https://github.com/marceloprates/prettymaps
Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely
https://github.com/marceloprates/prettymaps
cartography generative-art jupyter-notebook maps matplotlib openstreetmap python
Last synced: 6 days ago
JSON representation
Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely
- Host: GitHub
- URL: https://github.com/marceloprates/prettymaps
- Owner: marceloprates
- License: agpl-3.0
- Created: 2021-03-05T12:22:05.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2025-03-04T14:59:10.000Z (about 1 month ago)
- Last Synced: 2025-04-01T06:26:31.442Z (14 days ago)
- Topics: cartography, generative-art, jupyter-notebook, maps, matplotlib, openstreetmap, python
- Language: Jupyter Notebook
- Homepage: https://prettymaps.streamlit.app/
- Size: 255 MB
- Stars: 11,587
- Watchers: 77
- Forks: 547
- Open Issues: 54
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-github-repos - marceloprates/prettymaps - Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely (Jupyter Notebook)
- urban-and-regional-planning-resources - Prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. (Planning Coding Resources / Python)
- StarryDivineSky - marceloprates/prettymaps
- awesome-starred - marceloprates/prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries. (jupyter-notebook)
- my-awesome-github-stars - marceloprates/prettymaps - Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely (Jupyter Notebook)
- awesome - prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries. (python)
- awesome - prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries. (generative-art)
- awesome - marceloprates/prettymaps - Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely (Jupyter Notebook)
README
# prettymaps
A minimal Python library to draw customized maps from [OpenStreetMap](https://www.openstreetmap.org/#map=12/11.0733/106.3078) created using the [osmnx](https://github.com/gboeing/osmnx), [matplotlib](https://matplotlib.org/), [shapely](https://shapely.readthedocs.io/en/stable/index.html) and [vsketch](https://github.com/abey79/vsketch) packages.
# Prettymaps is now available as a streamlit app!
[](https://prettymaps.streamlit.app/)
[](https://prettymaps.streamlit.app/)
This work is [licensed](LICENSE) under a GNU Affero General Public License v3.0 (you can make commercial use, distribute and modify this project, but must **disclose** the source code with the license and copyright notice)
## Note about crediting and NFTs:
- Please keep the printed message on the figures crediting my repository and OpenStreetMap ([mandatory by their license](https://www.openstreetmap.org/copyright)).
- I am personally **against** NFTs for their [environmental impact](https://earth.org/nfts-environmental-impact/), the fact that they're a [giant money-laundering pyramid scheme](https://twitter.com/smdiehl/status/1445795667826208770) and the structural incentives they create for [theft](https://twitter.com/NFTtheft) in the open source and generative art communities.
- **I do not authorize in any way this project to be used for selling NFTs**, although I cannot legally enforce it. **Respect the creator**.
- The [AeternaCivitas](https://magiceden.io/marketplace/aeterna_civitas) and [geoartnft](https://www.geo-nft.com/) projects have used this work to sell NFTs and refused to credit it. See how they reacted after being exposed: [AeternaCivitas](etc/NFT_theft_AeternaCivitas.jpg), [geoartnft](etc/NFT_theft_geoart.jpg).
- **I have closed my other generative art projects on Github and won't be sharing new ones as open source to protect me from the NFT community**.
## As seen on [Hacker News](https://web.archive.org/web/20210825160918/https://news.ycombinator.com/news):
## [prettymaps subreddit](https://www.reddit.com/r/prettymaps_/)
## [Google Colaboratory Demo](https://colab.research.google.com/github/marceloprates/prettymaps/blob/master/notebooks/examples.ipynb)
# Installation
### Install locally:
Install prettymaps with:
```
pip install prettymaps
```
### Install on Google Colaboratory:
Install prettymaps with:
```
!pip install -e "git+https://github.com/marceloprates/prettymaps#egg=prettymaps"
```
Then **restart the runtime** (Runtime -> Restart Runtime) before importing prettymaps
# Run front-end
After prettymaps is installed, you can run the front-end (streamlit) application from the prettymaps repository using:
```
streamlit run app.py
```
# Tutorial
Plotting with prettymaps is very simple. Run:
```python
prettymaps.plot(your_query)
```
**your_query** can be:
1. An address (Example: "Porto Alegre"),
2. Latitude / Longitude coordinates (Example: (-30.0324999, -51.2303767))
3. A custom boundary in GeoDataFrame format
```python
%reload_ext autoreload
%autoreload 2
import prettymaps
plot = prettymaps.plot('Stad van de Zon, Heerhugowaard, Netherlands')
```
You can also choose from different "presets" (parameter combinations saved in JSON files)
See below an example using the "minimal" preset
```python
import prettymaps
plot = prettymaps.plot(
'Stad van de Zon, Heerhugowaard, Netherlands',
preset = 'minimal'
)
```
Run
```python
prettymaps.presets()
```
to list all available presets:
```python
import prettymaps
prettymaps.presets()
```
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preset
params
0
abraca-redencao
{'layers': {'perimeter': {}, 'streets': {'widt...
