class ArrayDisplay:
"""
Display a top-town view of a telescope array
"""
def __init__(self, telx, tely, mirrorarea,
axes=None, title="Array", autoupdate=True):
patches = [Circle(xy=(x, y), radius=np.sqrt(a))
for x, y, a in zip(telx, tely, mirrorarea)]
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches)
self.telescopes.set_clim(0, 100)
self.telescopes.set_array(np.zeros(len(telx)))
self.telescopes.set_cmap('spectral_r')
self.telescopes.set_edgecolor('none')
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.bar = plt.colorbar(self.telescopes)
self.bar.set_label("Value")
@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()
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)
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'))
class CameraDisplay:
"""
Camera Display using matplotlib.
Parameters
----------
geometry : `~ctapipe.instrument.CameraGeometry`
Definition of the Camera/Image
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
ax : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one
title : str (default "Camera")
Title to put on camera plot
norm : str or `matplotlib.color.Normalize` instance (default 'lin')
Normalization for the color scale.
Supported str arguments are
- 'lin': linear scale
- 'log': logarithmic scale (base 10)
cmap : str or `matplotlib.colors.Colormap` (default 'hot')
Color map to use (see `matplotlib.cm`)
allow_pick : bool (default False)
if True, allow user to click and select a pixel
autoupdate : bool (default True)
redraw automatically (otherwise need to call plt.draw())
autoscale : bool (default True)
rescale the vmin/vmax values when the image changes.
This is set to False if `set_limits_*` is called to explicity
set data limits.
antialiased : bool (default True)
whether to draw in antialiased mode or not.
Notes
-----
Speed:
CameraDisplay is not intended to be very fast (matplotlib
is not a very speed performant graphics library, it is
intended for nice output plots). However, most of the
slowness of CameraDisplay is in the constructor. Once one is
displayed, changing the image that is displayed is relatively
fast and efficient. Therefore it is best to initialize an
instance, and change the data, rather than generating new
CameraDisplays.
Pixel Implementation:
Pixels are rendered as a
`matplotlib.collections.PatchCollection` of Polygons (either 6
or 4 sided). You can access the PatchCollection directly (to
e.g. change low-level style parameters) via
`CameraDisplay.pixels`
Output:
Since CameraDisplay uses matplotlib, any display can be
saved to any output file supported via
plt.savefig(filename). This includes `.pdf` and `.png`.
"""
def __init__(
self,
geometry,
image=None,
ax=None,
title=None,
norm="lin",
cmap=None,
allow_pick=False,
autoupdate=True,
autoscale=True,
antialiased=True,
):
self.axes = ax if ax is not None else plt.gca()
self.geom = geometry
self.pixels = None
self.colorbar = None
self.autoupdate = autoupdate
self.autoscale = autoscale
self._active_pixel = None
self._active_pixel_label = None
if title is None:
title = geometry.cam_id
# initialize the plot and generate the pixels as a
# RegularPolyCollection
patches = []
if not hasattr(self.geom, "mask"):
self.geom.mask = np.ones_like(self.geom.pix_x.value, dtype=bool)
for xx, yy, aa in zip(
u.Quantity(self.geom.pix_x[self.geom.mask]).value,
u.Quantity(self.geom.pix_y[self.geom.mask]).value,
u.Quantity(np.array(self.geom.pix_area)[self.geom.mask]).value):
if self.geom.pix_type.startswith("hex"):
rr = sqrt(aa * 2 / 3 / sqrt(3)) + 2*PIXEL_EPSILON
poly = RegularPolygon(
(xx, yy), 6, radius=rr,
orientation=self.geom.pix_rotation.rad,
fill=True,
)
else:
rr = sqrt(aa) + PIXEL_EPSILON
poly = Rectangle(
(xx-rr/2., yy-rr/2.),
width=rr,
height=rr,
angle=self.geom.pix_rotation.deg,
fill=True,
)
patches.append(poly)
self.pixels = PatchCollection(patches, cmap=cmap, linewidth=0)
self.axes.add_collection(self.pixels)
self.pixel_highlighting = copy.copy(self.pixels)
self.pixel_highlighting.set_facecolor('none')
self.pixel_highlighting.set_linewidth(0)
self.axes.add_collection(self.pixel_highlighting)
# Set up some nice plot defaults
self.axes.set_aspect('equal', 'datalim')
self.axes.set_title(title)
self.axes.set_xlabel("X position ({})".format(self.geom.pix_x.unit))
self.axes.set_ylabel("Y position ({})".format(self.geom.pix_y.unit))
self.axes.autoscale_view()
# set up a patch to display when a pixel is clicked (and
# pixel_picker is enabled):
self._active_pixel = copy.copy(patches[0])
self._active_pixel.set_facecolor('r')
self._active_pixel.set_alpha(0.5)
self._active_pixel.set_linewidth(2.0)
self._active_pixel.set_visible(False)
self.axes.add_patch(self._active_pixel)
self._active_pixel_label = self.axes.text(self._active_pixel.xy[0],
self._active_pixel.xy[1],
"0",
horizontalalignment='center',
verticalalignment='center')
self._active_pixel_label.set_visible(False)
# enable ability to click on pixel and do something (can be
# enabled on-the-fly later as well:
if allow_pick:
self.enable_pixel_picker()
if image is not None:
self.image = image
else:
self.image = np.zeros_like(self.geom.pix_id, dtype=np.float)
self.norm = norm
def highlight_pixels(self, pixels, color='g', linewidth=1, alpha=0.75):
'''
Highlight the given pixels with a colored line around them
Parameters
----------
pixels : index-like
The pixels to highlight.
