"""
meca - Plot focal mechanisms.
"""
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,
data_kind,
dummy_context,
fmt_docstring,
kwargs_to_strings,
use_alias,
)
def data_format_code(convention, component="full"):
"""
Determine the data format code for meca -S option.
See the meca() method for explanations of the parameters.
Examples
--------
>>> data_format_code("aki")
'a'
>>> data_format_code("gcmt")
'c'
>>> data_format_code("partial")
'p'
>>> data_format_code("mt", component="full")
'm'
>>> data_format_code("mt", component="deviatoric")
'z'
>>> data_format_code("mt", component="dc")
'd'
>>> data_format_code("principal_axis", component="full")
'x'
>>> data_format_code("principal_axis", component="deviatoric")
't'
>>> data_format_code("principal_axis", component="dc")
'y'
>>> for code in ["a", "c", "m", "d", "z", "p", "x", "y", "t"]:
... assert data_format_code(code) == code
...
>>> data_format_code("invalid")
Traceback (most recent call last):
...
pygmt.exceptions.GMTInvalidInput: Invalid convention 'invalid'.
>>> data_format_code("mt", "invalid") # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
pygmt.exceptions.GMTInvalidInput:
Invalid component 'invalid' for convention 'mt'.
"""
# Codes for focal mechanism formats determined by "convention"
codes1 = {
"aki": "a",
"gcmt": "c",
"partial": "p",
}
# Codes for focal mechanism formats determined by both "convention" and
# "component"
codes2 = {
"mt": {
"deviatoric": "z",
"dc": "d",
"full": "m",
},
"principal_axis": {
"deviatoric": "t",
"dc": "y",
"full": "x",
},
}
if convention in codes1:
return codes1[convention]
if convention in codes2:
if component not in codes2[convention]:
raise GMTInvalidInput(
f"Invalid component '{component}' for convention '{convention}'."
)
return codes2[convention][component]
if convention in ["a", "c", "m", "d", "z", "p", "x", "y", "t"]:
return convention
raise GMTInvalidInput(f"Invalid convention '{convention}'.")
@fmt_docstring
@use_alias(
R="region",
J="projection",
A="offset",
B="frame",
N="no_clip",
V="verbose",
X="xshift",
Y="yshift",
c="panel",
p="perspective",
t="transparency",
)
@kwargs_to_strings(R="sequence", c="sequence_comma", p="sequence")
def meca(
self,
spec,
scale,
longitude=None,
latitude=None,
depth=None,
convention=None,
component="full",
plot_longitude=None,
plot_latitude=None,
**kwargs,
):
"""
Plot focal mechanisms.
Full option list at :gmt-docs:`supplements/seis/meca.html`
Note
----
Currently, labeling of beachballs with text strings is only supported
via providing a file to `spec` as input.
{aliases}
Parameters
----------
spec: dict, 1D array, 2D array, pd.DataFrame, or str
Either a filename containing focal mechanism parameters as columns, a
1- or 2-D array with the same, or a dictionary. If a filename or array,
`convention` is required so we know how to interpret the
columns/entries. If a dictionary, the following combinations of keys
are supported; these determine the convention. Dictionary may contain
values for a single focal mechanism or lists of values for many focal
mechanisms. A Pandas DataFrame may optionally contain columns latitude,
longitude, depth, plot_longitude, and/or plot_latitude instead of
passing them to the meca method.
- ``"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_exponent, t_azimuth, t_plunge, n_exponent,
n_azimuth, n_plunge, p_exponent, p_azimuth, p_plunge, exponent*
scale: str
Adjusts the scaling of the radius of the beachball, which is
proportional to the magnitude. Scale defines the size for magnitude = 5
(i.e. scalar seismic moment M0 = 4.0E23 dynes-cm)
longitude: int, float, list, or 1d numpy array
Longitude(s) of event location. Ignored if `spec` is not a dictionary.
List must be the length of the number of events. Ignored if `spec` is a
DataFrame and contains a 'longitude' column.
latitude: int, float, list, or 1d numpy array
Latitude(s) of event location. Ignored if `spec` is not a dictionary.
