Source code for gammapy.maps.axes

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import inspect
from collections.abc import Sequence
import numpy as np
import scipy
import astropy.units as u
from astropy.io import fits
from astropy.table import Column, Table, hstack
from astropy.time import Time
from astropy.utils import lazyproperty
import matplotlib.pyplot as plt
from gammapy.utils.interpolation import interpolation_scale
from gammapy.utils.time import time_ref_from_dict, time_ref_to_dict
from .utils import INVALID_INDEX, edges_from_lo_hi

__all__ = ["MapAxes", "MapAxis", "TimeMapAxis", "LabelMapAxis"]


def flat_if_equal(array):
    if array.ndim == 2 and np.all(array == array[0]):
        return array[0]
    else:
        return array


class AxisCoordInterpolator:
    """Axis coord interpolator"""

    def __init__(self, edges, interp="lin"):
        self.scale = interpolation_scale(interp)
        self.x = self.scale(edges)
        self.y = np.arange(len(edges), dtype=float)
        self.fill_value = "extrapolate"

        if len(edges) == 1:
            self.kind = 0
        else:
            self.kind = 1

    def coord_to_pix(self, coord):
        """Pix to coord"""
        interp_fn = scipy.interpolate.interp1d(
            x=self.x, y=self.y, kind=self.kind, fill_value=self.fill_value
        )
        return interp_fn(self.scale(coord))

    def pix_to_coord(self, pix):
        """Coord to pix"""
        interp_fn = scipy.interpolate.interp1d(
            x=self.y, y=self.x, kind=self.kind, fill_value=self.fill_value
        )
        return self.scale.inverse(interp_fn(pix))


PLOT_AXIS_LABEL = {
    "energy": "Energy",
    "energy_true": "True Energy",
    "offset": "FoV Offset",
    "rad": "Source Offset",
    "migra": "Energy / True Energy",
    "fov_lon": "FoV Lon.",
    "fov_lat": "FoV Lat.",
    "time": "Time",
}

DEFAULT_LABEL_TEMPLATE = "{quantity} [{unit}]"


[docs]class MapAxis: """Class representing an axis of a map. Provides methods for transforming to/from axis and pixel coordinates. An axis is defined by a sequence of node values that lie at the center of each bin. The pixel coordinate at each node is equal to its index in the node array (0, 1, ..). Bin edges are offset by 0.5 in pixel coordinates from the nodes such that the lower/upper edge of the first bin is (-0.5,0.5). Parameters ---------- nodes : `~numpy.ndarray` or `~astropy.units.Quantity` Array of node values. These will be interpreted as either bin edges or centers according to ``node_type``. interp : str Interpolation method used to transform between axis and pixel coordinates. Valid options are 'log', 'lin', and 'sqrt'. name : str Axis name node_type : str Flag indicating whether coordinate nodes correspond to pixel edges (node_type = 'edges') or pixel centers (node_type = 'center'). 'center' should be used where the map values are defined at a specific coordinate (e.g. differential quantities). 'edges' should be used where map values are defined by an integral over coordinate intervals (e.g. a counts histogram). unit : str String specifying the data units. """ # TODO: Cache an interpolation object? def __init__(self, nodes, interp="lin", name="", node_type="edges", unit=""): if not isinstance(name, str): raise TypeError(f"Name must be a string, got: {type(name)!r}") if len(nodes) != len(np.unique(nodes)): raise ValueError("MapAxis: node values must be unique") if ~(np.all(nodes == np.sort(nodes)) or np.all(nodes[::-1] == np.sort(nodes))): raise ValueError("MapAxis: node values must be sorted") if isinstance(nodes, u.Quantity): unit = nodes.unit if nodes.unit is not None else "" nodes = nodes.value else: nodes = np.array(nodes) self._name = name self._unit = u.Unit(unit) self._nodes = nodes.astype(float) self._node_type = node_type self._interp = interp if (self._nodes < 0).any() and interp != "lin": raise ValueError( f"Interpolation scaling {interp!r} only support for positive node values." ) # Set pixel coordinate of first node if node_type == "edges": self._pix_offset = -0.5 nbin = len(nodes) - 1 elif node_type == "center": self._pix_offset = 0.0 nbin = len(nodes) else: raise ValueError(f"Invalid node type: {node_type!r}") self._nbin = nbin self._use_center_as_plot_labels = None
[docs] def assert_name(self, required_name): """Assert axis name if a specific one is required. Parameters ---------- required_name : str Required """ if self.name != required_name: raise ValueError( "Unexpected axis name," f' expected "{required_name}", got: "{self.name}"' )
[docs] def is_aligned(self, other, atol=2e-2): """Check if other map axis is aligned. Two axes are aligned if their center coordinate values map to integers on the other axes as well and if the interpolation modes are equivalent. Parameters ---------- other : `MapAxis` Other map axis. atol : float Absolute numerical tolerance for the comparison measured in bins. Returns ------- aligned : bool Whether the axes are aligned """ pix = self.coord_to_pix(other.center) pix_other = other.coord_to_pix(self.center) pix_all = np.append(pix, pix_other) aligned = np.allclose(np.round(pix_all) - pix_all, 0, atol=atol) return aligned and self.interp == other.interp
[docs] def is_allclose(self, other, **kwargs): """Check if other map axis is all close. Parameters ---------- other : `MapAxis` Other map axis **kwargs : dict Keyword arguments forwarded to `~numpy.allclose` Returns ------- is_allclose : bool Whether other axis is allclose """ if not isinstance(other, self.__class__): return TypeError(f"Cannot compare {type(self)} and {type(other)}") if self.edges.shape != other.edges.shape: return False if not self.unit.is_equivalent(other.unit): return False return ( np.allclose(self.edges, other.edges, **kwargs) and self._node_type == other._node_type and self._interp == other._interp and self.name.upper() == other.name.upper() )
def __eq__(self, other): if not isinstance(other, self.__class__): return False return self.is_allclose(other, rtol=1e-6, atol=1e-6) def __ne__(self, other): return not self.__eq__(other) def __hash__(self): return id(self) @lazyproperty def _transform(self): """Interpolate coordinates to pixel""" return AxisCoordInterpolator(edges=self._nodes, interp=self.interp) @property def is_energy_axis(self): return self.name in ["energy", "energy_true"] @property def interp(self): """Interpolation scale of the axis.""" return self._interp @property def name(self): """Name of the axis.""" return self._name @lazyproperty def edges(self): """Return array of bin edges.""" pix = np.arange(self.nbin + 1, dtype=float) - 0.5 return u.Quantity(self.pix_to_coord(pix), self._unit, copy=False) @property def edges_min(self): """Return array of bin edges max values.""" return self.edges[:-1] @property def edges_max(self): """Return array of bin edges min values.""" return self.edges[1:] @property def bounds(self): """Bounds of the axis (~astropy.units.Quantity)""" idx = [0, -1] if self.node_type == "edges": return self.edges[idx] else: return self.center[idx] @property def as_plot_xerr(self): """Return tuple of xerr to be used with plt.errorbar()""" return ( self.center - self.edges_min, self.edges_max - self.center, ) @property def use_center_as_plot_labels(self): """Use center as plot labels""" if self._use_center_as_plot_labels is not None: return self._use_center_as_plot_labels return self.node_type == "center" @use_center_as_plot_labels.setter def use_center_as_plot_labels(self, value): """Use center as plot labels""" self._use_center_as_plot_labels = bool(value) @property def as_plot_labels(self): """Return list of axis plot labels""" if self.use_center_as_plot_labels: labels = [f"{val:.2e}" for val in self.center] else: labels = [ f"{val_min:.2e} - {val_max:.2e}" for val_min, val_max in self.iter_by_edges ] return labels @property def as_plot_edges(self): """Plot edges""" return self.edges @property def as_plot_center(self): """Plot center""" return self.center @property def as_plot_scale(self): """Plot axis scale""" mpl_scale = {"lin": "linear", "sqrt": "linear", "log": "log"} return mpl_scale[self.interp]
[docs] def to_node_type(self, node_type): """Return MapAxis copy changing its node type to node_type. Parameters ---------- node_type : str 'edges' or 'center' the target node type Returns ------- axis : `~gammapy.maps.MapAxis` the new MapAxis """ if node_type == self.node_type: return self else: if node_type == "center": nodes = self.center else: nodes = self.edges return self.__class__( nodes=nodes, interp=self.interp, name=self.name, node_type=node_type, unit=self.unit, )
[docs] def rename(self, new_name): """Rename the axis. Parameters ---------- new_name : str The new name for the axis. Returns ------- axis : `~gammapy.maps.MapAxis` Renamed MapAxis """ return self.copy(name=new_name)
