def custom_plot(image, box=None, polygons=None):
#target.extra_fields['masks'].polygons è una lista in cui ogni elemento ha un .polygons
# che è una lista di tensor
fig, ax = plt.subplots(1)
ax.imshow(image)
if box is not None:
for bb in box:
rect = Rectangle((int(bb[0]), int(bb[1])),
int(bb[2]),
int(bb[3]),
linewidth=1,
edgecolor='r',
facecolor='none')
ax.add_patch(rect)
if polygons is not None:
patches = []
for p in polygons:
for p2 in p.polygons:
polygon = Polygon(p2.numpy().reshape((-1, 2)), False)
patches.append(polygon)
p = PatchCollection(patches, alpha=0.4)
p.set_linewidth(2.0)
p.set_edgecolor('r')
ax.add_collection(p)
plt.show()
def create_pixels(lons,lats,widths,heights,alphas,color,label): """ Plot of pixels using centers (lons,lats), with sizes (widhts,heights), angle alphas, and color color. :param lons: array of longitude coordinates at the center of the pixels :param lats: array of latitude coordinates at the center of the pixels :param widths: array of widhts of the pixels :param heights: array of heights of the pixels :param alphas: array of angles of the pixels :param color: tuple with RGB color values :return col: collection of rectangles with the pixels Developed in Python 2.7.15 :: Anaconda 4.5.10, on MACINTOSH. Angel Farguell ([email protected]) 2018-12-28 """ # Create pixels pixels=[] for x, y, h, w, a in zip(lons, lats, heights, widths, alphas): xpos = x - w/2 # The x position will be half the width from the center ypos = y - h/2 # same for the y position, but with height rect = Rectangle( (xpos, ypos), w, h, a) # Create a rectangle pixels.append(rect) # Add the rectangle patch to our list # Create a collection from the rectangles col = PatchCollection(pixels) # set the alpha for all rectangles col.set_alpha(0.5) # Set the colors using the colormap col.set_facecolor( [color]*len(lons) ) # No lines col.set_linewidth( 0 ) return col
def get_annotations(tiles_w, image_size, indices, coordinates): patches = [] colors = [] for index in range(canvas.shape[0]): x = index % tiles_w y = int(np.floor(index / tiles_w)) tile = int(indices[index]) if tile == -1: continue else: (lat, lon) = coordinates[tile] if np.isnan(lat) or np.isnan(lon): continue fc = lonlat2rgba(lon, lat) rect = mpatches.Rectangle((x * image_size, y * image_size), image_size, image_size) patches.append(rect) colors.append(fc) #image[((y + 1) * image_size, x * image_size):(x + 1) * image_size] = canvas[index] #colors = 100 * np.random.rand(len(patches)) collection = PatchCollection(patches, alpha=0.35) collection.set_facecolors(colors) collection.set_edgecolors(colors) collection.set_linewidth(0.1) #collection.set_array(np.array(colors)) return collection
def draw(self, ax, colour='k', alpha=1., linestyle='-', linewidth=1., markers=None, facecolor='none', dashes=(1, 1), zorder=0): """ Draw the polygon on ax """ # Add the last point ptstoplot = self.verts.tolist() ptstoplot.append(ptstoplot[0]) ptstoplot = np.array(ptstoplot) if facecolor == 'none': try: if linestyle == '--': ax.plot(ptstoplot[:, 0], ptstoplot[:, 1], c=colour, alpha=alpha, ls=linestyle, dashes=dashes, lw=linewidth, marker=markers, zorder=zorder) else: ax.plot(ptstoplot[:, 0], ptstoplot[:, 1], c=colour, alpha=alpha, ls=linestyle, lw=linewidth, marker=markers, zorder=zorder) except IndexError: pass else: patches = [] filled_pol = mp.Polygon(self.verts) patches.append(filled_pol) collection = PatchCollection(patches, facecolors=facecolor) ax.add_collection(collection) collection.set_alpha(alpha) collection.set_edgecolor(colour) collection.set_linewidth(linewidth) collection.set_linestyle(linestyle)
def mark_pixel(pixels, color='g', ax=None, linewidth=None):
''' surrounds pixels given by pixels with a border '''
pixel_x, pixel_y = get_pixel_coords()
if ax is None:
ax = plt.gca()
patches = []
for xy in zip(pixel_x[pixels], pixel_y[pixels]):
patches.append(
RegularPolygon(
xy=xy,
numVertices=6,
radius=9.5 / np.sqrt(3),
orientation=0., # in radians
fill=False,
))
if linewidth is None:
linewidth = calc_linewidth(ax=ax)
collection = PatchCollection(patches, picker=0)
collection.set_linewidth(linewidth)
collection.set_edgecolors(color)
collection.set_facecolor('none')
ax.add_collection(collection)
plt.draw_if_interactive()
return collection
def add_circles(self, ax, centers, radis):
circles = []
for c, r in zip(centers, radis):
circles.append(patches.Circle(tuple(c), r))
patch_collection = PatchCollection(circles)
patch_collection.set_facecolor("none")
patch_collection.set_edgecolor("blue")
patch_collection.set_linewidth(1.5)
patch_collection.set_linestyle("dashed")
ax.add_collection(patch_collection)
print "added %d circles" % len(circles)
def plotpatchcollection(self, mypatches, mycolors=[], mylw=[]):
self.canvas.figure.clf()
ax = self.canvas.figure.subplots()
p = PatchCollection(mypatches) # , alpha=0.4)
p.set_facecolor(tuple(mycolors))
p.set_linewidth(mylw)
ax.add_collection(p)
ax.autoscale(enable=True, axis="both", tight=None)
self.canvas.draw()
def get_mpas_patch_collection(nVertices,
vertexDegree,
interiorVertex,
cellsOnVertex,
xCell,
yCell,
mpasArray,
cmap,
vmin,
vmax,
limitPatches=False,
rasterizePatches=False):
patches = []
colours = []
minval = 1.0e30
maxval = -1.0e30
nPatches = 0
for iVertex in range(0, nVertices):
if (interiorVertex[iVertex] == 1 and
(math.fabs(mpasArray[iVertex]) > 1e-3 or not limitPatches)):
polygonVertices = []
for iVertexDegree in range(0, vertexDegree):
iCell = cellsOnVertex[iVertex, iVertexDegree] - 1
polygonVertices.append((xCell[iCell], yCell[iCell]))
polygon = Polygon(polygonVertices)
patches.append(polygon)
colours.append(mpasArray[iVertex])
minval = min(minval, mpasArray[iVertex])
maxval = max(maxval, mpasArray[iVertex])
nPatches = nPatches + 1
print("nPatches: ", nPatches, limitPatches)
patchCollection = PatchCollection(patches,
cmap=cmap,
rasterized=rasterizePatches)
patchCollection.set_array(np.array(colours))
patchCollection.set_linewidth(0)
patchCollection.set_clim(vmin=vmin, vmax=vmax)
return patchCollection, minval, maxval
def draw_bboxes(bboxes, ax=None, color='red', linewidth=1, **kw):
if ax is None:
ax = plt.gca()
patches = [
mpatches.Rectangle((i[1], i[0]), i[3] - i[1], i[2] - i[0])
for i in bboxes
]
boxcoll = PatchCollection(patches, **kw)
boxcoll.set_facecolor('none')
boxcoll.set_edgecolor(color)
boxcoll.set_linewidth(linewidth)
ax.collections = []
ax.add_collection(boxcoll)
def get_shape_collections(data: Dict):
shapes = []
for points in data.values():
shapes.append(
Polygon(np.array([points['lons'], points['lats']]).T, closed=True))
collection = PatchCollection(shapes)
collection.set_facecolor('#eeeeee')
collection.set_edgecolor('black')
collection.set_linewidth(0.2)
return collection
def VoronoiPlot(points: Sequence,
values: Sequence,
vmin: float = None,
vmax: float = None,
cmap=None):
""" plot the voronoi regions of the poins with the given colormap """
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from scipy.spatial import Voronoi, voronoi_plot_2d
from matplotlib import cm
if cmap is None:
cmap = cm.get_cmap('viridis')
vor = Voronoi(points)
# %%
patches = []
dist_list = []
excluded_indices = []
for index, p in enumerate(points):
# print(index)
reg = vor.regions[vor.point_region[index]]
if -1 in reg:
# plt.plot(p[0], p[1], 'ok', alpha=0.3, ms=1)
excluded_indices.append(index)
continue
distances = np.linalg.norm(np.array([vor.vertices[i]
for i in reg]) - p,
axis=1)
if np.max(distances) > 2:
# plt.plot(p[0], p[1], 'ok', alpha=0.3, ms=1)
excluded_indices.append(index)
continue
region = np.array([vor.vertices[i] for i in reg])
polygon = Polygon(region, True)
patches.append(polygon)
dists = values[index]
dist_list.append(dists)
# plt.plot(p[0], p[1], 'ok', alpha=0.3, ms=1)
p = PatchCollection(patches, cmap=cmap)
p.set_clim([vmin, vmax])
p.set_array(np.array(dist_list))
p.set_linewidth(10)
plt.gca().add_collection(p)
plt.xticks([])
plt.yticks([])
return p, excluded_indices
def zoneLines(self, edgecolour='black'): #was 'red'
"""
Set boundary colour for defined ESRI shapes
edgecolour -- HTML colour name for boundary
"""
pc2 = PatchCollection(self.patches, match_original=True)
pc2.set_facecolor('none')
pc2.set_edgecolor(edgecolour)
pc2.set_alpha(0.5) #5.0
pc2.set_linewidth(0.5)
pc2.set_zorder(25) # 500
sq2 = self.ax.add_collection(pc2)
def plot_trajectory_ellipse(frame, varx="attr_VARX", vary="attr_VARY", covxy="attr_COVXY", opacity_factor=1):
"""
Draw the trajectory and uncertainty ellipses around teach point.
1) Scatter of points
2) Trajectory lines
3) Ellipses
:param frame: Trajectory
:param opacity_factor: all opacity values are multiplied by this. Useful when used to plot multiple Trajectories in
an overlay plot.
