Ridge plots of ridges
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:
pip install ridge_map
Or live on the edge and install from github with
pip install git+git://github.com/colcarroll/ridge_map.git
RidgeMap.preprocessormethod 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 RidgeMapRidgeMap().plot_map()
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')
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'))
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
bonelook nice.
from ridge_map import FontManagerfont = FontManager('https://github.com/google/fonts/blob/master/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')
I have been using this website. I find an area I like, draw a rectangle, then copy and paste the coordinates into the
RidgeMapconstructor.
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)
You might really have to tune the
water_ntileand
lake_flatnessto 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)
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)
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
axto
RidgeMap.plot_map.
import numpy as np fig, axes = plt.subplots(ncols=2, figsize=(20, 5)) x = np.linspace(-2, 2) y = x * xaxes[0].plot(x, y, 'o')
rm = RidgeMap() rm.plot_map(label_size=24, background_color=(1, 1, 1), ax=axes[1])
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 npbgcolor = 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)
Elevation data used by
ridge_mapcomes 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_mapvia the python package
SRTM.py(link). SRTM data is not available for latitudes greater than N 60° or less than S 60°: