Repository: ec-jrc/pyPoseidon (language: python)

Issue:
Mismatch face<->side, edge<->node?
https://github.com/ec-jrc/pyPoseidon/blob/06cdcaa21f839f8531e067e9c633cc292fdbe30f/pyposeidon/schism.py#L1184for 

I think the code should look like this:

``` python
    for key in tfs[0].variables:
        if 'nSCHISM_hgrid_face' in tfs[0][key].dims :
            r = combine_(key,tfs,melems,'nSCHISM_hgrid_face')
            el.append(r)
        elif 'nSCHISM_hgrid_node' in tfs[0][key].dims :
            r = combine_(key,tfs,mnodes,'nSCHISM_hgrid_node')
            node.append(r)
        elif 'nSCHISM_hgrid_edge' in tfs[0][key].dims :
            r = combine_(key,tfs,msides,'nSCHISM_hgrid_edge')
            side.append(r)
        elif len(tfs[0][key].dims) == 1:
            single.append(tfs[0][key])
```


Discussion / hints:

Fix patch:
diff --git a/environment.yml b/environment.yml
index fa90620e..6985f5d5 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,16 +4,18 @@ channels:
   - conda-forge
 
 dependencies:
-  - python =3.8
+  - python=3.8
   - mayavi
   - pyresample
 # - pydap
   - dask
   - pygeos >=0.6
   - geoviews
-  - geopandas
+  - geopandas>=0.10.1
   - rasterio
-  - jigsaw =0.9.12
+  - jigsaw=0.9.12
+  - gmsh>=4.8.4
+  - python-gmsh
   - hvplot
   - descartes
   - netCDF4
@@ -38,5 +40,4 @@ dependencies:
   - mkdocs-material
   - pip
   - pip:
-    - gmsh
-    - "--editable=git+https://github.com/pydap/pydap.git@3f6aa190c59e3bbc6c834377a61579b20275ff69#egg=Pydap"
+    - "git+https://github.com/pydap/pydap.git@3f6aa190c59e3bbc6c834377a61579b20275ff69#egg=Pydap"
diff --git a/poetry.lock b/poetry.lock
index 6e984c4e..bdca3c10 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -9,14 +9,6 @@ python-versions = "*"
 [package.extras]
 test = ["pytest (>=3.0)", "pytest-cov", "pydocstyle", "coveralls"]
 
-[[package]]
-name = "apipkg"
-version = "1.5"
-description = "apipkg: namespace control and lazy-import mechanism"
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-
 [[package]]
 name = "appdirs"
 version = "1.4.4"
@@ -54,17 +46,17 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "attrs"
-version = "20.3.0"
+version = "21.2.0"
 description = "Classes Without Boilerplate"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "furo", "sphinx", "pre-commit"]
-docs = ["furo", "sphinx", "zope.interface"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit"]
+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface"]
+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins"]
 
 [[package]]
 name = "backcall"
@@ -76,14 +68,14 @@ python-versions = "*"
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.9.3"
+version = "4.10.0"
 description = "Screen-scraping library"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">3.0.0"
 
 [package.dependencies]
-soupsieve = {version = ">1.2", markers = "python_version >= \"3.0\""}
+soupsieve = ">1.2"
 
 [package.extras]
 html5lib = ["html5lib"]
@@ -113,21 +105,20 @@ d = ["aiohttp (>=3.3.2)", "aiohttp-cors"]
 
 [[package]]
 name = "bokeh"
-version = "2.3.1"
+version = "2.4.0"
 description = "Interactive plots and applications in the browser from Python"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
-Jinja2 = ">=2.7"
+Jinja2 = ">=2.9"
 numpy = ">=1.11.3"
 packaging = ">=16.8"
 pillow = ">=7.1.0"
-python-dateutil = ">=2.1"
 PyYAML = ">=3.10"
 tornado = ">=5.1"
-typing_extensions = ">=3.7.4"
+typing-extensions = ">=3.10.0"
 
 [[package]]
 name = "branca"
@@ -177,7 +168,7 @@ tests = ["flufl.lock", "pytest (>=3.0.0)"]
 
 [[package]]
 name = "certifi"
-version = "2020.12.5"
+version = "2021.10.8"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
@@ -185,7 +176,7 @@ python-versions = "*"
 
 [[package]]
 name = "cffi"
-version = "1.14.5"
+version = "1.14.6"
 description = "Foreign Function Interface for Python calling C code."
 category = "main"
 optional = false
@@ -196,25 +187,25 @@ pycparser = "*"
 
 [[package]]
 name = "cfgrib"
-version = "0.9.9.0"
+version = "0.9.9.1"
 description = "Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes."
 category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [package.dependencies]
 attrs = ">=19.2"
 click = "*"
-eccodes = "*"
+eccodes = ">=0.9.8"
 numpy = "*"
 
 [package.extras]
-tests = ["dask", "flake8", "pytest", "pytest-cov", "scipy", "xarray (>=0.12.0)"]
-xarray = ["xarray (>=0.12.0)"]
+tests = ["dask", "flake8", "pytest", "pytest-cov", "scipy", "xarray (>=0.15)"]
+xarray = ["xarray (>=0.15)"]
 
 [[package]]
 name = "cftime"
-version = "1.4.1"
+version = "1.5.1"
 description = "Time-handling functionality from netcdf4-python"
 category = "main"
 optional = false
@@ -224,20 +215,27 @@ python-versions = "*"
 numpy = "*"
 
 [[package]]
-name = "chardet"
-version = "4.0.0"
-description = "Universal encoding detector for Python 2 and 3"
+name = "charset-normalizer"
+version = "2.0.6"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.5.0"
+
+[package.extras]
+unicode_backport = ["unicodedata2"]
 
 [[package]]
 name = "click"
-version = "7.1.2"
+version = "8.0.2"
 description = "Composable command line interface toolkit"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.6"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
 
 [[package]]
 name = "click-plugins"
@@ -255,25 +253,25 @@ dev = ["pytest (>=3.6)", "pytest-cov", "wheel", "coveralls"]
 
 [[package]]
 name = "cligj"
-version = "0.7.1"
+version = "0.7.2"
 description = "Click params for commmand line interfaces to GeoJSON"
 category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, <4"
 
 [package.dependencies]
-click = ">=4.0,<8"
+click = ">=4.0"
 
 [package.extras]
 test = ["pytest-cov"]
 
 [[package]]
 name = "cloudpickle"
-version = "1.6.0"
+version = "2.0.0"
 description = "Extended pickling support for Python objects"
 category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [[package]]
 name = "colorama"
@@ -327,14 +325,14 @@ six = "*"
 
 [[package]]
 name = "coverage"
-version = "5.5"
+version = "6.0.1"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
+python-versions = ">=3.6"
 
 [package.extras]
-toml = ["toml"]
+toml = ["tomli"]
 
 [[package]]
 name = "cycler"
@@ -349,7 +347,7 @@ six = "*"
 
 [[package]]
 name = "dask"
-version = "2021.4.1"
+version = "2021.9.1"
 description = "Parallel PyData with Task Scheduling"
 category = "main"
 optional = false
@@ -358,20 +356,22 @@ python-versions = ">=3.7"
 [package.dependencies]
 bokeh = {version = ">=1.0.0,<2.0.0 || >2.0.0", optional = true, markers = "extra == \"complete\""}
 cloudpickle = ">=1.1.1"
-distributed = {version = ">=2021.04.1", optional = true, markers = "extra == \"complete\""}
+distributed = {version = "2021.09.1", optional = true, markers = "extra == \"complete\""}
 fsspec = ">=0.6.0"
-numpy = {version = ">=1.16", optional = true, markers = "extra == \"complete\""}
-pandas = {version = ">=0.25.0", optional = true, markers = "extra == \"complete\""}
+jinja2 = {version = "*", optional = true, markers = "extra == \"complete\""}
+numpy = {version = ">=1.18", optional = true, markers = "extra == \"complete\""}
+packaging = ">=20.0"
+pandas = {version = ">=1.0", optional = true, markers = "extra == \"complete\""}
 partd = ">=0.3.10"
 pyyaml = "*"
 toolz = ">=0.8.2"
 
 [package.extras]
-array = ["numpy (>=1.16)"]
-complete = ["bokeh (>=1.0.0,!=2.0.0)", "distributed (>=2021.04.1)", "numpy (>=1.16)", "pandas (>=0.25.0)"]
-dataframe = ["numpy (>=1.16)", "pandas (>=0.25.0)"]
-diagnostics = ["bokeh (>=1.0.0,!=2.0.0)"]
-distributed = ["distributed (>=2021.04.1)"]
+array = ["numpy (>=1.18)"]
+complete = ["bokeh (>=1.0.0,!=2.0.0)", "distributed (==2021.09.1)", "jinja2", "numpy (>=1.18)", "pandas (>=1.0)"]
+dataframe = ["numpy (>=1.18)", "pandas (>=1.0)"]
+diagnostics = ["bokeh (>=1.0.0,!=2.0.0)", "jinja2"]
+distributed = ["distributed (==2021.09.1)"]
 test = ["pytest", "pytest-rerunfailures", "pytest-xdist"]
 
 [[package]]
@@ -427,7 +427,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*"
 
 [[package]]
 name = "distributed"
-version = "2021.4.1"
+version = "2021.9.1"
 description = "Distributed scheduler for Dask"
 category = "main"
 optional = false
@@ -436,7 +436,8 @@ python-versions = ">=3.7"
 [package.dependencies]
 click = ">=6.6"
 cloudpickle = ">=1.5.0"
-dask = ">=2021.03.0"
+dask = "2021.09.1"
+jinja2 = "*"
 msgpack = ">=0.6.0"
 psutil = ">=5.0"
 pyyaml = "*"
@@ -459,7 +460,7 @@ python-versions = "*"
 
 [[package]]
 name = "eccodes"
-version = "1.3.2"
+version = "1.3.3"
 description = "Python interface to the ecCodes GRIB and BUFR decoder/encoder"
 category = "main"
 optional = false
@@ -473,21 +474,18 @@ numpy = "*"
 
 [[package]]
 name = "execnet"
-version = "1.8.0"
+version = "1.9.0"
 description = "execnet: rapid multi-Python deployment"
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
-[package.dependencies]
-apipkg = ">=1.4"
-
 [package.extras]
 testing = ["pre-commit"]
 
 [[package]]
 name = "f90nml"
-version = "1.2"
+version = "1.3.1"
 description = "Fortran 90 namelist parser"
 category = "main"
 optional = false
@@ -498,7 +496,7 @@ yaml = ["pyyaml"]
 
 [[package]]
 name = "findlibs"
-version = "0.0.1"
+version = "0.0.2"
 description = "A packages to search for shared libraries on various platforms"
 category = "main"
 optional = false
@@ -506,7 +504,7 @@ python-versions = "*"
 