Can either be a list or array of integers or a
boolean mask of length number of pixels
color: a matplotlib conform color
the color for the pixel highlighting
linewidth: float
linewidth of the highlighting in points
alpha: 0 <= alpha <= 1
The transparency
'''
l = np.zeros_like(self.image)
l[pixels] = linewidth
self.pixel_highlighting.set_linewidth(l)
self.pixel_highlighting.set_alpha(alpha)
self.pixel_highlighting.set_edgecolor(color)
self._update()
def enable_pixel_picker(self):
""" enable ability to click on pixels """
self.pixels.set_picker(True) # enable click
self.pixels.set_pickradius(sqrt(u.Quantity(self.geom.pix_area[0])
.value) / np.pi)
self.pixels.set_snap(True) # snap cursor to pixel center
self.axes.figure.canvas.mpl_connect('pick_event', self._on_pick)
def set_limits_minmax(self, zmin, zmax):
""" set the color scale limits from min to max """
self.pixels.set_clim(zmin, zmax)
self.autoscale = False
self._update()
def set_limits_percent(self, percent=95):
""" auto-scale the color range to percent of maximum """
zmin = self.pixels.get_array().min()
zmax = self.pixels.get_array().max()
dz = zmax - zmin
frac = percent / 100.0
self.autoscale = False
self.set_limits_minmax(zmin, zmax - (1.0 - frac) * dz)
@property
def norm(self):
'''
The norm instance of the Display
Possible values:
- "lin": linear scale
- "log": log scale (cannot have negative values)
- "symlog": symmetric log scale (negative values are ok)
- any matplotlib.colors.Normalize instance, e. g. PowerNorm(gamma=-2)
'''
return self.pixels.norm
@norm.setter
def norm(self, norm):
if norm == 'lin':
self.pixels.norm = Normalize()
elif norm == 'log':
self.pixels.norm = LogNorm()
self.pixels.autoscale() # this is to handle matplotlib bug #5424
elif norm == 'symlog':
self.pixels.norm = SymLogNorm(linthresh=1.0)
self.pixels.autoscale()
elif isinstance(norm, Normalize):
self.pixels.norm = norm
else:
raise ValueError("Unsupported norm: '{}', options are 'lin',"
"'log','symlog', or a matplotlib Normalize object"
.format(norm))
self.update(force=True)
self.pixels.autoscale()
@property
def cmap(self):
"""
Color map to use. Either a name or `matplotlib.colors.ColorMap`
instance, e.g. from `matplotlib.pyplot.cm`
"""
return self.pixels.get_cmap()
@cmap.setter
def cmap(self, cmap):
self.pixels.set_cmap(cmap)
self._update()
@property
def image(self):
"""The image displayed on the camera (1D array of pixel values)"""
return self.pixels.get_array()
@image.setter
def image(self, image):
"""
Change the image displayed on the Camera.
Parameters
----------
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
"""
image = np.asanyarray(image)
if image.shape != self.geom.pix_x.shape:
raise ValueError(
"Image has a different shape {} than the "
"given CameraGeometry {}"
.format(image.shape, self.geom.pix_x.shape)
)
self.pixels.set_array(image[self.geom.mask])
self.pixels.changed()
if self.autoscale:
self.pixels.autoscale()
self._update()
def _update(self, force=False):
""" signal a redraw if autoupdate is turned on """
if self.autoupdate:
self.update(force)
def update(self, force=False):
""" redraw the display now """
self.axes.figure.canvas.draw()
if self.colorbar is not None:
if force is True:
self.colorbar.update_bruteforce(self.pixels)
else:
self.colorbar.update_normal(self.pixels)
self.colorbar.draw_all()
def add_colorbar(self, **kwargs):
"""
add a colobar to the camera plot
kwargs are passed to `figure.colorbar(self.pixels, **kwargs)`
See matplotlib documentation for the supported kwargs:
http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
"""
if self.colorbar is not None:
raise ValueError(
'There is already a colorbar attached to this CameraDisplay'
)
else:
self.colorbar = self.axes.figure.colorbar(self.pixels, **kwargs)
self.update()
def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0,
**kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid, width=length, height=width,
angle=np.degrees(angle), fill=False, **kwargs)
self.axes.add_patch(ellipse)
self.update()
return ellipse
def overlay_moments(self, momparams, with_label=True, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
cen_x = u.Quantity(momparams.cen_x).value
cen_y = u.Quantity(momparams.cen_y).value
length = u.Quantity(momparams.length).value
width = u.Quantity(momparams.width).value
el = self.add_ellipse(centroid=(cen_x, cen_y),
length=length*2,
width=width*2, angle=momparams.psi.rad,
**kwargs)
if with_label:
self.axes.text(cen_x, cen_y,
("({:.02f},{:.02f})\n"
"[w={:.02f},l={:.02f}]")
.format(momparams.cen_x,
momparams.cen_y,
momparams.width, momparams.length),
color=el.get_edgecolor())
def _on_pick(self, event):
""" handler for when a pixel is clicked """
pix_id = event.ind[-1]
xx, yy, aa = u.Quantity(self.geom.pix_x[pix_id]).value, \
u.Quantity(self.geom.pix_y[pix_id]).value, \
u.Quantity(np.array(self.geom.pix_area)[pix_id])
if self.geom.pix_type.startswith("hex"):
self._active_pixel.xy = (xx, yy)
else:
rr = sqrt(aa)
self._active_pixel.xy = (xx - rr / 2., yy - rr / 2.)