:return: axis
"""
ellipses = []
segments = []
start_point = None
for i, pnt in frame.iterrows():
# The ellipse
U, s, V = np.linalg.svd(np.array([[pnt[varx], pnt[covxy]],
[pnt[covxy], pnt[vary]]]), full_matrices=True)
w, h = s**.5
theta = np.arctan(V[1][0]/V[0][0]) # == np.arccos(-V[0][0])
ellipse = {"xy":pnt[list(frame.geo_cols)].values, "width":w, "height":h, "angle":theta}
ellipses.append(Ellipse(**ellipse))
# The line segment
x, y = pnt[list(frame.geo_cols)][:2]
if start_point:
segments.append([start_point, (x, y)])
start_point = (x, y)
ax = plt.gca()
ellipses = PatchCollection(ellipses)
ellipses.set_facecolor('none')
ellipses.set_color("green")
ellipses.set_linewidth(2)
ellipses.set_alpha(.4*opacity_factor)
ax.add_collection(ellipses)
frame.plot(kind="scatter", x=frame.geo_cols[0], y=frame.geo_cols[1], marker=".", ax=plt.gca(), alpha=opacity_factor)
lines = LineCollection(segments)
lines.set_color("gray")
lines.set_linewidth(1)
lines.set_alpha(.2*opacity_factor)
ax.add_collection(lines)
return ax
def get_mpas_patch_collection(nVertices, vertexDegree, cellsOnVertex, xCell,
yCell, zCell, latVertex, mpasArray, cmap, vmin,
vmax, minX, maxX, minY, maxY):
minval = 1.0e30
maxval = -1.0e30
patches = []
colours = []
for iVertex in range(0, nVertices):
if (math.fabs(latVertex[iVertex]) > math.radians(20.0)):
useVertex = False
polygonVertices = []
for iCellOnVertex in range(0, vertexDegree[iVertex]):
iCell = cellsOnVertex[iVertex, iCellOnVertex]
x, y, z = ortho_projection(xCell[iCell], yCell[iCell],
zCell[iCell])
if (x >= minX and x <= maxX and \
y >= minY and y <= maxY and \
z > 0.0):
useVertex = True
polygonVertices.append((x, y))
# create patch and add to collection
if (useVertex):
polygon = Polygon(polygonVertices)
patches.append(polygon)
colours.append(mpasArray[iVertex])
minval = min(minval, mpasArray[iVertex])
maxval = max(maxval, mpasArray[iVertex])
patchCollection = PatchCollection(patches, cmap=cmap, rasterized=True)
patchCollection.set_array(np.array(colours))
patchCollection.set_linewidth(0)
patchCollection.set_clim(vmin=vmin, vmax=vmax)
return patchCollection, minval, maxval
def zoneColour(self, colours):
"""
Set display colours for defined ESRI shapes
colours -- list containing HTML colour names
"""
self.colours = colours
if not (isinstance(self.colours, list)):
raise Exception('Invalid list of zone colours')
pc = PatchCollection(self.patches, match_original=True)
pc.set_facecolor(self.colours)
pc.set_edgecolor('none')
pc.set_alpha(0.5)
pc.set_linewidth(0.5)
pc.set_zorder(20)
sq = self.ax.add_collection(pc)
def get_mpas_patch_collection(nVertices, vertexDegree, cellsOnVertex, xCell,
yCell, zCell, latVertex, mpasArray, cmap, vmin,
vmax, minX, maxX, minY, maxY):
patches = []
colours = []
minval = 1.0e30
maxval = -1.0e30
for iVertex in range(0, nVertices):
if (latVertex[iVertex] > math.radians(20.0)):
polygonVertices = []
useVertex = False
for iCellOnVertex in range(0, vertexDegree[iVertex]):
iCell = cellsOnVertex[iVertex, iCellOnVertex]
polygonVertices.append((xCell[iCell], yCell[iCell]))
if (xCell[iCell] >= minX and xCell[iCell] <= maxX and \
yCell[iCell] >= minY and yCell[iCell] <= maxY):
useVertex = True
if (useVertex):
polygon = Polygon(polygonVertices)
patches.append(polygon)
colours.append(mpasArray[iVertex])
minval = min(minval, mpasArray[iVertex])
maxval = max(maxval, mpasArray[iVertex])
patchCollection = PatchCollection(patches, cmap=cmap, rasterized=True)
patchCollection.set_array(np.array(colours))
patchCollection.set_linewidth(0)
patchCollection.set_clim(vmin=vmin, vmax=vmax)
return patchCollection, minval, maxval
def _render_matplotlib(vertices, faces, face_color, borders, new_figure):
"""
Render the data in matplotlib: This is segmented to allow for openGL renderer
Input:
vertices (np.ndarray)
Array of vertices
faces (nd.array)
Array of Faces
face_color (nd.array)
RGBA array of color and alpha of all vertices
new_figure (bool)
Create new Figure or render in currrent axis?
"""
patches = []
for i in range(faces.shape[0]):
polygon = Polygon(vertices[faces[i],0:2], True)
patches.append(polygon)
p = PatchCollection(patches)
p.set_facecolor(face_color)
p.set_linewidth(0.0)
# Get the current axis and plot it
if new_figure:
fig = plt.figure(figsize=(7,7))
ax = plt.gca()
ax.add_collection(p)
xrang = [np.nanmin(vertices[:,0]),np.nanmax(vertices[:,0])]
yrang = [np.nanmin(vertices[:,1]),np.nanmax(vertices[:,1])]
ax.set_xlim(xrang[0],xrang[1])
ax.set_ylim(yrang[0],yrang[1])
ax.axis('equal')
ax.axis('off')
if borders is not None:
ax.plot(borders[:,0],borders[:,1],color='k',
marker='.', linestyle=None,
markersize=2,linewidth=0)
return ax
def get_mpas_patch_collection(nCells, nEdgesOnCell, verticesOnCell, xVertex,
yVertex, zVertex, mpasArray, cmap):
patches = []
colours = []
minval = 1.0e30
maxval = -1.0e30
for iCell in range(0, nCells):
polygonVertices = []
lUse = True
for iVertexOnCell in range(0, nEdgesOnCell[iCell]):
iVertex = verticesOnCell[iCell, iVertexOnCell] - 1
polygonVertices.append((xVertex[iVertex], zVertex[iVertex]))
if (yVertex[iVertex] < 0.0):
lUse = False
if (lUse):
polygon = Polygon(polygonVertices)
patches.append(polygon)
colours.append(mpasArray[iCell])
minval = min(minval, mpasArray[iCell])
maxval = max(maxval, mpasArray[iCell])
patchCollection = PatchCollection(patches, cmap=cmap, rasterized=False)
patchCollection.set_array(np.array(colours))
patchCollection.set_linewidth(0)
patchCollection.set_clim(vmin=0.0, vmax=1.0)
return patchCollection, minval, maxval
def draw_map(map: GeographicalMap, mode='random', data=None):
fig, ax = plt.subplots(figsize=(18, 10))
ax.set_xlim(map.xmin, map.xmax)
ax.set_ylim(map.ymin, map.ymax)
patches = PatchCollection(
[matplotlib.patches.Polygon(p, fill=False) for p in map.polygons])
to_line = lambda e: map.centroids[(e.v1, e.v2), :]
land_edges = [to_line(e) for e in map.edges.values() if e.type == 'land']
air_edges = [to_line(e) for e in map.edges.values() if e.type == 'air']
land_edges = LineCollection(land_edges, linewidths=0.3, colors='red')
air_edges = LineCollection(air_edges, linewidths=0.1, colors='green')
ax.add_collection(patches)
if mode == 'random':
patches.set_array(np.random.randint(0, 20, size=map.N))
patches.set_cmap(matplotlib.cm.jet)
elif mode == 'population':
patches.set_array(map.population)
patches.set_cmap(matplotlib.cm.jet)
patches.set_norm(matplotlib.colors.LogNorm())
plt.colorbar(patches, ax=ax)
elif mode == 'graph':
patches.set_color('black')
patches.set_facecolor('white')
patches.set_linewidth(0.1)
ax.scatter(map.centroids[:, 0], map.centroids[:, 1], s=5)
