astropy:docs

Source code for astropy.table.table

# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from ..extern import six
from ..extern.six.moves import zip as izip
from ..extern.six.moves import range as xrange

import abc
import collections
import functools
import sys
import operator
import platform
import warnings

from copy import deepcopy
from distutils import version

import numpy as np
from numpy import ma

from .. import log
from ..config import ConfigurationItem
from ..io import registry as io_registry
from ..units import Unit, Quantity
from ..utils import OrderedDict, isiterable, deprecated
from ..utils.console import color_print
from ..utils.exceptions import AstropyDeprecationWarning
from ..utils.metadata import MetaData
from . import operations, groups
from .pprint import (_pformat_table, _pformat_col, _pformat_col_iter,
                     _more_tabcol)
from .np_utils import fix_column_name


# Prior to Numpy 1.6.2, there was a bug (in Numpy) that caused
# sorting of structured arrays containing Unicode columns to
# silently fail.
_NUMPY_VERSION = version.LooseVersion(np.__version__)
_BROKEN_UNICODE_TABLE_SORT = _NUMPY_VERSION < version.LooseVersion('1.6.2')


__doctest_skip__ = ['Table.read', 'Table.write']


AUTO_COLNAME = ConfigurationItem(
    'auto_colname', 'col{0}',
    'The template that determines the name of a column if it cannot be '
    'determined. Uses new-style (format method) string formatting')

ERROR_COLUMN_ARGS_MESSAGE = """
The first argument to {class_name} is the string {first_arg}, which was probably intended
as the column name.  Starting in Astropy 0.3 the argument order for initializing
a {class_name} object is {class_name}(data=None, name=None, ...)."""


def _check_column_new_args(func):
    """
    Decorator for transition from 0.2 arg order (name, data, ..) to 0.3 order (data,
    name, ...).  Check if user provided a string as the first arg (note that a string
    cannot be valid as ``data``).  Raise an error with a useful message.
    """
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        if len(args) > 1 and isinstance(args[1], six.string_types):
            cls = args[0]  # Column or MaskedColumn class from __new__(cls, ..)
            raise ValueError(ERROR_COLUMN_ARGS_MESSAGE.format(class_name=cls.__name__,
                                                              first_arg=repr(args[1])))
        return func(*args, **kwargs)
    return wrapper


def _auto_names(n_cols):
    return [AUTO_COLNAME().format(i) for i in range(n_cols)]


[docs]class TableColumns(OrderedDict): """OrderedDict subclass for a set of columns. This class enhances item access to provide convenient access to columns by name or index, including slice access. It also handles renaming of columns. The initialization argument ``cols`` can be any structure that is valid for initializing a Python dict. This includes a dict, list of (key, val) tuple pairs, list of [key, val] lists, etc. Parameters ---------- cols : dict, list, tuple; optional Column objects as data structure that can init dict (see above) """ def __init__(self, cols={}): if isinstance(cols, (list, tuple)): cols = [(col.name, col) for col in cols] super(TableColumns, self).__init__(cols) def __getitem__(self, item): """Get items from a TableColumns object. :: tc = TableColumns(cols=[Column(name='a'), Column(name='b'), Column(name='c')]) tc['a'] # Column('a') tc[1] # Column('b') tc['a', 'b'] # <TableColumns names=('a', 'b')> tc[1:3] # <TableColumns names=('b', 'c')> """ if isinstance(item, six.string_types): return OrderedDict.__getitem__(self, item) elif isinstance(item, int): return self.values()[item] elif isinstance(item, tuple): return TableColumns([self[x] for x in item]) elif isinstance(item, slice): return TableColumns([self[x] for x in list(self)[item]]) else: raise IndexError('Illegal key or index value for TableColumns ' 'object') def __repr__(self): names = ("'{0}'".format(x) for x in six.iterkeys(self)) return "<TableColumns names=({0})>".format(",".join(names)) def _rename_column(self, name, new_name): if new_name in self: raise KeyError("Column {0} already exists".format(new_name)) mapper = {name: new_name} new_names = [mapper.get(name, name) for name in self] cols = list(six.itervalues(self)) self.clear() self.update(list(izip(new_names, cols))) # Define keys and values for Python 2 and 3 source compatibility
[docs] def keys(self): return list(OrderedDict.keys(self))
[docs] def values(self): return list(OrderedDict.values(self))
def _column_compare(op): """ Convenience function to return a function that properly does a comparison between a column object and something else. """ def compare(self, other): # We have to define this to ensure that we always return boolean arrays # (otherwise in some cases, Column objects are returned). if isinstance(other, BaseColumn): other = other.data return op(self.data, other) return compare @six.add_metaclass(abc.ABCMeta) class BaseColumn(object): meta = MetaData() # Define comparison operators __eq__ = _column_compare(operator.eq) __ne__ = _column_compare(operator.ne) __lt__ = _column_compare(operator.lt) __le__ = _column_compare(operator.le) __gt__ = _column_compare(operator.gt) __ge__ = _column_compare(operator.ge) def __setstate__(self, state): """ Restore the internal state of the Column/MaskedColumn for pickling purposes. This requires that the last element of ``state`` is a 5-tuple that has Column-specific state values. """ # Get the Column attributes and meta name, unit, format, description, meta = state[-1] state = state[:-1] # Using super(type(self), self).__setstate__() gives an infinite recursion. # Manually call the right super class to actually set up the array object. super_class = ma.MaskedArray if isinstance(self, ma.MaskedArray) else np.ndarray super_class.__setstate__(self, state) # Set the Column attributes and meta self._name = name self.unit = unit self.format = format self.description = description self.meta = meta def __reduce__(self): """ Return a 3-tuple for pickling a Column. Use the super-class functionality but then add in a 5-tuple of Column-specific values that get used in __setstate__. """ super_class = ma.MaskedArray if isinstance(self, ma.MaskedArray) else np.ndarray reconstruct_func, reconstruct_func_args, state = super_class.__reduce__(self) # Define Column-specific attrs and meta that gets added to state. column_state = (self.name, self.unit, self.format, self.description, self.meta) state = state + (column_state,) return reconstruct_func, reconstruct_func_args, state def __array_finalize__(self, obj): # Obj will be none for direct call to Column() creator if obj is None: return # Self was created from template (e.g. obj[slice] or (obj * 2)) # or viewcast e.g. obj.view(Column). In either case we want to # init Column attributes for self from obj if possible. self.parent_table = None for attr in ('name', 'unit', 'format', 'description'): val = getattr(obj, attr, None) setattr(self, attr, val) self.meta = deepcopy(getattr(obj, 'meta', {})) def __array_wrap__(self, out_arr, context=None): """ __array_wrap__ is called at the end of every ufunc. If the output is the same shape as the column then call the standard ndarray __array_wrap__ which will return a Column object. If instead the output shape is different (e.g. for reduction ufuncs like sum() or mean()) then return the output viewed as a standard np.ndarray. The "[()]" selects everything, but also converts a zero rank array to a scalar. For some reason np.sum() returns a zero rank scalar array while np.mean() returns a scalar. So the [()] is needed for this case. """ if self.shape == out_arr.shape: return np.ndarray.