self._active_pixel.set_visible(True)
self._active_pixel_label.set_x(xx)
self._active_pixel_label.set_y(yy)
self._active_pixel_label.set_text("{:003d}".format(pix_id))
self._active_pixel_label.set_visible(True)
self._update()
self.on_pixel_clicked(pix_id) # call user-function
def on_pixel_clicked(self, pix_id):
"""virtual function to overide in sub-classes to do something special
when a pixel is clicked
"""
print("Clicked pixel_id {}".format(pix_id))
def show(self):
self.axes.figure.show()
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()
class CameraDisplay:
"""Camera Display using matplotlib.
Parameters
----------
geometry : `~ctapipe.io.CameraGeometry`
Definition of the Camera/Image
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
ax : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one
title : str
Title to put on camera plot
allow_pick : bool (default False)
if True, allow user to click and select a pixel
autoupdate : bool (default True)
redraw automatically (otherwise need to call plt.draw())
antialiased : bool (default True)
whether to draw in antialiased mode or not.
Notes
-----
Speed:
CameraDisplay is not intended to be very fast (matplotlib
is not a very speed performant graphics library, it is
intended for nice output plots). However, most of the
slowness of CameraDisplay is in the constructor. Once one is
displayed, changing the image that is displayed is relatively
fast and efficient. Therefore it is best to initialize an
instance, and change the data, rather than generating new
CameraDisplays.
Pixel Implementation:
Pixels are rendered as a
`matplotlib.collections.PatchCollection` of Polygons (either 6
or 4 sided). You can access the PatchCollection directly (to
e.g. change low-level style parameters) via
`CameraDisplay.pixels`
Output:
Since CameraDisplay uses matplotlib, any display can be
saved to any output file supported via
plt.savefig(filename). This includes `.pdf` and `.png`.
"""
def __init__(self, geometry, image=None, ax=None, title="Camera",
allow_pick=False, autoupdate=True, antialiased=True):
self.axes = ax if ax is not None else plt.gca()
self.geom = geometry
self.pixels = None
self.autoupdate = autoupdate
self._active_pixel = None
self._active_pixel_label = None
# initialize the plot and generate the pixels as a
# RegularPolyCollection
patches = []
for xx, yy, aa in zip(u.Quantity(self.geom.pix_x).value,
u.Quantity(self.geom.pix_y).value,
u.Quantity(np.array(self.geom.pix_area))):
if self.geom.pix_type.startswith("hex"):
rr = sqrt(aa * 2 / 3 / sqrt(3))
poly = RegularPolygon((xx, yy), 6, radius=rr,
orientation=np.radians(0),
fill=True)
else:
rr = sqrt(aa)
poly = Rectangle((xx, yy), width=rr, height=rr,
angle=np.radians(0),
fill=True)
patches.append(poly)
self.pixels = PatchCollection(patches, cmap='hot', linewidth=0)
self.axes.add_collection(self.pixels)
# Set up some nice plot defaults
self.axes.set_aspect('equal', 'datalim')
self.axes.set_title(title)
self.axes.set_xlabel("X position ({})".format(self.geom.pix_x.unit))
self.axes.set_ylabel("Y position ({})".format(self.geom.pix_y.unit))
self.axes.autoscale_view()
# set up a patch to display when a pixel is clicked (and
# pixel_picker is enabled):
self._active_pixel = copy.copy(patches[0])
self._active_pixel.set_facecolor('r')
self._active_pixel.set_alpha(0.5)
self._active_pixel.set_linewidth(2.0)
self._active_pixel.set_visible(False)
self.axes.add_patch(self._active_pixel)
self._active_pixel_label = plt.text(self._active_pixel.xy[0],
self._active_pixel.xy[1],
"0",
horizontalalignment='center',
verticalalignment='center')
self._active_pixel_label.set_visible(False)
# enable ability to click on pixel and do something (can be
# enabled on-the-fly later as well:
if allow_pick:
self.enable_pixel_picker()
if image is not None:
self.image = image
else:
self.image = np.zeros_like(self.geom.pix_id, dtype=np.float)
def enable_pixel_picker(self):
""" enable ability to click on pixels """
self.pixels.set_picker(True) # enable click
self.pixels.set_pickradius(sqrt(u.Quantity(self.geom.pix_area[0])
.value) / np.pi)
self.pixels.set_snap(True) # snap cursor to pixel center
self.axes.figure.canvas.mpl_connect('pick_event', self._on_pick)
def set_limits_minmax(self, zmin, zmax):
""" set the color scale limits from min to max """
self.pixels.set_clim(zmin, zmax)
self.update()
def set_limits_percent(self, percent=95):
""" auto-scale the color range to percent of maximum """
zmin = self.pixels.get_array().min()
zmax = self.pixels.get_array().max()
dz = zmax - zmin
frac = percent / 100.0
self.set_limits_minmax(zmin, zmax - (1.0 - frac) * dz)
@property
def cmap(self):
return self.pixels.get_cmap()
@cmap.setter
def cmap(self, cmap):
""" Change the color map
Parameters
----------
self: type
description
cmap: `matplotlib.colors.ColorMap`
a color map, e.g. from `matplotlib.pyplot.cm.*`
"""
self.pixels.set_cmap(cmap)
self.update()
@property
def image(self):
return self.pixels.get_array()
@image.setter
def image(self, image):
"""
Change the image displayed on the Camera.
