A library for making ridge plots of... ridges. Choose a location, get an elevation map, and tinker with it to make something beautiful. Heavily inspired from Zach Cole's beautiful art, Jake Vanderplas' examples, and Joy Division's 1979 album "Unknown Pleasures".
Uses matplotlib, SRTM.py, numpy, and scikit-image (for lake detection).
Available on PyPI:
Or live on the edge and install from github with
pip install git+https://github.com/colcarroll/ridge_map.git
You can also make a copy of this colab.
RidgeMap.preprocessor method and find something nice, I would love to hear about it!The API allows you to download the data once, then edit the plot yourself, or allow the default processor to help you.
Plotting with all the defaults should give you a map of my favorite mountains.
from ridge_map import RidgeMap RidgeMap().plot_map()Download once and tweak settings
First you download the elevation data to get an array with shape (num_lines, elevation_pts), then you can use the preprocessor to automatically detect lakes, rivers, and oceans, and scale the elevations. Finally, there are options to style the plot
rm = RidgeMap((11.098251,47.264786,11.695633,47.453630))
values = rm.get_elevation_data(num_lines=150)
values=rm.preprocess(
values=values,
lake_flatness=2,
water_ntile=10,
vertical_ratio=240)
rm.plot_map(values=values,
label='Karwendelgebirge',
label_y=0.1,
label_x=0.55,
label_size=40,
linewidth=1)
If you are plotting a town that is super into burnt orange for whatever reason, you can respect that choice.
rm = RidgeMap((-97.794285,30.232226,-97.710171,30.334509))
values = rm.get_elevation_data(num_lines=80)
rm.plot_map(values=rm.preprocess(values=values, water_ntile=12, vertical_ratio=40),
label='Austin\nTexas',
label_x=0.75,
linewidth=6,
line_color='orange')
Plot with even more colors!
The line color accepts a matplotlib colormap, so really feel free to go to town.
rm = RidgeMap((-123.107300,36.820279,-121.519775,38.210130))
values = rm.get_elevation_data(num_lines=150)
rm.plot_map(values=rm.preprocess(values=values, lake_flatness=3, water_ntile=50, vertical_ratio=30),
label='The Bay\nArea',
label_x=0.1,
line_color = plt.get_cmap('spring'))
Plot with custom fonts and elevation colors!
You can find a good font from Google, and then get the path to the ttf file in the github repo.
If you pass a matplotlib colormap, you can specify kind="elevation" to color tops of mountains different from bottoms. ocean, gnuplot, and bone look nice.
from ridge_map import FontManager
font = FontManager('https://github.com/google/fonts/blob/main/ofl/uncialantiqua/UncialAntiqua-Regular.ttf?raw=true')
rm = RidgeMap((-156.250305,18.890695,-154.714966,20.275080), font=font.prop)
values = rm.get_elevation_data(num_lines=100)
rm.plot_map(values=rm.preprocess(values=values, lake_flatness=2, water_ntile=10, vertical_ratio=240),
label="Hawai'i",
label_y=0.85,
label_x=0.7,
label_size=60,
linewidth=2,
line_color=plt.get_cmap('ocean'),
kind='elevation')
How do I find a bounding box?
I have been using this website. I find an area I like, draw a rectangle, then copy and paste the coordinates into the RidgeMap constructor.
rm = RidgeMap((-73.509693,41.678682,-73.342838,41.761581))
values = rm.get_elevation_data()
rm.plot_map(values=rm.preprocess(values=values, lake_flatness=2, water_ntile=2, vertical_ratio=60),
label='Kent\nConnecticut',
label_y=0.7,
label_x=0.65,
label_size=40)
What about really flat areas?
You might really have to tune the water_ntile and lake_flatness to get the water right. You can set them to 0 if you do not want any water marked.
rm = RidgeMap((-71.167374,42.324286,-70.952454, 42.402672))
values = rm.get_elevation_data(num_lines=50)
rm.plot_map(values=rm.preprocess(values=values, lake_flatness=4, water_ntile=30, vertical_ratio=20),
label='Cambridge\nand Boston',
label_x=0.75,
label_size=40,
linewidth=1)
Yes, you can change the angle at which you look at the map. South to North is 0 degrees, East to West is 90 degrees and so forth with the rest of the compass. I really recommend playing around with this setting because of the really cool maps it can generate.
