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.gitattributes

@@ -1,35 +1,35 @@
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-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.github/workflows/sync-hf.yml

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://giswqs:[email protected]/spaces/giswqs/solara-geemap main

.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+private/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+        types: [python]
+      - id: trailing-whitespace
+      - id: requirements-txt-fixer
+      - id: check-added-large-files
+        args: ["--maxkb=500"]
+
+  - repo: https://github.com/psf/black
+    rev: 24.10.0
+    hooks:
+      - id: black-jupyter
+        language_version: python3
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.3.0
+    hooks:
+      - id: codespell
+        args:
+          [
+            "--ignore-words-list=gis,timeseries,sav,slowy",
+            "--skip=*.json,*.csv",
+          ]
+
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout

CSVs/esa_stats_ucs_rj.csv

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+0.0,0.0013829072661898929,0.0,0.0039116519815085545,0.04125014816863566,0.0,0.9525860365877751,1.9755818088427042E-4,6.716978150065195E-4,253.09,30.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MANGARATIBA,25312.3537777,1987,2.73219986964,N�O SE APLICA,0.02228427968,SIM,24,APA DE MANGARATIBA,8,1:20.000,00000000000000000007
+0.1413828689370485,0.0371517027863777,0.0,0.0,0.30856553147574817,0.0010319917440660474,0.42414860681114547,0.04850361197110422,0.039215686274509796,9.690000000000001,31.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MARICA,969.239448931,1984,0.30140385146,N�O SE APLICA,8.5341915E-4,SIM,33,APA DE MARICA,9,Sem info,00000000000000000008
+0.006794520547945206,0.096,2.1917808219178083E-4,0.0,0.30224657534246574,0.0,0.2533698630136986,0.20076712328767124,0.1406027397260274,91.25,32.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MASSAMBABA,9124.45193908,2011,0.8822736963,N�O SE APLICA,0.00803179984,SIM,32,APA DE MASSAMBABA,10,1:50.000,00000000000000000009
+0.006665378670788253,0.022024729520865528,0.0,0.0,0.04028207109737248,0.0,0.17793663060278206,0.01642194744976816,0.7366692426584235,103.52000000000001,36.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DO PAU-BRASIL,10353.1623171,2002,0.42686675166,N�O SE APLICA,0.00910866994,SIM,28,APA PAU BRASIL,11,1:50.000,0000000000000000000a
+0.0043668122270742364,0.11185757473967083,0.004702720859926101,0.0,0.5018474974806854,0.0,0.3574067853543837,0.008397715821296609,0.011420893516963388,59.53999999999999,27.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DA SERRA DE SAPIATIBA,5955.22943195,1990,0.34376268569,N�O SE APLICA,0.00523829411,SIM,29,APA DA SERRA DE SAPIATIBA,12,1:50.000,0000000000000000000b
+1.8844812965231321E-4,0.017081476894913246,0.0017364149089391717,0.0,0.3553593302015049,0.0,0.6150408528623925,0.0010903070358455264,0.009503169966752366,742.91,37.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DO RIO GUANDU,74295.2023405,2007,3.89480581005,N�O SE APLICA,0.06525411525,N�O,26,APA DO RIO GUANDU,13,1:50.000,0000000000000000000c
+0.0,0.0,0.0,0.0,0.034482758620689655,0.0,0.8620689655172413,0.0,0.10344827586206895,0.29000000000000004,38.0,USO SUSTENTAVEL,FLOR,ESTADUAL,FLORESTA ESTADUAL JOSE ZAGO,29.1364229911,2016,0.02136871955,N�O SE APLICA,2.549498E-5,N�O,37,FLOE JOSE ZAGO,14,1:25.000,0000000000000000000d
+0.017441860465116275,0.0697674418604651,0.0,0.0,0.3255813953488372,0.0,0.5581395348837208,0.029069767441860465,0.0,1.7200000000000002,33.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE SEPETIBA II,171.666213409,2004,0.05801156773,N�O SE APLICA,1.5116278E-4,N�O,34,APA DE SEPETIBA II,15,1:10.000,0000000000000000000e
+0.006845708267509216,0.0,0.0,0.0,0.0010531858873091102,0.0,0.061084781463928386,0.0,0.9310163243812534,18.99,39.0,USO SUSTENTAVEL,RDS,ESTADUAL,RESERVA DE DESENVOLVIMENTO SUSTENTAVEL DO AVENTUREIRO,1899.91188332,2014,0.21453857684,N�O SE APLICA,0.00167576192,N�O,39,RDS DO AVENTUREIRO,16,1:50.000,0000000000000000000f
+0.005580923389142567,2.536783358701167E-4,0.0,0.0,0.0025367833587011668,0.0,0.0025367833587011668,0.0010147133434804667,0.9880771182141046,39.42,40.0,USO SUSTENTAVEL,RESEX,ESTADUAL,RESERVA EXTRATIVISTA MARINHA DE ITAIPU,3942.53457822,2013,0.4358579572,N�O SE APLICA,0.00347203423,N�O,38,RESEX MARINHA DE ITAIPU,17,1:25.000,00000000000000000010
+0.0023963788053607887,0.026759563326528804,0.0,0.0176621993432147,0.014555782373302566,0.0,0.8882577438537322,0.0014200763291026895,0.04894825596875833,225.33999999999997,34.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE TAMOIOS,22545.1793314,1986,6.34858345439,N�O SE APLICA,0.01987470585,SIM,35,APA DE TAMOIOS,38,Sem info,00000000000000000025

CSVs/esa_stats_ucs_rj2 - esa_stats_ucs_rj2.csv.csv

@@ -0,0 +1,10 @@
+Classes,MONUMENTO NATURAL DA SERRA DA MARIA COMPRIDA,REFUGIO DA VIDA SILVESTRE ESTADUAL DO MEDIO PARAIBA,RESERVA ECOLOGICA ESTADUAL DA JUATINGA,PARQUE ESTADUAL DO MENDANHA,PARQUE ESTADUAL DA LAGOA DO ACU,MONUMENTO NATURAL DA SERRA DA BELEZA,MONUMENTO NATURAL DA SERRA DOS MASCATES,REFUGIO DA VIDA SILVESTRE ESTADUAL DA SERRA DA ESTRELA,REFUGIO DA VIDA SILVESTRE ESTADUAL LAGOA DA TURFEIRA,RESERVA BIOLOGICA ESTADUAL DA PRAIA DO SUL,RESERVA BIOLOGICA ESTADUAL DE GUARATIBA,RESERVA BIOLOGICA ESTADUAL DE ARARAS,PARQUE ESTADUAL DOS TRES PICOS,PARQUE ESTADUAL DA SERRA DA TIRIRICA,PARQUE ESTADUAL DA SERRA DA CONCORDIA,PARQUE ESTADUAL DA PEDRA SELADA,PARQUE ESTADUAL DA PEDRA BRANCA,PARQUE ESTADUAL DO GRAJAU,PARQUE ESTADUAL DO DESENGANO,PARQUE ESTADUAL CUNHAMBEBE,PARQUE ESTADUAL DA COSTA DO SOL,PARQUE ESTADUAL DA CHACRINHA,ESTACAO ECOLOGICA ESTADUAL DE GUAXINDIBA,PARQUE ESTADUAL DA ILHA GRANDE,AREA DE PROTECAO AMBIENTAL DO ALTO IGUACU,AREA DE PROTECAO AMBIENTAL DA BACIA DO RIO MACACU,AREA DE PROTECAO AMBIENTAL DA BACIA DOS FRADES,AREA DE PROTECAO AMBIENTAL DE MACAE DE CIMA,AREA DE PROTECAO AMBIENTAL DE GERICINO-MENDANHA,AREA DE PROTECAO AMBIENTAL DE MANGARATIBA,AREA DE PROTECAO AMBIENTAL DE MARICA,AREA DE PROTECAO AMBIENTAL DE MASSAMBABA,AREA DE PROTECAO AMBIENTAL DO PAU-BRASIL,AREA DE PROTECAO AMBIENTAL DA SERRA DE SAPIATIBA,AREA DE PROTECAO AMBIENTAL DO RIO GUANDU,FLORESTA ESTADUAL JOSE ZAGO,AREA DE PROTECAO AMBIENTAL DE SEPETIBA II,RESERVA DE DESENVOLVIMENTO SUSTENTAVEL DO AVENTUREIRO,RESERVA EXTRATIVISTA MARINHA DE ITAIPU,AREA DE PROTECAO AMBIENTAL DE TAMOIOS
+Área Degradada,0,0.06,0.08,0.0,1.65,0,0,0,0,0.14,0.85,0,0,0.08,0,0,0.0,0,0,0,4.26,0,0,0.15,0.37,0.04,0,0,0.04,0,1.37,0.62,0.69,0.26,0.14,0,0.03,0.13,0.22,0.54
+Área Urbana,0,0.86,0.03,0,0.15,0,0.0,0.03,0.02,0.0,0.18,0,0.01,0.09,0.02,0.0,0.1,0.02,0,0,1.4,0.0,0.0,0.05,8.45,2.96,0,0.32,0.05,0.35,0.36,8.76,2.28,6.66,12.69,0.0,0.12,0,0.01,06.03
+Agricultura,0,0.1,0,0,0.04,0,0,0,0,0,0,0,0.01,0,0.01,0.02,0,0,0,0,0.03,0,0.51,0,0.37,2.66,0.13,0.02,0,0,0,0.02,0,0.28,1.29,0,0,0,0,0
+Manguezais,0,0,0.45,0,0,0,0,0,0,0,18.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.57,0,0,0,0.99,0,0,0,0,0,0,0,0,0,3.98
+Vegetação rasteira,23.95,38.93,0.98,2.93,26.1,10.51,2.61,0.73,1.97,0.05,1.82,5.46,21.27,1.55,08.02,2.12,10.83,0.02,8.76,12.87,22.11,0,10.26,0.63,62.51,49.86,11.13,25.23,14.7,10.44,2.99,27.58,4.17,29.88,264.0,0.01,0.56,0.02,0.1,3.28
+Vegetação arbustiva,0,0,0,0,0,0,0,0,0,0,0,0.01,0,0,0,0.02,0,0,0,0,0.04,0,0,0,0,0,0,0,0,0,0.01,0,0,0,0,0,0,0,0,0
+Floresta,54.07,57.04,94.76,41.05,1.85,44.24,4.14,47.31,0.36,32.11,1.88,32.91,629.29,30.19,51.45,78.25,113.97,0.51,204.47,364.62,44.33,0.04,16.53,118.68,142.49,130.79,17.46,324.44,64.94,241.09,4.11,23.12,18.42,21.28,456.92,0.25,0.96,1.16,0.1,200.16
+Área Urbana,0,0,0.01,0,39.14,0,0,0,0.21,0.06,4.37,0,0,0.38,0,0,0,0,0,0,14.65,0,4.81,0.02,5.25,6.49,0,0,0,0.05,0.47,18.32,1.7,0.5,0.81,0,0.05,0,0.04,0.32
+Água,0,14.29,3.36,0,13.56,0,0,0.02,0.13,0.75,6.11,0,0.01,02.02,0.02,0,0.02,0,0,3.24,11.45,0,0.47,1.36,1.67,0.3,0.0,0,0.02,0.17,0.38,12.83,76.26,0.68,07.06,0.03,0,17.68,38.95,11.03

