Plotting datetime charts

PyGMT accepts a variety of datetime objects to plot data and create charts. Aside from the built-in Python datetime module, PyGMT supports inputs containing ISO formatted strings as well as objects generated with numpy, pandas, and xarray. These data types can be used to plot specific points as well as get passed into the region parameter to create a range of the data on an axis.

The following examples will demonstrate how to create plots using these different datetime objects.

import datetime

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

Using Python’s datetime

In this example, Python’s built-in datetime module is used to create data points stored in the list x. Additionally, dates are passed into the region parameter in the format [x_start, x_end, y_start, y_end], where the date range is plotted on the x-axis. An additional notable parameter is style, where it’s specified that data points are to be plotted in an X shape with a size of 0.3 centimeters.

x = [
    datetime.date(2010, 6, 1),
    datetime.date(2011, 6, 1),
    datetime.date(2012, 6, 1),
    datetime.date(2013, 6, 1),
]
y = [1, 2, 3, 5]

fig = pygmt.Figure()
fig.plot(
    projection="X10c/5c",
    region=[datetime.date(2010, 1, 1), datetime.date(2014, 12, 1), 0, 6],
    frame=["WSen", "afg"],
    x=x,
    y=y,
    style="x0.3c",
    pen="1p",
)
fig.show()

In addition to specifying the date, datetime supports the time at which the data points were recorded. Using datetime.datetime the region parameter as well as data points can be created with both date and time information.

Some notable differences to the previous example include:

• Modifying frame to only include West (left) and South (bottom) borders, and removing grid lines

• Using circles to plot data points defined by c in the argument passed through the style parameter

x = [
    datetime.datetime(2021, 1, 1, 3, 45, 1),
    datetime.datetime(2021, 1, 1, 6, 15, 1),
    datetime.datetime(2021, 1, 1, 13, 30, 1),
    datetime.datetime(2021, 1, 1, 20, 30, 1),
]
y = [5, 3, 1, 2]

fig = pygmt.Figure()
fig.plot(
    projection="X10c/5c",
    region=[
        datetime.datetime(2021, 1, 1, 0, 0, 0),
        datetime.datetime(2021, 1, 2, 0, 0, 0),
        0,
        6,
    ],
    frame=["WS", "af"],
    x=x,
    y=y,
    style="c0.4c",
    pen="1p",
    fill="blue",
)
fig.show()

Using ISO Format

In addition to Python’s datetime module, PyGMT also supports passing dates in ISO format. Basic ISO strings are formatted as YYYY-MM-DD with each - delineated section marking the four-digit year value, two-digit month value, and two-digit day value, respectively.

For including the time into an ISO string, the T character is used, as it can be seen in the following example. This character is immediately followed by a string formatted as hh:mm:ss where each : delineated section marking the two-digit hour value, two-digit minute value, and two-digit second value, respectively. The figure in the following example is plotted over a horizontal range of one year from 2016-01-01 to 2017-01-01.

x = ["2016-02-01", "2016-06-04T14", "2016-10-04T00:00:15", "2016-12-01T05:00:15"]
y = [1, 3, 5, 2]
fig = pygmt.Figure()
fig.plot(
    projection="X10c/5c",
    region=["2016-01-01", "2017-01-01", 0, 6],
    frame=["WSen", "afg"],
    x=x,
    y=y,
    style="a0.45c",
    pen="1p",
    fill="dodgerblue",
)
fig.show()

Note

PyGMT doesn’t recognize non-ISO datetime strings like “Jun 05, 2018”. If your data contain non-ISO datetime strings, you can convert them to a recognized format using pandas.to_datetime and then pass it to PyGMT.

Mixing and matching Python datetime and ISO dates

The following example provides context on how both datetime and ISO date data can be plotted using PyGMT. This can be helpful when dates and times are coming from different sources, meaning conversions do not need to take place between ISO and datetime in order to create valid plots.

x = ["2020-02-01", "2020-06-04", "2020-10-04", datetime.datetime(2021, 1, 15)]
y = [1.3, 2.2, 4.1, 3]
fig = pygmt.Figure()
fig.plot(
    projection="X10c/5c",
    region=[datetime.datetime(2020, 1, 1), datetime.datetime(2021, 3, 1), 0, 6],
    frame=["WSen", "afg"],
    x=x,
    y=y,
    style="i0.4c",
    pen="1p",
    fill="yellow",
)
fig.show()

Using pandas.date_range

In the following example, pandas.date_range produces a list of pandas.DatetimeIndex objects, which is used to pass date data to the PyGMT figure. Specifically x contains 7 different pandas.DatetimeIndex objects, with the number being manipulated by the periods parameter. Each period begins at the start of a business quarter as denoted by BQS when passed to the freq parameter. The initial date is the first argument that is passed to pandas.date_range and it marks the first data point in the list x that will be plotted.

x = pd.date_range("2018-03-01", periods=7, freq="BQS")
y = [4, 5, 6, 8, 6, 3, 5]

fig = pygmt.Figure()
fig.plot(
    projection="X10c/10c",
    region=[datetime.datetime(2017, 12, 31), datetime.datetime(2019, 12, 31), 0, 10],
    frame=["WSen", "ag"],
    x=x,
    y=y,
    style="i0.4c",
    pen="1p",
    fill="purple",
)
fig.show()

