ax=None, **kwargs):
polys = [(c, self.fields[c]) for c in self.fclasses if self.fields[c]['poly']]
if ax is None:
fig, ax = plt.subplots(1)
pgns = []
for c, f in polys:
label = f['names']
pg = Polygon(f['poly'], closed=True, **kwargs)
pgns.append(pg)
x, y = pg.get_xy().T
ax.annotate(c, xy=(np.nanmean(x), np.nanmean(y)))
ax.add_patch(pg)
def get_features_from_shape(basemap, path = 'data/shape/contour_bv_MRCC/Bassins_MRCC_utm18.shp', linewidth = 2,
edgecolor = 'k', face_color = "none", id_list = None, zorder = 0, alpha = 1):
driver = ogr.GetDriverByName("ESRI Shapefile")
dataStore = driver.Open(path, 0)
layer = dataStore.GetLayer(0)
latlong = osr.SpatialReference()
latlong.ImportFromProj4("+proj=latlong")
result = []
id_list_lower = []
if id_list is not None:
id_list_lower = map(lambda x: x.lower(), id_list)
feature = layer.GetNextFeature()
while feature:
geom = feature.GetGeometryRef()
geom.TransformTo(latlong)
#get fields of the feature
# for i in xrange(feature.GetFieldCount()):
# print feature.GetField(i)
# print geom.ExportToWkt()
polygon = loads(geom.ExportToWkt())
boundary = polygon.exterior
coords = np.zeros(( len(boundary.coords), 2))
currentId = None
for i, the_coord in enumerate(boundary.coords):
# if feature.GetFieldAsString('abr').lower() == 'rdo':
# print the_coord[0], the_coord[1]
if basemap is not None:
coords[i, 0], coords[i, 1] = basemap( the_coord[0], the_coord[1] )
currentId = feature.GetFieldAsString("abr").lower()
to_add = True
if id_list is not None:
to_add = currentId in id_list_lower
if to_add:
p = Polygon(coords,linewidth = linewidth, edgecolor = edgecolor,
facecolor=face_color, zorder = zorder, alpha = alpha)
p.basin_id = currentId
result.append(p)
feature = layer.GetNextFeature()
dataStore.Destroy()
return result
def __init__(self, ax, poly_xy=None, max_ds=10, line_width=2,
line_color=(0, 0, 1), face_color=(1, .5, 0)):
self.showverts = True
self.max_ds = max_ds
if poly_xy is None:
poly_xy = default_vertices(ax)
self.poly = Polygon(poly_xy, animated=True,
fc=face_color, ec='none', alpha=0.4)
ax.add_patch(self.poly)
ax.set_clip_on(False)
ax.set_title("Click and drag a point to move it; "
"'i' to insert; 'd' to delete.\n"
"Close figure when done.")
self.ax = ax
x, y = zip(*self.poly.xy)
#line_color = 'none'
color = np.array(line_color) * .6
marker_face_color = line_color
line_kwargs = {'lw': line_width, 'color': color, 'mfc': marker_face_color}
self.line = plt.Line2D(x, y, marker='o', alpha=0.8, animated=True, **line_kwargs)
self._update_line()
self.ax.add_line(self.line)
self.poly.add_callback(self.poly_changed)
self._ind = None # the active vert
canvas = self.poly.figure.canvas
canvas.mpl_connect('draw_event', self.draw_callback)
canvas.mpl_connect('button_press_event', self.button_press_callback)
canvas.mpl_connect('button_release_event', self.button_release_callback)
canvas.mpl_connect('key_press_event', self.key_press_callback)
canvas.mpl_connect('motion_notify_event', self.motion_notify_callback)
self.canvas = canvas
def __init__(self, ax, poly_xy=None, max_ds=10):
self.showverts = True
self.max_ds = max_ds
if poly_xy is None:
poly_xy = default_vertices(ax)
self.poly = Polygon(poly_xy, animated=True,
fc='y', ec='none', alpha=0.4)
ax.add_patch(self.poly)
ax.set_clip_on(False)
ax.set_title("Click and drag a point to move it; "
"'i' to insert; 'd' to delete.\n"
"Close figure when done.")
self.ax = ax
x, y = zip(*self.poly.xy)
self.line = plt.Line2D(x, y, color='none', marker='o', mfc='r',
alpha=0.2, animated=True)
self._update_line()
self.ax.add_line(self.line)
self.poly.add_callback(self.poly_changed)
self._ind = None # the active vert
canvas = self.poly.figure.canvas
canvas.mpl_connect('draw_event', self.draw_callback)
canvas.mpl_connect('button_press_event', self.button_press_callback)
canvas.mpl_connect('button_release_event', self.button_release_callback)
canvas.mpl_connect('key_press_event', self.key_press_callback)
canvas.mpl_connect('motion_notify_event', self.motion_notify_callback)
self.canvas = canvas
def _setup_patch(self):
self._patch = Polygon(np.array(list(zip([0, 1], [0, 1]))))
self._patch.set_zorder(100)
self._patch.set(**self.plot_opts)
self._axes.add_patch(self._patch)
self._patch.set_visible(False)
self._sync_patch()
def __init__(self, ax, Inpimg, Mask, max_ds=10,PolyAtStart = [(50,50),(100,50),(100,100),(50,100)]):
self.showverts = True
self.max_ds = max_ds
self.Mask = Mask
self.img = Inpimg
self.maskinvert = False
# imshow the image
self.imgplot = ax.imshow(np.ma.filled(np.ma.array(self.img,mask=self.Mask,fill_value=np.nan)), cmap=cm.gray)
self.poly = Polygon(PolyAtStart, animated=True,
fc='y', ec='none', alpha=0.5)
ax.add_patch(self.poly)
ax.set_clip_on(False)
ax.set_title("Click and drag a point to move it; "
"'i' to insert; 'd' to delete.\n"
"'n' to invert the region for masking, 'c' to confirm & apply the mask.")
self.ax = ax
x, y = zip(*self.poly.xy)
self.line = Line2D(x, y, color='none', marker='o', mfc='r',
alpha=0.7, animated=True)
# self._update_line()
self.ax.add_line(self.line)
self.poly.add_callback(self.poly_changed)
self._ind = None # the active vert
canvas = self.poly.figure.canvas
canvas.mpl_connect('draw_event', self.draw_callback)
canvas.mpl_connect('button_press_event', self.button_press_callback)
canvas.mpl_connect('button_release_event', self.button_release_callback)
canvas.mpl_connect('key_press_event', self.key_press_callback)
canvas.mpl_connect('motion_notify_event', self.motion_notify_callback)
self.canvas = canvas
def _setEdge(self, **kwargs):
self._dec_range = np.linspace(-90, 90, self._resolution)
self._ra_range = np.linspace(-180, 180, self._resolution) + self.proj.ra_0
# styling: frame needs to be on top of everything, must be transparent
facecolor = 'None'
zorder = 1000
lw = kwargs.pop('lw', 0.7)
edgecolor = kwargs.pop('edgecolor', 'k')
# if there is facecolor: clone the polygon and put it in as bottom layer
facecolor_ = kwargs.pop('facecolor', '#dddddd')
# polygon of the map edge: top, left, bottom, right
# don't draw poles if that's a single point
lines = [self._getMeridian(self.proj.ra_0 + 180, reverse=True), self._getMeridian(self.proj.ra_0 - 180)]
if not self.proj.poleIsPoint[-90]:
lines.insert(1, self._getParallel(-90, reverse=True))
if not self.proj.poleIsPoint[90]:
lines.insert(0, self._getParallel(90))
xy = np.concatenate(lines, axis=1).T
self._edge = Polygon(xy, closed=True, edgecolor=edgecolor, facecolor=facecolor, lw=lw, zorder=zorder,gid="edge", **kwargs)
self.ax.add_patch(self._edge)
if facecolor_ is not None:
zorder = -1000
edgecolor = 'None'
poly = Polygon(xy, closed=True, edgecolor=edgecolor, facecolor=facecolor_, zorder=zorder, gid="edge-background")
self.ax.add_patch(poly)
def __init__(self, axtmp, pntxy):
QtCore.QObject.__init__(self)
self.ax = axtmp
self.poly = Polygon([(1, 1)], animated=True)
self.ax.add_patch(self.poly)
self.canvas = self.poly.figure.canvas
self.poly.set_alpha(0.5)
self.pntxy = pntxy
self.ishist = True
self.background = self.canvas.copy_from_bbox(self.ax.bbox)
xtmp, ytmp = list(zip(*self.poly.xy))
self.line = Line2D(xtmp, ytmp, marker='o', markerfacecolor='r',
color='y', animated=True)
self.ax.add_line(self.line)
self.poly.add_callback(self.poly_changed)
self._ind = None # the active vert
self.canvas.mpl_connect('button_press_event',
self.button_press_callback)
self.canvas.mpl_connect('button_release_event',
self.button_release_callback)
self.canvas.mpl_connect('motion_notify_event',
self.motion_notify_callback)
def getCatalog(size=10000, surveyname=None):
# dummy catalog: uniform on sphere
# Marsaglia (1972)
xyz = np.random.normal(size=(size, 3))
r = np.sqrt((xyz**2).sum(axis=1))
dec = np.arccos(xyz[:,2]/r) / skm.DEG2RAD - 90
ra = - np.arctan2(xyz[:,0], xyz[:,1]) / skm.DEG2RAD
if surveyname is not None:
from matplotlib.patches import Polygon
# construct survey polygon
ra_fp, dec_fp = skm.survey_register[surveyname].getFootprint()
poly = Polygon(np.dstack((ra_fp,dec_fp))[0], closed=True)
inside = [poly.get_path().contains_point(Point(ra_,dec_)) for (ra_,dec_) in zip(ra,dec)]
ra = ra[inside]
dec = dec[inside]
return ra, dec
def pressContour(self, event):
if event.inaxes==None: return
if event.button != 1: return
# new contour
if self.poly is None:
self.poly = Polygon( [(event.xdata , event.ydata)] , animated=False , alpha = .3 , color = 'g')
self.ax.add_patch(self.poly)
self.line, = self.ax.plot([event.xdata] , [event.ydata] ,
color = 'g',
linewidth = 2 ,
marker = 'o' ,
markerfacecolor='g',
animated=False)
#~ self.canvas.draw()
self.redraw()
return
# event near a point
xy = asarray(self.poly.xy)
xyt = self.poly.get_transform().transform(xy)
xt, yt = xyt[:, 0], xyt[:, 1]
print '#######'
d = sqrt((xt-event.x)**2 + (yt-event.y)**2)
indseq = nonzero(equal(d, amin(d)))[0]
self._ind = indseq[0]
if d[self._ind]>=self.epsilon:
self._ind = None
self.a=list(numpy.copy(self.poly.xy))
for i,j in enumerate(self.a):
self.a[i]=tuple(j)
# new point
if self._ind is None:
b=float(event.xdata)
c=float(event.ydata)
d=(b,c)
e=[d]
#self.a=numpy.append(self.a,e)
self.a.extend(e)
self.line.set_xdata( list(self.line.get_xdata()) + [ event.xdata] )
self.line.set_ydata( list(self.line.get_ydata()) + [ event.ydata] )
#~ self.canvas.draw()
self.redraw()
#self.poly.xy=a
print self.a, type(self.a)
if self.a != None:
test=Path(self.a)
self.actualSelection, = where(test.contains_points(dot( self.data, self.projection )))
self.emit(SIGNAL('selectionChanged'))
def __init__(self, axes, roi=None):
super(MplPolygonalROI, self).__init__(axes, roi=roi)
self.plot_opts = {'edgecolor': PATCH_COLOR,
'facecolor': PATCH_COLOR,
'alpha': 0.3}
self._patch = Polygon(np.array(list(zip([0, 1], [0, 1]))), zorder=100)
self._patch.set_visible(False)
self._axes.add_patch(self._patch)
def __init__(self, ax):
"""
:param ax: A matplotlib Axes object to attach the graphical ROI to
"""
AbstractMplRoi.__init__(self, ax)
self.plot_opts = {'edgecolor': PATCH_COLOR, 'facecolor': PATCH_COLOR,
'alpha': 0.3}
self._patch = Polygon(np.array(zip([0, 1], [0, 1])))
self._patch.set_zorder(100)
self._patch.set(**self.plot_opts)
self._setup_patch()
def draw_new_poly(self):
coords = default_vertices(self.ax)
Poly = Polygon(coords, animated=True,
fc="white", ec='none', alpha=0.2, picker=True)
self.polyList.append(Poly)
self.ax.add_patch(Poly)
x, y = zip(*Poly.xy)
#line_color = 'none'
color = np.array((1,1,1))
marker_face_color = (1,1,1)
line_width = 4;
line_kwargs = {'lw': line_width, 'color': color, 'mfc': marker_face_color}
self.line.append(plt.Line2D(x, y, marker='o', alpha=1, animated=True, **line_kwargs))
self._update_line()
self.ax.add_line(self.line[-1])
Poly.add_callback(self.poly_changed)
Poly.num = self.poly_count
self._ind = None # the active vert
self.poly_count+=1;
def __init__(self, filename, **kwargs):
"""
Create a new coastline polygon.
