def get_dates(dt):
output = []
"""
Params
date_arg: a date in format datetime
Returns:
master_list_sorted: list of dates within
a week that includes the given date, plus matching day
from the previous week. If a Monday date is provided,'current' dates
will be the previous week, not the week that includes the Monday date
"""
if dt.isoweekday() == 1: #change dt to previous Sunday
dt = dt - timedelta(days=1)
else:
dt = dt + timedelta(days=(7 - dt.isoweekday())) #change target to following Sunday
for x in reversed(range(7)):
result = {
"current": dt - timedelta(days=x),
"previous": dt - timedelta(days=x, weeks=1),
}
output.append(result)
return output
def is_weekend(self, datetime):
""" Returns True if datetime lands on a weekend.
"""
for weekend in self._weekends:
if datetime.isoweekday() == weekend:
return True
return False
def is_weekend(self, datetime):
"""
Returns True if datetime lands on a weekend.
"""
for weekend in self.weekends:
if datetime.isoweekday() == weekend:
return True
return False
def isRecordingTime(dt, DAY_START = time(8, 30),DAY_END = time(15, 18),NIGHT_START = time(20, 30),NIGHT_END = time(2, 33)):
currentTime = dt.time()
recording = False
# 判断当前处于的时间段
if ((currentTime >= DAY_START and currentTime <= DAY_END) or
(currentTime >= NIGHT_START) or
(currentTime <= NIGHT_END)):
recording = True
# 过滤周末时间段
weekday = dt.isoweekday()
if ((weekday == 6 and currentTime > NIGHT_END) or weekday == 7):
recording = False
return recording
def generate_data_frame(self, df, debug=False):
def convert_to_target_group(value):
return 1 if str(value) == str(self.target_group) else 0
def convert_to_light(value):
light_aware_date_time = LightAwareDatetime(value, self.location)
if light_aware_date_time.is_light():
return 1
elif light_aware_date_time.is_dark():
return 0
else:
return -1
def convert_to_dusk(value):
light_aware_date_time = LightAwareDatetime(value, self.location)
return self.location.dusk(date=light_aware_date_time.date)
import time
start = time.time()
def timecheck(note=None):
if debug:
print(note, time.time() - start)
if self.datetime_column != "datetime":
if "datetime" in df.columns:
df.drop("datetime", axis=1, inplace=True)
df.rename(columns={self.datetime_column: "datetime"}, inplace=True)
timecheck("Before dropping na rows")
df.dropna(how="any", subset=["datetime", self.target_column],
inplace=True)
timecheck("After dropping na rows")
# set index to datetime
timecheck("Before setting index")
df.index = df.datetime
timecheck("After setting index")
timecheck("Before stripping non-evening hours")
df = self._strip_non_evening_hours(df)
timecheck("After stripping non-evening hours")
if self.dst_restrict:
timecheck("Before stripping non-seasonal days")
df = self._strip_non_seasonal_days(df)
df = self._strip_based_on_min_max_twilight(df)
timecheck("After stripping non-seasonal days")
timecheck("Before converting to target group")
if "original_target" in df.columns:
df.drop("original_target", axis=1, inplace=True)
df.rename(columns={self.target_column: "original_target"}, inplace=True)
if "target" in df.columns:
df.drop("target", axis=1, inplace=True)
df["target"] = df["original_target"].apply(convert_to_target_group)
timecheck("After converting to target group")
timecheck("Before converting to light")
df["light"] = df["datetime"].apply(convert_to_light)
if debug:
df["dusk"] = df["datetime"].apply(convert_to_dusk)
timecheck("After converting to light")
timecheck("Before stripping twilight hours")
df = self._strip_grey_hours(df)
timecheck("After stripping twilight hours")
timecheck("Before creating year column")
df["year"] = df["datetime"].apply(lambda dt: dt.year)
timecheck("After creating year column")
df["month"] = df["datetime"].apply(lambda dt: dt.month)
df["day_of_week"] = df["datetime"].apply(lambda dt: dt.isoweekday())
def time_in_seconds(dt):
start_of_day = dt.replace(hour=0, minute=0, second=0, microsecond=0)
return int(dt.timestamp() - start_of_day.timestamp())
df["time_in_seconds"] = df["datetime"].apply(time_in_seconds)
if self.officer_id_column:
df.rename(columns={self.officer_id_column: "officerid"},
inplace=True)
df.officerid = df.officerid.apply(str)
return df
contador += 1
fechaactual = fechadesde.strftime(formato)
print(contador, fechaactual, 'es martes')
fechadesde = fechadesde + timedelta(days=1)
except:
print('Fecha incorrecta')
print(input(" continuar?"))
