def f_solver():
tmp = numx.array((1., 2.), )
sys = multiminimize.gsl_multimin_function(my_f, tmp, 2)
solver = multiminimize.nmsimplex(sys, 2)
start_point = numx.array((5., 7.), )
initial_steps = numx.array((0.1, 0.1), )
solver.set(start_point, initial_steps)
print "Using solver ", solver.name()
for i in range(100):
status = solver.iterate()
if status != errno.GSL_SUCCESS:
break
ssval = solver.size()
rval = multiminimize.test_size (ssval, 1e-2);
if rval == 0:
print "converged to minimum at"
fval = solver.getf()
x = solver.getx()
t = (i, x[0], x[1], fval, ssval)
print "iter %3d x % 10.3e % 10.3e f() = %-10.3f ssize = %.3f" % t
if rval == 0:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def run_fsolver():
params = numx.array((1., 10.), numx.Float)
#solver = multiroots.hybrids(mysys, 2)
solver = multiroot.dnewton(2)
tmp = numx.array((-10., -5.), numx.Float)
solver.set(rosenbrock_f, tmp, params)
#solver = multiroots.broyden(mysys, 2)
#solver = multiroots.hybrid(mysys, 2)
print "# Testing solver ", solver.name(), solver.type()
print "# %5s %9s %9s %9s %10s" % ("iter", "x[0]", "x[1]", "f[0]", "f[1]")
for iter in range(100):
status = solver.iterate()
x = solver.root()
dx = solver.dx()
f = solver.f()
status = multiroot.test_residual(f, 1e-7)
if status == 0:
print "# Converged :"
print " %5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f[0], f[1])
if status == 0:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def fdf_solver():
params = numx.array((1., 2.), )
sys = multiminimize.gsl_multimin_function_fdf(my_f, my_df, my_fdf, params, 2)
#solver = multiminimize.conjugate_pr(sys, 2)
#solver = multiminimize.conjugate_fr(sys, 2)
solver = multiminimize.vector_bfgs(sys, 2)
#solver = multiminimize.steepest_descent(sys, 2)
start = numx.array((5., 7.), )
solver.set(start, 0.01, 1e-4)
print "Using solver ", solver.name()
print "%5s %9s %9s %9s %9s %9s" % ("iter", "x", "y", "f", "dx", "dy")
for iter in range(200):
status = solver.iterate()
gradient = solver.gradient()
x = solver.getx()
f = solver.getf()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == errno.GSL_SUCCESS:
print "Converged "
print "%5d % .7f % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f, gradient[0], gradient[1])
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def run_fdfsolver():
params = numx.array((1., 10.),)
mysys = multiroots.gsl_multiroot_function_fdf(rosenbrock_f, rosenbrock_df,
rosenbrock_fdf, params, 2)
#solver = multiroots.newton(mysys, 2)
solver = multiroots.gnewton(mysys, 2)
#solver = multiroots.hybridj(mysys, 2)
#solver = multiroots.hybridsj(mysys, 2)
tmp = numx.array((-10., -5.), )
solver.set(tmp)
print "# Testing solver ", solver.name()
print "# %5s %9s %9s %9s %10s" % ("iter", "x[0]", "x[1]", "f[0]", "f[1]")
for iter in range(100):
status = solver.iterate()
r = solver.root()
x = solver.getx()
f = solver.getf()
status = multiroots.test_residual(f, 1e-7)
if status == errno.GSL_SUCCESS:
print "# Converged :"
print " %5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f[0], f[1])
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def test_wmean(self):
self.failIf(wmean(numx.array([1,1,1]), numx.array([-1.,-3.,1.])) != -1.0)
self.failIf(wmean(numx.array([1,1,1]),[1,2,3]) != 2)
data = numx.array([1.,2.,3.,4.,5.,6.,7.,8.,9.,10.])
weight = numx.ones(data.shape)
self.failIf(wmean(weight,data) != 5.5)
self.failIf(wmean(weight[::2],data[::2]) != 5.0)
self.failIf(wmean(weight[::-1],data[::-1]) != 5.5)
self.failIf(wmean(weight[::-2],data[::-2]) != 6.0)
def test_mean(self):
self.failIf(mean(numx.array([-1.,-3.,1.])) != -1.0)
self.failIf(mean([1,2,3]) != 2)
# test stride != 1
# these are only valid tests when using NumPy,
# since otherwise the sequence is trnsformed to an
# contigous array anyway...
data = numx.array([1.,2.,3.,4.,5.,6.,7.,8.,9.,10.])
