66import pandas as pd
77from pygmt .clib import Session
88from pygmt .exceptions import GMTInvalidInput
9- from pygmt .helpers import build_arg_list , fmt_docstring , kwargs_to_strings , use_alias
9+ from pygmt .helpers import (
10+ build_arg_list ,
11+ data_kind ,
12+ fmt_docstring ,
13+ kwargs_to_strings ,
14+ use_alias ,
15+ )
1016from pygmt .src ._common import _FocalMechanismConvention
1117
1218
@@ -25,6 +31,82 @@ def _get_focal_convention(spec, convention, component) -> _FocalMechanismConvent
2531 return _FocalMechanismConvention (convention = convention , component = component )
2632
2733
34+ def _preprocess_spec (spec , colnames , override_cols ):
35+ """
36+ Preprocess the input data.
37+
38+ Parameters
39+ ----------
40+ spec
41+ The input data to be preprocessed.
42+ colnames
43+ The minimum required column names of the input data.
44+ override_cols
45+ Dictionary of column names and values to override in the input data. Only makes
46+ sense if ``spec`` is a dict or :class:`pandas.DataFrame`.
47+ """
48+ kind = data_kind (spec ) # Determine the kind of the input data.
49+
50+ # Convert pandas.DataFrame and numpy.ndarray to dict.
51+ if isinstance (spec , pd .DataFrame ):
52+ spec = {k : v .to_numpy () for k , v in spec .items ()}
53+ elif isinstance (spec , np .ndarray ):
54+ spec = np .atleast_2d (spec )
55+ # Optional columns that are not required by the convention. The key is the
56+ # number of extra columns, and the value is a list of optional column names.
57+ extra_cols = {
58+ 0 : [],
59+ 1 : ["event_name" ],
60+ 2 : ["plot_longitude" , "plot_latitude" ],
61+ 3 : ["plot_longitude" , "plot_latitude" , "event_name" ],
62+ }
63+ ndiff = spec .shape [1 ] - len (colnames )
64+ if ndiff not in extra_cols :
65+ msg = f"Input array must have { len (colnames )}
66+ raise GMTInvalidInput (msg )
67+ spec = dict (zip ([* colnames , * extra_cols [ndiff ]], spec .T , strict = False ))
68+
69+ # Now, the input data is a dict or an ASCII file.
70+ if isinstance (spec , dict ):
71+ # The columns can be overridden by the parameters given in the function
72+ # arguments. Only makes sense for dict/pandas.DataFrame input.
73+ if kind != "matrix" and override_cols is not None :
74+ spec .update ({k : v for k , v in override_cols .items () if v is not None })
75+ # Due to the internal implementation of the meca module, we need to convert the
76+ # ``plot_longitude``, ``plot_latitude``, and ``event_name`` columns into strings
77+ # if they exist.
78+ for key in ["plot_longitude" , "plot_latitude" , "event_name" ]:
79+ if key in spec :
80+ spec [key ] = np .array (spec [key ], dtype = str )
81+
82+ # Reorder columns to match convention if necessary. The expected columns are:
83+ # longitude, latitude, depth, focal_parameters, [plot_longitude, plot_latitude],
84+ # [event_name].
85+ extra_cols = []
86+ if "plot_longitude" in spec and "plot_latitude" in spec :
87+ extra_cols .extend (["plot_longitude" , "plot_latitude" ])
88+ if "event_name" in spec :
89+ extra_cols .append ("event_name" )
90+ cols = [* colnames , * extra_cols ]
91+ if list (spec .keys ()) != cols :
92+ spec = {k : spec [k ] for k in cols }
93+ return spec
94+
95+
96+ def _auto_offset (spec ) -> bool :
97+ """
98+ Determine if offset should be set based on the input data.
99+
100+ If the input data contains ``plot_longitude`` and ``plot_latitude``, then we set the
101+ ``offset`` parameter to ``True`` automatically.
102+ """
103+ return (
104+ isinstance (spec , dict | pd .DataFrame )
105+ and "plot_longitude" in spec
106+ and "plot_latitude" in spec
107+ )
108+
109+
28110@fmt_docstring
29111@use_alias (
30112 A = "offset" ,
@@ -45,7 +127,7 @@ def _get_focal_convention(spec, convention, component) -> _FocalMechanismConvent
45127 t = "transparency" ,
46128)
47129@kwargs_to_strings (R = "sequence" , c = "sequence_comma" , p = "sequence" )
48- def meca ( # noqa: PLR0912, PLR0913
130+ def meca ( # noqa: PLR0913
49131 self ,
50132 spec ,
51133 scale ,
@@ -248,78 +330,25 @@ def meca( # noqa: PLR0912, PLR0913
248330 {transparency}
249331 """
250332 kwargs = self ._preprocess (** kwargs )
251-
252333 # Determine the focal mechanism convention from the input data or parameters.
