yasuit21/Coupe_in_PyGMT.ipynb Secret

## coupe.py
import numpy as np
import pandas as pd

from pygmt.clib import Session
from pygmt.exceptions import GMTError, GMTInvalidInput
from pygmt.helpers import build_arg_string, fmt_docstring, kwargs_to_strings, use_alias
from pygmt.src.meca import convention_params, convention_code


@fmt_docstring
@use_alias(
    A="section",
    B="frame",
    C="cmap",
    E="extensionfill",
    Fr="labelbox",
    G="compressionfill",
    J="projection",
    L="outline",
    N="no_clip",
    Q="no_file",
    R="region",
    T="nodal",
    V="verbose",
    W="pen",
    c="panel",
    p="perspective",
    t="transparency",
)
@kwargs_to_strings(A="sequence", R="sequence", c="sequence_comma", p="sequence")
def coupe(
    self,
    spec,
    scale,
    convention=None,
    component="full",
    longitude=None,
    latitude=None,
    depth=None,
    section_format='lonlat_lonlat',
    event_name=None,
    **kwargs
):
    r"""
    Plot focal mechanisms in a vertical cross section.
    This function/method is copied from `pygmt.src.meca.meca()`

    Full option list at :gmt-docs:`supplements/seis/coupe.html`

    {aliases}

    Parameters
    ----------
    spec : str, 1-D array, 2-D array, dict, or pd.DataFrame
        Data that contains focal mechanism parameters.

        ``spec`` can be specified in either of the following types:

        - *str*: a file name containing focal mechanism parameters as
          columns. The meaning of each column is:

          - Columns 1 and 2: event longitude and latitude
          - Column 3: event depth (in km)
          - Columns 4 to 3+n: focal mechanism parameters. The number of columns
            *n* depends on the choice of ``convention``, which will be
            described below.
          - Columns 4+n and 5+n: longitude, latitude at which to place
            beachball. Using ``0 0`` will plot the beachball at the longitude,
            latitude given in columns 1 and 2. [optional and requires
            ``offset=True`` to take effect].
          - Text string to appear near the beachball [optional].

        - *1-D array*: focal mechanism parameters of a single event.
          The meanings of columns are the same as above.
        - *2-D array*: focal mechanism parameters of multiple events.
          The meanings of columns are the same as above.
        - *dictionary or pd.DataFrame*: The dictionary keys or pd.DataFrame
          column names determine the focal mechanism convention. For
          different conventions, the following combination of keys are allowed:

          - ``"aki"``: *strike, dip, rake, magnitude*
          - ``"gcmt"``: *strike1, dip1, rake1, strike2, dip2, rake2, mantissa,*
            *exponent*
          - ``"mt"``: *mrr, mtt, mff, mrt, mrf, mtf, exponent*
          - ``"partial"``: *strike1, dip1, strike2, fault_type, magnitude*
          - ``"principal_axis"``: *t_value, t_azimuth, t_plunge, n_value,
            n_azimuth, n_plunge, p_value, p_azimuth, p_plunge, exponent*

          A dictionary may contain values for a single focal mechanism or
          lists of values for multiple focal mechanisms.

          Both dictionary and pd.DataFrame may optionally contain
          keys/column names: ``latitude``, ``longitude``, ``depth``,
          ``plot_longitude``, ``plot_latitude``, and/or ``event_name``.