# Plot edges.
ax.add_collection(land_edges)
ax.add_collection(air_edges)
elif mode == 'data':
patches.set_array(data)
patches.set_cmap(matplotlib.cm.jet)
plt.colorbar(patches, ax=ax)
plt.show()
class ArrayDisplay:
"""
Display a top-town view of a telescope array
"""
def __init__(self,
telx,
tely,
tel_type=None,
radius=20,
axes=None,
title="Array",
autoupdate=True):
if tel_type is None:
tel_type = np.ones(len(telx))
patches = [
Rectangle(xy=(x - radius / 2, y - radius / 2),
width=radius,
height=radius,
fill=False) for x, y in zip(telx, tely)
]
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_clim(1, 9)
rgb = matplotlib.cm.Set1((tel_type - 1) / 9)
self.telescopes.set_edgecolor(rgb)
self.telescopes.set_linewidth(2.0)
self.axes = axes if axes is not None else plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self.axes.set_xlim(-1000, 1000)
self.axes.set_ylim(-1000, 1000)
self.axes_hillas = axes if axes is not None else plt.gca()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def add_ellipse(self, centroid, length, width, angle, **kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid,
width=length,
height=width,
angle=np.degrees(angle),
fill=True,
**kwargs)
self.axes.add_patch(ellipse)
return ellipse
def add_polygon(self, centroid, radius, nsides=3, **kwargs):
"""
plot a polygon on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
radius: float
radius
nsides: int
Number of points on polygon
kwargs:
any MatPlotLib style arguments to pass to the RegularPolygon patch
"""
polygon = RegularPolygon(xy=centroid,
radius=radius,
numVertices=nsides,
**kwargs)
self.axes.add_patch(polygon)
return polygon
def overlay_moments(self, momparams, tel_position, scale_fac, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
scale_fac: float
scaling factor to apply to width and length when overlaying moments
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
ellipse_list = list()
size_list = list()
i = 0
for h in momparams:
length = u.Quantity(momparams[h].length).value * scale_fac
width = u.Quantity(momparams[h].width).value * scale_fac
size_list.append(u.Quantity(momparams[h].size).value)
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
i += 1
ellipse = Ellipse(xy=(tel_x, tel_y),
width=length,
height=width,
angle=np.degrees(momparams[h].psi.rad))
ellipse_list.append(ellipse)
patches = PatchCollection(ellipse_list, **kwargs)
patches.set_clim(0, 1000) # Set ellipse colour based on image size
patches.set_array(np.asarray(size_list))
self.axes_hillas.add_collection(patches)
def overlay_axis(self, momparams, tel_position, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
line_list = list()
size_list = list()
i = 0
for h in momparams:
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
psi = u.Quantity(momparams[h].psi).value
x_sc = [tel_x - np.cos(psi) * 10000, tel_x + np.cos(psi) * 10000]
y_sc = [tel_y - np.sin(psi) * 10000, tel_y + np.sin(psi) * 10000]
i += 1
self.axes_hillas.add_line(
Line2D(x_sc, y_sc, linestyle='dashed', color='black'))
def factcamera(self,
data,
pixelcoords=None,
cmap='gray',
vmin=None,
vmax=None,
pixelset=None,
pixelsetcolour='g',
linewidth=None,
intersectcolour='b',
picker=True,
):
"""
Attributes
----------
data : array like with shape 1440
the data you want to plot into the pixels
pixelset : boolean array with shape 1440
the pixels where pixelset is True are marked with 'pixelsetcolour'
[default: None]
pixelsetcolour : a matplotlib conform colour representation
the colour for the pixels in 'pixelset',
[default: green]
pixelcoords : the coordinates for the pixels in form [x-values, y-values]
if None, the package resource is used
[default: None]
cmap : str or matplotlib colormap instance
the colormap to use for plotting the 'dataset'
[default: gray]
vmin : float
the minimum for the colorbar, if None min(dataset[event]) is used
[default: None]
vmax : float
the maximum for the colorbar, if None max(dataset[event]) is used
[default: None]
picker: bool
if True then the the pixel are made clickable to show information
"""
self.set_aspect('equal')
if picker is True:
fig = self.get_figure()
fig.canvas.mpl_connect("pick_event", onpick)
# if the axes limit is still (0,1) assume new axes
if self.get_xlim() == (0, 1) and self.get_ylim() == (0, 1):
self.set_xlim(-200, 200)
self.set_ylim(-200, 200)
if pixelcoords is None:
pixel_x, pixel_y = get_pixel_coords()
else:
pixel_x, pixel_y = pixelcoords
if vmin is None:
vmin = np.min(data)
if vmax is None:
vmax = np.max(data)
edgecolors = np.array(1440*["k"])
if pixelset is None:
pixelset = np.zeros(1440, dtype=np.bool)
_pixelset = np.array(pixelset)
if _pixelset.ndim == 1:
if _pixelset.shape != (1440,):
pixelset = np.zeros(1440, dtype=np.bool)
pixelset[_pixelset] = True
else:
pixelset = np.array(_pixelset, dtype=np.bool)
edgecolors[pixelset] = pixelsetcolour
elif _pixelset.ndim == 2:
for pixelset, colour in zip(_pixelset, pixelsetcolour):
edgecolors[pixelset] = colour
intersect = np.logical_and(_pixelset[0], _pixelset[1])
edgecolors[intersect] = intersectcolour
else:
raise ValueError(
"""pixelset needs to be one of:
1. list of pixelids
2. 1d bool array with shape (1440,)
3. 2d bool array with shape (2, 1440)
"""
)
patches = []
for x, y, ec in zip(pixel_x, pixel_y, edgecolors):
patches.append(
RegularPolygon(
xy=(x, y),
numVertices=6,
radius=0.95*9.5/np.sqrt(3),
orientation=0., # in radians
)
)
if linewidth is None:
linewidth = calc_linewidth(self)
collection = PatchCollection(patches, picker=0)
collection.set_linewidth(linewidth)
collection.set_edgecolors(edgecolors)
collection.set_cmap(cmap)
collection.set_array(data)
collection.set_clim(vmin, vmax)
self.add_collection(collection)
return collection
def plot_multifolder_old(folder_list, Zlist, pointing, ap):
rw = 0.5
rh = 0.1
numfree = 1334 - 5 - 1
minchi = np.inf
maxchi = 0
for f in folder_list:
stepfile = glob("{:}/P{:}_*{:02n}_steps.dat".format(f, pointing, ap))[0]
print stepfile
chi = np.loadtxt(stepfile, usecols=(12,), unpack=True)
chi = chi[chi == chi]
upperlim = np.median(chi) + 1.3 * np.min(chi)
if chi[-1] < minchi:
minchi = chi[-1]
if upperlim > maxchi:
maxchi = upperlim
fig = plt.figure()
grid = ImageGrid(
fig, 111, nrows_ncols=(1, 1), cbar_mode="each", cbar_location="top", cbar_pad="1%", axes_class=None
)
ax = grid[0]
ax.set_xlabel("$Z/Z_{\odot}$")
ax.set_ylabel("MLWA")
goodage = []
bigZ = np.zeros(len(folder_list))
for z, f in enumerate(folder_list):
stepfile = glob("{:}/P{:}_*{:02n}_steps.dat".format(f, pointing, ap))[0]
print stepfile
MLWA, chi = np.loadtxt(stepfile, usecols=(6, 12), unpack=True)
idx = chi == chi
chi = chi[idx]
MLWA = MLWA[idx]
idx = MLWA == MLWA
chi = chi[idx]
MLWA = MLWA[idx]
# good = np.where(chi < 80)
# chi = chi[good]
# MLWA = MLWA[good]
chi -= minchi
patches = []
for i in range(MLWA.size):
# patches.append(Circle((z,MLWA[i]),radius=0.1,linewidth=0))
width = rw * ((minchi + 5000) / (chi[i] + 5000))
# width = rw * (minchi/chi[i])
patches.append(Rectangle((z - width / 2, MLWA[i] - rh / 2), width, rh))
collection = PatchCollection(
np.array(patches)[::-1],
cmap=plt.get_cmap("gnuplot"),
norm=matplotlib.colors.Normalize(vmin=minchi, vmax=maxchi),
)
collection.set_alpha(0.7)
collection.set_linewidth(0.0)
collection.set_array(chi[::-1])
ax.add_collection(collection)
# ax.axhline(y=MLWA[-1],color='k',ls=':',alpha=0.6)
ax.hlines(MLWA[-1], z - 0.3, z + 0.3, color="g", lw=2)
prob = 1 - ss.chi2.cdf(chi[-1] * numfree, numfree)
if prob >= 0.68:
goodage.append(MLWA[-1])
bigZ[z] = 1
ax.text(z + 0.5, MLWA[-1], "{:4.2f}\n{:4.2f}".format(prob, chi[-1]), fontsize=10)
collection.set_alpha(1.0)
cbar = ax.cax.colorbar(collection)
ci = [68.27, 50.0, 80.0, 40.0]
dc = [4.72, 3.36, 5.99, 2.75]
# for (c, d) in zip(ci, dc):
# ax.cax.axvline(x = np.log10(d), color='g')
# ax.cax.text(np.log10(d) - 0.03, 2, '{}'.format(c),
# transform=ax.cax.transData,
# fontsize=8)
collection.set_alpha(0.1)
cbar.set_label_text(r"$\Delta\chi^2_{\nu}$")
ax.set_xlim(-1, len(Zlist) + 1)
ax.set_ylim(0, 12)
ax.set_xticks(np.arange(len(Zlist)))
ax.set_xticklabels(Zlist)
# fig.show()
return fig, np.mean(goodage), np.std(goodage), bigZ
class CameraPlot(object):
'''A Class for a camera pixel'''
def __init__(
self,
telescope,
ax,
data=None,
cmap='gray',
vmin=None,
vmax=None,
):
'''
:telescope: the telescope class for the pixel
:data: array-like with one value for each pixel
:cmap: a matpixellib colormap string or instance
:vmin: minimum value of the colormap
:vmax: maximum value of the colormap
'''
self.telescope = telescope
if data is None:
data = np.zeros(telescope.n_pixel)
patches = []
if telescope.pixel_shape == 'hexagon':
for xy in zip(telescope.pixel_x, telescope.pixel_y):
patches.append(
RegularPolygon(
xy=xy,
numVertices=6,
radius=telescope.pixel_size,
orientation=telescope.pixel_orientation,
)
)
self.pixel = PatchCollection(patches)
self.pixel.set_linewidth(0)
self.pixel.set_cmap(cmap)
self.pixel.set_array(data)
self.pixel.set_clim(vmin, vmax)
self.vmin = vmin
self.vmax = vmax
self.ax = ax
self.ax.add_collection(self.pixel)
self.ax.set_xlim(
self.telescope.pixel_x.min() - 2 * self.telescope.pixel_size,
self.telescope.pixel_x.max() + 2 * self.telescope.pixel_size,
)
self.ax.set_ylim(
self.telescope.pixel_y.min() - 2 * self.telescope.pixel_size,
self.telescope.pixel_y.max() + 2 * self.telescope.pixel_size,
)
@property
def data(self):
return self.pixel.get_array()
@data.setter
def data(self, data):
self.pixel.set_array(data)
if not self.vmin or not self.vmax:
self.pixel.autoscale()
self.pixel.changed()
def draw(self,
ax,
draw_polygons=True,
colour='k',
line_alpha=1.,
fill_alpha=1.,
linestyle='-',
linewidth=1.,
markers=None,
values=None,
precompv=None,
vmin=None,
vmax=None,
title=None,
cmap=None,
cblabel=None,
logscale=False,
allowed_cbticklabels=(None, ),
extend='neither',
zorder=0):
"""
Draw the diagram
"""
drawvalues = values is not None
if drawvalues:
patches = []
colours = np.zeros(self.nc)
for i, pol in self.polygons.items():
# Colours and filled polygons
if drawvalues:
filled_pol = mp.Polygon(self.cellverts[i])
patches.append(filled_pol)
if values == 'areas':
colours[i] = pol.area
if values == 'inv_areas':
colours[i] = 1. / self.free_areas[i]
if values == 'free_areas':
colours[i] = self.free_areas[i]
if values == 'precomputed':
if logscale:
colours[i] = np.abs(precompv[i])
else:
colours[i] = precompv[i]
# Colour for the polygon contours
if colour == 'same_as_facecolors':
pc_ = colours[i]
else:
pc_ = colour
# Draw the polygon
if draw_polygons:
pol.draw(ax=ax,
colour=pc_,
alpha=line_alpha,
linestyle=linestyle,
linewidth=linewidth,
markers=markers,
zorder=zorder)
# Line colours
if drawvalues:
if vmin is None:
vmin = colours.min()
if vmax is None:
vmax = colours.max()
if logscale:
vmin = np.abs(vmin)
vmax = np.abs(vmax)
norm = LogNorm(vmin=vmin, vmax=vmax)
else:
norm = Normalize(vmin=vmin, vmax=vmax)
m = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
line_colours = m.to_rgba(colours)
print(line_colours)
if drawvalues:
collection = PatchCollection(patches, cmap=cmap, norm=norm)
ax.add_collection(collection)
collection.set_alpha(fill_alpha)
collection.set_array(colours)
if logscale:
collection.set_clim(vmin=vmin, vmax=vmax)
else:
collection.set_clim(vmin=0., vmax=vmax)
if colour == 'same_as_facecolors':
collection.set_edgecolors(line_colours)
else:
collection.set_edgecolor(colour)
collection.set_linewidth(linewidth)
if values == 'precomputed':
collection.set_clim(vmin=vmin, vmax=vmax)
# Color bar
if logscale:
l_f = LogFormatter(10, labelOnlyBase=False)
cb = plt.colorbar(collection,
ax=ax,
orientation='vertical',
format=l_f,
extend=extend)