Parameters
----------
color : color, optional, default 'gray'
Line color of the coastline.
land : color, optional, default 'seashell'
Fill color of the land polygons.
Other parameters
----------------
kwargs : polygon properties
Other parameters passed on to :class:`~matplotlib.patches.Polygon`,
e.g. zorder=N to control drawing the land polygons above/below
other data.
"""
color = kwargs.pop('color', 'gray')
land = kwargs.pop('land', 'seashell')
self.data = []
self.extents = None
if not color:
color = 'none'
if not land:
land = 'none'
xy = [[None, None], [None, None]]
Polygon.__init__(self, xy, edgecolor=color, facecolor=land, **kwargs)
datapath = os.path.join(os.path.dirname(__file__), 'data')
coastfile = _find(filename, datapath)
if coastfile:
file = shapefile.Reader(coastfile)
for shape in file.shapes():
for points in _split(shape.points, shape.parts):
self.data += [Path(points)]
else:
raise Warning('coastline "%s" not found in directory "%s"' % (filename, datapath))
def fill_between(x, y1, y2, ax=P.gca(), alpha=0.5, **kwargs):
"""Plot distribution to a head surface, derived from some sensor locations.
The sensor locations are first projected onto the best fitting sphere and
finally projected onto a circle (by simply ignoring the z-axis).
:Parameters:
x:
,y1,y2
ax: mpl axes
axes to plot to. Standard is pylab.
view: one of 'top' and 'rear'
Defines from where the head is viewed.
**kwargs:
All additional arguments will be passed to `pylab.imshow()`.
:Returns:
(map, head, sensors)
The corresponding matplotlib objects are returned if plotted, ie.
if plothead is set to `False`, `head` will be `None`.
map
The colormap that makes the actual plot, a
matplotlib.image.AxesImage instance.
head
What is returned by `plot_head_outline()`.
sensors
The dots marking the electrodes, a matplotlib.lines.Line2d
instance.
"""
# add x,y2 in reverse order for proper polygon filling
verts = zip(x,y1) + [(x[i], y2[i]) for i in range(len(x)-1,-1,-1)]
poly = Polygon(verts, **kwargs)
poly.set_alpha(alpha)
ax.add_patch(poly)
ax.autoscale_view()
return poly
def draw(self, renderer):
if isSupportedRenderer(renderer):
renderer.use_gl = True
glcanvas = get_glcanvas()
if self.axes is not None:
tag = self.axes
trans = self.axes.transAxes
elif self.figure is not None:
tag = self.figure
trans = self.figure.transFigure
gc = renderer.new_gc()
rgbFace = self._facecolor
gc.set_foreground(self._edgecolor, isRGBA=True)
glcanvas.frame_request(self, trans)
# if not glcanvas.has_vbo_data(self):
glcanvas.start_draw_request(self)
if self._invalidz:
if glcanvas.has_vbo_data(self):
d = glcanvas.get_vbo_data(self)
d[0]['v'].need_update = True
self._invalidz = False
renderer.gl_draw_path(gc, self._verts3d, trans,
rgbFace = rgbFace,
stencil_test = self.do_stencil_test)
# glcanvas.update_gc(self, gc)
glcanvas.end_draw_request()
# else:
# glcanvas.update_gc(self, gc)
gc.restore()
renderer.use_gl = False
finish_gl_drawing(glcanvas, renderer, tag, trans)
else:
Polygon.draw(self, renderer)
def plot_category_chart(ax, res, show_legend=True):
"""Plot stacked bar chart of remapping response categories
Arguments:
ax -- axis to plot into
res -- trends results dictionary
Keyword arguments:
show_legend -- whether to draw legend
"""
from matplotlib.patches import Polygon
labels = res['mismatch_labels']
N = res['N_mismatch']
x = np.arange(1, N+1)
stacked = np.empty((len(CL_LABELS)+1, N), 'd')
stacked[0] = 0.0
stacked[-1] = 1.0
cats = res['categories']
for i in xrange(1,len(CL_LABELS)):
stacked[i] = stacked[i-1] + cats[CL_LABELS[i-1]]
poly_kwargs = dict(aa=True, lw=1.5, alpha=0.85)
xloop = np.concatenate((x, x[::-1]))
for i in xrange(len(CL_LABELS)):
yloop = np.concatenate((stacked[i], stacked[i+1,::-1]))
p = Polygon(np.c_[xloop, yloop], fc=CL_COLORS[i],
ec=CL_COLORS[i], label=CL_LABELS[i], **poly_kwargs)
ax.add_artist(p)
p.set_clip_box(ax.bbox)
ax.axis([1, N, 0, 1])
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlabel('Mismatch')
ax.set_ylabel('Response Fraction')
if show_legend:
ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.0))
return ax
def overlapping_bricks(self, candidates, map_petals=False):
"""Convert a list of potentially overlapping bricks into actual overlaps.
Parameters
----------
candidates : :class:`list`
A list of candidate bricks.
map_petals : bool, optional
If ``True`` a map of petal number to a list of overlapping bricks
is returned.
Returns
-------
:class:`list`
A list of Polygon objects.
"""
petals = self.petals()
petal2brick = dict()
bricks = list()
for b in candidates:
b_ra1, b_ra2 = self.brick_offset(b)
brick_corners = np.array([[b_ra1, b.dec1],
[b_ra2, b.dec1],
[b_ra2, b.dec2],
[b_ra1, b.dec2]])
brick = Polygon(brick_corners, closed=True, facecolor='r')
for i, p in enumerate(petals):
if brick.get_path().intersects_path(p.get_path()):
brick.set_facecolor('g')
if i in petal2brick:
petal2brick[i].append(b.id)
else:
petal2brick[i] = [b.id]
bricks.append(brick)
if map_petals:
return petal2brick
return bricks
def on_press(self, event):
#Comprobamos si hemos pulsado en un punto antiguo para moverlo
for circle in self.list_circles:
contains, attr = circle.contains(event)
if contains:
self.touched_circle = circle
self.exists_touched_circle = True
self.pressed_event = event
self.touched_x0, self.touched_y0 = circle.center
return
#Limpiamos los ejes y pintamos todos los puntos
plt.cla()
if self.cont==2:
self.list_circles.remove(self.list_circles[0])
c = Circle((event.xdata,event.ydata),0.02)
ax.add_patch(Circle((0,0),1,color='blue', fill = False))
self.list_circles.append(c)
for circle in self.list_circles :
self.ax.add_patch(circle)
#Guardamos el nuevo punto (borrando el primero si ya habia dos)
new_point = [event.xdata,event.ydata]
if not(self.polygon_exist) :
self.polygon.append(new_point)
self.P = Polygon(self.polygon, closed = False,color = 'red', fill = False)
self.polygon_exist = True
else :
if self.cont==2:
self.polygon.remove(self.polygon[0])
self.polygon.append(new_point)
self.P.set_xy(self.polygon)
self.ax.add_patch(self.P)
self.fig.canvas.draw()
#Si a1 y a2 se encuentran dentro del disco unidad:
if self.cont<2:
self.cont=self.cont+1
if self.cont==2:
a1 = self.polygon[0]
a2 = self.polygon[1]
if np.linalg.norm(a1)<=1 and np.linalg.norm(a2)<=1:
self.calculate_Blaschke3(a1,a2)
def _createWidgets(self):
# Create the mpl Figure and FigCanvas objects.
# 5x4 inches, 100 dots-per-inch
#
self.dpi = 100
self.fig = Figure((1.0, 1.0), dpi=self.dpi)
self.fig.autolayout = True
self.canvas = FigCanvas(self, -1, self.fig)
self.axes = self.fig.add_axes([0.0, 0.0, 1, 1])
pol = Polygon(list(zip([0], [0])), animated=True)
self.axes.add_patch(pol)
self.polygon_drawer = PolygonDrawer(self.axes, pol=pol, empty_pol=True)
self.zoom_wheel_coords = None
self.zoom_rect_coords = None
self.zoom_rect = Polygon(list(zip([0], [0])), facecolor='none')
self.zoom_rect.set_visible(False)
self.axes.add_patch(self.zoom_rect)
def pressContour(self, event):
if event.inaxes==None: return
if event.button != 1: return
# new contour
if self.poly is None:
self.poly = Polygon( [[event.xdata , event.ydata]] , animated=False , alpha = .3 , color = 'g')
self.ax.add_patch(self.poly)
self.line, = self.ax.plot([event.xdata] , [event.ydata] ,
color = 'g',
linewidth = 2 ,
marker = 'o' ,
markerfacecolor='g',
animated=False)
self.redraw()
return
# event near a point
xy = np.asarray(self.poly.xy)
xyt = self.poly.get_transform().transform(xy)
xt, yt = xyt[:, 0], xyt[:, 1]
d = np.sqrt((xt-event.x)**2 + (yt-event.y)**2)
indseq = np.nonzero(np.equal(d, np.amin(d)))[0]
self._ind = indseq[0]
if d[self._ind]>=self.epsilon:
self._ind = None
# new point
if self._ind is None:
self.poly.xy = np.array( list(self.poly.xy) + [[event.xdata , event.ydata]])
self.line.set_xdata( np.array(list(self.line.get_xdata()) + [ event.xdata]) )
self.line.set_ydata( np.array(list(self.line.get_ydata()) + [ event.ydata]) )
self.redraw()
self.actualSelection = points_inside_poly(np.dot( self.data, self.projection ), self.poly.xy)
self.selection_changed.emit()
def _init_boundary_interactor(self):
"""Send polyclick thread to boundary interactor"""