__array_wrap__(self, out_arr, context) else: return out_arr.view(np.ndarray)[()] @property def name(self): return self._name @name.setter def name(self, val): val = fix_column_name(val) if self.parent_table is not None: table = self.parent_table table.columns._rename_column(self.name, val) table._data.dtype.names = list(table.columns) if table.masked: table._data.mask.dtype.names = list(table.columns) self._name = val @property def descr(self): """Array-interface compliant full description of the column. This returns a 3-tuple (name, type, shape) that can always be used in a structured array dtype definition. """ return (self.name, self.dtype.str, self.shape[1:]) def __repr__(self): unit = None if self.unit is None else six.text_type(self.unit) out = "<{0} name={1} unit={2} format={3} " \ "description={4}>\n{5}".format( self.__class__.__name__, repr(self.name), repr(unit), repr(self.format), repr(self.description), repr(self.data)) return out def iter_str_vals(self): """ Return an iterator that yields the string-formatted values of this column. Returns ------- str_vals : iterator Column values formatted as strings """ # pprint._pformat_col_iter(col, max_lines, show_name, show_unit, outs) # Iterate over formatted values with no max number of lines, no column # name, no unit, and ignoring the returned header info in outs. for str_val in _pformat_col_iter(self, -1, False, False, {}): yield str_val def attrs_equal(self, col): """Compare the column attributes of ``col`` to this object. The comparison attributes are: name, unit, dtype, format, description, and meta. Parameters ---------- col : Column Comparison column Returns ------- equal: boolean True if all attributes are equal """ if not isinstance(col, BaseColumn): raise ValueError('Comparison `col` must be a Column or MaskedColumn object') attrs = ('name', 'unit', 'dtype', 'format', 'description', 'meta') equal = all(getattr(self, x) == getattr(col, x) for x in attrs) return equal def pformat(self, max_lines=None, show_name=True, show_unit=False): """Return a list of formatted string representation of column values. If no value of `max_lines` is supplied then the height of the screen terminal is used to set `max_lines`. If the terminal height cannot be determined then the default will be determined using the `astropy.table.pprint.MAX_LINES` configuration item. If a negative value of `max_lines` is supplied then there is no line limit applied. Parameters ---------- max_lines : int Maximum lines of output (header + data rows) show_name : bool Include column name (default=True) show_unit : bool Include a header row for unit (default=False) Returns ------- lines : list List of lines with header and formatted column values """ lines, n_header = _pformat_col(self, max_lines, show_name, show_unit) return lines def pprint(self, max_lines=None, show_name=True, show_unit=False): """Print a formatted string representation of column values. If no value of `max_lines` is supplied then the height of the screen terminal is used to set `max_lines`. If the terminal height cannot be determined then the default will be determined using the `astropy.table.pprint.MAX_LINES` configuration item. If a negative value of `max_lines` is supplied then there is no line limit applied. Parameters ---------- max_lines : int Maximum number of values in output show_name : bool Include column name (default=True) show_unit : bool Include a header row for unit (default=False) """ lines, n_header = _pformat_col(self, max_lines, show_name, show_unit) for i, line in enumerate(lines): if i < n_header: color_print(line, 'red') else: print(line) def more(self, max_lines=None, show_name=True, show_unit=False): """Interactively browse column with a paging interface. Supported keys:: f, <space> : forward one page b : back one page r : refresh same page n : next row p : previous row < : go to beginning > : go to end q : quit browsing h : print this help Parameters ---------- max_lines : int Maximum number of lines in table output show_name : bool Include a header row for column names (default=True) show_unit : bool Include a header row for unit (default=False) """ _more_tabcol(self, max_lines=max_lines, show_name=show_name, show_unit=show_unit) @property def unit(self): """ The unit associated with this column. May be a string or a `astropy.units.UnitBase` instance. Setting the `unit` property does not change the values of the data. To perform a unit conversion, use `convert_unit_to`. """ return self._unit @unit.setter def unit(self, unit): if unit is None: self._unit = None else: self._unit = Unit(unit, parse_strict='silent') @unit.deleter def unit(self): self._unit = None @property @deprecated('0.3', alternative=':attr:`Column.unit`') def units(self): return self.unit @units.setter @deprecated('0.3', alternative=':attr:`Column.unit`') def units(self, unit): self.unit = unit @units.deleter @deprecated('0.3', alternative=':attr:`Column.unit`') def units(self): del self.unit def convert_unit_to(self, new_unit, equivalencies=[]): """ Converts the values of the column in-place from the current unit to the given unit. To change the unit associated with this column without actually changing the data values, simply set the `unit` property. Parameters ---------- new_unit : str or `astropy.units.UnitBase` instance The unit to convert to. equivalencies : list of equivalence pairs, optional A list of equivalence pairs to try if the unit are not directly convertible. See :ref:`unit_equivalencies`. Raises ------ astropy.units.UnitsError If units are inconsistent """ if self.unit is None: raise ValueError("No unit set on column") self.data[:] = self.unit.to( new_unit, self.data, equivalencies=equivalencies) self.unit = new_unit def __unicode__(self): lines, n_header = _pformat_col(self) return '\n'.join(lines) if six.PY3: __str__ = __unicode__ def __bytes__(self): return six.text_type(self).encode('utf-8') if six.PY2: __str__ = __bytes__ @property def groups(self): if not hasattr(self, '_groups'): self._groups = groups.ColumnGroups(self) return self._groups def group_by(self, keys): """ Group this column by the specified ``keys`` This effectively splits the column into groups which correspond to unique values of the ``keys`` grouping object. The output is a new `Column` or `MaskedColumn` which contains a copy of this column but sorted by row according to ``keys``. The ``keys`` input to `group_by` must be a numpy array with the same length as this column. Parameters ---------- keys : numpy array Key grouping object Returns ------- out : Column New column with groups attribute set accordingly """ return groups.column_group_by(self, keys) def _copy_groups(self, out): """ Copy current groups into a copy of self ``out`` """ if self.parent_table: if hasattr(self.parent_table, '_groups'): out._groups = groups.ColumnGroups(out, indices=self.parent_table._groups._indices) elif hasattr(self, '_groups'): out._groups = groups.ColumnGroups(out, indices=self._groups._indices)
[docs]class Column(BaseColumn, np.ndarray): """Define a data column for use in a Table object. Parameters ---------- data : list, ndarray or None Column data values name : str Column name and key for reference within Table dtype : numpy.dtype compatible value Data type for column shape : tuple or () Dimensions of a single row element in the column data length : int or 0 Number of row elements in column data description : str or None Full description of column unit : str or None Physical unit format : str or None or function Format string for outputting column values. This can be an "old-style" (``format % value``) or "new-style" (`str.format`) format specification string or a function that accepts a single value and returns a string. meta : dict-like or None Meta-data associated with the column Examples -------- A Column can be created in two different ways: - Provide a ``data`` value but not ``shape`` or ``length`` (which are inferred from the data). Examples:: col = Column(data=[1, 2], name='name') # shape=(2,) col = Column(data=[[1, 2], [3, 4]], name='name') # shape=(2, 2) col = Column(data=[1, 2], name='name', dtype=float) col = Column(data=np.array([1, 2]), name='name') col = Column(data=['hello', 'world'], name='name') The ``dtype`` argument can be any value which is an acceptable fixed-size data-type initializer for the numpy.dtype() method. See `<http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html>`_. Examples include: - Python non-string type (float, int, bool) - Numpy non-string type (e.g. np.float32, np.int64, np.bool) - Numpy.dtype array-protocol type strings (e.g. 