          If ``spec`` is either a str, a 1-D array or a 2-D array, the
          ``convention`` parameter is required so we know how to interpret the
          columns. If ``spec`` is a dictionary or a pd.DataFrame,
          ``convention`` is not needed and is ignored if specified.
    scale : int, float, or str
        *scale*\ [**+a**\ *angle*][**+f**\ *font*][**+j**\ *justify*]\
        [**+l**][**+m**][**+o**\ *dx*\ [/\ *dy*]][**+s**\ *reference*].
        Adjust scaling of the radius of the beachball, which is
        proportional to the magnitude. By default, *scale* defines the
        size for magnitude = 5 (i.e., scalar seismic moment
        M0 = 4.0E23 dynes-cm). If **+l** is used the radius will be
        proportional to the seismic moment instead. Use **+s** and give
        a *reference* to change the reference magnitude (or moment), and
        use **+m** to plot all beachballs with the same size. A text
        string can be specified to appear near the beachball
        (corresponding to column or parameter ``event_name``).
        Append **+a**\ *angle* to change the angle of the text string;
        append **+f**\ *font* to change its font (size,fontname,color);
        append **+j**\ *justify* to change the text location relative
        to the beachball [Default is ``"TC"``, i.e., Top Center];
        append **+o** to offset the text string by *dx*\ /*dy*.
    section : list, or str
        Cross-section parameters.
        *section*\ a|b|c|dparams[+c[n|t]][+ddip][+r[a|e|dx]][+wwidth]\
        [+z[s]a|e|dz|min/max].
        a, b, c, and d are specified by *section_format*.
        a: List of four float values of the longitude and latitude of points 1 and 2
        limiting the length of the cross-section.
        b: List of four float values of the longitude and latitude of
        the beginning of the cross-section, strike is the azimuth of
        the direction of the cross-section, and length is the length
        along which the cross-section is made (in km).
        c: List of four float values the same as `a` option
        with x and y given as Cartesian coordinates.
        d: List of four float values the same as `b` option
        with x and y given as Cartesian coordinates.
    section_format : str, `"lonlat_lonlat"`
        `"lonlat_lonlat"`: a
        `"lonlat_strlen"`: b
        `"xy_xy"`: c
        `"xy_strlen"`: d
    no_file : bool, default to False
        If True, creates no output files in the current path.
    convention : str
        Focal mechanism convention. Choose from:

        - ``"aki"`` (Aki & Richards)
        - ``"gcmt"`` (global CMT)
        - ``"mt"`` (seismic moment tensor)
        - ``"partial"`` (partial focal mechanism)
        - ``"principal_axis"`` (principal axis)

        Ignored if ``spec`` is a dictionary or pd.DataFrame.
    component : str
        The component of the seismic moment tensor to plot.

        - ``"full"``: the full seismic moment tensor
        - ``"dc"``: the closest double couple defined from the moment tensor
          (zero trace and zero determinant)
        - ``"deviatoric"``: deviatoric part of the moment tensor (zero trace)
    longitude : int, float, list, or 1-D numpy array
        Longitude(s) of event location(s). Must be the same length as the
        number of events. Will override the ``longitude`` values
        in ``spec`` if ``spec`` is a dictionary or pd.DataFrame.
    latitude : int, float, list, or 1-D numpy array
        Latitude(s) of event location(s). Must be the same length as the
        number of events. Will override the ``latitude`` values
        in ``spec`` if ``spec`` is a dictionary or pd.DataFrame.
    depth : int, float, list, or 1-D numpy array
        Depth(s) of event location(s) in kilometers. Must be the same length
        as the number of events. Will override the ``depth`` values in ``spec``
        if ``spec`` is a dictionary or pd.DataFrame.
    event_name : str or list of str, or 1-D numpy array
        Text string(s), e.g., event name(s) to appear near the beachball(s).
        List must be the same length as the number of events. Will override
        the ``event_name`` labels in ``spec`` if ``spec`` is a dictionary
        or pd.DataFrame.
    labelbox : bool or str
        [*fill*].
        Draw a box behind the label if given. Use *fill* to give a fill color
        [Default is ``"white"``].
    offset : bool or str
        [**+p**\ *pen*][**+s**\ *size*].
        Offset beachball(s) to longitude(s) and latitude(s) specified in the
        the last two columns of the input file or array, or by
        ``plot_longitude`` and ``plot_latitude`` if provided. A small circle
        is plotted at the initial location and a line connects the beachball
        to the circle. Use **+s**\ *size* to set the diameter of the circle
        [Default is no circle]. Use **+p**\ *pen* to set the pen attributes
        for this feature [Default is set via ``pen``]. The fill of the
        circle is set via ``compressionfill`` or ``cmap``, i.e.,
        corresponds to the fill of the compressive quadrants.
    compressionfill : str
        Set color or pattern for filling compressive quadrants
        [Default is ``"black"``]. This setting also applies to the fill of
        the circle defined via ``offset``.
    extensionfill : str
        Set color or pattern for filling extensive quadrants
        [Default is ``"white"``].
    pen : str
        Set pen attributes for all lines related to beachball [Default is
        ``"0.25p,black,solid"``]. This setting applies to ``outline``,
        ``nodal``, and ``offset``, unless overruled by arguments passed to
        those parameters. Draws circumference of beachball.
    outline : bool or str
        [*pen*].
        Draw circumference and nodal planes of beachball. Use *pen* to set
        the pen attributes for this feature [Default is set via ``pen``].
    nodal : bool, int, or str
        [*nplane*][/*pen*].
        Plot the nodal planes and outline the bubble which is transparent.
        If *nplane* is

        - ``0`` or ``True``: both nodal planes are plotted [Default].
        - ``1``: only the first nodal plane is plotted.
        - ``2``: only the second nodal plane is plotted.

