Python numpy 模块，string_() 实例源码
我们从Python开源项目中，提取了以下44个代码示例，用于说明如何使用numpy.string_()。
def categorize_columns(self, df):
"""Categorize columns of dataframe by data type
:param df: input (pandas) data frame
"""
# check presence and data type of requested columns
# sort columns into numerical, timestamp and category based
for c in self.columns:
for col in c:
if col not in df.columns:
raise KeyError('column "{0:s}" not in dataframe "{1:s}"'.format(col, self.read_key))
dt = self.get_data_type(df, col)
if col not in self.var_dtype:
self.var_dtype[col] = dt.type
if (self.var_dtype[col] is np.string_) or (self.var_dtype[col] is np.object_):
self.var_dtype[col] = str
if not any(dt in types for types in (STRING_SUBSTR, NUMERIC_SUBSTR, TIME_SUBSTR)):
raise TypeError('cannot process column "{0:s}" of data type "{1:s}"'.format(col, str(dt)))
is_number = isinstance(dt.type(), np.number)
is_timestamp = isinstance(dt.type(), np.datetime64)
colset = self.num_cols if is_number else self.dt_cols if is_timestamp else self.str_cols
if col not in colset:
colset.append(col)
self.log().debug('Data type of column "%s" is "%s"', col, self.var_dtype[col])
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def test_from_string_array(self):
A = np.array(asbytes_nested([['abc', 'foo'],
['long ', '0123456789']]))
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, asbytes('#'))
assert_array_equal(C.startswith(asbytes('#')), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(asbytes('#'))))
C = np.char.ljust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([['FOO ', 'FOO '],
['FOO ', 'FOO ']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lstrip(self):
tgt = asbytes_nested([['abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']])
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = asbytes_nested([[' abc', ''],
['2345', 'ixedCase'],
['23 \t 345 \x00', 'UPPER']])
assert_array_equal(self.A.lstrip(asbytes_nested(['1', 'M'])), tgt)
tgt = [[sixu('\u03a3 '), ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = asbytes_nested([[' abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_array_equal(self.A.rstrip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['1234', 'MixedCase'],
['123 \t 345 \x00', 'UPP']
])
assert_array_equal(self.A.rstrip(asbytes_nested(['5', 'ER'])), tgt)
tgt = [[sixu(' \u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = asbytes_nested([['abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['234', 'ixedCas'],
['23 \t 345 \x00', 'UPP']])
assert_array_equal(self.A.strip(asbytes_nested(['15', 'EReM'])), tgt)
tgt = [[sixu('\u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def from_array(self, data, labels):
"""Fill matrix from 2 dimensional array
Args:
data (np.array): 2 dimensional square array with scores
labels (list): List of labels for each column and row index
"""
labels = [np.string_(d) for d in labels]
self.labels = self.h5file.create_carray('/', 'labels', obj=labels, filters=self.filters)
self.h5file.flush()
self.build_label_cache()
nr_frags = len(labels)
self.scores = self.h5file.create_carray('/', 'scores', atom=tables.UInt16Atom(),
shape=(nr_frags, nr_frags), chunkshape=(1, nr_frags),
filters=self.filters)
self.scores[0:nr_frags, 0:nr_frags] = (data * self.score_precision).astype('uint16')
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def test_from_string_array(self):
A = np.array(asbytes_nested([['abc', 'foo'],
['long ', '0123456789']]))
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, asbytes('#'))
assert_array_equal(C.startswith(asbytes('#')), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(asbytes('#'))))
C = np.char.ljust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([['FOO ', 'FOO '],
['FOO ', 'FOO ']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lstrip(self):
tgt = asbytes_nested([['abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']])
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = asbytes_nested([[' abc', ''],
['2345', 'ixedCase'],
['23 \t 345 \x00', 'UPPER']])
assert_array_equal(self.A.lstrip(asbytes_nested(['1', 'M'])), tgt)
tgt = [[sixu('\u03a3 '), ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = asbytes_nested([[' abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_array_equal(self.A.rstrip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['1234', 'MixedCase'],
['123 \t 345 \x00', 'UPP']
])
assert_array_equal(self.A.rstrip(asbytes_nested(['5', 'ER'])), tgt)
tgt = [[sixu(' \u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = asbytes_nested([['abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['234', 'ixedCas'],
['23 \t 345 \x00', 'UPP']])
assert_array_equal(self.A.strip(asbytes_nested(['15', 'EReM'])), tgt)
tgt = [[sixu('\u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def fen2state(fen):
'''
transfer the fen string to chessboard
fen: fen string
return: state of the chessboard
'''
fenstrlist = fen.split()
cstate = chessboradstate()
cstate.state = np.zeros([10, 9], np.string_)
