"""
High-level operations for numpy structured arrays.
join(): Perform a database join of two numpy ndarrays.
hstack(): Horizontally stack a list of numpy ndarrays.
vstack(): Vertically stack a list of numpy ndarrays.
Some code and inspiration taken from numpy.lib.recfunctions.join_by().
Redistribution license restrictions apply.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from ..extern import six
from ..extern.six.moves import zip as izip
from ..utils.decorators import deprecated
from itertools import chain
import collections
from collections import OrderedDict, Counter
import numpy as np
import numpy.ma as ma
from . import _np_utils
__all__ = ['join', 'hstack', 'vstack', 'TableMergeError']
DEPRECATION_MESSAGE = ('The %(func)s %(obj_type)s is deprecated and may '
'be removed in a future version. '
'Contact the Astropy developers if you need '
'continued support for this function.')
[docs]class TableMergeError(ValueError):
pass
def get_col_name_map(arrays, common_names, uniq_col_name='{col_name}_{table_name}',
table_names=None):
"""
Find the column names mapping when merging the list of structured ndarrays
``arrays``. It is assumed that col names in ``common_names`` are to be
merged into a single column while the rest will be uniquely represented
in the output. The args ``uniq_col_name`` and ``table_names`` specify
how to rename columns in case of conflicts.
Returns a dict mapping each output column name to the input(s). This takes the form
{outname : (col_name_0, col_name_1, ...), ... }. For key columns all of input names
will be present, while for the other non-key columns the value will be (col_name_0,
None, ..) or (None, col_name_1, ..) etc.
"""
col_name_map = collections.defaultdict(lambda: [None] * len(arrays))
col_name_list = []
if table_names is None:
table_names = [six.text_type(ii + 1) for ii in range(len(arrays))]
for idx, array in enumerate(arrays):
table_name = table_names[idx]
for name in array.dtype.names:
out_name = name
if name in common_names:
# If name is in the list of common_names then insert into
# the column name list, but just once.
if name not in col_name_list:
col_name_list.append(name)
else:
# If name is not one of the common column outputs, and it collides
# with the names in one of the other arrays, then rename
others = list(arrays)
others.pop(idx)
if any(name in other.dtype.names for other in others):
out_name = uniq_col_name.format(table_name=table_name, col_name=name)
col_name_list.append(out_name)
col_name_map[out_name][idx] = name
# Check for duplicate output column names
col_name_count = Counter(col_name_list)
repeated_names = [name for name, count in six.iteritems(col_name_count) if count > 1]
if repeated_names:
raise TableMergeError('Merging column names resulted in duplicates: {0}. '
'Change uniq_col_name or table_names args to fix this.'
.format(repeated_names))
# Convert col_name_map to a regular dict with tuple (immutable) values
col_name_map = OrderedDict((name, col_name_map[name]) for name in col_name_list)
return col_name_map
def get_descrs(arrays, col_name_map):
"""
Find the dtypes descrs resulting from merging the list of arrays' dtypes,
using the column name mapping ``col_name_map``.
Return a list of descrs for the output.
"""
out_descrs = []
for out_name, in_names in six.iteritems(col_name_map):
# List of input arrays that contribute to this output column
in_cols = [arr[name] for arr, name in izip(arrays, in_names) if name is not None]
# List of names of the columns that contribute to this output column.
names = [name for name in in_names if name is not None]
# Output dtype is the superset of all dtypes in in_arrays
try:
dtype = common_dtype(in_cols)
except TableMergeError as tme:
# Beautify the error message when we are trying to merge columns with incompatible
# types by including the name of the columns that originated the error.
raise TableMergeError("The '{0}' columns have incompatible types: {1}"
.format(names[0], tme._incompat_types))
# Make sure all input shapes are the same
uniq_shapes = set(col.shape[1:] for col in in_cols)
if len(uniq_shapes) != 1:
raise TableMergeError('Key columns {0!r} have different shape'.format(name))
shape = uniq_shapes.pop()
out_descrs.append((fix_column_name(out_name), dtype, shape))
return out_descrs
def common_dtype(cols):
"""
Use numpy to find the common dtype for a list of structured ndarray columns.
