Update README, notebook, and images from CI

parks and sport pitches that are outside of parks, and whatnot.

.github/workflows/docs.yml

@@ -1,4 +1,8 @@
-name: Deploy Docs
+# Deploy Docs to GitHub Pages using MkDocs
+# This workflow builds documentation from the 'docs/' folder using mkdocs.yml in the repo root
+# and deploys the static site to GitHub Pages.
+
+name: Build and Deploy Docs
 
 on:
   push:
@@ -6,20 +10,31 @@ on:
       - main
 
 jobs:
-  deploy:
+  build-and-deploy-docs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.11'
+          python-version: '3.12'
+      # Ensure mkdocs.yml exists in the repo root
+      - name: Check for mkdocs.yml in root
+        run: |
+          if [ ! -f mkdocs.yml ]; then
+            echo "Error: mkdocs.yml not found in repository root. Please add it." && exit 1
+          fi
       - name: Install dependencies
         run: |
           pip install -r docs/requirements.txt
-      - name: Build docs
+      - name: Build docs with MkDocs
         run: |
           mkdocs build
+      - name: List files after build
+        run: |
+          pwd
+          ls -l
+          ls -l site || true
       - name: Deploy to GitHub Pages
         uses: peaceiris/actions-gh-pages@v4
         with:

.github/workflows/generate-readme.yml

@@ -20,7 +20,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.11'
+          python-version: '3.12'
 
       - name: Install nbconvert
         run: |
@@ -36,7 +36,7 @@ jobs:
 
       - name: Execute the notebook (run all cells)
         run: |
-          jupyter nbconvert --to notebook --execute notebooks/examples.ipynb --output notebooks/examples.ipynb
+          jupyter nbconvert --to notebook --execute notebooks/examples.ipynb --output examples.ipynb --output-dir notebooks
 
       - name: Run notebooks_to_readme.sh
         run: |

.github/workflows/publish-package.yml

@@ -25,10 +25,10 @@ jobs:
       with:
         fetch-depth: 0
 
-    - name: Set up Python 3.11
+    - name: Set up Python 3.12
       uses: actions/setup-python@v2
       with:
-        python-version: 3.11
+        python-version: 3.12
 
     - name: Install build and twine
       run: |

.github/workflows/test.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.11"]
+        python-version: ["3.12"]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4

.gitignore

@@ -47,3 +47,4 @@ SRTM1/
 # Ignore test and temp files
 *.tmp
 *.tmp.png
+prettymaps/presets/my-preset.json

README.md

@@ -2,7 +2,9 @@
 
 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)
+![](https://github.com/marceloprates/prettymaps/raw/main/pictures/heerhugowaard.png)
+
+# [![Docs](https://img.shields.io/badge/docs-GitHub%20Pages-blue?logo=github)](https://marceloprates.github.io/prettymaps/) [![PyPI](https://img.shields.io/pypi/v/prettymaps)](https://pypi.org/project/prettymaps/) [![Python](https://img.shields.io/badge/python-3.12%2B-blue)](https://www.python.org/) [![License](https://img.shields.io/badge/license-AGPL%20v3.0-green)](LICENSE)
 
 
 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)
@@ -17,8 +19,7 @@ This work is [licensed](LICENSE) under a GNU Affero General Public License v3.0
 <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)
-
+![](https://github.com/marceloprates/prettymaps/raw/main/pictures/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)
 
@@ -70,7 +71,7 @@ import prettymaps
 plot = prettymaps.plot('Stad van de Zon, Heerhugowaard, Netherlands')
 ```
 
-    Fetching geodataframes took 2.04 seconds
+    Fetching geodataframes took 14.43 seconds
 
 
 
@@ -93,7 +94,7 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 0.81 seconds
+    Fetching geodataframes took 5.48 seconds
 
 
 
@@ -185,21 +186,11 @@ prettymaps.presets()
     </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>pytest-temp-preset</td>
-      <td>{'layers': {'building': {'tags': {'building': ...</td>
-    </tr>
-    <tr>
-      <th>11</th>
+      <th>9</th>
       <td>tijuca</td>
       <td>{'layers': {'perimeter': {}, 'streets': {'widt...</td>
     </tr>
@@ -221,7 +212,7 @@ 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': '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})
+    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})
 
 
 
@@ -378,7 +369,7 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 15.97 seconds
+    Fetching geodataframes took 20.74 seconds
 
 
 
@@ -402,7 +393,7 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 1.28 seconds
+    Fetching geodataframes took 14.80 seconds
 
 
 
@@ -422,7 +413,7 @@ plot = prettymaps.plot('Centro Histórico, Porto Alegre', show = False)
 plot.geodataframes['building']
 ```
 