[docs] def format_plot_xaxis(self, ax): """Format plot axis Parameters ---------- ax : `~matplotlib.pyplot.Axis` Plot axis to format Returns ------- ax : `~matplotlib.pyplot.Axis` Formatted plot axis """ ax.set_xscale(self.as_plot_scale) xlabel = DEFAULT_LABEL_TEMPLATE.format( quantity=PLOT_AXIS_LABEL.get(self.name, self.name.capitalize()), unit=ax.xaxis.units, ) ax.set_xlabel(xlabel) xmin, xmax = self.bounds if not xmin == xmax: ax.set_xlim(self.bounds) return ax
[docs] def format_plot_yaxis(self, ax): """Format plot axis Parameters ---------- ax : `~matplotlib.pyplot.Axis` Plot axis to format Returns ------- ax : `~matplotlib.pyplot.Axis` Formatted plot axis """ ax.set_yscale(self.as_plot_scale) ylabel = DEFAULT_LABEL_TEMPLATE.format( quantity=PLOT_AXIS_LABEL.get(self.name, self.name.capitalize()), unit=ax.yaxis.units, ) ax.set_ylabel(ylabel) ax.set_ylim(self.bounds) return ax
@property def iter_by_edges(self): """Iterate by intervals defined by the edges""" for value_min, value_max in zip(self.edges[:-1], self.edges[1:]): yield (value_min, value_max) @lazyproperty def center(self): """Return array of bin centers.""" pix = np.arange(self.nbin, dtype=float) return u.Quantity(self.pix_to_coord(pix), self._unit, copy=False) @lazyproperty def bin_width(self): """Array of bin widths.""" return np.diff(self.edges) @property def nbin(self): """Return number of bins.""" return self._nbin @property def nbin_per_decade(self): """Return number of bins.""" if self.interp != "log": raise ValueError("Bins per decade can only be computed for log-spaced axes") if self.node_type == "edges": values = self.edges else: values = self.center ndecades = np.log10(values.max() / values.min()) return (self._nbin / ndecades).value @property def node_type(self): """Return node type ('center' or 'edges').""" return self._node_type @property def unit(self): """Return coordinate axis unit.""" return self._unit
[docs] @classmethod def from_bounds(cls, lo_bnd, hi_bnd, nbin, **kwargs): """Generate an axis object from a lower/upper bound and number of bins. If node_type = 'edges' then bounds correspond to the lower and upper bound of the first and last bin. If node_type = 'center' then bounds correspond to the centers of the first and last bin. Parameters ---------- lo_bnd : float Lower bound of first axis bin. hi_bnd : float Upper bound of last axis bin. nbin : int Number of bins. interp : {'lin', 'log', 'sqrt'} Interpolation method used to transform between axis and pixel coordinates. Default: 'lin'. """ nbin = int(nbin) interp = kwargs.setdefault("interp", "lin") node_type = kwargs.setdefault("node_type", "edges") if node_type == "edges": nnode = nbin + 1 elif node_type == "center": nnode = nbin else: raise ValueError(f"Invalid node type: {node_type!r}") if interp == "lin": nodes = np.linspace(lo_bnd, hi_bnd, nnode) elif interp == "log": nodes = np.exp(np.linspace(np.log(lo_bnd), np.log(hi_bnd), nnode)) elif interp == "sqrt": nodes = np.linspace(lo_bnd**0.5, hi_bnd**0.5, nnode) ** 2.0 else: raise ValueError(f"Invalid interp: {interp}") return cls(nodes, **kwargs)
[docs] @classmethod def from_energy_edges(cls, energy_edges, unit=None, name=None, interp="log"): """Make an energy axis from adjacent edges. Parameters ---------- energy_edges : `~astropy.units.Quantity`, float Energy edges unit : `~astropy.units.Unit` Energy unit name : str Name of the energy axis, either 'energy' or 'energy_true' interp: str interpolation mode. Default is 'log'. Returns ------- axis : `MapAxis` Axis with name "energy" and interp "log". """ energy_edges = u.Quantity(energy_edges, unit) if not energy_edges.unit.is_equivalent("TeV"): raise ValueError( f"Please provide a valid energy unit, got {energy_edges.unit} instead." ) if name is None: name = "energy" if name not in ["energy", "energy_true"]: raise ValueError("Energy axis can only be named 'energy' or 'energy_true'") return cls.from_edges(energy_edges, unit=unit, interp=interp, name=name)
[docs] @classmethod def from_energy_bounds( cls, energy_min, energy_max, nbin, unit=None, per_decade=False, name=None, node_type="edges", ): """Make an energy axis. Used frequently also to make energy grids, by making the axis, and then using ``axis.center`` or ``axis.edges``. Parameters ---------- energy_min, energy_max : `~astropy.units.Quantity`, float Energy range nbin : int Number of bins unit : `~astropy.units.Unit` Energy unit per_decade : bool Whether `nbin` is given per decade. name : str Name of the energy axis, either 'energy' or 'energy_true' Returns ------- axis : `MapAxis` Axis with name "energy" and interp "log". """ energy_min = u.Quantity(energy_min, unit) energy_max = u.Quantity(energy_max, unit) if unit is None: unit = energy_max.unit energy_min = energy_min.to(unit) if not energy_max.unit.is_equivalent("TeV"): raise ValueError( f"Please provide a valid energy unit, got {energy_max.unit} instead." ) if per_decade: nbin = np.ceil(np.log10(energy_max / energy_min).value * nbin) if name is None: name = "energy" if name not in ["energy", "energy_true"]: raise ValueError("Energy axis can only be named 'energy' or 'energy_true'") return cls.from_bounds( energy_min.value, energy_max.value, nbin=nbin, unit=unit, interp="log", name=name, node_type=node_type, )
[docs] @classmethod def from_nodes(cls, nodes, **kwargs): """Generate an axis object from a sequence of nodes (bin centers). This will create a sequence of bins with edges half-way between the node values. This method should be used to construct an axis where the bin center should lie at a specific value (e.g. a map of a continuous function). Parameters ---------- nodes : `~numpy.ndarray` Axis nodes (bin center). interp : {'lin', 'log', 'sqrt'} Interpolation method used to transform between axis and pixel coordinates. Default: 'lin'. """ if len(nodes) < 1: raise ValueError("Nodes array must have at least one element.") return cls(nodes, node_type="center", **kwargs)
[docs] @classmethod def from_edges(cls, edges, **kwargs): """Generate an axis object from a sequence of bin edges. This method should be used to construct an axis where the bin edges should lie at specific values (e.g. a histogram). The number of bins will be one less than the number of edges. Parameters ---------- edges : `~numpy.ndarray` Axis bin edges. interp : {'lin', 'log', 'sqrt'} Interpolation method used to transform between axis and pixel coordinates. Default: 'lin'. """ if len(edges) < 2: raise ValueError("Edges array must have at least two elements.") return cls(edges, node_type="edges", **kwargs)
[docs] def append(self, axis): """Append another map axis to this axis Name, interp type and node type must agree between the axes. If the node type is "edges", the edges must be contiguous and non-overlapping. Parameters ---------- axis : `MapAxis` Axis to append. Returns ------- axis : `MapAxis` Appended axis """ if self.node_type != axis.node_type: raise ValueError( f"Node type must agree, got {self.node_type} and {axis.node_type}" ) if self.name != axis.name: raise ValueError(f"Names must agree, got {self.name} and {axis.name} ") if self.interp != axis.interp: raise ValueError( f"Interp type must agree, got {self.interp} and {axis.interp}" ) if self.node_type == "edges": edges = np.append(self.edges, axis.edges[1:]) return self.from_edges(edges=edges, interp=self.interp, name=self.name) else: nodes = np.append(self.center, axis.center) return self.from_nodes(nodes=nodes, interp=self.interp, name=self.name)
[docs] def pad(self, pad_width): """Pad axis by a given number of pixels Parameters ---------- pad_width : int or tuple of int A single int pads in both direction of the axis, a tuple specifies, which number of bins to pad at the low and high edge of the axis. Returns ------- axis : `MapAxis` Padded axis """ if isinstance(pad_width, tuple): pad_low, pad_high = pad_width else: pad_low, pad_high = pad_width, pad_width if self.node_type == "edges": pix = np.arange(-pad_low, self.nbin + pad_high + 1) - 0.5 edges = self.pix_to_coord(pix) return self.from_edges(edges=edges, interp=self.interp, name=self.name) else: pix = np.arange(-pad_low, self.nbin + pad_high) nodes = self.pix_to_coord(pix) return self.from_nodes(nodes=nodes, interp=self.interp, name=self.name)
[docs] @classmethod def from_stack(cls, axes): """Create a map axis by merging a list of other map axes. If the node type is "edges" the bin edges in the provided axes must be contiguous and non-overlapping. Parameters ---------- axes : list of `MapAxis` List of map axis to merge. Returns ------- axis : `MapAxis` Merged axis """ ax_stacked = axes[0] for ax in axes[1:]: ax_stacked = ax_stacked.append(ax) return ax_stacked
