code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
# no Test case for these
# elif isinstance(t, typedesc.Argument):
# elif isinstance(t, typedesc.CvQualifiedType):
# elif isinstance(t, typedesc.Variable):
# return "%s" % self.type_name(t.typ, generate)
# elif isinstance(t, typedesc.Enumeration):
# re... | def type_name(self, t, generate=True) | Returns a string containing an expression that can be used to
refer to the type. Assumes the 'from ctypes import *'
namespace is available. | 2.982191 | 2.920622 | 1.021081 |
# FIXME
if self.generate_comments:
self.print_comment(alias)
print("%s = %s # alias" % (alias.name, alias.alias), file=self.stream)
self._aliases += 1
return | def Alias(self, alias) | Handles Aliases. No test cases yet | 7.401573 | 7.226116 | 1.024281 |
if macro.location is None:
log.info('Ignoring %s with no location', macro.name)
return
if self.generate_locations:
print("# %s:%s" % (macro.location), file=self.stream)
if self.generate_comments:
self.print_comment(macro)
print("%s... | def Macro(self, macro) | Handles macro. No test cases else that #defines. | 4.218747 | 4.064962 | 1.037832 |
if item in self.done:
return None
if isinstance(item, typedesc.FundamentalType):
return None
if isinstance(item, typedesc.PointerType):
return self.get_undeclared_type(item.typ)
if isinstance(item, typedesc.ArrayType):
return self.... | def get_undeclared_type(self, item) | Checks if a typed has already been declared in the python output
or is a builtin python type. | 3.383924 | 3.169788 | 1.067555 |
log.debug('HERE in FundamentalType for %s %s', _type, _type.name)
if _type.name in ["None", "c_long_double_t", "c_uint128", "c_int128"]:
self.enable_fundamental_type_wrappers()
return _type.name
return "ctypes.%s" % (_type.name) | def FundamentalType(self, _type) | Returns the proper ctypes class name for a fundamental type
1) activates generation of appropriate headers for
## int128_t
## c_long_double_t
2) return appropriate name for type | 6.302347 | 4.665782 | 1.350759 |
if item in self.done:
return
# verbose output with location.
if self.generate_locations and item.location:
print("# %s:%d" % item.location, file=self.stream)
if self.generate_comments:
self.print_comment(item)
log.debug("generate %s, %... | def _generate(self, item, *args) | wraps execution of specific methods. | 4.4408 | 4.32977 | 1.025643 |
ldata = 200
degrees = np.arange(ldata+1, dtype=float)
degrees[0] = np.inf
power = degrees**(-1)
clm1 = pyshtools.SHCoeffs.from_random(power, exact_power=False)
clm2 = pyshtools.SHCoeffs.from_random(power, exact_power=True)
fig, ax = plt.subplots()
ax.plot(clm1.spectrum(unit='per_l... | def example() | Plot random phase and Gaussian random variable spectra. | 3.644089 | 3.421985 | 1.064905 |
if ax is None:
fig, axes = self.rad.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if show:
fig.show()
if fname is not None:
... | def plot_rad(self, colorbar=True, cb_orientation='vertical',
cb_label='$g_r$, m s$^{-2}$', ax=None, show=True, fname=None,
**kwargs) | Plot the radial component of the gravity field.
Usage
-----
x.plot_rad([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
... | 1.73666 | 2.046936 | 0.848419 |
if ax is None:
fig, axes = self.theta.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False,
**kwargs)
if show:
... | def plot_theta(self, colorbar=True, cb_orientation='vertical',
cb_label='$g_\\theta$, m s$^{-2}$', ax=None, show=True,
fname=None, **kwargs) | Plot the theta component of the gravity field.
Usage
-----
x.plot_theta([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30... | 1.713806 | 1.998569 | 0.857517 |
if ax is None:
fig, axes = self.phi.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if show:
fig.show()
if fname is not None:
... | def plot_phi(self, colorbar=True, cb_orientation='vertical',
cb_label='$g_\phi$, m s$^{-2}$', ax=None, show=True,
fname=None, **kwargs) | Plot the phi component of the gravity field.
Usage
-----
x.plot_phi([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
... | 1.761052 | 2.096796 | 0.839878 |
if self.normal_gravity is True:
if cb_label is None:
cb_label = 'Gravity disturbance, mGal'
else:
if cb_label is None:
cb_label = 'Gravity disturbance, m s$^{-2}$'
if ax is None:
if self.normal_gravity is True:
... | def plot_total(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the total gravity disturbance.
Usage
-----
x.plot_total([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
... | 1.79297 | 1.736164 | 1.032719 |
if ax is None:
fig, axes = self.pot.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if show:
fig.show()
if fname is not None:
... | def plot_pot(self, colorbar=True, cb_orientation='vertical',
cb_label='Potential, m$^2$ s$^{-2}$', ax=None, show=True,
fname=None, **kwargs) | Plot the gravitational potential.
Usage
-----
x.plot_pot([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Int... | 1.706247 | 2.034143 | 0.838804 |
if colorbar is True:
if cb_orientation == 'horizontal':
scale = 0.8
else:
scale = 0.5
else:
scale = 0.6
figsize = (_mpl.rcParams['figure.figsize'][0],
_mpl.rcParams['figure.figsize'][0] * scale)
... | def plot(self, colorbar=True, cb_orientation='horizontal',
tick_interval=[60, 60], minor_tick_interval=[20, 20],
xlabel='Longitude', ylabel='Latitude',
axes_labelsize=9, tick_labelsize=8, show=True, fname=None,
**kwargs) | Plot the three vector components of the gravity field and the gravity
disturbance.
Usage
-----
x.plot([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname, **kwargs])
... | 1.477131 | 1.558381 | 0.947863 |
if type(grid) != str:
raise ValueError('grid must be a string. ' +
'Input type was {:s}'
.format(str(type(grid))))
if nmax is None:
nmax = self.nmax
if self.galpha.kind == 'cap':
shcoeffs = _... | def expand(self, nmax=None, grid='DH2', zeros=None) | Expand the function on a grid using the first n Slepian coefficients.
Usage
-----
f = x.expand([nmax, grid, zeros])
Returns
-------
f : SHGrid class instance
Parameters
----------
nmax : int, optional, default = x.nmax
The number of ... | 2.636478 | 2.569355 | 1.026125 |
if type(normalization) != str:
raise ValueError('normalization must be a string. ' +
'Input type was {:s}'
.format(str(type(normalization))))
if normalization.lower() not in set(['4pi', 'ortho', 'schmidt']):
rais... | def to_shcoeffs(self, nmax=None, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients using the first n Slepian
coefficients.