Play around with interpolation, lock_rotation, and crop to polish out the map.
rm = RidgeMap((-124.848974,46.292035,-116.463262,49.345786))
values = rm.get_elevation_data(elevation_pts=300, num_lines=300, viewpoint_angle=11)
values=rm.preprocess(
values=values,
lake_flatness=2,
water_ntile=10,
vertical_ratio=240
)
rm.plot_map(values=values,
label='Washington',
label_y=0.8,
label_x=0.05,
label_size=40,
linewidth=2
)
It is that pleasant kettle pond in the bottom right of this map, looking entirely comfortable with its place in Western writing and thought.
rm = RidgeMap((-71.418858,42.427511,-71.310024,42.481719))
values = rm.get_elevation_data(num_lines=100)
rm.plot_map(values=rm.preprocess(values=values, water_ntile=15, vertical_ratio=30),
label='Concord\nMassachusetts',
label_x=0.1,
label_size=30)
Do you play nicely with other matplotlib figures?
Of course! If you really want to put a stylized elevation map in a scientific plot you are making, I am not going to stop you, and will actually make it easier for you. Just pass an argument for ax to RidgeMap.plot_map.
import numpy as np fig, axes = plt.subplots(ncols=2, figsize=(20, 5)) x = np.linspace(-2, 2) y = x * x axes[0].plot(x, y, 'o') rm = RidgeMap() rm.plot_map(label_size=24, background_color=(1, 1, 1), ax=axes[1])Annotating, changing background color, custom text
This example shows how to annotate a lat/long on the map, and updates the color of the label text to allow for a dark background. Thanks to kratsg for contributing.
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
bgcolor = np.array([65,74,76])/255.
scipp = (-122.060510, 36.998776)
rm = RidgeMap((-122.087116,36.945365,-121.999226,37.023250))
scipp_coords = ((scipp[0] - rm.longs[0])/(rm.longs[1] - rm.longs[0]),(scipp[1] - rm.lats[0])/(rm.lats[1] - rm.lats[0]))
values = rm.get_elevation_data(num_lines=150)
ridges = rm.plot_map(values=rm.preprocess(values=values,
lake_flatness=1,
water_ntile=0,
vertical_ratio=240),
label='Santa Cruz\nMountains',
label_x=0.75,
label_y=0.05,
label_size=36,
kind='elevation',
linewidth=1,
background_color=bgcolor,
line_color = plt.get_cmap('cool'))
# Bit of a hack to update the text label color
for child in ridges.get_children():
if isinstance(child, matplotlib.text.Text) and 'Santa Cruz' in child._text:
label_artist = child
break
label_artist.set_color('white')
ridges.text(scipp_coords[0]+0.005, scipp_coords[1]+0.005, 'SCIPP',
fontproperties=rm.font,
size=20,
color="white",
transform=ridges.transAxes,
verticalalignment="bottom",
zorder=len(values)+10)
ridges.plot(*scipp_coords, 'o',
color='white',
transform=ridges.transAxes,
ms=6,
zorder=len(values)+10) Updated Annotation and Custom Text Color
The above code still works, but now there is a simplified method (Shown Below) that will produce the same image.
import matplotlib.pyplot as plt
import numpy as np
from ridge_map import RidgeMap
bgcolor = np.array([65,74,76])/255.
rm = RidgeMap((-122.087116,36.945365,-121.999226,37.023250))
values = rm.get_elevation_data(num_lines=150)
values = rm.preprocess(
values=values,
lake_flatness=1,
water_ntile=0,
vertical_ratio=240
)
rm.plot_map(
values=values,
label='Santa Cruz\nMountains',
label_x=0.75,
label_y=0.05,
label_size=36,
label_color='white',
kind='elevation',
linewidth=1,
background_color=bgcolor,
line_color = plt.get_cmap('cool')
)
rm.plot_annotation(
label='SCIPP',
coordinates=(-122.060510, 36.998776),
x_offset=0.005,
y_offset=0.005,
label_size=20,
annotation_size=6,
color='white',
background=False
)
Elevation data used by ridge_map comes from NASA's Shuttle Radar Topography Mission (SRTM), high resolution topographic data collected in 2000, and released in 2015. SRTM data are sampled at a resolution of 1 arc-second (about 30 meters). SRTM data is provided to ridge_map via the python package SRTM.py (link). SRTM data is not available for latitudes greater than N 60° or less than S 60°:
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