prettymaps

GitHub

Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely

12,308 stars Jupyter Notebook Markdown Skills CodeWiki #cartography#generative-art#jupyter-notebook#maps
Documentation Agent Skills & Rules CodeWiki
AI Prompts & Endpoints
Agent Skills View CodeWiki Knowledge Base

Repository: marceloprates/prettymaps


Stars: 12234

README.md

prettymaps

A minimal Python library to draw customized maps from OpenStreetMap created using the osmnx, matplotlib, shapely and vsketch packages.

![](https://github.com/marceloprates/prettymaps/raw/main/pictures/heerhugowaard.png)

![Docs](https://marceloprates.github.io/prettymaps/) ![PyPI](https://pypi.org/project/prettymaps/) ![Python](https://www.python.org/) ![License](LICENSE)


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.

<a href='https://ko-fi.com/marceloprates_' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>

As seen on Hacker News:


![](https://github.com/marceloprates/prettymaps/raw/main/pictures/hackernews-prettymaps.png)

prettymaps subreddit


Google Colaboratory Demo

Installation

Install locally:


Install prettymaps with:

text
pip install prettymaps

Install on Google Colaboratory:

Install prettymaps with:

text
!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:

text
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')

Fetching geodataframes took 14.43 seconds


!png


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'
)

Fetching geodataframes took 5.48 seconds


!png


Run

python
prettymaps.presets()

to list all available presets:


python
import prettymaps
prettymaps.presets()


<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

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

.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>preset</th>
<th>params</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>abraca-redencao</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
<tr>
<th>1</th>
<td>barcelona</td>
<td>{'layers': {'perimeter': {'circle': False}, 's...</td>
</tr>
<tr>
<th>2</th>
<td>barcelona-plotter</td>
<td>{'layers': {'streets': {'width': {'primary': 5...</td>
</tr>
<tr>
<th>3</th>
<td>cb-bf-f</td>
<td>{'layers': {'streets': {'width': {'trunk': 6, ...</td>
</tr>
<tr>
<th>4</th>
<td>default</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
<tr>
<th>5</th>
<td>heerhugowaard</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
<tr>
<th>6</th>
<td>macao</td>
<td>{'layers': {'perimeter': {}, 'streets': {'cust...</td>
</tr>
<tr>
<th>7</th>
<td>minimal</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
<tr>
<th>8</th>
<td>plotter</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
<tr>
<th>9</th>
<td>tijuca</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
</tbody>
</table>
</div>

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': ['dog_park', 'disc_golf_course', 'garden', 'golf_course', 'park', 'pitch', 'sports_centre', 'track']}}, '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,
        }
    }
)

Fetching geodataframes took 20.74 seconds


!png


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,
)

Fetching geodataframes took 14.80 seconds


!png


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']

Fetching geodataframes took 14.56 seconds

<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

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

.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>geometry</th>
<th>bicycle</th>
<th>highway</th>
<th>leisure</th>
<th>addr:housenumber</th>
<th>addr:street</th>
<th>amenity</th>
<th>operator</th>
<th>website</th>
<th>check_date</th>
<th>...</th>
<th>payment:lightning_contactless</th>
<th>payment:onchain</th>
<th>bus</th>
<th>smoothness</th>
<th>inscription</th>
<th>type</th>
<th>boat</th>
<th>name:fr</th>
<th>building:part</th>
<th>architect</th>
</tr>
</thead>
<tbody>
<tr>
<th>(node, 2407915698)</th>
<td>POINT (-51.23212 -30.0367)</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>820</td>
<td>Rua Washington Luiz</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(relation, 2798271)</th>
<td>POLYGON ((-51.23097 -30.03377, -51.2309 -30.03...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>Praça Marechal Deodoro</td>
<td>NaN</td>
<td>NaN</td>
<td>https://www.estado.rs.gov.br/</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>Palais Piratini</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(relation, 2895718)</th>
<td>POLYGON ((-51.23445 -30.03076, -51.23441 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>736</td>
<td>Rua dos Andradas</td>
<td>arts_centre</td>
<td>NaN</td>
<td>https://www.ccmq.com.br/</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
<td>no</td>
<td>NaN</td>
</tr>
<tr>
<th>(relation, 3532262)</th>
<td>POLYGON ((-51.22935 -30.03693, -51.22923 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(relation, 3532263)</th>
<td>POLYGON ((-51.22916 -30.037, -51.22903 -30.036...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>parking</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>(way, 1082776706)</th>
<td>POLYGON ((-51.22975 -30.02912, -51.22974 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(way, 1082776707)</th>
<td>POLYGON ((-51.22992 -30.02954, -51.22987 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(way, 1082787655)</th>
<td>POLYGON ((-51.22601 -30.03038, -51.22602 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(way, 1354523569)</th>
<td>POLYGON ((-51.23248 -30.03341, -51.23244 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>pharmacy</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>(way, 1423336172)</th>
<td>POLYGON ((-51.23399 -30.03092, -51.23389 -30.0...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>788</td>
<td>Rua dos Andradas</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>2415 rows × 137 columns</p>
</div>

