API¶
Main functions¶
-
class
meshslice.meshslice_sph.MeshPlotSph(mesh, lat: Optional[float] = None, lon: Optional[float] = None, rotangle: float = 0.0, depth: Optional[float] = None, rotlat: Optional[float] = None, rotlon: Optional[float] = None, get_mean: bool = True, opacity: bool = False, meshname: str = 'RF', cmapname: str = 'seismic', cmapsym: bool = True, clim: Optional[List[float]] = None, debug: bool = True, outputdir: str = '.', use_ipyvtk: bool = False, figures_only: bool = False, test: bool = False, fmt: str = 'pdf')[source]¶
Utilities¶
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meshslice.utils.cart2geo.cart2geo(x: float, y: float, z: float) -> (<class 'float'>, <class 'float'>, <class 'float'>)[source]¶ Computes Cartesian coordinates from geographical coordinates.
- Parameters
- xfloat or numpy.ndarray or list
Radius
- yfloat or numpy.ndarray or list
Latitude (-90, 90)
- zfloat or numpy.ndarray or list
Longitude (-180, 180)
- Returns
- float or np.ndarray or list, float or np.ndarray or list, float or np.ndarray or list
(r, theta, phi)
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meshslice.utils.cart2sph.cart2sph(x: float, y: float, z: float) -> (<class 'float'>, <class 'float'>, <class 'float'>)[source]¶ Computes Cartesian coordinates from geographical coordinates.
- Parameters
- xfloat or numpy.ndarray or list
Radius
- yfloat or numpy.ndarray or list
Latitude (-90, 90)
- zfloat or numpy.ndarray or list
Longitude (-180, 180)
- Returns
- float or np.ndarray or list, float or np.ndarray or list, float or np.ndarray or list
(r, theta, phi)
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meshslice.utils.geo2cart.geo2cart(r: float, theta: float, phi: float) -> (<class 'float'>, <class 'float'>, <class 'float'>)[source]¶ Computes Cartesian coordinates from geographical coordinates.
- Parameters
- rfloat or numpy.ndarray or list
Radius
- thetafloat or numpy.ndarray or list
Latitude (-90, 90)
- phifloat or numpy.ndarray or list
Longitude (-180, 180)
- Returns
- float or np.ndarray or list, float or np.ndarray or list, float or np.ndarray or list
(x, y, z)
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meshslice.utils.map_axes.map_axes(proj: str = 'moll', central_longitude=0.0) → matplotlib.axes._axes.Axes[source]¶ Creates matplotlib axes with map projection taken from cartopy.
- Parameters
- proj: str, optional
shortname for mapprojection ‘moll’, ‘carr’, by default “moll”
- Returns
- plt.Axes
Matplotlib axes with projection
- Raises
- ValueError
If non supported shortname for axes is given
Notes
- Author
Lucas Sawade (lsawade@princeton.edu)
- Last Modified
2021.01.13 20.30
Examples
>>> from lwsspy import map_axes >>> map_axes()
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meshslice.utils.plot_map.plot_map(fill=True, zorder=None, labelstopright: bool = True, labelsbottomleft: bool = True, borders: bool = True, rivers: bool = False, lakes: bool = False)[source]¶ Plots map into existing axes.
- Parameters
- fillbool, optional
fills the continents in light gray, by default True
- zorderint, optional
zorder of the map, by default -10
- projectioncartopy.crs.projection, optional
projection to be used for the map.
- labelstoprightbool, optional
flag to turn on or off the ticks
- labelsbottomleftbool, optional
flag to turn on or off the ticks
- bordersbool
plot borders. Default True
- riversbool
plot rivers. Default False
- lakesbool
plot lakes. Default True
- Returns
- matplotlib.pyplot.Axes
Axes in which the map was plotted
Notes
- Author
Lucas Sawade (lsawade@princeton.edu)
- Last Modified
2020.01.07 16.30
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meshslice.utils.rotation_matrix.rotation_matrix(axis, theta)[source]¶ Return the rotation matrix associated with counterclockwise rotation about the given axis by theta radians.
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meshslice.utils.sph2cart.sph2cart(r: float, theta: float, phi: float) -> (<class 'float'>, <class 'float'>, <class 'float'>)[source]¶ Computes Cartesian coordinates from geographical coordinates.
- Parameters
- rfloat or numpy.ndarray or list
Radius
- thetafloat or numpy.ndarray or list
Theta (0, pi)
- phifloat or numpy.ndarray or list
phi (0, 2*pi)
- Returns
- float or np.ndarray or list, float or np.ndarray or list, float or np.ndarray or list
(x, y, z)
-
class
meshslice.utils.SphericalNN.SphericalNN(lat, lon)[source]¶ Spherical nearest neighbour queries using scipy’s fast kd-tree implementation.
Notes
- Authors
Lucas Sawade (lsawade@princeton.edu)
- Last Modified
2020.01.07 19.00
- Attributes
- datanumpy.ndarray
cartesian point data array [x,y,z]
- kd_treescipy.spatial.cKDTree
a KDTree used to query data
Methods
query(qlat, qlon)
Query a set of latitudes and longitudes
query_pairs(maximum_distance)
Find pairs of points that are within a certain distance of each other
interp(data, qlat, qlon)
Use the kdtree to interpolate data corresponding to the points of the Kdtree onto a new set of points using nearest neighbor interpolation or weighted nearest neighbor interpolation (default).
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meshslice.utils.updaterc.updaterc(rebuild=False)[source]¶ Updates the rcParams to something generic that looks ok good out of the box.
Args:
- rebuild (bool):
Rebuilds fontcache incase it needs it.
Last modified: Lucas Sawade, 2020.09.15 01.00 (lsawade@princeton.edu)