Documentation: See docs folder
Source Code: Available on GitHub
Feedback: I welcome any and all feedback! See the Development Notes below for more details.
matplotlib_map_utils is intended to be a package that provides various functions and objects that assist with the the creation of maps using matplotlib.
As of v3.x (the current version), this includes three-ish elements:
north_arrow.py, for adding a north arrow to a given plot.
scale_bar.py, for adding a scale bar to a given plot.
inset_map.py, for adding inset maps and detail/extent indicators to a given plot.
The three elements listed above are all intended to be high-resolution, easily modifiable, and context-aware, relative to your specific plot.
This package also contains a single utility object:
usa.py, which contains a class that helps filter for states and territories within the USA based on given characteristics.Together, these allow for the easy creation of a map such as the following:
This package is available on PyPi, and can be installed like so:
pip install matplotlib-map-utils
The requirements for this package are:
python >= 3.10 (due to the use of the pipe operator to concatenate dictionaries and types)
matplotlib >= 3.9 (might work with lower versions but not guaranteed)
cartopy >= 0.23 (due to earlier bug with calling copy() on CRS objects)
package_name/ ├── __init__.py │ ├── core/ │ ├── __init__.py │ ├── inset_map.py │ ├── north_arrow.py │ ├── scale_bar.py ├── validation/ │ ├── __init__.py │ ├── functions.py │ └── inset_map.py │ ├── north_arrow.py │ └── scale_bar.py ├── defaults/ │ ├── __init__.py │ ├── north_arrow.py │ └── scale_bar.py │ └── inset_map.py ├── utils/ │ ├── __init__.py │ ├── usa.py │ └── usa.json
Where:
core contains the main functions and classes for each object
validation contains type hints for each variable and functions to validate inputs
defaults contains default settings for each object at different paper sizes
utils contains utility functions and objects
Importing the North Arrow functions and classes can be done like so:
from matplotlib_map_utils.core.north_arrow import NorthArrow, north_arrow from matplotlib_map_utils.core import NorthArrow, north_arrow # also valid from matplotlib_map_utils import NorthArrow, north_arrow # also valid
The quickest way to add a single north arrow to a single plot is to use the north_arrow function:
# Setting up a plot
fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(5,5), dpi=150)
# Adding a north arrow to the upper-right corner of the axis, without any rotation (see Rotation under Formatting Components for details)
north_arrow.north_arrow(ax=ax, location="upper right", rotation={"degrees":0})
An object-oriented approach is also supported:
# Setting up a plot
fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(5,5), dpi=150)
# Creating a north arrow for the upper-right corner of the axis, without any rotation (see Rotation under Formatting Components for details)
na = north_arrow.NorthArrow(location="upper right", rotation={"degrees":0})
# Adding the artist to the plot
ax.add_artist(na)
Both of these will create an output like the following:
Both the object-oriented and functional approaches can be customized to allow for fine-grained control over formatting:
north_arrow(
ax,
location = "upper right", # accepts a valid string from the list of locations
scale = 0.5, # accepts a valid positive float or integer
# each of the follow accepts arguments from a customized style dictionary
base = {"facecolor":"green"},
fancy = False,
label = {"text":"North"},
shadow = {"alpha":0.8},
pack = {"sep":6},
aob = {"pad":2},
rotation = {"degrees": 35}
)
This will create an output like the following:
Refer to docs\howto_north_arrow for details on how to customize each facet of the north arrow.
The north arrow object is also capable of pointing towards "true north", given a CRS and reference point:
Instructions for how to do so can be found in docs\howto_north_arrow.
Importing the Scale Bar functions and classes can be done like so:
from matplotlib_map_utils.core.scale_bar import ScaleBar, scale_bar from matplotlib_map_utils.core import ScaleBar, scale_bar # also valid from matplotlib_map_utils import ScaleBar, scale_bar # also valid
There are two available styles for the scale bars: boxes and ticks. The quickest way to add one to a single plot is to use the scale_bar function:
# Setting up a plot
# NOTE: you MUST set the desired DPI here, when the subplots are created
# so that the scale_bar's DPI matches!
fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(5,5), dpi=150)
# Adding a scale bar to the upper-right corner of the axis, in the same projection as whatever geodata you plotted
# Here, this scale bar will have the "boxes" style
scale_bar(ax=ax, location="upper right", style="boxes", bar={"projection":3857})
An object-oriented approach is also supported:
# Setting up a plot
# NOTE: you MUST set the desired DPI here, when the subplots are created
# so that the scale_bar's DPI matches!
fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(5,5), dpi=150)
# Adding a scale bar to the upper-right corner of the axis, in the same projection as whatever geodata you plotted
# Here, we change the boxes to "ticks"
sb = ScaleBar(location="upper right", style="ticks", bar={"projection":3857})
# Adding the artist to the plot
ax.add_artist(sb)
Both of these will create an output like the following (function is left, class is right):
Both the object-oriented and functional approaches can be customized to allow for fine-grained control over formatting:
scale_bar(
ax,
location = "upper right", # accepts a valid string from the list of locations
style = "boxes", # accepts a valid positive float or integer
# each of the follow accepts arguments from a customized style dictionary
bar = {"unit":"mi", "length":2}, # converting the units to miles, and changing the length of the bar (in inches)
labels = {"style":"major", "loc":"below"}, # placing a label on each major division, and moving them below the bar
units = {"loc":"text"}, # changing the location of the units text to the major division labels
text = {"fontfamily":"monospace"}, # changing the font family of all the text to monospace
)
This will create an output like the following:
Refer to docs\howto_scale_bar for details on how to customize each facet of the scale bar.