Usage
-----
s = x.to_shcoeffs([nmax])
Returns
-------
s : SHCoeffs class instance
The spherical harmonic coefficients obtained from using the first
n Slep... | 2.430209 | 2.405719 | 1.01018 |
'''
Determine error message to print when a SHTOOLS Fortran 95 routine exits
improperly.
'''
if (status == 1):
errmsg = 'Improper dimensions of input array.'
elif (status == 2):
errmsg = 'Improper bounds for input variable.'
elif (status == 3):
errmsg = 'Error allocat... | def _shtools_status_message(status) | Determine error message to print when a SHTOOLS Fortran 95 routine exits
improperly. | 4.836959 | 2.881132 | 1.67884 |
if theta_degrees:
tapers, eigenvalues, taper_order = _shtools.SHReturnTapers(
_np.radians(theta), lmax)
else:
tapers, eigenvalues, taper_order = _shtools.SHReturnTapers(
theta, lmax)
return SlepianCap(theta, tapers, eigenvalues, t... | def from_cap(cls, theta, lmax, clat=None, clon=None, nmax=None,
theta_degrees=True, coord_degrees=True, dj_matrix=None) | Construct spherical cap Slepian functions.
Usage
-----
x = Slepian.from_cap(theta, lmax, [clat, clon, nmax, theta_degrees,
coord_degrees, dj_matrix])
Returns
-------
x : Slepian class instance
Parameters
------... | 3.593344 | 4.116504 | 0.872912 |
if nmax is None:
nmax = (lmax + 1)**2
else:
if nmax > (lmax + 1)**2:
raise ValueError('nmax must be less than or equal to ' +
'(lmax + 1)**2. lmax = {:d} and nmax = {:d}'
.format(lmax, nmax... | def from_mask(cls, dh_mask, lmax, nmax=None) | Construct Slepian functions that are optimally concentrated within
the region specified by a mask.
Usage
-----
x = Slepian.from_mask(dh_mask, lmax, [nmax])
Returns
-------
x : Slepian class instance
Parameters
----------
dh_mask :ndarray... | 2.963971 | 2.83879 | 1.044097 |
if nmax is None:
nmax = (self.lmax+1)**2
elif nmax is not None and nmax > (self.lmax+1)**2:
raise ValueError(
"nmax must be less than or equal to (lmax+1)**2 " +
"where lmax is {:s}. Input value is {:s}"
.format(repr(self.l... | def expand(self, flm, nmax=None) | Return the Slepian expansion coefficients of the input function.
Usage
-----
s = x.expand(flm, [nmax])
Returns
-------
s : SlepianCoeff class instance
The Slepian expansion coefficients of the input function.
Parameters
----------
fl... | 3.337901 | 3.275884 | 1.018931 |
if alpha is None:
if nmax is None:
nmax = self.nmax
spectra = _np.zeros((self.lmax+1, nmax))
for iwin in range(nmax):
coeffs = self.to_array(iwin)
spectra[:, iwin] = _spectrum(coeffs, normalization='4pi',
... | def spectra(self, alpha=None, nmax=None, convention='power', unit='per_l',
base=10.) | Return the spectra of one or more Slepian functions.
Usage
-----
spectra = x.spectra([alpha, nmax, convention, unit, base])
Returns
-------
spectra : ndarray, shape (lmax+1, nmax)
A matrix with each column containing the spectrum of a Slepian
f... | 2.673187 | 2.767267 | 0.966002 |
if self.coeffs is None:
self.rotate(clat=90., clon=0., nrot=nmax)
falpha = _shtools.SlepianCoeffs(self.coeffs, coeffsin, self.nrot)
else:
falpha = _shtools.SlepianCoeffs(self.coeffs, coeffsin, self.nrot)
return SlepianCoeffs(falpha, self) | def _expand(self, coeffsin, nmax) | Determine the Slepian expansion coefficients of a function. | 5.011766 | 4.501101 | 1.113453 |
taperm = self.orders[alpha]
coeffs = _np.zeros((2, self.lmax + 1, self.lmax + 1))
if taperm < 0:
coeffs[1, :, abs(taperm)] = self.tapers[:, alpha]
else:
coeffs[0, :, abs(taperm)] = self.tapers[:, alpha]
return coeffs | def _taper2coeffs(self, alpha) | Return the spherical harmonic coefficients of the unrotated Slepian
function i as an array, where i = 0 is the best concentrated function. | 3.938465 | 3.9328 | 1.001441 |
if self.coeffs is None:
coeffs = _np.copy(self._taper2coeffs(alpha))
else:
if alpha > self.nrot - 1:
raise ValueError('alpha must be less than or equal to ' +
'nrot - 1. alpha = {:d}, nrot = {:d}'
... | def _to_array(self, alpha, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients of Slepian function i as an
array, where i = 0 is the best concentrated function. | 3.279573 | 3.184323 | 1.029912 |
falpha = _shtools.SlepianCoeffs(self.tapers, coeffsin, nmax)
return SlepianCoeffs(falpha, self) | def _expand(self, coeffsin, nmax) | Determine the Slepian expansion coefficients of a function. | 10.318316 | 7.798131 | 1.323178 |
coeffs = _shtools.SHVectorToCilm(self.tapers[:, alpha])
if normalization == 'schmidt':
for l in range(self.lmax + 1):
coeffs[:, l, :l+1] *= _np.sqrt(2.0 * l + 1.0)
elif normalization == 'ortho':
coeffs *= _np.sqrt(4.0 * _np.pi)
if csphas... | def _to_array(self, alpha, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients of Slepian function i as an
array, where i=0 is the best concentrated function. | 3.433356 | 3.399576 | 1.009936 |
if ax is None:
fig, axes = self.total.plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
... | def plot_total(self, colorbar=True, cb_orientation='vertical',
cb_label='$|B|$, nT', ax=None, show=True, fname=None,
**kwargs) | Plot the total magnetic intensity.
Usage
-----
x.plot_total([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
... | 1.715566 | 2.058744 | 0.833307 |
if _np.iscomplexobj(coeffs):
raise TypeError('The input array must be real.')
if type(normalization) != str:
raise ValueError('normalization must be a string. '
'Input type was {:s}'
.format(str(type(normalizatio... | def from_array(self, coeffs, r0, errors=None, normalization='schmidt',
csphase=1, lmax=None, copy=True) | Initialize the class with spherical harmonic coefficients from an input
array.