[docs] def pix_to_coord(self, pix): """Transform from pixel to axis coordinates. Parameters ---------- pix : `~numpy.ndarray` Array of pixel coordinate values. Returns ------- coord : `~numpy.ndarray` Array of axis coordinate values. """ pix = pix - self._pix_offset values = self._transform.pix_to_coord(pix=pix) return u.Quantity(values, unit=self.unit, copy=False)
[docs] def pix_to_idx(self, pix, clip=False): """Convert pix to idx Parameters ---------- pix : `~numpy.ndarray` Pixel coordinates. clip : bool Choose whether to clip indices to the valid range of the axis. If false then indices for coordinates outside the axi range will be set -1. Returns ------- idx : `~numpy.ndarray` Pixel indices. """ if clip: idx = np.clip(pix, 0, self.nbin - 1) else: condition = (pix < 0) | (pix >= self.nbin) idx = np.where(condition, -1, pix) return idx
[docs] def coord_to_pix(self, coord): """Transform from axis to pixel coordinates. Parameters ---------- coord : `~numpy.ndarray` Array of axis coordinate values. Returns ------- pix : `~numpy.ndarray` Array of pixel coordinate values. """ coord = u.Quantity(coord, self.unit, copy=False).value pix = self._transform.coord_to_pix(coord=coord) return np.array(pix + self._pix_offset, ndmin=1)
[docs] def coord_to_idx(self, coord, clip=False): """Transform from axis coordinate to bin index. Parameters ---------- coord : `~numpy.ndarray` Array of axis coordinate values. clip : bool Choose whether to clip the index to the valid range of the axis. If false then indices for values outside the axis range will be set -1. Returns ------- idx : `~numpy.ndarray` Array of bin indices. """ coord = u.Quantity(coord, self.unit, copy=False, ndmin=1).value edges = self.edges.value idx = np.digitize(coord, edges) - 1 if clip: idx = np.clip(idx, 0, self.nbin - 1) else: with np.errstate(invalid="ignore"): idx[coord > edges[-1]] = INVALID_INDEX.int idx[~np.isfinite(coord)] = INVALID_INDEX.int return idx
[docs] def slice(self, idx): """Create a new axis object by extracting a slice from this axis. Parameters ---------- idx : slice Slice object selecting a subselection of the axis. Returns ------- axis : `~MapAxis` Sliced axis object. """ center = self.center[idx].value idx = self.coord_to_idx(center) # For edge nodes we need to keep N+1 nodes if self._node_type == "edges": idx = tuple(list(idx) + [1 + idx[-1]]) nodes = self._nodes[(idx,)] return MapAxis( nodes, interp=self._interp, name=self._name, node_type=self._node_type, unit=self._unit, )
[docs] def squash(self): """Create a new axis object by squashing the axis into one bin. Returns ------- axis : `~MapAxis` Sliced axis object. """ # TODO: Decide on handling node_type=center # See https://github.com/gammapy/gammapy/issues/1952 return MapAxis.from_bounds( lo_bnd=self.edges[0].value, hi_bnd=self.edges[-1].value, nbin=1, interp=self._interp, name=self._name, unit=self._unit, )
def __repr__(self): str_ = self.__class__.__name__ str_ += "\n\n" fmt = "\t{:<10s} : {:<10s}\n" str_ += fmt.format("name", self.name) str_ += fmt.format("unit", "{!r}".format(str(self.unit))) str_ += fmt.format("nbins", str(self.nbin)) str_ += fmt.format("node type", self.node_type) vals = self.edges if self.node_type == "edges" else self.center str_ += fmt.format(f"{self.node_type} min", "{:.1e}".format(vals.min())) str_ += fmt.format(f"{self.node_type} max", "{:.1e}".format(vals.max())) str_ += fmt.format("interp", self._interp) return str_ def _init_copy(self, **kwargs): """Init map axis instance by copying missing init arguments from self.""" argnames = inspect.getfullargspec(self.__init__).args argnames.remove("self") for arg in argnames: value = getattr(self, "_" + arg) kwargs.setdefault(arg, copy.deepcopy(value)) return self.__class__(**kwargs)
[docs] def copy(self, **kwargs): """Copy `MapAxis` instance and overwrite given attributes. Parameters ---------- **kwargs : dict Keyword arguments to overwrite in the map axis constructor. Returns ------- copy : `MapAxis` Copied map axis. """ return self._init_copy(**kwargs)
[docs] def round(self, coord, clip=False): """Round coord to nearest axis edge. Parameters ---------- coord : `~astropy.units.Quantity` Coordinates clip : bool Choose whether to clip indices to the valid range of the axis. Returns ------- coord : `~astropy.units.Quantity` Rounded coordinates """ edges_pix = self.coord_to_pix(coord) if clip: edges_pix = np.clip(edges_pix, -0.5, self.nbin - 0.5) edges_idx = np.round(edges_pix + 0.5) - 0.5 return self.pix_to_coord(edges_idx)
[docs] def group_table(self, edges): """Compute bin groups table for the map axis, given coarser bin edges. Parameters ---------- edges : `~astropy.units.Quantity` Group bin edges. Returns ------- groups : `~astropy.table.Table` Map axis group table. """ # TODO: try to simplify this code if self.node_type != "edges": raise ValueError("Only edge based map axis can be grouped") edges_pix = self.coord_to_pix(edges) edges_pix = np.clip(edges_pix, -0.5, self.nbin - 0.5) edges_idx = np.round(edges_pix + 0.5) - 0.5 edges_idx = np.unique(edges_idx) edges_ref = self.pix_to_coord(edges_idx) groups = Table() groups[f"{self.name}_min"] = edges_ref[:-1] groups[f"{self.name}_max"] = edges_ref[1:] groups["idx_min"] = (edges_idx[:-1] + 0.5).astype(int) groups["idx_max"] = (edges_idx[1:] - 0.5).astype(int) if len(groups) == 0: raise ValueError("No overlap between reference and target edges.") groups["bin_type"] = "normal " edge_idx_start, edge_ref_start = edges_idx[0], edges_ref[0] if edge_idx_start > 0: underflow = { "bin_type": "underflow", "idx_min": 0, "idx_max": edge_idx_start, f"{self.name}_min": self.pix_to_coord(-0.5), f"{self.name}_max": edge_ref_start, } groups.insert_row(0, vals=underflow) edge_idx_end, edge_ref_end = edges_idx[-1], edges_ref[-1] if edge_idx_end < (self.nbin - 0.5): overflow = { "bin_type": "overflow", "idx_min": edge_idx_end + 1, "idx_max": self.nbin - 1, f"{self.name}_min": edge_ref_end, f"{self.name}_max": self.pix_to_coord(self.nbin - 0.5), } groups.add_row(vals=overflow) group_idx = Column(np.arange(len(groups))) groups.add_column(group_idx, name="group_idx", index=0) return groups
[docs] def upsample(self, factor): """Upsample map axis by a given factor. When up-sampling for each node specified in the axis, the corresponding number of sub-nodes are introduced and preserving the initial nodes. For node type "edges" this results in nbin * factor new bins. For node type "center" this results in (nbin - 1) * factor + 1 new bins. Parameters ---------- factor : int Upsampling factor. Returns ------- axis : `MapAxis` Usampled map axis. """ if self.node_type == "edges": pix = self.coord_to_pix(self.edges) nbin = int(self.nbin * factor) + 1 pix_new = np.linspace(pix.min(), pix.max(), nbin) edges = self.pix_to_coord(pix_new) return self.from_edges(edges, name=self.name, interp=self.interp) else: pix = self.coord_to_pix(self.center) nbin = int((self.nbin - 1) * factor) + 1 pix_new = np.linspace(pix.min(), pix.max(), nbin) nodes = self.pix_to_coord(pix_new) return self.from_nodes(nodes, name=self.name, interp=self.interp)
[docs] def downsample(self, factor): """Downsample map axis by a given factor. When down-sampling each n-th (given by the factor) bin is selected from the axis while preserving the axis limits. For node type "edges" this requires nbin to be dividable by the factor, for node type "center" this requires nbin - 1 to be dividable by the factor. Parameters ---------- factor : int Downsampling factor. Returns ------- axis : `MapAxis` Downsampled map axis. """ if self.node_type == "edges": nbin = self.nbin / factor if np.mod(nbin, 1) > 0: raise ValueError( f"Number of {self.name} bins is not divisible by {factor}" ) edges = self.edges[::factor] return self.from_edges(edges, name=self.name, interp=self.interp) else: nbin = (self.nbin - 1) / factor if np.mod(nbin, 1) > 0: raise ValueError( f"Number of {self.name} bins - 1 is not divisible by {factor}" ) nodes = self.center[::factor] return self.from_nodes(nodes, name=self.name, interp=self.interp)
[docs] def to_header(self, format="ogip", idx=0): """Create FITS header Parameters ---------- format : {"ogip"} Format specification idx : int Column index of the axis. Returns ------- header : `~astropy.io.fits.Header` Header to extend. """ header = fits.Header() if format in ["ogip", "ogip-sherpa"]: header["EXTNAME"] = "EBOUNDS", "Name of this binary table extension" header["TELESCOP"] = "DUMMY", "Mission/satellite name" header["INSTRUME"] = "DUMMY", "Instrument/detector" header["FILTER"] = "None", "Filter information" header["CHANTYPE"] = "PHA", "Type of channels (PHA, PI etc)" header["DETCHANS"] = self.nbin, "Total number of detector PHA channels" header["HDUCLASS"] = "OGIP", "Organisation devising file format" header["HDUCLAS1"] = "RESPONSE", "File relates to response of instrument" header["HDUCLAS2"] = "EBOUNDS", "This is an EBOUNDS extension" header["HDUVERS"] = "1.2.0", "Version of file format" elif format in ["gadf", "fgst-ccube", "fgst-template"]: key = f"AXCOLS{idx}" name = self.name.upper() if self.name == "energy" and self.node_type == "edges": header[key] = "E_MIN,E_MAX" elif self.name == "energy" and self.node_type == "center": header[key] = "ENERGY" elif self.node_type == "edges": header[key] = f"{name}_MIN,{name}_MAX" elif self.node_type == "center": header[key] = name else: raise ValueError(f"Invalid node type {self.node_type!r}") key_interp = f"INTERP{idx}" header[key_interp] = self.interp else: raise ValueError(f"Unknown format {format}") return header