Dockerfile

@@ -0,0 +1,21 @@
+FROM jupyter/base-notebook:latest
+
+RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \
+    fix-permissions "${CONDA_DIR}" && \
+    fix-permissions "/home/${NB_USER}"
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+RUN mkdir ./pages
+COPY /pages ./pages
+
+ENV PROJ_LIB='/opt/conda/share/proj'
+
+USER root
+RUN chown -R ${NB_UID} ${HOME}
+USER ${NB_USER}
+
+EXPOSE 8765
+
+CMD ["solara", "run", "./pages", "--host=0.0.0.0"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Open Geospatial Solutions
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,58 @@
----
-title: Solara Ucs
-emoji: 📚
-colorFrom: purple
-colorTo: pink
-sdk: docker
-pinned: false
-license: cc-by-4.0
-short_description: Uso de solo em unidades de conservação estaduais
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Solara Geemap
+emoji: 🏃
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+license: mit
+app_port: 8765
+---
+
+## Earth Engine Web Apps
+
+### Introduction
+
+**A collection of Earth Engine web apps developed using [Solara](https://github.com/widgetti/solara) and geemap**
+
+- Web App: <https://giswqs-solara-geemap.hf.space>
+- GitHub: <https://github.com/opengeos/solara-geemap>
+- Hugging Face: <https://huggingface.co/spaces/giswqs/solara-geemap>
+
+### How to deploy this app on Hugging Face Spaces
+
+1. Go to <https://huggingface.co/spaces/giswqs/solara-geemap/tree/main> and duplicate the space to your own space.
+
+   ![](https://i.imgur.com/gTg4V2x.png)
+
+2. You need to set `EARTHENGINE_TOKEN` in order to use Earth Engine. The token value should be copied from the following file depending on your operating system:
+
+   ```text
+   Windows: C:\\Users\\USERNAME\\.config\\earthengine\\credentials
+   Linux: /home/USERNAME/.config/earthengine/credentials
+   MacOS: /Users/USERNAME/.config/earthengine/credentials
+   ```
+
+   Simply open the file and copy **ALL** the content to the `EARTHENGINE_TOKEN` environment variable.
+
+   ![](https://i.imgur.com/i04gzyH.png)
+
+   ![](https://i.imgur.com/Ex37Ut7.png)
+
+   Alternatively, you can run the following code to retrieve your Earth Engine token:
+
+   ```python
+   import geemap
+   geemap.get_ee_token()
+   ```
+
+   Copy all the content of the printed token and set it as the `EARTHENGINE_TOKEN` environment variable.
+
+3. After the space is built successfully, click the `Embed this Space` menu and find the `Direct URL` for the app, such as <https://giswqs-solara-geemap.hf.space>.
+
+   ![](https://i.imgur.com/DNM36sk.png)
+
+   ![](https://i.imgur.com/KX82lSf.png)
+
+4. Add your own apps (\*.py) to the `pages` folder.
+5. Commit and push your changes to the repository. Wait for the space to be built successfully.

notebooks/01_basic.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/01_basic.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geemap\n",
+    "import solara\n",
+    "\n",
+    "zoom = solara.reactive(4)\n",
+    "center = solara.reactive((40, -100))\n",
+    "bounds = solara.reactive(None)\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    # Isolation is required to prevent the map from overlapping navigation (when screen width < 960px)\n",
+    "    with solara.Column(\n",
+    "        style={\"min-width\": \"500px\", \"height\": \"780px\", \"isolation\": \"isolate\"}\n",
+    "    ):\n",
+    "        # solara components support reactive variables\n",
+    "        solara.SliderInt(label=\"Zoom level\", value=zoom, min=1, max=20)\n",
+    "        # using 3rd party widget library require wiring up the events manually\n",
+    "        # using zoom.value and zoom.set\n",
+    "        geemap.Map.element(  # type: ignore\n",
+    "            zoom=zoom.value,\n",
+    "            on_zoom=zoom.set,\n",
+    "            center=center.value,\n",
+    "            on_center=center.set,\n",
+    "            on_bounds=bounds.set,\n",
+    "            scroll_wheel_zoom=True,\n",
+    "            height=\"600px\",\n",
+    "        )\n",
+    "        solara.Text(f\"Zoom: {zoom.value}\")\n",
+    "        solara.Text(f\"Center: {center.value}\")\n",
+    "        solara.Text(f\"Bounds: {bounds.value}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/02_inspector.ipynb

@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/02_inspector.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import geemap\n",
+    "import solara\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_ee_data()\n",
+    "        self.add(\"layer_manager\")\n",
+    "        self.add(\"inspector\")\n",
+    "\n",
+    "    def add_ee_data(self):\n",
+    "        # Add Earth Engine dataset\n",
+    "        dem = ee.Image(\"USGS/SRTMGL1_003\")\n",
+    "        landsat7 = ee.Image(\"LANDSAT/LE7_TOA_5YEAR/1999_2003\").select(\n",
+    "            [\"B1\", \"B2\", \"B3\", \"B4\", \"B5\", \"B7\"]\n",
+    "        )\n",
+    "        states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
+    "\n",
+    "        # Set visualization parameters.\n",
+    "        vis_params = {\n",
+    "            \"min\": 0,\n",
+    "            \"max\": 4000,\n",
+    "            \"palette\": [\"006633\", \"E5FFCC\", \"662A00\", \"D8D8D8\", \"F5F5F5\"],\n",
+    "        }\n",
+    "\n",
+    "        # Add Earth Engine layers to Map\n",
+    "        self.addLayer(\n",
+    "            landsat7,\n",
+    "            {\"bands\": [\"B4\", \"B3\", \"B2\"], \"min\": 20, \"max\": 200, \"gamma\": 2.0},\n",
+    "            \"Landsat 7\",\n",
+    "            True,\n",
+    "        )\n",
+    "        self.addLayer(dem, vis_params, \"SRTM DEM\", True, 1)\n",
+    "        self.addLayer(states, {}, \"US States\")\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
+    "        Map.element(\n",
+    "            center=[40, -100],\n",
+    "            zoom=4,\n",
+    "            height=\"600px\",\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/03_plotting.ipynb

@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/03_plotting.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import geemap\n",
+    "import solara\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_ee_data()\n",
+    "        self.add_plot_gui()\n",
+    "\n",
+    "    def add_ee_data(self):\n",
+    "        landsat7 = ee.Image(\"LANDSAT/LE7_TOA_5YEAR/1999_2003\").select(\n",
+    "            [\"B1\", \"B2\", \"B3\", \"B4\", \"B5\", \"B7\"]\n",
+    "        )\n",
+    "\n",
+    "        landsat_vis = {\"bands\": [\"B4\", \"B3\", \"B2\"], \"gamma\": 1.4}\n",
+    "        self.addLayer(landsat7, landsat_vis, \"Landsat\")\n",
+    "\n",
+    "        hyperion = ee.ImageCollection(\"EO1/HYPERION\").filter(\n",
+    "            ee.Filter.date(\"2016-01-01\", \"2017-03-01\")\n",
+    "        )\n",
+    "\n",
+    "        hyperion_vis = {\n",
+    "            \"min\": 1000.0,\n",
+    "            \"max\": 14000.0,\n",
+    "            \"gamma\": 2.5,\n",
+    "        }\n",
+    "        self.addLayer(hyperion, hyperion_vis, \"Hyperion\")\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
+    "        Map.element(\n",
+    "            center=[40, -100],\n",
+    "            zoom=4,\n",
+    "            height=\"600px\",\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/04_split_map.ipynb

@@ -0,0 +1,115 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/04_split_map.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import geemap\n",
+    "import solara\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_ee_data()\n",
+    "\n",
+    "    def add_ee_data(self):\n",
+    "        # Select the eight NLCD epochs after 2000.\n",
+    "        years = [\"2001\", \"2004\", \"2006\", \"2008\", \"2011\", \"2013\", \"2016\", \"2019\"]\n",
+    "\n",
+    "        # Get an NLCD image by year.\n",
+    "        def getNLCD(year):\n",
+    "            # Import the NLCD collection.\n",
+    "            dataset = ee.ImageCollection(\"USGS/NLCD_RELEASES/2019_REL/NLCD\")\n",
+    "\n",
+    "            # Filter the collection by year.\n",
+    "            nlcd = dataset.filter(ee.Filter.eq(\"system:index\", year)).first()\n",
+    "\n",
+    "            # Select the land cover band.\n",
+    "            landcover = nlcd.select(\"landcover\")\n",
+    "            return landcover\n",
+    "\n",
+    "        ## Create an NLCD image collection for the selected years.\n",
+    "        collection = ee.ImageCollection(ee.List(years).map(lambda year: getNLCD(year)))\n",
+    "\n",
+    "        # Create a list of labels to populate the dropdown list.\n",
+    "        labels = [f\"NLCD {year}\" for year in years]\n",
+    "\n",
+    "        # Add a split-panel map for visualizing NLCD land cover change.\n",
+    "        self.ts_inspector(\n",
+    "            left_ts=collection,\n",
+    "            right_ts=collection,\n",
+    "            left_names=labels,\n",
+    "            right_names=labels,\n",
+    "        )\n",
+    "\n",
+    "        # Add the NLCD legend to the map.\n",
+    "        self.add_legend(\n",
+    "            title=\"NLCD Land Cover Type\",\n",
+    "            builtin_legend=\"NLCD\",\n",
+    "            height=\"460px\",\n",
+    "            add_header=False,\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
+    "        Map.element(\n",
+    "            center=[40, -100],\n",
+    "            zoom=4,\n",
+    "            height=\"600px\",\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/05_timelapse.ipynb

@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/05_timelapse.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geemap\n",
+    "import solara\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_basemap(\"Esri.WorldImagery\")\n",
+    "        self.add_gui(\"timelapse\", basemap=None)\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
+    "        Map.element(\n",
+    "            center=[20, -0],\n",
+    "            zoom=2,\n",
+    "            height=\"750px\",\n",
+    "            zoom_ctrl=False,\n",
+    "            measure_ctrl=False,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/06_timeseries.ipynb