-    Fetching geodataframes took 1.59 seconds
+    Fetching geodataframes took 14.56 seconds
 
 
 
@@ -455,16 +446,16 @@ plot.geodataframes['building']
       <th>amenity</th>
       <th>operator</th>
       <th>website</th>
-      <th>historic</th>
+      <th>check_date</th>
       <th>...</th>
-      <th>contact:website</th>
+      <th>payment:lightning_contactless</th>
+      <th>payment:onchain</th>
       <th>bus</th>
       <th>smoothness</th>
       <th>inscription</th>
-      <th>ways</th>
+      <th>type</th>
       <th>boat</th>
       <th>name:fr</th>
-      <th>type</th>
       <th>building:part</th>
       <th>architect</th>
     </tr>
@@ -472,7 +463,7 @@ plot.geodataframes['building']
   <tbody>
     <tr>
       <th>(node, 2407915698)</th>
-      <td>POINT (-51.23212 -30.03670)</td>
+      <td>POINT (-51.23212 -30.0367)</td>
       <td>NaN</td>
       <td>NaN</td>
       <td>NaN</td>
@@ -495,14 +486,62 @@ plot.geodataframes['building']
       <td>NaN</td>
     </tr>
     <tr>
-      <th>(way, 126665330)</th>
-      <td>POLYGON ((-51.23518 -30.03275, -51.23512 -30.0...</td>
+      <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>387</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>place_of_worship</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>
@@ -512,23 +551,23 @@ plot.geodataframes['building']
       <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>(way, 126665331)</th>
-      <td>POLYGON ((-51.23167 -30.03066, -51.23160 -30.0...</td>
+      <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>1001</td>
-      <td>Rua dos Andradas</td>
       <td>NaN</td>
       <td>NaN</td>
-      <td>https://www.ruadapraiashopping.com.br/</td>
+      <td>parking</td>
+      <td>NaN</td>
+      <td>NaN</td>
       <td>NaN</td>
       <td>...</td>
       <td>NaN</td>
@@ -536,55 +575,7 @@ plot.geodataframes['building']
       <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>multipolygon</td>
       <td>NaN</td>
       <td>NaN</td>
       <td>NaN</td>
@@ -615,8 +606,8 @@ plot.geodataframes['building']
       <td>...</td>
     </tr>
     <tr>
-      <th>(relation, 6760281)</th>
-      <td>POLYGON ((-51.23238 -30.03337, -51.23223 -30.0...</td>
+      <th>(way, 1082776706)</th>
+      <td>POLYGON ((-51.22975 -30.02912, -51.22974 -30.0...</td>
       <td>NaN</td>
       <td>NaN</td>
       <td>NaN</td>
@@ -631,16 +622,16 @@ plot.geodataframes['building']
       <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>
       <td>NaN</td>
       <td>NaN</td>
     </tr>
     <tr>
-      <th>(relation, 6760282)</th>
-      <td>POLYGON ((-51.23203 -30.03340, -51.23203 -30.0...</td>
+      <th>(way, 1082776707)</th>
+      <td>POLYGON ((-51.22992 -30.02954, -51.22987 -30.0...</td>
       <td>NaN</td>
       <td>NaN</td>
       <td>NaN</td>
@@ -655,16 +646,16 @@ plot.geodataframes['building']
       <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>
       <td>NaN</td>
       <td>NaN</td>
     </tr>
     <tr>
-      <th>(relation, 6760283)</th>
-      <td>POLYGON ((-51.23284 -30.03367, -51.23288 -30.0...</td>
+      <th>(way, 1082787655)</th>
+      <td>POLYGON ((-51.22601 -30.03038, -51.22602 -30.0...</td>
       <td>NaN</td>
       <td>NaN</td>
       <td>NaN</td>
@@ -679,64 +670,64 @@ plot.geodataframes['building']
       <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>
+      <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>1044</td>
-      <td>Rua Siqueira Campos</td>
       <td>NaN</td>
       <td>NaN</td>
-      <td>https://www.sefaz.rs.gov.br</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>[236213286, 1081974882]</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>(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>2420 rows × 167 columns</p>
+<p>2415 rows × 137 columns</p>
 </div>
 