 [[package]]
 name = "fiona"
-version = "1.8.19"
+version = "1.8.20"
 description = "Fiona reads and writes spatial data files"
 category = "main"
 optional = false
@@ -515,21 +513,21 @@ python-versions = "*"
 [package.dependencies]
 attrs = ">=17"
 certifi = "*"
-click = ">=4.0,<8"
+click = ">=4.0"
 click-plugins = ">=1.0"
 cligj = ">=0.5"
 munch = "*"
 six = ">=1.7"
 
 [package.extras]
-all = ["pytest (>=3)", "pytest-cov", "shapely", "boto3 (>=1.2.4)", "mock"]
+all = ["pytest (>=3)", "boto3 (>=1.2.4)", "pytest-cov", "shapely", "mock"]
 calc = ["shapely"]
 s3 = ["boto3 (>=1.2.4)"]
 test = ["pytest (>=3)", "pytest-cov", "boto3 (>=1.2.4)", "mock"]
 
 [[package]]
 name = "fsspec"
-version = "2021.4.0"
+version = "2021.10.0"
 description = "File-system specification"
 category = "main"
 optional = false
@@ -538,62 +536,73 @@ python-versions = ">=3.6"
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dropbox = ["dropboxdrivefs", "requests", "dropbox"]
 entrypoints = ["importlib-metadata"]
+fuse = ["fusepy"]
 gcs = ["gcsfs"]
 git = ["pygit2"]
 github = ["requests"]
 gs = ["gcsfs"]
+gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
 http = ["requests", "aiohttp"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
 s3 = ["s3fs"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
 
-[[package]]
-name = "future"
-version = "0.18.2"
-description = "Clean single-source support for Python 3 and 2"
-category = "dev"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-
 [[package]]
 name = "geopandas"
-version = "0.8.2"
+version = "0.10.1"
 description = "Geographic pandas extensions"
 category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.7"
 
 [package.dependencies]
-fiona = "*"
-pandas = ">=0.23.0"
+fiona = ">=1.8"
+pandas = ">=0.25.0"
 pyproj = ">=2.2.0"
-shapely = "*"
+shapely = ">=1.6"
 
 [[package]]
 name = "geoviews"
-version = "1.9.1"
+version = "1.9.2"
 description = "GeoViews is a Python library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
-bokeh = ">=2.3.0,<2.4"
-cartopy = ">=0.17.0"
+bokeh = ">=2.4.0,<2.5"
+cartopy = ">=0.18.0"
 holoviews = ">=1.14.2"
 
 [package.extras]
-all = ["codecov", "datashader", "flake8", "gdal", "geopandas", "ipython (>=7.0)", "iris", "jupyter", "matplotlib (>2.2)", "mock", "nbsite (>=0.6.1)", "nbsmoke (>=0.2.0)", "netcdf4", "nose", "pandas", "pyct", "pytest", "pytest-cov", "scipy", "selenium", "shapely", "sphinx-holoviz-theme", "xarray", "xesmf"]
-build = ["param (>=1.9.2)", "pyct (>=0.4.4)", "bokeh (>=2.3.0,<2.4.0)", "pyviz-comms (>=0.6.0)"]
-doc = ["datashader", "geopandas", "gdal", "netcdf4", "jupyter", "matplotlib (>2.2)", "pandas", "pyct", "scipy", "shapely", "xarray", "iris", "xesmf", "mock", "nbsite (>=0.6.1)", "sphinx-holoviz-theme", "selenium"]
-examples_extra = ["datashader", "geopandas", "gdal", "netcdf4", "jupyter", "matplotlib (>2.2)", "pandas", "pyct", "scipy", "shapely", "xarray", "iris", "xesmf", "mock"]
-recommended = ["datashader", "geopandas", "gdal", "netcdf4", "jupyter", "matplotlib (>2.2)", "pandas", "pyct", "scipy", "shapely", "xarray"]
-tests = ["pytest-cov", "codecov", "flake8", "nbsmoke (>=0.2.0)", "ipython (>=7.0)", "nose", "pytest"]
+all = ["cartopy (>=0.20.0)", "codecov", "datashader", "fiona", "flake8", "geopandas-base", "graphviz", "iris", "jupyter", "lxml", "matplotlib (>2.2)", "mock", "nbsite (>=0.7.1)", "nbsmoke (>=0.2.0)", "netcdf4", "pandas", "pooch", "pyct", "pytest", "pytest-cov", "scipy", "selenium", "shapely", "xarray", "xesmf"]
+build = ["param (>=1.9.2)", "pyct (>=0.4.4)", "bokeh (>=2.4,<2.5)", "pyviz-comms (>=0.6.0)"]
+doc = ["datashader", "geopandas-base", "netcdf4", "jupyter", "matplotlib (>2.2)", "pandas", "pyct", "scipy", "shapely", "xarray", "iris", "xesmf", "mock", "fiona", "nbsite (>=0.7.1)", "cartopy (>=0.20.0)", "graphviz", "lxml", "selenium", "pooch"]
+examples_extra = ["datashader", "geopandas-base", "netcdf4", "jupyter", "matplotlib (>2.2)", "pandas", "pyct", "scipy", "shapely", "xarray", "iris", "xesmf", "mock", "fiona"]
+recommended = ["datashader", "geopandas-base", "netcdf4", "jupyter", "matplotlib (>2.2)", "pandas", "pyct", "scipy", "shapely", "xarray"]
+tests = ["pytest-cov", "codecov", "flake8", "nbsmoke (>=0.2.0)", "pytest", "fiona"]
+
+[[package]]
+name = "ghp-import"
+version = "2.0.2"
+description = "Copy your docs directly to the gh-pages branch."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+python-dateutil = ">=2.8.1"
+
+[package.extras]
+dev = ["twine", "markdown", "flake8", "wheel"]
 
 [[package]]
 name = "gmsh"
@@ -613,7 +622,7 @@ python-versions = "*"
 
 [[package]]
 name = "holoviews"
-version = "1.14.3"
+version = "1.14.6"
 description = "Stop plotting your data - annotate your data and let it visualize itself."
 category = "main"
 optional = false
@@ -622,27 +631,27 @@ python-versions = ">=2.7"
 [package.dependencies]
 colorcet = "*"
 numpy = ">=1.0"
-pandas = "*"
+pandas = ">=0.20.0"
 panel = ">=0.8.0"
 param = ">=1.9.3,<2.0"
 pyviz-comms = ">=0.7.4"
 
 [package.extras]
-all = ["ipython (>=5.4.0)", "datashader (>=0.11.1)", "keyring", "mock", "notebook", "matplotlib (>=2.2)", "nbsmoke (>=0.2.0)", "pillow", "dask", "coveralls", "bokeh (>=1.1.0)", "plotly (>=4.0)", "streamz (>=0.5.0)", "shapely", "scikit-image", "dash (>=1.16)", "ffmpeg", "xarray (>=0.10.4,<0.17.0)", "deepdiff", "twine", "matplotlib (>=2.2,<3.1)", "networkx", "scipy", "nose", "flake8 (==3.6.0)", "awscli", "pyarrow (<1.0)", "ibis-framework (>=1.3)", "nbconvert (<6)", "cftime", "rfc3986", "nbconvert (==5.3.1)", "jsonschema (==2.6.0)", "netcdf4", "path.py", "ipython (==5.4.1)", "spatialpandas"]
-basic_tests = ["nose", "mock", "flake8 (==3.6.0)", "coveralls", "path.py", "matplotlib (>=2.2,<3.1)", "nbsmoke (>=0.2.0)", "nbconvert (<6)", "twine", "rfc3986", "keyring", "matplotlib (>=2.1)", "bokeh (>=1.1.0)", "pandas", "ipython (>=5.4.0)", "notebook"]
-build = ["param (>=1.7.0)", "setuptools (>=30.3.0)", "pyct (>=0.4.4)", "python (<3.8)"]
-doc = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=2.2)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4,<0.17.0)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "spatialpandas", "pyarrow (<1.0)", "ibis-framework (>=1.3)", "nbsite (>=0.6.8a36)", "sphinx", "sphinx-holoviz-theme", "mpl-sample-data (>=3.1.3)", "awscli", "pscript", "graphviz", "bokeh (>2.2)", "nbconvert (<6.0)"]
-examples = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=2.2)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4,<0.17.0)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "spatialpandas", "pyarrow (<1.0)", "ibis-framework (>=1.3)"]
-extras = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=2.2)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4,<0.17.0)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "spatialpandas", "pyarrow (<1.0)", "ibis-framework (>=1.3)", "pscript (==0.7.1)"]
-nbtests = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=2.2)", "bokeh (>=1.1.0)", "nose", "awscli", "deepdiff", "nbconvert (==5.3.1)", "jsonschema (==2.6.0)", "ipython (==5.4.1)"]
+all = ["deepdiff", "ipython (>=5.4.0)", "xarray (>=0.10.4)", "scipy", "awscli", "scikit-image", "matplotlib (>=3)", "pyarrow", "coveralls", "rfc3986", "flake8", "bokeh (>=1.1.0)", "pillow", "networkx", "shapely", "notebook", "datashader (>=0.11.1)", "nbsmoke (>=0.2.0)", "streamz (>=0.5.0)", "nose", "nbconvert (<6)", "ibis-framework (>=1.3)", "path.py", "keyring", "cftime", "twine", "ffmpeg", "netcdf4", "dask", "plotly (>=4.0)", "mock", "dash (>=1.16)"]
+basic_tests = ["nose", "mock", "flake8", "coveralls", "path.py", "matplotlib (>=3)", "nbsmoke (>=0.2.0)", "nbconvert (<6)", "twine", "rfc3986", "keyring", "bokeh (>=1.1.0)", "pandas", "ipython (>=5.4.0)", "notebook"]
+build = ["param (>=1.7.0)", "setuptools (>=30.3.0)", "pyct (>=0.4.4)"]
+doc = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=3)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "pyarrow", "ibis-framework (>=1.3)", "nbsite (>=0.6.8a36)", "sphinx", "sphinx-holoviz-theme", "mpl-sample-data (>=3.1.3)", "awscli", "pscript", "graphviz", "bokeh (>2.2)"]
+examples = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=3)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "pyarrow", "ibis-framework (>=1.3)"]
+extras = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=3)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "pyarrow", "ibis-framework (>=1.3)", "pscript (==0.7.1)"]
+nbtests = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=3)", "bokeh (>=1.1.0)", "nose", "awscli", "deepdiff"]
 notebook = ["ipython (>=5.4.0)", "notebook"]
-recommended = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=2.2)", "bokeh (>=1.1.0)"]
-tests = ["nose", "mock", "flake8 (==3.6.0)", "coveralls", "path.py", "matplotlib (>=2.2,<3.1)", "nbsmoke (>=0.2.0)", "nbconvert (<6)", "twine", "rfc3986", "keyring"]
-unit_tests = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=2.2)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4,<0.17.0)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "spatialpandas", "pyarrow (<1.0)", "ibis-framework (>=1.3)", "nose", "mock", "flake8 (==3.6.0)", "coveralls", "path.py", "matplotlib (>=2.2,<3.1)", "nbsmoke (>=0.2.0)", "nbconvert (<6)", "twine", "rfc3986", "keyring"]
+recommended = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=3)", "bokeh (>=1.1.0)"]
+tests = ["nose", "mock", "flake8", "coveralls", "path.py", "matplotlib (>=3)", "nbsmoke (>=0.2.0)", "nbconvert (<6)", "twine", "rfc3986", "keyring"]
+unit_tests = ["ipython (>=5.4.0)", "notebook", "matplotlib (>=3)", "bokeh (>=1.1.0)", "networkx", "pillow", "xarray (>=0.10.4)", "plotly (>=4.0)", "dash (>=1.16)", "streamz (>=0.5.0)", "datashader (>=0.11.1)", "ffmpeg", "cftime", "netcdf4", "dask", "scipy", "shapely", "scikit-image", "pyarrow", "ibis-framework (>=1.3)", "nose", "mock", "flake8", "coveralls", "path.py", "nbsmoke (>=0.2.0)", "nbconvert (<6)", "twine", "rfc3986", "keyring"]
 