Parameters
----------
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
"""
image = np.asanyarray(image)
if image.shape != self.geom.pix_x.shape:
raise ValueError(
"Image has a different shape {} than the"
"given CameraGeometry {}"
.format(image.shape, self.geom.pix_x.shape)
)
self.pixels.set_array(image)
self.axes._sci(self.pixels)
self.update()
def set_image(self, image):
logger.warn("set_image(x) is deprecated:"
" use CameraDisplay.image = x instead")
self.image = image
def update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def add_colorbar(self):
""" add a colobar to the camera plot """
self.axes.figure.colorbar(self.pixels)
def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0,
**kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float,float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt "up" about the centroid, clockwise, 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=width, height=length,
angle=np.degrees(angle), fill=False, **kwargs)
self.axes.add_patch(ellipse)
self.update()
return ellipse
def overlay_moments(self, momparams, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
el = self.add_ellipse(centroid=(momparams.cen_x, momparams.cen_y),
length=momparams.length,
width=momparams.width, angle=momparams.psi,
**kwargs)
self.axes.text(momparams.cen_x, momparams.cen_y,
("({:.02f},{:.02f})\n"
"[w={:.02f},l={:.02f}]")
.format(momparams.cen_x,
momparams.cen_y,
momparams.width, momparams.length),
color=el.get_edgecolor())
def _on_pick(self, event):
""" handler for when a pixel is clicked """
pix_id = event.ind.pop()
xx, yy = u.Quantity(self.geom.pix_x[pix_id]).value,\
u.Quantity(self.geom.pix_y[pix_id]).value
self._active_pixel.xy = (xx, yy)
self._active_pixel.set_visible(True)
self._active_pixel_label.set_x(xx)
self._active_pixel_label.set_y(yy)
self._active_pixel_label.set_text("{:003d}".format(pix_id))
self._active_pixel_label.set_visible(True)
self.update()
self.on_pixel_clicked(pix_id) # call user-function
def on_pixel_clicked(self, pix_id):
"""virtual function to overide in sub-classes to do something special
when a pixel is clicked
"""
print("Clicked pixel_id {}".format(pix_id))
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
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)
x = x_0 + np.cos(params.psi) * r
y = y_0 + np.sin(params.psi) * 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.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)
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'))
class CameraDisplay:
"""
Camera Display using matplotlib.
Parameters
----------
geometry : `~ctapipe.instrument.CameraGeometry`
Definition of the Camera/Image
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
ax : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one
title : str (default "Camera")
Title to put on camera plot
norm : str or `matplotlib.color.Normalize` instance (default 'lin')
Normalization for the color scale.
Supported str arguments are
- 'lin': linear scale
- 'log': logarithmic scale (base 10)
cmap : str or `matplotlib.colors.Colormap` (default 'hot')
Color map to use (see `matplotlib.cm`)
allow_pick : bool (default False)
if True, allow user to click and select a pixel
autoupdate : bool (default True)
redraw automatically (otherwise need to call plt.draw())
autoscale : bool (default True)
rescale the vmin/vmax values when the image changes.
This is set to False if `set_limits_*` is called to explicity
set data limits.
antialiased : bool (default True)
whether to draw in antialiased mode or not.
Notes
-----
Speed:
CameraDisplay is not intended to be very fast (matplotlib
is not a very speed performant graphics library, it is
intended for nice output plots). However, most of the
slowness of CameraDisplay is in the constructor. Once one is
displayed, changing the image that is displayed is relatively
fast and efficient. Therefore it is best to initialize an
instance, and change the data, rather than generating new
CameraDisplays.
Pixel Implementation:
Pixels are rendered as a
`matplotlib.collections.PatchCollection` of Polygons (either 6
or 4 sided). You can access the PatchCollection directly (to
e.g. change low-level style parameters) via
`CameraDisplay.pixels`
Output:
Since CameraDisplay uses matplotlib, any display can be
saved to any output file supported via
plt.savefig(filename). This includes `.pdf` and `.png`.
