# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
from itertools import repeat
import numpy as np
import scipy.interpolate
import scipy.ndimage as ndi
import scipy.signal
import astropy.units as u
from astropy.convolution import Tophat2DKernel
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.nddata import block_reduce
from regions import PixCoord, PointPixelRegion, PointSkyRegion, SkyRegion
import matplotlib.colors as mpcolors
import matplotlib.pyplot as plt
import gammapy.utils.parallel as parallel
from gammapy.utils.interpolation import ScaledRegularGridInterpolator
from gammapy.utils.units import unit_from_fits_image_hdu
from gammapy.visualization.utils import add_colorbar
from ..geom import pix_tuple_to_idx
from ..utils import INVALID_INDEX
from .core import WcsMap
from .geom import WcsGeom
__all__ = ["WcsNDMap"]
log = logging.getLogger(__name__)
C_MAP_MASK = mpcolors.ListedColormap(["black", "white"], name="mask")
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class WcsNDMap(WcsMap):
"""WCS map with any number of non-spatial dimensions.
This class uses an ND numpy array to store map values. For maps with
non-spatial dimensions and variable pixel size it will allocate an
array with dimensions commensurate with the largest image plane.
Parameters
----------
geom : `~gammapy.maps.WcsGeom`
WCS geometry object.
data : `~numpy.ndarray`
Data array. If none then an empty array will be allocated.
dtype : str, optional
Data type, default is float32
meta : `dict`
Dictionary to store meta data.
unit : str or `~astropy.units.Unit`
The map unit
"""
def __init__(self, geom, data=None, dtype="float32", meta=None, unit=""):
# TODO: Figure out how to mask pixels for integer data types
data_shape = geom.data_shape
if data is None:
data = self._make_default_data(geom, data_shape, dtype)
super().__init__(geom, data, meta, unit)
@staticmethod
def _make_default_data(geom, shape_np, dtype):
# Check whether corners of each image plane are valid
data = np.zeros(shape_np, dtype=dtype)
if not geom.is_regular or geom.is_allsky:
coords = geom.get_coord()
is_nan = np.isnan(coords.lon)
data[is_nan] = np.nan
return data
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@classmethod
def from_hdu(cls, hdu, hdu_bands=None, format=None):
"""Make a WcsNDMap object from a FITS HDU.
Parameters
----------
hdu : `~astropy.io.fits.BinTableHDU` or `~astropy.io.fits.ImageHDU`
The map FITS HDU.
hdu_bands : `~astropy.io.fits.BinTableHDU`
The BANDS table HDU.
format : {'gadf', 'fgst-ccube','fgst-template'}
FITS format convention.
Returns
-------
map : `WcsNDMap`
WCS map.
"""
geom = WcsGeom.from_header(hdu.header, hdu_bands, format=format)
shape = geom.axes.shape
shape_wcs = tuple([np.max(geom.npix[0]), np.max(geom.npix[1])])
meta = cls._get_meta_from_header(hdu.header)
unit = unit_from_fits_image_hdu(hdu.header)
# TODO: Should we support extracting slices?
if isinstance(hdu, fits.BinTableHDU):
map_out = cls(geom, meta=meta, unit=unit)
pix = hdu.data.field("PIX")
pix = np.unravel_index(pix, shape_wcs[::-1])
vals = hdu.data.field("VALUE")
if "CHANNEL" in hdu.data.columns.names and shape:
chan = hdu.data.field("CHANNEL")
chan = np.unravel_index(chan, shape[::-1])
idx = chan + pix
else:
idx = pix
map_out.set_by_idx(idx[::-1], vals)
else:
if any(x in hdu.name.lower() for x in ["mask", "is_ul", "success"]):
data = hdu.data.astype(bool)
else:
data = hdu.data
map_out = cls(geom=geom, meta=meta, data=data, unit=unit)
return map_out
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def get_by_idx(self, idx):
idx = pix_tuple_to_idx(idx)
return self.data.T[idx]
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def interp_by_coord(
self, coords, method="linear", fill_value=None, values_scale="lin"
):
"""Interpolate map values at the given map coordinates.
