def test():
# get the package
import gsl
# the terms
α = 2
β = 3
# the vector x
x = gsl.vector(shape=3)
x[0], x[1], x[2] = 1,2,3
# the vector y
y = gsl.vector(shape=3)
y[0], y[1], y[2] = 2,4,6
# the matrix A
A = gsl.matrix(shape=(3,3)).identity()
A[0,1], A[0,2], A[1,2] = 2,3,2
# compute the form
y = gsl.blas.dsymv(A.upperTriangular, α, A, x, β, y)
# check
# print(tuple(y))
assert tuple(y) == (34, 32, 38)
# all done
return
def test():
# math
from math import pi, sqrt, exp
# access the package
import gsl
# build a random number generator
rng = gsl.rng()
# the order of the distribution
K = 10
# the weight vectors
α = gsl.vector(shape=K).fill(K**(-1/2))
# build a gaussian distribution
dirichlet = gsl.pdf.dirichlet(alpha=α, rng=rng)
# make a vector
v = gsl.vector(shape=K)
# fill it with random numbers
v.random(pdf=dirichlet)
# make a matrix
m = gsl.matrix(shape=(50, K))
# fill it with random numbers
m.random(pdf=dirichlet)
return dirichlet
def test():
# get the package
import gsl
# the terms
α = 2
β = 3
# the vector x
x = gsl.vector(shape=3)
x[0], x[1], x[2] = 1,2,3
# the vector y
y = gsl.vector(shape=3)
y[0], y[1], y[2] = 2,4,6
# the matrix A
A = gsl.matrix(shape=(3,3)).identity()
A[0,1], A[0,2], A[1,2] = 2,3,2
A[1,0], A[2,0], A[2,1] = 2,3,2
# compute the form
y = gsl.blas.dgemv(A.opNoTrans, α, A, x, β, y)
# check
# print(tuple(y))
assert tuple(y) == (34, 32, 38)
# all done
return
def test():
# package access
import gsl
# make a matrix
m = gsl.matrix(shape=(100, 50))
# fill it with random values
m.random(pdf=gsl.pdf.gaussian(mean=0.0, sigma=2, rng=gsl.rng()))
# rows
i = 3
# set the ith row to all {i}
for j in range(m.columns):
m[i, j] = i
# get the row using the interface
row = m.getRow(i)
# check that it is a vector
assert row.shape == m.columns
# full of {i}
assert row == gsl.vector(shape=m.columns).fill(i)
# columns
j = 2
# set the jth column to all {j}
for i in range(m.rows):
m[i, j] = j
# get the column using the interface
column = m.getColumn(j)
# check that it is a vector
assert column.shape == m.rows
# full of {j}
assert column == gsl.vector(shape=m.rows).fill(j)
# shape
rows = 100
columns = 200
# make another matrix
m = gsl.matrix(shape=(rows, columns)).zero()
# make a vector of ones
ones = gsl.vector(shape=columns).fill(1.0)
# set the middle column
m.setRow(rows / 2, ones)
# verify it was done properly
assert m.getRow(rows / 2) == ones
# make a vector of twos
twos = gsl.vector(shape=rows).fill(2.0)
# set the middle column
m.setColumn(columns / 2, twos)
# verify it was done properly
assert m.getColumn(columns / 2) == twos
# all done
return m
def test():
# package access
import gsl
# make a matrix
m = gsl.matrix(shape=(100,50))
# fill it with random values
m.random(pdf=gsl.pdf.gaussian(mean=0.0, sigma=2, rng=gsl.rng()))
# rows
i = 3
# set the ith row to all {i}
for j in range(m.columns) : m[i,j] = i
# get the row using the interface
row = m.getRow(i)
# check that it is a vector
assert row.shape == m.columns
# full of {i}
assert row == gsl.vector(shape=m.columns).fill(i)
# columns
j = 2
# set the jth column to all {j}
for i in range(m.rows): m[i,j] = j
# get the column using the interface
column = m.getColumn(j)
# check that it is a vector
assert column.shape == m.rows
# full of {j}
assert column == gsl.vector(shape=m.rows).fill(j)
# shape
rows = 100
columns = 200
# make another matrix
m = gsl.matrix(shape=(rows,columns)).zero()
# make a vector of ones
ones = gsl.vector(shape=columns).fill(1.0)
# set the middle column
m.setRow(rows/2, ones)
# verify it was done properly
assert m.getRow(rows/2) == ones
# make a vector of twos
twos = gsl.vector(shape=rows).fill(2.0)
