Source code for

# Licensed under a 3-clause BSD style license - see PYFITS.rst

import copy
import operator
import warnings
import weakref
from contextlib import suppress
from functools import reduce

import numpy as np
from numpy import char as chararray

from astropy.utils import lazyproperty

from .column import (
from .util import _rstrip_inplace, decode_ascii, encode_ascii

[docs] class FITS_record: """ FITS record class. `FITS_record` is used to access records of the `FITS_rec` object. This will allow us to deal with scaled columns. It also handles conversion/scaling of columns in ASCII tables. The `FITS_record` class expects a `FITS_rec` object as input. """ def __init__( self, input, row=0, start=None, end=None, step=None, base=None, **kwargs ): """ Parameters ---------- input : array The array to wrap. row : int, optional The starting logical row of the array. start : int, optional The starting column in the row associated with this object. Used for subsetting the columns of the `FITS_rec` object. end : int, optional The ending column in the row associated with this object. Used for subsetting the columns of the `FITS_rec` object. """ self.array = input self.row = row if base: width = len(base) else: width = self.array._nfields s = slice(start, end, step).indices(width) self.start, self.end, self.step = s self.base = base def __getitem__(self, key): if isinstance(key, str): indx = _get_index(self.array.names, key) if indx < self.start or indx > self.end - 1: raise KeyError(f"Key '{key}' does not exist.") elif isinstance(key, slice): return type(self)(self.array, self.row, key.start, key.stop, key.step, self) else: indx = self._get_index(key) if indx > self.array._nfields - 1: raise IndexError("Index out of bounds") return self.array.field(indx)[self.row] def __setitem__(self, key, value): if isinstance(key, str): indx = _get_index(self.array.names, key) if indx < self.start or indx > self.end - 1: raise KeyError(f"Key '{key}' does not exist.") elif isinstance(key, slice): for indx in range(slice.start, slice.stop, slice.step): indx = self._get_indx(indx) self.array.field(indx)[self.row] = value else: indx = self._get_index(key) if indx > self.array._nfields - 1: raise IndexError("Index out of bounds") self.array.field(indx)[self.row] = value def __len__(self): return len(range(self.start, self.end, self.step)) def __repr__(self): """ Display a single row. """ outlist = [] for idx in range(len(self)): outlist.append(repr(self[idx])) return f"({', '.join(outlist)})"
[docs] def field(self, field): """ Get the field data of the record. """ return self.__getitem__(field)
[docs] def setfield(self, field, value): """ Set the field data of the record. """ self.__setitem__(field, value)
@lazyproperty def _bases(self): bases = [weakref.proxy(self)] base = self.base while base: bases.append(base) base = base.base return bases def _get_index(self, indx): indices = np.ogrid[: self.array._nfields] for base in reversed(self._bases): if base.step < 1: s = slice(base.start, None, base.step) else: s = slice(base.start, base.end, base.step) indices = indices[s] return indices[indx]
[docs] class FITS_rec(np.recarray): """ FITS record array class. `FITS_rec` is the data part of a table HDU's data part. This is a layer over the `~numpy.recarray`, so we can deal with scaled columns. It inherits all of the standard methods from `numpy.ndarray`. """ _record_type = FITS_record _character_as_bytes = False _load_variable_length_data = True def __new__(subtype, input): """ Construct a FITS record array from a recarray. """ # input should be a record array if input.dtype.subdtype is None: self = np.recarray.__new__( subtype, input.shape, input.dtype, ) else: self = np.recarray.__new__( subtype, input.shape, input.dtype,, strides=input.strides ) self._init() if self.dtype.fields: self._nfields = len(self.dtype.fields) return self def __setstate__(self, state): meta = state[-1] column_state = state[-2] state = state[:-2] super().__setstate__(state) self._col_weakrefs = weakref.WeakSet() for attr, value in zip(meta, column_state): setattr(self, attr, value) def __reduce__(self): """ Return a 3-tuple for pickling a FITS_rec. Use the super-class functionality but then add in a tuple of FITS_rec-specific values that get used in __setstate__. """ reconst_func, reconst_func_args, state = super().__reduce__() # Define FITS_rec-specific attrs that get added to state column_state = [] meta = [] for attrs in [ "_converted", "_heapoffset", "_heapsize", "_nfields", "_gap", "_uint", "parnames", "_coldefs", ]: with suppress(AttributeError): # _coldefs can be Delayed, and file objects cannot be # picked, it needs to be deepcopied first if attrs == "_coldefs": column_state.append(self._coldefs.