def test_union(self):
ex = self.tm_windows_examples()
tests = [
(ex['Tbase'], 'Tbase', 'Tbase'),
(TimeWindow(ex['Tsneigh'].since,
ex['Tbase'].until), 'Tbase', 'Tsneigh'),
(TimeWindow(
ex['Tbase'].since,
ex['Teneigh'].until,
), 'Tbase', 'Teneigh'),
([ex['Tsneigh'], ex['Teneigh']], 'Tsneigh', 'Teneigh'),
(TimeWindow(ex['Tbase'].since,
ex['Toverl'].until), 'Tbase', 'Toverl'),
(ex['Tsupset'], 'Tbase', 'Tsupset'),
(ex['Tbase'], 'Tbase', 'Tsubset'),
]
for result, twname1, twname2 in tests:
tw1 = ex[twname1]
tw2 = ex[twname2]
self.assertEqual(
result, tw1.union(tw2),
'Checking "{0}".union("{1}")'.format(twname1, twname2))
self.assertEqual(
result, tw1 | tw2,
'Checking "{0}".union("{1}")'.format(twname1, twname2))
self.assertEqual(
result, tw2.union(tw1),
'Checking "{0}".union("{1}")'.format(twname2, twname1))
self.assertEqual(
result, tw2 | tw1,
'Checking "{0}".union("{1}")'.format(twname2, twname1))
def test_smallestpossible(self):
ex = self.tm_windows_examples()
tests = [
(ex['Tbase'], [ex['Tbase']]),
(ex['Tbase'], [ex['Tbase'], ex['Tbase']]),
(TimeWindow(ex['Tsneigh'].since,
ex['Tbase'].until), [ex['Tbase'], ex['Tsneigh']]),
(TimeWindow(
ex['Tbase'].since,
ex['Teneigh'].until,
), [ex['Tbase'], ex['Teneigh']]),
(TimeWindow(
ex['Tsneigh'].since,
ex['Teneigh'].until,
), [ex['Tsneigh'], ex['Teneigh']]),
(TimeWindow(
ex['Tsneigh'].since,
ex['Teneigh'].until,
), [ex['Tsneigh'], ex['Tbase'], ex['Teneigh']]),
(TimeWindow(
ex['Tbase'].since,
ex['Toverl'].until,
), [ex['Toverl'], ex['Tbase']]),
(ex['Tsupset'], [ex['Tsupset'], ex['Tbase']]),
(ex['Tbase'], [ex['Tsubset'], ex['Tbase']]),
]
for result, tw_set in tests:
self.assertEqual(result, TimeWindow.smallest_possible(tw_set))
def tm_windows_examples(self):
"""
Generate a set of fixed time windows with the following
relationships between them. The actual time scale and point in time
may vary.
