kopia lustrzana https://github.com/marceloprates/prettymaps
1054 wiersze
27 KiB
Markdown
1054 wiersze
27 KiB
Markdown
# prettymaps
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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.
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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)
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## Note about crediting and NFTs:
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- Please keep the printed message on the figures crediting my repository and OpenStreetMap ([mandatory by their license](https://www.openstreetmap.org/copyright)).
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- 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.
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- **I do not authorize in any way this project to be used for selling NFTs**, although I cannot legally enforce it. **Respect the creator**.
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- 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](https://github.com/marceloprates/prettymaps/raw/main/pictures/NFT_theft_AeternaCivitas.jpg), [geoartnft](https://github.com/marceloprates/prettymaps/raw/main/pictures/NFT_theft_geoart.jpg).
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- **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**.
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<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>
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## As seen on [Hacker News](https://web.archive.org/web/20210825160918/https://news.ycombinator.com/news):
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## [prettymaps subreddit](https://www.reddit.com/r/prettymaps_/)
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## [Google Colaboratory Demo](https://colab.research.google.com/github/marceloprates/prettymaps/blob/master/notebooks/examples.ipynb)
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# Installation
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### Install locally:
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Install prettymaps with:
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```
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pip install prettymaps
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```
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### Install on Google Colaboratory:
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Install prettymaps with:
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```
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!pip install -e "git+https://github.com/marceloprates/prettymaps#egg=prettymaps"
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```
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Then **restart the runtime** (Runtime -> Restart Runtime) before importing prettymaps
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# Run front-end
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After prettymaps is installed, you can run the front-end (streamlit) application from the prettymaps repository using:
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```
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streamlit run app.py
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```
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# Tutorial
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Plotting with prettymaps is very simple. Run:
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```python
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prettymaps.plot(your_query)
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```
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**your_query** can be:
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1. An address (Example: "Porto Alegre"),
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2. Latitude / Longitude coordinates (Example: (-30.0324999, -51.2303767))
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3. A custom boundary in GeoDataFrame format
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```python
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%reload_ext autoreload
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%autoreload 2
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import prettymaps
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plot = prettymaps.plot('Stad van de Zon, Heerhugowaard, Netherlands')
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```
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Fetching geodataframes took 2.04 seconds
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You can also choose from different "presets" (parameter combinations saved in JSON files)
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See below an example using the "minimal" preset
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```python
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import prettymaps
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plot = prettymaps.plot(
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'Stad van de Zon, Heerhugowaard, Netherlands',
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preset = 'minimal'
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)
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```
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Fetching geodataframes took 0.81 seconds
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Run
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```python
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prettymaps.presets()
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```
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to list all available presets:
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```python
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import prettymaps
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prettymaps.presets()
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```
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<div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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vertical-align: middle;
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}
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.dataframe tbody tr th {
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vertical-align: top;
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}
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.dataframe thead th {
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text-align: right;
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}
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</style>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>preset</th>
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<th>params</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>abraca-redencao</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
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</tr>
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<tr>
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<th>1</th>
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<td>barcelona</td>
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<td>{'layers': {'perimeter': {'circle': False}, 's...</td>
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</tr>
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<tr>
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<th>2</th>
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<td>barcelona-plotter</td>
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<td>{'layers': {'streets': {'width': {'primary': 5...</td>
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</tr>
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<tr>
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<th>3</th>
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<td>cb-bf-f</td>
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<td>{'layers': {'streets': {'width': {'trunk': 6, ...</td>
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</tr>
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<tr>
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<th>4</th>
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<td>default</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
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</tr>
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<tr>
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<th>5</th>
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<td>heerhugowaard</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
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</tr>
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<tr>
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<th>6</th>
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<td>macao</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'cust...</td>
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</tr>
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<tr>
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<th>7</th>
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<td>minimal</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
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</tr>
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<tr>
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<th>8</th>
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<td>my-preset</td>
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<td>{'layers': {'building': {'tags': {'building': ...</td>
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</tr>
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<tr>
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<th>9</th>
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<td>plotter</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
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</tr>
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<tr>
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<th>10</th>
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<td>pytest-temp-preset</td>
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<td>{'layers': {'building': {'tags': {'building': ...</td>
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</tr>
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<tr>
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<th>11</th>
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<td>tijuca</td>
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<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
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</tr>
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</tbody>
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</table>
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</div>
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To examine a specific preset, run:
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```python
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import prettymaps
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prettymaps.preset('default')
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```
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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})
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Insted of using the default configuration you can customize several parameters. The most important are:
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- layers: A dictionary of OpenStreetMap layers to fetch.