__deepcopy__(None)) else: column_state.append(getattr(self, attrs)) meta.append(attrs) state = state + (column_state, meta) return reconst_func, reconst_func_args, state def __array_finalize__(self, obj): if obj is None: return if isinstance(obj, FITS_rec): self._character_as_bytes = obj._character_as_bytes if isinstance(obj, FITS_rec) and obj.dtype == self.dtype: self._converted = obj._converted self._heapoffset = obj._heapoffset self._heapsize = obj._heapsize self._col_weakrefs = obj._col_weakrefs self._coldefs = obj._coldefs self._nfields = obj._nfields self._gap = obj._gap self._uint = obj._uint elif self.dtype.fields is not None: # This will allow regular ndarrays with fields, rather than # just other FITS_rec objects self._nfields = len(self.dtype.fields) self._converted = {} self._heapoffset = getattr(obj, "_heapoffset", 0) self._heapsize = getattr(obj, "_heapsize", 0) self._gap = getattr(obj, "_gap", 0) self._uint = getattr(obj, "_uint", False) self._col_weakrefs = weakref.WeakSet() self._coldefs = ColDefs(self) # Work around chicken-egg problem. Column.array relies on the # _coldefs attribute to set up ref back to parent FITS_rec; however # in the above line the self._coldefs has not been assigned yet so # this fails. This patches that up... for col in self._coldefs: del col.array col._parent_fits_rec = weakref.ref(self) else: self._init() def _init(self): """Initializes internal attributes specific to FITS-isms.""" self._nfields = 0 self._converted = {} self._heapoffset = 0 self._heapsize = 0 self._col_weakrefs = weakref.WeakSet() self._coldefs = None self._gap = 0 self._uint = False
[docs] @classmethod def from_columns(cls, columns, nrows=0, fill=False, character_as_bytes=False): """ Given a `ColDefs` object of unknown origin, initialize a new `FITS_rec` object. .. note:: This was originally part of the ``new_table`` function in the table module but was moved into a class method since most of its functionality always had more to do with initializing a `FITS_rec` object than anything else, and much of it also overlapped with ``FITS_rec._scale_back``. Parameters ---------- columns : sequence of `Column` or a `ColDefs` The columns from which to create the table data. If these columns have data arrays attached that data may be used in initializing the new table. Otherwise the input columns will be used as a template for a new table with the requested number of rows. nrows : int Number of rows in the new table. If the input columns have data associated with them, the size of the largest input column is used. Otherwise the default is 0. fill : bool If `True`, will fill all cells with zeros or blanks. If `False`, copy the data from input, undefined cells will still be filled with zeros/blanks. """ if not isinstance(columns, ColDefs): columns = ColDefs(columns) # read the delayed data for column in columns: arr = column.array if isinstance(arr, Delayed): if is None: column.array = None else: column.array = _get_recarray_field(, arr.field) # Reset columns._arrays (which we may want to just do away with # altogether del columns._arrays # use the largest column shape as the shape of the record if nrows == 0: for arr in columns._arrays: if arr is not None: dim = arr.shape[0] else: dim = 0 if dim > nrows: nrows = dim raw_data = np.empty(columns.dtype.itemsize * nrows, dtype=np.uint8) raw_data.fill(ord(columns._padding_byte)) data = np.recarray(nrows, dtype=columns.dtype, buf=raw_data).view(cls) data._character_as_bytes = character_as_bytes # Previously this assignment was made from hdu.columns, but that's a # bug since if a _TableBaseHDU has a FITS_rec in its .data attribute # the _TableBaseHDU.columns property is actually returned from # .data._coldefs, so this assignment was circular! Don't make that # mistake again. # All of this is an artifact of the fragility of the FITS_rec class, # and that it can't just be initialized by columns... data._coldefs = columns # If fill is True we don't copy anything from the column arrays. We're # just using them as a template, and returning a table filled with # zeros/blanks if fill: return data # Otherwise we have to fill the recarray with data from the input # columns for idx, column in enumerate(columns): # For each column in the ColDef object, determine the number of # rows in that column. This will be either the number of rows in # the ndarray associated with the column, or the number of rows # given in the call to this function, which ever is smaller. If # the input FILL argument is true, the number of rows is set to # zero so that no data is copied from the original input data. arr = column.array if arr is None: array_size = 0 else: array_size = len(arr) n = min(array_size, nrows) # TODO: At least *some* of this logic is mostly redundant with the # _convert_foo methods in this class; see if we can eliminate some # of that duplication. if not n: # The input column had an empty array, so just use the fill # value continue field = _get_recarray_field(data, idx) name = fitsformat = column.format recformat = fitsformat.recformat outarr = field[:n] inarr = arr[:n] if isinstance(recformat, _FormatX): # Data is a bit array if inarr.shape[-1] == recformat.repeat: _wrapx(inarr, outarr, recformat.repeat) continue elif isinstance(recformat, _FormatP): data._cache_field(name, _makep(inarr, field, recformat, nrows=nrows)) continue # TODO: Find a better way of determining that the column is meant # to be FITS L formatted elif recformat[-2:] == FITS2NUMPY["L"] and inarr.dtype == bool: # column is boolean # The raw data field should be filled with either 'T' or 'F' # (not 0). Use 'F' as a default field[:] = ord("F") # Also