Tbase = |_______________|
Tsneigh= |__|
Teneigh= |__|
Toverl = |_____________|
Tsupset= |_____________________|
Tsubset= |_____|
"""
examples = {}
now = self.now
examples['Tbase'] = TimeWindow.from_timedelta(now,
timedelta(minutes=10))
examples['Tsneigh'] = TimeWindow.from_timedelta(
examples['Tbase'].since, -timedelta(minutes=1))
examples['Teneigh'] = TimeWindow.from_timedelta(
examples['Tbase'].until, timedelta(minutes=1))
examples['Toverl'] = TimeWindow.from_timedelta(
examples['Tbase'].since + examples['Tbase'].delta / 2,
examples['Tbase'].delta)
examples['Tsupset'] = TimeWindow(
examples['Tbase'].since - timedelta(minutes=1),
examples['Tbase'].until + timedelta(minutes=1))
examples['Tsubset'] = TimeWindow(
examples['Tbase'].since + timedelta(minutes=1),
examples['Tbase'].until - timedelta(minutes=1))
return examples
def test_time_window_to_timestamps(self):
start = 1420063200
end = start + 120
tw = TimeWindow(utcfromtimestamp_tzaware(start),
utcfromtimestamp_tzaware(end))
tw_timestamps = time_window_to_timestamps(tw)
self.assertTupleEqual(tw_timestamps, (start, end))
def start_simulate(self, iteration):
#self.log('start simulating')
self.__iteration_number = iteration
for _ in range(iteration):
#self.log("found a new block")
random_number = random.random()
# Mining Process
if random_number < self._alpha:
self.calculating_weight(True)
self.start_selfish_mining()
else:
self.calculating_weight(False)
self.start_honest_mining()
block_creation_response = self.time_window.create_a_block()
if block_creation_response == BlockCreationStatus.EndTow:
self.chain_evaluation()
self.reset_tow()
elif block_creation_response == BlockCreationStatus.EndTimeWindow:
self.time_window = TimeWindow(self.tow_number,
self.min_tow_block_number,
self.max_tow_block_number)
self.chain_evaluation()
self.reset_tow()
else:
self.__current_block_tow += 1
self.calculating_output()
return
def convert_to_loc_avail(self, db_loc):
la = LocationAvailability()
la.location_name = db_loc[HASH_KEY]
if 'is_muted' in db_loc.keys():
la.is_muted = db_loc['is_muted']
if 'windows' in db_loc.keys():
for w in json.loads(db_loc['windows']):
tw = TimeWindow(IsMuted=w["is_muted"],
ical=w['ical'],
Priority=w['priority'])
la.add_window(tw)
if 'last_activity' in db_loc.keys():
la.last_activity = arrow.get(db_loc['last_activity'])
logging.info('Last motion was ' + la.last_activity.isoformat())
if ((arrow.utcnow() - la.last_activity).seconds > 60):
la.is_motion = False
else:
la.is_motion = True
if 'input_capabilities' in db_loc.keys():
for k in json.loads(db_loc['input_capabilities']):
i = json.loads(db_loc['input_capabilities'])[k]
la.add_input_capability(HardwareId=k,
Name=i['name'],
Color=i['color'],
Style=i['style'],
Type=i['type'])
return la
def test_time_window_from_timestamps(self):
start = 1420063200
end = start + 120
check_tw = time_window_from_timestamps((start, end))
tw = TimeWindow(utcfromtimestamp_tzaware(start),
utcfromtimestamp_tzaware(end))
self.assertEqual(check_tw, tw)
def adjust_segments(consumption_segments: dict,
weight_series: pd.Series) -> dict:
"""
Realign consumption segments to end one timestamp before a new product arrives on a scale and start on timestamp
of product arrival.
Args:
consumption_segments: Dictionary of TimeWindow segments.
weight_series: Weight measurements as a Pandas Series.
Returns: A dictionary of adjusted TimeWindow segments.
