def get_monthly_cycles(self):
temp = {}
if os.path.exists(os.path.dirname(os.path.abspath(__file__))+"/pv/"+self.lang+"/"+self.mp_title+".txt"):
filename = "pv/"+self.lang+"/"+self.mp_title+".txt"
with open(filename) as doc:
for line in doc:
cols = line.strip('\n').split("\t")
timestamp = int(cols[0])
hour_views = int(cols[2])
dt = datetime.utcfromtimestamp(timestamp)
if dt.strftime("%B_%Y") not in temp:
temp[dt.strftime("%B_%Y")] = {}
temp[dt.strftime("%B_%Y")]["hourly_totals"] = np.zeros(24).tolist()
temp[dt.strftime("%B_%Y")]["hourly_num"] = np.zeros(24).tolist()
temp[dt.strftime("%B_%Y")]["weekly_totals"] = np.zeros(7*24).tolist()
temp[dt.strftime("%B_%Y")]["weekly_num"] = np.zeros(7*24).tolist()
temp[dt.strftime("%B_%Y")]["hourly_totals"][dt.hour] += hour_views
temp[dt.strftime("%B_%Y")]["hourly_num"][dt.hour] += 1
temp[dt.strftime("%B_%Y")]["weekly_totals"][24*dt.weekday()+dt.hour] += hour_views
temp[dt.strftime("%B_%Y")]["weekly_num"][24*dt.weekday()+dt.hour] += 1
for month in temp:
self.monthly_cycles[month] = {}
self.monthly_cycles[month]["hourly"] = calc_average(temp[month]["hourly_totals"],temp[month]["hourly_num"])
self.monthly_cycles[month]["weekly"] = calc_average(temp[month]["weekly_totals"],temp[month]["weekly_num"])
def mml9_damage(rnge, art):
"""
Calculates MML9 damage.
We assumes a switch from LRM to SRM at range 6.
"""
if rnge <= 6:
return 2 * calc_average(9, art)
else:
return calc_average(9, art)
def hag20_damage(rnge):
"""
Calculates HAG20 damage.
"""
if rnge <= 8:
return calc_average(20, 2)
elif rnge <= 16:
return calc_average(20, 0)
else:
return calc_average(20, -2)
def atm12_damage(rnge):
"""
Calculates ATM12 damage.
We assumes a switch to HE at range 6, and normal at range 10.
"""
if rnge <= 6:
return 3 * calc_average(12, 2)
elif rnge <= 10:
return 2 * calc_average(12, 2)
else:
return calc_average(12, 2)
def get_cycles(self):
hourly_totals = np.zeros(24).tolist()
hourly_norm_totals = np.zeros(24).tolist()
hourly_num = np.zeros(24).tolist()
weekly_totals = np.zeros(7*24).tolist()
weekly_norm_totals = np.zeros(7*24).tolist()
weekly_num = np.zeros(7*24).tolist()
hour_norm = np.zeros(24).tolist()
week_norm = np.zeros(7*24).tolist()
if os.path.exists(os.path.dirname(os.path.abspath(__file__))+"/pv/"+self.lang+"_total.txt"):
filename = "pv/"+self.lang+"_total.txt"
with open(filename) as doc:
for line in doc:
cols = line.strip('\n').split("\t")
timestamp = int(cols[0])
hour_views = int(cols[2])
dt = datetime.utcfromtimestamp(timestamp)
if dt.hour == 0 and sum(hour_norm) > 0:
temp = [x/sum(hour_norm) for x in hour_norm]
hour_norm = np.zeros(24).tolist()
for x,views in enumerate(temp):
hourly_norm_totals[x] += views
if dt.weekday() == 0:
temp_week = [x/sum(week_norm) for x in week_norm]
week_norm = np.zeros(7*24).tolist()
for x,views in enumerate(temp_week):
weekly_norm_totals[x] += views
hourly_totals[dt.hour] += hour_views
hour_norm[dt.hour] = hour_views
hourly_num[dt.hour] += 1
weekly_totals[24*dt.weekday()+dt.hour] += hour_views
week_norm[24*dt.weekday()+dt.hour] += hour_views
weekly_num[24*dt.weekday()+dt.hour] += 1
self.hourly = calc_average(hourly_totals,hourly_num)
self.hourly_norm = calc_average(hourly_norm_totals,hourly_num)
self.weekly = calc_average(weekly_totals,weekly_num)
self.weekly_norm = calc_average(weekly_norm_totals,weekly_num)
"T", 1, 8, 5, ""],
"(IS) LB 10-X AC": ["LB10", "DB", [148, 19], 1, 400000,
2, (lambda x, y: 10), [0, 6, 12, 18],
"T", 1, 11, 6, ""],
"(IS) LB 20-X AC": ["LB20", "DB", [237, 30], 1, 600000,
6, (lambda x, y: 20), [0, 4, 8, 12],
"T", 1, 14, 11, ""],
"(IS) Light AC/2": ["LAC2", "DB", [30, 4], 1, 100000,
1, (lambda x, y: 2), [0, 6, 12, 18],
"T", 1, 4, 1, ""],
"(IS) Light AC/5": ["LAC5", "DB", [62, 8], 1, 150000,
1, (lambda x, y: 5), [0, 5, 10, 15],
"T", 1, 5, 2, ""],
"(IS) Rotary AC/2":
["RAC2", "DB", [118, 15], 1, 175000,
6, (lambda x, y: 2 * calc_average(6, 0)), [0, 6, 12, 18],
"T", 6, 8, 3, ""],
"(IS) Rotary AC/5":
["RAC5", "DB", [247, 31], 1, 275000,
6, (lambda x, y: 5 * calc_average(6, 0)), [0, 5, 10, 15],
"T", 6, 10, 6, ""],
"(IS) Ultra AC/2":
["UAC2", "DB", [56, 7], 1, 120000,
2, (lambda x, y: 2 * calc_average(2, 0)), [3, 8, 17, 25],
"T", 2, 7, 3, ""],
"(IS) Ultra AC/5":
["UAC5", "DB", [112, 14], 1, 200000,
2, (lambda x, y: 5 * calc_average(2, 0)), [2, 6, 13, 20],
"T", 2, 9, 5, ""],
"(IS) Ultra AC/10":
["UAC10", "DB", [210, 26], 1, 320000,
