def scipy_discrete_sequence(n,distribution=False):
"""
Return sample sequence of length n from a given discrete distribution.
distribution=histogram of values, will be normalized
"""
try:
import scipy.stats as stats
except ImportError:
print("Import error: not able to import scipy")
return
import bisect
if not distribution:
return "no distribution specified"
p=distribution
random._inst = random.Random()
# make CDF out of distribution to use for sample
cdf=[]
cdf.append(0.0)
psum=float(sum(p))
for i in range(0,len(p)):
cdf.append(cdf[i]+p[i]/psum)
# get a uniform random number
stats.seed(random.randint(1,2**30),random.randint(1,2**30))
inputseq=stats.uniform(size=n)
# choose from CDF
seq=[bisect.bisect_left(cdf,s)-1 for s in inputseq]
return seq
def scipy_discrete_sequence(n, distribution=False):
"""
Return sample sequence of length n from a given discrete distribution
distribution=histogram of values, will be normalized
"""
try:
import scipy.stats as stats
except ImportError:
print "Import error: not able to import scipy"
return
import bisect
if not distribution:
return "no distribution specified"
p = distribution
random._inst = random.Random()
# make CDF out of distribution to use for sample
cdf = []
cdf.append(0.0)
psum = float(sum(p))
for i in range(0, len(p)):
cdf.append(cdf[i] + p[i] / psum)
# get a uniform random number
stats.seed(random.randint(1, 2**30), random.randint(1, 2**30))
inputseq = stats.uniform(size=n)
# choose from CDF
seq = [bisect.bisect_left(cdf, s) - 1 for s in inputseq]
return seq
def scipy_pareto_sequence(n,exponent=1.0):
"""
Return sample sequence of length n from a Pareto distribution.
"""
try:
import scipy.stats as stats
except ImportError:
print("Import error: not able to import scipy")
return
random._inst = random.Random()
stats.seed(random.randint(1,2**30),random.randint(1,2**30))
return stats.pareto(exponent,size=n)
def scipy_uniform_sequence(n):
"""
Return sample sequence of length n from a uniform distribution.
"""
try:
import scipy.stats as stats
except ImportError:
print("Import error: not able to import scipy")
return
random._inst = random.Random()
stats.seed(random.randint(1,2**30),random.randint(1,2**30))
return stats.uniform(size=n)
def scipy_poisson_sequence(n,mu=1.0):
"""
Return sample sequence of length n from a Poisson distribution.
"""
try:
import scipy.stats as stats
except ImportError:
print "Import error: not able to import scipy"
return
random._inst = random.Random()
stats.seed(random.randint(1,2**30),random.randint(1,2**30))
return stats.poisson(mu,size=n)
def scipy_uniform_sequence(n):
"""
Return sample sequence of length n from a uniform distribution.
"""
try:
import scipy.stats as stats
except ImportError:
print "Import error: not able to import scipy"
return
random._inst = random.Random()
stats.seed(random.randint(1, 2**30), random.randint(1, 2**30))
return stats.uniform(size=n)
def scipy_poisson_sequence(n, mu=1.0):
"""
Return sample sequence of length n from a Poisson distribution.
"""
try:
import scipy.stats as stats
except ImportError:
print "Import error: not able to import scipy"
return
random._inst = random.Random()
stats.seed(random.randint(1, 2**30), random.randint(1, 2**30))
return stats.poisson(mu, size=n)
def scipy_powerlaw_sequence(n, exponent=2.0):
"""
Return sample sequence of length n from a power law distribution.
"""
try:
import scipy.stats as stats
except ImportError:
print "Import error: not able to import scipy"
return
random._inst = random.Random()
stats.seed(random.randint(1, 2**30), random.randint(1, 2**30))
return stats.pareto(exponent - 1, size=n)
