# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Tools to create profiles (i.e. 1D "slices" from 2D images)."""
import numpy as np
import scipy.ndimage
from astropy import units as u
from astropy.convolution import Box1DKernel, Gaussian1DKernel
from astropy.coordinates import Angle
from astropy.table import Table
import matplotlib.pyplot as plt
from .core import Estimator
__all__ = ["ImageProfile", "ImageProfileEstimator"]
# TODO: implement measuring profile along arbitrary directions
# TODO: think better about error handling. e.g. MC based methods
[docs]class ImageProfileEstimator(Estimator):
"""Estimate profile from image.
Parameters
----------
x_edges : `~astropy.coordinates.Angle`
Coordinate edges to define a custom measument grid (optional).
method : ['sum', 'mean']
Compute sum or mean within profile bins.
axis : ['lon', 'lat', 'radial']
Along which axis to estimate the profile.
center : `~astropy.coordinates.SkyCoord`
Center coordinate for the radial profile option.
Examples
--------
This example shows how to compute a counts profile for the Fermi galactic
center region::
import matplotlib.pyplot as plt
from gammapy.estimators import ImageProfileEstimator
from gammapy.maps import Map
from astropy import units as u
# load example data
filename = '$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz'
fermi_cts = Map.read(filename)
# set up profile estimator and run
p = ImageProfileEstimator(axis='lon', method='sum')
profile = p.run(fermi_cts)
# smooth profile and plot
smoothed = profile.smooth(kernel='gauss')
smoothed.peek()
plt.show()
"""
tag = "ImageProfileEstimator"
def __init__(self, x_edges=None, method="sum", axis="lon", center=None):
if method not in ["sum", "mean"]:
raise ValueError("Not a valid method, choose either 'sum' or 'mean'")
if axis not in ["lon", "lat", "radial"]:
raise ValueError("Not a valid axis, choose either 'lon', 'lat' or 'radial'")
if axis == "radial" and center is None:
raise ValueError("Please provide center coordinate for radial profiles")
self._x_edges = x_edges
self.parameters = {"method": method, "axis": axis, "center": center}
def _get_x_edges(self, image):
if self._x_edges is not None:
return self._x_edges
p = self.parameters
coordinates = image.geom.get_coord(mode="edges").skycoord
if p["axis"] == "lat":
x_edges = coordinates[:, 0].data.lat
elif p["axis"] == "lon":
lon = coordinates[0, :].data.lon
x_edges = lon.wrap_at("180d")
elif p["axis"] == "radial":
rad_step = image.geom.pixel_scales.mean()
corners = [0, 0, -1, -1], [0, -1, 0, -1]
rad_max = coordinates[corners].separation(p["center"]).max()
x_edges = Angle(np.arange(0, rad_max.deg, rad_step.deg), unit="deg")
return x_edges
def _estimate_profile(self, image, image_err, mask):
p = self.parameters
labels = self._label_image(image, mask)
profile_err = None
index = np.arange(1, len(self._get_x_edges(image)))
if p["method"] == "sum":
profile = scipy.ndimage.sum(image.data, labels.data, index)
if image.unit.is_equivalent("counts"):
profile_err = np.sqrt(profile)
elif image_err:
# gaussian error propagation
err_sum = scipy.ndimage.sum(image_err.data**2, labels.data, index)
profile_err = np.sqrt(err_sum)
elif p["method"] == "mean":
# gaussian error propagation
profile = scipy.ndimage.mean(image.data, labels.data, index)
if image_err:
N = scipy.ndimage.sum(~np.isnan(image_err.data), labels.data, index)
err_sum = scipy.ndimage.sum(image_err.data**2, labels.data, index)
profile_err = np.sqrt(err_sum) / N
return profile, profile_err
def _label_image(self, image, mask=None):
p = self.parameters
coordinates = image.geom.get_coord().skycoord
x_edges = self._get_x_edges(image)
if p["axis"] == "lon":
lon = coordinates.data.lon.wrap_at("180d")
data = np.digitize(lon.degree, x_edges.deg)
elif p["axis"] == "lat":
lat = coordinates.data.lat
data = np.digitize(lat.degree, x_edges.deg)
elif p["axis"] == "radial":
separation = coordinates.separation(p["center"])
data = np.digitize(separation.degree, x_edges.deg)
if mask is not None:
# assign masked values to background
data[mask.data] = 0
return image.copy(data=data)
[docs] def run(self, image, image_err=None, mask=None):
"""Run image profile estimator.