Usage
-----
x = SHMagCoeffs.from_array(array, r0, [errors, normalization, csphase,
lmax, copy])
Returns
-------
x : SHMagCoeffs class in... | 2.029154 | 1.843296 | 1.100829 |
# Ensure that the type is correct
values = _np.array(values)
ls = _np.array(ls)
ms = _np.array(ms)
mneg_mask = (ms < 0).astype(_np.int)
self.coeffs[mneg_mask, ls, _np.abs(ms)] = values | def set_coeffs(self, values, ls, ms) | Set spherical harmonic coefficients in-place to specified values.
Usage
-----
x.set_coeffs(values, ls, ms)
Parameters
----------
values : float (list)
The value(s) of the spherical harmonic coefficient(s).
ls : int (list)
The degree(s) of... | 4.069285 | 4.255247 | 0.956298 |
if normalization is None:
normalization = self.normalization
if csphase is None:
csphase = self.csphase
if lmax is None:
lmax = self.lmax
coeffs = _convert(self.coeffs, normalization_in=self.normalization,
normalizat... | def to_array(self, normalization=None, csphase=None, lmax=None) | Return spherical harmonic coefficients (and errors) as a numpy array.
Usage
-----
coeffs, [errors] = x.to_array([normalization, csphase, lmax])
Returns
-------
coeffs : ndarry, shape (2, lmax+1, lmax+1)
numpy ndarray of the spherical harmonic coefficients.
... | 1.57257 | 1.599988 | 0.982864 |
if normalization is None:
normalization = self.normalization
if csphase is None:
csphase = self.csphase
if lmax is None:
lmax = self.lmax
# check argument consistency
if type(normalization) != str:
raise ValueError('normal... | def convert(self, normalization=None, csphase=None, lmax=None) | Return an SHMagCoeffs class instance with a different normalization
convention.
Usage
-----
clm = x.convert([normalization, csphase, lmax])
Returns
-------
clm : SHMagCoeffs class instance
Parameters
----------
normalization : str, optio... | 1.819914 | 1.698618 | 1.071408 |
clm = self.copy()
if lmax <= self.lmax:
clm.coeffs = clm.coeffs[:, :lmax+1, :lmax+1]
clm.mask = clm.mask[:, :lmax+1, :lmax+1]
if self.errors is not None:
clm.errors = clm.errors[:, :lmax+1, :lmax+1]
else:
clm.coeffs = _np.... | def pad(self, lmax) | Return an SHMagCoeffs class where the coefficients are zero padded or
truncated to a different lmax.
Usage
-----
clm = x.pad(lmax)
Returns
-------
clm : SHMagCoeffs class instance
Parameters
----------
lmax : int
Maximum sphe... | 1.786165 | 1.832826 | 0.974541 |
if lmax is None:
lmax = self.lmax
clm = self.pad(lmax)
if r0 is not None and r0 != self.r0:
for l in _np.arange(lmax+1):
clm.coeffs[:, l, :l+1] *= (self.r0 / r0)**(l+2)
if self.errors is not None:
clm.errors[:... | def change_ref(self, r0=None, lmax=None) | Return a new SHMagCoeffs class instance with a different reference r0.
Usage
-----
clm = x.change_ref([r0, lmax])
Returns
-------
clm : SHMagCoeffs class instance.
Parameters
----------
r0 : float, optional, default = self.r0
The ref... | 2.857186 | 2.89627 | 0.986505 |
if a is None:
a = self.r0
if f is None:
f = 0.
if lmax is None:
lmax = self.lmax
if lmax_calc is None:
lmax_calc = lmax
if self.errors is not None:
coeffs, errors = self.to_array(normalization='schmidt', csphas... | def expand(self, a=None, f=None, lmax=None, lmax_calc=None, sampling=2) | Create 2D cylindrical maps on a flattened and rotating ellipsoid of all
three components of the magnetic field, the total magnetic intensity,
and the magnetic potential, and return as a SHMagGrid class instance.
Usage
-----
mag = x.expand([a, f, lmax, lmax_calc, sampling])
... | 3.104939 | 2.567515 | 1.209317 |
if line.isspace():
return True
elif len(line.split()) >= 3:
try: # python 3 str
if line.split()[0].isdecimal() and line.split()[1].isdecimal():
return False
except: # python 2 str
if (line.decode().split()[0].isdecimal() and
... | def _iscomment(line) | Determine if a line is a comment line. A valid line contains at least three
words, with the first two being integers. Note that Python 2 and 3 deal
with strings differently. | 3.248134 | 2.876065 | 1.129367 |
# ---- split f2py document in its parts
# 0=Call Signature
# 1=Parameters
# 2=Other (optional) Parameters (only if present)
# 3=Returns
docparts = re.split('\n--', f2pydoc)
if len(docparts) == 4:
doc_has_optionals = True
elif len(docparts) == 3:
doc_has_optionals = ... | def process_f2pydoc(f2pydoc) | this function replace all optional _d0 arguments with their default values
in the function signature. These arguments are not intended to be used and
signify merely the array dimensions of the associated argument. | 4.543833 | 4.115574 | 1.104058 |
if theta_degrees:
tapers, eigenvalues, taper_order = _shtools.SHReturnTapers(
_np.radians(theta), lwin)
else:
tapers, eigenvalues, taper_order = _shtools.SHReturnTapers(
theta, lwin)
return SHWindowCap(theta, tapers, eigenvalues, ... | def from_cap(cls, theta, lwin, clat=None, clon=None, nwin=None,
theta_degrees=True, coord_degrees=True, dj_matrix=None,
weights=None) | Construct spherical cap localization windows.
Usage
-----
x = SHWindow.from_cap(theta, lwin, [clat, clon, nwin, theta_degrees,
coord_degrees, dj_matrix, weights])
Returns
-------
x : SHWindow class instance
Parameters... | 3.622627 | 3.680443 | 0.984291 |
if nwin is None:
nwin = (lwin + 1)**2
else:
if nwin > (lwin + 1)**2:
raise ValueError('nwin must be less than or equal to ' +
'(lwin + 1)**2. lwin = {:d} and nwin = {:d}'
.format(lwin, nwin... | def from_mask(cls, dh_mask, lwin, nwin=None, weights=None) | Construct localization windows that are optimally concentrated within
the region specified by a mask.