List must be the length of the number of events. Ignored if `spec` is a
DataFrame and contains a 'latitude' column.
depth: int, float, list, or 1d numpy array
Depth(s) of event location in kilometers. Ignored if `spec` is not a
dictionary. List must be the length of the number of events. Ignored if
`spec` is a DataFrame and contains a 'depth' column.
convention: str
``"aki"`` (Aki & Richards), ``"gcmt"`` (global CMT), ``"mt"`` (seismic
moment tensor), ``"partial"`` (partial focal mechanism), or
``"principal_axis"`` (principal axis). Ignored if `spec` is a
dictionary or dataframe.
component: str
The component of the seismic moment tensor to plot. ``"full"`` (the
full seismic moment tensor), ``"dc"`` (the closest double couple with
zero trace and zero determinant), ``"deviatoric"`` (zero trace)
plot_longitude: int, float, list, or 1d numpy array
Longitude(s) at which to place beachball, only used if `spec` is a
dictionary. List must be the length of the number of events. Ignored if
`spec` is a DataFrame and contains a 'plot_longitude' column.
plot_latitude: int, float, list, or 1d numpy array
Latitude(s) at which to place beachball, only used if `spec` is a
dictionary. List must be the length of the number of events. Ignored if
`spec` is a DataFrame and contains a 'plot_latitude' column.
offset: bool or str
Offsets beachballs to the longitude, latitude specified in 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. Specify pen
and optionally append ``+ssize`` to change the line style and/or size
of the circle.
no_clip : bool
Does NOT skip symbols that fall outside frame boundary specified by
*region* [Default is False, i.e. plot symbols inside map frame only].
{J}
{R}
{B}
{V}
{XY}
{c}
{p}
{t}
"""
# pylint warnings that need to be fixed
# pylint: disable=too-many-locals
# pylint: disable=too-many-nested-blocks
# pylint: disable=too-many-branches
# pylint: disable=too-many-statements
def set_pointer(data_pointers, spec):
"""
Set optional parameter pointers based on DataFrame or dict, if those
parameters are present in the DataFrame or dict.
"""
for param in list(data_pointers):
if param in spec:
# set pointer based on param name
data_pointers[param] = spec[param]
def update_pointers(data_pointers):
"""
Updates variables based on the location of data, as the following data
can be passed as parameters or it can be contained in `spec`.
"""
# update all pointers
longitude = data_pointers["longitude"]
latitude = data_pointers["latitude"]
depth = data_pointers["depth"]
plot_longitude = data_pointers["plot_longitude"]
plot_latitude = data_pointers["plot_latitude"]
return (longitude, latitude, depth, plot_longitude, plot_latitude)
kwargs = self._preprocess(**kwargs) # pylint: disable=protected-access
# Check the spec and parse the data according to the specified
# convention
if isinstance(spec, (dict, pd.DataFrame)):
# dicts and DataFrames are handed similarly but not identically
if (longitude is None or latitude is None or depth is None) and not isinstance(
spec, (dict, pd.DataFrame)
):
raise GMTError("Location not fully specified.")
param_conventions = {
"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_exponent",
"t_azimuth",
"t_plunge",
"n_exponent",
"n_azimuth",
"n_plunge",
"p_exponent",
"p_azimuth",
"p_plunge",
"exponent",
],
}
# to keep track of where optional parameters exist
data_pointers = {
"longitude": longitude,
"latitude": latitude,
"depth": depth,
"plot_longitude": plot_longitude,
"plot_latitude": plot_latitude,
}
# make a DataFrame copy to check convention if it contains other params
# check if a copy is necessary
copy = False
drop_list = []
for pointer in data_pointers:
if pointer in spec:
copy = True
drop_list.append(pointer)
if copy:
spec_conv = spec.copy()
# delete optional parameters from copy for convention check
for item in drop_list:
del spec_conv[item]
else:
spec_conv = spec
# set convention and focal parameters based on spec convention
for conv in list(param_conventions):
if set(spec_conv) == set(param_conventions[conv]):
convention = conv.lower()
foc_params = param_conventions[conv]
break
else: # if there is no convention assigned
raise GMTError(
"Parameters in spec dictionary do not match known conventions."
