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trolldbois/ctypeslib | ctypeslib/codegen/codegenerator.py | Generator.enable_fundamental_type_wrappers | def enable_fundamental_type_wrappers(self):
"""
If a type is a int128, a long_double_t or a void, some placeholders need
to be in the generated code to be valid.
"""
# 2015-01 reactivating header templates
#log.warning('enable_fundamental_type_wrappers deprecated - replaced by generate_headers')
# return # FIXME ignore
self.enable_fundamental_type_wrappers = lambda: True
import pkgutil
headers = pkgutil.get_data(
'ctypeslib',
'data/fundamental_type_name.tpl').decode()
from clang.cindex import TypeKind
size = str(self.parser.get_ctypes_size(TypeKind.LONGDOUBLE) // 8)
headers = headers.replace('__LONG_DOUBLE_SIZE__', size)
print(headers, file=self.imports)
return | python | def enable_fundamental_type_wrappers(self):
"""
If a type is a int128, a long_double_t or a void, some placeholders need
to be in the generated code to be valid.
"""
# 2015-01 reactivating header templates
#log.warning('enable_fundamental_type_wrappers deprecated - replaced by generate_headers')
# return # FIXME ignore
self.enable_fundamental_type_wrappers = lambda: True
import pkgutil
headers = pkgutil.get_data(
'ctypeslib',
'data/fundamental_type_name.tpl').decode()
from clang.cindex import TypeKind
size = str(self.parser.get_ctypes_size(TypeKind.LONGDOUBLE) // 8)
headers = headers.replace('__LONG_DOUBLE_SIZE__', size)
print(headers, file=self.imports)
return | [
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trolldbois/ctypeslib | ctypeslib/codegen/codegenerator.py | Generator.enable_pointer_type | def enable_pointer_type(self):
"""
If a type is a pointer, a platform-independent POINTER_T type needs
to be in the generated code.
"""
# 2015-01 reactivating header templates
#log.warning('enable_pointer_type deprecated - replaced by generate_headers')
# return # FIXME ignore
self.enable_pointer_type = lambda: True
import pkgutil
headers = pkgutil.get_data('ctypeslib', 'data/pointer_type.tpl').decode()
import ctypes
from clang.cindex import TypeKind
# assuming a LONG also has the same sizeof than a pointer.
word_size = self.parser.get_ctypes_size(TypeKind.POINTER) // 8
word_type = self.parser.get_ctypes_name(TypeKind.ULONG)
# pylint: disable=protected-access
word_char = getattr(ctypes, word_type)._type_
# replacing template values
headers = headers.replace('__POINTER_SIZE__', str(word_size))
headers = headers.replace('__REPLACEMENT_TYPE__', word_type)
headers = headers.replace('__REPLACEMENT_TYPE_CHAR__', word_char)
print(headers, file=self.imports)
return | python | def enable_pointer_type(self):
"""
If a type is a pointer, a platform-independent POINTER_T type needs
to be in the generated code.
"""
# 2015-01 reactivating header templates
#log.warning('enable_pointer_type deprecated - replaced by generate_headers')
# return # FIXME ignore
self.enable_pointer_type = lambda: True
import pkgutil
headers = pkgutil.get_data('ctypeslib', 'data/pointer_type.tpl').decode()
import ctypes
from clang.cindex import TypeKind
# assuming a LONG also has the same sizeof than a pointer.
word_size = self.parser.get_ctypes_size(TypeKind.POINTER) // 8
word_type = self.parser.get_ctypes_name(TypeKind.ULONG)
# pylint: disable=protected-access
word_char = getattr(ctypes, word_type)._type_
# replacing template values
headers = headers.replace('__POINTER_SIZE__', str(word_size))
headers = headers.replace('__REPLACEMENT_TYPE__', word_type)
headers = headers.replace('__REPLACEMENT_TYPE_CHAR__', word_char)
print(headers, file=self.imports)
return | [
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trolldbois/ctypeslib | ctypeslib/codegen/codegenerator.py | Generator.Alias | def Alias(self, alias):
"""Handles Aliases. No test cases yet"""
# FIXME
if self.generate_comments:
self.print_comment(alias)
print("%s = %s # alias" % (alias.name, alias.alias), file=self.stream)
self._aliases += 1
return | python | def Alias(self, alias):
"""Handles Aliases. No test cases yet"""
# FIXME
if self.generate_comments:
self.print_comment(alias)
print("%s = %s # alias" % (alias.name, alias.alias), file=self.stream)
self._aliases += 1
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trolldbois/ctypeslib | ctypeslib/codegen/codegenerator.py | Generator.get_undeclared_type | def get_undeclared_type(self, item):
"""
Checks if a typed has already been declared in the python output
or is a builtin python type.
"""
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.get_undeclared_type(item.typ)
# else its an undeclared structure.
return item | python | def get_undeclared_type(self, item):
"""
Checks if a typed has already been declared in the python output
or is a builtin python type.
"""
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.get_undeclared_type(item.typ)
# else its an undeclared structure.
return item | [
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trolldbois/ctypeslib | ctypeslib/codegen/codegenerator.py | Generator.FundamentalType | 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
"""
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) | python | 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
"""
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) | [
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trolldbois/ctypeslib | ctypeslib/codegen/codegenerator.py | Generator._generate | def _generate(self, item, *args):
""" wraps execution of specific methods."""
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, %s", item.__class__.__name__, item.name)
#
#log.debug('generate: %s( %s )', type(item).__name__, name)
#if name in self.known_symbols:
# log.debug('item is in known_symbols %s'% name )
# mod = self.known_symbols[name]
# print >> self.imports, "from %s import %s" % (mod, name)
# self.done.add(item)
# if isinstance(item, typedesc.Structure):
# self.done.add(item.get_head())
# self.done.add(item.get_body())
# return
#
# to avoid infinite recursion, we have to mark it as done
# before actually generating the code.
self.done.add(item)
# go to specific treatment
mth = getattr(self, type(item).__name__)
mth(item, *args)
return | python | def _generate(self, item, *args):
""" wraps execution of specific methods."""
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, %s", item.__class__.__name__, item.name)
#
#log.debug('generate: %s( %s )', type(item).__name__, name)
#if name in self.known_symbols:
# log.debug('item is in known_symbols %s'% name )
# mod = self.known_symbols[name]
# print >> self.imports, "from %s import %s" % (mod, name)
# self.done.add(item)
# if isinstance(item, typedesc.Structure):
# self.done.add(item.get_head())
# self.done.add(item.get_body())
# return
#
# to avoid infinite recursion, we have to mark it as done
# before actually generating the code.
self.done.add(item)
# go to specific treatment
mth = getattr(self, type(item).__name__)
mth(item, *args)
return | [
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SHTOOLS/SHTOOLS | examples/python/ClassInterface/exact_power.py | example | def example():
"""Plot random phase and Gaussian random variable spectra."""
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'), label='Normal distributed power')
ax.plot(clm2.spectrum(unit='per_l'), label='Exact power')
ax.set(xscale='log', yscale='log', xlabel='degree l',
ylabel='power per degree l')
ax.grid(which='both')
ax.legend()
plt.show() | python | def example():
"""Plot random phase and Gaussian random variable spectra."""
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'), label='Normal distributed power')
ax.plot(clm2.spectrum(unit='per_l'), label='Exact power')
ax.set(xscale='log', yscale='log', xlabel='degree l',
ylabel='power per degree l')
ax.grid(which='both')
ax.legend()
plt.show() | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravgrid.py | SHGravGrid.plot_rad | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_r$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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:
fig.savefig(fname)
return fig, axes
else:
self.rad.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_r$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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:
fig.savefig(fname)
return fig, axes
else:
self.rad.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Label for the longitude axis.
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Label for the latitude axis.
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A single matplotlib axes object where the plot will appear.
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If True, plot a colorbar.
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Orientation of the colorbar: either 'vertical' or 'horizontal'.
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Text label for the colorbar.
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravgrid.py | SHGravGrid.plot_theta | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_\\theta$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
if ax is None:
fig, axes = self.theta.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)
return fig, axes
else:
self.theta.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_\\theta$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
if ax is None:
fig, axes = self.theta.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)
return fig, axes
else:
self.theta.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_\\theta$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravgrid.py | SHGravGrid.plot_phi | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_\phi$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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:
fig.savefig(fname)
return fig, axes
else:
self.phi.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_\phi$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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:
fig.savefig(fname)
return fig, axes
else:
self.phi.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$g_\phi$, m s$^{-2}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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"field",
"."
] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravgrid.py#L229-L279 | train | 203,809 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravgrid.py | SHGravGrid.plot_total | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'gravity disturbance'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
Notes
-----
If the normal gravity is removed from the total gravitational
acceleration, the output will be displayed in mGals.
"""
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:
fig, axes = (self.total*1.e5).plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
else:
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)
return fig, axes
else:
if self.normal_gravity is True:
(self.total*1.e5).plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs)
else:
self.total.plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'gravity disturbance'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
Notes
-----
If the normal gravity is removed from the total gravitational
acceleration, the output will be displayed in mGals.
"""
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:
fig, axes = (self.total*1.e5).plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, show=False, **kwargs)
else:
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)
return fig, axes
else:
if self.normal_gravity is True:
(self.total*1.e5).plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs)
else:
self.total.plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
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x.plot_total([tick_interval, xlabel, ylabel, ax, colorbar,
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Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'gravity disturbance'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
Notes
-----
If the normal gravity is removed from the total gravitational
acceleration, the output will be displayed in mGals. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravgrid.py#L281-L354 | train | 203,810 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravgrid.py | SHGravGrid.plot_pot | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'potential, m s$^{-1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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:
fig.savefig(fname)
return fig, axes
else:
self.pot.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'potential, m s$^{-1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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:
fig.savefig(fname)
return fig, axes
else:
self.pot.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'potential, m s$^{-1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravgrid.py#L356-L406 | train | 203,811 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravgrid.py | SHGravGrid.plot | 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])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(2, 2, figsize=figsize)
self.plot_rad(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_theta(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_phi(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
minor_tick_interval=minor_tick_interval,
tick_labelsize=tick_labelsize,**kwargs)
self.plot_total(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[3], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | python | 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])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(2, 2, figsize=figsize)
self.plot_rad(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_theta(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_phi(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
minor_tick_interval=minor_tick_interval,
tick_labelsize=tick_labelsize,**kwargs)
self.plot_total(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[3], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | [
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Usage
-----
x.plot([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepiancoeffs.py | SlepianCoeffs.expand | 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 expansion coefficients to use when calculating the
spherical harmonic coefficients.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, 'DH2'
for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ' for a
Gauss-Legendre quadrature grid.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
"""
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 = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.coeffs, nmax)
else:
shcoeffs = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.tapers, nmax)
if grid.upper() in ('DH', 'DH1'):
gridout = _shtools.MakeGridDH(shcoeffs, sampling=1,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'DH2':
gridout = _shtools.MakeGridDH(shcoeffs, sampling=2,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'GLQ':
if zeros is None:
zeros, weights = _shtools.SHGLQ(self.galpha.lmax)
gridout = _shtools.MakeGridGLQ(shcoeffs, zeros,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='GLQ', copy=False)
else:
raise ValueError(
"grid must be 'DH', 'DH1', 'DH2', or 'GLQ'. " +
"Input value was {:s}".format(repr(grid))) | python | 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 expansion coefficients to use when calculating the
spherical harmonic coefficients.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, 'DH2'
for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ' for a
Gauss-Legendre quadrature grid.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
"""
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 = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.coeffs, nmax)
else:
shcoeffs = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.tapers, nmax)
if grid.upper() in ('DH', 'DH1'):
gridout = _shtools.MakeGridDH(shcoeffs, sampling=1,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'DH2':
gridout = _shtools.MakeGridDH(shcoeffs, sampling=2,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'GLQ':
if zeros is None:
zeros, weights = _shtools.SHGLQ(self.galpha.lmax)
gridout = _shtools.MakeGridGLQ(shcoeffs, zeros,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='GLQ', copy=False)
else:
raise ValueError(
"grid must be 'DH', 'DH1', 'DH2', or 'GLQ'. " +
"Input value was {:s}".format(repr(grid))) | [
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Usage
-----
f = x.expand([nmax, grid, zeros])
Returns
-------
f : SHGrid class instance
Parameters
----------
nmax : int, optional, default = x.nmax
The number of expansion coefficients to use when calculating the
spherical harmonic coefficients.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, 'DH2'
for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ' for a
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zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepiancoeffs.py | SlepianCoeffs.to_shcoeffs | 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 Slepian expansion coefficients.
Parameters
----------
nmax : int, optional, default = x.nmax
The maximum number of expansion coefficients to use when
calculating the spherical harmonic coefficients.
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho' or 'schmidt' for
geodesy 4pi-normalized, orthonormalized, or Schmidt semi-normalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
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']):
raise ValueError(
"normalization must be '4pi', 'ortho' " +
"or 'schmidt'. Provided value was {:s}"
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase))
)
if nmax is None:
nmax = self.nmax
if self.galpha.kind == 'cap':
shcoeffs = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.coeffs, nmax)
else:
shcoeffs = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.tapers, nmax)
temp = SHCoeffs.from_array(shcoeffs, normalization='4pi', csphase=1)
if normalization != '4pi' or csphase != 1:
return temp.convert(normalization=normalization, csphase=csphase)
else:
return temp | python | 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 Slepian expansion coefficients.
Parameters
----------
nmax : int, optional, default = x.nmax
The maximum number of expansion coefficients to use when
calculating the spherical harmonic coefficients.
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho' or 'schmidt' for
geodesy 4pi-normalized, orthonormalized, or Schmidt semi-normalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
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']):
raise ValueError(
"normalization must be '4pi', 'ortho' " +
"or 'schmidt'. Provided value was {:s}"
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase))
)
if nmax is None:
nmax = self.nmax
if self.galpha.kind == 'cap':
shcoeffs = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.coeffs, nmax)
else:
shcoeffs = _shtools.SlepianCoeffsToSH(self.falpha,
self.galpha.tapers, nmax)
temp = SHCoeffs.from_array(shcoeffs, normalization='4pi', csphase=1)
if normalization != '4pi' or csphase != 1:
return temp.convert(normalization=normalization, csphase=csphase)
else:
return temp | [
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Returns
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s : SHCoeffs class instance
The spherical harmonic coefficients obtained from using the first
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Parameters
----------
nmax : int, optional, default = x.nmax
The maximum number of expansion coefficients to use when
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normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho' or 'schmidt' for
geodesy 4pi-normalized, orthonormalized, or Schmidt semi-normalized
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csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
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SHTOOLS/SHTOOLS | pyshtools/shtools/__init__.py | _shtools_status_message | def _shtools_status_message(status):
'''
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 allocating memory.'
elif (status == 4):
errmsg = 'File IO error.'
else:
errmsg = 'Unhandled Fortran 95 error.'
return errmsg | python | def _shtools_status_message(status):
'''
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 allocating memory.'
elif (status == 4):
errmsg = 'File IO error.'
else:
errmsg = 'Unhandled Fortran 95 error.'
return errmsg | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepian.py | Slepian.from_cap | 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
----------
theta : float
Angular radius of the spherical-cap localization domain (default
in degrees).
lmax : int
Spherical harmonic bandwidth of the Slepian functions.
clat, clon : float, optional, default = None
Latitude and longitude of the center of the rotated spherical-cap
Slepian functions (default in degrees).
nmax : int, optional, default (lmax+1)**2
Number of Slepian functions to compute.
theta_degrees : bool, optional, default = True
True if theta is in degrees.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
"""
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, taper_order, clat, clon,
nmax, theta_degrees, coord_degrees, dj_matrix,
copy=False) | python | 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
----------
theta : float
Angular radius of the spherical-cap localization domain (default
in degrees).
lmax : int
Spherical harmonic bandwidth of the Slepian functions.
clat, clon : float, optional, default = None
Latitude and longitude of the center of the rotated spherical-cap
Slepian functions (default in degrees).
nmax : int, optional, default (lmax+1)**2
Number of Slepian functions to compute.
theta_degrees : bool, optional, default = True
True if theta is in degrees.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
"""
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, taper_order, clat, clon,
nmax, theta_degrees, coord_degrees, dj_matrix,
copy=False) | [
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x = Slepian.from_cap(theta, lmax, [clat, clon, nmax, theta_degrees,
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Returns
-------
x : Slepian class instance
Parameters
----------
theta : float
Angular radius of the spherical-cap localization domain (default
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lmax : int
Spherical harmonic bandwidth of the Slepian functions.
clat, clon : float, optional, default = None
Latitude and longitude of the center of the rotated spherical-cap
Slepian functions (default in degrees).
nmax : int, optional, default (lmax+1)**2
Number of Slepian functions to compute.
theta_degrees : bool, optional, default = True
True if theta is in degrees.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepian.py | Slepian.from_mask | 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, shape (nlat, nlon)
A Driscoll and Healy (1994) sampled grid describing the
concentration region R. All elements should either be 1 (for inside
the concentration region) or 0 (for outside the concentration
region). The grid must have dimensions nlon=nlat or nlon=2*nlat,
where nlat is even.
lmax : int
The spherical harmonic bandwidth of the Slepian functions.
nmax : int, optional, default = (lmax+1)**2
The number of best-concentrated eigenvalues and eigenfunctions to
return.
"""
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))
if dh_mask.shape[0] % 2 != 0:
raise ValueError('The number of latitude bands in dh_mask ' +
'must be even. nlat = {:d}'
.format(dh_mask.shape[0]))
if dh_mask.shape[1] == dh_mask.shape[0]:
_sampling = 1
elif dh_mask.shape[1] == 2 * dh_mask.shape[0]:
_sampling = 2
else:
raise ValueError('dh_mask must be dimensioned as (n, n) or ' +
'(n, 2 * n). Input shape is ({:d}, {:d})'
.format(dh_mask.shape[0], dh_mask.shape[1]))
mask_lm = _shtools.SHExpandDH(dh_mask, sampling=_sampling, lmax_calc=0)
area = mask_lm[0, 0, 0] * 4 * _np.pi
tapers, eigenvalues = _shtools.SHReturnTapersMap(dh_mask, lmax,
ntapers=nmax)
return SlepianMask(tapers, eigenvalues, area, copy=False) | python | 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, shape (nlat, nlon)
A Driscoll and Healy (1994) sampled grid describing the
concentration region R. All elements should either be 1 (for inside
the concentration region) or 0 (for outside the concentration
region). The grid must have dimensions nlon=nlat or nlon=2*nlat,
where nlat is even.
lmax : int
The spherical harmonic bandwidth of the Slepian functions.
nmax : int, optional, default = (lmax+1)**2
The number of best-concentrated eigenvalues and eigenfunctions to
return.
"""
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))
if dh_mask.shape[0] % 2 != 0:
raise ValueError('The number of latitude bands in dh_mask ' +
'must be even. nlat = {:d}'
.format(dh_mask.shape[0]))
if dh_mask.shape[1] == dh_mask.shape[0]:
_sampling = 1
elif dh_mask.shape[1] == 2 * dh_mask.shape[0]:
_sampling = 2
else:
raise ValueError('dh_mask must be dimensioned as (n, n) or ' +
'(n, 2 * n). Input shape is ({:d}, {:d})'
.format(dh_mask.shape[0], dh_mask.shape[1]))
mask_lm = _shtools.SHExpandDH(dh_mask, sampling=_sampling, lmax_calc=0)
area = mask_lm[0, 0, 0] * 4 * _np.pi
tapers, eigenvalues = _shtools.SHReturnTapersMap(dh_mask, lmax,
ntapers=nmax)
return SlepianMask(tapers, eigenvalues, area, copy=False) | [
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Returns
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x : Slepian class instance
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dh_mask :ndarray, shape (nlat, nlon)
A Driscoll and Healy (1994) sampled grid describing the
concentration region R. All elements should either be 1 (for inside
the concentration region) or 0 (for outside the concentration
region). The grid must have dimensions nlon=nlat or nlon=2*nlat,
where nlat is even.
lmax : int
The spherical harmonic bandwidth of the Slepian functions.
nmax : int, optional, default = (lmax+1)**2
The number of best-concentrated eigenvalues and eigenfunctions to
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepian.py | Slepian.expand | 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
----------
flm : SHCoeffs class instance
The input function to expand in Slepian functions.
nmax : int, optional, default = (x.lmax+1)**2
The number of Slepian expansion coefficients to compute.
Description
-----------
The global function f is input using its spherical harmonic
expansion coefficients flm. The expansion coefficients of the function
f using Slepian functions g is given by
f_alpha = sum_{lm}^{lmax} f_lm g(alpha)_lm
"""
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.lmax), repr(nmax))
)
coeffsin = flm.to_array(normalization='4pi', csphase=1, lmax=self.lmax)
return self._expand(coeffsin, nmax) | python | 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
----------
flm : SHCoeffs class instance
The input function to expand in Slepian functions.
nmax : int, optional, default = (x.lmax+1)**2
The number of Slepian expansion coefficients to compute.
Description
-----------
The global function f is input using its spherical harmonic
expansion coefficients flm. The expansion coefficients of the function
f using Slepian functions g is given by
f_alpha = sum_{lm}^{lmax} f_lm g(alpha)_lm
"""
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.lmax), repr(nmax))
)
coeffsin = flm.to_array(normalization='4pi', csphase=1, lmax=self.lmax)
return self._expand(coeffsin, nmax) | [
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flm : SHCoeffs class instance
The input function to expand in Slepian functions.
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The number of Slepian expansion coefficients to compute.
Description
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The global function f is input using its spherical harmonic
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepian.py | Slepian.spectra | 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
function, and where the functions are arranged with increasing
concentration factors. If alpha is set, only a single vector is
returned, whereas if nmax is set, the first nmax spectra are
returned.
Parameters
----------
alpha : int, optional, default = None
The function number of the output spectrum, where alpha=0
corresponds to the best concentrated Slepian function.
nmax : int, optional, default = 1
The number of best concentrated Slepian function power spectra
to return.
convention : str, optional, default = 'power'
The type of spectrum to return: 'power' for power spectrum,
'energy' for energy spectrum, and 'l2norm' for the l2 norm
spectrum.
unit : str, optional, default = 'per_l'
If 'per_l', return the total contribution to the spectrum for each
spherical harmonic degree l. If 'per_lm', return the average
contribution to the spectrum for each coefficient at spherical
harmonic degree l. If 'per_dlogl', return the spectrum per log
interval dlog_a(l).
base : float, optional, default = 10.
The logarithm base when calculating the 'per_dlogl' spectrum.
Description
-----------
This function returns either the power spectrum, energy spectrum, or
l2-norm spectrum of one or more of the Slepian funtions. Total power
is defined as the integral of the function squared over all space,
divided by the area the function spans. If the mean of the function is
zero, this is equivalent to the variance of the function. The total
energy is the integral of the function squared over all space and is
4pi times the total power. The l2-norm is the sum of the magnitude of
the coefficients squared.
The output spectrum can be expresed using one of three units. 'per_l'
returns the contribution to the total spectrum from all angular orders
at degree l. 'per_lm' returns the average contribution to the total
spectrum from a single coefficient at degree l. The 'per_lm' spectrum
is equal to the 'per_l' spectrum divided by (2l+1). 'per_dlogl' returns
the contribution to the total spectrum from all angular orders over an
infinitessimal logarithmic degree band. The contrubution in the band
dlog_a(l) is spectrum(l, 'per_dlogl')*dlog_a(l), where a is the base,
and where spectrum(l, 'per_dlogl) is equal to
spectrum(l, 'per_l')*l*log(a).
"""
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',
convention=convention, unit=unit,
base=base)
else:
coeffs = self.to_array(alpha)
spectra = _spectrum(coeffs, normalization='4pi',
convention=convention, unit=unit, base=base)
return spectra | python | 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
function, and where the functions are arranged with increasing
concentration factors. If alpha is set, only a single vector is
returned, whereas if nmax is set, the first nmax spectra are
returned.
Parameters
----------
alpha : int, optional, default = None
The function number of the output spectrum, where alpha=0
corresponds to the best concentrated Slepian function.
nmax : int, optional, default = 1
The number of best concentrated Slepian function power spectra
to return.
convention : str, optional, default = 'power'
The type of spectrum to return: 'power' for power spectrum,
'energy' for energy spectrum, and 'l2norm' for the l2 norm
spectrum.
unit : str, optional, default = 'per_l'
If 'per_l', return the total contribution to the spectrum for each
spherical harmonic degree l. If 'per_lm', return the average
contribution to the spectrum for each coefficient at spherical
harmonic degree l. If 'per_dlogl', return the spectrum per log
interval dlog_a(l).
base : float, optional, default = 10.
The logarithm base when calculating the 'per_dlogl' spectrum.
Description
-----------
This function returns either the power spectrum, energy spectrum, or
l2-norm spectrum of one or more of the Slepian funtions. Total power
is defined as the integral of the function squared over all space,
divided by the area the function spans. If the mean of the function is
zero, this is equivalent to the variance of the function. The total
energy is the integral of the function squared over all space and is
4pi times the total power. The l2-norm is the sum of the magnitude of
the coefficients squared.
The output spectrum can be expresed using one of three units. 'per_l'
returns the contribution to the total spectrum from all angular orders
at degree l. 'per_lm' returns the average contribution to the total
spectrum from a single coefficient at degree l. The 'per_lm' spectrum
is equal to the 'per_l' spectrum divided by (2l+1). 'per_dlogl' returns
the contribution to the total spectrum from all angular orders over an
infinitessimal logarithmic degree band. The contrubution in the band
dlog_a(l) is spectrum(l, 'per_dlogl')*dlog_a(l), where a is the base,
and where spectrum(l, 'per_dlogl) is equal to
spectrum(l, 'per_l')*l*log(a).
"""
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',
convention=convention, unit=unit,
base=base)
else:
coeffs = self.to_array(alpha)
spectra = _spectrum(coeffs, normalization='4pi',
convention=convention, unit=unit, base=base)
return spectra | [
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Returns
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spectra : ndarray, shape (lmax+1, nmax)
A matrix with each column containing the spectrum of a Slepian
function, and where the functions are arranged with increasing
concentration factors. If alpha is set, only a single vector is
returned, whereas if nmax is set, the first nmax spectra are
returned.
Parameters
----------
alpha : int, optional, default = None
The function number of the output spectrum, where alpha=0
corresponds to the best concentrated Slepian function.
nmax : int, optional, default = 1
The number of best concentrated Slepian function power spectra
to return.
convention : str, optional, default = 'power'
The type of spectrum to return: 'power' for power spectrum,
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unit : str, optional, default = 'per_l'
If 'per_l', return the total contribution to the spectrum for each
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base : float, optional, default = 10.
The logarithm base when calculating the 'per_dlogl' spectrum.
Description
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This function returns either the power spectrum, energy spectrum, or
l2-norm spectrum of one or more of the Slepian funtions. Total power
is defined as the integral of the function squared over all space,
divided by the area the function spans. If the mean of the function is
zero, this is equivalent to the variance of the function. The total
energy is the integral of the function squared over all space and is
4pi times the total power. The l2-norm is the sum of the magnitude of
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The output spectrum can be expresed using one of three units. 'per_l'
returns the contribution to the total spectrum from all angular orders
at degree l. 'per_lm' returns the average contribution to the total
spectrum from a single coefficient at degree l. The 'per_lm' spectrum
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the contribution to the total spectrum from all angular orders over an
infinitessimal logarithmic degree band. The contrubution in the band
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SHTOOLS/SHTOOLS | pyshtools/shclasses/slepian.py | SlepianCap._taper2coeffs | 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.
"""
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 | python | 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.
"""
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 | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmaggrid.py | SHMagGrid.plot_total | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$|B|$, nT'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
return fig, axes
else:
self.total.plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$|B|$, nT'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
return fig, axes
else:
self.total.plot(
colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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... | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$|B|$, nT'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
"Plot",
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"magnetic",
"intensity",
"."
] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shmaggrid.py#L269-L321 | train | 203,821 |
SHTOOLS/SHTOOLS | pyshtools/expand/spharm_functions.py | spharm_lm | def spharm_lm(l, m, theta, phi, normalization='4pi', kind='real', csphase=1,
degrees=True):
"""
Compute the spherical harmonic function for a specific degree and order.
Usage
-----
ylm = spharm (l, m, theta, phi, [normalization, kind, csphase, degrees])
Returns
-------
ylm : float or complex
The spherical harmonic function ylm, where l and m are the spherical
harmonic degree and order, respectively.
Parameters
----------
l : integer
The spherical harmonic degree.
m : integer
The spherical harmonic order.
theta : float
The colatitude in degrees.
phi : float
The longitude in degrees.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized spherical
harmonic functions, respectively.
kind : str, optional, default = 'real'
'real' or 'complex' spherical harmonic coefficients.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the spherical
harmonic functions.
degrees : optional, bool, default = True
If True, colat and phi are expressed in degrees.
Description
-----------
spharm_lm will calculate the spherical harmonic function for a specific
degree l and order m, and for a given colatitude theta and longitude phi.
Three parameters determine how the spherical harmonic functions are
defined. normalization can be either '4pi' (default), 'ortho', 'schmidt',
or 'unnorm' for 4pi normalized, orthonormalized, Schmidt semi-normalized,
or unnormalized spherical harmonic functions, respectively. kind can be
either 'real' or 'complex', and csphase determines whether to include or
exclude (default) the Condon-Shortley phase factor.