'i4', 'f8', 'S15') If no ``dtype`` value is provide then the type is inferred using ``np.array(data)``. - Provide ``length`` and optionally ``shape``, but not ``data`` Examples:: col = Column(name='name', length=5) col = Column(name='name', dtype=int, length=10, shape=(3,4)) The default ``dtype`` is ``np.float64``. The ``shape`` argument is the array shape of a single cell in the column. .. warning:: In the next major release of `astropy` (0.3), the order of function arguments for creating a |Column| will change. Currently the order is ``Column(name, data, ...)``, but in 0.3 and later it will be ``Column(data, name, ...)``. This improves consistency with |Table| and `numpy`. In order to use the same code for Astropy 0.2 and 0.3, column objects should always be created using named keyword arguments for ``data`` and ``name``, for instance ``c = Column(data=[1, 2], name='col')``. When Astropy 0.3 is released then the the keyword identifiers can be dropped, allowing for ``c = Column([1, 2], 'c')``. """ @_check_column_new_args def __new__(cls, data=None, name=None, dtype=None, shape=(), length=0, description=None, unit=None, format=None, meta=None, dtypes=None, units=None): if dtypes is not None: dtype = dtypes warnings.warn("'dtypes' has been renamed to the singular 'dtype'.", AstropyDeprecationWarning) if units is not None: unit = units warnings.warn("'units' has been renamed to the singular 'unit'.", AstropyDeprecationWarning) if data is None: dtype = (np.dtype(dtype).str, shape) self_data = np.zeros(length, dtype=dtype) elif isinstance(data, Column): self_data = np.asarray(data.data, dtype=dtype) if description is None: description = data.description if unit is None: unit = unit or data.unit if format is None: format = data.format if meta is None: meta = deepcopy(data.meta) if name is None: name = data.name elif isinstance(data, MaskedColumn): raise TypeError("Cannot convert a MaskedColumn to a Column") elif isinstance(data, Quantity): if unit is None: self_data = np.asarray(data, dtype=dtype) unit = data.unit else: self_data = np.asarray(data.to(unit), dtype=dtype) else: self_data = np.asarray(data, dtype=dtype) self = self_data.view(cls) self._name = fix_column_name(name) self.unit = unit self.format = format self.description = description self.parent_table = None self.meta = meta return self @property
[docs] def data(self): return self.view(np.ndarray)
[docs] def copy(self, order='C', data=None, copy_data=True): """Return a copy of the current Column instance. If ``data`` is supplied then a view (reference) of ``data`` is used, and ``copy_data`` is ignored. """ if data is None: data = self.view(np.ndarray) if copy_data: data = data.copy(order) out = Column(name=self.name, data=data, unit=self.unit, format=self.format, description=self.description, meta=deepcopy(self.meta)) self._copy_groups(out) return out
[docs]class MaskedColumn(BaseColumn, ma.MaskedArray): """Define a masked data column for use in a Table object. Parameters ---------- data : list, ndarray or None Column data values name : str Column name and key for reference within Table mask : list, ndarray or None Boolean mask for which True indicates missing or invalid data fill_value : float, int, str or None Value used when filling masked column elements dtype : numpy.dtype compatible value Data type for column shape : tuple or () Dimensions of a single row element in the column data length : int or 0 Number of row elements in column data description : str or None Full description of column unit : str or None Physical unit format : str or None or function Format string for outputting column values. This can be an "old-style" (``format % value``) or "new-style" (`str.format`) format specification string or a function that accepts a single value and returns a string. meta : dict-like or None Meta-data associated with the column Examples -------- A MaskedColumn is similar to a Column except that it includes ``mask`` and ``fill_value`` attributes. It can be created in two different ways: - Provide a ``data`` value but not ``shape`` or ``length`` (which are inferred from the data). Examples:: col = MaskedColumn(data=[1, 2], name='name') col = MaskedColumn(data=[1, 2], name='name', mask=[True, False]) col = MaskedColumn(data=[1, 2], name='name', dtype=float, fill_value=99) The ``mask`` argument will be cast as a boolean array and specifies which elements are considered to be missing or invalid. The ``dtype`` argument can be any value which is an acceptable fixed-size data-type initializer for the numpy.dtype() method. See `<http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html>`_. Examples include: - Python non-string type (float, int, bool) - Numpy non-string type (e.g. np.float32, np.int64, np.bool) - Numpy.dtype array-protocol type strings (e.g. 'i4', 'f8', 'S15') If no ``dtype`` value is provide then the type is inferred using ``np.array(data)``. When ``data`` is provided then the ``shape`` and ``length`` arguments are ignored. - Provide ``length`` and optionally ``shape``, but not ``data`` Examples:: col = MaskedColumn(name='name', length=5) col = MaskedColumn(name='name', dtype=int, length=10, shape=(3,4)) The default ``dtype`` is ``np.float64``. The ``shape`` argument is the array shape of a single cell in the column. .. warning:: In the next major release of `astropy` (0.3), the order of function arguments for creating a |MaskedColumn| will change. Currently the order is ``MaskedColumn(name, data, ...)``, but in 0.3 and later it will be ``MaskedColumn(data, name, ...)``. This improves consistency with |Table| and `numpy`. In order to use the same code for Astropy 0.2 and 0.3, column objects should always be created using named keyword arguments for ``data`` and ``name``, for instance ``c = MaskedColumn(data=[1, 2], name='col')``. When Astropy 0.3 is released then the the keyword identifiers can be dropped, allowing for ``c = MaskedColumn([1, 2], 'c')``. """ @_check_column_new_args def __new__(cls, data=None, name=None, mask=None, fill_value=None, dtype=None, shape=(), length=0, description=None, unit=None, format=None, meta=None, units=None, dtypes=None): if dtypes is not None: dtype = dtypes warnings.warn("'dtypes' has been renamed to the singular 'dtype'.", AstropyDeprecationWarning) if units is not None: unit = units warnings.warn("'units' has been renamed to the singular 'unit'.", AstropyDeprecationWarning) if data is None: dtype = (np.dtype(dtype).str, shape) self_data = ma.zeros(length, dtype=dtype) elif isinstance(data, (Column, MaskedColumn)): self_data = ma.asarray(data.data, dtype=dtype) if description is None: description = data.description if unit is None: unit = unit or data.unit if format is None: format = data.format if meta is None: meta = deepcopy(data.meta) if name is None: name = data.name elif isinstance(data, Quantity): if unit is None: self_data = ma.asarray(data, dtype=dtype) unit = data.unit else: self_data = ma.asarray(data.to(unit), dtype=dtype) else: self_data = ma.asarray(data, dtype=dtype) self = self_data.view(MaskedColumn) if mask is None and hasattr(data, 'mask'): mask = data.mask if fill_value is None and hasattr(data, 'fill_value'): fill_value = data.fill_value self.mask = mask self.fill_value = fill_value self._name = fix_column_name(name) self.unit = unit self.format = format self.description = description self.parent_table = None self.meta = meta return self def __array_finalize__(self, obj): BaseColumn.__array_finalize__(self, obj) ma.MaskedArray.__array_finalize__(self, obj) # Surprisingly, MaskedArray is buggy and does not call __array_finalize__ # after __getitem__, but instead does call _update_from, so we override # this instead to copy over the column metadata. def _update_from(self, obj): BaseColumn.__array_finalize__(self, obj) ma.MaskedArray._update_from(self, obj) # We also need to fix this for take(), which *also* does not call # __array_finalize__. Fun times.