# set the middle column
m.setColumn(columns/2, twos)
# verify it was done properly
assert m.getColumn(columns/2) == twos
# all done
return m
def test():
# get the package
import gsl
# a couple of values
x = 3
y = 2
# make a couple of vectors
v1 = gsl.vector(shape=10).fill(x)
v2 = gsl.vector(shape=10).fill(y)
# compute the dot product
assert gsl.blas.ddot(v1, v2) == x * y * v1.shape
# all done
return
def test():
# get the package
import gsl
# a couple of values
x = 3
y = 2
# make a couple of vectors
v1 = gsl.vector(shape=10).fill(x)
v2 = gsl.vector(shape=10).fill(y)
# compute the dot product
assert gsl.blas.ddot(v1, v2) == x*y*v1.shape
# all done
return
def test():
# access the package
import gsl
# build a random number generator
rng = gsl.rng()
# build a uniform distribution
uniform = gsl.pdf.uniform_pos(rng=rng)
# sample it
sample = uniform.sample()
# assert the sample is within (0, 1)
assert sample > 0 and sample < 1
density = uniform.density(0)
assert density == 1
# make a vector
v = gsl.vector(1000)
# fill it with random numbers
uniform.vector(vector=v)
# assert all the samples are positive
for sample in v: assert sample > 0 and sample < 1
# make a matrix
m = gsl.matrix(shape=(100, 100))
# fill it with random numbers
uniform.matrix(matrix=m)
# assert all the samples are positive
for sample in m: assert sample > 0 and sample < 1
return uniform
def test():
# package access
import gsl
# make a couple of vectors and initialize them
v1 = gsl.vector(shape=100).fill(value=1)
v2 = gsl.vector(shape=100).fill(value=2)
# check
for e in v1: assert e == 1
for e in v2: assert e == 2
# add them and store the result in v2
v2 -= v1
# check
for e in v1: assert e == 1
for e in v2: assert e == 1
# all done
return v1, v2
def test():
# math
from math import pi, sqrt, exp
# access the package
import gsl
# the σ of the distribution
σ = 2
# build a random number generator
rng = gsl.rng()
# build a gaussian distribution
gaussian = gsl.pdf.gaussian(mean=0.0, sigma=σ, rng=rng)
# sample it
sample = gaussian.sample()
# compute the density
x = 0
density = gaussian.density(x)
assert density == 1 / sqrt(2 * pi * σ**2) * exp(-x**2 / (2 * σ**2))
# make a vector
v = gsl.vector(1000)
# fill it with random numbers
gaussian.vector(vector=v)
# make a matrix
m = gsl.matrix(shape=(50, 200))
# fill it with random numbers
gaussian.matrix(matrix=m)
return gaussian
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# all done
return v
def test():
# access the package
import gsl
# build a random number generator
rng = gsl.rng()
# build a uniform distribution
uniform = gsl.pdf.uniform_pos(rng=rng)
# sample it
sample = uniform.sample()
# assert the sample is within (0, 1)
assert sample > 0 and sample < 1
density = uniform.density(0)
assert density == 1
# make a vector
v = gsl.vector(1000)
# fill it with random numbers
uniform.vector(vector=v)
# assert all the samples are positive
for sample in v:
assert sample > 0 and sample < 1
# make a matrix
m = gsl.matrix(shape=(100, 100))
# fill it with random numbers
uniform.matrix(matrix=m)
# assert all the samples are positive
for sample in m:
assert sample > 0 and sample < 1
return uniform
def test():
# access the package
import gsl
# the support of the distribution
support = (-1,1)
# build a random number generator
rng = gsl.rng()
# build a uniform distribution
uniform = gsl.pdf.uniform(support=support, rng=rng)
# sample it
sample = uniform.sample()
assert sample >= support[0] and sample < support[1]
density = uniform.density(0)
assert density == 1/(support[1]-support[0])
# make a vector
v = gsl.vector(1000)
# fill it with random numbers
uniform.vector(vector=v)
# make a matrix
m = gsl.matrix(shape=(100, 100))
