diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 96a6dc8..61b92e9 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -23,7 +23,7 @@ jobs:
           docker buildx create --use
           docker buildx build \
             --file ./Dockerfile \
-            --tag ${{ secrets.DOCKERHUB_ORG }}/somospie_openvisus:latest \
+            --tag ${{ secrets.DOCKERHUB_ORG }}/somospie_openvisus:tutorial \
             --push .
 
     env:
diff --git a/.gitignore b/.gitignore
index c666075..ab72cda 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,2 @@
 .ipynb*
-GEOtiled/
-openvisuspy/
-.DS_Store
\ No newline at end of file
+.DS_Store
diff --git a/Dockerfile b/Dockerfile
index 8c2403d..a00acc2 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,16 @@
-FROM python:3.10
+FROM --platform=linux/amd64 continuumio/miniconda3:23.10.0-1
+
+RUN mkdir app
+WORKDIR /app
+
+COPY files/ /app/files/
+COPY idx_data/ /app/idx_data/
+COPY openvisuspy/ /app/openvisuspy/
+COPY GEOtiled/geotiled /app/geotiled/
+COPY Tutorial.ipynb /app/
+COPY Explore_Data.ipynb /app/
+COPY *.py /app/
+COPY environment.yml /app/
 
 # Install base utilities
 RUN apt-get update \
@@ -8,43 +20,39 @@ RUN apt-get update \
     && rm -rf /var/lib/apt/lists/*
 
 # Install miniconda
-ENV CONDA_DIR /opt/conda
-RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
-    /bin/bash ~/miniconda.sh -b -p /opt/conda
+# ENV CONDA_DIR /opt/conda
+# RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+#     /bin/bash ~/miniconda.sh -b -p /opt/conda
 
 # Put conda in path so we can use conda activate
-ENV PATH=$CONDA_DIR/bin:$PATH
-
-RUN mkdir app
-WORKDIR /app
-
-COPY setup_openvisuspy.sh /app
+#ENV PATH=$CONDA_DIR/bin:$PATH
 
-RUN chmod +x setup_openvisuspy.sh
-RUN ./setup_openvisuspy.sh
+#RUN conda update -n base conda && conda install -n base conda-libmamba-solver && conda config --set solver libmamba
 
-COPY environment.yml /app/
+WORKDIR /app
 
-RUN conda env create -f environment.yml 
+RUN conda env create -f environment.yml
 SHELL ["conda", "run", "-n", "somospie", "/bin/bash", "-c"]
 
-RUN apt-get update && apt-get install -y grass grass-doc
 RUN pip install openvisus
 
-RUN git clone https://github.com/TauferLab/GEOtiled.git
-WORKDIR /app/GEOtiled/geotiled
+WORKDIR /app/geotiled/
 RUN pip install -e .
 
-WORKDIR /app
+WORKDIR /app/openvisuspy
+RUN echo "export PATH=\$PATH:$(pwd)/src" >> ~/.bashrc && \
+    echo "export PYTHONPATH=\$PYTHONPATH:$(pwd)/src" >> ~/.bashrc && \
+    echo "export BOKEH_ALLOW_WS_ORIGIN='*'" && \
+    echo "export BOKEH_RESOURCES='cdn'" && \
+    echo "export VISUS_CACHE=/tmp/visus-cache/nsdf-services/somospie" && \
+    echo "export VISUS_CPP_VERBOSE=1" && \
+    echo "export VISUS_NETSERVICE_VERBOSE=1" && \
+    echo "export VISUS_VERBOSE_DISKACCESS=1" && \
+    . ~/.bashrc
 
-COPY Tutorial.ipynb /app
-COPY Explore_Data.ipynb /app
-COPY *.py /app/
+WORKDIR /app
 
 EXPOSE 8989 5000
 
-COPY /files/ /app/files/
-COPY /idx_data/ /app/idx_data/
-
 RUN conda init
 CMD ["conda", "run","-n", "somospie","jupyter", "lab", "--port=5000", "--no-browser", "--ip=0.0.0.0", "--allow-root", "--NotebookApp.token=''","--NotebookApp.password=''"]
diff --git a/GEOtiled/README.md b/GEOtiled/README.md
new file mode 100644
index 0000000..f9ebbd4
--- /dev/null
+++ b/GEOtiled/README.md
@@ -0,0 +1,133 @@
+# GEOtiled
+
+## About
+
+Terrain parameters such as slope, aspect, and hillshading are essential in various applications, including agriculture, forestry, 
+and hydrology. However, generating high-resolution terrain parameters is computationally intensive, making it challenging to 
+provide these value-added products to communities in need. We present a scalable workflow called GEOtiled that leverages data 
+partitioning to accelerate the computation of terrain parameters from digital elevation models, while preserving accuracy.
+
+This repository contains the library for all functions used for GEOtiled, and includes a Jupyter Notebook walking through 
+GEOtiled's workflow and function features.
+
+
+## Dependencies
+
+### Supported Operating Systems
+
+1. [Linux](https://www.linux.org/pages/download/)
+
+### Required Software
+> Note: These have to be installed on your own
+
+1. [Git](https://git-scm.com/downloads)
+2. [Python](https://www.python.org/downloads/)
+3. [Conda](https://www.anaconda.com/download/)
+4. [Jupyter Notebook](https://jupyter.org/install)
+
+### Required Libraries
+> Note: These will be installed with GEOtiled
+
+1. numpy
+2. tqdm
+3. pandas
+4. geopandas
+5. matplotlib
+6. GDAL
+
+## Installation
+
+### Install Conda
+> If you already have Conda installed on your machine, skip to Install GEOtiled
+1. Download Anaconda
+```
+wget https://repo.anaconda.com/archive/Anaconda3-2023.09-0-Linux-x86_64.sh
+```
+2. Run the downloaded file and agree to all prompts
+```
+bash ./Anaconda3-2023.09-0-Linux-x86_64.sh
+```
+3. Restart the shell to complete the installation
+
+### Install GEOtiled
+
+1. Create a new conda environment
+   > Note: This process might take some time
+```
+conda create -n geotiled -c conda-forge gdal=3.8.0
+```
+2. Change to the new environment
+```
+conda activate geotiled
+```
+3. Clone the repository in a desired working directory
+```
+git clone https://github.com/TauferLab/GEOtiled
+```
+4. Change to the geotiled directory
+   > Note: `your_path` should be replaced with your working directory
+```
+cd your_path/GEOtiled/geotiled
+```
+5. Install editable library
+```
+pip install -e .
+```
+
+> Note: Installations can be verified with `conda list`
+
+## How to Use the Library
+
+1. Ensure you are in the correct conda environment
+```
+conda activate geotiled
+```
+2. Place the following code snippet towards the top of any Python code to use GEOtiled functions
+```
+import geotiled
+```
+> Note: Documentation on functions can be found under docs/build/html/index.html
+
+## How to Run the Demo
+
+1. Install Jupyter Notebook in the geotiled conda environment
+```
+pip install notebook
+```
+2. Go to the GEOtiled directory
+```
+cd your_path/GEOtiled
+```
+3. Launch Jupyter Notebook
+```
+jupyter notebook
+```
+4. Navigate to the 'demo' folder and run the notebook 'demo.ipynb'
+
+## Publications
+
+Camila Roa, Paula Olaya, Ricardo Llamas, Rodrigo Vargas, and Michela Taufer. 2023. **GEOtiled: A Scalable Workflow
+for Generating Large Datasets of High-Resolution Terrain Parameters.** *In Proceedings of the 32nd International Symposium 
+on High-Performance Parallel and Distributed Computing* (HPDC '23). Association for Computing Machinery, New York, NY, USA, 
+311–312. [https://doi.org/10.1145/3588195.3595941](https://doi.org/10.1145/3588195.3595941)
+
+## Copyright and License
+
+Copyright (c) 2024, Global Computing Lab
+
+## Acknowledgements
+
+SENSORY is funded by the National Science Foundation (NSF) under grant numbers [#1724843](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724843&HistoricalAwards=false), 
+[#1854312](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854312&HistoricalAwards=false), [#2103836](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103836&HistoricalAwards=false), 
+[#2103845](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103845&HistoricalAwards=false), [#2138811](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138811&HistoricalAwards=false), 
+and [#2334945](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334945&HistoricalAwards=false).
+Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) 
+and do not necessarily reflect the views of the National Science Foundation. 
+
+## Contact Info
+
+Dr. Michela Taufer: mtaufer@utk.edu
+
+Jay Ashworth: washwor1@vols.utk.edu
+
+Gabriel Laboy: glaboy@vols.utk.edu
diff --git a/GEOtiled/demo/demo.ipynb b/GEOtiled/demo/demo.ipynb
new file mode 100644
index 0000000..0b07d22
--- /dev/null
+++ b/GEOtiled/demo/demo.ipynb
@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GEOtiled Demo: A Scalable Workflow for Generating Large Datasets of High-Resolution Terrain Parameters\n",
+    "\n",
+    "The GEOtiled workflow is comprised of three stages: \n",
+    "1. Reproject and partition a Digital Elevation Model (DEM) into tiles, each with a buffer region\n",
+    "2. Compute the terrain parameters for each individual tile concurrently\n",
+    "3. Mosaic each parameter's tiles together\n",
+    "\n",
+    "<p style=\"text-align:center\">\n",
+    "    <img src=\"demo_images/workflow.png\" width=\"500\"/>\n",
+    "</p>\n",
+    "<p style=\"text-align:center\">\n",
+    "    <b>Figure 1. </b>GEOtiled Workflow\n",
+    "</p>\n",
+    "    \n",
+    "Terrain parameters are computed using DEMs from [USGS 3DEP Products](https://www.usgs.gov/3d-elevation-program/about-3dep-products-services) to compute 3 topographic parameters: aspect, hillshade, and slope. By default, this demo uses 3DEP products covering the entirety of the US state of Tennessee at a 30m resolution.\n",
+    "\n",
+    "If you would like to work with a different region of data, go to the [USGS Data Download Application](https://apps.nationalmap.gov/downloader/#/elevation) and use the map to look for available DEM data. Data should be downloaded using the TXT button located under the *Products* tab, and the text file should be stored in your working directory.\n",
+    "\n",
+    "**IMPORTANT NOTE: Larger regions or higher resolutions will significantly increase the size of the data and the time to compute it.** "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Environment Setup\n",
+    "\n",
+    "The first cell below imports required libraries to run the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/exouser/GEOtiled/geotiled/src/geotiled.py:1: SyntaxWarning: invalid escape sequence '\\ '\n",
+      "  '''\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import glob\n",
+    "import os\n",
+    "import shutil\n",
+    "import multiprocessing\n",
+    "import geotiled\n",
+    "\n",
+    "geotiled.gdal.UseExceptions() # Used to silence a deprecation warning. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Settings\n",
+    "\n",
+    "In the following cell you may specify variables such as what data to download, the number of intermediary tiles to use for computation, and the file paths where data will be stored. Comments for what each variable is for is included.\n",
+    "\n",
+    "**Important Notes**\n",
+    "* For DEM download, three different methods are available: from a text file with a list of USGS download links, based off a shape file, or a specified latitude and longitude box\n",
+    "  * A text file from the USGS page should be stored in the working directory or child directory\n",
+    "  * Shape files are available for all US states and Washington DC. For the shapefile variable, specify the state abbreviation to use the correlating shapefile (e.g. TN for Tennessee)\n",
+    "  * A bounding box can be specified using the following syntax: {\"xmin\": val,\"ymin\": val,\"xmax\": val,\"ymax\": val}. X values correlate to longitude and Y values correlate to latitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_list = './download_urls.txt'                                    # Path to list of URLs for DEM download\n",
+    "shape_file = 'TN'                                                        # State shape file to use for DEM download\n",
+    "bounding_box = {\"xmin\": -90.4,\"ymin\": 34.8,\"xmax\": -81.55,\"ymax\": 36.8}  # Bounding box to use for DEM download\n",
+    "n_tiles = 16                                                             # Number of tiles to split the DEM mosaic into\n",
+    "\n",
+    "root_folder = './computed_data/'                                         # Root folder where GEOtiled data will be stored\n",
+    "dem_directory = 'dem_tiles'                                              # Folder where downloaded DEM tiles will be stored\n",
+    "rep_directory = 'elevation_tiles'                                        # Folder where reprojected, split DEM tiles will be stored\n",
+    "\n",
+    "dem_mosaic = 'mosaic'                                                    # Name of the mosaicked DEM file\n",
+    "rep_mosaic = 'elevation'                                                 # Name of the reprojected DEM file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Directories\n",
+    "\n",
+    "The following cell creates needed file paths after specifying the path and file names above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Root, DEM, and reprojected file paths creation\n",
+    "tiles_folder = os.path.join(root_folder, dem_directory)\n",
+    "elevation_tiles = os.path.join(root_folder, rep_directory)\n",
+    "Path(root_folder).mkdir(parents=True, exist_ok=True)\n",
+    "Path(tiles_folder).mkdir(parents=True, exist_ok=True)\n",
+    "Path(elevation_tiles).mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Update mosaic and reprojected DEM file paths\n",
+    "dem_mosaic = os.path.join(root_folder, dem_mosaic + '.tif')\n",
+    "rep_mosaic = os.path.join(root_folder, rep_mosaic + '.tif')\n",
+    "\n",
+    "# Update shape_file variable to have path to correlating shape file\n",
+    "shape_file = glob.glob('../shape_files/' + shape_file + '/*.shp')\n",
+    "\n",
+    "# Terrain parameter file paths creation\n",
+    "aspect_tiles = os.path.join(root_folder, 'aspect_tiles')\n",
+    "hillshading_tiles = os.path.join(root_folder, 'hillshading_tiles')\n",
+    "slope_tiles = os.path.join(root_folder, 'slope_tiles')\n",
+    "Path(aspect_tiles).mkdir(parents=True, exist_ok=True)\n",
+    "Path(hillshading_tiles).mkdir(parents=True, exist_ok=True)\n",
+    "Path(slope_tiles).mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-processing of the DEM\n",
+    "\n",
+    "### Fetch Data\n",
+    "\n",
+    "`fetch_dem()` pulls DEM data directly from the USGS webpage with a specified shape file or bounding box and a desired dataset. It will return a text file with the download URLs which can be saved or immediately downloaded from. **Note that if both a shape file and bounding box are given, the shape file will take precedent.**\n",
+    "\n",
+    "`download_files()` downloads the DEMs from a supplied text file with URLs or a Python list of strings containing the URLs. If you would like to use your own text file to download DEMs, skip `fetch_dem()` and only run this function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./download_urls.txt\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|\u001b[32m██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\u001b[0m| 1.72G/1.72G [00:15<00:00, 115MB/s]\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a text file with download URLs from a shape file\n",
+    "geotiled.fetch_dem(shapeFile=shape_file[0],txtPath=download_list, dataset=\"National Elevation Dataset (NED) 1 arc-second Current\")\n",
+    "\n",
+    "# Download files from the created text file\n",
+    "geotiled.download_files(download_list, tiles_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mosaic and Reproject DEMs\n",
+    "\n",
+    "`build_mosaic` creates a mosaic from a list of GeoTIFF files.\n",
+    "\n",
+    "`reproject` reprojects a specified GeoTIFF raster dataset from its original coordinate system to a new specified projection. DEMs from USGS are projected using the Global Coordinate System (GCS) by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40.."
+     ]
+    }
+   ],
+   "source": [
+    "# Variable storing all DEM tiles into a list\n",
+    "raster_list = glob.glob(tiles_folder + '/*')\n",
+    "\n",
+    "# Build mosaic from DEMs\n",
+    "geotiled.build_mosaic(raster_list, dem_mosaic)\n",
+    "\n",
+    "# Reproject the mosaic to Projected Coordinate System (PCS) EPSG:9822 which is the Albers Conic Equal Area projection \n",
+    "geotiled.reproject(dem_mosaic, rep_mosaic, 'EPSG:9822')\n",
+    "\n",
+    "# Cleanup: delete downloaded DEM tiles, vertex file used for mosaic, and mosaic file\n",
+    "shutil.rmtree(tiles_folder)\n",
+    "os.remove('./merged.vrt')\n",
+    "os.remove(dem_mosaic)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute Parameters with GEOtiled\n",
+    "\n",
+    "`crop_into_tiles()` splits a GeoTIFF file into a specified number of tiles.\n",
+    "\n",
+    "`compute_geotiled()` concurrently computes terrain parameters slope, aspect, and hillshading for all provided elevation model files.\n",
+    "\n",
+    "`build_mosaic_filtered()` is similar to `build_mosaic()` but includes extra logic to handle averaging of buffer regions made from cropping the mosaic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90.."
+     ]
+    }
+   ],
+   "source": [
+    "# Crop reprojected mosaic into specified number of intermediary elevation tiles\n",
+    "geotiled.crop_into_tiles(rep_mosaic, elevation_tiles, n_tiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Put intermediary elevation tiles into a list\n",
+    "#elev_tile_list = glob.glob(elevation_tiles + '/*.tif')\n",
+    "\n",
+    "# Run GEOtiled to compute all terrain parameters for each tile\n",
+    "pool = multiprocessing.Pool(processes=n_tiles) \n",
+    "pool.map(geotiled.compute_geotiled, sorted(glob.glob(elevation_tiles + '/*.tif')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build the mosaic for each of the terrain parameters\n",
+    "geotiled.build_mosaic_filtered(sorted(glob.glob(aspect_tiles + '/*.tif')), os.path.join(root_folder, 'aspect.tif'))\n",
+    "geotiled.build_mosaic_filtered(sorted(glob.glob(hillshading_tiles + '/*.tif')), os.path.join(root_folder,'hillshading.tif'))\n",
+    "geotiled.build_mosaic_filtered(sorted(glob.glob(slope_tiles + '/*.tif')), os.path.join(root_folder, 'slope.tif'))\n",
+    "\n",
+    "# Cleanup: remove intermediary terrain parameter tiles\n",
+    "shutil.rmtree(aspect_tiles)\n",
+    "shutil.rmtree(hillshading_tiles)\n",
+    "shutil.rmtree(slope_tiles)\n",
+    "shutil.rmtree(elevation_tiles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize the Results\n",
+    "\n",
+    "`generate_img()` plots the GeoTIFF data. A wide variety of parameters are available for this function, and details on what each one does can be found in the function documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reprojecting..\n",
+      ".100 - done.\n",
+      "0...10...20...30...40...50..Cropping with combined shapefiles...\n",
+      ".60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...Done.\n",
+      "Reading in tif for visualization...\n",
+      "Done.\n",
+      "Plotting data...\n",
+      "Done. (image should appear soon...)\n",
+      "Reprojecting..\n",
+      "100 - done.\n",
+      "0...10...20...30...40...50...Cropping with combined shapefiles...\n",
+      "60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...Done.\n",
+      "Reading in tif for visualization...\n",
+      "Done.\n",
+      "Plotting data...\n",
+      "Done. (image should appear soon...)\n",
+      "Reprojecting..\n",
+      "100 - done.\n",
+      "0...10...20...30...40...50..Cropping with combined shapefiles...\n",
+      ".60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...Done.\n",
+      "Reading in tif for visualization...\n",
+      "Done.\n",
+      "Plotting data...\n",
+      "Done. (image should appear soon...)\n",
+      "Reprojecting..\n",
+      "100 - done.\n",
+      "0...10...20...30...40...50..Cropping with combined shapefiles...\n",
+      ".60...70...80...90...100 - done.\n",
+      "0...10...20...30...40...50...60...70...80...90...Done.\n",
+      "Reading in tif for visualization...\n",
+      "Done.\n",
+      "Plotting data...\n",
+      "Done. (image should appear soon...)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "masked_array(\n",
+       "  data=[[--, --, --, ..., --, --, --],\n",
+       "        [--, --, --, ..., --, --, --],\n",
+       "        [--, --, --, ..., --, --, --],\n",
+       "        ...,\n",
+       "        [--, --, --, ..., --, --, --],\n",
+       "        [--, --, --, ..., --, --, --],\n",
+       "        [--, --, --, ..., --, --, --]],\n",
+       "  mask=[[ True,  True,  True, ...,  True,  True,  True],\n",
+       "        [ True,  True,  True, ...,  True,  True,  True],\n",
+       "        [ True,  True,  True, ...,  True,  True,  True],\n",
+       "        ...,\n",
+       "        [ True,  True,  True, ...,  True,  True,  True],\n",
+       "        [ True,  True,  True, ...,  True,  True,  True],\n",
+       "        [ True,  True,  True, ...,  True,  True,  True]],\n",
+       "  fill_value=1e+20,\n",
+       "  dtype=float32)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# File paths to the mosaics\n",
+    "hillshade = os.path.join(root_folder, 'hillshading.tif')\n",
+    "aspect = os.path.join(root_folder, 'aspect.tif')\n",
+    "slope = os.path.join(root_folder, 'slope.tif')\n",
+    "\n",
+    "# Create plots for all terrain parameters\n",
+    "geotiled.generate_img(rep_mosaic, downsample=5, reproject_gcs=True, shp_files=shape_file, title=\"Elevation Data for TN @ 1 Arc-Second/30m Resolution\",    zunit=\"Meter\",  xyunit=\"Degree\", ztype=\"Elevation\",   crop_shp=True) \n",
+    "geotiled.generate_img(hillshade,  downsample=5, reproject_gcs=True, shp_files=shape_file, title=\"Hillshading Data for TN @ 1 Arc-Second/30m Resolution\",  zunit=\"Level\",  xyunit=\"Degree\", ztype=\"Hillshading\", crop_shp=True)\n",
+    "geotiled.generate_img(aspect,     downsample=5, reproject_gcs=True, shp_files=shape_file, title=\"Aspect Data for Rhode TN @ 1 Arc-Second/30m Resolution\", zunit=\"Degree\", xyunit=\"Degree\", ztype=\"Aspect\",      crop_shp=True)\n",
+    "geotiled.generate_img(slope,      downsample=5, reproject_gcs=True, shp_files=shape_file, title=\"Slope Data for TN @ 1 Arc-Second/30m Resolution\",        zunit=\"Degree\", xyunit=\"Degree\", ztype=\"Slope\",       crop_shp=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## End of Demo"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GEOtiled/demo/demo_images/workflow.png b/GEOtiled/demo/demo_images/workflow.png
new file mode 100644
index 0000000..ecbfe83
Binary files /dev/null and b/GEOtiled/demo/demo_images/workflow.png differ
diff --git a/GEOtiled/docs/Makefile b/GEOtiled/docs/Makefile
new file mode 100644
index 0000000..d0c3cbf
--- /dev/null
+++ b/GEOtiled/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/GEOtiled/docs/make.bat b/GEOtiled/docs/make.bat
new file mode 100644
index 0000000..747ffb7
--- /dev/null
+++ b/GEOtiled/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/GEOtiled/docs/source/conf.py b/GEOtiled/docs/source/conf.py
new file mode 100644
index 0000000..a44ebfb
--- /dev/null
+++ b/GEOtiled/docs/source/conf.py
@@ -0,0 +1,39 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+import os
+import sys
+
+project = 'GEOtiled'
+copyright = '2024 GCLab'
+author = 'GCLab'
+release = '0.0.1'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    'sphinx.ext.autodoc',
+    # other extensions you might have
+    'sphinx_rtd_theme',
+]
+
+
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']
+
+
+sys.path.insert(0, os.path.abspath('/home/exouser/GEOtiled/geotiled/src'))
diff --git a/GEOtiled/docs/source/index.rst b/GEOtiled/docs/source/index.rst
new file mode 100644
index 0000000..29ee287
--- /dev/null
+++ b/GEOtiled/docs/source/index.rst
@@ -0,0 +1,30 @@
+.. SOMOSPIE_lib documentation master file, created by
+   sphinx-quickstart on Fri Sep 22 19:03:44 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to GEOtiled!
+========================================
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
+General documentation
+=====================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+.. automodule:: geotiled
+    :members:
+
+
+
+
+
+
diff --git a/GEOtiled/geotiled/LICENSE b/GEOtiled/geotiled/LICENSE
new file mode 100755
index 0000000..3c88c01
--- /dev/null
+++ b/GEOtiled/geotiled/LICENSE
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2024, Global Computing Lab
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/GEOtiled/geotiled/README.md b/GEOtiled/geotiled/README.md
new file mode 100755
index 0000000..6b619a5
--- /dev/null
+++ b/GEOtiled/geotiled/README.md
@@ -0,0 +1,3 @@
+# GEOtiled Library!!!
+
+Package with all of the required functions and classes for GEOtiled.
diff --git a/GEOtiled/geotiled/pyproject.toml b/GEOtiled/geotiled/pyproject.toml
new file mode 100755
index 0000000..07de284
--- /dev/null
+++ b/GEOtiled/geotiled/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
\ No newline at end of file
diff --git a/GEOtiled/geotiled/setup.cfg b/GEOtiled/geotiled/setup.cfg
new file mode 100755
index 0000000..8ec67d5
--- /dev/null
+++ b/GEOtiled/geotiled/setup.cfg
@@ -0,0 +1,33 @@
+[metadata]
+name = geotiled
+version = 0.0.1
+author = Camila Roa and compiled by Jay Ashworth of GCLab
+author_email = taufer@utk.edu
+description = Package with all of the required functions and classes for GEOtiled and its associated workflows.
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/TauferLab/GEOtiled
+classifiers =
+    Programming Language :: Python :: 3
+    License :: OSI Approved :: BSD License
+    Operating System :: OS Independent
+
+[options]
+python_requires = >=3.7
+package_dir =
+    =src
+packages = find:
+install_requires =
+    numpy
+    matplotlib
+    tqdm
+    geopandas
+    GDAL
+    pandas
+
+
+[options.package_data]
+* = *.md
+
+[options.packages.find]
+where = src
diff --git a/GEOtiled/geotiled/src/__init__.py b/GEOtiled/geotiled/src/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/GEOtiled/geotiled/src/__pycache__/geotiled.cpython-310.pyc b/GEOtiled/geotiled/src/__pycache__/geotiled.cpython-310.pyc
new file mode 100644
index 0000000..c8bb393
Binary files /dev/null and b/GEOtiled/geotiled/src/__pycache__/geotiled.cpython-310.pyc differ
diff --git a/GEOtiled/geotiled/src/geotiled.egg-info/PKG-INFO b/GEOtiled/geotiled/src/geotiled.egg-info/PKG-INFO
new file mode 100644
index 0000000..0961547
--- /dev/null
+++ b/GEOtiled/geotiled/src/geotiled.egg-info/PKG-INFO
@@ -0,0 +1,21 @@
+Metadata-Version: 2.1
+Name: geotiled
+Version: 0.0.1
+Summary: Package with all of the required functions and classes for GEOtiled and its associated workflows.
+Home-page: https://github.com/TauferLab/GEOtiled
+Author: Camila Roa and compiled by Jay Ashworth of GCLab
+Author-email: taufer@utk.edu
+License: UNKNOWN
+Platform: UNKNOWN
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+License-File: LICENSE
+
+# GEOtiled Library!!!
+
+Package with all of the required functions and classes for GEOtiled.
+
+
diff --git a/GEOtiled/geotiled/src/geotiled.egg-info/SOURCES.txt b/GEOtiled/geotiled/src/geotiled.egg-info/SOURCES.txt
new file mode 100644
index 0000000..f2c9c95
--- /dev/null
+++ b/GEOtiled/geotiled/src/geotiled.egg-info/SOURCES.txt
@@ -0,0 +1,9 @@
+LICENSE
+README.md
+pyproject.toml
+setup.cfg
+src/geotiled.egg-info/PKG-INFO
+src/geotiled.egg-info/SOURCES.txt
+src/geotiled.egg-info/dependency_links.txt
+src/geotiled.egg-info/requires.txt
+src/geotiled.egg-info/top_level.txt
\ No newline at end of file
diff --git a/GEOtiled/geotiled/src/geotiled.egg-info/dependency_links.txt b/GEOtiled/geotiled/src/geotiled.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/GEOtiled/geotiled/src/geotiled.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/GEOtiled/geotiled/src/geotiled.egg-info/requires.txt b/GEOtiled/geotiled/src/geotiled.egg-info/requires.txt
new file mode 100644
index 0000000..d643b80
--- /dev/null
+++ b/GEOtiled/geotiled/src/geotiled.egg-info/requires.txt
@@ -0,0 +1,7 @@
+GDAL
+geopandas
+grass-session
+matplotlib
+numpy
+pandas
+tqdm
diff --git a/GEOtiled/geotiled/src/geotiled.egg-info/top_level.txt b/GEOtiled/geotiled/src/geotiled.egg-info/top_level.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/GEOtiled/geotiled/src/geotiled.egg-info/top_level.txt
@@ -0,0 +1 @@
+
diff --git a/GEOtiled/geotiled/src/geotiled.py b/GEOtiled/geotiled/src/geotiled.py
new file mode 100755
index 0000000..9def36c
--- /dev/null
+++ b/GEOtiled/geotiled/src/geotiled.py
@@ -0,0 +1,1381 @@
+"""
+::
+
+     ____    ____    _____   ______     ___              __     
+    /\  _`\ /\  _`\ /\  __`\/\__  _\__ /\_ \            /\ \    
+    \ \ \L\_\ \ \L\_\ \ \/\ \/_/\ \/\_\\//\ \      __   \_\ \   
+     \ \ \L_L\ \  _\L\ \ \ \ \ \ \ \/\ \ \ \ \   /'__`\ /'_` \  
+      \ \ \/, \ \ \L\ \ \ \_\ \ \ \ \ \ \ \_\ \_/\  __//\ \L\ \ 
+       \ \____/\ \____/\ \_____\ \ \_\ \_\/\____\ \____\ \___,_\ 
+        \/___/  \/___/  \/_____/  \/_/\/_/\/____/\/____/\/__,_ /
+
+
+GEOtiled: A Scalable Workflow for Generating Large Datasets of
+High-Resolution Terrain Parameters
+
+Refactored library. Compiled by Jay Ashworth
+v0.0.1
+GCLab 2023
+
+Derived from original work by: Camila Roa (@CamilaR20), Eric Vaughan (@VaughanEric), Andrew Mueller (@Andym1098), Sam Baumann (@sam-baumann), David Huang (@dhuang0212), Ben Klein (@robobenklein)
+
+`Read the paper here <https://dl.acm.org/doi/pdf/10.1145/3588195.3595941>`_
+
+Terrain parameters such as slope, aspect, and hillshading are essential in various applications, including agriculture, forestry, and
+hydrology. However, generating high-resolution terrain parameters is computationally intensive, making it challenging to provide
+these value-added products to communities in need. We present a
+scalable workflow called GEOtiled that leverages data partitioning
+to accelerate the computation of terrain parameters from digital elevation models, while preserving accuracy. We assess our workflow
+in terms of its accuracy and wall time by comparing it to SAGA,
+which is highly accurate but slow to generate results, and to GDAL,
+which supports memory optimizations but not data parallelism. We
+obtain a coefficient of determination (𝑅2) between GEOtiled and
+SAGA of 0.794, ensuring accuracy in our terrain parameters. We
+achieve an X6 speedup compared to GDAL when generating the
+terrain parameters at a high-resolution (10 m) for the Contiguous
+United States (CONUS).
+"""
+
+import os
+import math
+import subprocess
+import requests
+import pandas as pd
+from osgeo import gdal
+from osgeo import osr
+from osgeo import ogr
+import numpy as np
+import matplotlib.pyplot as plt
+import concurrent.futures
+from tqdm import tqdm
+import geopandas as gpd
+
+# In Ubuntu: sudo apt-get install grass grass-doc
+# pip install grass-session
+# from grass_session import Session
+# import grass.script as gscript
+# import tempfile
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def bash(argv):
+    """
+    Execute bash commands using Popen.
+    ----------------------------------
+
+    This function acts as a wrapper to execute bash commands using the subprocess Popen method. Commands are executed synchronously, 
+    and errors are caught and raised.
+
+    Required Parameters
+    -------------------
+    argv : List
+        List of arguments for a bash command. They should be in the order that you would arrange them in the command line (e.g., ["ls", "-lh", "~/"]).
+
+    Outputs
+    -------
+    None
+        The function does not return any value.
+
+    Error States
+    ------------
+    RuntimeError
+        Raises a RuntimeError if Popen returns with an error, detailing the error code, stdout, and stderr.
+
+    Notes
+    -----
+    - It's essential to ensure that the arguments in the 'argv' list are correctly ordered and formatted for the desired bash command.
+    """
+    arg_seq = [str(arg) for arg in argv]
+    proc = subprocess.Popen(arg_seq, stdout=subprocess.PIPE, stderr=subprocess.PIPE)#, shell=True)
+    proc.wait() #... unless intentionally asynchronous
+    stdout, stderr = proc.communicate()
+
+    # Error catching: https://stackoverflow.com/questions/5826427/can-a-python-script-execute-a-function-inside-a-bash-script
+    if proc.returncode != 0:
+        raise RuntimeError("'%s' failed, error code: '%s', stdout: '%s', stderr: '%s'" % (
+            ' '.join(arg_seq), proc.returncode, stdout.rstrip(), stderr.rstrip()))
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def build_mosaic(input_files, output_file, description = "Elevation"):
+    """
+    Build a mosaic of geo-tiles using the GDAL library.
+    ---------------------------------------------------
+
+    This function creates a mosaic from a list of geo-tiles. 
+    It is an integral part of the GEOTILED workflow and is frequently used for merging tiles into a single mosaic file.
+
+    Required Parameters
+    -------------------
+    input_files : list of str
+        List of strings containing paths to the geo-tiles that are to be merged.
+    output_file : str
+        String representing the desired location and filename for the output mosaic.
+
+    Optional Parameters
+    -------------------
+    description : str
+        Description to attach to the output raster band. Default is "Elevation".
+
+    Outputs
+    -------
+    None
+        The function does not return any value. 
+        Generates a .tif file representing the created mosaic. This file will be placed at the location specified by 'output_file'.
+
+    Notes
+    -----
+    - Ensure the input geo-tiles are compatible for merging.
+    - The function utilizes the GDAL library's capabilities to achieve the desired mosaic effect.
+    """
+    # input_files: list of .tif files to merge
+    vrt = gdal.BuildVRT("merged.vrt", input_files)
+    translate_options = gdal.TranslateOptions(creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES', 'NUM_THREADS=ALL_CPUS'])#,callback=gdal.TermProgress_nocb)
+    gdal.Translate(output_file, vrt, options=translate_options)
+    vrt = None  # closes file
+    dataset = gdal.Open(output_file)
+    band = dataset.GetRasterBand(1)
+    band.SetDescription(description)
+    dataset = None
+
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def build_mosaic_filtered(input_files, output_file):
+    """
+    Build a mosaic of geo-tiles using the GDAL library with added logic to handle overlapping regions.
+    ---------------------------------------------------------------------------------------------------
+
+    This function creates a mosaic from a list of geo-tiles and introduces extra logic to handle averaging when regions overlap.
+    The function is similar to `build_mosaic` but provides additional capabilities to ensure the integrity of data in overlapping regions.
+
+    Required Parameters
+    -------------------
+    input_files : list of str
+        List of strings containing paths to the geo-tiles to be merged.
+    output_file : str
+        String representing the desired location and filename for the output mosaic.
+
+    Outputs
+    -------
+    None
+        The function does not return any value.
+        Generates a .tif file representing the created mosaic. This file is placed at the location specified by 'output_file'.
+
+    Notes
+    -----
+    - The function makes use of the GDAL library's capabilities and introduces Python-based pixel functions to achieve the desired averaging effect.
+    - The function is particularly useful when there are multiple sources of geo-data with possible overlapping regions,
+      ensuring a smooth transition between tiles.
+    - Overlapping regions in the mosaic are handled by averaging pixel values.
+    """
+    vrt = gdal.BuildVRT('merged.vrt', input_files)
+    vrt = None  # closes file
+
+    with open('merged.vrt', 'r') as f:
+        contents = f.read()
+
+    if '<NoDataValue>' in contents:
+        nodata_value = contents[contents.index('<NoDataValue>') + len(
+            '<NoDataValue>'): contents.index('</NoDataValue>')]  # To add averaging function
+    else:
+        nodata_value = 0
+
+    code = '''band="1" subClass="VRTDerivedRasterBand">
+  <PixelFunctionType>average</PixelFunctionType>
+  <PixelFunctionLanguage>Python</PixelFunctionLanguage>
+  <PixelFunctionCode><![CDATA[
+import numpy as np
+
+def average(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize,raster_ysize, buf_radius, gt, **kwargs):
+    data = np.ma.array(in_ar, mask=np.equal(in_ar, {}))
+    np.mean(data, axis=0, out=out_ar, dtype="float32")
+    mask = np.all(data.mask,axis = 0)
+    out_ar[mask] = {}
+]]>
+  </PixelFunctionCode>'''.format(nodata_value, nodata_value)
+
+    sub1, sub2 = contents.split('band="1">', 1)
+    contents = sub1 + code + sub2
+
+    with open('merged.vrt', 'w') as f:
+        f.write(contents)
+
+    cmd = ['gdal_translate', '-co', 'COMPRESS=LZW', '-co', 'TILED=YES', '-co', 
+           'BIGTIFF=YES', '--config', 'GDAL_VRT_ENABLE_PYTHON', 'YES', 'merged.vrt', output_file]
+    bash(cmd)
+
+    
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def build_stack(input_files, output_file):
+    """
+    Stack a list of .tif files into a single .tif file with multiple bands.
+    ------------------------------------------------------------------------
+
+    This function takes multiple .tif files and combines them into a single .tif file where each input file represents a separate band. 
+    This operation is useful when multiple datasets, each represented by a separate .tif file, need to be combined into a single multi-band raster.
+
+    Required Parameters
+    -------------------
+    input_files : list of str
+        List of strings containing paths to the .tif files to be stacked.
+    output_file : str
+        String representing the desired location and filename for the output stacked raster.
+
+    Outputs
+    -------
+    None
+        The function does not return any value.
+        A multi-band .tif file is generated at the location specified by 'output_file'.
+
+    Notes
+    -----
+    - The function makes use of the GDAL library's capabilities to achieve the stacking operation.
+    - Each input .tif file becomes a separate band in the output .tif file, retaining the order of the `input_files` list.
+    """
+    # input_files: list of .tif files to stack
+    vrt_options = gdal.BuildVRTOptions(separate=True)
+    vrt = gdal.BuildVRT("stack.vrt", input_files, options=vrt_options)
+    translate_options = gdal.TranslateOptions(creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])#,callback=gdal.TermProgress_nocb)
+    gdal.Translate(output_file, vrt, options=translate_options)
+    vrt = None  # closes file
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def change_raster_format(input_file, output_file, raster_format):
+    """
+    Convert the format of a given raster file.
+    -------------------------------------------
+
+    This function leverages the GDAL library to facilitate raster format conversion.
+    It allows users to convert the format of their .tif raster files to several supported formats, 
+    specifically highlighting the GTiff and NC4C formats.
+
+    Required Parameters
+    -------------------
+    input_file : str
+        String containing the path to the input .tif file.
+    output_file : str
+        String representing the desired location and filename for the output raster.
+    raster_format : str
+        String indicating the desired output format for the raster conversion. 
+        Supported formats can be found at `GDAL Raster Formats <https://gdal.org/drivers/raster/index.html>`.
+        This function explicitly supports GTiff and NC4C.
+
+    Outputs
+    -------
+    None
+        The function does not return any value.
+        A raster file in the desired format is generated at the location specified by 'output_file'.
+
+    Notes
+    -----
+    - While GTiff and NC4C formats have been explicitly mentioned, 
+      the function supports several other formats as listed in the GDAL documentation.
+    - The function sets specific creation options for certain formats. 
+      For example, the GTiff format will use LZW compression, tiling, and support for large files.
+    """
+
+    # Supported formats: https://gdal.org/drivers/raster/index.html
+    # SAGA, GTiff
+    if raster_format == 'GTiff':
+        translate_options = gdal.TranslateOptions(format=raster_format, creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])#,callback=gdal.TermProgress_nocb)
+    elif raster_format == 'NC4C':
+        translate_options = gdal.TranslateOptions(format=raster_format, creationOptions=['COMPRESS=DEFLATE'])#,callback=gdal.TermProgress_nocb)
+    else:
+        translate_options = gdal.TranslateOptions(format=raster_format)#,callback=gdal.TermProgress_nocb)
+    
+    gdal.Translate(output_file, input_file, options=translate_options)
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def compute_geotiled(input_file):
+    """
+    Generate terrain parameters for an elevation model.
+    ---------------------------------------------------
+
+    This function uses the GDAL library to compute terrain parameters like slope, aspect, and hillshading 
+    from a provided elevation model in .tif format.
