The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def test_leak_in_structured_dtype_comparison(self):
# gh-6250
recordtype = np.dtype([('a', np.float64),
('b', np.int32),
('d', (np.str, 5))])
# Simple case
a = np.zeros(2, dtype=recordtype)
for i in range(100):
a == a
assert_(sys.getrefcount(a) < 10)
# The case in the bug report.
before = sys.getrefcount(a)
u, v = a[0], a[1]
u == v
del u, v
gc.collect()
after = sys.getrefcount(a)
assert_equal(before, after) Example 2
def add_node_attribute(inFile, pedgraph, animal=1, atCol=4, atName="attr1"):
"""
inFile - pedigree as .txt file
pedgraph - Pedigree as a networkX graph object
animal - column for the animal ID
atCol - column for the attribute
atName - name for the attribute
"""
ped_df = pd.read_table(inFile, header=None, delim_whitespace=True)
#print ped_df
dic_ped = dict(zip(ped_df[animal - 1], ped_df[atCol - 1]))
#print dic_ped
correct_dic_ped = {str(k):int(v) for k,v in dic_ped.items()}
#print correct_dic_ped
for node, value in dic_ped.items():
pedgraph.node[str(node)]["EBV"] = value
return correct_dic_ped Example 3
def add_ebv_attribute(inFile, pedgraph, animal=1, atCol=4, atName="attr1"):
"""
inFile - pedigree as .txt file
pedgraph - Pedigree as a networkX graph object
animal - column for the animal ID
atCol - column for the attribute
atName - name for the attribute
"""
ped_df = pd.read_table(inFile, header=None, delim_whitespace=True)
#print ped_df
dic_ped = dict(zip(ped_df[animal - 1], ped_df[atCol - 1]))
#print dic_ped
correct_dic_ped = {str(k):int(-v) for k,v in dic_ped.items()}
#print correct_dic_ped
for node, value in dic_ped.items():
pedgraph.node[str(node)]["EBV"] = value
return correct_dic_ped Example 4
def __init__(self,filename='word2vec.pklz'):
"""
Py Word2vec??
"""
super().__init__()
self.name='word2vec'
self.load(filename)
self.vocab_cnt=len(self)
self.dims=self[list(self.keys())[0]].shape[0]
print('???:' + str(self.vocab_cnt))
print('???:' + str(self.dims))
self.word2idx= {w: i for i, w in enumerate(self.keys())}
self.idx2word= {i: w for i, w in enumerate(self.keys())}
self._matrix =np.array(list(self.values()))
print(self._matrix.shape) Example 5
def get_antonyms(self,wordA:str, topk:int=10,ispositive:bool=True):
seed=[['??','??'],['??','??'],['??','??'],['??','??'],['??','??']]
proposal={}
for pair in seed:
if ispositive:
result=self.analogy(pair[0],pair[1],wordA,topk)
print(w2v.find_nearest_word((self[pair[0]] + self[pair[1]]) / 2, 3))
else:
result = self.analogy(pair[1], pair[0], wordA, topk)
print(w2v.find_nearest_word((self[pair[0]] + self[pair[1]]) / 2, 3))
for item in result:
term_products = np.argwhere(self[wordA] * self[item[0]] < 0)
#print(item[0] + ':' +wordA + str(term_products))
#print(item[0] + ':' +wordA+'('+str(pair)+') '+ str(len(term_products)))
if len(term_products)>=self.dims/4:
if item[0] not in proposal:
proposal[item[0]] = item[1]
elif item[1]> proposal[item[0]]:
proposal[item[0]] +=item[1]
for k,v in proposal.items():
proposal[k]=v/len(seed)
sortitems=sorted(proposal.items(), key=lambda d: d[1],reverse=True)
return [sortitems[i] for i in range(min(topk,len(sortitems)))] Example 6
def test_leak_in_structured_dtype_comparison(self):
# gh-6250
recordtype = np.dtype([('a', np.float64),
('b', np.int32),
('d', (np.str, 5))])
# Simple case
a = np.zeros(2, dtype=recordtype)
for i in range(100):
a == a
assert_(sys.getrefcount(a) < 10)
# The case in the bug report.
before = sys.getrefcount(a)
u, v = a[0], a[1]
u == v
del u, v
gc.collect()
after = sys.getrefcount(a)
assert_equal(before, after) Example 7
def __init__(self):
#??????????????data????????
current = os.getcwd()
folder = os.path.join(current, 'data')
if os.path.exists(folder) == False:
os.mkdir(folder)
os.chdir(folder)
#??tushare?????A???