"""
def __init__(
self,
geometry,
image=None,
ax=None,
title=None,
norm="lin",
cmap=None,
allow_pick=False,
autoupdate=True,
autoscale=True,
antialiased=True,
):
self.axes = ax if ax is not None else plt.gca()
self.geom = geometry
self.pixels = None
self.colorbar = None
self.autoupdate = autoupdate
self.autoscale = autoscale
self._active_pixel = None
self._active_pixel_label = None
self._axes_overlays = []
if title is None:
title = geometry.cam_id
# initialize the plot and generate the pixels as a
# RegularPolyCollection
patches = []
if not hasattr(self.geom, "mask"):
self.geom.mask = np.ones_like(self.geom.pix_x.value, dtype=bool)
for xx, yy, aa in zip(
u.Quantity(self.geom.pix_x[self.geom.mask]).value,
u.Quantity(self.geom.pix_y[self.geom.mask]).value,
u.Quantity(np.array(self.geom.pix_area)[self.geom.mask]).value):
if self.geom.pix_type.startswith("hex"):
rr = sqrt(aa * 2 / 3 / sqrt(3)) + 2*PIXEL_EPSILON
poly = RegularPolygon(
(xx, yy), 6, radius=rr,
orientation=self.geom.pix_rotation.rad,
fill=True,
)
else:
rr = sqrt(aa) + PIXEL_EPSILON
poly = Rectangle(
(xx-rr/2., yy-rr/2.),
width=rr,
height=rr,
angle=self.geom.pix_rotation.deg,
fill=True,
)
patches.append(poly)
self.pixels = PatchCollection(patches, cmap=cmap, linewidth=0)
self.axes.add_collection(self.pixels)
self.pixel_highlighting = copy.copy(self.pixels)
self.pixel_highlighting.set_facecolor('none')
self.pixel_highlighting.set_linewidth(0)
self.axes.add_collection(self.pixel_highlighting)
# Set up some nice plot defaults
self.axes.set_aspect('equal', 'datalim')
self.axes.set_title(title)
self.axes.set_xlabel("X position ({})".format(self.geom.pix_x.unit))
self.axes.set_ylabel("Y position ({})".format(self.geom.pix_y.unit))
self.axes.autoscale_view()
# set up a patch to display when a pixel is clicked (and
# pixel_picker is enabled):
self._active_pixel = copy.copy(patches[0])
self._active_pixel.set_facecolor('r')
self._active_pixel.set_alpha(0.5)
self._active_pixel.set_linewidth(2.0)
self._active_pixel.set_visible(False)
self.axes.add_patch(self._active_pixel)
self._active_pixel_label = self.axes.text(self._active_pixel.xy[0],
self._active_pixel.xy[1],
"0",
horizontalalignment='center',
verticalalignment='center')
self._active_pixel_label.set_visible(False)
# enable ability to click on pixel and do something (can be
# enabled on-the-fly later as well:
if allow_pick:
self.enable_pixel_picker()
if image is not None:
self.image = image
else:
self.image = np.zeros_like(self.geom.pix_id, dtype=np.float)
self.norm = norm
def highlight_pixels(self, pixels, color='g', linewidth=1, alpha=0.75):
'''
Highlight the given pixels with a colored line around them
Parameters
----------
pixels : index-like
The pixels to highlight.
Can either be a list or array of integers or a
boolean mask of length number of pixels
color: a matplotlib conform color
the color for the pixel highlighting
linewidth: float
linewidth of the highlighting in points
alpha: 0 <= alpha <= 1
The transparency
'''
l = np.zeros_like(self.image)
l[pixels] = linewidth
self.pixel_highlighting.set_linewidth(l)
self.pixel_highlighting.set_alpha(alpha)
self.pixel_highlighting.set_edgecolor(color)
self._update()
def enable_pixel_picker(self):
""" enable ability to click on pixels """
self.pixels.set_picker(True) # enable click
self.pixels.set_pickradius(sqrt(u.Quantity(self.geom.pix_area[0])
.value) / np.pi)
self.pixels.set_snap(True) # snap cursor to pixel center
self.axes.figure.canvas.mpl_connect('pick_event', self._on_pick)
def set_limits_minmax(self, zmin, zmax):
""" set the color scale limits from min to max """
self.pixels.set_clim(zmin, zmax)
self.autoscale = False
self._update()
def set_limits_percent(self, percent=95):
""" auto-scale the color range to percent of maximum """
zmin = self.pixels.get_array().min()
zmax = self.pixels.get_array().max()
dz = zmax - zmin
frac = percent / 100.0
self.autoscale = False
self.set_limits_minmax(zmin, zmax - (1.0 - frac) * dz)
@property
def norm(self):
'''
The norm instance of the Display
Possible values:
- "lin": linear scale
- "log": log scale (cannot have negative values)
- "symlog": symmetric log scale (negative values are ok)
- any matplotlib.colors.Normalize instance, e. g. PowerNorm(gamma=-2)
'''
return self.pixels.norm
@norm.setter
def norm(self, norm):
if norm == 'lin':
self.pixels.norm = Normalize()
elif norm == 'log':
self.pixels.norm = LogNorm()
self.pixels.autoscale() # this is to handle matplotlib bug #5424
elif norm == 'symlog':
self.pixels.norm = SymLogNorm(linthresh=1.0)
self.pixels.autoscale()
elif isinstance(norm, Normalize):
self.pixels.norm = norm
else:
raise ValueError("Unsupported norm: '{}', options are 'lin',"
"'log','symlog', or a matplotlib Normalize object"
.format(norm))
self.update(force=True)
self.pixels.autoscale()
@property
def cmap(self):
"""
Color map to use. Either a name or `matplotlib.colors.ColorMap`
instance, e.g. from `matplotlib.pyplot.cm`
"""
return self.pixels.get_cmap()
@cmap.setter
def cmap(self, cmap):
self.pixels.set_cmap(cmap)
self._update()
@property
def image(self):
"""The image displayed on the camera (1D array of pixel values)"""
return self.pixels.get_array()
@image.setter
def image(self, image):
"""
Change the image displayed on the Camera.