Importing the Inset Map functions and classes can be done like so:
from matplotlib_map_utils.core.inset_map import InsetMap, inset_map, ExtentIndicator, indicate_extent, DetailIndicator, indicate_detail from matplotlib_map_utils.core import InsetMap, inset_map, ExtentIndicator, indicate_extent, DetailIndicator, indicate_detail # also valid from matplotlib_map_utils import InsetMap, inset_map, ExtentIndicator, indicate_extent, DetailIndicator, indicate_detail # also valid
The quickest way to add a single inset map to an existing plot is the inset_map function:
# Setting up a plot fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(5,5), dpi=150) # Adding an inset map to the upper-right corner of the axis iax = inset_map(ax=ax, location="upper right", size=0.75, pad=0, xticks=[], yticks=[]) # You can now plot additional data to iax as desired
An object-oriented approach is also supported:
# Setting up a plot fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(5,5), dpi=150) # Creating an object for the inset map im = InsetMap(location="upper right", size=0.75, pad=0, xticks=[], yticks=[]) # Adding the inset map template to the plot iax = im.create(ax=ax) # You can now plot additional data to iax as desired
Both of these will create an output like the following:
Extent and Detail IndicatorsInset maps can be paired with either an extent or detail indicator, to provide additional geographic context to the inset map
indicate_extent(inset_axis, parent_axis, inset_crs, parent_crs, ...) indicate_detail(parent_axis, inset_axis, parent_crs, inset_crs, ...)
This will create an output like the following (extent indicator on the left, detail indicator on the right):
Refer to docs\howto_inset_map for details on how to customize the inset map and indicators to your liking.
Importing the bundled utility functions and classes can be done like so:
from matplotlib_map_utils.utils import USA
As of v2.1.0, there is only one utility class available: USA, an object to help quickly filter for subsets of US states and territories. This utility class is still in beta, and might change.
An example:
# Loading the object usa = USA() # Getting a list FIPS codes for US States usa.filter(states=True, to_return="fips") # Getting a list of State Names for states in the South and Midwest regions usa.filter(region=["South","Midtwest"], to_return="name")
Refer to docs\howto_utils for details on how to use this class, including with pandas.apply().
This project was heavily inspired by matplotlib-scalebar, and much of the code is either directly copied or a derivative of that project, since it uses the same "artist"-based approach.
Two more projects assisted with the creation of this script:
EOmaps provided code for calculating the rotation required to point to "true north" for an arbitrary point and CRS for the north arrow.
Cartopy fixed an issue inherent to calling .copy() on CRS objects.
v1.0.x: Initial releases featuring the North Arrow element, along with some minor bug fixes.
v2.0.0: Initial release of the Scale Bar element.
v2.0.1: Fixed a bug in the dual_bars() function that prevented empty dictionaries to be passed. Also added a warning when auto-calculated bar widths appear to be exceeding the dimension of the axis (usually occurs when the axis is <2 kilometers or miles long, depending on the units selected).
v2.0.2: Changed f-string formatting to alternate double and single quotes, so as to maintain compatibility with versions of Python before 3.12 (see here). However, this did reveal that another aspect of the code, namely concatenating type in function arguments, requires 3.10, and so the minimum python version was incremented.
v2.1.0: Added a utility class, USA, for filtering subsets of US states and territories based on FIPS code, name, abbreviation, region, division, and more. This is considered a beta release, and might be subject to change later on.
v3.0.0: Release of inset map and extent and detail indicator classes and functions.
With the release of v3.x, this project has achieved full coverage of the "main" map elements I think are necessary.
If I continue development of this project, I will be looking to add or fix the following features:
For all: switch to a system based on Pydantic for easier type validation
North Arrow:
Copy the image-rendering functionality of the Scale Bar to allow for rotation of the entire object, label and arrow together
Create more styles for the arrow, potentially including a compass rose and a line-only arrow
Scale Bar:
Allow for custom unit definitions (instead of just metres/feet/miles/kilometres/etc.), so that the scale bar can be used on arbitrary plots (such as inches/cm/mm, mathmatical plots, and the like)
Fix/improve the dual_bars() function, which currently doesn't work great with rotations
Clean up the variable naming scheme (consistency on loc vs position, style vs type, etc.)
Create more styles for the bar, potentially including dual boxes and a sawtooth bar
Inset Map:
Clean up the way that connectors are drawn for detail indicators
New functionality for placing multiple inset maps at once (with context-aware positioning to prevent overlap with each other)
Utils:
(USA): Stronger fuzzy search mechanics, so that it will accept flexible inputs for FIPS/abbr/name
(USA): More integrated class types to allow for a more fully-formed object model (USA being a Country, with subclasses related to State and Territory that have their own classes of attributes, etc.)
(USA): Stronger typing options, so you don't have to recall which region or division types are available, etc.
Future releases (if the project is continued) will probably focus on other functions that I have created myself that give more control in the formatting of maps. I am also open to ideas for other extensions to create!
Support and ContributionsIf you notice something is not working as intended or if you'd like to add a feature yourself, I welcome PRs - just be sure to be descriptive as to what you are changing and why, including code examples!
If you are having issues using this script, feel free to leave a post explaining your issue, and I will try and assist, though I have no guaranteed SLAs as this is just a hobby project.
I know nothing about licensing, so I went with the GPL license. If that is incompatible with any of the dependencies, please let me know.
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