save the original boolean array in data._converted so # that it doesn't have to be re-converted converted = np.zeros(field.shape, dtype=bool) converted[:n] = inarr data._cache_field(name, converted) # TODO: Maybe this step isn't necessary at all if _scale_back # will handle it? inarr = np.where(inarr == np.False_, ord("F"), ord("T")) elif columns[idx]._physical_values and columns[idx]._pseudo_unsigned_ints: # Temporary hack... bzero = column.bzero converted = np.zeros(field.shape, dtype=inarr.dtype) converted[:n] = inarr data._cache_field(name, converted) if n < nrows: # Pre-scale rows below the input data field[n:] = -bzero inarr = inarr - bzero elif isinstance(columns, _AsciiColDefs): # Regardless whether the format is character or numeric, if the # input array contains characters then it's already in the raw # format for ASCII tables if fitsformat._pseudo_logical: # Hack to support converting from 8-bit T/F characters # Normally the column array is a chararray of 1 character # strings, but we need to view it as a normal ndarray of # 8-bit ints to fill it with ASCII codes for 'T' and 'F' outarr = field.view(np.uint8, np.ndarray)[:n] elif arr.dtype.kind not in ("S", "U"): # Set up views of numeric columns with the appropriate # numeric dtype # Fill with the appropriate blanks for the column format data._cache_field(name, np.zeros(nrows, dtype=arr.dtype)) outarr = data._converted[name][:n] outarr[:] = inarr continue if inarr.shape != outarr.shape: if ( inarr.dtype.kind == outarr.dtype.kind and inarr.dtype.kind in ("U", "S") and inarr.dtype != outarr.dtype ): inarr_rowsize = inarr[0].size inarr = inarr.flatten().view(outarr.dtype) # This is a special case to handle input arrays with # non-trivial TDIMn. # By design each row of the outarray is 1-D, while each row of # the input array may be n-D if outarr.ndim > 1: # The normal case where the first dimension is the rows inarr_rowsize = inarr[0].size inarr = inarr.reshape(n, inarr_rowsize) outarr[:, :inarr_rowsize] = inarr else: # Special case for strings where the out array only has one # dimension (the second dimension is rolled up into the # strings outarr[:n] = inarr.ravel() else: outarr[:] = inarr # Now replace the original column array references with the new # fields # This is required to prevent the issue reported in # for idx in range(len(columns)): columns._arrays[idx] = data.field(idx) return data
def __repr__(self): # Force use of the normal ndarray repr (rather than the new # one added for recarray in Numpy 1.10) for backwards compat return np.ndarray.__repr__(self) def __getattribute__(self, attr): # First, see if ndarray has this attr, and return it if so. Note that # this means a field with the same name as an ndarray attr cannot be # accessed by attribute, this is Numpy's default behavior. # We avoid using np.recarray.__getattribute__ here because after doing # this check it would access the columns without doing the conversions # that we need (with .field, see below). try: return object.__getattribute__(self, attr) except AttributeError: pass # attr might still be a fieldname. If we have column definitions, # we should access this via .field, as the data may have to be scaled. if self._coldefs is not None and attr in self.columns.names: return self.field(attr) # If not, just let the usual np.recarray override deal with it. return super().__getattribute__(attr) def __getitem__(self, key): if self._coldefs is None: return super().__getitem__(key) if isinstance(key, str): return self.field(key) # Have to view as a recarray then back as a FITS_rec, otherwise the # circular reference fix/hack in FITS_rec.field() won't preserve # the slice. out = self.view(np.recarray)[key] if type(out) is not np.recarray: # Oops, we got a single element rather than a view. In that case, # return a Record, which has no __getstate__ and is more efficient. return self._record_type(self, key) # We got a view; change it back to our class, and add stuff out = out.view(type(self)) out._uint = self._uint out._coldefs = ColDefs(self._coldefs) arrays = [] out._converted = {} for idx, name in enumerate(self._coldefs.names): # # Store the new arrays for the _coldefs object # arrays.append(self._coldefs._arrays[idx][key]) # Ensure that the sliced FITS_rec will view the same scaled # columns as the original; this is one of the few cases where # it is not necessary to use _cache_field() if name in self._converted: dummy = self._converted[name] field = np.ndarray.__getitem__(dummy, key) out._converted[name] = field out._coldefs._arrays = arrays return out def __setitem__(self, key, value): if self._coldefs is None: return super().__setitem__(key, value) if isinstance(key, str): self[key][:] = value return if isinstance(key, slice): end = min(len(self), key.stop or len(self)) end = max(0, end) start = max(0, key.start or 0) end = min(end, start + len(value)) for idx in range(start, end): self.__setitem__(idx, value[idx - start]) return if isinstance(value, FITS_record): for idx in range(self._nfields): self.field(self.names[idx])[key] = value.field(self.names[idx]) elif isinstance(value, (tuple, list, np.void)): if self._nfields == len(value): for idx in range(self._nfields): self.field(idx)[key] = value[idx] else: raise ValueError( f"Input tuple or list required to have {self._nfields} elements." ) else: raise TypeError( "Assignment requires a FITS_record, tuple, or list as input." ) def _ipython_key_completions_(self): return self.names