The spherical harmonic functions are calculated using the standard
three-term recursion formula, and in order to prevent overflows, the
scaling approach of Holmes and Featherstone (2002) is utilized.
The resulting functions are accurate to about degree 2800. See Wieczorek
and Meschede (2018) for exact definitions on how the spherical harmonic
functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018.
"""
if l < 0:
raise ValueError(
"The degree l must be greater or equal than 0. Input value was {:s}."
.format(repr(l))
)
if m > l:
raise ValueError(
"The order m must be less than or equal to the degree l. " +
"Input values were l={:s} and m={:s}.".format(repr(l), repr(m))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if kind.lower() not in ('real', 'complex'):
raise ValueError(
"kind must be 'real' or 'complex'. " +
"Input value was {:s}.".format(repr(kind))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
ind = (l*(l+1))//2 + abs(m)
if degrees is True:
theta = _np.deg2rad(theta)
phi = _np.deg2rad(phi)
if kind.lower() == 'real':
p = _legendre(l, _np.cos(theta), normalization=normalization,
csphase=csphase, cnorm=0, packed=True)
if m >= 0:
ylm = p[ind] * _np.cos(m*phi)
else:
ylm = p[ind] * _np.sin(abs(m)*phi)
else:
p = _legendre(l, _np.cos(theta), normalization=normalization,
csphase=csphase, cnorm=1, packed=True)
ylm = p[ind] * (_np.cos(m*phi) + 1j * _np.sin(abs(m)*phi)) # Yl|m|
if m < 0:
ylm = ylm.conj()
if _np.mod(m, 2) == 1:
ylm = - ylm
return ylm | python | def spharm_lm(l, m, theta, phi, normalization='4pi', kind='real', csphase=1,
degrees=True):
"""
Compute the spherical harmonic function for a specific degree and order.
Usage
-----
ylm = spharm (l, m, theta, phi, [normalization, kind, csphase, degrees])
Returns
-------
ylm : float or complex
The spherical harmonic function ylm, where l and m are the spherical
harmonic degree and order, respectively.
Parameters
----------
l : integer
The spherical harmonic degree.
m : integer
The spherical harmonic order.
theta : float
The colatitude in degrees.
phi : float
The longitude in degrees.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized spherical
harmonic functions, respectively.
kind : str, optional, default = 'real'
'real' or 'complex' spherical harmonic coefficients.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the spherical
harmonic functions.
degrees : optional, bool, default = True
If True, colat and phi are expressed in degrees.
Description
-----------
spharm_lm will calculate the spherical harmonic function for a specific
degree l and order m, and for a given colatitude theta and longitude phi.
Three parameters determine how the spherical harmonic functions are
defined. normalization can be either '4pi' (default), 'ortho', 'schmidt',
or 'unnorm' for 4pi normalized, orthonormalized, Schmidt semi-normalized,
or unnormalized spherical harmonic functions, respectively. kind can be
either 'real' or 'complex', and csphase determines whether to include or
exclude (default) the Condon-Shortley phase factor.
The spherical harmonic functions are calculated using the standard
three-term recursion formula, and in order to prevent overflows, the
scaling approach of Holmes and Featherstone (2002) is utilized.
The resulting functions are accurate to about degree 2800. See Wieczorek
and Meschede (2018) for exact definitions on how the spherical harmonic
functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018.
"""
if l < 0:
raise ValueError(
"The degree l must be greater or equal than 0. Input value was {:s}."
.format(repr(l))
)
if m > l:
raise ValueError(
"The order m must be less than or equal to the degree l. " +
"Input values were l={:s} and m={:s}.".format(repr(l), repr(m))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if kind.lower() not in ('real', 'complex'):
raise ValueError(
"kind must be 'real' or 'complex'. " +
"Input value was {:s}.".format(repr(kind))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
ind = (l*(l+1))//2 + abs(m)
if degrees is True:
theta = _np.deg2rad(theta)
phi = _np.deg2rad(phi)
if kind.lower() == 'real':
p = _legendre(l, _np.cos(theta), normalization=normalization,
csphase=csphase, cnorm=0, packed=True)
if m >= 0:
ylm = p[ind] * _np.cos(m*phi)
else:
ylm = p[ind] * _np.sin(abs(m)*phi)
else:
p = _legendre(l, _np.cos(theta), normalization=normalization,
csphase=csphase, cnorm=1, packed=True)
ylm = p[ind] * (_np.cos(m*phi) + 1j * _np.sin(abs(m)*phi)) # Yl|m|
if m < 0:
ylm = ylm.conj()
if _np.mod(m, 2) == 1:
ylm = - ylm
return ylm | [
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Usage
-----
ylm = spharm (l, m, theta, phi, [normalization, kind, csphase, degrees])
Returns
-------
ylm : float or complex
The spherical harmonic function ylm, where l and m are the spherical
harmonic degree and order, respectively.
Parameters
----------
l : integer
The spherical harmonic degree.
m : integer
The spherical harmonic order.
theta : float
The colatitude in degrees.
phi : float
The longitude in degrees.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized spherical
harmonic functions, respectively.
kind : str, optional, default = 'real'
'real' or 'complex' spherical harmonic coefficients.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the spherical
harmonic functions.
degrees : optional, bool, default = True
If True, colat and phi are expressed in degrees.
Description
-----------
spharm_lm will calculate the spherical harmonic function for a specific
degree l and order m, and for a given colatitude theta and longitude phi.
Three parameters determine how the spherical harmonic functions are
defined. normalization can be either '4pi' (default), 'ortho', 'schmidt',
or 'unnorm' for 4pi normalized, orthonormalized, Schmidt semi-normalized,
or unnormalized spherical harmonic functions, respectively. kind can be
either 'real' or 'complex', and csphase determines whether to include or
exclude (default) the Condon-Shortley phase factor.
The spherical harmonic functions are calculated using the standard
three-term recursion formula, and in order to prevent overflows, the
scaling approach of Holmes and Featherstone (2002) is utilized.
The resulting functions are accurate to about degree 2800. See Wieczorek
and Meschede (2018) for exact definitions on how the spherical harmonic
functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmagcoeffs.py | SHMagCoeffs.set_coeffs | 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 the coefficient(s) that should be set.
ms : int (list)
The order(s) of the coefficient(s) that should be set. Positive
and negative values correspond to the cosine and sine
components, respectively.
Examples
--------
x.set_coeffs(10., 1, 1) # x.coeffs[0, 1, 1] = 10.
x.set_coeffs(5., 1, -1) # x.coeffs[1, 1, 1] = 5.
x.set_coeffs([1., 2], [1, 2], [0, -2]) # x.coeffs[0, 1, 0] = 1.
# x.coeffs[1, 2, 2] = 2.
"""
# 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 | python | 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 the coefficient(s) that should be set.
ms : int (list)
The order(s) of the coefficient(s) that should be set. Positive
and negative values correspond to the cosine and sine
components, respectively.
Examples
--------
x.set_coeffs(10., 1, 1) # x.coeffs[0, 1, 1] = 10.
x.set_coeffs(5., 1, -1) # x.coeffs[1, 1, 1] = 5.
x.set_coeffs([1., 2], [1, 2], [0, -2]) # x.coeffs[0, 1, 0] = 1.
# x.coeffs[1, 2, 2] = 2.
"""
# 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 | [
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Usage
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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 the coefficient(s) that should be set.
ms : int (list)
The order(s) of the coefficient(s) that should be set. Positive
and negative values correspond to the cosine and sine
components, respectively.
Examples
--------
x.set_coeffs(10., 1, 1) # x.coeffs[0, 1, 1] = 10.
x.set_coeffs(5., 1, -1) # x.coeffs[1, 1, 1] = 5.
x.set_coeffs([1., 2], [1, 2], [0, -2]) # x.coeffs[0, 1, 0] = 1.
# x.coeffs[1, 2, 2] = 2. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmagcoeffs.py | SHMagCoeffs.convert | 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, optional, default = x.normalization
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention for the output class: 1 to exclude
the phase factor, or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method will return a new class instance of the spherical
harmonic coefficients using a different normalization and
Condon-Shortley phase convention. A different maximum spherical
harmonic degree of the output coefficients can be specified, and if
this maximum degree is smaller than the maximum degree of the original
class, the coefficients will be truncated. Conversely, if this degree
is larger than the maximum degree of the original class, the
coefficients of the new class will be zero padded.
"""
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('normalization must be a string. '
'Input type was {:s}'
.format(str(type(normalization))))
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"normalization must be '4pi', 'ortho', 'schmidt', or "
"'unnorm'. Provided value was {:s}"
.format(repr(normalization)))
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase)))
if self.errors is not None:
coeffs, errors = self.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
return SHMagCoeffs.from_array(
coeffs, r0=self.r0, errors=errors,
normalization=normalization.lower(),
csphase=csphase, copy=False)
else:
coeffs = self.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
return SHMagCoeffs.from_array(
coeffs, r0=self.r0, normalization=normalization.lower(),
csphase=csphase, copy=False) | python | 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, optional, default = x.normalization
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention for the output class: 1 to exclude
the phase factor, or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method will return a new class instance of the spherical
harmonic coefficients using a different normalization and
Condon-Shortley phase convention. A different maximum spherical
harmonic degree of the output coefficients can be specified, and if
this maximum degree is smaller than the maximum degree of the original
class, the coefficients will be truncated. Conversely, if this degree
is larger than the maximum degree of the original class, the
coefficients of the new class will be zero padded.
"""
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('normalization must be a string. '
'Input type was {:s}'
.format(str(type(normalization))))
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"normalization must be '4pi', 'ortho', 'schmidt', or "
"'unnorm'. Provided value was {:s}"
.format(repr(normalization)))
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase)))
if self.errors is not None:
coeffs, errors = self.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
return SHMagCoeffs.from_array(
coeffs, r0=self.r0, errors=errors,
normalization=normalization.lower(),
csphase=csphase, copy=False)
else:
coeffs = self.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
return SHMagCoeffs.from_array(
coeffs, r0=self.r0, normalization=normalization.lower(),
csphase=csphase, copy=False) | [
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Usage
-----
clm = x.convert([normalization, csphase, lmax])
Returns
-------
clm : SHMagCoeffs class instance
Parameters
----------
normalization : str, optional, default = x.normalization
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention for the output class: 1 to exclude
the phase factor, or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method will return a new class instance of the spherical
harmonic coefficients using a different normalization and
Condon-Shortley phase convention. A different maximum spherical
harmonic degree of the output coefficients can be specified, and if
this maximum degree is smaller than the maximum degree of the original
class, the coefficients will be truncated. Conversely, if this degree
is larger than the maximum degree of the original class, the
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmagcoeffs.py | SHMagCoeffs.pad | 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 spherical harmonic degree to output.
"""
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.pad(clm.coeffs, ((0, 0), (0, lmax - self.lmax),
(0, lmax - self.lmax)), 'constant')
if self.errors is not None:
clm.errors = _np.pad(
clm.errors, ((0, 0), (0, lmax - self.lmax),
(0, lmax - self.lmax)), 'constant')
mask = _np.zeros((2, lmax + 1, lmax + 1), dtype=_np.bool)
for l in _np.arange(lmax + 1):
mask[:, l, :l + 1] = True
mask[1, :, 0] = False
clm.mask = mask
clm.lmax = lmax
return clm | python | 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 spherical harmonic degree to output.
"""
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.pad(clm.coeffs, ((0, 0), (0, lmax - self.lmax),
(0, lmax - self.lmax)), 'constant')
if self.errors is not None:
clm.errors = _np.pad(
clm.errors, ((0, 0), (0, lmax - self.lmax),
(0, lmax - self.lmax)), 'constant')
mask = _np.zeros((2, lmax + 1, lmax + 1), dtype=_np.bool)
for l in _np.arange(lmax + 1):
mask[:, l, :l + 1] = True
mask[1, :, 0] = False
clm.mask = mask
clm.lmax = lmax
return clm | [
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Usage
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clm = x.pad(lmax)
Returns
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clm : SHMagCoeffs class instance
Parameters
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lmax : int
Maximum spherical harmonic degree to output. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmagcoeffs.py | SHMagCoeffs.change_ref | 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 reference radius of the spherical harmonic coefficients.
lmax : int, optional, default = self.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method returns a new class instance of the magnetic potential,
but using a difference reference r0. When changing the reference
radius r0, the spherical harmonic coefficients will be upward or
downward continued under the assumption that the reference radius is
exterior to the body.
"""
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[:, l, :l+1] *= (self.r0 / r0)**(l+2)
clm.r0 = r0
return clm | python | 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 reference radius of the spherical harmonic coefficients.
lmax : int, optional, default = self.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method returns a new class instance of the magnetic potential,
but using a difference reference r0. When changing the reference
radius r0, the spherical harmonic coefficients will be upward or
downward continued under the assumption that the reference radius is
exterior to the body.
"""
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[:, l, :l+1] *= (self.r0 / r0)**(l+2)
clm.r0 = r0
return clm | [
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clm = x.change_ref([r0, lmax])
Returns
-------
clm : SHMagCoeffs class instance.
Parameters
----------
r0 : float, optional, default = self.r0
The reference radius of the spherical harmonic coefficients.
lmax : int, optional, default = self.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method returns a new class instance of the magnetic potential,
but using a difference reference r0. When changing the reference
radius r0, the spherical harmonic coefficients will be upward or
downward continued under the assumption that the reference radius is
exterior to the body. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmagcoeffs.py | SHMagCoeffs.expand | 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])
Returns
-------
mag : SHMagGrid class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree, which determines the number
of samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps of the three
components of the magnetic field, the total field, and the magnetic
potential, and return these as an SHMagGrid class instance. Each
map is stored as an SHGrid class instance using Driscoll and Healy
grids that are either equally sampled (n by n) or equally spaced
(n by 2n) in latitude and longitude. All grids use geocentric
coordinates, and the units are either in nT (for the magnetic field),
or nT m (for the potential),
The magnetic potential is given by
V = r0 Sum_{l=1}^lmax (r0/r)^{l+1} Sum_{m=-l}^l g_{lm} Y_{lm}
and the magnetic field is
B = - Grad V.
The coefficients are referenced to a radius r0, and the function is
computed on a flattened ellipsoid with semi-major axis a (i.e., the
mean equatorial radius) and flattening f.
"""
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', csphase=1)
else:
coeffs = self.to_array(normalization='schmidt', csphase=1)
rad, theta, phi, total, pot = _MakeMagGridDH(
coeffs, self.r0, a=a, f=f, lmax=lmax,
lmax_calc=lmax_calc, sampling=sampling)
return _SHMagGrid(rad, theta, phi, total, pot, a, f, lmax, lmax_calc) | python | 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])
Returns
-------
mag : SHMagGrid class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree, which determines the number
of samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps of the three
components of the magnetic field, the total field, and the magnetic
potential, and return these as an SHMagGrid class instance. Each
map is stored as an SHGrid class instance using Driscoll and Healy
grids that are either equally sampled (n by n) or equally spaced
(n by 2n) in latitude and longitude. All grids use geocentric
coordinates, and the units are either in nT (for the magnetic field),
or nT m (for the potential),
The magnetic potential is given by
V = r0 Sum_{l=1}^lmax (r0/r)^{l+1} Sum_{m=-l}^l g_{lm} Y_{lm}
and the magnetic field is
B = - Grad V.
The coefficients are referenced to a radius r0, and the function is
computed on a flattened ellipsoid with semi-major axis a (i.e., the
mean equatorial radius) and flattening f.
"""
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', csphase=1)
else:
coeffs = self.to_array(normalization='schmidt', csphase=1)
rad, theta, phi, total, pot = _MakeMagGridDH(
coeffs, self.r0, a=a, f=f, lmax=lmax,
lmax_calc=lmax_calc, sampling=sampling)
return _SHMagGrid(rad, theta, phi, total, pot, a, f, lmax, lmax_calc) | [
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and the magnetic potential, and return as a SHMagGrid class instance.
Usage
-----
mag = x.expand([a, f, lmax, lmax_calc, sampling])
Returns
-------
mag : SHMagGrid class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree, which determines the number
of samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps of the three
components of the magnetic field, the total field, and the magnetic
potential, and return these as an SHMagGrid class instance. Each
map is stored as an SHGrid class instance using Driscoll and Healy
grids that are either equally sampled (n by n) or equally spaced
(n by 2n) in latitude and longitude. All grids use geocentric
coordinates, and the units are either in nT (for the magnetic field),
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The magnetic potential is given by
V = r0 Sum_{l=1}^lmax (r0/r)^{l+1} Sum_{m=-l}^l g_{lm} Y_{lm}
and the magnetic field is
B = - Grad V.
The coefficients are referenced to a radius r0, and the function is
computed on a flattened ellipsoid with semi-major axis a (i.e., the
mean equatorial radius) and flattening f. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shmagcoeffs.py | SHMagCoeffs.tensor | def tensor(self, a=None, f=None, lmax=None, lmax_calc=None, sampling=2):
"""
Create 2D cylindrical maps on a flattened ellipsoid of the 9
components of the magnetic field tensor in a local north-oriented
reference frame, and return an SHMagTensor class instance.
Usage
-----
tensor = x.tensor([a, f, lmax, lmax_calc, sampling])
Returns
-------
tensor : SHMagTensor class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number of
samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps for the 9
components of the magnetic field tensor and return an SHMagTensor
class instance. The components are
(Vxx, Vxy, Vxz)
(Vyx, Vyy, Vyz)
(Vzx, Vzy, Vzz)
where the reference frame is north-oriented, where x points north, y
points west, and z points upward (all tangent or perpendicular to a
sphere of radius r, where r is the local radius of the flattened
ellipsoid). The magnetic potential is defined as
V = r0 Sum_{l=0}^lmax (r0/r)^(l+1) Sum_{m=-l}^l C_{lm} Y_{lm},
where r0 is the reference radius of the spherical harmonic coefficients
Clm, and the vector magnetic field is
B = - Grad V.
The components of the tensor are calculated according to eq. 1 in
Petrovskaya and Vershkov (2006), which is based on eq. 3.28 in Reed
(1973) (noting that Reed's equations are in terms of latitude and that
the y axis points east):
Vzz = Vrr
Vxx = 1/r Vr + 1/r^2 Vtt
Vyy = 1/r Vr + 1/r^2 /tan(t) Vt + 1/r^2 /sin(t)^2 Vpp
Vxy = 1/r^2 /sin(t) Vtp - cos(t)/sin(t)^2 /r^2 Vp
Vxz = 1/r^2 Vt - 1/r Vrt
Vyz = 1/r^2 /sin(t) Vp - 1/r /sin(t) Vrp
where r, t, p stand for radius, theta, and phi, respectively, and
subscripts on V denote partial derivatives. The output grids are in
units of nT / m.
References
----------
Reed, G.B., Application of kinematical geodesy for determining
the short wave length components of the gravity field by satellite
gradiometry, Ohio State University, Dept. of Geod. Sciences, Rep. No.
201, Columbus, Ohio, 1973.
Petrovskaya, M.S. and A.N. Vershkov, Non-singular expressions for the
gravity gradients in the local north-oriented and orbital reference
frames, J. Geod., 80, 117-127, 2006.
"""
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', csphase=1)
else:
coeffs = self.to_array(normalization='schmidt', csphase=1)
vxx, vyy, vzz, vxy, vxz, vyz = _MakeMagGradGridDH(
coeffs, self.r0, a=a, f=f, lmax=lmax, lmax_calc=lmax_calc,
sampling=sampling)
return _SHMagTensor(vxx, vyy, vzz, vxy, vxz, vyz, a, f, lmax,
lmax_calc) | python | def tensor(self, a=None, f=None, lmax=None, lmax_calc=None, sampling=2):
"""
Create 2D cylindrical maps on a flattened ellipsoid of the 9
components of the magnetic field tensor in a local north-oriented
reference frame, and return an SHMagTensor class instance.
Usage
-----
tensor = x.tensor([a, f, lmax, lmax_calc, sampling])
Returns
-------
tensor : SHMagTensor class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number of
samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps for the 9
components of the magnetic field tensor and return an SHMagTensor
class instance. The components are
(Vxx, Vxy, Vxz)
(Vyx, Vyy, Vyz)
(Vzx, Vzy, Vzz)
where the reference frame is north-oriented, where x points north, y
points west, and z points upward (all tangent or perpendicular to a
sphere of radius r, where r is the local radius of the flattened
ellipsoid). The magnetic potential is defined as
V = r0 Sum_{l=0}^lmax (r0/r)^(l+1) Sum_{m=-l}^l C_{lm} Y_{lm},
where r0 is the reference radius of the spherical harmonic coefficients
Clm, and the vector magnetic field is
B = - Grad V.
The components of the tensor are calculated according to eq. 1 in
Petrovskaya and Vershkov (2006), which is based on eq. 3.28 in Reed
(1973) (noting that Reed's equations are in terms of latitude and that
the y axis points east):
Vzz = Vrr
Vxx = 1/r Vr + 1/r^2 Vtt
Vyy = 1/r Vr + 1/r^2 /tan(t) Vt + 1/r^2 /sin(t)^2 Vpp
Vxy = 1/r^2 /sin(t) Vtp - cos(t)/sin(t)^2 /r^2 Vp
Vxz = 1/r^2 Vt - 1/r Vrt
Vyz = 1/r^2 /sin(t) Vp - 1/r /sin(t) Vrp
where r, t, p stand for radius, theta, and phi, respectively, and
subscripts on V denote partial derivatives. The output grids are in
units of nT / m.
References
----------
Reed, G.B., Application of kinematical geodesy for determining
the short wave length components of the gravity field by satellite
gradiometry, Ohio State University, Dept. of Geod. Sciences, Rep. No.
201, Columbus, Ohio, 1973.
Petrovskaya, M.S. and A.N. Vershkov, Non-singular expressions for the
gravity gradients in the local north-oriented and orbital reference
frames, J. Geod., 80, 117-127, 2006.
"""
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', csphase=1)
else:
coeffs = self.to_array(normalization='schmidt', csphase=1)
vxx, vyy, vzz, vxy, vxz, vyz = _MakeMagGradGridDH(
coeffs, self.r0, a=a, f=f, lmax=lmax, lmax_calc=lmax_calc,
sampling=sampling)
return _SHMagTensor(vxx, vyy, vzz, vxy, vxz, vyz, a, f, lmax,
lmax_calc) | [
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... | Create 2D cylindrical maps on a flattened ellipsoid of the 9
components of the magnetic field tensor in a local north-oriented
reference frame, and return an SHMagTensor class instance.
Usage
-----
tensor = x.tensor([a, f, lmax, lmax_calc, sampling])
Returns
-------
tensor : SHMagTensor class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number of
samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps for the 9
components of the magnetic field tensor and return an SHMagTensor
class instance. The components are
(Vxx, Vxy, Vxz)
(Vyx, Vyy, Vyz)
(Vzx, Vzy, Vzz)
where the reference frame is north-oriented, where x points north, y
points west, and z points upward (all tangent or perpendicular to a
sphere of radius r, where r is the local radius of the flattened
ellipsoid). The magnetic potential is defined as
V = r0 Sum_{l=0}^lmax (r0/r)^(l+1) Sum_{m=-l}^l C_{lm} Y_{lm},
where r0 is the reference radius of the spherical harmonic coefficients
Clm, and the vector magnetic field is
B = - Grad V.
The components of the tensor are calculated according to eq. 1 in
Petrovskaya and Vershkov (2006), which is based on eq. 3.28 in Reed
(1973) (noting that Reed's equations are in terms of latitude and that
the y axis points east):
Vzz = Vrr
Vxx = 1/r Vr + 1/r^2 Vtt
Vyy = 1/r Vr + 1/r^2 /tan(t) Vt + 1/r^2 /sin(t)^2 Vpp
Vxy = 1/r^2 /sin(t) Vtp - cos(t)/sin(t)^2 /r^2 Vp
Vxz = 1/r^2 Vt - 1/r Vrt
Vyz = 1/r^2 /sin(t) Vp - 1/r /sin(t) Vrp
where r, t, p stand for radius, theta, and phi, respectively, and
subscripts on V denote partial derivatives. The output grids are in
units of nT / m.
References
----------
Reed, G.B., Application of kinematical geodesy for determining
the short wave length components of the gravity field by satellite
gradiometry, Ohio State University, Dept. of Geod. Sciences, Rep. No.
201, Columbus, Ohio, 1973.
Petrovskaya, M.S. and A.N. Vershkov, Non-singular expressions for the
gravity gradients in the local north-oriented and orbital reference
frames, J. Geod., 80, 117-127, 2006. | [
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SHTOOLS/SHTOOLS | pyshtools/legendre/legendre_functions.py | legendre | def legendre(lmax, z, normalization='4pi', csphase=1, cnorm=0, packed=False):
"""
Compute all the associated Legendre functions up to a maximum degree and
order.
Usage
-----
plm = legendre (lmax, z, [normalization, csphase, cnorm, packed])
Returns
-------
plm : float, dimension (lmax+1, lmax+1) or ((lmax+1)*(lmax+2)/2)
An array of associated Legendre functions, plm[l, m], where l and m
are the degree and order, respectively. If packed is True, the array
is 1-dimensional with the index corresponding to l*(l+1)/2+m.
Parameters
----------
lmax : integer
The maximum degree of the associated Legendre functions to be computed.
z : float
The argument of the associated Legendre functions.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for use with geodesy 4pi
normalized, orthonormalized, Schmidt semi-normalized, or unnormalized
spherical harmonic functions, respectively.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the associated
Legendre functions.
cnorm : optional, integer, default = 0
If 1, the complex normalization of the associated Legendre functions
will be used. The default is to use the real normalization.
packed : optional, bool, default = False
If True, return a 1-dimensional packed array with the index
corresponding to l*(l+1)/2+m, where l and m are respectively the
degree and order.
Description
-----------
legendre` will calculate all of the associated Legendre functions up to
degree lmax for a given argument. The Legendre functions are used typically
as a part of the spherical harmonic functions, and three parameters
determine how they are defined. `normalization` can be either '4pi'
(default), 'ortho', 'schmidt', or 'unnorm' for use with 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized spherical
harmonic functions, respectively. csphase determines whether to include
or exclude (default) the Condon-Shortley phase factor. cnorm determines
whether to normalize the Legendre functions for use with real (default)
or complex spherical harmonic functions.
By default, the routine will return a 2-dimensional array, p[l, m]. If the
optional parameter `packed` is set to True, the output will instead be a
1-dimensional array where the indices correspond to `l*(l+1)/2+m`. The
Legendre functions are calculated using the standard three-term recursion
formula, and in order to prevent overflows, the scaling approach of Holmes
and Featherstone (2002) is utilized. The resulting functions are accurate
to about degree 2800. See Wieczorek and Meschede (2018) for exact
definitions on how the Legendre functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018.
"""
if lmax < 0:
raise ValueError(
"lmax must be greater or equal to 0. Input value was {:s}."
.format(repr(lmax))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if cnorm != 0 and cnorm != 1:
raise ValueError(
"cnorm must be either 0 or 1. Input value was {:s}."
.format(repr(cnorm))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
if normalization == '4pi':
p = _PlmBar(lmax, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'ortho':
p = _PlmON(lmax, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'schmidt':
p = _PlmSchmidt(lmax, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'unnorm':
p = _PLegendreA(lmax, z, csphase=csphase, cnorm=cnorm)
if packed is True:
return p
else:
plm = _np.zeros((lmax+1, lmax+1))
for l in range(lmax+1):
for m in range(l+1):
plm[l, m] = p[(l*(l+1))//2+m]
return plm | python | def legendre(lmax, z, normalization='4pi', csphase=1, cnorm=0, packed=False):
"""
Compute all the associated Legendre functions up to a maximum degree and
order.
Usage
-----
plm = legendre (lmax, z, [normalization, csphase, cnorm, packed])
Returns
-------
plm : float, dimension (lmax+1, lmax+1) or ((lmax+1)*(lmax+2)/2)
An array of associated Legendre functions, plm[l, m], where l and m
are the degree and order, respectively. If packed is True, the array
is 1-dimensional with the index corresponding to l*(l+1)/2+m.
Parameters
----------
lmax : integer
The maximum degree of the associated Legendre functions to be computed.
z : float
The argument of the associated Legendre functions.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for use with geodesy 4pi
normalized, orthonormalized, Schmidt semi-normalized, or unnormalized
spherical harmonic functions, respectively.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the associated
Legendre functions.
cnorm : optional, integer, default = 0
If 1, the complex normalization of the associated Legendre functions
will be used. The default is to use the real normalization.
packed : optional, bool, default = False
If True, return a 1-dimensional packed array with the index
corresponding to l*(l+1)/2+m, where l and m are respectively the
degree and order.
Description
-----------
legendre` will calculate all of the associated Legendre functions up to
degree lmax for a given argument. The Legendre functions are used typically
as a part of the spherical harmonic functions, and three parameters
determine how they are defined. `normalization` can be either '4pi'
(default), 'ortho', 'schmidt', or 'unnorm' for use with 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized spherical
harmonic functions, respectively. csphase determines whether to include
or exclude (default) the Condon-Shortley phase factor. cnorm determines
whether to normalize the Legendre functions for use with real (default)
or complex spherical harmonic functions.