"""
segments = consumption_segments.copy()
for segment_index in range(len(segments) - 1):
current_segment_data = create_segment_data(segments[segment_index],
weight_series)
next_segment_data = create_segment_data(segments[segment_index + 1],
weight_series)
current_end_weight = current_segment_data["end_weight"]
next_start_weight = next_segment_data["start_weight"]
if current_end_weight >= next_start_weight:
weight_sub_range = weight_series[current_segment_data[
"min_weight_time"]:next_segment_data["end_time"]]
sub_range_values = np.array(weight_sub_range.values)
sub_range_times = weight_sub_range.index
peaks, plateaus = signal.find_peaks(sub_range_values,
plateau_size=1)
first_maxima_index = peaks[0]
if plateaus["left_edges"]:
plateaus_left_edge = plateaus["left_edges"]
first_maxima_index = np.minimum(peaks, plateaus_left_edge)[0]
adjusted_current_end_time = sub_range_times[first_maxima_index - 1]
adjusted_next_start_time = sub_range_times[first_maxima_index]
adjusted_current_segment = TimeWindow(
current_segment_data["start_time"], adjusted_current_end_time)
adjusted_next_segment = TimeWindow(adjusted_next_start_time,
next_segment_data["end_time"])
segments[segment_index] = adjusted_current_segment
segments[segment_index + 1] = adjusted_next_segment
else:
continue
return segments
def test_split_per_week(self):
# Single time test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 1, 15, 30, 40, 54),
)
self.assertEqual(tw.split_per_week(), [tw])
# Single day test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 1, 16, 4, 3, 1),
)
self.assertEqual(tw.split_per_week(), [tw])
# Span one week, with period less than 7 days
tw = TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 3, 10))
self.assertEqual(
tw.split_per_week(),
[TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 3, 10))])
# Span two weeks, with period greater than 7 days
tw = TimeWindow(datetime(2015, 1, 1, 23),
datetime(2015, 1, 11, 23, 30))
print(tw.split_per_week())
self.assertEqual(tw.split_per_week(), [
TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 5, 0)),
TimeWindow(datetime(2015, 1, 5, 0), datetime(2015, 1, 11, 23, 30))
])
def test_constructor(self):
now = datetime.now()
until = now + timedelta(minutes=5)
tw = TimeWindow(now, until)
self.assertEqual(now, tw.since)
self.assertEqual(until, tw.until)
self.assertEqual(until, tw.until)
with self.assertRaises(ValueError):
# Test reverse order
TimeWindow(until, now)
with self.assertRaises(TypeError):
TimeWindow("string", until)
with self.assertRaises(TypeError):
TimeWindow(now, "string")
def __init__(self):
'''
obj -> None
This method initializes an object
'''
self.conti = True
self.wrong = False
self.element_1 = StartingWindow()
if self.element_1.name != 'quit' and len(self.element_1.name) > 0:
self.element_2 = TimeWindow()
self.get_chart()
self.get_rep()
if self.element_2 != None:
a = Represent()
def create_consumption_segments(weight_series: pd.Series, peaks: list) -> dict:
"""
Create and return a dictionary of time windows representing consumption segments of individual products.
Args:
weight_series: Weight measurements as a Pandas Series.
peaks: Peaks in data as a list of time stamps.
Returns: Dictionary of time_window.TimeWindows 'windows'.
"""
weight_schema = pas.weight_series
try:
weight_schema(weight_series)
except pa.errors.SchemaErrors:
raise
time_stamps = weight_series.index
consumption_segments = {}