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]

/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/geopandas/geoseries.py:772: 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)


!svg

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.projection.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')

Fetching geodataframes took 16.54 seconds


!png


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
)

Fetching geodataframes took 12.52 seconds


Use plotter mode to export a pen plotter-compatible SVG (thanks to abey79's amazing 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,
)

Fetching geodataframes took 4.82 seconds


!png


Some other examples


python
import prettymaps
plot = prettymaps.plot(
    'Barra da Tijuca',
    dilate = 0,
    figsize = (22,10),
    preset = 'tijuca',
    adjust_aspect_ratio = False
)

Fetching geodataframes took 23.53 seconds


!png


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)
)

Fetching geodataframes took 8.95 seconds


Fetching geodataframes took 7.03 seconds


Fetching geodataframes took 8.45 seconds


!png


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,
    }},
)

Fetching geodataframes took 37.53 seconds


make: Entering directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'
curl -s -o spool/N21/N21W158.hgt.gz.temp https://s3.amazonaws.com/elevation-tiles-prod/skadi/N21/N21W158.hgt.gz && mv spool/N21/N21W158.hgt.gz.temp spool/N21/N21W158.hgt.gz


gunzip spool/N21/N21W158.hgt.gz 2>/dev/null || touch spool/N21/N21W158.hgt
gdal_translate -q -co TILED=YES -co COMPRESS=DEFLATE -co ZLEVEL=9 -co PREDICTOR=2 spool/N21/N21W158.hgt cache/N21/N21W158.tif 2>/dev/null || touch cache/N21/N21W158.tif


rm spool/N21/N21W158.hgt
make: Leaving directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'
make: Entering directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'
gdalbuildvrt -q -overwrite SRTM1.vrt cache/N21/N21W158.tif
make: Leaving directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'
make: Entering directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'
cp SRTM1.vrt SRTM1.3faa36cc8cab4dfda9edabe3b5a4ddc1.vrt
make: Leaving directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'
make: Entering directory '/home/runner/work/prettymaps/prettymaps/notebooks/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 SRTM1.3faa36cc8cab4dfda9edabe3b5a4ddc1.vrt /home/runner/work/prettymaps/prettymaps/notebooks/elevation.tif
rm -f SRTM1.3faa36cc8cab4dfda9edabe3b5a4ddc1.vrt
make: Leaving directory '/home/runner/work/prettymaps/prettymaps/notebooks/SRTM1'


WARNING:matplotlib.axes._base:Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.


!png


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']}},
        }
    },
)

Fetching geodataframes took 17.09 seconds


!png

Explore Related Technologies & Topics

View All 9,111+ Repositories →

Discover similar high-velocity repositories, agent skills, and OpenAPI specifications across the ecosystem.

#cartography Browse cartography catalog → #generative-art Browse generative-art catalog → #jupyter-notebook Browse jupyter-notebook catalog → #maps Browse maps catalog → #matplotlib Browse matplotlib catalog → #openstreetmap Browse openstreetmap catalog → ⚡ MCP Servers Model Context Protocol → 🧠 Agent Skills Prompt & rule files → 🔌 OpenAPI Specs REST & Swagger schemas →