 
@@ -750,7 +741,7 @@ plot.geodataframes['building'][
 ].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]`
+    /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)
 
 
@@ -777,7 +768,7 @@ 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 = 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
@@ -797,7 +788,7 @@ for ax,building in zip(np.concatenate(axes),buildings):
     ax.autoscale(); ax.axis('off'); ax.axis('equal')
 ```
 
-    Fetching geodataframes took 2.01 seconds
+    Fetching geodataframes took 16.54 seconds
 
 
 
@@ -828,7 +819,7 @@ _ = plot.ax.set_title(
 )
 ```
 
-    Fetching geodataframes took 3.78 seconds
+    Fetching geodataframes took 12.52 seconds
 
 
 Use **plotter** mode to export a pen plotter-compatible SVG (thanks to abey79's amazing [vsketch](https://github.com/abey79/vsketch) library)
@@ -847,7 +838,7 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 3.89 seconds
+    Fetching geodataframes took 4.82 seconds
 
 
 
@@ -871,7 +862,7 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 16.55 seconds
+    Fetching geodataframes took 23.53 seconds
 
 
 
@@ -972,14 +963,18 @@ plot = prettymaps.multiplot(
 )
 ```
 
-    Fetching geodataframes took 0.97 seconds
-    Fetching geodataframes took 1.15 seconds
-    Fetching geodataframes took 0.79 seconds
+    Fetching geodataframes took 8.95 seconds
+
+
+    Fetching geodataframes took 7.03 seconds
+
+
+    Fetching geodataframes took 8.45 seconds
 
 
 
     
-![png](pictures/README/temp_readme_files/temp_readme_33_1.png)
+![png](pictures/README/temp_readme_files/temp_readme_33_3.png)
     
 
 
@@ -1002,15 +997,29 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 24.92 seconds
+    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
-    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
+    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.
@@ -1018,7 +1027,7 @@ plot = prettymaps.plot(
 
 
     
-![png](pictures/README/temp_readme_files/temp_readme_35_2.png)
+![png](pictures/README/temp_readme_files/temp_readme_35_5.png)
     
 
 
@@ -1043,7 +1052,7 @@ plot = prettymaps.plot(
 )
 ```
 
-    Fetching geodataframes took 21.47 seconds
+    Fetching geodataframes took 17.09 seconds
 
 
 

app.py

@@ -5,6 +5,16 @@ import sys
 import os
 import io
 
+
+def download_svg():
+    """
+    Creates additional map in SVG format
+    """
+    fig_path = "/tmp/generated_map_download.svg"
+    plt.savefig(fig_path, format="svg", bbox_inches="tight", dpi=150)
+    return fig_path
+
+
 # Set Streamlit to use the wide layout
 st.set_page_config(layout="wide")
 
@@ -65,8 +75,10 @@ with cols[0]:
     page_size_col, dpi_col = st.columns(2)
     with page_size_col:
         page_size = st.selectbox(
-            "Page Size", ["A4", "A5", "Square"], index = 0
-            #, "A3", "A2", "A1", "Custom"], index=0
+            "Page Size",
+            ["A4", "A5", "Square"],
+            index=0,
+            # , "A3", "A2", "A1", "Custom"], index=0
         )
     with dpi_col:
         dpi = st.number_input("DPI", min_value=50, max_value=300, value=100, step=50)
@@ -130,7 +142,7 @@ with cols[1]:
         use_container_width=True,
     )
 
-    if button:  # or "last_image" in st.session_state:
+    if button:
         hillshade_params = (
             {
                 "azdeg": azdeg,
@@ -166,23 +178,43 @@ with cols[1]:
             with open(fig_path, "wb") as f:
                 f.write(st.session_state.last_image.getbuffer())
 
-            # Provide a download button
-            with open(fig_path, "rb") as file:
-                btn = st.download_button(
-                    label="Download Map",
-                    data=file,
-                    file_name=f"{query}.png",
-                    mime="image/png",
-                    use_container_width=True,
-                )
+            # Save SVG for persistent download
+            svg_path = download_svg()
+            st.session_state.last_png_path = fig_path
+            st.session_state.last_svg_path = svg_path
 