+
+    Required Parameters
+    --------------------
+    input_file : str
+        String containing the path to the input elevation model .tif.
+
+    Returns
+    -------
+    None
+        The function does not return any value.
+        Generates terrain parameter files at the specified paths for slope, aspect, and hillshading.
+
+    Notes
+    -----
+    - The function currently supports the following terrain parameters:
+      - Slope
+      - Aspect
+      - Hillshading
+    - The generated parameter files adopt the following GDAL creation options: 'COMPRESS=LZW', 'TILED=YES', and 'BIGTIFF=YES'.
+    - The hillshading file undergoes an additional step to change its datatype to match that of the other parameters and 
+      also sets its nodata value. The intermediate file used for this process is removed after the conversion.
+    """
+
+    out_folder = os.path.dirname(os.path.dirname(input_file))
+    out_file = os.path.join(out_folder,'slope_tiles', os.path.basename(input_file))
+    # Slope
+    dem_options = gdal.DEMProcessingOptions(format='GTiff', creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])
+    gdal.DEMProcessing(out_file, input_file, processing='slope', options=dem_options)
+
+    #Adding 'Slope' name to band description 
+    dataset = gdal.Open(out_file)
+    band = dataset.GetRasterBand(1)
+    band.SetDescription("Slope")
+    dataset = None
+
+    # Aspect
+    out_file = os.path.join(out_folder,'aspect_tiles', os.path.basename(input_file))
+    dem_options = gdal.DEMProcessingOptions(zeroForFlat=False, format='GTiff', creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])
+    gdal.DEMProcessing(out_file, input_file, processing='aspect', options=dem_options)
+
+    #Adding 'Aspect' name to band description 
+    dataset = gdal.Open(out_file)
+    band = dataset.GetRasterBand(1)
+    band.SetDescription("Aspect")
+    dataset = None
+    
+    # Hillshading
+    out_file = os.path.join(out_folder,'hillshading_tiles', os.path.basename(input_file))
+    dem_options = gdal.DEMProcessingOptions(format='GTiff', creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])
+    gdal.DEMProcessing(out_file, input_file, processing='hillshade', options=dem_options)
+
+    #Adding 'Hillshading' name to band description 
+    dataset = gdal.Open(out_file)
+    band = dataset.GetRasterBand(1)
+    band.SetDescription("Hillshading")
+    dataset = None
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+# def compute_params(input_prefix, parameters):
+#     """
+#     Compute various topographic parameters using GDAL and GRASS GIS.
+#     ----------------------------------------------------------------
+
+#     This function computes a range of topographic parameters such as slope, aspect, and hillshading for a given Digital Elevation Model (DEM) using GDAL and GRASS GIS libraries.
+
+#     Required Parameters
+#     -------------------
+#     input_prefix : str
+#         Prefix path for the input DEM (elevation.tif) and the resulting parameter files.
+#         For instance, if input_prefix is "/path/to/dem/", then the elevation file should be 
+#         "/path/to/dem/elevation.tif" and the resulting slope will be at "/path/to/dem/slope.tif", etc.
+#     parameters : list of str
+#         List of strings specifying which topographic parameters to compute. Possible values are:
+#         'slope', 'aspect', 'hillshading', 'twi', 'plan_curvature', 'profile_curvature', 
+#         'convergence_index', 'valley_depth', 'ls_factor'.
+
+#     Outputs
+#     -------
+#     None
+#         Files are written to the `input_prefix` directory based on the requested parameters.
+
+#     Notes
+#     -----
+#     - GDAL is used for slope, aspect, and hillshading computations.
+#     - GRASS GIS is used for other parameters including 'twi', 'plan_curvature', 'profile_curvature', and so on.
+#     - The function creates a temporary GRASS GIS session for processing.
+#     - Assumes the input DEM is named 'elevation.tif' prefixed by `input_prefix`.
+
+#     Error states
+#     ------------
+#     - If an unsupported parameter is provided in the 'parameters' list, it will be ignored.
+#     """
+
+#     # Slope
+#     if 'slope' in parameters:
+#         dem_options = gdal.DEMProcessingOptions(format='GTiff', creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'], callback=gdal.TermProgress_nocb)
+#         gdal.DEMProcessing(input_prefix + 'slope.tif', input_prefix + 'elevation.tif', processing='slope', options=dem_options)
+#     # Aspect
+#     if 'aspect' in parameters:
+#         dem_options = gdal.DEMProcessingOptions(zeroForFlat=True, format='GTiff', creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'], callback=gdal.TermProgress_nocb)
+#         gdal.DEMProcessing(input_prefix + 'aspect.tif', input_prefix + 'elevation.tif', processing='aspect', options=dem_options)
+#     # Hillshading
+#     if 'hillshading' in parameters:
+#         dem_options = gdal.DEMProcessingOptions(format='GTiff', creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'], callback=gdal.TermProgress_nocb)
+#         gdal.DEMProcessing(input_prefix + 'hillshading.tif', input_prefix + 'elevation.tif', processing='hillshade', options=dem_options)
+
+#     # Other parameters with GRASS GIS
+#     if any(param in parameters for param in ['twi', 'plan_curvature', 'profile_curvature']):
+#         # define where to process the data in the temporary grass-session
+#         tmpdir = tempfile.TemporaryDirectory()
+
+#         s = Session()
+#         s.open(gisdb=tmpdir.name, location='PERMANENT', create_opts=input_prefix + 'elevation.tif')
+#         creation_options = 'BIGTIFF=YES,COMPRESS=LZW,TILED=YES' # For GeoTIFF files
+
+#         # Load raster into GRASS without loading it into memory (else use r.import or r.in.gdal)
+#         gscript.run_command('r.external', input=input_prefix + 'elevation.tif', output='elevation', overwrite=True)
+#         # Set output folder for computed parameters
+#         gscript.run_command('r.external.out', directory=os.path.dirname(input_prefix), format="GTiff", option=creation_options)
+
+#         if 'twi' in parameters:
+#             gscript.run_command('r.topidx', input='elevation', output='twi.tif', overwrite=True)
+
+#         if 'plan_curvature' in parameters:
+#             gscript.run_command('r.slope.aspect', elevation='elevation', tcurvature='plan_curvature.tif', overwrite=True)
+
+#         if 'profile_curvature' in parameters:
+#             gscript.run_command('r.slope.aspect', elevation='elevation', pcurvature='profile_curvature.tif', overwrite=True)
+
+#         if 'convergence_index' in parameters:
+#             gscript.run_command('r.convergence', input='elevation', output='convergence_index.tif', overwrite=True)
+
+#         if 'valley_depth' in parameters:
+#             gscript.run_command('r.valley.bottom', input='elevation', mrvbf='valley_depth.tif', overwrite=True)
+
+#         if 'ls_factor' in parameters:
+#             gscript.run_command('r.watershed', input='elevation', length_slope='ls_factor.tif', overwrite=True)
+
+
+#         tmpdir.cleanup()
+#         s.close()
+        
+#         # Slope and aspect with GRASS GIS (uses underlying GDAL implementation)
+#         #vgscript.run_command('r.slope.aspect', elevation='elevation', aspect='aspect.tif', slope='slope.tif', overwrite=True)
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+# def compute_params_concurrently(input_prefix, parameters):
+#     """
+#     Compute various topographic parameters concurrently using multiple processes.
+#     ------------------------------------------------------------------------------
+
+#     This function optimizes the performance of the `compute_params` function by concurrently computing 
+#     various topographic parameters. It utilizes Python's concurrent futures for parallel processing.
+
+#     Required Parameters
+#     -------------------
+#     input_prefix : str
+#         Prefix path for the input DEM (elevation.tif) and the resulting parameter files.
+#         E.g., if `input_prefix` is "/path/to/dem/", the elevation file is expected at 
+#         "/path/to/dem/elevation.tif", and the resulting slope at "/path/to/dem/slope.tif", etc.
+#     parameters : list of str
+#         List of strings specifying which topographic parameters to compute. Possible values include:
+#         'slope', 'aspect', 'hillshading', 'twi', 'plan_curvature', 'profile_curvature', 
+#         'convergence_index', 'valley_depth', 'ls_factor'.
+
+#     Outputs
+#     -------
+#     None
+#         Files are written to the `input_prefix` directory based on the requested parameters.
+
+#     Notes
+#     -----
+#     - Utilizes a process pool executor with up to 20 workers for parallel computations.
+#     - Invokes the `compute_params` function for each parameter in the list concurrently.
+
+#     Error states
+#     ------------
+#     - Unsupported parameters are ignored in the `compute_params` function.
+#     - Potential for resource contention: possible if multiple processes attempt simultaneous disk writes or read shared input files.
+#     """
+#     with concurrent.futures.ProcessPoolExecutor(max_workers=20) as executor:
+#         for param in parameters:
+#             executor.submit(compute_params, input_prefix, param)
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def crop_coord(input_file, output_file, upper_left, lower_right):
+    """
+    Crop a raster file to a specific region using upper-left and lower-right coordinates.
+    --------------------------------------------------------------------------------------
+
+    This function uses the GDAL library to crop a raster file based on specified coordinates.
+
+    Required Parameters
+    -------------------
+    input_file : str
+        Path to the input raster file intended for cropping.
+    output_file : str
+        Destination path where the cropped raster file will be saved.
+    upper_left : tuple of float
+        (x, y) coordinates specifying the upper-left corner of the cropping window.
+        Must be in the same projection as the input raster.
+    lower_right : tuple of float
+        (x, y) coordinates specifying the lower-right corner of the cropping window.
+        Must be in the same projection as the input raster.
+
+    Outputs
+    -------
+    None
+        Generates a cropped raster file saved at the designated `output_file` location.
+
+    Notes
+    -----
+    - The `upper_left` and `lower_right` coordinates define the bounding box for cropping.
+    - Employs GDAL's Translate method with specific creation options for cropping.
+    - For shapefiles, ensure they are unzipped. Using zipped files can lead to GDAL errors.
+
+    Error states
+    ------------
+    - GDAL might raise an error if provided coordinates fall outside the input raster's bounds.
+    """
+
+    # upper_left = (x, y), lower_right = (x, y)
+    # Coordinates must be in the same projection as the raster
+    window = upper_left + lower_right
+    translate_options = gdal.TranslateOptions(projWin=window, creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])#,callback=gdal.TermProgress_nocb)
+    gdal.Translate(output_file, input_file, options=translate_options)
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def crop_into_tiles(mosaic, out_folder, n_tiles, buffer = 10):
+    """
+    Splits a mosaic image into smaller, equally-sized tiles and saves them to a specified folder.
+    ----------------------------------------------------------------------------------------------
+
+    The function divides the mosaic into a specified number of tiles (n_tiles), taking care
+    to adjust the dimensions of edge tiles and add a buffer to each tile. 
+
+    Required Parameters 
+    --------------------
+        mosaic : str
+            The file path of the mosaic image.
+        out_folder : str
+            The directory path where the tile images will be saved.
+        n_tiles : int 
+            The total number of tiles to produce. Must be a perfect square number.
+
+    Optional Parameters
+    --------------------
+        buffer : int
+            Specifies the size of the buffer region between tiles in pixels. Default is 10.
+
+    Returns:
+    ---------
+        None: Tiles are saved to the specified directory and no value is returned.
+
+    Notes
+    ------
+        - The function will automatically create a buffer of overlapping pixels that is included in the borders between two tiles. This is customizable with the "buffer" kwarg. 
+    """
+    n_tiles = math.sqrt(n_tiles)
+
+    ds = gdal.Open(mosaic, 0)
+    cols = ds.RasterXSize
+    rows = ds.RasterYSize
+    x_win_size = int(math.ceil(cols / n_tiles))
+    y_win_size = int(math.ceil(rows / n_tiles))
+
+    tile_count = 0
+
+    for i in range(0, rows, y_win_size):
+        if i + y_win_size < rows:
+            nrows = y_win_size
+        else:
+            nrows = rows - i
+
+        for j in range(0, cols, x_win_size):
+            if j + x_win_size < cols:
+                ncols = x_win_size
+            else:
+                ncols = cols - j
+
+            tile_file = out_folder + '/tile_' + '{0:04d}'.format(tile_count) + '.tif'
+            win = [j, i, ncols, nrows]
+
+            # Upper left corner
+            win[0] = max(0, win[0] - buffer)
+            win[1] = max(0, win[1] - buffer)
+
+            w = win[2] + 2 * buffer
+            win[2] = w if win[0] + w < cols else cols - win[0]
+
+            h = win[3] + 2 * buffer
+            win[3] = h if win[1] + h < rows else rows - win[1]  
+
+
+            crop_pixels(mosaic, tile_file, win)
+            tile_count += 1
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def crop_region(input_file, shp_file, output_file):
+    """
+    Crop a raster file based on a region defined by a shapefile.
+    ------------------------------------------------------------------
+
+    This function uses the GDAL library to crop a raster file according to the boundaries 
+    specified in a shapefile.
+
+    Required Parameters
+    -------------------
+    input_file : str
+        Path to the input raster file intended for cropping.
+    shp_file : str
+        Path to the shapefile that outlines the cropping region.
+    output_file : str
+        Destination path where the cropped raster file will be saved.
+
+    Outputs
+    -------
+    None
+        Produces a cropped raster file at the designated `output_file` location using boundaries 
+        from the `shp_file`.
+
+    Notes
+    -----
+    - Utilizes GDAL's Warp method, setting the `cutlineDSName` option and enabling `cropToCutline` 
+      for shapefile-based cropping.
+
+    Error states
+    ------------
+    - GDAL may generate an error if the shapefile's boundaries exceed the input raster's limits.
+    - GDAL can also report errors if the provided shapefile is invalid or devoid of geometries.
+    """
+    warp_options = gdal.WarpOptions(cutlineDSName=shp_file, cropToCutline=True, creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])#,callback=gdal.TermProgress_nocb)
+    warp = gdal.Warp(output_file, input_file, options=warp_options)
+    warp = None
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def crop_to_valid_data(input_file, output_file, block_size=512):
+    """
+    Crops a border region of NaN values from a GeoTIFF file. 
+    -----------------------------------------------------------
+
+    Using a blocking method, the function will scan through the GeoTIFF file to determine the extent of valid data in order to crop of excess border NaN values.
+
+    Required Parameters 
+    --------------------
+        input_file : str
+            Path to the input GeoTIFF file.
+        output_file : str
+            Desired path for the output cropped GeoTIFF file.
+
+    Optional Parameters
+    --------------------
+        block_size : int
+            Specifies the size of blocks used in computing the extent. Default is 512. This means that a max 512x512 pixel area will be loaded into memory at any time. 
+
+    Returns:
+    ---------
+        None: Saves a cropped GeoTIFF file to the specified output path.
+
+    Notes
+    ------
+        - block_size is used to minimize RAM usage with a blocking technique. Adjust to fit your performance needs.
+    """
+    src_ds = gdal.Open(input_file, gdal.GA_ReadOnly)
+    src_band = src_ds.GetRasterBand(1)
+    
+    no_data_value = src_band.GetNoDataValue()
+    
+    gt = src_ds.GetGeoTransform()
+    
+    # Initialize bounding box variables to None
+    x_min, x_max, y_min, y_max = None, None, None, None
+
+    for i in range(0, src_band.YSize, block_size):
+        # Calculate block height to handle boundary conditions
+        if i + block_size < src_band.YSize:
+            actual_block_height = block_size
+        else:
+            actual_block_height = src_band.YSize - i
+
+        for j in range(0, src_band.XSize, block_size):
+            # Calculate block width to handle boundary conditions
+            if j + block_size < src_band.XSize:
+                actual_block_width = block_size
+            else:
+                actual_block_width = src_band.XSize - j
+
+            block_data = src_band.ReadAsArray(j, i, actual_block_width, actual_block_height)
+            
+            rows, cols = np.where(block_data != no_data_value)
+            
+            if rows.size > 0 and cols.size > 0:
+                if x_min is None or j + cols.min() < x_min:
+                    x_min = j + cols.min()
+                if x_max is None or j + cols.max() > x_max:
+                    x_max = j + cols.max()
+                if y_min is None or i + rows.min() < y_min:
+                    y_min = i + rows.min()
+                if y_max is None or i + rows.max() > y_max:
+                    y_max = i + rows.max()
+
+    # Convert pixel coordinates to georeferenced coordinates
+    min_x = gt[0] + x_min * gt[1]
+    max_x = gt[0] + (x_max + 1) * gt[1]
+    min_y = gt[3] + (y_max + 1) * gt[5]
+    max_y = gt[3] + y_min * gt[5]
+    
+    out_ds = gdal.Translate(output_file, src_ds, projWin=[min_x, max_y, max_x, min_y], projWinSRS='EPSG:4326')
+    
+    out_ds = None
+    src_ds = None
+
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+
+def crop_pixels(input_file, output_file, window):
+    """
+    Crop a raster file to a specific region using provided pixel window.
+    ---------------------------------------------------------------------
+
+    This function uses the GDAL library to perform the cropping operation based on pixel coordinates 
+    rather than geospatial coordinates.
+
+    Required Parameters
+    -------------------
+    input_file : str
+        String representing the path to the input raster file to be cropped.
+    output_file : str
+        String representing the path where the cropped raster file should be saved.
+    window : list or tuple
+        List or tuple specifying the window to crop by in the format [left_x, top_y, width, height].
+        Here, left_x and top_y are the pixel coordinates of the upper-left corner of the cropping window,
+        while width and height specify the dimensions of the cropping window in pixels.
+
+    Outputs
+    -------
+    None
+        A cropped raster file saved at the specified output_file path.
+
+    Notes
+    -----
+    - The function uses GDAL's Translate method with the `srcWin` option to perform the pixel-based cropping.
+    - Must ensure that GDAL is properly installed to utilize this function.
+
+    Error States
+    ------------
+    - If the specified pixel window is outside the bounds of the input raster, an error might be raised by GDAL.
+    """
+
+    # Window to crop by [left_x, top_y, width, height]
+    translate_options = gdal.TranslateOptions(srcWin=window, creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES'])#,callback=gdal.TermProgress_nocb)
+    gdal.Translate(output_file, input_file, options=translate_options)
+
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+
+def download_file(url, folder, pbar):
+    """
+    Download a single file given its URL and store it in a specified path.
+    -----------------------------------------------------------------------
+
+    This is a utility function that facilitates the downloading of files, especially within iterative download operations.
+
+    Required Parameters
+    -------------------
+    url : str
+        String containing the URL of the file intended for downloading.
+    folder : str
+        String specifying the path where the downloaded file will be stored.
+    pbar : tqdm object
+        Reference to the tqdm progress bar, typically used in a parent function to indicate download progress.
+
+    Outputs
+    -------
+    int
+        Returns an integer representing the number of bytes downloaded.
+    None
+        Creates a file in the designated 'folder' upon successful download.
+
+    Notes
+    -----
+    - This function is meant to be used inside of `download_files()`. If you use it outside of that, YMMV. 
+    - If the file already exists in the specified folder, no download occurs, and the function returns 0.
+    - Utilizes the requests library for file retrieval and tqdm for progress visualization.
+    """
+    local_filename = os.path.join(folder, url.split('/')[-1])
+    if os.path.exists(local_filename):
+        return 0
+
+    response = requests.get(url, stream=True)
+    downloaded_size = 0
+    with open(local_filename, 'wb') as f:
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+            downloaded_size += len(chunk)
+            pbar.update(len(chunk))
+    return downloaded_size
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+
+def download_files(input, folder="./"):
+    """
+    Download one or multiple files from provided URLs.
+    ---------------------------------------------------
+
+    This function allows for the simultaneous downloading of files using threading, and showcases download progress via a tqdm progress bar.
+
+    Required Parameters
+    -------------------
+    input : str or list of str
+        Can either be:
+        1. A string specifying the path to a .txt file. This file should contain URLs separated by newlines.
+        2. A list of strings where each string is a URL.
+    
+    Optional Parameters
+    -------------------
+    folder : str
+        String denoting the directory where the downloaded files will be stored. Default is the current directory.
+
+    Outputs
+    -------
+    None
+        Downloads files and stores them in the specified 'folder'.
+
+    Notes
+    -----
+    - The function uses `ThreadPoolExecutor` from the `concurrent.futures` library to achieve multi-threaded downloads for efficiency.
+    - The tqdm progress bar displays the download progress.
+    - If the 'input' argument is a string, it's assumed to be the path to a .txt file containing URLs.
+    - Will not download files if the file already exists, but the progress bar will not reflect it. 
+    """
+    if isinstance(input, str):
+        with open(input, 'r', encoding='utf8') as dsvfile:
+            urls = [url.strip().replace("'$", "")
+                    for url in dsvfile.readlines()]
+    else:
+        urls = input
+    print(input)
+    total_size = sum(get_file_size(url.strip()) for url in urls)
+    downloaded_size = 0
+
+    with tqdm(total=total_size, unit='B', unit_scale=True, ncols=1000, desc="Downloading", colour='green') as pbar:
+        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
+            futures = [executor.submit(
+                download_file, url, folder, pbar) for url in urls]
+            for future in concurrent.futures.as_completed(futures):
+                size = future.result()
+                downloaded_size += size
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def extract_raster(csv_file, raster_file, band_names):
+    """
+    Extract raster values corresponding to the coordinates specified in the CSV file.
+    --------------------------------------------------------------------------------------------
+
+    This function reads the x and y coordinates from a CSV file and extracts the raster values 
+    corresponding to those coordinates. The extracted values are added to the CSV file as new columns.
+
+    Required Parameters
+    -------------------
+    csv_file : str
+        String representing the path to the CSV file containing 'x' and 'y' columns with coordinates.
+    raster_file : str
+        String representing the path to the raster file to extract values from.
+    band_names : list of str
+        List of strings specifying the column names for each band's extracted values.
+
+    Outputs
+    -------
+    None
+        Modifies the provided CSV file to include new columns with extracted raster values based on band_names.
+
+    Notes
+    -----
+    - The CSV file must contain columns named 'x' and 'y' specifying the coordinates.
+    - The order of band_names should correspond to the order of bands in the raster_file.
+    - Ensure that GDAL and pandas are properly installed to utilize this function.
+
+    Error States
+    ------------
+    - If the CSV file does not have 'x' and 'y' columns, a KeyError will occur.
+    - If the specified coordinates in the CSV file are outside the bounds of the raster, incorrect or no values may be extracted.
+    """
+    # Extract values from raster corresponding to
+    df = pd.read_csv(csv_file)
+
+    ds = gdal.Open(raster_file, 0)
+    gt = ds.GetGeoTransform()
+
+    n_bands = ds.RasterCount
+    bands = np.zeros((df.shape[0], n_bands))
+
+    for i in range(df.shape[0]):
+        px = int((df['x'][i] - gt[0]) / gt[1])
+        py = int((df['y'][i] - gt[3]) / gt[5])
+
+        for j in range(n_bands):
+            band = ds.GetRasterBand(j + 1)
+            val = band.ReadAsArray(px, py, 1, 1)
+            bands[i, j] = val[0]
+    ds = None
+
+    for j in range(n_bands):
+        df[band_names[j]] = bands[:, j]
+
+    df.to_csv(csv_file, index=None)
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def fetch_dem(bbox={"xmin": -84.0387, "ymin": 35.86, "xmax": -83.815, "ymax": 36.04}, dataset="National Elevation Dataset (NED) 1/3 arc-second Current", prod_format="GeoTIFF", download=False, txtPath="download_urls.txt", saveToTxt=True, downloadFolder='./', shapeFile = None):
+    """
+    Queries the USGS API for DEM data given specified parameters and optionally extracts download URLs.
+    ----------------------------------------------------------------------------------------------------
+
+    The function targets the USGS National Map API, fetching Digital Elevation Model (DEM) data based on provided parameters. It can automatically download these files or save their URLs to a .txt file.
+
+    Optional Parameters
+    -------------------
+    bbox : dict
+        Dictionary containing bounding box coordinates to query. Consists of xmin, ymin, xmax, ymax. Default is {"xmin": -84.0387, "ymin": 35.86, "xmax": -83.815, "ymax": 36.04}.
+    dataset : str
+        Specifies the USGS dataset to target. Default is "National Elevation Dataset (NED) 1/3 arc-second Current".
+    prod_format : str
+        Desired file format for the downloads. Default is "GeoTIFF".
+    download : bool
+        If set to True, triggers automatic file downloads. Default is False.
+    txtPath : str
+        Designated path to save the .txt file containing URLs. Default is "download_urls.txt".
+    saveToTxt : bool
+        Flag to determine if URLs should be saved to a .txt file. Default is True.
+    downloadFolder : str
+        Destination folder for downloads (used if `download` is True). Default is the current directory.
+    shapeFile : str
+        Path to a shapefile with which a bounding box will be generated. Overrides the 'bbox' parameter if set. 
+
+    Outputs
+    -------
+    None
+        Depending on configurations, either saves URLs to a .txt file or initiates downloads using the `download_files` function.
+
+    Notes
+    -----
+    - If both `bbox` and `shapefile` are provided, `bbox` will take precedence.
+    - Uses the USGS National Map API for data fetching. Ensure the chosen dataset and product format are valid.
+    """
+    if shapeFile is not None:
+        coords = get_extent(shapeFile)
+        bbox['xmin'] = coords[0][0]
+        bbox['ymax'] = coords[0][1]
+        bbox['xmax'] = coords[1][0]
+        bbox['ymin'] = coords[1][1]
+    
+    base_url = "https://tnmaccess.nationalmap.gov/api/v1/products"
+
+    # Construct the query parameters
+    params = {
+        "bbox": f"{bbox['xmin']},{bbox['ymin']},{bbox['xmax']},{bbox['ymax']}",
+        "datasets": dataset,
+        "prodFormats": prod_format
+    }
+
+    # Make a GET request
+    response = requests.get(base_url, params=params)
+
+    # Check for a successful request
+    if response.status_code != 200:
+        raise Exception(
+            f"Failed to fetch data. Status code: {response.status_code}")
+
+    # Convert JSON response to Python dict
+    data = response.json()
+
+    # Extract download URLs
+    download_urls = [item['downloadURL'] for item in data['items']]
+
+    if saveToTxt is True:
+        with open(txtPath, "w") as file:
+            for url in download_urls:
+                file.write(f"{url}\n")
+
+    if download is True:
+        download_files(download_urls, folder=downloadFolder)
+
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def generate_img(tif, cmap='inferno', dpi=150, downsample=1, verbose=False, clean=False, title=None, 
+                 nancolor='green', ztype="Z", zunit=None, xyunit=None, reproject_gcs=False, 
+                 shp_files =None, crop_shp=False, bordercolor="black", borderlinewidth=1.5, saveDir = None):
+    """
+    Plot a GeoTIFF image using matplotlib.
+    --------------------------------------
+
+    This function is a powerful plotting tool for GEOTiff files that uses GDAL, OSR, numpy, matplotlib, and geopandas. 
+    We have tried to create a simple interface for the end user where you can input a tif file and an informational image will be generated.
+    If the default image is not suited for your needs or if any of the information is incorrect, there are a series of keyword arguments that allow for user customizability. 
+
+    Several major features that are not enabled by default include:
+
+    - Automatic PCS to GCS conversion using the ``reproject_gcs`` flag.
+    - Automated cropping with a shapefile using the ``shp_file`` parameter in addition to the ``crop_shp``.
+    - Downsampling in order to reduce computation time using the ``downsample`` flag. 
+    - A verbose mode that will print additional spatial information about the geotiff file using the ``verbose`` flag.
+    - A clean mode that will print an image of the geotiff with no other information using the ``clean`` flag. 
+
+    Required Parameters
+    --------------------
+    tif : str
+        Path to the GeoTIFF file.
+
+    Optional Parameters
+    -------------------
+    cmap : str
+        Colormap used for visualization. Default is 'inferno'.
+    dpi : int
+        Resolution in dots per inch for the figure. Default is 150.
+    downsample : int
+        Factor to downsample the image by. Default is 10.
+    verbose : bool
+        If True, print geotransform and spatial reference details. Default is False.
+    clean : bool
+        If True, no extra data will be shown besides the plot image. Default is False.
+    title : str
+        Title for the plot. Default will display the projection name.
+    nancolor : str
+        Color to use for NaN values. Default is 'green'.
+    ztype : str
+        Data that is represented by the z-axis. Default is 'Z'.
+    zunit : str
+        Units for the data values (z-axis). Default is None and inferred from spatial reference.
+    xyunit : str
+        Units for the x and y axes. Default is None and inferred from spatial reference.
+    reproject_gcs : bool
+        Reproject a given raster from a projected coordinate system (PCS) into a geographic coordinate system (GCS).
+    shp_file : str
+        Path to the shapefile used for cropping. Default is None.
+    crop_shp : bool
+        Flag to indicate if the shapefile should be used for cropping. Default is False.
+    bordercolor : str
+        Color for the shapefile boundary. Default is "black".
+    borderlinewidth : float
+        Line width for the shapefile boundary. Default is 1.5.
+
+    Returns
+    -------
+    raster_array: np.ndarray
+        Returns the raster array that was used for visualization. 
+
+    Notes
+    -----
+    - Alternative colormaps can be found in the `matplotlib documentation <https://matplotlib.org/stable/users/explain/colors/colormaps.html>`_.
+    - Shapemap cannot be in a .zip form. GDAL will throw an error if you use a .zip. We recommend using .shp. It can also cause issues if you don't have the accompanying files with the .shp file. (.dbf, .prj, .shx).
+    - Must be used with Jupyter Notebooks to display results properly. Will Implement a feature to save output to dir eventually. 
+    - Using ``shp_file`` without setting ``crop_shp`` will allow you to plot the outline of the shapefile without actually cropping anything. 
+    """
+
+    # Initial setup
+    tif_dir_changed = False
+    
+    # Reproject raster into geographic coordinate system if needed
+    if reproject_gcs:
+        print("Reprojecting..")
+        base_dir = os.path.dirname(tif)
+        new_path = os.path.join(base_dir, "vis.tif")
+        reproject(tif, new_path, "EPSG:4326")
+        if crop_shp is False:
+            new_path_crop = os.path.join(base_dir, "vis_trim_crop.tif")
+            print("Cropping NaN values...")
+            crop_to_valid_data(new_path,new_path_crop)
+            print("Done.")
+            os.remove(new_path)
+            tif = new_path_crop
+        else:
+            tif = new_path
+        tif_dir_changed = True
+
+    # Crop using shapefiles if needed
+    if crop_shp and shp_files:
+        # Check if the list is not empty
+        if not shp_files:
+            print("Shapefile list is empty. Skipping shapefile cropping.")
+        else:
+            # Read each shapefile, clean any invalid geometries, and union them
+            gdfs = [gpd.read_file(shp_file).buffer(0) for shp_file in shp_files]
+            combined_geom = gdfs[0].unary_union
+            for gdf in gdfs[1:]:
+                combined_geom = combined_geom.union(gdf.unary_union)
+            
+            combined_gdf = gpd.GeoDataFrame(geometry=[combined_geom], crs=gdfs[0].crs)
+                
+            # Save the combined shapefile temporarily for cropping
+            temp_combined_shp = os.path.join(os.path.dirname(tif), "temp_combined.shp")
+            combined_gdf.to_file(temp_combined_shp)
+                
+            print("Cropping with combined shapefiles...")
+            base_dir = os.path.dirname(tif)
+            new_path = os.path.join(base_dir, "crop.tif")
+            crop_region(tif, temp_combined_shp, new_path)
+            if tif_dir_changed:
+                os.remove(tif)
+            tif = new_path
+            tif_dir_changed = True
+            print("Done.")
+            
+            # Remove the temporary combined shapefile
+            os.remove(temp_combined_shp)
+
+    print("Reading in tif for visualization...")
+    dataset = gdal.Open(tif)
+    band = dataset.GetRasterBand(1)
+
+    geotransform = dataset.GetGeoTransform()
+    spatial_ref = osr.SpatialReference(wkt=dataset.GetProjection())
+
+    # Extract spatial information about raster
+    proj_name = spatial_ref.GetAttrValue('PROJECTION')
+    proj_name = proj_name if proj_name else "GCS, No Projection"
+    data_unit = zunit or spatial_ref.GetLinearUnitsName()
+    coord_unit = xyunit or spatial_ref.GetAngularUnitsName()
+    z_type = ztype if band.GetDescription() == '' else band.GetDescription()
+
+
+    if verbose:
+        print(f"Geotransform:\n{geotransform}\n\nSpatial Reference:\n{spatial_ref}\n\nDocumentation on spatial reference format: https://docs.ogc.org/is/18-010r11/18-010r11.pdf\n")
+
+    raster_array = gdal.Warp('', tif, format='MEM', 
+                             width=int(dataset.RasterXSize/downsample), 
+                             height=int(dataset.RasterYSize/downsample)).ReadAsArray()
+
+    # Mask nodata values
+    raster_array = np.ma.array(raster_array, mask=np.equal(raster_array, band.GetNoDataValue()))
+
+    print("Done.\nPlotting data...")
+
+    # Set up plotting environment
+    cmap_instance = plt.get_cmap(cmap)
+    cmap_instance.set_bad(color=nancolor)
+
+    # Determine extent
+    ulx, xres, _, uly, _, yres = geotransform
+    lrx = ulx + (dataset.RasterXSize * xres)
+    lry = uly + (dataset.RasterYSize * yres)
+
+    # Plot
+    fig, ax = plt.subplots(dpi=dpi)
+    sm = ax.imshow(raster_array, cmap=cmap_instance, vmin=np.nanmin(raster_array), vmax=np.nanmax(raster_array), 
+                   extent=[ulx, lrx, lry, uly])
+    if clean:
+        ax.axis('off')
+    else:
+        # Adjust colorbar and title
+        cbar = fig.colorbar(sm, fraction=0.046*raster_array.shape[0]/raster_array.shape[1], pad=0.04)
+        cbar_ticks = np.linspace(np.nanmin(raster_array), np.nanmax(raster_array), 8)
+        cbar.set_ticks(cbar_ticks)
+        cbar.set_label(f"{z_type} ({data_unit}s)")
+
+        ax.set_title(title if title else f"Visualization of GEOTiff data using {proj_name}.", fontweight='bold')
+        ax.tick_params(axis='both', which='both', bottom=True, top=False, left=True, right=False, color='black', length=5, width=1)
+
+        ax.set_title(title or f"Visualization of GEOTiff data using {proj_name}.", fontweight='bold')
+
+    # Set up the ticks for x and y axis
+    x_ticks = np.linspace(ulx, lrx, 5)
+    y_ticks = np.linspace(lry, uly, 5)
+
+    # Format the tick labels to two decimal places
+    x_tick_labels = [f'{tick:.2f}' for tick in x_ticks]
+    y_tick_labels = [f'{tick:.2f}' for tick in y_ticks]
+
+    ax.set_xticks(x_ticks)
+    ax.set_yticks(y_ticks)
+    ax.set_xticklabels(x_tick_labels)
+    ax.set_yticklabels(y_tick_labels)
+
+
+    # Determine x and y labels based on whether data is lat-long or projected
+    y_label = f"Latitude ({coord_unit}s)" if spatial_ref.EPSGTreatsAsLatLong() else f"Northing ({coord_unit}s)"
+    x_label = f"Longitude ({coord_unit}s)" if spatial_ref.EPSGTreatsAsLatLong() else f"Easting ({coord_unit}s)"
+    ax.set_ylabel(y_label)
+    ax.set_xlabel(x_label)
+
+    # ax.ticklabel_format(style='plain', axis='both')  # Prevent scientific notation on tick labels
+    ax.set_aspect('equal')
+
+    if shp_files:
+        for shp_file in shp_files:
+            overlay = gpd.read_file(shp_file)
+            overlay.boundary.plot(color=bordercolor, linewidth=borderlinewidth, ax=ax)
+
+    print("Done. (image should appear soon...)")
+
+    if saveDir is not None:
+        fig.savefig(saveDir)
+
+    if tif_dir_changed:
+        os.remove(tif)
+
+    return raster_array
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def get_extent(shp_file):
+    """
+    Get the bounding box (extent) of a shapefile.
+    ----------------------------------------------
+
+    This function extracts the extent or bounding box of a shapefile. The extent is returned as 
+    the upper left and lower right coordinates.
+
+    Required Parameters
+    -------------------
+    shp_file : str
+        String representing the path to the shapefile.
+
+    Outputs
+    -------
+    tuple of tuple
+        Returns two tuples, the first representing the upper left (x, y) coordinate and the second 
+        representing the lower right (x, y) coordinate.
+
+    Notes
+    -----
+    - Ensure that OGR is properly installed to utilize this function.
+
+    Error States
+    ------------
+    - If the provided file is not a valid shapefile or cannot be read, OGR may raise an error.
+    """
+    ds = ogr.Open(shp_file)
+    layer = ds.GetLayer()
+    ext = layer.GetExtent()
+    upper_left = (ext[0], ext[3])
+    lower_right = (ext[1], ext[2])
+
+    return upper_left, lower_right  
+
+    
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def get_file_size(url):
+    """
+    Retrieve the size of a file at a given URL in bytes.
+    ----------------------------------------------------
+
+    This function sends a HEAD request to the provided URL and reads the 'Content-Length' header to determine the size of the file. 
+    It's primarily designed to support the `download_files` function to calculate download sizes beforehand.
+
+    Required Parameters
+    -------------------
+    url : str
+        String representing the URL from which the file size needs to be determined.
+
+    Outputs
+    -------
+    int
+        Size of the file at the specified URL in bytes. Returns 0 if the size cannot be determined.
+
+    Notes
+    -----
+    - This function relies on the server's response headers to determine the file size.
+    - If the server doesn't provide a 'Content-Length' header or there's an error in the request, the function will return 0.
+    - This function's primary use is with `download_files()`. 
+    """
+    try:
+        response = requests.head(url)
+        return int(response.headers.get('Content-Length', 0))
+    except:
+        return 0
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+
+def reproject(input_file, output_file, projection):
+    """
+    Reproject a geospatial raster dataset (GeoTIFF) using the GDAL library.
+    -------------------------------------------------------------------------
+
+    This function reprojects a given GeoTIFF raster dataset from its original coordinate system to a new specified projection. The result is saved as a new raster file. The projection can be provided in multiple formats, including standard EPSG codes or WKT format.
+
+    Required Parameters
+    -------------------
+    input_file : str
+        String representing the file location of the GeoTIFF to be reprojected.
+    output_file : str
+        String representing the desired location and filename for the output reprojected raster.
+    projection : str
+        String indicating the desired target projection. This can be a standard GDAL format code (e.g., EPSG:4326) or the path to a .wkt file.
+
+    Outputs
+    -------
+    None
+        Generates a reprojected GeoTIFF file at the specified 'output_file' location.
+
+    Notes
+    -----
+    - The function supports multi-threading for improved performance on multi-core machines.
+    - The source raster data remains unchanged; only a new reprojected output file is generated.
+    """
+    # Projection can be EPSG:4326, .... or the path to a wkt file
+    warp_options = gdal.WarpOptions(dstSRS=projection, creationOptions=['COMPRESS=LZW', 'TILED=YES', 'BIGTIFF=YES', 'NUM_THREADS=ALL_CPUS'],
+                                    multithread=True, warpOptions=['NUM_THREADS=ALL_CPUS'])#,callback=gdal.TermProgress_nocb)
+    warp = gdal.Warp(output_file, input_file, options=warp_options)
+    warp = None  # Closes the files
+
+# ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+def tif2csv(raster_file, band_names=['elevation'], output_file='params.csv'):
+    """
+    Convert raster values from a TIF file into CSV format.
+    ------------------------------------------------------
+
+    This function reads raster values from a specified raster TIF file and exports them into a CSV format.
+    The resulting CSV file will contain columns representing the x and y coordinates, followed by columns
+    for each band of data in the raster.
+
+    Required Parameters
+    -------------------
+    raster_file : str
+        Path to the input raster TIF file to be converted.
+
+    Optional Parameters
+    -------------------
+    band_names : list
+        Names for each band in the raster. The order should correspond to the bands in the raster file. 
+        Default is ['elevation'].
+    output_file : str
+        Path where the resultant CSV file will be saved. Default is 'params.csv'.
+
+    Outputs
+    -------
+    None
+        The function will generate a CSV file saved at the specified `output_file` path, containing the raster values 
+        and their corresponding x and y coordinates.
+
+    Notes
+    -----
+    - The x and y coordinates in the output CSV correspond to the center of each pixel.
+    - NaN values in the CSV indicate that there's no data or missing values for a particular pixel.
+
+    Error States
+    ------------
+    - If the provided raster file is not present, invalid, or cannot be read, GDAL may raise an error.