Using xarray.DataArray

In this example, instead of using a list of pandas.DatetimeIndex objects, x is initialized as an xarray.DataArray object. This object provides a wrapper around regular PyData formats. It also allows the data to have labeled dimensions while supporting operations that use various pieces of metadata. The following code uses pandas.date_range to fill the DataArray with data, but this is not essential for the creation of a valid DataArray.

x = xr.DataArray(data=pd.date_range(start="2020-01-01", periods=4, freq="Q"))
y = [4, 7, 5, 6]

fig = pygmt.Figure()
fig.plot(
    projection="X10c/10c",
    region=[datetime.datetime(2020, 1, 1), datetime.datetime(2021, 4, 1), 0, 10],
    frame=["WSen", "ag"],
    x=x,
    y=y,
    style="n0.4c",
    pen="1p",
    fill="red",
)
fig.show()

Using numpy.datetime64

In this example, instead of using pd.date_range, x is initialized as an np.array object. Similar to xarray.DataArray this wraps the dataset before passing it as an argument. However, np.array objects use less memory and allow developers to specify data types.

x = np.array(["2010-06-01", "2011-06-01T12", "2012-01-01T12:34:56"], dtype="datetime64")
y = [2, 7, 5]

fig = pygmt.Figure()
fig.plot(
    projection="X10c/10c",
    region=[datetime.datetime(2010, 1, 1), datetime.datetime(2012, 6, 1), 0, 10],
    frame=["WS", "ag"],
    x=x,
    y=y,
    style="s0.5c",
    pen="1p",
    fill="blue",
)
fig.show()

Generating an automatic region

Another way of creating charts involving datetime data can be done by automatically generating the region of the plot. This can be done by passing the DataFrame to pygmt.info, which will find the maximum and minimum values for each column and create a list that could be passed as region. Additionally, the spacing parameter can be used to increase the range past the maximum and minimum data points.

data = [
    ["20200712", 1000],
    ["20200714", 1235],
    ["20200716", 1336],
    ["20200719", 1176],
    ["20200721", 1573],
    ["20200724", 1893],
    ["20200729", 1634],
]
df = pd.DataFrame(data, columns=["Date", "Score"])
df.Date = pd.to_datetime(df["Date"], format="%Y%m%d")

fig = pygmt.Figure()
region = pygmt.info(
    data=df[["Date", "Score"]], per_column=True, spacing=(700, 700), coltypes="T"
)

fig.plot(
    region=region,
    projection="X15c/10c",
    frame=["WSen", "afg"],
    x=df.Date,
    y=df.Score,
    style="c0.4c",
    pen="1p",
    fill="green3",
)

fig.show()

Setting Primary and Secondary Time Axes

This example focuses on annotating the axes and setting the interval in which the annotations should appear. All of these modifications are passed to the frame parameter and each item in that list modifies a specific aspect of the frame.

Adding "WS" means that only the Western/Left (W) and Southern/Bottom (S) borders of the plot are annotated. For more information on this, please refer to the Frames, ticks, titles, and labels tutorial.

Another important item in the list passed to frame is "sxa1Of1D". This string modifies the secondary annotation (s) of the x-axis (x). Specifically, it sets the main annotation and major tick spacing interval to one month (a1O) (capital letter O, not zero). Additionally, it sets the minor tick spacing interval to 1 day (f1D). To use the month’s name instead of its number set FORMAT_DATE_MAP to o. More information on configuring date formats can be found at FORMAT_DATE_MAP, FORMAT_DATE_IN, and FORMAT_DATE_OUT.

x = pd.date_range("2013-05-02", periods=10, freq="2D")
y = [4, 5, 6, 8, 9, 5, 8, 9, 4, 2]

fig = pygmt.Figure()
with pygmt.config(FORMAT_DATE_MAP="o"):
    fig.plot(
        projection="X15c/10c",
        region=[datetime.datetime(2013, 5, 1), datetime.datetime(2013, 5, 25), 0, 10],
        frame=["WS", "sxa1Of1D", "pxa5d", "sy+lLength", "pya1+ucm"],
        x=x,
        y=y,
        style="c0.4c",
        pen="1p",
        fill="green3",
    )

fig.show()

The same concept shown above can be applied to smaller as well as larger intervals. In this example, data are plotted for different times throughout two days. The primary x-axis annotations are modified to repeat every 6 hours, and the secondary x-axis annotations repeat every day and show the day of the week.

Another notable mention in this example is setting FORMAT_CLOCK_MAP to -hhAM which specifies the format used for time. In this case, leading zeros are removed using (-), and only hours are displayed. Additionally, an AM/PM system is used instead of a 24-hour system. More information on configuring time formats can be found at FORMAT_CLOCK_MAP, FORMAT_CLOCK_IN, and FORMAT_CLOCK_OUT.

x = pd.date_range("2021-04-15", periods=8, freq="6H")
y = [2, 5, 3, 1, 5, 7, 9, 6]

fig = pygmt.Figure()
with pygmt.config(FORMAT_CLOCK_MAP="-hhAM"):
    fig.plot(
        projection="X15c/10c",
        region=[
            datetime.datetime(2021, 4, 14, 23, 0, 0),
            datetime.datetime(2021, 4, 17),
            0,
            10,
        ],
        frame=["WS", "sxa1K", "pxa6H", "sy+lSpeed", "pya1+ukm/h"],
        x=x,
        y=y,
        style="n0.4c",
        pen="1p",
        fill="lightseagreen",
    )
fig.show()