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 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 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 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_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)
