diff --git a/.github/workflows/build-openvisuspy.yml b/.github/workflows/build-openvisuspy.yml new file mode 100644 index 0000000..941dbfc --- /dev/null +++ b/.github/workflows/build-openvisuspy.yml @@ -0,0 +1,50 @@ +name: build-openvisus +on: [push] + +env: + PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }} + PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + + +jobs: + + build-ubuntu: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: [ '3.9', '3.10'] + steps: + + # git clone + - uses: actions/checkout@v2 + + # login in Docker + - uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKER_USERNAME }} + password: ${{ secrets.DOCKER_TOKEN }} + + # build + - uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + - run: | + set -ex + export PYTHON_VERSION=${{ matrix.python-version }} + source scripts/ubuntu.sh + + + + + + + + + + + + + + diff --git a/.gitignore b/.gitignore index cb135c6..4c84ffe 100644 --- a/.gitignore +++ b/.gitignore @@ -11,5 +11,10 @@ build .vscode **/*.npz **/*.npy +deploy/.env +deploy/inventory.ini + + + diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..d7663f7 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.10 + +RUN python3 -m pip install --upgrade pip + +COPY requirements.txt ./ +RUN python3 -m pip install -r requirements.txt + +RUN python3 -m pip install --upgrade OpenVisusNoGui + +ARG GIT_TAG +RUN python3 -m pip install openvisuspy==$GIT_TAG + +CMD ["jupyter", "lab", "--allow-root", "--notebook-dir='/usr/local/lib/python3.10/site-packages/openvisuspy/notebooks'", "--port 8888", "--NotebookApp.token=''", "--NotebookApp.allow_origin='*'", "--ip", "0.0.0.0""] + + + + diff --git a/README.md b/README.md index bd23640..60d5131 100644 --- a/README.md +++ b/README.md @@ -1,170 +1,125 @@ # Instructions +Important: -## Windows PIP installation +- 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 (see https://github.com/bokeh/jupyter_bokeh/issues/197) -(OPTONAL) Remove old environment: +## Installation -```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 +In Windows: ```bash -python -m venv .venv -.venv\Scripts\activate +set PATH=c:\Python310;%PATH% where python -# OPTIONAL -# python.exe -m pip install --upgrade pip +python.exe -m venv .venv +.venv\Scripts\activate -# 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==3.3.4 ipywidgets-bokeh panel==1.3.8 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" +python.exe -m pip install --upgrade pip +python.exe -m pip install -r requirements.txt +# install the latest version of OpenVisus +python.exe -m pip install --upgrade OpenVisusNoGui openvisuspy +``` -# (OPTIONAL) in debug just use local paths -# set PYTHONPATH=C:\projects\OpenVisus\build\RelWithDebInfo;.\src +In MacOs/Linux: -# or install binaries... -python -m pip install OpenVisusNoGui openvisuspy +```bash -# test import -python -c "import OpenVisus" -python -c "import openvisuspy" +python3 -m venv .venv +source .venv/Scripts/activate +python3 -m pip install --upgrade pip +python3 -m pip install -r requirements.txt -# (OPTIONAL) save the output for the future -# pip freeze +# install the latest version of OpenVisus +python3 -m pip install --upgrade OpenVisusNoGui openvisuspy ``` -## Run Dashboards - +## Dashboards -Change path and script as needed: +In Windows: ```bash .venv\Scripts\activate +python.exe -m panel serve ./app --dev --args ./json/dashboards.json +``` -# set PYTHONPATH=C:\projects\OpenVisus\build\RelWithDebInfo;.\src +In MacOs/Linux: -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 +```bash +source .venv/bin/activate +python.exe -m panel serve ./app --dev --args ./json/dashboards.json +``` + +## Notebooks -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" +In Windows: -# 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" +```bash +.venv\Scripts\activate +jupyter lab ./notebooks ``` -## Chess demos +In MacOs/Linux: ```bash -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" - -python -m panel serve src/openvisuspy/dashboards --dev --args "https://raw.githubusercontent.com/nsdf-fabric/nsdf-slac/main/dashboards.json" +source .venv/bin/activate +jupyter lab ./notebooks ``` -## Signal1d demos +Important: if Panel is not working you may have to do: ```bash +pskill python +jupyter nbconvert --clear-output --inplace notebooks/test-panel.ipynb +jupyter trust notebooks/test-panel.ipynb +``` + -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/signal1d_slac/visus.idx -python -m panel serve src/openvisuspy/dashboards --dev --args "https://maritime.sealstorage.io/api/v0/s3/utah/visus-datasets/signal1d_slac/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" -python -m panel serve src/openvisuspy/dashboards --dev --args c:\big\visus-datasets\signal1d_slac_max\visus.idx -python -m panel serve src/openvisuspy/dashboards --dev --args "https://maritime.sealstorage.io/api/v0/s3/utah/visus-datasets/signal1d_slac_max/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" +## Volume Rendering -# single NUMPY signals (NOTE: it's compunting min-max so it will be slow) -python -m panel serve src/openvisuspy/dashboards --dev --args "https://maritime.sealstorage.io/api/v0/s3/utah/supercdms-data/CDMS/UMN/R68/Raw/07180816_1648/07180816_1648_F0006/events/00135/banks/SCD0/data.npz?profile=sealstorage_ro&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" +```bash +python test/test-pyvista.py +python test/test-vtkvolume.py ``` -## Run notebooks +## Debug openvisuspy -### Setup Jupyter Lab +Debug mode in Windows ```bash +.venv\Scripts\activate -# check jupyter paths -where jupyter - -jupyter kernelspec list -# Available kernels: -# python3 C:\projects\openvisuspy\.venv\share\jupyter\kernels\python3 - -# 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 - -# if `Build recommended`, or `needs to be included in 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 +set PATH=c:\Python310;%PATH% -# empty all output cells -python scripts/run_command.py "jupyter nbconvert --clear-output --inplace {notebook}" "examples/notebooks/*.ipynb" +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 -# trust notebook -python scripts/run_command.py "jupyter trust {notebook}" "examples/notebooks/*.ipynb" +set PYTHONPATH=C:\projects\OpenVisus\build\RelWithDebInfo;.\src -jupyter lab . +# dashboards +python.exe -m panel serve ./app --dev --args ./json/dashboards.debug.json -# [OK ] test-bokeh.ipynb -# [OK ] test-ipywidgets.ipynb -# [OK ] test-matplotlib.ipynb -# [OK ] test-panel.ipynb -# [OK ] test-pyvista.ipynb +# jupyter lab +python.exe -m jupyter lab notebooks/ov-dashboards.ipynb -# [OK ] ov-dashboards.ipynb -# [OK ] ov-retina-rabbit-matplot.ipynb -# [OK ] ov-signal.ipynb -# [ERR] ov-vr.ipynb **THIS DOES NOT WORK IN CHROME, problems with VTK?** +python.exe -m panel serve ./app --dev --args "https://atlantis.sci.utah.edu/mod_visus?action=readdataset&dataset=chess-intro&cached=arco" --probe ``` -## (OPTIONAL) Test Volume rendering +Deploy binaries ```bash -# test pyvista -python examples/python/test-pyvista.py +./scripts/new_tag.sh -# test vtk volume -python examples/python/test-vtkvolume.py +# you may want to change the tag in docker-compose.yml too ``` -## Developers only - -Deploy new binaries -- **Update the `PROJECT_VERSION` inside `pyproject.toml`** - -```bash -# source .venv/bin/activate -./scripts/new_tag.sh -``` diff --git a/TODO.md b/TODO.md deleted file mode 100644 index 8715a47..0000000 --- a/TODO.md +++ /dev/null @@ -1,23 +0,0 @@ - - -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/src/openvisuspy/dashboards/__init__.py b/app/__init__.py similarity index 100% rename from src/openvisuspy/dashboards/__init__.py rename to app/__init__.py diff --git a/src/openvisuspy/dashboards/app_hooks.py b/app/app_hooks.py similarity index 100% rename from src/openvisuspy/dashboards/app_hooks.py rename to app/app_hooks.py diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000..ae29514 --- /dev/null +++ b/app/main.py @@ -0,0 +1,43 @@ +import os +import sys +import logging +import base64 +import json +import panel as pn + +from openvisuspy import SetupLogger, Slice, ProbeTool,cbool + + +# //////////////////////////////////////////////////////////////////////////// +if __name__.startswith('bokeh'): + + pn.extension( + "ipywidgets", + "floatpanel", + "codeeditor", + log_level="DEBUG", + notifications=True, + sizing_mode="stretch_width" + ) + + query_params = {k: v for k,v in pn.state.location.query_params.items()} + + 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]) + + # load a whole scene + if "load" in query_params: + body = json.loads(base64.b64decode(query_params['load']).decode("utf-8")) + slice.setBody(body) + + # select from list of choices + elif "dataset" in query_params: + scene_name = query_params["dataset"] + slice.scene.value = scene_name + + main_layout = slice.getMainLayout() + main_layout.servable() + diff --git a/src/openvisuspy/dashboards/templates/index.html b/app/templates/index.html similarity index 100% rename from src/openvisuspy/dashboards/templates/index.html rename to app/templates/index.html diff --git a/src/openvisuspy/dashboards/templates/styles.css b/app/templates/styles.css similarity index 100% rename from src/openvisuspy/dashboards/templates/styles.css rename to app/templates/styles.css diff --git a/data/ironProt.vtk b/data/ironProt.vtk deleted file mode 100644 index b4dcd4f..0000000 Binary files a/data/ironProt.vtk and /dev/null differ diff --git a/deploy/ReadMe.md b/deploy/ReadMe.md new file mode 100644 index 0000000..167222b --- /dev/null +++ b/deploy/ReadMe.md @@ -0,0 +1,106 @@ +# Instructions + +## Docker Compose + +```bash + +cd deploy + +# Create an `.env` file with a token: +cat < .env +NSDF_TOKEN=whatever-but-secure +EOF + +# this is needed for WSL2 +export ANSIBLE_CONFIG=${PWD}/ansible.cfg + +ln -s ../notebooks ./notebooks +ln -s ../json ./json + +sudo docker compose up chess1_service +sudo docker compose up chess2_service +sudo docker compose up jupyter_service +sudo docker compose up +``` + +You can check if it's working going to any of the URL: + +- http://localhost/ +- http://localhost/chess1 +- http://localhost/chess2 +- http://localhost/lab + +## Ansible + +```bash + +cd deploy + +# Create an `.env` file with a token: +cat < .env +NSDF_TOKEN=whatever-but-secure +EOF + +# only needed for ansible (!) +cat < ./inventory.ini +[all:vars] +ansible_ssh_connection=ssh +ansible_ssh_user=root +ansible_ssh_private_key_file=~/.ssh/id_rsa + +[my_vps] +127.0.0.1 +EOF + +# this is needed for WSL2 +export ANSIBLE_CONFIG=${PWD}/ansible.cfg + +# check connectivity +ansible all -m ping + +# ansible-playbook options +# --tags "configuration,packages" +# --limit= +# -l +# -vvv + +ansible-playbook ./setup.yml # --tags "restart" + +ansible-playbook ./benchmark.yml --verbose + +# (OPTIONAL) Clean up notebooks +for it in $(find ./notebooks/*.ipynb) ; do + jupyter nbconvert --clear-output --inplace ${it} + jupyter trust ${it} +done + +ansible-playbook ./run.yml + +# if you need to restart +ansible-playbook ./run.yml --tags restart -l 5.161.228.121 + +# if you need to check all services +ansible-playbook ./run.yml --tags check + +# you can run it later... +# OPTIONAL, you can even use without precaching (cached=arco will cache blocks on demand) +ansible-playbook ./precache.yml + +# check docker ps +ansible --become-user root --become all -m shell -a 'cd /root/deploy && docker compose ps' +ansible --become-user root --become all -m shell -a 'df -h' | grep "/dev/sda1" +``` + +if you want to debug: + +```bash + +# check connectivity +ansible-playbook ./run.yml --tags check + +# enter in the machine and see `docker compose` +ssh -i root@vps-ip +cd deploy +docker ps +docker exec -it bash +``` \ No newline at end of file diff --git a/deploy/ansible.cfg b/deploy/ansible.cfg new file mode 100644 index 0000000..27e4032 --- /dev/null +++ b/deploy/ansible.cfg @@ -0,0 +1,5 @@ +[defaults] +host_key_checking = False +deprecation_warnings = False +inventory = ./inventory.ini +command_warnings = False \ No newline at end of file diff --git a/deploy/benchmark.yml b/deploy/benchmark.yml new file mode 100644 index 0000000..7a991cd --- /dev/null +++ b/deploy/benchmark.yml @@ -0,0 +1,20 @@ + +--- +- hosts: all + become: false + gather_facts: yes + + tasks: + + - name: Run geekbench + ansible.builtin.shell: | + curl -Ls https://cdn.geekbench.com/Geekbench-4.4.4-Linux.tar.gz | tar xvz + ./Geekbench-4.4.4-Linux/geekbench_x86_64 + + - name: Run ookla + ansible.builtin.shell: | + curl -Ls https://packagecloud.io/install/repositories/ookla/speedtest-cli/script.deb.sh | bash + apt-get install speedtest + speedtest --progress=no --accept-gdpr --accept-license + + diff --git a/deploy/docker-compose.yml b/deploy/docker-compose.yml new file mode 100644 index 0000000..7fe3d98 --- /dev/null +++ b/deploy/docker-compose.yml @@ -0,0 +1,92 @@ +services: + + # Old Dashboard + chess1_service: + restart: unless-stopped + image: docker.io/nsdf/nsdf-chess:0.1 + ports: + - "10334:10334" + environment: + - VISUS_CACHE=/mnt/data/visus-cache + volumes: + - /mnt/data:/mnt/data + - ./json/dashboards.chess.json:/root/json/dashboards.json + command: python3 -m bokeh serve app --use-xheaders --allow-websocket-origin='*' --port 10334 --num-procs 0 --args /root/json/dashboards.json + healthcheck: + test: curl --fail http://localhost:10334 || exit 1 + interval: 10s + timeout: 5s + retries: 5 + + # New Dashboard + chess2_service: + restart: unless-stopped + image: docker.io/nsdf/openvisuspy:1.0.64 + ports: + - "10335:10335" + environment: + - BOKEH_ALLOW_WS_ORIGIN=* + - BOKEH_RESOURCES=cdn + - PYTHONUNBUFFERED=True + - VISUS_CPP_VERBOSE=0 + - VISUS_NETSERVICE_VERBOSE=0 + - VISUS_VERBOSE_DISKACCESS=0 + - VISUS_CACHE=/mnt/data/visus-cache + volumes: + - /mnt/data:/mnt/data + - ./json/dashboards.chess.json:/root/json/dashboards.json + command: python3 -m panel serve /usr/local/lib/python3.10/site-packages/openvisuspy/app --port 10335 --address 0.0.0.0 --allow-websocket-origin='*' --use-xheaders --num-procs 0 --args /root/json/dashboards.json + healthcheck: + test: curl --fail http://localhost:10335 || exit 1 + interval: 10s + timeout: 5s + retries: 5 + + # Jupyter Lab + jupyter_service: + restart: unless-stopped + image: docker.io/nsdf/openvisuspy:1.0.64 + ports: + - "8888:8888" + environment: + - VISUS_CACHE=/mnt/data/visus-cache + - NSDF_TOKEN=${NSDF_TOKEN} + volumes: + - /mnt/data:/mnt/data + - ./notebooks:/home/notebooks + command: jupyter lab --allow-root --notebook-dir="/home/notebooks" --port 8888 --NotebookApp.token='${NSDF_TOKEN}' --NotebookApp.allow_origin='*' --ip '0.0.0.0' --NotebookApp.base_url=jupyter + healthcheck: + test: curl --fail http://localhost:8888/jupyter || exit 1 + interval: 10s + timeout: 5s + retries: 5 + + # nginx + nginx_service: + restart: unless-stopped + image: nginx + ports: + - 80:80 + volumes: + - /var/log/nginx:/var/log/nginx + - ./nginx.conf:/etc/nginx/conf.d/default.conf + - ./html:/usr/share/nginx/html + depends_on: + chess1_service: + condition: service_healthy + chess2_service: + condition: service_healthy + jupyter_service: + condition: service_healthy + + # autoheal_service + # see https://sdr-enthusiasts.gitbook.io/ads-b/useful-extras/auto-restart-unhealthy-containers + autoheal_service: + image: willfarrell/autoheal:latest + tty: true + container_name: autoheal + restart: unless-stopped + environment: + - AUTOHEAL_CONTAINER_LABEL=all + volumes: + - /var/run/docker.sock:/var/run/docker.sock diff --git a/deploy/html/index.html b/deploy/html/index.html new file mode 100644 index 0000000..0cd6ea8 --- /dev/null +++ b/deploy/html/index.html @@ -0,0 +1,99 @@ + + + + + + + NSDF@CHESS - 2024 CHESS User Meeting + + + + + + +
+
+
+
+
+ +
+
+
+
+
+ +
+ +
+
+ JUNE 4TH and 5TH, 2024 AT CORNELL UNIVERSITY +
+ + +
+ National Science Data Fabric
+ +
+
+ +
+

NSDF Dashboards v2

+

+ https://chess-tutorial.nationalsciencedatafabric.org/chess2
+
+
+ + +
+

NSDF Dashboards v1

+

+ https://chess-tutorial.nationalsciencedatafabric.org/chess1
+
+
+ +
+

NSDF Poster

+

+ Come and see our poster here +

+
+ +
+

NSDF Jupyter

+

+ + Work in progress... +

+
+ +
+ This material is based upon work supported by the National Science Foundation under Grant No. 2138811. + 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. + Copyright © 2024 National Science Data Fabric
+ + +
+ +
+ + + + \ No newline at end of file diff --git a/deploy/html/resources/chess.png b/deploy/html/resources/chess.png new file mode 100644 index 0000000..6d05162 Binary files /dev/null and b/deploy/html/resources/chess.png differ diff --git a/deploy/html/resources/dashboards-v1.png b/deploy/html/resources/dashboards-v1.png new file mode 100644 index 0000000..5b36034 Binary files /dev/null and b/deploy/html/resources/dashboards-v1.png differ diff --git a/deploy/html/resources/dashboards-v2.png b/deploy/html/resources/dashboards-v2.png new file mode 100644 index 0000000..43632cb Binary files /dev/null and b/deploy/html/resources/dashboards-v2.png differ diff --git a/deploy/html/resources/header.jpg b/deploy/html/resources/header.jpg new file mode 100644 index 0000000..ebcf8fd Binary files /dev/null and b/deploy/html/resources/header.jpg differ diff --git a/deploy/html/resources/jupyter.png b/deploy/html/resources/jupyter.png new file mode 100644 index 0000000..5471f1c Binary files /dev/null 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scale3d(1,1,1);}.is-ultimenu-canvas--active .is-ultimenu-canvas-on{-webkit-transition:-webkit-transform 500ms ease;transition:transform 450ms ease;-ms-transform:translate3d(0,0,0);-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0);}.is-ultimenu-canvas.is-ultimenu-expanded .is-ultimenu-canvas-on{-ms-transform:translate3d(320px,0,0) scale3d(1,1,1);-webkit-transform:translate3d(320px,0,0) scale3d(1,1,1);transform:translate3d(320px,0,0) scale3d(1,1,1);}@media only screen and (max-width:58.999em){.is-ultimenu-canvas .is-ultimenu-canvas-off{max-width:82%;-ms-transform:translate3d(210%,0,0) scale3d(0.9,0.9,0.9);-webkit-transform:translate3d(210%,0,0) scale3d(0.9,0.9,0.9);transform:translate3d(210%,0,0) scale3d(0.9,0.9,0.9);}.is-ultimenu-canvas.is-ultimenu-expanded .is-ultimenu-canvas-off{-ms-transform:translate3d(0,0,0) scale3d(1,1,1);-webkit-transform:translate3d(0,0,0) scale3d(1,1,1);transform:translate3d(0,0,0) scale3d(1,1,1);}.is-ultimenu-canvas.is-ultimenu-expanded .is-ultimenu-canvas-on{-ms-transform:translate3d(82%,0,0) scale3d(1,1,1);-webkit-transform:translate3d(82%,0,0) scale3d(1,1,1);transform:translate3d(82%,0,0) scale3d(1,1,1);}} diff --git a/deploy/nginx.conf b/deploy/nginx.conf new file mode 100644 index 0000000..6100ec7 --- /dev/null +++ b/deploy/nginx.conf @@ -0,0 +1,89 @@ +upstream jupyter_upstream { + server jupyter_service:8888 max_fails=0 fail_timeout=30s; +} + +upstream chess1_upstream { + server chess1_service:10334 max_fails=0 fail_timeout=30s; +} + +upstream chess2_upstream { + server chess2_service:10335 max_fails=0 fail_timeout=30s; +} + +server { + + # http + listen 80; + + access_log /var/log/nginx/access.log combined; + error_log /var/log/nginx/error.log; + + # see https://docs.bokeh.org/en/3.1.0/docs/user_guide/server/deploy.html#nginx + large_client_header_buffers 4 24k; + + # avoid “413 – Request Entity Too Large” + client_max_body_size 200M; + + # http://127.0.0.1/jupyter + location /jupyter/ { + proxy_pass http://jupyter_upstream; + proxy_set_header Host $host:$server_port; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + + # # websocket headers + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection "upgrade"; + proxy_set_header X-Forwarded-Proto $scheme; + + proxy_buffering off; + } + + + # http://127.0.0.1/chess1 + location /chess1 { + proxy_pass http://chess1_upstream/app; + proxy_redirect /app /chess1; + proxy_set_header Host $host:$server_port; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + + # # websocket headers + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection "upgrade"; + proxy_set_header X-Forwarded-Proto $scheme; + + proxy_buffering off; + } + + # http://127.0.0.1/chess2 + location /chess2 { + proxy_pass http://chess2_upstream/app; + proxy_redirect /app /chess2; + proxy_set_header Host $host:$server_port; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + + # # websocket headers + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection "upgrade"; + proxy_set_header X-Forwarded-Proto $scheme; + + proxy_buffering off; + } + + # health + location /health { + add_header 'Content-Type' 'text/plain'; + return 200 "healthy\n"; + } + + + # http://127.0.0.1 + location / { + root /usr/share/nginx/html; + index index.html; + } + + +} \ No newline at end of file diff --git a/deploy/precache.yml b/deploy/precache.yml new file mode 100644 index 0000000..8e17043 --- /dev/null +++ b/deploy/precache.yml @@ -0,0 +1,18 @@ + +--- +- hosts: all + become: true + gather_facts: false + + tasks: + + # to sync to S3" + # aws s3 --no-verify-ssl --profile sealstorage --endpoint-url https://maritime.sealstorage.io/api/v0/s3 sync --size-only .workflow/umich/datasets/ s3://utah/chess/arco/ + + - name: precache data + environment: + AWS_ACCESS_KEY_ID: any + AWS_SECRET_ACCESS_KEY: any + ansible.builtin.shell: | + aws s3 --no-verify-ssl --endpoint-url https://maritime.sealstorage.io/api/v0/s3 sync --size-only s3://utah/chess/arco/ /mnt/data/visus-cache/DiskAccess/maritime.sealstorage.io/443/zip/api/v0/s3/utah/chess/arco/ + chmod a+rwX -R /mnt/data/visus-cache \ No newline at end of file diff --git a/deploy/run.yml b/deploy/run.yml new file mode 100644 index 0000000..7af2421 --- /dev/null +++ b/deploy/run.yml @@ -0,0 +1,103 @@ +--- +- hosts: all + become: true + gather_facts: false + + tasks: + + # data directory (mainly for cache) + - name: Creates data directory + ansible.builtin.file: + path: /mnt/data + state: directory + + - name: Copy files to node + copy: + src: "{{item}}" + dest: /root/deploy/ + loop: + - ./docker-compose.yml + - ./.env + - ./nginx.conf + tags: + - copy-files + + - name: Copy json + copy: + src: "{{ item }}" + dest: /root/deploy/json/ + with_fileglob: "../json/*.json" + tags: + - copy-files + + # copy html files + - name: Copy html files + ansible.builtin.copy: + src: ./html + dest: /root/deploy/ + tags: + - copy-files + + - name: Copy notebooks + copy: + src: "{{ item }}" + dest: /root/deploy/notebooks/ + with_fileglob: "../notebooks/*.ipynb" + tags: + - copy-files + + - name: Copy NASA notebooks + copy: + src: "{{ item }}" + dest: /root/deploy/notebooks/nasa/ + with_fileglob: "../notebooks/nasa/*.ipynb" + tags: + - copy-files + + # create nginx log directory + - name: Creates nginx log directory + ansible.builtin.file: + path: /var/log/nginx + state: directory + + # remove containers + - name: Remove all containers + ansible.builtin.shell: | + docker rm -v -f $(docker ps -qa) || true + # optional: remove all images + # docker image remove -f $(sudo docker images -a -q) || true + tags: + - stop + - restart + + # docker-compose up + - name: docker-compose up + community.docker.docker_compose: + project_src: /root/deploy + remove_orphans: true + recreate: always + pull: true + restarted: true + tags: + - start + - restart + + - name: Pause for 10 seconds + pause: + seconds: 10 + + - name: Confirm services are up + uri: + url: "http://{{ ansible_host }}{{ item }}" + follow_redirects: all + status_code: 200 + loop: + - :8888/jupyter + - :10334 + - :10335 + - /health + - /jupyter + - /chess1 + - /chess2 + tags: + - check \ No newline at end of file diff --git a/deploy/setup.yml b/deploy/setup.yml new file mode 100644 index 0000000..5f9377a --- /dev/null +++ b/deploy/setup.yml @@ -0,0 +1,62 @@ + +--- +- hosts: all + become: true + become_user: root + gather_facts: yes + + tasks: + + - name: Install aptitude + apt: + name: aptitude + state: latest + update_cache: true + + - name: Install required system packages + apt: + pkg: + - apt-transport-https + - ca-certificates + - curl + - software-properties-common + - python3 + - python3-pip + - virtualenv + - python3-setuptools + state: latest + update_cache: true + + + - name: Add Docker GPG Key + apt_key: + url: https://download.docker.com/linux/ubuntu/gpg + state: present + + - name: Add Docker Repository + apt_repository: + repo: deb https://download.docker.com/linux/ubuntu focal stable + state: present + + - name: Install Docker + apt: + name: docker-ce + state: latest + update_cache: true + + - name: Install docker-compose + get_url: + url : https://github.com/docker/compose/releases/download/v2.27.0/docker-compose-linux-x86_64 + dest: /usr/local/bin/docker-compose + mode: 'a+x' + + # this need to be fixed... + - name: install pip modules + ansible.builtin.pip: + name: + - docker==6.1.3 + - docker-compose==1.29.2 + - urllib3==1.26.18 + - requests==2.31.0 + - jinja2<3.1.0 + - awscli diff --git a/docs/create_streamable.drawio b/docs/create_streamable.drawio new file mode 100644 index 0000000..8c1501a --- /dev/null +++ b/docs/create_streamable.drawio @@ -0,0 +1,137 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/create_streamable.png b/docs/create_streamable.png new file mode 100644 index 0000000..ed273e8 Binary files /dev/null and b/docs/create_streamable.png differ diff --git a/diagram.drawio b/docs/diagram.drawio similarity index 100% rename from diagram.drawio rename to docs/diagram.drawio diff --git a/examples/create_streamable/ReadMe.md b/examples/create_streamable/ReadMe.md deleted file mode 100644 index e622ae5..0000000 --- a/examples/create_streamable/ReadMe.md +++ /dev/null @@ -1,2 +0,0 @@ -Links: -- https://github.com/silx-kit/hdf5plugin/blob/main/doc/hdf5plugin_EuropeanHUG2021.ipynb diff --git a/examples/create_streamable/diagram.drawio b/examples/create_streamable/diagram.drawio deleted file mode 100644 index df0c918..0000000 --- a/examples/create_streamable/diagram.drawio +++ /dev/null @@ -1 +0,0 @@ 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 \ No newline at end of file diff --git a/examples/create_streamable/diagram.drawio.png b/examples/create_streamable/diagram.drawio.png deleted file mode 100644 index 29b8fa4..0000000 Binary files a/examples/create_streamable/diagram.drawio.png and /dev/null differ diff --git a/examples/create_streamable/run.ipynb b/examples/create_streamable/run.ipynb deleted file mode 100644 index 22b3614..0000000 --- a/examples/create_streamable/run.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "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/examples/create_streamable/xarray_backend.py b/examples/create_streamable/xarray_backend.py deleted file mode 100644 index fd865cf..0000000 --- a/examples/create_streamable/xarray_backend.py +++ /dev/null @@ -1,314 +0,0 @@ -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/examples/notebooks/ov-dashboards.ipynb b/examples/notebooks/ov-dashboards.ipynb deleted file mode 100644 index 9793588..0000000 --- a/examples/notebooks/ov-dashboards.ipynb +++ /dev/null @@ -1,1243 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test openvisuspy dashboards\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import and configure OpenVisus" - ] - }, - { - "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": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: xmltodict in c:\\python310\\lib\\site-packages (0.13.0)\n", - "Collecting boto3\n", - " Downloading boto3-1.34.84-py3-none-any.whl.metadata (6.6 kB)\n", - "Collecting botocore<1.35.0,>=1.34.84 (from boto3)\n", - " Downloading botocore-1.34.84-py3-none-any.whl.metadata (5.7 kB)\n", - "Collecting jmespath<2.0.0,>=0.7.1 (from boto3)\n", - " Downloading jmespath-1.0.1-py3-none-any.whl.metadata (7.6 kB)\n", - "Collecting s3transfer<0.11.0,>=0.10.0 (from boto3)\n", - " Downloading s3transfer-0.10.1-py3-none-any.whl.metadata (1.7 kB)\n", - "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in 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------------------------------------- -- 11.4/12.1 MB 8.6 MB/s eta 0:00:01\n", - " -------------------------------------- - 11.7/12.1 MB 8.5 MB/s eta 0:00:01\n", - " --------------------------------------- 12.1/12.1 MB 8.5 MB/s eta 0:00:01\n", - " ---------------------------------------- 12.1/12.1 MB 8.4 MB/s eta 0:00:00\n", - "Using cached jmespath-1.0.1-py3-none-any.whl (20 kB)\n", - "Downloading s3transfer-0.10.1-py3-none-any.whl (82 kB)\n", - " ---------------------------------------- 0.0/82.2 kB ? eta -:--:--\n", - " ---------------------------------------- 82.2/82.2 kB 4.8 MB/s eta 0:00:00\n", - "Installing collected packages: jmespath, botocore, s3transfer, boto3\n", - "Successfully installed boto3-1.34.84 botocore-1.34.84 jmespath-1.0.1 s3transfer-0.10.1\n" - ] - } - ], - "source": [ - "!{sys.executable} -m pip install xmltodict boto3" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "c:\\Python310\\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 = '3.4.0'.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', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}}});\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([\"notyf\"], function() {\n", - "\ton_load()\n", - " })\n", - " root._bokeh_is_loading = css_urls.length + 8;\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/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.4.0/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['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", - " var urls = ['https://cdn.holoviz.org/panel/1.4.0/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-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/notificationarea/notyf@3/notyf.min.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/panel.min.js\"];\n", - " var js_modules = [];\n", - " var js_exports = {};\n", - " var css_urls = [\"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.css?v=1.4.0\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/notificationarea/notyf@3/notyf.min.css?v=1.4.0\", \"https://cdn.holoviz.org/panel/1.4.0/dist/bundled/font-awesome/css/all.min.css?v=1.4.0\", \"static/extensions/panel/css/notifications.css?v=1.4.0\"];\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 = '3.4.0'.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', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}}});\n require([\"jspanel\"], 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*/\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 = 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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": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "" - ], - "text/plain": [ - "NotificationArea()" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "57dce9e8-97ac-45b9-83d6-5f1fb69a6628" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1713277749.016731 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": "markdown", - "metadata": {}, - "source": [ - "If you have problems running the following cell, see # https://github.com/holoviz/holoviews/issues/4861\n", - " - Restart and Clear All Cells \n", - " - save the notebook \n", - " - kill jupyter lab \n", - " - restart" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "" - ], - "text/plain": [ - "Row(sizing_mode='stretch_width')\n", - " [0] Button(name='Is Panel working? C..., sizing_mode='stretch_width')" - ] - }, - "execution_count": 7, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "177ecae2-366a-4f19-abd6-1cb9551845a8" - } - }, - "output_type": "execute_result" - } - ], - "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" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Example of loading data from Object storage" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "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": 9, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "" - ], - "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='9..., 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', value=300, 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] TextInput(sizing_mode='stretch_width', visible=False)\n", - " [5] TextInput(sizing_mode='stretch_width', visible=False)" - ] - }, - "execution_count": 9, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "36df74b4-a7d9-4da2-a609-a452b3007ab1" - } - }, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NONE COLORMAP\n" - ] - } - ], - "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/examples/notebooks/ov-nasa-dashboards.ipynb b/examples/notebooks/ov-nasa-dashboards.ipynb deleted file mode 100644 index 5df39ad..0000000 --- a/examples/notebooks/ov-nasa-dashboards.ipynb +++ /dev/null @@ -1,1159 +0,0 @@ -{ - "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', 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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": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "" - ], - "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": [ - "
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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: THIS NOTEBOOK DOES NOT WORK in Chrome, please use Firefox\n", - "\n", - "the problem seems related to Chrome security/context creation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d1a6f936", - "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/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/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/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/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", 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'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 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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 || 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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, 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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": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "" - ], - "text/plain": [ - "NotificationArea()" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "48f6505c-0e29-4c0c-8991-007d9131392c" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1710749588.004326 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", - "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": [ - "#
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": 2, - "id": "c43a4cc7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "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': {'vtk': 'https://cdn.jsdelivr.net/npm/vtk.js@20.0.1/vtk', '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': {'vtk': {'exports': 'vtk'}, 'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", - " require([\"vtk\"], function() {\n", - "\ton_load()\n", - " })\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 + 10;\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['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n", - " var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/abstractvtkplot/vtk.js@20.0.1/vtk.js'];\n", - " for (var i = 0; i < urls.length; i++) {\n", - " skip.push(urls[i])\n", - " }\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.holoviz.org/panel/1.3.8/dist/bundled/abstractvtkplot/vtk.js@20.0.1/vtk.js\", \"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/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/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': {'vtk': 'https://cdn.jsdelivr.net/npm/vtk.js@20.0.1/vtk', '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': {'vtk': {'exports': 'vtk'}, 'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"vtk\"], function() {\n\ton_load()\n })\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 + 10;\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['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/abstractvtkplot/vtk.js@20.0.1/vtk.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\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 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\n", - "" - ], - "text/plain": [ - "NotificationArea()" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "6af6f7cb-eff3-4dd6-bba0-387aec857ed1" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded dataset\n", - " fields=['data']\n", - " logic_box=([0, 0, 0], [2000, 2000, 2160])\n", - "Got data shape=(67, 125, 125) dtype=uint8\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
\n", - "
\n", - "
\n", - "" - ], - "text/plain": [ - "Column(css_classes=['panel-widget-box'], height=800, sizing_mode='stretch_width', width_policy='max')\n", - " [0] IntSlider(name='Experiment', sizing_mode='stretch_width')\n", - " [1] IntSlider(end=67, name='i-slice', sizing_mode='stretch_width', value=33)\n", - " [2] IntSlider(end=125, name='j-slice', sizing_mode='stretch_width', value=62)\n", - " [3] IntSlider(end=125, name='k-slice', sizing_mode='stretch_width', value=62)\n", - " [4] Row(sizing_mode='stretch_width')\n", - " [0] Checkbox(name='Show Volume', sizing_mode='stretch_width', value=True)\n", - " [1] Checkbox(name='Show Slices', sizing_mode='stretch_width', value=True)\n", - " [5] VTKVolume(ndarray, controller_expanded=False, display_slices=True, orientation_widget=True, sizing_mode='stretch_both', slice_i=33, slice_j=62, slice_k=62)\n", - " [6] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [7] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [8] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [9] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [10] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [11] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')" - ] - }, - "execution_count": 2, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "3b2a7d71-1223-43fe-ae38-af849bf11945" - } - }, - "output_type": "execute_result" - } - ], - "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(\n", - " 'http://atlantis.sci.utah.edu:80/mod_visus?dataset=fly_scan_time-s-midx/data&cached=idx', \n", - " endh=21, \n", - " height=800)" - ] - }, - { - "cell_type": "markdown", - "id": "3668c1e2-2122-4f51-b236-44a888f0ca0f", - "metadata": {}, - "source": [ - "#
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": 3, - "id": "dba26bf5-3865-4499-b5b3-d3dd1cef38e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded dataset\n", - " fields=['data']\n", - " logic_box=([0, 0, 0], [1193, 402, 330])\n", - "Got data shape=(41, 50, 298) dtype=float32\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
\n", - "
\n", - "
\n", - "" - ], - "text/plain": [ - "Column(css_classes=['panel-widget-box'], height=800, sizing_mode='stretch_width', width_policy='max')\n", - " [0] IntSlider(name='Experiment', sizing_mode='stretch_width')\n", - " [1] IntSlider(end=41, name='i-slice', sizing_mode='stretch_width', value=20)\n", - " [2] IntSlider(end=50, name='j-slice', sizing_mode='stretch_width', value=25)\n", - " [3] IntSlider(end=298, name='k-slice', sizing_mode='stretch_width', value=149)\n", - " [4] Row(sizing_mode='stretch_width')\n", - " [0] Checkbox(name='Show Volume', sizing_mode='stretch_width', value=True)\n", - " [1] Checkbox(name='Show Slices', sizing_mode='stretch_width', value=True)\n", - " [5] VTKVolume(ndarray, controller_expanded=False, display_slices=True, orientation_widget=True, sizing_mode='stretch_both', slice_i=20, slice_j=25, slice_k=149)\n", - " [6] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [7] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [8] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [9] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [10] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [11] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')" - ] - }, - "execution_count": 3, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "9c6b4654-d785-4dbc-8acb-80cec8173c60" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "MyApp(\n", - " url='http://atlantis.sci.utah.edu:80/mod_visus?dataset=chess-zip&cached=1', \n", - " endh=21, \n", - " height=800)" - ] - }, - { - "cell_type": "markdown", - "id": "4c071ff0-ec00-44a1-864c-1b2c07712b21", - "metadata": {}, - "source": [ - "#
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": 4, - "id": "921d4c52", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded dataset\n", - " fields=['data']\n", - " logic_box=([0, 0, 0], [912, 816, 2025])\n", - "Got data shape=(126, 102, 114) dtype=float32\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
\n", - "
\n", - "
\n", - "" - ], - "text/plain": [ - "Column(css_classes=['panel-widget-box'], height=800, sizing_mode='stretch_width', width_policy='max')\n", - " [0] IntSlider(name='Experiment', sizing_mode='stretch_width')\n", - " [1] IntSlider(end=126, name='i-slice', sizing_mode='stretch_width', value=63)\n", - " [2] IntSlider(end=102, name='j-slice', sizing_mode='stretch_width', value=51)\n", - " [3] IntSlider(end=114, name='k-slice', sizing_mode='stretch_width', value=57)\n", - " [4] Row(sizing_mode='stretch_width')\n", - " [0] Checkbox(name='Show Volume', sizing_mode='stretch_width', value=True)\n", - " [1] Checkbox(name='Show Slices', sizing_mode='stretch_width', value=True)\n", - " [5] VTKVolume(ndarray, controller_expanded=False, display_slices=True, orientation_widget=True, sizing_mode='stretch_both', slice_i=63, slice_j=51, slice_k=57)\n", - " [6] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [7] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [8] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [9] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [10] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')\n", - " [11] ParamFunction(function, _pane=Markdown, defer_load=False, sizing_mode='stretch_width')" - ] - }, - "execution_count": 4, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "4e6d0d4e-c6cc-4b00-a8eb-da4601913074" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "MyApp(\n", - " url='https://atlantis.sci.utah.edu/mod_visus?dataset=chess-recon_combined_1_2_3_fullres_zip&cached=1', \n", - " endh=21, \n", - " 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/json/dashboards.chess.json b/json/dashboards.chess.json new file mode 100644 index 0000000..c67eea7 --- /dev/null +++ b/json/dashboards.chess.json @@ -0,0 +1,85675 @@ +{ + "datasets": [ + + { + "name": "chess-intro", + "color-mapper-type": "linear", + "range-mode": "user", + "range-min": 9600, + "range-max": 14855, + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/chess-intro/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" + }, + + { + "name": "3scans_HKLI", + "color-mapper-type": "log", + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/3scans_HKLI/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" + }, + + { + "name": "ff1_000231", + "color-mapper-type": "log", + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/ff1_000231/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" + }, + + { + "name": "recon_combined_1_2_3_fullres", + "color-mapper-type": "linear", + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/recon_combined_1_2_3_fullres/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" + }, + + { + "name": "reconstructed_data", + "color-mapper-type": "linear", + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/reconstructed_data/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" + }, + + { + "name": "test-k", + "color-mapper-type": "linear", + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/test-k/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3" + }, + { + "name": "ti25nb_22", + "url": "https://maritime.sealstorage.io/api/v0/s3/utah/chess/arco/ti25nb_22/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3", + "color-mapper-type": "linear", + "metadata": [ + { + "type": "json-object", + "filename": "/dev/null", + "query": "{\"Technique\": \"Tomography\"}", + "records": [ + { + "Affiliation": [ + "AirForce" + ], + "Alignment": false, + "AttenThickness": 0, + "BTR": "z-3234-A", + "BeamEnergy": 41.991, + "BeamSlitHorizontalPosition": 0, + "BeamSlitHorizontalSize": 0, + "BeamSlitVerticalPosition": 0, + "BeamSlitVerticalSize": 0, + "Beamline": [ + "3A" + ], + "BeamlineFundingPartner": [ + "MSNC_AFRL" + ], + "CESRConditions": [ + "9BunchMode" + ], + "Calibration": false, + "Cycle": "2021-3", + "DataLocationBeamtimeNotes": "/nfs/chess/scratch/user/rv43/2021-3/id3a/z-3234-A/tenstom_1304r-1/metadata/low_ic0_log.txt", + "DataLocationMeta": "/nfs/chess/scratch/user/rv43/2021-3/id3a/z-3234-A/tenstom_1304r-1/metadata", + "DataLocationRaw": "/nfs/chess/scratch/user/rv43/2021-3/id3a/z-3234-A/tenstom_1304r-1", + "DataLocationReduced": "/nfs/chess/scratch/user/rv43/2021-3/id3a/z-3234-A/tenstom_1304r-1/reduced", + "DataLocationScratch": "/nfs/chess/scratch/user/rv43/2021-3/id3a/z-3234-A/tenstom_1304r-1/scratch", + "Date": 1694619926, + "Description": "Tomo test", + "Detectors": [ + "Retiga" + ], + "EnergyFoil": [ + "scrn" + ], + "EnergyScan": false, + "ExperimentType": [ + "Imaging" + ], + "Experimenters": "", + "Facility": "CHESS", + "Focusing": [ + "Collimator" + ], + "Furnace": [ + "RAMSII" + ], + "GuardSlitHorizontalPosition": 0, + "GuardSlitHorizontalSize": 0, + "GuardSlitVerticalPosition": 0, + "GuardSlitVerticalSize": 0, + "InSitu": false, + "MaterialSafetyHazardousSamples": false, + "MechanicalGrips": [ + "Wedge" + ], + "MechanicalLoadFrame": [ + "RAMSII" + ], + "MechanicalTest": true, + "MechanicalTestType": [ + "Tension" + ], + "Monochromator": [ + "MultiLayer15HE" + ], + "PI": "Verberg", + "PreSlitHorizontalPosition": 0, + "PreSlitHorizontalSize": 0, + "PreSlitVerticalPosition": 0, + "PreSlitVerticalSize": 0, + "Processing": [ + "NISTTestBed" + ], + "ReferenceCalibrantSampleName": "", + "ReferenceCalibrantScanNumber": 0, + "ReferenceEnergyScanNumber": 0, + "ReferenceEnergyScanSampleName": "", + "SampleChemicalFormula": "freetext", + "SampleCommonName": "n/a", + "SampleGeometry": "n/a", + "SampleMatPedHeatTreatment": "None", + "SampleMatPedProcessingRoute": "None", + "SampleName": "tenstom_1304r-1", + "SampleSpaceGroup": 136, + "SampleUnitCell": [ + 2.5 + ], + "Schema": "ID3A", + "SchemaFile": "/mnt/chessdata/dev/schemas/ID3A.json", + "StaffScientist": [ + "NygrenKE" + ], + "Technique": [ + "Tomography" + ], + "UndulatorScan": false, + "User": "test", + "_id": "6501d91ad2f7654ee374b797", + "dataset": "/2021-3/3A/z-3234-A/tenstom_1304r-1", + "did": 1694619926714200274, + "path": "/nfs/chess/scratch/user/rv43/2021-3/id3a/z-3234-A/tenstom_1304r-1" + }, + { + "Affiliation": [ + "AirForce" + ], + "Alignment": false, + "AttenThickness": 0, + "BTR": "z-3234-A", + "BeamEnergy": 41.991, + "BeamSlitHorizontalPosition": 0, + "BeamSlitHorizontalSize": 0, + "BeamSlitVerticalPosition": 0, + "BeamSlitVerticalSize": 0, + "Beamline": [ + "3A" + ], + "BeamlineFundingPartner": [ + "MSNC_AFRL" + ], + "CESRConditions": [ + "9BunchMode" + ], + "Calibration": false, + "Cycle": "2021-3", + "DataLocationBeamtimeNotes": "/tmp", + "DataLocationMeta": "/tmp", + "DataLocationRaw": "/tmp", + "DataLocationReduced": "/tmp", + "DataLocationScratch": "/tmp", + "Date": 1693492165, + "Description": "None", + "Detectors": [ + "Retiga" + ], + "EnergyFoil": [ + "scrn" + ], + "EnergyScan": false, + "ExperimentType": [ + "Imaging" + ], + "Experimenters": "", + "Facility": "CHESS", + "Focusing": [ + "Collimator" + ], + "Furnace": [ + "RAMSII" + ], + "GuardSlitHorizontalPosition": 0, + "GuardSlitHorizontalSize": 0, + "GuardSlitVerticalPosition": 0, + "GuardSlitVerticalSize": 0, + "InSitu": false, + "MaterialSafetyHazardousSamples": false, + "MechanicalGrips": [ + "Wedge" + ], + "MechanicalLoadFrame": [ + "RAMSII" + ], + "MechanicalTest": true, + "MechanicalTestType": [ + "Tension" + ], + "Monochromator": [ + "MultiLayer15HE" + ], + "PI": "Valentin", + "PreSlitHorizontalPosition": 0, + "PreSlitHorizontalSize": 0, + "PreSlitVerticalPosition": 0, + "PreSlitVerticalSize": 0, + "Processing": [ + "NISTTestBed", + "Stratasys3DPrinter", + "Other" + ], + "ReferenceCalibrantSampleName": "", + "ReferenceCalibrantScanNumber": 0, + "ReferenceEnergyScanNumber": 0, + "ReferenceEnergyScanSampleName": "", + "SampleChemicalFormula": "Cr0.31Mo0.07Mn0.02Fe0.52Ni0.08", + "SampleCommonName": 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"url":"D:/visus-datasets/david_subsampled/visus.idx"}, + {"name":"2kbit1", "url":"D:/visus-datasets/2kbit1/zip/hzorder/visus.idx"} + ] +} diff --git a/json/dashboards.json b/json/dashboards.json new file mode 100644 index 0000000..6764b9f --- /dev/null +++ b/json/dashboards.json @@ -0,0 +1,49 @@ +{ + "datasets": [ + + { + "name": "chess-intro", + "url": "https://atlantis.sci.utah.edu/mod_visus?action=readdataset&dataset=chess-intro&cached=arco", + "color-mapper-type": "linear" + }, + + {"name":"david", "url":"http://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled&cached=idx"}, + {"name":"2kbit1", "url":"http://atlantis.sci.utah.edu/mod_visus?dataset=2kbit1&cached=idx"}, + {"name":"retina", "url":"http://atlantis.sci.utah.edu/mod_visus?dataset=rabbit&cached=idx"}, + + + {"name":"NASA Salinity (Salt)", 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Temperature(theta)", "url":"https://maritime.sealstorage.io/api/v0/s3/utah/nasa/dyamond/mit_output/llc2160_theta/llc2160_theta.idx?access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3&cached=arco"}, + + { + "name": "chess-recon_combined_1_2_3_fullres_zip", + "url": "https://atlantis.sci.utah.edu/mod_visus?action=readdataset&dataset=chess-recon_combined_1_2_3_fullres_zip&cached=arco", + "color-mapper-type": "linear" + }, + { + "name": "chess-zip", + "url": "https://atlantis.sci.utah.edu/mod_visus?action=readdataset&dataset=chess-zip&cached=arco", + "color-mapper-type": "log" + }, + { + "name": "chess-test-k", + "url": "https://atlantis.sci.utah.edu/mod_visus?action=readdataset&dataset=chess-test-k&cached=arco", + "color-mapper-type": "linear" + }, + + {"name": "slac-07180816_1648_F0006-00001", "url": 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"https://atlantis.sci.utah.edu/mod_visus?dataset=david_subsampled&cached=arco"}, + {"name": "2kbit1", "url": "https://atlantis.sci.utah.edu/mod_visus?dataset=2kbit1&cached=arco"} + + ] +} \ No newline at end of file diff --git a/config.json b/json/nasa.json similarity index 100% rename from config.json rename to json/nasa.json diff --git a/notebooks/convert-hdf5-data.ipynb b/notebooks/convert-hdf5-data.ipynb new file mode 100644 index 0000000..2f6a3e4 --- /dev/null +++ b/notebooks/convert-hdf5-data.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-large.png) \n", + "[National Science Data Fabric](https://nationalsciencedatafabric.org/) \n", + "\n", + "# Converting HDF5 data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os ,sys, time, logging,shutil\n", + "from datetime import datetime\n", + "import numpy as np\n", + "import boto3\n", + "import h5py\n", + "from pprint import pprint\n", + "\n", + "#sys.path.append(\"C:/projects/OpenVisus/build/RelWithDebInfo\")\n", + "#sys.path.append(\"C:/projects/openvisuspy/src\")\n", + "\n", + "import OpenVisus as ov\n", + "import openvisuspy\n", + "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n", + "logger= logging.getLogger(\"OpenVisus\")\n", + "\n", + "# uncomment for debugging\n", + "# ov.SetupLogger(logger, stream=True)\n", + "\n", + "from openvisuspy.utils import DownloadObject\n", + "\n", + "print(\"OpenVisus imported\")\n", + "\n", + "# /////////////////////////////////////////////////////////////////////////////////\n", + "def Traverse(cur):\n", + " \"\"\"\n", + " Utility to show the internal of an HDF5 file\n", + " \"\"\"\n", + " \n", + " if isinstance(cur,h5py.Dataset):\n", + " return {\n", + " '__type':type(cur),\n", + " 'shape':cur.shape,\n", + " 'dtype':cur.dtype,\n", + " 'size':cur.size,\n", + " 'ndim':cur.ndim,\n", + " 'nbytes':cur.nbytes,\n", + " }\n", + " else:\n", + " return {key: Traverse(cur[key])for key in cur.keys()}\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I need an HDF5 file to convert.