Plotting datetime charts

PyGMT accepts a variety of datetime objects to plot data and create charts. Aside from the built-in Python datetime object, PyGMT supports input using ISO formatted strings, pandas, xarray, as well as numpy. 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 the 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 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()

Out:

<IPython.core.display.Image object>

In addition to specifying the date, datetime supports the exact 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) border, and removing grid lines

• Using circles to plot data points defined through c in 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",
    color="blue",
)
fig.show()

Out:

<IPython.core.display.Image object>

Using ISO Format

In addition to Python’s datetime library, PyGMT also supports passing times 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.

When including time of day into ISO strings, the T character is used, as 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 1/1/2016 to 1/1/2017.

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", "2017-01-1", 0, 6],
    frame=["WSen", "afg"],
    x=x,
    y=y,
    style="a0.45c",
    pen="1p",
    color="dodgerblue",
)
fig.show()

Out:

<IPython.core.display.Image object>

Mixing and matching datetime and ISO

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",
    color="yellow",
)
fig.show()

Out:

<IPython.core.display.Image object>

Using pandas.date_range

In the following example, a pandas.date_range object is used to pass date data to the PyGMT figure. This object is set using the pandas method date_range(). This particular object 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 periods parameter. The inital date is the first argument that is passed to pandas.date_range and it marks the first data in the object 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",
    color="purple",
)
fig.show()

Out:

<IPython.core.display.Image object>

Using xarray.DataArray

In this example, instead of using a pandas.date_range object, x is initialized as an xarray.DataArray object. Such object provides a wrapper around traditional data formats allowing this data to have varying labeled dimensions and support operations that use various pieces of metadata. The following code uses a pandas.date_range object to fill the DataArray with data, yet 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",
    color="red",
)
fig.show()

Out:

<IPython.core.display.Image object>

Using numpy.datetime64

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

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",
    color="blue",
)
fig.show()

Out:

<IPython.core.display.Image object>

Generating 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 maximum and minimum values for each column and create a list that could be passed as region. Additionally, a spacing argument can be passed 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(
    table=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",
    color="green3",
)

fig.show()

Out:

<IPython.core.display.Image object>

Setting Primary and Secondary Time Axes

This example focuses on labeling the axes and setting intervals at which the labels are expected to appear. All of these modifications are added to the frame parameter and each item in that list modifies a specific section of the plot.

Starting off with WS, adding this string means that only Western/Left (W) and Southern/Bottom (S) borders of the plot will be shown. For more information on this, please refer to frame instructions.

The other important item in the frame list is "sxa1Of1D". This string modifies the secondary labeling (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). The labeling of this axis is also modified using pygmt.config(FORMAT_DATE_MAP="o") to use the month’s name instead of its number.

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",
        color="green3",
    )

fig.show()

Out:

<IPython.core.display.Image object>

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

Another notable mention in this example is pygmt.config(FORMAT_CLOCK_MAP="-hhAM") which specifies the used format for time. In this case, leading zeros are removed using (-), and only hours are displayed. Additionally, an AM/PM system is being used instead of a 24-hour system.

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",
        color="lightseagreen",
    )
fig.show()

Out:

<IPython.core.display.Image object>