Parameters
----------
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
"""
image = np.asanyarray(image)
if image.shape != self.geom.pix_x.shape:
raise ValueError(
"Image has a different shape {} than the "
"given CameraGeometry {}"
.format(image.shape, self.geom.pix_x.shape)
)
self.pixels.set_array(image[self.geom.mask])
self.pixels.changed()
if self.autoscale:
self.pixels.autoscale()
self._update()
def _update(self, force=False):
""" signal a redraw if autoupdate is turned on """
if self.autoupdate:
self.update(force)
def update(self, force=False):
""" redraw the display now """
self.axes.figure.canvas.draw()
if self.colorbar is not None:
if force is True:
self.colorbar.update_bruteforce(self.pixels)
else:
self.colorbar.update_normal(self.pixels)
self.colorbar.draw_all()
def add_colorbar(self, **kwargs):
"""
add a colobar to the camera plot
kwargs are passed to `figure.colorbar(self.pixels, **kwargs)`
See matplotlib documentation for the supported kwargs:
http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
"""
if self.colorbar is not None:
raise ValueError(
'There is already a colorbar attached to this CameraDisplay'
)
else:
self.colorbar = self.axes.figure.colorbar(self.pixels, **kwargs)
self.update()
def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0,
**kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid, width=length, height=width,
angle=np.degrees(angle), fill=False, **kwargs)
self.axes.add_patch(ellipse)
self.update()
return ellipse
def overlay_moments(self, momparams, with_label=True, keep_old=False, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
with_label: bool
If True, show coordinates of centroid and width and length
keep_old: bool
If True, to not remove old overlays
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
if not keep_old:
self.clear_overlays()
# strip off any units
cen_x = u.Quantity(momparams.cen_x).value
cen_y = u.Quantity(momparams.cen_y).value
length = u.Quantity(momparams.length).value
width = u.Quantity(momparams.width).value
el = self.add_ellipse(centroid=(cen_x, cen_y),
length=length*2,
width=width*2, angle=momparams.psi.rad,
**kwargs)
self._axes_overlays.append(el)
if with_label:
text = self.axes.text(cen_x, cen_y,
("({:.02f},{:.02f})\n"
"[w={:.02f},l={:.02f}]")
.format(momparams.cen_x,
momparams.cen_y,
momparams.width, momparams.length),
color=el.get_edgecolor())
self._axes_overlays.append(text)
def clear_overlays(self):
''' Remove added overlays from the axes '''
while self._axes_overlays:
overlay = self._axes_overlays.pop()
overlay.remove()
def _on_pick(self, event):
""" handler for when a pixel is clicked """
pix_id = event.ind[-1]
xx, yy, aa = u.Quantity(self.geom.pix_x[pix_id]).value, \
u.Quantity(self.geom.pix_y[pix_id]).value, \
u.Quantity(np.array(self.geom.pix_area)[pix_id])
if self.geom.pix_type.startswith("hex"):
self._active_pixel.xy = (xx, yy)
else:
rr = sqrt(aa)
self._active_pixel.xy = (xx - rr / 2., yy - rr / 2.)
self._active_pixel.set_visible(True)
self._active_pixel_label.set_x(xx)
self._active_pixel_label.set_y(yy)
self._active_pixel_label.set_text("{:003d}".format(pix_id))
self._active_pixel_label.set_visible(True)
self._update()
self.on_pixel_clicked(pix_id) # call user-function
def on_pixel_clicked(self, pix_id):
"""virtual function to overide in sub-classes to do something special
when a pixel is clicked
"""
print("Clicked pixel_id {}".format(pix_id))
def show(self):
self.axes.figure.show()
class CameraDisplay:
"""
Camera Display using matplotlib.
Parameters
----------
geometry : `~ctapipe.instrument.CameraGeometry`
Definition of the Camera/Image
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
ax : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one
title : str (default "Camera")
Title to put on camera plot
norm : str or `matplotlib.colors.Normalize` instance (default 'lin')
Normalization for the color scale.
Supported str arguments are
- 'lin': linear scale
- 'log': logarithmic scale (base 10)
cmap : str or `matplotlib.colors.Colormap` (default 'hot')
Color map to use (see `matplotlib.cm`)
allow_pick : bool (default False)
if True, allow user to click and select a pixel
autoupdate : bool (default True)
redraw automatically (otherwise need to call plt.draw())
autoscale : bool (default True)
rescale the vmin/vmax values when the image changes.
This is set to False if ``set_limits_*`` is called to explicity
set data limits.
Notes
-----
Speed:
CameraDisplay is not intended to be very fast (matplotlib
is not a very speed performant graphics library, it is
intended for nice output plots). However, most of the
slowness of CameraDisplay is in the constructor. Once one is
displayed, changing the image that is displayed is relatively
fast and efficient. Therefore it is best to initialize an
instance, and change the data, rather than generating new
CameraDisplays.
Pixel Implementation:
Pixels are rendered as a
`matplotlib.collections.PatchCollection` of Polygons (either 6
or 4 sided). You can access the PatchCollection directly (to
e.g. change low-level style parameters) via
``CameraDisplay.pixels``
Output:
Since CameraDisplay uses matplotlib, any display can be
saved to any output file supported via
plt.savefig(filename). This includes ``.pdf`` and ``.png``.