By default, the routine will return a 2-dimensional array, p[l, m]. If the
optional parameter `packed` is set to True, the output will instead be a
1-dimensional array where the indices correspond to `l*(l+1)/2+m`. The
Legendre functions are calculated using the standard three-term recursion
formula, and in order to prevent overflows, the scaling approach of Holmes
and Featherstone (2002) is utilized. The resulting functions are accurate
to about degree 2800. See Wieczorek and Meschede (2018) for exact
definitions on how the Legendre functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018.
"""
if lmax < 0:
raise ValueError(
"lmax must be greater or equal to 0. Input value was {:s}."
.format(repr(lmax))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if cnorm != 0 and cnorm != 1:
raise ValueError(
"cnorm must be either 0 or 1. Input value was {:s}."
.format(repr(cnorm))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
if normalization == '4pi':
p = _PlmBar(lmax, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'ortho':
p = _PlmON(lmax, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'schmidt':
p = _PlmSchmidt(lmax, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'unnorm':
p = _PLegendreA(lmax, z, csphase=csphase, cnorm=cnorm)
if packed is True:
return p
else:
plm = _np.zeros((lmax+1, lmax+1))
for l in range(lmax+1):
for m in range(l+1):
plm[l, m] = p[(l*(l+1))//2+m]
return plm | [
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order.
Usage
-----
plm = legendre (lmax, z, [normalization, csphase, cnorm, packed])
Returns
-------
plm : float, dimension (lmax+1, lmax+1) or ((lmax+1)*(lmax+2)/2)
An array of associated Legendre functions, plm[l, m], where l and m
are the degree and order, respectively. If packed is True, the array
is 1-dimensional with the index corresponding to l*(l+1)/2+m.
Parameters
----------
lmax : integer
The maximum degree of the associated Legendre functions to be computed.
z : float
The argument of the associated Legendre functions.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for use with geodesy 4pi
normalized, orthonormalized, Schmidt semi-normalized, or unnormalized
spherical harmonic functions, respectively.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the associated
Legendre functions.
cnorm : optional, integer, default = 0
If 1, the complex normalization of the associated Legendre functions
will be used. The default is to use the real normalization.
packed : optional, bool, default = False
If True, return a 1-dimensional packed array with the index
corresponding to l*(l+1)/2+m, where l and m are respectively the
degree and order.
Description
-----------
legendre` will calculate all of the associated Legendre functions up to
degree lmax for a given argument. The Legendre functions are used typically
as a part of the spherical harmonic functions, and three parameters
determine how they are defined. `normalization` can be either '4pi'
(default), 'ortho', 'schmidt', or 'unnorm' for use with 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized spherical
harmonic functions, respectively. csphase determines whether to include
or exclude (default) the Condon-Shortley phase factor. cnorm determines
whether to normalize the Legendre functions for use with real (default)
or complex spherical harmonic functions.
By default, the routine will return a 2-dimensional array, p[l, m]. If the
optional parameter `packed` is set to True, the output will instead be a
1-dimensional array where the indices correspond to `l*(l+1)/2+m`. The
Legendre functions are calculated using the standard three-term recursion
formula, and in order to prevent overflows, the scaling approach of Holmes
and Featherstone (2002) is utilized. The resulting functions are accurate
to about degree 2800. See Wieczorek and Meschede (2018) for exact
definitions on how the Legendre functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/legendre/legendre_functions.py#L17-L138 | train | 203,829 |
SHTOOLS/SHTOOLS | pyshtools/legendre/legendre_functions.py | legendre_lm | def legendre_lm(l, m, z, normalization='4pi', csphase=1, cnorm=0):
"""
Compute the associated Legendre function for a specific degree and order.
Usage
-----
plm = legendre_lm (l, m, z, [normalization, csphase, cnorm])
Returns
-------
plm : float
The associated Legendre functions for degree l and order m.
Parameters
----------
l : integer
The spherical harmonic degree.
m : integer
The spherical harmonic order.
z : float
The argument of the associated Legendre functions.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for use with geodesy 4pi
normalized, orthonormalized, Schmidt semi-normalized, or unnormalized
spherical harmonic functions, respectively.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the associated
Legendre functions.
cnorm : optional, integer, default = 0
If 1, the complex normalization of the associated Legendre functions
will be used. The default is to use the real normalization.
Description
-----------
legendre_lm will calculate the associated Legendre function for a specific
degree l and order m. The Legendre functions are used typically as a part
of the spherical harmonic functions, and three parameters determine how
they are defined. normalization can be either '4pi' (default), 'ortho',
'schmidt', or 'unnorm' for use with 4pi normalized, orthonormalized,
Schmidt semi-normalized, or unnormalized spherical harmonic functions,
respectively. csphase determines whether to include or exclude (default)
the Condon-Shortley phase factor. cnorm determines whether to normalize
the Legendre functions for use with real (default) or complex spherical
harmonic functions.
The Legendre functions are calculated using the standard three-term
recursion formula, and in order to prevent overflows, the scaling approach
of Holmes and Featherstone (2002) is utilized. The resulting functions are
accurate to about degree 2800. See Wieczorek and Meschede (2018) for exact
definitions on how the Legendre functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018.
"""
if l < 0:
raise ValueError(
"The degree l must be greater or equal to 0. Input value was {:s}."
.format(repr(l))
)
if m < 0:
raise ValueError(
"The order m must be greater or equal to 0. Input value was {:s}."
.format(repr(m))
)
if m > l:
raise ValueError(
"The order m must be less than or equal to the degree l. " +
"Input values were l={:s} and m={:s}.".format(repr(l), repr(m))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if cnorm != 0 and cnorm != 1:
raise ValueError(
"cnorm must be either 0 or 1. Input value was {:s}."
.format(repr(cnorm))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
if normalization == '4pi':
p = _PlmBar(l, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'ortho':
p = _PlmON(l, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'schmidt':
p = _PlmSchmidt(l, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'unnorm':
p = _PLegendreA(l, z, csphase=csphase, cnorm=cnorm)
return p[(l*(l+1))//2+m] | python | def legendre_lm(l, m, z, normalization='4pi', csphase=1, cnorm=0):
"""
Compute the associated Legendre function for a specific degree and order.
Usage
-----
plm = legendre_lm (l, m, z, [normalization, csphase, cnorm])
Returns
-------
plm : float
The associated Legendre functions for degree l and order m.
Parameters
----------
l : integer
The spherical harmonic degree.
m : integer
The spherical harmonic order.
z : float
The argument of the associated Legendre functions.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for use with geodesy 4pi
normalized, orthonormalized, Schmidt semi-normalized, or unnormalized
spherical harmonic functions, respectively.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the associated
Legendre functions.
cnorm : optional, integer, default = 0
If 1, the complex normalization of the associated Legendre functions
will be used. The default is to use the real normalization.
Description
-----------
legendre_lm will calculate the associated Legendre function for a specific
degree l and order m. The Legendre functions are used typically as a part
of the spherical harmonic functions, and three parameters determine how
they are defined. normalization can be either '4pi' (default), 'ortho',
'schmidt', or 'unnorm' for use with 4pi normalized, orthonormalized,
Schmidt semi-normalized, or unnormalized spherical harmonic functions,
respectively. csphase determines whether to include or exclude (default)
the Condon-Shortley phase factor. cnorm determines whether to normalize
the Legendre functions for use with real (default) or complex spherical
harmonic functions.
The Legendre functions are calculated using the standard three-term
recursion formula, and in order to prevent overflows, the scaling approach
of Holmes and Featherstone (2002) is utilized. The resulting functions are
accurate to about degree 2800. See Wieczorek and Meschede (2018) for exact
definitions on how the Legendre functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018.
"""
if l < 0:
raise ValueError(
"The degree l must be greater or equal to 0. Input value was {:s}."
.format(repr(l))
)
if m < 0:
raise ValueError(
"The order m must be greater or equal to 0. Input value was {:s}."
.format(repr(m))
)
if m > l:
raise ValueError(
"The order m must be less than or equal to the degree l. " +
"Input values were l={:s} and m={:s}.".format(repr(l), repr(m))
)
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if cnorm != 0 and cnorm != 1:
raise ValueError(
"cnorm must be either 0 or 1. Input value was {:s}."
.format(repr(cnorm))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
if normalization == '4pi':
p = _PlmBar(l, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'ortho':
p = _PlmON(l, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'schmidt':
p = _PlmSchmidt(l, z, csphase=csphase, cnorm=cnorm)
elif normalization == 'unnorm':
p = _PLegendreA(l, z, csphase=csphase, cnorm=cnorm)
return p[(l*(l+1))//2+m] | [
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Usage
-----
plm = legendre_lm (l, m, z, [normalization, csphase, cnorm])
Returns
-------
plm : float
The associated Legendre functions for degree l and order m.
Parameters
----------
l : integer
The spherical harmonic degree.
m : integer
The spherical harmonic order.
z : float
The argument of the associated Legendre functions.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for use with geodesy 4pi
normalized, orthonormalized, Schmidt semi-normalized, or unnormalized
spherical harmonic functions, respectively.
csphase : optional, integer, default = 1
If 1 (default), the Condon-Shortley phase will be excluded. If -1, the
Condon-Shortley phase of (-1)^m will be appended to the associated
Legendre functions.
cnorm : optional, integer, default = 0
If 1, the complex normalization of the associated Legendre functions
will be used. The default is to use the real normalization.
Description
-----------
legendre_lm will calculate the associated Legendre function for a specific
degree l and order m. The Legendre functions are used typically as a part
of the spherical harmonic functions, and three parameters determine how
they are defined. normalization can be either '4pi' (default), 'ortho',
'schmidt', or 'unnorm' for use with 4pi normalized, orthonormalized,
Schmidt semi-normalized, or unnormalized spherical harmonic functions,
respectively. csphase determines whether to include or exclude (default)
the Condon-Shortley phase factor. cnorm determines whether to normalize
the Legendre functions for use with real (default) or complex spherical
harmonic functions.
The Legendre functions are calculated using the standard three-term
recursion formula, and in order to prevent overflows, the scaling approach
of Holmes and Featherstone (2002) is utilized. The resulting functions are
accurate to about degree 2800. See Wieczorek and Meschede (2018) for exact
definitions on how the Legendre functions are defined.
References
----------
Holmes, S. A., and W. E. Featherstone, A unified approach to the Clenshaw
summation and the recursive computation of very high degree and order
normalised associated Legendre functions, J. Geodesy, 76, 279-299,
doi:10.1007/s00190-002-0216-2, 2002.
Wieczorek, M. A., and M. Meschede. SHTools — Tools for working with
spherical harmonics, Geochem., Geophys., Geosyst., 19, 2574-2592,
doi:10.1029/2018GC007529, 2018. | [
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SHTOOLS/SHTOOLS | pyshtools/shio/shread.py | _iscomment | 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.
"""
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
line.split()[1].decode().isdecimal()):
return False
return True
else:
return True | python | 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.
"""
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
line.split()[1].decode().isdecimal()):
return False
return True
else:
return True | [
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SHTOOLS/SHTOOLS | pyshtools/make_docs.py | process_f2pydoc | 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.
"""
# ---- 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 = False
else:
print('-- uninterpretable f2py documentation --')
return f2pydoc
# ---- replace arguments with _d suffix with empty string in ----
# ---- function signature (remove them): ----
docparts[0] = re.sub('[\[(,]\w+_d\d', '', docparts[0])
# ---- replace _d arguments of the return arrays with their default value:
if doc_has_optionals:
returnarray_dims = re.findall('[\[(,](\w+_d\d)', docparts[3])
for arg in returnarray_dims:
searchpattern = arg + ' : input.*\n.*Default: (.*)\n'
match = re.search(searchpattern, docparts[2])
if match:
default = match.group(1)
docparts[3] = re.sub(arg, default, docparts[3])
docparts[2] = re.sub(searchpattern, '', docparts[2])
# ---- remove all optional _d# from optional argument list:
if doc_has_optionals:
searchpattern = '\w+_d\d : input.*\n.*Default: (.*)\n'
docparts[2] = re.sub(searchpattern, '', docparts[2])
# ---- combine doc parts to a single string
processed_signature = '\n--'.join(docparts)
return processed_signature | python | 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.
"""
# ---- 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 = False
else:
print('-- uninterpretable f2py documentation --')
return f2pydoc
# ---- replace arguments with _d suffix with empty string in ----
# ---- function signature (remove them): ----
docparts[0] = re.sub('[\[(,]\w+_d\d', '', docparts[0])
# ---- replace _d arguments of the return arrays with their default value:
if doc_has_optionals:
returnarray_dims = re.findall('[\[(,](\w+_d\d)', docparts[3])
for arg in returnarray_dims:
searchpattern = arg + ' : input.*\n.*Default: (.*)\n'
match = re.search(searchpattern, docparts[2])
if match:
default = match.group(1)
docparts[3] = re.sub(arg, default, docparts[3])
docparts[2] = re.sub(searchpattern, '', docparts[2])
# ---- remove all optional _d# from optional argument list:
if doc_has_optionals:
searchpattern = '\w+_d\d : input.*\n.*Default: (.*)\n'
docparts[2] = re.sub(searchpattern, '', docparts[2])
# ---- combine doc parts to a single string
processed_signature = '\n--'.join(docparts)
return processed_signature | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.from_cap | 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
----------
theta : float
Angular radius of the spherical cap localization domain (default
in degrees).
lwin : int
Spherical harmonic bandwidth of the localization windows.
clat, clon : float, optional, default = None
Latitude and longitude of the center of the rotated spherical cap
localization windows (default in degrees).
nwin : int, optional, default (lwin+1)**2
Number of localization windows.
theta_degrees : bool, optional, default = True
True if theta is in degrees.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
weights : ndarray, optional, default = None
Taper weights used with the multitaper spectral analyses.
"""
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, taper_order,
clat, clon, nwin, theta_degrees, coord_degrees,
dj_matrix, weights, copy=False) | python | 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
----------
theta : float
Angular radius of the spherical cap localization domain (default
in degrees).
lwin : int
Spherical harmonic bandwidth of the localization windows.
clat, clon : float, optional, default = None
Latitude and longitude of the center of the rotated spherical cap
localization windows (default in degrees).
nwin : int, optional, default (lwin+1)**2
Number of localization windows.
theta_degrees : bool, optional, default = True
True if theta is in degrees.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
weights : ndarray, optional, default = None
Taper weights used with the multitaper spectral analyses.
"""
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, taper_order,
clat, clon, nwin, theta_degrees, coord_degrees,
dj_matrix, weights, copy=False) | [
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Returns
-------
x : SHWindow class instance
Parameters
----------
theta : float
Angular radius of the spherical cap localization domain (default
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lwin : int
Spherical harmonic bandwidth of the localization windows.
clat, clon : float, optional, default = None
Latitude and longitude of the center of the rotated spherical cap
localization windows (default in degrees).
nwin : int, optional, default (lwin+1)**2
Number of localization windows.
theta_degrees : bool, optional, default = True
True if theta is in degrees.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
weights : ndarray, optional, default = None
Taper weights used with the multitaper spectral analyses. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.from_mask | 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_mask :ndarray, shape (nlat, nlon)
A Driscoll and Healy (1994) sampled grid describing the
concentration region R. All elements should either be 1 (for inside
the concentration region) or 0 (for outside the concentration
region). The grid must have dimensions nlon=nlat or nlon=2*nlat,
where nlat is even.
lwin : int
The spherical harmonic bandwidth of the localization windows.
nwin : int, optional, default = (lwin+1)**2
The number of best concentrated eigenvalues and eigenfunctions to
return.
weights ndarray, optional, default = None
Taper weights used with the multitaper spectral analyses.
"""
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))
if dh_mask.shape[0] % 2 != 0:
raise ValueError('The number of latitude bands in dh_mask ' +
'must be even. nlat = {:d}'
.format(dh_mask.shape[0]))
if dh_mask.shape[1] == dh_mask.shape[0]:
_sampling = 1
elif dh_mask.shape[1] == 2 * dh_mask.shape[0]:
_sampling = 2
else:
raise ValueError('dh_mask must be dimensioned as (n, n) or ' +
'(n, 2 * n). Input shape is ({:d}, {:d})'
.format(dh_mask.shape[0], dh_mask.shape[1]))
mask_lm = _shtools.SHExpandDH(dh_mask, sampling=_sampling, lmax_calc=0)
area = mask_lm[0, 0, 0] * 4 * _np.pi
tapers, eigenvalues = _shtools.SHReturnTapersMap(dh_mask, lwin,
ntapers=nwin)
return SHWindowMask(tapers, eigenvalues, weights, area, copy=False) | python | 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_mask :ndarray, shape (nlat, nlon)
A Driscoll and Healy (1994) sampled grid describing the
concentration region R. All elements should either be 1 (for inside
the concentration region) or 0 (for outside the concentration
region). The grid must have dimensions nlon=nlat or nlon=2*nlat,
where nlat is even.
lwin : int
The spherical harmonic bandwidth of the localization windows.
nwin : int, optional, default = (lwin+1)**2
The number of best concentrated eigenvalues and eigenfunctions to
return.
weights ndarray, optional, default = None
Taper weights used with the multitaper spectral analyses.
"""
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))
if dh_mask.shape[0] % 2 != 0:
raise ValueError('The number of latitude bands in dh_mask ' +
'must be even. nlat = {:d}'
.format(dh_mask.shape[0]))
if dh_mask.shape[1] == dh_mask.shape[0]:
_sampling = 1
elif dh_mask.shape[1] == 2 * dh_mask.shape[0]:
_sampling = 2
else:
raise ValueError('dh_mask must be dimensioned as (n, n) or ' +
'(n, 2 * n). Input shape is ({:d}, {:d})'
.format(dh_mask.shape[0], dh_mask.shape[1]))
mask_lm = _shtools.SHExpandDH(dh_mask, sampling=_sampling, lmax_calc=0)
area = mask_lm[0, 0, 0] * 4 * _np.pi
tapers, eigenvalues = _shtools.SHReturnTapersMap(dh_mask, lwin,
ntapers=nwin)
return SHWindowMask(tapers, eigenvalues, weights, area, copy=False) | [
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Returns
-------
x : SHWindow class instance
Parameters
----------
dh_mask :ndarray, shape (nlat, nlon)
A Driscoll and Healy (1994) sampled grid describing the
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the concentration region) or 0 (for outside the concentration
region). The grid must have dimensions nlon=nlat or nlon=2*nlat,
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lwin : int
The spherical harmonic bandwidth of the localization windows.
nwin : int, optional, default = (lwin+1)**2
The number of best concentrated eigenvalues and eigenfunctions to
return.
weights ndarray, optional, default = None
Taper weights used with the multitaper spectral analyses. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.to_array | 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 coefficients of the
window.
Parameters
----------
itaper : int
Taper number, where itaper=0 is the best concentrated.
normalization : str, optional, default = '4pi'
Normalization of the output coefficients: '4pi', 'ortho' or
'schmidt' for geodesy 4pi normalized, orthonormalized, or Schmidt
semi-normalized coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
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 ValueError(
"normalization must be '4pi', 'ortho' " +
"or 'schmidt'. Provided value was {:s}"
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase))
)
return self._to_array(
itaper, normalization=normalization.lower(), csphase=csphase) | python | 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 coefficients of the
window.
Parameters
----------
itaper : int
Taper number, where itaper=0 is the best concentrated.
normalization : str, optional, default = '4pi'
Normalization of the output coefficients: '4pi', 'ortho' or
'schmidt' for geodesy 4pi normalized, orthonormalized, or Schmidt
semi-normalized coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
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 ValueError(
"normalization must be '4pi', 'ortho' " +
"or 'schmidt'. Provided value was {:s}"
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase))
)
return self._to_array(
itaper, normalization=normalization.lower(), csphase=csphase) | [
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Returns
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coeffs : ndarray, shape (2, lwin+1, lwin+11)
3-D numpy ndarray of the spherical harmonic coefficients of the
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Parameters
----------
itaper : int
Taper number, where itaper=0 is the best concentrated.
normalization : str, optional, default = '4pi'
Normalization of the output coefficients: '4pi', 'ortho' or
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csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.to_shcoeffs | 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
Taper number, where itaper=0 is the best concentrated.
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho' or 'schmidt' for
geodesy 4pi-normalized, orthonormalized, or Schmidt semi-normalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
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']):
raise ValueError(
"normalization must be '4pi', 'ortho' " +
"or 'schmidt'. Provided value was {:s}"
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase))
)
coeffs = self.to_array(itaper, normalization=normalization.lower(),
csphase=csphase)
return SHCoeffs.from_array(coeffs, normalization=normalization.lower(),
csphase=csphase, copy=False) | python | 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
Taper number, where itaper=0 is the best concentrated.
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho' or 'schmidt' for
geodesy 4pi-normalized, orthonormalized, or Schmidt semi-normalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
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']):
raise ValueError(
"normalization must be '4pi', 'ortho' " +
"or 'schmidt'. Provided value was {:s}"
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase))
)
coeffs = self.to_array(itaper, normalization=normalization.lower(),
csphase=csphase)
return SHCoeffs.from_array(coeffs, normalization=normalization.lower(),
csphase=csphase, copy=False) | [
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class instance.
Usage
-----
clm = x.to_shcoeffs(itaper, [normalization, csphase])
Returns
-------
clm : SHCoeffs class instance
Parameters
----------
itaper : int
Taper number, where itaper=0 is the best concentrated.
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho' or 'schmidt' for
geodesy 4pi-normalized, orthonormalized, or Schmidt semi-normalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.to_shgrid | 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
Taper number, where itaper=0 is the best concentrated.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, 'DH2'
for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ' for a
Gauss-Legendre quadrature grid.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
Description
-----------
For more information concerning the spherical harmonic expansions and
the properties of the output grids, see the documentation for
SHExpandDH and SHExpandGLQ.
"""
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,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'DH2':
gridout = _shtools.MakeGridDH(self.to_array(itaper), sampling=2,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'GLQ':
if zeros is None:
zeros, weights = _shtools.SHGLQ(self.lwin)
gridout = _shtools.MakeGridGLQ(self.to_array(itaper), zeros,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='GLQ', copy=False)
else:
raise ValueError(
"grid must be 'DH', 'DH1', 'DH2', or 'GLQ'. " +
"Input value was {:s}".format(repr(grid))) | python | 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
Taper number, where itaper=0 is the best concentrated.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, 'DH2'
for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ' for a
Gauss-Legendre quadrature grid.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
Description
-----------
For more information concerning the spherical harmonic expansions and
the properties of the output grids, see the documentation for
SHExpandDH and SHExpandGLQ.
"""
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,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'DH2':
gridout = _shtools.MakeGridDH(self.to_array(itaper), sampling=2,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='DH', copy=False)
elif grid.upper() == 'GLQ':
if zeros is None:
zeros, weights = _shtools.SHGLQ(self.lwin)
gridout = _shtools.MakeGridGLQ(self.to_array(itaper), zeros,
norm=1, csphase=1)
return SHGrid.from_array(gridout, grid='GLQ', copy=False)
else:
raise ValueError(
"grid must be 'DH', 'DH1', 'DH2', or 'GLQ'. " +
"Input value was {:s}".format(repr(grid))) | [
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Usage
-----
f = x.to_shgrid(itaper, [grid, zeros])
Returns
-------
f : SHGrid class instance
Parameters
----------
itaper : int
Taper number, where itaper=0 is the best concentrated.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, 'DH2'
for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ' for a
Gauss-Legendre quadrature grid.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
Description
-----------
For more information concerning the spherical harmonic expansions and
the properties of the output grids, see the documentation for
SHExpandDH and SHExpandGLQ. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.multitaper_spectrum | 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
-------
mtse : ndarray, shape (lmax-lwin+1)
The localized multitaper spectrum estimate, where lmax is the
spherical-harmonic bandwidth of clm, and lwin is the
spherical-harmonic bandwidth of the localization windows.
sd : ndarray, shape (lmax-lwin+1)
The standard error of the localized multitaper spectrum
estimate.
Parameters
----------
clm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the global field to analyze.
k : int
The number of tapers to be utilized in performing the multitaper
spectral analysis.
convention : str, optional, default = 'power'
The type of output spectra: 'power' for power spectra, and
'energy' for energy spectra.
unit : str, optional, default = 'per_l'
The units of the output spectra. If 'per_l', the spectra contain
the total contribution for each spherical harmonic degree l. If
'per_lm', the spectra contain the average contribution for each
coefficient at spherical harmonic degree l.
lmax : int, optional, default = clm.lmax
The maximum spherical-harmonic degree of clm to use.
taper_wt : ndarray, optional, default = None
1-D numpy array of the weights used in calculating the multitaper
spectral estimates and standard error.
clat, clon : float, optional, default = 90., 0.
Latitude and longitude of the center of the spherical-cap
localization windows.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
"""
return self._multitaper_spectrum(clm, k, convention=convention,
unit=unit, **kwargs) | python | 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
-------
mtse : ndarray, shape (lmax-lwin+1)
The localized multitaper spectrum estimate, where lmax is the
spherical-harmonic bandwidth of clm, and lwin is the
spherical-harmonic bandwidth of the localization windows.
sd : ndarray, shape (lmax-lwin+1)
The standard error of the localized multitaper spectrum
estimate.
Parameters
----------
clm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the global field to analyze.
k : int
The number of tapers to be utilized in performing the multitaper
spectral analysis.
convention : str, optional, default = 'power'
The type of output spectra: 'power' for power spectra, and
'energy' for energy spectra.
unit : str, optional, default = 'per_l'
The units of the output spectra. If 'per_l', the spectra contain
the total contribution for each spherical harmonic degree l. If
'per_lm', the spectra contain the average contribution for each
coefficient at spherical harmonic degree l.
lmax : int, optional, default = clm.lmax
The maximum spherical-harmonic degree of clm to use.
taper_wt : ndarray, optional, default = None
1-D numpy array of the weights used in calculating the multitaper
spectral estimates and standard error.
clat, clon : float, optional, default = 90., 0.
Latitude and longitude of the center of the spherical-cap
localization windows.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
"""
return self._multitaper_spectrum(clm, k, convention=convention,
unit=unit, **kwargs) | [
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Usage
-----
mtse, sd = x.multitaper_spectrum(clm, k, [convention, unit, lmax,
taper_wt, clat, clon,
coord_degrees])
Returns
-------
mtse : ndarray, shape (lmax-lwin+1)
The localized multitaper spectrum estimate, where lmax is the
spherical-harmonic bandwidth of clm, and lwin is the
spherical-harmonic bandwidth of the localization windows.
sd : ndarray, shape (lmax-lwin+1)
The standard error of the localized multitaper spectrum
estimate.
Parameters
----------
clm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the global field to analyze.
k : int
The number of tapers to be utilized in performing the multitaper
spectral analysis.
convention : str, optional, default = 'power'
The type of output spectra: 'power' for power spectra, and
'energy' for energy spectra.
unit : str, optional, default = 'per_l'
The units of the output spectra. If 'per_l', the spectra contain
the total contribution for each spherical harmonic degree l. If
'per_lm', the spectra contain the average contribution for each
coefficient at spherical harmonic degree l.
lmax : int, optional, default = clm.lmax
The maximum spherical-harmonic degree of clm to use.
taper_wt : ndarray, optional, default = None
1-D numpy array of the weights used in calculating the multitaper
spectral estimates and standard error.
clat, clon : float, optional, default = 90., 0.
Latitude and longitude of the center of the spherical-cap
localization windows.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shwindow.py#L418-L467 | train | 203,838 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.multitaper_cross_spectrum | 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, clon,
coord_degrees])
Returns
-------
mtse : ndarray, shape (lmax-lwin+1)
The localized multitaper cross-spectrum estimate, where lmax is the
smaller of the two spherical-harmonic bandwidths of clm and slm,
and lwin is the spherical-harmonic bandwidth of the localization
windows.
sd : ndarray, shape (lmax-lwin+1)
The standard error of the localized multitaper cross-spectrum
estimate.
Parameters
----------
clm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the first global field to analyze.
slm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the second global field to analyze.
k : int
The number of tapers to be utilized in performing the multitaper
spectral analysis.
convention : str, optional, default = 'power'
The type of output spectra: 'power' for power spectra, and
'energy' for energy spectra.
unit : str, optional, default = 'per_l'
The units of the output spectra. If 'per_l', the spectra contain
the total contribution for each spherical harmonic degree l. If
'per_lm', the spectra contain the average contribution for each
coefficient at spherical harmonic degree l.
lmax : int, optional, default = min(clm.lmax, slm.lmax)
The maximum spherical-harmonic degree of the input coefficients
to use.
taper_wt : ndarray, optional, default = None
The weights used in calculating the multitaper cross-spectral
estimates and standard error.
clat, clon : float, optional, default = 90., 0.
Latitude and longitude of the center of the spherical-cap
localization windows.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
"""
return self._multitaper_cross_spectrum(clm, slm, k,
convention=convention,
unit=unit, **kwargs) | python | 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, clon,
coord_degrees])
Returns
-------
mtse : ndarray, shape (lmax-lwin+1)
The localized multitaper cross-spectrum estimate, where lmax is the
smaller of the two spherical-harmonic bandwidths of clm and slm,
and lwin is the spherical-harmonic bandwidth of the localization
windows.
sd : ndarray, shape (lmax-lwin+1)
The standard error of the localized multitaper cross-spectrum
estimate.