@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/06_timeseries.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geemap\n",
+    "import ipywidgets as widgets\n",
+    "import solara\n",
+    "from geemap import get_current_year, jslink_slider_label\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_basemap(\"Esri.WorldImagery\")\n",
+    "        self.add_ts_gui(position=\"topright\")\n",
+    "\n",
+    "    def clean_up(self):\n",
+    "        if hasattr(self, \"slider_ctrl\") and self.slider_ctrl is not None:\n",
+    "            self.remove(self.slider_ctrl)\n",
+    "            delattr(self, \"slider_ctrl\")\n",
+    "\n",
+    "        layer = self.find_layer(\"Time series\")\n",
+    "        if layer is not None:\n",
+    "            self.remove(layer)\n",
+    "        layer = self.find_layer(\"Image X\")\n",
+    "        if layer is not None:\n",
+    "            self.remove(layer)\n",
+    "\n",
+    "        draw_layer = self.find_layer(\"Drawn Features\")\n",
+    "        if draw_layer is not None:\n",
+    "            self.remove(draw_layer)\n",
+    "\n",
+    "    def add_ts_gui(self, position=\"topright\", **kwargs):\n",
+    "\n",
+    "        widget_width = \"350px\"\n",
+    "        padding = \"0px 0px 0px 5px\"  # upper, right, bottom, left\n",
+    "        style = {\"description_width\": \"initial\"}\n",
+    "        current_year = get_current_year()\n",
+    "\n",
+    "        collection = widgets.Dropdown(\n",
+    "            options=[\n",
+    "                \"Landsat TM-ETM-OLI Surface Reflectance\",\n",
+    "            ],\n",
+    "            value=\"Landsat TM-ETM-OLI Surface Reflectance\",\n",
+    "            description=\"Collection:\",\n",
+    "            layout=widgets.Layout(width=widget_width, padding=padding),\n",
+    "            style=style,\n",
+    "        )\n",
+    "        bands = widgets.Dropdown(\n",
+    "            description=\"Bands:\",\n",
+    "            options=[\n",
+    "                \"Red/Green/Blue\",\n",
+    "                \"NIR/Red/Green\",\n",
+    "                \"SWIR2/SWIR1/NIR\",\n",
+    "                \"NIR/SWIR1/Red\",\n",
+    "                \"SWIR2/NIR/Red\",\n",
+    "                \"SWIR2/SWIR1/Red\",\n",
+    "                \"SWIR1/NIR/Blue\",\n",
+    "                \"NIR/SWIR1/Blue\",\n",
+    "                \"SWIR2/NIR/Green\",\n",
+    "                \"SWIR1/NIR/Red\",\n",
+    "            ],\n",
+    "            value=\"SWIR1/NIR/Red\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(width=\"195px\", padding=padding),\n",
+    "        )\n",
+    "\n",
+    "        frequency = widgets.Dropdown(\n",
+    "            description=\"Frequency:\",\n",
+    "            options=[\"year\", \"quarter\", \"month\"],\n",
+    "            value=\"year\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(width=\"150px\", padding=padding),\n",
+    "        )\n",
+    "\n",
+    "        start_year = widgets.IntSlider(\n",
+    "            description=\"Start Year:\",\n",
+    "            value=1984,\n",
+    "            min=1984,\n",
+    "            max=current_year,\n",
+    "            readout=False,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(width=\"138px\", padding=padding),\n",
+    "        )\n",
+    "\n",
+    "        start_year_label = widgets.Label(\"1984\")\n",
+    "        jslink_slider_label(start_year, start_year_label)\n",
+    "\n",
+    "        end_year = widgets.IntSlider(\n",
+    "            description=\"End Year:\",\n",
+    "            value=current_year,\n",
+    "            min=1984,\n",
+    "            max=current_year,\n",
+    "            readout=False,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(width=\"138px\", padding=padding),\n",
+    "        )\n",
+    "        end_year_label = widgets.Label(str(current_year))\n",
+    "        jslink_slider_label(end_year, end_year_label)\n",
+    "\n",
+    "        start_month = widgets.IntSlider(\n",
+    "            description=\"Start Month:\",\n",
+    "            value=5,\n",
+    "            min=1,\n",
+    "            max=12,\n",
+    "            readout=False,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(width=\"145px\", padding=padding),\n",
+    "        )\n",
+    "\n",
+    "        start_month_label = widgets.Label(\n",
+    "            \"5\",\n",
+    "            layout=widgets.Layout(width=\"20px\", padding=padding),\n",
+    "        )\n",
+    "        jslink_slider_label(start_month, start_month_label)\n",
+    "\n",
+    "        end_month = widgets.IntSlider(\n",
+    "            description=\"End Month:\",\n",
+    "            value=10,\n",
+    "            min=1,\n",
+    "            max=12,\n",
+    "            readout=False,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(width=\"155px\", padding=padding),\n",
+    "        )\n",
+    "\n",
+    "        end_month_label = widgets.Label(\"10\")\n",
+    "        jslink_slider_label(end_month, end_month_label)\n",
+    "\n",
+    "        output = widgets.Output()\n",
+    "\n",
+    "        button_width = \"113px\"\n",
+    "        apply_btn = widgets.Button(\n",
+    "            description=\"Time slider\",\n",
+    "            button_style=\"primary\",\n",
+    "            tooltip=\"Click to create timeseries\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=\"0px\", width=button_width),\n",
+    "        )\n",
+    "\n",
+    "        split_btn = widgets.Button(\n",
+    "            description=\"Split map\",\n",
+    "            button_style=\"primary\",\n",
+    "            tooltip=\"Click to create timeseries\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=\"0px\", width=button_width),\n",
+    "        )\n",
+    "\n",
+    "        reset_btn = widgets.Button(\n",
+    "            description=\"Reset\",\n",
+    "            button_style=\"primary\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=\"0px\", width=button_width),\n",
+    "        )\n",
+    "\n",
+    "        vbox = widgets.VBox(\n",
+    "            [\n",
+    "                collection,\n",
+    "                widgets.HBox([bands, frequency]),\n",
+    "                widgets.HBox([start_year, start_year_label, end_year, end_year_label]),\n",
+    "                widgets.HBox(\n",
+    "                    [start_month, start_month_label, end_month, end_month_label]\n",
+    "                ),\n",
+    "                widgets.HBox([apply_btn, split_btn, reset_btn]),\n",
+    "                output,\n",
+    "            ]\n",
+    "        )\n",
+    "        self.add_widget(vbox, position=position, add_header=True)\n",
+    "\n",
+    "        def apply_btn_click(change):\n",
+    "\n",
+    "            if hasattr(self, \"slider_ctrl\") and self.slider_ctrl is not None:\n",
+    "                self.remove(self.slider_ctrl)\n",
+    "                delattr(self, \"slider_ctrl\")\n",
+    "\n",
+    "            with output:\n",
+    "                output.clear_output()\n",
+    "                if self.user_roi is None:\n",
+    "                    output.append_stdout(\"Please draw a ROI first.\")\n",
+    "                else:\n",
+    "                    output.append_stdout(\"Creating time series...\")\n",
+    "                    collection = geemap.landsat_timeseries(\n",
+    "                        roi=self.user_roi,\n",
+    "                        start_year=start_year.value,\n",
+    "                        end_year=end_year.value,\n",
+    "                        start_date=str(start_month.value).zfill(2) + \"-01\",\n",
+    "                        end_date=str(end_month.value).zfill(2) + \"-01\",\n",
+    "                        frequency=frequency.value,\n",
+    "                    )\n",
+    "                    vis_params = {\n",
+    "                        \"bands\": bands.value.split(\"/\"),\n",
+    "                        \"min\": 0,\n",
+    "                        \"max\": 0.4,\n",
+    "                    }\n",
+    "\n",
+    "                    if frequency.value == \"year\":\n",
+    "                        date_format = \"YYYY\"\n",
+    "                    elif frequency.value == \"quarter\":\n",
+    "                        date_format = \"YYYY-MM\"\n",
+    "                    elif frequency.value == \"month\":\n",
+    "                        date_format = \"YYYY-MM\"\n",
+    "\n",
+    "                    self.add_time_slider(\n",
+    "                        collection,\n",
+    "                        region=self.user_roi,\n",
+    "                        vis_params=vis_params,\n",
+    "                        date_format=date_format,\n",
+    "                    )\n",
+    "                    self._draw_control.clear()\n",
+    "                    draw_layer = self.find_layer(\"Drawn Features\")\n",
+    "                    if draw_layer is not None:\n",
+    "                        self.remove(draw_layer)\n",
+    "                    output.clear_output()\n",
+    "\n",
+    "        apply_btn.on_click(apply_btn_click)\n",
+    "\n",
+    "        def split_btn_click(change):\n",
+    "\n",
+    "            if hasattr(self, \"slider_ctrl\") and self.slider_ctrl is not None:\n",
+    "                self.remove(self.slider_ctrl)\n",
+    "                delattr(self, \"slider_ctrl\")\n",
+    "\n",
+    "            with output:\n",
+    "                output.clear_output()\n",
+    "                if self.user_roi is None:\n",
+    "                    output.append_stdout(\"Please draw a ROI first.\")\n",
+    "                else:\n",
+    "                    output.append_stdout(\"Creating time series...\")\n",
+    "                    collection = geemap.landsat_timeseries(\n",
+    "                        roi=self.user_roi,\n",
+    "                        start_year=start_year.value,\n",
+    "                        end_year=end_year.value,\n",
+    "                        start_date=str(start_month.value).zfill(2) + \"-01\",\n",
+    "                        end_date=str(end_month.value).zfill(2) + \"-01\",\n",
+    "                        frequency=frequency.value,\n",
+    "                    )\n",
+    "                    vis_params = {\n",
+    "                        \"bands\": bands.value.split(\"/\"),\n",
+    "                        \"min\": 0,\n",
+    "                        \"max\": 0.4,\n",
+    "                    }\n",
+    "\n",
+    "                    if frequency.value == \"year\":\n",
+    "                        date_format = \"YYYY\"\n",
+    "                        dates = geemap.image_dates(collection, date_format).getInfo()\n",
+    "                    elif frequency.value == \"quarter\":\n",
+    "                        date_format = \"YYYY-MM\"\n",
+    "                        dates = geemap.image_dates(collection, date_format).getInfo()\n",
+    "                    elif frequency.value == \"month\":\n",
+    "                        date_format = \"YYYY-MM\"\n",
+    "                        dates = geemap.image_dates(collection, date_format).getInfo()\n",
+    "\n",
+    "                    self.ts_inspector(\n",
+    "                        collection,\n",
+    "                        left_names=dates,\n",
+    "                        left_vis=vis_params,\n",
+    "                        add_close_button=True,\n",
+    "                    )\n",
+    "                    output.clear_output()\n",
+    "\n",
+    "                    try:\n",
+    "                        self._draw_control.clear()\n",
+    "                        draw_layer = self.find_layer(\"Drawn Features\")\n",
+    "                        if draw_layer is not None:\n",
+    "                            self.remove(draw_layer)\n",
+    "                    except Exception as e:\n",
+    "                        print(e)\n",
+    "\n",
+    "        split_btn.on_click(split_btn_click)\n",
+    "\n",
+    "        def reset_btn_click(change):\n",
+    "            output.clear_output()\n",
+    "            self.clean_up()\n",
+    "\n",
+    "        reset_btn.on_click(reset_btn_click)\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\"}):\n",
+    "        Map.element(\n",
+    "            center=[20, -0],\n",
+    "            zoom=2,\n",
+    "            height=\"750px\",\n",
+    "            zoom_ctrl=False,\n",
+    "            measure_ctrl=False,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/07_jrc.ipynb