        Use /*pen* to set the pen attributes for this feature [Default is
        set via ``pen``].
        For double couple mechanisms, ``nodal`` renders the beachball
        transparent by drawing only the nodal planes and the circumference.
        For non-double couple mechanisms, ``nodal=0`` overlays best
        double couple transparently.
    cmap : str
        File name of a CPT file or a series of comma-separated colors (e.g.,
        *color1,color2,color3*) to build a linear continuous CPT from those
        colors automatically. The color of the compressive quadrants is
        determined by the z-value (i.e., event depth or the third column for
        an input file). This setting also applies to the fill of the circle
        defined via ``offset``.
    no_clip : bool
        Do **not** skip symbols that fall outside the frame boundaries
        [Default is ``False``, i.e., plot symbols inside the frame
        boundaries only].
    {projection}
    {region}
    {frame}
    {verbose}
    {panel}
    {perspective}
    {transparency}
    """
    kwargs = self._preprocess(**kwargs)

    ## The cross-sectional profile
    if kwargs.get("A") is None:
        raise GMTInvalidInput("The `section` parameter must be specified.")
    kwargs["A"] = section_convention_code(section_format) + kwargs["A"]

    # Convert spec to pandas.DataFrame unless it's a file
    if isinstance(spec, (dict, pd.DataFrame)):  # spec is a dict or pd.DataFrame
        # determine convention from dict keys or pd.DataFrame column names
        for conv in ["aki", "gcmt", "mt", "partial", "pricipal_axis"]:
            if set(convention_params(conv)).issubset(set(spec.keys())):
                convention = conv
                break
        else:
            if isinstance(spec, dict):
                msg = "Keys in dict 'spec' do not match known conventions."
            else:
                msg = "Column names in pd.DataFrame 'spec' do not match known conventions."
            raise GMTError(msg)

        # convert dict to pd.DataFrame so columns can be reordered
        if isinstance(spec, dict):
            # convert values to ndarray so pandas doesn't complain about "all
            # scalar values". See
            # https://github.com/GenericMappingTools/pygmt/pull/2174
            spec = pd.DataFrame(
                {key: np.atleast_1d(value) for key, value in spec.items()}
            )

    # Now spec is a pd.DataFrame or a file
    if isinstance(spec, pd.DataFrame):
        # override the values in pd.DataFrame if parameters are given
        if longitude is not None:
            spec["longitude"] = np.atleast_1d(longitude)
        if latitude is not None:
            spec["latitude"] = np.atleast_1d(latitude)
        if depth is not None:
            spec["depth"] = np.atleast_1d(depth)
        if event_name is not None:
            spec["event_name"] = np.atleast_1d(event_name)

        # Due to the internal implementation of the meca module, we need to
        # convert the following columns to strings if they exist
        if "event_name" in spec.columns:
            spec["event_name"] = spec["event_name"].astype(str)

        # Reorder columns in DataFrame to match convention if necessary
        # expected columns are:
        # longitude, latitude, depth, focal_parameters,
        #   [event_name]
        newcols = ["longitude", "latitude", "depth"] + convention_params(convention)
        if "event_name" in spec.columns:
            newcols += ["event_name"]
        # reorder columns in DataFrame
        if spec.columns.tolist() != newcols:
            spec = spec.reindex(newcols, axis=1)

    data_format = convention_code(convention=convention, component=component)
    kwargs["S"] = f"{data_format}{scale}"

    with Session() as lib:
        # Choose how data will be passed into the module
        file_context = lib.virtualfile_from_data(check_kind="vector", data=spec)
        with file_context as fname:
            lib.call_module(module="coupe", args=build_arg_string(kwargs, infile=fname))


def section_convention_code(section_format):

    codes = {
        "lonlat_lonlat": "a",
        "lonlat_strlen": "b",
        "xy_xy": "c",
        "xy_strlen": "d"
    }

    if section_format in codes:
        return codes[section_format]
    else:
        raise GMTInvalidInput(f"Invalid section format '{section_format}'.")

