Source code for gammapy.maps.axes

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import html
import inspect
import logging
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, INVALID_VALUE, edges_from_lo_hi

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

log = logging.getLogger(__name__)


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


class AxisCoordInterpolator:
    """Axis coordinate 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):
        """Transform coordinate to pixel."""
        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):
        """Transform pixel to coordinate."""
        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}]"
UNIT_STRING_FORMAT = "latex_inline"


[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 : {'lin', 'log', 'sqrt'} Interpolation method used to transform between axis and pixel coordinates. Default is 'lin'. name : str, optional Axis name. Default is "". node_type : str, optional 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). Default is "edges". unit : str, optional String specifying the data units. Default is "". """ # 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 def _repr_html_(self): try: return self.to_html() except AttributeError: return f"<pre>{html.escape(str(self))}</pre>"
[docs] def assert_name(self, required_name): """Assert axis name if a specific one is required. Parameters ---------- required_name : str Required name. """ 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 the 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, optional Absolute numerical tolerance for the comparison measured in bins. Default is 2e-2. 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 the other map axis is all close. Parameters ---------- other : `MapAxis` Other map axis. **kwargs : dict, optional Keyword arguments passed to `~numpy.allclose`. Returns ------- is_allclose : bool Whether the 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): """Whether this is an energy axis.""" 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 an 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 an array of bin edges maximum values.""" return self.edges[:-1] @property def edges_max(self): """Return an array of bin edges minimum values.""" return self.edges[1:] @property def bounds(self): """Bounds of the axis as a `~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 a tuple of x-error to be passed to `~matplotlib.pyplot.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 a 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 a copy of the `MapAxis` instance with a node type set to node_type. Parameters ---------- node_type : str The target node type. It can be either 'center' or 'edges'. 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. Return a copy of the `MapAxis` instance with name set to new_name. Parameters ---------- new_name : str The new name for the axis. Returns ------- axis : `~gammapy.maps.MapAxis` The new MapAxis. """ return self.copy(name=new_name)
[docs] def format_plot_xaxis(self, ax): """Format the x-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.to_string(UNIT_STRING_FORMAT), ) 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 y-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.to_string(UNIT_STRING_FORMAT), ) 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 an 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 the number of bins.""" return self._nbin @property def nbin_per_decade(self): """Return the number of bins per decade.""" 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, either 'center' or 'edges'.""" return self._node_type @property def unit(self): """Return the 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'. ***kwargs : dict, optional Keyword arguments passed to `MapAxis`. """ 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.geomspace(lo_bnd, 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` or float Energy edges. unit : `~astropy.units.Unit`, optional Energy unit. Default is None. name : str, optional Name of the energy axis, either 'energy' or 'energy_true'. Default is None. interp: str, optional 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", strict_bounds=True, ): """Make an energy axis from energy bounds. The interpolation is always 'log'. 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`, optional Energy unit. Default is None. per_decade : bool, optional Whether `nbin` is given per decade. Default is False. name : str, optional Name of the energy axis, either 'energy' or 'energy_true'. Default is None. node_type : str, optional Node type, either 'edges' or 'center'. Default is 'edges'. strict_bounds : bool, optional Whether to strictly end the binning at 'energy_max' when `per_decade=True`. If True, the number of bins per decade might be slightly increased to match the bounds. If False, 'energy_max' might be reduced so the number of bins per decade is exactly the given input. Default is True. Returns ------- axis : `MapAxis` Create MapAxis from the given input parameters. """ 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: if strict_bounds: nbin = np.ceil(np.log10(energy_max / energy_min).value * nbin) else: bin_per_decade = nbin nbin = np.floor( np.log10(energy_max / energy_min).value * bin_per_decade ) if np.log10(energy_max / energy_min).value % (1 / bin_per_decade) != 0: energy_max = energy_min * 10 ** (nbin / bin_per_decade) 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): # TODO: What to do with interp in docstring but not in signature? """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 is 'lin'. **kwargs : dict, optional Keyword arguments passed to `MapAxis`. """ 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'. **kwargs : dict, optional Keyword arguments passed to `MapAxis`. """ if len(edges) < 2: raise ValueError("Edges array must have at least two elements.") return cls(edges, node_type="edges", **kwargs)