self.failIf(mean(data) != 5.5)
self.failIf(mean(data[::2]) != 5.0)
self.failIf(mean(data[::-1]) != 5.5)
self.failIf(mean(data[::-2]) != 6.0)
def run_fdfsolver():
A = 1.
lambda_ = .1
b = .5
n = 40
p = 3
t = numx.arange(n);
y = testfunc(t, A, lambda_, b)
sigma = numx.ones(n) * 0.1
data = numx.array((t,y,sigma), numx.Float)
#mysys = multifit_nlin.gsl_multifit_function_fdf(exp_f, exp_df, exp_fdf,
# data, n,p)
pygsl.set_debug_level(0)
solver = multifit_nlin.lmsder(n, p)
pygsl.set_debug_level(0)
#solver = multifit_nlin.lmder(mysys, n, p)
x = numx.array((1.0, 0.0, 0.0))
pygsl.set_debug_level(0)
solver.set(exp_f, exp_df, exp_fdf, x, data)
pygsl.set_debug_level(0)
print "# Testing solver ", solver.name()
print "# %5s %9s %9s %9s %10s" % ("iter", "A", "lambda", "b", "|f(x)|")
for iter in range(20):
status = solver.iterate()
x = solver.x()
dx = solver.dx()
f = solver.f()
J = solver.J()
#tdx = multifit_nlin.gradient(J, f)
#status = multifit_nlin.test_delta(dx, x, 1e-8, 1e-8)
status = solver.test_delta(1e-8, 1e-8)
fn = numx.sqrt(numx.sum(f*f))
if status == 0:
print "# Convereged :"
if status == 0:
break
print " %5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], x[2], fn)
else:
raise ValueError, "Number of Iterations exceeded!"
J = solver.J()
covar = multifit_nlin.covar(solver.J(), 0.0)
print "# A = % .5f +/- % .5f" % (x[0], covar[0,0])
print "# lambda = % .5f +/- % .5f" % (x[1], covar[1,1])
print "# b = % .5f +/- % .5f" % (x[2], covar[2,2])
def run_fdfsolver():
a = 1.0
b = 0.0
c = -5.0
mysys = roots.gsl_function_fdf(quadratic, quadratic_deriv, quadratic_fdf,
numx.array((a,b,c)))
solver = roots.newton(mysys)
#solver = roots.secant(mysys)
#solver = roots.steffenson(mysys)
x = 5.0
solver.set(x)
r_expected = numx.sqrt(5.0)
print "# Using solver ", solver.name()
print "# %5s %9s %10s %9s" % ("iter", "root", "err", "err(est)")
ok = 1
for iter in range(10):
status = solver.iterate()
x0 = x
x = solver.root()
status = roots.test_delta(x, x0, 0.0, 1e-3)
r = solver.root()
if status == errno.GSL_SUCCESS:
print "# Convereged :"
print "%5d %.7f % .6f % .6f" %(iter, r, r -r_expected, x - x0)
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError, "Exeeded maximum number of iterations!"
