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zPM6ji>^Nv8kXMb6#u`f2XeKFh=I5xP%Q^E?Bvt23%T_|pliTDx**Q0V_F|i9axfYl hMzAL&0$bAdh!{p#Ld&#PXM_(2$lIJJlW!xR{U31Y>cap4 literal 0 HcmV?d00001 diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 00000000..e69de29b diff --git a/_modules/astrodata.html b/_modules/astrodata.html new file mode 100644 index 00000000..696f74b3 --- /dev/null +++ b/_modules/astrodata.html @@ -0,0 +1,198 @@ + + + + + + + astrodata — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +

+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/astrodata/_version.html b/_modules/astrodata/_version.html new file mode 100644 index 00000000..bdc4c758 --- /dev/null +++ b/_modules/astrodata/_version.html @@ -0,0 +1,137 @@ + + + + + + + astrodata._version — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for astrodata._version

+#!/usr/bin/env python
+"""
+Holds the DRAGONS version to be propagated throught all the DRAGONS package
+and to be used in the documentation.
+"""
+
+# --- Setup Version Here ---
+API = 3
+FEATURE = 2
+BUG = 0
+TAG = ""
+
+
+

+[docs]
+def version(short=False, tag=TAG):
+    """
+    Returns DRAGONS's version based on the api,
+    feature and bug numbers.
+
+    Returns
+    -------
+    str : formatted version
+    """
+
+    if short:
+        _version = "{:d}.{:d}".format(API, FEATURE)
+
+    else:
+        _tag = "_{:s}".format(tag) if tag else ""
+        _version = "{:d}.{:d}.{:d}".format(API, FEATURE, BUG) + _tag
+
+    return _version

+
+
+ +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/astrodata/core.html b/_modules/astrodata/core.html new file mode 100644 index 00000000..b5d6e8a5 --- /dev/null +++ b/_modules/astrodata/core.html @@ -0,0 +1,1624 @@ + + + + + + + astrodata.core — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for astrodata.core

+"""This is the core module of the AstroData package. It provides the
+`AstroData` class, which is the main interface to manipulate astronomical
+data sets.
+"""
+import inspect
+import logging
+import os
+import re
+import textwrap
+import warnings
+from collections import OrderedDict
+from contextlib import suppress
+from copy import deepcopy
+from functools import partial
+
+import numpy as np
+
+from astropy.io import fits
+from astropy.nddata import NDData
+from astropy.table import Table
+from astropy.utils import format_doc
+
+from .fits import (
+    DEFAULT_EXTENSION,
+    FitsHeaderCollection,
+    _process_table,
+    read_fits,
+    write_fits,
+)
+from .nddata import ADVarianceUncertainty
+from .nddata import NDAstroData as NDDataObject
+from .utils import (
+    assign_only_single_slice,
+    astro_data_descriptor,
+    deprecated,
+    normalize_indices,
+    returns_list,
+)
+
+NO_DEFAULT = object()
+
+
+_ARIT_DOC = """
+    Performs {name} by evaluating ``self {op} operand``.
+
+    Parameters
+    ----------
+    oper : number or object
+        The operand to perform the operation  ``self {op} operand``.
+
+    Returns
+    --------
+    `AstroData` instance
+"""
+
+
+

+[docs]
+class AstroData:
+    """Base class for the AstroData software package. It provides an interface
+    to manipulate astronomical data sets.
+
+    Parameters
+    ----------
+    nddata : `astrodata.NDAstroData` or list of `astrodata.NDAstroData`
+        List of NDAstroData objects.
+
+    tables : dict[name, `astropy.table.Table`]
+        Dict of table objects.
+
+    phu : `astropy.io.fits.Header`
+        Primary header.
+
+    indices : list of int
+        List of indices mapping the `astrodata.NDAstroData` objects that this
+        object will access to. This is used when slicing an object, then the
+        sliced AstroData will have the ``.nddata`` list from its parent and
+        access the sliced NDAstroData through this list of indices.
+    """
+
+    # Derived classes may provide their own __keyword_dict. Being a private
+    # variable, each class will preserve its own, and there's no risk of
+    # overriding the whole thing
+    __keyword_dict = {
+        "instrument": "INSTRUME",
+        "object": "OBJECT",
+        "telescope": "TELESCOP",
+        "ut_date": "DATE-OBS",
+    }
+
+    def __init__(
+        self, nddata=None, tables=None, phu=None, indices=None, is_single=False
+    ):
+        if nddata is None:
+            nddata = []
+
+        # Check that nddata is either a single or iterable of NDAstroData
+        # objects
+        is_nddata = isinstance(nddata, NDDataObject)
+
+        try:
+            is_nddata_iterable = isinstance(nddata[0], NDDataObject)
+
+        except IndexError:
+            # Fall back on checking if it's a list or tuple---could be empty.
+            is_nddata_iterable = isinstance(nddata, (list, tuple))
+
+        if not (is_nddata or is_nddata_iterable):
+            raise TypeError(
+                f"nddata must be an NDAstroData object or a list of "
+                f"NDAstroData objects, not {type(nddata)} ({nddata})."
+            )
+
+        # If nddata is a single NDAstroData object, make it a list.
+        if not isinstance(nddata, (list, tuple)):
+            nddata = [nddata]
+
+        # _all_nddatas contains all the extensions from the original file or
+        # object.  And _indices is used to map extensions for sliced objects.
+        self._all_nddatas = nddata
+        self._indices = indices
+
+        # TODO: Is there no way to know if this is a single frame without
+        # passing an arg?
+        self.is_single = is_single
+
+        # If this data provider represents a single slice out of a whole
+        # dataset, return True. Otherwise, return False.
+        if tables is not None and not isinstance(tables, dict):
+            raise ValueError("tables must be a dict")
+
+        self._tables = tables or {}
+
+        self._phu = phu or fits.Header()
+        self._fixed_settable = {
+            "data",
+            "uncertainty",
+            "mask",
+            "variance",
+            "wcs",
+            "path",
+            "filename",
+        }
+        self._logger = logging.getLogger(__name__)
+        self._orig_filename = None
+        self._path = None
+
+    def __deepcopy__(self, memo):
+        """Returns a new instance of this class.
+
+        Parameters
+        ----------
+        memo : dict
+            See the documentation on `deepcopy` for an explanation on how
+            this works.
+
+        """
+        obj = self.__class__()
+
+        for attr in ("_phu", "_path", "_orig_filename", "_tables"):
+            obj.__dict__[attr] = deepcopy(self.__dict__[attr])
+
+        obj.__dict__["_all_nddatas"] = [deepcopy(nd) for nd in self._nddata]
+        return obj
+
+    def _keyword_for(self, name):
+        """Returns the FITS keyword name associated to ``name``.
+
+        Parameters
+        ----------
+        name : str
+            The common "key" name for which we want to know the associated
+            FITS keyword.
+
+        Returns
+        -------
+        str
+            The desired keyword name.
+
+        Raises
+        ------
+        AttributeError
+            If there is no keyword for the specified ``name``.
+
+        """
+        for cls in self.__class__.mro():
+            with suppress(AttributeError, KeyError):
+                # __keyword_dict is a mangled variable
+                return getattr(self, f"_{cls.__name__}__keyword_dict")[name]
+
+        raise AttributeError(f"No match for '{name}'")
+
+    def _process_tags(self):
+        """Return the tag set (as a set of str) for the current instance."""
+        results = []
+        # Calling inspect.getmembers on `self` would trigger all the
+        # properties (tags, phu, hdr, etc.), and that's undesirable. To
+        # prevent that, we'll inspect the *class*.
+        members = inspect.getmembers(
+            self.__class__, lambda x: hasattr(x, "tag_method")
+        )
+
+        for _, method in members:
+            ts = method(self)
+            if ts.add or ts.remove or ts.blocks:
+                results.append(ts)
+
+        # Sort by the length of substractions... those that substract
+        # from others go first
+        results = sorted(
+            results, key=lambda x: len(x.remove) + len(x.blocks), reverse=True
+        )
+
+        # Sort by length of blocked_by, those that are never disabled go first
+        results = sorted(results, key=lambda x: len(x.blocked_by))
+
+        # Sort by length of if_present... those that need other tags to
+        # be present go last
+        results = sorted(results, key=lambda x: len(x.if_present))
+
+        tags = set()
+        removals = set()
+        blocked = set()
+        for plus, minus, blocked_by, blocks, is_present in results:
+            if is_present:
+                # If this TagSet requires other tags to be present, make
+                # sure that all of them are. Otherwise, skip...
+                if len(tags & is_present) != len(is_present):
+                    continue
+
+            allowed = (len(tags & blocked_by) + len(plus & blocked)) == 0
+            if allowed:
+                # This set is not being blocked by others...
+                removals.update(minus)
+                tags.update(plus - removals)
+                blocked.update(blocks)
+
+        return tags
+
+    @staticmethod
+    def _matches_data(source):
+        # This one is trivial. Will be more specific for subclasses.
+        logging.debug("Using default _matches_data with %s", source)
+        return True
+
+    @property
+    def path(self):
+        """Return the file path."""
+        return self._path
+
+    @path.setter
+    def path(self, value):
+        if self._path is None and value is not None:
+            self._orig_filename = os.path.basename(value)
+        self._path = value
+
+    @property
+    def filename(self):
+        """Return the file name."""
+        if self.path is not None:
+            return os.path.basename(self.path)
+
+        return self.path
+
+    @filename.setter
+    def filename(self, value):
+        if os.path.isabs(value):
+            raise ValueError("Cannot set the filename to an absolute path!")
+
+        if self.path is None:
+            self.path = os.path.abspath(value)
+
+        else:
+            dirname = os.path.dirname(self.path)
+            self.path = os.path.join(dirname, value)
+
+    @property
+    def orig_filename(self):
+        """Return the original file name (before it was modified)."""
+        return self._orig_filename
+
+    @orig_filename.setter
+    def orig_filename(self, value):
+        self._orig_filename = value
+
+    @property
+    def phu(self):
+        """Return the primary header."""
+        return self._phu
+
+    @phu.setter
+    def phu(self, phu):
+        self._phu = phu
+
+    @property
+    def hdr(self):
+        """Return all headers, as a `astrodata.fits.FitsHeaderCollection`."""
+        if not self.nddata:
+            return None
+        headers = [nd.meta["header"] for nd in self._nddata]
+        return headers[0] if self.is_single else FitsHeaderCollection(headers)
+
+    @property
+    @deprecated(
+        "Access to headers through this property is deprecated and "
+        "will be removed in the future. Use '.hdr' instead."
+    )
+    def header(self):
+        """Deprecated header access. Use ``.hdr`` instead."""
+        return [self.phu] + [ndd.meta["header"] for ndd in self._nddata]
+
+    @property
+    def tags(self):
+        """A set of strings that represent the tags defining this instance."""
+        return self._process_tags()
+
+    @property
+    def descriptors(self):
+        """Returns a sequence of names for the methods that have been
+        decorated as descriptors.
+
+        Returns
+        --------
+        tuple of str
+        """
+        members = inspect.getmembers(
+            self.__class__, lambda x: hasattr(x, "descriptor_method")
+        )
+        return tuple(mname for (mname, method) in members)
+
+    @property
+    def id(self):
+        """Returns the extension identifier (1-based extension number)
+        for sliced objects.
+        """
+        if self.is_single:
+            return self._indices[0] + 1
+
+        raise ValueError(
+            "Cannot return id for an AstroData object "
+            "that is not a single slice"
+        )
+
+    @property
+    def indices(self):
+        """Returns the extensions indices for sliced objects."""
+        return self._indices if self._indices else list(range(len(self)))
+
+    @property
+    def is_sliced(self):
+        """If this data provider instance represents the whole dataset, return
+        False. If it represents a slice out of the whole, return True.
+        """
+        return self._indices is not None
+
+

+[docs]
+    def is_settable(self, attr):
+        """Return True if the attribute is meant to be modified."""
+        if self.is_sliced and attr in {"path", "filename"}:
+            return False
+        return attr in self._fixed_settable or attr.isupper()

+
+
+    @property
+    def _nddata(self):
+        """Return the list of `astrodata.NDAstroData` objects. Contrary to
+        ``self.nddata`` this always returns a list.
+        """
+        if self._indices is not None:
+            return [self._all_nddatas[i] for i in self._indices]
+
+        return self._all_nddatas
+
+    @property
+    def nddata(self):
+        """Return the list of `astrodata.NDAstroData` objects.
+
+        If the `AstroData` object is sliced, this returns only the NDData
+        objects of the sliced extensions. And if this is a single extension
+        object, the NDData object is returned directly (i.e. not a list).
+        """
+        return self._nddata[0] if self.is_single else self._nddata
+
+

+[docs]
+    def table(self):
+        """Return a dictionary of `astropy.table.Table` objects.
+
+        Notes
+        -----
+        This returns a _copy_ of the tables, so modifying them will not
+        affect the original ones.
+        """
+        # FIXME: do we need this in addition to .tables ?
+        return self._tables.copy()

+
+
+    @property
+    def tables(self):
+        """Return the names of the `astropy.table.Table` objects associated to
+        the top-level object.
+        """
+        return set(self._tables)
+
+    @property
+    def ext_tables(self):
+        """Return the names of the `astropy.table.Table` objects associated to
+        an extension.
+        """
+        if not self.is_single:
+            raise AttributeError("this is only available for extensions")
+
+        return set(
+            key
+            for key, obj in self.nddata.meta["other"].items()
+            if isinstance(obj, Table)
+        )
+
+    @property
+    @returns_list
+    def shape(self):
+        """Return the shape of the data array for each extension as a list of
+        shapes.
+        """
+        return [nd.shape for nd in self._nddata]
+
+    @property
+    @returns_list
+    def data(self):
+        """A list of the arrays (or single array, if this is a single slice)
+        corresponding to the science data attached to each extension.
+        """
+        return [nd.data for nd in self._nddata]
+
+    @data.setter
+    @assign_only_single_slice
+    def data(self, value):
+        # Setting the ._data in the NDData is a bit kludgy, but we're all
+        # grown adults and know what we're doing, isn't it?
+        if hasattr(value, "shape"):
+            self.nddata._data = value
+
+        else:
+            raise AttributeError(
+                "Trying to assign data to be something with no shape"
+            )
+
+    @property
+    @returns_list
+    def uncertainty(self):
+        """A list of the uncertainty objects (or a single object, if this is
+        a single slice) attached to the science data, for each extension.
+
+        The objects are instances of AstroPy's `astropy.nddata.NDUncertainty`,
+        or `None` where no information is available.
+
+        See also
+        --------
+        variance : The actual array supporting the uncertainty object.
+
+        """
+        return [nd.uncertainty for nd in self._nddata]
+
+    @uncertainty.setter
+    @assign_only_single_slice
+    def uncertainty(self, value):
+        self.nddata.uncertainty = value
+
+    @property
+    @returns_list
+    def mask(self):
+        """A list of the mask arrays (or a single array, if this is a single
+        slice) attached to the science data, for each extension.
+
+        For objects that miss a mask, `None` will be provided instead.
+        """
+        return [nd.mask for nd in self._nddata]
+
+    @mask.setter
+    @assign_only_single_slice
+    def mask(self, value):
+        self.nddata.mask = value
+
+    @property
+    @returns_list
+    def variance(self):
+        """A list of the variance arrays (or a single array, if this is a
+        single slice) attached to the science data, for each extension.
+
+        For objects that miss uncertainty information, `None` will be provided
+        instead.
+
+        See also
+        ---------
+        uncertainty : The uncertainty objects used under the hood.
+
+        """
+        return [nd.variance for nd in self._nddata]
+
+    @variance.setter
+    @assign_only_single_slice
+    def variance(self, value):
+        if value is None:
+            self.nddata.uncertainty = None
+        else:
+            self.nddata.uncertainty = ADVarianceUncertainty(value)
+
+    @property
+    def wcs(self):
+        """Returns the list of WCS objects for each extension."""
+        if self.is_single:
+            return self.nddata.wcs
+
+        raise ValueError(
+            "Cannot return WCS for an AstroData object "
+            "that is not a single slice"
+        )
+
+    @wcs.setter
+    @assign_only_single_slice
+    def wcs(self, value):
+        self.nddata.wcs = value
+
+    def __iter__(self):
+        if self.is_single:
+            yield self
+        else:
+            for n in range(len(self)):
+                yield self[n]
+
+    def __getitem__(self, idx):
+        """Returns a sliced view of the instance. It supports the standard
+        Python indexing syntax.
+
+        Parameters
+        ----------
+        slice : int, `slice`
+            An integer or an instance of a Python standard `slice` object
+
+        Raises
+        -------
+        TypeError
+            If trying to slice an object when it doesn't make sense (e.g.
+            slicing a single slice)
+
+        ValueError
+            If `slice` does not belong to one of the recognized types
+
+        IndexError
+            If an index is out of range
+        """
+        if self.is_single:
+            raise TypeError("Can't slice a single slice!")
+
+        indices, _ = normalize_indices(idx, nitems=len(self))
+
+        if self._indices:
+            indices = [self._indices[i] for i in indices]
+
+        is_single = not isinstance(idx, (tuple, slice))
+
+        obj = self.__class__(
+            self._all_nddatas,
+            tables=self._tables,
+            phu=self.phu,
+            indices=indices,
+            is_single=is_single,
+        )
+
+        obj._path = self.path
+        obj._orig_filename = self.orig_filename
+
+        return obj
+
+    def __delitem__(self, idx):
+        """Called to implement deletion of ``self[idx]``.  Supports standard
+        Python syntax (including negative indices).
+
+        Parameters
+        ----------
+        idx : int
+            This index represents the order of the element that you want
+            to remove.
+
+        Raises
+        -------
+        IndexError
+            If `idx` is out of range.
+        """
+        if self.is_sliced:
+            raise TypeError("Can't remove items from a sliced object")
+        del self._all_nddatas[idx]
+
+    def __getattr__(self, attribute):
+        """Called when an attribute lookup has not found the attribute in the
+        usual places (not an instance attribute, and not in the class tree for
+        ``self``).
+
+        Parameters
+        ----------
+        attribute : str
+            The attribute's name.
+
+        Raises
+        -------
+        AttributeError
+            If the attribute could not be found/computed.
+        """
+        # I we're working with single slices, let's look some things up
+        # in the ND object
+        if self.is_single and attribute.isupper():
+            with suppress(KeyError):
+                return self.nddata.meta["other"][attribute]
+
+        if attribute in self._tables:
+            return self._tables[attribute]
+
+        raise AttributeError(
+            f"{self.__class__.__name__!r} object has no "
+            f"attribute {attribute!r}"
+        )
+
+    def __setattr__(self, attribute, value):
+        """Called when an attribute assignment is attempted, instead of the
+        normal mechanism.
+
+        Parameters
+        ----------
+        attribute : str
+            The attribute's name.
+
+        value : object
+            The value to be assigned to the attribute.
+        """
+
+        def _my_attribute(attr):
+            return attr in self.__dict__ or attr in self.__class__.__dict__
+
+        if (
+            attribute.isupper()
+            and self.is_settable(attribute)
+            and not _my_attribute(attribute)
+        ):
+            # This method is meant to let the user set certain attributes of
+            # the NDData objects. First we check if the attribute belongs to
+            # this object's dictionary.  Otherwise, see if we can pass it down.
+            #
+            if self.is_sliced and not self.is_single:
+                raise TypeError(
+                    "This attribute can only be "
+                    "assigned to a single-slice object"
+                )
+
+            if attribute == DEFAULT_EXTENSION:
+                raise AttributeError(
+                    f"{attribute} extensions should be "
+                    "appended with .append"
+                )
+
+            if attribute in {"DQ", "VAR"}:
+                raise AttributeError(
+                    f"{attribute} should be set on the " "nddata object"
+                )
+
+            add_to = self.nddata if self.is_single else None
+            self._append(value, name=attribute, add_to=add_to)
+
+            return
+
+        super().__setattr__(attribute, value)
+
+    def __delattr__(self, attribute):
+        """Implements attribute removal."""
+        if not attribute.isupper():
+            super().__delattr__(attribute)
+            return
+
+        if self.is_sliced:
+            if not self.is_single:
+                raise TypeError("Can't delete attributes on non-single slices")
+
+            other = self.nddata.meta["other"]
+            if attribute in other:
+                del other[attribute]
+            else:
+                raise AttributeError(
+                    f"{self.__class__.__name__!r} sliced "
+                    "object has no attribute {attribute!r}"
+                )
+        else:
+            if attribute in self._tables:
+                del self._tables[attribute]
+            else:
+                raise AttributeError(
+                    f"'{attribute}' is not a global table " "for this instance"
+                )
+
+    def __contains__(self, attribute):
+        """Implements the ability to use the ``in`` operator with an
+        `AstroData` object.
+
+        Parameters
+        ----------
+        attribute : str
+            An attribute name.
+
+        Returns
+        --------
+        bool
+        """
+        return attribute in self.exposed
+
+    def __len__(self):
+        """Return the number of independent extensions stored by the object."""
+        if self._indices is not None:
+            return len(self._indices)
+
+        return len(self._all_nddatas)
+
+    @property
+    def exposed(self):
+        """A collection of strings with the names of objects that can be
+        accessed directly by name as attributes of this instance, and that are
+        not part of its standard interface (i.e. data objects that have been
+        added dynamically).
+
+        Examples
+        ---------
+        >>> ad[0].exposed  # doctest: +SKIP
+        set(['OBJMASK', 'OBJCAT'])
+
+        """
+        exposed = set(self._tables)
+        if self.is_single:
+            exposed |= set(self.nddata.meta["other"])
+
+        return exposed
+
+    def _pixel_info(self):
+        for idx, nd in enumerate(self._nddata):
+            other_objects = []
+            uncer = nd.uncertainty
+            fixed = (
+                ("variance", None if uncer is None else uncer),
+                ("mask", nd.mask),
+            )
+
+            for name, other in fixed + tuple(sorted(nd.meta["other"].items())):
+                if other is None:
+                    continue
+
+                if isinstance(other, Table):
+                    other_objects.append(
+                        {
+                            "attr": name,
+                            "type": "Table",
+                            "dim": str((len(other), len(other.columns))),
+                            "data_type": "n/a",
+                        }
+                    )
+
+                else:
+                    dim = ""
+                    if hasattr(other, "dtype"):
+                        dt = other.dtype.name
+                        dim = str(other.shape)
+
+                    elif hasattr(other, "data"):
+                        dt = other.data.dtype.name
+                        dim = str(other.data.shape)
+
+                    elif hasattr(other, "array"):
+                        dt = other.array.dtype.name
+                        dim = str(other.array.shape)
+
+                    else:
+                        dt = "unknown"
+
+                    obj_dict = {
+                        "attr": name,
+                        "type": type(other).__name__,
+                        "dim": dim,
+                        "data_type": dt,
+                    }
+
+                    other_objects.append(obj_dict)
+
+            main_dict = {
+                "content": "science",
+                "type": type(nd).__name__,
+                "dim": str(nd.data.shape),
+                "data_type": nd.data.dtype.name,
+            }
+
+            out_dict = {
+                "idx": f"[{idx:2}]",
+                "main": main_dict,
+                "other": other_objects,
+            }
+
+            yield out_dict
+
+

+[docs]
+    def info(self):
+        """Prints out information about the contents of this instance."""
+
+        print(f"Filename: {self.path if self.path else 'Unknown'}")
+        # This is fixed. We don't support opening for update
+        # print("Mode: readonly")
+
+        text = "Tags: " + " ".join(sorted(self.tags))
+        textwrapper = textwrap.TextWrapper(width=80, subsequent_indent="    ")
+        for line in textwrapper.wrap(text):
+            print(line)
+
+        if len(self) > 0:
+            main_fmt = "{:6} {:24} {:17} {:14} {}"
+            other_fmt = "          .{:20} {:17} {:14} {}"
+            print("\nPixels Extensions")
+            print(
+                main_fmt.format(
+                    "Index", "Content", "Type", "Dimensions", "Format"
+                )
+            )
+            for pi in self._pixel_info():
+                main_obj = pi["main"]
+                print(
+                    main_fmt.format(
+                        pi["idx"],
+                        main_obj["content"][:24],
+                        main_obj["type"][:17],
+                        main_obj["dim"],
+                        main_obj["data_type"],
+                    )
+                )
+                for other in pi["other"]:
+                    print(
+                        other_fmt.format(
+                            other["attr"][:20],
+                            other["type"][:17],
+                            other["dim"],
+                            other["data_type"],
+                        )
+                    )
+
+        # NOTE: This covers tables, only. Study other cases before
+        # implementing a more general solution
+        if self._tables:
+            print("\nOther Extensions")
+            print("               Type        Dimensions")
+            for name, table in sorted(self._tables.items()):
+                if isinstance(table, list):
+                    # This is not a free floating table
+                    continue
+
+                print(
+                    f".{name[:13]:13s} {'Table':11s} {len(table), len(table.columns)}"
+                )

+
+
+    def _oper(self, operator, operand):
+        ind = self.indices
+        ndd = self._all_nddatas
+        if isinstance(operand, AstroData):
+            if len(operand) != len(self):
+                raise ValueError("Operands are not the same size")
+            for n in range(len(self)):
+                try:
+                    data = (
+                        operand.nddata
+                        if operand.is_single
+                        else operand.nddata[n]
+                    )
+                    ndd[ind[n]] = operator(ndd[ind[n]], data)
+                except TypeError:
+                    # This may happen if operand is a sliced, single
+                    # AstroData object
+                    ndd[ind[n]] = operator(ndd[ind[n]], operand.nddata)
+            op_table = operand.table()
+            ltab, rtab = set(self._tables), set(op_table)
+            for tab in rtab - ltab:
+                self._tables[tab] = op_table[tab]
+
+        else:
+            for n in range(len(self)):
+                ndd[ind[n]] = operator(ndd[ind[n]], operand)
+
+    def _standard_nddata_op(self, fn, operand):
+        return self._oper(
+            partial(fn, handle_mask=np.bitwise_or, handle_meta="first_found"),
+            operand,
+        )
+
+    @format_doc(_ARIT_DOC, name="addition", op="+")
+    def __add__(self, oper):
+        copy = deepcopy(self)
+        copy += oper
+        return copy
+
+    @format_doc(_ARIT_DOC, name="subtraction", op="-")
+    def __sub__(self, oper):
+        copy = deepcopy(self)
+        copy -= oper
+        return copy
+
+    @format_doc(_ARIT_DOC, name="multiplication", op="*")
+    def __mul__(self, oper):
+        copy = deepcopy(self)
+        copy *= oper
+        return copy
+
+    @format_doc(_ARIT_DOC, name="division", op="/")
+    def __truediv__(self, oper):
+        copy = deepcopy(self)
+        copy /= oper
+        return copy
+
+    @format_doc(_ARIT_DOC, name="inplace addition", op="+=")
+    def __iadd__(self, oper):
+        self._standard_nddata_op(NDDataObject.add, oper)
+        return self
+
+    @format_doc(_ARIT_DOC, name="inplace subtraction", op="-=")
+    def __isub__(self, oper):
+        self._standard_nddata_op(NDDataObject.subtract, oper)
+        return self
+
+    @format_doc(_ARIT_DOC, name="inplace multiplication", op="*=")
+    def __imul__(self, oper):
+        self._standard_nddata_op(NDDataObject.multiply, oper)
+        return self
+
+    @format_doc(_ARIT_DOC, name="inplace division", op="/=")
+    def __itruediv__(self, oper):
+        self._standard_nddata_op(NDDataObject.divide, oper)
+        return self
+
+    add = __iadd__
+    subtract = __isub__
+    multiply = __imul__
+    divide = __itruediv__
+
+    __radd__ = __add__
+    __rmul__ = __mul__
+
+    def __rsub__(self, oper):
+        copy = (deepcopy(self) - oper) * -1
+        return copy
+
+    def _rdiv(self, ndd, operand):
+        # Divide method works with the operand first
+        return NDDataObject.divide(operand, ndd)
+
+    def __rtruediv__(self, oper):
+        obj = deepcopy(self)
+        obj._oper(obj._rdiv, oper)
+        return obj
+
+    def _process_pixel_plane(
+        self, pixim, name=None, top_level=False, custom_header=None
+    ):
+        # Assume that we get an ImageHDU or something that can be
+        # turned into one
+        if isinstance(pixim, fits.ImageHDU):
+            nd = NDDataObject(pixim.data, meta={"header": pixim.header})
+        elif isinstance(pixim, NDDataObject):
+            nd = pixim
+        else:
+            nd = NDDataObject(pixim)
+
+        if custom_header is not None:
+            nd.meta["header"] = custom_header
+
+        header = nd.meta.setdefault("header", fits.Header())
+        currname = header.get("EXTNAME")
+
+        if currname is None:
+            header["EXTNAME"] = name if name is not None else DEFAULT_EXTENSION
+
+        if top_level:
+            nd.meta.setdefault("other", OrderedDict())
+
+        return nd
+
+    def _append_array(self, data, name=None, header=None, add_to=None):
+        if name in {"DQ", "VAR"}:
+            raise ValueError(
+                f"'{name}' need to be associated to a "
+                f"'{DEFAULT_EXTENSION}' one"
+            )
+
+        if add_to is None:
+            # Top level extension
+            if name is not None:
+                hname = name
+            elif header is not None:
+                hname = header.get("EXTNAME", DEFAULT_EXTENSION)
+            else:
+                hname = DEFAULT_EXTENSION
+
+            hdu = fits.ImageHDU(data, header=header)
+            hdu.header["EXTNAME"] = hname
+            ret = self._append_imagehdu(
+                hdu, name=hname, header=None, add_to=None
+            )
+        else:
+            ret = add_to.meta["other"][name] = data
+
+        return ret
+
+    def _append_imagehdu(self, hdu, name, header, add_to):
+        if name in {"DQ", "VAR"} or add_to is not None:
+            return self._append_array(hdu.data, name=name, add_to=add_to)
+
+        nd = self._process_pixel_plane(
+            hdu, name=name, top_level=True, custom_header=header
+        )
+        return self._append_nddata(nd, name, add_to=None)
+
+    def _append_raw_nddata(self, raw_nddata, name, header, add_to):
+        logging.debug("Appending data to nddata: %s", name)
+
+        # We want to make sure that the instance we add is whatever we specify
+        # as NDDataObject, instead of the random one that the user may pass
+        top_level = add_to is None
+
+        if not isinstance(raw_nddata, NDDataObject):
+            raw_nddata = NDDataObject(raw_nddata)
+
+        processed_nddata = self._process_pixel_plane(
+            raw_nddata, top_level=top_level, custom_header=header
+        )
+        return self._append_nddata(processed_nddata, name=name, add_to=add_to)
+
+    def _append_nddata(self, new_nddata, name, add_to):
+        # NOTE: This method is only used by others that have constructed NDData
+        # according to our internal format. We don't accept new headers at this
+        # point, and that's why it's missing from the signature.  'name' is
+        # ignored. It's there just to comply with the _append_XXX signature.
+        if add_to is not None:
+            raise TypeError(
+                "You can only append NDData derived instances "
+                "at the top level"
+            )
+
+        hd = new_nddata.meta["header"]
+        hname = hd.get("EXTNAME", DEFAULT_EXTENSION)
+
+        if hname == DEFAULT_EXTENSION:
+            self._all_nddatas.append(new_nddata)
+
+        else:
+            raise ValueError(
+                f"Arbitrary image extensions can only be added "
+                f"in association to a '{DEFAULT_EXTENSION}'"
+            )
+
+        logging.debug("Appending data to nddata: %s", name)
+
+        return new_nddata
+
+    def _append_table(self, new_table, name, header, add_to):
+        tb = _process_table(new_table, name, header)
+        hname = tb.meta["header"].get("EXTNAME")
+
+        def find_next_num(tables):
+            table_num = 1
+            while f"TABLE{table_num}" in tables:
+                table_num += 1
+            return f"TABLE{table_num}"
+
+        if add_to is None:
+            # Find table names for all extensions
+            ext_tables = set()
+            for nd in self._nddata:
+                ext_tables |= set(
+                    key
+                    for key, obj in nd.meta["other"].items()
+                    if isinstance(obj, Table)
+                )
+
+            if hname is None:
+                hname = find_next_num(set(self._tables) | ext_tables)
+            elif hname in ext_tables:
+                raise ValueError(
+                    f"Cannot append table '{hname}' because it "
+                    "would hide an extension table"
+                )
+
+            self._tables[hname] = tb
+        else:
+            if hname in self._tables:
+                raise ValueError(
+                    f"Cannot append table '{hname}' because it "
+                    "would hide a top-level table"
+                )
+
+            add_to.meta["other"][hname] = tb
+
+        return tb
+
+    def _append_astrodata(self, ad, name, header, add_to):
+        logging.debug("Appending astrodata object: %s", name)
+
+        if not ad.is_single:
+            raise ValueError(
+                "Cannot append AstroData instances that are "
+                "not single slices"
+            )
+
+        if add_to is not None:
+            raise ValueError(
+                "Cannot append an AstroData slice to another slice"
+            )
+
+        new_nddata = deepcopy(ad.nddata)
+        if header is not None:
+            new_nddata.meta["header"] = deepcopy(header)
+
+        return self._append_nddata(new_nddata, name=None, add_to=None)
+
+    def _append(self, ext, name=None, header=None, add_to=None):
+        """
+        Internal method to dispatch to the type specific methods. This is
+        called either by ``.append`` to append on top-level objects only or
+        by ``__setattr__``. In the second case ``name`` cannot be None, so
+        this is always the case when appending to extensions (add_to != None).
+        """
+        dispatcher = (
+            (NDData, self._append_raw_nddata),
+            ((Table, fits.TableHDU, fits.BinTableHDU), self._append_table),
+            (fits.ImageHDU, self._append_imagehdu),
+            (AstroData, self._append_astrodata),
+        )
+
+        for bases, method in dispatcher:
+            if isinstance(ext, bases):
+                return method(ext, name=name, header=header, add_to=add_to)
+
+        # Assume that this is an array for a pixel plane
+        return self._append_array(ext, name=name, header=header, add_to=add_to)
+
+

+[docs]
+    def append(self, ext, name=None, header=None):
+        """
+        Adds a new top-level extension.
+
+        Parameters
+        ----------
+        ext : array, `astropy.nddata.NDData`, `astropy.table.Table`, other
+            The contents for the new extension. The exact accepted types depend
+            on the class implementing this interface. Implementations specific
+            to certain data formats may accept specialized types (eg. a FITS
+            provider will accept an `astropy.io.fits.ImageHDU` and extract the
+            array out of it).
+        name : str, optional
+            A name that may be used to access the new object, as an attribute
+            of the provider. The name is typically ignored for top-level
+            (global) objects, and required for the others. If the name cannot
+            be derived from the metadata associated to ``ext``, you will
+            have to provider one.
+            It can consist in a combination of numbers and letters, with the
+            restriction that the letters have to be all capital, and the first
+            character cannot be a number ("[A-Z][A-Z0-9]*").
+
+        Returns
+        --------
+        The same object, or a new one, if it was necessary to convert it to
+        a more suitable format for internal use.
+
+        Raises
+        -------
+        TypeError
+            If adding the object in an invalid situation (eg. ``name`` is
+            `None` when adding to a single slice).
+        ValueError
+            Raised if the extension is of a proper type, but its value is
+            illegal somehow.
+
+        """
+        if self.is_sliced:
+            raise TypeError(
+                "Can't append objects to slices, use "
+                "'ext.NAME = obj' instead"
+            )
+
+        # NOTE: Most probably, if we want to copy the input argument, we
+        #       should do it here...
+        if isinstance(ext, fits.PrimaryHDU):
+            raise ValueError(
+                "Only one Primary HDU allowed. "
+                "Use .phu if you really need to set one"
+            )
+
+        if isinstance(ext, Table):
+            raise ValueError(
+                "Tables should be set directly as attribute, "
+                "i.e. 'ad.MYTABLE = table'"
+            )
+
+        if name is not None and not name.isupper():
+            warnings.warn(
+                f"extension name '{name}' should be uppercase", UserWarning
+            )
+            name = name.upper()
+
+        return self._append(ext, name=name, header=header)

+
+
+

+[docs]
+    @classmethod
+    def read(cls, source, extname_parser=None):
+        """Read from a file, file object, HDUList, etc."""
+        return read_fits(cls, source, extname_parser=extname_parser)

+
+
+    load = read  # for backward compatibility
+
+

+[docs]
+    def write(self, filename=None, overwrite=False):
+        """
+        Write the object to disk.
+
+        Parameters
+        ----------
+        filename : str, optional
+            If the filename is not given, ``self.path`` is used.
+        overwrite : bool
+            If True, overwrites existing file.
+
+        """
+        if filename is None:
+            if self.path is None:
+                raise ValueError("A filename needs to be specified")
+            filename = self.path
+
+        write_fits(self, filename, overwrite=overwrite)

+
+
+

+[docs]
+    def operate(self, operator, *args, **kwargs):
+        """
+        Applies a function to the main data array on each extension, replacing
+        the data with the result. The data will be passed as the first argument
+        to the function.
+
+        It will be applied to the mask and variance of each extension, too, if
+        they exist.
+
+        This is a convenience method, which is equivalent to::
+
+            for ext in ad:
+                ext.data = operator(ext.data, *args, **kwargs)
+                if ext.mask is not None:
+                    ext.mask = operator(ext.mask, *args, **kwargs)
+                if ext.variance is not None:
+                    ext.variance = operator(ext.variance, *args, **kwargs)
+
+        with the additional advantage that it will work on single slices, too.
+
+        Parameters
+        ----------
+        operator : callable
+            A function that takes an array (and, maybe, other arguments)
+            and returns an array.
+        args, kwargs : optional
+            Additional arguments to be passed to the ``operator``.
+
+        Examples
+        ---------
+        >>> import numpy as np
+        >>> ad.operate(np.squeeze)  # doctest: +SKIP
+
+        """
+        # Ensure we can iterate, even on a single slice
+        for ext in [self] if self.is_single else self:
+            ext.data = operator(ext.data, *args, **kwargs)
+            if ext.mask is not None:
+                ext.mask = operator(ext.mask, *args, **kwargs)
+            if ext.variance is not None:
+                ext.variance = operator(ext.variance, *args, **kwargs)

+
+
+

+[docs]
+    def reset(self, data, mask=NO_DEFAULT, variance=NO_DEFAULT, check=True):
+        """
+        Sets the ``.data``, and optionally ``.mask`` and ``.variance``
+        attributes of a single-extension AstroData slice. This function will
+        optionally check whether these attributes have the same shape.
+
+        Parameters
+        ----------
+        data : ndarray
+            The array to assign to the ``.data`` attribute ("SCI").
+        mask : ndarray, optional
+            The array to assign to the ``.mask`` attribute ("DQ").
+        variance: ndarray, optional
+            The array to assign to the ``.variance`` attribute ("VAR").
+        check: bool
+            If set, then the function will check that the mask and variance
+            arrays have the same shape as the data array.
+
+        Raises
+        -------
+        TypeError
+            if an attempt is made to set the .mask or .variance attributes
+            with something other than an array
+        ValueError
+            if the .mask or .variance attributes don't have the same shape as
+            .data, OR if this is called on an AD instance that isn't a single
+            extension slice
+
+        """
+        if not self.is_single:
+            raise ValueError("Trying to reset a non-sliced AstroData object")
+
+        # In case data is an NDData object
+        try:
+            self.data = data.data
+        except AttributeError:
+            self.data = data
+        # Set mask, with checking if required
+        try:
+            if mask.shape != self.data.shape and check:
+                raise ValueError("Mask shape incompatible with data shape")
+
+        except AttributeError as err:
+            if mask is None:
+                self.mask = mask
+
+            elif mask == NO_DEFAULT:
+                if hasattr(data, "mask"):
+                    self.mask = data.mask
+
+            else:
+                raise TypeError("Attempt to set mask inappropriately") from err
+
+        else:
+            self.mask = mask
+
+        # Set variance, with checking if required
+        try:
+            if variance.shape != self.data.shape and check:
+                raise ValueError("Variance shape incompatible with data shape")
+
+        except AttributeError as err:
+            if variance is None:
+                self.uncertainty = None
+
+            elif variance == NO_DEFAULT:
+                if hasattr(data, "uncertainty"):
+                    self.uncertainty = data.uncertainty
+
+            else:
+                raise TypeError(
+                    "Attempt to set variance inappropriately"
+                ) from err
+
+        else:
+            self.variance = variance
+
+        if hasattr(data, "wcs"):
+            self.wcs = data.wcs

+
+
+

+[docs]
+    def update_filename(self, prefix=None, suffix=None, strip=False):
+        """Update the "filename" attribute of the AstroData object.
+
+        A prefix and/or suffix can be specified. If ``strip=True``, these will
+        replace the existing prefix/suffix; if ``strip=False``, they will
+        simply be prepended/appended.
+
+        The current filename is broken down into its existing prefix, root, and
+        suffix using the ``ORIGNAME`` phu keyword, if it exists and is
+        contained within the current filename. Otherwise, the filename is split
+        at the last underscore and the part before is assigned as the root and
+        the underscore and part after the suffix. No prefix is assigned.
+
+        Note that, if ``strip=True``, a prefix or suffix will only be stripped
+        if '' is specified.
+
+        Parameters
+        ----------
+        prefix: str, optional
+            New prefix (None => leave alone)
+
+        suffix: str, optional
+            New suffix (None => leave alone)
+
+        strip: bool, optional
+            Strip existing prefixes and suffixes if new ones are given?
+
+        Raises
+        ------
+        ValueError
+            If the filename cannot be determined
+        """
+        if self.filename is None:
+            if "ORIGNAME" in self.phu:
+                self.filename = self.phu["ORIGNAME"]
+            else:
+                raise ValueError(
+                    "A filename needs to be set before it can be updated"
+                )
+
+        # Set the ORIGNAME keyword if it's not there
+        if "ORIGNAME" not in self.phu:
+            self.phu.set(
+                "ORIGNAME",
+                self.orig_filename,
+                "Original filename prior to processing",
+            )
+
+        if strip:
+            root, filetype = os.path.splitext(self.phu["ORIGNAME"])
+            filename, filetype = os.path.splitext(self.filename)
+            m = re.match(f"(.*){re.escape(root)}(.*)", filename)
+
+            # Do not strip a prefix/suffix unless a new one is provided
+            if m:
+                if prefix is None:
+                    prefix = m.groups()[0]
+
+                existing_suffix = m.groups()[1]
+
+                if "_" in existing_suffix:
+                    last_underscore = existing_suffix.rfind("_")
+                    root += existing_suffix[:last_underscore]
+                    existing_suffix = existing_suffix[last_underscore:]
+
+            else:
+                try:
+                    root, existing_suffix = filename.rsplit("_", 1)
+                    existing_suffix = "_" + existing_suffix
+
+                except ValueError as err:
+                    logging.info(
+                        "Could not split filename (ValueError): %s", err
+                    )
+                    root, existing_suffix = filename, ""
+
+            if suffix is None:
+                suffix = existing_suffix
+
+        else:
+            root, filetype = os.path.splitext(self.filename)
+
+        # Cope with prefix or suffix as None
+        self.filename = (prefix or "") + root + (suffix or "") + filetype

+
+
+    def _crop_nd(self, nd, x1, y1, x2, y2):
+        y_start, y_end = y1, y2 + 1
+        x_start, x_end = x1, x2 + 1
+
+        nd.data = nd.data[y_start:y_end, x_start:x_end]
+
+        if nd.uncertainty is not None:
+            nd.uncertainty = nd.uncertainty[y_start:y_end, x_start:x_end]
+
+        if nd.mask is not None:
+            nd.mask = nd.mask[y_start:y_end, x_start:x_end]
+
+

+[docs]
+    def crop(self, x1, y1, x2, y2):
+        """Crop the NDData objects given indices.
+
+        Parameters
+        ----------
+        x1, y1, x2, y2 : int
+            Minimum and maximum indices for the x and y axis.
+
+        """
+        # TODO: Consider cropping of objects in the meta section
+        for nd in self._nddata:
+            orig_shape = nd.data.shape
+            self._crop_nd(nd, x1, y1, x2, y2)
+            for o in nd.meta["other"].values():
+                try:
+                    if o.shape == orig_shape:
+                        self._crop_nd(o, x1, y1, x2, y2)
+                except AttributeError:
+                    # No 'shape' attribute in the object. It's probably
+                    # not array-like
+                    pass

+
+
+

+[docs]
+    @astro_data_descriptor
+    def instrument(self):
+        """Returns the name of the instrument making the observation."""
+        return self.phu.get(self._keyword_for("instrument"))

+
+
+

+[docs]
+    @astro_data_descriptor
+    def object(self):
+        """Returns the name of the object being observed."""
+        return self.phu.get(self._keyword_for("object"))

+
+
+

+[docs]
+    @astro_data_descriptor
+    def telescope(self):
+        """Returns the name of the telescope."""
+        return self.phu.get(self._keyword_for("telescope"))

+

+
+
+ +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/astrodata/factory.html b/_modules/astrodata/factory.html new file mode 100644 index 00000000..af96bde9 --- /dev/null +++ b/_modules/astrodata/factory.html @@ -0,0 +1,350 @@ + + + + + + + astrodata.factory — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for astrodata.factory

+"""Factory for AstroData objects."""
+import logging
+import os
+from contextlib import contextmanager
+from copy import deepcopy
+
+from astropy.io import fits
+
+from .utils import deprecated
+
+LOGGER = logging.getLogger(__name__)
+
+
+

+[docs]
+class AstroDataError(Exception):
+    """Exception raised when there is a problem with the AstroData class."""

+
+
+
+class AstroDataFactory:
+    """Factory class for AstroData objects."""
+
+    _file_openers = (fits.open,)
+
+    def __init__(self):
+        self._registry = set()
+
+    @staticmethod
+    @deprecated(
+        "Renamed to _open_file, please use that method instead: "
+        "astrodata.factory.AstroDataFactory._open_file"
+    )
+    @contextmanager
+    def _openFile(source):  # pylint: disable=invalid-name
+        return AstroDataFactory._open_file(source)
+
+    @staticmethod
+    @contextmanager
+    def _open_file(source):
+        """Internal static method that takes a ``source``, assuming that it is
+        a string pointing to a file to be opened.
+
+        If this is the case, it will try to open the file and return an
+        instance of the appropriate native class to be able to manipulate it
+        (eg. ``HDUList``).
+
+        If ``source`` is not a string, it will be returned verbatim, assuming
+        that it represents an already opened file.
+        """
+        if isinstance(source, (str, os.PathLike)):
+            # Check that the file exists.
+            if not os.path.isfile(source):
+                raise FileNotFoundError(f"Path is not a file: {source}")
+
+            # Check that the file has nonzero size.
+            stats = os.stat(source)
+
+            if stats.st_size == 0:
+                LOGGER.warning("File %s is zero size", source)
+
+            if not AstroDataFactory._file_openers:
+                raise AstroDataError(
+                    "No file openers registered. Register some using"
+                    " 'add_class' method."
+                )
+
+            # try vs all handlers
+            for func in AstroDataFactory._file_openers:
+                try:
+                    fp = func(source)
+                    yield fp
+
+                except Exception as err:  # pylint: disable=broad-except
+                    # TODO: Should be more specific than this.
+                    # Log the exception, if it's a serious error then
+                    # re-raise it, e.g., user exits with Ctrl-C.
+                    LOGGER.error(
+                        "Failed to open %s with %s, got error: %s",
+                        source,
+                        func,
+                        err,
+                    )
+
+                    # Handle nonexistent files.
+                    if isinstance(err, FileNotFoundError):
+                        raise err
+
+                else:
+                    if hasattr(fp, "close"):
+                        fp.close()
+
+                    return
+
+            raise AstroDataError(
+                f"No access, or not supported format for: {source}"
+            )
+
+        yield source
+
+    @deprecated(
+        "Renamed to add_class, please use that method instead: "
+        "astrodata.factory.AstroDataFactory.add_class"
+    )
+    def addClass(self, cls):  # pylint: disable=invalid-name
+        """Add a new class to the AstroDataFactory registry. It will be used
+        when instantiating an AstroData class for a FITS file.
+        """
+        self.add_class(cls)
+
+    def add_class(self, cls):
+        """Add a new class to the AstroDataFactory registry. It will be used
+        when instantiating an AstroData class for a FITS file.
+        """
+        if not hasattr(cls, "_matches_data"):
+            raise AttributeError(
+                f"Class '{cls.__name__}' has no '_matches_data' method"
+            )
+
+        self._registry.add(cls)
+
+    def remove_class(self, cls: type | str):
+        """Remove a class from the AstroDataFactory registry."""
+        if isinstance(cls, str):
+            cls = next((c for c in self._registry if c.__name__ == cls), None)
+
+        self._registry.remove(cls)
+
+    @deprecated(
+        "Renamed to get_astro_data, please use that method instead: "
+        "astrodata.factory.AstroDataFactory.get_astro_data"
+    )
+    def getAstroData(self, source):  # pylint: disable=invalid-name
+        """Deprecated, see |get_astro_data|."""
+        self.get_astro_data(source)
+
+    def get_astro_data(self, source):
+        """Takes either a string (with the path to a file) or an HDUList as
+        input, and tries to return an AstroData instance.
+
+        It will raise exceptions if the file is not found, or if there is no
+        match for the HDUList, among the registered AstroData classes.
+
+        Returns an instantiated object, or raises AstroDataError if it was
+        not possible to find a match
+
+        Parameters
+        ----------
+        source : `str` or `pathlib.Path` or `fits.HDUList`
+            The file path or HDUList to read.
+        """
+        candidates = []
+        with self._open_file(source) as opened:
+            for adclass in self._registry:
+                try:
+                    # TODO: accessing protected member
+                    # pylint: disable=protected-access
+                    if adclass._matches_data(opened):
+                        candidates.append(adclass)
+
+                except Exception as err:  # pylint: disable=broad-except
+                    # TODO: Should be more specific than this.
+                    LOGGER.error(
+                        "Failed to open %s with %s, got error: %s",
+                        source,
+                        adclass,
+                        err,
+                    )
+
+        # For every candidate in the list, remove the ones that are base
+        # classes for other candidates. That way we keep only the more
+        # specific ones.
+        final_candidates = []
+        for cnd in candidates:
+            if any(cnd in x.mro() for x in candidates if x != cnd):
+                continue
+
+            final_candidates.append(cnd)
+
+        if len(final_candidates) > 1:
+            raise AstroDataError(
+                "More than one class is candidate for this dataset"
+            )
+
+        if not final_candidates:
+            raise AstroDataError("No class matches this dataset")
+
+        return final_candidates[0].read(source)
+
+    @deprecated(
+        "Renamed to create_from_scratch, please use that method instead: "
+        "astrodata.factory.AstroDataFactory.create_from_scratch"
+    )
+    def createFromScratch(
+        self,
+        phu,
+        extensions=None,
+    ):  # pylint: disable=invalid-name
+        """Deprecated, see |create_from_scratch|."""
+        self.create_from_scratch(phu=phu, extensions=extensions)
+
+    def create_from_scratch(self, phu, extensions=None):
+        """Creates an AstroData object from a collection of objects.
+
+        Parameters
+        ----------
+        phu : `fits.PrimaryHDU` or `fits.Header` or `dict` or `list`
+            FITS primary HDU or header, or something that can be used to create
+            a fits.Header (a dict, a list of "cards").
+
+        extensions : list of HDUs
+            List of HDU objects.
+
+        Returns
+        -------
+        `astrodata.AstroData`
+            An AstroData instance.
+
+        Raises
+        ------
+        ValueError
+            If ``phu`` is not a valid object.
+        """
+        lst = fits.HDUList()
+        if phu is not None:
+            if isinstance(phu, fits.PrimaryHDU):
+                lst.append(deepcopy(phu))
+
+            elif isinstance(phu, fits.Header):
+                lst.append(fits.PrimaryHDU(header=deepcopy(phu)))
+
+            elif isinstance(phu, (dict, list, tuple)):
+                p = fits.PrimaryHDU()
+                p.header.update(phu)
+                lst.append(p)
+
+            else:
+                raise ValueError(
+                    "phu must be a PrimaryHDU or a valid header object"
+                )
+
+        # TODO: Verify the contents of extensions...
+        if extensions is not None:
+            for ext in extensions:
+                lst.append(ext)
+
+        return self.get_astro_data(lst)
+
+ +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/astrodata/fits.html b/_modules/astrodata/fits.html new file mode 100644 index 00000000..4e6582b9 --- /dev/null +++ b/_modules/astrodata/fits.html @@ -0,0 +1,1301 @@ + + + + + + + astrodata.fits — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for astrodata.fits

+"""Functions used when interacting with FITS files and HDUs.
+
+.. |NDData| replace:: :class:`~astropy.nddata.NDData`
+.. |NDDataRef| replace:: :class:`~astropy.nddata.NDDataRef`
+.. |BinTableHDU| replace:: :class:`~astropy.io.fits.BinTableHDU`
+.. |TableHDU| replace:: :class:`~astropy.io.fits.TableHDU`
+.. |NDAstroData| replace:: :class:`~astrodata.nddata.NDAstroData`
+.. |NDAstroDataRef| replace:: :class:`~astrodata.nddata.NDAstroDataRef`
+"""
+from collections import OrderedDict
+from copy import deepcopy
+from io import BytesIO
+from itertools import product as cart_product, zip_longest
+import gc
+import logging
+import os
+import traceback
+import warnings
+
+from astropy import units as u
+from astropy.io import fits
+from astropy.io.fits import (
+    BinTableHDU,
+    Column,
+    DELAYED,
+    HDUList,
+    ImageHDU,
+    PrimaryHDU,
+    TableHDU,
+)
+from astropy.nddata import NDData
+
+# NDDataRef is still not in the stable astropy, but this should be the one
+# we use in the future...
+# from astropy.nddata import NDData, NDDataRef as NDDataObject
+from astropy.table import Table
+
+import asdf
+import astropy
+import jsonschema
+import numpy as np
+
+
+from gwcs.wcs import WCS as gWCS
+
+from .nddata import ADVarianceUncertainty, NDAstroData as NDDataObject
+from .utils import deprecated
+from .wcs import fitswcs_to_gwcs, gwcs_to_fits
+
+DEFAULT_EXTENSION = "SCI"
+NO_DEFAULT = object()
+LOGGER = logging.getLogger(__name__)
+
+
+class FitsHeaderCollection:
+    """Group access to a list of FITS Header-like objects.
+
+    It exposes a number of methods (``set``, ``get``, etc.) that operate over
+    all the headers at the same time. It can also be iterated.
+
+    Parameters
+    ----------
+    headers : list of `astropy.io.fits.Header`
+        List of Header objects.
+    """
+
+    def __init__(self, headers):
+        self._headers = list(headers)
+
+    def _insert(self, idx, header):
+        self._headers.insert(idx, header)
+
+    def __iter__(self):
+        yield from self._headers
+
+    def __setitem__(self, key, value):
+        if isinstance(value, tuple):
+            self.set(key, value=value[0], comment=value[1])
+        else:
+            self.set(key, value=value)
+
+    def set(self, key, value=None, comment=None):
+        """Set a keyword in all the headers."""
+        for header in self._headers:
+            header.set(key, value=value, comment=comment)
+
+    def __getitem__(self, key):
+        missing_at = []
+        ret = []
+        for n, header in enumerate(self._headers):
+            try:
+                ret.append(header[key])
+
+            except KeyError:
+                logging.debug(
+                    "Assigning None to header missing keyword %s", key
+                )
+
+                missing_at.append(n)
+                ret.append(None)
+
+        if missing_at:
+            error = KeyError(
+                f"The keyword couldn't be found at headers: "
+                f"{tuple(missing_at)}"
+            )
+
+            error.missing_at = missing_at
+            error.values = ret
+            raise error
+
+        return ret
+
+    def get(self, key, default=None):
+        """Get a keyword, defaulting to None."""
+        try:
+            return self[key]
+        except KeyError as err:
+            vals = err.values
+            for n in err.missing_at:
+                vals[n] = default
+            return vals
+
+    def __delitem__(self, key):
+        self.remove(key)
+
+    def remove(self, key):
+        """Remove a keyword from all the headers."""
+        deleted = 0
+        for header in self._headers:
+            try:
+                del header[key]
+                deleted = deleted + 1
+            except KeyError:
+                pass
+        if not deleted:
+            raise KeyError(f"'{key}' is not on any of the extensions")
+
+    def get_comment(self, key):
+        """Get the comment for a keyword, from all the headers, as a list."""
+        return [header.comments[key] for header in self._headers]
+
+    def set_comment(self, key, comment):
+        """Set the comment for a keyword in all the headers."""
+
+        def _inner_set_comment(header):
+            if key not in header:
+                raise KeyError(f"Keyword {key!r} not available")
+
+            header.set(key, comment=comment)
+
+        for n, header in enumerate(self._headers):
+            try:
+                _inner_set_comment(header)
+            except KeyError as err:
+                raise KeyError(f"{err.args[0]} at header {n}") from err
+
+    def __contains__(self, key):
+        return any(tuple(key in h for h in self._headers))
+
+
+def new_imagehdu(data, header, name=None):
+    """Create a new ImageHDU from data and header.
+
+    Parameters
+    ----------
+    data : `numpy.ndarray`
+        The data array.
+
+    header : `astropy.io.fits.Header`
+        The header.
+
+    name : str
+        The extension name.
+
+    Notes
+    -----
+    Assigning data in a delayed way, won't reset BZERO/BSCALE in the header,
+    for some reason. Need to investigated. Maybe astropy.io.fits bug. Figure
+    out WHY were we delaying in the first place.
+
+    Example:
+    >> i = ImageHDU(data=DELAYED, header=header.copy(), name=name)
+    >> i.data = data
+    """
+    # Assigning data in a delayed way, won't reset BZERO/BSCALE in the header,
+    # for some reason. Need to investigated. Maybe astropy.io.fits bug. Figure
+    # out WHY were we delaying in the first place.
+    #    i = ImageHDU(data=DELAYED, header=header.copy(), name=name)
+    #    i.data = data
+    return ImageHDU(data=data, header=header.copy(), name=name)
+
+
+def table_to_bintablehdu(table, extname=None):
+    """Convert an astropy Table object to a BinTableHDU before writing to disk.
+
+    Parameters
+    ----------
+    table: astropy.table.Table instance
+        the table to be converted to a BinTableHDU
+
+    extname: str
+        name to go in the EXTNAME field of the FITS header
+
+    Returns
+    -------
+    BinTableHDU
+    """
+    # remove header to avoid warning from table_to_hdu
+    table_header = table.meta.pop("header", None)
+
+    # table_to_hdu sets units only if the unit conforms to the FITS standard,
+    # otherwise it issues a warning, which we catch here.
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        hdu = fits.table_to_hdu(table)
+
+    # And now we try to set the units that do not conform to the standard,
+    # using unit.to_string() without the format='fits' argument.
+    for col in table.itercols():
+        if col.unit and not hdu.columns[col.name].unit:
+            hdu.columns[col.name].unit = col.unit.to_string()
+
+    if table_header is not None:
+        # Update with cards from table.meta, but skip structural FITS
+        # keywords since those have been set by table_to_hdu
+        exclude = (
+            "SIMPLE",
+            "XTENSION",
+            "BITPIX",
+            "NAXIS",
+            "EXTEND",
+            "PCOUNT",
+            "GCOUNT",
+            "TFIELDS",
+            "TFORM",
+            "TSCAL",
+            "TZERO",
+            "TNULL",
+            "TTYPE",
+            "TUNIT",
+            "TDISP",
+            "TDIM",
+            "THEAP",
+            "TBCOL",
+        )
+        hdr = fits.Header(
+            [
+                card
+                for card in table_header.cards
+                if not card.keyword.startswith(exclude)
+            ]
+        )
+        update_header(hdu.header, hdr)
+        # reset table's header
+        table.meta["header"] = table_header
+    if extname:
+        hdu.header["EXTNAME"] = (extname, "added by AstroData")
+    return hdu
+
+
+def header_for_table(table):
+    """Return a FITS header for a table."""
+    table_header = table.meta.pop("header", None)
+    fits_header = fits.table_to_hdu(table).header
+    if table_header:
+        table.meta["header"] = table_header  # restore original meta
+        fits_header = update_header(table_header, fits_header)
+    return fits_header
+
+
+

