Source code for pygmt.src.x2sys_cross

x2sys_cross - Calculate crossovers between track data files.
import contextlib
import os
from pathlib import Path

import pandas as pd
from pygmt.clib import Session
from pygmt.exceptions import GMTInvalidInput
from pygmt.helpers import (

def tempfile_from_dftrack(track, suffix):
    Saves pandas.DataFrame track table to a temporary tab-separated ASCII text
    file with a unique name (to prevent clashes when running x2sys_cross),
    adding a suffix extension to the end.

    track : pandas.DataFrame
        A table holding track data with coordinate (x, y) or (lon, lat) values,
        and (optionally) time (t).
    suffix : str
        File extension, e.g. xyz, tsv, etc.

    tmpfilename : str
        A temporary tab-separated value file with a unique name holding the
        track data. E.g. 'track-1a2b3c4.tsv'.
        tmpfilename = f"track-{unique_name()[:7]}.{suffix}"
        yield tmpfilename

[docs]@fmt_docstring @use_alias( A="combitable", C="runtimes", D="override", I="interpolation", R="region", S="speed", T="tag", Q="coe", V="verbose", W="numpoints", Z="trackvalues", ) @kwargs_to_strings(R="sequence") def x2sys_cross(tracks=None, outfile=None, **kwargs): r""" Calculate crossovers between track data files. Determines all intersections between ("external cross-overs") or within ("internal cross-overs") tracks (Cartesian or geographic), and report the time, position, distance along track, heading and speed along each track segment, and the crossover error (COE) and mean values for all observables. By default, :meth:`pygmt.x2sys_cross` will look for both external and internal COEs. As an option, you may choose to project all data using one of the map projections prior to calculating the COE. Full option list at :gmt-docs:`supplements/x2sys/x2sys_cross.html` {aliases} Parameters ---------- tracks : pandas.DataFrame or str or list A table or a list of tables with (x, y) or (lon, lat) values in the first two columns. Track(s) can be provided as pandas DataFrame tables or file names. Supported file formats are ASCII, native binary, or COARDS netCDF 1-D data. More columns may also be present. If the filenames are missing their file extension, we will append the suffix specified for this TAG. Track files will be searched for first in the current directory and second in all directories listed in $X2SYS_HOME/TAG/TAG_paths.txt (if it exists). [If $X2SYS_HOME is not set it will default to $GMT_SHAREDIR/x2sys]. (Note: MGD77 files will also be looked for via $MGD77_HOME/mgd77_paths.txt and .gmt files will be searched for via $GMT_SHAREDIR/mgg/gmtfile_paths). outfile : str Optional. The file name for the output ASCII txt file to store the table in. tag : str Specify the x2sys TAG which identifies the attributes of this data type. combitable : str Only process the pair-combinations found in the file *combitable* [Default process all possible combinations among the specified files]. The file *combitable* is created by :gmt-docs:`x2sys_get's -L option <supplements/x2sys/x2sys_get.html#l>`. runtimes : bool or str Compute and append the processing run-time for each pair to the progress message (use ``runtimes=True``). Pass in a filename (e.g. ``runtimes="file.txt"``) to save these run-times to file. The idea here is to use the knowledge of run-times to split the main process in a number of sub-processes that can each be launched in a different processor of your multi-core machine. See the MATLAB function `split_file4coes.m <>`_. override : bool or str **S**\|\ **N**. Control how geographic coordinates are handled (Cartesian data are unaffected). By default, we determine if the data are closer to one pole than the other, and then we use a cylindrical polar conversion to avoid problems with longitude jumps. You can turn this off entirely with ``override`` and then the calculations uses the original data (we have protections against longitude jumps). However, you can force the selection of the pole for the projection by appending **S** or **N** for the south or north pole, respectively. The conversion is used because the algorithm used to find crossovers is inherently a Cartesian algorithm that can run into trouble with data that has large longitudinal range at higher latitudes. interpolation : str **l**\|\ **a**\|\ **c**. Sets the interpolation mode for estimating values at the crossover. Choose among: - **l** - Linear interpolation [Default]. - **a** - Akima spline interpolation. - **c** - Cubic spline interpolation. coe : str Use **e** for external COEs only, and **i** for internal COEs only [Default is all COEs]. {R} speed : str or list **l**\|\ **u**\|\ **h**\ *speed*. Defines window of track speeds. If speeds are outside this window we do not calculate a COE. Specify: - **l** sets lower speed [Default is 0]. - **u** sets upper speed [Default is infinity]. - **h** does not limit the speed but sets a lower speed below which headings will not be computed (i.e., set to NaN) [Default calculates headings regardless of speed]. For example, you can use ``speed=["l0", "u10", "h5"]`` to set a lower speed of 0, upper speed of 10, and disable heading calculations for speeds below 5. {V} numpoints : int Give the maximum number of data points on either side of the crossover to use in the spline interpolation [Default is 3]. trackvalues : bool Report the values of each track at the crossover [Default reports the crossover value and the mean value]. Returns ------- crossover_errors : :class:`pandas.DataFrame` or None Table containing crossover error information. Return type depends on whether the ``outfile`` parameter is set: - :class:`pandas.DataFrame` with (x, y, ..., etc) if ``outfile`` is not set - None if ``outfile`` is set (track output will be stored in the set in ``outfile``) """ with Session() as lib: file_contexts = [] for track in tracks: kind = data_kind(track) if kind == "file": file_contexts.append(dummy_context(track)) elif kind == "matrix": # find suffix (-E) of trackfiles used (e.g. xyz, csv, etc) from # $X2SYS_HOME/TAGNAME/TAGNAME.tag file lastline = ( Path(os.environ["X2SYS_HOME"], kwargs["T"], f"{kwargs['T']}.tag") .read_text() .strip() .split("\n")[-1] ) # e.g. "-Dxyz -Etsv -I1/1" for item in sorted(lastline.split()): # sort list alphabetically if item.startswith(("-E", "-D")): # prefer -Etsv over -Dxyz suffix = item[2:] # e.g. tsv (1st choice) or xyz (2nd choice) # Save pandas.DataFrame track data to temporary file file_contexts.append(tempfile_from_dftrack(track=track, suffix=suffix)) else: raise GMTInvalidInput(f"Unrecognized data type: {type(track)}") with GMTTempFile(suffix=".txt") as tmpfile: with contextlib.ExitStack() as stack: fnames = [stack.enter_context(c) for c in file_contexts] if outfile is None: outfile = arg_str = " ".join([*fnames, build_arg_string(kwargs), "->" + outfile]) lib.call_module(module="x2sys_cross", args=arg_str) # Read temporary csv output to a pandas table if outfile == # if outfile isn't set, return pd.DataFrame # Read the tab-separated ASCII table table = pd.read_csv(, sep="\t", header=2, # Column names are on 2nd row comment=">", # Skip the 3rd row with a ">" parse_dates=[2, 3], # Datetimes on 3rd and 4th column ) # Remove the "# " from "# x" in the first column table = table.rename(columns={table.columns[0]: table.columns[0][2:]}) elif outfile != # if outfile is set, output in outfile only table = None return table