#df0=ts.get_stock_basics()
df0=pd.read_csv('bases.csv',dtype={'code':np.str})
self.bases=df0.sort_values('timeToMarket',ascending=False)
#????? ????????????
self.cxg=self.bases[(self.bases['timeToMarket']>20170101) & (self.bases['timeToMarket']<20170401)]
self.codes= self.cxg['code'].values Example 8
def getBigDeal(self, code,vol):
df = ts.get_today_ticks(code)
t= df[df['volume']>vol]
s=df[df['amount']>100000000]
print '\n'
if t.size!=0:
print "Big volume"
print self.base[self.base['code']==str(code)]['name'].values[0]
print t
if s.size!=0:
print "Big amount: "
print self.base[self.base['code']==str(code)]['name'].values[0]
print s
r=df[df['volume']>vol*10]
if r.size!=0:
print "Super amount:"
print self.base[self.base['code']==str(code)]['name'].values[0]
print r Example 9
def years(self):
df_list=[]
k=[str(i) for i in range(1,13)]
print k
j=[i for i in range(1,13)]
result=[]
for i in range(1,13):
filename='2016-%s.xls' %str(i).zfill(2)
#print filename
t=pd.read_table(filename,encoding='gbk',dtype={u'????':np.str})
fee=t[u'???'].sum()+t[u'???'].sum()+t[u'????'].sum()
print i," fee: "
print fee
df_list.append(t)
result.append(fee)
df=pd.concat(df_list,keys=k)
#print df
#df.to_excel('2016_delivery_order.xls')
self.caculation(df)
plt.plot(j,result)
plt.show() Example 10
def getTotal():
path=os.path.join(os.getcwd(),'data')
os.chdir(path)
all=pd.read_csv('bases.csv',dtype={'code':np.str})
#print all
all_code=all['code'].values
#print all_code
lists=[]
for i in all_code:
df=ts.get_k_data(i,start='2017-07-17',end='2017-07-17')
lists.append(df)
all_df=pd.DataFrame(lists)
print all_df
all_df.to_csv('2017-all.csv',encoding='gbk')
all_df.to_excel('2017-excel.xls') Example 11
def add_code_redis():
rds = redis.StrictRedis(REDIS_HOST, 6379, db=0)
rds_1 = redis.StrictRedis(REDIS_HOST, 6379, db=1)
df = ts.get_stock_basics()
df = df.reset_index()
# ?????
if rds.dbsize() != 0:
rds.flushdb()
if rds_1.dbsize() != 0:
rds_1.flushdb()
for i in range(len(df)):
code, name, timeToMarket = df.loc[i]['code'], df.loc[i]['name'], df.loc[i]['timeToMarket']
# print str(timeToMarket)
d = dict({code: ':'.join([name, str(timeToMarket)])})
# print d
rds.set(code, name)
rds_1.lpush('codes', d) Example 12
def read_cufflinks(sample_path, isoforms=False):
''' Function for reading a Cufflinks quantification result.
Returns
-------
A pandas.Series with the expression values in the sample.