[docs] def to_table(self, format="ogip"): """Convert `~astropy.units.Quantity` to OGIP ``EBOUNDS`` extension. See https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#tth_sEc3.2 # noqa: E501 The 'ogip-sherpa' format is equivalent to 'ogip' but uses keV energy units. Parameters ---------- format : {"ogip", "ogip-sherpa", "gadf-dl3", "gtpsf"} Format specification Returns ------- table : `~astropy.table.Table` Table HDU """ table = Table() edges = self.edges if format in ["ogip", "ogip-sherpa"]: self.assert_name("energy") if format == "ogip-sherpa": edges = edges.to("keV") table["CHANNEL"] = np.arange(self.nbin, dtype=np.int16) table["E_MIN"] = edges[:-1] table["E_MAX"] = edges[1:] elif format in ["ogip-arf", "ogip-arf-sherpa"]: self.assert_name("energy_true") if format == "ogip-arf-sherpa": edges = edges.to("keV") table["ENERG_LO"] = edges[:-1] table["ENERG_HI"] = edges[1:] elif format == "gadf-sed": if self.is_energy_axis: table["e_ref"] = self.center table["e_min"] = self.edges_min table["e_max"] = self.edges_max elif format == "gadf-dl3": from gammapy.irf.io import IRF_DL3_AXES_SPECIFICATION if self.name == "energy": column_prefix = "ENERG" else: for column_prefix, spec in IRF_DL3_AXES_SPECIFICATION.items(): if spec["name"] == self.name: break if self.node_type == "edges": edges_hi, edges_lo = edges[:-1], edges[1:] else: edges_hi, edges_lo = self.center, self.center table[f"{column_prefix}_LO"] = edges_hi[np.newaxis] table[f"{column_prefix}_HI"] = edges_lo[np.newaxis] elif format == "gtpsf": if self.name == "energy_true": table["Energy"] = self.center.to("MeV") elif self.name == "rad": table["Theta"] = self.center.to("deg") else: raise ValueError( "Can only convert true energy or rad axis to" f"'gtpsf' format, got {self.name}" ) else: raise ValueError(f"{format} is not a valid format") return table
[docs] def to_table_hdu(self, format="ogip"): """Convert `~astropy.units.Quantity` to OGIP ``EBOUNDS`` extension. See https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#tth_sEc3.2 # noqa: E501 The 'ogip-sherpa' format is equivalent to 'ogip' but uses keV energy units. Parameters ---------- format : {"ogip", "ogip-sherpa", "gtpsf"} Format specification Returns ------- hdu : `~astropy.io.fits.BinTableHDU` Table HDU """ table = self.to_table(format=format) if format == "gtpsf": name = "THETA" else: name = None hdu = fits.BinTableHDU(table, name=name) if format in ["ogip", "ogip-sherpa"]: hdu.header.update(self.to_header(format=format)) return hdu
[docs] @classmethod def from_table(cls, table, format="ogip", idx=0, column_prefix=""): """Instantiate MapAxis from table HDU Parameters ---------- table : `~astropy.table.Table` Table format : {"ogip", "ogip-arf", "fgst-ccube", "fgst-template", "gadf", "gadf-dl3"} Format specification idx : int Column index of the axis. column_prefix : str Column name prefix of the axis, used for creating the axis. Returns ------- axis : `MapAxis` Map Axis """ if format in ["ogip", "fgst-ccube"]: energy_min = table["E_MIN"].quantity energy_max = table["E_MAX"].quantity energy_edges = ( np.append(energy_min.value, energy_max.value[-1]) * energy_min.unit ) axis = cls.from_edges(energy_edges, name="energy", interp="log") elif format == "ogip-arf": energy_min = table["ENERG_LO"].quantity energy_max = table["ENERG_HI"].quantity energy_edges = ( np.append(energy_min.value, energy_max.value[-1]) * energy_min.unit ) axis = cls.from_edges(energy_edges, name="energy_true", interp="log") elif format in ["fgst-template", "fgst-bexpcube"]: allowed_names = ["Energy", "ENERGY", "energy"] for colname in table.colnames: if colname in allowed_names: tag = colname break nodes = table[tag].data axis = cls.from_nodes( nodes=nodes, name="energy_true", unit="MeV", interp="log" ) elif format == "gadf": axcols = table.meta.get("AXCOLS{}".format(idx + 1)) colnames = axcols.split(",") node_type = "edges" if len(colnames) == 2 else "center" # TODO: check why this extra case is needed if colnames[0] == "E_MIN": name = "energy" else: name = colnames[0].replace("_MIN", "").lower() # this is need for backward compatibility if name == "theta": name = "rad" interp = table.meta.get("INTERP{}".format(idx + 1), "lin") if node_type == "center": nodes = np.unique(table[colnames[0]].quantity) else: edges_min = np.unique(table[colnames[0]].quantity) edges_max = np.unique(table[colnames[1]].quantity) nodes = edges_from_lo_hi(edges_min, edges_max) axis = MapAxis(nodes=nodes, node_type=node_type, interp=interp, name=name) elif format == "gadf-dl3": from gammapy.irf.io import IRF_DL3_AXES_SPECIFICATION spec = IRF_DL3_AXES_SPECIFICATION[column_prefix] name, interp = spec["name"], spec["interp"] # background models are stored in reconstructed energy hduclass = table.meta.get("HDUCLAS2") if hduclass in {"BKG", "RAD_MAX"} and column_prefix == "ENERG": name = "energy" edges_lo = table[f"{column_prefix}_LO"].quantity[0] edges_hi = table[f"{column_prefix}_HI"].quantity[0] if np.allclose(edges_hi, edges_lo): axis = MapAxis.from_nodes(edges_hi, interp=interp, name=name) else: edges = edges_from_lo_hi(edges_lo, edges_hi) axis = MapAxis.from_edges(edges, interp=interp, name=name) elif format == "gtpsf": try: energy = table["Energy"].data * u.MeV axis = MapAxis.from_nodes(energy, name="energy_true", interp="log") except KeyError: rad = table["Theta"].data * u.deg axis = MapAxis.from_nodes(rad, name="rad") elif format == "gadf-sed-energy": if "e_min" in table.colnames and "e_max" in table.colnames: e_min = flat_if_equal(table["e_min"].quantity) e_max = flat_if_equal(table["e_max"].quantity) edges = edges_from_lo_hi(e_min, e_max) axis = MapAxis.from_energy_edges(edges) elif "e_ref" in table.colnames: e_ref = flat_if_equal(table["e_ref"].quantity) axis = MapAxis.from_nodes(e_ref, name="energy", interp="log") else: raise ValueError( "Either 'e_ref', 'e_min' or 'e_max' column " "names are required" ) elif format == "gadf-sed-norm": # TODO: guess interp here nodes = flat_if_equal(table["norm_scan"][0]) axis = MapAxis.from_nodes(nodes, name="norm") elif format == "gadf-sed-counts": if "datasets" in table.colnames: labels = np.unique(table["datasets"]) axis = LabelMapAxis(labels=labels, name="dataset") else: shape = table["counts"].shape edges = np.arange(shape[-1] + 1) - 0.5 axis = MapAxis.from_edges(edges, name="dataset") elif format == "profile": if "datasets" in table.colnames: labels = np.unique(table["datasets"]) axis = LabelMapAxis(labels=labels, name="dataset") else: x_ref = table["x_ref"].quantity axis = MapAxis.from_nodes(x_ref, name="projected-distance") else: raise ValueError(f"Format '{format}' not supported") return axis
[docs] @classmethod def from_table_hdu(cls, hdu, format="ogip", idx=0): """Instantiate MapAxis from table HDU Parameters ---------- hdu : `~astropy.io.fits.BinTableHDU` Table HDU format : {"ogip", "ogip-arf", "fgst-ccube", "fgst-template"} Format specification idx : int Column index of the axis. Returns ------- axis : `MapAxis` Map Axis """ table = Table.read(hdu) return cls.from_table(table, format=format, idx=idx)
[docs]class MapAxes(Sequence): """MapAxis container class. Parameters ---------- axes : list of `MapAxis` List of map axis objects. """ def __init__(self, axes, n_spatial_axes=None): unique_names = [] for ax in axes: if ax.name in unique_names: raise ( ValueError(f"Axis names must be unique, got: '{ax.name}' twice.") ) unique_names.append(ax.name) self._axes = axes self._n_spatial_axes = n_spatial_axes @property def primary_axis(self): """Primary extra axis, defined as the one longest Returns ------- axis : `MapAxis` Map axis """ # get longest axis idx = np.argmax(self.shape) return self[int(idx)] @property def is_flat(self): """Whether axes is flat""" shape = np.array(self.shape) return np.all(shape == 1) @property def is_unidimensional(self): """Whether axes is unidimensional""" shape = np.array(self.shape) non_zero = np.count_nonzero(shape > 1) return self.is_flat or non_zero == 1 @property def reverse(self): """Reverse axes order""" return MapAxes(self[::-1]) @property def iter_with_reshape(self): """Iterate by shape""" for idx, axis in enumerate(self): # Extract values for each axis, default: nodes shape = [1] * len(self) shape[idx] = -1 if self._n_spatial_axes: shape = ( shape[::-1] + [ 1, ] * self._n_spatial_axes ) yield tuple(shape), axis
