# Install prettymaps using pip:
#!pip install prettymaps

prettymaps

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)

Note about crediting and NFTs:

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:

prettymaps subreddit

Google Colaboratory Demo

Installation

To enable plotter mode:

pip install git+https://github.com/abey79/vsketch@1.0.0

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

Tutorial

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

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()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	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	plotter	{'layers': {'perimeter': {}, 'streets': {'widt...
9	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}}, 'building': {'tags': {'building': True, 'landuse': 'construction'}}, 'water': {'tags': {'natural': ['water', 'bay']}}, 'forest': {'tags': {'landuse': 'forest'}}, 'green': {'tags': {'landuse': ['grass', 'orchard'], 'natural': ['island', 'wood'], 'leisure': 'park'}}, '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': 3}, '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}}, '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.Axes

Here'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 = False

import 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']

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

		addr:housenumber	addr:street	amenity	operator	website	geometry	addr:postcode	name	office	opening_hours	...	contact:phone	bus	public_transport	source:name	government	ways	name:fr	type	building:part	architect
element_type	osmid
node	2407915698	820	Rua Washington Luiz	NaN	NaN	NaN	POINT (-51.23212 -30.0367)	90010-460	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
way	126665330	387	Rua dos Andradas	place_of_worship	NaN	NaN	POLYGON ((-51.23518 -30.03275, -51.23512 -30.0...	90020-002	Igreja Nossa Senhora das Dores	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	126665331	1001	Rua dos Andradas	NaN	NaN	http://www.ruadapraiashopping.com.br	POLYGON ((-51.23167 -30.03066, -51.2316 -30.03...	90020-015	Rua da Praia Shopping	NaN	Mo-Fr 09:00-21:00; Sa 08:00-20:00	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	129176990	1020	Rua 7 de Setembro	NaN	NaN	http://www.memorial.rs.gov.br	POLYGON ((-51.23117 -30.02891, -51.2312 -30.02...	90010-191	Memorial do Rio Grande do Sul	NaN	Tu-Sa 10:00-18:00	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	129176991	NaN	Praça da Alfândega	NaN	NaN	http://www.margs.rs.gov.br	POLYGON ((-51.23153 -30.02914, -51.23156 -30.0...	90010-150	Museu de Arte do Rio Grande do Sul	NaN	Tu-Su 10:00-19:00	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
relation	6760281	NaN	NaN	NaN	NaN	NaN	POLYGON ((-51.23238 -30.03337, -51.23223 -30.0...	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	[457506887, 457506886]	NaN	multipolygon	NaN	NaN
	6760282	NaN	NaN	NaN	NaN	NaN	POLYGON ((-51.23203 -30.0334, -51.23203 -30.03...	NaN	Atheneu Espírita Cruzeiro do Sul	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	[457506875, 457506889, 457506888]	NaN	multipolygon	NaN	NaN
	6760283	NaN	NaN	NaN	NaN	NaN	POLYGON ((-51.23284 -30.03367, -51.23288 -30.0...	NaN	Palacete Chaves	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	[457506897, 457506896]	NaN	multipolygon	NaN	Theodor Wiederspahn
	6760284	NaN	NaN	NaN	NaN	NaN	POLYGON ((-51.23499 -30.03412, -51.23498 -30.0...	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	[457506910, 457506913]	NaN	multipolygon	NaN	NaN
	14393526	1044	Rua Siqueira Campos	NaN	NaN	https://www.sefaz.rs.gov.br	POLYGON ((-51.23125 -30.02813, -51.23128 -30.0...	NaN	Secretaria Estadual da Fazenda	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	[236213286, 1081974882]	NaN	multipolygon	NaN	NaN

2423 rows × 105 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:720: 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',
)

# 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(
    # City name
    'Barra da Tijuca',
    dilate = 0,
    figsize = (22,10),
    preset = 'tijuca',
)

import prettymaps

plot = prettymaps.plot(
    'Stad van de Zon, Heerhugowaard, Netherlands',
    preset = 'heerhugowaard',
)

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.delete_preset() to delete presets:

# Show presets before deletion
print('Before deletion:')
display(prettymaps.presets())
# Delete 'my-preset'
prettymaps.delete_preset('my-preset')
# Show presets after deletion
print('After deletion:')
display(prettymaps.presets())

Before deletion:

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	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...

After deletion:

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	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	plotter	{'layers': {'perimeter': {}, 'streets': {'widt...
9	tijuca	{'layers': {'perimeter': {}, 'streets': {'widt...

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',
        style={'building': {'palette': ['#EEE4E1', '#E7D8C9', '#E6BEAE']}}
    ),
    # Load a global preset
    preset='cb-bf-f',
    # Figure size
    figsize=(12, 12)
)

<Figure size 3600x3600 with 0 Axes>



<Figure size 3600x3600 with 0 Axes>



<Figure size 3600x3600 with 0 Axes>

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README.md

README.md

prettymaps

Note about crediting and NFTs:

As seen on Hacker News:

prettymaps subreddit

Google Colaboratory Demo

Installation

Install locally:

Install on Google Colaboratory:

Tutorial

Files

README.md

Latest commit

History

README.md

File metadata and controls

prettymaps

Note about crediting and NFTs:

As seen on Hacker News:

prettymaps subreddit

Google Colaboratory Demo

Installation

Install locally:

Install on Google Colaboratory:

Tutorial