1
barcelona
{'layers': {'perimeter': {'circle': False}, 's...
2
barcelona-plotter
{'layers': {'streets': {'width': {'primary': 5...
3
cb-bf-f
{'layers': {'streets': {'width': {'trunk': 6, ...
4
default
{'layers': {'perimeter': {}, 'streets': {'widt...
5
heerhugowaard
{'layers': {'perimeter': {}, 'streets': {'widt...
6
macao
{'layers': {'perimeter': {}, 'streets': {'cust...
7
minimal
{'layers': {'perimeter': {}, 'streets': {'widt...
8
my-preset
{'layers': {'building': {'tags': {'building': ...
9
plotter
{'layers': {'perimeter': {}, 'streets': {'widt...
10
tijuca
{'layers': {'perimeter': {}, 'streets': {'widt...
To examine a specific preset, run:
```python
import prettymaps
prettymaps.preset('default')
```
Preset(params={'layers': {'perimeter': {}, 'streets': {'width': {'motorway': 5, 'trunk': 5, 'primary': 4.5, 'secondary': 4, 'tertiary': 3.5, 'cycleway': 3.5, 'residential': 3, 'service': 2, 'unclassified': 2, 'pedestrian': 2, 'footway': 1}}, 'waterway': {'tags': {'waterway': ['river', 'stream']}, 'width': {'river': 20, 'stream': 10}}, 'building': {'tags': {'building': True, 'landuse': 'construction'}}, 'water': {'tags': {'natural': ['water', 'bay']}}, 'sea': {}, 'forest': {'tags': {'landuse': 'forest'}}, 'green': {'tags': {'landuse': ['grass', 'orchard'], 'natural': ['island', 'wood', 'wetland'], 'leisure': 'park'}}, 'rock': {'tags': {'natural': 'bare_rock'}}, 'beach': {'tags': {'natural': 'beach'}}, 'parking': {'tags': {'amenity': 'parking', 'highway': 'pedestrian', 'man_made': 'pier'}}}, 'style': {'perimeter': {'fill': False, 'lw': 0, 'zorder': 0}, 'background': {'fc': '#F2F4CB', 'zorder': -1}, 'green': {'fc': '#8BB174', 'ec': '#2F3737', 'hatch_c': '#A7C497', 'hatch': 'ooo...', 'lw': 1, 'zorder': 1}, 'forest': {'fc': '#64B96A', 'ec': '#2F3737', 'lw': 1, 'zorder': 2}, 'water': {'fc': '#a8e1e6', 'ec': '#2F3737', 'hatch_c': '#9bc3d4', 'hatch': 'ooo...', 'lw': 1, 'zorder': 99}, 'sea': {'fc': '#a8e1e6', 'ec': '#2F3737', 'hatch_c': '#9bc3d4', 'hatch': 'ooo...', 'lw': 1, 'zorder': 99}, 'waterway': {'fc': '#a8e1e6', 'ec': '#2F3737', 'hatch_c': '#9bc3d4', 'hatch': 'ooo...', 'lw': 1, 'zorder': 200}, 'beach': {'fc': '#FCE19C', 'ec': '#2F3737', 'hatch_c': '#d4d196', 'hatch': 'ooo...', 'lw': 1, 'zorder': 3}, 'parking': {'fc': '#F2F4CB', 'ec': '#2F3737', 'lw': 1, 'zorder': 3}, 'streets': {'fc': '#2F3737', 'ec': '#475657', 'alpha': 1, 'lw': 0, 'zorder': 4}, 'building': {'palette': ['#433633', '#FF5E5B'], 'ec': '#2F3737', 'lw': 0.5, 'zorder': 5}, 'rock': {'fc': '#BDC0BA', 'ec': '#2F3737', 'lw': 1, 'zorder': 6}}, 'circle': None, 'radius': 500})
Insted of using the default configuration you can customize several parameters. The most important are:
- layers: A dictionary of OpenStreetMap layers to fetch.