[docs] def concatenate(self, axis): """Concatenate another `MapAxis` to this `MapAxis` into a new `MapAxis` object. 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 concatenate with. Returns ------- axis : `MapAxis` Concatenation of the two 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 the axis by a given number of pixels. Parameters ---------- pad_width : int or tuple of int A single integer 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.concatenate(ax) return ax_stacked
[docs] def pix_to_coord(self, pix): """Transform 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 pixel to index. Parameters ---------- pix : `~numpy.ndarray` Pixel coordinates. clip : bool, optional Choose whether to clip indices to the valid range of the axis. Default is False. If False, indices for coordinates outside the axis range will be set to -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 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 axis coordinate to bin index. Parameters ---------- coord : `~numpy.ndarray` Array of axis coordinate values. clip : bool, optional Choose whether to clip the index to the valid range of the axis. Default is False. If False, then indices for values outside the axis range will be set to -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 sub-selection 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` Squashed 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) if arg not in kwargs: kwargs[arg] = copy.deepcopy(value) return self.__class__(**kwargs)
[docs] def copy(self, **kwargs): """Copy `MapAxis` instance and overwrite given attributes. Parameters ---------- **kwargs : dict, optional 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 coordinate to the nearest axis edge. Parameters ---------- coord : `~astropy.units.Quantity` Coordinates to be rounded. clip : bool, optional Choose whether to clip the index to the valid range of the axis. Default is False. If False, then indices for values outside the axis range will be set to -1. 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 upsampling 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` Upsampled 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 downsampling 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"}, optional Format specification. Default is "ogip". idx : int, optional Column index of the axis. Default is 0. 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. Default is "ogip". 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. Default is "ogip". 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 a table HDU. Parameters ---------- table : `~astropy.table.Table` Table. format : {"ogip", "ogip-arf", "fgst-ccube", "fgst-template", "gadf", "gadf-dl3"} Format specification. Default is "ogip". idx : int, optional Column index of the axis. Default is 0. column_prefix : str, optional Column name prefix of the axis, used for creating the axis. Default is "". 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] # for a single-valued array, it can happen that the value is stored/extracted as a scalar if edges_lo.isscalar: log.warning( f"'{column_prefix}' axis is stored as a scalar -- converting to 1D array." ) edges_lo = edges_lo[np.newaxis] edges_hi = edges_hi[np.newaxis] 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. Default is "ogip". idx : int, optional Column index of the axis. Default is 0. 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 def _repr_html_(self): try: return self.to_html() except AttributeError: return f"<pre>{html.escape(str(self))}</pre>" @property def primary_axis(self): """Primary extra axis, defined as the longest one. 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): # TODO: The name is misleading. Maybe iter_axis_and_shape? """Generator that iterates over axes and their 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. Default is "center". axis_name : str, optional Axis name for which mode='edges' applies. Default is None. Returns ------- coords : dict of `~astropy.units.Quantity`. Map coordinates as a dictionary. """ 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): """Shapes 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. In order to be replaced, the name of the new axis must match the name of the old axis. Parameters ---------- axis : `MapAxis` Map axis. Returns ------- axes : MapAxes Map axes. """ 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 Downsampling factor. axis_name : str Axis to downsample. 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 axis. 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 without the `axis_name`. """ 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. clip : bool, optional Choose whether to clip indices to the valid range of the axis. Default is True. If False, then indices for coordinates outside the axis range will be set to -1. 