# Get rid old mpl connections.
self._ax.figure.canvas.mpl_disconnect(self._key_id)
self._ax.figure.canvas.mpl_disconnect(self._click_id)
# Shade the selected region in a polygon
self._poly = Polygon(self.verts, alpha=0.2, fc='k', animated=True)
self._ax.add_patch(self._poly)
# modify the line properties
self._line.set_markerfacecolor('k')
# get the canvas and connect the callback events
cid = self._poly.add_callback(self._poly_changed)
self._ind = None # the active vert
self._canvas.mpl_connect('draw_event', self._draw_callback)
self._canvas.mpl_connect('button_press_event', self._button_press_callback)
self._canvas.mpl_connect('key_press_event', self._key_press_callback)
self._canvas.mpl_connect('button_release_event', self._button_release_callback)
self._canvas.mpl_connect('motion_notify_event', self._motion_notify_callback)
def update(self):
# print "updating"
if len(self.vertices) >= 3:
xy = numpy.array([v.circle.center for v in self.vertices])
# bug in matplotlib? patch is not closed without this
xy = numpy.concatenate([xy, [xy[0]]])
if self.poly is None:
self.poly = Polygon(xy, closed=True, alpha=0.5, facecolor="pink")
self.ax.add_patch(self.poly)
else:
self.poly.set_xy(xy)
if self.background is not None:
self.canvas.restore_region(self.background)
if self.poly is not None:
self.ax.draw_artist(self.poly)
for vertex in self.vertices:
self.ax.draw_artist(vertex.circle)
# print ">>>", self.ax.bbox
self.canvas.blit(self.ax.bbox)
def _init_boundary_interactor(self):
"""Send polyclick thread to boundary interactor"""
# Get rid old mpl connections.
self._ax.figure.canvas.mpl_disconnect(self._key_id)
self._ax.figure.canvas.mpl_disconnect(self._click_id)
# Shade the selected region in a polygon
self._poly = Polygon(self.verts, alpha=0.1, fc='k', animated=True)
self._ax.add_patch(self._poly)
# change line to animated
# self._line.set_animated(True)
# self._line.set_markerfacecolor('k')
self._line.set_marker('None')
# Link in the two lines that will show the beta values
# pline for positive turns, mline for negative (minus) turns.
self._pline = Line2D([], [], marker='^', ms=12, mfc='g', animated=True, lw=0)
self._mline = Line2D([], [], marker='v', ms=12, mfc='r', animated=True, lw=0)
self._zline = Line2D([], [], marker='o', mfc='k', animated=True, lw=0)
self._sline = Line2D([], [], marker='s', mfc='k', animated=True, lw=0)
self._update_beta_lines()
self._ax.add_artist(self._pline)
self._ax.add_artist(self._mline)
self._ax.add_artist(self._zline)
self._ax.add_artist(self._sline)
# get the canvas and connect the callback events
cid = self._poly.add_callback(self._poly_changed)
self._ind = None # the active vert
self._canvas.mpl_connect('draw_event', self._draw_callback)
self._canvas.mpl_connect('button_press_event', self._button_press_callback)
self._canvas.mpl_connect('key_press_event', self._key_press_callback)
self._canvas.mpl_connect('button_release_event', self._button_release_callback)
self._canvas.mpl_connect('motion_notify_event', self._motion_notify_callback)
def create_figure():
f = plt.figure(frameon=False)
f.set_size_inches(1, 1)
ax = plt.Axes(f, [0.0, 0.0, 1.0, 1.0])
#ax.set_axis_off()
f.add_axes(ax)
ax.set_xlim([0, 50])
ax.set_ylim([0, 50])
#
# Circe of random size
#
f.patch.set_facecolor("black")
ax.patch.set_facecolor("black")
n = np.random.randint(0, 20)
for i in range(n):
r = np.random.uniform(5, 10)
x, y = np.random.uniform(0, 100, size=2)
circle = plt.Circle((x, y), r, color="white")
ax.add_artist(circle)
#
# Random triangle
#
n = np.random.randint(0, 20)
for i in range(n):
r = np.random.uniform(2, 10)
x, y = np.random.uniform(0, 100, size=2)
v0 = np.array([x, y])
v1 = v0 + np.array([r, 0])
v2 = np.array([x + 0.5 * r, y + r * np.sin(60.0 / 180.0 * np.pi)])
points = np.stack([v0, v1, v2])
midpoint = (v0 + v1 + v2) / 3.0
polygon = Polygon(points, color="white")
theta = np.random.uniform(0, 360)
r = Affine2D().rotate_around(midpoint[0], midpoint[1], theta)
tra = r + ax.transData
polygon.set_transform(tra)
ax.add_artist(polygon)
#
# Random line
#
n = np.random.randint(0, 20)
for i in range(n):
l = 2 * np.random.normal() + 10
x, y = np.random.uniform(0, 100, size=2)
theta = np.random.uniform(0, 360)
v0 = np.array([x, y])
v1 = v0 + np.array([np.cos(theta)])
v0 = np.array([x, y])
theta = np.random.uniform(0, 2 * np.pi)
v1 = v0 + np.array([l * np.cos(theta), l * np.sin(theta)])
ax.plot([v0[0], v1[0]], [v0[1], v1[1]], c="white", lw=3)
return f
map = Basemap(projection='merc',llcrnrlat=50,urcrnrlat=55,\
llcrnrlon=-7,urcrnrlon=2,lat_ts=52,resolution='c')
map.drawmapboundary(fill_color='#99ccff')
map.readshapefile('countries/countries', 'countries', drawbounds=False)
patches = []
for info, shape in zip(map.countries_info, map.countries):
if info['NAME'] in [
'United Kingdom', 'Ireland', 'France', 'Belgium', 'Luxembourg',
'Netherlands', 'Norway'
]:
patches.append(
Polygon(np.array(shape),
True,
facecolor='#ccffcc',
edgecolor='none'))
ax.add_collection(PatchCollection(patches, zorder=2, match_original=True))
map.plot(t['longitude'],
t['latitude'],
'k.',
latlon=True,
ax=ax,
alpha=0.1,
markersize=1)
map.plot(-1.5788031, 53.8164596, 'ro', latlon=True, markersize=5, alpha=0.5)
fig.savefig('uk_map.png', bbox_inches='tight', dpi=150)
def display_instances(image, boxes, masks, class_ids, class_names,poses,
scores=None, title="",title1='',
figsize=(16, 16), axs=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
poses: [num_instance, (tetax,tetay,tetaz,x,y,z)] in radians and cm.
masks: [height, width, num_instances]
class_ids: [NUM_FEATURES,num_instances] ids/feature
class_names: [NUM_FEATURES], list of class names of the dataset/feature
scores: [NUM_FEATURES,num_instances],(optional) confidence scores for each box/feature
figsize: (optional) the size of the image.
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
return 0
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids[0].shape[0]==class_ids[1].shape[0]==class_ids[2].shape[0]==\
class_ids[3].shape[0]==class_ids[4].shape[0]==class_ids[5].shape[0]==scores[0].shape[0]==scores[1].shape[0]==scores[2].shape[0]==\
scores[3].shape[0]==scores[4].shape[0]==scores[5].shape[0]==poses.shape[0]
if not np.any(axs):
_, axs = plt.subplots(1,2, figsize=figsize)
ax=axs[0]
ax1=axs[1]
# Generate random colors
colors = random_colors(N)
titles=[title,title1]
# Show area outside image boundaries.
height, width = image.shape[:2]
for i in range(axs.size):
axs[i].set_ylim(height + 10, -10)
axs[i].set_xlim(-10, width + 10)
axs[i].axis('off')
axs[i].set_title(titles[i])
masked_image = image.astype(np.uint32).copy()
back_img=image.astype(np.uint32).copy()*0+255
x3=10
y3=10
#class_ids[len(class_ids)-1]=class_ids[len(class_ids)-1]+1
#best_relations=np.argmax(scores[len(class_ids)-1],axis=1)
#best_relations=utils.relation_graph(boxes)
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
caption=str(i)+'. '
if scores!=None and False:
for j in range(len(class_ids)-1):
caption=caption+class_names[j][class_ids[j][i]]+' '+str(scores[j][i])+'\n'
else:
for j in range(len(class_ids)-1):
caption=caption+class_names[j][class_ids[j][i]]+'\n'
caption=caption+'Orientation: ('+str(poses[i][0])+','+str(poses[i][1])+','+str(poses[i][2])+')\n'
caption=caption+'Position: ('+str(poses[i][3])+','+str(poses[i][4])+','+str(poses[i][5])+')'
x = random.randint(x1, (x1 + x2) // 2)
ax.text(x1, y1 + 8, caption,
color='black', size=7, backgroundcolor="none")
#object relationship
for k in range(N):
#k=best_relations[i]
if(class_ids[len(class_ids)-1][i][k]!=0 and i!=k):
caption=str(i)+'.'+class_names[0][class_ids[0][i]]+' is '+class_names[5][class_ids[len(class_ids)-1][i][k]]+' '+str(k)+\
'.'+class_names[0][class_ids[0][k]]+': '+str((scores[len(class_ids)-1][i][k]))+'.'
print('RELATION:'+caption)
ax1.text(x3, y3, caption,color='black', size=8, backgroundcolor="none")
y3+=30
# Mask
mask = masks[:, :, i]
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
ax1.imshow(back_img)
plt.show()
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 fisher_jenks_map(coords, y, k, title='Fisher-Jenks', sampled=False):
"""
coords: Map_Projection instance
y: array
variable to map
k: int
number of classes
title: string
map title
sampled: binary
if True classification bins obtained on a sample of y and then
applied. Useful for large n arrays
"""
if sampled:
classification = ps.esda.mapclassify.Fisher_Jenks_Sampled(y,k)
else:
classification = ps.Fisher_Jenks(y,k)
fig = plt.figure()
ax = fig.add_subplot(111)
patches = []
colors = []
i = 0
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[:]
#print boundaries
#print min(shape_colors) > 0.0
if min(shape_colors) > 0.0:
boundaries.insert(0,0)
else:
boundaries.insert(0, boundaries[0] - boundaries[1])
#print boundaries
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
ea_file = 'EA_prj.shp'
land_cover = fiona.open(shapefile_dir + land_cover_file)
vjr = fiona.open(shapefile_dir + vjr_file)
ea = fiona.open(shapefile_dir + ea_file)
patches = []
colours = ['#30A000', '#85EE58', '#E756A8', '0.75']
for poly in land_cover:
color_iter = colours[poly['properties']['HCS_CLass'] - 1]
if poly['geometry']['type'] == 'MultiPolygon':
Npoly = len(poly['geometry']['coordinates'][1])
for nn in range(0, Npoly):
polygon = Polygon(poly['geometry']['coordinates'][1][nn],
True,
ec='None',
fc=color_iter)
patches.append(polygon)
else:
polygon = Polygon(poly['geometry']['coordinates'][0],
True,
ec='None',
fc=color_iter)
patches.append(polygon)
VJR_poly = vjr[0]
polygon = Polygon(VJR_poly['geometry']['coordinates'][0],
True,
ec='#1A2CCE',
fc='None')
patches.append(polygon)
def display_instances(image,
boxes,
masks,
class_ids,
class_names,
scores=None,
title="",
figsize=(16, 16),
ax=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
figsize: (optional) the size of the image.