- Keys: layer names (arbitrary)
- Values: dicts representing OpenStreetMap queries
- style: Matplotlib style parameters
- Keys: layer names (the same as before)
- Values: dicts representing Matplotlib style parameters
```python
plot = prettymaps.plot(
# Your query. Example: "Porto Alegre" or (-30.0324999, -51.2303767) (GPS coords)
your_query,
# Dict of OpenStreetMap Layers to plot. Example:
# {'building': {'tags': {'building': True}}, 'water': {'tags': {'natural': 'water'}}}
# Check the /presets folder for more examples
layers,
# Dict of style parameters for matplotlib. Example:
# {'building': {'palette': ['#f00','#0f0','#00f'], 'edge_color': '#333'}}
style,
# Preset to load. Options include:
# ['default', 'minimal', 'macao', 'tijuca']
preset,
# Save current parameters to a preset file.
# Example: "my-preset" will save to "presets/my-preset.json"
save_preset,
# Whether to update loaded preset with additional provided parameters. Boolean
update_preset,
# Plot with circular boundary. Boolean
circle,
# Plot area radius. Float
radius,
# Dilate the boundary by this amount. Float
dilate
)
```
**plot** is a python dataclass containing:
```python
@dataclass
class Plot:
# A dictionary of GeoDataFrames (one for each plot layer)
geodataframes: Dict[str, gp.GeoDataFrame]
# A matplotlib figure
fig: matplotlib.figure.Figure
# A matplotlib axis object
ax: matplotlib.axes.Axes
```
Here's an example of running prettymaps.plot() with customized parameters:
```python
import prettymaps
plot = prettymaps.plot(
'Praça Ferreira do Amaral, Macau',
circle = True,
radius = 1100,
layers = {
"green": {
"tags": {
"landuse": "grass",
"natural": ["island", "wood"],
"leisure": "park"
}
},
"forest": {
"tags": {
"landuse": "forest"
}
},
"water": {
"tags": {
"natural": ["water", "bay"]
}
},
"parking": {
"tags": {
"amenity": "parking",
"highway": "pedestrian",
"man_made": "pier"
}
},
"streets": {
"width": {
"motorway": 5,
"trunk": 5,
"primary": 4.5,
"secondary": 4,
"tertiary": 3.5,
"residential": 3,
}
},
"building": {
"tags": {"building": True},
},
},
style = {
"background": {
"fc": "#F2F4CB",
"ec": "#dadbc1",
"hatch": "ooo...",
},
"perimeter": {
"fc": "#F2F4CB",
"ec": "#dadbc1",
"lw": 0,
"hatch": "ooo...",
},
"green": {
"fc": "#D0F1BF",
"ec": "#2F3737",
"lw": 1,
},
"forest": {
"fc": "#64B96A",
"ec": "#2F3737",
"lw": 1,
},
"water": {
"fc": "#a1e3ff",
"ec": "#2F3737",
"hatch": "ooo...",
"hatch_c": "#85c9e6",
"lw": 1,
},
"parking": {
"fc": "#F2F4CB",
"ec": "#2F3737",
"lw": 1,
},
"streets": {
"fc": "#2F3737",
"ec": "#475657",
"alpha": 1,
"lw": 0,
},
"building": {
"palette": [
"#FFC857",
"#E9724C",
"#C5283D"
],
"ec": "#2F3737",
"lw": 0.5,
}
}
)
```
In order to plot an entire region and not just a rectangular or circular area, set
```python
radius = False
```
```python
import prettymaps
plot = prettymaps.plot(
'Bom Fim, Porto Alegre, Brasil', radius = False,
)
```
You can access layers's GeoDataFrames directly like this:
```python
import prettymaps
# Run prettymaps in show = False mode (we're only interested in obtaining the GeoDataFrames)
plot = prettymaps.plot('Centro Histórico, Porto Alegre', show = False)
plot.geodataframes['building']
```
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}
geometry
bicycle
highway
leisure
addr:housenumber
addr:street
amenity
operator
website
historic
...
contact:website
bus
smoothness
inscription
ways
boat
name:fr
type
building:part
architect
(node, 2407915698)
POINT (-51.23212 -30.03670)
NaN
NaN
NaN
820
Rua Washington Luiz
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
(way, 126665330)
POLYGON ((-51.23518 -30.03275, -51.23512 -30.0...
NaN
NaN
NaN
387
Rua dos Andradas
place_of_worship
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
(way, 126665331)
POLYGON ((-51.23167 -30.03066, -51.23160 -30.0...