[docs] def take(self, indices, axis=None, out=None, mode='raise', **kwargs): """ Return an array formed from the elements of `a` at the given indices. Refer to `numpy.take` for full documentation. See Also -------- numpy.take : equivalent function """ out = ma.MaskedArray.take(self, indices, axis=axis, out=out, mode=mode, **kwargs) if isinstance(out, MaskedColumn): # don't do this for scalars BaseColumn.__array_finalize__(out, self) return out
def _fix_fill_value(self, val): """Fix a fill value (if needed) to work around a bug with setting the fill value of a string array in MaskedArray with Python 3.x. See https://github.com/numpy/numpy/pull/2733. This mimics the check in numpy.ma.core._check_fill_value() (version < 1.7) which incorrectly sets fill_value to a default if self.dtype.char is 'U' (which is the case for Python 3). Here we change the string to a byte string so that in Python 3 the isinstance(val, basestring) part fails. """ if isinstance(val, six.string_types) and (self.dtype.char not in 'SV'): val = val.encode() return val @property def fill_value(self): return self.get_fill_value() # defer to native ma.MaskedArray method @fill_value.setter
[docs] def fill_value(self, val): """Set fill value both in the masked column view and in the parent table if it exists. Setting one or the other alone doesn't work.""" val = self._fix_fill_value(val) if self.parent_table: self.parent_table._data[self._name].fill_value = val # Yet another ma bug workaround: If the value of fill_value for a string array is # requested but not yet set then it gets created as 'N/A'. From this point onward # any new fill_values are truncated to 3 characters. Note that this does not # occur if the masked array is a structured array (as in the previous block that # deals with the parent table). # # >>> x = ma.array(['xxxx']) # >>> x.fill_value # fill_value now gets represented as an 'S3' array # 'N/A' # >>> x.fill_value='yyyy' # >>> x.fill_value # 'yyy' # # To handle this we are forced to reset a private variable first: self._fill_value = None self.set_fill_value(val) # defer to native ma.MaskedArray method
@property
[docs] def data(self): out = self.view(ma.MaskedArray) # The following is necessary because of a bug in Numpy, which was # fixed in numpy/numpy#2703. The fix should be included in Numpy 1.8.0. out.fill_value = self.fill_value return out
[docs] def filled(self, fill_value=None): """Return a copy of self, with masked values filled with a given value. Parameters ---------- fill_value : scalar; optional The value to use for invalid entries (None by default). If None, the `fill_value` attribute of the array is used instead. Returns ------- filled_column : Column A copy of ``self`` with masked entries replaced by `fill_value` (be it the function argument or the attribute of ``self``). """ if fill_value is None: fill_value = self.fill_value fill_value = self._fix_fill_value(fill_value) data = super(MaskedColumn, self).filled(fill_value) out = Column(name=self.name, data=data, unit=self.unit, format=self.format, description=self.description, meta=deepcopy(self.meta)) return out
[docs] def copy(self, order='C', data=None, copy_data=True): """ Return a copy of the current MaskedColumn instance. If ``data`` is supplied then a view (reference) of ``data`` is used, and ``copy_data`` is ignored. Parameters ---------- data : array; optional Data to use when creating MaskedColumn copy. If not supplied the column data array is used. copy_data : bool; optional Make a copy of input data instead of using a reference (default=True) Returns ------- column : MaskedColumn A copy of ``self`` """ if data is None: data = self.view(ma.MaskedArray) if copy_data: data = data.copy(order) out = MaskedColumn(name=self.name, data=data, unit=self.unit, format=self.format, # Do not include mask=self.mask since `data` has the mask fill_value=self.fill_value, description=self.description, meta=deepcopy(self.meta)) self._copy_groups(out) return out
[docs]class Row(object): """A class to represent one row of a Table object. A Row object is returned when a Table object is indexed with an integer or when iterating over a table:: >>> table = Table([(1, 2), (3, 4)], names=('a', 'b'), ... dtype=('int32', 'int32')) >>> row = table[1] >>> row <Row 1 of table values=(2, 4) dtype=[('a', '<i4'), ('b', '<i4')]> >>> row['a'] 2 >>> row[1] 4 """ def __init__(self, table, index): self._table = table self._index = index try: self._data = table._data[index] # MaskedArray __getitem__ has a strange behavior where if a # row mask is all False then it returns a np.void which # has no mask attribute. This makes it impossible to then set # the mask. Here we recast back to mvoid. This was fixed in # Numpy following issue numpy/numpy#483, and the fix should be # included in Numpy 1.8.0. if self._table.masked and isinstance(self._data, np.void): self._data = ma.core.mvoid(self._data, mask=self._table._mask[index]) except ValueError as err: # Another bug (or maybe same?) that is fixed in 1.8 prevents accessing # a row in masked array if it has object-type members. # >>> x = np.ma.empty(1, dtype=[('a', 'O')]) # >>> x['a'] = 1 # >>> x['a'].mask = True # >>> x[0] # ValueError: Setting void-array with object members using buffer. [numpy.ma.core] # # All we do here is re-raise with a more informative message if (six.text_type(err).startswith('Setting void-array with object members') and version.LooseVersion(np.__version__) < version.LooseVersion('1.8')): raise ValueError('Cannot access table row with Object type columns, due to ' 'a bug in numpy {0}. Please upgrade to numpy 1.8 or newer.' .format(np.__version__)) else: raise def __getitem__(self, item): return self.data[item] def __setitem__(self, item, val): self.data[item] = val def __eq__(self, other): if self._table.masked: # Sent bug report to numpy-discussion group on 2012-Oct-21, subject: # "Comparing rows in a structured masked array raises exception" # No response, so this is still unresolved. raise ValueError('Unable to compare rows for masked table due to numpy.ma bug') return self.data == other def __ne__(self, other): if self._table.masked: raise ValueError('Unable to compare rows for masked table due to numpy.ma bug') return self.data != other @property def _mask(self): return self._data.mask def __array__(self, dtype=None): """Support converting Row to np.array via np.array(table). Coercion to a different dtype via np.array(table, dtype) is not supported and will raise a ValueError. """ if dtype is not None: raise ValueError('Datatype coercion is not allowed') return np.array(self._data) def __len__(self): return len(self._data.dtype) @property
[docs] def table(self): return self._table
@property
[docs] def index(self): return self._index
@property
[docs] def data(self): return self._data
@property
[docs] def meta(self): return self.table.meta
@property
[docs] def columns(self): return self.table.columns
@property
[docs] def colnames(self): return self.table.colnames
@property
[docs] def dtype(self): return self.data.dtype
@property @deprecated('0.3', alternative=':attr:`Row.dtype`', pending=False)
[docs] def dtypes(self): return self.dtype
def __repr__(self): return "<Row {0} of table\n values={1!r}\n dtype={2}>".format( self.index, self.data, self.dtype)
collections.Sequence.register(Row)
[docs]class Table(object): """A class to represent tables of heterogeneous data. `Table` provides a class for heterogeneous tabular data, making use of a `numpy` structured array internally to store the data values. A key enhancement provided by the `Table` class is the ability to easily modify the structure of the table by adding or removing columns, or adding new rows of data. In addition table and column metadata are fully supported. `Table` differs from `NDData` by the assumption that the input data consists of columns of homogeneous data, where each column has a unique identifier and may contain additional metadata such as the data unit, format, and description. Parameters ---------- data : numpy ndarray, dict, list, or Table, optional Data to initialize table. masked : bool, optional Specify whether the table is masked. names : list, optional Specify column names dtype : list, optional Specify column data types meta : dict, optional Metadata associated with the table. copy : bool, optional Copy the input data (default=True). """ meta = MetaData() def __init__(self, data=None, masked=None, names=None, dtype=None, meta=None, copy=True, dtypes=None): if dtypes is not None: dtype = dtypes warnings.warn("'dtypes' has been renamed to the singular 'dtype'.", AstropyDeprecationWarning) # Set up a placeholder empty table self._data = None self._set_masked(masked) self.columns = TableColumns() self.meta = meta # Must copy if dtype are changing if not copy and dtype is not None: raise ValueError('Cannot specify dtype when copy=False') # Infer the type of the input data and set up the initialization # function, number of columns, and potentially the default col names default_names = None if isinstance(data, Row): data = data._table[data._index:data._index + 1] if isinstance(data, (list, tuple)): init_func = self._init_from_list if data and all(isinstance(row, dict) for row in data): n_cols = len(data[0]) else: n_cols = len(data) elif