-            st.image(st.session_state.last_image, use_container_width=True)
+    # Always show download buttons (disabled if no image)
+    png_ready = "last_png_path" in st.session_state and os.path.exists(
+        st.session_state["last_png_path"]
+    )
+    svg_ready = "last_svg_path" in st.session_state and os.path.exists(
+        st.session_state["last_svg_path"]
+    )
+    btn_cols = st.columns(2)
+    with btn_cols[0]:
+        st.download_button(
+            label="Download PNG",
+            data=open(st.session_state["last_png_path"], "rb") if png_ready else b"",
+            file_name=f"{query}.png",
+            mime="image/png",
+            use_container_width=True,
+            disabled=not png_ready,
+        )
+    with btn_cols[1]:
+        st.download_button(
+            label="Download SVG",
+            data=open(st.session_state["last_svg_path"], "rb") if svg_ready else b"",
+            file_name=f"{query}.svg",
+            mime="image/svg",
+            use_container_width=True,
+            disabled=not svg_ready,
+        )
 
+    # Always show image (generated or placeholder)
+    if st.session_state.get("last_image"):
+        st.image(st.session_state.last_image, use_container_width=True)
     else:
-        if st.session_state.last_image:
-            st.image(st.session_state.last_image, use_container_width=True)
-        else:
-            st.image(
-                "https://github.com/marceloprates/prettymaps/blob/main/prints/app_placeholder.png?raw=true",
-                use_container_width=True,
-            )
+        st.image(
+            "https://github.com/marceloprates/prettymaps/blob/main/pictures/app_placeholder.png?raw=true",
+            use_container_width=True,
+        )

docs/api.md

@@ -1,21 +1,7 @@
 # API Reference
 
-This page documents the main modules and functions of the `prettymaps` library. The API documentation is auto-generated from the code and includes all public classes and functions.
-
-## Modules
-
-- **draw**: Core drawing and plotting functions, including map rendering, layer management, and style handling.
-- **fetch**: Functions for fetching and processing OpenStreetMap data and elevation.
-- **utils**: Utility functions for logging, timing, and other helpers.
-
----
+This section is auto-generated from the Prettymaps source code.
 
 ::: prettymaps.draw
 
----
-
-::: prettymaps.fetch
-
----
-
-::: prettymaps.utils 
+::: prettymaps.fetch 

docs/index.md

@@ -1,27 +1,24 @@
-# prettymaps
+# Prettymaps Documentation
 
-A minimal Python library to draw pretty maps from OpenStreetMap data.
+Welcome to the Prettymaps documentation site.
 
-- [Usage](usage.md)
-- [API Reference](api.md)
+Prettymaps is a minimal Python library to draw beautiful maps from OpenStreetMap data.  
+It allows you to easily generate artistic, customizable maps for any location in the world.
 
 ## Features
-- Draw beautiful maps from OSM data
-- Highly customizable layers and styles
-- Preset management
-- Keypoint highlighting
 
-## Installation
-```bash
-pip install prettymaps
-```
+- Fetches and visualizes OpenStreetMap data with minimal code
+- Highly customizable layers and styles
+- Supports elevation, hillshading, and keypoints
+- Preset system for reusable map styles
+- Export to PNG, SVG, and plotter-friendly formats
 
 ## Quick Example
+
 ```python
 import prettymaps
-prettymaps.plot("Porto Alegre")
+
+prettymaps.plot("Porto Alegre, Brazil")
 ```
 
----
-
-Data © OpenStreetMap contributors 
+Explore the [Usage](usage.md) and [API Reference](api.md) for more details. 

docs/usage.md

@@ -1,54 +1,41 @@
 # Usage
 
+## Installation
+
+Install Prettymaps and its dependencies:
+
+```sh
+pip install prettymaps
+```
+
 ## Basic Usage
 
-The main entry point is the `plot()` function:
+Generate a map for a location:
 
 ```python
 import prettymaps
-prettymaps.plot("Porto Alegre")
-```
 
-This will generate a map for the given location using default layers and styles.
+prettymaps.plot("Paris, France")
+```
 
 ## Customizing Layers and Styles
 
-You can customize which OpenStreetMap layers are shown and how they are styled:
+You can customize which map layers to show and their appearance:
 