def test_evolve_bsimp():
dimension = 2
step = odeiv.step_bsimp(dimension, func,jac, mu)
control = odeiv.control_y_new(step, 1e-6, 1e-6)
evolve = odeiv.evolve(step, control, dimension)
step1 = odeiv.step_rkf45(dimension, func,jac,mu)
control1 = odeiv.control_y_new(step1, 1e-6, 1e-6)
evolve1 = odeiv.evolve(step1, control1, dimension)
h = 1
t = 0.0
t1 = 100.0
y = numx.array((1.0, 0.0))
while t<t1:
t, h, y = evolve.apply(t, t1, h, y)
sys.stdout = file
h = 1
t = 0.0
t1 = 100.0
y = (1.0, 0.0)
while t<t1:
t, h, y = evolve1.apply(t, t1, h, y)
def run():
def f2(x,y):
return numx.sin(x) / x
sys2 = integrate.gsl_function(f2, None)
def f1(x,y):
return 1 / x
sys1 = integrate.gsl_function(f1, None)
def f3(x,y):
return 1 / -x
sys3 = integrate.gsl_function(f3, None)
w = integrate.workspace(1000000)
cyclew = integrate.workspace(1000000)
table1 = integrate.qawo_table(1, 100, integrate.SINE, 100)
table2 = integrate.qawo_table(-1, 100, integrate.SINE, 100)
# Borders and singualrity for gagp
pts = numx.array((-numx.pi, 0, numx.pi))
flag, result1, error = integrate.qagp(sys2, pts, 1e-8, 1e-8, 100000, w)
flag, result2, error = integrate.qawf(sys1, numx.pi, 1e-8, 100, w,
cyclew, table1)
flag, result3, error = integrate.qawf(sys3, numx.pi, 1e-8, 100, w,
cyclew, table2)
print "Result of integration is :", result1 + result2 + result3
def generate_data():
r = pygsl.rng.mt19937()
a = numx.arange(20) / 10.# + .1
y0 = numx.exp(a)
sigma = 0.1 * y0
dy = numx.array(map(r.gaussian, sigma))
return a, y0+dy, sigma
def test_qagp(self):
def f1(x,y):
return x / x
sys = integrate.gsl_function(f1, None)
pts = Numeric.array((-1, 0, 1))
flag, result, error = integrate.qagp(sys, pts, 1e-8, 1e-8, 100000, self.w)
assert(Numeric.absolute(result - 2.) < 1e-3)
assert(error<1e-8)
def setUp(self):
t = Numeric.arange(self._getn());
y = testfunc(t, self.A, self.lambda_, self.b)
sigma = Numeric.ones(self._getn()) * 0.1
self.data = Numeric.array((t,y,sigma), Float)
self.sys = multifit_nlin.gsl_multifit_function_fdf(exp_f, exp_df,
exp_fdf, self.data,
self._getn(),
self._getp())
def setUp(self):
self.dim = 100
self.param = 2
self.a = 1000.1
self.b = 100
self.x = Numeric.arange(self.dim)
x = self.x
self.y = self.a + self.b * self.x
self.w = Numeric.ones((self.dim,))
self.ws = multifit.linear_workspace(self.dim, self.param)
self.X = Numeric.transpose(Numeric.array((Numeric.ones(self.dim,), x)))
def _run(self, solver):
start_point = Numeric.array((5., 7.), Float)
initial_steps = Numeric.array((0.1, 0.1), Float)
solver.set(start_point, initial_steps)
for i in range(100):
status = solver.iterate()
if status:
break
ssval = solver.size()
rval = multiminimize.test_size (ssval, 1e-2);
#if rval == 0:
# print "converged to minimum at"
fval = solver.getf()
x = solver.getx()
t = (i, x[0], x[1], fval, ssval)
#print "iter %3d x % 10.3e % 10.3e f() = %-10.3f ssize = %.3f" % t
if rval == 0:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def exp_df(x, params):
A = x[0]
lambda_ = x[1]
b = x[2]
t = params[0]
yi = params[1]
sigma = params[2]
e = exp(-lambda_ * t)
e_s = e/sigma
df = numx.array((e_s, -t * A * e_s, 1/sigma))
df = numx.transpose(df)
return df
def test_evolve():
dimension = 2
step = odeiv.step_bsimp(dimension, func, jac, mu)