[docs] def copy(self, order="C"): """ The Numpy documentation lies; `numpy.ndarray.copy` is not equivalent to `numpy.copy`. Differences include that it re-views the copied array as self's ndarray subclass, as though it were taking a slice; this means ``__array_finalize__`` is called and the copy shares all the array attributes (including ``._converted``!). So we need to make a deep copy of all those attributes so that the two arrays truly do not share any data. """ new = super().copy(order=order) new.__dict__ = copy.deepcopy(self.__dict__) return new
@property def columns(self): """A user-visible accessor for the coldefs.""" return self._coldefs @property def _coldefs(self): # This used to be a normal internal attribute, but it was changed to a # property as a quick and transparent way to work around the reference # leak bug fixed in # # See the long comment in the Column.array property for more details # on this. But in short, FITS_rec now has a ._col_weakrefs attribute # which is a WeakSet of weakrefs to each Column in _coldefs. # # So whenever ._coldefs is set we also add each Column in the ColDefs # to the weakrefs set. This is an easy way to find out if a Column has # any references to it external to the FITS_rec (i.e. a user assigned a # column to a variable). If the column is still in _col_weakrefs then # there are other references to it external to this FITS_rec. We use # that information in __del__ to save off copies of the array data # for those columns to their Column.array property before our memory # is freed. return self.__dict__.get("_coldefs") @_coldefs.setter def _coldefs(self, cols): self.__dict__["_coldefs"] = cols if isinstance(cols, ColDefs): for col in cols.columns: self._col_weakrefs.add(col) @_coldefs.deleter def _coldefs(self): try: del self.__dict__["_coldefs"] except KeyError as exc: raise AttributeError(exc.args[0]) def __del__(self): try: del self._coldefs if self.dtype.fields is not None: for col in self._col_weakrefs: if col.array is not None: col.array = col.array.copy() # See issues #4690 and #4912 except (AttributeError, TypeError): # pragma: no cover pass @property def names(self): """List of column names.""" if self.dtype.fields: return list(self.dtype.names) elif getattr(self, "_coldefs", None) is not None: return self._coldefs.names else: return None @property def formats(self): """List of column FITS formats.""" if getattr(self, "_coldefs", None) is not None: return self._coldefs.formats return None @property def _raw_itemsize(self): """ Returns the size of row items that would be written to the raw FITS file, taking into account the possibility of unicode columns being compactified. Currently for internal use only. """ if _has_unicode_fields(self): total_itemsize = 0 for field in self.dtype.fields.values(): itemsize = field[0].itemsize if field[0].kind == "U": itemsize = itemsize // 4 total_itemsize += itemsize return total_itemsize else: # Just return the normal itemsize return self.itemsize
[docs] def field(self, key): """ A view of a `Column`'s data as an array. """ # NOTE: The *column* index may not be the same as the field index in # the recarray, if the column is a phantom column column = self.columns[key] name = format = column.format if format.dtype.itemsize == 0: warnings.warn( f"Field {key!r} has a repeat count of 0 in its format code, " "indicating an empty field." ) return np.array([], dtype=format.dtype) # If field's base is a FITS_rec, we can run into trouble because it # contains a reference to the ._coldefs object of the original data; # this can lead to a circular reference; see ticket #49 base = self while isinstance(base, FITS_rec) and isinstance(base.base, np.recarray): base = base.base # base could still be a FITS_rec in some cases, so take care to # use rec.recarray.field to avoid a potential infinite # recursion field = _get_recarray_field(base, name) if name not in self._converted: recformat = format.recformat # TODO: If we're now passing the column to these subroutines, do we # really need to pass them the recformat? if isinstance(recformat, _FormatP) and self._load_variable_length_data: # for P format converted = self._convert_p(column, field, recformat) else: # Handle all other column data types which are fixed-width # fields converted = self._convert_other(column, field, recformat) # Note: Never assign values directly into the self._converted dict; # always go through self._cache_field; this way self._converted is # only used to store arrays that are not already direct views of # our own data. self._cache_field(name, converted) return converted return self._converted[name]