'''
if isoforms:
quant_file = sample_path + '/isoforms.fpkm_tracking'
else:
quant_file = sample_path + '/genes.fpkm_tracking'
df = pd.read_table(quant_file, engine='c',
usecols=['tracking_id', 'FPKM'],
index_col=0,
dtype={'tracking_id': np.str, 'FPKM': np.float64})
df['tracking_id'] = df.index
df = df.groupby('tracking_id').sum()
df['TPM'] = df['FPKM'] / df['FPKM'].sum() * 1e6
df = df.rename(columns={'tracking_id': 'target_id'})
return df['TPM'] Example 13
def tensor2state(tensor_frd, tensor_emy):
'''
transform tensor 2 state
tensor_frd, tensor_emy ndarray [9,10,16]
return state ndarray [10,9]
'''
assert tensor_frd.shape == tensor_emy.shape
state = np.zeros((10,9), dtype=np.str)
chessfrdplayer = 'KAABBNNRRCCPPPPP'
chessemyplayer = 'kaabbnnrrccppppp'
for i in range(tensor_frd.shape[0]):
for j in range(tensor_frd.shape[1]):
if ~(tensor_frd[i][j] == 0).all():
layer = np.argmax(tensor_frd[i][j])
state[j][i] = chessfrdplayer[layer]
elif ~(tensor_emy[i][j] == 0).all():
layer = np.argmax(tensor_emy[i][j])
state[j][i] = chessemyplayer[layer]
else:
state[j][i] = ' '
return state Example 14
def tensor2state(tensor_frd, tensor_emy):
'''
transform tensor 2 state
tensor_frd, tensor_emy ndarray [9,10,16]
return state ndarray [10,9]
'''
assert tensor_frd.shape == tensor_emy.shape
state = np.zeros((10,9), dtype=np.str)
chessfrdplayer = 'KAABBNNRRCCPPPPP'
chessemyplayer = 'kaabbnnrrccppppp'
for i in range(tensor_frd.shape[0]):
for j in range(tensor_frd.shape[1]):
if ~(tensor_frd[i][j] == 0).all():
layer = np.argmax(tensor_frd[i][j])
state[j][i] = chessfrdplayer[layer]
elif ~(tensor_emy[i][j] == 0).all():
layer = np.argmax(tensor_emy[i][j])
state[j][i] = chessemyplayer[layer]
else:
state[j][i] = ' '
return state Example 15
def tensor2state(tensor_frd, tensor_emy):
'''
transform tensor 2 state
tensor_frd, tensor_emy ndarray [9,10,16]
return state ndarray [10,9]
'''
assert tensor_frd.shape == tensor_emy.shape
state = np.zeros((10,9), dtype=np.str)
chessfrdplayer = 'KAABBNNRRCCPPPPP'
chessemyplayer = 'kaabbnnrrccppppp'
for i in range(tensor_frd.shape[0]):
for j in range(tensor_frd.shape[1]):
if ~(tensor_frd[i][j] == 0).all():
layer = np.argmax(tensor_frd[i][j])
state[j][i] = chessfrdplayer[layer]
elif ~(tensor_emy[i][j] == 0).all():
layer = np.argmax(tensor_emy[i][j])
state[j][i] = chessemyplayer[layer]
else:
state[j][i] = ' '
return state Example 16
def tensor2state(tensor_frd, tensor_emy):
'''
transform tensor 2 state
tensor_frd, tensor_emy ndarray [9,10,16]
return state ndarray [10,9]
'''
assert tensor_frd.shape == tensor_emy.shape
state = np.zeros((10,9), dtype=np.str)
chessfrdplayer = 'KAABBNNRRCCPPPPP'
chessemyplayer = 'kaabbnnrrccppppp'
for i in range(tensor_frd.shape[0]):
for j in range(tensor_frd.shape[1]):
if ~(tensor_frd[i][j] == 0).all():
layer = np.argmax(tensor_frd[i][j])
state[j][i] = chessfrdplayer[layer]
elif ~(tensor_emy[i][j] == 0).all():
layer = np.argmax(tensor_emy[i][j])
state[j][i] = chessemyplayer[layer]
else:
state[j][i] = ' '
return state Example 17
def tensor2state(tensor_frd, tensor_emy):
'''
transform tensor 2 state
tensor_frd, tensor_emy ndarray [9,10,16]
return state ndarray [10,9]
'''
assert tensor_frd.shape == tensor_emy.shape
state = np.zeros((10,9), dtype=np.str)
chessfrdplayer = 'KAABBNNRRCCPPPPP'
chessemyplayer = 'kaabbnnrrccppppp'
for i in range(tensor_frd.shape[0]):
for j in range(tensor_frd.shape[1]):
if ~(tensor_frd[i][j] == 0).all():
layer = np.argmax(tensor_frd[i][j])
state[j][i] = chessfrdplayer[layer]
elif ~(tensor_emy[i][j] == 0).all():
layer = np.argmax(tensor_emy[i][j])
state[j][i] = chessemyplayer[layer]
else:
state[j][i] = ' '
return state Example 18
def _get_value(self, var: str):
"""
Utility method to return the value of the specified variable for this instance in the backing xarray data set.
Parameters
----------
var: str
Name of the variable. There should be no reason to pass a str directly. Instead, the names defined in the
_DataVar class should be used.
Returns
-------
depending on variable
The value of the specified variable for this instance
"""
return self._data[var][dict(instance=self._instance)] Example 19
def contains(self,
filename: str,
chunk_nr: int) -> bool:
"""
Check whether this data set contains an instance with the specified filename and chunk number.