+    - If the number of provided `band_names` does not match the number of bands in the raster, the resulting CSV 
+      might contain columns without headers or may be missing some data.
+    """
+    ds = gdal.Open(raster_file, 0)
+    xmin, res, _, ymax, _, _ = ds.GetGeoTransform()
+    xsize = ds.RasterXSize
+    ysize = ds.RasterYSize
+    xstart = xmin + res / 2
+    ystart = ymax - res / 2
+
+    x = np.arange(xstart, xstart + xsize * res, res)
+    y = np.arange(ystart, ystart - ysize * res, -res)
+    x = np.tile(x[:xsize], ysize)
+    y = np.repeat(y[:ysize], xsize)
+
+    n_bands = ds.RasterCount
+    bands = np.zeros((x.shape[0], n_bands))
+    for k in range(1, n_bands + 1):
+        band = ds.GetRasterBand(k)
+        data = band.ReadAsArray()
+        data = np.ma.array(data, mask=np.equal(data, band.GetNoDataValue()))
+        data = data.filled(np.nan)
+        bands[:, k-1] = data.flatten()
+
+    column_names = ['x', 'y'] + band_names
+    stack = np.column_stack((x, y, bands))
+    df = pd.DataFrame(stack, columns=column_names)
+    df.dropna(inplace=True)
+    df.to_csv(output_file, index=None)
diff --git a/environment.yml b/environment.yml
index 8a495e3..14532d3 100644
--- a/environment.yml
+++ b/environment.yml
@@ -5,32 +5,20 @@ channels:
 dependencies:
   - python=3.10
   - gdal
+  - ipykernel==6.29.2
+  - ipywidgets==8.1.2
+  - xmltodict
+  - requests
+  - colorcet
+  - jupyterlab
+  - tifffile
+  - rasterio
+  - imagecodecs
+  - boto3
+  - param==2.0.2
+  - bokeh==3.3.4
+  - ipywidgets-bokeh==1.5.0
   - pip
   - pip:
-      - pandas
-      - pyspark
-      - findspark
-      - scikit-learn
-      - matplotlib
-      - grass-session
-      - xmltodict
-      - requests
-      - colorcet
-      - geopandas
-      - tqdm
-      - jupyterlab
-      - grass-session
-      - tifffile
-      - rasterio
-      - imagecodecs
-      - numpy 
-      - boto3  
-      - param==2.0.2 
-      - bokeh==3.3.4 
-      - ipywidgets-bokeh==1.5.0 
-      - panel==1.3.8 
-      - OpenVisusNoGui==2.2.128 
-      - ipykernel==6.29.2 
-      - ipywidgets==8.1.2 
-      - matplotlib==3.8.2 
-      - ipywidgets-bokeh==1.5.0
+      - panel==1.3.8
+      - OpenVisusNoGui==2.2.128
diff --git a/openvisuspy/.gitignore b/openvisuspy/.gitignore
new file mode 100644
index 0000000..2849393
--- /dev/null
+++ b/openvisuspy/.gitignore
@@ -0,0 +1,13 @@
+~*
+.DS_Store
+.vs/
+**/__pycache__/
+**/.ipynb_checkpoints/
+**/*.egg-info/
+dist/
+.venv/
+build
+.workflow
+.vscode
+
+
diff --git a/openvisuspy/LICENSE b/openvisuspy/LICENSE
new file mode 100644
index 0000000..a93a123
--- /dev/null
+++ b/openvisuspy/LICENSE
@@ -0,0 +1,36 @@
+Copyright (c) 2010-2018 ViSUS L.L.C., 
+Scientific Computing and Imaging Institute of the University of Utah
+
+ViSUS L.L.C., 50 W. Broadway, Ste. 300, 84101-2044 Salt Lake City, UT
+University of Utah, 72 S Central Campus Dr, Room 3750, 84112 Salt Lake City, UT
+ 
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation
+and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+contributors may be used to endorse or promote products derived from
+this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+For additional information about this project contact: pascucci@acm.org
+For support: support@visus.net
+
diff --git a/openvisuspy/README.md b/openvisuspy/README.md
new file mode 100644
index 0000000..72833db
--- /dev/null
+++ b/openvisuspy/README.md
@@ -0,0 +1,134 @@
+# Instructions
+
+
+## Windows PIP installation
+
+(OPTONAL) Remove old environment:
+
+```bat
+.venv\Scripts\deactivate
+rmdir /s /q  .venv
+rmdir /s /q  "%USERPROFILE%\.jupyter"
+```
+
+Install new version:
+- use python `3.10` or `3.11`. 
+- **DO NOT** use `python312` does not have a good match for `libzmq`
+- use  JupyterLab v3 (**NOT v4**) because bokeh does not work
+  - https://github.com/bokeh/jupyter_bokeh/issues/197
+
+```bash
+python -m venv .venv
+.venv\Scripts\activate
+where python
+
+# OPTIONAL
+# python.exe -m pip install --upgrade pip
+
+# removed `itkwidgets`, since pyvista seems to be better maintained/compatible
+#   see https://github.com/imjoy-team/imjoy-jupyterlab-extension/issues/6#issuecomment-1898703563
+#      `imjoy` is needed for itkwidgets on jupyterlab
+python -m pip install --verbose --no-cache --no-warn-script-location boto3 colorcet fsspec numpy imageio urllib3 pillow xarray xmltodict  plotly requests scikit-image scipy seaborn tifffile pandas tqdm matplotlib  zarr altair cartopy dash fastparquet folium geodatasets geopandas geoviews lxml numexpr scikit-learn sqlalchemy statsmodels vega_datasets xlrd yfinance pyarrow pydeck h5py hdf5plugin netcdf4 nexpy nexusformat nbgitpuller intake ipysheet ipywidgets bokeh ipywidgets-bokeh panel holoviews hvplot datashader vtk pyvista trame trame-vtk trame-vuetify notebook "jupyterlab==3.6.6" jupyter_bokeh jupyter-server-proxy  jupyterlab-system-monitor "pyviz_comms>=2.0.0,<3.0.0" "jupyterlab-pygments>=0.2.0,<0.3.0" 
+
+
+# in debug just use local paths
+# set PYTHONPATH=C:\projects\OpenVisus\build\RelWithDebInfo;.\src
+python -m pip install OpenVisusNoGui openvisuspy
+
+# test import 
+python -c "import OpenVisus"
+python -c "import openvisuspy"
+
+# save the output for the future
+pip freeze 
+```
+
+## Test Volume rendering
+
+```bash
+# test pyvista
+python examples/python/test-pyvista.py
+
+# test vtk volume
+python examples/python/test-vtkvolume.py 
+```
+
+
+## Run Dashboards 
+
+
+Change as needed:
+
+```bash
+set BOKEH_ALLOW_WS_ORIGIN=*
+set BOKEH_LOG_LEVEL=debug
+set VISUS_CPP_VERBOSE=1
+set VISUS_NETSERVICE_VERBOSE=1
+set VISUS_VERBOSE_DISKACCESS=0
+set VISUS_CACHE=c:/tmp/visus-cache
+
+python -m panel serve src/openvisuspy/dashboards --dev --args "D:/visus-datasets/david_subsampled/visus.idx"
+python -m panel serve src/openvisuspy/dashboards --dev --args "D:/visus-datasets/2kbit1/zip/hzorder/visus.idx"
+
+python -m panel serve src/openvisuspy/dashboards --dev --args "D:/visus-datasets/signal1d/visus.idx"
+
+python -m panel serve src/openvisuspy/dashboards --dev --args "D:/visus-datasets/chess/nsdf-group/dashboards.json"
+
+
+python -m panel serve src/openvisuspy/dashboards --dev --args "D:\visus-datasets\chess\nsdf-group\datasets\near-field-nexus\visus.idx"
+
+# not sure why I cannot cache in arco an IDX that is NON arco
+python -m panel serve src/openvisuspy/dashboards --dev --args "https://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled&cached=idx" 
+python -m panel serve src/openvisuspy/dashboards --dev --args "https://atlantis.sci.utah.edu/mod_visus?dataset=2kbit1&cached=idx"
+
+```
+
+## Run notebooks
+
+### Setup Jupyter Lab
+
+```bash
+
+# check jupyter paths
+where jupyter
+jupyter kernelspec list
+
+# Check extensions:
+#   **all extensions should show `enabled ok...`**
+#   e.g you will need @bokeh/jupyter_bokeh    for bokeh   (installed by `jupyter_bokeh``)
+#   e.g you will need @pyviz/jupyterlab_pyviz for panel   (installed by `pyviz_comms``)
+#   avoid any message `is not compatible with the current JupyterLab` message at the bottom
+jupyter labextension list
+
+# Build recommended, please run `jupyter lab build`:
+#   @plotly/dash-jupyterlab needs to be included in build
+pip install nodejs-bin[cmd]
+jupyter lab clean --all
+jupyter lab build 
+# rmdir /s /q C:\Users\scrgi\AppData\Local\Yarn 
+# Look also for additional extensions loaded from here
+# dir .venv\share\jupyter\lab\extensions\*
+jupyter labextension list
+```
+
+```bash
+
+# is this avoiding any caching/security problem? not sure
+python scripts/run_command.py "jupyter nbconvert --clear-output --inplace {notebook}" "examples/notebooks/*.ipynb"
+python scripts/run_command.py "jupyter trust {notebook}"                              "examples/notebooks/*.ipynb"
+
+jupyter lab .
+```
+
+
+## Developers only
+
+Deploy new binaries
+
+- **Update the `PROJECT_VERSION` inside `pyproject.toml`**
+
+```bash
+# source .venv/bin/activate
+./scripts/new_tag.sh
+```
+
diff --git a/openvisuspy/TODO.md b/openvisuspy/TODO.md
new file mode 100644
index 0000000..2c028c0
--- /dev/null
+++ b/openvisuspy/TODO.md
@@ -0,0 +1,23 @@
+
+
+Todo
+- [DONE] remove `viewmode` and parent/child
+- [DONE] copy url does not work
+- [DONE] add `show-options`
+- [DONE] File menu in  jupyter lab does not show floating panels
+- [DONE] holoviews "param" tracking
+- [DONE] probe as a tool on top of Slice
+- [DONE] pick of a range with a rectable (or even for a point)... at full res. Prototype working...
+- [DONE] addTool breajs jupyter lab
+- [DONE] 1D big signals
+
+- [TODO] Linked View
+- [TODO] helper - with all query parameters
+- [TODO] not so sure about the `onlychanged=True` since I am missing some events
+
+ON HOLD:
+- [todo] sometimes dialog boxes do not work in jupyter lab (i.e. details). What to do?
+- [TODO] opick a value (or onhover?). ASK valerio ... not clear, I already include the point value in the dynamic range
+
+
+
diff --git a/openvisuspy/data/ironProt.vtk b/openvisuspy/data/ironProt.vtk
new file mode 100644
index 0000000..b4dcd4f
Binary files /dev/null and b/openvisuspy/data/ironProt.vtk differ
diff --git a/openvisuspy/diagram.drawio b/openvisuspy/diagram.drawio
new file mode 100644
index 0000000..09f9828
--- /dev/null
+++ b/openvisuspy/diagram.drawio
@@ -0,0 +1 @@
+<mxfile host="Electron" modified="2023-03-23T23:09:01.413Z" agent="5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/20.8.10 Chrome/106.0.5249.199 Electron/21.3.5 Safari/537.36" etag="a9atDN72UHo_MvGlw3hC" version="20.8.10" type="device"><diagram name="Page-1" id="E84A8r-Dovyr7iGDvByx">7V1td5o8GP41nrN9sId38GNb37qtq53d+rgvOwipZqJhEK3u1z+JEoXGOmrVRLSetuQOCFzXnfslJKGkXw+njcgN+7fIB0FJU/xpSa+WNE1TVZP8o5LZQlIxnIWgF0F/IVJXgjb8CxKhkkjH0AdxZkeMUIBhmBV6aDQCHs7I3ChCz9ndnlCQPWvo9gAnaHtuwEsfoY/7C6ljKit5E8Ben51ZVZKaoct2TgRx3/XRc0qk10r6dYQQXmwNp9cgoOAxXBbH1V+pXV5YBEY4zwFjDz787Dfrv3+Bxn2n3AnhZ6OsVpKLwzN2x8AnACRFFOE+6qGRG9RW0itvHE0A/VaVFFa7fEEoTIS/AcazhEx3jBER9fEwSGrJBUez/0hBYYUOLVyYrFidpiurs6TE33ECQozGkQc23WaiOW7UA3jDfvpiPwpB6gQJng2AhoBcD9khAoGL4SSrI26iar3lfis2yEZCyBvI0S2R5KiykcMakizsOGd20nDYUrFjaGd20nDI1XaS7524wTg5U2tG0B7R4+gfl/x2ifMGUUmzAnJjV1261aNbrc5d9aZa4+iN0HjkL9l77kMM2qE7R++ZxCZZpp5gEFyjAEXzY/Unk36oHI1wSr74IfIYR2gAUjXW/IfWMLeubiJyAiIMphuRZyGTnVCVBEw6CzWeV+GHykxhPxV6WMq+yDKFxgir1tPJNJ71TQlMIU4dRkod9o1ke3UQLbBjfDfuL691wTSL9fQNusLpRNWin3e1Zz1ne1YNudqzLUBFDoCyJRXKOmc170Iw+gHjcVxUa2hUZLOGJkdCALtElemBZfJLShGI44B3XD9u2t/bv77WHubw7JQwxwOet46YrmMaprIHYhwnS4zhmPmIMfZFjMURU48AuBnSBP8VLm5uLxu197ORwfZFm6kp9LMfzE1dNOY2h/k9Nl9Du/H95oixrojG2uGwZjHzerhbnYfm3dci2horrxPYGxcs/EqRsaSBsYCIb55Q3xzOWB0517Kap80HExCQ2GeR/oTjCNB/LC8i/AzmpqyYjn7p2Fl7s9Zw7BzS0Rti05639SBksxZmG5PTqFvZyn/3PSQe959hdAKkJFG0IbRXdSte1+WYUjMuV95kiMhOT4xxyXp/hfbNv5dxZf4jO+OOVIyrfPr3ekRG9vsAhp73MV9g5qFhCAOQBGaPl+3bRWgTQxqZFTMeW8Zfm+KxyiHjMU2oFX/jE53cfcZLArdvsSzz+fcDVFWqJqsd0wNUOQnV5CJU6GCSQhAq2QAU5UzoOwmV6zmdzvcFngl9G6FydWboQjupCkGoXH0V5jGZ3A3pyNaZ62tKcgBVMCpyqcIxDf8smCqYcpl585hy36KpglwhnHlMWXPRVEGu9Mw8pny7aKogV9eLdQ4bxamCXN2q1omGjcunZgJVwZLMKpxo2CiFKsgVK1gnGjZKoQpyZRDWiYaNUqiCXP0K9jE9DyiYKrB5frKogkwOwj41XZDLQ9gyeYiT0wW5XIRzoj0LUqiCXM8mnXO0IE4V5Bpgza5703Dbr3cPtau7u8/5Btl+GoczDKIvEBd2jtPLKbPqmiG1B53LvLyFFYtXBIv+TgnwTeD4xjqgHa2r80CnGmNlN7iX1ReTy3QtJ/La3pDXOORb7oiunVZw5PNO3dwF8kZF+/358d4xH8p19bbcsOvN5pqlZ7ZwaitWlO282lpjvqULybuaha2s5+pAHoNfvSLlMVLwW3/GdEm8uTaW4zmAl2SHSjhd+I2kmvkNGM4mKBgP6SR/d0gbxKgbhykXk3U7u/QsT0/a+pnQvtW1TBHNy87rUvZn2PiZ0S0CW0BQKLppc4Rjz69NQpyK7+52dRgJoc8dSe0Pen762QQP6He+jIqJuYIxdvdvnd64TsMhrJMtmiadX7CEI6h62W5e3V1+q+bLW5KwTfkQEHY+8vVhUu+hEYWObF1cXOyWaYkyHO3CzFC+dtrgQXMctgbILimftb0IhvhkWLSFr7rFhmbvPHrEA3JPJG6Oz+Ej4dkQbaBNPj3rm+Gs6AGMVhEOPN8hAenSaRAVHvu8Lmp/2POp8dDFYYBwALuFh18XDj+fstKVIrxJmS2+VXAGFOEM8IlrOEM+9EG5j3FYdAJU4XmRxbvd2IMDiMtwsX7mfGnTeDAXBcCNCt8oDOGhEBtLnOWk+LGQIb418P546kaRW3joTeGewOJ98Wy3ebaMsAsPgSzeAf8lGl944IWH/szW5V627LrVemXVsl12TcjXd+wIN002b5rcALuw+K1EeOeEzZun7h+aHxcdekv4QBWbf7BFYtBln2nB4T/kaJWNw/7SmTHtFyp8KGod8mm612481tH4/udt91Pz4Vc9GJjf2UChw44LWr2+yE6/v0gpbXp/0XKIbHqwfL5RsvNSC0SQYAai0ntfR8Ye08iz1A4prl6xOa9LvahUr/0P</diagram></mxfile>
\ No newline at end of file
diff --git a/openvisuspy/examples/create_streamable/ReadMe.md b/openvisuspy/examples/create_streamable/ReadMe.md
new file mode 100644
index 0000000..fe3d186
--- /dev/null
+++ b/openvisuspy/examples/create_streamable/ReadMe.md
@@ -0,0 +1,2 @@
+Links:
+- https://github.com/silx-kit/hdf5plugin/blob/main/doc/hdf5plugin_EuropeanHUG2021.ipynb
diff --git a/openvisuspy/examples/create_streamable/create_streamable.py b/openvisuspy/examples/create_streamable/create_streamable.py
new file mode 100644
index 0000000..6311395
--- /dev/null
+++ b/openvisuspy/examples/create_streamable/create_streamable.py
@@ -0,0 +1,187 @@
+import os,sys,time, h5py
+import numpy as np
+
+import OpenVisus as ov
+
+# //////////////////////////////////////////////////////////////////////////////////
+class Streamable:
+
+	compression="zip"
+	arco="4mb"
+
+	def __init__(self, src_file:h5py.File, dst_file:h5py.File, idx_urls:dict=None, compression=None, arco=None):
+		self.src_file=src_file
+		self.dst_file=dst_file
+		assert(idx_urls and "local" in idx_urls) # I need to create local files
+
+		# if not specified the first one will be the default
+		if not "default" in idx_urls:
+			idx_urls["default"]=idx_urls.keys()[0]
+
+		# default it's just an alias/key
+		assert(idx_urls["default"] in idx_urls)
+
+		self.idx_urls=idx_urls 
+		self.idx_datasets={}
+		self.compression=compression or self.compression
+		self.arco=arco or self.arco
+	
+	def copyAttribues(self, src, dst):
+		for k,v in src.attrs.items():
+			dst.attrs[k]=v
+
+	def createIdx(self, idx_url, src):
+
+		if True:
+			data_dir=os.path.splitext(idx_url)[0]	
+			print(f"DANGEROUS but needed: removing any old data file from {data_dir}")
+			import shutil
+			shutil.rmtree(data_dir, ignore_errors=True)
+		
+		t1=time.time()
+		data = src[...]
+		vmin,vmax=np.min(data),np.max(data)
+		print(f"Read data in {time.time()-t1} seconds shape={data.shape} dtype={data.dtype} vmin={vmin} vmax={vmax}")
+
+		# e.g. 1x1441x676x2048 -> 1441x676x2048
+		if len(data.shape)==4:
+			assert(data.shape[0]==1)
+			data=data[0,...]
+
+		basename=src.name.split("/")[-1]
+		ov_field=ov.Field.fromString(f"""{basename} {str(data.dtype)} format(row_major) min({vmin}) max({vmax})""")
+
+		
+		idx_axis=["X", "Y", "Z"]
+		D,H,W=data.shape
+		idx_physic_box=[0,W,0,H,0,D] 
+
+		# this is the NEXUS conventions where I have axes information 
+		axes=[str(it) for it in src.parent.attrs.get("axes",[])]
+		if axes:
+			idx_axis,idx_physic_box=[], []
+			for ax in reversed(axes):
+				sub=src.parent.get(ax)
+				idx_physic_box.extend([sub[0],sub[-1]])
+				idx_axis.append(ax)
+
+		idx_axis=" ".join([str(it) for it in idx_axis])
+		idx_physic_box=" ".join([str(it) for it in idx_physic_box])
+
+		db=ov.CreateIdx(
+			url=idx_url, 
+			dims=list(reversed(data.shape)), 
+			fields=[ov_field], 
+			compression="raw", # first I need to write uncompressed
+			physic_box=ov.BoxNd.fromString(idx_physic_box),
+			axis=idx_axis,
+			arco=self.arco
+		)
+		print(f"Created IDX idx_url=[{idx_url}] idx_axis=[{idx_axis}] idx_physic_box=[{idx_physic_box}]")
+
+		t1=time.time()
+		db.write(data)
+		print(f"Wrote IDX data in {time.time()-t1} seconds")
+
+		if self.compression and self.compression!="raw":
+			t1 = time.time()
+			print(f"Compressing dataset compression={self.compression}...")
+			db.compressDataset([self.compression])
+			print(f"Compressed dataset to {self.compression} in {time.time()-t1} seconds")
+
+	def doCopy(self, src):
+
+		if isinstance(src,h5py.Group):
+			dst=self.dst_file if src.name=="/" else self.dst_file.create_group(src.name)
+			self.copyAttribues(src,dst)
+			for it in src.keys():
+				self.doCopy(src[it])
+			return
+
+		if isinstance(src,h5py.Dataset):
+
+			shape, dtype=src.shape, src.dtype
+			if len(shape)==3 or (len(shape)==4 and shape[0]==1):
+
+				if src in self.idx_datasets:
+					dst, urls=self.idx_datasets[src]
+					self.dst_file[src.name]=dst
+					print(f"Found already converted dataset, using the link {src.name}->{dst.name}")
+				else:
+					dst=self.dst_file.create_dataset(src.name, shape=shape, dtype=dtype, data=None)
+					urls={k:v.replace("\\","/").replace("{name}",dst.name.lstrip("/")) for k,v in self.idx_urls.items()}
+					self.idx_datasets[src]=(dst,urls)
+					# need "local" to generate local datasets
+					self.createIdx(urls["local"],src) 
+
+				self.copyAttribues(src, dst)
+				# I am setting the idx_url at the parent level
+				dst.parent.attrs["idx_urls"] =str(urls)
+
+				idx_url=urls[urls["default"]]
+				dst.parent.attrs["idx_url" ] =str(idx_url)
+				
+			else:
+				# just copy the dataset
+				dst=self.dst_file.create_dataset(src.name, shape=shape, dtype=dtype, data=src[...])
+				self.copyAttribues(src,dst)
+
+			return
+		
+		raise NotImplementedError(f"doCopy of {src} not supported")
+
+
+	@staticmethod 
+	def SaveRemoteToLocal(remote_url, profile=None, endpoint_url=None):
+		import s3fs, tempfile
+		fs = s3fs.S3FileSystem(profile=profile,client_kwargs={'endpoint_url': endpoint_url})
+		key=remote_url[len(endpoint_url):]
+		key=key.lstrip("/")
+		key=key.split("?")[0]
+		with fs.open(key, mode='rb') as fin:
+			with tempfile.NamedTemporaryFile(suffix=os.path.splitext(key)[1]) as tmpfile: temporary_filename=tmpfile.name
+			with open(temporary_filename,"wb") as fout: fout.write(fin.read())
+		return temporary_filename
+
+	@staticmethod 
+	def Create(src_filename:str,dst_filename:str, **kwargs):
+		
+		if os.path.isfile(dst_filename): 
+				os.remove(dst_filename)
+		
+		os.makedirs(os.path.dirname(dst_filename),exist_ok=True)
+
+		with h5py.File(src_filename, 'r') as src_file:
+			with h5py.File(dst_filename,'w') as dst_file:
+				streamable=Streamable(src_file, dst_file, **kwargs)
+				streamable.doCopy(src_file)
+
+		print(f"new-size/old-size={os.path.getsize(dst_filename):,}/{os.path.getsize(src_filename):,}")
+
+	@staticmethod
+	def Print(src, links={}, nrec=0):
+
+		if isinstance(src,str):
+			with h5py.File(src, 'r') as f:
+				return Streamable.Print(f)
+
+		print("  "*nrec, f"{src.name}",end="")
+		is_link=src in links
+		if is_link:
+			print(f" link={links[src].name}")
+		else:
+			links[src]=src
+
+		if isinstance(src,h5py.Dataset):
+			print(f" shape='{src.shape}'",end="")
+			print(f" dtype='{src.dtype}'",end="")
+		print()
+
+		for k,v in src.attrs.items():
+			print("  "*(nrec+1), f"@{k}={v}")
+
+		if not is_link and not isinstance(src,h5py.Dataset):
+			for I,it in enumerate(src.keys()):
+				Streamable.Print(src[it], links, nrec+1)
+
+
diff --git a/openvisuspy/examples/create_streamable/diagram.drawio b/openvisuspy/examples/create_streamable/diagram.drawio
new file mode 100644
index 0000000..df0c918
--- /dev/null
+++ b/openvisuspy/examples/create_streamable/diagram.drawio
@@ -0,0 +1 @@
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</diagram></mxfile>
\ No newline at end of file
diff --git a/openvisuspy/examples/create_streamable/diagram.drawio.png b/openvisuspy/examples/create_streamable/diagram.drawio.png
new file mode 100644
index 0000000..29b8fa4
Binary files /dev/null and b/openvisuspy/examples/create_streamable/diagram.drawio.png differ
diff --git a/openvisuspy/examples/create_streamable/run.ipynb b/openvisuspy/examples/create_streamable/run.ipynb
new file mode 100644
index 0000000..f4c6f33
--- /dev/null
+++ b/openvisuspy/examples/create_streamable/run.ipynb
@@ -0,0 +1,274 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# HDF5 streamable version\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os,sys,time\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "# import openvisus\n",
+    "if os.path.isdir(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\"):\n",
+    "\tsys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n",
+    "\n",
+    "import OpenVisus as ov\n",
+    "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n",
+    "\n",
+    "from create_streamable import Streamable\n",
+    "from xarray_backend import OpenVisusBackendEntrypoint\n",
+    "\n",
+    "# NEEDED\n",
+    "#   OpenVisus need credentials that will extract from s3 config file\n",
+    "#   you need to have a `~/.aws/config` file with the profile\n",
+    "assert(os.path.isfile(os.path.expanduser(\"~/.aws/config\")))\n",
+    "\n",
+    "# *** CHANGE AS NEEDED ****\n",
+    "#   NOTE:  always better to have a directory which contains all h5 and OpenVisus file, for this reason I am using `dirname` for templates below\n",
+    "\n",
+    "# original file\n",
+    "h5_filename         = './reconstructed_data.nxs'\n",
+    "expression          ='/shanks-3731-a/data/reconstructed_data'\n",
+    "group,fieldname     = expression.rsplit(\"/\",maxsplit=1) # xarray needs to read one level-up (i.e. at group level)\n",
+    "\n",
+    "# create streamable local version, where each 3d field will be an OpenVisus dataset\n",
+    "local_url           = f\"./streamable/hdf5/reconstructed_data/visus.nxs\"\n",
+    "\n",
+    "# upload to S3\n",
+    "profile             = \"sealstorage\"\n",
+    "endpoint_url        = f\"https://maritime.sealstorage.io/api/v0/s3\"\n",
+    "\n",
+    "# this is where to get the file from the network \n",
+    "# -   NOTE: OpenVisus server does not support serving files such as HDF5 directly, we need a solution on apache\n",
+    "remote_url          = f\"https://maritime.sealstorage.io/api/v0/s3/utah/streamable/hdf5/reconstructed_data/visus.nxs?profile=\" + profile\n",
+    "\n",
+    "# {name} is the internal HDF5 expression to reach the data\n",
+    "idx_urls={\n",
+    "\n",
+    "\t# alias to a dic item that will be used for the `public`\n",
+    "\t\"default\":         \"remote\",\n",
+    "\n",
+    "\t# this is needed to generate interal local dtaset\n",
+    "\t\"local\":            os.path.splitext(local_url)[0]+\"/{name}/visus.idx\",\n",
+    "\n",
+    "\t# network s3 storage\n",
+    "\t\"remote\":           os.path.splitext(remote_url)[0]+\"/{name}/visus.idx?cached=arco&profile=\" + profile, \n",
+    "\n",
+    "\t# **TODO** this is missing the {name} in case of multiple fielcs inside the H5\n",
+    "\t\"remote-atlantis\": \"https://atlantis.sci.utah.edu/mod_visus?action=readdataset&dataset=reconstructed_data&cached=arco?cached=arco\" \n",
+    "}\n",
+    "\n",
+    "from pprint import pprint\n",
+    "pprint(idx_urls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read from original HDF5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset(h5_filename, group=group)\n",
+    "field=ds[fieldname]\n",
+    "data=field[...].values\n",
+    "print(\"Got data\",\"type\",type(data),\"shape\",data.shape,\"dtype\",data.dtype,\"min\",np.min(data),\"max\",np.max(data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use H5glance too\n",
+    "- Execute `!{sys.executable} -m pip install --quiet h5glance` if needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from h5glance import H5Glance\n",
+    "H5Glance(h5_filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create streamable version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arco  = \"2mb\"\n",
+    "compression = \"zip\"\n",
+    "Streamable.Create(h5_filename, local_url, arco=arco, compression=compression, idx_urls=idx_urls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Streamable.Print(local_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read  local "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset(local_url, group=group, engine=OpenVisusBackendEntrypoint, prefer=\"local\")\n",
+    "field=ds[fieldname]\n",
+    "timestep,res=0,27\n",
+    "data=field[timestep,...,res].values\n",
+    "print(\"Got data\",\"type\",type(data),\"shape\",data.shape,\"dtype\",data.dtype,\"min\",np.min(data),\"max\",np.max(data))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(data[100,...]) \n",
+    "plt.colorbar(im)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Upload all folder (H5 files and IDX data) to S3\n",
+    "\n",
+    "It is important to have an unique folder to simplify the upload\n",
+    "- **TODO** OpenVisus server would need a modification to the `visus.config` file , so it's not easy to make the upload automatic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!{sys.executable} -m pip install --quiet awscli-plugin-endpoint\n",
+    "!aws s3 sync --no-progress --endpoint-url {endpoint_url} --profile {profile} --size-only {os.path.dirname(local_url)}/ s3:/{os.path.dirname(remote_url)[len(endpoint_url):]}/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Read from S3\n",
+    "\n",
+    "- the streamable file already contains `cached=arco` so it should automatically cache data\n",
+    "- check your `~/visus/` directory for cache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# directly opening the stream using f3fs is causing some problems with `xr.open_dataset` so I am saving the file locally first\n",
+    "temp_local_url=Streamable.SaveRemoteToLocal(remote_url, profile=profile, endpoint_url=endpoint_url)\n",
+    "\n",
+    "ds=xr.open_dataset(temp_local_url, group=group, engine=OpenVisusBackendEntrypoint, prefer=\"remote\")\n",
+    "field=ds[fieldname]\n",
+    "timestep,res=0,27\n",
+    "data=field[timestep,...,res].values\n",
+    "print(\"Got data\",\"type\",type(data),\"shape\",data.shape,\"dtype\",data.dtype,\"min\",np.min(data),\"max\",np.max(data))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(data[100,...]) \n",
+    "plt.colorbar(im)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read from atlantis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OpenVisus server does not support serving any file such as streamable HDF5, so I need another place\n",
+    "ds=xr.open_dataset(local_url, group=group, engine=OpenVisusBackendEntrypoint, prefer=\"remote-atlantis\")\n",
+    "field=ds[fieldname]\n",
+    "timestep,res=0,27\n",
+    "data=field[timestep,...,res].values\n",
+    "print(\"Got data\",\"type\",type(data),\"shape\",data.shape,\"dtype\",data.dtype,\"min\",np.min(data),\"max\",np.max(data))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(data[100,...]) \n",
+    "plt.colorbar(im)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TODO\n",
+    "\n",
+    "- support of direct HDF5  (i.e. using `h5py` with `HDF5_PLUGIN_PATH`)\n",
+    "- support of direct NEXUS (i.e. ?)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/openvisuspy/examples/create_streamable/xarray_backend.py b/openvisuspy/examples/create_streamable/xarray_backend.py
new file mode 100644
index 0000000..068540f
--- /dev/null
+++ b/openvisuspy/examples/create_streamable/xarray_backend.py
@@ -0,0 +1,314 @@
+import xarray as xr
+import numpy  as np
+import pandas as pd
+import concurrent.futures
+
+import os
+
+# !pip install OpenVisusNoGui
+import OpenVisus as ov
+
+# see https://xarray.pydata.org/en/stable/internals/how-to-add-new-backend.html
+
+
+# ////////////////////////////////////////////////////////////
+class OpenVisusBackendArray(xr.backends.common.BackendArray):
+#     TODO: add num_refinements,quality
+#     TODO: adding it for normalized coordinates
+
+		# constructor
+		def __init__(self,db, shape, dtype, timesteps,resolution,fieldname):
+				self.db    = db
+				self.shape = shape
+				self.fieldname=fieldname
+				self.dtype = dtype
+				self.pdim=db.getPointDim()
+				self.timesteps=timesteps
+				self.resolution=resolution
+
+		# _getKeyRange
+		def _getXRange(self, value):
+				if self.pdim==2:
+						A = value.start if isinstance(value, slice) else value    ; A = int(0)             if A is None else A
+						B = value.stop  if isinstance(value, slice) else value + 1; B = int(self.shape[2]) if B is None else B
+				if self.pdim==3:
+						A = value.start if isinstance(value, slice) else value    ; A = int(0)             if A is None else A
+						B = value.stop  if isinstance(value, slice) else value + 1; B = int(self.shape[3]) if B is None else B
+				return (A,B)
+		def _getYRange(self, value):
+				if self.pdim==2:
+						A = value.start if isinstance(value, slice) else value    ; A = 0             if A is None else A
+						B = value.stop  if isinstance(value, slice) else value + 1; B = int(self.shape[1]) if B is None else B
+				if self.pdim==3:
+						A = value.start if isinstance(value, slice) else value    ; A = int(0)             if A is None else A
+						B = value.stop  if isinstance(value, slice) else value + 1; B = int(self.shape[2]) if B is None else B
+				return (A,B)
+		
+		def _getZRange(self, value):
+			
+				A = value.start if isinstance(value, slice) else value    ; A = int(0)             if A is None else A
+				B = value.stop  if isinstance(value, slice) else value + 1; B = int(self.shape[1]) if B is None else B
+				return (A,B)
+
+		def _getResRange(self, value):
+				A = value.start if isinstance(value, slice) else value    ; A =0         if A is None else A
+				B = value.stop  if isinstance(value, slice) else value + 1; B =  self.db.getMaxResolution()  +1 if B is None else B
+				return (A,B)
+		
+		def _getTRange(self, value):
+
+				A =  value.start if isinstance(value, slice) else value    ;A= int(self.shape[0])-1 if A is None else A
+				B =  value.stop  if isinstance(value, slice) else value + 1; B=1 if B is None else B
+
+				return (A,B)
+		# __readSamples
+		def _raw_indexing_method(self, key: tuple) -> np.typing.ArrayLike:
+
+				def fetch_data( time, res, x1, y1, x2, y2, fieldname, max_attempts=5, retry_delay=5):
+						attempt = 0
+						while attempt < max_attempts:
+								try:
+										if attempt>0:
+												print(f'Attempt: {attempt} out of {max_attempts}')
+										d=self.db.read(time=time, max_resolution=res, logic_box=[(x1, y1), (x2, y2)], field=fieldname)
+										return d
+								except Exception as e:  # Consider specifying the exception type if possible
+										print(f"Retry {attempt + 1}/{max_attempts} - Error fetching data: {e}")
+										attempt += 1
+										time.sleep(retry_delay)
+										if attempt == max_attempts:
+												print(f"Failed to fetch data after {max_attempts} attempts")
+												return None
+
+				def fetch_all_data(t1, t2, res, x1, y1, x2, y2, fieldname, max_workers=8):
+						data = []
+						with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+								futures = [executor.submit(fetch_data, time, res, x1, y1, x2, y2, fieldname) for time in range(t1, t2)]
+								for future in futures:
+										data.append(future.result())
+						return data
+				max_workers = 20 
+
+
+				if self.pdim==2:
+						t1,t2=self._getTRange(key[0])
+						y1,y2=self._getYRange(key[1])
+						x1,x2=self._getXRange(key[2])
+
+						data=[]
+						if isinstance(self.resolution,int):
+								res=self.resolution
+						else:
+								res,res1=self._getResRange(key[3])
+								if res==0:  
+										res= self.db.getMaxResolution()
+										print('Using Max Resolution: ',res)
+
+						if isinstance(self.timesteps,int):
+								data=self.db.read(time=self.timesteps,max_resolution=res, logic_box=[(x1,y1),(x2,y2)],field=self.fieldname)
+						else:
+								if isinstance(t1,int) and isinstance(res,int) and isinstance(t2,int):
+										data = fetch_all_data(t1, t2, res, x1, y1, x2, y2, self.fieldname, max_workers)
+
+								else:
+										data=self.db.read(logic_box=[(x1,y1),(x2,y2)],max_resolution=self.db.getMaxResolution(),field=self.fieldname)
+								
+				elif self.pdim==3:
+						
+						t1,t2=self._getTRange(key[0])
+						z1,z2=self._getZRange(key[1])
+						y1,y2=self._getYRange(key[2])
+						print(self.resolution)
+
+						
+						
+						if isinstance(self.resolution,int):
+								res=self.resolution
+						else:
+								res,res1=self._getResRange(key[4])
+
+								if res==0:
+										self.shape.pop()
+										res= self.db.getMaxResolution()
+										print('Using Max Resolution: ',res)
+
+						if isinstance(self.timesteps,int):
+								x1,x2=self._getXRange(key[3])
+								data=self.db.read(time=self.timesteps,max_resolution=res, logic_box=[(x1,y1,z1),(x2,y2,z2)],field=self.fieldname)
+						elif len(self.timesteps)==1:
+								x1,x2=self._getXRange(key[3])
+								data=self.db.read(max_resolution=res,logic_box=[(x1,y1,z1),(x2,y2,z2)],field=self.fieldname)
+
+ 
+						else:
+								
+								if isinstance(t1, int) and isinstance(res,int):
+										x1,x2=self._getXRange(key[3])
+
+										data=self.db.read(time=t1, max_resolution=res,logic_box=[(x1,y1,z1),(x2,y2,z2)])
+								else:
+										data=self.db.read(logic_box=[(x1,y1,z1),(x2,y2,z2)],field=self.fieldname)
+										
+										
+				else:
+						raise Exception("dimension error")
+
+
+				
+				return np.array(data)
+		# __getitem__
+		def __getitem__(self, key: xr.core.indexing.ExplicitIndexer) -> np.typing.ArrayLike:
+				return xr.core.indexing.explicit_indexing_adapter(key,self.shape,
+																													xr.core.indexing.IndexingSupport.BASIC,
+																													self._raw_indexing_method)
+
+
+# ////////////////////////////////////////////////////////////////////////////////
+class OpenVisusBackendEntrypoint(xr.backends.common.BackendEntrypoint):
+
+		# needed bu xarray (list here all arguments specific for the backend)
+		open_dataset_parameters = ["filename_or_obj", "drop_variables", "resolution", "timesteps","coordinates","prefer"]
+		
+		# open_dataset (needed by the backend)
+		def open_dataset(self,filename_or_obj,*, resolution=None, timesteps=None,drop_variables=None,coords=None,attrs=None,dims=None, prefer=None, **kwargs):
+
+				self.resolution=resolution
+				
+				self.coordinates=coords
+				data_vars={}
+
+				ds=xr.open_dataset(filename_or_obj,decode_times=False, **kwargs)
+				if drop_variables!= None:
+						for i in drop_variables:
+								ds=ds.drop(i)
+				if 'time' in ds:
+						ds=ds.drop('time')
+
+				# i can have multiple versions of urls {remote:..., "local":...}
+				idx_urls=eval(ds.attrs.get("idx_urls","{}"))
+				if prefer is not None:
+					idx_url=idx_urls[prefer]
+				elif idx_urls:
+					if 'idx_url' not in ds.attrs: 
+						raise Exception("`idx_url` not found in dataset attributes")
+					idx_url=ds.attrs['idx_url']
+				print(f"ov.LoadDataset({idx_url})")
+				db=ov.LoadDataset(idx_url)
+				# if self.resolution==None:
+				#     self.resolution=db.getMaxResolution()
+				self.timesteps=timesteps
+				dim=db.getPointDim()
+				dims=db.getLogicSize()
+				
+				if self.timesteps==None:
+						self.timesteps=db.getTimesteps()
+						
+				# convert OpenVisus fields into xarray variables
+				for fieldname in db.getFields():
+						field=db.getField(fieldname)
+						
+						ncomponents=field.dtype.ncomponents()
+						atomic_dtype=field.dtype.get(0)
+
+						dtype=self.toNumPyDType(atomic_dtype)
+						shape=list(reversed(dims))
+					 
+						
+						if self.coordinates==None:
+								if ds[fieldname].coords:
+										labels=[i for i in ds[fieldname].coords]
+								else:
+										labels=[i for i in ds[fieldname].dims]
+
+						
+
+								if ncomponents>1:
+										labels.append("channel")
+										shape.append(ncomponents)
+								labels.insert(0,"time")
+								labels.append("resolution")
+								if isinstance(self.resolution,int):
+										
+										shape.append(self.resolution+1)
+								else:
+										shape.append(db.getMaxResolution()+1)
+								if isinstance(self.timesteps, int):
+										shape.insert(0,self.timesteps+1)
+								else:
+										shape.insert(0,len(self.timesteps))
+								
+
+				
+						data_vars[fieldname]=xr.Variable(
+								labels,
+								xr.core.indexing.LazilyIndexedArray(OpenVisusBackendArray(db=db, shape=shape,dtype=dtype,
+								fieldname=fieldname,
+																																					timesteps=self.timesteps,
+																																					resolution=self.resolution)),
+								attrs=ds[fieldname].attrs
+						)
+						print(resolution)
+						print("Adding field ",fieldname,"shape ",shape,"dtype ",dtype,"labels ",labels,
+								 "Max Resolution ", db.getMaxResolution())
+				
+						
+				ds1 = xr.Dataset(data_vars=data_vars,attrs=ds.attrs)
+				coord_name=[i for i in ds.coords]
+
+				for coord in coord_name:
+						ds1[coord]=ds.coords[coord].values
+						if coord in ds1.coords:
+								ds1[coord].attrs=ds[coord].attrs
+						
+				ds1.attrs=ds.attrs
+
+				ds1.set_close(self.close_method)
+				
+				return ds1
+		
+		# toNumPyDType (always pass the atomic OpenVisus type i.e. uint8[8] should not be accepted)
+		def toNumPyDType(self,atomic_dtype):
+				"""
+				convert an Openvisus dtype to numpy dtype
+				"""
+
+				# dtype  (<: little-endian, >: big-endian, |: not-relevant) ; integer providing the number of bytes  ; i (integer) u (unsigned integer) f (floating point)
+				return np.dtype("".join([
+						"|" if atomic_dtype.getBitSize()==8 else "<",
+						"f" if atomic_dtype.isDecimal() else ("u" if atomic_dtype.isUnsigned() else "i"),
+						str(int(atomic_dtype.getBitSize()/8))
+				]))
+
+		# close_method (needed for the OpenVisus backend)
+		def close_method(self):
+				print("nothing to do here")
+		
+		# guess_can_open (needed for the OpenVisus backend)
+		def guess_can_open(self, filename_or_obj):
+				print("guess_can_open",filename_or_obj)
+
+				# todo: extend to S3 datasets
+				if "mod_visus" in filename_or_obj:
+						return True
+				
+				# using this backend, anything that goes to the network will be S3
+				if filename_or_obj.startswith("http"):
+					return True
+				 
+				# local files
+				try:
+						_, ext = os.path.splitext(filename_or_obj)
+				except TypeError:
+						return False
+				return ext.lower()==".idx"
+
+
+
+
+
+
+
+
+
+
+
diff --git a/openvisuspy/examples/experimental/2kbit1.html b/openvisuspy/examples/experimental/2kbit1.html
new file mode 100644
index 0000000..c04ea0e
--- /dev/null
+++ b/openvisuspy/examples/experimental/2kbit1.html
@@ -0,0 +1,64 @@
+<html>
+	<head>
+		<title>Panel Example</title>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/@holoviz/panel@0.14.4/dist/panel.min.js" ></script>
+		<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css"  />
+		<script defer src="https://pyscript.net/releases/2023.03.1/pyscript.js"></script>
+	</head>
+<body>
+
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-config>
+
+terminal = false
+
+packages=[
+	"numpy",
+	"pandas",
+	"requests",
+	"xmltodict",
+	"xyzservices",
+	"pyodide-http",
+	"colorcet",
+	"panel==0.14.4",
+	"https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl",
+	"openvisuspy==0.0.20",
+]
+
+</py-config>
+
+<!-- where the app is displayed-->
+<div id="my_app"></div>
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-script>
+
+import os,sys,datetime,logging,time,pyodide_http
+
+from openvisuspy import *
+SetupLogger()
+pyodide_http.patch_all() 
+
+import panel as pn
+pn.extension(sizing_mode='stretch_both')
+
+url="https://atlantis.sci.utah.edu/mod_visus?dataset=2kbit1"
+view=Slices(is_panel=True)
+view.setDataset(url)
+view.setPalette("Greys256") 
+view.setPaletteRange([0,255])
+app=view.getMainLayout()
+app.servable(target='my_app')
+
+</py-script>
+
+<!--  <py-terminal/> -->
+<!--  <py-repl/>     -->
+
+
+</body>
+</html>
\ No newline at end of file
diff --git a/openvisuspy/examples/experimental/README.md b/openvisuspy/examples/experimental/README.md
new file mode 100644
index 0000000..23d3627
--- /dev/null
+++ b/openvisuspy/examples/experimental/README.md
@@ -0,0 +1,87 @@
+
+## (EXPERIMENTAL and DEPRECATED) Use Pure Python Backend
+
+This version may be used for cpython too in case you cannot install C++ OpenVisus (e.g., WebAssembly).