+[docs]
+def add_header_to_table(table):
+    """Add a FITS header to a table."""
+    header = header_for_table(table)
+    table.meta["header"] = header
+    return header

+
+
+
+def _process_table(table, name=None, header=None):
+    """Convert a BinTableHDU or TableHDU to an astropy Table object.
+
+    Arguments
+    ---------
+    table : |BinTableHDU| or |TableHDU| or |Table|
+        The table to convert. If it's already an |Table|, it will be returned
+        as is.
+
+    name : str
+        The name to assign to the table.
+
+    header : `astropy.io.fits.Header`
+        The header to assign to the table.
+    """
+    if isinstance(table, (BinTableHDU, TableHDU)):
+        obj = Table(table.data, meta={"header": header or table.header})
+        for i, col in enumerate(obj.columns, start=1):
+            try:
+                obj[col].unit = u.Unit(obj.meta["header"][f"TUNIT{i}"])
+            except (KeyError, TypeError, ValueError):
+                pass
+    elif isinstance(table, Table):
+        obj = Table(table)
+        if header is not None:
+            obj.meta["header"] = deepcopy(header)
+        elif "header" not in obj.meta:
+            obj.meta["header"] = header_for_table(obj)
+    else:
+        raise ValueError(f"{table.__class__} is not a recognized table type")
+
+    if name is not None:
+        obj.meta["header"]["EXTNAME"] = name
+
+    return obj
+
+
+def card_filter(cards, include=None, exclude=None):
+    """Filter a list of cards, lazily returning only those that match the
+    criteria.
+
+    Parameters
+    ----------
+    cards : iterable
+        The cards to filter.
+
+    include : iterable of str
+        Only cards with these keywords will be returned.
+
+    exclude : iterable of str
+        Cards with these keywords will be skipped.
+
+    Yields
+    ------
+    card : tuple
+        A card that matches the criteria.
+    """
+    for card in cards:
+        if include is not None and card[0] not in include:
+            continue
+
+        if exclude is not None and card[0] in exclude:
+            continue
+
+        yield card
+
+
+def update_header(headera, headerb):
+    """Update headera with the cards from headerb, but only if they are
+    different.
+
+    Parameters
+    ----------
+    headera : `astropy.io.fits.Header`
+        The header to update.
+
+    headerb : `astropy.io.fits.Header`
+        The header to update from.
+    """
+    cardsa = tuple(tuple(cr) for cr in headera.cards)
+    cardsb = tuple(tuple(cr) for cr in headerb.cards)
+
+    if cardsa == cardsb:
+        return headera
+
+    # Ok, headerb differs somehow. Let's try to bring the changes to headera
+    # Updated keywords that should be unique
+    difference = set(cardsb) - set(cardsa)
+    headera.update(card_filter(difference, exclude={"HISTORY", "COMMENT", ""}))
+    # Check the HISTORY and COMMENT cards, just in case
+    for key in ("HISTORY", "COMMENT"):
+        fltcardsa = card_filter(cardsa, include={key})
+        fltcardsb = card_filter(cardsb, include={key})
+        # assume we start with two headers that are mostly the same and
+        # that will have added comments/history at the end (in headerb)
+        for ca, cb in zip_longest(fltcardsa, fltcardsb):
+            if ca is None:
+                headera.update((cb,))
+
+    return headera
+
+
+def fits_ext_comp_key(ext):
+    """Returns a pair (int, str) that will be used to sort extensions."""
+    if isinstance(ext, PrimaryHDU):
+        # This will guarantee that the primary HDU goes first
+        ret = (-1, "")
+    else:
+        # When two extensions share version number, we'll use their names
+        # to sort them out. Choose a suitable key so that:
+        #
+        #  - SCI extensions come first
+        #  - unnamed extensions come last
+        #
+        # We'll resort to add 'z' in front of the usual name to force
+        # SCI to be the "smallest"
+        name = ext.name
+        if name == "":
+            name = "zzzz"
+        elif name != DEFAULT_EXTENSION:
+            name = "z" + name
+
+        ver = ext.header.get("EXTVER")
+        if ver in (-1, None):
+            # In practice, this number should be larger than any EXTVER found
+            # in real life HDUs, pushing unnumbered HDUs to the end.
+            ver = 2**32 - 1
+
+        # For the general case, just return version and name, to let them
+        # be sorted naturally
+        ret = (ver, name)
+
+    return ret
+
+
+class FitsLazyLoadable:
+    """Class to delay loading of data from a FITS file."""
+
+    def __init__(self, obj):
+        """Initializes the object.
+
+        Parameters
+        ----------
+        obj : `astropy.io.fits.ImageHDU` or `astropy.io.fits.BinTableHDU`
+            The HDU to delay loading from.
+        """
+        self._obj = obj
+        self.lazy = True
+
+    def _create_result(self, shape):
+        """Create an empty array to hold the data."""
+        return np.empty(shape, dtype=self.dtype)
+
+    def _scale(self, data):
+        """Scale the data, if necessary."""
+        # TODO: It would be goot to access these differently. Is this always an
+        # object we control? Even if so, should access through a property, not
+        # a protected member. No friends in python...
+        # pylint: disable=protected-access
+        bscale = self._obj._orig_bscale
+        bzero = self._obj._orig_bzero
+
+        if bscale == 1 and bzero == 0:
+            return data
+
+        return (bscale * data + bzero).astype(self.dtype)
+
+    def __getitem__(self, arr_slice):
+        # TODO: We may want (read: should) create an empty result array before
+        # scaling
+        return self._scale(self._obj.section[arr_slice])
+
+    @property
+    def header(self):
+        """The header of the HDU."""
+        return self._obj.header
+
+    @property
+    def data(self):
+        """The data of the HDU."""
+        res = self._create_result(self.shape)
+        res[:] = self._scale(self._obj.data)
+        return res
+
+    @property
+    def shape(self):
+        """The shape of the data."""
+        return self._obj.shape
+
+    @property
+    def dtype(self):
+        """Need to to some overriding of astropy.io.fits since it doesn't
+        know about BITPIX=8
+        """
+        # TODO: It would be goot to access these differently. Is this always an
+        # object we control? Even if so, should access through a property, not
+        # a protected member. No friends in python... These are scattered
+        # throughout the function.
+        # pylint: disable=protected-access
+        bitpix = self._obj._orig_bitpix
+        if self._obj._orig_bscale == 1 and self._obj._orig_bzero == 0:
+            dtype = fits.BITPIX2DTYPE[bitpix]
+        else:
+            # this method from astropy will return the dtype if the data
+            # needs to be converted to unsigned int or scaled to float
+            dtype = self._obj._dtype_for_bitpix()
+
+        if dtype is None:
+            if bitpix < 0:
+                dtype = np.dtype(f"float{abs(bitpix)}")
+
+        if (
+            self._obj.header["EXTNAME"] == "DQ"
+            or self._obj._uint
+            and self._obj._orig_bscale == 1
+            and bitpix == 8
+        ):
+            dtype = np.uint16
+
+        return dtype
+
+
+def _prepare_hdulist(hdulist, default_extension="SCI", extname_parser=None):
+    """Prepare an HDUList for reading.
+
+    Parameters
+    ----------
+    hdulist : `astropy.io.fits.HDUList`
+        The HDUList to prepare.
+
+    default_extension : str
+        The name of the default extension.
+
+    extname_parser : callable
+        A function to parse the EXTNAME of an HDU.
+
+    Returns
+    -------
+    hdulist : `astropy.io.fits.HDUList`
+        The prepared HDUList.
+    """
+    new_list = []
+    highest_ver = 0
+    recognized = set()
+
+    if len(hdulist) > 1 or (len(hdulist) == 1 and hdulist[0].data is None):
+        # MEF file
+        # First get HDUs for which EXTVER is defined
+        for hdu in hdulist:
+            if extname_parser:
+                extname_parser(hdu)
+            ver = hdu.header.get("EXTVER")
+            if ver not in (-1, None) and hdu.name:
+                highest_ver = max(highest_ver, ver)
+            elif not isinstance(hdu, PrimaryHDU):
+                continue
+
+            new_list.append(hdu)
+            recognized.add(hdu)
+
+        # Then HDUs that miss EXTVER
+        for hdu in hdulist:
+            if hdu in recognized:
+                continue
+
+            if isinstance(hdu, ImageHDU):
+                highest_ver += 1
+                if "EXTNAME" not in hdu.header:
+                    hdu.header["EXTNAME"] = (
+                        default_extension,
+                        "Added by AstroData",
+                    )
+
+                if hdu.header.get("EXTVER") in (-1, None):
+                    hdu.header["EXTVER"] = (highest_ver, "Added by AstroData")
+
+            new_list.append(hdu)
+            recognized.add(hdu)
+
+    else:
+        # Uh-oh, a single image FITS file
+        new_list.append(PrimaryHDU(header=hdulist[0].header))
+        image = ImageHDU(header=hdulist[0].header, data=hdulist[0].data)
+        # Fudge due to apparent issues with assigning ImageHDU from data
+        # TODO: protected members
+        # pylint: disable=protected-access
+        image._orig_bscale = hdulist[0]._orig_bscale
+        image._orig_bzero = hdulist[0]._orig_bzero
+
+        for keyw in ("SIMPLE", "EXTEND"):
+            if keyw in image.header:
+                del image.header[keyw]
+
+        image.header["EXTNAME"] = (default_extension, "Added by AstroData")
+        image.header["EXTVER"] = (1, "Added by AstroData")
+        new_list.append(image)
+
+    return HDUList(sorted(new_list, key=fits_ext_comp_key))
+
+
+def read_fits(cls, source, extname_parser=None):
+    """Takes either a string (with the path to a file) or an HDUList as input,
+    and tries to return a populated AstroData (or descendant) instance.
+
+    It will raise exceptions if the file is not found, or if there is no match
+    for the HDUList, among the registered AstroData classes.
+
+    Parameters
+    ----------
+    cls : class
+        The class to instantiate.
+
+    source : str or `astropy.io.fits.HDUList`
+        The path to the file, or an HDUList.
+
+    extname_parser : callable
+        A function to parse the EXTNAME of an HDU.
+
+    Returns
+    -------
+    ad : `astrodata.AstroData` or descendant
+        The populated AstroData object. This is of the type specified by cls.
+    """
+
+    ad = cls()
+
+    if isinstance(source, (str, os.PathLike)):
+        hdulist = fits.open(
+            source, memmap=True, do_not_scale_image_data=True, mode="readonly"
+        )
+
+        ad.path = source
+
+    else:
+        hdulist = source
+
+        try:
+            ad.path = source[0].header.get("ORIGNAME")
+
+        except AttributeError as err:
+            logging.info("Attribute error in read_fits: %s", err)
+            ad.path = None
+
+    # TODO: This is a hack to get around the fact that we don't have a
+    # proper way to pass the original filename to the object. This is
+    # needed for the writer to be able to write the ORIGNAME keyword.
+    # pylint: disable=protected-access
+    _file = hdulist._file
+
+    hdulist = _prepare_hdulist(
+        hdulist,
+        default_extension=DEFAULT_EXTENSION,
+        extname_parser=extname_parser,
+    )
+
+    if _file is not None:
+        hdulist._file = _file
+
+    # Initialize the object containers to a bare minimum
+    # pylint: disable=no-member
+    if "ORIGNAME" not in hdulist[0].header and ad.orig_filename is not None:
+        hdulist[0].header.set(
+            "ORIGNAME",
+            ad.orig_filename,
+            "Original filename prior to processing",
+        )
+
+    ad.phu = hdulist[0].header
+
+    # This is hashable --- we can use it to check if we've seen this object
+    # before.
+    # pylint: disable=unhashable-member
+    seen = {hdulist[0]}
+
+    skip_names = {DEFAULT_EXTENSION, "REFCAT", "MDF"}
+
+    def associated_extensions(ver):
+        for hdu in hdulist:
+            if hdu.header.get("EXTVER") == ver and hdu.name not in skip_names:
+                yield hdu
+
+    # Only SCI HDUs
+    sci_units = [hdu for hdu in hdulist[1:] if hdu.name == DEFAULT_EXTENSION]
+
+    seen_vers = []
+
+    for hdu in sci_units:
+        seen.add(hdu)
+        ver = hdu.header.get("EXTVER", -1)
+
+        if ver > -1 and seen_vers.count(ver) == 1:
+            LOGGER.warning("Multiple SCI extension with EXTVER %s", ver)
+
+        seen_vers.append(ver)
+        parts = {
+            "data": hdu,
+            "uncertainty": None,
+            "mask": None,
+            "wcs": None,
+            "other": [],
+        }
+
+        # For each SCI HDU find if it has an associated variance, mask, wcs
+        for extra_unit in associated_extensions(ver):
+            seen.add(extra_unit)
+            name = extra_unit.name
+            if name == "DQ":
+                parts["mask"] = extra_unit
+            elif name == "VAR":
+                parts["uncertainty"] = extra_unit
+            elif name == "WCS":
+                parts["wcs"] = extra_unit
+            else:
+                parts["other"].append(extra_unit)
+
+        header = parts["data"].header
+        lazy = hdulist._file is not None and hdulist._file.memmap
+
+        for part_name in ("data", "mask", "uncertainty"):
+            if parts[part_name] is not None:
+                if lazy:
+                    # Use FitsLazyLoadable to delay loading of the data
+                    parts[part_name] = FitsLazyLoadable(parts[part_name])
+                else:
+                    # Otherwise use the data array
+                    # parts[part_name] = parts[part_name].data
+                    # TODO: we open the file with do_not_scale_data=True, so
+                    #  the data array does not have the correct data values.
+                    #  AstroData handles scaling internally, and we can ensure
+                    #  it does that by making the data a FitsLazyLoadable; the
+                    #  side-effect of this is that the is_lazy() function will
+                    #  return True, but this has minimal knock-on effects.
+                    #  Hopefully astropy will handle this better in future.
+                    if hdulist._file is not None:  # probably compressed
+                        parts[part_name] = FitsLazyLoadable(parts[part_name])
+                    else:  # for astrodata.create() files
+                        parts[part_name] = parts[part_name].data
+
+        # handle the variance if not lazy
+        if parts["uncertainty"] is not None and not isinstance(
+            parts["uncertainty"], FitsLazyLoadable
+        ):
+            parts["uncertainty"] = ADVarianceUncertainty(parts["uncertainty"])
+
+        # Create the NDData object
+        nd = NDDataObject(
+            data=parts["data"],
+            uncertainty=parts["uncertainty"],
+            mask=parts["mask"],
+            meta={"header": header},
+        )
+
+        ad.append(nd, name=DEFAULT_EXTENSION)
+
+        # This is used in the writer to keep track of the extensions that
+        # were read from the current object.
+        nd.meta["parent_ad"] = id(ad)
+
+        for other in parts["other"]:
+            if not other.name:
+                warnings.warn(f"Skip HDU {other} because it has no EXTNAME")
+            else:
+                setattr(ad[-1], other.name, other)
+
+        if parts["wcs"] is not None:
+            # Load the gWCS object from the ASDF extension
+            nd.wcs = asdftablehdu_to_wcs(parts["wcs"])
+        if nd.wcs is None:
+            # Fallback to the data header
+            nd.wcs = fitswcs_to_gwcs(nd)
+            if nd.wcs is None:
+                # In case WCS info is in the PHU
+                nd.wcs = fitswcs_to_gwcs(hdulist[0].header)
+
+    for other in hdulist:
+        if other in seen:
+            continue
+        name = other.header.get("EXTNAME")
+        try:
+            ad.append(other, name=name)
+        except ValueError as e:
+            warnings.warn(f"Discarding {name} :\n {e}")
+
+    return ad
+
+
+def ad_to_hdulist(ad):
+    """Creates an HDUList from an AstroData object."""
+    hdul = HDUList()
+    hdul.append(PrimaryHDU(header=ad.phu, data=DELAYED))
+
+    # Find the maximum EXTVER for extensions that belonged with this
+    # object if it was read from a FITS file
+    # TODO: Is there a way to access _nddata without using the protected
+    # variable? Should it be a protected variable?
+    # pylint: disable=protected-access
+    maxver = max(
+        (
+            nd.meta["header"].get("EXTVER", 0)
+            for nd in ad._nddata
+            if nd.meta.get("parent_ad") == id(ad)
+        ),
+        default=0,
+    )
+
+    for ext in ad._nddata:
+        header = ext.meta["header"].copy()
+
+        if not isinstance(header, fits.Header):
+            header = fits.Header(header)
+
+        if ext.meta.get("parent_ad") == id(ad):
+            # If the extension belonged with this object, use its
+            # original EXTVER
+            ver = header["EXTVER"]
+        else:
+            # Otherwise renumber the extension
+            ver = header["EXTVER"] = maxver + 1
+            maxver += 1
+
+        wcs = ext.wcs
+
+        if isinstance(wcs, gWCS):
+            # We don't have access to the AD tags so see if it's an image
+            # Catch ValueError as any sort of failure
+            try:
+                wcs_dict = gwcs_to_fits(ext, ad.phu)
+
+            except (ValueError, NotImplementedError) as e:
+                LOGGER.warning(e)
+
+            else:
+                # Must delete keywords if image WCS has been downscaled
+                # from a higher number of dimensions
+                for i in range(1, 5):
+                    for kw in (
+                        f"CDELT{i}",
+                        f"CRVAL{i}",
+                        f"CUNIT{i}",
+                        f"CTYPE{i}",
+                        f"NAXIS{i}",
+                    ):
+                        if kw in header:
+                            del header[kw]
+
+                    for j in range(1, 5):
+                        for kw in (f"CD{i}_{j}", f"PC{i}_{j}", f"CRPIX{j}"):
+                            if kw in header:
+                                del header[kw]
+
+                # Delete this if it's left over from a previous save
+                if "FITS-WCS" in header:
+                    del header["FITS-WCS"]
+
+                try:
+                    extensions = wcs_dict.pop("extensions")
+
+                except KeyError:
+                    pass
+
+                else:
+                    for k, v in extensions.items():
+                        ext.meta["other"][k] = v
+
+                header.update(wcs_dict)
+
+                # Use "in" here as the dict entry may be (value, comment)
+                if "APPROXIMATE" not in wcs_dict.get("FITS-WCS", ""):
+                    wcs = None  # There's no need to create a WCS extension
+
+        hdul.append(new_imagehdu(ext.data, header, "SCI"))
+
+        if ext.uncertainty is not None:
+            hdul.append(new_imagehdu(ext.uncertainty.array, header, "VAR"))
+
+        if ext.mask is not None:
+            hdul.append(new_imagehdu(ext.mask, header, "DQ"))
+
+        if isinstance(wcs, gWCS):
+            hdul.append(wcs_to_asdftablehdu(ext.wcs, extver=ver))
+
+        for name, other in ext.meta.get("other", {}).items():
+            if isinstance(other, Table):
+                hdu = table_to_bintablehdu(other, extname=name)
+
+            elif isinstance(other, np.ndarray):
+                hdu = new_imagehdu(other, header, name=name)
+
+            elif isinstance(other, NDDataObject):
+                hdu = new_imagehdu(other.data, ext.meta["header"])
+
+            else:
+                raise ValueError(
+                    "I don't know how to write back an object "
+                    f"of type {type(other)}"
+                )
+
+            hdu.ver = ver
+            hdul.append(hdu)
+
+    if ad._tables is not None:
+        for name, table in sorted(ad._tables.items()):
+            hdul.append(table_to_bintablehdu(table, extname=name))
+
+    # Additional FITS compatibility, add to PHU
+    # pylint: disable=no-member
+    hdul[0].header["NEXTEND"] = len(hdul) - 1
+
+    return hdul
+
+
+def write_fits(ad, filename, overwrite=False):
+    """Writes the AstroData object to a FITS file."""
+    hdul = ad_to_hdulist(ad)
+    hdul.writeto(filename, overwrite=overwrite)
+
+
+@deprecated(
+    "Renamed to 'windowed_operation', this is just an alias for now, "
+    "and will be removed in a future version."
+)
+def windowedOp(*args, **kwargs):  # pylint: disable=invalid-name
+    """Alias for windowed_operation."""
+    return windowed_operation(*args, **kwargs)
+
+
+# TODO: Need to refactor this function
+def windowed_operation(
+    func,
+    sequence,
+    kernel,
+    shape=None,
+    dtype=None,
+    with_uncertainty=False,
+    with_mask=False,
+    **kwargs,
+):
+    """Apply function on a NDData obbjects, splitting the data in chunks to
+    limit memory usage.
+
+    Parameters
+    ----------
+    func : callable
+        The function to apply.
+
+    sequence : list of NDData
+        List of NDData objects.
+
+    kernel : tuple of int
+        Shape of the blocks.
+
+    shape : tuple of int
+        Shape of inputs. Defaults to ``sequence[0].shape``.
+
+    dtype : str or dtype
+        Type of the output array. Defaults to ``sequence[0].dtype``.
+
+    with_uncertainty : bool
+        Compute uncertainty?
+
+    with_mask : bool
+        Compute mask?
+
+    **kwargs
+        Additional args are passed to ``func``.
+    """
+
+    def generate_boxes(shape, kernel):
+        if len(shape) != len(kernel):
+            raise AssertionError(
+                f"Incompatible shape ({shape}) and kernel ({kernel})"
+            )
+
+        ticks = [
+            [(x, x + step) for x in range(0, axis, step)]
+            for axis, step in zip(shape, kernel)
+        ]
+
+        return list(cart_product(*ticks))
+
+    if shape is None:
+        if len({x.shape for x in sequence}) > 1:
+            raise ValueError(
+                "Can't calculate final shape: sequence elements "
+                "disagree on shape, and none was provided"
+            )
+
+        shape = sequence[0].shape
+
+    if dtype is None:
+        dtype = sequence[0].window[:1, :1].data.dtype
+
+    result = NDDataObject(
+        np.empty(shape, dtype=dtype),
+        variance=np.zeros(shape, dtype=dtype) if with_uncertainty else None,
+        mask=np.empty(shape, dtype=np.uint16) if with_mask else None,
+        meta=sequence[0].meta,
+        wcs=sequence[0].wcs,
+    )
+
+    # Delete other extensions because we don't know what to do with them
+    result.meta["other"] = OrderedDict()
+
+    # The Astropy logger's "INFO" messages aren't warnings, so have to fudge
+    # pylint: disable=no-member
+    log_level = astropy.logger.conf.log_level
+    astropy.log.setLevel(astropy.logger.WARNING)
+
+    boxes = generate_boxes(shape, kernel)
+
+    try:
+        for coords in boxes:
+            section = tuple(slice(start, end) for (start, end) in coords)
+            out = func(
+                [element.window[section] for element in sequence], **kwargs
+            )
+            result.set_section(section, out)
+
+            # propagate additional attributes
+            if out.meta.get("other"):
+                for k, v in out.meta["other"].items():
+                    if len(boxes) > 1:
+                        result.meta["other"][k, coords] = v
+                    else:
+                        result.meta["other"][k] = v
+
+            gc.collect()
+
+    finally:
+        astropy.log.setLevel(log_level)  # and reset
+
+    # Now if the input arrays where splitted in chunks, we need to gather
+    # the data arrays for the additional attributes.
+    other = result.meta["other"]
+    if other:
+        if len(boxes) > 1:
+            for (name, coords), obj in list(other.items()):
+                if not isinstance(obj, NDData):
+                    raise ValueError("only NDData objects are handled here")
+                if name not in other:
+                    other[name] = NDDataObject(
+                        np.empty(shape, dtype=obj.data.dtype)
+                    )
+                section = tuple(slice(start, end) for (start, end) in coords)
+                other[name].set_section(section, obj)
+                del other[name, coords]
+
+        for name in other:
+            # To set the name of our object we need to save it as an ndarray,
+            # otherwise for a NDData one AstroData would use the name of the
+            # AstroData object.
+            other[name] = other[name].data
+
+    return result
+
+
+# ---------------------------------------------------------------------------
+# gWCS <-> FITS WCS helper functions go here
+# ---------------------------------------------------------------------------
+# Could parametrize some naming conventions in the following two functions if
+# done elsewhere for hard-coded names like 'SCI' in future, but they only have
+# to be self-consistent with one another anyway.
+
+
+def wcs_to_asdftablehdu(wcs, extver=None):
+    """Serialize a gWCS object as a FITS TableHDU (ASCII) extension.
+
+    The ASCII table is actually a mini ASDF file. The constituent AstroPy
+    models must have associated ASDF "tags" that specify how to serialize them.
+
+    In the event that serialization as pure ASCII fails (this should not
+    happen), a binary table representation will be used as a fallback.
+
+    Returns None (issuing a warning) if the WCS object cannot be serialized,
+    so the rest of the file can still be written.
+
+    Parameters
+    ----------
+    wcs : gWCS
+        The gWCS object to serialize.
+
+    extver : int
+        The EXTVER to assign to the extension.
+
+    Returns
+    -------
+    hdu : TableHDU or BinTableHDU
+        The FITS table extension containing the serialized WCS object.
+    """
+    # Create a small ASDF file in memory containing the WCS object
+    # representation because there's no public API for generating only the
+    # relevant YAML subsection and an ASDF file handles the "tags" properly.
+    try:
+        af = asdf.AsdfFile({"wcs": wcs})
+    except jsonschema.exceptions.ValidationError as err:
+        # (The original traceback also gets printed here)
+        raise TypeError(
+            f"Cannot serialize model(s) for 'WCS' extension " f"{extver or ''}"
+        ) from err
+
+    # ASDF can only dump YAML to a binary file object, so do that and read
+    # the contents back from it for storage in a FITS extension:
+    with BytesIO() as fd:
+        with af:
+            # Generate the YAML, dumping any binary arrays as text:
+            af.write_to(fd, all_array_storage="inline")
+        fd.seek(0)
+        wcsbuf = fd.read()
+
+    # Convert the bytes to readable lines of text for storage (falling back to
+    # saving as binary in the unexpected event that this is not possible):
+    try:
+        wcsbuf = wcsbuf.decode("ascii").splitlines()
+
+    except UnicodeDecodeError as err:
+        # This should not happen, but if the ASDF contains binary data in
+        # spite of the 'inline' option above, we have to dump the bytes to
+        # a non-human-readable binary table rather than an ASCII one:
+        LOGGER.warning(
+            "Could not convert WCS %s ASDF to ASCII; saving table "
+            "as binary (error was %s)",
+            extver or "",
+            err,
+        )
+
+        hduclass = BinTableHDU
+        fmt = "B"
+        wcsbuf = np.frombuffer(wcsbuf, dtype=np.uint8)
+
+    else:
+        hduclass = TableHDU
+        fmt = f"A{max(len(line) for line in wcsbuf)}"
+
+    # Construct the FITS table extension:
+    col = Column(
+        name="gWCS", format=fmt, array=wcsbuf, ascii=hduclass is TableHDU
+    )
+
+    return hduclass.from_columns([col], name="WCS", ver=extver)
+
+
+def asdftablehdu_to_wcs(hdu):
+    """Recreate a gWCS object from its serialization in a FITS table extension.
+
+    Returns None (issuing a warning) if the extension cannot be parsed, so
+    the rest of the file can still be read.
+    """
+    ver = hdu.header.get("EXTVER", -1)
+
+    if isinstance(hdu, (TableHDU, BinTableHDU)):
+        try:
+            colarr = hdu.data["gWCS"]
+
+        except KeyError as err:
+            LOGGER.warning(
+                "Ignoring 'WCS' extension %s with no 'gWCS' table "
+                "column (error was %s)",
+                ver,
+                err,
+            )
+
+            return None
+
+        # If this table column contains text strings as expected, join the rows
+        # as separate lines of a string buffer and encode the resulting YAML as
+        # bytes that ASDF can parse. If AstroData has produced another format,
+        # it will be a binary dump due to the unexpected presence of non-ASCII
+        # data, in which case we just extract unmodified bytes from the table.
+        if colarr.dtype.kind in ("U", "S"):
+            sep = os.linesep
+            # Just in case io.fits ever produces 'S' on Py 3 (not the default):
+            # join lines as str & avoid a TypeError with unicode linesep; could
+            # also use astype('U') but it assumes an encoding implicitly.
+            if colarr.dtype.kind == "S" and not isinstance(sep, bytes):
+                colarr = np.char.decode(
+                    np.char.rstrip(colarr), encoding="ascii"
+                )
+            wcsbuf = sep.join(colarr).encode("ascii")
+        else:
+            wcsbuf = colarr.tobytes()
+
+        # Convert the stored text to a Bytes file object that ASDF can open:
+        with BytesIO(wcsbuf) as fd:
+            # Try to extract a 'wcs' entry from the YAML:
+            try:
+                af = asdf.open(fd)
+
+            except IOError:
+                LOGGER.warning(
+                    "Ignoring 'WCS' extension %s: failed to parse "
+                    "ASDF.\nError was as follows:\n%s",
+                    ver,
+                    traceback.format_exc(),
+                )
+
+                return None
+
+            with af:
+                try:
+                    wcs = af.tree["wcs"]
+
+                except KeyError as err:
+                    LOGGER.warning(
+                        "Ignoring 'WCS' extension %s: missing "
+                        "'wcs' dict entry. Error was %s",
+                        ver,
+                        err,
+                    )
+
+                    return None
+
+    else:
+        LOGGER.warning("Ignoring non-FITS-table 'WCS' extension %s", ver)
+
+        return None
+
+    return wcs
+
+ +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/astrodata/nddata.html b/_modules/astrodata/nddata.html new file mode 100644 index 00000000..0632ae74 --- /dev/null +++ b/_modules/astrodata/nddata.html @@ -0,0 +1,780 @@ + + + + + + + astrodata.nddata — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for astrodata.nddata

+"""This module implements a derivative class based on NDData with some Mixins,
+implementing windowing and on-the-fly data scaling.
+"""
+
+
+import warnings
+from copy import deepcopy
+from functools import reduce
+
+import numpy as np
+
+from astropy.io.fits import ImageHDU
+from astropy.modeling import Model, models
+from astropy.nddata import (
+    NDArithmeticMixin,
+    NDData,
+    NDSlicingMixin,
+    VarianceUncertainty,
+)
+from gwcs.wcs import WCS as gWCS
+from .wcs import remove_axis_from_frame
+
+INTEGER_TYPES = (int, np.integer)
+
+__all__ = ["NDAstroData"]
+
+
+class ADVarianceUncertainty(VarianceUncertainty):
+    """Subclass VarianceUncertainty to check for negative values."""
+
+    @VarianceUncertainty.array.setter
+    def array(self, value):
+        if value is not None and np.any(value < 0):
+            warnings.warn(
+                "Negative variance values found. Setting to zero.",
+                RuntimeWarning,
+            )
+            value = np.where(value >= 0.0, value, 0.0)
+        VarianceUncertainty.array.fset(self, value)
+
+
+

+[docs]
+class AstroDataMixin:
+    """A Mixin for ``NDData``-like classes (such as ``Spectrum1D``) to enable
+    them to behave similarly to ``AstroData`` objects.
+
+    These behaviors are:
+        1.  ``mask`` attributes are combined with bitwise, not logical, or,
+            since the individual bits are important.
+        2.  The WCS must be a ``gwcs.WCS`` object and slicing results in
+            the model being modified.
+        3.  There is a settable ``variance`` attribute.
+        4.  Additional attributes such as OBJMASK can be extracted from
+            the .meta['other'] dict
+    """
+
+    def __getattr__(self, attribute):
+        """Allow access to attributes stored in self.meta['other'], as we do
+        with AstroData objects.
+        """
+        if attribute.isupper():
+            try:
+                return self.meta["other"][attribute]
+
+            # Does this ever happen? If so under what circumstances?
+            except KeyError:
+                pass
+
+        raise AttributeError(
+            f"{self.__class__.__name__!r} object has no "
+            f"attribute {attribute!r}"
+        )
+
+    def _arithmetic(
+        self,
+        operation,
+        operand,
+        propagate_uncertainties=True,
+        handle_mask=np.bitwise_or,
+        handle_meta=None,
+        uncertainty_correlation=0,
+        compare_wcs="first_found",
+        **kwds,
+    ):
+        """Override the NDData method so that "bitwise_or" becomes the default
+        operation to combine masks, rather than "logical_or"
+        """
+        # TODO: What is this doing, and where?
+        return super()._arithmetic(
+            operation,
+            operand,
+            propagate_uncertainties=propagate_uncertainties,
+            handle_mask=handle_mask,
+            handle_meta=handle_meta,
+            uncertainty_correlation=uncertainty_correlation,
+            compare_wcs=compare_wcs,
+            **kwds,
+        )
+
+    def _slice_wcs(self, slices):
+        """The ``__call__()`` method of gWCS doesn't appear to conform to the
+        APE 14 interface for WCS implementations, and doesn't react to slicing
+        properly. We override NDSlicing's method to do what we want.
+        """
+        if not isinstance(self.wcs, gWCS):
+            return self.wcs
+
+        # Sanitize the slices, catching some errors early
+        if not isinstance(slices, (tuple, list)):
+            slices = (slices,)
+        slices = list(slices)
+        ndim = len(self.shape)
+        if len(slices) > ndim:
+            raise ValueError(
+                f"Too many dimensions specified in slice {slices}"
+            )
+
+        if Ellipsis in slices:
+            if slices.count(Ellipsis) > 1:
+                raise IndexError(
+                    "Only one ellipsis can be specified in a slice"
+                )
+
+            ell_index = slices.index(Ellipsis) + 1
+            slice_fill = [slice(None)] * (ndim - len(slices) + 1)
+            slices[ell_index:ell_index] = slice_fill
+
+        slices.extend([slice(None)] * (ndim - len(slices)))
+
+        mods = []
+        mapped_axes = []
+        for i, (slice_, length) in enumerate(zip(slices[::-1], self.shape)):
+            model = []
+            if isinstance(slice_, slice):
+                if slice_.step and slice_.step > 1:
+                    raise IndexError("Cannot slice with a step")
+                if slice_.start:
+                    start = (
+                        length + slice_.start
+                        if slice_.start < 1
+                        else slice_.start
+                    )
+                    if start > 0:
+                        model.append(models.Shift(start))
+                mapped_axes.append(max(mapped_axes) + 1 if mapped_axes else 0)
+            elif isinstance(slice_, INTEGER_TYPES):
+                model.append(models.Const1D(slice_))
+                mapped_axes.append(-1)
+            else:
+                raise IndexError("Slice not an integer or range")
+            if model:
+                mods.append(reduce(Model.__or__, model))
+            else:
+                # If the previous model was an Identity, we can hang this
+                # one onto that without needing to append a new Identity
+                if i > 0 and isinstance(mods[-1], models.Identity):
+                    mods[-1] = models.Identity(mods[-1].n_inputs + 1)
+                else:
+                    mods.append(models.Identity(1))
+
+        slicing_model = reduce(Model.__and__, mods)
+        if mapped_axes != list(np.arange(ndim)):
+            slicing_model = (
+                models.Mapping(tuple(max(ax, 0) for ax in mapped_axes))
+                | slicing_model
+            )
+            slicing_model.inverse = models.Mapping(
+                tuple(ax for ax in mapped_axes if ax != -1), n_inputs=ndim
+            )
+
+        if (
+            isinstance(slicing_model, models.Identity)
+            and slicing_model.n_inputs == ndim
+        ):
+            return self.wcs  # Unchanged!
+        new_wcs = deepcopy(self.wcs)
+        input_frame = new_wcs.input_frame
+        for axis, mapped_axis in reversed(list(enumerate(mapped_axes))):
+            if mapped_axis == -1:
+                input_frame = remove_axis_from_frame(input_frame, axis)
+        new_wcs.pipeline[0].frame = input_frame
+        new_wcs.insert_transform(
+            new_wcs.input_frame, slicing_model, after=True
+        )
+        return new_wcs
+
+    @property
+    def variance(self):
+        """A convenience property to access the contents of ``uncertainty``."""
+        return getattr(self.uncertainty, "array", None)
+
+    @variance.setter
+    def variance(self, value):
+        self.uncertainty = (
+            ADVarianceUncertainty(value) if value is not None else None
+        )
+
+    @property
+    def wcs(self):
+        """The WCS of the data. This is a gWCS object, not a FITS WCS object.
+
+        This is returning wcs from an inhertited class, see NDData.wcs for more
+        details.
+        """
+        return super().wcs
+
+    @wcs.setter
+    def wcs(self, value):
+        if value is not None and not isinstance(value, gWCS):
+            raise TypeError("wcs value must be None or a gWCS object")
+        self._wcs = value
+
+    @property
+    def shape(self):
+        """The shape of the data."""
+        return self._data.shape
+
+    @property
+    def size(self):
+        """The size of the data."""
+        return self._data.size

+
+
+
+class FakeArray:
+    """A class that pretends to be an array, but is actually a lazy-loaded"""
+
+    def __init__(self, very_faked):
+        self.data = very_faked
+        self.shape = (100, 100)  # Won't matter. This is just to fool NDData
+        self.dtype = np.float32  # Same here
+
+    def __getitem__(self, index):
+        return None
+
+    def __array__(self):
+        return self.data
+
+
+class NDWindowing:
+    """A class to allow "windowed" access to some properties of an
+    ``NDAstroData`` instance. In particular, ``data``, ``uncertainty``,
+    ``variance``, and ``mask`` return clipped data.
+    """
+
+    def __init__(self, target):
+        self._target = target
+
+    def __getitem__(self, window_slice):
+        return NDWindowingAstroData(self._target, window=window_slice)
+
+
+class NDWindowingAstroData(
+    AstroDataMixin, NDArithmeticMixin, NDSlicingMixin, NDData
+):
+    """Allows "windowed" access to some properties of an ``NDAstroData``
+    instance.  In particular, ``data``, ``uncertainty``, ``variance``, and
+    ``mask`` return clipped data.
+    """
+
+    # pylint: disable=super-init-not-called
+    def __init__(self, target, window):
+        self._target = target
+        self._window = window
+
+        # TODO: __init__ exists in parent classes, but we don't call it.
+        #       Is this a problem?
+
+    def __getattr__(self, attribute):
+        """Allow access to attributes stored in self.meta['other'], as we do
+        with AstroData objects.
+        """
+        if attribute.isupper():
+            try:
+                return self._target._get_simple(
+                    attribute, section=self._window
+                )
+            except KeyError:
+                pass
+        raise AttributeError(
+            f"{self.__class__.__name__!r} object has no "
+            f"attribute {attribute!r}"
+        )
+
+    @property
+    def unit(self):
+        return self._target.unit
+
+    @property
+    def wcs(self):
+        # TODO: Accessing protected member from _target
+        # pylint: disable=protected-access
+        return self._target._slice_wcs(self._window)
+
+    @property
+    def data(self):
+        # TODO: Accessing protected member from _target
+        # pylint: disable=protected-access
+        return self._target._get_simple("_data", section=self._window)
+
+    @property
+    def uncertainty(self):
+        # TODO: Accessing protected member from _target
+        # pylint: disable=protected-access
+        return self._target._get_uncertainty(section=self._window)
+
+    @property
+    def variance(self):
+        if self.uncertainty is not None:
+            return self.uncertainty.array
+
+        return None
+
+    @property
+    def mask(self):
+        # TODO: Accessing protected member from _target
+        # pylint: disable=protected-access
+        return self._target._get_simple("_mask", section=self._window)
+
+
+def is_lazy(item):
+    """Returns True if the item is a lazy-loaded object, False otherwise."""
+    return isinstance(item, ImageHDU) or getattr(item, "lazy", False)
+
+
+

+[docs]
+class NDAstroData(AstroDataMixin, NDArithmeticMixin, NDSlicingMixin, NDData):
+    """Implements ``NDData`` with all Mixins, plus some ``AstroData``
+    specifics.
+
+    This class implements an ``NDData``-like container that supports reading
+    and writing as implemented in the ``astropy.io.registry`` and also slicing
+    (indexing) and simple arithmetics (add, subtract, divide and multiply).
+
+    A very important difference between ``NDAstroData`` and ``NDData`` is that
+    the former attempts to load all its data lazily. There are also some
+    important differences in the interface (eg. ``.data`` lets you reset its
+    contents after initialization).
+
+    Documentation is provided where our class differs.
+
+    See also
+    --------
+    NDData
+    NDArithmeticMixin
+    NDSlicingMixin
+
+    Examples
+    --------
+
+    The mixins allow operation that are not possible with ``NDData`` or
+    ``NDDataBase``, i.e. simple arithmetics::
+
+        >>> from astropy.nddata import StdDevUncertainty
+        >>> import numpy as np
+        >>> data = np.ones((3,3), dtype=float)
+        >>> ndd1 = NDAstroData(data, uncertainty=StdDevUncertainty(data))
+        >>> ndd2 = NDAstroData(data, uncertainty=StdDevUncertainty(data))
+        >>> ndd3 = ndd1.add(ndd2)
+        >>> ndd3.data
+        array([[2., 2., 2.],
+            [2., 2., 2.],
+            [2., 2., 2.]])
+        >>> ndd3.uncertainty.array
+        array([[1.41421356, 1.41421356, 1.41421356],
+            [1.41421356, 1.41421356, 1.41421356],
+            [1.41421356, 1.41421356, 1.41421356]])
+
+    see ``NDArithmeticMixin`` for a complete list of all supported arithmetic
+    operations.
+
+    But also slicing (indexing) is possible::
+
+        >>> ndd4 = ndd3[1,:]
+        >>> ndd4.data
+        array([2., 2., 2.])
+        >>> ndd4.uncertainty.array
+        array([1.41421356, 1.41421356, 1.41421356])
+
+    See ``NDSlicingMixin`` for a description how slicing works (which
+    attributes) are sliced.
+    """
+
+    def __init__(
+        self,
+        data,
+        uncertainty=None,
+        mask=None,
+        wcs=None,
+        meta=None,
+        unit=None,
+        copy=False,
+        variance=None,
+    ):
+        """Initialize an ``NDAstroData`` instance.
+
+        Parameters
+        ----------
+        data : array-like
+            The actual data. This can be a numpy array, a memmap, or a
+            ``fits.ImageHDU`` object.
+
+        uncertainty : ``NDUncertainty``-like object, optional
+            An object that represents the uncertainty of the data. If not
+            specified, the uncertainty will be set to None.
+
+        mask : array-like, optional
+            An array that represents the mask of the data. If not specified,
+            the mask will be set to None.
+
+        wcs : ``gwcs.WCS`` object, optional
+            The WCS of the data. If not specified, the WCS will be set to None.
+
+        meta : dict-like, optional
+            A dictionary-like object that holds the meta data. If not
+            specified, the meta data will be set to None.
+
+        unit : ``astropy.units.Unit`` object, optional
+            The unit of the data. If not specified, the unit will be set to
+            None.
+
+        copy : bool, optional
+            If True, the data, uncertainty, mask, wcs, meta, and unit will be
+            copied. Otherwise, they will be referenced. Default is False.
+
+        variance : array-like, optional
+            An array that represents the variance of the data. If not
+            specified, the variance will be set to None.
+
+        Raises
+        ------
+        ValueError
+            If ``uncertainty`` and ``variance`` are both specified.
+
+        Notes
+        -----
+        The ``uncertainty`` and ``variance`` parameters are mutually exclusive.
+        """
+        if variance is not None:
+            if uncertainty is not None:
+                raise ValueError(
+                    f"Cannot specify both uncertainty and variance"
+                    f"({uncertainty = }, {variance = })."
+                )
+
+            uncertainty = ADVarianceUncertainty(variance)
+
+        super().__init__(
+            FakeArray(data) if is_lazy(data) else data,
+            None if is_lazy(uncertainty) else uncertainty,
+            mask,
+            wcs,
+            meta,
+            unit,
+            copy,
+        )
+
+        if is_lazy(data):
+            self.data = data
+        if is_lazy(uncertainty):
+            self.uncertainty = uncertainty
+
+    def __deepcopy__(self, memo):
+        new = self.__class__(
+            self._data if is_lazy(self._data) else deepcopy(self.data, memo),
+            self._uncertainty if is_lazy(self._uncertainty) else None,
+            self._mask if is_lazy(self._mask) else deepcopy(self.mask, memo),
+            deepcopy(self.wcs, memo),
+            None,
+            self.unit,
+        )
+        new.meta = deepcopy(self.meta, memo)
+        # Needed to avoid recursion because of uncertainty's weakref to self
+        if not is_lazy(self._uncertainty):
+            new.variance = deepcopy(self.variance)
+        return new
+
+    @property
+    def window(self):
+        """Interface to access a section of the data, using lazy access
+        whenever possible.
+
+        Returns
+        --------
+        An instance of ``NDWindowing``, which provides ``__getitem__``,
+        to allow the use of square brackets when specifying the window.
+        Ultimately, an ``NDWindowingAstrodata`` instance is returned.
+
+        Examples
+        ---------
+
+        >>> ad[0].nddata.window[100:200, 100:200]  # doctest: +SKIP
+        <NDWindowingAstrodata .....>
+        """
+        return NDWindowing(self)
+
+    def _get_uncertainty(self, section=None):
+        """Return the ADVarianceUncertainty object, or a slice of it."""
+        if self._uncertainty is not None:
+            if is_lazy(self._uncertainty):
+                if section is None:
+                    self.uncertainty = ADVarianceUncertainty(
+                        self._uncertainty.data
+                    )
+                    return self.uncertainty
+
+                return ADVarianceUncertainty(self._uncertainty[section])
+
+            if section is not None:
+                return self._uncertainty[section]
+
+            return self._uncertainty
+
+        return None
+
+    def _get_simple(self, target, section=None):
+        """Only use 'section' for image-like objects that have the same shape
+        as the NDAstroData object; otherwise, return the whole object"""
+        source = getattr(self, target)
+        if source is not None:
+            if is_lazy(source):
+                if section is None:
+                    ret = np.empty(source.shape, dtype=source.dtype)
+                    ret[:] = source.data
+                    setattr(self, target, ret)
+
+                else:
+                    ret = source[section]
+
+                return ret
+
+            if hasattr(source, "shape"):
+                if section is None or source.shape != self.shape:
+                    return np.array(source, copy=False)
+
+                return np.array(source, copy=False)[section]
+
+            return source
+
+        return None
+
+    @property
+    def data(self):
+        """An array representing the raw data stored in this instance. It
+        implements a setter.
+        """
+        return self._get_simple("_data")
+
+    @data.setter
+    def data(self, value):
+        if value is None:
+            raise ValueError(f"Cannot set data to {value}.")
+
+        if is_lazy(value):
+            self.meta["header"] = value.header
+
+        self._data = value
+
+    @property
+    def uncertainty(self):
+        return self._get_uncertainty()
+
+    @uncertainty.setter
+    def uncertainty(self, value):
+        if value is not None and not is_lazy(value):
+            # TODO: Accessing protected member from value
+            # pylint: disable=protected-access
+            if value._parent_nddata is not None:
+                value = value.__class__(value, copy=False)
+
+            value.parent_nddata = self
+
+        self._uncertainty = value
+
+    @property
+    def mask(self):
+        """Get or set the mask of the data."""
+        return self._get_simple("_mask")
+
+    @mask.setter
+    def mask(self, value):
+        self._mask = value
+
+    @property
+    def variance(self):
+        """A convenience property to access the contents of ``uncertainty``,
+        squared (as the uncertainty data is stored as standard deviation).
+        """
+        # TODO: Is this supposed to be squared?
+        arr = self._get_uncertainty()
+
+        if arr is not None:
+            return arr.array
+
+        return arr
+
+    @variance.setter
+    def variance(self, value):
+        self.uncertainty = (
+            ADVarianceUncertainty(value) if value is not None else None
+        )
+
+

+[docs]
+    def set_section(self, section, input_data):
+        """Sets only a section of the data. This method is meant to prevent
+        fragmentation in the Python heap, by reusing the internal structures
+        instead of replacing them with new ones.
+
+        Args
+        -----
+        section : ``slice``
+            The area that will be replaced
+
+        input_data : ``NDData``-like instance
+            This object needs to implement at least ``data``, ``uncertainty``,
+            and ``mask``. Their entire contents will replace the data in the
+            area defined by ``section``.
+
+        Examples
+        ---------
+
+        >>> def setup():
+        ...     sec = NDData(np.zeros((100,100)))
+        ...     ad[0].nddata.set_section(
+        ...         (slice(None,100),slice(None,100)),
+        ...         sec
+        ...     )
+        ...
+        >>> setup()  # doctest: +SKIP
+
+        """
+        self.data[section] = input_data.data
+
+        if self.uncertainty is not None:
+            self.uncertainty.array[section] = input_data.uncertainty.array
+
+        if self.mask is not None:
+            self.mask[section] = input_data.mask

+
+
+    def __repr__(self):
+        if is_lazy(self._data):
+            return self.__class__.__name__ + "(Memmapped)"
+
+        return super().__repr__()
+
+    # This is a common idiom in numpy, so keep the name.
+    # pylint: disable=invalid-name
+    @property
+    def T(self):
+        """Transpose the data. This is not a copy of the data."""
+        return self.transpose()
+
+

+[docs]
+    def transpose(self):
+        """Transpose the data. This is not a copy of the data."""
+        unc = self.uncertainty
+        new_wcs = deepcopy(self.wcs)
+        inframe = new_wcs.input_frame
+        new_wcs.insert_transform(
+            inframe,
+            models.Mapping(tuple(reversed(range(inframe.naxes)))),
+            after=True,
+        )
+        return self.__class__(
+            self.data.T,
+            uncertainty=None if unc is None else unc.__class__(unc.array.T),
+            mask=None if self.mask is None else self.mask.T,
+            wcs=new_wcs,
+            copy=False,
+        )

+

+
+
+ +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/astrodata/utils.html b/_modules/astrodata/utils.html new file mode 100644 index 00000000..ee52b0be --- /dev/null +++ b/_modules/astrodata/utils.html @@ -0,0 +1,608 @@ + + + + + + + astrodata.utils — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for astrodata.utils

+"""Utility functions and classes for AstroData objects."""
+import inspect
+import logging
+import warnings
+from collections import namedtuple
+from functools import wraps
+from traceback import format_stack
+
+import numpy as np
+
+INTEGER_TYPES = (int, np.integer)
+
+__all__ = (
+    "assign_only_single_slice",
+    "astro_data_descriptor",
+    "AstroDataDeprecationWarning",
+    "astro_data_tag",
+    "deprecated",
+    "normalize_indices",
+    "returns_list",
+    "TagSet",
+    "Section",
+)
+
+
+class AstroDataDeprecationWarning(DeprecationWarning):
+    """Warning class for deprecated AstroData methods."""
+
+
+warnings.simplefilter("always", AstroDataDeprecationWarning)
+
+
+def deprecated(reason):
+    """Marks a function as deprecated.
+
+    Parameters
+    ----------
+    reason : str
+        The reason why the function is deprecated
+
+    Returns
+    -------
+    function
+        The decorated function
+
+    Usage
+    -----
+
+    >>> @deprecated("Use another function instead")
+    ... def my_function():
+    ...     pass
+    """
+
+    def decorator_wrapper(fn):
+        @wraps(fn)
+        def wrapper(*args, **kw):
+            current_source = "|".join(format_stack(inspect.currentframe()))
+            if current_source not in wrapper.seen:
+                wrapper.seen.add(current_source)
+                warnings.warn(reason, AstroDataDeprecationWarning)
+            return fn(*args, **kw)
+
+        wrapper.seen = set()
+        return wrapper
+
+    return decorator_wrapper
+
+
+def normalize_indices(slc, nitems):
+    """Normalize a slice or index to a list of indices."""
+    multiple = True
+    if isinstance(slc, slice):
+        start, stop, step = slc.indices(nitems)
+        indices = list(range(start, stop, step))
+    elif isinstance(slc, INTEGER_TYPES) or (
+        isinstance(slc, tuple)
+        and all(isinstance(i, INTEGER_TYPES) for i in slc)
+    ):
+        if isinstance(slc, INTEGER_TYPES):
+            slc = (int(slc),)  # slc's type m
+            multiple = False
+
+        else:
+            multiple = True
+
+        # Normalize negative indices...
+        indices = [(x if x >= 0 else nitems + x) for x in slc]
+
+    else:
+        raise ValueError(f"Invalid index: {slc}")
+
+    if any(i >= nitems for i in indices):
+        raise IndexError("Index out of range")
+
+    return indices, multiple
+
+
+