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
contourlist = []
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1),
x2 - x1,
y2 - y1,
linewidth=2,
alpha=0.7,
linestyle="dashed",
edgecolor=color,
facecolor='none')
# ax.add_patch(p)
# Label
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:.3f}".format(label, score) if score else label
# ax.text(x1, y1 + 8, caption,color='w', size=11, backgroundcolor="none")
# Mask
mask = masks[:, :, i]
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2),
dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
# contourlist.append(contours)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
contourlist.append(p.get_xy())
ax.add_patch(p)
break
image1 = cv2.imread(
'/home/sumedh/Machine Learning/Mask_RCNN/images/cool.jpg', -1)
resultant = image1.copy()
image = Image.open(
'/home/sumedh/Machine Learning/Mask_RCNN/images/cool.jpg')
blurred_image = image.filter(ImageFilter.GaussianBlur(radius=3))
blurred_image.show()
# blur = cv2.GaussianBlur(resultant, (39, 39), 0)
# blur = cv2.blur(resultant, (5, 5))
# mask defaulting to black for 3-channel and transparent for 4-channel
# (of course replace corners with yours)
a = np.array(contourlist)
mask4 = np.zeros(image1.shape, dtype=np.uint8)
roi_corners = np.array(contourlist, dtype=np.int32)
# fill the ROI so it doesn't get wiped out when the mask is applied
channel_count = image1.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255, ) * channel_count
cv2.fillPoly(mask4, roi_corners, ignore_mask_color)
# from Masterfool: use cv2.fillConvexPoly if you know it's convex
# resultant[mask4] = mask4
# apply the mask
mask_to_pil = Image.fromarray(np.uint8(255 * mask4))
background = cv2.bitwise_not(mask4)
fore_ground_image = cv2.bitwise_and(image1, mask4)
background_image = cv2.bitwise_and(image1, background)
blur = cv2.GaussianBlur(background_image, (49, 49), 0)
blur_original = cv2.GaussianBlur(image1, (49, 49), 0)
cv2.imwrite("background.png", blur)
cv2.imwrite("foreground.png", fore_ground_image)
bck = Image.open("background.png")
fgk = Image.open("foreground.png")
blended = Image.blend(bck, fgk, alpha=15.0)
blended.save("resultant.png")
cv2.imwrite("mask.png", mask4)
cv2.imwrite("blur_original.png", blur_original)
callling()
#cv2.imwrite("final_image.png",vis)
# save the result
# cv2.imwrite('image_masked.png', background)
# m_image = Image.open('image_masked.png')
# blurred_image.paste(m_image, box=None, mask=mask_to_pil)
# blurred_image.show()
# store_cropped_image(masked_image,contourlist)
ax.imshow(masked_image.astype(np.uint8))
plt.show()
def compare_mesh_topo ():
# File paths
# I know the copy-paste of 6 meshes is terrible programing practice.
# But I am in a hurry.
mesh_path_A = '../FESOM_mesh/low_res_fixsmooth/output/'
mesh_path_B = '../FESOM_mesh/high_res_fixsmooth/output/'
mesh_path_C = '../FESOM_mesh/extra_high_res_less_amery/output/'
mesh_path_D = '../FESOM_mesh/extra_high_res_less_amery_more_acc/output/'
mesh_path_E = '../FESOM_mesh/extra_high_res/output/'
mesh_path_F = '../FESOM_mesh/extra_high_res_more_acc/output/'
rtopo_data_file = '../FESOM_mesh/RTopo105/RTopo105_data.nc'
rtopo_aux_file = '../FESOM_mesh/RTopo105/RTopo105_aux.nc'
# Maximum j-index to read from RTopo (can skip most of the world)
rtopo_max_j = 2000
# Degrees to radians conversion factor
deg2rad = pi/180.0
# Name of each region
region_names = ['Filchner-Ronne Ice Shelf', 'Eastern Weddell Region', 'Amery Ice Shelf', 'Australian Sector', 'Ross Sea', 'Amundsen Sea', 'Bellingshausen Sea', 'Larsen Ice Shelves']
num_regions = len(region_names)
# Beginning of filenames for figures
fig_heads = ['filchner_ronne', 'eweddell', 'amery', 'australian', 'ross', 'amundsen', 'bellingshausen', 'larsen']
# Bounds for each region (using polar coordinate transformation as below)
x_min = [-14, -8, 15.25, 12, -9.5, -15.5, -20.25, -22.5]
x_max = [-4.5, 13, 20.5, 25.5, 4, -10.5, -15.5, -14.5]
y_min = [1, 12, 4.75, -20, -13, -11.25, -4.5, 8.3]
y_max = [10, 21, 8, 4, -4.75, -2.25, 7.6, 13]
# Size of each plot in the y direction
ysize = [12, 9, 10, 13, 10, 13, 15, 10]
# Grey colourmap for RTopo land mask
grey_cmap = ListedColormap([(0.6, 0.6, 0.6)])
print 'Reading mesh A'
# Build mesh
elements_A = fesom_grid(mesh_path_A, circumpolar=True, cross_180=True)
# Build patches for ocean, open ocean, and ice shelves
patches_A = []
shelf_patches_A = []
ocean_patches_A = []
for elm in elements_A:
coord = transpose(vstack((elm.x, elm.y)))
if elm.cavity:
shelf_patches_A.append(Polygon(coord, True, linewidth=0.))
else:
ocean_patches_A.append(Polygon(coord, True, linewidth=0.))
patches_A.append(Polygon(coord, True, linewidth=0.))
# Calculate bathy, draft, wct at each element
bathy_A = []
draft_A = []
wct_A = []
for elm in elements_A:
# Bathymetry is depth of bottom layer, averaged over 3 Nodes
bathy_A.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
if elm.cavity:
# Ice shelf draft is depth of surface layer
draft_A.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
# Water column thickness is depth of bottom layer minus depth of
# surface layer
wct_A.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
print 'Reading mesh B'
elements_B = fesom_grid(mesh_path_B, circumpolar=True, cross_180=True)
patches_B = []
shelf_patches_B = []
ocean_patches_B = []
for elm in elements_B:
coord = transpose(vstack((elm.x, elm.y)))
if elm.cavity:
shelf_patches_B.append(Polygon(coord, True, linewidth=0.))
else:
ocean_patches_B.append(Polygon(coord, True, linewidth=0.))
patches_B.append(Polygon(coord, True, linewidth=0.))
bathy_B = []
draft_B = []
wct_B = []
for elm in elements_B:
bathy_B.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
if elm.cavity:
draft_B.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
wct_B.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
print 'Reading mesh C'
elements_C = fesom_grid(mesh_path_C, circumpolar=True, cross_180=True)
patches_C = []
shelf_patches_C = []
ocean_patches_C = []
for elm in elements_C:
coord = transpose(vstack((elm.x, elm.y)))
if elm.cavity:
shelf_patches_C.append(Polygon(coord, True, linewidth=0.))
else:
ocean_patches_C.append(Polygon(coord, True, linewidth=0.))
patches_C.append(Polygon(coord, True, linewidth=0.))
bathy_C = []
draft_C = []
wct_C = []
for elm in elements_C:
bathy_C.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
if elm.cavity:
draft_C.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
wct_C.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
print 'Reading mesh D'
elements_D = fesom_grid(mesh_path_D, circumpolar=True, cross_180=True)
patches_D = []
shelf_patches_D = []
ocean_patches_D = []
for elm in elements_D:
coord = transpose(vstack((elm.x, elm.y)))
if elm.cavity:
shelf_patches_D.append(Polygon(coord, True, linewidth=0.))
else:
ocean_patches_D.append(Polygon(coord, True, linewidth=0.))
patches_D.append(Polygon(coord, True, linewidth=0.))
bathy_D = []
draft_D = []
wct_D = []
for elm in elements_D:
bathy_D.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
if elm.cavity:
draft_D.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
wct_D.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
print 'Reading mesh E'
elements_E = fesom_grid(mesh_path_E, circumpolar=True, cross_180=True)
patches_E = []
shelf_patches_E = []
ocean_patches_E = []
for elm in elements_E:
coord = transpose(vstack((elm.x, elm.y)))
if elm.cavity:
shelf_patches_E.append(Polygon(coord, True, linewidth=0.))
else:
ocean_patches_E.append(Polygon(coord, True, linewidth=0.))
patches_E.append(Polygon(coord, True, linewidth=0.))
bathy_E = []
draft_E = []
wct_E = []
for elm in elements_E:
bathy_E.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
if elm.cavity:
draft_E.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
wct_E.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
print 'Reading mesh F'
elements_F = fesom_grid(mesh_path_F, circumpolar=True, cross_180=True)
patches_F = []
shelf_patches_F = []
ocean_patches_F = []
for elm in elements_F:
coord = transpose(vstack((elm.x, elm.y)))
if elm.cavity:
shelf_patches_F.append(Polygon(coord, True, linewidth=0.))
else:
ocean_patches_F.append(Polygon(coord, True, linewidth=0.))
patches_F.append(Polygon(coord, True, linewidth=0.))
bathy_F = []
draft_F = []
wct_F = []
for elm in elements_F:
bathy_F.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
if elm.cavity:
draft_F.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
wct_F.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
print 'Reading RTopo'
# Read grid, bathy, draft
id = Dataset(rtopo_data_file, 'r')
rtopo_lon = id.variables['lon'][:]
rtopo_lat = id.variables['lat'][:rtopo_max_j]
rtopo_bathy = -1*id.variables['bathy'][:rtopo_max_j,:]
rtopo_draft = -1*id.variables['draft'][:rtopo_max_j,:]
id.close()
# Calculate wct
rtopo_wct = rtopo_bathy - rtopo_draft
# Read composite mask
id = Dataset(rtopo_aux_file, 'r')
rtopo_amask = id.variables['amask'][:rtopo_max_j,:]
id.close()
# Create ocean and ice shelf masks
rtopo_omask = zeros(shape(rtopo_amask))
rtopo_imask = zeros(shape(rtopo_amask))
index = rtopo_amask == 0
rtopo_omask[index] = 1
index = rtopo_amask == 2
rtopo_omask[index] = 1
rtopo_imask[index] = 1
# Create land mask for plotting in grey
rtopo_land = ma.masked_where(rtopo_amask==0, rtopo_amask)
# Apply masks to each variable
rtopo_bathy = ma.masked_where(rtopo_omask==0, rtopo_bathy)
rtopo_draft = ma.masked_where(rtopo_imask==0, rtopo_draft)
rtopo_wct = ma.masked_where(rtopo_imask==0, rtopo_wct)
# Calculate polar coordinate transformation
rtopo_lon2d, rtopo_lat2d = meshgrid(rtopo_lon, rtopo_lat)
rtopo_x = -(rtopo_lat2d+90)*cos(rtopo_lon2d*deg2rad+pi/2)
rtopo_y = (rtopo_lat2d+90)*sin(rtopo_lon2d*deg2rad+pi/2)
# Loop over regions
for index in range(num_regions):
print 'Processing ' + region_names[index]
# Set up a grey square to fill the background with land
x_reg, y_reg = meshgrid(linspace(x_min[index], x_max[index], num=100), linspace(y_min[index], y_max[index], num=100))
land_square = zeros(shape(x_reg))
# Find bounds on variables in this region
# Start with RTopo
loc = (rtopo_x >= x_min[index])*(rtopo_x <= x_max[index])*(rtopo_y >= y_min[index])*(rtopo_y <= y_max[index])
bathy_min = amin(rtopo_bathy[loc])
bathy_max = amax(rtopo_bathy[loc])
draft_min = amin(rtopo_draft[loc])
draft_max = amax(rtopo_draft[loc])
wct_min = amin(rtopo_wct[loc])
wct_max = amax(rtopo_wct[loc])
# Modify with each mesh
i = 0
j = 0
for elm in elements_A:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if bathy_A[i] < bathy_min:
bathy_min = bathy_A[i]
if bathy_A[i] > bathy_max:
bathy_max = bathy_A[i]
if elm.cavity:
if draft_A[j] < draft_min:
draft_min = draft_A[j]
if draft_A[j] > draft_max:
draft_max = draft_A[j]
if wct_A[j] < wct_min:
wct_min = wct_A[j]
if wct_A[j] > wct_max:
wct_max = wct_A[j]
i += 1
if elm.cavity:
j += 1
i = 0
j = 0
for elm in elements_B:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if bathy_B[i] < bathy_min:
bathy_min = bathy_B[i]
if bathy_B[i] > bathy_max:
bathy_max = bathy_B[i]
if elm.cavity:
if draft_B[j] < draft_min:
draft_min = draft_B[j]
if draft_B[j] > draft_max:
draft_max = draft_B[j]
if wct_B[j] < wct_min:
wct_min = wct_B[j]
if wct_B[j] > wct_max:
wct_max = wct_B[j]
i += 1
if elm.cavity:
j += 1
i = 0
j = 0
for elm in elements_C:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if bathy_C[i] < bathy_min:
bathy_min = bathy_C[i]
if bathy_C[i] > bathy_max:
bathy_max = bathy_C[i]
if elm.cavity:
if draft_C[j] < draft_min:
draft_min = draft_C[j]
if draft_C[j] > draft_max:
draft_max = draft_C[j]
if wct_C[j] < wct_min:
wct_min = wct_C[j]
if wct_C[j] > wct_max:
wct_max = wct_C[j]
i += 1
if elm.cavity:
j += 1
i = 0
j = 0
for elm in elements_D:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if bathy_D[i] < bathy_min:
bathy_min = bathy_D[i]
if bathy_D[i] > bathy_max:
bathy_max = bathy_D[i]
if elm.cavity:
if draft_D[j] < draft_min:
draft_min = draft_D[j]
if draft_D[j] > draft_max:
draft_max = draft_D[j]
if wct_D[j] < wct_min:
wct_min = wct_D[j]
if wct_D[j] > wct_max:
wct_max = wct_D[j]
i += 1
if elm.cavity:
j += 1
i = 0
j = 0