start_time_stamp = time_stamps[0]
end_index = (time_stamps.get_loc(peaks[0]) - 1
) # end the segment 1 timestamp before peak
end_time_stamp = time_stamps[end_index]
segment_0 = TimeWindow(start_time_stamp, end_time_stamp)
consumption_segments[0] = segment_0
for index, break_point in enumerate(peaks):
start_time_stamp = break_point
if index < len(peaks) - 1:
end_index = (time_stamps.get_loc(peaks[index + 1]) - 1
) # end the segment 1 timestamp before peak
end_time_stamp = time_stamps[end_index]
else:
end_time_stamp = time_stamps[-1]
consumption_segments[index + 1] = TimeWindow(start_time_stamp,
end_time_stamp)
return consumption_segments
def test_complement(self):
ex = self.tm_windows_examples()
tests = [
(None, 'Tbase', 'Tbase'),
(ex['Tbase'], 'Tbase', 'Tsneigh'),
(ex['Tbase'], 'Tbase', 'Teneigh'),
(TimeWindow(ex['Tbase'].since,
ex['Toverl'].since), 'Tbase', 'Toverl'),
(None, 'Tbase', 'Tsupset'),
([
TimeWindow(ex['Tbase'].since, ex['Tsubset'].since),
TimeWindow(ex['Tsubset'].until, ex['Tbase'].until)
], 'Tbase', 'Tsubset'),
]
for result, twname1, twname2 in tests:
tw1 = ex[twname1]
tw2 = ex[twname2]
self.assertEqual(
result, tw1.complement(tw2),
'Checking "{0}".complement("{1}")'.format(twname1, twname2))
self.assertEqual(
result, tw1 - tw2,
'Checking "{0}".complement("{1}")'.format(twname1, twname2))
def __init__(self,
tow_number,
min_tow_block_number,
max_tow_block_number,
show_log=False):
self._alpha = 0
self._gamma = 0
self.tow_number = tow_number
self.min_tow_block_number = min_tow_block_number
self.max_tow_block_number = max_tow_block_number
self.time_window = TimeWindow(tow_number, min_tow_block_number,
max_tow_block_number)
self.__show_log = show_log
random.seed(None)
self.__public_chain_length = 0
self.__private_chain_length = 0
self.__delta = 0
self.__selfish_miners_win_block = 0
self.__honest_miners_win_block = 0
self.__selfish_miner_revenue = 0
self.__honest_miner_revenue = 0
self.__total_mined_block = 0
self.__total_stale_block = 0
self.__iteration_number = 0
self.__predicted_K = 2
self.__published = False
self.__private_chain_weight = [0 for _ in range(max_tow_block_number)]
self.__public_chain_weight = [0 for _ in range(max_tow_block_number)]
self.__current_block_tow = 1
self.__average_tow = self.time_window.get_average_tow()
self.__fork_created = False
def helper_time_periods_to_time_window_list(self, periods):
return [
TimeWindow(*list(map(utcfromtimestamp_tzaware, period)))
for period in periods
]
def test_split_per_day(self):
# Single time test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 1, 15, 30, 40, 54),
)
self.assertEqual(tw.split_per_day(), [tw])
# Single day test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 1, 16, 4, 3, 1),
)
self.assertEqual(tw.split_per_day(), [tw])
# Span two days, with period less than 24 hours
tw = TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 2, 10))
self.assertEqual(tw.split_per_day(), [
TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 2, 0)),
TimeWindow(datetime(2015, 1, 2, 0), datetime(2015, 1, 2, 10))
])
# Span two days, with period greater than 24 hours
tw = TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 2, 23, 30))
self.assertEqual(tw.split_per_day(), [
TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 2, 0)),
TimeWindow(datetime(2015, 1, 2, 0), datetime(2015, 1, 2, 23, 30))
])
# A big test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 2, 3, 5, 4, 3, 1),
)