isinstance(data, np.ndarray): if data.dtype.names: init_func = self._init_from_ndarray # _struct n_cols = len(data.dtype.names) default_names = data.dtype.names else: init_func = self._init_from_ndarray # _homog n_cols = data.shape[1] elif isinstance(data, dict): init_func = self._init_from_dict default_names = list(data) n_cols = len(default_names) elif isinstance(data, Table): init_func = self._init_from_table n_cols = len(data.colnames) default_names = data.colnames elif data is None: if names is None: return # Empty table else: init_func = self._init_from_list n_cols = len(names) data = [[]] * n_cols else: raise ValueError('Data type {0} not allowed to init Table' .format(type(data))) # Set up defaults if names and/or dtype are not specified. # A value of None means the actual value will be inferred # within the appropriate initialization routine, either from # existing specification or auto-generated. if names is None: names = default_names or [None] * n_cols if dtype is None: dtype = [None] * n_cols # Numpy does not support Unicode column names on Python 2, or # bytes column names on Python 3, so fix them up now. names = [fix_column_name(name) for name in names] self._check_names_dtype(names, dtype, n_cols) # Finally do the real initialization init_func(data, names, dtype, n_cols, copy) # Whatever happens above, the masked property should be set to a boolean if type(self.masked) != bool: raise TypeError("masked property has not been set to True or False") def __getstate__(self): return (self.columns.values(), self.meta) def __setstate__(self, state): columns, meta = state self.__init__(columns, meta=meta) @property def mask(self): return self._data.mask if self.masked else None @mask.setter
[docs] def mask(self, val): self._data.mask = val
@property def _mask(self): """This is needed due to intricacies in numpy.ma, don't remove it.""" return self._data.mask
[docs] def filled(self, fill_value=None): """Return a copy of self, with masked values filled. If input ``fill_value`` supplied then that value is used for all masked entries in the table. Otherwise the individual ``fill_value`` defined for each table column is used. Returns ------- filled_table : Table New table with masked values filled """ if self.masked: data = [col.filled(fill_value) for col in six.itervalues(self.columns)] else: data = self return self.__class__(data, meta=deepcopy(self.meta))
def __array__(self, dtype=None): """Support converting Table to np.array via np.array(table). Coercion to a different dtype via np.array(table, dtype) is not supported and will raise a ValueError. """ if dtype is not None: raise ValueError('Datatype coercion is not allowed') # This limitation is because of the following unexpected result that # should have made a table copy while changing the column names. # # >>> d = astropy.table.Table([[1,2],[3,4]]) # >>> np.array(d, dtype=[('a', 'i8'), ('b', 'i8')]) # array([(0, 0), (0, 0)], # dtype=[('a', '<i8'), ('b', '<i8')]) return self._data.data if self.masked else self._data def _rebuild_table_column_views(self): """ Some table manipulations can corrupt the Column views of self._data. This function will cleanly rebuild the columns and self.columns. This is a slightly subtle operation, see comments. """ cols = [] for col in six.itervalues(self.columns): # First make a new column based on the name and the original column. This # step is needed because the table manipulation may have changed the table # masking so that the original data columns no longer correspond to # self.ColumnClass. This uses data refs, not copies. newcol = self.ColumnClass(name=col.name, data=col) # Now use the copy() method to copy the column and its metadata, but at # the same time set the column data to a view of self._data[col.name]. # Somewhat confusingly in this case copy() refers to copying the # column attributes, but the data are used by reference. newcol = newcol.copy(data=self._data[col.name]) # Make column aware of the parent table newcol.parent_table = self cols.append(newcol) self.columns = TableColumns(cols) def _check_names_dtype(self, names, dtype, n_cols): """Make sure that names and dtype are boths iterable and have the same length as data. """ for inp_list, inp_str in ((dtype, 'dtype'), (names, 'names')): if not isiterable(inp_list): raise ValueError('{0} must be a list or None'.format(inp_str)) if len(names) != n_cols or len(dtype) != n_cols: raise ValueError( 'Arguments "names" and "dtype" must match number of columns' .format(inp_str)) def _set_masked_from_cols(self, cols): if self.masked is None: if any(isinstance(col, (MaskedColumn, ma.MaskedArray)) for col in cols): self._set_masked(True) else: self._set_masked(False) elif not self.masked: if any(isinstance(col, (MaskedColumn, ma.MaskedArray)) for col in cols): self._set_masked(True) def _init_from_list(self, data, names, dtype, n_cols, copy): """Initialize table from a list of columns. A column can be a Column object, np.ndarray, or any other iterable object. """ if not copy: raise ValueError('Cannot use copy=False with a list data input') # Set self.masked appropriately, then get class to create column instances. self._set_masked_from_cols(data) cols = [] def_names = _auto_names(n_cols) if data and all(isinstance(row, dict) for row in data): names_from_data = set() for row in data: names_from_data.update(row) cols = {} for name in names_from_data: cols[name] = [] for i, row in enumerate(data): try: cols[name].append(row[name]) except KeyError: raise ValueError('Row {0} has no value for column {1}'.format(i, name)) if all(name is None for name in names): names = sorted(names_from_data) self._init_from_dict(cols, names, dtype, n_cols, copy) return for col, name, def_name, dtype in zip(data, names, def_names, dtype): if isinstance(col, (Column, MaskedColumn)): col = self.ColumnClass(name=(name or col.name), data=col, dtype=dtype) elif isinstance(col, np.ndarray) or isiterable(col): col = self.ColumnClass(name=(name or def_name), data=col, dtype=dtype) else: raise ValueError('Elements in list initialization must be ' 'either Column or list-like') cols.append(col) self._init_from_cols(cols) def _init_from_ndarray(self, data, names, dtype, n_cols, copy): """Initialize table from an ndarray structured array""" data_names = data.dtype.names or _auto_names(n_cols) struct = data.dtype.names is not None names = [name or data_names[i] for i, name in enumerate(names)] cols = ([data[name] for name in data_names] if struct else [data[:, i] for i in range(n_cols)]) # Set self.masked appropriately, then get class to create column instances. self._set_masked_from_cols(cols) if copy: self._init_from_list(cols, names, dtype, n_cols, copy) else: dtype = [(name, col.dtype) for name, col in zip(names, cols)] self._data = data.view(dtype).ravel() columns = TableColumns() for name in names: columns[name] = self.ColumnClass(name=name, data=self._data[name]) columns[name].parent_table = self self.columns = columns def _init_from_dict(self, data, names, dtype, n_cols, copy): """Initialize table from a dictionary of columns""" if not copy: raise ValueError('Cannot use copy=False with a dict data input') data_list = [data[name] for name in names] self._init_from_list(data_list, names, dtype, n_cols, copy) def _init_from_table(self, data, names, dtype, n_cols, copy): """Initialize table from an existing Table object """ table = data # data is really a Table, rename for clarity data_names = table.colnames self.meta.clear() self.meta.update(deepcopy(table.meta)) cols = list(six.itervalues(table.columns)) # Set self.masked appropriately from cols self._set_masked_from_cols(cols) if copy: self._init_from_list(cols, names, dtype, n_cols, copy) else: names = [vals[0] or vals[1] for vals in zip(names, data_names)] dtype = [(name, col.dtype) for name, col in zip(names, cols)] data = table._data.view(dtype) self._update_table_from_cols(self, data, cols, names) def _init_from_cols(self, cols): """Initialize table from a list of Column objects""" lengths = set(len(col.data) for col in cols) if len(lengths) != 1: raise ValueError('Inconsistent data column lengths: {0}' .format(lengths)) self._set_masked_from_cols(cols) cols = [self.ColumnClass(name=col.name, data=col) for col in cols] names = [col.name for col in cols] dtype = [col.descr for col in cols] empty_init = ma.empty if self.masked else np.empty data = empty_init(lengths.pop(), dtype=dtype) for col in cols: data[col.name] = col.data self._update_table_from_cols(self, data, cols, names) def _new_from_slice(self, slice_): """Create a new table as a referenced slice from self.""" table = self.__class__(masked=self.masked) table.meta.clear() table.meta.update(deepcopy(self.meta)) cols = list(six.itervalues(self.columns)) names = [col.name for col in cols] data = self._data[slice_] self._update_table_from_cols(table, data, cols, names) return table @staticmethod def _update_table_from_cols(table, data, cols, names): """Update the existing ``table`` so that it represents the given ``data`` (a structured ndarray) with ``cols`` and ``names``.""" columns = TableColumns() table._data = data for name, col in zip(names, cols): newcol = col.copy(data=data[name], copy_data=False) newcol.name = name newcol.parent_table = table columns[name] = newcol table.columns = columns def __repr__(self): names = ("'{0}'".format(x) for x in self.colnames) s = "<{3} rows={0} names=({1})>\n{2}".format( self.__len__(), ','.join(names), repr(self._data), self.__class__.__name__) return s def __unicode__(self): lines, n_header = _pformat_table(self) return '\n'.join(lines) if six.PY3: __str__ = __unicode__ def __bytes__(self): return six.text_type(self).encode('utf-8') if six.PY2: __str__ = __bytes__