\n", + "\n", + "I can download from the cloud but NOTE: **it's 33GB file**, it will take a lot to download" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hdf5_filename='/mnt/data/chess/assets/ff1_000231.h5'\n", + "DownloadObject(\"s3://utah/assets/ff1_000231.h5\", hdf5_filename)\n", + "f = h5py.File(hdf5_filename, 'r') \n", + "pprint(Traverse(f))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read binary data\n", + "\n", + "A single dataset is 33GB. \n", + "\n", + "**Better to now load all in memory**; here I am reading only a little portion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy import ndimage\n", + "from matplotlib.colors import LogNorm\n", + "\n", + "# ///////////////////////////////////////////////////////////////\n", + "def ShowImage(img, histogram=True):\n", + "\tprint(\"dtype\",img.dtype,\"shape\",img.shape,\"m\",np.min(img),\"M\",np.max(img))\n", + "\tfig = plt.figure()\n", + "\tfig.set_size_inches(18.5, 10.5)\n", + "\n", + "\tax = fig.add_subplot(1, 2, 1) # nrows, ncols, index\n", + "\timgplot = plt.imshow(img)\n", + "\n", + "\tplt.colorbar(orientation='horizontal')\n", + "\tif histogram:\n", + "\t\tax = fig.add_subplot(1, 2, 2)\n", + "\t\thistogram, bin_edges = np.histogram(img, bins=256, range=(np.min(img), np.max(img)))\n", + "\t\tplt.title(\"Histogram\")\n", + "\t\tplt.xlabel(\"value\")\n", + "\t\tplt.ylabel(\"pixel count\")\n", + "\t\tplt.xlim([np.min(img), np.max(img)])\n", + "\t\tplt.plot(bin_edges[0:-1], histogram)\n", + "\n", + "\n", + "images=f[\"imageseries\"][\"images\"]\n", + "D,H,W=images.shape\n", + "print(f\"Images dtype={images.dtype} shape={images.shape} \")\n", + "\n", + "Z=300\n", + "img=images[Z,:,:]\n", + "print(np.min(slice),np.max(slice))\n", + "\n", + "from skimage import io,exposure\n", + "ShowImage(exposure.equalize_hist(img))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create OpenVisus file\n", + "\n", + "NOTE **Converting only a litte portion of data (don't want to have 33GB in memory)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# note: creating the dataset in a temporary local directory , that is safe to remove\n", + "idx_filename=\"./remove-me/hdf5-example/visus.idx\"\n", + "\n", + "reduced_depth=D//3\n", + "print(\"Reduced depth to\",reduced_depth)\n", + "\n", + "import os,sys,shutil\n", + "assert(\"remove-me\" in idx_filename)\n", + "shutil.rmtree(os.path.dirname(idx_filename), ignore_errors=True)\n", + "\n", + "data=images[0:reduced_depth,:,:]\n", + "\n", + "fields=[ov.Field(\"data\",str(data.dtype),\"row_major\")]\n", + "db=ov.CreateIdx(\n", + "\turl=idx_filename,\n", + "\tdims=[W,H,reduced_depth],\n", + "\tfields=fields,\n", + " # NOTE: we are first creating the dataset with no-compression\n", + "\tcompression=\"raw\")\n", + "\n", + "print(db.getDatasetBody().toString())\n", + "print(\"Dataset created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write data to OpenVisus" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t1 = time.time()\n", + "db.write(data)\n", + "print(f\"db.write done in {time.time() - t1} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (OPTIONAL) Compress using zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# in production is better to compress the dataset\n", + "do_compress=False\n", + "if do_compress:\n", + " t1 = time.time()\n", + " db.compressDataset([\"zip\"])\n", + " print(f\"db.compressDataset done in {time.time()-t1} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show OpenVisus data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os,sys\n", + "img=db.read(x=[0,W],y=[0,H],z=[Z,Z+1], num_refinements=1)[0,:,:]\n", + "print(img.dtype,img.shape)\n", + "ShowImage(exposure.equalize_hist(img))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show coarse to fine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os,sys\n", + "for img in db.read(x=[0,W],y=[0,H],z=[Z,Z+1], num_refinements=3):\n", + "\tShowImage(exposure.equalize_hist(img[0,:,:]))" + ] + }, + { + "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/notebooks/convert-in-memory.ipynb b/notebooks/convert-in-memory.ipynb new file mode 100644 index 0000000..b2bb63c --- /dev/null +++ b/notebooks/convert-in-memory.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "cde69193-94db-4295-9cc6-ff44a7b5089c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os,sys\n", + "\n", + "from PIL import Image\n", + "from urllib.request import urlopen\n", + "import matplotlib.pyplot as plt\n", + "import shutil\n", + "import numpy as np\n", + "\n", + "# //////////////////////////////////////////////////////\n", + "def ShowImage(data):\n", + "\tdata=np.flip(data,axis=0)\n", + "\tprint(\"shape\",data.shape,\"dtype\",str(data.dtype))\n", + "\tfig = plt.figure()\n", + "\tax = fig.add_subplot(1,1,1)\n", + "\tax.imshow(data, origin='lower')\n", + "\tplt.show()\n", + "\n", + "sys.path.append(\"C:/projects/OpenVisus/build/RelWithDebInfo\")\n", + "import OpenVisus as ov\n", + "print(\"OpenVisus imported\")" + ] + }, + { + "cell_type": "markdown", + "id": "3457e2c7-8db4-464a-bcfd-e48e94a7e503", + "metadata": {}, + "source": [ + "# Load original data from *.nc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "301e191e", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import xarray as xr\n", + "\n", + "# Variable Name -> List of elevations\n", + "variables = {\n", + " 'precipitation': [0], \n", + " 'pressure': [0, 100, 200, 500],\n", + " 'temperature': [2, 30, 40, 60, 80, 100, 200, 300, 500, 1000],\n", + " 'virtual_potential_temperature': [2, 30, 40, 60, 80, 100, 200, 300, 500, 1000],\n", + " 'winddirection': [10, 30, 32, 40, 44, 47, 54, 57, 60, 80, 100, 120, 140, 160, 180, 200, 250, 300, 500, 1000],\n", + " 'windspeed': [10, 30, 32, 40, 44, 47, 54, 57, 60, 80, 100, 120, 140, 160, 180, 200, 250, 300, 500, 1000]\n", + "}\n", + "\n", + "# Create fields for each variable and elevation\n", + "fields = [\n", + " ov.Field('latitude', 'float32'), \n", + " ov.Field('longitude', 'float32'),\n", + "]\n", + "\n", + "# Go through each variable and elevation combination\n", + "for variable in variables:\n", + " for elevation in variables[variable]:\n", + " fields.append(ov.Field(f'{variable}_{elevation}m', \"int16\"))\n", + " \n", + "nc_file = xr.open_dataset(\n", + " './cstm_d01_2016-05-01_00_00_00.nc', \n", + "\n", + " # important because the original data is int16 (otherwise xarray cast )\n", + " # see https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html\n", + " mask_and_scale=False\n", + " )\n", + "data={}\n", + "shape=None\n", + "for field in fields:\n", + " # print(field.name, data[field.name].shape,data[field.name].dtype)\n", + " # use isel(time=0) since each field is 3d but only has a depth of 1\n", + " data[field.name] = nc_file[field.name].isel(time=0).values\n", + " assert(shape is None or shape==data[field.name].shape)\n", + " shape=data[field.name].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bd0bcd0", + "metadata": {}, + "outputs": [], + "source": [ + "# check if compressing using pure python gives back the same compression ratio\n", + "np.savez_compressed(\"check-compression-size.npz\",data,compression=\"gzip\",compression_opts=9)" + ] + }, + { + "cell_type": "markdown", + "id": "a77cc199-e0bf-4287-8eb9-ec94c686f4cb", + "metadata": {}, + "source": [ + "# Create IDX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074b88f1", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "import struct,io,time\n", + "from io import BytesIO\n", + "\n", + "# since I am writing/reading in multiple threads, I will not have collisions and I will not need file locks\n", + "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n", + "\n", + "# /////////////////////////////////////////////////////////////\n", + "def CreateRamAccess(db):\n", + " ret=db.createAccess(ov.StringTree.fromString(\"\"))\n", + " ret.disableWriteLocks()\n", + " assert(ret.bDisableWriteLocks==True)\n", + " assert(ret.compression==\"\")\n", + " assert(str(ret.getAccessTypeName())==\"class Visus::RamAccess\")\n", + " return ret\n", + "\n", + "dims=list(reversed(shape))\n", + "\n", + "# using a temporary filename , so I am sure I can remove the directory\n", + "idx_filename='visus-remove-me/visus.idx'\n", + "assert('visus-remove-me' in idx_filename)\n", + "shutil.rmtree(os.path.dirname(idx_filename), ignore_errors=True)\n", + "\n", + "db=ov.CreateIdx(\n", + " url=idx_filename, \n", + " dims=dims, \n", + " fields=fields,\n", + " # writing at the beginning should be uncompressed \n", + " compression=\"raw\"\n", + " )\n", + "\n", + "# make sure the file has been created\n", + "assert(os.path.isfile(idx_filename))\n", + "\n", + "blocksperfile=db.idxfile.blocksperfile\n", + "total_blocks=db.getTotalNumberOfBlocks()\n", + "\n", + "nfields=len(fields)\n", + "pdim=db.getPointDim()\n", + "num_files=total_blocks//blocksperfile\n", + "file_header_size=10*4\n", + "block_header_size=10*4\n", + "header0=np.zeros(shape=[file_header_size+nfields*blocksperfile*block_header_size,],dtype=np.uint8)\n", + "assert(num_files*blocksperfile==total_blocks)\n", + "\n", + "print(f\"header0 ={header0.nbytes}\")\n", + "print(f\"total_blocks ={total_blocks}\")\n", + "print(f\"pdim ={pdim}\")\n", + "print(f\"num_files ={num_files}\")\n", + "print(f\"dims ={dims}\")\n", + "print(f\"nfields ={nfields}\")\n", + "print(f\"blocksperfile ={blocksperfile}\")\n", + "print(f\"header0 ={header0.nbytes}\")\n", + "print(\"\")\n", + "\n", + "print(db.getDatasetBody().toString())\n", + "\n", + "# db.write(data,field=db.getField(\"temperature\"))\n", + "# db.write(data,field=db.getField(\"pressure\"))" + ] + }, + { + "cell_type": "markdown", + "id": "fd0db3ac-e3d2-4986-91d3-5cce65180b21", + "metadata": {}, + "source": [ + "# Write data in memory\n", + " - uncompressed\n", + " - not using any disk IO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d3268a9-37d9-47e7-970e-cfc9401e1367", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# by specifying an `access` I tell OpenVisus to write in memory and not on disk\n", + "ram_access=CreateRamAccess(db)\n", + "\n", + "# for each timestep\n", + "for timestep in db.getTimesteps():\n", + " \n", + " # for each field\n", + " for F,field in enumerate(db.getFields()):\n", + "\n", + " # here I am writing the HDF5 (or numpy data, or whatever)\n", + " # this is called BoxQuery (equivalent to writing a Region of Interest)\n", + " print(f\"Writing timestep={timestep} field={field} F={F}...\",end=\"\")\n", + " logic_box=db.getLogicBox()\n", + " query = db.createBoxQuery(ov.BoxNi(ov.PointNi(logic_box[0]),ov.PointNi(logic_box[1])), db.getField(field), timestep, ord('w'))\n", + " db.beginBoxQuery(query)\n", + " assert(query.isRunning())\n", + " query.buffer = ov.Array.fromNumPy(data[field],TargetDim=pdim, bShareMem=True)\n", + " assert(db.executeBoxQuery(ram_access, query))\n", + " print(\"done\")\n", + "\n", + "# Here I am reding blocks (all in memory) and converting them into numpy arrays\n", + "# using a dict to make block looking simplier\n", + "blocks={}\n", + "for timestep in db.getTimesteps():\n", + " for field in db.getFields():\n", + " for blockid in range(total_blocks):\n", + " read_block = db.createBlockQuery(blockid, db.getField(field), timestep, ord('r'))\n", + " if db.executeBlockQueryAndWait(ram_access, read_block): \n", + " # scrgiorgio: if I share the memory here I have a problem... why????\n", + " blocks[(timestep, field, blockid)]=ov.Array.toNumPy(read_block.buffer, bShareMem=False) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa198188", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# verify\n", + "if True:\n", + " for field in db.getFields():\n", + " check=db.read(time=timestep,field=db.getField(field), access=ram_access)\n", + " assert(np.array_equal(data[field], check))\n", + " # ShowImage(check)\n", + " print(\"Verification ok\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac14fbc9", + "metadata": {}, + "source": [ + "# Write blocks to disk using Python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aecf62d9-f2e6-49e7-ac9a-cd825bfb31f6", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "import zlib\n", + "\n", + "NoCompression = 0x00\n", + "ZipMask = 0x03\n", + "ZfpCompression = 0x08\n", + "CompressionMask = 0x0f\n", + "\n", + "RowMajor = 0x10\n", + "\n", + "# use empty string or `zip` here\n", + "compression=\"zip\"\n", + "\n", + "# TODO (!)\n", + "assert(compression!=\"zfp\") \n", + "\n", + "# at the beginning I am using a disk access to generate filename, later on to verify the data\n", + "disk_access=db.createAccess()\n", + "\n", + "#using bytesIO does not help at all (scrgiorgio: at least from today tests 20240527)\n", + "use_bytes_io=False\n", + "\n", + "# block statistics\n", + "statistics={}\n", + "\n", + "# for each timestep\n", + "for timestep in db.getTimesteps():\n", + "\n", + "\t# for each block inside file\n", + "\tfor fileid, BLOCKID in enumerate(range(0,total_blocks,blocksperfile)):\n", + "\n", + "\t\t# create the file\n", + "\t\tfilename=disk_access.getFilename(db.getField(), timestep, BLOCKID)\n", + "\t\t\n", + "\t\tos.makedirs(os.path.dirname(filename),exist_ok =True)\n", + "\t\tif os.path.isfile(filename):\n", + "\t\t\tos.remove(filename)\n", + "\t\t\n", + "\t\t# using BytesIO does not seem to help at all\n", + "\t\twith (BytesIO() if use_bytes_io else open(filename,\"wb\")) as stream:\n", + "\n", + "\t\t\tprint(f\"Writing filename={filename}...\")\n", + "\n", + "\t\t\tstream.write(header0.tobytes())\n", + "\t\t\tfor field_index,field in enumerate(db.getFields()):\n", + "\t\t\t\tfor B,blockid in enumerate(range(BLOCKID,BLOCKID+blocksperfile)):\n", + "\n", + "\t\t\t\t\tkey=(timestep,field,blockid)\n", + "\t\t\t\t\tblock=blocks.get(key,None)\n", + "\t\t\t\t\tif block is None: continue\n", + "\n", + "\t\t\t\t\t# i do not want to write all zero blocks\n", + "\t\t\t\t\tall_zeros = not np.any(block)\n", + "\t\t\t\t\tif all_zeros:\n", + "\t\t\t\t\t\tcontinue\n", + "\n", + "\t\t\t\t\tflags=RowMajor\n", + "\n", + "\t\t\t\t\t# no need to compress\n", + "\t\t\t\t\tif compression in [\"\",\"raw\"]:\n", + "\t\t\t\t\t\tblock=block.tobytes()\n", + "\t\t\t\t\t\tflags|=0\n", + "\n", + "\t\t\t\t\t# need to compress in zip\n", + "\t\t\t\t\telif compression==\"zip\":\n", + "\t\t\t\t\t\tblock=zlib.compress(block,level=9)\n", + "\t\t\t\t\t\tflags|=ZipMask\n", + "\n", + "\t\t\t\t\t# write the binary data\n", + "\t\t\t\t\tstream.seek(0, io.SEEK_END)\n", + "\t\t\t\t\toffset=stream.tell()\n", + "\t\t\t\t\tsize=len(block)\n", + "\t\t\t\t\tstream.write(block)\n", + "\n", + "\t\t\t\t\t# write the file header\n", + "\t\t\t\t\tstream.seek(file_header_size+block_header_size*(field_index*blocksperfile+B), io.SEEK_SET)\n", + "\t\t\t\t\tstream.write(struct.pack('>IIIIIIIIII', 0,0, offset & 0xffffffff, offset>>32, size, flags,0,0,0,0))\n", + "\n", + "\t\t\t\t\t# print(f\"Wrote block timestep={timestep} field={field} field_index={field_index} blockid={blockid} B={B} offset={offset} size={size} flags={flags}\")\n", + "\t\t\t\t\tstatistics[key]=(offset,size)\n", + "\n", + "\t\t\t# need to do the real IO \n", + "\t\t\tif use_bytes_io:\n", + "\t\t\t\twith open(filename, \"wb\") as f:\n", + "\t\t\t\t\tf.write(stream.getbuffer())\t\n", + "\n", + "\t\t\tprint(f\"Writing filename={filename} DONE\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f85098f", + "metadata": {}, + "outputs": [], + "source": [ + "if True:\n", + "\tfor field in db.getFields():\n", + "\t\tcheck=db.read(field=field, access=disk_access)\n", + "\t\tassert(np.array_equal(data[field], check))\n", + "\t\t# ShowImage(check)\n", + "\tprint(\"Verification ok\")" + ] + }, + { + "cell_type": "markdown", + "id": "d4f9e77d", + "metadata": {}, + "source": [ + "# Plot block statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5d1a3b1", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "df = px.data.tips()\n", + "\n", + "size_int16=2\n", + "uncompressed_single_timestep_size=np.product(shape)*size_int16*len(db.getFields())\n", + "uncompressed_block_size=(1< -np.inf])\n", + " vmax = np.nanmax(img[img < np.inf])\n", + " \n", + " norm=colors.LogNorm(max(vmin, 0.01), max(vmax, 0.01))\n", + " pos=ax.imshow(np.flip(img,axis=0),origin=\"upper\", norm=norm, cmap=cmap, extent=[x1,x2,y1,y2])\n", + " \n", + " ax.set_aspect('equal')\n", + " ax.set_xlim(x1,x2)\n", + " ax.set_ylim(y1,y2)\n", + " fig.colorbar(pos, ax=ax,location='right')\n", + " plt.autoscale(True)\n", + " plt.show()\n", + "\n", + "\n", + "# find an item with axes and signal as childs\n", + "# note: nxdata is the entry in the nexus tree containing axes and signal\n", + "nxdata=[node for depth, node in ConvertNexus.traverse(nx) if isinstance(node,NXdata) and \"axes\" in node.attrs and \"signal\" in node.attrs][0]\n", + "\n", + "# X-slice\n", + "ShowSlice(nxdata, A=0, img=nxdata[nxdata.attrs[\"signal\"]][:,:,200]) \n", + "\n", + "# Y slice\n", + "ShowSlice(nxdata, A=1, img=nxdata[nxdata.attrs[\"signal\"]][:,200,:]) \n", + "\n", + "# Z slice\n", + "ShowSlice(nxdata, A=2, img=nxdata[nxdata.attrs[\"signal\"]][200,:,:]) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a Streamable OpenVisus version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os,sys\n", + "\n", + "t1=time.time()\n", + "\n", + "# binary data will be converted to the IDX file format\n", + "local_idx_filename =\"./remove-me/streamable/3scans_HKLI/visus.idx\"\n", + "\n", + "# I am creating a new NEXUS file without binary data which reference OpenVISUS binary data\n", + "# this version is called ` streamable`\n", + "streamable_filename=\"./remove-me/streamable/3scans_HKLI/visus.nxs\"\n", + "\n", + "# ************ dangerous (make sure you are in a tmp directory) ************ \n", + "import os,sys,shutil\n", + "assert(\"remove-me\" in local_idx_filename)\n", + "assert(\"remove-me\" in streamable_filename)\n", + "shutil.rmtree(os.path.dirname(local_idx_filename), ignore_errors=True)\n", + "shutil.rmtree(os.path.dirname(streamable_filename), ignore_errors=True)\n", + "\n", + "streamable=ConvertNexus(\n", + " local_nexus_filename, \n", + " local_idx_filename,\n", + " streamable=streamable_filename, \n", + " compression=\"raw\" # \n", + ").run()\n", + "\n", + "print(f\"Created NEXUS streamable in {time.time()-t1} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show data using OpenVisus API\n", + "\n", + "If we use OpenVisus we don't know real physical coordinates; and we show pixels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ShowOpenVisusSlice(img):\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " vmin = np.nanmin(img[img > -np.inf])\n", + " vmax = np.nanmax(img[img < np.inf])\n", + " norm=colors.LogNorm(max(vmin, 0.01), max(vmax, 0.01))\n", + " pos=ax.imshow(np.flip(img,axis=0),origin=\"upper\", norm=norm, cmap=\"viridis\")\n", + " fig.colorbar(pos, ax=ax,location='right')\n", + " plt.autoscale(True)\n", + " plt.show()\n", + "\n", + "db=ov.LoadDataset(local_idx_filename)\n", + "\n", + "D,H,W=1419,1419,309\n", + "X,Y,Z=W//2,H//2,D//2\n", + "ShowOpenVisusSlice(db.read(x=[0,W], y=[0,H], z=[Z,Z+1], num_refinements=1)[0,:,:])\n", + "ShowOpenVisusSlice(db.read(x=[0,W], y=[Y,Y+1],z=[0,D] , num_refinements=1)[:,0,:])\n", + "ShowOpenVisusSlice(db.read(x=[X,X+1], y=[0,H], z=[0,D] , num_refinements=1)[:,:,0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show slice using streamable version\n", + "\n", + "The streamable version is a Nexus file (with proper metadata) without binary data.\n", + "\n", + "\n", + "The binary data is stored in OpenVisus datasets. But since Nexus library **DOES NOT** support reading OpenVisus binary, here we are *internally* using OpenVisus API to read binary data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nx=nxload(streamable_filename)\n", + "pprint(nx.tree)\n", + "nxdata=[node for depth, node in ConvertNexus.traverse(nx) if isinstance(node,NXdata) and \"axes\" in node.attrs and \"signal\" in node.attrs][0]\n", + "db=ov.LoadDataset(local_idx_filename)\n", + "ShowSlice(nxdata, A=0, img=db.read(x=[0,W], y=[0,H], z=[Z,Z+1], num_refinements=1)[0,:,:]) \n", + "ShowSlice(nxdata, A=1, img=db.read(x=[0,W], y=[Y,Y+1],z=[0,D] , num_refinements=1)[:,0,:]) \n", + "ShowSlice(nxdata, A=2, img=db.read(x=[X,X+1], y=[0,H], z=[0,D] , num_refinements=1)[:,:,0]) " + ] + }, + { + "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/notebooks/convert-numpy-data.ipynb b/notebooks/convert-numpy-data.ipynb new file mode 100644 index 0000000..8f522be --- /dev/null +++ b/notebooks/convert-numpy-data.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-large.png) \n", + "[National Science Data Fabric](https://nationalsciencedatafabric.org/) \n", + "\n", + "# Converting numpy data \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os ,sys, time, logging,shutil\n", + "from datetime import datetime\n", + "import numpy as np\n", + "\n", + "# sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "# sys.path.append(r\"C:\\projects\\openvisuspy\\src\")\n", + "\n", + "# sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "import OpenVisus as ov\n", + "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n", + "logger= logging.getLogger(\"OpenVisus\")\n", + "\n", + "# uncomment for debugging\n", + "# ov.SetupLogger(logger, output_stdout=True) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download a numpy file to convert\n", + "\n", + "NOTE: **it's 6GB file** and will take a lot to download" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "numpy_filename=\"/mnt/data/chess/assets/recon_combined_1_2_3_fullres.npy\"\n", + "\n", + "from openvisuspy.utils import DownloadObject\n", + "DownloadObject(\"s3://utah/assets/recon_combined_1_2_3_fullres.npy\",numpy_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load the data in memory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t1 = time.time()\n", + "data=np.load(numpy_filename)\n", + "shape,dtype=data.shape,data.dtype\n", + "D,H,W=data.shape\n", + "m,M=np.min(data),np.max(data)\n", + "print(f\"np.load done in {time.time()-t1} seconds \\ndtype={data.dtype} \\nshape={data.shape} \\nc_size={W*H*D*4:,} \\nm={m} M={M}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show a slice along Z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy import ndimage\n", + "\n", + "def ShowData(data,cmap=\"viridis\"):\n", + "\tprint(\"ShowImage\",data.dtype,data.shape,np.min(data),np.max(data))\n", + "\tplt.imshow(data, cmap=cmap)\n", + "\tplt.colorbar()\n", + "\tplt.show()\n", + "\n", + "Z=200\n", + "slice=data[Z,:,:]\n", + "ShowData(slice,cmap=\"viridis\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create the IDX file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "idx_filename=\"./remove-me/example-numpy/visus.idx\"\n", + "\n", + "# ************ dangerous (make sure you are in a tmp directory) ************ \n", + "import os,sys,shutil\n", + "assert(\"remove-me\" in idx_filename)\n", + "shutil.rmtree(os.path.dirname(idx_filename), ignore_errors=True)\n", + "\n", + "fields=[ov.Field(\"data\",str(data.dtype),\"row_major\")]\n", + "db=ov.CreateIdx(\n", + "\turl=idx_filename,\n", + "\tdims=[W,H,D],\n", + "\tfields=fields,\n", + "\tcompression=\"raw\")\n", + "\n", + "print(db.getDatasetBody().toString())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write data to OpenVisus" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t1 = time.time()\n", + "db.write(data)\n", + "print(f\"db.write done in {time.time() - t1} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (OPTIONAL) Compress dataset using zip\n", + "\n", + "Here the compression is disabled, but better to compress data in production" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "do_compress=False\n", + "if do_compress:\n", + " t1 = time.time()\n", + " db.compressDataset([\"zip\"])\n", + " print(f\"db.compressDataset done in {time.time()-t1} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show OpenVisus data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "slice=db.read(x=[0,W],y=[0,H],z=[Z,Z+1], num_refinements=1)[0,:,:]\n", + "ShowData(slice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show coarse to fine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os,sys\n", + "num_refinements=3\n", + "for slice in db.read(x=[0,W],y=[0,H],z=[Z,Z+1], num_refinements=num_refinements):\n", + "\tShowData(slice[0,:,:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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/notebooks/convert-streamable.ipynb b/notebooks/convert-streamable.ipynb new file mode 100644 index 0000000..e43f6b2 --- /dev/null +++ b/notebooks/convert-streamable.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-large.png) \n", + "[National Science Data Fabric](https://nationalsciencedatafabric.org/) \n", + "\n", + "# Create Streamable" + ] + }, + { + "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", + "from pprint import pprint\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#sys.path.append(\"C:/projects/OpenVisus/build/RelWithDebInfo\")\n", + "#sys.path.append(\"C:/projects/openvisuspy/src\")\n", + "\n", + "import OpenVisus as ov\n", + "import openvisuspy as ovy\n", + "\n", + "def ShowImage(slice):\n", + " fig, ax = plt.subplots()\n", + " im = ax.imshow(slice) \n", + " plt.colorbar(im)\n", + "\n", + "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n", + "\n", + "print(\"OpenVisus imported\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download Nexus file\n", + "\n", + "Note **it's a 5 GB** file so it will take a lot to download" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "h5_filename = '/mnt/data/chess/assets/reconstructed_data.nxs'\n", + "from openvisuspy import DownloadObject\n", + "DownloadObject(\"s3://utah/assets/ff1_000231.h5\",h5_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read from original HDF5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# how to reach the binary data inside the HDF5\n", + "expression ='/shanks-3731-a/data/reconstructed_data'\n", + "\n", + "# xarray needs to read one level-up (i.e. at group level)\n", + "group,fieldname = expression.rsplit(\"/\",maxsplit=1) \n", + "\n", + "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": [ + "# Show the HDF5 structure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install --quiet h5glance\n", + "from h5glance import H5Glance\n", + "H5Glance(h5_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show the binary data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z=100\n", + "slice=data[Z,:,:]\n", + "ShowImage(slice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create streamable version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openvisuspy.create_streamable import Streamable\n", + "\n", + "# create streamable local version, where each 3d field will be an OpenVisus dataset\n", + "local_url = f\"./remove-me/streamable/reconstructed_data/visus.nxs\"\n", + "\n", + "Streamable.Create(\n", + " \n", + " # original hdf5\n", + " h5_filename, \n", + "\n", + " # local streamable version\n", + " local_url, \n", + " \n", + " # arco\n", + " arco=\"2mb\", \n", + "\n", + " # copmpression\n", + " compression=\"zip\", \n", + "\n", + " # I need to predeclare what will be the options to open/read the binary data\n", + " # I can choose later (by using `prefer` argument) which one to use \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 internal local dtaset\n", + " # ./remove-me/streamable/reconstructed_data/visus/{name}/visus.idx\n", + " \t\"local\": \"./remove-me/streamable/reconstructed_data/visus/{name}/visus.idx\",\n", + " \n", + " \t# network s3 storage\n", + " # e.g https://maritime.sealstorage.io/api/v0/s3/utah/streamable/hdf5/reconstructed_data/visus/{name}/visus.idx\n", + " \t\"remote\": \"https://maritime.sealstorage.io/api/v0/s3/utah/streamable/hdf5/reconstructed_data/visus/{name}/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3&cached=arco\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show streamable version has exactly the same metadata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I want to see any extra metadata that H5Glance does not show\n", + "Streamable.Print(local_url)\n", + "# H5Glance(local_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read from local streamable (i.e. OpenVisus reads binary data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openvisuspy.xarray_backend import OpenVisusBackendEntrypoint\n", + "ds = xr.open_dataset(\n", + " local_url, \n", + " group=group, \n", + "\n", + " # this is telling xarray to open binary data using OpenVisus\n", + " engine=OpenVisusBackendEntrypoint, \n", + "\n", + " # since I know I have data locally, I will open preferring local storage\n", + " prefer=\"local\"\n", + ")\n", + "\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", + "Z=100\n", + "slice=data[Z,...]\n", + "ShowImage(slice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (OPTIONAL SINCE ALREADY UPLOADED) Upload data to S3\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "do_upload=False\n", + "if do_upload:\n", + " os.system(f'aws s3 sync --no-verify-ssl --endpoint-url \"https://maritime.sealstorage.io/api/v0/s3\" --profile sealstorage --size-only ./remove-me/streamable/reconstructed_data/ s3://utah/streamable/hdf5/reconstructed_data/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read the data from S3\n", + "\n", + "NOTE: the first time will be slow, the second will be a lot faster thanks to the cache" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I already have the file locally, but in case you need to get it\n", + "# NOTE directly opening the stream using f3fs is causing some problems with `xr.open_dataset` so I am saving the file locally first\n", + "\n", + "from openvisuspy import DownloadObject\n", + "DownloadObject(\"s3://utah/streamable/hdf5/reconstructed_data/visus.nxs\",local_url)\n", + "\n", + "# cache will be filled here...\n", + "if not \"VISUS_CACHE\" in os.environ:\n", + " os.environ[\"VISUS_CACHE\"]=\"./remove-me/streamable/reconstructed_data/visus-cache\"\n", + "\n", + "ds=xr.open_dataset(\n", + " local_url, \n", + " group=group, \n", + " engine=OpenVisusBackendEntrypoint, \n", + " # NOTE: this time I have the data remotely (and will cache it too!)\n", + " prefer=\"remote\"\n", + ")\n", + "\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))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show streamable data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z=100\n", + "slice=data[Z,...]\n", + "ShowImage(slice)" + ] + }, + { + "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/notebooks/convert-tiff-data.ipynb b/notebooks/convert-tiff-data.ipynb new file mode 100644 index 0000000..5313179 --- /dev/null +++ b/notebooks/convert-tiff-data.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-large.png) \n", + "[National Science Data Fabric](https://nationalsciencedatafabric.org/) \n", + "\n", + "# Converting TIFF data \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os,sys,glob,argparse,time\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from skimage import io\n", + "\n", + "import os,sys\n", + "\n", + "# sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "# sys.path.append(r\"C:\\projects\\openvisuspy\\src\")\n", + "\n", + "import OpenVisus as ov\n", + "import logging\n", + "os.environ[\"VISUS_DISABLE_WRITE_LOCK\"]=\"1\"\n", + "logger= logging.getLogger(\"OpenVisus\")\n", + "\n", + "# uncomment for debugging\n", + "# ov.SetupLogger(logger, output_stdout=True) \n", + "print(\"OpenVisus imported\")\n", + "\n", + "# ///////////////////////////////////////////////////////////////\n", + "def ShowImage(img):\n", + " \"\"\"\n", + " Utility to show an image using matplot lib\n", + " \"\"\"\n", + " print(\"dtype\",img.dtype,\"shape\",img.shape,\"m\",np.min(img),\"M\",np.max(img))\n", + " fig = plt.figure()\n", + " fig.set_size_inches(18.5, 10.5)\n", + "\n", + " ax = fig.add_subplot(1, 2, 1) # nrows, ncols, index\n", + " imgplot = plt.imshow(img)\n", + "\n", + " plt.colorbar(orientation='horizontal')\n", + "\n", + " # sho historgram\n", + " ax = fig.add_subplot(1, 2, 2)\n", + " histogram, bin_edges = np.histogram(img, bins=256, range=(np.min(img), np.max(img)))\n", + " plt.title(\"Histogram\")\n", + " plt.xlabel(\"value\")\n", + " plt.ylabel(\"pixel count\")\n", + " plt.xlim([np.min(img), np.max(img)])\n", + " plt.plot(bin_edges[0:-1], histogram)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Donwload a TIFF stack \n", + "\n", + "NOTE: **it's 4GB file** and will take a lot to download and unzip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filenames=list(sorted(glob.glob(\"/mnt/data/chess/assets/image-stack/*.tif\")))\n", + "if not filenames:\n", + "\n", + " from openvisuspy.utils import DownloadObject\n", + " DownloadObject(\"s3://utah/assets/image-stack.zip\", \"/mnt/data/chess/assets/image-stack.zip\")\n", + "\n", + " import zipfile\n", + " with zipfile.ZipFile(\"/mnt/data/chess/assets/image-stack.zip\", 'r') as zip_ref:\n", + " zip_ref.extractall(\"/mnt/data/chess/assets/\")\n", + " filenames=list(sorted(glob.glob(\"/mnt/data/chess/assets/image-stack/*.tif\")))\n", + "\n", + "D=len(filenames)\n", + "assert(D)\n", + "img = io.imread(filenames[D//2])\n", + "H,W=img.shape\n", + "print(\"W\",W,\"H\",H,\"D\",D,\"dtype\",img.dtype)\n", + "ShowImage(np.clip(img, 0, 70))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create the IDX file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "idx_filename=\"./remove-me/image-stack/visus.idx\"\n", + "\n", + "# ************ dangerous (make sure you are in a tmp directory) ************ \n", + "import os,sys,shutil\n", + "assert('remove-me' in idx_filename)\n", + "shutil.rmtree(os.path.dirname(idx_filename), ignore_errors=True)\n", + "\n", + "fields=[ov.Field(\"data\",str(img.dtype),\"row_major\")]\n", + "db=ov.CreateIdx(\n", + "\turl=idx_filename,\n", + "\tdims=[W,H,D],\n", + "\tfields=fields,\n", + "\tcompression=\"raw\")\n", + "\n", + "print(type(db))\n", + "print(db.getDatasetBody().toString())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write data to OpenVisus" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generateSlices():\n", + " for Z in range(D): \n", + " # print(f\"Writing {Z}/{D}...\")\n", + " yield io.imread(filenames[Z])\n", + "\n", + "t1 = time.time()\n", + "db.writeSlabs(generateSlices())\n", + "print(f\"db.write done in {time.time() - t1} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show OpenVisus data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z=D//2\n", + "slice=db.read(x=[0,W],y=[0,H],z=[Z,Z+1], num_refinements=1)[0,:,:]\n", + "print(slice.shape,slice.dtype)\n", + "ShowImage(np.clip(slice, 0, 70))" + ] + }, + { + "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/notebooks/nasa/cmip6_IDX_01_read_data.ipynb b/notebooks/nasa/cmip6_IDX_01_read_data.ipynb new file mode 100644 index 0000000..2b676b2 --- /dev/null +++ b/notebooks/nasa/cmip6_IDX_01_read_data.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a26a3fa-8d08-4c39-a788-d3fe5a239858", + "metadata": {}, + "source": [ + "| [National Science Data Fabric](https://nationalsciencedatafabric.org/) + [NASA Open Climate Workbench (OCW)](https://esto.nasa.gov/forums/estf2024/estf24Abstracts/lee.html) + [WIRED Global Center](https://resilience.utah.edu/)
created by [Valerio Pascucci](http://cedmav.org/) |![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-smaller.PNG) |![NASA](https://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg)| ![wired](https://www.sci.utah.edu/~pascucci/public/wired-logo-small.png) |\n", + "|:---|:---:|:---:|---:|" + ] + }, + { + "cell_type": "markdown", + "id": "fe480733-1dab-4104-9c0c-b87c5b68b3a6", + "metadata": { + "tags": [] + }, + "source": [ + "## Uncomment the line below if any library is missing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19daabb1-a63f-4ace-88bc-16bee2bc9340", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!pip install --upgrade openvisuspy numpy matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5929a3e4-cba2-4854-8e10-6f95ade55cdf", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VISUS_CACHE is c:/tmp/visus-cache\n" + ] + } + ], + "source": [ + "import os,sys\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "#Stores the OpenVisus cache in the local direcrtory \n", + "\n", + "#sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "import OpenVisus as ov\n", + "\n", + "os.environ[\"VISUS_CACHE\"]=os.environ.get(\"VISUS_CACHE\",\"./visus_cache_can_be_erased\")\n", + "print(\"VISUS_CACHE is \",os.environ[\"VISUS_CACHE\"])" + ] + }, + { + "cell_type": "markdown", + "id": "5aaabd9a-7550-4eda-937a-d7d97d467855", + "metadata": { + "tags": [] + }, + "source": [ + "## Load the data **`dataset_name`** into the numpy array **`data`**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59bb623-7161-4da2-8fcb-1dc8b15c4321", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model = \"ACCESS-CM2\"\n", + "\n", + "variables = {\n", + " \"hurs\": \"Near-Surface Relative Humidity\",\n", + " \"huss\": \"Near-Surface Specific Humidity\",\n", + " \"pr\": \"Precipitation\",\n", + " \"rlds\" : \"Surface Downwelling Longwave Radiation\",\n", + " \"rsds\": \"Surface Downwelling Shortwave Radiation\",\n", + " \"sfcWind\": \"Daily-Mean Near-Surface Wind Speed\",\n", + " \"tas\": \"Daily Near-Surface Air Temperature\",\n", + " \"tasmax\": \"Daily Maximum Near-Surface Air Temperature\",\n", + " \"tasmin\": \"Daily Minimum Near-Surface Air Temperature\"\n", + "}\n", + "\n", + "variable = \"tas\" \n", + "\n", + "year = 2020 \n", + "# 2015 is the year whne the data switches from historical to simulated\n", + "scenario = \"historical\" if year < 2015 else \"ssp585\"\n", + "\n", + "# Open (connect to) dataset\n", + "dataset_name = f\"{variable}_day_{model}_{scenario}_r1i1p1f1_gn\"\n", + "db = ov.LoadDataset(f\"http://atlantis.sci.utah.edu/mod_visus?dataset={dataset_name}&cached=arco\")\n", + "\n", + "# Corresponds to July 21. See https://nsidc.org/data/user-resources/help-center/day-year-doy-calendar\n", + "day_of_the_year = 202 \n", + "timestep =year*365 + day_of_the_year\n", + "quality = 0 # 0 = load full resolution (-4 coarse, -8 coarser)\n", + "data=db.read(time=timestep,quality=quality) #load anta in a numpy array\n", + "np.info(data) # print array information" + ] + }, + { + "cell_type": "markdown", + "id": "10728a98-a616-403f-a126-51c218da541d", + "metadata": {}, + "source": [ + "## Plot the data if you like" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "465c0f7c-d871-43ff-988c-74de52e83bea", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "my_cmap = 'Oranges'\n", + "plt.imshow(np.flip(data, 0), cmap=my_cmap) # matplotlib puts the origin on the top left instead of bottom left\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a923c7f-6256-4921-8ccd-a3176a69c085", + "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/notebooks/nasa/cmip6_IDX_02_read_data_multiresoluton.ipynb b/notebooks/nasa/cmip6_IDX_02_read_data_multiresoluton.ipynb new file mode 100644 index 0000000..ddedbab --- /dev/null +++ b/notebooks/nasa/cmip6_IDX_02_read_data_multiresoluton.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a26a3fa-8d08-4c39-a788-d3fe5a239858", + "metadata": {}, + "source": [ + "| [National Science Data Fabric](https://nationalsciencedatafabric.org/) + [NASA Open Climate Workbench (OCW)](https://esto.nasa.gov/forums/estf2024/estf24Abstracts/lee.html) + [WIRED Global Center](https://resilience.utah.edu/)
created by [Valerio Pascucci](http://cedmav.org/) |![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-smaller.PNG) |![NASA](https://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg)| ![wired](https://www.sci.utah.edu/~pascucci/public/wired-logo-small.png) |\n", + "|:---|:---:|:---:|---:|" + ] + }, + { + "cell_type": "markdown", + "id": "fe480733-1dab-4104-9c0c-b87c5b68b3a6", + "metadata": { + "tags": [] + }, + "source": [ + "## Uncomment the line below if any library is missing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19daabb1-a63f-4ace-88bc-16bee2bc9340", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!pip install --upgrade openvisuspy xarray openvisuspy matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5929a3e4-cba2-4854-8e10-6f95ade55cdf", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VISUS_CACHE is c:/tmp/visus-cache\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os,sys\n", + "\n", + "# sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "import OpenVisus as ov\n", + "os.environ[\"VISUS_CACHE\"]=os.environ.get(\"VISUS_CACHE\",\"./visus_cache_can_be_erased\")\n", + "print(\"VISUS_CACHE is \",os.environ[\"VISUS_CACHE\"])" + ] + }, + { + "cell_type": "markdown", + "id": "fd657f99-1a67-4a9f-bf72-139b5b9a79a1", + "metadata": { + "tags": [] + }, + "source": [ + "# More complex examples" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5c67ab5-6b34-45b6-b432-ffc84f2992f8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tas_day_ACCESS-CM2_ssp585_r1i1p1f1_gn\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d2d8e7c2692e41c3bf5f2628480917d1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/6 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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rVqwosA1xcXFi0KBBwmQyCS8vLzFo0CBx7NixPP13/fp10a9fP+Ht7S28vLzEf//7X3Hz5k2b7SnK/SiEEBaLRQQGBor33nuvwPYzDMOUBpIQDoYGYxiGyYcff/wR4eHhuHr1KofbZoqF1atX4/nnn8eFCxcQGBhY2s1hGIbJA2uoGYYpVsLCwlCrVi05jDbDFJWPPvoII0aM4ME0wzBlFp6hZhiGYRiGYZgiwDPUDMMwDMMwDFMEeEDNMAzDMAzDMEWAB9QMwzAMwzAMUwR4QM0wDMMwDMMwRaBSBnaxWq24efMmPD09ixzql2EYhmEYpqgIIZCcnIygoCDodKU735meno7MzEy7yxuNRri4uJRgi8o+lXJAffPmTVWoY4ZhGIZhmLLAtWvXUKNGjVI7fnp6Onxd3ZAG+53ABQQE4NKlS5V6UF0pB9Senp4Asm9ak8lUyq1hGIZhGKayk5SUhJo1a8pjlNIiMzMTaRAYBHcYUbAVPxMCP8bEIDMzkwfUlY0cmYfJZOIBNcMwDMMwZYayIkV1gw5GO9pi4HAmACrpgJphGIZhGIbRRpIAnR1jewmAA+qQCgsPqBmGYRiGYRgVOtjnCo7dxWXDA2qGYRiGYRhGhU6SoLND8qEDeIYaPKBmGIZhGIZhcsEz1I7BA2qGYRiGYRhGhc5ODTUPqLPhATXDMAzD3EfE9dPKB6ObkjaQr+S0JPVOBqOS1pNymelKWqdX0pYs5Xhmkr59g5TXGC2Zzcqh2j2pVLlzuVLGTfGQJbmSc3AibtNyeX+Q3DyUTfdSlQ1WC9nHqqSd3WznZ5GAIxIZzpFzpv0iMu7ZzNd3ekbZ9Y+FSn63IUr+9qWqc9A/+jwqCzxD7Rg8oGYYhmEYhmFUSJJklwu/suHkr/ThATXDMAzDMAyjgmeoHYMH1CWEefpQdUY6Mcv5+CjptDQl7e6upC2KCcwwfo7tY0wZrJSJWFSoduaHZf9a1WfJRWmfuHVFyQ+sqxQiJkSRlqLk6xVTpL51j2Js5f1DXPlL+UDNmkZnpUxCrJzW1WtzP5rFMMx9wvr3bjmta/IfJf/iMTktGV2VHcg7E5lEdmAl8oV7irRDpJPvg9zBMpyI5IPKH4icQaQSmUhcjI0zAPRPKN9NltVfKRvoTKS7IuewbPxWyXcl55OuSDYEladQOYqT8m4EABEfrXwgMhR6bpK7t5JvTSblFRmKSsKRQfqM5lMZCYUcy7LlR5tFqPwDCfG266kE6CXAYMf0s7ngIpUCHlAzDMMwDMMwKhxym8eUrX6wWCyYNGkSgoOD4erqigceeADTp0+HIL/UhRCYPHkyAgMD4erqitDQUJw7d64UW80wDMMwDFOx0Dnwx5SxGeqPPvoI8+bNw6JFi9CkSRMcPnwYL730Ery8vDBq1CgAwMyZM/HFF19g0aJFCA4OxqRJk9C9e3ecOnUKLi4uBRyh+Ml67Qk5LXl7KRsCAzX3Mbz1pZy2fDdZTutfmebQsUtC5kHRt+9dovWXN6TazQouU7WWnBbXzygbiOlT8g+W09YLR+W07oGHithChmFsQaUa1EOErmXXAvdVSd+IXMD65zYln3ijkOq2Usoc2yynxT1FAqfv0E+pn3qRoB47MjLUDdGSfBB5oP6p15TsOeOVMsQLh2XRDNv1UIlJLJFmUJmHB2kTbQ/1LkI9lVhyiQGcFTmM5Kl4/IDeyfb+OpJ29VT2JZIUpCmyEJUUhPQ3lacgnZQxkjEDPWfqCeTlCFRW2G2eY5SpAfW+ffvQp08f9OrVCwBQp04d/PTTTzh48CCA7Nnp2bNn47333kOfPn0AAIsXL4a/vz9Wr16NgQMHllrbGYZhGIZhKgq8KNExylQ/dOjQAVu3bsU///wDAPjzzz+xZ88ePPFE9izwpUuXEBMTg9DQUHkfLy8vtGvXDlFRUZr1ZmRkICkpSfXHMAzDMAzD2EYHye4/pozNUE+YMAFJSUlo2LAh9Ho9LBYL3n//fYSFhQEAYmKyVy37+/ur9vP395e32SIyMhIRESVjtnGa/1uR9ndU5lFesez4Sflg8pWTElkRLpLvKmXMillW3+W5Em2bvViPb1U+UNOlh7ecFsRUKLkTCRA9z/ibShkfRRok7hKTIzWVGhRzqOQd4GizSxzL7hVyWv+fp5X8Dd8o+b2G39c2MRULlbyCPA8qaLAP5Db/E28RRJ6gClRCF19RKYCkMe9E8nUN29ssQmUeKq8WBHsDhVh+/kzZ55mxBZbXh39sM9884xXbO1DJpBN559B+pefgoUgwQAK2UM8mkgd5BwIAeVdK5B2qClpDZR70mlBJCg1aQ/aVqOcRelzq8YOez9XzsIle4x6rZLDkwzHKVD/8/PPPWLJkCZYuXYqjR49i0aJF+OSTT7BoUdG0whMnTkRiYqL8d+3atWJqMcMwDMMwTMWDFyU6RpmaoR4/fjwmTJgga6GbNWuGK1euIDIyEoMHD0ZAQPbs3K1btxBIFv3dunULLVu21KzX2dkZzs7OmtsZhmEYhmEYBZ6hdowyNaBOS0uDTqe+NHq9HtZ/neAHBwcjICAAW7dulQfQSUlJOHDgAF5//fX73VzGBpajf9jMLyuyDYr15E45rWvaWcmnQRqo3KJqkLIzNUsSEzKVsKgwU3M0eUORgDcqMyMN/JB7pXwZgAY+0HcbIqetl08o+SzzYIqAyrsGlRrQ4CfEI8T9fsdYDm6wnb/t/5QP1PuFVqARO7FH5mEPhve+K5Z6LHPfVj7UUDwXqbx/5JZO6DTedzSfyjy0fCDTfCKzUwWYoVDvKTeUoGRIJZIc2m4rkQtVYrIDuxQ8otZDFFimMlCmBtRPPfUU3n//fdSqVQtNmjTBsWPH8Nlnn+Hll18GkB1XfsyYMZgxYwbq168vu80LCgpC3759S7fxDMMwDMMwFQT28uEYZWpA/eWXX2LSpEl44403EBsbi6CgILz66quYPFnx1fz2228jNTUVw4cPR0JCAh555BFs2rSpVHxQMwzDMAzDVERY8uEYZWpA7enpidmzZ2P27NmaZSRJwrRp0zBtWuXwjsEwDMMwDHO/sdclHrvNy6ZMDaiZ8gON8IjGSmQwGgGsuFC5qyNoRTmjbtxotCyq9QUAcfuGss++VcoGJ2LtMJLFrMQdFHWrJVEdtOrFouimBdFEq3TWgkY/06iG6AtFwi2ooGsOVDpEkk/dTakirNl242WPa77cfSnv66ZEMBMxF5UNGcSdVUqCah+Rquge9Q/3KvDYTMXFsutn5QN1OUejBlK3ZyRN3coBUEfco/plEllQ9Zy4KG7c9L1tr8mxbPlR+aA1dUcjFz72gpJNIiKqdNbkmcHd2zbbA0DtWo66qSN9cD+j24o7d+S0RPXHxAGAoO8bqN+V9LlXXSvqslDrnUavJ12fQl0lppH7h+Lrp6QzSd+9Fmm7fCWGZ6gdgwfUDMMwDMMwjAoJsGvumeens+EfFgzDMAzDMIyKnBlqe/4cYd68eWjevDlMJhNMJhNCQkLw229KkLz09HSEh4fD19cXHh4eGDBgAG7dUltnr169il69esHNzQ1+fn4YP348zObS9YglCSEqnb+TpKQkeHl5ITExESaTqeAdihnziCeVDwHEvE5dpbkSc18KMV0RM5thjm23TWURrWh6muU1XFJpRi2jmDXMw9TdFqB2xaX1GLiTaGBU8kFcPknUXOlKytNoitQkSs+BpqkUhEo5aNNyR4mjn7XqpRVQUyntD3o8anLNIn3pROQvtB6tvqOuqjKUY4nkeFUxkRRnuy4S9Qz3SFupqyt6fVOVaHj64e/bbhPB/NYAOW349NcCy5cVzNOHKh88lftNcibXzdXNdpreL/S+teRy6Ua/mMi3pf7pNx1trk1ULvGoZMGOd4Nmnf+Xy2RP7z8KlRHQ59KTRPWjrsLIva7vE2772DQSrLOrzTL6kL62y9O+phKz3O4yLRrR/kg0QhqZUNDnh+6bRp4TDemWo5g/UiQykhu536qqoxrDp6qSpu9TCn0PGjRc69H3BH1HUfnHHRI9me6r9c54uWSiKTtCaY9NcrdjvqcvXO34zr0nrHgtOc7udq9btw56vR7169eHEAKLFi3Cxx9/jGPHjqFJkyZ4/fXXsWHDBixcuBBeXl4YMWIEdDod9u7dCwCwWCxo2bIlAgIC8PHHHyM6Ohovvvgihg0bhg8++KDI519YWPLBMAzDMAzDqCgpDfVTTz2l+vz+++9j3rx52L9/P2rUqIHvv/8eS5cuxWOPPQYAWLBgARo1aoT9+/ejffv2+OOPP3Dq1Cls2bIF/v7+aNmyJaZPn4533nkHU6dOhdFotHXYEoclHwzDMAzDMIwKHQC9HX85A8mkpCTVXwZdTKyBxWLBsmXLkJqaipCQEBw5cgRZWVkIDQ2VyzRs2BC1atVCVFQUACAqKgrNmjWDv79iAenevTuSkpLw999/F8OZFw6eoS5Gsl57Qk5L3l7qjSRUOqoSsxcxj0hkVbdIJ6uVnYjZi5h4zR8T8yOVi1ATIrmhDdMW22y3+bNRShtM3nJa/4raNaHly7eUD7lXoNvKp6ZPYl6mq92pjEDyrKLkU5Oe1bYuSmVCpREadXSVOLnFnXL9aqVmUCJ5kDxIO9yJ+YqaCsnqdcmNyDxUsgvy056ab+lqfa0IYTSfkmvVvMrEqYo8prGPSqpB82l/k3xqfqX5OtueTWhkSUFX3NNzM6ivg8ozSGaG7XJGci/R65icqKTJvqoobgQRG6t88FKeUfM4IjWg9x714JJFJELkfhH31FHVMm/cldPua6JstsMezNNeVj4QzwmSv4ZMjJrHqczDg9zDWhKm3GZdVUQ7JU2fM4leUyp1Is+6uPaPkm/yUfZ1JV4q3JRjq6ReVLJB5Ul6DfM99d4AANSLxB3lutN7TKISC1ov8aKhKfOg3oSoBIz2Bekj1TvK3VtJU4kavffuqb1U6Lu/DFtYNnyj7E7fB1rvEPL8WE/vU4o36mC7vD0QWaIgackz1/dgiiKxAH3fU+8mRg1pGfHIoSU5E38ds1neMJ14Z2HsRidJ0NkRKTHHbV7NmjVV+VOmTMHUqVNt7vPXX38hJCQE6enp8PDwwKpVq9C4cWMcP34cRqMR3t7eqvL+/v6IicmW8cTExKgG0znbc7aVFjygZhiGYRiGYVQ46uXj2rVrKg21M5kIyE2DBg1w/PhxJCYmYsWKFRg8eDB27txZpPaWNjygZhiGYRiGYVQ4OqDO8dphD0ajEfXq1QMAtG7dGocOHcLnn3+OZ599FpmZmUhISFDNUt+6dQsB/zpxCAgIwMGDB1X15XgBCQgoOI5CScED6iJy7/kuctqp0QNyWvLxUZUTVF5QhZi6UknACy29Ef2VR8X2VNpBzVv/+1YpMmWwzSqpzENlviarns2zx6h3stg2v0meGg8QNcdSjwLUlEs8bQhq1qXlqZSBmlCjVpN8jQAnVNaQ2xuFKtiD0pciNUGpVq8hySBSFUH2lag0gZYn7RMq7wPppAj1+KGxuj23bIVCz48Gp1H1DSlPTf5UnkHOX+Xxw5nICIiURtVuigspn6nUr5L2ABBZGt4YJA3PI5n38pYF1PcMNQNTd0vkeVMfi1wr8lzllnPI+ZlKGWum2kOGZFTakfJkO2UfC5EJOSnX1OCp3DMuS7YrFSURyQI1pdK2ViUyB1dyPYm8QiW9Iv2ikl0Ycn0VaHqMIdDrSyVNpH1StepKPn0fUMixJSqxotec5KukRFR5lrv+e+RaEzmMyhuKyvsOvQeU95Jl47e2yxs1AkDResj7QzLafiYlct2s544ru2pIPHKj7zVcaSuV02l5OSH3g0hUvEZZDiluyxAf7XA7cpCCyDWnAWgAwKcaKWi7v3HjitK+JCLp0jDlGz4h0pu+DjQUQMZLik5XMhBpU6ryXexUTS1bsSQp95Xzgi2OHbCccT/9UFutVmRkZKB169ZwcnLC1q1bMWBAtiems2fP4urVqwgJCQEAhISE4P3330dsbCz8/LLfgZs3b4bJZELjxo2LoTWFw64B9YkTJxyuuHHjxjDkfkkzDMMwDMMwZR5JktRrJbTKOTiknjhxIp544gnUqlULycnJWLp0KXbs2IHff/8dXl5eGDp0KMaOHQsfHx+YTCaMHDkSISEhaN++PQCgW7duaNy4MQYNGoSZM2ciJiYG7733HsLDw/OVmZQ0do14W7ZsCUmSYK/Lap1Oh3/++Qd169YtUuMYhmEYhmGY+09JzVDHxsbixRdfRHR0NLy8vNC8eXP8/vvvePzxxwEAs2bNgk6nw4ABA5CRkYHu3btj7ty58v56vR7r16/H66+/jpCQELi7u2Pw4MGYNm2a1iHvC3YFdtHpdDh48CCqVatWUFEIIdC0aVOcOHGizA6oi+o8Pf2Fx+S0gZqDgoOVdG5TMQ3UEq8EtjBMXSinzVOHKGXcFTOg5KJhoqRmbZUHCnJJNUzZ1PuHKl/DXAlALfmg5Uj7JC9izqeeBryJCdqajwwjB3qe1BGmXiMwBYWaqLU8XABqzxY62/IMataWNDwZaHlLoCZeQU2aFjsCyhAPFxL1tJHfbIFWkBgakEUVKEGj3gQikXAm50a8cahlFxqBY6jJmVw3EXdD1WyRQj11kH2ojID2mVXjeNRUfP2qkk+CIdF73ZJGngGtflV5mxE2s10Wb7W9732AenhQyTysGkFAaJpKIkxqGY5qW0BtJU2eJx3NpxIjeo8JDe8x1HMPlVvRoD9UYpROpXH3bObnkXxoBQaiz6I9gaJcbHsrUklpqCyLtFuiZagshD57xMsHldwhTpFdAIC+5zA5bdn2f0r+Yy8o+b//oOxA36H0+pD3lUTe0bqmnVEcqL7HqIcqAFK3/soH+kwT6QmuXJCT9gRoKi6ofJPK2CQn9T2SFad4MfFYu79Y21DWArss8a4GNzuekTRhRVjC7VJvd2lj1wx1586dUa9evTxuTLTo1KkTXF013KoxDMMwDMMwZRpJyn8ORy5X8k0pF9g1oN6+fXvBhQgbN24sVGMYhmEYhmGY0kf695895Rj28pEvaU8/IqeNLRrIaUMDIu0gJmTqEUJQmQYAw6jPbB7D/NYA5QMN+ELlFtSLAl3JT2UhNOgEDQpDJQ8kX1DzK5V/ZGp45gDU0hBqyiXHFnRVOz0H2qbcUhK5fnIOKtO/JW9ZQG2uVXkFKTigAQD1Kn1qsqXnmZwgJwVJq6Qg3sTTAr0H6PloHYsEbxA0EAVBaElYch1PomZdDQ8EKvkITbuQ1fjUU0k6MUFnErO5KiANaR8JLKHyZpKSoOSnEokHAMRcU9JUqkA9BND7zdl28CCReNd2eV9fJfvGTTmt9/VWypBnCXFxSpo8e4Yv1uJ+ogoUZSJyIxKQRqIeg2h/0WeAmPXFdUUWo/LGUU/9vqLvGUkj0IZIUvpJqlpDKaMVUEQi15a2jwZ3ovsSSQC9l6iXGJVgKvdUGq2LPg9a7xMqQbBotJXKjVRejIi0gchZdC272j5WIbDs+dXmsS2rv1LyybWmUhBVPcSbh0o+U0yoZIzvD1NvPEQm5xq3UtLkWt1PmQfFdekOu8qV3pK3+48O9oXT5pDb2Tg8oLZYLFi4cCG2bt2K2NhYWK3qL/ht27YVW+MYhmEYhmGY+48O6uVFmuXs81dR4XF4QD169GgsXLgQvXr1QtOmTe1yqcIwDMMwDMOUH1jy4RgOD6iXLVuGn3/+GT179iyJ9pQp9O7EuKPlIcOJei8gpuJcZkVqvlUFVyDO5JGimP8NE79WqloQQSoiZsYMcvlUXieISTxVqVMFbR+Vi6Sl5S2bA7VGUBmKlhRES3pBTbHUxK/yyEHqoedGy9MyWl5BqLk6PZd5UyJyBrqPlvcPKrch5yASYpV8InlQ9bFKykBWQdNzJvIPVZ/e0whGkqtNgp4r6Q+papBShngqUf0Yvkf6ggahoSbhLA3PMARxl3gIuXtbSWcQ+UduzyYp5Ni0P5ISlDSRdujDJtg8Njo/qzSPBjQi97fkptRDA0KYJwxU8r9ab7v++4w5Sel73T3lvtKnK30pqETLqvYKIUPeUYbJihcI80evK/XsVAeoMMz8WSlHzPZSHSV4FfxJAA/yzEheisRG5WFGwxuOKk3uN5UEij7f5B0oUc8mybm+1DW8EwhVQCeapp5kNLykaMlCiLxLH9LX5nGLiv6RATbzLT/blhNa/liofCDvBn2X54qzWflCg4zlB33+8LjtYGRM6cBDZftxWPpCw0WWBDdu3MALL7wAX19fuLq6olmzZjh8+LC8XQiByZMnIzAwEK6urggNDcW5c+dKrD0MwzAMwzCVjRwvH/b8MYUYUL/11lv4/PPP7Q7y4gh3795Fx44d4eTkhN9++w2nTp3Cp59+iipk4c3MmTPxxRdfYP78+Thw4ADc3d3RvXt3pKdrhFllGIZhGIZhHEJy4I+xM7BL//79VZ+3bdsGHx8fNGnSBE65HLevXLmy0I2ZMGEC9u7di927d9vcLoRAUFAQ3nrrLYwbNw4AkJiYCH9/fyxcuBADBw60uV9uCuM8nQZzcfk/ZeFl1qs95LTkSVbZ55J8GGatsl3vi8oqcIM3MYlSn9+exEzvRoIdqLwdENMqlaQQuYCgPzqIvISWN98kZnqLesGp1WxbbiHpld9lOmdFnqH3pp4jiMmWelSg/sq1bkWaT6UG5N6TvLyVfOrtgHqayO3lg6LyjEKOR4PTOBNPAXo7vIpQqByDlk/TkHnQNJW85D4HlScMcr2oVwOV9wfyvFJZCJWh0OMRs7tKzpFJ7rEs2zIXfW9FUlBSqIJIUOkS6WPDtMUl3o6yRtqzneS02/JdcpoGr7DXq4GjWJZ9rHyooQT30tVqqOS7Ea8llDRF/iPuacjVqBzD6GK7TG4yNSZcqISD3MeCPn9asjTqxYdKvYjURE9kSMWJZedy5UMceS7pu0XLUwl9Xq22JXr6AaOLoZVMfqQ82U5Oe6w/AKDsBXZZ7RMAd13B866pViv6xseUertLG7tmqL28vFR//fr1Q+fOnVG1atU824rC2rVr0aZNG/z3v/+Fn58fWrVqhW+/VTRYly5dQkxMDEJDQ1Vta9euHaKiojTrzcjIQFJSkuqPYRiGYRiGsQ3PUDuGXYsSFyxYUNLtAABcvHgR8+bNw9ixY/Huu+/i0KFDGDVqFIxGIwYPHoyYmBgAgL+/v2o/f39/eZstIiMjERERobmdYRiGYRiGUbA7UiKPqAEUwsvHY489hpUrV+YJQ56UlIS+ffsWyQ+11WpFmzZt8MEHHwAAWrVqhZMnT2L+/PkYPLjwK38nTpyIsWPHqtpas2ZNh+rIuqPMalv6dZDTOlfFbK4nEglhVcsXtDpa70ZM+PSupOZrIskQRDohScSMSVfHU8lHKvEQQT14WGiABsXMas1QjmtJ0dal64h3EnqmljQiH0lW9tcZlR6QSNrgSUy2VGpA+4J6F6ESI+phhVodqLzEnoAvgNprCU1reUmhnlpoEB+6rzsxa3sS6w09NyrhoCba6rWVdDyR4RhzhRWgZmd3Iu2gnlRoUAwvpYygpt90cp94V1PS1DSfTAKn3FPuJX2v4bifmCcSLwXUvGiHabKiQYNPua3Yo6SJzINSUjIPin7geDltjnxVTlubEG84AbXkpOSnvIslD28lTYMNUWkGlbpR6RHyUS+6K/VS2YZITVDyiVRDkoj8Q0vyoZJP0eA3ilzEcoB4jNEIeKNv+4TN/NxYjmxS9iFSEsv2pbZ3MNuWQKlkHlQKQ/rSsuRDZVctrzqMwyR2aSmnde75SBDLCDpI0Nkx/2xPmcqAw99AO3bsQCZ11fQv6enpmtpnewkMDETjxo1VeY0aNcLVq1cBAAEBAQCAW7duqcrcunVL3mYLZ2dnmEwm1R/DMAzDMAxjG51k/x/jwAz1iRMn5PSpU6dUEguLxYJNmzahevXqtna1m44dO+Ls2bOqvH/++Qe1a2fP1gUHByMgIABbt25Fy5YtAWTPNh84cACvv17yC6AYhmEYhmEqA/bqo3k8nY3dA+qWLVtCkiRIkoTHHnssz3ZXV1d8+eWXRWrMm2++iQ4dOuCDDz7AM888g4MHD+Kbb77BN998AyA7EMWYMWMwY8YM1K9fH8HBwZg0aRKCgoLQt2/fIh27IDw3Kb6wU4nkw5pOgkZQU34uUVHmK91sbtO5aUgeqKeJe8QDBdlXqAKeEFMk9eZB97VoBDTQgMo6AEBQrx/kJ6kqOA0pY/BWPGRY0+gKeuXYVFYiOdk2iYospTyVi9DyEpV5aHkLyeWRRuUZRSPQjeStuGwUGRoSGL9AJW0gx0hOJHVqBHyhbahGrCx0X2quvZ0reIcHkZJYC76mIjFe+eClBMVQeVSgQV5SSDtooIw021IYy3eT5bT+lWkFtqdQaEk7yH1v+EDDDF6OSH6irZz2/O2QzTJU5lEWoQGqzG/2UzbQyZdOiuRBUnmnoYvcST6VXdB3YO6v9XSNgEjkGaXebQSVZOiU96ZE5XTU8ZFO+SAZqBRLSQsqSdEIHGPZrQQYou/33IFc9K0Vj1KW375XNlBPRAbyHqSeiCzUOxI5Tyqfoe8P8l6xrJuv5MffUaof/B4Y28R3aCqnnaoo14d+Z3luOCinU/uEZP+fZTtgVmnBA2rHsHtAfenSJQghULduXRw8eBDVqik6S6PRCD8/P+jtcR+WD23btsWqVaswceJETJs2DcHBwZg9ezbCwsLkMm+//TZSU1MxfPhwJCQk4JFHHsGmTZvg4mKn+ySGYRiGYRgmXzj0uGPYPaCuXbs2srKyMHjwYPj6+soyjOLmySefxJNPPqm5XZIkTJs2DdOmldDsF8MwDMMwTCWHvXw4hl2BXSje3t44duwYgoODS6pNJU6O0/KbTz4Mk5MB7iv3Ktseb60UJKuhDcRso3NTTHr0RhJmdSAUFaSgpCeSD3dSFw1yQs3a9BJRE6dWGRqUgC4gJflUdmHNVPKtGSR9L+/iU7mtJJgLlXyo86nnDCJVsQibZfTEY4oq4AuVSNhYEJtdEbkQ1FpBvdHklnxoBAIBCZ4jeSmSD5Wcgx6PmITh66ekkxKUNPHOIS6dV6oJCFLKUNMt3ZdKLQy5fgO7kH6q4kvapBF4hbabnnMA+YFMPY9QTwHUZH3tou36iVRFkGA5hjGzURKY31LM4oZPfy2RY9giuUcbOU3lYLmh7xP6nHnv/LNkGlYOMX+krH/R9R4kpyX/Okohet9SKQeVLFhymcvpfWmm3kBsf/uLDOIFiXq/IM+ASsJBJRJaIwr6zND3GN3XbPsdjSTiVQeA/uk3lapWf6VsoO8NrWAu9H1AdSuqZ50cm/YdlXelkMA7acp1MLxVNLlnRSMupImc1pHvvip7T8rppG7Ku0H/7zggKcuMoA2HSj1ASs4YaUu1ILsDu4Tevlnq7S5tHPby0adPH6xevboEmsIwDMMwDMOUBTiwi2M47Ie6fv36mDZtGvbu3YvWrVvDnYaRBjBq1KhiaxzDMAzDMAxTCvzriMKeckwhJB/5ST0kScLFixc1t5cVcswZOeYJuppeJVkgaRrARVXG2bZnDipryN5H2wNIDnoiJclj2ncAa6piaqcSDpVDf43LTs3SKq8egMqDB0VyUaQQOiel3ToXJU0D3UjEBKZzJjIKKtUwaji915K5UDkH9WySW+ZBIQtrKZIvyacyDxpghQZeId4yVKbiq+RZIKZiQc2yJCCN5E+8fNDjUu8ihlznQ4O5aL3UtILkUC8A3uQcPL2VNPXyQT2KxJMgHfeICT76upwUJJCQ4Z15ttvGVHqy3ugpp6WGDZV0A8VTglSrIWwhEpT7UKqqdtsqaCAilZcdIsNwIu8cKsNwsv3+kYgnEEGlXlYqsyPyClqGPsdURiE03stEMpW9TUNSSGRw1BORRN+VRP4hiLTDMOozOW2e/KJSnu5L5CKGyJ+U8iTAEs1n7ONuR+X+zpGC5B6blBY57djuVx0edkg+UqxWPBp7o9TbXdo4PGq7dOlSSbSDYRiGYRiGKSNIds5Q2zWLXQko/DQogJzJbe5MhmEYhmGYioO9URA5UmI2hRpQL168GB9//DHOnTsHAHjwwQcxfvx4DBo0qIA9yxapz4dC72SA3qSsFBc0UAuVMhBpB7XKUfkCtcjpPdQmQ9WPDqo7p+Z/InOw3o5T6iVSCyo3oaZ8S6piljMnktXqBB0JikID0tB8zWMB2k8NlYIYiOyFyDzoMeyCdjKVgnh6Kmkq50gmwUjilL5TrXSnQVQAIIWYXYlnD9WqeTciqUgkJmRqjnXLtJ2fpeGR5LYiHZFq2XY/Ke7E2syXPDzVGaRN1AOBRNtNJRlUMkJlHvTmpTIPagZPIP1KA8/cU+43/chP5bR5yuC8J8AwuXCau9FmvnnGK8qHS+eUNPVm46nIkESuYFWSOwnaQuUf9J1APYbQwC4W8s6hwbTMGoE3aD3UQw99TqgHHPqOovI+6t0o90QVfSdqveNocfqBtpvUYw7vpTRjzgab9aiqGf9f2/WMUFzdGr5aX2A9jJocb0BJ5oKDc91PJJ2kGuNoluNliQAKMaD+7LPPMGnSJIwYMQIdO3YEAOzZswevvfYa7ty5gzfffLOAGhiGYRiGYZiyTEXzQ7127VqH93n88cfhSn/o5oPDA+ovv/wS8+bNw4svKgsYevfujSZNmmDq1Kk8oGYYhmEYhinnVLQBdd++fR0qL0kSzp07h7p169pV3uEBdXR0NDp06JAnv0OHDoiOjna0ulJFZFkhYFF5mtB5kaAexDOHPpAE7FAFGUi3mc7jIcNETPDUNEllCESCoPNQ2mG+o3iCMCcqkgIqybCkKMf2/O2QnE7s0pKUUSQBKpmHE/HAQfJpnYBaJkL7wFhNkSFQLyE60j6rRZE/SE7EREnNSamKdIDWo/a8ckspr/KqovS3NV3ZV29STLdS7tXKxAuF5eIVZR/qqcaDXDcaJIZKO65dVtL0GDQIDc2nkh/qEYBCzbh0hX5uGYmGCVqkEgkMlcNQ83Kqcr9JCfHKvknETE3Pge5LJTLU/Ey5d892fj6Yx/RVDjd7tcP7MxUH65VrclpHvdPcJnIo+izFqmVSgmyT6LPrF6iktYI1eZhs59MgS1QORb12UMlHivLuFvQc6PuAPD+WBOVZpe/63Kg8UFE5HQ0gZlTeG9SDlCVVea9b0pTnO/O//5HTbr/sltNpz3ZS8pfv0mwT4xg0yItMUhLg5ZU3v5SoiIsSY2Ji4OfnV3BBAJ6engUXIjgc2KVevXr4+eef8+QvX74c9evXd7Q6hmEYhmEYpoyRM0Ntz195YPDgwXbLNwDghRdecMgNoMMz1BEREXj22Wexa9cuWUO9d+9ebN261eZAm2EYhmEYhilfVLQZ6gULFthVLiUlBR4eHpg3z7H4CQ4PqAcMGIADBw5g1qxZcgjyRo0a4eDBg2jVqpWj1ZUqwmqFsEgQGitrJTfF60bmP1fJfraDlLit2COnMwZ3VdWl9yRrrqnMQyPAiuUuMf0lKdIEj7X7bZbXwmvHcTmd3OthOa3l8YOaGXP3S0aG8tnFTTGVZsUrK9z1pM+onEMl2yBBb6Ssglc1m4kHExqcRh1sRzmWIaiqUp7Ia6Tmue5P4lVFbz5scx+VJ5AspZ8sdxKUfT2IRwDqkYSUt6YpZladl2JGUq32p7+cqdSCmrVz3y9UGkIlGTExSprKVqhEhO5LPZukaMhFiCnSMHoWCoRIoGjwDpV0CIDzD5uVelnmwfyLJZk8M/Q5JFItawZ5dyWpJRJ66qWJpKWa1IsNkWEQWYjUqAU5Hjk2lYiogsVovMeotIOeA3nu004rwZCSbyvv0sxMtWwwnQTp0pP3qYuL8hx7VVHeIVRaJ8h7ln4nJHRqLqe9d52weQos86i86HUS9HZ4+dCXIy8fs2bNynetX3JyMnr06IG9e/c6XHeh3Oa1bt0a//d//1eYXRmGYRiGYZgyTkVblAgA7777Lnx9fVWONXJITU1Fjx49EKe1LqgACjWgtlqtOH/+PGJjY2G1qn9Fd+rUSWMvhmEYhmEYpjwgwU7JRzmaof7xxx8xaNAgeHt7o3fv3nJ+amoqunfvjtu3b2Pnzp2FqtvhAfX+/fvx/PPP48qVK3KkxBwkSYJFy/TFMAzDMAzDlAskndqxjWY528rVMsnTTz+NhIQEPPfcc9iwYQO6dOkiz0zfunULO3fuRGBgYMEV2cDhAfVrr72GNm3aYMOGDQgMDCw3YnRb6Jz00DkZVPoyqoO2h6SuD8np9LBH5bTLku2a+5gnDLS9gWhaaeTDlJuK5s8Dhcdzw0E5fb1lQzntXUPRxnq0VTy1JO07o9o/LU3R8KWmKmk3N+U2Mt5TdIUG4pLJyYfogKmbJ6HcP9RVHtVvOzeqI6elesSTTAbRVpu8SZ3k6a6tlBdbc0XwIppGa4Li3oq6UbSkKccQdxV9I3VJpYpYGa9cK6obp7p7c4ziok4yEPdXRBOuOoekJFI+1yNLNdv0xyyNIkn2V1GtmpKm7vuIucsw07GFxhkvhcpp5wVbHNqXYSh0wkaQZ1WQ94SZ6KxFrvUYmVRTTXSgBrJOhEbA1fsTjTNdU0AjMzqRZ5RaZ81kXQBxqSmuK/poy03FrV/GzQQ5nRJnWzeddk+91iArS+kPv2pKO7LMSn5ygtIferKuJPDIadhCSzfNFB+pfULktMplLP3e+Pd7JtmONUX3FTsXJZYrzQeAV155BfHx8ejTpw/WrFmDyZMn4+bNm9i5cyeCgoIKXa/DA+pz585hxYoVqFevXqEPyjAMwzAMw5RdKqKGOoe3334b8fHx6Nq1K+rUqYMdO3agRo0aRarT4QF1u3btcP78eR5QMwzDMAzDVFCyB9T2uM27D40pJvr376/67OTkhKpVq2L06NGq/JUrVzpct8MD6pEjR+Ktt95CTEwMmjVrBidqWgbQvHlzjT3LHhm3k5Fu0MM36m85L6ZNIzkdcNi2mYxi2nrUZj51UQeoXUC51vOX03oPxc3RvX9uyun0BMVsaCYmvYRHmimVEkkBlQg4+SrCEBpB0OUBRRfk30EJpZlxXZEgWBMVN2nUTR4AZGQqny2kTdRtU1aWcjydjriZI1IQYvSCVcMFIZW8WG8rEgQpIUGpv7ni2kr8o1wrcUsxrWat3qDk5zKnUYmFzlW5j6krLvqmoGWonEPlfo5GKjMo+RK5PlZyTQRxIUijllGoS0CRpY5emRWnRCel50PvAZ0rcdNIZSE3bihtunRZ2Xf+bzbbYQ9ZcYrZ3DmfcgxTEOnXlPdS1i1FtuRU1bbwzeDpovpsJdIyc7LyPjV4K3IOKi2z3lXkWrobilRDoq7ydLYjmIJEOBRxd5TjXlXcV2bGKudgpdI4IvvKyFDeDfS9D6jflckpyjuKujWjIpF6Z86DKX2olJG6cqTSJacq2fe0U5btyLelRUWcofbKFYnyueeeK7a6C+WHGgBefvllOU+SJAghin1R4ocffoiJEydi9OjRmD17NgAgPT0db731FpYtW4aMjAx0794dc+fOhb+/f/6VMQzDMAzDMHahkyTo7Bgt21OmrGBvcJfC4PCA+tKlSyXRjjwcOnQIX3/9dZ4Z7zfffBMbNmzAL7/8Ai8vL4wYMQL9+/cvlBNuhmEYhmEYJi8VcYa6JHF4QF27du2SaIeKlJQUhIWF4dtvv8WMGTPk/MTERHz//fdYunQpHnvsMQDZvzYaNWqE/fv3o3379g4dx2frwTxx2u2ReWgR3VqRi2SZ1f65a/151uY+qf06yGlq8s8kcgt6s6alKUY9jypkJTqRBVAvFVR2kHL0opw2VlOi9TkHesvp9KuKvMIjQCkDAHfuKGZTurKcSkN8qpE2EWmDlaxYF0Q6Qt1XqmQU1NpJ5BJSrVpK9rl/lDJuynGp7IBKTQxepG0AnKqqr718jKaKrEZcuaxsIJEPVengYDmpp5EIiXcNy1VFXkEjK1IzMI1eqZKXkPz0WMWcnHsfakI0fvs7CiK1f0eb+zrZKpwPVN6kp55KGKYI+Ow7KaepFM8YoJhsqZSDytsA9XPjUsNHTltSFDmZwVd5x1mSFG8bliuK5MNQW3m+VZFGk5VnV1xWJprSjyrvelX0Rqtt32L30pX3YWoqia6aq7zRSXk/xicq7zVX4nGo5bXLNo/BlB70HqD3KP0ezJEAZWlEbS4tdDoJOjsiJepE+RhR9+/fHwsXLswz7tMiLCwMs2bNgp+fn13l7fAweP8JDw9Hr169EBoaqso/cuQIsrKyVPkNGzZErVq1EBUVpVlfRkYGkpKSVH8MwzAMwzCMbXJmqO35Kw+sWbMGt2/fzjMetPWXmJiIdevWIYW47CyIQkVKLEmWLVuGo0eP4tChQ3m2xcTEwGg0wtvbW5Xv7++PmJiYPOVziIyMRERERHE3lWEYhmEYpkIi6SSVswDNcuVkhloIgQcffLDE6i9TA+pr165h9OjR2Lx5M1xcXArewU4mTpyIsWPHyp+TkpJQs2ZN3H60LdL1etUN4+LtaqsKWNMVU19KouJpofoxJfhJOjHdBf9N5Aj54L5qn818b5L+p4HiojCDtCPoqDrwSg40aIvRqBgh6GryhIuKdEB3WZF5mIKrymlLitqjRM06iqnVtY5SLuP6XTlNTVoSMf/TQCjU1KVzse05Q++uXH8qW8mI+lMpQ6QThqZKe5y7PqI02kJMtDT4CwBxVzlvpKUp+TGK5wxomYZoUAdq8SBBUagXACqFoJIU6ujfnKi0AYrDAVW/UHM1AAhi1qZBYpIeb62cwuYjNk8hPTpBTjt5OPa8JXRWPKyoJEPXlPNXC2wYpvDcTSABls4pXnxcPZTnKiUlWbUPffeZSVAmp6qKzCP90i05Td8z1CuT/jx5lzsT3zXkuc+4qLwzVMFmyPuQSuNokKykZEXmkUE8Ebm7qL+ek+/Z9gCRVdaCgTAqquxVpEu32jaW0/6HTuUpK5KSgFxeKEqTiqah3r5dO+CeFtWrV7e7bJkaUB85cgSxsbF46CEl+qDFYsGuXbvw1Vdf4ffff0dmZiYSEhJUs9S3bt1CQECAZr3Ozs5wdmYnXgzDMAzDMPZQ0bx8dO7cuUTrd1hDfe3aNVwn4VQPHjyIMWPG4JtvvilyY7p27Yq//voLx48fl//atGmDsLAwOe3k5IStW7fK+5w9exZXr15FSEhIPjUzDMMwDMMw9lJSGurIyEi0bdsWnp6e8PPzQ9++fXH2rNpxQ0xMDAYNGoSAgAC4u7vjoYcewq+//qoqEx8fj7CwMJhMJnh7e2Po0KEOaZ6LG4dnqJ9//nkMHz4cgwYNQkxMDB5//HE0adIES5YsQUxMDCZPnlzoxnh6eqJp06aqPHd3d/j6+sr5Q4cOxdixY+Hj4wOTyYSRI0ciJCTEYQ8fAOBscoWLQQ+9m2I2zLitmA0tZJU19bph0Vitba/Mw1EePOuYg34Pk3I+6cQrSGambdNgOjk3VyJHyB0oQe+uzPJTmUc6NY8SbyNGd+LxgZg+9R5KPUYiF6ByBlomi1yTS38qWvngh4KUtpIgJZaLl+W0rroSzAb3yIp7QOU9RJX28LCdn6qYjVXQ8jTQUSLRbZB6dM5Kf2XeUspQU7HK/Jyp7eyfbtNTLyl26N7u3VOuu2+UIgu51ETRmLm7K68ICwlCQ83prst2KukCj8owjtPovOKh6GLj+nKaBpsy6NXzQympyrPhKSnvFiqzou8lc7zyfFN5Rta243Lahb6vEonXozSlfrovlQFSmQd9594j8jEreU+kpaoDPdFH2kBGMG1jroMpH1jI/Xa3ozLWyZH1JZUxLx+SJNkZKdGxEfXOnTsRHh6Otm3bwmw2491330W3bt1w6tQpuLu7AwBefPFFJCQkYO3atahatSqWLl2KZ555BocPH0arVq0AZHvhiI6OxubNm5GVlYWXXnoJw4cPx9KlSx0/2WLA4QH1yZMn8fDD2W6yfv75ZzRt2hR79+7FH3/8gddee61IA2p7mDVrFnQ6HQYMGKAK7MIwDMMwDMMUDxLs1FD/+39uD2pacttNmzapPi9cuBB+fn44cuQIOnXqBADYt28f5s2bJ48333vvPcyaNQtHjhxBq1atcPr0aWzatAmHDh1CmzZtAABffvklevbsiU8++QRBQUG43zgs+cjKypI7aMuWLejduzeAbPd10dHR+e1aKHbs2CFHSQQAFxcXzJkzB/Hx8UhNTcXKlSvz1U8zDMMwDMMwjpEzQ23PHwDUrFkTXl5e8l9kZKRdx0n815rr46P4i+/QoQOWL1+O+Ph4WK1WLFu2DOnp6ejSpQsAICoqCt7e3vJgGgBCQ0Oh0+lw4MCBYuoBx3B4hrpJkyaYP38+evXqhc2bN2P69OkAgJs3b8LX17fYG1iiWAVgFSqvC+nEiwZ15GAwkMApxPZ2pXkDOU1/yWkFcgGA0/Xq2qyLOvKnJk5HuRunSAecSDAAKseggVmoqfRONOmLywmqeoNb15DTElmBbjQTTx2kE1JiFKmGV7Byb6TfVOrNuKX8ojVQryCk3dfOxctpM5EdnDukyDyMRuXHXN32SvAhHZVd0DQAaHmSMZDHggZyoPn0YhO5CapUsblv5vXbSjPOK2kaAMjkpfyST01STMhUYmTQq6cLqMzDKkjQAA1ZEqXGcdteYih+B/OuRGeY0qTuqXM286kJHQBu3FAkHPeIhwwPIr2gz5aevIupPI6qvqRExcMIfddR6QmVeVCvHRZSEXn9qvNJRCsd1M/6o7E3wJQ/LjdTJHT+zZRZ00zyPaj7Vx6pL2MeWyQ7A7vkeEq7du2aKmiKPc4grFYrxowZg44dO6okvz///DOeffZZ+Pr6wmAwwM3NDatWrUK9etlez2JiYvIEXDEYDPDx8cnXjTLFbDZjx44duHDhAp5//nl4enri5s2bMJlM8KBSTjtxeED90UcfoV+/fvj4448xePBgtGiR7T5r7dq18tQ8wzAMwzAMU36RdNl/9pQDAJPJZHcUwhzCw8Nx8uRJ7NmzR5U/adIkJCQkYMuWLahatSpWr16NZ555Brt370azZs00arOfK1euoEePHrh69SoyMjLw+OOPw9PTEx999BEyMjIwf/58h+t0eEDdpUsX3LlzB0lJSahCZuOGDx8ONzf2PMswDMMwDFPeKalFiTmMGDEC69evx65du1CjhmIBv3DhAr766iucPHkSTZo0AQC0aNECu3fvxpw5czB//nwEBAQgNjZWVZ/ZbEZ8fLxdMuDRo0ejTZs2+PPPP1Xqin79+mHYsGGFOp9C+aEWQuDIkSOqaXKj0VjuBtSZqRnIMOhV5nFq3tORX2ZCw4JuJNIEar6nZh4AMGcRLxfETE+tKfQYVBaSmUm0J7ROcjx6Q3t4KBIMaop0Mmj81KSyECJt0ZvV5U/tuSKnGzyk3LBO3rave0acEqgkK962+dXVVbkFM1IVmQPtL2cSFMZKJB9U/pGcqngzObZF8YrSqL63nPbo0EjdQC05R5ZSl+omSCaBI6hcxFMJFGG9ck3Z1UvJtyQpHgGSU5T6BTX3knP29lZMZSmptLz6FLLIPu4kyMs9ct1j2ijnHXD4NAqipLzVMExJcuWq2l1Wy2uX5fTJOsFyOpHIqdzdlXcllWq4uRlIecXbBpXlmYlugz6HWaRMBpFhZZB8KvOgHjvouESgYNkWc//Z7a8M/P5zq2APK94+iu+juFOKFIF+tTilZ7/jM8qYlw/oJLs8RtlVhiCEwMiRI7Fq1Srs2LEDwcHBqu1p/wZa0+nUYxC9Xg/rv1rckJAQJCQk4MiRI2jdOjuQ2bZt22C1WtGuXbsC27B7927s27cPRqNRlV+nTh3cuFE4eZXDA+qSmCZnGIZhGIZhyhAlFCoxPDwcS5cuxZo1a+Dp6Slrnr28vODq6oqGDRuiXr16ePXVV/HJJ5/A19cXq1evxubNm7F+/XoAQKNGjdCjRw8MGzYM8+fPR1ZWFkaMGIGBAwfa5eHDarXCYsn7A+b69evwJJNkjuCwl4+cafK7d+/C1VX55dWvXz9VwBWGYRiGYRimfOKolw97mTdvHhITE9GlSxcEBgbKf8uXLwcAODk5YePGjahWrRqeeuopNG/eHIsXL8aiRYvQs2dPuZ4lS5agYcOG6Nq1K3r27IlHHnnE7iCD3bp1U3mQkyQJKSkpmDJliuoYjuDwDHVJTJOXFhkZFhjN2h48qHlPp/HTg5ahkooMjSAqgNrMn0VkC7QuYilUrT6nMgd6Ezs7K8emzuOpGZOGNaHnSWUX1IuENZeniDbRV+X0sRqKJw2Tp3IvULkJlWdY0hXZAvU2QuUMnmTfi5cV8y3tetqrGcQNi5GsnHAhEpYz5xOU9pzbpzofiayib/tca2VDlm1JhkSCtpxcsl85nosiSalNPKHoiAQjMVqRi6isYxovovh4xVOLC/Gokrs4lQPRa0091AQdLVjmwTDlHSrxAICj1ZV31EM3FLnagYCactrXR5FumYkUL5NIOCxE2nH3nm35VafY4g+ustE3sOBCzH2nKvm+i/KvabOMkXzHUcmiEwmIRb1r5XjdSrMxY1qqlKDkoyDq16+fJzJibnx8fAodxOWTTz5Bjx490LhxY6Snp+P555/HuXPnULVqVfz000+FqtPhAXVJTJMzDMMwDMMwZYgSknyUBWrWrIk///wTy5cvx59//omUlBQMHToUYWFhKvWFIzg8oM6ZJs+ZVi+OaXKGYRiGYRim7CDpJNnHdEHlyhNZWVlo2LAh1q9fj7CwMISFhRVLvQ4PqD/99FN07969WKfJS4vUNDMknVUlbaByBA+yApyaANVSEBIMIEPxGkGlGQCQRSQgVEmhChpA7kl6f1JpBz12UhYJUKAsREfX24r0ZqdfdTltIhKETA1JirubtrxgH1nd3EFjdfMhYk51clJMXQmJSgNpvVQuEUcC0phJx2TSlfUagQ90kpKfQpzjU7lI7kee1rV36SE5Ta9c7uAKORipDId4W7l2TOl7kye5f0h/m0yKyTCNeDyhMp+qVRVTtFaQCQDIIp5KMpJIEAki+6kBpiywgZjwe8UVf1RZRg2VeRwJqiWn28Vcs5lfo7oSyCE5hbxQNbhnte19qbiwsJePMgkNurbLT3m70phb5CsBIkN5L9NxBJVp6rKydy7pe8pRJJ0Okr7gpXaSHYHEyhJOTk5IT08vuKCDODygrlGjBv78808sW7YMJ06cKJZpcoZhGIZhGKYMUYElH+Hh4fjoo4/w3XffwWAolAfpPBSqFoPBgBdeeKFYGsAwDMMwDMOUMXSwc1Fiibek2Dl06BC2bt2KP/74A82aNYO7u7tq+8qVKx2us1AD6h9//BFff/01Ll68iKioKNSuXRuzZs1C3bp10adPn8JUWSpkZFjgpBOq+8VqUT4kC2VFN/0BZiRShrR0IvMg5pzckg8qC9DyJEKhgQVoIJQMYsp3Uv0qtF1PEllAalbiGcCVtMFA2kCD09DzyX2EP6oqfh6ziA7Bg5iHOl+7hsLye1XbfiTpsZzo6ZOm0mebSjOkXPIN1c1P+jKdmt3ILsQZDNydlXsgNYN611Dal5amXDfar1TmkUikMKr7kNSTTqREhlzBeeh9de+e0g7qkYVCr1u3OzdtlmFKBpZ5lB6tbyrPw14iXetIpGt/1qojp6nnosQM5Rl1NyjPfXIJB+F4Ki5Gc9taHyWwVu947XJMyUK/jzJosB7yxUG/g4xCSWeQ7+McqWlmWZN8lHCkxNLE29sbAwYMKNY6HR5Qz5s3D5MnT8aYMWMwY8YM2eNHlSpVMHv27HI1oGYYhmEYhmFsUEJu88oCCxYsKPY6HZ6o//LLL/Htt9/if//7n0p30qZNG/z111/F2jiGYRiGYRimFMjRUNvzxzg+Q33p0iW0atUqT76zszNSU1OLpVH3C7PZCrMOMFMvEsRso0+ncgGFTFLGjUgcaD36XDeYVjAXC8nXkX2oXIAGMKEmJurxIYO4iNhUVfEmQM2GW6spHj/oOViyqHcI0s5cchR6RvT86I/TDLIPlW10d1BeQMuv81XMmxaNxcS0X6g0I5NIeIy5rknua5QD9f5hVfUxuU+IbIMe20iuW7qGtCc2UbH1uWpEDMrIVMrQZmZZzapy7WMck9WwzKNiwaZ/x+mo4aGoxdXLcvrvusFymga7upOhyABLYlJupY+/nM4jUaNeJNgDSKmxpZryvZasIcG0quR7Spp8HcGJfGfr/v0Kzihrkg9d9p895cobwcHB+UpVLl68qLlNC4cH1MHBwTh+/Dhq166tyt+0aRMaNWrkcAMYhmEYhmGYMkYF9vIxZswY1eesrCwcO3YMmzZtwvjx4wtVp8MD6rFjxyI8PBzp6ekQQuDgwYP46aefEBkZie+++65QjWAYhmEYhmHKDhU1sAsAjB492mb+nDlzcPjw4ULV6fCA+pVXXoGrqyvee+89pKWl4fnnn0dQUBA+//xzDBw4sFCNKC1SzBYInVCZ/qlsI0MoUghahsoAaHl6T+U2yDkTm0i6hlmHOnCgwUyopMBKaqb1aAWIocRmKebKui7OcjqFmK0yiKTAYOevTqGRdlTmoYXWancaKEO12pr0HV1hbZCUFfoA4ExOj/aBPbII6tCfyoTukZX/qRrmQOoJJYEEZgm9rRx3N/FE8B8NEzXDaMk8qFma3leMfaQQ6Ra1Zvt5Ku/NO8kFB3+h/OTtJ6fpe/L5hFg53T/+lkN1AsAqIvvpx7KfEocGX0klXrSMRL5H5YX0e9RM0pk2vl5Ty5jkA3pd9l9BWMuh5kODJ554AhMnTizUokWHesFsNmPx4sUIDQ3FuXPnkJKSgpiYGFy/fh1Dhw51+OC2iIyMRNu2beHp6Qk/Pz/07dsXZ8+eVZVJT09HeHg4fH194eHhgQEDBuDWLcdfRAzDMAzDMExectzm2fNXUVixYgV8fHwKta9DM9QGgwGvvfYaTp8+DQBwc3ODm5tboQ6sxc6dOxEeHo62bdvCbDbj3XffRbdu3XDq1CnZ8fabb76JDRs24JdffoGXlxdGjBiB/v37Y+/evcXaFoZhGIZhmEpJBXab16pVK9UPASEEYmJicPv2bcydO7dQdTos+Xj44Ydx7NixPIsSi4tNmzapPi9cuBB+fn44cuQIOnXqhMTERHz//fdYunQpHnvsMQDZ/gQbNWqE/fv3o3379nYfK8FiQaZVQOtWoGY5GkSElqf3kY44bafeOwAgTSgmRGrUoXW5EYMBXcVNq6LyAjPJpw7h6U2ylJgZqWmRBvjwosEKaCCYXOegta6cSmDEfVx9To9E+5vKP9QBX9TmtHTy0dGgG2lWpZ/SSQfQ808ikg8aSCeB9LGTxi/74pR5FMXbClM+YZlH0WhHvOcs9qomp02ZimzOnZjCf66ieOewqiR6CoZcX95FgXoDoTIR2g76zqGyObovff/kF0iGUchUyTypFJR8N5Py9LuJSj70VCL6b5F7ooxJPmCvS7zyN6Du06ePaqyk0+lQrVo1dOnSBQ0bNixUnQ4PqN944w289dZbuH79Olq3bp0nXGPz5s0L1RAtEhMTAUCegj9y5AiysrIQGhoql2nYsCFq1aqFqKgomwPqjIwMZJCwRElJScXaRoZhGIZhmIpERY6UOHXq1GKv0+EBdc7Cw1GjRsl5kiRBCAFJkuTIicWB1WrFmDFj0LFjRzRt2hQAEBMTA6PRCG9vb1VZf39/xMTY/oUdGRmJiIiIYmsXwzAMwzBMhaYCSz70ej2io6Ph5+enyo+Li4Ofn1+hxrKFCuxyvwgPD8fJkyexZ8+eItUzceJEjB07Vv6clJSEmjVrItNqhUHK5cGD7EfNYaqAL+TesdhWF+SRS2h56jAS7x9ZqiAv5BikvEUVdIRKDcixiPzDh0SzXGhSTJdDkm7L6dP16srptEQqHVGdgupck8jNVhZWlmfZYUJ1VNaRH/c0vLBkkk6iXlgyyc1BTb8SWRlOvZYUta3LiNTHWSN4TGlBZUiAWorEMGWNFxNv28ynXjtMekU2F01kITWcneR0PPEARO95Ws9zdj4LWt5Anrlb8OJ8ezyJsOcQ+6DfwQbynrVofDerZYp58zOLKAUqbiryDLWW7CojIwNGo7FQdTo8oC4p7XRuRowYgfXr12PXrl2oUUNxIRYQEIDMzEwkJCSoZqlv3bqFgIAAGzVlR3F0dna2uY1hGIZhGIbJRQWcof7iiy8AZP8I+O677+Dh4SFvs1gs2LVr1/3TUK9du9ZmviRJcHFxQb169RAcHGyzjD0IITBy5EisWrUKO3bsyFNX69at4eTkhK1bt2LAgAEAgLNnz+Lq1asICQkp9HEZhmEYhmGYf6mAkRJnzZoFIHusOX/+fOiJdcloNKJOnTqYP39+oep2eEDdt29fWTNNoTrqRx55BKtXr0aVKlUcblB4eDiWLl2KNWvWwNPTU9ZFe3l5wdXVFV5eXhg6dCjGjh0LHx8fmEwmjBw5EiEhIQ55+KBQeQa9LYjTDujJFpq218MF3UK9Qjjpba8YVklJSJrmZ2oEM0kwK/oCD1K/s8avyNRUZU2yG1m5fiVDHbjAHnPi/eRJIov4laxuV0t4SsaEpmUGpebbFHKxPIhOiAaRofdCVSeHH0dNBhLTMV35v5y079lSklqwxIOpCNgjz1BJr8h7aYdfdTlN5SJl4fkEWOaRH1RZm0m+XtKJpIfK+gzka5eONaikVNjYXhaoiJESc2TLjz76KFauXFmocaoWDosrN2/ejLZt22Lz5s1ITExEYmIiNm/ejHbt2skSjbi4OIwbN65QDZo3bx4SExPRpUsXBAYGyn/Lly+Xy8yaNQtPPvkkBgwYgE6dOiEgIAArV64s1PEYhmEYhmGYXOglJVpivn/lZ0Cdw/bt24t1MA0UYoZ69OjR+Oabb9ChQwc5r2vXrnBxccHw4cPx999/Y/bs2Xj55ZcL1SB7/HO6uLhgzpw5mDNnTqGOwTAMwzAMw2hTkRclAsD169exdu1aXL16FZmZaov8Z5995nB9Dg+oL1y4AJPJlCffZDLh4sWLAID69evjzp07DjeGYRiGYRiGKQNUwEWJOWzduhW9e/dG3bp1cebMGTRt2hSXL1+GEAIPPfRQoep0eEDdunVrjB8/HosXL0a1atlu2G7fvo23334bbdu2BQCcO3cONWvWLFSD7ieWf/+o7oW6vqOT5Tpyw1DXdRKoDsp2FEMAyLDajoCkjuqnlKHZtIxZwx0P1VP7GxVNXjWiy6XunKi2b6CGVq+dzdxsVhBd7tNlQFttJNeHXpP77YaIXis30ibqeou6jaMaaq17pDD8qhG5TVdOZxIYpjyi9W6l0EiMHvric3FJ66V6XXu03/9H9n1Bw21gZUVP0i7kHZ9moe/ZguuhrldzdNa5IyyXOhVwUWIOEydOxLhx4xAREQFPT0/8+uuv8PPzQ1hYGHr06FGoOh1+er///ntcunQJNWrUQL169VCvXj3UqFEDly9fxnfffQcASElJwXvvvVeoBjEMwzAMwzClTM6A2p6/csbp06fx4osvAgAMBgPu3bsHDw8PTJs2DR999FGh6nR4hrpBgwY4deoU/vjjD/zzzz9y3uOPPw7dv47N+/btW6jGMAzDMAzDMGUBewfL5W9A7e7uLuumAwMDceHCBTRp0gQACi1ZLpSfLp1Ohx49eqBLly5wdnYut4L0exYrIKld3FBShWKCN5GeotP6ZlKGmtNzT/1TQw49mg5aUgWl3lQiBaAu9Kg7Hmp6ou7x7pF93Ugkp3RS/1oSFYu6nMu9cDeBuAWyx5R5P7lL2kbdUNkTFaykoNEoKWYNCc+AYpTOaNVF3fpRd3plzSViSfEDiRb6ssb1YZj7CZVjuEiOSz6otIPuTd12epMvC/rc0/cPlQcWn/is4kHfrfR9SmWHard51M2u7YjJOdK/9LIm+dDpsv/sKVfOaN++Pfbs2YNGjRqhZ8+eeOutt/DXX39h5cqVhXbB7HAvWK1WTJ8+HdWrV4eHh4fs02/SpEn4/vvvC9UIhmEYhmEYpgxRgSUfn332Gdq1y14pFhERga5du2L58uWoU6dOoceyDg+oZ8yYgYULF2LmzJmqeOdNmzaVNdQMwzAMwzBMOaaCDqgtFguuX7+OWrVqAciWf8yfPx8nTpzAr7/+itq1axeqXoclH4sXL8Y333yDrl274rXXXpPzW7RogTNnzhSqEaWF9d+/JAuVRdg21SSZaXwkBXeNVdm5by9aypmYR4zkGLQuKvmgHkPUcgElP41oQeq7uMhpHy/lR09MQrqcthBzII38eC8fTxPUI0VZ40WNlejU20VxSirsacd3nlXl9CvJd2yWud+o5Upl93qWFCzzYMoaWl43FhJ5EqCWkGl58Egm32UdqnjI6R1xyXKavuGpbDA2U/mO8yOeotjjhzb02tHrRb1LUTUEfefSd3GOXFRX1rTIen32X4HlypdISK/Xo1u3bjh9+jS8vb2LrV6HZ6hv3LiBevXq5cm3Wq3IysqysQfDMAzDMAxTrqigM9RAtqoiJ3ZKceHwgLpx48bYvXt3nvwVK1agVatWxdIohmEYhmEYphSpwAPqGTNmYNy4cVi/fj2io6ORlJSk+isMDks+Jk+ejMGDB+PGjRuwWq1YuXIlzp49i8WLF2P9+vWFakRpMyo1zmY+NdlT3Ig0Q+s2yh1QhJajztuNqrqI/EOnmFkEsbjEEekJlWB4G5R6bhNLQUaCYorJIOUTST2ZGpKS3AqP11LKX/RLfRl50Kk5UMv7R1Hrpd5dtMzINJhAcbajIqEl1WGY+0l+zyeVjVEvTV7EPH8wIUVO00AjZvIOqOOiSAKTLMp3Ap1po98PVGpSmtK1sgi9XjR4F5VjUm9c5UJyV4EDu/Ts2RMA0Lt3b5WnOiEEJEmCxWJb5psfDg+o+/Tpg3Xr1mHatGlwd3fH5MmT8dBDD2HdunV4/PHHHW4AwzAMwzAMU8aowG7ztm/fXux1FsoP9X/+8x9s3ry5uNvCMAzDMAzDlAUq8Ax1586di73OQg2oKwov3bwMk8mkuV3L3DvfQzEJe5DoJ3QtbKpFveqVflKZfbKItw1yU6okIiRNvZDQNF3pfT5dkXw0dVN+OSYTE0a8WWkRbQ+VkWhJYcoTfeNjSvwYy4h5j/YfvX9oGS2omfB5DcnG0lz1eBLJ0F0NTzQUlnkUDMs8mPJENScnOZ2l8iKhfA+YyHsihXwbZTkoO6DzkD/Z8U7Tkp5VdLTe3wWRlJSEEV5exdyaIiDBzgF1ibekRNi9eze+/vprXLx4Eb/88guqV6+OH3/8EcHBwXjkkUccrs+uefoqVarAx8fHrj+GYRiGYRimnFOBFyX++uuv6N69O1xdXXH06FFkZGQAABITE/HBBx8Uqk67Zqhnz54tp+Pi4jBjxgx0794dISEhAICoqCj8/vvvmDRpUqEawTAMwzAMw5QdJJ0Okh36aHvKlDVmzJiB+fPn48UXX8SyZcvk/I4dO2LGjBmFqtOuAfXgwYPl9IABAzBt2jSMGDFCzhs1ahS++uorbNmyBW+++WahGlKeoDIPGvyFyjrsdXNOTW7UYE/lI0Zys3oR816q1baJ30id9ROPH3S1sUHjF2VFkHncbwYS8x5dBU89cBiJrIaaSqlJ1B6ZhzHXdaP3j1aQIXugkpSBldRMW97hAByVB/puCTAqX+N3ssxyWqfxjndTBRAjEhFSnkoCafAXK7HtG2F7X6YCIdm5KFEqfwPqs2fPolOnTnnyvby8kJCQUKg6He6F33//HT169MiT36NHD2zZsqVQjWAYhmEYhmHKEBVY8hEQEIDz58/nyd+zZw/q1q1bqDodHlD7+vpizZo1efLXrFkDX1/fQjWCYRiGYRiGKUPkuM2z56+cMWzYMIwePRoHDhyAJEm4efMmlixZgnHjxuH1118vVJ0Oe/mIiIjAK6+8gh07dqBdu3YAgAMHDmDTpk349ttvC9WI8gZ1dE8d5tPfaLklFdRsRj1yWOxYZU3L3LMqpjgjMbMIYn5r6OYsp+OJ5wfquN9K6hyewjKP4oJKMoxEGpRmoV5VbL98qBmXSoaohCcLamiglpeJBw8tWYkWWrIVDt5QfmCZR8WGyrI616wip3dfv2uzvNYQh34X0WBfKvmHRj30e8NMvvGsGt9j3+QKjjacPeiULyqw27wJEybAarWia9euSEtLQ6dOneDs7Ixx48Zh5MiRharT4Z8VQ4YMwd69e2EymbBy5UqsXLkSJpMJe/bswZAhQwrViMIwZ84c1KlTBy4uLmjXrh0OHjx4347NMAzDMAxToSkhyUdkZCTatm0LT09P+Pn5oW/fvjh79myeclFRUXjsscfg7u4Ok8mETp064d69e/L2+Ph4hIWFwWQywdvbG0OHDkVKSkqeemyfmoT//e9/iI+Px8mTJ7F//37cvn0b06dPd+hcKIXyQ92uXTssWbKk0ActKsuXL8fYsWMxf/58tGvXDrNnz0b37t1x9uxZ+PkV7BuTYRiGYRiGyYcSmqHeuXMnwsPD0bZtW5jNZrz77rvo1q0bTp06BXd3dwDZg+kePXpg4sSJ+PLLL2EwGPDnn39CRyy8YWFhiI6OxubNm5GVlYWXXnoJw4cPx9KlS+1ui9FohKenJzw9PeHh4eHQeeRGEqJgzUFSUlK+AVByk5ycDE9PzyI1LD/atWuHtm3b4quvvgIAWK1W1KxZEyNHjsSECRMK3D8pKQleXl5ITEx06LxyoCZxT73ij8OqIc0A1CZ8KgehtyFN01XTLiRNV2inWOhKbKVMLFnp7UHK38lSJB9vpLDp7X7yHTF90mtFg+pQ2QU1larMr7leXPSeowFJlhPz8LOkXuoxhHqSoWWoXITKjahUxUzyaTAbDhzDMCUDfYeYDMrTS+VkWlIxlQcqYTvfrDEUUEk+NOpRlSfHzW0CN2vs8xp/HwEo+tikuNsRHzEUJhdjweXTM+Ez5ftCt/v27dvw8/PDzp07Zc8b7du3x+OPP645Y3z69Gk0btwYhw4dQps2bQAAmzZtQs+ePXH9+nUEBQXle0yz2YyIiAh88cUX8qy2h4cHRo4ciSlTpsCJeFSzF7sDu8TG2u9Kq3r16rh48aLDjbGHzMxMHDlyBKGhoXKeTqdDaGgooqKibO6TkZGBpKQk1R/DMAzDMAyjgYOSj9zjrJxgKQWRmJgIAHJwwNjYWBw4cAB+fn7o0KED/P390blzZ+zZs0feJyoqCt7e3vJgGgBCQ0Oh0+lw4MCBAo85cuRIfPPNN5g5cyaOHTuGY8eOYebMmfj+++8xatQou7uIYpfkQwiB7777zu7p8Kys3Eunio87d+7AYrHA399fle/v748zZ87Y3CcyMhIREREl1iaGYRiGYZgKhYOSj5o1a6qyp0yZgqlTp+a7q9VqxZgxY9CxY0c0bdoUAOQJ2alTp+KTTz5By5YtsXjxYnTt2hUnT55E/fr1ERMTk0fiazAY4OPjg5iYmAKbvHTpUixbtgxPPPGEnNe8eXPUrFkTzz33HObNm1dgHbmxa0Bdq1Ythzx4BAQEFGq6vKSYOHEixo4dK39OSkrKc+EdgXo+oCuvqTneKddNmKFhTtOSeVDcqayEePMgTiSQKRRjnA8xB9KV2yzzKD2o9IZKJLSCqNi7Gv4H4hmEkkqOQb2HaEky7ClD73UTuScbmRSvMjS4CDXvmjWkKQzD2EegUflOpR6bKFpeOAwa2kIq29CSdlBU3qs0xln51eOiIUOh7590DS2JliyEyti0gmMxhUSvA/R6+8oBuHbtmkry4ezsrLWHTHh4OE6ePKmafbb+K5l99dVX8dJLLwEAWrVqha1bt+KHH35AZGSkI2dhE2dnZ9SpUydPfnBwMIzGgmUutrBrQH358uVCVV4SVK1aFXq9Hrdu3VLl37p1CwEBATb3cXZ2tuvCMgzDMAzDMHB4htpkMjmkoR4xYgTWr1+PXbt2oUaNGnJ+YGAgAKBx48aq8o0aNcLVq1cBZE/c5pYim81mxMfHa44Fcx97+vTpWLBggTw+zMjIwPvvv6+KBO4I5c4bt9FoROvWrbF161Y5z2q1YuvWrQgJCSnFljEMwzAMw1QQSshtnhACI0aMwKpVq7Bt2zYEBwerttepUwdBQUF5XOn9888/qF27NgAgJCQECQkJOHLkiLx927ZtsFqtcoyU/Dh27BjWr1+PGjVqIDQ0FKGhoahRowbWrVuHP//8E/3795f/7KVQbvNKm7Fjx2Lw4MFo06YNHn74YcyePRupqamyaeB+QmUa9NeJIVfwDmpAyCAeQOhtqOVwJc6seO0wEekALe6msy0poJ5AmNJDS9pBJRI0MAeVcrycj+cMrW1a+dR7CJWVaMk8qGcB6mGmZ1y0zbaqPZIo6RrEXL2lmnr19dUMZc2FFznGgLtqKxTDVGYSiczDn0gqL6QrC7+ozIo+f1reNSgqyaEdHj8ozjrb34O5pY5achDaPipTpIGyPnH1kdNUQsceQkoQyc4oiJJjc7Ph4eFYunQp1qxZA09PT1nz7OXlBVdXV0iShPHjx2PKlClo0aIFWrZsiUWLFuHMmTNYsWIFgOzZ6h49emDYsGGYP38+srKyMGLECAwcOLBADx8A4O3tjQEDBqjyiiIFBsrpgPrZZ5/F7du3MXnyZMTExKBly5bYtGlTnoWKDMMwDMMwTCEoIT/UOQv+unTpospfsGCBHCBwzJgxSE9Px5tvvon4+Hi0aNECmzdvxgMPPCCXX7JkCUaMGIGuXbtCp9NhwIAB+OKLL+xqw4IFCxxqsz2UywE1kK1/KazOhWEYhmEYhsmHEhpQ2xH+BEB2ePD8Yov4+Pg4FMSlpCm3A+qywjPELP1rFWWGPD8DiDMxodBV2fSe1HKaT/PTyb4GQVd0K+k0lnyUaajMY6WPcv/QgC90BTyVXQDq+88ehtsR/EVLFqKF1r1O79WbmWaSr7652esHwxQM9WBxul5dOR1L3NSmkvd9JnkADRrjHa2AYzRt1SjvrrNdKX26DbkGWun2tIlUkEbeFfTd56Z3bADHFBJJZ5+cw0HJR1kgLi4OkydPxvbt2xEbGyt7FskhPj7e4ToLNaDevXs3vv76a1y4cAErVqxA9erV8eOPPyI4OBiPPPJIYapkGIZhGIZhygo6SS3Gz69cOWPQoEE4f/48hg4dCn9/f0gOzrLbwuEB9a+//opBgwYhLCwMx44dkyPhJCYm4oMPPsDGjRuL3CiGYRiGYRimFKnAM9S7d+/Gnj170KJFi2Kr0+EB9YwZMzB//ny8+OKLWLZsmZzfsWNHzJgxo9gaVh5JJSaDKgZtZ+gSMWlR8xj9feSpp8FZlHr15FcU9exxz2pb2uFtKH83emWlf3zxebX4mciPqCzkJyLzeK6IQWVy0PIQQqUjdLX+y7nqX0w8ndCgSY6idc4MU9G4kZgup1tV85TT+24lyWkXlZcPEvBFwxOIQUNyqCXNoB48aCAzi8axctel08h30Zjt1Gnsy5QgJaShLgs0bNgQ9+7dK9Y6Hb4vz549i06dOuXJ9/LyQkJCQnG0iWEYhmEYhilN9Hr7/8oZc+fOxf/+9z/s3LkTcXFxSEpKUv0VBodnqAMCAnD+/Pk8IRv37NmDunXr2t6JYRiGYRiGKT9UYMmHt7c3kpKS8Nhjj6nyhRCQJAkW4vfdXhweUA8bNgyjR4/GDz/8AEmScPPmTURFRWHcuHGYNGmSww2oSHiTX2nGXDcYlW1QK5hRY2U1NZtRpwhZZG9qZKGXXuu3or3BQpjyj5bkQUvmQdHy8hHpogRW8CSr7Eekxtmshz4BWrIQoGgyDwrLPJjKQujtm3L6RquGctrltvJcmjUCpJgttiUZ1CuIluwik8pFSP49Ydt7hzmXRx8qU6Tfd+lEsqh1bMoLxfTOYApAgp2SjxJvSbETFhYGJycnLF26tPQWJU6YMAFWqxVdu3ZFWloaOnXqBGdnZ4wbNw4jR44scoMYhmEYhmGYUkZnZ6REe8qUMU6ePIljx46hQYMGxVanwwNqSZLwv//9D+PHj8f58+eRkpKCxo0bw8PDo9gaxTAMwzAMw5QiFXhRYps2bXDt2rXSHVDnYDQa0bhx42JrSEWAmrAEtCMBUTkH9c2RRfJ1pC4tCUeWhkkvU2P1taO+8JcRjxCA2il/fiZ8pnww10ORdryRokg76H08xVhFThvJJES6HfGC7DHdVlb+j3g2YfM1UxSOXk2Q08283OS0jrzwY5Mz5fSF9Aw5TR9jKuegshD6HOtU5YXN/DRSaW4PIfQ7xE1nO59Cj11c0jDGASqwhnrkyJEYPXo0xo8fj2bNmsHJyUm1vXnz5g7XadeAun///nZXuHLlSocbwTAMwzAMw5QhJDsDu5TDGepnn30WAPDyyy/LeZIklfyiRC8vLzkthMCqVavg5eWFNm3aAACOHDmChIQEhwbeDMMwDMMwTBmlAs9QX7p0qdjrtGtAvWDBAjn9zjvv4JlnnsH8+fOh/9erhcViwRtvvAGTyVTsDSzrrPMNkNN6stQ1waz+dUPN6M7kF1+mhqlLaIR8SSW/mlI1VmV76pWbO5OY37SCv1CWE5lH7t+cuVdsM+WPj1wVTx30Ffi+iyLtcCb3ahUSGCjZQu4fOyYkojOVe7UsephZSu715+3wfqIFlW8A2hIOeryyLvMoi9eLsc1TcTFy+lBATTld7wFlIsxAnuPUOOU5vp6RJafp+z3RbCX5yrE89La/x3xI/UkachEAcCPfTSnkfUIli0Y7vh/LK1H+yvUJuXWtFFtiJxVYQ127du1ir9PhnxU//PADxo0bJw+mAUCv12Ps2LH44YcfirVxDMMwDMMwTClQgQO7AMCPP/6Ijh07IigoCFeuXAEAzJ49G2vWrClUfQ4PqM1mM86cOZMn/8yZM7DaMQPKMAzDMAzDlHFyZqjt+StnzJs3D2PHjkXPnj2RkJAga6a9vb0xe/bsQtXpsJePl156CUOHDsWFCxfw8MMPAwAOHDiADz/8EC+99FKhGsEwDMMwDMOUISqwhvrLL7/Et99+i759++LDDz+U89u0aYNx48YVqk6HB9SffPIJAgIC8OmnnyI6OhoAEBgYiPHjx+Ott94qVCPKM1UNiquVJKJvzj1XT13cORMBKv1hR7VqFqIdywTVpCk3rrvetraaal3t0YZSDag7lfLkKvdsEXSmTOkx3VnRR1P3VPQeNasindHonbCZT5WNVG9L63cneklXootcnEtzXFrusIqim17ro6ydsFcPXZTj3W9YN10+aRtjW5d7q/4Dcrq2h7OcdiffJ7FZip76LtFQU/d4d7KUtDfRTevI28SDPPf6XDOX9P1AddNO5P1A1/pUNJt3udBNU3R2evkohy5SL126hFatWuXJd3Z2RmpqaqHqdHhArdPp8Pbbb+Ptt99GUlISAFTKxYgMwzAMwzAVFkmyc4a6/A2og4ODcfz48TyLEzdt2oRGjRoVqs5CB3YBeCDNMAzDMAxTIamAXj6mTZuGcePGYezYsQgPD0d6ejqEEDh48CB++uknREZG4rvvvitU3Q4PqIODgyHl03kXL14sVEPKK9SEs8+/hpxOs6p/1VlUUaWI5IOUsQrFwEUlItQsZyXGdi3zvaOmZWqypqZspvwyyehtM19lmSO6DereippizRpeq2j+7SxFbkTverXkQ0ln5XKYXx5dtPWOjym4EMOUEW4kpMtpPzdFpljFRUlfyVCiKdJ3QBr5LkojcgwDeYxddYpAkLrDMwv1WIFGADaSTbQUlZa9kFh+ZFIVkgqooY6IiMBrr72GV155Ba6urnjvvfeQlpaG559/HkFBQfj8888xcODAQtXt8IB6zJgxqs9ZWVk4duwYNm3ahPHjxxeqETn1vPfee9i4cSMuXrwILy8vhIaG4sMPP0RQUJBcLj4+HiNHjsS6deug0+kwYMAAfP755/Dw8Cj0sRmGYRiGYRhCBdRQC/IDMSwsDGFhYUhLS0NKSgr8/Pzy2bNgHB5Qjx492mb+nDlzcPjw4UI3JC0tDUePHsWkSZPQokUL3L17F6NHj0bv3r1V9YaFhSE6OhqbN29GVlYWXnrpJQwfPhxLly4t9LEZhmEYhmEYQgWcoQaQR2Xh5uYGNze3ItdbJA015YknnsDEiRNVURUdwcvLC5s3b1blffXVV3j44Ydx9epV1KpVC6dPn8amTZtw6NAhOez5l19+iZ49e+KTTz5RzWSXBtS7hjrSoVry4UQuJpVwCGp2J0YwGi2KBiukK6idi/ALkUZwo1EWMypYlKqKwFyPqqrPcURuoSUBonIO6rWDyjZUK+41PHsYNG6xTOqdhtyg9HmgK/eNuV9mpH0rqvjL6afv3rJ9wCKw3jdQTj8ZF13s9Zcmm6oq59bjTsU6N6ZoUM8bQUHucvripSSb5alXniTyuFIpolHlmUPJp9FVc0NlHvT7SzXA4Yi8ZQd7g7aUs8AuDz74YL7SZSBbDeEoxTagXrFiBXx8fAou6ACJiYmQJAne3t4AgKioKHh7e8uDaQAIDQ2FTqfDgQMH0K9fP5v1ZGRkICMjQ/6c452EYRiGYRiGsYW9QVvKj+QDyNZRe3l5FXu9Dg+oW7VqpRrZCyEQExOD27dvY+7cucXWsPT0dLzzzjt47rnnZG8iMTExeTQuBoMBPj4+iInRXiQUGRmJiIiIYmsbwzAMwzBMhaaCSj4GDhxYZL20LRweUPfp00c1oNbpdKhWrRq6dOmChg0b2l3PkiVL8Oqrr8qff/vtN/znP/8BkL1A8ZlnnoEQAvPmzXO0iXmYOHEixo4dK39OSkpCzZo1i1xvbrrfuSmnfyWma0Bt0qKO8qnZXSK/8qykjJvOtqN8GkhGV4RfiNSsT70xPBV301ZxphShMgoASNAIwEBNvFSqQT3GUKhiiEpEqASI7klNv7RF8VnKpwxSxqIROAYA0okcRFfC7pcqmsyDwjIPRotEM5GGpStpDw/Fy0d6yj05Td8HPkTC4aYhLXSy87Glz34akYm8nMTePMokFdBtXkFSj6Lg8IB66tSpxXLg3r17o127dvLn6tWrA1AG01euXMG2bdtUvq4DAgIQG6t+8MxmM+Lj4xEQoO3uzdnZGc7OzprbGYZhGIZhGIJOl/1nT7lygihBjb7DA2q9Xo/o6Og80+VxcXHw8/ODJZePWS08PT3h6empyssZTJ87dw7bt2+Hr6+vantISAgSEhJw5MgRtG7dGgCwbds2WK1W1eCcYRiGYRiGKQIVcIbaai25gPYOD6i1RvcZGRkwGo2FbkhWVhaefvppHD16FOvXr4fFYpF10T4+PjAajWjUqBF69OiBYcOGYf78+cjKysKIESMwcODAUvfwkRuTQb3qNZWY6q0afUjz6f7ORJ9EvXlQs3vfIgSaeKYEvCkwJUNuxyvUbEqlHbQclQPRMkaNYC40X+s1qddYlU/lIlkWJT9T5Z1GXZe7xgrxdb6K1empOA6kwjCFxYt8n6SkZsnpi3cVmYeJSDuoZ6k0icq76PeYUj+dRqPvD9dcEpGBDgYdY0qZChx6vCSwe0D9xRdfAMjWn3z33XeqQCoWiwW7du1ySEOdmxs3bmDt2rUAgJYtW6q2bd++HV26dAGQrb0eMWIEunbtKgd2yWkbwzAMwzAMUwxUwBnqksTuAfWsWbMAZM9Qz58/H3oyq2Q0GlGnTh3Mnz+/0A2pU6eOXdoWHx8fDuLCMAzDMAxTklRQLx8lhd0D6kuXLgEAHn30UaxcuRJVqlQpsUZVBFxyifSpCS1T2DbTu5AfKVmkvDu5SjTYSu4AGUzFp7aL+pGlwVxSLLa1YWqvMjR4kGPQ2i0aXj7o/UzNvWmkbTqonw2J1EC9ALjpylewAIYpq1TxUhblpxLJB5VnuAgtCRj9zlHqpB6GqHce+k6qblS8iDDlkAoa2KWkcFhDvX379pJoB8MwDMMwDFNWYMmHQ9g1oB47diymT58Od3d3lT9nW3z22WfF0jCGYRiGYRimlGDJh0PYNaA+duwYsrKyzURHjx4tUcfYFYX/3Lqu+ry1WnU57UkjTcJ2wAt3Ytr3JM737yYoIdQ7xaqPwVR8ns+1Sv47z6py+kqG7TUIVIZBPcNkEq2GkbjeoN5mDES6RGUe1MsHNfZpuSNNsVDZibqd1AOIk0aQIYZhCk9gXUWiuePgNTlNA774GJTvnEzyvUQddRgk5Wl3J0GfcgecyoEGO2PKH5Ik2TXe4zFhNnYNqKnMY8eOHSXVFoZhGIZhGKYswDPUDuFwL7z88stITk7Ok5+amoqXX365WBrFMAzDMAzDlCI5A2p7/hjHFyUuWrQIH374YZ4oh/fu3cPixYvxww8/FFvjKhLU44GXuyLh0JF8NzclPyNDMcUlJmXK6TbRV0uqiUw5xNdJeYSTiKyCyjO0jHF0ZT4NyEKDL9DgKhL5/Z0pbJt4BfEUYJCo2Vhb8kEDRFCvA2lW+6KuMgyTP/fiUuW0mTxvJuKdgUqsDOQZTSeR5ZzIuIm+D6hXKzb+VyAkSa35ya8cY/+AOikpCUIICCGQnJwMFxcXeZvFYsHGjRvzhCNnGIZhGIZhyiEs+XAIuwfU3t7eskD9wQcfzLNdkiREREQUa+MYhmEYhmGYUoDd5jmE3QPq7du3QwiBxx57DL/++it8fHzkbUajEbVr10ZQUFCJNLIi0IF4/djnX0NOU/kHdbh/755ZTre+yTIPxjaeZKX9g65KqBYqydCBepVR6BcfU2D9T8XZLrPBN1BOW0mtWiINE/EiYsknIqozmenoxh4CGKbQHA6sJacbPax4mXK/mCCnqcSKDomo/IOOlSzCtlcqF7J36G1+bisMkmTnDDUPqAEHBtSdO3cGkB0xsWbNmtCxSyuGYRiGYZiKiU6f/WdPOcbxRYm1a9cGAKSlpeHq1avIzMxUbW/evHnxtIxhGIZhGIYpHXR2Lkq0p0wlwOEB9e3bt/HSSy/ht99+s7ndYuGV+QXR4RYHZGGKh9Iyr/aKi5bTO/wUczINHEO9d1DPHrklH25EtpJh1ZaDlAZUnsXPLVOecCIuOVJuJsppT+LZg0rD6JNH7c/Ug4eZPJ+P3r5RPA1lyi68KNEhHO6FMWPGICEhAQcOHICrqys2bdqERYsWoX79+li7dm1JtJFhGIZhGIa5n+QsSrTnzwEiIyPRtm1beHp6ws/PD3379sXZs2dtlhVC4IknnoAkSVi9erVq29WrV9GrVy+4ubnBz88P48ePh9lstlnP/cDhGept27ZhzZo1aNOmDXQ6HWrXro3HH38cJpMJkZGR6NWrV0m0k2EYhmEYhrlflNAM9c6dOxEeHo62bdvCbDbj3XffRbdu3XDq1Cm4u7urys6ePdtmaHOLxYJevXohICAA+/btQ3R0NF588UU4OTnhgw8+cKg9xYXDA+rU1FTZ33SVKlVw+/ZtPPjgg2jWrBmOHj1a7A1kGKZs0yXWtun3UEBNm/l6vfrlm5ylzCiExpUtDwFU5kHPh77gOdgSUxbx9VFiRdDgYM4G8vyRyTw6ZjGSZzTToshCDKyVrVw46DYvKSlJle3s7AxnZ+c8xTdt2qT6vHDhQvj5+eHIkSPo1KmTnH/8+HF8+umnOHz4MAIDA1X7/PHHHzh16hS2bNkCf39/tGzZEtOnT8c777yDqVOnwmg04n7jsOSjQYMG8tR8ixYt8PXXX+PGjRuYP39+nhNmGIZhGIZhyiEOhh6vWbMmvLy85L/IyEi7DpOYmK3xp+6Y09LS8Pzzz2POnDkICAjIs09UVBSaNWsGf39/Oa979+5ISkrC33//XZSzLjQOz1CPHj0a0dHZC5KmTJmCHj16YMmSJTAajVi4cGFxt49hGIZhGIa53+h02X/2lANw7do1mEwmOdvW7HRurFYrxowZg44dO6Jp06Zy/ptvvokOHTqgT58+NveLiYlRDaYByJ9jYgqOsVASODygfuGFF+R069atceXKFZw5cwa1atVC1apVi7VxDMOUX9rGXJPTx2rUltOtrl9RlTugIQ0pa5hMigmxwT8XSrElDFMw1MuHNVXxzuHsTPKttr3vPHRD/YwylZOc6Nj2lAMAk8mkGlDbQ3h4OE6ePIk9e/bIeWvXrsW2bdtw7NgxxxpcyhTZ14mbmxseeuihYh9Mv/baa5AkCbNnz1blx8fHIywsDCaTCd7e3hg6dChSUlKK9dgMwzAMwzCVGp1OCe6S71/hhpIjRozA+vXrsX37dtSoobgo3bZtGy5cuABvb28YDAYYDNlzvwMGDECXLl0AAAEBAbh165aqvpzPtiQi9wO7ZqjHjh1rd4WfffZZoRuTw6pVq7B//36boczDwsIQHR2NzZs3IysrCy+99BKGDx+OpUuXFvm4DMMwDMMwDADY6eXDwblZIQRGjhyJVatWYceOHQgODlZtnzBhAl555RVVXrNmzTBr1iw89dRTAICQkBC8//77iI2NlR1lbN68GSaTCY0bN3aoPcWFXQNqe6fd7TENFMSNGzcwcuRI/P7773lc8J0+fRqbNm3CoUOH0KZNGwDAl19+iZ49e+KTTz6xOQBnGKb0oTKPmw81VG0zGopsKCtWTj1QV06npyuBqtgMzpR1brRSni2jsxLAxYvIlbLMJJiLVfH+0fTypRJuHVPucNDLh72Eh4dj6dKlWLNmDTw9PWXNs5eXF1xdXREQEGBzlrlWrVry4Ltbt25o3LgxBg0ahJkzZyImJgbvvfcewsPD7dJulwR2Dai3b99e0u0AkC1OHzRoEMaPH48mTZrk2R4VFQVvb295MA0AoaGh0Ol0OHDgAPr162ez3oyMDGRkZMifc7t2YRiGYRiGYQgOLkq0l3nz5gGALN/IYcGCBRgyZIhddej1eqxfvx6vv/46QkJC4O7ujsGDB2PatGkOtaU4cXhRYkny0UcfwWAwYNSoUTa3x8TEyFP7ORgMBvj4+OS7qjMyMhIRERHF2laGYRiGYZgKSwnNUAshCi5kxz61a9fGxo0bHa6rpCi1AfWSJUvw6quvyp83bNiAzz//HEePHi0W6Qhl4sSJKh14UlISatYsH54FGKaiEXT0jPpzKbWDcqFRfTmdmpolp6mnksJwvGYdOd3y2uUi1cUwBWEhXjuMLk5yOiM9XU4nJWbaLM8weSihSIkVlVIbUPfu3Rvt2rWTP//yyy+IjY1FrVq15DyLxYK33noLs2fPxuXLlxEQEIDY2FhVPWazGfHx8fmu6tSK1sMwDMMwDMPYoIRmqCsqpTag9vT0hKenp/x5+PDh8urNHLp3745BgwbhpZdeApC9qjMhIQFHjhxB69atAWS7V7FararBOcMwDMMwDFMUpH//7CnHlBkNta+vL3x9fVV5Tk5OCAgIQIMGDQAAjRo1Qo8ePTBs2DDMnz8fWVlZGDFiBAYOHMgePhiGcYi/6wbbzC+qzIPCMg/mfmLQKwMbS6ZZTqcQGRO1zre4fPl+NIspr/AMtUOUO+HLkiVL0LBhQ3Tt2hU9e/bEI488gm+++aa0m8UwDMMwDFNxyNFQ2/PHlJ0ZaltctvHr2cfHh4O4MAzDMAzDlCQ8Q+0QZXpAzTAMwzAMw5QCLKF2CB5QMwxTKWlykSPDMRULA4k6qtMpoxxnoxI1MT1DcZV3pv4DcrrhuQsl3Dqm/MEjakfgATXDMAzDMAyjhiUfDsEDaoZhGIZhGEaNBDsH1CXeknIBD6gZhmEYpoJhsVjltNGoSEGEUOQfdU+du69tYsobLPlwBB5QMwzDMAzDMGpY8uEQPKBmGIZhGIZhcsEz1I7AA2qGYRiGqQAYiTePe/eUSInU44ejMo+4kCZyWiIzkT77ThamiUx5QqfL/rOnHMMDaoZhGIZhGCY3PEPtCDygZhiGYRiGYVRIkqSySuRXjuEBNcMwDMNUCLz3/CWnRcemcloyKFKQ+A5Kvj2yDd+ov4updUUj+Ym2ctrzt0Ol2JJKBC9KdAgeUDMMwzAMwzC5YMmHI/CAmmEYhmEYhsmFnTPUPKAGwANqhmEYhqlwVNlrW86R0Kn5fW5J8cAyj1KAJR8OwQNqhmEYhmEYJhcs+XAEHlAzDMMwDMMwaniG2iF4QM0wDMMwlQTvXSdKuwlMeYEnqB2CB9QMwzAMwzCMGkmX/WdPOYYH1AzDMAzDMEwuWPLhEDygZhiGYRiGYXLBmg9HKHPz9KdPn0bv3r3h5eUFd3d3tG3bFlevXpW3p6enIzw8HL6+vvDw8MCAAQNw69atUmwxwzAMwzBMBUOCMkud719pN7RsUKYG1BcuXMAjjzyChg0bYseOHThx4gQmTZoEFxcXucybb76JdevW4ZdffsHOnTtx8+ZN9O/fvxRbzTAMwzAMU8GwazBtb/CXio8khBCl3YgcBg4cCCcnJ/z44482tycmJqJatWpYunQpnn76aQDAmTNn0KhRI0RFRaF9+/Z2HScpKQleXl5ITEyEyWQqtvYzDMMwDMMUhrIyNpHbcfOKXe1ISkqCV1DtUm93aVNmNNRWqxUbNmzA22+/je7du+PYsWMIDg7GxIkT0bdvXwDAkSNHkJWVhdDQUHm/hg0bolatWvkOqDMyMpCRkSF/TkxMBJB9EzAMwzAMw5Q2OWOSsjLPmZSSYtfsc1JKyn1oTdmnzAyoY2NjkZKSgg8//BAzZszARx99hE2bNqF///7Yvn07OnfujJiYGBiNRnh7e6v29ff3R0xMjGbdkZGRiIiIyJNfs2bN4j4NhmEYhmGYQpOcnAwvL69SO77RaERAQABqPtjE7n0CAgJgNBpLsFVln1IbUC9ZsgSvvvqq/HnDhg0AgD59+uDNN98EALRs2RL79u3D/Pnz0blz50Ifa+LEiRg7dqz82Wq14sqVK2jZsiWuXbtWqU0UjpCUlISaNWtyn9kJ95fjcJ85BveX43CfOQb3l+MUts+EEEhOTkZQUFAJtq5gXFxccOnSJWRmZtq9j9FoVK13q4yU2oC6d+/eaNeunfy5WrVqMBgMaNy4sapco0aNsGfPHgDZv4AyMzORkJCgmqW+desWAgICNI/l7OwMZ2dnVZ5Ol70e02Qy8UvCQbjPHIP7y3G4zxyD+8txuM8cg/vLcQrTZ6U5M01xcXGp9ANkRym1AbWnpyc8PT1VeW3btsXZs2dVef/88w9q164NAGjdujWcnJywdetWDBgwAABw9uxZXL16FSEhIfen4QzDMAzDMAxDKDMaagAYP348nn32WXTq1AmPPvooNm3ahHXr1mHHjh0Asn+5DR06FGPHjoWPjw9MJhNGjhyJkJAQuz18MAzDMAzDMExxUqYG1P369cP8+fMRGRmJUaNGoUGDBvj111/xyCOPyGVmzZoFnU6HAQMGICMjA927d8fcuXMdPpazszOmTJmSRwrCaMN95hjcX47DfeYY3F+Ow33mGNxfjsN9VjkpU36oGYZhGIZhGKa8UaYiJTIMwzAMwzBMeYMH1AzDMAzDMAxTBHhAzTAMwzAMwzBFgAfUDMMwDMMwDFMEKu2Aes6cOahTpw5cXFzQrl07HDx4sLSbVCaYOnUqJElS/TVs2FDenp6ejvDwcPj6+sLDwwMDBgzArVu3SrHF959du3bhqaeeQlBQECRJwurVq1XbhRCYPHkyAgMD4erqitDQUJw7d05VJj4+HmFhYTCZTPD29sbQoUORkpJyH8/i/lFQfw0ZMiTPPdejRw9VmcrUX5GRkWjbti08PT3h5+eHvn375vHPb89zePXqVfTq1Qtubm7w8/PD+PHjYTab7+ep3Dfs6bMuXbrkuc9ee+01VZnK0mfz5s1D8+bN5cAjISEh+O233+TtfH/lpaA+4/uLqZQD6uXLl2Ps2LGYMmUKjh49ihYtWqB79+6IjY0t7aaVCZo0aYLo6Gj5LydSJQC8+eabWLduHX755Rfs3LkTN2/eRP/+/Uuxtfef1NRUtGjRAnPmzLG5febMmfjiiy8wf/58HDhwAO7u7ujevTvS09PlMmFhYfj777+xefNmrF+/Hrt27cLw4cPv1yncVwrqLwDo0aOH6p776aefVNsrU3/t3LkT4eHh2L9/PzZv3oysrCx069YNqampcpmCnkOLxYJevXohMzMT+/btw6JFi7Bw4UJMnjy5NE6pxLGnzwBg2LBhqvts5syZ8rbK1Gc1atTAhx9+iCNHjuDw4cN47LHH0KdPH/z9998A+P6yRUF9BvD9VekRlZCHH35YhIeHy58tFosICgoSkZGRpdiqssGUKVNEixYtbG5LSEgQTk5O4pdffpHzTp8+LQCIqKio+9TCsgUAsWrVKvmz1WoVAQEB4uOPP5bzEhIShLOzs/jpp5+EEEKcOnVKABCHDh2Sy/z2229CkiRx48aN+9b20iB3fwkhxODBg0WfPn0096nM/SWEELGxsQKA2LlzpxDCvudw48aNQqfTiZiYGLnMvHnzhMlkEhkZGff3BEqB3H0mhBCdO3cWo0eP1tynsvdZlSpVxHfffcf3lwPk9JkQfH8xQlS6GerMzEwcOXIEoaGhcp5Op0NoaCiioqJKsWVlh3PnziEoKAh169ZFWFgYrl69CgA4cuQIsrKyVH3XsGFD1KpVi/vuXy5duoSYmBhVH3l5eaFdu3ZyH0VFRcHb2xtt2rSRy4SGhkKn0+HAgQP3vc1lgR07dsDPzw8NGjTA66+/jri4OHlbZe+vxMREAICPjw8A+57DqKgoNGvWDP7+/nKZ7t27IykpSTWjVlHJ3Wc5LFmyBFWrVkXTpk0xceJEpKWlydsqa59ZLBYsW7YMqampCAkJ4fvLDnL3WQ58f1VuylSkxPvBnTt3YLFYVDc1APj7++PMmTOl1KqyQ7t27bBw4UI0aNAA0dHRiIiIwH/+8x+cPHkSMTExMBqN8Pb2Vu3j7++PmJiY0mlwGSOnH2zdXznbYmJi4Ofnp9puMBjg4+NTKfuxR48e6N+/P4KDg3HhwgW8++67eOKJJxAVFQW9Xl+p+8tqtWLMmDHo2LEjmjZtCgB2PYcxMTE278GcbRUZW30GAM8//zxq166NoKAgnDhxAu+88w7Onj2LlStXAqh8ffbXX38hJCQE6enp8PDwwKpVq9C4cWMcP36c7y8NtPoM4PuLqYQDaiZ/nnjiCTndvHlztGvXDrVr18bPP/8MV1fXUmwZU1EZOHCgnG7WrBmaN2+OBx54ADt27EDXrl1LsWWlT3h4OE6ePKlax8Dkj1afUc19s2bNEBgYiK5du+LChQt44IEH7nczS50GDRrg+PHjSExMxIoVKzB48GDs3LmztJtVptHqs8aNG/P9xVS+RYlVq1aFXq/Ps2L51q1bCAgIKKVWlV28vb3x4IMP4vz58wgICEBmZiYSEhJUZbjvFHL6Ib/7KyAgIM8CWLPZjPj4eO5HAHXr1kXVqlVx/vx5AJW3v0aMGIH169dj+/btqFGjhpxvz3MYEBBg8x7M2VZR0eozW7Rr1w4AVPdZZeozo9GIevXqoXXr1oiMjESLFi3w+eef8/2VD1p9ZovKfn9VRirdgNpoNKJ169bYunWrnGe1WrF161aVForJJiUlBRcuXEBgYCBat24NJycnVd+dPXsWV69e5b77l+DgYAQEBKj6KCkpCQcOHJD7KCQkBAkJCThy5IhcZtu2bbBarfJLuDJz/fp1xMXFITAwEEDl6y8hBEaMGIFVq1Zh27ZtCA4OVm235zkMCQnBX3/9pfohsnnzZphMJtlEXZEoqM9scfz4cQBQ3WeVqc9yY7VakZGRwfeXA+T0mS34/qqElPaqyNJg2bJlwtnZWSxcuFCcOnVKDB8+XHh7e6tW31ZW3nrrLbFjxw5x6dIlsXfvXhEaGiqqVq0qYmNjhRBCvPbaa6JWrVpi27Zt4vDhwyIkJESEhISUcqvvL8nJyeLYsWPi2LFjAoD47LPPxLFjx8SVK1eEEEJ8+OGHwtvbW6xZs0acOHFC9OnTRwQHB4t79+7JdfTo0UO0atVKHDhwQOzZs0fUr19fPPfcc6V1SiVKfv2VnJwsxo0bJ6KiosSlS5fEli1bxEMPPSTq168v0tPT5ToqU3+9/vrrwsvLS+zYsUNER0fLf2lpaXKZgp5Ds9ksmjZtKrp16yaOHz8uNm3aJKpVqyYmTpxYGqdU4hTUZ+fPnxfTpk0Thw8fFpcuXRJr1qwRdevWFZ06dZLrqEx9NmHCBLFz505x6dIlceLECTFhwgQhSZL4448/hBB8f9kivz7j+4sRQohKOaAWQogvv/xS1KpVSxiNRvHwww+L/fv3l3aTygTPPvusCAwMFEajUVSvXl08++yz4vz58/L2e/fuiTfeeENUqVJFuLm5iX79+ono6OhSbPH9Z/v27QJAnr/BgwcLIbJd502aNEn4+/sLZ2dn0bVrV3H27FlVHXFxceK5554THh4ewmQyiZdeekkkJyeXwtmUPPn1V1pamujWrZuoVq2acHJyErVr1xbDhg3L8+O2MvWXrb4CIBYsWCCXsec5vHz5snjiiSeEq6urqFq1qnjrrbdEVlbWfT6b+0NBfXb16lXRqVMn4ePjI5ydnUW9evXE+PHjRWJioqqeytJnL7/8sqhdu7YwGo2iWrVqomvXrvJgWgi+v2yRX5/x/cUIIYQkhBD3bz6cYRiGYRiGYSoWlU5DzTAMwzAMwzDFCQ+oGYZhGIZhGKYI8ICaYRiGYRiGYYoAD6gZhmEYhmEYpgjwgJphGIZhGIZhigAPqBmGYRiGYRimCPCAmmEYhmEYhmGKAA+oGYZhGIZhGKYI8ICaYSoZXbp0wZgxYyrUcYcMGYK+ffsWqY46depAkiRIkoSEhATNcgsXLoS3t3eRjsWo2bFjh9z3Rb2ODMMwpQEPqBmGuS+sXLkS06dPlz/XqVMHs2fPLr0G2WDatGmIjo6Gl5dXaTelwmLrunfo0AHR0dF45plnSqdRDMMwRcRQ2g1gGKZy4OPjU9pNKBBPT08EBASUdjMAAFlZWXBycirtZtwXjEYjAgIC4OrqioyMjNJuDsMwjMPwDDXDVHLu3r2LF198EVWqVIGbmxueeOIJnDt3Tt6eI3H4/fff0ahRI3h4eKBHjx6Ijo6Wy5jNZowaNQre3t7w9fXFO++8g8GDB6vM91Ty0aVLF1y5cgVvvvmmbOoHgKlTp6Jly5aq9s2ePRt16tSRP1ssFowdO1Y+1ttvvw0hhGofq9WKyMhIBAcHw9XVFS1atMCKFSsK1T8LFy5ErVq14Obmhn79+iEuLi5PmTVr1uChhx6Ci4sL6tati4iICJjNZnn7mTNn8Mgjj8DFxQWNGzfGli1bIEkSVq9eDQC4fPkyJEnC8uXL0blzZ7i4uGDJkiUAgO+++w6NGjWCi4sLGjZsiLlz56qOfe3aNTzzzDPw9vaGj48P+vTpg8uXL8vbd+zYgYcffhju7u7w9vZGx44dceXKFc3zLai+Q4cO4fHHH0fVqlXh5eWFzp074+jRo/J2IQSmTp2KWrVqwdnZGUFBQRg1ahQA7evOMAxT3uEBNcNUcoYMGYLDhw9j7dq1iIqKghACPXv2RFZWllwmLS0Nn3zyCX788Ufs2rULV69exbhx4+TtH330EZYsWYIFCxZg7969SEpKkgeLtli5ciVq1KghSyzo4LwgPv30UyxcuBA//PAD9uzZg/j4eKxatUpVJjIyEosXL8b8+fPx999/480338QLL7yAnTt32t8xAA4cOIChQ4dixIgROH78OB599FHMmDFDVWb37t148cUXMXr0aJw6dQpff/01Fi5ciPfffx9A9g+Avn37ws3NDQcOHMA333yD//3vfzaPN2HCBIwePRqnT59G9+7dsWTJEkyePBnvv/8+Tp8+jQ8++ACTJk3CokWLAGTPYnfv3h2enp7YvXs39u7dK//gyczMhNlsRt++fdG5c2ecOHECUVFRGD58uOZAtqD6ACA5ORmDBw/Gnj17sH//ftSvXx89e/ZEcnIyAODXX3/FrFmz8PXXX+PcuXNYvXo1mjVrBqBo151hGKZMIxiGqVR07txZjB49WgghxD///CMAiL1798rb79y5I1xdXcXPP/8shBBiwYIFAoA4f/68XGbOnDnC399f/uzv7y8+/vhj+bPZbBa1atUSffr0sXlcIYSoXbu2mDVrlqptU6ZMES1atFDlzZo1S9SuXVv+HBgYKGbOnCl/zsrKEjVq1JCPlZ6eLtzc3MS+fftU9QwdOlQ899xzmv1iqz3PPfec6Nmzpyrv2WefFV5eXvLnrl27ig8++EBV5scffxSBgYFCCCF+++03YTAYRHR0tLx98+bNAoBYtWqVEEKIS5cuCQBi9uzZqnoeeOABsXTpUlXe9OnTRUhIiHycBg0aCKvVKm/PyMgQrq6u4vfffxdxcXECgNixY4fmeedud3712cJisQhPT0+xbt06IYQQn376qXjwwQdFZmamzfK2+jmHwYMHq+4ZhmGY8gLPUDNMJeb06dMwGAxo166dnOfr64sGDRrg9OnTcp6bmxseeOAB+XNgYCBiY2MBAImJibh16xYefvhhebter0fr1q2Lvb2JiYmIjo5WtddgMKBNmzby5/PnzyMtLQ2PP/44PDw85L/FixfjwoULDh3v9OnTqmMBQEhIiOrzn3/+iWnTpqmONWzYMERHRyMtLQ1nz55FzZo1Vdps2lcUeh6pqam4cOEChg4dqqp7xowZ8nn8+eefOH/+PDw9PeXtPj4+SE9Px4ULF+Dj44MhQ4age/fueOqpp/D555/nOytcUH0AcOvWLQwbNgz169eHl5cXTCYTUlJScPXqVQDAf//7X9y7dw9169bFsGHDsGrVKpX8hWEYpiLCixIZhimQ3IvjJEnKo1suDnQ6XZ56qfTEHlJSUgAAGzZsQPXq1VXbnJ2di9ZAjeNFRESgf//+eba5uLg4VJe7u7uqXgD49ttv8wzq9Xq9XKZ169ay3ppSrVo1AMCCBQswatQobNq0CcuXL8d7772HzZs3o3379jbPpaD6Bg8ejLi4OHz++eeoXbs2nJ2dERISIktCatasibNnz2LLli3YvHkz3njjDXz88cfYuXNnpVlkyTBM5YMH1AxTiWnUqBHMZjMOHDiADh06AADi4uJw9uxZNG7c2K46vLy84O/vj0OHDqFTp04AsnXDR48ezbPAkGI0GmGxWFR51apVQ0xMDIQQss73+PHjqmMFBgbiwIED8rHMZjOOHDmChx56CADQuHFjODs74+rVq+jcubNd56BFo0aNcODAAVXe/v37VZ8feughnD17FvXq1bNZR4MGDXDt2jXcunUL/v7+ALIX9hWEv78/goKCcPHiRYSFhdks89BDD2H58uXw8/ODyWTSrKtVq1Zo1aoVJk6ciJCQECxdutTmgNqe+vbu3Yu5c+eiZ8+eALIXMd65c0dVxtXVFU899RSeeuophIeHo2HDhvjrr7/w0EMP2bzuDMMw5R2WfDBMJaZ+/fro06cPhg0bhj179uDPP//ECy+8gOrVq6NPnz521zNy5EhERkZizZo1OHv2LEaPHo27d+/m68WhTp062LVrF27cuCEPyLp06YLbt29j5syZuHDhAubMmYPffvtNtd/o0aPx4YcfYvXq1Thz5gzeeOMNVSAWT09PjBs3Dm+++SYWLVqECxcu4OjRo/jyyy/lxXz2kjOz+8knn+DcuXP46quvsGnTJlWZyZMnY/HixYiIiMDff/+N06dPY9myZXjvvfcAAI8//jgeeOABDB48GCdOnMDevXvlbQV5uYiIiEBkZCS++OIL/PPPP/jrr7+wYMECfPbZZwCAsLAwVK1aFX369MHu3btx6dIl7NixA6NGjcL169dx6dIlTJw4EVFRUbhy5Qr++OMPnDt3Do0aNbJ5vILqA7LvmR9//BGnT5/GgQMHEBYWBldXV7mOhQsX4vvvv8fJkydx8eJF/N///R9cXV1Ru3ZtALavO8MwTHmHB9QMU8lZsGABWrdujSeffBIhISEQQmDjxo0OmeffeecdPPfcc3jxxRcREhICDw8PdO/ePV/Jw7Rp03D58mU88MADspygUaNGmDt3LubMmYMWLVrg4MGDKm8iAPDWW29h0KBBGDx4MEJCQuDp6Yl+/fqpykyfPh2TJk1CZGQkGjVqhB49emDDhg0IDg52oGeA9u3b49tvv8Xnn3+OFi1a4I8//pAHwzl0794d69evxx9//IG2bduiffv2mDVrljyA1Ov1WL16NVJSUtC2bVu88sorspePgiQhr7zyCr777jssWLAAzZo1Q+fOnbFw4UL5PNzc3LBr1y7UqlUL/fv3R6NGjTB06FCkp6fDZDLBzc0NZ86cwYABA/Dggw9i+PDhCA8Px6uvvmrzeAXVBwDff/897t69i4ceegiDBg3CqFGj4OfnJ9fh7e2Nb7/9Fh07dkTz5s2xZcsWrFu3Dr6+vgBsX3eGYZjyjiRKQgjJMEylxmq1olGjRnjmmWdU0RHLMnXq1MGYMWPuS1j2vXv34pFHHsH58+dViz0rO0OGDEFCQkK+LhcZhmHKIjxDzTBMkbly5Qq+/fZbWZbw+uuv49KlS3j++edLu2kO8c4778DDwwOJiYnFWu+qVauwefNmXL58GVu2bMHw4cPRsWNHHkz/y+7du+Hh4WFzMSTDMEx5gBclMgxTZHQ6HRYuXIhx48ZBCIGmTZtiy5YtmlrdssjOnTtljyKenp7FWndycjLeeecdXL16FVWrVkVoaCg+/fTTYj1GeaZNmzby4lMPD4/SbQzDMEwhYMkHwzAMwzAMwxQBlnwwDMMwDMMwTBHgATXDMAzDMAzDFAEeUDMMwzAMwzBMEeABNcMwDMMwDMMUAR5QMwzDMAzDMEwR4AE1wzAMwzAMwxQBHlAzDMMwDMMwTBHgATXDMAzDMAzDFIH/B5HQdNDF7ubQAAAAAElFTkSuQmCC", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tqdm.notebook import tqdm\n", + "\n", + "variables = {\n", + " \"hurs\": \"Near-Surface Relative Humidity\",\n", + " \"huss\": \"Near-Surface Specific Humidity\",\n", + " \"pr\": \"Precipitation\",\n", + " \"rlds\" : \"Surface Downwelling Longwave Radiation\",\n", + " \"rsds\": \"Surface Downwelling Shortwave Radiation\",\n", + " \"sfcWind\": \"Daily-Mean Near-Surface Wind Speed\",\n", + " \"tas\": \"Daily Near-Surface Air Temperature\",\n", + " \"tasmax\": \"Daily Maximum Near-Surface Air Temperature\",\n", + " \"tasmin\": \"Daily Minimum Near-Surface Air Temperature\"\n", + "}\n", + "model = \"ACCESS-CM2\"\n", + "year=2015 # This is the year whne the data switches from historical to simulated\n", + "scenario = \"historical\" if year < 2015 else \"ssp585\"\n", + "my_cmap = 'Reds'\n", + "variable = \"tas\"\n", + "\n", + "# Open (connect to) dataset\n", + "dataset_name = f\"{variable}_day_{model}_{scenario}_r1i1p1f1_gn\"\n", + "print(dataset_name)\n", + "db = ov.LoadDataset(f\"http://atlantis.sci.utah.edu/mod_visus?dataset={dataset_name}&cached=arco\")\n", + "data=db.read()\n", + "y_max,x_max = data.shape \n", + "\n", + "xlabels = [str(x) for x in range( 0,360,50)] \n", + "ylabels = [str(x) for x in range(-60, 90,20)] \n", + "\n", + "for quality in tqdm([0,-2,-4,-6,-8,-10]): # use negative even numbers for resolution \n", + " my_fig, my_plt = plt.subplots(figsize=(9, 3))\n", + " data=db.read(time=year*365+364,quality=quality)\n", + " y,x = data.shape \n", + " xticks = [i*x/x_max for i in range(0,x_max, 200)] \n", + " yticks = [i*y/y_max for i in range(y_max,0,-80)] \n", + "\n", + " plot = my_plt.imshow(np.flip(data, 0), cmap=my_cmap)\n", + " my_plt.set_xticks(xticks, labels=xlabels)\n", + " my_plt.set_yticks(yticks, labels=ylabels)\n", + " my_plt.set(xlabel='longitude [degrees east]', ylabel='latitude [degrees north]',\n", + " title=f\"{variable} ({1.0/(2**(-1*quality)):.03f} of the data)\")\n", + "\n", + " # add a colorbar to our figure, based on the plot we just made above\n", + " my_fig.colorbar(plot,location='right', label='Temperature [K]')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be01fe0f-2993-4b2a-8afa-8129aef790c6", + "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/notebooks/nasa/cmip6_IDX_03_read_data_variables.ipynb b/notebooks/nasa/cmip6_IDX_03_read_data_variables.ipynb new file mode 100644 index 0000000..cddccd7 --- /dev/null +++ b/notebooks/nasa/cmip6_IDX_03_read_data_variables.ipynb @@ -0,0 +1,275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a26a3fa-8d08-4c39-a788-d3fe5a239858", + "metadata": {}, + "source": [ + "| [National Science Data Fabric](https://nationalsciencedatafabric.org/) + [NASA Open Climate Workbench (OCW)](https://esto.nasa.gov/forums/estf2024/estf24Abstracts/lee.html) + [WIRED Global Center](https://resilience.utah.edu/)
created by [Valerio Pascucci](http://cedmav.org/) |![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-smaller.PNG) |![NASA](https://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg)| ![wired](https://www.sci.utah.edu/~pascucci/public/wired-logo-small.png) |\n", + "|:---|:---:|:---:|---:|" + ] + }, + { + "cell_type": "markdown", + "id": "fe480733-1dab-4104-9c0c-b87c5b68b3a6", + "metadata": { + "tags": [] + }, + "source": [ + "## Uncomment the line below if any library is missing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19daabb1-a63f-4ace-88bc-16bee2bc9340", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!pip install --upgrade openvisuspy xarray openvisuspy matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5929a3e4-cba2-4854-8e10-6f95ade55cdf", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VISUS_CACHE is c:/tmp/visus-cache\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os,sys\n", + "\n", + "# # sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "import OpenVisus as ov\n", + "os.environ[\"VISUS_CACHE\"]=os.environ.get(\"VISUS_CACHE\",\"./visus_cache_can_be_erased\")\n", + "print(\"VISUS_CACHE is \",os.environ[\"VISUS_CACHE\"])" + ] + }, + { + "cell_type": "markdown", + "id": "fd657f99-1a67-4a9f-bf72-139b5b9a79a1", + "metadata": { + "tags": [] + }, + "source": [ + "# More complex examples" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5c67ab5-6b34-45b6-b432-ffc84f2992f8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "600 1440\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "47038281acd54943aca5c7b6728ca7b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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u+1VXXUUA6MUXX3Tbenp66Mtf/jKNGjWKRo4cSUceeSTNnTu3Xyu+xmPOmzePTjzxRNp8882pvb2dRo0aRTvttBPdeOONg2rr17/+ddp44437bD/66KMJQNN/oU2bnFOjdVuWZXTxxRfT+PHjqVwu08SJE+n3v/990zY89dRTtP/++1NbWxuNHj2aPv/5z9OcOXP67DeUYzbD1KlTadddd6XW1lbq6Oigj370o/T000/n9hmKFR8R0W9+8xvaaKONKIqinC3f+PHj6ZBDDumz/x577EF77LFHbtvChQvpzDPPpI033pjK5TKtvvrqtMsuu9CPfvQjqtfrA35+lmV0/vnn09prr02tra2055570lNPPUXjx4/vY8V3+umnu/123XVXevDBB5u25y9/+QttueWWFMdx7rt9+umnad9996URI0bQ6quvTl/5ylfoiSeeaPr9f/rTn6bddtttMJewQIECBZY5FNEySN8vUKBAgSZ44YUXsPnmm+OOO+7APvvss6KbU+A9gDlz5mDChAm44YYbish1gQIFVgoU5LpAgQLLFSeccAKee+65AZPTChQYLM444wzcd999ePjhh1d0UwoUKFAAQEGuCxQoUKBAgQIFChQYNhRuIQUKFChQoECBAgUKDBMKcl2gQIECBQoUKFCgwDChINcFChQoUKBAgQIFCgwTCnJdoECBAgUKFChQoMAw4X1ZRMYYgzfeeAMjR44clpLCBQoUKFCgQIECwwUiwsKFC7HOOutA6xUfB61Wq6jX64Pat1wuo6WlZRm3aOXG+5Jcv/HGG1h//fVXdDMKFChQoECBAgX6xauvvor11ltvhbahWq2itX01wFQHtf9aa62FF1988X1NsN+X5HrkyJEA+Kbt6OhYwa0pUKBAgQIFChTw6Orqwvrrr+/4yopEvV4HTBUt63wc0KWBdzYJ5rxxC+r1ekGu328QKUhHR0dBrgsUKFCgQIECKyVWJumqjlqgdHnAfUhFy6k1Kzfel+S6QIECBQoUKFCgwOChlIZSi9F/L+719wkKcl2gQIECBQoUKFBgQCilFh9JX4ki7SsSBbkuUKBAgQIFChQosBhoLN7BuYhcAwW5LlCgQIECBQoUKLAYFLKQwaMg1wUKFChQoECBAgUGREGuB4+CXBcoUKBAgQIFChQYEAoaqpCFDAoFuS5QoECBAgUKFCgwIIrI9eBRkOsCBQoUKFCgwCqHZxbcBkMAAVDgn/WM3Soy4n9EbGCxw7hDV2RT3xNQGAS5LiLXAFYycp1lGc477zz8/ve/x5w5c7DOOuvgmGOOwfe+9z1n/0JEOPfcc/Gb3/wGnZ2d2HXXXTFp0iRssskmK7j1BQoUKFCgwPLF3N5bUTf8u5igGQIyUlCKECkmmBkpZASkBths9EdXWHtDvNZ9KxS4vSkpkCXKWvlzIXiCbCxhNpR/PSMgUvyvpAmJ4Ve14vdFCnjinclMtuGPQ6Sw85qHLO/TXmWhlIZeTJEYU0SuAaxk5PrSSy/FpEmTcM0112DixIn429/+hmOPPRajRo3C17/+dQDAD3/4Q/zsZz/DNddcgwkTJuDss8/GAQccgKeffvp9XWqzQIECBQqsuuis3wkFBUDZ/zQIBgQDQ6ndi5CRgQKgLYmJFNASMWHkPYDEAAoEIoWaYUKdBQT70XmTQdS3DdsvYXT3sXmTUdLAVmP4/U+/exuUArawJP4/nfx3SQOxIqw/4mMAgMQo17Y0aI8GE2Nt36Mtk47sTyHkdk7httcNvzfShDRjsh5pf13kvcp+hlF9L8Idr96Og9Y/eImuw3sdg5GFLD6y/f7ASkWuH3jgARx22GE45BCeSW644Yb4wx/+gIcffhgAR61/8pOf4Hvf+x4OO+wwAMC1116LNddcE7fccgs+85nPrLC2FyhQoECBAkNFZ+129zspbeUN/B/s7/ZVZGSQOLJMNgqrnDSCLOGUn/Jaajz5BoAd1xgaib7rtdtR0uQIbwQgIX/Milaoa8LDcydDKTs9sNFibSPHCkCaAgYKnfMnu+i6tD1r4LmRkHFNiJWPZsu+HMFWNirvI9Jy7plR0AowRLZNnmADNqLdcJ73z5mCSjQ4gn3jC3cgMQqf3/jAIV3LVRkFuR48Vipyvcsuu+DXv/41nnnmGWy66aZ44oknMHv2bPz4xz8GALz44ouYM2cO9t13X/eeUaNGYaeddsKDDz7YL7mu1Wqo1Wru766urmV7Iu9z/KvzNhABW662ciw9Lgu8W5sMBQ2tSlAqwsjS3iu6SQUKFFhJ8FbvrZZUKmhVwuhy/wRsdIVJ3NvVWwFLDtdo/Zh7/eVFt4JsxDkxCo2x1pDKhNrjzHgZSNUwid1z7cFJIKa+fju0YjJdUkBrbCPF9l+kgZbIkn9LkDOjUA+OEUFkGYTIyTo98U0ISI1CYvx7tAIqEaGkAK3Jfh7vG54jBYRcJB4IjivvYyjoJhFqmSjMnDPFTj4UYg1ULKGf/uYUGHvN65lCb8a/1zKFL212AI7c6KBBXcv3EgpyPXisVOT6jDPOQFdXFzbffHNEUYQsy3DRRRfh85//PABgzpw5AIA111wz974111zTvdYMl1xyCc4///xl1/Ah4sQH7kNbTLhsx30AACc/eB80gHLkt63K2GIl0fMNN17oug0VO6AYAlpjgxiAgsHC+r0YWV71v7sCBQosHV7rvtXJGAACUR3v1qZAQcEgQ0aEcS1Mnl9edCvGW4nEuJaP4cWFtyE1wBqt/ngLE19OWmhLwEdZqwyWgiQGqBuF3ddaMh3x//znLgBASfMnxRqIFLkIs7aaZq24LWE0G4DbB+DIc6wJWilHtMPoeUkBpYjQGtlouPLHijWY3IMjzCJhMbBaa8VkWAEgBWiJomtCkikm5IbJe0YcLU+NPXZwDgJpd2IUFtr38LUFatn7Kzo9MIoKjYPFSkWub7zxRlx33XW4/vrrMXHiRDz++OM49dRTsc466+Doo49e4uOeeeaZ+MY3vuH+7urqwvrrrz8cTcYn7p2Jsn1Qr99zj9xrJz5wH7Tizu5Xu+7lti+oR/jFLn7fK3Yuop6rAjbq6H/S8G5tCubXboNWJUSqDI0Y7aXdl2PrChQosDJgvXYmyy8uvM1FWAEDrSQxT6E7uc1Fov/deRs2twGJrnpeE60siUxt5BmwJNCSydQo1DLeJ7NR4U9suOQR1dd7PDFyRBpe+8zbyRJnoKQ8SQ1/ClGOlQpItifcJdWXUEeKtdJCqEUbDfhfQhkHKWqQwvAkoyUiZEQuol23uu5apnLtFETB34nha9ubKnSnHKUOI+vvdxSR68FjpSLX3/rWt3DGGWc4ecdWW22Fl19+GZdccgmOPvporLXWWgCAt956C2uvvbZ731tvvYVtttmm3+NWKhVUKpVl0uY/78ME6jPTZuAz02bghr2YNH9l9jTwchTc37/ZjQn27xtIeIFVH6tVBo4ULUzucwlKhYSkQIH3PiaMzE/G/9N5m3Pp+Mf8yS75DwAenjsZBhzzS4xCBpZzJAGprtgor0gzQrJoCPj4EEn1uY9Ozf2tFcGQcnKQxs9IiVDRooNmYh1rT5L9cTg6HCk+Jke/PcEu2f1VQKqVfV8jsVYB8ZV9QpcQSWrUAAw4wq6QJ8xlTYisNCQvF/HnLTCk0JsqLEwUvrbFAUO6nu8HFOR68FipyHVPTw+0zn8xURTBGJ46TpgwAWuttRbuvfdeR6a7urrw17/+FSeccMLybm4OQqpDaIVcxLrA+xcFoS5QYMXjH/MngwBsPWb5ex6H9ndbNXz+OzXtEuimvHK70xmnNmoqQZre1DpsLKVc4bt/m4qyzuuQtSXAQKiv7qtXFhLsI855aYiGJ9aN0epIk30NjgQ7Mt1EFy3RexV8niAk2SIbER12aLcnkWtxJqnZn4BPqBT0ZgrdSRGt7g+DqdC4+AqO7w+sVOT6ox/9KC666CJssMEGmDhxIv7+97/jxz/+Mb70pS8B4OW0U089FRdeeCE22WQTZ8W3zjrr4OMf//iKbXwDfrPbXvjSzOkruhkFChQoUMCikdSuDLjt5TsQCCBQsx7NQvoyYukHwBFhA+BTE5YumW5eLXLkWoPJb0tEKGsfjTYNXDcxvPaGVCHWBCOpk9oTcCHWsSbn9FFSnE8kUhIm1dQ0Qi0kGsF2Id462E+kIHLdhFzL+8UHW4Mj5cz3yCaYElJlkyQNoTfV6M1YCpLayUSsgF88fRcixZOYUz64/1Jd7/cKlFKDiFyrAV9/v0ARNXO7XDFYuHAhzj77bNx8882YO3cu1llnHXz2s5/FOeecg3K5DMAXkfn1r3+Nzs5O7LbbbvjlL3+JTTfddNCf09XVhVGjRmHBggXo6OhYVqdT4H2CUDM5EBYlLBVicF6/UhGIMhBlRUJkgQIF+sWfX7pjqfTUjThu1nSUAlJdjgixIpz7X/u5fX7wxD3OkUPgotGKUIlYl13W1uXDknMh1JWIpSTliJzHdaixHohANyPdls47+z7RnYs1X2b/Du39MsMTFO8i4jXq9Uzh7arG/JrCwkTDwJ9XWRO+sdWKI9UrE0+Rtqw78bvQ0cD1RExWxev/vGilaPeKxEpFrpcXVqabtsCqjde7b4VSwDptHxtwvwX1uyDFIYCwjKwMF3C/k/hxAYCSghJRkSBZoMAw48G3pgypQt8jb0/OSRS0GpzE5MG3puTI3d7rrLpVAb/z8L1oiw1GlsgSamuhp/nvsgZaY0JrzKRdKifGllzL71o1IdtOdy19ZbMoKPeRxvp8i4d3SpL86SUhmXUNqVsXlbpNUBSCXTNAV53JdW/mHVEkmfPbWxfkOmzLehPPHhS5fu2fF6wU7V6RWKlkIQXem3jq3dtApFbKJdmh4s2eW3NJNZJB/3b11mCJMz8wKCjAFYdgEFKAfCU2gVIaGrEL2/Q/wKxcmF+7ja3GyMCgb9GKddsHnnwUKLCs8Le3fcGSkEjf9drtiJTCfW9MATA4wrvDElYw3HnNQzD19dv7RIEHg4fnTkZ1Kez1hhuXBnaxP33qbpQ0ueTGMIJdttIQF6G27/Ea6gbrPUjVSRX0mY39nwQfMkSKYMi45MUIee01F9GRwjLiGMKym97U668NAZWI21MPfLcNKfzwybtXKMFe2VAkNA4eBbkusEwQFpL54CpYTObFhbe5pcowm12iK0C+y29GqhkcXVFkQEqDq0TY3luxZyghsxXNIrs3CyxHxD4ZdmFy37I50WHCmMqq9x0XeH+gv5LeB6y3fEtc77vukn3eUKspLk+80h2ho+Sj2JVIoT0mdJQMYqtzNkKgkXfxEAmIBCtYq5sPJBCob29KfbMNRR6SGI5gS/JiWPgm3BcAEpvMmATvy8hrtvmYBVEMUZDrwaMg1wWGFU/On4xY0SpbnfGFrtv6aAABW8gAvgJaGKUGOBeAiGBsbDo/INhjkN9fQUERF0BQUJaAU7+668JtpECBAisb/nunfXDyg/ehbgzGVgxKmpx7CAAnCXEOIcpGlxtcP1g/TVDIoJRyAQkADfRaYKUhyFdpbNRcAzYoogmwiaJSZbE3VehKlKvOmFLejaQlIpy/3b59Pvn9jMItZPAoyHWBpcb0N6dgZIm1divC4mo48PS7t+WKIHBGu4JRQoo5gi2aPJfV3pCyMNCSr+zvibuBCENWq/S9bgvr9wLAsCc6LqjfhVHlwsO1wKqBu167nbWyDRHIxJKlyEoSBMOZ9Ncfpr85xXk2A8jJTaRsdlgl8KPj37ulsq/YeW989f5p9rsxiJVGrLloTgTWLpO2BWAMO41kBCjiDpWC1UAAULCrhSofqGik2CI/Sw1XqRQLPu5bffXGjAAyUrVR2f25Xa0R0AuFasZOLLohobNAA5S2K66L2WcImDRpEiZNmoSXXnoJADBx4kScc845OOggfmaq1SpOP/103HDDDajVajjggAPwy1/+Mlep+5VXXsEJJ5yAadOmYcSIETj66KNxySWXII5XHMUtyHWBpUZLxIRxWWqqH39nMgBgm7FL/hl3vXa7y14PraBY96dcdrqU3U1V6Oeq7PImBZXT+LjhwBBy7UaenVGQHU+iRSS2twrQWb/TyUsUFLrq93A0IIh6209275ECNSNKew54Dbrq90AjxoL63SAYEGWQePziCuEUKLAiUM8UFqWqz9K+IeDoTdnnWcp2R4pw3XN3uqX9ofhAX/PMna4E9pc2G3jyWcsUSpolD40T6j3XZn11YuC8q5vhlpfucLrgRmu9//nPXahEhPaYciXGG6UsD8+dvFLIRn616174yuxpNgKcwUAjNQaGgPYS0AJLoi3vYskFYJTKRYsBXwFS+krA93SNJcsladEEOeBECmQTHH2FzPz7YgWkYH24fH9C0gv0j2UhC1lvvfXwgx/8AJtssgmICNdccw0OO+ww/P3vf8fEiRNx2mmnYcqUKbjpppswatQonHTSSfjEJz6B+++/HwCQZRkOOeQQrLXWWnjggQfw5ptv4qijjkKpVMLFF1+8xOe6tCjIdYElxm0v34FRFYOy9sUOlgWenD/Zda5Pzp+8xNHxcGC649XbbZa7t46S5UxlibZ3BCAb1YD1SfXRbPH7aCYDAfJkWyqJybFldZTgEwKFUBPgNNkakU1ubFiSCzTesr2aPtSHhBMIRAYEg1i3AQAi2aYyd7hFyTSMKHmd99Li3doUGMpQN365NrOD6maDsC4sUAAAPjr+INz4AntBCxH64iZ50tyb8f2llXJ2cBpMmAEmYGkgHxCcuKUn0Zn1OA6rDfaHA9Y7GHe8entTP2gAiyXWU1+/HSNKcM9DiBtfuAMjSrYKomZbOCBfSTDEI29PdsVSVmTSI1dxBBYmGq2RQUkplK0rCGKgRRNLL4xCpMiVaweQk99pKCjlEyFlO9Akeh1Y7rFziLJl5a0G217f7oS9rAG+rpmNVEMDpTJhfk2h2lBQpkBfsM/1wBOQofpcf/Sj+bHgoosuwqRJk/DQQw9hvfXWw+9+9ztcf/312HtvlkZeddVV2GKLLfDQQw/hwx/+MO6++248/fTTmDp1KtZcc01ss802uOCCC/Cd73wH5513nrNxXt4oyHWBJcZHxx+EO169HWmE3LLscOLJ+ZNzBHUwxPqhuZOR2Y61lqmmiUsHrX8wbnnpDkRKubK88jsAt6TJFcWUS2YUss0R7TwJd1Fp+xmNFcf6lPaF1x9mRFBWXegz5gGAYJBBU5QP5bg9hHQrS7ajILLtE4K4w5OEScBQCqUiAMpGr4dXKyfRd600ytogJY7Sw/AKwL86b3NLtKKVpAY3hwIFBEduNLCsIg1cHmqZslZqPgkZyJcSF1zxz7tdpDTWXvY1GAxEnhd3jH3XPZilJTyLxh2v3u6S68o6Xzqcj8e/z5wzBVHu+DxLl/Oa/uYU7Ln2inmGmlUj/sXTd6GjRBhdJqQlg4oGMk2I7AkEjqNN+tK87zXQ3DdJvlLRWksBGaUIxspBtCJkpNGbKrdaAADHb+4nV+c+OrX5BxRwaHS36m8fgO37QlQqFVQqlQHfm2UZbrrpJnR3d2PnnXfGo48+iiRJsO++Xvu++eabY4MNNsCDDz6ID3/4w3jwwQex1VZb5WQiBxxwAE444QT885//xLbbbjvU0xwWFOS6wFJhoAFmafDovMm5vwfq8/729uScIwDZpT2teNlv6uu3OwumUI9ZN7yPVETTNmoiCEv4iu9prIEICpGmQDLiiXdYCjh2pFwi4v5cwsE3tI/y1cnyziNMsGUvZYlxcAxQLlINmLxfNuAbQGQlISkMpY5waxVjYf3ephrvBfW7naNJrtGATSvy7ZD2k9JuPzl/pYgnJcE5C7EGgPvneD/gVdkLuMDyRbMKehc9fg8AjmSHiFXetaIZ/vfZO/tExwcDWRGrRISZc6YMGEkW+QjgfZeBfL8TVmkMS3LLJCAsLS4VFx+ay32nhl8tW1HSkbm9ETrrhN7MoD3h/nBETBhZNigp78yhVZ5MS0Ag7CslttCf1EOi2GSlIjXDVRd7bfKiIaA7ZaJd0jwJC3H+dvvi3Een4oK/34Ozty10102hIygdDbwP8evrr79+bvO5556L8847r+lb/vGPf2DnnXdGtVrFiBEjcPPNN2PLLbfE448/jnK5jNGjR+f2X3PNNTFnzhwAwJw5c3LEWl6X11YUCnK9gnHkfTNd5xJr1n+Vtc+2To1CNbP/jEKaAdMP2W3FNno5oLG0ESeqMOnebvX8QKEUE+xwPxFQGADQhIxYgHHLS3e45eEQjXZNIWEu2QpjZZ3v/OWzpA3K6riZWHuyjeA9rs3B54TQIv9wtny+UTxQ2kQdygAVIedhQsGBiRzR9ddUJCAEogyGUhhKbPu4UqRWMRYl0+1exr1Hq9hGoks2T9/LSRSkHeFnGRik4ImA6vuFhucMwLFtKJQAJIDTraaGB8rEKNQzttCSwVKW+A0B33wf+9Ge8uB9iDW5pCz2HGYSUbbFPfzKC3DcYjTF7wXIM13PONImhDVRAYEFrJTEyi7s86MV8Ifn7xyyblv6cSGOD1ui2x+5bWbPF0rW+DyU22/KK7fjkA34d155A8oRYDShYhtfCsYPeSylHUI6l2V0+yuzpyEzCq2xwS924Yjj+Y/dgzVbDVojQq8CUNe2aqOffMvFl1XDkHADfftQ6TGkaxHtdQZYT2uF7lShO1FIghLyhhQWJSyjaUThDrIYKGCxC5x2Evjqq6/misgMFLXebLPN8Pjjj2PBggX44x//iKOPPhozZsxY+vauQBTkejlil5tno1IBxlYM2mKDWAEdJZYeCLEjUlBW4wvwQADw/Vo3aiCO8p7C9uMOdYRZIImEIR5rINt/e3syIgVn06QtWUNETvYh1kuh9lISk2SiU9F+MJakIllmpqAtJU05zbUUlcmR7oZz61uJTNt9fURA/FxdRNoVVPBSD6LMJjLKheHQDWu25QrkrqCLZrNpoHGf65f6lEuOVNKTKmUL2yj3fn6PssS6Qd+NDAYJNMWcsERcNKexX5brlr+mTAJZPuJnCzJRibWCJkKpRDkdbWMU6r2Oo2ZMRyXi0tX8z9+vQrABOC1uSj5iG2vClf+6y+mSZQIYuuXI9RY5xh9fvAOGFi/PGAoak/GGWi1xcZDo47mPTrVSAOUYWbjqxFphwskTecIhiY2C655j3fZgSHYoQZNqjv0FBfpDe4kcuTQEJEGnL8QaAD4erMJNeeV2JFajnZByLiaA/14jZYu7RNxP9mTLRqP9m9326rOtmrFTR2vkpS4SrdeKnFY6jMhru8oVkm3Ay+0keEJ2wiDSoLD6YmKsRAiEUgyn8y4Z/vejJ+9+X0/Khwyl+g7CzfYB0NHRMegKjeVyGRtvvDEAYLvttsMjjzyCn/70p/j0pz+Ner2Ozs7OXPT6rbfewlprrQUAWGuttfDwww/njvfWW2+511YUCnK9HHDgXbMRK8KYdh7YWmyJWJm1x5oC0uEpUWqYaJdFdweDeqRw4F2z3d6+fCzhln1X/vLYU1+/He0xIbakVIaNZpXPROrxSECyGycX/9UwYMl7/mYHNlvCBSUA2maGlzRyS6yAj1pXIrZnUopApFC2Eb8QEuGKFFzCjuwiEhAhi+GEQJqeEQ8M3DL2dtUNBrCuTLrKk1e5AOSi55YgkwavKmdgbbWcYOMSQJ50S7UzKEAjtsQ6C0i0dgQ6vPiewKdMvMHSFeVSNuEIPIGrNvrYtNdERtbTlgLSY8ATzgpk4AViA6R2ciTaVEMEZAop+Dv40ZN3o+astrglPJlStn0c0VqUalQz9rq9do890Qz73znbDeYy4Ic/U+KkqDQF7v/48l1J+srsaRhV5pWUcpC8J4h13mdYEnKdhEBzqWopQe3cKGDv/5gjrxmY8BoC1mv3kgP5rMzKGAwBu/ZD0uTZbaZO0oqfU/n+Y837kyVAmVHI4AuECPmS34E8wewPstwvE7FLdugbnbzg7/e43xNrESdOJIuDVHjUKh8R3mHcoXj8ncmun3tsHp/r4kh2GLXFIN0rQtINwMlNuF0yRnjSrRRhRLz8IjV1o9CbgYvMECEBAKPcvWeC71YrKTyj8kVnXCTfr0TE2ko/rPxjQd2SeHtu3QmT7IoGejO+b767TSH5WGIMgVwvDYwxqNVq2G677VAqlXDvvffik5/8JADgP//5D1555RXsvPPOAICdd94ZF110EebOnYs11lgDAHDPPfego6MDW2655VK3ZUlRkOtlgD0mz0YlBtpig7aYsF4bQSlCb6pRt51HpMkt4+bJtY1SZ7xfDIVybFAhhfYSDziGfJa1kEKtgGNmTnd/G1L4/Z57LMez7osr/3UXtMonjSxKuINrjZGLrjw0d7Ijqo12exScL9DcfuqxeZNzRFsCufycs1yDy+Py9ZPExRAl+11UIrhiCOHqqiTeheV85V9YOEGi0tq6qITkUSJZrp3oR0+utEtUlEgyt8P4BtmoMhNdA0XaRpfDq+XPoFEmEoIog7GVcjhqrYOfGSSiLaJHr/H2Jx2rVqSmBxoxSMVQlrgbMrnEJRcZBXOHkvIuDkSeKEYRoUSAicgmqBJqGZNnrThaHSnK2WdJ1EoIcN0mNKUES6g16sZHzL4wfYZ73noyxUlR9jgaeYmWVnCvawXoGIgiYO87ZiPLgN4ewsNHfqTfa7y0+NLM6WiNDTpK5NoVq/yzIXpSIdRtsbjhcMRSPKEr9j4vR/zFlJRfNZDzDb8zgaw02D3chOPJ+ZMRWqgB/P0QAT02wbCsvetOOCGVzxAtLXsgA1oTNCkmYshLt2SicNdrt+dWosKVjFAzvbjl/tQo/OCJe1DWhLY4d5KLxUC7ynnKtJmAHOFuZi1a0uQishmWTMIRyk3ue2OKI66pJEmSQrYMHZ4aESsbTc6A+TWN1ojQXiK0K57IpfDyL614YlzShBThKqNyE2QAyCzJZucRJti1TDXV3xcYJmgsXhYyxLz4M888EwcddBA22GADLFy4ENdffz2mT5+Ou+66C6NGjcKXv/xlfOMb38CYMWPQ0dGBk08+GTvvvDM+/OEPAwD2339/bLnllvjiF7+IH/7wh5gzZw6+973v4cQTT1xsAuWyxKDI9ZNPPjnkA2+55ZYr1MB7RaIUA6PLBqPKmY0u8cDeEpGNHPGgVDccdYvJR+xCcuz6PksE2DqI3L4y0AN50i3vFT03wAPRDXstHdk+85Gpub994h4P1LEd2IW4StGVX//7LsRKomKhx6k/TslGQjPy0TIPP3r1FyEj8NJrswFbKUC4tLEFBvrjmQp8Hq0B8cgMR+myhveEVcDk84SchB7YRF4bLP2Oi+I6skBOX6gbkhWhFBPn8O9QhkEmYEDKEfK8VMPHjpWNmytoRKpszyUBUYrUUZl89Fyi5I7AU+rPQcWIUHaR94R6vJYbGkopRPa7Dc/dn7eN5pPP9g/JiAL4CzQKRhMTY8iExvvksiSH0AsgzRRSIdlG2ciZwqKEo9Z8jyoXsTWAe77k+REpVmYvbyORBfw2Y7/nD/3vTBgDpBmQpYRnjh++Ce7/7L4nTrh/GlLjiapEoiWqx9FnoDUmtFpNq1hNavucamW93rUn066CHrxMRJLhBKF0Xn4XEhjaUgJ+EikSK2kbwX+/je47sNdSW59ivifIPSPhSgL/zRMMtqojR6ol+nndc3f2K+U47aH73DHKdjVRkBEPjINJarzvjSm5yZfg8Xcm587NyEWBvxaNEC//MJI7HNZwIpnioIKyEyKODj/41hQoxZPX3kwtcYn2Rhx69yy0xYQRscGoMgeZ+Pui3HkZ4klzRkDN6qTD10T6ldgJlKw4hpNAY9+7oK5x0faFZnqZQinQMEeu586di6OOOgpvvvkmRo0aha233hp33XUX9tuPVxguv/xyaK3xyU9+MldERhBFESZPnowTTjgBO++8M9rb23H00Ufj+9///pBPbzihiBav4tWaB8hB7Or2f+aZZ7DRRhstdQOXBbq6ujBq1CgsWLBg0JqgweLQu2ehJSJ0lAzaS6yrTu0AD/hBkAdt5QawkJABQTKHCqQfDZ+VkkQDFP5n9z3d9s9Mm8GvB+RbBuLrhxjNPvORqZ5skM9oF7IviX5SnEUGlEh5QinyiUrE+wnRDh04XPQSHEWUTjSvv+sbxZFlap9hnm+/RMPC6LdEr5uhpAltJUJ7zARGdpOItfxuLAGQ7ylS3vor/J6czZy9fiEhceQU/hj+OJYkqxgagSa6wdvagB0/AGJZh4rAyYfWkk8p53FNMH62QZKUyFZ8KVWRUZbTk8cSPZc2WG22UtpFwaUIjUIErUpOLpKYRY5gG2TuNUMpqlmG3lShbqTQgx/85ToBgbQm+P5EzlHPvBtAzciysB+A60ahJ1XoSTV6M/7ZkyqXxBRbEg1wGxrhvivD0o9IA7pBHeOirZZMJ3VCkgJkyBFskwHGEDIDvHDC8BFtwUWP34OKs5Lk82qNvLxDXpOk3DBCLfZzpYZE3ZAkNvZJ4TZ5iYLvTaQikSIkxlfNIyGL8P2dTFiihs8Pv+/M6sUz45+bZm0SHa6QxFoG5xLRbctcL0z4HgjfG9tIbhggqNjrFwf9U+gCJBOVWOf7pjA6X9Jhn+Qj+o0uQQQfrf/gauz3+7e3JyOxMorhcMyZ8srtuRwTuU/knilHhBYbLV+UaCe/WRonqCPvm4mWyKA95rGwLebfW2OWo5Qjdg1pL3Elx+5EoSvR6E75uxP9ND+7yq1OVe1Eoc3mGsiq06U7Dm8V25UBy5KnLGlbNtr5cui4dcB9TdqLFx48baVo94rEoEPLf/3rXzFu3LjF7kdE+OAHP7hUjVoVseeU2RhdMRhd5g6jzUapjY3IiU0S0HwwBxojntSwlNqcDDQ71pJGqCWaI+C26D4RdcB6wkrEBkIqbfTMMtwwohYeU95XsgSnNwUWBmRafjYOXI3gqItEu3yUU3YNB/9GYp01HEuiKQlxAQPRZocfGwVtyw3wllCmxvp9N7Q1p9cNGtZ4StLOxO7E0ot6QLa1J81QjsjKAXmoUTaSDQARwsvP9njhT3n8CRolAITMvjc1ACmDSBlEiu+ByBaz4Xh0ZMm5zumyRS4S61YYKiGjOkAZ6lnd6T7LGtAlQimTRCcv7QjttIC8vEeBiU3q7ouQKBFipZDau8BVXbMT2zS4tkQ8ICvlE1ABuGVyF5W1xFqIoSL+O3w9cDfEI59mSchW18x0x2kmUdz01zNcsGJpI9uSaBtqqTlq7QlUOIFtJHoKdgIIq/W1x23WRTWu3MTBufnngVwlUilzLZPRMGKpVXOyCZXX35PiQUpp+15FaDYxNsH+vOLEky+BSHzklpfzDCP/QopddFWF0WPl+rKM/DPdn6Wf64ts5Do8tzDZOUywFGw/7lBMf3PKsBJrQdinRiovH3lo7mQ3AYv14q0EB4JWfB/GVv7YHhNGlMjJQVojO5nRfN+1xoSaIfTayU/NcOGXRSk7ZckqVHeqsSjRzhBgRUsf33fobzBu3KfA4Mj1HnvsgY033riP12B/2H333dHaOvDs5r2EPafMxogSoS0mtMbGRkM4oiukSgzuU+Jkq8QoXL37nk2Pd9ys6X2INd+vfklcxg2teEA5Zub0fo/XDF+9f5rr0L1+Ox/V4W35yKIgJfFVVUFn7ZOOZIm+EnHUekTJOK2nDEzdNkoC+E5fPtt7Suef1QffmtL0fNz1oDwxkGiXtF9IdXguEpGXaHlqWDZQ0azpjbQsl3uSFyZkshGdQmwJdkimGnWrchwn1HBt49UBCiTVyr6vbshKbjJEKkNYAcvpssW6jzJOBqLYfqh2+7GGuhmUbWOMCBkyylzEsXk/GVjxSWRceZ9tYwm2goZWMQgGkaqDAFQzchG7lJQlhkBquHpbT+pLGeeIl72WGZgg14xo5/2SMeAnevI9cnv875HmiLIQ5Lo9Da35NUOcqJhl+e9PKybWmb1hTeZJt1bI6Vf/cXTfxOKNr5yBja+cIZcMzxw/uOTjg++ehY6SwciSwYiYHKkQ8lKxK0LhypCTI9jnKJSAyD0ll9aQTNP4WxXFVBhVDa+BvE8mDbGV9YTbQ+10Zq97Rv65D4/vJu32/WEqBOdJ+HwJIeiizRe4Z5Ds5MHK6ErBcyL9UaOEJpTEyIpb2PdlYHeacIIMeM1+fzAkUXhlpWj2flH5cyQAmzepVjocVnlhxNpNsHQw6Wp4tj+8xqFuJbBZgvlQIAGeY2ZOhwbQEhFGVzhizSuXftUzspPuWkTojcglI8vEuG4UulM95FXXJcV//WEWHvusz53Y8cZZ0BGgtYIx5Pto4uf+0c8suzyLlQ7LKaHxvYBBketp06YN6aC333774nd6D6GtRBhhB0AhrFWrHZOItRBrSajqz6VA9lUULKOqvM5QVLPG/p0CGKCfz+Ers/132bjM6gY4IJekniOi8rdRqEMGHI5GJsafq5Dm1ggY12LQGpMj3r1ZPooFyODmE1lM0K6QdDsXBHljQzZ9s+T6JPisUNoSIvRLZq2n/R7sYBhGKEUjXLKkQgZSIySjITpliKOc4TUOZSG+Xfk2hclhQlakEZ7QZDaOnQUR7QgGKTRFPqpsG+R01O5nBFKWBJOBQQJCj7MYjK0kRKuY/yGG2O05rbdlVF6iIneojaaTsUSdJytyDWoE1DI4CVFmk6yqlpBJpFXD+/M6TS188ppEbxOjsCDhyJYkM2aGJSTVzEubiKxcw34vRJyYSMH3JCAimAzI0gZCZz97sAPrc1/zxGDz384c1HsA4Pb9Bz7+T5+620UDK0G+QygDkQmyrHLxCo0/hpCsyMocGp09Sna7QZB4be/5OPIE1ZN3v6qSEfUhwhLZFnItRT+kbLU8B+Kq4z4zsGb0GQT8nUlfJs96uMJWtsRaKjA2QvqX0Ae8P4QEu9l2IgUTPNkEWdmyxN99Jr+22ag8sX78nclNkxyXFOFqg0wgSoonW3GT8/jwMBebqWYK2n6DhoC124x1CPHXgogj1W51j2BdfzjJuJm137JEI7FWioMe8r3qiFedTLpcm7VyQKHvkmuzfQoUbiFLg53/PBstLcCoMuu/YttJ12zRAhPxSC2dWGoHuIGIdQhDHPUwFJTlBnJM2oCXyfuTmjRDI6FuJJyuuELwGb5NvG8KQpopZIqTZOrGkv4mLD8joLOugkFTuWM1oqRt5EL5rHB5WkOdoI/m59EsUajR13qgJKEwMpWBcuVRwp8N5Vvce4C8Y4K0nux5E3zUrrEtcqy8NMafJwG2jDj/0TjpUjbKp4it/Qw4EVJZUqzkW83ptr1XNSkmyIpYqiEWkEpFiFQLONFSA8ggxWVyhWnASZIGKTKqg2BgAmaV2GXfxOQjot1Wf13NfHKiJLyWFDsmJAS3ZBxrcmWj5Ur2Wi2mkO5qppw7TzXj4jOijwashtcAxkbShTyHkyJlzz+0VJQI9dKsfP77