@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/07_jrc.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import geemap\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import display\n",
+    "import solara\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_basemap(\"Esri.WorldImagery\")\n",
+    "        self.add_ee_data()\n",
+    "        self.add_buttons(add_header=True)\n",
+    "\n",
+    "    def add_ee_data(self):\n",
+    "\n",
+    "        dataset = ee.Image(\"JRC/GSW1_4/GlobalSurfaceWater\")\n",
+    "        image = dataset.select([\"occurrence\"])\n",
+    "        vis_params = {\n",
+    "            \"min\": 0.0,\n",
+    "            \"max\": 100.0,\n",
+    "            \"palette\": [\"ffffff\", \"ffbbbb\", \"0000ff\"],\n",
+    "        }\n",
+    "        self.addLayer(image, vis_params, \"Occurrence\")\n",
+    "        self.add_colorbar(\n",
+    "            vis_params, label=\"Water occurrence (%)\", layer_name=\"Occurrence\"\n",
+    "        )\n",
+    "\n",
+    "    def add_buttons(self, position=\"topright\", **kwargs):\n",
+    "        padding = \"0px 5px 0px 5px\"\n",
+    "        widget = widgets.VBox(layout=widgets.Layout(padding=padding))\n",
+    "        layout = widgets.Layout(width=\"auto\")\n",
+    "        style = {\"description_width\": \"initial\"}\n",
+    "        hist_btn = widgets.Button(description=\"Occurrence\", layout=layout)\n",
+    "        bar_btn = widgets.Button(description=\"Monthly history\", layout=layout)\n",
+    "        reset_btn = widgets.Button(description=\"Reset\", layout=layout)\n",
+    "        scale = widgets.IntSlider(\n",
+    "            min=30, max=1000, value=90, description=\"Scale\", layout=layout, style=style\n",
+    "        )\n",
+    "        month_slider = widgets.IntRangeSlider(\n",
+    "            description=\"Months\",\n",
+    "            value=[5, 10],\n",
+    "            min=1,\n",
+    "            max=12,\n",
+    "            step=1,\n",
+    "            layout=layout,\n",
+    "            style=style,\n",
+    "        )\n",
+    "        widget.children = [\n",
+    "            widgets.HBox([hist_btn, bar_btn, reset_btn]),\n",
+    "            month_slider,\n",
+    "            scale,\n",
+    "        ]\n",
+    "        self.add_widget(widget, position=position, **kwargs)\n",
+    "        output = widgets.Output()\n",
+    "        self.add_widget(output, position=\"bottomleft\", add_header=False)\n",
+    "\n",
+    "        def hist_btn_click(b):\n",
+    "            region = self.user_roi\n",
+    "            if region is not None:\n",
+    "                output.clear_output()\n",
+    "                output.append_stdout(\"Computing histogram...\")\n",
+    "                image = ee.Image(\"JRC/GSW1_4/GlobalSurfaceWater\").select([\"occurrence\"])\n",
+    "                self.default_style = {\"cursor\": \"wait\"}\n",
+    "                hist = geemap.image_histogram(\n",
+    "                    image,\n",
+    "                    region,\n",
+    "                    scale=scale.value,\n",
+    "                    height=350,\n",
+    "                    width=550,\n",
+    "                    x_label=\"Water Occurrence (%)\",\n",
+    "                    y_label=\"Pixel Count\",\n",
+    "                    layout_args={\n",
+    "                        \"title\": dict(x=0.5),\n",
+    "                        \"margin\": dict(l=0, r=0, t=10, b=0),\n",
+    "                    },\n",
+    "                    return_df=False,\n",
+    "                )\n",
+    "\n",
+    "                with output:\n",
+    "                    output.clear_output()\n",
+    "                    display(hist)\n",
+    "                self.default_style = {\"cursor\": \"default\"}\n",
+    "            else:\n",
+    "                output.clear_output()\n",
+    "                with output:\n",
+    "                    output.append_stdout(\"Please draw a region of interest first.\")\n",
+    "\n",
+    "        hist_btn.on_click(hist_btn_click)\n",
+    "\n",
+    "        def bar_btn_click(b):\n",
+    "            region = self.user_roi\n",
+    "            if region is not None:\n",
+    "                self.default_style = {\"cursor\": \"wait\"}\n",
+    "                output.clear_output()\n",
+    "                output.append_stdout(\"Computing monthly history...\")\n",
+    "                bar = geemap.jrc_hist_monthly_history(\n",
+    "                    region=region,\n",
+    "                    scale=scale.value,\n",
+    "                    height=350,\n",
+    "                    width=550,\n",
+    "                    layout_args={\n",
+    "                        \"title\": dict(x=0.5),\n",
+    "                        \"margin\": dict(l=0, r=0, t=10, b=0),\n",
+    "                    },\n",
+    "                    frequency=\"month\",\n",
+    "                    start_month=month_slider.value[0],\n",
+    "                    end_month=month_slider.value[1],\n",
+    "                    denominator=1e4,\n",
+    "                    y_label=\"Area (ha)\",\n",
+    "                )\n",
+    "\n",
+    "                with output:\n",
+    "                    output.clear_output()\n",
+    "                    display(bar)\n",
+    "                self.default_style = {\"cursor\": \"default\"}\n",
+    "            else:\n",
+    "                output.clear_output()\n",
+    "                with output:\n",
+    "                    output.append_stdout(\"Please draw a region of interest first.\")\n",
+    "\n",
+    "        bar_btn.on_click(bar_btn_click)\n",
+    "\n",
+    "        def reset_btn_click(b):\n",
+    "            self._draw_control.clear()\n",
+    "            output.clear_output()\n",
+    "\n",
+    "        reset_btn.on_click(reset_btn_click)\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\"}):\n",
+    "        Map.element(\n",
+    "            center=[20, -0],\n",
+    "            zoom=2,\n",
+    "            height=\"750px\",\n",
+    "            zoom_ctrl=False,\n",
+    "            measure_ctrl=False,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/08_compare.ipynb