+
+It **will not work with S3 cloud-storage blocks**.
+
+Bokeh dashboards:
+
+```
+python3 -m bokeh serve "dashboards"  --dev --address localhost --port 8888 --args --py  --single
+python3 -m bokeh serve "dashboards"  --dev --address localhost --port 8888 --args --py  --multi
+```
+
+Panel dashboards:
+
+```
+python -m panel serve "dashboards"  --dev --address localhost --port 8888 --args --py --single
+python -m panel serve "dashboards"  --dev --address localhost --port 8888 --args --py --multi
+```
+
+Jupyter notebooks:
+
+```
+export VISUS_BACKEND=py
+python3 -m jupyter notebook ./examples/notebooks
+```
+
+### Demos
+
+REMEMBER to resize the browswe  window, **otherwise it will not work**:
+
+- https://scrgiorgio.it/david_subsampled.html
+- https://scrgiorgio.it/2kbit1.html
+- https://scrgiorgio.it/chess_zip.html
+
+DEVELOPERS notes:
+- grep for `openvisuspy==` and **change the version consistently**.
+
+### PyScript
+
+Serve local directory
+
+```
+export VISUS_BACKEND=py
+python3 examples/server.py --directory ./
+```
+
+Open the urls in your Google Chrome browser:
+
+- http://localhost:8000/examples/pyscript/index.html 
+- http://localhost:8000/examples/pyscript/2kbit1.html 
+- http://localhost:8000/examples/pyscript/chess_zip.html 
+- http://localhost:8000/examples/pyscript/david_subsampled.html
+
+### JupyterLite
+
+```
+export VISUS_BACKEND=py
+ENV=/tmp/openvisuspy-lite-last
+python3 -m venv ${ENV}
+source ${ENV}/bin/activate
+
+# Right now jupyter lite seems to build the output based on installed packages. 
+# There should be other ways (e.g., JSON file or command line) for specifying packages, but for now creating a virtual env is good enough\
+# you need to have exactly the same package version inside your jupyter notebook (see `12-jupyterlite.ipynb`)
+python3 -m pip install \
+    jupyterlite==0.1.0b20 pyviz_comms numpy pandas requests xmltodict xyzservices pyodide-http colorcet \
+    https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl \
+    panel==0.14.2 \
+    openvisuspy==1.0.100 \
+    jupyter_server 
+
+rm -Rf ${ENV}/_output 
+jupyter lite build --contents /mnt/c/projects/openvisuspy/examples/notebooks --output-dir ${ENV}/_output
+
+# change port to avoid caching
+PORT=14445
+python3 -m http.server --directory ${ENV}/_output --bind localhost ${PORT}
+
+# or serve
+jupyter lite serve --contents ./examples/notebooks --output-dir ${ENV}/_output --port ${PORT} 
+
+# copy the files somewhere for testing purpouse
+rsync -arv ${ENV}/_output/* <username>@<hostname>:jupyterlite-demos/
+```
+
diff --git a/openvisuspy/examples/experimental/chess_zip.html b/openvisuspy/examples/experimental/chess_zip.html
new file mode 100644
index 0000000..f30bc8d
--- /dev/null
+++ b/openvisuspy/examples/experimental/chess_zip.html
@@ -0,0 +1,64 @@
+<html>
+	<head>
+		<title>Panel Example</title>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/@holoviz/panel@0.14.4/dist/panel.min.js" ></script>
+		<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css"  />
+		<script defer src="https://pyscript.net/releases/2023.03.1/pyscript.js"></script>
+	</head>
+<body>
+
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-config>
+
+terminal = false
+
+packages=[
+	"numpy",
+	"pandas",
+	"requests",
+	"xmltodict",
+	"xyzservices",
+	"pyodide-http",
+	"colorcet",
+	"panel==0.14.4",
+	"https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl",
+	"openvisuspy==0.0.20",
+]
+
+</py-config>
+
+<!-- where the app is displayed-->
+<div id="my_app"></div>
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-script>
+
+import os,sys,datetime,logging,time,pyodide_http
+
+from openvisuspy import *
+SetupLogger()
+pyodide_http.patch_all() 
+
+import panel as pn
+pn.extension(sizing_mode='stretch_both')
+
+url="https://atlantis.sci.utah.edu/mod_visus?dataset=chess-zip"
+view=Slices(is_panel=True)
+view.setDataset(url)
+view.setPalette("Viridis256") 
+view.setPaletteRange([-0.017141795,0.012004322]) 
+app=view.getMainLayout()
+app.servable(target='my_app')
+
+</py-script>
+
+<!--  <py-terminal/> -->
+<!--  <py-repl/>     -->
+
+
+</body>
+</html>
\ No newline at end of file
diff --git a/openvisuspy/examples/experimental/david_subsampled.html b/openvisuspy/examples/experimental/david_subsampled.html
new file mode 100644
index 0000000..a10b12c
--- /dev/null
+++ b/openvisuspy/examples/experimental/david_subsampled.html
@@ -0,0 +1,64 @@
+<html>
+	<head>
+		<title>Panel Example</title>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/@holoviz/panel@0.14.4/dist/panel.min.js" ></script>
+		<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css"  />
+		<script defer src="https://pyscript.net/releases/2023.03.1/pyscript.js"></script>
+	</head>
+<body>
+
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-config>
+
+terminal = false
+
+packages=[
+	"numpy",
+	"pandas",
+	"requests",
+	"xmltodict",
+	"xyzservices",
+	"pyodide-http",
+	"colorcet",
+	"panel==0.14.4",
+	"https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl",
+	"openvisuspy==0.0.20",
+]
+
+</py-config>
+
+<!-- where the app is displayed-->
+<div id="my_app"></div>
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-script>
+
+import os,sys,datetime,logging,time,pyodide_http
+
+from openvisuspy import *
+SetupLogger()
+pyodide_http.patch_all() 
+
+import panel as pn
+pn.extension(sizing_mode='stretch_both')
+
+url="https://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled"
+view=Slices(is_panel=True)
+view.setDataset(url)
+view.setPalette("Greys256") 
+view.setPaletteRange([0,255])
+app=view.getMainLayout()
+app.servable(target='my_app')
+
+</py-script>
+
+<!--  <py-terminal/> -->
+<!--  <py-repl/>     -->
+
+
+</body>
+</html>
\ No newline at end of file
diff --git a/openvisuspy/examples/experimental/index.html b/openvisuspy/examples/experimental/index.html
new file mode 100644
index 0000000..67c0681
--- /dev/null
+++ b/openvisuspy/examples/experimental/index.html
@@ -0,0 +1,77 @@
+<html>
+	<head>
+		<title>Panel Example</title>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js" ></script>
+		<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/@holoviz/panel@0.14.4/dist/panel.min.js" ></script>
+		<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css"  />
+		<script defer src="https://pyscript.net/releases/2023.03.1/pyscript.js"></script>
+	</head>
+<body>
+
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-config>
+
+terminal = false
+
+packages=[
+	"numpy",
+	"pandas",
+	"requests",
+	"xmltodict",
+	"xyzservices",
+	"pyodide-http",
+	"colorcet",
+	"panel==0.14.4",
+	"https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl",
+]
+[[fetch]]
+from = '/src/openvisuspy/'
+to_folder = '/home/pyodide/openvisuspy/'
+files = [
+	"backend.py",
+	"backend_py.py",
+	"backend_cpp.py",
+	"app.py",
+	"canvas.py",
+	"slice.py",
+	"slices.py",
+	"utils.py",
+	"widgets.py",
+	"__init__.py",
+]
+</py-config>
+
+<!-- where the app is displayed-->
+<div id="my_app"></div>
+
+<!-- # ////////////////////////////////////////////////////////////////// / -->
+<py-script>
+
+import os,sys,datetime,logging,time,pyodide_http
+
+from openvisuspy import *
+# SetupLogger()
+pyodide_http.patch_all() 
+
+import panel as pn
+pn.extension(sizing_mode='stretch_both')
+
+url="https://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled&cached=1"
+view=Slices(is_panel=True)
+view.setDataset(url)
+view.setPalette("Greys256") 
+view.setPaletteRange([0,255])
+app=view.getMainLayout()
+app.servable(target='my_app')
+
+</py-script>
+
+<!--  <py-terminal/> -->
+<!--  <py-repl/>     -->
+
+
+</body>
+</html>
\ No newline at end of file
diff --git a/openvisuspy/examples/experimental/jupyterlite-example.ipynb b/openvisuspy/examples/experimental/jupyterlite-example.ipynb
new file mode 100644
index 0000000..5fdfac3
--- /dev/null
+++ b/openvisuspy/examples/experimental/jupyterlite-example.ipynb
@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Configure JupyterLite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil, os, micropip,logging,time\n",
+    "\n",
+    "# I have no ide why I have to do this, there is some confusion between bokeh version\n",
+    "_fix=[shutil.rmtree(f\"/lib/python3.10/site-packages/{it}\", ignore_errors=True) for it in os.listdir(\"/lib/python3.10/site-packages\") if it.startswith(\"bokeh\")]\n",
+    "await micropip.install(\"https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl\")\n",
+    "await micropip.install(\"bokeh==2.4.3\")\n",
+    "await micropip.install([\n",
+    "    \"pyviz_comms\",\n",
+    "    \"numpy\", \n",
+    "    \"pandas\", \n",
+    "    \"requests\", \n",
+    "    \"xmltodict\",\n",
+    "    \"xyzservices\", \n",
+    "    \"pyodide-http\", \n",
+    "    \"colorcet\",\n",
+    "    \"https://cdn.holoviz.org/panel/0.14.3/dist/wheels/bokeh-2.4.3-py3-none-any.whl\", \n",
+    "    \"panel==0.14.2\", \n",
+    "    \"openvisuspy==0.0.20\",])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test if Panel is working"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bokeh\n",
+    "print(bokeh.__version__)\n",
+    "import panel as pn;print(pn.__version__)\n",
+    "pn.extension('vega')\n",
+    "\n",
+    "button = bokeh.models.widgets.Button(label=\"Panel is working? Push...\", sizing_mode='stretch_width')\n",
+    "def OnClick(evt=None): button.label=\"YES!\"\n",
+    "button.on_click(OnClick)    \n",
+    "pn.pane.Bokeh(button) # NOTE: bokeh will not work (complaining about tornado)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# David (2D-uint8[3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openvisuspy\n",
+    "from openvisuspy import Slice, Slices, GetBackend, SetupLogger\n",
+    "logger=SetupLogger()\n",
+    "logger.info(f\"GetBackend={GetBackend()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view=Slice() \n",
+    "view.setDataset(\"https://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled&cached=1\" )   \n",
+    "view.setPalette(\"Greys256\")\n",
+    "view.setPaletteRange([0,255])\n",
+    "view.getPanelLayout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2kbit1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view=Slice() \n",
+    "view.setDataset(\"https://atlantis.sci.utah.edu/mod_visus?dataset=2kbit1&cached=1\") \n",
+    "view.setPalette(\"Greys256\")\n",
+    "view.setPaletteRange((0,255))\n",
+    "view.getPanelLayout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rabbit "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view=Slice()\n",
+    "view.setDataset('https://atlantis.sci.utah.edu/mod_visus?dataset=rabbit&cached=1')   \n",
+    "view.setPalette(\"Greys256\")\n",
+    "view.setPaletteRange((0,255))\n",
+    "view.getPanelLayout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view=Slice() \n",
+    "view.setDataset('https://atlantis.sci.utah.edu/mod_visus?dataset=chess-zip&cached=1')   \n",
+    "view.setPalette(\"Viridis256\")\n",
+    "view.setPaletteRange([-0.017141795,0.012004322])\n",
+    "view.getPanelLayout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "python",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/openvisuspy/examples/experimental/server.py b/openvisuspy/examples/experimental/server.py
new file mode 100644
index 0000000..f9c967c
--- /dev/null
+++ b/openvisuspy/examples/experimental/server.py
@@ -0,0 +1,14 @@
+import http.server
+
+class MyHttpServer(http.server.SimpleHTTPRequestHandler):
+	
+	def end_headers(self):
+		self.send_header("Cache-Control", "no-cache, no-store, must-revalidate")
+		self.send_header("Pragma", "no-cache")
+		self.send_header("Expires", "0")
+		super().end_headers()
+
+
+if __name__ == '__main__':
+	# NOTE: I am disabling cache in http.server (useful for debugging mode)
+	http.server.test(HandlerClass=MyHttpServer)
\ No newline at end of file
diff --git a/openvisuspy/examples/notebooks/ov-dashboards.ipynb b/openvisuspy/examples/notebooks/ov-dashboards.ipynb
new file mode 100644
index 0000000..5df39ad
--- /dev/null
+++ b/openvisuspy/examples/notebooks/ov-dashboards.ipynb
@@ -0,0 +1,1159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import OpenVisus\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "endpoint_url=\"https://maritime.sealstorage.io/api/v0/s3\"\n",
+    "\n",
+    "datasets={\n",
+    "     \"datasets\": [\n",
+    "         {\"name\":\"david\", \"url\":\"http://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled&cached=idx\"},\n",
+    "         {\"name\":\"2kbit1\", \"url\":\"http://atlantis.sci.utah.edu/mod_visus?dataset=2kbit1&cached=idx\"},\n",
+    "         {\"name\":\"retina\", \"url\":\"http://atlantis.sci.utah.edu/mod_visus?dataset=rabbit&cached=idx\"},\n",
+    "         {\n",
+    "             \"name\":\"chess-zip\",\"url\":\"http://atlantis.sci.utah.edu:80/mod_visus?dataset=chess-zip&cached=idx\",\n",
+    "             \"palette\" :\"Viridis256\", \"range-min\": -0.017141795, \"range-max\": +0.012004322,\n",
+    "         },\n",
+    "         {\n",
+    "             \"name\":\"chess-recon\",\"url\":\"http://atlantis.sci.utah.edu:80/mod_visus?dataset=chess-recon_combined_1_2_3_fullres_zip&cached=idx\",\n",
+    "             \"palette\" :\"Plasma256\", \"range-min\": -0.0014, \"range-max\": +0.0020,   \n",
+    "         },\n",
+    "         {\n",
+    "            \"name\": \"llc2160_arco\",\"url\": f\"{endpoint_url}/utah/nasa/dyamond/mit_output/llc2160_arco/visus.idx?cached=idx& access_key=any&secret_key=any&endpoint_url={endpoint_url}\",\n",
+    "            \"palette\":\"colorcet.coolwarm\", \"range-min\":-0.25256651639938354, \"range-max\":+0.3600933849811554,\n",
+    "            \"timestep-delta\":10, \"timestep\": 2015, \"resolution\": -6, \n",
+    "         },\n",
+    "         {\n",
+    "            \"name\":\"bellows\", \"url\": \"http://atlantis.sci.utah.edu/mod_visus?dataset=bellows_CT_NASA_JHochhalter&cached=idx\",\n",
+    "            \"palette\":\"Greys256\", \"range-min\":0, \"range-max\":65536\n",
+    "         } \n",
+    "         ] + [ \n",
+    "            {\n",
+    "                \"name\": f\"diamond-{zone}\",   \"url\": f\"{endpoint_url}/utah/nasa/dyamond/idx_arco/face{zone}/u_face_{zone}_depth_52_time_0_10269.idx?cached=idx& access_key=any&secret_key=any&endpoint_url={endpoint_url}\",\n",
+    "                \"palette\": \"Turbo256\", \"range-min\":-30.0, \"range-max\":60.0,\n",
+    "                \"timestep-delta\":10, \"resolution\": -6,  \"directions\": {'Long':0, 'Lat':1, 'Depth':2},\n",
+    "                \"logic-to-physic\":[(0.0,1.0), (0.0,1.0), (0.0,10.0)],               \n",
+    "            }\n",
+    "            for zone in range(6)\n",
+    "        ] \n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\projects\\openvisuspy\\.venv\\Scripts\\python.exe\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  var force = true;\n",
+       "  var py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+       "  var reloading = false;\n",
+       "  var Bokeh = root.Bokeh;\n",
+       "\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "    if (js_modules == null) js_modules = [];\n",
+       "    if (js_exports == null) js_exports = {};\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (!reloading) {\n",
+       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    }\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "    window._bokeh_on_load = on_load\n",
+       "\n",
+       "    function on_error() {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    var skip = [];\n",
+       "    if (window.requirejs) {\n",
+       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
+       "      require([\"jspanel\"], function(jsPanel) {\n",
+       "\twindow.jsPanel = jsPanel\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-modal\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-tooltip\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-hint\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-layout\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-contextmenu\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-dock\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"gridstack\"], function(GridStack) {\n",
+       "\twindow.GridStack = GridStack\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"notyf\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      root._bokeh_is_loading = css_urls.length + 9;\n",
+       "    } else {\n",
+       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
+       "    }\n",
+       "\n",
+       "    var existing_stylesheets = []\n",
+       "    var links = document.getElementsByTagName('link')\n",
+       "    for (var i = 0; i < links.length; i++) {\n",
+       "      var link = links[i]\n",
+       "      if (link.href != null) {\n",
+       "\texisting_stylesheets.push(link.href)\n",
+       "      }\n",
+       "    }\n",
+       "    for (var i = 0; i < css_urls.length; i++) {\n",
+       "      var url = css_urls[i];\n",
+       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
+       "\ton_load()\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    var existing_scripts = []\n",
+       "    var scripts = document.getElementsByTagName('script')\n",
+       "    for (var i = 0; i < scripts.length; i++) {\n",
+       "      var script = scripts[i]\n",
+       "      if (script.src != null) {\n",
+       "\texisting_scripts.push(script.src)\n",
+       "      }\n",
+       "    }\n",
+       "    for (var i = 0; i < js_urls.length; i++) {\n",
+       "      var url = js_urls[i];\n",
+       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (var i = 0; i < js_modules.length; i++) {\n",
+       "      var url = js_modules[i];\n",
+       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (const name in js_exports) {\n",
+       "      var url = js_exports[name];\n",
+       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      element.textContent = `\n",
+       "      import ${name} from \"${url}\"\n",
+       "      window.${name} = ${name}\n",
+       "      window._bokeh_on_load()\n",
+       "      `\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    if (!js_urls.length && !js_modules.length) {\n",
+       "      on_load()\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/panel.min.js\"];\n",
+       "  var js_modules = [];\n",
+       "  var js_exports = {};\n",
+       "  var css_urls = [\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.css\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.css\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/font-awesome/css/all.min.css\"];\n",
+       "  var inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"debug\");\n",
+       "    },\n",
+       "function(Bokeh) {} // ensure no trailing comma for IE\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
+       "      for (var i = 0; i < inline_js.length; i++) {\n",
+       "\ttry {\n",
+       "          inline_js[i].call(root, root.Bokeh);\n",
+       "\t} catch(e) {\n",
+       "\t  if (!reloading) {\n",
+       "\t    throw e;\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "      // Cache old bokeh versions\n",
+       "      if (Bokeh != undefined && !reloading) {\n",
+       "\tvar NewBokeh = root.Bokeh;\n",
+       "\tif (Bokeh.versions === undefined) {\n",
+       "\t  Bokeh.versions = new Map();\n",
+       "\t}\n",
+       "\tif (NewBokeh.version !== Bokeh.version) {\n",
+       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
+       "\t}\n",
+       "\troot.Bokeh = Bokeh;\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    }\n",
+       "    root._bokeh_is_initializing = false\n",
+       "  }\n",
+       "\n",
+       "  function load_or_wait() {\n",
+       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
+       "    // versions of Bokeh and its dependencies at the same time.\n",
+       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
+       "    // to determine whether there is an ongoing attempt to initialize\n",
+       "    // bokeh, however for backward compatibility we also try to ensure\n",
+       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
+       "    // before older versions are fully initialized.\n",
+       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
+       "      root._bokeh_is_initializing = false;\n",
+       "      root._bokeh_onload_callbacks = undefined;\n",
+       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
+       "      load_or_wait();\n",
+       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
+       "      setTimeout(load_or_wait, 100);\n",
+       "    } else {\n",
+       "      root._bokeh_is_initializing = true\n",
+       "      root._bokeh_onload_callbacks = []\n",
+       "      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
+       "      if (!reloading && !bokeh_loaded) {\n",
+       "\troot.Bokeh = undefined;\n",
+       "      }\n",
+       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
+       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "\trun_inline_js();\n",
+       "      });\n",
+       "    }\n",
+       "  }\n",
+       "  // Give older versions of the autoload script a head-start to ensure\n",
+       "  // they initialize before we start loading newer version.\n",
+       "  setTimeout(load_or_wait, 100)\n",
+       "}(window));"
+      ],
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.css\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.css\", \"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/font-awesome/css/all.min.css\"];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"debug\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    function JupyterCommManager() {\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        comm_manager.register_target(comm_id, function(comm) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        });\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        });\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "          function processIteratorResult(result) {\n",
+       "            var message = result.value;\n",
+       "            console.log(message)\n",
+       "            var content = {data: message.data, comm_id};\n",
+       "            var buffers = []\n",
+       "            for (var buffer of message.buffers || []) {\n",
+       "              buffers.push(new DataView(buffer))\n",
+       "            }\n",
+       "            var metadata = message.metadata || {};\n",
+       "            var msg = {content, buffers, metadata}\n",
+       "            msg_handler(msg);\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "          return messages.next().then(processIteratorResult);\n",
+       "        })\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (comm_id in window.PyViz.comms) {\n",
+       "        return window.PyViz.comms[comm_id];\n",
+       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
+       "        if (msg_handler) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        }\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
+       "        comm.open();\n",
+       "        if (msg_handler) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        }\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        }) \n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
+       "  if (server_id !== null) {\n",
+       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
+       "    return;\n",
+       "  } else if (comm !== null) {\n",
+       "    comm.send({event_type: 'delete', 'id': id});\n",
+       "  }\n",
+       "  delete PyViz.plot_index[id];\n",
+       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
+       "    var doc = window.Bokeh.index[id].model.document\n",
+       "    doc.clear();\n",
+       "    const i = window.Bokeh.documents.indexOf(doc);\n",
+       "    if (i > -1) {\n",
+       "      window.Bokeh.documents.splice(i, 1);\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle kernel restart event\n",
+       " */\n",
+       "function handle_kernel_cleanup(event, handle) {\n",
+       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
+       "  window.PyViz.plot_index = {}\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle update_display_data messages\n",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function register_renderer(events, OutputArea) {\n",
+       "  function append_mime(data, metadata, element) {\n",
+       "    // create a DOM node to render to\n",
+       "    var toinsert = this.create_output_subarea(\n",
+       "    metadata,\n",
+       "    CLASS_NAME,\n",
+       "    EXEC_MIME_TYPE\n",
+       "    );\n",
+       "    this.keyboard_manager.register_events(toinsert);\n",
+       "    // Render to node\n",
+       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "    render(props, toinsert[0]);\n",
+       "    element.append(toinsert);\n",
+       "    return toinsert\n",
+       "  }\n",
+       "\n",
+       "  events.on('output_added.OutputArea', handle_add_output);\n",
+       "  events.on('output_updated.OutputArea', handle_update_output);\n",
+       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
+       "  events.on('delete.Cell', handle_clear_output);\n",
+       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
+       "\n",
+       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "    safe: true,\n",
+       "    index: 0\n",
+       "  });\n",
+       "}\n",
+       "\n",
+       "if (window.Jupyter !== undefined) {\n",
+       "  try {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  } catch(err) {\n",
+       "  }\n",
+       "}\n"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265'>\n",
+       "  <div id=\"a3fa2199-b65c-484c-abf3-ff90c1dbb334\" data-root-id=\"1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"040eabb9-e8d2-49e5-8c12-ddb4594c587a\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"f2dfe534-c60d-4085-a54f-d97c78d01e04\",\"attributes\":{\"plot_id\":\"1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265\",\"comm_id\":\"fe73d93342fe4bbc88f56bb79bba0750\",\"client_comm_id\":\"fae790e0d03b4bf799dbe893415ec2fd\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"040eabb9-e8d2-49e5-8c12-ddb4594c587a\",\"roots\":{\"1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265\":\"a3fa2199-b65c-484c-abf3-ff90c1dbb334\"},\"root_ids\":[\"1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['jsPanel'] !== undefined))\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "1fdc8b0b-2bff-4728-aaa3-0cb2c8d83265"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='aae709a2-75af-4de4-a84b-3a6d684f448d'>\n",
+       "  <div id=\"b1e6d74d-331b-4cbe-87bb-8a65b2b20b2e\" data-root-id=\"aae709a2-75af-4de4-a84b-3a6d684f448d\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"84db0fd7-3756-4535-96c7-9ae54c627089\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.reactive_html.ReactiveHTML\",\"id\":\"aae709a2-75af-4de4-a84b-3a6d684f448d\",\"attributes\":{\"subscribed_events\":{\"type\":\"set\",\"entries\":[\"dom_event\"]},\"stylesheets\":[{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"fa22221e-da92-4829-b8d5-a01ec8f714eb\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"7e97457a-2f6a-4f2a-a059-c68438769c10\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/font-awesome/css/all.min.css\"}},\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHN0eWxlPSJtYXJnaW46IGF1dG87IGJhY2tncm91bmQ6IG5vbmU7IGRpc3BsYXk6IGJsb2NrOyBzaGFwZS1yZW5kZXJpbmc6IGF1dG87IiB2aWV3Qm94PSIwIDAgMTAwIDEwMCIgcHJlc2VydmVBc3BlY3RSYXRpbz0ieE1pZFlNaWQiPiAgPGNpcmNsZSBjeD0iNTAiIGN5PSI1MCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSIjYzNjM2MzIiBzdHJva2Utd2lkdGg9IjEwIiByPSIzNSIgc3Ryb2tlLWRhc2hhcnJheT0iMTY0LjkzMzYxNDMxMzQ2NDE1IDU2Ljk3Nzg3MTQzNzgyMTM4Ij4gICAgPGFuaW1hdGVUcmFuc2Zvcm0gYXR0cmlidXRlTmFtZT0idHJhbnNmb3JtIiB0eXBlPSJyb3RhdGUiIHJlcGVhdENvdW50PSJpbmRlZmluaXRlIiBkdXI9IjFzIiB2YWx1ZXM9IjAgNTAgNTA7MzYwIDUwIDUwIiBrZXlUaW1lcz0iMDsxIj48L2FuaW1hdGVUcmFuc2Zvcm0+ICA8L2NpcmNsZT48L3N2Zz4=\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"c1e5a0dc-6040-4c32-89ed-3cd3ced486af\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/css/loading.css\"}},{\"id\":\"7e97457a-2f6a-4f2a-a059-c68438769c10\"},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"b32737e5-92fd-4495-bf9e-1dea0021d746\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/theme/default.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"f7fd3723-5638-4f11-ad19-19ba1efb0833\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/theme/native.css\"}}],\"margin\":0,\"align\":\"start\",\"data\":{\"type\":\"object\",\"name\":\"NotificationArea1\",\"id\":\"9206afe2-0a90-44d2-952d-78e53a4413dc\",\"attributes\":{\"name\":\"NotificationArea00113\"}},\"scripts\":{\"type\":\"map\",\"entries\":[[\"render\",[\"var [y, x] = data.position.split(&#x27;-&#x27;)\\nstate.toaster = new Notyf({\\n  dismissible: true,\\n  position: {x: x, y: y},\\n  types: data.types\\n})\"]],[\"notifications\",[\"var notification = state.current || data.notifications[data.notifications.length-1]\\nif (notification._destroyed) {\\n  return\\n}\\nvar config = {\\n  duration: notification.duration,\\n  type: notification.notification_type,\\n  message: notification.message\\n}\\nif (notification.background != null) {\\n  config.background = notification.background;\\n}\\nif (notification.icon != null) {\\n  config.icon = notification.icon;\\n}\\nvar toast = state.toaster.open(config);\\nfunction destroy() {\\n  if (state.current !== notification) {\\n    notification._destroyed = true;\\n  }\\n}\\ntoast.on(&#x27;dismiss&#x27;, destroy)\\nif (notification.duration) {\\n  setTimeout(destroy, notification.duration)\\n}\\nif (notification.properties === undefined)\\n  return\\nview.connect(notification.properties._destroyed.change, function () {\\n  state.toaster.dismiss(toast)\\n})\"]],[\"_clear\",[\"state.toaster.dismissAll()\"]],[\"position\",[\"script(&#x27;_clear&#x27;);\\nscript(&#x27;render&#x27;);\\nfor (notification of data.notifications) {\\n  state.current = notification;\\n  script(&#x27;notifications&#x27;);\\n}\\nstate.current = undefined\"]]]}}},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"980fccfa-cbf6-4de8-b2f6-a3211cbc2481\",\"attributes\":{\"plot_id\":\"aae709a2-75af-4de4-a84b-3a6d684f448d\",\"comm_id\":\"dd38e4e1c3924083b5c399438a216a0c\",\"client_comm_id\":\"1bfb492d39e24387bbcac7251c7c5034\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"84db0fd7-3756-4535-96c7-9ae54c627089\",\"roots\":{\"aae709a2-75af-4de4-a84b-3a6d684f448d\":\"b1e6d74d-331b-4cbe-87bb-8a65b2b20b2e\"},\"root_ids\":[\"aae709a2-75af-4de4-a84b-3a6d684f448d\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['jsPanel'] !== undefined))\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       "NotificationArea()"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "aae709a2-75af-4de4-a84b-3a6d684f448d"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1707843287.7283635 OpenVisus imported\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os,sys,logging,time\n",
+    "import numpy as np\n",
+    "\n",
+    "print(sys.executable)\n",
+    "\n",
+    "os.environ[\"BOKEH_ALLOW_WS_ORIGIN\"]=\"*\"\n",
+    "os.environ[\"BOKEH_LOG_LEVEL\"]=\"debug\"\n",
+    "os.environ[\"VISUS_CPP_VERBOSE\"]=\"0\"\n",
+    "os.environ[\"VISUS_NETSERVICE_VERBOSE\"]=\"0\"\n",
+    "os.environ[\"VISUS_VERBOSE_DISKACCESS\"]=\"0\"\n",
+    "\n",
+    "import panel as pn\n",
+    "\n",
+    "pn.extension(\"ipywidgets\",\n",
+    "             \"floatpanel\",\n",
+    "             log_level=\"DEBUG\",\n",
+    "             notifications=True, \n",
+    "             sizing_mode=\"stretch_width\")\n",
+    "\n",
+    "if True:\n",
+    "    sys.path.append(\"c:/projects/openvisus/build/RelWithDebInfo\")\n",
+    "    sys.path.append(\"c:/projects/openvisuspy/src\")\n",
+    "\n",
+    "from openvisuspy import Slice, SetupJupyterLogger, LoadDataset, ExecuteBoxQuery\n",
+    "logger=SetupJupyterLogger(logging_level=logging.DEBUG) \n",
+    "print(time.time(),\"OpenVisus imported\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='beef48f6-7252-41e0-af2f-86edc5c935ff'>\n",
+       "  <div id=\"b9416684-37cf-464a-9e06-9652b11f9ea8\" data-root-id=\"beef48f6-7252-41e0-af2f-86edc5c935ff\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"e65b591a-8357-4412-935c-2ed2418bdd8a\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"beef48f6-7252-41e0-af2f-86edc5c935ff\",\"attributes\":{\"name\":\"Row00163\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"3485ea2d-3369-4de2-9407-2652e7d25d90\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/css/loading.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"ab8e3084-64d0-408d-9a41-3d24d2d5ee57\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/font-awesome/css/all.min.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"a0e44d56-8435-4fa1-bbf1-a494c6af9317\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/css/listpanel.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"03a5f69a-243a-4a62-b7c8-b164769b82b6\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/theme/default.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"3cd06fa2-1f21-415a-a555-07e8e53731e3\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/bundled/theme/native.css\"}}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\",\"children\":[{\"type\":\"object\",\"name\":\"panel.models.widgets.Button\",\"id\":\"24ceeb60-9add-426f-bc95-48f92d274976\",\"attributes\":{\"subscribed_events\":{\"type\":\"set\",\"entries\":[\"button_click\"]},\"css_classes\":[\"solid\"],\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"3485ea2d-3369-4de2-9407-2652e7d25d90\"},{\"id\":\"ab8e3084-64d0-408d-9a41-3d24d2d5ee57\"},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"91b4361d-094e-45e9-a6b2-80adbe6309e0\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.3.8/dist/css/button.css\"}},{\"id\":\"03a5f69a-243a-4a62-b7c8-b164769b82b6\"},{\"id\":\"3cd06fa2-1f21-415a-a555-07e8e53731e3\"}],\"margin\":[5,10],\"sizing_mode\":\"stretch_width\",\"align\":\"start\",\"label\":\"Is Panel working? 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+       "  var render_items = [{\"docid\":\"e65b591a-8357-4412-935c-2ed2418bdd8a\",\"roots\":{\"beef48f6-7252-41e0-af2f-86edc5c935ff\":\"b9416684-37cf-464a-9e06-9652b11f9ea8\"},\"root_ids\":[\"beef48f6-7252-41e0-af2f-86edc5c935ff\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['jsPanel'] !== undefined))\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       "Row(sizing_mode='stretch_width')\n",
+       "    [0] Button(name='Is Panel working? C..., sizing_mode='stretch_width')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "beef48f6-7252-41e0-af2f-86edc5c935ff"
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# if you have problems here see # https://github.com/holoviz/holoviews/issues/4861\n",
+    "# you need to (1) Restart and Clear All Cells (2) save the notebook (3) kill jupyter lab (4) restart\n",
+    "button = pn.widgets.Button(name=\"Is Panel working? Click me...\")\n",
+    "def onClick(evt):\n",
+    "    button.name=\"Yes\"\n",
+    "button.on_click(onClick)\n",
+    "app=pn.Row(button)\n",
+    "app"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example of loading data from Object storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "logic box 8640 6480 90\n",
+      "palette_range (-0.25256651639938354, 0.3600933849811554)\n",
+      "resolution 34\n"
+     ]
+    }
+   ],
+   "source": [
+    "url=f\"{endpoint_url}/utah/nasa/dyamond/mit_output/llc2160_arco/visus.idx?cached=idx& access_key=any&secret_key=any&endpoint_url={endpoint_url}\"\n",
+    "\n",
+    "db=LoadDataset(url)\n",
+    "W,H,D=db.getLogicBox()[1]\n",
+    "access=db.createAccess()\n",
+    "\n",
+    "# get a Z slice in the middle to compute the range of the data\n",
+    "endh=db.getMaxResolution()-6\n",
+    "Z=D//2\n",
+    "logic_box, delta, num_pixels=db.getAlignedBox([[0,0,Z],[W,H,Z]], endh, slice_dir=2)\n",
+    "data=list(ExecuteBoxQuery(db, access=access, logic_box=logic_box,  endh=endh, num_refinements=1))[0]['data']\n",
+    "palette_range = np.min(data)/4, np.max(data)/4  \n",
+    "print(\"logic box\",W,H,D) \n",
+    "print(\"palette_range\",palette_range)\n",
+    "print(\"resolution\",db.getMaxResolution())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Show single slice of a RGB 2D dataset (David)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='c6b4edda-2138-4938-b913-fd5868851b45'>\n",
+       "  <div id=\"ea7edc3c-deca-4912-b3d2-92e7e17f8034\" data-root-id=\"c6b4edda-2138-4938-b913-fd5868851b45\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"d4ce0c0e-c2e8-4368-b7e7-bba911086acd\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.layout.Column\",\"id\":\"c6b4edda-2138-4938-b913-fd5868851b45\",\"attributes\":{\"name\":\"Column00192\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"d4ce0c0e-c2e8-4368-b7e7-bba911086acd\",\"roots\":{\"c6b4edda-2138-4938-b913-fd5868851b45\":\"ea7edc3c-deca-4912-b3d2-92e7e17f8034\"},\"root_ids\":[\"c6b4edda-2138-4938-b913-fd5868851b45\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['jsPanel'] !== undefined))\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       "Column(height=800, sizing_mode='stretch_width')\n",