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 pixel to pixel indices. Parameters ---------- pix : tuple of `~numpy.ndarray` Pixel coordinates. clip : bool, optional Choose whether to clip indices to the valid range of the axis. Default is False. If False, then indices for coordinates outside the axi range will be set to -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 Dictionary 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 dictionary 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. Default is "gadf". 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. Default is "gadf". 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. Default is "gadf". hdu_bands : str, optional Name of the bands HDU to use. Default is None. 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. format : {"gadf", "gadf-dl3", "fgst-ccube", "fgst-template", "fgst-bexcube", "ogip-arf"} Format to use. Default is "gadf". 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. Default is "gadf". 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, IndexError): 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. Parameters ---------- axes : list of `~MapAxis` or `~numpy.ndarray` Sequence of axis or edges defining the axes. n_spatial_axes : int, optional Number of spatial axes. Default is None. Returns ------- axes : `MapAxes` Map axes object. """ 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 names. """ 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 : str or list of str Names of the axis. new_names : str or list of 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, optional 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): """Initialize a new 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 is the time delta w.r.t. to the reference time. edges_max : `~astropy.units.Quantity` Array of edge time values. This is the time delta w.r.t. to the reference time. reference_time : `~astropy.time.Time` Reference time to use. name : str, optional Axis name. Default is "time". interp : {'lin'} Interpolation method used to transform between axis and pixel coordinates. For now only 'lin' is supported. Default is 'lin'. """ node_type = "intervals" def __init__(self, edges_min, edges_max, reference_time, name="time", interp="lin"): self._name = name self._time_format = "iso" 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.") def _repr_html_(self): try: return self.to_html() except AttributeError: return f"<pre>{html.escape(str(self))}</pre>" @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): """Axis unit.""" return self.edges_max.unit @property def interp(self): """Interpolation scale of the axis.""" return self._interp @property def reference_time(self): """Return reference time used for the axis.""" return self._reference_time @property def name(self): """Return the axis name.""" return self._name @property def nbin(self): """Return the number of bins in the axis.""" return len(self.edges_min.flatten()) @property def edges_min(self): """Return the array of bin edges maximum values.""" return self._edges_min @property def edges_max(self): """Return the array of bin edges minimum values.""" return self._edges_max @property def edges(self): """Return the 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 as a ~astropy.units.Quantity.""" return self.edges_min[0], self.edges_max[-1] @property def time_bounds(self): """Bounds of the axis as a ~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 `~astropy.time.Time` object.""" return self._edges_min + self.reference_time @property def time_max(self): """Return axis upper edges as a `~astropy.time.Time` object.""" return self._edges_max + self.reference_time @property def time_delta(self): """Return axis time bin width as a `~astropy.time.TimeDelta` object.""" return self.time_max - self.time_min @property def time_mid(self): """Return time bin center as a `~astropy.time.Time` object.""" return self.time_min + 0.5 * self.time_delta @property def time_edges(self): """Time edges as a `~astropy.time.Time` object.""" return self.reference_time + self.edges @property def time_format(self): # TODO: The docsttring for time format and its setter are not showing up in the docs. """The time format to use for the axis.""" return self._time_format @time_format.setter def time_format(self, val): """The time format to use for the axis.""" if val not in ["iso", "mjd"]: raise ValueError(f"Invalid time_format: {self.time_format}") self._time_format = val @property def as_plot_xerr(self): """Return x errors for plotting.""" 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() else: x_errn = xn.to("day") x_errp = xp.to("day") return x_errn, x_errp @property def as_plot_labels(self): """Return labels for plotting.""" 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): """Return edges for plotting.""" if self.time_format == "iso": edges = self.time_edges.to_datetime() else: edges = self.time_edges.mjd * u.day return edges @property def as_plot_center(self): """Return center for plotting.""" if self.time_format == "iso": center = self.time_mid.datetime else: center = self.time_mid.mjd * u.day return center