@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/08_compare.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U geemap solara"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import geemap\n",
+    "import ipywidgets as widgets\n",
+    "import solara\n",
+    "from datetime import date\n",
+    "\n",
+    "\n",
+    "class Map(geemap.Map):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.add_basemap(\"Esri.WorldImagery\")\n",
+    "        self.add_gui_widget(add_header=True)\n",
+    "\n",
+    "    def clean_up(self):\n",
+    "\n",
+    "        layers = [\n",
+    "            \"Pre-event Image\",\n",
+    "            \"Post-event Image\",\n",
+    "            \"Pre-event NDWI\",\n",
+    "            \"Post-event NDWI\",\n",
+    "            \"Pre-event Water\",\n",
+    "            \"Post-event Water\",\n",
+    "            \"Disappeared Water\",\n",
+    "            \"New Water\",\n",
+    "        ]\n",
+    "        for layer_name in layers:\n",
+    "            layer = self.find_layer(layer_name)\n",
+    "            if layer is not None:\n",
+    "                self.remove(layer)\n",
+    "\n",
+    "    def add_gui_widget(self, position=\"topright\", **kwargs):\n",
+    "\n",
+    "        widget = widgets.VBox(layout=widgets.Layout(padding=\"0px 5px 0px 5px\"))\n",
+    "        pre_widget = widgets.HBox()\n",
+    "        post_widget = widgets.HBox()\n",
+    "        layout = widgets.Layout(width=\"auto\")\n",
+    "        style = {\"description_width\": \"initial\"}\n",
+    "        padding = \"0px 5px 0px 5px\"\n",
+    "        pre_start_date = widgets.DatePicker(\n",
+    "            description=\"Start\",\n",
+    "            value=date(2014, 1, 1),\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
+    "        )\n",
+    "        pre_end_date = widgets.DatePicker(\n",
+    "            description=\"End\",\n",
+    "            value=date(2014, 12, 31),\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
+    "        )\n",
+    "        pre_cloud_cover = widgets.IntSlider(\n",
+    "            description=\"Cloud\",\n",
+    "            min=0,\n",
+    "            max=100,\n",
+    "            value=25,\n",
+    "            step=1,\n",
+    "            readout=False,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"130px\"),\n",
+    "        )\n",
+    "        pre_cloud_label = widgets.Label(value=str(pre_cloud_cover.value))\n",
+    "        geemap.jslink_slider_label(pre_cloud_cover, pre_cloud_label)\n",
+    "        pre_widget.children = [\n",
+    "            pre_start_date,\n",
+    "            pre_end_date,\n",
+    "            pre_cloud_cover,\n",
+    "            pre_cloud_label,\n",
+    "        ]\n",
+    "        post_start_date = widgets.DatePicker(\n",
+    "            description=\"Start\",\n",
+    "            value=date(2024, 1, 1),\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
+    "        )\n",
+    "        post_end_date = widgets.DatePicker(\n",
+    "            description=\"End\",\n",
+    "            value=date(2024, 12, 31),\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
+    "        )\n",
+    "        post_cloud_cover = widgets.IntSlider(\n",
+    "            description=\"Cloud\",\n",
+    "            min=0,\n",
+    "            max=100,\n",
+    "            value=30,\n",
+    "            step=1,\n",
+    "            readout=False,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"130px\"),\n",
+    "        )\n",
+    "        post_cloud_label = widgets.Label(value=str(post_cloud_cover.value))\n",
+    "        geemap.jslink_slider_label(post_cloud_cover, post_cloud_label)\n",
+    "        post_widget.children = [\n",
+    "            post_start_date,\n",
+    "            post_end_date,\n",
+    "            post_cloud_cover,\n",
+    "            post_cloud_label,\n",
+    "        ]\n",
+    "\n",
+    "        apply_btn = widgets.Button(description=\"Apply\", layout=layout)\n",
+    "        reset_btn = widgets.Button(description=\"Reset\", layout=layout)\n",
+    "        buttons = widgets.HBox([apply_btn, reset_btn])\n",
+    "        output = widgets.Output()\n",
+    "\n",
+    "        use_split = widgets.Checkbox(\n",
+    "            value=False,\n",
+    "            description=\"Split map\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"100px\"),\n",
+    "        )\n",
+    "\n",
+    "        use_ndwi = widgets.Checkbox(\n",
+    "            value=False,\n",
+    "            description=\"Compute NDWI\",\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
+    "        )\n",
+    "\n",
+    "        ndwi_threhold = widgets.FloatSlider(\n",
+    "            description=\"Threshold\",\n",
+    "            min=-1,\n",
+    "            max=1,\n",
+    "            value=0,\n",
+    "            step=0.05,\n",
+    "            readout=True,\n",
+    "            style=style,\n",
+    "            layout=widgets.Layout(padding=padding, width=\"230px\"),\n",
+    "        )\n",
+    "\n",
+    "        options = widgets.HBox(\n",
+    "            [\n",
+    "                use_split,\n",
+    "                use_ndwi,\n",
+    "                ndwi_threhold,\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
+    "        widget.children = [pre_widget, post_widget, options, buttons, output]\n",
+    "        self.add_widget(widget, position=position, **kwargs)\n",
+    "\n",
+    "        def apply_btn_click(b):\n",
+    "\n",
+    "            marker_layer = self.find_layer(\"Search location\")\n",
+    "            if marker_layer is not None:\n",
+    "                self.remove(marker_layer)\n",
+    "            self.clean_up()\n",
+    "\n",
+    "            if self.user_roi is None:\n",
+    "                output.clear_output()\n",
+    "                output.append_stdout(\"Please draw a ROI first.\")\n",
+    "            elif (\n",
+    "                pre_start_date.value is None\n",
+    "                or pre_end_date.value is None\n",
+    "                or post_start_date.value is None\n",
+    "                or post_end_date.value is None\n",
+    "            ):\n",
+    "                output.clear_output()\n",
+    "                output.append_stdout(\"Please select start and end dates.\")\n",
+    "\n",
+    "            elif self.user_roi is not None:\n",
+    "                output.clear_output()\n",
+    "                output.append_stdout(\"Computing... Please wait.\")\n",
+    "                roi = ee.FeatureCollection(self.user_roi)\n",
+    "                vis_params = {\"bands\": [\"B6\", \"B5\", \"B4\"], \"min\": 0, \"max\": 0.4}\n",
+    "                if pre_start_date.value.strftime(\"%Y-%m-%d\") < \"2013-04-11\":\n",
+    "                    pre_col = geemap.landsat_timeseries(\n",
+    "                        roi,\n",
+    "                        start_year=pre_start_date.value.year,\n",
+    "                        end_year=pre_end_date.value.year,\n",
+    "                    ).select([\"SWIR1\", \"NIR\", \"Red\", \"Green\"], [\"B6\", \"B5\", \"B4\", \"B3\"])\n",
+    "                else:\n",
+    "                    pre_col = (\n",
+    "                        ee.ImageCollection(\"NASA/HLS/HLSL30/v002\")\n",
+    "                        .filterBounds(roi)\n",
+    "                        .filterDate(\n",
+    "                            pre_start_date.value.strftime(\"%Y-%m-%d\"),\n",
+    "                            pre_end_date.value.strftime(\"%Y-%m-%d\"),\n",
+    "                        )\n",
+    "                        .filter(ee.Filter.lt(\"CLOUD_COVERAGE\", pre_cloud_cover.value))\n",
+    "                    )\n",
+    "\n",
+    "                if post_start_date.value.strftime(\"%Y-%m-%d\") < \"2013-04-11\":\n",
+    "                    post_col = geemap.landsat_timeseries(\n",
+    "                        roi,\n",
+    "                        start_year=post_start_date.value.year,\n",
+    "                        end_year=post_end_date.value.year,\n",
+    "                    ).select([\"SWIR1\", \"NIR\", \"Red\", \"Green\"], [\"B6\", \"B5\", \"B4\", \"B3\"])\n",
+    "                else:\n",
+    "                    post_col = (\n",
+    "                        ee.ImageCollection(\"NASA/HLS/HLSL30/v002\")\n",
+    "                        .filterBounds(roi)\n",
+    "                        .filterDate(\n",
+    "                            post_start_date.value.strftime(\"%Y-%m-%d\"),\n",
+    "                            post_end_date.value.strftime(\"%Y-%m-%d\"),\n",
+    "                        )\n",
+    "                        .filter(ee.Filter.lt(\"CLOUD_COVERAGE\", post_cloud_cover.value))\n",
+    "                    )\n",
+    "\n",
+    "                pre_img = pre_col.median().clip(roi)\n",
+    "                post_img = post_col.median().clip(roi)\n",
+    "\n",
+    "                if use_split.value:\n",
+    "                    left_layer = geemap.ee_tile_layer(\n",
+    "                        pre_img, vis_params, \"Pre-event Image\"\n",
+    "                    )\n",
+    "                    right_layer = geemap.ee_tile_layer(\n",
+    "                        post_img, vis_params, \"Post-event Image\"\n",
+    "                    )\n",
+    "                    self.split_map(\n",
+    "                        left_layer,\n",
+    "                        right_layer,\n",
+    "                        add_close_button=True,\n",
+    "                        left_label=\"Pre-event\",\n",
+    "                        right_label=\"Post-event\",\n",
+    "                    )\n",
+    "                else:\n",
+    "                    pre_img = pre_col.median().clip(roi)\n",
+    "                    post_img = post_col.median().clip(roi)\n",
+    "                    self.add_layer(pre_img, vis_params, \"Pre-event Image\")\n",
+    "                    self.add_layer(post_img, vis_params, \"Post-event Image\")\n",
+    "\n",
+    "                if use_ndwi.value and (not use_split.value):\n",
+    "                    pre_ndwi = pre_img.normalizedDifference([\"B3\", \"B6\"]).rename(\"NDWI\")\n",
+    "                    post_ndwi = post_img.normalizedDifference([\"B3\", \"B6\"]).rename(\n",
+    "                        \"NDWI\"\n",
+    "                    )\n",
+    "                    ndwi_vis = {\"min\": -1, \"max\": 1, \"palette\": \"ndwi\"}\n",
+    "                    self.add_layer(pre_ndwi, ndwi_vis, \"Pre-event NDWI\", False)\n",
+    "                    self.add_layer(post_ndwi, ndwi_vis, \"Post-event NDWI\", False)\n",
+    "\n",
+    "                    pre_water = pre_ndwi.gt(ndwi_threhold.value)\n",
+    "                    post_water = post_ndwi.gt(ndwi_threhold.value)\n",
+    "                    self.add_layer(\n",
+    "                        pre_water.selfMask(), {\"palette\": \"blue\"}, \"Pre-event Water\"\n",
+    "                    )\n",
+    "                    self.add_layer(\n",
+    "                        post_water.selfMask(), {\"palette\": \"red\"}, \"Post-event Water\"\n",
+    "                    )\n",
+    "                    new_water = post_water.subtract(pre_water).gt(0)\n",
+    "                    disappear_water = pre_water.subtract(post_water).gt(0)\n",
+    "                    self.add_layer(\n",
+    "                        disappear_water.selfMask(),\n",
+    "                        {\"palette\": \"brown\"},\n",
+    "                        \"Disappeared Water\",\n",
+    "                    )\n",
+    "                    self.add_layer(\n",
+    "                        new_water.selfMask(), {\"palette\": \"cyan\"}, \"New Water\"\n",
+    "                    )\n",
+    "\n",
+    "                    with output:\n",
+    "                        output.clear_output()\n",
+    "\n",
+    "                output.clear_output()\n",
+    "\n",
+    "        apply_btn.on_click(apply_btn_click)\n",
+    "\n",
+    "        def reset_btn_click(b):\n",
+    "            self.clean_up()\n",
+    "            self._draw_control.clear()\n",
+    "            draw_layer = self.find_layer(\"Drawn Features\")\n",
+    "            if draw_layer is not None:\n",
+    "                self.remove(draw_layer)\n",
+    "            output.clear_output()\n",
+    "\n",
+    "        reset_btn.on_click(reset_btn_click)\n",
+    "\n",
+    "\n",
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column(style={\"min-width\": \"500px\"}):\n",
+    "        Map.element(\n",
+    "            center=[20, -0],\n",
+    "            zoom=2,\n",
+    "            height=\"750px\",\n",
+    "            zoom_ctrl=False,\n",
+    "            measure_ctrl=False,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Page()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/09_teste.ipynb

@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import geemap\n",
+    "import ee\n",
+    "import plotly.express as px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "geemap.ee_initialize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "esa = ee.ImageCollection('ESA/WorldCover/v200').first()\n",
+    "\n",
+    "ucs = ee.FeatureCollection(\"projects/ee-curso-gee-rhamon/assets/ucs_estaduais_rj\")\n",
+    "\n",
+    "       \n",
+    "esa = esa.clipToCollection(ucs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computing ... \n",
+      "Generating URL ...\n",
+      "Downloading data from https://earthengine.googleapis.com/v1/projects/346107197273/tables/60f4e2523a74ad9da945ec21ae745a37-75ffe80c0d13be48ca8789157bb6c245:getFeatures\n",
+      "Please wait ...\n",
+      "Data downloaded to c:\\Users\\Rhamon\\Desktop\\Solara\\solara-geemap\\notebooks\\esa_stats_ucs_rj.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "esa_stats = 'esa_stats_ucs_rj.csv'\n",
+    "\n",
+    "geemap.zonal_stats_by_group(\n",
+    "    esa,\n",
+    "    ucs,\n",
+    "    esa_stats,\n",
+    "    statistics_type='PERCENTAGE',\n",
+    "    denominator=1e6,\n",
+    "    decimal_places=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:1: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:1: SyntaxWarning: invalid escape sequence '\\e'\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Rhamon\\AppData\\Local\\Temp\\ipykernel_21848\\2857369937.py:1: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  df = 'CSVs\\esa_stats_ucs_rj.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = 'CSVs\\esa_stats_ucs_rj.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "DataFrame constructor not properly called!",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_21848\\385931256.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Class_10'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nome\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Cobertura Florestal UCs Estaduais RJ'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\plotly\\express\\_chart_types.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(data_frame, names, values, color, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, color_discrete_sequence, color_discrete_map, hover_name, hover_data, custom_data, category_orders, labels, title, template, width, height, opacity, hole)\u001b[0m\n\u001b[0;32m   1478\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcolor_discrete_sequence\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1479\u001b[0m         \u001b[0mlayout_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"piecolorway\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcolor_discrete_sequence\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1480\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1481\u001b[0m         \u001b[0mlayout_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1482\u001b[1;33m     return make_figure(\n\u001b[0m\u001b[0;32m   1483\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1484\u001b[0m         \u001b[0mconstructor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPie\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1485\u001b[0m         \u001b[0mtrace_patch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshowlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhole\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhole\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\plotly\\express\\_core.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[0;32m   2086\u001b[0m     \u001b[0mtrace_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrace_patch\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2087\u001b[0m     \u001b[0mlayout_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlayout_patch\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2088\u001b[0m     \u001b[0mapply_default_cascade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2089\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2090\u001b[1;33m     \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuild_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2091\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mconstructor\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTreemap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSunburst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIcicle\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"path\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2092\u001b[0m         \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess_dataframe_hierarchy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2093\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mconstructor\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPie\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\plotly\\express\\_core.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(args, constructor)\u001b[0m\n\u001b[0;32m   1344\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"to_pandas_df\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1345\u001b[0m             \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_pandas_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1346\u001b[0m             \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m             \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1349\u001b[0m             \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mdf_provided\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1351\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    882\u001b[0m                 \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    883\u001b[0m         \u001b[1;31m# For data is scalar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    885\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 886\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"DataFrame constructor not properly called!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    889\u001b[0m             \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: DataFrame constructor not properly called!"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/esa_stats_ucs_rj2.csv

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+0,0.0,0.02,0,2.12,0.02,78.25,80.41,0,0,11.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA PEDRA SELADA,8041.49819897,2012,1.66658109283,SIM,0.00704996872,SIM,18,PE DA PEDRA SELADA,26,1:25.000,00000000000000000019
+0.0,0.1,0,0,10.83,0,113.97,124.91999999999999,0,0.02,10.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA PEDRA BRANCA,12494.2417207,1974,1.8845293254,SIM,0.01100042314,SIM,10,PE DA PEDRA BRANCA,27,Sem Info,0000000000000000001a
+0,0.02,0,0,0.02,0,0.51,0.55,0,0,15.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DO GRAJAU,54.7290261485,1978,0.05699572081,SIM,4.817604E-5,SIM,14,PE DO GRAJAU,30,Sem Info,0000000000000000001d
+0,0,0,0,8.76,0,204.47,213.23,0,0,14.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DO DESENGANO,21325.7388368,1970,1.75405439943,SIM,0.01863237605,SIM,16,PE DO DESENGANO,31,Sem info,0000000000000000001e
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+4.26,1.4,0.03,0,22.11,0.04,44.33,98.27000000000001,14.65,11.45,7.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA COSTA DO SOL,9828.61554842,2011,3.29315109101,SIM,0.0086502762,SIM,17,PE DA COSTA DO SOL,33,1:25.000,00000000000000000020
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+0,0.0,0.51,0,10.26,0,16.53,32.58,4.81,0.47,1.0,PROTECAO INTEGRAL,EEE,ESTADUAL,ESTACAO ECOLOGICA ESTADUAL DE GUAXINDIBA,3259.22390746,2002,0.47132646001,SIM,0.00283886233,SIM,1,EEE DE GUAXINDIBA,35,1:50.000,00000000000000000022
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pages/00_home.py