Parameters
----------
clm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the first global field to analyze.
slm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the second global field to analyze.
k : int
The number of tapers to be utilized in performing the multitaper
spectral analysis.
convention : str, optional, default = 'power'
The type of output spectra: 'power' for power spectra, and
'energy' for energy spectra.
unit : str, optional, default = 'per_l'
The units of the output spectra. If 'per_l', the spectra contain
the total contribution for each spherical harmonic degree l. If
'per_lm', the spectra contain the average contribution for each
coefficient at spherical harmonic degree l.
lmax : int, optional, default = min(clm.lmax, slm.lmax)
The maximum spherical-harmonic degree of the input coefficients
to use.
taper_wt : ndarray, optional, default = None
The weights used in calculating the multitaper cross-spectral
estimates and standard error.
clat, clon : float, optional, default = 90., 0.
Latitude and longitude of the center of the spherical-cap
localization windows.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
"""
return self._multitaper_cross_spectrum(clm, slm, k,
convention=convention,
unit=unit, **kwargs) | [
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Usage
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mtse, sd = x.multitaper_cross_spectrum(clm, slm, k, [convention, unit,
lmax, taper_wt,
clat, clon,
coord_degrees])
Returns
-------
mtse : ndarray, shape (lmax-lwin+1)
The localized multitaper cross-spectrum estimate, where lmax is the
smaller of the two spherical-harmonic bandwidths of clm and slm,
and lwin is the spherical-harmonic bandwidth of the localization
windows.
sd : ndarray, shape (lmax-lwin+1)
The standard error of the localized multitaper cross-spectrum
estimate.
Parameters
----------
clm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the first global field to analyze.
slm : SHCoeffs class instance
SHCoeffs class instance containing the spherical harmonic
coefficients of the second global field to analyze.
k : int
The number of tapers to be utilized in performing the multitaper
spectral analysis.
convention : str, optional, default = 'power'
The type of output spectra: 'power' for power spectra, and
'energy' for energy spectra.
unit : str, optional, default = 'per_l'
The units of the output spectra. If 'per_l', the spectra contain
the total contribution for each spherical harmonic degree l. If
'per_lm', the spectra contain the average contribution for each
coefficient at spherical harmonic degree l.
lmax : int, optional, default = min(clm.lmax, slm.lmax)
The maximum spherical-harmonic degree of the input coefficients
to use.
taper_wt : ndarray, optional, default = None
The weights used in calculating the multitaper cross-spectral
estimates and standard error.
clat, clon : float, optional, default = 90., 0.
Latitude and longitude of the center of the spherical-cap
localization windows.
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shwindow.py#L469-L525 | train | 203,839 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.spectra | 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
localization window, and where the windows are arranged with
increasing concentration factors. If itaper is set, only a single
vector is returned, whereas if nwin is set, the first nwin spectra
are returned.
Parameters
----------
itaper : int, optional, default = None
The taper number of the output spectrum, where itaper=0
corresponds to the best concentrated taper.
nwin : int, optional, default = 1
The number of best concentrated localization window power spectra
to return.
convention : str, optional, default = 'power'
The type of spectrum to return: 'power' for power spectrum,
'energy' for energy spectrum, and 'l2norm' for the l2 norm
spectrum.
unit : str, optional, default = 'per_l'
If 'per_l', return the total contribution to the spectrum for each
spherical harmonic degree l. If 'per_lm', return the average
contribution to the spectrum for each coefficient at spherical
harmonic degree l. If 'per_dlogl', return the spectrum per log
interval dlog_a(l).
base : float, optional, default = 10.
The logarithm base when calculating the 'per_dlogl' spectrum.
Description
-----------
This function returns either the power spectrum, energy spectrum, or
l2-norm spectrum of one or more of the localization windows.
Total power is defined as the integral of the function squared over all
space, divided by the area the function spans. If the mean of the
function is zero, this is equivalent to the variance of the function.
The total energy is the integral of the function squared over all space
and is 4pi times the total power. The l2-norm is the sum of the
magnitude of the coefficients squared.
The output spectrum can be expresed using one of three units. 'per_l'
returns the contribution to the total spectrum from all angular orders
at degree l. 'per_lm' returns the average contribution to the total
spectrum from a single coefficient at degree l. The 'per_lm' spectrum
is equal to the 'per_l' spectrum divided by (2l+1). 'per_dlogl' returns
the contribution to the total spectrum from all angular orders over an
infinitessimal logarithmic degree band. The contrubution in the band
dlog_a(l) is spectrum(l, 'per_dlogl')*dlog_a(l), where a is the base,
and where spectrum(l, 'per_dlogl) is equal to
spectrum(l, 'per_l')*l*log(a).
"""
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',
convention=convention, unit=unit,
base=base)
else:
coeffs = self.to_array(itaper)
spectra = _spectrum(coeffs, normalization='4pi',
convention=convention, unit=unit, base=base)
return spectra | python | 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
localization window, and where the windows are arranged with
increasing concentration factors. If itaper is set, only a single
vector is returned, whereas if nwin is set, the first nwin spectra
are returned.
Parameters
----------
itaper : int, optional, default = None
The taper number of the output spectrum, where itaper=0
corresponds to the best concentrated taper.
nwin : int, optional, default = 1
The number of best concentrated localization window power spectra
to return.
convention : str, optional, default = 'power'
The type of spectrum to return: 'power' for power spectrum,
'energy' for energy spectrum, and 'l2norm' for the l2 norm
spectrum.
unit : str, optional, default = 'per_l'
If 'per_l', return the total contribution to the spectrum for each
spherical harmonic degree l. If 'per_lm', return the average
contribution to the spectrum for each coefficient at spherical
harmonic degree l. If 'per_dlogl', return the spectrum per log
interval dlog_a(l).
base : float, optional, default = 10.
The logarithm base when calculating the 'per_dlogl' spectrum.
Description
-----------
This function returns either the power spectrum, energy spectrum, or
l2-norm spectrum of one or more of the localization windows.
Total power is defined as the integral of the function squared over all
space, divided by the area the function spans. If the mean of the
function is zero, this is equivalent to the variance of the function.
The total energy is the integral of the function squared over all space
and is 4pi times the total power. The l2-norm is the sum of the
magnitude of the coefficients squared.
The output spectrum can be expresed using one of three units. 'per_l'
returns the contribution to the total spectrum from all angular orders
at degree l. 'per_lm' returns the average contribution to the total
spectrum from a single coefficient at degree l. The 'per_lm' spectrum
is equal to the 'per_l' spectrum divided by (2l+1). 'per_dlogl' returns
the contribution to the total spectrum from all angular orders over an
infinitessimal logarithmic degree band. The contrubution in the band
dlog_a(l) is spectrum(l, 'per_dlogl')*dlog_a(l), where a is the base,
and where spectrum(l, 'per_dlogl) is equal to
spectrum(l, 'per_l')*l*log(a).
"""
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',
convention=convention, unit=unit,
base=base)
else:
coeffs = self.to_array(itaper)
spectra = _spectrum(coeffs, normalization='4pi',
convention=convention, unit=unit, base=base)
return spectra | [
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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
localization window, and where the windows are arranged with
increasing concentration factors. If itaper is set, only a single
vector is returned, whereas if nwin is set, the first nwin spectra
are returned.
Parameters
----------
itaper : int, optional, default = None
The taper number of the output spectrum, where itaper=0
corresponds to the best concentrated taper.
nwin : int, optional, default = 1
The number of best concentrated localization window power spectra
to return.
convention : str, optional, default = 'power'
The type of spectrum to return: 'power' for power spectrum,
'energy' for energy spectrum, and 'l2norm' for the l2 norm
spectrum.
unit : str, optional, default = 'per_l'
If 'per_l', return the total contribution to the spectrum for each
spherical harmonic degree l. If 'per_lm', return the average
contribution to the spectrum for each coefficient at spherical
harmonic degree l. If 'per_dlogl', return the spectrum per log
interval dlog_a(l).
base : float, optional, default = 10.
The logarithm base when calculating the 'per_dlogl' spectrum.
Description
-----------
This function returns either the power spectrum, energy spectrum, or
l2-norm spectrum of one or more of the localization windows.
Total power is defined as the integral of the function squared over all
space, divided by the area the function spans. If the mean of the
function is zero, this is equivalent to the variance of the function.
The total energy is the integral of the function squared over all space
and is 4pi times the total power. The l2-norm is the sum of the
magnitude of the coefficients squared.
The output spectrum can be expresed using one of three units. 'per_l'
returns the contribution to the total spectrum from all angular orders
at degree l. 'per_lm' returns the average contribution to the total
spectrum from a single coefficient at degree l. The 'per_lm' spectrum
is equal to the 'per_l' spectrum divided by (2l+1). 'per_dlogl' returns
the contribution to the total spectrum from all angular orders over an
infinitessimal logarithmic degree band. The contrubution in the band
dlog_a(l) is spectrum(l, 'per_dlogl')*dlog_a(l), where a is the base,
and where spectrum(l, 'per_dlogl) is equal to
spectrum(l, 'per_l')*l*log(a). | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.coupling_matrix | 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, shape (lmax+lwin+1, lmax+1) or (lmax+1, lmax+1) or
(lmax-lwin+1, lmax+1)
Parameters
----------
lmax : int
Spherical harmonic bandwidth of the global power spectrum.
nwin : int, optional, default = x.nwin
Number of tapers used in the mutlitaper spectral analysis.
weights : ndarray, optional, default = x.weights
Taper weights used with the multitaper spectral analyses.
mode : str, opitonal, default = 'full'
'full' returns a biased output spectrum of size lmax+lwin+1. The
input spectrum is assumed to be zero for degrees l>lmax.
'same' returns a biased output spectrum with the same size
(lmax+1) as the input spectrum. The input spectrum is assumed to be
zero for degrees l>lmax.
'valid' returns a biased spectrum with size lmax-lwin+1. This
returns only that part of the biased spectrum that is not
influenced by the input spectrum beyond degree lmax.
"""
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),
nwin))
else:
if len(weights) != self.nwin:
raise ValueError(
'Length of weights must be equal to nwin. ' +
'len(weights) = {:d}, nwin = {:d}'.format(len(weights),
self.nwin))
if mode == 'full':
return self._coupling_matrix(lmax, nwin=nwin, weights=weights)
elif mode == 'same':
cmatrix = self._coupling_matrix(lmax, nwin=nwin,
weights=weights)
return cmatrix[:lmax+1, :]
elif mode == 'valid':
cmatrix = self._coupling_matrix(lmax, nwin=nwin,
weights=weights)
return cmatrix[:lmax - self.lwin+1, :]
else:
raise ValueError("mode has to be 'full', 'same' or 'valid', not "
"{}".format(mode)) | python | 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, shape (lmax+lwin+1, lmax+1) or (lmax+1, lmax+1) or
(lmax-lwin+1, lmax+1)
Parameters
----------
lmax : int
Spherical harmonic bandwidth of the global power spectrum.
nwin : int, optional, default = x.nwin
Number of tapers used in the mutlitaper spectral analysis.
weights : ndarray, optional, default = x.weights
Taper weights used with the multitaper spectral analyses.
mode : str, opitonal, default = 'full'
'full' returns a biased output spectrum of size lmax+lwin+1. The
input spectrum is assumed to be zero for degrees l>lmax.
'same' returns a biased output spectrum with the same size
(lmax+1) as the input spectrum. The input spectrum is assumed to be
zero for degrees l>lmax.
'valid' returns a biased spectrum with size lmax-lwin+1. This
returns only that part of the biased spectrum that is not
influenced by the input spectrum beyond degree lmax.
"""
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),
nwin))
else:
if len(weights) != self.nwin:
raise ValueError(
'Length of weights must be equal to nwin. ' +
'len(weights) = {:d}, nwin = {:d}'.format(len(weights),
self.nwin))
if mode == 'full':
return self._coupling_matrix(lmax, nwin=nwin, weights=weights)
elif mode == 'same':
cmatrix = self._coupling_matrix(lmax, nwin=nwin,
weights=weights)
return cmatrix[:lmax+1, :]
elif mode == 'valid':
cmatrix = self._coupling_matrix(lmax, nwin=nwin,
weights=weights)
return cmatrix[:lmax - self.lwin+1, :]
else:
raise ValueError("mode has to be 'full', 'same' or 'valid', not "
"{}".format(mode)) | [
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... | 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, shape (lmax+lwin+1, lmax+1) or (lmax+1, lmax+1) or
(lmax-lwin+1, lmax+1)
Parameters
----------
lmax : int
Spherical harmonic bandwidth of the global power spectrum.
nwin : int, optional, default = x.nwin
Number of tapers used in the mutlitaper spectral analysis.
weights : ndarray, optional, default = x.weights
Taper weights used with the multitaper spectral analyses.
mode : str, opitonal, default = 'full'
'full' returns a biased output spectrum of size lmax+lwin+1. The
input spectrum is assumed to be zero for degrees l>lmax.
'same' returns a biased output spectrum with the same size
(lmax+1) as the input spectrum. The input spectrum is assumed to be
zero for degrees l>lmax.
'valid' returns a biased spectrum with size lmax-lwin+1. This
returns only that part of the biased spectrum that is not
influenced by the input spectrum beyond degree lmax. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindow.plot_coupling_matrix | 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, ax, fname])
Parameters
----------
lmax : int
Spherical harmonic bandwidth of the global power spectrum.
nwin : int, optional, default = x.nwin
Number of tapers used in the mutlitaper spectral analysis.
weights : ndarray, optional, default = x.weights
Taper weights used with the multitaper spectral analyses.
mode : str, opitonal, default = 'full'
'full' returns a biased output spectrum of size lmax+lwin+1. The
input spectrum is assumed to be zero for degrees l>lmax.
'same' returns a biased output spectrum with the same size
(lmax+1) as the input spectrum. The input spectrum is assumed to be
zero for degrees l>lmax.
'valid' returns a biased spectrum with size lmax-lwin+1. This
returns only that part of the biased spectrum that is not
influenced by the input spectrum beyond degree lmax.
axes_labelsize : int, optional, default = None
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = None
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
ax : matplotlib axes object, optional, default = None
An array of matplotlib axes objects where the plots will appear.
fname : str, optional, default = None
If present, save the image to the specified file.
"""
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']
if ax is None:
fig = _plt.figure(figsize=figsize)
axes = fig.add_subplot(111)
else:
axes = ax
axes.imshow(self.coupling_matrix(lmax, nwin=nwin, weights=weights,
mode=mode), aspect='auto')
axes.set_xlabel('Input power', fontsize=axes_labelsize)
axes.set_ylabel('Output power', fontsize=axes_labelsize)
axes.tick_params(labelsize=tick_labelsize)
axes.minorticks_on()
if ax is None:
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes | python | 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, ax, fname])
Parameters
----------
lmax : int
Spherical harmonic bandwidth of the global power spectrum.
nwin : int, optional, default = x.nwin
Number of tapers used in the mutlitaper spectral analysis.
weights : ndarray, optional, default = x.weights
Taper weights used with the multitaper spectral analyses.
mode : str, opitonal, default = 'full'
'full' returns a biased output spectrum of size lmax+lwin+1. The
input spectrum is assumed to be zero for degrees l>lmax.
'same' returns a biased output spectrum with the same size
(lmax+1) as the input spectrum. The input spectrum is assumed to be
zero for degrees l>lmax.
'valid' returns a biased spectrum with size lmax-lwin+1. This
returns only that part of the biased spectrum that is not
influenced by the input spectrum beyond degree lmax.
axes_labelsize : int, optional, default = None
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = None
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
ax : matplotlib axes object, optional, default = None
An array of matplotlib axes objects where the plots will appear.
fname : str, optional, default = None
If present, save the image to the specified file.
"""
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']
if ax is None:
fig = _plt.figure(figsize=figsize)
axes = fig.add_subplot(111)
else:
axes = ax
axes.imshow(self.coupling_matrix(lmax, nwin=nwin, weights=weights,
mode=mode), aspect='auto')
axes.set_xlabel('Input power', fontsize=axes_labelsize)
axes.set_ylabel('Output power', fontsize=axes_labelsize)
axes.tick_params(labelsize=tick_labelsize)
axes.minorticks_on()
if ax is None:
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes | [
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x.plot_coupling_matrix(lmax, [nwin, weights, mode, axes_labelsize,
tick_labelsize, show, ax, fname])
Parameters
----------
lmax : int
Spherical harmonic bandwidth of the global power spectrum.
nwin : int, optional, default = x.nwin
Number of tapers used in the mutlitaper spectral analysis.
weights : ndarray, optional, default = x.weights
Taper weights used with the multitaper spectral analyses.
mode : str, opitonal, default = 'full'
'full' returns a biased output spectrum of size lmax+lwin+1. The
input spectrum is assumed to be zero for degrees l>lmax.
'same' returns a biased output spectrum with the same size
(lmax+1) as the input spectrum. The input spectrum is assumed to be
zero for degrees l>lmax.
'valid' returns a biased spectrum with size lmax-lwin+1. This
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axes_labelsize : int, optional, default = None
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = None
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
ax : matplotlib axes object, optional, default = None
An array of matplotlib axes objects where the plots will appear.
fname : str, optional, default = None
If present, save the image to the specified file. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shwindow.py#L1020-L1089 | train | 203,842 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindowCap._taper2coeffs | def _taper2coeffs(self, itaper):
"""
Return the spherical harmonic coefficients of the unrotated taper i
as an array, where i = 0 is the best concentrated.
"""
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 | python | def _taper2coeffs(self, itaper):
"""
Return the spherical harmonic coefficients of the unrotated taper i
as an array, where i = 0 is the best concentrated.
"""
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 | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shwindow.py | SHWindowCap.rotate | 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 : float
Latitude and longitude of the center of the rotated spherical-cap
localization windows (default in degrees).
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
nwinrot : int, optional, default = (lwin+1)**2
The number of best concentrated windows to rotate, where lwin is
the spherical harmonic bandwidth of the localization windows.
Description
-----------
This function will take the spherical-cap localization windows
centered at the North pole (and saved in the attributes tapers and
orders), rotate each function to the coordinate (clat, clon), and save
the spherical harmonic coefficients in the attribute coeffs. Each
column of coeffs contains a single window, and the coefficients are
ordered according to the convention in SHCilmToVector.
"""
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:
angles = _np.radians(_np.array([0., -(90. - clat), -clon]))
else:
angles = _np.array([0., -(_np.pi/2. - clat), -clon])
if dj_matrix is None:
if self.dj_matrix is None:
self.dj_matrix = _shtools.djpi2(self.lwin + 1)
dj_matrix = self.dj_matrix
else:
dj_matrix = self.dj_matrix
if ((coord_degrees is True and clat == 90. and clon == 0.) or
(coord_degrees is False and clat == _np.pi/2. and clon == 0.)):
for i in range(self.nwinrot):
coeffs = self._taper2coeffs(i)
self.coeffs[:, i] = _shtools.SHCilmToVector(coeffs)
else:
coeffs = _shtools.SHRotateTapers(self.tapers, self.orders,
self.nwinrot, angles, dj_matrix)
self.coeffs = coeffs | python | 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 : float
Latitude and longitude of the center of the rotated spherical-cap
localization windows (default in degrees).
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
nwinrot : int, optional, default = (lwin+1)**2
The number of best concentrated windows to rotate, where lwin is
the spherical harmonic bandwidth of the localization windows.
Description
-----------
This function will take the spherical-cap localization windows
centered at the North pole (and saved in the attributes tapers and
orders), rotate each function to the coordinate (clat, clon), and save
the spherical harmonic coefficients in the attribute coeffs. Each
column of coeffs contains a single window, and the coefficients are
ordered according to the convention in SHCilmToVector.
"""
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:
angles = _np.radians(_np.array([0., -(90. - clat), -clon]))
else:
angles = _np.array([0., -(_np.pi/2. - clat), -clon])
if dj_matrix is None:
if self.dj_matrix is None:
self.dj_matrix = _shtools.djpi2(self.lwin + 1)
dj_matrix = self.dj_matrix
else:
dj_matrix = self.dj_matrix
if ((coord_degrees is True and clat == 90. and clon == 0.) or
(coord_degrees is False and clat == _np.pi/2. and clon == 0.)):
for i in range(self.nwinrot):
coeffs = self._taper2coeffs(i)
self.coeffs[:, i] = _shtools.SHCilmToVector(coeffs)
else:
coeffs = _shtools.SHRotateTapers(self.tapers, self.orders,
self.nwinrot, angles, dj_matrix)
self.coeffs = coeffs | [
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Usage
-----
x.rotate(clat, clon [coord_degrees, dj_matrix, nwinrot])
Parameters
----------
clat, clon : float
Latitude and longitude of the center of the rotated spherical-cap
localization windows (default in degrees).
coord_degrees : bool, optional, default = True
True if clat and clon are in degrees.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
nwinrot : int, optional, default = (lwin+1)**2
The number of best concentrated windows to rotate, where lwin is
the spherical harmonic bandwidth of the localization windows.
Description
-----------
This function will take the spherical-cap localization windows
centered at the North pole (and saved in the attributes tapers and
orders), rotate each function to the coordinate (clat, clon), and save
the spherical harmonic coefficients in the attribute coeffs. Each
column of coeffs contains a single window, and the coefficients are
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vxx | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = False
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xx}$'
Text label for the colorbar..
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vxx.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = False
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xx}$'
Text label for the colorbar..
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vxx.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = False
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xx}$'
Text label for the colorbar..
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vyy | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vyy.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vyy.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vzz | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vzz.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vzz.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vxy | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vxy.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vxy.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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"Vxy",
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"."
] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L285-L337 | train | 203,848 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vyx | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yx}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vyx.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yx}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vyx.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yx}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L339-L391 | train | 203,849 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vxz | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vxz.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vxz.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{xz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L393-L445 | train | 203,850 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vzx | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zx}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vzx.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zx}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vzx.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zx}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vyz | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vyz.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vyz.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{yz}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_vzy | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vzy.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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 to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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 show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.vzy.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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x.plot_vzy([tick_interval, xlabel, ylabel, ax, colorbar,
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Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$V_{zy}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_invar | 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
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(2, 2, figsize=figsize)
self.plot_i0(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_i1(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_i2(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_i(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[3], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | python | 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
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(2, 2, figsize=figsize)
self.plot_i0(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_i1(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_i2(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_i(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[3], tick_interval=tick_interval,
xlabel=xlabel, ylabel=ylabel,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | [
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Usage
-----
x.plot_invar([tick_interval, minor_tick_interval, xlabel, ylabel,
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tick_labelsize, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eig1 | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_1$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eig1.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_1$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eig1.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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x.plot_eig1([tick_interval, xlabel, ylabel, ax, colorbar,
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Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_1$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eig2 | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_2$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eig2.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_2$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eig2.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_2$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L1132-L1188 | train | 203,856 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eig3 | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_3$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eig3.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_3$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eig3.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_3$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eigs | 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
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(3, 1, figsize=figsize)
self.plot_eig1(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eig2(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eig3(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | python | 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
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(3, 1, figsize=figsize)
self.plot_eig1(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eig2(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eig3(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
axes_labelsize=axes_labelsize,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | [
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Usage
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x.plot_eigs([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eigh1 | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{h1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eigh1.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{h1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eigh1.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{h1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L1336-L1392 | train | 203,859 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eigh2 | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{h2}$, Eotvos$^{-1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eigh2.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{h2}$, Eotvos$^{-1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eigh2.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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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]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{h2}$, Eotvos$^{-1}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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"."
] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L1394-L1450 | train | 203,860 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eighh | 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, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{hh}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eighh.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | python | 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, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{hh}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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,
cb_label=cb_label, show=False,
**kwargs)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, axes
else:
self.eighh.plot(colorbar=colorbar, cb_orientation=cb_orientation,
cb_label=cb_label, ax=ax, **kwargs) | [
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Usage
-----
x.plot_eighh([tick_interval, xlabel, ylabel, ax, colorbar,
cb_orientation, cb_label, show, fname])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = '$\lambda_{hh}$'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shtensor.py#L1452-L1508 | train | 203,861 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shtensor.py | Tensor.plot_eigh | 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, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(3, 1, figsize=figsize)
self.plot_eigh1(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eigh2(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eighh(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | python | 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, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
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)
fig, ax = _plt.subplots(3, 1, figsize=figsize)
self.plot_eigh1(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[0], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eigh2(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[1], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
self.plot_eighh(colorbar=colorbar, cb_orientation=cb_orientation,
ax=ax.flat[2], xlabel=xlabel, ylabel=ylabel,
tick_interval=tick_interval,
tick_labelsize=tick_labelsize,
minor_tick_interval=minor_tick_interval,
**kwargs)
fig.tight_layout(pad=0.5)
if show:
fig.show()
if fname is not None:
fig.savefig(fname)
return fig, ax | [
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Usage
-----
x.plot_eigh([tick_interval, minor_tick_interval, xlabel, ylabel,
colorbar, cb_orientation, cb_label, axes_labelsize,
tick_labelsize, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [60, 60]
Intervals to use when plotting the major x and y ticks. If set to
None, major ticks will not be plotted.
minor_tick_interval : list or tuple, optional, default = [20, 20]
Intervals to use when plotting the minor x and y ticks. If set to
None, minor ticks will not be plotted.
xlabel : str, optional, default = 'Longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'Latitude'
Label for the latitude axis.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = None
Text label for the colorbar.
axes_labelsize : int, optional, default = 9
The font size for the x and y axes labels.
tick_labelsize : int, optional, default = 8
The font size for the x and y tick labels.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods. | [
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SHTOOLS/SHTOOLS | setup.py | get_version | 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
"""
d = os.path.dirname(__file__)
# get release number from VERSION
with open(os.path.join(d, 'VERSION')) as f:
vre = re.compile('.Version: (.+)$', re.M)
version = vre.search(f.read()).group(1)
if os.path.isdir(os.path.join(d, '.git')):
# Get the version using "git describe".
cmd = 'git describe --tags'
try:
git_version = check_output(cmd.split()).decode().strip()[1:]
except CalledProcessError:
print('Unable to get version number from git tags\n'
'Setting to x.x')
git_version = 'x.x'
# PEP440 compatibility
if '-' in git_version:
git_revision = check_output(['git', 'rev-parse', 'HEAD'])
git_revision = git_revision.strip().decode('ascii')
# add post0 if the version is released
# otherwise add dev0 if the version is not yet released
if ISRELEASED:
version += '.post0+' + git_revision[:7]
else:
version += '.dev0+' + git_revision[:7]
return version | python | def get_version():
"""Get version from git and VERSION file.
In the case where the version is not tagged in git, this function appends
.post0+commit if the version has been released and .dev0+commit if the
version has not yet been released.
Derived from: https://github.com/Changaco/version.py
"""
d = os.path.dirname(__file__)
# get release number from VERSION
with open(os.path.join(d, 'VERSION')) as f:
vre = re.compile('.Version: (.+)$', re.M)
version = vre.search(f.read()).group(1)
if os.path.isdir(os.path.join(d, '.git')):
# Get the version using "git describe".
cmd = 'git describe --tags'
try:
git_version = check_output(cmd.split()).decode().strip()[1:]
except CalledProcessError:
print('Unable to get version number from git tags\n'
'Setting to x.x')
git_version = 'x.x'
# PEP440 compatibility
if '-' in git_version:
git_revision = check_output(['git', 'rev-parse', 'HEAD'])
git_revision = git_revision.strip().decode('ascii')
# add post0 if the version is released
# otherwise add dev0 if the version is not yet released
if ISRELEASED:
version += '.post0+' + git_revision[:7]
else:
version += '.dev0+' + git_revision[:7]
return version | [
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SHTOOLS/SHTOOLS | setup.py | get_compiler_flags | def get_compiler_flags():
"""Set fortran flags depending on the compiler."""
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 = ['-m64', '-fpp', '-free', '-O3', '-Tf']
elif compiler == 'g95':
flags = ['-O3', '-fno-second-underscore']
elif compiler == 'pg':
flags = ['-fast']
else:
flags = ['-m64', '-O3']
return flags | python | def get_compiler_flags():
"""Set fortran flags depending on the compiler."""
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 = ['-m64', '-fpp', '-free', '-O3', '-Tf']
elif compiler == 'g95':
flags = ['-O3', '-fno-second-underscore']
elif compiler == 'pg':
flags = ['-fast']
else:
flags = ['-m64', '-O3']
return flags | [
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SHTOOLS/SHTOOLS | setup.py | configuration | def configuration(parent_package='', top_path=None):
"""Configure all packages that need to be built."""
