Python numpy 模块，chararray() 实例源码
我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.chararray()。
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == asbytes('abc'))
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == asbytes('abc'))
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert sl1.base is arr
assert sl1.base.base is arr.base
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert sl2.base is arr
assert sl2.base.base is arr.base
assert arr[0, 0] == asbytes('abc')
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert sl1.base is arr
assert sl1.base.base is arr.base
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert sl2.base is arr
assert sl2.base.base is arr.base
assert arr[0, 0] == asbytes('abc')
def getHaploidIndividualSequence(self,msa,ind):
"""
Extract individuals "ind" sequence(s) from MSA dictionary
------------------------------------------------------------------------
Parameters:
- msa: dictionary
- ind: dict(indexREP,seqDEscription)
Returns:
- sequence of the individual
"""
# ind: [indID,indexREP,seqDescription]
self.appLogger.debug("getHaploidIndividualSequence(self,msa,ind)")
seqSize=len(msa["{0}_{1}".format(str(1),str(0))][str(0)]['sequence'])
fullInd=None; speciesID=None; tipID=None; tmp=None
fullInd=np.chararray(shape=(1,seqSize), itemsize=1)
speciesID=ind["spID"].strip()
locusID=ind["locID"].strip()
tipID=ind["geneID"].strip()
tmp=list(msa["{0}_{1}".format(str(speciesID), str(locusID))][str(tipID)]['sequence'])
fullInd=[item for item in tmp]
return fullInd
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == asbytes('abc'))
def lisa_sig_vals(pvals, quads, threshold):
"""
Produce Moran's I classification based of n
"""
sig = (pvals <= threshold)
lisa_sig = np.empty(len(sig), np.chararray)
for idx, val in enumerate(sig):
if val:
lisa_sig[idx] = map_quads(quads[idx])
else:
lisa_sig[idx] = 'Not significant'
return lisa_sig
def lisa_sig_vals(pvals, quads, threshold):
"""
Produce Moran's I classification based of n
"""
sig = (pvals <= threshold)
lisa_sig = np.empty(len(sig), np.chararray)
for idx, val in enumerate(sig):
if val:
lisa_sig[idx] = map_quads(quads[idx])
else:
lisa_sig[idx] = 'Not significant'
return lisa_sig
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == asbytes('abc'))
def converttochars(pixarray):
#array of chars in increasing darnkess
chars = [' ', '.','-','~','=','!',']','}','#','$','%','&','@',]
procarray = numpy.chararray(pixarray.shape)
k = 0
for row in pixarray:
j = 0
for val in row:
val = 255.0-val
i = ((len(chars)-1)*(val/255.0))
i = int(round(i))
procarray[k][j] = chars[i]
j+=1
k+=1
return procarray
#get array of pixel values
def teams_to_seat_arr(teams, seats_arr, allocated_seats):
if isinstance(teams.values()[0], int):
# plot the team dist
teams_seats_arr = np.zeros(seats_arr.shape)
else:
teams_seats_arr = np.chararray(seats_arr.shape)
for person, seat in allocated_seats.iteritems():
# get location for the seat
y, x = np.where(seats_arr == seat)
# now get the team for the
team = teams[person]
teams_seats_arr[y, x] = team
return teams_seats_arr
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == asbytes('abc'))
def test_chararray_rstrip(self,level=rlevel):
# Ticket #222
x = np.chararray((1,), 5)
x[0] = asbytes('a ')
x = x.rstrip()
assert_equal(x[0], asbytes('a'))
def setUp(self):
self.A = np.array([['abc ', '123 '],
['789 ', 'xyz ']]).view(np.chararray)
self.B = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
def setUp(self):
self.A = np.array('abc1', dtype='c').view(np.chararray)
def setUp(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '123 '],
['051', 'tuv']]).view(np.chararray)
def setUp(self):
TestComparisons.setUp(self)
self.B = np.array([['efg', '123 '],
['051', 'tuv']], np.unicode_).view(np.chararray)
def setUp(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]).view(np.chararray)
self.B = np.array([[sixu(' \u03a3 '), sixu('')],
[sixu('12345'), sixu('MixedCase')],
[sixu('123 \t 345 \0 '), sixu('UPPER')]]).view(np.chararray)
def setUp(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']],
dtype='S').view(np.chararray)
self.B = np.array([[sixu(' \u03a3 '), sixu('')],
[sixu('12345'), sixu('MixedCase')],
[sixu('123 \t 345 \0 '), sixu('UPPER')]]).view(np.chararray)
def setUp(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '456'],