[docs] def get_coord(self, mode="center", axis_name=None): """Get axes coordinates Parameters ---------- mode : {"center", "edges"} Coordinate center or edges axis_name : str Axis name for which mode='edges' applies Returns ------- coords : dict of `~astropy.units.Quanity` Map coordinates """ coords = {} for shape, axis in self.iter_with_reshape: if mode == "edges" and axis.name == axis_name: coord = axis.edges else: coord = axis.center coords[axis.name] = coord.reshape(shape) return coords
[docs] def bin_volume(self): """Bin axes volume Returns ------- bin_volume : `~astropy.units.Quantity` Bin volume """ bin_volume = np.array(1) for shape, axis in self.iter_with_reshape: bin_volume = bin_volume * axis.bin_width.reshape(shape) return bin_volume
@property def shape(self): """Shape of the axes""" return tuple([ax.nbin for ax in self]) @property def names(self): """Names of the axes""" return [ax.name for ax in self]
[docs] def index(self, axis_name): """Get index in list""" return self.names.index(axis_name)
[docs] def index_data(self, axis_name): """Get data index of the axes Parameters ---------- axis_name : str Name of the axis. Returns ------- idx : int Data index """ idx = self.names.index(axis_name) return len(self) - idx - 1
def __len__(self): return len(self._axes) def __add__(self, other): return self.__class__(list(self) + list(other))
[docs] def upsample(self, factor, axis_name): """Upsample axis by a given factor Parameters ---------- factor : int Upsampling factor. axis_name : str Axis to upsample. Returns ------- axes : `MapAxes` Map axes """ axes = [] for ax in self: if ax.name == axis_name: ax = ax.upsample(factor=factor) axes.append(ax.copy()) return self.__class__(axes=axes)
[docs] def replace(self, axis): """Replace a given axis Parameters ---------- axis : `MapAxis` Map axis Returns ------- axes : MapAxes Map axe """ axes = [] for ax in self: if ax.name == axis.name: ax = axis axes.append(ax) return self.__class__(axes=axes)
[docs] def resample(self, axis): """Resample axis binning. This method groups the existing bins into a new binning. Parameters ---------- axis : `MapAxis` New map axis. Returns ------- axes : `MapAxes` Axes object with resampled axis. """ axis_self = self[axis.name] groups = axis_self.group_table(axis.edges) # Keep only normal bins groups = groups[groups["bin_type"] == "normal "] edges = edges_from_lo_hi( groups[axis.name + "_min"].quantity, groups[axis.name + "_max"].quantity, ) axis_resampled = MapAxis.from_edges( edges=edges, interp=axis.interp, name=axis.name ) axes = [] for ax in self: if ax.name == axis.name: axes.append(axis_resampled) else: axes.append(ax.copy()) return self.__class__(axes=axes)
[docs] def downsample(self, factor, axis_name): """Downsample axis by a given factor Parameters ---------- factor : int Upsampling factor. axis_name : str Axis to upsample. Returns ------- axes : `MapAxes` Map axes """ axes = [] for ax in self: if ax.name == axis_name: ax = ax.downsample(factor=factor) axes.append(ax.copy()) return self.__class__(axes=axes)
[docs] def squash(self, axis_name): """Squash axis. Parameters ---------- axis_name : str Axis to squash. Returns ------- axes : `MapAxes` Axes with squashed axis. """ axes = [] for ax in self: if ax.name == axis_name: ax = ax.squash() axes.append(ax.copy()) return self.__class__(axes=axes)
[docs] def pad(self, axis_name, pad_width): """Pad axes Parameters ---------- axis_name : str Name of the axis to pad. pad_width : int or tuple of int Pad width Returns ------- axes : `MapAxes` Axes with squashed axis. """ axes = [] for ax in self: if ax.name == axis_name: ax = ax.pad(pad_width=pad_width) axes.append(ax) return self.__class__(axes=axes)
[docs] def drop(self, axis_name): """Drop an axis. Parameters ---------- axis_name : str Name of the axis to remove. Returns ------- axes : `MapAxes` Axes with squashed axis. """ axes = [] for ax in self: if ax.name == axis_name: continue axes.append(ax.copy()) return self.__class__(axes=axes)
def __getitem__(self, idx): if isinstance(idx, int): return self._axes[idx] elif isinstance(idx, str): for ax in self._axes: if ax.name == idx: return ax raise KeyError(f"No axes: {idx!r}") elif isinstance(idx, slice): axes = self._axes[idx] return self.__class__(axes=axes) elif isinstance(idx, list): axes = [] for name in idx: axes.append(self[name]) return self.__class__(axes=axes) else: raise TypeError(f"Invalid type: {type(idx)!r}")
[docs] def coord_to_idx(self, coord, clip=True): """Transform from axis to pixel indices. Parameters ---------- coord : dict of `~numpy.ndarray` or `MapCoord` Array of axis coordinate values. Returns ------- pix : tuple of `~numpy.ndarray` Array of pixel indices values. """ return tuple([ax.coord_to_idx(coord[ax.name], clip=clip) for ax in self])
[docs] def coord_to_pix(self, coord): """Transform from axis to pixel coordinates. Parameters ---------- coord : dict of `~numpy.ndarray` Array of axis coordinate values. Returns ------- pix : tuple of `~numpy.ndarray` Array of pixel coordinate values. """ return tuple([ax.coord_to_pix(coord[ax.name]) for ax in self])
[docs] def pix_to_coord(self, pix): """Convert pixel coordinates to map coordinates. Parameters ---------- pix : tuple Tuple of pixel coordinates. Returns ------- coords : tuple Tuple of map coordinates. """ return tuple([ax.pix_to_coord(p) for ax, p in zip(self, pix)])
[docs] def pix_to_idx(self, pix, clip=False): """Convert pix to idx Parameters ---------- pix : tuple of `~numpy.ndarray` Pixel coordinates. clip : bool Choose whether to clip indices to the valid range of the axis. If false then indices for coordinates outside the axi range will be set -1. Returns ------- idx : tuple `~numpy.ndarray` Pixel indices. """ idx = [] for pix_array, ax in zip(pix, self): idx.append(ax.pix_to_idx(pix_array, clip=clip)) return tuple(idx)
[docs] def slice_by_idx(self, slices): """Create a new geometry by slicing the non-spatial axes. Parameters ---------- slices : dict Dict of axes names and integers or `slice` object pairs. Contains one element for each non-spatial dimension. For integer indexing the corresponding axes is dropped from the map. Axes not specified in the dict are kept unchanged. Returns ------- geom : `~Geom` Sliced geometry. """ axes = [] for ax in self: ax_slice = slices.get(ax.name, slice(None)) # in the case where isinstance(ax_slice, int) the axes is dropped if isinstance(ax_slice, slice): ax_sliced = ax.slice(ax_slice) axes.append(ax_sliced.copy()) return self.__class__(axes=axes)
[docs] def to_header(self, format="gadf"): """Convert axes to FITS header Parameters ---------- format : {"gadf"} Header format Returns ------- header : `~astropy.io.fits.Header` FITS header. """ header = fits.Header() for idx, ax in enumerate(self, start=1): header_ax = ax.to_header(format=format, idx=idx) header.update(header_ax) return header
[docs] def to_table(self, format="gadf"): """Convert axes to table Parameters ---------- format : {"gadf", "gadf-dl3", "fgst-ccube", "fgst-template", "ogip", "ogip-sherpa", "ogip-arf", "ogip-arf-sherpa"} # noqa E501 Format to use. Returns ------- table : `~astropy.table.Table` Table with axis data """ if format == "gadf-dl3": tables = [] for ax in self: tables.append(ax.to_table(format=format)) table = hstack(tables) elif format in ["gadf", "fgst-ccube", "fgst-template"]: table = Table() table["CHANNEL"] = np.arange(np.prod(self.shape)) axes_ctr = np.meshgrid(*[ax.center for ax in self]) axes_min = np.meshgrid(*[ax.edges_min for ax in self]) axes_max = np.meshgrid(*[ax.edges_max for ax in self]) for idx, ax in enumerate(self): name = ax.name.upper() if name == "ENERGY": colnames = ["ENERGY", "E_MIN", "E_MAX"] else: colnames = [name, name + "_MIN", name + "_MAX"] for colname, v in zip(colnames, [axes_ctr, axes_min, axes_max]): # do not store edges for label axis if ax.node_type == "label" and colname != name: continue table[colname] = np.ravel(v[idx]) if isinstance(ax, TimeMapAxis): ref_dict = time_ref_to_dict(ax.reference_time) table.meta.update(ref_dict) elif format in ["ogip", "ogip-sherpa", "ogip", "ogip-arf"]: energy_axis = self["energy"] table = energy_axis.to_table(format=format) else: raise ValueError(f"Unsupported format: '{format}'") return table