control = odeiv.control_y_new(step, 1e-6, 1e-6)
evolve = odeiv.evolve(step, control, dimension)
h = 1
t = 0.0
t1 = 1.0
y = numx.array([1.0, 0.0])
print step.name(), step.order()
while t<t1:
t, h, y = evolve.apply(t, t1, h, y)
dimension = 2
steps = (
odeiv.step_rk2,
odeiv.step_rk4,
odeiv.step_rkf45,
odeiv.step_rkck,
odeiv.step_rk8pd,
odeiv.step_rk2imp,
odeiv.step_rk4imp,
odeiv.step_gear1,
odeiv.step_gear2)
for s in steps:
step = s(dimension, func, None, mu)
print step.name(), step.order()
control = odeiv.control_y_new(step, 1e-6, 1e-6)
print control.name()
evolve = odeiv.evolve(step, control, dimension)
h = 1
t = 0.0
t1 = 1.0
y = (1.0, 0.0)
while t<t1/2.0:
t, h, y = evolve.apply(t, t1, h, y)
y, yerr, dydt = step.apply(t, h, y, None)
h, msg = control.hadjust(y, yerr, dydt, h)
assert(msg == odeiv.HADJ_DEC or
msg == odeiv.HADJ_INC or
msg == odeiv.HADJ_NIL
)
step.reset()
evolve.reset()
while t<t1:
t, h, y = evolve.apply(t, t1, h, y)
y = y
def run():
r = rng.rng()
bw = bspline(4, nbreak)
# Data to be fitted
x = 15. / (N-1) * numx.arange(N)
y = numx.cos(x) * numx.exp(0.1 * x)
sigma = .1
w = 1.0 / sigma**2 * numx.ones(N)
dy = r.gaussian(sigma, N)
y = y + dy
# use uniform breakpoints on [0, 15]
bw.knots_uniform(0.0, 15.0)
X = numx.zeros((N, ncoeffs))
for i in range(N):
B = bw.eval(x[i])
X[i,:] = B
# do the fit
c, cov, chisq = multifit.wlinear(X, w, y, multifit.linear_workspace(N, ncoeffs))
# output the smoothed curve
res_y = []
res_y_err = []
for i in range(N):
B = bw.eval(x[i])
yi, yi_err = multifit.linear_est(B, c, cov)
res_y.append(yi)
res_y_err.append(yi_err)
#print yi, yerr
res_y = numx.array(res_y)
res_y_err = numx.array(res_y_err)
return (x, y,), (x, res_y), res_y_err
def _run(self, solver):
tmp = Numeric.array((-10., -5.), Float)
solver.set(tmp)
for iter in range(100):
status = solver.iterate()
r = solver.root()
x = solver.getx()
f = solver.getf()
status = multiroots.test_residual(f, 1e-7)
if status == 0:
break
else:
raise ValueError, "Number of Iterations exceeded!"
assert(Numeric.absolute(x[0] - 1)<1e-6)
assert(Numeric.absolute(x[1] - 1)<1e-6)
assert(Numeric.absolute(f[0])<1e-6)
assert(Numeric.absolute(f[1])<1e-6)
def _run(self, solver):
#x = Numeric.array((1.0, .4, .1))
x = Numeric.array((1.0, 0.0, 0.0))
solver.set(x)
#g.title('Start')
#g.plot(Gnuplot.Data(self.data[0], self.data[1]),
# Gnuplot.Data(self.data[0], testfunc(self.data[0]),
# with = 'line'),
# )
#raw_input()
#print "Testing solver ", solver.name()
#print "%5s %9s %9s %9s %10s" % ("iter", "A", "lambda", "b", "|f(x)|")
for iter in range(20):
status = solver.iterate()
assert(status == 0 or status == -2)
x = solver.getx()
dx = solver.getdx()
f = solver.getf()
J = solver.getJ()
tdx = multifit_nlin.gradient(J, f)
status = multifit_nlin.test_delta(dx, x, 1e-8, 1e-8)
#status = multifit_nlin.test_gradient(dx, 1e-4)
fn = Numeric.sqrt(Numeric.sum(f*f))
#g.title('Iteration')
if status == 0:
break
#print "%5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], x[2], fn)
#g.plot(Gnuplot.Data(self.data[0], self.data[1]),
# Gnuplot.Data(self.data[0],
# testfunc(self.data[0], x[0], x[1], x[2]),
# with = 'line', title='iteration ' + str(iter)),
# )
#raw_input()
else:
raise ValueError, "Number of Iterations exceeded!"