def _cache_field(self, name, field): """ Do not store fields in _converted if one of its bases is self, or if it has a common base with self. This results in a reference cycle that cannot be broken since ndarrays do not participate in cyclic garbage collection. """ base = field while True: self_base = self while True: if self_base is base: return if getattr(self_base, "base", None) is not None: self_base = self_base.base else: break if getattr(base, "base", None) is not None: base = base.base else: break self._converted[name] = field def _update_column_attribute_changed(self, column, idx, attr, old_value, new_value): """ Update how the data is formatted depending on changes to column attributes initiated by the user through the `Column` interface. Dispatches column attribute change notifications to individual methods for each attribute ``_update_column_<attr>`` """ method_name = f"_update_column_{attr}" if hasattr(self, method_name): # Right now this is so we can be lazy and not implement updaters # for every attribute yet--some we may not need at all, TBD getattr(self, method_name)(column, idx, old_value, new_value) def _update_column_name(self, column, idx, old_name, name): """Update the dtype field names when a column name is changed.""" dtype = self.dtype # Updating the names on the dtype should suffice dtype.names = dtype.names[:idx] + (name,) + dtype.names[idx + 1 :] def _convert_x(self, field, recformat): """Convert a raw table column to a bit array as specified by the FITS X format. """ dummy = np.zeros(self.shape + (recformat.repeat,), dtype=np.bool_) _unwrapx(field, dummy, recformat.repeat) return dummy def _convert_p(self, column, field, recformat): """Convert a raw table column of FITS P or Q format descriptors to a VLA column with the array data returned from the heap. """ if column.dim: vla_shape = tuple( reversed(tuple(map(int, column.dim.strip("()").split(",")))) ) dummy = _VLF([None] * len(self), dtype=recformat.dtype) raw_data = self._get_raw_data() if raw_data is None: raise OSError( f"Could not find heap data for the {!r} variable-length " "array column." ) for idx in range(len(self)): offset = field[idx, 1] + self._heapoffset count = field[idx, 0] if recformat.dtype == "S": dt = np.dtype(recformat.dtype + str(1)) arr_len = count * dt.itemsize da = raw_data[offset : offset + arr_len].view(dt) da = np.char.array(da.view(dtype=dt), itemsize=count) dummy[idx] = decode_ascii(da) else: dt = np.dtype(recformat.dtype) arr_len = count * dt.itemsize dummy[idx] = raw_data[offset : offset + arr_len].view(dt) if column.dim and len(vla_shape) > 1: # The VLA is reshaped consistently with TDIM instructions if vla_shape[0] == 1: dummy[idx] = dummy[idx].reshape(1, len(dummy[idx])) else: vla_dim = vla_shape[1:] vla_first = int(len(dummy[idx]) / dummy[idx] = dummy[idx].reshape((vla_first,) + vla_dim) dummy[idx].dtype = dummy[idx].dtype.newbyteorder(">") # Each array in the field may now require additional # scaling depending on the other scaling parameters # TODO: The same scaling parameters apply to every # array in the column so this is currently very slow; we # really only need to check once whether any scaling will # be necessary and skip this step if not # TODO: Test that this works for X format; I don't think # that it does--the recformat variable only applies to the P # format not the X format dummy[idx] = self._convert_other(column, dummy[idx], recformat) return dummy def _convert_ascii(self, column, field): """ Special handling for ASCII table columns to convert columns containing numeric types to actual numeric arrays from the string representation. """ format = column.format recformat = getattr(format, "recformat", ASCII2NUMPY[format[0]]) # if the string = TNULL, return ASCIITNULL nullval = str(column.null).strip().encode("ascii") if len(nullval) > format.width: nullval = nullval[: format.width] # Before using .replace make sure that any trailing bytes in each # column are filled with spaces, and *not*, say, nulls; this causes # functions like replace to potentially leave gibberish bytes in the # array buffer. dummy = np.char.ljust(field, format.width) dummy = np.char.replace(dummy, encode_ascii("D"), encode_ascii("E")) null_fill = encode_ascii(str(ASCIITNULL).rjust(format.width)) # Convert all fields equal to the TNULL value (nullval) to empty fields. # TODO: These fields really should be converted to NaN or something else undefined. # Currently they are converted to empty fields, which are then set to zero. dummy = np.where(np.char.strip(dummy) == nullval, null_fill, dummy) # always replace empty fields, see if nullval != b"": dummy = np.where(np.char.strip(dummy) == b"", null_fill, dummy) try: dummy = np.array(dummy, dtype=recformat) except ValueError as exc: indx = self.names.index( raise ValueError( f"{exc}; the header may be missing the necessary TNULL{indx + 1} " "keyword or the table contains invalid data" ) return dummy def _convert_other(self, column, field, recformat): """Perform conversions on any other fixed-width column data types. This may not perform any conversion at all if it's not necessary, in which case the original column array is returned. """ if isinstance(recformat, _FormatX): # special handling for the X format return self._convert_x(field, recformat) scale_factors = self._get_scale_factors(column) _str, _bool, _number, _scale, _zero, bscale, bzero, dim = scale_factors indx = self.names.index( # ASCII table, convert strings to numbers # TODO: # For now, check