def __init__(self, a=None, b=None, bins=50):
"""
a and b can be in either datetime or unix time
"""
self._a = UnixTime(a)
self._b = UnixTime(b)
self.bins = bins
def __init__(self, distribution_beginning, distribution_ending,
bins=50, relation_formula=None):
if not isinstance(distribution_beginning, rv_frozen):
raise TypeError("'distribution_beginning' should be a scipy frozen distribution")
if not isinstance(distribution_ending, rv_frozen):
raise TypeError("'distribution_ending' should be a scipy frozen distribution")
self._distribution_beginning = distribution_beginning
self._distribution_ending = distribution_ending
a, beginning = calculate_bounds_of_probability_distribution(distribution_beginning)
ending, b = calculate_bounds_of_probability_distribution(distribution_ending)
self._beginning = UnixTime(beginning)
self._ending = UnixTime(ending)
self.membership_function = MembershipFunction(self)
bins_beginning = bins / 2
bins_ending = bins - bins_beginning
self.interval_beginning = TimeInterval(a, beginning, bins_beginning)
self.interval_ending = TimeInterval(ending, b, bins_ending)
list.__init__(self, self.interval_beginning + self.interval_ending)
TimeInterval.__init__(self, a, b, bins)
if relation_formula is None:
relation_formula = RelationFormulaGeometricMean()
elif not isinstance(relation_formula, BaseRelationFormula):
raise TypeError("'relation_formula' should be of type 'BaseRelationFormula'")
relation_formula.bounds[distribution_beginning] = self.a, self.beginning
relation_formula.bounds[distribution_ending] = self.ending, self.b
self._formula_creator = FormulaCreator(relation_formula)
def __init__(self,
distribution_beginning,
distribution_ending,
bins=50,
distribution_integral_limit=DISTRIBUTION_INTEGRAL_LIMIT):
if not isinstance(distribution_beginning, rv_frozen):
raise TypeError(
"'distribution_beginning' should be a scipy frozen distribution"
)
if not isinstance(distribution_ending, rv_frozen):
raise TypeError(
"'distribution_ending' should be a scipy frozen distribution")
if not 0 < distribution_integral_limit <= 1:
raise TypeError(
"'distribution_integral_limit' should be within (0, 1]")
self._distribution_beginning = distribution_beginning
self._distribution_ending = distribution_ending
a, beginning = calculate_bounds_of_probability_distribution(
distribution_beginning, distribution_integral_limit)
ending, b = calculate_bounds_of_probability_distribution(
distribution_ending, distribution_integral_limit)
self._beginning = UnixTime(beginning)
self._ending = UnixTime(ending)
self.membership_function = MembershipFunction(self)
bins_beginning = bins / 2
bins_ending = bins - bins_beginning
self.interval_beginning = TimeInterval(a, beginning, bins_beginning)
self.interval_ending = TimeInterval(ending, b, bins_ending)
list.__init__(self, self.interval_beginning + self.interval_ending)
TimeInterval.__init__(self, a, b, bins)
def plot(self):
import matplotlib.pyplot as plt
from spatiotemporal.unix_time import UnixTime
plt.plot(
[UnixTime(self.a).to_datetime(),
UnixTime(self.b).to_datetime()], [1, 1])
return plt
def to_list(self):
result = []
for i, time_step in enumerate(self):
if i == len(self) - 1 and self.output_list[len(self) - 1] == 0:
result.append(UnixTime(time_step - EPSILON))
result.append(time_step)
if i == 0 and self.output_list[0] == 0:
result.append(UnixTime(time_step + EPSILON))
return result
def plot(self):
import matplotlib.pyplot as plt
x_axis, y_axis = [], []
for time_step in self:
x_axis.append(UnixTime(time_step - EPSILON).to_datetime())
x_axis.append(UnixTime(time_step + EPSILON).to_datetime())
y_axis.append(self.pdf(time_step - EPSILON))
y_axis.append(self.pdf(time_step + EPSILON))
plt.plot(x_axis, y_axis)
return plt
def __init__(self,
a,
b,
beginning=None,
ending=None,
beginning_factor=None,
ending_factor=None):
"""
start and end can be in either datetime or unix time
"""
a, b = UnixTime(a), UnixTime(b)
assert a < b, "'b' should be greater than 'a'"
if (beginning, ending) != (None, None):
assert (beginning_factor, ending_factor) == (None, None), "PiecewiseTemporalEvent() only accepts " \
"either 'beginning_factor' and 'ending_factor' " \
"or 'beginning' and 'ending'"
if beginning_factor is not None:
assert beginning_factor > 0
self.beginning_factor = beginning_factor
if ending_factor is not None:
assert ending_factor > 0
self.ending_factor = ending_factor
if (beginning, ending) != (None, None):
beginning = UnixTime(beginning)
ending = UnixTime(ending)
else:
beginning, ending = 0, 0
while not a < beginning < ending < b:
beginning = random_time(
a,
b,
probability_distribution=t(
# df, mean, variance
4,
a + float(b - a) / self.beginning_factor,
float(b - a) / self.beginning_factor))
ending = random_time(
a,
b,
probability_distribution=t(
# df, mean, variance
4,
b - float(b - a) / self.ending_factor,
float(b - a) / self.ending_factor))