"""
def __init__(
self,
geometry,
image=None,
ax=None,
title=None,
norm="lin",
cmap=None,
allow_pick=False,
autoupdate=True,
autoscale=True,
show_frame=True,
):
self.axes = ax if ax is not None else plt.gca()
self.pixels = None
self.colorbar = None
self.autoupdate = autoupdate
self.autoscale = autoscale
self._active_pixel = None
self._active_pixel_label = None
self._axes_overlays = []
self.geom = geometry
if title is None:
title = f"{geometry.camera_name}"
# initialize the plot and generate the pixels as a
# RegularPolyCollection
patches = []
if hasattr(self.geom, "mask"):
self.mask = self.geom.mask
else:
self.mask = np.ones_like(self.geom.pix_x.value, dtype=bool)
pix_x = self.geom.pix_x.value[self.mask]
pix_y = self.geom.pix_y.value[self.mask]
pix_width = self.geom.pixel_width.value[self.mask]
for x, y, w in zip(pix_x, pix_y, pix_width):
if self.geom.pix_type == PixelShape.HEXAGON:
r = w / np.sqrt(3)
patch = RegularPolygon(
(x, y),
6,
radius=r,
orientation=self.geom.pix_rotation.to_value(u.rad),
fill=True,
)
elif self.geom.pix_type == PixelShape.CIRCLE:
patch = Circle((x, y), radius=w / 2, fill=True)
elif self.geom.pix_type == PixelShape.SQUARE:
patch = Rectangle(
(x - w / 2, y - w / 2),
width=w,
height=w,
angle=self.geom.pix_rotation.to_value(u.deg),
fill=True,
)
else:
raise ValueError(
f"Unsupported pixel_shape {self.geom.pix_type}")
patches.append(patch)
self.pixels = PatchCollection(patches, cmap=cmap, linewidth=0)
self.axes.add_collection(self.pixels)
self.pixel_highlighting = copy.copy(self.pixels)
self.pixel_highlighting.set_facecolor("none")
self.pixel_highlighting.set_linewidth(0)
self.axes.add_collection(self.pixel_highlighting)
# Set up some nice plot defaults
self.axes.set_aspect("equal", "datalim")
self.axes.set_title(title)
self.axes.autoscale_view()
if show_frame:
self.add_frame_name()
# set up a patch to display when a pixel is clicked (and
# pixel_picker is enabled):
self._active_pixel = copy.copy(patches[0])
self._active_pixel.set_facecolor("r")
self._active_pixel.set_alpha(0.5)
self._active_pixel.set_linewidth(2.0)
self._active_pixel.set_visible(False)
self.axes.add_patch(self._active_pixel)
if hasattr(self._active_pixel, "xy"):
center = self._active_pixel.xy
else:
center = self._active_pixel.center
self._active_pixel_label = self.axes.text(*center,
"0",
horizontalalignment="center",
verticalalignment="center")
self._active_pixel_label.set_visible(False)
# enable ability to click on pixel and do something (can be
# enabled on-the-fly later as well:
if allow_pick:
self.enable_pixel_picker()
if image is not None:
self.image = image
else:
self.image = np.zeros_like(self.geom.pix_id, dtype=np.float64)
self.norm = norm
self.auto_set_axes_labels()
def highlight_pixels(self, pixels, color="g", linewidth=1, alpha=0.75):
"""
Highlight the given pixels with a colored line around them
Parameters
----------
pixels : index-like
The pixels to highlight.
Can either be a list or array of integers or a
boolean mask of length number of pixels
color: a matplotlib conform color
the color for the pixel highlighting
linewidth: float
linewidth of the highlighting in points
alpha: 0 <= alpha <= 1
The transparency
"""
l = np.zeros_like(self.image)
l[pixels] = linewidth
self.pixel_highlighting.set_linewidth(l)
self.pixel_highlighting.set_alpha(alpha)
self.pixel_highlighting.set_edgecolor(color)
self._update()
def enable_pixel_picker(self):
""" enable ability to click on pixels """
self.pixels.set_picker(True)
self.pixels.set_pickradius(self.geom.pixel_width.value[0] / 2)
self.axes.figure.canvas.mpl_connect("pick_event", self._on_pick)
def set_limits_minmax(self, zmin, zmax):
""" set the color scale limits from min to max """
self.pixels.set_clim(zmin, zmax)
self.autoscale = False
self._update()
def set_limits_percent(self, percent=95):
""" auto-scale the color range to percent of maximum """
zmin = np.nanmin(self.pixels.get_array())
zmax = np.nanmax(self.pixels.get_array())
dz = zmax - zmin
frac = percent / 100.0
self.autoscale = False
self.set_limits_minmax(zmin, zmax - (1.0 - frac) * dz)
@property
def norm(self):
"""
The norm instance of the Display
Possible values:
- "lin": linear scale
- "log": log scale (cannot have negative values)
- "symlog": symmetric log scale (negative values are ok)
- any matplotlib.colors.Normalize instance, e. g. PowerNorm(gamma=-2)
"""
return self.pixels.norm
@norm.setter
def norm(self, norm):
if norm == "lin":
self.pixels.norm = Normalize()
elif norm == "log":
self.pixels.norm = LogNorm()
self.pixels.autoscale() # this is to handle matplotlib bug #5424
elif norm == "symlog":
self.pixels.norm = SymLogNorm(linthresh=1.0, base=10)
self.pixels.autoscale()
elif isinstance(norm, Normalize):
self.pixels.norm = norm
else:
raise ValueError(
"Unsupported norm: '{}', options are 'lin',"
"'log','symlog', or a matplotlib Normalize object".format(
norm))
self.update(force=True)
self.pixels.autoscale()
@property
def cmap(self):
"""
Color map to use. Either name or `matplotlib.colors.Colormap`
"""
return self.pixels.get_cmap()
@cmap.setter
def cmap(self, cmap):
self.pixels.set_cmap(cmap)
self._update()
@property
def image(self):
"""The image displayed on the camera (1D array of pixel values)"""
return self.pixels.get_array()
@image.setter
def image(self, image):
"""
Change the image displayed on the Camera.
