Source code for pygmt.datasets.tutorial

"""
Functions to load sample data from the GMT tutorials.
"""
import pandas as pd

from .. import which


[docs]def load_japan_quakes(): """ Load a table of earthquakes around Japan as a pandas.Dataframe. Data is from the NOAA NGDC database. This is the ``@tut_quakes.ngdc`` dataset used in the GMT tutorials. The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the first time you invoke this function. Afterwards, it will load the data from the cache. So you'll need an internet connection the first time around. Returns ------- data : pandas.Dataframe The data table. Columns are year, month, day, latitude, longitude, depth (in km), and magnitude of the earthquakes. """ fname = which("@tut_quakes.ngdc", download="c") data = pd.read_csv(fname, header=1, sep=r"\s+") data.columns = [ "year", "month", "day", "latitude", "longitude", "depth_km", "magnitude", ] return data
[docs]def load_sample_bathymetry(): """ Load a table of ship observations of bathymetry off Baja California as a pandas.DataFrame. This is the ``@tut_ship.xyz`` dataset used in the GMT tutorials. The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the first time you invoke this function. Afterwards, it will load the data from the cache. So you'll need an internet connection the first time around. Returns ------- data : pandas.Dataframe The data table. Columns are longitude, latitude, and bathymetry. """ fname = which("@tut_ship.xyz", download="c") data = pd.read_csv( fname, sep="\t", header=None, names=["longitude", "latitude", "bathymetry"] ) return data
[docs]def load_usgs_quakes(): """ Load a table of global earthquakes form the USGS as a pandas.Dataframe. This is the ``@usgs_quakes_22.txt`` dataset used in the GMT tutorials. The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the first time you invoke this function. Afterwards, it will load the data from the cache. So you'll need an internet connection the first time around. Returns ------- data : pandas.Dataframe The data table. Use ``print(data.describe())`` to see the available columns. """ fname = which("@usgs_quakes_22.txt", download="c") data = pd.read_csv(fname) return data