# fill it with random numbers
uniform.matrix(matrix=m)
return uniform
def test():
# package access
import gsl
# make a couple of vectors and initialize them
v1 = gsl.vector(shape=100).fill(value=1)
v2 = gsl.vector(shape=100).fill(value=2)
# check
for e in v1: assert e == 1
for e in v2: assert e == 2
# add them and store the result in v2
v2 -= v1
# check
for e in v1: assert e == 1
for e in v2: assert e == 1
# all done
return v1, v2
def test():
# access the package
import gsl
# the support of the distribution
support = (-1, 1)
# build a random number generator
rng = gsl.rng()
# build a uniform distribution
uniform = gsl.pdf.uniform(support=support, rng=rng)
# sample it
sample = uniform.sample()
assert sample >= support[0] and sample < support[1]
density = uniform.density(0)
assert density == 1 / (support[1] - support[0])
# make a vector
v = gsl.vector(1000)
# fill it with random numbers
uniform.vector(vector=v)
# make a matrix
m = gsl.matrix(shape=(100, 100))
# fill it with random numbers
uniform.matrix(matrix=m)
return uniform
def test():
# package access
import gsl
# make a couple of vectors and initialize them
v1 = gsl.vector(shape=100).fill(value=2)
v2 = gsl.vector(shape=100).fill(value=2)
# check
for e in v1: assert e == 2
for e in v2: assert e == 2
# multiply them and store the result in v1
v1 *= v2
# check
for e in v1: assert e == 4
for e in v2: assert e == 2
# all done
return v1, v2
def test():
# math
from math import pi, sqrt, exp
# access the package
import gsl
# the σ of the distribution
σ = 2
# build a random number generator
rng = gsl.rng()
# build a gaussian distribution
gaussian = gsl.pdf.gaussian(mean=0.0, sigma=σ, rng=rng)
# sample it
sample = gaussian.sample()
# compute the density
x = 0
density = gaussian.density(x)
assert density == 1/sqrt(2*pi*σ**2) * exp(-x**2/ (2*σ**2))
# make a vector
v = gsl.vector(1000)
# fill it with random numbers
gaussian.vector(vector=v)
# make a matrix
m = gsl.matrix(shape=(50, 200))
# fill it with random numbers
gaussian.matrix(matrix=m)
return gaussian
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# all done
return v
def test():
# setup the workload
samplesPerTask = 8
workload = samplesPerTask
# externals
import mpi
import gsl
# initialize
mpi.init()
# get the world communicator
world = mpi.world
# figure out its geometry
rank = world.rank
tasks = world.size
# decide which task is the source
source = 0
# at the source task
if rank == source:
# allocate a vector
θ = gsl.vector(shape=tasks * samplesPerTask)
# initialize it
for task in range(tasks):
for sample in range(samplesPerTask):
offset = task * samplesPerTask + sample
θ[offset] = offset
# print it out
# θ.print(format="{}")
# the other tasks
else:
# have a dummy source vector
θ = None
# make a partition
part = gsl.vector(shape=workload)
part.excerpt(communicator=world, source=source, vector=θ)
# verify that i got the correct part
for index in range(samplesPerTask):
assert part[index] == rank * samplesPerTask + index
# all done
return
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# set it to random values
v.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=gsl.rng()))
# all done
return v
def test():
# get the package
import gsl
# a couple of values
a = 2
x = 3
y = 4
# make a couple of vectors
v1 = gsl.vector(shape=10).fill(x)
v2 = gsl.vector(shape=10).fill(y)
# compute the form
gsl.blas.daxpy(a, v1, v2)
# verify v1 was left alone
assert v1 == gsl.vector(shape=10).fill(x)
# and that v2 has the right value
assert v2 == gsl.vector(shape=10).fill(a*x+y)
# all done
return
def test():
# get the package
import gsl
# a couple of values
a = 2
x = 3
y = 4