+[docs]
+class TagSet(namedtuple("TagSet", "add remove blocked_by blocks if_present")):
+    """Named tuple that is used by tag methods to return which actions should
+    be performed on a tag set.
+
+    All the attributes are optional, and any combination of them can be used,
+    allowing to create complex tag structures.  Read the documentation on the
+    tag-generating algorithm if you want to better understand the interactions.
+
+    The simplest TagSet, though, tends to just add tags to the global set.
+
+    It can be initialized by position, like any other tuple (the order of the
+    arguments is the one in which the attributes are listed below). It can
+    also be initialized by name.
+
+    Attributes
+    ----------
+    add : set of str, optional
+        Tags to be added to the global set
+
+    remove : set of str, optional
+        Tags to be removed from the global set
+
+    blocked_by : set of str, optional
+        Tags that will prevent this TagSet from being applied
+
+    blocks : set of str, optional
+        Other TagSets containing these won't be applied
+
+    if_present : set of str, optional
+        This TagSet will be applied only *all* of these tags are present
+
+    Examples
+    ---------
+    >>> TagSet()  # doctest: +SKIP
+    TagSet(
+        add=set(),
+        remove=set(),
+        blocked_by=set(),
+        blocks=set(),
+        if_present=set()
+    )
+    >>> TagSet({'BIAS', 'CAL'})  # doctest: +SKIP
+    TagSet(
+        add={'BIAS', 'CAL'},
+        remove=set(),
+        blocked_by=set(),
+        blocks=set(),
+        if_present=set()
+    )
+    >>> TagSet(remove={'BIAS', 'CAL'}) # doctest: +SKIP
+    TagSet(
+        add=set(),
+        remove={'BIAS', 'CAL'},
+        blocked_by=set(),
+        blocks=set(),
+        if_present=set()
+    )
+    """
+
+    def __new__(
+        cls,
+        add=None,
+        remove=None,
+        blocked_by=None,
+        blocks=None,
+        if_present=None,
+    ):
+        return super().__new__(
+            cls,
+            add or set(),
+            remove or set(),
+            blocked_by or set(),
+            blocks or set(),
+            if_present or set(),
+        )

+
+
+
+

+[docs]
+def astro_data_descriptor(fn):
+    """Decorator that will mark a class method as an AstroData descriptor.
+    Useful to produce list of descriptors, for example.
+
+    If used in combination with other decorators, this one *must* be the
+    one on the top (ie. the last one applying). It doesn't modify the
+    method in any other way.
+
+    Args
+    -----
+    fn : method
+        The method to be decorated
+
+    Returns
+    --------
+    The tagged method (not a wrapper)
+    """
+    fn.descriptor_method = True
+    return fn

+
+
+
+

+[docs]
+def returns_list(fn):
+    """Decorator to ensure that descriptors that should return a list (of one
+    value per extension) only returns single values when operating on single
+    slices; and vice versa.
+
+    This is a common case, and you can use the decorator to simplify the
+    logic of your descriptors.
+
+    Args
+    -----
+    fn : method
+        The method to be decorated
+
+    Returns
+    --------
+    A function
+    """
+
+    @wraps(fn)
+    def wrapper(self, *args, **kwargs):
+        ret = fn(self, *args, **kwargs)
+        if self.is_single:
+            if isinstance(ret, list):
+                if len(ret) > 1:
+                    logging.warning(
+                        "Descriptor %s returned a list "
+                        "of %s elements when operating on "
+                        "a single slice",
+                        fn.__name__,
+                        len(ret),
+                    )
+
+                return ret[0]
+
+            return ret
+
+        if isinstance(ret, list):
+            if len(ret) == len(self):
+                return ret
+
+            raise IndexError(
+                f"Incompatible numbers of extensions and "
+                f"elements in {fn.__name__}"
+            )
+
+        return [ret] * len(self)
+
+    return wrapper

+
+
+
+def assign_only_single_slice(fn):
+    """Raise `ValueError` if assigning to a non-single slice."""
+
+    @wraps(fn)
+    def wrapper(self, *args, **kwargs):
+        if not self.is_single:
+            raise ValueError(
+                "Trying to assign to an AstroData object that "
+                "is not a single slice"
+            )
+        return fn(self, *args, **kwargs)
+
+    return wrapper
+
+
+

+[docs]
+def astro_data_tag(fn):
+    """Decorator that marks methods of an `AstroData` derived class as part of
+    the tag-producing system.
+
+    It wraps the method around a function that will ensure a consistent return
+    value: the wrapped method can return any sequence of sequences of strings,
+    and they will be converted to a TagSet. If the wrapped method
+    returns None, it will be turned into an empty TagSet.
+
+    Args
+    -----
+    fn : method
+        The method to be decorated
+
+    Returns
+    --------
+    A wrapper function
+    """
+
+    @wraps(fn)
+    def wrapper(self):
+        try:
+            ret = fn(self)
+            if ret is not None:
+                if not isinstance(ret, TagSet):
+                    raise TypeError(
+                        f"Tag function {fn.__name__} didn't return a TagSet"
+                    )
+
+                return TagSet(*tuple(set(s) for s in ret))
+
+        except KeyError:
+            pass
+
+        # Return empty TagSet for the "doesn't apply" case
+        return TagSet()
+
+    wrapper.tag_method = True
+    return wrapper

+
+
+
+

+[docs]
+class Section(tuple):
+    """A class to handle n-dimensional sections"""
+
+    def __new__(cls, *args, **kwargs):
+        # Ensure that the order of keys is what we want
+        axis_names = [x for axis in "xyzuvw" for x in (f"{axis}1", f"{axis}2")]
+
+        _dict = dict(zip(axis_names, args + ("",) * len(kwargs)))
+
+        _dict.update(kwargs)
+
+        if list(_dict.values()).count("") or (len(_dict) % 2):
+            raise ValueError("Cannot initialize 'Section' object")
+
+        instance = tuple.__new__(cls, tuple(_dict.values()))
+        instance._axis_names = tuple(_dict.keys())
+
+        if not all(np.diff(instance)[::2] > 0):
+            raise ValueError(
+                "Not all 'Section' end coordinates exceed the "
+                "start coordinates"
+            )
+
+        return instance
+
+    @property
+    def axis_dict(self):
+        return dict(zip(self._axis_names, self))
+
+    def __getnewargs__(self):
+        return tuple(self)
+
+    def __getattr__(self, attr):
+        if attr in self._axis_names:
+            return self.axis_dict[attr]
+
+        raise AttributeError(f"No such attribute '{attr}'")
+
+    def __repr__(self):
+        return (
+            "Section("
+            + ", ".join([f"{k}={self.axis_dict[k]}" for k in self._axis_names])
+            + ")"
+        )
+
+    @property
+    def ndim(self):
+        """The number of dimensions in the section."""
+        return len(self) // 2
+
+

+[docs]
+    @staticmethod
+    def from_shape(value):
+        """Produce a Section object defining a given shape."""
+        return Section(*[y for x in reversed(value) for y in (0, x)])

+
+
+

+[docs]
+    @staticmethod
+    def from_string(value):
+        """The inverse of __str__, produce a Section object from a string."""
+        return Section(
+            *[
+                y
+                for x in value.strip("[]").split(",")
+                for start, end in [x.split(":")]
+                for y in (
+                    None if start == "" else int(start) - 1,
+                    None if end == "" else int(end),
+                )
+            ]
+        )

+
+
+

+[docs]
+    @deprecated(
+        "Renamed to 'as_iraf_section', this is just an alias for now "
+        "and will be removed in a future version."
+    )
+    def asIRAFsection(self):  # pylint: disable=invalid-name
+        """Deprecated, see as_iraf_section"""
+        return self.as_iraf_section()

+
+
+

+[docs]
+    def as_iraf_section(self):
+        """Produce string of style '[x1:x2,y1:y2]' that is 1-indexed
+        and end-inclusive
+        """
+        return (
+            "["
+            + ",".join(
+                [
+                    ":".join(
+                        [
+                            str(self.axis_dict[axis] + 1),
+                            str(self.axis_dict[axis.replace("1", "2")]),
+                        ]
+                    )
+                    for axis in self._axis_names[::2]
+                ]
+            )
+            + "]"
+        )

+
+
+

+[docs]
+    def asslice(self, add_dims=0):
+        """Return the Section object as a slice/list of slices.  Higher
+        dimensionality can be achieved with the add_dims parameter.
+        """
+        return (slice(None),) * add_dims + tuple(
+            slice(self.axis_dict[axis], self.axis_dict[axis.replace("1", "2")])
+            for axis in reversed(self._axis_names[::2])
+        )

+
+
+

+[docs]
+    def contains(self, section):
+        """Return True if the supplied section is entirely within self"""
+        if self.ndim != section.ndim:
+            raise ValueError("Sections have different dimensionality")
+
+        con1 = all(s2 >= s1 for s1, s2 in zip(self[::2], section[::2]))
+
+        if not con1:
+            return False
+
+        con2 = all(s2 <= s1 for s1, s2 in zip(self[1::2], section[1::2]))
+
+        return con1 and con2

+
+
+

+[docs]
+    def is_same_size(self, section):
+        """Return True if the Sections are the same size"""
+        return np.array_equal(np.diff(self)[::2], np.diff(section)[::2])

+
+
+

+[docs]
+    def overlap(self, section):
+        """Determine whether the two sections overlap. If so, the Section
+        common to both is returned, otherwise None
+        """
+        if self.ndim != section.ndim:
+            raise ValueError("Sections have different dimensionality")
+
+        mins = [max(s1, s2) for s1, s2 in zip(self[::2], section[::2])]
+        maxs = [min(s1, s2) for s1, s2 in zip(self[1::2], section[1::2])]
+
+        try:
+            return self.__class__(
+                *[v for pair in zip(mins, maxs) for v in pair]
+            )
+
+        except ValueError as err:
+            logging.warning(
+                "Sections do not overlap, recieved %s: %s",
+                err.__class__.__name__,
+                err,
+            )
+
+            return None

+
+
+

+[docs]
+    def shift(self, *shifts):
+        """Shift a section in each direction by the specified amount"""
+        if len(shifts) != self.ndim:
+            raise ValueError(
+                f"Number of shifts {len(shifts)} incompatible "
+                f"with dimensionality {self.ndim}"
+            )
+        return self.__class__(
+            *[
+                x + s
+                for x, s in zip(self, [ss for s in shifts for ss in [s] * 2])
+            ]
+        )

+

+
+
+ +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_modules/index.html b/_modules/index.html new file mode 100644 index 00000000..4f07ef11 --- /dev/null +++ b/_modules/index.html @@ -0,0 +1,106 @@ + + + + + + + Overview: module code — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