Usage
-----
x = SHWindow.from_mask(dh_mask, lwin, [nwin, weights])
Returns
-------
x : SHWindow class instance
Parameters
----------
dh... | 3.098976 | 2.832755 | 1.093979 |
if type(normalization) != str:
raise ValueError('normalization must be a string. ' +
'Input type was {:s}'
.format(str(type(normalization))))
if normalization.lower() not in ('4pi', 'ortho', 'schmidt'):
raise Val... | def to_array(self, itaper, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients of taper i as a numpy
array.
Usage
-----
coeffs = x.to_array(itaper, [normalization, csphase])
Returns
-------
coeffs : ndarray, shape (2, lwin+1, lwin+11)
3-D numpy ndarray of the spherical harmonic coeffic... | 2.031988 | 2.130975 | 0.953549 |
if type(normalization) != str:
raise ValueError('normalization must be a string. ' +
'Input type was {:s}'
.format(str(type(normalization))))
if normalization.lower() not in set(['4pi', 'ortho', 'schmidt']):
rais... | def to_shcoeffs(self, itaper, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients of taper i as a SHCoeffs
class instance.
Usage
-----
clm = x.to_shcoeffs(itaper, [normalization, csphase])
Returns
-------
clm : SHCoeffs class instance
Parameters
----------
itaper : int
... | 1.996986 | 2.057261 | 0.970702 |
if type(grid) != str:
raise ValueError('grid must be a string. ' +
'Input type was {:s}'
.format(str(type(grid))))
if grid.upper() in ('DH', 'DH1'):
gridout = _shtools.MakeGridDH(self.to_array(itaper), sampling=1... | def to_shgrid(self, itaper, grid='DH2', zeros=None) | Evaluate the coefficients of taper i on a spherical grid and return
a SHGrid class instance.
Usage
-----
f = x.to_shgrid(itaper, [grid, zeros])
Returns
-------
f : SHGrid class instance
Parameters
----------
itaper : int
Tape... | 2.314469 | 2.214828 | 1.044988 |
return self._multitaper_spectrum(clm, k, convention=convention,
unit=unit, **kwargs) | def multitaper_spectrum(self, clm, k, convention='power', unit='per_l',
**kwargs) | Return the multitaper spectrum estimate and standard error.
Usage
-----
mtse, sd = x.multitaper_spectrum(clm, k, [convention, unit, lmax,
taper_wt, clat, clon,
coord_degrees])
Returns
... | 2.410269 | 5.533176 | 0.435603 |
return self._multitaper_cross_spectrum(clm, slm, k,
convention=convention,
unit=unit, **kwargs) | def multitaper_cross_spectrum(self, clm, slm, k, convention='power',
unit='per_l', **kwargs) | Return the multitaper cross-spectrum estimate and standard error.
Usage
-----
mtse, sd = x.multitaper_cross_spectrum(clm, slm, k, [convention, unit,
lmax, taper_wt,
clat, cl... | 2.28215 | 4.49076 | 0.508188 |
return self._biased_spectrum(power, k, convention=convention,
unit=unit, **kwargs) | def biased_spectrum(self, power, k, convention='power', unit='per_l',
**kwargs) | Calculate the multitaper (cross-)spectrum expectation of a
localized function.
Usage
-----
outspectrum = x.biased_spectrum(spectrum, k, [unit, power, taper_wt,
save_cg, ldata])
Returns
-------
outspectrum : n... | 2.634682 | 6.345843 | 0.415182 |
if itaper is None:
if nwin is None:
nwin = self.nwin
spectra = _np.zeros((self.lwin+1, nwin))
for iwin in range(nwin):
coeffs = self.to_array(iwin)
spectra[:, iwin] = _spectrum(coeffs, normalization='4pi',
... | def spectra(self, itaper=None, nwin=None, convention='power', unit='per_l',
base=10.) | Return the spectra of one or more localization windows.
Usage
-----
spectra = x.spectra([itaper, nwin, convention, unit, base])
Returns
-------
spectra : ndarray, shape (lwin+1, nwin)
A matrix with each column containing the spectrum of a
local... | 2.518591 | 2.397177 | 1.050649 |
if weights is not None:
if nwin is not None:
if len(weights) != nwin:
raise ValueError(
'Length of weights must be equal to nwin. ' +
'len(weights) = {:d}, nwin = {:d}'.format(len(weights),
... | def coupling_matrix(self, lmax, nwin=None, weights=None, mode='full') | Return the coupling matrix of the first nwin tapers. This matrix
relates the global power spectrum to the expectation of the localized
multitaper spectrum.
Usage
-----
Mmt = x.coupling_matrix(lmax, [nwin, weights, mode])
Returns
-------
Mmt : ndarray, sh... | 1.841015 | 1.787617 | 1.029871 |
figsize = (_mpl.rcParams['figure.figsize'][0],
_mpl.rcParams['figure.figsize'][0])
if axes_labelsize is None:
axes_labelsize = _mpl.rcParams['axes.labelsize']
if tick_labelsize is None:
tick_labelsize = _mpl.rcParams['xtick.labelsize']
... | def plot_coupling_matrix(self, lmax, nwin=None, weights=None, mode='full',
axes_labelsize=None, tick_labelsize=None,
show=True, ax=None, fname=None) | Plot the multitaper coupling matrix.
This matrix relates the global power spectrum to the expectation of
the localized multitaper spectrum.
Usage
-----
x.plot_coupling_matrix(lmax, [nwin, weights, mode, axes_labelsize,
tick_labelsize, show,... | 1.603733 | 1.770346 | 0.905887 |
taperm = self.orders[itaper]
coeffs = _np.zeros((2, self.lwin + 1, self.lwin + 1))
if taperm < 0:
coeffs[1, :, abs(taperm)] = self.tapers[:, itaper]
else:
coeffs[0, :, abs(taperm)] = self.tapers[:, itaper]
return coeffs | def _taper2coeffs(self, itaper) | Return the spherical harmonic coefficients of the unrotated taper i
as an array, where i = 0 is the best concentrated. | 4.114479 | 3.992618 | 1.030522 |
if self.coeffs is None:
coeffs = _np.copy(self._taper2coeffs(itaper))
else:
if itaper > self.nwinrot - 1:
raise ValueError('itaper must be less than or equal to ' +
'nwinrot - 1. itaper = {:d}, nwinrot = {:d}'
... | def _to_array(self, itaper, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients of taper i as an
array, where i = 0 is the best concentrated. | 3.349682 | 3.210892 | 1.043224 |
self.coeffs = _np.zeros(((self.lwin + 1)**2, self.nwin))
self.clat = clat
self.clon = clon
self.coord_degrees = coord_degrees
if nwinrot is not None:
self.nwinrot = nwinrot
else:
self.nwinrot = self.nwin
if self.coord_degrees:
... | def rotate(self, clat, clon, coord_degrees=True, dj_matrix=None,
nwinrot=None) | Rotate the spherical-cap windows centered on the North pole to clat
and clon, and save the spherical harmonic coefficients in the
attribute coeffs.