Parameters
----------
coords : tuple, dict or `~gammapy.maps.MapCoord`
Coordinate arrays for each dimension of the map. Tuple
should be ordered as (lon, lat, x_0, ..., x_n) where x_i
are coordinates for non-spatial dimensions of the map.
"lon" and "lat" are optional and will be taken at the center
of the region by default.
method : {"linear", "nearest"}
Method to interpolate data values. By default linear
interpolation is performed.
fill_value : None or float value
The value to use for points outside of the interpolation domain.
If None, values outside the domain are extrapolated.
values_scale : {"lin", "log", "sqrt"}
Optional value scaling. Default is "lin".
Returns
-------
vals : `~numpy.ndarray`
Interpolated pixel values.
"""
if self.geom.is_regular:
pix = self.geom.coord_to_pix(coords)
return self.interp_by_pix(
pix, method=method, fill_value=fill_value, values_scale=values_scale
)
else:
return self._interp_by_coord_griddata(coords, method=method)
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def interp_by_pix(self, pix, method="linear", fill_value=None, values_scale="lin"):
if not self.geom.is_regular:
raise ValueError("interp_by_pix only supported for regular geom.")
grid_pix = [np.arange(n, dtype=float) for n in self.data.shape[::-1]]
if np.any(np.isfinite(self.data)):
data = self.data.copy().T
data[~np.isfinite(data)] = 0.0
else:
data = self.data.T
fn = ScaledRegularGridInterpolator(
grid_pix,
data,
fill_value=None,
bounds_error=False,
method=method,
values_scale=values_scale,
)
interp_data = fn(tuple(pix), clip=False)
if fill_value is not None:
idxs = self.geom.pix_to_idx(pix, clip=False)
invalid = np.broadcast_arrays(*[idx == -1 for idx in idxs])
mask = np.any(invalid, axis=0)
if not interp_data.shape:
mask = mask.squeeze()
interp_data[mask] = fill_value
interp_data[~np.isfinite(interp_data)] = fill_value
return interp_data
def _interp_by_coord_griddata(self, coords, method="linear"):
grid_coords = self.geom.get_coord()
data = self.data[np.isfinite(self.data)]
vals = scipy.interpolate.griddata(
tuple(grid_coords.flat), data, tuple(coords), method=method
)
m = ~np.isfinite(vals)
if np.any(m):
vals_fill = scipy.interpolate.griddata(
tuple(grid_coords.flat),
data,
tuple([c[m] for c in coords]),
method="nearest",
)
vals[m] = vals_fill
return vals
def _resample_by_idx(self, idx, weights=None, preserve_counts=False):
idx = pix_tuple_to_idx(idx)
msk = np.all(np.stack([t != INVALID_INDEX.int for t in idx]), axis=0)
idx = [t[msk] for t in idx]
if weights is not None:
if isinstance(weights, u.Quantity):
weights = weights.to_value(self.unit)
weights = weights[msk]
idx = np.ravel_multi_index(idx, self.data.T.shape)
idx, idx_inv = np.unique(idx, return_inverse=True)
weights = np.bincount(idx_inv, weights=weights).astype(self.data.dtype)
if not preserve_counts:
weights /= np.bincount(idx_inv).astype(self.data.dtype)
self.data.T.flat[idx] += weights
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def fill_by_idx(self, idx, weights=None):
return self._resample_by_idx(idx, weights=weights, preserve_counts=True)
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def set_by_idx(self, idx, vals):
idx = pix_tuple_to_idx(idx)
self.data.T[idx] = vals
def _pad_spatial(
self, pad_width, axis_name=None, mode="constant", cval=0, method="linear"
):
if axis_name is None:
if np.isscalar(pad_width):
pad_width = (pad_width, pad_width)
if len(pad_width) == 2:
pad_width += (0,) * (self.geom.ndim - 2)
geom = self.geom._pad_spatial(pad_width[:2])
if self.geom.is_regular and mode != "interp":
return self._pad_np(geom, pad_width, mode, cval)
else:
return self._pad_coadd(geom, pad_width, mode, cval, method)
def _pad_np(self, geom, pad_width, mode, cval):
"""Pad a map using `~numpy.pad`.
This method only works for regular geometries but should be more
efficient when working with large maps.