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- Keys: layer names (arbitrary)
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- Values: dicts representing OpenStreetMap queries
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- style: Matplotlib style parameters
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- Keys: layer names (the same as before)
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- Values: dicts representing Matplotlib style parameters
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```python
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plot = prettymaps.plot(
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# Your query. Example: "Porto Alegre" or (-30.0324999, -51.2303767) (GPS coords)
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your_query,
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# Dict of OpenStreetMap Layers to plot. Example:
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# {'building': {'tags': {'building': True}}, 'water': {'tags': {'natural': 'water'}}}
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# Check the /presets folder for more examples
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layers,
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# Dict of style parameters for matplotlib. Example:
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# {'building': {'palette': ['#f00','#0f0','#00f'], 'edge_color': '#333'}}
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style,
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# Preset to load. Options include:
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# ['default', 'minimal', 'macao', 'tijuca']
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preset,
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# Save current parameters to a preset file.
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# Example: "my-preset" will save to "presets/my-preset.json"
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save_preset,
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# Whether to update loaded preset with additional provided parameters. Boolean
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update_preset,
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# Plot with circular boundary. Boolean
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circle,
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# Plot area radius. Float
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radius,
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# Dilate the boundary by this amount. Float
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dilate
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)
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```
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**plot** is a python dataclass containing:
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```python
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@dataclass
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class Plot:
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# A dictionary of GeoDataFrames (one for each plot layer)
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geodataframes: Dict[str, gp.GeoDataFrame]
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# A matplotlib figure
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fig: matplotlib.figure.Figure
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# A matplotlib axis object
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ax: matplotlib.axes.Axes
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```
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Here's an example of running prettymaps.plot() with customized parameters:
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```python
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import prettymaps
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plot = prettymaps.plot(
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'Praça Ferreira do Amaral, Macau',
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circle = True,
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radius = 1100,
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layers = {
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"green": {
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"tags": {
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"landuse": "grass",
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"natural": ["island", "wood"],
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"leisure": "park"
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}
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},
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"forest": {
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"tags": {
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"landuse": "forest"
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}
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},
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"water": {
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"tags": {
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"natural": ["water", "bay"]
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}
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},
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"parking": {
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"tags": {
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"amenity": "parking",
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"highway": "pedestrian",
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"man_made": "pier"
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}
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},
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"streets": {
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"width": {
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"motorway": 5,
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"trunk": 5,
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"primary": 4.5,
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"secondary": 4,
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"tertiary": 3.5,
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"residential": 3,
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}
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},
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"building": {
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"tags": {"building": True},
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},
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},
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style = {
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"background": {
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"fc": "#F2F4CB",
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"ec": "#dadbc1",
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"hatch": "ooo...",
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},
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"perimeter": {
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"fc": "#F2F4CB",
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"ec": "#dadbc1",
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"lw": 0,
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"hatch": "ooo...",
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},
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"green": {
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"fc": "#D0F1BF",
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"ec": "#2F3737",
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"lw": 1,
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},
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"forest": {
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"fc": "#64B96A",
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"ec": "#2F3737",
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"lw": 1,
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},
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"water": {
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"fc": "#a1e3ff",
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"ec": "#2F3737",
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"hatch": "ooo...",
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"hatch_c": "#85c9e6",
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"lw": 1,
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},
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"parking": {
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"fc": "#F2F4CB",
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"ec": "#2F3737",
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"lw": 1,
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},
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"streets": {
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"fc": "#2F3737",
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"ec": "#475657",
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"alpha": 1,
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"lw": 0,
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},
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"building": {
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"palette": [
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"#FFC857",
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"#E9724C",
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"#C5283D"
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],
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"ec": "#2F3737",