Usage
-----
x.rotate(clat, clon [coord_degrees, dj_matrix, nwinrot])
Parameters
----------
clat, clon : fl... | 2.795526 | 2.263902 | 1.234826 |
if nwin is None:
nwin = self.nwin
if weights is None:
weights = self.weights
if weights is None:
return _shtools.SHMTCouplingMatrix(lmax, self.tapers**2, k=nwin)
else:
return _shtools.SHMTCouplingMatrix(lmax, self.tapers**2, k=nw... | def _coupling_matrix(self, lmax, nwin=None, weights=None) | Return the coupling matrix of the first nwin tapers. | 3.385814 | 3.065946 | 1.104329 |
if lmax is None:
lmax = clm.lmax
if (clat is not None and clon is not None and clat == self.clat and
clon == self.clon and coord_degrees is self.coord_degrees and
k <= self.nwinrot):
# use the already stored coeffs
pass
... | def _multitaper_spectrum(self, clm, k, convention='power', unit='per_l',
clat=None, clon=None, coord_degrees=True,
lmax=None, taper_wt=None) | Return the multitaper spectrum estimate and standard error for an
input SHCoeffs class instance. | 2.328032 | 2.252114 | 1.03371 |
if lmax is None:
lmax = min(clm.lmax, slm.lmax)
if (clat is not None and clon is not None and clat == self.clat and
clon == self.clon and coord_degrees is self.coord_degrees and
k <= self.nwinrot):
# use the already stored coeffs
... | def _multitaper_cross_spectrum(self, clm, slm, k, convention='power',
unit='per_l', clat=None, clon=None,
coord_degrees=True, lmax=None,
taper_wt=None) | Return the multitaper cross-spectrum estimate and standard error for
two input SHCoeffs class instances. | 2.228705 | 2.166496 | 1.028714 |
coeffs = _shtools.SHVectorToCilm(self.tapers[:, itaper])
if normalization == 'schmidt':
for l in range(self.lwin + 1):
coeffs[:, l, :l+1] *= _np.sqrt(2.0 * l + 1.0)
elif normalization == 'ortho':
coeffs *= _np.sqrt(4.0 * _np.pi)
if cspha... | def _to_array(self, itaper, normalization='4pi', csphase=1) | Return the spherical harmonic coefficients of taper i as an
array, where i=0 is the best concentrated. | 3.741787 | 3.465992 | 1.079572 |
if nwin is None:
nwin = self.nwin
if weights is None:
weights = self.weights
tapers_power = _np.zeros((self.lwin+1, nwin))
for i in range(nwin):
tapers_power[:, i] = _spectrum(self.to_array(i),
norm... | def _coupling_matrix(self, lmax, nwin=None, weights=None) | Return the coupling matrix of the first nwin tapers. | 4.22186 | 4.00645 | 1.053766 |
if lmax is None:
lmax = clm.lmax
sh = clm.to_array(normalization='4pi', csphase=1, lmax=lmax)
if taper_wt is None:
mtse, sd = _shtools.SHMultiTaperMaskSE(sh, self.tapers, lmax=lmax,
k=k)
else:
... | def _multitaper_spectrum(self, clm, k, convention='power', unit='per_l',
lmax=None, taper_wt=None) | Return the multitaper spectrum estimate and standard error for an
input SHCoeffs class instance. | 2.48546 | 2.354989 | 1.055402 |
if lmax is None:
lmax = min(clm.lmax, slm.lmax)
sh1 = clm.to_array(normalization='4pi', csphase=1, lmax=lmax)
sh2 = slm.to_array(normalization='4pi', csphase=1, lmax=lmax)
if taper_wt is None:
mtse, sd = _shtools.SHMultiTaperMaskCSE(sh1, sh2, self.taper... | def _multitaper_cross_spectrum(self, clm, slm, k, convention='power',
unit='per_l', lmax=None, taper_wt=None) | Return the multitaper cross-spectrum estimate and standard error for
two input SHCoeffs class instances. | 2.302921 | 2.186738 | 1.053131 |
# The equation is not modified if the in- and out- spectra are power
# or energy. However, the convention can not be l2norm, which depends
# upon the normalization of the coefficients.
if (convention != 'power' and convention != 'energy'):
raise ValueError(
... | def _biased_spectrum(self, spectrum, k, convention='power', unit='per_l',
**kwargs) | Calculate the multitaper (cross-) spectrum expectation of a function
localized by arbitary windows. | 3.929859 | 3.944344 | 0.996328 |
self.i0 = self.vxx + self.vyy + self.vzz
self.i1 = (self.vxx*self.vyy + self.vyy*self.vzz + self.vxx*self.vzz -
self.vxy**2 - self.vyz**2 - self.vxz**2)
self.i2 = (self.vxx*(self.vyy*self.vzz - self.vyz**2) +
self.vxy*(self.vyz*self.vxz - self.vxy*s... | def compute_invar(self) | Compute the three invariants (I0, I1, I2) of the tensor, as well as
the quantity I = -(I2/2)**2 / (I1/3)**3. | 2.395625 | 2.021655 | 1.184982 |
self.eig1 = _SHGrid.from_array(_np.zeros_like(self.vxx.data),
grid='DH')
self.eig2 = _SHGrid.from_array(_np.zeros_like(self.vxx.data),
grid='DH')
self.eig3 = _SHGrid.from_array(_np.zeros_like(self.vxx.data),
... | def compute_eig(self) | Compute the three eigenvalues of the tensor: eig1, eig2, ei3. | 1.758176 | 1.67733 | 1.048199 |
self.eigh1 = _SHGrid.from_array(_np.zeros_like(self.vxx.data),
grid='DH')
self.eigh2 = _SHGrid.from_array(_np.zeros_like(self.vxx.data),
grid='DH')
self.eighh = _SHGrid.from_array(_np.zeros_like(self.vxx.dat... | def compute_eigh(self) | Compute the two horizontal eigenvalues of the tensor (eigh1, and
eigh2), as well as the combined maximum absolute value of the two
(eighh). | 1.946044 | 1.784612 | 1.090458 |
if cb_label is None:
cb_label = self._vxx_label
if ax is None:
fig, axes = self.vxx.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vxx(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vxx component of the tensor.