)
# create a dict type pointer for easier to read code
if isinstance(spec, dict):
dict_type_pointer = list(spec.values())[0]
elif isinstance(spec, pd.DataFrame):
# use df.values as pointer for DataFrame behavior
dict_type_pointer = spec.values
# assemble the 1D array for the case of floats and ints as values
if isinstance(dict_type_pointer, (int, float)):
# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
(
longitude,
latitude,
depth,
plot_longitude,
plot_latitude,
) = update_pointers(data_pointers)
# Construct the array (order matters)
spec = [longitude, latitude, depth] + [spec[key] for key in foc_params]
# Add in plotting options, if given, otherwise add 0s
for arg in plot_longitude, plot_latitude:
if arg is None:
spec.append(0)
else:
if "A" not in kwargs:
kwargs["A"] = True
spec.append(arg)
# or assemble the 2D array for the case of lists as values
elif isinstance(dict_type_pointer, list):
# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
(
longitude,
latitude,
depth,
plot_longitude,
plot_latitude,
) = update_pointers(data_pointers)
# before constructing the 2D array lets check that each key
# of the dict has the same quantity of values to avoid bugs
list_length = len(list(spec.values())[0])
for value in list(spec.values()):
if len(value) != list_length:
raise GMTError(
"Unequal number of focal mechanism "
"parameters supplied in 'spec'."
)
# lets also check the inputs for longitude, latitude,
# and depth if it is a list or array
if (
isinstance(longitude, (list, np.ndarray))
or isinstance(latitude, (list, np.ndarray))
or isinstance(depth, (list, np.ndarray))
):
if (len(longitude) != len(latitude)) or (
len(longitude) != len(depth)
):
raise GMTError(
"Unequal number of focal mechanism " "locations supplied."
)
# values are ok, so build the 2D array
spec_array = []
for index in range(list_length):
# Construct the array one row at a time (note that order
# matters here, hence the list comprehension!)
row = [longitude[index], latitude[index], depth[index]] + [
spec[key][index] for key in foc_params
]
# Add in plotting options, if given, otherwise add 0s as
# required by GMT
for arg in plot_longitude, plot_latitude:
if arg is None:
row.append(0)
else:
if "A" not in kwargs:
kwargs["A"] = True
row.append(arg[index])
spec_array.append(row)
spec = spec_array
# or assemble the array for the case of pd.DataFrames
elif isinstance(dict_type_pointer, np.ndarray):
# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
(
longitude,
latitude,
depth,
plot_longitude,
plot_latitude,
) = update_pointers(data_pointers)
# lets also check the inputs for longitude, latitude, and depth
# just in case the user entered different length lists
if (
isinstance(longitude, (list, np.ndarray))
or isinstance(latitude, (list, np.ndarray))
or isinstance(depth, (list, np.ndarray))
):
if (len(longitude) != len(latitude)) or (len(longitude) != len(depth)):
raise GMTError(
"Unequal number of focal mechanism locations supplied."
)
# values are ok, so build the 2D array in the correct order
spec_array = []
for index in range(len(spec)):
# Construct the array one row at a time (note that order
# matters here, hence the list comprehension!)
row = [longitude[index], latitude[index], depth[index]] + [
spec[key][index] for key in foc_params
]
# Add in plotting options, if given, otherwise add 0s as
# required by GMT
for arg in plot_longitude, plot_latitude:
if arg is None:
row.append(0)
else:
if "A" not in kwargs:
kwargs["A"] = True
row.append(arg[index])
spec_array.append(row)
spec = spec_array
else:
raise GMTError("Parameter 'spec' contains values of an unsupported type.")
# determine data_foramt from convection and component
data_format = data_format_code(convention=convention, component=component)
# Assemble -S flag
kwargs["S"] = data_format + scale
kind = data_kind(spec)
with Session() as lib:
if kind == "matrix":
file_context = lib.virtualfile_from_matrix(np.atleast_2d(spec))
elif kind == "file":
file_context = dummy_context(spec)
else:
raise GMTInvalidInput(f"Unrecognized data type: {type(spec)}")
with file_context as fname:
lib.call_module("meca", build_arg_string(kwargs, infile=fname))