that these are ASCII columns by checking the coldefs # type; in the future all columns (for binary tables, ASCII tables, or # otherwise) should "know" what type they are already and how to handle # converting their data from FITS format to native format and vice # versa... if not _str and isinstance(self._coldefs, _AsciiColDefs): field = self._convert_ascii(column, field) # Test that the dimensions given in dim are sensible; otherwise # display a warning and ignore them if dim: # See if the dimensions already match, if not, make sure the # number items will fit in the specified dimensions if field.ndim > 1: actual_shape = field.shape[1:] if _str: actual_shape = actual_shape + (field.itemsize,) else: actual_shape = field.shape[0] if dim == actual_shape: # The array already has the correct dimensions, so we # ignore dim and don't convert dim = None else: nitems = reduce(operator.mul, dim) if _str: actual_nitems = field.itemsize elif len(field.shape) == 1: # No repeat count in TFORMn, equivalent to 1 actual_nitems = 1 else: actual_nitems = field.shape[1] if nitems > actual_nitems and not isinstance(recformat, _FormatP): warnings.warn( f"TDIM{indx + 1} value {self._coldefs[indx].dims:d} does not " f"fit with the size of the array items ({actual_nitems:d}). " f"TDIM{indx + 1:d} will be ignored." ) dim = None # further conversion for both ASCII and binary tables # For now we've made columns responsible for *knowing* whether their # data has been scaled, but we make the FITS_rec class responsible for # actually doing the scaling # TODO: This also needs to be fixed in the effort to make Columns # responsible for scaling their arrays to/from FITS native values if not column.ascii and column.format.p_format: format_code = column.format.p_format else: # TODO: Rather than having this if/else it might be nice if the # ColumnFormat class had an attribute guaranteed to give the format # of actual values in a column regardless of whether the true # format is something like P or Q format_code = column.format.format if _number and (_scale or _zero) and not column._physical_values: # This is to handle pseudo unsigned ints in table columns # TODO: For now this only really works correctly for binary tables # Should it work for ASCII tables as well? if self._uint: if bzero == 2**15 and format_code == "I": field = np.array(field, dtype=np.uint16) elif bzero == 2**31 and format_code == "J": field = np.array(field, dtype=np.uint32) elif bzero == 2**63 and format_code == "K": field = np.array(field, dtype=np.uint64) bzero64 = np.uint64(2**63) else: field = np.array(field, dtype=np.float64) else: field = np.array(field, dtype=np.float64) if _scale: np.multiply(field, bscale, field) if _zero: if self._uint and format_code == "K": # There is a chance of overflow, so be careful test_overflow = field.copy() try: test_overflow += bzero64 except OverflowError: warnings.warn( f"Overflow detected while applying TZERO{indx + 1:d}. " "Returning unscaled data." ) else: field = test_overflow else: field += bzero # mark the column as scaled column._physical_values = True elif _bool and field.dtype != bool: field = np.equal(field, ord("T")) elif _str: if not self._character_as_bytes: with suppress(UnicodeDecodeError): field = decode_ascii(field) if dim and not isinstance(recformat, _FormatP): # Apply the new field item dimensions nitems = reduce(operator.mul, dim) if field.ndim > 1: field = field[:, :nitems] if _str: fmt = field.dtype.char dtype = (f"|{fmt}{dim[-1]}", dim[:-1]) field.dtype = dtype else: field.shape = (field.shape[0],) + dim return field def _get_heap_data(self): """ Returns a pointer into the table's raw data to its heap (if present). This is returned as a numpy byte array. """ if self._heapsize: raw_data = self._get_raw_data().view(np.ubyte) heap_end = self._heapoffset + self._heapsize return raw_data[self._heapoffset : heap_end] else: return np.array([], dtype=np.ubyte) def _get_raw_data(self): """ Returns the base array of self that "raw data array" that is the array in the format that it was first read from a file before it was sliced or viewed as a different type in any way. This is determined by walking through the bases until finding one that has at least the same number of bytes as self, plus the heapsize. This may be the immediate .base but is not always. This is used primarily for variable-length array support which needs to be able to find the heap (the raw data *may* be larger than nbytes + heapsize if it contains a gap or padding). May return ``None`` if no array resembling the "raw data" according to the stated criteria can be found. """ raw_data_bytes = self.nbytes + self._heapsize base = self while hasattr(base, "base") and base.base is not None: base = base.base # Variable-length-arrays: should take into account the case of # empty arrays if hasattr(base, "_heapoffset"): if hasattr(base, "nbytes") and base.nbytes > raw_data_bytes: return base # non variable-length-arrays else: if hasattr(base, "nbytes") and base.nbytes >= raw_data_bytes: return base def _get_scale_factors(self, column): """Get all the scaling flags and factors for one column.""" # TODO: Maybe this should be a method/property on Column? Or