 ```python
 layers = {
     "perimeter": {},
     "streets": {"width": 8},
     "buildings": {},
+    "water": {},
 }
 style = {
-    "perimeter": {"fc": "#f2efe9"},
-    "streets": {"fc": "#cccccc", "ec": "#333333"},
-    "buildings": {"fc": "#bdbdbd"},
+    "perimeter": {"fc": "#f2efe9", "ec": "#333"},
+    "streets": {"fc": "#cccccc"},
+    "buildings": {"fc": "#b0b0b0"},
+    "water": {"fc": "#aadaff"},
 }
-prettymaps.plot("Porto Alegre", layers=layers, style=style)
-```
-
-## Using Presets
-
-Presets are reusable configurations for layers and styles. You can load, save, or update presets:
-
-```python
-prettymaps.plot("Porto Alegre", preset="default")
-```
-
-You can also create your own presets and save them for later use.
-
-## Highlighting Keypoints
-
-You can highlight specific keypoints (e.g., landmarks) on the map:
-
-```python
-keypoints = {
-    "tags": {"tourism": "attraction"},
-    "kwargs": {"bbox": {"fc": "yellow"}},
-}
-prettymaps.plot("Porto Alegre", keypoints=keypoints)
+prettymaps.plot("Berlin, Germany", layers=layers, style=style)
 ```
 
 ## Saving Maps
@@ -56,9 +43,13 @@ prettymaps.plot("Porto Alegre", keypoints=keypoints)
 You can save the generated map to a file:
 