# Set minor ticks
# We need to nomalize the tick locations so that they're in the range from 0-1...
# minorticks = p.norm(np.arange(1, 10, 2))
# Minimum and maximum exponents
min_exp = int(np.log10(min(vmin, vmax)))
max_exp = int(np.log10(max(vmin, vmax))) + 1
# Ticks for the colorbar in case it's logscale
minorticks = 10.**np.arange(min_exp, max_exp + 1, 1)
minorticks_ = minorticks.tolist()
for i in range(2, 10, 1):
minorticks_ = minorticks_ + (float(i) *
minorticks).tolist()
minorticks_.sort()
minorticks = np.array(minorticks_)
minorticks = minorticks[np.where(minorticks >= vmin)]
minorticks = minorticks[np.where(minorticks <= vmax)]
print(minorticks)
cb.set_ticks(minorticks)
cb.set_ticklabels([
str(int(x)) if x in allowed_cbticklabels else ''
for x in minorticks
])
else:
cb = plt.colorbar(collection,
ax=ax,
orientation='vertical',
extend=extend)
cb.set_label(cblabel)
if title is not None:
ax.set_title(title)
ax.set_xlabel(r'$\rm x$ ($\rm \mu m$)')
ax.set_ylabel(r'$\rm y$ ($\rm \mu m$)')
color = 'y'
elif zone == 'C':
color = 'c'
elif zone == 'M':
color = 'darkmagenta'
elif zone == 'O':
color = 'ivory'
elif zone == 'P':
color = 'forestgreen'
patches = [mplPolygon(np.array(shape), True)]
pc = PatchCollection(patches)
pc.set_alpha(.7)
pc.set_facecolor(color)
pc.set_zorder(2)
pc.set_linewidth(.1)
pc.set_edgecolor('k')
ax.add_collection(pc)
#####################################################################
# Add Roads shapefile to map #
#####################################################################
map.readshapefile('RoadCenterlines/RoadCenterlines_2',
'RoadCenterlines_2',
linewidth=.2)
#####################################################################
# TRY CODE HERE #
#####################################################################
c = fiona.open('ZoningDistrict/ZoningDistrict_test_2.shp')
def plotter(self, mesh=True):
"""
Plots solution and overlays mesh
"""
# approx=self.nodal_values()
xy = np.array([self.x[:], self.y[:]]).T
patches = []
for coords in xy[self.con_array[:]]:
quad = Polygon(coords, facecolor='none', fill=False)
patches.append(quad)
n = 50
X, Y = np.meshgrid(np.linspace(min(self.x), max(self.x), n),
np.linspace(min(self.y), max(self.y), n))
# X=X.ravel(); Y=Y.ravel()
# Z=np.zeros(n**2)
# print(X,Y)
# for i in range(n**2):
# print(self.approx_function(X[i],Y[i]),i)
# Z[i]=self.approx_function(X[i],Y[i])
# f=scipy.interpolate.interp2d(self.x,self.y,self.approx)
# Z=f(np.linspace(min(self.x),max(self.x),n),np.linspace(min(self.y),max(self.y),n))
# X=X.reshape((n,n)); Y=Y.reshape((n,n)); Z=Z.reshape((n,n))
Z = scipy.interpolate.griddata((self.x, self.y), self.approx, (X, Y))
ticks = np.linspace(min(self.approx), max(self.approx), 5)
# return (X,Y,Z.astype('float'))
fig, ax = plt.subplots()
cs = ax.contourf(X,
Y,
Z,
cmap="coolwarm",
levels=np.linspace(min(self.approx), max(self.approx),
30))
cbar = fig.colorbar(cs, ax=ax, ticks=ticks)
p = PatchCollection(patches, match_original=True)
p.set_linewidth(0.1)
if mesh == False:
p.set_linewidth(0)
plt.xticks(np.linspace(-400, 400, 5))
plt.yticks(np.linspace(-400, 400, 5))
ax.add_collection(p)
ax.set_aspect('equal')
ax.set_xlim([-400, 400])
ax.set_ylim([-400, 400])
props = dict(boxstyle='square', facecolor='white', alpha=1.)
plt.gcf().subplots_adjust(bottom=0.15)
plt.text(0.5,
0.9,
'Degrees of freedom= %s' % len(self.F),
ha='center',
va='center',
transform=ax.transAxes,
bbox=props,
fontsize=14)
plt.xlabel('x [m]', fontsize=18)
plt.ylabel('y [m]', fontsize=18)
plt.tick_params(labelsize=18)
cbar.ax.tick_params(labelsize=18)
cbar.set_label('Pressure [MPa]', labelpad=10, size=18)
return plt
polygon = Polygon(np.array([x, y]).transpose(),
True,
color='gray')
patches_polygon.append(polygon)
pcc = PatchCollection(patches_circle, match_original=False)
pcp = PatchCollection(patches_polygon, match_original=False)
if args.collision_status:
pcc.set_color('#85878b')
pcp.set_color('#85878b')
pcc.set_alpha(0.3)
pcp.set_alpha(0.3)
pcc.set_linestyle('dashed')
pcp.set_linestyle('dashed')
pcc.set_edgecolor('#73cdc9')
pcp.set_edgecolor('#73cdc9')
pcc.set_linewidth(0.4)
pcp.set_linewidth(0.4)
pcc.set_joinstyle('round')
pcp.set_joinstyle('round')
else:
pcc.set_color('gray')
pcp.set_color('gray')
ax.add_collection(pcc)
ax.add_collection(pcp)
if args.safety_certificate:
xyrs = []
for line in open(args.safety_certificate, 'r').readlines():
l = line.strip()
if not l:
continue
xyrs.append([float(x) for x in l.split(' ')])
def plot_multiZ(prefix, minval=0, maxval=200, bluefree=702):
fraclist = np.array([0.005, 0.02, 0.2, 0.4, 1, 2.5])
rw = 0.5
rh = 0.05
minchi = np.inf
for z in range(fraclist.size):
stepfile = "{}_Z{:04}_steps.dat".format(prefix, int(fraclist[z] * 1000))
blueChi = np.loadtxt(stepfile, usecols=(17,), unpack=True)
blueChi *= bluefree
if blueChi[-1] < minchi:
minchi = blueChi[-1]
fig = plt.figure()
grid = ImageGrid(
fig, 111, nrows_ncols=(1, 1), cbar_mode="each", cbar_location="top", cbar_pad="1%", axes_class=None
)
ax = grid[0]
ax.set_xlabel("$Z/Z_{\odot}$")
ax.set_ylabel("MLWA")
for z in range(fraclist.size):
stepfile = "{}_Z{:04}_steps.dat".format(prefix, int(fraclist[z] * 1000))
MLWA, TAUV, Chi, blueChi = np.loadtxt(stepfile, usecols=(12, 13, 15, 17), unpack=True)
blueChi *= bluefree
# good = np.where(blueChi < 80)
# blueChi = blueChi[good]
# MLWA = MLWA[good]
blueChi -= minchi
patches = []
print np.log10(blueChi.min()), np.log10(blueChi.max())
for i in range(MLWA.size):
# patches.append(Circle((z,MLWA[i]),radius=0.1,linewidth=0))
width = rw * ((minchi + 5000) / (blueChi[i] + 5000))
# width = rw * (minchi/blueChi[i])
patches.append(Rectangle((z - width / 2, MLWA[i] - rh / 2), width, rh))
collection = PatchCollection(
np.array(patches)[::-1],
cmap=plt.get_cmap("gnuplot"),
norm=matplotlib.colors.Normalize(vmin=minval, vmax=maxval),
)
collection.set_alpha(0.1)
collection.set_linewidth(0.0)
collection.set_array(np.log10(blueChi)[::-1])
ax.add_collection(collection)
# ax.axhline(y=MLWA[-1],color='k',ls=':',alpha=0.6)
ax.hlines(MLWA[-1], z - 0.5, z + 0.5, color="g", lw=2)
ax.text(
z + 0.5,
MLWA[-1],
"{:5.2f}%, {:4.1f}".format(100 - 100 * ss.chi2.cdf(blueChi[-1], bluefree + 5), blueChi[-1]),
)
collection.set_alpha(1.0)
cbar = ax.cax.colorbar(collection)
ci = [68.27, 50.0, 80.0, 40.0]
dc = [4.72, 3.36, 5.99, 2.75]
# for (c, d) in zip(ci, dc):
# ax.cax.axvline(x = np.log10(d), color='g')
# ax.cax.text(np.log10(d) - 0.03, 2, '{}'.format(c),
# transform=ax.cax.transData,
# fontsize=8)
collection.set_alpha(0.1)
cbar.set_label_text("Log( $\Delta\chi^2_{\mathrm{blue}}$ )")
ax.set_xlim(-1, fraclist.size + 1)
ax.set_ylim(0, 12)
ax.set_xticks(np.arange(fraclist.size))
ax.set_xticklabels(fraclist)
fig.show()
return fig
#ax.set_xlim(-(seg_bar_height+1.25), (seg_bar_height+1.25))
#ax.set_ylim(-(seg_bar_height+1.25), (seg_bar_height+1.25))
chrom_set = set()
for i in path:
chrom_set.add(segSeqD[i[0]][0])
sorted_chrom = sorted(chrom_set, key=lambda x: x.rsplit("chr")[-1])
sorted_chrom_colors = [chromosome_colors[x] for x in sorted_chrom]
legend_patches = []
for chrom, color in zip(sorted_chrom, sorted_chrom_colors):
legend_patches.append(mpatches.Patch(color=color, label=chrom))
# plt.legend(handles=legend_patches,fontsize=8,loc=3,bbox_to_anchor=(-.3,.15))
plt.legend(
handles=legend_patches, fontsize=10,
bbox_to_anchor=(0,
0)) # bbox_to_anchor=(0,-1.5))#,bbox_to_anchor=(.09,-1.5))
p = PatchCollection(patches)
p.set_facecolor(f_color_v)
p.set_edgecolor(e_color_v)
p.set_linewidth(lw_v)
ax.add_collection(p)
ax.set_aspect(1.0)
plt.axis('off')
plt.savefig(fname + '.png', dpi=600)
plt.savefig(fname + '.pdf', format='pdf')
plt.close()
print("finished")
#! /home/kshan/anaconda2/bin/python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection
fig, ax = plt.subplots()
patches = []
colors = ["red"]
collection = PatchCollection(patches, alpha=1)
collection.set_color(colors)
collection.set_edgecolor("black")
collection.set_linewidth(1)
ax.add_collection(collection)
plt.plot((13.96, 8.05175), (7.93333, 7.34158), color='r')
plt.plot((13.96, 19.754), (7.93333, 7.2273), color='r')
plt.plot((8.05175, 8.87409), (7.34158, 4.66392), color='g')
plt.plot((8.05175, 9.09689), (7.34158, 9.79644), color='g')
plt.plot((19.754, 18.3777), (7.2273, 5.10356), color='g')
plt.plot((19.754, 19.7798), (7.2273, 10.7015), color='g')
plt.plot((6.1265, 9.30722), (3.40278, 3.47205), color='m')
plt.plot((8.87409, 9.30722), (4.66392, 3.47205), color='m')
plt.plot((8.87409, 9.17591), (4.66392, 5.9871), color='m')
plt.plot((12.3072, 9.17591), (6.10582, 5.9871), color='m')
plt.plot((7.46506, 10.1029), (9.78961, 9.17742), color='m')
plt.plot((9.09689, 10.1029), (9.79644, 9.17742), color='m')
plt.plot((9.09689, 8.6289), (9.79644, 10.9534), color='m')
class ArrayDisplay:
"""
Display a top-town view of a telescope array.