@@ -0,0 +1,59 @@
+import solara
+
+
+@solara.component
+def Page():
+    with solara.Column(align="center"):
+        markdown = """
+        ## Earth Engine Web Apps
+
+        ### Introduction
+
+        **A collection of Earth Engine web apps developed using [Solara](https://github.com/widgetti/solara) and geemap**
+
+        - Web App: <https://giswqs-solara-geemap.hf.space>
+        - GitHub: <https://github.com/opengeos/solara-geemap>
+        - Hugging Face: <https://huggingface.co/spaces/giswqs/solara-geemap>
+
+
+        ### How to deploy this app on Hugging Face Spaces
+
+        1. Go to <https://huggingface.co/spaces/giswqs/solara-geemap/tree/main> and duplicate the space to your own space.
+
+            ![](https://i.imgur.com/gTg4V2x.png)
+
+        2. You need to set `EARTHENGINE_TOKEN` in order to use Earth Engine. The token value should be copied from the following file depending on your operating system:
+
+            ```text
+            Windows: C:\\Users\\USERNAME\\.config\\earthengine\\credentials
+            Linux: /home/USERNAME/.config/earthengine/credentials
+            MacOS: /Users/USERNAME/.config/earthengine/credentials
+            ```
+
+            Simply open the file and copy **ALL** the content to the `EARTHENGINE_TOKEN` environment variable.
+
+            ![](https://i.imgur.com/i04gzyH.png)
+
+            ![](https://i.imgur.com/Ex37Ut7.png)
+
+
+            ```python
+            import geemap
+            geemap.get_ee_token()
+            ```
+
+            Copy all the content of the printed token and set it as the `EARTHENGINE_TOKEN` environment variable.
+
+        3. After the space is built successfully, click the `Embed this Space` menu and find the `Direct URL` for the app, such as <https://giswqs-solara-geemap.hf.space>.
+
+            ![](https://i.imgur.com/DNM36sk.png)
+
+            ![](https://i.imgur.com/KX82lSf.png)
+
+        4. Add your own apps (*.py) to the `pages` folder.
+
+        5. Commit and push your changes to the repository. Wait for the space to be built successfully.
+
+        """
+
+        solara.Markdown(markdown)

pages/01_basic.py

@@ -0,0 +1,30 @@
+import geemap
+import solara
+
+zoom = solara.reactive(4)
+center = solara.reactive((40, -100))
+bounds = solara.reactive(None)
+
+
+@solara.component
+def Page():
+    # Isolation is required to prevent the map from overlapping navigation (when screen width < 960px)
+    with solara.Column(
+        style={"min-width": "500px", "height": "780px", "isolation": "isolate"}
+    ):
+        # solara components support reactive variables
+        solara.SliderInt(label="Zoom level", value=zoom, min=1, max=20)
+        # using 3rd party widget library require wiring up the events manually
+        # using zoom.value and zoom.set
+        geemap.Map.element(  # type: ignore
+            zoom=zoom.value,
+            on_zoom=zoom.set,
+            center=center.value,
+            on_center=center.set,
+            on_bounds=bounds.set,
+            scroll_wheel_zoom=True,
+            height="600px",
+        )
+        solara.Text(f"Zoom: {zoom.value}")
+        solara.Text(f"Center: {center.value}")
+        solara.Text(f"Bounds: {bounds.value}")

pages/02_inspector.py

@@ -0,0 +1,47 @@
+import ee
+import geemap
+
+import solara
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_ee_data()
+        self.add("layer_manager")
+        self.add("inspector")
+
+    def add_ee_data(self):
+        # Add Earth Engine dataset
+        dem = ee.Image("USGS/SRTMGL1_003")
+        landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003").select(
+            ["B1", "B2", "B3", "B4", "B5", "B7"]
+        )
+        states = ee.FeatureCollection("TIGER/2018/States")
+
+        # Set visualization parameters.
+        vis_params = {
+            "min": 0,
+            "max": 4000,
+            "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
+        }
+
+        # Add Earth Engine layers to Map
+        self.addLayer(
+            landsat7,
+            {"bands": ["B4", "B3", "B2"], "min": 20, "max": 200, "gamma": 2.0},
+            "Landsat 7",
+            True,
+        )
+        self.addLayer(dem, vis_params, "SRTM DEM", True, 1)
+        self.addLayer(states, {}, "US States")
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[40, -100],
+            zoom=4,
+            height="600px",
+        )

pages/03_plotting.py

@@ -0,0 +1,40 @@
+import ee
+import geemap
+
+import solara
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_ee_data()
+        self.add_plot_gui()
+
+    def add_ee_data(self):
+        landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003").select(
+            ["B1", "B2", "B3", "B4", "B5", "B7"]
+        )
+
+        landsat_vis = {"bands": ["B4", "B3", "B2"], "gamma": 1.4}
+        self.addLayer(landsat7, landsat_vis, "Landsat")
+
+        hyperion = ee.ImageCollection("EO1/HYPERION").filter(
+            ee.Filter.date("2016-01-01", "2017-03-01")
+        )
+
+        hyperion_vis = {
+            "min": 1000.0,
+            "max": 14000.0,
+            "gamma": 2.5,
+        }
+        self.addLayer(hyperion, hyperion_vis, "Hyperion")
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[40, -100],
+            zoom=4,
+            height="600px",
+        )

pages/04_split_map.py

@@ -0,0 +1,58 @@
+import ee
+import geemap
+
+import solara
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_ee_data()
+
+    def add_ee_data(self):
+        # Select the eight NLCD epochs after 2000.
+        years = ["2001", "2004", "2006", "2008", "2011", "2013", "2016", "2019"]
+
+        # Get an NLCD image by year.
+        def getNLCD(year):
+            # Import the NLCD collection.
+            dataset = ee.ImageCollection("USGS/NLCD_RELEASES/2019_REL/NLCD")
+
+            # Filter the collection by year.
+            nlcd = dataset.filter(ee.Filter.eq("system:index", year)).first()
+
+            # Select the land cover band.
+            landcover = nlcd.select("landcover")
+            return landcover
+
+        ## Create an NLCD image collection for the selected years.
+        collection = ee.ImageCollection(ee.List(years).map(lambda year: getNLCD(year)))
+
+        # Create a list of labels to populate the dropdown list.
+        labels = [f"NLCD {year}" for year in years]
+
+        # Add a split-panel map for visualizing NLCD land cover change.
+        self.ts_inspector(
+            left_ts=collection,
+            right_ts=collection,
+            left_names=labels,
+            right_names=labels,
+        )
+
+        # Add the NLCD legend to the map.
+        self.add_legend(
+            title="NLCD Land Cover Type",
+            builtin_legend="NLCD",
+            height="460px",
+            add_header=False,
+        )
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[40, -100],
+            zoom=4,
+            height="600px",
+        )

pages/05_timelapse.py

@@ -0,0 +1,21 @@
+import geemap
+import solara
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_basemap("Esri.WorldImagery")
+        self.add_gui("timelapse", basemap=None)
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px", "isolation": "isolate"}):
+        Map.element(
+            center=[20, -0],
+            zoom=2,
+            height="750px",
+            zoom_ctrl=False,
+            measure_ctrl=False,
+        )