# make a couple of vectors
v1 = gsl.vector(shape=10).fill(x)
v2 = gsl.vector(shape=10).fill(y)
# compute the form
gsl.blas.daxpy(a, v1, v2)
# verify v1 was left alone
assert v1 == gsl.vector(shape=10).fill(x)
# and that v2 has the right value
assert v2 == gsl.vector(shape=10).fill(a*x+y)
# all done
return
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# set it to random values
v.random(pdf=gsl.pdf.uniform(support=(-1, 1), rng=gsl.rng()))
# all done
return v
def test():
# setup the workload
samplesPerTask = 8
workload = samplesPerTask
# externals
import mpi
import gsl
# get the world communicator
world = mpi.world
# figure out its geometry
rank = world.rank
tasks = world.size
# decide which task is the source
source = 0
# at the source task
if rank == source:
# allocate a vector
θ = gsl.vector(shape=tasks*samplesPerTask)
# initialize it
for task in range(tasks):
for sample in range(samplesPerTask):
offset = task*samplesPerTask + sample
θ[offset] = offset
# print it out
# θ.print(format="{}")
# the other tasks
else:
# have a dummy source vector
θ = None
# make a partition
part = gsl.vector(shape=workload)
part.excerpt(communicator=world, source=source, vector=θ)
# verify that i got the correct part
for index in range(samplesPerTask):
assert part[index] == rank*samplesPerTask + index
# all done
return
def test():
# package access
import gsl
# make two vectors
length = 100
v1 = gsl.vector(shape=length)
v2 = gsl.vector(shape=length)
# set them to random values
rng = gsl.rng()
v1.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
v2.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
# call correlation
covariance = gsl.stats.covariance(v1, v2)
# covariance between a vector and itself is its variance
assert gsl.stats.covariance(v1, v1) == v1.variance(mean=v1.mean())
# all done
return covariance
def test():
# package access
import gsl
# make a vector
v1 = gsl.vector(shape=100)
# set it to some value
v1.fill(value=2)
# verify it happened
for e in v1: assert e == 2
# make and initialize another vector
v2 = gsl.vector(shape=100).zero()
# fill it
v2.fill(range(100))
# verify
assert v2.tuple() == tuple(range(100))
# all done
return v1, v2
def test():
# package access
import gsl
# make a matrix
v = gsl.vector(shape=3)
# set it to some value
v.fill(value=2)
# print it
v.print(indent=' '*4)
# all done
return v
def test():
# package access
import gsl
# make a matrix
v = gsl.vector(shape=3)
# set it to some value
v.fill(value=2)
# print it
v.print(indent=' ' * 4)
# all done
return v
def test():
# package access
import gsl
# make two vectors
length = 100
v1 = gsl.vector(shape=length)
v2 = gsl.vector(shape=length)
# set them to random values
rng = gsl.rng()
v1.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
v2.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
# call correlation
correlation = gsl.stats.correlation(v1, v2)
# correlation of a vector with itself should be one
assert gsl.stats.correlation(v1, v1) == 1.0
# all done
return correlation
def test():
# package access
import gsl
# make a couple of vectors and initialize them
v1 = gsl.vector(shape=100).fill(value=2)
v2 = gsl.vector(shape=100).fill(value=2)
# check
for e in v1:
assert e == 2
for e in v2:
assert e == 2
# multiply them and store the result in v1
v1 *= v2
# check
for e in v1:
assert e == 4
for e in v2:
assert e == 2
# all done
return v1, v2
def test():
# package access
import gsl
# make two vectors
length = 100
v1 = gsl.vector(shape=length)
v2 = gsl.vector(shape=length)
# set them to random values
rng = gsl.rng()
v1.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
v2.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