"""
kwargs = {}
if mode == "constant":
kwargs["constant_values"] = cval
pad_width = [(t, t) for t in pad_width]
data = np.pad(self.data, pad_width[::-1], mode, **kwargs)
return self._init_copy(geom=geom, data=data)
def _pad_coadd(self, geom, pad_width, mode, cval, method):
"""Pad a map manually by co-adding the original map with the new map."""
idx_in = self.geom.get_idx(flat=True)
idx_in = tuple([t + w for t, w in zip(idx_in, pad_width)])[::-1]
idx_out = geom.get_idx(flat=True)[::-1]
map_out = self._init_copy(geom=geom, data=None)
map_out.coadd(self)
if mode == "constant":
pad_msk = np.zeros_like(map_out.data, dtype=bool)
pad_msk[idx_out] = True
pad_msk[idx_in] = False
map_out.data[pad_msk] = cval
elif mode == "interp":
coords = geom.pix_to_coord(idx_out[::-1])
m = self.geom.contains(coords)
coords = tuple([c[~m] for c in coords])
vals = self.interp_by_coord(coords, method=method)
map_out.set_by_coord(coords, vals)
else:
raise ValueError(f"Invalid mode: {mode!r}")
return map_out
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def crop(self, crop_width):
if np.isscalar(crop_width):
crop_width = (crop_width, crop_width)
geom = self.geom.crop(crop_width)
if self.geom.is_regular:
slices = [slice(None)] * len(self.geom.axes)
slices += [
slice(crop_width[1], int(self.geom.npix[1][0] - crop_width[1])),
slice(crop_width[0], int(self.geom.npix[0][0] - crop_width[0])),
]
data = self.data[tuple(slices)]
map_out = self._init_copy(geom=geom, data=data)
else:
# FIXME: This could be done more efficiently by
# constructing the appropriate slices for each image plane
map_out = self._init_copy(geom=geom, data=None)
map_out.coadd(self)
return map_out
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def upsample(self, factor, order=0, preserve_counts=True, axis_name=None):
if factor == 1 or factor is None:
return self
geom = self.geom.upsample(factor, axis_name=axis_name)
idx = geom.get_idx()
if axis_name is None:
pix = (
(idx[0] - 0.5 * (factor - 1)) / factor,
(idx[1] - 0.5 * (factor - 1)) / factor,
) + idx[2:]
else:
pix = list(idx)
idx_ax = self.geom.axes_names.index(axis_name)
pix[idx_ax] = (pix[idx_ax] - 0.5 * (factor - 1)) / factor
if preserve_counts:
data = self.data / self.geom.bin_volume().value
else:
data = self.data
data = ndi.map_coordinates(data.T, tuple(pix), order=order, mode="nearest")
if preserve_counts:
data *= geom.bin_volume().value
return self._init_copy(geom=geom, data=data.astype(self.data.dtype))
[docs]
def downsample(self, factor, preserve_counts=True, axis_name=None, weights=None):
if factor == 1 or factor is None:
return self
geom = self.geom.downsample(factor, axis_name=axis_name)
if axis_name is None:
block_size = (1,) * len(self.geom.axes) + (factor, factor)
else:
block_size = [1] * self.data.ndim
idx = self.geom.axes.index_data(axis_name)
block_size[idx] = factor
func = np.nansum if preserve_counts else np.nanmean
if weights is None:
weights = 1
else:
weights = weights.data
data = block_reduce(self.data * weights, tuple(block_size), func=func)
return self._init_copy(geom=geom, data=data.astype(self.data.dtype))
[docs]
def plot(
self,
ax=None,
fig=None,
add_cbar=False,
stretch="linear",
axes_loc=None,
kwargs_colorbar=None,
**kwargs,
):
"""
Plot image on matplotlib WCS axes.
Parameters
----------
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object to plot on. Default is None.
fig : `~matplotlib.figure.Figure`, optional
Figure object. Default is None.
add_cbar : bool, optional
Add color bar. Default is False.
stretch : str, optional
Passed to `astropy.visualization.simple_norm`.
Default is "linear".
axes_loc : dict, optional
Keyword arguments passed to `~mpl_toolkits.axes_grid1.axes_divider.AxesDivider.append_axes`.
kwargs_colorbar : dict, optional
Keyword arguments passed to `~matplotlib.pyplot.colorbar`.
