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Commit fa3f0f56 authored by Maximilian Schanner's avatar Maximilian Schanner
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Handle models with uncertainties/covariances available. field now takes a type argument

parent 84364631
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pymagglobal/core.py
View file @ fa3f0f56
......@@ -27,7 +27,7 @@ import numpy as np
from scipy.interpolate import BSpline
from pymagglobal.utils import nez2dif, REARTH, i2lm_l, i2lm_m, lmax2N, lm2i, \
dsh_basis
dsh_basis, grad_d, grad_i, grad_f
__all__ = ['built_in_models', 'Model', 'master_curve', 'coefficients',
......@@ -79,6 +79,9 @@ class Model(object):
A large array containing coefficients at the (inner) knots.
splines : scipy.interpolate.BSpline
A `BSpline` instance representing the model.
cov_splines : scipy.interpolate.BSpline
A `BSPline` instance giving the covariances. Only available for certain
models. Default is `None`.
'''
def __init__(self, name, fname=None):
self.name = name
......@@ -107,6 +110,7 @@ class Model(object):
self.splines = BSpline(self.knots,
self.coeffs,
3)
self.cov_splines = None
def valid_epoch(self, epoch):
'''Check whether the given epoch is in the range of the model.
......@@ -124,12 +128,21 @@ class Model(object):
epoch = np.asarray(epoch)
out_of_range = epoch[(epoch < self.t_min) + (self.t_max < epoch)]
if np.any(out_of_range):
warn(f'Epochs {out_of_range} are outside of the models range [{self.t_min},'
f' {self.t_max}]. The result will be extrapolated and not '
f'robust.',
warn(f'Epochs {out_of_range} are outside of the models range '
f'[{self.t_min}, {self.t_max}]. The result will be '
f'extrapolated and not robust.',
category=OutOfRangeWarning)
return epoch
@property
def _has_covariances(self):
return (self.cov_splines is not None)
@_has_covariances.setter
def _has_covariances(self, value):
raise RuntimeError("Private property '_has_covariances' can not be "
" set manually.")
def valid_degrees_orders(self, degrees, orders):
'''Check whether the given degrees and orders are valid for the model
and calculate the corresponding indices.
......@@ -163,7 +176,8 @@ class Model(object):
return inds
def master_curve(times, loc, splines, ser_type='dif', check=True):
def master_curve(times, loc, splines, cov_splines=None, ser_type='dif',
check=True):
'''Create master curves from a splines object.
Parameters
......@@ -177,6 +191,10 @@ def master_curve(times, loc, splines, ser_type='dif', check=True):
ser_type : {'dif', 'nez'}
The type of the master curves. May be either 'dif' (default) for
declination, inclination and intensity or 'nez' for north, east, down.
cov_splines : scipy.interpolate.BSpline, optional
A BSpline interpolating the covariance matrices of the coefficients.
If a model is passed to splines, this argument will be ignored and set,
if the model's covariances are available.
check : bool, optional
If a Model is given, check the input validity.
......@@ -184,18 +202,26 @@ def master_curve(times, loc, splines, ser_type='dif', check=True):
-------
ndarray
The first component master curve. Either declination or north,
depending on the ser_type kwarg.
depending on the ser_type kwarg. If cov_splines is given, a tuple
containing the component and the standard deviation is returned.
ndarray
The second component master curve. Either inclination or east,
depending on the ser_type kwarg.
depending on the ser_type kwarg. If cov_splines is given, a tuple
containing the component and the standard deviation is returned.
ndarray
The third component master curve. Either intensity or down,
depending on the ser_type kwarg.
depending on the ser_type kwarg. If cov_splines is given, a tuple
containing the component and the standard deviation is returned.