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.FCompiler doesn't support .F95 extension
compiler = FCompiler(get_default_fcompiler())
compiler.src_extensions.append('.F95')
compiler.language_map['.F95'] = 'f90'
# collect all Fortran sources
files = os.listdir('src')
exclude_sources = ['PlanetsConstants.f95', 'PythonWrapper.f95']
sources = [os.path.join('src', file) for file in files if
file.lower().endswith(('.f95', '.c')) and file not in
exclude_sources]
# (from http://stackoverflow.com/questions/14320220/
# testing-python-c-libraries-get-build-path)):
build_lib_dir = "{dirname}.{platform}-{version[0]}.{version[1]}"
dirparams = {'dirname': 'temp',
'platform': sysconfig.get_platform(),
'version': sys.version_info}
libdir = os.path.join('build', build_lib_dir.format(**dirparams))
print('searching SHTOOLS in:', libdir)
# Fortran compilation
config.add_library('SHTOOLS',
sources=sources,
**kwargs)
# SHTOOLS
kwargs['libraries'].extend(['SHTOOLS'])
kwargs['include_dirs'].extend([libdir])
kwargs['library_dirs'].extend([libdir])
# FFTW info
fftw_info = get_info('fftw', notfound_action=2)
dict_append(kwargs, **fftw_info)
if sys.platform != 'win32':
kwargs['libraries'].extend(['m'])
# BLAS / Lapack info
lapack_info = get_info('lapack_opt', notfound_action=2)
blas_info = get_info('blas_opt', notfound_action=2)
dict_append(kwargs, **blas_info)
dict_append(kwargs, **lapack_info)
config.add_extension('pyshtools._SHTOOLS',
sources=['src/pyshtools.pyf',
'src/PythonWrapper.f95'],
**kwargs)
return config | python | def configuration(parent_package='', top_path=None):
"""Configure all packages that need to be built."""
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.FCompiler doesn't support .F95 extension
compiler = FCompiler(get_default_fcompiler())
compiler.src_extensions.append('.F95')
compiler.language_map['.F95'] = 'f90'
# collect all Fortran sources
files = os.listdir('src')
exclude_sources = ['PlanetsConstants.f95', 'PythonWrapper.f95']
sources = [os.path.join('src', file) for file in files if
file.lower().endswith(('.f95', '.c')) and file not in
exclude_sources]
# (from http://stackoverflow.com/questions/14320220/
# testing-python-c-libraries-get-build-path)):
build_lib_dir = "{dirname}.{platform}-{version[0]}.{version[1]}"
dirparams = {'dirname': 'temp',
'platform': sysconfig.get_platform(),
'version': sys.version_info}
libdir = os.path.join('build', build_lib_dir.format(**dirparams))
print('searching SHTOOLS in:', libdir)
# Fortran compilation
config.add_library('SHTOOLS',
sources=sources,
**kwargs)
# SHTOOLS
kwargs['libraries'].extend(['SHTOOLS'])
kwargs['include_dirs'].extend([libdir])
kwargs['library_dirs'].extend([libdir])
# FFTW info
fftw_info = get_info('fftw', notfound_action=2)
dict_append(kwargs, **fftw_info)
if sys.platform != 'win32':
kwargs['libraries'].extend(['m'])
# BLAS / Lapack info
lapack_info = get_info('lapack_opt', notfound_action=2)
blas_info = get_info('blas_opt', notfound_action=2)
dict_append(kwargs, **blas_info)
dict_append(kwargs, **lapack_info)
config.add_extension('pyshtools._SHTOOLS',
sources=['src/pyshtools.pyf',
'src/PythonWrapper.f95'],
**kwargs)
return config | [
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SHTOOLS/SHTOOLS | pyshtools/shio/icgem.py | _time_variable_part | 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).
"""
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 / period * delta_t)
elif trifunc == 'asin':
periodic_sum += coeffs * _np.sin(2 * _np.pi / period * delta_t)
return trend + periodic_sum | python | 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).
"""
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 / period * delta_t)
elif trifunc == 'asin':
periodic_sum += coeffs * _np.sin(2 * _np.pi / period * delta_t)
return trend + periodic_sum | [
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SHTOOLS/SHTOOLS | src/create_wrapper.py | modify_subroutine | def modify_subroutine(subroutine):
"""loops through variables of a subroutine and modifies them"""
# 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 return variables with 'py'
if varname == subroutine['name']:
subroutine['vars']['py' + varname] = subroutine['vars'].pop(varname)
varname = 'py' + varname
# print('prefix added:',varname)
#-- change assumed to explicit:
if has_assumed_shape(varattribs):
make_explicit(subroutine, varname, varattribs)
# print('assumed shape variable modified to:',varname,varattribs['dimension'])
#-- add py prefix to subroutine:
subroutine['name'] = 'py' + subroutine['name'] | python | def modify_subroutine(subroutine):
"""loops through variables of a subroutine and modifies them"""
# 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 return variables with 'py'
if varname == subroutine['name']:
subroutine['vars']['py' + varname] = subroutine['vars'].pop(varname)
varname = 'py' + varname
# print('prefix added:',varname)
#-- change assumed to explicit:
if has_assumed_shape(varattribs):
make_explicit(subroutine, varname, varattribs)
# print('assumed shape variable modified to:',varname,varattribs['dimension'])
#-- add py prefix to subroutine:
subroutine['name'] = 'py' + subroutine['name'] | [
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SHTOOLS/SHTOOLS | pyshtools/utils/figstyle.py | figstyle | 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.
screen_dpi : int, optional, default = 114
The screen resolution of the display in dpi, which determines the
size of the plot on the display.
aspect_ratio : float, optional, default = 4/3
The aspect ratio of the plot.
max_width : float, optional, default = 7.48031
The maximum width of the usable area of a journal page in inches.
Description
-----------
This function sets a variety of matplotlib parameters for creating
publication quality graphics. The default parameters are tailored to
AGU/Wiley-Blackwell journals that accept relative widths of 0.5, 0.75,
and 1. To reset the maplotlib parameters to their default values, use
matplotlib.pyplot.style.use('default')
"""
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',
'Verdana', 'Arial', 'Helvetica'],
'axes.titlesize': 10,
'axes.labelsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'legend.fontsize': 9,
'text.usetex': False,
'axes.formatter.limits': (-3, 3),
# figure
'figure.dpi': screen_dpi,
'figure.figsize': (width_x, width_y),
# line and tick widths
'axes.linewidth': 1,
'lines.linewidth': 1.5,
'xtick.major.width': 0.6,
'ytick.major.width': 0.6,
'xtick.minor.width': 0.6,
'xtick.minor.width': 0.6,
'xtick.top': True,
'ytick.right': True,
# grids
'grid.linewidth': 0.3,
'grid.color': 'k',
'grid.linestyle': '-',
# legends
'legend.framealpha': 1.,
'legend.edgecolor': 'k',
# images
'image.lut': 65536, # 16 bit
# savefig
'savefig.bbox': 'tight',
'savefig.pad_inches': 0.02,
'savefig.dpi': 600,
'savefig.format': 'pdf'
}
_plt.style.use([shtools]) | python | 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.
screen_dpi : int, optional, default = 114
The screen resolution of the display in dpi, which determines the
size of the plot on the display.
aspect_ratio : float, optional, default = 4/3
The aspect ratio of the plot.
max_width : float, optional, default = 7.48031
The maximum width of the usable area of a journal page in inches.
Description
-----------
This function sets a variety of matplotlib parameters for creating
publication quality graphics. The default parameters are tailored to
AGU/Wiley-Blackwell journals that accept relative widths of 0.5, 0.75,
and 1. To reset the maplotlib parameters to their default values, use
matplotlib.pyplot.style.use('default')
"""
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',
'Verdana', 'Arial', 'Helvetica'],
'axes.titlesize': 10,
'axes.labelsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'legend.fontsize': 9,
'text.usetex': False,
'axes.formatter.limits': (-3, 3),
# figure
'figure.dpi': screen_dpi,
'figure.figsize': (width_x, width_y),
# line and tick widths
'axes.linewidth': 1,
'lines.linewidth': 1.5,
'xtick.major.width': 0.6,
'ytick.major.width': 0.6,
'xtick.minor.width': 0.6,
'xtick.minor.width': 0.6,
'xtick.top': True,
'ytick.right': True,
# grids
'grid.linewidth': 0.3,
'grid.color': 'k',
'grid.linestyle': '-',
# legends
'legend.framealpha': 1.,
'legend.edgecolor': 'k',
# images
'image.lut': 65536, # 16 bit
# savefig
'savefig.bbox': 'tight',
'savefig.pad_inches': 0.02,
'savefig.dpi': 600,
'savefig.format': 'pdf'
}
_plt.style.use([shtools]) | [
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Usage
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Parameters
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rel_width : float, optional, default = 0.75
The relative width of the plot (from 0 to 1) wih respect to max_width.
screen_dpi : int, optional, default = 114
The screen resolution of the display in dpi, which determines the
size of the plot on the display.
aspect_ratio : float, optional, default = 4/3
The aspect ratio of the plot.
max_width : float, optional, default = 7.48031
The maximum width of the usable area of a journal page in inches.
Description
-----------
This function sets a variety of matplotlib parameters for creating
publication quality graphics. The default parameters are tailored to
AGU/Wiley-Blackwell journals that accept relative widths of 0.5, 0.75,
and 1. To reset the maplotlib parameters to their default values, use
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.from_zeros | 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
The highest spherical harmonic degree l of the coefficients.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
kind : str, optional, default = 'real'
'real' or 'complex' spherical harmonic coefficients.
"""
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'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
nl = lmax + 1
if kind.lower() == 'real':
coeffs = _np.zeros((2, nl, nl))
else:
coeffs = _np.zeros((2, nl, nl), dtype=complex)
for cls in self.__subclasses__():
if cls.istype(kind):
return cls(coeffs, normalization=normalization.lower(),
csphase=csphase) | python | 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
The highest spherical harmonic degree l of the coefficients.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
kind : str, optional, default = 'real'
'real' or 'complex' spherical harmonic coefficients.
"""
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'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients " +
"are stable only for degrees less than or equal " +
"to 85. lmax for the coefficients will be set to " +
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
nl = lmax + 1
if kind.lower() == 'real':
coeffs = _np.zeros((2, nl, nl))
else:
coeffs = _np.zeros((2, nl, nl), dtype=complex)
for cls in self.__subclasses__():
if cls.istype(kind):
return cls(coeffs, normalization=normalization.lower(),
csphase=csphase) | [
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Returns
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Parameters
----------
lmax : int
The highest spherical harmonic degree l of the coefficients.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized
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csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
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kind : str, optional, default = 'real'
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.to_file | 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, default = 'shtools'
'shtools' or 'npy'. See method from_file() for more information.
header : str, optional, default = None
A header string written to an 'shtools'-formatted file directly
before the spherical harmonic coefficients.
**kwargs : keyword argument list, optional for format = 'npy'
Keyword arguments of numpy.save().
Description
-----------
If format='shtools', the coefficients will be written to an ascii
formatted file. The first line of the file is an optional user provided
header line, and the spherical harmonic coefficients are then listed,
with increasing degree and order, with the format
l, m, coeffs[0, l, m], coeffs[1, l, m]
where l and m are the spherical harmonic degree and order,
respectively.
If format='npy', the spherical harmonic coefficients will be saved to
a binary numpy 'npy' file using numpy.save().
"""
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}, {:.16e}\n'
.format(l, m, self.coeffs[0, l, m],
self.coeffs[1, l, m]))
elif format is 'npy':
_np.save(filename, self.coeffs, **kwargs)
else:
raise NotImplementedError(
'format={:s} not implemented'.format(repr(format))) | python | 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, default = 'shtools'
'shtools' or 'npy'. See method from_file() for more information.
header : str, optional, default = None
A header string written to an 'shtools'-formatted file directly
before the spherical harmonic coefficients.
**kwargs : keyword argument list, optional for format = 'npy'
Keyword arguments of numpy.save().
Description
-----------
If format='shtools', the coefficients will be written to an ascii
formatted file. The first line of the file is an optional user provided
header line, and the spherical harmonic coefficients are then listed,
with increasing degree and order, with the format
l, m, coeffs[0, l, m], coeffs[1, l, m]
where l and m are the spherical harmonic degree and order,
respectively.
If format='npy', the spherical harmonic coefficients will be saved to
a binary numpy 'npy' file using numpy.save().
"""
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}, {:.16e}\n'
.format(l, m, self.coeffs[0, l, m],
self.coeffs[1, l, m]))
elif format is 'npy':
_np.save(filename, self.coeffs, **kwargs)
else:
raise NotImplementedError(
'format={:s} not implemented'.format(repr(format))) | [
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Parameters
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filename : str
Name of the output file.
format : str, optional, default = 'shtools'
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header : str, optional, default = None
A header string written to an 'shtools'-formatted file directly
before the spherical harmonic coefficients.
**kwargs : keyword argument list, optional for format = 'npy'
Keyword arguments of numpy.save().
Description
-----------
If format='shtools', the coefficients will be written to an ascii
formatted file. The first line of the file is an optional user provided
header line, and the spherical harmonic coefficients are then listed,
with increasing degree and order, with the format
l, m, coeffs[0, l, m], coeffs[1, l, m]
where l and m are the spherical harmonic degree and order,
respectively.
If format='npy', the spherical harmonic coefficients will be saved to
a binary numpy 'npy' file using numpy.save(). | [
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"a",
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"."
] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shcoeffsgrid.py#L543-L592 | train | 203,870 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.to_array | 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
----------
normalization : str, optional, default = x.normalization
Normalization of the output coefficients: '4pi', 'ortho',
'schmidt', or 'unnorm' for geodesy 4pi normalized, orthonormalized,
Schmidt semi-normalized, or unnormalized coefficients,
respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output. If lmax is greater
than x.lmax, the array will be zero padded.
Description
-----------
This method will return an array of the spherical harmonic coefficients
using a different normalization and Condon-Shortley phase convention,
and a different maximum spherical harmonic degree. If the maximum
degree is smaller than the maximum degree of the class instance, the
coefficients will be truncated. Conversely, if this degree is larger
than the maximum degree of the class instance, the output array will be
zero padded.
"""
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,
normalization_out=normalization,
csphase_in=self.csphase, csphase_out=csphase,
lmax=lmax)
return coeffs | python | 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
----------
normalization : str, optional, default = x.normalization
Normalization of the output coefficients: '4pi', 'ortho',
'schmidt', or 'unnorm' for geodesy 4pi normalized, orthonormalized,
Schmidt semi-normalized, or unnormalized coefficients,
respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output. If lmax is greater
than x.lmax, the array will be zero padded.
Description
-----------
This method will return an array of the spherical harmonic coefficients
using a different normalization and Condon-Shortley phase convention,
and a different maximum spherical harmonic degree. If the maximum
degree is smaller than the maximum degree of the class instance, the
coefficients will be truncated. Conversely, if this degree is larger
than the maximum degree of the class instance, the output array will be
zero padded.
"""
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,
normalization_out=normalization,
csphase_in=self.csphase, csphase_out=csphase,
lmax=lmax)
return coeffs | [
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Usage
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coeffs = x.to_array([normalization, csphase, lmax])
Returns
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coeffs : ndarry, shape (2, lmax+1, lmax+1)
numpy ndarray of the spherical harmonic coefficients.
Parameters
----------
normalization : str, optional, default = x.normalization
Normalization of the output coefficients: '4pi', 'ortho',
'schmidt', or 'unnorm' for geodesy 4pi normalized, orthonormalized,
Schmidt semi-normalized, or unnormalized coefficients,
respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output. If lmax is greater
than x.lmax, the array will be zero padded.
Description
-----------
This method will return an array of the spherical harmonic coefficients
using a different normalization and Condon-Shortley phase convention,
and a different maximum spherical harmonic degree. If the maximum
degree is smaller than the maximum degree of the class instance, the
coefficients will be truncated. Conversely, if this degree is larger
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.volume | 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, default = x.lmax
The maximum spherical harmonic degree to use when calculating the
volume.
Description
-----------
If the function is the real shape of an object, this method will
calculate the volume of the body exactly by integration. This routine
raises the function to the nth power, with n from 1 to 3, and
calculates the spherical harmonic degree and order 0 term. To avoid
aliases, the function is first expand on a grid that can resolve
spherical harmonic degrees up to 3*lmax.
"""
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 the object can not be calculated '
'for complex functions.')
if lmax is None:
lmax = self.lmax
r0 = self.coeffs[0, 0, 0]
grid = self.expand(lmax=3*lmax) - r0
h200 = (grid**2).expand(lmax_calc=0).coeffs[0, 0, 0]
h300 = (grid**3).expand(lmax_calc=0).coeffs[0, 0, 0]
volume = 4 * _np.pi / 3 * (h300 + 3 * r0 * h200 + r0**3)
return volume | python | 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, default = x.lmax
The maximum spherical harmonic degree to use when calculating the
volume.
Description
-----------
If the function is the real shape of an object, this method will
calculate the volume of the body exactly by integration. This routine
raises the function to the nth power, with n from 1 to 3, and
calculates the spherical harmonic degree and order 0 term. To avoid
aliases, the function is first expand on a grid that can resolve
spherical harmonic degrees up to 3*lmax.
"""
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 the object can not be calculated '
'for complex functions.')
if lmax is None:
lmax = self.lmax
r0 = self.coeffs[0, 0, 0]
grid = self.expand(lmax=3*lmax) - r0
h200 = (grid**2).expand(lmax_calc=0).coeffs[0, 0, 0]
h300 = (grid**3).expand(lmax_calc=0).coeffs[0, 0, 0]
volume = 4 * _np.pi / 3 * (h300 + 3 * r0 * h200 + r0**3)
return volume | [
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Usage
-----
volume = x.volume([lmax])
Returns
-------
volume : float
The volume of the object.
Parameters
----------
lmax : int, optional, default = x.lmax
The maximum spherical harmonic degree to use when calculating the
volume.
Description
-----------
If the function is the real shape of an object, this method will
calculate the volume of the body exactly by integration. This routine
raises the function to the nth power, with n from 1 to 3, and
calculates the spherical harmonic degree and order 0 term. To avoid
aliases, the function is first expand on a grid that can resolve
spherical harmonic degrees up to 3*lmax. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.rotate | 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])
Returns
-------
x_rotated : SHCoeffs class instance
Parameters
----------
alpha, beta, gamma : float
The three Euler rotation angles in degrees.
degrees : bool, optional, default = True
True if the Euler angles are in degrees, False if they are in
radians.
convention : str, optional, default = 'y'
The convention used for the rotation of the second angle, which
can be either 'x' or 'y' for a rotation about the x or y axes,
respectively.
body : bool, optional, default = False
If true, rotate the physical body and not the coordinate system.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
Description
-----------
This method will take the spherical harmonic coefficients of a
function, rotate the coordinate frame by the three Euler anlges, and
output the spherical harmonic coefficients of the new function. If
the optional parameter body is set to True, then the physical body will
be rotated instead of the coordinate system.
The rotation of a coordinate system or body can be viewed in two
complementary ways involving three successive rotations. Both methods
have the same initial and final configurations, and the angles listed
in both schemes are the same.
Scheme A:
(I) Rotation about the z axis by alpha.
(II) Rotation about the new y axis by beta.
(III) Rotation about the new z axis by gamma.
Scheme B:
(I) Rotation about the z axis by gamma.
(II) Rotation about the initial y axis by beta.
(III) Rotation about the initial z axis by alpha.
Here, the 'y convention' is employed, where the second rotation is with
respect to the y axis. When using the 'x convention', the second
rotation is instead with respect to the x axis. The relation between
the Euler angles in the x and y conventions is given by
alpha_y=alpha_x-pi/2, beta_y=beta_x, and gamma_y=gamma_x+pi/2.
To perform the inverse transform associated with the three angles
(alpha, beta, gamma), one would perform an additional rotation using
the angles (-gamma, -beta, -alpha).
The rotations can be viewed either as a rotation of the coordinate
system or the physical body. To rotate the physical body without
rotation of the coordinate system, set the optional parameter body to
True. This rotation is accomplished by performing the inverse rotation
using the angles (-gamma, -beta, -alpha).
"""
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(
"convention must be either 'x' or 'y'. " +
"Provided value was {:s}".format(repr(convention))
)
if convention is 'y':
if body is True:
angles = _np.array([-gamma, -beta, -alpha])
else:
angles = _np.array([alpha, beta, gamma])
elif convention is 'x':
if body is True:
angles = _np.array([-gamma - _np.pi/2, -beta,
-alpha + _np.pi/2])
else:
angles = _np.array([alpha - _np.pi/2, beta, gamma + _np.pi/2])
if degrees:
angles = _np.radians(angles)
if self.lmax > 1200:
_warnings.warn("The rotate() method is accurate only to about" +
" spherical harmonic degree 1200. " +
"lmax = {:d}".format(self.lmax),
category=RuntimeWarning)
rot = self._rotate(angles, dj_matrix)
return rot | python | 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])
Returns
-------
x_rotated : SHCoeffs class instance
Parameters
----------
alpha, beta, gamma : float
The three Euler rotation angles in degrees.
degrees : bool, optional, default = True
True if the Euler angles are in degrees, False if they are in
radians.
convention : str, optional, default = 'y'
The convention used for the rotation of the second angle, which
can be either 'x' or 'y' for a rotation about the x or y axes,
respectively.
body : bool, optional, default = False
If true, rotate the physical body and not the coordinate system.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
Description
-----------
This method will take the spherical harmonic coefficients of a
function, rotate the coordinate frame by the three Euler anlges, and
output the spherical harmonic coefficients of the new function. If
the optional parameter body is set to True, then the physical body will
be rotated instead of the coordinate system.
The rotation of a coordinate system or body can be viewed in two
complementary ways involving three successive rotations. Both methods
have the same initial and final configurations, and the angles listed
in both schemes are the same.
Scheme A:
(I) Rotation about the z axis by alpha.
(II) Rotation about the new y axis by beta.
(III) Rotation about the new z axis by gamma.
Scheme B:
(I) Rotation about the z axis by gamma.
(II) Rotation about the initial y axis by beta.
(III) Rotation about the initial z axis by alpha.
Here, the 'y convention' is employed, where the second rotation is with
respect to the y axis. When using the 'x convention', the second
rotation is instead with respect to the x axis. The relation between
the Euler angles in the x and y conventions is given by
alpha_y=alpha_x-pi/2, beta_y=beta_x, and gamma_y=gamma_x+pi/2.
To perform the inverse transform associated with the three angles
(alpha, beta, gamma), one would perform an additional rotation using
the angles (-gamma, -beta, -alpha).
The rotations can be viewed either as a rotation of the coordinate
system or the physical body. To rotate the physical body without
rotation of the coordinate system, set the optional parameter body to
True. This rotation is accomplished by performing the inverse rotation
using the angles (-gamma, -beta, -alpha).
"""
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(
"convention must be either 'x' or 'y'. " +
"Provided value was {:s}".format(repr(convention))
)
if convention is 'y':
if body is True:
angles = _np.array([-gamma, -beta, -alpha])
else:
angles = _np.array([alpha, beta, gamma])
elif convention is 'x':
if body is True:
angles = _np.array([-gamma - _np.pi/2, -beta,
-alpha + _np.pi/2])
else:
angles = _np.array([alpha - _np.pi/2, beta, gamma + _np.pi/2])
if degrees:
angles = _np.radians(angles)
if self.lmax > 1200:
_warnings.warn("The rotate() method is accurate only to about" +
" spherical harmonic degree 1200. " +
"lmax = {:d}".format(self.lmax),
category=RuntimeWarning)
rot = self._rotate(angles, dj_matrix)
return rot | [
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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])
Returns
-------
x_rotated : SHCoeffs class instance
Parameters
----------
alpha, beta, gamma : float
The three Euler rotation angles in degrees.
degrees : bool, optional, default = True
True if the Euler angles are in degrees, False if they are in
radians.
convention : str, optional, default = 'y'
The convention used for the rotation of the second angle, which
can be either 'x' or 'y' for a rotation about the x or y axes,
respectively.
body : bool, optional, default = False
If true, rotate the physical body and not the coordinate system.
dj_matrix : ndarray, optional, default = None
The djpi2 rotation matrix computed by a call to djpi2.
Description
-----------
This method will take the spherical harmonic coefficients of a
function, rotate the coordinate frame by the three Euler anlges, and
output the spherical harmonic coefficients of the new function. If
the optional parameter body is set to True, then the physical body will
be rotated instead of the coordinate system.
The rotation of a coordinate system or body can be viewed in two
complementary ways involving three successive rotations. Both methods
have the same initial and final configurations, and the angles listed
in both schemes are the same.
Scheme A:
(I) Rotation about the z axis by alpha.
(II) Rotation about the new y axis by beta.
(III) Rotation about the new z axis by gamma.
Scheme B:
(I) Rotation about the z axis by gamma.
(II) Rotation about the initial y axis by beta.
(III) Rotation about the initial z axis by alpha.
Here, the 'y convention' is employed, where the second rotation is with
respect to the y axis. When using the 'x convention', the second
rotation is instead with respect to the x axis. The relation between
the Euler angles in the x and y conventions is given by
alpha_y=alpha_x-pi/2, beta_y=beta_x, and gamma_y=gamma_x+pi/2.
To perform the inverse transform associated with the three angles
(alpha, beta, gamma), one would perform an additional rotation using
the angles (-gamma, -beta, -alpha).
The rotations can be viewed either as a rotation of the coordinate
system or the physical body. To rotate the physical body without
rotation of the coordinate system, set the optional parameter body to
True. This rotation is accomplished by performing the inverse rotation
using the angles (-gamma, -beta, -alpha). | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.convert | 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, optional, default = x.normalization
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention for the output class: 1 to exclude
the phase factor, or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output.
kind : str, optional, default = clm.kind
'real' or 'complex' spherical harmonic coefficients for the output
class.
check : bool, optional, default = True
When converting complex coefficients to real coefficients, if True,
check if function is entirely real.
Description
-----------
This method will return a new class instance of the spherical
harmonic coefficients using a different normalization and
Condon-Shortley phase convention. The coefficients can be converted
between real and complex form, and a different maximum spherical
harmonic degree of the output coefficients can be specified. If this
maximum degree is smaller than the maximum degree of the original
class, the coefficients will be truncated. Conversely, if this degree
is larger than the maximum degree of the original class, the
coefficients of the new class will be zero padded.
"""
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(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', 'unnorm'):
raise ValueError(
"normalization must be '4pi', 'ortho', 'schmidt', or " +
"'unnorm'. Provided value was {:s}"
.format(repr(normalization)))
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase)))
if (kind != self.kind):
if (kind == 'complex'):
temp = self._make_complex()
else:
temp = self._make_real(check=check)
coeffs = temp.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
else:
coeffs = self.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
return SHCoeffs.from_array(coeffs,
normalization=normalization.lower(),
csphase=csphase, copy=False) | python | 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, optional, default = x.normalization
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention for the output class: 1 to exclude
the phase factor, or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output.
kind : str, optional, default = clm.kind
'real' or 'complex' spherical harmonic coefficients for the output
class.
check : bool, optional, default = True
When converting complex coefficients to real coefficients, if True,
check if function is entirely real.
Description
-----------
This method will return a new class instance of the spherical
harmonic coefficients using a different normalization and
Condon-Shortley phase convention. The coefficients can be converted
between real and complex form, and a different maximum spherical
harmonic degree of the output coefficients can be specified. If this
maximum degree is smaller than the maximum degree of the original
class, the coefficients will be truncated. Conversely, if this degree
is larger than the maximum degree of the original class, the
coefficients of the new class will be zero padded.
"""
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(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', 'unnorm'):
raise ValueError(
"normalization must be '4pi', 'ortho', 'schmidt', or " +
"'unnorm'. Provided value was {:s}"
.format(repr(normalization)))
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be 1 or -1. Input value was {:s}"
.format(repr(csphase)))
if (kind != self.kind):
if (kind == 'complex'):
temp = self._make_complex()
else:
temp = self._make_real(check=check)
coeffs = temp.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
else:
coeffs = self.to_array(normalization=normalization.lower(),
csphase=csphase, lmax=lmax)
return SHCoeffs.from_array(coeffs,
normalization=normalization.lower(),
csphase=csphase, copy=False) | [
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"s... | 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, optional, default = x.normalization
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = x.csphase
Condon-Shortley phase convention for the output class: 1 to exclude
the phase factor, or -1 to include it.
lmax : int, optional, default = x.lmax
Maximum spherical harmonic degree to output.
kind : str, optional, default = clm.kind
'real' or 'complex' spherical harmonic coefficients for the output
class.
check : bool, optional, default = True
When converting complex coefficients to real coefficients, if True,
check if function is entirely real.
Description
-----------
This method will return a new class instance of the spherical
harmonic coefficients using a different normalization and
Condon-Shortley phase convention. The coefficients can be converted
between real and complex form, and a different maximum spherical
harmonic degree of the output coefficients can be specified. If this
maximum degree is smaller than the maximum degree of the original
class, the coefficients will be truncated. Conversely, if this degree
is larger than the maximum degree of the original class, the
coefficients of the new class will be zero padded. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shcoeffsgrid.py#L1129-L1210 | train | 203,874 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHCoeffs.expand | 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])
Returns
-------
f : SHGrid class instance
g : float, ndarray, or list
Parameters
----------
lat : int, float, ndarray, or list, optional, default = None
Latitude coordinates where the function is to be evaluated.
colat : int, float, ndarray, or list, optional, default = None
Colatitude coordinates where the function is to be evaluated.
lon : int, float, ndarray, or list, optional, default = None
Longitude coordinates where the function is to be evaluated.
degrees : bool, optional, default = True
True if lat, colat and lon are in degrees, False if in radians.
grid : str, optional, default = 'DH'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon,
'DH2' for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ'
for a Gauss-Legendre quadrature grid.
lmax : int, optional, default = x.lmax
The maximum spherical harmonic degree, which determines the grid
spacing of the output grid.
lmax_calc : int, optional, default = x.lmax
The maximum spherical harmonic degree to use when evaluating the
function.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
Description
-----------
This method either (1) evaluates the spherical harmonic coefficients on
a global grid and returns an SHGrid class instance, or (2) evaluates
the spherical harmonic coefficients for a list of (co)latitude and
longitude coordinates. For the first case, the grid type is defined
by the optional parameter grid, which can be 'DH', 'DH2' or 'GLQ'.For
the second case, the optional parameters lon and either colat or lat
must be provided.