Parameters
----------
image : `~gammapy.maps.Map`
Input image to run profile estimator on.
image_err : `~gammapy.maps.Map`
Input error image to run profile estimator on.
mask : `~gammapy.maps.Map`
Optional mask to exclude regions from the measurement.
Returns
-------
profile : `ImageProfile`
Result image profile object.
"""
p = self.parameters
if image.unit.is_equivalent("count"):
image_err = image.copy(data=np.sqrt(image.data))
profile, profile_err = self._estimate_profile(image, image_err, mask)
result = Table()
x_edges = self._get_x_edges(image)
result["x_min"] = x_edges[:-1]
result["x_max"] = x_edges[1:]
result["x_ref"] = (x_edges[:-1] + x_edges[1:]) / 2
result["profile"] = profile * image.unit
if profile_err is not None:
result["profile_err"] = profile_err * image.unit
result.meta["PROFILE_TYPE"] = p["axis"]
return ImageProfile(result)
[docs]class ImageProfile:
"""Image profile class.
The image profile data is stored in `~astropy.table.Table` object, with the
following columns:
* `x_ref` Coordinate bin center (required).
* `x_min` Coordinate bin minimum (optional).
* `x_max` Coordinate bin maximum (optional).
* `profile` Image profile data (required).
* `profile_err` Image profile data error (optional).
Parameters
----------
table : `~astropy.table.Table`
Table instance with the columns specified as above.
"""
def __init__(self, table):
self.table = table
[docs] def smooth(self, kernel="box", radius="0.1 deg", **kwargs):
r"""Smooth profile with error propagation.
Smoothing is described by a convolution:
.. math::
x_j = \sum_i x_{(j - i)} h_i
Where :math:`h_i` are the coefficients of the convolution kernel.
The corresponding error on :math:`x_j` is then estimated using Gaussian
error propagation, neglecting correlations between the individual
:math:`x_{(j - i)}`:
.. math::
\Delta x_j = \sqrt{\sum_i \Delta x^{2}_{(j - i)} h^{2}_i}
Parameters
----------
kernel : {'gauss', 'box'}
Kernel shape
radius : `~astropy.units.Quantity`, str or float
Smoothing width given as quantity or float. If a float is given it
is interpreted as smoothing width in pixels. If an (angular) quantity
is given it is converted to pixels using `xref[1] - x_ref[0]`.
kwargs : dict
Keyword arguments passed to `~scipy.ndimage.uniform_filter`
('box') and `~scipy.ndimage.gaussian_filter` ('gauss').
Returns
-------
profile : `ImageProfile`
Smoothed image profile.