NaN
NaN
NaN
1001
Rua dos Andradas
NaN
NaN
https://www.ruadapraiashopping.com.br/
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
(way, 129176990)
POLYGON ((-51.23117 -30.02891, -51.23120 -30.0...
NaN
NaN
NaN
1020
Rua 7 de Setembro
NaN
NaN
http://www.memorial.rs.gov.br
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
(way, 129176991)
POLYGON ((-51.23153 -30.02914, -51.23156 -30.0...
NaN
NaN
NaN
NaN
Praça da Alfândega
NaN
NaN
https://www.margs.rs.gov.br/
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
(relation, 6760281)
POLYGON ((-51.23238 -30.03337, -51.23223 -30.0...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
[457506887, 457506886]
NaN
NaN
multipolygon
NaN
NaN
(relation, 6760282)
POLYGON ((-51.23203 -30.03340, -51.23203 -30.0...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
[457506875, 457506889, 457506888]
NaN
NaN
multipolygon
NaN
NaN
(relation, 6760283)
POLYGON ((-51.23284 -30.03367, -51.23288 -30.0...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
[457506897, 457506896]
NaN
NaN
multipolygon
NaN
Theodor Wiederspahn
(relation, 6760284)
POLYGON ((-51.23499 -30.03412, -51.23498 -30.0...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
[457506910, 457506913]
NaN
NaN
multipolygon
NaN
NaN
(relation, 14393526)
POLYGON ((-51.23125 -30.02813, -51.23128 -30.0...
NaN
NaN
NaN
1044
Rua Siqueira Campos
NaN
NaN
https://www.sefaz.rs.gov.br
NaN
...
NaN
NaN
NaN
NaN
[236213286, 1081974882]
NaN
NaN
multipolygon
NaN
NaN
2420 rows × 167 columns
Search a building by name and display it:
```python
plot.geodataframes['building'][
plot.geodataframes['building'].name == 'Catedral Metropolitana Nossa Senhora Mãe de Deus'
].geometry[0]
```
/home/marcelo/anaconda3/envs/prettymaps/lib/python3.11/site-packages/geopandas/geoseries.py:648: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
val = getattr(super(), mtd)(*args, **kwargs)
Plot mosaic of building footprints
```python
import prettymaps
import numpy as np
import osmnx as ox
from matplotlib import pyplot as plt
# Run prettymaps in show = False mode (we're only interested in obtaining the GeoDataFrames)
plot = prettymaps.plot('Porto Alegre', show = False)
# Get list of buildings from plot's geodataframes dict
buildings = plot.geodataframes['building']
# Project from lat / long
buildings = ox.project_gdf(buildings)
buildings = [b for b in buildings.geometry if b.area > 0]
# Draw Matplotlib mosaic of n x n building footprints
n = 6
fig,axes = plt.subplots(n,n, figsize = (7,6))
# Set background color
fig.patch.set_facecolor('#5cc0eb')
# Figure title
fig.suptitle(
'Buildings of Porto Alegre',
size = 25,
color = '#fff'
)
# Draw each building footprint on a separate axis
for ax,building in zip(np.concatenate(axes),buildings):
ax.plot(*building.exterior.xy, c = '#ffffff')
ax.autoscale(); ax.axis('off'); ax.axis('equal')
```
Access plot.ax or plot.fig to add new elements to the matplotlib plot:
```python
import prettymaps
plot = prettymaps.plot(
(41.39491,2.17557),
preset = 'barcelona',
show = False # We don't want to render the map yet
)
# Change background color
plot.fig.patch.set_facecolor('#F2F4CB')
# Add title
_ = plot.ax.set_title(
'Barcelona',
font = 'serif',
size = 50
)
```
Use **plotter** mode to export a pen plotter-compatible SVG (thanks to abey79's amazing [vsketch](https://github.com/abey79/vsketch) library)
```python
import prettymaps
plot = prettymaps.plot(
(41.39491,2.17557),
mode = 'plotter',
layers = dict(perimeter = {}),
preset = 'barcelona-plotter',
scale_x = .6,
scale_y = -.6,
)
```
Some other examples
```python
import prettymaps
plot = prettymaps.plot(
'Barra da Tijuca',
