pygrt.plot

file:

plot.py

author:

Zhu Dengda (zhudengda@mail.iggcas.ac.cn)

date:

2024-07-24

计算结果与断层几何的辅助绘图及配色

pygrt.plot.plot_statsdata(statsdata: ndarray, dist: float, srctype: str, ptype: str, RorI: bool | int = True, fig: Figure | None = None, axs: Axes | None = None)[源代码]

Based on the data read by the read_statsfile function, plot the kernel function \(F(k,\omega)\), the integrand \(F(k,\omega)J_m(kr)k\), and calculate the cumulative integral \(\sum F(k,\omega)J_m(kr)k\) .

备注

Not every source type corresponds to every order and every integration type, see 格林函数分类 for details.

参数:

  • statsdata – return value of read_statsfile function

  • dist – epicentral distance (km)

  • srctype – abbreviation of source type, including EX, VF, HF, DD, DS, SS

  • ptype – integration type (0,1,2,3)

  • RorI – whether to plot real or imaginary part, default is real part, pass 2 to plot both

  • fig – user-defined matplotlib.Figure object, default is None

  • axs – user-defined matplotlib.Axes object array (three elements), default is None

返回:

  • fig - matplotlib.Figure object

  • (ax1,ax2,ax3) - matplotlib.Axes object array

pygrt.plot.plot_statsdata_ptam(statsdata1: ndarray, statsdata2: ndarray, statsdata_ptam: ndarray, dist: float, srctype: str, ptype: str, RorI: bool | int = True, fig: Figure | None = None, axs: Axes | None = None)[源代码]

Based on data read by the read_statsfile_ptam function, simply calculate and plot the cumulative integral as well as the peak/trough positions used by PTAM.

备注

Not every source type corresponds to every order and every integration type, see 格林函数分类 for details.

参数:

  • statsdata1 – integral process data during DWM or FIM

  • statsdata2 – integral process data during PTAM

  • statsdata_ptam – peak/trough positions and amplitudes from PTAM

  • dist – epicentral distance (km)

  • srctype – abbreviation of source type, including EX, VF, HF, DD, DS, SS

  • ptype – integration type (0, 1, 2, 3)

  • RorI – whether to plot real or imaginary part, default is real part, pass 2 to plot both

  • fig – user-defined matplotlib.Figure object, default is None

  • axs – user-defined matplotlib.Axes object array (three elements), default is None

返回:

  • fig - matplotlib.Figure object

  • (ax1, ax2, ax3) - matplotlib.Axes object array

pygrt.plot.plot_dispersion(data: dict, *, varname: str | None = None, title: str | None = None, ax: Axes | None = None, outpath: str | PathLike | None = None)[源代码]

Plot phase- or group-velocity dispersion curves.

参数:

  • data – Dictionary from read_dispersion, or a mapping from panel title to such dictionaries (drawn side by side).

  • varname – Variable to plot, c for phase velocity or u for group velocity. Default is u when present, otherwise c.

  • title – Panel title when data is a single dictionary.

  • ax – Existing matplotlib Axes. Only valid for a single dataset.

  • outpath – If given, save the figure to this path.

返回:

  • fig - matplotlib.Figure object

  • axs - tuple of matplotlib.Axes

pygrt.plot.plot_eigenfunction(data: dict, *, scale: float = 2.0, ifreq: int = 0, title: str | None = None, ax: Axes | None = None, outpath: str | PathLike | None = None)[源代码]

Plot eigenfunctions of different modes at one frequency.

Rayleigh-wave panels show the vertical component as a solid line and the radial component as a dashed line. Love-wave panels show the transverse component. Each mode is offset horizontally by its mode number.

参数:

  • data – Dictionary from read_eigenfunction, or a mapping from panel title to such dictionaries (drawn side by side).

  • scale – Amplitude scale of the plotted eigenfunctions.

  • ifreq – Index among frequencies that have at least one mode. Default is the first frequency.

  • title – Panel title when data is a single dictionary.

  • ax – Existing matplotlib Axes. Only valid for a single dataset.

  • outpath – If given, save the figure to this path.

