def __init__(self, x, y, name, maxhealth, speed, damage, image, attacktimer, mass, attack_range):
self.x = x
self.y = y
self.name = name
self.maxhealth = maxhealth
self.speed = speed
self.damage = damage
self.image = image
self.attacktimer = attacktimer
self.mass = mass
self.uuid = core.Var.uuid_gen
core.Var.uuid_gen += 1
self.health = self.maxhealth
self.frame = self.image.get_rect()
self.size = (self.image.get_width() / 2) ** 2
self.vect = core.p.math.Vector2(core.Gameobj.hero.x - self.x, core.Gameobj.hero.y - self.y)
self.vect_long = core.p.math.Vector2(core.Gameobj.hero.x - self.x, core.Gameobj.hero.y - self.y)
self.time_attacked = 0
self.attack_bool = False
self.kickback = 0
self.collision_with_other = [False, 1, 1]
self.kick_direction = [0, 0]
self.attack_range = (core.dpp(attack_range) + core.sqrt(self.size) + core.sqrt(core.Gameobj.hero.size)) ** 2
def corrcoef(x, y=None, rowvar=True, bias=False, allow_masked=True):
"""
The correlation coefficients formed from the array x, where the
rows are the observations, and the columns are variables.
corrcoef(x,y) where x and y are 1d arrays is the same as
corrcoef(transpose([x,y]))
Parameters
----------
x : ndarray
Input data. If x is a 1D array, returns the variance.
If x is a 2D array, returns the covariance matrix.
y : {None, ndarray} optional
Optional set of variables.
rowvar : {False, True} optional
If True, then each row is a variable with observations in columns.
If False, each column is a variable and the observations are in the rows.
bias : {False, True} optional
Whether to use a biased (True) or unbiased (False) estimate of the
covariance.
If True, then the normalization is by N, the number of non-missing
observations.
Otherwise, the normalization is by (N-1).
allow_masked : {True, False} optional
If True, masked values are propagated pair-wise: if a value is masked
in x, the corresponding value is masked in y.
If False, raises an exception.
See Also
--------
cov
"""
# Get the data
(x, xnotmask, rowvar) = _covhelper(x, y, rowvar, allow_masked)
# Compute the covariance matrix
if not rowvar:
fact = np.dot(xnotmask.T, xnotmask) * 1.0 - (1 - bool(bias))
c = (dot(x.T, x.conj(), strict=False) / fact).squeeze()
else:
fact = np.dot(xnotmask, xnotmask.T) * 1.0 - (1 - bool(bias))
c = (dot(x, x.T.conj(), strict=False) / fact).squeeze()
# Check whether we have a scalar
try:
diag = ma.diagonal(c)
except ValueError:
return 1
#
if xnotmask.all():
_denom = ma.sqrt(ma.multiply.outer(diag, diag))
else:
_denom = diagflat(diag)
n = x.shape[1 - rowvar]
if rowvar:
for i in range(n - 1):
for j in range(i + 1, n):
_x = mask_cols(vstack((x[i], x[j]))).var(axis=1, ddof=1 - bias)
_denom[i, j] = _denom[j, i] = ma.sqrt(ma.multiply.reduce(_x))
else:
for i in range(n - 1):
for j in range(i + 1, n):
_x = mask_cols(vstack((x[:, i], x[:, j]))).var(axis=1, ddof=1 - bias)
_denom[i, j] = _denom[j, i] = ma.sqrt(ma.multiply.reduce(_x))
return c / _denom
def corrcoef(x, y=None, rowvar=True, bias=False, allow_masked=True):
"""The correlation coefficients formed from the array x, where the
rows are the observations, and the columns are variables.
corrcoef(x,y) where x and y are 1d arrays is the same as
corrcoef(transpose([x,y]))
Parameters
----------
x : ndarray
Input data. If x is a 1D array, returns the variance.
If x is a 2D array, returns the covariance matrix.
y : {None, ndarray} optional
Optional set of variables.
rowvar : {False, True} optional
If True, then each row is a variable with observations in columns.
If False, each column is a variable and the observations are in the rows.
bias : {False, True} optional
Whether to use a biased (True) or unbiased (False) estimate of the
covariance.
If True, then the normalization is by N, the number of non-missing
observations.
Otherwise, the normalization is by (N-1).
allow_masked : {True, False} optional
If True, masked values are propagated pair-wise: if a value is masked
in x, the corresponding value is masked in y.
If False, raises an exception.
See Also
--------
cov
"""
# Get the data
(x, xnotmask, rowvar) = _covhelper(x, y, rowvar, allow_masked)
# Compute the covariance matrix
if not rowvar:
fact = np.dot(xnotmask.T, xnotmask)*1. - (1 - bool(bias))
c = (dot(x.T, x.conj(), strict=False) / fact).squeeze()
else:
fact = np.dot(xnotmask, xnotmask.T)*1. - (1 - bool(bias))
c = (dot(x, x.T.conj(), strict=False) / fact).squeeze()
# Check whether we have a scalar
try:
diag = ma.diagonal(c)
except ValueError:
return 1
#
if xnotmask.all():
_denom = ma.sqrt(ma.multiply.outer(diag, diag))
else:
_denom = diagflat(diag)
n = x.shape[1-rowvar]
if rowvar:
for i in range(n-1):
for j in range(i+1,n):
_x = mask_cols(vstack((x[i], x[j]))).var(axis=1,
ddof=1-bias)
_denom[i,j] = _denom[j,i] = ma.sqrt(ma.multiply.reduce(_x))
else:
for i in range(n-1):
for j in range(i+1,n):
_x = mask_cols(vstack((x[:,i], x[:,j]))).var(axis=1,
ddof=1-bias)
_denom[i,j] = _denom[j,i] = ma.sqrt(ma.multiply.reduce(_x))
return c/_denom