All modules for which code is available

+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/_sources/api/astrodata.AstroData.rst.txt b/_sources/api/astrodata.AstroData.rst.txt new file mode 100644 index 00000000..24707c0c --- /dev/null +++ b/_sources/api/astrodata.AstroData.rst.txt @@ -0,0 +1,101 @@ +AstroData +========= + +.. currentmodule:: astrodata + +.. autoclass:: AstroData + :show-inheritance: + + .. rubric:: Attributes Summary + + .. autosummary:: + + ~AstroData.data + ~AstroData.descriptors + ~AstroData.exposed + ~AstroData.ext_tables + ~AstroData.filename + ~AstroData.hdr + ~AstroData.header + ~AstroData.id + ~AstroData.indices + ~AstroData.is_sliced + ~AstroData.mask + ~AstroData.nddata + ~AstroData.orig_filename + ~AstroData.path + ~AstroData.phu + ~AstroData.shape + ~AstroData.tables + ~AstroData.tags + ~AstroData.uncertainty + ~AstroData.variance + ~AstroData.wcs + + .. rubric:: Methods Summary + + .. autosummary:: + + ~AstroData.add + ~AstroData.append + ~AstroData.crop + ~AstroData.divide + ~AstroData.info + ~AstroData.instrument + ~AstroData.is_settable + ~AstroData.load + ~AstroData.multiply + ~AstroData.object + ~AstroData.operate + ~AstroData.read + ~AstroData.reset + ~AstroData.subtract + ~AstroData.table + ~AstroData.telescope + ~AstroData.update_filename + ~AstroData.write + + .. rubric:: Attributes Documentation + + .. autoattribute:: data + .. autoattribute:: descriptors + .. autoattribute:: exposed + .. autoattribute:: ext_tables + .. autoattribute:: filename + .. autoattribute:: hdr + .. autoattribute:: header + .. autoattribute:: id + .. autoattribute:: indices + .. autoattribute:: is_sliced + .. autoattribute:: mask + .. autoattribute:: nddata + .. autoattribute:: orig_filename + .. autoattribute:: path + .. autoattribute:: phu + .. autoattribute:: shape + .. autoattribute:: tables + .. autoattribute:: tags + .. autoattribute:: uncertainty + .. autoattribute:: variance + .. autoattribute:: wcs + + .. rubric:: Methods Documentation + + .. automethod:: add + .. automethod:: append + .. automethod:: crop + .. automethod:: divide + .. automethod:: info + .. automethod:: instrument + .. automethod:: is_settable + .. automethod:: load + .. automethod:: multiply + .. automethod:: object + .. automethod:: operate + .. automethod:: read + .. automethod:: reset + .. automethod:: subtract + .. automethod:: table + .. automethod:: telescope + .. automethod:: update_filename + .. automethod:: write diff --git a/_sources/api/astrodata.AstroDataError.rst.txt b/_sources/api/astrodata.AstroDataError.rst.txt new file mode 100644 index 00000000..b93c97b1 --- /dev/null +++ b/_sources/api/astrodata.AstroDataError.rst.txt @@ -0,0 +1,6 @@ +AstroDataError +============== + +.. currentmodule:: astrodata + +.. autoexception:: AstroDataError diff --git a/_sources/api/astrodata.AstroDataMixin.rst.txt b/_sources/api/astrodata.AstroDataMixin.rst.txt new file mode 100644 index 00000000..a9892094 --- /dev/null +++ b/_sources/api/astrodata.AstroDataMixin.rst.txt @@ -0,0 +1,23 @@ +AstroDataMixin +============== + +.. currentmodule:: astrodata + +.. autoclass:: AstroDataMixin + :show-inheritance: + + .. rubric:: Attributes Summary + + .. autosummary:: + + ~AstroDataMixin.shape + ~AstroDataMixin.size + ~AstroDataMixin.variance + ~AstroDataMixin.wcs + + .. rubric:: Attributes Documentation + + .. autoattribute:: shape + .. autoattribute:: size + .. autoattribute:: variance + .. autoattribute:: wcs diff --git a/_sources/api/astrodata.NDAstroData.rst.txt b/_sources/api/astrodata.NDAstroData.rst.txt new file mode 100644 index 00000000..65784550 --- /dev/null +++ b/_sources/api/astrodata.NDAstroData.rst.txt @@ -0,0 +1,39 @@ +NDAstroData +=========== + +.. currentmodule:: astrodata + +.. autoclass:: NDAstroData + :show-inheritance: + + .. rubric:: Attributes Summary + + .. autosummary:: + + ~NDAstroData.T + ~NDAstroData.data + ~NDAstroData.mask + ~NDAstroData.uncertainty + ~NDAstroData.variance + ~NDAstroData.window + + .. rubric:: Methods Summary + + .. autosummary:: + + ~NDAstroData.set_section + ~NDAstroData.transpose + + .. rubric:: Attributes Documentation + + .. autoattribute:: T + .. autoattribute:: data + .. autoattribute:: mask + .. autoattribute:: uncertainty + .. autoattribute:: variance + .. autoattribute:: window + + .. rubric:: Methods Documentation + + .. automethod:: set_section + .. automethod:: transpose diff --git a/_sources/api/astrodata.Section.rst.txt b/_sources/api/astrodata.Section.rst.txt new file mode 100644 index 00000000..1fa174a9 --- /dev/null +++ b/_sources/api/astrodata.Section.rst.txt @@ -0,0 +1,45 @@ +Section +======= + +.. currentmodule:: astrodata + +.. autoclass:: Section + :show-inheritance: + + .. rubric:: Attributes Summary + + .. autosummary:: + + ~Section.axis_dict + ~Section.ndim + + .. rubric:: Methods Summary + + .. autosummary:: + + ~Section.asIRAFsection + ~Section.as_iraf_section + ~Section.asslice + ~Section.contains + ~Section.from_shape + ~Section.from_string + ~Section.is_same_size + ~Section.overlap + ~Section.shift + + .. rubric:: Attributes Documentation + + .. autoattribute:: axis_dict + .. autoattribute:: ndim + + .. rubric:: Methods Documentation + + .. automethod:: asIRAFsection + .. automethod:: as_iraf_section + .. automethod:: asslice + .. automethod:: contains + .. automethod:: from_shape + .. automethod:: from_string + .. automethod:: is_same_size + .. automethod:: overlap + .. automethod:: shift diff --git a/_sources/api/astrodata.TagSet.rst.txt b/_sources/api/astrodata.TagSet.rst.txt new file mode 100644 index 00000000..2d6191cd --- /dev/null +++ b/_sources/api/astrodata.TagSet.rst.txt @@ -0,0 +1,7 @@ +TagSet +====== + +.. currentmodule:: astrodata + +.. autoclass:: TagSet + :show-inheritance: diff --git a/_sources/api/astrodata.add_header_to_table.rst.txt b/_sources/api/astrodata.add_header_to_table.rst.txt new file mode 100644 index 00000000..920b0cae --- /dev/null +++ b/_sources/api/astrodata.add_header_to_table.rst.txt @@ -0,0 +1,6 @@ +add_header_to_table +=================== + +.. currentmodule:: astrodata + +.. autofunction:: add_header_to_table diff --git a/_sources/api/astrodata.astro_data_descriptor.rst.txt b/_sources/api/astrodata.astro_data_descriptor.rst.txt new file mode 100644 index 00000000..80688c15 --- /dev/null +++ b/_sources/api/astrodata.astro_data_descriptor.rst.txt @@ -0,0 +1,6 @@ +astro_data_descriptor +===================== + +.. currentmodule:: astrodata + +.. autofunction:: astro_data_descriptor diff --git a/_sources/api/astrodata.astro_data_tag.rst.txt b/_sources/api/astrodata.astro_data_tag.rst.txt new file mode 100644 index 00000000..1f224c4d --- /dev/null +++ b/_sources/api/astrodata.astro_data_tag.rst.txt @@ -0,0 +1,6 @@ +astro_data_tag +============== + +.. currentmodule:: astrodata + +.. autofunction:: astro_data_tag diff --git a/_sources/api/astrodata.create.rst.txt b/_sources/api/astrodata.create.rst.txt new file mode 100644 index 00000000..ae5daa40 --- /dev/null +++ b/_sources/api/astrodata.create.rst.txt @@ -0,0 +1,6 @@ +create +====== + +.. currentmodule:: astrodata + +.. autofunction:: create diff --git a/_sources/api/astrodata.from_file.rst.txt b/_sources/api/astrodata.from_file.rst.txt new file mode 100644 index 00000000..fb9081fa --- /dev/null +++ b/_sources/api/astrodata.from_file.rst.txt @@ -0,0 +1,6 @@ +from_file +========= + +.. currentmodule:: astrodata + +.. autofunction:: from_file diff --git a/_sources/api/astrodata.open.rst.txt b/_sources/api/astrodata.open.rst.txt new file mode 100644 index 00000000..37110c63 --- /dev/null +++ b/_sources/api/astrodata.open.rst.txt @@ -0,0 +1,6 @@ +open +==== + +.. currentmodule:: astrodata + +.. autofunction:: open diff --git a/_sources/api/astrodata.returns_list.rst.txt b/_sources/api/astrodata.returns_list.rst.txt new file mode 100644 index 00000000..60a14c66 --- /dev/null +++ b/_sources/api/astrodata.returns_list.rst.txt @@ -0,0 +1,6 @@ +returns_list +============ + +.. currentmodule:: astrodata + +.. autofunction:: returns_list diff --git a/_sources/api/astrodata.version.rst.txt b/_sources/api/astrodata.version.rst.txt new file mode 100644 index 00000000..553303d6 --- /dev/null +++ b/_sources/api/astrodata.version.rst.txt @@ -0,0 +1,6 @@ +version +======= + +.. currentmodule:: astrodata + +.. autofunction:: version diff --git a/_sources/api_short.rst.txt b/_sources/api_short.rst.txt new file mode 100644 index 00000000..e19dc12a --- /dev/null +++ b/_sources/api_short.rst.txt @@ -0,0 +1,13 @@ +Common API for Users +==================== + +This package contains the common API for users of the AstroData system. It +provides a single interface to the data, regardless of the format in which it +is stored. + +If you would like a more extensive description of the astrodata package as a +user, see the |UserGuide|. If you are interested in developing with AstroData, +see the |DeveloperGuide|. + +.. automodapi:: astrodata + :no-inheritance-diagram: diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..1fcf4a3d --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,19 @@ +astrodata Documentation +----------------------- + +This is the documentation for astrodata. + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + manuals/index + api_short + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/_sources/manuals/appendix_descriptors.rst.txt b/_sources/manuals/appendix_descriptors.rst.txt new file mode 100644 index 00000000..b808fd8a --- /dev/null +++ b/_sources/manuals/appendix_descriptors.rst.txt @@ -0,0 +1,246 @@ +.. descriptors.rst + +*********************************** +List of Gemini Standard Descriptors +*********************************** + +To run and re-use Gemini primitives and functions this list of Standard +Descriptors must be defined for input data. This also applies to data +that is to be served by the Gemini Observatory Archive (GOA). + +For any ``AstroData`` objects, to get the list of the descriptors that are +defined use the ``AstroData.descriptors`` attribute:: + + >> import astrodata + >> import gemini_instruments + >> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >> ad.descriptors + ('airmass', 'amp_read_area', 'ao_seeing', ..., 'well_depth_setting') + +To get the values:: + + >> ad.airmass() + + >> for descriptor in ad.descriptors: + ... print(descriptor, getattr(ad, descriptor)()) + +Note that not all of the descriptors below are defined for all of the +instruments. For example, ``shuffle_pixels`` is defined only for GMOS data +since only GMOS offers a Nod & Shuffle mode. + + +.. tabularcolumns:: |l|p{3.0in}|l| + + ++--------------------------------+----------------------------------------------------------------+-----------------+ +| **Descriptor** | **Short Definition** | **Python type** | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| | | ad[0].desc() | +| | +-----------------+ +| | | ad.desc() | ++================================+================================================================+=================+ +| airmass | Airmass of the observation. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| amp_read_area | Combination of amplifier name and 1-indexed section relative | str | +| | to the detector. +-----------------+ +| | | list of str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| ao_seeing | Estimate of the natural seeing as calculated from the | float | +| | adaptive optics systems. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| array_name | Name assigned to the array generated by a given amplifier, | str | +| | one array per amplifier. +-----------------+ +| | | list of str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| array_section | Section covered by the array(s), in 0-indexed pixels, relative | Section | +| | to the detector frame (e.g. position of multiple amps read +-----------------+ +| | within a CCD). Uses ``namedtuple`` "Section" defined in | list of Section | +| | ``gemini_instruments.common``. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| azimuth | Pointing position in azimuth, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| calibration_key | Key used in the database that the ``getProcessed*`` primitives | str | +| | use to store previous calibration association information. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| camera | Name of the camera. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| cass_rotator_pa | Position angle of the Cassegrain rotator, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| central_wavelength | Central wavelength, in meters. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| coadds | Number of co-adds. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| data_label | Gemini data label. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| data_section | Section where the sky-exposed data falls, in 0-indexed pixels. | Section | +| | Uses ``namedtuple`` "Section" defined in +-----------------+ +| | ``gemini_instruments.common`` | list of Section | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| dec | Declination of the center of the field, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| decker | Name of the decker. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_name | Name assigned to the detector. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_roi_setting | Human readable Region of Interest (ROI) setting | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_rois_requested | Section defining the Regions of Interest, in 0-indexed pixels. | list of Section | +| | Uses ``namedtuple`` "Section" defined in | | +| | ``gemini_instruments.common``. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_section | Section covered by the detector(s), in 0-indexed pixels, | list | +| | relative to the whole mosaic of detectors. +-----------------+ +| | Uses ``namedtuple`` "Section" defined in | list of Section | +| | ``gemini_instruments.common``. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_x_bin | X-axis binning. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_x_offset | Telescope offset along the detector X-axis, in pixels. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_y_bin | Y-axis binning. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| detector_y_offset | Telescope offset along the detector Y-axis, in pixels. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| disperser | Name of the disperser. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| dispersion | Value for the dispersion, in meters per pixel. | float | +| | +-----------------+ +| | | list of float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| dispersion_axis | Dispersion axis. | int | +| | +-----------------+ +| | | list of int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| effective_wavelength | Wavelength representing the bandpass or the spectrum coverage. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| elevation | Pointing position in elevation, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| exposure_time | Exposure time, in seconds. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| filter_name | Name of the filter combination. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| focal_plane_mask | Name of the mask in the focal plane. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| gain | Gain in electrons per ADU | float | +| | +-----------------+ +| | | list of float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| gain_setting | Human readable gain setting (eg. low, high) | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| gcal_lamp | Returns the name of the GCAL lamp being used, or "Off" if no | str | +| | lamp is in use. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| group_id | Gemini observation group ID that identifies compatible data. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| instrument | Name of the instrument | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| is_ao | Whether or not the adaptive optics system was used. | bool | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| is_coadds_summed | Whether co-adds are summed or averaged. | bool | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| local_time | Local time. | datetime | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| lyot_stop | Name of the lyot stop. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| mdf_row_id | Mask Definition File row ID of a cut MOS or XD spectrum. | int ?? | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| nod_count | Number of nods to A and B positions. | tuple of int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| nod_offsets | Nod offsets to A and B positions, in arcseconds | tuple of float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| nominal_atmospheric_extinction | Nomimal atmospheric extinction, from model. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| nominal_photometric_zeropoint | Nominal photometric zeropoint. | float | +| | +-----------------+ +| | | list of float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| non_linear_level | Lower boundary of the non-linear regime. | float | +| | +-----------------+ +| | | list of int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| object | Name of the target (as entered by the user). | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| observation_class | Gemini class name for the observation | str | +| | (eg. 'science', 'acq', 'dayCal'). | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| observation_epoch | Observation epoch. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| observation_id | Gemini observation ID. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| observation_type | Gemini observation type (eg. 'OBJECT', 'FLAT', 'ARC'). | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| overscan_section | Section where the overscan data falls, in 0-indexed pixels. | Section | +| | Uses namedtuple "Section" defined in +-----------------+ +| | ``gemini_instruments.common``. | list of Section | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| pixel_scale | Pixel scale in arcsec per pixel. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| program_id | Gemini program ID. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| pupil_mask | Name of the pupil mask. | str ?? | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| qa_state | Gemini quality assessment state (eg. pass, usable, fail). | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| ra | Right ascension, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| raw_bg | Gemini sky background band. | int ?? | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| raw_cc | Gemini cloud coverage band. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| raw_iq | Gemini image quality band. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| raw_wv | Gemini water vapor band. | int ?? | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| read_mode | Gemini name for combination for gain setting and read setting. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| read_noise | Read noise in electrons. | float | +| | +-----------------+ +| | | list of float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| read_speed_setting | human readable read mode setting (eg. slow, fast). | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| requested_bg | PI requested Gemini sky background band. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| requested_cc | PI requested Gemini cloud coverage band. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| requested_iq | PI requested Gemini image quality band. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| requested_wv | PI requested Gemini water vapor band. | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| saturation_level | Saturation level. | int | +| | +-----------------+ +| | | list of int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| shuffle_pixels | Charge shuffle, in pixels. (nod and shuffle mode) | int | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| slit | Name of the slit. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| target_dec | Declination of the target, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| target_ra | Right Ascension of the target, in degrees. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| telescope | Name of the telescope. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| telescope_x_offset | Offset along the telescope's x-axis. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| telescope_y_offset | Offset along the telescope's y-axis. | float | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| ut_date | UT date of the observation. | datetime.date | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| ut_datetime | UT date and time of the observation. | datetime | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| ut_time | UT time of the observation. | datetime.time | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| wavefront_sensor | Wavefront sensor used for the observation. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| wavelength_band | Band associated with the filter or the central wavelength. | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| wcs_dec | Declination of the center of field from the WCS keywords. | float | +| | In degrees. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| wcs_ra | Right Ascension of the center of field from the WCS keywords. | float | +| | In degrees. | | ++--------------------------------+----------------------------------------------------------------+-----------------+ +| well_depth_setting | Human readable well depth setting (eg. shallow, deep) | str | ++--------------------------------+----------------------------------------------------------------+-----------------+ diff --git a/_sources/manuals/cheatsheet.rst.txt b/_sources/manuals/cheatsheet.rst.txt new file mode 100644 index 00000000..624f6364 --- /dev/null +++ b/_sources/manuals/cheatsheet.rst.txt @@ -0,0 +1,463 @@ +.. cheatsheet + +.. _cheatsheet: + +*********** +Cheat Sheet +*********** + +.. admonition:: Document ID + + PIPE-USER-105_AstrodataCheatSheet + +A data package is available for download if you wish to run the examples +included in this cheat sheet. Download it at: + + ``_ + +To unpack:: + + $ cd + $ tar xvf ad_usermanual_datapkg-v1.tar + $ bunzip2 ad_usermanual/playdata/*.bz2 + +Then go to the ``ad_usermanual/playground`` directory to run the examples. + +Imports +======= + +Import :mod:`astrodata` and :mod:`gemini_instruments`:: + + >> import astrodata + >> import gemini_instruments + +Basic read and write operations +=============================== + +Open a file:: + + >> ad = astrodata.open('../playdata/N20170609S0154.fits') + +Get path and filename:: + + >> ad.path + '../playdata/N20170609S0154.fits' + >> ad.filename + 'N20170609S0154.fits' + +Write to a new file:: + + >> ad.write(filename='new154.fits') + >> ad.filename + N20170609S0154.fits + +Overwrite the file:: + + >> adnew = astrodata.open('new154.fits') + >> adnew.filename + new154.fits + >> adnew.write(overwrite=True) + +Object structure +================ + +Description +----------- +The |AstroData| object is assigned by "tags" that describe the +type of data it contains. The tags are drawn from rules defined in +|gemini_instruments| and are based on header information. + +When mapping a FITS file, each science pixel extension is loaded as a +|NDAstroData| object. The list is zero-indexed. So FITS +extension 1 becomes element 0 of the |AstroData| object. If a ``VAR`` +extension is present, it is loaded to the variance attribute of the +|NDAstroData|. If a ``DQ`` extension is present, it is loaded to the ``.mask`` +attribute of the |NDAstroData|. ``SCI``, ``VAR`` and ``DQ`` are associated +through the ``EXTVER`` keyword value. + +In the file below, each |AstroData| "extension" contains the pixel data, +then an error plane (``.variance``) and a bad pixel mask plane (``.mask``). +|Table| can be attached to an extension, like OBJCAT, or to the +|AstroData| object globally, like REFCAT. (In this case, OBJCAT is a +catalogue of the sources detected in the image, REFCAT is a reference catalog +for the area covered by the whole file.) If other 2D data needs to be +associated with an extension this can also be done, like here with OBJMASK, +a 2D mask matching the sources in the image. + +:: + + >> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >> ad.info() + Filename: ../playdata/N20170609S0154_varAdded.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED + PREPARED SIDEREAL + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) int16 + .OBJCAT Table (6, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 1] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) int16 + .OBJCAT Table (8, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 2] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) int16 + .OBJCAT Table (7, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 3] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) int16 + .OBJCAT Table (5, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + Other Extensions + Type Dimensions + .REFCAT Table (245, 16) + + + +Modifying the structure +----------------------- + +Let's first get our play data loaded. You are encouraged to do a +:meth:`~astrodata.AstroData.info` before and after each structure-modification +step, to see how things change. + +:: + + >> from copy import deepcopy + >> ad = astrodata.open('../playdata/N20170609S0154.fits') + >> adcopy = deepcopy(ad) + >> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + +Append an extension:: + + >> adcopy.append(advar[3]) + >> adcopy.append(advar[3].data) + + +Delete an extension:: + + >> del adcopy[5] + +Delete and add variance and mask planes:: + + >> var = adcopy[4].variance + >> adcopy[4].variance = None + >> adcopy[4].variance = var + +Attach a table to an extension:: + + >> adcopy[3].SMAUG = advar[0].OBJCAT.copy() + + +Attach a table to the |AstroData| object:: + + >> adcopy.DROGON = advar.REFCAT.copy() + +Delete a table:: + + >> del adcopy[3].SMAUG + >> del adcopy.DROGON + + + +Astrodata tags +============== + +:: + + >> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >> ad.tags + {'GMOS', 'OVERSCAN_SUBTRACTED', 'SIDEREAL', 'NORTH', 'OVERSCAN_TRIMMED', + 'PREPARED', 'IMAGE', 'GEMINI'} + + >> type(ad.tags) + + + >> {'IMAGE', 'PREPARED'}.issubset(ad.tags) + True + >> 'PREPARED' in ad.tags + True + + +Headers +======= + +The use of descriptors is favored over direct header access when retrieving +values already represented by descriptors, and when writing instrument agnostic +routines. + +Descriptors +----------- + +:: + + >> ad = astrodata.open('../playdata/N20170609S0154.fits') + >> ad.filter_name() + 'open1-6&g_G0301' + >> ad.filter_name(pretty=True) + 'g' + >> ad.gain() # uses a look-up table to get the correct values + [2.03, 1.97, 1.96, 2.01] + >> ad.hdr['GAIN'] + [1.0, 1.0, 1.0, 1.0] # the wrong values contained in the raw data. + >> ad[0].gain() + 2.03 + >> ad.gain()[0] + 2.03 + + >> ad.descriptors + ('airmass', 'amp_read_area', 'ao_seeing', ... + ...) + + +Direct access to header keywords +-------------------------------- + +:: + + >> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + +Primary Header Unit +******************* + +To see a print out of the full PHU: + + >> ad.phu + +Get value from PHU:: + + >> ad.phu['EXPTIME'] + 1.0 + + >> default = 5. + >> ad.phu.get('BOGUSKEY', default) + 5.0 + +Set PHU keyword, with and without comment:: + + >> ad.phu['NEWKEY'] = 50. + >> ad.phu['ANOTHER'] = (30., 'Some comment') + +Delete PHU keyword:: + + >> del ad.phu['NEWKEY'] + + + +Pixel extension header +********************** +To see a print out of the full header for an extension or all the extensions: + + >> ad[0].hdr + >> list(ad.hdr) + +Get value from an extension header:: + + >> ad[0].hdr['OVERSCAN'] + 469.7444308769482 + >> ad[0].hdr.get('OVERSCAN', default) + +Get keyword value for all extensions:: + + >> ad.hdr['OVERSCAN'] + [469.7444308769482, 469.656175780001, 464.9815279808291, 467.5701178951787] + >> ad.hdr.get('BOGUSKEY', 5.) + [5.0, 5.0, 5.0, 5.0] + +Set extension header keyword, with and without comment:: + + >> ad[0].hdr['NEWKEY'] = 50. + >> ad[0].hdr['ANOTHER'] = (30., 'Some comment') + +Delete an extension keyword:: + + >> del ad[0].hdr['NEWKEY'] + +Table header +************ +See the :ref:`cheatsheet_tables` section. + + +Pixel data +========== + +Arithmetics +----------- +Arithmetics with variance and mask propagation is offered for +``+``, ``-``, ``*``, ``/``, and ``**``. + +:: + + >> ad_hcont = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >> ad_halpha = astrodata.open('../playdata/N20170521S0926_forStack.fits') + + >> adsub = ad_halpha - ad_hcont + + >> ad_halpha[0].data.mean() + 646.11896 + >> ad_hcont[0].data.mean() + 581.81342 + >> adsub[0].data.mean() + 64.305862 + + >> ad_halpha[0].variance.mean() + 669.80664 + >> ad_hcont[0].variance.mean() + 598.46667 + >> adsub[0].variance.mean() + 1268.274 + + + # In place multiplication + >> ad_mult = deepcopy(ad) + >> ad_mult.multiply(ad) + >> ad_mult.multiply(5.) + + + # Using descriptors to operate in-place on extensions. + >> from copy import deepcopy + >> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >> ad_gain = deepcopy(ad) + >> for (ext, gain) in zip(ad_gain, ad_gain.gain()): + ... ext.multiply(gain) + >> ad_gain[0].data.mean() + 366.39545 + >> ad[0].data.mean() + 180.4904 + >> ad[0].gain() + 2.03 + + +Other pixel data operations +--------------------------- + +:: + + >> import numpy as np + >> ad_halpha[0].mask[300:350,300:350] = 1 + >> np.mean(ad_halpha[0].data[ad_halpha[0].mask==0]) + 657.1994 + >> np.mean(ad_halpha[0].data) + 646.11896 + + +.. _cheatsheet_tables: + +Tables +====== + +Tables are stored as :class:`astropy.table.Table` class. FITS tables are +represented in :mod:`astrodata` as |Table| and FITS headers are stored in the +|NDAstroData| ``meta`` attribute. Most table +access should be done through the |Table| interface. The best reference is the +|astropy| documentation itself. Below are just a few examples. + +:: + + >> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + +Get column names:: + + >> ad.REFCAT.colnames + +Get column content:: + + >> ad.REFCAT['zmag'] + >> ad.REFCAT['zmag', 'zmag_err'] + +Get content of row:: + + >> ad.REFCAT[4] # 5th row + >> ad.REFCAT[4:6] # 5th and 6th rows + + +Get content from specific row and column:: + + >> ad.REFCAT['zmag'][4] + +Add a column:: + + >> new_column = [0] * len(ad.REFCAT) + >> ad.REFCAT['new_column'] = new_column + +Add a row:: + + >> new_row = [0] * len(ad.REFCAT.colnames) + >> new_row[1] = '' # Cat_Id column is of "str" type. + >> ad.REFCAT.add_row(new_row) + +Selecting value from criterion:: + + >> ad.REFCAT['zmag'][ad.REFCAT['Cat_Id'] == '1237662500002005475'] + >> ad.REFCAT['zmag'][ad.REFCAT['zmag'] < 18.] + +Rejecting :class:`numpy.nan` before doing something with the values:: + + >> t = ad.REFCAT # to save typing. + >> t['zmag'][np.where(np.isnan(t['zmag']), 99, t['zmag']) < 18.] + + >> t['zmag'].mean() + nan + >> t['zmag'][np.where(~np.isnan(t['zmag']))].mean() + 20.377306 + +If for some reason you need to access the FITS table headers, here is how to do it. + +To see the FITS headers:: + + >> ad.REFCAT.meta + >> ad[0].OBJCAT.meta + +To retrieve a specific FITS table header:: + + >> ad.REFCAT.meta['header']['TTYPE3'] + 'RAJ2000' + >> ad[0].OBJCAT.meta['header']['TTYPE3'] + 'Y_IMAGE' + +To retrieve all the keyword names matching a selection:: + + >> keynames = [key for key in ad.REFCAT.meta['header'] if key.startswith('TTYPE')] + + +Create new AstroData object +=========================== + +Basic header and data array set to zeros:: + + >> from astropy.io import fits + + >> phu = fits.PrimaryHDU() + >> pixel_data = np.zeros((100,100)) + + >> hdu = fits.ImageHDU() + >> hdu.data = pixel_data + >> ad = astrodata.create(phu) + >> ad.append(hdu, name='SCI') + +or another way:: + + >> hdu = fits.ImageHDU(data=pixel_data, name='SCI') + >> ad = astrodata.create(phu, [hdu]) + +A |Table| as an |AstroData| object:: + + >> from astropy.table import Table + + >> my_astropy_table = Table(list(np.random.rand(2,100)), names=['col1', 'col2']) + >> phu = fits.PrimaryHDU() + + >> ad = astrodata.create(phu) + >> ad.SMAUG = my_astropy_table + + >> phu = fits.PrimaryHDU() + >> ad = astrodata.create(phu) + >> ad.SMAUG = my_fits_table + +WARNING: This last line will not run like the others as we have not defined +``my_fits_table``. This is nonetheless how it is done if you had a FITS table. diff --git a/_sources/manuals/full_api.rst.txt b/_sources/manuals/full_api.rst.txt new file mode 100644 index 00000000..776bbae2 --- /dev/null +++ b/_sources/manuals/full_api.rst.txt @@ -0,0 +1,8 @@ +.. _api: + +************* +Reference API +************* + +.. need to make this a reference for all the modules/functions/private + methods/attrs diff --git a/_sources/manuals/index.rst.txt b/_sources/manuals/index.rst.txt new file mode 100644 index 00000000..6d065a60 --- /dev/null +++ b/_sources/manuals/index.rst.txt @@ -0,0 +1,55 @@ +.. Astrodata Master Manual + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + Manually edited by SC December 2020 + +################ +Astrodata Manual +################ + +.. admonition:: Document ID + + PIPE-USER-120_AstrodataMasterManual + +.. toctree:: + :hidden: + :numbered: + + cheatsheet + usermanual/index + progmanual/index + full_api + +This documentation provides different levels of information: + +- :doc:`cheatsheet` - A refresher on common astrodata operations +- :doc:`usermanual/index` - How to code with astrodata +- :doc:`progmanual/index` - How to code for astrodata +- :doc:`full_api` - The full API documentation for developers. + + +.. raw:: latex + + % Set up the appendix mode and modify the LaTeX toc behavior + \appendix + \noappendicestocpagenum + \addappheadtotoc + + +.. rubric:: Appendix + +.. toctree:: + :maxdepth: 1 + + appendix_descriptors + api + +.. ****************** +.. Indices and tables +.. ****************** + +.. * :ref:`genindex` +.. * :ref:`modindex` +.. * :ref:`search` + +.. todolist:: diff --git a/_sources/manuals/progmanual/adclass.rst.txt b/_sources/manuals/progmanual/adclass.rst.txt new file mode 100644 index 00000000..5fb36f65 --- /dev/null +++ b/_sources/manuals/progmanual/adclass.rst.txt @@ -0,0 +1,397 @@ +.. astrodata.rst + +.. _astrodata: + +************************* +AstroData and Derivatives +************************* + +The |AstroData| class is the main interface to the package. When opening files +or creating new objects, a derivative of this class is returned, as the +|AstroData| class is not intended to be used directly. It provides the logic to +calculate the :ref:`tag set ` for an image, which is common to all +data products. Aside from that, it lacks any kind of specialized knowledge +about the different instruments that produce the FITS files. More importantly, +it defines two methods (``info`` and ``load``) as abstract, meaning that the +class cannot be instantiated directly: a derivative must implement those +methods in order to be useful. Such derivatives can also implement descriptors, +which provide processed metadata in a way that abstracts the user from the raw +information (e.g., the keywords in FITS headers). + +|AstroData| does define a common interface, though. Much of it consists on +implementing semantic behavior (access to components through indices, like a +list; arithmetic using standard operators; etc), mostly by implementing +standard Python methods: + +* Defines a common ``__init__`` function. + +* Implements ``__deepcopy__``. + +* Implements ``__iter__`` to allow sequential iteration over the main set of + components (e.g., FITS science HDUs). + +* Implements ``__getitem__`` to allow data slicing (e.g., ``ad[2:4]`` returns + a new |AstroData| instance that contains only the third and fourth main + components). + +* Implements ``__delitem__`` to allow for data removal based on index. It does + not define ``__setitem__``, though. The basic AstroData series of classes + only allows to append new data blocks, not to replace them in one sweeping + move. + +* Implements ``__iadd__``, ``__isub__``, ``__imul__``, ``__itruediv__``, and + their not-in-place versions, based on them. + +There are a few other methods. For a detailed discussion, please refer to the +:ref:`api`. + +.. _tags_prop_entry: + +The ``tags`` Property +===================== + +Additionally, and crucial to the package, AstroData offers a ``tags`` property, +that under the hood calculates textual tags that describe the object +represented by an instance, and returns a set of strings. Returning a set (as +opposed to a list, or other similar structure) is intentional, because it is +fast to compare sets, e.g., testing for membership; or calculating intersection, +etc., to figure out if a certain dataset belongs to an arbitrary category. + +The implementation for the tags property is just a call to +``AstroData._process_tags()``. This function implements the actual logic behind +calculating the tag set (described :ref:`below `). A derivative class +could redefine the algorithm, or build upon it. + + +Writing an ``AstroData`` Derivative +=================================== + +The first step when creating new |AstroData| derivative hierarchy would be to +create a new class that knows how to deal with some kind of specific data in a +broad sense. + +|AstroData| implements both ``.info()`` and ``.load()`` in ways that are +specific to FITS files. It also introduces a number of FITS-specific methods +and properties, e.g.: + +* The properties ``phu`` and ``hdr``, which return the primary header and + a list of headers for the science HDUs, respectively. + +* A ``write`` method, which will write the data back to a FITS file. + +* A ``_matches_data`` **static** method, which is very important, involved in + guiding for the automatic class choice algorithm during data loading. We'll + talk more about this when dealing with :ref:`registering our classes + `. + +It also defines the first few descriptors, which are common to all Gemini data: +``instrument``, ``object``, and ``telescope``, which are good examples of simple +descriptors that just map a PHU keyword without applying any conversion. + +A typical AstroData programmer will extend this class (|AstroData|). Any of +the classes under the ``gemini_instruments`` package can be used as examples, +but we'll describe the important bits here. + + +Create a package for it +----------------------- + +This is not strictly necessary, but simplifies many things, as we'll see when +talking about *registration*. The package layout is up to the designer, so you +can decide how to do it. For DRAGONS we've settled on the following +recommendation for our internal process (just to keep things familiar):: + + gemini_instruments + __init__.py + instrument_name + __init__.py + adclass.py + lookup.py + +Where ``instrument_name`` would be the package name (for Gemini we group all +our derivative packages under ``gemini_instruments``, and we would import +``gemini_instruments.gmos``, for example). ``__init__.py`` and ``adclass.py`` +would be the only required modules under our recommended layout, with +``lookup.py`` being there just to hold hard-coded values in a module separate +from the main logic. + +``adclass.py`` would contain the declaration of the derivative class, and +``__init__.py`` will contain any code needed to register our class with the +|AstroData| system upon import. + + +Create your derivative class +---------------------------- + +This is an excerpt of a typical derivative module:: + + from astrodata import astro_data_tag, astro_data_descriptor, TagSet + from astrodata import AstroData + + from . import lookup + + class AstroDataInstrument(AstroData): + __keyword_dict = dict( + array_name = 'AMPNAME', + array_section = 'CCDSECT' + ) + + @staticmethod + def _matches_data(source): + return source[0].header.get('INSTRUME', '').upper() == 'MYINSTRUMENT' + + @astro_data_tag + def _tag_instrument(self): + return TagSet(['MYINSTRUMENT']) + + @astro_data_tag + def _tag_image(self): + if self.phu.get('GRATING') == 'MIRROR': + return TagSet(['IMAGE']) + + @astro_data_tag + def _tag_dark(self): + if self.phu.get('OBSTYPE') == 'DARK': + return TagSet(['DARK'], blocks=['IMAGE', 'SPECT']) + + @astro_data_descriptor + def array_name(self): + return self.phu.get(self._keyword_for('array_name')) + + @astro_data_descriptor + def amp_read_area(self): + ampname = self.array_name() + detector_section = self.detector_section() + return "'{}':{}".format(ampname, detector_section) + +.. note:: + An actual Gemini Facility Instrument class will derive from + ``gemini_instruments.AstroDataGemini``, but this is irrelevant + for the example. + +The class typically relies on functionality declared elsewhere, in some +ancestor, e.g., the tag set computation and the ``_keyword_for`` method are +defined at |AstroData|. + +Some highlights: + +* ``__keyword_dict``\ [#keywdict]_ defines one-to-one mappings, assigning a more + readable moniker for an HDU header keyword. The idea here is to prevent + hard-coding the names of the keywords, in the actual code. While these are + typically quite stable and not prone to change, it's better to be safe than + sorry, and this can come in useful during instrument development, which is + the more likely source of instability. The actual value can be extracted by + calling ``self._keyword_for('moniker')``. + +* ``_matches_data`` is a static method. It does not have any knowledge about + the class itself, and it does not work on an *instance* of the class: it's + a member of the class just to make it easier for the AstroData registry to + find it. This method is passed some object containing cues of the internal + structure and contents of the data. This could be, for example, an instance + of ``HDUList``. Using these data, ``_matches_data`` must return a boolean, + with ``True`` meaning "I know how to handle this data". + + Note that ``True`` **does not mean "I have full knowledge of the data"**. It + is acceptable for more than one class to claim compatibility. For a GMOS FITS + file, the classes that will return ``True`` are: |AstroData| (because it is + a FITS file that comply with certain minimum requirements), + `~gemini_instruments.gemini.AstroDataGemini` (the data contains Gemini + Facility common metadata), and `~gemini_instruments.gmos.AstroDataGmos` (the + actual handler!). + + But this does not mean that multiple classes can be valid "final" candidates. + If AstroData's automatic class discovery finds more than one class claiming + matching with the data, it will start discarding them on the basis of + inheritance: any class that appears in the inheritance tree of another one is + dropped, because the more specialized one is preferred. If at some point the + algorithm cannot find more classes to drop, and there is more than one left + in the list, an exception will occur, as AstroData will have no way to choose + one over the other. + +* A number of "tag methods" have been declared. Their naming is a convention, + at the end of the day (the "``_tag_``" prefix, and the related "``_status_``" + one, are *just hints* for the programmer): each team should establish + a convention that works for them. What is important here is to **decorate** + them using `~astrodata.astro_data_tag`, which earmarks the method so that it + can be discovered later, and ensures that it returns an appropriate value. + + A tag method will return either a `~astrodata.TagSet` instance (which can be + empty), or ``None``, which is the same as returning an empty + `~astrodata.TagSet`\ [#tagset1]_. + + **All** these methods will be executed when looking up for tags, and it's up + to the tag set construction algorithm (see :ref:`ad_tags`) to figure out the final + result. In theory, one **could** provide *just one* big method, but this is + feasible only when the logic behind deciding the tag set is simple. The + moment that there are a few competing alternatives, with some conditions + precluding other branches, one may end up with a rather complicated dozens of + lines of logic. Let the algorithm do the heavy work for you: split the tags + as needed to keep things simple, with an easy to understand logic. + + Also, keeping the individual (or related) tags in separate methods lets you + exploit the inheritance, keeping common ones at a higher level, and + redefining them as needed later on, at derived classes. + + Please, refer to `~gemini_instruments.gemini.AstroDataGemini`, + `~gemini_instruments.gmos.AstroDataGmos`, and + `~gemini_instruments.gnirs.AstroDataGnirs` for examples using most of the + features. + +* The `astrodata.AstroData.read` method calls the `astrodata.fits.read_fits` + function, which uses metadata in the FITS headers to determine how the data + should be stored in the |AstroData| object. In particular, the ``EXTNAME`` + and ``EXTVER`` keywords are used to assign individual FITS HDUs, using the + same names (``SCI``, ``DQ``, and ``VAR``) as Gemini-IRAF for the ``data``, + ``mask``, and ``variance`` planes. A ``SCI`` HDU *must* exist if there is + another HDU with the same ``EXTVER``, or else an error will occur. + + If the raw data do not conform to this format, the `astrodata.AstroData.read` + method can be overridden by your class, by having it call the + `astrodata.fits.read_fits` function with an additional parameter, + ``extname_parser``, that provides a function to modify the header. This + function will be called on each HDU before further processing. As an example, + the SOAR Adaptive Module Imager (SAMI) instrument writes raw data as + a 4-extension MEF file, with the extensions having ``EXTNAME`` values + ``im1``, ``im2``, etc. These need to be modified to ``SCI``, and an + appropriate ``EXTVER`` keyword added` [#extver]_\. This can be done by + writing a suitable ``read`` method for the ``AstroDataSami`` class:: + + @classmethod + def read(cls, source, extname_parser=None): + def sami_parser(hdu): + m = re.match('im(\d)', hdu.header.get('EXTNAME', '')) + if m: + hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData') + hdu.header['EXTVER'] = (int(m.group(1)), 'Added by AstroData') + + return super().read(source, extname_parser=extname_parser) + + +* *Descriptors* will make the bulk of the class: again, the name is arbitrary, + and it should be descriptive. What *may* be important here is to use + `~astrodata.astro_data_descriptor` to decorate them. This is *not required*, + because unlike tag methods, descriptors are meant to be called explicitly by + the programmer, but they can still be marked (using this decorator) to be + listed when calling the ``descriptors`` property. The decorator does not + alter the descriptor input or output in any way, so it is always safe to use + it, and you probably should, unless there's a good reason against it (e.g., + if a descriptor is deprecated and you don't want it to show up in lookups). + + More detailed information can be found in :ref:`ad_descriptors`. + + +.. _class_registration: + +Register your class +------------------- + +Finally, you need to include your class in the **AstroData Registry**. This is +an internal structure with a list of all the |AstroData|\-derived classes that +we want to make available for our programs. Including the classes in this +registry is an important step, because a file should be opened using +`astrodata.open` or `astrodata.create`, which uses the registry to identify +the appropriate class (via the ``_matches_data`` methods), instead of having +the user specify it explicitly. + +The version of AstroData prior to DRAGONS had an auto-discovery mechanism, that +explored the source tree looking for the relevant classes and other related +information. This forced a fixed directory structure (because the code needed +to know where to look for files), and gave the names of files and classes +semantic meaning (to know *which* files to look into, for example). Aside from +the rigidness of the scheme, this introduced all sort of inefficiencies, +including an unacceptably high overhead when importing the AstroData package +for the first time during execution. + +In this new version of AstroData we've introduced a more manageable scheme, +that places the discovery responsibility on the programmer. A typical +``__init__.py`` file on an instrument package will look like this:: + + __all__ = ['AstroDataMyInstrument'] + + from astrodata import factory + from .adclass import AstroDataMyInstrument + + factory.addClass(AstroDataMyInstrument) + +The call to ``factory.addClass`` is the one registering the class. This step +**needs** to be done **before** the class can be used effectively in the +AstroData system. Placing the registration step in the ``__init__.py`` file is +convenient, because importing the package will be enough! + +Thus, a script making use of DRAGONS' AstroData to manipulate GMOS data +could start like this:: + + import astrodata + from gemini_instruments import gmos + + ... + + ad = astrodata.open(some_file) + +The first import line is not needed, technically, because the ``gmos`` package +will import it too, anyway, but we'll probably need the ``astrodata`` package +in the namespace anyway, and it's always better to be explicit. Our +typical DRAGONS scripts and modules start like this, instead:: + + import astrodata + import gemini_instruments + +``gemini_instruments`` imports all the packages under it, making knowledge +about all Gemini instruments available for the script, which is perfect for a +multi-instrument pipeline, for example. Loading all the instrument classes is +not typically a burden on memory, though, so it's easier for everyone to take +the more general approach. It also makes things easier on the end user, because +they won't need to know internal details of our packages (like their naming +scheme). We suggest this "*cascade import*" scheme for all new source trees, +letting the user decide which level of detail they need. + +As an additional step, the ``__init__.py`` file in a package may do extra +initialization. For example, for the Gemini modules, one piece of functionality +that is shared across instruments is a descriptor that translates a filter's +name (say "u" or "FeII") to its central wavelength (e.g., +0.35µm, 1.644µm). As it is a rather common function for us, it is implemented +by `~gemini_instruments.gemini.AstroDataGemini`. This class **does not know** +about its daughter classes, though, meaning that it **cannot know** about the +filters offered by their instruments. Instead, we offer a function that can +be used to update the filter → wavelength mapping in +`gemini_instruments.gemini.lookup` so that it is accessible by the +`~gemini_instruments.gemini.AstroDataGemini`\-level descriptor. So our +``gmos/__init__.py`` looks like this:: + + __all__ = ['AstroDataGmos'] + + from astrodata import factory + from ..gemini import addInstrumentFilterWavelengths + from .adclass import AstroDataGmos + from .lookup import filter_wavelengths + + factory.addClass(AstroDataGmos) + # Use the generic GMOS name for both GMOS-N and GMOS-S + addInstrumentFilterWavelengths('GMOS', filter_wavelengths) + +where `~gemini_instruments.gemini.addInstrumentFilterWavelengths` is provided +by the ``gemini`` package to perform the update in a controlled way. + +We encourage package maintainers and creators to follow such explicit +initialization methods, driven by the modules that add functionality +themselves, as opposed to active discovery methods on the core code. This +favors decoupling between modules, which is generally a good idea. + +.. rubric:: Footnotes + +.. [#keywdict] Note that the keyword dictionary is a "private" property of the + class (due to the double-underscore prefix). Each class can define its own + set, which will not be replaced by derivative classes. ``_keyword_for`` is + aware of this and will look up each class up the inheritance chain, in turn, + when looking up for keywords. + +.. [#tagset1] Notice that the example functions will return only + a `~astrodata.TagSet`, if appropriate. This is OK, remember that *every + function* in Python returns a value, which will be ``None``, implicitly, if + you don't specify otherwise. + +.. [#extver] An ``EXTVER`` keyword is not formally required as the + `astrodata.fits.read_fits` method will assign the lowest available integer + to a ``SCI`` header with no ``EXTVER`` keyword (or if its value is -1). But + we wish to be able to identify the original ``im1`` header by assigning it + an ``EXTVER`` of 1, etc. diff --git a/_sources/manuals/progmanual/containers.rst.txt b/_sources/manuals/progmanual/containers.rst.txt new file mode 100644 index 00000000..fe2a1a26 --- /dev/null +++ b/_sources/manuals/progmanual/containers.rst.txt @@ -0,0 +1,96 @@ +.. containers.rst + +.. _containers: + +*************** +Data Containers +*************** + +A third, and very important part of the AstroData core package is the data +container. We have chosen to extend Astropy's |NDData| with our own +requirements, particularly lazy-loading of data using by opening the FITS files +in read-only, memory-mapping mode, and exploiting the windowing capability of +`astropy.io.fits` (using ``section``) to reduce our memory requirements, which +becomes important when reducing data (e.g., stacking). + +We'll describe here how we depart from |NDData|, and how do we integrate the +data containers with the rest of the package. Please refer to |NDData| for the +full interface. + +Our main data container is `astrodata.NDAstroData`. Fundamentally, it is +a derivative of `astropy.nddata.NDData`, plus a number of mixins to add +functionality:: + + class NDAstroData(AstroDataMixin, NDArithmeticMixin, NDSlicingMixin, NDData): + ... + +This allows us out of the box to have proper arithmetic with error +propagation, and slicing the data with the array syntax. + +Our first customization is ``NDAstroData.__init__``. It relies mostly on the +upstream initialization, but customizes it because our class is initialized +with lazy-loaded data wrapped around a custom class +(`astrodata.fits.FitsLazyLoadable`) that mimics a `astropy.io.fits` HDU +instance just enough to play along with |NDData|'s initialization code. + +``FitsLazyLoadable`` is an integral part of our memory-mapping scheme, and +among other things it will scale data on the fly, as memory-mapped FITS data +can only be read unscaled. Our NDAstroData redefines the properties ``data``, +``uncertainty``, and ``mask``, in two ways: + +* To deal with the fact that our class is storing ``FitsLazyLoadable`` + instances, not arrays, as |NDData| would expect. This is to keep data out + of memory as long as possible. + +* To replace lazy-loaded data with a real in-memory array, under certain + conditions (e.g., if the data is modified, as we won't apply the changes to the + original file!) + +Our obsession with lazy-loading and discarding data is directed to reduce +memory fragmentation as much as possible. This is a real problem that can hit +applications dealing with large arrays, particularly when using Python. Given +the choice to optimize for speed or for memory consumption, we've chosen the +latter, which is the more pressing issue. + +We've added another new property, ``window``, that can be used to +explicitly exploit the `astropy.io.fits`'s ``section`` property, to (again) +avoid loading unneeded data to memory. This property returns an instance of +``NDWindowing`` which, when sliced, in turn produces an instance of +``NDWindowingAstroData``, itself a proxy of ``NDAstroData``. This scheme may +seem complex, but it was deemed the easiest and cleanest way to achieve the +result that we were looking for. + +The base ``NDAstroData`` class provides the memory-mapping functionality, +with other important behaviors added by the ``AstroDataMixin``, which can +be used with other |NDData|-like classes (such as ``Spectrum1D``) to add +additional convenience. + +One addition is the ``variance`` property, which allows direct access and +setting of the data's uncertainty, without the user needing to explicitly wrap +it as an ``NDUncertainty`` object. Internally, the variance is stored as an +``ADVarianceUncertainty`` object, which is subclassed from Astropy's standard +``VarianceUncertainty`` class with the addition of a check for negative values +whenever the array is accessed. + +``NDAstroDataMixin`` also changes the default method of combining the ``mask`` +attributes during arithmetic operations from ``logical_or`` to ``bitwise_or``, +since the individual bits in the mask have separate meanings. + +The way slicing affects the ``wcs`` is also changed since DRAGONS regularly +uses the callable nature of ``gWCS`` objects and this is broken by the standard +slicing method. + +Finally, the additional image planes and tables stored in the ``meta`` dict +are exposed as attributes of the ``NDAstroData`` object, and any image planes +that have the same shape as the parent ``NDAstroData`` object will be handled +by ``NDWindowingAstroData``. Sections will be ignored when accessing image +planes with a different shape, as well as tables. + + +.. note:: + + We expect to make changes to ``NDAstroData`` in future releases. In particular, + we plan to make use of the ``unit`` attribute provided by the + |NDData| class and increase the use of memory-mapping by default. These + changes mostly represent increased functionality and we anticipate a high + (and possibly full) degree of backward compatibility. diff --git a/_sources/manuals/progmanual/descriptors.rst.txt b/_sources/manuals/progmanual/descriptors.rst.txt new file mode 100644 index 00000000..e9ae03a2 --- /dev/null +++ b/_sources/manuals/progmanual/descriptors.rst.txt @@ -0,0 +1,65 @@ +.. descriptors.rst + +.. _ad_descriptors: + +*********** +Descriptors +*********** + +Descriptors are just regular methods that translate metadata from the raw +storage (e.g., cards from FITS headers) to values useful for the user, +potentially doing some processing in between. They exist to: + +* Abstract the actual organization of the metadata; e.g. + `~gemini_instruments.gemini.AstroDataGemini` takes the detector gain from + a keyword in the FITS PHU, where `~gemini_instruments.niri.AstroDataNiri` + overrides this to provide a hard-coded value. + + More complex implementations also exist. In order to determine the gain of + a GMOS observation, `~gemini_instruments.gmos.AstroDataGmos` uses the + observation date (provided by a descriptor) to select a particular lookup + table, and then uses the values of other descriptors to select the correct + entry in the table. + +* Provide a common interface to a set of instruments. This simplifies user + training (no need to learn a different API for each instrument), and + facilitates the reuse of code for pipelines, etc. + +* Also, since FITS header keywords are limited to 8 characters, for simple + keyword → value mappings, they provide a more meaningful and readable name. + +Descriptors **should** be decorated using `~astrodata.astro_data_descriptor`. +The only function of this decorator is to ensure that the descriptor is marked +as such: it does not alter its input or output in any way. This lets the user +explore the API of an |AstroData| object via the +`~astrodata.AstroData.descriptors` property. + +Descriptors **can** be decorated with `~astrodata.core.returns_list` to +eliminate the need to code some logic. Some descriptors return single values, +while some return lists, one per extension. Typically, the former are +descriptors that refer to the entire observation (and, for MEF files, are +usually extracted from metadata in the PHU, such as ``airmass``), while the +latter are descriptors where different extensions might return different values +(and typically come from metadata in the individual HDUs, such as ``gain``). +A list is returned even if there is only one extension in the |AstroData| +object, as this allows code to be written generically to iterate over the +|AstroData| object and the descriptor return, without needing to know how many +extensions there are. The `~astrodata.core.returns_list` decorator ensures that +the descriptor returns an appropriate object (value or list), using the +following rules: + +* If the |AstroData| object is not a single slice: + + * If the undecorated descriptor returns a list, an exception is raised + if the list is not the same length as the number of extensions. + * If the undecorated descriptor returns a single value, the decorator + will turn it into a list of the correct length by copying this value. + +* If the |AstroData| object is a single slice and the undecorated + descriptor returns a list, only the first element is returned. + +An example of the use of this decorator is the NIRI +`~gemini_instruments.niri.AstroDataNiri.gain` descriptor, which reads the +value from a lookup table and simply returns it. A single value is only +appropriate if the |AstroData| object is singly-sliced and the decorator ensures +that a list is returned otherwise. diff --git a/_sources/manuals/progmanual/design.rst.txt b/_sources/manuals/progmanual/design.rst.txt new file mode 100644 index 00000000..bab79e65 --- /dev/null +++ b/_sources/manuals/progmanual/design.rst.txt @@ -0,0 +1,82 @@ +.. design.rst + +.. _design: + +************** +General Design +************** + +As astronomical instruments have become more complex, there +has been an increasing need for bespoke reduction packages and pipelines to +deal with the specific needs of each instrument. Despite this +complexity, many of the reduction steps can be very similar and the overall +effort could be reduced significantly by sharing code. In practice, however, +there are often issues regarding the manner in which the data are stored +internally. The purpose of AstroData is to provide a uniform interface to the data +and metadata, in a manner that is independent both of the specific instrument +and the way the data are stored on disk, thereby facilitating this code-sharing. +It is *not* a new astronomical data format. + +One of the main features of AstroData is the use of *descriptors*, which +provide a level of abstraction between the metadata and the code accessing it. +Somebody using the AstroData interface who wishes to know the exposure time +of a particular astronomical observation represented by the ``AstroData`` object +``ad`` can simply write ``ad.exposure_time()`` without needing to concern +themselves about how that value is stored internally, for example, the name +of the FITS header keyword. These are discussed further in :ref:`ad_descriptors`. + +AstroData also provides a clearer representation of the relationships +between different parts of the data produced from a single astronomical +observation. Modern astronomical instruments often contain multiple +detectors that are read out separately and the multi-extension FITS (MEF) +format used by many institutions, including Gemini Observatory, handles +the raw data well. In this format, each detector's data and metadata is +assigned to its own extension, +while there is also a separate extension (the Primary Header Unit, +or PHU) containing additional metadata that applies to the entire +observation. However, as the data are processed, more data and/or +metadata may be added whose relationship is obscured by the limitations +of the MEF format. One example is the creation and propagation of information +describing the quality and uncertainty of the scientific data: while +this was a feature of +Gemini IRAF\ [#iraf]_, the coding required to implement it was cumbersome +and AstroData uses the `astropy.nddata.NDData` class, +as discussed in :ref:`containers`. This makes the relationship between these +data much clearer, and AstroData creates a syntax that makes readily apparent the +roles of other data and metadata that may be created during the reduction +process. + +An ``AstroData`` object therefore consists of one or more self-contained +"extensions" (data and metadata) plus additional data and metadata that is +relevant to all the extensions. In many data reduction processes, the same +operation will be performed on each extension (e.g., subtracting an overscan +region from a CCD frame) and an axiom of AstroData is that iterating over +the extensions produces AstroData "slices" which retain knowledge of the +top-level data and metadata. Since a slice has one (or more) extensions +plus this top-level (meta)data, it too is an ``AstroData`` object and, +specifically, an instance of the same subclass as its parent. + + +A final feature of AstroData is the implementation of very high-level metadata. +These data, called ``tags``, facilitate a key part of the Gemini data reduction +system, DRAGONS, by linking the astronomical data to the recipes +required to process them. They are explained in detail in :ref:`ad_tags` and the +Recipe System Programmers Manual\ [#rsprogman]_. + +.. note:: + + AstroData and DRAGONS have been developed for the reduction of data from + Gemini Observatory, which produces data in the FITS format that is still the + most widely-used format for astronomical data. In light of this, and the + limited resources in the Science User Support Department, we have only + *developed* support for FITS, even though the AstroData format is designed + to be independent of the file format. In some cases, this has led to + uncertainty and internal disagreement over where precisely to engage in + abstraction and, should AstroData support a different file format, we + may find alternative solutions that result in small, but possibly + significant, changes to the API. + + +.. [#iraf] ``_ + +.. [#rsprogman] |RSProgManual| diff --git a/_sources/manuals/progmanual/index.rst.txt b/_sources/manuals/progmanual/index.rst.txt new file mode 100644 index 00000000..751abd29 --- /dev/null +++ b/_sources/manuals/progmanual/index.rst.txt @@ -0,0 +1,22 @@ +.. Astrodata Programmer's Manual documentation master file, created by + sphinx-quickstart on Fri Jun 1 11:08:23 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +=================== +Programmer's Manual +=================== + +.. admonition:: Document ID + + PIPE-USER-104_AstrodataProgManual + +.. toctree:: + :maxdepth: 2 + + intro + design + adclass + containers + tags + descriptors diff --git a/_sources/manuals/progmanual/intro.rst.txt b/_sources/manuals/progmanual/intro.rst.txt new file mode 100644 index 00000000..d2b9a939 --- /dev/null +++ b/_sources/manuals/progmanual/intro.rst.txt @@ -0,0 +1,45 @@ +.. intro.rst + +.. _intro_progmanual: + +************************* +Precedents and Motivation +************************* + + +The Gemini Observatory has produced a number of tools for data processing. +Historically this has translated into a number of IRAF\ [#IRAF]_ packages but +the lack of long-term support for IRAF, coupled with the well-known +difficulty in creating robust reduction pipelines within the IRAF +environment, led to a decision +to adopt Python as a programming tool and a new +package was born: Gemini Python. Gemini Python provided tools to load and +manipulate Gemini-produced multi-extension FITS\ [#FITS]_ (MEF) files, +along with a pipeline that +allowed the construction of reduction recipes. At the center of this package +was the AstroData subpackage, which supported the abstraction of the FITS +files. + +Gemini Python reached version 1.0.1, released during November 2014. In 2015 +the Science User Support Department (SUSD) was created at Gemini, which took on the +responsibility of maintaining the software reduction tools, and started +planning future steps. With improved oversight and time and thought, it became +evident that the design of Gemini Python and, specially, of AstroData, made +further development a daunting task. + +In 2016 a decision was reached to overhaul Gemini Python. While the +principles behind AstroData were sound, the coding involved unnecessary +layers of abstraction and eschewed features of the Python language in favor +of its own implementation. Thus, +|DRAGONS| was born, with a new, simplified (and backward *incompatible*) +AstroData v2.0 (which we will refer to simply as AstroData) + +This manual documents both the high level design and some implementation +details of AstroData, together with an explanation of how to extend the +package to work for new environments. + +.. rubric:: Footnotes + +.. [#IRAF] http://iraf.net +.. [#FITS] The `Flexible Image Transport System `_ +.. [#DRAGONS] The `Data Reduction for Astronomy from Gemini Observatory North and South `_ package diff --git a/_sources/manuals/progmanual/tags.rst.txt b/_sources/manuals/progmanual/tags.rst.txt new file mode 100644 index 00000000..58bef846 --- /dev/null +++ b/_sources/manuals/progmanual/tags.rst.txt @@ -0,0 +1,160 @@ +.. tags.rst + +.. _ad_tags: + +**** +Tags +**** + +We described :ref:`in previous section ` how to generate tags for an +AstroData derivative. In this section we'll describe the algorithm that +generates the complete tag set out of the individual ``TagSet`` instances. The +algorithm collects all the tags in a list and then decides whether to apply +them or not following certain rules, but let's talk about ``TagSet`` first. + +``TagSet`` is actually a standard named tuple customized to generate default +values (``None``) for its missing members. Its signature is:: + + TagSet(add=None, remove=None, blocked_by=None, blocks=None, + if_present=None) + +The most common ``TagSet`` is an **additive** one: ``TagSet(['FOO', 'BAR'])``. +If all you need is to add tags, then you're done here. But the real power of +our tag generating system is that you can specify some conditions to apply a +certain ``TagSet``, or put restrictions on others. The different arguments to +``TagSet`` all expect a list (or some others work in the following way): + +* ``add``: if this ``TagSet`` is selected, then add all these members to the tag + set. +* ``remove``: if this ``TagSet`` is selected, then prevent all these members + from joining the tag set. +* ``blocked_by``: if any of the tags listed in here exist in the tag set, then + discard this ``TagSet`` altogether. +* ``blocks``: discard from the list of unprocessed ones any ``TagSet`` that + would add any of the tags listed here. +* ``if_present``: process this tag only if all the tags listed in here exist in + the tag set at this point. + +Note that ``blocked_by`` and ``blocks`` look like two sides of the same coin. +This is intentional: which one to use is up to the programmer, depending on +what will reduce the amount of typing and/or make the logic easier (sometimes one +wants to block a bunch of other tags from a single one; sometimes one wants a +tag to be blocked by a bunch of others). Furthermore, while ``blocks`` and +``blocked_by`` prevent the entire ``TagSet`` from being added if it contains a +tag affected by these, ``remove`` only affects the specific tag. + +Now, the algorithm works like this: + +#. Collect all the ``TagSet`` generated by methods in the instance that are + decorated using ``astro_data_tag``. +#. Then we sort them out: + + #. Those that subtract tags from the tag set go first (the ones with + non-empty ``remove`` or ``blocks``), allowing them to act early on + #. Those with non-empty ``blocked_by`` are moved to the end of the list, to + ensure that other tags can be generated before them. + #. Those with non-empty ``if_present`` are moved behind those with + ``blocked_by``. + +#. Now that we've sorted the tags, process them sequentially and for each one: + + #. If they require other tags to be present, make sure that this is the case. + If the requirements are not met, drop the tagset. If not... + #. Figure out if any other tag is blocking the tagset. This will be the + case if *any* of the tags to be added is in the "blocked" list, or if + any of the tags added by previous tag sets are in the ``blocked_by`` + list of the one being processed. Then... + #. If all the previous hurdles have been passed, apply the changes declared + by this tag (add, remove, and/or block others). + +Note that Python's sort algorithm is stable. This means, that if two elements +are indistinguishable from the point of view of the sorting algorithm, they are +guaranteed to stay in the same relative position. To better understand how this +affects our tags, and the algorithm itself, let's follow up with an example taken +from real code (the Gemini-generic and GMOS modules):: + + # Simple tagset, with only a constant, additive content + @astro_data_tag + def _tag_instrument(self): + return TagSet(['GMOS']) + + # Simple tagset, also with additive content. This one will + # check if the frame fits the requirements to be classified + # as "GMOS imaging". It returns a value conditionally: + # if this is not imaging, then it will return None, which + # means the algorithm will ignore the value + @astro_data_tag + def _tag_image(self): + if self.phu.get('GRATING') == 'MIRROR': + return TagSet(['IMAGE']) + + # This is a slightly more complex TagSet (but fairly simple, anyway), + # inherited by all Gemini instruments. + @astro_data_tag + def _type_gcal_lamp(self): + if self.phu.get('GCALLAMP') == 'IRhigh': + shut = self.phu.get('GCALSHUT') + if shut == 'OPEN': + return TagSet(['GCAL_IR_ON', 'LAMPON'], + blocked_by=['PROCESSED']) + elif shut == 'CLOSED': + return TagSet(['GCAL_IR_OFF', 'LAMPOFF'], + blocked_by=['PROCESSED']) + + # This tagset is only active when we detect that the frame is + # a bias. In that case we want to prevent the frame from being + # classified as "imaging" or "spectroscopy", which depend on the + # configuration of the instrument + @astro_data_tag + def _tag_bias(self): + if self.phu.get('OBSTYPE') == 'BIAS': + return TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']) + +These four simple tag methods will serve to illustrate the algorithm. Let's pretend +that the requirements for all four of them are somehow met, meaning that we get four +``TagSet`` instances in our list, in some random order. After step 1 in the algorithm, +then, we may have collected the following list:: + + [ TagSet(['GMOS']), + TagSet(['GCAL_IR_OFF', 'LAMPOFF'], blocked_by=['PROCESSED']), + TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']), + TagSet(['IMAGE']) ] + +The algorithm then proceeds to sort them. First, it will promote the ``TagSet`` +with non-empty ``blocks`` or ``remove``:: + + [ TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']), + TagSet(['GMOS']), + TagSet(['GCAL_IR_OFF', 'LAMPOFF'], blocked_by=['PROCESSED']), + TagSet(['IMAGE']) ] + +Note that the other three ``TagSet`` stay in exactly the same order. Now the +algorithm will sort the list again, moving the ones with non-empty +``blocked_by`` to the end:: + + [ TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']), + TagSet(['GMOS']), TagSet(['IMAGE']), + TagSet(['GCAL_IR_OFF', 'LAMPOFF'], blocked_by=['PROCESSED']) ] + +Note that at each step, all the instances (except the ones "being moved") have +kept the same position relative to each other -here's where the "stability" of +the sorting comes into play,- ensuring that each step does not affect the previous +one. Finally, there are no ``if_present`` in our example, so no more instances are +moved around. + +Now the algorithm prepares three empty sets (``tags``, ``removals``, and ``blocked``), +and starts iterating over the ``TagSet`` list. + + 1. For the first ``TagSet`` there are no blocks or removals, so we just add its + contents to the current sets: ``tags = {'BIAS', 'CAL'}``, + ``blocked = {'IMAGE', 'SPECT'}``. + 2. Then comes ``TagSet(['GMOS'])``. Again, there are no removals in place, and + ``GMOS`` is not in the list of blocked tags. Thus, we just add it to the current + tag set: ``tags = {'BIAS', 'CAL', 'GMOS'}``. + 3. When processing ``TagSet(['IMAGE'])``, the algorithm observes that this ``IMAGE`` + is in the ``blocked`` set, and stops processing this tag set. + 4. Finally, neither ``GCAL_IR_OFF`` nor ``LAMPOFF`` are in ``blocked``, and + ``PROCESSED`` is not in ``tags``, meaning that we can add this tag set to + the final one. + +Our result will look something like: ``{'BIAS', 'CAL', 'GMOS', 'GCAL_IR_OFF', 'LAMPOFF'}`` diff --git a/_sources/manuals/usermanual/data.rst.txt b/_sources/manuals/usermanual/data.rst.txt new file mode 100644 index 00000000..0cc7d026 --- /dev/null +++ b/_sources/manuals/usermanual/data.rst.txt @@ -0,0 +1,903 @@ +.. data.rst + +.. _pixel-data: + +********** +Pixel Data +********** + +**Try it yourself** + +Download the data package (:ref:`datapkg`) if you wish to follow along and run the +examples. Then :: + + $ cd /ad_usermanual/playground + $ python + +Then import core astrodata and the Gemini astrodata configurations. :: + + >>> import astrodata + >>> import gemini_instruments + + +Operate on Pixel Data +===================== +The pixel data are stored in the ``AstroData`` object as a list of +``NDAstroData`` objects. The ``NDAstroData`` is a subclass of Astropy's +``NDData`` class which combines in one "package" the pixel values, the +variance, and the data quality plane or mask (as well as associated meta-data). +The data can be retrieved as a standard NumPy ``ndarray``. + +In the sections below, we will present several typical examples of data +manipulation. But first let's start with a quick example on how to access +the pixel data. :: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >>> the_data = ad[1].data + >>> type(the_data) + + + >>> # Loop through the extensions + >>> for ext in ad: + ... the_data = ext.data + ... print(the_data.sum()) + 333071030 + 335104458 + 333170484 + 333055206 + +In this example, we first access the pixels for the second extensions. +Remember that in Python, list are zero-indexed, hence we access the second +extension as ``ad[1]``. The ``.data`` attribute contains a NumPy ``ndarray``. +In the for-loop, for each extension, we get the data and use the NumPy +``.sum()`` method to sum the pixel values. Anything that can be done +with a ``ndarray`` can be done on ``AstroData`` pixel data. + + +Arithmetic on AstroData Objects +=============================== +``AstroData`` objects support basic in-place arithmetics with these methods: + ++----------------+-------------+ +| addition | .add() | ++----------------+-------------+ +| subtraction | .subtract() | ++----------------+-------------+ +| multiplication | .multiply() | ++----------------+-------------+ +| division | .divide() | ++----------------+-------------+ + +Normal, not in-place, arithmetics is also possible using the standard +operators, ``+``, ``-``, ``*``, and ``/``. + +The big advantage of using ``AstroData`` to do arithmetics is that the +variance and mask, if present, will be propagated through to the output +``AstroData`` object. We will explore the variance propagation in the next +section and mask usage later in this chapter. + +Simple operations +----------------- +Here are a few examples of arithmetics on ``AstroData`` objects.:: + + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + + >>> # Addition + >>> ad.add(50.) + >>> ad = ad + 50. + >>> ad += 50. + + >>> # Subtraction + >>> ad.subtract(50.) + >>> ad = ad - 50. + >>> ad -= 50. + + >>> # Multiplication (Using a descriptor) + >>> ad.multiply(ad.exposure_time()) + >>> ad = ad * ad.exposure_time() + >>> ad *= ad.exposure_time() + + >>> # Division (Using a descriptor) + >>> ad.divide(ad.exposure_time()) + >>> ad = ad / ad.exposure_time() + >>> ad /= ad.exposure_time() + +When the syntax ``adout = adin + 1`` is used, the output variable is a copy +of the original. In the examples above we reassign the result back onto the +original. The two other forms, ``ad.add()`` and ``ad +=`` are in-place +operations. + +When a descriptor returns a list because the value changes for each +extension, a for-loop is needed:: + + >>> for (ext, gain) in zip(ad, ad.gain()): + ... ext.multiply(gain) + +If you want to do the above but on a new object, leaving the original unchanged, +use ``deepcopy`` first. :: + + >>> from copy import deepcopy + >>> adcopy = deepcopy(ad) + >>> for (ext, gain) in zip(adcopy, adcopy.gain()): + ... ext.multiply(gain) + + +Operator Precedence +------------------- +The ``AstroData`` arithmetics methods can be stringed together but beware that +there is no operator precedence when that is done. For arithmetics that +involve more than one operation, it is probably safer to use the normal +Python operator syntax. Here is a little example to illustrate the difference. + +:: + + >>> ad.add(5).multiply(10).subtract(5) + + >>> # means: ad = ((ad + 5) * 10) - 5 + >>> # NOT: ad = ad + (5 * 10) - 5 + +This is because the methods modify the object in-place, one operation after +the other from left to right. This also means that the original is modified. + +This example applies the expected operator precedence:: + + >>> ad = ad + ad * 3 - 40. + >>> # means: ad = ad + (ad * 3) - 40. + +If you need a copy, leaving the original untouched, which is sometimes useful +you can use ``deepcopy`` or just use the normal operator and assign to a new +variable.:: + + >>> adnew = ad + ad * 3 - 40. + + +Variance +======== +When doing arithmetic on an ``AstroData`` object, if a variance is present +it will be propagated appropriately to the output no matter which syntax +you use (the methods or the Python operators). + +Adding a Variance Plane +----------------------- +In this example, we will add the poisson noise to an ``AstroData`` dataset. +The data is still in ADU, therefore the poisson noise as variance is +``signal / gain``. We want to set the variance for each of the pixel +extensions. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >>> for (extension, gain) in zip(ad, ad.gain()): + ... extension.variance = extension.data / gain + +Check ``ad.info()``, you will see a variance plane for each of the four +extensions. + +Automatic Variance Propagation +------------------------------ +As mentioned before, if present, the variance plane will be propagated to the +resulting ``AstroData`` object when doing arithmetics. The variance +calculation assumes that the data are not correlated. + +Let's look into an example. + +:: + + >>> # output = x * x + >>> # var_output = var * x^2 + var * x^2 + >>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + + >>> ad[1].data[50,50] + 56.160931 + >>> ad[1].variance[50,50] + 96.356529 + >>> adout = ad * ad + >>> adout[1].data[50,50] + 3154.05 + >>> adout[1].variance[50,50] + 607826.62 + +Data Quality Plane +================== +The NDData ``mask`` stores the data quality plane. The simplest form is a +True/False array of the same size at the pixel array. In Astrodata we favor +a bit array that allows for additional information about why the pixel is being +masked. For example at Gemini here is our bit mapping for bad pixels. + ++---------------+-------+ +| Meaning | Value | ++===============+=======+ +| Bad pixel | 1 | ++---------------+-------+ +| Non Linear | 2 | ++---------------+-------+ +| Saturated | 4 | ++---------------+-------+ +| Cosmic Ray | 8 | ++---------------+-------+ +| No Data | 16 | ++---------------+-------+ +| Overlap | 32 | ++---------------+-------+ +| Unilluminated | 64 | ++---------------+-------+ + +(These definitions are located in ``geminidr.gemini.lookups.DQ_definitions``.) + +So a pixel marked 10 in the mask, would be a "non-linear" "cosmic ray". The +``AstroData`` masks are propagated with bitwise-OR operation. For example, +let's say that we are stacking frames. A pixel is set as bad (value 1) +in one frame, saturated in another (value 4), and fine in all the other +the frames (value 0). The mask of the resulting stack will be assigned +a value of 5 for that pixel. + +These bitmasks will work like any other NumPy True/False mask. There is a +usage example below using the mask. + +The mask can be accessed as follow:: + + >>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >>> ad.info() + + >>> ad[2].mask + + +Display +======= +Since the data is stored in the ``AstroData`` object as a NumPy ``ndarray`` +any tool that works on ``ndarray`` can be used. To display to DS9 there +is the ``imexam`` package. The ``numdisplay`` package is still available for +now but it is no longer supported by STScI. We will show +how to use ``imexam`` to display and read the cursor position. Read the +documentation on that tool to learn more about what else it has +to offer. + +Displaying with imexam +---------------------- + +Here is an example how to display pixel data to DS9 with ``imexam``. You must +start ``ds9`` before running this example. + +:: + + >>> import imexam + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + + # Connect to the DS9 window (should already be opened.) + >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0]) + + >>> ds9.view(ad[0].data) + + # To scale "a la IRAF" + >>> ds9.view(ad[0].data) + >>> ds9.scale('zscale') + + # To set the mininum and maximum scale values + >>> ds9.view(ad[0].data) + >>> ds9.scale('limits 0 2000') + + +Retrieving cursor position with imexam +-------------------------------------- + +The function ``readcursor()`` can be used to retrieve cursor +position in pixel coordinates. Note that it will **not** respond to +mouse clicks, **only** keyboard entries are acknowledged. + +When invoked, ``readcursor()`` will stop the flow of the program and wait +for the user to put the cursor on top of the image and type a key. A +tuple with three values will be returned: the x and +y coordinates **in 0-based system**, and the value of the key the user +hit. + +:: + + >>> import imexam + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + + # Connect to the DS9 window (should already be opened.) + # and display + >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0]) + >>> ds9.view(ad[0].data) + >>> ds9.scale('zscale') + + + >>> cursor_coo = ds9.readcursor() + >>> print(cursor_coo) + + # To extract only the x,y coordinates + >>> (xcoo, ycoo) = cursor_coo[:2] + >>> print(xcoo, ycoo) + + # If you are also interested in the keystroke + >>> keystroke = cursor_coo[2] + >>> print('You pressed this key: %s' % keystroke) + + +Useful tools from the NumPy, SciPy, and Astropy Packages +======================================================== +Like for the Display section, this section is not really specific to +Astrodata but is rather a quick show-and-tell of a few things that can +be done on the pixels with the big scientific packages NumPy, SciPy, +and Astropy. + +Those three packages are very large and rich. They have their own +extensive documentation and it is highly recommend for the users to learn about what +they have to offer. It might save you from re-inventing the wheel. + +The pixels, the variance, and the mask are stored as NumPy ``ndarray``'s. +Let us go through some basic examples, just to get a feel for how the +data in an ``AstroData`` object can be manipulated. + +ndarray +------- +The data are contained in NumPy ``ndarray`` objects. Any tools that works +on an ``ndarray`` can be used with Astrodata. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >>> data = ad[0].data + + >>> # Shape of the array. (equivalent to NAXIS2, NAXIS1) + >>> data.shape + (2112, 288) + + >>> # Value of a pixel at "IRAF" or DS9 coordinates (100, 50) + >>> data[49,99] + 455 + + >>> # Data type + >>> data.dtype + dtype('uint16') + +The two most important thing to remember for users coming from the IRAF +world or the Fortran world are that the array has the y-axis in the first +index, the x-axis in the second, and that the array indices are zero-indexed, +not one-indexed. The examples above illustrate those two critical +differences. + +It is sometimes useful to know the data type of the values stored in the +array. Here, the file is a raw dataset, fresh off the telescope. No +operations has been done on the pixels yet. The data type of Gemini raw +datasets is always "Unsigned integer (0 to 65535)", ``uint16``. + +.. warning:: + Beware that doing arithmetic on ``uint16`` can lead to unexpected + results. This is a NumPy behavior. If the result of an operation + is higher than the range allowed by ``uint16``, the output value will + be "wrong". The data type will not be modified to accommodate the large + value. A workaround, and a safety net, is to multiply the array by + ``1.0`` to force the conversion to a ``float64``. :: + + >>> a = np.array([65535], dtype='uint16') + >>> a + a + array([65534], dtype=uint16) + >>> 1.0*a + a + array([ 131070.]) + + + +Simple Numpy Statistics +----------------------- +A lot of functions and methods are available in NumPy to probe the array, +too many to cover here, but here are a couple examples. + +:: + + >>> import numpy as np + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> data = ad[0].data + + >>> data.mean() + >>> np.average(data) + >>> np.median(data) + +Note how ``mean()`` is called differently from the other two. ``mean()`` +is a ``ndarray`` method, the others are NumPy functions. The implementation +details are clearly well beyond the scope of this manual, but when looking +for the tool you need, keep in mind that there are two sets of functions to +look into. Duplications like ``.mean()`` and ``np.average()`` can happen, +but they are not the norm. The readers are strongly encouraged to refer to +the NumPy documentation to find the tool they need. + + +Clipped Statistics +------------------ +It is common in astronomy to apply clipping to the statistics, a clipped +average, for example. The NumPy ``ma`` module can be used to create masks +of the values to reject. In the examples below, we calculated the clipped +average of the first pixel extension with a rejection threshold set to ++/- 3 times the standard deviation. + +Before Astropy, it was possible to do something like that with only +NumPy tools, like in this example:: + + >>> import numpy as np + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> data = ad[0].data + + >>> stddev = data.std() + >>> mean = data.mean() + + >>> clipped_mean = np.ma.masked_outside(data, mean-3*stddev, mean+3*stddev).mean() + +There is no iteration in that example. It is a straight one-time clipping. + +For something more robust, there is an Astropy function that can help, in +particular by adding an iterative process to the calculation. Here is +how it is done:: + + >>> import numpy as np + >>> from astropy.stats import sigma_clip + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> data = ad[0].data + + >>> clipped_mean = np.ma.mean(sigma_clip(data, sigma=3)) + + +Filters with SciPy +------------------ +Another common operation is the filtering of an image, for example convolving +with a gaussian filter. The SciPy module ``ndimage.filters`` offers +several functions for image processing. See the SciPy documentation for +more information. + +The example below applies a gaussian filter to the pixel array. + +:: + + >>> from scipy.ndimage import filters + >>> import imexam + + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >>> data = ad[0].data + + >>> # We need to prepare an array of the same size and shape as + >>> # the data array. The result will be put in there. + >>> convolved_data = np.zeros(data.size).reshape(data.shape) + + >>> # We now apply the convolution filter. + >>> sigma = 10. + >>> filters.gaussian_filter(data, sigma, output=convolved_data) + + >>> # Let's visually compare the convolved image with the original + >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0]) + >>> ds9.view(data) + >>> ds9.scale('zscale') + >>> ds9.frame(2) + >>> ds9.view(convolved_data) + >>> ds9.scale('zscale') + >>> ds9.blink() + >>> # When you are convinced it's been convolved, stop the blinking. + >>> ds9.blink(blink=False) + +Note that there is an Astropy way to do this convolution, with tools in +``astropy.convolution`` package. Beware that for this particular kernel +we have found that the Astropy ``convolve`` function is extremely slow +compared to the SciPy solution. +This is because the SciPy function is optimized for a Gaussian convolution +while the generic ``convolve`` function in Astropy can take in any kernel. +Being able to take in any kernel is a very powerful feature, but the cost +is time. The lesson here is do your research, and find the best tool for +your needs. + + +Many other tools +---------------- +There are many, many other tools available out there. Here are the links to +the three big projects we have featured in this section. + +* NumPy: `www.numpy.org `_ +* SciPy: `www.scipy.org `_ +* Astropy: `www.astropy.org `_ + +Using the Astrodata Data Quality Plane +====================================== +Let us look at an example where the use of the Astrodata mask is +necessary to get correct statistics. A GMOS imaging frame has large sections +of unilluminated pixels; the edges are not illuminated and there are two +bands between the three CCDs that represent the physical gap between the +CCDs. Let us have a look at the pixels to have a better sense of the +data:: + + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >>> import imexam + >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0]) + + >>> ds9.view(ad[0].data) + >>> ds9.scale('zscale') + +See how the right and left portions of the frame are not exposed to the sky, +and the 45 degree angle cuts of the four corners. The chip gaps too. +If we wanted to do statistics on the whole frames, we certainly would not want +to include those unilluminated areas. We would want to mask them out. + +Let us have a look at the mask associated with that image:: + + >>> ds9.view(ad[0].mask) + >>> ds9.scale('zscale') + +The bad sections are all white (pixel value > 0). There are even some +illuminated pixels that have been marked as bad for a reason or another. + +Let us use that mask to reject the pixels with no or bad information and +do calculations only on the good pixels. For the sake of simplicity we will +just do an average. This is just illustrative. We show various ways to +accomplish the task; choose the one that best suits your need or that you +find most readable. + +:: + + >>> import numpy as np + + >>> # For clarity... + >>> data = ad[0].data + >>> mask = ad[0].mask + + >>> # Reject all flagged pixels and calculate the mean + >>> np.mean(data[mask == 0]) + >>> np.ma.masked_array(data, mask).mean() + + >>> # Reject only the pixels flagged "no_data" (bit 16) + >>> np.mean(data[(mask & 16) == 0]) + >>> np.ma.masked_array(data, mask & 16).mean() + >>> np.ma.masked_where(mask & 16, data).mean() + +The "long" form with ``np.ma.masked_*`` is useful if you are planning to do +more than one operation on the masked array. For example:: + + >>> clean_data = np.ma.masked_array(data, mask) + >>> clean_data.mean() + >>> np.ma.median(clean_data) + >>> clean_data.max() + + +Manipulate Data Sections +======================== +So far we have shown examples using the entire data array. It is possible +to work on sections of that array. If you are already familiar with +Python, you probably already know how to do most if not all of what is in +this section. For readers new to Python, and especially those coming +from IRAF, there are a few things that are worth explaining. + +When indexing a NumPy ``ndarray``, the left most number refers to the +highest dimension's axis. For example, in a 2D array, the IRAF section +are in (x-axis, y-axis) format, while in Python they are in +(y-axis, x-axis) format. Also important to remember is that the ``ndarray`` +is 0-indexed, rather than 1-indexed like in Fortran or IRAF. + +Putting it all together, a pixel position (x,y) = (50,75) in IRAF or from +the cursor on a DS9 frame, is accessed in Python as ``data[74,49]``. +Similarly, the IRAF section [10:20, 30:40] translate in Python to +[9:20, 29:40]. Also remember that when slicing in Python, the upper limit +of the slice is not included in the slice. This is why here we request +20 and 40 rather 19 and 39. + +Let's put it in action. + +Basic Statistics on Section +--------------------------- +In this example, we do simple statistics on a section of the image. + +:: + + >>> import numpy as np + + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >>> data = ad[0].data + + >>> # Get statistics for a 25x25 pixel-wide box centered on pixel + >>> # (50,75) (DS9 frame coordinate) + >>> xc = 49 + >>> yc = 74 + >>> buffer = 25 + >>> (xlow, xhigh) = (xc - buffer//2, xc + buffer//2 + 1) + >>> (ylow, yhigh) = (yc - buffer//2, yc + buffer//2 + 1) + >>> # The section is [62:87, 37:62] + >>> stamp = data[ylow:yhigh, xlow:xhigh] + >>> mean = stamp.mean() + >>> median = np.median(stamp) + >>> stddev = stamp.std() + >>> minimum = stamp.min() + >>> maximum = stamp.max() + + >>> print(' Mean Median Stddev Min Max\n \ + ... %.2f %.2f %.2f %.2f %.2f' % \ + ... (mean, median, stddev, minimum, maximum)) + +Have you noticed that the median is calculated with a function rather +than a method? This is simply because the ``ndarray`` object does not +have a method to calculate the median. + +Example - Overscan Subtraction with Trimming +-------------------------------------------- +Several concepts from previous sections and chapters are used in this +example. The Descriptors are used to retrieve the overscan section and +the data section information from the headers. Statistics are done on the +NumPy ``ndarray`` representing the pixel data. Astrodata arithmetics is +used to subtract the overscan level. Finally, the overscan section is +trimmed off and the modified ``AstroData`` object is written to a new file +on disk. + +To make the example more complete, and to show that when the pixel data +array is trimmed, the variance (and mask) arrays are also trimmed, let us +add a variance plane to our raw data frame. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >>> for (extension, gain) in zip(ad, ad.gain()): + ... extension.variance = extension.data / gain + ... + + >>> # Here is how the data structure looks like before the trimming. + >>> ad.info() + Filename: ../playdata/N20170609S0154.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED + + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + [ 1] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + [ 2] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + [ 3] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + + >>> # Let's operate on the first extension. + >>> # + >>> # The section descriptors return the section in a Python format + >>> # ready to use, 0-indexed. + >>> oversec = ad[0].overscan_section() + >>> datasec = ad[0].data_section() + + >>> # Measure the overscan level + >>> mean_overscan = ad[0].data[oversec.y1: oversec.y2, oversec.x1: oversec.x2].mean() + + >>> # Subtract the overscan level. The variance will be propagated. + >>> ad[0].subtract(mean_overscan) + + >>> # Trim the data to remove the overscan section and keep only + >>> # the data section. Note that the WCS will be automatically + >>> # adjusted when the trimming is done. + >>> # + >>> # Here we work on the NDAstroData object to have the variance + >>> # trimmed automatically to the same size as the science array. + >>> # To reassign the cropped NDAstroData, we use the reset() method. + >>> ad[0].reset(ad[0].nddata[datasec.y1:datasec.y2, datasec.x1:datasec.x2] + + >>> # Now look at the dimensions of the first extension, science + >>> # and variance. That extension is smaller than the others. + >>> ad.info() + Filename: ../playdata/N20170609S0154.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED + + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 256) float64 + .variance ndarray (2112, 256) float64 + [ 1] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + [ 2] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + [ 3] science NDAstroData (2112, 288) uint16 + .variance ndarray (2112, 288) float64 + + >>> # We can write this to a new file + >>> ad.write('partly_overscan_corrected.fits') + +A new feature presented in this example is the ability to work on the +``NDAstroData`` object directly. This is particularly useful when cropping +the science pixel array as one will want the variance and the mask arrays +cropped exactly the same way. Taking a section of the ``NDAstroData`` +object (ad[0].nddata[y1:y2, x1:x2]), instead of just the ``.data`` array, +does all that for us. + +To reassign the cropped ``NDAstroData`` to the extension one uses the +``.reset()`` method as shown in the example. + +Of course to do the overscan correction correctly and completely, one would +loop over all four extensions. But that's the only difference. + +Data Cubes +========== +Reduced Integral Field Unit (IFU) data is commonly represented as a cube, +a three-dimensional array. The ``data`` component of an ``AstroData`` +object extension can be such a cube, and it can be manipulated and explored +with NumPy, AstroPy, SciPy, imexam, like we did already in this section +with 2D arrays. We can use matplotlib to plot the 1D spectra represented +in the third dimension. + +In Gemini IFU cubes, the first axis is the X-axis, the second, the Y-axis, +and the wavelength is in the third axis. Remember that in a ``ndarray`` +that order is reversed (wlen, y, x). + +In the example below we "collapse" the cube along the wavelenth axis to +create a "white light" image and display it. Then we plot a 1D spectrum +from a given (x,y) position. + +:: + + >>> import imexam + >>> import matplotlib.pyplot as plt + + >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0]) + + >>> adcube = astrodata.open('../playdata/gmosifu_cube.fits') + >>> adcube.info() + + >>> # Sum along the wavelength axis to create a "white light" image + >>> summed_image = adcube[0].data.sum(axis=0) + >>> ds9.view(summed_image) + >>> ds9.scale('minmax') + + >>> # Plot a 1-D spectrum from the spatial position (14,25). + >>> plt.plot(adcube[0].data[:,24,13]) + >>> plt.show() # might be needed, depends on matplotlibrc interactive setting + + +Now that is nice but it would be nicer if we could plot the x-axis in units +of Angstroms instead of pixels. We use the AstroData's WCS handler, which is +based on ``gwcs.wcs.WCS`` to get the necessary information. A particularity +of ``gwcs.wcs.WCS`` is that it refers to the axes in the "natural" way, +(x, y, wlen) contrary to Python's (wlen, y, x). It truly requires you to pay +attention. + +:: + + >>> import matplotlib.pyplot as plt + + >>> adcube = astrodata.open('../playdata/gmosifu_cube.fits') + + # We get the wavelength axis in Angstroms at the position we want to + # extract, x=13, y=24. + # The wcs call returns a 3-element list, the third element ([2]) contains + # the wavelength values for each pixel along the wavelength axis. + + >>> length_wlen_axis = adcube[0].shape[0] # (wlen, y, x) + >>> wavelengths = adcube[0].wcs(13, 24, range(length_wlen_axis))[2] # (x, y, wlen) + + # We get the intensity along that axis + >>> intensity = adcube[0].data[:, 24, 13] # (wlen, y, x) + + # We plot + plt.clf() + plt.plot(wavelengths, intensity) + plt.show() + + +Plot Data +========= +The main plotting package in Python is ``matplotlib``. We have used it in the +previous section on data cubes to plot a spectrum. There is also the project +called ``imexam`` which provides astronomy-specific tools for the +exploration and measurement of data. We have also used that package above to +display images to DS9. + +In this section we absolutely do not aim at covering all the features of +either package but rather to give a few examples that can get the readers +started in their exploration of the data and of the visualization packages. + +Refer to the projects web pages for full documentation. + +* Matplotlib: `https://matplotlib.org `_ +* imexam: `https://github.com/spacetelescope/imexam `_ + +Matplotlib +---------- +With Matplotlib you have full control on your plot. You do have to do a bit +for work to get it perfect though. However it can produce publication +quality plots. Here we just scratch the surface of Matplotlib. + +:: + + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> from astropy import wcs + + >>> ad_image = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >>> ad_spectrum = astrodata.open('../playdata/estgsS20080220S0078.fits') + + >>> # Line plot from image. Row #1044 (y-coordinate) + >>> line_index = 1043 + >>> line = ad_image[0].data[line_index, :] + >>> plt.clf() + >>> plt.plot(line) + >>> plt.show() + + >>> # Column plot from image, averaging across 11 pixels around colum #327 + >>> col_index = 326 + >>> width = 5 + >>> xlow = col_index - width + >>> xhigh = col_index + width + 1 + >>> thick_column = ad_image[0].data[:, xlow:xhigh] + >>> plt.clf() + >>> plt.plot(thick_column.mean(axis=1)) # mean along the width. + >>> plt.show() + >>> plt.ylim(0, 50) # Set the y-axis range + >>> plt.plot(thick_column.mean(axis=1)) + >>> plt.show() + + >>> # Contour plot for a section of an image. + >>> center = (1646, 2355) + >>> width = 15 + >>> xrange = (center[1]-width//2, center[1] + width//2 + 1) + >>> yrange = (center[0]-width//2, center[0] + width//2 + 1) + >>> blob = ad_image[0].data[yrange[0]:yrange[1], xrange[0]:xrange[1]] + >>> plt.clf() + >>> plt.imshow(blob, cmap='gray', origin='lower') + >>> plt.contour(blob) + >>> plt.show() + + >>> # Spectrum in pixels + >>> plt.clf() + >>> plt.plot(ad_spectrum[0].data) + >>> plt.show() + + >>> # Spectrum in Angstroms + >>> spec_wcs = wcs.WCS(ad_spectrum[0].hdr) + >>> pixcoords = np.array(range(ad_spectrum[0].data.shape[0])) + >>> wlen = spec_wcs.wcs_pix2world(pixcoords, 0)[0] + >>> plt.clf() + >>> plt.plot(wlen, ad_spectrum[0].data) + >>> plt.show() + + +imexam +------ +For those who have used IRAF, ``imexam`` is a well-known tool. The Python +``imexam`` reproduces many of of the features of its IRAF predecesor, the interactive mode of +course, but it also offers programmatic tools. One can even control DS9 +from Python. As for Matplotlib, here we really just scratch the surface of +what ``imexam`` has to offer. + +:: + + >>> import imexam + >>> from imexam.imexamine import Imexamine + + >>> ad_image = astrodata.open('../playdata/N20170521S0925_forStack.fits') + + # Display the image + >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0]) + >>> ds9.view(ad_image[0].data) + >>> ds9.scale('zscale') + + # Run in interactive mode. Try the various commands. + >>> ds9.imexam() + + # Use the programmatic interface + # First initialize an Imexamine object. + >>> plot = Imexamine() + + # Line plot from image. Row #1044 (y-coordinate) + >>> line_index = 1043 + >>> plot.plot_line(0, line_index, ad_image[0].data) + + # Column plot from image, averaging across 11 pixels around colum #327 + # There is no setting for this, so we have to do something similar + # to what we did with matplotlib. + >>> col_index = 326 + >>> width = 5 + >>> xlow = col_index - width + >>> xhigh = col_index + width + 1 + >>> thick_column = ad_image[0].data[:, xlow:xhigh] + >>> mean_column = thick_column.mean(axis=1) + >>> plot.plot_column(0, 0, np.expand_dims(mean_column, 1)) + + >>> # Contour plot for a section of an image. + >>> center = (1646, 2355) # in python coordinates + >>> width = 15 + >>> plot.contour_pars['ncolumns'][0] = width + >>> plot.contour_pars['nlines'][0] = width + >>> plot.contour(center[1], center[0], ad_image[0].data) diff --git a/_sources/manuals/usermanual/headers.rst.txt b/_sources/manuals/usermanual/headers.rst.txt new file mode 100644 index 00000000..fec6cdee --- /dev/null +++ b/_sources/manuals/usermanual/headers.rst.txt @@ -0,0 +1,302 @@ +.. headers.rst + +.. _headers: + +******************** +Metadata and Headers +******************** + +**Try it yourself** + +Download the data package (:ref:`datapkg`) if you wish to follow along and run the +examples. Then :: + + $ cd /ad_usermanual/playground + $ python + +You need to import Astrodata and the Gemini instrument configuration package. + +:: + + >>> import astrodata + >>> import gemini_instruments + +Astrodata Descriptors +===================== + +We show in this chapter how to use the Astrodata Descriptors. But first +let's explain what they are. + +Astrodata Descriptors provide a "header-to-concept" mapping that allows the +user to access header information from a unique interface, regardless of +which instrument the dataset is from. Like for the Astrodata Tags, the +mapping is coded in a configuration package separate from core Astrodata. +For Gemini instruments, that package is named ``gemini_instruments``. + +For example, if the user is interested to know the effective filter used +for an observation, normally one needs to know which specific keyword or +set of keywords to look at for that instrument. However, once the concept +of "filter" is coded as a Descriptor, the user only needs to call the +``filter_name()`` descriptor to retrieve the information. + +The Descriptors are closely associated with the Astrodata Tags. In fact, +they are implemented in the same ``AstroData`` class as the tags. Once +the specific ``AstroData`` class is selected (upon opening the file), all +the tags and descriptors for that class are defined. For example, all the +descriptor functions of GMOS data, ie. the functions that map a descriptor +concept to the actual header content, are defined in the ``AstroDataGmos`` +class. + +This is all completely transparent to the user. One simply opens the data +file and all the descriptors are ready to be used. + +.. note:: + Of course if the Descriptors have not been implemented for that specific + data, they will not work. They should all be defined for Gemini data. + For other sources, the headers can be accessed directly, one keyword at + a time. This type of access is discussed below. This is also useful + when the information needed is not associated with one of the standard + descriptors. + +To get the list of descriptors available for an ``AstroData`` object:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> ad.descriptors + ('airmass', 'amp_read_area', 'ao_seeing', ... + ...) + +Most Descriptor names are readily understood, but one can get a short +description of what the Descriptor refers to by calling the Python help +function. For example:: + + >>> help(ad.airmass) + >>> help(ad.filter_name) + +The full list of standard descriptors is available in the Appendix +:ref:`descriptors`. + +Accessing Metadata +================== + +Accessing Metadata with Descriptors +----------------------------------- +Whenever possible the Descriptors should be used to get information from +headers. This allows for maximum re-usability of the code as it will then +work on any datasets with an ``AstroData`` class. + +Here are a few examples using Descriptors:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >>> #--- print a value + >>> print('The airmass is : ', ad.airmass()) + The airmass is : 1.089 + + >>> #--- use a value to control the flow + >>> if ad.exposure_time() < 240.: + ... print('This is a short exposure.') + ... else: + ... print('This is a long exposure.') + This is a short exposure. + + >>> #--- multiply all extensions by their respective gain + >>> for ext, gain in zip(ad, ad.gain()): + ... ext *= gain + + >>> #--- do arithmetics + >>> fwhm_pixel = 3.5 + >>> fwhm_arcsec = fwhm_pixel * ad.pixel_scale() + +The return values for Descriptors depend on the nature of the information +being requested and the number of extensions in the ``AstroData`` object. +When the value has words, it will be string, if it is a number +it will be a float or an integer. +The dataset used in this section has 4 extensions. When the descriptor +value can be different for each extension, the descriptor will return a +Python list. + +:: + + >>> ad.airmass() + 1.089 + >>> ad.gain() + [2.03, 1.97, 1.96, 2.01] + >>> ad.filter_name() + 'open1-6&g_G0301' + +Some descriptors accept arguments. For example:: + + >>> ad.filter_name(pretty=True) + 'g' + +A full list of standard descriptors is available in the Appendix +:ref:`descriptors`. + + +Accessing Metadata Directly +--------------------------- +Not all header content is mapped to Descriptors, nor should it. Direct access +is available for header content falling outside the scope of the descriptors. + +One important thing to keep in mind is that the PHU (Primary Header Unit) and +the extension headers are accessed slightly differently. The attribute +``phu`` needs to be used for the PHU, and ``hdr`` for the extension headers. + +Here are some examples of direct header access:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + + >>> #--- Get keyword value from the PHU + >>> ad.phu['AOFOLD'] + 'park-pos.' + + >>> #--- Get keyword value from a specific extension + >>> ad[0].hdr['CRPIX1'] + 511.862999160781 + + >>> #--- Get keyword value from all the extensions in one call. + >>> ad.hdr['CRPIX1'] + [511.862999160781, 287.862999160781, -0.137000839218696, -224.137000839219] + + + +Whole Headers +------------- +Entire headers can be retrieved as ``fits`` ``Header`` objects:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> type(ad.phu) + + >>> type(ad[0].hdr) + + +In interactive mode, it is possible to print the headers on the screen as +follows:: + + >>> ad.phu + SIMPLE = T / file does conform to FITS standard + BITPIX = 16 / number of bits per data pixel + NAXIS = 0 / number of data axes + .... + + >>> ad[0].hdr + XTENSION= 'IMAGE ' / IMAGE extension + BITPIX = 16 / number of bits per data pixel + NAXIS = 2 / number of data axes + .... + + + +Updating, Adding and Deleting Metadata +====================================== +Header cards can be updated, added to, or deleted from the headers. The PHU +and the extensions headers are again accessed in a mostly identical way +with ``phu`` and ``hdr``, respectively. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + +Add and update a keyword, without and with comment:: + + >>> ad.phu['NEWKEY'] = 50. + >>> ad.phu['NEWKEY'] = (30., 'Updated PHU keyword') + + >>> ad[0].hdr['NEWKEY'] = 50. + >>> ad[0].hdr['NEWKEY'] = (30., 'Updated extension keyword') + +Delete a keyword:: + + >>> del ad.phu['NEWKEY'] + >>> del ad[0].hdr['NEWKEY'] + + +World Co-ordinate System attribute +================================== + +The ``wcs`` of an extension's ``nddata`` attribute (eg. ``ad[0].nddata.wcs``; +see :ref:`pixel-data`) is stored as an instance of ``astropy.wcs.WCS`` (a +standard FITS WCS object) or ``gwcs.WCS`` (a `"Generalized WCS" or gWCS +`_ object). This defines a transformation +between array indices and some other co-ordinate system such as "World" +co-ordinates (see `APE 14 +`_). GWCS allows +multiple, almost arbitrary co-ordinate mappings from different calibration +steps (eg. CCD mosaicking, distortion correction & wavelength calibration) to +be combined in a single, reversible transformation chain --- but this +information cannot always be represented as a FITS standard WCS. If a gWCS +object is too complex to be defined by the basic FITS keywords, it gets stored +as a table extension named 'WCS' when the ``AstroData`` instance is saved to a +file (with the same EXTVER as the corresponding 'SCI' array) and the FITS +header keywords are updated to provide an approximation to the true WCS and an +additional keyword ``FITS-WCS`` is added with the value 'APPROXIMATE'. +The representation in the table is produced using +`ASDF `_, with one line of text per row. Likewise, +when the file is re-opened, the gWCS object gets recreated in ``wcs`` from the +table. If the transformation defined by the gWCS object can be accurately +described by standard FITS keywords, then no WCS extension is created as the +gWCS object can be created from these keywords when the file is re-opened. + +In future, it is intended to improve the quality of the FITS approximation +using the Simple Imaging Polynomial convention +(`SIP `_) or +a discrete sampling of the World co-ordinate +values will be stored as part of the FITS WCS, following `Greisen et al. (2006) +`_, S6 (in addition to the +definitive 'WCS' table), allowing standard FITS readers to report accurate +World co-ordinates for each pixel. + + +Adding Descriptors [Advanced Topic] +=================================== +For proper and complete instructions on how to create Astrodata Descriptors, +the reader is invited to refer to the Astrodata Programmer Manual. Here we +provide a simple introduction that might help some readers better understand +Astrodata Descriptors, or serve as a quick reference for those who have +written Astrodata Descriptors in the past but need a little refresher. + +The Astrodata Descriptors are defined in an ``AstroData`` class. The +``AstroData`` class specific to an instrument is located in a separate +package, not in ``astrodata``. For example, for Gemini instruments, all the +various ``AstroData`` classes are contained in the ``gemini_instruments`` +package. + +An Astrodata Descriptor is a function within the instrument's ``AstroData`` +class. The descriptor function is distinguished from normal functions by +applying the ``@astro_data_descriptor`` decorator to it. The descriptor +function returns the value(s) using a Python type, ``int``, ``float``, +``string``, ``list``; it depends on the value being returned. There is no +special "descriptor" type. + +Here is an example of code defining a descriptor:: + + class AstroDataGmos(AstroDataGemini): + ... + @astro_data_descriptor + def detector_x_bin(self): + def _get_xbin(b): + try: + return int(b.split()[0]) + except (AttributeError, ValueError): + return None + + binning = self.hdr.get('CCDSUM') + if self.is_single: + return _get_xbin(binning) + else: + xbin_list = [_get_xbin(b) for b in binning] + # Check list is single-valued + return xbin_list[0] if xbin_list == xbin_list[::-1] else None + +This descriptor returns the X-axis binning as a integer when called on a +single extension, or an object with only one extension, for example after the +GMOS CCDs have been mosaiced. If there are more than one extensions, it +will return a Python list or an integer if the binning is the same for all +the extensions. + +Gemini has defined a standard list of descriptors that should be defined +one way or another for each instrument to ensure the re-usability of our +algorithms. That list is provided in the Appendix :ref:`descriptors`. + +For more information on adding to Astrodata, see the Astrodata Programmer +Manual. diff --git a/_sources/manuals/usermanual/index.rst.txt b/_sources/manuals/usermanual/index.rst.txt new file mode 100644 index 00000000..9c5dff7a --- /dev/null +++ b/_sources/manuals/usermanual/index.rst.txt @@ -0,0 +1,23 @@ +.. Astrodata User Manual master file, created from team template + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + Manually edited by KL Wed Jan 18 2017 + +=========== +User Manual +=========== + +.. admonition:: Document ID + + PIPE-USER-106_AstrodataUserManual + +.. toctree:: + :maxdepth: 2 + + intro + structure + iomef + tags + headers + data + tables diff --git a/_sources/manuals/usermanual/intro.rst.txt b/_sources/manuals/usermanual/intro.rst.txt new file mode 100644 index 00000000..8981afb4 --- /dev/null +++ b/_sources/manuals/usermanual/intro.rst.txt @@ -0,0 +1,225 @@ +.. intro.rst + +.. _intro_usermanual: + +************ +Introduction +************ + +This is the AstroData User's Manual. AstroData is a DRAGONS package. +The current chapter covers basic concepts +like what is the |astrodata| package and how to install it (together with the +other DRAGONS' packages). :ref:`Chapter 2 ` +explains with more details what is |AstroData| and how the data is represented +using it. :ref:`Chapter 3 ` describes input and output operations and +how multi-extension (MEF) FITS files are represented. :ref:`Chapter 4 ` +provides information regarding the |TagSet| class, its usage and a few advanced +topics. In :ref:`Chapter 5 ` you will find information about the FITS +headers and how to access/modify the metadata. The last two chapters, +:ref:`Chapter 6 ` and :ref:`Chapter 7 ` cover more details +about how to read, manipulate and write pixel data and tables, respectively. + + +If you are looking for a quick reference, please, have a look on the +:doc:`../cheatsheet`. + +Reference Documents +=================== + + - |DRAGONS| + - :doc:`../cheatsheet` + - |RSUserManual| + - |RSProgManual| + +What is |astrodata|? +==================== + +|astrodata| is a package that wraps together tools to represent internally +astronomical datasets stored on disks and to properly parse their metadata +using the |AstroData| and the |TagSet| classes. |astrodata| provides uniform +interfaces for working on datasets from different +instruments. Once a dataset has been opened with |astrodata|, the object +"knows about itself". Information like instrument, observation mode, and how +to access headers, is readily available through the uniform interface. All +the details are coded inside the class associated with the instrument, that +class then provides the interface. The appropriate class is selected +automatically when the file is opened and inspected by |astrodata|. + +Currently |astrodata| implements a representation for Multi-Extension FITS +(MEF) files. (Other representations can be implemented.) + + +.. _install: + +Installing Astrodata +==================== + +The |astrodata| package has a few dependencies, |astropy|, |numpy| and others. +The best way to get everything you need is to install Anaconda_, and the +|dragons| stack from conda-forge and Gemini's public conda channel. + +|astrodata| itself is part of |DRAGONS|. It is available from the +repository, as a tar file, or as a conda package. The bare |astrodata| package +does not do much by itself, it needs a companion instrument definitions +package. For Gemini, this is ``gemini_instruments``, also included in +|DRAGONS|. + +Installing Anaconda and the DRAGONS stack +----------------------------------------- +This is required whether you are installing |DRAGONS| from the +repository, the tar file or the conda package. + +#. Install Anaconda. + Go to https://www.anaconda.com/download/ and install the latest 64-bit + Anaconda. + +#. Open a bash session. + Anaconda requires bash. If you are not familiar with bash, note that the + shell configuration files are named ``.bash_profile`` and ``.bashrc``. + During the installation, a PATH setting has been added to your + ``.bash_profile`` or ``.bashrc`` to add the Anaconda bin directory to + the ``PATH``. + +#. Activate Anaconda. + Normal Python 3 installation puts the software in ``~/anaconda3/``.:: + + $ conda init + +#. Set up conda channels. + Configure the ``conda`` package manager to look in conda-forge, and in the + GEMINI Conda Channel. This is a one-time step. It affects current and + future Anaconda installations belonging to the same user on the same + machine.:: + + $ conda config --add channels conda-forge + $ conda config --add channels http://astroconda.gemini.edu/public + +#. Create an environment. + To keep things clean, Anaconda offers virtual environments. Each project + can use its own environment. For example, if you do not want to modify + the software packages needed for a previous project, just create a new + environment for the new project. + + Here we set up an environment where the ``DRAGONS`` dependencies can + be installed without affecting the rest of the system when not using that + virtual environement. The new virtual environment here is named + ``dragons``. The software has been tested with Python 3.10 hence we + recommend that you use this specific version of Python with DRAGONS. + :: + + $ conda create -n dragons python=3.10 dragons ds9 + + +#. Activate your new virtual environment. + :: + + $ conda activate dragons + + +#. Configure DRAGONS. + These configurations are not strictly required when using only |astrodata|. + It is however likely that if you are using |astrodata| you will be using + DRAGONS too at some point. So let's configure it to have it ready to go. + + DRAGONS requires a configuration file located in ``~/.dragons/``. The + ``dragonsrc`` file contains basic configuration for DRAGONS local calibration + manager used by ``reduce``. + + :: + + $ cd ~ + $ mkdir .dragons + $ cd .dragons + $ touch dragonsrc + + Open ``dragonsrc`` with your favorite editor and add these lines:: + [interactive] + browser = one of "safari", "chrome", "firefox" + + [calibs] + databases = ~/.dragons/dragons.db + + Next time you start a DRAGONS project, set the ``databases`` to a path + of your liking, this is where the local calibration database will be written. + + Then configure buffers for ``ds9``:: + + $ cd ~/ + $ cp $CONDA_PREFIX/lib/python3.10/site-packages/gempy/numdisplay/imtoolrc ~/.imtoolrc + $ vi .bash_profile (or use your favority editor) + Add this line to the .bash_profile: + export IMTOOLRC=~/.imtoolrc + + +Update an existing DRAGONS installation +--------------------------------------- +To check which version of DRAGONS you have installed:: + + $ conda list dragons + +To check for newer version:: + + $ conda search dragons + +To update to the newest version:: + + $ conda update dragons + + +Smoke test the Astrodata installation +------------------------------------- +From the configured bash shell:: + + $ type python + python is hashed (/anaconda3/envs/dragons/python) + + Make sure that python is indeed pointing to the Anaconda environment you + have just set up. + +:: + + $ python + >>> import astrodata + >>> import gemini_instruments + + Expected result: Just a python prompt and no error messages. + +Source code availability +------------------------ +The source code is available on Github: + + ``_ + +.. _datapkg: + +Try it yourself +=============== + +**Try it yourself** + +Download the data package if you wish to follow along and run the +examples presented in this manual. It is available at: + + ``_ + +Unpack it:: + + $ cd + $ tar xvf ad_usermanual_datapkg-v1.tar + $ bunzip2 ad_usermanual/playdata/*.bz2 + +Then :: + + $ cd ad_usermanual/playground + $ python + + +Astrodata Support +================= + +Astrodata is developed and supported by staff at the Gemini Observatory. +Questions about the reduction of Gemini data should be directed to the +Gemini Helpdesk system at +``_ +The github issue tracker can be used to report software bugs in DRAGONS +(``_). diff --git a/_sources/manuals/usermanual/iomef.rst.txt b/_sources/manuals/usermanual/iomef.rst.txt new file mode 100644 index 00000000..8bb228da --- /dev/null +++ b/_sources/manuals/usermanual/iomef.rst.txt @@ -0,0 +1,546 @@ +.. iomef.rst + +.. _iomef: + +************************************************************ +Input and Output Operations and Extension Manipulation - MEF +************************************************************ + +|AstroData| is not intended to be Multi-Extension FITS (MEF) centric. The core +is independent of the file format. At Gemini, our data model uses MEF. +Therefore we have implemented a FITS handler that maps a MEF to the +internal |AstroData| representation. A different handler can be implemented +for a different file format. + +In this chapter, we present examples that will help the reader understand how +to access the information stored in a MEF with the |AstroData| object and +understand that mapping. + +**Try it yourself** + +Download the data package (:ref:`datapkg`) if you wish to follow along and run the +examples. Then :: + + $ cd /ad_usermanual/playground + $ python + + +Imports +======= + +Before doing anything, you need to import |AstroData| and the Gemini instrument +configuration package |gemini_instruments|. + +:: + + >>> import astrodata + >>> import gemini_instruments + + +Open and access existing dataset +================================ + +Read in the dataset +------------------- + +The file on disk is loaded into the |AstroData| class associated with the +instrument the data is from. This association is done automatically based on +header content. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> type(ad) + + +From now on, ``ad`` knows it is GMOS data. It knows how to access its headers +and when using the Recipe System (|recipe_system|), it will trigger the +selection of the GMOS primitives and recipes. + +The original path and filename are stored in the object. If you were to write +the |AstroData| object to disk without specifying anything, those path and +filename would be used. :: + + >>> ad.path + '../playdata/N20170609S0154.fits' + >>> ad.filename + 'N20170609S0154.fits' + + +Accessing the content of a MEF file +----------------------------------- + +Accessing pixel data, headers, and tables will be covered in detail in the +following chapters. Here we just introduce the basic content interface. + +For details on the |AstroData| structure, please refer to the +:ref:`previous chapter `. + +|AstroData| uses |NDData| as the core of its structure. Each FITS extension +becomes a |NDAstroData| object, subclassed from |NDData|, and is added to +a list. + +Pixel data +^^^^^^^^^^ + +To access pixel data, the list index and the ``.data`` attribute are used. That +returns a :class:`numpy.ndarray`. The list of |NDAstroData| is zero-indexed. +*Extension number 1 in a MEF is index 0 in an |AstroData| object*. :: + + >>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >>> data = ad[0].data + >>> type(data) + + >>> data.shape + (2112, 256) + +Remember that in a :class:`~numpy.ndarray` the y-axis is the first number. + +The variance and data quality planes, the VAR and DQ planes in Gemini MEF +files, are represented by the ``.variance`` and ``.mask`` attributes, +respectively. They are not their own "extension", they don't have their +own index in the list, unlike in a MEF. They are attached to the pixel data, +packaged together by the |NDAstroData| object. They are represented as +:class:`numpy.ndarray` just like the pixel data :: + + >>> var = ad[0].variance + >>> dq = ad[0].mask + +Tables +^^^^^^ +Tables in the MEF file will also be loaded into the |AstroData| object. If a table +is associated with a specific science extension through the EXTVER header keyword, that +table will be packaged within the same AstroData extension as the pixel data. +The |AstroData| "extension" is the |NDAstroData| object plus any table or other pixel +array. If the table is not associated with a specific extension and applies +globally, it will be added to the AstroData object as a global addition. No +indexing will be required to access it. In the example below, one ``OBJCAT`` is +associated with each extension, while the ``REFCAT`` has a global scope :: + + >>> ad.info() + Filename: ../playdata/N20170609S0154_varAdded.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED + PREPARED SIDEREAL + + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (6, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 1] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (8, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 2] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (7, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 3] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (5, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + + Other Extensions + Type Dimensions + .REFCAT Table (245, 16) + + +The tables are stored internally as :class:`astropy.table.Table` objects. :: + + >>> ad[0].OBJCAT + + NUMBER X_IMAGE Y_IMAGE ... REF_MAG_ERR PROFILE_FWHM PROFILE_EE50 + int32 float32 float32 ... float32 float32 float32 + ------ ------- ------- ... ----------- ------------ ------------ + 1 283.461 55.4393 ... 0.16895 -999.0 -999.0 + ... + >>> type(ad[0].OBJCAT) + + + >>> refcat = ad.REFCAT + >>> type(refcat) + + + +Headers +^^^^^^^ +Headers are stored in the |NDAstroData| ``.meta`` attribute as :class:`astropy.io.fits.Header` objects, +which is a form of Python ordered dictionaries. Headers associated with extensions +are stored with the corresponding |NDAstroData| object. The MEF Primary Header +Unit (PHU) is stored "globally" in the |AstroData| object. Note that when slicing an |AstroData| object, +for example copying over just the first extension, the PHU will follow. The +slice of an |AstroData| object is an |AstroData| object. +Headers can be accessed directly, or for some predefined concepts, the use of +Descriptors is preferred. See the chapters on headers for details. + +Using Descriptors:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> ad.filter_name() + 'open1-6&g_G0301' + >>> ad.filter_name(pretty=True) + 'g' + +Using direct header access:: + + >>> ad.phu['FILTER1'] + 'open1-6' + >>> ad.phu['FILTER2'] + 'g_G0301' + +Accessing the extension headers:: + + >>> ad.hdr['CCDSEC'] + ['[1:512,1:4224]', '[513:1024,1:4224]', '[1025:1536,1:4224]', '[1537:2048,1:4224]'] + >>> ad[0].hdr['CCDSEC'] + '[1:512,1:4224]' + + With descriptors: + >>> ad.array_section(pretty=True) + ['[1:512,1:4224]', '[513:1024,1:4224]', '[1025:1536,1:4224]', '[1537:2048,1:4224]'] + + +Modify Existing MEF Files +========================= +Before you start modify the structure of an |AstroData| object, you should be +familiar with it. Please make sure that you have read the previous chapter +on :ref:`the structure of the AstroData object `. + +Appending an extension +---------------------- +In this section, we take an extension from one |AstroData| object and append it +to another. + +Here is an example appending a whole AstroData extension, with pixel data, +variance, mask and tables. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + + >>> ad.info() + Filename: ../playdata/N20170609S0154.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 288) uint16 + [ 1] science NDAstroData (2112, 288) uint16 + [ 2] science NDAstroData (2112, 288) uint16 + [ 3] science NDAstroData (2112, 288) uint16 + + >>> ad.append(advar[3]) + >>> ad.info() + Filename: ../playdata/N20170609S0154.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 288) uint16 + [ 1] science NDAstroData (2112, 288) uint16 + [ 2] science NDAstroData (2112, 288) uint16 + [ 3] science NDAstroData (2112, 288) uint16 + [ 4] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) int16 + .OBJCAT Table (5, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + + >>> ad[4].hdr['EXTVER'] + 4 + >>> advar[3].hdr['EXTVER'] + 4 + +As you can see above, the fourth extension of ``advar``, along with everything +it contains was appended at the end of the first |AstroData| object. However, +note that, because the EXTVER of the extension in ``advar`` was 4, there are +now two extensions in ``ad`` with this EXTVER. This is not a problem because +EXTVER is not used by |AstroData| (it uses the index instead) and it is handled +only when the file is written to disk. + +In this next example, we are appending only the pixel data, leaving behind the other +associated data. One can attach the headers too, like we do here. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + + >>> ad.append(advar[3].data, header=advar[3].hdr) + >>> ad.info() + Filename: ../playdata/N20170609S0154.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 288) uint16 + [ 1] science NDAstroData (2112, 288) uint16 + [ 2] science NDAstroData (2112, 288) uint16 + [ 3] science NDAstroData (2112, 288) uint16 + [ 4] science NDAstroData (2112, 256) float32 + +Notice how a new extension was created but ``variance``, ``mask``, the OBJCAT +table and OBJMASK image were not copied over. Only the science pixel data was +copied over. + +Please note, there is no implementation for the "insertion" of an extension. + +Removing an extension or part of one +------------------------------------ +Removing an extension or a part of an extension is straightforward. The +Python command :func:`del` is used on the item to remove. Below are a few +examples, but first let us load a file :: + + >>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >>> ad.info() + +As you go through these examples, check the new structure with :func:`ad.info()` +after every removal to see how the structure has changed. + +Deleting a whole |AstroData| extension, the fourth one :: + + >>> del ad[3] + +Deleting only the variance array from the second extension :: + + >>> ad[1].variance = None + +Deleting a table associated with the first extension :: + + >>> del ad[0].OBJCAT + +Deleting a global table, not attached to a specific extension :: + + >>> del ad.REFCAT + + + +Writing back to disk +==================== +The :class:`~astrodata.AstroData` layer takes care of converting +the |AstroData| object back to a MEF file on disk. When writing to disk, +one should be aware of the path and filename information associated +with the |AstroData| object. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> ad.path + '../playdata/N20170609S0154.fits' + >>> ad.filename + 'N20170609S0154.fits' + +Writing to a new file +--------------------- +There are various ways to define the destination for the new FITS file. +The most common and natural way is :: + + >>> ad.write('new154.fits') + + >>> ad.write('new154.fits', overwrite=True) + +This will write a FITS file named 'new154.fits' in the current directory. +With ``overwrite=True``, it will overwrite the file if it already exists. +A path can be prepended to the filename if the current directory is not +the destination. +Note that ``ad.filename`` and ``ad.path`` have not changed, we have just +written to the new file, the |AstroData| object is in no way associated +with that new file. :: + + >>> ad.path + '../playdata/N20170609S0154.fits' + >>> ad.filename + 'N20170609S0154.fits' + +If you want to create that association, the ``ad.filename`` and ``ad.path`` +needs to be modified first. For example:: + + >>> ad.filename = 'new154.fits' + >>> ad.write(overwrite=True) + + >>> ad.path + '../playdata/new154.fits' + >>> ad.filename + 'new154.fits' + +Changing ``ad.filename`` also changes the filename in the ``ad.path``. The +sequence above will write 'new154.fits' not in the current directory but +rather to the directory that is specified in ``ad.path``. + +WARNING: :func:`ad.write` has an argument named ``filename``. Setting ``filename`` +in the call to :func:`ad.write`, as in ``ad.write(filename='new154.fits')`` will NOT +modify ``ad.filename`` or ``ad.path``. The two "filenames", one a method argument +the other a class attribute have no association to each other. + + +Updating an existing file on disk +---------------------------------- +Updating an existing file on disk requires explicitly allowing overwrite. + +If you have not written 'new154.fits' to disk yet (from previous section) :: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> ad.write('new154.fits', overwrite=True) + +Now let's open 'new154.fits', and write to it :: + + >>> adnew = astrodata.open('new154.fits') + >>> adnew.write(overwrite=True) + + +A note on FITS header keywords +------------------------------ + +.. _fitskeys: + +When writing an |AstroData| object to disk as a FITS file, it is necessary to add or +update header keywords to represent some of the internally-stored information. Any +extensions that did not originally belong to this |AstroData| will be assigned new +EXTVER keywords to avoid conflicts with existing extensions, and the internal WCS is +converted to the appropriate FITS keywords. Note that in some cases it may not be +possible for standard FITS keywords to accurately represent the true WCS. In such +cases, the FITS keywords are written as an approximation to the true WCS, together +with an additional keyword :: + + FITS-WCS= 'APPROXIMATE' / FITS WCS is approximate + +to indicate this. The accurate WCS is written as an additional FITS extension with +``EXTNAME='WCS'`` that AstroData will recognize when the file is read back in. The +``WCS`` extension will not be written to disk if there is an accurate FITS +representation of the WCS (e.g., for a simple image). + + +Create New MEF Files +==================== + +A new MEF file can be created from an existing, maybe modified, file or it +can be created from scratch. We discuss both cases here. + +Create New Copy of MEF Files +---------------------------- +To create a new copy of a MEF file, modified or not, the user has already +been given most of the tools in the sections above. Yet, let's throw a +couple examples for completeness. + +Basic example +^^^^^^^^^^^^^ +As seen above, a MEF file can be opened with |astrodata|, the |AstroData| +object can be modified (or not), and then written back to disk under a +new name. :: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + ... optional modifications here ... + >>> ad.write('newcopy.fits') + + +Needing true copies in memory +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Sometimes it is a true copy in memory that is needed. This is not specific +to MEF. In Python, doing something like ``adnew = ad`` does not create a +new copy of the AstrodData object; it just gives it a new name. If you +modify ``adnew`` you will be modifying ``ad`` too. They point to the same +block of memory. + +To create a true independent copy, the ``deepcopy`` utility needs to be used. :: + + >>> from copy import deepcopy + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> adcopy = deepcopy(ad) + +Be careful using ``deepcopy``, your memory could balloon really fast. Use it +only when truly needed. + + +Create New MEF Files from Scratch +--------------------------------- +Before one creates a new MEF file on disk, one has to create the AstroData +object that will be eventually written to disk. The |AstroData| object +created also needs to know that it will have to be written using the MEF +format. This is fortunately handled fairly transparently by |astrodata|. + +The key to associating the FITS data to the |AstroData| object is simply to +create the |AstroData| object from :mod:`astropy.io.fits` header objects. Those +will be recognized by |astrodata| as FITS and the constructor for FITS will be +used. The user does not need to do anything else special. Here is how it is +done. + +Create a MEF with basic header and data array set to zeros +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +:: + + >>> import numpy as np + >>> from astropy.io import fits + + >>> phu = fits.PrimaryHDU() + + >>> pixel_data = np.zeros((100,100)) + + >>> hdu = fits.ImageHDU() + >>> hdu.data = pixel_data + + >>> ad = astrodata.create(phu) + >>> ad.append(hdu, name='SCI') + + or another way to do the last two blocs: + >>> hdu = fits.ImageHDU(data=pixel_data, name='SCI') + >>> ad = astrodata.create(phu, [hdu]) + +Then it is just a matter of calling ``ad.write('somename.fits')`` on that +new ``Astrodata`` object. + +Associate a pixel array with a science pixel array +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Only main science ("SCI") pixel arrays are added as slices to an astrodata +object. It not uncommon to have pixels information associated with those +main science pixels, for example an object mask where marked pixels in the mask +are directly associated with sources in the science array. + +Such pixel arrays are added to specific slice of the astrodata object they are +associated with. + +Building on the astrodata object we created in the previous subsection, one +would add a pixel array to the first slice of the astrodata object as +follows: + + >>> extra_data = np.ones((100, 100)) + >>> ad[0].EXTRADATA = extra_data + +When the file is written to disk as a MEF, an extension will be created with +``EXTNAME = EXTRADATA`` and an ``EXTVER`` that matches the slice's ``EXTVER``, +in this case is would be ``1``. + +Represent a table as a FITS binary table in an ``AstroData`` object +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +One first needs to create a table, either an :class:`astropy.table.Table` +or a :class:`~astropy.io.fits.BinTableHDU`. See the |astropy| documentation +on tables and this manual's :ref:`section ` dedicated to tables for +more information. + +In the first example, we assume that ``my_astropy_table`` is +a :class:`~astropy.table.Table` ready to be attached to an |AstroData| +object. (Warning: we have not created ``my_astropy_table`` therefore the +example below will not run, though this is how it would be done.) + +:: + + >>> phu = fits.PrimaryHDU() + >>> ad = astrodata.create(phu) + + >>> astrodata.add_header_to_table(my_astropy_table) + >>> ad.append(my_astropy_table, name='SMAUG') + + +In the second example, we start with a FITS :class:`~astropy.io.fits.BinTableHDU` +and attach it to a new |AstroData| object. (Again, we have not created +``my_fits_table`` so the example will not run.) :: + + >>> phu = fits.PrimaryHDU() + >>> ad = astrodata.create(phu) + >>> ad.append(my_fits_table, name='DROGON') + +As before, once the |AstroData| object is constructed, the ``ad.write()`` +method can be used to write it to disk as a MEF file. diff --git a/_sources/manuals/usermanual/structure.rst.txt b/_sources/manuals/usermanual/structure.rst.txt new file mode 100644 index 00000000..591edd03 --- /dev/null +++ b/_sources/manuals/usermanual/structure.rst.txt @@ -0,0 +1,190 @@ +.. structure.rst + +.. _structure: + +******************** +The AstroData Object +******************** + +The |AstroData| object is an internal representation of a file on disk. +As of this version, only a FITS layer has been written, but |AstroData| itself +is not limited to FITS. + +The internal structure of the |AstroData| object makes uses of +:class:`astropy.nddata.NDData`, :mod:`astropy.table`, and +:class:`astropy.io.fits.Header`, the latter simply because it is a +convenient ordered dictionary. + +**Try it yourself** + +Download the data package (:ref:`datapkg`) if you wish to follow along and run the +examples. Then :: + + $ cd /ad_usermanual/playground + $ python + + +Global vs Extension-specific +============================ +At the very top level, the structure is divided in two types of information. +In the first category, there is the information that applies to the data +globally, for example the information that would be stored in a FITS Primary +Header Unit, a table from a catalog that matches the RA and DEC of the field, +etc. In the second category, there is the information specific to individual +science pixel extensions, for example the gain of the amplifier, the data +themselves, the error on those data, etc. + +Let us look at an example. The :meth:`~astrodata.AstroData.info` method shows +the content of the |AstroData| object and its organization, from the user's +perspective.:: + + >>> import astrodata + >>> import gemini_instruments + + >>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >>> ad.info() + Filename: N20170609S0154_varAdded.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED + PREPARED SIDEREAL + + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (6, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 1] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (8, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 2] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (7, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + [ 3] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (5, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + + Other Extensions + Type Dimensions + .REFCAT Table (245, 16) + + +The "Pixel Extensions" contain the pixel data. Each extension is represented +individually in a list (0-indexed like all Python lists). The science pixel +data, its associated metadata (extension header), and any other pixel or table +extensions directly associated with that science pixel data are stored in +a |NDAstroData| object which is a subclass of astropy |NDData|. We will +return to this structure later. An |AstroData| extension is accessed like +any list: ``ad[0]``. To access the science pixels, one uses ``ad[0].data``; for +the object mask of the first extension, ``ad[0].OBJMASK``. + +In the example above, the "Other Extensions" at the bottom of the +:meth:`~astrodata.AstroData.info` display contains a ``REFCAT`` table which in +this case is a list of stars from a catalog that overlaps the field of view +covered by the pixel data. The "Other Extensions" are global extensions. They +are not attached to any pixel extension in particular. To access a global +extension one simply uses the name of that extension: ``ad.REFCAT``. + + +Organization of the Global Information +====================================== +All the global information is stored in attributes of the |AstroData| object. +The global headers, or Primary Header Unit (PHU), is stored in the ``phu`` +attribute as an :class:`astropy.io.fits.Header`. + +Any global tables, like ``REFCAT`` above, are stored in the private attribute +``_tables`` as a Python dictionary with the name (eg. "REFCAT") as the key. +All tables are stored as :class:`astropy.table.Table`. Access to those table +is done using the key directly as if it were a normal attribute, eg. +``ad.REFCAT``. Header information for the table, if read in from a FITS table, +is stored in the ``meta`` attribute of the :class:`astropy.table.Table`, eg. +``ad.REFCAT.meta['header']``. It is for information only, it is not used. + + +Organization of the Extension-specific Information +================================================== +The pixel data are stored in the |AstroData| attribute ``nddata`` as a list +of |NDAstroData| object. The |NDAstroData| object is a subclass of astropy +|NDData| and it is fully compatible with any function expecting an |NDData| as +input. The pixel extensions are accessible through slicing, eg. ``ad[0]`` or +even ``ad[0:2]``. A slice of an AstroData object is an AstroData object, and +all the global attributes are kept. For example:: + + >>> ad[0].info() + Filename: N20170609S0154_varAdded.fits + Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED + PREPARED SIDEREAL + + Pixels Extensions + Index Content Type Dimensions Format + [ 0] science NDAstroData (2112, 256) float32 + .variance ndarray (2112, 256) float32 + .mask ndarray (2112, 256) uint16 + .OBJCAT Table (6, 43) n/a + .OBJMASK ndarray (2112, 256) uint8 + + Other Extensions + Type Dimensions + .REFCAT Table (245, 16) + +Note how ``REFCAT`` is still present. + +The science data is accessed as ``ad[0].data``, the variance as ``ad[0].variance``, +and the data quality plane as ``ad[0].mask``. Those familiar with astropy +|NDData| will recognize the structure "data, error, mask", and will notice +some differences. First |AstroData| uses the variance for the error plane, not +the standard deviation. Another difference will be evident only when one looks +at the content of the mask. |NDData| masks contain booleans, |AstroData| masks +are ``uint16`` bit mask that contains information about the type of bad pixels +rather than just flagging them a bad or not. Since ``0`` is equivalent to +``False`` (good pixel), the |AstroData| mask is fully compatible with the +|NDData| mask. + +Header information for the extension is stored in the |NDAstroData| ``meta`` +attribute. All table and pixel extensions directly associated with the +science extension are also stored in the ``meta`` attribute. + +Technically, an extension header is located in ``ad.nddata[0].meta['header']``. +However, for obviously needed convenience, the normal way to access that header +is ``ad[0].hdr``. + +Tables and pixel arrays associated with a science extension are +stored in ``ad.nddata[0].meta['other']`` as a dictionary keyed on the array +name, eg. ``OBJCAT``, ``OBJMASK``. As it is for global tables, astropy tables +are used for extension tables. The extension tables and extra pixel arrays are +accessed, like the global tables, by using the table name rather than the long +format, for example ``ad[0].OBJCAT`` and ``ad[0].OBJMASK``. + +When reading a FITS Table, the header information is stored in the +``meta['header']`` of the table, eg. ``ad[0].OBJCAT.meta['header']``. That +information is not used, it is simply a place to store what was read from disk. + +The header of a pixel extension directly associated with the science extension +should match that of the science extension. Therefore such headers are not +stored in |AstroData|. For example, the header of ``ad[0].OBJMASK`` is the +same as that of the science, ``ad[0].hdr``. + +The world coordinate system (WCS) is stored internally in the ``wcs`` attribute +of the |NDAstroData| object. It is constructed from the header keywords when +the FITS file is read from disk, or directly from the ``WCS`` extension if +present (see :ref:`the next chapter `). If the WCS is modified (for +example, by refining the pointing or attaching a more accurate wavelength +calibration), the FITS header keywords are not updated and therefore they should +never be used to determine the world coordinates of any pixel. These keywords are +only updated when the object is written to disk as a FITS file. The WCS is +retrieved as follows: ``ad[0].wcs``. + + +A Note on Memory Usage +====================== +When an file is opened, the headers are loaded into memory, but the pixels +are not. The pixel data are loaded into memory only when they are first +needed. This is not real "memory mapping", more of a delayed loading. This +is useful when someone is only interested in the metadata, especially when +the files are very large. diff --git a/_sources/manuals/usermanual/tables.rst.txt b/_sources/manuals/usermanual/tables.rst.txt new file mode 100644 index 00000000..20316d38 --- /dev/null +++ b/_sources/manuals/usermanual/tables.rst.txt @@ -0,0 +1,228 @@ +.. tables.rst + +.. _tables: + +********** +Table Data +********** +**Try it yourself** + +Download the data package (:ref:`datapkg`) if you wish to follow along and run the +examples. Then :: + + $ cd /ad_usermanual/playground + $ python + +Then import core astrodata and the Gemini astrodata configurations. :: + + >>> import astrodata + >>> import gemini_instruments + +Tables and Astrodata +==================== +Tables are stored as ``astropy.table`` ``Table`` class. FITS tables too +are represented in Astrodata as ``Table`` and FITS headers are stored in +the NDAstroData `.meta` attribute. Most table access should be done +through the ``Table`` interface. The best reference on ``Table`` is the +Astropy documentation itself. In this chapter we covers some common +examples to get the reader started. + +The ``astropy.table`` documentation can be found at: ``_ + + +Operate on a Table +================== + +Let us open a file with tables. Some tables are associated with specific +extensions, and there is one table that is global to the `AstroData` object. + +:: + + >>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits') + >>> ad.info() + +To access the global table named ``REFCAT``:: + + >>> ad.REFCAT + +To access the ``OBJCAT`` table in the first extension :: + + >>> ad[0].OBJCAT + + +Column and Row Operations +------------------------- +Columns are named. Those names are used to access the data as columns. +Rows are not names and are simply represented as a sequential list. + +Read columns and rows ++++++++++++++++++++++ +To get the names of the columns present in the table:: + + >>> ad.REFCAT.colnames + ['Id', 'Cat_Id', 'RAJ2000', 'DEJ2000', 'umag', 'umag_err', 'gmag', + 'gmag_err', 'rmag', 'rmag_err', 'imag', 'imag_err', 'zmag', 'zmag_err', + 'filtermag', 'filtermag_err'] + +Then it is easy to request the values for specific columns:: + + >>> ad.REFCAT['zmag'] + >>> ad.REFCAT['zmag', 'zmag_err'] + +To get the content of a specific row, row 10 in this case:: + + >>> ad.REFCAT[9] + +To get the content of a specific row(s) from a specific column(s):: + + >>> ad.REFCAT['zmag'][4] + >>> ad.REFCAT['zmag'][4:10] + >>> ad.REFCAT['zmag', 'zmag_err'][4:10] + +Change values ++++++++++++++ +Assigning new values works in a similar way. When working on multiple elements +it is important to feed a list that matches in size with the number of elements +to replace. + +:: + + >>> ad.REFCAT['imag'][4] = 20.999 + >>> ad.REFCAT['imag'][4:10] = [5, 6, 7, 8, 9, 10] + + >>> overwrite_col = [0] * len(ad.REFCAT) # a list of zeros, size = nb of rows + >>> ad.REFCAT['imag_err'] = overwrite_col + +Add a row ++++++++++ +To append a row, there is the ``add_row()`` method. The length of the row +should match the number of columns:: + + >>> new_row = [0] * len(ad.REFCAT.colnames) + >>> new_row[1] = '' # Cat_Id column is of "str" type. + >>> ad.REFCAT.add_row(new_row) + +Add a column +++++++++++++ +Adding a new column can be more involved. If you need full control, please +see the AstroPy Table documentation. For a quick addition, which might be +sufficient for your use case, we simply use the "dictionary" technique. Please +note that when adding a column, it is important to ensure that all the +elements are of the same type. Also, if you are planning to use that table +in IRAF/PyRAF, we recommend not using 64-bit types. + +:: + + >>> import numpy as np + + >>> new_column = [0] * len(ad.REFCAT) + >>> # Ensure that the type is int32, otherwise it will default to int64 + >>> # which generally not necessary. Also, IRAF 32-bit does not like it. + >>> new_column = np.array(new_column).astype(np.int32) + >>> ad.REFCAT['my_column'] = new_column + +If you are going to write that table back to disk as a FITS Bintable, then +some additional headers need to be set. Astrodata will take care of that +under the hood when the `write` method is invoked. + +:: + + >>> ad.write('myfile_with_modified_table.fits') + + +Selection and Rejection Operations +---------------------------------- +Normally, one does not know exactly where the information needed is located +in a table. Rather some sort of selection needs to be done. This can also +be combined with various calculations. We show two such examples here. + +Select a table element from criterion ++++++++++++++++++++++++++++++++++++++ + +:: + + >>> # Get the magnitude of a star selected by ID number + >>> ad.REFCAT['zmag'][ad.REFCAT['Cat_Id'] == '1237662500002005475'] + + >>> # Get the ID and magnitude of all the stars brighter than zmag 18. + >>> ad.REFCAT['Cat_Id', 'zmag'][ad.REFCAT['zmag'] < 18.] + + +Rejection and selection before statistics ++++++++++++++++++++++++++++++++++++++++++ + +:: + + >>> t = ad.REFCAT # to save typing + + >>> # The table has "NaN" values. ("Not a number") We need to ignore them. + >>> t['zmag'].mean() + nan + >>> # applying rejection of NaN values: + >>> t['zmag'][np.where(~np.isnan(t['zmag']))].mean() + 20.377306 + + + +Accessing FITS table headers directly +------------------------------------- +If for some reason you need to access the FITS table headers directly, here +is how to do it. It is very unlikely that you will need this. + +To see the FITS headers:: + + >>> ad.REFCAT.meta['header'] + >>> ad[0].OBJCAT.meta['header'] + +To retrieve a specific FITS table header:: + + >>> ad.REFCAT.meta['header']['TTYPE3'] + 'RAJ2000' + >>> ad[0].OBJCAT.meta['header']['TTYPE3'] + 'Y_IMAGE' + +To retrieve all the keyword names matching a selection:: + + >>> keynames = [key for key in ad.REFCAT.meta['header'] if key.startswith('TTYPE')] + + + +Create a Table +============== + +To create a table that can be added to an ``AstroData`` object and eventually +written to disk as a FITS file, the first step is to create an Astropy +``Table``. + +Let us first add our data to NumPy arrays, one array per column:: + + >>> import numpy as np + + >>> snr_id = np.array(['S001', 'S002', 'S003']) + >>> feii = np.array([780., 78., 179.]) + >>> pabeta = np.array([740., 307., 220.]) + >>> ratio = pabeta / feii + +Then build the table from that data:: + + >>> from astropy.table import Table + + >>> my_astropy_table = Table([snr_id, feii, pabeta, ratio], + ... names=('SNR_ID', 'FeII', 'PaBeta', 'ratio')) + + +Now we append this Astropy ``Table`` to a new ``AstroData`` object. + +:: + + >>> # Since we are going to write a FITS, we build the AstroData object + >>> # from FITS objects. + >>> from astropy.io import fits + + >>> phu = fits.PrimaryHDU() + >>> ad = astrodata.create(phu) + >>> ad.MYTABLE = my_astropy_table + >>> ad.info() + >>> ad.MYTABLE + + >>> ad.write('new_table.fits') diff --git a/_sources/manuals/usermanual/tags.rst.txt b/_sources/manuals/usermanual/tags.rst.txt new file mode 100644 index 00000000..6caaf387 --- /dev/null +++ b/_sources/manuals/usermanual/tags.rst.txt @@ -0,0 +1,172 @@ +.. tags.rst + +.. _tags: + +************** +Astrodata Tags +************** + +What are the Astrodata Tags? +============================ +The Astrodata Tags identify the data represented in the |AstroData| object. +When a file on disk is opened with |astrodata|, the headers are inspected to +identify which specific |AstroData| class needs to be loaded, +:class:`~gemini_instruments.gmos.AstroDataGmos`, +:class:`~gemini_instruments.niri.AstroDataNiri`, etc. Based on the class the data is +associated with, a list of "tags" will be defined. The tags will tell whether the +file is a flatfield or a dark, if it is a raw dataset, or if it has been processed by the +recipe system, if it is imaging or spectroscopy. The tags will tell the +users and the system what that data is and also give some information about +the processing status. + +As a side note, the tags are used by DRAGONS Recipe System to match recipes +and primitives to the data. + +Using the Astrodata Tags +======================== +**Try it yourself** + +Download the data package (:ref:`datapkg`) if you wish to follow along and run the +examples. Then :: + + $ cd /ad_usermanual/playground + $ python + +Before doing anything, you need to import |astrodata| and the Gemini instrument +configuration package (|gemini_instruments|). + +:: + + >>> import astrodata + >>> import gemini_instruments + +Let us open a Gemini dataset and see what tags we get:: + + >>> ad = astrodata.open('../playdata/N20170609S0154.fits') + >>> ad.tags + {'RAW', 'GMOS', 'GEMINI', 'NORTH', 'SIDEREAL', 'UNPREPARED', 'IMAGE', 'ACQUISITION'} + +The file we loaded is raw, GMOS North data. It is a 2D image and it is an +acquisition image, not a science observation. The "UNPREPARED" tag indicates +that the file has never been touched by the Recipe System which runs a +"prepare" primitive as the first step of each recipe. + +Let's try another :: + + >>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits') + >>> ad.tags + {'GMOS', 'GEMINI', 'NORTH', 'SIDEREAL', 'OVERSCAN_TRIMMED', 'IMAGE', + 'OVERSCAN_SUBTRACTED', 'PREPARED'} + +This file is a science GMOS North image. It has been processed by the +Recipe System. The overscan level has been subtracted and the overscan section +has been trimmed away. The tags do NOT include all the processing steps. Rather, +at least from the time being, it focuses on steps that matter when associating +calibrations. + +The tags can be used when coding. For example:: + + >>> if 'GMOS' in ad.tags: + ... print('I am GMOS') + ... else: + ... print('I am these instead:', ad.tags) + ... + +And:: + + >>> if {'IMAGE', 'GMOS'}.issubset(ad.tags): + ... print('I am a GMOS Image.') + ... + +Using typewalk +============== +In DRAGONS, there is a convenience tool that will list the Astrodata tags +for all the FITS file in a directory. + +To try it, from the shell, not Python, go to the "playdata" directory and +run typewalk:: + + % cd /ad_usermanual/playdata + % typewalk + + directory: /data/workspace/ad_usermanual/playdata + N20170521S0925_forStack.fits ...... (GEMINI) (GMOS) (IMAGE) (NORTH) (OVERSCAN_SUBTRACTED) (OVERSCAN_TRIMMED) (PREPARED) (SIDEREAL) + N20170521S0926_forStack.fits ...... (GEMINI) (GMOS) (IMAGE) (NORTH) (OVERSCAN_SUBTRACTED) (OVERSCAN_TRIMMED) (PREPARED) (PROCESSED) (PROCESSED_SCIENCE) (SIDEREAL) + N20170609S0154.fits ............... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED) + N20170609S0154_varAdded.fits ...... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (OVERSCAN_SUBTRACTED) (OVERSCAN_TRIMMED) (PREPARED) (SIDEREAL) + estgsS20080220S0078.fits .......... (GEMINI) (GMOS) (LONGSLIT) (LS) (PREPARED) (PROCESSED) (PROCESSED_SCIENCE) (SIDEREAL) (SOUTH) (SPECT) + gmosifu_cube.fits ................. (GEMINI) (GMOS) (IFU) (NORTH) (ONESLIT_RED) (PREPARED) (PROCESSED) (PROCESSED_SCIENCE) (SIDEREAL) (SPECT) + new154.fits ....................... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED) + Done DataSpider.typewalk(..) + +``typewalk`` can be used to select specific data based on tags, and even create +lists:: + + % typewalk --tags RAW + directory: /data/workspace/ad_usermanual/playdata + N20170609S0154.fits ............... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED) + new154.fits ....................... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED) + Done DataSpider.typewalk(..) + +:: + + % typewalk --tags RAW -o rawfiles.lis + % cat rawfiles.lis + # Auto-generated by typewalk, vv2.0 (beta) + # Written: Tue Mar 6 13:06:06 2018 + # Qualifying types: RAW + # Qualifying logic: AND + # ----------------------- + /Users/klabrie/data/tutorials/ad_usermanual/playdata/N20170609S0154.fits + /Users/klabrie/data/tutorials/ad_usermanual/playdata/new154.fits + + + +Creating New Astrodata Tags [Advanced Topic] +============================================ +For proper and complete instructions on how to create Astrodata Tags and +the |AstroData| class that hosts the tags, the reader is invited to refer to the +Astrodata Programmer Manual. Here we provide a simple introduction that +might help some readers better understand Astrodata Tags, or serve as a +quick reference for those who have written Astrodata Tags in the past but need +a little refresher. + +The Astrodata Tags are defined in an |AstroData| class. The |AstroData| +class specific to an instrument is located in a separate package, not in +|astrodata|. For example, for Gemini instruments, all the various |AstroData| +classes are contained in the |gemini_instruments| package. + +An Astrodata Tag is a function within the instrument's |AstroData| class. +The tag function is distinguished from normal functions by applying the +:func:`~astrodata.astro_data_tag` decorator to it. +The tag function returns a :class:`astrodata.TagSet`. + +For example:: + + class AstroDataGmos(AstroDataGemini): + ... + @astro_data_tag + def _tag_arc(self): + if self.phu.get('OBSTYPE) == 'ARC': + return TagSet(['ARC', 'CAL']) + +The tag function looks at the headers and if the keyword "OBSTYPE" is set +to "ARC", the tags "ARC" and "CAL" (for calibration) will be assigned to the +|AstroData| object. + +A whole suite of such tag functions is needed to fully characterize all +types of data an instrument can produce. + +Tags are about what the dataset is, not it's flavor. The Astrodata +"descriptors" (see the section on :ref:`headers`) will describe the flavor. +For example, tags will say that the data is an image, but the descriptor +will say whether it is B-band or R-band. Tags are used for recipe and +primitive selection. A way to understand the difference between a tag and +a descriptor is in terms of the recipe that will be selected: A GMOS image +will use the same recipe whether it's a B-band or R-band image. However, +a GMOS longslit spectrum will need a very different recipe. A bias is +reduced differently from a science image, there should be a tag differentiating +a bias from a science image. (There is for GMOS.) + +For more information on adding to Astrodata, see the Astrodata Programmer +Manual. diff --git a/_static/alabaster.css b/_static/alabaster.css new file mode 100644 index 00000000..517d0b29 --- /dev/null +++ b/_static/alabaster.css @@ -0,0 +1,703 @@ +@import url("basic.css"); + +/* -- page layout ----------------------------------------------------------- */ + +body { + font-family: Georgia, serif; + font-size: 17px; + background-color: #fff; + color: #000; + margin: 0; + padding: 0; +} + + +div.document { + width: 940px; + margin: 30px auto 0 auto; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 220px; +} + +div.sphinxsidebar { + width: 220px; + font-size: 14px; + line-height: 1.5; +} + +hr { + border: 1px solid #B1B4B6; +} + +div.body { + background-color: #fff; + color: #3E4349; + padding: 0 30px 0 30px; +} + +div.body > .section { + text-align: left; +} + +div.footer { + width: 940px; + margin: 20px auto 30px auto; 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Without this, docutils' magical list fuckery causes inconsistent + * formatting between different release sub-lists. + */ +div#changelog > div.section > ul > li > p:only-child { + margin-bottom: 0; +} + +/* Hide fugly table cell borders in ..bibliography:: directive output */ +table.docutils.citation, table.docutils.citation td, table.docutils.citation th { + border: none; + /* Below needed in some edge cases; if not applied, bottom shadows appear */ + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + + +/* relbar */ + +.related { + line-height: 30px; + width: 100%; + font-size: 0.9rem; +} + +.related.top { + border-bottom: 1px solid #EEE; + margin-bottom: 20px; +} + +.related.bottom { + border-top: 1px solid #EEE; +} + +.related ul { + padding: 0; + margin: 0; + list-style: none; +} + +.related li { + display: inline; +} + +nav#rellinks { + float: right; +} + +nav#rellinks li+li:before { + content: "|"; +} + +nav#breadcrumbs li+li:before { + content: "\00BB"; +} + +/* Hide certain items when printing */ +@media print { + div.related { + display: none; + } +} \ No newline at end of file diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 00000000..30fee9d0 --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_static/custom.css b/_static/custom.css new file mode 100644 index 00000000..2a924f1d --- /dev/null +++ b/_static/custom.css @@ -0,0 +1 @@ +/* This file intentionally left blank. */ diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 00000000..d06a71d7 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 00000000..b1611d5c --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '3.2.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; 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+ + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file mode 100644 index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK literal 0 HcmV?d00001 diff --git a/_static/plus.png b/_static/plus.png new file mode 100644 index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu>-2 m3q%Vub%g%s<8sJhVPMczOq}xhg9DJoz~JfX=d#Wzp$Pyb1r*Kz literal 0 HcmV?d00001 diff --git a/_static/pygments.css b/_static/pygments.css new file mode 100644 index 00000000..57c7df37 --- /dev/null +++ b/_static/pygments.css @@ -0,0 +1,84 @@ +pre { line-height: 125%; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/api/astrodata.AstroData.html b/api/astrodata.AstroData.html new file mode 100644 index 00000000..b08a77aa --- /dev/null +++ b/api/astrodata.AstroData.html @@ -0,0 +1,700 @@ + + + + + + + + AstroData — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

