def get_percentage_genes_covered_at_this_fraction(self, this):
assert this <= 1 and this >= 0
icol = self.coverage_column
X = pylab.linspace(0, 1, 101)
N = float(len(self.df))
Y = np.array([sum(self.df[icol] > x) / N * 100 for x in X])
return np.interp(this, X, Y)
def target_distribution(self, xprime):
"""The target distribution
Compute histogram. Get X, Y. Given xprime, interpolate to get yprime
use e.g. np.interp
"""
return np.interp(xprime, self.X[1:self.bins + 1], self.Y)
def target_distribution(self, xprime):
"""The target distribution
Compute histogram. Get X, Y. Given xprime, interpolate to get yprime
use e.g. np.interp
"""
return np.interp(xprime, self.X[1:self.bins+1], self.Y)
def simulate(self, n=100000, burning=20000, step=None, x0=None):
if step is None:
self.step = (self.upper_bound - self.lower_bound) / 100.
step = self.step
if x0 is None:
self.x0 = (self.upper_bound -
self.lower_bound) / 2 + self.lower_bound
self.aprob = []
# function target profile
NS = self.Ytarget / sum(self.Ytarget)
sdnorm = lambda x: np.interp(x, self.Xtarget, NS)
x = self.x0
vec = [x] # starting seed
# a gaussian jump centered on 0 is used as a random inovation
# if the candidate is outside of the boundaries, we try another
# candidate
def jumper(x):
#jump = uniform(-step, step)
jump = gauss(0, step)
xprime = x + jump
while xprime < self.lower_bound or xprime > self.upper_bound:
jump = gauss(0, step)
xprime = x + jump
return xprime
for i in range(1, n * 2 + burning):
xprime = jumper(x)
aprob = min([1.,
sdnorm(xprime) / sdnorm(x)]) #acceptance probability
u = uniform(0, 1)
if u < aprob:
x = xprime
vec.append(x)
self.aprob.append(aprob)
if len(vec) == n + burning:
break
self.burning_vector = vec[0:burning]
self.vec = vec[burning:]
return vec[burning:]
def simulate(self, n=100000, burning=20000, step=None, x0=None):
if step is None:
self.step = (self.upper_bound - self.lower_bound) / 100.
step = self.step
if x0 is None:
self.x0 = (self.upper_bound - self.lower_bound) / 2 + self.lower_bound
self.aprob = []
# function target profile
NS = self.Ytarget / sum(self.Ytarget)
sdnorm = lambda x: np.interp(x, self.Xtarget, NS)
x = self.x0
vec = [x] # starting seed
# a gaussian jump centered on 0 is used as a random inovation
# if the candidate is outside of the boundaries, we try another
# candidate
def jumper(x):
#jump = uniform(-step, step)
jump = gauss(0, step)
xprime = x + jump
while xprime < self.lower_bound or xprime>self.upper_bound:
jump = gauss(0, step)
xprime = x + jump
return xprime
for i in range(1, n*2+burning):
xprime = jumper(x)
aprob = min([1., sdnorm(xprime)/sdnorm(x)]) #acceptance probability
u = uniform(0, 1)
if u < aprob:
x = xprime
vec.append(x)
self.aprob.append(aprob)
if len(vec) == n + burning:
break
self.burning_vector = vec[0:burning]
self.vec = vec[burning:]
return vec[burning:]