def read_spikes_from_file(file_path,
min_atom=0,
max_atom=float('inf'),
min_time=0,
max_time=float('inf'),
split_value="\t"):
""" Read spikes from a file formatted as:\
<time>\t<neuron ID>
:param file_path: absolute path to a file containing spike values
:type file_path: str
:param min_atom: min neuron ID to which neurons to read in
:type min_atom: int
:param max_atom: max neuron ID to which neurons to read in
:type max_atom: int
:param min_time: min time slot to read neurons values of.
:type min_time: int
:param max_time: max time slot to read neurons values of.
:type max_time: int
:param split_value: the pattern to split by
:type split_value: str
:return:\
a numpy array with max_atom elements each of which is a list of\
spike times.
:rtype: numpy.array(int, int)
"""
# pylint: disable=too-many-arguments
# For backward compatibility as previous version tested for None rather
# than having default values
if min_atom is None:
min_atom = 0
if max_atom is None:
max_atom = float('inf')
if min_time is None:
min_time = 0
if max_time is None:
max_time = float('inf')
data = []
with open(file_path, 'r') as fsource:
read_data = fsource.readlines()
evaluator = SafeEval()
for line in read_data:
if line.startswith('#'):
continue
values = line.split(split_value)
time = float(evaluator.eval(values[0]))
neuron_id = float(evaluator.eval(values[1]))
if (min_atom <= neuron_id < max_atom and min_time <= time < max_time):
data.append([neuron_id, time])
data.sort()
return numpy.array(data)
def read_spikes_from_file(file_path, min_atom=0, max_atom=float('inf'),
min_time=0, max_time=float('inf'), split_value="\t"):
""" Read spikes from a file formatted as:\
<time>\t<neuron ID>
:param file_path: absolute path to a file containing spike values
:type file_path: str
:param min_atom: min neuron ID to which neurons to read in
:type min_atom: int
:param max_atom: max neuron ID to which neurons to read in
:type max_atom: int
:param min_time: min time slot to read neurons values of.
:type min_time: int
:param max_time: max time slot to read neurons values of.
:type max_time: int
:param split_value: the pattern to split by
:type split_value: str
:return:\
a numpy array with max_atom elements each of which is a list of\
spike times.
:rtype: numpy.array(int, int)
"""
# pylint: disable=too-many-arguments
# For backward compatibility as previous version tested for None rather
# than having default values
if min_atom is None:
min_atom = 0
if max_atom is None:
max_atom = float('inf')
if min_time is None:
min_time = 0
if max_time is None:
max_time = float('inf')
data = []
with open(file_path, 'r') as fsource:
read_data = fsource.readlines()
evaluator = SafeEval()
for line in read_data:
if line.startswith('#'):
continue
values = line.split(split_value)
time = float(evaluator.eval(values[0]))
neuron_id = float(evaluator.eval(values[1]))
if (min_atom <= neuron_id < max_atom and
min_time <= time < max_time):
data.append([neuron_id, time])
data.sort()
return numpy.array(data)
def test_environment_eval():
evaluator = SafeEval(Abc)
assert evaluator.eval("Abc(1)") == 2
with pytest.raises(NameError):
evaluator.eval("Def(1)")
with pytest.raises(TypeError):
evaluator.eval("Abc(1,2)")
def read_in_data_from_file(file_path,
min_atom,
max_atom,
min_time,
max_time,
extra=False):
""" Read in a file of data values where the values are in a format of:
<time>\t<atom ID>\t<data value>
:param str file_path: absolute path to a file containing the data
:param int min_atom: min neuron ID to which neurons to read in
:param int max_atom: max neuron ID to which neurons to read in
:param extra:
:param min_time: min time slot to read neurons values of.
:type min_time: float or int
:param max_time: max time slot to read neurons values of.