+       "    [0] Column(sizing_mode='stretch_both')\n",
+       "        [0] Column(sizing_mode='stretch_width')\n",
+       "            [0] Row(sizing_mode='stretch_width')\n",
+       "                [0] Button(icon='file-upload', width=20)\n",
+       "                [1] Button(icon='file-download', width=20)\n",
+       "                [2] Button(icon='info-circle', width=20)\n",
+       "                [3] Button(icon='copy', width=20)\n",
+       "                [4] Button(icon='logout', width=20)\n",
+       "                [5] Select(name='Scene', options=['david', '2kbit1', ...], value='david', width=120)\n",
+       "                [6] IntSlider(end=3, name='Time', sizing_mode='stretch_width')\n",
+       "                [7] Select(name='Speed', options=[1, 2, 4, 8, 1, ...], value=1, width=50)\n",
+       "                [8] ColorMap(name='Palette', ncols=5, options={'Blues256': ('#08306b', ...}, value=('#440154', '#440255', ..., value_name='Viridis256', width=180)\n",
+       "                [9] Select(name='Mapper', options=['linear', 'log'], value='linear', width=60)\n",
+       "                [10] IntSlider(end=32, name='Res', sizing_mode='stretch_width', start=20, value=26)\n",
+       "                [11] Select(name='ViewDep', options={'Yes': True, ...}, value=True, width=80)\n",
+       "                [12] IntSlider(end=4, name='#Ref', value=2, width=80)\n",
+       "            [1] Row(sizing_mode='stretch_width')\n",
+       "                [0] Select(name='Field', options=['data'], value='data', width=80)\n",
+       "                [1] Select(name='Direction', options={'X': 0, 'Y': 1, 'Z': 2}, value=2, width=80)\n",
+       "                [2] EditableFloatSlider(end=0, format=NumeralTickFormatter(id='1..., name='Offset', sizing_mode='stretch_width', step=1)\n",
+       "                [3] Select(name='Range', options=['metadata', 'user', ...], value='dynamic-acc', width=120)\n",
+       "                [4] FloatInput(name='Min', width=80)\n",
+       "                [5] FloatInput(name='Max', width=80)\n",
+       "        [1] Column(sizing_mode='stretch_both')\n",
+       "            [0] Row(sizing_mode='stretch_both')\n",
+       "                [0] Row(sizing_mode='stretch_both')\n",
+       "                    [0] Bokeh(figure, sizing_mode='stretch_width')\n",
+       "        [2] Column(sizing_mode='stretch_width')\n",
+       "            [0] Row(sizing_mode='stretch_width')\n",
+       "                [0] TextInput(sizing_mode='stretch_width')\n",
+       "                [1] TextInput(sizing_mode='stretch_width')\n",
+       "        [3] Column(sizing_mode='stretch_width', visible=False)\n",
+       "        [4] IntInput(sizing_mode='stretch_width', visible=False)\n",
+       "        [5] IntInput(sizing_mode='stretch_width', visible=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "c6b4edda-2138-4938-b913-fd5868851b45"
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def CreateApp():\n",
+    "    view=Slice()\n",
+    "    view.load(datasets)\n",
+    "    return pn.Column(view.getMainLayout(),sizing_mode=\"stretch_width\",height=800)\n",
+    "\n",
+    "app=CreateApp()\n",
+    "app.servable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "81794d4967e6c3204c66dcd87b604927b115b27c00565d3d43f05ba2f3a2cb0d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/openvisuspy/examples/notebooks/ov-retina-rabbit-matplot.ipynb b/openvisuspy/examples/notebooks/ov-retina-rabbit-matplot.ipynb
new file mode 100644
index 0000000..e4c7483
--- /dev/null
+++ b/openvisuspy/examples/notebooks/ov-retina-rabbit-matplot.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ecb8dcf0",
+   "metadata": {},
+   "source": [
+    "| ![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-smaller.PNG)  | [National Science Data Fabric](https://nationalsciencedatafabric.org/) [Jupyter notebook](https://jupyter.org/) <br> created by  [Valerio Pascucci](http://cedmav.com/) and  [Giorgio Scorzelli](https://www.sci.utah.edu/people/scrgiorgio.html)  |  \n",
+    "|---|:---:|\n",
+    "\n",
+    "\n",
+    "# Distribution of the data related by the following book chapter: \n",
+    "\n",
+    "### Chapter 1.18 - Retinal Connectomics \n",
+    "\n",
+    "__Authors:__  _[Bryan W. Jones](http://marclab.org/outreach/people/bryan-w-jones/), and [Robert E. Marc.](https://marclab.org/outreach/people/robert-e-marc/)_ \n",
+    "\n",
+    "__Published in:__  The Senses: A Comprehensive Reference, Elsevier, 2nd Edition - September 8, 2020, Pages 320-343, ISBN  9780128054086\n",
+    "\n",
+    "https://www.elsevier.com/books/the-senses-a-comprehensive-reference/fritzsch/978-0-12-805408-6\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bb12ff1",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#  <center> This is a preview of the 6.4 TB of EM data\n",
+    "![Connectomics EM Data](https://www.sci.utah.edu/~pascucci/public/RabbitRetinaEM.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d7b8f7",
+   "metadata": {},
+   "source": [
+    "# Import OpenVisus and Load dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4963287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os,sys\n",
+    "import matplotlib.pyplot as plt, skimage\n",
+    "\n",
+    "# %matplotlib notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#if you are debugging in local mode...\n",
+    "if True:\n",
+    "    sys.path.append(\"c:/projects/openvisus/build/RelWithDebInfo\")\n",
+    "    sys.path.append(\"c:/projects/openvisuspy/src\")\n",
+    "\n",
+    "import IPython\n",
+    "from IPython.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
+    "\n",
+    "def ShowData(data,extent):\n",
+    "    fig, ax = plt.subplots()\n",
+    "    im = ax.imshow(data, extent=extent) \n",
+    "    plt.colorbar(im)\n",
+    "    return fig,im,ax\n",
+    "\n",
+    "from openvisuspy import LoadDataset, ExecuteBoxQuery,SetupJupyterLogger\n",
+    "SetupJupyterLogger()\n",
+    "\n",
+    "\n",
+    "db=LoadDataset('https://atlantis.sci.utah.edu/mod_visus?dataset=rabbit&cached=1')\n",
+    "print(f\"Loaded dataset \\nfields={db.getFields()} \\nlogic_box={db.getLogicBox()}\")\n",
+    "W,H=db.getLogicBox()[1]\n",
+    "access=db.createAccess()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd739ab4",
+   "metadata": {},
+   "source": [
+    "# Display a single XY slice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "015e1412",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ReadData(cx, cy, zoom=1.0, resolution=0, timestep=1, verbose=True): \n",
+    "    x1,x2 = int(cx - zoom*W*0.5), int(cx + zoom*W*0.5)\n",
+    "    y1,y2 = int(cy - zoom*H*0.5), int(cy + zoom*H*0.5)  \n",
+    "    aligned_box, delta, num_pixels = db.getAlignedBox([[x1,y1],[x2,y2]], resolution)\n",
+    "    (x1,y1),(x2,y2)=aligned_box\n",
+    "    extent=[x1,x2,y1,y2]     \n",
+    "    \n",
+    "    for it in ExecuteBoxQuery(db, access=access, logic_box=aligned_box, timestep=timestep, endh=resolution, num_refinements=1):\n",
+    "        data=it['data']\n",
+    "        if verbose: print(f\"data.shape={data.shape} extent={extent}\")\n",
+    "        return data, extent\n",
+    "\n",
+    "data,extent = ReadData(W//2, H//2, resolution=db.getMaxResolution() -12, zoom=1.0)\n",
+    "ShowData(data,extent)\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a1b52af",
+   "metadata": {},
+   "source": [
+    "# Mark the area of interest and show it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45b408ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "piece_data, piece_extent = ReadData(W//2,H//2,zoom=0.1,resolution=db.getMaxResolution()-12)\n",
+    "ShowData(data,extent)\n",
+    "\n",
+    "x1,x2,y1,y2=piece_extent\n",
+    "plt.plot([x1,x1],[ 0, H], color='r')\n",
+    "plt.plot([x2,x2],[ 0, H], color='r')\n",
+    "plt.plot([ 0, W],[y1,y1], color='g')  \n",
+    "plt.plot([ 0, W],[y2,y2], color='g')  \n",
+    "plt.show()\n",
+    "\n",
+    "ShowData(piece_data,piece_extent)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "759d3dc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/notebooks/ov-signal.ipynb b/openvisuspy/examples/notebooks/ov-signal.ipynb
new file mode 100644
index 0000000..88f1007
--- /dev/null
+++ b/openvisuspy/examples/notebooks/ov-signal.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9360e08f-5933-47b7-b887-249e28a7667f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N=1,073,741,824\n",
+      "NUMPY dtype float64 shape (1073741824,) vmin 1.6153667292684304e-10 vmax 0.9999999999331494\n",
+      "IDX write uncompressed done logic_box 0 1073741824\n",
+      "compress dataset done\n",
+      "IDX read done dtype float64 shape (1073741824,) vmin 1.6153667292684304e-10 vmax 0.9999999999331494\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import os,sys\n",
+    "import shutil\n",
+    "\n",
+    "sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n",
+    "import OpenVisus as ov\n",
+    "\n",
+    "sys.path.append(r\"C:\\projects\\openvisuspy\\src\")\n",
+    "import openvisuspy as ovy\n",
+    "\n",
+    "os.environ[\"VISUS_VERBOSE_DISKACCESS\"]=\"0\"\n",
+    "os.environ[\"VISUS_CPP_VERBOSE\"]=\"0\"\n",
+    "\n",
+    "# since I am the only one writing...\n",
+    "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n",
+    "\n",
+    "GB=1024*1024*1024\n",
+    "memsize=8*GB\n",
+    "N=memsize//8\n",
+    "print(f\"N={N:,}\")\n",
+    "\n",
+    "signal = np.random.uniform(low=0.0,high=1.0,size=[N])\n",
+    "print(\"NUMPY dtype\",signal.dtype,\"shape\",signal.shape,\"vmin\",np.min(signal),\"vmax\",np.max(signal))\n",
+    "\n",
+    "idx_filename=r'D:/visus-datasets/signal1d/visus.idx'\n",
+    "shutil.rmtree(os.path.dirname(idx_filename), ignore_errors=True)\n",
+    "db=ov.CreateIdx(url=idx_filename, dims=[N],fields=[ov.Field('data',ov.convert_dtype(signal.dtype),'row_major')], compression=\"raw\", arco=f\"{1024*1024}\")\n",
+    "assert(os.path.isfile(idx_filename))\n",
+    "\n",
+    "logic_box=logic_box=ov.BoxNi(ov.PointNi([0]),ov.PointNi([N]))\n",
+    "db.write(signal,  logic_box=logic_box)\n",
+    "print(\"IDX write uncompressed done\",\"logic_box\",logic_box.toString())\n",
+    "\n",
+    "# use the python version\n",
+    "db=ov.LoadDataset(idx_filename)\n",
+    "db.compressDataset(\"zip\") \n",
+    "print(\"compress dataset done\")\n",
+    "\n",
+    "data=db.read(logic_box=logic_box)\n",
+    "print(\"IDX read done\",\"dtype\",data.dtype,\"shape\",data.shape,\"vmin\",np.min(data),\"vmax\",np.max(data))\n",
+    "assert(list(data.shape)==[N])\n",
+    "assert(np.min(data)==np.min(signal))\n",
+    "assert(np.max(data)==np.max(signal))\n",
+    "assert(data.dtype==signal.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "169a98d5-7548-4343-b8cf-889d2638a4b2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "logic_box ([0], [134217728])\n",
+      "db.getMaxResolution() 27\n",
+      "IDX read done dtype=float64 shape=(256,) vmin=0.0003777226978680659 vmax=0.9978663834143826\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def ShowSignal(data):\n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.plot(np.arange(data.shape[0]), data)\n",
+    "    my_cmap = plt.get_cmap(\"viridis\")\n",
+    "    plt.show()\n",
+    "    \n",
+    "logic_box=db.getLogicBox()\n",
+    "print(\"logic_box\",logic_box)\n",
+    "print(\"db.getMaxResolution()\",db.getMaxResolution())\n",
+    "\n",
+    "resolution=8\n",
+    "data=db.read(logic_box=logic_box, max_resolution=resolution)\n",
+    "print(f\"IDX read done dtype={data.dtype} shape={data.shape} vmin={np.min(data)} vmax={np.max(data)}\")\n",
+    "ShowSignal(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aacae798-fcb2-4ac7-9c7c-990e4fee9191",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Copy blocks to S3. For example:\n",
+    "\n",
+    "```\n",
+    "\n",
+    "python -m pip install --quiet awscli-plugin-endpoint\n",
+    "aws s3 sync --deub --endpoint-url https://maritime.sealstorage.io/api/v0/s3 --profile sealstorage --size-only  /mnt/d/visus-datasets/signal1d/ s3://utah/visus-datasets/signal1d/\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21a5cf75-212b-484c-bc87-d73d7633dd2d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/notebooks/ov-vr.ipynb b/openvisuspy/examples/notebooks/ov-vr.ipynb
new file mode 100644
index 0000000..1afe6f1
--- /dev/null
+++ b/openvisuspy/examples/notebooks/ov-vr.ipynb
@@ -0,0 +1,244 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3dab248e",
+   "metadata": {},
+   "source": [
+    "| ![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-smaller.PNG)  | [National Science Data Fabric](https://nationalsciencedatafabric.org/) [Jupyter notebook](https://jupyter.org/) <br> created by  [Valerio Pascucci](http://cedmav.com/) and  [Giorgio Scorzelli](https://www.sci.utah.edu/people/scrgiorgio.html)  |  \n",
+    "|---|:---:|\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a2b55e",
+   "metadata": {},
+   "source": [
+    "# IMPORTANT, READ CAREFULLY\n",
+    "\n",
+    "## this notebook does NOT work in Chrome, please use Firefox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1a6f936",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os,sys,logging,time\n",
+    "import numpy as np\n",
+    "\n",
+    "print(sys.executable)\n",
+    "\n",
+    "os.environ[\"BOKEH_ALLOW_WS_ORIGIN\"]=\"*\"\n",
+    "os.environ[\"BOKEH_LOG_LEVEL\"]=\"debug\"\n",
+    "os.environ[\"VISUS_CPP_VERBOSE\"]=\"0\"\n",
+    "os.environ[\"VISUS_NETSERVICE_VERBOSE\"]=\"0\"\n",
+    "os.environ[\"VISUS_VERBOSE_DISKACCESS\"]=\"0\"\n",
+    "\n",
+    "import panel as pn\n",
+    "pn.extension(log_level=\"DEBUG\",notifications=True, sizing_mode=\"stretch_width\")\n",
+    "\n",
+    "if True:\n",
+    "    sys.path.append(\"c:/projects/openvisus/build/RelWithDebInfo\")\n",
+    "    sys.path.append(\"c:/projects/openvisuspy/src\")\n",
+    "\n",
+    "from openvisuspy import Slice, SetupJupyterLogger, LoadDataset, ExecuteBoxQuery\n",
+    "logger=SetupJupyterLogger(logging_level=logging.DEBUG) \n",
+    "print(time.time(),\"OpenVisus imported\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5488f75a-876b-4057-96db-a6f7ce509cd8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#  <center> This is a preview of the data:\n",
+    "![Visualization of Covid-19 cases](https://www.sci.utah.edu/~pascucci/public/DRP-preview.gif)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c43a4cc7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "# VTK is needed for VTKVolumePlot \n",
+    "# see https://panel.holoviz.org/reference/panes/VTKVolume.html\n",
+    "pn.extension('vtk') \n",
+    "\n",
+    "def MyApp(url, endh=21, height=800):\n",
+    "    \n",
+    "    db=LoadDataset(url)\n",
+    "    print(f\"Loaded dataset\")\n",
+    "    print(f\"  fields={db.getFields()}\")\n",
+    "    print(f\"  logic_box={db.getLogicBox()}\")\n",
+    "    W,H,D=db.getLogicBox()[1]\n",
+    "    \n",
+    "    access=db.createAccess()\n",
+    "    data=list(ExecuteBoxQuery(db, access=access, endh=endh,timestep=0,num_refinements=1))[0]['data']\n",
+    "    print(f\"Got data shape={data.shape} dtype={data.dtype}\")\n",
+    "\n",
+    "     # generate a panel for numpy data\n",
+    "    volume = pn.panel(\n",
+    "        data, \n",
+    "        sizing_mode='stretch_both',\n",
+    "        orientation_widget =True,\n",
+    "        display_slices=True,\n",
+    "        spacing = (1, 1, 1),\n",
+    "        controller_expanded=False)\n",
+    "\n",
+    "    # other widgets, with callbacks\n",
+    "    experiment  = pn.widgets.IntSlider(name='Experiment', start=0, end=1, step=1, value=0 )\n",
+    "    @pn.depends(experiment)\n",
+    "    def experiment_callback(value):\n",
+    "        nonlocal volume\n",
+    "        data=list(ExecuteBoxQuery(db, access=access, endh=endh,timestep=value,num_refinements=1))[0]['data']\n",
+    "        volume.object = data\n",
+    "        return \" \"    \n",
+    "    \n",
+    "    slider_i = pn.widgets.IntSlider(name='i-slice', start=0, end=data.shape[0], value = data.shape[0]//2 )\n",
+    "    @pn.depends(slider_i)\n",
+    "    def slider_i_callback(value):\n",
+    "        nonlocal volume\n",
+    "        volume.slice_i = value \n",
+    "        return \" \"    \n",
+    "    \n",
+    "    slider_j = pn.widgets.IntSlider(name='j-slice', start=0, end=data.shape[1], value = data.shape[1]//2 )\n",
+    "    @pn.depends(slider_j)\n",
+    "    def slider_j_callback(value):\n",
+    "        nonlocal volume\n",
+    "        volume.slice_j = value \n",
+    "        return \" \"    \n",
+    "    \n",
+    "    slider_k = pn.widgets.IntSlider(name='k-slice', start=0, end=data.shape[2], value = data.shape[2]//2 )\n",
+    "    @pn.depends(slider_k)\n",
+    "    def slider_k_callback(value):\n",
+    "        nonlocal volume\n",
+    "        volume.slice_k = value \n",
+    "        return \" \"    \n",
+    "    \n",
+    "    show_volume = pn.widgets.Checkbox(name='Show Volume',value=True)\n",
+    "    @pn.depends(show_volume)\n",
+    "    def show_volume_callback(value):\n",
+    "        nonlocal volume\n",
+    "        volume.display_volume = value \n",
+    "        return \" \"    \n",
+    "    \n",
+    "    show_slices = pn.widgets.Checkbox(name='Show Slices',value=True)\n",
+    "    @pn.depends(show_slices)\n",
+    "    def show_slices_calback(value):\n",
+    "        nonlocal volume\n",
+    "        volume.display_slices = value \n",
+    "        return \" \"    \n",
+    "\n",
+    "    main_layout=pn.Column(\n",
+    "        experiment,\n",
+    "        slider_i,\n",
+    "        slider_j,\n",
+    "        slider_k,\n",
+    "        pn.Row(\n",
+    "            show_volume,\n",
+    "            show_slices\n",
+    "        ),\n",
+    "        volume, \n",
+    "        experiment_callback,\n",
+    "        slider_i_callback,\n",
+    "        slider_j_callback,\n",
+    "        slider_k_callback,\n",
+    "        show_volume_callback,\n",
+    "        show_slices_calback,\n",
+    "        height = height,\n",
+    "        css_classes=['panel-widget-box'],\n",
+    "        sizing_mode='stretch_width',\n",
+    "        width_policy='max')\n",
+    "\n",
+    "    return main_layout\n",
+    "\n",
+    "MyApp('http://atlantis.sci.utah.edu:80/mod_visus?dataset=fly_scan_time-s-midx/data&cached=idx', endh=21, height=800)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3668c1e2-2122-4f51-b236-44a888f0ca0f",
+   "metadata": {},
+   "source": [
+    "#  <center> This is a preview of the data\n",
+    "![Visualization of Covid-19 cases](https://www.sci.utah.edu/~pascucci/public/CHESS-visus_recon_combined_1_fullres_zip.gif)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba26bf5-3865-4499-b5b3-d3dd1cef38e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "MyApp(url='http://atlantis.sci.utah.edu:80/mod_visus?dataset=chess-zip&cached=1', endh=21, height=800)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c071ff0-ec00-44a1-864c-1b2c07712b21",
+   "metadata": {},
+   "source": [
+    "#  <center> This is a preview of the data:\n",
+    "![Visualization of Covid-19 cases](https://www.sci.utah.edu/~pascucci/public/CHESS-visus_recon_combined_1_2_3_fullres_zip.gif)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "921d4c52",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "MyApp(url='https://atlantis.sci.utah.edu/mod_visus?dataset=chess-recon_combined_1_2_3_fullres_zip&cached=1', endh=21, height=800)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "523f7cc8-24b7-4945-a12a-e62b2f72b33b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/notebooks/test-bokeh.ipynb b/openvisuspy/examples/notebooks/test-bokeh.ipynb
new file mode 100644
index 0000000..4e647e1
--- /dev/null
+++ b/openvisuspy/examples/notebooks/test-bokeh.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e17398cc-4f4a-4a44-bc20-b3299adc9c82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os,sys,time\n",
+    "\n",
+    "# import bokeh\n",
+    "# TODO: enforce security here\n",
+    "os.environ[\"BOKEH_ALLOW_WS_ORIGIN\"]=\"*\"\n",
+    "os.environ[\"BOKEH_LOG_LEVEL\"]=\"error\" \n",
+    "\n",
+    "import bokeh\n",
+    "import bokeh.io\n",
+    "import bokeh.plotting\n",
+    "import bokeh.layouts\n",
+    "\n",
+    "bokeh.io.output_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "148506ff-4a9c-4694-952d-ee3c222df61b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def MyApp(doc):\n",
+    "    button = bokeh.models.Button(label = \"Is Bokeh Working?\") \n",
+    "    def onButtonClick(evt=None):\n",
+    "        button.label=\"Yes, it is working\"\n",
+    "    button.on_click(onButtonClick) \n",
+    "    doc.add_root(bokeh.layouts.row(button))\n",
+    "bokeh.io.show(MyApp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deb46de0-4588-426d-be5c-371d17b123c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def MyApp(doc):\n",
+    "    \n",
+    "    import yaml\n",
+    "    from bokeh.layouts import column\n",
+    "    from bokeh.models import ColumnDataSource, Slider\n",
+    "    from bokeh.plotting import figure\n",
+    "    from bokeh.themes import Theme\n",
+    "    from bokeh.io import show, output_notebook\n",
+    "\n",
+    "    from bokeh.sampledata.sea_surface_temperature import sea_surface_temperature\n",
+    "    \n",
+    "    df = sea_surface_temperature.copy()\n",
+    "    source = ColumnDataSource(data=df)\n",
+    "    plot = figure(x_axis_type='datetime', y_range=(0, 25),\n",
+    "                  y_axis_label='Temperature (Celsius)',\n",
+    "                  title=\"Sea Surface Temperature at 43.18, -70.43\")\n",
+    "    plot.line('time', 'temperature', source=source)\n",
+    "    def callback(attr, old, new):\n",
+    "        if new == 0:\n",
+    "            data = df\n",
+    "        else:\n",
+    "            data = df.rolling('{0}D'.format(new)).mean()\n",
+    "        source.data = ColumnDataSource.from_df(data)\n",
+    "\n",
+    "    slider = Slider(start=0, end=30, value=0, step=1, title=\"Smoothing by N Days\")\n",
+    "    slider.on_change('value', callback)\n",
+    "    doc.add_root(column(slider, plot))\n",
+    "    doc.theme = Theme(json=yaml.load(\"\"\"\n",
+    "        attrs:\n",
+    "            figure:\n",
+    "                background_fill_color: \"#DDDDDD\"\n",
+    "                outline_line_color: white\n",
+    "                toolbar_location: above\n",
+    "                height: 500\n",
+    "                width: 800\n",
+    "            Grid:\n",
+    "                grid_line_dash: [6, 4]\n",
+    "                grid_line_color: white\n",
+    "    \"\"\", Loader=yaml.FullLoader))\n",
+    "    \n",
+    "bokeh.io.show(MyApp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdffe5f4-f295-43cb-8df6-dc2c1fefa99b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/notebooks/test-ipywidgets.ipynb b/openvisuspy/examples/notebooks/test-ipywidgets.ipynb
new file mode 100644
index 0000000..0045d88
--- /dev/null
+++ b/openvisuspy/examples/notebooks/test-ipywidgets.ipynb
@@ -0,0 +1,51 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a441cf52-3600-4ef1-a9d5-a9801ef2ddf7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
+    "import ipywidgets\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import display\n",
+    "\n",
+    "print(ipywidgets.__version__)\n",
+    "\n",
+    "def func3(a,b,c): \n",
+    "    display(a+b+c)\n",
+    "    \n",
+    "w = interactive(func3,  \n",
+    "                a=widgets.IntSlider(min=0, max=50, value=0, step=1),    \n",
+    "                b=widgets.IntSlider(min=0, max=50, value=0, step=1),\n",
+    "                c=widgets.IntSlider(min=0, max=50, value=0, step=1))\n",
+    "display(w)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/notebooks/test-matplotlib.ipynb b/openvisuspy/examples/notebooks/test-matplotlib.ipynb
new file mode 100644
index 0000000..2c36a7e
--- /dev/null
+++ b/openvisuspy/examples/notebooks/test-matplotlib.ipynb
@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "t = np.linspace(0, 2 * np.pi, 1024)\n",
+    "data2d = np.sin(t)[:, np.newaxis] * np.cos(t)[np.newaxis, :]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(data2d)\n",
+    "ax.set_title('Pan on the colorbar to shift the color mapping\\n'\n",
+    "             'Zoom on the colorbar to scale the color mapping')\n",
+    "\n",
+    "fig.colorbar(im, ax=ax, label='Interactive colorbar')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/openvisuspy/examples/notebooks/test-panel.ipynb b/openvisuspy/examples/notebooks/test-panel.ipynb
new file mode 100644
index 0000000..f1a04fd
--- /dev/null
+++ b/openvisuspy/examples/notebooks/test-panel.ipynb
@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29d32f52-73b2-426f-8fe3-4bfab2a28cf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os,sys,time\n",
+    "import panel as pn\n",
+    "print(pn.__version__)\n",
+    "pn.extension()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7efb4fa8-fd17-4006-aa66-9ce6066e0466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "button = pn.widgets.Button(name=\"Is Panel working? Click me...\")\n",
+    "def onClick(evt):\n",
+    "    button.name=\"Yes\"\n",
+    "button.on_click(onClick)\n",
+    "app=pn.Row(button)\n",
+    "app\n",
+    "#app.servable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc6c8c5f-0af2-43e4-acf6-342085ac1029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "csv_file = (\"https://raw.githubusercontent.com/holoviz/panel/main/examples/assets/occupancy.csv\")\n",
+    "data = pd.read_csv(csv_file, parse_dates=[\"date\"], index_col=\"date\")\n",
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9623809c-2f78-40d2-948c-e1c1df8d5a71",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/notebooks/test-pyvista.ipynb b/openvisuspy/examples/notebooks/test-pyvista.ipynb
new file mode 100644
index 0000000..35dd6c3
--- /dev/null
+++ b/openvisuspy/examples/notebooks/test-pyvista.ipynb
@@ -0,0 +1,73 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "109b671c-7a23-4eeb-a32a-edfd94c3e069",
+   "metadata": {},
+   "source": [
+    "# Important\n",
+    "\n",
+    "This demo should work in Chrome and Firefox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bf6da98-d4bf-4526-bd41-ec0caa25bc00",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import pyvista as pv\n",
+    "import imageio.v2 as imageio\n",
+    "pl = pv.Plotter()\n",
+    "vol = pl.add_volume(imageio.imread('imageio:stent.npz'))\n",
+    "pl.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4367a4f7-a532-4b6a-a0c7-d50ef568e2d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pyvista as pv\n",
+    "print(pv.Report())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cba5966b-8b67-40c3-ad08-ccb0efe9c0c3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/openvisuspy/examples/python/test-pyvista.py b/openvisuspy/examples/python/test-pyvista.py
new file mode 100644
index 0000000..1ec4758
--- /dev/null
+++ b/openvisuspy/examples/python/test-pyvista.py
@@ -0,0 +1,6 @@
+from pyvista import examples
+import pyvista as pv
+bolt_nut = examples.download_bolt_nut()
+pl = pv.Plotter()
+_ = pl.add_volume(bolt_nut, cmap="coolwarm")
+pl.show()
\ No newline at end of file
diff --git a/openvisuspy/examples/python/test-vtkvolume.py b/openvisuspy/examples/python/test-vtkvolume.py
new file mode 100644
index 0000000..dada8b7
--- /dev/null
+++ b/openvisuspy/examples/python/test-vtkvolume.py
@@ -0,0 +1,87 @@
+
+# noinspection PyUnresolvedReferences
+import vtkmodules.vtkInteractionStyle
+from vtkmodules.vtkCommonColor import vtkNamedColors
+from vtkmodules.vtkCommonDataModel import vtkPiecewiseFunction
+from vtkmodules.vtkIOLegacy import vtkStructuredPointsReader
+from vtkmodules.vtkRenderingCore import (
+    vtkColorTransferFunction,
+    vtkRenderWindow,
+    vtkRenderWindowInteractor,
+    vtkRenderer,
+    vtkVolume,
+    vtkVolumeProperty
+)
+from vtkmodules.vtkRenderingVolume import vtkFixedPointVolumeRayCastMapper
+# noinspection PyUnresolvedReferences
+from vtkmodules.vtkRenderingVolumeOpenGL2 import vtkOpenGLRayCastImageDisplayHelper
+
+
+def main():
+    import sys
+    fileName = "./data/ironProt.vtk"
+
+    colors = vtkNamedColors()
+
+    # This is a simple volume rendering example that
+    # uses a vtkFixedPointVolumeRayCastMapper
+
+    # Create the standard renderer, render window
+    # and interactor.
+    ren1 = vtkRenderer()
+
+    renWin = vtkRenderWindow()
+    renWin.AddRenderer(ren1)
+
+    iren = vtkRenderWindowInteractor()
+    iren.SetRenderWindow(renWin)
+
+    # Create the reader for the data.
+    reader = vtkStructuredPointsReader()
+    reader.SetFileName(fileName)
+
+    # Create transfer mapping scalar value to opacity.
+    opacityTransferFunction = vtkPiecewiseFunction()
+    opacityTransferFunction.AddPoint(20, 0.0)
+    opacityTransferFunction.AddPoint(255, 0.2)
+
+    # Create transfer mapping scalar value to color.
+    colorTransferFunction = vtkColorTransferFunction()
+    colorTransferFunction.AddRGBPoint(0.0, 0.0, 0.0, 0.0)
+    colorTransferFunction.AddRGBPoint(64.0, 1.0, 0.0, 0.0)
+    colorTransferFunction.AddRGBPoint(128.0, 0.0, 0.0, 1.0)
+    colorTransferFunction.AddRGBPoint(192.0, 0.0, 1.0, 0.0)
+    colorTransferFunction.AddRGBPoint(255.0, 0.0, 0.2, 0.0)
+
+    # The property describes how the data will look.
+    volumeProperty = vtkVolumeProperty()
+    volumeProperty.SetColor(colorTransferFunction)
+    volumeProperty.SetScalarOpacity(opacityTransferFunction)
+    volumeProperty.ShadeOn()
+    volumeProperty.SetInterpolationTypeToLinear()
+
+    # The mapper / ray cast function know how to render the data.
+    volumeMapper = vtkFixedPointVolumeRayCastMapper()
+    volumeMapper.SetInputConnection(reader.GetOutputPort())
+
+    # The volume holds the mapper and the property and
+    # can be used to position/orient the volume.
+    volume = vtkVolume()
+    volume.SetMapper(volumeMapper)
+    volume.SetProperty(volumeProperty)
+
+    ren1.AddVolume(volume)
+    ren1.SetBackground(colors.GetColor3d('Wheat'))
+    ren1.GetActiveCamera().Azimuth(45)
+    ren1.GetActiveCamera().Elevation(30)
+    ren1.ResetCameraClippingRange()
+    ren1.ResetCamera()
+
+    renWin.SetSize(600, 600)
+    renWin.SetWindowName('SimpleRayCast')
+    renWin.Render()
+
+    iren.Start()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/openvisuspy/favicon.ico b/openvisuspy/favicon.ico
new file mode 100644
index 0000000..c695939
Binary files /dev/null and b/openvisuspy/favicon.ico differ
diff --git a/openvisuspy/openvisus.env b/openvisuspy/openvisus.env
new file mode 100644
index 0000000..224df04
--- /dev/null
+++ b/openvisuspy/openvisus.env
@@ -0,0 +1,247 @@
+aiofiles==22.1.0
+aiohttp==3.9.3
+aiosignal==1.3.1
+aiosqlite==0.19.0
+altair==5.2.0
+ansi2html==1.9.1
+anyio==4.2.0
+appdirs==1.4.4
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asciitree==0.3.3
+asteval==0.9.31
+asttokens==2.4.1
+async-timeout==4.0.3
+attrs==23.2.0
+Babel==2.14.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+blinker==1.7.0
+bokeh==3.3.4
+boto3==1.34.34
+botocore==1.34.34
+branca==0.7.1
+Cartopy==0.22.0
+certifi==2024.2.2
+cffi==1.16.0
+cftime==1.6.3
+charset-normalizer==3.3.2
+click==8.1.7
+click-plugins==1.1.1
+cligj==0.7.2
+cloudpickle==3.0.0
+colorama==0.4.6
+colorcet==3.0.1
+comm==0.2.1
+contourpy==1.2.0
+cramjam==2.8.1
+cycler==0.12.1
+dash==2.15.0
+dash-core-components==2.0.0
+dash-html-components==2.0.0
+dash-table==5.0.0
+dask==2024.1.1
+datashader==0.16.0
+debugpy==1.8.0
+decorator==5.1.1
+defusedxml==0.7.1
+entrypoints==0.4
+exceptiongroup==1.2.0
+executing==2.0.1
+fasteners==0.19
+fastjsonschema==2.19.1
+fastparquet==2023.10.1
+fiona==1.9.5
+Flask==3.0.2
+folium==0.15.1
+fonttools==4.47.2
+fqdn==1.5.1
+frozendict==2.4.0
+frozenlist==1.4.1
+fsspec==2024.2.0
+future==0.13.1
+geodatasets==2023.12.0
+geopandas==0.14.3
+geoviews==1.11.0
+greenlet==3.0.3
+h5py==3.10.0
+hdf5plugin==4.3.0
+holoviews==1.18.2
+html5lib==1.1
+hvplot==0.9.2
+idna==3.6
+imageio==2.33.1
+importlib-metadata==7.0.1
+intake==2.0.0
+ipykernel==6.29.0
+ipysheet==0.7.0
+ipython==8.21.0
+ipython-genutils==0.2.0
+ipywidgets==8.1.1
+ipywidgets-bokeh==1.5.0
+isoduration==20.11.0
+itsdangerous==2.1.2
+jedi==0.19.1
+Jinja2==3.1.3
+jmespath==1.0.1
+joblib==1.3.2
+json5==0.9.14
+jsonpointer==2.4
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+jupyter-bokeh==3.0.7
+jupyter-events==0.9.0
+jupyter-resource-usage==0.7.2
+jupyter-ydoc==0.2.5
+jupyter_client==7.4.9
+jupyter_core==5.7.1
+jupyter_server==2.12.5
+jupyter_server_fileid==0.9.1
+jupyter_server_proxy==4.1.0
+jupyter_server_terminals==0.5.2
+jupyter_server_ydoc==0.8.0
+jupyterlab==3.6.6
+jupyterlab-pygments==0.2.2
+jupyterlab-system-monitor==0.8.0
+jupyterlab-topbar==0.6.1
+jupyterlab-widgets==3.0.9
+jupyterlab_server==2.25.2
+kiwisolver==1.4.5
+lazy_loader==0.3
+linkify-it-py==2.0.3
+llvmlite==0.42.0
+lmfit==1.2.2
+locket==1.0.0
+lxml==5.1.0
+Markdown==3.5.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+mdit-py-plugins==0.4.0
+mdurl==0.1.2
+mistune==3.0.2
+more-itertools==10.2.0
+mplcursors==0.5.3
+multidict==6.0.5
+multipledispatch==1.0.0
+multitasking==0.0.11
+nbclassic==1.0.0
+nbclient==0.9.0
+nbconvert==7.14.2
+nbformat==5.9.2
+nbgitpuller==1.2.0
+nest-asyncio==1.6.0
+netCDF4==1.6.5
+networkx==3.2.1
+NeXpy==1.0.6
+nexusformat==1.0.3
+nodejs-bin==18.4.0a4
+nodejs-cmd==0.0.1a0
+notebook==6.5.6
+notebook_shim==0.2.3
+numba==0.59.0
+numcodecs==0.12.1
+numexpr==2.9.0
+numpy==1.26.3
+overrides==7.7.0
+packaging==23.2
+pandas==2.2.0
+pandocfilters==1.5.1
+panel==1.3.8
+param==2.0.2
+parso==0.8.3
+partd==1.4.1
+patsy==0.5.6
+peewee==3.17.0
+pillow==10.2.0
+platformdirs==4.2.0
+plotly==5.18.0
+pooch==1.8.0
+prometheus-client==0.19.0
+prompt-toolkit==3.0.43
+psutil==5.9.8
+pure-eval==0.2.2
+pyarrow==15.0.0
+pycparser==2.21
+pyct==0.5.0
+pydeck==0.8.0
+Pygments==2.17.2
+pylatexenc==2.10
+pyparsing==3.1.1
+pyproj==3.6.1
+pyshp==2.3.1
+python-dateutil==2.8.2
+python-json-logger==2.0.7
+pytz==2024.1
+pyvista==0.43.2
+pyviz-comms==2.3.2
+pywin32==306
+pywinpty==2.0.12
+PyYAML==6.0.1
+pyzmq==24.0.1
+qtconsole==5.5.1
+QtPy==2.4.1
+referencing==0.33.0
+requests==2.31.0
+retrying==1.3.4
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.17.1
+s3transfer==0.10.0
+scikit-image==0.22.0
+scikit-learn==1.4.0
+scipy==1.12.0
+scooby==0.9.2
+seaborn==0.13.2
+Send2Trash==1.8.2
+shapely==2.0.2
+simpervisor==1.0.0
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.25
+stack-data==0.6.3
+statsmodels==0.14.1
+tenacity==8.2.3
+terminado==0.18.0
+threadpoolctl==3.2.0
+tifffile==2024.1.30
+tinycss2==1.2.1
+tomli==2.0.1
+toolz==0.12.1
+tornado==6.4
+tqdm==4.66.1
+traitlets==5.14.1
+trame==3.5.2
+trame-client==2.15.0
+trame-server==2.16.0
+trame-vtk==2.8.0
+trame-vuetify==2.4.2
+types-python-dateutil==2.8.19.20240106
+typing_extensions==4.9.0
+tzdata==2023.4
+uc-micro-py==1.0.2
+uncertainties==3.1.7
+uri-template==1.3.0
+urllib3==2.0.7
+vega-datasets==0.9.0
+vtk==9.3.0
+wcwidth==0.2.13
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.7.0
+Werkzeug==3.0.1
+widgetsnbextension==4.0.9
+wslink==1.12.4
+xarray==2024.1.1
+xlrd==2.0.1
+xmltodict==0.13.0
+xyzservices==2023.10.1
+y-py==0.6.2
+yarl==1.9.4
+yfinance==0.2.36
+ypy-websocket==0.8.4
+zarr==2.16.1
+zipp==3.17.0
\ No newline at end of file
diff --git a/openvisuspy/pyproject.toml b/openvisuspy/pyproject.toml
new file mode 100644
index 0000000..ce39e6e
--- /dev/null
+++ b/openvisuspy/pyproject.toml
@@ -0,0 +1,16 @@
+
+[project]
+name = "openvisuspy"
+version = "1.0.28"
+authors = [{ name="OpenVisus developers"},]
+description = "openvisuspy"
+readme = "README.md"
+requires-python = ">=3.6"
+
+[project.urls]
+"Homepage" = "https://github.com/sci-visus/openvisuspy"
+"Bug Tracker" = "https://github.com/sci-visus/openvisuspy"
+
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
diff --git a/openvisuspy/scripts/new_tag.py b/openvisuspy/scripts/new_tag.py
new file mode 100644
index 0000000..26200a4
--- /dev/null
+++ b/openvisuspy/scripts/new_tag.py
@@ -0,0 +1,31 @@
+
+import os,sys, tomli
+
+BODY="""
+[project]
+name = "openvisuspy"
+version = "{version}"
+authors = [{ name="OpenVisus developers"},]
+description = "openvisuspy"
+readme = "README.md"
+requires-python = ">=3.6"
+
+[project.urls]
+"Homepage" = "https://github.com/sci-visus/openvisuspy"
+"Bug Tracker" = "https://github.com/sci-visus/openvisuspy"
+
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+"""
+
+# ////////////////////////////////////////////////////////////
+if __name__=="__main__":
+	with open("pyproject.toml", "rb") as f: config = tomli.load(f)
+	old_version=config['project']['version']
+	v=old_version.split('.')