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.imshow`.
Returns
-------
ax : `~astropy.visualization.wcsaxes.WCSAxes`
WCS axes object.
"""
from astropy.visualization import simple_norm
if not self.geom.is_flat:
raise TypeError("Use .plot_interactive() for Map dimension > 2")
ax = self._plot_default_axes(ax=ax)
if fig is None:
fig = plt.gcf()
if self.geom.is_image:
data = self.data.astype(float)
else:
axis = tuple(np.arange(len(self.geom.axes)))
data = np.squeeze(self.data, axis=axis).astype(float)
kwargs.setdefault("interpolation", "nearest")
kwargs.setdefault("origin", "lower")
kwargs.setdefault("cmap", "afmhot")
kwargs_colorbar = kwargs_colorbar or {}
mask = np.isfinite(data)
if self.is_mask:
kwargs.setdefault("vmin", 0)
kwargs.setdefault("vmax", 1)
kwargs["cmap"] = C_MAP_MASK
if mask.any():
min_cut, max_cut = kwargs.pop("vmin", None), kwargs.pop("vmax", None)
try:
norm = simple_norm(data[mask], stretch, vmin=min_cut, vmax=max_cut)
except TypeError:
# astropy <6.1
norm = simple_norm(
data[mask], stretch, min_cut=min_cut, max_cut=max_cut
)
kwargs.setdefault("norm", norm)
im = ax.imshow(data, **kwargs)
if add_cbar:
label = str(self.unit)
kwargs_colorbar.setdefault("label", label)
add_colorbar(im, ax=ax, axes_loc=axes_loc, **kwargs_colorbar)
if self.geom.is_allsky:
ax = self._plot_format_allsky(ax)
else:
ax = self._plot_format(ax)
# without this the axis limits are changed when calling scatter
ax.autoscale(enable=False)
return ax
[docs]
def plot_mask(self, ax=None, **kwargs):
"""Plot the mask as a shaded area.
Parameters
----------
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object to plot on. Default is None.
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.contourf`.
Returns
-------
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object to plot on.
"""
if not self.geom.is_flat:
raise TypeError("Use .plot_interactive() for Map dimension > 2")
if not self.is_mask:
raise ValueError(
"`.plot_mask()` only supports maps containing boolean values."
)
ax = self._plot_default_axes(ax=ax)
kwargs.setdefault("alpha", 0.5)
kwargs.setdefault("colors", "w")
data = np.squeeze(self.data).astype(float)
ax.contourf(data, levels=[0, 0.5], **kwargs)
if self.geom.is_allsky:
ax = self._plot_format_allsky(ax)
else:
ax = self._plot_format(ax)
# without this the axis limits are changed when calling scatter
ax.autoscale(enable=False)
return ax
def _plot_default_axes(self, ax):
from astropy.visualization.wcsaxes.frame import EllipticalFrame
if ax is None:
fig = plt.gcf()
if self.geom.projection in ["AIT"]:
ax = fig.add_subplot(
1, 1, 1, projection=self.geom.wcs, frame_class=EllipticalFrame
)
else:
ax = fig.add_subplot(1, 1, 1, projection=self.geom.wcs)
return ax
@staticmethod
def _plot_format(ax):
try:
ax.coords["glon"].set_axislabel("Galactic Longitude")
ax.coords["glat"].set_axislabel("Galactic Latitude")
except KeyError:
ax.coords["ra"].set_axislabel("Right Ascension")
ax.coords["dec"].set_axislabel("Declination")
except AttributeError:
log.info("Can't set coordinate axes. No WCS information available.")
return ax
def _plot_format_allsky(self, ax):
# Remove frame
ax.coords.frame.set_linewidth(0)
# Set plot axis limits
xmin, _ = self.geom.to_image().coord_to_pix({"lon": 180, "lat": 0})
xmax, _ = self.geom.to_image().coord_to_pix({"lon": -180, "lat": 0})
_, ymin = self.geom.to_image().coord_to_pix({"lon": 0, "lat": -90})
_, ymax = self.geom.to_image().coord_to_pix({"lon": 0, "lat": 90})
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(ymin[0], ymax[0])
ax.text(0, ymax[0], self.geom.frame + " coords")
# Grid and ticks
glon_spacing, glat_spacing = 45, 15
lon, lat = ax.coords
lon.set_ticks(spacing=glon_spacing * u.deg, color="w", alpha=0.8)
lat.set_ticks(spacing=glat_spacing * u.deg)
lon.set_ticks_visible(False)
lon.set_major_formatter("d")
lat.set_major_formatter("d")
lon.set_ticklabel(color="w", alpha=0.8)
lon.grid(alpha=0.2, linestyle="solid", color="w")
lat.grid(alpha=0.2, linestyle="solid", color="w")
return ax
[docs]
def cutout_and_mask_region(self, region=None):
"""Compute cutout and mask for a given region of the map.