 [[package]]
 name = "hvplot"
-version = "0.7.1"
+version = "0.7.3"
 description = "A high-level plotting API for the PyData ecosystem built on HoloViews."
 category = "main"
 optional = false
@@ -656,20 +665,19 @@ numpy = ">=1.15"
 pandas = "*"
 
 [package.extras]
-all = ["coveralls", "dask", "datashader (>=0.6.5)", "flake8", "geopandas", "geoviews (>=1.6.0)", "intake", "intake-parquet", "intake-xarray", "nbsite (>=0.5.1)", "nbsmoke (>=0.2.0)", "networkx", "nose", "notebook (>=5.4)", "panel", "param (>=1.6.1)", "parameterized", "pillow", "pyct (>=0.4.4)", "pyepsg", "pygraphviz", "pytest", "python-snappy", "rasterio", "s3fs", "scikit-image", "scipy", "selenium", "setuptools", "spatialpandas", "sphinx-holoviz-theme", "streamz (>=0.3.0)", "tornado (<6.0)", "xarray (<0.17.0)"]
+all = ["coveralls", "dask", "datashader (>=0.6.5)", "flake8", "geopandas", "geoviews (>=1.6.0)", "intake", "intake-parquet", "intake-xarray", "keyring", "nbsite (>=0.5.1)", "nbsmoke (>=0.2.0)", "networkx", "nose", "notebook (>=5.4)", "numba (>=0.51.0)", "numpy (>=1.7)", "param (>=1.6.1)", "parameterized", "pillow", "pyct (>=0.4.4)", "pyepsg", "pytest", "python-snappy", "rasterio", "rfc3986", "s3fs", "scikit-image", "scipy", "selenium", "setuptools", "spatialpandas", "streamz (>=0.3.0)", "twine", "xarray"]
 build = ["param (>=1.6.1)", "pyct (>=0.4.4)", "setuptools"]
-doc = ["geoviews (>=1.6.0)", "panel", "geopandas", "xarray (<0.17.0)", "networkx", "streamz (>=0.3.0)", "intake", "intake-parquet", "intake-xarray", "dask", "datashader (>=0.6.5)", "notebook (>=5.4)", "rasterio", "s3fs", "scipy", "pillow", "selenium", "spatialpandas", "scikit-image", "python-snappy", "pyepsg", "pygraphviz", "nbsite (>=0.5.1)", "sphinx-holoviz-theme", "tornado (<6.0)"]
-examples = ["geoviews (>=1.6.0)", "panel", "geopandas", "xarray (<0.17.0)", "networkx", "streamz (>=0.3.0)", "intake", "intake-parquet", "intake-xarray", "dask", "datashader (>=0.6.5)", "notebook (>=5.4)", "rasterio", "s3fs", "scipy", "pillow", "selenium", "spatialpandas", "scikit-image", "python-snappy", "pyepsg"]
-examples_extra = ["geoviews (>=1.6.0)", "panel", "geopandas", "xarray (<0.17.0)", "networkx", "streamz (>=0.3.0)", "intake", "intake-parquet", "intake-xarray", "dask", "datashader (>=0.6.5)", "notebook (>=5.4)", "rasterio", "s3fs", "scipy", "pillow", "selenium", "spatialpandas", "scikit-image", "python-snappy", "pyepsg", "pygraphviz"]
-tests = ["coveralls", "nose", "flake8", "parameterized", "pytest", "nbsmoke (>=0.2.0)"]
+doc = ["geoviews (>=1.6.0)", "numba (>=0.51.0)", "geopandas", "xarray", "networkx", "streamz (>=0.3.0)", "intake", "intake-parquet", "intake-xarray", "dask", "datashader (>=0.6.5)", "notebook (>=5.4)", "rasterio", "s3fs", "scipy", "pillow", "selenium", "spatialpandas", "scikit-image", "python-snappy", "pyepsg", "nbsite (>=0.5.1)"]
+examples = ["geoviews (>=1.6.0)", "numba (>=0.51.0)", "geopandas", "xarray", "networkx", "streamz (>=0.3.0)", "intake", "intake-parquet", "intake-xarray", "dask", "datashader (>=0.6.5)", "notebook (>=5.4)", "rasterio", "s3fs", "scipy", "pillow", "selenium", "spatialpandas", "scikit-image", "python-snappy", "pyepsg"]
+tests = ["coveralls", "nose", "flake8", "parameterized", "pytest", "nbsmoke (>=0.2.0)", "twine", "rfc3986", "keyring", "numpy (>=1.7)"]
 
 [[package]]
 name = "idna"
-version = "2.10"
+version = "3.2"
 description = "Internationalized Domain Names in Applications (IDNA)"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.5"
 
 [[package]]
 name = "imageio"
@@ -692,7 +700,7 @@ itk = ["itk"]
 
 [[package]]
 name = "imageio-ffmpeg"
-version = "0.4.3"
+version = "0.4.5"
 description = "FFMPEG wrapper for Python"
 category = "main"
 optional = false
@@ -700,7 +708,7 @@ python-versions = ">=3.4"
 
 [[package]]
 name = "importlib-metadata"
-version = "4.0.1"
+version = "4.8.1"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
@@ -712,7 +720,8 @@ zipp = ">=0.5"
 
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
+perf = ["ipython"]
+testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
 
 [[package]]
 name = "iniconfig"
@@ -724,7 +733,7 @@ python-versions = "*"
 
 [[package]]
 name = "ipython"
-version = "7.22.0"
+version = "7.28.0"
 description = "IPython: Productive Interactive Computing"
 category = "main"
 optional = false
@@ -736,6 +745,7 @@ backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
 jedi = ">=0.16"
+matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
 pickleshare = "*"
 prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
@@ -743,7 +753,7 @@ pygments = "*"
 traitlets = ">=4.2"
 
 [package.extras]
-all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.16)", "pygments", "qtconsole", "requests", "testpath"]
+all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.17)", "pygments", "qtconsole", "requests", "testpath"]
 doc = ["Sphinx (>=1.3)"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
@@ -751,15 +761,7 @@ nbformat = ["nbformat"]
 notebook = ["notebook", "ipywidgets"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.16)"]
-
-[[package]]
-name = "ipython-genutils"
-version = "0.2.0"
-description = "Vestigial utilities from IPython"
-category = "main"
-optional = false
-python-versions = "*"
+test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.17)"]
 
 [[package]]
 name = "jedi"
@@ -778,45 +780,25 @@ testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<6.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "2.11.3"
+version = "3.0.2"
 description = "A very fast and expressive template engine."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
-MarkupSafe = ">=0.23"
+MarkupSafe = ">=2.0"
 
 [package.extras]
-i18n = ["Babel (>=0.8)"]
-
-[[package]]
-name = "joblib"
-version = "1.0.1"
-description = "Lightweight pipelining with Python functions"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
+i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "kiwisolver"
-version = "1.3.1"
+version = "1.3.2"
 description = "A fast implementation of the Cassowary constraint solver"
 category = "main"
 optional = false
-python-versions = ">=3.6"
-
-[[package]]
-name = "livereload"
-version = "2.6.3"
-description = "Python LiveReload is an awesome tool for web developers"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-six = "*"
-tornado = {version = "*", markers = "python_version > \"2.7\""}
+python-versions = ">=3.7"
 
 [[package]]
 name = "llvmlite"
@@ -834,22 +816,6 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
-[[package]]
-name = "lunr"
-version = "0.5.8"
-description = "A Python implementation of Lunr.js"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-future = ">=0.16.0"
-nltk = {version = ">=3.2.5", optional = true, markers = "python_version > \"2.7\" and extra == \"languages\""}
-six = ">=1.11.0"
-
-[package.extras]
-languages = ["nltk (>=3.2.5,<3.5)", "nltk (>=3.2.5)"]
-
 [[package]]
 name = "markdown"
 version = "3.3.4"
@@ -866,15 +832,15 @@ testing = ["coverage", "pyyaml"]
 
 [[package]]
 name = "markupsafe"
-version = "1.1.1"
+version = "2.0.1"
 description = "Safely add untrusted strings to HTML/XML markup."
 category = "main"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*"
+python-versions = ">=3.6"
 
 [[package]]
 name = "matplotlib"
-version = "3.4.1"
+version = "3.4.3"
 description = "Python plotting package"
 category = "main"
 optional = false
@@ -888,26 +854,51 @@ pillow = ">=6.2.0"
 pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
 
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.3"
+description = "Inline Matplotlib backend for Jupyter"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+traitlets = "*"
+
+[[package]]
+name = "mergedeep"
+version = "1.3.4"
+description = "A deep merge function for 🐍."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "mkdocs"
-version = "1.1.2"
+version = "1.2.2"
 description = "Project documentation with Markdown."
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [package.dependencies]
 click = ">=3.3"
+ghp-import = ">=1.0"
+importlib-metadata = ">=3.10"
 Jinja2 = ">=2.10.1"
-livereload = ">=2.5.1"
-lunr = {version = "0.5.8", extras = ["languages"]}
 Markdown = ">=3.2.1"
+mergedeep = ">=1.3.4"
+packaging = ">=20.5"
 PyYAML = ">=3.10"
-tornado = ">=5.0"
+pyyaml-env-tag = ">=0.1"
+watchdog = ">=2.0"
+
+[package.extras]
+i18n = ["babel (>=2.9.0)"]
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "0.1.1"
+version = "0.2.1"
 description = "Automatically link across pages in MkDocs."
 category = "dev"
 optional = false
@@ -919,7 +910,7 @@ mkdocs = ">=1.1,<2.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "7.1.3"
+version = "7.3.0"
 description = "A Material Design theme for MkDocs"
 category = "dev"
 optional = false
@@ -927,36 +918,33 @@ python-versions = "*"
 