+	new_version=f"{v[0]}.{v[1]}.{int(v[2])+1}"
+	body=BODY.replace("{version}",new_version)
+	with open("pyproject.toml", "wt") as f: f.write(body)
+	print(new_version)
+	sys.exit(0)
\ No newline at end of file
diff --git a/openvisuspy/scripts/new_tag.sh b/openvisuspy/scripts/new_tag.sh
new file mode 100755
index 0000000..42f89c7
--- /dev/null
+++ b/openvisuspy/scripts/new_tag.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+#export PYPI_USERNAME="..."
+#export PYPI_PASSWORD="..."
+
+TAG=$(python3 scripts/new_tag.py) && echo ${TAG}
+
+git commit -a -m "New tag ($TAG)" 
+git tag -a $TAG -m "$TAG"
+git push origin $TAG
+git push origin
+
+rm -f dist/*  
+python -m build .
+python -m twine upload --username "${PYPI_USERNAME}"  --password "${PYPI_PASSWORD}" --skip-existing "dist/*.whl" --verbose 
\ No newline at end of file
diff --git a/openvisuspy/scripts/run_command.py b/openvisuspy/scripts/run_command.py
new file mode 100644
index 0000000..ba868f4
--- /dev/null
+++ b/openvisuspy/scripts/run_command.py
@@ -0,0 +1,7 @@
+import os,sys,glob
+cmd,pattern=sys.argv[1:]
+for notebook in glob.glob(pattern,recursive=True): 
+  s=cmd.format(notebook=notebook)
+  print(s)
+  os.system(s)
+exit()
\ No newline at end of file
diff --git a/openvisuspy/src/openvisuspy/__init__.py b/openvisuspy/src/openvisuspy/__init__.py
new file mode 100644
index 0000000..8d90a19
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/__init__.py
@@ -0,0 +1,4 @@
+from .utils      import *
+from .backend    import *
+from .slice      import *
+from .probe      import *
\ No newline at end of file
diff --git a/openvisuspy/src/openvisuspy/backend.py b/openvisuspy/src/openvisuspy/backend.py
new file mode 100644
index 0000000..4606395
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/backend.py
@@ -0,0 +1,201 @@
+import os,sys,copy,math,time,logging,types,requests,zlib,xmltodict,urllib,queue,types,threading
+import numpy as np
+
+from . utils import *
+
+logger = logging.getLogger(__name__)
+
+# //////////////////////////////////////////////////////////////////////////
+class BaseDataset:
+
+	# getAlignedBox
+	def getAlignedBox(self, logic_box, endh, slice_dir:int=None):
+		p1,p2=copy.deepcopy(logic_box)
+		pdim=self.getPointDim()
+		maxh=self.getMaxResolution()
+		bitmask=self.getBitmask()
+		delta=[1,1,1]
+		
+		for K in range(maxh,endh,-1):
+			bit=ord(bitmask[K])-ord('0')
+			delta[bit]*=2
+
+		for I in range(pdim):
+			p1[I]=delta[I]*(p1[I]//delta[I])
+			p2[I]=delta[I]*(p2[I]//delta[I])
+			p2[I]=max(p1[I]+delta[I], p2[I])
+		
+		num_pixels=[(p2[I]-p1[I])//delta[I] for I in range(pdim)]
+
+
+		#  force to be a slice?
+		# REMOVE THIS!!!
+		if pdim==3 and slice_dir is not None:
+			offset=p1[slice_dir]
+			p2[slice_dir]=offset+0
+			p2[slice_dir]=offset+1
+		
+		# print(f"getAlignedBox logic_box={logic_box} endh={endh} slice_dir={slice_dir} (p1,p2)={(p1,p2)} delta={delta} num_pixels={num_pixels}")
+
+		return (p1,p2), delta, num_pixels
+
+	# createBoxQuery
+	def createBoxQuery(self,
+		timestep=None, 
+		field=None, 
+		logic_box=None,
+		max_pixels=None, 
+		endh=None, 
+		num_refinements=1, 
+		aborted=None,
+		full_dim=False,
+	):
+
+		pdim=self.getPointDim()
+		assert pdim in [1,2,3]
+
+		maxh=self.getMaxResolution()
+		bitmask=self.getBitmask()
+		dims=self.getLogicSize()
+
+		if timestep is None:
+			timestep=self.getTimestep()
+
+		if field is None:
+			field=self.getField()
+
+		if logic_box is None:
+			logic_box=self.getLogicBox()
+
+		if endh is None and not max_pixels:
+			endh=maxh
+
+		if aborted is None:
+			aborted=Aborted()
+
+		logger.info(f"begin timestep={timestep} field={field} logic_box={logic_box} num_refinements={num_refinements} max_pixels={max_pixels} endh={endh}")
+
+		# if box is not specified get the all box
+		if logic_box is None:
+			W,H,D=[int(it) for it in self.getLogicSize()]
+			logic_box=[[0,0,0],[W,H,D]]
+		
+		# crop logic box
+		if True:
+			p1,p2=list(logic_box[0]),list(logic_box[1])
+			slice_dir=None
+			for I in range(pdim):
+				
+				# *************** is a slice? *******************
+				if not full_dim and  pdim==3 and (p2[I]-p1[I])==1:
+					assert slice_dir is None 
+					slice_dir=I
+					p1[I]=Clamp(p1[I],0,dims[I])
+					p2[I]=p1[I]+1
+				else:
+					p1[I]=Clamp(int(math.floor(p1[I])),     0,dims[I])
+					p2[I]=Clamp(int(math.ceil (p2[I])) ,p1[I],dims[I])
+				if not p1[I]<p2[I]:
+					return None
+			logic_box=(p1,p2)
+		
+		# is view dependent? if so guess max resolution and endh is IGNORED and overwritten 
+		if max_pixels:
+
+			if IsIterable(max_pixels):
+				max_pixels=int(np.prod(max_pixels,dtype=np.int64))
+
+			original_box=logic_box
+			for __endh in range(maxh,0,-1):
+				aligned_box, delta, num_pixels=self.getAlignedBox(original_box,__endh, slice_dir=slice_dir)
+				tot_pixels=np.prod(num_pixels, dtype=np.int64)
+				if tot_pixels<=max_pixels:
+					endh=__endh
+					logger.info(f"Guess resolution endh={endh} original_box={original_box} aligned_box={aligned_box} delta={delta} num_pixels={repr(num_pixels)} tot_pixels={tot_pixels:,} max_pixels={max_pixels:,} end={endh}")
+					logic_box=aligned_box
+					break
+		else:
+			original_box=logic_box
+			aligned_box, delta, num_pixels=self.getAlignedBox(original_box,endh, slice_dir=slice_dir)
+
+		# this is the query I need
+		end_resolutions=list(reversed([endh-pdim*I for I in range(num_refinements) if endh-pdim*I>=0]))
+
+		# scrgiorgio: end_resolutions[0] is wrong, I need to align to the finest resolution
+		logic_box, delta, num_pixels=self.getAlignedBox(logic_box, end_resolutions[-1], slice_dir=slice_dir)
+
+		logic_box=[
+			[int(it) for it in logic_box[0]],
+			[int(it) for it in logic_box[1]]
+		]
+
+		query=types.SimpleNamespace()
+		query.logic_box=logic_box
+		query.timestep=timestep
+		query.field=field
+		query.end_resolutions=end_resolutions
+		query.slice_dir=slice_dir
+		query.aborted=aborted
+		query.t1=time.time()
+		query.cursor=0
+		return query
+
+	# beginBoxQuery
+	def beginBoxQuery(self,query):
+		logger.info(f"beginBoxQuery timestep={query.timestep} field={query.field} logic_box={query.logic_box} end_resolutions={query.end_resolutions}")	
+		query.cursor=0	
+
+	# isQueryRunning (if cursor==0 , means I have to begin, if cursor==1 means I have the first level ready etc)
+	def isQueryRunning(self,query):
+		return query is not None and query.cursor>=0 and query.cursor<len(query.end_resolutions)
+		
+	 # getQueryCurrentResolution
+	def getQueryCurrentResolution(self,query):
+		if query is None: return -1
+		last=query.cursor-1
+		return query.end_resolutions[last]  if last>=0 and last<len(query.end_resolutions) else -1
+
+	# returnBoxQueryData
+	def returnBoxQueryData(self,access, query, data):
+		
+		if query is None or data is None:
+			logger.info(f"read done {query} {data}")
+			return None
+
+		# is a slice? I need to reduce the size (i.e. from 3d data to 2d data)
+		if query.slice_dir is not None:
+			dims=list(reversed(data.shape))
+			assert dims[query.slice_dir]==1
+			del dims[query.slice_dir]
+			while len(dims)>2 and dims[-1]==1: dims=dims[0:-1] # remove right `1`
+			data=data.reshape(list(reversed(dims)))
+
+		H=self.getQueryCurrentResolution(query)
+		msec=int(1000*(time.time()-query.t1))
+		logger.info(f"got data cursor={query.cursor} end_resolutions{query.end_resolutions} timestep={query.timestep} field={query.field} H={H} data.shape={data.shape} data.dtype={data.dtype} logic_box={query.logic_box} m={np.min(data)} M={np.max(data)} ms={msec}")
+		
+		return {
+			"I": query.cursor,
+			"timestep": query.timestep,
+			"field": query.field, 
+			"logic_box": query.logic_box, 
+			"H": H, 
+			"data": data,
+			"msec": msec,
+			}
+
+	# nextBoxQuery
+	def nextBoxQuery(self,query):
+		if not self.isQueryRunning(query): return
+		query.cursor+=1
+
+# //////////////////////////////////////////////////
+from .utils import GetBackend
+backend=GetBackend()
+
+logger.info(f"openvisuspy backend={backend}")
+
+if backend=="py":
+	from .backend_py import *
+else:
+	from .backend_cpp import *
\ No newline at end of file
diff --git a/openvisuspy/src/openvisuspy/backend_cpp.py b/openvisuspy/src/openvisuspy/backend_cpp.py
new file mode 100644
index 0000000..74137c2
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/backend_cpp.py
@@ -0,0 +1,335 @@
+import os,sys,time,threading,queue
+import OpenVisus as ov
+
+from .utils import *
+from .backend import BaseDataset
+
+logger = logging.getLogger(__name__)
+
+# ///////////////////////////////////////////////////////////////////
+class Aborted:
+	
+	# constructor
+	def __init__(self,value=False):
+		self.inner=ov.Aborted()
+		if value: self.inner.setTrue()
+
+	# setTrue
+	def setTrue(self):
+		self.inner.setTrue()
+
+# ///////////////////////////////////////////////////////////////////
+class Stats:
+	
+	# constructor
+	def __init__(self):
+		self.lock = threading.Lock()
+		self.num_running=0
+		
+	# isRunning
+	def isRunning(self):
+		with self.lock:
+			return self.num_running>0
+
+	# readStats
+	def readStats(self):
+
+		io =ov.File.global_stats()
+		net=ov.NetService.global_stats()
+
+		ret= {
+			"io": {
+				"r":io.getReadBytes(),
+				"w":io.getWriteBytes(),
+				"n":io.getNumOpen(),
+			},
+			"net":{
+				"r":net.getReadBytes(), 
+				"w":net.getWriteBytes(),
+				"n":net.getNumRequests(),
+			}
+		}
+
+		ov.File      .global_stats().resetStats()
+		ov.NetService.global_stats().resetStats()
+
+		return ret
+			
+
+	# startCollecting
+	def startCollecting(self):
+		with self.lock:
+			self.num_running+=1
+			if self.num_running>1: return
+		self.t1=time.time()
+		self.readStats()
+			
+	# stopCollecting
+	def stopCollecting(self):
+		with self.lock:
+			self.num_running-=1
+			if self.num_running>0: return
+		self.printStatistics()
+
+	# printStatistics
+	def printStatistics(self):
+		sec=max(time.time()-self.t1,1e-8)
+		stats=self.readStats()
+		logger.info(f"Stats::printStatistics enlapsed={sec} seconds" )
+		for k,v in stats.items():
+			w,r,n=v['w'],v['r'],v['n']
+			logger.info(" ".join([f"  {k:4}",
+						f"r={HumanSize(r)} r_sec={HumanSize(r/sec)}/sec",
+						f"w={HumanSize(w)} w_sec={HumanSize(w/sec)}/se ",
+						f"n={n:,} n_sec={int(n/sec):,}/sec"]))
+
+
+# /////////////////////////////////////////////////////////////////////////////////////////////////
+class QueryNode:
+
+	# shared by all instances (and must remain this way!)
+	stats=Stats()
+
+	# constructor
+	def __init__(self):
+		self.iqueue=queue.Queue()
+		self.oqueue=queue.Queue()
+		self.wait_for_oqueue=False
+		self.thread=None
+
+	# disableOutputQueue
+	def disableOutputQueue(self):
+		self.oqueue=None
+
+	# start
+	def start(self):
+		# already running
+		if not self.thread is None:
+			return
+		self.thread = threading.Thread(target=self._threadLoop,daemon=True)
+		self.thread.start()
+
+	# stop
+	def stop(self):
+		self.iqueue.join()
+		self.iqueue.put((None,None))
+		if self.thread is not None:
+			self.thread.join()
+			self.thread=None
+
+	# waitIdle
+	def waitIdle(self):
+		self.iqueue.join()
+
+	# pushJob
+	def pushJob(self, db, **kwargs):
+		self.iqueue.put([db,kwargs])
+
+	# popResult
+	def popResult(self, last_only=True):
+		assert self.oqueue is not None
+		ret=None
+		while not self.oqueue.empty():
+			ret=self.oqueue.get()
+			self.oqueue.task_done()
+			if not last_only: break
+		return ret
+
+	# _threadLoop
+	def _threadLoop(self):
+
+		logger.info("entering _threadLoop ...")
+
+		is_aborted=ov.Aborted()
+		is_aborted.setTrue()
+
+		t1=None
+		while True:
+
+			if t1 is None or (time.time()-t1)>5.0:
+				logger.info("_threadLoop is Alive")
+				t1=time.time()
+
+			db, kwargs=self.iqueue.get()
+			if db is None: 
+				logger.info("exiting _threadLoop...")
+				return 
+			
+			self.stats.startCollecting() 
+
+			access=kwargs['access'];del kwargs['access']
+			query=db.createBoxQuery(**kwargs)
+			db.beginBoxQuery(query)
+			while db.isQueryRunning(query):
+				try:
+					result=db.executeBoxQuery(access, query)
+				except Exception as ex:
+					if not query.aborted == is_aborted:
+						logger.info(f"db.executeBoxQuery failed {ex}")
+					break
+
+				if result is None: 
+					break
+				
+				if query.aborted == is_aborted:
+					break 
+
+				
+				db.nextBoxQuery(query)
+				result["running"]=db.isQueryRunning(query)
+
+				if self.oqueue:
+					self.oqueue.put(result)
+					if self.wait_for_oqueue:
+						self.oqueue.join()
+				
+				time.sleep(0.01)
+
+				# remove me
+				# break
+
+			logger.info("Query finished")
+			self.iqueue.task_done()
+			self.stats.stopCollecting()
+
+
+
+# ///////////////////////////////////////////////////////////////////
+class Dataset (BaseDataset):
+	
+	# coinstructor
+	def __init__(self,url):
+		self.url=url
+
+		# handle security
+		if all([
+				url.startswith("http"),
+				"mod_visus" in url,
+			  "MODVISUS_USERNAME" in os.environ,
+				"MODVISUS_PASSWORD" in os.environ,
+				"~auth_username" not in url,
+				"~auth_password" not in url,
+		 	]) :
+
+			url=url + f"&~auth_username={os.environ['MODVISUS_USERNAME']}&~auth_password={os.environ['MODVISUS_PASSWORD']}"
+
+		self.inner=ov.LoadDataset(url)
+		
+	# getUrl
+	def getUrl(self):
+		return self.url       
+
+	# getPointDim
+	def getPointDim(self):
+		return self.inner.getPointDim()
+
+	# getLogicBox
+	def getLogicBox(self):
+		return self.inner.getLogicBox()
+
+	# getMaxResolution
+	def getMaxResolution(self):
+		return self.inner.getMaxResolution()
+
+	# getBitmask
+	def getBitmask(self):
+		return self.inner.getBitmask().toString()
+
+	# getLogicSize
+	def getLogicSize(self):
+		return self.inner.getLogicSize()
+	
+	# getTimesteps
+	def getTimesteps(self):
+		return self.inner.getTimesteps() 
+
+	# getTimestep
+	def getTimestep(self):
+		return self.inner.getTime()
+
+	# getFields
+	def getFields(self):
+		return self.inner.getFields()
+
+	# createAccess
+	def createAccess(self):
+		return self.inner.createAccess()
+
+	# getField
+	def getField(self,field=None):
+		return self.inner.getField(field) if field is not None else self.inner.getField()
+
+	# getDatasetBody
+	def getDatasetBody(self):
+		return self.inner.getDatasetBody()
+
+	# ///////////////////////////////////////////////////////////////////////////
+
+	def createBoxQuery(self, *args,**kwargs):
+
+		query=super().createBoxQuery(*args,**kwargs)
+		
+		if query is None:
+			return None
+
+		query.inner  = self.inner.createBoxQuery(
+			ov.BoxNi(ov.PointNi(query.logic_box[0]), ov.PointNi(query.logic_box[1])), 
+			self.inner.getField(query.field), 
+			query.timestep, 
+			ord('r'), 
+			query.aborted.inner)
+
+		if not query.inner:
+			return None
+
+		for H in query.end_resolutions:
+			query.inner.end_resolutions.push_back(H)
+
+		return query
+
+	# begin
+	def beginBoxQuery(self,query):
+		if query is None: return
+		super().beginBoxQuery(query)
+		self.inner.beginBoxQuery(query.inner)
+
+	# isRunning
+	def isQueryRunning(self,query):
+		if query is None: return False
+		return query.inner.isRunning() 
+
+	# getQueryCurrentResolution
+	def getQueryCurrentResolution(self, query):
+		return query.inner.getCurrentResolution() if self.isQueryRunning(query) else -1
+
+	# executeBoxQuery
+	def executeBoxQuery(self,access, query):
+		assert self.isQueryRunning(query)
+		if not self.inner.executeBoxQuery(access, query.inner):
+			return None
+		data=ov.Array.toNumPy(query.inner.buffer, bShareMem=False) 
+		return super().returnBoxQueryData(access,query,data)
+
+	# nextBoxQuery
+	def nextBoxQuery(self,query):
+		if not self.isQueryRunning(query): return
+		self.inner.nextBoxQuery(query.inner)
+		super().nextBoxQuery(query)
+
+# ///////////////////////////////////////////////////////////////////
+def LoadDataset(url):
+	return Dataset(url)
+
+# ////////////////////////////////////////////////////////////////////////////////////////////////////////////
+def ExecuteBoxQuery(db,*args,**kwargs):
+	access=kwargs['access'];del kwargs['access']
+	query=db.createBoxQuery(*args,**kwargs)
+	t1=time.time()
+	I,N=0,len(query.end_resolutions)
+	db.beginBoxQuery(query)
+	while db.isQueryRunning(query):
+		result=db.executeBoxQuery(access, query)
+		if result is None: break
+		db.nextBoxQuery(query)
+		result["running"]=db.isQueryRunning(query)
+		yield result
\ No newline at end of file
diff --git a/openvisuspy/src/openvisuspy/backend_py.py b/openvisuspy/src/openvisuspy/backend_py.py
new file mode 100644
index 0000000..e48333d
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/backend_py.py
@@ -0,0 +1,453 @@
+import os,sys,xmltodict,urllib,zlib,requests
+from threading import Lock
+
+from .utils import *
+from .backend import BaseDataset
+
+logger = logging.getLogger(__name__)
+
+# ///////////////////////////////////////////////////////////////////
+class Aborted:
+	
+	# constructor
+	def __init__(self):
+		self.value=False
+		self.on_aborted=None
+
+	# setTrue
+	def setTrue(self):
+
+		if self.value==True: 
+			return
+		
+		self.value=True
+
+		if self.on_aborted is not None:
+			try:
+				self.on_aborted()
+			except:
+				pass
+
+# ///////////////////////////////////////////////////////////////////
+class Stats:
+	
+	# constructor
+	def __init__(self):
+		self.lock = Lock()
+		self.num_running=0
+		
+	# readStats
+	def readStats(self):
+
+		# TODO
+		return {
+			"io": {
+				"r": 0,
+				"w": 0,
+				"n": 0,
+			},
+			"net":{
+				"r": 0, 
+				"w": 0,
+				"n": 0,
+			}
+		}
+
+		# todo reset
+
+	# isRunning
+	def isRunning(self):
+		with self.lock:
+			return self.num_running>0
+
+	# startCollecting
+	def startCollecting(self):
+		with self.lock:
+			self.num_running+=1
+			if self.num_running>1: return
+		self.t1=time.time()
+		self.readStats()
+			
+	# stopCollecting
+	def stopCollecting(self):
+		with self.lock:
+			self.num_running-=1
+			if self.num_running>0: return
+		self.printStatistics()
+
+	# printStatistics
+	def printStatistics(self):
+		sec=time.time()-self.t1
+		stats=self.readStats()
+		logger.info(f"Stats::printStatistics enlapsed={sec} seconds" )
+		try: # division by zero
+			for k,v in stats.items():
+				logger.info(f"   {k}  r={HumanSize(v['r'])} r_sec={HumanSize(v['r']/sec)}/sec w={HumanSize(v['w'])} w_sec={HumanSize(v['w']/sec)}/sec n={v.n:,} n_sec={int(v/sec):,}/sec")
+		except:
+			pass
+
+# /////////////////////////////////////////////////////////////////////////////////////////////////
+class QueryNode:
+
+	# shared by all instances (and must remain this way!)
+	stats=Stats()
+
+	# constructor
+	def __init__(self):
+		self.job=[None,None]
+		self.result=None
+		self.task=None
+		
+	# disableOutputQueue
+	def disableOutputQueue(self):
+		pass
+
+	# asyncExecuteQuery
+	async def asyncExecuteQuery(self):
+		(db,kwargs)=self.popJob()
+		if db is None: return
+		self.stats.startCollecting() 
+		access=kwargs['access']
+		del kwargs['access']
+		query=db.createBoxQuery(**kwargs)
+		db.beginBoxQuery(query)
+		while db.isQueryRunning(query):
+			result=None 
+			try:
+				result=await db.executeBoxQuery(access, query)
+			except Exception as ex: # was without Exception
+				logger.info(f"db.executeBoxQuery FAILED {ex}")
+			except: # this is needed for pyoidide
+				logger.info(f"db.executeBoxQuery FAILED unknown-error")
+				
+			if result is None: break
+			db.nextBoxQuery(query)
+			result["running"]=db.isQueryRunning(query)
+			self.pushResult(result)
+			await SleepMsec(0)
+		self.stats.stopCollecting()
+
+	# start
+	def start(self):
+		self.task=AddAsyncLoop(f"{self}.QueryNodeLoop",self.asyncExecuteQuery, msec=50) 
+
+	# stop
+	def stop(self):
+		if self.task:
+			self.task.cancel()
+			self.task=None
+
+	# waitIdle
+	def waitIdle(self):
+		pass # I don't think I need this
+
+	# pushJob
+	def pushJob(self, db, **kwargs):
+		logger.info(f"pushed new job {db}")
+		self.job=[db,kwargs]
+
+	# popJob
+	def popJob(self):
+		ret,self.job=self.job,[None,None]
+		return ret
+
+	def pushResult(self, result):
+		self.result=result
+
+	# popResult
+	def popResult(self, last_only=True):
+		ret, self.result = self.result, None
+		return ret
+
+# ///////////////////////////////////////////////////////////////////
+class Dataset(BaseDataset):
+	
+	# constructor
+	def __init__(self):
+		pass
+	
+	# getUrl
+	def getUrl(self):
+		return self.url
+	
+	# getPointDim
+	def getPointDim(self):
+		return self.pdim
+
+	# getLogicBox
+	def getLogicBox(self):
+		return self.logic_box
+
+	# getMaxResolution
+	def getMaxResolution(self):
+		return self.max_resolution
+
+	# getBitmask
+	def getBitmask(self):
+		return self.bitmask
+
+	# getLogicSize
+	def getLogicSize(self):
+		return self.logic_size
+	
+	# getTimesteps
+	def getTimesteps(self):
+		return self.timesteps
+
+	# getTimestep
+	def getTimestep(self):
+		return self.timesteps[0]
+
+	# getFields
+	def getFields(self):
+		return [it['name'] for it in self.fields]
+
+	# createAccess
+	def createAccess(self):
+		return None # I don't have the access
+
+	# getField
+	def getField(self,field=None):
+		if field is None:
+			return self.fields[0]['name']
+		else:
+			raise Exception("internal error")
+
+	# getDatasetBody
+	def getDatasetBody(self):
+		return self.body
+		
+	# /////////////////////////////////////////////////////////////////////////////////
+
+	async def executeBoxQuery(self,access, query, verbose=False):
+
+		"""
+		Links:
+
+		- https://blog.jonlu.ca/posts/async-python-http
+		- https://requests.readthedocs.io/en/latest/user/advanced/
+		- https://lwebapp.com/en/post/pyodide-fetch
+		- https://stackoverflow.com/questions/31998421/abort-a-get-request-in-python-when-the-server-is-not-responding
+		- https://developer.mozilla.org/en-US/docs/Web/API/fetch#options
+		- https://pyodide.org/en/stable/usage/packages-in-pyodide.html
+		"""
+
+		if not self.isQueryRunning(query):
+			return
+
+		H=query.end_resolutions[query.cursor]
+
+		url=self.getUrl()
+		timestep=query.timestep
+		field=query.field
+		logic_box=query.logic_box
+		toh=H
+		compression="zip"
+
+		parsed=urllib.parse.urlparse(url)
+		
+		scheme=parsed.scheme
+		path=parsed.path;assert(path=="/mod_visus")
+		params=urllib.parse.parse_qs(parsed.query)
+		
+		for k,v in params.items():
+			if isinstance(v,list):
+				params[k]=v[0]
+		
+		# remove array in values
+		params={k:(v[0] if isinstance(v,list)  else v) for k,v in params.items()}
+
+		def SetParam(key,value):
+			nonlocal params
+			if not key in params:
+				params[key]=value
+		
+		SetParam('action',"boxquery")
+		SetParam('box'," ".join([f"{a} {b-1}" for a,b in zip(*logic_box)]).strip())
+		SetParam('compression',compression)
+		SetParam('field',field)
+		SetParam('time',timestep)
+		SetParam('toh',toh)
+	
+		if verbose:
+			logger.info("Sending params={params.items()}")
+			
+		url=f"{scheme}://{parsed.netloc}{path}?" + urllib.parse.urlencode(params)
+
+		aborted=query.aborted
+		if aborted.value: return None
+
+		if IsPyodide():
+				# see pyfetch (https://github.com/pyodide/pyodide/blob/main/src/py/pyodide/http.py)
+				import js
+				import pyodide
+				import pyodide.http
+				import pyodide.ffi 
+				import pyodide.webloop
+				
+				options=pyodide.ffi.to_js({"method":"GET", "mode":"cors","cache":"no-cache","redirect":"follow",},dict_converter=js.Object.fromEntries)
+				# https://github.com/pyodide/pyodide/issues/2923
+				def OnError(err): print(f'there were error: {err.message}')
+				js_future = js.fetch(url, options).catch(OnError)
+				assert(isinstance(js_future,pyodide.webloop.PyodideFuture))
+				def OnAborted(): js_future.cancel()
+				aborted.on_aborted=OnAborted
+				response=pyodide.http.FetchResponse(url,await js_future)
+				response.status_code=response.status
+				response.headers=response.js_response.headers
+
+		else:
+			import httpx
+			client = httpx.AsyncClient(verify=False)
+			def OnAborted(): client.close()
+			aborted.on_aborted=OnAborted
+			response = await client.get(url)
+				
+		if aborted.value:
+			return None
+
+		logger.info(f"[{response.status_code}] {response.url}")
+		if response.status_code!=200:
+			if not aborted.value: logger.info(f"Got unvalid response {response.status_code}")
+			return None
+
+		# get the body
+		try:
+			if IsPyodide():
+				body=await response.bytes()
+			else:
+				body=response.content
+		except Exception as ex:
+			if not aborted.value: logger.info(f"Got unvalid response {ex}")
+			return None			
+		except:	# this is needed for pyoidide
+			if not aborted.value: logger.info(f"Got unvalid response unknown-error")
+			return None
+
+		if verbose:
+			logger.info(f"Got body len={len(body)}")
+			logger.info(f"response headers {response.headers.items()}")
+
+		dtype     = response.headers["visus-dtype"].strip()
+		compression=response.headers["visus-compression"].strip()
+
+		if compression=="raw" or compression=="":
+			pass
+
+		elif compression=="zip":
+			body=zlib.decompress(body)
+			if verbose:
+				logger.info(f"data after decompression {type(body)} {len(body)}")
+		else:
+			raise Exception("internal error")
+		
+		nsamples=[int(it) for it in response.headers["visus-nsamples"].strip().split()]
+
+		# example uint8[3]
+		shape=list(reversed(nsamples))
+		if "[" in dtype:
+			assert dtype[-1]==']'
+			dtype,N=dtype[0:-1].split("[")
+			shape.append(int(N))
+
+		if verbose:
+			logger.info(f"numpy array dtype={dtype} shape={shape}")
+
+		data=np.frombuffer(body,dtype=np.dtype(dtype)).reshape(shape)   
+
+		# full-dimension
+		return super().returnBoxQueryData(access, query, data)
+
+# /////////////////////////////////////////////////////////////////////////////////
+def LoadDataset(url):
+	
+	# i don't support block access
+	if not "mod_visus" in url:
+		raise Exception(f"{repr(url)} is not a mod_visus dataset")
+
+	response=requests.get(url,params={'action':'readdataset','format':'xml'},verify=False) 
+	if response.status_code!=200:
+		raise Exception(f"requests.get({url}) returned {response.status_code}")
+
+	assert(response.status_code==200)
+	body=response.text
+	logger.info(f"Got response {body}")
+	
+	def RemoveAt(cursor):
+		if isinstance(cursor,dict):
+			return {(k[1:] if k.startswith("@") else k):RemoveAt(v) for k,v in cursor.items()}
+		elif isinstance(cursor,list):
+			return [RemoveAt(it) for it in cursor]
+		else:
+			return cursor
+
+	d=RemoveAt(xmltodict.parse(body)["dataset"]["idxfile"])
+	# pprint(d)
+
+	ret=Dataset()
+	ret.url=url
+	ret.body=body
+	ret.bitmask=d["bitmask"]["value"]
+	ret.pdim=3 if '2' in ret.bitmask else 2
+	ret.max_resolution=len(ret.bitmask)-1
+
+	# logic_box (X1 X2 Y1 Y2 Z1 Z2)
+	v=[int(it) for it in d["box"]["value"].strip().split()]
+	p1=[v[I] for I in range(0,len(v),2)]
+	p2=[v[I] for I in range(1,len(v),2)]
+	ret.logic_box=[p1,p2]
+	
+	# logic_size
+	ret.logic_size=[(b-a) for a,b in zip(p1,p2)]
+
+	# timesteps
+	ret.timesteps=[]
+	v=d["timestep"]
+	if not isinstance(v,list): v=[v]
+	for T,timestep in enumerate(v):
+		if "when" in timestep:
+			ret.timesteps.append(int(timestep["when"]))
+		else:
+			assert("from" in timestep)
+			for T in range(int(timestep["from"]),int(timestep["to"]),int(timestep["step"])):
+				ret.timesteps.append(T)
+
+	# fields
+	v=d["field"]
+	if not isinstance(v,list):
+		v=[v]
+	ret.fields=[{"name":field["name"],"dtype": field["dtype"]} for field in v]
+	
+	logger.info(f"LoadDataset returned:\n" + str({
+			"url":ret.url,
+			"bitmask":ret.bitmask,
+			"pdim":ret.pdim,
+			"max_resolution":ret.max_resolution,
+			"timesteps": ret.timesteps,
+			"fields":ret.fields,
+			"logic_box":ret.logic_box,
+			"logic_size":ret.logic_size,
+		}))
+	
+
+	#box,delta,num_pixels=ret.getAlignedBox(logic_box=[[0,0,539],[2048,2048,540]],endh=22,slice_dir=2)
+	
+	return ret
+
+# ////////////////////////////////////////////////////////////////////////////////////////////////////////////
+def ExecuteBoxQuery(db,*args,**kwargs):
+	access=kwargs['access']
+	del kwargs['access']
+	
+	query=db.createBoxQuery(*args,**kwargs)
+	t1=time.time()
+	I,N=0,len(query.end_resolutions)
+	db.beginBoxQuery(query)
+	while db.isQueryRunning(query):
+		result=RunAsync(db.executeBoxQuery(access, query))
+		if result is None: break
+		db.nextBoxQuery(query)
+		result["running"]=db.isQueryRunning(query)
+		yield result
+
+
diff --git a/openvisuspy/src/openvisuspy/dashboards/__init__.py b/openvisuspy/src/openvisuspy/dashboards/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/openvisuspy/src/openvisuspy/dashboards/app_hooks.py b/openvisuspy/src/openvisuspy/dashboards/app_hooks.py
new file mode 100644
index 0000000..e57cc40
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/dashboards/app_hooks.py
@@ -0,0 +1,15 @@
+def on_server_loaded(server_context):
+	# If present, this function executes when the server starts.
+	pass
+
+def on_server_unloaded(server_context):
+	# If present, this function executes when the server shuts down.
+	pass
+
+def on_session_created(session_context):
+	# If present, this function executes when the server creates a session.
+	pass
+
+def on_session_destroyed(session_context):
+	# If present, this function executes when the server closes a session.