"""
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=degrees,
lmax_calc=lmax_calc)
return values
if colat is not None and lon is not None:
if lmax_calc is None:
lmax_calc = self.lmax
if type(colat) is list:
lat = list(map(lambda x: 90 - x, colat))
else:
lat = 90 - colat
values = self._expand_coord(lat=lat, lon=lon, degrees=degrees,
lmax_calc=lmax_calc)
return values
else:
if lmax is None:
lmax = self.lmax
if lmax_calc is None:
lmax_calc = lmax
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 = self._expandDH(sampling=1, lmax=lmax,
lmax_calc=lmax_calc)
elif grid.upper() == 'DH2':
gridout = self._expandDH(sampling=2, lmax=lmax,
lmax_calc=lmax_calc)
elif grid.upper() == 'GLQ':
gridout = self._expandGLQ(zeros=zeros, lmax=lmax,
lmax_calc=lmax_calc)
else:
raise ValueError(
"grid must be 'DH', 'DH1', 'DH2', or 'GLQ'. " +
"Input value was {:s}".format(repr(grid)))
return gridout | python | 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])
Returns
-------
f : SHGrid class instance
g : float, ndarray, or list
Parameters
----------
lat : int, float, ndarray, or list, optional, default = None
Latitude coordinates where the function is to be evaluated.
colat : int, float, ndarray, or list, optional, default = None
Colatitude coordinates where the function is to be evaluated.
lon : int, float, ndarray, or list, optional, default = None
Longitude coordinates where the function is to be evaluated.
degrees : bool, optional, default = True
True if lat, colat and lon are in degrees, False if in radians.
grid : str, optional, default = 'DH'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon,
'DH2' for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ'
for a Gauss-Legendre quadrature grid.
lmax : int, optional, default = x.lmax
The maximum spherical harmonic degree, which determines the grid
spacing of the output grid.
lmax_calc : int, optional, default = x.lmax
The maximum spherical harmonic degree to use when evaluating the
function.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
Description
-----------
This method either (1) evaluates the spherical harmonic coefficients on
a global grid and returns an SHGrid class instance, or (2) evaluates
the spherical harmonic coefficients for a list of (co)latitude and
longitude coordinates. For the first case, the grid type is defined
by the optional parameter grid, which can be 'DH', 'DH2' or 'GLQ'.For
the second case, the optional parameters lon and either colat or lat
must be provided.
"""
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=degrees,
lmax_calc=lmax_calc)
return values
if colat is not None and lon is not None:
if lmax_calc is None:
lmax_calc = self.lmax
if type(colat) is list:
lat = list(map(lambda x: 90 - x, colat))
else:
lat = 90 - colat
values = self._expand_coord(lat=lat, lon=lon, degrees=degrees,
lmax_calc=lmax_calc)
return values
else:
if lmax is None:
lmax = self.lmax
if lmax_calc is None:
lmax_calc = lmax
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 = self._expandDH(sampling=1, lmax=lmax,
lmax_calc=lmax_calc)
elif grid.upper() == 'DH2':
gridout = self._expandDH(sampling=2, lmax=lmax,
lmax_calc=lmax_calc)
elif grid.upper() == 'GLQ':
gridout = self._expandGLQ(zeros=zeros, lmax=lmax,
lmax_calc=lmax_calc)
else:
raise ValueError(
"grid must be 'DH', 'DH1', 'DH2', or 'GLQ'. " +
"Input value was {:s}".format(repr(grid)))
return gridout | [
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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])
Returns
-------
f : SHGrid class instance
g : float, ndarray, or list
Parameters
----------
lat : int, float, ndarray, or list, optional, default = None
Latitude coordinates where the function is to be evaluated.
colat : int, float, ndarray, or list, optional, default = None
Colatitude coordinates where the function is to be evaluated.
lon : int, float, ndarray, or list, optional, default = None
Longitude coordinates where the function is to be evaluated.
degrees : bool, optional, default = True
True if lat, colat and lon are in degrees, False if in radians.
grid : str, optional, default = 'DH'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon,
'DH2' for an equidistant lat/lon grid with nlon=2*nlat, or 'GLQ'
for a Gauss-Legendre quadrature grid.
lmax : int, optional, default = x.lmax
The maximum spherical harmonic degree, which determines the grid
spacing of the output grid.
lmax_calc : int, optional, default = x.lmax
The maximum spherical harmonic degree to use when evaluating the
function.
zeros : ndarray, optional, default = None
The cos(colatitude) nodes used in the Gauss-Legendre Quadrature
grids.
Description
-----------
This method either (1) evaluates the spherical harmonic coefficients on
a global grid and returns an SHGrid class instance, or (2) evaluates
the spherical harmonic coefficients for a list of (co)latitude and
longitude coordinates. For the first case, the grid type is defined
by the optional parameter grid, which can be 'DH', 'DH2' or 'GLQ'.For
the second case, the optional parameters lon and either colat or lat
must be provided. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHRealCoeffs._make_complex | def _make_complex(self):
"""Convert the real SHCoeffs class to the complex class."""
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),
dtype='complex')
complex_coeffs[0, :, :] = (rcomplex_coeffs[0, :, :] + 1j *
rcomplex_coeffs[1, :, :])
complex_coeffs[1, :, :] = complex_coeffs[0, :, :].conjugate()
for m in self.degrees():
if m % 2 == 1:
complex_coeffs[1, :, m] = - complex_coeffs[1, :, m]
# complex_coeffs is initialized in this function and can be
# passed as reference
return SHCoeffs.from_array(complex_coeffs,
normalization=self.normalization,
csphase=self.csphase, copy=False) | python | def _make_complex(self):
"""Convert the real SHCoeffs class to the complex class."""
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),
dtype='complex')
complex_coeffs[0, :, :] = (rcomplex_coeffs[0, :, :] + 1j *
rcomplex_coeffs[1, :, :])
complex_coeffs[1, :, :] = complex_coeffs[0, :, :].conjugate()
for m in self.degrees():
if m % 2 == 1:
complex_coeffs[1, :, m] = - complex_coeffs[1, :, m]
# complex_coeffs is initialized in this function and can be
# passed as reference
return SHCoeffs.from_array(complex_coeffs,
normalization=self.normalization,
csphase=self.csphase, copy=False) | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHRealCoeffs._expand_coord | def _expand_coord(self, lat, lon, lmax_calc, degrees):
"""Evaluate the function at the coordinates lat and lon."""
if self.normalization == '4pi':
norm = 1
elif self.normalization == 'schmidt':
norm = 2
elif self.normalization == 'unnorm':
norm = 3
elif self.normalization == 'ortho':
norm = 4
else:
raise ValueError(
"Normalization must be '4pi', 'ortho', 'schmidt', or " +
"'unnorm'. Input value was {:s}"
.format(repr(self.normalization)))
if degrees is True:
latin = lat
lonin = lon
else:
latin = _np.rad2deg(lat)
lonin = _np.rad2deg(lon)
if type(lat) is not type(lon):
raise ValueError('lat and lon must be of the same type. ' +
'Input types are {:s} and {:s}'
.format(repr(type(lat)), repr(type(lon))))
if type(lat) is int or type(lat) is float or type(lat) is _np.float_:
return _shtools.MakeGridPoint(self.coeffs, lat=latin, lon=lonin,
lmax=lmax_calc, norm=norm,
csphase=self.csphase)
elif type(lat) is _np.ndarray:
values = _np.empty_like(lat, dtype=float)
for v, latitude, longitude in _np.nditer([values, latin, lonin],
op_flags=['readwrite']):
v[...] = _shtools.MakeGridPoint(self.coeffs, lat=latitude,
lon=longitude,
lmax=lmax_calc, norm=norm,
csphase=self.csphase)
return values
elif type(lat) is list:
values = []
for latitude, longitude in zip(latin, lonin):
values.append(
_shtools.MakeGridPoint(self.coeffs, lat=latitude,
lon=longitude,
lmax=lmax_calc, norm=norm,
csphase=self.csphase))
return values
else:
raise ValueError('lat and lon must be either an int, float, ' +
'ndarray, or list. ' +
'Input types are {:s} and {:s}'
.format(repr(type(lat)), repr(type(lon)))) | python | def _expand_coord(self, lat, lon, lmax_calc, degrees):
"""Evaluate the function at the coordinates lat and lon."""
if self.normalization == '4pi':
norm = 1
elif self.normalization == 'schmidt':
norm = 2
elif self.normalization == 'unnorm':
norm = 3
elif self.normalization == 'ortho':
norm = 4
else:
raise ValueError(
"Normalization must be '4pi', 'ortho', 'schmidt', or " +
"'unnorm'. Input value was {:s}"
.format(repr(self.normalization)))
if degrees is True:
latin = lat
lonin = lon
else:
latin = _np.rad2deg(lat)
lonin = _np.rad2deg(lon)
if type(lat) is not type(lon):
raise ValueError('lat and lon must be of the same type. ' +
'Input types are {:s} and {:s}'
.format(repr(type(lat)), repr(type(lon))))
if type(lat) is int or type(lat) is float or type(lat) is _np.float_:
return _shtools.MakeGridPoint(self.coeffs, lat=latin, lon=lonin,
lmax=lmax_calc, norm=norm,
csphase=self.csphase)
elif type(lat) is _np.ndarray:
values = _np.empty_like(lat, dtype=float)
for v, latitude, longitude in _np.nditer([values, latin, lonin],
op_flags=['readwrite']):
v[...] = _shtools.MakeGridPoint(self.coeffs, lat=latitude,
lon=longitude,
lmax=lmax_calc, norm=norm,
csphase=self.csphase)
return values
elif type(lat) is list:
values = []
for latitude, longitude in zip(latin, lonin):
values.append(
_shtools.MakeGridPoint(self.coeffs, lat=latitude,
lon=longitude,
lmax=lmax_calc, norm=norm,
csphase=self.csphase))
return values
else:
raise ValueError('lat and lon must be either an int, float, ' +
'ndarray, or list. ' +
'Input types are {:s} and {:s}'
.format(repr(type(lat)), repr(type(lon)))) | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHComplexCoeffs._make_real | def _make_real(self, check=True):
"""Convert the complex SHCoeffs class to the real class."""
# Test if the coefficients correspond to a real grid.
# This is not very elegant, and the equality condition
# is probably not robust to round off errors.
if check:
for l in self.degrees():
if self.coeffs[0, l, 0] != self.coeffs[0, l, 0].conjugate():
raise RuntimeError('Complex coefficients do not ' +
'correspond to a real field. ' +
'l = {:d}, m = 0: {:e}'
.format(l, self.coeffs[0, l, 0]))
for m in _np.arange(1, l + 1):
if m % 2 == 1:
if (self.coeffs[0, l, m] != -
self.coeffs[1, l, m].conjugate()):
raise RuntimeError('Complex coefficients do not ' +
'correspond to a real field. ' +
'l = {:d}, m = {:d}: {:e}, {:e}'
.format(
l, m, self.coeffs[0, l, 0],
self.coeffs[1, l, 0]))
else:
if (self.coeffs[0, l, m] !=
self.coeffs[1, l, m].conjugate()):
raise RuntimeError('Complex coefficients do not ' +
'correspond to a real field. ' +
'l = {:d}, m = {:d}: {:e}, {:e}'
.format(
l, m, self.coeffs[0, l, 0],
self.coeffs[1, l, 0]))
coeffs_rc = _np.zeros((2, self.lmax + 1, self.lmax + 1))
coeffs_rc[0, :, :] = self.coeffs[0, :, :].real
coeffs_rc[1, :, :] = self.coeffs[0, :, :].imag
real_coeffs = _shtools.SHctor(coeffs_rc, convention=1,
switchcs=0)
return SHCoeffs.from_array(real_coeffs,
normalization=self.normalization,
csphase=self.csphase) | python | def _make_real(self, check=True):
"""Convert the complex SHCoeffs class to the real class."""
# Test if the coefficients correspond to a real grid.
# This is not very elegant, and the equality condition
# is probably not robust to round off errors.
if check:
for l in self.degrees():
if self.coeffs[0, l, 0] != self.coeffs[0, l, 0].conjugate():
raise RuntimeError('Complex coefficients do not ' +
'correspond to a real field. ' +
'l = {:d}, m = 0: {:e}'
.format(l, self.coeffs[0, l, 0]))
for m in _np.arange(1, l + 1):
if m % 2 == 1:
if (self.coeffs[0, l, m] != -
self.coeffs[1, l, m].conjugate()):
raise RuntimeError('Complex coefficients do not ' +
'correspond to a real field. ' +
'l = {:d}, m = {:d}: {:e}, {:e}'
.format(
l, m, self.coeffs[0, l, 0],
self.coeffs[1, l, 0]))
else:
if (self.coeffs[0, l, m] !=
self.coeffs[1, l, m].conjugate()):
raise RuntimeError('Complex coefficients do not ' +
'correspond to a real field. ' +
'l = {:d}, m = {:d}: {:e}, {:e}'
.format(
l, m, self.coeffs[0, l, 0],
self.coeffs[1, l, 0]))
coeffs_rc = _np.zeros((2, self.lmax + 1, self.lmax + 1))
coeffs_rc[0, :, :] = self.coeffs[0, :, :].real
coeffs_rc[1, :, :] = self.coeffs[0, :, :].imag
real_coeffs = _shtools.SHctor(coeffs_rc, convention=1,
switchcs=0)
return SHCoeffs.from_array(real_coeffs,
normalization=self.normalization,
csphase=self.csphase) | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHComplexCoeffs._expandGLQ | def _expandGLQ(self, zeros, lmax, lmax_calc):
"""Evaluate the coefficients on a Gauss-Legendre quadrature grid."""
if self.normalization == '4pi':
norm = 1
elif self.normalization == 'schmidt':
norm = 2
elif self.normalization == 'unnorm':
norm = 3
elif self.normalization == 'ortho':
norm = 4
else:
raise ValueError(
"Normalization must be '4pi', 'ortho', 'schmidt', or " +
"'unnorm'. Input value was {:s}"
.format(repr(self.normalization)))
if zeros is None:
zeros, weights = _shtools.SHGLQ(self.lmax)
data = _shtools.MakeGridGLQC(self.coeffs, zeros, norm=norm,
csphase=self.csphase, lmax=lmax,
lmax_calc=lmax_calc)
gridout = SHGrid.from_array(data, grid='GLQ', copy=False)
return gridout | python | def _expandGLQ(self, zeros, lmax, lmax_calc):
"""Evaluate the coefficients on a Gauss-Legendre quadrature grid."""
if self.normalization == '4pi':
norm = 1
elif self.normalization == 'schmidt':
norm = 2
elif self.normalization == 'unnorm':
norm = 3
elif self.normalization == 'ortho':
norm = 4
else:
raise ValueError(
"Normalization must be '4pi', 'ortho', 'schmidt', or " +
"'unnorm'. Input value was {:s}"
.format(repr(self.normalization)))
if zeros is None:
zeros, weights = _shtools.SHGLQ(self.lmax)
data = _shtools.MakeGridGLQC(self.coeffs, zeros, norm=norm,
csphase=self.csphase, lmax=lmax,
lmax_calc=lmax_calc)
gridout = SHGrid.from_array(data, grid='GLQ', copy=False)
return gridout | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHGrid.from_array | def from_array(self, array, grid='DH', copy=True):
"""
Initialize the class instance from an input array.
Usage
-----
x = SHGrid.from_array(array, [grid, copy])
Returns
-------
x : SHGrid class instance
Parameters
----------
array : ndarray, shape (nlat, nlon)
2-D numpy array of the gridded data, where nlat and nlon are the
number of latitudinal and longitudinal bands, respectively.
grid : str, optional, default = 'DH'
'DH' or 'GLQ' for Driscoll and Healy grids or Gauss Legendre
Quadrature grids, respectively.
copy : bool, optional, default = True
If True (default), make a copy of array when initializing the class
instance. If False, initialize the class instance with a reference
to array.
"""
if _np.iscomplexobj(array):
kind = 'complex'
else:
kind = 'real'
if type(grid) != str:
raise ValueError('grid must be a string. ' +
'Input type was {:s}'
.format(str(type(grid))))
if grid.upper() not in set(['DH', 'GLQ']):
raise ValueError(
"grid must be 'DH' or 'GLQ'. Input value was {:s}."
.format(repr(grid))
)
for cls in self.__subclasses__():
if cls.istype(kind) and cls.isgrid(grid):
return cls(array, copy=copy) | python | def from_array(self, array, grid='DH', copy=True):
"""
Initialize the class instance from an input array.
Usage
-----
x = SHGrid.from_array(array, [grid, copy])
Returns
-------
x : SHGrid class instance
Parameters
----------
array : ndarray, shape (nlat, nlon)
2-D numpy array of the gridded data, where nlat and nlon are the
number of latitudinal and longitudinal bands, respectively.
grid : str, optional, default = 'DH'
'DH' or 'GLQ' for Driscoll and Healy grids or Gauss Legendre
Quadrature grids, respectively.
copy : bool, optional, default = True
If True (default), make a copy of array when initializing the class
instance. If False, initialize the class instance with a reference
to array.
"""
if _np.iscomplexobj(array):
kind = 'complex'
else:
kind = 'real'
if type(grid) != str:
raise ValueError('grid must be a string. ' +
'Input type was {:s}'
.format(str(type(grid))))
if grid.upper() not in set(['DH', 'GLQ']):
raise ValueError(
"grid must be 'DH' or 'GLQ'. Input value was {:s}."
.format(repr(grid))
)
for cls in self.__subclasses__():
if cls.istype(kind) and cls.isgrid(grid):
return cls(array, copy=copy) | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHGrid.from_file | def from_file(self, fname, binary=False, **kwargs):
"""
Initialize the class instance from gridded data in a file.
Usage
-----
x = SHGrid.from_file(fname, [binary, **kwargs])
Returns
-------
x : SHGrid class instance
Parameters
----------
fname : str
The filename containing the gridded data. For text files (default)
the file is read using the numpy routine loadtxt(), whereas for
binary files, the file is read using numpy.load(). The dimensions
of the array must be nlon=nlat or nlon=2*nlat for Driscoll and
Healy grids, or nlon=2*nlat-1 for Gauss-Legendre Quadrature grids.
binary : bool, optional, default = False
If False, read a text file. If True, read a binary 'npy' file.
**kwargs : keyword arguments, optional
Keyword arguments of numpy.loadtxt() or numpy.load().
"""
if binary is False:
data = _np.loadtxt(fname, **kwargs)
elif binary is True:
data = _np.load(fname, **kwargs)
else:
raise ValueError('binary must be True or False. '
'Input value is {:s}'.format(binary))
if _np.iscomplexobj(data):
kind = 'complex'
else:
kind = 'real'
if (data.shape[1] == data.shape[0]) or (data.shape[1] ==
2 * data.shape[0]):
grid = 'DH'
elif data.shape[1] == 2 * data.shape[0] - 1:
grid = 'GLQ'
else:
raise ValueError('Input grid must be dimensioned as ' +
'(nlat, nlon). For DH grids, nlon = nlat or ' +
'nlon = 2 * nlat. For GLQ grids, nlon = ' +
'2 * nlat - 1. Input dimensions are nlat = ' +
'{:d}, nlon = {:d}'.format(data.shape[0],
data.shape[1]))
for cls in self.__subclasses__():
if cls.istype(kind) and cls.isgrid(grid):
return cls(data) | python | def from_file(self, fname, binary=False, **kwargs):
"""
Initialize the class instance from gridded data in a file.
Usage
-----
x = SHGrid.from_file(fname, [binary, **kwargs])
Returns
-------
x : SHGrid class instance
Parameters
----------
fname : str
The filename containing the gridded data. For text files (default)
the file is read using the numpy routine loadtxt(), whereas for
binary files, the file is read using numpy.load(). The dimensions
of the array must be nlon=nlat or nlon=2*nlat for Driscoll and
Healy grids, or nlon=2*nlat-1 for Gauss-Legendre Quadrature grids.
binary : bool, optional, default = False
If False, read a text file. If True, read a binary 'npy' file.
**kwargs : keyword arguments, optional
Keyword arguments of numpy.loadtxt() or numpy.load().
"""
if binary is False:
data = _np.loadtxt(fname, **kwargs)
elif binary is True:
data = _np.load(fname, **kwargs)
else:
raise ValueError('binary must be True or False. '
'Input value is {:s}'.format(binary))
if _np.iscomplexobj(data):
kind = 'complex'
else:
kind = 'real'
if (data.shape[1] == data.shape[0]) or (data.shape[1] ==
2 * data.shape[0]):
grid = 'DH'
elif data.shape[1] == 2 * data.shape[0] - 1:
grid = 'GLQ'
else:
raise ValueError('Input grid must be dimensioned as ' +
'(nlat, nlon). For DH grids, nlon = nlat or ' +
'nlon = 2 * nlat. For GLQ grids, nlon = ' +
'2 * nlat - 1. Input dimensions are nlat = ' +
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for cls in self.__subclasses__():
if cls.istype(kind) and cls.isgrid(grid):
return cls(data) | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHGrid.to_file | def to_file(self, filename, binary=False, **kwargs):
"""
Save gridded data to a file.
Usage
-----
x.to_file(filename, [binary, **kwargs])
Parameters
----------
filename : str
Name of output file. For text files (default), the file will be
saved automatically in gzip compressed format if the filename ends
in .gz.
binary : bool, optional, default = False
If False, save as text using numpy.savetxt(). If True, save as a
'npy' binary file using numpy.save().
**kwargs : keyword arguments, optional
Keyword arguments of numpy.savetxt() and numpy.save().
"""
if binary is False:
_np.savetxt(filename, self.data, **kwargs)
elif binary is True:
_np.save(filename, self.data, **kwargs)
else:
raise ValueError('binary must be True or False. '
'Input value is {:s}'.format(binary)) | python | def to_file(self, filename, binary=False, **kwargs):
"""
Save gridded data to a file.
Usage
-----
x.to_file(filename, [binary, **kwargs])
Parameters
----------
filename : str
Name of output file. For text files (default), the file will be
saved automatically in gzip compressed format if the filename ends
in .gz.
binary : bool, optional, default = False
If False, save as text using numpy.savetxt(). If True, save as a
'npy' binary file using numpy.save().
**kwargs : keyword arguments, optional
Keyword arguments of numpy.savetxt() and numpy.save().
"""
if binary is False:
_np.savetxt(filename, self.data, **kwargs)
elif binary is True:
_np.save(filename, self.data, **kwargs)
else:
raise ValueError('binary must be True or False. '
'Input value is {:s}'.format(binary)) | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHGrid.lats | def lats(self, degrees=True):
"""
Return the latitudes of each row of the gridded data.
Usage
-----
lats = x.lats([degrees])
Returns
-------
lats : ndarray, shape (nlat)
1-D numpy array of size nlat containing the latitude of each row
of the gridded data.
Parameters
-------
degrees : bool, optional, default = True
If True, the output will be in degrees. If False, the output will
be in radians.
"""
if degrees is False:
return _np.radians(self._lats())
else:
return self._lats() | python | def lats(self, degrees=True):
"""
Return the latitudes of each row of the gridded data.
Usage
-----
lats = x.lats([degrees])
Returns
-------
lats : ndarray, shape (nlat)
1-D numpy array of size nlat containing the latitude of each row
of the gridded data.
Parameters
-------
degrees : bool, optional, default = True
If True, the output will be in degrees. If False, the output will
be in radians.
"""
if degrees is False:
return _np.radians(self._lats())
else:
return self._lats() | [
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-------
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHGrid.lons | def lons(self, degrees=True):
"""
Return the longitudes of each column of the gridded data.
Usage
-----
lons = x.get_lon([degrees])
Returns
-------
lons : ndarray, shape (nlon)
1-D numpy array of size nlon containing the longitude of each row
of the gridded data.
Parameters
-------
degrees : bool, optional, default = True
If True, the output will be in degrees. If False, the output will
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"""
if degrees is False:
return _np.radians(self._lons())
else:
return self._lons() | python | def lons(self, degrees=True):
"""
Return the longitudes of each column of the gridded data.
Usage
-----
lons = x.get_lon([degrees])
Returns
-------
lons : ndarray, shape (nlon)
1-D numpy array of size nlon containing the longitude of each row
of the gridded data.
Parameters
-------
degrees : bool, optional, default = True
If True, the output will be in degrees. If False, the output will
be in radians.
"""
if degrees is False:
return _np.radians(self._lons())
else:
return self._lons() | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | SHGrid.expand | def expand(self, normalization='4pi', csphase=1, **kwargs):
"""
Expand the grid into spherical harmonics.
Usage
-----
clm = x.expand([normalization, csphase, lmax_calc])
Returns
-------
clm : SHCoeffs class instance
Parameters
----------
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
lmax_calc : int, optional, default = x.lmax
Maximum spherical harmonic degree to return.
"""
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', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
return self._expand(normalization=normalization, csphase=csphase,
**kwargs) | python | def expand(self, normalization='4pi', csphase=1, **kwargs):
"""
Expand the grid into spherical harmonics.
Usage
-----
clm = x.expand([normalization, csphase, lmax_calc])
Returns
-------
clm : SHCoeffs class instance
Parameters
----------
normalization : str, optional, default = '4pi'
Normalization of the output class: '4pi', 'ortho', 'schmidt', or
'unnorm', for geodesy 4pi normalized, orthonormalized, Schmidt
semi-normalized, or unnormalized coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
lmax_calc : int, optional, default = x.lmax
Maximum spherical harmonic degree to return.
"""
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', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
return self._expand(normalization=normalization, csphase=csphase,
**kwargs) | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shcoeffsgrid.py#L2775-L2818 | train | 203,885 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shcoeffsgrid.py | DHRealGrid._expand | def _expand(self, normalization, csphase, **kwargs):
"""Expand the grid into real spherical harmonics."""
if normalization.lower() == '4pi':
norm = 1
elif normalization.lower() == 'schmidt':
norm = 2
elif normalization.lower() == 'unnorm':
norm = 3
elif normalization.lower() == 'ortho':
norm = 4
else:
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
cilm = _shtools.SHExpandDH(self.data, norm=norm, csphase=csphase,
sampling=self.sampling,
**kwargs)
coeffs = SHCoeffs.from_array(cilm,
normalization=normalization.lower(),
csphase=csphase, copy=False)
return coeffs | python | def _expand(self, normalization, csphase, **kwargs):
"""Expand the grid into real spherical harmonics."""
if normalization.lower() == '4pi':
norm = 1
elif normalization.lower() == 'schmidt':
norm = 2
elif normalization.lower() == 'unnorm':
norm = 3
elif normalization.lower() == 'ortho':
norm = 4
else:
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', " +
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
cilm = _shtools.SHExpandDH(self.data, norm=norm, csphase=csphase,
sampling=self.sampling,
**kwargs)
coeffs = SHCoeffs.from_array(cilm,
normalization=normalization.lower(),
csphase=csphase, copy=False)
return coeffs | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgeoid.py | SHGeoid.plot | def plot(self, colorbar=True, cb_orientation='vertical',
cb_label='geoid, m', show=True, **kwargs):
"""
Plot the geoid.
Usage
-----
x.plot([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'geoid, m'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
return self.geoid.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=True, **kwargs) | python | def plot(self, colorbar=True, cb_orientation='vertical',
cb_label='geoid, m', show=True, **kwargs):
"""
Plot the geoid.
Usage
-----
x.plot([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
cb_label, show, fname, **kwargs])
Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
ticks will not be plotted.
xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'geoid, m'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
and plt.imshow() methods.