AstroData

+
+
+class astrodata.AstroData(nddata=None, tables=None, phu=None, indices=None, is_single=False)[source]
+

Bases: object

+

Base class for the AstroData software package. It provides an interface +to manipulate astronomical data sets.

+
+
Parameters:
+
    +

  • nddata (astrodata.NDAstroData or list of astrodata.NDAstroData) – List of NDAstroData objects.

    • +

  • tables (dict[name, astropy.table.Table]) – Dict of table objects.

    • +

  • phu (astropy.io.fits.Header) – Primary header.

    • +

  • indices (list of int) – List of indices mapping the astrodata.NDAstroData objects that this +object will access to. This is used when slicing an object, then the +sliced AstroData will have the .nddata list from its parent and +access the sliced NDAstroData through this list of indices.

    • +
+
+
+

Attributes Summary

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

data

A list of the arrays (or single array, if this is a single slice) corresponding to the science data attached to each extension.

descriptors

Returns a sequence of names for the methods that have been decorated as descriptors.

exposed

A collection of strings with the names of objects that can be accessed directly by name as attributes of this instance, and that are not part of its standard interface (i.e. data objects that have been added dynamically).

ext_tables

Return the names of the astropy.table.Table objects associated to an extension.

filename

Return the file name.

hdr

Return all headers, as a astrodata.fits.FitsHeaderCollection.

header

Deprecated header access.

id

Returns the extension identifier (1-based extension number) for sliced objects.

indices

Returns the extensions indices for sliced objects.

is_sliced

If this data provider instance represents the whole dataset, return False.

mask

A list of the mask arrays (or a single array, if this is a single slice) attached to the science data, for each extension.

nddata

Return the list of astrodata.NDAstroData objects.

orig_filename

Return the original file name (before it was modified).

path

Return the file path.

phu

Return the primary header.

shape

Return the shape of the data array for each extension as a list of shapes.

tables

Return the names of the astropy.table.Table objects associated to the top-level object.

tags

A set of strings that represent the tags defining this instance.

uncertainty

A list of the uncertainty objects (or a single object, if this is a single slice) attached to the science data, for each extension.

variance

A list of the variance arrays (or a single array, if this is a single slice) attached to the science data, for each extension.

wcs

Returns the list of WCS objects for each extension.

+

Methods Summary

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

add(oper)

Performs inplace addition by evaluating self += operand.

append(ext[, name, header])

Adds a new top-level extension.

crop(x1, y1, x2, y2)

Crop the NDData objects given indices.

divide(oper)

Performs inplace division by evaluating self /= operand.

info()

Prints out information about the contents of this instance.

instrument()

Returns the name of the instrument making the observation.

is_settable(attr)

Return True if the attribute is meant to be modified.

load(source[, extname_parser])

Read from a file, file object, HDUList, etc.

multiply(oper)

Performs inplace multiplication by evaluating self *= operand.

object()

Returns the name of the object being observed.

operate(operator, *args, **kwargs)

Applies a function to the main data array on each extension, replacing the data with the result.

read(source[, extname_parser])

Read from a file, file object, HDUList, etc.

reset(data[, mask, variance, check])

Sets the .data, and optionally .mask and .variance attributes of a single-extension AstroData slice.

subtract(oper)

Performs inplace subtraction by evaluating self -= operand.

table()

Return a dictionary of astropy.table.Table objects.

telescope()

Returns the name of the telescope.

update_filename([prefix, suffix, strip])

Update the "filename" attribute of the AstroData object.

write([filename, overwrite])

Write the object to disk.

+

Attributes Documentation

+
+
+data
+

A list of the arrays (or single array, if this is a single slice) +corresponding to the science data attached to each extension.

+
+ +
+
+descriptors
+

Returns a sequence of names for the methods that have been +decorated as descriptors.

+
+
Return type:
+

tuple of str

+
+
+
+ +
+
+exposed
+

A collection of strings with the names of objects that can be +accessed directly by name as attributes of this instance, and that are +not part of its standard interface (i.e. data objects that have been +added dynamically).

+

Examples

+
>>> ad[0].exposed  
+set(['OBJMASK', 'OBJCAT'])
+
+
+
+ +
+
+ext_tables
+

Return the names of the astropy.table.Table objects associated to +an extension.

+
+ +
+
+filename
+

Return the file name.

+
+ +
+
+hdr
+

Return all headers, as a astrodata.fits.FitsHeaderCollection.

+
+ +
+
+header
+

Deprecated header access. Use .hdr instead.

+
+ +
+
+id
+

Returns the extension identifier (1-based extension number) +for sliced objects.

+
+ +
+
+indices
+

Returns the extensions indices for sliced objects.

+
+ +
+
+is_sliced
+

If this data provider instance represents the whole dataset, return +False. If it represents a slice out of the whole, return True.

+
+ +
+
+mask
+

A list of the mask arrays (or a single array, if this is a single +slice) attached to the science data, for each extension.

+

For objects that miss a mask, None will be provided instead.

+
+ +
+
+nddata
+

Return the list of astrodata.NDAstroData objects.

+

If the AstroData object is sliced, this returns only the NDData +objects of the sliced extensions. And if this is a single extension +object, the NDData object is returned directly (i.e. not a list).

+
+ +
+
+orig_filename
+

Return the original file name (before it was modified).

+
+ +
+
+path
+

Return the file path.

+
+ +
+
+phu
+

Return the primary header.

+
+ +
+
+shape
+

Return the shape of the data array for each extension as a list of +shapes.

+
+ +
+
+tables
+

Return the names of the astropy.table.Table objects associated to +the top-level object.

+
+ +
+
+tags
+

A set of strings that represent the tags defining this instance.

+
+ +
+
+uncertainty
+

A list of the uncertainty objects (or a single object, if this is +a single slice) attached to the science data, for each extension.

+

The objects are instances of AstroPy’s astropy.nddata.NDUncertainty, +or None where no information is available.

+
+

See also

+
+
variance

The actual array supporting the uncertainty object.

+
+
+
+
+ +
+
+variance
+

A list of the variance arrays (or a single array, if this is a +single slice) attached to the science data, for each extension.

+

For objects that miss uncertainty information, None will be provided +instead.

+
+

See also

+
+
uncertainty

The uncertainty objects used under the hood.

+
+
+
+
+ +
+
+wcs
+

Returns the list of WCS objects for each extension.

+
+ +

Methods Documentation

+
+
+add(oper)
+

Performs inplace addition by evaluating self += operand.

+
+
Parameters:
+

oper (number or object) – The operand to perform the operation self += operand.

+
+
Return type:
+

AstroData instance

+
+
+
+ +
+
+append(ext, name=None, header=None)[source]
+

Adds a new top-level extension.

+
+
Parameters:
+
    +

  • ext (array, astropy.nddata.NDData, astropy.table.Table, other) – The contents for the new extension. The exact accepted types depend +on the class implementing this interface. Implementations specific +to certain data formats may accept specialized types (eg. a FITS +provider will accept an astropy.io.fits.ImageHDU and extract the +array out of it).

    • +

  • name (str, optional) – A name that may be used to access the new object, as an attribute +of the provider. The name is typically ignored for top-level +(global) objects, and required for the others. If the name cannot +be derived from the metadata associated to ext, you will +have to provider one. +It can consist in a combination of numbers and letters, with the +restriction that the letters have to be all capital, and the first +character cannot be a number (“[A-Z][A-Z0-9]*”).

    • +
+
+
Returns:
+

    +

  • The same object, or a new one, if it was necessary to convert it to

    • +

  • a more suitable format for internal use.

    • +
+

+
+
Raises:
+
    +

  • TypeError – If adding the object in an invalid situation (eg. name is + None when adding to a single slice).

    • +

  • ValueError – Raised if the extension is of a proper type, but its value is + illegal somehow.

    • +
+
+
+
+ +
+
+crop(x1, y1, x2, y2)[source]
+

Crop the NDData objects given indices.