Only allow columns within the following fundamental numpy data types:
np.bool_, np.object_, np.number, np.character, np.void
"""
np_types = (np.bool_, np.object_, np.number, np.character, np.void)
uniq_types = set(tuple(issubclass(col.dtype.type, np_type) for np_type in np_types)
for col in cols)
if len(uniq_types) > 1:
# Embed into the exception the actual list of incompatible types.
incompat_types = [col.dtype.name for col in cols]
tme = TableMergeError('Columns have incompatible types {0}'
.format(incompat_types))
tme._incompat_types = incompat_types
raise tme
arrs = [np.empty(1, dtype=col.dtype) for col in cols]
# For string-type arrays need to explicitly fill in non-zero
# values or the final arr_common = .. step is unpredictable.
for arr in arrs:
if arr.dtype.kind in ('S', 'U'):
arr[0] = '0' * arr.itemsize
arr_common = np.array([arr[0] for arr in arrs])
return arr_common.dtype.str
@deprecated('1.0', message=DEPRECATION_MESSAGE)
def join(left, right, keys=None, join_type='inner',
uniq_col_name='{col_name}_{table_name}',
table_names=['1', '2'],
col_name_map=None):
"""
Perform a join of the left and right numpy structured array on specified keys.
Parameters
----------
left : structured array
Left side table in the join
right : structured array
Right side table in the join
keys : str or list of str
Name(s) of column(s) used to match rows of left and right tables.
Default is to use all columns which are common to both tables.
join_type : str
Join type ('inner' | 'outer' | 'left' | 'right'), default is 'inner'
uniq_col_name : str or None
String generate a unique output column name in case of a conflict.
The default is '{col_name}_{table_name}'.
table_names : list of str or None
Two-element list of table names used when generating unique output
column names. The default is ['1', '2'].
col_name_map : empty dict or None
If passed as a dict then it will be updated in-place with the
mapping of output to input column names.
"""
# Store user-provided col_name_map until the end
_col_name_map = col_name_map
if join_type not in ('inner', 'outer', 'left', 'right'):
raise ValueError("The 'join_type' argument should be in 'inner', "
"'outer', 'left' or 'right' (got '{0}' instead)".
format(join_type))
# If we have a single key, put it in a tuple
if keys is None:
keys = tuple(name for name in left.dtype.names if name in right.dtype.names)
if len(keys) == 0:
raise TableMergeError('No keys in common between left and right tables')
elif isinstance(keys, six.string_types):
keys = (keys,)
# Check the key columns
for arr, arr_label in ((left, 'Left'), (right, 'Right')):
for name in keys:
if name not in arr.dtype.names:
raise TableMergeError('{0} table does not have key column {1!r}'
.format(arr_label, name))
if hasattr(arr[name], 'mask') and np.any(arr[name].mask):
raise TableMergeError('{0} key column {1!r} has missing values'
.format(arr_label, name))
# Make sure we work with ravelled arrays
left = left.ravel()
right = right.ravel()
len_left, len_right = len(left), len(right)
left_names, right_names = left.dtype.names, right.dtype.names
# Joined array dtype as a list of descr (name, type_str, shape) tuples
col_name_map = get_col_name_map([left, right], keys, uniq_col_name, table_names)
out_descrs = get_descrs([left, right], col_name_map)
# Make an array with just the key columns
out_keys_dtype = [descr for descr in out_descrs if descr[0] in keys]
out_keys = np.empty(len_left + len_right, dtype=out_keys_dtype)
for key in keys:
out_keys[key][:len_left] = left[key]
out_keys[key][len_left:] = right[key]
idx_sort = out_keys.argsort(order=keys)
out_keys = out_keys[idx_sort]
# Get all keys
diffs = np.concatenate(([True], out_keys[1:] != out_keys[:-1], [True]))
idxs = np.flatnonzero(diffs)
# Main inner loop in Cython to compute the cartesion product
# indices for the given join type
int_join_type = {'inner': 0, 'outer': 1, 'left': 2, 'right': 3}[join_type]
masked, n_out, left_out, left_mask, right_out, right_mask = \
_np_utils.join_inner(idxs, idx_sort, len_left, int_join_type)
# If either of the inputs are masked then the output is masked
if any(isinstance(array, ma.MaskedArray) for array in (left, right)):
masked = True
if masked:
out = ma.empty(n_out, dtype=out_descrs)
else:
out = np.empty(n_out, dtype=out_descrs)