'''
if ser_type not in ['nez', 'dif']:
raise ValueError(f"{ser_type} is not a valid series type. 'dif' and "
f"'nez' are supported.")
if isinstance(splines, Model):
if check:
for it in np.atleast_1d(times):
splines.valid_epoch(it)
if splines._has_covariances:
cov_splines = splines.cov_splines
splines = splines.splines
try:
n_plt = len(times)
......@@ -208,27 +234,31 @@ def master_curve(times, loc, splines, ser_type='dif', check=True):
z_mst[2] = REARTH
z_mst[3] = times
# the master curves are just field values at a constant location
mst_cv = field(z_mst, splines)
mst_cv = field(z_mst, splines, cov_splines=cov_splines,
field_type=ser_type)
if ser_type == 'dif':
return nez2dif(*mst_cv)
elif ser_type == 'nez':
if cov_splines is None:
return mst_cv[0], mst_cv[1], mst_cv[2]
else:
raise ValueError(f"{ser_type} is not a valid series type. 'dif' and "
f"'nez' are supported.")
return (mst_cv[0][0], mst_cv[1][0]), (mst_cv[0][1], mst_cv[1][1]), \
(mst_cv[0][2], mst_cv[1][2])
def coefficients(epoch, splines, check=True):
def coefficients(epoch, splines, cov_splines=None, check=True):
'''Evaluate splines at an epoch and return the coefficients, together with
the appropriate degrees and orders.
Parameters
----------
epoch : float
The epoch at which to return the coefficients, in years.
epoch : float or array
The epoch(s) at which to return the coefficients, in years.
splines : scipy.interpolate.BSpline or Model
An instance of Model or splines specifying the model.
cov_splines : scipy.interpolate.BSpline, optional
A BSpline interpolating the covariance matrices of the coefficients.
If a model is passed to splines, this argument will be ignored and set,
if the model's covariances are available. If cov_splines is given,
the standard deviations are returned as a fourth return value.
check : bool, optional
If a Model is given, check the input validity.
......@@ -246,16 +276,27 @@ def coefficients(epoch, splines, check=True):
if check:
for it in np.atleast_1d(epoch):
splines.valid_epoch(epoch)
if splines._has_covariances:
cov_splines = splines.cov_splines
splines = splines.splines
coeffs = splines(epoch)
# set up degrees and orders for output purposes
ls = [i2lm_l(it) for it in range(splines.c.shape[1])]
ms = [i2lm_m(it) for it in range(splines.c.shape[1])]
if cov_splines is None:
return ls, ms, coeffs
return ls, ms, coeffs
covs = cov_splines(epoch)
try:
covs = np.diagonal(covs, axis1=1, axis2=2)
except np.AxisError:
covs = np.diagonal(covs)
return ls, ms, coeffs, np.sqrt(covs)
def dipole_series(times, splines, check=True):
def dipole_series(times, splines, cov_splines=None, check=True):
'''Create a dipole-moment time series from a splines object.
Parameters
......@@ -264,28 +305,54 @@ def dipole_series(times, splines, check=True):
The times for which to create the master curve.
splines : scipy.interpolate.BSpline or Model
An instance of Model or splines specifying the model.
cov_splines : scipy.interpolate.BSpline, optional
A BSpline interpolating the covariance matrices of the coefficients.
If a model is passed to splines, this argument will be ignored and set,
if the model's covariances are available. If cov_splines is given,
the 16- and 84-percentiles are returned along with the series.
check : bool, optional
If a Model is given, check the input validity.
Returns
-------
ndarray
An array containing the dipole-moment time series.
An array containing the dipole-moment time series. If cov_splines is
given, the 16- and 84-percentiles are returned along with the series.