## Coupe_in_PyGMT.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de3e29a1-9dee-4c72-abaf-ccced5b24237",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import pygmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fc2feea-6de1-4eaf-82c5-5adbe1b52c70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sys.path.append('.')\n",
    "from coupe import coupe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5418f09d-92ab-4479-94ed-6ac66933ec6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_meca = pd.DataFrame(\n",
    "    [\n",
    "        [133.3498, 35.2752, 11.26, 150, 85, -9, 241, 81, -175, 4.97, 18,],\n",
    "        [133.33, 35.3, 5.26, 151, 29, -122, 5, 67, -74, 8.62, 18],\n",
    "        [133.3668, 35.255, 8.26, 223.5, 61.2, 98, 24.9, 30.3, 72.1, 6.12, 18],\n",
    "    ],\n",
    "    columns=[\n",
    "        'longitude','latitude','depth',\n",
    "        'strike1','dip1','rake1','strike2','dip2','rake2',\n",
    "        \"mantissa\",\"exponent\",\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fea5c80-2cff-406a-ae1d-4d02dd570365",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pygmt.Figure()\n",
    "\n",
    "pygmt.config(\n",
    "    FONT_TITLE=\"15p,Helvetica,black\",\n",
    "    MAP_FRAME_TYPE=\"plain\",\n",
    "    MAP_FRAME_PEN=\"1.5p\",\n",
    "    FORMAT_GEO_MAP=\"ddd.xx\",\n",
    "    FONT_ANNOT=\"11p\",\n",
    "    MAP_TICK_LENGTH=\"6p\"\n",
    ")\n",
    "\n",
    "fig.basemap(\n",
    "    region=[133.3,133.4,35.22,35.32],\n",
    "    projection=\"T133.25/10c\",\n",
    "    frame=[\"WSne\",\"xa0.05f0.01\", \"ya0.05f0.01\"],\n",
    ")\n",
    "\n",
    "center = [133.32,35.31]\n",
    "azimuth = 145\n",
    "\n",
    "lineA = pygmt.project(\n",
    "    generate='5k',\n",
    "    center=center,\n",
    "    azimuth=azimuth,\n",
    "    length=[0,10],\n",
    "    unit=True\n",
    ")\n",
    "\n",
    "fig.plot(\n",
    "    lineA,\n",
    "    pen='2p'\n",
    ")\n",
    "fig.text(x=lineA[:1].r,y=lineA[:1].s,text='A',font='15p')\n",
    "fig.text(x=lineA[-1:].r,y=lineA[-1:].s,text='A\\'',font='15p')\n",
    "\n",
    "pygmt.makecpt(cmap='jet', series=[4,14], reverse=True)\n",
    "fig.colorbar(frame='xa2+lDepth [km]', position=\"JMR+o0.5c/-1.5c+w-6c+ebf\",)\n",
    "\n",
    "fig.meca(\n",
    "    df_meca,\n",
    "    scale=\"1.5c\",\n",
    "    cmap=True,\n",
    "    pen=\"0.5p,black,solid\",\n",
    "    transparency=30,\n",
    "    event_name=['1','2','3'],\n",
    ")\n",
    "\n",
    "\n",
    "fig.shift_origin(yshift=\"14.c\")\n",
    "fig.basemap(\n",
    "    region=[0, 10, 0, 20],  # x_min, x_max, y_min, y_max\n",
    "    projection=\"X11c/-3c\",\n",
    "    frame=[\"WSrt\", \"xa5f1+lDistance [km]\", \"ya5+lDepth [km]\"],\n",
    ")\n",
    "\n",
    "coupe(\n",
    "    fig,\n",
    "    df_meca,\n",
    "    \"1.5c\",\n",
    "    section=center+[azimuth,10],\n",
    "    section_format='lonlat_strlen',\n",
    "    cmap=True,\n",
    "    no_file=True,\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pygmt",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
	import numpy as np
	import pandas as pd

	from pygmt.clib import Session
	from pygmt.exceptions import GMTError, GMTInvalidInput
	from pygmt.helpers import build_arg_string, fmt_docstring, kwargs_to_strings, use_alias
	from pygmt.src.meca import convention_params, convention_code