This can be used in two ways: by default, you get a display of all
telescopes in the subarray, colored by telescope type, however you can
also color the telescopes by a value (like trigger pattern, or some other
scalar per-telescope parameter). To set the color value, simply set the
``value`` attribute, and the fill color will be updated with the value. You
might want to set the border color to zero to avoid confusion between the
telescope type color and the value color (
``array_disp.telescope.set_linewidth(0)``)
To display a vector field over the telescope positions, e.g. for
reconstruction, call `set_vector_uv()` to set cartesian vectors,
or `set_vector_rho_phi()` to set polar coordinate vectors.
These both take an array of length N_tels, or a single value.
Parameters
----------
subarray: ctapipe.instrument.SubarrayDescription
the array layout to display
axes: matplotlib.axes.Axes
matplotlib axes to plot on, or None to use current one
title: str
title of array plot
tel_scale: float
scaling between telescope mirror radius in m to displayed size
autoupdate: bool
redraw when the input changes
radius: Union[float, list, None]
set telescope radius to value, list/array of values. If None, radius
is taken from the telescope's mirror size.
"""
def __init__(
self,
subarray,
axes=None,
autoupdate=True,
tel_scale=2.0,
alpha=0.7,
title=None,
radius=None,
frame=GroundFrame(),
):
self.frame = frame
self.subarray = subarray
self.axes = axes or plt.gca()
# get the telescope positions. If a new frame is set, this will
# transform to the new frame.
self.tel_coords = subarray.tel_coords.transform_to(frame).cartesian
self.unit = self.tel_coords.x.unit
# set up colors per telescope type
tel_types = [str(tel) for tel in subarray.tels.values()]
if radius is None:
# set radius to the mirror radius (so big tels appear big)
radius = [
np.sqrt(tel.optics.mirror_area.to("m2").value) * tel_scale
for tel in subarray.tel.values()
]
self.radii = radius
else:
self.radii = np.ones(len(tel_types)) * radius
if title is None:
title = subarray.name
# get default matplotlib color cycle (depends on the current style)
color_cycle = cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"])
# map a color to each telescope type:
tel_type_to_color = {}
for tel_type in list(set(tel_types)):
tel_type_to_color[tel_type] = next(color_cycle)
tel_color = [tel_type_to_color[ttype] for ttype in tel_types]
patches = []
for x, y, r, c in zip(
list(self.tel_coords.x.to_value("m")),
list(self.tel_coords.y.to_value("m")),
list(radius),
tel_color,
):
patches.append(
Circle(xy=(x, y), radius=r, fill=True, color=c, alpha=alpha))
# build the legend:
legend_elements = []
for ttype in list(set(tel_types)):
color = tel_type_to_color[ttype]
legend_elements.append(
Line2D(
[0],
[0],
marker="o",
color=color,
label=ttype,
markersize=10,
alpha=alpha,
linewidth=0,
))
self.axes.legend(handles=legend_elements)
self.add_radial_grid()
# create the plot
self.tel_colors = tel_color
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_linewidth(2.0)
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
xunit = self.tel_coords.x.unit.to_string("latex")
yunit = self.tel_coords.y.unit.to_string("latex")
xname, yname, _ = frame.get_representation_component_names().keys()
self.axes.set_xlabel(f"{xname} [{xunit}] $\\rightarrow$")
self.axes.set_ylabel(f"{yname} [{yunit}] $\\rightarrow$")
self._labels = []
self._quiver = None
self.axes.autoscale_view()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
"""set the telescope colors to display"""
self.telescopes.set_array(np.ma.masked_invalid(values))
self._update()
def add_radial_grid(self, spacing=100 * u.m):
"""add some dotted rings for distance estimation. The number of rings
is estimated automatically from the spacing and the array footprint.
Parameters
----------
spacing: Quantity
spacing between rings
"""
n_circles = np.round(
(np.sqrt(self.subarray.footprint / np.pi) / spacing).to_value(""),
0,
)
circle_radii = np.arange(1, n_circles + 2, 1) * spacing.to_value(
self.unit)
circle_patches = PatchCollection(
[
Circle(
xy=(0, 0),
radius=r,
fill=False,
fc="none",
linestyle="dotted",
color="gray",
alpha=0.1,
lw=1,
) for r in circle_radii
],
color="#eeeeee",
ls="dotted",
fc="none",
lw=3,
)
self.axes.add_collection(circle_patches)
def set_vector_uv(self, uu, vv, c=None, **kwargs):
"""sets the vector field U,V and color for all telescopes
Parameters
----------
uu: array[num_tels]
x-component of direction vector
vv: array[num_tels]
y-component of direction vector
c: color or list of colors
vector color for each telescope (or one for all)
kwargs:
extra args passed to plt.quiver(), ignored on subsequent updates
"""
coords = self.tel_coords
uu = u.Quantity(uu).to_value("m")
vv = u.Quantity(vv).to_value("m")
N = len(coords.x)
# matplotlib since 3.2 does not allow scalars anymore
# if quiver was already created with a certain number of arrows
if np.isscalar(uu):
uu = np.full(N, uu)
if np.isscalar(vv):
vv = np.full(N, vv)
# passing in None for C does not work, we need to provide
# a variadic number of arguments
args = [coords.x.to_value("m"), coords.y.to_value("m"), uu, vv]
if c is None:
# use colors by telescope type if the user did not provide any
kwargs["color"] = kwargs.get("color", self.tel_colors)
else:
# same as above, enable use of scalar to set all values at once
if np.isscalar(c):
c = np.full(N, c)
args.append(c)
if self._quiver is None:
self._quiver = self.axes.quiver(*args,
scale_units="xy",
angles="xy",
scale=1,
**kwargs)
else:
self._quiver.set_UVC(uu, vv, c)
def set_vector_rho_phi(self, rho, phi, c=None, **kwargs):
"""sets the vector field using R, Phi for each telescope
Parameters
----------
rho: float or array[float]
vector magnitude for each telescope
phi: array[Angle]
vector angle for each telescope
c: color or list of colors
vector color for each telescope (or one for all)
"""
phi = Angle(phi).rad
uu, vv = polar_to_cart(rho, phi)
self.set_vector_uv(uu, vv, c=c, **kwargs)
def set_vector_hillas(self, hillas_dict, core_dict, length, time_gradient,
angle_offset):
"""
Function to set the vector angle and length from a set of Hillas parameters.
In order to proper use the arrow on the ground, also a dictionary with the time
gradients for the different telescopes is needed. If the gradient is 0 the arrow
is not plotted on the ground, whereas if the value of the gradient is negative,
the arrow is rotated by 180 degrees (Angle(angle_offset) not added).
This plotting behaviour has been tested with the timing_parameters function
in ctapipe/image.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
core_dict : Dict[int, CoreParameters]
mapping of tel_id to CoreParametersContainer
length: Float
length of the arrow (in meters)
time_gradient: Dict[int, value of time gradient (no units)]
dictionary for value of the time gradient for each telescope
angle_offset: Float
This should be the ``event.pointing.array_azimuth`` parameter
"""
# rot_angle_ellipse is psi parameter in HillasParametersContainer
rho = np.zeros(self.subarray.num_tels) * u.m
rot_angle_ellipse = np.zeros(self.subarray.num_tels) * u.deg
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
rho[idx] = u.Quantity(length, u.m)
psi = core_dict[tel_id]
if time_gradient[tel_id] > 0.01:
angle_offset = Angle(angle_offset)
rot_angle_ellipse[idx] = psi + angle_offset + 180 * u.deg
elif time_gradient[tel_id] < -0.01:
rot_angle_ellipse[idx] = psi + angle_offset
else:
rho[idx] = 0 * u.m
self.set_vector_rho_phi(rho=rho, phi=rot_angle_ellipse)
def set_line_hillas(self, hillas_dict, core_dict, range, **kwargs):
"""
Plot the telescope-wise direction of the shower as a segment.