['051', 'tuv']]).view(np.chararray)
def test_add(self):
AB = np.array([['abcefg', '123456'],
['789051', 'xyztuv']]).view(np.chararray)
assert_array_equal(AB, (self.A + self.B))
assert_(len((self.A + self.B)[0][0]) == 6)
def test_radd(self):
QA = np.array([['qabc', 'q123'],
['q789', 'qxyz']]).view(np.chararray)
assert_array_equal(QA, ('q' + self.A))
def test_rmul(self):
A = self.A
for r in (2, 3, 5, 7, 197):
Ar = np.array([[A[0, 0]*r, A[0, 1]*r],
[A[1, 0]*r, A[1, 1]*r]]).view(np.chararray)
assert_array_equal(Ar, (r * self.A))
for ob in [object(), 'qrs']:
try:
ob * A
except ValueError:
pass
else:
self.fail("chararray can only be multiplied by integers")
def test_mod(self):
"""Ticket #856"""
F = np.array([['%d', '%f'], ['%s', '%r']]).view(np.chararray)
C = np.array([[3, 7], [19, 1]])
FC = np.array([['3', '7.000000'],
['19', '1']]).view(np.chararray)
assert_array_equal(FC, F % C)
A = np.array([['%.3f', '%d'], ['%s', '%r']]).view(np.chararray)
A1 = np.array([['1.000', '1'], ['1', '1']]).view(np.chararray)
assert_array_equal(A1, (A % 1))
A2 = np.array([['1.000', '2'], ['3', '4']]).view(np.chararray)
assert_array_equal(A2, (A % [[1, 2], [3, 4]]))
def test_rmod(self):
assert_(("%s" % self.A) == str(self.A))
assert_(("%r" % self.A) == repr(self.A))
for ob in [42, object()]:
try:
ob % self.A
except TypeError:
pass
else:
self.fail("chararray __rmod__ should fail with "
"non-string objects")
def test_empty_indexing():
"""Regression test for ticket 1948."""
# Check that indexing a chararray with an empty list/array returns an
# empty chararray instead of a chararray with a single empty string in it.
s = np.chararray((4,))
assert_(s[[]].size == 0)
def test_chararray_rstrip(self,level=rlevel):
# Ticket #222
x = np.chararray((1,), 5)
x[0] = asbytes('a ')
x = x.rstrip()
assert_equal(x[0], asbytes('a'))
def setUp(self):
self.A = np.array([['abc ', '123 '],
['789 ', 'xyz ']]).view(np.chararray)
self.B = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
def setUp(self):
self.A = np.array('abc1', dtype='c').view(np.chararray)
def setUp(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '123 '],
['051', 'tuv']]).view(np.chararray)
def setUp(self):
TestComparisons.setUp(self)
self.B = np.array([['efg', '123 '],
['051', 'tuv']], np.unicode_).view(np.chararray)
def setUp(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]).view(np.chararray)
self.B = np.array([[sixu(' \u03a3 '), sixu('')],
[sixu('12345'), sixu('MixedCase')],
[sixu('123 \t 345 \0 '), sixu('UPPER')]]).view(np.chararray)
def setUp(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']],
dtype='S').view(np.chararray)
self.B = np.array([[sixu(' \u03a3 '), sixu('')],
[sixu('12345'), sixu('MixedCase')],
[sixu('123 \t 345 \0 '), sixu('UPPER')]]).view(np.chararray)
def setUp(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '456'],
['051', 'tuv']]).view(np.chararray)
def test_add(self):
AB = np.array([['abcefg', '123456'],
['789051', 'xyztuv']]).view(np.chararray)
assert_array_equal(AB, (self.A + self.B))
assert_(len((self.A + self.B)[0][0]) == 6)
def test_radd(self):
QA = np.array([['qabc', 'q123'],
['q789', 'qxyz']]).view(np.chararray)
assert_array_equal(QA, ('q' + self.A))
def test_rmul(self):
A = self.A
for r in (2, 3, 5, 7, 197):
Ar = np.array([[A[0, 0]*r, A[0, 1]*r],
[A[1, 0]*r, A[1, 1]*r]]).view(np.chararray)
assert_array_equal(Ar, (r * self.A))
for ob in [object(), 'qrs']:
try:
ob * A
except ValueError:
pass
else:
self.fail("chararray can only be multiplied by integers")
def test_mod(self):
"""Ticket #856"""
F = np.array([['%d', '%f'], ['%s', '%r']]).view(np.chararray)
C = np.array([[3, 7], [19, 1]])
FC = np.array([['3', '7.000000'],
['19', '1']]).view(np.chararray)
assert_array_equal(FC, F % C)
A = np.array([['%.3f', '%d'], ['%s', '%r']]).view(np.chararray)
A1 = np.array([['1.000', '1'], ['1', '1']]).view(np.chararray)
assert_array_equal(A1, (A % 1))
A2 = np.array([['1.000', '2'], ['3', '4']]).view(np.chararray)
assert_array_equal(A2, (A % [[1, 2], [3, 4]]))
def test_rmod(self):
assert_(("%s" % self.A) == str(self.A))
assert_(("%r" % self.A) == repr(self.A))
for ob in [42, object()]:
try:
ob % self.A
except TypeError:
pass
else:
self.fail("chararray __rmod__ should fail with "
"non-string objects")
def test_empty_indexing():
"""Regression test for ticket 1948."""