	@fmt_docstring
	@use_alias(
	A="section",
	B="frame",
	C="cmap",
	E="extensionfill",
	Fr="labelbox",
	G="compressionfill",
	J="projection",
	L="outline",
	N="no_clip",
	Q="no_file",
	R="region",
	T="nodal",
	V="verbose",
	W="pen",
	c="panel",
	p="perspective",
	t="transparency",
	)
	@kwargs_to_strings(A="sequence", R="sequence", c="sequence_comma", p="sequence")
	def coupe(
	self,
	spec,
	scale,
	convention=None,
	component="full",
	longitude=None,
	latitude=None,
	depth=None,
	section_format='lonlat_lonlat',
	event_name=None,
	**kwargs
	):
	r"""
	Plot focal mechanisms in a vertical cross section.
	This function/method is copied from `pygmt.src.meca.meca()`

	Full option list at :gmt-docs:`supplements/seis/coupe.html`

	{aliases}

	Parameters
	----------
	spec : str, 1-D array, 2-D array, dict, or pd.DataFrame
	Data that contains focal mechanism parameters.

	``spec`` can be specified in either of the following types:

	- str: a file name containing focal mechanism parameters as
	columns. The meaning of each column is:

	- Columns 1 and 2: event longitude and latitude
	- Column 3: event depth (in km)
	- Columns 4 to 3+n: focal mechanism parameters. The number of columns
	n depends on the choice of ``convention``, which will be
	described below.
	- Columns 4+n and 5+n: longitude, latitude at which to place
	beachball. Using ``0 0`` will plot the beachball at the longitude,
	latitude given in columns 1 and 2. [optional and requires
	``offset=True`` to take effect].
	- Text string to appear near the beachball [optional].

	- 1-D array: focal mechanism parameters of a single event.
	The meanings of columns are the same as above.
	- 2-D array: focal mechanism parameters of multiple events.
	The meanings of columns are the same as above.
	- dictionary or pd.DataFrame: The dictionary keys or pd.DataFrame
	column names determine the focal mechanism convention. For
	different conventions, the following combination of keys are allowed:

	- ``"aki"``: strike, dip, rake, magnitude
	- ``"gcmt"``: strike1, dip1, rake1, strike2, dip2, rake2, mantissa,
	exponent
	- ``"mt"``: mrr, mtt, mff, mrt, mrf, mtf, exponent
	- ``"partial"``: strike1, dip1, strike2, fault_type, magnitude
	- ``"principal_axis"``: *t_value, t_azimuth, t_plunge, n_value,
	n_azimuth, n_plunge, p_value, p_azimuth, p_plunge, exponent*

	A dictionary may contain values for a single focal mechanism or
	lists of values for multiple focal mechanisms.

	Both dictionary and pd.DataFrame may optionally contain
	keys/column names: ``latitude``, ``longitude``, ``depth``,
	``plot_longitude``, ``plot_latitude``, and/or ``event_name``.