[docs] def pprint(self, max_lines=None, max_width=None, show_name=True, show_unit=False): """Print a formatted string representation of the table. If no value of `max_lines` is supplied then the height of the screen terminal is used to set `max_lines`. If the terminal height cannot be determined then the default is taken from the configuration item `astropy.table.pprint.MAX_LINES`. If a negative value of `max_lines` is supplied then there is no line limit applied. The same applies for max_width except the configuration item is `astropy.table.pprint.MAX_WIDTH`. Parameters ---------- max_lines : int Maximum number of lines in table output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names (default=True) show_unit : bool Include a header row for unit (default=False) """ lines, n_header = _pformat_table(self, max_lines, max_width, show_name, show_unit) for i, line in enumerate(lines): if i < n_header: color_print(line, 'red') else: print(line)
[docs] def show_in_browser(self, css="table,th,td,tr,tbody {border: 1px solid black; border-collapse: collapse;}", max_lines=5000, jsviewer=False, jskwargs={}, tableid=None, browser='default'): """ Render the table in HTML and show it in a web browser. In order to make a persistent html file, i.e. one that survives refresh, the returned file object must be kept in memory. Parameters ---------- css : string A valid CSS string declaring the formatting for the table max_lines : int Maximum number of rows to export to the table (set low by default to avoid memory issues, since the browser view requires duplicating the table in memory). A negative value of `max_lines` indicates no row limit jsviewer : bool If True, prepends some javascript headers so that the table is rendered as a https://datatables.net data table. This allows in-browser searching & sorting. See `JSViewer` jskwargs : dict Passed to the `JSViewer` init tableid : str or None An html ID tag for the table. Default is "table{id}", where id is the unique integer id of the table object, id(self) browser : str Any legal browser name, e.g. 'firefox','chrome','safari' (for mac, you may need to use 'open -a "/Applications/Google Chrome.app" %s' for Chrome). If 'default', will use the system default browser. Returns ------- A :py:`tempfile.NamedTemporaryFile` object pointing to the html file on disk. """ import webbrowser import tempfile from .jsviewer import JSViewer tmp = tempfile.NamedTemporaryFile(suffix='.html') if tableid is None: tableid = 'table{id}'.format(id=id(self)) linelist = self.pformat(html=True, max_width=np.inf, max_lines=max_lines, tableid=tableid) if jsviewer: jsv = JSViewer(**jskwargs) js = jsv.command_line(tableid=tableid) else: js = [] css = ["<style>{0}</style>".format(css)] html = "\n".join(['<!DOCTYPE html>','<html>'] + css + js + linelist + ['</html>']) try: tmp.write(html) except TypeError: tmp.write(html.encode('utf8')) tmp.flush() if browser == 'default': webbrowser.open("file://"+tmp.name) else: webbrowser.get(browser).open("file://"+tmp.name) return tmp
[docs] def pformat(self, max_lines=None, max_width=None, show_name=True, show_unit=False, html=False, tableid=None): """Return a list of lines for the formatted string representation of the table. If no value of `max_lines` is supplied then the height of the screen terminal is used to set `max_lines`. If the terminal height cannot be determined then the default is taken from the configuration item `astropy.table.pprint.MAX_LINES`. If a negative value of `max_lines` is supplied then there is no line limit applied. The same applies for max_width except the configuration item is `astropy.table.pprint.MAX_WIDTH`. Parameters ---------- max_lines : int or None Maximum number of rows to output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names (default=True) show_unit : bool Include a header row for unit (default=False) html : bool Format the output as an HTML table (default=False) tableid : str or None An ID tag for the table; only used if html is set. Default is "table{id}", where id is the unique integer id of the table object, id(self) Returns ------- lines : list Formatted table as a list of strings """ lines, n_header = _pformat_table(self, max_lines, max_width, show_name, show_unit, html, tableid=tableid) return lines
[docs] def more(self, max_lines=None, max_width=None, show_name=True, show_unit=False): """Interactively browse table with a paging interface. Supported keys:: f, <space> : forward one page b : back one page r : refresh same page n : next row p : previous row < : go to beginning > : go to end q : quit browsing h : print this help Parameters ---------- max_lines : int Maximum number of lines in table output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names (default=True) show_unit : bool Include a header row for unit (default=False) """ _more_tabcol(self, max_lines, max_width, show_name, show_unit)
def _repr_html_(self): # Since the user cannot provide input, need a sensible default tableid = 'table{id}'.format(id=id(self)) lines = self.pformat(html=True, tableid=tableid) return ''.join(lines) def __getitem__(self, item): if isinstance(item, six.string_types): return self.columns[item] elif isinstance(item, int): return Row(self, item) elif isinstance(item, (tuple, list)) and all(x in self.colnames for x in item): out = self.__class__([self[x] for x in item], meta=deepcopy(self.meta)) out._groups = groups.TableGroups(out, indices=self.groups._indices, keys=self.groups._keys) return out elif (isinstance(item, slice) or isinstance(item, np.ndarray) or isinstance(item, list) or isinstance(item, tuple) and all(isinstance(x, np.ndarray) for x in item)): # here for the many ways to give a slice; a tuple of ndarray # is produced by np.where, as in t[np.where(t['a'] > 2)] # For all, a new table is constructed with slice of all columns return self._new_from_slice(item) else: raise ValueError('Illegal type {0} for table item access' .format(type(item))) def __setitem__(self, item, value): # If the item is a string then it must be the name of a column. # If that column doesn't already exist then create it now. if isinstance(item, six.string_types) and item not in self.colnames: NewColumn = MaskedColumn if self.masked else Column # Make sure value is an ndarray so we can get the dtype if not isinstance(value, np.ndarray): value = np.asarray(value) # Make new column and assign the value. If the table currently has no rows # (len=0) of the value is already a Column then define new column directly # from value. In the latter case this allows for propagation of Column # metadata. Otherwise define a new column with the right length and shape and # then set it from value. This allows for broadcasting, e.g. t['a'] = 1. if isinstance(value, BaseColumn): new_column = value.copy(copy_data=False) new_column.name = item elif len(self) == 0: new_column = NewColumn(name=item, data=value) else: new_column = NewColumn(name=item, length=len(self), dtype=value.dtype, shape=value.shape[1:]) new_column[:] = value if isinstance(value, Quantity): new_column.unit = value.unit # Now add new column to the table self.add_column(new_column) elif isinstance(value, Row): # Value is another row self._data[item] = value.data else: # Otherwise just delegate to the numpy item setter. self._data[item] = value def __delitem__(self, item): if isinstance(item, six.string_types): self.remove_column(item) elif isinstance(item, tuple): self.remove_columns(item) def __iter__(self): self._iter_index = 0 return self def __next__(self): """Python 3 iterator""" if self._iter_index < len(self._data): val = self[self._iter_index] self._iter_index += 1 return val else: raise StopIteration if sys.version_info[0] < 3: # pragma: py2 next = __next__
[docs] def field(self, item): """Return column[item] for recarray compatibility.""" return self.columns[item]
@property def masked(self): return self._masked @masked.setter
[docs] def masked(self, masked): raise Exception('Masked attribute is read-only (use t = Table(t, masked=True)' ' to convert to a masked table)')
def _set_masked(self, masked): """ Set the table masked property. Parameters ---------- masked : bool State of table masking (True or False) """ if hasattr(self, '_masked'): # The only allowed change is from None to False or True, or False to True if self._masked is None and masked in [False, True]: self._masked = masked elif self._masked is False and masked is True: log.info("Upgrading Table to masked Table") self._masked = masked elif self._masked is masked: raise Exception("Masked attribute is already set to {0}".format(masked)) else: raise Exception("Cannot change masked attribute to {0} once it is set to {1}" .format(masked, self._masked)) else: if masked in [True, False, None]: self._masked = masked else: raise ValueError("masked should be one of True, False, None") if self._masked: self._column_class = MaskedColumn else: self._column_class = Column @property