:type max_time: float or int
:return: a numpy array of (time stamp, atom ID, data value)
:rtype: ~numpy.ndarray(tuple(float, int, float))
"""
times = list()
atom_ids = list()
data_items = list()
evaluator = SafeEval()
with open(file_path, 'r') as f:
for line in f.readlines():
if line.startswith('#'):
continue
if extra:
time, neuron_id, data_value, extra = line.split("\t")
else:
time, neuron_id, data_value = line.split("\t")
time = float(evaluator.eval(time))
neuron_id = int(evaluator.eval(neuron_id))
data_value = float(evaluator.eval(data_value))
if (min_atom <= neuron_id < max_atom
and min_time <= time < max_time):
times.append(time)
atom_ids.append(neuron_id)
data_items.append(data_value)
else:
print("failed to enter {}:{}".format(neuron_id, time))
result = numpy.dstack((atom_ids, times, data_items))[0]
return result[numpy.lexsort((times, atom_ids))]
def test_multi_environment():
s = Support()
evaluator = SafeEval(Abc, s.Def)
assert evaluator.eval("x+y*z", x=1, y=2, z=3) == 7
assert evaluator.eval("Def(Abc(x))", x=3) == 8
with pytest.raises(NameError):
assert evaluator.eval("Ghi(Def(Abc(y)))", y=3) == 1
assert evaluator.eval("Ghi(Def(Abc(y)))", y=3, Ghi=s.Ghi) == 4.0
assert evaluator.eval("Ghi(Def(Abc(y)))", y=3, Ghi=s.Def) == 16
# Not sure if the next test should pass; Python, huh...
assert evaluator.eval("Ghi.__name__", y=3, Ghi=s.Def) == "Def"
def read_in_data_from_file(
file_path, min_atom, max_atom, min_time, max_time, extra=False):
""" Read in a file of data values where the values are in a format of:
<time>\t<atom ID>\t<data value>
:param file_path: absolute path to a file containing the data
:param min_atom: min neuron ID to which neurons to read in
:param max_atom: max neuron ID to which neurons to read in
:param min_time: min time slot to read neurons values of.
:param max_time: max time slot to read neurons values of.
:return: a numpy array of (time stamp, atom ID, data value)
"""
times = list()
atom_ids = list()
data_items = list()
evaluator = SafeEval()
with open(file_path, 'r') as f:
for line in f.readlines():
if line.startswith('#'):
continue
if extra:
time, neuron_id, data_value, extra = line.split("\t")
else:
time, neuron_id, data_value = line.split("\t")
time = float(evaluator.eval(time))
neuron_id = int(evaluator.eval(neuron_id))
data_value = float(evaluator.eval(data_value))
if (min_atom <= neuron_id < max_atom and
min_time <= time < max_time):
times.append(time)
atom_ids.append(neuron_id)
data_items.append(data_value)
else:
print("failed to enter {}:{}".format(neuron_id, time))
result = numpy.dstack((atom_ids, times, data_items))[0]
return result[numpy.lexsort((times, atom_ids))]
# support for arbitrary expression for the distance dependence
_d_expr_context = SafeEval(math,
numpy,
arccos,
arcsin,
arctan,
arctan2,
ceil,
cos,
cosh,
exp,
fabs,
floor,
fmod,
hypot,
ldexp,
log,
log10,
modf,
power,
sin,
sinh,
sqrt,
tan,
tanh,
maximum,
minimum,
e=e,
pi=pi)
# global objects
logger = FormatAdapter(logging.getLogger(__name__))
_expr_context = SafeEval(math,
numpy,
numpy.arccos,
numpy.arcsin,
numpy.arctan,
numpy.arctan2,
numpy.ceil,
numpy.cos,
numpy.cosh,
numpy.exp,
numpy.fabs,
numpy.floor,
numpy.fmod,
numpy.hypot,
numpy.ldexp,
numpy.log,
numpy.log10,
numpy.modf,
numpy.power,
numpy.sin,
numpy.sinh,
numpy.sqrt,
numpy.tan,
numpy.tanh,
numpy.maximum,
numpy.minimum,
e=numpy.e,
pi=numpy.pi)
def test_defined_names():
evaluator = SafeEval(math, x=123.25)
assert evaluator.eval("math.floor(x+d)", d=0.875) == 124.0
def test_packages():
evaluator = SafeEval(math)
assert evaluator.eval("math.floor(d)", d=2.5) == 2.0
def test_state_sensitive():
s = Support()
evaluator = SafeEval(s.Jkl)
assert evaluator.eval("Jkl(Jkl(Jkl(x)))", x=2.5) == 16.5
def test_simple_eval():
evaluator = SafeEval()
assert evaluator.eval("1+2*3") == 7
assert evaluator.eval("x+y*z", x=1, y=2, z=3) == 7