Each segment will be centered with a point on the telescope position
and will be 2*range long.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
core_dict : Dict[int, CoreParameters]
mapping of tel_id to CoreParametersContainer
range: float
half of the length of the segments to be plotted (in meters)
"""
coords = self.tel_coords
c = self.tel_colors
r = np.array([-range, range])
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
x_0 = coords[idx].x.to_value(u.m)
y_0 = coords[idx].y.to_value(u.m)
psi = core_dict[tel_id]
x = x_0 + np.cos(psi).value * r
y = y_0 + np.sin(psi).value * r
self.axes.plot(x, y, color=c[idx], **kwargs)
self.axes.scatter(x_0, y_0, color=c[idx])
def add_labels(self):
px = self.tel_coords.x.to_value("m")
py = self.tel_coords.y.to_value("m")
for tel, x, y, r in zip(self.subarray.tels, px, py, self.radii):
name = str(tel)
lab = self.axes.text(
x,
y - r * 1.8,
name,
fontsize=8,
clip_on=True,
horizontalalignment="center",
verticalalignment="top",
)
self._labels.append(lab)
def remove_labels(self):
for lab in self._labels:
lab.remove()
self._labels = []
def _update(self):
"""signal a redraw if necessary"""
if self.autoupdate:
plt.draw()
def background_contour(self, x, y, background, **kwargs):
"""
Draw image contours in background of the display, useful when likelihood fitting
Parameters
----------
x: ndarray
array of image X coordinates
y: ndarray
array of image Y coordinates
background: ndarray
Array of image to use in background
kwargs: key=value
any style keywords to pass to matplotlib
"""
# use zorder to ensure the contours appear under the telescopes.
self.axes.contour(x, y, background, zorder=0, **kwargs)
# for i in range(1,24):
# chrom_set.add("chr" + str(i))
sorted_chrom = sorted(chrom_set,key=lambda x:int(x.rsplit("chr")[-1]))
sorted_chrom_colors = [chromosome_colors[x] for x in sorted_chrom]
legend_patches = []
for chrom,color in zip(sorted_chrom,sorted_chrom_colors):
legend_patches.append(mpatches.Patch(color=color,label=chrom))
plt.legend(handles=legend_patches,fontsize=8,loc=3,bbox_to_anchor=(-.3,.15))
p = PatchCollection(patches)
p.set_facecolor(f_color_v)
p.set_edgecolor(e_color_v)
p.set_linewidth(lw_v)
ax.add_collection(p)
ax.set_aspect(1.0)
plt.axis('off')
plt.savefig(fname + '.png',dpi=600)
plt.savefig(fname + '.pdf',format='pdf')
plt.close()
print "done plotting bionano"
#handles basic (non-bionano case)
if args.feature_labels:
#make a separate figure
plt.clf()
fig, ax = plt.subplots()
patches = []
def Plot2D(self, xy=None, elecon=None, u=None, color=None, ax=None, show=0,
weight=None, colorby=None, linestyle='-', label=None, xlim=None,
ylim=None, filename=None, **kwds):
assert self.dimensions == 2
from matplotlib.patches import Polygon
import matplotlib.lines as mlines
from matplotlib.collections import PatchCollection
from matplotlib.cm import coolwarm, Spectral
import matplotlib.pyplot as plt
if xy is None:
xy = array(self.coord)
if elecon is None:
elecon = []
for blk in self.eleblx:
elecon.extend(blk.elecon.tolist())
elecon = asarray(elecon)
if u is not None:
xy += u.reshape(xy.shape)
patches = []
for points in xy[elecon[:]]:
quad = Polygon(points, True)
patches.append(quad)
if ax is None:
fig, ax = plt.subplots()
#colors = 100 * random.rand(len(patches))
p = PatchCollection(patches, linewidth=weight, **kwds)
if colorby is not None:
colorby = asarray(colorby).flatten()
if len(colorby) == len(xy):
# average value in element
colorby = array([average(colorby[points]) for points in elecon])
p.set_cmap(Spectral) #coolwarm)
p.set_array(colorby)
p.set_clim(vmin=colorby.min(), vmax=colorby.max())
fig.colorbar(p)
else:
p.set_edgecolor(color)
p.set_facecolor('None')
p.set_linewidth(weight)
p.set_linestyle(linestyle)
if label:
ax.plot([], [], color=color, linestyle=linestyle, label=label)
ax.add_collection(p)
if not ylim:
ymin, ymax = amin(xy[:,1]), amax(xy[:,1])
dy = max(abs(ymin*.05), abs(ymax*.05))
ax.set_ylim([ymin-dy, ymax+dy])
else:
ax.set_ylim(ylim)
if not xlim:
xmin, xmax = amin(xy[:,0]), amax(xy[:,0])
dx = max(abs(xmin*.05), abs(xmax*.05))
ax.set_xlim([xmin-dx, xmax+dx])
else:
ax.set_xlim(xlim)
ax.set_aspect('equal')
if show:
plt.show()
if filename is not None:
plt.legend()
plt.savefig(filename, transparent=True,
bbox_inches="tight", pad_inches=0)
return ax
class ArrayDisplay:
"""
Display a top-town view of a telescope array.
This can be used in two ways: by default, you get a display of all
telescopes in the subarray, colored by telescope type, however you can
also color the telescopes by a value (like trigger pattern, or some other
scalar per-telescope parameter). To set the color value, simply set the
`value` attribute, and the fill color will be updated with the value. You
might want to set the border color to zero to avoid confusion between the
telescope type color and the value color (
`array_disp.telescope.set_linewidth(0)`)
To display a vector field over the telescope positions, e.g. for
reconstruction, call `set_uv()` to set cartesian vectors, or `set_r_phi()`
to set polar coordinate vectors. These both take an array of length
N_tels, or a single value.
Parameters
----------
subarray: ctapipe.instrument.SubarrayDescription
the array layout to display
axes: matplotlib.axes.Axes
matplotlib axes to plot on, or None to use current one
title: str
title of array plot
tel_scale: float
scaling between telescope mirror radius in m to displayed size
autoupdate: bool
redraw when the input changes
radius: Union[float, list, None]
set telescope radius to value, list/array of values. If None, radius
is taken from the telescope's mirror size.
"""
def __init__(self, subarray, axes=None, autoupdate=True,
tel_scale=2.0, alpha=0.7, title=None,
radius=None, frame=GroundFrame()):
self.frame = frame
self.subarray = subarray
# get the telescope positions. If a new frame is set, this will
# transform to the new frame.
self.tel_coords = subarray.tel_coords.transform_to(frame)
# set up colors per telescope type
tel_types = [str(tel) for tel in subarray.tels.values()]
if radius is None:
# set radius to the mirror radius (so big tels appear big)
radius = [np.sqrt(tel.optics.mirror_area.to("m2").value) * tel_scale
for tel in subarray.tel.values()]
if title is None:
title = subarray.name
# get default matplotlib color cycle (depends on the current style)
color_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])
# map a color to each telescope type:
tel_type_to_color = {}
for tel_type in list(set(tel_types)):
tel_type_to_color[tel_type] = next(color_cycle)
tel_color = [tel_type_to_color[ttype] for ttype in tel_types]
patches = []
for x, y, r, c in zip(list(self.tel_coords.x.value),
list(self.tel_coords.y.value),
list(radius),
tel_color):
patches.append(
Circle(
xy=(x, y),
radius=r,
fill=True,
color=c,
alpha=alpha,
)
)
# build the legend:
legend_elements = []
for ttype in list(set(tel_types)):
color = tel_type_to_color[ttype]
legend_elements.append(
Line2D([0], [0], marker='o', color=color,
label=ttype, markersize=10, alpha=alpha,
linewidth=0)
)
plt.legend(handles=legend_elements)
self.tel_colors = tel_color
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_linewidth(2.0)
self.axes = axes or plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self._labels = []
self._quiver = None
self.axes.autoscale_view()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def set_vector_uv(self, u, v, c=None, **kwargs):
""" sets the vector field U,V and color for all telescopes
Parameters
----------
u: array[num_tels]
x-component of direction vector
v: array[num_tels]
y-component of direction vector
c: color or list of colors
vector color for each telescope (or one for all)
kwargs:
extra args passed to plt.quiver(), ignored on subsequent updates
"""
if c is None:
c = self.tel_colors
if self._quiver is None:
coords = self.tel_coords
self._quiver = self.axes.quiver(
coords.x, coords.y,
u, v,
color=c,
scale_units='xy',
angles='xy',
scale=1,
**kwargs
)
else:
self._quiver.set_UVC(u, v)
def set_vector_rho_phi(self, rho, phi, c=None, **kwargs):
"""sets the vector field using R, Phi for each telescope
Parameters
----------
rho: float or array[float]
vector magnitude for each telescope
phi: array[Angle]
vector angle for each telescope
c: color or list of colors
vector color for each telescope (or one for all)
"""
phi = Angle(phi).rad
u, v = polar_to_cart(rho, phi)
self.set_vector_uv(u, v, c=c, **kwargs)
def set_vector_hillas(self, hillas_dict, length, time_gradient, angle_offset):
"""
Function to set the vector angle and length from a set of Hillas parameters.
In order to proper use the arrow on the ground, also a dictionary with the time
gradients for the different telescopes is needed. If the gradient is 0 the arrow
is not plotted on the ground, whereas if the value of the gradient is negative,
the arrow is rotated by 180 degrees (Angle(angle_offset) not added).
This plotting behaviour has been tested with the timing_parameters function
in ctapipe/image.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
length: Float
length of the arrow (in meters)
time_gradient: Dict[int, value of time gradient (no units)]
dictionary for value of the time gradient for each telescope
angle_offset: Float
This should be the event.mcheader.run_array_direction[0] parameter
"""
# rot_angle_ellipse is psi parameter in HillasParametersContainer
rho = np.zeros(self.subarray.num_tels) * u.m
rot_angle_ellipse = np.zeros(self.subarray.num_tels) * u.deg
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
rho[idx] = length * u.m
if time_gradient[tel_id] > 0.01:
params.psi = Angle(params.psi)
angle_offset = Angle(angle_offset)
rot_angle_ellipse[idx] = params.psi + angle_offset + 180 * u.deg
elif time_gradient[tel_id] < -0.01:
rot_angle_ellipse[idx] = params.psi + angle_offset
else:
rho[idx] = 0 * u.m
self.set_vector_rho_phi(rho=rho, phi=rot_angle_ellipse)
def set_line_hillas(self, hillas_dict, range, **kwargs):
"""
Function to plot a segment of length 2*range for each telescope from a set of Hillas parameters.
The segment is centered on the telescope position.