Parameters
----------
filename: str
The filename of the instance
chunk_nr: int
The chunk number of the instance
Returns
-------
bool
True, if this data set contains an instance with the specified filename and chunk number, False otherwise
"""
if filename not in self._data[_DataVar.FILENAME].values:
return False
instances_with_filename = self._data.where(self._data[_DataVar.FILENAME] == filename)
return chunk_nr in instances_with_filename[_DataVar.CHUNK_NR].values Example 20
def labels_nominal(self) -> np.ndarray:
"""
Returns the nominal labels of all instances in this data set as a NumPy array.
The order of labels in the returned array matches the order in which instances are stored in this data set.
Returns
-------
numpy.ndarray
The nominal labels of the instances in this data set
Raises
------
AttributeError
If the data set is not fully labeled
"""
if not self.is_fully_labeled:
raise AttributeError("data set does not have label information")
return self._data[_DataVar.LABEL_NOMINAL].values.astype(np.str) Example 21
def save(self, path: Path):
"""
Writes this data set to the specified path.
Any directories in the path that do not exist are automatically created.
Parameters
----------
path: pathlib.Path
"""
if not path.parent.exists():
path.parent.mkdir(parents=True)
self.log.info("writing data set as netCDF4 to %s", path)
self._data.to_netcdf(path=str(path),
engine="netcdf4",
format="NETCDF4") Example 22
def test_leak_in_structured_dtype_comparison(self):
# gh-6250
recordtype = np.dtype([('a', np.float64),
('b', np.int32),
('d', (np.str, 5))])
# Simple case
a = np.zeros(2, dtype=recordtype)
for i in range(100):
a == a
assert_(sys.getrefcount(a) < 10)
# The case in the bug report.
before = sys.getrefcount(a)
u, v = a[0], a[1]
u == v
del u, v
gc.collect()
after = sys.getrefcount(a)
assert_equal(before, after) Example 23
def test_leak_in_structured_dtype_comparison(self):
# gh-6250
recordtype = np.dtype([('a', np.float64),
('b', np.int32),
('d', (np.str, 5))])
# Simple case
a = np.zeros(2, dtype=recordtype)
for i in range(100):
a == a
assert_(sys.getrefcount(a) < 10)
# The case in the bug report.
before = sys.getrefcount(a)
u, v = a[0], a[1]
u == v
del u, v
gc.collect()
after = sys.getrefcount(a)
assert_equal(before, after) Example 24
def _read_xz(self, filepath):
dtype = {
'applicant_id': np.str,
'batch_number': np.str,
'cnpj_cpf': np.str,
'congressperson_document': np.str,
'congressperson_id': np.str,
'document_id': np.str,
'document_number': np.str,
'document_type': np.str,
'leg_of_the_trip': np.str,
'passenger': np.str,
'reimbursement_number': np.str,
'subquota_group_description': np.str,
'subquota_group_id': np.str,
'subquota_number': np.str,
'term_id': np.str,
}
return pd.read_csv(filepath, dtype=dtype) Example 25
def read_csv(self, name):
filepath = os.path.join(self.path, name)
log.info('Loading {}…'.format(name))
dtype = {
'applicant_id': np.str,
'batch_number': np.str,
'cnpj_cpf': np.str,
'congressperson_document': np.str,
'congressperson_id': np.str,
'document_id': np.str,
'document_number': np.str,
'document_type': np.str,
'leg_of_the_trip': np.str,
'passenger': np.str,
'reimbursement_number': np.str,
'subquota_group_description': np.str,
'subquota_group_id': np.str,
'subquota_number': np.str,
'term_id': np.str,
}
return pd.read_csv(filepath, dtype=dtype) Example 26
def pcaCreate(image_files,dir,name_num, dir_list):
image_list = []
new_file_name = dir
save_dir = dir_list + new_file_name
save_dir_tt = save_dir + "\\"
for image_file in image_files:
image_list.append(misc.imread(image_file))
for image in image_list:
img = np.asarray(image, dtype='float32')
img = img / 255.