maybe # it's not really needed at all... _str = column.format.format == "A" _bool = column.format.format == "L" _number = not (_bool or _str) bscale = column.bscale bzero = column.bzero _scale = bscale not in ("", None, 1) _zero = bzero not in ("", None, 0) # ensure bscale/bzero are numbers if not _scale: bscale = 1 if not _zero: bzero = 0 # column._dims gives a tuple, rather than column.dim which returns the # original string format code from the FITS header... dim = column._dims return (_str, _bool, _number, _scale, _zero, bscale, bzero, dim) def _scale_back(self, update_heap_pointers=True): """ Update the parent array, using the (latest) scaled array. If ``update_heap_pointers`` is `False`, this will leave all the heap pointers in P/Q columns as they are verbatim--it only makes sense to do this if there is already data on the heap and it can be guaranteed that that data has not been modified, and there is not new data to add to the heap. Currently this is only used as an optimization for CompImageHDU that does its own handling of the heap. """ # Running total for the new heap size heapsize = 0 for indx, name in enumerate(self.dtype.names): column = self._coldefs[indx] recformat = column.format.recformat raw_field = _get_recarray_field(self, indx) # add the location offset of the heap area for each # variable length column if isinstance(recformat, _FormatP): # Irritatingly, this can return a different dtype than just # doing np.dtype(recformat.dtype); but this returns the results # that we want. For example if recformat.dtype is 'S' we want # an array of characters. dtype = np.array([], dtype=recformat.dtype).dtype if update_heap_pointers and name in self._converted: # The VLA has potentially been updated, so we need to # update the array descriptors raw_field[:] = 0 # reset npts = [ for arr in self._converted[name]] raw_field[: len(npts), 0] = npts raw_field[1:, 1] = ( np.add.accumulate(raw_field[:-1, 0]) * dtype.itemsize ) raw_field[:, 1][:] += heapsize heapsize += raw_field[:, 0].sum() * dtype.itemsize # Even if this VLA has not been read or updated, we need to # include the size of its constituent arrays in the heap size # total if type(recformat) == _FormatP and heapsize >= 2**31: raise ValueError( "The heapsize limit for 'P' format has been reached. " "Please consider using the 'Q' format for your file." ) if isinstance(recformat, _FormatX) and name in self._converted: _wrapx(self._converted[name], raw_field, recformat.repeat) continue scale_factors = self._get_scale_factors(column) _str, _bool, _number, _scale, _zero, bscale, bzero, _ = scale_factors field = self._converted.get(name, raw_field) # conversion for both ASCII and binary tables if _number or _str: if _number and (_scale or _zero) and column._physical_values: dummy = field.copy() if _zero: # Cast before subtracting to avoid overflow problems. dummy -= np.array(bzero).astype(dummy.dtype, casting="unsafe") if _scale: dummy /= bscale # This will set the raw values in the recarray back to # their non-physical storage values, so the column should # be mark is not scaled column._physical_values = False elif _str or isinstance(self._coldefs, _AsciiColDefs): dummy = field else: continue # ASCII table, convert numbers to strings if isinstance(self._coldefs, _AsciiColDefs): self._scale_back_ascii(indx, dummy, raw_field) # binary table string column elif isinstance(raw_field, chararray.chararray): self._scale_back_strings(indx, dummy, raw_field) # all other binary table columns else: if len(raw_field) and isinstance(raw_field[0], np.integer): dummy = np.around(dummy) if raw_field.shape == dummy.shape: raw_field[:] = dummy else: # Reshaping the data is necessary in cases where the # TDIMn keyword was used to shape a column's entries # into arrays raw_field[:] = dummy.ravel().view(raw_field.dtype) del dummy # ASCII table does not have Boolean type elif _bool and name in self._converted: choices = ( np.array([ord("F")], dtype=np.int8)[0], np.array([ord("T")], dtype=np.int8)[0], ) raw_field[:] = np.choose(field, choices) # Store the updated heapsize self._heapsize = heapsize def _scale_back_strings(self, col_idx, input_field, output_field): # There are a few possibilities this has to be able to handle properly # The input_field, which comes from the _converted column is of dtype # 'Un' so that elements read out of the array are normal str # objects (i.e. unicode strings) # # At the other end the *output_field* may also be of type 'S' or of # type 'U'. It will *usually* be of type 'S' because when reading # an existing FITS table the raw data is just ASCII strings, and # represented in Numpy as an S array. However, when a user creates # a new table from scratch, they *might* pass in a column containing # unicode strings (dtype 'U'). Therefore the output_field of the # raw array is actually a unicode array. But we still want to make # sure the data is encodable as ASCII. Later when we write out the # array we use, in the dtype 'U' case, a different write routine # that writes row by row and encodes any 'U' columns to ASCII. # If the output_field is non-ASCII we will worry about ASCII encoding # later when writing; otherwise we can do it right here