for elm in elements_E:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if bathy_E[i] < bathy_min:
bathy_min = bathy_E[i]
if bathy_E[i] > bathy_max:
bathy_max = bathy_E[i]
if elm.cavity:
if draft_E[j] < draft_min:
draft_min = draft_E[j]
if draft_E[j] > draft_max:
draft_max = draft_E[j]
if wct_E[j] < wct_min:
wct_min = wct_E[j]
if wct_E[j] > wct_max:
wct_max = wct_E[j]
i += 1
if elm.cavity:
j += 1
i = 0
j = 0
for elm in elements_F:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if bathy_F[i] < bathy_min:
bathy_min = bathy_F[i]
if bathy_F[i] > bathy_max:
bathy_max = bathy_F[i]
if elm.cavity:
if draft_F[j] < draft_min:
draft_min = draft_F[j]
if draft_F[j] > draft_max:
draft_max = draft_F[j]
if wct_F[j] < wct_min:
wct_min = wct_F[j]
if wct_F[j] > wct_max:
wct_max = wct_F[j]
i += 1
if elm.cavity:
j += 1
# Plot
fig = figure(figsize=(20, ysize[index]))
fig.patch.set_facecolor('white')
gs = GridSpec([3,7])
gs.update(left=0.05, right=0.9, bottom=0.05, top=0.9, wspace=0.05, hspace=0.1)
# RTopo
# Bathymetry
ax = subplot(gs[0,0], aspect='equal')
# First plot land in grey
pcolor(rtopo_x, rtopo_y, rtopo_land, cmap=grey_cmap)
# Now plot bathymetry
pcolor(rtopo_x, rtopo_y, rtopo_bathy, vmin=bathy_min, vmax=bathy_max)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Ice shelf draft
ax = subplot(gs[1,0], aspect='equal')
pcolor(rtopo_x, rtopo_y, rtopo_land, cmap=grey_cmap)
pcolor(rtopo_x, rtopo_y, rtopo_draft, vmin=draft_min, vmax=draft_max)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Water column thickness
ax = subplot(gs[2,0], aspect='equal')
pcolor(rtopo_x, rtopo_y, rtopo_land, cmap=grey_cmap)
pcolor(rtopo_x, rtopo_y, rtopo_wct, vmin=wct_min, vmax=wct_max)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Add RTopo label on bottom
xlabel('RTopo')
# Mesh A
# Bathymetry
ax = subplot(gs[0,1], aspect='equal')
# Start with land background
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
# Add bathymetry data
img = PatchCollection(patches_A, cmap='jet')
img.set_array(array(bathy_A))
img.set_edgecolor('face')
img.set_clim(vmin=bathy_min, vmax=bathy_max)
ax.add_collection(img)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Ice shelf draft
ax = subplot(gs[1,1], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_A, cmap='jet')
img.set_array(array(draft_A))
img.set_edgecolor('face')
img.set_clim(vmin=draft_min, vmax=draft_max)
ax.add_collection(img)
# Mask out the open ocean in white
overlay = PatchCollection(ocean_patches_A, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Water column thickness in cavity
ax = subplot(gs[2,1], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_A, cmap='jet')
img.set_array(array(wct_A))
img.set_edgecolor('face')
img.set_clim(vmin=wct_min, vmax=wct_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_A, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Mesh title
xlabel('Mesh A')
# Mesh B
# Bathymetry
ax = subplot(gs[0,2], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches_B, cmap='jet')
img.set_array(array(bathy_B))
img.set_edgecolor('face')
img.set_clim(vmin=bathy_min, vmax=bathy_max)
ax.add_collection(img)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Ice shelf draft
ax = subplot(gs[1,2], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_B, cmap='jet')
img.set_array(array(draft_B))
img.set_edgecolor('face')
img.set_clim(vmin=draft_min, vmax=draft_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_B, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Water column thickness in cavity
ax = subplot(gs[2,2], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_B, cmap='jet')
img.set_array(array(wct_B))
img.set_edgecolor('face')
img.set_clim(vmin=wct_min, vmax=wct_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_B, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
xlabel('Mesh B')
# Mesh C
# Bathymetry
ax = subplot(gs[0,3], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches_C, cmap='jet')
img.set_array(array(bathy_C))
img.set_edgecolor('face')
img.set_clim(vmin=bathy_min, vmax=bathy_max)
ax.add_collection(img)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Variable title
title('Bathymetry (m)')
# Ice shelf draft
ax = subplot(gs[1,3], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_C, cmap='jet')
img.set_array(array(draft_C))
img.set_edgecolor('face')
img.set_clim(vmin=draft_min, vmax=draft_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_C, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
title('Ice shelf draft (m)')
# Water column thickness in cavity
ax = subplot(gs[2,3], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_C, cmap='jet')
img.set_array(array(wct_C))
img.set_edgecolor('face')
img.set_clim(vmin=wct_min, vmax=wct_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_C, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
title('Water column thickness (m)')
xlabel('Mesh C')
# Mesh D
# Bathymetry
ax = subplot(gs[0,4], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches_D, cmap='jet')
img.set_array(array(bathy_D))
img.set_edgecolor('face')
img.set_clim(vmin=bathy_min, vmax=bathy_max)
ax.add_collection(img)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Ice shelf draft
ax = subplot(gs[1,4], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_D, cmap='jet')
img.set_array(array(draft_D))
img.set_edgecolor('face')
img.set_clim(vmin=draft_min, vmax=draft_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_D, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Water column thickness in cavity
ax = subplot(gs[2,4], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_D, cmap='jet')
img.set_array(array(wct_D))
img.set_edgecolor('face')
img.set_clim(vmin=wct_min, vmax=wct_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_D, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
xlabel('Mesh D')
# Mesh E
# Bathymetry
ax = subplot(gs[0,5], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches_E, cmap='jet')
img.set_array(array(bathy_E))
img.set_edgecolor('face')
img.set_clim(vmin=bathy_min, vmax=bathy_max)
ax.add_collection(img)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Ice shelf draft
ax = subplot(gs[1,5], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_E, cmap='jet')
img.set_array(array(draft_E))
img.set_edgecolor('face')
img.set_clim(vmin=draft_min, vmax=draft_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_E, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Water column thickness in cavity
ax = subplot(gs[2,5], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_E, cmap='jet')
img.set_array(array(wct_E))
img.set_edgecolor('face')
img.set_clim(vmin=wct_min, vmax=wct_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_E, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
xlabel('Mesh E')
# Mesh F
# Bathymetry
ax = subplot(gs[0,6], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches_F, cmap='jet')
img.set_array(array(bathy_F))
img.set_edgecolor('face')
img.set_clim(vmin=bathy_min, vmax=bathy_max)
ax.add_collection(img)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
# Colourbar on right
cbaxes = fig.add_axes([0.92, 0.7, 0.01, 0.15])
cbar = colorbar(img, cax=cbaxes)
# Ice shelf draft
ax = subplot(gs[1,6], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_F, cmap='jet')
img.set_array(array(draft_F))
img.set_edgecolor('face')
img.set_clim(vmin=draft_min, vmax=draft_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_F, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
cbaxes = fig.add_axes([0.92, 0.4, 0.01, 0.15])
cbar = colorbar(img, cax=cbaxes)
# Water column thickness in cavity
ax = subplot(gs[2,6], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(shelf_patches_F, cmap='jet')
img.set_array(array(wct_F))
img.set_edgecolor('face')
img.set_clim(vmin=wct_min, vmax=wct_max)
ax.add_collection(img)
overlay = PatchCollection(ocean_patches_F, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
ax.set_xticks([])
ax.set_yticks([])
xlabel('Mesh F')
cbaxes = fig.add_axes([0.92, 0.1, 0.01, 0.15])
cbar = colorbar(img, cax=cbaxes)
# Main title
suptitle(region_names[index])
fig.show()
def showAnns(self, anns, draw_bbox=False):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
else:
raise Exception('datasetType not supported')
if datasetType == 'instances':
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in anns:
c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
if 'segmentation' in ann:
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape(
(int(len(seg) / 2), 2))
polygons.append(Polygon(poly))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = maskUtils.frPyObjects([ann['segmentation']],
t['height'],
t['width'])
else:
rle = [ann['segmentation']]
m = maskUtils.decode(rle)
img = np.ones((m.shape[0], m.shape[1], 3))
if ann['iscrowd'] == 1:
color_mask = np.array([2.0, 166.0, 101.0]) / 255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:, :, i] = color_mask[i]
ax.imshow(np.dstack((img, m * 0.5)))
if 'keypoints' in ann and type(ann['keypoints']) == list:
# turn skeleton into zero-based index
sks = np.array(
self.loadCats(ann['category_id'])[0]['skeleton']) - 1
kp = np.array(ann['keypoints'])
x = kp[0::3]
y = kp[1::3]
v = kp[2::3]
for sk in sks:
if np.all(v[sk] > 0):
plt.plot(x[sk], y[sk], linewidth=3, color=c)
plt.plot(x[v > 0],
y[v > 0],
'o',
markersize=8,
markerfacecolor=c,
markeredgecolor='k',
markeredgewidth=2)
plt.plot(x[v > 1],
y[v > 1],
'o',
markersize=8,
markerfacecolor=c,
markeredgecolor=c,
markeredgewidth=2)
if draw_bbox:
[bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox']
poly = [[bbox_x, bbox_y], [bbox_x, bbox_y + bbox_h],
[bbox_x + bbox_w, bbox_y + bbox_h],
[bbox_x + bbox_w, bbox_y]]
np_poly = np.array(poly).reshape((4, 2))
polygons.append(Polygon(np_poly))
color.append(c)
p = PatchCollection(polygons,
facecolor=color,
linewidths=0,
alpha=0.4)
ax.add_collection(p)
p = PatchCollection(polygons,
facecolor='none',
edgecolors=color,
linewidths=2)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print(ann['caption'])
'perim',
drawbounds=False)
# fire August 6th, 2016
patches = []
for info, shape in zip(m.perim_info, m.perim):
#if info['FIRENAME'] == 'PIONEER' and info['SHAPENUM']==1772:
if info['FIRENAME'] == 'PIONEER' and info['PERDATTIME'] == [
2016, 8, 6
] and np.shape(shape)[0] > 6000 and info['SHAPENUM'] == 1914:
x, y = zip(*shape)
print info['FIRENAME'], info['SHAPENUM'], info['PERDATTIME'], info[
'DATECRNT']
print 'GIS Acres', info['GISACRES']
#m.plot(x, y, marker=None,color=colors[colorI],linewidth=lw)
patches.append(Polygon(np.array(shape), True))
print ""
ax.add_collection(
PatchCollection(patches,
facecolor='indianred',
alpha=.75,
edgecolor='k',
linewidths=1.5,
zorder=1))
# fire August 4th 2016
patches2 = []
for info, shape in zip(m.perim_info, m.perim):
#if info['FIRENAME'] == 'PIONEER' and info['SHAPENUM']==1772:
if info['FIRENAME'] == 'PIONEER' and info['PERDATTIME'] == [
2016, 8, 4
def display_instances(image,
boxes,
masks,
class_ids,
class_names,
scores=None,
title="",
figsize=(16, 16),
ax=None,
show_mask=True,
show_bbox=True,
colors=None,
captions=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
# If no axis is passed, create one and automatically call show()
auto_show = False
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
auto_show = True
# Generate random colors
colors = colors or random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
if show_bbox:
p = patches.Rectangle((x1, y1),
x2 - x1,
y2 - y1,
linewidth=2,
alpha=0.7,
linestyle="dashed",
edgecolor=color,
facecolor='none')
ax.add_patch(p)
# Label
if not captions:
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:.3f}".format(label, score) if score else label
else:
caption = captions[i]
ax.text(x1,
y1 + 8,
caption,
color='w',
size=11,
backgroundcolor="none")
# Mask
mask = masks[:, :, i]
if show_mask:
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2),
dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
if auto_show:
plt.show()
def draw_boxes(image,
boxes=None,
refined_boxes=None,
masks=None,
captions=None,
visibilities=None,
title="",
ax=None):
"""Draw bounding boxes and segmentation masks with different
customizations.
boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates.
refined_boxes: Like boxes, but draw with solid lines to show
that they're the result of refining 'boxes'.
masks: [N, height, width]
captions: List of N titles to display on each box
visibilities: (optional) List of values of 0, 1, or 2. Determine how
prominent each bounding box should be.
title: An optional title to show over the image
ax: (optional) Matplotlib axis to draw on.
"""
# Number of boxes
assert boxes is not None or refined_boxes is not None
N = boxes.shape[0] if boxes is not None else refined_boxes.shape[0]
# Matplotlib Axis
if not ax:
_, ax = plt.subplots(1, figsize=(12, 12))
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
margin = image.shape[0] // 10
ax.set_ylim(image.shape[0] + margin, -margin)
ax.set_xlim(-margin, image.shape[1] + margin)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
# Box visibility
visibility = visibilities[i] if visibilities is not None else 1
if visibility == 0:
color = "gray"
style = "dotted"
alpha = 0.5
elif visibility == 1:
color = colors[i]
style = "dotted"
alpha = 1
elif visibility == 2:
color = colors[i]
style = "solid"
alpha = 1
# Boxes
if boxes is not None:
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1),
x2 - x1,
y2 - y1,
linewidth=2,
alpha=alpha,
linestyle=style,
edgecolor=color,
facecolor='none')
ax.add_patch(p)
# Refined boxes
if refined_boxes is not None and visibility > 0:
ry1, rx1, ry2, rx2 = refined_boxes[i].astype(np.int32)
p = patches.Rectangle((rx1, ry1),
rx2 - rx1,
ry2 - ry1,
linewidth=2,
edgecolor=color,
facecolor='none')
ax.add_patch(p)
# Connect the top-left corners of the anchor and proposal
if boxes is not None:
ax.add_line(lines.Line2D([x1, rx1], [y1, ry1], color=color))
# Captions
if captions is not None:
caption = captions[i]
# If there are refined boxes, display captions on them
if refined_boxes is not None:
y1, x1, y2, x2 = ry1, rx1, ry2, rx2
x = random.randint(x1, (x1 + x2) // 2)
ax.text(x1,
y1,
caption,
size=11,
verticalalignment='top',
color='w',
backgroundcolor="none",
bbox={
'facecolor': color,
'alpha': 0.5,
'pad': 2,
'edgecolor': 'none'
})
# Masks
if masks is not None:
mask = masks[:, :, i]
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2),
dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
# bmap = Basemap(llcrnrlon=80.33,
# llcrnrlat=3.01,
# urcrnrlon=138.16,
# urcrnrlat=56.123,
# resolution='h', projection='cass', lat_0 = 42.5,lon_0=120)
#
shp_info = bmap.readshapefile("D:\\GoogleDownload\\CHN_adm_shp\\CHN_adm1",
'states',
drawbounds=False)
for info, shp in zip(bmap.states_info, bmap.states):
proid = info['NAME_1']
if proid == 'Guangdong':
poly = Polygon(shp, facecolor='g', edgecolor='b', lw=0.8)
ax1.add_patch(poly)
bmap.drawcoastlines()
#bmap.drawcountries()
bmap.drawparallels(np.arange(3, 55, 10), labels=[1, 0, 0, 0])
bmap.drawmeridians(np.arange(80, 140, 10), labels=[0, 0, 0, 1])
plt.title('Province')
plt.show()
plt.savefig('d:\\fig_province.png', dpi=100, bbox_inches='tight')
plt.clf()
plt.close()
end = time.clock()
print(end - start)
def plotproj(plotdef, data, outdir):
'''
Plot map.
'''
axes = plt.axes()
bounds = (plotdef['lonmin'], plotdef['latmin'], plotdef['lonmax'],
plotdef['latmax'])
for geom in data.filter(bbox=bounds):
temp_pol = shape(geom['geometry'])
box = Polygon([
(plotdef['lonmin'], plotdef['latmin']),
(plotdef['lonmin'], plotdef['latmax']),
(plotdef['lonmax'], plotdef['latmax']),
(plotdef['lonmax'], plotdef['latmin']),
])
try:
temp_pol = temp_pol.intersection(box)
except Exception as e:
continue
if plotdef['type'] == 'poly':
if isinstance(temp_pol, MultiPolygon):
polys = [resample_polygon(polygon) for polygon in temp_pol]
pol = MultiPolygon(polys)
else:
pol = resample_polygon(temp_pol)
else:
pol = temp_pol
trans = functools.partial(project_xy,
proj_string=plotdef['projstring'])
proj_geom = transform(trans, pol)
if plotdef['type'] == 'poly':
patch = PolygonPatch(proj_geom, fc=COLOR_LAND, zorder=0)
axes.add_patch(patch)
else:
x, y = proj_geom.xy
axes.plot(x, y, color=COLOR_COAST, linewidth=0.5)
# Plot frame
frame = [
parallel(plotdef['latmin'], plotdef['lonmin'], plotdef['lonmax']),
parallel(plotdef['latmax'], plotdef['lonmin'], plotdef['lonmax']),
]
for line in frame:
line = project(line, plotdef['projstring'])
x = line[:, 0]
y = line[:, 1]
axes.plot(x, y, '-k')
graticule = build_graticule(
plotdef['lonmin'],
plotdef['lonmax'],
plotdef['latmin'],
plotdef['latmax'],
)
# Plot graticule
for feature in graticule:
feature = project(feature, plotdef['projstring'])
x = feature[:, 0]
y = feature[:, 1]
axes.plot(x, y, color=COLOR_GRAT, linewidth=0.4)
# Switch off the axis lines...
plt.axis('off')
# ... and additionally switch off the visibility of the axis lines and
# labels so they can be removed by "bbox_inches='tight'" when saving
axes.get_xaxis().set_visible(False)
axes.get_yaxis().set_visible(False)
font = {'family': 'serif', 'color': 'black', 'style': 'italic', 'size': 12}
# Make sure the plot is not stretched
axes.set_aspect('equal')
if not os.path.exists(outdir):
os.makedirs(outdir)
plt.savefig(outdir + '/' + plotdef['filename'],
dpi=400,
bbox_inches='tight')
# Clean up
axes = None
del axes
plt.close()
zorder=7,
ls="solid",
lw=LW,
color="black",
alpha=1.0,
)
################################################################################
# Draw triangles between bottom and top layer
# dark colored isosceles triangles
triangles = []
for i, j1, j2 in lines_bottom_to_top:
p0 = points_bottom[i]
p1 = points_top[j1]
p2 = points_top[j2]
triangles.append(Polygon([p0, p1, p2], closed=True))
collection = PatchCollection(
triangles,
zorder=3,
facecolors=FACECOLOR,
linewidths=0.0,
alpha=0.65,
)
ax.add_collection(collection)
# Draw triangles on the top layer
# light colored equilateral triangles
trios = [
[0, 2, 3],
[1, 3, 4],
[2, 5, 6],
drawbounds=True)
# collect the state names from the shapefile attributes so we can look up the shape obect for a state by it's name
names = []
colors = {}
i = 0
for shape_dict in m.states_info:
names.append(shape_dict['HASC_1'])
if shape_dict['HASC_1'] in corSTs.keys():
colors[
shape_dict['HASC_1']] = corSTs[shape_dict['HASC_1']] / 2 + 0.5
else:
colors[shape_dict['HASC_1']] = 0.5
ax = plt.gca() # get current axes instance
for nshape, seg in enumerate(m.states):
poly = Polygon(seg,
facecolor=cm.BrBG(colors[names[nshape]]),
edgecolor='k')
ax.add_patch(poly)
ARshp = m.readshapefile(
'/Volumes/Data_Archive/Data/adminBoundaries/GADM/ARG_adm_shp/ARG_adm1',
name='states',
drawbounds=True)
# collect the state names from the shapefile attributes so we can look up the shape obect for a state by it's name
names = []
colors = {}
i = 0
for shape_dict in m.states_info:
names.append(shape_dict['HASC_1'])
if shape_dict['HASC_1'] in corSTs.keys():
colors[
shape_dict['HASC_1']] = corSTs[shape_dict['HASC_1']] / 2 + 0.5
0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25, 27.5,
30, 32.5, 35
])
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
cbr = plt.colorbar(im, cax=cax)
cbr.set_label('Sidescan Intensity [dBW]')
m.readshapefile(
r"C:\workspace\Reach_4a\Multibeam\mb_sed_class\output\shapefiles\visual_seg_2_geo",
"layer",
drawbounds=False)
#sand, sand/gravel, gravel, sand/rock, rock
s_patch, g_patch, r_patch, = [], [], []
for info, shape in zip(m.layer_info, m.layer):
if info['substrate'] == 'sand':
s_patch.append(Polygon(np.asarray(shape), True))
if info['substrate'] == 'gravel':
g_patch.append(Polygon(np.asarray(shape), True))
if info['substrate'] == 'boulders':
r_patch.append(Polygon(np.asarray(shape), True))
ax.add_collection(
PatchCollection(s_patch,
facecolor=colors[4],
alpha=a_val,
edgecolor='none',
zorder=10))
ax.add_collection(
PatchCollection(g_patch,
facecolor=colors[2],
def _drawPolygonEvent(self, points):
polygon = Polygon(np.array(points), color=np.random.rand(3, ))
self._addPatch(polygon)
forward = False
poly, text = None, None
while True:
forward = not forward
if text is None:
text = plt.figtext(0.05, 0.25, "") # initialize text section
for i in range(b_min * 10, b_max * 10):
b = i / 10 if forward else (b_min + b_max) - i / 10
# Make the shaded region
ix = np.linspace(a, b)
iy = func(ix)
verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]
if poly is not None:
poly.remove()
poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')
ax.add_patch(poly)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
fig.texts.remove(text)
text = plt.figtext(.3,
0.5,
s=r"$\int_a^b f(x)\mathrm{d}x=$ " + r"$\int_{" +
str(round(a, 2)) + "}^{" + str(round(b, 2)) +
r"} f(x)\mathrm{d}x$",
horizontalalignment='center',
fontsize=15)
ax.set_xticks((a, b))
ax.set_xticklabels(('$a$', '$b$'))
meridians = np.arange(-180.,180.,20.)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=8)
ny = 180; nx = 360
lons, lats = m.makegrid(nx, ny)
#get lat/lons of ny by nx evenly space grid.
x, y = m(lons, lats)
from matplotlib.patches import Polygon
m.readshapefile("/Users/zhengkai/slm5/RasterT_Int_slm9_SplitLine3",'states',drawbounds=False)
# m.readshapefile("./slm/land_sea",'states',drawbounds=False)
# m.readshapefile("./CHN_adm/CHN_adm1",'states',drawbounds=False)
for info, shp in zip(m.states_info, m.states):
# proid = info['NAME_0']
# if proid == 'China':
poly = Polygon(shp,facecolor='w',fill=False,edgecolor='k', lw=1.0)
ax.add_patch(poly)
#compute map proj coordinates.
