def grow_plants(play_area, num, plant_type, bias):
"""
:param play_area:
:param num:
:param plant_type:
:param bias:
:return: a list of spawned plants of given type
"""
normal_distribution = NormalDist(0.5, 0.15)
plant_db = {}
dist_rolls = normal_distribution.samples(num)
if bias == 'edges':
dist_rolls = inverse_probabilities(dist_rolls)
for i in range(num):
location = get_location_in_circle(dist_rolls[i], play_area)
new_p = Decoration(location, plant_type)
plant_db[location] = new_p
clean_up_db = {}
for p in plant_db.values():
p_loc = p.rect.left, p.rect.top
if p_loc not in clean_up_db:
clean_up_db[p_loc] = p
return clean_up_db
def get_control(x_):
seed = x_[0]["close"]
generated = [{"datetime": r["datetime"], "close": r["close"]} for r in x_]
try:
(mean_, stdev_) = dist(x_, 0, len(x_) - 1)
except (StatisticsError, ValueError, ZeroDivisionError):
mean_, stdev_ = (0, 1)
dist_ = NormalDist(mean_, stdev_)
samples = dist_.samples(len(x_))
for i in range(1, len(samples)):
seed *= round(exp(samples[i]), 2)
generated[i]["close"] = seed
control = rsi(generated)
return (control, generated)
def normal_distribution_flower_spawning_strategy(play_area, flower_num):
"""
Returns a normal-distribution generated list of flowers
:param play_area:
:return: list of spawned flowers
"""
normal_distribution = NormalDist(0.5, 0.15)
flower_database = {}
dist_rolls = normal_distribution.samples(flower_num)
for i in range(flower_num):
location = get_location_in_circle(dist_rolls[i], play_area)
new_f = Flower(location)
flower_database[location] = new_f
clean_up_table = {}
for f in flower_database.values():
f_loc = f.rect.left, f.rect.top
if f_loc not in clean_up_table:
clean_up_table[f_loc] = f
return clean_up_table