# call correlation
covariance = gsl.stats.covariance(v1, v2)
# covariance between a vector and itself is its variance
assert gsl.stats.covariance(v1, v1) == v1.variance(mean=v1.mean())
# all done
return covariance
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# zero it out
v.zero()
# verify it happened
for e in v: assert e == 0
# all done
return v
def test():
# package access
import gsl
# make two vectors
length = 100
v1 = gsl.vector(shape=length)
v2 = gsl.vector(shape=length)
# set them to random values
rng = gsl.rng()
v1.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
v2.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=rng))
# call correlation
correlation = gsl.stats.correlation(v1, v2)
# correlation of a vector with itself should be one
assert gsl.stats.correlation(v1, v1) == 1.0
# all done
return correlation
def test():
# externals
import gsl
import math
# a value
x = 3
# make a couple of vectors
v = gsl.vector(shape=10).fill(x)
# compute the dot product
assert gsl.blas.dnrm2(v) == math.sqrt(v.shape * x**2)
# all done
return
def test():
# externals
import gsl
import math
# a value
x = -3
# make a couple of vectors
v = gsl.vector(shape=10).fill(x)
# compute the dot product
assert gsl.blas.dasum(v) == v.shape * abs(x)
# all done
return
def test():
# externals
import gsl
import math
# a value
x = -3
# make a couple of vectors
v = gsl.vector(shape=10).fill(x)
# compute the dot product
assert gsl.blas.dasum(v) == v.shape * abs(x)
# all done
return
def test():
# package access
import gsl
# make a vector and initialize it
v = gsl.vector(shape=100)
# prime
for index in range(v.shape): v[index] = 2*index+1
# find the min
small = v.min()
# check it
assert small == 1
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# set an element to some value
v[50] = 10
# verify it happened
assert v[50] == 10
# check that it can be found
assert 10 in v
# all done
return v
def test():
# package access
import gsl
# make a vector and initialize it
v = gsl.vector(shape=100)
# prime
for index in range(v.shape): v[index] = 2*index+1
# find the max
big = v.max()
# check it
assert big == 2*(v.shape-1)+1
# all done
return v
def test():
# package access
import gsl
# make a vector and initialize it
v = gsl.vector(shape=100)
# prime
for index in range(v.shape): v[index] = 2*index+1
# find both the min and the max
small, big = v.minmax()
# check it
assert small == 1
assert big == 2*(v.shape-1)+1
# all done
return v
def test():
# package access
import gsl
# make a vectors and initialize it
v = gsl.vector(shape=100).fill(value=1)
# check
for e in v:
assert e == 1
# scale it
v *= 2
# check
for e in v:
assert e == 2
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# set it to some value
v.random(pdf=gsl.pdf.uniform(support=(-1,1), rng=gsl.rng()))
# clone it
u = v.clone()
# and check it
assert u == v
# all done
return v, u
def test():
# package access
import gsl
# make a matrix
v = gsl.vector(shape=3)
# set some values
v[0] = 0
v[1] = 1
v[2] = 2
# verify the tuple rep
assert v.tuple() == (0, 1, 2)
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# set it to some value
v.random(pdf=gsl.pdf.uniform(support=(-1, 1), rng=gsl.rng()))
# clone it
u = v.clone()
# and check it
assert u == v
# all done
return v, u
def test():
# package access
import gsl
# make a matrix
v = gsl.vector(shape=3)
# set some values
v[0] = 0
v[1] = 1
v[2] = 2
# verify the tuple rep
assert v.tuple() == (0,1,2)
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# fill with a test pattern
for i in range(len(v)): v[i] = i
# verify it happened
assert v[50] == 50
# access through reflection
v[-99] == v[1]
