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 (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="Camera",
norm="lin",
cmap="hot",
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
# 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=self.geom.pix_rotation.rad,
fill=True,
)
else:
rr = sqrt(aa)
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
- 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 isinstance(norm, Normalize):
self.pixels.norm = norm
else:
raise ValueError('Unsupported norm: {}'.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.pixels.changed()
if self.autoscale:
self.pixels.autoscale()
self.update()
def update(self, force=False):
""" signal a redraw if necessary """
if self.autoupdate:
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()
self.axes.figure.canvas.draw()
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 "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.value,
momparams.cen_y.value),
length=momparams.length.value,
width=momparams.width.value,
angle=momparams.psi.to(u.rad).value,
**kwargs)
self.axes.text(momparams.cen_x.value,
momparams.cen_y.value,
("({:.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 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.
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,
):
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 = 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_area = self.geom.pix_area.value[self.mask]
for x, y, area in zip(pix_x, pix_y, pix_area):
if self.geom.pix_type.startswith("hex"):
r = sqrt(area * 2 / 3 / sqrt(3)) + 2 * PIXEL_EPSILON
poly = RegularPolygon(
(x, y),
6,
radius=r,
orientation=self.geom.pix_rotation.to_value(u.rad),
fill=True,
)
else:
r = sqrt(area) + PIXEL_EPSILON
poly = Rectangle(
(x - r / 2, y - r / 2),
width=r,
height=r,
angle=self.geom.pix_rotation.to_value(u.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(f"X position ({self.geom.pix_x.unit})")
self.axes.set_ylabel(f"Y position ({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 = 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)
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(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 `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.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]
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 / 2.0, yy - rr / 2.0)
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(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 plot(self, frequency='Y', filepath='stripes.png', lineplot=True):
"""Plot resampled means and anomalies to re-create climate stripes
**Arguments**
*frequency*
Frequency for plotting stripes. Supported frequencies are
yearly ('Y'; default),monthly ('M'), weekly ('W') and N days (N)
where N is a Natural number less than 1000
*path*
Path and filename for saving the figure, including a
matplotlib-supported extension.
Default: 'stripes.png' in current directory
*lineplot*
Boolean argument to plot the actual data as a line plot
overlaying the stripes.
Default: True
**Returns**
matplotlib figure, current format is .png
**Example**
from climate_stripes import climate_stripes
stripes = climate_stripes(station_id, '2001-01-01', '2003-07-03',
Token)
stripes.plot(10)
"""
if frequency in ['Y', 'M', 'W']:
pass
elif isinstance(frequency, int) & (frequency > 0) & (frequency < 1000):
frequency = str(frequency) + 'D'
else:
raise Exception('Unsupported resampling frequency')
# Define a new dataframe for resampled data
new_DF = []
# Get the resampled mean temperature and mean anomalies
new_DF = self.data.groupby(
pd.Grouper(
key='date',
freq=frequency)).mean()
# empty figure
fig = plt.figure(figsize=(12, 5))
ax = fig.add_axes([0, 0, 1, 1])
# create a collection with a rectangle for each epoch
col = PatchCollection([Rectangle((y, 0), 1, 1)
for y in range(0, len(new_DF.index))])
# add data to collection, and set colormap and color limits
col.set_array(new_DF['anom'])
col.set_cmap('RdBu_r')
col.set_clim(new_DF['anom'].min(), new_DF['anom'].max())
ax.add_collection(col)
# set labels, ticks, limits and title
ax.set_ylim(0, 1)
ax.set_xlim(0, len(new_DF.index))
ax.set_yticks([])
ax.set_xlabel('Time', fontsize=14)
ax.set_title('GHCN Station ID - ' + self.stnid, fontsize=14)
# Colorbar settings
cmap = col.get_cmap()
norm = mpl.colors.Normalize(
vmin=new_DF['anom'].min(),
vmax=new_DF['anom'].max())
cbar = plt.colorbar(
col,
ax=ax,
cmap=cmap,
norm=norm,
orientation='horizontal',
shrink=0.5)
cbar.set_label('Anomaly ($^{o}C$)', fontsize=12)
# Get and set locations and labels
locs, labels = plt.xticks()
labels = new_DF.index.strftime('%Y-%m-%d')
locs = locs[locs < len(new_DF.index)]
ax.set_xticklabels(labels[locs.astype(int)], fontsize=12)
# Overlay line plot if lineplot == True
if lineplot:
# secondary y axis for the line plot
ax2 = ax.twinx() # instantiate second axes sharing the same x-axis
ax2.plot(range(0,
len(new_DF.index)),
new_DF['average'],
color='xkcd:chartreuse',
linestyle='--',
marker='s')
ax2.set_ylabel('Temperature ($^{o}C$)', fontsize=14)
# save figure at user defined location
plt.savefig(filepath, bbox_inches="tight")
plt.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.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 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))