The function will estimate the minimal size of the cutout, which encloses
the region.
Parameters
----------
region: `~regions.Region`, optional
Extended region. Default is None.
Returns
-------
cutout, mask : tuple of `WcsNDMap`
Cutout and mask map.
"""
from gammapy.maps import RegionGeom
if region is None:
region = self.geom.footprint_rectangle_sky_region
geom = RegionGeom.from_regions(regions=region, wcs=self.geom.wcs)
cutout = self.cutout(position=geom.center_skydir, width=geom.width)
mask = cutout.geom.to_image().region_mask([region])
return self.__class__(data=cutout.data, geom=cutout.geom, unit=self.unit), mask
[docs]
def to_region_nd_map(
self, region=None, func=np.nansum, weights=None, method="nearest"
):
"""Get region ND map in a given region.
By default, the whole map region is considered.
Parameters
----------
region: `~regions.Region` or `~astropy.coordinates.SkyCoord`, optional
Region. Default is None.
func : numpy.func, optional
Function to reduce the data. Default is np.nansum.
For boolean Map, use np.any or np.all.
weights : `WcsNDMap`, optional
Array to be used as weights. The geometry must be equivalent.
Default is None.
method : {"nearest", "linear"}, optional
How to interpolate if a position is given.
Default is "nearest".
Returns
-------
spectrum : `~gammapy.maps.RegionNDMap`
Spectrum in the given region.
"""
from gammapy.maps import RegionGeom, RegionNDMap
if region is None:
region = self.geom.footprint_rectangle_sky_region
if weights is not None:
if not self.geom == weights.geom:
raise ValueError("Incompatible spatial geoms between map and weights")
geom = RegionGeom.from_regions(
regions=region, axes=self.geom.axes, wcs=self.geom.wcs
)
if geom.is_all_point_sky_regions:
coords = geom.get_coord()
data = self.interp_by_coord(coords=coords, method=method)
if weights is not None:
data *= weights.interp_by_coord(coords=coords, method=method)
# Casting needed as interp_by_coord transforms boolean
data = data.astype(self.data.dtype)
else:
cutout, mask = self.cutout_and_mask_region(region=region)
if weights is not None:
weights_cutout = weights.cutout(
position=geom.center_skydir, width=geom.width
)
cutout.data *= weights_cutout.data
idx_y, idx_x = np.where(mask)
data = func(cutout.data[..., idx_y, idx_x], axis=-1)
return RegionNDMap(geom=geom, data=data, unit=self.unit, meta=self.meta.copy())
[docs]
def to_region_nd_map_histogram(
self, region=None, bins_axis=None, nbin=100, density=False
):
"""Convert map into region map by histogramming.
By default, it creates a linearly spaced axis with 100 bins between
(-max(abs(data)), max(abs(data))) within the given region.
Parameters
----------
region: `~regions.Region`, optional
Region to histogram over. Default is None.
bins_axis : `MapAxis`, optional
Binning of the histogram. Default is None.
nbin : int, optional
Number of bins to use if no bins_axis is given.
Default is 100.
density : bool, optional
Normalize integral of the histogram to 1.
Default is False.
Examples
--------
This is how to use the method to create energy dependent histograms:
::
from gammapy.maps import MapAxis, Map
import numpy as np
random_state = np.random.RandomState(seed=0)
energy_axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=3)
data = Map.create(axes=[energy_axis], width=10, unit="cm2 s-1", binsz=0.02)
data.data = random_state.normal(
size=data.data.shape, loc=0, scale=np.array([1.0, 2.0, 3.0]).reshape((-1, 1, 1))
)
hist = data.to_region_nd_map_histogram()
hist.plot(axis_name="bins")
Returns
-------
region_map : `RegionNDMap`
Region map with histogram.