# Check that indexing a chararray with an empty list/array returns an
# empty chararray instead of a chararray with a single empty string in it.
s = np.chararray((4,))
assert_(s[[]].size == 0)
def __init__(self, filename):
self.filename = filename
self.xyzfile = open(self.filename, 'r')
self.offsets = []
self.n_atoms = self._n_atoms()
self.n_frames = self._n_frames()
self.box_size = np.empty([self.n_frames, 3], dtype=np.float)
self.atom_names = np.chararray([self.n_frames, self.n_atoms, 1], itemsize=3)
self.coords = np.empty([self.n_frames, self.n_atoms, 3], dtype=np.float)
self.read_all_frames()
def echantillonnage (fichier):
data = np.genfromtxt(fichier,delimiter=',')
clen, rlen = data.shape
new_tab=np.chararray([clen,rlen],itemsize=25)
for c in range(0,rlen):
petit=str(c)+'_1'
moyen=str(c)+'_2'
grand=str(c)+'_3'
#----------------- sur le rang --------------------------
data_sort=np.sort(data[:,c])
x=len(data_sort)/3.
tiers=data_sort[int(x)]
deux_tiers=data_sort[2*int(x)]
#-----------------------------------------------------------
for l in range(0,clen):
if data[l,c]<tiers:
new_tab[l,c]=petit
elif data[l,c]<deux_tiers:
new_tab[l,c]=moyen
else :
new_tab[l,c]=grand
return new_tab
def echantillonnage_glucose (fichier):
data = np.genfromtxt(fichier,delimiter=',')
clen, rlen = data.shape
rlen=rlen+1
diab= np.genfromtxt('glucose_a_traiter.csv',delimiter=',')
new_tab=np.chararray([clen,rlen],itemsize=25)
for c in range(0,rlen-1):
petit=str(c)+'_1'
moyen=str(c)+'_2'
grand=str(c)+'_3'
#----------------- sur le rang --------------------------
data_sort=np.sort(data[:,c])
x=len(data_sort)/3.
tiers=data_sort[int(x)]
deux_tiers=data_sort[2*int(x)]
#-----------------------------------------------------------
for l in range(0,clen):
if data[l,c]<tiers:
new_tab[l,c]=petit
elif data[l,c]<deux_tiers:
new_tab[l,c]=moyen
else :
new_tab[l,c]=grand
for l in range (0,clen):
if diab[0,l]<6.5:
if diab[1,l]==0 and diab[2,l]==0 :
new_tab[l,-1]=petit
else :
new_tab[l,-1]=moyen
else : new_tab[l,-1]=grand
return new_tab
def fill_matrix(matrix, width, value='n/a'):
if matrix.shape[0] < width:
nraters = matrix.shape[1]
nas = np.chararray((1, nraters), itemsize=len(value))
nas[:] = value
matrix = np.vstack(tuple([matrix] + [nas] * (width - matrix.shape[0])))
return matrix
def one_hot_encoding_sequences(seqs):
CHARS = 'acgt'
CHARS_COUNT = len(CHARS)
maxlen = max(map(len, seqs))
res = numpy.zeros((len(seqs), CHARS_COUNT * maxlen), dtype=numpy.uint8)
for si, seq in enumerate(seqs):
seqlen = len(seq)
arr = numpy.chararray((seqlen,), buffer=seq)
for ii, char in enumerate(CHARS):
res[si][ii*seqlen:(ii+1)*seqlen][arr == char] = 1
return res
def one_hot_encoding_sequences(seqs, sequenceLength):
"""
input: genome sequences
output: one_hot encoded sequence array
"""
le = preprocessing.LabelEncoder()
one_hot_sequences = []
le.fit_transform(['a','t','g','c'])
for si, seq in enumerate(seqs):
seqlen = len(seq)
arr = np.chararray((seqlen,), buffer=seq)
a = le.transform(arr)
a = np.array(a)
b = np.zeros((len(a), 4))
b[np.arange(len(a)), a] = 1
#b = np.array(b)
b = b.transpose()
if b.shape[1] == sequenceLength:
one_hot_sequences.append(b)
return one_hot_sequences
#print one_hot_encoding_sequences(['atgctgc','gctatgc'])
#print numpy.arange(8).reshape((4,8/4), order = 'F')
def write_kmers(kmers, filename):
char_kmers = np.chararray(kmers.shape)
for _char, _int in six.iteritems(ALPHABET):
char_kmers[kmers == _int] = _char
with open(filename, 'w') as fh:
for i, kmer in enumerate(char_kmers):
print('>%d' % i, file=fh)
print(kmer.tostring().decode(), file=fh)
def test_chararray_rstrip(self,level=rlevel):
# Ticket #222
x = np.chararray((1,), 5)
x[0] = asbytes('a ')
x = x.rstrip()
assert_equal(x[0], asbytes('a'))
def setUp(self):
self.A = np.array([['abc ', '123 '],
['789 ', 'xyz ']]).view(np.chararray)
self.B = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)