 [package.dependencies]
 markdown = ">=3.2"
-mkdocs = ">=1.1"
+mkdocs = ">=1.2.2"
 mkdocs-material-extensions = ">=1.0"
 Pygments = ">=2.4"
 pymdown-extensions = ">=7.0"
 
 [[package]]
 name = "mkdocs-material-extensions"
-version = "1.0.1"
+version = "1.0.3"
 description = "Extension pack for Python Markdown."
 category = "dev"
 optional = false
-python-versions = ">=3.5"
-
-[package.dependencies]
-mkdocs-material = ">=5.0.0"
+python-versions = ">=3.6"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.15.0"
+version = "0.15.2"
 description = "Automatic documentation from sources, for MkDocs."
 category = "dev"
 optional = false
 python-versions = ">=3.6,<4.0"
 
 [package.dependencies]
-Jinja2 = ">=2.11,<3.0"
+Jinja2 = ">=2.11.1,<4.0"
 Markdown = ">=3.3,<4.0"
-MarkupSafe = ">=1.1,<2.0"
-mkdocs = ">=1.1,<2.0"
-mkdocs-autorefs = ">=0.1,<0.2"
+MarkupSafe = ">=1.1,<3.0"
+mkdocs = ">=1.1.1,<2.0.0"
+mkdocs-autorefs = ">=0.1,<0.3"
 pymdown-extensions = ">=6.3,<9.0"
 pytkdocs = ">=0.2.0,<0.12.0"
 
@@ -1029,7 +1017,7 @@ python-versions = "*"
 
 [[package]]
 name = "netcdf4"
-version = "1.5.6"
+version = "1.5.7"
 description = "Provides an object-oriented python interface to the netCDF version 4 library."
 category = "main"
 optional = false
@@ -1039,28 +1027,6 @@ python-versions = "*"
 cftime = "*"
 numpy = ">=1.9"
 
-[[package]]
-name = "nltk"
-version = "3.6.2"
-description = "Natural Language Toolkit"
-category = "dev"
-optional = false
-python-versions = ">=3.5.*"
-
-[package.dependencies]
-click = "*"
-joblib = "*"
-regex = "*"
-tqdm = "*"
-
-[package.extras]
-all = ["matplotlib", "twython", "scipy", "numpy", "gensim (<4.0.0)", "python-crfsuite", "pyparsing", "scikit-learn", "requests"]
-corenlp = ["requests"]
-machine_learning = ["gensim (<4.0.0)", "numpy", "python-crfsuite", "scikit-learn", "scipy"]
-plot = ["matplotlib"]
-tgrep = ["pyparsing"]
-twitter = ["twython"]
-
 [[package]]
 name = "numba"
 version = "0.53.1"
@@ -1075,19 +1041,19 @@ numpy = ">=1.15"
 
 [[package]]
 name = "numpy"
-version = "1.20.2"
+version = "1.21.2"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.7,<3.11"
 
 [[package]]
 name = "packaging"
-version = "20.9"
+version = "21.0"
 description = "Core utilities for Python packages"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
 pyparsing = ">=2.0.2"
@@ -1135,14 +1101,15 @@ tests = ["flake8", "parameterized", "pytest", "scipy", "nbsmoke (>=0.2.0)", "pyt
 
 [[package]]
 name = "param"
-version = "1.10.1"
+version = "1.11.1"
 description = "Make your Python code clearer and more reliable by declaring Parameters."
 category = "main"
 optional = false
 python-versions = ">=2.7"
 
 [package.extras]
-all = ["flake8", "nose"]
+all = ["flake8", "graphviz", "myst-parser", "myst-nb (==0.12.2)", "nbconvert (<6.0)", "nbsite (>=0.6.1)", "nose", "pydata-sphinx-theme", "pygraphviz"]
+doc = ["pygraphviz", "nbsite (>=0.6.1)", "pydata-sphinx-theme", "myst-parser", "nbconvert (<6.0)", "graphviz", "myst-nb (==0.12.2)"]
 tests = ["nose", "flake8"]
 
 [[package]]
@@ -1174,11 +1141,11 @@ complete = ["numpy (>=1.9.0)", "pandas (>=0.19.0)", "pyzmq", "blosc"]
 
 [[package]]
 name = "pathspec"
-version = "0.8.1"
+version = "0.9.0"
 description = "Utility library for gitignore style pattern matching of file paths."
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
 [[package]]
 name = "pexpect"
@@ -1201,7 +1168,7 @@ python-versions = "*"
 
 [[package]]
 name = "pillow"
-version = "8.2.0"
+version = "8.3.2"
 description = "Python Imaging Library (Fork)"
 category = "main"
 optional = false
@@ -1209,17 +1176,18 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "pluggy"
-version = "0.13.1"
+version = "1.0.0"
 description = "plugin and hook calling mechanisms for python"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
 importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
 
 [package.extras]
 dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "proglog"
@@ -1234,11 +1202,11 @@ tqdm = "*"
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.18"
+version = "3.0.20"
 description = "Library for building powerful interactive command lines in Python"
 category = "main"
 optional = false
-python-versions = ">=3.6.1"
+python-versions = ">=3.6.2"
 
 [package.dependencies]
 wcwidth = "*"
@@ -1346,7 +1314,7 @@ test = ["pytest"]
 
 [[package]]
 name = "pygments"
-version = "2.8.1"
+version = "2.10.0"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "main"
 optional = false
@@ -1365,7 +1333,7 @@ numpy = "*"
 
 [[package]]
 name = "pymdown-extensions"
-version = "8.1.1"
+version = "8.2"
 description = "Extension pack for Python Markdown."
 category = "dev"
 optional = false
@@ -1384,18 +1352,18 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "pyproj"
-version = "3.0.1"
+version = "3.2.1"
 description = "Python interface to PROJ (cartographic projections and coordinate transformations library)"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 certifi = "*"
 
 [[package]]
 name = "pyresample"
-version = "1.19.0"
+version = "1.21.1"
 description = "Geospatial image resampling in Python"
 category = "main"
 optional = false
@@ -1427,7 +1395,7 @@ python-versions = ">= 2.7"
 
 [[package]]
 name = "pytest"
-version = "6.2.3"
+version = "6.2.5"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
@@ -1440,7 +1408,7 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<1.0.0a1"
+pluggy = ">=0.12,<2.0"
 py = ">=1.8.2"
 toml = "*"
 
@@ -1449,7 +1417,7 @@ testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xm
 
 [[package]]
 name = "pytest-cov"
-version = "2.11.1"
+version = "2.12.1"
 description = "Pytest plugin for measuring coverage."
 category = "dev"
 optional = false
@@ -1458,9 +1426,10 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 [package.dependencies]
 coverage = ">=5.2.1"
 pytest = ">=4.6"
+toml = "*"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests (==2.0.2)", "six", "pytest-xdist", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "pytest-forked"
@@ -1476,11 +1445,11 @@ pytest = ">=3.10"
 
 [[package]]
 name = "pytest-xdist"
-version = "2.2.1"
+version = "2.4.0"
 description = "pytest xdist plugin for distributed testing and loop-on-failing modes"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [package.dependencies]
 execnet = ">=1.1"
@@ -1489,11 +1458,12 @@ pytest-forked = "*"
 
 [package.extras]
 psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
 testing = ["filelock"]
 
 [[package]]
 name = "python-dateutil"
-version = "2.8.1"
+version = "2.8.2"
 description = "Extensions to the standard Python datetime module"
 category = "main"
 optional = false
@@ -1520,7 +1490,7 @@ numpy-style = ["docstring_parser (>=0.7.3,<0.8.0)"]
 
 [[package]]
 name = "pytz"
-version = "2021.1"
+version = "2021.3"
 description = "World timezone definitions, modern and historical"
 category = "main"
 optional = false
@@ -1528,7 +1498,7 @@ python-versions = "*"
 
 [[package]]
 name = "pyviz-comms"
-version = "2.0.1"
+version = "2.1.0"
 description = "Bidirectional communication for the HoloViz ecosystem."
 category = "main"
 optional = false
@@ -1538,8 +1508,8 @@ python-versions = "*"
 param = "*"
 
 [package.extras]
-all = ["flake8", "jupyter-packaging (>=0.7.9,<0.8.0)", "jupyterlab (>=3.0,<4.0)", "nose", "setuptools"]
-build = ["setuptools", "jupyterlab (>=3.0,<4.0)", "jupyter-packaging (>=0.7.9,<0.8.0)"]
+all = ["flake8", "jupyter-packaging (>=0.7.9,<0.8.0)", "jupyterlab (>=3.0,<4.0)", "keyring", "nose", "rfc3986", "setuptools", "twine"]
+build = ["setuptools", "jupyterlab (>=3.0,<4.0)", "jupyter-packaging (>=0.7.9,<0.8.0)", "twine", "rfc3986", "keyring"]
 tests = ["flake8", "nose"]
 
 [[package]]
@@ -1550,9 +1520,20 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
+[[package]]
+name = "pyyaml-env-tag"
+version = "0.1"
+description = "A custom YAML tag for referencing environment variables in YAML files. "
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+pyyaml = "*"
+
 [[package]]
 name = "rasterio"
-version = "1.2.3"
+version = "1.2.9"
 description = "Fast and direct raster I/O for use with Numpy and SciPy"
 category = "main"
 optional = false
@@ -1562,14 +1543,14 @@ python-versions = ">=3.6"
 affine = "*"
 attrs = "*"
 certifi = "*"
-click = ">=4.0,<8"
+click = ">=4.0"
 click-plugins = "*"
 cligj = ">=0.5"
 numpy = "*"
 snuggs = ">=1.4.1"
 
 [package.extras]
-all = ["pytest-cov (>=2.2.0)", "ipython (>=2.0)", "matplotlib", "boto3 (>=1.2.4)", "numpydoc", "packaging", "hypothesis", "shapely", "ghp-import", "sphinx-rtd-theme", "sphinx", "pytest (>=2.8.2)"]
+all = ["pytest (>=2.8.2)", "sphinx-rtd-theme", "hypothesis", "packaging", "shapely", "sphinx", "boto3 (>=1.2.4)", "matplotlib", "numpydoc", "ghp-import", "pytest-cov (>=2.2.0)", "ipython (>=2.0)"]
 docs = ["ghp-import", "numpydoc", "sphinx", "sphinx-rtd-theme"]
 ipython = ["ipython (>=2.0)"]
 plot = ["matplotlib"]
@@ -1578,7 +1559,7 @@ test = ["boto3 (>=1.2.4)", "hypothesis", "packaging", "pytest-cov (>=2.2.0)", "p
 
 [[package]]
 name = "regex"
-version = "2021.4.4"
+version = "2021.10.8"
 description = "Alternative regular expression module, to replace re."
 category = "dev"
 optional = false
@@ -1586,25 +1567,25 @@ python-versions = "*"
 