# If either input array was zero length then stub a new version
# with one row. In this case the corresponding left_out or right_out
# will contain all zeros with mask set to true. This allows the
# take(*_out) method calls to work as expected.
if len(left) == 0:
left = left.__class__(1, dtype=left.dtype)
if len(right) == 0:
right = right.__class__(1, dtype=right.dtype)
for out_name, left_right_names in six.iteritems(col_name_map):
left_name, right_name = left_right_names
if left_name and right_name: # this is a key which comes from left and right
out[out_name] = np.where(right_mask,
left[left_name].take(left_out),
right[right_name].take(right_out))
continue
elif left_name: # out_name came from the left table
name, array, array_out, array_mask = left_name, left, left_out, left_mask
elif right_name:
name, array, array_out, array_mask = right_name, right, right_out, right_mask
else:
raise TableMergeError('Unexpected column names (maybe one is ""?)')
out[out_name] = array[name].take(array_out, axis=0)
if masked:
if isinstance(array, ma.MaskedArray):
array_mask = array_mask | array[name].mask.take(array_out)
out[out_name].mask = array_mask
# If col_name_map supplied as a dict input, then update.
if isinstance(_col_name_map, collections.Mapping):
_col_name_map.update(col_name_map)
return out
def _check_for_sequence_of_structured_arrays(arrays):
err = '`arrays` arg must be a sequence (e.g. list) of structured arrays'
if not isinstance(arrays, collections.Sequence):
raise TypeError(err)
for array in arrays:
# Must be structured array
if not isinstance(array, np.ndarray) or array.dtype.names is None:
raise TypeError(err)
if len(arrays) == 0:
raise ValueError('`arrays` arg must include at least one array')
@deprecated('1.0', message=DEPRECATION_MESSAGE)
def vstack(arrays, join_type='inner', col_name_map=None):
"""
Stack structured arrays vertically (by rows)
A ``join_type`` of 'exact' (default) means that the arrays must all
have exactly the same column names (though the order can vary). If
``join_type`` is 'inner' then the intersection of common columns will
be output. A value of 'outer' means the output will have the union of
all columns, with array values being masked where no common values are
available.
Parameters
----------
arrays : list of structured arrays
Structured array(s) to stack by rows (vertically)
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'exact'
col_name_map : empty dict or None
If passed as a dict then it will be updated in-place with the
mapping of output to input column names.
Examples
--------
To stack two structured arrays by rows do::
>>> from astropy.table import np_utils
>>> t1 = np.array([(1, 2),
... (3, 4)], dtype=[(str('a'), 'i4'), (str('b'), 'i4')])
>>> t2 = np.array([(5, 6),
... (7, 8)], dtype=[(str('a'), 'i4'), (str('b'), 'i4')])
>>> np_utils.vstack([t1, t2])
array([(1, 2),
(3, 4),
(5, 6),
(7, 8)],
dtype=[('a', '<i4'), ('b', '<i4')])
"""
# Store user-provided col_name_map until the end
_col_name_map = col_name_map
# Input validation
if join_type not in ('inner', 'exact', 'outer'):
raise ValueError("`join_type` arg must be one of 'inner', 'exact' or 'outer'")
_check_for_sequence_of_structured_arrays(arrays)
# Trivial case of one input array
if len(arrays) == 1:
return arrays[0]
# Start by assuming an outer match where all names go to output
names = set(chain(*[arr.dtype.names for arr in arrays]))
col_name_map = get_col_name_map(arrays, names)
# If require_match is True then the output must have exactly the same
# number of columns as each input array
if join_type == 'exact':
for names in six.itervalues(col_name_map):
if any(x is None for x in names):
raise TableMergeError('Inconsistent columns in input arrays '
"(use 'inner' or 'outer' join_type to "
"allow non-matching columns)")
join_type = 'outer'
# For an inner join, keep only columns where all input arrays have that column
if join_type == 'inner':
col_name_map = OrderedDict((name, in_names) for name, in_names in six.iteritems(col_name_map)
if all(x is not None for x in in_names))
if len(col_name_map) == 0:
raise TableMergeError('Input arrays have no columns in common')