+	pass
\ No newline at end of file
diff --git a/openvisuspy/src/openvisuspy/dashboards/main.py b/openvisuspy/src/openvisuspy/dashboards/main.py
new file mode 100644
index 0000000..161f6d4
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/dashboards/main.py
@@ -0,0 +1,45 @@
+import os, sys
+import argparse,json
+import panel as pn
+import logging
+import base64,json
+sys.path.append('/app/openvisuspy/src')
+from openvisuspy import SetupLogger, Slice, ProbeTool, GetQueryParams
+
+# //////////////////////////////////////////////////////////////////////////////////////
+if __name__.startswith('bokeh'):
+
+	# https://github.com/holoviz/panel/issues/3404
+	# https://panel.holoviz.org/api/config.html
+	pn.extension(
+		"ipywidgets",
+		"floatpanel",
+		log_level ="DEBUG",
+		notifications=True, 
+		sizing_mode="stretch_width",
+		# template="fast",
+		#theme="default",
+	)
+
+	log_filename=os.environ.get("OPENVISUSPY_DASHBOARDS_LOG_FILENAME","/tmp/openvisuspy-dashboards.log")
+	logger=SetupLogger(log_filename=log_filename,logging_level=logging.DEBUG)
+
+	
+	slice = Slice()
+	slice.load(sys.argv[1])
+	
+	query_params=GetQueryParams()
+	if "load" in query_params:
+		body=json.loads(base64.b64decode(query_params['load']).decode("utf-8"))
+		slice.setSceneBody(body)
+	elif "dataset" in query_params:
+		scene_name=query_params["dataset"]
+		slice.scene.value=scene_name
+
+	if False:
+		app = ProbeTool(slice).getMainLayout()
+	else:
+		app = slice.getMainLayout()
+
+	app.servable()
+
diff --git a/openvisuspy/src/openvisuspy/dashboards/templates/index.html b/openvisuspy/src/openvisuspy/dashboards/templates/index.html
new file mode 100644
index 0000000..abfaef0
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/dashboards/templates/index.html
@@ -0,0 +1,13 @@
+{% extends base %}
+
+{% block title %}NSDF Dashboard{% endblock %}
+
+{% block postamble %}
+  <style>
+    {% include 'styles.css' %}
+  </style>
+{% endblock %}
+
+{% block contents %}
+{{ super() }}
+{% endblock %}
\ No newline at end of file
diff --git a/openvisuspy/src/openvisuspy/dashboards/templates/styles.css b/openvisuspy/src/openvisuspy/dashboards/templates/styles.css
new file mode 100644
index 0000000..accae16
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/dashboards/templates/styles.css
@@ -0,0 +1,6 @@
+html {
+	width: 100%;
+}
+body {
+	margin: auto;
+}
diff --git a/openvisuspy/src/openvisuspy/probe.py b/openvisuspy/src/openvisuspy/probe.py
new file mode 100644
index 0000000..d3b7d05
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/probe.py
@@ -0,0 +1,462 @@
+import os,sys,logging
+
+logger = logging.getLogger(__name__)
+
+import numpy as np
+from statistics import mean, median
+
+from .slice  import  Slice, EPSILON
+from .backend import ExecuteBoxQuery
+from .utils   import *
+
+import bokeh.plotting 
+import bokeh.events
+import bokeh.models.scales
+
+import param
+import panel as pn
+
+# //////////////////////////////////////////////////////////////////////////////////////
+class Probe:
+
+	def __init__(self):
+		self.pos = None
+		self.enabled = True
+
+# //////////////////////////////////////////////////////////////////////////////////////
+class ProbeTool(param.Parameterized):
+
+	# widgets
+	slider_x_pos         = pn.widgets.FloatSlider     (name="X coordinate", value=0.0, start=0.0, end=1.0, step=1.0, width=160)
+	slider_y_pos         = pn.widgets.FloatSlider     (name="Y coordinate", value=0, start=0, end=1, step=1, width=160)
+	slider_z_range       = pn.widgets.RangeSlider     (name="Range", start=0.0, end=1.0, value=(0.0, 1.0), width=250,format="0.001")
+	slider_num_points_x  = pn.widgets.IntSlider       (name="#x", start=1, end=8, step=1, value=2, width=60)
+	slider_num_points_y  = pn.widgets.IntSlider       (name="#y", start=1, end=8, step=1, value=2, width=60)
+	slider_z_res         = pn.widgets.IntSlider       (name="Res", start=20, end=99, step=1, value=24, width=60)
+	slider_z_op          = pn.widgets.RadioButtonGroup(name="", options=["avg", "mM", "med", "*"], value="avg")
+
+	# constructor
+	def __init__(self, slice):
+		self.slice=slice
+		self.probes = {}
+		self.renderers = {"offset": None}
+		for dir in range(3):
+			self.probes[dir] = []
+			for I in range(len(COLORS)):
+				probe = Probe()
+				self.probes[dir].append(probe)
+				self.renderers[probe] = {
+					"canvas": [], # i am drwing on slice.canva s
+					"fig": []     # or probe fig
+				}
+		self.createGui()
+		
+		# to add probes
+		slice.canvas.on_event(bokeh.events.DoubleTap,SafeCallback(self.onCanvasDoubleTap))
+
+		self.slice.offset.param.watch(SafeCallback(lambda evt: self.refresh()),"value", onlychanged=True,queued=True) # display the new offset
+		self.slice.scene.param.watch(SafeCallback(lambda evt: self.recompute()),"value", onlychanged=True,queued=True)
+		self.slice.direction.param.watch(SafeCallback(lambda evt: self.recompute()),"value", onlychanged=True,queued=True)
+
+		# new data, important for the range
+		self.slice.render_id.param.watch(SafeCallback(lambda evt: self.refresh()), "value", onlychanged=True,queued=True) 
+
+	# createFigure
+	def createFigure(self):
+
+		x1, x2 = self.slider_z_range.value
+		y1, y2 = (self.slice.color_bar.color_mapper.low, self.slice.color_bar.color_mapper.high) if self.slice.color_bar else (0.0,1.0)
+
+		self.fig=bokeh.plotting.figure(
+			title=None,
+			sizing_mode="stretch_both",
+			active_scroll="wheel_zoom",
+			toolbar_location=None,
+			x_axis_label="Z", x_range=[x1,x2],x_axis_type="linear",
+			y_axis_label="f", y_range=[y2,y2],y_axis_type=self.slice.color_mapper_type.value
+			)
+
+		# change the offset on the proble plot (NOTE evt.x in is physic domain)
+		def handleDoubleTap(evt): self.slice.offset.value=evt.x
+		self.fig.on_event(bokeh.events.DoubleTap, handleDoubleTap)
+
+		self.fig_placeholder[:]=[]
+		self.fig_placeholder.append(self.fig)
+
+	# createGui
+	def createGui(self):
+
+		self.slot = None
+		self.button_css = [None] * len(COLORS)
+		self.fig_placeholder = pn.Column(sizing_mode='stretch_both')
+
+		self.slider_x_pos.param.watch(SafeCallback(lambda new: self.onProbeXYChange()), "value_throttled", onlychanged=True,queued=True)
+		self.slider_y_pos.param.watch(SafeCallback(lambda new: self.onProbeXYChange()), "value_throttled", onlychanged=True,queued=True)
+		self.slider_z_range.param.watch(SafeCallback(lambda evt: self.recompute()), "value_throttled", onlychanged=True,queued=True)
+		self.slider_num_points_x.param.watch(SafeCallback(lambda evt: self.recompute()), 'value_throttled', onlychanged=True,queued=True)
+		self.slider_num_points_y.param.watch(SafeCallback(lambda evt: self.recompute()), 'value_throttled', onlychanged=True,queued=True)
+		self.slider_z_res.param.watch(SafeCallback(lambda evt: self.recompute()), 'value_throttled', onlychanged=True,queued=True)
+		self.slider_z_op.param.watch(SafeCallback(lambda evt: self.recompute()), "value", onlychanged=True,queued=True)
+
+		# create buttons
+		self.buttons = []
+		for slot, color in enumerate(COLORS):
+			button=pn.widgets.Button(name=color, sizing_mode="stretch_width")
+			button.on_click(SafeCallback(lambda evt, slot=slot: self.onProbeButtonClick(slot)))
+			self.buttons.append(button)
+
+		self.createFigure()
+
+		self.main_layout = pn.Row(
+			self.slice.getMainLayout(),
+				pn.Column(
+					pn.Row(
+						self.slider_x_pos,
+						self.slider_y_pos,
+						self.slider_z_range,
+						self.slider_z_op,
+						self.slider_z_res,
+						self.slider_num_points_x,
+						self.slider_num_points_y,
+						sizing_mode="stretch_width"
+					),
+					pn.Row(
+						*[button for button in self.buttons], 
+						sizing_mode="stretch_width"
+					),
+					self.fig_placeholder,
+					sizing_mode="stretch_both"
+				)
+		)
+
+	# getMainLayout
+	def getMainLayout(self):
+		return self.main_layout
+
+	# removeRenderer
+	def removeRenderer(self, target, value):
+		if value in target.renderers:
+			target.renderers.remove(value)
+
+	# onProbeXYChange
+	def onProbeXYChange(self):
+		dir = self.slice.direction.value
+		slot = self.slot
+		if slot is None: return
+		probe = self.probes[dir][slot]
+		probe.pos = (self.slider_x_pos.value, self.slider_y_pos.value)
+		self.addProbe(probe)
+
+	# onCanvasDoubleTap
+	def onCanvasDoubleTap(self, evt):
+		x,y=evt.x,evt.y
+		logger.info(f"[{self.slice.id}] x={x} y={y}")
+		dir = self.slice.direction.value
+		slot = self.slot
+		if slot is None: slot = 0
+		probe = self.probes[dir][slot]
+		probe.pos = [x, y]
+		self.addProbe(probe)
+
+	# onProbeButtonClick
+	def onProbeButtonClick(self, slot):
+		dir = self.slice.direction.value
+		probe = self.probes[dir][slot]
+		logger.info(f"[{self.slice.id}] slot={slot} self.slot={self.slot} probe.pos={probe.pos} probe.enabled={probe.enabled}")
+
+		# when I click on the same slot, I am disabling the probe
+		if self.slot == slot:
+			self.removeProbe(probe)
+			self.slot = None
+		else:
+			# when I click on a new slot..
+			self.slot = slot
+
+			# automatically enable a disabled probe
+			if not probe.enabled and probe.pos is not None:
+				self.addProbe(probe)
+
+		self.refresh()
+
+	# findProbe
+	def findProbe(self, probe):
+		for dir in range(3):
+			for slot in range(len(COLORS)):
+				if self.probes[dir][slot] == probe:
+					return dir, slot
+		return None
+
+	# addProbe
+	def addProbe(self, probe):
+		dir, slot = self.findProbe(probe)
+		logger.info(f"[{self.slice.id}] dir={dir} slot={slot} probe.pos={probe.pos}")
+		self.removeProbe(probe)
+		probe.enabled = True
+
+		vt = [self.slice.logic_to_physic[I][0] for I in range(3)]
+		vs = [self.slice.logic_to_physic[I][1] for I in range(3)]
+
+		def LogicToPhysic(P):
+			ret = [vt[I] + vs[I] * P[I] for I in range(3)]
+			last = ret[dir]
+			del ret[dir]
+			ret.append(last)
+			return ret
+
+		def PhysicToLogic(p):
+			ret = [it for it in p]
+			last = ret[2]
+			del ret[2]
+			ret.insert(dir, last)
+			return [(ret[I] - vt[I]) / vs[I] for I in range(3)]
+
+		# __________________________________________________________
+		# here is all in physical coordinates
+		assert (probe.pos is not None)
+		x, y = probe.pos
+		z1, z2 = self.slider_z_range.value
+		p1 = (x, y, z1)
+		p2 = (x, y, z2)
+
+		# logger.info(f"Add Probe vs={vs} vt={vt} p1={p1} p2={p2}")
+
+		# automatically update the XY slider values
+		self.slider_x_pos.value = x
+		self.slider_y_pos.value = y
+
+		# keep the status for later
+
+		# __________________________________________________________
+		# here is all in logical coordinates
+		# compute x1,y1,x2,y2 but eigther extrema included (NOTE: it's working at full-res)
+
+		# compute delta
+		Delta = [1, 1, 1]
+		endh = self.slider_z_res.value
+		maxh = self.slice.db.getMaxResolution()
+		bitmask = self.slice.db.getBitmask()
+		for K in range(maxh, endh, -1):
+			Delta[ord(bitmask[K]) - ord('0')] *= 2
+
+		P1 = PhysicToLogic(p1)
+		P2 = PhysicToLogic(p2)
+		# print(P1,P2)
+
+		# align to the bitmask
+		(X, Y, Z), titles = self.slice.getLogicAxis()
+
+		def Align(idx, p):
+			return int(Delta[idx] * (p[idx] // Delta[idx]))
+
+		P1[X] = Align(X, P1)
+		P2[X] = Align(X, P2) + (self.slider_num_points_x.value) * Delta[X]
+
+		P1[Y] = Align(Y, P1)
+		P2[Y] = Align(Y, P2) + (self.slider_num_points_y.value) * Delta[Y]
+
+		P1[Z] = Align(Z, P1)
+		P2[Z] = Align(Z, P2) + Delta[Z]
+
+		logger.info(f"Add Probe aligned is P1={P1} P2={P2}")
+
+		# invalid query
+		if not all([P1[I] < P2[I] for I in range(3)]):
+			return
+
+		color = COLORS[slot]
+
+		# for debugging draw points
+		if True:
+			xs, ys = [[], []]
+			for _Z in range(P1[2], P2[2], Delta[2]) if dir != 2 else (P1[2],):
+				for _Y in range(P1[1], P2[1], Delta[1]) if dir != 1 else (P1[1],):
+					for _X in range(P1[0], P2[0], Delta[0]) if dir != 0 else (P1[0],):
+						x, y, z = LogicToPhysic([_X, _Y, _Z])
+						xs.append(x)
+						ys.append(y)
+
+			x1, x2 = min(xs), max(xs);
+			cx = (x1 + x2) / 2.0
+			y1, y2 = min(ys), max(ys);
+			cy = (y1 + y2) / 2.0
+
+			fig = self.slice.canvas.fig
+			self.renderers[probe]["canvas"] = [
+				fig.scatter(xs, ys, color=color),
+				fig.line([x1, x2, x2, x1, x1], [y2, y2, y1, y1, y2], line_width=1, color=color),
+				fig.line(self.slice.getPhysicBox()[X], [cy, cy], line_width=1, color=color),
+				fig.line([cx, cx], self.slice.getPhysicBox()[Y], line_width=1, color=color),
+			]
+
+		# execute the query
+		access = self.slice.db.createAccess()
+		logger.info(f"ExecuteBoxQuery logic_box={[P1, P2]} endh={endh} num_refinements={1} full_dim={True}")
+		multi = ExecuteBoxQuery(self.slice.db, access=access, logic_box=[P1, P2], endh=endh, num_refinements=1,
+								full_dim=True)  # full_dim means I am not quering a slice
+		data = list(multi)[0]['data']
+
+		# render probe
+		if dir == 2:
+			xs = list(np.linspace(z1, z2, num=data.shape[0]))
+			ys = []
+			for Y in range(data.shape[1]):
+				for X in range(data.shape[2]):
+					ys.append(list(data[:, Y, X]))
+
+		elif dir == 1:
+			xs = list(np.linspace(z1, z2, num=data.shape[1]))
+			ys = []
+			for Z in range(data.shape[0]):
+				for X in range(data.shape[2]):
+					ys.append(list(data[Z, :, X]))
+
+		else:
+			xs = list(np.linspace(z1, z2, num=data.shape[2]))
+			ys = []
+			for Z in range(data.shape[0]):
+				for Y in range(data.shape[1]):
+					ys.append(list(data[Z, Y, :]))
+
+		if True:
+			op = self.slider_z_op.value
+
+			if op == "avg":
+				ys = [[mean(p) for p in zip(*ys)]]
+
+			if op == "mM":
+				ys = [
+					[min(p) for p in zip(*ys)],
+					[max(p) for p in zip(*ys)]
+				]
+
+			if op == "med":
+				ys = [[median(p) for p in zip(*ys)]]
+
+			if op == "*":
+				ys = [it for it in ys]
+
+			for it in ys:
+				if self.slice.color_mapper_type.value=="log":
+					it = [max(EPSILON, value) for value in it]
+				self.renderers[probe]["fig"].append(
+					self.fig.line(xs, it, line_width=2, legend_label=color, line_color=color))
+
+		self.refresh()
+
+	# removeProbe
+	def removeProbe(self, probe):
+		fig = self.slice.canvas.fig
+		for r in self.renderers[probe]["canvas"]:
+			self.removeRenderer(fig, r)
+		self.renderers[probe]["canvas"] = []
+
+		for r in self.renderers[probe]["fig"]:
+			self.removeRenderer(self.fig, r)
+		self.renderers[probe]["fig"] = []
+
+		probe.enabled = False
+		self.refresh()
+
+	# refresh
+	def refresh(self):
+
+		# changing y_scale DOES NOT WORK (!!!)
+		# self.fig.y_scale=bokeh.models.scales.LogScale() if self.slice.color_mapper_type.value=="log" else bokeh.models.scales.LinearScale()
+		
+		is_log=self.slice.color_mapper_type.value=="log"
+		fig_log=isinstance(self.fig.y_scale, bokeh.models.scales.LogScale)
+		if is_log!=fig_log:
+			self.createFigure()
+
+		pbox = self.slice.getPhysicBox()
+		pdim=self.slice.getPointDim()
+		(X, Y, Z), titles = self.slice.getLogicAxis()
+		X1,X2=(pbox[X][0],pbox[X][1])
+		Y1,Y2=(pbox[Y][0],pbox[Y][1])
+		Z1,Z2=(pbox[Z][0],pbox[Z][1]) if pdim==3 else (0,1)
+
+		self.slider_z_res.end = self.slice.db.getMaxResolution()
+
+		if self.slider_x_pos.name!=titles[0]:
+			self.slider_x_pos.name = titles[0]
+			self.slider_x_pos.start = X1
+			self.slider_x_pos.end   = X2
+			self.slider_x_pos.step  = (X2 - X1) / 10000
+			self.slider_x_pos.value  = X1
+
+		if self.slider_y_pos.name!=titles[1]:
+			self.slider_y_pos.name = titles[1]
+			self.slider_y_pos.start = Y1
+			self.slider_y_pos.end   = Y2
+			self.slider_y_pos.step  = (Y2 - Y1) / 10000
+			self.slider_y_pos.value  = Y1
+
+		if self.slider_z_range.name!=titles[2]:
+			self.slider_z_range.name = titles[2]
+			self.slider_z_range.start = Z1 
+			self.slider_z_range.end   = Z2
+			self.slider_z_range.step  = (Z2 - Z1) / 10000
+			self.slider_z_range.value    = (Z1,Z2)
+
+		z1, z2 = self.slider_z_range.value
+		self.fig.xaxis.axis_label = self.slider_z_range.name
+		self.fig.x_range.start = z1
+		self.fig.x_range.end   = z2
+
+		self.fig.y_range.start = self.slice.color_bar.color_mapper.low  if self.slice.color_bar else 0.0
+		self.fig.y_range.end   = self.slice.color_bar.color_mapper.high if self.slice.color_bar else 1.0
+
+		# buttons
+		dir = self.slice.direction.value
+		for slot, button in enumerate(self.buttons):
+			color = COLORS[slot]
+			probe = self.probes[dir][slot]
+
+			css = [".bk-btn-default {"]
+
+			if slot == self.slot:
+				css.append("font-weight: bold;")
+				css.append("border: 2px solid black;")
+
+			if slot == self.slot or (probe.pos is not None and probe.enabled):
+				css.append("background-color: " + color + " !important;")
+
+			css.append("}")
+			css = " ".join(css)
+
+			if self.button_css[slot] != css:
+				self.button_css[slot] = css
+				button.stylesheets = [css]
+
+		# draw figure line for offset
+		offset = self.slice.offset.value
+		self.removeRenderer(self.fig, self.renderers["offset"])
+		self.renderers["offset"] = self.fig.line(
+			[offset, offset],
+			[self.fig.y_range.start, self.fig.y_range.end],
+			line_width=1, color="black")
+
+
+	# recompute
+	def recompute(self):
+		
+		self.refresh()
+
+		# remove all old probes
+		was_enabled = {}
+		for dir in range(3):
+			for probe in self.probes[dir]:
+				was_enabled[probe] = probe.enabled
+				self.removeProbe(probe)
+
+		# restore enabled
+		for dir in range(3):
+			for probe in self.probes[dir]:
+				probe.enabled = was_enabled[probe]
+
+		# add the probes only if sibile
+		dir = self.slice.direction.value
+		for slot, probe in enumerate(self.probes[dir]):
+	
+			if probe.pos is not None and probe.enabled:
+				self.addProbe(probe)
diff --git a/openvisuspy/src/openvisuspy/slice.py b/openvisuspy/src/openvisuspy/slice.py
new file mode 100644
index 0000000..a1084ff
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/slice.py
@@ -0,0 +1,1381 @@
+
+import os,sys,logging,copy,traceback,colorcet
+import base64
+import types
+import logging
+import copy
+import traceback
+import io 
+import threading
+import time
+from urllib.parse import urlparse, urlencode
+
+import numpy as np
+
+
+import bokeh
+import bokeh.models
+import bokeh.events
+import bokeh.plotting
+import bokeh.models.callbacks
+from bokeh.plotting import figure
+from bokeh.models import ColumnDataSource, ColorBar, LinearColorMapper
+from bokeh.transform import transform
+
+import param 
+
+import panel as pn
+from panel.layout import FloatPanel
+from panel import Column,Row,GridBox,Card
+from panel.pane import HTML,JSON,Bokeh
+
+from .utils   import *
+from .backend import Aborted,LoadDataset,ExecuteBoxQuery,QueryNode
+
+
+logger = logging.getLogger(__name__)
+
+SLICE_ID=0
+EPSILON = 0.001
+
+DEFAULT_SHOW_OPTIONS={
+	"top": [
+		["open_button","save_button","info_button","copy_url_button",  "scene", "timestep", "timestep_delta", "palette",  "color_mapper_type", "resolution", "view_dependent", "num_refinements"],
+		["field","direction", "offset", "range_mode", "range_min",  "range_max"]
+	],
+	"bottom": [
+		["request","response"]
+	]
+}
+
+class ViewportUpdate: 
+	pass
+
+# ////////////////////////////////////////////////////////////////////////////////////
+class Canvas:
+  
+	# constructor
+	def __init__(self, id):
+		self.id=id
+		self.fig=None
+		self.pdim=2
+
+		# events
+		self.events={
+			bokeh.events.Tap: [],
+			bokeh.events.DoubleTap: [],
+			bokeh.events.SelectionGeometry: [],
+			ViewportUpdate: []
+		}
+
+		self.fig_layout=Row(sizing_mode="stretch_both")	
+		self.createFigure() 
+
+		# since I cannot track consistently inner_width,inner_height (particularly on Jupyter) I am using a timer
+		self.last_W=0
+		self.last_H=0
+		self.last_viewport=None
+		self.setViewport([0,0,256,256])
+		
+	# onIdle
+	def onIdle(self):
+		
+		# I need to wait until I get a decent size
+		W,H=self.getWidth(),self.getHeight()
+		if W==0 or H==0:  
+			return
+
+		# some zoom in/out or panning happened (handled by bokeh) 
+		# note: no need to fix the aspect ratio in this case
+		x=self.fig.x_range.start
+		w=self.fig.x_range.end-x
+
+		y=self.fig.y_range.start
+		h=self.fig.y_range.end-y
+
+		# nothing todo
+		if [x,y,w,h]==self.last_viewport and [self.last_W,self.last_H]==[W,H]:
+			return
+
+		# I need to fix the aspect ratio 
+		if self.pdim==2 and [self.last_W,self.last_H]!=[W,H]:
+			x+=0.5*w
+			y+=0.5*h
+			if (w/W) > (h/H): 
+				h=w*(H/W) 
+			else: 
+				w=h*(W/H)
+			x-=0.5*w
+			y-=0.5*h
+
+		self.last_W=W
+		self.last_H=H
+		self.last_viewport=[x,y,w,h]
+
+		if not all([
+			self.fig.x_range.start==x, self.fig.x_range.end==x+w,
+			self.fig.y_range.start==y, self.fig.y_range.end==y+h
+		]):
+			self.fig.x_range.start, self.fig.x_range.end = x,x+w
+			self.fig.y_range.start, self.fig.y_range.end = y,y+h
+
+		[fn(None) for fn in self.events[ViewportUpdate]]
+
+	# on_event
+	def on_event(self, evt, callback):
+		self.events[evt].append(callback)
+
+	# createFigure
+	def createFigure(self):
+		old=self.fig
+
+		self.pan_tool               = bokeh.models.PanTool()
+		self.wheel_zoom_tool        = bokeh.models.WheelZoomTool()
+		self.box_select_tool        = bokeh.models.BoxSelectTool()
+		self.box_select_tool_helper = bokeh.models.TextInput()
+
+		self.fig=bokeh.plotting.figure(tools=[self.pan_tool,self.wheel_zoom_tool,self.box_select_tool]) 
+		self.fig.toolbar_location="right" 
+		self.fig.toolbar.active_scroll = self.wheel_zoom_tool
+		self.fig.toolbar.active_drag    = self.pan_tool
+		self.fig.toolbar.active_inspect = None
+		self.fig.toolbar.active_tap     = None
+
+		# try to preserve the old status
+		self.fig.x_range = bokeh.models.Range1d(0,512) if old is None else old.x_range
+		self.fig.y_range = bokeh.models.Range1d(0,512) if old is None else old.y_range
+		self.fig.sizing_mode = 'stretch_both'          if old is None else old.sizing_mode
+		self.fig.yaxis.axis_label  = "Latitude"               if old is None else old.xaxis.axis_label
+		self.fig.xaxis.axis_label  = "Longitude"               if old is None else old.yaxis.axis_label
+		self.fig.on_event(bokeh.events.Tap      , lambda evt: [fn(evt) for fn in self.events[bokeh.events.Tap      ]])
+		self.fig.on_event(bokeh.events.DoubleTap, lambda evt: [fn(evt) for fn in self.events[bokeh.events.DoubleTap]])
+
+		# replace the figure from the fig_layout (so that later on I can replace it)
+		self.fig_layout[:]=[]
+		self.fig_layout.append(Bokeh(self.fig))
+		
+		self.enableSelection()
+
+		self.last_renderer={}
+
+	# enableSelection
+	def enableSelection(self,use_python_events=False):
+		if use_python_events:
+			# python event DOES NOT work
+			self.fig.on_event(bokeh.events.SelectionGeometry, lambda s: print("JHERE"))
+		else:
+			def handleSelectionGeometry(attr,old,new):
+				j=json.loads(new)
+				x,y=float(j["x0"]),float(j["y0"])
+				w,h=float(j["x1"])-x,float(j["y1"])-y
+				evt=types.SimpleNamespace()
+				evt.new=[x,y,w,h]
+				[fn(evt) for fn in self.events[bokeh.events.SelectionGeometry]]
+				logger.info(f"HandleSeletionGeometry {evt}")
+
+			self.box_select_tool_helper.on_change('value', handleSelectionGeometry)
+
+			self.fig.js_on_event(bokeh.events.SelectionGeometry, bokeh.models.callbacks.CustomJS(
+				args=dict(widget=self.box_select_tool_helper), 
+				code="""
+					console.log("Setting widget value for selection...");
+					widget.value=JSON.stringify(cb_obj.geometry, undefined, 2);
+					console.log("Setting widget value for selection DONE");
+					"""
+			))	
+
+	# setAxisLabels
+	def setAxisLabels(self,x,y):
+		self.fig.xaxis.axis_label  = 'Longitude'
+		self.fig.yaxis.axis_label  = 'Latitude'		
+
+	# getWidth (this is number of pixels along X for the canvas)
+	def getWidth(self):
+		try:
+			return self.fig.inner_width
+		except:
+			return 0
+
+	# getHeight (this is number of pixels along Y  for the canvas)
+	def getHeight(self):
+		try:
+			return self.fig.inner_height
+		except:
+			return 0
+
+	# getViewport [(x1,x2),(y1,y2)]
+	def getViewport(self):
+		x=self.fig.x_range.start
+		y=self.fig.y_range.start
+		w=self.fig.x_range.end-x
+		h=self.fig.y_range.end-y
+		return [x,y,w,h]
+
+	  # setViewport
+	def setViewport(self,value):
+		x,y,w,h=value
+		self.last_W,self.last_H=0,0 # force a fix viewport
+		self.fig.x_range.start, self.fig.x_range.end = x, x+w
+		self.fig.y_range.start, self.fig.y_range.end = y, y+h
+		# NOTE: the event will be fired inside onIdle
+
+	# setImage
+	def showData(self, data, viewport,color_bar=None):
+
+		x,y,w,h=viewport
+
+		# 1D signal
+		if len(data.shape)==1:
+			self.pdim=1
+			self.wheel_zoom_tool.dimensions="width"
+			vmin,vmax=np.min(data),np.max(data)
+			self.fig.y_range.start=0.5*(vmin+vmax)-1.2*0.5*(vmax-vmin)
+			self.fig.y_range.end  =0.5*(vmin+vmax)+1.2*0.5*(vmax-vmin)
+			self.fig.renderers.clear()
+			xs=np.arange(x,x+w,w/data.shape[0])
+			ys=data
+			self.fig.line(xs,ys)
+			
+		# 2d image (eventually multichannel)
+		else:	
+			assert(len(data.shape) in [2,3])
+			self.pdim=2
+			self.wheel_zoom_tool.dimensions="both"
+			img=ConvertDataForRendering(data)
+			dtype=img.dtype
+			
+			# compatible with last rendered image?
+			if all([
+				self.last_renderer.get("source",None) is not None,
+				self.last_renderer.get("dtype",None)==dtype,
+				self.last_renderer.get("color_bar",None)==color_bar
+			]):
+				self.last_renderer["source"].data={"image":[img], "Longitude":[x], "Latitude":[y], "dw":[w], "dh":[h]}
+			else:
+				self.createFigure()
+				source = bokeh.models.ColumnDataSource(data={"image":[img], "Longitude":[x], "Latitude":[y], "dw":[w], "dh":[h]})
+				if img.dtype==np.uint32:	
+					self.fig.image_rgba("image", source=source, x="Longitude", y="Latitude", dw="dw", dh="dh") 
+				else:
+					self.fig.image("image", source=source, x="Longitude", y="Latitude", dw="dw", dh="dh", color_mapper=color_bar.color_mapper) 
+				self.fig.add_layout(color_bar, 'right')
+				self.last_renderer={
+					"source": source,
+					"dtype":img.dtype,
+					"color_bar":color_bar
+				}
+    
+
+
+# ////////////////////////////////////////////////////////////////////////////////////
+class Slice(param.Parameterized):
+
+	# whenever some new result is available
+	render_id              = pn.widgets.IntSlider          (name="RenderId", value=0)
+
+		# current scene as JSON
+	scene_body             = pn.widgets.TextAreaInput(name='Current',sizing_mode="stretch_width",height=520,)
+
+	# core query
+	scene                  = pn.widgets.Select             (name="Scene", options=[], width=120)
+	timestep               = pn.widgets.IntSlider          (name="Time", value=0, start=0, end=1, step=1, sizing_mode="stretch_width")
+	timestep_delta         = pn.widgets.Select             (name="Speed", options=[1, 2, 4, 8, 1, 32, 64, 128], value=1, width=50)
+	field                  = pn.widgets.Select             (name='Field', options=[], value='data', width=80)
+	resolution             = pn.widgets.IntSlider          (name='Res', value=21, start=20, end=99,  sizing_mode="stretch_width")
+	view_dependent         = pn.widgets.Select             (name="ViewDep",options={"Yes":True,"No":False}, value=True,width=80)
+	num_refinements        = pn.widgets.IntSlider          (name='#Ref', value=0, start=0, end=4, width=80)
+	direction              = pn.widgets.Select             (name='Direction', options={'X':0, 'Y':1, 'Z':2}, value=2, width=80)
+	offset                 = pn.widgets.EditableFloatSlider(name="Offset", start=0.0, end=1024.0, step=1.0, value=0.0,  sizing_mode="stretch_width", format=bokeh.models.formatters.NumeralTickFormatter(format="0.01"))
+	viewport               = pn.widgets.TextInput          (name="Viewport",value="")
+
+	# palette thingy
+	range_mode             = pn.widgets.Select             (name="Range", options=["metadata", "user", "dynamic", "dynamic-acc"], value="user", width=120)
+	range_min              = pn.widgets.FloatInput         (name="Min", width=80,value=0)
+	range_max              = pn.widgets.FloatInput         (name="Max", width=80,value=300)
+
+	palette                = pn.widgets.ColorMap           (name="Palette", options=GetPalettes(), value_name=DEFAULT_PALETTE, ncols=5,  width=180)
+	color_mapper_type      = pn.widgets.Select             (name="Mapper", options=["linear", "log", ],width=60)
+	
+	# play thingy
+	play_button            = pn.widgets.Button             (name="Play", width=8)
+	play_sec               = pn.widgets.Select             (name="Frame delay", options=["0.00", "0.01", "0.1", "0.2", "0.1", "1", "2"], value="0.01")
+
+	# bottom status bar
+	request                = pn.widgets.TextInput          (name="", sizing_mode='stretch_width', disabled=False)
+	response               = pn.widgets.TextInput          (name="", sizing_mode='stretch_width', disabled=False)
+
+	# toolbar thingy
+	info_button            = pn.widgets.Button   (icon="info-circle",width=20)
+	open_button            = pn.widgets.Button   (icon="file-upload",width=20)
+	save_button            = pn.widgets.Button   (icon="file-download",width=20)
+	copy_url_button        = pn.widgets.Button   (icon="copy",width=20)
+	logout_button          = pn.widgets.Button   (icon="logout",width=20)
+
+	# internal use only
+	save_button_helper = pn.widgets.TextInput(visible=False)
+	copy_url_button_helper = pn.widgets.TextInput(visible=False)
+	file_name_input=  pn.widgets.TextInput(name="Numpy_File", placeholder='Numpy File Name')
+
+
+	# constructor
+	def __init__(self):
+
+		self.on_change_callbacks={}
+
+		self.num_hold=0
+		global SLICE_ID
+		self.id=SLICE_ID
+		SLICE_ID += 1
+		
+		self.db = None
+		self.access = None
+		self.detailed_data=None
+		self.selected_physic_box=None
+		self.selected_logic_box=None
+
+		# translate and scale for each dimension
+		self.logic_to_physic        = [(0.0, 1.0)] * 3
+		self.metadata_range         = [0.0, 255.0]
+		self.scenes                 = {}
+
+		self.scene_body.stylesheets=[""".bk-input {background-color: rgb(48, 48, 64);color: white;font-size: small;}"""]
+
+		self.createGui()
+
+		def onSceneChange(evt): 
+			logger.info(f"onSceneChange {evt}")
+			body=self.scenes[evt.new]
+			self.setSceneBody(body)
+		self.scene.param.watch(SafeCallback(onSceneChange),"value", onlychanged=True,queued=True)
+
+		def onTimestepChange(evt):
+			self.refresh()
+		self.timestep.param.watch(SafeCallback(onTimestepChange), "value", onlychanged=True,queued=True)
+
+		def onTimestepDeltaChange(evt):
+			if bool(getattr(self,"setting_timestep_delta",False)): return
+			setattr("setting_timestep_delta",True)
+			value=int(evt.new)
+			A = self.timestep.start
+			B = self.timestep.end
+			T = self.getTimestep()
+			T = A + value * int((T - A) / value)
+			T = min(B, max(A, T))
+			self.timestep.step = value
+			self.setTimestep(T)
+			setattr("setting_timestep_delta",False)
+		self.timestep_delta.param.watch(SafeCallback(onTimestepDeltaChange),"value", onlychanged=True,queued=True)
+
+		def onFieldChange(evt):
+			self.refresh()
+		self.field.param.watch(SafeCallback(onFieldChange),"value", onlychanged=True,queued=True)
+
+		def onPaletteChange(evt):
+			self.color_bar=None
+			self.refresh()
+		self.palette.param.watch(SafeCallback(onPaletteChange),"value_name", onlychanged=True,queued=True)
+
+		def onRangeModeChange(evt):
+			mode=evt.new
+			self.color_map=None
+
+			if mode == "metadata":   
+				self.range_min.value = self.metadata_range[0]
+				self.range_max.value = self.metadata_range[1]
+
+			if mode == "dynamic-acc":
+				self.range_min.value = 0.0
+				self.range_max.value = 0.0
+			
+			self.range_min.disabled = False if mode == "user" else True
+			self.range_max.disabled = False if mode == "user" else True
+			self.refresh()
+		self.range_mode.param.watch(SafeCallback(onRangeModeChange),"value", onlychanged=True,queued=True)
+
+		def onRangeChange(evt):
+			self.color_map=None
+			self.color_bar=None
+			self.refresh()
+		self.range_min.param.watch(SafeCallback(onRangeChange),"value", onlychanged=True,queued=True)
+		self.range_max.param.watch(SafeCallback(onRangeChange),"value", onlychanged=True,queued=True)
+
+		def onColorMapperTypeChange(evt):
+			self.color_bar=None 
+			self.refresh()
+		self.color_mapper_type.param.watch(SafeCallback(onColorMapperTypeChange),"value", onlychanged=True,queued=True)
+		
+		self.resolution.param.watch(SafeCallback(lambda evt: self.refresh()),"value", onlychanged=True,queued=True)
+		self.view_dependent.param.watch(SafeCallback(lambda evt: self.refresh()),"value", onlychanged=True,queued=True)
+
+		self.num_refinements.param.watch(SafeCallback(lambda evt: self.refresh()),"value", onlychanged=True,queued=True)
+
+		def onDirectionChange(evt):
+			value=evt.new
+			logger.debug(f"id={self.id} value={value}")
+			pdim = self.getPointDim()
+			if pdim in (1,2): value = 2 # direction value does not make sense in 1D and 2D
+			dims = [int(it) for it in self.db.getLogicSize()]
+
+			# default behaviour is to guess the offset
+			offset_value,offset_range=self.guessOffset(value)
+			self.offset.start=offset_range[0]
+			self.offset.end  =offset_range[1]
+			self.offset.step=1e-16 if self.offset.editable and offset_range[2]==0.0 else offset_range[2] #  problem with editable slider and step==0
+			self.offset.value=offset_value
+			self.setQueryLogicBox(([0]*pdim,dims))
+			self.refresh()
+		self.direction.param.watch(SafeCallback(onDirectionChange),"value", onlychanged=True,queued=True)
+
+		self.offset.param.watch(SafeCallback(lambda evt: self.refresh()),"value", onlychanged=True,queued=True)
+
+		self.info_button.on_click(SafeCallback(lambda evt: self.showInfo()))
+		self.open_button.on_click(SafeCallback(lambda evt: self.showOpen()))
+		self.save_button.on_click(SafeCallback(lambda evt: self.save()))
+		self.copy_url_button.on_click(SafeCallback(lambda evt: self.copyUrl()))
+		self.play_button.on_click(SafeCallback(lambda evt: self.togglePlay()))
+
+		self.setShowOptions(DEFAULT_SHOW_OPTIONS)
+
+		self.canvas.on_event(bokeh.events.SelectionGeometry, SafeCallback(self.showDetails))
+
+		self.start()
+
+
+	# showDetails
+	def showDetails(self,evt=None):
+		from matplotlib.figure import Figure
+		import openvisuspy as ovy
+		import panel as pn
+		import numpy as np
+		from matplotlib.colors import LinearSegmentedColormap
+
+		x,y,h,w=evt.new
+		logic_box=self.toLogic([x,y,w,h])
+		data=list(ovy.ExecuteBoxQuery(self.db, access=self.db.createAccess(), field=self.field.value,logic_box=logic_box,num_refinements=1))[0]["data"]
+		print('Selected logic box here...')
+		print(logic_box)
+		self.selected_logic_box=logic_box
+		self.selected_physic_box=[[x,x+w],[y,y+h]]
+		print('Physical box here')
+		print(f'{x} {y} {x+w} {y+h}')
+		self.detailed_data=data
+		save_numpy_button = pn.widgets.Button(name='Save Data as Numpy', button_type='primary')
+		save_numpy_button.on_click(self.save_data)
+		if self.range_mode.value=="dynamic-acc":
+			vmin,vmax=np.min(data),np.max(data)
+			self.range_min.value = min(self.range_min.value, vmin)
+			self.range_max.value = max(self.range_max.value, vmax)
+			logger.info(f"Updating range with selected area vmin={vmin} vmax={vmax}")
+		p = figure(x_range=(self.selected_physic_box[0][0], self.selected_physic_box[0][1]), y_range=(self.selected_physic_box[1][0], self.selected_physic_box[1][1]))
+		palette_name = self.palette.value_name 
+		mapper = LinearColorMapper(palette=palette_name, low=self.range_min.value, high=self.range_max.value)
+        
+		data_flipped = data # Flip data to match imshow orientation
+		source = ColumnDataSource(data=dict(image=[data_flipped]))
+		dw = abs(self.selected_physic_box[0][1] -self.selected_physic_box[0][0])
+		dh = abs(self.selected_physic_box[1][1] - self.selected_physic_box[1][0])
+		p.image(image='image', x=self.selected_physic_box[0][0], y=self.selected_physic_box[1][0], dw=dw, dh=dh, color_mapper=mapper, source=source)  
+		color_bar = ColorBar(color_mapper=mapper, label_standoff=12, location=(0,0))
+		p.add_layout(color_bar, 'right')
+		p.xaxis.axis_label = "Longitude"
+		p.yaxis.axis_label = "Latitude"
+
+
+        # Display using Panel
+		self.showDialog(
+            pn.Column(
+                self.file_name_input,  # Assuming this is defined elsewhere in your class
+                save_numpy_button,
+                pn.pane.Bokeh(p),
+                sizing_mode="stretch_both"
+            ), 
+            width=1024, height=768, name="Details"
+        )
+
+  
+
+	def save_data(self, event):
+		if self.detailed_data is not None:
+			file_name = f"{self.file_name_input.value}.npz"
+			print(file_name)
+			np.savez(file_name, data=self.detailed_data, lon_lat=self.selected_physic_box)
+			ShowInfoNotification('Data Saved successfully to current directory!')
+			print("Data saved successfully.") 
+		else:
+			print("No data to save.")
+	# open
+	def showOpen(self):
+
+		def onLoadClick(evt):
+			body=value.decode('ascii')
+			self.scene_body.value=body
+			ShowInfoNotification('Load done. Press `Eval`')
+		file_input = pn.widgets.FileInput(description="Load", accept=".json")
+		file_input.param.watch(SafeCallback(onLoadClick),"value", onlychanged=True,queued=True)
+
+		def onEvalClick(evt):
+			self.setSceneBody(json.loads(self.scene_body.value))
+			ShowInfoNotification('Eval done')
+		eval_button = pn.widgets.Button(name="Eval", align='end')
+		eval_button.on_click(SafeCallback(onEvalClick))
+
+		self.showDialog(
+			Column(
+				self.scene_body,
+				Row(file_input, eval_button, align='end'),
+				sizing_mode="stretch_both",align="end"
+			), 
+			width=600, height=700, name="Open")
+	
+
+	# save
+	def save(self):
+		body=json.dumps(self.getSceneBody(),indent=2)
+		self.save_button_helper.value=body
+		ShowInfoNotification('Save done')
+		print(body)
+
+	# copy url
+	def copyUrl(self):
+		self.copy_url_button_helper.value=self.getShareableUrl()
+		ShowInfoNotification('Copy url done')
+
+	
+	# createGui
+	def createGui(self):
+
+		self.save_button.js_on_click(args={"source":self.save_button_helper}, code="""
+			function jsSave() {
+				console.log('Test scene values');
+				console.log(source.value);
+				const link = document.createElement("a");
+				const file = new Blob([source.value], { type: 'text/plain' });
+				link.href = URL.createObjectURL(file);
+				link.download = "save_scene.json";
+				link.click();
+				URL.revokeObjectURL(link.href);
+			}
+			setTimeout(jsSave,300);
+		""")
+
+
+		self.copy_url_button.js_on_click(args={"source": self.copy_url_button_helper}, code="""
+			function jsCopyUrl() {
+				console.log(source);
+				navigator.clipboard.writeText(source.value);
+			} 
+			setTimeout(jsCopyUrl,300);
+		""")
+
+		self.logout_button = pn.widgets.Button(icon="logout",width=20)
+		self.logout_button.js_on_click(args={"source": self.logout_button}, code="""
+			console.log("logging out...")
