A minimal Python library to draw customized maps from OpenStreetMap created using the osmnx, matplotlib, shapely and vsketch packages.
This work is licensed 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)
- Please keep the printed message on the figures crediting my repository and OpenStreetMap (mandatory by their license).
- I am personally against NFTs for their environmental impact, the fact that they're a giant money-laundering pyramid scheme and the structural incentives they create for theft 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 and geoartnft projects have used this work to sell NFTs and refused to credit it. See how they reacted after being exposed: AeternaCivitas, geoartnft.
- 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:
Install prettymaps with:
pip install prettymaps
Install prettymaps with:
!pip install -e "git+https://github.com/marceloprates/prettymaps#egg=prettymaps"
Then restart the runtime (Runtime -> Restart Runtime) before importing prettymaps
After prettymaps is installed, you can run the front-end (streamlit) application from the prettymaps repository using:
streamlit run app.py
Plotting with prettymaps is very simple. Run:
prettymaps.plot(your_query)your_query can be:
- An address (Example: "Porto Alegre"),
- Latitude / Longitude coordinates (Example: (-30.0324999, -51.2303767))
- A custom boundary in GeoDataFrame format
%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
import prettymaps
plot = prettymaps.plot(
'Stad van de Zon, Heerhugowaard, Netherlands',
preset = 'minimal'
)
Run
prettymaps.presets()to list all available presets:
import prettymaps
prettymaps.presets()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
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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:
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
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:
@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.AxesHere's an example of running prettymaps.plot() with customized parameters:
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
radius = Falseimport prettymaps
plot = prettymaps.plot(
'Bom Fim, Porto Alegre, Brasil', radius = False,
)
You can access layers's GeoDataFrames directly like this:
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']
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| 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:
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
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:
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 library)
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
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:
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
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)
)
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.
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']}},
}
},
)