# If there are any output columns where one or more input arrays are missing
# then the output must be masked. If any input arrays are masked then
# output is masked.
masked = any(isinstance(arr, ma.MaskedArray) for arr in arrays)
for names in six.itervalues(col_name_map):
if any(x is None for x in names):
masked = True
break
lens = [len(arr) for arr in arrays]
n_rows = sum(lens)
out_descrs = get_descrs(arrays, col_name_map)
if masked:
# Make a masked array with all values initially masked. Note
# that setting an array value automatically unmasks it.
# See comment in hstack for heritage of this code.
out = ma.masked_array(np.zeros(n_rows, out_descrs),
mask=np.ones(n_rows, ma.make_mask_descr(out_descrs)))
else:
out = np.empty(n_rows, dtype=out_descrs)
for out_name, in_names in six.iteritems(col_name_map):
idx0 = 0
for name, array in izip(in_names, arrays):
idx1 = idx0 + len(array)
if name in array.dtype.names:
out[out_name][idx0:idx1] = array[name]
idx0 = idx1
# If col_name_map supplied as a dict input, then update.
if isinstance(_col_name_map, collections.Mapping):
_col_name_map.update(col_name_map)
return out
@deprecated('1.0', message=DEPRECATION_MESSAGE)
def hstack(arrays, join_type='exact', uniq_col_name='{col_name}_{table_name}',
table_names=None, col_name_map=None):
"""
Stack structured arrays by horizontally (by columns)
A ``join_type`` of 'exact' (default) means that the arrays must all
have exactly the same number of rows. If ``join_type`` is 'inner' then
the intersection of rows will be output. A value of 'outer' means
the output will have the union of all rows, with array values being
masked where no common values are available.
Parameters
----------
arrays : List of structured array objects
Structured arrays to stack by columns (horizontally)
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'exact'
uniq_col_name : str or None
String generate a unique output column name in case of a conflict.
The default is '{col_name}_{table_name}'.
table_names : list of str or None
Two-element list of table names used when generating unique output
column names. The default is ['1', '2', ..].
Examples
--------
To stack two arrays horizontally (by columns) do::
>>> from astropy.table import np_utils
>>> t1 = np.array([(1, 2),
... (3, 4)], dtype=[(str('a'), 'i4'), (str('b'), 'i4')])
>>> t2 = np.array([(5, 6),
... (7, 8)], dtype=[(str('c'), 'i4'), (str('d'), 'i4')])
>>> np_utils.hstack([t1, t2])
array([(1, 2, 5, 6),
(3, 4, 7, 8)],
dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<i4'), ('d', '<i4')])
"""
# Store user-provided col_name_map until the end
_col_name_map = col_name_map
# Input validation
if join_type not in ('inner', 'exact', 'outer'):
raise ValueError("join_type arg must be either 'inner', 'exact' or 'outer'")
_check_for_sequence_of_structured_arrays(arrays)
if table_names is None:
table_names = ['{0}'.format(ii + 1) for ii in range(len(arrays))]
if len(arrays) != len(table_names):
raise ValueError('Number of arrays must match number of table_names')
# Trivial case of one input arrays
if len(arrays) == 1:
return arrays[0]
col_name_map = get_col_name_map(arrays, [], uniq_col_name, table_names)
# If require_match is True then all input arrays must have the same length
arr_lens = [len(arr) for arr in arrays]
if join_type == 'exact':
if len(set(arr_lens)) > 1:
raise TableMergeError("Inconsistent number of rows in input arrays "
"(use 'inner' or 'outer' join_type to allow "
"non-matching rows)")
join_type = 'outer'
# For an inner join, keep only columns where all input arrays have that column
if join_type == 'inner':
min_arr_len = min(arr_lens)
arrays = [arr[:min_arr_len] for arr in arrays]
arr_lens = [min_arr_len for arr in arrays]