[docs] def format_plot_xaxis(self, ax): """Format the x-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")) else: ax.xaxis.set_major_formatter("{x:,.5f}") 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 name. """ 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 time map axis is all close. Parameters ---------- other : `TimeMapAxis` Other time map axis. **kwargs : dict, optional Keyword arguments forwarded to `~numpy.allclose`. Returns ------- is_allclose : bool Whether the other time map 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): """Not supported for time axis.""" 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 time axis 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 time 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 pix_to_coord(self, pix): """Transform from pixel position to time coordinate. Currently, works only for linear interpolation scheme. Parameters ---------- pix : `~numpy.ndarray` Array of pixel positions. Returns ------- coord : `~astropy.time.Time` Array of time axis coordinate values. """ shape = np.shape(pix) pix = np.atleast_1d(pix) coords = np.zeros_like(pix) frac, idx = np.modf(pix) idx1 = idx.astype(int) valid = np.logical_and(idx >= 0, idx < self.nbin, np.isfinite(idx)) idx_valid = np.where(valid) idx_invalid = np.where(~valid) coords[idx_valid] = ( frac[idx_valid] * self.time_delta[idx1[valid]] + self.edges_min[idx1[valid]] ).value coords = coords * self.unit + self.reference_time coords[idx_invalid] = Time(INVALID_VALUE.time, scale=self.reference_time.scale) return coords.reshape(shape)
[docs] def coord_to_pix(self, coord, **kwargs): """Transform time axis coordinate to pixel position. Pixels of time values falling outside time bins will be set to -1. Parameters ---------- coord : `~astropy.time.Time` Array of time 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): # TODO: Is this useful at all? return pix
@property def center(self): """Return interval centers as a `~astropy.units.Quantity`.""" return self.edges_min + 0.5 * self.bin_width @property def bin_width(self): """Return time interval width as a `~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): """Not supported for time axis.""" raise NotImplementedError
[docs] def downsample(self): """Not supported for time axis.""" 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) if arg not in kwargs: kwargs[arg] = copy.deepcopy(value) return self.__class__(**kwargs)
[docs] def copy(self, **kwargs): """Copy `TimeMapAxis` instance and overwrite given attributes. Parameters ---------- **kwargs : dict, optional Keyword arguments to overwrite in the map axis constructor. Returns ------- copy : `TimeMapAxis` Copied time 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 sub-selection of the axis. Returns ------- axis : `~TimeMapAxis` Sliced time map 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` Squashed time map 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 a `~astropy.time.Time` object. 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, optional The unit to convert the edges to. Default is 'd' (day). interp : {'lin'} Interpolation method used to transform between axis and pixel coordinates. Currently, only 'lin' is supported. Default is 'lin'. name : str, optional Axis name. Default is "time". 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. Default is "gadf". idx : int Axis index. Default is 0. 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" time_ref_dict = dict( MJDREFF=table.meta.get("MJDREFF", 0), MJDREFI=table.meta.get("MJDREFI", 0), TIMESYS=table.meta.get("TIMESYS", "utc"), TIMEUNIT=table.meta.get("TIMEUNIT", "d"), ) reference_time = time_ref_from_dict(time_ref_dict, format="mjd") time_min = reference_time + table["time_min"].data * u.Unit( time_ref_dict["TIMEUNIT"] ) time_max = reference_time + table["time_max"].data * u.Unit( time_ref_dict["TIMEUNIT"] ) if reference_time.mjd == 0: # change to a more recent reference time reference_time = Time( "2001-01-01T00:00:00", scale=time_ref_dict["TIMESYS"] ) 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 : `~gammapy.data.GTI` GTI table. name : str, optional Axis name. Default is "time". 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_gti_bounds(cls, gti, t_delta, name="time"): """Create a time axis from an input GTI. The unit for the axis is taken from the t_delta quantity. Parameters ---------- gti : `~gammapy.data.GTI` GTI table. t_delta : `~astropy.units.Quantity` Time binning. name : str, optional Axis name. Default is "time". Returns ------- axis : `TimeMapAxis` Time map axis. """ time_min = gti.time_start[0] time_max = gti.time_stop[-1] nbin = int(((time_max - time_min) / t_delta).to("")) return TimeMapAxis.from_time_bounds( time_min=time_min, time_max=time_max, nbin=nbin, name=name, unit=t_delta.unit, )
[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. unit : `~astropy.units.Unit` or str, optional The unit to convert the edges to. Default is 'd' (day). name : str, optional Name of the axis. Default is "time". """ 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_gti(self): """Convert the axis to a GTI table. Returns ------- gti : `~gammapy.data.GTI` GTI table. """ from gammapy.data import GTI return GTI.create( self.edges_min, self.edges_max, reference_time=self.reference_time )