	If ``spec`` is either a str, a 1-D array or a 2-D array, the
	``convention`` parameter is required so we know how to interpret the
	columns. If ``spec`` is a dictionary or a pd.DataFrame,
	``convention`` is not needed and is ignored if specified.
	scale : int, float, or str
	scale\ [+a\ angle][+f\ font][+j\ justify]\
	[+l][+m][+o\ dx\ [/\ dy]][+s\ reference].
	Adjust scaling of the radius of the beachball, which is
	proportional to the magnitude. By default, scale defines the
	size for magnitude = 5 (i.e., scalar seismic moment
	M0 = 4.0E23 dynes-cm). If +l is used the radius will be
	proportional to the seismic moment instead. Use +s and give
	a reference to change the reference magnitude (or moment), and
	use +m to plot all beachballs with the same size. A text
	string can be specified to appear near the beachball
	(corresponding to column or parameter ``event_name``).
	Append +a\ angle to change the angle of the text string;
	append +f\ font to change its font (size,fontname,color);
	append +j\ justify to change the text location relative
	to the beachball [Default is ``"TC"``, i.e., Top Center];
	append +o to offset the text string by dx\ /dy.
	section : list, or str
	Cross-section parameters.
	section\ a\|b\|c\|dparams[+c[n\|t]][+ddip][+r[a\|e\|dx]][+wwidth]\
	[+z[s]a\|e\|dz\|min/max].
	a, b, c, and d are specified by section_format.
	a: List of four float values of the longitude and latitude of points 1 and 2
	limiting the length of the cross-section.
	b: List of four float values of the longitude and latitude of
	the beginning of the cross-section, strike is the azimuth of
	the direction of the cross-section, and length is the length
	along which the cross-section is made (in km).
	c: List of four float values the same as `a` option
	with x and y given as Cartesian coordinates.
	d: List of four float values the same as `b` option
	with x and y given as Cartesian coordinates.
	section_format : str, `"lonlat_lonlat"`
	`"lonlat_lonlat"`: a
	`"lonlat_strlen"`: b
	`"xy_xy"`: c
	`"xy_strlen"`: d
	no_file : bool, default to False
	If True, creates no output files in the current path.
	convention : str
	Focal mechanism convention. Choose from:

	- ``"aki"`` (Aki & Richards)
	- ``"gcmt"`` (global CMT)
	- ``"mt"`` (seismic moment tensor)
	- ``"partial"`` (partial focal mechanism)
	- ``"principal_axis"`` (principal axis)

	Ignored if ``spec`` is a dictionary or pd.DataFrame.
	component : str
	The component of the seismic moment tensor to plot.

	- ``"full"``: the full seismic moment tensor
	- ``"dc"``: the closest double couple defined from the moment tensor
	(zero trace and zero determinant)
	- ``"deviatoric"``: deviatoric part of the moment tensor (zero trace)
	longitude : int, float, list, or 1-D numpy array
	Longitude(s) of event location(s). Must be the same length as the
	number of events. Will override the ``longitude`` values
	in ``spec`` if ``spec`` is a dictionary or pd.DataFrame.
	latitude : int, float, list, or 1-D numpy array
	Latitude(s) of event location(s). Must be the same length as the
	number of events. Will override the ``latitude`` values
	in ``spec`` if ``spec`` is a dictionary or pd.DataFrame.
	depth : int, float, list, or 1-D numpy array
	Depth(s) of event location(s) in kilometers. Must be the same length
	as the number of events. Will override the ``depth`` values in ``spec``
	if ``spec`` is a dictionary or pd.DataFrame.
	event_name : str or list of str, or 1-D numpy array
	Text string(s), e.g., event name(s) to appear near the beachball(s).
	List must be the same length as the number of events. Will override
	the ``event_name`` labels in ``spec`` if ``spec`` is a dictionary
	or pd.DataFrame.
	labelbox : bool or str
	[fill].
	Draw a box behind the label if given. Use fill to give a fill color
	[Default is ``"white"``].
	offset : bool or str
	[+p\ pen][+s\ size].
	Offset beachball(s) to longitude(s) and latitude(s) specified in the
	the last two columns of the input file or array, or by
	``plot_longitude`` and ``plot_latitude`` if provided. A small circle
	is plotted at the initial location and a line connects the beachball
	to the circle. Use +s\ size to set the diameter of the circle
	[Default is no circle]. Use +p\ pen to set the pen attributes
	for this feature [Default is set via ``pen``]. The fill of the
	circle is set via ``compressionfill`` or ``cmap``, i.e.,
	corresponds to the fill of the compressive quadrants.
	compressionfill : str
	Set color or pattern for filling compressive quadrants
	[Default is ``"black"``]. This setting also applies to the fill of
	the circle defined via ``offset``.
	extensionfill : str
	Set color or pattern for filling extensive quadrants
	[Default is ``"white"``].
	pen : str
	Set pen attributes for all lines related to beachball [Default is
	``"0.25p,black,solid"``]. This setting applies to ``outline``,
	``nodal``, and ``offset``, unless overruled by arguments passed to
	those parameters. Draws circumference of beachball.
	outline : bool or str
	[pen].
	Draw circumference and nodal planes of beachball. Use pen to set
	the pen attributes for this feature [Default is set via ``pen``].
	nodal : bool, int, or str
	[nplane][/pen].
	Plot the nodal planes and outline the bubble which is transparent.
	If nplane is

	- ``0`` or ``True``: both nodal planes are plotted [Default].
	- ``1``: only the first nodal plane is plotted.
	- ``2``: only the second nodal plane is plotted.