[docs] def ColumnClass(self): if self._column_class is None: return Column else: return self._column_class
@property
[docs] def dtype(self): return self._data.dtype
@property
[docs] def colnames(self): return list(self.columns.keys())
[docs] def keys(self): return list(self.columns.keys())
def __len__(self): if self._data is None: return 0 else: return len(self._data)
[docs] def create_mask(self): if isinstance(self._data, ma.MaskedArray): raise Exception("data array is already masked") else: self._data = ma.array(self._data)
[docs] def index_column(self, name): """ Return the positional index of column ``name``. Parameters ---------- name : str column name Returns ------- index : int Positional index of column ``name``. Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Get index of column 'b' of the table:: >>> t.index_column('b') 1 """ try: return self.colnames.index(name) except ValueError: raise ValueError("Column {0} does not exist".format(name))
[docs] def add_column(self, col, index=None): """ Add a new Column object ``col`` to the table. If ``index`` is supplied then insert column before ``index`` position in the list of columns, otherwise append column to the end of the list. Parameters ---------- col : Column Column object to add. index : int or None Insert column before this position or at end (default) Examples -------- Create a table with two columns 'a' and 'b':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> print(t) a b --- --- 1 0.1 2 0.2 3 0.3 Create a third column 'c' and append it to the end of the table:: >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> t.add_column(col_c) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Add column 'd' at position 1. Note that the column is inserted before the given index:: >>> col_d = Column(name='d', data=['a', 'b', 'c']) >>> t.add_column(col_d, 1) >>> print(t) a d b c --- --- --- --- 1 a 0.1 x 2 b 0.2 y 3 c 0.3 z To add several columns use add_columns. """ if index is None: index = len(self.columns) self.add_columns([col], [index])
[docs] def add_columns(self, cols, indexes=None): """ Add a list of new Column objects ``cols`` to the table. If a corresponding list of ``indexes`` is supplied then insert column before each ``index`` position in the *original* list of columns, otherwise append columns to the end of the list. Parameters ---------- cols : list of Columns Column objects to add. indexes : list of ints or None Insert column before this position or at end (default) Examples -------- Create a table with two columns 'a' and 'b':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> print(t) a b --- --- 1 0.1 2 0.2 3 0.3 Create column 'c' and 'd' and append them to the end of the table:: >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> col_d = Column(name='d', data=['u', 'v', 'w']) >>> t.add_columns([col_c, col_d]) >>> print(t) a b c d --- --- --- --- 1 0.1 x u 2 0.2 y v 3 0.3 z w Add column 'c' at position 0 and column 'd' at position 1. Note that the columns are inserted before the given position:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> col_d = Column(name='d', data=['u', 'v', 'w']) >>> t.add_columns([col_c, col_d], [0, 1]) >>> print(t) c a d b --- --- --- --- x 1 u 0.1 y 2 v 0.2 z 3 w 0.3 """ if indexes is None: indexes = [len(self.columns)] * len(cols) elif len(indexes) != len(cols): raise ValueError('Number of indexes must match number of cols') if self._data is None: # No existing table data, init from cols newcols = cols else: newcols = list(self.columns.values()) new_indexes = list(range(len(newcols) + 1)) for col, index in zip(cols, indexes): i = new_indexes.index(index) new_indexes.insert(i, None) newcols.insert(i, col) self._init_from_cols(newcols)
[docs] def remove_row(self, index): """ Remove a row from the table. Parameters ---------- index : int Index of row to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove row 1 from the table:: >>> t.remove_row(1) >>> print(t) a b c --- --- --- 1 0.1 x 3 0.3 z To remove several rows at the same time use remove_rows. """ # check the index against the types that work with np.delete if not isinstance(index, (six.integer_types, np.integer)): raise TypeError("Row index must be an integer") self.remove_rows(index)
[docs] def remove_rows(self, row_specifier): """ Remove rows from the table. Parameters ---------- row_specifier : slice, int, or array of ints Specification for rows to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove rows 0 and 2 from the table:: >>> t.remove_rows([0, 2]) >>> print(t) a b c --- --- --- 2 0.2 y Note that there are no warnings if the slice operator extends outside the data:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> t.remove_rows(slice(10, 20, 1)) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z """ try: table = np.delete(self._data, row_specifier, axis=0) except (ValueError, IndexError): # Numpy <= 1.7 raises ValueError while Numpy >= 1.8 raises IndexError raise IndexError('Removing row(s) {0} from table with {1} rows failed' .format(row_specifier, len(self._data))) self._data = table # after updating the row data, the column views will be out of date # and should be updated: self._rebuild_table_column_views() # Revert groups to default (ungrouped) state if hasattr(self, '_groups'): del self._groups
[docs] def remove_column(self, name): """ Remove a column from the table. This can also be done with:: del table[name] Parameters ---------- name : str Name of column to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove column 'b' from the table:: >>> t.remove_column('b') >>> print(t) a c --- --- 1 x 2 y 3 z To remove several columns at the same time use remove_columns. """ self.remove_columns([name])
[docs] def remove_columns(self, names): ''' Remove several columns from the table Parameters ---------- names : list A list containing the names of the columns to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove columns 'b' and 'c' from the table:: >>> t.remove_columns(['b', 'c']) >>> print(t) a --- 1 2 3 Specifying only a single column also works. Remove column 'b' from the table:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> t.remove_columns('b') >>> print(t) a c --- --- 1 x 2 y 3 z This gives the same as using remove_column. ''' for name in names: if name not in self.columns: raise KeyError("Column {0} does not exist".format(name)) for name in names: self.columns.pop(name) newdtype = [(name, self._data.dtype[name]) for name in self._data.dtype.names if name not in names] newdtype = np.dtype(newdtype) if newdtype: if self.masked: table = np.ma.empty(self._data.shape, dtype=newdtype) else: table = np.empty(self._data.shape, dtype=newdtype) for field in newdtype.fields: table[field] = self._data[field] if self.masked: table[field].fill_value = self._data[field].fill_value else: table = None self._data = table
[docs] def keep_columns(self, names): ''' Keep only the columns specified (remove the others). Parameters ---------- names : list A list containing the names of the columns to keep. All other columns will be removed. Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Specifying only a single column name keeps only this column. Keep only column 'a' of the table:: >>> t.keep_columns('a') >>> print(t) a --- 1 2 3 Specifying a list of column names is keeps is also possible. Keep columns 'a' and 'c' of the table:: >>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> t.keep_columns(['a', 'c']) >>> print(t) a c --- --- 1 x 2 y 3 z ''' if isinstance(names, six.string_types): names = [names] for name in names: if name not in self.columns: raise KeyError("Column {0} does not exist".format(name)) remove = list(set(self.keys()) - set(names)) self.remove_columns(remove)
[docs] def rename_column(self, name, new_name): ''' Rename a column. This can also be done directly with by setting the ``name`` attribute for a column:: table[name].name = new_name Parameters ---------- name : str The current name of the column. new_name : str The new name for the column Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c')) >>> t.pprint() a b c --- --- --- 1 3 5 2 4 6 Renaming column 'a' to 'aa':: >>> t.rename_column('a' , 'aa') >>> t.pprint() aa b c --- --- --- 1 3 5 2 4 6 ''' if name not in self.keys(): raise KeyError("Column {0} does not exist".format(name)) self.columns[name].name = new_name