self.assertEqual(tw.split_per_day(), [
TimeWindow(datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 2, 0, 0)),
TimeWindow(datetime(2015, 1, 2, 0, 0), datetime(2015, 1, 3, 0, 0)),
TimeWindow(datetime(2015, 1, 3, 0, 0), datetime(2015, 1, 4, 0, 0)),
TimeWindow(datetime(2015, 1, 4, 0, 0), datetime(2015, 1, 5, 0, 0)),
TimeWindow(datetime(2015, 1, 5, 0, 0), datetime(2015, 1, 6, 0, 0)),
TimeWindow(datetime(2015, 1, 6, 0, 0), datetime(2015, 1, 7, 0, 0)),
TimeWindow(datetime(2015, 1, 7, 0, 0), datetime(2015, 1, 8, 0, 0)),
TimeWindow(datetime(2015, 1, 8, 0, 0), datetime(2015, 1, 9, 0, 0)),
TimeWindow(datetime(2015, 1, 9, 0, 0), datetime(2015, 1, 10, 0,
0)),
TimeWindow(datetime(2015, 1, 10, 0, 0), datetime(
2015, 1, 11, 0, 0)),
TimeWindow(datetime(2015, 1, 11, 0, 0), datetime(
2015, 1, 12, 0, 0)),
TimeWindow(datetime(2015, 1, 12, 0, 0), datetime(
2015, 1, 13, 0, 0)),
TimeWindow(datetime(2015, 1, 13, 0, 0), datetime(
2015, 1, 14, 0, 0)),
TimeWindow(datetime(2015, 1, 14, 0, 0), datetime(
2015, 1, 15, 0, 0)),
TimeWindow(datetime(2015, 1, 15, 0, 0), datetime(
2015, 1, 16, 0, 0)),
TimeWindow(datetime(2015, 1, 16, 0, 0), datetime(
2015, 1, 17, 0, 0)),
TimeWindow(datetime(2015, 1, 17, 0, 0), datetime(
2015, 1, 18, 0, 0)),
TimeWindow(datetime(2015, 1, 18, 0, 0), datetime(
2015, 1, 19, 0, 0)),
TimeWindow(datetime(2015, 1, 19, 0, 0), datetime(
2015, 1, 20, 0, 0)),
TimeWindow(datetime(2015, 1, 20, 0, 0), datetime(
2015, 1, 21, 0, 0)),
TimeWindow(datetime(2015, 1, 21, 0, 0), datetime(
2015, 1, 22, 0, 0)),
TimeWindow(datetime(2015, 1, 22, 0, 0), datetime(
2015, 1, 23, 0, 0)),
TimeWindow(datetime(2015, 1, 23, 0, 0), datetime(
2015, 1, 24, 0, 0)),
TimeWindow(datetime(2015, 1, 24, 0, 0), datetime(
2015, 1, 25, 0, 0)),
TimeWindow(datetime(2015, 1, 25, 0, 0), datetime(
2015, 1, 26, 0, 0)),
TimeWindow(datetime(2015, 1, 26, 0, 0), datetime(
2015, 1, 27, 0, 0)),
TimeWindow(datetime(2015, 1, 27, 0, 0), datetime(
2015, 1, 28, 0, 0)),
TimeWindow(datetime(2015, 1, 28, 0, 0), datetime(
2015, 1, 29, 0, 0)),
TimeWindow(datetime(2015, 1, 29, 0, 0), datetime(
2015, 1, 30, 0, 0)),
TimeWindow(datetime(2015, 1, 30, 0, 0), datetime(
2015, 1, 31, 0, 0)),
TimeWindow(datetime(2015, 1, 31, 0, 0), datetime(2015, 2, 1, 0,
0)),
TimeWindow(datetime(2015, 2, 1, 0, 0), datetime(2015, 2, 2, 0, 0)),
TimeWindow(datetime(2015, 2, 2, 0, 0), datetime(2015, 2, 3, 0, 0)),
TimeWindow(datetime(2015, 2, 3, 0, 0),
datetime(2015, 2, 3, 5, 4, 3, 1))
])
def test_split_per_month(self):
# Single time test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 1, 15, 30, 40, 54),
)
self.assertEqual(tw.split_per_month(), [tw])
# Single day test
tw = TimeWindow(
datetime(2015, 1, 1, 15, 30, 40, 54),
datetime(2015, 1, 1, 16, 4, 3, 1),
)
self.assertEqual(tw.split_per_month(), [tw])
# Span one month, with period less than 30 days
tw = TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 15, 10))
self.assertEqual(
tw.split_per_month(),
[TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 1, 15, 10))])
# Span two months, with period greater than 30 days
tw = TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 2, 5, 23, 30))
self.assertEqual(tw.split_per_month(), [
TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 2, 1, 0, 0)),
TimeWindow(datetime(2015, 2, 1, 0, 0), datetime(
2015, 2, 5, 23, 30))
])
# Span three months, with period greater than 30 days
tw = TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 3, 5, 23, 30))