	Use /pen to set the pen attributes for this feature [Default is
	set via ``pen``].
	For double couple mechanisms, ``nodal`` renders the beachball
	transparent by drawing only the nodal planes and the circumference.
	For non-double couple mechanisms, ``nodal=0`` overlays best
	double couple transparently.
	cmap : str
	File name of a CPT file or a series of comma-separated colors (e.g.,
	color1,color2,color3) to build a linear continuous CPT from those
	colors automatically. The color of the compressive quadrants is
	determined by the z-value (i.e., event depth or the third column for
	an input file). This setting also applies to the fill of the circle
	defined via ``offset``.
	no_clip : bool
	Do not skip symbols that fall outside the frame boundaries
	[Default is ``False``, i.e., plot symbols inside the frame
	boundaries only].
	{projection}
	{region}
	{frame}
	{verbose}
	{panel}
	{perspective}
	{transparency}
	"""
	kwargs = self._preprocess(**kwargs)

	## The cross-sectional profile
	if kwargs.get("A") is None:
	raise GMTInvalidInput("The `section` parameter must be specified.")
	kwargs["A"] = section_convention_code(section_format) + kwargs["A"]

	# Convert spec to pandas.DataFrame unless it's a file
	if isinstance(spec, (dict, pd.DataFrame)): # spec is a dict or pd.DataFrame
	# determine convention from dict keys or pd.DataFrame column names
	for conv in ["aki", "gcmt", "mt", "partial", "pricipal_axis"]:
	if set(convention_params(conv)).issubset(set(spec.keys())):
	convention = conv
	break
	else:
	if isinstance(spec, dict):
	msg = "Keys in dict 'spec' do not match known conventions."
	else:
	msg = "Column names in pd.DataFrame 'spec' do not match known conventions."
	raise GMTError(msg)

	# convert dict to pd.DataFrame so columns can be reordered
	if isinstance(spec, dict):
	# convert values to ndarray so pandas doesn't complain about "all
	# scalar values". See
	# https://github.com/GenericMappingTools/pygmt/pull/2174
	spec = pd.DataFrame(
	{key: np.atleast_1d(value) for key, value in spec.items()}
	)

	# Now spec is a pd.DataFrame or a file
	if isinstance(spec, pd.DataFrame):
	# override the values in pd.DataFrame if parameters are given
	if longitude is not None:
	spec["longitude"] = np.atleast_1d(longitude)
	if latitude is not None:
	spec["latitude"] = np.atleast_1d(latitude)
	if depth is not None:
	spec["depth"] = np.atleast_1d(depth)
	if event_name is not None:
	spec["event_name"] = np.atleast_1d(event_name)

	# Due to the internal implementation of the meca module, we need to
	# convert the following columns to strings if they exist
	if "event_name" in spec.columns:
	spec["event_name"] = spec["event_name"].astype(str)

	# Reorder columns in DataFrame to match convention if necessary
	# expected columns are:
	# longitude, latitude, depth, focal_parameters,
	# [event_name]
	newcols = ["longitude", "latitude", "depth"] + convention_params(convention)
	if "event_name" in spec.columns:
	newcols += ["event_name"]
	# reorder columns in DataFrame
	if spec.columns.tolist() != newcols:
	spec = spec.reindex(newcols, axis=1)

	data_format = convention_code(convention=convention, component=component)
	kwargs["S"] = f"{data_format}{scale}"

	with Session() as lib:
	# Choose how data will be passed into the module
	file_context = lib.virtualfile_from_data(check_kind="vector", data=spec)
	with file_context as fname:
	lib.call_module(module="coupe", args=build_arg_string(kwargs, infile=fname))


	def section_convention_code(section_format):

	codes = {
	"lonlat_lonlat": "a",
	"lonlat_strlen": "b",
	"xy_xy": "c",
	"xy_strlen": "d"
	}

	if section_format in codes:
	return codes[section_format]
	else:
	raise GMTInvalidInput(f"Invalid section format '{section_format}'.")