#draw filled contours.
# clevs = [-2,-1,0,1,2.5,5,7.5,10,15,20,30,40,50,70,100,150,200,250,300,400,500,600,750]
#clevs = [-1,-0.75,-0.5,-0.25,0,0.25,0.5,0.75,1]
# clevs = [0,1,5,10,20,30,45,50,70,85,100,130,150,170,190,210,230]
#cs = m.contourf(x,y,prcpvar[0,:,:],clevs,linewidths=0.5,colors='k',animated=True)
clevs = np.arange(1032,1043,1)
#clevs = [-1,0,0.5,0.75,1,15,20,30,40,70]
lonnumber=360# data.shape[2];
latnumber=180
prcpvar1=np.zeros(shape=(latnumber,lonnumber))
for i in range(int(lonnumber/2),int(lonnumber)):
prcpvar1[:,i]= prcpvar[0,:,i-int(lonnumber/2)]
from matplotlib.backends.backend_agg import (
FigureCanvasAgg as FigureCanvas)
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
fig = figure.Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(1,1,1)
m = Basemap(projection='cyl', lat_0 = 50, lon_0 = -100,
ax=ax, resolution = 'l', area_thresh = 1000,
llcrnrlat=-90,urcrnrlat=90, llcrnrlon=-180,urcrnrlon=180)
border_color = '0.8'
m.drawcoastlines(color=border_color)
m.drawcountries(color=border_color)
m.drawmapboundary(color=border_color)
shp_info = m.readshapefile('st99_d00', 'states', drawbounds=True)
ax = fig.gca() # get current axes instance
non_states = ['District of Columbia','Puerto Rico']
statenames = [sp['NAME'] for sp in m.states_info if sp['NAME'] not in non_states]
cls = ['r','g','b','y']
for nshape,seg in enumerate(m.states):
poly = Polygon(seg, ec='k', fc=cls[nshape%4])
ax.add_patch(poly)
canvas.print_figure('../figures/mapshape.png',
facecolor='lightgray')
def plot():
"""
info: plots the results and saves them
input: -
output: -
"""
# info: Fig1 - plot degree over N
ax1.plot(Ns, np.divide(degMaxsPre, Ns))
ax1.plot(Ns, np.divide(degMaxsPost, Ns))
ax1.set_xscale('log')
ax1.set_xlabel('sample size $N$')
ax1.set_ylabel('maximum degree $d_{max}$')
if withTitle > 0:
ax1.set_title('degMaxsPre')
alphaVal = 0.5
# info: Fig. 2 - plot clustersize of N
ax2.plot(Ns, np.divide(clustersizesMaxPre, Ns), color='blue', zorder=0)
ax2.plot(Ns,
np.divide(clustersizesMaxPost, Ns),
color='orange',
zorder=0)
#Nperfect = np.linspace(0.5, 10**5, num = 1000)
ax2.legend(['fit', 'Pre selection', 'Post selection'], frameon=False)
ax2.set_xlim(min(Ns), max(Ns))
ax2.set_ylim(-1.0, 1.0)
ax2.set_xscale('log')
ax2.set_xlabel('cluster size')
ax2.set_ylabel('frequency')
ax2.set_title('clustersizes')
xText = 0.7
yText = 0.7
dxText = 0.2
dyText = 0.2
ax2.add_patch(
Polygon(
[[xText, yText], [xText + dxText, yText],
[xText + dxText, yText + dyText], [xText, yText + dyText]],
closed=False,
fill=True,
color='white',
alpha=0.9,
transform=ax2.transAxes,
zorder=2))
# info: digits - number of digits for the parameters of the fit
digits = 2
content = "a = " + digNum(popt1[0], digits)+ "(" + digNum(pcov1[0][0], digits), \
+ ")" + "\n b = " + digNum(popt1[1], digits) + "(", \
+ digNum(pcov1[1][1], digits) + ")"
content = "a = " + sci(popt1[0], digits) + "\n b = " + sci(
popt1[1], digits)
if withParams > 0:
ax2.text(xText, yText, content, transform=ax2.transAxes, zorder=3)
# info: Fig. 6 - probability, that everything is connected
ax6.plot(Ns, isConnectedsPre, color='blue', zorder=0)
ax6.plot(Ns, isConnectedsPost, color='orange', zorder=0)
ax6.set_xlim(min(Ns), max(Ns))
ax6.set_ylim(0.0, 1.0)
ax6.set_xlabel('sample size $N$')
ax6.set_ylabel('probability $p$')
# info: save
fig1.savefig('degreeNum.png', dpi=300, bbox_inches="tight")
fig2.savefig('clusterProb.png', dpi=300, bbox_inches="tight")
fig3.savefig('diameters.png', dpi=300, bbox_inches="tight")
fig6.savefig('connectedProb.png', dpi=300, bbox_inches="tight")
plt.show()
def display_instances(n,
image,
boxes,
masks,
class_ids,
class_names,
scores=None,
title="",
figsize=(16, 16),
ax=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [num_instances, height, width]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
figsize: (optional) the size of the image.
"""
N = 3
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
# Generate random colors
if n == 1:
colors = random_colors(N)
elif n == 2:
colors = random_colors(1)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
ax.imshow(image.astype(np.uint8))
plt.savefig("D:\FYP\myfyp\static\img\original.png")
masked_image = image.astype(np.uint32).copy()
list_num = 0
label_list = ['a', 'b', 'c']
for i in range(N):
if n == 1:
color = colors[i]
elif n == 2:
color = colors[0]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1),
x2 - x1,
y2 - y1,
linewidth=2,
alpha=0.7,
linestyle="dashed",
edgecolor=color,
facecolor='none')
ax.add_patch(p)
# Label
class_id = class_ids[i]
# print("------------------------------------")
# print(class_id)
# print("------------------------------------")
score = scores[i] if scores is not None else None
label = class_names[class_id]
label_list[list_num] = label
list_num = list_num + 1
# x = random.randint(x1, (x1 + x2) // 2)
# caption = "{} {:.3f}".format(label, score) if score else label
# ax.text(x1, y1 + 8, caption,
# color='w', size=11, backgroundcolor="none")
# Mask
mask = masks[:, :, i]
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2),
dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
# plt.show()
plt.savefig("D:\FYP\myfyp\static\img\mask.png")
plt.close()
return label_list
stepCount = 12 # Aufteilung der Bewegung in 12 Abschnitte / Frames
step = 2 * np.pi / 32 / stepCount # Schrittweite Drehung des großen Zahnrades
fig = plt.figure(figsize=(3, 2))
ax = fig.add_axes([-.05, -.05, 1.1, 1.1])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_aspect("equal")
plt.margins(0, 0)
plt.xlim(-9.5, 17.6)
plt.ylim(-9.5, 9.5)
ims = []
for i in range(stepCount):
g1 *= np.exp(1j * step)
g2 *= np.exp(-2j * step)
ims.append([
ax.add_patch(
Polygon(g1.view(float).reshape(g1.size, 2), color=(.1, 0, 1, .5))),
ax.add_patch(
Polygon(g2.view(float).reshape(g2.size, 2) + [d2, 0],
color=(1, 0, .1, .5)))
])
ani = animation.ArtistAnimation(fig, ims, interval=1000. * 2 / 3 / stepCount)
plt.show()
self.ax.draw_artist(self.poly)
self.ax.draw_artist(self.line)
self.canvas.blit(self.ax.bbox)
if __name__ == '__main__':
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
fig = plt.figure()
theta = np.arange(0, 2 * np.pi, 0.1)
r = 1.5
xs = r * np.cos(theta)
ys = r * np.sin(theta)
poly = Polygon(zip(
xs,
ys,
), animated=True)
ax = plt.subplot(111)
ax.add_patch(poly)
p = PolygonInteractor(ax, poly)
#ax.add_line(p.line)
ax.set_title('Click and drag a point to move it')
ax.set_xlim((-2, 2))
ax.set_ylim((-2, 2))
plt.show()
def save_image_keypoint_and_mask(image,
boxes,
keypoints,
masks,
class_ids,
class_names,
filename=None,
skeleton=[],
scores=None,
title="",
figsize=(16, 9),
ax=None,
save_picture=False):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
keypoints: [num_instance, num_keypoints] Every value is a int label which indicates the position ([0,56*56)) of the joint
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
"""