"""
from gammapy.maps import MapAxis, RegionGeom, RegionNDMap
if isinstance(region, (PointSkyRegion, SkyCoord)):
raise ValueError("Histogram method not supported for point regions")
cutout, mask = self.cutout_and_mask_region(region=region)
idx_y, idx_x = np.where(mask)
quantity = cutout.quantity[..., idx_y, idx_x]
value = np.abs(quantity).max()
if bins_axis is None:
bins_axis = MapAxis.from_bounds(
-value,
value,
nbin=nbin,
interp="lin",
unit=self.unit,
name="bins",
)
if not bins_axis.unit.is_equivalent(self.unit):
raise ValueError("Unit of bins_axis must be equivalent to unit of map.")
axes = [bins_axis] + list(self.geom.axes)
geom_hist = RegionGeom(region=region, axes=axes, wcs=self.geom.wcs)
# This is likely not the most efficient way to do this
data = np.apply_along_axis(
lambda a: np.histogram(a, bins=bins_axis.edges.value, density=density)[0],
axis=-1,
arr=quantity.to_value(bins_axis.unit),
)
unit = 1.0 / bins_axis.unit if density else ""
return RegionNDMap.from_geom(geom=geom_hist, data=data, unit=unit)
[docs]
def mask_contains_region(self, region):
"""Check if input region is contained in a boolean mask map.
Parameters
----------
region: `~regions.SkyRegion` or `~regions.PixRegion`
Region or list of Regions (pixel or sky regions accepted).
Returns
-------
contained : bool
Whether region is contained in the mask.
"""
if not self.is_mask:
raise ValueError("mask_contains_region is only supported for boolean masks")
if not self.geom.is_image:
raise ValueError("Method only supported for 2D images")
if isinstance(region, SkyRegion):
region = region.to_pixel(self.geom.wcs)
if isinstance(region, PointPixelRegion):
lon, lat = region.center.x, region.center.y
contains = self.get_by_pix((lon, lat))
else:
idx = self.geom.get_idx()
coords_pix = PixCoord(idx[0][self.data], idx[1][self.data])
contains = region.contains(coords_pix)
return np.any(contains)
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def binary_erode(self, width, kernel="disk", use_fft=True):
"""Binary erosion of boolean mask removing a given margin.
Parameters
----------
width : `~astropy.units.Quantity`, str or float
If a float is given it interpreted as width in pixels. If an (angular)
quantity is given it converted to pixels using ``geom.wcs.wcs.cdelt``.
The width corresponds to radius in case of a disk kernel, and
the side length in case of a box kernel.
kernel : {'disk', 'box'}, optional
Kernel shape. Default is "disk".
use_fft : bool, optional
Use `scipy.signal.fftconvolve` if True. Otherwise, use
`scipy.ndimage.binary_erosion`.
Default is True.
Returns
-------
map : `WcsNDMap`
Eroded mask map.
"""
if not self.is_mask:
raise ValueError("Binary operations only supported for boolean masks")
structure = self.geom.binary_structure(width=width, kernel=kernel)
if use_fft:
return self.convolve(structure.squeeze(), method="fft") > (
structure.sum() - 1
)
data = ndi.binary_erosion(self.data, structure=structure)
return self._init_copy(data=data)
[docs]
def binary_dilate(self, width, kernel="disk", use_fft=True):
"""Binary dilation of boolean mask adding a given margin.
Parameters
----------
width : tuple of `~astropy.units.Quantity`
Angular sizes of the margin in (lon, lat) in that specific order.
If only one value is passed, the same margin is applied in (lon, lat).
kernel : {'disk', 'box'}, optional
Kernel shape. Default is "disk".
use_fft : bool, optional
Use `scipy.signal.fftconvolve` if True. Otherwise, use
`scipy.ndimage.binary_dilation`.
Default is True.
Returns
-------
map : `WcsNDMap`
Dilated mask map.