Usage
-----
x.plot_vxx([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.737735 | 2.025666 | 0.857858 |
if cb_label is None:
cb_label = self._vyy_label
if ax is None:
fig, axes = self.vyy.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vyy(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vyy component of the tensor.
Usage
-----
x.plot_vyy([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.73791 | 2.036034 | 0.853576 |
if cb_label is None:
cb_label = self._vzz_label
if ax is None:
fig, axes = self.vzz.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vzz(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vzz component of the tensor.
Usage
-----
x.plot_vzz([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.745698 | 2.047911 | 0.852429 |
if cb_label is None:
cb_label = self._vxy_label
if ax is None:
fig, axes = self.vxy.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vxy(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vxy component of the tensor.
Usage
-----
x.plot_vxy([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.756477 | 2.017079 | 0.870802 |
if cb_label is None:
cb_label = self._vyx_label
if ax is None:
fig, axes = self.vyx.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vyx(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vyx component of the tensor.
Usage
-----
x.plot_vyx([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.78252 | 2.087936 | 0.853724 |
if cb_label is None:
cb_label = self._vxz_label
if ax is None:
fig, axes = self.vxz.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vxz(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vxz component of the tensor.
Usage
-----
x.plot_vxz([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.753365 | 2.041861 | 0.858709 |
if cb_label is None:
cb_label = self._vzx_label
if ax is None:
fig, axes = self.vzx.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vzx(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vzx component of the tensor.
Usage
-----
x.plot_vzx([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.742828 | 2.024994 | 0.860658 |
if cb_label is None:
cb_label = self._vyz_label
if ax is None:
fig, axes = self.vyz.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vyz(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vyz component of the tensor.
Usage
-----
x.plot_vyz([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.772613 | 2.108889 | 0.840543 |
if cb_label is None:
cb_label = self._vzy_label
if ax is None:
fig, axes = self.vzy.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
if... | def plot_vzy(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the Vzy component of the tensor.
Usage
-----
x.plot_vzy([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals... | 1.731398 | 2.032582 | 0.851822 |
if cb_label is None:
cb_label = self._i0_label
if self.i0 is None:
self.compute_invar()
if ax is None:
fig, axes = self.i0.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_i0(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the first invariant I0 (the trace) of the tensor
I0 = vxx + vyy + vzz
which should be identically zero.
Usage
-----
x.plot_i0([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
cb_label, show, fname])
Parameters
---------... | 1.980922 | 2.275219 | 0.870651 |
if cb_label is None:
cb_label = self._i1_label
if self.i1 is None:
self.compute_invar()
if ax is None:
fig, axes = self.i1.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_i1(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the second invariant I1 of the tensor:
I1 = vxx*vyy + vyy*vzz + vxx*vzz - vxy**2 - vyz**2 - vxz**2
Usage
-----
x.plot_i1([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
cb_label, show, fname])
Parameters
----------
tick... | 1.969382 | 2.223373 | 0.885763 |
if cb_label is None:
cb_label = self._i2_label
if self.i2 is None:
self.compute_invar()
if ax is None:
fig, axes = self.i2.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_i2(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the third invariant I2 (the determinant) of the tensor:
I2 = vxx*(vyy*vzz - vyz**2) + vxy*(vyz*vxz - vxy*vzz)
+ vxz*(vxy*vyz - vxz*vyy)
Usage
-----
x.plot_i2([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
cb_label, show, fname])... | 1.972461 | 2.224842 | 0.886562 |
if cb_label is None:
cb_label = self._i_label
if self.i is None:
self.compute_invar()
if ax is None:
fig, axes = self.i.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb... | def plot_i(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the dimensionless quantity I of Pedersen and Rasmussen (1990)
I = -(I2/2)**2 / (I1/3)**3
that is bounded by 0 and 1.
Usage
-----
x.plot_i([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
cb_label, show, fname])
Parameters
... | 1.986243 | 2.260373 | 0.878724 |
if colorbar is True:
if cb_orientation == 'horizontal':
scale = 0.8
else:
scale = 0.5
else:
scale = 0.6
figsize = (_mpl.rcParams['figure.figsize'][0],
_mpl.rcParams['figure.figsize'][0] * scale)
... | def plot_invar(self, colorbar=True, cb_orientation='horizontal',
tick_interval=[60, 60], minor_tick_interval=[20, 20],
xlabel='Longitude', ylabel='Latitude',
axes_labelsize=9, tick_labelsize=8, show=True, fname=None,
**kwargs) | Plot the three invariants of the tensor and the derived quantity I.
Usage
-----
x.plot_invar([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname, **kwargs])
Parameters
... | 1.430316 | 1.488696 | 0.960785 |
if cb_label is None:
cb_label = self._eig1_label
if self.eig1 is None:
self.compute_eig()
if ax is None:
fig, axes = self.eig1.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_eig1(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the first eigenvalue of the tensor.
Usage
-----
x.plot_eig1([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Inte... | 1.777364 | 2.094292 | 0.848671 |
if cb_label is None:
cb_label = self._eig2_label
if self.eig2 is None:
self.compute_eig()
if ax is None:
fig, axes = self.eig2.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_eig2(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the second eigenvalue of the tensor.
Usage
-----
x.plot_eig2([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Int... | 1.789871 | 2.113045 | 0.847058 |
if cb_label is None:
cb_label = self._eig3_label
if self.eig3 is None:
self.compute_eig()
if ax is None:
fig, axes = self.eig3.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_eig3(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the third eigenvalue of the tensor.
