Datetime inputs

Datetime inputs of the following types are supported in PyGMT:

• numpy.datetime64

• pandas.DatetimeIndex

• xarray.DataArray: datetimes included in a xarray.DataArray

• raw datetime strings in ISO format (e.g. "YYYY-MM-DD", "YYYY-MM-DDTHH", and "YYYY-MM-DDTHH:MM:SS")

• Python built-in datetime.datetime and datetime.date

We can pass datetime inputs based on one of the types listed above directly to the x and y arguments of e.g. the pygmt.Figure.plot method:

The region argument has to include the \(x\) and \(y\) axis limits as str in the form date_min/date_max/ymin/ymax.

Out:

<IPython.core.display.Image object>

import datetime

import numpy as np
import pandas as pd
import pygmt
import xarray as xr

fig = pygmt.Figure()

# create a basemap with limits of 2010-01-01 to 2020-06-01 on the x axis and
# 0 to 10 on the y axis
fig.basemap(
    projection="X15c/5c", region="2010-01-01/2020-06-01/0/10", frame=["WSen", "af"]
)

# numpy.datetime64 types
x = np.array(["2010-06-01", "2011-06-01T12", "2012-01-01T12:34:56"], dtype="datetime64")
y = [1, 2, 3]
fig.plot(x, y, style="c0.4c", pen="1p", color="red3")

# pandas.DatetimeIndex
x = pd.date_range("2013", periods=3, freq="YS")
y = [4, 5, 6]
fig.plot(x, y, style="t0.4c", pen="1p", color="gold")

# xarray.DataArray
x = xr.DataArray(data=pd.date_range(start="2015-03", periods=3, freq="QS"))
y = [7.5, 6, 4.5]
fig.plot(x, y, style="s0.4c", pen="1p")

# raw datetime strings
x = ["2016-02-01", "2016-06-04T14", "2016-10-04T00:00:15"]
y = [7, 8, 9]
fig.plot(x, y, style="a0.4c", pen="1p", color="dodgerblue")

# the Python built-in datetime and date
x = [datetime.date(2018, 1, 1), datetime.datetime(2019, 6, 1, 20, 5, 45)]
y = [6.5, 4.5]
fig.plot(x, y, style="i0.4c", pen="1p", color="seagreen")

fig.show()