def test_loglog():
Ndat = 100
x = np.linspace(50, 150, Ndat)
y = 5e7 * x**(-2.5)
N = 4
if os.path.isdir('new_dir/'):
shutil.rmtree('new_dir/')
sol = smooth(x, y, N, model_type='loglog_polynomial')
param_plotter(sol.optimum_params,
sol.optimum_signs,
x,
y,
N,
samples=10,
base_dir='new_dir/',
model_type='loglog_polynomial',
data_plot=True,
center_plot=True)
assert (os.path.exists('new_dir/Parameter_plot.pdf') is True)
def test_files():
np.random.seed(0)
Ndat = 100
x = np.linspace(0, 1, Ndat)
y = 1 + x + x**2 + x**3
N = 6
if os.path.isdir('new_dir/'):
shutil.rmtree('new_dir/')
res = smooth(x, y, N, zero_crossings=[4])
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
base_dir='new_dir/',
samples=10,
zero_crossings=[4])
assert (os.path.exists('new_dir/') is True)
assert (os.path.exists('new_dir/Parameter_plot.pdf') is True)
N = 10
if os.path.isdir('new_dir/'):
shutil.rmtree('new_dir/')
res = smooth(x, y, N)
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
base_dir='new_dir/',
samples=10,
gridlines=True)
assert (os.path.exists('new_dir/') is True)
assert (os.path.exists('new_dir/Parameter_plot.pdf') is True)
def test_keywords():
np.random.seed(0)
Ndat = 100
x = np.linspace(0, 1, Ndat)
y = 1 + x + x**2 + x**3
N = 4
if os.path.isdir('new_dir/'):
shutil.rmtree('new_dir/')
res = smooth(x, y, N, base_dir='new_dir/', data_save=True)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
color='pink')
with pytest.raises(Exception):
param_plotter(res.optimum_params, res.optimum_signs, x, y, 5.5)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
model_type='banana')
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
pivot_point='string')
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
pivot_point=len(x) + 10)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
base_dir=5)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
base_dir='string')
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
constraints=3.3)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
constraints=20)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
zero_crossings=[3.3])
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
zero_crossings=[1])
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
samples=50.2)
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
width='string')
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
warnings='string')
with pytest.raises(Exception):
param_plotter(res.optimum_params,
res.optimum_signs,
x,
y,
N,
gridlines=9)
def test_new_basis_without_args():
np.random.seed(0)
Ndat = 100
x = np.linspace(-1, 1, Ndat)
y = 1 + x + x**2 + x**3 + np.random.normal(0, 0.05, 100)
N = 4
def basis_functions(x, y, pivot_point, N, *args):
phi = np.empty([len(x), N])
for h in range(len(x)):
for i in range(N):
phi[h, i] = (x[h])**i
return phi
def model(x, y, pivot_point, N, params, *args):
y_sum = np.sum([params[i] * (x)**i for i in range(N)], axis=0)
return y_sum
def derivative(m, x, y, N, pivot_point, params, *args):
mth_order_derivative = []
for i in range(N):
if i <= m - 1:
mth_order_derivative.append([0] * len(x))
for i in range(N - m):
mth_order_derivative_term = np.math.factorial(m+i) / \
np.math.factorial(i) * \
params[int(m)+i]*(x)**i
mth_order_derivative.append(mth_order_derivative_term)
return mth_order_derivative
def derivative_pre(m, x, y, N, pivot_point, *args):
mth_order_derivative = []
for i in range(N):
if i <= m - 1:
mth_order_derivative.append([0] * len(x))
for i in range(N - m):
mth_order_derivative_term = np.math.factorial(m+i) / \
np.math.factorial(i) * \
(x)**i
mth_order_derivative.append(mth_order_derivative_term)
return mth_order_derivative
sol = smooth(x,
y,
N,
basis_functions=basis_functions,
model=model,
derivatives=derivative,
der_pres=derivative_pre)
if os.path.isdir('new_dir/'):
shutil.rmtree('new_dir/')
param_plotter(sol.optimum_params,
sol.optimum_signs,
x,
y,
N,
samples=10,
base_dir='new_dir/',
basis_functions=basis_functions,
model=model,
derivatives=derivative,
der_pres=derivative_pre)
assert (os.path.exists('new_dir/') is True)
assert (os.path.exists('new_dir/Parameter_plot.pdf') is True)
with pytest.raises(Exception):
param_plotter(sol.optimum_params,
sol.optimum_signs,
x,
y,
N,
samples=10,
base_dir='new_dir/',
basis_functions=basis_functions,
model=None,
derivatives=derivative,
der_pres=None)
optimise for alongside the parameters :math:`{a_k}`. The coloured valid regions
therefore correspond to a specific combination of :math:`{\pm_m}` for the problem.
:math:`{\pm_m}` is also referred to as :math:`{\mathbf{s}}` in the theory
section and the ``maxsmooth`` paper.
We can import the function like so,
"""
from maxsmooth.parameter_plotter import param_plotter
"""
and access it using,
"""
param_plotter(result.optimum_params,
result.optimum_signs,
x,
y,
N,
base_dir='examples/')
"""
The function takes in the optimum parameters and signs found after the fit
as well as the data and order of the fit. There are a number of keyword arguments
detailed in the following section and the resultant fit is shown below. The
function by default samples the parameter ranges 50% either side of the optimum
and calculates 50 spamples for each parameter. In each panel the two
labelled parameters are varied while the others are maintained at their optimum
values.
.. image:: https://github.com/htjb/maxsmooth/raw/master/docs/images/Parameter_plot.png
We are also able to plot the data, fit and residuals alongside the parameter
plot and this can be done by setting data_plot=True. We can also highlight the