A point is added at each telescope position for better visualization.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
range: float
half of the length of the segments to be plotted (in meters)
"""
coords = self.tel_coords
c = self.tel_colors
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
x_0 = coords[idx].x.value
y_0 = coords[idx].y.value
m = np.tan(Angle(params.psi))
x = x_0 + np.linspace(-range, range, 50)
y = y_0 + m * (x - x_0)
distance = np.sqrt((x - x_0) ** 2 + (y - y_0) ** 2)
mask = np.ma.masked_where(distance < range, distance).mask
self.axes.plot(x[mask], y[mask], color=c[idx], **kwargs)
self.axes.scatter(x_0, y_0, color=c[idx])
def add_labels(self):
px = self.tel_coords.x.value
py = self.tel_coords.y.value
for tel, x, y in zip(self.subarray.tels, px, py):
name = str(tel)
lab = self.axes.text(x, y, name, fontsize=8, clip_on=True)
self._labels.append(lab)
def remove_labels(self):
for lab in self._labels:
lab.remove()
self._labels = []
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def background_contour(self, x, y, background, **kwargs):
"""
Draw image contours in background of the display, useful when likelihood fitting
Parameters
----------
x: ndarray
array of image X coordinates
y: ndarray
array of image Y coordinates
background: ndarray
Array of image to use in background
kwargs: key=value
any style keywords to pass to matplotlib
"""
# use zorder to ensure the contours appear under the telescopes.
self.axes.contour(x, y, background, zorder=0, **kwargs)
def s_mu_tpcf_patches(tpcf, s_bins, mu_bins, cmap=cm.cool, vmin=None, vmax=None):
"""
Return a collection of patches that can be used to plot the tpcf in s and mu bins.
Parameters
----------
tcpf : np.array
numpy array with dimension len(s_bins) by len(mu_bins).
This is the output of tpcf_s_mu()
s_bins : np.array
numpy array of shape (num_s_bin_edges, ) storing the :math:`s`
boundaries defining the bins in which pairs are counted.
mu_bins : np.array
numpy array of shape (num_mu_bin_edges, ) storing the
:math:`\cos(\theta_{\rm LOS})` boundaries defining the bins in
which pairs are counted. All values must be between [0,1].
cmap : matplotlib.colors.LinearSegmentedColormap object
color map to use for coloring the patches
vmin : float
minimum value of tpcf used to normalize luminance data. Default is min(tpcf).
vmax : float
maximum value of tpcf used to normalize luminance data. Default is max(tpcf).
Returns
-------
p : matplotlib.collections.PatchCollection object
collection of patches
Example
-------
For demonstration purposes we create a randomly distributed set of points within a
periodic cube of length 250 Mpc/h.
>>> Npts = 100
>>> Lbox = 250.
>>> x = np.random.uniform(0, Lbox, Npts)
>>> y = np.random.uniform(0, Lbox, Npts)
>>> z = np.random.uniform(0, Lbox, Npts)
We transform our *x, y, z* points into the array shape used by the pair-counter by
taking the transpose of the result of `numpy.vstack`. This boilerplate transformation
is used throughout the `~halotools.mock_observables` sub-package:
>>> sample1 = np.vstack((x,y,z)).T
First, we calculate the correlation function using
`~halotools.mock_observables.s_mu_tpcf`.
>>> from halotools.mock_observables import s_mu_tpcf
>>> s_bins = np.linspace(0.01, 25, 10)
>>> mu_bins = np.linspace(0, 1, 15)
>>> xi_s_mu = s_mu_tpcf(sample1, s_bins, mu_bins, period=Lbox)
Get a collection of patches taht represent the tpcf in s and mu bins:
>>> p = s_mu_tpcf_patches(xi_s_mu, s_bins, mu_bins)
Now, you can plot the tpcf:
>>> fig, ax = plt.subplots()
>>> ax.add_collection(p)
>>> ax.set_xlim([0,25])
>>> ax.set_ylim([0,25])
>>> plt.show()
"""
#normalize tpcf
if vmin is None:
min_tpcf = 1.0*np.min(tpcf)
else:
min_tpcf = vmin
if vmax is None:
max_tpcf = 1.0*np.max(tpcf)
else:
max_tpcf = vmax
#set values over and under the vmin and vmax values
mask = (tpcf>max_tpcf)
tpcf[mask] = max_tpcf
mask = (tpcf<min_tpcf)
tpcf[mask] = min_tpcf
#create lists to store wedges and color
wedges = []
colors=[]
#set center of wedges to be the origin
center = (0.0,0.0)
#convert to degrees
theta_bins = np.degrees(np.arccos(mu_bins))
#change convention to start theta=0.0 on the positive x-axis
#instead of the positive y-axis as is the theta_LOS convention
theta_bins = 90.0 - theta_bins
#loop over s bins
for i, (s_low, s_high) in enumerate(zip(s_bins[:-1], s_bins[1:])):
#loop over mu_bins
for j, (theta_high, theta_low) in enumerate(zip(theta_bins[:-1], theta_bins[1:])):
#set radial extent
r = s_high
dr = s_high-s_low
#set angular extent
theta1 = theta_high
theta2 = theta_low
#set color
c = 1.0*(tpcf[i,j]-min_tpcf)/max_tpcf
#store wedges
colors.append(c)
wedges.append(Wedge(center, r, theta1, theta2, width=dr))
#create a collection of patches
p = PatchCollection(wedges, cmap=cmap)
colors = np.array(colors)
p.set_array(colors)
p.set_linewidth(0.0)
return p
class ArrayDisplay:
"""
Display a top-town view of a telescope array
"""
def __init__(self, telx, tely, tel_type=None, radius=20,
axes=None, title="Array", autoupdate=True):
if tel_type is None:
tel_type = np.ones(len(telx))
patches = [Rectangle(xy=(x-radius/2, y-radius/2), width=radius, height=radius, fill=False)
for x, y in zip(telx, tely)]
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_clim(1, 9)
rgb = matplotlib.cm.Set1((tel_type-1)/9)
self.telescopes.set_edgecolor(rgb)
self.telescopes.set_linewidth(2.0)
self.axes = axes if axes is not None else plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self.axes.set_xlim(-1000, 1000)
self.axes.set_ylim(-1000, 1000)
self.axes_hillas = axes if axes is not None else plt.gca()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def add_ellipse(self, centroid, length, width, angle, **kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid, width=length, height=width,
angle=np.degrees(angle), fill=True, **kwargs)
self.axes.add_patch(ellipse)
return ellipse
def add_polygon(self, centroid, radius, nsides=3, **kwargs):
"""
plot a polygon on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
radius: float
radius
nsides: int
Number of points on polygon
kwargs:
any MatPlotLib style arguments to pass to the RegularPolygon patch
"""
polygon = RegularPolygon(xy=centroid, radius=radius, numVertices=nsides, **kwargs)
self.axes.add_patch(polygon)
return polygon
def overlay_moments(self, momparams, tel_position, scale_fac, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
scale_fac: float
scaling factor to apply to width and length when overlaying moments
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
ellipse_list = list()
size_list = list()
i = 0
for h in momparams:
length = u.Quantity(momparams[h].length).value * scale_fac
width = u.Quantity(momparams[h].width).value * scale_fac
size_list.append(u.Quantity(momparams[h].size).value)
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
i += 1
ellipse = Ellipse(xy=(tel_x,tel_y), width=length, height=width,
angle=np.degrees(momparams[h].psi.rad))
ellipse_list.append(ellipse)
patches = PatchCollection(ellipse_list, **kwargs)
patches.set_clim(0, 1000) # Set ellipse colour based on image size
patches.set_array(np.asarray(size_list))
self.axes_hillas.add_collection(patches)
def overlay_axis(self, momparams, tel_position, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
i = 0
for h in momparams:
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
psi = u.Quantity(momparams[h].psi).value
x_sc = [tel_x - np.cos(psi) * 10000, tel_x + np.cos(psi) * 10000]
y_sc = [tel_y - np.sin(psi) * 10000, tel_y + np.sin(psi) * 10000]
i += 1
self.axes_hillas.add_line(Line2D(x_sc, y_sc, linestyle='dashed', color='black'))
import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
if __name__ == '__main__':
data = np.genfromtxt("polygon.txt", delimiter=',')
triangle_indices = np.genfromtxt("triangle_indices.txt", delimiter=',')
triangle_indices = triangle_indices.astype(int)
fig, ax = plt.subplots()
patches = []
# Plot polygon
convex_polygon = Polygon(data, closed=True)
patches.append(convex_polygon)
# Plot triangle vertices
for i in range(np.size(triangle_indices, 0)):
triangle = Polygon(data[triangle_indices[i, :]], closed=True)
patches.append(triangle)
polygons = PatchCollection(patches, alpha=0.4)
polygons.set_facecolor('red')
polygons.set_edgecolor('green')
polygons.set_linewidth(2)
ax.add_collection(polygons)
ax.autoscale_view()
plt.show()
class ArrayDisplay:
"""
Display a top-town view of a telescope array.
This can be used in two ways: by default, you get a display of all
telescopes in the subarray, colored by telescope type, however you can
also color the telescopes by a value (like trigger pattern, or some other
scalar per-telescope parameter). To set the color value, simply set the
`value` attribute, and the fill color will be updated with the value. You
might want to set the border color to zero to avoid confusion between the
telescope type color and the value color (
`array_disp.telescope.set_linewidth(0)`)
To display a vector field over the telescope positions, e.g. for
reconstruction, call `set_uv()` to set cartesian vectors, or `set_r_phi()`
to set polar coordinate vectors. These both take an array of length
N_tels, or a single value.
Parameters
----------
subarray: ctapipe.instrument.SubarrayDescription
the array layout to display
axes: matplotlib.axes.Axes
matplotlib axes to plot on, or None to use current one
title: str
title of array plot
tel_scale: float
scaling between telescope mirror radius in m to displayed size
autoupdate: bool
redraw when the input changes
radius: Union[float, list, None]
set telescope radius to value, list/array of values. If None, radius
is taken from the telescope's mirror size.