uMFFrQeDUz64P/7w/J1ojbx8SqLXAF83uf9FWtZYmAfwLiLhqhjAxHtkmfWykU0elXtUk31WiSwx53vNe6YTNDKQ4ieFk2/Jykl8RJv7FCmgZRO9A/YcqfA549/km5c+QryOgfzkvqQJJoKbfBnkZSUhWQ6/02b9iHsPJJIun+MtI6H5Wkc633c41xDiAAAB2HRU34i1AidmG1r66PEhGxyM216+o0Ef7s9PKeAJm3hJALYeM/xVHG/Yaw8cM3M6FiYaVaPQGidYo4X11vKsyXcVu1UY1a/cZnliuxtm2QmT7WczoFTiVa77P/4+ilaHiOwS30Cgxbz+PsGQyXWWZbj66qtx7733Yu7cuTANoZ777ruvn3e+t7DdDbNQKgGA4mRB5clOWG6XI9hMqhc3A//MtBlokQx/5XWAbNnnrbdCMLnmQe70v94LgNtxWZDg8Z2H73VLbDx4AQjdSIJjAfY18hEdId4hEXckVAFRphArHpZjrdwkg6NdwMKEZTDRIMLrqTX91wqoNZxrODjESrltfM7kzr2R+Mg+EiEdLDFKbYp/STHxlGMrSLzYn6cM/hyRJrfiAMBZ87EeVeWWwxH8Lsdwr2leVs7ppoMl7dC9QT7HER9LtrUyiGBgKHHyESbcETRiq38X4pPBUILEGOslTEgpRYkWIVa9iFWLS2p0HtaUOZ21j1bzFRDi5K8U38tSUEL0uJmxiWsmLyti3Su5iVFq/WsTI44YBl117QqkyOqQwBVV0UBLzAQ+g5d3ZCmcE0ikedBUbkKhoLUlQ5oTFDOTJ+Ari7SwngGdpJ0MZGHiXVAi5VduapnmynU2AbRmeGWJJQMK7TFhXIsQaUKPdTgCvCTHk1YePGSimBgA2iACy5akCBKUBij190jDkrEGf0cZwski6+lTE04e/bUnRS7aDS3PKcu4DAGZCiQemXd0SQ1Zf/S+E+yMmn+Z/fVZYT8iBBvgSQJH/8UHHjYTAjBQ+OBiCOyHhjFqDQAfHc8+4DPnTHGrqHWj0JtoaMVSRtHON7M4HQ5cvfue+Or909Civd96iEixo4whlvJwrQHCu1j8mLks8b6SewwWCkXkepAYMrk+5ZRTcPXVV+OQQw7BBz/4wT6d5Xsdu9w8G0RAS4tCHPMgL4S47BwIeNlLomaL6yD2v3M2WiLCiBICoh4SbLIe0PnkPongJkZZzaqPDn/3b1PdANsSMaHWCkCm2DZM5aUIjRHs0E1BtotMxSf4WA1hpmxb7blr/pkYXvY3jqzb6KLyESMmAEw6w8RJeS0Pf6+F2ft8PVR+m9sPuchNIxkPISQuTGIyBOsNyyQ7UgTSXgMaBRMpN+EAAMoTMQWvIQ1jcKE9nZMBSXtM8F65Ako17bvCiGJj0lqmjNfaUsryG4pAqgSNElyhGku8I0vSpE31DDA6g1Hd0EoSKj2J9n7XBKViEKXIiNwSv4ItumK8NCExwCJb/EGij4mxBJiUs5FLjHYEEeBiMmGkcUSJ0GXLmKbE+kzRaLpERhu1JVJIEnb1kPssjuD0lPK7SEac1jhSUJqgjf1dAcYeYJebZ+OBw5dvEZlGdCV8xzChlOfXvy6loGvBCk7FJpn1pgrzqhHKERPrSsSFk2LN0cU4SOoVS7TM8LK4VjyRJrITE8DeS1YKoCJXWEgp7ZcNAmjFvuyRnX65JGmrc86CCRfvL0QVACg30XfTU/tDVrpiw9egpHyF1oxU7pjGRdAb25dfQWqcUIX7i/Vo1rTvWrGcQxITw5wVLgM+fPZ7A+FXu+6FE+6fZmWB/OxLLkB4rWoGzt3lvegAssqjSGgcNIZMrm+44QbceOONOPjgZfNAvv766/jOd76DO+64Az09Pdh4441x1VVXYfvttwfAGshzzz0Xv/nNb9DZ2Yldd90VkyZNwiabbLJM2rPt9bPQ1qYwqo0thDZcjVDP8oM24MmwsX9LIsZgiLUUmylrcpHJZuDIlLfmq9jqgUJiWdPtCXJIIGOtYDJLmmFJss3ClnORyJ9W/JpAhkRJCnPbCYCVcSTGR+lFChApHtQqNvoVK0+k/QSh+fkK0XSf03Ad0uAYQtDF8zhMSOL9fSUwieZJRFPIt1wzGSAliirRa5nYtMZcNlfbwTsixatgmpBloebUEwHAR5X7nCN5km3IE82+52CPExxP/nYew0oi2ipHrsVLWvZnEpZCqxRaVa3NHmuq+bMiVKLM2whS6NOdBVRaeAzrs3mfups+yL0p91ZiPMmuGzibNCkKEysm2FIoJtbh5M4TxtagamY5IrQaoNtOcnpsIQm5l/+8j5dg7HX7bHftIgXEJSbUcYmtvcoRR9WrWiFNkEtO1Vrh/k+uWBJ9sLX5FBuy1tjYdmubRM3fe+z6JL8CAMAm8PFk/J2aRlpVOf99gEn4/Kr2tpma80ZKNorP+mtyKyyJ4UTmWBNbtrr/eHVDIwKUyI68XESsHBVxXFdWe+S+AWxkXDXotO3NJfd6ZP3P3T1npVGSAOETFXklRluSnTWxlfUBBr8tI9WUWBvArsY1TtQ50GHVV9DoW0V2RWI4y8sPFZN23Qvfevhe9GYKr3UTxlYMVm8xaC9JwqhyK1CLkkJasFIijPQMtE+BoZPrsAb8cOPdd9/Frrvuir322gt33HEHxo0bh2effRarrbaa2+eHP/whfvazn+Gaa67BhAkTcPbZZ+OAAw7A008/jZaWlmFtz9bXzkRrq0K5DHSUDPtqElAukSXPMuhbQm2JqUTMatni7zKtOPLdElSCC6OXgCUnkkyhrOVWFFRes1ngPSkfLyztHQ4WrKV2ylu3TWQg9SCyKJn0jcmMfWATG23szEWAtCLEdtmZdeKeSMvPzBYJkGPLYN3nI/pZsnXtB5weNIxaN2pHw0i3ePyye4nKDZypUU6r6qEQa/5Ow8z7igayiCcMcggvByF3HUOiDfhIdTixEZs4Sc5qnGhlRuWOzefNCtTwM0J/XZ7QKDehUIr1yv7WZKJd1ikiFTtdRGRlPsa1x0ehw8ix/E3w5JmIn41IWQmSfb9IQWoZk2pjr3FG7G0LeFmVISbfMhnj15hUG40Bo21fvX9aU+ecSgQk2keqKxW2AJNCTwCsZRtgIuCBj61YMh1i91tnQ0csnalnTK7rBqhaxwWRj5WD+1nDJjVq70HfbXXr1Ux7NwYA9Uihq65QsUQ6teWkNbiv6SiRmySXtZckZaLPJ7CNI1JXmAgApMqnFCJif3UvqGZlRwQggQJZ6YaXEUXKKvmVX0kInUwAWRHyxFuTHJdhAGjbd4cFZ8J+Jb9i5SP8EIkXGnzug/fJCpcUZilpDjAMd3Jgf/jD83einvF3IX1TJSJ8YhCl4Zc3LttxHxw3iyuNLkwy1IzC6LLNTdL8zHfWdRG1XklBWHwRGSrYNYAlINenn346fvrTn+LnP//5sEtCLr30Uqy//vq46qqr3LYJEya434kIP/nJT/C9730Phx12GADg2muvxZprrolbbrkFn/nMZ4atLZv/diba2hWiWOW8XBsJp/xdyzTShmhIdRDkWpKvALHes6NDQGBcBTML0aQlpJAEy5slyUZyDiXB5zcEalz0mmy0OXxN/nYDTf69OSIKcmu5BkCLHWRSS7ASQ3apH0gbSKwkBIXyEwC5ZeDGz8udA3mpSaOdVuN7ay6an49e17SPTMkxE5tQJVErKRstZNvAFipRCiZmU7rU+AixJwI+ogZ40ivtE4It/xJL6v21UH2kKnKtpKiNkCeJMLoItiXVsK4fkfJyESHFcokJsGQ4zbkopcYXwcmhYQKWKl/FMrHvqWY82agbWxQo+E57LXELv8fE5N0s5LsS/+vWIJJ6yAYDr5r9ate9+n2trQLU7TMteTnVTHO0EexUMvXAlYdUC2Z+bDd8+E+zkMYKScmvOJUzoCUybnIgy+yxJdzlzEeleeLPk+gFVqsOAInm57KesdvKBbZU+JX/ugtlG6nurPtcktFldkdqi+HcX2oZ+4NUogyEKlyFUKXhhlurk2Kv9gB2u6EESmVWdsP3k7KyE8nfAPpKMTSI11O0J74Kvs+MFFBC3+AABfdbaNEZkuzGvkiey7A/zdy+ykqZgH3XXX4RYkNw44AxsnKo8OeX7kBF02Kfl+WN335kT/f7aQ/d51YO22KDaqbw3zsVxHqlRSELGTQGRa4/8YlP5P6+7777cMcdd2DixIkocVafw5///Oclbsytt96KAw44AEcccQRmzJiBddddF//v//0/fOUrXwEAvPjii5gzZw723deXOB01ahR22mknPPjgg/2S61qthlqt5v7u6upabFv+fdzu+K8/zEJsNdVS2U0i06GEoppp60Xs5QOp1VsvDkkKLILGLfsufkC/4O/3wARRPolSizyFo9oKkY2CNhuMQrkF4CPGEm0PqzqGutPQrzlM6NKkfLEF+z6daWjFtoS92h9PyFi+0qEnUIJGjWW49BqSS4FEOnl/T7aFVDTuL76qAKBF9hF8fpr7fOuZGwfXzzJmQ4C2EaO68v2OTLhEWiLvA+AqWYZabXE6yJf+5XNNyG+Xf+FSNRdiYD142colYu3ttkqkgMiWkDZcPEa8u/nM8+cdBsvDAiChL7H8lO9H7o2MvG6610o7elOuvCbSD9Zc8qcmJl+amb9L1l3K8nAl8q8trhR1fzj07lkwAO48YNVOUHrokx/BjjfOwuzD+Dz2vXM2V9YjDRORJcs+ai0EO7zXGicen7h3JkbEBgvqXCioJ/VPwte2OKDftvz5pTswpmJcApouEXqsJIz7oown+3YCLBIRXwQJLslR6zITZKMA1GCUQQZZKVGu2IpY6TWuoBB44qgIUEY5oqzQXI5FkCg3ub+B/KqRIWpCuGWiS9aqU+Wi2ADf/x9fQRHjxFhXFCggsxNpKEx55XZXiXM4CtMMJy7/8N7u94/dM8uNswVWUigUspBBYlDketSoUbm/Dz/88GXSmBdeeAGTJk3CN77xDZx11ll45JFH8PWvfx3lchlHH3005syZAwBYc801c+9bc8013WvNcMkll+D8888fcnsizXpMSTIEWAtWdwUseBk2BQDi5VVxsRCZxd53zEa9Dsw+rDl5nnHo4KNkVUtYEgOUMuUi2touRYYVxIz9KZEqIbFhpTT5l9p/daNs+Wa4cwDyVmSAj4wqcVhQgBGyCltYwk0sNHpSX22yMXIdllYH+k56RSfqs/H7upxIZcRIeau0SHEik5QkB5C7Pp70KvdaSPrd5ytZmVBOvxy2zZ+HJcOmb/vybVVIIrJaWB+VDqUokmwlzg5inSaR+ZLV2cTB9azZfRmcLMSV+iyBICCzUWwhLGG0n2UFeUJC9nPrWSANgZAPJiWiq/bOHxyVXpSw9GNhwsu8MvALJNlRvk8N4Pzt/KRZcO6jUxFrykW6h4Ij75uJyfvvvvgdVxE8fKSfIEw9cDcceNdspMTOHmnwnIcTRJn8TDu4b18TatKHgkbJwZRXbsfYFoPWyOc3lOxqCldBrCNWMYAMEUoQO0i2gVSIEAOKJSOxqiHTfI8khu9Fd28CzjteCg3JZEIDiCKyeQL+fhF5FtD3/s2tyikh3jxZ9z7MsvJCiBESbT+pl20DFZZZVijZJE3pwEoqLzHkXA6fV7Ky4tb9Vu3J7/sCRRGZQWNQ5DqUaSxLGGOw/fbb4+KLLwYAbLvttnjqqadw5ZVX4uijj17i45555pn4xje+4f7u6urC+uuvv9j3JSlXautRyhHUuxuWjA+8azZiuzTKCRm+c02NgjH9E+tmCJOWwtKupz10H8pa2YRJjqy4BKZAQ5iRZPUrR6wlGi06cSmu4SQhxPukGZAkQqYtKTOeXAvk+dKRcuQ6i4A0smVqAaRESAyhaplqrPxA20iwgfzAFL7Og6Il1sgPhuL/LUUi0mC7VgREXFFLJkbipCDHdwTPfkYYvZaIuWh+pWJfPhmScomVNTf5sYNccK5CzI2SSLePwIffl8gjnEzEeF12rLyjjHgZN65KyGfL0rCUtjYRoUwSMeR9xRWkEiloW6o6LCKSGJZ1JEH0Ogv+CYFJyEo/jG/DgrpCzagBo5+DQTPCvTgcNWM6l1POFG7ce8+l+vyVHb0JcOcBTJD3up2djJIESBPKFcQJK0guC4j0YPqbU1yfUo4Iq5XZlaSuCS1Rys+iMlCIbbIjE2xJcNSIEakMQOomnnJPSr+jyDuGuEIzKu+QIknVLkptocFWfjIRCSVkjmxbsu5F3WQ15t7qT6LX/FlBRHsF8Ao3waC+riaGgAS8wjgY7+hbXrpjhUXeC6wCKGQhg8aQNdd77703/vznP/cphd7V1YWPf/zjS+Vzvfbaa2PLLbfMbdtiiy3wpz/9CQCw1lprAQDeeustrL322m6ft956C9tss02/x61UKqhUKkNuz+OfX/xM+s4DdsNet8920dzQE3pJtJvitlE3Cl+YPgNlTa68cUpADIWUiH8aHlnCJEbRP4rMQ8i1SD6EyIXb5X1pCmQpD8oSGXL/Ahan7MOjFEFLIpjiKHY1VugtaUS27HnZVuMKE67EE1z0od6tw0cx+1wXhJE41mJqG51OtYKJuLy6lJ7310QhUXlpCexniMtJGEGV18S+TzyV/XttyW4Tylt89D3sV8Tb22+zMhFxbCFPyOUYQrDzxB+uTLC/T/hnapTTIksyYFk3SH/IO34I2fC8gZeQM1KIGgiYrHxkAcmvBtIV8QyuZflofWqAozddMgnH0uLI+2ZiVJmQZKppUuN7DeHKVxiZ/tD/znSE9IkvLr/IvZTt/sXTd2F0mTA/JqzVlqE1UmiLxeUkZes9K1swVAerpA0yqln7PkuWTfDcw8uS5D52E33lJUoEv+IkkW7AH4eP7StHSjRbXsuob/BNwcpYlCRPWqINT7RlYnvXa7fjgPWWn875UxMOwnXP3ZnbJs+lQK7PH56/E5/9QPNn83+fvROxVgPuU+B9joJcDxpDJtfTp09HvV7vs71arWLWrFlL1Zhdd90V//nPf3LbnnnmGYwfPx4AJzeutdZauPfeex2Z7urqwl//+leccMIJS/XZS4N63VZuijypWZpIkRBsSTpkUm23K45Sp2Cize4cntwkxlc4S0k528CUFJJMOUcQsQp0pDvjKH2SgktCG0+ygXz0R6iZCp4zGYyiGKjV2D+4GitorRBZb2CtPeEW+69YkZfSoGGZFvnJCoLXRd8eK4W6JtSNRoudhEhyV147bfXT4bERSmOUI9mxZleVWBPqSqE3YweGkKCLC4fIKhrlJSItiZSybi4+Kh8mTYaRaimUIiiFExfli6nUDWCg2CkmYhLSGhGMnWhVIt+ecsSV4txSOnxSpbQlA1fZk/PyCVr5VUAp9CJaaZmoeb0027x9fuMVMzB/6t6ZaLER/d73uXazVhveSpBDxYlb8orFL56+C92pwpqtBuNa+CnOCKhEGVoiAlHqHGoEhjiOXdKsyQ5zMKImq9JMoP3zJU4mMlEXN5vMrrCEbxQCTrbAkyPPdhfXHdkHJ3Lvtf7etu/lw5Jtv8LU12+HVstP4xwWLuM2+J9hFNsQcM0zd+Ym8wKtFGKbq/OLp+9y32GBZYdNfjUDz351jxXdjMGjkIUMGoMm108++aT7/emnn85pnLMsw5133ol11113qRpz2mmnYZdddsHFF1+MI488Eg8//DB+/etf49e//jUAQCmFU089FRdeeCE22WQTZ8W3zjrr4OMf//hSffbS4P6P74bd/uI9dAHADJJc73jjrJyO0nWK8J0hWf9jbRP3DLyEgiMU8l6WfaRW5iG/C5mrGybbXiJii3ZkPEGo1wlZSkgz2NLQfFzKM2sAgNYKqiFCqhSQGRs1VjzZEFItke1aSaFUUqjHyslEpFhOM02gEM+QvGrFkwtJNtRGoRsIovzUp4xzru1yrZE/tk/ItAQ78+SZP9u/N/QGLgUz9XAf0Z2KIVnJtV31kXTI9wdwVK098tvFtYOTIflN5UiWzPnv1pgTJXszS7A1uaQwWVZvJAwy8EqFSSk57iQx4PfFipAqf/0B5dwbWm0i3acmrJil5E/cO9N91+L9nGR8b331/mkDOoe8l7EiiXWIN3oidNa5amRGwNptBilxKmNqDMqRcQnGkugYKY2SNkgMTw6BvMY6tv1J2FvI5DSMYAN+PyGSWggwJIjh73sFYg8/Ztt5+Rr6SkyYbFuHoMD7zwQ7zZwzxRVwWZY4cqODcMtLd/QhzI3ISLz08wV4eIxgCQnA48MPn7wbiVH47jb7LdvGv08x4RczUC4rbP7bmVCaV42fOX4lJ9qS3LC4fQoMnlxvs802XBxDKey99959Xm9tbcUVV1yxVI3ZYYcdcPPNN+PMM8/E97//fUyYMAE/+clP8PnPf97t8+1vfxvd3d04/vjj0dnZid122w133nnnsHtc94fNfjMT//lK34ErTSmXRNOEjzaFjrjKm9ZAHHPENyXWz7kkNuLooxsQgui1RpigmI9GS1RR5CFOf00+Ip5lDXIQ+ZdxiWjAR4IE8jdl/nelPGnL7Hkh89Z0MuGNUnCBjrJCHCvEkXLkuhGNVSG18hFriUiFQ2ysCeWMXOJk4zPebLWqMbKjFfj6GhUQdEssg/dVrddwY9VM0W5rRWiNCZEM+pQvmS6IREOvwEUuFHKewoLelH22W2Pe3p2wpjk1wKgy3w/dKU8KKrahEvGrkY1KiYex8Uvnsh/ZSG9FhRr1QJZifZU1sSymlvnr0VVfcb2pIW6bTKx6U41YcyLnMpYZFxgELtp+X5xw/zT0pBkSo9CbZhjTYqy1onIe485SUgFaRYhVBK0zV1iG4KPMJR1BqxhcqIaj30Zl0OQddoUja6UBu6fSxk0kub/0Diup/SCJYIePqjwrpqGfk11Exx3ZvioLVk20Ah6aO5l/B79/h3HLxv9a+gyZCEtie2hJmBGhrphkx+QnLpKILRN57msBNETECwwfnAOX5vF2pSfWQF5nNdA+BaCoWViyCV5++WUQETbaaCM8/PDDGDdunHutXC5jjTXWQBRFAxxh5UFXVxdGjRqFBQsWoKOjY0jv3fy3MwH0jQz91x9m4bHPDi3b+cN/muUlEzbSG0fiTkKONMgAJCXWG5MCASttCBIXE9M3ii3kWiDkmgk2udLQmQGylMl1490RVqwbCDry+8t7iNiBRWsgihUizTISrb2X+EDPbdQYCYeXTABw9mONr/m/+143T9L90jLg3WEaB0vAk3Ap0lLWlIuwAV760h4TKpEt+uNIK+8kCVha8aDWbQn0mAoXb5HERXHKKCk/YKaGy14bAkaWyFUtDFcA5FqE9nxi1+f0p8qfr1JwBKdspSVersJ667rhKojdicZHx6+4xKePT52JsgZGxAaRlaN0JRqjSwatscEvdukbACiwYvHdv03FGi0GI0vskz2mQhhdNtCKy9hLbgnnTjCBZvIspniyalK2ZdYZhhIYSuFlGcZJmiKloIOESdjqkOzKTahlPGkU20yRToUWlAg+vXG1Sba736n5Pr6wk98PALYfJqJ93xtT0OhWYmxOiaDUMOn2+3l5WmiNKWNIT6rwja32H5Z2FvDY4GczEEV+XGuUhywNTxluSFsmHP5r6FLbgPuapAcv3nz8StHuFYlBR67Hjx+PJElw9NFHY+zYsU4H/X5EsyXXoRJrwJNKsbVrRGqUJ3+Sxh6+PyBJQqp9eekGTXVAvgG/zNoIR3I1oOzOMmCE+upwPwr2azxWqMt2nboBKCGYSCEznNjYKOVqdj2MUY5YhzaA4lkdK466e/cAgrZLn7GUbUOefEtiZGYdVKIg6VKuqVF+VSLS5HSXsiogD1EsNgbu8/suLQN+RSK2wzLrJRVag8TEeVWNCSMzjIjJaag76wq9tsJeqNkGgHomn+c/MFKwRUB8eyrB9xjZdibkEyZTYytRGqn+6BMsVxZiDfB33RIZlCJOXuyy5ZLFWaXAyoeLtvfOL+c/dg9GlwlrtBqs2ZrxM0iAtiszkc2ulfLpBLLRanEXMY50a8SAUqzfhtzfnlU2rmFIxUhQhlhn3CdpgjIKKcQDm99JLBaRA+WfZ5mghsdWfVf67Fv5J+X/fuTtyVAYHpItwYOSbVBCvqy4vA54v/DwXMTitaJ9gaqE2HEnbtIXF1h6vPL1PJleFfTXpBVoMYG1xb3+fsGQEhpLpRJuvvlmnHPOOcuqPSs9Biv3WBx2uXl2jlhLRFZgZXG8bJkpZkJNCLbsK9rsPppro5ycxO1PoQtI33PSllgqW71BIs4CqczpotORPYbxEpE4AuKSctXWTBYULlHN8yJCL22v987LNaR0tTLoc/2MZtlILJpraohsi8OIYpIcK0+ie+0EpJpq6JJBS2Q4EqTJPiQ+Ou6IOwIHFHtuUmpaIkaNLiW1zJNrQ778eqQII8s8ML5V1e57er1Hoz3m6HVnXaM7yRemqdjvICOFcqDDh/3ORattjAIyjo5XNLn3SdukTRUN1A2BSDunk1omHtwKXYlCd7LiO09J3CxrwsJEO/vKsPpbgZUX5/7Xfjjh/mlIDKt8tcqwRgtbW5YVgYhAYN1ZpEpcfEZpR6xBACGDQRjVBqA0IpTsCk4dKRkYKxeR6LFWCkRC3jmiq4j12CpYrdEAMlCu/1T+k/rkTeR04MpHwIny7wN8nyu0/8G3puSuT7iKNlAZ9Smv3A5J1g6TGrXiqpRJcG0aK+G6QINdqYolP8RGrbWR60P4w/N3Ot/9WoYi2XEJsOHPZ7gx84UT+pLoJFkFCHaR0DhoDNkt5LDDDsMtt9yC0047bVm0Z6WHjoAtr2JpyNPHcgR7y6tmut9D/NcfZuUit8aS1CjmCGykPTF00pCGYxhSSEEwGbtOiL66ERKxa4xUpw26ZSBvseeItu3llR0x/DOkXEff7JnJbdM8iMSRP8dmlR3d57hz7OsCEP4eXhMygAFZ/b/VVhIvrRHBVotUMMhLQHTwMywqATDZLmke2EFgVxWt0CZSD8Algsqkp5rxqkKb1VaL/MO5k2RcRKWW8tJ0rLwvtQEcAa9lGjoltEZsqTc/U+issbvCvKpGa8ySh+6Eo9aLbORaSLyLVrmS93kNJcB6bZesad8ba18tUqq3CUoarpBGYvgz67Zo0eiywZgKOw4sStUKG2Rv3Ht3HDdrOlIDtMcGmVEFsV7FMGnXvXDKg/chpQwlTRhZYsmbRE4BcAVHVUakuBKwoQxElvJakiyJkCIVkah2RsYRZcD2EzYineu2bOyC7GpSao+YwgWwYbvCXMn0/jCYyLWBt/8LjyVThci+lqF//PmlO8D+9/z8RjYYkgAuuCDnJ62QlS65vn33gXs9ijh4IM8+wCtYkQJ+/I+7C6nIILHB5dOAiO1pyTRflQWAl07aAxtfOQOb/noGlFJ45NPbLNd2DgoKi9dUF9wawBKQ60022QTf//73cf/992O77bZDe3t77vWvf/3rw9a4lRGl2BO/ra9lkh1H/nfAEsmgt8qC7QCs7Zny+zaZDDp7PfKkW3S4UnYdgEu6yyUqOns3vy0LItSuXHXmSXa43bVJ+6RFf27KvRYi0r6jjmKvoXZZ+7Y3Dz+jETzRULnXfdvyoxUF1xAQ0g1/UaA4F8clHPooTTO9OsDXVmtCpINy98F+oatJOSi5vjDRSAzrntticlFVQ1zquzflSY54fZc0kBiy1Re5t+rWhO4kggEwqmSQWP/qxCh0pnyM3oyXbyuRt8zTYttILA1JnDuM15FWorxHdkoclUoNUNPKEnVlHUco5yKiiTAiBhD7yUFiz7uWKfzoybuRksIZH2JHgQv+fg/O3nb5uAt01rVzbXk/eFq/F/HTnffGCfdPg1ZMritR5gMICtCqhEiVEClJWK/DIAUZjlpreG022/bVYShFYjKkJIWS/KQzC/s4e0SOZod9qk1qhPydJ7miyw6h4FfKQgKec+lpINaNpDqng7Y/wxyZRiys82vlYBVKLENDhH2f2ITydWhC7hsmD9KHpIb7l8h+BsDSnrpROblPgb545bS9MP6KGf77T/vfV/KRAGC7a+5fDq0bIlSTAbTZPgWGTq5/97vfYfTo0Xj00Ufx6KOP5l5TSr2nyPWGP5/BSV4lljjEklAX9F6O/DWEMshQjmDLvkQiefDkUCzrGhEu72tl5RUBue7PUi6MYgupluh0Y/TaGOLt/VgrSPQ6/DuMtgOiXc5Hn0XiEk4eGh1H5Kd3E+kb0ffXV+XOpdm10oajTzKhkGsjbZTobGaUHSwV0iDiC9jCNsFkRpZMy5qJcyVIJu3NvI94WFylo+wbGCtCa8xykLpRqCaerstxAaA7YZeLSsSR8Lrh44uftEg8+FwIpsk14KVgKezDUaxK8B2FSZoadulX+QTG1sgny/K7fTuFPEQKgGEiXrK+XSUi/OjJu/m4SuGCv98DAI5kn/zgfbhi5+FPMGSdtcEfg0qmBVY9TLJ2iVf8826uagpOeixp3ykpKGRIkFENRKkVaxAyqjs9tlYlEBkYJM6vWu5luX8JtuelPEcQ4p0Fz1qY9KuQd4MC8v0Q2f0yyr9ODfsNRKxNw3PONqv9F3WpGWWf6fyqVSN3EzIcHj90EQHCglj+94R8USuRhIjHfW/mK/sWWDwoNaBI95vxuvGVM6w01HIC7WWXKxWaRaea7VNg6OT6xRdfXBbtWOkgM81GaYOU/AbyGmFS+cisoBlpbiTYSp459E1S9G8CAJaIaJegYvcjIdi+004defakOldxkQgma/6sh1Hu/s6h2Tn25/ghBLqRv4fEWq5rHMMRbIEhgGyCpUTb+2ubI9/BddQQBxafFe8PHk4cmFi2RYS22OQizUJCxe7KkEIlMtaWjiUgsSJ0lJmkpgY2MZATFUeWWBv8bs3KLIzCmLJBrFhSIolchjgiK6XrBXFwnrI0GwcFbVpjlnrISoZopWUQlTLuCP4WRAoYETOpD+8pwN+PBNgiG+yfHUbn2bqLtZgy2QijWctqAK5nwB8P7CvHKrBq4pmu2MoPUmjFHthKVQFDMCpjjbV1BjGUIKXUPgtVlHQEUhkUNCJVAVBDGvQ4Up0U8H1hiCzQWsv9HvaLjcTabQ9/pyAarjyxbiYBCQk14IsxhUGShGBXrJp/NssM+QApAWkm55D/rPCZlmqwgC+VDnjnImmDPPe9mSfUdau3TskXKSvI9eDwyml7Dfj6c1/bA5v8akafFdmVDgW5HjSGTK5DyFK9Whz7WsUw/ooZAPJkMdJMABt1wY0yh/4itI1/C8FWigmjS+RTniDlCI78Tb7KoMgdGv2gjSXHoexDCLUkMIakOpSFNEbgRf5BATMmY6cFQXRZ67wUJpx4CLQNIfkosSfWYkkkxDq0yDP2ukjS50BJpc1uRSGpYURbbA21goset1mCqwG0l3jPWqbwbo0vQkeZyTTrG3nfxPB30B4D7TF79WrFpLrXyjkSzbrrjDgK3AYgTXxEutuGmiTJkF1I8gNkneDJs90mMpVKlNeXSxEJIb8CjkoFJDuIUNUMUDLe4US6eAIP/N2pYi9i8Dlx8iNLW5hcE2pGN62ymQ7wfS0Nph+y2+J3KrDK4Iqd98ZpD91nJW4pFDKMrtRBOkWm6iBiKUhGPHmtWeIXa6AlyhDrXsRKQ1t9dj2TIIPKSTMao9Iuwmz3yRruV3l/WJjGyUtkn+Cni5JTQNphi9UEJD0x+fyIkGzL+0RW9qMn78Y3t/Ya5x88cQ9aI0/CtW1JWO3VNETIhTBL26X4lf9MLxuRvrKWKZcUX88UqpnCT5fBKlQBrjcRRQBSglEKaUKLfc9yR0GuB40lItfXXnstLrvsMjz77LMAgE033RTf+ta38MUvfnFYG7c8sdEkJtRkvKTB+yrbTigjJwlx0iPVVx8corFzC+EILymYjJeBRGsonXNIFl2HLZ8PuPK78roUhWGCTTk3kD5awCbtbbotdwyfTNgMoQxEro9sl+P7aDVfW63zxFpIo0wyYAuzNE7oU/IkvHGSEcKQeDTzAaQqpER+dfB7S8QJPL2pcmRX5BuR4gGxpJlIt8ZenwzwQKQVVy3sTVk6sSDRqCY80JUjQjVT6LFJiYtSjS4rrWiLCB1l47TcbnIRRPwb82BK1mavFkS2ZLAOy60DdmCH1+bDVolsj9luq6uuoZVBC8h5XhsCejJv/ycYVeZ4uAYwv6ZQs9e91ZL5C7bLa66LvrbAYPFqd4xFNlrbmyqsPyJDe0yIdd2RVpekTTxxjjU/Vy0RUIkMIlVDbCeIPSnnNSjwyhQFEWrA35sykQzB7+nbRgW7ChYGVihPsIVY10yDHIMCHTN5yUWOVNvjiCykN+P9Lnr8nlw7akakXX5/g+ZRcIFUlZWIM/cJwCU79K+b/ur902AI+M1uA0dfCywdXjxxj9zfXV1dGHXqimlLfyDF/xa3T4ElINc//vGPcfbZZ+Okk07CrrvuCgCYPXs2vva1r2HevHmrpIvIxlfO8DKEnFbYEmlJ1rMZhhKhpUC+kHPesKS4FANxpJCl5KK7sq/srxr014DVDVs5h9L8Oe614PghiRRNdWa4+EuW9iXROY1gE90XEQbUNfv9uL39PUOh3KMRod2gkHSvuRZS6SswMsHOE0WAzy227xVbPICctzdrzhWQefItUV9OLiSoTDm9cVlztLo9JqSGB/fYWv8BHAVemGjUbBvKGqgQSzFKtg2t1o+6N+PvpjtVeTIgDiMxoWo12G65GFZKYo8fh1pwq58XIqGJiXVmB9+atdgDfDSspFVOZ54E0XsfubKfZYl0xU4cS4qXmzNjNeU2Opgavt9rVmteD5JItfL2gwUKLCn+uM/uOGrGdLxd1XjsnRhvVTXWbs0wusyrQpF1JBLvdukSJIotk92SLSxUDyazziEDgXwKvl8Jo9vSL0XoG50WIi4kWgXvEwmI6JVrmSQY+wqIiVFWZuGJcH8QAt1Y0tyQ3xa6RPn+z/clYUJ3bN8jfdJ/77TPYr+TX+3aP6n+1L0z8cd9CmnW+wbvocj1rbfeOuT37LfffmhtbR3UvkMm11dccQUmTZqEo446ym372Mc+hokTJ+K8885bpcj1tv8zG1Fbe47whTCGXNRapFBEcGEGA9Vc/mCX1rmUOCGKgShSTk+cpUCacclxkZoIsYU9fGrX0rW2iZQN8ow+iTNC2DNCmsF5S5tgwHHn1U9nniPiQyRKzQaZPvZ6DcRaisCE2yXxTshliPBrypNqhmuyYW16ahSq1qWjHOimteJkOJ1yxDjW7JFcsZHg1hJZCzzWPKZGYUzFIFYGKXE1uY4yJ11x5DgfncrswFezy6hVG1nm6nNkf+e2V+E14XxOPADLEm5K/hiOeCtA2wo/4eCbBsvAvqS8j2yFFdoiBaREzpUkUrBJStyGKPjiOEKvXDGZ3pSJgRCHkrVP06q5BnOgwblAgUZcu8eeAIBvPXwvnu9SOG4ztnu84p93o6NkMH5kxrZ9kOdEOXec1Mh9zvdh3fiJp2wrR4QW+/xlYtsJT4xDLbMh5IIIjbI/A4CMtyvNDBNrmYA2JgUmxJNh0VKHhb008lVVw0h3GM32EWlfkZefR51boZPqvuzpL/7//J7hSjA24Gqpt+xbEOz3BZpZmzXbZxXAxz/+8SHtr5TCs88+i4022mhQ+w+ZXL/55pvYZZdd+mzfZZdd8Oabbw71cCsUUUkhilWOSBrjSS2AnDOGi64G0hC5j0Q/7KLK9phaW4IcA5XY+oZqhcgAifZa41hZK7UkH3mONC9lNrunnczDkCPsQqhdBFrIOJrrp9Fke7OotURwKSBPZABlpSyR4mi4avCBkgqOjcS6v+fPBANKvnR5vnALSxd8EZjMMAE14AivSwolzp7XxP9CAjq6bDC2kmF02SDWhJrJ654jJUQ6Q2ddOYeNzjoAsP0ee0WLzpoHUhlMmy3LylK2kFAh97HyziEAL2f3pNpFpLQi57PtyhRHLOnosVZ/+S+MULbEo2p9xESzrbliBhfTUUxGJALvdNjBRM/YpROZ0AmxrjmZDcuR6sZPJAoUWFpctmM+qvpKd4QN2oHOGqFkJ5cJ8X0rSX+GABPlredGlQ267SSZ72vlJrAM5aLUAiKFzJJuQpATY3zEG/ZnQl4jLVIOiUz3pgoJqSBizfkY8nzXDXJkuCWiYGLs+48wl8EEK1mZkdUxn2AocjkDlnVpw9ahsd33f3bfc+m/HACH3j0LWvExD7xrtk2qLHIhhgMbTZoBVe9e0c3oi/dQ5BoA5syZgzXWWGNQ+44cOXJIxx4yud54441x44034qyzzspt/7//+z9ssskmQz3cCgUTXwDaR20lMURHVv9bYlmHeE82WsjlZA4aiKyGWKLWYbJnSuBKebBkvCz7AYiYyHUrhWqVFx7JdlZE1Ce6LqRV9slSQpJyVNwYyiVaujaTckmoobxE3Ef4c/zvoee0T4TMa64bI/d9/m4g1uF+gizzRWCg4Iq5SARbB4NNWUsCnz+AaK1Tskk3KVyFR6VhDUFUoOGGI+VVozDPJizGClhQMhhVNjY5kZP2AKCieT+WSChoGFRsgYVerVzyYGK9q2W1QLxnpX3GKPSmGnWjMLJkbHVHn5BYtREviUDJxCHSoiv3BF1+ClkPJyC+DyQY4s9DJhUleXtJB0mjZDWkVnriLQCZOEgCZ0YK9czbcsng35UofHeb5eNvXeD9hWNmTkdrRAA05vSyu07dsKsOAGshySsotQgYWQZaNLlgR6S5aFNv6mVUkeIciDBpPHLPK5PTEpTNbyGnCRHJR0jEpXqpRM9rxtvV9abeYSMxfgK6KNFs5WmlK0AQUVfWErThOkjfJYGEqn1Gc5rroNS5CSa69Yyf657UH/Xgu2e5dkswZ/ZhgyPGe06ZjXIQSJHJOBGw6y2zcf/HC4I9VEixGXHNWikjwJYvLXafVQBHH330oCUeAPCFL3wBHR0dg95/yOT6/PPPx6c//WnMnDnTaa7vv/9+3HvvvbjxxhuHergViiyFK6MtjiDKXhHngKF8GW+A9ylXOBItBDGyPW2jXVq9rmCMJ6cZ+F+YuJdaYp9aYtMeE6I2he4eBVP3S5NhOXCBDB5pQqjXWUoS2iiFJVbZszsfklZWBhESaS+FUTbZ0n+ut8EjxFbqopAn6nwM5F09Ggh2I3F3SUXKJ9gQAXXFURdJPtQKqGtC2ZD7O8xk78kUehKFet1PBPj7YWmNJCZKAmOsrFYTXqv4ek+MlogwsmQwpsLLz5LMCFjib1h/HVpktcfsgy2WVqzBBqoRy1PqdsIA8Pm2xiYnfTEE1Cm/TBxLcqPO69CNJbip8lHwlJhQyFKwROAXJnxuPSL5UBzFKkeEuiH0akIlYrvAmpEiMgpvVTVXdiS/giBtd8mSsNryRFvP6QIFhh9X774nPjNtBgC+39piQqmmnCxJiLVYZbZGLGWSKqYi8VqYKDdRzEgBqXLuPhVN7NSkfDJwIg1oUkkxL9NgAi02n72prc5qCbZ43NcDUpzIc5vLEQGqtl8MC1aFSdqyque01cH4FfbVWisnNZO+o5popBmw9x2z7b7K5fZwYKbJkmU/GCg6vdNNswZ9nAIeYte34c/5Xm+WF7XC8R6ShVx11VWD2m/RokUYMWIEJk2aNKTjD5lcf/KTn8Rf//pXXH755bjlllsAAFtssQUefvhhbLvttkM93AqFyci5dMg9E5eU66TK7OiEuKTQ2sLkBQA6yhk0eMlNKibWg0hjauUdIUE3BFfmSwVERY7ZkyjUwQNFW0RIKwpZmk8yNBmT3SePYn3bttfPcpHrNCWkKZdQ7e9c5cPd82F43HATBzs7MDZaLnptiVqbxLA2QCkkxAumSiknDVEEkBOD+wg9AFDDuUMHBNvuI0uuIZGPNJBon1yqbaJeOJFxy6IJULcTDfmsOOLIdbUGJKlGawloiw06SpYwErt2iPd0ZmCLuPDAWInI+V6PLHHUTCJUnXXtrPx6Ui5P3xoz8a9ZjWWs+PNMqp0URDeQ02Z9qEyQwqg94LP8ecBUOYkJ4JfDM4J1JtHoyRQSK5kR9YhMSAAbMYsIiTEwUOhONObVtF92hi8RD7CjiviAVzOFzkQXuuoCyxRdiUZbxCSxq65zHvBakVu50VZeNaJENtpt8yishztvyycDupyEgNCGk0jZD/DSKFdoxfaRifEJir1WqiVFpuoGqGYsARH5VqwAo6TqqbZEWfch0pw707fCrgmSbsLKtvw6NeTqKNef5qxg5RoEUsLhwF+P+MjwHOh9ipdO2oNlISu6IU1AWnMxnMXss6rg8ssvHzBPcOHChTjwwANx//1Dr5a5RFZ82223HX7/+98vyVtXKiitEJdgOyHloteGPClsbVNYvcWgbhQWJmzPJqSknjEh7uzlzqsUwzl0SPS2ZI8P+NdCuznXFsXv6U2V029HMXtdEnFUIc2Ap4/1iSN//5zvxDaaNKNfYr3B5dN8j60p93AIKVYKuYmGEGtjkyNBBCQGKiOQBijm6La0HWCSL9eQDIEUk9VwIitSGrLEPiTSjYMHEUePwkpV/XmqS1GcNCgyI5Uew+I0InWY0xtjRGzQlWh0V4GkTsgMuxEkFYV6hQdIkVpINHtEbNBio9GLUo3uRLvXqhkwv86kWSJLWWCVJZUcy06aYb8D7a+NfCeNGf+NcBEt8hU7q5nKkQyJlIWOBAbeuUAgn2VIO6lJLdPBdgVoQmrveyESZXs9C2JdYFlD7CAX2BWjUH8M8AQ31B3LCk7VKFslldyzu1rZoDX2q1Gxtv1ukABciQhlrZoSbH6PX1WUiHVoq5eLcpNUQlWBlpqDC/KaMUy+jWEr1aROLpoc9mlA3o1JAjh9q/lRbkU2ipVbTeTxifN0MgPnZlVg5cELJ+yBjS6/fUU3oy/eQ7IQADjrrLMwduzYnEGHoLu7GwceeCDeeeedJTr2EpFrYwyee+45zJ07F6ahbvbuu686WcPSScUlhVIp0Afboh6lssLoisH8ukb3IkJqowXvRgpxiTvaRm3ZLjfPduRQogdl1xH3tV0Soja2YrAo1baz9rKGUE/9ry/1f21FNtIUickzTk0ugu1sqESLbXzk3tgESakCohIOb6hIgwxHeJpZ/WU2WUgqT4YrScb+j8k82detBMX4SL0Jmqxy9XzJfRYZcscH4CwSJWIdlxRaWliysVZrhvbY4PWeGKtVMiSGibVUsswMD2ipkmuvkJXleLxC0VVnIh1rwgjncU2WkMJ9x7VMoSvRfby1Ae53WiJCGhHKdkAWfWWYxOS2W/kKb/fR7FiT9axmO7+M2IkgTLZiiY3XZcaSFElgW0DFelJjI3mLrN5a7tMwKi7H0ODIe2+q0YvhS44qUGAgVKtATSmkJnL3eBwUguqxNpc9tix33fBKVq3GfUS5ojCmjbB6JUPdZBjX4jtMeb6k0JNEukeWqE8BJOkvs0x5Rw+IXIOfwVxRGFiXIvAEVQh6ao/F+9mJuF3xTOqEapVQq3H/nybow64lEd7+6VdIje8ftVaIYu4LS2UO9ADcrycp97+Sp/Ofr/DYstlvZtr6A8A/j1l1xvL3IlZGVch7LaHxf//3f/HFL34Ro0ePxsc+9jG3vbu7GwcccADefvttzJgxY4mOPWRy/dBDD+Fzn/scXn75ZUfIBEopZNlALG/lgjHkiJxSCnHZR03LFYURLUwwOjsp5/5Rt/KDZnjg8N2wy82zncVeZoDURnjDaYghoEV5rTCRclELcQvhjpXbOBCxBvqXgwAAiKDsKEEwQEm7wi3hMiFLTIRk24h1YphUy07anlRikFqSHWXSuSsfQVF+6ZLt9rzloEhRtJKoeV7bLZH6gaIpYRRGPkN+xhHQ2qqwQUeG8SMSjCobvFON0JlolCPCu7XIJTO1RASqKGitUHZk2kfvpXR8FrG7S7ddWZhf4/Z3lAwWpQpAhBExoSXiayV9kBS4MMZH8etGocVQLvnQEQV7n3ACIkfgRE/ql6zFz5tlG9xmloloe2/1iEyJrAVgoJuG/YwwwGDgJ4EyUYgVoW6tAGO72lKOyN3HVxfEusByQq1GaGlRTv4VxQDARDpWHKHODJNTAC6hPLXDUZbxM9GZaCeJaokIPZlCh01iXmATJMuR12F7GZYkPdrnSPnJrkSvxYIzdCqS93o7VHKa6d6UV8cWpQrVKlCtMqlO6oRaDUA1hcoIyva3KvMHN6UIxpp9U0nntYZiAg5CXOYAT5oBSd0HLkzmCXWIep36FDMpUMDhPaS5BoBPfepT6OzsxGc/+1lMmTIFe+65p4tYv/XWW5gxYwbWXnvtJTr2kMn11772NWy//faYMmUK1l577X6X6VcJkPWc1oS6lXDUakwKlQIWZAqlWOGxzzZP3tj62plNtz9w+G7Y4f9moVxW1uaM+kQThaD0ZFYPJ/pmK8WQiHWaLZ5YLw6vfMdX39po0gzufyMmwlma11YzoZRRA7Y3loYRqBJBSeOrKbKShomFrJOLmAD5ZEUYsiXTvTc4NXlOXYA9ZpcWiYLbQziYzLu5hMeINK84lMus0/zHuxUXbR5bybB+WwqtWOYxvxZhYaIxv65RjXROiiHJQgQfRS/byVe9zpMrkwELYo1SzFH6nkRBWQG5k8okXi/pZEcRAHCyJgC3ZCxROLHnkii3EG+ACbVE7QSx4ki7XKNFqUaP1ZCXXXTPE/dQ+y0OCWID1pVoGOKoed26mqSkoIk1ogC37fo9Bz8A7zF5NmYcWrgHFFhyGCdfsE48xNalDIVajQl4mrCzk1Is0xP5hMkIi7qBekmjp6LRk7JTT90ovFPjvqEtINslTegosWSkO1UoKUlWZtIqkKTKkEgLtAJi8AQVyPtR91jHkEUpt723l9C9yKBaBZNq+487IF5aU9IfJ4ZX6RSAWIMi27GUNKglZrINTtZPTQRT0kgS7udfPrn/53aDy6e5pLoCKxbjr5iRj8atLHiPRa4B4LjjjsP8+fNx2GGH4S9/+QvOOeccvPHGG5gxYwbWWWedJT7ukMn1s88+iz/+8Y/YeOONl/hDVxaI9ixJFDJDePQzeRK76a9n4Jnj+++M6nXgQ/87E098sS/5feTTH8FW18xEW5tCS6tCxV5psV6TCoJlDVRN2F0zXER5mB+wF07YA7veMtv5bnd1E+a/w5mERnM0Mwsj1kH4WFVTqO6EjZFb7AklBlTSjmRHMRARD4I6aohSKwCGHMEOEeqs5TVxVXGWWsEzG+oNy2WVcycplXhystB4wtlRJiyoaywA6ylHxIS12zKsr1K8W2cJx7u1CD1WV9wWkdVI8wDYk/D2cgS0VQwMKcxbpNDbS6gCaGnxNodaK7ZDNHnyr5TXgKd2MsMqHYUUcB7RQuy1tiRb+Yx/QKLc3sUjtdt6Uu3suWQC56T2dgm6UZdt1U3sKJLqPGm39yoAl8BogCERa4BXSA68azZ6ExQku8ASgQxBRxpKAS2VMMnbO12IaxLqhFIJzvZU5GM6Uk7OVk21s0wFWPI1osSabLeapNgeNTHgzsd4gi35hBK1DhF686d29ahqVyhrmUJnPUJXnV2NRFtd7SVUewmqJwXqGVSSuRwXF+CIOSFIJQbQAJU4k1wpBYIBlf1SpKrZ6EM94xVEQ3jl2wNXYyyI9coDrQHVR0e/EsCu2ix2n1UM3/72tzF//nzss88+2HDDDTF9+nSst956S3XMIZPrnXbaCc8999x7gly3tSuoikKS5BMFBQMRa4AjJcYAW141E6UYfUj2P47O/33gXbOdVlYrcmW4W2KF1DpucJSUyX6SAv8+bvh1b6IT3+v22RgzEqhUIuduUrf2fws6FZJ36tYdBMz+M+7YKTNQdp2TIg2VGiCLQCVCajRMpBDHPoqtwxrC4MHOlZCXQaqh+I1oyLWVeJRivjblinLaQSHXI0tkbfPCZENl/aHzEd622GBEzKRRCkuw9lqhNTaItMKLi0ro7pEKmrKS4EvYL4gUSmWFEa3cJr525DT7TPRZYyntFJlJWHkzXGFzdoSWQYu6qg7Rr/vVgDjySZGuxLHVVlcz5TxnJW9V7Lvk9dSIx6+CIdZcy/SubthZpDrE6PRAyDLgzoMKUl1gySD9q3tWABh5nqyzUWLzMqIYqFVZVqHEM9qu0pVASFKeBPf2ApWKQlK3ORMVhe5KhFFl41aGWiImxmxr6fsR8XxvTBQOwStEvAq1KNXotY5EixKNhXWF3h7WVCcpS/9qNTCxrqYs/8hEH2d8qcYs49ekIyrZqDXAOTSZAdUzqFS5Mq+qZoCI8PJ3Cw/6FY3xV8xApcL3nbiSKcVjbjjmKQ10dChEgSf5ygLSCrSYyPTiXl+Z8IlPfCL3d6lUwuqrr45TTjklt/3Pf/7zkI89ZHJ98skn4/TTT8ecOXOw1VZboSRMx2LrrbceciP6ww9+8AOceeaZOOWUU/CTn/wEAFCtVnH66afjhhtuQK1WwwEHHIBf/vKXWHPNNYd8fK0V4nJ+mW8oEG3aRpNmoDRi8TfUnQfshgPvmu2W4WGsZISAFkXorGkkNgmnp5vQ27tk7Rosnn82RVu7Rls7nLShvY0dVMrlCG9RGdnbVah6xsS6ngEpL0mSUkCJi+mSjnjZEgASA6OBehxBlTVKJUJECoi9awpgpSeBDrsRYbS3FAPrjiGMKRtEVubQZu3jpACLVDzrrGsQKaxWyZyLx+gyYX5N492aRmei8XZVoTUm9KYKVVsy+J1uG5ku+2hWZrwDikTUkzohixSSlFCr+si5sy60ch6/8uBPzmXqk08gDf3TJaMf8G0AmEiENo4mA7JYoe68bDnallr/a5MBcQkuKdGQQk8mqyU+GcwgX2petKspDT06PRCKaHWBpcHTx+6Ora+dyZFprUB2yBFXDZHQCe+MYy5HLgFfZaO7ScqT9UzxhK97kXF5FllKqCuFHq1R1pKIyFVcxeISkUI8wFghyegSzRYrzO6E+53Ouka1ymQ6sTk19Rqh3ms4Wl3PvK7aEEetjdVcp5TTl1GkOVJd0t4fEPCaa6N8g7JlO44UGBxKJV7JjEvKJZNKAKQUA6qskCSEeo0lgLqfvK4ViveYLGTUqFG5vz/72c8O27GXyOcaAL70pS+5beL2MJwJjY888gh+9atf9SHrp512GqZMmYKbbroJo0aNwkknnYRPfOITS+RDuGihwQvHLb0npxRr2fjKGXjuawOTkvkLCGuM5t9TUqinYtXGiSf1ukQyCNSbYvxF9yyzqMN6G/DXbwxLQ6rdLA+pjNBYY80I41bXeLNegXq7h5cZZeQgQNUzJojloP65NUpVCXfqhBJSpTnZjrwUJCSe0n2I7tu9ZrdHmu0Qx1ZSECksqGvECui1xFn0xAAnF67dZuULBLxdjfB0dxmGgHm1CIuqymXJh5/J7SL2xDbKaTQlcSnUTMclZYkwD+KSFJqmTIwlEdM7mNjzyyRh1EfpnVVW4LcXbpdr1ZjsKQQ9U4ApAbDFIIC+iZ5JJsUwfORba9FdkyteJJprrQprvQIrH3SkkGZAbOCKRElOikT+IkuKOTeCVySzjEl2mnLhq3IJMOL1Hynn7SyVcusZsChVMKSdDV9bRDAwiBQ520zXLoWcREQqHsrEtmoLOHVax6lq1a/QpQlxAqZI8PL+ffyT4DupxiiE7BPGh1IDpLDRb+4IX7rgkOH5EgosFaQvr9XI5d8o5eWOZK1g4xJb8CbdKyG5fo8lNA62kMySYMjk+sUXX1wW7chh0aJF+PznP4/f/OY3uPDCC932BQsW4He/+x2uv/567L333gD44myxxRZ46KGH8OEPf3hIn/P4l4c3ohYS6wm/mNE067paJXSnGhW75Liol6Mvj3y6L8nf4NKpAxLrTX41w5Il1vjKoAMAVDd45fT+SdKut8xGR5uPsqy2mkZXrNC9iFCfV8eri2K0rxZh1GiNBbUKou7ER1VIuUgqKcXFY3w6PENzT+IIodV0RwFZdPZ8gYyByFdoZCtChTQFXu0uoXMRD0blssLINmDCyARtMb9Z8i97U9ayz+mN8HpPjEVV1kUzqTWuPUrxgC2RYtfpJZ6gNrZP7P1ECx4F1TzFGosnmnB6Tp+gCTsBBVKlRDHi3E0yA6Q1cpGMKOZjSYcsevQ089EOrW0CbkDIxaWmVOobMd7r9tleKgLReGuMrWQoR+xq8tOd9+73nilQYEWhXmcHpaROaG3lezxJvdWe1pKjoYLnl1Ct+rwVeRbb2jjhOUv5GNUqS0NkoiwWlKGDT6wVato67zT40kfWxSezgeKaUW7FKLO/Z5m4TNmVp4yLfrl+07Is0hHrrZ1ODJ7BBzNtjmYb5tRu5m8leobw0qWHLqdvpsBgwU425II0Svs+G/BmXDqyxc+qK7S5zfEe87lelhgyuR4/fvyyaEcOJ554Ig455BDsu+++OXL96KOPIkkS7Luvd7/YfPPNscEGG+DBBx/sl1zXajXUajX3d1dX17JrvEWDWsZBKW8XlaXsLNIfVIPJ6gaXTgVaYlRafJKckMIsZR2zWOxFLREmXj0TceRJZGurQkcrH7NcVm6lUeQn41bXGDMGmD+/gp43qujpqrH0I9Kg1piz1w0AWCYby8AAKCJQKKy2S5sSZTWGoIzKkVaBRJ2EGBoiVzwnAmsoe3uDaxtzYmhmB8G22FhJiMLb1QiLEo3uVKGnm1Crk1syDr3AdcSZ/EJgGyPIYRvFQ1Yi2oByRFcpLlAjcg1JkhX3jmbfv5BpQRq0S7anNb7OoWxEIuhuHxu1q9cIpTJH1LUmxCWFBw7vO1kjYjJBBjAlHvxTwxUl38o0btx7+PT9W18701USLVBgaVGtEtKFKbKOGKUyr9QI4VY8R/T9n3teFeKMn0dvFWotUlN5nsg9k4CfEFerhFizr31ZG6RGoWYIFYNc2XT5ZwzsPspJ1MSJ6N2aRm8PoV7nfi1N4V2ZxKJIKw5aGC7y5exPpc5AZgJhNznJhwqj3bbs5IuXH7acvpUCQ4HJWFIYaR4rVMz3rEwQY+svLvfoSunEppWP7gy0zyqAT3ziE7j66qvR0dExqP0///nP4/LLL8caa6wxqP2XqIjMssQNN9yAxx57DI888kif1+bMmYNyuYzRo0fntq+55pqYM2dOv8e85JJLcP755w93UwdEf8mQkvS4w//NQq22+GWf8RfezRo769DBDyOcNkDFmoMZykcsxaGCZQ4sTSiV+J7vTZVNovCOFCbjwaRW56hOKQaotQRl6kAtg6rXmVi7iivgz7bZdtRe4qqPMkiEuqzMIFOaqz9mZAlgfnJAREisNENI7oiRGpUKt2eNFrbJ4uear1mLdbLoTDRe7YkxrxphUa/XM2apsdHqUFZhoz7KF78xGVt3lWIv+QiTCmU/rSWx0JNtABgRE7sM2Gpw1UzhnV7tvtvIRiXES11FfkAXoixuKuHqb7gCrCVKnvpJAFuLsTafl5nJRdaf+GJfYr3LzbObTuR2uXk25poID31yeEsWEwHbXj8rV0W0QIElRVpl1ltfkKALJRe9llwHHSlroaqcSi2y+RBSeTbSPDFPU7tfRlCanX7EZpOMlVIlcEWg6lbynBiFnhQcVICv3ui9ruF01gvqGnOrEeYtZG23SFS4jgHyhb0AkAaUlWqxpIMj0+53wwEMAKy1Nuze1CdS0V9J1wIrHoaQJgqZ4iBIlgmZ5pcl6EbEstAnj9kVo05Zcc1timWgub7kkkvw5z//Gf/+97/R2tqKXXbZBZdeeik222wzt8+cOXPwrW99C/fccw8WLlyIzTbbDN/97nedTBkA5s+fj5NPPhm33XYbtNb45Cc/iZ/+9KcYMWJE08/9y1/+grfffntQbSQi3HbbbbjgggtWTXL96quv4pRTTsE999yDlpaWYTvumWeeiW984xvu766uLqy//vrDdvwlwTvzDVpbFTb51Qw8+9XmRBzVFK56B3HvTcqTVnbrgE9yaZAaKK2giJAk7GSR2FLsOlIuiqK0QqXMA0xXF0d5kwQot2uklRaY7gRqXmoTY3xyjCJikmiLGCitQC1xX5Ew4EqhE3GeTRxTjlyHHttKAWNGKnR08ODZZuUKdcP+s2NLBuWIUM+UTcgjtEjp48Rn33NZX7iKZRIJkCU3iRBo7eUeccxRf7bgMjZq5QdQwFvjAWEpc0JrbNAKwJDCuJYMvanCnN4Y1Tp3nkkiUTFyJNtk5CZAEr0Otdbhd6m1QgaeCNRqttBE1S+Ji+6/P/S3QtLbS06DPpwwhIJYFxg2qO7ELd/UFyTQuoSWFoWsia1nmAxMhqwkyyYM22cxs/d8KQZUiZ+hlojQXVdIqxwEqNWAnpJCe+y9rA3lqzIaAnozhUUJF5jqqmu8XYswvxZhQTfQ1WXQ2xPK48gHKRphPaxRy6ASY12Y+AMVcR8KDU5irNkkcxkH7Gsv/ObTy+w7KLCUSOx3ZVc+M7sKrFT+Hs6yZdMnDwdIKc9BBthnKJgxYwZOPPFE7LDDDkjTFGeddRb2339/PP3002hvbwcAHHXUUejs7MStt96K1VdfHddffz2OPPJI/O1vf8O2224LgCPLb775Ju655x4kSYJjjz0Wxx9/PK6//vrm7STCpptuOqS2DgUrFbl+9NFHMXfuXPzXf/2X25ZlGWbOnImf//znuOuuu1Cv19HZ2ZmLXr/11ltYa621+j1upVJBpVJZlk0fMoQMbf7bmf0mQr58wUHY8Hu3s3WdeLKVI5CG1ymIRkC2wUssAF/lsU4+ulmpeC2vsgRZKwUdsTY5ivn9pTJQi0owb/e4qEkfmPx2aokRtWhkqQ3lhK/ZXdMmARch1uLW8corGcgALa0KbW0Ka48BTMyFYXTK5ber1gprbCXDJh0JnlclvNtJ7tKEnyHEulziaJZE9xtdSUaXDMa2ZGixy74L6vw5PalyZcHlrAwp6NigVPLnKVUUW2PCem0pOku8LBxp1osCoqdG7vvRuQi3j5QDTPyNYUlHvc6FJrKMl7r7kx8NBhOvnuksoLb4n5lLXawoPK4xi9+vQIHB4uXv7Y/x598FRAoqZS11Swv3D/U6F3Sq29WxcKUJ4GcqjmxRGS3J9/7YnM+gXHElkdrVE6BU0ugoGVcZ1ZlxEFCz/YEj1gkT6ze6I3R1kau4mNWDh8EgSPqAs9xTCRNrVc+gUv4pBBqAc2dCSTuGr5KMjxcBKgKev+ozy+W7KLCEsAEylDlUTYYXgX19BL4/0xR45ZQ9l4t8dchYBprrO++8M/f31VdfjTXWWAOPPvoodt+dx6QHHngAkyZNwo477ggA+N73vofLL78cjz76KLbddlv861//wp133olHHnkE22+/PQDgiiuuwMEHH4wf/ehHTYvBTJs2bWgNBbDuuusOet+Vilzvs88++Mc//pHbduyxx2LzzTfHd77zHay//voolUq499573XLAf/7zH7zyyivYeeedV0SThwUDOoxkBCjiTlWEt6WY/wb4YbXJMFHMcg8uFcybq1WCqdqwaEkjNYRqLw8ilRaFkR3+SSAb/W1tVVY2QiiXFKotMdCduCRFAKyvtnoGZQCyg4CuaMQx0NqmEWmN3l5b2CH4jJybhsq/lmbA/LkplM0a6unmaHp7WwStIgAZWmMfOdKKK54ZYmJcb9OoRbawRMQJkSJ/Ebu7yEaoBVoBq7cbrN2aoi1mDbJUOTSw1nUK6DFwJcVFm1k3XE65o2Rc4YiQgMcKWK1i0BNpTm6ychE511B6Evp/G+NzmEpWAyoWf/7aWY26oUE51TTin8dwx7XxlTNcYudwIV0ZbaQKrNJ4+dwDMP77lmB3J1gYlTBqtA8UiEd/bEc1SfYm4mRqtr/jqKBMZjPDuSjtMRPnap0n3mnG/Wi1ClRbNQwyb22ZSP/DziBcwpw11nOrEboXsVwrTVkT7iDVZYz9ZwvEqBrb8DnXkMTqUGzEGoD3s7YEnF1BbN9PwPPXDp+FWIFliMwAiYIRSyutoDSPSRrLpq7FsGIIbiGNk4PBBjkXLFgAABgzZozbtssuu+D//u//cMghh2D06NG48cYbUa1WseeeewIAHnzwQYwePdoRawDYd999obXGX//6Vxx++OF9PmePPYY2Xg4VQybXr776KpRSrnrNww8/jOuvvx5bbrkljj/++KVqzMiRI/HBD34wt629vR1jx45127/85S/jG9/4BsaMGYOOjg6cfPLJ2HnnnYfsFLKyYHEPk6pxR0q6BBoZAxnhlW+yo8MGP5vBO2n2Nc7m9OLl8w/k1354L7PJNPPlymti8cEdd10B83rKKLdqlySZprwcqiOgXgUqFYDWbEfWwgmN+p1evzYaWb/ViCUhanQF5bL/CK2BkR0aJiMsXERIE39eLrpkSa7IQtIEgK3EJoQzzRTefdcgzTRMe4RySk6OUbHSDUMK46IMI2wUuSvR6Eo0enq9TEQrq6uOOEI+IubiM+2xQVvMUpOeVKGWRVZnyUS6rAkdJQLA5cCdn7XhJeZ5ica7VvveEvtqhiJn0YrQm3CfU2mxUeg6eZeU2C8Lij2hLBtGGujpARJLVsVthUtAk9vPZOxQE5Y43vy3HJn+z1cGvsfSlAnJkhD0Rmx1zUzUqrTUxylQoBlePucAjL/oHiAzMJ11LEAZHR3sciSVCLNMbIeIGUukUc7ClSMFLcEABSwkoLeXSbaOAGMnwGyVp1zhJdZTA4a0q4KaEtCdcl8jdnvdPYS6leIpBS5RLnPNUDIXEutqysRaItJp5iQfAGzGtO1366bBzmj5XPsCS4dXvrNv7u8NLp/mknAXJ+tbaTAEzXWj9Pbcc8/FeeedN+BbjTE49dRTseuuu+a44I033ohPf/rTGDt2LOI4RltbG26++WZXzHDOnDl9tNBxHGPMmDED5uMtSwyZXH/uc5/D8ccfjy9+8YuYM2cO9ttvP0ycOBHXXXcd5syZg3POOWdZtNPh8ssvd2L1sIjMcGCDH90HlDReOWVPAFxRiQhOa6xEd1zL8PIFBw3LZy4OL/74Y/2+9srX+38g1cI6lxbL2V5IWNXqkOsZML+KpKRRby+j3K6dzCC2BDSKuVpUrVKB1i3AhiPYXWRuD1Q9gxnXhsrIyI0ZtZpY2RFMplCp8AuViurj7xzH3vZOlpKUsvsSnJl++wiNjpHsSbuwrpAkikk/8Qrb6LJxZHtUyaAtNlitrFE3wMJWjWqm8XaVpRnrtaVotQVoBCkxGYdthihdNFiHGVlnklgTRpcMdJn370048myI3T2M4SIX5REKLSUDbThyXTfWczdlTbgkUkrHymTdV5mjINfJRADVOfHKZFzqXLLLw32VTbZUxJMurYGXThpch22MPY8lHKS3vnam9e3lCDvVi9G+wLLDy9/djxO9UwPzThVJWyt3c9UUqpqBWjh7UfUkQCkCRnBxLgmaSZ6FrBhVq9YiM/PdZbXKuSe9vYR3ujVaohgjYuN4RUiuuxKNRVWFapULf9VrNpDgrJKCxme2pHniC3OpWuZLnrvCMeT76pg7SVU3vnJjCCJ84HPX4/nrP7eMr3yB4cQqWW5+COT61VdfzTlxDCZqfeKJJ+Kpp57C7Nmzc9vPPvtsdHZ2YurUqVh99dVxyy234Mgjj8SsWbOw1VZbDf08lgOGTK6feuopp3u58cYb8cEPfhD3338/7r77bnzta18bdnI9ffr03N8tLS34xS9+gV/84hfDcvwNLp/mb5aSBlLCBj+dzg4StsodRZqX7yMFZIFN0soMIdKynNjoQW2TI1VmuGPvTZFUy0BHGSNGSllh3l2SAXt7OTloxEiF0pgRqNd9Yp2LEiUGWaRhyhpaMzmWaHalolwikRSMkVKw5ZLXFouEo7uHNeKtbQrlCKilwPx3DNraFBbVgZ5eJpkLR2lEETC6lasOLkw0KpZAr9/OjPUDI9mjVitC3XBGf0+mUMuUaw9XOFS5S2VgzQHARBuWlMeaUI+V7WvknBSqGSdDtscGhjTmp9oVtqiDKzsCrGkn8oS2XgfqgZRCHA6Q8u+lko9km4TdA1LKazmUnbgMNOlqCiJEkcKS1H/a5FczUCpzAaQUAPUkfSI0BQoMN1Q1ZYciAIvm8zKSqlqCmhoOLBC7cCA10FZGRwQn1XCxBnvf10DuuevtIZieFIuyGIBBlim0tMRoiYGWyOTyLxZVFbp7WF9dr9nCMGHZciK/epgZdvkQCYgEboRg21VFJ/koc7tVd+JJukhLwse/wba1QIFlAmX/LW4fAB0dHYO2uQOAk046CZMnT8bMmTOdMgIAnn/+efz85z/HU089hYkTJwIAPvShD2HWrFn4xS9+gSuvvBJrrbUW5s6dmztemqaYP3/+gPl4yxJDJtdJkrgZyNSpU/Gxj3FkdfPNN8ebb745vK1bxph45WzE7e2OVCgFUAygmiJTCmiPnS2blbatGsRaUIp4QiCduxSBAQLvN3CkJDEA6qDMwLS3gozXKyYpy05UDUi7EiyaT6CKLXmeEhBzkpFzMCkDlAKZVkisv2wUc+RWkjYAHsTKFYWWFu/UIU3LDLDaaIWFC3nQmm/Lwdd6DBYtYjI9YiT/5IqWQL0GVDs4wtQWE2oZWaLNzh9sKWuXeUkhscu9Mi5x0hKT5Vh5XXfVaCs94f1Edw0wyY7d/oQ2q50j4gTI2BahqILLNhNx9S2p1ChFYdKUXBSaCKDUwIg1e0nzBKaWIQ1nPOHSsJWWLNEQawj1GtyKTX/Y4PJpPiFLSEMtQ60SAeVo1YzEFFgl8dKFB7P+GmAJRdXK55SCSg0o1ky0Yw2KYVfNFKJIIU1lBc2zhCzjPkQpa5XXm0AlBkSERSi5XJSWFoVSWXsrT8P+1SGxptQGLFzJcuRWC7nabeY11ZLMKO4gLqsZLAFJMqiMfCK7Tbx0SRkAnv/jF5bbtS+wfLDlpFkrugl9QFqBFhO5XtzrffYnwsknn4ybb74Z06dPx4QJE3Kv9/T0AAB0Q2JQFEUwNjq18847o7OzE48++ii22247AMB9990HYwx22mmnIbVnuDBkcj1x4kRceeWVOOSQQ3DPPffgggsuAAC88cYbGDt27LA3cFlCvishdUxqiDvFegrTm/oKWUrxhCw1Tte8UqOFv1pVzzyhHoh5EUEtqoNi9miWomFKcSUpIo6+VrMY6EmdXhAEKPLJGZw1yKn6cZkfMhnMXJJ85L1lxfM6SeASH9lzmqPnSQos7DKov1WFqqaIqhmopJG0l9CtWqAU0D5Co1yy+y4kdEca7W1s4zeiZJCSdnJzgQTay5rQ4pZ6bVRa5fexddCQ2mprQsZjO/6J7ju1FdnkfUys2UmAi0bAWSxpbb1z64TUat2NgU1msow7sgxfkpgAP1myFl1uwJWiNUtQeGAwpHj8Rffw4J9aEhApvHThwUP+rAIFhgsvn3OA+338JVNZPqEV0F1nSQjAXv2pAbVEqNcUVGuMclmsL30/lyRgOZN18FC2uAsnGNZQ69aotpfQ0kJoafHJkFnKxDxJ7IqSJCmK3R7BPzMJE2upogjRWGcmqCHgybWqmlyfraBAmnKT6oJUr7oYf+6dQEmDWnmwUylx7lJZI4rZ1Wqlg4zvi9tnCDjxxBNx/fXX4y9/+QtGjhzpNNKjRo1Ca2srNt98c2y88cb46le/ih/96EcYO3YsbrnlFtxzzz2YPHkyAGCLLbbAgQceiK985Su48sorkSQJTjrpJHzmM59p6hTSDGmaYvr06Xj++efxuc99DiNHjsQbb7yBjo6Ofr2yB8KQyfWll16Kww8/HJdddhmOPvpofOhDHwIA3HrrrU4usqpAaSbWWQZnHacSm4UdaJMdJNq7kmODS6dyZcVa5qPTjc+psVETl3DDFRRValgvaDUILS2EyBLguKQwsqzQWy4hrYqshFwmu6qmoBJytoAyECV1colzrpJhxgl8RvtSxEoBaGF9dU+P2GopUHsZqjeF6q5DAaB6hjqAaFwL3p1vMGasRmurspUdCfW6Qhop9GQRE+jIJ0EKUktIy9Z2L1ZcpIZdPwitVi9tL5ez6hLi3Gb12LVMIQn2A4CqUViUsCSkpIG6rPTKudY58SkVh4MyR8XqiUaW2iVsQwAIiDST6HrWZ+XEDbhuM3EyK4BXvr3P0G6cgUAsMXr53OWTa1CgwFDw8pksRRp/4d3Ok18lVkeVEZNtrUC9MWqtMfdtIzkCndo8CAlEUEl7WYdEnRMDBaBKsfOkN1kwIRYttSHnV63cz2BbKAUJI9UGTiOmDKxPW8MqIzinAgCe+8tRy+vSFlgGGH/2HUBLDKpEUGUef6mVEwFkbDTp4o+z3DEEWchgMWnSJABwzh+Cq666CscccwxKpRJuv/12nHHGGfjoRz+KRYsWYeONN8Y111yDgw/2AZ7rrrsOJ510EvbZZx+Xl/ezn/1sUG14+eWXceCBB+KVV15BrVbDfvvth5EjR+LSSy9FrVbDlVdeObSTwhKQ6z333BPz5s1DV1cXVlttNbf9+OOPR1tb25AbsCKRpYCpw5eWzTi6gNQw8bTRWIoC01StMP77d+WiJisb9MI6V/HK/Dk4uE7ad9xkLd1gANWTQL3dw5HvzKDaXkbcpjGinUsHZymxTjrSqNW0j/YYuLrDqpqB2hTSOpBorrJIxMVPWFNNMMZXRmQbLV5ibWlRqMKX9dZaoffdBHpuNy/92spmygBRlVcXqKOCrriESDPBNhQUbsmANMoTZK1gI81w8o9wrp2YsIKkglKWlMcGZRu9BoCeTEHL7wnLQAA+djkilpeAkxu7oK2/Nx+vXgfSbkuWDSGNFVKJVgskuUmsFN0KRDDBM3YwtidAQwsa9Ivx594Jaoudfvrl7+0/PAcuUGBZIjFQ3QlUZgtuZcQaZ60Axe5LJjVASfMqnFicVlPpILjvE1JtbGQZ1iozyZCVvI+nKz8u9QaCSHWfRMUkGFfsfkh9f8bvDwi9RKjldwM8N/mY5XMdCywTbPidyTw2lzSoLUa5lZNtUdJ2Bd0GqjSAaIADrSBohcXbtg6RXFM4nvWDTTbZBH/6058G3GfMmDH9FoxZHE455RRsv/32eOKJJ3IKjMMPPxxf+cpXluiYS+RzTUR49NFHc+Hzcrm8ypFrIik1Sz7SIREH2afZMjsBG37vdiAjvHTJIcuxxYNELbPk2Fb2MuAl05BYBxF40ZVTpKBIQb9b5Q2GgKgH2egWVNdvQ/sIJq5ibVcqAQl8ohDpGKhnfBz2rEJNlRGXbNJQZpxxfpbyYKc1YGrGdS61Gkd0JcmIbIEFVbVaRQAutT8F9Ns9oIV1pAvLeIdGYOwYjbZ2/s5KpUCKAibWVVsMIlZB1Noop7MuE6w2WzvNNaByVYWFy6aGC0cYMElvi73XdTXz+u3UsG9uvcYa60ULDapdGZOAWgpqK/GXYCuvKVvJC4Cv2uZkN8ovFSv4WYG9ZZW/dZcKKjMuYaxAgVUFL59/IDY8cwrUvF4o8cKzqy7QCpQRVEmzJnthnftFBV95NrF9TOqlWEKclTJAaok4EFSpIfRJXkysxV44tjRWZpQCMhKhlpdEpx0OPUoVxHoVx4bfuo19rVMDqkSIW7TNQWKe4aT2Ed9aabb4Yy5vDMHmepXCrFmz8MADD6As7gsWG264IV5//fUlOuaQyfWyCJ+vUHgG1RSOnAJ+eU6i2YpYN2UJj6pxFvhLl310sR874aSb3bFe/Pnhw3MuADY69v+Asa2cXQ5wBy0lxeQcrF5XojkukqIVDzoAk7zMAAkQzetBDYAe345SiZMQe3tZZ9jWrpDUbYZ8rKFKmovWSFGE7gTVqMQE3sBHkay+mgyxzCMuWf0i+1ATAVTLOJLem1p9d7PILSfVqbd7Qd0J5o5rw+jxLTAGKJUVKhWFUe0sARGtdDkYE3syJs4+uJ+vxGhIccKi9dJ20W7yrkRi31fNFDuK2Ai5HDtWhN4eQs2S62ovQXdW7TXm70JFClTixECyqwZucI8U/64AZFLqWOW/1yaC+qVZYXnpwoP53i5QYBUDtfJye9ivbnTcTfzMVDgcqJIMFGkfVU4l8kx+chtKPbSCfbD9M2flWspGpV2UW1ZBRU8dRqldIy0JDyPVdjs30O+3SrKVAjlM+MatfoI3oozy6mWUyuzPTnJfWImlMZzkZFZCCep7lVwbY5A1sct67bXXMHLkyCU65pBDUxI+f/fdd9Ha2uq2H3744bj33nuXqBErDKJ/g4/45cp864DIAHkSrhVHPQBv/E/o60E6ECy5nXDKLZhw8s1MuIcLsiwpkXjRkEuUBeh3UkGR4mWrSPMdUsugO6vo6Tao17nZ5RJfk+5FbOMW2WlauQzE7ZEfaHoSmHdrLIGopjzAJJl3LiE7cFVTV+RGac64Vz0Jf0ehVEKSJuOI/0VSMCKD6qpBv9WNBf9ZhAVvp+y5TFzARYrBlAOS3J0ql6Qo/8Q9JCVPsnsytu6rWtu+2Mo+BCNigxG2QmNqFObXNTp7WTdeTYHOXoVa3dvuqc4aR61tp6oS6y7Qk0AtqkO/28tRNRm0Q+1lCK3Yxxfw+tCGf85RYQmwSiTuFijQgJfPOQDUXspte+G3R+TzaIyVi1gbPCk5rrLAY7rZyk3Yh9oERdQyXwzG/lOSxCj72uOqzK/EuT4vrLgozy6Cn7qIWq/qMKMqoDGtMGuPAI0so163FpILarzKK3p8G5RqmvO1EoBljYv/t6ph//33x09+8hP3t1IKixYtwrnnnpvTdQ8FQ45cL4vw+QqDRCbs747EAM2JzAAgpaDAUYYNv3mri0q8ePlhzd8QNT+ei2jDyzde+NWn3OsbHf1//JqQU1v6HAAo1lxVBXAk3y1NVuAjnY0PbqMHtkS1I+VcKFRXHdGz76K67kgko0vsP93KvtX1OqGlVbFPrGHv6u7RLVBzFtnIDbHOjHgSQkomJrxc6xwvbLuzVPkotyQDBRp4p/MA3MqDaJdVvQrqTqAW1NBdGwGggjFjFGoA/n97Zx4nRXG+8aeqe2Z2l70EhAXlZiMQFeUQ8cIICmIiRPLzIoqKeAGiCFESL5QENZ4xKJ6gBqKJgniFiAp4ISrgFZHsLghqQBRkF9jdmemu+v1RR3fPzrIzy97U18/IznRPT03PTPfbbz3v88YIQYat3j5Bps29bsQy4A7LgkZVOaQ6MsYglDbqZW0CMMKRZXNk2Qx7ZBFiaVT4RbuuLF5Uf4tEGPbsdGHtrPDta5ElIy6gep6LJhheepxUepUtWqak3AZUsRXn4NTy3G1kgGACZMMBiU/ap4l5M0X6+KsCaP8xkQLEBXhEWpn6G4j57UtVIsVf0OjflspM66JFeSxTrjtcHF6L3/Q0nT1HzhN/cPiM7ZtekGVIDxJ1xHcpzkBktoeo4zYhIC7V5wEOiM+9Cdr+ttTM9d13340RI0agT58+qKysxPnnn4+ioiK0bdsWf//732u1zbSD6/pInzcWoppcfoHlAZEr67l0vyF6ml4EhtwCQGkg0NbTgir7qjt4VT1gc0L0WLpf+k/vdeTBnltUaAq5EOdxqS3kIStBs8sBVxbV+IoyxQ4gopiAkWCATYieltz48JjA2+w2aTFclg2WFwbAEZJy4coKDtsWjiIA0CqboNyiIDFfz3N5oiDSJJZn2L6gm+mZAl4eh/V9uS7O09Ou6oTIufB/ZT79uD6ZyWKiSgekwkHFNhvf5oSR0T0HWa0IKsPCwSQWA0IhgsyQkG3EfOdWhxNUOt5BIhYTOnOWQZAR9qz1HAfYu5cjP180lyiPCp1cNCqa34SkUHvvXuHvvXcPB9lVGfi8xUmU6/3Nsyx5kGXgIV9hlZQk+b+bQrYjtsFtX5atiWY9DIaG4uu7z6zy2MZ556D7hOfFxapKSLQKg2fZIoPsKyzk0pIP8M0sxV1d2xDUTvvkI/L3XPJs8m6JPUfOE/rpN5MXSRW/dnHV9dOZDTU0SbjM6tCfKsS5TJ+DAQIuAmqp/1f6/KbYU6OlBtedOnXCp59+iueeew6ffvop9uzZg/Hjx2Ps2LEBhUY6pB1cq/T5o48+CqBu0ueNhrJCknI6ACLA83+n/ZIQv86VJywHwBkBkboqLyAiItDmAIEvC8GrCYBkkEioyPZyTryKdEAeyGWxmUWASlc0dAlbQKXQ5ZKYKzXhvgy37/34NbuEi0Ca68o/7/1serCqFnzTX3+NblNfghsSBXhRRnX1cCwGEMJFp0UXQFZITH2q7HWGBcRtMb6QJSyvQlRMhXFxwUApwPfEQX6qAMmwxUVEeVxc4btqv0F7Lgc0if5ffoyBVFaAhCxgRwWiZTFUtMmEVZCJdu2o7BYJVBLhCCI+P6CcEUSjXotx5Z8NSKtAKoJqZaW3u4wjGiVo3Vq0V49La72Y9O0WxZwc4ZBo8oLsELA3LrIXPnhY7A8SFT7eQlYT97LboN5np4pTVRaOcfEd0VkQpusCusz8Nzbf0nSdbQyGhoRUxL2LUYuIY0+WLfyFXQZUONpaFI6vVXmIShmIowsRxQZ9kYTMUpc8N7ba108Mnmui+LWL0XPkPPQc8SSKl16S7ts1NAG6TntJyCV0DQ0V3zFVtCPP0xwQ9zkAyO9dU4N6Bgj7Wqc5EY/H0atXL7zyyisYO3Ysxo6t/vebDmkH1/fccw+GDx9ep+nzRkMVMyZKNPx+1v7KNQJPkuDyKkErbBrMMqiDM0/YlrJ8Uqv6pxgVLgep8KVPfZkUQP4YQ1S0vK5w9cmBq+05rmeRx+V0pAyeiXwPnFFdZEFAPb/kGjIlm+49E92mvgSWI6RB3BI+1KBAZQzCt7M8LoL3iKV1jcKCyAIqHJBoDKSSiit6FSxS2f5cdkdDNOrpt+QVPlwEA0vl1a2kJfpfKvpxOyK7S7fuAf2pEmxnBf5Xno9WB1nIyiLIyRF2gDaRyXAmLjQiEQLmclCLwObCNpBSEYBXVHCUlTI43+4FdTnKwxaceCYsiyA3lwiLR5cjFvdauu/axWUHUAssJwwaF7pMblFx0SE9w/X3weXSomgfpv1+h4I4F8WhKmhgHAiRYBGVwXCAU/K389Dj3IUgYOCWpWcCYcuCYkpEFlv+jnhcJTMIiDp2+zPYPnlavTV0cX3nCkOzovsl/wANUShLKW5RgLkgrooppCxSzUQrWR/gnQ+aEKn0kGluwXUoFEJlZWWdbzft4PrQQw/Fp59+imeffRafffZZnaTPG4uApMD3GCckEOwG3EL87Wz9Mg74sok+Zw4Ood/Tej1/1tsfVLMkga0/K8L868jFcQaEKUi5g5KHvWnI7pc/7wXjrief4ASBjAtxuVe4CKaz2OINcXSbvDhp9hoQATYAdLt2iTjx7BXBtLJvozFZtBiyRJaIQxTphSzwViGQ0qjIBMmLGy1nqXSkjAZeYKh05bq6nusCoYBeW62rLoJCFmBzT+O4JwYadUF3ViJqU8Q4x868CNwuechvZ6OgHcWemNCSi26RRBZFAtE9DJmZBBYFdhSVg+6oAN0d03roeGUuoh2zUV7KYWVQhMME1BJ+3RUVHLG9TGhGCJEepyGprSP6ookj+PkTBvBQcLaEcFFdHsjcQxah2sGjmslYGwxCVrfx8f/T90uePV8E2DFRVMhDFMi0vWO3sudTx2tKpL2eV6CoAh9Oxe+0+MUL6+8NcCTXkBuaND1++3cQSrxZYRcglmpSxL2stU3BwTx5pq4Da3rBdUuVhUycOBF33nknHn/8cdh2rRyqq1Crrdi2jd/+tgW0XY26IHACB0oQIrJ/Siriy5j6fX8DXtEcINF41YyvJWUPrlvFoSPwfH9QrW2f5GO+4Es/T0ojFDwh877xkd+g+yX/kBpnnwNKYiCqiuEgLw7UOr5xdp/wPDY+5hVUJkJ2VYKELSj7qY1Png1AFmYSAOWOiNcpAc+wwHMiYsxqGpbKjBEBIOUf8BXwiffpy1ID3mwAh+xQ6HtfDqv665aZbA5fG2L52dK9cdCdldiTG8aGtlkgnIPlZ4DEGaz8ENwfKgGXg+6OoTw/AvpTJaxKR1xMlEvtpk1By6LaNtDtlo+KnLAeRtzX8VL9y3PC3uev2ptTIgYlH+chGvz85QmfCFsSrzumLqxyRUAesZrmlKLB0Aj4C4IVShPd4/yFICELPJPJmUcEkiPEb9MnixH1hS103XO9Uvz6ePQc+hh6Dn2sWq22oWnQ4zd/886zhAAWtKyTWxTgruyMLE8OtkzAIei3zima5DG8pQbXH330Ed588028/vrrOOKII9CqVavA8kWLFqW9zVoF18888wweeeQRbNy4EatWrUKXLl1w3333oXv37hg1alRtNtkokKgDwhwvO6F8KAERFAOB6TjiMF9mF0k108QfKCubiLgb1D77i2Kq0137CxKT6bT9LXPtqvMwG588Gz0ufFZoHXRlvJSASO2XKoIE5LVENWKqHhc+i5Knz026bOO8c5I+vumvv0a3a5d4aX+5P0m5kHzoToIWFfZUyqaPK12k954D02Oq+l5n4KnUcvh+9ap5hD/opuJ/XEXVsnhJNA6KCWu8HRUAIbAIAWIOELJEsxn5WnRLmQ6COQUQtsHaZoK4TGTllf0XFwWMjiMy1qTSAd0bExIYwHM8sXyyDVlJriRI3H9hlUQzT3z7RzXKUPuJVDrVu9QYDAcYJX87r/qFLgfZHQPJtMFzwuKiX/YrIMoij8NrYGVTIRvxy/oagOI3J6DwlMca7gUNtUOdi6COy96MomhYB2FIoF2+RL8McF9sAYBQ0kQLGmu22muOVnz5+fkYM2ZMzSumQdrB9cMPP4ybb74Z11xzDWbNmqWdQw466CDcf//9zSu4dhgIVVN9ACdWMKhVQWli+/DEgMefUU1YTjj3phP9JGSfq7PmCzyPJf7LQBwObaCcyJ6YyGRa4odMLOJl0bnQ53JGxAYJSf5tkO4UPc5ZsM9CnWTQ7eV6BkA3p5EQQDRMCVGxj6JiXMr7WXRIYzpDrfE3cZDFH4RB6MV5wmeiDnJAsOhRrWKJ96c9zmPM91yIykXZWAcUUr4irwpCFljrDPAMC3R7VKwfc/X2WbmDGAC6qxI84tuxIUsXeJJKJ/DdIDLrrjLxPGR5F2VqKlFq8tRFHInLCzG/E00TPCgbDE0SQkCiLmhZFG52GDzT1jZ5+njkcG/mCbIDrbLIa0gnD+Ma0qTpcdbT3h2/XaxOgMCLFSjAVZInzsR51qJalhpobNSEICkUNNZY8NgEmTdvXp1vM+3g+sEHH8Rjjz2G0aNH44477tCPDxgwANOmTavTwdU7fgs3abvMLaK9ULnfok4Vs3AE9G+BTLXPczigofb5Wern+Xyb/RlkHfwlykOYvPJNbKErl/c462mULArq/koWXYieZz4lvKUjlshiA16ho7+YMzGIBWR2Pp0dGqRkwT4yRvugx5hnpHwjeDWv971MPoMQ30WMXO53b6FJTkaJQTYhMpvtW8Xx7nMVVGfZXoY4xsDyIqJl+/ZysV8pRGFpXtjLhukgXbq7cMgGOmK/K2cPUilmNrTFnsryu1w7h2hpCBfZ/4AG0y9PirlV7BMNBkNytDzk7L+Bhiywg7O8i1X/7BAAMA6eFQJCFkhMetW7HMX/Ht8gYy1aflmDvI4hfXqOftqb1FaJHn8Nk+VbWZ1edEG+yHATmSQj0ligaWauW6YspD5IO7jetGkTjj766CqPRyIR7N27t04G1WAwBIJH4jKgEuIHEbK8Dls+rbVy6AhKNLiWiZA485b5JQ1KK6tf2/cckRAR27eAREs878XhZal19pp7mcwkFL80Dj1HPSUz3I4IFlVXPx58DRJ3PQcRDt97YDqAJ1EXxa9eXM0OrRtKXrgAPUc86R141IWNr8BIFB15wXXgQqRKgWOSfakuMNQ66rkJEAbwuAtS6npSjrDMKO+NC324TfWFFM8KieBXfS5xT5+ppUaUADEu5UK+70dMakNjVBaack+XrjIe/lkQ9f2Ry0xQbTDUjpJ//FZkHhkHb52hjyfcovJim3u9CSwi5CMJdpqGAxhfckonZyxx7uEMgEv1fajgWZ73tdGAOte5vm02MVpqcN2tW7d9ylk2btyY9jbTDq67deuGTz75BF26dAk8vnTpUvTu3TvtATQqqjgQ0Foo4shsoZKLUKKzjaLYjoggSWUTZcMPAN70YaJMJPE3EpCUIFiwZvu8bmTXSKKyta4vG57svVRHhSPHLOykOJEyEMJBwGQWVY5ZWlQFWqYDSWUVfnoOf0K2Jacofmlc9WNJkeKll4htao01vGlYBq9wkXOpu5ZPpD4ducxwc5U18E/VAd7BDhAXLa4qllTr+9ZVgbVNwTOFtppEXWH559e+x5l0QKHa/UNc2HAdIAf2oWx4E5zdYMLPPMP2DrT+xjH6YsPbTHVFp11vfA1fz2pm/vMGQyNQsuhC9Dh/IViG7c0YWUT4dIJ6SROXA1k2UB5vUN21oWnS84x54g/1XfDPOoMLOSZjop+Cr+sxmAioiWzVGQiy1fImhpowrmmd5sY111wTuB+Px7Fu3TosXboU06dPr9U20w6up06diokTJ6KyshKcc3z44Yf4+9//jtmzZ+Pxxx+v1SAaDX/wKLPM3BLFYjwi9cDljifhiEvfagqvzbi/oJdC/HAYfB7EYtta/gF4jh9AUHZhy8BQBYAx7klB/AWMSJCjSHqe+VTSwFZVm4ve31JuYEHqlwG4Qg/NVYGFGrv/NdQlKyVeVtkfhBKipQo9fzkfxa9clNpnsA/UdGvP4U94WXQms0hMHozkRYKedvMXP8pqbSEXUVlj+a9FtB5cNRMKeJKr50MG3ODi4GhT8IglAmseXF8UQ6nCJyL2d5x5xZyqEtySGWh14QToCygi/icOzntFZowTqZdPDM6lNWR1RaXdL/kHvpbuLQaDoWZKFp6PHhc+C5YrXI1A5O+PimMOLALiiEYz2lXKcMDSc+Q8nQQDkCDlJF6yR+mpOfHOQQB0jwaOYJCtttHEUC0/9gVvhr+JKVOmJH18zpw5+Pjjj2u1zbSD60svvRSZmZm48cYbUV5ejvPPPx8dO3bEAw88gHPPTe4o0VQhDgNR0bHujAjhjbw7BsK45w5hkeCUfJxJq7QE9b4sfCGqmtyflQ5RsT2tn0348ajAXAVeSvfnsGBQrZ6rAneX69bi1b7XCkcU1skLBWUHxCnEtyAGEIuA2/J8ofyxEzs8EnhX3yqT7L+UdcVO7PnL+QBQJ0G2LjBSMACQrcBV0JlsPBZENpoDPCSzUHFXuqZQcWGkAmR9Q9X9qAJ7B14HREd+jkqmky0a6iAmu7kxCKvHuMhCq66YJO6CVvi6vClfbr/2HyTQEl1Y7/nGxNi+HRAkG01gbTCkTcnT56LHuQvBMy0gww7UyqjfvXY3aoLZRUMD4gaTJGAEnPrOPzp5xrQzFgcT5ytV78SkVEQe9wlRM+JN77tFafDaIBm8huXNidNPPx0zZsyoVcFjWsG14zhYuHAhhg8fjrFjx6K8vBx79uxBu3bt0n7h6pg9ezYWLVqEr776CpmZmTjuuONw55134rDDDtPrVFZW4rrrrsOzzz6LaDSK4cOH46GHHkL79u3TezF/QaMKUgERzKkMpMxUE/WN8rXcJg4HbN80vQqmLRZ4LgDxQwpRIdFIyHhqlJxBtWX32c5VyVSrIJBD+6/CotVmjYvevxI9T3tCZNzVtJXcLhdm2IAjf9Dqvatx+gsuARm8qqkvFQHCy+L7qpzrom0v8Qeeaj9wLh6gsjuhv7LaotpJBFJGQQiAqByzA299lZFX79FXMFn8UtX92HP006Jrm3QyUfo6HhNyEC4voES2Wa5TwXTATVRBo9Rb84jsWumfwVD7POHEnUpAbTAY9h+yOwrwsHBIU62qAe/CXQXWjRBcFw55FOAcRW9f3uCvbfDoOXKeyDj7M9dEzExzpZ1WNVTqXESIOGPKh0TmmivhtW9WsnG+WzVBqOxUWsM6LYXnn38erVu3rtVz0wqubdvGFVdcgfXr1wMAsrKykJWVVasXro6VK1di4sSJGDhwIBzHwe9//3ucdtpp+PLLL7Wx97XXXotXX30V//znP5GXl4dJkybhrLPOwnvvvZfei7lcpjV5IGgjDqoGskRYMImgWkRCnDHAoYEWuABE222LABFLZz14qxDonpiWIFSZ4pfjIcQXmCspSGJWNfFvGVgLWQZFz9OeQPHrSSrYXQaeERFtt5UFncPE+yUyk80TxubPBKvHKdHZ4yroCnsxvuI39r+SXlXJF/7iURStlH8fP1fa6HljJVLDxl35ntSQ1MWPf4rOVe+Lg1NxkZGSVpxxkD1x8EwLdK/vAMhFRK4uTogtM+PS3YX4ZxlcDoRtoCIOEmOi4MUiVawOe5z9N5T8owU0azIYmhnFr14sCrhtKhpsyW62AEQApaxaG8EuzX8MLHrvigZ/fYPE9TUUUucTGSgTee7U3RmVnoLCS87J3gxcSRQB6HOqbBTW1GipBY1HH310oKCRc45t27bhhx9+wEMPPVSrbaYtCznmmGOwbt26KgWNdcXSpUsD9+fPn4927dphzZo1OOmkk1BaWoonnngCCxcuxCmnnAJAeBT27t0bH3zwAY499tjUX8zhgPJ4VlljBlm0woISAUpEwKaDWIBwUfioOwSqDDMAzqV9W4YtAjKXiYyw7KKn4JQjkDlVmXF/sO8v6FO3QFabA5zJq2WRPk7WzYs4XChJIpawGgQTV5lqSosQEOb7ZSgtRrIfiwqw/cG0H8a9wLeO8FtRFb13hddUQXVNIwTgTOxfCjmHBU8+I4NYPV7OxYkyypJeJySj+KVx6HnmU/p1xbZ8B9CYmPXgasYjxoRPtvxclF82HPE4cRhKnk8eQJvA2mBoRGwKVDogGbb4rVZK33uLAjlhwOEgdhw9hz2O4jcuTWvThQMeFH9Q4XpUtPqqtIdnAutGRgXVypNaN4bxZnyJDJy5X4OtaoVkcE38lr8SnigFbCK01OB61KhRgeCaUoqDDz4YJ598Mnr16lWrbaYdXF911VW47rrr8O2336J///5V2kQeeeSRtRpIdZSWlgKATs2vWbMG8Xgcw4YN0+v06tULnTt3xqpVq5IG19FoFNFoVN8vKysDIKfoLS/zqDK5SYNrW1rpRGVKVLk6MO8HFLDj4Rzkpyh4hisKzVuFRWDtBKd79I9P6oPVdrTeyi9XUYWVcnyq0YEOEpWOupovt8p49DjraRH0R7nOWFd5LeI7ECSTowBIzFL776d7sqkNXElbLCIOXIFByvEx4l07qcDaEVIRHqYgMZa2taAOsNX3hBDR0ZFbutWyarMOeJ8LAJGlCEk5S5SheEkK2XKDwdCg9PzlfOEIIm1LA915QxQ8LJpB8Sw7YECUKpwzcM5AOEXxmil1NWxDA0KYDKpVTZTy2lXWuur7QuQiFWT7XatUJpsFT9oE8HTcTYkUguvmWOR766231vk20z4unHvuudi0aROuvvpqHH/88TjqqKNw9NFH63/rEsYYrrnmGhx//PE4/PDDAQDbtm1DOBxGfn5+YN327dtj27ZtSbcze/Zs5OXl6VunTp3EgrjIMqobVJGKnuqRtmoqqFUxpsp6Jv6rBy5/dHEXZI9srf1TpXgN//Z93RyJI5xISNz1Cif9P16/nIRxL7BOXO6KZfvKGpcsulCsH7HAbQoepp4FoM8VJNDqXb1e4v7gkMWPKpOexgdcB+j9wHz7QC+Ub0A95tfGcdEMprYFl8UvjQt+zuUOiPK9drkobFTj8QXaQkudZKxJ6DHmmVqNzWAw7B/Fr1wEUhoFKY+D7I7pC3LEZIFyhiWOlxELrF0r9Pjt39PaPiFU3wzNFBVUMwbGHHDmBDogA5DnSt+5Wc3ouix47vbf4m7wnNGE0OqWGm7NDcuysH379iqP79ixA5ZlJXlGzdSqiUxDMXHiRHzxxRd4991392s7M2bMwNSpU/X9srIyEWDHHIC7voJEKQ1RAZuEAMIHmhKAWiJwUkGlTYO+lomaaHDAAUiFEwxWAe+KVWXF/ZIPJKyr1tcBvfzR+p0uGMQ0o78SuRrInjhY+ywxJAbRKKWKe4l6D77X948pMYCX+yCp3rueEDNrvh2rdOrSW1RdPpK4zHRLL27E3f3urFa89BIU/uJRcafCEXIbxoW2yH9gZNKyy1He1dz7vKuhx5hnUPLCBfs1PoPBUHu0Feiwx0F3VOiCap5pg7fNAql0hOQr7PPZT4HCQQ8BlIJwiqKPJtbP4NVr/eJR8JAFZIXE8Wd3TM9gGvaPoneuEJ8loC+SuJIlEksEEsx3blKFi8ky2Ug4z5LG0fPXREuVhfDE2EcSjUYRDodrtc20g+v60lonMmnSJLzyyit4++23ceihh+rHCwoKEIvFsGvXrkD2+vvvv0dBQUHSbUUiEUQikSqPkzgTTVQSSSzq8z/mDzhVJ0Nw7+CaWGwoF6O6KR79TaTB5ysSg23dFdL3fK2/hnAvYQBcisJj5qDow+QH7+LXLkb3S/4hOgpWOkAcwQx8MncSP4njImiwNsB+hJOILwusNPMqIeRCH6gIkXpJVoctixl0QK+9qFVGW7nF2L6xVQp/bB6iQKT6n58JrA2GpoFf4lZ43MMgu2NydpGBhCzw3Ai4TUXBdQ0tyguPmeNpcxsgdjIt0xsAQr28GffJQxLdtlQ79MQg2+8SopJk4No4oSlBKFDTZEtzmoz5y1/+AkAk6B5//HFkZ2frZa7r4u233244zfVLL72U9HFCCDIyMtCzZ09069atVoMBxBXE5MmTsXjxYqxYsaLKtvr3749QKIQ333wTY8aIds8bNmzAli1bMHjw4PRezG+bp/AHyar6lxBhKiKn+AmTVjsqiKaqiAFJ3TwCmXCf5oqrLKqyevL/mPy6Zl+mWMs9VMOaxKhXFfNJn+3CUx5D0VvBwkbFxifPRvdxzwn7uErf5aY/yPaPxT/nQ+AdLBoJz1sU3kFJ6s81+mKBALZweimqy8y6z4MclSIjzSO2mAWIufJgSgNTg6DSbcDhtSqGSoeew59olIseg6ElUvT+leg5TDZLoxYQtsSMlU3BWmei8KRHPCtTQJ4DhGxAy0DUNHMzCkIM1aAL5mXWmvgaC+nTOff+9QXW3t8qfvCd81X9VxOjpWWu77vvPgAi7pw7d25AAhIOh9G1a1fMnTu3VttOO7gePXq0yAAmpNHVY4QQnHDCCXjxxRdx0EEHpT2giRMnYuHChViyZAlycnK0jjovLw+ZmZnIy8vD+PHjMXXqVLRu3Rq5ubmYPHkyBg8enJ5TCBAMIIEED2VUKfQjUb+/GxCI4pJkdj29tm+7DNIwXhS1CW0z8XTLyfAH6doDuZr3lNAWuybdFnEY2EEZMthjwUIMhZKY+BrmJPOAbmiKll8mTmYKdSFCfPfV/pZa56KVdVxhL7PWqtq/cPBDIETo7jhzQKgtdNiA74IJAJiQCwHVWyfWBc3pSGcwNAMSL4Z7jn5aWK5aRMwE+vsbAOCRCGjUAaIuuOuAMAqELRS9Y9w+mj0+SUdAP+8vXPQn7JT3NSAlIzLgVgRihaYYXJOAq0Z16zQXlMz5F7/4BRYtWlSrmLU60r52XrZsGQYOHIhly5ahtLQUpaWlWLZsGQYNGqRlHDt27MC0adNqNaCHH34YpaWlOPnkk9GhQwd9e+655/Q69913H375y19izJgxOOmkk1BQUIBFixal/2J+3+HEwBoQe6fKdE2SbfgcPKoE7IH15N9SSkAcHix887++vyrAH1j7t6cLKiCvnhnAGbgsrmBuDCAkGIAmULLgPFFo6XJh/2PRqjcFgXiP5fFqt9coJH42HJ7+XH6GRe9dUT8nM5cHbLSKVl0lvjM2FY4AzAF34uBOHMyNiaIXfyv7uAsSd0VDgvqgCTYiMBhaEsUvXgiiehjEXX2sKXrnCpGUqXCEuwghYNwRx+mmFzcZ0qTw+Ln6nKzcXzhnCbLOJE/0xxrq3MVQNQZpgodu1aGxpltzY/ny5XUaWAO1yFxPmTIFjz76KI477jj92NChQ5GRkYHLLrsM//nPf3D//ffjkktq15WvOmG5n4yMDMyZMwdz5syp1Wt4LwZf4WLC66orTpX1TEDIROQvR7lrAF7WlMpMPnzSjYDUgnh732Fedlh7ZcLXrAXeMnVBkNiMhjO4LKZ/4JTKjVc64LlV9eYBXAYeUTZy6iJBvhd/gO3ytG3r6h2/fEdlAvyfFyEoev/Kenv5pJp2mXFg3AFjLgiIzmpQ+aETF7LQUUhJ4DL0HDkPxa/V3f4tPGYOiqvR3BsMhrqjOtchdezpOexx8IJWsH6k4pjVBJ0gDGmizsXyeM+5+Jdwpc3cV6LNk5wG5CBA1XqnJkRLk4X4+fbbb/HSSy9hy5YtiMVigWX33ntv2ttLO7guKSlBbm5ulcdzc3OxceNGAEBhYSF+/PHHtAfT4FRxx0gIzPyZa583JU/0mtHygyTfqsTXUMG8kob4X9MfWAOBrKPyrhZBGfMy5vIHzZgjbtwV2VJw2DQithe20HPkPJAfypNWp5O9cRGAhyxpI+fJVbhNhHVfE6XonStEBgGQF0RJDlYNjcpKQ3wulNo+oxim9XmAtORilshuZdh1qpGurpjVYDA0LMVvXIoeZ/8NyAmDxEQzMdUEi4dk7U3UFcuibq2ayhgaGClD5JyJGQnIDDZjIDJYoNROXuGna6l4ILbQxgQETTLATsVqrzla8b355ps488wz0b17d3z11Vc4/PDD8fXXX4Nzjn79+tVqm2kn8Pv374/p06fjhx9+0I/98MMP+N3vfoeBAwcCAIqKijwv6aaOf3pG3U/E/+VXl27+m3pegvczUfIEv+6aceGFyRAscuQQB1i1TZ+ntXoNLQ1JmGrinIGDgYPLwJqBuTHxg5d6bp4him+SQaKi0Q3CVBTnyEwq2Rtr0oF1ALXfLHnz78uGhjHA4XKKUBQziSFSfSAWn5f8uzIqTqwV8TovYikc9BAKB9eufavBYKg7Sv7xW+GZ7XrHdB6inpuR8uG3hdOTofnA5XGecRcui4tZZMjH3JiWBPpvyiNb10k5vsC6sc5dNUCRgs91Yw+yFsyYMQPTpk3D559/joyMDLzwwgv45ptvMGTIEPzf//1frbaZdub6iSeewKhRo3DooYfqAPqbb75B9+7dsWTJEgDAnj17cOONN9ZqQA2KCoT39R0mSb4tSQJwEUgzgIkOiYHuibrJiQh+AZmBjkvxvyW7KlpEVJqr58jXqhJUa+s9T+elMtccsvMXtWGHssT6Lhf6bpui61F34utPrg8OPuaC/FgBEmeiOUpzgzHdSQ2+/Uk4GqXiumjVVSgc8GBgP3c6clagaQTxZa4ZHFAHIMwGYULLV1etjU0GzGBoOiT+rguPmQOELPAMS7hHZRBh8+c0vcDKkACDtNOzQTkDgwPOPbmPSnrpQsdqEtEEEIWOgMhyq4x2k8xcc1Cy73HVtLwpsn79evz976IRlG3bqKioQHZ2Nm677TaMGjUKV16ZvrQ07eD6sMMOw5dffonXX38d//3vf/Vjp556KqhUso8ePTrtgTQnqv3uuNzzrvT5H2sbNlXwAF9lsc9qj1tEWLg5LFjA6NdgqQJMHXt7WWslEaE0jJCdKQ7aeRGvGyTnoNRGj373gVIbRR9PFmPZVYni1+pPl1zfFK26SjRLUPNRUiPOXdZkOrEKaQgRswuciWs6QvVnxsFEwM2b43W/wWCoDUq6VTj4IeE04hg9drOBAiozRxEWNVjc1V7XieceXmXK2fentPHznkOrDcYbk5YqC2nVqpXWWXfo0AElJSX4+c9/DgC1ljinHVwDAKUUI0aMwMknn4xIJNKsrFfSgkFE0slM4ZOhGgP4pSAqsPbZ6lBqi5JaXcwgZCCkLCZbo/rlIrxqcO27olVSAxW0q+1bdgZ4pvx4Q6LFOXE5LDtDv65qMlOfBX8NRdHyyzwNo8+6sOjtyxt8LN2O/jMsGuzqtPmTGwAA3fvdA5fFvc9MHlAJoUDYFt+hlvp7MhgMSSladRUKB84BbCVva+wRGWrELwlVD3mdYeQqUntN7MA52r9MJcgCXtlSUtjUIKhZ9tEcz17HHnss3n33XfTu3RsjR47Eddddh88//xyLFi1K3+JZknaajDGG22+/HYcccgiys7O1T+BNN92EJ554olaDaCw4c+WXGNK+jfmKBeEFtwkBbRXLPr+ln/KnTJzSUXIA1eLIX8RICIjLhI923A2+bqIziNJ1+zcNCkosIQWxMhEO5QAZtnAsybSFjjrDFrprmwZet6XBqZhWDVykNDCb1k2vciBVbFx7HWwaCXii6oIYlwOUNsoFgcFgaFyKPpoIHhM2py0h4XFAoM/7oojRtjIQsjIDx3hV3KhkgX55oA6sAV2Lw7ijnaaaGkoWUtOtuXHvvfdi0KBBAICZM2di6NCheO6559C1a9dax7VpB9ezZs3C/PnzcddddwV6rh9++OF4/PHHazWIxsJlMVF4oIoMpHaZu44oOnR8WWd/oKaKEdVyf/CtPLP92WsgIAnRl3/JspRq2+p140yMxXVl4O3zsk7IVttWBCE7S2SqM23w7JDOtPOsENAqHHQksdILrpt8YVxF3LtAibFG7XBVsvbaape5zLP5EQdXKRVx4ihaadoVGwwHKoRQIObUvGKKFA40hZH1RdH7V6Jo9VVC2kMoqBUGsWxxS8heE2p7tyQBNpPnc38Nlf880VSosZgxBdlIU8N1XXz77bfo3LkzACERmTt3Lj777DO88MIL6NKlS622m7Ys5Omnn8ajjz6KoUOH4oorvOKMvn374quvvqrVIBoLFZwqGx39GJcHOQZYLCza1VoEIHKuzq+l9jUqCWS0/ffldI+e8lHLE11IqBRrq207POA2wSGujhOznhwMlNgg4ZBuYEIqXdHIJCci/KtjLrhNZAJdvnaYonDIo1UCusLj5wqtNgEQZ8JNJO42eS1g0aqrRMOcEAUcVmdFgXWNv9Cxy1F3aMlIfVM46CGA8aR2jAaDofEp+ngyCgfOQeGgh2pdjNy93z2gbfMBl6HY91v3B9rMje0zAWBIj6KPJqJwwIPChk8F0LJ+hlDbcwABRF0Nh0iSIaEGCxB2ujLp0tSwibjtC97MgmvLsnDaaadh/fr1yM/Pr7Ptph1cf/fdd+jZs2eVxxljiMebWOe+GhAFBFQHreqLrq8euQvGHNg8AwQhIAQR9MZ8sg/H9b5tDvf9iLzAOpC1BuRjKhCXTiGE6NII0aFGbEM9T48NwQ5QjIsrXssOC82ujM95RHhWk/K4kIMAIDE5BkrBwwTIDAEWEd0BXSYKAR0GZNpA1PEOBuriIZlfZ1Mj8SKniUMaWKFmAmuDoWmzP7/R7v3uEc5R239EqFVecCFnQFYYKI+BEBoItosSgnDXrcTGtdfVehwHKpwzMLcSlNiex7U/sFbJLQbARSBZpgNtWYvTFNXLhHCQGmQfNS1vihx++OHYuHEjunXrVmfbTDta6tOnD955550qjz///PM4+uij62RQDQUltrau81fycnDdjMVlcaF/ikeBijgQdcHiUTAnBu5KOUksLm7Mqd5uJ2EqyLdA6KDDFrhNwW2yT7mG3ydZBdb+K1xuyW1QAp4lpCE8MyS02pUOeKswWJsMkZlWshTZMAYxV/hbMw4Sc0GijpBZxIQWvTkEZkUrLxMtiJuJZrGKLWI9Ymz5DIbmRc8RT6a1fsCNKuai0xG3ectaZwHlMVTGShGN70Y0Vqpvnfv+CZ2OnIVOR85CNFaKuFOBLkfdgW5H/xk9+t1Xp++ppVL08WRvdlmbFkCc360ES18ujBKoTyZCia2D7YZOuqRKS5SFAELuPG3aNLzyyivYunUrysrKArfakHbm+uabb8a4cePw3XffgTGGRYsWYcOGDXj66afxyiuv1GoQjYUweY+DMUd+uWWGV1X8yhbiSpbh+lpiEkJhkTCILQNXmawOltMSqdWWQbWqBBcbEBnkEBUezZTIK1yIIFeNUVntJWS/vYw2B2MOovHdiFRQEITAc8IAJSAM4ISAxEXXL54XEcV+5U6g+yMIhP2fw72sr1+aguaV8Sxa1fhBZLej/4xN66Y39jD2SeGAB7Udo8FgaHoUL70k5XVVEEypDUpsIMMCjYdROOghsEOyQXeWoyK6I6lrBUFQauhfVsVCzlAtG9deJ2YP3JgOtCnC3nlfnXcTZ7Ml3gw1b5KyEIqaM7LNYH67CiNHjgQAnHnmmQH3O845CCFw3fQlsWkH16NGjcLLL7+M2267Da1atcLNN9+Mfv364eWXX8app56a9gAaE1E44IJxEVxzOZVDiSWqf4klpCHcDRYogAR9qrV9kvxQ/FZ6yp6PQlzJWlK2QFWWmvou94QWi1scxGKAExxr4o9N/zhlNtuJl8M6qLWvy6IMlGMM/KAMkPK4KJDkXATcqlOYlrgkOKWINxuwEWyu1GVjllTYtG56kw6wC096RGr8DQZDc6Vw0EPgTlw0MglFgDhAIxG40QrQ3FyE7dZCcrj5J1TGSpNuQ1iCIhBgE3keVJnU7v3uAQE1Ou0U0XVcBGBOTBc1AtCN5FQNlm42A3mRQ7wmY02NltpEZvny5XW+zVr5XJ944olYtmxZXY+lwQk4NXAGQhg4d8ASrr0ItQPFiIRQ4WMcsrzOgIAX0CZOiyhNNueiE5NFRGBtU1F0KINzAoggW7XvZgDlYTA3psfr/1fckdOAKjvOAeJykbF2OXilI7TYISocQ6IuSCW01COAr4BSHRwIoS1Ce6d05w1JUw2sAQBRVzewMBgMzZOi1VehZ/8HgKwQeHYIdLclZilVsX6bLLCSrXCcCv0cQqiejSWEgtKwbnKlgj0lU+BgcN2YtJA1s12poM6XaiaBcUcUOjKxT/0EZqbB9CxCU5WGtNQmMkOGDKnzbdYquG5JfPv5rUkf73TEbUFvaoiMNaW2F1hTKtqVK3Thn6+oLmmBnbLC8zmHQBQREyqy2dziIDYAzkG5LbLrvgJHv8E8oTYsGoJlZYgGKuVxkLDIXpMYB88AyO6YcP6ocEAq4kDMhetWimET7+JB+Gs6+qKjika8mULiTTMT0Gg0k4JPg8Gwb4rXTEHPYY+LGUvHBt9TDjvcCpwQ8JwQ7FAWHKciULDv/zdstwK1wgAB4rE9MtD2JWt8gTZjDnr2fwDFa6Y02vttLpSsvVZc+HAp85Ct0f0BdKI0RM0iUGKD06YXngVUr/tYpznyzjvv4JFHHsHGjRvxz3/+E4cccgieeeYZdOvWDSeccELa20spcjrooIPQunXrlG7NiS/eq36K65vPb4ZtRWBbEVBiaT0250xkggGwigrPg9p1PX/qmCvkFw4X/8ak97LjNYYhHMKhg3EQh4G4XGie5WOBDHbIgm1lwKKhpMGul0mnohlNRVxKPCAkIHvjIDsrQXZUgOyOApVesWY8vhcxZy/ibgUctxIui4siTum72VA2cfVNYzZmKTxmDgqPqT+/2cIT56b9HJO1NhhaDqIIXfRdoNQGL2gF1iYTZHcc8die4Lq+wnpCKKgdBjJEozGLhoVOmNogdgi0VSbs3DzQjAzR3VdiihxTo3jNFGxaNx2UWLBoSLiISPmpNjmALGYM2PE5+OydpjdDUB8FjbNnz8bAgQORk5ODdu3aYfTo0diwYUOV9VatWoVTTjkFrVq1Qm5uLk466SRUVHgzMjt37sTYsWORm5uL/Px8jB8/Hnv27KmynWS88MILGD58ODIzM7F27VpEo1EAQGlpKf70pz+l94YkKV0a3X///frvHTt2YNasWRg+fDgGDx4MQLzpf//737jppptqNYimir/Qg4OJAw+1ReDKZCcl19FX9sLXkusGL349FSA7KXK5yy0x6aO6n+sMt+u17g5kF6l4LidM3PTDImvNORPZ2bhoMENcLiQnGTZQ6YiOhQDgivHFnL26/TZnwn2EJ1xrffP5zfW0Zw8wlKf4fnjX7hOHofCYOeCuYzJKBsOBiMN0MobnZ4K1zgDZHQfdUooYiwP+okWlsSYUkXAeuOsgVlaKSERY9ynZCKVStkgJYNsgcQZaCdFkDdA6bH+gbiQjydm0bjoKBzwIACnvo9q6VNQn9aG5XrlyJSZOnIiBAwfCcRz8/ve/x2mnnYYvv/wSrVq1AiBizBEjRmDGjBl48MEHYds2Pv30U/EdlYwdOxZbt27FsmXLEI/HcfHFF+Oyyy7DwoULaxzDrFmzMHfuXFx44YV49tln9ePHH388Zs2aldb7URDO05sfHjNmDH7xi19g0qRJgcf/+te/4o033sCLL75Yq4E0JGVlZcjLy0NpaSlyc3PTem73fvfAcaOgxAIHl1ltn6cloHXLfpcPHYCrrIE8cAUKIRX+j4RBBvMiKFb2e4y7sGlETOdRAriu7ugUdyoQsjNhhTKBViGxHSVfibsAY6iMlYJzRwT8VljorFks0LLVBNepUzjk0Wob1xQOfkh81i6vU4vAwmPm+ApXm5eji8FgqDt6Dn0MyIkIu9iIDZ4TBi3aiVhsN4Cgn75ymAJniITztBQwFMoW5xL/+UdZxQKyQzB0DRAgkjsBfNlXczzaP/YnTqmvsYxb+grCMuCtjtjevXhqxC9rPe4ffvgB7dq1w8qVK3HSSScBAI499liceuqpuP3225M+Z/369ejTpw8++ugjDBgwAACwdOlSjBw5Et9++y06duy4z9fMysrCl19+ia5duyInJweffvopunfvjo0bN6JPnz6orKxM+32kLaj997//jREjRlR5fMSIEXjjjTfSHkBzQxUrcHA9zROQavgKAnU7U1U06bfU4xBBs8O87odxf8t1eC3QfVAipu4oscSBLSsEhCmQGRKNcGR7dMYcuPEKcbB1mGwuI15XBeGJlkt+DZgJrNOjaOVlKHrvChQeX1WiUbTqKk8yVEcUHjNHF8SCMa2fNxgMByA2BY9YID9VgmeFQLfu0XIQVajoyRBE7RChNqLx3VUDa3meAGNg8Sjcir1wynfDiZfDccp1nwWdJLJ8xeLy/AOY1ustEZW5rukGoIpXtJJa1ERpqXC1UTLj7du3Y/Xq1WjXrh2OO+44tG/fHkOGDMG7776rn7Nq1Srk5+frwBoAhg0bBkopVq9eXeNrFhQUoLi4uMrj7777Lrp3757SuBNJO7hu06YNlixZUuXxJUuWoE2bNrUaRHNjy6e/R8jKFDKRJIG1P4hW9nlVCgT9hQwq8FIBdWIbdQrhW20JHRyxRVAPQoWem0HIRiKRKl7YPBYH31sJVLraDURslmp7IH3wlVN81RV5GlKAykx1Ii4PZoT2g8JBDwUcZQC0CEcXg8FQS0KWSKJELJCfKoDdsSqrJNrJqgQNB4frVoI7cRFUWxaYG0PcKUfcrUDcqYDjRuGyeGA7OklUHZxpKYShZZCO5rpTp07Iy8vTt9mzZ9e4fcYYrrnmGhx//PE4/PDDAQAbN24EANx6662YMGECli5din79+mHo0KEoKioCAGzbtg3t2rULbMu2bbRu3Rrbtm2r8XUnTJiAKVOmYPXq1SCE4H//+x8WLFiAadOm4corazfbnHY56syZM3HppZdixYoVGDRoEABg9erVWLp0KR577LFaDaI5Qiyx6winVQzhldtGYH2fzZF+LNHAQrVKZQSgvuCaEMCSy+UUHWU82JTGYYAtClHUQVA8XejnuLLzkxXfIri2pUOIrGKmNr759Pf7uWcObIreuQKFQx6t4qtdtPqqOsnk6MJI2xKfPUvf3N5gMLQceo6cB946Q2StM0MgZVFt2ZBo5+YPsDlnejnnDAwOiMtAQxmgrq1nOP3PDbhbECERoQiL+5R6M61N1KfZsH+kY8X3zTffBGQhkUikxu1PnDgRX3zxRSArzeR36vLLL8fFF18MADj66KPx5ptv4sknn0wpaK+JG264AYwxDB06FOXl5TjppJMQiUQwbdo0TJ5cuzqCtDPXF110Ed577z3k5uZi0aJFWLRoEXJzc/Huu+/ioosuqtUgasOcOXPQtWtXZGRkYNCgQfjwww8b7LX3hedVWf1BDVANbHzSEd90mi5sVM1cLKJvPMMSlks2ke1Vfa8TsmBFMkGpjVAoG7aVoSUqLovBYVHEnQq4LB4ouCSEYsunv8cWE1jXCVoikpC1URrE2mRzCgc8KIJzv0xIzVpAdIQ0GAwHFoUnzhXnhJCo6SB7YuCxOJgT0zOrypkicVbTLxWxaFjIDakNxJykCSL/c/XfYKIbIWfgrnc+A0k7tDA0A2iKNwDIzc0N3GoKridNmoRXXnkFy5cvx6GHHqof79ChAwCgT58+gfV79+6NLVu2ABCyju3btweWO46DnTt3oqCgoMb3RQjBH/7wB+zcuRNffPEFPvjgA/zwww/VarxToVZGioMGDcKCBQtq/aL7y3PPPYepU6di7ty5GDRoEO6//34MHz4cGzZsqDI1UB/oQjIGSBNLACoTQD2jx8Q2sz5vS91SlicE3n73EIXqpGgBJCZ12dIOEIyJcdgUPDsMZNoI/wggyxadGStiOpOtXEEIqD5sEkJbjN1eU4Nzhu797gEl9v67eKhZDWpJ/Tz0587iqWnZDAZDy6HwuIfBD84Cwjbod3uEzWo8OJOVLKnjf1z/GwmLc1qMwY1XTcAolAe2kj6KJ6OKPIT7ih6797vHyNZaCCQFtxCSplsI5xyTJ0/G4sWLsWLFCnTr1i2wvGvXrujYsWMVe77//ve/OP300wEAgwcPxq5du7BmzRr0798fAPDWW2+BMaYVFqkQDoeRk5ODnJwcZGdnp/U+EkkpuC4rK0ur6nP37t3Iycmp9aBq4t5778WECRP0FMHcuXPx6quv4sknn8QNN1QNFKPRaEBMv98WN4wBxApoaAM6tITHAl2XtOYaXoBdHeq4qAJtm4Lbol269/2lYj2LiINcyALPDgHZYWBPDBbLBKIQB0siJSwQWXICii2f3bhfu8JQPSQcApXnGH+2OlUrpp79H9B/q++VdodhslV9NA6XxfD1J9fX3cANBkOTpnDQQ2CHZIO3zgT9uhSkNKrbait0IicxyVNdVtmRBYw+WaG/k6P/voKDg3BW1TXEv46RiLQY6qND48SJE7Fw4UIsWbIEOTk5WiOdl5eHzMxMEEIwffp03HLLLejbty+OOuooPPXUU/jqq6/w/PPPAxBZ7BEjRmDChAmYO3cu4vE4Jk2ahHPPPbdGpxBAZLlnzpyJv/zlL9obOzs7G5MnT8Ytt9yCUCiU3ptCGk1kElPu++KQQw7RIvS6JhaLYc2aNRg2bJh+jFKKYcOGYdWqVUmfM3v27ICwvlOnTvs1BpfFwJ24mAaTU2GA0CwHAmqfSXwyEj1CNX6Pa1nQyDNt8LDlPQYEgntwDlQ44BYBz4uAZ4bAD24FhCzxOrpiXHbfMlZ79U7RqqtAwuJHqWRAiSenfeGymL4B0vaKwGtIxBgc4xJiMBxQFA6cA54dAs/PAN1eDrKrwpNmcKbdPJRjlZ9gUSPVN7eyHE7l3qQ66+rwy0b8XR8TJY+GlkM6spBUefjhh1FaWoqTTz4ZHTp00LfnnntOr3PNNddgxowZuPbaa9G3b1+8+eabWLZsGXr06KHXWbBgAXr16oWhQ4di5MiROOGEE/Doo4+mNIbJkyfj0UcfxV133YV169Zh3bp1uOuuu/DEE0/g6quvTvMdCVLKXHPO8fjjj6ecJo/H4zWvVEt+/PFHuK6L9u3bBx5v3749vvrqq6TPmTFjBqZOnarvl5WV7VeAzTmDy4WmLTDNpvysYQelH0g4qCUcsKocwJQsRBmk2xTIsLWHKRgHl10ddYt1Ii8pCQEiotiNlEZF+3Qq2qeDu+K1pMbaUP8UrboKhQMerHLSSpXAxRehuoEDY97JtDptpMFgaHlw5oAflCcahEWdwLmmpmBWa633EQIRkMAxxW/hB6DK8YaD66J+tSyx5kjVhHDOzCxbM0Yal9W4Tjqk2mrlhhtuSKpMULRu3TqlhjHJWLhwIZ599lktMwGAI488Ep06dcJ5552Hhx9+OO1tphRcd+7cOS0nkIKCglql0euLSCSSUqVqqgRkHmo6jDMdDBNW9SCXWEAYCKiTZQeSPMTzIqJFesynq0vIYpOYK4JwDrEeIbrpjPawNlKQhiVsA5XpB9eb1k1Ht6P/LB1dHHCn6jYa6kSV6H5iMBgaB84ZkGGBlkZBdkV1qOvPHifLOO8rC50O/uDb010naLvBwbmrg2y/drvLUXcAgKn1aYZYlMOi+w6Ga1reFIlEIujatWuVx7t164ZwOFyrbaYUXH/99de12nh90LZtW1iWhe+//z7w+Pfff59SVWhdELjqV64dDvEFxBSEU6/AUZIYYIuNqX/VSupF5PZ0+1m5nuzWSJjXKl0E2ETIBSwC8lOlyF4DQNwNTM+ZwLrhKXr/ShQOeghObC8AkcXZtG56Ss9NnFpN9QRZeOJc3aCoLloS88ooCgc8aNobGwyNSM/+D4BxB9Thgc6+qpA+MbBOlIH48c+sBh1AeNUGY0ierkzUdSee3zi4CMZV4O/LeqsgGzCBdnMhFdlHc/SJmTRpEm6//XbMmzdPJ2Kj0Sj++Mc/VulGniq1cgtpTMLhMPr3748333wTo0ePBiB8EN98881a74R0KVl7LQBZpOZwIBQ80IHIAxKnQWlI4tdOyT78ziD+KTfVVCYkiieJCxEw6Q2SwLqEALzSCdr4uRwOi4JzZgLrRqRo9VXo2f8BLQ9RGWkCqr9PyVCZaX0i8p249hloU4qiVXWbaWbMqRJgq0JNzhkItU3LY4OhHileMwWFA+fAzbRAymLivJDoP419B9X+x6sLsBNRQbL3XC97rYLvxPuJ4wn4aidsL52Eg6HxoCm4hdS0vCmybt06vPnmmzj00EPRt29fAMCnn36KWCyGoUOH4qyzztLrLlq0KKVtNrvgGgCmTp2KcePGYcCAATjmmGNw//33Y+/evdo9pMFQB7A4ExnsJMv8V/Q1Zh0pRAMZvwZJaqm5TUEqHekUIZdbRMTiuvsfASwqbPgIAbctkEqn2qyDoWEh4RBI1AlM325cl5pFVbLMTo9+91X/hFjqhZOpULxminAvoQnf4cwQUBEHoTY4c9Cj332i6xtn+28/aDAYqsBbZ4C4HPSnSoBVndlSmWQve+zNoCa6WlUJsPdRv6EC4qRdhrHvhjWJr02JFRgvICz71HLGXZPNboLUh1tIUyA/Px9jxowJPLa/xhfNMrg+55xz8MMPP+Dmm2/Gtm3bcNRRR2Hp0qVVihzrm6KPJorGHpwBLvW8rykAEFASBqqJcThnokOj8i8GdEMQkYL2ZcMtIooXxRMBl3kBuMpQAyKrTdUYREMBEALbyjBFJE2AolVXAcB+FTj62VfGW70WINwF6iKj7A+WC0+cK9xrWmcCyATPsEH2xEDLokDUBQHVwT8HMz63BkNdEbKAvXGRVAGqFDQmk5H5A+zqMtSqKF/cSaLZ9gfWSBKo11CoX1OiKVHS0rnvn2BbEdkITdQZmUL8xoWkEFyTZhhcz5s3r8632RzlMQCERmbz5s2IRqNYvXp1WkbhdUnRRxO9TolKJ60CbIuA2qLrFaV2wKovQLJqWSUJcRjInjhQHgcpFwdU4vg6OAL69XnYEhrtkE+n3Ww/4ZZL0ceTQUnDXdcWfTRx31nu2mzznStQ/OYEFC8ZB7J9L+i3u4FKR1hAts0CMmzQUER/37sedWdAY2kwGNKncPBDItniMC0RVA1ddOG6+i+JFV6V+6imqYz8LxnJguikN2nzR6X9a2B9VA3S1fgSg3UOrrfZ6chZKe4pQ31gpXgzmNCrbmBM7EnORaAbpjKwldnskAVYlpxTocmD7GQBtpJ7MCay1S4HcVVmQR74VIWBzHIT6SRClFe2w830fBOkeM0UWLR2VcipUDj4ocD9fWW595eiVVeh6L0rwH4qA/txF8j2PYBFwPMjQNtsWPm5sNu0RjgzF4UDHjRBtsFQW1wuGolVyinRsOyY6HOE0h7TCQF2YtOymrLdgXUTtldTME2JXSWplJixVuur1/QH0+p9qE6RAMDcGBiLoePPTe1QY6E01zXdmhs7duzAxIkT0adPH7Rt2xatW7cO3GpDrdJn77zzDh555BGUlJTg+eefxyGHHIJnnnkG3bp1wwknnFCrgTRnij6e7LVElw4f4rjFvKy2K4NkJnvFcoipNzXHwnnwXyUNka57hAmZCA9ZgMvEfQmX/taEcSDuglAvY83c/ZcfGOqH+nTe8MtCGoqNa69D9373wGUxkFJHNL2xLPC8CBAS3UVJnCFc2SzVaAZDk4DsjYsZyuwQYFHQXYBbHgsG0jr7a+n7hNCqAfY+JCKqADFVH32d9aa2kJckZrhR9fWre5wzRz5m63H6m+J0/PmNMngPG6lIA9JSNdcXXHABiouLMX78eLRv3x6kDrQtaZ/lXnjhBVxwwQUYO3Ys1q1bp9uKl5aW4k9/+hNee+21/R5Us8RSshAhySAEAJcfEJEBNSEAlQGzX2sNyGAbXkaac69To9q2y8V0IERArS8Q1XMckd2GK/ytEY3DYVF0OnKWcQoxNAie763oMAkGWD9xUfAbtkWhZcgyvtkGQy0o+lDUTvQ4Z4FIqFTEhfyQ2uAswXsaXoOXZI4iep2aNNOcgRBLy038hYyUBGdh9d+Ueuc0LgJuAlTbol3km6o2wmHMEetU04a9rry7DanRUoPrd955B++++652CqkL0v5mzpo1C3PnzsVjjz0WaBRz/PHHY+3atXU2sGaJyjYrpBUeABEgqyDZkt9Qlen2tz1S61nBbygnAA9REUBz6XPNuRe/y/s6wGZVtWuGpk/hcel3gmpK+Iuc/NO5PBYH270XbrQCzu6yKt9vg8GQBi4Hz7SkRETIQSwaqiK/UHIOJRkJBN5J7iuSyz48HXZ10pAqhZD+pHeS4F5lpBl3dOGiP0utOtGCUB3Ic86E3ESGL0Zm1nD4w5N93ZobvXr1QkVFRZ1uM+3M9YYNG3DSSSdVeTwvLw+7du2qizE1S4pWXSV0rpR6emd1LFHZZ0YASGlIoi7JQjBrTeQ6MiAnHOARS9vw8RAFqXT1N5kTiKmM7LAIrisdOG6lvvI3NA+K3r8ycF+50TQna7tEFwFVcJXoGFJ43MNV3q/BYKiZkud/i+4XPwdk2kB2CFZZGIwHs7mJHRsTG7ooArKPJN0d/Y1hkvppq1nYQCBddczJ9N/+wkZl4cmJeC2VneY8mO22rAwhOQNAibVPf25D3WJTDruGDow1LW+KPPTQQ7jhhhtw88034/DDD6/SYTw3NzftbaYdXBcUFKC4uLhKq8h3330X3bt3T3sALYq460k/FEreQagso6UiaHZ83RWJL2sNCNlI3BWFkBHLcw2pkPZLLgcybHCbgcQZuE1A4kzo8MIWyI4KxOK7xSZp02lDb0if5taUxR9Adzv6z/rEl6xBRNH7V6LnsMdR/MalDTY+g6GlQFwOnhkCOzgLtNwBdQDm935N7AaMqs1batJTV6eTBrxg2es2LBewJHZ+ct3EpmrC/g8BPbgqjlQBtCrQ9DLkws3EtjJACDVdYxuQlmrFl5+fj7KyMpxyyimBxznnIITAdd20t5l2cD1hwgRMmTIFTz75JAgh+N///odVq1Zh2rRpuOmmm9IeQEuCMQfUkU4hSv7h8KAXNeAF1X6obx0mA+UMYa1HuCu240IG0EQcyGwKbst/IxBt0ffG4DjlCNut4LiVoMSu9uBoMNQnqXRcK37jUhSeOBdF7xj9tcGQDiVPn4uu018GdRn4QRkgOzlsZMCVUgrhSy0LGn1BdHUBdRUf64TAOtkMKOMOKJNFjP7stfLM9mmk/V7bAHTwnJgVt2hI6rCZzqSn1ITNUO+kYrXXHK34xo4di1AohIULFzZeQeMNN9wAxhiGDh2K8vJynHTSSYhEIpg2bRomTzZXkIEiRCa9pgGRba5OjMS5lIxA2/nxDEtkpS1fi3TpYQ0AiMorKYsAlY5YNzssmsaEWwFcHGhVsYjB0FQpeueKKm3VDQZDzZCYC56fAS59r8kuBtvKCDSp0oEqEoLsauz3kgWxiT7YgBd8c87A3ZgIln1FkH5JYrALJNP2gYlBMyU2GHc8KQq1QXnV12bchctipmV6A9NSCxq/+OILrFu3DocddlidbTPtS0FCCP7whz9g586d+OKLL/DBBx/ghx9+wO23315ng2qulKy9FsiwwCO2CIotX0DsD6wTr4pUllutn+m75vG1OucEWhaiCxhdLuQjnIPsFs4tiFiiLXVmSHuM9uz/QL2+d4NhfzCBtcGQPpseGA2eZYO1zQJv30r3VPA3byFESCxUUSKwbws+v/+0IhCIIxhgBzTeqohZ66WDFnqBIFtmtoll69f1LyOEwrYisK0ILBqCRUOBv01g3fC0VJ/rAQMG4JtvvqnTbdbacDYcDqNPnz51OZYWAY/YMlCm4FEHhPEqtYsAZCdH5RYifan9QbcltWg+P2tVKMkJRJU4E0E2Z1xkyGNMWJ4p3OALdzv6z+aAZDAYDC0IsisKdnAWSFkUyLCBqAvKw572WbdOoOBEdnJEsuYxll5PZY6TBeHaMg9MyA6TOJAk2uolOld57iJVt824G7gQIISK9udyG5TY9doUy1A9qbiBNEe3kMmTJ2PKlCmYPn06jjjiiCoFjUceeWTa20wpuD7rrLNS3uCiRYvSHkSLQjZ0gQx4ucNEu3IF9xUyqkBYBtbKVk85jXBlKqLaqfsLIKl4Df0cpeuWVn0qq81lMQi1wqCmqtpgMBhaFJv++mt0+eMysIOzAABWyU8iwnFF0yaiAt59HP91wM24dutQGWwVROt1E7PP8ByB/I/51/H7bQM+lxFxp8o2VCG+RcOilskX+JvAuvFoqbKQc845BwBwySWX6McIIfVf0JiXl6f/5pxj8eLFyMvLw4ABAwAAa9aswa5du9IKwlssyr/aTVK0yJgXKPuRgTXhEM+jCF7RJxHvkKgjAvCQvPpnMqi2pXm/w3XGQjQYcKpuxGAwGAzNns1/OBVdbvs3eNgCz40Ae2LCvjVHFNeTnRWA68kzCIgObv1BMedMGlqJQFhJS5IhZB3e3377PIWyAFR/q3WVHptQG+pEZdGw0IpLr2vbytCFkIamQUsNrjdt2lTn20wpuJ43b57++/rrr8fZZ5+NuXPnwrLE1aTrurjqqqtq5QXYkig85TEgJCNjmVmuIglRxYsBuz6AuPAyzpwEg2vLl9W2ZMt0GVwTiwAu08E8D4lshbgvq7ghC7lN5tqQJh1/fiNcFtf31UnUoiH87z+zUtpGz/4PNBuPboOhuUIqHLBDckA4B/mhAtzXuIzEXJC9BJYrAmAGR9jjqecmNH9SEhFVXKi01YnZ6WQt1/347fr8zlUui8GiYRCfo4gKuvW2fPIUi4bBOTNZ60bG3+9uX+s0N7p06VLn20x7Pzz55JOYNm2aDqwBwLIsTJ06FU8++WSdDq45UXjKY+IP2aYcTN6UvZ5qKqMCaAbPs9phIvPsJq7jk3gAIrBmXGetQeE9R2bLCZPbUwdW6gXqxsrIkA6djpwVKEjyaylTDawNBkPDYG0qBRwGlhsBz4+AtckEK8gGa5sJ56j2YJ1ygVYR0FAEFg3r5yU7L/i7OorOiZ7XtP85SvKR+B/jrnfPF3Sr17WtDP18/ZoJ6zFXOJ74iyYNjYtNOEI13OxmWNAIAM888wyOP/54dOzYEZs3bwYA3H///ViyZEmttpf2N9ZxHHz11VdVHv/qq6/A2AGcGVUBsMNAYi4Qd0EcFlyuAmjZnlzfVICsUIG3fp4obCRxVwTWKngmRDzfIuBhS3yaSnPNID22VTVL7Q5QhSc9gp4jnkThoIdqsVMMzZWuR90Jzh04LArHKYfrVgaCbIPB0LQoefZ8WF/vAkIU7OAsEVy3CoGHLbBWIbg/OwjOUe3ADskGaZWhm7AolNsHZ474vbsxuCwuigkBOCyqLf48rTX3Lrx9xwWlkU6UiYhlNohla59rlT73ujYScDC4LAbHrRRFjswxWesmgJKF1HRrbjz88MOYOnUqRo4ciV27dmmNdX5+Pu6///5abTNtt5CLL74Y48ePR0lJCY455hgAwOrVq3HHHXfg4osvrtUgWgI8Ygs7vJgrMsjJVvIXMQLBrLZaDniXPH7Ndox51d+UAuBeASMhQo6SYYv1KAXAANvytu3KJjS1IV7bJxqaMoccfrM+uX77+a2BZUoKQokNKrNN+oRK0guu426FCNbBsfmTG/Zz1AaDoTo2zjsH3a98AeygDCBiiaRLiIJUOiCVLmBRuN3zQfbGQSpsUGbB9Tl5qNkpNWOlHmcyqHYBEF9xo781OiWWbxvBjLVy/mDMAbXDsg+EJ1sU6wSTP5TaIhBPouU2NA4tVXP94IMP4rHHHsPo0aNxxx136McHDBiAadOm1WqbaQfXd999NwoKCnDPPfdg69atAIAOHTpg+vTpuO6662p4dsuk8BePykJGKjLGcTdYuKgcPiwiAl/lTw0EOzcCniWf31WEQh+MEHfBeVw+VVoTlTOQsLQADMsxgHqZbFdsK10v4cKBc8SBmRAUrb6qNrvG0ERp3/t6nV3i4GjXS1g0ihOapa2ybCsCSixvipinf5JznHJxkqS2sYM0GOqZjQ+PAQB0m7wYLD8DyLDBs0Kitmd3TNTf5GeA7InDJlmwAcRje0RATCgIF1prxt1gIxifLIQSC5YVBiFhgDNQGgpIRri000tsFEOtsDfralmA6+qsd3XHFlPU2HQQatN9yz6aoxXfpk2bcPTRR1d5PBKJYO/evbXaZtrBNaUUv/vd7/C73/0OZWVlAHDAFzLqhjGuX0/tAkxG1xSyJ6gWP4uCRub7FpIkGW31WIwBMVfqp6n2+xT+n45oc14pp9kyQ15wHvfGU/ThxJTfT+Exc8RUnU1EQxp33z8mQ/NDnfDUCVRnq1xHZqtVISyRBU3iO0AhlnXo83sQQmvUXhf0mSGCeCJPyD6tp8FgqD82Pfhr9DjrafCDs+C2a+XV4cQZ2CE5gEVAv90NVDpCJ62KCeHJPJQkgye0L6dWONg8hnktytVFO+BdrFNii8BapTV9tUTqdSixPfcRGUwnar0NjUtLzVx369YNn3zySZXCxqVLl6J379612matm8gAJqjW6ADUy0Yr/RohFODSHg++A4sKsEGCemtl4WfLzHO5IwJ1KmUfAIgVFtlxh8FyhM0e446owK6A6NIlt5lOxrlw4Bw5PiktCdsAISh6+7Ja7xpD0+PgXteBgILBywgx5uipX05sMC6+a2paVqFPltQGITY6HTkLhFBs+fT31b4epbY+GZuTpMHQcJQsuhAA0OP8hXA75YJn2CDxmJgBzQmDFbQCiTGEf7AQ3bNTnEuYo9uLq/OY0F6LZZTaCAFwffIQxmJSSy1/55yJYwR8BZCciXOh0JYA8DWXAQ0cHxy3UvRnoLY5ZjQhWlpwfdttt2HatGmYOnUqJk6ciMrKSnDO8eGHH+Lvf/87Zs+ejccff7xW2077W9utWzd079692ltticfjuP7663HEEUegVatW6NixIy688EL873//C6y3c+dOjB07Frm5ucjPz8f48eOxZ8+eWr9uXcAzQyLAVg1kCEloCYtgAO3PUgcKGZnnHOIwkVFw4p7ZftwFKMCpDL7DNhC2QMIhWFaGvPKnupAxbSkHlZpwy5LvByZr3QIRhUoxuG6lLlzi3AHnospfnERjYHIdx62Ey2L65OqyuMhmyWlcSix0PepOdDoymMXu+PMbYdEQCLF1BhyEovDEuY3xtg2GA5aShefD3rATpNIBLCL01y4Hb5MJlhcBOyQb4S6HICO7LSxZ6JgY1Ko26gD0jGncrfCOB74LdWW95w+ORYZb3URRv1824l+PEjvgKGKKqJsGLa2gcebMmdizZw8uvfRS3HnnnbjxxhtRXl6O888/Hw8//DAeeOABnHvuubXadtqZ62uuuSZwPx6PY926dVi6dCmmT6+9lrK8vBxr167FTTfdhL59++Knn37ClClTcOaZZ+Ljjz/W640dOxZbt27FsmXLEI/HcfHFF+Oyyy7DwoULa/3a+41NwRkDCVERAFtETJPBq8AGLE8/rY4T6l+/PZ9sAMNZVBxk7LD3PLWOBVGowiE00XtjACUgXB4MOUtbX60JWcJ9xBLacCUHMDR/2hROltOscjpWT/OKglXlacs5B5cOAYSIdTj3FRxJORJlNkJ2JjhE8Eypjc59/6Rfz7az5Dao9MYOi8LbxOZKBoOh3il+8UL0OHchWMdsoNIV/REYBzIsYE9M9E7IsJFB2gC7vUYvwmdadEwUMhFX1GC4UZGxpmHhPMIcAJUgxIZFQ9pZRPlkU2oHAmV/sK6DZ8sC4RTcEQkAdezYuPbArOdqaqgeeTWt01zgvpq3sWPHYuzYsSgvL8eePXvQrl27/dp22sH1lCnJm0HMmTMnEASnS15eHpYtWxZ47K9//SuOOeYYbNmyBZ07d8b69euxdOlSfPTRR7o75IMPPoiRI0fi7rvvRseOHWv9+vtDybPno8eYZ0SQbVEQIjtX+eNSHRyT4GP+QJuJFrG6UJHaYhsWpAOI2AaRHtq6q6N8HISi6KPUtdVJsYjIjFsEABFab0PLQJ7kABY4qBAZ7FLYIL5ZFyAha6S+upyBuQycMsABXDcWyFARiAy1mBYWQbcIrKUW0+UoHPwQilaZIlmDoSEhe2IiqFY/cZeL80vEAvYS/RiHkGSAAS5RTWIcGViLGSvGhUSEymJEyi0w5sCiVLc7F/+6QoedkHxWM1mc+TtHiqQRISJh5W88Y2h8KOGgNRQ01rS8qUESkj1ZWVnIysra7+3WmZjp9NNPxwsvvFBXmwMAlJaWghCC/Px8AMCqVauQn5+vA2sAGDZsGCilWL16dbXbiUajKCsrC9zqBQ4p16BSM01B7JCQb6jgmMJzBFEfqu7MGDTsZ8zRxYugctuqWNFhYmpPwdj+B9aqAQ0hQt9tUyMLaSG0PUx4xHraRiJdQYhXiEQtUBoGpWEp5RBTu9XBOdNTwzFnL+JOhZSNKJkJ01krWJaeFQm45RgMhgaj+JWLgDgTM5/q/GMT8AxbuIqEhYMHpeGAa5BK+ujCZxZsLOXPRnMw7ZENILgevPX8cKj6JNFZmHFHdGU0gXWTwiap3ZoTP/vZz9C6det93mrDfhU0+nn++edrPYhkVFZW4vrrr8d5552nCye3bdtWJVVv2zZat26Nbdu2Vbut2bNnY+bMmXU2tmSUvHABepz1NEhMaKKJPxB2ufzWicw2ABAifaepr925PpBBriODksRtJc7LuNzTZe8PnAHM8qySOFD09uX7v11Do3FQzyuDGWX1b8JXSGWbk+ofuVeUpIJtvR4IOLj2sRWWWi4cNwpgL2wrgnAoB5SFxfdU6vi566B7v3vAOTPWfAZDA0Iq4mBtMkEqHBCHgUddkEoHnIqZ0EQdtAiYxRS6P/kDAIRTuCwOQmww7mppopKcMVe0MecJXtWU2rCI55+v64Wk6xYltkhMcZ6W05WhflGGMzWt05yYOXMm8vLy6ny7aQfXRx99dCCNzjnHtm3b8MMPP+Chh1Lv4rdgwQJcfrkXuP3rX//CiSeeCEDouM8++2xwzvHwww+nO8QqzJgxA1OnTtX3y8rK0KlTp/3ebiIliy5EzzPmAXF4GboYF24fNvUKEVVrdAovsJYz5gQ2iJ6Gpz7piM8X24Vn11fpyAxEHWQCKfV1fuRGEtKMyeshHF6IX3tEfAE2sb1ZEV8wrTPNUNO2wZOtdgpB1cyTP5vluOV6CtlSmsyYGAMYC1hsFQ6cA1DjpW4wNAQk6gIh2WAmLjsKxxiIDLIdFvNZcBL9W/dnrRVaNubr0ui3zhMWe2F98Z0Il7pudazRY7RDAAWKVpnAuinREmUh55577n7rq5ORdnA9atSoQHBNKcXBBx+Mk08+Gb169Up5O2eeeSYGDRqk7x9yyCEAvMB68+bNeOuttwJ2fwUFBdi+fXtgO47jYOfOnSgoKKj2tSKRCCKRSMpjqxMYPB11YlaZyOw2E39rz2tlzQfPJ1S3Svc3m1GXhv4Zs/28XCwcOEdk1wkR0hBKUPzGpfu1TUPD0OZnU6S+0Tc9CxqYgiWggW5ogHc55mWhfUG1fh4BpG9tYD31nU4yLSwkJmEAsao2WhxCj+3XUhIIKZXBYKh/XCG94DnScz7ORB1PBYSTCKFy5gmyEDkkgmTugKlMNvcSL4R4xxYmm8eos5Ga6eLyaKNdgwBt61fFCYQJuZmh6dHSrPgS9dZ1SdrB9a233lonL5yTk4OcnJzAYyqwLioqwvLly9GmTZvA8sGDB2PXrl1Ys2YN+vfvDwB46623wBgLBOqNSfGrF6PwlMc8Sz1KqnZhrA4q5+X8WmwXgUJHcWBKcFxQGu5aUjjgQSE/0V0kiSh6MTRp2h52rfedIBTw2WERQgAutNUqsPbLQ7TGEaiShfa3Mk4sUqwJ9VxxEvYy3JwzLzFFIMbjP6k6DIXHz0XRe1fsxx4xGAw1YhGQCke0SLcIUOGARB3w7DDIrmig1kf9/i0aBiMxUCo7tcLf/MVKbtsnu7Lqi3T/cnXMsSwQRj0bCke8rm1n7X8NkaHOaWnBNU81NqsFaQfXlmVh69atVdLoO3bsQLt27eC6tZMSxONx/OY3v8HatWvxyiuvwHVdraNu3bo1wuEwevfujREjRmDChAmYO3cu4vE4Jk2ahHPPPbfRnEKSooq1KPVakHMO4nBwePuHExmvcCnrSPygdQt0yKl7n/c041W+xYXHPYyi969Mf7whqesOWeBStkJMcN2kaXvYtbprWtWvDZeFiiThBOdrQ5xQqJiondbSD58cpEoRknIDUH+rLm6+LLjYeIJnbuLxjEN6uHMUHjPHaCwNhvqEAWRPHKRVCDxiixqIsCV++TFXFyIGajO4C2qFYUP42jNl1QkxS+XvvEoQPO748ctFAMhCfUuf65gTQ8naa+vvvRv2C+XHUNM6zQXG6i/OSTu4ri7Sj0ajCIdr39r4u+++w0svvQQAOOqoowLLli9fjpNPPhmA0GpPmjQJQ4cOBaUUY8aMwV/+8pdav269oLLWNhWtyzmXjWQYiEODmWb12VIEbfqUpzUgruiV04LDhIabyHUsX5a7ltnrolVXofC4hwEiM+1hC6gwwXVThnOmPav9+uVkzfbk4AAANF1JREFU8g7AcwlJFiT719VBd4LGWndqpKLglicUNkFmq4U3bkK7YjXr4q8l8MHcmAjKmbDv69n/ARSvSW75aTAYak/PUU8BeRHAZSC7Y+C5EZA4A0IU3BX/IoqA7AvwBcwWBUXQyYr65GbKehO+Y4fL4j5ZSDAbLhJRzNhyNhdICmFGM8pc1ycpB9cqgCWE4PHHH0d2drZe5rou3n777bQ014l07do1pRR969atG7dhTCowpg8aQnMNmcWmXqGj49NRK1s+JbVWchBABOgRSxdDEqWJVtlxf3C+P1McfmvAmIui5ableXPAH+CqQiNKbelZjarFir7GDX78sg+v6UtInAAJDc6ScC62A083zSGC58TObsp+i3FHOwkQiHH5Xw8cWn9p2h0bDPUHtwh4qxB4qxCIy8Blssf6oQKIi4YxrtRcK8TvHeBENJUB9VnscQZXtj9H4LfPhZOIT2etL9KB2jc6MzQasvtFjesY0giu77vvPgAicz137lxYlld0Fw6H0bVrV8yda9oaAzITPOghEayqY03ifIo/M00TgmxAfENtAoQpeMgSGmiHgYcoQEIg5fFAYL2/V/5F712BwhPngsSSWP0ZmhQFfWYAnOm2w6kEo0r3KJq9JAmuA/rqKj59Se7T4MNcvQb3eWl7eutEFwEAOmAndgjEEXUFKjtu9NcGQ93Ds8PgrcJg7VuBlMdBKl1wiwgZYEUcCFFQ1/akIaqhC/E5hRAquigieDHNuAMK1XHRAedBGZq/eNHMTDVPSAqZa9OAV5BycL1p0yYAwC9+8QssWrQIBx10UL0NqkXQKiQ01Y6vMQuDF0ir4kXX11DDgldUqDolAiKYiYimLjxsicA65gJO3U6nFb1jgpnmwLYvZyO/xxW6ot6vbxatykWHUB3kglYJwAOSEV9QncxmD0C1syKqPbL6m3Cmp4T9vtgcoutbwMMd8IxTifR7ZxDOIYSg8MS55jtpMNQh3KbgOWHPhq/SBS2LgvxYIS9wxcWxbQl3LcZdbbUHqHborj6uBLbtcw7ypB+ibbo+xoRDRgLSjLEIh1WD1V5Nyw8U0tZcL1++vD7G0eLgmTZACHjMDWb4RJn1Pp7IwSn1NNYWBbdJ0IM6bHkFKIYDGr+NleqOptxAREtiL+tEiBV4rvax9ckz/NO2UN6z/lREDdIjv/TE4l4Nhsp6KTkJ4QxyPlr8JiwipFCy2RJk06TCkx4xjYwMhjqCxGW9DiUgcVc0jqEEJM7AbSLqHpwwCKM6SFazTkzKusBknkhnqb3Oi+q+ftw/W0VrnmEzNG2MLCR1Ugqup06dittvvx2tWrUKNGNJxr333lsnA2v2hCxx4HLkQUtmBeAqBxC/Yz7xAmd1/OH+zo0y4JDaOG5TEIuKAkTDAYlFQ6DEBoPQWSeThnDOwbkDxgBCKUAsaYnHqhQV+iUc1cpMkgTWVTxqoQJsGxb1Tq6cOXAJk7IUeeLm1NsmI4HfAbdVwG++4wZDnWFT8IgNErHAQxboXtmiPDsEEnWAOANsAuLaIByAanVORETNiS1iZAZwkIBriCh2q96SD4QIVyBDsyUx11LdOoYUg+t169YhHhc/wrVr16I+jbdbCjxMvYycQ8BDUtcmfTx1gK0kIJYINAiTwYYqiKTE+5T8+92mRht9AGNZGbBZHC6LVQlwAwWFnOmvjbbNg5rmRSCbnTSw5vp/1TYB9TeuAYEubKTEFidlqIJHXwabSgmJK7PjSi4FIZciYDrANhgMdQPPtMEzxKwn3VUJsjcOsks2jNkT95ys1GHBpaAk7HVzBcC4uHjm3AHcmPa8VhIwxl1wNwqLMjguRcii4hjEucleN3NM5jp1UvqmL1++HPn5+QCAFStWYPny5dXeDIKSf/w2mOlTBYxKY2pTkUUICR01MmwhAaG+igH1dPUcAIjKK3/q02QbDji2f/VnhEM5CNutYFsZsKywcAnxecsK/SMPdFH0tysW63jf0aqBtVxP2khyZb+V0O44GaqxjV+XqccAYR8o7LwgHHUcOavjMM9Jh3FhAW8wGOoEnmEDtgVe4YDsjoFuKQP9rgxkV6VwuQLkDCr1kj9U3Lekn7VtRYSTkJUhjj00pB2IKLFg0RBsKwJKw17BY8gSHSEz0laiGpoQqolMTbd0mD17NgYOHIicnBy0a9cOo0ePxoYNG5KuyznH6aefDkIIXnzxxcCyLVu24IwzzkBWVhbatWuH6dOnw3GcWr7T/Sfty8hLLrkEu3fvrvL43r17cckll9TJoFoKJS9coLWjhHFpmE+9wNoWmmpYSpNGROFiRAbbYdncJWyBRywRWMuAvfiVi1D85oTGe3OGRicSzkMolA2LhkQzB+JrFpOs2IgzXaDkb1keyDxDWPox5gQC8UQdZXV4RZREB9iB5/mCcg7ZeEbewJQnvLwQlY2NDAbD/tN93HNgBdmA48L6uhQk6goXKpcLr2tKPdmi3yZWJXcsC9QK6yDboiFxXwbYthXRF/lEuoNYNARihwCbovjNCaZ+oplDUrylw8qVKzFx4kR88MEHWLZsGeLxOE477TTs3bu3yrr3339/UuWE67o444wzEIvF8P777+Opp57C/PnzcfPNN6c5mroj7eD6qaeeQkVFRZXHKyoq8PTTT9fJoFoSxa9cJK7aLaF141miKxYPycCBQBzg4q4IojNtaZckfEh5lg12UAZYm0zwnBAQsVDy/G8b+20ZmgCRUA5CdivYdhYsK6IPOsIii8iMkk/24ctei+DZqRJgJ6Iz0L4guab1VZBv0VBVOz4En+fPputMtjqhh63m1UvXYGjCsIMzwUMUZE8ctDSqZ0GZE4O7e7d2oNLBtQqw1UyrRUTBMaWgVhiU2LBpBLYVgW1nyeNQBgjxWp6L3zRvXm37DNWirPhquqXD0qVLcdFFF+HnP/85+vbti/nz52PLli1Ys2ZNYL1PPvkE99xzD5588skq23j99dfx5Zdf4m9/+xuOOuoonH766bj99tsxZ84cxGKx/XnLtSblr3xZWRlKS0vBOcfu3btRVlambz/99BNee+21Ki3RDZKIBWSJwBghy/s3bIlMdUhkqnnYAjJt8CxbTN+FKHirsFjOuWgTazBIitdM0VO0lFbNWovsUThp+3NFoIU5fJZ6hIoiJMsSJ1TLArE82YnappKiUGqDWmEQy9YBtrhZOoulA23mCGs+n8Y7EHRTaRlm12KO0WAwJCcszjl0T0xIQnZWgJTH4TDZMIYzEUBnhrwZVssXLSm5iE1F0aMdArXDsK0MhKxMOYNGvKJleVxhTsw0JWshpJO59seIZWVliEajyTZZhdLSUgCiYaCivLwc559/PubMmYOCgoIqz1m1ahWOOOIItG/fXj82fPhwlJWV4T//+U/a77MuSFkAlZ+fD0IICCH42c9+VmU5IQQzZ86s08G1FIqXjEOPMc+IO5a0Hosg6BJiSYcES7qCuFzosTNEQE3KhXaoZOH5jfIeDE0Ti8ppWJfKjJFsSayyRzKoVprIxAYxKsMkllHdulhuxJsSVhaSyosaEFZ6HF4JeVhMKxNGtSOIJXWXLlOWXo4nSZHFlKpDI/Fb/xFphmOKdg2GOoFnhYREsTQK7I3J+gaOkJUpVghZIpkTtmRhMjyJlj/7TGiwCRqnIC6H5QpdtoZQFH00seHeoKHeSaegsVOnToHHb7nlFtx66637fC5jDNdccw2OP/54HH744frxa6+9FscddxxGjRqV9Hnbtm0LBNYA9P1t27bVMOL6IeXgevny5eCc45RTTsELL7wQuKoIh8Po0qULOnbsWC+DbAmUvHCBCLCVh68/mAaETEQVjzCvyAwhC4i5IBVxbJx3TiON3tBUKV4zBV2OugM28xVucAZCbd0VDaguqA76W1Nqe4VM2jVAXvzZqsjWZyepvqbC9Fas50rtpurYqCz3IFwEGINXTMllW3Sl07ZDYjuO12xGN1gyGAy1pvul/wQvPAio9B0n5O+akLBI5rQKg+eGQWJMBNryt06EB1/weYDnKAICWBxwSaCI3zSLaXmkUrColn/zzTfIzc3Vj0cikRq3P3HiRHzxxRd499139WMvvfQS3nrrLaxbt65WY24sUg6uhwwZAkB0auzUqROosdSpHYSI4kTpCsIjlgimpWsIcTnguiKoiTOQyhgQc7HxKRNYG5ITsjK9Bi3M1n8T2SkRQKAZhL9JDCEUm9ZNT+l1Ck95DDqlxZjwpgZkigteMC5dJAGIrBYTGXawmGhooxaBw2Vx4TBAwr5ujXK7cRfFr168v7vHYDjg2fj4/6HzPctBYq6QJQKeNZ4qtFcF9DEm/uUcIFxOTrFgAzT/BbjeFjzHEUOLhBAOWkMHRiKX5+bmBoLrmpg0aRJeeeUVvP322zj00EP142+99RZKSkq0Y51izJgxOPHEE7FixQoUFBTgww8/DCz//vvvASCpjKQhSNsXp0uXLgCEBmbLli1VxOJHHnlk3YysJWJTkTnIFHpqlDsgjivN9SlIuaNlIDp77TITWBv2CYmEEUEOHLcS1I2CW0x6Tgc11v4ixG8+uzHt1yl6S7jTFJ7ymOcsAIjOipY82Sp3j7jrLScUBNASkYDziAy1hSREajo5B4m6KHp9fNpjNBgMySF7YiBtMkRPBUVY2r9KK1gEpFhEXziLOmMufuuAqItItI2lAOFWjV1cDc2XdGQhqcI5x+TJk7F48WKsWLEC3bp1Cyy/4YYbcOmllwYeO+KII3DffffhV7/6FQBg8ODB+OMf/4jt27fr2r9ly5YhNzcXffr0SXNEdUPawfUPP/yAiy++GP/617+SLndd04GpWqSdEYm54HEmujeGZYbAlcVke5k4yLkMG588u5EHbGgWRCwQhBFybFhOWEgtVEtzBJ0+StZeu98vV/TWBBFgyzha2Wv1HPa4OAE7vuyV6B6hPa8tLlsmM0c2wJHHC8sSJ3rZTMnYTBoMdUf3i58DaZuFcIcMxBkXVrCA+H1GLPDMkNRRe17XnKpASR4/HAbiz1wTn0aAqmOMkJIUvxEMhgwtg1TcQNJ1C5k4cSIWLlyIJUuWICcnR2uk8/LykJmZiYKCgqTZ586dO+tA/LTTTkOfPn1wwQUX4K677sK2bdtw4403YuLEiSnJUeqDtIPra665Brt27cLq1atx8sknY/Hixfj+++8xa9Ys3HPPPfUxxpYFEZpqQmUXR4ti4+P/BwDofvnz2PjIbxp5gIbmBg8JmRFxOCizQF2OovevrNfXVFnsavF3eYNPi6nP3TZsQrWERWfCwhYQbTzjf4OhJUJcDhJz4bgAqXREptqnvyaqLbkqUAZEgG0JswIltuUxBLsLa/21/NcFit8wM04tFYqaLebSFQw//PDDAICTTz458Pi8efNw0UUXpbQNy7Lwyiuv4Morr8TgwYPRqlUrjBs3Drfddluao6k70g6u33rrLSxZsgQDBgwApRRdunTBqaeeitzcXMyePRtnnHFGfYyzRVCT04cJrA21ItMWBUmMo/i1xtMoF79xKXoOfQxEnXRVQM15VX02B0AsUbsh5SCEA9w1mk2Doa7hhIDlZ4AyAFkh8NIoiJKHWNTrlArIeiD5RErBKYRUC3IdhwUy0z1HztNZ7OJXxzXYezI0PPWRuea1kBEle06XLl3w2muvpb2t+iLt4Hrv3r1a03LQQQfhhx9+wM9+9jMcccQRWLt2bZ0P0GAw7JviJU3ohGZRcMIBykG4CrB9Vn4qi80IACZsvSwi3Amkk46RhBgMdUyIgueEwcrisKKyqDHOgs5UCvkYAcAtSCtO4llzJuQmG/OC3tCw1IfmuqWStuXHYYcdpvu+9+3bF4888gi+++47zJ07Fx06dKjzARoMhuZD8evjdQMKHqLgqjupTbymFFR1exNdSnlmCCw/AmSFULz0ksZ+CwZDi6L7xc+B50g3HpeJ32VeRDQts4gXMSkrWKWjVl0apdUmlxfBCFvo+cv5jfeGDI1GfXRobKmknbmeMmUKtm7dCkCYgo8YMQILFixAOBzG/Pnz63p8BoOhmVG89BL0PGOeJw1RShDX51/NuDiJKwuwDBvcMZIQg6Eu6X7580DYAsuVRV2Mg+eEQXZVikJGIGifJ3+zxP9T9Mm6uHQFIsxEUAciJnOdOmkH17/97W/13/3798fmzZvx1VdfoXPnzmjbtm2dDs5gMDRPeMTy7sh0BlHBM+deFsyWk2emWYzBUOdwi4rixQxbyEDiTHhdU9HEjLgcUHWNah6bESHtCmyIe0WLcnmPs55GyaILG+qtGJoA6TSROdDZ704wWVlZ6NevX50H1ldccQUIIbj//vsDj+/cuRNjx45Fbm4u8vPzMX78eOzZs6dOX9tgMOwfJYsulFlpW+g7wxQ8y0bxixcKjXhWSDzucpQsOA8lz56Pkn/8tuYNGwyGlOh6/StCax2Stpy7YyAVcRFkO6p40XdTc/o+yRYilncRrHzoZbMnE1gfeFDZRKammyHFzPXUqVNT3uC9995b68EoFi9ejA8++CBpO/WxY8di69atWLZsGeLxOC6++GJcdtllWLhw4X6/rsFgqDv2FSyX/O28BhxJ3dFj7N9RsqB5jt1wYPH1nb9E1+kvg2fYwv7VYSBxJmz3VEdFX+MmAJ6US3VgVM4h0qmv5Nl9O14ZWjZGFpI6KQXXqfZ0J3WgZP/uu+8wefJk/Pvf/65i67d+/XosXboUH330EQYMGAAAePDBBzFy5EjcfffdSYNxg8FgqAt6XPgseMRC90v+YRo8GZoHNhUBsss8WQclQobFkDwSUgG2W7N9rOHAoj6s+FoqKQXXy5cvr+9xAAAYY7jgggswffp0/PznP6+yfNWqVcjPz9eBNQAMGzYMlFKsXr0av/71r5NuNxqNIhqN6vtlZWV1P3iDwdCiKXn6XHS/8gWQvfHGHorBkBpSwgHGZUAtb1IqIjLYsmBRZbJVAaNpY25IwGSuU2e/Ndd1yZ133gnbtnH11VcnXb5t2zbtsa2wbRutW7fWLTOTMXv2bOTl5elbp06d6nTcBoPhwIDsjaPk6XMbexgGQ0pwQgJNYnhEOIfwzBB4hg0esUXxcZh6EhAlCbFMmGQIQlO8GWrhFlJXLFiwAJdffrm+/+qrr+KBBx7A2rVr60Re4mfGjBkB3XhZWZkJsA0GQ9qYwNrQXOj6+1eFjzyF6JoaouLmcnAqOi5yAhCHgMcZNj7+f409ZENTJxUfa3NNBqARLzLOPPNMfPLJJ/r2/vvvY/v27ejcuTNs24Zt29i8eTOuu+46dO3aFQBQUFCA7du3B7bjOA527tyJgoKCal8rEokgNzc3cDMYDE2D7hOeb+whJKXbVYsaewgGQ635+k9n6OwzD1HwsCWaOtkiyOYRS2SvM2yTpTakCEnxZmi0zHVOTg5ycnL0/csuuwy/+tWvAusMHz4cF1xwAS6+WLRXHTx4MHbt2oU1a9agf//+AIC33noLjDEMGjSo4QZvMBj2i67TXgIA0N0xbHzsN408mqp0n/A8kB1Ct6kvYdO9Zzb2cAyG2mFR0RAm5oLEXSETCVFhCMIckJgrfK+Nz7whBYj8r6Z1DI0YXCfSpk0btGnTJvBYKBRCQUEBDjvsMABA7969MWLECEyYMAFz585FPB7HpEmTcO655xqnEIOhGfH13Wei26TF2PhI0wusAYBnWNh036jGHobBUHcwDkIBVDiAw4Q1X7kjnERcju7jnsPGp85p7FEamjCEUBCyb8FDTcsPFJrdXliwYAF69eqFoUOHYuTIkTjhhBPw6KOPNvawDAZDmmz6a3J3n8am27VLjCOIoWXBOHhIdE0lURdkbxxkT1wE1tIVxATWhpoQeWtaw81kroEmlLlOxtdff13lsdatW5uGMQaDoc7pNnkxiMOw6eExjT0Ug6HuYNz71+VVLfYIAUImIDKkgjHjS5UmHVwbDAZDfdN1+sugZVEQSrDRBNaGFkCXW5YKbXXYApF6ahJzRYDtML2eaYZkSAcjC0kdE1wbDIYDlm5TXgRhHNwyJwRDC4IScNkohlOZS3Rl+/OYC8SZ8L82GNLCZK5TxQTXBoPhgGXTA6PR/dJ/Go9fQ4uhy6zXRXxjWwAFSKXrSUOUK4hFm6RLj6FpY9xCUsdcuhoMhgMaE1gbWhKbbzwNPCcM2ASQUhDiMi0PATWtzQ21g6T4n8EE1waDwWAwtCwsCsSZ8LiWGmtuU/AMS7c+737lC407RkMzxDRATxWzFwwGg8FgaEGQ3THAZeAUwe6LhIBbBAhb4BZFt0mLG22MhuYHISSlm8Forg0Gg8FgaBF0mfW68LRuFdYFjLCokIIA4l9XZLKbqs+8oSljChpTxQTXBoPBYDC0FKhocQ4KobmOMV3QSOJs3881GPYBgQUCq8Z1DEYWYjAYDAZDi2DzjacBhIAQAAw6qOYRGzzDBo9Y4BTYdO+ZjTpOQ/PEFDSmjslcGwwGg8HQUqAEnHGROpMtzxF3AZuCU4LNs89o1OEZmi+paKqN5lpggmuDwWAwGFoInMJrde76ZCCMe37XBkOtMJrrVDGyEIPBYDAYWgqEiMCa+W4cQotN9y/w6fLHZXUzRkOzRIg+aA03E1wDJrg2GAwGg6HFsOV3Q72MtXQK2XzLcGy+ZThACbrM/Hett735D6fW0SgNzROS4s1ggmuDwWAwGFoQW64fBgDgNhEBtsIigG1O+4baYXyuU8f8ygwGg8FgaGFs+d1QwKKiaYyEhyi4TdD5zjcacWSG5ovJXKeKKWg0GAwGg6EFsmXaKQCAzne/JbLWmSEQAnAOdH5gBbZMOblxB2hoVihddU3rGExwbTAYDAZDi0YF2YrOD6wwziGGWmDcQlLFBNcGg8FgMBxAmIy1oTZQQkHJvjPTNS0/UDDBtcFgMBgMBoOhBihqLtUzwTVggmuDwWAwGAwGQw2k0t7c+FwLmtwlxvr163HmmWciLy8PrVq1wsCBA7Flyxa9vLKyEhMnTkSbNm2QnZ2NMWPG4Pvvv2/EERsMBoPBYDC0dIxbSKo0qeC6pKQEJ5xwAnr16oUVK1bgs88+w0033YSMjAy9zrXXXouXX34Z//znP7Fy5Ur873//w1lnndWIozYYDAaDwWBo2Rif69QhnPMmUzJ87rnnIhQK4Zlnnkm6vLS0FAcffDAWLlyI3/zmNwCAr776Cr1798aqVatw7LHHpvQ6ZWVlyMvLQ2lpKXJzc+ts/AaDwWAwGAz7S1OKU9RYdpV+jNzc7BrW3YP8vAFNYtyNSZPRXDPG8Oqrr+J3v/sdhg8fjnXr1qFbt26YMWMGRo8eDQBYs2YN4vE4hg0bpp/Xq1cvdO7ceZ/BdTQaRTQa1fdLS0sBiC+MwWAwGAwGQ1NCxSdNKP+J3WV7a9RU7y7b20Cjado0meB6+/bt2LNnD+644w7MmjULd955J5YuXYqzzjoLy5cvx5AhQ7Bt2zaEw2Hk5+cHntu+fXts27at2m3Pnj0bM2fOrPJ4p06d6vptGAwGg8FgMNQJu3fvRl5eXqOOIRwOo6CgAJ06DUlp/YKCAoTD4XoeVdOm0YLrBQsW4PLLL9f3X331VQDAqFGjcO211wIAjjrqKLz//vuYO3cuhgxJ7UNNxowZMzB16lR9f9euXejSpQu2bNnS6F/a5kJZWRk6deqEb7755oCe6kkHs8/Sx+yz9DH7LH3MPksfs8/SZ3/2Geccu3fvRseOHetpdKmTkZGBTZs2IRaLpbR+OBwO1ModiDRacH3mmWdi0KBB+v7BBx8M27bRp0+fwHq9e/fGu+++C0BcDcViMezatSuQvf7+++9RUFBQ7WtFIhFEIpEqj+fl5ZmDRJrk5uaafZYmZp+lj9ln6WP2WfqYfZY+Zp+lT233WVNK/mVkZBzwAXM6NFpwnZOTg5ycnMBjAwcOxIYNGwKP/fe//0WXLl0AAP3790coFMKbb76JMWPGAAA2bNiALVu2YPDgwQ0zcIPBYDAYDAaDoRqajOYaAKZPn45zzjkHJ510En7xi19g6dKlePnll7FixQoA4ipu/PjxmDp1Klq3bo3c3FxMnjwZgwcPTtkpxGAwGAwGg8FgqC+aVHD961//GnPnzsXs2bNx9dVX47DDDsMLL7yAE044Qa9z3333gVKKMWPGIBqNYvjw4XjooYfSep1IJIJbbrklqVTEkByzz9LH7LP0Mfssfcw+Sx+zz9LH7LP0MfvswKVJ+VwbDAaDwWAwGAzNmSbVodFgMBgMBoPBYGjOmODaYDAYDAaDwWCoI0xwbTAYDAaDwWAw1BEmuDYYDAaDwWAwGOqIAy64njNnDrp27YqMjAwMGjQIH374YWMPqclw6623ghASuPXq1Usvr6ysxMSJE9GmTRtkZ2djzJgx+P777xtxxA3P22+/jV/96lfo2LEjCCF48cUXA8s557j55pvRoUMHZGZmYtiwYSgqKgqss3PnTowdOxa5ubnIz8/H+PHjsWfPngZ8Fw1LTfvsoosuqvK9GzFiRGCdA22fzZ49GwMHDkROTg7atWuH0aNHV+kBkMrvccuWLTjjjDOQlZWFdu3aYfr06XAcpyHfSoORyj47+eSTq3zXrrjiisA6B9I+e/jhh3HkkUfqJieDBw/Gv/71L73cfMeC1LS/zPfLoDigguvnnnsOU6dOxS233IK1a9eib9++GD58OLZv397YQ2sy/PznP8fWrVv1TXXHBIBrr70WL7/8Mv75z39i5cqV+N///oezzjqrEUfb8Ozduxd9+/bFnDlzki6/66678Je//AVz587F6tWr0apVKwwfPhyVlZV6nbFjx+I///kPli1bhldeeQVvv/02LrvssoZ6Cw1OTfsMAEaMGBH43v39738PLD/Q9tnKlSsxceJEfPDBB1i2bBni8ThOO+007N27V69T0+/RdV2cccYZiMVieP/99/HUU09h/vz5uPnmmxvjLdU7qewzAJgwYULgu3bXXXfpZQfaPjv00ENxxx13YM2aNfj4449xyimnYNSoUfjPf/4DwHzHEqlpfwHm+2WQ8AOIY445hk+cOFHfd12Xd+zYkc+ePbsRR9V0uOWWW3jfvn2TLtu1axcPhUL8n//8p35s/fr1HABftWpVA42waQGAL168WN9njPGCggL+5z//WT+2a9cuHolE+N///nfOOedffvklB8A/+ugjvc6//vUvTgjh3333XYONvbFI3Geccz5u3Dg+atSoap9zoO8zzjnfvn07B8BXrlzJOU/t9/jaa69xSinftm2bXufhhx/mubm5PBqNNuwbaAQS9xnnnA8ZMoRPmTKl2ucc6PuMc84POugg/vjjj5vvWIqo/cW5+X4ZPA6YzHUsFsOaNWswbNgw/RilFMOGDcOqVasacWRNi6KiInTs2BHdu3fH2LFjsWXLFgDAmjVrEI/HA/uvV69e6Ny5s9l/kk2bNmHbtm2BfZSXl4dBgwbpfbRq1Srk5+djwIABep1hw4aBUorVq1c3+JibCitWrEC7du1w2GGH4corr8SOHTv0MrPPgNLSUgBA69atAaT2e1y1ahWOOOIItG/fXq8zfPhwlJWVBTJtLZXEfaZYsGAB2rZti8MPPxwzZsxAeXm5XnYg7zPXdfHss89i7969GDx4sPmO1UDi/lKY75cBaGIdGuuTH3/8Ea7rBr7UANC+fXt89dVXjTSqpsWgQYMwf/58HHbYYdi6dStmzpyJE088EV988QW2bduGcDiM/Pz8wHPat2+Pbdu2Nc6AmxhqPyT7jqll27ZtQ7t27QLLbdtG69atD9j9OGLECJx11lno1q0bSkpK8Pvf/x6nn346Vq1aBcuyDvh9xhjDNddcg+OPPx6HH344AKT0e9y2bVvS76Ja1pJJts8A4Pzzz0eXLl3QsWNHfPbZZ7j++uuxYcMGLFq0CMCBuc8+//xzDB48GJWVlcjOzsbixYvRp08ffPLJJ+Y7loTq9hdgvl8GjwMmuDbUzOmnn67/PvLIIzFo0CB06dIF//jHP5CZmdmIIzO0ZM4991z99xFHHIEjjzwSPXr0wIoVKzB06NBGHFnTYOLEifjiiy8C9Q+GfVPdPvPr9I844gh06NABQ4cORUlJCXr06NHQw2wSHHbYYfjkk09QWlqK559/HuPGjcPKlSsbe1hNlur2V58+fcz3y6A5YGQhbdu2hWVZVSqdv//+exQUFDTSqJo2+fn5+NnPfobi4mIUFBQgFoth165dgXXM/vNQ+2Ff37GCgoIqBbSO42Dnzp1mP0q6d++Otm3bori4GMCBvc8mTZqEV155BcuXL8ehhx6qH0/l91hQUJD0u6iWtVSq22fJGDRoEAAEvmsH2j4Lh8Po2bMn+vfvj9mzZ6Nv37544IEHzHesGqrbX8kw368DlwMmuA6Hw+jfvz/efPNN/RhjDG+++WZAL2Xw2LNnD0pKStChQwf0798foVAosP82bNiALVu2mP0n6datGwoKCgL7qKysDKtXr9b7aPDgwdi1axfWrFmj13nrrbfAGNMH4gOdb7/9Fjt27ECHDh0AHJj7jHOOSZMmYfHixXjrrbfQrVu3wPJUfo+DBw/G559/HrgwWbZsGXJzc/U0dkuipn2WjE8++QQAAt+1A2mfJYMxhmg0ar5jKaL2VzLM9+sAprErKhuSZ599lkciET5//nz+5Zdf8ssuu4zn5+cHKncPZK677jq+YsUKvmnTJv7ee+/xYcOG8bZt2/Lt27dzzjm/4ooreOfOnflbb73FP/74Yz548GA+ePDgRh51w7J7926+bt06vm7dOg6A33vvvXzdunV88+bNnHPO77jjDp6fn8+XLFnCP/vsMz5q1CjerVs3XlFRobcxYsQIfvTRR/PVq1fzd999lxcWFvLzzjuvsd5SvbOvfbZ7924+bdo0vmrVKr5p0yb+xhtv8H79+vHCwkJeWVmpt3Gg7bMrr7yS5+Xl8RUrVvCtW7fqW3l5uV6npt+j4zj88MMP56eddhr/5JNP+NKlS/nBBx/MZ8yY0Rhvqd6paZ8VFxfz2267jX/88cd806ZNfMmSJbx79+78pJNO0ts40PbZDTfcwFeuXMk3bdrEP/vsM37DDTdwQgh//fXXOefmO5bIvvaX+X4Z/BxQwTXnnD/44IO8c+fOPBwO82OOOYZ/8MEHjT2kJsM555zDO3TowMPhMD/kkEP4Oeecw4uLi/XyiooKftVVV/GDDjqIZ2Vl8V//+td869atjTjihmf58uUcQJXbuHHjOOfCju+mm27i7du355FIhA8dOpRv2LAhsI0dO3bw8847j2dnZ/Pc3Fx+8cUX8927dzfCu2kY9rXPysvL+WmnncYPPvhgHgqFeJcuXfiECROqXPAeaPss2f4CwOfNm6fXSeX3+PXXX/PTTz+dZ2Zm8rZt2/LrrruOx+PxBn43DUNN+2zLli38pJNO4q1bt+aRSIT37NmTT58+nZeWlga2cyDts0suuYR36dKFh8NhfvDBB/OhQ4fqwJpz8x1LZF/7y3y/DH4I55w3XJ7cYDAYDAaDwWBouRwwmmuDwWAwGAwGg6G+McG1wWAwGAwGg8FQR5jg2mAwGAwGg8FgqCNMcG0wGAwGg8FgMNQRJrg2GAwGg8FgMBjqCBNcGwwGg8FgMBgMdYQJrg0Gg8FgMBgMhjrCBNcGg8FgMBgMBkMdYYJrg+EA5OSTT8Y111zTol73oosuwujRo/drG127dgUhBIQQ7Nq1q9r15s+fj/z8/P16LUOQFStW6H2/v5+jwWAwNCYmuDYYDA3GokWLcPvtt+v7Xbt2xf333994A0rCbbfdhq1btyIvL6+xh9JiSfa5H3fccdi6dSvOPvvsxhmUwWAw1BF2Yw/AYDAcOLRu3bqxh1AjOTk5KCgoaOxhAADi8ThCoVBjD6NBCIfDKCgoQGZmJqLRaGMPx2AwGGqNyVwbDAb89NNPuPDCC3HQQQchKysLp59+OoqKivRyJYP497//jd69eyM7OxsjRozA1q1b9TqO4+Dqq69Gfn4+2rRpg+uvvx7jxo0LTPH7ZSEnn3wyNm/ejGuvvVbLAQDg1ltvxVFHHRUY3/3334+uXbvq+67rYurUqfq1fve734FzHngOYwyzZ89Gt27dkJmZib59++L555+v1f6ZP38+OnfujKysLPz617/Gjh07qqyzZMkS9OvXDxkZGejevTtmzpwJx3H08q+++gonnHACMjIy0KdPH7zxxhsghODFF18EAHz99dcghOC5557DkCFDkJGRgQULFgAAHn/8cfTu3RsZGRno1asXHnroocBrf/PNNzj77LORn5+P1q1bY9SoUfj666/18hUrVuCYY45Bq1atkJ+fj+OPPx6bN2+u9v3WtL2PPvoIp556Ktq2bYu8vDwMGTIEa9eu1cs557j11lvRuXNnRCIRdOzYEVdffTWA6j93g8FgaCmY4NpgMOCiiy7Cxx9/jJdeegmrVq0C5xwjR45EPB7X65SXl+Puu+/GM888g7fffhtbtmzBtGnT9PI777wTCxYswLx58/Dee++hrKxMB47JWLRoEQ499FAtw/AH6jVxzz33YP78+XjyySfx7rvvYufOnVi8eHFgndmzZ+Ppp5/G3Llz8Z///AfXXnstfvvb32LlypWp7xgAq1evxvjx4zFp0iR88skn+MUvfoFZs2YF1nnnnXdw4YUXYsqUKfjyyy/xyCOPYP78+fjjH/8IQFwMjB49GllZWVi9ejUeffRR/OEPf0j6ejfccAOmTJmC9evXY/jw4ViwYAFuvvlm/PGPf8T69evxpz/9CTfddBOeeuopACK7PXz4cOTk5OCdd97Be++9py9+YrEYHMfB6NGjMWTIEHz22WdYtWoVLrvssmqD2pq2BwC7d+/GuHHj8O677+KDDz5AYWEhRo4cid27dwMAXnjhBdx333145JFHUFRUhBdffBFHHHEEgP373A0Gg6FZwA0GwwHHkCFD+JQpUzjnnP/3v//lAPh7772nl//44488MzOT/+Mf/+Cccz5v3jwOgBcXF+t15syZw9u3b6/vt2/fnv/5z3/W9x3H4Z07d+ajRo1K+rqcc96lSxd+3333BcZ2yy238L59+wYeu++++3iXLl30/Q4dOvC77rpL34/H4/zQQw/Vr1VZWcmzsrL4+++/H9jO+PHj+XnnnVftfkk2nvPOO4+PHDky8Ng555zD8/Ly9P2hQ4fyP/3pT4F1nnnmGd6hQwfOOef/+te/uG3bfOvWrXr5smXLOAC+ePFizjnnmzZt4gD4/fffH9hOjx49+MKFCwOP3X777Xzw4MH6dQ477DDOGNPLo9Eoz8zM5P/+97/5jh07OAC+YsWKat934rj3tb1kuK7Lc3Jy+Msvv8w55/yee+7hP/vZz3gsFku6frL9rBg3blzgO2MwGAzNDZO5NhgOcNavXw/btjFo0CD9WJs2bXDYYYdh/fr1+rGsrCz06NFD3+/QoQO2b98OACgtLcX333+PY445Ri+3LAv9+/ev8/GWlpZi69atgfHato0BAwbo+8XFxSgvL8epp56K7OxsfXv66adRUlKS1uutX78+8FoAMHjw4MD9Tz/9FLfddlvgtSZMmICtW7eivLwcGzZsQKdOnQJabv++8uN/H3v37kVJSQnGjx8f2PasWbP0+/j0009RXFyMnJwcvbx169aorKxESUkJWrdujYsuugjDhw/Hr371KzzwwAP7zBbXtD0A+P777zFhwgQUFhYiLy8Pubm52LNnD7Zs2QIA+L//+z9UVFSge/fumDBhAhYvXhyQyBgMBkNLxhQ0GgyGlEgsrCOEVNE51wWU0irb9ctTUmHPnj0AgFdffRWHHHJIYFkkEtm/AVbzejNnzsRZZ51VZVlGRkZa22rVqlVguwDw2GOPVQnwLcvS6/Tv31/rs/0cfPDBAIB58+bh6quvxtKlS/Hcc8/hxhtvxLJly3DssccmfS81bW/cuHHYsWMHHnjgAXTp0gWRSASDBw/WspFOnTphw4YNeOONN7Bs2TJcddVV+POf/4yVK1ceMAWaBoPhwMUE1wbDAU7v3r3hOA5Wr16N4447DgCwY8cObNiwAX369ElpG3l5eWjfvj0++ugjnHTSSQCEznjt2rVVihP9hMNhuK4beOzggw/Gtm3bwDnXuuBPPvkk8FodOnTA6tWr9Ws5joM1a9agX79+AIA+ffogEolgy5YtGDJkSErvoTp69+6N1atXBx774IMPAvf79euHDRs2oGfPnkm3cdhhh+Gbb77B999/j/bt2wMQRYE10b59e3Ts2BEbN27E2LFjk67Tr18/PPfcc2jXrh1yc3Or3dbRRx+No48+GjNmzMDgwYOxcOHCpMF1Ktt777338NBDD2HkyJEARAHkjz/+GFgnMzMTv/rVr/CrX/0KEydORK9evfD555+jX79+ST93g8FgaCkYWYjBcIBTWFiIUaNGYcKECXj33Xfx6aef4re//S0OOeQQjBo1KuXtTJ48GbNnz8aSJUuwYcMGTJkyBT/99NM+3SC6du2Kt99+G999950Ozk4++WT88MMPuOuuu1BSUoI5c+bgX//6V+B5U6ZMwR133IEXX3wRX331Fa666qpA05ecnBxMmzYN1157LZ566imUlJRg7dq1ePDBB3UhYKqojO/dd9+NoqIi/PWvf8XSpUsD69x88814+umnMXPmTPznP//B+vXr8eyzz+LGG28EAJx66qno0aMHxo0bh88++wzvvfeeXlaTW8bMmTMxe/Zs/OUvf8F///tffP7555g3bx7uvfdeAMDYsWPRtm1bjBo1Cu+88w42bdqEFStW4Oqrr8a3336LTZs2YcaMGVi1ahU2b96M119/HUVFRejdu3fS16tpe4D4zjzzzDNYv349Vq9ejbFjxyIzM1NvY/78+XjiiSfwxRdfYOPGjfjb3/6GzMxMdOnSBUDyz91gMBhaCia4NhgMmDdvHvr3749f/vKXGDx4MDjneO2119Kawr/++utx3nnn4cILL8TgwYORnZ2N4cOH71MWcdttt+Hrr79Gjx49tOSgd+/eeOihhzBnzhz07dsXH374YcCVBACuu+46XHDBBRg3bhwGDx6MnJwc/PrXvw6sc/vtt+Omm27C7Nmz0bt3b4wYMQKvvvoqunXrlsaeAY499lg89thjeOCBB9C3b1+8/vrrOjBWDB8+HK+88gpef/11DBw4EMceeyzuu+8+HUxaloUXX3wRe/bswcCBA3HppZdqt5CaZCOXXnopHn/8ccybNw9HHHEEhgwZgvnz5+v3kZWVhbfffhudO3fGWWedhd69e2P8+PGorKxEbm4usrKy8NVXX2HMmDH42c9+hssuuwwTJ07E5ZdfnvT1atoeADzxxBP46aef0K9fP1xwwQW4+uqr0a5dO72N/Px8PPbYYzj++ONx5JFH4o033sDLL7+MNm3aAEj+uRsMBkNLgfD6EE0aDIYDHsYYevfujbPPPjvQlbEp07VrV1xzzTU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", 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foECBAgMJwpj5EOZfoECBAQUufvLZZ5/Nl+Cw6667Dsceeyw++OCDouR3gX7BbbfdhgMPPBDvvPOOC1otUKBAgUUBhee6QIEC84yDDjoIq622WidPd4ECfcX555+P4447riDWBQoUWORQeK4LFCgwz5BS4pVXXlnYzSiwGGHs2LELuwkFChQo0CcUynWBAgUKFChQoECBAv2EwnNdoECBAgUKFChQoEA/oVCuCxQoUKBAgQIFChToJxTkukCBAgUKFChQoECBfsISGdCotcYnn3yCIUOGzHPp4QIFChQoUKBAgf6EMQazZs3CSiutBCkXvg5arVZRr9d7tG2pVEKlUpnPLRrYWCLJ9SeffIJVV111YTejQIECBQoUKFCgS3z44YdYZZVVFmobqtUqWoYsA2TtPdp+xIgRmDBhwhJNsJdIcj1kyBAAdNMOHTp0IbemQIECBQoUKFDAo62tDauuuqrjKwsT9XodyNpRHnU4EJW631jVMfHVq1Gv1wtyvaSBrSBDhw4tyHWBAgUKFChQYEBiQFlXkwpEVO52EzMALCwDAUskuS5QoECBAgUKFCjQCwhB/+a2TYGCXBcoUKBAgQIFChSYC4Skf3PbpkBBrgsUKFCgQIECBQrMBYVy3WMU5LpAgQIFChQoUKDAXNAD5boonwKgINcFChQoUKBAgQIF5oZCue4xCnJdoECBAgUKFChQoHsUnuseoyDXBQoUKFCgQIECBbpHoVz3GAW5LlCgQIECBQoscnjpw1kwBjAABOhnpjQAwBhAGwNjiO9t8bnhC7GliwkK5brHGFDkWimFs846C3/7298wceJErLTSSjjssMNw2mmnuUTqxhiceeaZuOqqqzBjxgxss802GDNmDNZZZ52F3PoCBQoUKFBgweLtyR2OUIbjpDZEOFlI1AbQ2kAbg41WXfhV/wBg/KdzIISAMQZKGwBwZDg8F0YjYQaIUBv7ghRAJIXbl7CvCQE8M2GmI+J8fYwx2HqdpRbkKS/akBH9m9s2BQYWuT7//PMxZswYXHPNNRg1ahSeeeYZHH744Rg2bBh++MMfAgAuuOAC/OEPf8A111yDNddcE6effjpGjx6N1157bYkutVmgQIECBRZdfDCt5siwgP3FkkBtPPHk36Vll1IApVjC8kkiqsYruUoRoVbauM8//e7MHGll9FXdHffuTERS4ItrUMXj599vgxACG69GJP6lD2cBIOIbSYGRK7YCAJQl+0z6Q0ghICxZZqItBAANGOEJMr0u3P74OKkyMMZAyrxNga+Lu8YNeGD8FOw4ctk+XYfFHkL0QLkubCHAACPXTzzxBPbaay/svvvuAIA11lgD//jHP/D0008DoIfp4osvxmmnnYa99toLAHDttddihRVWwG233YYDDjhgobW9QIECBQoU6C0+mFp1vxshAGdzsISa37PEWGljSSmNiaEaC8C9Hv7OxJqx5drDe9XGh8ZPdWRXgn4q4/cZSwGpBZ58e4bjVkIIPDNhpvucEAIqI4X9uffanAKtg7aGYBU6kiK3j1B1bnYN+HdtDBFp05zvuUlMgMffmo5YyB4R7LtfmQxlDPbYcIVeXctFGlLQv7ltU2Bgkeutt94aV155Jd58802su+66ePHFF/HYY4/hd7/7HQBgwoQJmDhxInbaaSf3mWHDhmGLLbbA2LFjuyTXtVoNtVrN/d3W1jZ/T2QJxwsfzIIxBpusPnRhN2W+4b0pVdf5CyGw0vDSwm5SgQIFBgjemtQOKQSkJYerLV3uctvVlqEV13cmdwAwMADWXr7Fvf/6J3OILFpS3ag3h1zGhIq1JdRKG2TaQMNg23WX7lH7H35jKoQl0ZEQSKSEht0fDGL7Gh3TODIbEnghjFXihWtjSHyVMe6nOxfQ9YqEgICw5Du0hpimEwkm5rw/Oo7fr+hEo+EmCo+9OZ0+AwMpBGIpEJkIj745zZH/zGikivatjMbeG62Ir22wfI+u5WKFwnPdYwwocv2zn/0MbW1tGDlyJKIoglIKv/zlL3HQQQcBACZOnAgAWGGF/ExxhRVWcO81w3nnnYezzz57/jW8l/jdI++ikkgcs/UaAICLH30XQggkkXCvLcrgpcDFDa9+PBtx5AeUUky/CwF8PKOOlQuCXaDAEo/xn86BFAIahiwMxuD9qVUIAMr6nj9nyfPrn8zBeiuRReJzy7fg1Y9nd1Jwq6nq9niZ9mSa/3153b75iG976VMA3nLiSC6IwJJwSa+53y1JBQAN434X9n1pAC086WdEgkh0AnrP7ctOSpiQs1rNYDuHNoAEydLCEXDhiDKTdybo2hg3WeBjMVjdVsagFkwSlKGJyRKlTneHIltIjzGgyPVNN92Ev//977j++usxatQovPDCCzjxxBOx0kor4dBDD+3zfk899VScfPLJ7u+2tjasuuqq/dFknHr3G0giAQmBs0bngyp/98i7kAJItcEp23/OvT6rpnDydmu5v0/cdi0UGPgYtfLgLt97f2oVE6ZUEUmrfEiBFYYmC7B1BQoUGAhgP/GrH88GAEfUQs7x2idznBL94gez8AUrSHTUFYyB80SHwX5MLwUsWQSRx0wTmWQSueuo5frc9pm11P0uLAmVwpNfgMk1/YwCksrqMG3vVW9HmOF/j4J9CJH3V/Oxw2DMZoRNGsCArk8EwBgy0iRG5BRtpYloZ4YsKbJBxRYCzh+ijIHSQKo16loj07qTF3yJRqFc9xgDilyfcsop+NnPfubsHRtuuCHef/99nHfeeTj00EMxYsQIAMCkSZOw4oorus9NmjQJG2+8cZf7LZfLKJe7XpabF5z3tc8DAM7475s4479v4pzR6wIAzn/oHQB+ye78h97BT3cggn3mLkVmk8UNqy/TfTDtJzPqkIIGv8JCUqDA4o/GyfhLH85yWTqee6/NBf8BwJNvz3BBdmxrYOVVW1odC4lEEoHUMEFGDCLwu6zfO1L9l6ffz/3NRF4IAQkOjDRQ8NaOWOaJtSffItiPJ9UCohPBDhXv0DLC5L0TsQ7aF77ufdbWj27N6gY07moIUrYlKdvCyE52ESDfdmMMUq1RUwr7bbxSr67nEoFCue4xBhS5bm9vh5T5WU8URdCaZpxrrrkmRowYgQceeMCR6ba2Njz11FM4+uijF3Rzc2BSHUIK5BTrAksuCkJdoMDCx3PvtcEA2HSNBR8PEqa/+2LD8edkmQugu//1KWRr0D7YTwgAEZBm3lc8L3aFq558H3GUJ0ESgAh81Nr+FEJABlGBYXq7UKXOKdfh66IJGQ+IcldkOkSzbYT1mYRU2ZFtdFauw+vmsq/A5Gw4rFgXanUXKJTrHmNAkes99tgDv/zlL7Haaqth1KhReP755/G73/0O3/3udwHQA3biiSfi3HPPxTrrrONS8a200krYe++9F27jG/DTHT6HXz3w9sJuRoECBQoUsGgktQMB973+We7vTHvixz/5tch6jXcbNW/BdDOqGZLAghFJoBxJxBGrzsL7WSy0Jaup1tBCIBIAJBA1IdYhmY5skCB7qbsj1fwagte7It+sVksIp1q77VzGFV499j5wYVcQafICKKOQalKslabJRCSAf77wCQSAzBgcsMnK83S9FxsUynWPMaDI9SWXXILTTz8dxxxzDCZPnoyVVloJRx11FM444wy3zU9+8hPMmTMHRx55JGbMmIEvf/nLuOeeewZkjuuf77j2wm5CgQWA0DPZHT6dWXcDB3f8zlNpUAREFiiwBGLn9fJ2jq480/e8+lmvrR9d4ZTtP4dfP/gO4kigLK3dw3ZOh2zm45Gue+bDIMWdQaqMI7uRFIi0J8+RkI5QQ9rARuH7OQ5SbPRZA70j2hzcaAwgrM8cGoD0v2sYNxGBBrjH1aCJgBEGsQAyo1G3HutqpqANnZcRAoDGt7+4Sr9c78UGRRGZHkOYZpnkF3O0tbVh2LBhmDlzJoYOHXhKRoFFB29MbIcAsO6IQd1u9+G0Wj6vahAoFAYqhcua/BoPREWAZIEC/Ysn3preqwp9T70zA0DeM9wTi8kTb013QYjGANt9vmcp8QYirhj7HsqxRCWSiKV0AY6xFIiFRCwpTV8ipc1Rnc8AEuat5lSmvbGKNOa0ZkWdC9E05vbmVIRKU1BjmKov0wY1pdCeKaRKM0d3wZzf2XThkeuBxFO4LeWdfg2RdC9kmrSK2v0/GxDtXpgYUMp1gcUTz7/fBmMG5pJsb/HWpHYAcBXBOLr9nckdXaowtCopgrJggNEGRgTvWfDgw8gR8gGMCVOqNotBfsBjfH4uk48CBeYXnnpnhrsnQyL90PipEAJ45I1pAHpGePtawXDrdZbCw29M9YF3vcCTb89ANg/p9fobP9hqDff7Dc9/7KwfTsG25NkXgKFtw37MK9fN1WzuFxsJtjG+OqOxdhBhAxcN21OCVHrapePzfuvMWkAofzW9FwkBEcm8H9sY/O3ZjxYqwR5wKGwhPUZBrgvMF4SFZBbFYjKvfjy7k5ISqivA3KPZAWtbFIAwBkZQUQTu+Y2NnIfxxRbCwXfFYd4m8smM+vw50X7CmssOPFtWgQJA14R4h5HLLNB2bP/5vh2vt9UUFySmtNfRWoqcih0JjXIkUbbebYDIsLBG6CjoHEPhwKnVjb5q97/w786TE23ggxe5gqX2ea7zQY/0lwpIt7bWPANvPaHc4Xqers9iByF6ENBYkGugINcF+hnPvtcGKbBIEmrA56Ztpj4b29OHgwAr2OxJbBwMkPvbuO0pFshAcyiQ3V9Xvusi20iBAgUGGo7bZk1c/Oi7yEoRBpcAGUUuSwiAJqp0Z+LFogJABNf1kWGmlAbk0/BR36u1/+f6Yvt5GRjwtK2ySCn3tCfZOn+sUiTx3c1X65frtNigyBbSYxTkusA849E3p6ESR4hlz/yHAxHPv98W5Fn1aaZgvJLhotBzK2O9DVnwAUFaePVmjSbK78dWre7vQMcPp9WwajflmAsUGEh4aPxU8srqfI5il6YOwj27wLwVUekpHn1zmsvZDCBnN+Gy2WGVwMagxcUJJ267Fi58+B1oGAwtUfYQqQQkNISQ9Dd3phou77QtsQijGy0jvo+kv5sfl60+Tqm2hFpYewhXb9QGMFrbtHtcwpzLugtAA3VtEEnq6w8vCHXXmA+2kDFjxmDMmDF47733AACjRo3CGWecgd122w0AUK1W8aMf/Qg33HADarUaRo8ejcsvvzxXqfuDDz7A0UcfjYceegiDBw/GoYceivPOOw9xvPAobkGuC8wzEkm6wPz0VD8zYSYAYLM1h/V5Hw+Nn0oVFEW+/C3/rizxpQHT/84EmNUXl/YpUKEZueXHJsuXQggIS9QF/O8hPphW83YUAB9Przn/IdDg5Q72a4zBiGHdE/GPp9cgpcBH02su8IdT2M6tEE6BAgsDmdGoK53z8HNFwr02osJiXLZbQODOlye5gk29yQN9+0sTbZo2g703WrHbbZWNmaBc0Pn3tl13aTz8xlQoY1zu6ma497XP6BmE6ZRa77aXPkUkJMqRzJUYb7SyPPn2jAFhGzll+8/h/IfescVv2KYRQcOgLCPEIFECks6D8mgLGGUgRP4C0vn2jGATqc5/B6xmc//WCGNoAqAgEEtaPwSkrc64xOV36B3mg3K9yiqr4Ne//jXWWWcdGGNwzTXXYK+99sLzzz+PUaNG4aSTTsJdd92Ff/7znxg2bBiOO+447Lvvvnj88ccBAEop7L777hgxYgSeeOIJfPrppzjkkEOQJAl+9atf9fVM5xkFuS7QZ9z3+mdoiWJEUszXTunZ99pyv/dVHQ8HpgfGT7EleDkfa+dOnX+XwrgBjlXrkCSzT4/RVQIeXvo08MuU/DkOCGRCbeA92dwe/j2EJ/4CgMDU2Zlj3bxluHxaim2RCAin/DA+nVnP+bznFe9PreY9kEFEf1hQo0CB7rDzesvh7lcmQwofJLvnhiNy26S2UIgAcsVKbn9pIgBWkZGzCwDAN4MqfMYGxwl0weQC7DByGTwwfgqkEU0DFOdGrB9+YypKkXSVGEPc/cpklGRkz4GCBAF02a6n3pnh/MYLM+hRCsrRXc0UEmkDHLVAaslrLIXrA4wVMBrB/S3tr7MHu7H/CzOGmKB/CfscZQzqWiFVfr/KGMrTDYFyFEHpzGUXKdAN5oNyvccee+T+/uUvf4kxY8bgySefxCqrrII///nPuP766/HVr34VAHD11VdjvfXWw5NPPoktt9wS9957L1577TXcf//9WGGFFbDxxhvjF7/4BX7605/irLPOQqm0cCyVBbku0GfsvN5yeGD8FMRG5gJV+hPPvteWI6s9IdZj357hl/+0aRq4tOPIZXHva585NVrC+wIB5P5mUhv+BPLpuAS8At1M0abX/fv8OfZoh7aTMGKeCXmzDisk1uzb5rRYDE5QYuw++Epqze31eWOb+SH7ClbfKeetba89uNEU8BoGHbHK1Ju0aAWWHHxtg+6LprD9gn/ney98HlQDsQaAG222C96eJ7g9QXfkWc6FoG//+WXIPmL/fmD8FEcSOec0T/wB//tjb07vnEEoCNh79M1p2HbdhZPmr1k14n++8AnKkURLHKFkIsRGQGthbSLI9T2hoMG/83sM0YSQh30ak2z+nDbaTZoMtM0S4j/zjS/4FYq/PP0+ivDFuaAXynVbW1vu5XK5jHK5ezuiUgr//Oc/MWfOHGy11VZ49tlnkaYpdtppJ7fNyJEjsdpqq2Hs2LHYcsstMXbsWGy44YY5m8jo0aNx9NFH49VXX8Umm2zSy5PsHxTkusA8obsBZl7w9Lszc393N1Q99c6MXEYA7rAliNg9/MZUl4Ip9GNmjmCG3s3gmGzdsEuynHJKiPxA3JhGqrscrrzfENzRGwN3fMH/hD8nZX2EXqnO7yOvVDcJrgw20HYg19oTDinI593M4/3R9FruuLkhLpj8uCAiAMZOOnjCYDgrSkNbw+Xbx9+a7vLQLsq5gAssWDSroHftMx8C8BNmRtRkBagRd7w8sZM63hPwilgkiQh3pySzfQTgAD0fwMf9joaBNML5hRlcmZD7Gz5mBIGxb8+g/cD3CwvLOjKrnqJDSqTaoCQ1pKBAwXIU2ZVDbmNzUh3+za91ZfXwKfTg0uylWiNV9NPAoKa0m3RlDZlAvvul1fGXp9/HNeM+wKGF77ophJQQci7k2r6/6qqr5l4+88wzcdZZZzX9yMsvv4ytttoK1WoVgwcPxq233or1118fL7zwAkqlEoYPH57bfoUVVsDEibQqNXHixByx5vf5vYWFglwvZJx2z5vO8xpbP28ipSNVShvUlUEt06grg0wZXP6N9RduoxcAGjtQtl48/e5MfGmtvO9aCOGKO/DfCHzNAFyp3ntf+8yRt/zxYAcw3gd9H0LQMnMsqCBCo7LlVOpQxW4g1gC6/JxsGONDMs52Doa0GoyXtIUfmOxR3BK1aSDAoNzafC05up7tPHQ8AykFJs6kQMqQ9IYTBmMavh/RWU/i5V+3727WW0PSrg2l61IwzreqjXH5ZzO7/E+DpW+HAXDgElxN7ff/m4AoqLTHwVpsK4htXAQTln2/0L2neHEA+XrhSJQQAhKAditNwnqz2fYVKqQCd70yqde+7dwkHOSJBromt83S84WWNYD6Jd7u/tenYKf1SNDglbdYSBgBWMeXEwDsh4GgHUw656e6ff5D70Bpg0oscfJ2awEArh73AYYksY3PMQAUYiFzQeTc2fGkISTcbptmyrV9iQMa2WrDXv26zQjC9hvqR7QrKR/iu19avf8uxGII0fCcdLERAODDDz/MFZHpTrX+/Oc/jxdeeAEzZ87EzTffjEMPPRSPPPJIv7R5YaEg1wsQR9z4MipJhOEtMSqxRCSBwaUIEZdtBRzR5kIimTIANLQRuQT3izu2+NzwHGEG2DqRf7DHNZDtp96ZASmsp5g+REurkmwJxghIY1xAFEPDB0kJYUkKyC5SspXGuDiCCdrSSLijRraMZj7pzvmzG7fjtjtFOhhkjPN6GHAaKybZEl4NajZB8QMRgolE4yTAZ0cx8KWOEXyeJxFucAyOoY3duSXi4II5omGC4Y7nz0s0nmewqiDAvnMgElHOR5stIc8F4xf3vY1SLFCOBEqxRDn2Kyyc9QCgq0cTEu0U20gI3PzCJ97yxFMzdw/419mO8Z9XJ8OYudszeoPGYLzeVkucG1h9/MvT79O9bgwUAMB4ixf8/f+tTWjCwYGNjDtfngSgZyQ7tKBx/9WVKNAVyjLytjBQgCCDiTWAXCn0+1+fAm2of1KiIZ+0/cmFXWIh8dQ7M5Cq+ePR/ukOne0hdaWhYoMEvs/QvAoH4T3TgSJP6n3wd9D38HlxP5cZnwmEhRIeL4UAImurYQGFJlQG1z/30RI9Ke81wmXH7rYBMHTo0B5XaCyVSlh77bUBAJtuuinGjRuH3//+9/jWt76Fer2OGTNm5NTrSZMmYcQIWlkaMWIEnn766dz+Jk2a5N5bWCjI9QLASXeMRySApQYliKVAKRJIIoGYIioQNyxTMinKLNFLrOwqhUApkjjpjvFu20h4X/Cvd//8Aj2vvuDhN6Y6shqiWaEHfi0k2Y2EcfOGASv8DHW+wqmhEgJa0DKrNibXSWhLT2MhkVjV2RiDOJKd1GVpGaGwqqAIvj+nOnexdGl/s0cTtriMJZUm8G3bLRv928EuAgWbj2kHp+D4zShn2DYm8RrkgySfoi+8wIGejbCLAxQYJL3qHJJjGONIvDEhMW/4h8D3DT6uQCwBYaQbCLUBpPZL5NoIABrKXsfrn/vIKdtKE4GKhAgUK2pvR12jpgzqmcHpO6/d5AoBJ97+uieobhLjf2YayBSpX3/af4Om+5hfOP+hdzCkLBHbfoQDchlRoC7xZCkk1lL4jDkukBe+NHUiKYbCGCK8BsDwMlmFxr49w32/OrjHtumCFPOz27DQ4X6G70dSuGqKPEnje7rZ70CeYHYFXu5Xmj5/1JZrdNrmmnEfuN/5ftmjh7YQrvAoBHKK8BafG+6yHAEkBBhgriSbifXcVnxChKQbgLObAN5iIg2vlNF9XI57l9VhXkDWDINYGpRgoDQ1iFX23HcLOn9pf7rsTE7J1+68IiHI+mH/dWQKytDqLwDUrUIdS4FUUS9/yGarNmtigR6gN8r1vEBrjVqthk033RRJkuCBBx7AN77xDQDAG2+8gQ8++ABbbbUVAGCrrbbCL3/5S0yePBnLL0+T//vuuw9Dhw7F+usvvFX+glzPB/zg5ldRSSQqMf1bYXACIYBqqpFqYxUEItWNJWIBQBuBVBlIQ71hIgVKMTDIUKL7sLR0SFjOvf/t3BLZmbuss2BPvAE3v/AJhBC5oJG60jAGSEyetI59e4Yjqo3p9niwZTRLPzXu3Zk5oh2qr7wQCTvAUPnczh0Ak4s4ku47CQkW2yTyynPeymEP4wJ2XLq7oF3hkbvsh5yqCM9k+WLAKjYIVPNA1Q8H5JCUdzdOc9R9xrnF+JiWbPNg77f3JIAPlEQS9Uw7smdsu3Rw/u4a2GvHSjSL3ezLpu9DIDLCES1lDJTwZYx5RSe8O1wxHwOkxiAFnxeR6boySJVxVoCz732LttUG1VRbGwoL74by4AZskN+XAijFErEBjrv1dWRKo72W4dqDvtD1RZ5H/OqBt1GJJVpLEkkknEodrm4LwWkmiVAnUjqbgluNEWwVockkXevO93GzQFd3v9m/+D7g4lHh/Z3aZ72utW1rvtR1iJBI+vuA017STx2sNvGqyUPjp9pAReMme8ZQ+r7QMz235X5lDK575kPEdnLRG3THJfh68FMrgBzhbpZalBRW/0z2xcIR2k0eeYPybmfQEMYGSZoFuwoaS56UarRnoDENEUo2qwiXKc80B6Ny/+FXFAEfKwIEHnXQhDvVGpkxTf33BfoH84Ncn3rqqdhtt92w2mqrYdasWbj++uvx8MMP47///S+GDRuGI444AieffDKWXnppDB06FMcffzy22morbLnllgCAXXbZBeuvvz4OPvhgXHDBBZg4cSJOO+00HHvssXMNoJyf6BG5fumll3q94/XXX3+hJvBemCjFEoPLEYaUIySS1KWaMijHEpE2SCJKSZRqg8wAsfaKGA+UxHHsYGOXL6UUSCQghLRLYX4bJt3KeMWH/dwAEEngnNHrztN5/fHJ93J/83Id545m24SPcKf2/evFTxEJIBISiZTQsMUVgv2wnzhUy5qhK4XMgIIg88Q1X/zFvmiZYdfn6YhToMhqI6yHOCCY8CSTz5cHfV/gopHY+qqMxnBKqlCt6mwtEQBMFwSHSSkpPVYPbyA5rE7nCb7JqdKcucNv5Umu4MYFBF4HMx4pQKswth21TDsvt19+N36wDMm/4GBHWNtO42TK+8kB2NzCxnplbepE1w6BJJKA0lDwE1GlPXnuSDVqmfbpzYLvK7IFJUjlM+4ZVfb5jBuILAD3Gp/D/n99DtoGz2ZK499HfQn9hZ/vuDZ+88i7UBpIInqm2VsdrpzQa9Ip0DyZF/AEO5ZeuW4Mug3Jc4iQAIe/Nz4DfF1pIDaIhci9py1xbsy+wzsJ7wNh/0HbKbLIkyohaMmf25EZTfvXEne+PKlLK8clj01wh0wiWhFk8Ffck6DGR96Y5s49zBDyzISZuXNzk+RuwKSbuyiaPs47Ac4M9bTG0EqnTzlIfS2vVqVa97lEeyN+fOd4VBKJQYnEkHKESizdxI8mDfY7c9YuuKBzHhmM8e8z+dbB/Rf2b5k26EgVvr9l95OoAvOG+UGuJ0+ejEMOOQSffvophg0bho022gj//e9/sfPOOwMALrroIkgp8Y1vfCNXRIYRRRH+/e9/4+ijj8ZWW22F1tZWHHrooTjnnHN6fX79CWF6sO4kpezVEpWUEm+++SbWWmuteW7g/EBbWxuGDRuGmTNn9tgT1FP8+M7xKFt1aVAiXQ7ojAmA3Y4HbVbBQkIGhJkn4JTAxptaaeqG6pnGz3f0y9tn/PdN+76f5TMBPmt079TsPz75niNw+TRHcOpXIgUiKe3A7TNpODIH2jYS0v60RByejJNP2g+QvDSb9991VnF4mdqLi/lrFBIAE7zW1Z0cCYFyIolcR7IpgTCBehISEp5chE0IC7XohrZ0TkPlVzJcbmtJCqQjHsgvG7vKZMZvK7kd9gPGeOU9BCvSypDFgfO88hTCKY3SK6GsOnsh3TgbB/vPDYBaqhzBVgbuPW0M6plGPdPIVH6JP7w+QP4acbtSre3zRMu9qfY/U23LIBtagq5mGtWMSHU106imPogpEsJN7prlaOfvTdkBn+/ZsJ38zBIx0KinGqnSLgtLxs+4/XnPMVt0cdf1HddatZXvBybbPJENAxwpgMwTWinIChSunDWurIRXRjS8lrMXNekfwvuJnwG+t0TQDr6uocrrRQOfH93tp0mbwklf/p6ggLaq0qhmCtXMZ4jgSQd/tywQkMovneWOgwu5H+KJClsanO0iUOfDZzic7DbyjvD6bLI6jUVPvTMDStM93B8Zc+5/fUpO/eUJJfetse2XDYCaUs6iMS+ZoE67501KwVeSGFySKMdUECeREiV7X5YiiZL1l9eVRk0pV46cA5lT+90p+7XV7fdXSSRKVqTKtMEPtlpj3i7SAMT85Cl9bcuQb/wRImnpdluTdmDWv44aEO1emOixtPzUU09hueXm7m8zxmCDDRasB3Eg4Jh/vYahFVKrB5WkDVgUzoeaBJ1qVwVXcoqnJdaJI1uedISfb7avvirUrOYwvPUkr6gDZGnh9kgBiHCbgHw0+nWp4wYi+z4t5/lI7lAFahy4GsGqCymz7gidtmtGrBvnibzkrISByOjcI2VyR2WrRzhx4BK7wl0v07StfA7hBKqZAkBuBwPAB1c2S+lH16bhXIxVxY33XofFz/ge4p8+p7dxWUDY36iNgdFWlRa+lDCfv5R8Lp6o8dUXABK7SpNpA6WIUHP7I0lBeJEki0YWVN9rNvHg1QcBS4qFgTB5EkgkiNIVIiB5YREbY8j7aez3xKolI5c5JSDWvMLAA7kUnA1F5yZc133nCwCA/f7yrL9OAmh0IH39j0+7Y8yrss3PWOilTqS1jYjOE9h8AK1fXVLGrzCgCyGl8ZVwhcUYryBznxdJb6XShhVLf2xuv99H52PT+waC7zchOj273DZh/8VCAlIj0yLYD/WlnFKDm87XjSePfD25gqAAV+xmE5Z/hjSMO+eu4O8P3090et/kr+0WnxuOR9+c1q/EOjweB2oLkbePjH17Bk3AJHqUSrA7SAFUEkHWRyFQjohUJ1KiFPmJH09QEints6Vpsq8N6kqhPdWoZ8bZsTrqGu2pRiWhzy5s6+MSB37I5rZNgZ6R6+222w5rr712p1yDXWHbbbdFS0v3s5vFCcf86zVHqCuJdEpIIoXtmL0CoziVmDI4bafmwVS/fvCdTsSay24bk1+SpcFb4Nz73+5yf81w4cPvOHIXeh0ZPPhwMBr9HpB6KZAEA6UQBkJ7YsQBaezv5IpjrKQZA9SNyg22PPQ05pQOn9Un3pre9Hy64tY59RPBueR8urz86ImlVNoHfcnOChS/ZoQnUJEElfQNFT37M6+wGXZZuPe9zcK4NoUExCuM+Ywp7Mv2kwyyQUTSUm8mxI5Zd752/JKURMaVJcRSADLq/IHwmnJblTEBkWhUJv32aeZT6Clt7DlJ92zUM+1IWFOVzx4rsxYWJttpYEPR9hqz2mUM3O+RFMiU8fegXVLi66sNLNnn78ESbgg7UdBQ8KosZzcIq+zd/N1NO12z3cY8hd3GPOW+z38ftXnnL6IJfnTHeLSWJVpLEQYl0mUZEpZEx1ZxDVeG+D9+jkILCJBXp9mi4aZGIm/3CL/rZl7pUHWm6xQq1X4Cxtal3PNo8n1KeL39Pn3mCyboTNhD8PPElidhSGFmNYAtQ+HzEz47gs8vOBc7xXUmlPCeZGtFV+AJnIEIngsbOCzy231htc7VSvsjVV6oWFMfElSvbGgHAGy19nC3EtgswLw3YIHn3PvfhhAUxF+JIkeo2e/vYlsAZJFAqiWU0HbMhLN0ddR1r1dd+4oDr3sB1x+8sfv7kL+/SO2UwhaqodeNVdj//p2Nm+5nccSCCmhcHNAjcv3QQw/1aqd33313nxqzqKIlkRhUkmgtS3fj1TMNbaP5AXjyYAOquspSQNvS4KANIAz55HKKtv1FGArQIyLTs7ae/9A7/g+Ttytwp8GEPWyP/90PHACNXaQ8eA+1hidsiRRotflNmYykqnMqPCbRTFS5rhbZLIXbRga/0yl0HmQboY0n00ykG2EMgvftP+HJQLPMEWyXYNXMXZeAvAj4/Ku54zS0PSTa4bmEx6XrR6yQyT6n6eLrx/cCq4dcuj28zuFP9gpLq1xT+irt1PI4tJpITmeVtwAw2AKRO4BxNwzYgyvs+5kyqGfKWaeMgbNTUNv8eXF+XrbBSEHBl+x35qCo9lShI9VuWZ1/1q067uxN2k6o7HHjyJdmzt8XVsEWprMNAj0fWP9ztLeE7HXVuB59BgB+u+fIbt+/4fmPnRrIJBrI20DcZMbdeyY3GWDyygS88blkuxOvavj3hFuB4u34dSbSYZEiwN/HPLkJ+x62zzRObBhS0P3v4jPs66FlhJ91PioTOWjq0yLRcNMin06y0RbTiCA5Zv51+zKtjAk36dLwk5ewj+Y+pJFYPzNhZtMgx74ivITStiQsD9+Irfq52ExdacyhtPnQAIaWEnB2mrCLyAxPAugactafaqabpvabn2gk1uRJhxsgaZWPMgYtaWDRpPuNFkxbBjqWzIjDfsJ3b3gZLSWygpQiXwK8bkexku3YOHiKFbfuiHUIDQqsMsb7PRs7RG0EIm2genFDNw5qjQqSsWpgqF53+qwWVnk0iIVEGhlANn/wjAE6Mq9S88CX4zGWmEaOgAlo2Ti4+uVMYTqraEBz+0iYTQDoPkgoVKaMEU07ikYyHfo9paUVuQwLorPiFpJst1/TWDShMXsDb5MTuV2rvGWEfzfOM+0HUz85EWgg8IIIsgD95FSRwql+AAc/8bE7rQoIq+5wmeHgvsoUVUtTOk9Ua6n3yPL3xwGvkd2GC8cAbCeyK/yazikVnpArbVBTxmXnqWW0tBxOCJTxXmhjDDKVXy0I7RPhdQ9jAfqK27/fM9W6Jzhgk5Vx1yuTXOEp5/t1Fg1//3NWhcbCPIC1CgguquL3H0mgHEUoRzKnXEv7PSsBiChQx2XDdRP+3nD3CPw9xyWrIwBaCsTGOC++ypEZ+rwEqb+8+sPbhLnOg/mcnXhJyMinb2z2zNE18GhGPBna2MBAfgxsf6UswzYI+gGInHWMyTdgsNGqnRVrgAKzjTHzrB7vtN6yuO/1zxyhDkUYfu6fmTDTPU+brtH//thzRq+Lc+9/G3PqCnWlkUiJIUmMcuQLpfH9FglefdEDgp8d9LcXKNOUMW7MKEUSUgr8af8NF3LrFg6kmHuFRjO38uhLCHpNrpVS+Otf/4oHHngAkydPhm4oIfrggw/2W+MGMg762wsuwjySPLhQRxEHS+n1zKBqFbK5zcDP+O+b5EeLeID0AW6R5IEz/xltACUBpQUufXwCABpQjtl6DbfNFWPfs8FVpCSnmkaFRrt2jnwar17nysqCgzCZIAvUpEGUGSCmQSdUsLQBqkq55ee5gZP+02CVfy9UO1xGEuHf478biU+o1hrTc2Kk6ISJINiBiT2goTrF+2XSy0prrsCKEI4YNAZl5Ql38J72x/XXIH9OeZiAFPrfaXXDp9MLvc98PCaQ7DPWhiwcWgtEigKL4ki6oMZwsuC0PCbp8EokK+ihjckH85rcdpxuT9tJFpMknhhpQ/lrlTHW/iABUF5bm6zE2T/oWKTcx5KCVNnfzfaOTBlnZ4kaiKGwfxtjIFlVDVRXYN4Idn+Cvi/lve+KybJd3bAqvatyaXzQXyK9klmOJCpxjJJN31fX2qWl42ff3ZzSnz/dN/SaMPCqsr1YRvt7pHHyzddeGx+/AFCb89lJ2FbinzdDbJtymkMghl91ogk4ZRiJhC8c0jjR9vds8wl3V31WeB8Qp7b9Ich7HpJo/qkh5kpg+1O1BoCd16M4qcfenO76ZaUNqlpBKAoqZKtMsxSn/YHTdlobFz78jhWgOvddHNfB1rpIS8r6k6oFrlqHWJLsHj0GKzJz26ZA78n1CSecgL/+9a/YfffdscEGG8x9iWAxwxE3vgxjgJZSTF7imKKWY/vPKzNUXbGm5k6qT7z9dZQiyi4SyzyxFrbD4XRbXFEM8IOVywGs/aBx1ZPvWzIIlCKfeQQAOJVxqOSFxBmGOjtXfANMpDpnBNASkBkp2EIAiIjMIWiX0V7543bzYCstkTZ8DNCbrLp2hTB63xGiZq8JuNyueeWm884diYNviIGwn/eWFWlJqZXSAO092Hwt+bq59toD55a2TcPEJVCEAUC7tprcgNTsmRN8rsJbdYRtr2qiakfCr4i4+0mwjcQvfbKFgpXEKDxPwHlpifAY70sMCLQQnkC7DCJWUU61zwzDaqqBcfmZtdGOINL18wGjQpCSxBkg2ALCHk1vI6GvyRiaJNRT7b4b97xJmyGCLSr8LMBnTdHuenn7yBE3vow/f2vhqlhVRXUHXYYY5AMFmVC74EzQeZcjiVRrzEkzxFLCIEYkNOJEWsU6X+yJrW3cBwhB90MUtIWzybB1TRnb7zAjbgBPwH0gJdwKGPtbwxUQnuAa2xYYH4/iAoYFIO2qUyKDfgiA1DbDi/DfL3+3jVY1Pp5zsYvO/QbfR9IEpB/NJ78Lc6TkwMQwZsXAoKZUv6Xf6w6nbP85/OaRd2FAQcU1qd1qR3hdMm1Q1xq1TC2WGUAWeYi5e67NXN5fUtBrcn3DDTfgpptuwte+9rX50R58/PHH+OlPf4r//Oc/aG9vx9prr42rr74am222GQDqZM8880xcddVVmDFjBrbZZhuMGTMG66wzf4Idvn3t82itJBhWidFSklh1eKVTGj2ABpPYBkQBfqDvCbHmYjNx1NzqwJDg9FHUIcWSaAanLlLCWO8aD0R+Z5EAjDA2uAwwNj8vZ+pg8kw5fZHLcMAEUDV6zCwBVYKWcuuw+44AppCSS/EKm8oNnOOC0KUyBO8n7uSrtuqxsMdngi5sFgkpODeqvW6CiYdX8xpTabmwJcubXUU4q0jx4JpIichmquDlal5NIFLrrz1zCj6jTmkC4Qdj5xfVQTuQn8h0vgbIHYPJMZ2zcESalexwGyUEpGai7a0jAAKFK7+cz5O3sC3ub77elsC5lH0aUFq7zzPBZhtIzQYPsoJJ97K2ajQdSFv1OhICUJJyPNv7JhYSSWRQtzORaqpRy0j9VtrgvK/5qqXH3vKau5YRBJJYIpYSSSxRiSk+op7RhDjNNIzR7l6TUuLqBVyJsRE/umM8SrFAJabUZpVYoBJMyCPh70uAJ6/eBsEkN1Uas+oKSlNsSCmWaLXRycpotGcZIm0DzoRwq2fG2ADU4LnMlIZ0gWnCC1uWaYZpJHnQDeeQwrCu6+8lZ6WQAkbDZSLySrG/x1m5Zpc3F4zSMI5g0wQbMEZASF6p6dzv8H0Wdjdsp2kk1tr2PXS+vvIrBHvbfTB2YxXZhYn+LC/fW/x4u7Vw+RPvIVUaM+sSg+IYrUlM6rntgyO7WtGxJBqaFwH0JKBxSRNcu0KvyXVYA76/MX36dGyzzTbYYYcd8J///AfLLbcc3nrrLSy1lO8QLrjgAvzhD3/ANddcgzXXXBOnn346Ro8ejddeew2VSqVf2/PNq5/DoHJMeavLFDRkDNk+mDwTuaKbiVIJCZf6q5aZuRyBbsRSTP9cOrMGBmOM9/lxIn7OZBEJgdgu+aa2AlfOP218oCGrPl7Bte01Pk2ZT0dm0Jh7u4nwBID8riKjdWENuIEwCnywpHDyoG/PEz7wiFXrZlXDOr0W/C2tOiacOtvcKx4Sael+cvaSfPo8LjySPy5/t8pdd6lt1LsR0MFg7dKHCf93qJrTKXjlmpW5xrzAjZ0U56UG8kq4P0ZI7I37vtnuIIRw5dZDiZzJUyx9I0Pi4oLSDKnp4YpC6BEPt80UXScOTgzPL1Uada2Dcsd8/1rbjlUmU6NzEx9jQOTICGw/smu17cKH38FpO3VOR1mKJeWhtkp1JaHiFolddaLzsUWetMAfvzlwfJVH/fNVUtVt9VYKjJaoKeOyCkUSrnKjsN9TbFcAOBd23c6Q65lx+feFMkgjUjFjnnwpqm7Jqxic6QHwqSH5Hub+LpI04c2r5vD3HnjFyk+vNWwed2uB8mkTfd/RaI0CGgi6IDKugolebtIJuiZamFzBmXCSzytWYXETY0myP4dgW+N/h/DPrYANQAfw5bUXDJG965VJbsWH+6ZISOw6au6pcxc0jtl6Dfz6wXdQiQWGtdDqUktEax9SCFfCvFCtByYKct1z9Jpc/+hHP8Lvf/97XHrppf1+Ec8//3ysuuqquPrqq91ra665pvvdGIOLL74Yp512Gvbaay8AwLXXXosVVlgBt912Gw444IB+a8teV41DayWxarLvkHnpj8F/11U+Ah/wA1l3oPRjpLUkkpZjIe2AA0/MQkLDuWxZsfbKlB++qL3UYGkMVMNxnVIa/GRo0G74fJorx7xxOEBplIyAjCWUJtKduKV+n8EibENIqE3D6+54XVy78LNMHGlC0LmtmR39mHyzGqVM58h1Dvhi1YrTm/G1koIUbSFIKzNGku/TfldadL5mro3orPyySs7fJ7/uph9u4PeEgO9FygjhC/PQ/gPF2vpgERyb28lN9KoyEIZ7scqcU/IaJmvCEgm+NkywU+WtGhz5z/vh4h7+OOyHtcfQ/jvluyKRwp3rTut1X9zilO27Xi0aVIpQt8F/PCmuKZ+hpK4M/rD3et3uf2Hgj98chcP/8RIy60dVhgM3yd7BWYk4TV9szy+J/AoA3e+UrWdWTbmVqCyieyXTlG3le1usDgC4+YVP3L1flcqR5pY4QjmKUIrpOmoDm97QT5RlkBWGeyTXMwUTMim8jYKLYvG2bDmi7USuf2AwsTagyaBXlBvWxATrzB5dpehsfM4a03ZCGMjcfuxPq5wrmAVitWBoAzcOaJ48S417Xv0MsZz787Kg8bOv+ufzkscmuL6oEkvUlMZx26zZzacLLFQIzN3fVHBrAD0k1/vuu2/u7wcffBD/+c9/MGrUKCRJknvvlltu6XNj7rjjDowePRrf/OY38cgjj2DllVfGMcccg+9///sAgAkTJmDixInYaaed3GeGDRuGLbbYAmPHju2SXNdqNdRqNfd3W1vbXNty+/c3x4HXvYBYSpQjUosAymbAaYPYQlGDRqrDggQhae4eXHHq17vPfUC/ZtwHcFHxWoP91hw8J4NBNCQrIcUnIpcfLOhcfA5uJqeh15E/EiqwrCppq/LqiNgc+XZpuTgNCoQQqfWEl0l0YyEcVm6BkJAGEwaEuitnEeGB3X8uChhkuL1bYjZAxp7JoENQJjw+kdNE5hUvbdMgCBgYob03U8Cl4MpVH7TH4WpojcUznD84GNQNSEFXRueWrMOl6tiuMEQSiLV05JnvxdyllbxqkPctu+ui895ulx7PhG3yqnvj58OCLamdbJL1g4IOWYXmnNTksda574fVbg5Q5NR/gMSeX1gefcGP7xwPY4CL5pLWbqDj6m9vhEP+/iKuPIjsKT+87XWUtC3+Y3yMBCvXvBoRBkE3TjxOvfsNDEKEWTVFwcfBcvx+G6/UZVvuefUzDNKRS3UnS5HNUS7cxI4DZoXgSoggYh1MZIUgO50xgp4avt9MfpItbN/hFGIRjOHCpy2F9gGE9FZn45nrXxrEIdq3D0Y0djYQEm4aBew+eOJr8nvfZb2FoxjzOMCB2MIW8bn/9SmuP+iPwjT9ieO/7In0T/79husbCgxMFMp1z9Ejcj1s2LDc3/vss898acy7776LMWPG4OSTT8bPf/5zjBs3Dj/84Q9RKpVw6KGHYuLEiQCAFVZYIfe5FVZYwb3XDOeddx7OPvvsXrcnEuTL5GVzAGhPfZnlSJBPU2lShlMYtx0tfxscd+vrqGcKV36zuV/ziv1G9bg9dWVs1TODVPhBLFR36Ng+OFAZm9rKEjUeDNhjzd5Ulw840zklBvAkO198wQ/gQgBGchsMVTYUANN6IWhSEkcip/josK1NyBrvzxVACbYPEfpN3WAMQ8v/OZXMXh948hxWk+Ttwt3zgM6V+cKh2geIejKgm7QvhJYK2khwdUr+DkIrCgfTcSAaB2QJwE2iImEg4dNZZdogC9YnqCy9RGIkAOm+9zA40q+KNFhH4FdkwqDAZsU/2AYVroBwyemaLWnckSk38LvrYBVw/j6lAL77pdU7Xa+/PP0+IiGQir55ME+75038Zo9Fm1SHuPagL7jf/7D3ejjpjvGU/SG4f1jV9tYF+uWyfdfvtL/Qk94bNFoO7n99CuWz50xHdiUljtivLdzs2q+w+Ocussss2mgKLjQUiKu0X4VxKxkN/QXHDRBfZruKv9ca+5SQEJuGh93ACw68C2ns6ozdRhoBYwOCyWriJ8NdxZDMT1AhJZH7m/8kIcHGoKCbjmkA4IKv9+1eLLDgUJDrnqNH5Dq0acxPaK2x2Wab4Ve/+hUAYJNNNsErr7yCK664Aoceemif93vqqafi5JNPdn+3tbVh1VVXnevnUqWRKY2qAFJNnfbFe+UV5pPuGO86dg3vMwZ8Lt2uiHUzhEFLYWnXSx6bYL2goOVPwHXqVI3PLg0aDuYzjlizGs2KYqdUaMaT8FRpO7jkiXU4CLkBTXpSlgmBOJJWZTVQWiKNBGpWPueKlZHsnMkA8CoWkH8/DMZrtK/wuScGCFMWuImAJosGW2w88fbHtmIXgHx2ElbLBWiQVfa6ukmFIJ815+QG4ALx8oQ9PyExGk7p5nPgz/ifgGnyN08iIgHK3ctzDmFy++LfAe2JQiQQBYSDrzdZB6S1uBB5sZeDLB1K58g1E2vdcI9k9tz5/Y5MITOmW/WzJ2hGuOeGX9z3NiqJQC0zOHfXzt7rxQkddYWL7AT92FtesxY1bdMo5vui+Qm2Hjz65jRHNGMhMagUoZzQZFJZog3DVU9pLYOtIwAF1sZG2FzX+X6BwSSb35dgAu5zufP+Gu1ZjjRrtqk1nIiBX9ZyBN5OQg0r1l695kA8fm1hpGfk1UQJ3z+FcS1KC2/TmQvufe0z7LL+wPNqFxgYKMh1z9Frz/VXv/pV3HLLLZ1Kobe1tWHvvfeepzzXK664ItZfP6+urLfeevjXv/4FABgxYgQAYNKkSVhxxRXdNpMmTcLGG2/c5X7L5TLK5XKv23PDoZvMdZuL9hyJY295zakrzF+0QZ+8m5wmL9UGZ9/7FpKI0v0lkpbLYylseWtSyzlHbEhAlU1vxqVjWZnOAoKdKW9F4M9lmnL/amtPYJIdKpWAJ79CcFo6GlSkVao6IonY+kAT6ZeIKaWgdASb/aFBleKcQh4iVKx5wJWW5KpIoATvO45sB6Ats9SBGuvPwWev0A37Z5UsEgLaBpF5wm993QGpDtMINiw0u+Nqm8XEKW429RfnHg7Vau/59gpbLPOdGltTDDRieCWcl+lD9ZwDtQyTAyb/NliLrDt5qwrdEyZn9dCG/NJ8DnTtTE4l5df32mhEp+9wQeC0e97EkHKEVBmcttP8CbweSAhXvkJlev+/Pueek5sO++ICaw+X7f7nC5+gEkVozySGlBIkkUA5icgbHktEIH+4BuXBFvb+V0q7wEQSs7mIhw3Ghe+TGgvA5OwmYJ4cTiiN21ZKgUAHydu3rAKeS6UJaxmxf0hL0llUCbpgPDR+KnboJuC2v7HbqOVx58uTfANA/QPHmVCTqR++65VJ2H2DFZru546XJyISotttCizZEFJAyM7jc+M2BfpArh9++GHU6/VOr1erVfzvf/+bp8Zss802eOONN3Kvvfnmm1h9dVKv1lxzTYwYMQIPPPCAI9NtbW146qmncPTRR8/TsecFtUxTOqEon4qvr2BFhpcvlTaIBWgZ0i7zQQtEnFYKPm0dLcsbT65tBhAOZGIvbON7mfLKtSvq0WRpOURYxY4HsTjyOYPjYImYrQdJpJFIyo4SMxnmVHaGBzl/rFDZBvzSJnsII2mQaoFU0T6Z0IOXyZ1jt3NgaN4a4wMLqc1EVDMJR9ZZzabje2U7zKrCy9z8nQiYoMCQLy+toTsp0/SZ/CSGbT98DANvUSGeQtchiaxvVZCvmy09sZA+8LBh32T98P5SbW1NoeeeVTsgX+jF+/ODfNCC/J57bLhwBub/+8+bKNngvGpjBaIlDLVU9WslyN7im3bF4p8vfIKa0hhS4qEmgjaanlNbwKSz99k/h8JQTIdTtnOzZJ7kezNGuCueXIRWkSzXL/tiSACnmeSnl1Vp3yYh8j5vfmZELgyYiO3Db0yFwILzOFPQqjfdNQY+A3BxG7e/NDE3mWcIKxpoQ9/bN+dx1anA3LH7FU/jrh98aWE3o8colOueo8fk+qWXXnK/v/baazmPs1IK99xzD1ZeeeV5asxJJ52ErbfeGr/61a+w//774+mnn8aVV16JK6+8EgB9aSeeeCLOPfdcrLPOOi4V30orrYS99957no49L/jT/hvgyH++AgQe2J6S60P+/mLOR0kkVgSKqrDeV1r9F8bnbwV8pgb+LCnVXqFmSwir1an2/t7Uqdga9YwS92e2cl1Y8KMZsWbfpD9NLkdM5KYutPNjSymcsp3ElNKwrKWzifgiHZ2vjw+qo785JZYSNMmQmgbBDhCxL8fCFdxp1m4gHxypTV415/dj4ScPoV2FyXkpEi4LQ6j45iYc/J6GI8PGXStfcIjB3yMp8DL3ugGlVYt5EmNJMNOKxL6X2qDEWEhPmJsdyyp00jIGAwpo9KsCQRlrEaQXZA1PkPqeSCLUu43qW7DhvOLUu99wE49yJFGxlRiTSODCh9/pNnPI4oyFSaxDzKimKMcKNaUceWUPNhcl4tR+/BTxqhY/N5B+8uqqjrpJHyG0KXXK7Q0WJrwNhFfjwuqJzgpi+46QTLNqnc9U4gm4CdatwuiAx96c7gq4zE98bYPlce9rnwWEOeiEAhiQl52WWE1DX+gnysoY/O3Zj6CNwSGbzd1CWaD3GH3ZkyglEfa6ahyEEMiUxr+PGthEuyDXPUePyfXGG2/sLuxXv/rVTu+3tLTgkksumafGbL755rj11ltx6qmn4pxzzsGaa66Jiy++GAcddJDb5ic/+QnmzJmDI488EjNmzMCXv/xl3HPPPf2e47or7HHlONx5ZOeBK800dIPvtyeIpMARN75sI/uly6FNgWLsJ4Qv/tKgXguRT9XGanRmEJBkm/5M5RVa9tRm9idX1nOFPmyursZ81y4LhTGOFHNKLRiyq0ghkKmQoNJgmCqJNJLItMyr202uTWjX4GPwtpyH215tupYKqCsK5GtUqbnd4faAj7APfxfCuNy6jWm/GIkkIs8DbKPlRAjfDl5dMG5Qg/NJC1Bwog7Ur3IkEYv8FUm1JoXaku66VmQpEQItcUReW0PbMAG3XxEyYyCkz2ftc5n78zaWEfBkB/DEgVcWMnu/ReB7lF6vqcZEjwsOxgBRBFRkhFIsUE1pUldJZI+fwQLzD9/fcnX85pF3MaysXfaYQXFsc4tT5hzuA8J/XFCLJ3ueHNMqDU/+eFKo3KoXISTqbj/Gq9gArSRpN7tuTrDd/uxbQuRVahYVIITzZIuGdox9e4b7XQhgi88N76/LmwNXywztc9xv8jkYIyC0geGiN/C2NibmTM0pVqUgSvMLfJsIQf3pQCfWQEGue4Mek+sJEybAGIO11loLTz/9NJZbzgc9lEolLL/88oiiqJs99Axf//rX8fWvf73L94UQOOecc3DOOefM87H6AikoB3ajMlTLNK7/9ka92tfh/3gJUSQ7ZaHQoKV/K3palccucVr1GpryqbrPGM4G4om1J9rG/23Cz/hz4gIV3jvui8g081s37qNRdfY81g5ahoIttSXtqaLqb2wjaSyK0gyhvYTUK+M829wGY4BUqCATRt4S0vjch8cLSz2HHYRPV86DskEmBKqZD9TkPMN8rfhvKaRXsEEDGavW0p0zqb9a0TUvycj5pyNJaSDLUeQmWNqQMp2BggY5Z7Sw91GNl7WFLyiSGTgC7SwmlrkY44PCtCUhsRQ+C4o9+9hmdKgrCsjceSGlHAOAn931BuJIYGglctdoRkeGIWUqDHPydmsttLYVyOPH9ru46sn3YQC0ZxlKMsKgJEJLHEFmAuWE8ncDdI/H1jYSqsXO9hT0FwJ2ZQWsQvuAW/