"""
if not self.is_mask:
raise ValueError("Binary operations only supported for boolean masks")
structure = self.geom.binary_structure(width=width, kernel=kernel)
if use_fft:
return self.convolve(structure.squeeze(), method="fft") > 1
data = ndi.binary_dilation(self.data, structure=structure)
return self._init_copy(data=data)
[docs]
def convolve(self, kernel, method="fft", mode="same"):
"""Convolve map with a kernel.
If the kernel is two-dimensional, it is applied to all image planes likewise.
If the kernel is higher dimensional, it should either match the map in the number of
dimensions or the map must be an image (no non-spatial axes). In that case, the
corresponding kernel is selected and applied to every image plane or to the single
input image respectively.
Parameters
----------
kernel : `~gammapy.irf.PSFKernel` or `numpy.ndarray`
Convolution kernel.
method : str, optional
The method used by `~scipy.signal.convolve`.
Default is 'fft'.
mode : str, optional
The convolution mode used by `~scipy.signal.convolve`.
Default is 'same'.
Returns
-------
map : `WcsNDMap`
Convolved map.
"""
from gammapy.irf import PSFKernel
if self.geom.is_image and not isinstance(kernel, PSFKernel):
if kernel.ndim > 2:
raise ValueError(
"Image convolution with 3D kernel requires a PSFKernel object"
)
geom = self.geom.copy()
if isinstance(kernel, PSFKernel):
kmap = kernel.psf_kernel_map
if not np.allclose(
self.geom.pixel_scales.deg, kmap.geom.pixel_scales.deg, rtol=1e-5
):
raise ValueError("Pixel size of kernel and map not compatible.")
kernel = kmap.data.astype(np.float32)
if self.geom.is_image:
geom = geom.to_cube(kmap.geom.axes)
if mode == "full":
pad_width = [0.5 * (width - 1) for width in kernel.shape[-2:]]
geom = geom.pad(pad_width, axis_name=None)
elif mode == "valid":
raise NotImplementedError(
"WcsNDMap.convolve: mode='valid' is not supported."
)
shape_axes_kernel = kernel.shape[slice(0, -2)]
if len(shape_axes_kernel) > 0:
if not geom.shape_axes == shape_axes_kernel:
raise ValueError(
f"Incompatible shape between data {geom.shape_axes}"
" and kernel {shape_axes_kernel}"
)
if self.geom.is_image and kernel.ndim == 3:
indexes = range(kernel.shape[0])
images = repeat(self.data.astype(np.float32))
else:
indexes = list(self.iter_by_image_index())
images = (self.data[idx] for idx in indexes)
kernels = (
kernel[Ellipsis] if kernel.ndim == 2 else kernel[idx] for idx in indexes
)
convolved = parallel.run_multiprocessing(
self._convolve,
zip(
images,
kernels,
repeat(method),
repeat(mode),
),
task_name="Convolution",
)
data = np.empty(geom.data_shape, dtype=np.float32)
for idx_res, idx in enumerate(indexes):
data[idx] = convolved[idx_res]
return self._init_copy(data=data, geom=geom)
@staticmethod
def _convolve(image, kernel, method, mode):
"""Convolve using `~scipy.signal.convolve` without kwargs for parallel evaluation."""
return scipy.signal.convolve(image, kernel, method=method, mode=mode)
[docs]
def smooth(self, width, kernel="gauss", **kwargs):
"""Smooth the map.
Iterates over 2D image planes, processing one at a time.
Parameters
----------
width : `~astropy.units.Quantity`, str or float
Smoothing width given as quantity or float. If a float is given it
interpreted as smoothing width in pixels. If an (angular) quantity
is given it converted to pixels using ``geom.wcs.wcs.cdelt``.
It corresponds to the standard deviation in case of a Gaussian kernel,
the radius in case of a disk kernel, and the side length in case
of a box kernel.
kernel : {'gauss', 'disk', 'box'}, optional
Kernel shape. Default is "gauss".
kwargs : dict
Keyword arguments passed to `~ndi.uniform_filter`
('box'), `~ndi.gaussian_filter` ('gauss') or
`~ndi.convolve` ('disk').
Returns
-------
image : `WcsNDMap`
Smoothed image (a copy, the original object is unchanged).