"""
def __init__(self,
subarray,
axes=None,
autoupdate=True,
tel_scale=2.0,
alpha=0.7,
title=None,
radius=None,
frame=GroundFrame()):
self.frame = frame
self.subarray = subarray
# get the telescope positions. If a new frame is set, this will
# transform to the new frame.
self.tel_coords = subarray.tel_coords.transform_to(frame)
# set up colors per telescope type
tel_types = [str(tel) for tel in subarray.tels.values()]
if radius is None:
# set radius to the mirror radius (so big tels appear big)
radius = [
np.sqrt(tel.optics.mirror_area.to("m2").value) * tel_scale
for tel in subarray.tel.values()
]
if title is None:
title = subarray.name
# get default matplotlib color cycle (depends on the current style)
color_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])
# map a color to each telescope type:
tel_type_to_color = {}
for tel_type in list(set(tel_types)):
tel_type_to_color[tel_type] = next(color_cycle)
tel_color = [tel_type_to_color[ttype] for ttype in tel_types]
patches = []
for x, y, r, c in zip(list(self.tel_coords.x.value),
list(self.tel_coords.y.value), list(radius),
tel_color):
patches.append(
Circle(
xy=(x, y),
radius=r,
fill=True,
color=c,
alpha=alpha,
))
# build the legend:
legend_elements = []
for ttype in list(set(tel_types)):
color = tel_type_to_color[ttype]
legend_elements.append(
Line2D([0], [0],
marker='o',
color=color,
label=ttype,
markersize=10,
alpha=alpha,
linewidth=0))
plt.legend(handles=legend_elements)
self.tel_colors = tel_color
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_linewidth(2.0)
self.axes = axes or plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self._labels = []
self._quiver = None
self.axes.autoscale_view()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def set_vector_uv(self, u, v, c=None, **kwargs):
""" sets the vector field U,V and color for all telescopes
Parameters
----------
u: array[num_tels]
x-component of direction vector
v: array[num_tels]
y-component of direction vector
kwargs:
extra args passed to plt.quiver(), ignored on subsequent updates
"""
if c is None:
c = self.tel_colors
if self._quiver is None:
coords = self.tel_coords
self._quiver = self.axes.quiver(coords.x,
coords.y,
u,
v,
color=c,
**kwargs)
else:
self._quiver.set_UVC(u, v)
def set_vector_rho_phi(self, rho, phi, c=None, **kwargs):
"""sets the vector field using R, Phi for each telescope
Parameters
----------
rho: float or array[float]
vector magnitude for each telescope
phi: array[Angle]
vector angle for each telescope
c: color or list of colors
vector color for each telescope (or one for all)
"""
phi = Angle(phi).rad
u, v = polar_to_cart(rho, phi)
self.set_vector_uv(u, v, c=c, **kwargs)
def set_vector_hillas(self, hillas_dict, angle_offset=180 * u.deg):
"""
helper function to set the vector angle and length from a set of
Hillas parameters.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
"""
rho = np.zeros(self.subarray.num_tels) * u.m
phi = np.zeros(self.subarray.num_tels) * u.deg
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
rho[idx] = 1.0 * u.m # params.length
phi[idx] = Angle(params.phi) + Angle(angle_offset)
self.set_vector_rho_phi(rho=rho, phi=phi)
def add_labels(self):
px = self.tel_coords.x.value
py = self.tel_coords.y.value
for tel, x, y in zip(self.subarray.tels, px, py):
name = str(tel)
lab = self.axes.text(x, y, name, fontsize=8)
self._labels.append(lab)
def remove_labels(self):
for lab in self._labels:
lab.remove()
self._labels = []
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def background_contour(self, x, y, background, **kwargs):
"""
Draw image contours in background of the display, useful when likelihood fitting
Parameters
----------
x: ndarray
array of image X coordinates
y: ndarray
array of image Y coordinates
background: ndarray
Array of image to use in background
kwargs: key=value
any style keywords to pass to matplotlib
"""
# use zorder to ensure the contours appear under the telescopes.
self.axes.contour(x, y, background, zorder=0, **kwargs)
class ArrayDisplay:
"""
Display a top-town view of a telescope array.
This can be used in two ways: by default, you get a display of all
telescopes in the subarray, colored by telescope type, however you can
also color the telescopes by a value (like trigger pattern, or some other
scalar per-telescope parameter). To set the color value, simply set the
`value` attribute, and the fill color will be updated with the value. You
might want to set the border color to zero to avoid confusion between the
telescope type color and the value color (
`array_disp.telescope.set_linewidth(0)`)
To display a vector field over the telescope positions, e.g. for
reconstruction, call `set_uv()` to set cartesian vectors, or `set_r_phi()`
to set polar coordinate vectors. These both take an array of length
N_tels, or a single value.
Parameters
----------
subarray: ctapipe.instrument.SubarrayDescription
the array layout to display
axes: matplotlib.axes.Axes
matplotlib axes to plot on, or None to use current one
title: str
title of array plot
tel_scale: float
scaling between telescope mirror radius in m to displayed size
autoupdate: bool
redraw when the input changes
radius: Union[float, list, None]
set telescope radius to value, list/array of values. If None, radius
is taken from the telescope's mirror size.
"""
def __init__(self,
subarray,
axes=None,
autoupdate=True,
tel_scale=2.0,
alpha=0.7,
title=None,
radius=None,
frame=GroundFrame()):
self.frame = frame
self.subarray = subarray
# get the telescope positions. If a new frame is set, this will
# transform to the new frame.
self.tel_coords = subarray.tel_coords.transform_to(frame)
# set up colors per telescope type
tel_types = [str(tel) for tel in subarray.tels.values()]
if radius is None:
# set radius to the mirror radius (so big tels appear big)
radius = [
np.sqrt(tel.optics.mirror_area.to("m2").value) * tel_scale
for tel in subarray.tel.values()
]
if title is None:
title = subarray.name
# get default matplotlib color cycle (depends on the current style)
color_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])
# map a color to each telescope type:
tel_type_to_color = {}
for tel_type in list(set(tel_types)):
tel_type_to_color[tel_type] = next(color_cycle)
tel_color = [tel_type_to_color[ttype] for ttype in tel_types]
patches = []
for x, y, r, c in zip(list(self.tel_coords.x.value),
list(self.tel_coords.y.value), list(radius),
tel_color):
patches.append(
Circle(
xy=(x, y),
radius=r,
fill=True,
color=c,
alpha=alpha,
))
# build the legend:
legend_elements = []
for ttype in list(set(tel_types)):
color = tel_type_to_color[ttype]
legend_elements.append(
Line2D([0], [0],
marker='o',
color=color,
label=ttype,
markersize=10,
alpha=alpha,
linewidth=0))
plt.legend(handles=legend_elements)
self.tel_colors = tel_color
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_linewidth(2.0)
self.axes = axes or plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self._labels = []
self._quiver = None
self.axes.autoscale_view()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def set_vector_uv(self, u, v, c=None, **kwargs):
""" sets the vector field U,V and color for all telescopes
Parameters
----------
u: array[num_tels]
x-component of direction vector
v: array[num_tels]
y-component of direction vector
c: color or list of colors
vector color for each telescope (or one for all)
kwargs:
extra args passed to plt.quiver(), ignored on subsequent updates
"""
if c is None:
c = self.tel_colors
if self._quiver is None:
coords = self.tel_coords
self._quiver = self.axes.quiver(coords.x,
coords.y,
u,
v,
color=c,
scale_units='xy',
angles='xy',
scale=1,
**kwargs)
else:
self._quiver.set_UVC(u, v)
def set_vector_rho_phi(self, rho, phi, c=None, **kwargs):
"""sets the vector field using R, Phi for each telescope
Parameters
----------
rho: float or array[float]
vector magnitude for each telescope
phi: array[Angle]
vector angle for each telescope
c: color or list of colors
vector color for each telescope (or one for all)
"""
phi = Angle(phi).rad
u, v = polar_to_cart(rho, phi)
self.set_vector_uv(u, v, c=c, **kwargs)
def set_vector_hillas(self, hillas_dict, length, time_gradient,
angle_offset):
"""
Function to set the vector angle and length from a set of Hillas parameters.
In order to proper use the arrow on the ground, also a dictionary with the time
gradients for the different telescopes is needed. If the gradient is 0 the arrow
is not plotted on the ground, whereas if the value of the gradient is negative,
the arrow is rotated by 180 degrees (Angle(angle_offset) not added).
This plotting behaviour has been tested with the timing_parameters function
in ctapipe/image.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
length: Float
length of the arrow (in meters)
time_gradient: Dict[int, value of time gradient (no units)]
dictionary for value of the time gradient for each telescope
angle_offset: Float
This should be the event.mcheader.run_array_direction[0] parameter
"""
# rot_angle_ellipse is psi parameter in HillasParametersContainer
rho = np.zeros(self.subarray.num_tels) * u.m
rot_angle_ellipse = np.zeros(self.subarray.num_tels) * u.deg
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
rho[idx] = length * u.m
if time_gradient[tel_id] > 0.01:
params.psi = Angle(params.psi)
angle_offset = Angle(angle_offset)
rot_angle_ellipse[
idx] = params.psi + angle_offset + 180 * u.deg
elif time_gradient[tel_id] < -0.01:
rot_angle_ellipse[idx] = params.psi + angle_offset
else:
rho[idx] = 0 * u.m
self.set_vector_rho_phi(rho=rho, phi=rot_angle_ellipse)
def set_line_hillas(self, hillas_dict, range, **kwargs):
"""
Function to plot a segment of length 2*range for each telescope from a set of Hillas parameters.
The segment is centered on the telescope position.
A point is added at each telescope position for better visualization.
Parameters
----------
hillas_dict: Dict[int, HillasParametersContainer]
mapping of tel_id to Hillas parameters
range: float
half of the length of the segments to be plotted (in meters)
"""
coords = self.tel_coords
c = self.tel_colors
for tel_id, params in hillas_dict.items():
idx = self.subarray.tel_indices[tel_id]
x_0 = coords[idx].x.value
y_0 = coords[idx].y.value
m = np.tan(Angle(params.psi))
x = x_0 + np.linspace(-range, range, 50)
y = y_0 + m * (x - x_0)
distance = np.sqrt((x - x_0)**2 + (y - y_0)**2)
mask = np.ma.masked_where(distance < range, distance).mask
self.axes.plot(x[mask], y[mask], color=c[idx], **kwargs)
self.axes.scatter(x_0, y_0, color=c[idx])
def add_labels(self):
px = self.tel_coords.x.value
py = self.tel_coords.y.value
for tel, x, y in zip(self.subarray.tels, px, py):
name = str(tel)
lab = self.axes.text(x, y, name, fontsize=8, clip_on=True)
self._labels.append(lab)
def remove_labels(self):
for lab in self._labels:
lab.remove()
self._labels = []
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def background_contour(self, x, y, background, **kwargs):
"""
Draw image contours in background of the display, useful when likelihood fitting
Parameters
----------
x: ndarray
array of image X coordinates
y: ndarray
array of image Y coordinates
background: ndarray
Array of image to use in background
kwargs: key=value
any style keywords to pass to matplotlib
"""
# use zorder to ensure the contours appear under the telescopes.
self.axes.contour(x, y, background, zorder=0, **kwargs)