"""
return self.geoid.plot(colorbar=colorbar,
cb_orientation=cb_orientation,
cb_label=cb_label, show=True, **kwargs) | [
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x.plot([tick_interval, xlabel, ylabel, ax, colorbar, cb_orientation,
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Parameters
----------
tick_interval : list or tuple, optional, default = [30, 30]
Intervals to use when plotting the x and y ticks. If set to None,
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xlabel : str, optional, default = 'longitude'
Label for the longitude axis.
ylabel : str, optional, default = 'latitude'
Label for the latitude axis.
ax : matplotlib axes object, optional, default = None
A single matplotlib axes object where the plot will appear.
colorbar : bool, optional, default = True
If True, plot a colorbar.
cb_orientation : str, optional, default = 'vertical'
Orientation of the colorbar: either 'vertical' or 'horizontal'.
cb_label : str, optional, default = 'geoid, m'
Text label for the colorbar.
show : bool, optional, default = True
If True, plot the image to the screen.
fname : str, optional, default = None
If present, and if axes is not specified, save the image to the
specified file.
kwargs : optional
Keyword arguements that will be sent to the SHGrid.plot()
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgeoid.py#L107-L145 | train | 203,887 |
SHTOOLS/SHTOOLS | pyshtools/utils/datetime.py | _yyyymmdd_to_year_fraction | def _yyyymmdd_to_year_fraction(date):
"""Convert YYYMMDD.DD date string or float to YYYY.YYY"""
date = str(date)
if '.' in date:
date, residual = str(date).split('.')
residual = float('0.' + residual)
else:
residual = 0.0
date = _datetime.datetime.strptime(date, '%Y%m%d')
date += _datetime.timedelta(days=residual)
year = date.year
year_start = _datetime.datetime(year=year, month=1, day=1)
next_year_start = _datetime.datetime(year=year + 1, month=1, day=1)
year_duration = next_year_start - year_start
year_elapsed = date - year_start
fraction = year_elapsed / year_duration
return year + fraction | python | def _yyyymmdd_to_year_fraction(date):
"""Convert YYYMMDD.DD date string or float to YYYY.YYY"""
date = str(date)
if '.' in date:
date, residual = str(date).split('.')
residual = float('0.' + residual)
else:
residual = 0.0
date = _datetime.datetime.strptime(date, '%Y%m%d')
date += _datetime.timedelta(days=residual)
year = date.year
year_start = _datetime.datetime(year=year, month=1, day=1)
next_year_start = _datetime.datetime(year=year + 1, month=1, day=1)
year_duration = next_year_start - year_start
year_elapsed = date - year_start
fraction = year_elapsed / year_duration
return year + fraction | [
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SHTOOLS/SHTOOLS | examples/python/GlobalSpectralAnalysis/GlobalSpectralAnalysis.py | example | def example():
"""
example that plots the power spectrum of Mars topography data
"""
# --- input data filename ---
infile = os.path.join(os.path.dirname(__file__),
'../../ExampleDataFiles/MarsTopo719.shape')
coeffs, lmax = shio.shread(infile)
# --- plot grid ---
grid = expand.MakeGridDH(coeffs, csphase=-1)
fig_map = plt.figure()
plt.imshow(grid)
# ---- compute spectrum ----
ls = np.arange(lmax + 1)
pspectrum = spectralanalysis.spectrum(coeffs, unit='per_l')
pdensity = spectralanalysis.spectrum(coeffs, unit='per_lm')
# ---- plot spectrum ----
fig_spectrum, ax = plt.subplots(1, 1)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('degree l')
ax.grid(True, which='both')
ax.plot(ls[1:], pspectrum[1:], label='power per degree l')
ax.plot(ls[1:], pdensity[1:], label='power per degree l and order m')
ax.legend()
fig_map.savefig('SHRtopography_mars.png')
fig_spectrum.savefig('SHRspectrum_mars.png')
print('mars topography and spectrum saved') | python | def example():
"""
example that plots the power spectrum of Mars topography data
"""
# --- input data filename ---
infile = os.path.join(os.path.dirname(__file__),
'../../ExampleDataFiles/MarsTopo719.shape')
coeffs, lmax = shio.shread(infile)
# --- plot grid ---
grid = expand.MakeGridDH(coeffs, csphase=-1)
fig_map = plt.figure()
plt.imshow(grid)
# ---- compute spectrum ----
ls = np.arange(lmax + 1)
pspectrum = spectralanalysis.spectrum(coeffs, unit='per_l')
pdensity = spectralanalysis.spectrum(coeffs, unit='per_lm')
# ---- plot spectrum ----
fig_spectrum, ax = plt.subplots(1, 1)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('degree l')
ax.grid(True, which='both')
ax.plot(ls[1:], pspectrum[1:], label='power per degree l')
ax.plot(ls[1:], pdensity[1:], label='power per degree l and order m')
ax.legend()
fig_map.savefig('SHRtopography_mars.png')
fig_spectrum.savefig('SHRspectrum_mars.png')
print('mars topography and spectrum saved') | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.from_zeros | def from_zeros(self, lmax, gm, r0, omega=None, errors=False,
normalization='4pi', csphase=1):
"""
Initialize the class with spherical harmonic coefficients set to zero
from degree 1 to lmax, and set the degree 0 term to 1.
Usage
-----
x = SHGravCoeffs.from_zeros(lmax, gm, r0, [omega, errors,
normalization, csphase])
Returns
-------
x : SHGravCoeffs class instance.
Parameters
----------
lmax : int
The maximum spherical harmonic degree l of the coefficients.
gm : float
The gravitational constant times the mass that is associated with
the gravitational potential coefficients.
r0 : float
The reference radius of the spherical harmonic coefficients.
omega : float, optional, default = None
The angular rotation rate of the body.
errors : bool, optional, default = False
If True, initialize the attribute errors with zeros.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', "
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients "
"are stable only for degrees less than or equal "
"to 85. lmax for the coefficients will be set to "
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
coeffs = _np.zeros((2, lmax + 1, lmax + 1))
coeffs[0, 0, 0] = 1.0
if errors is False:
clm = SHGravRealCoeffs(coeffs, gm=gm, r0=r0, omega=omega,
normalization=normalization.lower(),
csphase=csphase)
else:
clm = SHGravRealCoeffs(coeffs, gm=gm, r0=r0, omega=omega,
errors=_np.zeros((2, lmax + 1, lmax + 1)),
normalization=normalization.lower(),
csphase=csphase)
return clm | python | def from_zeros(self, lmax, gm, r0, omega=None, errors=False,
normalization='4pi', csphase=1):
"""
Initialize the class with spherical harmonic coefficients set to zero
from degree 1 to lmax, and set the degree 0 term to 1.
Usage
-----
x = SHGravCoeffs.from_zeros(lmax, gm, r0, [omega, errors,
normalization, csphase])
Returns
-------
x : SHGravCoeffs class instance.
Parameters
----------
lmax : int
The maximum spherical harmonic degree l of the coefficients.
gm : float
The gravitational constant times the mass that is associated with
the gravitational potential coefficients.
r0 : float
The reference radius of the spherical harmonic coefficients.
omega : float, optional, default = None
The angular rotation rate of the body.
errors : bool, optional, default = False
If True, initialize the attribute errors with zeros.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
or -1 to include it.
"""
if normalization.lower() not in ('4pi', 'ortho', 'schmidt', 'unnorm'):
raise ValueError(
"The normalization must be '4pi', 'ortho', 'schmidt', "
"or 'unnorm'. Input value was {:s}."
.format(repr(normalization))
)
if csphase != 1 and csphase != -1:
raise ValueError(
"csphase must be either 1 or -1. Input value was {:s}."
.format(repr(csphase))
)
if normalization.lower() == 'unnorm' and lmax > 85:
_warnings.warn("Calculations using unnormalized coefficients "
"are stable only for degrees less than or equal "
"to 85. lmax for the coefficients will be set to "
"85. Input value was {:d}.".format(lmax),
category=RuntimeWarning)
lmax = 85
coeffs = _np.zeros((2, lmax + 1, lmax + 1))
coeffs[0, 0, 0] = 1.0
if errors is False:
clm = SHGravRealCoeffs(coeffs, gm=gm, r0=r0, omega=omega,
normalization=normalization.lower(),
csphase=csphase)
else:
clm = SHGravRealCoeffs(coeffs, gm=gm, r0=r0, omega=omega,
errors=_np.zeros((2, lmax + 1, lmax + 1)),
normalization=normalization.lower(),
csphase=csphase)
return clm | [
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Usage
-----
x = SHGravCoeffs.from_zeros(lmax, gm, r0, [omega, errors,
normalization, csphase])
Returns
-------
x : SHGravCoeffs class instance.
Parameters
----------
lmax : int
The maximum spherical harmonic degree l of the coefficients.
gm : float
The gravitational constant times the mass that is associated with
the gravitational potential coefficients.
r0 : float
The reference radius of the spherical harmonic coefficients.
omega : float, optional, default = None
The angular rotation rate of the body.
errors : bool, optional, default = False
If True, initialize the attribute errors with zeros.
normalization : str, optional, default = '4pi'
'4pi', 'ortho', 'schmidt', or 'unnorm' for geodesy 4pi normalized,
orthonormalized, Schmidt semi-normalized, or unnormalized
coefficients, respectively.
csphase : int, optional, default = 1
Condon-Shortley phase convention: 1 to exclude the phase factor,
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.from_shape | def from_shape(self, shape, rho, gm, nmax=7, lmax=None, lmax_grid=None,
lmax_calc=None, omega=None):
"""
Initialize a class of gravitational potential spherical harmonic
coefficients by calculuting the gravitational potential associatiated
with relief along an interface.
Usage
-----
x = SHGravCoeffs.from_shape(shape, rho, gm, [nmax, lmax, lmax_grid,
lmax_calc, omega])
Returns
-------
x : SHGravCoeffs class instance.
Parameters
----------
shape : SHGrid or SHCoeffs class instance
The shape of the interface, either as an SHGrid or SHCoeffs class
instance. If the input is an SHCoeffs class instance, this will be
expaned on a grid using the optional parameters lmax_grid and
lmax_calc.
rho : int, float, or ndarray, or an SHGrid or SHCoeffs class instance
The density contrast associated with the interface in kg / m3. If
the input is a scalar, the density contrast is constant. If
the input is an SHCoeffs or SHGrid class instance, the density
contrast will vary laterally.
gm : float
The gravitational constant times the mass that is associated with
the gravitational potential coefficients.
nmax : integer, optional, default = 7
The maximum order used in the Taylor-series expansion when
calculating the potential coefficients.
lmax : int, optional, shape.lmax
The maximum spherical harmonic degree of the output spherical
harmonic coefficients.
lmax_grid : int, optional, default = lmax
If shape or rho is of type SHCoeffs, this parameter determines the
maximum spherical harmonic degree that is resolvable when expanded
onto a grid.
lmax_calc : optional, integer, default = lmax
If shape or rho is of type SHCoeffs, this parameter determines the
maximum spherical harmonic degree that will be used when expanded
onto a grid.
omega : float, optional, default = None
The angular rotation rate of the body.
Description
-----------
Initialize an SHGravCoeffs class instance by calculating the spherical
harmonic coefficients of the gravitational potential associated with
the shape of a density interface. The potential is calculated using the
finite-amplitude technique of Wieczorek and Phillips (1998) for a
constant density contrast and Wieczorek (2007) for a density contrast
that varies laterally. The output coefficients are referenced to the
mean radius of shape, and the potential is strictly valid only when it
is evaluated at a radius greater than the maximum radius of shape.
The input shape (and density contrast rho for variable density) can be
either an SHGrid or SHCoeffs class instance. The routine makes direct
use of gridded versions of these quantities, so if the input is of type
SHCoeffs, it will first be expanded onto a grid. This exansion will be
performed on a grid that can resolve degrees up to lmax_grid, with only
the first lmax_calc coefficients being used. The input shape must
correspond to absolute radii as the degree 0 term determines the
reference radius of the coefficients.
As an intermediate step, this routine calculates the spherical harmonic
coefficients of the interface raised to the nth power, i.e.,
(shape-r0)**n, where r0 is the mean radius of shape. If the input shape
is bandlimited to degree L, the resulting function will thus be
bandlimited to degree L*nmax. This subroutine assumes implicitly that
the maximum spherical harmonic degree of the input shape (when
SHCoeffs) or maximum resolvable spherical harmonic degree of shape
(when SHGrid) is greater or equal to this value. If this is not the
case, aliasing will occur. In practice, for accurate results, the
effective bandwidth needs only to be about three times the size of L,
though this should be verified for each application. The effective
bandwidth of shape (when SHCoeffs) can be increased by preprocessing
with the method pad(), or by increaesing the value of lmax_grid (when
SHGrid).
"""
mass = gm / _G.value
if type(shape) is not _SHRealCoeffs and type(shape) is not _DHRealGrid:
raise ValueError('shape must be of type SHRealCoeffs '
'or DHRealGrid. Input type is {:s}'
.format(repr(type(shape))))
if (not issubclass(type(rho), float) and type(rho) is not int
and type(rho) is not _np.ndarray and
type(rho) is not _SHRealCoeffs and
type(rho is not _DHRealGrid)):
raise ValueError('rho must be of type float, int, ndarray, '
'SHRealCoeffs or DHRealGrid. Input type is {:s}'
.format(repr(type(rho))))
if type(shape) is _SHRealCoeffs:
shape = shape.expand(lmax=lmax_grid, lmax_calc=lmax_calc)
if type(rho) is _SHRealCoeffs:
rho = rho.expand(lmax=lmax_grid, lmax_calc=lmax_calc)
if type(rho) is _DHRealGrid:
if shape.lmax != rho.lmax:
raise ValueError('The grids for shape and rho must have the '
'same size. '
'lmax of shape = {:d}, lmax of rho = {:d}'
.format(shape.lmax, rho.lmax))
cilm, d = _CilmPlusRhoHDH(shape.data, nmax, mass, rho.data,
lmax=lmax)
else:
cilm, d = _CilmPlusDH(shape.data, nmax, mass, rho, lmax=lmax)
clm = SHGravRealCoeffs(cilm, gm=gm, r0=d, omega=omega,
normalization='4pi', csphase=1)
return clm | python | def from_shape(self, shape, rho, gm, nmax=7, lmax=None, lmax_grid=None,
lmax_calc=None, omega=None):
"""
Initialize a class of gravitational potential spherical harmonic
coefficients by calculuting the gravitational potential associatiated
with relief along an interface.
Usage
-----
x = SHGravCoeffs.from_shape(shape, rho, gm, [nmax, lmax, lmax_grid,
lmax_calc, omega])
Returns
-------
x : SHGravCoeffs class instance.
Parameters
----------
shape : SHGrid or SHCoeffs class instance
The shape of the interface, either as an SHGrid or SHCoeffs class
instance. If the input is an SHCoeffs class instance, this will be
expaned on a grid using the optional parameters lmax_grid and
lmax_calc.
rho : int, float, or ndarray, or an SHGrid or SHCoeffs class instance
The density contrast associated with the interface in kg / m3. If
the input is a scalar, the density contrast is constant. If
the input is an SHCoeffs or SHGrid class instance, the density
contrast will vary laterally.
gm : float
The gravitational constant times the mass that is associated with
the gravitational potential coefficients.
nmax : integer, optional, default = 7
The maximum order used in the Taylor-series expansion when
calculating the potential coefficients.
lmax : int, optional, shape.lmax
The maximum spherical harmonic degree of the output spherical
harmonic coefficients.
lmax_grid : int, optional, default = lmax
If shape or rho is of type SHCoeffs, this parameter determines the
maximum spherical harmonic degree that is resolvable when expanded
onto a grid.
lmax_calc : optional, integer, default = lmax
If shape or rho is of type SHCoeffs, this parameter determines the
maximum spherical harmonic degree that will be used when expanded
onto a grid.
omega : float, optional, default = None
The angular rotation rate of the body.
Description
-----------
Initialize an SHGravCoeffs class instance by calculating the spherical
harmonic coefficients of the gravitational potential associated with
the shape of a density interface. The potential is calculated using the
finite-amplitude technique of Wieczorek and Phillips (1998) for a
constant density contrast and Wieczorek (2007) for a density contrast
that varies laterally. The output coefficients are referenced to the
mean radius of shape, and the potential is strictly valid only when it
is evaluated at a radius greater than the maximum radius of shape.
The input shape (and density contrast rho for variable density) can be
either an SHGrid or SHCoeffs class instance. The routine makes direct
use of gridded versions of these quantities, so if the input is of type
SHCoeffs, it will first be expanded onto a grid. This exansion will be
performed on a grid that can resolve degrees up to lmax_grid, with only
the first lmax_calc coefficients being used. The input shape must
correspond to absolute radii as the degree 0 term determines the
reference radius of the coefficients.
As an intermediate step, this routine calculates the spherical harmonic
coefficients of the interface raised to the nth power, i.e.,
(shape-r0)**n, where r0 is the mean radius of shape. If the input shape
is bandlimited to degree L, the resulting function will thus be
bandlimited to degree L*nmax. This subroutine assumes implicitly that
the maximum spherical harmonic degree of the input shape (when
SHCoeffs) or maximum resolvable spherical harmonic degree of shape
(when SHGrid) is greater or equal to this value. If this is not the
case, aliasing will occur. In practice, for accurate results, the
effective bandwidth needs only to be about three times the size of L,
though this should be verified for each application. The effective
bandwidth of shape (when SHCoeffs) can be increased by preprocessing
with the method pad(), or by increaesing the value of lmax_grid (when
SHGrid).
"""
mass = gm / _G.value
if type(shape) is not _SHRealCoeffs and type(shape) is not _DHRealGrid:
raise ValueError('shape must be of type SHRealCoeffs '
'or DHRealGrid. Input type is {:s}'
.format(repr(type(shape))))
if (not issubclass(type(rho), float) and type(rho) is not int
and type(rho) is not _np.ndarray and
type(rho) is not _SHRealCoeffs and
type(rho is not _DHRealGrid)):
raise ValueError('rho must be of type float, int, ndarray, '
'SHRealCoeffs or DHRealGrid. Input type is {:s}'
.format(repr(type(rho))))
if type(shape) is _SHRealCoeffs:
shape = shape.expand(lmax=lmax_grid, lmax_calc=lmax_calc)
if type(rho) is _SHRealCoeffs:
rho = rho.expand(lmax=lmax_grid, lmax_calc=lmax_calc)
if type(rho) is _DHRealGrid:
if shape.lmax != rho.lmax:
raise ValueError('The grids for shape and rho must have the '
'same size. '
'lmax of shape = {:d}, lmax of rho = {:d}'
.format(shape.lmax, rho.lmax))
cilm, d = _CilmPlusRhoHDH(shape.data, nmax, mass, rho.data,
lmax=lmax)
else:
cilm, d = _CilmPlusDH(shape.data, nmax, mass, rho, lmax=lmax)
clm = SHGravRealCoeffs(cilm, gm=gm, r0=d, omega=omega,
normalization='4pi', csphase=1)
return clm | [
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"... | Initialize a class of gravitational potential spherical harmonic
coefficients by calculuting the gravitational potential associatiated
with relief along an interface.
Usage
-----
x = SHGravCoeffs.from_shape(shape, rho, gm, [nmax, lmax, lmax_grid,
lmax_calc, omega])
Returns
-------
x : SHGravCoeffs class instance.
Parameters
----------
shape : SHGrid or SHCoeffs class instance
The shape of the interface, either as an SHGrid or SHCoeffs class
instance. If the input is an SHCoeffs class instance, this will be
expaned on a grid using the optional parameters lmax_grid and
lmax_calc.
rho : int, float, or ndarray, or an SHGrid or SHCoeffs class instance
The density contrast associated with the interface in kg / m3. If
the input is a scalar, the density contrast is constant. If
the input is an SHCoeffs or SHGrid class instance, the density
contrast will vary laterally.
gm : float
The gravitational constant times the mass that is associated with
the gravitational potential coefficients.
nmax : integer, optional, default = 7
The maximum order used in the Taylor-series expansion when
calculating the potential coefficients.
lmax : int, optional, shape.lmax
The maximum spherical harmonic degree of the output spherical
harmonic coefficients.
lmax_grid : int, optional, default = lmax
If shape or rho is of type SHCoeffs, this parameter determines the
maximum spherical harmonic degree that is resolvable when expanded
onto a grid.
lmax_calc : optional, integer, default = lmax
If shape or rho is of type SHCoeffs, this parameter determines the
maximum spherical harmonic degree that will be used when expanded
onto a grid.
omega : float, optional, default = None
The angular rotation rate of the body.
Description
-----------
Initialize an SHGravCoeffs class instance by calculating the spherical
harmonic coefficients of the gravitational potential associated with
the shape of a density interface. The potential is calculated using the
finite-amplitude technique of Wieczorek and Phillips (1998) for a
constant density contrast and Wieczorek (2007) for a density contrast
that varies laterally. The output coefficients are referenced to the
mean radius of shape, and the potential is strictly valid only when it
is evaluated at a radius greater than the maximum radius of shape.
The input shape (and density contrast rho for variable density) can be
either an SHGrid or SHCoeffs class instance. The routine makes direct
use of gridded versions of these quantities, so if the input is of type
SHCoeffs, it will first be expanded onto a grid. This exansion will be
performed on a grid that can resolve degrees up to lmax_grid, with only
the first lmax_calc coefficients being used. The input shape must
correspond to absolute radii as the degree 0 term determines the
reference radius of the coefficients.
As an intermediate step, this routine calculates the spherical harmonic
coefficients of the interface raised to the nth power, i.e.,
(shape-r0)**n, where r0 is the mean radius of shape. If the input shape
is bandlimited to degree L, the resulting function will thus be
bandlimited to degree L*nmax. This subroutine assumes implicitly that
the maximum spherical harmonic degree of the input shape (when
SHCoeffs) or maximum resolvable spherical harmonic degree of shape
(when SHGrid) is greater or equal to this value. If this is not the
case, aliasing will occur. In practice, for accurate results, the
effective bandwidth needs only to be about three times the size of L,
though this should be verified for each application. The effective
bandwidth of shape (when SHCoeffs) can be increased by preprocessing
with the method pad(), or by increaesing the value of lmax_grid (when
SHGrid). | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravcoeffs.py#L676-L794 | train | 203,891 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.to_file | def to_file(self, filename, format='shtools', header=None, errors=False,
**kwargs):
"""
Save spherical harmonic coefficients to a file.
Usage
-----
x.to_file(filename, [format='shtools', header, errors])
x.to_file(filename, [format='npy', **kwargs])
Parameters
----------
filename : str
Name of the output file.
format : str, optional, default = 'shtools'
'shtools' or 'npy'. See method from_file() for more information.
header : str, optional, default = None
A header string written to an 'shtools'-formatted file directly
before the spherical harmonic coefficients.
errors : bool, optional, default = False
If True, save the errors in the file (for 'shtools' formatted
files only).
**kwargs : keyword argument list, optional for format = 'npy'
Keyword arguments of numpy.save().
Description
-----------
If format='shtools', the coefficients and meta-data will be written to
an ascii formatted file. The first line is an optional user provided
header line, and the following line provides the attributes r0, gm,
omega, and lmax. The spherical harmonic coefficients are then listed,
with increasing degree and order, with the format
l, m, coeffs[0, l, m], coeffs[1, l, m]
where l and m are the spherical harmonic degree and order,
respectively. If the errors are to be saved, the format of each line
will be
l, m, coeffs[0, l, m], coeffs[1, l, m], error[0, l, m], error[1, l, m]
If format='npy', the spherical harmonic coefficients (but not the
meta-data nor errors) will be saved to a binary numpy 'npy' file using
numpy.save().
"""
if format is 'shtools':
if errors is True and self.errors is None:
raise ValueError('Can not save errors when then have not been '
'initialized.')
if self.omega is None:
omega = 0.
else:
omega = self.omega
with open(filename, mode='w') as file:
if header is not None:
file.write(header + '\n')
file.write('{:.16e}, {:.16e}, {:.16e}, {:d}\n'.format(
self.r0, self.gm, omega, self.lmax))
for l in range(self.lmax+1):
for m in range(l+1):
if errors is True:
file.write('{:d}, {:d}, {:.16e}, {:.16e}, '
'{:.16e}, {:.16e}\n'
.format(l, m, self.coeffs[0, l, m],
self.coeffs[1, l, m],
self.errors[0, l, m],
self.errors[1, l, m]))
else:
file.write('{:d}, {:d}, {:.16e}, {:.16e}\n'
.format(l, m, self.coeffs[0, l, m],
self.coeffs[1, l, m]))
elif format is 'npy':
_np.save(filename, self.coeffs, **kwargs)
else:
raise NotImplementedError(
'format={:s} not implemented'.format(repr(format))) | python | def to_file(self, filename, format='shtools', header=None, errors=False,
**kwargs):
"""
Save spherical harmonic coefficients to a file.
Usage
-----
x.to_file(filename, [format='shtools', header, errors])
x.to_file(filename, [format='npy', **kwargs])
Parameters
----------
filename : str
Name of the output file.
format : str, optional, default = 'shtools'
'shtools' or 'npy'. See method from_file() for more information.
header : str, optional, default = None
A header string written to an 'shtools'-formatted file directly
before the spherical harmonic coefficients.
errors : bool, optional, default = False
If True, save the errors in the file (for 'shtools' formatted
files only).
**kwargs : keyword argument list, optional for format = 'npy'
Keyword arguments of numpy.save().
Description
-----------
If format='shtools', the coefficients and meta-data will be written to
an ascii formatted file. The first line is an optional user provided
header line, and the following line provides the attributes r0, gm,
omega, and lmax. The spherical harmonic coefficients are then listed,
with increasing degree and order, with the format
l, m, coeffs[0, l, m], coeffs[1, l, m]
where l and m are the spherical harmonic degree and order,
respectively. If the errors are to be saved, the format of each line
will be
l, m, coeffs[0, l, m], coeffs[1, l, m], error[0, l, m], error[1, l, m]
If format='npy', the spherical harmonic coefficients (but not the
meta-data nor errors) will be saved to a binary numpy 'npy' file using
numpy.save().
"""
if format is 'shtools':
if errors is True and self.errors is None:
raise ValueError('Can not save errors when then have not been '
'initialized.')
if self.omega is None:
omega = 0.
else:
omega = self.omega
with open(filename, mode='w') as file:
if header is not None:
file.write(header + '\n')
file.write('{:.16e}, {:.16e}, {:.16e}, {:d}\n'.format(
self.r0, self.gm, omega, self.lmax))
for l in range(self.lmax+1):
for m in range(l+1):
if errors is True:
file.write('{:d}, {:d}, {:.16e}, {:.16e}, '
'{:.16e}, {:.16e}\n'
.format(l, m, self.coeffs[0, l, m],
self.coeffs[1, l, m],
self.errors[0, l, m],
self.errors[1, l, m]))
else:
file.write('{:d}, {:d}, {:.16e}, {:.16e}\n'
.format(l, m, self.coeffs[0, l, m],
self.coeffs[1, l, m]))
elif format is 'npy':
_np.save(filename, self.coeffs, **kwargs)
else:
raise NotImplementedError(
'format={:s} not implemented'.format(repr(format))) | [
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Usage
-----
x.to_file(filename, [format='shtools', header, errors])
x.to_file(filename, [format='npy', **kwargs])
Parameters
----------
filename : str
Name of the output file.
format : str, optional, default = 'shtools'
'shtools' or 'npy'. See method from_file() for more information.
header : str, optional, default = None
A header string written to an 'shtools'-formatted file directly
before the spherical harmonic coefficients.
errors : bool, optional, default = False
If True, save the errors in the file (for 'shtools' formatted
files only).
**kwargs : keyword argument list, optional for format = 'npy'
Keyword arguments of numpy.save().
Description
-----------
If format='shtools', the coefficients and meta-data will be written to
an ascii formatted file. The first line is an optional user provided
header line, and the following line provides the attributes r0, gm,
omega, and lmax. The spherical harmonic coefficients are then listed,
with increasing degree and order, with the format
l, m, coeffs[0, l, m], coeffs[1, l, m]
where l and m are the spherical harmonic degree and order,
respectively. If the errors are to be saved, the format of each line
will be
l, m, coeffs[0, l, m], coeffs[1, l, m], error[0, l, m], error[1, l, m]
If format='npy', the spherical harmonic coefficients (but not the
meta-data nor errors) will be saved to a binary numpy 'npy' file using
numpy.save(). | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravcoeffs.py#L853-L930 | train | 203,892 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.change_ref | def change_ref(self, gm=None, r0=None, lmax=None):
"""
Return a new SHGravCoeffs class instance with a different reference gm
or r0.
Usage
-----
clm = x.change_ref([gm, r0, lmax])
Returns
-------
clm : SHGravCoeffs class instance.
Parameters
----------
gm : float, optional, default = self.gm
The gravitational constant time the mass that is associated with
the gravitational potential coefficients.
r0 : float, optional, default = self.r0
The reference radius of the spherical harmonic coefficients.
lmax : int, optional, default = self.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method returns a new class instance of the gravitational
potential, but using a difference reference gm or r0. When
changing the reference radius r0, the spherical harmonic coefficients
will be upward or downward continued under the assumption that the
reference radius is exterior to the body.
"""
if lmax is None:
lmax = self.lmax
clm = self.pad(lmax)
if gm is not None and gm != self.gm:
clm.coeffs *= self.gm / gm
clm.gm = gm
if self.errors is not None:
clm.errors *= self.gm / gm
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
if self.errors is not None:
clm.errors[:, l, :l+1] *= (self.r0 / r0)**l
clm.r0 = r0
return clm | python | def change_ref(self, gm=None, r0=None, lmax=None):
"""
Return a new SHGravCoeffs class instance with a different reference gm
or r0.
Usage
-----
clm = x.change_ref([gm, r0, lmax])
Returns
-------
clm : SHGravCoeffs class instance.
Parameters
----------
gm : float, optional, default = self.gm
The gravitational constant time the mass that is associated with
the gravitational potential coefficients.
r0 : float, optional, default = self.r0
The reference radius of the spherical harmonic coefficients.
lmax : int, optional, default = self.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method returns a new class instance of the gravitational
potential, but using a difference reference gm or r0. When
changing the reference radius r0, the spherical harmonic coefficients
will be upward or downward continued under the assumption that the
reference radius is exterior to the body.
"""
if lmax is None:
lmax = self.lmax
clm = self.pad(lmax)
if gm is not None and gm != self.gm:
clm.coeffs *= self.gm / gm
clm.gm = gm
if self.errors is not None:
clm.errors *= self.gm / gm
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
if self.errors is not None:
clm.errors[:, l, :l+1] *= (self.r0 / r0)**l
clm.r0 = r0
return clm | [
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or r0.