Parameters
----------
image: array_like
array of values corresponding to the pixels in the CameraGeometry.
"""
image = np.asanyarray(image)
if image.shape != self.geom.pix_x.shape:
raise ValueError(
("Image has a different shape {} than the "
"given CameraGeometry {}").format(image.shape,
self.geom.pix_x.shape))
self.pixels.set_array(np.ma.masked_invalid(image[self.mask]))
self.pixels.changed()
if self.autoscale:
self.pixels.autoscale()
self._update()
def _update(self, force=False):
""" signal a redraw if autoupdate is turned on """
if self.autoupdate:
self.update(force)
def update(self, force=False):
""" redraw the display now """
self.axes.figure.canvas.draw()
if self.colorbar is not None:
if force is True:
self.colorbar.update_bruteforce(self.pixels)
else:
self.colorbar.update_normal(self.pixels)
self.colorbar.draw_all()
def add_colorbar(self, **kwargs):
"""
add a colorbar to the camera plot
kwargs are passed to ``figure.colorbar(self.pixels, **kwargs)``
See matplotlib documentation for the supported kwargs:
http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
"""
if self.colorbar is not None:
raise ValueError(
"There is already a colorbar attached to this CameraDisplay")
else:
if "ax" not in kwargs:
kwargs["ax"] = self.axes
self.colorbar = self.axes.figure.colorbar(self.pixels, **kwargs)
self.update()
def add_ellipse(self,
centroid,
length,
width,
angle,
asymmetry=0.0,
**kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(
xy=centroid,
width=length,
height=width,
angle=np.degrees(angle),
fill=False,
**kwargs,
)
self.axes.add_patch(ellipse)
self.update()
return ellipse
def overlay_moments(self,
hillas_parameters,
with_label=True,
keep_old=False,
**kwargs):
"""helper to overlay ellipse from a `~ctapipe.containers.HillasParametersContainer` structure
Parameters
----------
hillas_parameters: `HillasParametersContainer`
structuring containing Hillas-style parameterization
with_label: bool
If True, show coordinates of centroid and width and length
keep_old: bool
If True, to not remove old overlays
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
if not keep_old:
self.clear_overlays()
# strip off any units
cen_x = u.Quantity(hillas_parameters.x).value
cen_y = u.Quantity(hillas_parameters.y).value
length = u.Quantity(hillas_parameters.length).value
width = u.Quantity(hillas_parameters.width).value
el = self.add_ellipse(
centroid=(cen_x, cen_y),
length=length * 2,
width=width * 2,
angle=hillas_parameters.psi.to_value("rad"),
**kwargs,
)
self._axes_overlays.append(el)
if with_label:
text = self.axes.text(
cen_x,
cen_y,
"({:.02f},{:.02f})\n[w={:.02f},l={:.02f}]".format(
hillas_parameters.x,
hillas_parameters.y,
hillas_parameters.width,
hillas_parameters.length,
),
color=el.get_edgecolor(),
)
self._axes_overlays.append(text)
def clear_overlays(self):
""" Remove added overlays from the axes """
while self._axes_overlays:
overlay = self._axes_overlays.pop()
overlay.remove()
def _on_pick(self, event):
""" handler for when a pixel is clicked """
pix_id = event.ind[-1]
x = self.geom.pix_x[pix_id].value
y = self.geom.pix_y[pix_id].value
if self.geom.pix_type in (PixelShape.HEXAGON, PixelShape.CIRCLE):
self._active_pixel.xy = (x, y)
else:
w = self.geom.pixel_width.value[0]
self._active_pixel.xy = (x - w / 2.0, y - w / 2.0)
self._active_pixel.set_visible(True)
self._active_pixel_label.set_x(x)
self._active_pixel_label.set_y(y)
self._active_pixel_label.set_text(f"{pix_id:003d}")
self._active_pixel_label.set_visible(True)
self._update()
self.on_pixel_clicked(pix_id) # call user-function
def on_pixel_clicked(self, pix_id):
"""virtual function to overide in sub-classes to do something special
when a pixel is clicked
"""
print(f"Clicked pixel_id {pix_id}")
def show(self):
self.axes.figure.show()
def auto_set_axes_labels(self):
""" set the axes labels based on the Frame attribute"""
axes_labels = ("X", "Y")
if self.geom.frame is not None:
axes_labels = list(
self.geom.frame.get_representation_component_names().keys())
self.axes.set_xlabel(f"{axes_labels[0]} ({self.geom.pix_x.unit})")
self.axes.set_ylabel(f"{axes_labels[1]} ({self.geom.pix_y.unit})")
def add_frame_name(self, color="grey"):
""" label the frame type of the display (e.g. CameraFrame) """
frame_name = (self.geom.frame.__class__.__name__
if self.geom.frame is not None else "Unknown Frame")
self.axes.text( # position text relative to Axes
1.0,
0.0,
frame_name,
ha="right",
va="bottom",
transform=self.axes.transAxes,
color=color,
fontsize="smaller",
)