fenstr1st = fenstrlist[0].split('/')
for i in range(len(fenstr1st)):
current = 0
for j in range(len(fenstr1st[i])):
if fenstr1st[i][j].isdigit():
num = int(fenstr1st[i][j])
for k in range(num):
cstate.state[i][current+k] = ' '
current += num
else:
cstate.state[i][current] = fenstr1st[i][j]
current += 1
cstate.turn = fenstrlist[1]
cstate.roundcnt = int(fenstrlist[5])
return cstate
def move(cstate, move):
'''
move the chess according to the move action
state: the current chessborad, numpy.array[10][9], dtype=string_
move: the action to move, string format as:'D5-E5'
'''
src = []
des = []
src.append(9 - int(move[1]))
src.append(ord(move[0]) - ord('A'))
des.append(9 - int(move[4]))
des.append(ord(move[3]) - ord('A'))
# print src, des
chess = cstate.state[src[0]][src[1]]
cstate.state[src[0]][src[1]] = ' '
cstate.state[des[0]][des[1]] = chess
cstate.roundcnt += 1
if cstate.turn == 'b':
cstate.turn = 'w'
else:
cstate.turn = 'b'
def saveSparseHDF5(matrix, prefix, fname):
""" matrix: sparse matrix
prefix: prefix of dataset
fname : name of h5py file where matrix will be saved
"""
assert matrix.__class__==csr_matrix or matrix.__class__==csc_matrix,'Expecting csc/csr'
with h5py.File(fname,mode='a') as f:
for info in ['data','indices','indptr','shape']:
key = '%s_%s'%(prefix,info)
try:
data = getattr(matrix, info)
except:
assert False,'Expecting attribute '+info+' in matrix'
"""
For empty arrays, data, indicies and indptr will be []
To deal w/ this use np.nan in its place
"""
if len(data)==0:
f.create_dataset(key,data=np.array([np.nan]))
else:
f.create_dataset(key,data= data)
key = prefix+'_type'
val = matrix.__class__.__name__
f.attrs[key] = np.string_(val)
def test_select_dtypes_str_raises(self):
df = DataFrame({'a': list('abc'),
'g': list(u('abc')),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.date_range('now', periods=3).values})
string_dtypes = set((str, 'str', np.string_, 'S1',
'unicode', np.unicode_, 'U1'))
try:
string_dtypes.add(unicode)
except NameError:
pass
for dt in string_dtypes:
with tm.assertRaisesRegexp(TypeError,
'string dtypes are not allowed'):
df.select_dtypes(include=[dt])
with tm.assertRaisesRegexp(TypeError,
'string dtypes are not allowed'):
df.select_dtypes(exclude=[dt])
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def test_from_string_array(self):
A = np.array(asbytes_nested([['abc', 'foo'],
['long ', '0123456789']]))
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_join(self):
if sys.version_info[0] >= 3:
# NOTE: list(b'123') == [49, 50, 51]
# so that b','.join(b'123') results to an error on Py3
A0 = self.A.decode('ascii')
else:
A0 = self.A
A = np.char.join([',', '#'], A0)
if sys.version_info[0] >= 3:
assert_(issubclass(A.dtype.type, np.unicode_))
else:
assert_(issubclass(A.dtype.type, np.string_))
tgt = np.array([[' ,a,b,c, ', ''],
['1,2,3,4,5', 'M#i#x#e#d#C#a#s#e'],
['1,2,3, ,\t, ,3,4,5, ,\x00, ', 'U#P#P#E#R']])
assert_array_equal(np.char.join([',', '#'], A0), tgt)
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, asbytes('#'))
assert_array_equal(C.startswith(asbytes('#')), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(asbytes('#'))))
C = np.char.ljust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([['FOO ', 'FOO '],
['FOO ', 'FOO ']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_rjust(self):
assert_(issubclass(self.A.rjust(10).dtype.type, np.string_))
C = self.A.rjust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.rjust(20, asbytes('#'))
assert_(np.all(C.startswith(asbytes('#'))))
assert_array_equal(C.endswith(asbytes('#')),
[[False, True], [False, False], [False, False]])
C = np.char.rjust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([[' FOO', ' FOO'],
[' FOO', ' FOO']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = asbytes_nested([[' abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_array_equal(self.A.rstrip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['1234', 'MixedCase'],
['123 \t 345 \x00', 'UPP']
])
assert_array_equal(self.A.rstrip(asbytes_nested(['5', 'ER'])), tgt)
tgt = [[sixu(' \u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def test_from_string_array(self):
A = np.array(asbytes_nested([['abc', 'foo'],
['long ', '0123456789']]))
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, asbytes('#'))
assert_array_equal(C.startswith(asbytes('#')), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(asbytes('#'))))
C = np.char.ljust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([['FOO ', 'FOO '],
['FOO ', 'FOO ']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lstrip(self):
tgt = asbytes_nested([['abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']])
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = asbytes_nested([[' abc', ''],
['2345', 'ixedCase'],
['23 \t 345 \x00', 'UPPER']])
assert_array_equal(self.A.lstrip(asbytes_nested(['1', 'M'])), tgt)