[docs] def add_row(self, vals=None, mask=None): """Add a new row to the end of the table. The ``vals`` argument can be: sequence (e.g. tuple or list) Column values in the same order as table columns. mapping (e.g. dict) Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype. None All values filled with np.zeros for the column dtype. This method requires that the Table object "owns" the underlying array data. In particular one cannot add a row to a Table that was initialized with copy=False from an existing array. The ``mask`` attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if ``vals`` is an iterable, then ``mask`` should also be an iterable with the same length, and if ``vals`` is a mapping, then ``mask`` should be a dictionary. Parameters ---------- vals : tuple, list, dict or None Use the specified values in the new row Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c')) >>> t.pprint() a b c --- --- --- 1 4 7 2 5 8 Adding a new row with entries '3' in 'a', '6' in 'b' and '9' in 'c':: >>> t.add_row([3,6,9]) >>> t.pprint() a b c --- --- --- 1 4 7 2 5 8 3 6 9 """ def _is_mapping(obj): """Minimal checker for mapping (dict-like) interface for obj""" attrs = ('__getitem__', '__len__', '__iter__', 'keys', 'values', 'items') return all(hasattr(obj, attr) for attr in attrs) newlen = len(self._data) + 1 if vals is None: vals = np.zeros(1, dtype=self._data.dtype)[0] if mask is not None and not self.masked: self._set_masked(True) # Create a table with one row to test the operation on test_data = (ma.zeros if self.masked else np.zeros)(1, dtype=self._data.dtype) if _is_mapping(vals): if mask is not None and not _is_mapping(mask): raise TypeError("Mismatch between type of vals and mask") # Now check that the mask is specified for the same keys as the # values, otherwise things get really confusing. if mask is not None and set(vals.keys()) != set(mask.keys()): raise ValueError('keys in mask should match keys in vals') if self.masked: # We set the mask to True regardless of whether a mask value # is specified or not - that is, any cell where a new row # value is not specified should be treated as missing. test_data.mask[-1] = (True,) * len(test_data.dtype) # First we copy the values for name, val in six.iteritems(vals): try: test_data[name][-1] = val except IndexError: raise ValueError("No column {0} in table".format(name)) if mask: test_data[name].mask[-1] = mask[name] elif isiterable(vals): if mask is not None and (not isiterable(mask) or _is_mapping(mask)): raise TypeError("Mismatch between type of vals and mask") if len(self.columns) != len(vals): raise ValueError('Mismatch between number of vals and columns') if not isinstance(vals, tuple): vals = tuple(vals) test_data[-1] = vals if mask is not None: if len(self.columns) != len(mask): raise ValueError('Mismatch between number of masks and columns') if not isinstance(mask, tuple): mask = tuple(mask) test_data.mask[-1] = mask else: raise TypeError('Vals must be an iterable or mapping or None') # If no errors have been raised, then the table can be resized if self.masked: if newlen == 1: self._data = ma.empty(1, dtype=self._data.dtype) else: self._data = ma.resize(self._data, (newlen,)) else: self._data.resize((newlen,), refcheck=False) # Assign the new row self._data[-1:] = test_data self._rebuild_table_column_views() # Revert groups to default (ungrouped) state if hasattr(self, '_groups'): del self._groups
[docs] def argsort(self, keys=None, kind=None): """ Return the indices which would sort the table according to one or more key columns. This simply calls the `numpy.argsort` function on the table with the ``order`` parameter set to `keys`. Parameters ---------- keys : str or list of str The column name(s) to order the table by kind : {'quicksort', 'mergesort', 'heapsort'}, optional Sorting algorithm. Returns ------- index_array : ndarray, int Array of indices that sorts the table by the specified key column(s). """ if isinstance(keys, six.string_types): keys = [keys] kwargs = {} if keys: kwargs['order'] = keys if kind: kwargs['kind'] = kind data = self._data if _BROKEN_UNICODE_TABLE_SORT and keys is not None and any( data.dtype[i].kind == 'U' for i in xrange(len(data.dtype))): return np.lexsort([data[key] for key in keys[::-1]]) else: return data.argsort(**kwargs)
[docs] def sort(self, keys): ''' Sort the table according to one or more keys. This operates on the existing table and does not return a new table. Parameters ---------- keys : str or list of str The key(s) to order the table by Examples -------- Create a table with 3 columns:: >>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'], ... [12,15,18]], names=('firstname','name','tel')) >>> t.pprint() firstname name tel --------- ------- --- Max Miller 12 Jo Miller 15 John Jackson 18 Sorting according to standard sorting rules, first 'name' then 'firstname':: >>> t.sort(['name','firstname']) >>> t.pprint() firstname name tel --------- ------- --- John Jackson 18 Jo Miller 15 Max Miller 12 ''' if type(keys) is not list: keys = [keys] data = self._data if _BROKEN_UNICODE_TABLE_SORT and any( data.dtype[i].kind == 'U' for i in xrange(len(data.dtype))): # Use an alternate sort implementation that uses argsort ordering = self.argsort(keys=keys) data[:] = data[ordering] else: data.sort(order=keys) self._rebuild_table_column_views()
[docs] def reverse(self): ''' Reverse the row order of table rows. The table is reversed in place and there are no function arguments. Examples -------- Create a table with three columns:: >>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'], ... [12,15,18]], names=('firstname','name','tel')) >>> t.pprint() firstname name tel --------- ------- --- Max Miller 12 Jo Miller 15 John Jackson 18 Reversing order:: >>> t.reverse() >>> t.pprint() firstname name tel --------- ------- --- John Jackson 18 Jo Miller 15 Max Miller 12 ''' self._data[:] = self._data[::-1].copy() self._rebuild_table_column_views()
@classmethod
[docs] def read(cls, *args, **kwargs): """ Read and parse a data table and return as a Table. This function provides the Table interface to the astropy unified I/O layer. This allows easily reading a file in many supported data formats using syntax such as:: >>> from astropy.table import Table >>> dat = Table.read('table.dat', format='ascii') >>> events = Table.read('events.fits', format='fits') The arguments and keywords (other than ``format``) provided to this function are passed through to the underlying data reader (e.g. `~astropy.io.ascii.ui.read`). """ return io_registry.read(cls, *args, **kwargs)
[docs] def write(self, *args, **kwargs): """ Write this Table object out in the specified format. This function provides the Table interface to the astropy unified I/O layer. This allows easily writing a file in many supported data formats using syntax such as:: >>> from astropy.table import Table >>> dat = Table([[1, 2], [3, 4]], names=('a', 'b')) >>> dat.write('table.dat', format='ascii') The arguments and keywords (other than ``format``) provided to this function are passed through to the underlying data reader (e.g. `~astropy.io.ascii.ui.write`). """ io_registry.write(self, *args, **kwargs)
[docs] def copy(self, copy_data=True): ''' Return a copy of the table Parameters ---------- copy_data : bool If True (the default), copy the underlying data array. Otherwise, use the same data array ''' out = self.__class__(self, copy=copy_data) # If the current table is grouped then do the same in the copy if hasattr(self, '_groups'): out._groups = groups.TableGroups(out, indices=self._groups._indices, keys=self._groups._keys) return out
def __deepcopy__(self, memo=None): return self.copy(True) def __copy__(self): return self.copy(False) def __lt__(self, other): if six.PY3: return super(Table, self).__lt__(other) else: raise TypeError("unorderable types: Table() < {0}".format(str(type(other)))) def __gt__(self, other): if six.PY3: return super(Table, self).__gt__(other) else: raise TypeError("unorderable types: Table() > {0}".format(str(type(other)))) def __le__(self, other): if six.PY3: return super(Table, self).__le__(other) else: raise TypeError("unorderable types: Table() <= {0}".format(str(type(other)))) def __ge__(self, other): if six.PY3: return super(Table, self).__ge__(other) else: raise TypeError("unorderable types: Table() >= {0}".format(str(type(other)))) def __eq__(self, other): if isinstance(other, Table): other = other._data if self.masked: if isinstance(other, np.ma.MaskedArray): result = self._data == other else: # If mask is True, then by definition the row doesn't match # because the other array is not masked. false_mask = np.zeros(1, dtype=[(n, bool) for n in self.dtype.names]) result = (self._data.data == other) & (self.mask == false_mask) else: if isinstance(other, np.ma.MaskedArray): # If mask is True, then by definition the row doesn't match # because the other array is not masked. false_mask = np.zeros(1, dtype=[(n, bool) for n in other.dtype.names]) result = (self._data == other.data) & (other.mask == false_mask) else: result = self._data == other return result def __ne__(self, other): return ~self.__eq__(other) @property
[docs] def groups(self): if not hasattr(self, '_groups'): self._groups = groups.TableGroups(self) return self._groups
[docs] def group_by(self, keys): """ Group this table by the specified ``keys`` This effectively splits the table into groups which correspond to unique values of the ``keys`` grouping object. The output is a new `GroupedTable` which contains a copy of this table but sorted by row according to ``keys``. The ``keys`` input to `group_by` can be specified in different ways: - String or list of strings corresponding to table column name(s) - Numpy array (homogeneous or structured) with same length as this table - `Table` with same length as this table Parameters ---------- keys : str, list of str, numpy array, or Table Key grouping object Returns ------- out : Table New table with groups set """ return groups.table_group_by(self, keys)

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