+
+
Parameters:
+
    +

  • x1 (int) – Minimum and maximum indices for the x and y axis.

    • +

  • y1 (int) – Minimum and maximum indices for the x and y axis.

    • +

  • x2 (int) – Minimum and maximum indices for the x and y axis.

    • +

  • y2 (int) – Minimum and maximum indices for the x and y axis.

    • +
+
+
+
+ +
+
+divide(oper)
+

Performs inplace division by evaluating self /= operand.

+
+
Parameters:
+

oper (number or object) – The operand to perform the operation self /= operand.

+
+
Return type:
+

AstroData instance

+
+
+
+ +
+
+info()[source]
+

Prints out information about the contents of this instance.

+
+ +
+
+instrument()[source]
+

Returns the name of the instrument making the observation.

+
+ +
+
+is_settable(attr)[source]
+

Return True if the attribute is meant to be modified.

+
+ +
+
+classmethod load(source, extname_parser=None)
+

Read from a file, file object, HDUList, etc.

+
+ +
+
+multiply(oper)
+

Performs inplace multiplication by evaluating self *= operand.

+
+
Parameters:
+

oper (number or object) – The operand to perform the operation self *= operand.

+
+
Return type:
+

AstroData instance

+
+
+
+ +
+
+object()[source]
+

Returns the name of the object being observed.

+
+ +
+
+operate(operator, *args, **kwargs)[source]
+

Applies a function to the main data array on each extension, replacing +the data with the result. The data will be passed as the first argument +to the function.

+

It will be applied to the mask and variance of each extension, too, if +they exist.

+

This is a convenience method, which is equivalent to:

+
for ext in ad:
+    ext.data = operator(ext.data, *args, **kwargs)
+    if ext.mask is not None:
+        ext.mask = operator(ext.mask, *args, **kwargs)
+    if ext.variance is not None:
+        ext.variance = operator(ext.variance, *args, **kwargs)
+
+
+

with the additional advantage that it will work on single slices, too.

+
+
Parameters:
+
    +

  • operator (callable) – A function that takes an array (and, maybe, other arguments) +and returns an array.

    • +

  • args (optional) – Additional arguments to be passed to the operator.

    • +

  • kwargs (optional) – Additional arguments to be passed to the operator.

    • +
+
+
+

Examples

+
>>> import numpy as np
+>>> ad.operate(np.squeeze)  
+
+
+
+ +
+
+classmethod read(source, extname_parser=None)[source]
+

Read from a file, file object, HDUList, etc.

+
+ +
+
+reset(data, mask=<object object>, variance=<object object>, check=True)[source]
+

Sets the .data, and optionally .mask and .variance +attributes of a single-extension AstroData slice. This function will +optionally check whether these attributes have the same shape.

+
+
Parameters:
+
    +

  • data (ndarray) – The array to assign to the .data attribute (“SCI”).

    • +

  • mask (ndarray, optional) – The array to assign to the .mask attribute (“DQ”).

    • +

  • variance (ndarray, optional) – The array to assign to the .variance attribute (“VAR”).

    • +

  • check (bool) – If set, then the function will check that the mask and variance +arrays have the same shape as the data array.

    • +
+
+
Raises:
+
    +

  • TypeError – if an attempt is made to set the .mask or .variance attributes + with something other than an array

    • +

  • ValueError – if the .mask or .variance attributes don’t have the same shape as + .data, OR if this is called on an AD instance that isn’t a single + extension slice

    • +
+
+
+
+ +
+
+subtract(oper)
+

Performs inplace subtraction by evaluating self -= operand.

+
+
Parameters:
+

oper (number or object) – The operand to perform the operation self -= operand.

+
+
Return type:
+

AstroData instance

+
+
+
+ +
+
+table()[source]
+

Return a dictionary of astropy.table.Table objects.

+

Notes

+

This returns a _copy_ of the tables, so modifying them will not +affect the original ones.

+
+ +
+
+telescope()[source]
+

Returns the name of the telescope.

+
+ +
+
+update_filename(prefix=None, suffix=None, strip=False)[source]
+

Update the “filename” attribute of the AstroData object.

+

A prefix and/or suffix can be specified. If strip=True, these will +replace the existing prefix/suffix; if strip=False, they will +simply be prepended/appended.

+

The current filename is broken down into its existing prefix, root, and +suffix using the ORIGNAME phu keyword, if it exists and is +contained within the current filename. Otherwise, the filename is split +at the last underscore and the part before is assigned as the root and +the underscore and part after the suffix. No prefix is assigned.

+

Note that, if strip=True, a prefix or suffix will only be stripped +if ‘’ is specified.

+
+
Parameters:
+
    +

  • prefix (str, optional) – New prefix (None => leave alone)

    • +

  • suffix (str, optional) – New suffix (None => leave alone)

    • +

  • strip (bool, optional) – Strip existing prefixes and suffixes if new ones are given?

    • +
+
+
Raises:
+

ValueError – If the filename cannot be determined

+
+
+
+ +
+
+write(filename=None, overwrite=False)[source]
+

Write the object to disk.

+
+
Parameters:
+
    +

  • filename (str, optional) – If the filename is not given, self.path is used.

    • +

  • overwrite (bool) – If True, overwrites existing file.

    • +
+
+
+
+ + + + + + + + + + + +
+ +
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.AstroDataError.html b/api/astrodata.AstroDataError.html new file mode 100644 index 00000000..7598d961 --- /dev/null +++ b/api/astrodata.AstroDataError.html @@ -0,0 +1,117 @@ + + + + + + + + AstroDataError — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

AstroDataError

+
+
+exception astrodata.AstroDataError[source]
+

Exception raised when there is a problem with the AstroData class.

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.AstroDataMixin.html b/api/astrodata.AstroDataMixin.html new file mode 100644 index 00000000..043c293b --- /dev/null +++ b/api/astrodata.AstroDataMixin.html @@ -0,0 +1,175 @@ + + + + + + + + AstroDataMixin — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

AstroDataMixin

+
+
+class astrodata.AstroDataMixin[source]
+

Bases: object

+

A Mixin for NDData-like classes (such as Spectrum1D) to enable +them to behave similarly to AstroData objects.

+
+
These behaviors are:
    +

  1. mask attributes are combined with bitwise, not logical, or, +since the individual bits are important.

    2. +

  2. The WCS must be a gwcs.WCS object and slicing results in +the model being modified.

    3. +

  3. There is a settable variance attribute.

    4. +

  4. Additional attributes such as OBJMASK can be extracted from +the .meta[‘other’] dict

    5. +
+
+
+

Attributes Summary

+ + + + + + + + + + + + + + + +

shape

The shape of the data.

size

The size of the data.

variance

A convenience property to access the contents of uncertainty.

wcs

The WCS of the data.

+

Attributes Documentation

+
+
+shape
+

The shape of the data.

+
+ +
+
+size
+

The size of the data.

+
+ +
+
+variance
+

A convenience property to access the contents of uncertainty.

+
+ +
+
+wcs
+

The WCS of the data. This is a gWCS object, not a FITS WCS object.

+

This is returning wcs from an inhertited class, see NDData.wcs for more +details.

+
+ +
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.NDAstroData.html b/api/astrodata.NDAstroData.html new file mode 100644 index 00000000..efef1745 --- /dev/null +++ b/api/astrodata.NDAstroData.html @@ -0,0 +1,313 @@ + + + + + + + + NDAstroData — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

NDAstroData

+
+
+class astrodata.NDAstroData(data, uncertainty=None, mask=None, wcs=None, meta=None, unit=None, copy=False, variance=None)[source]
+

Bases: AstroDataMixin, NDArithmeticMixin, NDSlicingMixin, NDData

+

Implements NDData with all Mixins, plus some AstroData +specifics.

+

This class implements an NDData-like container that supports reading +and writing as implemented in the astropy.io.registry and also slicing +(indexing) and simple arithmetics (add, subtract, divide and multiply).

+

A very important difference between NDAstroData and NDData is that +the former attempts to load all its data lazily. There are also some +important differences in the interface (eg. .data lets you reset its +contents after initialization).

+

Documentation is provided where our class differs.

+
+

See also

+

NDData, NDArithmeticMixin, NDSlicingMixin

+
+

Examples

+

The mixins allow operation that are not possible with NDData or +NDDataBase, i.e. simple arithmetics:

+
>>> from astropy.nddata import StdDevUncertainty
+>>> import numpy as np
+>>> data = np.ones((3,3), dtype=float)
+>>> ndd1 = NDAstroData(data, uncertainty=StdDevUncertainty(data))
+>>> ndd2 = NDAstroData(data, uncertainty=StdDevUncertainty(data))
+>>> ndd3 = ndd1.add(ndd2)
+>>> ndd3.data
+array([[2., 2., 2.],
+    [2., 2., 2.],
+    [2., 2., 2.]])
+>>> ndd3.uncertainty.array
+array([[1.41421356, 1.41421356, 1.41421356],
+    [1.41421356, 1.41421356, 1.41421356],
+    [1.41421356, 1.41421356, 1.41421356]])
+
+
+

see NDArithmeticMixin for a complete list of all supported arithmetic +operations.

+

But also slicing (indexing) is possible:

+
>>> ndd4 = ndd3[1,:]
+>>> ndd4.data
+array([2., 2., 2.])
+>>> ndd4.uncertainty.array
+array([1.41421356, 1.41421356, 1.41421356])
+
+
+

See NDSlicingMixin for a description how slicing works (which +attributes) are sliced.

+

Initialize an NDAstroData instance.

+
+
Parameters:
+
    +

  • data (array-like) – The actual data. This can be a numpy array, a memmap, or a +fits.ImageHDU object.

    • +

  • uncertainty (NDUncertainty-like object, optional) – An object that represents the uncertainty of the data. If not +specified, the uncertainty will be set to None.

    • +

  • mask (array-like, optional) – An array that represents the mask of the data. If not specified, +the mask will be set to None.

    • +

  • wcs (gwcs.WCS object, optional) – The WCS of the data. If not specified, the WCS will be set to None.

    • +

  • meta (dict-like, optional) – A dictionary-like object that holds the meta data. If not +specified, the meta data will be set to None.

    • +

  • unit (astropy.units.Unit object, optional) – The unit of the data. If not specified, the unit will be set to +None.

    • +

  • copy (bool, optional) – If True, the data, uncertainty, mask, wcs, meta, and unit will be +copied. Otherwise, they will be referenced. Default is False.

    • +

  • variance (array-like, optional) – An array that represents the variance of the data. If not +specified, the variance will be set to None.

    • +
+
+
Raises:
+

ValueError – If uncertainty and variance are both specified.

+
+
+

Notes

+

The uncertainty and variance parameters are mutually exclusive.

+

Attributes Summary

+ + + + + + + + + + + + + + + + + + + + + +

T

Transpose the data.

data

An array representing the raw data stored in this instance.

mask

Get or set the mask of the data.

uncertainty

Uncertainty in the dataset, if any.

variance

A convenience property to access the contents of uncertainty, squared (as the uncertainty data is stored as standard deviation).

window

Interface to access a section of the data, using lazy access whenever possible.

+

Methods Summary

+ + + + + + + + + +

set_section(section, input_data)

Sets only a section of the data.

transpose()

Transpose the data.

+

Attributes Documentation

+
+
+T
+

Transpose the data. This is not a copy of the data.

+
+ +
+
+data
+

An array representing the raw data stored in this instance. It +implements a setter.

+
+ +
+
+mask
+

Get or set the mask of the data.

+
+ +
+
+uncertainty
+
+ +
+
+variance
+

A convenience property to access the contents of uncertainty, +squared (as the uncertainty data is stored as standard deviation).

+
+ +
+
+window
+

Interface to access a section of the data, using lazy access +whenever possible.

+
+
Returns:
+

    +

  • An instance of NDWindowing, which provides __getitem__,

    • +

  • to allow the use of square brackets when specifying the window.

    • +

  • Ultimately, an NDWindowingAstrodata instance is returned.

    • +
+

+
+
+

Examples

+
>>> ad[0].nddata.window[100:200, 100:200]  
+<NDWindowingAstrodata .....>
+
+
+
+ +

Methods Documentation

+
+
+set_section(section, input_data)[source]
+

Sets only a section of the data. This method is meant to prevent +fragmentation in the Python heap, by reusing the internal structures +instead of replacing them with new ones.

+
+
Parameters:
+
    +

  • section (slice) – The area that will be replaced

    • +

  • input_data (NDData-like instance) – This object needs to implement at least data, uncertainty, +and mask. Their entire contents will replace the data in the +area defined by section.

    • +
+
+
+

Examples

+
>>> def setup():
+...     sec = NDData(np.zeros((100,100)))
+...     ad[0].nddata.set_section(
+...         (slice(None,100),slice(None,100)),
+...         sec
+...     )
+...
+>>> setup()  
+
+
+
+ +
+
+transpose()[source]
+

Transpose the data. This is not a copy of the data.

+
+ +
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.Section.html b/api/astrodata.Section.html new file mode 100644 index 00000000..ff573fc4 --- /dev/null +++ b/api/astrodata.Section.html @@ -0,0 +1,231 @@ + + + + + + + + Section — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Section

+
+
+class astrodata.Section(*args, **kwargs)[source]
+

Bases: tuple

+

A class to handle n-dimensional sections

+

Attributes Summary

+ + + + + + + + + +

axis_dict

ndim

The number of dimensions in the section.

+

Methods Summary

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

asIRAFsection()

Deprecated, see as_iraf_section

as_iraf_section()

Produce string of style '[x1:x2,y1:y2]' that is 1-indexed and end-inclusive

asslice([add_dims])

Return the Section object as a slice/list of slices.

contains(section)

Return True if the supplied section is entirely within self

from_shape(value)

Produce a Section object defining a given shape.

from_string(value)

The inverse of __str__, produce a Section object from a string.

is_same_size(section)

Return True if the Sections are the same size

overlap(section)

Determine whether the two sections overlap.

shift(*shifts)

Shift a section in each direction by the specified amount

+

Attributes Documentation

+
+
+axis_dict
+
+ +
+
+ndim
+

The number of dimensions in the section.

+
+ +

Methods Documentation

+
+
+asIRAFsection()[source]
+

Deprecated, see as_iraf_section

+
+ +
+
+as_iraf_section()[source]
+

Produce string of style ‘[x1:x2,y1:y2]’ that is 1-indexed +and end-inclusive

+
+ +
+
+asslice(add_dims=0)[source]
+

Return the Section object as a slice/list of slices. Higher +dimensionality can be achieved with the add_dims parameter.

+
+ +
+
+contains(section)[source]
+

Return True if the supplied section is entirely within self

+
+ +
+
+static from_shape(value)[source]
+

Produce a Section object defining a given shape.

+
+ +
+
+static from_string(value)[source]
+

The inverse of __str__, produce a Section object from a string.

+
+ +
+
+is_same_size(section)[source]
+

Return True if the Sections are the same size

+
+ +
+
+overlap(section)[source]
+

Determine whether the two sections overlap. If so, the Section +common to both is returned, otherwise None

+
+ +
+
+shift(*shifts)[source]
+

Shift a section in each direction by the specified amount

+
+ +
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.TagSet.html b/api/astrodata.TagSet.html new file mode 100644 index 00000000..6c9f67cd --- /dev/null +++ b/api/astrodata.TagSet.html @@ -0,0 +1,207 @@ + + + + + + + + TagSet — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

TagSet

+
+
+class astrodata.TagSet(add=None, remove=None, blocked_by=None, blocks=None, if_present=None)[source]
+

Bases: TagSet

+

Named tuple that is used by tag methods to return which actions should +be performed on a tag set.

+

All the attributes are optional, and any combination of them can be used, +allowing to create complex tag structures. Read the documentation on the +tag-generating algorithm if you want to better understand the interactions.

+

The simplest TagSet, though, tends to just add tags to the global set.

+

It can be initialized by position, like any other tuple (the order of the +arguments is the one in which the attributes are listed below). It can +also be initialized by name.

+
+
+add
+

Tags to be added to the global set

+
+
Type:
+

set of str, optional

+
+
+
+ +
+
+remove
+

Tags to be removed from the global set

+
+
Type:
+

set of str, optional

+
+
+
+ +
+
+blocked_by
+

Tags that will prevent this TagSet from being applied

+
+
Type:
+

set of str, optional

+
+
+
+ +
+
+blocks
+

Other TagSets containing these won’t be applied

+
+
Type:
+

set of str, optional

+
+
+
+ +
+
+if_present
+

This TagSet will be applied only all of these tags are present

+
+
Type:
+

set of str, optional

+
+
+
+ +

Examples

+
>>> TagSet()  
+TagSet(
+    add=set(),
+    remove=set(),
+    blocked_by=set(),
+    blocks=set(),
+    if_present=set()
+)
+>>> TagSet({'BIAS', 'CAL'})  
+TagSet(
+    add={'BIAS', 'CAL'},
+    remove=set(),
+    blocked_by=set(),
+    blocks=set(),
+    if_present=set()
+)
+>>> TagSet(remove={'BIAS', 'CAL'}) 
+TagSet(
+    add=set(),
+    remove={'BIAS', 'CAL'},
+    blocked_by=set(),
+    blocks=set(),
+    if_present=set()
+)
+
+
+

Create new instance of TagSet(add, remove, blocked_by, blocks, if_present)

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.add_header_to_table.html b/api/astrodata.add_header_to_table.html new file mode 100644 index 00000000..17a04875 --- /dev/null +++ b/api/astrodata.add_header_to_table.html @@ -0,0 +1,117 @@ + + + + + + + + add_header_to_table — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

add_header_to_table

+
+
+astrodata.add_header_to_table(table)[source]
+

Add a FITS header to a table.

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.astro_data_descriptor.html b/api/astrodata.astro_data_descriptor.html new file mode 100644 index 00000000..57c5f756 --- /dev/null +++ b/api/astrodata.astro_data_descriptor.html @@ -0,0 +1,129 @@ + + + + + + + + astro_data_descriptor — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

astro_data_descriptor

+
+
+astrodata.astro_data_descriptor(fn)[source]
+

Decorator that will mark a class method as an AstroData descriptor. +Useful to produce list of descriptors, for example.

+

If used in combination with other decorators, this one must be the +one on the top (ie. the last one applying). It doesn’t modify the +method in any other way.

+
+
Parameters:
+

fn (method) – The method to be decorated

+
+
Return type:
+

The tagged method (not a wrapper)

+
+
+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.astro_data_tag.html b/api/astrodata.astro_data_tag.html new file mode 100644 index 00000000..ae5b8869 --- /dev/null +++ b/api/astrodata.astro_data_tag.html @@ -0,0 +1,130 @@ + + + + + + + + astro_data_tag — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

astro_data_tag

+
+
+astrodata.astro_data_tag(fn)[source]
+

Decorator that marks methods of an AstroData derived class as part of +the tag-producing system.

+

It wraps the method around a function that will ensure a consistent return +value: the wrapped method can return any sequence of sequences of strings, +and they will be converted to a TagSet. If the wrapped method +returns None, it will be turned into an empty TagSet.

+
+
Parameters:
+

fn (method) – The method to be decorated

+
+
Return type:
+

A wrapper function

+
+
+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.create.html b/api/astrodata.create.html new file mode 100644 index 00000000..a309159c --- /dev/null +++ b/api/astrodata.create.html @@ -0,0 +1,119 @@ + + + + + + + + create — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

create

+
+
+astrodata.create(*args, **kwargs)[source]
+

Return an AstroData object from data.

+

For implementation details, see +create_from_scratch()

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.from_file.html b/api/astrodata.from_file.html new file mode 100644 index 00000000..54d50a90 --- /dev/null +++ b/api/astrodata.from_file.html @@ -0,0 +1,119 @@ + + + + + + + + from_file — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

from_file

+
+
+astrodata.from_file(*args, **kwargs)[source]
+

Return an AstroData object from a file.

+

For implementation details, see +get_astro_data().

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.open.html b/api/astrodata.open.html new file mode 100644 index 00000000..a14d06ea --- /dev/null +++ b/api/astrodata.open.html @@ -0,0 +1,118 @@ + + + + + + + + open — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

open

+
+
+astrodata.open(*args, **kwargs)[source]
+

Return an AstroData object from a file (deprecated, use +from_file()).

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.returns_list.html b/api/astrodata.returns_list.html new file mode 100644 index 00000000..e0be8e27 --- /dev/null +++ b/api/astrodata.returns_list.html @@ -0,0 +1,129 @@ + + + + + + + + returns_list — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

returns_list

+
+
+astrodata.returns_list(fn)[source]
+

Decorator to ensure that descriptors that should return a list (of one +value per extension) only returns single values when operating on single +slices; and vice versa.

+

This is a common case, and you can use the decorator to simplify the +logic of your descriptors.

+
+
Parameters:
+

fn (method) – The method to be decorated

+
+
Return type:
+

A function

+
+
+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api/astrodata.version.html b/api/astrodata.version.html new file mode 100644 index 00000000..b88c5d3d --- /dev/null +++ b/api/astrodata.version.html @@ -0,0 +1,126 @@ + + + + + + + + version — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

version

+
+
+astrodata.version(short=False, tag='')[source]
+

Returns DRAGONS’s version based on the api, +feature and bug numbers.

+
+
Returns:
+

str

+
+
Return type:
+

formatted version

+
+
+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/api_short.html b/api_short.html new file mode 100644 index 00000000..937d0dce --- /dev/null +++ b/api_short.html @@ -0,0 +1,178 @@ + + + + + + + + Common API for Users — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Common API for Users

+

This package contains the common API for users of the AstroData system. It +provides a single interface to the data, regardless of the format in which it +is stored.

+

If you would like a more extensive description of the astrodata package as a +user, see the User Guide. If you are interested in developing with AstroData, +see the Developer Guide.

+
+

astrodata Package

+

This package adds an abstraction layer to astronomical data by parsing the +information contained in the headers as attributes. To do so, one must subclass +astrodata.AstroData and add parse methods accordingly to the +TagSet received.

+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

add_header_to_table(table)

Add a FITS header to a table.

astro_data_descriptor(fn)

Decorator that will mark a class method as an AstroData descriptor.

astro_data_tag(fn)

Decorator that marks methods of an AstroData derived class as part of the tag-producing system.

from_file(*args, **kwargs)

Return an AstroData object from a file.

create(*args, **kwargs)

Return an AstroData object from data.

returns_list(fn)

Decorator to ensure that descriptors that should return a list (of one value per extension) only returns single values when operating on single slices; and vice versa.

version([short, tag])

Returns DRAGONS's version based on the api, feature and bug numbers.

open(*args, **kwargs)

Return an AstroData object from a file (deprecated, use from_file()).

+
+
+

Classes

+ + + + + + + + + + + + + + + + + + + + + +

AstroData([nddata, tables, phu, indices, ...])

Base class for the AstroData software package.

AstroDataError

Exception raised when there is a problem with the AstroData class.

AstroDataMixin()

A Mixin for NDData-like classes (such as Spectrum1D) to enable them to behave similarly to AstroData objects.

NDAstroData(data[, uncertainty, mask, wcs, ...])

Implements NDData with all Mixins, plus some AstroData specifics.

Section(*args, **kwargs)

A class to handle n-dimensional sections

TagSet([add, remove, blocked_by, blocks, ...])

Named tuple that is used by tag methods to return which actions should be performed on a tag set.

+
+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..a5aadf07 --- /dev/null +++ b/genindex.html @@ -0,0 +1,461 @@ + + + + + + + Index — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ + +

Index

+ +
+ A + | B + | C + | D + | E + | F + | H + | I + | L + | M + | N + | O + | P + | R + | S + | T + | U + | V + | W + +
+

A

+ + + +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

H

+ + + +
+ +

I

+ + + +
+ +

L

+ + +
+ +

M

+ + + +
+ +

N

+ + + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + + +
+ +

V

+ + + +
+ +

W

+ + + +
+ + + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..3f7ce152 --- /dev/null +++ b/index.html @@ -0,0 +1,123 @@ + + + + + + + + astrodata Documentation — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

astrodata Documentation

+

This is the documentation for astrodata.

+
+

Contents:

+ +
+
+

Indices and tables

+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/appendix_descriptors.html b/manuals/appendix_descriptors.html new file mode 100644 index 00000000..56ad6c2b --- /dev/null +++ b/manuals/appendix_descriptors.html @@ -0,0 +1,532 @@ + + + + + + + + List of Gemini Standard Descriptors — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

List of Gemini Standard Descriptors

+

To run and re-use Gemini primitives and functions this list of Standard +Descriptors must be defined for input data. This also applies to data +that is to be served by the Gemini Observatory Archive (GOA).

+

For any AstroData objects, to get the list of the descriptors that are +defined use the AstroData.descriptors attribute:

+
>> import astrodata
+>> import gemini_instruments
+>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>> ad.descriptors
+('airmass', 'amp_read_area', 'ao_seeing', ..., 'well_depth_setting')
+
+
+

To get the values:

+
>> ad.airmass()
+
+>> for descriptor in ad.descriptors:
+...     print(descriptor, getattr(ad, descriptor)())
+
+
+

Note that not all of the descriptors below are defined for all of the +instruments. For example, shuffle_pixels is defined only for GMOS data +since only GMOS offers a Nod & Shuffle mode.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Descriptor

Short Definition

Python type

ad[0].desc()

ad.desc()

airmass

Airmass of the observation.

float

amp_read_area

Combination of amplifier name and 1-indexed section relative +to the detector.

str

list of str

ao_seeing

Estimate of the natural seeing as calculated from the +adaptive optics systems.

float

array_name

Name assigned to the array generated by a given amplifier, +one array per amplifier.

str

list of str

array_section

Section covered by the array(s), in 0-indexed pixels, relative +to the detector frame (e.g. position of multiple amps read +within a CCD). Uses namedtuple “Section” defined in +gemini_instruments.common.

Section

list of Section

azimuth

Pointing position in azimuth, in degrees.

float

calibration_key

Key used in the database that the getProcessed* primitives +use to store previous calibration association information.

str

camera

Name of the camera.

str

cass_rotator_pa

Position angle of the Cassegrain rotator, in degrees.

float

central_wavelength

Central wavelength, in meters.

float

coadds

Number of co-adds.

int

data_label

Gemini data label.

str

data_section

Section where the sky-exposed data falls, in 0-indexed pixels. +Uses namedtuple “Section” defined in +gemini_instruments.common

Section

list of Section

dec

Declination of the center of the field, in degrees.

float

decker

Name of the decker.

str

detector_name

Name assigned to the detector.

str

detector_roi_setting

Human readable Region of Interest (ROI) setting

str

detector_rois_requested

Section defining the Regions of Interest, in 0-indexed pixels. +Uses namedtuple “Section” defined in +gemini_instruments.common.

list of Section

detector_section

Section covered by the detector(s), in 0-indexed pixels, +relative to the whole mosaic of detectors. +Uses namedtuple “Section” defined in +gemini_instruments.common.

list

list of Section

detector_x_bin

X-axis binning.

int

detector_x_offset

Telescope offset along the detector X-axis, in pixels.

float

detector_y_bin

Y-axis binning.

int

detector_y_offset

Telescope offset along the detector Y-axis, in pixels.

float

disperser

Name of the disperser.

str

dispersion

Value for the dispersion, in meters per pixel.

float

list of float

dispersion_axis

Dispersion axis.

int

list of int

effective_wavelength

Wavelength representing the bandpass or the spectrum coverage.

float

elevation

Pointing position in elevation, in degrees.

float

exposure_time

Exposure time, in seconds.

float

filter_name

Name of the filter combination.

str

focal_plane_mask

Name of the mask in the focal plane.

str

gain

Gain in electrons per ADU

float

list of float

gain_setting

Human readable gain setting (eg. low, high)

str

gcal_lamp

Returns the name of the GCAL lamp being used, or “Off” if no +lamp is in use.

str

group_id

Gemini observation group ID that identifies compatible data.

str

instrument

Name of the instrument

str

is_ao

Whether or not the adaptive optics system was used.

bool

is_coadds_summed

Whether co-adds are summed or averaged.

bool

local_time

Local time.

datetime

lyot_stop

Name of the lyot stop.

str

mdf_row_id

Mask Definition File row ID of a cut MOS or XD spectrum.

int ??

nod_count

Number of nods to A and B positions.

tuple of int

nod_offsets

Nod offsets to A and B positions, in arcseconds

tuple of float

nominal_atmospheric_extinction

Nomimal atmospheric extinction, from model.

float

nominal_photometric_zeropoint

Nominal photometric zeropoint.

float

list of float

non_linear_level

Lower boundary of the non-linear regime.

float

list of int

object

Name of the target (as entered by the user).

str

observation_class

Gemini class name for the observation +(eg. ‘science’, ‘acq’, ‘dayCal’).

str

observation_epoch

Observation epoch.

float

observation_id

Gemini observation ID.

str

observation_type

Gemini observation type (eg. ‘OBJECT’, ‘FLAT’, ‘ARC’).

str

overscan_section

Section where the overscan data falls, in 0-indexed pixels. +Uses namedtuple “Section” defined in +gemini_instruments.common.

Section

list of Section

pixel_scale

Pixel scale in arcsec per pixel.

float

program_id

Gemini program ID.

str

pupil_mask

Name of the pupil mask.

str ??

qa_state

Gemini quality assessment state (eg. pass, usable, fail).

str

ra

Right ascension, in degrees.

float

raw_bg

Gemini sky background band.

int ??

raw_cc

Gemini cloud coverage band.

int

raw_iq

Gemini image quality band.

int

raw_wv

Gemini water vapor band.

int ??

read_mode

Gemini name for combination for gain setting and read setting.

str

read_noise

Read noise in electrons.

float

list of float

read_speed_setting

human readable read mode setting (eg. slow, fast).

str

requested_bg

PI requested Gemini sky background band.

int

requested_cc

PI requested Gemini cloud coverage band.

int

requested_iq

PI requested Gemini image quality band.

int

requested_wv

PI requested Gemini water vapor band.

int

saturation_level

Saturation level.

int

list of int

shuffle_pixels

Charge shuffle, in pixels. (nod and shuffle mode)

int

slit

Name of the slit.

str

target_dec

Declination of the target, in degrees.

float

target_ra

Right Ascension of the target, in degrees.

float

telescope

Name of the telescope.

str

telescope_x_offset

Offset along the telescope’s x-axis.

float

telescope_y_offset

Offset along the telescope’s y-axis.

float

ut_date

UT date of the observation.

datetime.date

ut_datetime

UT date and time of the observation.

datetime

ut_time

UT time of the observation.

datetime.time

wavefront_sensor

Wavefront sensor used for the observation.

str

wavelength_band

Band associated with the filter or the central wavelength.

str

wcs_dec

Declination of the center of field from the WCS keywords. +In degrees.

float

wcs_ra

Right Ascension of the center of field from the WCS keywords. +In degrees.

float

well_depth_setting

Human readable well depth setting (eg. shallow, deep)

str

+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/cheatsheet.html b/manuals/cheatsheet.html new file mode 100644 index 00000000..4d0e0bd8 --- /dev/null +++ b/manuals/cheatsheet.html @@ -0,0 +1,535 @@ + + + + + + + + 1. Cheat Sheet — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

1. Cheat Sheet

+
+

Document ID

+

PIPE-USER-105_AstrodataCheatSheet

+
+

A data package is available for download if you wish to run the examples +included in this cheat sheet. Download it at:

+
+
+

To unpack:

+
$ cd <somewhere_convenient>
+$ tar xvf ad_usermanual_datapkg-v1.tar
+$ bunzip2 ad_usermanual/playdata/*.bz2
+
+
+

Then go to the ad_usermanual/playground directory to run the examples.

+
+

1.1. Imports

+

Import astrodata and gemini_instruments:

+
>> import astrodata
+>> import gemini_instruments
+
+
+
+
+

1.2. Basic read and write operations

+

Open a file:

+
>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+
+

Get path and filename:

+
>> ad.path
+'../playdata/N20170609S0154.fits'
+>> ad.filename
+'N20170609S0154.fits'
+
+
+

Write to a new file:

+
>> ad.write(filename='new154.fits')
+>> ad.filename
+N20170609S0154.fits
+
+
+

Overwrite the file:

+
>> adnew = astrodata.open('new154.fits')
+>> adnew.filename
+new154.fits
+>> adnew.write(overwrite=True)
+
+
+
+
+

1.3. Object structure

+
+

1.3.1. Description

+

The AstroData object is assigned by “tags” that describe the +type of data it contains. The tags are drawn from rules defined in +|gemini_instruments| and are based on header information.

+

When mapping a FITS file, each science pixel extension is loaded as a +NDAstroData object. The list is zero-indexed. So FITS +extension 1 becomes element 0 of the AstroData object. If a VAR +extension is present, it is loaded to the variance attribute of the +NDAstroData. If a DQ extension is present, it is loaded to the .mask +attribute of the NDAstroData. SCI, VAR and DQ are associated +through the EXTVER keyword value.

+

In the file below, each AstroData “extension” contains the pixel data, +then an error plane (.variance) and a bad pixel mask plane (.mask). +|Table| can be attached to an extension, like OBJCAT, or to the +AstroData object globally, like REFCAT. (In this case, OBJCAT is a +catalogue of the sources detected in the image, REFCAT is a reference catalog +for the area covered by the whole file.) If other 2D data needs to be +associated with an extension this can also be done, like here with OBJMASK, +a 2D mask matching the sources in the image.

+
>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>> ad.info()
+Filename: ../playdata/N20170609S0154_varAdded.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED
+    PREPARED SIDEREAL
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    int16
+          .OBJCAT               Table             (6, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 1]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    int16
+          .OBJCAT               Table             (8, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 2]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    int16
+          .OBJCAT               Table             (7, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 3]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    int16
+          .OBJCAT               Table             (5, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+Other Extensions
+               Type        Dimensions
+.REFCAT        Table       (245, 16)
+
+
+
+
+

1.3.2. Modifying the structure

+

Let’s first get our play data loaded. You are encouraged to do a +info() before and after each structure-modification +step, to see how things change.

+
>> from copy import deepcopy
+>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>> adcopy = deepcopy(ad)
+>> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+
+
+

Append an extension:

+
>> adcopy.append(advar[3])
+>> adcopy.append(advar[3].data)
+
+
+

Delete an extension:

+
>> del adcopy[5]
+
+
+

Delete and add variance and mask planes:

+
>> var = adcopy[4].variance
+>> adcopy[4].variance = None
+>> adcopy[4].variance = var
+
+
+

Attach a table to an extension:

+
>> adcopy[3].SMAUG = advar[0].OBJCAT.copy()
+
+
+

Attach a table to the AstroData object:

+
>> adcopy.DROGON = advar.REFCAT.copy()
+
+
+

Delete a table:

+
>> del adcopy[3].SMAUG
+>> del adcopy.DROGON
+
+
+
+
+
+

1.4. Astrodata tags

+
>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>> ad.tags
+{'GMOS', 'OVERSCAN_SUBTRACTED', 'SIDEREAL', 'NORTH', 'OVERSCAN_TRIMMED',
+'PREPARED', 'IMAGE', 'GEMINI'}
+
+>> type(ad.tags)
+<class 'set'>
+
+>> {'IMAGE', 'PREPARED'}.issubset(ad.tags)
+True
+>> 'PREPARED' in ad.tags
+True
+
+
+
+
+

1.5. Headers

+

The use of descriptors is favored over direct header access when retrieving +values already represented by descriptors, and when writing instrument agnostic +routines.

+
+

1.5.1. Descriptors

+
>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>> ad.filter_name()
+'open1-6&g_G0301'
+>> ad.filter_name(pretty=True)
+'g'
+>> ad.gain()   # uses a look-up table to get the correct values
+[2.03, 1.97, 1.96, 2.01]
+>> ad.hdr['GAIN']
+[1.0, 1.0, 1.0, 1.0]    # the wrong values contained in the raw data.
+>> ad[0].gain()
+2.03
+>> ad.gain()[0]
+2.03
+
+>> ad.descriptors
+('airmass', 'amp_read_area', 'ao_seeing', ...
+ ...)
+
+
+
+
+

1.5.2. Direct access to header keywords

+
>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+
+
+
+

1.5.2.1. Primary Header Unit

+

To see a print out of the full PHU:

+
+

>> ad.phu

+
+

Get value from PHU:

+
>> ad.phu['EXPTIME']
+1.0
+
+>> default = 5.
+>> ad.phu.get('BOGUSKEY', default)
+5.0
+
+
+

Set PHU keyword, with and without comment:

+
>> ad.phu['NEWKEY'] = 50.
+>> ad.phu['ANOTHER'] = (30., 'Some comment')
+
+
+

Delete PHU keyword:

+
>> del ad.phu['NEWKEY']
+
+
+
+
+

1.5.2.2. Pixel extension header

+

To see a print out of the full header for an extension or all the extensions:

+
+

>> ad[0].hdr +>> list(ad.hdr)

+
+

Get value from an extension header:

+
>> ad[0].hdr['OVERSCAN']
+469.7444308769482
+>> ad[0].hdr.get('OVERSCAN', default)
+
+
+

Get keyword value for all extensions:

+
>> ad.hdr['OVERSCAN']
+[469.7444308769482, 469.656175780001, 464.9815279808291, 467.5701178951787]
+>> ad.hdr.get('BOGUSKEY', 5.)
+[5.0, 5.0, 5.0, 5.0]
+
+
+

Set extension header keyword, with and without comment:

+
>> ad[0].hdr['NEWKEY'] = 50.
+>> ad[0].hdr['ANOTHER'] = (30., 'Some comment')
+
+
+

Delete an extension keyword:

+
>> del ad[0].hdr['NEWKEY']
+
+
+
+
+

1.5.2.3. Table header

+

See the Tables section.

+
+
+
+
+

1.6. Pixel data

+
+

1.6.1. Arithmetics

+

Arithmetics with variance and mask propagation is offered for ++, -, *, /, and **.

+
>> ad_hcont = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>> ad_halpha = astrodata.open('../playdata/N20170521S0926_forStack.fits')
+
+>> adsub = ad_halpha - ad_hcont
+
+>> ad_halpha[0].data.mean()
+646.11896
+>> ad_hcont[0].data.mean()
+581.81342
+>> adsub[0].data.mean()
+64.305862
+
+>> ad_halpha[0].variance.mean()
+669.80664
+>> ad_hcont[0].variance.mean()
+598.46667
+>> adsub[0].variance.mean()
+1268.274
+
+
+# In place multiplication
+>> ad_mult = deepcopy(ad)
+>> ad_mult.multiply(ad)
+>> ad_mult.multiply(5.)
+
+
+# Using descriptors to operate in-place on extensions.
+>> from copy import deepcopy
+>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>> ad_gain = deepcopy(ad)
+>> for (ext, gain) in zip(ad_gain, ad_gain.gain()):
+...     ext.multiply(gain)
+>> ad_gain[0].data.mean()
+366.39545
+>> ad[0].data.mean()
+180.4904
+>> ad[0].gain()
+2.03
+
+
+
+
+

1.6.2. Other pixel data operations

+
>> import numpy as np
+>> ad_halpha[0].mask[300:350,300:350] = 1
+>> np.mean(ad_halpha[0].data[ad_halpha[0].mask==0])
+657.1994
+>> np.mean(ad_halpha[0].data)
+646.11896
+
+
+
+
+
+

1.7. Tables

+

Tables are stored as astropy.table.Table class. FITS tables are +represented in astrodata as |Table| and FITS headers are stored in the +NDAstroData meta attribute. Most table +access should be done through the |Table| interface. The best reference is the +|astropy| documentation itself. Below are just a few examples.

+
>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+
+
+

Get column names:

+
>> ad.REFCAT.colnames
+
+
+

Get column content:

+
>> ad.REFCAT['zmag']
+>> ad.REFCAT['zmag', 'zmag_err']
+
+
+

Get content of row:

+
>> ad.REFCAT[4]     # 5th row
+>> ad.REFCAT[4:6]   # 5th and 6th rows
+
+
+

Get content from specific row and column:

+
>> ad.REFCAT['zmag'][4]
+
+
+

Add a column:

+
>> new_column = [0] * len(ad.REFCAT)
+>> ad.REFCAT['new_column'] = new_column
+
+
+

Add a row:

+
>> new_row = [0] * len(ad.REFCAT.colnames)
+>> new_row[1] = ''   # Cat_Id column is of "str" type.
+>> ad.REFCAT.add_row(new_row)
+
+
+

Selecting value from criterion:

+
>> ad.REFCAT['zmag'][ad.REFCAT['Cat_Id'] == '1237662500002005475']
+>> ad.REFCAT['zmag'][ad.REFCAT['zmag'] < 18.]
+
+
+

Rejecting numpy.nan before doing something with the values:

+
>> t = ad.REFCAT   # to save typing.
+>> t['zmag'][np.where(np.isnan(t['zmag']), 99, t['zmag']) < 18.]
+
+>> t['zmag'].mean()
+nan
+>> t['zmag'][np.where(~np.isnan(t['zmag']))].mean()
+20.377306
+
+
+

If for some reason you need to access the FITS table headers, here is how to do it.

+

To see the FITS headers:

+
>> ad.REFCAT.meta
+>> ad[0].OBJCAT.meta
+
+
+

To retrieve a specific FITS table header:

+
>> ad.REFCAT.meta['header']['TTYPE3']
+'RAJ2000'
+>> ad[0].OBJCAT.meta['header']['TTYPE3']
+'Y_IMAGE'
+
+
+

To retrieve all the keyword names matching a selection:

+
>> keynames = [key for key in ad.REFCAT.meta['header'] if key.startswith('TTYPE')]
+
+
+
+
+

1.8. Create new AstroData object

+

Basic header and data array set to zeros:

+
>> from astropy.io import fits
+
+>> phu = fits.PrimaryHDU()
+>> pixel_data = np.zeros((100,100))
+
+>> hdu = fits.ImageHDU()
+>> hdu.data = pixel_data
+>> ad = astrodata.create(phu)
+>> ad.append(hdu, name='SCI')
+
+
+

or another way:

+
>> hdu = fits.ImageHDU(data=pixel_data, name='SCI')
+>> ad = astrodata.create(phu, [hdu])
+
+
+

A |Table| as an AstroData object:

+
>> from astropy.table import Table
+
+>> my_astropy_table = Table(list(np.random.rand(2,100)), names=['col1', 'col2'])
+>> phu = fits.PrimaryHDU()
+
+>> ad = astrodata.create(phu)
+>> ad.SMAUG = my_astropy_table
+
+>> phu = fits.PrimaryHDU()
+>> ad = astrodata.create(phu)
+>> ad.SMAUG = my_fits_table
+
+
+

WARNING: This last line will not run like the others as we have not defined +my_fits_table. This is nonetheless how it is done if you had a FITS table.

+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/full_api.html b/manuals/full_api.html new file mode 100644 index 00000000..64ec89c4 --- /dev/null +++ b/manuals/full_api.html @@ -0,0 +1,111 @@ + + + + + + + + 4. Reference API — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

4. Reference API

+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/index.html b/manuals/index.html new file mode 100644 index 00000000..5a75032c --- /dev/null +++ b/manuals/index.html @@ -0,0 +1,128 @@ + + + + + + + + Astrodata Manual — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Astrodata Manual

+
+

Document ID

+

PIPE-USER-120_AstrodataMasterManual

+
+
+
+

This documentation provides different levels of information:

+ +

Appendix

+
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/adclass.html b/manuals/progmanual/adclass.html new file mode 100644 index 00000000..c2df88ca --- /dev/null +++ b/manuals/progmanual/adclass.html @@ -0,0 +1,471 @@ + + + + + + + + 3.3. AstroData and Derivatives — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3.3. AstroData and Derivatives

+

The AstroData class is the main interface to the package. When opening files +or creating new objects, a derivative of this class is returned, as the +AstroData class is not intended to be used directly. It provides the logic to +calculate the tag set for an image, which is common to all +data products. Aside from that, it lacks any kind of specialized knowledge +about the different instruments that produce the FITS files. More importantly, +it defines two methods (info and load) as abstract, meaning that the +class cannot be instantiated directly: a derivative must implement those +methods in order to be useful. Such derivatives can also implement descriptors, +which provide processed metadata in a way that abstracts the user from the raw +information (e.g., the keywords in FITS headers).

+

AstroData does define a common interface, though. Much of it consists on +implementing semantic behavior (access to components through indices, like a +list; arithmetic using standard operators; etc), mostly by implementing +standard Python methods:

+
    +

  • Defines a common __init__ function.

    • +

  • Implements __deepcopy__.

    • +

  • Implements __iter__ to allow sequential iteration over the main set of +components (e.g., FITS science HDUs).

    • +

  • Implements __getitem__ to allow data slicing (e.g., ad[2:4] returns +a new AstroData instance that contains only the third and fourth main +components).

    • +

  • Implements __delitem__ to allow for data removal based on index. It does +not define __setitem__, though. The basic AstroData series of classes +only allows to append new data blocks, not to replace them in one sweeping +move.

    • +

  • Implements __iadd__, __isub__, __imul__, __itruediv__, and +their not-in-place versions, based on them.

    • +
+

There are a few other methods. For a detailed discussion, please refer to the +Reference API.

+
+

3.3.1. The tags Property

+

Additionally, and crucial to the package, AstroData offers a tags property, +that under the hood calculates textual tags that describe the object +represented by an instance, and returns a set of strings. Returning a set (as +opposed to a list, or other similar structure) is intentional, because it is +fast to compare sets, e.g., testing for membership; or calculating intersection, +etc., to figure out if a certain dataset belongs to an arbitrary category.

+

The implementation for the tags property is just a call to +AstroData._process_tags(). This function implements the actual logic behind +calculating the tag set (described below). A derivative class +could redefine the algorithm, or build upon it.

+
+
+

3.3.2. Writing an AstroData Derivative

+

The first step when creating new AstroData derivative hierarchy would be to +create a new class that knows how to deal with some kind of specific data in a +broad sense.

+

AstroData implements both .info() and .load() in ways that are +specific to FITS files. It also introduces a number of FITS-specific methods +and properties, e.g.:

+
    +

  • The properties phu and hdr, which return the primary header and +a list of headers for the science HDUs, respectively.

    • +

  • A write method, which will write the data back to a FITS file.

    • +

  • A _matches_data static method, which is very important, involved in +guiding for the automatic class choice algorithm during data loading. We’ll +talk more about this when dealing with registering our classes.

    • +
+

It also defines the first few descriptors, which are common to all Gemini data: +instrument, object, and telescope, which are good examples of simple +descriptors that just map a PHU keyword without applying any conversion.

+

A typical AstroData programmer will extend this class (AstroData). Any of +the classes under the gemini_instruments package can be used as examples, +but we’ll describe the important bits here.

+
+

3.3.2.1. Create a package for it

+

This is not strictly necessary, but simplifies many things, as we’ll see when +talking about registration. The package layout is up to the designer, so you +can decide how to do it. For DRAGONS we’ve settled on the following +recommendation for our internal process (just to keep things familiar):

+
gemini_instruments
+    __init__.py
+    instrument_name
+        __init__.py
+        adclass.py
+        lookup.py
+
+
+

Where instrument_name would be the package name (for Gemini we group all +our derivative packages under gemini_instruments, and we would import +gemini_instruments.gmos, for example). __init__.py and adclass.py +would be the only required modules under our recommended layout, with +lookup.py being there just to hold hard-coded values in a module separate +from the main logic.

+

adclass.py would contain the declaration of the derivative class, and +__init__.py will contain any code needed to register our class with the +AstroData system upon import.

+
+
+

3.3.2.2. Create your derivative class

+

This is an excerpt of a typical derivative module:

+
from astrodata import astro_data_tag, astro_data_descriptor, TagSet
+from astrodata import AstroData
+
+from . import lookup
+
+class AstroDataInstrument(AstroData):
+    __keyword_dict = dict(
+        array_name = 'AMPNAME',
+        array_section = 'CCDSECT'
+    )
+
+    @staticmethod
+    def _matches_data(source):
+        return source[0].header.get('INSTRUME', '').upper() == 'MYINSTRUMENT'
+
+    @astro_data_tag
+    def _tag_instrument(self):
+       return TagSet(['MYINSTRUMENT'])
+
+    @astro_data_tag
+    def _tag_image(self):
+        if self.phu.get('GRATING') == 'MIRROR':
+            return TagSet(['IMAGE'])
+
+    @astro_data_tag
+    def _tag_dark(self):
+        if self.phu.get('OBSTYPE') == 'DARK':
+            return TagSet(['DARK'], blocks=['IMAGE', 'SPECT'])
+
+    @astro_data_descriptor
+    def array_name(self):
+        return self.phu.get(self._keyword_for('array_name'))
+
+    @astro_data_descriptor
+    def amp_read_area(self):
+        ampname = self.array_name()
+        detector_section = self.detector_section()
+        return "'{}':{}".format(ampname, detector_section)
+
+
+
+

Note

+

An actual Gemini Facility Instrument class will derive from +gemini_instruments.AstroDataGemini, but this is irrelevant +for the example.

+
+

The class typically relies on functionality declared elsewhere, in some +ancestor, e.g., the tag set computation and the _keyword_for method are +defined at AstroData.

+

Some highlights:

+
    +

  • __keyword_dict[1] defines one-to-one mappings, assigning a more +readable moniker for an HDU header keyword. The idea here is to prevent +hard-coding the names of the keywords, in the actual code. While these are +typically quite stable and not prone to change, it’s better to be safe than +sorry, and this can come in useful during instrument development, which is +the more likely source of instability. The actual value can be extracted by +calling self._keyword_for('moniker').

    • +

  • _matches_data is a static method. It does not have any knowledge about +the class itself, and it does not work on an instance of the class: it’s +a member of the class just to make it easier for the AstroData registry to +find it. This method is passed some object containing cues of the internal +structure and contents of the data. This could be, for example, an instance +of HDUList. Using these data, _matches_data must return a boolean, +with True meaning “I know how to handle this data”.

    +

    Note that True does not mean “I have full knowledge of the data”. It +is acceptable for more than one class to claim compatibility. For a GMOS FITS +file, the classes that will return True are: AstroData (because it is +a FITS file that comply with certain minimum requirements), +~gemini_instruments.gemini.AstroDataGemini (the data contains Gemini +Facility common metadata), and ~gemini_instruments.gmos.AstroDataGmos (the +actual handler!).

    +

    But this does not mean that multiple classes can be valid “final” candidates. +If AstroData’s automatic class discovery finds more than one class claiming +matching with the data, it will start discarding them on the basis of +inheritance: any class that appears in the inheritance tree of another one is +dropped, because the more specialized one is preferred. If at some point the +algorithm cannot find more classes to drop, and there is more than one left +in the list, an exception will occur, as AstroData will have no way to choose +one over the other.

    +
    • +

  • A number of “tag methods” have been declared. Their naming is a convention, +at the end of the day (the “_tag_” prefix, and the related “_status_” +one, are just hints for the programmer): each team should establish +a convention that works for them. What is important here is to decorate +them using ~astrodata.astro_data_tag, which earmarks the method so that it +can be discovered later, and ensures that it returns an appropriate value.

    +

    A tag method will return either a ~astrodata.TagSet instance (which can be +empty), or None, which is the same as returning an empty +~astrodata.TagSet[2].

    +

    All these methods will be executed when looking up for tags, and it’s up +to the tag set construction algorithm (see Tags) to figure out the final +result. In theory, one could provide just one big method, but this is +feasible only when the logic behind deciding the tag set is simple. The +moment that there are a few competing alternatives, with some conditions +precluding other branches, one may end up with a rather complicated dozens of +lines of logic. Let the algorithm do the heavy work for you: split the tags +as needed to keep things simple, with an easy to understand logic.

    +

    Also, keeping the individual (or related) tags in separate methods lets you +exploit the inheritance, keeping common ones at a higher level, and +redefining them as needed later on, at derived classes.

    +

    Please, refer to ~gemini_instruments.gemini.AstroDataGemini, +~gemini_instruments.gmos.AstroDataGmos, and +~gemini_instruments.gnirs.AstroDataGnirs for examples using most of the +features.

    +
    • +

  • The astrodata.AstroData.read method calls the astrodata.fits.read_fits +function, which uses metadata in the FITS headers to determine how the data +should be stored in the AstroData object. In particular, the EXTNAME +and EXTVER keywords are used to assign individual FITS HDUs, using the +same names (SCI, DQ, and VAR) as Gemini-IRAF for the data, +mask, and variance planes. A SCI HDU must exist if there is +another HDU with the same EXTVER, or else an error will occur.

    +

    If the raw data do not conform to this format, the astrodata.AstroData.read +method can be overridden by your class, by having it call the +astrodata.fits.read_fits function with an additional parameter, +extname_parser, that provides a function to modify the header. This +function will be called on each HDU before further processing. As an example, +the SOAR Adaptive Module Imager (SAMI) instrument writes raw data as +a 4-extension MEF file, with the extensions having EXTNAME values +im1, im2, etc. These need to be modified to SCI, and an +appropriate EXTVER keyword added` [3]. This can be done by +writing a suitable read method for the AstroDataSami class:

    +
    @classmethod
+def read(cls, source, extname_parser=None):
+    def sami_parser(hdu):
+        m = re.match('im(\d)', hdu.header.get('EXTNAME', ''))
+        if m:
+            hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData')
+            hdu.header['EXTVER'] = (int(m.group(1)), 'Added by AstroData')
+
+    return super().read(source, extname_parser=extname_parser)
+
    +
    +
    • +

  • Descriptors will make the bulk of the class: again, the name is arbitrary, +and it should be descriptive. What may be important here is to use +~astrodata.astro_data_descriptor to decorate them. This is not required, +because unlike tag methods, descriptors are meant to be called explicitly by +the programmer, but they can still be marked (using this decorator) to be +listed when calling the descriptors property. The decorator does not +alter the descriptor input or output in any way, so it is always safe to use +it, and you probably should, unless there’s a good reason against it (e.g., +if a descriptor is deprecated and you don’t want it to show up in lookups).

    +

    More detailed information can be found in Descriptors.

    +
    • +
+
+
+

3.3.2.3. Register your class

+

Finally, you need to include your class in the AstroData Registry. This is +an internal structure with a list of all the AstroData-derived classes that +we want to make available for our programs. Including the classes in this +registry is an important step, because a file should be opened using +astrodata.open or astrodata.create, which uses the registry to identify +the appropriate class (via the _matches_data methods), instead of having +the user specify it explicitly.

+

The version of AstroData prior to DRAGONS had an auto-discovery mechanism, that +explored the source tree looking for the relevant classes and other related +information. This forced a fixed directory structure (because the code needed +to know where to look for files), and gave the names of files and classes +semantic meaning (to know which files to look into, for example). Aside from +the rigidness of the scheme, this introduced all sort of inefficiencies, +including an unacceptably high overhead when importing the AstroData package +for the first time during execution.

+

In this new version of AstroData we’ve introduced a more manageable scheme, +that places the discovery responsibility on the programmer. A typical +__init__.py file on an instrument package will look like this:

+
__all__ = ['AstroDataMyInstrument']
+
+from astrodata import factory
+from .adclass import AstroDataMyInstrument
+
+factory.addClass(AstroDataMyInstrument)
+
+
+

The call to factory.addClass is the one registering the class. This step +needs to be done before the class can be used effectively in the +AstroData system. Placing the registration step in the __init__.py file is +convenient, because importing the package will be enough!

+

Thus, a script making use of DRAGONS’ AstroData to manipulate GMOS data +could start like this:

+
import astrodata
+from gemini_instruments import gmos
+
+...
+
+ad = astrodata.open(some_file)
+
+
+

The first import line is not needed, technically, because the gmos package +will import it too, anyway, but we’ll probably need the astrodata package +in the namespace anyway, and it’s always better to be explicit. Our +typical DRAGONS scripts and modules start like this, instead:

+
import astrodata
+import gemini_instruments
+
+
+

gemini_instruments imports all the packages under it, making knowledge +about all Gemini instruments available for the script, which is perfect for a +multi-instrument pipeline, for example. Loading all the instrument classes is +not typically a burden on memory, though, so it’s easier for everyone to take +the more general approach. It also makes things easier on the end user, because +they won’t need to know internal details of our packages (like their naming +scheme). We suggest this “cascade import” scheme for all new source trees, +letting the user decide which level of detail they need.