#print "Convereged :"
#print "%5d % .7f % .7f %.7f % .7f" %(iter, x[0], x[1], x[2], fn)
assert(Numeric.absolute(x[0] - self.A) < _eps)
assert(Numeric.absolute(x[1] - self.lambda_) < _eps)
assert(Numeric.absolute(x[2] - self.b) < _eps)
#J = solver.getJ()
#print "shape = ", J.shape
covar = multifit_nlin.covar(solver.getJ(), 0.0)
def test_qawf(self):
def f2(x,y):
return Numeric.sin(x) / x
sys2 = integrate.gsl_function(f2, None)
def f1(x,y):
return 1 / x
sys1 = integrate.gsl_function(f1, None)
def f3(x,y):
return 1 / -x
sys3 = integrate.gsl_function(f3, None)
pts = Numeric.array((-Numeric.pi, 0, Numeric.pi))
flag, result1, error = integrate.qagp(sys2, pts, 1e-8, 1e-8, 100000, self.w)
table1 = integrate.qawo_table(1, 100, integrate.SINE, 100)
cyclew = integrate.workspace(1000000)
flag, result2, error = integrate.qawf(sys1, Numeric.pi, 1e-8, 100, self.w, cyclew, table1)
table2 = integrate.qawo_table(-1, 100, integrate.SINE, 100)
flag, result3, error = integrate.qawf(sys3, Numeric.pi, 1e-8, 100, self.w, cyclew, table2)
assert(Numeric.absolute(result1+result2+result3 - Numeric.pi) < 1e-8)
def _run(self, solver):
tmp = Numeric.array((5., 7.), Float)
solver.set(tmp, 0.01, 1e-4)
#print "Testing solver ", solver.name()
#print "%5s %9s %9s %9s %9s %9s" % ("iter", "x", "y", "f", "dx", "dy")
for iter in range(200):
status = solver.iterate()
gradient = solver.gradient()
x = solver.getx()
f = solver.getf()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == 0:
break
#print "%5d % .7f % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f, gradient[0], gradient[1])
else:
raise ValueError, "Number of Iterations exceeded!"
assert(Numeric.absolute(x[0] - 1)<1e-3)
assert(Numeric.absolute(x[1] - 2)<1e-3)
assert(Numeric.absolute(f - 30)<1e-3)
assert(Numeric.absolute(gradient[0])<1e-3)
assert(Numeric.absolute(gradient[1])<1e-3)
def fdf_solver():
#solver = multiminimize.conjugate_pr(sys, 2)
#solver = multiminimize.conjugate_fr(sys, 2)
#solver = multiminimize.vector_bfgs(sys, 2)
#solver = multiminimize.vector_bfgs2(sys, 2)
solver = multiminimize.steepest_descent(sys, 2)
start = numx.array((5., 7.), )
solver.set(start, 0.01, 1e-4)
print( "Using solver ", solver.name() )
print( "%5s %9s %9s %9s %9s %9s" % ("iter", "x", "y", "f", "dx", "dy"))
for iter in range(200):
status = solver.iterate()
gradient = solver.gradient()
x = solver.getx()
f = solver.getf()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == errno.GSL_SUCCESS:
print( "Converged ")
print( "%5d % .7f % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f, gradient[0], gradient[1]))
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError("Number of Iterations exceeded!")
def test_variance_long(self):
self.failIf(long.variance(numx.array([-1,-3,1])) != 4.0)
return
def test_variance_m_long(self):
self.failIf(long.variance_m(numx.array([-1,-3,1]),
long.mean(numx.array([-1,-3,1]))) != 4)
return
def test_variance_m(self):
self.failIf(variance_m(numx.array([-1.,-3.,1.]),
mean(numx.array([-1.,-3.,1.]))) != 4.0)
return
def test_mean_uchar(self):
self.failIf(uchar.mean(numx.array([1,2,3], UInt8)) != 2)
self.failIf(uchar.mean([1,2,3]) != 2)
def test_min_long(self):
self.failIf(long.min(numx.array([1,2,3])) != 1)
def test_sd_m(self):
self.failIf(sd_m(numx.array([-1.,-3.,1.]),
mean(numx.array([-1.,-3.,1.]))) != 2.0)
def setUp(self):
a = 1.0
b = 0.0
c = -5.0