返回:

  • fig - matplotlib.Figure object

  • axs - tuple of matplotlib.Axes

pygrt.plot.plot_sensitivity(data: dict, *, modes: int | Sequence[int] | None = 0, title: str | None = None, outpath: str | PathLike | None = None)[源代码]

Plot dimensionless phase- or group-velocity sensitivity kernels.

Three panels correspond to \(\alpha\), \(\beta\) and \(\rho\). By default only the fundamental mode (modes=0) is drawn; pass None to draw all modes.

参数:

  • data – Dictionary from read_sensitivity.

  • modes – Mode number, a sequence of mode numbers, or None for all modes. Default is the fundamental mode.

  • title – Figure suptitle.

  • outpath – If given, save the figure to this path.

返回:

  • fig - matplotlib.Figure object

  • axs - tuple of matplotlib.Axes

pygrt.plot.plot_secfunc(data: dict, *, title: str | None = None, ax: Axes | None = None, outpath: str | PathLike | None = None)[源代码]

Plot the real and imaginary parts of secular functions.

参数:

  • data – One dictionary from the list returned by read_secfunc (index the list before passing), or a mapping from panel title to such dictionaries (drawn side by side).

  • title – Panel title when data is a single dictionary.

  • ax – Existing matplotlib Axes. Only valid for a single dataset.

  • outpath – If given, save the figure to this path.

返回:

  • fig - matplotlib.Figure object

  • axs - tuple of matplotlib.Axes

pygrt.plot.plot_sCoulomb_faults_geometry(input_path: str | PathLike, *, outpath: str | PathLike | None = None)[源代码]

Plot horizontal projection and 3D geometry of sCoulomb-format faults.

Coordinates follow sCoulomb conventions: X east, Y north and depth downward, in km. Fault records are read using read_sCoulomb_faults(); row-end text is ignored when plotting.

参数:

  • input_path – sCoulomb-format fault file with two header lines

  • outpath – If given, save the figure to this path

返回:

Figure and a tuple of horizontal and 3D Axes

pygrt.plot.get_anatolia_cmap()[源代码]

Return the Coulomb Anatolia colormap, from blue through white to red.

Colors follow the Coulomb_anatolia.cpt palette used in the gallery. The midpoint is white, suitable for symmetric ranges around zero.

返回:

A Matplotlib LinearSegmentedColormap

pygrt.plot.plot_static_displacement(data: dict, *, src_fault: str | PathLike | None = None, title: str | None = None, clim_ratio: float = 0.5, outpath: str | PathLike | None = None)[源代码]

Quickly inspect static ZNE displacement on a horizontal grid or at arbitrary points.

Color shows vertical displacement and arrows show horizontal displacement. This is a quick auxiliary plot using the Anatolia colormap. Color limits are ±clim_ratio times the maximum absolute vertical displacement. Values outside this symmetric range saturate at the endpoint colors. For nonzero horizontal displacement, a reference arrow above the axes uses Matplotlib’s automatic quiver scale, with a length of 1 cm or a multiple of 10 cm.

参数:

  • data – Variable dictionary from read_nc_variables(), with ZNE components

  • src_fault – Optional sCoulomb fault file whose top edges are drawn in black

  • title – Optional plot title

  • clim_ratio – Color-range fraction of the maximum absolute vertical displacement, in (0, 1]

  • outpath – If given, save the figure to this path

返回:

Figure and Axes

pygrt.plot.plot_static_coulomb(data: dict, *, src_fault: str | PathLike | None = None, clim_ratio: float = 0.5, outpath: str | PathLike | None = None)[源代码]

Quickly inspect shear, normal and Coulomb stress changes on a horizontal grid, in MPa.

This is a quick auxiliary plot using a shared Anatolia color scale. Color limits are ±clim_ratio times the maximum absolute stress across all three components. Values outside this symmetric range saturate at the endpoint colors.

参数:

  • data – Variable dictionary from read_nc_variables() after static Coulomb calculation

  • src_fault – Optional sCoulomb fault file whose top edges are drawn in black

  • clim_ratio – Shared color-range fraction of the maximum absolute stress, in (0, 1]

  • outpath – If given, save the figure to this path

返回:

Figure and a tuple of three Axes