253334 _convention = _get_focal_convention (spec , convention , component )
254-
255- # Convert spec to pandas.DataFrame unless it's a file
256- if isinstance (spec , dict | pd .DataFrame ): # spec is a dict or pd.DataFrame
257- # convert dict to pd.DataFrame so columns can be reordered
258- if isinstance (spec , dict ):
259- # convert values to ndarray so pandas doesn't complain about "all
260- # scalar values". See
261- # https://github.com/GenericMappingTools/pygmt/pull/2174
262- spec = pd .DataFrame (
263- {key : np .atleast_1d (value ) for key , value in spec .items ()}
264- )
265- elif isinstance (spec , np .ndarray ): # spec is a numpy array
266- # Convert array to pd.DataFrame and assign column names
267- spec = pd .DataFrame (np .atleast_2d (spec ))
268- colnames = ["longitude" , "latitude" , "depth" , * _convention .params ]
269- # check if spec has the expected number of columns
270- ncolsdiff = len (spec .columns ) - len (colnames )
271- if ncolsdiff == 0 :
272- pass
273- elif ncolsdiff == 1 :
274- colnames += ["event_name" ]
275- elif ncolsdiff == 2 :
276- colnames += ["plot_longitude" , "plot_latitude" ]
277- elif ncolsdiff == 3 :
278- colnames += ["plot_longitude" , "plot_latitude" , "event_name" ]
279- else :
280- msg = (
281- f"Input array must have { len (colnames )} { len (colnames ) + 3 }
282- )
283- raise GMTInvalidInput (msg )
284- spec .columns = colnames
285-
286- # Now spec is a pd.DataFrame or a file
287- if isinstance (spec , pd .DataFrame ):
288- # override the values in pd.DataFrame if parameters are given
289- for arg , name in [
290- (longitude , "longitude" ),
291- (latitude , "latitude" ),
292- (depth , "depth" ),
293- (plot_longitude , "plot_longitude" ),
294- (plot_latitude , "plot_latitude" ),
295- (event_name , "event_name" ),
296- ]:
297- if arg is not None :
298- spec [name ] = np .atleast_1d (arg )
299-
300- # Due to the internal implementation of the meca module, we need to
301- # convert the following columns to strings if they exist
302- if "plot_longitude" in spec .columns and "plot_latitude" in spec .columns :
303- spec ["plot_longitude" ] = spec ["plot_longitude" ].astype (str )
304- spec ["plot_latitude" ] = spec ["plot_latitude" ].astype (str )
305- if "event_name" in spec .columns :
306- spec ["event_name" ] = spec ["event_name" ].astype (str )
307-
308- # Reorder columns in DataFrame to match convention if necessary
309- # expected columns are:
310- # longitude, latitude, depth, focal_parameters,
311- # [plot_longitude, plot_latitude] [event_name]
312- newcols = ["longitude" , "latitude" , "depth" , * _convention .params ]
313- if "plot_longitude" in spec .columns and "plot_latitude" in spec .columns :
314- newcols += ["plot_longitude" , "plot_latitude" ]
315- if kwargs .get ("A" ) is None :
316- kwargs ["A" ] = True
317- if "event_name" in spec .columns :
318- newcols += ["event_name" ]
319- # reorder columns in DataFrame
320- if spec .columns .tolist () != newcols :
321- spec = spec .reindex (newcols , axis = 1 )
322-
335+ # Preprocess the input data.
336+ spec = _preprocess_spec (
337+ spec ,
338+ # The minimum expected columns for the input data.
339+ colnames = ["longitude" , "latitude" , "depth" , * _convention .params ],
340+ override_cols = {
341+ "longitude" : longitude ,
342+ "latitude" : latitude ,
343+ "depth" : depth ,
344+ "plot_longitude" : plot_longitude ,
345+ "plot_latitude" : plot_latitude ,
346+ "event_name" : event_name ,
347+ },
348+ )
349+ # Determine the offset parameter if not provided.
350+ if kwargs .get ("A" ) is None :
351+ kwargs ["A" ] = _auto_offset (spec )
323352 kwargs ["S" ] = f"{ _convention .code } { scale }
324353 with Session () as lib :
325354 with lib .virtualfile_in (check_kind = "vector" , data = spec ) as vintbl :
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