self.sys = roots.gsl_function_fdf(quadratic, quadratic_deriv, quadratic_fdf, Numeric.array((a,b,c)))
def test_max_index(self):
self.failIf(long.max_index(numx.array([1,2,3])) != 2)
def test_max_long(self):
self.failIf(long.max(numx.array([1,2,3])) != 3)
def test_mean_float(self):
self.failIf(float.mean(numx.array([-1.,-3.,1.], 'f')) != -1)
self.failIf(float.mean([1.,2.,3.]) != 2)
def test_mean_char(self):
tmp = (ord("1") + ord("2") + ord("3")) / 3
self.failIf(char.mean(numx.array([1,2,3], Int8)) != 2)
self.failIf(char.mean([1,2,3]) != 2)
def setUp(self):
self._assertIsNotNone(self._t_type)
tmp = numx.array((1., 2.), )
self._dim = 2
self._sys = multiminimize.gsl_multimin_function(my_f, tmp, self._dim)
self._solver = self._t_type(self._sys, self._dim)
def test_sd_long(self):
self.failIf(long.sd(numx.array([-1,-3,1])) != 2)
def test_sd(self):
self.failIf(sd(numx.array([-1.,-3.,1.])) != 2.0)
def setUp(self):
tmp = Numeric.array((1., 2.), Float)
self.sys = multiminimize.gsl_multimin_function(my_f, tmp, self._getsize())
City("Phoenix", -112.07, 33.54,),
City("Albuquerque", -106.62, 35.12,),
City("Clovis", -103.20, 34.41,),
City("Durango", -107.87, 37.29,),
City("Dallas", -96.77, 32.79,),
City("Tesuque", -105.92, 35.77,),
City("Grants", -107.84, 35.15,),
City("Los Alamos", -106.28, 35.89,),
City("Las Cruces", -106.76, 32.34,),
City("Cortez", -108.58, 37.35,),
City("Gallup", -108.74, 35.52,))
n_cities = len(cities)
distance_matrix = numx.zeros((n_cities, n_cities))
cities_vec = numx.array(map(lambda x: x.GetData(), cities))
for i in range(len(cities)):
cities[i].SetNumber(i)
earth_radius = 6375.000
#n_cities = 6
# distance between two cities
def city_distance(c):
la = c[:,0]
lo = c[:,1]
sla = sin(la*pi/180)
cla = cos(la*pi/180)
slo = sin(lo*pi/180)
clo = sin(lo*pi/180)
def test_sd_m_long(self):
self.failIf(long.sd_m(numx.array([-1,-3,1]),
long.mean(numx.array([-1,-3,1]))) != 2)
def setUp(self):
tmp = Numeric.array((1., 10.), Float)
self.sys = multiroots.gsl_multiroot_function_fdf(
rosenbrock_f, rosenbrock_df, rosenbrock_fdf, tmp, self._getsize())
def generate_data():
r = pygsl.rng.mt19937()
a = numx.arange(20) / 10. # + .1
y0 = numx.exp(a)
sigma = 0.1 * y0
tmp = tuple(map(r.gaussian, sigma))
dy = numx.array(tmp)
tmp = y0 + dy
return a, tmp, sigma
if __name__ == '__main__':
x, y, sigma = generate_data()
data = (x, y, sigma)
data = numx.array(data).transpose()
for tmp in data:
print("%e %e %e" % tuple(tmp))
#c, cov , chisq = calculate(x, y, sigma)
#import Gnuplot
#g = Gnuplot.Gnuplot()
#xref = numx.arange(100) / 50.
#yref = c[0] + c[1] * xref + c[2] * xref **2
#t1 = Gnuplot.Data(x,y, with='points')
#t2 = Gnuplot.Data(xref, yref, with='line')
#g.plot(t1,t2)
#print "Press return !"
#raw_input()
-106.76,
32.34,
), City(
"Cortez",
-108.58,
37.35,
), City(
"Gallup",
-108.74,
35.52,
))
n_cities = len(cities)
distance_matrix = numx.zeros((n_cities, n_cities))
cities_vec = numx.array(map(lambda x: x.GetData(), cities))
for i in range(len(cities)):
cities[i].SetNumber(i)
earth_radius = 6375.000
#n_cities = 6
# distance between two cities
def city_distance(c):
la = c[:, 0]
lo = c[:, 1]
sla = sin(la * pi / 180)
cla = cos(la * pi / 180)
slo = sin(lo * pi / 180)
def test_mean_long(self):
self.failIf(long.mean(numx.array([-1,-3,1])) != -1)
self.failIf(long.mean([1,2,3]) != 2)
def test_mean_short(self):
self.failIf(short.mean([1,2,3]) != 2)
self.failIf(short.mean(numx.array([-1,-3,1], Int16)) != -1)