 ```python
-prettymaps.plot("Porto Alegre", save_as="map.png")
+prettymaps.plot("Tokyo, Japan", save_as="tokyo_map.png")
 ```
 
----
+## Advanced Features
 
-See the [API Reference](api.md) for details on all functions and parameters. 
+- **Keypoints:** Highlight specific places or features on the map.
+- **Presets:** Save and reuse your favorite map styles.
+- **Hillshade:** Add elevation shading for a 3D effect.
+
+See the [API Reference](api.md) for all available options. 

prettymaps/draw.py

@@ -277,7 +277,7 @@ def gdf_to_shapely(
 
     # Project gdf if applicable
     if not gdf.empty and gdf.crs is not None:
-        gdf = ox.project_gdf(gdf)
+        gdf = ox.projection.project_gdf(gdf)
 
     if layer in ["streets", "railway", "waterway"]:
         geometries = graph_to_shapely(gdf, width)
@@ -426,7 +426,7 @@ def plot_legends(
     texts = []
     for i in range(len(keypoints_df)):
         x, y = np.concatenate(
-            ox.project_gdf(keypoints_df.iloc[[i]]).geometry.iloc[0].centroid.xy
+            ox.projection.project_gdf(keypoints_df.iloc[[i]]).geometry.iloc[0].centroid.xy
         )
         name = keypoints_df.name.iloc[i]
         kwargs = keypoints_df.kwargs.iloc[i]
@@ -554,7 +554,7 @@ def draw_hillshade(
 
         min_x, max_x = ax.get_xlim()
         min_y, max_y = ax.get_ylim()
-        min_lon, min_lat, max_lon, max_lat = ox.project_gdf(
+        min_lon, min_lat, max_lon, max_lat = ox.projection.project_gdf(
             gdfs["perimeter"]
         ).total_bounds
         ax.imshow(
@@ -601,7 +601,7 @@ def create_background(
 
     background = shapely.affinity.scale(
         box(
-            *shapely.ops.unary_union(ox.project_gdf(gdfs["perimeter"]).geometry).bounds
+            *shapely.ops.unary_union(ox.projection.project_gdf(gdfs["perimeter"]).geometry).bounds
         ),
         background_pad,
         background_pad,
@@ -670,7 +670,7 @@ def transform_gdfs(
     """
     # Project geometries
     gdfs = {
-        name: ox.project_gdf(gdf) if len(gdf) > 0 else gdf for name, gdf in gdfs.items()
+        name: ox.projection.project_gdf(gdf) if len(gdf) > 0 else gdf for name, gdf in gdfs.items()
     }
     # Create geometry collection from gdfs' geometries
     collection = GeometryCollection(
@@ -685,7 +685,7 @@ def transform_gdfs(
         gdfs[layer].geometry = list(collection.geoms[i].geoms)
         # Reproject
         if len(gdfs[layer]) > 0:
-            gdfs[layer] = ox.project_gdf(gdfs[layer], to_crs="EPSG:4326")
+            gdfs[layer] = ox.projection.project_gdf(gdfs[layer], to_crs="EPSG:4326")
 
     return gdfs
 

prettymaps/fetch.py

@@ -95,7 +95,7 @@ def get_keypoints(
     Returns:
     geopandas.GeoDataFrame: A GeoDataFrame containing the keypoints that match the specified tags within the given perimeter.
     """
-    keypoints_df = ox.features_from_polygon(perimeter, tags=tags)
+    keypoints_df = ox.features.features_from_polygon(perimeter, tags=tags)
 
     return keypoints_df
 
@@ -150,7 +150,7 @@ def obtain_elevation(gdf):
 
     raster = rxr.open_rasterio(output_file).squeeze()
 
-    raster = raster.rio.reproject(CRS.from_string(ox.project_gdf(gdf).crs.to_string()))
+    raster = raster.rio.reproject(CRS.from_string(ox.projection.project_gdf(gdf).crs.to_string()))
 
     # convert to numpy array
     elevation_data = raster.data
@@ -200,9 +200,9 @@ def parse_query(query):
 def get_boundary(query, radius, circle=False, rotation=0):
 
     # Get point from query
-    point = query if parse_query(query) == "coordinates" else ox.geocode(query)
+    point = query if parse_query(query) == "coordinates" else ox.geocoder.geocode(query)
     # Create GeoDataFrame from point
-    boundary = ox.project_gdf(
+    boundary = ox.projection.project_gdf(
         GeoDataFrame(geometry=[Point(point[::-1])], crs="EPSG:4326")
     )
 
@@ -252,19 +252,19 @@ def get_perimeter(
             perimeter = query
         else:
             # Fetch perimeter from OSM
-            perimeter = ox.geocode_to_gdf(
+            perimeter = ox.geocoder.geocode_to_gdf(
                 query,
                 by_osmid=by_osmid,
                 **kwargs,
             )
 
     # Scale according to aspect ratio
-    perimeter = ox.project_gdf(perimeter)
+    perimeter = ox.projection.project_gdf(perimeter)
     perimeter.loc[0, "geometry"] = scale(perimeter.loc[0, "geometry"], aspect_ratio, 1)
     perimeter = perimeter.to_crs(4326)
 
     # Apply dilation
-    perimeter = ox.project_gdf(perimeter)
+    perimeter = ox.projection.project_gdf(perimeter)
     if dilate is not None:
         perimeter.geometry = perimeter.geometry.buffer(dilate)
     perimeter = perimeter.to_crs(4326)
@@ -404,7 +404,7 @@ def get_gdf(
 
     # Apply tolerance to the perimeter
     perimeter_with_tolerance = (
-        ox.project_gdf(perimeter).buffer(perimeter_tolerance).to_crs(4326)
+        ox.projection.project_gdf(perimeter).buffer(perimeter_tolerance).to_crs(4326)
     )
     perimeter_with_tolerance = unary_union(perimeter_with_tolerance.geometry).buffer(0)
 