[docs] def to_header(self, format="gadf", idx=0): """Create FITS header. Parameters ---------- format : {"ogip"} Format specification. Default is "gadf". idx : int, optional Column index of the axis. Default is 0. Returns ------- header : `~astropy.io.fits.Header` Corresponding FITS header. """ 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] def group_table(self, interval_edges): """Compute bin groups table for the TimeMapAxis, given coarser bin edges. Parameters ---------- interval_edges : list of `~astropy.time.Time` or `~astropy.units.Quantity` Start and stop time for each interval to compute the LC. Returns ------- groups : `~astropy.table.Table` Group table. Bin groups are divided in: *"normal" for the bins containing data *"underflow" for the bins falling below the minimum axis threshold *"overflow" for the bins falling above the maximum axis threshold *"outflow" for other states """ for _, edge in enumerate(interval_edges): if not isinstance(edge, Time): interval_edges[_] = self.reference_time + interval_edges[_] time_intervals = list(zip(interval_edges[::2], interval_edges[1::2])) group_table = Table( names=("idx_min", "idx_max", "time_min", "time_max", "bin_type"), dtype=("i8", "i8", "f8", "f8", "S10"), ) for time_interval in time_intervals: mask1 = self.time_min >= time_interval[0] mask2 = self.time_max <= time_interval[1] mask = mask1 & mask2 if np.any(mask): idx_min = np.where(mask)[0][0] idx_max = np.where(mask)[0][-1] bin_type = "normal " else: idx_min = idx_max = -1 if np.any(mask1): bin_type = "overflow" elif np.any(mask2): bin_type = "underflow" else: bin_type = "outflow" time_min = self.time_min[idx_min] time_max = self.time_max[idx_max] group_table.add_row( [idx_min, idx_max, time_min.mjd, time_max.mjd, bin_type] ) return group_table
[docs]class LabelMapAxis: """Map axis using labels. Parameters ---------- labels : list of str Labels to be used for the axis nodes. name : str, optional Name of the axis. Default is "". """ 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): # TODO: should we allow units for label axis? """Unit of the axis.""" 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 name. """ 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 pixel to label coordinate. 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 label coordinate 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 label coordinate to pixel coordinate. 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 pixel to idx Parameters ---------- pix : tuple of `~numpy.ndarray` Pixel coordinates. clip : bool, optional Choose whether to clip indices to the valid range of the axis. Default is False. If False, then indices for coordinates outside the axis range will be set to -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.""" # TODO: Why not return self._labels here? 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): """Return labels for plotting.""" return 0.5 * np.ones(self.nbin) @property def as_plot_labels(self): """Return labels for plotting.""" return self._labels.tolist() @property def as_plot_center(self): """Return labels for plotting.""" return np.arange(self.nbin) @property def as_plot_edges(self): """Return labels for plotting.""" 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. Default is "gadf". idx : int, optional Column index of the axis. Default is 0. 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. idx : int Axis index. Default is 0. 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) def _repr_html_(self): try: return self.to_html() except AttributeError: return f"<pre>{html.escape(str(self))}</pre>"
[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): """Not supported for label 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): """Not supported for label 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): """Not supported for label 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): """Not supported for label axis.""" raise NotImplementedError("Padding a LabelMapAxis is not supported")
[docs] def copy(self): """Copy the 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 sub-selection of the axis. Returns ------- axis : `~LabelMapAxis` Sliced axis object. """ return self.__class__( labels=self._labels[idx], name=self.name, )
[docs] @classmethod def from_stack(cls, axes): """Create a label map axis by merging a list of label axis. Parameters ---------- axes : list of `LabelMapAxis` List of label map axis to be merged. Returns ------- axis : `LabelMapAxis` Stacked axis. """ axis_stacked = axes[0] for ax in axes[1:]: axis_stacked = axis_stacked.concatenate(ax) return axis_stacked
[docs] def concatenate(self, axis): """Concatenate another label map axis to this one into a new instance of `LabelMapAxis`. Names must agree between the axes. Labels must be unique. Parameters ---------- axis : `LabelMapAxis` Axis to concatenate with. Returns ------- axis : `LabelMapAxis` Concatenation of the two axis. """ if not isinstance(axis, LabelMapAxis): raise TypeError( f"axis must be an instance of LabelMapAxis, got {axis.__class__.__name__} instead." ) if self.name != axis.name: raise ValueError(f"Names must agree, got {self.name} and {axis.name} ") merged_labels = np.append(self.center, axis.center) return LabelMapAxis(merged_labels, self.name)
[docs] def squash(self): """Create a new axis object by squashing the axis into one bin. The label of the new axis is given as "first-label...last-label". Returns ------- axis : `LabelMapAxis` Squashed label map axis. """ return LabelMapAxis( labels=[self.center[0] + "..." + self.center[-1]], name=self._name )