# Plot keypoints first.
# Number of persons
N = boxes.shape[0]
keypoints = np.array(keypoints).astype(int)
print("keypoint_shape:", np.shape(keypoints))
if not N:
print("\n*** No persons to display *** \n")
else:
assert boxes.shape[0] == keypoints.shape[0] == class_ids.shape[0]
if not ax:
fig, ax = plt.subplots(1, figsize=figsize)
fig.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0)
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
final_image = image.astype(np.float32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1),
x2 - x1,
y2 - y1,
linewidth=2,
alpha=0.7,
linestyle="dashed",
edgecolor=color,
facecolor='none')
ax.add_patch(p)
# Label
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
# x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:.3f}".format(label, score) if score else label
ax.text(x1,
y1 + 8,
caption,
color='w',
size=11,
backgroundcolor="none")
# # Keypoints: num_person, num_keypoint, 3
# for Joint in keypoints[i]:
# if(Joint[2]!=0):
# circle = patches.Circle((Joint[0],Joint[1]),radius=1,edgecolor=color,facecolor='none')
# ax.add_patch(circle)
#
# # Skeleton: 11*2
# limb_colors = [[0, 0, 255], [0, 170, 255], [0, 255, 170], [0, 255, 0], [170, 255, 0],
# [255, 170, 0], [255, 0, 0], [255, 0, 170], [170, 0, 255], [170,170,0],[170,0,170]]
# if(len(skeleton)):
# skeleton = np.reshape(skeleton,(-1,2))
# neck = np.array((keypoints[i, 5, :] + keypoints[i,6,:])/2).astype(int)
# if(keypoints[i, 5, 2] == 0 or keypoints[i,6,2] == 0):
# neck = [0,0,0]
# limb_index = -1
# for limb in skeleton:
# limb_index += 1
# start_index, end_index = limb # connection joint index from 0 to 16
# if(start_index == -1):
# Joint_start = neck
# else:
# Joint_start = keypoints[i][start_index]
# if(end_index == -1):
# Joint_end = neck
# else:
# Joint_end = keypoints[i][end_index]
# # both are Annotated
# # Joint:(x,y,v)
# if ((Joint_start[2] != 0) & (Joint_end[2] != 0)):
# # print(color)
# cv2.line(final_image, tuple(Joint_start[:2]), tuple(Joint_end[:2]), limb_colors[limb_index],5)
# Mask
mask = masks[:, :, i]
final_image = apply_mask(final_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2),
dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(final_image.astype(np.uint8))
# plt.show()
if save_picture:
plt.savefig(filename)
plt.close(fig)
def visualize_tone(args):
with open('output/tone_analyzer.json') as data_file:
outputs = json.load(data_file)
emotions = [
e['tone_name']
for e in outputs[0]['tone']['tone_categories'][0]['tones']
]
nostalgic_emotion = np.array([0.0] * len(emotions))
nostalgic_emotion_lst = []
nostalgic_count = 0
ordinary_emotion = np.array([0.0] * len(emotions))
ordinary_emotion_lst = []
ordinary_count = 0
for idx, t in enumerate(outputs):
score = [s['score'] for s in t['tone']['tone_categories'][0]['tones']]
if t['tag'] == 'nostalgic':
nostalgic_emotion += np.array(score)
nostalgic_emotion_lst.append(score)
nostalgic_count += 1
elif t['tag'] == 'ordinary':
ordinary_emotion += np.array(score)
ordinary_emotion_lst.append(score)
ordinary_count += 1
print(emotions)
print(nostalgic_emotion, nostalgic_count)
print(ordinary_emotion, ordinary_count)
ind = np.arange(len(emotions)) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind,
nostalgic_emotion / nostalgic_count,
width,
color='darkkhaki')
rects2 = ax.bar(ind + width,
ordinary_emotion / ordinary_count,
width,
color='royalblue')
# add some text for labels, title and axes ticks
ax.set_ylabel('Average Tone Score')
ax.set_title('Emotion Score from Tone Analyzer')
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(emotions)
ax.legend((rects1[0], rects2[0]), ('Nostalgic', 'Ordinary'))
if args.save_fig:
plt.savefig('output/tone_analyzer_emotion_comparison.pdf',
bbox_inches='tight')
else:
plt.show()
plt.close()
# create box plot
nostalgic_emotion_lst = np.array(nostalgic_emotion_lst)
ordinary_emotion_lst = np.array(ordinary_emotion_lst)
data_for_box_plot = []
for i in range(len(emotions)):
data_for_box_plot.append(nostalgic_emotion_lst[:, i])
data_for_box_plot.append(ordinary_emotion_lst[:, i])
fig, ax1 = plt.subplots(figsize=(10, 6))
plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
bp = plt.boxplot(data_for_box_plot,
notch=0,
sym='+',
vert=1,
whis=1.5,
showmeans=True)
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='red', marker='+')
# Add a horizontal grid to the plot, but make it very light in color
# so we can use it for reading data values but not be distracting
ax1.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.5)
# Hide these grid behind plot objects
ax1.set_axisbelow(True)
ax1.set_title('Comparison of Emotion Tones')
ax1.set_xlabel('Emotions')
ax1.set_ylabel('Scores')
# Now fill the boxes with desired colors
boxColors = ['darkkhaki', 'royalblue']
numBoxes = len(emotions) * 2
medians = list(range(numBoxes))
for i in range(numBoxes):
box = bp['boxes'][i]
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = list(zip(boxX, boxY))
# Alternate between Dark Khaki and Royal Blue
k = i % 2
boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
ax1.add_patch(boxPolygon)
# Set the axes ranges and axes labels
ax1.set_xlim(0.5, numBoxes + 0.5)
top = 1.2
bottom = -0.2
ax1.set_ylim(bottom, top)
xtickNames = plt.setp(ax1, xticklabels=np.repeat(emotions, 2))
plt.setp(xtickNames, rotation=45, fontsize=10)
# Finally, add a basic legend
plt.figtext(0.15,
0.8,
'Nostalgic',
backgroundcolor=boxColors[0],
color='black',
weight='roman',
size='small')
plt.figtext(0.15,
0.75,
'Ordinary',
backgroundcolor=boxColors[1],
color='white',
weight='roman',
size='small')
if args.save_fig:
plt.savefig('output/tone_analyzer_emotion_boxplot.pdf',
bbox_inches='tight')
else:
plt.show()
plt.close()
plt.show()
def vis_one_image(im,
im_name,
output_dir,
boxes,
segms=None,
keypoints=None,
thresh=0.9,
kp_thresh=2,
dpi=200,
box_alpha=0.0,
dataset=None,
show_class=False,
ext='pdf',
thresh_lo=None,
range_thresh=False,
scale=None,
classes=None):
"""Visual debugging of detections.
:param range_thresh:
:param thresh_lo:
"""
output_name = os.path.basename(im_name) + '.' + ext
fig_path = os.path.join(output_dir, '{}'.format(output_name))
if os.path.isfile(fig_path):
return
if not os.path.exists(output_dir):
try:
os.makedirs(output_dir)
except:
pass
if isinstance(boxes, list):
boxes, segms, keypoints, classes = convert_from_cls_format(
boxes, segms, keypoints)
if range_thresh:
if boxes is None or boxes.shape[0] == 0 or (
max(boxes[:, 4]) <= thresh_lo and min(boxes[:, 4]) > thresh):
return
elif boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
return
dataset_keypoints, _ = keypoint_utils.get_keypoints()
if segms is not None:
masks = mask_util.decode(segms)
if scale:
im_size_max = max(masks.shape)
if np.round(scale * im_size_max) > cfg.TRAIN.MAX_SIZE:
scale = float(cfg.TRAIN.MAX_SIZE) / float(im_size_max)
masks = cv2.resize(masks,
None,
None,
fx=scale,
fy=scale,
interpolation=cv2.INTER_LINEAR)
if len(masks.shape) == 2:
masks = np.expand_dims(masks, -1)
boxes = boxes * scale
color_list = colormap(rgb=True) / 255
kp_lines = kp_connections(dataset_keypoints)
cmap = plt.get_cmap('rainbow')
colors = [cmap(i) for i in np.linspace(0, 1, len(kp_lines) + 2)]
fig = plt.figure(frameon=False)
fig.set_size_inches(im.shape[1] / dpi, im.shape[0] / dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.axis('off')
fig.add_axes(ax)
ax.imshow(im)
# Display in largest to smallest order to reduce occlusion
areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
sorted_inds = np.argsort(-areas)
mask_color_id = 0
for i in sorted_inds:
bbox = boxes[i, :4]
score = boxes[i, -1]
if range_thresh:
if not (thresh_lo < score <= thresh):
continue
elif score < thresh:
continue
# show box (off by default)
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor='b',
linewidth=0.2,
alpha=0.3))
if show_class:
ax.text(bbox[0],
bbox[1] - 2,
get_class_string(classes[i], score, dataset),
fontsize=2,
family='serif',
bbox=dict(facecolor='g',
alpha=0.2,
pad=0,
edgecolor='none'),
color='white')
# show mask
if segms is not None and len(segms) > i:
img = np.ones(im.shape)
color_mask = color_list[mask_color_id % len(color_list), 0:3]
mask_color_id += 1
w_ratio = .1
for c in range(3):
color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio
for c in range(3):
img[:, :, c] = color_mask[c]
e = masks[:, :, i]
_, contour, hier = cv2.findContours(e.copy(), cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_NONE)
for c in contour:
polygon = Polygon(c.reshape((-1, 2)),
fill=True,
facecolor=color_mask,
edgecolor='b',
linewidth=0.2,
alpha=0.2)
ax.add_patch(polygon)
# show keypoints
if keypoints is not None and len(keypoints) > i:
kps = keypoints[i]
plt.autoscale(False)
for l in range(len(kp_lines)):
i1 = kp_lines[l][0]
i2 = kp_lines[l][1]
if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh:
x = [kps[0, i1], kps[0, i2]]
y = [kps[1, i1], kps[1, i2]]
line = plt.plot(x, y)
plt.setp(line, color=colors[l], linewidth=1.0, alpha=0.7)
if kps[2, i1] > kp_thresh:
plt.plot(kps[0, i1],
kps[1, i1],
'.',
color=colors[l],
markersize=3.0,
alpha=0.7)
if kps[2, i2] > kp_thresh:
plt.plot(kps[0, i2],
kps[1, i2],
'.',
color=colors[l],
markersize=3.0,
alpha=0.7)
# add mid shoulder / mid hip for better visualization
mid_shoulder = (
kps[:2, dataset_keypoints.index('right_shoulder')] +
kps[:2, dataset_keypoints.index('left_shoulder')]) / 2.0
sc_mid_shoulder = np.minimum(
kps[2, dataset_keypoints.index('right_shoulder')],
kps[2, dataset_keypoints.index('left_shoulder')])
mid_hip = (kps[:2, dataset_keypoints.index('right_hip')] +
kps[:2, dataset_keypoints.index('left_hip')]) / 2.0
sc_mid_hip = np.minimum(
kps[2, dataset_keypoints.index('right_hip')],
kps[2, dataset_keypoints.index('left_hip')])
if (sc_mid_shoulder > kp_thresh
and kps[2, dataset_keypoints.index('nose')] > kp_thresh):
x = [mid_shoulder[0], kps[0, dataset_keypoints.index('nose')]]
y = [mid_shoulder[1], kps[1, dataset_keypoints.index('nose')]]
line = plt.plot(x, y)
plt.setp(line,
color=colors[len(kp_lines)],
linewidth=1.0,
alpha=0.7)
if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh:
x = [mid_shoulder[0], mid_hip[0]]
y = [mid_shoulder[1], mid_hip[1]]
line = plt.plot(x, y)
plt.setp(line,
color=colors[len(kp_lines) + 1],
linewidth=1.0,
alpha=0.7)
fig.savefig(fig_path, dpi=dpi)
plt.close('all')
import sim
import cache
deg = np.pi / 180.0
path = "./results/efficiency"
if len(sys.argv) > 1:
path = sys.argv[1]
files = glob.glob(path + "/*.root")
files.sort()
# Load famous model
famousModel = sim.FamousModel(files[0])
pixels = famousModel.pixels_to_cart(polygons=False)
pixel_centers = famousModel.pixel_centers_to_cart()
pixels = [Polygon(p) for p in pixels]
def find(point, pixels):
for i, p in enumerate(pixels):
if p.contains_point(point):
return i
return None
def read(filename):
# Open root file and retreive tree
file = TFile(filename)
hits = file.Get("wicoBackHits")
n = hits.GetEntriesFast()
pos = np.zeros((n, 3))
for i in xrange(n):
hits.GetEntry(i)
this_point[2] / this_point[3], this_point[3] / this_point[3]
])
triangle_arr.append(perspective_point)
#append the color as well
triangle_arr.append(triangle[3])
model.append(triangle_arr)
# next need to use the painters algoritm (z sorting)
model.sort(key=sort_avgz, reverse=True)
# now we plot points that are only within the z range of 0 to 1
fig, ax = plt.subplots()
patches = []
for triangle in model:
if (triangle[0][2] >= 0 and triangle[1][2] >= 0
and triangle[2][2] >= 0) or (triangle[0][2] <= 1
and triangle[1][2] <= 1
and triangle[2][2] <= 1):
#if meets above condition of being between 0 and 1 we plot the triangle with its corresponding color
polygon_triangle = Polygon(array([[triangle[0][0], triangle[0][1]],
[triangle[1][0], triangle[1][1]],
[triangle[2][0], triangle[2][1]]]),
True,
color=rgb_color(triangle[3]))
patches.append(polygon_triangle)
p = PatchCollection(patches, match_original=True)
# colors = array([100,100,100])
# p.set_array(colors)
ax.add_collection(p)
plt.show()