dilate = 0,
figsize = (22,10),
preset = 'tijuca',
adjust_aspect_ratio = False
)
```
Use prettymaps.create_preset() to create a preset:
```python
import prettymaps
prettymaps.create_preset(
"my-preset",
layers = {
"building": {
"tags": {
"building": True,
"leisure": [
"track",
"pitch"
]
}
},
"streets": {
"width": {
"trunk": 6,
"primary": 6,
"secondary": 5,
"tertiary": 4,
"residential": 3.5,
"pedestrian": 3,
"footway": 3,
"path": 3
}
},
},
style = {
"perimeter": {
"fill": False,
"lw": 0,
"zorder": 0
},
"streets": {
"fc": "#F1E6D0",
"ec": "#2F3737",
"lw": 1.5,
"zorder": 3
},
"building": {
"palette": [
"#fff"
],
"ec": "#2F3737",
"lw": 1,
"zorder": 4
}
}
)
prettymaps.preset('my-preset')
```
Preset(params={'layers': {'building': {'tags': {'building': True, 'leisure': ['track', 'pitch']}}, 'streets': {'width': {'trunk': 6, 'primary': 6, 'secondary': 5, 'tertiary': 4, 'residential': 3.5, 'pedestrian': 3, 'footway': 3, 'path': 3}}}, 'style': {'perimeter': {'fill': False, 'lw': 0, 'zorder': 0}, 'streets': {'fc': '#F1E6D0', 'ec': '#2F3737', 'lw': 1.5, 'zorder': 3}, 'building': {'palette': ['#fff'], 'ec': '#2F3737', 'lw': 1, 'zorder': 4}}, 'circle': None, 'radius': None, 'dilate': None})
Use **prettymaps.multiplot** and **prettymaps.Subplot** to draw multiple regions on the same canvas
```python
import prettymaps
# Draw several regions on the same canvas
plot = prettymaps.multiplot(
prettymaps.Subplot(
'Cidade Baixa, Porto Alegre',
style={'building': {'palette': ['#49392C', '#E1F2FE', '#98D2EB']}}
),
prettymaps.Subplot(
'Bom Fim, Porto Alegre',
style={'building': {'palette': ['#BA2D0B', '#D5F2E3', '#73BA9B', '#F79D5C']}}
),
prettymaps.Subplot(
'Farroupilha, Porto Alegre',
layers = {'building': {'tags': {'building': True}}},
style={'building': {'palette': ['#EEE4E1', '#E7D8C9', '#E6BEAE']}}
),
# Load a global preset
preset='cb-bf-f',
# Figure size
figsize=(12, 12)
)
```
# Add hillshade
```python
plot = prettymaps.plot(
'Honolulu',
radius = 5500,
figsize = 'a4',
layers = {'hillshade': {
'azdeg': 315,
'altdeg': 45,
'vert_exag': 1,
'dx': 1,
'dy': 1,
'alpha': 0.75,
}},
)
```
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
make: Entering directory '/home/marcelo/.cache/elevation/SRTM1'
make: Nothing to be done for 'download'.
make: Leaving directory '/home/marcelo/.cache/elevation/SRTM1'
make: Entering directory '/home/marcelo/.cache/elevation/SRTM1'
make: Nothing to be done for 'all'.
make: Leaving directory '/home/marcelo/.cache/elevation/SRTM1'
make: Entering directory '/home/marcelo/.cache/elevation/SRTM1'
cp SRTM1.vrt SRTM1.2d5b6f11e0e74b44a9386ba897fb0852.vrt
make: Leaving directory '/home/marcelo/.cache/elevation/SRTM1'
make: Entering directory '/home/marcelo/.cache/elevation/SRTM1'
gdal_translate -q -co TILED=YES -co COMPRESS=DEFLATE -co ZLEVEL=9 -co PREDICTOR=2 -projwin -157.90125854957773 21.364471426268267 -157.81006761682832 21.244615177105388 SRTM1.2d5b6f11e0e74b44a9386ba897fb0852.vrt /home/marcelo/Projects/Art/prettymaps/notebooks/elevationa.tif
rm -f SRTM1.2d5b6f11e0e74b44a9386ba897fb0852.vrt
make: Leaving directory '/home/marcelo/.cache/elevation/SRTM1'
WARNING:matplotlib.axes._base:Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.
# Add keypoints
```python
plot = prettymaps.plot(
'Garopaba',
radius = 5000,
figsize = 'a4',
layers = {'building': False},
keypoints = {
# Search for general keypoints specified by OSM tags
'tags': {'natural': ['beach']},
# Or, search by specific name or free-text search
# pretymaps will use a fuzzy string matching to search for the specified name
'specific': {
'pedra branca': {'tags': {'natural': ['peak']}},
}
},
)
```