[docs] def to_table_hdu(self, format="gadf", hdu_bands=None): """Make FITS table columns for map axes. Parameters ---------- format : {"gadf", "fgst-ccube", "fgst-template"} Format to use. hdu_bands : str Name of the bands HDU to use. Returns ------- hdu : `~astropy.io.fits.BinTableHDU` Bin table HDU. """ # FIXME: Check whether convention is compatible with # dimensionality of geometry and simplify!!! if format in ["fgst-ccube", "ogip", "ogip-sherpa"]: hdu_bands = "EBOUNDS" elif format == "fgst-template": hdu_bands = "ENERGIES" elif format == "gadf" or format is None: if hdu_bands is None: hdu_bands = "BANDS" else: raise ValueError(f"Unknown format {format}") table = self.to_table(format=format) header = self.to_header(format=format) return fits.BinTableHDU(table, name=hdu_bands, header=header)
[docs] @classmethod def from_table_hdu(cls, hdu, format="gadf"): """Create MapAxes from BinTableHDU Parameters ---------- hdu : `~astropy.io.fits.BinTableHDU` Bin table HDU Returns ------- axes : `MapAxes` Map axes object """ if hdu is None: return cls([]) table = Table.read(hdu) return cls.from_table(table, format=format)
[docs] @classmethod def from_table(cls, table, format="gadf"): """Create MapAxes from table Parameters ---------- table : `~astropy.table.Table` Bin table HDU format : {"gadf", "gadf-dl3", "fgst-ccube", "fgst-template", "fgst-bexcube", "ogip-arf"} Format to use. Returns ------- axes : `MapAxes` Map axes object """ from gammapy.irf.io import IRF_DL3_AXES_SPECIFICATION axes = [] # Formats that support only one energy axis if format in [ "fgst-ccube", "fgst-template", "fgst-bexpcube", "ogip", "ogip-arf", ]: axes.append(MapAxis.from_table(table, format=format)) elif format == "gadf": # This limits the max number of axes to 5 for idx in range(5): axcols = table.meta.get("AXCOLS{}".format(idx + 1)) if axcols is None: break # TODO: what is good way to check whether it is a given axis type? try: axis = LabelMapAxis.from_table(table, format=format, idx=idx) except (KeyError, TypeError): try: axis = TimeMapAxis.from_table(table, format=format, idx=idx) except (KeyError, ValueError): axis = MapAxis.from_table(table, format=format, idx=idx) axes.append(axis) elif format == "gadf-dl3": for column_prefix in IRF_DL3_AXES_SPECIFICATION: try: axis = MapAxis.from_table( table, format=format, column_prefix=column_prefix ) except KeyError: continue axes.append(axis) elif format == "gadf-sed": for axis_format in ["gadf-sed-norm", "gadf-sed-energy", "gadf-sed-counts"]: try: axis = MapAxis.from_table(table=table, format=axis_format) except KeyError: continue axes.append(axis) elif format == "lightcurve": axes.extend(cls.from_table(table=table, format="gadf-sed")) axes.append(TimeMapAxis.from_table(table, format="lightcurve")) elif format == "profile": axes.extend(cls.from_table(table=table, format="gadf-sed")) axes.append(MapAxis.from_table(table, format="profile")) else: raise ValueError(f"Unsupported format: '{format}'") return cls(axes)
[docs] @classmethod def from_default(cls, axes, n_spatial_axes=None): """Make a sequence of `~MapAxis` objects.""" if axes is None: return cls([]) axes_out = [] for idx, ax in enumerate(axes): if isinstance(ax, np.ndarray): ax = MapAxis(ax) if ax.name == "": ax._name = f"axis{idx}" axes_out.append(ax) return cls(axes_out, n_spatial_axes=n_spatial_axes)
[docs] def assert_names(self, required_names): """Assert required axis names and order Parameters ---------- required_names : list of str Required """ message = ( "Incorrect axis order or names. Expected axis " f"order: {required_names}, got: {self.names}." ) if not len(self) == len(required_names): raise ValueError(message) try: for ax, required_name in zip(self, required_names): ax.assert_name(required_name) except ValueError: raise ValueError(message)
[docs] def rename_axes(self, names, new_names): """Rename the axes. Parameters ---------- names : list or str Names of the axes new_names : list or str New names of the axes (list must be of same length than `names`). Returns ------- axes : `MapAxes` Renamed Map axes object """ axes = self.copy() if isinstance(names, str): names = [names] if isinstance(new_names, str): new_names = [new_names] for name, new_name in zip(names, new_names): axes[name]._name = new_name return axes
@property def center_coord(self): """Center coordinates""" return tuple([ax.pix_to_coord((float(ax.nbin) - 1.0) / 2.0) for ax in self])
[docs] def is_allclose(self, other, **kwargs): """Check if other map axes are all close. Parameters ---------- other : `MapAxes` Other map axes **kwargs : dict Keyword arguments forwarded to `~MapAxis.is_allclose` Returns ------- is_allclose : bool Whether other axes are all close """ if not isinstance(other, self.__class__): return TypeError(f"Cannot compare {type(self)} and {type(other)}") return np.all([ax0.is_allclose(ax1, **kwargs) for ax0, ax1 in zip(other, self)])
def __eq__(self, other): if not isinstance(other, self.__class__): return False return self.is_allclose(other, rtol=1e-6, atol=1e-6) def __ne__(self, other): return not self.__eq__(other)
[docs] def copy(self): """Init map axes instance by copying each axis.""" return self.__class__([_.copy() for _ in self])
[docs]class TimeMapAxis: """Class representing a time axis. Provides methods for transforming to/from axis and pixel coordinates. A time axis can represent non-contiguous sequences of non-overlapping time intervals. Time intervals must be provided in increasing order. Parameters ---------- edges_min : `~astropy.units.Quantity` Array of edge time values. This the time delta w.r.t. to the reference time. edges_max : `~astropy.units.Quantity` Array of edge time values. This the time delta w.r.t. to the reference time. reference_time : `~astropy.time.Time` Reference time to use. name : str Axis name interp : str Interpolation method used to transform between axis and pixel coordinates. For now only 'lin' is supported. """ node_type = "intervals" time_format = "iso" def __init__(self, edges_min, edges_max, reference_time, name="time", interp="lin"): self._name = name edges_min = u.Quantity(edges_min, ndmin=1) edges_max = u.Quantity(edges_max, ndmin=1) if not edges_min.unit.is_equivalent("s"): raise ValueError( f"Time edges min must have a valid time unit, got {edges_min.unit}" ) if not edges_max.unit.is_equivalent("s"): raise ValueError( f"Time edges max must have a valid time unit, got {edges_max.unit}" ) if not edges_min.shape == edges_max.shape: raise ValueError( "Edges min and edges max must have the same shape," f" got {edges_min.shape} and {edges_max.shape}." ) if not np.all(edges_max > edges_min): raise ValueError("Edges max must all be larger than edge min") if not np.all(edges_min == np.sort(edges_min)): raise ValueError("Time edges min values must be sorted") if not np.all(edges_max == np.sort(edges_max)): raise ValueError("Time edges max values must be sorted") if interp != "lin": raise NotImplementedError( f"Non-linear scaling scheme are not supported yet, got {interp}" ) self._edges_min = edges_min self._edges_max = edges_max self._reference_time = Time(reference_time) self._pix_offset = -0.5 self._interp = interp delta = edges_min[1:] - edges_max[:-1] if np.any(delta < 0 * u.s): raise ValueError("Time intervals must not overlap.") @property def is_contiguous(self): """Whether the axis is contiguous""" return np.all(self.edges_min[1:] == self.edges_max[:-1])
[docs] def to_contiguous(self): """Make the time axis contiguous Returns ------- axis : `TimeMapAxis` Contiguous time axis """ edges = np.unique(np.stack([self.edges_min, self.edges_max])) return self.__class__( edges_min=edges[:-1], edges_max=edges[1:], reference_time=self.reference_time, name=self.name, interp=self.interp, )
@property def unit(self): """Axes unit""" return self.edges_max.unit @property def interp(self): """Interp""" return self._interp @property def reference_time(self): """Return reference time used for the axis.""" return self._reference_time @property def name(self): """Return axis name.""" return self._name @property def nbin(self): """Return number of bins in the axis.""" return len(self.edges_min.flatten()) @property def edges_min(self): """Return array of bin edges max values.""" return self._edges_min @property def edges_max(self): """Return array of bin edges min values.""" return self._edges_max @property def edges(self): """Return array of bin edges values.""" if not self.is_contiguous: raise ValueError("Time axis is not contiguous") return edges_from_lo_hi(self.edges_min, self.edges_max) @property def bounds(self): """Bounds of the axis (~astropy.units.Quantity)""" return self.edges_min[0], self.edges_max[-1] @property def time_bounds(self): """Bounds of the axis (~astropy.units.Quantity)""" t_min, t_max = self.bounds return t_min + self.reference_time, t_max + self.reference_time @property def time_min(self): """Return axis lower edges as Time objects.""" return self._edges_min + self.reference_time @property def time_max(self): """Return axis upper edges as Time objects.""" return self._edges_max + self.reference_time @property def time_delta(self): """Return axis time bin width (`~astropy.time.TimeDelta`).""" return self.time_max - self.time_min @property def time_mid(self): """Return time bin center (`~astropy.time.Time`).""" return self.time_min + 0.5 * self.time_delta @property def time_edges(self): """Time edges""" return self.reference_time + self.edges @property def as_plot_xerr(self): """Plot x error""" xn, xp = self.time_mid - self.time_min, self.time_max - self.time_mid if self.time_format == "iso": x_errn = xn.to_datetime() x_errp = xp.to_datetime() elif self.time_format == "mjd": x_errn = xn.to("day") x_errp = xp.to("day") else: raise ValueError(f"Invalid time_format: {self.time_format}") return x_errn, x_errp @property def as_plot_labels(self): """Plot labels""" labels = [] for t_min, t_max in self.iter_by_edges: label = f"{getattr(t_min, self.time_format)} - {getattr(t_max, self.time_format)}" labels.append(label) return labels @property def as_plot_edges(self): """Plot edges""" if self.time_format == "iso": edges = self.time_edges.to_datetime() elif self.time_format == "mjd": edges = self.time_edges.mjd * u.day else: raise ValueError(f"Invalid time_format: {self.time_format}") return edges @property def as_plot_center(self): """Plot center""" if self.time_format == "iso": center = self.time_mid.datetime elif self.time_format == "mjd": center = self.time_mid.mjd * u.day return center
[docs] def format_plot_xaxis(self, ax): """Format plot axis Parameters ---------- ax : `~matplotlib.pyplot.Axis` Plot axis to format Returns ------- ax : `~matplotlib.pyplot.Axis` Formatted plot axis """ from matplotlib.dates import DateFormatter xlabel = DEFAULT_LABEL_TEMPLATE.format( quantity=PLOT_AXIS_LABEL.get(self.name, self.name.capitalize()), unit=self.time_format, ) ax.set_xlabel(xlabel) if self.time_format == "iso": ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d %H:%M:%S")) plt.setp( ax.xaxis.get_majorticklabels(), rotation=30, ha="right", rotation_mode="anchor", ) return ax
[docs] def assert_name(self, required_name): """Assert axis name if a specific one is required. Parameters ---------- required_name : str Required """ if self.name != required_name: raise ValueError( "Unexpected axis name," f' expected "{required_name}", got: "{self.name}"' )
[docs] def is_allclose(self, other, **kwargs): """Check if other map axis is all close. Parameters ---------- other : `TimeMapAxis` Other map axis **kwargs : dict Keyword arguments forwarded to `~numpy.allclose` Returns ------- is_allclose : bool Whether other axis is allclose """ if not isinstance(other, self.__class__): return TypeError(f"Cannot compare {type(self)} and {type(other)}") if self._edges_min.shape != other._edges_min.shape: return False # This will test equality at microsec level. delta_min = self.time_min - other.time_min delta_max = self.time_max - other.time_max return ( np.allclose(delta_min.to_value("s"), 0.0, **kwargs) and np.allclose(delta_max.to_value("s"), 0.0, **kwargs) and self._interp == other._interp and self.name.upper() == other.name.upper() )
def __eq__(self, other): if not isinstance(other, self.__class__): return False return self.is_allclose(other=other, atol=1e-6) def __ne__(self, other): return not self.__eq__(other) def __hash__(self): return id(self)
[docs] def is_aligned(self, other, atol=2e-2): raise NotImplementedError
@property def iter_by_edges(self): """Iterate by intervals defined by the edges""" for time_min, time_max in zip(self.time_min, self.time_max): yield (time_min, time_max)
[docs] def coord_to_idx(self, coord, **kwargs): """Transform from axis time coordinate to bin index. Indices of time values falling outside time bins will be set to -1. Parameters ---------- coord : `~astropy.time.Time` or `~astropy.units.Quantity` Array of axis coordinate values. The quantity is assumed to be relative to the reference time. Returns ------- idx : `~numpy.ndarray` Array of bin indices. """ if isinstance(coord, u.Quantity): coord = self.reference_time + coord time = Time(coord[..., np.newaxis]) delta_plus = (time - self.time_min).value > 0.0 delta_minus = (time - self.time_max).value <= 0.0 mask = np.logical_and(delta_plus, delta_minus) idx = np.asanyarray(np.argmax(mask, axis=-1)) idx[~np.any(mask, axis=-1)] = INVALID_INDEX.int return idx
[docs] def coord_to_pix(self, coord, **kwargs): """Transform from time to coordinate to pixel position. Pixels of time values falling outside time bins will be set to -1. Parameters ---------- coord : `~astropy.time.Time` Array of axis coordinate values. Returns ------- pix : `~numpy.ndarray` Array of pixel positions. """ if isinstance(coord, u.Quantity): coord = self.reference_time + coord idx = np.atleast_1d(self.coord_to_idx(coord)) valid_pix = idx != INVALID_INDEX.int pix = np.atleast_1d(idx).astype("float") # TODO: is there the equivalent of np.atleast1d for astropy.time.Time? if coord.shape == (): coord = coord.reshape((1,)) relative_time = coord[valid_pix] - self.reference_time scale = interpolation_scale(self._interp) valid_idx = idx[valid_pix] s_min = scale(self._edges_min[valid_idx]) s_max = scale(self._edges_max[valid_idx]) s_coord = scale(relative_time.to(self._edges_min.unit)) pix[valid_pix] += (s_coord - s_min) / (s_max - s_min) pix[~valid_pix] = INVALID_INDEX.float return pix - 0.5
[docs] @staticmethod def pix_to_idx(pix, clip=False): return pix
@property def center(self): """Return `~astropy.units.Quantity` at interval centers.""" return self.edges_min + 0.5 * self.bin_width @property def bin_width(self): """Return time interval width (`~astropy.units.Quantity`).""" return self.time_delta.to("h") def __repr__(self): str_ = self.__class__.__name__ + "\n" str_ += "-" * len(self.__class__.__name__) + "\n\n" fmt = "\t{:<14s} : {:<10s}\n" str_ += fmt.format("name", self.name) str_ += fmt.format("nbins", str(self.nbin)) str_ += fmt.format("reference time", self.reference_time.iso) str_ += fmt.format("scale", self.reference_time.scale) str_ += fmt.format("time min.", self.time_min.min().iso) str_ += fmt.format("time max.", self.time_max.max().iso) str_ += fmt.format("total time", np.sum(self.bin_width)) return str_.expandtabs(tabsize=2)
[docs] def upsample(self): raise NotImplementedError
[docs] def downsample(self): raise NotImplementedError
def _init_copy(self, **kwargs): """Init map axis instance by copying missing init arguments from self.""" argnames = inspect.getfullargspec(self.__init__).args argnames.remove("self") for arg in argnames: value = getattr(self, "_" + arg) kwargs.setdefault(arg, copy.deepcopy(value)) return self.__class__(**kwargs)
[docs] def copy(self, **kwargs): """Copy `MapAxis` instance and overwrite given attributes. Parameters ---------- **kwargs : dict Keyword arguments to overwrite in the map axis constructor. Returns ------- copy : `MapAxis` Copied map axis. """ return self._init_copy(**kwargs)
[docs] def slice(self, idx): """Create a new axis object by extracting a slice from this axis. Parameters ---------- idx : slice Slice object selecting a subselection of the axis. Returns ------- axis : `~TimeMapAxis` Sliced axis object. """ return TimeMapAxis( self._edges_min[idx].copy(), self._edges_max[idx].copy(), self.reference_time, interp=self._interp, name=self.name, )
[docs] def squash(self): """Create a new axis object by squashing the axis into one bin. Returns ------- axis : `~MapAxis` Sliced axis object. """ return TimeMapAxis( self._edges_min[0], self._edges_max[-1], self.reference_time, interp=self._interp, name=self._name, )
# TODO: if we are to allow log or sqrt bins the reference time should always # be strictly lower than all times # Should we define a mechanism to ensure this is always correct?
[docs] @classmethod def from_time_edges(cls, time_min, time_max, unit="d", interp="lin", name="time"): """Create TimeMapAxis from the time interval edges defined as `~astropy.time.Time`. The reference time is defined as the lower edge of the first interval. Parameters ---------- time_min : `~astropy.time.Time` Array of lower edge times. time_max : `~astropy.time.Time` Array of lower edge times. unit : `~astropy.units.Unit` or str The unit to convert the edges to. Default is 'd' (day). interp : str Interpolation method used to transform between axis and pixel coordinates. Valid options are 'log', 'lin', and 'sqrt'. name : str Axis name Returns ------- axis : `TimeMapAxis` Time map axis. """ unit = u.Unit(unit) reference_time = time_min[0] edges_min = time_min - reference_time edges_max = time_max - reference_time return cls( edges_min.to(unit), edges_max.to(unit), reference_time, interp=interp, name=name, )