limpiar()
print("#########################################################")
print("Obtener día de la semana por su número")
print(
"La función isoweekday() devuelve el número de día de la semana a que corresponda la fecha indicada, según los siguientes valores por día: 1-Lunes, 2-Martes, 3-Miércoles, 4-Jueves, 5-Viernes, 6-Sábado y 7-Domingo."
)
dia_semana = datetime.isoweekday(fecha1)
print(fecha1, "->", dia_semana, "->", tupla_diassem[dia_semana - 1])
print(input(" continuar?"))
limpiar()
print("#########################################################")
print("Dado el ordinal se obtiene la fecha correspondiente")
print("Si la fecha 01-01-0001 tiene el ordinal 1 entonces...")
for num in range(1, 7):
print("El día", 10**num, "se corresponde con", date.fromordinal(10**num))
print(input(" continuar?"))
limpiar()
print("#########################################################")
print("Dada una fecha se obtiene un ordinal (01-01-0001 -> 1)")
fecha3 = datetime(1, 1, 1, 0, 0, 0)
print("La fecha", fecha3, "tiene el ordinal", date.toordinal(fecha3))
def get_weekday(i):
return weekday[str(datetime.isoweekday(i))]
def OpenAllShutter():
print( 'Ouverture')
# if(datetime.isoweekday(datetime.now())<=5): # Only Monday to Friday
if(datetime.isoweekday(datetime.now())>10): # Never
# if(datetime.isoweekday(datetime.now())>0): # Always
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x06&entree=0x0E&data0=0x26' # Volet Salon
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 10 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x0A&entree=0x0B&data0=0x26' # VR Sdb 1
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 3 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x06&entree=0x09&data0=0x52' # VR haut cuisine
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
# if(datetime.isoweekday(datetime.now())<=5): # Only Monday to Friday
if(datetime.isoweekday(datetime.now())>10): # Never
# if(datetime.isoweekday(datetime.now())>0): # Always
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x02&entree=0x02&data0=0x26' # Chambre 3
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x02&entree=0x07&data0=0x26' # Chambre 4
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x04&entree=0x06&data0=0x26' # VR Fixe Chambre Nord
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x04&entree=0x01&data0=0x26' # VR FChambre Nord
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x0A&entree=0x04&data0=0x26' # VR Fixe Chambre Terrasse
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x0A&entree=0x06&data0=0x26' # VR Chambre Terrasse
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
time.sleep( 0.5 )
url='http://localhost/domocan/www/php/CmdVR.php?carte=0x02&entree=0x09&data0=0x26' # VR Sdb 2 + Garage
url_response = urllib2.urlopen(url)
html = url_response.read()
url_response.close() # best practice to close the file
import datetime
from datetime import datetime, date, time
import time
import calendar
test_day = datetime.strptime('19901204', '%Y%m%d').date()
today = date.today()
month_ago = date(2018, 4, 15)
plus_one_year = date(2019, 4, 15)
print(test_day) #for question #3
print(today - month_ago) #for question #4
print(datetime.isoweekday(plus_one_year)) #4 what day plus one year
def generate_data_frame(self, df, debug=False):
def convert_to_target_group(value):
return 1 if str(value) == str(self.target_group) else 0
def convert_to_light(value):
light_aware_date_time = LightAwareDatetime(value, self.location)
if light_aware_date_time.is_light():
return 1
elif light_aware_date_time.is_dark():
return 0
else:
return -1
@lru_cache
def cached_location_lookup(location, time_of_day, date, local):
if time_of_day == "sunset":