8ebRg5AuytWywgMME2ENZLbSe8DQTbmDzdDMk2T8abZQzh7kQIgaffnen2219E+5E3ptmMSV5uN8j397zSlsh85hBW8ZXx+d6NtYFl2uAfz32Eb39xlX5pZwEPpQ3qqcpVxx3wEJj7fGsROZX5jR6T69VXXx1pmuLQQw/FMsss43zQSyKaLblef/DGvd6PS3/WoHwylGZy2KjAEbEOrRRMqsM8wz6Ysfsc1rk9B4MAVy5kpdu/F6ohoRLU2Mq8TcKVJNcGqdEuWNBVTwwuQjOSLXkJWefTAPK2kRQwyrjXIgkYQT7CGEQWQ6IdEnC+Rj6rCZ1XBr86QMfwpbdDPzwgcu9J+PSADPddwafVQ/AzkT4wcU6aYZlKGaVIIhYU8NiRKUekQ/9zFb46aHjdXAEcwQq5Dc50XlW4QDL2mWstoKRAxqsN8PdUpg3qSqOu1UIl1gC1l4unpMpgTp3Uc/LZFxiI+P6Wfsy4etwHaEkjDEliDE4SGxMACCEpG5KNT+Dc2Ab51JGcFQnwwYk+OwpTZ0Jjv8QrS8auVLEDSxgupU6BlkzY+eNsFYH72x+NIYQn583Q+PpT78wA0D8kO8xkBHDMh6+H6TIXSUonGJ4L9yWx9H290oCSGlIVbGl+4IHjt8r9vSj4rwvluufoVUBjkiS49dZbccYZZ8yv9gx4zEOcYg5H3PiyJS/5XMMMtnr0oMijs07kbSE2SJE9yw3n4CPumw8+HMzG0mUzVddnDbGDH3zO4tj6q4WgpVcirv7zoomKHEb/e783PCnWxpWu5mwg4fXTBoiZ4FqVCQH55sGZC2uwMsyluZWmoj6yLFGyvlBhYMmzb7uzp9iBLG7wX8edPKKBCm58zmoDqrbJ3ulyRJlUZtUz+p5gMLOWohRJVxa4rrVX8uAHUi59zN8v+FVtEAkJA40UgFC2ZLtdU3dpBBV7tWnywsRDCLgKi9oYVDMi1wsbQoACWKXAnJp2fuuw+luBgYvDN18Nv3nkXaw0hJ8TYIj0PmzXPwkfFB0S63A1DAgsIUIgkaROZ8qnHA3ztwvXOcBP0uHtHPQWMUxHtpv0+0IEx0UgPgR/dCVi8Ev8zhNvTW/Yt+8ztlp7eJfX8f7XpzhrWi72AzbAOLCihUGO9IvIfVYGKT21MRDSQNqc/He9Mskp28qYItixD9jlsifdd3TPMVt0ej9VesAT7IJc9xy9zhay11574bbbbsNJJ500P9oz4BFJgX3+9AwA4NbvbQYA2OdPz7jfQxx43Quu8wJscJkgr6GUXrHNWRga7ktWerUxSEC5lbXpfPP6AhJeqc6cF9F76gBPrDkFXD4NnbAR8JZkI8wI0vl6uAfJyh2cwYLP0ak7nZRp/zsH3YXv+9+9NzC8HkLA+aLZFiIMKV5CwJVC9/vj43ZuC3k84ZZEU2WQSINKIu0xSR01bG2BgMqoQmYlARIZETEOlogzW0gls9eSB3DbfEfAq1pDaquWC4n2TKOqFAYnMeakGRIpoQ0pxlycha8fZwoJVXDOCKBz32m+SqbUnC+bPK+ceSW8HjzxUlatzoyGAPm7B4kIt780EXWtF9oge+6u6+LXD76DzAAtJSo/XxDrRQs/3m4t/P5/E1yMSSWOKANSSFjBk2F/fzNhFWB11m7bQGh5dcfw9pZYN64ESu5L7OqT0tRPcwEbPlbow+7Ozt9dMHVIqptVm/Vq+NyPc8+rnzn7GPeFMHZVEz6VoO9Kw0m/P54WvmKrPweaaJPAoKE06fmcTaSwivQcO/z+CZtBiwNlm5PPe4/dEruNeQpf/+PTEELg7wcMvKI6Aj0g14UvBEAfyPU666yDc845B48//jg23XRTtLa25t7/4Q9/2G+NG4jgghsA8M2rnwMAxJF0vwNwhJmhwk5OAhFLqvDEuvGGtRZAKGNsAJ8N3pL8mnD7NAauJDt7r6mUtifWnHUD8L5gXwyEXvdEmK0E9GLYtHy1NI8whVscXCMeQMLKle5aNCDcr/88fy4/yhh3DW0OXDRkaJHkr4zglRm3QtBsAgO4lHmRpKwbSHUnEu6+h0i661i1FQgTyUGMFJxqQGpEqrX1Z/sUgtrA5bgGgJoQqKs6tAEGJRG0IcWZC7KkWrsUi7HginT0HZOaZ6spGs6PbVxRoVj6oVPbJd8MtATMOby5oiMTCHcPGKAEiZK94wx8usBMa1z/3EdQ2uBgm1HgmnEf4NDNV+v85c4HzKopytoixRKR03pxxAlfWRO/eeRdmqhGEZIosf0S3bEc4Mh+7IytbpYwRwJuEi+ED+JObb5+VqbDjErcX4R9EGfDYTFBN/Q3IYwBGt8VCIMvPTEO+61GYt1IqnMVFBv66mbgQGJ+dpWBLSST35crLCN8O6n7zK8Ohm3kv3lywv7r0MJ49bgPkCmTs/sU6IyHTtgaO1061n0jSne9HB1ZYQoADvjrswugdb1DoVz3HL0m13/+858xfPhwPPvss3j22fyXL4RYrMj1Lpc9CQEgiSnYhkljaJFoJKyMxnLPvC2rgYDmKgqOtDWCO2YF651TTJSBMFuG7wDpc0ys2SvrVGrjlwaduqOtqt2F34XV6/DvxpzP/Fok/IDFFpfQAuKWW5En0dLOhoUQnRT9MM81k8Vm4w1n4uDrIC355rRUQvggQ6V9OyLjrwfg7R4c3BfZz1F2EFJ6+Wqk2iDTmuwkSruJQDkI7OWyzkKQTadufMcaW383ANS1RiQESpFEIiUybZBC0f5NfjDunLGAX4ddAQBS7bfln1ypkq07bKtJpEAspC3w49Oi8THihokR4P3hACAjgeuf+8hZia4Z9wEAOJJ98aPv4sRt+z/AcE5dAaUIvwwqmRZY9MABjzc+/7G7p1pKQCz9cyQEfFYj25EaO0GNwen76JlVmS/e5MQLCNsXGh/Y3PAQhX1lKDqwPa6x38kRZ7Bi3fn93Gvomlhz/xwSXKVNl0VdMju5YKHGGE6zl99OCHot3D/3AeH15X3w7+FqqDZkNSSSTRN9riJbYO5QysBIdDl+7TbmqZw1lIP3BxqKIjI9R6/J9YQJE+ZHOwYcdrp0rFWK89YGLvkN5DuivF/PozlpzhNsygDiLRjNAgQlKO0atIFmAuvIkvdV82czmzmEBwxXRdD4ZVI3mDT0xjwIuWPP5VlpVJsbz5kJtBZEdBtfD68rLwH7CYzwy8DaLwt31TZtjP0uiFgz2eT9sg89aF2+PQKoxBKVWOaUZiansZTuOC2xhBSxs4BQkF3krByRoKIVSSRREQJVoTAnVWiv0+A0zGa6KEcSJdsGA6AjU66wD4P96nxsvjcBOwGwZNf55aFtKXq4oFke0vnv8LxLEeUfDwdZ2tZPcNi7yvd9LKUjCvaJQF3RZCNUs7L5NP7WM4OL91q8y5svSfh0Vs0Sioojy1JSFVJteFXOuBiOzP30RZQgfOaeMNCELV2AFUIa7klvJcnb5vzn597+Zmk7+VCNFpCQUHObQoINwKdQ7ULppBUm6hg4lqNRQAHCccJbAQGvaAM+doNtZdxfcF2ETNuUrcYXkOIV0gJzx0MnbN3t+/85egvsfsXTwYrswEShXPccvSbXIcKiG4sTdrp0LIA8WQyLHTC8GpsnmF3lRg73yQRbCCLL0ib15yj5kETy3zCcQ9rvi0uHh75qV2HRdCbUucGjYSmQfYr59gr3HiMkVLzM2JhOKJx4MKQNNAyrLHJRiTgSbkAN1WOAVGht0wR2NfNvvL4hUvJgWL86vRaWZo/tYFyJpbveJas+K60xJ6Xl10FJBK7c6Acjmz4vkihHnuDWtXEDozZeCY4l5WBWdcpTXTIGNeujjoNr7crS80Cpmejb78KtGpB3OjxvX4xGuiAl952E2QMChSrTBpEw3vrC3y2s71prxEJCgs5LgPJpZ7a9UgjUlCVBTSZr8wOXf2P9uW9UYJHBiduuhUsem2CtTQZC0JRTa4lMsVLq+zcO3qZnQiIT+Yl5Zol4SG6B5kGGId/u6j0/3s2dbOfjWoKVt+DZUME2TGpdX2zFEO5Drn/uIxwYeJyve+ZDsspoba1dvE/fP/OxGfw8h8V2gPx5uz4WnA2JK8nS9axlBid8Zc3uT75An5ApbYU7siOmPclmsIDBq9Bz26ZAH8n1tddeiwsvvBBvvfUWAGDdddfFKaecgoMPPrhfG7cgsevlTwFgH58lLtITPoBTtQFsM2Cy2Kj2hXdXSDQag0Y84fVR8sxDnFob7osVZ7D3mYqdhJ/hojBa+0DFxuVJp5A0GSGaDRphwCO3qSs1WwhvBeHrE14SYzqr1ZHME2tfKMYqPFY1JrrrD8wDK+e6DicZjedUt6kJASKxYZCUqwgHUnAzO6CF1RATawdhRZrtG+EkgsuSJ5KyeyRSoj1VqBsNY2g/dWVQtZ6NjlRjTp1+r8QSrSWqWkk+ajrVCP6eaLzmHMyojAa0hB2ec5YkVqjzA7gBQN9jSQooo1FTdB4JZGCL4ZSO7DGBbWvk2tOeZZSRBlRYxxiB722+ekM7m98rBQo0YuKsFO2pdrEKS1VKKCdRLn1eaN/gfpOrciaBdc8Yg1qmXcYP158EE/S5E4XmG0gBlwqwWQAjH4djYZwdAz7Iku0WynQm1fQ7nAVDG4Nrn/kwdwwqImVcj8iEmHPYhyuZDJ+mz6vbyhgcteUaXV6DCx9+B9oAP92hCBien/jvsVvm/m5ra8OwkxZSY7oAj+9z26ZAH8j17373O5x++uk47rjjsM022wAAHnvsMfzgBz/AlClTFsksIruNeSpnQwDyNxH7b7nX402NDFKhmTxppUpi1j+rtFN3aVvf0dLk1PuvAcAI7/8TVk1kcKdNXmiPsMNWXMK8gUSH/WyzQBlW0xs9gZ23o8CfqIuHKLR7NCJMN8hLTALeeiGtGk8WDTt5aVBauP2RU7v9e6xmadjBTxmXhYUHV+UUWm2DpYg8lyLK+qGMRGptHsKQkhBJiWqmkBlPuI1B7viUAswGUxmgZr939i6yRaOSSNQz9i36a6qMcXmynTIOsgQpTWp4JARERA8u5eGmIC4Bn//awBPvxsG0mXIV2Qmat5n4oK7MkHJFObKNU1QyYydxTDCQvx8LFOgLfrnbuvjFfW9jZi1DZjowq55haClBSxzRfSl9MGLYH2SKMlrwZJcrkWZKo575DDtAflWG9xWC90GT9u4lanqUfP/KzxeT1sxOrPn5dxmd7POj5tLXctBzo7c5FF5owkHHc/1f8Dxz30d9Fe2H+6Tjtpm7Cn3K9l2T6v/7z5v45W6FNWuJQQ+U6wHsasnhjjvu6PVndt55Z7S0tPRo216T60suuQRjxozBIYcc4l7bc889MWrUKJx11lmLFLne78/PoDRocI68htDa5AgxrN/UqdEaMKJzah0pqVAHlxOPI4HIlvr1vl8iwD4DBSnS0Jag8YAgBYzp7IXNgsC40OqhbJAd+xI5YDE8xa5W6UNS3VUqqa5geKQCq82d0+vlA2h8pUXR8Dqp1/41V5glZ8khW0L42VyT7cBVt/MWSnXng0fn1BXaU1KMY0FqLAcrVuykSBtSbevGYFAkEVm1ikh4hEoUuUCrUJ1i9VhpjZrSqFvTcRL5i0DlnQXqdgkwsvdMSGJZpasrg3pGZB8REBlASJ89wadgzBe7cbYad/3z5EIIQBtF2USksekGtQsoZSRSunOqWyWb/Jf0JHAKwmYkAOh+cC5QoBGn70yZXy5/4j3UVQ37fmFFABTwWIkiLFUp2dUTriLLwdvU74X9OVuaQsTCBya7XPnwxNjAZhiCL1TDaFSpDWCzkvgVIn4WM6M7BQWyNzrVpM6Hhb2k8Fa5sD3hNqGIA/hMUKmiZ7NmJ+0AXOVYV1Je+H6lvwKMtTH42V1v4Ne7D7y0cQX6H4uT53rvvffu1fZCCLz11ltYa62ePTu9Jteffvoptt66szl/6623xqefftrb3S1UJDFlAAk7S/aHcgcdZsZgu0PuPXsfcafIigJn35BBoF4loVzIqTSItUAqNaVBiyRiSQFadaVzynOkBUzUfDnGBeho4wh7GPATqpRsJQk/x2h8vRn5bkylx9uHWTXYMhGC/eKNxLqr58/ZOrTIKS5CwJHpSPrKg74IDJWgJ0Js8ueiKZ2hFMItsQLAkHKE4S0xhpSlCz5k/zXnti1JiXI5QkemnJrbkSm7b+NyRQsBZAq5AVQ3XC/+TKaMC/KjwEmaTMTBUkAt06jaJe3MWDXfsA8SUEIDUkJpjaoNIsyDcncbY1Dle1p4L3skOLsLF5AQlpDTp53SbQBjNWk3sbPEgQNDuXx7ZicCBQr0B47Zeo3c31M7UizTQqkvwxWZSAhnXTIAYpNPvdkSRzYegCd/GkoFfZAxnVbawpR9gH8u2MMdrnNq+zqTXiLV9DykzrPs+wXyLtPznQZ2tUQKlGLft9mm0XGNX21kEYUq8LI9Bq54GOcNNwaoZhoSFNNCufU1fr5j/6St/PGd413A80l3jLcrBaaIhegH7Hr5UzD1OQu7GZ0geqBcLyLcGgAwceJELL/88j3adsiQIb3ad6/J9dprr42bbroJP//5z3Ov33jjjVhnnUUrJZZLdyN8EAcrpuwBTmIKpmHi05hCLrQ5SAHISLolyVDRACiDhxaevAsROXUbUmBQSUKkQIc2gOuoSU2k45jcsVix5hRVaeb91twR8yDDbQ4tIuzN5fK/xpiARPu2cxBd6AN31hjkV4F4SZXRSKzD7Yi2+fPiLB/K8KBp08AJv48kEijFEoGwDQVPalNlUEu1mySFy8dsC+Hrbwyl0JvR4b+TQYlESxK54MTQdz27nrnBXFqyn0Eh1cLZRHggJfWLjw+nMGk74KXKoLUckbIUfEd165mvZX6SoKz6Li251qBUexo0CaOlYKt8BasBEuRXN/a6AFzeHUBEQbSR+x6tRUkaQMBlBGAlnmmGgXFZAzhoFiALzCE213WBAv2Jc+9/2xV0SmSGckSpKjtsnmdalaK+PJMGlShCLP3zLiU9xx1WfFDgaqTSrUIyuXYVX8Hlz1mtprZwPxj2cSF55nzQTKyZXHOlyNROQNtTjWpqn98Gcu0nus3FFBYS6tYCEhJxp3CLMDjdINW0z2rqx5Af3THe9ZmZoniNK7+5QY++k2P+9RpKse+7U8VihsH3bnoFf9q/Z/sp4MHFZlxQ7gBkqSwWdgeziATZHHrooT22eADAd77zHQwdOrTH2/eaXJ999tn41re+hUcffdR5rh9//HE88MADuOmmm3q7u4UKpTUiY5wyTUoedeLGGFdmO4nzCnWlFCGWvogIJ32PA8aX2iU6JtjGkC8Q8JlHYglkEJYgA4BBS0LkfHY1g2aFFMYp4aESyoNHmmnUM4V6ph25NSZfYpVzdocQNJrkiLQLrITPzKFhPYPa20Y42T0XM6Hj2qVRa6exLc5VeeRrm8uR7XzupIJzMCYHbUbW0kHKs0A983YatkKkmgIFO1KNWqZyXvMokoijyAY8WfuFVYtrmYEUlqAag8kaKEUCreUIw8oR2UakCK4PAGlQzVQumpsDHL2PmTz35YiWq5XNV03nLFBJRK6sOt0f+SXgWIDUs4iJunCKFBeMYWKttEFky4HHkZ8U1C2Rr2ba+TATKZBoIiJpJBALjSSSyKRxk4RZaYbUllrngFEEbeDvThugrpQLzCxQoL9x2k5r44z/vgmAnulyLCGFIhsIfLpMTpXJxZzC4kjKUH8cppmrQ9vYBOqLBeg5c2lDTcBa0ZAFJ7Bp8ITTTfC5OqsxNj7BOBJdzzRqynujWWnmyXeNunw3SSZBI69aM3nPNFyhnNCu4iYU0gd7C0GkPlMGx936ekDUbf+ptBufeoLu1OlDr3+px/sp4MHp+na57EkA6LL2xMLE4qRcX3311T3abvbs2Rg8eDDGjBnTq/33mlx/4xvfwFNPPYWLLroIt912GwBgvfXWw9NPP41NNtmkt7tbqMiUQWTJJBPsJGabiEDJZkVIYolBpQglK/MNLpHXtmp9sOx54wCvTBlbVMQ4X3GYZonLeQshUI4AQKAj1eQPTgQqsUSWRC4QkouncEf4z8O/CAD49rXPO9JetxaCexsijv25andMp+ZaIsdLrDxJ4KBGlyPbKrGpmywAsUtbJx0pFsbbCehN9kt776L3AXuCHS65OiIftC1Un/l65oM8aaDhSUYt026/sZQQ2qCa0uSjpRShEgu0RhG4Klt7qtFhc08rbVCJpSPr5Vi4vNeVWKIceZV8Vl3ZQEaBqsrcoB5J4YKVIglUEsqiwVYQe1v56yY6d6JSkL+bAzwZpLx5RT60mPA5U94QUsGrwfIzD9R8zVIhgNSuBkSaKkNmRJZndCh3PbThkvZ0zcuRcHnA68pgVk0VvuoC8xVzahqVhPqa9lQ5pRSAnWT7wL0kIisIF2gSwlZOBeeD97EBtLJmAE33M5lGDKQRjlsDcCn0jPHHBPLp9PiZT51q7Quu1DM/gQfCgOjAN43AFgefVtWvIuVtIQy2erm22nEnlj5WJwyohvb7BuCthKZzP9QXXHPgRv2ynyUV9x67pcteNtCwOCnXAHDRRRd1Gyc4a9Ys7Lrrrnj88cd7ve8+peLbdNNN8be//a0vHx1QkFLYyoveoxcJAS084Wwtx1hqUIxUGcypK5Qs8QJInU6VQVs1Q6aoWAinUmL1tmT3D3gbR5hujsEqLBN2IYjsphmR9EyR9ePW723mPvOPQ/xkZtfLn+qSWO/w+ydcxywEnbeG9+Wx2s2qtRRchManuzKGspCwEi+5tjh8x54pH5zHxVyoumRwza2qwmRcsz3EAMZ4b29YyCYk0l0FS/BkIAs/JyitHA80UnKVNoGp7RkGJRJz6gqzawr1VHn/ZilCqiNUI+2WaMuRRGtZoiWRNqOIQUeq0V7XKNs0enUYKK0ghHDElP2PAKwnmVL0JYG/msfJsOJmY8R/5/OFU7k5JWE9o9UYJhmZMk4lM27ANsgUYCRddwBQWkAbH6zLwVEcqMmqGXusM+VTnyltCmJdYL6jaifMs6sK1VTn7mcA9l70vmMO5uNVrkoi3LPbWooc2QbYHqghlJ/sc9BjLnd/k7Sqro8MbCGN2Za4f42kgAyIsxCATml1LgVcyfZMa9RsOsLMxt801k5gQs1Eulk/IYIxLY6k63eNXYXklTIWcQoMHNxzzBbY+XcPLuxmdMLiFNAIAD//+c+xzDLL5BJ0MObMmYNdd90VU6dO7dO++0SutdZ4++23MXnyZOiGSOxtt922Tw1ZGJDWD5vEVJ2OZ/bWjYFyIjG0EmFmVWFWR4rMnmssJRKr3DZ6y4648WVHDqVV+1hBaRbgVoppUBjeEqM91c4fy4oxZxVJlcZtAbFuRLPUegwVdJ5S+uBIUuvp9bwX2+8zs4Sa9+MsH0a6bSV/1u6AVSQpOMAzsIxQKUqnNhljnC+YO37FFpQGlSgED2o8cAF5bzV/Ry2lCC2JxLKtCVoSicmzUwytRG6y5CpZGoN6qp2lRhtA2e9YCIEOqTG7TiXQI0kFZdizzvmg+Tuup2STqGU+6C8K2laOidDGWiCT3l/Jab807KqK9VnzwKn5egufaozvKWWANKjIyNuz6sVWGG1CFcuXplcaaNea1DtW4hQRFQBuH8I+M9XMoJqh34KjChToDh11haqdALtnQBCBplVEup+rKVlFUkUZl6qpgjEGlVKE4S0JhrdESLXB0LIf+nieGwakJ9KgApkj14C3sFGWKN9XdCbZeX92bGNmjPEPpwz7Gm3jZpRGPdXoqGeopsr1/Y3kOg4URC6bLYVXtL01hIh1KTEoBSuTqaKVTo7TufPIzQEAe1w5ztUduOWITfv7ayzQCwzECc/iRq6vu+46HHzwwRg+fDj23HNP9/qcOXMwevRofPbZZ3jkkUf6tO9ek+snn3wSBx54IN5///3cAw/QRVU2yGRRACuLWhuIWKAcc1onhUopQmuZFOtps2u57B/kb25+nn/+1oY44saXnQLMqiLQkIXDUJ5m8sMamHpQ0teqFZmmIEWlTbfEGkCXqrU9VDAJkohsEF1YVTEkVPS3VV4VtYe34UwoSmmk0vrFpa9eGfGIIURDUKH3ghth/Y38u8iTe7K6mE73VwgpWXU3APJBm7GUGFSOsdKwElYaWkJLEmF2PcOsmkIiBdqqyhY8IYuDSWwZ8jjfXl86Xrvqbx0prSy0VUmhbi1JdHTQ9WkpkTLm90E+0JqiYg/Kqv6ZVihFEpHKZ0KhFRQ4axFAGUQ4nRYXypGAW2JmmyTZQXx+araCGEP7CDOBAJ0DTY3xQbs8UYikQJqRVzW2k4BE+qI2p+1UEOsCCwbVVKGlFDn7V2yrSmlF5JiD+zgOgvt1FkQyG3cwq0b3fqoMyjGR8dZShJYkQj3LAFgrGYBMCGfZEvbZYkFCGwEYb/fj6othVUTf4wooThtoA4fJdkZtardkuqOu0FHLULeTgjRVLuA9s5Y87hKjSCCy6TyjSAb9n80Fbv8uxRHiSCNTEvVIusBKFRDqEPVUdSpmUqAAg8WYuW2zqGC//fbDjBkz8O1vfxt33XUXtt9+e6dYT5o0CY888ghWXHHFPu271+T6Bz/4ATbbbDPcddddWHHFFRepWUojjKGl7jTTkFLBGEnqCJMglSKJJK4/eOOmn//m1c81ff3P39oQB//tRZTiuFM2iHA5HyAFkJfluHoe56rO7NL83Ij13PC/H33Z/b7r5U85ywnl3NY5b3WoGhv3u/87jiNHetNUQUdE1rUWlvBagq09SQVoUIHJq9lMssMpOv8aRwKZ6npyom2O8CSWuXswEgKlRKKcSMypa7w1pUrprYTA8JYII4YmEIICENuqCnPqCm1VhZrigYcCUzlYyEX6C59hpJaSt1tpg5mRdFXhOqzyDfjZe5pp55fkFHjaGh+FJjVLwgYaak7pR0o4qdwSdUVeUQAuO0C4DMz+TbpGQSYCbZxXmsuys0oGePVP2utdjsgmo217UqUtibdJROxEJlMGZ43ueWagH9z8Kq7Yb1SPty9QoBFsYzCWxBojbP54uperKSnbTLy5iBevtihtMKsjRS2N7KqLRiLJvjWjQ2F4S4RKIi3ZtkHHET0fNaWtuk0WDuq4vFUqjKcRrCDAxkpI4SbKYT7qakqkmp5VhfZahlkdKTrqRKrTVKFe167vdRV3rbDhYmes3U1KItulUoQo8kH5JJpI1w/df9xWXV7jHX7/hAuqK7BwsdOlY7sVlxYWBHqgXC8qVWQsvve972HatGnYa6+9cPvtt+OMM87AJ598gkceeQQrrbRSn/fba3L91ltv4eabb8baay/6qhV3WqnSUFWDvzeQ2K//8Wn8+6gvdfn5eqaw/1+fw02HfbHTe9d95wvY7y/PorWSUBBdwhlFhC2uQkv5iSWmjcEkdAN3r972BfccswW+d9MrLu/2zPYUn7VVLeESiEC+W2XzbYeHr9c1qlXlOnGArCJRJO0/bxeREs5i4JXrvNc7TOTXGEAJwHkJebwKK0Lya1IKlOPIEnv6XBJR+sQ5mvNRSwwuRZhV05hV0yjFAoMSg6VbYizXmmBOXaGmNNqqynk7K7GklFeKgh07bMnyUixQiSNoE2PqnDraaxk6ALSU4ly7uXhPEpPvmzOm0IAorB2FVHAjAKUM6nYxhIk9baesZcQTd0+YrSpmvfLVjArWsBWEl6v5emlDQY5Ke2U9sb55UvZULoOJL+ZDntXIfm+9IdYATeROumM8OuqqINkF+gRtjFsdqySR6ytYreZUpLSqSH0ArzayfYxtUQYGtVS65xQAZtcVBpXIHui9ylQwKm+5I4LNL7FajdwWNpOOEDBaQ4NSa/LzOatGcR61TKFuvdUd9QzttQz1ukKWaWSZdjY8JtZSShv7YiCEQWRrNNA4RkHm3EdkmaL+wyneBo+e/GV0h4JYDxx4EWZgYXFTrhk/+clPMG3aNOy4445YY4018PDDD2OVVVaZp332mlxvscUWePvttxcLcj24JUZUipA2BAoyuiPWAFCtk3q5z5+eQRLLTiT75u/mPWsn3TEesaRMEhGIXEeCvN2smrJqrAylerv9+52X7uYV7BM/9pbXsHRrCeUksr4+oGbtLtNm1dBm7TDOW6g5qNGXGZZSQCmDODbQWkJHhlRx41Vs4mf+iWOvMhDmbs0XvwmL+Qwqx26wLJci5x3kAbe1FGFoJcLgkkQppmvZbnNdhwpvJAUqMeWyZkVKZQZzaqTqcEGXj2fWMbtaQ6Z8NpjMDXAUHFlOJIZUYmjtM8OEfmhj4D3UxttMUq1RS5VL88jN8+kI/TWCBurwuXcBIsRxJJAotovYz2uu5qhztg66xt57Xc/oXJJIQEsDbey+LT9IWV3LeqdOd4dMaVy6T0GqC/QN3L/6Z4CIq9IameY4FU5zKsmvXFeOJPMqHSCdf1mZDJUkQj1l8huhI40wpBzZZ5hWtwyULbjk+xF+VjlTCNC58Ja2K5SpNuioa1Qzg/ZUob2uMbtGSjUr7aki9bpep3+eUCNHrrVWOaId2SBFwE6etUGaakhJBDxJhCPZj5+y6MRCLa7Y6dKxqCQRKklks5LR91ZLefxgW6LAsNYSRGngWWwXN8/1vvvum/s7SRIsu+yyOOGEE3Kv33LLLb3ed6/J9fHHH48f/ehHmDhxIjbccEMkSZJ7f6ON+i8Nz69//WuceuqpOOGEE3DxxRcDAKrVKn70ox/hhhtuQK1Ww+jRo3H55ZdjhRVW6PX+IylQYumuD2Bv2q6XP+UCHLvDRXuOxEl3jIcEKdawy/bSen/bqorSp6UKc6op5tSyPretJ3j9oxkY0pKgtZLQEqoABpcTJLFEOY5gDDBzVhVpqgPfn1+SjCzh9QqQhlJAKjSiSCCOpc3GYlX7qIFgw6vSjfCkk1SolYaXMawSO99vJZEoOcuNcBXPZtUox/XQCqVOrMQRWuII7WmGOanCrJrC9HaFSiJQTY0rTz6tPQVANgwm9soYZBlPLmiwq6caStJg3lFTTjl3Efig5WtPkn3Ar88bbmDsV8vbhRH9QD7HaZiqTwgBpQVUJGxhCM5jS5M1Vq6TmAJ0U22gU41qSh5QttmwlYTKIvs21G3e7P4i1gAKtbrAPOHW722Gb179HNJMI5IKxvYnmaYgZLbQcYahki38pQKCKgXlco6kgLIrRbOyFGWbFzNTBjWhkUQCcSpcwO/gEiWZ56DtxgDHEAZwnmttjO3LaaI/q6Ywq0YKNRPqTFGK0GqgVntLXt4KEpJs9lXHMVvyfP/AFhHelvqvgWcvWBKRWAthYseY1GYGA3iFU1Ae9NSS6nQgkuvFS7keNmxY7u9vf/vb/bbvPuW5BoDvfve77rUwyrq/AhrHjRuHP/7xj53I+kknnYS77roL//znPzFs2DAcd9xx2HffffuUh3DmnBT3HbXxPLeVi7XsNuYp/OfoLbrdduqsGpYfWgZAZCjLtJvpxZGgynyZRtWqGNtc+Oh8Ux3WXIHKeWpt8FlbFXOqGYwxaK0kWHHpQVhheAvqmUKa1twyJfulM1uwJo4bCTbZHZRNa0VeXwPjbBsir86y8qM5sp7YNg8HkRRoLccY3hLDGGBWjZSkambQWpIuWwAAtJYklmpJqLANgLZahvemV2EMMKMjw+yaclHy7piBXzK258AeTQkyhbNCJQR1ghGrx7ZDlEIg06QYsS80LJID+ODIsER9qKyxTc0XhaC/G4tDGGNgBJXCyJRwk7ow4JbTgQHoVI6cMwtwFhNlZfVEUkCvFBKnbL9m326oAgXmE1zueG2gTZCe1Kat4xSnGpT+NImMI7CciSOOaGVLWFtUJLwdj8lrPTPoENr1NQBQiQV0BFQiV7PRtSsUB1zMin3+UlvvgCb92nqqM7dCl9rsIGzBC0lyp2c+eKbz24hcH0EB6CEZB549fYf59K0U6A2koMJqvKrikgAE4yHHEaWZRq1j/oprfcHiplz3tJBMX9Brcj1hwoT50Y4cZs+ejYMOOghXXXUVzj33XPf6zJkz8ec//xnXX389vvrVrwKgi7PeeuvhySefxJZb9i7K+V/zGCjYiJBYj77syaZR1x11hY5Uo2SL1cyqZqinGtd95wudtv3Kbx/rlljvfsXTLrhNaar+5XISZxoPn7hNl5/93k2vYGhL4gaBZYZUEEd1zOpIMWNWFXOqKYYPLmPpwWVkmUatpgLlxJM5Gly8TYQRxzZvcjDYaFtuO/QBA35fEWcfMT4nuBA0gE6clWLGnDrqSqMcRxjaEmPlYSVUYrqFtc2VnWqNqjaY2p5h8qzULcEyqTVuv1b1ddYJCjKSmXb+7bDiJODT+0kpHMGmz1rSGlF2AR7o+XgATZxon5K2kX4JMJY0yGepCgr6CEe+2dbBpe55GykFqjajgLCedvaoA7KTYnzsLa/lrCI1Rb6T4S1UvbKWGZzwlYJYFxh4qKXKKtUKg8oxhKCA22pdwcBPGmMhcs+v0pkjntw3tlYSlK26zX5nLfiZo9UeVVdulUwKiUhoZFIgtpNbgCuUuuR8TrGm0ufsCYctL+6r6DLZb5xkR5FwPmmOueHnGjA5Ek0WPVot9BY9ItfGAM+f+dUF+wUVmCvYuiQEqdjCFvkJ+2QhgUhGZB+tLszWNsfiplzPT/SaXK+++urzox05HHvssdh9992x00475cj1s88+izRNsdNOO7nXRo4cidVWWw1jx47tklzXajXUajX3d1tb2/xrvEVXNhEh/EOWKYM/f2vDLvfRmEP8K799DEki0VKKc9lHqnVSZJn0SSnQUoqx75+ftWnY6LXWcowhNr8r2x+UNmi39pMVhrVg2aEVTGmrYvK0dnwyeTZiSyZLpQhpqlwgYkhSw5ULhldbPDkUwsCo/Ha8LRfWEUJApxqppolCHAEdNZWzyCQRFXhQmlTlcixRywxm1xWmdWRotwGIs6sZDco8kAXKDy3vSjd4iYAs0/fkf+fUVqxoA5TnNtNBzlkhHAEOU2Z1/v6pLHEoKGeBYsVKWpb6ipqAV9mMoQqNAE2gaiktI5asX1xa5aPZfWUMlUM3BtCxtDYRWpqstWc4d9d1mze6D/jm1c+5SqIFCswrOuoK7dUUrS0JSkkEbWMXMmv5gF1B4j5QSJroZjYLUJgqVAq4Aircb8VBNh5jgI56RpmHIm0rmNqS44IILZdNl3aVzFiPdaaNs6jNqWvMrGZoq9IEv5ZS0GWmtMvKFFo96FE3Lo4FQIMlhK4F9620kti5z3r5F358LDBwkCkuSmaLrAmbm91OEDl3Od+rA1EBFoEg1RUGYiBmM+y7777461//iqFDh/Zo+4MOOggXXXQRll9++R5t36ciMvMTN9xwA5577jmMGzeu03sTJ05EqVTC8OHDc6+vsMIKmDhxYpf7PO+883D22Wf3d1O7RVfBkBz0ePDfXkS1B56qrS941Pmbk0Q6tZKJK2Xo4KpdRKwSG/EeWz+eFLRUKkAZJdijzHYOpQ066gq1VKG1QsGDpRL5iNOUvICcczUkzKSwKJTLscs7LQSTUa/6ZkJDG2ELo/iCB4BXqOsZAChnkxg6KEElidBaSbD0oAgVa8HgAL5SRMVcZtUUJralmFHNMKua9zNmSruCKYBfavWKMPmLKZe0dJaP0LbC7YukcIGFUXB+gxKJQYlEKabUYDWlMb09c745TovFChUr98IIt4wsBGX90MGkxZNzX4wIijq32OarndWRYk41dekC+TyaZa854saXmxLuI258GZ/NMrj62/1bstgYg29f+3yuimiBAn1FzU7sZ9vYiFarXhujkWkqI85pKrUAIhDJLgfFniJJz0dq+0COP2gpRSjHkcsqUlMU+1JTxqXFVBrQkgKSAYlYeuWan1nns7axH9PaM0ydTauBYZ/EtQzCCTj1SfkgRq11jlhz/xXbYLg0VZ0I2NyIT4GFB2NIWMs0VbnNVL66Zqo0oGhMrKUKNx++KYaduHDb3Ij5YQs577zzcMstt2D8+PFoaWnB1ltvjfPPPx+f//zn3TYTJ07EKaecgvvuuw+zZs3C5z//efzf//2fsykDwLRp03D88cfjzjvvhJQS3/jGN/D73/8egwcPbnrc22+/HZ999lmP2miMwZ133olf/OIXiya5/vDDD3HCCSfgvvvuQ6VS6bf9nnrqqTj55JPd321tbVh11VX7bf99wWdtVQwqx9j9iqdx1w+aE/F6XbtiAcbAeekAT9bCJdAw2phzGxsBZxepK41YksJZyyhSXdoBqKUUYWZ73VUJaynHKCUROqoZZs2q5dJCAUwAuYiBhhBEyPMeRLi2GkHVGbURiIwvXsMDGhNNKQSGDiph+KAShCC/Yz0zSJVCaynC8JbYZVzRxua1joUrLpEqX3gnC9ob2iZChYCzfySxdNXMYglXKjm0fgBkO+HSxzVlA6AigUosUYkBjQhLD4pRTQ2mtqfoqJMyXIfOqVSRoCXjWIhcsQvumELPNZduh6JMLNWUyrV31DMY0DVg339X6GqFpL2WOTW+P6ENCmJdoN9Qq2UurmN2e4rIrs5xer0QWhubZJp9rGTB46whdaWdMpxEEgmv9tk872mmKa4jVahmEi1a2okvxTqQ/YOc1wZEuOtao6ZIrZ7enmFmTWFmO1nZ2muZs4w0qtCN7c4y4+Jb8t7p0D4iXfCj1soRHiGANy/42vz8GgrMAyg/uQRg069ynIzIZ5vJbGrJgYj5YQt55JFHcOyxx2LzzTdHlmX4+c9/jl122QWvvfYaWltbAQCHHHIIZsyYgTvuuAPLLrssrr/+euy///545plnsMkmNM4cdNBB+PTTT3HfffchTVMcfvjhOPLII3H99dc3Pa4xBuuu23+rtY0YUOT62WefxeTJk/HFL3rlTSmFRx99FJdeein++9//ol6vY8aMGTn1etKkSRgxYkSX+y2XyyiXy/Oz6b0Gk6G9rhrXZSDkuP/bHpud+xC0FgC0ixDnjjT0PrMnD4CzWABwZLmWwqmblSRyVg4jbA5qKW1gHg1GlUQ4L2NbW61Lm0NjkE2SRC61n2oIpjOGvNHQ9mcAJtYlGyD57qRZMMagpRyjtRJjxWEVUq7qGhJwOWM1DIZXYqy+VAkfCmDq7Jq7PlIYVyWNiXXJZkLh1F5h4GEplhhSjjC8hdL9SUHKeN3mqNXGTwYAWy1RCCSlwEICQAGoJALLD04wq0ZFaigTB6nZcRCEyVU4XYlzq75JKRyJT+w1SZVGLVPoqCuXJrC77AVzw75/ftYpbnv/6Zl5LlYU7rcx92+BAvOCJ36yLbb89SOWYGt01BVaSjFaSjFqqSLxIFPIlO+3/OoYZWVKbDYek+UJANu5VCAKVFPyR5ciidZS5FYHBZhUA5m1Z3hirTC9PcPk2SlmtNfRUaOqi/Wsc8Yg7ntc6lWlA2JtctlDANt/RcLlttbaQGW0gklL9RLv/m73BfFVFOgjWCCLYx/no7SPBeBxW2mDB3+49QKxr/YW80O5vueee3J///Wvf8Xyyy+PZ599FttuSzFnTzzxBMaMGYMvfYmEyNNOOw0XXXQRnn32WWyyySZ4/fXXcc8992DcuHHYbDMaxy655BJ87Wtfw29+85umxWAeeuihXrUTAFZeeeUebzugyPWOO+6Il19+Offa4YcfjpEjR+KnP/0pVl11VSRJggceeMAtB7zxxhv44IMPsNVWXVeeGujoLsMIK9Rx7FMsJQmlYAJgCwpQpa5YSls6t4aWUgwpyKtYq7NFQaCWwnmsW0oxhrWW3LEMSP0dVI5d4E05ITtKrZaftXq1RLjOHgBKSYQ4ovLjkRSYY8v5umNYbyLvJySHXH590owOd36zqykypTG4nLhtKwmpVRqU0aOuiCAOKUeopQmqUtllWIFImtyycGzV6Jy/GgJLt1JBmUpCdpOOukZ7xhlMbMXCVFNqO166NbRkrI1Ba4ny40q7gsBznkgKDK1QVThljPsujLGZQ1SQesv4/N9cHVIIoGQod29mK7PJ3HdA2/YkU00jbjmC8rDvNuapXD7w/kCaDUzlpcCiiyd/th22Ov8RsrXVMrRJgaUGlx1Jrac8eSXZmp9bbQOOaUk+n/9eGYNBSYyWhMSFjrpGEktkmoIlO+oZaip29rBMAwba/a2NQdUWmppd15jWkTm7Vj3TbkWK20P9pa9+S2q1Qppqm+mjc/EYwNs9iIRraKWhlFWtjcCEiwpivSiAa0WESQB49RTAfKlr0Z/ojXLdODnoqcg5c+ZMAMDSSy/tXtt6661x4403Yvfdd8fw4cNx0003oVqtYvvttwcAjB07FsOHD3fEGgB22mknSCnx1FNPYZ999ul0nO22226ubZkX9Jpcf/jhhxBCuOo1Tz/9NK6//nqsv/76OPLII+epMUOGDMEGG2yQe621tRXLLLOMe/2II47AySefjKWXXhpDhw7F8ccfj6222qrXmUIGCub2MLF6IUSESiWB1gaPnERZQHa8ZCxYsU4iiWkzOvD0z7cHAGz7u8dcYAx30FnGlg56yDs6UrTXMlRKEQVJgpTuaqpo+TRTKCcRhg+voFSKUK8rzJ5ddwMD51TlgMfBrSWUbCCnAXUaw1tLUNqgrT11gZzchlB5FbBFF2xgpiOpmgj31FlVZLoEbRK0p1RAJZHkdU44BVcs0WIrYc6pK8ypa8ypZa70rwR50uNIoBRHGJRIDK3Qz0oikdqyxHXnsyRlPJESrVadTm3WFC7KkqoMaUal1I2hgkCVWNjrR1kDJGiSIwXQUqbqcvXUq1JxlC9fjiAINJICc6p+gmLTo+e82RysOfqyJ3Mljve6ahy0Ae48svt7jJcn+0LQG7HfX55FRz2b5/0UKNAMY3+6Hba58FFrD6kDAIa1ljC7Sqs5NGn1gcVsnSvFEvWMnuE4kq7fkQJoMwbtNS8IVFN6HlKlUVe+6mldGWj4YygNV7BqTl25dHuzq6lLDyiFAKJ8uj76iRyxJnKtHfmi332aViblOcVaiNw+Cwxs/O9H+QqZO/z+CReAOzdb30BBb5TrRuvtmWeeibPOOqvbz2qtceKJJ2KbbbbJccGbbroJ3/rWt7DMMssgjmMMGjQIt956qytmOHHixE5e6DiOsfTSS3cbjzc/0WtyfeCBB+LII4/EwQcfjIkTJ2LnnXfGqFGj8Pe//x0TJ07EGWecMT/a6XDRRRc5s3pYRKY/sN1FjyOKBB78IZWB3enSsbkAE+780lRj3P9t3y/HnBteOqfryO8Hju9are/oyJAkMjfLdFYQ2xlTsGId1VigoxyjpRy7LCexHZBiKTB8UAmVJCK1ZwVgTi3DzJmU+3ro0DIGtyRuaatq08kx6avYIj2VUgRd80FHrCRzNUNAIAbNeitJ5II6AGBIS4Jhg0pQxrhc1ZWElmpLscDgcoREEuEeUo5QSQSGZRFSZTCnnrggw0iSVaOSSJQD5VppgzlWUc571ym3LS/VRRIYUiZf+Zw6LU1rQ77LqiXLtUwgaS2hHAlkwiASNBBzQJOrEGcDTXk1gvPeAqyIUdu0IZLOAVipglsGB2z5Y9DkhMqk06QrkgL3NkkF2QxKGze56Qu+efVzrv0d9QxZoVoXmI94/JRtsfUFj0Ipg7bZNUrNB1iSqpAkEaQUqNczRJFEpRLbAjLUF3GVU56gdtQVBBSygMx2WO91ezXDtPYUJVvZlUk559xWhiq8zq4pV8K8mir3nIeBiEB+LAntH2G5cx5zjDYwMJDWCpJlil5reE6NMVj9h3fi/T/ssWC+gAL9gkWx3HxvyPWHH36Yy8TRE9X62GOPxSuvvILHHnss9/rpp5+OGTNm4P7778eyyy6L2267Dfvvvz/+97//YcMNu864tjDRa3L9yiuvON/LTTfdhA022ACPP/447r33XvzgBz/od3L98MMP5/6uVCq47LLLcNlll/XL/nf4/ROOgHK1q6/+4QlXTckYwEgKjmMitCiQByGQUzfCB4KK/Wi3nFqtUh7rtEWhpSXB0JaSVX/89kKQxSOWEkNbSlh2SAU160vkwgi8XykpbRznYy5bwl5OIheoYexSLZeCLVkfNCvikRCYXUsRS4nWcoxSLFBNNaa0VdFaSdCWUgVLYwyWGlxGHAkMrdDy7Zw6+afLkcCIIVRBdNXhJSQ2y0emyENdzTRqgZpP5clNbkJCCjv9XookYAt6xoIGWyEQeDupQuKgRKKlREFQ1SotIVMlOOWKbvGkgtXreqZQS/19xRaR1P6eRD6lWJrZgbuhuKgQ9JnuJl3NwOWUeWWjN9j9iqdpEhZJIv411UmhKVCgv1GvK2eVmD67ZkUP5TzLScLlpWn1jkUDes4p4wcHkvHKTbWu3HPXXstQrWW+2JLSaCnFKNuJeRh/MbumMLuWoqNGGZfSBltHSLC1NqjXlbOAeGuItoW5bJYQ2z9HtoJkrVrPZTtqTH3aX8XbChToDr2xhQwdOrTHae4A4LjjjsO///1vPProo84ZAQDvvPMOLr30UrzyyisYNYpqN3zhC1/A//73P1x22WW44oorMGLECEyePDm3vyzLMG3atG7j8eYnek2u0zR1M5D7778fe+65JwDKN/3pp5/2b+vmM/b849MoDxrsOlC+KTj9XItN9xS+pwZoFG8zRBzAE3j8Qg8fW0YoZ6pBRweR5MGVxBIuCg5KlUYSSdSEwJyOFLPm1BDHkUsXJaV0aaPIHy4p0l0Y5xGOpERmdC7YUgqBsrWkcKYOep0GvGUGlzGznaqafdZGqlB7jTyNUgoMG1RCFBHZrKaGqloqIreV2KAeC8ypa5QiyihCwSN2mdd464er0sYeOAGnvmtbzjzhhLaAKxABEMmmc6LPV2L6PHmobVlzl7lAwiTkR2bvJwcm1pkwgwbt0M4TRRKZylxVTIAnT9p716Vvb29h7CoBr9h0hR1+/0SOLBgDZJlCHEeIY7lIKjEFFk08c9oO2Or8RwBQ3EmaKht/QhP8KKJiLJRpSbjnLI4oLiWSeXKaKY1aSis4mSPA9Ly1gfrAQeXYEWwd9Km1TKMjUKxZkeY+MZxEN1OpWcVWipRptoMIIaAyBZWp3Gtcv9Zo44pFfnx5Z09pgUUbX7/iqYXdhE6YHwGNxhgcf/zxuPXWW/Hwww9jzTXXzL3f3t4OgOLKQkRR5GqBbLXVVpgxYwaeffZZbLopxRI9+OCD0Fpjiy0WjuWm1+R61KhRuOKKK7D77rvjvvvuwy9+8QsAwCeffIJlllmm3xs4P+Gq4VmyZ0BBZlqTB65eVy4IxZNr43zNAxmlEikerIYA3vfXDMZQuqsokrY8q4+4Z6WkFEtkiUS97gNxiGhpp6SwB1sIoGRVFx7MXACHEGgpxyjbAjVSWH+jXRHgnNNso5g5p45pMztQr9OyLy31RlTIQZBtpBRThpKZ7SlmS4HBlRiVmHJQZ1qgrvIPPBefSCKBspDuNcDfD5m1brBFg5VtVroid57k+6Z8037fsW1fR+qrQ7r0S1LAZHTOwk4mVLBczNeRi0Xwaokj0dqA/fZCdK562Rv0hBRvc+GjrrQyr048c9oOvT9YgQL9hLE/9QFJX/7N/xBF9DxUqwY1W7q8WlVWyY6QZRolm9M6U/z8BMGOmV/N01q7Z6+jQ6NmM3+0lDK0lGjYpGfW273SzCvR9HmfIcLbQJSbOPtUe6RUs2ptrEiR1lPk5srMLYwnMAWpXnTxpV89jDimFLZ0L2gkCQkVsZR2tXNggceluW3TGxx77LG4/vrrcfvtt2PIkCHOIz1s2DC0tLRg5MiRWHvttXHUUUfhN7/5DZZZZhncdtttuO+++/Dvf/8bALDeeuth1113xfe//31cccUVSNMUxx13HA444ICmmUKaIcsyPPzww3jnnXdw4IEHYsiQIfjkk08wdOjQLnNld4dek+vzzz8f++yzDy688EIceuih+MIXvgAAuOOOO5xdZFEBk0FOcxZ2gtwpEryyvSjEjnzlt4/ZXKi+2Ewj6Qpzp0rJAXKk+vCyppQCLSJypbiTSGJ4axntMRVJ4UwV/DBRUQOZm922lDzBzpRGHElXDp0C8eCCFlNlldhSTF7Gakr2ESlQLse20iZlOskyunWHDalg6qwalh1awaBy7CwTtVQ7+0ciSbnmIEhGaoluElkfplWg2btciYXbJlw+5mI2FVsVsZ4ZpCI/gallGu0pDbCJ9XfWtaaJhKHMBrOrGeoZkYBSHKGlElG77WycCDRPVoRTvDqDk4PR8bf9HfnVHj25/+wZ/D08+bPt5r5xgQILGI/9+CsA4LzY7Gvm/r1Wo/6qVIpQL5Ene3BLQqsvHAhp+44okq68uJRhulObMYSrJxo/IQ5tIJyvWje815i7OlSqjSGPNR+DSTbgxQ1ouNLrE6/cb0Fe3gL9jM1/+TBKJelW/YzRKJUSN17zvTXQQLE9c9+mNxgzZgwAuMwfjKuvvhqHHXYYkiTB3XffjZ/97GfYY489MHv2bKy99tq45ppr8LWv+bzuf//733Hcccdhxx13dHF5f/jDH3rUhvfffx+77rorPvjgA9RqNey8884YMmQIzj//fNRqNVxxxRW9PKs+kOvtt98eU6ZMQVtbG5Zaain3+pFHHolBgwb1ugELE5nWMJnOdYqeXAdKa8NsbavzH8mpJgMNHR0pkkS6SUKjh5h++gmDlBJUHYyWQ9vaaiiVeICJUSnFGNKSkLVDaVRKRLir1uPInT8r1mmqaBDLKOtIayWGAQU7KkO5aCnnNvvajasYSWQ8c2W9pRSYObuGmTNrbumXvpsMaapQqykMGpRguvVpDyrHMDC+WIQWyKQABf8BShKBZ6U5Epyqy1+jTHnfNV+/RAogkUg02UkAyrXNuag5DzYAS+JtyjwpUJIC2laS01Zp5qVkZRWrNNXoqOXvM74v09QPuuF3aFvYpbd+XvClXz2Mcjl2/uknfrJtv+y3QIH5iSwjlZnzxWstXCVDIeA82XFMpJutc6mteksqN1vp8oW7ajWyitQj728OvdT0t1eqGy0goQ3PGAOtvGLtn21jswX55zp8f9JV31yQl7NAP2OTsx8kZToWKJViVEq04hpFwq6Y+n58XmoYzC/IHrSrt+3uSbabddZZB//617+63WbppZfusmDM3HDCCSdgs802w4svvphzYOyzzz74/ve/36d99inPtTEGzz77bE4+L5VKixy51sZAaJNTFlhxYDQjK8bAFncxeO6Mry7IJvcIvPzJxAsQLlgTaCyvDbDqyZaDOXPqmDOHA90EBg0qIY4EhrQk0AauAmISeRWYqzVmmbaR+j5dVBJTMB6TxQywKrVNWWetHrEUqKbA7GrmMnbojL4XDlYCfECmUsCsWRrVaoaOjgzGAMsNraC1Qrd1yarkHISRaYOaotR4dUtikwgQ1sYRCVK3ObF/6GGOgvuACbfSQFs9I2+1FKjEwllValaF50qMmSJPeKYN2trrmNWeolbLkKYaZZuBhH2iHGwKcLou7Y4ZBWn7aBNuV56AzyvCFYkCBRYVPP3z7fHFcx50KUPzE38BramviaIIHR0ZIlvQiVcp6WfmfNOAJ860nYAQ2v0N+LR6YTyCy0cdjC1MpBmsWLNvlC0g/HpjEPqkPxXEelHGxmc94MauJIlRthm4tBaQkXCF1HgMGYiFuHoT0Lgo4X//+x+eeOIJlEql3OtrrLEGPv744z7ts9fken7I5wsToSrQ1fuAcGQjzO8shMCXfvWwIzycq/SFs3ac63HX//m9rgN97Ve79Nv5fO5Hd2Pw4DKqVbJPhIVG6G8/GLCfnAcEqgIGAGQpYbVl1qwaACBavhVJJFFOIrTXMqRKY3AlcRHyUSSQxDFqdQrAIVU7Qyx9sRlWkfLXl4h9qigrSywpHR753jPUaqrBOx4u09JkYtYssoy0za5hleUHQ2uDUhKhkkQYNiiBavA7A9R5VVNj820DgEFHilwmAA0iyLEtRsEZOzirAJcyN8agngGlGJAggl61lRcjQekLq6lCpgzaaxnmzKnnBl8paQKUJBJJEuWy0kSRsMvawgWNeqLdtZ9+XlZYnjltB3zpVw/36bMFCixMlMsRqlWZ61fXOeU/pA7GPhZFSpF7BgFWnkPvdX5lKAw09oTduBWoMIAx9G6zSs0IFWxWqvl1wIs6jSS7wKKJjc64363strTEGD6kglIsMauauoldyC2oYNHAS54wPwIaBwK01k0z7nz00UcYMmRIn/Yp575JHiyfT58+HS0tLe71ffbZBw888ECfGrGwEC75NSNujcGMoaLNZIj3w+SvN5NN3vcGp92HUf93L9b/+b3zeEYeTM7CqHT+Fw4kzUDnJl1nkGUa7e11zOpIUcvIN1y2xVpmdaRU9dB6mUuxREs5dgNNraYwaw6VAmZbh28T21aEy9DC10Upg3o9Q5blFVS26ZD6JF1HRIFHKWbOrOLdj2bis5lVdNhUfR1WRU8kF5whktyRUnGZ1Fo9mNxTBhEi1sYYVG3587odHCNJ1g/GoISK17CaPbOqMLMjg9Lk/W6rkkedr3t7e2qXmFkto3Ov1xU6OjLMnl13mVt4GZvPPQRNhqTdR/475n+cUaEvWBQCdwsUaMTYn26HcjmvG7114W4N9gsSQ8JnLCTarHY3wlk63Eqnsv907p+yFRSNNs5Xzf9UppDWU6T1FFmaIcsyKKVo+0aLCArVenHAoEEJBg8uYamlWlCpJKhlCtNn19DenqJez1zhIL53uP8eaJCiZ/8WNeyyyy64+OKL3d9CCMyePRtnnnlmztfdG/RauZ4f8vnCgtZkC+Hf83aQ/LZzWyKn2RqpwV848wFHHF/+RfMiMF3tjxVtwFfhe+P8Xd37a518t22vdqmYOEVNFEnEQT5XwFd4jOMwLV/oFcyfa5hDlbcHKE3fxImzsfTSLRhsbSKt5RiZouC8lnKMat0WjilFSAeVMGNG1Sk6FLQB59GOIn8dwgkMz9jJ/905X2zjhMd7I+l6zM7Ih93RkSKzwYLLDaugmmpK/ZeQx1uBAhKdkg9qe2InFNJev5q2uWk1TSj4uJEl9ZVYoBJLtNsE1m3W0kK+bRn8TvufPruG2bPr7lqzEs2DqpQCSRLlgkXrdT+j9ooWXKqxLPOf5evD160gyAWWRIR9OaNxpQjw/XB+BZOen1JJQsooF5gYWj9YWAyDFt2+tFemua92JJuJt+1np193kGvjct+9MbcPyKIC4+KANLX1H5R2lknutyl9rXEcApCQcoCm/RU9UKYXQXL9m9/8BrvuuivWX399VKtVHHjggXjrrbew7LLL4h//+Eef9tlrcj0/5POFBaW0I9esRniS3Ls7hBVVJqO8fBISbfbWhcTVLw/mvdDe2ySwzin/ccfx1hQJZZTz6TFBi6Kok2eXB4fGoDc+hmh4YEJf1fhfj86dJ5P/1kE0uUoiyg7SUcsoQNGS+yEtCdraarmCOz6NnIYxVDKdSTe3TwiyQMycWc2dA6en4+/KK7UmP5hpg5qqI61L1OsKM2ZU8UElwSojBqO1kqAcS7SUYtQyhSSSaClF5ItWviqiMkAt9Tmka5myPvMYLSXpUutl2mB2R4qlB5dRTiTaa1TlrZoqxFLakssaHXXKyz2rmmLOnDSYKJjgfqFjxbEn1qWSdJMHngSE92YYJBXl/O+mKbkoUGBJwYtnd7bmvfPbr2Hdn9yDNPUFaMrlGKVS5OIaQrWYn6FGRRFoXnUx7Is+uGTPpu1a7rs3AgI5Qh3is798q9P2Bble9EF9ucHs2XWbj51e53E7y4AoYl+/htYDk1wvrp7rVVddFS+++CJuvPFGvPjii5g9ezaOOOIIHHTQQTmHRm/Qa3LN8vmVV14JoH/k84UFlWlI6z0OSW049QotIaEfircPFWhjEKS1yxNX2l52UqUbESq0TDyN8cfg4gRSSkgpkdZTxDY3ZmoJIfsJmaTlrS5e3fRKZ3OS/eovO3vBX/vVLtjojPudLYM9yACRUCGIcGtjUC5HSK0dwhggSSIbnKgRx9Ltgwe2KKIAx2qVPMm+jLFyShF/V0opFyAUThr49yzLkNZTVOMIs2fV0NGRYvDgEpYe3oIVl2pBOYmgDdk2dPCdaE1ZUKo2ewDnzwZsEGZqi0zYVHoz2+uopgrLDq0gEgJ1W2WRKi5GLpizFFO59kolRq2W5dRoAO56kBpN90m1atx3SPcZTao4ONXkAqlowuLVcLo3t/z1I0X6vAIFLOr1zIkQ7LkulSIX5xBWfgyFiSiSLrBaa+3zT4dEwgoHH166V5fHbyTPc8Nnf/kWlvvujVj28Bsw5eoDenm2BQYCvnDmA25cBnz2sVDIC2OiOFtslg5Acm3/m9s2ixLSNMXIkSPx73//GwcddBAOOqj5xLe36DW5/u1vf4vRo0f3q3y+sMDkJIryN0NItpnohkQUEJ3UQfZgM9nzAS0m2Je3AHCRAX69kWdzRx+qzOHxtPYkLK1nSJKyU8Z5yYnPI1TlhfA/jfEPOT3Q9LDPTSh56ZydsNEZ96Nis3JEEeWhFkJgdpYijiVqNRqkYlvSnCr5EYGs1RSqVSKYpVKUu25K++poWarc4OVyvQI5DyMM5X5lYi2EAAQQi9gRcJUpzJyeYc6cGubMqaNazzB8cBmtlRjDWkoo2XzVqfIKVCWJ3MTBGKrsxsp7ey3D9Nk1fDa1HVpTwGeaacQRVY1UGfm265lyJd2nza5B2CDESiXOFYsJbSD8/YUEuSsLUbhMzasBfgLoq3AWKFCA8N7FX8dqx98BBQUpY7cSGEXC9mOREyf4+eHJLfdBytYPAPKBh/OroAuvyhVY9LD2j//jEh6E4pzW3g7pV7LzRHsgrj6GFZa7gl7ETNdJkqBarfb7fntNrldZZRW8+OKLuOGGG/DSSy/1i3y+sMDEJMtM7rWQyBHy2UK8Ytg5c0VIppm0dlo2bEKqQ2uIO2qgIrMaHm6jlEYcx6jX6hgfLEN+/qf3OLIWttNPDvxDTEGBfLx8eqlR/3dvU/UaIIINABueTlHQ1arKEUH2N9KgRQp8tZra6oox2tspSppJeBQJpwzxdWL7EV1HGVw/06lMcPBVOYIdiQgIVO5aRw1ZmmH27BqiKIIxBoMGJVh22VYsP7wFKy/dgll1hUHlGDWrMnTUKcVfW0eKQeUYkRCY8MlMzJpVR7WaIa1TVpY01VhqqQpmzqmjnEQo2TRLtUxjTi2zOa2NW5Eo2SpvfE/R7/kglkbvu78u3ucZrqA0kvBCsS5QgDKFvHXhbu7vDy7ZE6sdfweyTCFNqX8ql4WzmnkinR8POK6D+x7AT/o//eM35lv7WYwpsGhhjRP/be8Pv6LoEyH4rE8cM9VouRiI5HpxtYUce+yxOP/88/GnP/0JcdynDNWd0Ke9xHGM73znO/3SgIWJTGmYIIuDv9m9SpFTTBuIT0iM2doQgrNaaK1yqiTvq6vfPcE1OfLlPs9Ey+Wnzid9eeP8XbH2j/8TWFj8klMjYSdfl7T7AVgq5s+s+5N78OYFu6IrtLfXnX1Fa4O3f0OD2Po/vxdCAPW6v7ZJEqGlhW45XoZlsikEFWkwhtL15a+zyS3FcnorG/tBC1XsQw685QwhBIQUkJBEsrMafVYA9Wods2fXMKmlhPeGlGGMQWtrCUppDB5UwsxZVShlUK1maG1NMHt2aovXZKjX6mTPiSQ6Oijqu15XWH75VlQqiYuaDite8s+Wlthd4ywzLtCTVhvodc6GEt6fxvquw8Asvu/Yfx3HslCsCxSwCPsTBnuiV//hndaOFbk886EYoQMiTRYR5SbrwIIJNpx6zbex1MF/x1IH/71Lr3aBgYGVj7k1t4oq7MongEAg8pYQEkRoDGbhC4BbxR1omB9FZAYCxo0bhwceeAD33nsvNtxwQ7S2tubev+WWW3q9zz6R6+uuuw5//OMf8e6772Ls2LFYffXVcdFFF2GttdbCXnt17TcbaEhTBSV81Lb3QcFZRcLOk5RH/3qzfjV8ncljGGlO23TeNr+PvNc7vz0vGfmSuTLqnFHx7d/shjVPugsqUxAuMj7Keb981o3w4e6MNU+6CxMu2r3pe+/8trnP/rVf7YINT78fxnCuZmojVznj60jkUdm0WDSw1es+UJN9jP7amNwyqRSSlCSj8/VURN6DLYQgIm7C74+WeFWmiGTP6nCfydIMURzlJldTp2iolK6nEAJxEmPwkAq0NqhWU5f+yxjYsujapSCsVhVKJenawhMdJsEcSe47Zq9q521F3EnDHYvtP0zC01R3maWmQIElDe9d/PUu39OailBRcDUFN3K9Aioo5kujA0AURcjSrFNBmPmN6dcdhOHf+dsCO16BvoEzeBnQT2mkG5/9JE04vgFw/61z4y+NDQNvtWJxVa6HDx+Ob3yjf1efek2ux4wZgzPOOAMnnngizj33XLd0v9RSS+Hiiy9epMi1UgZwRI1Sp4WZGMKbn+GtH3k7CH+G/bIhkQ6jzP1n8sS6K19tXi33nwWs97ibTr7WUbPqtyTlVkqvxBjYh54D4Dp7z/nchRBY9bjbuw3UaYa2tg5HTiPOvReAPNjCDV6sDAFk5XDexuD0OIUVtU04FZ8uSLBzS7BZ3XfEmz3cVu02wn9Xyqbu42Nqpem62YwoUkr3xERxhNbBZSRJhLa2qlu94PunWiO7yJw5VIqeQSsZxk0iwqIUNHnLF4mhqHHjghhDddvYiQctOearVxb4//bOPMyJKmvjby1JOr3bIjQoqwvLqKio2OoACgriKAguKCogwugAoggqM26oM6gzCuqH4ILgAooLy4iKogKKIiriDowiigsNCNJNb0mq6n5/3Lq3qpJ0d9Kk9/PjydOkqlJ1U0mqTp16z3sIonrEhXR5OZer+f2a/ZsUzWG8d4kAyOMp+Gmj7mh4iUzCRZtrXNlN911icS5y3W0VCwmZn6I4dyFFBrshHsdjZJiVLNPYmDdvXsrXmXRw/cgjj+CJJ57A4MGDce+998rpJ554IiZPnpzSwdU2bg20yP45t+Gd2zeimFHcLoyWabiDXscA3t0K19vly71t8WWNLZDxykNEIBUvmGaMoc01i/HbnCGe6b/NGYL8MS8jYkSg+3QZPLp9laMz8dEXEgeSnPnpofNq9LpDr10ig+uYfWa59pste4HFixrBILP00ShOVO35qyiKzGYL3FaTIqj2B3xyvxmGifR0PzRNRXFxhRxnJGwgmO6HZTG7HTuTn784cIpCRt52mQfFwlFFvFVNZjqYLFqVHZKZ4//t3i/8L19/tH0iQRDxEfKQw8YthaapyM4OSDlITDKDAf6ADk3XYBqmvHv4+/y6cfHYt6DxSzGbKq3/+op3ggiolVjpprtwUWSCRXwBuB2xGt7VVFPNXNcGSQfX27Ztw/HHHx8zPRAIoLS0NCWDqiuig0fLEu25YVsvcYszd1ZZBDsicObriQ2kowPxeAFxvOy3qrrlGtHjdfR/7m0riC7AdCh84kLkj30ZlmkhwiJQFAWa3QI4+jX8wiC2gE6cRNpcsxhGxMCuuRdXt2sPiF9nX4AWo16A2wkkOrAW2WRhS6i4tOJez27Fa5flmi7fv1tOEoWQjpTbAbSicjkI7z7Ju6yJ4kjGGPx+XWrHRRbZqf52CldEVzj39yMSEVZNlkdv7WitxWfknOzdF3cUVBNEzfhl1mC0uWYxRM2FWxNrWQpE/Yv4q/t1REIR584Y0axx32G1bCmkkA8yi8FS7fOUonjO1yKhFS/Ibohfraaque7YsWOVGfcffvgh6XUmHVx37NgRn3/+Odq3b++ZvmLFCnTt2jXpAdQrngCZT7Asxc4qspgAif8QTFc2MdY6xy0JcTuEAF7bJjmEqPmKosb4rLr11dEyCfd6KiMSinD9MPPa1SmKYltSqZ734Wh9vc4n7r/RtBj5AhRVgaZpKHziwur2fLX8Pm8YWox8wSneZN59aZmWcA6U2QEA3gOXIrT0wg7FO37xvgFHw16Zh60IrDVNk04fRsSQRSrcGlGTwS9373AyEN596axXZK7ddzf4Z27Kok/3a7wdHZ31VFZ0euI9q/DprWckutsJotny25whaH/dq/D5NHmMd7vwOHcQGfwBP8IV4SqPu0TzoOXoFwFEJbwUXkDPwHiQzRR5Z9U9z10b5ATYzt3rhoaCuLmqmGUaG9dff73neSQSwcaNG7FixQpMmTKlRutMOrieNGkSxo0bh4oKrjP9+OOP8fzzz2P69Ol48sknazSI+sKduRbBsip8jXV+gA3bVmwiK82DG1O2GXdHus4tE8UbWLt+VAA8AZz74Ky4/J75ck67XHeALV4XfWDPH/Ny3MBWVJv7/D65fVVVYZomFEsBU52gWmw3OjvvroCWWWX3RYPCNeAmTLS6+iXsfPKiGnwiXsTt1hYjX3CCa4u7pHhs+BTntptnfyqKbB+suPSSgCurwGzduusixvN6OIUoKlOhabxhD3cV8H4GPr/uaRQgmua4ZR5iXnTLZPe2xfsK2bptx47Pe5Embl9XVlR6xOQ3pHsLQRDV89PD56HjDa8hGOTHSndGEeABkGlathOU1ij1pUTqOOSqRTHWjPw/gMlMKTsUHZVVpnqki/Lc6enVwOc1xAu3pqq5njhxYtzps2bNwqefflqjdSYdXF999dUIBoO49dZbUVZWhssuuwxt2rTBQw89hGHDGlcHKdO0YIkiNlGMaEsOKioiYAwwbDmACIhEcCuCpuhCRCdAZ55gGAyO1VOVUhHIIN40uWuGaZoxrxFBu9iOyKBWRiTEddcyGy9++OKHYHDzd1546GTqLcsZLwAZyMoMuq0tk+/BYmAKQ6urXwKAlATZ7iJG93uHwi9I3PvBPR5VVQGL36bTFA2Kqkj3FE3RnAsW5r0Qktty6ZxFEM4zy347OHb2TSAY4LvRsFxFmqbrgswpNgyHXV3e7OluyQtDrEbfrb9jzKrSAUFAgTVBJM+2Geei3YT/wufXZYMngfjdR8JmzHGJaH4wVwIMgOf8qKqqPJYDsA0FLG4JyyxvkA3vcd99J7kh0RSbyFTFOeecg6lTp9ao4DGp4NowDCxcuBD9+/fH8OHDUVZWhpKSErRs2TLpDVfG9OnTsXjxYmzevBnBYBCnnnoq7rvvPnTu3FkuU1FRgRtvvBEvvPACQqEQ+vfvj0cffRStWrVKaluWxdufu7XFAKCYCoyIk7V0SwtEwAsFsDRNan/dwbT40TDLCZB422pVtt+Ne1B26WpF8CW7EEa/xi2XsF1DFFWpNGtc8uJIHDzieSfAtuUTQjMoLYQsBiNib4K5Ar44GV3+xJkmrYZcVc6paNvrCTxdgbA4iBmGESv3kJ2wLCc7EGHy/2KMbomM2N9iW/H2Y+u/vmLbdPHvgViPcDYRLczd2eZwWHTThCyEEn91Hy+Qiv5sxWfB3xD/k0hATRDEgVNRVgHGAmDM6c4LuO5mmd47iXVJzmXPgjGG4uevrPNtEw6HXLVIJl3cmWsROIs7xMIpRDo7wXKkIa6MtXg9k03eGp5bCNC8ChZffvll5OXl1ei1SQXXuq7jmmuuwaZNmwAA6enpSE9Pr9GGK2PNmjUYN24cTjrpJBiGgb///e84++yz8e2330pj7xtuuAGvvfYaXnrpJeTk5GD8+PEYMmQIPvjgg6S2Jb7w0VlLi1lxgx2h0RVuEsxiUEznhyFfY/JldZ8u5wUCGioqDOeHGEfXa1kWYBtVuGUK0eOLnmaappOlhYWDRzyPPU9fGvN+LcuC7tdlBlYeGGz9crS1nVgm+v1VedvHJXdhjGHvM5cl+GlUjqiSzx3+HIoWXgEAyLrkabmf+KCcCwTLssBcmV4RhIuDmMgguN8fYywhrTizGEIVEfj8ukdzKbXgFuNabE2FZSnOSdj+rETbdt3HO2uKoFxV1Rirw8PGLcUvswYf0L4jCCJ5ds29GG2uWWzblfICcGFV6i6kNkyjzsfmPgbuXzSizrdPcJirqZBIpMk6H8XpKCzlHrYUUdQ+SW22qnrqhgBxN7PhBddNVRZy/PHHe8bNGENhYSF2796NRx99tEbrTFoWcvLJJ2Pjxo0xBY2pYsWKFZ7n8+fPR8uWLbFhwwb06tULRUVFmDt3LhYuXIgzzzwTAPco7Nq1Kz766COccsopCW+LB6VOYRwAJxNseTW1iqLAUiwZmPGF4SkS9GSXGaAGuO5W6LmNiOF4N9u4dcNS/xsd7Luy1+7uhG4dtsmcABtA3G5eIsvK3S4swPJmnIU8wRmcM8ZoogPueBKXVF95u62o9i8aIZsqWCYPpmWGPepiSO5LuKQW4iJCQcxnUhWFT1yI/DEvy+0C8BxATcPkwbWlyuc80Hb2sdC78wJIE78+ekHcbVFgTRD1h6qpMMIG4OfNY7iTFP/9pqWnwTIthENh5F25MOkkQvCCJ+yN8ONE2ctXJT0+CqzrFxFUiyBbBsoueYcInN1BtawTYqo850ZbyCqKAtYAfa5VBahO9dEYVSGDBg2Kuft9yCGHoE+fPujSpUuN1pl0cP23v/0NN954I3755Rf06NEjpk3kscceW6OBVEZRUREAyNT8hg0bEIlE0K+f04GuS5cuaNeuHdatWxc3uA6FQgiFQvJ5cXExAB5UKbpLIuG6xcNcchEAUv5hROxMhQg8meIpjpPSDmairKQcPj/3Rw6k+bgUIMqnWlYPi9aoihM4A06wL/8fHfy7JBOiS2FlV44i49HmmsXw+X38vSjeQNlTVAcn6Jew2IDao3e2n6ciY10dHm1btCbb9VeM2S3tUVVV+tUmay0oAmypuVa4cwhjDJEw19RYpuVpSuOWB6m6/V0yDBQ+fuDOKgRBpJZWV78EX8AnbUtF8ATwDLau67ZriA8lNdkAYwCzAMZQtnRs6gZO1BlCjmmaJmBZYIoqJR+wwO+iwiVL9BQtOvIQEXjHrN8wYzdazzTVzPWdd96Z8nXG9s2uhmHDhmHbtm247rrrcNppp+G4447D8ccfL/+mEsuycP311+O0007D0UcfDQAoLCyE3+9Hbm6uZ9lWrVqhsLAw7nqmT5+OnJwc+Wjbti0AyCyjeETCEZimKX8w4ha+O4gFYosJPUVwcDLQpmEiVB5CqCKE0pJynrm2byOJ9buvfIUW1zScK2IxFjE/ntZaPtwXBVVoAX+bMwSmaXK9r6ZB07l2XPpwuq6u+RviD/cFh0eyYnkD/rpEjEU22HG9b3nRIC5OXFXdjHH/6poWXBY+cSFfh93sJhwKI1wRhhE2bN26IwFyB9pCthI91ngceu2SGo2NIIgDY+eTF6GitALhijBCZSEppeO1Egw+Hy9m13UV2bmZ6HD98uQ2oCiAojYvAWsTQ56TLQuwTMAywUzTcx50n5/c50npAmY/3HGBafC/hlH3kqPqUBJ8NDY0TcOuXbtipu/Zsydud+lEqFETmbpi3Lhx+Prrr7F27doDWs/UqVMxadIk+by4uBht27aFGTHAVCNG0hEdnAo5gaqqgOpkKWG57PPgzf56JCImd+uI0Wbbdj3C0ik6W+5Zlg8kRr/r2ZZlSyGYy9u5EkLlIWQflCW3YRomTGY6WnDX9qLHUpkURLy/eHrv2sJ9Je0O+lVVlZl8ANIlRFVVaD6esT7Qzmq/zxuG3OFcmhIJRcB89udgOq3UAYCp9p0Fk2vexW3AqmQzh167BL/Oji8XIQii9hHHh7wrF6K0uFTKB30BHzJzMhEJR6BqKnRdTyqpkH7hU7xbGAPKF4+updFzcoc/B03X4E/zwzItVJRVyDuYxIGx/4UR/LME+IUSwO9IWBaYEicucBcvRmWy3cg6oAYpC2maTWQq+/2GQiH4/f4arTPp4Lq2tNbRjB8/HsuXL8d7772Hww47TE7Pz89HOBzGvn37PNnrnTt3Ij8/P+66AoEAAoFAzHTTtOLeeomWR7inRc+LpzWOJ5OoLJCStxqheV7vXp874I0ObGVhortI0uIBXXDoXJS/Ev/gvfupS3DE5DfgD+i8iYntflFZYB/vfbrHpShKnbUB9oxDZMzd0hnbDpBPEBZ43NNb9XP9c6rG6t73pmo6RYumIxkRV75CNsIYg6Zr0H2V//wosCaIhoFb4pZ58XxUlFVItx9N1xDMCELVVOQOf67aFuXBoXM9MsDahlqm1wGKCpdvqwy0xbncYwwQHVQrkM1k+Itc5/gGGFwrSvU3WxpTbP3www8D4DHCk08+iczMTDnPNE289957dae5/u9//xt3uqIoSEtLwxFHHIGOHTvWaDAA/2JNmDABS5YswerVq2PW1aNHD/h8PrzzzjsYOnQoAGDLli3Yvn07CgoKktuWFSfodQXEblsceXvHvtXv8Xp2jT1aMuKWTrjXqyjcT9stxxAuJGI5MZ7oW0yAk7GNF/TKdTEg9/LnsO+5+AfY7/9zDjpNeh2apiKieNcRL7CW2Xt4Dxz1hbT8i9rfiHovAPcRVVS+X1KZWXfbMLEwz0j7/D4pORKFleICQLqU2JKemhRDJUOLkS/Uy0UPQTRFSl4cibwrFwIAdEWH7uMPVVORkZ2B7EufcepyYEvSmC0bEDIQ+2K7MWpTCS/CIYRLJ5kdffJ58e70Vpq5jq5lYg01uG5amusZM2YA4Pt8zpw5HgmI3+9Hhw4dMGfOnBqtO+ngevDgwXGDOvfV2emnn46lS5fioIMOSnpA48aNw8KFC7Fs2TJkZWVJHXVOTg6CwSBycnIwevRoTJo0CXl5ecjOzsaECRNQUFCQlFMIYH/ZPROqyFpbjFeOu18bZd0Sve4YjbT40ShOEZ4sbqhCrxyTDY8uMnQvazne1YBXnhAP0zSRkRGAZTFUmBXSQcSNuxhPkIrmMAfKvgWXI/vSZ+Rz9/4Rz92fpWVZ2L9wRErHID43Ue2fcdE8LhuyGD+hqposcpQXWFG2S5VZJ6aExnOcI4hGQfTFcOu/vgKf4oOiKvCn+eUdK4HP70MkHOFBt+0wofk17H8htcciou7xnCuj4oHoBJg4H8kuwcJIwX0+8NRUNcTgumllroXM+YwzzsDixYtrFLNWRtIFjStXrsRJJ52ElStXoqioCEVFRVi5ciV69uwpZRx79uzB5MmTazSg2bNno6ioCH369EHr1q3lY9GiRXKZGTNm4C9/+QuGDh2KXr16IT8/H4sXL056W+5ivLiNWipx0vCuxP1j8Gayo7cl57mkA6J4Qco6hBw76ocaL0vubovOX2s5WRLLBEyu83YHoNH89NB5MAzL0ywn+uHeH5bF7acaEjGfjf2ZuAs09y8aUSsnM8uyPDZapS+NknclxGfBDAPMMAAzYn82lrz4EgWsh1y1qIqt1Jz6aHJBEM2JHY8NRag8JI/n4liz/4URYBaT3XEVRQFM0z5O1/eoiQMl65KnXXeYmevhLBMvZoiRmIrzflQMUheyoWQRHRqrezQ2Vq1aldLAGqhB5nrixIl4/PHHceqpp8ppffv2RVpaGsaOHYtvvvkGM2fOxFVXJe/bCST2hUpLS8OsWbMwa9asGm3Dva0qtdAMXm/kSl7rvlXi/itkJW6piVw/nNdYpuUEsYo3Q+zOQouCGukS4s52MwswDCfAVvlHGwlHEMwIVrkfePW7LjPz7i6F7gCbWSxp27raxnOrDbGfl6IoKHlxZK1tP56mXX6nTBOwwo4zAGD3o1DBTBNM4d27FFWBbuk45KpF2P3UJSkbW1Wae4IgUkdld/LEsSfvyoXIzstGaVEpGGMN0gmCSA4ZA8i+F+KvAkD1nrujXieIzmyLvw0xsAaanizEzS+//IL//ve/2L59O8JhbwLxwQcfTHp9SQfXW7duRXZ2dsz07Oxs/PDDDwCAI488Er///nvSg6lromUhMRpiJTZYBhBj+B4tI/HO9D51B+QaNO82owPrqB+heL3bHlD+oC0TsAwna60xwOeXfs6HXLUIJftK4lanhyrCCKYHoOkaX7/h+G+qmorf5gyJvwMbAPtfGCG7NUa7sdTbAYq5AmxbGiK/aBYDVJG5snWYOu/W6PP7UqqRpsCaIBoGe5+5DIeNW4pAegBGxIBiKLIJlq5zzbbbFrYmTWWIusXJOjOeSAHsu5XM6aSiak5iJeq1Qg7iji3kHewqpJ/1iYrq5Q5JyyEaAO+88w7OP/98dOrUCZs3b8bRRx+NH3/8EYwxnHDCCTVaZ9L7oUePHpgyZQp2794tp+3evRs33XQTTjrpJADAd999J72kGzqe2zOI/4X2XK0pzvNoGzj3o9LW5RbjWUvm9VxmjElLPqEPFlnqGGlK9BAZ4z9oZut8GeMSBLvxAbMYfH5fpe4URsTgdTa2g4WqqrzNd3moQQfWbuTFgJ1pd+/LukZIfjwyHYAfZJllZ7Ttz8w0YYRCMCIGb4duVq2RT5b0C59CxkXzUrpOgiCS55dZgxEqC3mO6bquc7md6TgMaZqG4NC59TxaIimY5SS3zAhgROxjvP1cSgLdD571Fha8/JwBT4zR0IiOfSp7NDamTp2KyZMn46uvvkJaWhpeeeUV/Pzzz+jduzcuuqhm9WVJZ67nzp2LQYMG4bDDDpMB9M8//4xOnTph2bJlAICSkhLceuutNRpQnSK0TlV8FyrzoYxZlcgkK86tHndDFxlw2YEWM02Y4suoOn+NiOHJfEYH52IaD74tW+Pl+mGL56oPCKQBcII9VVMRPPcRlL82wTN2I2KgpLgcpmGi8InG1zFQWGIB8OxPecCqY0pfGoXgBU949nNwwAyvPESxx6WogG0vbtia/axLnk5Za2PKgBFEwyH6dx0cOhe6rkP38+y1ovJGNY0xQGluSGcqVQM0CzABMFdyhNnTxDFfjT4Xidym5bxMUaUctSFmrhWl+vbmjfGru2nTJjz//PMA+AVveXk5MjMzcdddd2HQoEG49tprk15n0sF1586d8e233+Ktt97C//73PzntrLPOkpKGwYMHJz2QxkRlX3pRFChu6Yjss5ByyEwmACiO97FofarajQVMO7Pt1mrLwNqK8rQWt6FEUA0Amg/wpUHXdaRlpMmW64wxQNURHDQHUDWULxkDACgvKU+p1reuKX1pFHKHPyflOuJ7WFWTljpH1fhRR3xODFH+qArAYl14CIJougjpVsZF82Sjl+ocnoiGgUfOAR8/D8MuWIUam7SLLi6XwbZ9R9N9PoDaIIte1QSC60ZYz4iMjAyps27dujW2bt2KP/3pTwBQY4lz0sE1wIOXAQMGoE+fPggEAk33KtvOFkuNdTWZbmGv5842y8DaHeipGmA7WYhgyrJ496yYhihRWesYyz5FhfODdq3f74cv4APA5R6arvH1+v1yu6LgrTYL/uqKfQsulxpG92dQ/PyVdT6W4F9mAbrPM6389ev4vPNm81uE8kLLPggrKjSfT2bdCYJoPpS+NArBIXN59rqR3lpvbkTX+NgT4VHbykJ2zXuOds+TOm1X0oWZscs3AJpqQeMpp5yCtWvXomvXrhg4cCBuvPFGfPXVV1i8eHHSFs+CpDXXlmXh7rvvxqGHHorMzEzpE3jbbbdh7txGphOzXLZIDI4GKko/HWOx57LME89lp0CX3tqDlAOogKJ63UVszV0kHIFpmJ7tMotJq75K7f5UxQ7YNcAXANIy4PP7oCgK/AE/dJ/ONdd+XZqkN9UDuKqqfB8Ki8V6oHz5uEoPjOWvXgv4/N4iF1uHLZoT1ccFAUEQ9Uv54tGwItwTvykkPJoDnrvSqsLPv/407zFeJOfsc798AE5gDTi1OKZp/7/h3cEQmevqHo2NBx98ED179gQATJs2DX379sWiRYvQoUOHGse1SQfX99xzD+bPn4/777/f03P96KOPxpNPPlmjQdQbRoTb14kCA1EQaJpcE20b/ssfkI0IoEUBijuYlsFvlGel1EcDTsFCvCylHaiL9ZoGHwszTVhuqz0xXoGqAzr/YWu6Bn/Aj0AwIN1I/AEdgbSALPoDvM4kidDQC+MioYi8QDENs1701oLyZddUPtOIOP936eaZYaBo4RW1PziCIBomigIzEql+uQQJDmlkCa9GRMmLI1H28lVc2qOoXI6pafyhRGWvRfJLyAPd3VhEzZQ4t4vnRuq+B6nCPfSqHo0J0zTxyy+/oF27dgC4RGTOnDn48ssv8corr6B9+/Y1Wm/SspBnnnkGjz/+OPr27YtrrnECiO7du2Pz5s01GkS9IbKLZlQRgrBIA2DqOhRNkw1JAHi01KqqeosYXbZ8XpN45tJVeatu+QSnTba7yYzHbUJY/ERnPS0GaBpUn0/a50XCEaiairT0NN4dzOAFjWKsmsKlIjmXPRsT0GVd8jTSMtKgKIps420adnDfgCl9aRSyL30Gqq7CMq2UFQWmGk+h48CHpWSktkm/8Ckwi8W1YyQIov4pXzIGwSFzkX7hUzUuRg6eNxvpebmwLMvzW/cE2mak6gQAkRTli0cjeMET9jla81b+2VasjozE1lO7s9buc7r7jnoDQ1cU6NVEz2Yji641TcPZZ5+NTZs2ITc3N2XrTTq4/vXXX3HEEUfETLcsC5EUXnHXCYrKfwAiaLXcWWbR6dAA8wVg6To0aE42WWigbRcO8f/oro6ezomCqKy2tN+DBRWqp8hRZqct11931bHo+KXrsgsYYwy6T+fdFCvC0P38YxaFMqqqQtVU+AN+KKqCQ65axO2hVAWWacEX8Nn2fFEe33H8Ohsa0Rc5DZ46PhBRYE0QDZsD+Y0Gz5sNWAbKfv8demZUPwpmwZcWQKQiBCiqJ9iOCcIjYS5hI5KDWUDE5Nlr4XEdp18GY4w7hKgK7M5irkC7koLIBkAimelGFlsD4MqLH374AR07dkzZOpOOlrp164b3338/ZvrLL7+M448/PiWDqjM0zXbacF1BAo48xDIB0+AykUgY4VAYRsQAi4T5LRtbF2VFIlwrZ5mVX21KrVXUN0/hV06arkHTNKiaWrVcw+2TLFvpOhtVNVVaOglpiD/gB2MMkXAEgWAAGdkZCGYEpSxFZN9Nw5QSGCNiIBJ2ZBbRWZCGStHCK2AaZqPRLEbbItYmZMtHEI2LFqNeSO4F0o2KN6UJ9n9AzkrPzuSBdXkJUFEKlO+Xj+A5MxEcMINbhpbvByIVCA58GMG/zOLuUkS1lC8Z4yTqbNMCcX533/kGhI2f6grAVTsgV+LHCQ0EFQpUpZpHQ7wqqIZ77rkHkydPxvLly7Fjxw4UFxd7HjUh6cz17bffjhEjRuDXX3+FZVlYvHgxtmzZgmeeeQbLly+v0SDqDYsBsINixf5BAE7Fr7ShNLhDjlsDpaiArkLRdelv7TaA56uxm8HYV6GiElzMU1UVuq5D052GJ4qpwLBc8gtptRed/RYWbvaFQEUpwqoKP/xIS09znEjsoNmIGAhmBGEaJsKhsKfYT1EUXjTptuxzS1PQuDKepS+Nqu8hIPiXWbywsQETvOAJacdIEETD4/d5iXdrlUGwqgOaBt2vw6jwIf3Cp5DbIhdlxSVAaVF81wp3MCdPDXbgV0+F4Y2R8lev5XcPEJGBtgnX3WzP3fE4+1XOj2Nc0ABoqpnrgQMHAgDOP//8mIsgER8lS9LB9aBBg/Dqq6/irrvuQkZGBm6//XaccMIJePXVV3HWWWclPYB6hVlO5yRF5V9oUXQAi3tRiwy2p0DBkUgwxhz7JNX7A2KMSXs+EUwrqiKniwyzeK2QhKiaCsVUvLeF4v0YZYBtB+ChCviyMrg8RFVkcaVhGEjPTEM4xLXTYE6W2mMHaEZ1lQT4GBqSX3QNSWVjlkQoXz6uQQfY2Zc+wzMrBEE0WtIvfArMMABVg+LzgyEM1R+AVVGBYEYQYY3rff8o/J1npOMhLUFdJxxZgMfPdcHzZgOqQjrtRBHBmKUCRgRMZKkBp4ZK1GC5W6arCn+N0jDPuU3V53rVqlUpX2eNfK7//Oc/Y+XKlakeS90jDiqMOb6SIovtRtU8xYg8a61DtbPOcjFRkBj9ctUJxGGBB9Iab9EtpCBSc81UMJW3wAUDDAbwq2B4s9We9wAZ8AsnE1XhY4mEI9B9OjRNhT+gS7mHCK7deAooxcFBUZuE9k7ozuuShhpYA7wrp2hgQRBE46Ts5asQHPw4/GlcAlhRVmF3+eXH9oycDOzd/hsQqXBepKhOgKco3OVCuli4GpoI2aQVsl+j0t2uBBDnS3knwTQBFnH2qRv3nWnRzVFVANZwNddqtT7XdTSYFNK7d++Ur7PuI44GRvlbk+NOD/Z/wBtMA/Y3S5eBtapyXZvA7QAisr/xCuwU+1cjMt3RxQ4i+GaMQWMaTGYBigkozJutFqia3ZWRa6vDFWGZvbZMCz6/DxUVESkJEdpxRHhHImlwr6i2ntuIcTdp7FDXMy+NpuCTIIgqKV86FnlXLoTu0+EP+BEpK4OSFoSiKEgLBoBAGg+u3baw8q8KBIL8/KEAqCjj5zih/VUtHujJQNtEcPDjKF86tr7ebqOhfNk1CA5+HLzJm53AA5wAWlzIuGEWz1xrGv8cGhhNVRYCAO+//z4ee+wx/PDDD3jppZdw6KGH4tlnn0XHjh1x+umnJ72+hCKngw46CHl5eQk9GhO7Xq78Flf5mzcCup8/pFQEALOg+Xj3PaOiXHpQM9NbDCh8lk3DhBmJwDIMWKYlOzDKJjHMaRIj59tBuQiyVV3npvSaL36wq6iA7oOma7BMC+FQmGuEbFeSUEUIpcWlKC0u5ZmNsO3tbUaAcBkQKgPCFUAkxKeZhvTRriubuNqmPhuzBIfORXBo7fnNZg17OunXUNaaIJoOwsXKsixA1ZCdl42M7AxUlId4wOzGrbFWFEDnDcZ8fh/vLGsX5Cm6Dj0YhD87G1ogDXD1taAix8QoXzqW38EUCTDhg+2+U6Aqsd7YpoldL15dfwOvhNpoIjN9+nScdNJJyMrKQsuWLTF48GBs2bIlZrl169bhzDPPREZGBrKzs9GrVy+Ul5fL+Xv37sXw4cORnZ2N3NxcjB49GiUlJQmN4ZVXXkH//v0RDAbx2WefIRQKAQCKiorwr3/9K7k3ZJPQpdHMmTPl//fs2YN77rkH/fv3R0FBAQD+pt98803cdtttNRpEg0V88QF+1a7z22emaXIdsuikJG73MICBOT6Vbj0VAKgKGNPsuz8KDBgykBYFkbITY3RBg6o6GWaRcQCcHy2zZPMUWLz5jaqp8Pl9iIQjjg7czj4gVMavpsUtQlEY6aL8zRtrc+82GxRFgaZrB+RdWxWWafHg3TQpo0QQzRDLtGQyJpiVgYysdFSUh7B3516eLFFcMgOpsWZAWhZgmjDK9gPpWXy+fU5gKpcuyiSPoSICSMmg0GG77+ySZCQ+5cvHcR9sIOF9VFOXitpEsf9Vt0wyrFmzBuPGjcNJJ50EwzDw97//HWeffTa+/fZbZGRkAOAx5oABAzB16lQ88sgj0HUdX3zxhcdZbfjw4dixYwdWrlyJSCSCUaNGYezYsVi4cGG1Y7jnnnswZ84cXHnllXjhBceh57TTTsM999yT1PsRKCzJ+8NDhw7FGWecgfHjx3um/9///R/efvttLF26tEYDqUuKi4uRk5ODoqIiZGdnV/8CF8HzZgNGyNZh2wWQbk9LwOWR7XL5EAG4yBqIA5erEFLg+UgYZKdIqYUW67ez2YqqgJkm7zYJ8FuAvjQogQD8aTzbYEb4AdEwDH5hUF7i6Ms1n5PJdh8oKbhOmJzLnq20cU3GRfO4zMdiKbUIDA6d62k81JgcXQiCSB0HXbGANwwL8RqbQHoAu37ZBZSX8gU8biCuBFAwy5ECBtJlwb1AURRe/wP73MHAzxMCNUpD7Mq+0vHowDiQOKW2xjLt1Y1Iy8iqctmK0v2447zjazzu3bt3o2XLllizZg169eoFADjllFNw1lln4e677477mk2bNqFbt2745JNPcOKJJwIAVqxYgYEDB+KXX35BmzZtqtxmeno6vv32W3To0AFZWVn44osv0KlTJ/zwww/o1q0bKioqqnx9PJIW1L755psYMGBAzPQBAwbg7bffTnoAjQ1Z3MeYc5snpmOiKwh2tbd2gm0mg2bTNGXGWUhJhMuHaIHuQdNkBh2qBn+aH5quQQ/47UIIp30qC4UQCUVkcxsRhMkg3K33chezgLLWyVK08ArsXzQCWZfESjRKXxoV/7M8AIJD58piWFiWo58nCKLZoWkadJ+O0v2l8Kf5UbSnyJGDiGO7u9eCkDpWlMYG1uI8YVlgkTCMsjIYpSVAqILLB0WfBUUFVBWKu0jPZRlLrdebHu66sqoeAGK8ooXUojqKiooAQMqMd+3ahfXr16Nly5Y49dRT0apVK/Tu3Rtr166Vr1m3bh1yc3NlYA0A/fr1g6qqWL9+fbXbzM/Px/fffx8zfe3atejUqVNC444m6eD64IMPxrJly2KmL1u2DAcffHCNBtHYKH/jesCfBuh6/MDanbGWRSRRBYKuwFZotYXTR7zujYqty1J0HYquy6CeV4bbXRf9gRgvbCsSQaSc66ylGwhfscvE3mVcr6iVFnkSCaDwTHU04nNNBekXPiWtHIV0qCk4uhAEUTNEvY3u01G2vwyhsjiBTLSdrAiwGQMiYW7px8CDZTPCA+lwBb8TaoSdjLX7HFbVIY1ZUgpBNA2S0Vy3bdsWOTk58jF9+vRq129ZFq6//nqcdtppOProowEAP/zwAwDgzjvvxJgxY7BixQqccMIJ6Nu3L7777jsAQGFhIVq2bOlZl67ryMvLQ2FhYbXbHTNmDCZOnIj169dDURT89ttvWLBgASZPnoxrr63ZuTXpctRp06bh6quvxurVq9GzZ08AwPr167FixQo88UQz+iGJq3XZ3tx1lBFuG26iMsMAzzi6l2JuXZx8mfeKULRaD1uOvzbgtGG3VB1QDPcK7Ey6vV2L8fG5u0MJrbWq8QsHosbsf2EEci57NsZXu+zlq1KSyRGFkaLxkBmlkycIonlxyFWLkJGdgbL9ZfAH/CgvLXfOIzEdgaOSQWK+sIJjFlQ9DaaqAYjEvtadvLFUABEw8AJ/qKrTEyHaBYNoEiTjFvLzzz97ZCGBQKDa9Y8bNw5ff/21Jyst7vj+9a9/xahRvEHc8ccfj3feeQdPPfVUQkF7ddxyyy2wLAt9+/ZFWVkZevXqhUAggMmTJ2PChJp1UU46cz1y5Eh88MEHyM7OxuLFi7F48WJkZ2dj7dq1GDlyZI0GURNmzZqFDh06IC0tDT179sTHH39cZ9uuEnG7rKqDGuBkuRlzPfiXKDqDraiKfPj8Pug+XbZJd3cT0nQNaloaD5oD6VyTLSQqRoRLByIVvMBFHFjtbHr5G9dTYJ0ihEQkOmsjNIg1yeYEL3iCB+cuaYkRMWQ2PPiXWQcwYoIgGiNZw56Gz++DpvHai1B5CFYkwo/3uu0upeqxgbG4iyoeuo/fiVU1mJGIc26KhyfAZjyjzZgthWROoTzR5Ki29bn9AIDs7GzPo7rgevz48Vi+fDlWrVqFww47TE5v3bo1AKBbt26e5bt27Yrt27cD4LKOXbt2eeYbhoG9e/ciPz+/2velKAr+8Y9/YO/evfj666/x0UcfYffu3ZVqvBOhRkaKPXv2xIIFC2q80QNl0aJFmDRpEubMmYOePXti5syZ6N+/P7Zs2RJza6A2EIVkjDHuDiKOQcxyOiwB4J1fXLi9LaWDSJSXdFQLdb5ax1HEiBgwTRO6j390QketairSgmnwB/woYQz+gJ+3PS8rc27niQOmaO8O8MLFJmK31+BgjBfAatqBu3jYdzUUlWeshc2ipmkwSG9NEM2OzIvnIzM3E7pPx77fi1BRVgHLMLwLxUvquKfbfzW/H4qqwDRMsJBtySodr1x3x4QHtuUkg2DZfthuTOd58LzZJFtrItRGh0bGGCZMmIAlS5Zg9erV6Nixo2d+hw4d0KZNmxh7vv/9738455xzAAAFBQXYt28fNmzYgB49egAA3n33XViWJRUWieD3+5GVlYWsrCxkZmYm90aiSCi4Li4uTqrqc//+/cjKqrqi9EB48MEHMWbMGHmLYM6cOXjttdfw1FNP4ZZbbolZPhQKecT0B2xxY1lgireqWgasniJB1wFMBNZiOdUVYFeCu7kMANnV0V3RrdpBsqLy4FvTNQSCAQSCAZ7FsNJgVcB2AhGdKO2/ioLyFVS4WFuoPp+8vHJnqxO1YuINCGzEXQ3NB0VT5J0NIxwGjAjKX6vZrSuCIBof6Rc+hdwWucjICmLPzj9QXlIeY6XKzz9mrESjkqyyZfICRo8biLuTI8Alhe7VMQbeqruKUIIkIk2HBGQhyXaWHDduHBYuXIhly5YhKytLaqRzcnIQDPJmSFOmTMEdd9yB7t2747jjjsPTTz+NzZs34+WXXwbAs9gDBgzAmDFjMGfOHEQiEYwfPx7Dhg2r1ikE4FnuadOm4eGHH5be2JmZmZgwYQLuuOMO+OzeJsmQcBOZ6JR7VRx66KFShJ5qwuEwNmzYgH79+slpqqqiX79+WLduXdzXTJ8+3SOsb9u27YENwjB48YfwuRZBttuOz33LrbJLuSiPUAmDp6ARCuAL+GS2Wma1PYJtIBKKQFVVBDOC8Af8yMrN4u3ZVcURS8kCRrLaq21KXxoFVfwohf1VMhppI+I8APuzg2xIRC4hBNH8CA6Zi0AwgPTMIPbvK0X5/lIn08wsx83DrCawdlWgmeXlsMrLnGONXL6KSMljGevSWttuVW7nEKJpoEJJ6JEMs2fPRlFREfr06YPWrVvLx6JFi+Qy119/PaZOnYobbrgB3bt3xzvvvIOVK1fi8MMPl8ssWLAAXbp0Qd++fTFw4ECcfvrpePzxx+NtMoYJEybg8ccfx/3334+NGzdi48aNuP/++zF37lxcd13N7uwnlLlmjOHJJ59MOE0eiUSqX6iG/P777zBNE61atfJMb9WqFTZv3hz3NVOnTsWkSZPk8+Li4gMLsJkFGBbXqcnbbKKFuOI0e3FnpqMrtRH93IUtCxEG6Zqmwef3IRzibc1F4G2ZTjdHLhmwA3Efb2VbUVrBl1U1QDEB2L7WChUu1hWlL43iWevo27WJEn2xZjdwgBGOdaQhCKLpY5lIz0qHzyfcophzrqkumBVSj6ru3SuK95jidpMCYo83jAGIOhZFndNkTQiz6C5bI6Y22p8n6qJ1yy23xFUmCPLy8hJqGBOPhQsX4oUXXpAyEwA49thj0bZtW1x66aWYPXt20utMKLhu165dUk4g+fn5NUqj1xaBQCChStWEkTIPBVBFZtLi1dKA4xPqRurXWOw3NM5tOiXONzSYEYRlWtzMP3o5e3tmxEQkFOFt1Q3T1ucyT4FK+YobavS2iZqh+XwwaxBcly8fx09KIqiOzioBdXaiinY/IQiinmAWfH4fysrCKCsp80yXf+NJP1JVZOgOvt3uV54xOtJDuW3hfz3wYQCgWp9GiK4q0KsRVVc3vyESCATQoUOHmOkdO3aE3++v0ToTCq5//PHHGq28NmjRogU0TcPOnTs903fu3JlQVWhKcH15hGuHaZqOLR5gH3xMb4YgOsAGXJlJsYz91M5Gu9vPAk63Rrc3trDpMw0TqqqibH+ZlJCYhquZDSiwrg9KXhyJ9AufAqsoB8CzOOXLxyX24uhbqwmeILOGPQ3LtGAZRkpaEhuhEIIXPEHtjQmiHgkOfhwwTdnuXCIK6aMD62gZiBsrjqwD8DhXyXVUlo4UQbO7UNLTz4E5rlTiuXgvdpANUKDdWKiNzHVDYPz48bj77rsxb948mYgNhUL45z//GdONPFFq5BZSn/j9fvTo0QPvvPMOBg8eDIA7Zrzzzjs13gnJUr7sGgC8SM0yLY+EA+CBMVMUp6paEH1ws1/jcQZxp7wZf28+3Sdt+SxXFbbndXbXx0g44rHxs4QulzEKrOuRspev4idGO/ssM9KqIr9P8RCZaedE5DpxVRFoq6qK/S+kONNsmTEBtizUtOVH1PKYIGqP8qVjERwyFz6/joqyED/u2+5BHqoKqt3TKwuwo3EnhABv9loE39HPo8fj8dX2ri+phANRb6hwrPaqWqaxsXHjRrzzzjs47LDD0L17dwDAF198gXA4jL59+2LIkCFy2cWLFye0zkYXXAPApEmTMGLECJx44ok4+eSTMXPmTJSWlkr3kDrDPoCZhikzy955pveKvpqsowiW3Rok0Z1R0zREwhHuFGIfEBVVgcIc5wgFPMj3B/yAAuiajkg40jgvJZsgqs8Hy11kxCyUL0vshBIvsxMcNKfS5Y1IDTXelW1/6Vh+caB6v8N6wA8jFOa6fsvkY7K7vh2w/SBBEDFkZGfAshhK95faEsSo2h6RSRbnG4s5AXZ0h+CYALsK/auUNFZiNVtdbwf3tlXNO15wyz453zIpm90AaaqZ69zcXAwdOtQz7UCNLxplcH3JJZdg9+7duP3221FYWIjjjjsOK1asiClyrG3KF4/mjT2YBcuC9L5WFIW78ilV6M4Z4wdGV1dGt0OIaB4D8PVasvOV420tXuMucJRjgIJQeYivyxegIpIGQOlL/OLvgAocXVSV8RbbAri7QCoyyu5gOWvY09B9OjKyMwBkwOf3IVQeQnlpuQzsZfBvMfK5JYgUoekawhVhmWTxFM9XJiNzB9iVZagVFU7AHE+zrSCu5CTaOzt6vqC6RFOUpCV4zkxAD9j2f7yQmwrx6xcV1VvMNcb2QfPmzUv5OhWWaKlmE6K4uBg5OTkoKipKyr+7MoJD50ppiOyqKPTXluV01ZNZBVdFtWgOEudyT3RgVDUVuk+XGXLTMD3bAHiQLTo3ChcR0zBhGiasSISyiA0MoZ2sq6AzOGhOlcH4gZBx0Tyoqgrdr0NVVaiailB5iHeQFJaVFnc1oGwUQdScjIvmIesg3kOitKjU9rk3XA1dXFaf8vxihzvx5CHxZCHR+ut464sXoEcHzO5subAIFM+rkqS4z5GaL8Z3u7nIG1Mdp6RiLLNXfYNgZtU9TMpL9uPaM/7UIMZdnzTGi4yGh2XJbLOqqrwNuV3oqKoqVF2HYhcmQlUdr2kX8a5xhNxDFC9aliU11+4GM27Nt8gaujXaFFg3PMqXjuXthmuJjIu8V+K1FVgDPEu+f9EIVOwrRtnefSj5oxiqqiI9Mx0ZudkI5GQhLS8PakYWb+PuKmQiCCJxLMviXVnD/Div+Xw8ULWlFJ6/ojBRBt5R55jqst1uotfn7uPg7ueg2la0msbPc+LhXk7gbvfnnie2Y5mOfzfA/29GEOx33wHuRaKmKAk+Ght79uzBuHHj0K1bN7Ro0QJ5eXmeR02o0dn9/fffx2OPPYatW7fi5ZdfxqGHHopnn30WHTt2xOmnn16jgTRmypeMkS3RhcOHCIpVjWefhcMHAKdluqJK6YcIrqOlIZZlQYUKZjmBu2VZzi1B2NprhWuvTcMEU11ZbbP2PMeJA6M2nTfcspC6ovzVa7lu0jAQKilBSNWgaBqCGUFousaDAsOA0YBsOgmisRGqCEHVVASCASiqgnIAphHxZnyF97Ria5tFQBwdYFclEREFiIne3JYZcg3REg8+XYndfmXThXWs6hq/y0422O8+Pk/TSSpSh6hKAgWNjVB0fcUVV+D777/H6NGj0apVq7hKgmRJOrh+5ZVXcMUVV2D48OHYuHGjbCteVFSEf/3rX3j99dcPeFCNESnhUO2AmQEKc7LLDE7Ayyu8Ve8lHoNHHiLcP3gTGD6PWQymfWvN3QJdSlBMHnSbFrcFNMJhIBJGcMCMZnMrjahnZJZMBSwuCymzmJQ2GREDmq6RbzZB1IDyV3jtRNvxy6AoCsKhsH1HVHcCak/nRBFkVyLlABLQTNtBOmPO+gSa5n2dCEpUVZ7TwGAHyaKZGbzLMwaoUbpxgWV7Zce0YTft9dHN97qm8YXO1fP+++9j7dq10ikkFST9zbznnnswZ84cPPHEE55GMaeddho+++yzlA2sMSKyzQLGmNfZQ1U8AbgIyIUjCABHShLlyqAoCs9am7yYkVk8+HZruxnjwbeQkFTWLYtouGRePL++h3BguIucXLdzrUgE4dJSWBUViOzf7/XoJQgiKSzLgs+vOxIRZnF9srDBk5Z39nnA8vY7cOZFPRfEk31Erzd6enTBI+C1CIwX3DO7TbuoyxDZaTFeM2K7lKiOvER2HeZjIZlZ3eH5qKt4NDa6dOmC8vLylK4z6cz1li1b0KtXr5jpOTk52LdvXyrG1CgpfWkUL+xSVJl1djt6iGJHCxYU5g3CAUc7LZYXTWREAC4KFoW0RNe5zZ6qqJ7XB4IBMItxC75IyLnyJxoFJS+O9DwXbjSNytou2kXA4g2Voos3My+eH/N+CYKonl8fvQCH3/g6fAEfAsEAQpbpLRgEvAWK7uZl0UGu51wUR87hbgwT/VpXUb43kI4zaHdzGpGl9jTC0b2abpGttqKy3brfkYsICQpRJ2iKAq2aeKK6+Q2RRx99FLfccgtuv/12HH300TEdxmtSmJl0cJ2fn4/vv/8+plXk2rVr0alTp6QH0JSwDAOK4vPooWWgbUs+VNiOIqbjKiKLE8WBxgIMw4Cu69B8GtdrmxYioYjUW/v8Pukc4v6r+3SUFpUCFaV8XVqjdFskbBpbUxZ3AB38yyx54ovXIKLkxZHIu3Ih9j5zWZ2NjyCaCpbF4PfryMzNRDgU5vGs5bb4jNOxMboZTHV66sp00oCTFRerE3/d7lieZS3ENFVTVC4JgebME9lwEUCLLLZsTmNnrX0BQFGoa2wd4o5XqlqmsZGbm4vi4mKceeaZnukiRhNy3GRIOvIaM2YMJk6ciKeeegqKouC3337DunXrMHnyZNx2221JD6BJYZkwTVUWMSqqAmZ6s9iAKzPtRnGKGUVFuC5u+0UMKfVQNRWqj69f03iRmAiqGWMIlYeAcAUQCPLOjJpW+cGRIGqRRDqu7X3mMmQNezr13SQJoomzbca5OO7Od2BZDOlZ6SgtLgUQ4F1gRTGiLGhMoFFMtKwjOrCOp28WQYeqebPX0W3QLdPrtS1eA8jifsBel+bzupOI90H66nonETeQxhdaA8OHD4fP58PChQvrr6DxlltugWVZ6Nu3L8rKytCrVy8EAgFMnjwZEyZQoxJRhCg006Jzo2VZMTpq+RK76Yv4v/AMNg1Tvkb6Xdv2bcJyT1VVRMIRmIbJq8cVBUpakAfqPr9TLEIQDZT9L4yIaatOEET1GIaF9HQfTDOL267uL+XHfXeTKmZBFiF6guxK7PfiBbFiWrzgm1mAaTmaaLFuywQUe3vuIF0E2TEdH8GTQaZpB+emd53ugMcyASNCLdPrmKaauf7666+xceNGdO7cOWXrTPpSUFEU/OMf/8DevXvx9ddf46OPPsLu3btx9913p2xQjZXyZddA9+tSsqGoPCDWdM0TWEd/+USWWyzvCzh6H5HNFtprt+e1sOrTdG79V1FWAQDQfTr8AT/0gF8enIKDH6+DPUAQNYMCa4JInq/vOQt+v4asrACyDsqSPRWk37SUXuguWQWqtuBz+08L3Mt7ss+u4NijqTad15lmrNZazFNU23EkapsiY60HuMZa03lGWw/wv5qPAut6QE3w0dg48cQT8fPPP6d0nTUW5Pr9fnTr1i2VY2kS+Pw+6QbCItwtJG4TTAU8W+0qWnTfT1FVFUxzvLEBeLo/ur20xXPTMD0uDCxKDhL8yyw6IBEEQTQhysoiyM4OoLw8Ap/fByNiwNR9tvZZdd251LhFJrN4Itvti+32xBZBOOOFyDFYrmy0FlVQKBvARNnqRdv9SZeROOu2TO+FgCxutNehabXaFIuonKaauZ4wYQImTpyIKVOm4JhjjokpaDz22GOTXmdCwfWQIUMSXuHixYuTHkRTQgTJjDEe8MKUXRUBb4AsAmHxGo/HtT3d007dVRgp9Nzu14iOjPL/lqtbl+YDNJKHEARBNCW+/dfZOO3f7yErKwC0ORi7f9vDzw8Wg2mYAFO8WeV4SPs75mSURQZbBNGCeG3LLVcgHuNGIoJ4lwbbJQfhfSDgSEwAfr4CeBdbywSYE/hTYF1/NFXN9SWXXAIAuOqqq+Q0EX/VakFjTk6O/D9jDEuWLEFOTg5OPPFEAMCGDRuwb9++pILwporwr7YsK+Zbxr2nEfvtU1yBtMU/TKY4B7B4V4JG2OABvK5JuQhjvFkHGJxCSoBLQ6LtjAiCIIgmwQdTeqHgvjUoL1eRlpGGUHkIjDGkpacBCnixownIAFhRwG/gR3V1ZGK+BTDVkZbEg1k8Ey7+r6hRbiVwLADF/+XrbD22qvGOxQAPpA2Dj5FZXDsuCiGJBkEiPtaNMHGNbdu2pXydCQXX8+bNk/+/+eabcfHFF2POnDnQ7KtZ0zTxt7/9rUZegE2J3MufkwWH7qyzG1G8yFz2IQpzguNoez4AsvkMACk5iYQjAOPzRGdG0WjGNEwnuBcV2bDie48SRBUE+93HGzkIRBGS5kP52zcnto7Bjzcej26CaKSEQiby8oJgDNhfrDnNyBReAB+uCIOJbocm4DknSC22yFzb5w1RXCi01dEyD3cjmrhNYly+1W7nKiMC6D6eVXdrtkVwLq337Iy4zh1EKGtdv6hQoFaTm65ufkOkffv2KV9n0trzp556CpMnT5aBNQBomoZJkybhqaeeSungGhO5lz/H/6M43RJFF0XA8buWAbfdZVG0NBf+1e5lxHrEOkQjGt6Ri29LaLoVVZHrsExLHlgV1aVrIysjIgmCA2Y4WSbR+lj4VicYWBMEUTf8vrsEpskQDPqQnpGGzKw05OQGkZWVhnad8nFQy4PgD6ZB8fl5sCqIl2p0d0m0DKcoMdo1RFrmRT08xwxXVsdOPsEXcLYjt+kKznWfc1GvNtK2f00QVVESejRGnn32WZx22mlo06YNfvrpJwDAzJkzsWzZshqtL+loyzAMbN68OWb65s2bPcV3zQ5R42FavKDEMD06HREsx3u4g3CxLlGMKAJtoZ+LhCMwTVNmsy3LgqIq0H261AZ59NfRxvxJkn3pM2gx6gWkX9h8L5yaI8FzH7HtrsJApAIwQvwk6ymCIgiiobD9kfOxe3cpNE1BVlYAmZkBBAI6dF1FIKAjv3Uu2nbKR26LXPiCaXYTlqgGM+Ji2gjz4NY0HElhJMwzzoArW82c7LTHUcTVACYaTbPlJi6fa8DVRMbWeRuiyzAP1ilrXf801fbns2fPxqRJkzBw4EDs27dPxm65ubmYOXNmjdaZtFvIqFGjMHr0aGzduhUnn3wyAGD9+vW49957MWrUqBoNoimg+3WAQTZ8iYdw9RAIyYi7gJH/B97ngCP1gJPBZoxnvYUcxOf3SW9sy7J4d0dXYxpWQ9m1aZBeuykSPOt+eXItf2uyd6Zp355VNae4SGozk7yIDlfwYJ0xlL9+3QGMmCCIqtj6wEB0ueVNZGT44fOp0HUVuq4gHLYQiZhQVQWHtMpBuCKMSCjCf9+i4F4UzUc7fiiKczwAXFJD8RpbKiY8qd0NYNzLATzY1n1OXZJ7XaoiDUH4c92x6aOaoQaBkoAsRGmEspBHHnkETzzxBAYPHox7771XTj/xxBMxefLkKl5ZOUkH1//5z3+Qn5+PBx54ADt27AAAtG7dGlOmTMGNN95Yo0E0dnKHPycLGRnjGWZ3F0ahoXYv485Me3AXN7r014p94LEMA1ZUkBOxLFg+HxRVkZ7XokukaZpSOpKsl3BwyFxZMFn28lXVv4BoNATPuMc+sfHbt8E+tk+9ameURDW/7ncKYt2ayGSIVMj1kh0kQdQum+/tDwD40z/eQnq6H36/ikDA7oVQwX+/wcwgQuUhGEoagDSgoswOkm1XD2GJZ5lePbb4v7jo1nTbjcrvlYwwVyMYd6MYzSetZxVNAzNdDiWVOTJQUWODoSkXNB5//PEx0wOBAEpLS2u0zqSDa1VVcdNNN+Gmm25CcXExADT7QkZVU+WVuJRxmCaYsBqyg2p3sM0U5rnCc2epozPZpmHCiBi2flp1VXSr/IBkhmFadjergN/jPCLGU/7K6ITfT3DoXIBBtlVv1nKfpoqiOv6xQlsp9JWq7mSUxIlRnHTtecG+07ktVjXa6+CZ/3LWpWperSdBELXGN/88G22uWYysnHRkZ6dBVRWoqgLTtHBQXgZUVcUfu/+wi+OZq/FLlNRDWrq6gmvN59Vdm4ZzrPBko+2LdY0H48J+1lPsL7LkmgZAyEnsedFab6JeaarBdceOHfH555/HFDauWLECXbt2rdE6a9xEBqCgWhAdgDJZyMGv2hlTY1qcc29Pl3+1eK3t+iEyz+FQmF/dqyp8fh6YKKrCZSKmBUtkFU0TsAwYAFRdlweuZDLOwSFzxSAAlQfWUIDi5688sB1ENCiCvac5gbW7o5osIDJdAbHuPVqKk6XIRA+YASgKyt+4vvINiixXDXX/BEHUjN/mcHvc9te9iryDM+DzaTBNC6ZpIS3oQ3ZeNkzDRMk+HUbxH87FtjgeyKyy4Ux3XywLTFtiotkX7Yq4EHc1gmGWcy50Z8MBx41ETDfD9nlIp8C6AaHY/6pbprFw1113YfLkyZg0aRLGjRuHiooKMMbw8ccf4/nnn8f06dPx5JNP1mjdSX9rO3bsiE6dOlX6qCmRSAQ333wzjjnmGGRkZKBNmza48sor8dtvv3mW27t3L4YPH47s7Gzk5uZi9OjRKCkpqfF2U4Ev4JMFhdJGz2Nt5JV/eHyrXYG1ZVnSOcQyLUTCETDDkAcX0+D6alVVoWkadJ8O3adD9fm4J6jGNW+ikDFZKYdov67Y61YUJabLI9EEMCP8YYScwiUp+xAnVoM/opcRJ13xf4AH2ec+wgNtF8F+9/Hbxm6ZiaIia9jT9fCmCaL58tPD56Hwtz+k7joSsWBZDJlZQQQzgshpkYOcww6Dmp3HpWDizpYboasGvMXO7uMHwO9+Ces9ocMG7OMLz4Qzy/6/tNxTvedMTXM5ikQVSxL1hugrVN2jsTBt2jSUlJTg6quvxn333Ydbb70VZWVluOyyyzB79mw89NBDGDZsWI3WnXTm+vrrr/c8j0Qi2LhxI1asWIEpU6bUaBAAUFZWhs8++wy33XYbunfvjj/++AMTJ07E+eefj08//VQuN3z4cOzYsQMrV65EJBLBqFGjMHbsWCxcuLDG2z5QNE0Ds5j0mFZVFaZl30aXhR2aDLyd22H2H+bIN8C444gVifDX6T7P68RrdR/PTjOdIVQe4h25mDg4WUnrq+V70W1/VI1nGEDHtCZD8PRbvSfB6NutQl/paSah2JIQOCc/kfVWNcCX5gTPqobgOTOd9YmTozh56rrU8BMEUbfseGwo2k34L3IPSpfBNWMWfD4VoXIGI2Lw9unZB8EsEo1ebC9szQ4V3BfgLs97GZAbYTuDrTvOIrLbo+Y6Hwr/bM1ZL8B12IwBhsVfbx87yl+9ts73FxFLU8tcu5Oew4cPx/Dhw1FWVoaSkhK0bNnygNaddHA9ceLEuNNnzZrlCYKTJScnBytXrvRM+7//+z+cfPLJ2L59O9q1a4dNmzZhxYoV+OSTT2R3yEceeQQDBw7Ef/7zH7Rp06bG2z8Qtj9yPg69dgnXXmsqFMP2lnYlfeM2lXEt4/hiu6zOVA2iUYyqqnI5YeHnDtQZY/wgtDhxbXU8hDe22J5RkwI2omHi1k57voeuIiV38REQlTUSGSjTO13cEo5ubSyDbh3Qdai2xy2zGDIumofSl5qvuxBB1Aeh8hCsnKBj3mExqLaVa6giZE+zjw9Rga9HMgIAVsSRiImLbQvOBbo8zrjal7sRxwkpTxPHF9gykoi38QxR7zRFzXV0sic9PR3p6ekHvN6UiZnOOeccvPLKK6laHQCgqKgIiqIgNzcXALBu3Trk5ubKwBoA+vXrB1VVsX79+krXEwqFUFxc7HnUCnYRoKZr3HJPVaHoOjSfTwarbumI+FCdrLXl6Kdl8w5hkwSpwxbZbSPsCnwt64ADa9HQRlEU6H4dqqZSMWMTIdjrDv4fKc9QEC3X8DgAuLLRlcKYc2s4XM7/CkmJ+O7aWStF03jWWlXkBSJBEHXLzicvgmla0HWnwF5VFfj8Onx+Lm9kphkl51K8BY4xjaVctnsisBYe2YDrNcxl7xcVeljMrk9iPOlk2l0ZKbBuUGgKoClKNY/6HmVyHHXUUcjLy6vyURMOqKDRzcsvv1zjQcSjoqICN998My699FJZOFlYWBiTqtd1HXl5eSgsLKx0XdOnT8e0adNSNrZ4/Dr7ArS5ZjFguLTLihNIaBoPuFWNH1Qsy+IX9opjt8dEZlEVByquf3avi1lMVlwLeKvzFFwnMX4LUDibMMaomLGREzx1qjejLP/G0VOKoFvhLZMBzSsdEVno6OXdLgLuJhQAv10cSAfTfPzOjGJ/X00TwfNmcwkTWfMRRJ0RDpvIzAwgHOaWsYZhIRI2nDuh9l1Qz4U3omoyRDZbUZw7V5Yt9RBt1RXmHD/cNp4W5N0sALaVH7/zJf2vNQ2KXZifjNMVUbs0NVkIwHXXOTk5KV9v0sH18ccfH2MbV1hYiN27d+PRRx9NeD0LFizAX//6V/n8jTfewJ///GcAXMd98cUXgzGG2bNnJzvEGKZOnYpJkybJ58XFxWjbtu0Brzea3+YMQcvRL8KErbtm3NGDZwI0GXSLIkGn+JEHtBYsMGiA0E4rTqbaI/+wnOeGOCgiBVf4rnbpoiMk0TgJnnIT/4842QEAXEVDqubKNrmmuwuZFAU8wGZeqYfIaLlxt0tmzMliWwbPgvkCXIKpgBcyuSy2gkPmQlHJS50g6gIjYkLTFOi6CtNkMAwThmHAiBjcls+IxF5sA85v293QxZ25ln/hDbK1KlyCREBunwcFiq5Tf4UGSFOUhQwbNuyA9dXxSDq4HjRokCe4VlUVhxxyCPr06YMuXbokvJ7zzz8fPXv2lM8PPfRQAE5g/dNPP+Hdd9/12P3l5+dj165dnvUYhoG9e/ciPz+/0m0FAgEEAoGEx5YKRHEiz9Z5s4TC8UPqpm3Pa0VR7ISicyve7Z0tcOuvo6fVlOCQuTJDbpkWTMXE3mcuO6B1EnVD8M+3e09uAGSnNPH/uBKPKI10TIY76qQoT4JxTrjR2xIFUNF3VMSJV3NfRPJCWoIgah9x3klL49aupskTPpFQhJ9HRGEiYP9WdeeOlAiG3QF2tIuIvPOFWN22+9ghdNrRjdQsCywSTkW6iEgxClBtXroxxda1WVyfdHB95513pmTDWVlZyMrK8kwTgfV3332HVatW4eCDD/bMLygowL59+7Bhwwb06NEDAPDuu+/CsixPoF6f7Jp7MXIvf447fliW1Jgmguje6NZiW8K2SAQximLXjDhfCrd+uyYEL3gCima7magKWfA1EoK97nBllUUXRXumkH6IYNhTcGgH3uIrE2O5pSKmuDG60LEyxLbEidMdhIuLRgX2SdX5zlqmhaxLnsb+RSOS3xEEQSSMqqq2NMQHVVUQDpuIREwEggGUl5Tzu01uDTVUXosh5B9utyFmeX/nAnEcEPOiz0+2tFGxnbZEEb0pujT60w64hohIPSoUqNXEGtW1R29IJBqb1YSkg2tN07Bjx46YNPqePXvQsmVL58eRJJFIBBdeeCE+++wzLF++HKZpSh11Xl4e/H4/unbtigEDBmDMmDGYM2cOIpEIxo8fj2HDhtWbU0g8RGAqssmmybVtlukt4hKSkLhOInAVOiqQEhFFUWTAHa29zrx4PkpeHJn0eFVd9xRiihbuRMMl2OsOr/bRjQy4o09wbllIVKfEaO20p9hRiX8CdTvbuIucFHG3xtVJ1ONAEj1ewDIMWKqK4NC5pLEkiFqEMYZQyEAgoMHnU2FZDLquAtBhRAynEFHWZtjnLc0+ZoiLZ8DObPuceeJ17uOOG5m1dqSRmk9zznVGBOXLrqmNt02kgKaWua7Nwvqkg+vKIv1QKAS/31/jgfz666/473//CwA47rjjPPNWrVqFPn36AOBa7fHjx6Nv375QVRVDhw7Fww8/XOPt1gYia61qKkzDlAcO8UGKoBpw7U/FWwjgDrZVVZX+06LJDBS+jFsiUtPsdelLo5B58XxYpiW3FX0hQDQwpMbZpXWO1ki6T2xVBcmAN6iOXl7V7YIjBVBVHhy7XW0AAJpzu5hZ3kyVdBlwyU3cmBF+srW9cIODH0f50rEHuocIgogif+zLCGYEYVkMFRUGgkEfTNOCpvEgW9M1fkks7zi5/O7FsSPaycotN3MvJ44dotMjIBNFAADd6WxMtpyNhKYWXdciCQfXIoBVFAVPPvkkMjMz5TzTNPHee+8lpbmOpkOHDgml6PPy8uq1YUwiCB9qQGiueYAsPKSFlZ54v+7CRsDRawOQnRgVVeH2fooTvMtl4WS/a4pbWmIaJvYtuLzG6yLqkOgW5oAra4QoLbXLci+aeBIQzccr+hXVc5eEyeYRLmmHxQDFipWPiOy2abqmW1Hjsh1yxC3nxlYRQxCNCFVVEAhoCAR0WJZwqwJKikr5HUtNdzTXAkXlSWzGnN88U5x6CyEZcbsQCUs+9zFBcy7ua9rojKg/mqJbSG2RcHA9Y8YMAPzEOmfOHB7o2fj9fnTo0AFz5sxJ/QgbIaUvjUL6hU9JDTMAKf8QuANo4fTh0U4rPLDWdP4QrdF1u4o6XBH2BNYHeuW/f9EIZA17GizCDrg4kqhdgmf+yz6hGU5XxeqIlonEnR9V0CjnRS8LOLeL7deoFsBsK0m3nARwZa69lpGK1F36+J0SkfFWVdJfE0QtEAgGEAjoyMlJQyhkynbolsUQDoV55jqiA4rooWABiuYE1ZYJXsvBnN+rp0ARzv+jg2qXXpvuTDVSEnALodiak3BwvW3bNgDAGWecgcWLF+Oggw6qtUE1Bfxpfum8YZlON0VxVSeKF5nFnOJBFdJJBKor+FB4By1N06D5NIQrwjAiBizTSunttP0vUDDTGCh/9+8IFtziquiPcvQQxYNuZ494BUfu14hpqi6LjdxUeldEaLXFehTROMZ1a5ivwJZ+2AG5UKHYd3P474P/JkSL9KxhT9N3kiBSiKZpSEvToesq97eOAOXlEZQUlzt3U1WF+9MDXntNC3ZQHRU4C5h9gS3qhABHi20vq/p8JAFpxDRFK77aImnN9apVq2pjHE0Of4AH10bEgAGnk2K8wkU3jDFZXCikJKrmtCQXgbbu0z3rJZop7rbBimJnkzRHFymCbWY7iriJp9EW2mo5P84dl6pwa7XdRU7Msl0IVG4awngwzefZF5KWXV9gy5/AgOxLn6FGRgSRIkzDlHdITdNpYGYaJlRNhWqpsDSfc9yQx4+oOgrXYcdjxQk4F/diuji+0B3RRg/JQhInoeB60qRJuPvuu5GRkeFpxhKPBx98MCUDa+yI7Jtp8oOWyAowi8UUH7ot8OR00TjGDm402ypPtEEXgTbRTBHticU5Ld53wd3wweNLXUmgLeZHz5Orq8SZJBrZSl2H7PYmii+lcwmDuzmS8HkXTYyk7IxMawgiZaiaCp+PPzRNQSjEDyCBYABGxJBBtqUAYBq/02SZgCWOD67fu5SJoApJmSolIYqiwDIoIdSYocx14iQUXG/cuBGRSAQA8Nlnn1FQlwCarkk3D0uxpAOHYdsciQBbZKRVzZGJALwoUoXdzVHEOq7dLgJsopmiB7jm2l2JL4guKIRLmiH+Mgapp4y3rJzm1ARU2tVBugYo/CQsChs9nrj29pjCM9iqAjAFluWtNVChgikMJkxPXQdBEAeO36/B5+O/q7KyCEIhA2WlFQCAUHlIJnPEucWyYF8sW5A3St1e9oD3wtn9XNPtZdMARbMlJ3TOasyQWUjiJPRNX7VqFXJzcwEAq1evxqpVqyp9EJxfZg32BCNCDqKoimyDLuQfuk+Hz+/z6E/FawDI1wDgPqRwtKpE86R89W1AIAPwB7k+UmSy3a3JPS2JrdjngDcwj6uhtFsYWy77reh1xSPaV1tsSzxMUza9YRaTDjimadrFjYi5w0MQxIHh82nQNN44pqLCwN5d+7Bv9z6Ul5R7rGLFnVF5nlFV7hyk+/iFvabz447u5/IvEVgLOZiYbh8HdF1HWnoafH5fFaMjGjxKgo8kmD59Ok466SRkZWWhZcuWGDx4MLZs2RJ3WcYYzjnnHCiKgqVLl3rmbd++Heeeey7S09PRsmVLTJkyBUY93ilJ+jLyqquuwv79+2Oml5aW4qqrrkrJoJoKv86+wGkQY1seaRpv1CKCa3eQrKiOntrn90H38eYuuk+Hz2cb/Nux0M4nL8Ifzw6vx3dH1DtpmYA/3c4QRXVVrKzhi8gqyew1vJlnwGlzLJdzN4thiMmUu1FdTiFuPbcnKLcfFnO2ZZm87bF9EequPSAI4sDpNOl15OamwTQZdu8uRSRiSNtY0zB5kb3l/P4AJ9BWVRWKpjn2nLrPaR4jLu7dgbZ4aD4odpOyP54dTvUTjRwlwX/JsGbNGowbNw4fffQRVq5ciUgkgrPPPhulpaUxy86cOTNuwsU0TZx77rkIh8P48MMP8fTTT2P+/Pm4/fbba/xeD5Skg+unn34a5eXlMdPLy8vxzDPPpGRQTYmdT17EixM1Fbpfhz/ND92vQ9d1qctmFu+IqOs6/AE/AsEA/Gl+/gj4kZHhR2amH2nBAHSfjl8fvaC+3xbREEizM9e+NH5ic2esFcWbURLTZWBrOploMS8e8Wz1gMoDbFkUaftky9ex+IF59JiYU4MgbCcJgjhwsrIC0DQVFRUGysrC8i4ojAjC+0u45to0PRe4AJw7rbZ8EaroyqgBPjt77UtzjkPyIp9LwOgOVNNB5kyqeSTDihUrMHLkSPzpT39C9+7dMX/+fGzfvh0bNmzwLPf555/jgQcewFNPPRWzjrfeegvffvstnnvuORx33HE455xzcPfdd2PWrFkIh8Mxy9cFCQfXxcXFKCoqAmMM+/fvR3FxsXz88ccfeP3112NaohMc3ceDZt2nSxmIpmvQdd35v1+H5tPgC/jgD/jg8/ug6RoCaT5omsolbBrp1QiH8qVjvdkiKcUQ1fmaIxeJnidwB7zCEtLVbEax77LwrJVLduL+68pSOYG1a/louYrMirucBCy3PAXO3R3SaBJEStB1FbrOg+tQWQilxaUIV4SBiB18MN6h1xfwyTurbpmiyGKL36YiMtg+P+BPcy6mZSdH+zdtRKgpWRMhGVWIO0YsLi5GKBRKaBtFRUUAeMNAQVlZGS677DLMmjUL+fn5Ma9Zt24djjnmGLRq1UpO69+/P4qLi/HNN98k/T5TQcJWfLm5ubLw6KijjoqZrygKpk2bltLBNRUKH78Qh167BADPAshiRlE4oggLFOr7KwAAKVRJREFUMhWqyrtlWRaDpmnw+XhwEQ7zLMNPD59Xb++DaIDorsBa1VwSjyiJiGxrHl3N70o1qK4TI+DYP9pdRRljYLY/OwBupcfgONroGr8LY7kaT+i27lIUXroDa1E8KQqmXNZ/0hee6goIIiUEAryosLw8glBFSPZggD8NAKDqOgLBgHNHlTmOPsK5CnB6NMg6Z8ZgWRaYosATUigqyhePrts3SdQuSVQ0tm3b1jP5jjvuwJ133lnlSy3LwvXXX4/TTjsNRx99tJx+ww034NRTT8WgQYPivq6wsNATWAOQzwsLC6sZcO2QcHC9atUqMMZw5pln4pVXXvFcVfj9frRv3x5t2rSplUE2BX6dfQEOvXaJ9PB1B9MAoGmKHVh725hrGjf7D4dNbH1gYH0Nn2iglC8di+DAh53KfYAHrqpmN4RJIKh22WXBLmSSD9X5C8BjJ+kE2S6/XMvkt43toJvb/rk6uInxib+mKXXaii0Dkc0sAKfBEkEQNebIKW8gPz8LkYgj/1IUxc5A8w7AgWAAaelpMA2T1zowHuyIJmju1/H/iD/8+OC+8AZAzWKaIMn4XP/888/Izs6W0wOBQLXrHzduHL7++musXbtWTvvvf/+Ld999Fxs3bqzhqOuHhIPr3r17A+CdGtu2bUu3a2uKAvh8uh3bKNB1uyudxm/ZWRaDYTCoqgLTtBCJGDAMCz88SIE1UQn+NKfdsLDIEllrd3AdLcWws9rly8cltJncy5+DAgVM4ZkqhUW52th3YryBt8qdRnQft/JyN71hzM5oa4CuxrjlmIaJXXMvTskuIojmzHf/Pgd9Zn4Aw7Cg6/z3J6xioQC6psvaINPgNpjubLVY3v1bF30X+EyAqUw6jhBNE1WJ28A3ZhkAyM7O9gTX1TF+/HgsX74c7733Hg477DA5/d1338XWrVulY51g6NCh+POf/4zVq1cjPz8fH3/8sWf+zp07ASCujKQuSLpDY/v27QFwDcz27dtjxOLHHntsakbWBFE1FUbYgN+vQ9NUhMMmTDsrYJoM4bApZSDiIGZZjAJroko0v5/3WomEeEt0xmILEQFPMWH5ihuS3s6+57huMvfy56ApmnMitSCDYuHuYRqmM19R+UK6q/Mb4HUhccmkGGMwIgb2PH1p0mMkCCI+FRUGsrMC8m4pYDc7UxVpBeuWYonGTgLLsmTTKvddLcC+e2VnsCv1wycaP7VgdM0Yw4QJE7BkyRKsXr0aHTt29My/5ZZbcPXVV3umHXPMMZgxYwbOO4/LZAsKCvDPf/4Tu3btkrV/K1euRHZ2Nrp165bcgFJE0sH17t27MWrUKLzxxhtx55smtVSrDGFpZBgWTJPBNE3odrMZEYeEQiZUVYFlMXz/n3Pqd8BEo0D38Z+xqWmA4eNSC1nU6LXDK192zQFvb99zlyP38ufk3at9z/OgO+/KhfI2skBRFN6Exl1kCfDsuhEBGD9eKJomM2eMMbKZJIgUcviNryMrK4AWBwXtO6X8d6go3P7VF/BJz3kxXVEUWLCguRpLWeLC2FUbAfDEEQApJdn7zGV19+aIOqM22p+PGzcOCxcuxLJly5CVlSU10jk5OQgGg8jPz4+bfW7Xrp0MxM8++2x069YNV1xxBe6//34UFhbi1ltvxbhx4xKSo9QGSQfX119/Pfbt24f169ejT58+WLJkCXbu3Il77rkHDzzwQG2MsWmhOFlpXdegqgq++zcPojvfvAJb7htQn6MjGiEi+6SZGiyfDmYxlLw4sla3KbLYleHu8gbAo88GAKbYBZcWvxAQmTBN1xyLMIIgUgKXG1owTIZIxILu1wHXTWfTEPUQcLTU4k6SwmsqTIUvI2oshKQEgKujIwXWTZnaaH8+e/ZsAECfPn080+fNm4eRI0cmtA5N07B8+XJce+21KCgoQEZGBkaMGIG77rorucGkkKSD63fffRfLli3DiSeeCFVV0b59e5x11lnIzs7G9OnTce6559bGOJsE1Tl9UGBN1ARfwMe7HFoWdj91Sb2NY+8zl+GgKxbIE7Kn4DFKn80YA1N4EZRHq00FjASRchRFQXq6D4wx+P28kZmp8mBZtQuQpZ4ajpZaUe1MpSyT4N1U3QH0IVctkllsqpFo2tSCKsRTBHsgr2nfvj1ef/31pNdVWyQdXJeWlkpNy0EHHYTdu3fjqKOOwjHHHIPPPvss5QMkCKJqCh+/sL6HIHEH1tF2XUKXKYJty7JkEaOma9JJhyQhBJFaNE1BMOhDaXmEFzX6dJiGGTdIYYwBFmDB+X0yMGnNGU19XtATdUxtRNdNlKQtPzp37iz7vnfv3h2PPfYYfv31V8yZMwetW7dO+QAJgmg87Hn6Ut62XNNkF1LRkEJ2ebNP2KK9uT/gR3pmOvxpfvw+b1h9vwWCaFIcfuPrSEvzyVoeTVMQTPdD83E5mQiYpO2mq0hRXhSLBjJ287NWV79Uv2+KqBdqo/15UyXpzPXEiROxY8cOANwUfMCAAViwYAH8fj/mz5+f6vERBNHI+H3eMLQc/aL0bHd7Vkd7Y6uayguq/D4qhiaIFNP55hXQdRXBID/VM8aQluZDWVlYFkK7pVjRv1m+AHhRMhAj+SKaF7WhuW6qJB1cX365U8jUo0cP/PTTT9i8eTPatWuHFi1apHRwBEE0TsSJG4D0wxUuIsJfV2S1AWoWQxC1gaoq8Pk0+P0aTNOCYfDCRkVRuJe1xWDCduwRURGDrJGQMDjdUu35ba5ZjN/mDKnDd0PUN6QKSZwD7gSTnp6OE044IeWB9TXXXANFUTBz5kzP9L1792L48OHIzs5Gbm4uRo8ejZKSkpRumyCIA+O3OUNkVlr36dB1Hf6AHzseG4rCxy+EP80P3XY2+emh87D9kfPxy6zB9T1sgmgyHD/tXWga7wQMAOXlhuytYEVZ7rkfqqpKyZbuc6Rdcp4t66LAuvkR7/sS70EkmLmeNGlSwit88MEHazwYwZIlS/DRRx/Fbac+fPhw7NixAytXrkQkEsGoUaMwduxYLFy48IC3SxBE6qgqWP5x5l/qbiAppP3EV/HTQ1W7/hBEQ2DjHWfiuDvfgc/HnXhM05IPLssCoEA69QCOLEQGSqIpjG1vvf2R8+vt/RANgARkIZS65iQUXCfa0z0VVyy//vorJkyYgDfffDPG1m/Tpk1YsWIFPvnkE5x44okAgEceeQQDBw7Ef/7zn7jBOEEQRCroeMNr8Pl0HDH5DWrwRDQKVFWRhYyiMyM/TzO7kasi9dQCt+7654cH1cOoiYYKyUISJ6HgetWqVbU9DgDcgP6KK67AlClT8Kc//Slm/rp165CbmysDawDo168fVFXF+vXrccEFF8RdbygUQigUks+Li4tTP3iCIJo022aciy63vIlQiJrcEI0DEVzzLsCO+weXilhgzLmNL2oj3PaZBOGBouuEOWDNdSq57777oOs6rrvuurjzCwsLpce2QNd15OXlyZaZ8Zg+fTpycnLko23btikdN0EQzYNQyMC2GdQoi2gccJ20AsYAxnhX4GDQB79fg8/HH7pPk11eRYAd3WGVIACy4kuGpN1CUsWCBQvw17/+VT5/7bXX8NBDD+Gzzz5LuSB+6tSpHt14cXExBdgEQSQNBdZEY6HH3augaYoMsDWNPyyLubLVPLttmha++zdJnYiqISu+xKm3S9Pzzz8fn3/+uXx8+OGH2LVrF9q1awdd5+4CP/30E2688UZ06NABAJCfn49du3Z51mMYBvbu3Yv8/PxKtxUIBJCdne15EATRMDjqphX1PYS4dJ36Zn0PgSBqzIbbzoCm8UhH01T4fCp0XbUdRBTouiqz10KPTRBVoST4IOoxc52VlYWsrCz5fOzYsTjvPG8Vfv/+/XHFFVdg1KhRAICCggLs27cPGzZsQI8ePQAA7777LizLQs+ePetu8ARBHBDd73gHAFBREcH/7h9Qz6OJ5aibViAQ0HHs7W/jy7v61fdwCKJGCL21YVjS31rXFTCmIBKxYBgmDMOx5iOIKiHNdcLUW3AdzcEHH4yDDz7YM83n8yE/Px+dO3cGAHTt2hUDBgzAmDFjMGfOHEQiEYwfPx7Dhg0jpxCCaER8Ma0vuv39LWy5r+EF1gDg86n46m4KqommA69PZAiFeDBtmgzhsAnL4sWOnSa9jh8eHFjfwyQaMIloqklzzWl0FQsLFixAly5d0LdvXwwcOBCnn346Hn/88foeFkEQSfLtv86u7yHE5Zjb3kYoRK3YiaaDZTEpETEMC6GQgVDIkA4iACiwJqpFBaAq1Tzqe5ANhAaTuY7Hjz/+GDMtLy+PGsYQBJFy/vSPt2CaDJvv7V/fQyGIlCGCZ8ZgB9Pe+bzgsR4GRjQ6qKAxcRp0cE0QBFHbHHfnOygvj0BRFAqsiSbByf9aDV3nBYxCT20Ylu13bcnlqBkSkRwkuk4UCq4Jgmi2HH3rSjDGyC2BaFJw6z2ejha2e1xnzQsbTZO+80TyUOY6cSi4Jgii2fL1PWfhyClvkMcv0WQ49f73oCiQHteRiCmlICKLrapKg3TpIRo2lLdOHFJaEQTRrKHAmmhKfHhTL6Sl+aCqiksKwmRgrSgKtTYnaoTIXFf3ICi4JgiCIIgmBe+6yJ1ATJMH0pqmwOfTZOvzLrdQkyQiOaj9eeJQcE0QBEEQTYiKiohsc+7WVot257quQlUVdPv7W/U4SqLRQS0aE4Y01wRBEATRBDj1/vfAGEMgoMsCRlVV5K16RVGkPKSh+swTDRfSXCcOBdcEQRAE0URQFO4UoijMdgaxZEGjaVpVv5ggqkBVFKjViKqrm99cIFkIQRAEQTQBPryplywqY8zpvujzqfD5NOi6CkVR8OVd/ep5pESjhGQhCUOZa4IgCIJoIgjph8hgAzxjza35VHx2e5/6HSDRaCFZSOJQcE0QBEEQTQRutQeP/R4Qv/U5QSQDNZFJHJKFEARBEEQTQVFEIO1+wFPYWFNO+/d7qRkk0UhJxIaPomuAgmuCIAiCaDK8N+l0TydGRVHw0S298dEtvaGqCk65d02N1/3BlF6pGibRCKEmMolDwTVBEARBNCHev/F0AICqqh6fa1VVoGkU/RBEbUPBNUEQBEE0Md6bdDpU1dtERtNUqKqKPz+wth5HRjRWKHOdOFTQSBAEQRBNkDU3nAYA6D3jA6iqAr9fs236gDMf/hDvXndqPY+QaEwk0t6c2p9zKLgmCIIgiCaMCLIFZz78ITmHEElDbiGJQ8E1QRAEQTQjKGNN1AQKrhOHgmuCIAiCIAiiSkgWkjgUXBMEQRAEQRBVQpnrxGlwbiGbNm3C+eefj5ycHGRkZOCkk07C9u3b5fyKigqMGzcOBx98MDIzMzF06FDs3LmzHkdMEARBEATRtFESfBANLLjeunUrTj/9dHTp0gWrV6/Gl19+idtuuw1paWlymRtuuAGvvvoqXnrpJaxZswa//fYbhgwZUo+jJgiCIAiCaOJQdJ0wCmMNp2Z42LBh8Pl8ePbZZ+POLyoqwiGHHIKFCxfiwgsvBABs3rwZXbt2xbp163DKKacktJ3i4mLk5OSgqKgI2dnZKRs/QRAEQRDEgdKQ4hQxlsLfqx9LcXEx8ls0jHHXJw1Gc21ZFl577TXcdNNN6N+/PzZu3IiOHTti6tSpGDx4MABgw4YNiEQi6Nevn3xdly5d0K5duyqD61AohFAoJJ8XFRUB4F8CgiAIgiCIhoSITxpQ/hP79xdXq6nev5/iKqABBde7du1CSUkJ7r33Xtxzzz247777sGLFCgwZMgSrVq1C7969UVhYCL/fj9zcXM9rW7VqhcLCwkrXPX36dEybNi1metu2bVP9NgiCIAiCIFLC/v37kZOTU69j8Pv9yM/Px5EdE4uZ8vPz4ff7a3lUDZt6C64XLFiAv/71r/L5a6+9BgAYNGgQbrjhBgDAcccdhw8//BBz5sxB7969a7ytqVOnYtKkSfL5vn370L59e2zfvr3ev7SNheLiYrRt2xY///xzs77Vkwy0z5KH9lny0D5LHtpnyUP7LHkOZJ8xxrB//360adOmlkaXOGlpadi2bRvC4XBCy/v9fk+tXHOk3oLr888/Hz179pTPDznkEOi6jm7dunmW69q1K9auXQuAXw2Fw2Hs27fPk73euXMn8vPzK91WIBBAIBCImZ6Tk0MHiSTJzs6mfZYktM+Sh/ZZ8tA+Sx7aZ8lD+yx5arrPGlLyLy0trdkHzMlQb8F1VlYWsrKyPNNOOukkbNmyxTPtf//7H9q3bw8A6NGjB3w+H9555x0MHToUALBlyxZs374dBQUFdTNwgiAIgiAIgqiEBqO5BoApU6bgkksuQa9evXDGGWdgxYoVePXVV7F69WoA/Cpu9OjRmDRpEvLy8pCdnY0JEyagoKAgYacQgiAIgiAIgqgtGlRwfcEFF2DOnDmYPn06rrvuOnTu3BmvvPIKTj/9dLnMjBkzoKoqhg4dilAohP79++PRRx9NajuBQAB33HFHXKkIER/aZ8lD+yx5aJ8lD+2z5KF9ljy0z5KH9lnzpUH5XBMEQRAEQRBEY6ZBdWgkCIIgCIIgiMYMBdcEQRAEQRAEkSIouCYIgiAIgiCIFEHBNUEQBEEQBEGkiGYXXM+aNQsdOnRAWloaevbsiY8//ri+h9RguPPOO6EoiufRpUsXOb+iogLjxo3DwQcfjMzMTAwdOhQ7d+6sxxHXPe+99x7OO+88tGnTBoqiYOnSpZ75jDHcfvvtaN26NYLBIPr164fvvvvOs8zevXsxfPhwZGdnIzc3F6NHj0ZJSUkdvou6pbp9NnLkyJjv3YABAzzLNLd9Nn36dJx00knIyspCy5YtMXjw4JgeAIn8Hrdv345zzz0X6enpaNmyJaZMmQLDMOryrdQZieyzPn36xHzXrrnmGs8yzWmfzZ49G8cee6xsclJQUIA33nhDzqfvmJfq9hd9vwhBswquFy1ahEmTJuGOO+7AZ599hu7du6N///7YtWtXfQ+twfCnP/0JO3bskA/RHRMAbrjhBrz66qt46aWXsGbNGvz2228YMmRIPY627iktLUX37t0xa9asuPPvv/9+PPzww5gzZw7Wr1+PjIwM9O/fHxUVFXKZ4cOH45tvvsHKlSuxfPlyvPfeexg7dmxdvYU6p7p9BgADBgzwfO+ef/55z/zmts/WrFmDcePG4aOPPsLKlSsRiURw9tlno7S0VC5T3e/RNE2ce+65CIfD+PDDD/H0009j/vz5uP322+vjLdU6iewzABgzZoznu3b//ffLec1tnx122GG49957sWHDBnz66ac488wzMWjQIHzzzTcA6DsWTXX7C6DvF2HDmhEnn3wyGzdunHxumiZr06YNmz59ej2OquFwxx13sO7du8edt2/fPubz+dhLL70kp23atIkBYOvWraujETYsALAlS5bI55Zlsfz8fPbvf/9bTtu3bx8LBALs+eefZ4wx9u233zIA7JNPPpHLvPHGG0xRFPbrr7/W2djri+h9xhhjI0aMYIMGDar0Nc19nzHG2K5duxgAtmbNGsZYYr/H119/namqygoLC+Uys2fPZtnZ2SwUCtXtG6gHovcZY4z17t2bTZw4sdLXNPd9xhhjBx10EHvyySfpO5YgYn8xRt8vwqHZZK7D4TA2bNiAfv36yWmqqqJfv35Yt25dPY6sYfHdd9+hTZs26NSpE4YPH47t27cDADZs2IBIJOLZf126dEG7du1o/9ls27YNhYWFnn2Uk5ODnj17yn20bt065Obm4sQTT5TL9OvXD6qqYv369XU+5obC6tWr0bJlS3Tu3BnXXnst9uzZI+fRPgOKiooAAHl5eQAS+z2uW7cOxxxzDFq1aiWX6d+/P4qLiz2ZtqZK9D4TLFiwAC1atMDRRx+NqVOnoqysTM5rzvvMNE288MILKC0tRUFBAX3HqiF6fwno+0UADaxDY23y+++/wzRNz5caAFq1aoXNmzfX06gaFj179sT8+fPRuXNn7NixA9OmTcOf//xnfP311ygsLITf70dubq7nNa1atUJhYWH9DLiBIfZDvO+YmFdYWIiWLVt65uu6jry8vGa7HwcMGIAhQ4agY8eO2Lp1K/7+97/jnHPOwbp166BpWrPfZ5Zl4frrr8dpp52Go48+GgAS+j0WFhbG/S6KeU2ZePsMAC677DK0b98ebdq0wZdffombb74ZW7ZsweLFiwE0z3321VdfoaCgABUVFcjMzMSSJUvQrVs3fP755/Qdi0Nl+wug7xfh0GyCa6J6zjnnHPn/Y489Fj179kT79u3x4osvIhgM1uPIiKbMsGHD5P+POeYYHHvssTj88MOxevVq9O3btx5H1jAYN24cvv76a0/9A1E1le0zt07/mGOOQevWrdG3b19s3boVhx9+eF0Ps0HQuXNnfP755ygqKsLLL7+MESNGYM2aNfU9rAZLZfurW7du9P0iJM1GFtKiRQtomhZT6bxz507k5+fX06gaNrm5uTjqqKPw/fffIz8/H+FwGPv27fMsQ/vPQeyHqr5j+fn5MQW0hmFg7969tB9tOnXqhBYtWuD7778H0Lz32fjx47F8+XKsWrUKhx12mJyeyO8xPz8/7ndRzGuqVLbP4tGzZ08A8HzXmts+8/v9OOKII9CjRw9Mnz4d3bt3x0MPPUTfsUqobH/Fg75fzZdmE1z7/X706NED77zzjpxmWRbeeecdj16KcCgpKcHWrVvRunVr9OjRAz6fz7P/tmzZgu3bt9P+s+nYsSPy8/M9+6i4uBjr16+X+6igoAD79u3Dhg0b5DLvvvsuLMuSB+Lmzi+//II9e/agdevWAJrnPmOMYfz48ViyZAneffdddOzY0TM/kd9jQUEBvvrqK8+FycqVK5GdnS1vYzclqttn8fj8888BwPNda077LB6WZSEUCtF3LEHE/ooHfb+aMfVdUVmXvPDCCywQCLD58+ezb7/9lo0dO5bl5uZ6KnebMzfeeCNbvXo127ZtG/vggw9Yv379WIsWLdiuXbsYY4xdc801rF27duzdd99ln376KSsoKGAFBQX1POq6Zf/+/Wzjxo1s48aNDAB78MEH2caNG9lPP/3EGGPs3nvvZbm5uWzZsmXsyy+/ZIMGDWIdO3Zk5eXlch0DBgxgxx9/PFu/fj1bu3YtO/LII9mll15aX2+p1qlqn+3fv59NnjyZrVu3jm3bto29/fbb7IQTTmBHHnkkq6iokOtobvvs2muvZTk5OWz16tVsx44d8lFWViaXqe73aBgGO/roo9nZZ5/NPv/8c7ZixQp2yCGHsKlTp9bHW6p1qttn33//PbvrrrvYp59+yrZt28aWLVvGOnXqxHr16iXX0dz22S233MLWrFnDtm3bxr788kt2yy23MEVR2FtvvcUYo+9YNFXtL/p+EW6aVXDNGGOPPPIIa9euHfP7/ezkk09mH330UX0PqcFwySWXsNatWzO/388OPfRQdskll7Dvv/9ezi8vL2d/+9vf2EEHHcTS09PZBRdcwHbs2FGPI657Vq1axQDEPEaMGMEY43Z8t912G2vVqhULBAKsb9++bMuWLZ517Nmzh1166aUsMzOTZWdns1GjRrH9+/fXw7upG6raZ2VlZezss89mhxxyCPP5fKx9+/ZszJgxMRe8zW2fxdtfANi8efPkMon8Hn/88Ud2zjnnsGAwyFq0aMFuvPFGFolE6vjd1A3V7bPt27ezXr16sby8PBYIBNgRRxzBpkyZwoqKijzraU777KqrrmLt27dnfr+fHXLIIaxv374ysGaMvmPRVLW/6PtFuFEYY6zu8uQEQRAEQRAE0XRpNpprgiAIgiAIgqhtKLgmCIIgCIIgiBRBwTVBEARBEARBpAgKrgmCIAiCIAgiRVBwTRAEQRAEQRApgoJrgiAIgiAIgkgRFFwTBEEQBEEQRIqg4JogCIIgCIIgUgQF1wTRDOnTpw+uv/76JrXdkSNHYvDgwQe0jg4dOkBRFCiKgn379lW63Pz585Gbm3tA2yK8rF69Wu77A/0cCYIg6hMKrgmCqDMWL16Mu+++Wz7v0KEDZs6cWX8DisNdd92FHTt2ICcnp76H0mSJ97mfeuqp2LFjBy6++OL6GRRBEESK0Ot7AARBNB/y8vLqewjVkpWVhfz8/PoeBgAgEonA5/PV9zDqBL/fj/z8fASDQYRCofoeDkEQRI2hzDVBEPjjjz9w5ZVX4qCDDkJ6ejrOOeccfPfdd3K+kEG8+eab6Nq1KzIzMzFgwADs2LFDLmMYBq677jrk5ubi4IMPxs0334wRI0Z4bvG7ZSF9+vTBTz/9hBtuuEHKAQDgzjvvxHHHHecZ38yZM9GhQwf53DRNTJo0SW7rpptuAmPM8xrLsjB9+nR07NgRwWAQ3bt3x8svv1yj/TN//ny0a9cO6enpuOCCC7Bnz56YZZYtW4YTTjgBaWlp6NSpE6ZNmwbDMOT8zZs34/TTT0daWhq6deuGt99+G4qiYOnSpQCAH3/8EYqiYNGiRejduzfS0tKwYMECAMCTTz6Jrl27Ii0tDV26dMGjjz7q2fbPP/+Miy++GLm5ucjLy8OgQYPw448/yvmrV6/GySefjIyMDOTm5uK0007DTz/9VOn7rW59n3zyCc466yy0aNECOTk56N27Nz777DM5nzGGO++8E+3atUMgEECbNm1w3XXXAaj8cycIgmgqUHBNEARGjhyJTz/9FP/973+xbt06MMYwcOBARCIRuUxZWRn+85//4Nlnn8V7772H7du3Y/LkyXL+fffdhwULFmDevHn44IMPUFxcLAPHeCxevBiHHXaYlGG4A/XqeOCBBzB//nw89dRTWLt2Lfbu3YslS5Z4lpk+fTqeeeYZzJkzB9988w1uuOEGXH755VizZk3iOwbA+vXrMXr0aIwfPx6ff/45zjjjDNxzzz2eZd5//31ceeWVmDhxIr799ls89thjmD9/Pv75z38C4BcDgwcPRnp6OtavX4/HH38c//jHP+Ju75ZbbsHEiROxadMm9O/fHwsWLMDtt9+Of/7zn9i0aRP+9a9/4bbbbsPTTz8NgGe3+/fvj6ysLLz//vv44IMP5MVPOByGYRgYPHgwevfujS+//BLr1q3D2LFjKw1qq1sfAOzfvx8jRozA2rVr8dFHH+HII4/EwIEDsX//fgDAK6+8ghkzZuCxxx7Dd999h6VLl+KYY44BcGCfO0EQRKOAEQTR7OjduzebOHEiY4yx//3vfwwA++CDD+T833//nQWDQfbiiy8yxhibN28eA8C+//57ucysWbNYq1at5PNWrVqxf//73/K5YRisXbt2bNCgQXG3yxhj7du3ZzNmzPCM7Y477mDdu3f3TJsxYwZr3769fN66dWt2//33y+eRSIQddthhclsVFRUsPT2dffjhh571jB49ml166aWV7pd447n00kvZwIEDPdMuueQSlpOTI5/37duX/etf//Is8+yzz7LWrVszxhh74403mK7rbMeOHXL+ypUrGQC2ZMkSxhhj27ZtYwDYzJkzPes5/PDD2cKFCz3T7r77blZQUCC307lzZ2ZZlpwfCoVYMBhkb775JtuzZw8DwFavXl3p+44ed1Xri4dpmiwrK4u9+uqrjDHGHnjgAXbUUUexcDgcd/l4+1kwYsQIz3eGIAiisUGZa4Jo5mzatAm6rqNnz55y2sEHH4zOnTtj06ZNclp6ejoOP/xw+bx169bYtWsXAKCoqAg7d+7EySefLOdrmoYePXqkfLxFRUXYsWOHZ7y6ruPEE0+Uz7///nuUlZXhrLPOQmZmpnw888wz2Lp1a1Lb27Rpk2dbAFBQUOB5/sUXX+Cuu+7ybGvMmDHYsWMHysrKsGXLFrRt29aj5XbvKzfu91FaWoqtW7di9OjRnnXfc8898n188cUX+P7775GVlSXn5+XloaKiAlu3bkVeXh5GjhyJ/v3747zzzsNDDz1UZba4uvUBwM6dOzFmzBgceeSRyMnJQXZ2NkpKSrB9+3YAwEUXXYTy8nJ06tQJY8aMwZIlSzwSGYIgiKYMFTQSBJEQ0YV1iqLE6JxTgaqqMet1y1MSoaSkBADw2muv4dBDD/XMCwQCBzbASrY3bdo0DBkyJGZeWlpaUuvKyMjwrBcAnnjiiZgAX9M0uUyPHj2kPtvNIYccAgCYN28errvuOqxYsQKLFi3CrbfeipUrV+KUU06J+16qW9+IESOwZ88ePPTQQ2jfvj0CgQAKCgqkbKRt27bYsmUL3n77baxcuRJ/+9vf8O9//xtr1qxpNgWaBEE0Xyi4JohmTteuXWEYBtavX49TTz0VALBnzx5s2bIF3bp1S2gdOTk5aNWqFT755BP06tULANcZf/bZZzHFiW78fj9M0/RMO+SQQ1BYWAjGmNQFf/75555ttW7dGuvXr5fbMgwDGzZswAknnAAA6NatGwKBALZv347evXsn9B4qo2vXrli/fr1n2kcffeR5fsIJJ2DLli044ogj4q6jc+fO+Pnnn7Fz5060atUKAC8KrI5WrVqhTZs2+OGHHzB8+PC4y5xwwglYtGgRWrZsiezs7ErXdfzxx+P444/H1KlTUVBQgIULF8YNrhNZ3wcffIBHH30UAwcOBMALIH///XfPMsFgEOeddx7OO+88jBs3Dl26dMFXX32FE044Ie7nThAE0VQgWQhBNHOOPPJIDBo0CGPGjMHatWvxxRdf4PLLL8ehhx6KQYMGJbyeCRMmYPr06Vi2bBm2bNmCiRMn4o8//qjSDaJDhw5477338Ouvv8rgrE+fPti9ezfuv/9+bN26FbNmzcIbb7zhed3EiRNx7733YunSpdi8eTP+9re/eZq+ZGVlYfLkybjhhhvw9NNPY+vWrfjss8/wyCOPyELARBEZ3//85z/47rvv8H//939YsWKFZ5nbb78dzzzzDKZNm4ZvvvkGmzZtwgsvvIBbb70VAHDWWWfh8MMPx4gRI/Dll1/igw8+kPOqc8uYNm0apk+fjocffhj/+9//8NVXX2HevHl48MEHAQDDhw9HixYtMGjQILz//vvYtm0bVq9ejeuuuw6//PILtm3bhqlTp2LdunX46aef8NZbb+G7775D165d426vuvUB/Dvz7LPPYtOmTVi/fj2GDx+OYDAo1zF//nzMnTsXX3/9NX744Qc899xzCAaDaN++PYD4nztBEERTgYJrgiAwb9489OjRA3/5y19QUFAAxhhef/31pG7h33zzzbj00ktx5ZVXoqCgAJmZmejfv3+Vsoi77roLP/74Iw4//HApOejatSseffRRzJo1C927d8fHH3/scSUBgBtvvBFXXHEFRowYgYKCAmRlZeGCCy7wLHP33Xfjtttuw/Tp09G1a1cMGDAAr732Gjp27JjEngFOOeUUPPHEE3jooYfQvXt3vPXWWzIwFvTv3x/Lly/HW2+9hZNOOgmnnHIKZsyYIYNJTdOwdOlSlJSU4KSTTsLVV18t3UKqk41cffXVePLJJzFv3jwcc8wx6N27N+bPny/fR3p6Ot577z20a9cOQ4YMQdeuXTF69GhUVFQgOzsb6enp2Lx5M4YOHYqjjjoKY8eOxbhx4/DXv/417vaqWx8AzJ07F3/88QdOOOEEXHHFFbjuuuvQsmVLuY7c3Fw88cQTOO2003Dsscfi7bffxquvvoqDDz4YQPzPnSAIoqmgsNoQTRIE0eyxLAtdu3bFxRdf7OnK2JDp0KEDrr/++jppDf/BBx/g9NNPx/fff+8pFG3ujBw5Evv27avSxpEgCKIhQ5lrgiBSwk8//YQnnnhCSheuvfZabNu2DZdddll9Dy0pbr75ZmRmZqKoqCil612yZAlWrlyJH3/8EW+//TbGjh2L0047jQJrm/fffx+ZmZlxCykJgiAaE1TQSBBESlBVFfPnz8fkyZPBGMPRRx+Nt99+u1Jtb0NkzZo10pkkKysrpevev38/br75Zmzfvh0tWrRAv3798MADD6R0G42ZE088URauZmZm1u9gCIIgDgCShRAEQRAEQRBEiiBZCEEQBEEQBEGkCAquCYIgCIIgCCJFUHBNEARBEARBECmCgmuCIAiCIAiCSBEUXBMEQRAEQRBEiqDgmiAIgiAIgiBSBAXXBEEQBEEQBJEiKLgmCIIgCIIgiBTx/2dxgtkaO/+1AAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tqdm.notebook import tqdm\n", + "\n", + "variables = {\n", + " \"hurs\": \"Near-Surface Relative Humidity\",\n", + " \"huss\": \"Near-Surface Specific Humidity\",\n", + " \"pr\": \"Precipitation\",\n", + " \"rlds\" : \"Surface Downwelling Longwave Radiation\",\n", + " \"rsds\": \"Surface Downwelling Shortwave Radiation\",\n", + " \"sfcWind\": \"Daily-Mean Near-Surface Wind Speed\",\n", + " \"tas\": \"Daily Near-Surface Air Temperature\",\n", + " \"tasmax\": \"Daily Maximum Near-Surface Air Temperature\",\n", + " \"tasmin\": \"Daily Minimum Near-Surface Air Temperature\"\n", + "}\n", + "\n", + "\n", + "cmpas = [\"YlGnBu\",\"YlGnBu\",\"Blues\",\"rainbow\",\"rainbow\",\"turbo\",\"Reds\",\"Reds\",\"Reds\"]\n", + "\n", + "\n", + "model = \"ACCESS-CM2\"\n", + "year = 2015 # This is the year whne the data switches from historical to simulated\n", + "scenario = \"historical\" if year<2015 else \"ssp585\"\n", + "quality = 0\n", + "\n", + "# Open (connect to) dataset\n", + "variable=\"tas\"\n", + "dataset_name = f\"{variable}_day_{model}_{scenario}_r1i1p1f1_gn\"\n", + "db = ov.LoadDataset(f\"http://atlantis.sci.utah.edu/mod_visus?dataset={dataset_name}&cached=arco\")\n", + "data=db.read()\n", + "y_max,x_max = data.shape \n", + "print(y_max,x_max)\n", + "\n", + "xlabels = [str(x) for x in range(0,360,50)] \n", + "ylabels = [str(x) for x in range(-60,90,20)] \n", + "\n", + "for variable, my_cmap in tqdm(zip(variables,cmpas)): \n", + "\n", + " my_fig, my_plt = plt.subplots(figsize=(9, 3))\n", + " data=db.read(time=year*365+364,quality=quality)\n", + " y,x = data.shape \n", + " xticks = [i*x/x_max for i in range(0,x_max, 200)] \n", + " yticks = [i*y/y_max for i in range(y_max,0,-80)] \n", + "\n", + " plot = my_plt.imshow(np.flip(data, 0), cmap=my_cmap)\n", + " my_plt.set_xticks(xticks, labels=xlabels)\n", + " my_plt.set_yticks(yticks, labels=ylabels)\n", + " my_plt.set(xlabel='longitude [degrees east]', ylabel='latitude [degrees north]',\n", + " title=f\"{variable} ({1.0/(2**(-1*quality)):.03f} of the data)\")\n", + "\n", + " # add a colorbar to our figure, based on the plot we just made above\n", + " my_fig.colorbar(plot,location='right', label='Temperature [K]')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c69f9401-5b53-49c6-9baa-06023c22fef0", + "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/notebooks/nasa/cmip6_IDX_04_computer_temperature_trend.ipynb b/notebooks/nasa/cmip6_IDX_04_computer_temperature_trend.ipynb new file mode 100644 index 0000000..c434860 --- /dev/null +++ b/notebooks/nasa/cmip6_IDX_04_computer_temperature_trend.ipynb @@ -0,0 +1,36921 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a26a3fa-8d08-4c39-a788-d3fe5a239858", + "metadata": {}, + "source": [ + "| [National Science Data Fabric](https://nationalsciencedatafabric.org/) + [NASA Open Climate Workbench (OCW)](https://esto.nasa.gov/forums/estf2024/estf24Abstracts/lee.html) + [WIRED Global Center](https://resilience.utah.edu/)
created by [Valerio Pascucci](http://cedmav.org/) |![nsdf](https://www.sci.utah.edu/~pascucci/public/NSDF-smaller.PNG) |![NASA](https://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg)| ![wired](https://www.sci.utah.edu/~pascucci/public/wired-logo-small.png) |\n", + "|:---|:---:|:---:|---:|" + ] + }, + { + "cell_type": "markdown", + "id": "fe480733-1dab-4104-9c0c-b87c5b68b3a6", + "metadata": { + "tags": [] + }, + "source": [ + "## Uncomment the line below if any library is missing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19daabb1-a63f-4ace-88bc-16bee2bc9340", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!pip install --upgrade openvisuspy xarray openvisuspy matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5929a3e4-cba2-4854-8e10-6f95ade55cdf", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VISUS_CACHE is /mnt/data/visus-cache\n" + ] + } + ], + "source": [ + "import os,sys\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n", + "sys.path.append(r\"C:\\projects\\openvisuspy\\src\")\n", + "import openvisuspy as ovp\n", + "import OpenVisus as ov\n", + "from openvisuspy.create_netcdf_metadata import create_netcdf_metadata\n", + "from openvisuspy.xarray_backend import OpenVisusBackendEntrypoint\n", + "\n", + "os.environ[\"VISUS_CACHE\"]=os.environ.get(\"VISUS_CACHE\",\"./visus_cache_can_be_erased\")\n", + "print(\"VISUS_CACHE is \",os.environ[\"VISUS_CACHE\"])\n", + "\n", + "# if you want interactive plots\n", + "use_plotly=True \n", + "\n", + "# //////////////////////////////////////////////////////////\n", + "def ShowPlot(x, ys, ylabels=[], xaxis_title='',yaxis_title='', title=\"\"):\n", + " if use_plotly:\n", + " import plotly.graph_objects as go\n", + " fig = go.Figure()\n", + " for y,label in zip(ys,ylabels):\n", + " fig.add_trace(go.Scatter(x=x,y=y,name=label))\n", + " fig.update_layout(title=title,xaxis_title=xaxis_title,yaxis_title=yaxis_title, height=800, legend=dict( yanchor=\"top\", y=0.99, xanchor=\"left\", x=0.01))\n", + " fig.layout.dragmode='pan'\n", + " fig.show(config=dict({'scrollZoom': True,'displayModeBar': False}))\n", + " else:\n", + " fig, ax = plt.subplots()\n", + " for y,label in zip(ys,ylabels):\n", + " ax.plot(x, y , label = label)\n", + " ax.legend() \n", + " ax.set(xlabel=xaxis_title, ylabel=yaxis_title, title=title)\n", + " ax.grid()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3d5862ca-7293-4147-8e63-3e5dcfbf07ed", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f961d7484e0e4040bf26c6777d555e93", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=01 January/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f1085580e8ee4c87a4eb0b2a90661f3d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=31 January/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bbef8b02d17945cab4a0afef65a23d02", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=02 March/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "731759b6c3e2444691725e89ea9e2944", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=01 April/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5e0531faf20546afaa35b0d63ca966d1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=01 May/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "11934e31cfa74b4fbd34400cd8061e05", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=31 May/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "53ef4fba5c954a92a0d8cb3f6db92809", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=30 June/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8ff5bd602dda409daaac3bb0b10dfa18", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=30 July/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85461ae02a064b06b5cc9e168f5794d6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=29 August/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "16c60ea0ea0a48d89d236b618da3c2cc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=28 September/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c541de041fee44f7b33228615e30bd30", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=28 October/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "43e7a8db7a1c46feb1dd303ae57ed92c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=27 November/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7673a67d03ef480b988fbdee2ee02ead", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "day=27 December/quality=0 : 0%| | 0/150 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import time\n", + "from tqdm.notebook import tqdm\n", + "from datetime import datetime, timedelta\n", + "\n", + "# run multiple threads\n", + "from tqdm.contrib.concurrent import thread_map\n", + "\n", + "variables = {\n", + " \"hurs\": \"Near-Surface Relative Humidity\",\n", + " \"huss\": \"Near-Surface Specific Humidity\",\n", + " \"pr\": \"Precipitation\",\n", + " \"rlds\" : \"Surface Downwelling Longwave Radiation\",\n", + " \"rsds\": \"Surface Downwelling Shortwave Radiation\",\n", + " \"sfcWind\": \"Daily-Mean Near-Surface Wind Speed\",\n", + " \"tas\": \"Daily Near-Surface Air Temperature\",\n", + " \"tasmax\": \"Daily Maximum Near-Surface Air Temperature\",\n", + " \"tasmin\": \"Daily Minimum Near-Surface Air Temperature\"\n", + "}\n", + "\n", + "# configure as needed\n", + "variable = \"tas\"\n", + "db_hist =ov.LoadDataset(f'http://atlantis.sci.utah.edu/mod_visus?dataset={variable}_day_ACCESS-CM2_historical_r1i1p1f1_gn&cached=arco')\n", + "db_model=ov.LoadDataset(f\"http://atlantis.sci.utah.edu/mod_visus?dataset={variable}_day_ACCESS-CM2_ssp585_r1i1p1f1_gn&cached=arco\")\n", + "years=list(range(1950,2100))\n", + "\n", + "# every 30 days\n", + "days=[day for day in range(0,365,30)]\n", + "\n", + "# Lower the quality until it goes too far\n", + "qualities=[0,-2,-4,-6,-8,-10] \n", + "\n", + "# //////////////////////////////////////////////////////////\n", + "for day in days:\n", + " day_name=(datetime(year=2015, month=1, day=1) + timedelta(days=day)).strftime('%d %B')\n", + " ys,ylabels=[],[]\n", + " y0=None\n", + " for quality in qualities:\n", + " t0 = time.time()\n", + " data=db_hist.read(quality=quality) # here always history?\n", + " num_vals = (data.size)- np.isnan(data).sum()\n", + " def GetTemperature(year):\n", + " db=db_hist if year<2015 else db_model\n", + " data=db.read(time=year*365+day ,quality=quality)\n", + " return np.nansum(data)/num_vals-273.15\n", + " y=thread_map(GetTemperature, years, desc=f\"day={day_name}/quality={quality} \")\n", + " ys.append(y)\n", + " if quality==0: y0=y\n", + " rmse=np.sqrt(np.nanmean(np.square(np.subtract(y, y0))))\n", + " ylabels.append(f\"reductions={1/2**(-1*quality):.02%} seconds={time.time()-t0:.02f} rmse={rmse}\")\n", + " \n", + " ShowPlot(x=years, ys=ys, ylabels=ylabels, xaxis_title='year', yaxis_title='temperature (C)', title=f\"{variable}/{day_name}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "339ca570-45ff-4740-be0b-896b23203b8c", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/ov-dashboards.ipynb b/notebooks/ov-dashboards.ipynb new file mode 100644 index 0000000..564a3c3 --- /dev/null +++ b/notebooks/ov-dashboards.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test openvisuspy dashboards\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os,sys\n", + "import panel as pn\n", + "\n", + "print(sys.executable)\n", + "print(pn.__version__)\n", + "pn.extension(\"ipywidgets\", \"floatpanel\",\"codeeditor\",\n", + " log_level=\"DEBUG\",\n", + " notifications=True, \n", + " sizing_mode=\"stretch_width\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have problems running the following cell, see # https://github.com/holoviz/holoviews/issues/4861\n", + " - Restart and Clear All Cells \n", + " - save the notebook \n", + " - kill jupyter lab \n", + " - restart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "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.servable()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import and configure OpenVisus" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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", + " }\n", + "\n", + "print(\"datasets defined\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os,sys,logging,time\n", + "import numpy as np\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", + "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", + "metadata": {}, + "source": [ + "# Show single slice of a RGB 2D dataset (David)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "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": "markdown", + "metadata": {}, + "source": [ + "# Example: OpenVisus API to read data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "llc2160_url=[it for it in datasets[\"datasets\"] if it[\"name\"]==\"llc2160_arco\"][0][\"url\"]\n", + "db=LoadDataset(llc2160_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": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "menu_items = [('Option A', 'a'), ('Option B', 'b'), ('Option C', 'c'), None, ('Help', 'help')]\n", + "\n", + "menu_button = pn.widgets.MenuButton(name='Dropdown', items=menu_items, button_type='primary')\n", + "\n", + "pn.Column(menu_button, height=200)" + ] + }, + { + "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/examples/notebooks/ov-retina-rabbit-matplot.ipynb b/notebooks/ov-retina-rabbit-matplot.ipynb similarity index 92% rename from examples/notebooks/ov-retina-rabbit-matplot.ipynb rename to notebooks/ov-retina-rabbit-matplot.ipynb index 40ee6f5..9d83c97 100644 --- a/examples/notebooks/ov-retina-rabbit-matplot.ipynb +++ b/notebooks/ov-retina-rabbit-matplot.ipynb @@ -1,170 +1,168 @@ -{ - "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/)
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", - "#
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(\"\"))\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 -} +{ + "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/)
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", + "#
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", + "# 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(\"\"))\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/examples/notebooks/ov-signal.ipynb b/notebooks/ov-signal.ipynb similarity index 96% rename from examples/notebooks/ov-signal.ipynb rename to notebooks/ov-signal.ipynb index aa4bd43..ad1b451 100644 --- a/examples/notebooks/ov-signal.ipynb +++ b/notebooks/ov-signal.ipynb @@ -1,131 +1,131 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "9360e08f-5933-47b7-b887-249e28a7667f", - "metadata": { - "tags": [] - }, - "outputs": [], - "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": null, - "id": "169a98d5-7548-4343-b8cf-889d2638a4b2", - "metadata": { - "tags": [] - }, - "outputs": [], - "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 -} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "9360e08f-5933-47b7-b887-249e28a7667f", + "metadata": { + "tags": [] + }, + "outputs": [], + "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": null, + "id": "169a98d5-7548-4343-b8cf-889d2638a4b2", + "metadata": { + "tags": [] + }, + "outputs": [], + "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/notebooks/ov-vr.ipynb b/notebooks/ov-vr.ipynb new file mode 100644 index 0000000..733e816 --- /dev/null +++ b/notebooks/ov-vr.ipynb @@ -0,0 +1,253 @@ +{ + "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/)
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: THIS NOTEBOOK DOES NOT WORK in Chrome, please use Firefox\n", + "\n", + "the problem seems related to Chrome security/context creation" + ] + }, + { + "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": [ + "#
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(\n", + " 'http://atlantis.sci.utah.edu:80/mod_visus?dataset=fly_scan_time-s-midx/data&cached=idx', \n", + " endh=21, \n", + " height=800)" + ] + }, + { + "cell_type": "markdown", + "id": "3668c1e2-2122-4f51-b236-44a888f0ca0f", + "metadata": {}, + "source": [ + "#
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(\n", + " url='http://atlantis.sci.utah.edu:80/mod_visus?dataset=chess-zip&cached=1', \n", + " endh=21, \n", + " height=800)" + ] + }, + { + "cell_type": "markdown", + "id": "4c071ff0-ec00-44a1-864c-1b2c07712b21", + "metadata": {}, + "source": [ + "#
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(\n", + " url='https://atlantis.sci.utah.edu/mod_visus?dataset=chess-recon_combined_1_2_3_fullres_zip&cached=1', \n", + " endh=21, \n", + " 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/examples/notebooks/test-bokeh.ipynb b/notebooks/test-bokeh.ipynb similarity index 96% rename from examples/notebooks/test-bokeh.ipynb rename to notebooks/test-bokeh.ipynb index c69905a..abf597e 100644 --- a/examples/notebooks/test-bokeh.ipynb +++ b/notebooks/test-bokeh.ipynb @@ -1,131 +1,131 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cb4a1da5-4264-4b97-9aec-5791e1ae8023", - "metadata": {}, - "source": [ - "# Test bokeh" - ] - }, - { - "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 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cb4a1da5-4264-4b97-9aec-5791e1ae8023", + "metadata": {}, + "source": [ + "# Test bokeh" + ] + }, + { + "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/examples/notebooks/test-ipywidgets.ipynb b/notebooks/test-ipywidgets.ipynb similarity index 89% rename from examples/notebooks/test-ipywidgets.ipynb rename to notebooks/test-ipywidgets.ipynb index 093a455..ce42e71 100644 --- a/examples/notebooks/test-ipywidgets.ipynb +++ b/notebooks/test-ipywidgets.ipynb @@ -1,59 +1,67 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3b240d15-a382-4422-9779-c1159dd3d600", - "metadata": {}, - "source": [ - "# Test ipywidgets" - ] - }, - { - "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 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3b240d15-a382-4422-9779-c1159dd3d600", + "metadata": {}, + "source": [ + "# Test ipywidgets" + ] + }, + { + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "519eec09-d49a-4a49-95f2-1ad1945578af", + "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/examples/notebooks/test-matplotlib.ipynb b/notebooks/test-matplotlib.ipynb similarity index 95% rename from examples/notebooks/test-matplotlib.ipynb rename to notebooks/test-matplotlib.ipynb index 60acdd1..eb491d7 100644 --- a/examples/notebooks/test-matplotlib.ipynb +++ b/notebooks/test-matplotlib.ipynb @@ -1,75 +1,75 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test matplotlib" - ] - }, - { - "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 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test matplotlib" + ] + }, + { + "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/examples/notebooks/test-panel.ipynb b/notebooks/test-panel.ipynb similarity index 93% rename from examples/notebooks/test-panel.ipynb rename to notebooks/test-panel.ipynb index 6cb56f4..490f16e 100644 --- a/examples/notebooks/test-panel.ipynb +++ b/notebooks/test-panel.ipynb @@ -1,87 +1,86 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "80a1afcc-e7c4-4d88-8147-8fef29f02817", - "metadata": { - "tags": [] - }, - "source": [ - "# Test panel" - ] - }, - { - "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 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "80a1afcc-e7c4-4d88-8147-8fef29f02817", + "metadata": { + "tags": [] + }, + "source": [ + "# Test panel" + ] + }, + { + "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.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/examples/notebooks/test-pyvista.ipynb b/notebooks/test-pyvista.ipynb similarity index 95% rename from examples/notebooks/test-pyvista.ipynb rename to notebooks/test-pyvista.ipynb index 9054da9..25a7be4 100644 --- a/examples/notebooks/test-pyvista.ipynb +++ b/notebooks/test-pyvista.ipynb @@ -1,73 +1,73 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "109b671c-7a23-4eeb-a32a-edfd94c3e069", - "metadata": {}, - "source": [ - "# Test pyvista\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 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "109b671c-7a23-4eeb-a32a-edfd94c3e069", + "metadata": {}, + "source": [ + "# Test pyvista\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/openvisus.env b/openvisus.env deleted file mode 100644 index 7895f3c..0000000 --- a/openvisus.env +++ /dev/null @@ -1,247 +0,0 @@ -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/pyproject.toml b/pyproject.toml index c353c41..1bca187 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,16 +1,28 @@ - [project] name = "openvisuspy" -version = "1.0.36" +version = "1.0.64" authors = [{ name="OpenVisus developers"},] description = "openvisuspy" readme = "README.md" -requires-python = ">=3.6" +requires-python = ">=3.9" [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" +requires = ['hatchling'] +build-backend = 'hatchling.build' + +[tool.hatch.build.targets.wheel] +include = ["json", "src", 'notebooks', 'app', 'test', "doc" ] + +[tool.hatch.build.targets.wheel.sources] +"src/openvisuspy" = "openvisuspy" +"notebooks" = "openvisuspy/notebooks" +"app" = "openvisuspy/app" +"test" = "openvisuspy/test" +"json" = "openvisuspy/json" + +#[tool.hatch.build.targets.wheel.force-include] +"./LICENSE" = "openvisuspy/LICENSE" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..efc332b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,70 @@ +altair +awscli +awscli-plugin-endpoint +bokeh==3.3.4 +boto3 +cartopy +colorcet +dash +datashader +fastparquet +folium +fsspec +geodatasets +geopandas +geoviews +h5glance +h5netcdf +h5py +hdf5plugin +holoviews +hvplot +imageio +intake +ipysheet +ipywidgets +ipywidgets-bokeh +jupyter_bokeh +jupyter-server-proxy +jupyterlab-pygments>=0.2.0,<0.3.0 +jupyterlab-system-monitor +jupyterlab==3.6.6 +lxml +matplotlib +nbgitpuller +netcdf4 +nexpy +nexusformat +notebook +numexpr +numpy +opencv-python +pandas +panel==1.3.8 +pillow +plotly +pyarrow +pydeck +pyvista +pyviz_comms>=2.0.0,<3.0.0 +requests +scikit-image +scikit-learn +scipy +seaborn +sqlalchemy +statsmodels +tifffile +tqdm +trame +trame-vtk +trame-vuetify +urllib3 +vega_datasets +vtk +xarray +xlrd +xmltodict +yfinance +zarr + diff --git a/scripts/new_tag.py b/scripts/new_tag.py index 26200a4..6d56b5d 100644 --- a/scripts/new_tag.py +++ b/scripts/new_tag.py @@ -1,31 +1,27 @@ 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) + + 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) + + with open("pyproject.toml", "rt") as f: + lines = f.readlines() + + # version = "1.0.36" + lines=[(f'version = "{new_version}"\n' if line.startswith("version =") else line) for line in lines] + + body="".join(lines) + # print(body) + + 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/scripts/new_tag.sh b/scripts/new_tag.sh old mode 100755 new mode 100644 index 8147ac2..8d74123 --- a/scripts/new_tag.sh +++ b/scripts/new_tag.sh @@ -1,17 +1,8 @@ #!/bin/bash -#export PYPI_USERNAME="..." -#export PYPI_PASSWORD="..." +GIT_TAG=$(python3 ./scripts/new_tag.py) -TAG=$(python3 scripts/new_tag.py) && echo ${TAG} - -git commit -a -m "New tag ($TAG)" -git tag -a $TAG -m "$TAG" -git push origin $TAG -git push origin - -rm -f dist/* -python3 -m build . - -# this does not work in WSL2, use windows to just to the upload -python3 -m twine upload --username "${PYPI_USERNAME}" --password "${PYPI_PASSWORD}" --non-interactive --verbose --skip-existing --verbose "dist/*.whl" \ No newline at end of file +git commit -a -m "New tag ($GIT_TAG)" +git tag -a ${GIT_TAG} -m "${GIT_TAG}" +git push origin ${GIT_TAG} +git push origin \ No newline at end of file diff --git a/scripts/run_command.py b/scripts/run_command.py deleted file mode 100644 index f7a35d2..0000000 --- a/scripts/run_command.py +++ /dev/null @@ -1,7 +0,0 @@ -import os,sys,glob -cmd,pattern=sys.argv[1:] -for notebook in glob.glob(pattern,recursive=True): - s=cmd.format(notebook=notebook) - print(s) - os.system(s) -exit() \ No newline at end of file diff --git a/scripts/shorten.py b/scripts/shorten.py new file mode 100644 index 0000000..0b5dc18 --- /dev/null +++ b/scripts/shorten.py @@ -0,0 +1,15 @@ +import sys, contextlib, urllib +from urllib.parse import urlencode +from urllib.request import urlopen + +# //////////////////////////////////////////////////////////////////////////// +def ShortUlr(url): + request_url = 'http://tinyurl.com/api-create.php?' + urllib.parse.urlencode({'url':url}) + with contextlib.closing(urllib.request.urlopen(request_url)) as response: + return response.read().decode('utf-8 ') + +for url in sys.argv[1:]: + print(ShortUlr(url)) + + + diff --git a/scripts/ubuntu.sh b/scripts/ubuntu.sh new file mode 100644 index 0000000..a851276 --- /dev/null +++ b/scripts/ubuntu.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +python3 -m pip install --upgrade pip +python3 -m pip install hatch build keyring + +# disable keyring +export PYTHON_KEYRING_BACKEND="keyring.backends.null.Keyring" +python3 -m keyring --disable + +# will get the version from `pyproject.toml` +python3 -m build . --wheel +unzip -l dist/*.whl + +# publish only if there is a tag +GIT_TAG=`git describe --tags --exact-match 2>/dev/null || true` +if [[ "${GIT_TAG}" != "" ]] ; then + + # PyPi + hatch publish --yes --no-prompt --user "${PYPI_USERNAME}" --auth "${PYPI_TOKEN}" + + # DockerHub + if [[ "${PYTHON_VERSION}" == "3.10" ]] ; then + docker build --build-arg="GIT_TAG=${GIT_TAG}" --tag nsdf/openvisuspy:${GIT_TAG} --tag nsdf/openvisuspy:latest ./ + docker push nsdf/openvisuspy:${GIT_TAG} + docker push nsdf/openvisuspy:latest + fi + +fi diff --git a/src/openvisuspy/__main__.py b/src/openvisuspy/__main__.py new file mode 100644 index 0000000..719ded6 --- /dev/null +++ b/src/openvisuspy/__main__.py @@ -0,0 +1,9 @@ +import os, sys + +this_dir=os.path.dirname(os.path.abspath(__file__)) + +# ////////////////////////////////////////// +if __name__ == "__main__": + if len(sys.argv)>=2 and sys.argv[1]=="dirname": + print(this_dir) + sys.exit(0) diff --git a/src/openvisuspy/backend.py b/src/openvisuspy/backend.py index 778b314..73dcd65 100644 --- a/src/openvisuspy/backend.py +++ b/src/openvisuspy/backend.py @@ -430,7 +430,7 @@ def createBoxQuery(self, def beginBoxQuery(self,query): if query is None: return logic_box=BoxToPyList(query.logic_box) - logger.info(f"beginBoxQuery timestep={query.time} field={query.field} logic_box={logic_box} end_resolutions={[I for I in query.end_resolutions]}") + logger.info(f"beginBoxQuery timestep={query.time} field={query.field.name} logic_box={logic_box} end_resolutions={[I for I in query.end_resolutions]}") self.cursor=0 self.db.beginBoxQuery(query) @@ -466,7 +466,7 @@ def executeBoxQuery(self,access, query): logic_box=BoxToPyList(query.logic_box) H=self.getCurrentResolution(query) msec=int(1000*(time.time()-self.t1)) - logger.info(f"got data cursor={self.cursor} end_resolutions{[I for I in query.end_resolutions]} timestep={query.time} field={query.field} H={H} data.shape={data.shape} data.dtype={data.dtype} logic_box={logic_box} m={np.min(data)} M={np.max(data)} ms={msec}") + logger.info(f"got data cursor={self.cursor} end_resolutions{[I for I in query.end_resolutions]} timestep={query.time} field={query.field.name} H={H} data.shape={data.shape} data.dtype={data.dtype} logic_box={logic_box} m={np.min(data)} M={np.max(data)} ms={msec}") return { "I": self.cursor, diff --git a/src/openvisuspy/convert_nexus.py b/src/openvisuspy/convert_nexus.py new file mode 100644 index 0000000..4f8ce8a --- /dev/null +++ b/src/openvisuspy/convert_nexus.py @@ -0,0 +1,145 @@ + +import os ,sys, time, logging,shutil,copy +from datetime import datetime +import numpy as np +from nexusformat.nexus import * +import OpenVisus as ov + + +# //////////////////////////////////////////////////////////// +class ConvertNexus: + + # constructor + def __init__(self,src, dst, compression="raw",streamable=None, arco="modvisus"): + self.src=src + self.dst=dst + self.streamable=streamable + self.compression=compression + self.num_bin_fields=0 + self.arco=arco + + @staticmethod + def traverse(cur,nrec=0): + yield (nrec,cur) + + try: + childs=cur.entries.items() + except: + return + + for _k, child in childs: + yield from ConvertNexus.traverse(child,nrec+1) + + @staticmethod + def print(cur): + for depth, node in ConvertNexus.traverse(cur): + if isinstance(node,NXfield) and not isinstance(node,NXlinkfield) and(len(node.shape)==3 or len(node.shape)==4): + print(" "*(depth+0) + f"{node.nxname}::{type(node)} shape={node.shape} dtype={node.dtype} ***********************************") + else: + print(" "*(depth+0) + f"{node.nxname}::{type(node)}") + for k,v in node.attrs.items(): + print(" "*(depth+1) + f"@{k} = {v}") + + # run + def run(self): + print(f"ConvertNexus::run src={self.src} full-size={os.path.getsize(self.src):,}") + src=nxload(self.src) + dst=copy.deepcopy(src) + dst.attrs["streamable"]=True + self._convertNexusFieldsToOpenVisus(src, dst) + + if self.streamable: + t1=time.time() + print(f"Creating streamable version {self.streamable}") + if os.path.isfile(self.streamable): os.remove(self.streamable) + nxsave(self.streamable, dst , mode='w') + print(f"ConvertNexus::run streamable={self.streamable} reduced-size={os.path.getsize(self.streamable):,}") + return dst + + # _convertNexusFieldsToOpenVisus + def _convertNexusFieldsToOpenVisus(self, src, dst): + + if isinstance(src,NXfield) and not isinstance(src,NXlinkfield): + + src_field=src;src_parent=src_field.nxgroup + dst_field=dst;dst_parent=dst_field.nxgroup + + if len(src_field.shape)==3 or len(src_field.shape)==4: + + # TODO: support more than once + assert(self.num_bin_fields==0) + + # replace any 'big' field with something virtually empty + # TODO: read nexus by slabs + t1=time.time() + print(f"Reading Nexus field name={src_field.nxname} dtype={src_field.dtype} shape={src_field.shape} ...") + data = src_field.nxdata + vmin,vmax=np.min(data),np.max(data) + print(f"Read Nexus field in {time.time()-t1} seconds vmin={vmin} vmax={vmax}") + + # e.g. 1x1441x676x2048 -> 1x1441x676x2048 + if len(data.shape)==4: + assert(data.shape[0]==1) + data=data[0,:,:,:] + + t1=time.time() + print(f"Creating IDX file {self.dst}") + ov_field=ov.Field.fromString(f"""DATA {str(src_field.dtype)} format(row_major) min({vmin}) max({vmax})""") + + assert(isinstance(src_parent,NXdata)) + + if "axes" in src_parent.attrs: + idx_axis=[] + idx_physic_box=[] + axis=[src_parent[it] for it in src_parent.attrs["axes"]] + for it in axis: + idx_physic_box=[str(it.nxdata[0]),str(it.nxdata[-1])] + idx_physic_box + idx_axis=[it.nxname] +idx_axis + idx_axis=" ".join(idx_axis) + print(f"Found axis={idx_axis} idx_physic_box={idx_physic_box}") + idx_physic_box=" ".join(idx_physic_box) + else: + idx_axis="X Y Z" + D,H,W=data.shape + idx_physic_box=f"0 {W} 0 {H} 0 {D}" + + db=ov.CreateIdx( + url=self.dst, + dims=list(reversed(data.shape)), + fields=[ov_field], + compression="raw", + physic_box=ov.BoxNd.fromString(idx_physic_box), + arco=self.arco, + axis=idx_axis + ) + + t1=time.time() + print(f"Writing IDX data...") + 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") + + # this is the version without any data + dst_field=NXfield(value=None, shape=src_field.shape, dtype=src_field.dtype) + dst_field.attrs["openvisus"]=repr([self.dst]) + dst_parent[src_field.nxname]=dst_field + + else: + + # deepcopy does not seem to copy nxdata (maybe for the lazy evalation?) + dst_field.nxdata=copy.copy(src_field.nxdata) + + # recurse + try: + childs=src.entries + except: + childs=[] + for name in childs: + src_child=src.entries[name] + dst_child=dst.entries[name] + self._convertNexusFieldsToOpenVisus(src_child, dst_child) diff --git a/examples/create_streamable/create_streamable.py b/src/openvisuspy/create_streamable.py similarity index 95% rename from examples/create_streamable/create_streamable.py rename to src/openvisuspy/create_streamable.py index f6763fd..6062e9c 100644 --- a/examples/create_streamable/create_streamable.py +++ b/src/openvisuspy/create_streamable.py @@ -1,6 +1,5 @@ import os,sys,time, h5py import numpy as np - import OpenVisus as ov # ////////////////////////////////////////////////////////////////////////////////// @@ -9,6 +8,7 @@ class Streamable: compression="zip" arco="4mb" + # constructor 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 @@ -26,10 +26,12 @@ def __init__(self, src_file:h5py.File, dst_file:h5py.File, idx_urls:dict=None, c self.compression=compression or self.compression self.arco=arco or self.arco + # copyAttribues def copyAttribues(self, src, dst): for k,v in src.attrs.items(): dst.attrs[k]=v + # createIdx def createIdx(self, idx_url, src): if True: @@ -89,6 +91,7 @@ def createIdx(self, idx_url, src): db.compressDataset([self.compression]) print(f"Compressed dataset to {self.compression} in {time.time()-t1} seconds") + # doCopy def doCopy(self, src): if isinstance(src,h5py.Group): @@ -131,6 +134,7 @@ def doCopy(self, src): raise NotImplementedError(f"doCopy of {src} not supported") + # SaveRemoteToLocal @staticmethod def SaveRemoteToLocal(remote_url, profile=None, endpoint_url=None): import s3fs, tempfile @@ -143,6 +147,7 @@ def SaveRemoteToLocal(remote_url, profile=None, endpoint_url=None): with open(temporary_filename,"wb") as fout: fout.write(fin.read()) return temporary_filename + # Create @staticmethod def Create(src_filename:str,dst_filename:str, **kwargs): @@ -158,6 +163,7 @@ def Create(src_filename:str,dst_filename:str, **kwargs): print(f"new-size/old-size={os.path.getsize(dst_filename):,}/{os.path.getsize(src_filename):,}") + # Print @staticmethod def Print(src, links={}, nrec=0): diff --git a/src/openvisuspy/dashboards/dashboards.json b/src/openvisuspy/dashboards/dashboards.json deleted file mode 100644 index 26c2013..0000000 --- a/src/openvisuspy/dashboards/dashboards.