Usage
-----
clm = x.change_ref([gm, r0, lmax])
Returns
-------
clm : SHGravCoeffs class instance.
Parameters
----------
gm : float, optional, default = self.gm
The gravitational constant time the mass that is associated with
the gravitational potential coefficients.
r0 : float, optional, default = self.r0
The reference radius of the spherical harmonic coefficients.
lmax : int, optional, default = self.lmax
Maximum spherical harmonic degree to output.
Description
-----------
This method returns a new class instance of the gravitational
potential, but using a difference reference gm or r0. When
changing the reference radius r0, the spherical harmonic coefficients
will be upward or downward continued under the assumption that the
reference radius is exterior to the body. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.expand | def expand(self, a=None, f=None, lmax=None, lmax_calc=None,
normal_gravity=True, sampling=2):
"""
Create 2D cylindrical maps on a flattened and rotating ellipsoid of all
three components of the gravity field, the gravity disturbance, and the
gravitational potential, and return as a SHGravGrid class instance.
Usage
-----
grav = x.expand([a, f, lmax, lmax_calc, normal_gravity, sampling])
Returns
-------
grav : SHGravGrid class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree, which determines the number
of samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
normal_gravity : optional, bool, default = True
If True, the normal gravity (the gravitational acceleration on the
ellipsoid) will be subtracted from the total gravity, yielding the
"gravity disturbance." This is done using Somigliana's formula
(after converting geocentric to geodetic coordinates).
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps of the three
components of the gravity field, the total field, and the gravitational
potential, and return these as an SHGravGrid class instance. Each
map is stored as an SHGrid class instance using Driscoll and Healy
grids that are either equally sampled (n by n) or equally spaced
(n by 2n) in latitude and longitude. All grids use geocentric
coordinates, the output is in SI units, and the sign of the radial
components is positive when directed upwards. If the optional angular
rotation rate omega is specified in the SHGravCoeffs instance, the
potential and radial gravitational acceleration will be calculated in a
body-fixed rotating reference frame. If normal_gravity is set to True,
the normal gravity will be removed from the total field, yielding the
gravity disturbance.
The gravitational potential is given by
V = GM/r Sum_{l=0}^lmax (r0/r)^l Sum_{m=-l}^l C_{lm} Y_{lm},
and the gravitational acceleration is
B = Grad V.
The coefficients are referenced to the radius r0, and the function is
computed on a flattened ellipsoid with semi-major axis a (i.e., the
mean equatorial radius) and flattening f. To convert m/s^2 to mGals,
multiply the gravity grids by 10^5.
"""
if a is None:
a = self.r0
if f is None:
f = 0.
if normal_gravity is True:
ng = 1
else:
ng = 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='4pi', csphase=1)
else:
coeffs = self.to_array(normalization='4pi', csphase=1)
rad, theta, phi, total, pot = _MakeGravGridDH(
coeffs, self.gm, self.r0, a=a, f=f, lmax=lmax,
lmax_calc=lmax_calc, sampling=sampling, omega=self.omega,
normal_gravity=ng)
return _SHGravGrid(rad, theta, phi, total, pot, self.gm, a, f,
self.omega, normal_gravity, lmax, lmax_calc) | python | def expand(self, a=None, f=None, lmax=None, lmax_calc=None,
normal_gravity=True, sampling=2):
"""
Create 2D cylindrical maps on a flattened and rotating ellipsoid of all
three components of the gravity field, the gravity disturbance, and the
gravitational potential, and return as a SHGravGrid class instance.
Usage
-----
grav = x.expand([a, f, lmax, lmax_calc, normal_gravity, sampling])
Returns
-------
grav : SHGravGrid class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree, which determines the number
of samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
normal_gravity : optional, bool, default = True
If True, the normal gravity (the gravitational acceleration on the
ellipsoid) will be subtracted from the total gravity, yielding the
"gravity disturbance." This is done using Somigliana's formula
(after converting geocentric to geodetic coordinates).
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps of the three
components of the gravity field, the total field, and the gravitational
potential, and return these as an SHGravGrid class instance. Each
map is stored as an SHGrid class instance using Driscoll and Healy
grids that are either equally sampled (n by n) or equally spaced
(n by 2n) in latitude and longitude. All grids use geocentric
coordinates, the output is in SI units, and the sign of the radial
components is positive when directed upwards. If the optional angular
rotation rate omega is specified in the SHGravCoeffs instance, the
potential and radial gravitational acceleration will be calculated in a
body-fixed rotating reference frame. If normal_gravity is set to True,
the normal gravity will be removed from the total field, yielding the
gravity disturbance.
The gravitational potential is given by
V = GM/r Sum_{l=0}^lmax (r0/r)^l Sum_{m=-l}^l C_{lm} Y_{lm},
and the gravitational acceleration is
B = Grad V.
The coefficients are referenced to the radius r0, and the function is
computed on a flattened ellipsoid with semi-major axis a (i.e., the
mean equatorial radius) and flattening f. To convert m/s^2 to mGals,
multiply the gravity grids by 10^5.
"""
if a is None:
a = self.r0
if f is None:
f = 0.
if normal_gravity is True:
ng = 1
else:
ng = 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='4pi', csphase=1)
else:
coeffs = self.to_array(normalization='4pi', csphase=1)
rad, theta, phi, total, pot = _MakeGravGridDH(
coeffs, self.gm, self.r0, a=a, f=f, lmax=lmax,
lmax_calc=lmax_calc, sampling=sampling, omega=self.omega,
normal_gravity=ng)
return _SHGravGrid(rad, theta, phi, total, pot, self.gm, a, f,
self.omega, normal_gravity, lmax, lmax_calc) | [
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"="... | Create 2D cylindrical maps on a flattened and rotating ellipsoid of all
three components of the gravity field, the gravity disturbance, and the
gravitational potential, and return as a SHGravGrid class instance.
Usage
-----
grav = x.expand([a, f, lmax, lmax_calc, normal_gravity, sampling])
Returns
-------
grav : SHGravGrid class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree, which determines the number
of samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
normal_gravity : optional, bool, default = True
If True, the normal gravity (the gravitational acceleration on the
ellipsoid) will be subtracted from the total gravity, yielding the
"gravity disturbance." This is done using Somigliana's formula
(after converting geocentric to geodetic coordinates).
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps of the three
components of the gravity field, the total field, and the gravitational
potential, and return these as an SHGravGrid class instance. Each
map is stored as an SHGrid class instance using Driscoll and Healy
grids that are either equally sampled (n by n) or equally spaced
(n by 2n) in latitude and longitude. All grids use geocentric
coordinates, the output is in SI units, and the sign of the radial
components is positive when directed upwards. If the optional angular
rotation rate omega is specified in the SHGravCoeffs instance, the
potential and radial gravitational acceleration will be calculated in a
body-fixed rotating reference frame. If normal_gravity is set to True,
the normal gravity will be removed from the total field, yielding the
gravity disturbance.
The gravitational potential is given by
V = GM/r Sum_{l=0}^lmax (r0/r)^l Sum_{m=-l}^l C_{lm} Y_{lm},
and the gravitational acceleration is
B = Grad V.
The coefficients are referenced to the radius r0, and the function is
computed on a flattened ellipsoid with semi-major axis a (i.e., the
mean equatorial radius) and flattening f. To convert m/s^2 to mGals,
multiply the gravity grids by 10^5. | [
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SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.tensor | def tensor(self, a=None, f=None, lmax=None, lmax_calc=None, degree0=False,
sampling=2):
"""
Create 2D cylindrical maps on a flattened ellipsoid of the 9
components of the gravity "gradient" tensor in a local north-oriented
reference frame, and return an SHGravTensor class instance.
Usage
-----
tensor = x.tensor([a, f, lmax, lmax_calc, sampling])
Returns
-------
tensor : SHGravTensor class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number of
samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
degree0 : optional, default = False
If True, include the degree-0 term when calculating the tensor. If
False, set the degree-0 term to zero.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps for the 9
components of the gravity 'gradient' tensor and return an SHGravTensor
class instance. The components are
(Vxx, Vxy, Vxz)
(Vyx, Vyy, Vyz)
(Vzx, Vzy, Vzz)
where the reference frame is north-oriented, where x points north, y
points west, and z points upward (all tangent or perpendicular to a
sphere of radius r, where r is the local radius of the flattened
ellipsoid). The gravitational potential is defined as
V = GM/r Sum_{l=0}^lmax (r0/r)^l Sum_{m=-l}^l C_{lm} Y_{lm},
where r0 is the reference radius of the spherical harmonic coefficients
Clm, and the gravitational acceleration is
B = Grad V.
The components of the gravity tensor are calculated according to eq. 1
in Petrovskaya and Vershkov (2006), which is based on eq. 3.28 in Reed
(1973) (noting that Reed's equations are in terms of latitude and that
the y axis points east):
Vzz = Vrr
Vxx = 1/r Vr + 1/r^2 Vtt
Vyy = 1/r Vr + 1/r^2 /tan(t) Vt + 1/r^2 /sin(t)^2 Vpp
Vxy = 1/r^2 /sin(t) Vtp - cos(t)/sin(t)^2 /r^2 Vp
Vxz = 1/r^2 Vt - 1/r Vrt
Vyz = 1/r^2 /sin(t) Vp - 1/r /sin(t) Vrp
where r, t, p stand for radius, theta, and phi, respectively, and
subscripts on V denote partial derivatives. The output grids are in
units of Eotvos (10^-9 s^-2).
References
----------
Reed, G.B., Application of kinematical geodesy for determining
the short wave length components of the gravity field by satellite
gradiometry, Ohio State University, Dept. of Geod. Sciences, Rep. No.
201, Columbus, Ohio, 1973.
Petrovskaya, M.S. and A.N. Vershkov, Non-singular expressions for the
gravity gradients in the local north-oriented and orbital reference
frames, J. Geod., 80, 117-127, 2006.
"""
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='4pi', csphase=1)
else:
coeffs = self.to_array(normalization='4pi', csphase=1)
if degree0 is False:
coeffs[0, 0, 0] = 0.
vxx, vyy, vzz, vxy, vxz, vyz = _MakeGravGradGridDH(
coeffs, self.gm, self.r0, a=a, f=f, lmax=lmax,
lmax_calc=lmax_calc, sampling=sampling)
return _SHGravTensor(1.e9*vxx, 1.e9*vyy, 1.e9*vzz, 1.e9*vxy, 1.e9*vxz,
1.e9*vyz, self.gm, a, f, lmax, lmax_calc) | python | def tensor(self, a=None, f=None, lmax=None, lmax_calc=None, degree0=False,
sampling=2):
"""
Create 2D cylindrical maps on a flattened ellipsoid of the 9
components of the gravity "gradient" tensor in a local north-oriented
reference frame, and return an SHGravTensor class instance.
Usage
-----
tensor = x.tensor([a, f, lmax, lmax_calc, sampling])
Returns
-------
tensor : SHGravTensor class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number of
samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
degree0 : optional, default = False
If True, include the degree-0 term when calculating the tensor. If
False, set the degree-0 term to zero.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps for the 9
components of the gravity 'gradient' tensor and return an SHGravTensor
class instance. The components are
(Vxx, Vxy, Vxz)
(Vyx, Vyy, Vyz)
(Vzx, Vzy, Vzz)
where the reference frame is north-oriented, where x points north, y
points west, and z points upward (all tangent or perpendicular to a
sphere of radius r, where r is the local radius of the flattened
ellipsoid). The gravitational potential is defined as
V = GM/r Sum_{l=0}^lmax (r0/r)^l Sum_{m=-l}^l C_{lm} Y_{lm},
where r0 is the reference radius of the spherical harmonic coefficients
Clm, and the gravitational acceleration is
B = Grad V.
The components of the gravity tensor are calculated according to eq. 1
in Petrovskaya and Vershkov (2006), which is based on eq. 3.28 in Reed
(1973) (noting that Reed's equations are in terms of latitude and that
the y axis points east):
Vzz = Vrr
Vxx = 1/r Vr + 1/r^2 Vtt
Vyy = 1/r Vr + 1/r^2 /tan(t) Vt + 1/r^2 /sin(t)^2 Vpp
Vxy = 1/r^2 /sin(t) Vtp - cos(t)/sin(t)^2 /r^2 Vp
Vxz = 1/r^2 Vt - 1/r Vrt
Vyz = 1/r^2 /sin(t) Vp - 1/r /sin(t) Vrp
where r, t, p stand for radius, theta, and phi, respectively, and
subscripts on V denote partial derivatives. The output grids are in
units of Eotvos (10^-9 s^-2).
References
----------
Reed, G.B., Application of kinematical geodesy for determining
the short wave length components of the gravity field by satellite
gradiometry, Ohio State University, Dept. of Geod. Sciences, Rep. No.
201, Columbus, Ohio, 1973.
Petrovskaya, M.S. and A.N. Vershkov, Non-singular expressions for the
gravity gradients in the local north-oriented and orbital reference
frames, J. Geod., 80, 117-127, 2006.
"""
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='4pi', csphase=1)
else:
coeffs = self.to_array(normalization='4pi', csphase=1)
if degree0 is False:
coeffs[0, 0, 0] = 0.
vxx, vyy, vzz, vxy, vxz, vyz = _MakeGravGradGridDH(
coeffs, self.gm, self.r0, a=a, f=f, lmax=lmax,
lmax_calc=lmax_calc, sampling=sampling)
return _SHGravTensor(1.e9*vxx, 1.e9*vyy, 1.e9*vzz, 1.e9*vxy, 1.e9*vxz,
1.e9*vyz, self.gm, a, f, lmax, lmax_calc) | [
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components of the gravity "gradient" tensor in a local north-oriented
reference frame, and return an SHGravTensor class instance.
Usage
-----
tensor = x.tensor([a, f, lmax, lmax_calc, sampling])
Returns
-------
tensor : SHGravTensor class instance.
Parameters
----------
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number of
samples of the output grids, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
degree0 : optional, default = False
If True, include the degree-0 term when calculating the tensor. If
False, set the degree-0 term to zero.
sampling : optional, integer, default = 2
If 1 the output grids are equally sampled (n by n). If 2 (default),
the grids are equally spaced in degrees (n by 2n).
Description
-----------
This method will create 2-dimensional cylindrical maps for the 9
components of the gravity 'gradient' tensor and return an SHGravTensor
class instance. The components are
(Vxx, Vxy, Vxz)
(Vyx, Vyy, Vyz)
(Vzx, Vzy, Vzz)
where the reference frame is north-oriented, where x points north, y
points west, and z points upward (all tangent or perpendicular to a
sphere of radius r, where r is the local radius of the flattened
ellipsoid). The gravitational potential is defined as
V = GM/r Sum_{l=0}^lmax (r0/r)^l Sum_{m=-l}^l C_{lm} Y_{lm},
where r0 is the reference radius of the spherical harmonic coefficients
Clm, and the gravitational acceleration is
B = Grad V.
The components of the gravity tensor are calculated according to eq. 1
in Petrovskaya and Vershkov (2006), which is based on eq. 3.28 in Reed
(1973) (noting that Reed's equations are in terms of latitude and that
the y axis points east):
Vzz = Vrr
Vxx = 1/r Vr + 1/r^2 Vtt
Vyy = 1/r Vr + 1/r^2 /tan(t) Vt + 1/r^2 /sin(t)^2 Vpp
Vxy = 1/r^2 /sin(t) Vtp - cos(t)/sin(t)^2 /r^2 Vp
Vxz = 1/r^2 Vt - 1/r Vrt
Vyz = 1/r^2 /sin(t) Vp - 1/r /sin(t) Vrp
where r, t, p stand for radius, theta, and phi, respectively, and
subscripts on V denote partial derivatives. The output grids are in
units of Eotvos (10^-9 s^-2).
References
----------
Reed, G.B., Application of kinematical geodesy for determining
the short wave length components of the gravity field by satellite
gradiometry, Ohio State University, Dept. of Geod. Sciences, Rep. No.
201, Columbus, Ohio, 1973.
Petrovskaya, M.S. and A.N. Vershkov, Non-singular expressions for the
gravity gradients in the local north-oriented and orbital reference
frames, J. Geod., 80, 117-127, 2006. | [
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"SHGravTensor",... | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravcoeffs.py#L1708-L1815 | train | 203,895 |
SHTOOLS/SHTOOLS | pyshtools/shclasses/shgravcoeffs.py | SHGravCoeffs.geoid | def geoid(self, potref, a=None, f=None, r=None, omega=None, order=2,
lmax=None, lmax_calc=None, grid='DH2'):
"""
Create a global map of the height of the geoid and return an SHGeoid
class instance.
Usage
-----
geoid = x.geoid(potref, [a, f, r, omega, order, lmax, lmax_calc, grid])
Returns
-------
geoid : SHGeoid class instance.
Parameters
----------
potref : float
The value of the potential on the chosen geoid, in m2 / s2.
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
r : optional, float, default = self.r0
The radius of the reference sphere that the Taylor expansion of the
potential is calculated on.
order : optional, integer, default = 2
The order of the Taylor series expansion of the potential about the
reference radius r. This can be either 1, 2, or 3.
omega : optional, float, default = self.omega
The angular rotation rate of the planet.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number
of samples of the output grid, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, or
'DH2' for an equidistant lat/lon grid with nlon=2*nlat.
Description
-----------
This method will create a global map of the geoid height, accurate to
either first, second, or third order, using the method described in
Wieczorek (2007; equation 19-20). The algorithm expands the potential
in a Taylor series on a spherical interface of radius r, and computes
the height above this interface to the potential potref exactly from
the linear, quadratic, or cubic equation at each grid point. If the
optional parameters a and f are specified, the geoid height will be
referenced to a flattened ellipsoid with semi-major axis a and
flattening f. The pseudo-rotational potential is explicitly accounted
for by using the angular rotation rate omega of the planet in the
SHGravCoeffs class instance. If omega is explicitly specified for this
method, it will override the value present in the class instance.
Reference
----------
Wieczorek, M. A. Gravity and topography of the terrestrial planets,
Treatise on Geophysics, 10, 165-206, 2007.
"""
if a is None:
a = self.r0
if f is None:
f = 0.
if r is None:
r = self.r0
if lmax is None:
lmax = self.lmax
if lmax_calc is None:
lmax_calc = lmax
if grid.upper() in ('DH', 'DH1'):
sampling = 1
elif grid.upper() == 'DH2':
sampling = 2
else:
raise ValueError(
"grid must be 'DH', 'DH1', or 'DH2'. "
"Input value was {:s}".format(repr(grid)))
if self.errors is not None:
coeffs, errors = self.to_array(normalization='4pi', csphase=1)
else:
coeffs = self.to_array(normalization='4pi', csphase=1)
if omega is None:
omega = self.omega
geoid = _MakeGeoidGridDH(coeffs, self.r0, self.gm, potref, lmax=lmax,
omega=omega, r=r, order=order,
lmax_calc=lmax_calc, a=a, f=f,
sampling=sampling)
return _SHGeoid(geoid, self.gm, potref, a, f, omega, r, order,
lmax, lmax_calc) | python | def geoid(self, potref, a=None, f=None, r=None, omega=None, order=2,
lmax=None, lmax_calc=None, grid='DH2'):
"""
Create a global map of the height of the geoid and return an SHGeoid
class instance.
Usage
-----
geoid = x.geoid(potref, [a, f, r, omega, order, lmax, lmax_calc, grid])
Returns
-------
geoid : SHGeoid class instance.
Parameters
----------
potref : float
The value of the potential on the chosen geoid, in m2 / s2.
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
r : optional, float, default = self.r0
The radius of the reference sphere that the Taylor expansion of the
potential is calculated on.
order : optional, integer, default = 2
The order of the Taylor series expansion of the potential about the
reference radius r. This can be either 1, 2, or 3.
omega : optional, float, default = self.omega
The angular rotation rate of the planet.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number
of samples of the output grid, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, or
'DH2' for an equidistant lat/lon grid with nlon=2*nlat.
Description
-----------
This method will create a global map of the geoid height, accurate to
either first, second, or third order, using the method described in
Wieczorek (2007; equation 19-20). The algorithm expands the potential
in a Taylor series on a spherical interface of radius r, and computes
the height above this interface to the potential potref exactly from
the linear, quadratic, or cubic equation at each grid point. If the
optional parameters a and f are specified, the geoid height will be
referenced to a flattened ellipsoid with semi-major axis a and
flattening f. The pseudo-rotational potential is explicitly accounted
for by using the angular rotation rate omega of the planet in the
SHGravCoeffs class instance. If omega is explicitly specified for this
method, it will override the value present in the class instance.
Reference
----------
Wieczorek, M. A. Gravity and topography of the terrestrial planets,
Treatise on Geophysics, 10, 165-206, 2007.
"""
if a is None:
a = self.r0
if f is None:
f = 0.
if r is None:
r = self.r0
if lmax is None:
lmax = self.lmax
if lmax_calc is None:
lmax_calc = lmax
if grid.upper() in ('DH', 'DH1'):
sampling = 1
elif grid.upper() == 'DH2':
sampling = 2
else:
raise ValueError(
"grid must be 'DH', 'DH1', or 'DH2'. "
"Input value was {:s}".format(repr(grid)))
if self.errors is not None:
coeffs, errors = self.to_array(normalization='4pi', csphase=1)
else:
coeffs = self.to_array(normalization='4pi', csphase=1)
if omega is None:
omega = self.omega
geoid = _MakeGeoidGridDH(coeffs, self.r0, self.gm, potref, lmax=lmax,
omega=omega, r=r, order=order,
lmax_calc=lmax_calc, a=a, f=f,
sampling=sampling)
return _SHGeoid(geoid, self.gm, potref, a, f, omega, r, order,
lmax, lmax_calc) | [
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"'D... | Create a global map of the height of the geoid and return an SHGeoid
class instance.
Usage
-----
geoid = x.geoid(potref, [a, f, r, omega, order, lmax, lmax_calc, grid])
Returns
-------
geoid : SHGeoid class instance.
Parameters
----------
potref : float
The value of the potential on the chosen geoid, in m2 / s2.
a : optional, float, default = self.r0
The semi-major axis of the flattened ellipsoid on which the field
is computed.
f : optional, float, default = 0
The flattening of the reference ellipsoid: f=(a-b)/a.
r : optional, float, default = self.r0
The radius of the reference sphere that the Taylor expansion of the
potential is calculated on.
order : optional, integer, default = 2
The order of the Taylor series expansion of the potential about the
reference radius r. This can be either 1, 2, or 3.
omega : optional, float, default = self.omega
The angular rotation rate of the planet.
lmax : optional, integer, default = self.lmax
The maximum spherical harmonic degree that determines the number
of samples of the output grid, n=2lmax+2, and the latitudinal
sampling interval, 90/(lmax+1).
lmax_calc : optional, integer, default = lmax
The maximum spherical harmonic degree used in evaluating the
functions. This must be less than or equal to lmax.
grid : str, optional, default = 'DH2'
'DH' or 'DH1' for an equisampled lat/lon grid with nlat=nlon, or
'DH2' for an equidistant lat/lon grid with nlon=2*nlat.
Description
-----------
This method will create a global map of the geoid height, accurate to
either first, second, or third order, using the method described in
Wieczorek (2007; equation 19-20). The algorithm expands the potential
in a Taylor series on a spherical interface of radius r, and computes
the height above this interface to the potential potref exactly from
the linear, quadratic, or cubic equation at each grid point. If the
optional parameters a and f are specified, the geoid height will be
referenced to a flattened ellipsoid with semi-major axis a and
flattening f. The pseudo-rotational potential is explicitly accounted
for by using the angular rotation rate omega of the planet in the
SHGravCoeffs class instance. If omega is explicitly specified for this
method, it will override the value present in the class instance.
Reference
----------
Wieczorek, M. A. Gravity and topography of the terrestrial planets,
Treatise on Geophysics, 10, 165-206, 2007. | [
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] | 9a115cf83002df2ddec6b7f41aeb6be688e285de | https://github.com/SHTOOLS/SHTOOLS/blob/9a115cf83002df2ddec6b7f41aeb6be688e285de/pyshtools/shclasses/shgravcoeffs.py#L1817-L1913 | train | 203,896 |
toidi/hadoop-yarn-api-python-client | yarn_api_client/node_manager.py | NodeManager.node_application | def node_application(self, application_id):
"""
An application resource contains information about a particular
application that was run or is running on this NodeManager.
:param str application_id: The application id
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response`
"""
path = '/ws/v1/node/apps/{appid}'.format(appid=application_id)
return self.request(path) | python | def node_application(self, application_id):
"""
An application resource contains information about a particular
application that was run or is running on this NodeManager.
:param str application_id: The application id
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response`
"""
path = '/ws/v1/node/apps/{appid}'.format(appid=application_id)
return self.request(path) | [
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application that was run or is running on this NodeManager.
:param str application_id: The application id
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response` | [
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] | d245bd41808879be6637acfd7460633c0c7dfdd6 | https://github.com/toidi/hadoop-yarn-api-python-client/blob/d245bd41808879be6637acfd7460633c0c7dfdd6/yarn_api_client/node_manager.py#L58-L69 | train | 203,897 |
toidi/hadoop-yarn-api-python-client | yarn_api_client/node_manager.py | NodeManager.node_container | def node_container(self, container_id):
"""
A container resource contains information about a particular container
that is running on this NodeManager.
:param str container_id: The container id
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response`
"""
path = '/ws/v1/node/containers/{containerid}'.format(
containerid=container_id)
return self.request(path) | python | def node_container(self, container_id):
"""
A container resource contains information about a particular container
that is running on this NodeManager.
:param str container_id: The container id
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response`
"""
path = '/ws/v1/node/containers/{containerid}'.format(
containerid=container_id)
return self.request(path) | [
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that is running on this NodeManager.
:param str container_id: The container id
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response` | [
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] | d245bd41808879be6637acfd7460633c0c7dfdd6 | https://github.com/toidi/hadoop-yarn-api-python-client/blob/d245bd41808879be6637acfd7460633c0c7dfdd6/yarn_api_client/node_manager.py#L83-L95 | train | 203,898 |
toidi/hadoop-yarn-api-python-client | yarn_api_client/resource_manager.py | ResourceManager.cluster_application_statistics | def cluster_application_statistics(self, state_list=None,
application_type_list=None):
"""
With the Application Statistics API, you can obtain a collection of
triples, each of which contains the application type, the application
state and the number of applications of this type and this state in
ResourceManager context.
This method work in Hadoop > 2.0.0
:param list state_list: states of the applications, specified as a
comma-separated list. If states is not provided, the API will
enumerate all application states and return the counts of them.
:param list application_type_list: types of the applications,
specified as a comma-separated list. If application_types is not
provided, the API will count the applications of any application
type. In this case, the response shows * to indicate any
application type. Note that we only support at most one
applicationType temporarily. Otherwise, users will expect
an BadRequestException.
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response`
"""
path = '/ws/v1/cluster/appstatistics'
# TODO: validate state argument
states = ','.join(state_list) if state_list is not None else None
if application_type_list is not None:
application_types = ','.join(application_type_list)
else:
application_types = None
loc_args = (
('states', states),
('applicationTypes', application_types))
params = self.construct_parameters(loc_args)
return self.request(path, **params) | python | def cluster_application_statistics(self, state_list=None,
application_type_list=None):
"""
With the Application Statistics API, you can obtain a collection of
triples, each of which contains the application type, the application
state and the number of applications of this type and this state in
ResourceManager context.
This method work in Hadoop > 2.0.0
:param list state_list: states of the applications, specified as a
comma-separated list. If states is not provided, the API will
enumerate all application states and return the counts of them.
:param list application_type_list: types of the applications,
specified as a comma-separated list. If application_types is not
provided, the API will count the applications of any application
type. In this case, the response shows * to indicate any
application type. Note that we only support at most one
applicationType temporarily. Otherwise, users will expect
an BadRequestException.
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response`
"""
path = '/ws/v1/cluster/appstatistics'
# TODO: validate state argument
states = ','.join(state_list) if state_list is not None else None
if application_type_list is not None:
application_types = ','.join(application_type_list)
else:
application_types = None
loc_args = (
('states', states),
('applicationTypes', application_types))
params = self.construct_parameters(loc_args)
return self.request(path, **params) | [
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triples, each of which contains the application type, the application
state and the number of applications of this type and this state in
ResourceManager context.
This method work in Hadoop > 2.0.0
:param list state_list: states of the applications, specified as a
comma-separated list. If states is not provided, the API will
enumerate all application states and return the counts of them.
:param list application_type_list: types of the applications,
specified as a comma-separated list. If application_types is not
provided, the API will count the applications of any application
type. In this case, the response shows * to indicate any
application type. Note that we only support at most one
applicationType temporarily. Otherwise, users will expect
an BadRequestException.
:returns: API response object with JSON data
:rtype: :py:class:`yarn_api_client.base.Response` | [
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"... | d245bd41808879be6637acfd7460633c0c7dfdd6 | https://github.com/toidi/hadoop-yarn-api-python-client/blob/d245bd41808879be6637acfd7460633c0c7dfdd6/yarn_api_client/resource_manager.py#L122-L159 | train | 203,899 |
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