# If there are any output rows where one or more input arrays are missing
# then the output must be masked. If any input arrays are masked then
# output is masked.
masked = (any(isinstance(arr, ma.MaskedArray) for arr in arrays) or
len(set(arr_lens)) > 1)
n_rows = max(arr_lens)
out_descrs = get_descrs(arrays, col_name_map)
if masked:
# Adapted from ma.all_masked() code. Here the array is filled with
# zeros instead of empty. This avoids the bug reported here:
# https://github.com/numpy/numpy/issues/3276
out = ma.masked_array(np.zeros(n_rows, out_descrs),
mask=np.ones(n_rows, ma.make_mask_descr(out_descrs)))
else:
out = np.empty(n_rows, dtype=out_descrs)
for out_name, in_names in six.iteritems(col_name_map):
for name, array, arr_len in izip(in_names, arrays, arr_lens):
if name is not None:
out[out_name][:arr_len] = array[name]
# If col_name_map supplied as a dict input, then update.
if isinstance(_col_name_map, collections.Mapping):
_col_name_map.update(col_name_map)
return out
@deprecated('1.0', message=DEPRECATION_MESSAGE)
def get_groups(table, keys):
"""
Get groups for numpy structured array on specified keys.
Parameters
----------
table : structured array
Table to group
keys : str or list of str
Name(s) of column(s) used to match rows of table.
"""
if isinstance(keys, six.string_types):
keys = (keys,)
# Check the key columns
for name in keys:
if name not in table.dtype.names:
raise TableMergeError('Table does not have key column {1!r}'
.format(name))
if hasattr(table[name], 'mask') and np.any(table[name].mask):
raise TableMergeError('{0} key column {1!r} has missing values'
.format(name))
# Make sure we work with ravelled arrays
table = table.ravel()
len_table = len(table)
# joined array dtype as a list of descr (name, type_str, shape) tuples
col_name_map = get_col_name_map([table], keys)
out_descrs = get_descrs([table], col_name_map)
# Make an array with just the key columns
out_keys_dtype = [descr for descr in out_descrs if descr[0] in keys]
out_keys = np.empty(len_table, dtype=out_keys_dtype)
for key in keys:
out_keys[key] = table[key]
idx_sort = out_keys.argsort(order=keys)
out_keys = out_keys[idx_sort]
# Get all keys
diffs = np.concatenate(([True], out_keys[1:] != out_keys[:-1], [True]))
idxs = np.flatnonzero(diffs)
return idxs, out_keys
def fix_column_name(val):
"""
Fixes column names so that they are compatible with Numpy on
Python 2. Raises a ValueError exception if the column name
contains Unicode characters, which can not reasonably be used as a
column name.
"""
if val is not None:
try:
val = str(val)
except UnicodeEncodeError:
if not six.PY3:
raise ValueError(
"Column names must not contain Unicode characters "
"on Python 2")
raise
return val
def recarray_fromrecords(rec_list):
"""
Partial replacement for `~numpy.core.records.fromrecords` which includes
a workaround for the bug with unicode arrays described at:
https://github.com/astropy/astropy/issues/3052
This should not serve as a full replacement for the original function;
this only does enough to fulfill the needs of the table module.
"""
# Note: This is just copying what Numpy does for converting arbitrary rows
# to column arrays in the recarray module; it could be there is a better
# way
nfields = len(rec_list[0])
obj = np.array(rec_list, dtype=object)
array_list = [np.array(obj[..., i].tolist()) for i in range(nfields)]
formats = []
for obj in array_list:
formats.append(obj.dtype.str)
formats = ','.join(formats)
return np.rec.fromarrays(array_list, formats=formats)