"""
table = self.table.copy()
profile = table["profile"]
radius = u.Quantity(radius)
radius = np.abs(radius / np.diff(self.x_ref))[0]
width = 2 * radius.value + 1
if kernel == "box":
smoothed = scipy.ndimage.uniform_filter(
profile.astype("float"), width, **kwargs
)
# renormalize data
if table["profile"].unit.is_equivalent("count"):
smoothed *= int(width)
smoothed_err = np.sqrt(smoothed)
elif "profile_err" in table.colnames:
profile_err = table["profile_err"]
# use gaussian error propagation
box = Box1DKernel(width)
err_sum = scipy.ndimage.convolve(profile_err**2, box.array**2)
smoothed_err = np.sqrt(err_sum)
elif kernel == "gauss":
smoothed = scipy.ndimage.gaussian_filter(
profile.astype("float"), width, **kwargs
)
# use gaussian error propagation
if "profile_err" in table.colnames:
profile_err = table["profile_err"]
gauss = Gaussian1DKernel(width)
err_sum = scipy.ndimage.convolve(profile_err**2, gauss.array**2)
smoothed_err = np.sqrt(err_sum)
else:
raise ValueError("Not valid kernel choose either 'box' or 'gauss'")
table["profile"] = smoothed * self.table["profile"].unit
if "profile_err" in table.colnames:
table["profile_err"] = smoothed_err * self.table["profile"].unit
return self.__class__(table)
[docs] def plot(self, ax=None, **kwargs):
"""Plot image profile.
Parameters
----------
ax : `~matplotlib.axes.Axes`
Axes object
**kwargs : dict
Keyword arguments passed to `~matplotlib.axes.Axes.plot`
Returns
-------
ax : `~matplotlib.axes.Axes`
Axes object
"""
if ax is None:
ax = plt.gca()
y = self.table["profile"].data
x = self.x_ref.value
ax.plot(x, y, **kwargs)
ax.set_xlabel(self.table.meta.get("PROFILE_TYPE", "axis"))
ax.set_ylabel("profile")
ax.set_xlim(x.max(), x.min())
return ax
[docs] def plot_err(self, ax=None, **kwargs):
"""Plot image profile error as band.
Parameters
----------
ax : `~matplotlib.axes.Axes`
Axes object
**kwargs : dict
Keyword arguments passed to plt.fill_between()
Returns
-------
ax : `~matplotlib.axes.Axes`
Axes object
"""
if ax is None:
ax = plt.gca()
y = self.table["profile"].data
ymin = y - self.table["profile_err"].data
ymax = y + self.table["profile_err"].data
x = self.x_ref.value
# plotting defaults
kwargs.setdefault("alpha", 0.5)
ax.fill_between(x, ymin, ymax, **kwargs)
ax.set_xlabel("x [deg]")
ax.set_ylabel("profile")
return ax
@property
def x_ref(self):
"""Reference x coordinates."""
return self.table["x_ref"].quantity
@property
def x_min(self):
"""Min. x coordinates."""
return self.table["x_min"].quantity
@property
def x_max(self):
"""Max. x coordinates."""
return self.table["x_max"].quantity
@property
def profile(self):
"""Image profile quantity."""
return self.table["profile"].quantity
@property
def profile_err(self):
"""Image profile error quantity."""
try:
return self.table["profile_err"].quantity
except KeyError:
return None
[docs] def peek(self, figsize=(8, 4.5), **kwargs):
"""Show image profile and error.
Parameters
----------
figsize : tuple
Size of the figure.
**kwargs : dict
Keyword arguments passed to `ImageProfile.plot_profile()`
Returns
-------
ax : `~matplotlib.axes.Axes`
Axes object
"""
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax = self.plot(ax, **kwargs)
if "profile_err" in self.table.colnames:
ax = self.plot_err(ax, color=kwargs.get("c"))
return ax
[docs] def normalize(self, mode="peak"):
"""Normalize profile to peak value or integral.
Parameters
----------
mode : ['integral', 'peak']
Normalize image profile so that it integrates to unity ('integral')
or the maximum value corresponds to one ('peak').
Returns
-------
profile : `ImageProfile`
Normalized image profile.
"""
table = self.table.copy()
profile = self.table["profile"]
if mode == "peak":
norm = np.nanmax(profile)
elif mode == "integral":
norm = np.nansum(profile)
else:
raise ValueError(f"Invalid normalization mode: {mode!r}")
table["profile"] /= norm
if "profile_err" in table.colnames:
table["profile_err"] /= norm
return self.__class__(table)