The text is a detailed tutorial on creating zoom plots using Matplotlib. The author outlines a step-by-step process, from fetching and preparing data to creating the zoom plots with magnified views of areas of interest. The tutorial also includes code snippets and explanations for each step. This approach promises clear and informative visualizations for complex data.
“`html
Make beautiful multi-scale plots with matplotlib in 3 easy steps
Step 0: Get Data
Fetch the rain gauge records via the Open Science Framework.
df = pd.read_csv("https://osf.io/6mx3e/download") # download dataStep 1: Axes Grid
Our first plotting step is to initialize an outset.OutsetGrid instance to manage our lattice of magnification plots.
grid = otst.OutsetGrid(
data=[
(71.6, 0, 72.2, 2),
(59, 0, 90, 0.2),
(81.3, 0, 82.2, 16),
],
x="Time",
y="Precipitation (mm)",
aspect=2,
col_wrap=2,
marqueeplot_kws={"frame_outer_pad": 0, "mark_glyph_kws": {"zorder": 11}},
marqueeplot_source_kws={"zorder": 10, "frame_face_kws": {"zorder": 10}},
)Step 2: Plot Content
It’s time to put some content on our axes.
for y, color in zip([nwls, swls], ["fuchsia", "aquamarine"]):
grid.broadcast(
plt.stackplot,
march_df["Decimal DOY"],
march_df[y],
colors=[color],
labels=[y],
lw=2,
edgecolor=color,
alpha=0.4,
zorder=10,
)Step 3: Zoom Indicators
Now it’s time to add zoom indicator boxes, a.k.a. outset “marquees,” to show how the scales of our auxiliary plots relate to the scale of the main plot.
grid.marqueeplot(equalize_aspect=False)Our last business is to add a legend and switch out numeric x ticks for proper timestamps.
grid.source_axes.legend(
loc="upper left",
bbox_to_anchor=(0.02, 1.0),
frameon=True,
)Bonus: Want Insets Instead?
We can create insets by rearranging the magnification lattice axes into position over the main axes.
otst.inset_outsets(
grid,
insets=otst_util.layout_corner_insets(
3,
"NW",
inset_margin_size=(0.02, 0),
inset_grid_size=(0.67, 0.9),
),
equalize_aspect=False,
)
sns.move_legend(
grid.source_axes, "lower center", bbox_to_anchor=(0.5, 1.1), ncol=2
)Further Information
You can learn more about using outset in the library’s documentation at https://mmore500.com/outset. In particular, be sure to check out the quickstart guide.
Outset can be installed via pip as python3 -m pip install outset.
Authorship
This tutorial is contributed by me, Matthew Andres Moreno.
Citations
Evett, Steven R.; Marek, Gary W.; Copeland, Karen S.; Howell, Terry A. Sr.; Colaizzi, Paul D.; Brauer, David K.; Ruthardt, Brice B. (2023). Evapotranspiration, Irrigation, Dew/frost — Water Balance Data for The Bushland, Texas Soybean Datasets. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1528713. Accessed 2023–12–26.
J. D. Hunter, “Matplotlib: A 2D Graphics Environment”, Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007. https://doi.org/10.1109/MCSE.2007.55
Marek, G. W., Evett, S. R., Colaizzi, P. D., & Brauer, D. K. (2021). Preliminary crop coefficients for late planted short-season soybean: Texas High Plains. Agrosystems, Geosciences & Environment, 4(2). https://doi.org/10.1002/agg2.20177
Data structures for statistical computing in python, McKinney, Proceedings of the 9th Python in Science Conference, Volume 445, 2010. https://doi.org/10.25080/Majora-92bf1922-00a
Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, https://doi.org/10.21105/joss.03021.
Appendix
You can find the entire code as a gist here and as a notebook here.
To install dependencies for this exercise,
python3 -m pip install
matplotlib `# ==3.8.2`
numpy `# ==1.26.2`
outset `# ==0.1.6`
opytional `# ==0.1.0`
pandas `# ==2.1.3`
seaborn `# ==0.13.0`All images are works of the author.
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