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https://github.com/marceloprates/prettymaps
A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.
https://github.com/marceloprates/prettymaps
cartography generative-art jupyter-notebook maps matplotlib openstreetmap python
Last synced: 6 days ago
JSON representation
A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.
- Host: GitHub
- URL: https://github.com/marceloprates/prettymaps
- Owner: marceloprates
- License: agpl-3.0
- Created: 2021-03-05T12:22:05.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-06T13:17:45.000Z (6 months ago)
- Last Synced: 2024-12-30T17:30:42.098Z (13 days ago)
- Topics: cartography, generative-art, jupyter-notebook, maps, matplotlib, openstreetmap, python
- Language: Jupyter Notebook
- Homepage:
- Size: 176 MB
- Stars: 11,399
- Watchers: 82
- Forks: 538
- Open Issues: 64
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome - marceloprates/prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries. (Jupyter Notebook)
- awesome-github-repos - marceloprates/prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries. (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. (python)
- 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. (openstreetmap)
- my-awesome-github-stars - marceloprates/prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries. (Jupyter Notebook)
README
```python
# Install prettymaps using pip:
#!pip install prettymaps
```
# 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.
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
To enable plotter mode:
```
pip install git+https://github.com/abey79/[email protected]
```
### 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:
```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
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
plotter
{'layers': {'perimeter': {}, 'streets': {'widt...
9
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}}, '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
```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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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:
```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: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
```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',
)
# 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(
# City name
'Barra da Tijuca',
dilate = 0,
figsize = (22,10),
preset = 'tijuca',
)
```
```python
import prettymaps
plot = prettymaps.plot(
'Stad van de Zon, Heerhugowaard, Netherlands',
preset = 'heerhugowaard',
)
```
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.delete_preset() to delete presets:
```python
# 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:
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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...
After deletion:
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vertical-align: middle;
}
.dataframe tbody tr th {
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}
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text-align: right;
}
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
```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',
style={'building': {'palette': ['#EEE4E1', '#E7D8C9', '#E6BEAE']}}
),
# Load a global preset
preset='cb-bf-f',
# Figure size
figsize=(12, 12)
)
```
```python
```