+

As an additional step, the __init__.py file in a package may do extra +initialization. For example, for the Gemini modules, one piece of functionality +that is shared across instruments is a descriptor that translates a filter’s +name (say “u” or “FeII”) to its central wavelength (e.g., +0.35µm, 1.644µm). As it is a rather common function for us, it is implemented +by ~gemini_instruments.gemini.AstroDataGemini. This class does not know +about its daughter classes, though, meaning that it cannot know about the +filters offered by their instruments. Instead, we offer a function that can +be used to update the filter → wavelength mapping in +gemini_instruments.gemini.lookup so that it is accessible by the +~gemini_instruments.gemini.AstroDataGemini-level descriptor. So our +gmos/__init__.py looks like this:

+
__all__ = ['AstroDataGmos']
+
+from astrodata import factory
+from ..gemini import addInstrumentFilterWavelengths
+from .adclass import AstroDataGmos
+from .lookup import filter_wavelengths
+
+factory.addClass(AstroDataGmos)
+# Use the generic GMOS name for both GMOS-N and GMOS-S
+addInstrumentFilterWavelengths('GMOS', filter_wavelengths)
+
+
+

where ~gemini_instruments.gemini.addInstrumentFilterWavelengths is provided +by the gemini package to perform the update in a controlled way.

+

We encourage package maintainers and creators to follow such explicit +initialization methods, driven by the modules that add functionality +themselves, as opposed to active discovery methods on the core code. This +favors decoupling between modules, which is generally a good idea.

+

Footnotes

+ +
+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/containers.html b/manuals/progmanual/containers.html new file mode 100644 index 00000000..e302faf4 --- /dev/null +++ b/manuals/progmanual/containers.html @@ -0,0 +1,189 @@ + + + + + + + + 3.4. Data Containers — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3.4. Data Containers

+

A third, and very important part of the AstroData core package is the data +container. We have chosen to extend Astropy’s |NDData| with our own +requirements, particularly lazy-loading of data using by opening the FITS files +in read-only, memory-mapping mode, and exploiting the windowing capability of +astropy.io.fits (using section) to reduce our memory requirements, which +becomes important when reducing data (e.g., stacking).

+

We’ll describe here how we depart from |NDData|, and how do we integrate the +data containers with the rest of the package. Please refer to |NDData| for the +full interface.

+

Our main data container is astrodata.NDAstroData. Fundamentally, it is +a derivative of astropy.nddata.NDData, plus a number of mixins to add +functionality:

+
class NDAstroData(AstroDataMixin, NDArithmeticMixin, NDSlicingMixin, NDData):
+    ...
+
+
+

This allows us out of the box to have proper arithmetic with error +propagation, and slicing the data with the array syntax.

+

Our first customization is NDAstroData.__init__. It relies mostly on the +upstream initialization, but customizes it because our class is initialized +with lazy-loaded data wrapped around a custom class +(astrodata.fits.FitsLazyLoadable) that mimics a astropy.io.fits HDU +instance just enough to play along with |NDData|’s initialization code.

+

FitsLazyLoadable is an integral part of our memory-mapping scheme, and +among other things it will scale data on the fly, as memory-mapped FITS data +can only be read unscaled. Our NDAstroData redefines the properties data, +uncertainty, and mask, in two ways:

+
    +

  • To deal with the fact that our class is storing FitsLazyLoadable +instances, not arrays, as |NDData| would expect. This is to keep data out +of memory as long as possible.

    • +

  • To replace lazy-loaded data with a real in-memory array, under certain +conditions (e.g., if the data is modified, as we won’t apply the changes to the +original file!)

    • +
+

Our obsession with lazy-loading and discarding data is directed to reduce +memory fragmentation as much as possible. This is a real problem that can hit +applications dealing with large arrays, particularly when using Python. Given +the choice to optimize for speed or for memory consumption, we’ve chosen the +latter, which is the more pressing issue.

+

We’ve added another new property, window, that can be used to +explicitly exploit the astropy.io.fits’s section property, to (again) +avoid loading unneeded data to memory. This property returns an instance of +NDWindowing which, when sliced, in turn produces an instance of +NDWindowingAstroData, itself a proxy of NDAstroData. This scheme may +seem complex, but it was deemed the easiest and cleanest way to achieve the +result that we were looking for.

+

The base NDAstroData class provides the memory-mapping functionality, +with other important behaviors added by the AstroDataMixin, which can +be used with other |NDData|-like classes (such as Spectrum1D) to add +additional convenience.

+

One addition is the variance property, which allows direct access and +setting of the data’s uncertainty, without the user needing to explicitly wrap +it as an NDUncertainty object. Internally, the variance is stored as an +ADVarianceUncertainty object, which is subclassed from Astropy’s standard +VarianceUncertainty class with the addition of a check for negative values +whenever the array is accessed.

+

NDAstroDataMixin also changes the default method of combining the mask +attributes during arithmetic operations from logical_or to bitwise_or, +since the individual bits in the mask have separate meanings.

+

The way slicing affects the wcs is also changed since DRAGONS regularly +uses the callable nature of gWCS objects and this is broken by the standard +slicing method.

+

Finally, the additional image planes and tables stored in the meta dict +are exposed as attributes of the NDAstroData object, and any image planes +that have the same shape as the parent NDAstroData object will be handled +by NDWindowingAstroData. Sections will be ignored when accessing image +planes with a different shape, as well as tables.

+
+

Note

+

We expect to make changes to NDAstroData in future releases. In particular, +we plan to make use of the unit attribute provided by the +|NDData| class and increase the use of memory-mapping by default. These +changes mostly represent increased functionality and we anticipate a high +(and possibly full) degree of backward compatibility.

+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/descriptors.html b/manuals/progmanual/descriptors.html new file mode 100644 index 00000000..6a4eb413 --- /dev/null +++ b/manuals/progmanual/descriptors.html @@ -0,0 +1,168 @@ + + + + + + + + 3.6. Descriptors — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3.6. Descriptors

+

Descriptors are just regular methods that translate metadata from the raw +storage (e.g., cards from FITS headers) to values useful for the user, +potentially doing some processing in between. They exist to:

+
    +

  • Abstract the actual organization of the metadata; e.g. +~gemini_instruments.gemini.AstroDataGemini takes the detector gain from +a keyword in the FITS PHU, where ~gemini_instruments.niri.AstroDataNiri +overrides this to provide a hard-coded value.

    +

    More complex implementations also exist. In order to determine the gain of +a GMOS observation, ~gemini_instruments.gmos.AstroDataGmos uses the +observation date (provided by a descriptor) to select a particular lookup +table, and then uses the values of other descriptors to select the correct +entry in the table.

    +
    • +

  • Provide a common interface to a set of instruments. This simplifies user +training (no need to learn a different API for each instrument), and +facilitates the reuse of code for pipelines, etc.

    • +

  • Also, since FITS header keywords are limited to 8 characters, for simple +keyword → value mappings, they provide a more meaningful and readable name.

    • +
+

Descriptors should be decorated using ~astrodata.astro_data_descriptor. +The only function of this decorator is to ensure that the descriptor is marked +as such: it does not alter its input or output in any way. This lets the user +explore the API of an AstroData object via the +~astrodata.AstroData.descriptors property.

+

Descriptors can be decorated with ~astrodata.core.returns_list to +eliminate the need to code some logic. Some descriptors return single values, +while some return lists, one per extension. Typically, the former are +descriptors that refer to the entire observation (and, for MEF files, are +usually extracted from metadata in the PHU, such as airmass), while the +latter are descriptors where different extensions might return different values +(and typically come from metadata in the individual HDUs, such as gain). +A list is returned even if there is only one extension in the AstroData +object, as this allows code to be written generically to iterate over the +AstroData object and the descriptor return, without needing to know how many +extensions there are. The ~astrodata.core.returns_list decorator ensures that +the descriptor returns an appropriate object (value or list), using the +following rules:

+
    +

  • If the AstroData object is not a single slice:

    +
      +

    • If the undecorated descriptor returns a list, an exception is raised +if the list is not the same length as the number of extensions.

      • +

    • If the undecorated descriptor returns a single value, the decorator +will turn it into a list of the correct length by copying this value.

      • +
    +
    • +

  • If the AstroData object is a single slice and the undecorated +descriptor returns a list, only the first element is returned.

    • +
+

An example of the use of this decorator is the NIRI +~gemini_instruments.niri.AstroDataNiri.gain descriptor, which reads the +value from a lookup table and simply returns it. A single value is only +appropriate if the AstroData object is singly-sliced and the decorator ensures +that a list is returned otherwise.

+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/design.html b/manuals/progmanual/design.html new file mode 100644 index 00000000..30e1df4b --- /dev/null +++ b/manuals/progmanual/design.html @@ -0,0 +1,187 @@ + + + + + + + + 3.2. General Design — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3.2. General Design

+

As astronomical instruments have become more complex, there +has been an increasing need for bespoke reduction packages and pipelines to +deal with the specific needs of each instrument. Despite this +complexity, many of the reduction steps can be very similar and the overall +effort could be reduced significantly by sharing code. In practice, however, +there are often issues regarding the manner in which the data are stored +internally. The purpose of AstroData is to provide a uniform interface to the data +and metadata, in a manner that is independent both of the specific instrument +and the way the data are stored on disk, thereby facilitating this code-sharing. +It is not a new astronomical data format.

+

One of the main features of AstroData is the use of descriptors, which +provide a level of abstraction between the metadata and the code accessing it. +Somebody using the AstroData interface who wishes to know the exposure time +of a particular astronomical observation represented by the AstroData object +ad can simply write ad.exposure_time() without needing to concern +themselves about how that value is stored internally, for example, the name +of the FITS header keyword. These are discussed further in Descriptors.

+

AstroData also provides a clearer representation of the relationships +between different parts of the data produced from a single astronomical +observation. Modern astronomical instruments often contain multiple +detectors that are read out separately and the multi-extension FITS (MEF) +format used by many institutions, including Gemini Observatory, handles +the raw data well. In this format, each detector’s data and metadata is +assigned to its own extension, +while there is also a separate extension (the Primary Header Unit, +or PHU) containing additional metadata that applies to the entire +observation. However, as the data are processed, more data and/or +metadata may be added whose relationship is obscured by the limitations +of the MEF format. One example is the creation and propagation of information +describing the quality and uncertainty of the scientific data: while +this was a feature of +Gemini IRAF[1], the coding required to implement it was cumbersome +and AstroData uses the astropy.nddata.NDData class, +as discussed in Data Containers. This makes the relationship between these +data much clearer, and AstroData creates a syntax that makes readily apparent the +roles of other data and metadata that may be created during the reduction +process.

+

An AstroData object therefore consists of one or more self-contained +“extensions” (data and metadata) plus additional data and metadata that is +relevant to all the extensions. In many data reduction processes, the same +operation will be performed on each extension (e.g., subtracting an overscan +region from a CCD frame) and an axiom of AstroData is that iterating over +the extensions produces AstroData “slices” which retain knowledge of the +top-level data and metadata. Since a slice has one (or more) extensions +plus this top-level (meta)data, it too is an AstroData object and, +specifically, an instance of the same subclass as its parent.

+

A final feature of AstroData is the implementation of very high-level metadata. +These data, called tags, facilitate a key part of the Gemini data reduction +system, DRAGONS, by linking the astronomical data to the recipes +required to process them. They are explained in detail in Tags and the +Recipe System Programmers Manual[2].

+
+

Note

+

AstroData and DRAGONS have been developed for the reduction of data from +Gemini Observatory, which produces data in the FITS format that is still the +most widely-used format for astronomical data. In light of this, and the +limited resources in the Science User Support Department, we have only +developed support for FITS, even though the AstroData format is designed +to be independent of the file format. In some cases, this has led to +uncertainty and internal disagreement over where precisely to engage in +abstraction and, should AstroData support a different file format, we +may find alternative solutions that result in small, but possibly +significant, changes to the API.

+
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/index.html b/manuals/progmanual/index.html new file mode 100644 index 00000000..7fa123e5 --- /dev/null +++ b/manuals/progmanual/index.html @@ -0,0 +1,129 @@ + + + + + + + + 3. Programmer’s Manual — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3. Programmer’s Manual

+
+

Document ID

+

PIPE-USER-104_AstrodataProgManual

+
+
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/intro.html b/manuals/progmanual/intro.html new file mode 100644 index 00000000..9fc05b0c --- /dev/null +++ b/manuals/progmanual/intro.html @@ -0,0 +1,155 @@ + + + + + + + + 3.1. Precedents and Motivation — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3.1. Precedents and Motivation

+

The Gemini Observatory has produced a number of tools for data processing. +Historically this has translated into a number of IRAF[1] packages but +the lack of long-term support for IRAF, coupled with the well-known +difficulty in creating robust reduction pipelines within the IRAF +environment, led to a decision +to adopt Python as a programming tool and a new +package was born: Gemini Python. Gemini Python provided tools to load and +manipulate Gemini-produced multi-extension FITS[2] (MEF) files, +along with a pipeline that +allowed the construction of reduction recipes. At the center of this package +was the AstroData subpackage, which supported the abstraction of the FITS +files.

+

Gemini Python reached version 1.0.1, released during November 2014. In 2015 +the Science User Support Department (SUSD) was created at Gemini, which took on the +responsibility of maintaining the software reduction tools, and started +planning future steps. With improved oversight and time and thought, it became +evident that the design of Gemini Python and, specially, of AstroData, made +further development a daunting task.

+

In 2016 a decision was reached to overhaul Gemini Python. While the +principles behind AstroData were sound, the coding involved unnecessary +layers of abstraction and eschewed features of the Python language in favor +of its own implementation. Thus, +|DRAGONS| was born, with a new, simplified (and backward incompatible) +AstroData v2.0 (which we will refer to simply as AstroData)

+

This manual documents both the high level design and some implementation +details of AstroData, together with an explanation of how to extend the +package to work for new environments.

+

Footnotes

+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/progmanual/tags.html b/manuals/progmanual/tags.html new file mode 100644 index 00000000..7a2c1f4a --- /dev/null +++ b/manuals/progmanual/tags.html @@ -0,0 +1,267 @@ + + + + + + + + 3.5. Tags — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

3.5. Tags

+

We described in previous section how to generate tags for an +AstroData derivative. In this section we’ll describe the algorithm that +generates the complete tag set out of the individual TagSet instances. The +algorithm collects all the tags in a list and then decides whether to apply +them or not following certain rules, but let’s talk about TagSet first.

+

TagSet is actually a standard named tuple customized to generate default +values (None) for its missing members. Its signature is:

+
TagSet(add=None, remove=None, blocked_by=None, blocks=None,
+       if_present=None)
+
+
+

The most common TagSet is an additive one: TagSet(['FOO', 'BAR']). +If all you need is to add tags, then you’re done here. But the real power of +our tag generating system is that you can specify some conditions to apply a +certain TagSet, or put restrictions on others. The different arguments to +TagSet all expect a list (or some others work in the following way):

+
    +

  • add: if this TagSet is selected, then add all these members to the tag +set.

    • +

  • remove: if this TagSet is selected, then prevent all these members +from joining the tag set.

    • +

  • blocked_by: if any of the tags listed in here exist in the tag set, then +discard this TagSet altogether.

    • +

  • blocks: discard from the list of unprocessed ones any TagSet that +would add any of the tags listed here.

    • +

  • if_present: process this tag only if all the tags listed in here exist in +the tag set at this point.

    • +
+

Note that blocked_by and blocks look like two sides of the same coin. +This is intentional: which one to use is up to the programmer, depending on +what will reduce the amount of typing and/or make the logic easier (sometimes one +wants to block a bunch of other tags from a single one; sometimes one wants a +tag to be blocked by a bunch of others). Furthermore, while blocks and +blocked_by prevent the entire TagSet from being added if it contains a +tag affected by these, remove only affects the specific tag.

+

Now, the algorithm works like this:

+
    +

  1. Collect all the TagSet generated by methods in the instance that are +decorated using astro_data_tag.

    2. +

  2. Then we sort them out:

    +
      +

    1. Those that subtract tags from the tag set go first (the ones with +non-empty remove or blocks), allowing them to act early on

      2. +

    2. Those with non-empty blocked_by are moved to the end of the list, to +ensure that other tags can be generated before them.

      3. +

    3. Those with non-empty if_present are moved behind those with +blocked_by.

      4. +
    +
    3. +

  3. Now that we’ve sorted the tags, process them sequentially and for each one:

    +
      +

    1. If they require other tags to be present, make sure that this is the case. +If the requirements are not met, drop the tagset. If not…

      2. +

    2. Figure out if any other tag is blocking the tagset. This will be the +case if any of the tags to be added is in the “blocked” list, or if +any of the tags added by previous tag sets are in the blocked_by +list of the one being processed. Then…

      3. +

    3. If all the previous hurdles have been passed, apply the changes declared +by this tag (add, remove, and/or block others).

      4. +
    +
    4. +
+

Note that Python’s sort algorithm is stable. This means, that if two elements +are indistinguishable from the point of view of the sorting algorithm, they are +guaranteed to stay in the same relative position. To better understand how this +affects our tags, and the algorithm itself, let’s follow up with an example taken +from real code (the Gemini-generic and GMOS modules):

+
# Simple tagset, with only a constant, additive content
+@astro_data_tag
+def _tag_instrument(self):
+    return TagSet(['GMOS'])
+
+# Simple tagset, also with additive content. This one will
+# check if the frame fits the requirements to be classified
+# as "GMOS imaging". It returns a value conditionally:
+# if this is not imaging, then it will return None, which
+# means the algorithm will ignore the value
+@astro_data_tag
+def _tag_image(self):
+    if self.phu.get('GRATING') == 'MIRROR':
+        return TagSet(['IMAGE'])
+
+# This is a slightly more complex TagSet (but fairly simple, anyway),
+# inherited by all Gemini instruments.
+@astro_data_tag
+def _type_gcal_lamp(self):
+    if self.phu.get('GCALLAMP') == 'IRhigh':
+        shut = self.phu.get('GCALSHUT')
+        if shut == 'OPEN':
+            return TagSet(['GCAL_IR_ON', 'LAMPON'],
+                          blocked_by=['PROCESSED'])
+        elif shut == 'CLOSED':
+            return TagSet(['GCAL_IR_OFF', 'LAMPOFF'],
+                          blocked_by=['PROCESSED'])
+
+# This tagset is only active when we detect that the frame is
+# a bias. In that case we want to prevent the frame from being
+# classified as "imaging" or "spectroscopy", which depend on the
+# configuration of the instrument
+@astro_data_tag
+def _tag_bias(self):
+    if self.phu.get('OBSTYPE') == 'BIAS':
+        return TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT'])
+
+
+

These four simple tag methods will serve to illustrate the algorithm. Let’s pretend +that the requirements for all four of them are somehow met, meaning that we get four +TagSet instances in our list, in some random order. After step 1 in the algorithm, +then, we may have collected the following list:

+
[ TagSet(['GMOS']),
+  TagSet(['GCAL_IR_OFF', 'LAMPOFF'], blocked_by=['PROCESSED']),
+  TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']),
+  TagSet(['IMAGE']) ]
+
+
+

The algorithm then proceeds to sort them. First, it will promote the TagSet +with non-empty blocks or remove:

+
[ TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']),
+  TagSet(['GMOS']),
+  TagSet(['GCAL_IR_OFF', 'LAMPOFF'], blocked_by=['PROCESSED']),
+  TagSet(['IMAGE']) ]
+
+
+

Note that the other three TagSet stay in exactly the same order. Now the +algorithm will sort the list again, moving the ones with non-empty +blocked_by to the end:

+
[ TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'SPECT']),
+  TagSet(['GMOS']), TagSet(['IMAGE']),
+  TagSet(['GCAL_IR_OFF', 'LAMPOFF'], blocked_by=['PROCESSED']) ]
+
+
+

Note that at each step, all the instances (except the ones “being moved”) have +kept the same position relative to each other -here’s where the “stability” of +the sorting comes into play,- ensuring that each step does not affect the previous +one. Finally, there are no if_present in our example, so no more instances are +moved around.

+

Now the algorithm prepares three empty sets (tags, removals, and blocked), +and starts iterating over the TagSet list.

+
+
    +

  1. For the first TagSet there are no blocks or removals, so we just add its +contents to the current sets: tags = {'BIAS', 'CAL'}, +blocked = {'IMAGE', 'SPECT'}.

    2. +

  2. Then comes TagSet(['GMOS']). Again, there are no removals in place, and +GMOS is not in the list of blocked tags. Thus, we just add it to the current +tag set: tags = {'BIAS', 'CAL', 'GMOS'}.

    3. +

  3. When processing TagSet(['IMAGE']), the algorithm observes that this IMAGE +is in the blocked set, and stops processing this tag set.

    4. +

  4. Finally, neither GCAL_IR_OFF nor LAMPOFF are in blocked, and +PROCESSED is not in tags, meaning that we can add this tag set to +the final one.

    5. +
+
+

Our result will look something like: {'BIAS', 'CAL', 'GMOS', 'GCAL_IR_OFF', 'LAMPOFF'}

+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/data.html b/manuals/usermanual/data.html new file mode 100644 index 00000000..d12c2fbb --- /dev/null +++ b/manuals/usermanual/data.html @@ -0,0 +1,964 @@ + + + + + + + + 2.6. Pixel Data — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.6. Pixel Data

+

Try it yourself

+

Download the data package (Try it yourself) if you wish to follow along and run the +examples. Then

+
$ cd <path>/ad_usermanual/playground
+$ python
+
+
+

Then import core astrodata and the Gemini astrodata configurations.

+
>>> import astrodata
+>>> import gemini_instruments
+
+
+
+

2.6.1. Operate on Pixel Data

+

The pixel data are stored in the AstroData object as a list of +NDAstroData objects. The NDAstroData is a subclass of Astropy’s +NDData class which combines in one “package” the pixel values, the +variance, and the data quality plane or mask (as well as associated meta-data). +The data can be retrieved as a standard NumPy ndarray.

+

In the sections below, we will present several typical examples of data +manipulation. But first let’s start with a quick example on how to access +the pixel data.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>>> the_data = ad[1].data
+>>> type(the_data)
+<class 'numpy.ndarray'>
+
+>>> # Loop through the extensions
+>>> for ext in ad:
+...     the_data = ext.data
+...     print(the_data.sum())
+333071030
+335104458
+333170484
+333055206
+
+
+

In this example, we first access the pixels for the second extensions. +Remember that in Python, list are zero-indexed, hence we access the second +extension as ad[1]. The .data attribute contains a NumPy ndarray. +In the for-loop, for each extension, we get the data and use the NumPy +.sum() method to sum the pixel values. Anything that can be done +with a ndarray can be done on AstroData pixel data.

+
+
+

2.6.2. Arithmetic on AstroData Objects

+

AstroData objects support basic in-place arithmetics with these methods:

+ + + + + + + + + + + + + + + +

addition

.add()

subtraction

.subtract()

multiplication

.multiply()

division

.divide()

+

Normal, not in-place, arithmetics is also possible using the standard +operators, +, -, *, and /.

+

The big advantage of using AstroData to do arithmetics is that the +variance and mask, if present, will be propagated through to the output +AstroData object. We will explore the variance propagation in the next +section and mask usage later in this chapter.

+
+

2.6.2.1. Simple operations

+

Here are a few examples of arithmetics on AstroData objects.:

+
>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+
+>>> # Addition
+>>> ad.add(50.)
+>>> ad = ad + 50.
+>>> ad += 50.
+
+>>> # Subtraction
+>>> ad.subtract(50.)
+>>> ad = ad - 50.
+>>> ad -= 50.
+
+>>> # Multiplication (Using a descriptor)
+>>> ad.multiply(ad.exposure_time())
+>>> ad = ad * ad.exposure_time()
+>>> ad *= ad.exposure_time()
+
+>>> # Division (Using a descriptor)
+>>> ad.divide(ad.exposure_time())
+>>> ad = ad / ad.exposure_time()
+>>> ad /= ad.exposure_time()
+
+
+

When the syntax adout = adin + 1 is used, the output variable is a copy +of the original. In the examples above we reassign the result back onto the +original. The two other forms, ad.add() and ad += are in-place +operations.

+

When a descriptor returns a list because the value changes for each +extension, a for-loop is needed:

+
>>> for (ext, gain) in zip(ad, ad.gain()):
+...     ext.multiply(gain)
+
+
+

If you want to do the above but on a new object, leaving the original unchanged, +use deepcopy first.

+
>>> from copy import deepcopy
+>>> adcopy = deepcopy(ad)
+>>> for (ext, gain) in zip(adcopy, adcopy.gain()):
+...     ext.multiply(gain)
+
+
+
+
+

2.6.2.2. Operator Precedence

+

The AstroData arithmetics methods can be stringed together but beware that +there is no operator precedence when that is done. For arithmetics that +involve more than one operation, it is probably safer to use the normal +Python operator syntax. Here is a little example to illustrate the difference.

+
>>> ad.add(5).multiply(10).subtract(5)
+
+>>> # means:  ad = ((ad + 5) * 10) - 5
+>>> # NOT: ad = ad + (5 * 10) - 5
+
+
+

This is because the methods modify the object in-place, one operation after +the other from left to right. This also means that the original is modified.

+

This example applies the expected operator precedence:

+
>>> ad = ad + ad * 3 - 40.
+>>> # means: ad = ad + (ad * 3) - 40.
+
+
+

If you need a copy, leaving the original untouched, which is sometimes useful +you can use deepcopy or just use the normal operator and assign to a new +variable.:

+
>>> adnew = ad + ad * 3 - 40.
+
+
+
+
+
+

2.6.3. Variance

+

When doing arithmetic on an AstroData object, if a variance is present +it will be propagated appropriately to the output no matter which syntax +you use (the methods or the Python operators).

+
+

2.6.3.1. Adding a Variance Plane

+

In this example, we will add the poisson noise to an AstroData dataset. +The data is still in ADU, therefore the poisson noise as variance is +signal / gain. We want to set the variance for each of the pixel +extensions.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>>> for (extension, gain) in zip(ad, ad.gain()):
+...    extension.variance = extension.data / gain
+
+
+

Check ad.info(), you will see a variance plane for each of the four +extensions.

+
+
+

2.6.3.2. Automatic Variance Propagation

+

As mentioned before, if present, the variance plane will be propagated to the +resulting AstroData object when doing arithmetics. The variance +calculation assumes that the data are not correlated.

+

Let’s look into an example.

+
>>> #     output = x * x
+>>> # var_output = var * x^2 + var * x^2
+>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+
+>>> ad[1].data[50,50]
+56.160931
+>>> ad[1].variance[50,50]
+96.356529
+>>> adout = ad * ad
+>>> adout[1].data[50,50]
+3154.05
+>>> adout[1].variance[50,50]
+607826.62
+
+
+
+
+
+

2.6.4. Data Quality Plane

+

The NDData mask stores the data quality plane. The simplest form is a +True/False array of the same size at the pixel array. In Astrodata we favor +a bit array that allows for additional information about why the pixel is being +masked. For example at Gemini here is our bit mapping for bad pixels.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Meaning

Value

Bad pixel

1

Non Linear

2

Saturated

4

Cosmic Ray

8

No Data

16

Overlap

32

Unilluminated

64

+

(These definitions are located in geminidr.gemini.lookups.DQ_definitions.)

+

So a pixel marked 10 in the mask, would be a “non-linear” “cosmic ray”. The +AstroData masks are propagated with bitwise-OR operation. For example, +let’s say that we are stacking frames. A pixel is set as bad (value 1) +in one frame, saturated in another (value 4), and fine in all the other +the frames (value 0). The mask of the resulting stack will be assigned +a value of 5 for that pixel.

+

These bitmasks will work like any other NumPy True/False mask. There is a +usage example below using the mask.

+

The mask can be accessed as follow:

+
>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>>> ad.info()
+
+>>> ad[2].mask
+
+
+
+
+

2.6.5. Display

+

Since the data is stored in the AstroData object as a NumPy ndarray +any tool that works on ndarray can be used. To display to DS9 there +is the imexam package. The numdisplay package is still available for +now but it is no longer supported by STScI. We will show +how to use imexam to display and read the cursor position. Read the +documentation on that tool to learn more about what else it has +to offer.

+
+

2.6.5.1. Displaying with imexam

+

Here is an example how to display pixel data to DS9 with imexam. You must +start ds9 before running this example.

+
>>> import imexam
+>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+
+# Connect to the DS9 window (should already be opened.)
+>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
+
+>>> ds9.view(ad[0].data)
+
+# To scale "a la IRAF"
+>>> ds9.view(ad[0].data)
+>>> ds9.scale('zscale')
+
+# To set the mininum and maximum scale values
+>>> ds9.view(ad[0].data)
+>>> ds9.scale('limits 0 2000')
+
+
+
+
+

2.6.5.2. Retrieving cursor position with imexam

+

The function readcursor() can be used to retrieve cursor +position in pixel coordinates. Note that it will not respond to +mouse clicks, only keyboard entries are acknowledged.

+

When invoked, readcursor() will stop the flow of the program and wait +for the user to put the cursor on top of the image and type a key. A +tuple with three values will be returned: the x and +y coordinates in 0-based system, and the value of the key the user +hit.

+
>>> import imexam
+>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+
+# Connect to the DS9 window (should already be opened.)
+# and display
+>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
+>>> ds9.view(ad[0].data)
+>>> ds9.scale('zscale')
+
+
+>>> cursor_coo = ds9.readcursor()
+>>> print(cursor_coo)
+
+# To extract only the x,y coordinates
+>>> (xcoo, ycoo) = cursor_coo[:2]
+>>> print(xcoo, ycoo)
+
+# If you are also interested in the keystroke
+>>> keystroke = cursor_coo[2]
+>>> print('You pressed this key: %s' % keystroke)
+
+
+
+
+
+

2.6.6. Useful tools from the NumPy, SciPy, and Astropy Packages

+

Like for the Display section, this section is not really specific to +Astrodata but is rather a quick show-and-tell of a few things that can +be done on the pixels with the big scientific packages NumPy, SciPy, +and Astropy.

+

Those three packages are very large and rich. They have their own +extensive documentation and it is highly recommend for the users to learn about what +they have to offer. It might save you from re-inventing the wheel.

+

The pixels, the variance, and the mask are stored as NumPy ndarray’s. +Let us go through some basic examples, just to get a feel for how the +data in an AstroData object can be manipulated.

+
+

2.6.6.1. ndarray

+

The data are contained in NumPy ndarray objects. Any tools that works +on an ndarray can be used with Astrodata.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>>> data = ad[0].data
+
+>>> # Shape of the array.  (equivalent to NAXIS2, NAXIS1)
+>>> data.shape
+(2112, 288)
+
+>>> # Value of a pixel at "IRAF" or DS9 coordinates (100, 50)
+>>> data[49,99]
+455
+
+>>> # Data type
+>>> data.dtype
+dtype('uint16')
+
+
+

The two most important thing to remember for users coming from the IRAF +world or the Fortran world are that the array has the y-axis in the first +index, the x-axis in the second, and that the array indices are zero-indexed, +not one-indexed. The examples above illustrate those two critical +differences.

+

It is sometimes useful to know the data type of the values stored in the +array. Here, the file is a raw dataset, fresh off the telescope. No +operations has been done on the pixels yet. The data type of Gemini raw +datasets is always “Unsigned integer (0 to 65535)”, uint16.

+
+

Warning

+

Beware that doing arithmetic on uint16 can lead to unexpected +results. This is a NumPy behavior. If the result of an operation +is higher than the range allowed by uint16, the output value will +be “wrong”. The data type will not be modified to accommodate the large +value. A workaround, and a safety net, is to multiply the array by +1.0 to force the conversion to a float64.

+
>>> a = np.array([65535], dtype='uint16')
+>>> a + a
+array([65534], dtype=uint16)
+>>> 1.0*a + a
+array([ 131070.])
+
+
+
+
+
+

2.6.6.2. Simple Numpy Statistics

+

A lot of functions and methods are available in NumPy to probe the array, +too many to cover here, but here are a couple examples.

+
>>> import numpy as np
+
+>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> data = ad[0].data
+
+>>> data.mean()
+>>> np.average(data)
+>>> np.median(data)
+
+
+

Note how mean() is called differently from the other two. mean() +is a ndarray method, the others are NumPy functions. The implementation +details are clearly well beyond the scope of this manual, but when looking +for the tool you need, keep in mind that there are two sets of functions to +look into. Duplications like .mean() and np.average() can happen, +but they are not the norm. The readers are strongly encouraged to refer to +the NumPy documentation to find the tool they need.

+
+
+

2.6.6.3. Clipped Statistics

+

It is common in astronomy to apply clipping to the statistics, a clipped +average, for example. The NumPy ma module can be used to create masks +of the values to reject. In the examples below, we calculated the clipped +average of the first pixel extension with a rejection threshold set to ++/- 3 times the standard deviation.

+

Before Astropy, it was possible to do something like that with only +NumPy tools, like in this example:

+
>>> import numpy as np
+
+>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> data = ad[0].data
+
+>>> stddev = data.std()
+>>> mean = data.mean()
+
+>>> clipped_mean = np.ma.masked_outside(data, mean-3*stddev, mean+3*stddev).mean()
+
+
+

There is no iteration in that example. It is a straight one-time clipping.

+

For something more robust, there is an Astropy function that can help, in +particular by adding an iterative process to the calculation. Here is +how it is done:

+
>>> import numpy as np
+>>> from astropy.stats import sigma_clip
+
+>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> data = ad[0].data
+
+>>> clipped_mean = np.ma.mean(sigma_clip(data, sigma=3))
+
+
+
+
+

2.6.6.4. Filters with SciPy

+

Another common operation is the filtering of an image, for example convolving +with a gaussian filter. The SciPy module ndimage.filters offers +several functions for image processing. See the SciPy documentation for +more information.

+

The example below applies a gaussian filter to the pixel array.

+
>>> from scipy.ndimage import filters
+>>> import imexam
+
+>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>>> data = ad[0].data
+
+>>> # We need to prepare an array of the same size and shape as
+>>> # the data array.  The result will be put in there.
+>>> convolved_data = np.zeros(data.size).reshape(data.shape)
+
+>>> # We now apply the convolution filter.
+>>> sigma = 10.
+>>> filters.gaussian_filter(data, sigma, output=convolved_data)
+
+>>> # Let's visually compare the convolved image with the original
+>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
+>>> ds9.view(data)
+>>> ds9.scale('zscale')
+>>> ds9.frame(2)
+>>> ds9.view(convolved_data)
+>>> ds9.scale('zscale')
+>>> ds9.blink()
+>>> # When you are convinced it's been convolved, stop the blinking.
+>>> ds9.blink(blink=False)
+
+
+

Note that there is an Astropy way to do this convolution, with tools in +astropy.convolution package. Beware that for this particular kernel +we have found that the Astropy convolve function is extremely slow +compared to the SciPy solution. +This is because the SciPy function is optimized for a Gaussian convolution +while the generic convolve function in Astropy can take in any kernel. +Being able to take in any kernel is a very powerful feature, but the cost +is time. The lesson here is do your research, and find the best tool for +your needs.

+
+
+

2.6.6.5. Many other tools

+

There are many, many other tools available out there. Here are the links to +the three big projects we have featured in this section.

+ +
+
+
+

2.6.7. Using the Astrodata Data Quality Plane

+

Let us look at an example where the use of the Astrodata mask is +necessary to get correct statistics. A GMOS imaging frame has large sections +of unilluminated pixels; the edges are not illuminated and there are two +bands between the three CCDs that represent the physical gap between the +CCDs. Let us have a look at the pixels to have a better sense of the +data:

+
>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>>> import imexam
+>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
+
+>>> ds9.view(ad[0].data)
+>>> ds9.scale('zscale')
+
+
+

See how the right and left portions of the frame are not exposed to the sky, +and the 45 degree angle cuts of the four corners. The chip gaps too. +If we wanted to do statistics on the whole frames, we certainly would not want +to include those unilluminated areas. We would want to mask them out.

+

Let us have a look at the mask associated with that image:

+
>>> ds9.view(ad[0].mask)
+>>> ds9.scale('zscale')
+
+
+

The bad sections are all white (pixel value > 0). There are even some +illuminated pixels that have been marked as bad for a reason or another.

+

Let us use that mask to reject the pixels with no or bad information and +do calculations only on the good pixels. For the sake of simplicity we will +just do an average. This is just illustrative. We show various ways to +accomplish the task; choose the one that best suits your need or that you +find most readable.

+
>>> import numpy as np
+
+>>> # For clarity...
+>>> data = ad[0].data
+>>> mask = ad[0].mask
+
+>>> # Reject all flagged pixels and calculate the mean
+>>> np.mean(data[mask == 0])
+>>> np.ma.masked_array(data, mask).mean()
+
+>>> # Reject only the pixels flagged "no_data" (bit 16)
+>>> np.mean(data[(mask & 16) == 0])
+>>> np.ma.masked_array(data, mask & 16).mean()
+>>> np.ma.masked_where(mask & 16, data).mean()
+
+
+

The “long” form with np.ma.masked_* is useful if you are planning to do +more than one operation on the masked array. For example:

+
>>> clean_data = np.ma.masked_array(data, mask)
+>>> clean_data.mean()
+>>> np.ma.median(clean_data)
+>>> clean_data.max()
+
+
+
+
+

2.6.8. Manipulate Data Sections

+

So far we have shown examples using the entire data array. It is possible +to work on sections of that array. If you are already familiar with +Python, you probably already know how to do most if not all of what is in +this section. For readers new to Python, and especially those coming +from IRAF, there are a few things that are worth explaining.

+

When indexing a NumPy ndarray, the left most number refers to the +highest dimension’s axis. For example, in a 2D array, the IRAF section +are in (x-axis, y-axis) format, while in Python they are in +(y-axis, x-axis) format. Also important to remember is that the ndarray +is 0-indexed, rather than 1-indexed like in Fortran or IRAF.

+

Putting it all together, a pixel position (x,y) = (50,75) in IRAF or from +the cursor on a DS9 frame, is accessed in Python as data[74,49]. +Similarly, the IRAF section [10:20, 30:40] translate in Python to +[9:20, 29:40]. Also remember that when slicing in Python, the upper limit +of the slice is not included in the slice. This is why here we request +20 and 40 rather 19 and 39.

+

Let’s put it in action.

+
+

2.6.8.1. Basic Statistics on Section

+

In this example, we do simple statistics on a section of the image.

+
>>> import numpy as np
+
+>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>>> data = ad[0].data
+
+>>> # Get statistics for a 25x25 pixel-wide box centered on pixel
+>>> # (50,75)  (DS9 frame coordinate)
+>>> xc = 49
+>>> yc = 74
+>>> buffer = 25
+>>> (xlow, xhigh) = (xc - buffer//2, xc + buffer//2 + 1)
+>>> (ylow, yhigh) = (yc - buffer//2, yc + buffer//2 + 1)
+>>> # The section is [62:87, 37:62]
+>>> stamp = data[ylow:yhigh, xlow:xhigh]
+>>> mean = stamp.mean()
+>>> median = np.median(stamp)
+>>> stddev = stamp.std()
+>>> minimum = stamp.min()
+>>> maximum = stamp.max()
+
+>>> print(' Mean   Median  Stddev  Min   Max\n \
+... %.2f  %.2f   %.2f    %.2f  %.2f' % \
+... (mean, median, stddev, minimum, maximum))
+
+
+

Have you noticed that the median is calculated with a function rather +than a method? This is simply because the ndarray object does not +have a method to calculate the median.

+
+
+

2.6.8.2. Example - Overscan Subtraction with Trimming

+

Several concepts from previous sections and chapters are used in this +example. The Descriptors are used to retrieve the overscan section and +the data section information from the headers. Statistics are done on the +NumPy ndarray representing the pixel data. Astrodata arithmetics is +used to subtract the overscan level. Finally, the overscan section is +trimmed off and the modified AstroData object is written to a new file +on disk.

+

To make the example more complete, and to show that when the pixel data +array is trimmed, the variance (and mask) arrays are also trimmed, let us +add a variance plane to our raw data frame.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>>> for (extension, gain) in zip(ad, ad.gain()):
+...    extension.variance = extension.data / gain
+...
+
+>>> # Here is how the data structure looks like before the trimming.
+>>> ad.info()
+Filename: ../playdata/N20170609S0154.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
+
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+[ 1]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+[ 2]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+[ 3]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+
+>>> # Let's operate on the first extension.
+>>> #
+>>> # The section descriptors return the section in a Python format
+>>> # ready to use, 0-indexed.
+>>> oversec = ad[0].overscan_section()
+>>> datasec = ad[0].data_section()
+
+>>> # Measure the overscan level
+>>> mean_overscan = ad[0].data[oversec.y1: oversec.y2, oversec.x1: oversec.x2].mean()
+
+>>> # Subtract the overscan level.  The variance will be propagated.
+>>> ad[0].subtract(mean_overscan)
+
+>>> # Trim the data to remove the overscan section and keep only
+>>> # the data section.  Note that the WCS will be automatically
+>>> # adjusted when the trimming is done.
+>>> #
+>>> # Here we work on the NDAstroData object to have the variance
+>>> # trimmed automatically to the same size as the science array.
+>>> # To reassign the cropped NDAstroData, we use the reset() method.
+>>> ad[0].reset(ad[0].nddata[datasec.y1:datasec.y2, datasec.x1:datasec.x2]
+
+>>> # Now look at the dimensions of the first extension, science
+>>> # and variance.  That extension is smaller than the others.
+>>> ad.info()
+Filename: ../playdata/N20170609S0154.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
+
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 256)    float64
+          .variance             ndarray           (2112, 256)    float64
+[ 1]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+[ 2]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+[ 3]   science                  NDAstroData       (2112, 288)    uint16
+          .variance             ndarray           (2112, 288)    float64
+
+>>> # We can write this to a new file
+>>> ad.write('partly_overscan_corrected.fits')
+
+
+

A new feature presented in this example is the ability to work on the +NDAstroData object directly. This is particularly useful when cropping +the science pixel array as one will want the variance and the mask arrays +cropped exactly the same way. Taking a section of the NDAstroData +object (ad[0].nddata[y1:y2, x1:x2]), instead of just the .data array, +does all that for us.

+

To reassign the cropped NDAstroData to the extension one uses the +.reset() method as shown in the example.

+

Of course to do the overscan correction correctly and completely, one would +loop over all four extensions. But that’s the only difference.

+
+
+
+

2.6.9. Data Cubes

+

Reduced Integral Field Unit (IFU) data is commonly represented as a cube, +a three-dimensional array. The data component of an AstroData +object extension can be such a cube, and it can be manipulated and explored +with NumPy, AstroPy, SciPy, imexam, like we did already in this section +with 2D arrays. We can use matplotlib to plot the 1D spectra represented +in the third dimension.

+

In Gemini IFU cubes, the first axis is the X-axis, the second, the Y-axis, +and the wavelength is in the third axis. Remember that in a ndarray +that order is reversed (wlen, y, x).

+

In the example below we “collapse” the cube along the wavelenth axis to +create a “white light” image and display it. Then we plot a 1D spectrum +from a given (x,y) position.

+
>>> import imexam
+>>> import matplotlib.pyplot as plt
+
+>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
+
+>>> adcube = astrodata.open('../playdata/gmosifu_cube.fits')
+>>> adcube.info()
+
+>>> # Sum along the wavelength axis to create a "white light" image
+>>> summed_image = adcube[0].data.sum(axis=0)
+>>> ds9.view(summed_image)
+>>> ds9.scale('minmax')
+
+>>> # Plot a 1-D spectrum from the spatial position (14,25).
+>>> plt.plot(adcube[0].data[:,24,13])
+>>> plt.show()   # might be needed, depends on matplotlibrc interactive setting
+
+
+

Now that is nice but it would be nicer if we could plot the x-axis in units +of Angstroms instead of pixels. We use the AstroData’s WCS handler, which is +based on gwcs.wcs.WCS to get the necessary information. A particularity +of gwcs.wcs.WCS is that it refers to the axes in the “natural” way, +(x, y, wlen) contrary to Python’s (wlen, y, x). It truly requires you to pay +attention.

+
>>> import matplotlib.pyplot as plt
+
+>>> adcube = astrodata.open('../playdata/gmosifu_cube.fits')
+
+# We get the wavelength axis in Angstroms at the position we want to
+# extract, x=13, y=24.
+# The wcs call returns a 3-element list, the third element ([2]) contains
+# the wavelength values for each pixel along the wavelength axis.
+
+>>> length_wlen_axis = adcube[0].shape[0]   # (wlen, y, x)
+>>> wavelengths = adcube[0].wcs(13, 24, range(length_wlen_axis))[2] # (x, y, wlen)
+
+# We get the intensity along that axis
+>>> intensity = adcube[0].data[:, 24, 13]   # (wlen, y, x)
+
+# We plot
+plt.clf()
+plt.plot(wavelengths, intensity)
+plt.show()
+
+
+
+
+

2.6.10. Plot Data

+

The main plotting package in Python is matplotlib. We have used it in the +previous section on data cubes to plot a spectrum. There is also the project +called imexam which provides astronomy-specific tools for the +exploration and measurement of data. We have also used that package above to +display images to DS9.

+

In this section we absolutely do not aim at covering all the features of +either package but rather to give a few examples that can get the readers +started in their exploration of the data and of the visualization packages.

+

Refer to the projects web pages for full documentation.

+ +
+

2.6.10.1. Matplotlib

+

With Matplotlib you have full control on your plot. You do have to do a bit +for work to get it perfect though. However it can produce publication +quality plots. Here we just scratch the surface of Matplotlib.

+
>>> import numpy as np
+>>> import matplotlib.pyplot as plt
+>>> from astropy import wcs
+
+>>> ad_image = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>>> ad_spectrum = astrodata.open('../playdata/estgsS20080220S0078.fits')
+
+>>> # Line plot from image.  Row #1044 (y-coordinate)
+>>> line_index = 1043
+>>> line = ad_image[0].data[line_index, :]
+>>> plt.clf()
+>>> plt.plot(line)
+>>> plt.show()
+
+>>> # Column plot from image, averaging across 11 pixels around colum #327
+>>> col_index = 326
+>>> width = 5
+>>> xlow = col_index - width
+>>> xhigh = col_index + width + 1
+>>> thick_column = ad_image[0].data[:, xlow:xhigh]
+>>> plt.clf()
+>>> plt.plot(thick_column.mean(axis=1))  # mean along the width.
+>>> plt.show()
+>>> plt.ylim(0, 50)     # Set the y-axis range
+>>> plt.plot(thick_column.mean(axis=1))
+>>> plt.show()
+
+>>> # Contour plot for a section of an image.
+>>> center = (1646, 2355)
+>>> width = 15
+>>> xrange = (center[1]-width//2, center[1] + width//2 + 1)
+>>> yrange = (center[0]-width//2, center[0] + width//2 + 1)
+>>> blob = ad_image[0].data[yrange[0]:yrange[1], xrange[0]:xrange[1]]
+>>> plt.clf()
+>>> plt.imshow(blob, cmap='gray', origin='lower')
+>>> plt.contour(blob)
+>>> plt.show()
+
+>>> # Spectrum in pixels
+>>> plt.clf()
+>>> plt.plot(ad_spectrum[0].data)
+>>> plt.show()
+
+>>> # Spectrum in Angstroms
+>>> spec_wcs = wcs.WCS(ad_spectrum[0].hdr)
+>>> pixcoords = np.array(range(ad_spectrum[0].data.shape[0]))
+>>> wlen = spec_wcs.wcs_pix2world(pixcoords, 0)[0]
+>>> plt.clf()
+>>> plt.plot(wlen, ad_spectrum[0].data)
+>>> plt.show()
+
+
+
+
+

2.6.10.2. imexam

+

For those who have used IRAF, imexam is a well-known tool. The Python +imexam reproduces many of of the features of its IRAF predecesor, the interactive mode of +course, but it also offers programmatic tools. One can even control DS9 +from Python. As for Matplotlib, here we really just scratch the surface of +what imexam has to offer.

+
 >>> import imexam
+ >>> from imexam.imexamine import Imexamine
+
+ >>> ad_image = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+
+ # Display the image
+ >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
+ >>> ds9.view(ad_image[0].data)
+ >>> ds9.scale('zscale')
+
+ # Run in interactive mode.  Try the various commands.
+>>> ds9.imexam()
+
+ # Use the programmatic interface
+ # First initialize an Imexamine object.
+ >>> plot = Imexamine()
+
+ # Line plot from image.  Row #1044 (y-coordinate)
+ >>> line_index = 1043
+ >>> plot.plot_line(0, line_index, ad_image[0].data)
+
+ # Column plot from image, averaging across 11 pixels around colum #327
+ # There is no setting for this, so we have to do something similar
+ # to what we did with matplotlib.
+ >>> col_index = 326
+ >>> width = 5
+ >>> xlow = col_index - width
+ >>> xhigh = col_index + width + 1
+ >>> thick_column = ad_image[0].data[:, xlow:xhigh]
+ >>> mean_column = thick_column.mean(axis=1)
+ >>> plot.plot_column(0, 0, np.expand_dims(mean_column, 1))
+
+ >>> # Contour plot for a section of an image.
+ >>> center = (1646, 2355)  # in python coordinates
+ >>> width = 15
+ >>> plot.contour_pars['ncolumns'][0] = width
+ >>> plot.contour_pars['nlines'][0] = width
+ >>> plot.contour(center[1], center[0], ad_image[0].data)
+
+
+
+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/headers.html b/manuals/usermanual/headers.html new file mode 100644 index 00000000..47b9c106 --- /dev/null +++ b/manuals/usermanual/headers.html @@ -0,0 +1,379 @@ + + + + + + + + 2.5. Metadata and Headers — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.5. Metadata and Headers

+

Try it yourself

+

Download the data package (Try it yourself) if you wish to follow along and run the +examples. Then

+
$ cd <path>/ad_usermanual/playground
+$ python
+
+
+

You need to import Astrodata and the Gemini instrument configuration package.

+
>>> import astrodata
+>>> import gemini_instruments
+
+
+
+

2.5.1. Astrodata Descriptors

+

We show in this chapter how to use the Astrodata Descriptors. But first +let’s explain what they are.

+

Astrodata Descriptors provide a “header-to-concept” mapping that allows the +user to access header information from a unique interface, regardless of +which instrument the dataset is from. Like for the Astrodata Tags, the +mapping is coded in a configuration package separate from core Astrodata. +For Gemini instruments, that package is named gemini_instruments.

+

For example, if the user is interested to know the effective filter used +for an observation, normally one needs to know which specific keyword or +set of keywords to look at for that instrument. However, once the concept +of “filter” is coded as a Descriptor, the user only needs to call the +filter_name() descriptor to retrieve the information.

+

The Descriptors are closely associated with the Astrodata Tags. In fact, +they are implemented in the same AstroData class as the tags. Once +the specific AstroData class is selected (upon opening the file), all +the tags and descriptors for that class are defined. For example, all the +descriptor functions of GMOS data, ie. the functions that map a descriptor +concept to the actual header content, are defined in the AstroDataGmos +class.

+

This is all completely transparent to the user. One simply opens the data +file and all the descriptors are ready to be used.

+
+

Note

+

Of course if the Descriptors have not been implemented for that specific +data, they will not work. They should all be defined for Gemini data. +For other sources, the headers can be accessed directly, one keyword at +a time. This type of access is discussed below. This is also useful +when the information needed is not associated with one of the standard +descriptors.

+
+

To get the list of descriptors available for an AstroData object:

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> ad.descriptors
+('airmass', 'amp_read_area', 'ao_seeing', ...
+  ...)
+
+
+

Most Descriptor names are readily understood, but one can get a short +description of what the Descriptor refers to by calling the Python help +function. For example:

+
>>> help(ad.airmass)
+>>> help(ad.filter_name)
+
+
+

The full list of standard descriptors is available in the Appendix +Implementing Descriptors.

+
+
+

2.5.2. Accessing Metadata

+
+

2.5.2.1. Accessing Metadata with Descriptors

+

Whenever possible the Descriptors should be used to get information from +headers. This allows for maximum re-usability of the code as it will then +work on any datasets with an AstroData class.

+

Here are a few examples using Descriptors:

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>>> #--- print a value
+>>> print('The airmass is : ', ad.airmass())
+The airmass is :  1.089
+
+>>> #--- use a value to control the flow
+>>> if ad.exposure_time() < 240.:
+...     print('This is a short exposure.')
+... else:
+...     print('This is a long exposure.')
+This is a short exposure.
+
+>>> #--- multiply all extensions by their respective gain
+>>> for ext, gain in zip(ad, ad.gain()):
+...     ext *= gain
+
+>>> #--- do arithmetics
+>>> fwhm_pixel = 3.5
+>>> fwhm_arcsec = fwhm_pixel * ad.pixel_scale()
+
+
+

The return values for Descriptors depend on the nature of the information +being requested and the number of extensions in the AstroData object. +When the value has words, it will be string, if it is a number +it will be a float or an integer. +The dataset used in this section has 4 extensions. When the descriptor +value can be different for each extension, the descriptor will return a +Python list.

+
>>> ad.airmass()
+1.089
+>>> ad.gain()
+[2.03, 1.97, 1.96, 2.01]
+>>> ad.filter_name()
+'open1-6&g_G0301'
+
+
+

Some descriptors accept arguments. For example:

+
>>> ad.filter_name(pretty=True)
+'g'
+
+
+

A full list of standard descriptors is available in the Appendix +Implementing Descriptors.

+
+
+

2.5.2.2. Accessing Metadata Directly

+

Not all header content is mapped to Descriptors, nor should it. Direct access +is available for header content falling outside the scope of the descriptors.

+

One important thing to keep in mind is that the PHU (Primary Header Unit) and +the extension headers are accessed slightly differently. The attribute +phu needs to be used for the PHU, and hdr for the extension headers.

+

Here are some examples of direct header access:

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+>>> #--- Get keyword value from the PHU
+>>> ad.phu['AOFOLD']
+'park-pos.'
+
+>>> #--- Get keyword value from a specific extension
+>>> ad[0].hdr['CRPIX1']
+511.862999160781
+
+>>> #--- Get keyword value from all the extensions in one call.
+>>> ad.hdr['CRPIX1']
+[511.862999160781, 287.862999160781, -0.137000839218696, -224.137000839219]
+
+
+
+
+

2.5.2.3. Whole Headers

+

Entire headers can be retrieved as fits Header objects:

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> type(ad.phu)
+<class 'astropy.io.fits.header.Header'>
+>>> type(ad[0].hdr)
+<class 'astropy.io.fits.header.Header'>
+
+
+

In interactive mode, it is possible to print the headers on the screen as +follows:

+
>>> ad.phu
+SIMPLE  =                    T / file does conform to FITS standard
+BITPIX  =                   16 / number of bits per data pixel
+NAXIS   =                    0 / number of data axes
+....
+
+>>> ad[0].hdr
+XTENSION= 'IMAGE   '           / IMAGE extension
+BITPIX  =                   16 / number of bits per data pixel
+NAXIS   =                    2 / number of data axes
+....
+
+
+
+
+
+

2.5.3. Updating, Adding and Deleting Metadata

+

Header cards can be updated, added to, or deleted from the headers. The PHU +and the extensions headers are again accessed in a mostly identical way +with phu and hdr, respectively.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+
+
+

Add and update a keyword, without and with comment:

+
>>> ad.phu['NEWKEY'] = 50.
+>>> ad.phu['NEWKEY'] = (30., 'Updated PHU keyword')
+
+>>> ad[0].hdr['NEWKEY'] = 50.
+>>> ad[0].hdr['NEWKEY'] = (30., 'Updated extension keyword')
+
+
+

Delete a keyword:

+
>>> del ad.phu['NEWKEY']
+>>> del ad[0].hdr['NEWKEY']
+
+
+
+
+

2.5.4. World Co-ordinate System attribute

+

The wcs of an extension’s nddata attribute (eg. ad[0].nddata.wcs; +see Pixel Data) is stored as an instance of astropy.wcs.WCS (a +standard FITS WCS object) or gwcs.WCS (a “Generalized WCS” or gWCS object). This defines a transformation +between array indices and some other co-ordinate system such as “World” +co-ordinates (see APE 14). GWCS allows +multiple, almost arbitrary co-ordinate mappings from different calibration +steps (eg. CCD mosaicking, distortion correction & wavelength calibration) to +be combined in a single, reversible transformation chain — but this +information cannot always be represented as a FITS standard WCS. If a gWCS +object is too complex to be defined by the basic FITS keywords, it gets stored +as a table extension named ‘WCS’ when the AstroData instance is saved to a +file (with the same EXTVER as the corresponding ‘SCI’ array) and the FITS +header keywords are updated to provide an approximation to the true WCS and an +additional keyword FITS-WCS is added with the value ‘APPROXIMATE’. +The representation in the table is produced using +ASDF, with one line of text per row. Likewise, +when the file is re-opened, the gWCS object gets recreated in wcs from the +table. If the transformation defined by the gWCS object can be accurately +described by standard FITS keywords, then no WCS extension is created as the +gWCS object can be created from these keywords when the file is re-opened.