Usage
-----
x.plot_eig3([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Inte... | 1.785359 | 2.106384 | 0.847594 |
if colorbar is True:
if cb_orientation == 'horizontal':
scale = 2.3
else:
scale = 1.4
else:
scale = 1.65
figsize = (_mpl.rcParams['figure.figsize'][0],
_mpl.rcParams['figure.figsize'][0] * scale)
... | def plot_eigs(self, colorbar=True, cb_orientation='vertical',
tick_interval=[60, 60], minor_tick_interval=[20, 20],
xlabel='Longitude', ylabel='Latitude',
axes_labelsize=9, tick_labelsize=8, show=True, fname=None,
**kwargs) | Plot the three eigenvalues of the tensor.
Usage
-----
x.plot_eigs([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname, **kwargs])
Parameters
----------
... | 1.512364 | 1.597049 | 0.946974 |
if cb_label is None:
cb_label = self._eigh1_label
if self.eigh1 is None:
self.compute_eigh()
if ax is None:
fig, axes = self.eigh1.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_eigh1(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the first eigenvalue of the horizontal tensor.
Usage
-----
x.plot_eigh1([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
... | 1.747223 | 1.98045 | 0.882235 |
if cb_label is None:
cb_label = self._eigh2_label
if self.eigh2 is None:
self.compute_eigh()
if ax is None:
fig, axes = self.eigh2.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_eigh2(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the second eigenvalue of the horizontal tensor.
Usage
-----
x.plot_eigh2([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
... | 1.758707 | 1.9854 | 0.88582 |
if cb_label is None:
cb_label = self._eighh_label
if self.eighh is None:
self.compute_eigh()
if ax is None:
fig, axes = self.eighh.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
... | def plot_eighh(self, colorbar=True, cb_orientation='vertical',
cb_label=None, ax=None, show=True, fname=None, **kwargs) | Plot the maximum absolute value eigenvalue of the horizontal tensor.
Usage
-----
x.plot_eighh([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, defau... | 1.749699 | 1.986944 | 0.880598 |
if colorbar is True:
if cb_orientation == 'horizontal':
scale = 2.3
else:
scale = 1.4
else:
scale = 1.65
figsize = (_mpl.rcParams['figure.figsize'][0],
_mpl.rcParams['figure.figsize'][0] * scale)
... | def plot_eigh(self, colorbar=True, cb_orientation='vertical',
tick_interval=[60, 60], minor_tick_interval=[20, 20],
xlabel='Longitude', ylabel='Latitude',
axes_labelsize=9, tick_labelsize=8, show=True, fname=None,
**kwargs) | Plot the two eigenvalues and maximum absolute value eigenvalue of the
horizontal tensor.
Usage
-----
x.plot_eigh([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname... | 1.600249 | 1.697888 | 0.942494 |
d = os.path.dirname(__file__)
# get release number from VERSION
with open(os.path.join(d, 'VERSION')) as f:
vre = re.compile('.Version: (.+)$', re.M)
version = vre.search(f.read()).group(1)
if os.path.isdir(os.path.join(d, '.git')):
# Get the version using "git describe".
... | def get_version() | Get version from git and VERSION file.
In the case where the version is not tagged in git, this function appends
.post0+commit if the version has been released and .dev0+commit if the
version has not yet been released.
Derived from: https://github.com/Changaco/version.py | 3.36836 | 3.021211 | 1.114904 |
compiler = get_default_fcompiler()
if compiler == 'absoft':
flags = ['-m64', '-O3', '-YEXT_NAMES=LCS', '-YEXT_SFX=_',
'-fpic', '-speed_math=10']
elif compiler == 'gnu95':
flags = ['-m64', '-fPIC', '-O3', '-ffast-math']
elif compiler == 'intel':
flags = ['-m6... | def get_compiler_flags() | Set fortran flags depending on the compiler. | 5.475964 | 5.153579 | 1.062555 |
config = Configuration('', parent_package, top_path)
F95FLAGS = get_compiler_flags()
kwargs = {
'libraries': [],
'include_dirs': [],
'library_dirs': [],
}
kwargs['extra_compile_args'] = F95FLAGS
kwargs['f2py_options'] = ['--quiet']
# numpy.distutils.fcompiler.... | def configuration(parent_package='', top_path=None) | Configure all packages that need to be built. | 3.568209 | 3.572069 | 0.998919 |
print('---- BUILDING ----')
_build.run(self)
# build documentation
print('---- BUILDING DOCS ----')
docdir = os.path.join(self.build_lib, 'pyshtools', 'doc')
self.mkpath(docdir)
doc_builder = os.path.join(self.build_lib, 'pyshtools', 'make_docs.py')
... | def run(self) | Build the Fortran library, all python extensions and the docs. | 3.474157 | 3.065601 | 1.133271 |
print('---- CUSTOM DEVELOP ----')
_develop.run(self)
# build documentation
print('---- BUILDING DOCS ----')
docdir = os.path.join(self.setup_path, 'pyshtools', 'doc')
self.mkpath(docdir)
doc_builder = os.path.join(self.setup_path, 'pyshtools',
... | def run(self) | Build the Fortran library, all python extensions and the docs. | 4.631189 | 4.124975 | 1.122719 |
delta_t = epoch - ref_epoch
trend = trnd * delta_t
periodic_sum = _np.zeros_like(trnd)
for period in periodic:
for trifunc in periodic[period]:
coeffs = periodic[period][trifunc]
if trifunc == 'acos':
periodic_sum += coeffs * _np.cos(2 * _np.pi / peri... | def _time_variable_part(epoch, ref_epoch, trnd, periodic) | Return sum of the time-variable part of the coefficients
The formula is:
G(t) = G(t0) + trnd*(t-t0) +
asin1*sin(2pi/p1 * (t-t0)) + acos1*cos(2pi/p1 * (t-t0)) +
asin2*sin(2pi/p2 * (t-t0)) + acos2*cos(2pi/p2 * (t-t0))
This function computes all terms after G(t0). | 2.795751 | 2.757543 | 1.013856 |
# print('\n----',subroutine['name'],'----')
#-- use original function from shtools:
subroutine['use'] = {'shtools': {'map': {subroutine['name']: subroutine['name']}, 'only': 1}}
#-- loop through variables:
for varname, varattribs in subroutine['vars'].items():
#-- prefix function retu... | def modify_subroutine(subroutine) | loops through variables of a subroutine and modifies them | 6.842419 | 6.820755 | 1.003176 |
width_x = max_width * rel_width
width_y = max_width * rel_width / aspect_ratio
shtools = {
# fonts
'font.size': 10,
'font.family': 'sans-serif',
'font.sans-serif': ['Myriad Pro', 'DejaVu Sans',
'Bitstream Vera Sans',
... | def figstyle(rel_width=0.75, screen_dpi=114, aspect_ratio=4/3,
max_width=7.48031) | Set matplotlib parameters for creating publication quality graphics.
