Source code for pygmt.filtering

GMT modules for Filtering of 1-D and 2-D Data
import pandas as pd

from .clib import Session
from .exceptions import GMTInvalidInput
from .helpers import (

[docs]@fmt_docstring @use_alias(I="spacing", R="region", V="verbose") @kwargs_to_strings(R="sequence") def blockmedian(table, outfile=None, **kwargs): """ Block average (x,y,z) data tables by median estimation. Reads arbitrarily located (x,y,z) triples [or optionally weighted quadruples (x,y,z,w)] from a table and writes to the output a median position and value for every non-empty block in a grid region defined by the region and spacing arguments. Full option list at :gmt-docs:`blockmedian.html` {aliases} Parameters ---------- table : pandas.DataFrame or str Either a pandas dataframe with (x, y, z) or (longitude, latitude, elevation) values in the first three columns, or a file name to an ASCII data table. spacing : str ``'xinc[unit][+e|n][/yinc[unit][+e|n]]'``. x_inc [and optionally y_inc] is the grid spacing. region : str or list ``'xmin/xmax/ymin/ymax[+r][+uunit]'``. Specify the region of interest. outfile : str Required if 'table' is a file. The file name for the output ASCII file. {V} Returns ------- output : pandas.DataFrame or None Return type depends on whether the outfile parameter is set: - pandas.DataFrame table with (x, y, z) columns if outfile is not set - None if outfile is set (filtered output will be stored in outfile) """ kind = data_kind(table) with GMTTempFile(suffix=".csv") as tmpfile: with Session() as lib: if kind == "matrix": if not hasattr(table, "values"): raise GMTInvalidInput(f"Unrecognized data type: {type(table)}") file_context = lib.virtualfile_from_matrix(table.values) elif kind == "file": if outfile is None: raise GMTInvalidInput("Please pass in a str to 'outfile'") file_context = dummy_context(table) else: raise GMTInvalidInput(f"Unrecognized data type: {type(table)}") with file_context as infile: if outfile is None: outfile = arg_str = " ".join([infile, build_arg_string(kwargs), "->" + outfile]) lib.call_module(module="blockmedian", args=arg_str) # Read temporary csv output to a pandas table if outfile == # if user did not set outfile, return pd.DataFrame result = pd.read_csv(, sep="\t", names=table.columns) elif outfile != # return None if outfile set, output in outfile result = None return result