pages/06_timeseries.py

@@ -0,0 +1,283 @@
+import geemap
+import ipywidgets as widgets
+import solara
+from geemap import get_current_year, jslink_slider_label
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_basemap("Esri.WorldImagery")
+        self.add_ts_gui(position="topright")
+
+    def clean_up(self):
+        if hasattr(self, "slider_ctrl") and self.slider_ctrl is not None:
+            self.remove(self.slider_ctrl)
+            delattr(self, "slider_ctrl")
+
+        layer = self.find_layer("Time series")
+        if layer is not None:
+            self.remove(layer)
+        layer = self.find_layer("Image X")
+        if layer is not None:
+            self.remove(layer)
+
+        draw_layer = self.find_layer("Drawn Features")
+        if draw_layer is not None:
+            self.remove(draw_layer)
+
+    def add_ts_gui(self, position="topright", **kwargs):
+
+        widget_width = "350px"
+        padding = "0px 0px 0px 5px"  # upper, right, bottom, left
+        style = {"description_width": "initial"}
+        current_year = get_current_year()
+
+        collection = widgets.Dropdown(
+            options=[
+                "Landsat TM-ETM-OLI Surface Reflectance",
+            ],
+            value="Landsat TM-ETM-OLI Surface Reflectance",
+            description="Collection:",
+            layout=widgets.Layout(width=widget_width, padding=padding),
+            style=style,
+        )
+        bands = widgets.Dropdown(
+            description="Bands:",
+            options=[
+                "Red/Green/Blue",
+                "NIR/Red/Green",
+                "SWIR2/SWIR1/NIR",
+                "NIR/SWIR1/Red",
+                "SWIR2/NIR/Red",
+                "SWIR2/SWIR1/Red",
+                "SWIR1/NIR/Blue",
+                "NIR/SWIR1/Blue",
+                "SWIR2/NIR/Green",
+                "SWIR1/NIR/Red",
+            ],
+            value="SWIR1/NIR/Red",
+            style=style,
+            layout=widgets.Layout(width="195px", padding=padding),
+        )
+
+        frequency = widgets.Dropdown(
+            description="Frequency:",
+            options=["year", "quarter", "month"],
+            value="year",
+            style=style,
+            layout=widgets.Layout(width="150px", padding=padding),
+        )
+
+        start_year = widgets.IntSlider(
+            description="Start Year:",
+            value=1984,
+            min=1984,
+            max=current_year,
+            readout=False,
+            style=style,
+            layout=widgets.Layout(width="138px", padding=padding),
+        )
+
+        start_year_label = widgets.Label("1984")
+        jslink_slider_label(start_year, start_year_label)
+
+        end_year = widgets.IntSlider(
+            description="End Year:",
+            value=current_year,
+            min=1984,
+            max=current_year,
+            readout=False,
+            style=style,
+            layout=widgets.Layout(width="138px", padding=padding),
+        )
+        end_year_label = widgets.Label(str(current_year))
+        jslink_slider_label(end_year, end_year_label)
+
+        start_month = widgets.IntSlider(
+            description="Start Month:",
+            value=5,
+            min=1,
+            max=12,
+            readout=False,
+            style=style,
+            layout=widgets.Layout(width="145px", padding=padding),
+        )
+
+        start_month_label = widgets.Label(
+            "5",
+            layout=widgets.Layout(width="20px", padding=padding),
+        )
+        jslink_slider_label(start_month, start_month_label)
+
+        end_month = widgets.IntSlider(
+            description="End Month:",
+            value=10,
+            min=1,
+            max=12,
+            readout=False,
+            style=style,
+            layout=widgets.Layout(width="155px", padding=padding),
+        )
+
+        end_month_label = widgets.Label("10")
+        jslink_slider_label(end_month, end_month_label)
+
+        output = widgets.Output()
+
+        button_width = "113px"
+        apply_btn = widgets.Button(
+            description="Time slider",
+            button_style="primary",
+            tooltip="Click to create timeseries",
+            style=style,
+            layout=widgets.Layout(padding="0px", width=button_width),
+        )
+
+        split_btn = widgets.Button(
+            description="Split map",
+            button_style="primary",
+            tooltip="Click to create timeseries",
+            style=style,
+            layout=widgets.Layout(padding="0px", width=button_width),
+        )
+
+        reset_btn = widgets.Button(
+            description="Reset",
+            button_style="primary",
+            style=style,
+            layout=widgets.Layout(padding="0px", width=button_width),
+        )
+
+        vbox = widgets.VBox(
+            [
+                collection,
+                widgets.HBox([bands, frequency]),
+                widgets.HBox([start_year, start_year_label, end_year, end_year_label]),
+                widgets.HBox(
+                    [start_month, start_month_label, end_month, end_month_label]
+                ),
+                widgets.HBox([apply_btn, split_btn, reset_btn]),
+                output,
+            ]
+        )
+        self.add_widget(vbox, position=position, add_header=True)
+
+        def apply_btn_click(change):
+
+            if hasattr(self, "slider_ctrl") and self.slider_ctrl is not None:
+                self.remove(self.slider_ctrl)
+                delattr(self, "slider_ctrl")
+
+            with output:
+                output.clear_output()
+                if self.user_roi is None:
+                    output.append_stdout("Please draw a ROI first.")
+                else:
+                    output.append_stdout("Creating time series...")
+                    collection = geemap.landsat_timeseries(
+                        roi=self.user_roi,
+                        start_year=start_year.value,
+                        end_year=end_year.value,
+                        start_date=str(start_month.value).zfill(2) + "-01",
+                        end_date=str(end_month.value).zfill(2) + "-01",
+                        frequency=frequency.value,
+                    )
+                    vis_params = {
+                        "bands": bands.value.split("/"),
+                        "min": 0,
+                        "max": 0.4,
+                    }
+
+                    if frequency.value == "year":
+                        date_format = "YYYY"
+                    elif frequency.value == "quarter":
+                        date_format = "YYYY-MM"
+                    elif frequency.value == "month":
+                        date_format = "YYYY-MM"
+
+                    self.add_time_slider(
+                        collection,
+                        region=self.user_roi,
+                        vis_params=vis_params,
+                        date_format=date_format,
+                    )
+                    self._draw_control.clear()
+                    draw_layer = self.find_layer("Drawn Features")
+                    if draw_layer is not None:
+                        self.remove(draw_layer)
+                    output.clear_output()
+
+        apply_btn.on_click(apply_btn_click)
+
+        def split_btn_click(change):
+
+            if hasattr(self, "slider_ctrl") and self.slider_ctrl is not None:
+                self.remove(self.slider_ctrl)
+                delattr(self, "slider_ctrl")
+
+            with output:
+                output.clear_output()
+                if self.user_roi is None:
+                    output.append_stdout("Please draw a ROI first.")
+                else:
+                    output.append_stdout("Creating time series...")
+                    collection = geemap.landsat_timeseries(
+                        roi=self.user_roi,
+                        start_year=start_year.value,
+                        end_year=end_year.value,
+                        start_date=str(start_month.value).zfill(2) + "-01",
+                        end_date=str(end_month.value).zfill(2) + "-01",
+                        frequency=frequency.value,
+                    )
+                    vis_params = {
+                        "bands": bands.value.split("/"),
+                        "min": 0,
+                        "max": 0.4,
+                    }
+
+                    if frequency.value == "year":
+                        date_format = "YYYY"
+                        dates = geemap.image_dates(collection, date_format).getInfo()
+                    elif frequency.value == "quarter":
+                        date_format = "YYYY-MM"
+                        dates = geemap.image_dates(collection, date_format).getInfo()
+                    elif frequency.value == "month":
+                        date_format = "YYYY-MM"
+                        dates = geemap.image_dates(collection, date_format).getInfo()
+
+                    self.ts_inspector(
+                        collection,
+                        left_names=dates,
+                        left_vis=vis_params,
+                        add_close_button=True,
+                    )
+                    output.clear_output()
+
+                    try:
+                        self._draw_control.clear()
+                        draw_layer = self.find_layer("Drawn Features")
+                        if draw_layer is not None:
+                            self.remove(draw_layer)
+                    except Exception as e:
+                        print(e)
+
+        split_btn.on_click(split_btn_click)
+
+        def reset_btn_click(change):
+            output.clear_output()
+            self.clean_up()
+
+        reset_btn.on_click(reset_btn_click)
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[20, -0],
+            zoom=2,
+            height="750px",
+            zoom_ctrl=False,
+            measure_ctrl=False,
+        )

pages/07_jrc.py

@@ -0,0 +1,140 @@
+import ee
+import geemap
+import ipywidgets as widgets
+from IPython.display import display
+import solara
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_basemap("Esri.WorldImagery")
+        self.add_ee_data()
+        self.add_buttons(add_header=True)
+
+    def add_ee_data(self):
+
+        dataset = ee.Image("JRC/GSW1_4/GlobalSurfaceWater")
+        image = dataset.select(["occurrence"])
+        vis_params = {
+            "min": 0.0,
+            "max": 100.0,
+            "palette": ["ffffff", "ffbbbb", "0000ff"],
+        }
+        self.addLayer(image, vis_params, "Occurrence")
+        self.add_colorbar(
+            vis_params, label="Water occurrence (%)", layer_name="Occurrence"
+        )
+
+    def add_buttons(self, position="topright", **kwargs):
+        padding = "0px 5px 0px 5px"
+        widget = widgets.VBox(layout=widgets.Layout(padding=padding))
+        layout = widgets.Layout(width="auto")
+        style = {"description_width": "initial"}
+        hist_btn = widgets.Button(description="Occurrence", layout=layout)
+        bar_btn = widgets.Button(description="Monthly history", layout=layout)
+        reset_btn = widgets.Button(description="Reset", layout=layout)
+        scale = widgets.IntSlider(
+            min=30, max=1000, value=90, description="Scale", layout=layout, style=style
+        )
+        month_slider = widgets.IntRangeSlider(
+            description="Months",
+            value=[5, 10],
+            min=1,
+            max=12,
+            step=1,
+            layout=layout,
+            style=style,
+        )
+        widget.children = [
+            widgets.HBox([hist_btn, bar_btn, reset_btn]),
+            month_slider,
+            scale,
+        ]
+        self.add_widget(widget, position=position, **kwargs)
+        output = widgets.Output()
+        self.add_widget(output, position="bottomleft", add_header=False)
+
+        def hist_btn_click(b):
+            region = self.user_roi
+            if region is not None:
+                output.clear_output()
+                output.append_stdout("Computing histogram...")
+                image = ee.Image("JRC/GSW1_4/GlobalSurfaceWater").select(["occurrence"])
+                self.default_style = {"cursor": "wait"}
+                hist = geemap.image_histogram(
+                    image,
+                    region,
+                    scale=scale.value,
+                    height=350,
+                    width=550,
+                    x_label="Water Occurrence (%)",
+                    y_label="Pixel Count",
+                    layout_args={
+                        "title": dict(x=0.5),
+                        "margin": dict(l=0, r=0, t=10, b=0),
+                    },
+                    return_df=False,
+                )
+
+                with output:
+                    output.clear_output()
+                    display(hist)
+                self.default_style = {"cursor": "default"}
+            else:
+                output.clear_output()
+                with output:
+                    output.append_stdout("Please draw a region of interest first.")
+
+        hist_btn.on_click(hist_btn_click)
+
+        def bar_btn_click(b):
+            region = self.user_roi
+            if region is not None:
+                self.default_style = {"cursor": "wait"}
+                output.clear_output()
+                output.append_stdout("Computing monthly history...")
+                bar = geemap.jrc_hist_monthly_history(
+                    region=region,
+                    scale=scale.value,
+                    height=350,
+                    width=550,
+                    layout_args={
+                        "title": dict(x=0.5),
+                        "margin": dict(l=0, r=0, t=10, b=0),
+                    },
+                    frequency="month",
+                    start_month=month_slider.value[0],
+                    end_month=month_slider.value[1],
+                    denominator=1e4,
+                    y_label="Area (ha)",
+                )
+
+                with output:
+                    output.clear_output()
+                    display(bar)
+                self.default_style = {"cursor": "default"}
+            else:
+                output.clear_output()
+                with output:
+                    output.append_stdout("Please draw a region of interest first.")
+
+        bar_btn.on_click(bar_btn_click)
+
+        def reset_btn_click(b):
+            self._draw_control.clear()
+            output.clear_output()
+
+        reset_btn.on_click(reset_btn_click)
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[20, -0],
+            zoom=2,
+            height="750px",
+            zoom_ctrl=False,
+            measure_ctrl=False,
+        )