if input_field.dtype.kind == "U" and output_field.dtype.kind == "S": try: _ascii_encode(input_field, out=output_field) except _UnicodeArrayEncodeError as exc: raise ValueError( f"Could not save column '{self._coldefs[col_idx].name}': " "Contains characters that cannot be encoded as ASCII as required " "by FITS, starting at the index " f"{exc.index[0] if len(exc.index) == 1 else exc.index!r} of the " f"column, and the index {exc.start} of the string at that location." ) else: # Otherwise go ahead and do a direct copy into--if both are type # 'U' we'll handle encoding later input_field = input_field.flatten().view(output_field.dtype) output_field.flat[:] = input_field # Ensure that blanks at the end of each string are # converted to nulls instead of spaces, see Trac #15 # and #111 _rstrip_inplace(output_field) def _scale_back_ascii(self, col_idx, input_field, output_field): """ Convert internal array values back to ASCII table representation. The ``input_field`` is the internal representation of the values, and the ``output_field`` is the character array representing the ASCII output that will be written. """ starts = self._coldefs.starts[:] spans = self._coldefs.spans format = self._coldefs[col_idx].format # The index of the "end" column of the record, beyond # which we can't write end = super().field(-1).itemsize starts.append(end + starts[-1]) if col_idx > 0: lead = starts[col_idx] - starts[col_idx - 1] - spans[col_idx - 1] else: lead = 0 if lead < 0: warnings.warn( f"Column {col_idx + 1} starting point overlaps the previous column." ) trail = starts[col_idx + 1] - starts[col_idx] - spans[col_idx] if trail < 0: warnings.warn( f"Column {col_idx + 1} ending point overlaps the next column." ) # TODO: It would be nice if these string column formatting # details were left to a specialized class, as is the case # with FormatX and FormatP if "A" in format: _pc = "{:" else: _pc = "{:>" fmt = "".join([_pc, format[1:], ASCII2STR[format[0]], "}", (" " * trail)]) # Even if the format precision is 0, we should output a decimal point # as long as there is space to do so--not including a decimal point in # a float value is discouraged by the FITS Standard trailing_decimal = format.precision == 0 and format.format in ("F", "E", "D") # not using numarray.strings's num2char because the # result is not allowed to expand (as C/Python does). for jdx, value in enumerate(input_field): value = fmt.format(value) if len(value) > starts[col_idx + 1] - starts[col_idx]: raise ValueError( f"Value {value!r} does not fit into the output's itemsize of " f"{spans[col_idx]}." ) if trailing_decimal and value[0] == " ": # We have some extra space in the field for the trailing # decimal point value = value[1:] + "." output_field[jdx] = value # Replace exponent separator in floating point numbers if "D" in format: output_field[:] = output_field.replace(b"E", b"D")
[docs] def tolist(self): # Override .tolist to take care of special case of VLF column_lists = [self[name].tolist() for name in self.columns.names] return [list(row) for row in zip(*column_lists)]
def _get_recarray_field(array, key): """ Compatibility function for using the recarray base class's field method. This incorporates the legacy functionality of returning string arrays as Numeric-style chararray objects. """ # Numpy >= recarray no longer returns chararrays for strings # This is currently needed for backwards-compatibility and for # automatic truncation of trailing whitespace field = np.recarray.field(array, key) if field.dtype.char in ("S", "U") and not isinstance(field, chararray.chararray): field = field.view(chararray.chararray) return field class _UnicodeArrayEncodeError(UnicodeEncodeError): def __init__(self, encoding, object_, start, end, reason, index): super().__init__(encoding, object_, start, end, reason) self.index = index def _ascii_encode(inarray, out=None): """ Takes a unicode array and fills the output string array with the ASCII encodings (if possible) of the elements of the input array. The two arrays must be the same size (though not necessarily the same shape). This is like an inplace version of `np.char.encode` though simpler since it's only limited to ASCII, and hence the size of each character is guaranteed to be 1 byte. If any strings are non-ASCII an UnicodeArrayEncodeError is raised--this is just a `UnicodeEncodeError` with an additional attribute for the index of the item that couldn't be encoded. """ out_dtype = np.dtype((f"S{inarray.dtype.itemsize // 4}", inarray.dtype.shape)) if out is not None: out = out.view(out_dtype) op_dtypes = [inarray.dtype, out_dtype] op_flags = [["readonly"], ["writeonly", "allocate"]] it = np.nditer( [inarray, out], op_dtypes=op_dtypes, op_flags=op_flags, flags=["zerosize_ok"] ) try: for initem, outitem in it: outitem[...] = initem.item().encode("ascii") except UnicodeEncodeError as exc: index = np.unravel_index(it.iterindex, inarray.shape) raise _UnicodeArrayEncodeError(*(exc.args + (index,))) return it.operands[1] def _has_unicode_fields(array): """ Returns True if any fields in a structured array have Unicode dtype. """ dtypes = (d[0] for d in array.dtype.fields.values()) return any(d.kind == "U" for d in dtypes)