self.assertEqual(tw.split_per_month(), [
TimeWindow(datetime(2015, 1, 1, 23), datetime(2015, 2, 1, 0, 0)),
TimeWindow(datetime(2015, 2, 1, 0, 0), datetime(2015, 3, 1, 0, 0)),
TimeWindow(datetime(2015, 3, 1, 0, 0), datetime(
2015, 3, 5, 23, 30))
])
# Span three months, with period greater than 30 days,
# starting from the middle of the first month
tw = TimeWindow(datetime(2015, 1, 15, 5), datetime(2015, 3, 6, 15))
self.assertEqual(tw.split_per_month(), [
TimeWindow(datetime(2015, 1, 15, 5, 0), datetime(2015, 2, 1, 0,
0)),
TimeWindow(datetime(2015, 2, 1, 0, 0), datetime(2015, 3, 1, 0, 0)),
TimeWindow(datetime(2015, 3, 1, 0, 0), datetime(2015, 3, 6, 15, 0))
])
# Span five months, with period greater than 30 days,
# starting from the 10th day of the first month
tw = TimeWindow(datetime(2015, 5, 10, 5), datetime(2015, 7, 23, 15))
self.assertEqual(tw.split_per_month(), [
TimeWindow(datetime(2015, 5, 10, 5, 0), datetime(2015, 6, 1, 0,
0)),
TimeWindow(datetime(2015, 6, 1, 0, 0), datetime(2015, 7, 1, 0, 0)),
TimeWindow(datetime(2015, 7, 1, 0, 0), datetime(
2015, 7, 23, 15, 0))
])
# Span two months, with period less than 30 days,
# starting from December
tw = TimeWindow(datetime(2015, 12, 15, 5), datetime(2016, 1, 6, 15))
self.assertEqual(tw.split_per_month(), [
TimeWindow(datetime(2015, 12, 15, 5, 0), datetime(
2016, 1, 1, 0, 0)),
TimeWindow(datetime(2016, 1, 1, 0, 0), datetime(2016, 1, 6, 15, 0))
])
def __init__(self, wechat_id):
BaseCheckin.__init__(self, wechat_id)
self.class_list = self.init_class_records()
self.enter_time = time.strftime('%H:%M')
self.section_id = self.init_section_id(self.enter_time)
self.time_window = TimeWindow()
def test_bug_compress_spatial_time_area(self):
# This case was captured live on debugger.
tws = TimeWindowsCollection([
TimeWindow(datetime(2015, 3, 4, 16, 40, 31, 0),
datetime(2015, 3, 4, 16, 41, 31, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 43, 44, 0),
datetime(2015, 3, 4, 16, 44, 44, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 43, 57, 0),
datetime(2015, 3, 4, 16, 44, 57, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 44, 9, 0),
datetime(2015, 3, 4, 16, 45, 9, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 44, 22, 0),
datetime(2015, 3, 4, 16, 45, 22, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 44, 35, 0),
datetime(2015, 3, 4, 16, 45, 35, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 46, 23, 0),
datetime(2015, 3, 4, 16, 47, 23, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 46, 40, 0),
datetime(2015, 3, 4, 16, 47, 40, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 46, 56, 0),
datetime(2015, 3, 4, 16, 47, 56, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 47, 12, 0),
datetime(2015, 3, 4, 16, 48, 12, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 47, 28, 0),
datetime(2015, 3, 4, 16, 48, 28, 0)),
TimeWindow(datetime(2015, 3, 4, 16, 47, 44, 0),
datetime(2015, 3, 4, 16, 48, 44, 0))
])
res = tws.compressed()
self.assertEqual(res.time_windows, [
TimeWindow(datetime(2015, 3, 4, 16, 40, 31),
datetime(2015, 3, 4, 16, 41, 31)),
TimeWindow(datetime(2015, 3, 4, 16, 43, 44),
datetime(2015, 3, 4, 16, 45, 35)),
TimeWindow(datetime(2015, 3, 4, 16, 46, 23),
datetime(2015, 3, 4, 16, 48, 44))
])
def test_property_middle(self):
now = datetime.now()
until = now + timedelta(minutes=10)
tw = TimeWindow(now, until)
self.assertEqual(tw.middle, now + timedelta(minutes=5))