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# fill with a test pattern
for i in range(len(v)):
v[i] = i
# verify it happened
assert v[50] == 50
# access through reflection
v[-99] == v[1]
# all done
return v
def test():
# package access
import gsl
# make a vector and initialize it
v = gsl.vector(shape=100)
# prime
for index in range(v.shape):
v[index] = 2 * index + 1
# shuffle
vs = v.shuffle(rng=gsl.rng())
# check it
assert vs.mean() == v.mean()
assert vs.max() == v.max()
assert vs.min() == v.min()
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# fill it with our test pattern
v[:] = range(v.shape)
# verify it happened
for i in range(v.shape): assert v[i] == i
# and again
assert tuple(v[:]) == tuple(range(v.shape))
# also
assert tuple(v[10:20:2]) == tuple(range(10,20,2))
# and
for value, i in zip(v[10::2], range(10,2)): assert value == i
# all done
return v
def test():
# package access
import gsl
# make a vector
v = gsl.vector(shape=100)
# fill it with our test pattern
v[:] = range(v.shape)
# verify it happened
for i in range(v.shape): assert v[i] == i
# and again
assert tuple(v[:]) == tuple(range(v.shape))
# also
assert tuple(v[10:20:2]) == tuple(range(10,20,2))
# and
for value, i in zip(v[10::2], range(10,2)): assert value == i
# all done
return v
def test():
# get the package
import gsl
# the vector x
x = gsl.vector(shape=3)
x[0], x[1], x[2] = 1,2,3
# the matrix A
A = gsl.matrix(shape=(3,3)).identity()
# compute the form
y = gsl.blas.dtrmv(A.upperTriangular, A.opTrans, A.unitDiagonal, A, x.clone())
# check
# print(tuple(y))
assert tuple(y) == tuple(x)
# all done
return
def test():
# setup the workload
samplesPerTask = 8
workload = samplesPerTask
# externals
import mpi
import gsl
# initialize
mpi.init()
# get the world communicator
world = mpi.world
# figure out its geometry
rank = world.rank
tasks = world.size
# build my contribution
θ = gsl.vector(shape=workload)
# and initialize it
for index in range(samplesPerTask):
θ[index] = rank * samplesPerTask + index
# decide on the destination task
destination = 0
# exercise it
result = gsl.vector.collect(vector=θ,
communicator=world,
destination=destination)
# at the destination task
if rank == destination:
# verify that i got the correct parts
for task in range(tasks):
for index in range(samplesPerTask):
offset = task * samplesPerTask + index
assert result[offset] == offset
# print it out
# result.print(format='{}')
# all done
return
def test():
# setup the workload
parameters = 8
# externals
import mpi
import gsl
# get the world communicator
world = mpi.world
# figure out its geometry
rank = world.rank
tasks = world.size
# decide which task is the source
source = 0
# at the source task
if rank == source:
# allocate a matrix
θ = gsl.vector(shape=parameters)
# initialize it
for dof in range(parameters):
θ[dof] = dof
# print it out
# θ.print(format="{}")
# the other tasks
else:
# have a dummy source matrix
θ = None
# broadcast
result = gsl.vector.bcast(source=source, vector=θ)
# verify that i got the correct part
for dof in range(parameters):
assert result[dof] == dof
# all done
return
def test():
# package access
import gsl
# pick a size
n = 20
# make one
v = gsl.vector(shape=n)
# fill it
for i in range(n): v[i] = i
# show me
# print('v:')
# v.print(format='{:6.2f}', indent=' '*4)
# pick some parameters
start = int(n/4)
shape = n - start
# make a subvector
s = v.view(start=start, shape=shape)
# show me
# print('s:')
# s.print(format='{:6.2f}', indent=' '*4)
# check the length
assert len(s) == shape
# check the contents
for i in range(shape):
assert s[i] == start + i
# now modify
s.fill(0)
# and check
for i in range(shape):
assert v[start+i] == 0
# all done
return