"""
if isinstance(width, (u.Quantity, str)):
width = u.Quantity(width) / self.geom.pixel_scales.mean()
width = width.to_value("")
smoothed_data = np.empty(self.data.shape, dtype=float)
for img, idx in self.iter_by_image_data():
img = img.astype(float)
if kernel == "gauss":
data = ndi.gaussian_filter(img, width, **kwargs)
elif kernel == "disk":
disk = Tophat2DKernel(width)
disk.normalize("integral")
data = ndi.convolve(img, disk.array, **kwargs)
elif kernel == "box":
data = ndi.uniform_filter(img, width, **kwargs)
else:
raise ValueError(f"Invalid kernel: {kernel!r}")
smoothed_data[idx] = data
return self._init_copy(data=smoothed_data)
[docs]
def cutout(self, position, width, mode="trim", odd_npix=False, min_npix=1):
"""
Create a cutout around a given position.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Center position of the cutout region.
width : tuple of `~astropy.coordinates.Angle`
Angular sizes of the region in (lon, lat) in that specific order.
If only one value is passed, a square region is extracted.
mode : {'trim', 'partial', 'strict'}, optional
Mode option for Cutout2D, for details see `~astropy.nddata.utils.Cutout2D`.
Default is "trim".
odd_npix : bool, optional
Force width to odd number of pixels.
Default is False.
min_npix : bool, optional
Force width to a minimmum number of pixels.
Default is 1.
Returns
-------
cutout : `~gammapy.maps.WcsNDMap`
Cutout map.
"""
geom_cutout = self.geom.cutout(
position=position,
width=width,
mode=mode,
odd_npix=odd_npix,
min_npix=min_npix,
)
cutout_info = geom_cutout.cutout_slices(self.geom, mode=mode)
slices = cutout_info["parent-slices"]
parent_slices = Ellipsis, slices[0], slices[1]
slices = cutout_info["cutout-slices"]
cutout_slices = Ellipsis, slices[0], slices[1]
data = np.zeros(shape=geom_cutout.data_shape, dtype=self.data.dtype)
data[cutout_slices] = self.data[parent_slices]
return self._init_copy(geom=geom_cutout, data=data)
def _cutout_view(self, position, width, odd_npix=False):
"""
Create a cutout around a given position without copy of the data.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Center position of the cutout region.
width : tuple of `~astropy.coordinates.Angle`
Angular sizes of the region in (lon, lat) in that specific order.
If only one value is passed, a square region is extracted.
odd_npix : bool, optional
Force width to odd number of pixels.
Default is False.
Returns
-------
cutout : `~gammapy.maps.WcsNDMap`
Cutout map.
"""
geom_cutout = self.geom.cutout(
position=position, width=width, mode="trim", odd_npix=odd_npix
)
cutout_info = geom_cutout.cutout_slices(self.geom, mode="trim")
slices = cutout_info["parent-slices"]
parent_slices = Ellipsis, slices[0], slices[1]
return self.__class__.from_geom(
geom=geom_cutout, data=self.quantity[parent_slices]
)
[docs]
def stack(self, other, weights=None, nan_to_num=True):
"""Stack cutout into map.
Parameters
----------
other : `WcsNDMap`
Other map to stack.
weights : `WcsNDMap`, optional
Array to be used as weights. The spatial geometry must be equivalent
to `other` and additional axes must be broadcastable.
Default is None.
nan_to_num: bool, optional
Non-finite values are replaced by zero if True.
Default is True.
"""
if self.geom == other.geom:
parent_slices, cutout_slices = None, None
elif self.geom.is_aligned(other.geom):
cutout_slices = other.geom.cutout_slices(self.geom)
slices = cutout_slices["parent-slices"]
parent_slices = Ellipsis, slices[0], slices[1]
slices = cutout_slices["cutout-slices"]
cutout_slices = Ellipsis, slices[0], slices[1]
else:
raise ValueError(
"Can only stack equivalent maps or cutout of the same map."
)
data = other.quantity[cutout_slices].to_value(self.unit)
if nan_to_num:
not_finite = ~np.isfinite(data)
if np.any(not_finite):
data = data.copy()
data[not_finite] = 0
if weights is not None:
if not other.geom.to_image() == weights.geom.to_image():
raise ValueError("Incompatible spatial geoms between map and weights")
data = data * weights.data[cutout_slices]
self.data[parent_slices] += data