 [[package]]
 name = "requests"
-version = "2.25.1"
+version = "2.26.0"
 description = "Python HTTP for Humans."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
 [package.dependencies]
 certifi = ">=2017.4.17"
-chardet = ">=3.0.2,<5"
-idna = ">=2.5,<3"
+charset-normalizer = {version = ">=2.0.0,<2.1.0", markers = "python_version >= \"3\""}
+idna = {version = ">=2.5,<4", markers = "python_version >= \"3\""}
 urllib3 = ">=1.21.1,<1.27"
 
 [package.extras]
-security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"]
 socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
+use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"]
 
 [[package]]
 name = "scipy"
-version = "1.6.3"
+version = "1.7.1"
 description = "SciPy: Scientific Library for Python"
 category = "main"
 optional = false
@@ -1628,7 +1609,7 @@ vectorized = ["numpy"]
 
 [[package]]
 name = "six"
-version = "1.15.0"
+version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
 category = "main"
 optional = false
@@ -1651,7 +1632,7 @@ test = ["pytest", "hypothesis"]
 
 [[package]]
 name = "sortedcontainers"
-version = "2.3.0"
+version = "2.4.0"
 description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
 category = "main"
 optional = false
@@ -1699,12 +1680,15 @@ python-versions = ">= 3.5"
 
 [[package]]
 name = "tqdm"
-version = "4.60.0"
+version = "4.62.3"
 description = "Fast, Extensible Progress Meter"
 category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
 
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [package.extras]
 dev = ["py-make (>=0.1.0)", "twine", "wheel"]
 notebook = ["ipywidgets (>=6)"]
@@ -1712,15 +1696,12 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.0.5"
+version = "5.1.0"
 description = "Traitlets Python configuration system"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
-[package.dependencies]
-ipython-genutils = "*"
-
 [package.extras]
 test = ["pytest"]
 
@@ -1734,7 +1715,7 @@ python-versions = "*"
 
 [[package]]
 name = "typing-extensions"
-version = "3.7.4.3"
+version = "3.10.0.2"
 description = "Backported and Experimental Type Hints for Python 3.5+"
 category = "main"
 optional = false
@@ -1742,16 +1723,27 @@ python-versions = "*"
 
 [[package]]
 name = "urllib3"
-version = "1.26.4"
+version = "1.26.7"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
 
 [package.extras]
+brotli = ["brotlipy (>=0.6.0)"]
 secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
-brotli = ["brotlipy (>=0.6.0)"]
+
+[[package]]
+name = "watchdog"
+version = "2.1.6"
+description = "Filesystem events monitoring"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "wcwidth"
@@ -1775,21 +1767,21 @@ testing = ["pytest (>=3.1.0)", "coverage", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "xarray"
-version = "0.17.0"
+version = "0.19.0"
 description = "N-D labeled arrays and datasets in Python"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-numpy = ">=1.15"
-pandas = ">=0.25"
+numpy = ">=1.17"
+pandas = ">=1.0"
 
 [package.extras]
 accel = ["scipy", "bottleneck", "numbagg"]
-complete = ["netcdf4", "h5netcdf", "scipy", "pydap", "zarr", "fsspec", "cftime", "rasterio", "cfgrib", "bottleneck", "numbagg", "dask", "matplotlib", "seaborn", "nc-time-axis"]
-docs = ["netcdf4", "h5netcdf", "scipy", "pydap", "zarr", "fsspec", "cftime", "rasterio", "cfgrib", "bottleneck", "numbagg", "dask", "matplotlib", "seaborn", "nc-time-axis", "sphinx-autosummary-accessors", "sphinx-rtd-theme", "ipython", "ipykernel", "jupyter-client", "nbsphinx", "scanpydoc"]
-io = ["netcdf4", "h5netcdf", "scipy", "pydap", "zarr", "fsspec", "cftime", "rasterio", "cfgrib"]
+complete = ["netcdf4", "h5netcdf", "scipy", "pydap", "zarr", "fsspec", "cftime", "rasterio", "cfgrib", "pooch", "bottleneck", "numbagg", "dask", "matplotlib", "seaborn", "nc-time-axis"]
+docs = ["netcdf4", "h5netcdf", "scipy", "pydap", "zarr", "fsspec", "cftime", "rasterio", "cfgrib", "pooch", "bottleneck", "numbagg", "dask", "matplotlib", "seaborn", "nc-time-axis", "sphinx-autosummary-accessors", "sphinx-rtd-theme", "ipython", "ipykernel", "jupyter-client", "nbsphinx", "scanpydoc"]
+io = ["netcdf4", "h5netcdf", "scipy", "pydap", "zarr", "fsspec", "cftime", "rasterio", "cfgrib", "pooch"]
 parallel = ["dask"]
 viz = ["matplotlib", "seaborn", "nc-time-axis"]
 
@@ -1806,7 +1798,7 @@ heapdict = "*"
 
 [[package]]
 name = "zipp"
-version = "3.4.1"
+version = "3.6.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "main"
 optional = false
@@ -1814,22 +1806,18 @@ python-versions = ">=3.6"
 
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=4.6)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "pytest-enabler", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"]
+testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"]
 
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.7, <3.10"
-content-hash = "d9324f0bfeee22de1ac641b520ae500944d1a3434d339ca2697f10a9abd390c0"
+content-hash = "4beac8636b337fa397f535b92484b97bcc0adb3815796e5bee9718dc63670f68"
 
 [metadata.files]
 affine = [
     {file = "affine-2.3.0-py2.py3-none-any.whl", hash = "sha256:34b05b070d954c382e56f02c207a372d8a32621a87653cc30cdd31cd7f65799f"},
     {file = "affine-2.3.0.tar.gz", hash = "sha256:2e045def1aa29e613c42e801a7e10e0b9bacfed1a7c6af0cadf8843530a15102"},
 ]
-apipkg = [
-    {file = "apipkg-1.5-py2.py3-none-any.whl", hash = "sha256:58587dd4dc3daefad0487f6d9ae32b4542b185e1c36db6993290e7c41ca2b47c"},
-    {file = "apipkg-1.5.tar.gz", hash = "sha256:37228cda29411948b422fae072f57e31d3396d2ee1c9783775980ee9c9990af6"},
-]
 appdirs = [
     {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
     {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
@@ -1847,23 +1835,23 @@ atomicwrites = [
     {file = "atomicwrites-1.4.0.tar.gz", hash = "sha256:ae70396ad1a434f9c7046fd2dd196fc04b12f9e91ffb859164193be8b6168a7a"},
 ]
 attrs = [
-    {file = "attrs-20.3.0-py2.py3-none-any.whl", hash = "sha256:31b2eced602aa8423c2aea9c76a724617ed67cf9513173fd3a4f03e3a929c7e6"},
-    {file = "attrs-20.3.0.tar.gz", hash = "sha256:832aa3cde19744e49938b91fea06d69ecb9e649c93ba974535d08ad92164f700"},
+    {file = "attrs-21.2.0-py2.py3-none-any.whl", hash = "sha256:149e90d6d8ac20db7a955ad60cf0e6881a3f20d37096140088356da6c716b0b1"},
+    {file = "attrs-21.2.0.tar.gz", hash = "sha256:ef6aaac3ca6cd92904cdd0d83f629a15f18053ec84e6432106f7a4d04ae4f5fb"},
 ]
 backcall = [
     {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
     {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
 ]
 beautifulsoup4 = [
-    {file = "beautifulsoup4-4.9.3-py2-none-any.whl", hash = "sha256:4c98143716ef1cb40bf7f39a8e3eec8f8b009509e74904ba3a7b315431577e35"},
-    {file = "beautifulsoup4-4.9.3-py3-none-any.whl", hash = "sha256:fff47e031e34ec82bf17e00da8f592fe7de69aeea38be00523c04623c04fb666"},
-    {file = "beautifulsoup4-4.9.3.tar.gz", hash = "sha256:84729e322ad1d5b4d25f805bfa05b902dd96450f43842c4e99067d5e1369eb25"},
+    {file = "beautifulsoup4-4.10.0-py3-none-any.whl", hash = "sha256:9a315ce70049920ea4572a4055bc4bd700c940521d36fc858205ad4fcde149bf"},
+    {file = "beautifulsoup4-4.10.0.tar.gz", hash = "sha256:c23ad23c521d818955a4151a67d81580319d4bf548d3d49f4223ae041ff98891"},
 ]
 black = [
     {file = "black-20.8b1.tar.gz", hash = "sha256:1c02557aa099101b9d21496f8a914e9ed2222ef70336404eeeac8edba836fbea"},
 ]
 bokeh = [
-    {file = "bokeh-2.3.1.tar.gz", hash = "sha256:2f2fb8bab8ca8fa039f48b1f0bed56d3a847c75694ece1d206ffab645ff5beec"},
+    {file = "bokeh-2.4.0-py3-none-any.whl", hash = "sha256:91a7c3b8a42d9275df26f13269859a1add82dc9e5dda316f7476ad76a5a10851"},
+    {file = "bokeh-2.4.0.tar.gz", hash = "sha256:6fa00ed8baab5cca33f4175792c309fa2536eaae7e90abee884501ba8c90fddb"},
 ]
 branca = [
     {file = "branca-0.4.2-py3-none-any.whl", hash = "sha256:62c2e777f074fc1830cd40ba9e650beb941861075980babafead8d97856b1a4b"},
@@ -1881,102 +1869,110 @@ cartopy = [
     {file = "Cartopy-0.19.0.post1.tar.gz", hash = "sha256:4b8b4773a98ed7009fe17d9b6ec87ac3ac62b7d14634d7768c190eadc647d576"},
 ]
 certifi = [
-    {file = "certifi-2020.12.5-py2.py3-none-any.whl", hash = "sha256:719a74fb9e33b9bd44cc7f3a8d94bc35e4049deebe19ba7d8e108280cfd59830"},
-    {file = "certifi-2020.12.5.tar.gz", hash = "sha256:1a4995114262bffbc2413b159f2a1a480c969de6e6eb13ee966d470af86af59c"},
+    {file = "certifi-2021.10.8-py2.py3-none-any.whl", hash = "sha256:d62a0163eb4c2344ac042ab2bdf75399a71a2d8c7d47eac2e2ee91b9d6339569"},
+    {file = "certifi-2021.10.8.tar.gz", hash = "sha256:78884e7c1d4b00ce3cea67b44566851c4343c120abd683433ce934a68ea58872"},
 ]
 cffi = [
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+    {file = "watchdog-2.1.6.tar.gz", hash = "sha256:a36e75df6c767cbf46f61a91c70b3ba71811dfa0aca4a324d9407a06a8b7a2e7"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
@@ -2987,14 +3035,14 @@ webob = [
     {file = "WebOb-1.8.7.tar.gz", hash = "sha256:b64ef5141be559cfade448f044fa45c2260351edcb6a8ef6b7e00c7dcef0c323"},
 ]
 xarray = [
-    {file = "xarray-0.17.0-py3-none-any.whl", hash = "sha256:ce204fb5015c3d382036f7c065c927df5684276976810aef43d78908c3ceb440"},
-    {file = "xarray-0.17.0.tar.gz", hash = "sha256:9c2edad2a4e588f9117c666a4249920b9717fb75703b96998cf65fcd4f60551f"},
+    {file = "xarray-0.19.0-py3-none-any.whl", hash = "sha256:da0f0d7719b5ee95143a34804e3afb82cb8abbe5c10f9750a9dbed156ad30f00"},
+    {file = "xarray-0.19.0.tar.gz", hash = "sha256:3a365ce09127fc841ba88baa63f37ca61376ffe389a6c5e66d52f2c88c23a62b"},
 ]
 zict = [
     {file = "zict-2.0.0-py3-none-any.whl", hash = "sha256:26aa1adda8250a78dfc6a78d200bfb2ea43a34752cf58980bca75dde0ba0c6e9"},
     {file = "zict-2.0.0.tar.gz", hash = "sha256:8e2969797627c8a663575c2fc6fcb53a05e37cdb83ee65f341fc6e0c3d0ced16"},
 ]
 zipp = [
-    {file = "zipp-3.4.1-py3-none-any.whl", hash = "sha256:51cb66cc54621609dd593d1787f286ee42a5c0adbb4b29abea5a63edc3e03098"},
-    {file = "zipp-3.4.1.tar.gz", hash = "sha256:3607921face881ba3e026887d8150cca609d517579abe052ac81fc5aeffdbd76"},
+    {file = "zipp-3.6.0-py3-none-any.whl", hash = "sha256:9fe5ea21568a0a70e50f273397638d39b03353731e6cbbb3fd8502a33fec40bc"},
+    {file = "zipp-3.6.0.tar.gz", hash = "sha256:71c644c5369f4a6e07636f0aa966270449561fcea2e3d6747b8d23efaa9d7832"},
 ]
diff --git a/pyposeidon/bnd.py b/pyposeidon/bnd.py
index 6d204c0c..c5ac0c78 100644
--- a/pyposeidon/bnd.py
+++ b/pyposeidon/bnd.py
@@ -32,8 +32,18 @@ def __init__(self, **kwargs):
         tpath = kwargs.get("tpath", "./")
         tmodel = kwargs.get("tmodel", "./")
 