+			window.location=window.location.href + "/logout";
+		""")
+
+		# for icons see https://tabler.io/icons
+
+		# play time
+		self.play = types.SimpleNamespace()
+		self.play.is_playing = False
+
+		self.idle_callback = None
+		self.color_bar     = None
+		self.query_node    = None
+
+		self.t1=time.time()
+		self.aborted       = Aborted()
+		self.new_job       = False
+		self.current_img   = None
+		self.last_job_pushed =time.time()
+		self.query_node=QueryNode()
+
+		self.canvas = Canvas(self.id)
+		self.canvas.on_event(ViewportUpdate,              SafeCallback(self.onCanvasViewportChange))
+		self.canvas.on_event(bokeh.events.Tap           , SafeCallback(self.onCanvasSingleTap))
+		self.canvas.on_event(bokeh.events.DoubleTap     , SafeCallback(self.onCanvasDoubleTap))
+
+		self.top_layout=Column(sizing_mode="stretch_width")
+
+		self.middle_layout=Column(
+			Row(self.canvas.fig_layout, sizing_mode='stretch_both'),
+			sizing_mode='stretch_both'
+		)
+
+		self.bottom_layout=Column(sizing_mode="stretch_width")
+
+		self.dialogs=Column()
+		self.dialogs.visible=False
+
+		self.main_layout=Column(
+			self.top_layout,
+			self.middle_layout,
+			self.bottom_layout, 
+
+			self.dialogs,
+			self.copy_url_button_helper,
+			self.save_button_helper,
+
+			sizing_mode="stretch_both"
+		)
+
+	# onCanvasViewportChange
+	def onCanvasViewportChange(self, evt):
+		x,y,w,h=self.canvas.getViewport()
+		self.viewport.value=f"{x} {y} {w} {h}" # this way someone from the outside can watch for changes
+		self.refresh()
+
+	# onCanvasSingleTap
+	def onCanvasSingleTap(self, evt):
+		logger.info(f"Single tap {evt}")
+		pass
+
+	# onCanvasDoubleTap
+	def onCanvasDoubleTap(self, evt):
+		logger.info(f"Double tap {evt}")
+
+	# getShowOptions
+	def getShowOptions(self):
+		return self.show_options
+
+	# setShowOptions
+	def setShowOptions(self, value):
+		self.show_options=value
+		for layout, position in ((self.top_layout,"top"),(self.bottom_layout,"bottom")):
+			layout.clear()
+			for row in value.get(position,[[]]):
+				v=[]
+				for widget in row:
+					if isinstance(widget,str):
+						widget=getattr(self, widget.replace("-","_"),None)
+					if widget:
+						v.append(widget)
+				if v: layout.append(Row(*v,sizing_mode="stretch_width"))
+
+		# bottom
+
+	# getShareableUrl
+	def getShareableUrl(self):
+		body=self.getSceneBody()
+		load_s=base64.b64encode(json.dumps(body).encode('utf-8')).decode('ascii')
+		current_url=GetCurrentUrl()
+		o=urlparse(current_url)
+		return o.scheme + "://" + o.netloc + o.path + '?' + urlencode({'load': load_s})		
+
+	# stop
+	def stop(self):
+		self.aborted.setTrue()
+		self.query_node.stop()
+
+	# start
+	def start(self):
+		self.query_node.start()
+		if not self.idle_callback:
+			self.idle_callback = AddPeriodicCallback(self.onIdle, 1000 // 30)
+		self.refresh()
+
+	# getMainLayout
+	def getMainLayout(self):
+		return self.main_layout
+
+	# getLogicToPhysic
+	def getLogicToPhysic(self):
+		return self.logic_to_physic
+
+	# setLogicToPhysic
+	def setLogicToPhysic(self, value):
+		logger.debug(f"id={self.id} value={value}")
+		self.logic_to_physic = value
+		self.refresh()
+
+	# getPhysicBox
+	def getPhysicBox(self):
+		dims = self.db.getLogicSize()
+		vt = [it[0] for it in self.logic_to_physic]
+		vs = [it[1] for it in self.logic_to_physic]
+		return [[
+			0 * vs[I] + vt[I],
+			dims[I] * vs[I] + vt[I]
+		] for I in range(len(dims))]
+
+	# setPhysicBox
+	def setPhysicBox(self, value):
+		dims = self.db.getLogicSize()
+		def LinearMapping(a, b, A, B):
+			vs = (B - A) / (b - a)
+			vt = A - a * vs
+			return vt, vs
+		T = [LinearMapping(0, dims[I], *value[I]) for I in range(len(dims))]
+		self.setLogicToPhysic(T)
+		
+	# getSceneBody
+	def getSceneBody(self):
+		return {
+			"scene" : {
+				"name": self.scene.value, 
+				
+				# NOT needed.. they should come automatically from the dataset?
+				#   "timesteps": self.db.getTimesteps(),
+				#   "physic_box": self.getPhysicBox(),
+				#   "fields": self.field.options,
+				#   "directions" : self.direction.options,
+				# "metadata-range": self.metadata_range,
+
+				"timestep-delta": self.timestep_delta.value,
+				"timestep": self.timestep.value,
+				"direction": self.direction.value,
+				"offset": self.offset.value, 
+				"field": self.field.value,
+				"view-dependent": self.view_dependent.value,
+				"resolution": self.resolution.value,
+				"num-refinements": self.num_refinements.value,
+				"play-sec":self.play_sec.value,
+				"palette": self.palette.value_name,
+				"color-mapper-type": self.color_mapper_type.value,
+				"range-mode": self.range_mode.value,
+				"range-min": cdouble(self.range_min.value), # Object of type float32 is not JSON serializable
+				"range-max": cdouble(self.range_max.value),
+				"viewport": self.canvas.getViewport()
+			}
+		}
+
+	# hold
+	def hold(self):
+		self.num_hold=getattr(self,"num_hold",0) + 1
+		# if self.num_hold==1: self.doc.hold()
+
+	# unhold
+	def unhold(self):
+		self.num_hold-=1
+		# if self.num_hold==0: self.doc.unhold()
+
+	# load
+	def load(self, value):
+
+		if isinstance(value,str):
+			ext=os.path.splitext(value)[1].split("?")[0]
+			if ext==".json":
+				value=LoadJSON(value)
+			else:
+				value={"scenes": [{"name": os.path.basename(value), "url":value}]}
+
+		# from dictionary
+		elif isinstance(value,dict):
+			pass
+		else:
+			raise Exception(f"{value} not supported")
+
+		assert(isinstance(value,dict))
+		assert(len(value)==1)
+		root=list(value.keys())[0]
+
+		self.scenes={}
+		for it in value[root]:
+			if "name" in it:
+				self.scenes[it["name"]]={"scene": it}
+
+		self.scene.options = list(self.scenes)
+
+		if self.scenes:
+			first_scene_name=list(self.scenes)[0]
+			# I am not getting the event since it didn't change
+			if False:
+				self.scene.value=first_scene_name
+			else:
+				self.setSceneBody(self.scenes[first_scene_name])
+
+	# setSceneBody
+	def setSceneBody(self, scene):
+
+		logger.info(f"# //////////////////////////////////////////#")
+		logger.info(f"id={self.id} {scene} START")
+
+		# TODO!
+		# self.stop()
+
+		assert(isinstance(scene,dict))
+		assert(len(scene)==1 and list(scene.keys())==["scene"])
+
+		# go one level inside
+		scene=scene["scene"]
+
+		# the url should come from first load (for security reasons)
+		name=scene["name"]
+
+		assert(name in self.scenes)
+		default_scene=self.scenes[name]["scene"]
+		url =default_scene["url"]
+		urls=default_scene.get("urls",{})
+
+		# special case, I want to force the dataset to be local (case when I have a local dashboards and remove dashboards)
+		if "urls" in scene:
+
+			if "--prefer" in sys.argv:
+				prefer = sys.argv[sys.argv.index("--prefer") + 1]
+				prefers = [it for it in urls if it['id']==prefer]
+				if prefers:
+					logger.info(f"id={self.id} Overriding url from {prefers[0]['url']} since selected from --select command line")
+					url = prefers[0]['url']
+					
+			else:
+				locals=[it for it in urls if it['id']=="local"]
+				if locals and os.path.isfile(locals[0]["url"]):
+					logger.info(f"id={self.id} Overriding url from {locals[0]['url']} since it exists and is a local path")
+					url = locals[0]["url"]
+
+		logger.info(f"id={self.id} LoadDataset url={url}...")
+		db=LoadDataset(url=url) 
+
+		# update the GUI too
+		self.db    =db
+		self.access=db.createAccess()
+		self.scene.value=name
+
+		timesteps=self.db.getTimesteps()
+		self.timestep.start = timesteps[ 0]
+		self.timestep.end   = timesteps[-1]
+		self.timestep.step  = 1
+
+		self.field.options=list(self.db.getFields())
+
+		pdim = self.getPointDim()
+
+		if "logic-to-physic" in scene:
+			logic_to_physic=scene["logic-to-physic"]
+			self.setLogicToPhysic(logic_to_physic)
+		else:
+			physic_box = self.db.inner.idxfile.bounds.toAxisAlignedBox().toString().strip().split()
+			physic_box = [(float(physic_box[I]), float(physic_box[I + 1])) for I in range(0, pdim * 2, 2)]
+			self.setPhysicBox(physic_box)
+
+		if "directions" in scene:
+			directions=scene["directions"]
+		else:
+			directions = self.db.inner.idxfile.axis.strip().split()
+			directions = {it: I for I, it in enumerate(directions)} if directions else  {'X':0,'Y':1,'Z':2}
+		self.direction.options=directions
+
+		self.timestep_delta.value=int(scene.get("timestep-delta", 1))
+		self.timestep.value=int(scene.get("timestep", self.db.getTimesteps()[0]))
+		self.view_dependent.value = bool(scene.get('view-dependent', True))
+
+		resolution=int(scene.get("resolution", -6))
+		if resolution<0: resolution=self.db.getMaxResolution()+resolution
+		self.resolution.end = self.db.getMaxResolution()
+		self.resolution.value = resolution
+
+		self.field.value=scene.get("field", self.db.getField().name)
+		self.num_refinements.value=int(scene.get("num-refinements", 1 if pdim==1 else 2))
+
+		self.direction.value = int(scene.get("direction", 2))
+
+		default_offset_value,offset_range=self.guessOffset(self.direction.value)
+		self.offset.start=offset_range[0]
+		self.offset.end  =offset_range[1]
+		self.offset.step=1e-16 if self.offset.editable and offset_range[2]==0.0 else offset_range[2] #  problem with editable slider and step==0
+		self.offset.value=self.offset.value=float(scene.get("offset",default_offset_value))
+		self.setQueryLogicBox(([0]*self.getPointDim(),[int(it) for it in self.db.getLogicSize()]))
+
+		self.play_sec.value=float(scene.get("play-sec",0.01))
+		self.palette.value_name=scene.get("palette",DEFAULT_PALETTE)
+
+		db_field = self.db.getField(self.field.value)
+		self.metadata_range = list(scene.get("metadata-range",[db_field.getDTypeRange().From, db_field.getDTypeRange().To]))
+		assert(len(self.metadata_range))==2
+		self.color_map=None
+
+		self.range_mode.value=scene.get("range-mode","user")
+
+		self.color_mapper_type.value = scene.get("color-mapper-type","linear")	
+
+		viewport=scene.get("viewport",None)
+		if viewport is not None:
+			self.canvas.setViewport(viewport)
+
+		show_options=scene.get("show-options",DEFAULT_SHOW_OPTIONS)
+		self.setShowOptions(show_options)
+
+		self.start()
+
+		logger.info(f"id={self.id} END\n")
+
+
+	# showInfo
+	def showInfo(self):
+
+		logger.debug(f"Show info")
+		body=self.scenes[self.scene.value]
+		metadata=body["scene"].get("metadata", [])
+
+		cards=[]
+		for I, item in enumerate(metadata):
+
+			type = item["type"]
+			filename = item.get("filename",f"metadata_{I:02d}.bin")
+
+			if type == "b64encode":
+				# binary encoded in string
+				body = base64.b64decode(item["encoded"]).decode("utf-8")
+				body = io.StringIO(body)
+				body.seek(0)
+				internal_panel=HTML(f"<div><pre><code>{body}</code></pre></div>",sizing_mode="stretch_width",height=400)
+			elif type=="json-object":
+				obj=item["object"]
+				file = io.StringIO(json.dumps(obj))
+				file.seek(0)
+				internal_panel=JSON(obj,name="Object",depth=3, sizing_mode="stretch_width",height=400) 
+			else:
+				continue
+
+			cards.append(Card(
+					internal_panel,
+					pn.widgets.FileDownload(file, embed=True, filename=filename,align="end"),
+					title=filename,
+					collapsed=(I>0),
+					sizing_mode="stretch_width"
+				)
+			)
+
+		self.showDialog(*cards)
+
+	# showDialog
+	def showDialog(self, *args,**kwargs):
+		d={"position":"center", "width":1024, "height":600, "contained":False}
+		d.update(**kwargs)
+		float_panel=FloatPanel(*args, **d)
+		self.dialogs.append(float_panel)
+
+	# getMaxResolution
+	def getMaxResolution(self):
+		return self.db.getMaxResolution()
+
+	# setViewDependent
+	def setViewDependent(self, value):
+		logger.debug(f"id={self.id} value={value}")
+		self.view_dependent.value = value
+		self.refresh()
+
+	# getLogicAxis (depending on the projection XY is the slice plane Z is the orthogoal direction)
+	def getLogicAxis(self):
+		dir  = self.direction.value
+		directions = self.direction.options
+		# this is the projected slice
+		XY = list(directions.values())
+		if len(XY) == 3:
+			del XY[dir]
+		else:
+			assert (len(XY) == 2)
+		X, Y = XY
+		# this is the cross dimension
+		Z = dir if len(directions) == 3 else 2
+		titles = list(directions.keys())
+		return (X, Y, Z), (titles[X], titles[Y], titles[Z] if len(titles) == 3 else 'Z')
+
+	# guessOffset
+	def guessOffset(self, dir):
+
+		pdim = self.getPointDim()
+
+		# offset does not make sense in 1D and 2D
+		if pdim<=2:
+			return 0, [0, 0, 1] # (offset,range) 
+		else:
+			# 3d
+			vt = [self.logic_to_physic[I][0] for I in range(pdim)]
+			vs = [self.logic_to_physic[I][1] for I in range(pdim)]
+
+			if all([it == 0 for it in vt]) and all([it == 1.0 for it in vs]):
+				dims = [int(it) for it in self.db.getLogicSize()]
+				value = dims[dir] // 2
+				return value,[0, int(dims[dir]) - 1, 1]
+			else:
+				A, B = self.getPhysicBox()[dir]
+				value = (A + B) / 2.0
+				return value,[A, B, 0]
+
+	# toPhysic (i.e. logic box -> canvas viewport in physic coordinates)
+	def toPhysic(self, value):
+		dir = self.direction.value
+		pdim = self.getPointDim()
+		vt = [self.logic_to_physic[I][0] for I in range(pdim)]
+		vs = [self.logic_to_physic[I][1] for I in range(pdim)]
+		p1,p2=value
+		p1 = [vs[I] * p1[I] + vt[I] for I in range(pdim)]
+		p2 = [vs[I] * p2[I] + vt[I] for I in range(pdim)]
+
+		if pdim==1:
+			# todo: what is the y range? probably I shold do what I am doing with the colormap
+			assert(len(p1)==1 and len(p2)==1)
+			p1.append(0.0)
+			p2.append(1.0)
+
+		elif pdim==2:
+			assert(len(p1)==2 and len(p2)==2)
+
+		else:
+			assert(pdim==3 and len(p1)==3 and len(p2)==3)
+			del p1[dir]
+			del p2[dir]
+
+		x1,y1=p1
+		x2,y2=p2
+		return [x1,y1, x2-x1, y2-y1]
+
+	# toLogic
+	def toLogic(self, value):
+		pdim = self.getPointDim()
+		dir = self.direction.value
+		vt = [self.logic_to_physic[I][0] for I in range(pdim)]
+		vs = [self.logic_to_physic[I][1] for I in range(pdim)]
+		x,y,w,h=value
+		p1=[x  ,y  ]
+		p2=[x+w,y+h]
+
+		if pdim==1:
+			del p1[1]
+			del p2[1]
+		elif pdim==2:
+			pass # alredy in 2D
+		else:
+			assert(pdim==3) 
+			p1.insert(dir, 0) # need to add the missing direction
+			p2.insert(dir, 0)
+
+		assert(len(p1)==pdim and len(p2)==pdim)
+		p1 = [(p1[I] - vt[I]) / vs[I] for I in range(pdim)]
+		p2 = [(p2[I] - vt[I]) / vs[I] for I in range(pdim)]
+
+		# in 3d the offset is what I should return in logic coordinates (making the box full dim)
+		if pdim == 3:
+			p1[dir] = int((self.offset.value  - vt[dir]) / vs[dir])
+			p2[dir] = p1[dir]+1 
+		
+		return [p1, p2]
+
+	# togglePlay
+	def togglePlay(self):
+		if self.play.is_playing:
+			self.stopPlay()
+		else:
+			self.startPlay()
+
+	# startPlay
+	def startPlay(self):
+		logger.info(f"id={self.id}::startPlay")
+		self.play.is_playing = True
+		self.play.t1 = time.time()
+		self.play.wait_render_id = None
+		self.play.num_refinements = self.num_refinements.value
+		self.num_refinements.value = 1
+		self.setWidgetsDisabled(True)
+		self.play_button.disabled = False
+		self.play_button.label = "Stop"
+
+	# stopPlay
+	def stopPlay(self):
+		logger.info(f"id={self.id}::stopPlay")
+		self.play.is_playing = False
+		self.play.wait_render_id = None
+		self.num_refinements.value = self.play.num_refinements
+		self.setWidgetsDisabled(False)
+		self.play_button.disabled = False
+		self.play_button.label = "Play"
+
+	# playNextIfNeeded
+	def playNextIfNeeded(self):
+
+		if not self.play.is_playing:
+			return
+
+		# avoid playing too fast by waiting a minimum amount of time
+		t2 = time.time()
+		if (t2 - self.play.t1) < float(self.play_sec.value):
+			return
+
+		# wait
+		if self.play.wait_render_id is not None and self.render_id.value<self.play.wait_render_id:
+			return
+
+		# advance
+		T = int(self.timestep.value) + self.timestep_delta.value
+
+		# reached the end -> go to the beginning?
+		if T >= self.timestep.end:
+			T = self.timesteps.timestep.start
+
+		logger.info(f"id={self.id}::playing timestep={T}")
+
+		# I will wait for the resolution to be displayed
+		self.play.wait_render_id = self.render_id.value+1
+		self.play.t1 = time.time()
+		self.timestep.value= T
+
+	# onShowMetadataClick
+	def onShowMetadataClick(self):
+		self.metadata.visible = not self.metadata.visible
+
+	# setWidgetsDisabled
+	def setWidgetsDisabled(self, value):
+		self.scene.disabled = value
+		self.palette.disabled = value
+		self.timestep.disabled = value
+		self.timestep_delta.disabled = value
+		self.field.disabled = value
+		self.direction.disabled = value
+		self.offset.disabled = value
+		self.num_refinements.disabled = value
+		self.resolution.disabled = value
+		self.view_dependent.disabled = value
+		self.request.disabled = value
+		self.response.disabled = value
+		self.play_button.disabled = value
+		self.play_sec.disabled = value
+
+	# getPointDim
+	def getPointDim(self):
+		return self.db.getPointDim() if self.db else 2
+
+	# refresh
+	def refresh(self):
+		self.aborted.setTrue()
+		self.new_job=True
+
+	# getQueryLogicBox
+	def getQueryLogicBox(self):
+		viewport=self.canvas.getViewport()
+		return self.toLogic(viewport)
+
+	# setQueryLogicBox
+	def setQueryLogicBox(self,value):
+		viewport=self.toPhysic(value)
+		self.canvas.setViewport(viewport)
+		self.refresh()
+  
+	# getLogicCenter
+	def getLogicCenter(self):
+		pdim=self.getPointDim()  
+		p1,p2=self.getQueryLogicBox()
+		assert(len(p1)==pdim and len(p2)==pdim)
+		return [(p1[I]+p2[I])*0.5 for I in range(pdim)]
+
+	# getLogicSize
+	def getLogicSize(self):
+		pdim=self.getPointDim()
+		p1,p2=self.getQueryLogicBox()
+		assert(len(p1)==pdim and len(p2)==pdim)
+		return [(p2[I]-p1[I]) for I in range(pdim)]
+
+  # gotoPoint
+	def gotoPoint(self,point):
+		return  # COMMENTED OUT
+		"""
+		self.offset.value=point[self.direction.value]
+		
+		(p1,p2),dims=self.getQueryLogicBox(),self.getLogicSize()
+		p1,p2=list(p1),list(p2)
+		for I in range(self.getPointDim()):
+			p1[I],p2[I]=point[I]-dims[I]/2,point[I]+dims[I]/2
+		self.setQueryLogicBox([p1,p2])
+		self.canvas.renderPoints([self.toPhysic(point)]) 
+		"""
+  
+	# gotNewData
+	def gotNewData(self, result):
+
+		data=result['data']
+		try:
+			data_range=np.min(data),np.max(data)
+		except:
+			data_range=0.0,0.0
+
+		logic_box=result['logic_box'] 
+
+		# depending on the palette range mode, I need to use different color mapper low/high
+		mode=self.range_mode.value
+
+		# show the user what is the current offset
+		maxh=self.db.getMaxResolution()
+		dir=self.direction.value
+
+		pdim=self.getPointDim()
+		vt,vs=self.logic_to_physic[dir] if pdim==3 else (0.0,1.0)
+		endh=result['H']
+
+		user_physic_offset=self.offset.value
+
+		real_logic_offset=logic_box[0][dir] if pdim==3 else 0.0
+		real_physic_offset=vs*real_logic_offset + vt 
+		user_logic_offset=int((user_physic_offset-vt)/vs)
+
+		# update slider info
+		self.offset.name=" ".join([
+			f"Offset: {user_physic_offset:.3f}±{abs(user_physic_offset-real_physic_offset):.3f}",
+			f"Pixel: {user_logic_offset}±{abs(user_logic_offset-real_logic_offset)}",
+			f"Max Res: {endh}/{maxh}"
+		])
+
+		# refresh the range
+		if True:
+
+			# in dynamic mode, I need to use the data range
+			if mode=="dynamic":
+				self.range_min.value = data_range[0]
+				self.range_max.value = data_range[1]
+				
+			# in data accumulation mode I am accumulating the range
+			if mode=="dynamic-acc":
+				if self.range_min.value==self.range_max.value:
+					self.range_min.value=data_range[0]
+					self.range_max.value=data_range[1]
+				else:
+					self.range_min.value = min(self.range_min.value, data_range[0])
+					self.range_max.value = max(self.range_max.value, data_range[1])
+			# update the color bar
+			low =cdouble(self.range_min.value)
+			high=cdouble(self.range_max.value)
+			print(f'Min Value: {low} ;  Max Value: {high}')
+
+
+		# regenerate colormap
+		if self.color_bar is None:
+			print('NONE COLORMAP')
+			color_mapper_type=self.color_mapper_type.value
+			assert(color_mapper_type in ["linear","log"])
+			is_log=color_mapper_type=="log"
+			palette=self.palette.value
+			mapper_low =max(EPSILON, low ) if is_log else low
+			mapper_high=max(EPSILON, high) if is_log else high
+			self.color_bar = bokeh.models.ColorBar(color_mapper = 
+				bokeh.models.LogColorMapper   (palette=palette, low=mapper_low, high=mapper_high) if is_log else 
+				bokeh.models.LinearColorMapper(palette=palette, low=mapper_low, high=mapper_high)
+			)
+
+		logger.debug(f"id={self.id}::rendering result data.shape={data.shape} data.dtype={data.dtype} logic_box={logic_box} data-range={data_range} range={[low,high]}")
+
+		# update the image
+		self.canvas.showData(data, self.toPhysic(logic_box), color_bar=self.color_bar)
+
+		(X,Y,Z),(tX,tY,tZ)=self.getLogicAxis()
+		self.canvas.setAxisLabels(tX,tY)
+
+		# update the status bar
+		if True:
+			tot_pixels=np.prod(data.shape)
+			canvas_pixels=self.canvas.getWidth()*self.canvas.getHeight()
+			self.H=result['H']
+			query_status="running" if result['running'] else "FINISHED"
+			self.response.value=" ".join([
+				f"#{result['I']+1}",
+				f"{str(logic_box).replace(' ','')}",
+				str(data.shape),
+				f"Res={result['H']}/{maxh}",
+				f"{result['msec']}msec",
+				str(query_status)
+			])
+
+		# this way someone from the outside can watch for new results
+		self.render_id.value=self.render_id.value+1 
+  
+	# pushJobIfNeeded
+	def pushJobIfNeeded(self):
+
+		if not self.new_job:
+			return
+
+		canvas_w,canvas_h=(self.canvas.getWidth(),self.canvas.getHeight())
+		query_logic_box=self.getQueryLogicBox()
+		pdim=self.getPointDim()
+
+		# abort the last one
+		self.aborted.setTrue()
+		self.query_node.waitIdle()
+		num_refinements = self.num_refinements.value
+		if num_refinements==0:
+			num_refinements={
+				1: 1, 
+				2: 3, 
+				3: 4  
+			}[pdim]
+		self.aborted=Aborted()
+
+		# do not push too many jobs
+		if (time.time()-self.last_job_pushed)<0.2:
+			return
+		
+		# I will use max_pixels to decide what resolution, I am using resolution just to add/remove a little the 'quality'
+		if not self.view_dependent.value:
+			# I am not using the information about the pixel on screen
+			endh=self.resolution.value
+			max_pixels=None
+		else:
+
+			endh=None 
+			canvas_w,canvas_h=(self.canvas.getWidth(),self.canvas.getHeight())
+
+			# probably the UI is not ready yet
+			if not canvas_w or not canvas_h:
+				return
+
+			if pdim==1:
+				max_pixels=canvas_w
+			else:
+				delta=self.resolution.value-self.getMaxResolution()
+				a,b=self.resolution.value,self.getMaxResolution()
+				if a==b:
+					coeff=1.0
+				if a<b:
+					coeff=1.0/pow(1.3,abs(delta)) # decrease 
+				else:
+					coeff=1.0*pow(1.3,abs(delta)) # increase 
+				max_pixels=int(canvas_w*canvas_h*coeff)
+			
+		# new scene body
+		self.scene_body.value=json.dumps(self.getSceneBody(),indent=2)
+		
+		logger.debug("# ///////////////////////////////")
+		logger.debug(f"id={self.id} pushing new job query_logic_box={query_logic_box} max_pixels={max_pixels} endh={endh}..")
+
+		timestep=int(self.timestep.value)
+		field=self.field.value
+		box_i=[[int(it) for it in jt] for jt in query_logic_box]
+		self.request.value=f"t={timestep} b={str(box_i).replace(' ','')} {canvas_w}x{canvas_h}"
+		self.response.value="Running..."
+
+		self.query_node.pushJob(
+			self.db, 
+			access=self.access,
+			timestep=timestep, 
+			field=field, 
+			logic_box=query_logic_box, 
+			max_pixels=max_pixels, 
+			num_refinements=num_refinements, 
+			endh=endh, 
+			aborted=self.aborted
+		)
+		
+		self.last_job_pushed=time.time()
+		self.new_job=False
+		# logger.debug(f"id={self.id} pushed new job query_logic_box={query_logic_box}")
+
+	# onIdle
+	def onIdle(self):
+
+		if not self.db:
+			return
+
+		self.canvas.onIdle()
+
+		if self.canvas and  self.canvas.getWidth()>0 and self.canvas.getHeight()>0:
+			self.playNextIfNeeded()
+
+		if self.query_node:
+			result=self.query_node.popResult(last_only=True) 
+			if result is not None: 
+				self.gotNewData(result)
+			self.pushJobIfNeeded()
+
+
+
+
+
+
diff --git a/openvisuspy/src/openvisuspy/utils.py b/openvisuspy/src/openvisuspy/utils.py
new file mode 100644
index 0000000..c44027f
--- /dev/null
+++ b/openvisuspy/src/openvisuspy/utils.py
@@ -0,0 +1,438 @@
+
+import numpy as np
+import os,sys,logging,asyncio,time,json,xmltodict,urllib
+import urllib.request
+
+import requests
+from requests.auth import HTTPBasicAuth
+
+from pprint import pprint
+
+logger = logging.getLogger(__name__)
+
+COLORS = ["lime", "red", "green", "yellow", "orange", "silver", "aqua", "pink", "dodgerblue"]
+
+DEFAULT_PALETTE="Viridis256"
+
+import colorcet
+
+import bokeh
+import bokeh.core
+import bokeh.core.validation
+
+bokeh.core.validation.silence(bokeh.core.validation.warnings.EMPTY_LAYOUT, True)
+bokeh.core.validation.silence(bokeh.core.validation.warnings.FIXED_SIZING_MODE,True)
+
+import panel as pn
+
+# ////////////////////////////////////////////////////////
+def SafeCallback(fn):
+	def ReturnValue(evt):
+		try:
+			fn(evt)
+		except:
+			logger.error(traceback.format_exc())
+			raise
+	return ReturnValue
+
+
+# ///////////////////////////////////////////////
+def IsPyodide():
+	return "pyodide" in sys.modules
+
+# ///////////////////////////////////////////////
+def IsJupyter():
+	return hasattr(__builtins__,'__IPYTHON__') or 'ipykernel' in sys.modules
+
+# ///////////////////////////////////////////////
+def IsPanelServe():
+	return "panel.command.serve" in sys.modules 
+
+# ///////////////////////////////////////////////
+def GetBackend():
+	ret=os.environ.get("VISUS_BACKEND", "py" if IsPyodide() else "cpp")
+	assert(ret=="cpp" or ret=="py")
+	return ret
+
+# ///////////////////////////////////////////////////////////////////
+def Touch(filename):
+	from pathlib import Path
+	Path(filename).touch(exist_ok=True)
+
+# ///////////////////////////////////////////////////////////////////
+def LoadJSON(value):
+
+	# already a good json
+	if isinstance(value,dict):
+		return value
+
+	# remote file (maybe I need to setup credentials)
+	if value.startswith("http"):
+		url=value
+		username = os.environ.get("MODVISUS_USERNAME", "")
+		password = os.environ.get("MODVISUS_PASSWORD", "")
+		auth = None
+		if username and password: 
+			auth = HTTPBasicAuth(username, password) if username else None
+		response = requests.get(url, auth=auth)
+		body = response.body.decode('utf-8') 
+		return json.loads(body)
+	
+	if os.path.isfile(value):
+		url=value
+		with open(url, "r") as f:  body=f.read()
+		return json.loads(body)
+		
+	elif issintance(value,str):
+		body=value
+		return json.loads(body)
+
+	raise Exception(f"{value} not supported")
+
+
+# ///////////////////////////////////////////////////////////////////
+def SaveJSON(filename,d):
+	with open(filename,"wt") as fp:
+		json.dump(d, fp, indent=2)	
+
+# ///////////////////////////////////////////////////////////////////
+def LoadXML(filename):
+	with open(filename, 'rt') as file: 
+		body = file.read() 
+	return xmltodict.parse(body, process_namespaces=True) 	
+
+# ///////////////////////////////////////////////////////////////////
+def SaveFile(filename,body):
+	with open(filename,"wt") as f:
+		f.write(body)
+
+
+# ///////////////////////////////////////////////////////////////////
+def SaveXML(filename,d):
+	body=xmltodict.unparse(d, pretty=True)
+	SaveFile(filename,body)
+
+# ///////////////////////////////////////////////
+async def SleepMsec(msec):
+	await asyncio.sleep(msec/1000.0)
+
+# ///////////////////////////////////////////////
+def AddAsyncLoop(name, fn, msec):
+
+	# do I need this?
+	if False and not IsPyodide():
+		loop = asyncio.get_event_loop()
+		if loop is None:
+			logger.info(f"Setting new event loop")
+			loop=asyncio.new_event_loop() 
+			asyncio.set_event_loop(loop)
+
+	async def MyLoop():
+		t1=time.time()
+		while True:
+
+			# it's difficult to know what it running or not in the browser
+			if IsPyodide():
+				if (time.time()-t1)>5.0:
+					logger.info(f"{name} is alive...")
+					t1=time.time()
+			try:
+				await fn()
+			except Exception as ex:
+				logger.info(f"ERROR {fn} : {ex}")
+			await SleepMsec(msec)
+
+	return asyncio.create_task(MyLoop())
+
+
+# ////////////////////////////////////////////////////////////////////////////////////////////////////////////
+def RunAsync(coroutine_object):
+	try:
+		return asyncio.run(coroutine_object)
+	except RuntimeError:
+		pass
+
+	import nest_asyncio
+	nest_asyncio.apply()
+	return asyncio.run(coroutine_object)
+
+# //////////////////////////////////////////////////////////////////////////////////////
+def cdouble(value):
+	try:
+		return float(value)
+	except:
+		return 0.0
+
+
+
+# ///////////////////////////////////////////////////////////////////
+def cbool(value):
+	if isinstance(value,bool):
+		return value
+
+	if isinstance(value,int) or isinstance(value,float):
+		return bool(value)
+
+	if isinstance(value, str):
+		return value.lower().strip() in ['true', '1']
+	 
+	raise Exception("not supported")
+
+
+# ///////////////////////////////////////////////////////////////////
+def IsIterable(value):
+	try:
+		iter(value)
+		return True
+	except:
+		return False
+
+# ////////////////////////////////////////////////////////////////////////////////////////////////////////////
+def Clamp(value,a,b):
+	assert a<=b
+	if value<a: value=a
+	if value>b: value=b
+	return value
+
+# ///////////////////////////////////////////////////////////////////
+def HumanSize(size):
+	KiB,MiB,GiB,TiB=1024,1024*1024,1024*1024*1024,1024*1024*1024*1024
+	if size>TiB: return "{:.2f}TiB".format(size/TiB) 
+	if size>GiB: return "{:.2f}GiB".format(size/GiB) 
+	if size>MiB: return "{:.2f}MiB".format(size/MiB) 
+	if size>KiB: return "{:.2f}KiB".format(size/KiB) 
+	return str(size)
+
+# ////////////////////////////////////////////////////////////////
+class JupyterLoggingHandler(logging.Handler):
+
+	def __init__(self, stream=None):
+		logging.Handler.__init__(self)
+		self.stream = sys.__stdout__
+
+	def flush(self):
+		self.acquire()
+		try:
+			if self.stream and hasattr(self.stream, "flush"):
+					self.stream.flush()
+		finally:
+			self.release()
+
+	def emit(self, record):
+		try:
+			msg = self.format(record)
+			msg = msg.replace('"',"'")
+			stream = self.stream
+			stream.write(msg + "\n")
+
+			# it's producing some output to jupyter lab ... to solve
+			if False:
+				from IPython import get_ipython
+				msg=msg.replace("\n"," ") # weird, otherwise javascript fails
+				get_ipython().run_cell(f""" %%javascript\nconsole.log("{msg}");""")
+				self.flush()
+		except :
+			# self.handleError(record)
+			pass # just ignore
+
+	def setStream(self, stream):
+			raise Exception("internal error")
+		
+
+# ////////////////////////////////////////////////////////////////
+def SetupLogger(
+	logger=None, 
+	log_filename:str=None, 
+	logging_level=logging.INFO,
+	fmt="%(asctime)s %(levelname)s %(filename)s:%(lineno)d:%(funcName)s %(message)s",
+	datefmt="%Y-%M-%d- %H:%M:%S"
+	):
+
+	if logger is None:
+		logger=logging.getLogger("openvisuspy")
+
+	logger.handlers.clear()
+	logger.propagate=False
+
+	logger.setLevel(logging_level)
+	handler=logging.StreamHandler(stream=sys.stderr)
+	handler.setLevel(logging_level)
+	handler.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt))
+	logger.addHandler(handler)	
+	
+	# file
+	if log_filename:
+		os.makedirs(os.path.dirname(log_filename),exist_ok=True)
+		handler=logging.FileHandler(log_filename)
+		handler.setLevel(logging_level)
+		handler.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt))
+		logger.addHandler(handler)
+
+	return logger
+
+
+
+# ////////////////////////////////////////////////////////////////
+def SetupJupyterLogger(
+	logger=None, 
+	logging_level=logging.INFO,
+	fmt="%(asctime)s %(levelname)s %(filename)s:%(lineno)d:%(funcName)s %(message)s",
+	datefmt="%Y-%M-%d- %H:%M:%S"
+	):
+
+	if logger is None:
+		logger=logging.getLogger("openvisuspy")
+
+	logger.handlers.clear()
+	logger.propagate=False
+
+	logger.setLevel(logging_level)
+	handler=JupyterLoggingHandler()
+	handler.setLevel(logging_level)
+	handler.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt))
+	logger.addHandler(handler)	
+	
+	return logger
+
+
+# ///////////////////////////////////////////////////
+def SplitChannels(array):
+	return [array[...,C] for C in range(array.shape[-1])]
+
+# ///////////////////////////////////////////////////
+def InterleaveChannels(v):
+	N=len(v)
+	if N==0:
+		raise Exception("empty image")
+	if N==1: 
+		return v[0]
+	else:
+		ret=np.zeros(v[0].shape + (N,), dtype=v[0].dtype)
+		for C in range(N): 
+			ret[...,C]=v[C]
+		return ret 
+
+
+# ///////////////////////////////////////////////////
+def ConvertDataForRendering(data, normalize_float=True):
+	 
+	height,width=data.shape[0],data.shape[1]
+
+	# typycal case
+	if data.dtype==np.uint8:
+
+		# (height,width)::uint8... grayscale, I will apply the colormap
+		if len(data.shape)==2:
+			Gray=data
+			return Gray 
+
+		# (height,depth,channel)
+		if len(data.shape)!=3:
+			raise Exception(f"Wrong dtype={data.dtype} shape={data.shape}")
+
+		channels=SplitChannels(data)
+
+		if len(channels)==1:
+			Gray=channels[0]
+			return Gray
+
+		if len(channels)==2:
+			G,A=channels
+			return  InterleaveChannels([G,G,G,A]).view(dtype=np.uint32).reshape([height,width]) 
+	
+		elif len(channels)==3:
+			R,G,B=channels
+			A=np.full(channels[0].shape, 255, np.uint8)
+			return  InterleaveChannels([R,G,B,A]).view(dtype=np.uint32).reshape([height,width]) 
+
+		elif len(channels)==4:
+			R,G,B,A=channels
+			return InterleaveChannels([R,G,B,A]).view(dtype=np.uint32).reshape([height,width]) 
+		
+	else:
+
+		# (height,depth) ... I will apply matplotlib colormap 
+		if len(data.shape)==2:
+			G=data.astype(np.float32)
+			return G
+		
+		# (height,depth,channel)
+		if len(data.shape)!=3:
+			raise Exception(f"Wrong dtype={data.dtype} shape={data.shape}")  
+	
+		# convert all channels in float32
+		channels=SplitChannels(data)
+		channels=[channel.astype(np.float32) for channel in channels]
+
+		if normalize_float:
+			for C,channel in enumerate(channels):
+				m,M=np.min(channel),np.max(channel)
+				channels[C]=(channel-m)/(M-m)
+
+		if len(channels)==1:
+			G=channels[0]
+			return G
+
+		if len(channels)==2:
+			G,A=channels
+			return InterleaveChannels([G,G,G,A])
+	
+		elif len(channels)==3:
+			R,G,B=channels
+			A=np.full(channels[0].shape, 1.0, np.float32)
+			return InterleaveChannels([R,G,B,A])
+
+		elif len(channels)==4:
+			R,G,B,A=channels
+			return InterleaveChannels([R,G,B,A])
+	
+	raise Exception(f"Wrong dtype={data.dtype} shape={data.shape}") 
+
+
+
+
+# ///////////////////////////////////////////////////
+def GetPalettes():
+	ret = {}
+	for name in bokeh.palettes.__palettes__:
+		value=getattr(bokeh.palettes,name,None)
+		if value and len(value)>=256:
+			ret[name]=value
+
+	# for name in sorted(colorcet.palette):
+	# 	value=getattr(colorcet.palette,name,None)
+	# 	if value and len(value)>=256:
+	# 		# stupid criteria but otherwise I am getting too much palettes
+	# 		if len(name)>12: continue
+	# 		ret[name]=value
+
+	return ret
+
+# ////////////////////////////////////////////////////////
+def ShowInfoNotification(msg):  
+    pn.state.notifications.clear()
+    pn.state.notifications.info(msg)
+
+# ////////////////////////////////////////////////////////
+def GetCurrentUrl():
+	return pn.state.location.href
+
+# //////////////////////////////////////////////////////////////////////////////////////
+def GetQueryParams():
+	return {k: v for k,v in pn.state.location.query_params.items()}
+
+# ////////////////////////////////////////////////////////
+import traceback
+
+def CallPeriodicFunction(fn):
+	try:
+		fn()
+	except:
+		logger.error(traceback.format_exc())
+
+def AddPeriodicCallback(fn, period, name="AddPeriodicCallback"):
+	#if IsPyodide():
+	#	return AddAsyncLoop(name, fn,period )  
+	#else:
+
+	return pn.state.add_periodic_callback(lambda fn=fn: CallPeriodicFunction(fn), period=period)