tgt = [[sixu('\u03a3 '), ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = asbytes_nested([[' abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_array_equal(self.A.rstrip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['1234', 'MixedCase'],
['123 \t 345 \x00', 'UPP']
])
assert_array_equal(self.A.rstrip(asbytes_nested(['5', 'ER'])), tgt)
tgt = [[sixu(' \u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = asbytes_nested([['abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['234', 'ixedCas'],
['23 \t 345 \x00', 'UPP']])
assert_array_equal(self.A.strip(asbytes_nested(['15', 'EReM'])), tgt)
tgt = [[sixu('\u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def setup_openpmd_meshes_group( self, dset ) :
"""
Set the attributes that are specific to the mesh path
Parameter
---------
dset : an h5py.Group object that contains all the mesh quantities
"""
# Field Solver
dset.attrs["fieldSolver"] = np.string_("PSATD")
# Field boundary
dset.attrs["fieldBoundary"] = np.array([
np.string_("reflecting"), np.string_("reflecting"),
np.string_("reflecting"), np.string_("reflecting") ])
# Particle boundary
dset.attrs["particleBoundary"] = np.array([
np.string_("absorbing"), np.string_("absorbing"),
np.string_("absorbing"), np.string_("absorbing") ])
# Current Smoothing
dset.attrs["currentSmoothing"] = np.string_("Binomial")
dset.attrs["currentSmoothingParameters"] = \
np.string_("period=1;numPasses=1;compensator=false")
# Charge correction
dset.attrs["chargeCorrection"] = np.string_("spectral")
dset.attrs["chargeCorrectionParameters"] = np.string_("period=1")
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def test_from_string_array(self):
A = np.array(asbytes_nested([['abc', 'foo'],
['long ', '0123456789']]))
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, asbytes('#'))
assert_array_equal(C.startswith(asbytes('#')), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(asbytes('#'))))
C = np.char.ljust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([['FOO ', 'FOO '],
['FOO ', 'FOO ']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lstrip(self):
tgt = asbytes_nested([['abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']])
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = asbytes_nested([[' abc', ''],
['2345', 'ixedCase'],
['23 \t 345 \x00', 'UPPER']])
assert_array_equal(self.A.lstrip(asbytes_nested(['1', 'M'])), tgt)
tgt = [[sixu('\u03a3 '), ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = asbytes_nested([[' abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_array_equal(self.A.rstrip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['1234', 'MixedCase'],
['123 \t 345 \x00', 'UPP']
])
assert_array_equal(self.A.rstrip(asbytes_nested(['5', 'ER'])), tgt)
tgt = [[sixu(' \u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = asbytes_nested([['abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['234', 'ixedCas'],
['23 \t 345 \x00', 'UPP']])
assert_array_equal(self.A.strip(asbytes_nested(['15', 'EReM'])), tgt)
tgt = [[sixu('\u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def test_from_string_array(self):
A = np.array(asbytes_nested([['abc', 'foo'],
['long ', '0123456789']]))
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, asbytes('#'))
assert_array_equal(C.startswith(asbytes('#')), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(asbytes('#'))))
C = np.char.ljust(asbytes('FOO'), [[10, 20], [15, 8]])
tgt = asbytes_nested([['FOO ', 'FOO '],
['FOO ', 'FOO ']])
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lstrip(self):
tgt = asbytes_nested([['abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']])
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = asbytes_nested([[' abc', ''],
['2345', 'ixedCase'],
['23 \t 345 \x00', 'UPPER']])
assert_array_equal(self.A.lstrip(asbytes_nested(['1', 'M'])), tgt)
tgt = [[sixu('\u03a3 '), ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = asbytes_nested([[' abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_array_equal(self.A.rstrip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['1234', 'MixedCase'],
['123 \t 345 \x00', 'UPP']
])
assert_array_equal(self.A.rstrip(asbytes_nested(['5', 'ER'])), tgt)
tgt = [[sixu(' \u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = asbytes_nested([['abc', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']])
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = asbytes_nested([[' abc ', ''],
['234', 'ixedCas'],
['23 \t 345 \x00', 'UPP']])
assert_array_equal(self.A.strip(asbytes_nested(['15', 'EReM'])), tgt)
tgt = [[sixu('\u03a3'), ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def preprocess_writing(key, value):
if isinstance(value, dict):
# hack for storing dicts
value = np.array([str(value)])
else:
value = np.array(value)
if value.ndim == 0:
value = np.array([value])
# some output about the data to write
logg.m(key, type(value),
value.dtype, value.dtype.kind, value.shape,
v=6)
# make sure string format is chosen correctly
if value.dtype.kind == 'U':
value = value.astype(np.string_)
return key, value