-        dic = {"West": ba[:, 0], "East": ba[:, -1], "South": ba[0, :], "North": ba[-1, :]}
-        idic = {"West": [0, -99], "East": [lons.shape[0], -99], "South": [-99, 0], "North": [-99, lats.shape[0]]}
+        dic = {
+            "West": ba[:, 0],
+            "East": ba[:, -1],
+            "South": ba[0, :],
+            "North": ba[-1, :],
+        }
+        idic = {
+            "West": [0, -99],
+            "East": [lons.shape[0], -99],
+            "South": [-99, 0],
+            "North": [-99, lats.shape[0]],
+        }
 
         for bound in ["West", "East", "North", "South"]:
             chunks = []
diff --git a/pyposeidon/boundary.py b/pyposeidon/boundary.py
new file mode 100644
index 00000000..ed2b148f
--- /dev/null
+++ b/pyposeidon/boundary.py
@@ -0,0 +1,639 @@
+"""
+Geometry module of pyposeidon. It manages model boundaries.
+"""
+from pyposeidon.utils.stereo import to_stereo
+import pyposeidon.utils.pplot
+import pyposeidon.dem as pdem
+import pandas as pd
+import numpy as np
+import geopandas as gp
+import logging
+import shapely
+from tqdm import tqdm
+import sys
+import matplotlib.pyplot as plt
+
+logger = logging.getLogger("pyposeidon")
+
+
+def simplify(geo):
+
+    logger.info("merge dataset")
+    geo = geo.loc[:, ["geometry"]].explode(index_parts=True).droplevel(0).reset_index(drop=True)
+
+    # Just in case merge
+    if geo.geom_type.all() == "LineString":
+        geo_ = gp.GeoDataFrame(geometry=[shapely.ops.linemerge(geo.geometry.values)])
+        geo = geo_.explode(index_parts=True).droplevel(0).reset_index(drop=True)
+
+    if geo.geom_type.all() == "Polygon":
+        try:
+            geo_ = list(geo.buffer(0).unary_union)
+        except TypeError:
+            geo_ = [geo.buffer(0).unary_union]
+
+        geo = gp.GeoDataFrame(geometry=geo_)
+
+    return geo
+
+
+class get_boundaries:
+    def __init__(self, geometry, coastlines=None, cbuffer=None, blevels=None, **kwargs):
+        """
+        Set model boundaries
+
+        !!! danger ""
+            Due to a limitation of the Library rendering the docstrings, all arguments are marked
+            as `required`, nevertheless they are all `Optional` except geometry.
+
+        Args:
+            geometry (Union[dict, str, GeoDataFrame]): A `GeoDataFrame` or the path to a shapefile or
+                a dict defining the lat/lon window.
+            coastlines (Union[str, GeoDataFrame]): A `GeoDataFrame` or the path to a shapefile which
+                describes the coastlines. Defaults to `None`.
+            cbuffer (float): The buffer in arcs for extending the coastlines. Defaults to `None`.
+            levels (list[floats]): The range of DEM values for extracting the boundaries.
+                When one valus is present it defines inner coastlines. When two values exist they define
+               the extent. Defaults to `None`.
+            dem_source (str): Path or url to bathymetric data.
+        """
+
+        # COASTLINES
+        if coastlines is None:
+            logger.warning("coastlines not given")
+            self.coasts = None
+
+        elif isinstance(coastlines, str):
+            logger.info("reading {}".format(coastlines))
+            coasts = gp.GeoDataFrame.from_file(coastlines)
+            self.coasts = simplify(coasts)
+        elif isinstance(coastlines, gp.GeoDataFrame):
+            logger.warning("coastlines is not a file, trying with geopandas Dataset")
+            try:
+                self.coasts = simplify(coastlines)
+            except:
+                logger.error("coastlines argument not valid ")
+                sys.exit(1)
+
+        # GEOMETRY
+        if geometry is None:
+            if levels is None:
+                logger.error("geometry nor levels is given, exiting")
+                sys.exit(1)
+
+        if isinstance(geometry, dict):
+
+            if self.coasts is None:
+                logger.warning("coastlines might be required")
+
+            self.geometry = geometry
+
+        elif geometry == "global":
+
+            if self.coasts is None:
+                logger.warning("coastlines might be required")
+
+            self.geometry = "global"
+
+        else:
+
+            try:
+                self.geometry = gp.read_file(geometry)
+            except:
+                logger.warning("geometry is not a file, trying with geopandas Dataset")
+                if isinstance(geometry, gp.GeoDataFrame):
+                    self.geometry = geometry
+                else:
+                    logger.error("geometry argument not valid ")
+                    sys.exit(1)
+
+        # Define internal boundary as isovalue of DEM
+        if blevels:
+            dsource = kwargs.get("dem_source", None)
+            if dsource is None:
+                logger.error("dem_source is required")
+
+            dem = pdem.dem(geometry=self.geometry, dem_source=dsource)
+            dem_ = dem.Dataset
+
+            self.coasts = get_dem_contours(blevels, dem_)
+
+        # get boundaries
+        if isinstance(self.geometry, dict):
+            df = tag(self.geometry, self.coasts, cbuffer, blevels, **kwargs)
+        elif isinstance(self.geometry, str):
+            df = global_tag(self.coasts, cbuffer, blevels, **kwargs)
+        elif isinstance(self.geometry, gp.GeoDataFrame):
+            df = self.geometry
+
+        # line tag
+        df.loc[df.tag == "island", "lindex"] = np.arange(-df[df.tag == "island"].shape[0], 0)
+        df.loc[df.tag == "land", "lindex"] = 1000 + np.arange(1, df[df.tag == "land"].shape[0] + 1)
+        df.loc[df.tag == "open", "lindex"] = np.arange(1, df[df.tag == "open"].shape[0] + 1)
+        df = df.sort_values("lindex", ascending=False)
+        df.lindex = df.lindex.astype(int)
+
+        # number of points
+        df["nps"] = df.apply(lambda row: len(row.geometry.xy[1]) - 1, axis=1)
+
+        self.contours = df.reset_index(drop=True)
+
+    def show(self):
+
+        return self.contours.plot(
+            column="tag",
+            legend=True,
+            legend_kwds={
+                "loc": "upper center",
+                "bbox_to_anchor": (0.5, 1.15),
+                "ncol": 3,
+                "fancybox": True,
+                "shadow": True,
+            },
+        )
+
+
+def buffer_(coasts, cbuffer):
+
+    # check
+    if coasts.empty:
+        return coasts
+
+    # shrink the world so not to get over
+    limit = shapely.geometry.Polygon([[-180, -90], [180, -90], [180, 90], [-180, 90], [-180, -90]]).buffer(-cbuffer)
+    de = coasts.intersection(limit).explode(index_parts=True).droplevel(0)
+
+    # buffer
+    w_ = gp.GeoDataFrame(geometry=de.buffer(cbuffer))  # in arcs
+
+    # drop empty objects
+    empty = w_.is_empty
+    w_ = w_.loc[~empty]
+    w_ = w_.reset_index(drop=True)
+
+    # evaluate multiple boundaries
+    mls = []
+    for pos, pol in w_.itertuples():
+        bb = pol.boundary
+        try:
+            len(bb)
+            mls.append(pos)
+        except:
+            pass
+
+    # keep only exterior
+    for idx, val in w_.loc[mls].iterrows():
+        b = shapely.geometry.Polygon(val.geometry.exterior)
+        w_.loc[idx] = b
+
+    # join
+    wu = w_.unary_union
+    wu = gp.GeoDataFrame(geometry=[wu]).explode(index_parts=True).droplevel(0).reset_index(drop=True)
+
+    rings = wu.boundary.is_ring  # get multiple boundaries instance
+    wi = wu.loc[~rings]
+
+    # get exterior (if any)
+    for idx, val in wi.iterrows():
+        b = shapely.geometry.Polygon(val.geometry.exterior)
+        wu.loc[idx] = b
+
+    return wu
+
+
+def get_dem_contours(blevels, dem):
+
+    if len(blevels) == 1:
+        dvalues = [blevels[0] - 1, blevels[0]]
+    elif len(blevels) == 2:
+        dvalues = blevels
+    else:
+        logger.error("blevels not set properly")
+        sys.exit(1)
+
+    # check if in range
+    r_check = [dem.elevation.values.min() < x < dem.elevation.values.max() for x in dvalues]
+
+    if not any(r_check):
+        logger.warning(
+            "blevels not within DEM range {}:{}".format(dem.elevation.values.min(), dem.elevation.values.max())
+        )
+        return gp.GeoDataFrame(geometry=[])
+    try:
+        dem.adjusted.plot.contour(levels=dvalues)
+    except:
+        dem.elevation.plot.contour(levels=dvalues)
+
+    # get graph
+    cs = plt.gca()
+
+    # outer contour
+    outc = []
+    outl = cs.collections[0].get_paths()
+    for l in range(len(outl)):
+        v = outl[l].vertices
+        try:
+            outc.append(shapely.geometry.LineString(v))
+        except:
+            pass
+
+    # inner contour
+    inc = []
+    inl = cs.collections[1].get_paths()
+    for l in range(len(inl)):
+        v = inl[l].vertices
+        try:
+            inc.append(shapely.geometry.LineString(v))
+        except:
+            pass
+
+    # Get continuous area
+    lon_min = dem.longitude.min()
+    lon_max = dem.longitude.max()
+    lat_min = dem.latitude.min()
+    lat_max = dem.latitude.max()
+
+    # create a polygon of the lat/lon window
+    grp = shapely.geometry.Polygon([(lon_min, lat_min), (lon_min, lat_max), (lon_max, lat_max), (lon_max, lat_min)])
+
+    # outer contour
+    b1 = []
+    for geo_ in outc:
+        ee = shapely.ops.split(grp, geo_)
+        try:
+            b1.append(ee[1])
+        except:
+            pass
+    # inner contour
+    b2 = []
+    for geo_ in inc:
+        ee = shapely.ops.split(grp, geo_)
+        try:
+            b2.append(ee[1])
+        except:
+            pass
+
+    w1 = gp.GeoDataFrame(geometry=b1)
+    w1_ = w1.unary_union
+    w1 = gp.GeoDataFrame(geometry=[w1_])
+
+    w2 = gp.GeoDataFrame(geometry=b2)
+    w2_ = w2.unary_union
+    w2 = gp.GeoDataFrame(geometry=[w2_])
+
+    plt.close("all")
+    return w1.loc[~w1.is_empty]  # cleanup
+
+
+def tag(geometry, coasts, cbuffer, blevels, **kwargs):
+
+    try:
+        lon_min = geometry["lon_min"]
+        lon_max = geometry["lon_max"]
+        lat_min = geometry["lat_min"]
+        lat_max = geometry["lat_max"]
+    except:
+        logger.error("geometry not set properly")
+        sys.exit(1)
+
+    # create a polygon of the lat/lon window
+    grp = shapely.geometry.Polygon([(lon_min, lat_min), (lon_min, lat_max), (lon_max, lat_max), (lon_max, lat_min)])
+
+    # create a LineString of the grid
+    grl = shapely.geometry.LineString(
+        [
+            (lon_min, lat_min),
+            (lon_min, lat_max),
+            (lon_max, lat_max),
+            (lon_max, lat_min),
+            (lon_min, lat_min),
+        ]
+    )
+
+    # check -180/180 trespass
+    if np.mean([lon_min, lon_max]) < 0 and lon_min < -180.0:
+        flag = -1
+    elif np.mean([lon_min, lon_max]) > 0 and lon_max > 180.0:
+        flag = 1
+    else:
+        flag = 0
+
+    # adjust abd mask based on lat/lon window
+    if flag == 1:
+        block1 = coasts.cx[lon_min:180, lat_min:lat_max].copy()
+        block2 = coasts.cx[-180 : (lon_max - 360.0), lat_min:lat_max].copy()
+
+        for idx, poly in block2.iterrows():
+            block2.loc[idx, "geometry"] = shapely.ops.transform(lambda x, y, z=None: (x + 360.0, y), poly.geometry)
+
+        block = block1.append(block2)
+
+    elif flag == -1:
+
