Source code for pygmt.src.fitcircle

"""
fitcircle - Fit coordinators to create vectors on a sphere.
"""
import warnings

import pandas as pd
from pygmt.clib import Session
from pygmt.exceptions import GMTInvalidInput
from pygmt.helpers import GMTTempFile, build_arg_string, fmt_docstring, use_alias


[docs]@fmt_docstring @use_alias( L="normalize", S="small_circle", V="verbose", ) def fitcircle(data, output_type="pandas", outfile=None, **kwargs): r""" Fit coordinators to create vectors on a sphere. **fitcircle** reads lon,lat [or lat,lon] values from the first two columns on standard input [or *table*]. These are converted to Cartesian three-vectors on the unit sphere. Then two locations are found: the mean of the input positions, and the pole to the great circle which best fits the input positions. The user may choose one or both of two possible solutions to this problem. When the data are closely grouped along a great circle both solutions are similar. If the data have large dispersion, the pole to the great circle will be less well determined than the mean. Compare both solutions as a qualitative check. Setting `normalize` to **1** approximates the minimization of the sum of absolute values of cosines of angular distances. This solution finds the mean position as the Fisher average of the data, and the pole position as the Fisher average of the cross-products between the mean and the data. Averaging cross-products gives weight to points in proportion to their distance from the mean, analogous to the "leverage" of distant points in linear regression in the plane. Setting `normalize` to **2** approximates the minimization of the sum of squares of cosines of angular distances. It creates a 3 by 3 matrix of sums of squares of components of the data vectors. The eigenvectors of this matrix give the mean and pole locations. This method may be more subject to roundoff errors when there are thousands of data. The pole is given by the eigenvector corresponding to the smallest eigenvalue; it is the least-well represented factor in the data and is not easily estimated by either method. Full option list at :gmt-docs:`fitcircle.html` {aliases} Parameters ----------. output_type : str Determine the format the xyz data will be returned in [Default is ``pandas``]: - ``numpy`` - :class:`numpy.ndarray` - ``pandas``- :class:`pandas.DataFrame` - ``file`` - ASCII file (requires ``outfile``) outfile : str The file name for the output ASCII file. Returns ------- ret : pandas.DataFrame or numpy.ndarray or None Return type depends on ``outfile`` and ``output_type``: - None if ``outfile`` is set (output will be stored in file set by ``outfile``) - :class:`pandas.DataFrame` or :class:`numpy.ndarray` if ``outfile`` is not set (depends on ``output_type`` [Default is :class:`pandas.DataFrame`]) """ if "L" not in kwargs: raise GMTInvalidInput("""Pass a required argument to 'normalize'.""") if output_type not in ["numpy", "pandas", "file"]: raise GMTInvalidInput( """Must specify format as either numpy, pandas, or file.""" ) if outfile is not None and output_type != "file": msg = ( f"Changing `output_type` of fitcirle from '{output_type}' to 'file' " "since `outfile` parameter is set. Please use `output_type='file'` " "to silence this warning." ) warnings.warn(msg, category=RuntimeWarning, stacklevel=2) output_type = "file" elif output_type == "file" and outfile is None: raise GMTInvalidInput("""Must specify outfile for ASCII output.""") with GMTTempFile() as tmpfile: with Session() as lib: file_context = lib.virtualfile_from_data(check_kind="vector", data=data) with file_context as infile: if outfile is None: outfile = tmpfile.name arg_str = " ".join([infile, build_arg_string(kwargs), "->" + outfile]) lib.call_module("fitcircle", arg_str) # Read temporary csv output to a pandas table if outfile == tmpfile.name: # if user did not set outfile, return pd.DataFrame result = pd.read_csv( tmpfile.name, sep="\t", names=["longitutde", "latitude", "method"], comment=">", ) elif outfile != tmpfile.name: # return None if outfile set, output in outfile result = None if output_type == "numpy": result = result.to_numpy() return result