'''
if isinstance(splines, Model):
if check:
for it in np.atleast_1d(times):
splines.valid_epoch(it)
if splines._has_covariances:
cov_splines = splines.cov_splines
splines = splines.splines
# evaluate the splines
coeffs = splines(times).T
# calculate the dipole moment
dip_ser = np.sqrt(coeffs[0]**2 + coeffs[1]**2
+ coeffs[2]**2)
# get the units to 1e22 [m^2 A]
dip_ser *= 0.63712**3/1000
return dip_ser
# conversion factor from nT to 1e22 [m^2 A]
conversion = 0.63712**3/1000
if cov_splines is None:
# evaluate the splines
coeffs = splines(times).T
# calculate the dipole moment
dip_ser = np.sqrt(coeffs[0]**2 + coeffs[1]**2
+ coeffs[2]**2)
dip_ser *= conversion
return dip_ser
coeffs = splines(times)[:, :3]
covs = cov_splines(times)[:, :3, :3]
mus = np.empty(len(times))
errs_16 = np.empty(len(times))
errs_84 = np.empty(len(times))
for it, (mu, cov) in enumerate(zip(coeffs, covs)):
ens_dip = np.random.multivariate_normal(mean=mu,
cov=cov,
size=10000).T
ens_dip = ens_dip.reshape(-1, 3, 10000)
ens_m = np.sqrt(np.square(ens_dip).sum(axis=1))
ens_m *= conversion
mus[it] = ens_m.mean(axis=1)
errs_16[it], errs_84[it] = np.percentile(ens_m, (16, 84), axis=1)
return mus, errs_16, errs_84
def file2splines(fname):
......@@ -312,9 +379,8 @@ def file2splines(fname):
return c_splines
def field(z_at, splines, check=True):
'''Evaluate coefficient splines at locations z_at and return the field in
north, east, down components.
def field(z_at, splines, cov_splines=None, field_type='nez', check=True):
'''Evaluate coefficient splines at locations z_at and return the field.
Parameters
----------
......@@ -331,19 +397,30 @@ def field(z_at, splines, check=True):
points.
splines : scipy.interpolate.BSpline or Model
An instance of Model or splines specifying the model.
cov_splines : scipy.interpolate.BSpline, optional
A BSpline interpolating the covariance matrices of the coefficients.
If a model is passed to splines, this argument will be ignored and set,
if the model's covariances are available. If cov_splines is given,
the standard deviations are returned as a second return value.
field_type : {'dif', 'nez'}
The type of output. May be either 'nez' (default) for north, east and
down component or 'dif' for declination, inclination and intensity.
check : bool, optional
If a Model is given, check the input validity.
Returns
-------
array of shape (3, n)
An array containing the north, east and down component at the input
locations.
An array containing the field components at the input locations. If
cov_splines is given, the standard deviations are returned as a
second return value of identical shape.
'''
if isinstance(splines, Model):
if check:
for it in np.atleast_1d(z_at[3]):
splines.valid_epoch(it)
if splines._has_covariances:
cov_splines = splines.cov_splines
splines = splines.splines
# count the number of inputs
n_pts = z_at.shape[1]
......@@ -361,6 +438,45 @@ def field(z_at, splines, check=True):
# calculate the field as a dot product between coefficients and basis
field = np.einsum('ij, ijk -> jk',
coeffs,
spatial)
return field.T
spatial).T
if cov_splines is None:
if field_type == 'dif':
field = np.array(nez2dif(*field))
return field
covs = cov_splines(z_at[3]).T
if field_type == 'dif':
field_cov = np.einsum('ilk, ijl, jlm -> lkm',
spatial,
covs,
spatial)
grad_D = grad_d(*field)
cov_D = np.einsum('ij, ijk, ik -> i',
grad_D,
field_cov,
grad_D)
grad_I = grad_i(*field)
cov_I = np.einsum('ij, ijk, ik -> i',
grad_I,
field_cov,
grad_I)
grad_F = grad_f(*field)
cov_F = np.einsum('ij, ijk, ik -> i',
grad_F,
field_cov,
grad_F)
return np.array(nez2dif(*field)), \
np.sqrt(np.array([cov_D, cov_I, cov_F]))
field_cov = np.einsum('ilk, ijl, jlk -> lk',
spatial,
covs,
spatial)
return field, np.sqrt(field_cov).T
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