+        block1 = coasts.cx[lon_min + 360 : 180, lat_min:lat_max].copy()
+        block2 = coasts.cx[-180:lon_max, lat_min:lat_max].copy()
+
+        for idx, poly in block1.iterrows():
+            block1.loc[idx, "geometry"] = shapely.ops.transform(lambda x, y, z=None: (x - 360.0, y), poly.geometry)
+
+        block = block1.append(block2)
+
+    else:
+        block = coasts.cx[lon_min:lon_max, lat_min:lat_max]
+
+    # Fix polygons around international line
+    if flag != 0:
+
+        wc = block[(block.bounds.maxx == flag * 180) | (block.bounds.minx == flag * 180)]
+
+        cs = []  # adjust values around zero (in projection - international line in Platee Carree)
+        for idx, line in wc.itertuples():
+            try:
+                x_ = [
+                    flag * 180 if abs(x - flag * 180) < 1.0e-3 else x for (x, y) in line.exterior.coords[:]
+                ]  # polygons
+                y_ = [y for (x, y) in line.exterior.coords[:]]
+            except:
+                x_ = [flag * 180 if abs(x - flag * 180) < 1.0e-3 else x for (x, y) in line.coords[:]]  # LineStrings
+                y_ = [y for (x, y) in line.coords[:]]
+
+            cs.append(shapely.geometry.Polygon(list(zip(x_, y_))))
+
+        ww = gp.GeoDataFrame(geometry=cs)
+
+        try:
+            gw = gp.GeoDataFrame(
+                geometry=list(ww.buffer(0).unary_union)
+            )  # merge the polygons that are split (around -180/180)
+        except:
+            gw = gp.GeoDataFrame(geometry=list(ww.values))
+
+        if wc.geom_type.all() != "Polygon":
+            gw = gp.GeoDataFrame(geometry=gw.boundary.values)
+
+        block = pd.concat([block[~block.index.isin(wc.index)], gw])
+
+    # buffer the coastlines on demand
+    if cbuffer:
+        logger.info("Buffering...")
+        block = buffer_(block, cbuffer)
+        logger.info("...done")
+
+    # polygonize if need be for getting the symetric difference
+    if block.geom_type.all() == "LineString":
+        gg = shapely.ops.polygonize_full(block.geometry.values)
+        block = gp.GeoDataFrame(geometry=list(gg[0])).explode(index_parts=True).droplevel(0).reset_index(drop=True)
+
+    # bypass blocks in case of isodem
+    if blevels:
+        block = coasts.copy()
+
+    if not block.empty:
+        g = block.unary_union.symmetric_difference(grp)  # get the dif from the coasts
+    else:
+        g = grp
+
+    try:  # make geoDataFrame
+        t = gp.GeoDataFrame({"geometry": g})
+    except:
+        t = gp.GeoDataFrame({"geometry": [g]})
+    t["length"] = t["geometry"][:].length  # get length
+    t = t.sort_values(by="length", ascending=0)  # sort
+    t = t.reset_index(drop=True)
+
+    t["in"] = gp.GeoDataFrame(geometry=[grp] * t.shape[0]).contains(t)  # find the largest of boundaries
+    idx = np.where(t["in"] == True)[0][0]  # first(largest) boundary within lat/lon
+
+    b = t.iloc[idx].geometry  # get the largest
+
+    # set boundary
+
+    # open (water) boundaries
+
+    try:
+        water = b.boundary[0] - (b.boundary[0] - grl)
+    except:
+        water = b.boundary - (b.boundary - grl)
+
+    try:
+        cwater = shapely.ops.linemerge(water)
+    except:
+        cwater = water
+
+    df_water = gp.GeoDataFrame(geometry=[cwater]).explode(index_parts=True).droplevel(0).reset_index(drop=True)
+    df_water["tag"] = "open"
+
+    # land boundaries!!
+    try:
+        land = b.boundary[0] - grl
+    except:
+        land = b.boundary - grl
+
+    try:
+        cland = shapely.ops.linemerge(land)
+    except:
+        cland = land
+
+    df_land = gp.GeoDataFrame(geometry=[cland]).explode(index_parts=True).droplevel(0).reset_index(drop=True)
+    df_land["tag"] = "land"
+
+    ### interpolate if needed
+
+    # even = kwargs.get("even", False)
+    # ds = kwargs.get("ds", 0.001)
+
+    #    if even:
+
+    #        ibs = df_land.index.levels[0].shape[0]
+
+    #        if ibs < 0:
+    #            ## resample boundaries
+    # ndfs = {}
+    # for ic in tqdm(range(ibs)):
+    #     contour = df_land.index.levels[0][ic]
+    #     curve = df_land.loc[contour, ["lon", "lat"]]
+    #     di = spline(curve, ds=ds)
+    #     di["z"] = df_land.loc[contour].z.values[0]
+    #     di["tag"] = df_land.loc[contour].tag.values[0].astype(int)
+    #     ndfs.update({contour: di})
+    #
+    # df_land = pd.concat(ndfs, axis=0)
+    # df_land["z"] = df_land.z.values.astype(int)
+    # df_land["tag"] = df_land.tag.values.astype(int)
+
+    ndf = (
+        gp.GeoDataFrame(geometry=[shapely.ops.LineString(x) for x in b.interiors])
+        .explode(index_parts=True)
+        .droplevel(0)
+        .reset_index(drop=True)
+    )
+    ndf["tag"] = "island"
+
+    # ### interpolate if needed
+    # if even:
+    #
+    #     ibs = ndf.index.levels[0].shape[0]
+    #
+    #     ## resample boundaries
+    #     ndfs = {}
+    #     for ic in tqdm(range(ibs)):
+    #         contour = ndf.index.levels[0][ic]
+    #         curve = ndf.loc[contour, ["lon", "lat"]]
+    #         curve = pd.concat([curve, curve.loc[0:0]]).reset_index(
+    #             drop=True
+    #         )  # add the first point to do a better interpolation
+    #         di = spline(curve, ds=ds)
+    #         di["z"] = ndf.loc[contour].z.values[0]
+    #         di["tag"] = ndf.loc[contour].tag.values[0].astype(int)
+    #         ndfs.update({contour: di.drop_duplicates(["lon", "lat"])})  # remove duplicated points (see above)
+    #
+    #     ndf = pd.concat(ndfs, axis=0)
+    #
+    #     ndf["z"] = ndf.z.values.astype(int)
+    #     ndf["tag"] = ndf.tag.values.astype(int)
+
+    df = pd.concat([df_water, df_land, ndf]).reset_index(drop=True)
+
+    df = df.loc[~df.is_empty]  # clean up
+
+    return df
+
+
+def global_tag(geo, cbuffer, blevels, **kwargs):
+
+    # Manage coastlines
+    logger.info("preparing coastlines")
+
+    # buffer the coastlines on demand
+    if cbuffer:
+        logger.info("Buffering...")
+        geo = buffer_(geo, cbuffer)
+        logger.info("...done")
+
+    # ANTARTICA
+    anta_mask = geo.bounds.miny < geo.bounds.miny.min() + 0.1  # indentify antartica
+    anta = geo.loc[anta_mask]
+    indx = anta.index  # keep index
+
+    if anta.geom_type.iloc[0] == "Polygon":  # convert boundary values to pandas
+        anta = pd.DataFrame(anta.boundary.values[0].coords[:], columns=["lon", "lat"])
+    else:
+        anta = pd.DataFrame(anta.geometry.iloc[0].coords, columns=["lon", "lat"])
+
+    d1 = anta.where(anta.lon == anta.lon.max()).dropna().index[1:]  # get artificial boundaries as -180/180
+    d2 = anta.where(anta.lon == anta.lon.min()).dropna().index[1:]
+    anta = anta.drop(d1).drop(d2)  # drop the points
+    d3 = anta.where(anta.lat == anta.lat.min()).dropna().index  # drop lat=-90 line
+    anta = anta.drop(d3)
+    an = gp.GeoDataFrame(
+        {
+            "geometry": [shapely.geometry.LineString(anta.values)],
+            "length": shapely.geometry.LineString(anta.values).length,
+        },
+        index=indx,
+    )  # put together a LineString
+    geo.loc[indx] = shapely.geometry.LineString(anta.values)  # put it back to geo
+
+    # International Meridian
+    m1 = geo[geo.bounds.minx == geo.bounds.minx.min()].index
+    m2 = geo[geo.bounds.maxx == geo.bounds.maxx.max()].index
+    mm = np.concatenate((m1, m2))  # join them
+    mm = [j for j in mm if j != indx]  # subtract antartica
+
+    # convert to u,v (stereographic coordinates)
+    for idx, poly in geo.iterrows():
+        if idx == indx:
+            # resolve potential singularity
+            gk = geo.loc[indx].geometry.iloc[0]
+            gx = np.array([x for (x, y) in gk.coords[:]])
+            gy = np.array([y for (x, y) in gk.coords[:]])
+            jj = np.argwhere(gy > -86)
+            gx_, gy_ = to_stereo(gx[jj], gy[jj], R=1)  # project valid values only
+            geo.loc[idx, "geometry"] = shapely.geometry.LineString(list(zip(gx_, gy_)))
+        else:
+            geo.loc[idx, "geometry"] = shapely.ops.transform(
+                lambda x, y, z=None: to_stereo(x, y, R=kwargs.get("R", 1.0)),
+                poly.geometry,
+            )
+
+    w = geo.drop(indx)  # get all polygons
+
+    # join the split polygons
+    ww = w.loc[mm]  # split entities
+    qq = shapely.ops.polygonize_full(ww.geometry.values)  # polygonize in case of LineStrings
+    if len(qq[0]) > 0:
+        ww = gp.GeoDataFrame(geometry=list(qq[0]))  # convert to gp
+    elif len(qq[2]) > 0:
+        ww = gp.GeoDataFrame(geometry=list(qq[2]))  # convert to gp
+
+    cs = []  # adjust values around zero (in projection - international line in Platee Carree)
+    for idx, line in ww.itertuples():
+        try:
+            x_ = [x for (x, y) in line.exterior.coords[:]]  # polygons
+            y_ = [y if abs(y) > 1.0e-4 else 0 for (x, y) in line.exterior.coords[:]]
+        except:
+            x_ = [x for (x, y) in line.coords[:]]  # LineStrings
+            y_ = [y if abs(y) > 1.0e-4 else 0 for (x, y) in line.coords[:]]
+
+        cs.append(shapely.geometry.Polygon(list(zip(x_, y_))))
+
+    ww = gp.GeoDataFrame(geometry=cs)
+
+    gw = gp.GeoDataFrame(
+        geometry=list(ww.buffer(0).unary_union)
+    )  # merge the polygons that are split (around -180/180)
+
+    gw = gp.GeoDataFrame(geometry=gw.boundary.values)
+
+    w = w.drop(mm)
+
+    wp = w.loc[w.geom_type == "Polygon"]
+    w2 = w.loc[w.geom_type != "Polygon"]
+    w1 = gp.GeoDataFrame(geometry=wp.exterior.values)  # get boundaries if Polygons
+
+    # Check antartica LineString
+    if not geo.iloc[indx].geometry.values[0].is_ring:
+        ca = gp.GeoDataFrame(
+            geometry=[shapely.geometry.LinearRing(geo.loc[indx].geometry.values[0])],
+            index=indx,
+        )
+        ca["geometry"] = shapely.geometry.LineString(ca.geometry.values[0])
+    else:
+        ca = geo.loc[indx]
+
+    # PUT ALL TOGETHER
+    geo = pd.concat([w1, w2, gw, ca], ignore_index=True).reset_index(drop=True)
+
+    logger.info("storing boundaries")
+
+    geo["tag"] = "island"
+
+    geo["length"] = geo["geometry"][:].length  # sort with length
+    geo = geo.sort_values(by="length", ascending=0)
+
+    geo = gp.GeoDataFrame(geo)  # clean up
+
+    # idx = 0
+    # dic = {}
+    # for i, line in tqdm(geo.iterrows(), total=geo.shape[0]):
+    #     lon = []
+    #     lat = []
+    #
+    #     for x, y in line.geometry.coords[:]:
+    #         lon.append(x)
+    #         lat.append(y)
+    #     dic.update({"line{}".format(idx): {"lon": lon, "lat": lat, "tag": line.tag}})
+    #     idx += 1
+    #
+    # # Serialize data into file:
+    # json.dump( dic, open( "dich.json", 'w' ) )
+    # sys.exit()
+    #
+    # dict_of_df = {k: pd.DataFrame(v) for k, v in dic.items()}
+    #
+    # df = pd.concat(dict_of_df, axis=0)
+    #
+    # df["z"] = 0
+    # df = df.drop_duplicates()  # drop the repeat value on closed boundaries
+
+    return geo.loc[~geo.is_empty]
diff --git a/pyposeidon/d3d.py b/pyposeidon/d3d.py
index 64153ee7..dfdd8f86 100644
--- a/pyposeidon/d3d.py
+++ b/pyposeidon/d3d.py
@@ -75,7 +75,12 @@ def __init__(self, **kwargs):
                     logger.error("geometry argument not a valid geopandas file")
                     sys.exit(1)
 