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"lw": 0.5,
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}
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}
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)
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```
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Fetching geodataframes took 15.97 seconds
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In order to plot an entire region and not just a rectangular or circular area, set
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```python
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radius = False
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```
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```python
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import prettymaps
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plot = prettymaps.plot(
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'Bom Fim, Porto Alegre, Brasil', radius = False,
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)
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```
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Fetching geodataframes took 1.28 seconds
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You can access layers's GeoDataFrames directly like this:
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```python
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import prettymaps
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# Run prettymaps in show = False mode (we're only interested in obtaining the GeoDataFrames)
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plot = prettymaps.plot('Centro Histórico, Porto Alegre', show = False)
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plot.geodataframes['building']
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```
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Fetching geodataframes took 1.59 seconds
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<div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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vertical-align: middle;
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}
|
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.dataframe tbody tr th {
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vertical-align: top;
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}
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.dataframe thead th {
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text-align: right;
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}
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</style>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>geometry</th>
|
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<th>bicycle</th>
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<th>highway</th>
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<th>leisure</th>
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<th>addr:housenumber</th>
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<th>addr:street</th>
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<th>amenity</th>
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<th>operator</th>
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<th>website</th>
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<th>historic</th>
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<th>...</th>
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<th>contact:website</th>
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<th>bus</th>
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<th>smoothness</th>
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<th>inscription</th>
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<th>ways</th>
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<th>boat</th>
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<th>name:fr</th>
|
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<th>type</th>
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<th>building:part</th>
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<th>architect</th>
|
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</tr>
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</thead>
|
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<tbody>
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<tr>
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<th>(node, 2407915698)</th>
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<td>POINT (-51.23212 -30.03670)</td>
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<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>820</td>
|
||
<td>Rua Washington Luiz</td>
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||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>...</td>
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||
<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, 126665330)</th>
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||
<td>POLYGON ((-51.23518 -30.03275, -51.23512 -30.0...</td>
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||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>387</td>
|
||
<td>Rua dos Andradas</td>
|
||
<td>place_of_worship</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, 126665331)</th>
|
||
<td>POLYGON ((-51.23167 -30.03066, -51.23160 -30.0...</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>1001</td>
|
||
<td>Rua dos Andradas</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>https://www.ruadapraiashopping.com.br/</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, 129176990)</th>
|
||
<td>POLYGON ((-51.23117 -30.02891, -51.23120 -30.0...</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>1020</td>
|
||
<td>Rua 7 de Setembro</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>http://www.memorial.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>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
</tr>
|
||
<tr>
|
||
<th>(way, 129176991)</th>
|
||
<td>POLYGON ((-51.23153 -30.02914, -51.23156 -30.0...</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>Praça da Alfândega</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>https://www.margs.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>NaN</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>(relation, 6760281)</th>
|
||
<td>POLYGON ((-51.23238 -30.03337, -51.23223 -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>[457506887, 457506886]</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>multipolygon</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
</tr>
|
||
<tr>
|
||
<th>(relation, 6760282)</th>
|
||
<td>POLYGON ((-51.23203 -30.03340, -51.23203 -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>[457506875, 457506889, 457506888]</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>multipolygon</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
</tr>
|
||
<tr>
|
||
<th>(relation, 6760283)</th>
|
||
<td>POLYGON ((-51.23284 -30.03367, -51.23288 -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>[457506897, 457506896]</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>multipolygon</td>
|
||
<td>NaN</td>
|
||
<td>Theodor Wiederspahn</td>
|
||
</tr>
|
||
<tr>
|
||
<th>(relation, 6760284)</th>
|
||
<td>POLYGON ((-51.23499 -30.03412, -51.23498 -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>[457506910, 457506913]</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>multipolygon</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
</tr>
|
||
<tr>
|
||
<th>(relation, 14393526)</th>
|
||
<td>POLYGON ((-51.23125 -30.02813, -51.23128 -30.0...</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>1044</td>
|
||
<td>Rua Siqueira Campos</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>https://www.sefaz.rs.gov.br</td>
|
||
<td>NaN</td>
|
||
<td>...</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>[236213286, 1081974882]</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
<td>multipolygon</td>
|
||
<td>NaN</td>
|
||
<td>NaN</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>2420 rows × 167 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/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')
|
||
```
|
||
|
||
Fetching geodataframes took 2.01 seconds
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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 3.78 seconds
|
||
|
||
|
||
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,
|
||
)
|
||
```
|
||
|
||
Fetching geodataframes took 3.89 seconds
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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 16.55 seconds
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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 0.97 seconds
|
||
Fetching geodataframes took 1.15 seconds
|
||
Fetching geodataframes took 0.79 seconds
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
# 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 24.92 seconds
|
||
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
|
||
gdalbuildvrt -q -overwrite SRTM1.vrt cache/N21/N21W158.tif
|
||
cp SRTM1.vrt SRTM1.5af18c5270144c688522a27abf3b23a0.vrt
|
||
gdal_translate -q -co TILED=YES -co COMPRESS=DEFLATE -co ZLEVEL=9 -co PREDICTOR=2 -projwin -157.90125854957773 21.364471426268274 -157.81006761682832 21.244615177105377 SRTM1.5af18c5270144c688522a27abf3b23a0.vrt /Users/marceloprates/Projects/Art/07_Data_Visualization_and_Maps/prettymaps/notebooks/elevation.tif
|
||
rm -f SRTM1.5af18c5270144c688522a27abf3b23a0.vrt
|
||
|
||
|
||
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']}},
|
||
}
|
||
},
|
||
)
|
||
```
|
||
|
||
Fetching geodataframes took 21.47 seconds
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|