Usage
-----
figstyle([rel_width, screen_dpi, aspect_ratio, max_width])
Parameters
----------
rel_width : float, optional, default = 0.75
The relative width of the plot (from 0 to 1) wih respect to max_width.
scree... | 2.056563 | 2.177752 | 0.944351 |
if kind.lower() not in ('real', 'complex'):
raise ValueError(
"Kind must be 'real' or 'complex'. " +
"Input value was {:s}."
.format(repr(kind))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
... | def from_zeros(self, lmax, kind='real', normalization='4pi', csphase=1) | Initialize class with spherical harmonic coefficients set to zero from
degree 0 to lmax.
Usage
-----
x = SHCoeffs.from_zeros(lmax, [normalization, csphase])
Returns
-------
x : SHCoeffs class instance.
Parameters
----------
lmax : int
... | 2.117582 | 2.07353 | 1.021245 |
if _np.iscomplexobj(coeffs):
kind = 'complex'
else:
kind = 'real'
if type(normalization) != str:
raise ValueError('normalization must be a string. ' +
'Input type was {:s}'
.format(str(type(no... | def from_array(self, coeffs, normalization='4pi', csphase=1, lmax=None,
copy=True) | Initialize the class with spherical harmonic coefficients from an input
array.
Usage
-----
x = SHCoeffs.from_array(array, [normalization, csphase, lmax, copy])
Returns
-------
x : SHCoeffs class instance.
Parameters
----------
array : nd... | 2.131299 | 2.077052 | 1.026117 |
if format is 'shtools':
with open(filename, mode='w') as file:
if header is not None:
file.write(header + '\n')
for l in range(self.lmax+1):
for m in range(l+1):
file.write('{:d}, {:d}, {:.16e}, ... | def to_file(self, filename, format='shtools', header=None, **kwargs) | Save raw spherical harmonic coefficients to a file.
Usage
-----
x.to_file(filename, [format='shtools', header])
x.to_file(filename, [format='npy', **kwargs])
Parameters
----------
filename : str
Name of the output file.
format : str, optional... | 2.373002 | 2.10193 | 1.128963 |
if normalization is None:
normalization = self.normalization
if csphase is None:
csphase = self.csphase
if lmax is None:
lmax = self.lmax
coeffs = _convert(self.coeffs, normalization_in=self.normalization,
normalizat... | def to_array(self, normalization=None, csphase=None, lmax=None) | Return spherical harmonic coefficients as a numpy array.
Usage
-----
coeffs = x.to_array([normalization, csphase, lmax])
Returns
-------
coeffs : ndarry, shape (2, lmax+1, lmax+1)
numpy ndarray of the spherical harmonic coefficients.
Parameters
... | 1.897792 | 2.146807 | 0.884007 |
return _spectrum(self.coeffs, normalization=self.normalization,
convention=convention, unit=unit, base=base,
lmax=lmax) | def spectrum(self, lmax=None, convention='power', unit='per_l', base=10.) | Return the spectrum as a function of spherical harmonic degree.
Usage
-----
spectrum = x.spectrum([lmax, convention, unit, base])
Returns
-------
power : ndarray, shape (lmax+1)
1-D numpy ndarray of the spectrum, where lmax is the maximum
spheric... | 3.867964 | 5.565147 | 0.695034 |
if self.coeffs[0, 0, 0] == 0:
raise ValueError('The volume of the object can not be calculated '
'when the degree and order 0 term is equal to '
'zero.')
if self.kind == 'complex':
raise ValueError('The volume of... | def volume(self, lmax=None) | If the function is the real shape of an object, calculate the volume
of the body.
Usage
-----
volume = x.volume([lmax])
Returns
-------
volume : float
The volume of the object.
Parameters
----------
lmax : int, optional, defa... | 3.662796 | 3.199164 | 1.144923 |
if type(convention) != str:
raise ValueError('convention must be a string. ' +
'Input type was {:s}'
.format(str(type(convention))))
if convention.lower() not in ('x', 'y'):
raise ValueError(
"con... | def rotate(self, alpha, beta, gamma, degrees=True, convention='y',
body=False, dj_matrix=None) | Rotate either the coordinate system used to express the spherical
harmonic coefficients or the physical body, and return a new class
instance.
Usage
-----
x_rotated = x.rotate(alpha, beta, gamma, [degrees, convention,
body, dj_matrix])
Retur... | 2.574278 | 2.432061 | 1.058476 |
if normalization is None:
normalization = self.normalization
if csphase is None:
csphase = self.csphase
if lmax is None:
lmax = self.lmax
if kind is None:
kind = self.kind
# check argument consistency
if type(norma... | def convert(self, normalization=None, csphase=None, lmax=None, kind=None,
check=True) | Return a SHCoeffs class instance with a different normalization
convention.
Usage
-----
clm = x.convert([normalization, csphase, lmax, kind, check])
Returns
-------
clm : SHCoeffs class instance
Parameters
----------
normalization : str,... | 1.921493 | 1.811929 | 1.060468 |
if lat is not None and colat is not None:
raise ValueError('lat and colat can not both be specified.')
if lat is not None and lon is not None:
if lmax_calc is None:
lmax_calc = self.lmax
values = self._expand_coord(lat=lat, lon=lon, degrees=... | def expand(self, grid='DH', lat=None, colat=None, lon=None, degrees=True,
zeros=None, lmax=None, lmax_calc=None) | Evaluate the spherical harmonic coefficients either on a global grid
or for a list of coordinates.
Usage
-----
f = x.expand([grid, lmax, lmax_calc, zeros])
g = x.expand(lat=lat, lon=lon, [lmax_calc, degrees])
g = x.expand(colat=colat, lon=lon, [lmax_calc, degrees])
... | 1.859308 | 1.845897 | 1.007265 |
rcomplex_coeffs = _shtools.SHrtoc(self.coeffs,
convention=1, switchcs=0)
# These coefficients are using real floats, and need to be
# converted to complex form.
complex_coeffs = _np.zeros((2, self.lmax+1, self.lmax+1),
... | def _make_complex(self) | Convert the real SHCoeffs class to the complex class. | 4.270547 | 3.781253 | 1.1294 |
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