img_size = img.size / 3
img1 = img.reshape(img_size, 3)
img1 = np.transpose(img1)
img_cov = np.cov([img1[0], img1[1], img1[2]])
lamda, p = np.linalg.eig(img_cov)
p = np.transpose(p)
alpha1 = random.normalvariate(0, 0.3)
alpha2 = random.normalvariate(0, 0.3)
alpha3 = random.normalvariate(0, 0.3)
v = np.transpose((alpha1 * lamda[0], alpha2 * lamda[1], alpha3 * lamda[2]))
add_num = np.dot(p, v)
img2 = np.array([img[:, :, 0] + add_num[0], img[:, :, 1] + add_num[1], img[:, :, 2] + add_num[2]])
img2 = np.swapaxes(img2, 0, 2)
img2 = np.swapaxes(img2, 0, 1)
misc.imsave(save_dir_tt + np.str(name_num) + '.jpg', img2)
name_num += 1
return image_list Example 27
def dataset(self):
path = self.update_datasets()
self._dataset = pd.read_csv(path, dtype={'cnpj_cpf': np.str}, encoding='utf-8')
self.prepare_dataset()
return self._dataset Example 28
def setUp(self):
self.dataset = pd.read_csv('rosie/core/tests/fixtures/invalid_cnpj_cpf_classifier.csv',
dtype={'recipient_id': np.str})
self.subject = InvalidCnpjCpfClassifier() Example 29
def setUp(self):
self.full_dataset = pd.read_csv(
self.MONTHLY_SUBQUOTA_LIMIT_FIXTURE_FILE, dtype={'subquota_number': np.str})
self.dataset = self.full_dataset[
['applicant_id', 'subquota_number', 'issue_date', 'year', 'month', 'net_value']]
self.test_result_dataset = self.full_dataset[['expected_prediction', 'test_case_description']]
self.subject = MonthlySubquotaLimitClassifier()
self.subject.fit_transform(self.dataset)
self.prediction = self.subject.predict(self.dataset) Example 30
def setUp(self):
self.dataset = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/meal_price_outlier_classifier.csv',
dtype={'recipient_id': np.str})
self.subject = MealPriceOutlierClassifier()
self.subject.fit(self.dataset) Example 31
def setUp(self):
self.dataset = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/traveled_speeds_classifier.csv',
dtype={'recipient_id': np.str})
self.subject = TraveledSpeedsClassifier()
self.subject.fit(self.dataset) Example 32
def get_companies(self):
path = os.path.join(self.path, self.COMPANIES_DATASET)
dataset = pd.read_csv(path, dtype={'cnpj': np.str}, low_memory=False)
dataset['cnpj'] = dataset['cnpj'].str.replace(r'\D', '')
dataset['situation_date'] = pd.to_datetime(
dataset['situation_date'], errors='coerce')
return dataset Example 33
def load_test_data(ticker='000001'):
'''
Load test test_data for develop
:param ticker:
:return: ticker tradeDate turnoverVol closePrice highestPrice lowestPrice openPrice
'''
return pd.read_csv(BASE_DIR+'/tests/test_data/'+ticker+'.csv', dtype={"ticker": np.str}, index_col=0) Example 34
def load_mesh(filename):
"""
Open a json file and load the mesh into the target class
As long as there are no namespace conflicts, the target __class__
will be stored on the properties.HasProperties registry and may be
fetched from there.
:param str filename: name of file to read in
"""
with open(filename, 'r') as outfile:
jsondict = json.load(outfile)
data = BaseMesh.deserialize(jsondict, trusted=True)
return data Example 35
def _readUBC_3DMesh(TensorMesh, fileName):
"""Read UBC GIF 3D tensor mesh and generate same dimension TensorMesh.
:param string fileName: path to the UBC GIF mesh file
:rtype: TensorMesh
:return: The tensor mesh for the fileName.
"""
# Interal function to read cell size lines for the UBC mesh files.
def readCellLine(line):
line_list = []
for seg in line.split():
if '*' in seg:
sp = seg.split('*')
seg_arr = np.ones((int(sp[0]),)) * float(sp[1])
else:
seg_arr = np.array([float(seg)], float)
line_list.append(seg_arr)
return np.concatenate(line_list)
# Read the file as line strings, remove lines with comment = !
msh = np.genfromtxt(fileName, delimiter='\n', dtype=np.str, comments='!')
