Cartography/prettymaps
1
0
Fork 0
mirror of https://github.com/marceloprates/prettymaps.git synced 2025-01-16 20:48:13 +01:00
Code Issues Projects Releases Wiki Activity
prettymaps/README.md

1188 lines
28 KiB
Markdown
Executable File
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

```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.
![](https://github.com/marceloprates/prettymaps/raw/main/prints/heerhugowaard.png)
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**.
<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://web.archive.org/web/20210825160918/https://news.ycombinator.com/news):
![](https://github.com/marceloprates/prettymaps/raw/main/prints/hackernews-prettymaps.png)
## [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/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:
```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')
```
![png](README_files/README_8_0.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'
)
```
![png](README_files/README_10_0.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}}, '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,
}
}
)
```
![png](README_files/README_16_0.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,
)
```
![png](README_files/README_18_0.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']
```
<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></th>
<th>addr:housenumber</th>
<th>addr:street</th>
<th>amenity</th>
<th>operator</th>
<th>website</th>
<th>geometry</th>
<th>addr:postcode</th>
<th>name</th>
<th>office</th>
<th>opening_hours</th>
<th>...</th>
<th>contact:phone</th>
<th>bus</th>
<th>public_transport</th>
<th>source:name</th>
<th>government</th>
<th>ways</th>
<th>name:fr</th>
<th>type</th>
<th>building:part</th>
<th>architect</th>
</tr>
<tr>
<th>element_type</th>
<th>osmid</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>node</th>
<th>2407915698</th>
<td>820</td>
<td>Rua Washington Luiz</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>POINT (-51.23212 -30.0367)</td>
<td>90010-460</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 rowspan="4" valign="top">way</th>
<th>126665330</th>
<td>387</td>
<td>Rua dos Andradas</td>
<td>place_of_worship</td>
<td>NaN</td>
<td>NaN</td>
<td>POLYGON ((-51.23518 -30.03275, -51.23512 -30.0...</td>
<td>90020-002</td>
<td>Igreja Nossa Senhora das Dores</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>126665331</th>
<td>1001</td>
<td>Rua dos Andradas</td>
<td>NaN</td>
<td>NaN</td>
<td>http://www.ruadapraiashopping.com.br</td>
<td>POLYGON ((-51.23167 -30.03066, -51.2316 -30.03...</td>
<td>90020-015</td>
<td>Rua da Praia Shopping</td>
<td>NaN</td>
<td>Mo-Fr 09:00-21:00; Sa 08:00-20:00</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>129176990</th>
<td>1020</td>
<td>Rua 7 de Setembro</td>
<td>NaN</td>
<td>NaN</td>
<td>http://www.memorial.rs.gov.br</td>
<td>POLYGON ((-51.23117 -30.02891, -51.2312 -30.02...</td>
<td>90010-191</td>
<td>Memorial do Rio Grande do Sul</td>
<td>NaN</td>
<td>Tu-Sa 10:00-18:00</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>129176991</th>
<td>NaN</td>
<td>Praça da Alfândega</td>
<td>NaN</td>
<td>NaN</td>
<td>http://www.margs.rs.gov.br</td>
<td>POLYGON ((-51.23153 -30.02914, -51.23156 -30.0...</td>
<td>90010-150</td>
<td>Museu de Arte do Rio Grande do Sul</td>
<td>NaN</td>
<td>Tu-Su 10:00-19:00</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>
<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 rowspan="5" valign="top">relation</th>
<th>6760281</th>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>POLYGON ((-51.23238 -30.03337, -51.23223 -30.0...</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>[457506887, 457506886]</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>6760282</th>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>POLYGON ((-51.23203 -30.0334, -51.23203 -30.03...</td>
<td>NaN</td>
<td>Atheneu Espírita Cruzeiro do Sul</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>[457506875, 457506889, 457506888]</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>6760283</th>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>POLYGON ((-51.23284 -30.03367, -51.23288 -30.0...</td>
<td>NaN</td>
<td>Palacete Chaves</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>[457506897, 457506896]</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>Theodor Wiederspahn</td>
</tr>
<tr>
<th>6760284</th>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>POLYGON ((-51.23499 -30.03412, -51.23498 -30.0...</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>[457506910, 457506913]</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>14393526</th>
<td>1044</td>
<td>Rua Siqueira Campos</td>
<td>NaN</td>
<td>NaN</td>
<td>https://www.sefaz.rs.gov.br</td>
<td>POLYGON ((-51.23125 -30.02813, -51.23128 -30.0...</td>
<td>NaN</td>
<td>Secretaria Estadual da Fazenda</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>[236213286, 1081974882]</td>
<td>NaN</td>
<td>multipolygon</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>2423 rows × 105 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]
```
/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)
![svg](README_files/README_22_1.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.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')
```
![png](README_files/README_24_0.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',
)
# Change background color
plot.fig.patch.set_facecolor('#F2F4CB')
# Add title
_ = plot.ax.set_title(
'Barcelona',
font = 'serif',
size = 50
)
```
![png](README_files/README_26_0.png)
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,
)
```
![png](README_files/README_28_0.png)
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',
)
```
![png](README_files/README_31_0.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.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:
<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>my-preset</td>
<td>{'layers': {'building': {'tags': {'building': ...</td>
</tr>
<tr>
<th>9</th>
<td>plotter</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
<tr>
<th>10</th>
<td>tijuca</td>
<td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
</tr>
</tbody>
</table>
</div>
After deletion:
<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>
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)
)
```
![png](README_files/README_37_0.png)
<Figure size 3600x3600 with 0 Axes>
<Figure size 3600x3600 with 0 Axes>
<Figure size 3600x3600 with 0 Axes>
```python
```
Reference in New Issue View Git Blame Copy Permalink