# TODO: how configurable should that be? column names?
[docs] @classmethod def from_table(cls, table, format="gadf", idx=0): """Create time map axis from table Parameters ---------- table : `~astropy.table.Table` Bin table HDU format : {"gadf", "fermi-fgl", "lightcurve"} Format to use. Returns ------- axis : `TimeMapAxis` Time map axis. """ if format == "gadf": axcols = table.meta.get("AXCOLS{}".format(idx + 1)) colnames = axcols.split(",") name = colnames[0].replace("_MIN", "").lower() reference_time = time_ref_from_dict(table.meta) edges_min = np.unique(table[colnames[0]].quantity) edges_max = np.unique(table[colnames[1]].quantity) elif format == "fermi-fgl": meta = table.meta.copy() meta["MJDREFF"] = str(meta["MJDREFF"]).replace("D-4", "e-4") reference_time = time_ref_from_dict(meta=meta) name = "time" edges_min = table["Hist_Start"][:-1] edges_max = table["Hist_Start"][1:] elif format == "lightcurve": # TODO: is this a good format? It just supports mjd... name = "time" scale = table.meta.get("TIMESYS", "utc") time_min = Time(table["time_min"].data, format="mjd", scale=scale) time_max = Time(table["time_max"].data, format="mjd", scale=scale) reference_time = Time("2001-01-01T00:00:00") reference_time.format = "mjd" edges_min = (time_min - reference_time).to("s") edges_max = (time_max - reference_time).to("s") else: raise ValueError(f"Not a supported format: {format}") return cls( edges_min=edges_min, edges_max=edges_max, reference_time=reference_time, name=name, )
[docs] @classmethod def from_gti(cls, gti, name="time"): """Create a time axis from an input GTI. Parameters ---------- gti : `GTI` GTI table name : str Axis name Returns ------- axis : `TimeMapAxis` Time map axis. """ tmin = gti.time_start - gti.time_ref tmax = gti.time_stop - gti.time_ref return cls( edges_min=tmin.to("s"), edges_max=tmax.to("s"), reference_time=gti.time_ref, name=name, )
[docs] @classmethod def from_time_bounds(cls, time_min, time_max, nbin, unit="d", name="time"): """Create linearly spaced time axis from bounds Parameters ---------- time_min : `~astropy.time.Time` Lower bound time_max : `~astropy.time.Time` Upper bound nbin : int Number of bins name : str Name of the axis. """ delta = time_max - time_min time_edges = time_min + delta * np.linspace(0, 1, nbin + 1) return cls.from_time_edges( time_min=time_edges[:-1], time_max=time_edges[1:], interp="lin", unit=unit, name=name, )
[docs] def to_header(self, format="gadf", idx=0): """Create FITS header Parameters ---------- format : {"ogip"} Format specification idx : int Column index of the axis. Returns ------- header : `~astropy.io.fits.Header` Header to extend. """ header = fits.Header() if format == "gadf": key = f"AXCOLS{idx}" name = self.name.upper() header[key] = f"{name}_MIN,{name}_MAX" key_interp = f"INTERP{idx}" header[key_interp] = self.interp ref_dict = time_ref_to_dict(self.reference_time) header.update(ref_dict) else: raise ValueError(f"Unknown format {format}") return header
[docs]class LabelMapAxis: """Map axis using labels Parameters ---------- labels : list of str Labels to be used for the axis nodes. name : str Name of the axis. """ node_type = "label" def __init__(self, labels, name=""): unique_labels = np.unique(labels) if not len(unique_labels) == len(labels): raise ValueError("Node labels must be unique") self._labels = unique_labels self._name = name @property def unit(self): """Unit""" return u.Unit("") @property def name(self): """Name of the axis""" return self._name
[docs] def assert_name(self, required_name): """Assert axis name if a specific one is required. Parameters ---------- required_name : str Required """ if self.name != required_name: raise ValueError( "Unexpected axis name," f' expected "{required_name}", got: "{self.name}"' )
@property def nbin(self): """Number of bins""" return len(self._labels)
[docs] def pix_to_coord(self, pix): """Transform from pixel to axis coordinates. Parameters ---------- pix : `~numpy.ndarray` Array of pixel coordinate values. Returns ------- coord : `~numpy.ndarray` Array of axis coordinate values. """ idx = np.round(pix).astype(int) return self._labels[idx]
[docs] def coord_to_idx(self, coord, **kwargs): """Transform labels to indices If the label is not present an error is raised. Parameters ---------- coord : `~astropy.time.Time` Array of axis coordinate values. Returns ------- idx : `~numpy.ndarray` Array of bin indices. """ coord = np.array(coord)[..., np.newaxis] is_equal = coord == self._labels if not np.all(np.any(is_equal, axis=-1)): label = coord[~np.any(is_equal, axis=-1)] raise ValueError(f"Not a valid label: {label}") return np.argmax(is_equal, axis=-1)
[docs] def coord_to_pix(self, coord): """Transform from axis labels to pixel coordinates. Parameters ---------- coord : `~numpy.ndarray` Array of axis label values. Returns ------- pix : `~numpy.ndarray` Array of pixel coordinate values. """ return self.coord_to_idx(coord).astype("float")
[docs] def pix_to_idx(self, pix, clip=False): """Convert pix to idx Parameters ---------- pix : tuple of `~numpy.ndarray` Pixel coordinates. clip : bool Choose whether to clip indices to the valid range of the axis. If false then indices for coordinates outside the axi range will be set -1. Returns ------- idx : tuple `~numpy.ndarray` Pixel indices. """ if clip: idx = np.clip(pix, 0, self.nbin - 1) else: condition = (pix < 0) | (pix >= self.nbin) idx = np.where(condition, -1, pix) return idx
@property def center(self): """Center of the label axis""" return self._labels @property def edges(self): """Edges of the label axis""" raise ValueError("A LabelMapAxis does not define edges") @property def edges_min(self): """Edges of the label axis""" return self._labels @property def edges_max(self): """Edges of the label axis""" return self._labels @property def bin_width(self): """Bin width is unity""" return np.ones(self.nbin) @property def as_plot_xerr(self): """Plot labels""" return 0.5 * np.ones(self.nbin) @property def as_plot_labels(self): """Plot labels""" return self._labels.tolist() @property def as_plot_center(self): """Plot labels""" return np.arange(self.nbin) @property def as_plot_edges(self): """Plot labels""" return np.arange(self.nbin + 1) - 0.5
[docs] def format_plot_xaxis(self, ax): """Format plot axis. Parameters ---------- ax : `~matplotlib.pyplot.Axis` Plot axis to format. Returns ------- ax : `~matplotlib.pyplot.Axis` Formatted plot axis. """ ax.set_xticks(self.as_plot_center) ax.set_xticklabels( self.as_plot_labels, rotation=30, ha="right", rotation_mode="anchor", ) return ax
[docs] def to_header(self, format="gadf", idx=0): """Create FITS header Parameters ---------- format : {"ogip"} Format specification idx : int Column index of the axis. Returns ------- header : `~astropy.io.fits.Header` Header to extend. """ header = fits.Header() if format == "gadf": key = f"AXCOLS{idx}" header[key] = self.name.upper() else: raise ValueError(f"Unknown format {format}") return header
# TODO: how configurable should that be? column names?
[docs] @classmethod def from_table(cls, table, format="gadf", idx=0): """Create time map axis from table Parameters ---------- table : `~astropy.table.Table` Bin table HDU format : {"gadf"} Format to use. Returns ------- axis : `TimeMapAxis` Time map axis. """ if format == "gadf": colname = table.meta.get("AXCOLS{}".format(idx + 1)) column = table[colname] if not np.issubdtype(column.dtype, np.str_): raise TypeError(f"Not a valid dtype for label axis: '{column.dtype}'") labels = np.unique(column.data) else: raise ValueError(f"Not a supported format: {format}") return cls(labels=labels, name=colname.lower())
def __repr__(self): str_ = self.__class__.__name__ + "\n" str_ += "-" * len(self.__class__.__name__) + "\n\n" fmt = "\t{:<10s} : {:<10s}\n" str_ += fmt.format("name", self.name) str_ += fmt.format("nbins", str(self.nbin)) str_ += fmt.format("node type", self.node_type) str_ += fmt.format("labels", "{0}".format(list(self._labels))) return str_.expandtabs(tabsize=2)
[docs] def is_allclose(self, other, **kwargs): """Check if other map axis is all close. Parameters ---------- other : `LabelMapAxis` Other map axis Returns ------- is_allclose : bool Whether other axis is allclose """ if not isinstance(other, self.__class__): return TypeError(f"Cannot compare {type(self)} and {type(other)}") name_equal = self.name.upper() == other.name.upper() labels_equal = np.all(self.center == other.center) return name_equal & labels_equal
def __eq__(self, other): if not isinstance(other, self.__class__): return False return self.is_allclose(other=other) def __ne__(self, other): return not self.__eq__(other) # TODO: could create sub-labels here using dashes like "label-1-a", etc.
[docs] def upsample(self, *args, **kwargs): """Upsample axis""" raise NotImplementedError("Upsampling a LabelMapAxis is not supported")
# TODO: could merge labels here like "label-1-label2", etc.
[docs] def downsample(self, *args, **kwargs): """Downsample axis""" raise NotImplementedError("Downsampling a LabelMapAxis is not supported")
# TODO: could merge labels here like "label-1-label2", etc.
[docs] def resample(self, *args, **kwargs): """Resample axis""" raise NotImplementedError("Resampling a LabelMapAxis is not supported")
# TODO: could create new labels here like "label-10-a"
[docs] def pad(self, *args, **kwargs): """Resample axis""" raise NotImplementedError("Padding a LabelMapAxis is not supported")
[docs] def copy(self): """Copy axis""" return copy.deepcopy(self)
[docs] def slice(self, idx): """Create a new axis object by extracting a slice from this axis. Parameters ---------- idx : slice Slice object selecting a subselection of the axis. Returns ------- axis : `~LabelMapAxis` Sliced axis object. """ return self.__class__( labels=self._labels[idx], name=self.name, )