+

In future, it is intended to improve the quality of the FITS approximation +using the Simple Imaging Polynomial convention +(SIP) or +a discrete sampling of the World co-ordinate +values will be stored as part of the FITS WCS, following Greisen et al. (2006), S6 (in addition to the +definitive ‘WCS’ table), allowing standard FITS readers to report accurate +World co-ordinates for each pixel.

+
+
+

2.5.5. Adding Descriptors [Advanced Topic]

+

For proper and complete instructions on how to create Astrodata Descriptors, +the reader is invited to refer to the Astrodata Programmer Manual. Here we +provide a simple introduction that might help some readers better understand +Astrodata Descriptors, or serve as a quick reference for those who have +written Astrodata Descriptors in the past but need a little refresher.

+

The Astrodata Descriptors are defined in an AstroData class. The +AstroData class specific to an instrument is located in a separate +package, not in astrodata. For example, for Gemini instruments, all the +various AstroData classes are contained in the gemini_instruments +package.

+

An Astrodata Descriptor is a function within the instrument’s AstroData +class. The descriptor function is distinguished from normal functions by +applying the @astro_data_descriptor decorator to it. The descriptor +function returns the value(s) using a Python type, int, float, +string, list; it depends on the value being returned. There is no +special “descriptor” type.

+

Here is an example of code defining a descriptor:

+
class AstroDataGmos(AstroDataGemini):
+    ...
+    @astro_data_descriptor
+    def detector_x_bin(self):
+        def _get_xbin(b):
+            try:
+                return int(b.split()[0])
+            except (AttributeError, ValueError):
+                return None
+
+        binning = self.hdr.get('CCDSUM')
+        if self.is_single:
+            return _get_xbin(binning)
+        else:
+            xbin_list = [_get_xbin(b) for b in binning]
+            # Check list is single-valued
+            return xbin_list[0] if xbin_list == xbin_list[::-1] else None
+
+
+

This descriptor returns the X-axis binning as a integer when called on a +single extension, or an object with only one extension, for example after the +GMOS CCDs have been mosaiced. If there are more than one extensions, it +will return a Python list or an integer if the binning is the same for all +the extensions.

+

Gemini has defined a standard list of descriptors that should be defined +one way or another for each instrument to ensure the re-usability of our +algorithms. That list is provided in the Appendix Implementing Descriptors.

+

For more information on adding to Astrodata, see the Astrodata Programmer +Manual.

+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/index.html b/manuals/usermanual/index.html new file mode 100644 index 00000000..abd0b7b2 --- /dev/null +++ b/manuals/usermanual/index.html @@ -0,0 +1,176 @@ + + + + + + + + 2. User Manual — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2. User Manual

+
+

Document ID

+

PIPE-USER-106_AstrodataUserManual

+
+
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/intro.html b/manuals/usermanual/intro.html new file mode 100644 index 00000000..9ab3ea3c --- /dev/null +++ b/manuals/usermanual/intro.html @@ -0,0 +1,331 @@ + + + + + + + + 2.1. Introduction — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.1. Introduction

+

This is the AstroData User’s Manual. AstroData is a DRAGONS package. +The current chapter covers basic concepts +like what is the AstroData package and how to install it (together with the +other DRAGONS’ packages). Chapter 2 +explains with more details what is AstroData and how the data is represented +using it. Chapter 3 describes input and output operations and +how multi-extension (MEF) FITS files are represented. Chapter 4 +provides information regarding the TagSet class, its usage and a few advanced +topics. In Chapter 5 you will find information about the FITS +headers and how to access/modify the metadata. The last two chapters, +Chapter 6 and Chapter 7 cover more details +about how to read, manipulate and write pixel data and tables, respectively.

+

If you are looking for a quick reference, please, have a look on the +Cheat Sheet.

+
+

2.1.1. Reference Documents

+
+
+
+
+

2.1.2. What is AstroData?

+

AstroData is a package that wraps together tools to represent internally +astronomical datasets stored on disks and to properly parse their metadata +using the AstroData and the TagSet classes. AstroData provides uniform +interfaces for working on datasets from different +instruments. Once a dataset has been opened with AstroData, the object +“knows about itself”. Information like instrument, observation mode, and how +to access headers, is readily available through the uniform interface. All +the details are coded inside the class associated with the instrument, that +class then provides the interface. The appropriate class is selected +automatically when the file is opened and inspected by AstroData.

+

Currently AstroData implements a representation for Multi-Extension FITS +(MEF) files. (Other representations can be implemented.)

+
+
+

2.1.3. Installing Astrodata

+

The AstroData package has a few dependencies, |astropy|, |numpy| and others. +The best way to get everything you need is to install Anaconda_, and the +|dragons| stack from conda-forge and Gemini’s public conda channel.

+

AstroData itself is part of |DRAGONS|. It is available from the +repository, as a tar file, or as a conda package. The bare AstroData package +does not do much by itself, it needs a companion instrument definitions +package. For Gemini, this is gemini_instruments, also included in +|DRAGONS|.

+
+

2.1.3.1. Installing Anaconda and the DRAGONS stack

+

This is required whether you are installing |DRAGONS| from the +repository, the tar file or the conda package.

+
    +
  1. +
    Install Anaconda.

    Go to https://www.anaconda.com/download/ and install the latest 64-bit +Anaconda.

    +
    +
    +
    2. +
  2. +
    Open a bash session.

    Anaconda requires bash. If you are not familiar with bash, note that the +shell configuration files are named .bash_profile and .bashrc. +During the installation, a PATH setting has been added to your +.bash_profile or .bashrc to add the Anaconda bin directory to +the PATH.

    +
    +
    +
    3. +
  3. +
    Activate Anaconda.

    Normal Python 3 installation puts the software in ~/anaconda3/.:

    +
    $ conda init
+
    +
    +
    +
    +
    4. +
  4. +
    Set up conda channels.

    Configure the conda package manager to look in conda-forge, and in the +GEMINI Conda Channel. This is a one-time step. It affects current and +future Anaconda installations belonging to the same user on the same +machine.:

    +
    $ conda config --add channels conda-forge
+$ conda config --add channels http://astroconda.gemini.edu/public
+
    +
    +
    +
    +
    5. +
  5. +
    Create an environment.

    To keep things clean, Anaconda offers virtual environments. Each project +can use its own environment. For example, if you do not want to modify +the software packages needed for a previous project, just create a new +environment for the new project.

    +

    Here we set up an environment where the DRAGONS dependencies can +be installed without affecting the rest of the system when not using that +virtual environement. The new virtual environment here is named +dragons. The software has been tested with Python 3.10 hence we +recommend that you use this specific version of Python with DRAGONS.

    +
    $ conda create -n dragons python=3.10 dragons ds9
+
    +
    +
    +
    +
    6. +
  6. +
    Activate your new virtual environment.
    $ conda activate dragons
+
    +
    +
    +
    +
    7. +
  7. +
    Configure DRAGONS.

    These configurations are not strictly required when using only AstroData. +It is however likely that if you are using AstroData you will be using +DRAGONS too at some point. So let’s configure it to have it ready to go.

    +

    DRAGONS requires a configuration file located in ~/.dragons/. The +dragonsrc file contains basic configuration for DRAGONS local calibration +manager used by reduce.

    +
    $ cd ~
+$ mkdir .dragons
+$ cd .dragons
+$ touch dragonsrc
+
    +
    +
    +
    Open dragonsrc with your favorite editor and add these lines::

    [interactive] +browser = one of “safari”, “chrome”, “firefox”

    +

    [calibs] +databases = ~/.dragons/dragons.db

    +
    +
    +

    Next time you start a DRAGONS project, set the databases to a path +of your liking, this is where the local calibration database will be written.

    +

    Then configure buffers for ds9:

    +
    $ cd ~/
+$ cp $CONDA_PREFIX/lib/python3.10/site-packages/gempy/numdisplay/imtoolrc ~/.imtoolrc
+$ vi .bash_profile (or use your favority editor)
+     Add this line to the .bash_profile:
+        export IMTOOLRC=~/.imtoolrc
+
    +
    +
    +
    +
    8. +
+
+
+

2.1.3.2. Update an existing DRAGONS installation

+

To check which version of DRAGONS you have installed:

+
$ conda list dragons
+
+
+

To check for newer version:

+
$ conda search dragons
+
+
+

To update to the newest version:

+
$ conda update dragons
+
+
+
+
+

2.1.3.3. Smoke test the Astrodata installation

+

From the configured bash shell:

+
$ type python
+python is hashed (<home_path>/anaconda3/envs/dragons/python)
+
+Make sure that python is indeed pointing to the Anaconda environment you
+have just set up.
+
+
+
$ python
+>>> import astrodata
+>>> import gemini_instruments
+
+Expected result: Just a python prompt and no error messages.
+
+
+
+
+

2.1.3.4. Source code availability

+

The source code is available on Github:

+
+
+
+
+
+

2.1.4. Try it yourself

+

Try it yourself

+

Download the data package if you wish to follow along and run the +examples presented in this manual. It is available at:

+
+
+

Unpack it:

+
$ cd <somewhere_convenient>
+$ tar xvf ad_usermanual_datapkg-v1.tar
+$ bunzip2 ad_usermanual/playdata/*.bz2
+
+
+

Then

+
$ cd ad_usermanual/playground
+$ python
+
+
+
+
+

2.1.5. Astrodata Support

+

Astrodata is developed and supported by staff at the Gemini Observatory. +Questions about the reduction of Gemini data should be directed to the +Gemini Helpdesk system at +https://noirlab.atlassian.net/servicedesk/customer/portal/12 +The github issue tracker can be used to report software bugs in DRAGONS +(https://github.com/GeminiDRSoftware/DRAGONS).

+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/iomef.html b/manuals/usermanual/iomef.html new file mode 100644 index 00000000..b2ba0823 --- /dev/null +++ b/manuals/usermanual/iomef.html @@ -0,0 +1,606 @@ + + + + + + + + 2.3. Input and Output Operations and Extension Manipulation - MEF — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.3. Input and Output Operations and Extension Manipulation - MEF

+

AstroData is not intended to be Multi-Extension FITS (MEF) centric. The core +is independent of the file format. At Gemini, our data model uses MEF. +Therefore we have implemented a FITS handler that maps a MEF to the +internal AstroData representation. A different handler can be implemented +for a different file format.

+

In this chapter, we present examples that will help the reader understand how +to access the information stored in a MEF with the AstroData object and +understand that mapping.

+

Try it yourself

+

Download the data package (Try it yourself) if you wish to follow along and run the +examples. Then

+
$ cd <path>/ad_usermanual/playground
+$ python
+
+
+
+

2.3.1. Imports

+

Before doing anything, you need to import AstroData and the Gemini instrument +configuration package |gemini_instruments|.

+
>>> import astrodata
+>>> import gemini_instruments
+
+
+
+
+

2.3.2. Open and access existing dataset

+
+

2.3.2.1. Read in the dataset

+

The file on disk is loaded into the AstroData class associated with the +instrument the data is from. This association is done automatically based on +header content.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> type(ad)
+<class 'gemini_instruments.gmos.adclass.AstroDataGmos'>
+
+
+

From now on, ad knows it is GMOS data. It knows how to access its headers +and when using the Recipe System (|recipe_system|), it will trigger the +selection of the GMOS primitives and recipes.

+

The original path and filename are stored in the object. If you were to write +the AstroData object to disk without specifying anything, those path and +filename would be used.

+
>>> ad.path
+'../playdata/N20170609S0154.fits'
+>>> ad.filename
+'N20170609S0154.fits'
+
+
+
+
+

2.3.2.2. Accessing the content of a MEF file

+

Accessing pixel data, headers, and tables will be covered in detail in the +following chapters. Here we just introduce the basic content interface.

+

For details on the AstroData structure, please refer to the +previous chapter.

+

AstroData uses |NDData| as the core of its structure. Each FITS extension +becomes a NDAstroData object, subclassed from |NDData|, and is added to +a list.

+
+

2.3.2.2.1. Pixel data

+

To access pixel data, the list index and the .data attribute are used. That +returns a numpy.ndarray. The list of NDAstroData is zero-indexed. +Extension number 1 in a MEF is index 0 in an |AstroData| object.

+
>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>>> data = ad[0].data
+>>> type(data)
+<class 'numpy.ndarray'>
+>>> data.shape
+(2112, 256)
+
+
+

Remember that in a ndarray the y-axis is the first number.

+

The variance and data quality planes, the VAR and DQ planes in Gemini MEF +files, are represented by the .variance and .mask attributes, +respectively. They are not their own “extension”, they don’t have their +own index in the list, unlike in a MEF. They are attached to the pixel data, +packaged together by the NDAstroData object. They are represented as +numpy.ndarray just like the pixel data

+
>>> var = ad[0].variance
+>>> dq = ad[0].mask
+
+
+
+
+

2.3.2.2.2. Tables

+

Tables in the MEF file will also be loaded into the AstroData object. If a table +is associated with a specific science extension through the EXTVER header keyword, that +table will be packaged within the same AstroData extension as the pixel data. +The AstroData “extension” is the NDAstroData object plus any table or other pixel +array. If the table is not associated with a specific extension and applies +globally, it will be added to the AstroData object as a global addition. No +indexing will be required to access it. In the example below, one OBJCAT is +associated with each extension, while the REFCAT has a global scope

+
>>> ad.info()
+Filename: ../playdata/N20170609S0154_varAdded.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED
+    PREPARED SIDEREAL
+
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (6, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 1]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (8, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 2]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (7, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 3]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (5, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+
+Other Extensions
+               Type        Dimensions
+.REFCAT        Table       (245, 16)
+
+
+

The tables are stored internally as astropy.table.Table objects.

+
>>> ad[0].OBJCAT
+<Table length=6>
+NUMBER X_IMAGE Y_IMAGE ... REF_MAG_ERR PROFILE_FWHM PROFILE_EE50
+int32  float32 float32 ...   float32     float32      float32
+------ ------- ------- ... ----------- ------------ ------------
+     1 283.461 55.4393 ...     0.16895       -999.0       -999.0
+...
+>>> type(ad[0].OBJCAT)
+<class 'astropy.table.table.Table'>
+
+>>> refcat = ad.REFCAT
+>>> type(refcat)
+<class 'astropy.table.table.Table'>
+
+
+
+
+

2.3.2.2.3. Headers

+

Headers are stored in the NDAstroData .meta attribute as astropy.io.fits.Header objects, +which is a form of Python ordered dictionaries. Headers associated with extensions +are stored with the corresponding NDAstroData object. The MEF Primary Header +Unit (PHU) is stored “globally” in the AstroData object. Note that when slicing an AstroData object, +for example copying over just the first extension, the PHU will follow. The +slice of an AstroData object is an AstroData object. +Headers can be accessed directly, or for some predefined concepts, the use of +Descriptors is preferred. See the chapters on headers for details.

+

Using Descriptors:

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> ad.filter_name()
+'open1-6&g_G0301'
+>>> ad.filter_name(pretty=True)
+'g'
+
+
+

Using direct header access:

+
>>> ad.phu['FILTER1']
+'open1-6'
+>>> ad.phu['FILTER2']
+'g_G0301'
+
+
+

Accessing the extension headers:

+
>>> ad.hdr['CCDSEC']
+['[1:512,1:4224]', '[513:1024,1:4224]', '[1025:1536,1:4224]', '[1537:2048,1:4224]']
+>>> ad[0].hdr['CCDSEC']
+'[1:512,1:4224]'
+
+With descriptors:
+>>> ad.array_section(pretty=True)
+['[1:512,1:4224]', '[513:1024,1:4224]', '[1025:1536,1:4224]', '[1537:2048,1:4224]']
+
+
+
+
+
+
+

2.3.3. Modify Existing MEF Files

+

Before you start modify the structure of an AstroData object, you should be +familiar with it. Please make sure that you have read the previous chapter +on the structure of the AstroData object.

+
+

2.3.3.1. Appending an extension

+

In this section, we take an extension from one AstroData object and append it +to another.

+

Here is an example appending a whole AstroData extension, with pixel data, +variance, mask and tables.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+
+>>> ad.info()
+Filename: ../playdata/N20170609S0154.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 288)    uint16
+[ 1]   science                  NDAstroData       (2112, 288)    uint16
+[ 2]   science                  NDAstroData       (2112, 288)    uint16
+[ 3]   science                  NDAstroData       (2112, 288)    uint16
+
+>>> ad.append(advar[3])
+>>> ad.info()
+Filename: ../playdata/N20170609S0154.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 288)    uint16
+[ 1]   science                  NDAstroData       (2112, 288)    uint16
+[ 2]   science                  NDAstroData       (2112, 288)    uint16
+[ 3]   science                  NDAstroData       (2112, 288)    uint16
+[ 4]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    int16
+          .OBJCAT               Table             (5, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+
+>>> ad[4].hdr['EXTVER']
+4
+>>> advar[3].hdr['EXTVER']
+4
+
+
+

As you can see above, the fourth extension of advar, along with everything +it contains was appended at the end of the first AstroData object. However, +note that, because the EXTVER of the extension in advar was 4, there are +now two extensions in ad with this EXTVER. This is not a problem because +EXTVER is not used by AstroData (it uses the index instead) and it is handled +only when the file is written to disk.

+

In this next example, we are appending only the pixel data, leaving behind the other +associated data. One can attach the headers too, like we do here.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+
+>>> ad.append(advar[3].data, header=advar[3].hdr)
+>>> ad.info()
+Filename: ../playdata/N20170609S0154.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 288)    uint16
+[ 1]   science                  NDAstroData       (2112, 288)    uint16
+[ 2]   science                  NDAstroData       (2112, 288)    uint16
+[ 3]   science                  NDAstroData       (2112, 288)    uint16
+[ 4]   science                  NDAstroData       (2112, 256)    float32
+
+
+

Notice how a new extension was created but variance, mask, the OBJCAT +table and OBJMASK image were not copied over. Only the science pixel data was +copied over.

+

Please note, there is no implementation for the “insertion” of an extension.

+
+
+

2.3.3.2. Removing an extension or part of one

+

Removing an extension or a part of an extension is straightforward. The +Python command del() is used on the item to remove. Below are a few +examples, but first let us load a file

+
>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>>> ad.info()
+
+
+

As you go through these examples, check the new structure with ad.info() +after every removal to see how the structure has changed.

+

Deleting a whole AstroData extension, the fourth one

+
>>> del ad[3]
+
+
+

Deleting only the variance array from the second extension

+
>>> ad[1].variance = None
+
+
+

Deleting a table associated with the first extension

+
>>> del ad[0].OBJCAT
+
+
+

Deleting a global table, not attached to a specific extension

+
>>> del ad.REFCAT
+
+
+
+
+
+

2.3.4. Writing back to disk

+

The AstroData layer takes care of converting +the AstroData object back to a MEF file on disk. When writing to disk, +one should be aware of the path and filename information associated +with the AstroData object.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> ad.path
+'../playdata/N20170609S0154.fits'
+>>> ad.filename
+'N20170609S0154.fits'
+
+
+
+

2.3.4.1. Writing to a new file

+

There are various ways to define the destination for the new FITS file. +The most common and natural way is

+
>>> ad.write('new154.fits')
+
+>>> ad.write('new154.fits', overwrite=True)
+
+
+

This will write a FITS file named ‘new154.fits’ in the current directory. +With overwrite=True, it will overwrite the file if it already exists. +A path can be prepended to the filename if the current directory is not +the destination. +Note that ad.filename and ad.path have not changed, we have just +written to the new file, the AstroData object is in no way associated +with that new file.

+
>>> ad.path
+'../playdata/N20170609S0154.fits'
+>>> ad.filename
+'N20170609S0154.fits'
+
+
+

If you want to create that association, the ad.filename and ad.path +needs to be modified first. For example:

+
>>> ad.filename = 'new154.fits'
+>>> ad.write(overwrite=True)
+
+>>> ad.path
+'../playdata/new154.fits'
+>>> ad.filename
+'new154.fits'
+
+
+

Changing ad.filename also changes the filename in the ad.path. The +sequence above will write ‘new154.fits’ not in the current directory but +rather to the directory that is specified in ad.path.

+

WARNING: ad.write() has an argument named filename. Setting filename +in the call to ad.write(), as in ad.write(filename='new154.fits') will NOT +modify ad.filename or ad.path. The two “filenames”, one a method argument +the other a class attribute have no association to each other.

+
+
+

2.3.4.2. Updating an existing file on disk

+

Updating an existing file on disk requires explicitly allowing overwrite.

+

If you have not written ‘new154.fits’ to disk yet (from previous section)

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> ad.write('new154.fits', overwrite=True)
+
+
+

Now let’s open ‘new154.fits’, and write to it

+
>>> adnew = astrodata.open('new154.fits')
+>>> adnew.write(overwrite=True)
+
+
+
+
+

2.3.4.3. A note on FITS header keywords

+

When writing an AstroData object to disk as a FITS file, it is necessary to add or +update header keywords to represent some of the internally-stored information. Any +extensions that did not originally belong to this AstroData will be assigned new +EXTVER keywords to avoid conflicts with existing extensions, and the internal WCS is +converted to the appropriate FITS keywords. Note that in some cases it may not be +possible for standard FITS keywords to accurately represent the true WCS. In such +cases, the FITS keywords are written as an approximation to the true WCS, together +with an additional keyword

+
FITS-WCS= 'APPROXIMATE'        / FITS WCS is approximate
+
+
+

to indicate this. The accurate WCS is written as an additional FITS extension with +EXTNAME='WCS' that AstroData will recognize when the file is read back in. The +WCS extension will not be written to disk if there is an accurate FITS +representation of the WCS (e.g., for a simple image).

+
+
+
+

2.3.5. Create New MEF Files

+

A new MEF file can be created from an existing, maybe modified, file or it +can be created from scratch. We discuss both cases here.

+
+

2.3.5.1. Create New Copy of MEF Files

+

To create a new copy of a MEF file, modified or not, the user has already +been given most of the tools in the sections above. Yet, let’s throw a +couple examples for completeness.

+
+

2.3.5.1.1. Basic example

+

As seen above, a MEF file can be opened with AstroData, the AstroData +object can be modified (or not), and then written back to disk under a +new name.

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+... optional modifications here ...
+>>> ad.write('newcopy.fits')
+
+
+
+
+

2.3.5.1.2. Needing true copies in memory

+

Sometimes it is a true copy in memory that is needed. This is not specific +to MEF. In Python, doing something like adnew = ad does not create a +new copy of the AstrodData object; it just gives it a new name. If you +modify adnew you will be modifying ad too. They point to the same +block of memory.

+

To create a true independent copy, the deepcopy utility needs to be used.

+
>>> from copy import deepcopy
+>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> adcopy = deepcopy(ad)
+
+
+

Be careful using deepcopy, your memory could balloon really fast. Use it +only when truly needed.

+
+
+
+

2.3.5.2. Create New MEF Files from Scratch

+

Before one creates a new MEF file on disk, one has to create the AstroData +object that will be eventually written to disk. The AstroData object +created also needs to know that it will have to be written using the MEF +format. This is fortunately handled fairly transparently by AstroData.

+

The key to associating the FITS data to the AstroData object is simply to +create the AstroData object from astropy.io.fits header objects. Those +will be recognized by AstroData as FITS and the constructor for FITS will be +used. The user does not need to do anything else special. Here is how it is +done.

+
+

2.3.5.2.1. Create a MEF with basic header and data array set to zeros

+
>>> import numpy as np
+>>> from astropy.io import fits
+
+>>> phu = fits.PrimaryHDU()
+
+>>> pixel_data = np.zeros((100,100))
+
+>>> hdu = fits.ImageHDU()
+>>> hdu.data = pixel_data
+
+>>> ad = astrodata.create(phu)
+>>> ad.append(hdu, name='SCI')
+
+or another way to do the last two blocs:
+>>> hdu = fits.ImageHDU(data=pixel_data, name='SCI')
+>>> ad = astrodata.create(phu, [hdu])
+
+
+

Then it is just a matter of calling ad.write('somename.fits') on that +new Astrodata object.

+
+
+

2.3.5.2.2. Associate a pixel array with a science pixel array

+

Only main science (“SCI”) pixel arrays are added as slices to an astrodata +object. It not uncommon to have pixels information associated with those +main science pixels, for example an object mask where marked pixels in the mask +are directly associated with sources in the science array.

+

Such pixel arrays are added to specific slice of the astrodata object they are +associated with.

+

Building on the astrodata object we created in the previous subsection, one +would add a pixel array to the first slice of the astrodata object as +follows:

+
>>> extra_data = np.ones((100, 100))
+>>> ad[0].EXTRADATA = extra_data
+
+
+

When the file is written to disk as a MEF, an extension will be created with +EXTNAME = EXTRADATA and an EXTVER that matches the slice’s EXTVER, +in this case is would be 1.

+
+
+

2.3.5.2.3. Represent a table as a FITS binary table in an AstroData object

+

One first needs to create a table, either an astropy.table.Table +or a BinTableHDU. See the |astropy| documentation +on tables and this manual’s section dedicated to tables for +more information.

+

In the first example, we assume that my_astropy_table is +a Table ready to be attached to an AstroData +object. (Warning: we have not created my_astropy_table therefore the +example below will not run, though this is how it would be done.)

+
>>> phu = fits.PrimaryHDU()
+>>> ad = astrodata.create(phu)
+
+>>> astrodata.add_header_to_table(my_astropy_table)
+>>> ad.append(my_astropy_table, name='SMAUG')
+
+
+

In the second example, we start with a FITS BinTableHDU +and attach it to a new AstroData object. (Again, we have not created +my_fits_table so the example will not run.)

+
>>> phu = fits.PrimaryHDU()
+>>> ad = astrodata.create(phu)
+>>> ad.append(my_fits_table, name='DROGON')
+
+
+

As before, once the AstroData object is constructed, the ad.write() +method can be used to write it to disk as a MEF file.

+
+
+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/structure.html b/manuals/usermanual/structure.html new file mode 100644 index 00000000..84a42016 --- /dev/null +++ b/manuals/usermanual/structure.html @@ -0,0 +1,278 @@ + + + + + + + + 2.2. The AstroData Object — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.2. The AstroData Object

+

The AstroData object is an internal representation of a file on disk. +As of this version, only a FITS layer has been written, but AstroData itself +is not limited to FITS.

+

The internal structure of the AstroData object makes uses of +astropy.nddata.NDData, astropy.table, and +astropy.io.fits.Header, the latter simply because it is a +convenient ordered dictionary.

+

Try it yourself

+

Download the data package (Try it yourself) if you wish to follow along and run the +examples. Then

+
$ cd <path>/ad_usermanual/playground
+$ python
+
+
+
+

2.2.1. Global vs Extension-specific

+

At the very top level, the structure is divided in two types of information. +In the first category, there is the information that applies to the data +globally, for example the information that would be stored in a FITS Primary +Header Unit, a table from a catalog that matches the RA and DEC of the field, +etc. In the second category, there is the information specific to individual +science pixel extensions, for example the gain of the amplifier, the data +themselves, the error on those data, etc.

+

Let us look at an example. The info() method shows +the content of the AstroData object and its organization, from the user’s +perspective.:

+
>>> import astrodata
+>>> import gemini_instruments
+
+>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>>> ad.info()
+Filename: N20170609S0154_varAdded.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED
+    PREPARED SIDEREAL
+
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (6, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 1]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (8, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 2]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (7, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+[ 3]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (5, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+
+Other Extensions
+               Type        Dimensions
+.REFCAT        Table       (245, 16)
+
+
+

The “Pixel Extensions” contain the pixel data. Each extension is represented +individually in a list (0-indexed like all Python lists). The science pixel +data, its associated metadata (extension header), and any other pixel or table +extensions directly associated with that science pixel data are stored in +a NDAstroData object which is a subclass of astropy |NDData|. We will +return to this structure later. An AstroData extension is accessed like +any list: ad[0]. To access the science pixels, one uses ad[0].data; for +the object mask of the first extension, ad[0].OBJMASK.

+

In the example above, the “Other Extensions” at the bottom of the +info() display contains a REFCAT table which in +this case is a list of stars from a catalog that overlaps the field of view +covered by the pixel data. The “Other Extensions” are global extensions. They +are not attached to any pixel extension in particular. To access a global +extension one simply uses the name of that extension: ad.REFCAT.

+
+
+

2.2.2. Organization of the Global Information

+

All the global information is stored in attributes of the AstroData object. +The global headers, or Primary Header Unit (PHU), is stored in the phu +attribute as an astropy.io.fits.Header.

+

Any global tables, like REFCAT above, are stored in the private attribute +_tables as a Python dictionary with the name (eg. “REFCAT”) as the key. +All tables are stored as astropy.table.Table. Access to those table +is done using the key directly as if it were a normal attribute, eg. +ad.REFCAT. Header information for the table, if read in from a FITS table, +is stored in the meta attribute of the astropy.table.Table, eg. +ad.REFCAT.meta['header']. It is for information only, it is not used.

+
+
+

2.2.3. Organization of the Extension-specific Information

+

The pixel data are stored in the AstroData attribute nddata as a list +of NDAstroData object. The NDAstroData object is a subclass of astropy +|NDData| and it is fully compatible with any function expecting an |NDData| as +input. The pixel extensions are accessible through slicing, eg. ad[0] or +even ad[0:2]. A slice of an AstroData object is an AstroData object, and +all the global attributes are kept. For example:

+
>>> ad[0].info()
+Filename: N20170609S0154_varAdded.fits
+Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED
+    PREPARED SIDEREAL
+
+Pixels Extensions
+Index  Content                  Type              Dimensions     Format
+[ 0]   science                  NDAstroData       (2112, 256)    float32
+          .variance             ndarray           (2112, 256)    float32
+          .mask                 ndarray           (2112, 256)    uint16
+          .OBJCAT               Table             (6, 43)        n/a
+          .OBJMASK              ndarray           (2112, 256)    uint8
+
+Other Extensions
+               Type        Dimensions
+.REFCAT        Table       (245, 16)
+
+
+

Note how REFCAT is still present.

+

The science data is accessed as ad[0].data, the variance as ad[0].variance, +and the data quality plane as ad[0].mask. Those familiar with astropy +|NDData| will recognize the structure “data, error, mask”, and will notice +some differences. First AstroData uses the variance for the error plane, not +the standard deviation. Another difference will be evident only when one looks +at the content of the mask. |NDData| masks contain booleans, AstroData masks +are uint16 bit mask that contains information about the type of bad pixels +rather than just flagging them a bad or not. Since 0 is equivalent to +False (good pixel), the AstroData mask is fully compatible with the +|NDData| mask.

+

Header information for the extension is stored in the NDAstroData meta +attribute. All table and pixel extensions directly associated with the +science extension are also stored in the meta attribute.

+

Technically, an extension header is located in ad.nddata[0].meta['header']. +However, for obviously needed convenience, the normal way to access that header +is ad[0].hdr.

+

Tables and pixel arrays associated with a science extension are +stored in ad.nddata[0].meta['other'] as a dictionary keyed on the array +name, eg. OBJCAT, OBJMASK. As it is for global tables, astropy tables +are used for extension tables. The extension tables and extra pixel arrays are +accessed, like the global tables, by using the table name rather than the long +format, for example ad[0].OBJCAT and ad[0].OBJMASK.

+

When reading a FITS Table, the header information is stored in the +meta['header'] of the table, eg. ad[0].OBJCAT.meta['header']. That +information is not used, it is simply a place to store what was read from disk.

+

The header of a pixel extension directly associated with the science extension +should match that of the science extension. Therefore such headers are not +stored in AstroData. For example, the header of ad[0].OBJMASK is the +same as that of the science, ad[0].hdr.

+

The world coordinate system (WCS) is stored internally in the wcs attribute +of the NDAstroData object. It is constructed from the header keywords when +the FITS file is read from disk, or directly from the WCS extension if +present (see the next chapter). If the WCS is modified (for +example, by refining the pointing or attaching a more accurate wavelength +calibration), the FITS header keywords are not updated and therefore they should +never be used to determine the world coordinates of any pixel. These keywords are +only updated when the object is written to disk as a FITS file. The WCS is +retrieved as follows: ad[0].wcs.

+
+
+

2.2.4. A Note on Memory Usage

+

When an file is opened, the headers are loaded into memory, but the pixels +are not. The pixel data are loaded into memory only when they are first +needed. This is not real “memory mapping”, more of a delayed loading. This +is useful when someone is only interested in the metadata, especially when +the files are very large.

+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/tables.html b/manuals/usermanual/tables.html new file mode 100644 index 00000000..dd44a749 --- /dev/null +++ b/manuals/usermanual/tables.html @@ -0,0 +1,315 @@ + + + + + + + + 2.7. Table Data — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.7. Table Data

+

Try it yourself

+

Download the data package (Try it yourself) if you wish to follow along and run the +examples. Then

+
$ cd <path>/ad_usermanual/playground
+$ python
+
+
+

Then import core astrodata and the Gemini astrodata configurations.

+
>>> import astrodata
+>>> import gemini_instruments
+
+
+
+

2.7.1. Tables and Astrodata

+

Tables are stored as astropy.table Table class. FITS tables too +are represented in Astrodata as Table and FITS headers are stored in +the NDAstroData .meta attribute. Most table access should be done +through the Table interface. The best reference on Table is the +Astropy documentation itself. In this chapter we covers some common +examples to get the reader started.

+

The astropy.table documentation can be found at: http://docs.astropy.org/en/stable/table/index.html

+
+
+

2.7.2. Operate on a Table

+

Let us open a file with tables. Some tables are associated with specific +extensions, and there is one table that is global to the AstroData object.

+
>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
+>>> ad.info()
+
+
+

To access the global table named REFCAT:

+
>>> ad.REFCAT
+
+
+

To access the OBJCAT table in the first extension

+
>>> ad[0].OBJCAT
+
+
+
+

2.7.2.1. Column and Row Operations

+

Columns are named. Those names are used to access the data as columns. +Rows are not names and are simply represented as a sequential list.

+
+

2.7.2.1.1. Read columns and rows

+

To get the names of the columns present in the table:

+
>>> ad.REFCAT.colnames
+['Id', 'Cat_Id', 'RAJ2000', 'DEJ2000', 'umag', 'umag_err', 'gmag',
+'gmag_err', 'rmag', 'rmag_err', 'imag', 'imag_err', 'zmag', 'zmag_err',
+'filtermag', 'filtermag_err']
+
+
+

Then it is easy to request the values for specific columns:

+
>>> ad.REFCAT['zmag']
+>>> ad.REFCAT['zmag', 'zmag_err']
+
+
+

To get the content of a specific row, row 10 in this case:

+
>>> ad.REFCAT[9]
+
+
+

To get the content of a specific row(s) from a specific column(s):

+
>>> ad.REFCAT['zmag'][4]
+>>> ad.REFCAT['zmag'][4:10]
+>>> ad.REFCAT['zmag', 'zmag_err'][4:10]
+
+
+
+
+

2.7.2.1.2. Change values

+

Assigning new values works in a similar way. When working on multiple elements +it is important to feed a list that matches in size with the number of elements +to replace.

+
>>> ad.REFCAT['imag'][4] = 20.999
+>>> ad.REFCAT['imag'][4:10] = [5, 6, 7, 8, 9, 10]
+
+>>> overwrite_col = [0] * len(ad.REFCAT)  # a list of zeros, size = nb of rows
+>>> ad.REFCAT['imag_err'] = overwrite_col
+
+
+
+
+

2.7.2.1.3. Add a row

+

To append a row, there is the add_row() method. The length of the row +should match the number of columns:

+
>>> new_row = [0] * len(ad.REFCAT.colnames)
+>>> new_row[1] = ''   # Cat_Id column is of "str" type.
+>>> ad.REFCAT.add_row(new_row)
+
+
+
+
+

2.7.2.1.4. Add a column

+

Adding a new column can be more involved. If you need full control, please +see the AstroPy Table documentation. For a quick addition, which might be +sufficient for your use case, we simply use the “dictionary” technique. Please +note that when adding a column, it is important to ensure that all the +elements are of the same type. Also, if you are planning to use that table +in IRAF/PyRAF, we recommend not using 64-bit types.

+
>>> import numpy as np
+
+>>> new_column = [0] * len(ad.REFCAT)
+>>> # Ensure that the type is int32, otherwise it will default to int64
+>>> # which generally not necessary.  Also, IRAF 32-bit does not like it.
+>>> new_column = np.array(new_column).astype(np.int32)
+>>> ad.REFCAT['my_column'] = new_column
+
+
+

If you are going to write that table back to disk as a FITS Bintable, then +some additional headers need to be set. Astrodata will take care of that +under the hood when the write method is invoked.

+
>>> ad.write('myfile_with_modified_table.fits')
+
+
+
+
+
+

2.7.2.2. Selection and Rejection Operations

+

Normally, one does not know exactly where the information needed is located +in a table. Rather some sort of selection needs to be done. This can also +be combined with various calculations. We show two such examples here.

+
+

2.7.2.2.1. Select a table element from criterion

+
>>> # Get the magnitude of a star selected by ID number
+>>> ad.REFCAT['zmag'][ad.REFCAT['Cat_Id'] == '1237662500002005475']
+
+>>> # Get the ID and magnitude of all the stars brighter than zmag 18.
+>>> ad.REFCAT['Cat_Id', 'zmag'][ad.REFCAT['zmag'] < 18.]
+
+
+
+
+

2.7.2.2.2. Rejection and selection before statistics

+
>>> t = ad.REFCAT   # to save typing
+
+>>> # The table has "NaN" values.  ("Not a number")  We need to ignore them.
+>>> t['zmag'].mean()
+nan
+>>> # applying rejection of NaN values:
+>>> t['zmag'][np.where(~np.isnan(t['zmag']))].mean()
+20.377306
+
+
+
+
+
+

2.7.2.3. Accessing FITS table headers directly

+

If for some reason you need to access the FITS table headers directly, here +is how to do it. It is very unlikely that you will need this.

+

To see the FITS headers:

+
>>> ad.REFCAT.meta['header']
+>>> ad[0].OBJCAT.meta['header']
+
+
+

To retrieve a specific FITS table header:

+
>>> ad.REFCAT.meta['header']['TTYPE3']
+'RAJ2000'
+>>> ad[0].OBJCAT.meta['header']['TTYPE3']
+'Y_IMAGE'
+
+
+

To retrieve all the keyword names matching a selection:

+
>>> keynames = [key for key in ad.REFCAT.meta['header'] if key.startswith('TTYPE')]
+
+
+
+
+
+

2.7.3. Create a Table

+

To create a table that can be added to an AstroData object and eventually +written to disk as a FITS file, the first step is to create an Astropy +Table.

+

Let us first add our data to NumPy arrays, one array per column:

+
>>> import numpy as np
+
+>>> snr_id = np.array(['S001', 'S002', 'S003'])
+>>> feii = np.array([780., 78., 179.])
+>>> pabeta = np.array([740., 307., 220.])
+>>> ratio = pabeta / feii
+
+
+

Then build the table from that data:

+
>>> from astropy.table import Table
+
+>>> my_astropy_table = Table([snr_id, feii, pabeta, ratio],
+...                          names=('SNR_ID', 'FeII', 'PaBeta', 'ratio'))
+
+
+

Now we append this Astropy Table to a new AstroData object.

+
>>> # Since we are going to write a FITS, we build the AstroData object
+>>> # from FITS objects.
+>>> from astropy.io import fits
+
+>>> phu = fits.PrimaryHDU()
+>>> ad = astrodata.create(phu)
+>>> ad.MYTABLE = my_astropy_table
+>>> ad.info()
+>>> ad.MYTABLE
+
+>>> ad.write('new_table.fits')
+
+
+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/manuals/usermanual/tags.html b/manuals/usermanual/tags.html new file mode 100644 index 00000000..50d01549 --- /dev/null +++ b/manuals/usermanual/tags.html @@ -0,0 +1,264 @@ + + + + + + + + 2.4. Astrodata Tags — astrodata 3.2.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

2.4. Astrodata Tags

+
+

2.4.1. What are the Astrodata Tags?

+

The Astrodata Tags identify the data represented in the AstroData object. +When a file on disk is opened with AstroData, the headers are inspected to +identify which specific AstroData class needs to be loaded, +AstroDataGmos, +AstroDataNiri, etc. Based on the class the data is +associated with, a list of “tags” will be defined. The tags will tell whether the +file is a flatfield or a dark, if it is a raw dataset, or if it has been processed by the +recipe system, if it is imaging or spectroscopy. The tags will tell the +users and the system what that data is and also give some information about +the processing status.

+

As a side note, the tags are used by DRAGONS Recipe System to match recipes +and primitives to the data.

+
+
+

2.4.2. Using the Astrodata Tags

+

Try it yourself

+

Download the data package (Try it yourself) if you wish to follow along and run the +examples. Then

+
$ cd <path>/ad_usermanual/playground
+$ python
+
+
+

Before doing anything, you need to import AstroData and the Gemini instrument +configuration package (|gemini_instruments|).

+
>>> import astrodata
+>>> import gemini_instruments
+
+
+

Let us open a Gemini dataset and see what tags we get:

+
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
+>>> ad.tags
+{'RAW', 'GMOS', 'GEMINI', 'NORTH', 'SIDEREAL', 'UNPREPARED', 'IMAGE', 'ACQUISITION'}
+
+
+

The file we loaded is raw, GMOS North data. It is a 2D image and it is an +acquisition image, not a science observation. The “UNPREPARED” tag indicates +that the file has never been touched by the Recipe System which runs a +“prepare” primitive as the first step of each recipe.

+

Let’s try another

+
>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
+>>> ad.tags
+{'GMOS', 'GEMINI', 'NORTH', 'SIDEREAL', 'OVERSCAN_TRIMMED', 'IMAGE',
+'OVERSCAN_SUBTRACTED', 'PREPARED'}
+
+
+

This file is a science GMOS North image. It has been processed by the +Recipe System. The overscan level has been subtracted and the overscan section +has been trimmed away. The tags do NOT include all the processing steps. Rather, +at least from the time being, it focuses on steps that matter when associating +calibrations.

+

The tags can be used when coding. For example:

+
>>> if 'GMOS' in ad.tags:
+...   print('I am GMOS')
+... else:
+...   print('I am these instead:', ad.tags)
+...
+
+
+

And:

+
>>> if {'IMAGE', 'GMOS'}.issubset(ad.tags):
+...   print('I am a GMOS Image.')
+...
+
+
+
+
+

2.4.3. Using typewalk

+

In DRAGONS, there is a convenience tool that will list the Astrodata tags +for all the FITS file in a directory.

+

To try it, from the shell, not Python, go to the “playdata” directory and +run typewalk:

+
% cd <path>/ad_usermanual/playdata
+% typewalk
+
+directory:  /data/workspace/ad_usermanual/playdata
+ N20170521S0925_forStack.fits ...... (GEMINI) (GMOS) (IMAGE) (NORTH) (OVERSCAN_SUBTRACTED) (OVERSCAN_TRIMMED) (PREPARED) (SIDEREAL)
+ N20170521S0926_forStack.fits ...... (GEMINI) (GMOS) (IMAGE) (NORTH) (OVERSCAN_SUBTRACTED) (OVERSCAN_TRIMMED) (PREPARED) (PROCESSED) (PROCESSED_SCIENCE) (SIDEREAL)
+ N20170609S0154.fits ............... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
+ N20170609S0154_varAdded.fits ...... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (OVERSCAN_SUBTRACTED) (OVERSCAN_TRIMMED) (PREPARED) (SIDEREAL)
+ estgsS20080220S0078.fits .......... (GEMINI) (GMOS) (LONGSLIT) (LS) (PREPARED) (PROCESSED) (PROCESSED_SCIENCE) (SIDEREAL) (SOUTH) (SPECT)
+ gmosifu_cube.fits ................. (GEMINI) (GMOS) (IFU) (NORTH) (ONESLIT_RED) (PREPARED) (PROCESSED) (PROCESSED_SCIENCE) (SIDEREAL) (SPECT)
+ new154.fits ....................... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
+Done DataSpider.typewalk(..)
+
+
+

typewalk can be used to select specific data based on tags, and even create +lists:

+
% typewalk --tags RAW
+directory:  /data/workspace/ad_usermanual/playdata
+ N20170609S0154.fits ............... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
+ new154.fits ....................... (ACQUISITION) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
+Done DataSpider.typewalk(..)
+
+
+
% typewalk --tags RAW -o rawfiles.lis
+% cat rawfiles.lis
+# Auto-generated by typewalk, vv2.0 (beta)
+# Written: Tue Mar  6 13:06:06 2018
+# Qualifying types: RAW
+# Qualifying logic: AND
+# -----------------------
+/Users/klabrie/data/tutorials/ad_usermanual/playdata/N20170609S0154.fits
+/Users/klabrie/data/tutorials/ad_usermanual/playdata/new154.fits
+
+
+
+
+

2.4.4. Creating New Astrodata Tags [Advanced Topic]

+

For proper and complete instructions on how to create Astrodata Tags and +the AstroData class that hosts the tags, the reader is invited to refer to the +Astrodata Programmer Manual. Here we provide a simple introduction that +might help some readers better understand Astrodata Tags, or serve as a +quick reference for those who have written Astrodata Tags in the past but need +a little refresher.

+

The Astrodata Tags are defined in an AstroData class. The AstroData +class specific to an instrument is located in a separate package, not in +AstroData. For example, for Gemini instruments, all the various AstroData +classes are contained in the |gemini_instruments| package.

+

An Astrodata Tag is a function within the instrument’s AstroData class. +The tag function is distinguished from normal functions by applying the +astro_data_tag() decorator to it. +The tag function returns a astrodata.TagSet.

+

For example:

+
class AstroDataGmos(AstroDataGemini):
+    ...
+    @astro_data_tag
+    def _tag_arc(self):
+        if self.phu.get('OBSTYPE) == 'ARC':
+            return TagSet(['ARC', 'CAL'])
+
+
+

The tag function looks at the headers and if the keyword “OBSTYPE” is set +to “ARC”, the tags “ARC” and “CAL” (for calibration) will be assigned to the +AstroData object.

+

A whole suite of such tag functions is needed to fully characterize all +types of data an instrument can produce.

+

Tags are about what the dataset is, not it’s flavor. The Astrodata +“descriptors” (see the section on Metadata and Headers) will describe the flavor. +For example, tags will say that the data is an image, but the descriptor +will say whether it is B-band or R-band. Tags are used for recipe and +primitive selection. A way to understand the difference between a tag and +a descriptor is in terms of the recipe that will be selected: A GMOS image +will use the same recipe whether it’s a B-band or R-band image. However, +a GMOS longslit spectrum will need a very different recipe. A bias is +reduced differently from a science image, there should be a tag differentiating +a bias from a science image. (There is for GMOS.)

+

For more information on adding to Astrodata, see the Astrodata Programmer +Manual.

+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, . + + | + Powered by Sphinx 7.2.6 + & Alabaster 0.7.13 + + | + Page source +
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"table-data"]], "Tables and Astrodata": [[33, "tables-and-astrodata"]], "Operate on a Table": [[33, "operate-on-a-table"]], "Column and Row Operations": [[33, "column-and-row-operations"]], "Read columns and rows": [[33, "read-columns-and-rows"]], "Change values": [[33, "change-values"]], "Add a row": [[33, "add-a-row"]], "Add a column": [[33, "add-a-column"]], "Selection and Rejection Operations": [[33, "selection-and-rejection-operations"]], "Select a table element from criterion": [[33, "select-a-table-element-from-criterion"]], "Rejection and selection before statistics": [[33, "rejection-and-selection-before-statistics"]], "Accessing FITS table headers directly": [[33, "accessing-fits-table-headers-directly"]], "Create a Table": [[33, "create-a-table"]], "Astrodata Tags": [[34, "astrodata-tags"]], "What are the Astrodata Tags?": [[34, "what-are-the-astrodata-tags"]], "Using the Astrodata Tags": [[34, "using-the-astrodata-tags"]], "Using typewalk": [[34, "using-typewalk"]], "Creating New Astrodata Tags [Advanced Topic]": [[34, "creating-new-astrodata-tags-advanced-topic"]]}, "indexentries": {"astrodata (class in astrodata)": [[0, "astrodata.AstroData"]], "add() (astrodata.astrodata method)": [[0, "astrodata.AstroData.add"]], "append() (astrodata.astrodata method)": [[0, "astrodata.AstroData.append"]], "crop() (astrodata.astrodata method)": [[0, "astrodata.AstroData.crop"]], "data (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.data"]], "descriptors (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.descriptors"]], "divide() (astrodata.astrodata method)": [[0, "astrodata.AstroData.divide"]], "exposed (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.exposed"]], "ext_tables (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.ext_tables"]], "filename (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.filename"]], "hdr (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.hdr"]], "header (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.header"]], "id (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.id"]], "indices (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.indices"]], "info() (astrodata.astrodata method)": [[0, "astrodata.AstroData.info"]], "instrument() (astrodata.astrodata method)": [[0, "astrodata.AstroData.instrument"]], "is_settable() (astrodata.astrodata method)": [[0, "astrodata.AstroData.is_settable"]], "is_sliced (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.is_sliced"]], "load() (astrodata.astrodata class method)": [[0, "astrodata.AstroData.load"]], "mask (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.mask"]], "multiply() (astrodata.astrodata method)": [[0, "astrodata.AstroData.multiply"]], "nddata (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.nddata"]], "object() (astrodata.astrodata method)": [[0, "astrodata.AstroData.object"]], "operate() (astrodata.astrodata method)": [[0, "astrodata.AstroData.operate"]], "orig_filename (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.orig_filename"]], "path (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.path"]], "phu (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.phu"]], "read() (astrodata.astrodata class method)": [[0, "astrodata.AstroData.read"]], "reset() (astrodata.astrodata method)": [[0, "astrodata.AstroData.reset"]], "shape (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.shape"]], "subtract() (astrodata.astrodata method)": [[0, "astrodata.AstroData.subtract"]], "table() (astrodata.astrodata method)": [[0, "astrodata.AstroData.table"]], "tables (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.tables"]], "tags (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.tags"]], "telescope() (astrodata.astrodata method)": [[0, "astrodata.AstroData.telescope"]], "uncertainty (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.uncertainty"]], "update_filename() (astrodata.astrodata method)": [[0, "astrodata.AstroData.update_filename"]], "variance (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.variance"]], "wcs (astrodata.astrodata attribute)": [[0, "astrodata.AstroData.wcs"]], "write() (astrodata.astrodata method)": [[0, "astrodata.AstroData.write"]], "astrodataerror": [[1, "astrodata.AstroDataError"]], "astrodatamixin (class in astrodata)": [[2, "astrodata.AstroDataMixin"]], "shape (astrodata.astrodatamixin attribute)": [[2, "astrodata.AstroDataMixin.shape"]], "size (astrodata.astrodatamixin attribute)": [[2, "astrodata.AstroDataMixin.size"]], "variance (astrodata.astrodatamixin attribute)": [[2, "astrodata.AstroDataMixin.variance"]], "wcs (astrodata.astrodatamixin attribute)": [[2, "astrodata.AstroDataMixin.wcs"]], "ndastrodata (class in astrodata)": [[3, "astrodata.NDAstroData"]], "t (astrodata.ndastrodata attribute)": [[3, "astrodata.NDAstroData.T"]], "data (astrodata.ndastrodata attribute)": [[3, "astrodata.NDAstroData.data"]], "mask (astrodata.ndastrodata attribute)": [[3, "astrodata.NDAstroData.mask"]], "set_section() (astrodata.ndastrodata method)": [[3, "astrodata.NDAstroData.set_section"]], "transpose() (astrodata.ndastrodata method)": [[3, "astrodata.NDAstroData.transpose"]], "uncertainty (astrodata.ndastrodata attribute)": [[3, "astrodata.NDAstroData.uncertainty"]], "variance (astrodata.ndastrodata attribute)": [[3, "astrodata.NDAstroData.variance"]], "window (astrodata.ndastrodata attribute)": [[3, "astrodata.NDAstroData.window"]], "section (class in astrodata)": [[4, "astrodata.Section"]], "asirafsection() (astrodata.section method)": [[4, "astrodata.Section.asIRAFsection"]], "as_iraf_section() (astrodata.section method)": [[4, "astrodata.Section.as_iraf_section"]], "asslice() (astrodata.section method)": [[4, "astrodata.Section.asslice"]], "axis_dict (astrodata.section attribute)": [[4, "astrodata.Section.axis_dict"]], "contains() (astrodata.section method)": [[4, "astrodata.Section.contains"]], "from_shape() (astrodata.section static method)": [[4, "astrodata.Section.from_shape"]], "from_string() (astrodata.section static method)": [[4, "astrodata.Section.from_string"]], "is_same_size() (astrodata.section method)": [[4, "astrodata.Section.is_same_size"]], "ndim (astrodata.section attribute)": [[4, "astrodata.Section.ndim"]], "overlap() (astrodata.section method)": [[4, "astrodata.Section.overlap"]], "shift() (astrodata.section method)": [[4, "astrodata.Section.shift"]], "tagset (class in astrodata)": [[5, "astrodata.TagSet"]], "add (astrodata.tagset attribute)": [[5, "astrodata.TagSet.add"]], "blocked_by (astrodata.tagset attribute)": [[5, "astrodata.TagSet.blocked_by"]], "blocks (astrodata.tagset attribute)": [[5, "astrodata.TagSet.blocks"]], "if_present (astrodata.tagset attribute)": [[5, "astrodata.TagSet.if_present"]], "remove (astrodata.tagset attribute)": [[5, "astrodata.TagSet.remove"]], "add_header_to_table() (in module astrodata)": [[6, "astrodata.add_header_to_table"]], "astro_data_descriptor() (in module astrodata)": [[7, "astrodata.astro_data_descriptor"]], "astro_data_tag() (in module astrodata)": [[8, "astrodata.astro_data_tag"]], "create() (in module astrodata)": [[9, "astrodata.create"]], "from_file() (in module astrodata)": [[10, "astrodata.from_file"]], "open() (in module astrodata)": [[11, "astrodata.open"]], "returns_list() (in module astrodata)": [[12, "astrodata.returns_list"]], "version() (in module astrodata)": [[13, "astrodata.version"]], "astrodata": [[14, "module-astrodata"]], "module": [[14, "module-astrodata"]]}}) \ No newline at end of file
+
+
+ + +
+ +

Source code for astrodata

+"""This package adds an abstraction layer to astronomical data by parsing the
+information contained in the headers as attributes. To do so, one must subclass
+:class:`astrodata.AstroData` and add parse methods accordingly to the
+:class:`~astrodata.TagSet` received.
+
+"""
+from .core import AstroData
+from .factory import AstroDataFactory, AstroDataError
+from .fits import add_header_to_table
+from .nddata import NDAstroData, AstroDataMixin
+from .utils import (
+    Section,
+    TagSet,
+    astro_data_descriptor,
+    astro_data_tag,
+    returns_list,
+    deprecated,
+)
+from ._version import version
+
+__all__ = [
+    "AstroData",
+    "AstroDataError",
+    "AstroDataMixin",
+    "NDAstroData",
+    "Section",
+    "TagSet",
+    "__version__",
+    "add_header_to_table",
+    "astro_data_descriptor",
+    "astro_data_tag",
+    "from_file",
+    "create",
+    "returns_list",
+    "version",
+    # Below this are deprecated
+    "open",
+]
+
+# Make sure __all__does not have duplicates
+if len(__all__) != len(set(__all__)):
+    duplicates = [x for i, x in enumerate(__all__) if x in __all__[:i]]
+    raise ValueError(f"Duplicate entries in __all__: {', '.join(duplicates)}")
+
+__version__ = version()
+
+# TODO: This is pretty unacceptable, this makes it impossible to access the
+# factory classes, errors, and methods through any normal means. Which means
+# they are very difficult to write tests for, or to have users work on
+# themselves.
+factory = AstroDataFactory()
+# Let's make sure that there's at least one class that matches the data
+# (if we're dealing with a FITS file)
+factory.add_class(AstroData)
+
+
+
+
+
+
+

+[docs]
+def create(*args, **kwargs):
+    """Return an |AstroData| object from data.
+
+    For implementation details, see
+    :meth:`~astrodata.AstroDataFactory.create_from_scratch`
+    """
+    return factory.create_from_scratch(*args, **kwargs)

+
+
+
+# TODO: Need to replace this with a name that doesn't override the builtin.
+# This makes it so that the following will cause unexpected behavior:
+#     from astrodata import *
+#     file_stream = open("some_file.fits")
+# Without raising a warning or error.
+

+[docs]
+@deprecated(
+    "Use 'astrodata.from_file'. astrodata.open is deprecated, "
+    "and will be removed in a future version."
+)
+def open(*args, **kwargs):  # pylint: disable=redefined-builtin
+    """Return an |AstroData| object from a file (deprecated, use
+    :func:`~astrodata.from_file`).
+    """
+    return from_file(*args, **kwargs)

+
+
+ +
+ +
+
+ +
+