@@ -423,7 +423,7 @@ def get_gdf(
         elif layer == "sea":
             # Fetch geometries from OSM
             coastline = unary_union(
-                ox.features_from_polygon(
+                ox.features.features_from_polygon(
                     bbox, tags={"natural": "coastline"}
                 ).geometry.tolist()
             )
@@ -461,11 +461,11 @@ def get_gdf(
         else:
             if osmid is None:
                 # Fetch geometries from OSM
-                gdf = ox.features_from_polygon(
+                gdf = ox.features.features_from_polygon(
                     bbox, tags={tags: True} if type(tags) == str else tags
                 )
             else:
-                gdf = ox.geocode_to_gdf(osmid, by_osmid=True)
+                gdf = ox.geocoder.geocode_to_gdf(osmid, by_osmid=True)
     except Exception as e:
         # print(f"Error fetching {layer}: {e}")
         gdf = GeoDataFrame(geometry=[])
@@ -584,7 +584,7 @@ def unified_osm_request(
     dict: Dictionary of GeoDataFrames for each layer.
     """
     # Apply tolerance to the perimeter
-    perimeter_with_tolerance = ox.project_gdf(perimeter).buffer(0).to_crs(4326)
+    perimeter_with_tolerance = ox.projection.project_gdf(perimeter).buffer(0).to_crs(4326)
     perimeter_with_tolerance = unary_union(perimeter_with_tolerance.geometry).buffer(0)
 
     # Fetch from perimeter's bounding box, to avoid missing some geometries
@@ -605,7 +605,7 @@ def unified_osm_request(
 
     # Fetch all features in one request
     try:
-        all_features = ox.features_from_polygon(bbox, tags=combined_tags)
+        all_features = ox.features.features_from_polygon(bbox, tags=combined_tags)
     except Exception as e:
         all_features = GeoDataFrame(geometry=[])
 
@@ -624,7 +624,7 @@ def unified_osm_request(
             elif layer == "sea":
                 try:
                     coastline = unary_union(
-                        ox.features_from_polygon(
+                        ox.features.features_from_polygon(
                             bbox, tags={"natural": "coastline"}
                         ).geometry.tolist()
                     )
@@ -682,7 +682,7 @@ def unified_osm_request(
                                 ]
                             gdf = pd.concat([gdf, filtered_features], axis=0)
                 else:
-                    gdf = ox.geocode_to_gdf(kwargs.get("osmid"), by_osmid=True)
+                    gdf = ox.geocoder.geocode_to_gdf(kwargs.get("osmid"), by_osmid=True)
 
             gdf = gdf.copy()
             gdf.geometry = gdf.geometry.intersection(perimeter_with_tolerance)

prettymaps/presets/default.json

@@ -54,7 +54,16 @@
                     "wood",
                     "wetland"
                 ],
-                "leisure": "park"
+                "leisure": [
+                    "dog_park",
+                    "disc_golf_course",
+                    "garden",
+                    "golf_course",
+                    "park",
+                    "pitch",
+                    "sports_centre",
+                    "track"
+                ]
             }
         },
         "rock": {
@@ -162,4 +171,4 @@
     },
     "circle": null,
     "radius": 500
-}
+}

requirements.txt

@@ -1,7 +1,7 @@
-numpy>=1.20,<1.25
+numpy>=1.26.4
 matplotlib>=3.9.0
 shapely>=2.0.0
-osmnx>=1.9.3,<2.0
+osmnx>=2.0.5
 ipykernel>=6.29.5
 rasterio>=1.4.3
 rioxarray>=0.18.2
@@ -12,4 +12,4 @@ thefuzz>=0.22.1
 elevation>=1.1.3
 streamlit>=1.42.2
 tqdm>=4.67.1
-scikit-learn>=1.6.1
+scikit-learn>=1.6.1

scripts/notebooks_to_readme.sh

@@ -27,7 +27,7 @@ mv "$PICTURES_DIR/temp_readme.md" "$README_FILE"
 # (No-op if already in place)
 
 # Update image links in README.md to point to pictures/README/
-sed -i '' 's|(temp_readme_files/|(pictures/README/temp_readme_files/|g' "$README_FILE"
+sed -i 's|(temp_readme_files/|(pictures/README/temp_readme_files/|g' "$README_FILE"
 
 # Optionally, clean up any old temp_readme_files in root
 # rm -rf temp_readme_files

setup.py

@@ -22,5 +22,5 @@ setup(
     ],
     package_dir={"prettymaps": "prettymaps"},
     package_data={"prettymaps": ["presets/*.json"]},
-    python_requires=">=3.11",
+    python_requires=">=3.12",
 )

setup.sh

@@ -15,7 +15,7 @@ if [[ "$OS" == "Linux" ]]; then
   # Linux (Debian/Ubuntu)
   echo "Updating package list and installing dependencies for Linux..."
   sudo apt-get update
-  sudo apt-get install -y build-essential make gcc python3-dev libgdal-dev gdal-bin
+  sudo apt-get install -y build-essential make gcc libgdal-dev gdal-bin
   sudo apt-get clean
 
 elif [[ "$OS" == "Darwin" ]]; then
@@ -34,14 +34,10 @@ elif [[ "$OS" =~ "MINGW" || "$OS" =~ "MSYS" || "$OS" =~ "CYGWIN" ]]; then
   echo "- GDAL (https://gdal.org/download.html or via Conda: conda install -c conda-forge gdal)"
   echo "- Make and GCC (optional, for advanced features)"
   echo "- Python 3 and pip"
-  echo "Then run: pip install -e ."
   exit 0
 else
   echo "Unsupported OS: $OS"
   exit 1
 fi
 
-# Install Python dependencies
-pip install -e .
-
 echo "Setup complete!"