TemporalEventPiecewiseLinear.__init__(self, {
a: 0,
beginning: 1
}, {
ending: 1,
b: 0
})
def random_time(self, start=None, stop=None, probability_distribution=None):
if start is None:
start = self.a
else:
start = UnixTime(start)
assert self.a <= start <= self.b, "'start' should be within the interval of 'self'"
if stop is None:
stop = self.b
else:
stop = UnixTime(stop)
assert self.a <= stop <= self.b, "'stop' should be within the interval of 'self'"
return random_time(start, stop, probability_distribution)
def invoke_function_on(function, sequence_or_point):
result = []
try:
for point in sequence_or_point:
if type(point) is datetime:
point = UnixTime(point)
result.append(function(point))
except:
if type(sequence_or_point) is datetime:
sequence_or_point = UnixTime(sequence_or_point)
return function(sequence_or_point)
return result
def __init__(self,
a,
b,
beginning=None,
ending=None,
beginning_factor=None,
ending_factor=None):
"""
start and end can be in either datetime or unix time
"""
a, b = UnixTime(a), UnixTime(b)
assert a < b, "'b' should be greater than 'a'"
if (beginning, ending) != (None, None):
assert (beginning_factor, ending_factor) == (None, None), "PiecewiseTemporalEvent() only accepts " \
"either 'beginning_factor' and 'ending_factor' " \
"or 'beginning' and 'ending'"
if not a < beginning < ending < b:
raise AttributeError(
"The inputs should satisfy 'a < beginning < ending < b' relation"
)
if beginning_factor is not None:
assert beginning_factor > 0
self.beginning_factor = beginning_factor
if ending_factor is not None:
assert ending_factor > 0
self.ending_factor = ending_factor
if (beginning, ending) == (None, None):
beginning, ending = 0, 0
while not a < beginning < ending < b:
beginning = random_time(
a,
b,
probability_distribution=t(
# df, mean, variance
4,
a + float(b - a) / self.beginning_factor,
float(b - a) / self.beginning_factor))
ending = random_time(
a,
b,
probability_distribution=t(
# df, mean, variance
4,
b - float(b - a) / self.ending_factor,
float(b - a) / self.ending_factor))
TemporalEvent.__init__(self,
uniform(loc=a, scale=UnixTime(beginning - a)),
uniform(loc=ending, scale=UnixTime(b - ending)),
bins=4)
def plot(self):
import matplotlib.pyplot as plt
from spatiotemporal.unix_time import UnixTime
x_axis = [UnixTime(time).to_datetime() for time in self]
plt.plot(x_axis, self.membership_function)
return plt
def invoke_method_on(method, sequence_or_point):
if method is None:
return None
if not callable(method):
raise TypeError("'method' is not callable")
result = []
try:
for point in sequence_or_point:
if type(point) is datetime:
point = UnixTime(point)
result.append(method(point))
except TypeError:
if type(sequence_or_point) is datetime:
sequence_or_point = UnixTime(sequence_or_point)
return method(sequence_or_point)
return result
def __init__(self, iterable):
assert len(iterable) >= 2
iterable = sorted(iterable)
list_converted_to_unix_time = []
first_item = True
for current in iterable:
current = UnixTime(current)
list_converted_to_unix_time.append(current)
if first_item:
previous = current
first_item = False
continue
assert current > previous
previous = current
list.__init__(self, list_converted_to_unix_time)
def temporal_relation_between(temporal_event_1, temporal_event_2):
temporal_event_1 = TemporalEventTrapezium(1, 10)
temporal_event_2 = TemporalEventTrapezium(11, 16)
sum_times = sorted([
UnixTime(time).to_datetime()
for time in set(temporal_event_1.to_list() +
temporal_event_2.to_list())
])
sum_certainties = []
for time_step in sum_times:
sum_certainties.append(
temporal_event_1.membership_function(time_step) +
temporal_event_2.membership_function(time_step))
result = {}
formulas = {
'p': beforeFormula,
'm': meetsFormula,
'o': overlapsFormula,
'F': finished_byFormula,
'D': containsFormula,
's': startsFormula,
'e': equalsFormula,
'S': started_byFormula,
'd': duringFormula,
'f': finishesFormula,
'O': overlapped_byFormula,
'M': met_byFormula,
'P': afterFormula
}
dist1, dist2 = temporal_event_1.to_dict(), temporal_event_2.to_dict()
for name in formulas:
degree = formulas[name](dist1, dist2)
if degree > 0:
result[name] = degree
print result
temporal_event_1.plot()
plt = temporal_event_2.plot()
plt.plot(sum_times, sum_certainties)
plt.show()
return result
def a(self, value):
assert value > self[1]
self[0] = UnixTime(value)
def b(self, value):
assert value > self[-2]
self[-1] = UnixTime(value)
def __getitem__(self, index):
if index >= len(self):
raise IndexError
item = self.a + index * self._iter_step
return UnixTime(item)
def to_datetime_list(self):
if self._datetime is None:
self._datetime = [UnixTime(time).to_datetime() for time in self]
return self._datetime
def a(self, value):
self._a = UnixTime(value)
def b(self, value):
self._b = UnixTime(value)
def __getitem__(self, index):
value = self.a + index * self.iter_step
if value > self.b:
raise IndexError
return UnixTime(value)