pages/08_compare.py

@@ -0,0 +1,277 @@
+import ee
+import geemap
+import ipywidgets as widgets
+import solara
+from datetime import date
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_basemap("Esri.WorldImagery")
+        self.add_gui_widget(add_header=True)
+
+    def clean_up(self):
+
+        layers = [
+            "Pre-event Image",
+            "Post-event Image",
+            "Pre-event NDWI",
+            "Post-event NDWI",
+            "Pre-event Water",
+            "Post-event Water",
+            "Disappeared Water",
+            "New Water",
+        ]
+        for layer_name in layers:
+            layer = self.find_layer(layer_name)
+            if layer is not None:
+                self.remove(layer)
+
+    def add_gui_widget(self, position="topright", **kwargs):
+
+        widget = widgets.VBox(layout=widgets.Layout(padding="0px 5px 0px 5px"))
+        pre_widget = widgets.HBox()
+        post_widget = widgets.HBox()
+        layout = widgets.Layout(width="auto")
+        style = {"description_width": "initial"}
+        padding = "0px 5px 0px 5px"
+        pre_start_date = widgets.DatePicker(
+            description="Start",
+            value=date(2014, 1, 1),
+            style=style,
+            layout=widgets.Layout(padding=padding, width="160px"),
+        )
+        pre_end_date = widgets.DatePicker(
+            description="End",
+            value=date(2014, 12, 31),
+            style=style,
+            layout=widgets.Layout(padding=padding, width="160px"),
+        )
+        pre_cloud_cover = widgets.IntSlider(
+            description="Cloud",
+            min=0,
+            max=100,
+            value=25,
+            step=1,
+            readout=False,
+            style=style,
+            layout=widgets.Layout(padding=padding, width="130px"),
+        )
+        pre_cloud_label = widgets.Label(value=str(pre_cloud_cover.value))
+        geemap.jslink_slider_label(pre_cloud_cover, pre_cloud_label)
+        pre_widget.children = [
+            pre_start_date,
+            pre_end_date,
+            pre_cloud_cover,
+            pre_cloud_label,
+        ]
+        post_start_date = widgets.DatePicker(
+            description="Start",
+            value=date(2024, 1, 1),
+            style=style,
+            layout=widgets.Layout(padding=padding, width="160px"),
+        )
+        post_end_date = widgets.DatePicker(
+            description="End",
+            value=date(2024, 12, 31),
+            style=style,
+            layout=widgets.Layout(padding=padding, width="160px"),
+        )
+        post_cloud_cover = widgets.IntSlider(
+            description="Cloud",
+            min=0,
+            max=100,
+            value=30,
+            step=1,
+            readout=False,
+            style=style,
+            layout=widgets.Layout(padding=padding, width="130px"),
+        )
+        post_cloud_label = widgets.Label(value=str(post_cloud_cover.value))
+        geemap.jslink_slider_label(post_cloud_cover, post_cloud_label)
+        post_widget.children = [
+            post_start_date,
+            post_end_date,
+            post_cloud_cover,
+            post_cloud_label,
+        ]
+
+        apply_btn = widgets.Button(description="Apply", layout=layout)
+        reset_btn = widgets.Button(description="Reset", layout=layout)
+        buttons = widgets.HBox([apply_btn, reset_btn])
+        output = widgets.Output()
+
+        use_split = widgets.Checkbox(
+            value=False,
+            description="Split map",
+            style=style,
+            layout=widgets.Layout(padding=padding, width="100px"),
+        )
+
+        use_ndwi = widgets.Checkbox(
+            value=False,
+            description="Compute NDWI",
+            style=style,
+            layout=widgets.Layout(padding=padding, width="160px"),
+        )
+
+        ndwi_threhold = widgets.FloatSlider(
+            description="Threshold",
+            min=-1,
+            max=1,
+            value=0,
+            step=0.05,
+            readout=True,
+            style=style,
+            layout=widgets.Layout(padding=padding, width="230px"),
+        )
+
+        options = widgets.HBox(
+            [
+                use_split,
+                use_ndwi,
+                ndwi_threhold,
+            ]
+        )
+
+        widget.children = [pre_widget, post_widget, options, buttons, output]
+        self.add_widget(widget, position=position, **kwargs)
+
+        def apply_btn_click(b):
+
+            marker_layer = self.find_layer("Search location")
+            if marker_layer is not None:
+                self.remove(marker_layer)
+            self.clean_up()
+
+            if self.user_roi is None:
+                output.clear_output()
+                output.append_stdout("Please draw a ROI first.")
+            elif (
+                pre_start_date.value is None
+                or pre_end_date.value is None
+                or post_start_date.value is None
+                or post_end_date.value is None
+            ):
+                output.clear_output()
+                output.append_stdout("Please select start and end dates.")
+
+            elif self.user_roi is not None:
+                output.clear_output()
+                output.append_stdout("Computing... Please wait.")
+                roi = ee.FeatureCollection(self.user_roi)
+                vis_params = {"bands": ["B6", "B5", "B4"], "min": 0, "max": 0.4}
+                if pre_start_date.value.strftime("%Y-%m-%d") < "2013-04-11":
+                    pre_col = geemap.landsat_timeseries(
+                        roi,
+                        start_year=pre_start_date.value.year,
+                        end_year=pre_end_date.value.year,
+                    ).select(["SWIR1", "NIR", "Red", "Green"], ["B6", "B5", "B4", "B3"])
+                else:
+                    pre_col = (
+                        ee.ImageCollection("NASA/HLS/HLSL30/v002")
+                        .filterBounds(roi)
+                        .filterDate(
+                            pre_start_date.value.strftime("%Y-%m-%d"),
+                            pre_end_date.value.strftime("%Y-%m-%d"),
+                        )
+                        .filter(ee.Filter.lt("CLOUD_COVERAGE", pre_cloud_cover.value))
+                    )
+
+                if post_start_date.value.strftime("%Y-%m-%d") < "2013-04-11":
+                    post_col = geemap.landsat_timeseries(
+                        roi,
+                        start_year=post_start_date.value.year,
+                        end_year=post_end_date.value.year,
+                    ).select(["SWIR1", "NIR", "Red", "Green"], ["B6", "B5", "B4", "B3"])
+                else:
+                    post_col = (
+                        ee.ImageCollection("NASA/HLS/HLSL30/v002")
+                        .filterBounds(roi)
+                        .filterDate(
+                            post_start_date.value.strftime("%Y-%m-%d"),
+                            post_end_date.value.strftime("%Y-%m-%d"),
+                        )
+                        .filter(ee.Filter.lt("CLOUD_COVERAGE", post_cloud_cover.value))
+                    )
+
+                pre_img = pre_col.median().clip(roi)
+                post_img = post_col.median().clip(roi)
+
+                if use_split.value:
+                    left_layer = geemap.ee_tile_layer(
+                        pre_img, vis_params, "Pre-event Image"
+                    )
+                    right_layer = geemap.ee_tile_layer(
+                        post_img, vis_params, "Post-event Image"
+                    )
+                    self.split_map(
+                        left_layer,
+                        right_layer,
+                        add_close_button=True,
+                        left_label="Pre-event",
+                        right_label="Post-event",
+                    )
+                else:
+                    pre_img = pre_col.median().clip(roi)
+                    post_img = post_col.median().clip(roi)
+                    self.add_layer(pre_img, vis_params, "Pre-event Image")
+                    self.add_layer(post_img, vis_params, "Post-event Image")
+
+                if use_ndwi.value and (not use_split.value):
+                    pre_ndwi = pre_img.normalizedDifference(["B3", "B6"]).rename("NDWI")
+                    post_ndwi = post_img.normalizedDifference(["B3", "B6"]).rename(
+                        "NDWI"
+                    )
+                    ndwi_vis = {"min": -1, "max": 1, "palette": "ndwi"}
+                    self.add_layer(pre_ndwi, ndwi_vis, "Pre-event NDWI", False)
+                    self.add_layer(post_ndwi, ndwi_vis, "Post-event NDWI", False)
+
+                    pre_water = pre_ndwi.gt(ndwi_threhold.value)
+                    post_water = post_ndwi.gt(ndwi_threhold.value)
+                    self.add_layer(
+                        pre_water.selfMask(), {"palette": "blue"}, "Pre-event Water"
+                    )
+                    self.add_layer(
+                        post_water.selfMask(), {"palette": "red"}, "Post-event Water"
+                    )
+                    new_water = post_water.subtract(pre_water).gt(0)
+                    disappear_water = pre_water.subtract(post_water).gt(0)
+                    self.add_layer(
+                        disappear_water.selfMask(),
+                        {"palette": "brown"},
+                        "Disappeared Water",
+                    )
+                    self.add_layer(
+                        new_water.selfMask(), {"palette": "cyan"}, "New Water"
+                    )
+
+                    with output:
+                        output.clear_output()
+
+                output.clear_output()
+
+        apply_btn.on_click(apply_btn_click)
+
+        def reset_btn_click(b):
+            self.clean_up()
+            self._draw_control.clear()
+            draw_layer = self.find_layer("Drawn Features")
+            if draw_layer is not None:
+                self.remove(draw_layer)
+            output.clear_output()
+
+        reset_btn.on_click(reset_btn_click)
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[20, -0],
+            zoom=2,
+            height="750px",
+            zoom_ctrl=False,
+            measure_ctrl=False,
+        )

pages/09_teste.py

@@ -0,0 +1,134 @@
+import ee
+import geemap
+import plotly.express as px
+import pandas as pd
+import solara
+import dataclasses
+from typing import Callable, cast
+
+df = pd.read_csv('CSVs\esa_stats_ucs_rj2 - esa_stats_ucs_rj2.csv.csv')
+columns = list(df.columns)
+#df = px.data.iris()
+
+@dataclasses.dataclass
+class ClickPoint:
+    row_index: int
+    x_column: str
+    y_column: str
+
+def find_row_index(fig, click_data):
+    # goes from trace index and point index to row index in a dataframe
+    # requires passing df.index as to custom_data
+    trace_index = click_data["points"]["trace_indexes"][0]
+    point_index = click_data["points"]["point_indexes"][0]
+    trace = fig.data[trace_index]
+    return trace.customdata[point_index][0]
+
+
+
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.add_ee_data()
+        self.add("layer_manager")
+        self.add("inspector")
+
+    def add_ee_data(self):
+        # Add Earth Engine dataset
+
+        esa = ee.ImageCollection('ESA/WorldCover/v200').first()
+
+        ucs = ee.FeatureCollection("projects/ee-curso-gee-rhamon/assets/ucs_estaduais_rj")
+
+       
+        esa = esa.clipToCollection(ucs)
+
+
+
+        # Set visualization parameters.
+
+        visualization = {
+             "bands": ['Map'],
+        }   
+
+
+        fc_vis_params = {
+            "color": "000000",
+            "width": 1,
+            "Opacity": 0.8,
+        }
+
+        # Add Earth Engine layers to Map
+        
+        self.centerObject(ucs, zoom=8)
+        self.addLayer(ucs, fc_vis_params, "UCs Estaduais RJ")
+        self.addLayer(esa, visualization, "Landcover")
+        self.add_legend(title="Land Cover Type", builtin_legend='ESA_WorldCover', position="bottomright")
+
+
+@solara.component
+def Page():
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[40, -100],
+            zoom=4,
+            height="600px",
+        )
+        fig = px.pie(df,values= x, names="Classes", title='Cobertura Florestal UCs Estaduais RJ')
+        #fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
+        #solara.FigurePlotly(fig)
+
+@solara.component
+def ClickScatter(df, x, click_row, on_click: Callable[[ClickPoint], None]):
+    x, set_x = solara.use_state(x)
+    #y, set_y = solara.use_state(y)
+    #fig = px.scatter(df, x, y, color=color, custom_data=[df.index])
+    fig = px.pie(df,values= x , names='Classes', title='Cobertura Florestal UCs Estaduais RJ')
+
+    def on_click_trace(click_data):
+        # sanity checks
+        assert click_data["event_type"] == "plotly_click"
+        row_index = find_row_index(fig, click_data)
+        on_click(ClickPoint(row_index, x))
+
+    if click_row:
+        click_x = df[x].values[click_row]
+        #fig.add_trace(px.scatter(x=, y=[click_y], text=["⭐️"]).data[0])
+        fig.add_trace(px.pie(values= [click_x], names='Classes', title='Cobertura Florestal UCs Estaduais RJ').data[0])
+    # make the figure a bit smaller
+    fig.update_layout(width=1340,height=800)
+    with solara.Column(style={"width": "1540px"}) as main:
+        solara.FigurePlotly(fig, on_click=on_click_trace)
+        solara.Select(label="X-axis", value=x, values=columns, on_value=set_x)
+    return main
+
+@solara.component
+def Page():
+     with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[-22, -43],
+            zoom=8,
+            height="600px",
+        )
+
+
+        click_point, set_click_point = solara.use_state(cast(ClickPoint, None))
+        if click_point:
+            clicked_row = click_point.row_index
+        else:
+            clicked_row = None
+
+            with solara.Row(justify="center", style={"flex-wrap": "wrap"}):
+                ClickScatter(df, 'nome', clicked_row, on_click=set_click_point)
+            if click_point is not None:
+                clicked_row = click_point.row_index
+                solara.Success(f"Clicked on row {clicked_row}. Which is highlighted in the both plots.")
+                solara.Markdown(
+                    f"""
+                ```python
+                row_data = {df.iloc[clicked_row].to_dict()}
+                ```"""
+                )
+            else:
+                solara.Info("Click to select a point")

requirements.txt

@@ -0,0 +1,5 @@
+geemap
+geopandas
+pydantic< 2.0
+solara
+plotly