# Fist line is the size of the model
sizeM = np.array(msh[0].split(), dtype=float)
# Second line is the South-West-Top corner coordinates.
x0 = np.array(msh[1].split(), dtype=float)
# Read the cell sizes
h1 = readCellLine(msh[2])
h2 = readCellLine(msh[3])
h3temp = readCellLine(msh[4])
# Invert the indexing of the vector to start from the bottom.
h3 = h3temp[::-1]
# Adjust the reference point to the bottom south west corner
x0[2] = x0[2] - np.sum(h3)
# Make the mesh
tensMsh = TensorMesh([h1, h2, h3], x0=x0)
return tensMsh Example 36
def readUBC(TensorMesh, fileName, meshdim=None):
"""Wrapper to Read UBC GIF 2D and 3D tensor mesh and generate same dimension TensorMesh.
:param string fileName: path to the UBC GIF mesh file
:param int meshdim: expected dimension of the mesh, if unknown the default argument is None
:rtype: TensorMesh
:return: The tensor mesh for the fileName.
"""
# Check the expected mesh dimensions
if meshdim == None:
# Read the file as line strings, remove lines with comment = !
msh = np.genfromtxt(fileName, delimiter='\n', dtype=np.str, comments='!', max_rows=1)
# Fist line is the size of the model
sizeM = np.array(msh.ravel()[0].split(), dtype=float)
# Check if the mesh is a UBC 2D mesh
if sizeM.shape[0] == 1:
Tnsmsh = TensorMesh._readUBC_2DMesh(fileName)
# Check if the mesh is a UBC 3D mesh
elif sizeM.shape[0] == 3:
Tnsmsh = TensorMesh._readUBC_3DMesh(fileName)
else:
raise Exception('File format not recognized')
# expected dimension is 2
elif meshdim == 2:
Tnsmsh = TensorMesh._readUBC_2DMesh(fileName)
# expected dimension is 3
elif meshdim == 3:
Tnsmsh = TensorMesh._readUBC_3DMesh(fileName)
return Tnsmsh Example 37
def writeUBC(mesh, fileName, models=None):
"""Writes a TensorMesh to a UBC-GIF format mesh file.
:param string fileName: File to write to
:param dict models: A dictionary of the models
"""
assert mesh.dim == 3
s = ''
s += '{0:d} {1:d} {2:d}\n'.format(*tuple(mesh.vnC))
# Have to it in the same operation or use mesh.x0.copy(),
# otherwise the mesh.x0 is updated.
origin = mesh.x0 + np.array([0, 0, mesh.hz.sum()])
origin.dtype = float
s += '{0:.6f} {1:.6f} {2:.6f}\n'.format(*tuple(origin))
s += ('%.6f '*mesh.nCx+'\n')%tuple(mesh.hx)
s += ('%.6f '*mesh.nCy+'\n')%tuple(mesh.hy)
s += ('%.6f '*mesh.nCz+'\n')%tuple(mesh.hz[::-1])
f = open(fileName, 'w')
f.write(s)
f.close()
if models is None: return
assert type(models) is dict, 'models must be a dict'
for key in models:
assert type(key) is str, 'The dict key is a file name'
mesh.writeModelUBC(key, models[key]) Example 38
def test_pickle_py2_bytes_encoding(self):
# Check that arrays and scalars pickled on Py2 are
# unpickleable on Py3 using encoding='bytes'
test_data = [
# (original, py2_pickle)
(np.unicode_('\u6f2c'),
asbytes("cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n"
"(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\n"
"I0\ntp6\nbS',o\\x00\\x00'\np7\ntp8\nRp9\n.")),
(np.array([9e123], dtype=np.float64),
asbytes("cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\n"
"p1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\n"
"p7\n(S'f8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'<'\np11\nNNNI-1\nI-1\n"
"I0\ntp12\nbI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np13\ntp14\nb.")),
(np.array([(9e123,)], dtype=[('name', float)]),
asbytes("cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n"
"(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n"
"(S'V8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'name'\np12\ntp13\n"
"(dp14\ng12\n(g7\n(S'f8'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'<'\np18\nNNNI-1\n"
"I-1\nI0\ntp19\nbI0\ntp20\nsI8\nI1\nI0\ntp21\n"
"bI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np22\ntp23\nb.")),
]
if sys.version_info[:2] >= (3, 4):
# encoding='bytes' was added in Py3.4
for original, data in test_data:
result = pickle.loads(data, encoding='bytes')
assert_equal(result, original)
if isinstance(result, np.ndarray) and result.dtype.names:
for name in result.dtype.names:
assert_(isinstance(name, str)) Example 39
def test_mem_on_invalid_dtype(self):
"Ticket #583"
self.assertRaises(ValueError, np.fromiter, [['12', ''], ['13', '']], str) Example 40
def test_sign_bit(self, level=rlevel):
x = np.array([0, -0.0, 0])
assert_equal(str(np.abs(x)), '[ 0. 0. 0.]') Example 41
def test_unaligned_unicode_access(self, level=rlevel):
# Ticket #825
for i in range(1, 9):
msg = 'unicode offset: %d chars' % i
t = np.dtype([('a', 'S%d' % i), ('b', 'U2')])
x = np.array([(asbytes('a'), sixu('b'))], dtype=t)
if sys.version_info[0] >= 3:
assert_equal(str(x), "[(b'a', 'b')]", err_msg=msg)
else:
assert_equal(str(x), "[('a', u'b')]", err_msg=msg) Example 42
def test_zeros(self):