return location.sunset(date=date, local=local)
elif time_of_day == "sunrise":
return location.sunrise(date=date, local=local)
elif time_of_day == "dusk":
return location.dusk(date=date, local=local)
elif time_of_day == "dawn":
return location.dawn(date=date, local=local)
def convert_to_light_vector(
df, datetime_column: str, location: astral.Location
):
timezone = pytz.timezone(location.timezone)
df["_local_datetime"] = df[datetime_column].dt.tz_localize(timezone)
df["_date"] = df["_local_datetime"].dt.date
dates = df["_date"].tolist()
df["_sunrise"] = [
cached_location_lookup(location, "sunrise", date=date, local=True)
for date in dates
]
df["_sunset"] = [
cached_location_lookup(location, "sunset", date=date, local=True)
for date in dates
]
df["_dawn"] = [
cached_location_lookup(location, "dawn", date=date, local=True)
for date in dates
]
df["_dusk"] = [
cached_location_lookup(location, "dusk", date=date, local=True)
for date in dates
]
df["_light"] = (df["_sunrise"] <= df["_local_datetime"]) & (
df["_local_datetime"] <= df["_sunset"]
)
df["_dark"] = (df["_local_datetime"] < df["_dawn"]) | (
df["_local_datetime"] >= df["_dusk"]
)
df["_gray"] = ~(df["_light"] | df["_dark"])
df["light"] = (df["_light"]).astype(int)
return df
def convert_to_dusk(value):
light_aware_date_time = LightAwareDatetime(value, self.location)
return self.location.dusk(date=light_aware_date_time.date)
import time
start = time.time()
def timecheck(note=None):
if debug:
print(note, time.time() - start)
if self.datetime_column != "datetime":
if "datetime" in df.columns:
df.drop("datetime", axis=1, inplace=True)
df.rename(columns={self.datetime_column: "datetime"}, inplace=True)
timecheck("Before dropping na rows")
df.dropna(how="any", subset=["datetime", self.target_column], inplace=True)
timecheck("After dropping na rows")
# set index to datetime
timecheck("Before setting index")
df.index = df.datetime
timecheck("After setting index")
timecheck("Before stripping non-evening hours")
df = self._strip_non_evening_hours(df)
timecheck("After stripping non-evening hours")
if self.dst_restrict:
timecheck("Before stripping non-seasonal days")
df = self._strip_non_seasonal_days(df)
df = self._strip_based_on_min_max_twilight(df)
timecheck("After stripping non-seasonal days")
timecheck("Before converting to target group")
if "original_target" in df.columns:
df.drop("original_target", axis=1, inplace=True)
df.rename(columns={self.target_column: "original_target"}, inplace=True)
if "target" in df.columns:
df.drop("target", axis=1, inplace=True)
df["target"] = df["original_target"].apply(convert_to_target_group)
timecheck("After converting to target group")
timecheck("Before converting to light")
df = convert_to_light_vector(df, "datetime", self.location.location)
# df["light"] = df["datetime"].apply(convert_to_light)
if debug:
df["dusk"] = df["datetime"].apply(convert_to_dusk)
timecheck("After converting to light")
timecheck("Before stripping twilight hours")
df = self._strip_grey_hours(df)
timecheck("After stripping twilight hours")
timecheck("Before creating year column")
df["year"] = df["datetime"].apply(lambda dt: dt.year)
timecheck("After creating year column")
df["month"] = df["datetime"].apply(lambda dt: dt.month)
df["day_of_week"] = df["datetime"].apply(lambda dt: dt.isoweekday())
def time_in_seconds(dt):
start_of_day = dt.replace(hour=0, minute=0, second=0, microsecond=0)
return int(dt.timestamp() - start_of_day.timestamp())
df["time_in_seconds"] = df["datetime"].apply(time_in_seconds)
if self.officer_id_column:
df.rename(columns={self.officer_id_column: "officerid"}, inplace=True)
df.officerid = df.officerid.apply(str)
return df