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "datasets": [ - {"name": "subsampled", "url": "https://maritime.sealstorage.io/api/v0/s3/utah/visus-datasets/signal1d_slac/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3"}, - {"name": "max", "url": "https://maritime.sealstorage.io/api/v0/s3/utah/visus-datasets/signal1d_slac_max/visus.idx?cached=arco&access_key=any&secret_key=any&endpoint_url=https://maritime.sealstorage.io/api/v0/s3"} - ] -} \ No newline at end of file diff --git a/src/openvisuspy/dashboards/main.py b/src/openvisuspy/dashboards/main.py deleted file mode 100644 index e0f6ee6..0000000 --- a/src/openvisuspy/dashboards/main.py +++ /dev/null @@ -1,49 +0,0 @@ -import os -import sys -import logging -import base64 -import json -import panel as pn - -sys.path.append('/Users/aashishpanta/Research/openvisuspy_github/openvisuspy/src') -from openvisuspy import SetupLogger, Slice, ProbeTool, GetQueryParams - -class DashboardApp: - def __init__(self, config): - self.slice = Slice() - self.slice.load(config) - self.setup_logging() - self.setup_layout() - - def setup_logging(self): - log_filename = os.environ.get("OPENVISUSPY_DASHBOARDS_LOG_FILENAME", "/tmp/openvisuspy-dashboards.log") - self.logger = SetupLogger(log_filename=log_filename, logging_level=logging.DEBUG) - - def setup_layout(self): - query_params = GetQueryParams() - if "load" in query_params: - body = json.loads(base64.b64decode(query_params['load']).decode("utf-8")) - self.slice.setSceneBody(body) - elif "dataset" in query_params: - scene_name = query_params["dataset"] - self.slice.scene.value = scene_name - - if False: - self.app = ProbeTool(self.slice).getMainLayout() - else: - self.app = self.slice.getMainLayout() - - def servable(self): - return self.app - -if __name__.startswith('bokeh'): - pn.extension( - "ipywidgets", - "floatpanel", - log_level="DEBUG", - notifications=True, - sizing_mode="stretch_width" - ) - config = sys.argv[1] - app_instance = DashboardApp(config) - app_instance.servable().servable() diff --git a/src/openvisuspy/probe.py b/src/openvisuspy/probe.py index 666b135..37e6c4f 100644 --- a/src/openvisuspy/probe.py +++ b/src/openvisuspy/probe.py @@ -5,7 +5,7 @@ import numpy as np from statistics import mean, median -from .slice import Slice, EPSILON +from .slice import Slice from .backend import ExecuteBoxQuery from .utils import * @@ -19,70 +19,101 @@ # ////////////////////////////////////////////////////////////////////////////////////// class Probe: - def __init__(self): - self.pos = None + # constructor + def __init__(self, dir, slot): + self.dir = dir + self.slot = slot + 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() + for slot in range(len(COLORS)): + probe = Probe(dir, slot) 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) + # getActiveProbes + def getActiveProbes(self): + ret=[] + for dir in range(3): + for probe in self.probes[dir]: + if probe.enabled and probe.pos is not None: + ret.append(probe) + return ret + + # getBody + def getBody(self): + + ret= { + "nx": self.num_points_x.value, + "ny": self.num_points_y.value, + "op": self.z_op.value, + "res": self.z_res.value, + "probes":[ + { + "dir": probe.dir, + "slot": probe.slot, + "pos": probe.pos + } for probe in self.getActiveProbes() + ] + } - # new data, important for the range - self.slice.render_id.param.watch(SafeCallback(lambda evt: self.refresh()), "value", onlychanged=True,queued=True) + return ret + + # recomputeAllProbes + def setBody(self, body): + self.slot=None + self.removeAllProbes() + + self.num_points_x.value = body.get("nx",2) + self.num_points_y.value = body.get("ny",2) + self.z_op.value = body.get("z_op","avg") + self.z_res.value = body.get("z_res",24) + + for dir in range(3): + for probe in self.probes[dir]: + for it in body.get("probes",[]): + if probe.dir==it["dir"] and probe.slot==it["slot"]: + probe.pos=it["pos"] + probe.enabled=True + break # 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( + ret=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 + x_axis_label="Z", x_range=[0.0,256.0],x_axis_type="linear", + y_axis_label="f", y_range=[0.0,256.0],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) + def handleDoubleTap(evt): + self.slice.offset.value=evt.x + + ret.on_event(bokeh.events.DoubleTap, handleDoubleTap) - self.fig_placeholder[:]=[] - self.fig_placeholder.append(self.fig) + self.fig_placeholder[:]=[ret] + return ret + # createGui def createGui(self): @@ -90,13 +121,16 @@ def createGui(self): 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) + # widgets + self.num_points_x = pn.widgets.IntSlider (name="#x", start=1, end=8, step=1, value=2, width=90) + self.num_points_y = pn.widgets.IntSlider (name="#y", start=1, end=8, step=1, value=2, width=90) + self.z_res = pn.widgets.IntSlider (name="Res", start=20, end=99, step=1, value=24, sizing_mode="stretch_width") + self.z_op = pn.widgets.RadioButtonGroup(name="", options=["avg", "mM", "med", "*"], value="avg",max_width=200) + + self.num_points_x.param.watch (SafeCallback(lambda evt: self.recomputeAllProbes()), 'value_throttled', onlychanged=True,queued=True) + self.num_points_y.param.watch (SafeCallback(lambda evt: self.recomputeAllProbes()), 'value_throttled', onlychanged=True,queued=True) + self.z_res.param.watch (SafeCallback(lambda evt: self.recomputeAllProbes()), 'value_throttled', onlychanged=True,queued=True) + self.z_op.param.watch (SafeCallback(lambda evt: self.recomputeAllProbes()), "value", onlychanged=True,queued=True) # create buttons self.buttons = [] @@ -105,29 +139,55 @@ def createGui(self): 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=self.createFigure() + + # to add probes + self.slice.canvas.on_event(bokeh.events.DoubleTap,SafeCallback(self.onCanvasDoubleTap)) + + # watch for color mapper changes + def onColorMapperTypeChange(evt=None): + 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: return + if False: + # changing y_scale DOES NOT WORK (!!!) + self.fig.y_scale=bokeh.models.scales.LogScale() if is_log else bokeh.models.scales.LinearScale() + else: + self.fig=self.createFigure() + self.recomputeAllProbes() + self.slice.color_mapper_type.param.watch(SafeCallback(onColorMapperTypeChange),"value", onlychanged=True,queued=True) + + def onSliceOffsetChange(evt=None): + self.drawSliceOffset() + self.slice.offset.param.watch( SafeCallback(onSliceOffsetChange),"value", onlychanged=True,queued=True) # display the new offset + + self.slice.scene.param.watch( SafeCallback(lambda evt: self.recomputeAllProbes()),"value", onlychanged=True,queued=True) + self.slice.direction.param.watch(SafeCallback(lambda evt: self.recomputeAllProbes()),"value", onlychanged=True,queued=True) + + # new data, important for the range + def onRenderIdChange(evt=None): + self.refreshFigureRange() + self.slice.render_id.param.watch(SafeCallback(onRenderIdChange), "value", onlychanged=True,queued=True) + + top_row=pn.Row( + self.z_op, + self.z_res, + self.num_points_x, + self.num_points_y, + sizing_mode="stretch_width" + ) + + button_row=pn.Row( + *[button for button in self.buttons], + sizing_mode="stretch_width" + ) + + self.main_layout = pn.Column( + top_row, + button_row, self.fig_placeholder, sizing_mode="stretch_both" ) - ) # getMainLayout def getMainLayout(self): @@ -138,15 +198,6 @@ 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 @@ -176,7 +227,7 @@ def onProbeButtonClick(self, slot): if not probe.enabled and probe.pos is not None: self.addProbe(probe) - self.refresh() + self.updateButtons() # findProbe def findProbe(self, probe): @@ -188,11 +239,19 @@ def findProbe(self, probe): # 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 + 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) + 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)] @@ -213,16 +272,14 @@ def PhysicToLogic(p): # __________________________________________________________ # 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}") + x,y=probe.pos + z1,z2=Z1,Z2 + + p1 = (x, y, Z1) + p2 = (x, y, Z2) - # automatically update the XY slider values - self.slider_x_pos.value = x - self.slider_y_pos.value = y + # logger.info(f"Add Probe vs={vs} vt={vt} p1={p1} p2={p2}") # keep the status for later @@ -232,7 +289,7 @@ def PhysicToLogic(p): # compute delta Delta = [1, 1, 1] - endh = self.slider_z_res.value + endh = self.z_res.value maxh = self.slice.db.getMaxResolution() bitmask = self.slice.db.getBitmask() for K in range(maxh, endh, -1): @@ -242,17 +299,14 @@ def PhysicToLogic(p): 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] + P2[X] = Align(X, P2) + (self.num_points_x.value) * Delta[X] P1[Y] = Align(Y, P1) - P2[Y] = Align(Y, P2) + (self.slider_num_points_y.value) * Delta[Y] + P2[Y] = Align(Y, P2) + (self.num_points_y.value) * Delta[Y] P1[Z] = Align(Z, P1) P2[Z] = Align(Z, P2) + Delta[Z] @@ -318,7 +372,7 @@ def Align(idx, p): ys.append(list(data[Z, Y, :])) if True: - op = self.slider_z_op.value + op = self.z_op.value if op == "avg": ys = [[mean(p) for p in zip(*ys)]] @@ -337,11 +391,13 @@ def Align(idx, p): for it in ys: if self.slice.color_mapper_type.value=="log": - it = [max(EPSILON, value) for value in it] + it = [max(Slice.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() + self.refreshFigureRange() + self.drawSliceOffset() + self.updateButtons() # removeProbe def removeProbe(self, probe): @@ -355,18 +411,10 @@ def removeProbe(self, probe): self.renderers[probe]["fig"] = [] probe.enabled = False - self.refresh() + self.updateButtons() - # 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() + # refreshFigureRange + def refreshFigureRange(self): pbox = self.slice.getPhysicBox() pdim=self.slice.getPointDim() @@ -375,38 +423,25 @@ def refresh(self): 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.xaxis.axis_label = titles[2] + 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 + # drawSliceOffset + def drawSliceOffset(self): + 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") + + # updateButtons + def updateButtons(self): dir = self.slice.direction.value for slot, button in enumerate(self.buttons): color = COLORS[slot] @@ -426,28 +461,26 @@ def refresh(self): 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") + button.stylesheets = [css] - - # recompute - def recompute(self): - - self.refresh() - - # remove all old probes + # removeAllProbes + def removeAllProbes(self): was_enabled = {} for dir in range(3): for probe in self.probes[dir]: was_enabled[probe] = probe.enabled self.removeProbe(probe) + return was_enabled + + # recomputeAllProbes + def recomputeAllProbes(self): + + self.z_res.end = self.slice.db.getMaxResolution() + + self.drawSliceOffset() + + # remove all old probes + was_enabled = self.removeAllProbes() # restore enabled for dir in range(3): @@ -457,6 +490,8 @@ def recompute(self): # 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) + + self.refreshFigureRange() + self.updateButtons() \ No newline at end of file diff --git a/src/openvisuspy/show_details.py b/src/openvisuspy/show_details.py new file mode 100644 index 0000000..8dc001e --- /dev/null +++ b/src/openvisuspy/show_details.py @@ -0,0 +1,176 @@ +import openvisuspy as ovy +import panel as pn +import numpy as np +from bokeh.plotting import figure +from bokeh.models import LinearColorMapper +import bokeh.models +import logging + +logger = logging.getLogger(__name__) + +# //////////////////////////////////////////////////////////////////////// +def apply_min_cmap(self): + self.range_min.value=self.vmin + self.range_mode.value="user" + + logger.info('new min range applied') + ShowInfoNotification('New min range applied successfully') + +# //////////////////////////////////////////////////////////////////////// +def add_range(self): + if self.range_max.valueself.vmin: + self.range_min.value=self.vmin + + logger.info('Range added successfully') + ShowInfoNotification('Range Added successfully') + +# //////////////////////////////////////////////////////////////////////// +def apply_max_cmap(self): + self.range_max.value=self.vmax + self.range_mode.value="user" + + logger.info('new min range applied') + ShowInfoNotification('New max range applied successfully') + +# //////////////////////////////////////////////////////////////////////// +def apply_avg_min_cmap(self): + new_avg_min=(self.range_min.value+self.vmin)/2 + self.range_min.value=round(new_avg_min, 4) + self.range_mode.value="user" + + logger.info('new min range applied') + ShowInfoNotification('Average Min range applied successfully') + +# //////////////////////////////////////////////////////////////////////// +def apply_avg_max_cmap(self): + new_avg_max=(self.range_max.value+self.vmax)/2 + self.range_max.value=round(new_avg_max, 4) + self.range_mode.value="user" + + logger.info('new average max range applied') + ShowInfoNotification('Average Max range applied successfully') + +# //////////////////////////////////////////////////////////////////////// +def apply_cmap(self): + self.range_min.value=self.vmin + self.range_max.value=self.vmax + self.range_mode.value="user" + + logger.info('new range applied') + ShowInfoNotification('New Colormap Range applied successfully') + self.refresh("apply_cmap") + +# //////////////////////////////////////////////////////////////////////// +def download_script(self): + url=self.data_url + rounded_logic_box = [ + [int(self.logic_box[0][0]), int(self.logic_box[0][1]), self.logic_box[0][2]], + [int(self.logic_box[1][0] ), int(self.logic_box[1][1] ), self.logic_box[1][2]] + ] + + python_file_content = "/n".join([ + f"import OpenVisus", + f"import numpy as np", + f"data_url='{url}'", + f"db=OpenVisus.LoadDataset(data_url)", + f"data=db.read(time={self.timestep.value},logic_box={rounded_logic_box})", + f"np.savez('selected_data',data=data)", + ]) + + file_path = f'./download_script_{rounded_logic_box[0][0]}_{rounded_logic_box[0][1]}.py' + with open(file_path, 'w') as file: + file.write(python_file_content) + + ShowInfoNotification('Script to download selected data saved!') + logger.info("Script saved successfully.") + +# //////////////////////////////////////////////////////////////////////// +def save_data(self): + if self.detailed_data is not None: + if self.file_name_input.value: + file_name = f"{self.file_name_input.value}.npz" + else: + file_name = "test_region.npz" + np.savez(file_name, data=self.detailed_data, lon_lat=self.selected_physic_box) + ShowInfoNotification('Data Saved successfully to current directory!') + logger.info("Data saved successfully.") + else: + logger.info("No data to save.") + +# //////////////////////////////////////////////////////////////////////// +def ShowDetails(self,x,y,w,h): + + pdim=self.getPointDim() + + # todo for 2D dataset + assert(pdim==3) + + z=int(self.offset.value) + logic_box=self.toLogic([x,y,w,h]) + self.logic_box=logic_box + data=list(ovy.ExecuteBoxQuery(self.db, access=self.db.createAccess(), field=self.field.value,logic_box=logic_box,num_refinements=1))[0]["data"] + + self.selected_logic_box=self.logic_box + self.selected_physic_box=[[x,x+w],[y,y+h]] + logger.info(f'ShowDetails({x} {y} {x+w} {y+h}) logic_box={self.logic_box}...') + self.detailed_data=data + + save_numpy_button = pn.widgets.Button(name='Save Data as Numpy', button_type='primary') + save_numpy_button.on_click(lambda evt: save_data(self)) + + download_script_button = pn.widgets.Button(name='Download Script', button_type='primary') + download_script_button.on_click(lambda evt: download_script(self)) + + apply_colormap_button = pn.widgets.Button(name='Replace Existing Range', button_type='primary') + apply_colormap_button.on_click(lambda evt: apply_cmap(self)) + + apply_min_colormap_button = pn.widgets.Button(name='Replace Min Range', button_type='primary') + apply_min_colormap_button .on_click(lambda evt: apply_min_cmap(self)) + + apply_max_colormap_button = pn.widgets.Button(name='Replace Max Range', button_type='primary') + apply_max_colormap_button.on_click(lambda evt: apply_max_cmap(self)) + + apply_avg_min_colormap_button = pn.widgets.Button(name='Apply Average Min', button_type='primary') + apply_avg_min_colormap_button .on_click(lambda evt: apply_avg_min_cmap(self)) + + apply_avg_max_colormap_button = pn.widgets.Button(name='Apply Average Max', button_type='primary') + apply_avg_max_colormap_button.on_click(lambda evt: apply_avg_max_cmap(self)) + + self.vmin,self.vmax=np.min(data),np.max(data) + add_range_button=pn.widgets.Button(name='Add This Range',button_type='primary') + add_range_button.on_click(lambda evt: add_range(self)) + + if self.range_mode.value=="dynamic-acc": + self.vmin,self.vmax=np.min(data),np.max(data) + self.range_min.value = min(self.range_min.value, self.vmin) + self.range_max.value = max(self.range_max.value, self.vmax) + logger.info(f"Updating range with selected area vmin={self.vmin} vmax={self.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 if self.palette.value_name.endswith("256") else "Turbo256" + + mapper = LinearColorMapper(palette=palette_name, low=np.min(self.detailed_data), high=np.max(self.detailed_data)) + + # Flip data to match imshow orientation + data_flipped = data + source = bokeh.models.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 = bokeh.models.ColorBar(color_mapper=mapper, label_standoff=12, location=(0,0)) + p.add_layout(color_bar, 'right') + p.xaxis.axis_label = "X" + p.yaxis.axis_label = "Y" + + self.showDialog( + pn.Column( + self.file_name_input, + pn.Row(save_numpy_button,download_script_button), + pn.Row(apply_avg_min_colormap_button,apply_avg_max_colormap_button,add_range_button,apply_colormap_button), + pn.Row(pn.pane.Bokeh(p,sizing_mode="stretch_both")), + sizing_mode="stretch_both" + ) + , width=900, height=800, name=f"Palette: {palette_name} Min: {self.vmin}, Max: {self.vmax}") diff --git a/src/openvisuspy/slice.py b/src/openvisuspy/slice.py index 23d7e1a..4356277 100644 --- a/src/openvisuspy/slice.py +++ b/src/openvisuspy/slice.py @@ -29,28 +29,10 @@ from .utils import * from .backend import Aborted,LoadDataset,ExecuteBoxQuery +from .show_details import ShowDetails 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", "play_sec","play_button","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: @@ -60,70 +42,46 @@ def __init__(self, id): self.id=id self.fig=None self.pdim=2 + self.events={} - # events - self.events={ - bokeh.events.Tap: [], - bokeh.events.DoubleTap: [], - bokeh.events.SelectionGeometry: [], - ViewportUpdate: [] - } - + self.box_select_tool_helper = bokeh.models.TextInput(visible=False) 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]) + + # onFigureSizeChange + def onFigureSizeChange(self, __attr, __old, __new): + self.setViewport(self.getViewport()) + + # __fixAspectRatioIfNeeded + def __fixAspectRatioIfNeeded(self, value): + + W=self.getWidth() + H=self.getHeight() + + # does not apply to 1d signal + if self.pdim==2 and W>0 and H>0: + x,y,w,h=value + cx=x+0.5*w + cy=y+0.5*h + ratio=W/H + w,h=(w,w/ratio) if (w/W) > (h/H) else (h*ratio,h) + x1=cx-0.5*w + y1=cy-0.5*h + value=(x1,y1,w,h) + return value + # 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 # changing x+=0.5*w to x only, same for y+=0.5*h - 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]] + pass # on_event def on_event(self, evt, callback): + if not evt in self.events: + self.events[evt]=[] self.events[evt].append(callback) # createFigure @@ -133,8 +91,7 @@ def createFigure(self): 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.reset_fig= bokeh.models.ResetTool() + self.reset_fig = bokeh.models.ResetTool() self.fig=bokeh.plotting.figure(tools=[self.pan_tool,self.reset_fig,self.wheel_zoom_tool,self.box_select_tool]) @@ -150,42 +107,51 @@ def createFigure(self): self.fig.sizing_mode = 'stretch_both' if old is None else old.sizing_mode self.fig.yaxis.axis_label = "Y" if old is None else old.xaxis.axis_label self.fig.xaxis.axis_label = "X" 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]]) + + self.fig.on_event(bokeh.events.Tap , lambda evt: [fn(evt) for fn in self.events.get(bokeh.events.Tap,[]) ]) + self.fig.on_event(bokeh.events.DoubleTap, lambda evt: [fn(evt) for fn in self.events.get(bokeh.events.DoubleTap,[])]) + self.fig.on_event(bokeh.events.RangesUpdate, lambda evt: [fn(evt) for fn in self.events.get(bokeh.events.RangesUpdate,[])]) + + # tracl changes in the size + # see https://github.com/bokeh/bokeh/issues/9136 + self.fig.on_change('inner_width', self.onFigureSizeChange) + self.fig.on_change('inner_height', self.onFigureSizeChange) # 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.fig_layout[:]=[ + Bokeh(self.fig), + self.box_select_tool_helper, + ] 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"); - """ - )) + + """ + Implementing in javascript since this DOES NOT WORK + self.fig.on_event(bokeh.events.SelectionGeometry, lambda s: print("JHERE")) + """ + + 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): @@ -217,13 +183,11 @@ def getViewport(self): # setViewport def setViewport(self,value): - x,y,w,h=value - self.last_W,self.last_H=0,0 # force a fix viewport + x,y,w,h=self.__fixAspectRatioIfNeeded(value) 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 + # showData def showData(self, pdim, data, viewport, color_bar=None): x,y,w,h=viewport @@ -268,60 +232,36 @@ def showData(self, pdim, data, viewport, color_bar=None): "dtype":img.dtype, "color_bar":color_bar } - # //////////////////////////////////////////////////////////////////////////////////// class Slice(param.Parameterized): - def __init__(self): # just so that we can get new instances in each session + + ID=0 + + EPSILON = 0.001 + + show_options={ + "top": [ + [ "menu_button","scene", "timestep", "timestep_delta", "play_sec","play_button","palette", "color_mapper_type", "resolution","view_dependent", "num_refinements", "show_probe"], + ["field","direction", "offset", "range_mode", "range_min", "range_max"] + + ], + "bottom": [ + ["request","response"] + ] + } + + # constructor + def __init__(self): super().__init__() - self.render_id = pn.widgets.IntSlider(name="RenderId", value=0) - # current scene as json - self.scene_body = pn.widgets.TextAreaInput(name='Current', sizing_mode="stretch_width", height=520) - # core query - self.scene = pn.widgets.Select(name="Scene", options=[], width=120) - self.timestep = pn.widgets.IntSlider(name="Time", value=0, start=0, end=1, step=1, sizing_mode="stretch_width") - self.timestep_delta = pn.widgets.Select(name="Speed", options=[1, 2, 4, 8, 16, 32, 64, 128], value=1, width=50) - self.field = pn.widgets.Select(name='Field', options=[], value='data', width=80) - self.resolution = pn.widgets.IntSlider(name='Resolution', value=28, start=20, end=99, sizing_mode="stretch_width") - self.view_dependent = pn.widgets.Select(name="ViewDep", options={"Yes": True, "No": False}, value=True, width=80) - self.num_refinements = pn.widgets.IntSlider(name='#Ref', value=0, start=0, end=4, width=80) - self.direction = pn.widgets.Select(name='Direction', options={'X': 0, 'Y': 1, 'Z': 2}, value=2, width=80) - self.offset = pn.widgets.EditableFloatSlider(name="Depth", start=0.0, end=1024.0, step=1.0, value=0.0, sizing_mode="stretch_width", format=bokeh.models.formatters.NumeralTickFormatter(format="0.01")) - self.viewport = pn.widgets.TextInput(name="Viewport", value="") - # palette - self.range_mode = pn.widgets.Select(name="Range", options=["metadata", "user", "dynamic", "dynamic-acc"], value="dynamic", width=120) - self.range_min = pn.widgets.FloatInput(name="Min", width=80) - self.range_max = pn.widgets.FloatInput(name="Max", width=80) - self.palette = pn.widgets.ColorMap(name="Palette", options=GetPalettes(), value_name="Viridis256", ncols=5, width=180) - self.color_mapper_type = pn.widgets.Select(name="Mapper", options=["linear", "log"], width=60) - self.play_button = pn.widgets.Button(name="Play", width=10, sizing_mode='stretch_width') - self.play_sec = pn.widgets.Select(name="Frame delay", options=[0.00, 0.01, 0.1, 0.2, 0.1, 1, 2], value=0.01, width=120) - self.request = pn.widgets.TextInput(name="", sizing_mode='stretch_width', disabled=False) - self.response = pn.widgets.TextInput(name="", sizing_mode='stretch_width', disabled=False) - # toolbar - self.info_button = pn.widgets.Button(icon="info-circle", width=20) - self.open_button = pn.widgets.Button(icon="file-upload", width=20) - self.save_button = pn.widgets.Button(icon="file-download", width=20) - self.copy_url_button = pn.widgets.Button(icon="copy", width=20) - self.logout_button = pn.widgets.Button(icon="logout", width=20) - self.save_button_helper = pn.widgets.TextInput(visible=False) - self.copy_url_button_helper = pn.widgets.TextInput(visible=False) - self.file_name_input = pn.widgets.TextInput(name="Numpy_File", value='test', placeholder='Numpy File Name to save') + + self.id=Slice.ID+1 + Slice.ID += 1 + self.job_id=0 + self.vmin=None self.vmax=None - # internal use only - self.save_button_helper = pn.widgets.TextInput(visible=False) - self.copy_url_button_helper = pn.widgets.TextInput(visible=False) - self.file_name_input= pn.widgets.TextInput(name="Numpy_File", value='test',placeholder='Numpy File Name to save') - - self.on_change_callbacks={} - - self.num_hold=0 - global SLICE_ID - self.id=SLICE_ID - SLICE_ID += 1 - self.db = None self.access = None @@ -330,273 +270,134 @@ def __init__(self): # just so that we can get new instances in each session 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(self,"setting_timestep_delta",True) - value=int(evt.new) - A = self.timestep.start - B = self.timestep.end - T = self.timestep.value - T = A + value * int((T - A) / value) - T = min(B, max(A, T)) - self.timestep.step = value - self.timestep.value=T - setattr(self,"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) + self.aborted = Aborted() + self.new_job = False + self.current_img = None + self.last_job_pushed =time.time() - 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.createGui() - self.num_refinements.param.watch(SafeCallback(lambda evt: self.refresh()),"value", onlychanged=True,queued=True) + # createMenuButton + def createMenuButton(self): - 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()] + action_helper = pn.widgets.TextInput(visible=False) + save_button_helper = pn.widgets.TextInput(visible=False) + copy_url_button_helper = pn.widgets.TextInput(visible=False) + + main_button = pn.widgets.MenuButton( + name="File", items=[('Open', 'open'), ('Save', 'save'), ('Show Metadata', 'metadata'),('Copy Url','copy-url'), None, ("Refresh All","refresh-all"), None, ('Logout', 'logout')], + button_type='primary') - # 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) + # onClicked + def onClicked(action): + action_helper.value=action # this is needed for the javascript part - 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())) + if action=="metadata": + self.showMetadata() + return - self.setShowOptions(DEFAULT_SHOW_OPTIONS) + if action=="open": + self.showOpen() - self.canvas.on_event(bokeh.events.SelectionGeometry, SafeCallback(self.showDetails)) + if action=="save": + body=self.save() + save_button_helper.value=body # this is needed for the javascript part + return - self.start() + if action=="copy-url": + copy_url_button_helper.value=self.getShareableUrl() # this is needed for the javascript part + ShowInfoNotification(f'Copy Url done {copy_url_button_helper.value}') + return - # showDetails - def showDetails(self,evt=None): - import openvisuspy as ovy - import panel as pn - import numpy as np - - - x,y,w,h=evt.new - z=int(self.offset.value) - logic_box=self.toLogic([x,y,w,h]) - self.logic_box=logic_box - 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(self.logic_box) - self.selected_logic_box=self.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') - download_script_button = pn.widgets.Button(name='Download Script', button_type='primary') - apply_colormap_button = pn.widgets.Button(name='Replace Existing Range', button_type='primary') - - apply_min_colormap_button = pn.widgets.Button(name='Replace Min Range', button_type='primary') - apply_max_colormap_button = pn.widgets.Button(name='Replace Max Range', button_type='primary') - apply_avg_min_colormap_button = pn.widgets.Button(name='Apply Average Min', button_type='primary') - apply_avg_max_colormap_button = pn.widgets.Button(name='Apply Average Max', button_type='primary') - save_numpy_button.on_click(self.save_data) - download_script_button.on_click(self.download_script) - apply_colormap_button.on_click(self.apply_cmap) - apply_max_colormap_button.on_click(self.apply_max_cmap) - apply_min_colormap_button .on_click(self.apply_min_cmap) - apply_avg_max_colormap_button.on_click(self.apply_avg_max_cmap) - apply_avg_min_colormap_button .on_click(self.apply_avg_min_cmap) - self.vmin,self.vmax=np.min(data),np.max(data) - add_range_button=pn.widgets.Button(name='Add This Range',button_type='primary') - add_range_button.on_click(self.add_range) - - if self.range_mode.value=="dynamic-acc": - self.vmin,self.vmax=np.min(data),np.max(data) - self.range_min.value = min(self.range_min.value, self.vmin) - self.range_max.value = max(self.range_max.value, self.vmax) - logger.info(f"Updating range with selected area vmin={self.vmin} vmax={self.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 if self.palette.value_name.endswith("256") else "Turbo256" - - mapper = LinearColorMapper(palette=palette_name, low=np.min(self.detailed_data), high=np.max(self.detailed_data)) - - - data_flipped = data # Flip data to match imshow orientation - source = bokeh.models.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 = bokeh.models.ColorBar(color_mapper=mapper, label_standoff=12, location=(0,0)) - p.add_layout(color_bar, 'right') - p.xaxis.axis_label = "X" - p.yaxis.axis_label = "Y" + if action=="refresh-all": + self.refreshAll() + return - self.showDialog( - pn.Column( - self.file_name_input, - - pn.Row(save_numpy_button,download_script_button), - pn.Row(pn.pane.Bokeh(p),pn.Column( - pn.pane.Markdown(f"#### Palette Used: {palette_name}"), - pn.pane.Markdown(f"#### New Min/Max Found.."), - pn.pane.Markdown(f"#### Min: {self.vmin}, Max: {self.vmax}"), - pn.Row(apply_avg_min_colormap_button,apply_avg_max_colormap_button), - add_range_button, - apply_colormap_button)), - - sizing_mode="stretch_both" - ), - width=1048, height=748, name="Details" - ) - - def apply_min_cmap(self,event): - self.range_min.value=self.vmin - self.range_mode.value="user" - print('new min range applied') - ShowInfoNotification('New min range applied successfully') - def add_range(self,event): - if self.range_max.valueself.vmin: - self.range_min.value=self.vmin - print('Range added successfully') - ShowInfoNotification('Range Added successfully') - - def apply_max_cmap(self,event): - self.range_max.value=self.vmax - self.range_mode.value="user" - print('new min range applied') - ShowInfoNotification('New max range applied successfully') - - def apply_avg_min_cmap(self,event): - new_avg_min=(self.range_min.value+self.vmin)/2 - self.range_min.value=round(new_avg_min, 4) - self.range_mode.value="user" - print('new min range applied') - ShowInfoNotification('Average Min range applied successfully') - - def apply_avg_max_cmap(self,event): - new_avg_max=(self.range_max.value+self.vmax)/2 - self.range_max.value=round(new_avg_max, 4) - self.range_mode.value="user" - print('new average max range applied') - ShowInfoNotification('Average Max range applied successfully') - - def apply_cmap(self,event): - self.range_min.value=self.vmin - self.range_max.value=self.vmax - self.range_mode.value="user" - print('new range applied') - ShowInfoNotification('New Colormap Range applied successfully') - self.refresh() - - - def download_script(self,event): - url=self.data_url - rounded_logic_box = [ - [int(self.logic_box[0][0]), int(self.logic_box[0][1]), self.logic_box[0][2]], - [int(self.logic_box[1][0] ), int(self.logic_box[1][1] ), self.logic_box[1][2]] -] - python_file_content = f""" -import OpenVisus -import numpy as np + main_button.on_click(SafeCallback(lambda evt: onClicked(evt.new))) + main_button.js_on_click(args={ + "action_helper":action_helper, + "save_button_helper":save_button_helper, + "copy_url_button_helper": copy_url_button_helper + }, code=""" + + function jsCallFunction() { + + console.log("jsCallFunction value="+action_helper.value); + + if (action_helper.value=="save") { + console.log("save_button_helper.value=" + save_button_helper.value); + const link = document.createElement("a"); + const file = new Blob([save_button_helper.value], { type: 'text/plain' }); + link.href = URL.createObjectURL(file); + link.download = "save_scene.json"; + link.click(); + URL.revokeObjectURL(link.href); + return + } + + if (action_helper.value=="copy-url") { + console.log("copy_url_button_helper.value=" + copy_url_button_helper.value); + navigator.clipboard.writeText(copy_url_button_helper.value); + return; + } + + if (action_helper.value=="logout") { + console.log("window.location.href="+window.location.href); + window.location=window.location.href + "/logout"; + } + + } + + setTimeout(jsCallFunction,300); + """) + + return pn.Row( + main_button, + action_helper, + save_button_helper, + copy_url_button_helper, + max_width=120, + align=('start', 'end')) + + # refreshAll + def refreshAll(self): + viewport=self.canvas.getViewport() + self.canvas.setViewport(viewport) + self.refresh("refreshAll") + self.probe_tool.recomputeAllProbes() + + # createColorBar + def createColorBar(self): + color_mapper_type=self.color_mapper_type.value + assert(color_mapper_type in ["linear","log"]) + is_log=color_mapper_type=="log" + low =cdouble(self.range_min.value) + high=cdouble(self.range_max.value) + mapper_low =max(Slice.EPSILON, low ) if is_log else low + mapper_high=max(Slice.EPSILON, high) if is_log else high + self.color_bar = bokeh.models.ColorBar(color_mapper = + bokeh.models.LogColorMapper (palette=self.palette.value, low=mapper_low, high=mapper_high) if is_log else + bokeh.models.LinearColorMapper(palette=self.palette.value, low=mapper_low, high=mapper_high) + ) -data_url="{url}" -db=OpenVisus.LoadDataset(data_url) -data=db.read(time={self.timestep.value},logic_box={rounded_logic_box}) -np.savez('selected_data',data=data) -""" - file_path = f'./download_script_{rounded_logic_box[0][0]}_{rounded_logic_box[0][1]}.py' - - with open(file_path, 'w') as file: - file.write(python_file_content) - ShowInfoNotification('Script to download selected data saved!') - print("Script saved successfully.") - - def save_data(self, event): - if self.detailed_data is not None: - if self.file_name_input.value: - file_name = f"{self.file_name_input.value}.npz" - else: - file_name = "test_region.npz" - 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 + body=json.dumps(self.getBody(),indent=2) + self.scene_body.value=body + + def onFileInputChange(evt): + self.scene_body.value=file_input.value.decode('ascii') 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) + file_input.param.watch(SafeCallback(onFileInputChange),"value", onlychanged=True,queued=True) def onEvalClick(evt): - self.setSceneBody(json.loads(self.scene_body.value)) + self.setBody(json.loads(self.scene_body.value)) ShowInfoNotification('Eval done') eval_button = pn.widgets.Button(name="Eval", align='end') eval_button.on_click(SafeCallback(onEvalClick)) @@ -612,98 +413,174 @@ def onEvalClick(evt): # save def save(self): - body=json.dumps(self.getSceneBody(),indent=2) - self.save_button_helper.value=body + body=json.dumps(self.getBody(),indent=2) ShowInfoNotification('Save done') print(body) + return 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.play = types.SimpleNamespace() + self.play.is_playing = False + self.idle_callback = None + self.color_bar = None - 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.menu_button=self.createMenuButton() - 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"; - """) + self.dialogs=Column() + self.dialogs.visible=False - # for icons see https://tabler.io/icons + self.central_layout = Column(sizing_mode="stretch_both") - # play time - self.play = types.SimpleNamespace() - self.play.is_playing = False + self.main_layout=Row( + self.central_layout, + sizing_mode="stretch_both") - self.idle_callback = None - self.color_bar = None + # just so that we can get new instances in each session + self.render_id = pn.widgets.IntSlider(name="RenderId", value=0) - self.t1=time.time() - self.aborted = Aborted() - self.new_job = False - self.current_img = None - self.last_job_pushed =time.time() + # current scene as json + self.scene_body = pn.widgets.CodeEditor(name='Current', sizing_mode="stretch_width", height=520,language="json") + self.scene_body.stylesheets=[""".bk-input {background-color: rgb(48, 48, 64);color: white;font-size: small;}"""] + + # core query + self.scene = pn.widgets.Select(name="Scene", options=[], width=120) + def onSceneChange(evt): + logger.info(f"onSceneChange {evt}") + body=self.scenes[evt.new] + self.setBody(body) + self.scene.param.watch(SafeCallback(onSceneChange),"value", onlychanged=True,queued=True) - 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.timestep = pn.widgets.IntSlider(name="Time", value=0, start=0, end=1, step=1, sizing_mode="stretch_width") + def onTimestepChange(evt): + self.refresh(reason="onTimestepChange") + self.timestep.param.watch(SafeCallback(onTimestepChange), "value", onlychanged=True,queued=True) - self.top_layout=Column(sizing_mode="stretch_width") + self.timestep_delta = pn.widgets.Select(name="Speed", options=[1, 2, 4, 8, 16, 32, 64, 128], value=1, width=50) + def onTimestepDeltaChange(evt): + if bool(getattr(self,"setting_timestep_delta",False)): return + setattr(self,"setting_timestep_delta",True) + value=int(evt.new) + A = self.timestep.start + B = self.timestep.end + T = self.timestep.value + T = A + value * int((T - A) / value) + T = min(B, max(A, T)) + self.timestep.step = value + self.timestep.value=T + setattr(self,"setting_timestep_delta",False) + self.timestep_delta.param.watch(SafeCallback(onTimestepDeltaChange),"value", onlychanged=True,queued=True) - self.middle_layout=Column( - Row(self.canvas.fig_layout, sizing_mode='stretch_both'), - sizing_mode='stretch_both' - ) + self.field = pn.widgets.Select(name='Field', options=[], value='data', width=80) + def onFieldChange(evt): + self.refresh("onFieldChange") + self.field.param.watch(SafeCallback(onFieldChange),"value", onlychanged=True,queued=True) - self.bottom_layout=Column(sizing_mode="stretch_width") + self.resolution = pn.widgets.IntSlider(name='Resolution', value=28, start=20, end=99, sizing_mode="stretch_width") + self.resolution.param.watch(SafeCallback(lambda evt: self.refresh("resolution.param.watch")),"value", onlychanged=True,queued=True) - self.dialogs=Column() - self.dialogs.visible=False + self.view_dependent = pn.widgets.Select(name="ViewDep", options={"Yes": True, "No": False}, value=True, width=80) + self.view_dependent.param.watch(SafeCallback(lambda evt: self.refresh("view_dependent.param.watch")),"value", onlychanged=True,queued=True) + + self.num_refinements = pn.widgets.IntSlider(name='#Ref', value=0, start=0, end=4, width=80) + self.num_refinements.param.watch(SafeCallback(lambda evt: self.refresh("num_refinements.param.watch")),"value", onlychanged=True,queued=True) + self.direction = pn.widgets.Select(name='Direction', options={'X': 0, 'Y': 1, 'Z': 2}, value=2, width=80) + 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()] - self.main_layout=Column( - self.top_layout, - self.middle_layout, - self.bottom_layout, + # 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("onDirectionChange") + self.direction.param.watch(SafeCallback(onDirectionChange),"value", onlychanged=True,queued=True) - self.dialogs, - self.copy_url_button_helper, - self.save_button_helper, + self.offset = pn.widgets.EditableFloatSlider(name="Depth", start=0.0, end=1024.0, step=1.0, value=0.0, sizing_mode="stretch_width", format=bokeh.models.formatters.NumeralTickFormatter(format="0.01")) + self.offset.param.watch(SafeCallback(lambda evt: self.refresh("offset.param.watch")),"value", onlychanged=True,queued=True) + + # palette + self.range_mode = pn.widgets.Select(name="Range", options=["metadata", "user", "dynamic", "dynamic-acc"], value="dynamic", width=120) + def onRangeModeChange(evt): + mode=evt.new + 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("onRangeModeChange") + self.range_mode.param.watch(SafeCallback(onRangeModeChange),"value", onlychanged=True,queued=True) - sizing_mode="stretch_both" - ) + self.range_min = pn.widgets.FloatInput(name="Min", width=80,value=0.0) + self.range_max = pn.widgets.FloatInput(name="Max", width=80,value=0.0) # NOTE: in dynamic mode I need an empty range + def onUserRangeChange(evt): + mode=self.range_mode.value + if mode!="user": return + self.refresh("onUserRangeChange") + self.range_min.param.watch(SafeCallback(onUserRangeChange),"value", onlychanged=True,queued=True) + self.range_max.param.watch(SafeCallback(onUserRangeChange),"value", onlychanged=True,queued=True) + + self.palette = pn.widgets.ColorMap(name="Palette", options=GetPalettes(), value_name="Viridis256", ncols=3, width=180) + def onPaletteChange(evt): + self.createColorBar() + self.refresh("onPaletteChange") + self.palette.param.watch(SafeCallback(onPaletteChange),"value_name", onlychanged=True,queued=True) + + self.color_mapper_type = pn.widgets.Select(name="Mapper", options=["linear", "log"], width=60) + def onColorMapperTypeChange(evt): + self.createColorBar() + self.refresh("onColorMapperTypeChange") + self.color_mapper_type.param.watch(SafeCallback(onColorMapperTypeChange),"value", onlychanged=True,queued=True) + + self.play_button = pn.widgets.Button(name="Play", width=10) + self.play_button.on_click(SafeCallback(lambda evt: self.togglePlay())) + + self.play_sec = pn.widgets.Select(name="Delay", options=[0.00, 0.01, 0.1, 0.2, 0.1, 1, 2], value=0.01, width=90) + self.request = pn.widgets.TextInput(name="", sizing_mode='stretch_width', disabled=False) + self.response = pn.widgets.TextInput(name="", sizing_mode='stretch_width', disabled=False) + + self.file_name_input = pn.widgets.TextInput(name="Numpy_File", value='test', placeholder='Numpy File Name to save') + + self.canvas = Canvas(self.id) + self.canvas.on_event(bokeh.events.RangesUpdate , SafeCallback(self.onCanvasViewportChange)) + self.canvas.on_event(bokeh.events.Tap , SafeCallback(self.onCanvasSingleTap)) + self.canvas.on_event(bokeh.events.DoubleTap , SafeCallback(self.onCanvasDoubleTap)) + self.canvas.on_event(bokeh.events.SelectionGeometry, SafeCallback(self.onCanvasSelectionGeometry)) + + # probe_tool + from .probe import ProbeTool + self.probe_tool=ProbeTool(self) + + self.show_probe=pn.widgets.Toggle(name='Probe',value=False, width=60, align=('start', 'end'), button_style='outline', button_type='primary') + self.show_probe.param.watch(SafeCallback(lambda evt: self.setShowProbe(evt.new)),"value") + + self.setShowOptions(Slice.show_options) + self.start() + + # setShowProbe + def setShowProbe(self, value): + self.show_probe.value=value + if value: + self.main_layout[:]=[self.central_layout,self.probe_tool.getMainLayout()] + self.probe_tool.recomputeAllProbes() + else: + self.main_layout[:]=[self.central_layout] # 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() + self.refresh("onCanvasViewportChange") # onCanvasSingleTap def onCanvasSingleTap(self, evt): @@ -721,26 +598,40 @@ def getShowOptions(self): # 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 + + # [0,1) means 1 timestep + num_timesteps=max(1,len(self.db.getTimesteps())-1) if self.db else 1 + + def CreateWidgets(row): + ret=[] + for it in row: + widget=getattr(self, it.replace("-","_"),None) + if widget: + if num_timesteps==1 and widget in [self.timestep, self.timestep_delta, self.play_sec,self.play_button]: + continue + ret.append(widget) + + return ret + + top =[Row(*CreateWidgets(row),sizing_mode="stretch_width") for row in value.get("top" ,[[]])] + bottom=[Row(*CreateWidgets(row),sizing_mode="stretch_width") for row in value.get("bottom",[[]])] + + self.central_layout[:]=[ + *top, + self.canvas.fig_layout, + *bottom, + self.dialogs, + ] # getShareableUrl - def getShareableUrl(self): - body=self.getSceneBody() + def getShareableUrl(self, short=True): + body=self.getBody() 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}) + ret=o.scheme + "://" + o.netloc + o.path + '?' + urlencode({'load': load_s}) + ret=GetShortUrl(ret) if short else ret + return ret # stop def stop(self): @@ -754,7 +645,7 @@ def start(self): self.db.start() if not self.idle_callback: self.idle_callback = AddPeriodicCallback(self.onIdle, 1000 // 30) - self.refresh() + self.refresh("self.start") # getMainLayout def getMainLayout(self): @@ -768,7 +659,7 @@ def getLogicToPhysic(self): def setLogicToPhysic(self, value): logger.debug(f"id={self.id} value={value}") self.logic_to_physic = value - self.refresh() + self.refresh("self.setLogicToPhysic") # getPhysicBox def getPhysicBox(self): @@ -790,9 +681,9 @@ def LinearMapping(a, b, A, B): T = [LinearMapping(0, dims[I], *value[I]) for I in range(len(dims))] self.setLogicToPhysic(T) - # getSceneBody - def getSceneBody(self): - return { + # getBody + def getBody(self): + ret={ "scene" : { "name": self.scene.value, @@ -817,19 +708,15 @@ def getSceneBody(self): "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() + "viewport": self.canvas.getViewport(), + "show_probe": self.show_probe.value, } } - # hold - def hold(self): - self.num_hold=getattr(self,"num_hold",0) + 1 - # if self.num_hold==1: self.doc.hold() + if self.probe_tool.getActiveProbes(): + ret["scene"]["probe_tool"]=self.probe_tool.getBody() - # unhold - def unhold(self): - self.num_hold-=1 - # if self.num_hold==0: self.doc.unhold() + return ret # load def load(self, value): @@ -860,29 +747,27 @@ def load(self, value): 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]) + self.setBody(self.scenes[first_scene_name]) + else: + self.refreshAll() - # setSceneBody - def setSceneBody(self, scene): + # setBody + def setBody(self, body): logger.info(f"# //////////////////////////////////////////#") - logger.info(f"id={self.id} {scene} START") + logger.info(f"id={self.id} {body} START") # TODO! # self.stop() - assert(isinstance(scene,dict)) - assert(len(scene)==1 and list(scene.keys())==["scene"]) + assert(isinstance(body,dict)) + assert(len(body)==1 and list(body.keys())==["scene"]) # go one level inside - scene=scene["scene"] + body=body["scene"] # the url should come from first load (for security reasons) - name=scene["name"] + name=body["name"] assert(name in self.scenes) default_scene=self.scenes[name]["scene"] @@ -890,7 +775,7 @@ def setSceneBody(self, scene): 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 "urls" in body: if "--prefer" in sys.argv: prefer = sys.argv[sys.argv.index("--prefer") + 1] @@ -922,101 +807,116 @@ def setSceneBody(self, scene): pdim = self.getPointDim() - if "logic-to-physic" in scene: - logic_to_physic=scene["logic-to-physic"] + if "logic-to-physic" in body: + logic_to_physic=body["logic-to-physic"] self.setLogicToPhysic(logic_to_physic) else: physic_box=self.db.getPhysicBox() self.setPhysicBox(physic_box) - if "directions" in scene: - directions=scene["directions"] + if "directions" in body: + directions=body["directions"] else: directions=self.db.getAxis() 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)) + self.timestep_delta.value=int(body.get("timestep-delta", 1)) + self.timestep.value=int(body.get("timestep", self.db.getTimesteps()[0])) + self.view_dependent.value = bool(body.get('view-dependent', True)) - resolution=int(scene.get("resolution", -6)) + resolution=int(body.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()) + self.field.value=body.get("field", self.db.getField()) - self.num_refinements.value=int(scene.get("num-refinements", 1 if pdim==1 else 2)) + self.num_refinements.value=int(body.get("num-refinements", 1 if pdim==1 else 2)) - self.direction.value = int(scene.get("direction", 2)) + self.direction.value = int(body.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=float(scene.get("offset",default_offset_value)) + self.offset.value=float(body.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) + self.play_sec.value=float(body.get("play-sec",0.01)) + self.palette.value_name=body.get("palette",DEFAULT_PALETTE) - self.metadata_range = list(scene.get("metadata-range",self.db.getFieldRange())) + self.metadata_range = list(body.get("metadata-range",self.db.getFieldRange())) assert(len(self.metadata_range))==2 - self.color_map=None - self.range_mode.value=scene.get("range-mode","dynamic") - + self.range_mode.value=body.get("range-mode","dynamic") + self.range_min.value = body.get("range-min",0.0) + self.range_max.value = body.get("range-max",0.0) - self.color_mapper_type.value = scene.get("color-mapper-type","linear") + self.color_mapper_type.value = body.get("color-mapper-type","linear") - viewport=scene.get("viewport",None) + viewport=body.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) + # probe_tool + self.show_probe.value=body.get("show_probe",False) + self.probe_tool.setBody(body.get("probe_tool",{})) + + show_options=body.get("show-options",Slice.show_options) + self.setShowOptions(show_options) self.start() logger.info(f"id={self.id} END\n") + self.refreshAll() - # showInfo - def showInfo(self): + # onCanvasSelectionGeometry + def onCanvasSelectionGeometry(self, evt): + ShowInfoNotification('Reading data. Please wait...') + ShowDetails(self,*evt.new) + ShowInfoNotification('Data ready') + + # showMetadata + def showMetadata(self): logger.debug(f"Show info") body=self.scenes[self.scene.value] metadata=body["scene"].get("metadata", []) + if not metadata: + self.showDialog(HTML(f"
No metadata
",sizing_mode="stretch_width",height=400)) - 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"
{body}
",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" + else: + + 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"
{body}
",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) + self.showDialog(*cards) # showDialog def showDialog(self, *args,**kwargs): @@ -1033,7 +933,7 @@ def getMaxResolution(self): def setViewDependent(self, value): logger.debug(f"id={self.id} value={value}") self.view_dependent.value = value - self.refresh() + self.refresh("self.setViewDependent") # getLogicAxis (depending on the projection XY is the slice plane Z is the orthogoal direction) def getLogicAxis(self): @@ -1223,7 +1123,8 @@ def getPointDim(self): return self.db.getPointDim() if self.db else 2 # refresh - def refresh(self): + def refresh(self,reason=None): + logger.info(f"reason={reason}") self.aborted.setTrue() self.new_job=True @@ -1236,7 +1137,7 @@ def getQueryLogicBox(self): def setQueryLogicBox(self,value): viewport=self.toPhysic(value) self.canvas.setViewport(viewport) - self.refresh() + self.refresh("setQueryLogicBox") # getLogicCenter def getLogicCenter(self): @@ -1304,25 +1205,23 @@ def gotNewData(self, result): pdim = self.getPointDim() # 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]) + # 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) + # update the color bar + low =cdouble(self.range_min.value) + high=cdouble(self.range_max.value) # if pdim==1: @@ -1341,21 +1240,16 @@ def gotNewData(self, result): self.canvas.wheel_zoom_tool.dimensions="both" self.canvas.pan_tool.dimensions="both" - # 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} mode={mode} np-array-range={data_range} widget-range={[low,high]}") + color_mapper_type=self.color_mapper_type.value + assert(color_mapper_type in ["linear","log"]) + is_log=color_mapper_type=="log" + mapper_low =max(Slice.EPSILON, low ) if is_log else low + mapper_high=max(Slice.EPSILON, high) if is_log else high + + self.color_bar.color_mapper.low = mapper_low + self.color_bar.color_mapper.high = mapper_high + + logger.debug(f"id={self.id} job_id={self.job_id} rendering result data.shape={data.shape} data.dtype={data.dtype} logic_box={logic_box} mode={mode} np-array-range={data_range} widget-range={[low,high]}") # update the image self.canvas.showData(min(pdim,2), data, self.toPhysic(logic_box), color_bar=self.color_bar) @@ -1435,10 +1329,11 @@ def pushJobIfNeeded(self): max_pixels=int(canvas_w*canvas_h*coeff) # new scene body - self.scene_body.value=json.dumps(self.getSceneBody(),indent=2) + self.scene_body.value=json.dumps(self.getBody(),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}..") + self.job_id+=1 + logger.debug(f"id={self.id} job_id={self.job_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 diff --git a/src/openvisuspy/utils.py b/src/openvisuspy/utils.py index 502ed92..14bb256 100644 --- a/src/openvisuspy/utils.py +++ b/src/openvisuspy/utils.py @@ -4,15 +4,22 @@ import urllib.request import boto3 import urllib.parse +import sys, contextlib, urllib +from urllib.parse import urlencode +from urllib.request import urlopen + import requests from requests.auth import HTTPBasicAuth +import urllib3 +urllib3.disable_warnings() + from pprint import pprint logger = logging.getLogger(__name__) -COLORS = ["lime", "red", "green", "yellow", "orange", "silver", "aqua", "pink", "dodgerblue"] +COLORS = ["red", "lime", "green", "yellow", "orange", "silver", "aqua", "pink", "dodgerblue"] DEFAULT_PALETTE="Turbo256" @@ -84,12 +91,29 @@ def LoadJSON(value): with open(url, "r") as f: body=f.read() return json.loads(body) - elif issintance(value,str): + elif isinstance(value,str): body=value return json.loads(body) raise Exception(f"{value} not supported") +# /////////////////////////////////////////////////////////////////// +def DownloadObject(src,dst, aws_access_key_id='any',aws_secret_access_key='any', endpoint_url='https://maritime.sealstorage.io/api/v0/s3'): + if os.path.isfile(dst): return + print(f"DownloadObject {src} -> {dst}") + assert(src.startswith("s3://")) + session = boto3.session.Session() + s3_client = session.client( + service_name='s3', + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + endpoint_url=endpoint_url, + verify=False + ) + __bucket,__name=src[len("s3://"):].split("/",maxsplit=1) + os.makedirs(os.path.dirname(dst),exist_ok=True) + s3_client.download_file(__bucket,__name,dst) + # /////////////////////////////////////////////////////////////////// def DownloadFile(url, cache_dir=os.environ.get("VISUS_CACHE",os.path.expanduser("~/visus/cache")),verify=False): @@ -441,17 +465,16 @@ def ConvertDataForRendering(data, normalize_float=True): 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 + colors=getattr(bokeh.palettes,name,None) + if colors and len(colors)>=256: + ret[name]=colors + + if True: + for name in sorted(colorcet.palette): + if name.startswith("CET_") or len(name)>20: continue # stupid criteria but otherwise I am getting too much palettes + colors=getattr(colorcet.palette,name,None) + if colors and len(colors)>=256: + ret[name]=colors return ret # //////////////////////////////////////////////////////// @@ -463,9 +486,6 @@ def ShowInfoNotification(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 @@ -482,3 +502,10 @@ def AddPeriodicCallback(fn, period, name="AddPeriodicCallback"): #else: return pn.state.add_periodic_callback(lambda fn=fn: CallPeriodicFunction(fn), period=period) + + +# ////////////////////////////////////////////////////// +def GetShortUrl(url): + request_url = 'http://tinyurl.com/api-create.php?' + urllib.parse.urlencode({'url':url}) + with contextlib.closing(urllib.request.urlopen(request_url)) as response: + return response.read().decode('utf-8 ') diff --git a/examples/python/test-pyvista.py b/test/test-pyvista.py similarity index 100% rename from examples/python/test-pyvista.py rename to test/test-pyvista.py diff --git a/examples/python/test-vtkvolume.py b/test/test-vtkvolume.py similarity index 100% rename from examples/python/test-vtkvolume.py rename to test/test-vtkvolume.py