# Regression test for #1061.
# Set a size which cannot fit into a 64 bits signed integer
sz = 2 ** 64
good = 'Maximum allowed dimension exceeded'
try:
np.empty(sz)
except ValueError as e:
if not str(e) == good:
self.fail("Got msg '%s', expected '%s'" % (e, good))
except Exception as e:
self.fail("Got exception of type %s instead of ValueError" % type(e)) Example 43
def test_eq_string_and_object_array(self):
# From e-mail thread "__eq__ with str and object" (Keith Goodman)
a1 = np.array(['a', 'b'], dtype=object)
a2 = np.array(['a', 'c'])
assert_array_equal(a1 == a2, [True, False])
assert_array_equal(a2 == a1, [True, False]) Example 44
def test_refcount_error_in_clip(self):
# Ticket #1588
a = np.zeros((2,), dtype='>i2').clip(min=0)
x = a + a
# This used to segfault:
y = str(x)
# Check the final string:
assert_(y == "[0 0]") Example 45
def test_format_on_flex_array_element(self):
# Ticket #4369.
dt = np.dtype([('date', '<M8[D]'), ('val', '<f8')])
arr = np.array([('2000-01-01', 1)], dt)
formatted = '{0}'.format(arr[0])
assert_equal(formatted, str(arr[0])) Example 46
def test_run(self):
"""Only test hash runs at all."""
for t in [np.int, np.float, np.complex, np.int32, np.str, np.object,
np.unicode]:
dt = np.dtype(t)
hash(dt) Example 47
def test_dtypeattr(self):
assert_equal(self.one.dtype, np.dtype(np.int_))
assert_equal(self.three.dtype, np.dtype(np.float_))
assert_equal(self.one.dtype.char, 'l')
assert_equal(self.three.dtype.char, 'd')
self.assertTrue(self.three.dtype.str[0] in '<>')
assert_equal(self.one.dtype.str[1], 'i')
assert_equal(self.three.dtype.str[1], 'f') Example 48
def test_empty_subscript(self):
a, b = self.d
self.assertEqual(a[()], 0)
self.assertEqual(b[()], 'x')
self.assertTrue(type(a[()]) is a.dtype.type)
self.assertTrue(type(b[()]) is str) Example 49
def test_empty_unicode(self):
# don't throw decode errors on garbage memory
for i in range(5, 100, 5):
d = np.empty(i, dtype='U')
str(d) Example 50
def test_swapaxes(self):
a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
idx = np.indices(a.shape)
assert_(a.flags['OWNDATA'])
b = a.copy()
# check exceptions
assert_raises(ValueError, a.swapaxes, -5, 0)
assert_raises(ValueError, a.swapaxes, 4, 0)
assert_raises(ValueError, a.swapaxes, 0, -5)
assert_raises(ValueError, a.swapaxes, 0, 4)
for i in range(-4, 4):
for j in range(-4, 4):
for k, src in enumerate((a, b)):
c = src.swapaxes(i, j)
# check shape
shape = list(src.shape)
shape[i] = src.shape[j]
shape[j] = src.shape[i]
assert_equal(c.shape, shape, str((i, j, k)))
# check array contents
i0, i1, i2, i3 = [dim-1 for dim in c.shape]
j0, j1, j2, j3 = [dim-1 for dim in src.shape]
assert_equal(src[idx[j0], idx[j1], idx[j2], idx[j3]],
c[idx[i0], idx[i1], idx[i2], idx[i3]],
str((i, j, k)))
# check a view is always returned, gh-5260
assert_(not c.flags['OWNDATA'], str((i, j, k)))
# check on non-contiguous input array
if k == 1:
b = c 