-                self.lon_min, self.lat_min, self.lon_max, self.lat_max = geo.total_bounds
+                (
+                    self.lon_min,
+                    self.lat_min,
+                    self.lon_max,
+                    self.lat_max,
+                ) = geo.total_bounds
 
         start_date = kwargs.get("start_date", None)
         self.start_date = pd.to_datetime(start_date)
@@ -442,7 +447,12 @@ def bath(self, **kwargs):
 
         else:
             kwargs.update(
-                {"lon_min": self.lon_min, "lat_min": self.lat_min, "lon_max": self.lon_max, "lat_max": self.lat_max}
+                {
+                    "lon_min": self.lon_min,
+                    "lat_min": self.lat_min,
+                    "lon_max": self.lon_max,
+                    "lat_max": self.lat_max,
+                }
             )
             self.dem = pdem.dem(**kwargs)
 
@@ -538,7 +548,17 @@ def to_bnd(self):
                     bname = key + str(idx)
                     f.write(
                         "{0:<10s}{1:>12s}{2:>2s}{3:>6d}{4:>6d}{5:>6d}{6:>6d}   0.0000000e+00 {7:<s}{8:<g}A {9:<s}{10:<g}B\n".format(
-                            bname, nm[0], nm[1], k1[0] + 1, k1[1] + 1, k2[0] + 1, k2[1] + 1, key, idx, key, idx
+                            bname,
+                            nm[0],
+                            nm[1],
+                            k1[0] + 1,
+                            k1[1] + 1,
+                            k2[0] + 1,
+                            k2[1] + 1,
+                            key,
+                            idx,
+                            key,
+                            idx,
                         )
                     )  # fortran index ??
                     idx += 1
@@ -602,12 +622,27 @@ def to_obs(self, **kwargs):
 
         if ofilename:
 
-            obs_points = pd.read_csv(ofilename, delimiter="\t", header=None, names=["index", "Name", "lat", "lon"])
+            obs_points = pd.read_csv(
+                ofilename,
+                delimiter="\t",
+                header=None,
+                names=["index", "Name", "lat", "lon"],
+            )
             obs_points = obs_points.set_index("index", drop=True).reset_index(drop=True)  # reset index if any
 
             obs_points = obs_points[
-                (obs_points.lon.between(self.grid.Dataset.lons.values.min(), self.grid.Dataset.lons.values.max()))
-                & (obs_points.lat.between(self.grid.Dataset.lats.values.min(), self.grid.Dataset.lats.values.max()))
+                (
+                    obs_points.lon.between(
+                        self.grid.Dataset.lons.values.min(),
+                        self.grid.Dataset.lons.values.max(),
+                    )
+                )
+                & (
+                    obs_points.lat.between(
+                        self.grid.Dataset.lats.values.min(),
+                        self.grid.Dataset.lats.values.max(),
+                    )
+                )
             ]
 
             obs_points.reset_index(inplace=True, drop=True)
diff --git a/pyposeidon/dem.py b/pyposeidon/dem.py
index d25ab438..e85fb54c 100644
--- a/pyposeidon/dem.py
+++ b/pyposeidon/dem.py
@@ -52,7 +52,7 @@ def adjust(self, coastline, **kwargs):
 
 
 def normalize_coord_names(dataset: xr.Dataset) -> xr.Dataset:
-    """ Return a dataset with coords containing "longitude" and "latitude" """
+    """Return a dataset with coords containing "longitude" and "latitude" """
     coords = set(dataset.coords.keys())
     # longitude
     for lon_name in LONGITUDE_NAMES:
@@ -113,13 +113,18 @@ def dem_(source=None, lon_min=-180, lon_max=180, lat_min=-90, lat_max=90, **kwar
         lon0 = lon_min
         lon1 = lon_max
 
+    lon0 = lon_min + 360.0 if lon_min < -180 else lon_min
+    lon1 = lon_max + 360.0 if lon_max < -180 else lon_max
+
+    lon0 = lon0 - 360.0 if lon0 > 180 else lon0
+    lon1 = lon1 - 360.0 if lon1 > 180 else lon1
+
     if (lon_min < data.longitude.min()) or (lon_max > data.longitude.max()):
         logger.warning("Lon must be within {} and {}".format(data.longitude.min().values, data.longitude.max().values))
         logger.warning("compensating if global dataset available")
 
     if (lat_min < data.latitude.min()) or (lat_max > data.latitude.max()):
-        logger.warning("Lat must be within {} and {}".format(data.latitude.min().values, data.latitude.max().values))
-        logger.warning("compensating if global dataset available")
+        logger.warning("Lat is within {} and {}".format(data.latitude.min().values, data.latitude.max().values))
 
     # get idx
     i0 = np.abs(data.longitude.data - lon0).argmin()
@@ -198,13 +203,23 @@ def dem_(source=None, lon_min=-180, lon_max=180, lat_min=-90, lat_max=90, **kwar
 
         if len(grid_x.shape) > 1:
             idem = xr.Dataset(
-                {"ival": (["k", "l"], itopo), "ilons": (["k", "l"], grid_x), "ilats": (["k", "l"], grid_y)}
+                {
+                    "ival": (["k", "l"], itopo),
+                    "ilons": (["k", "l"], grid_x),
+                    "ilats": (["k", "l"], grid_y),
+                }
             )  # ,
         #                           coords={'ilon': ('ilon', grid_x[0,:]),
         #                                   'ilat': ('ilat', grid_y[:,0])})
 
         elif len(grid_x.shape) == 1:
-            idem = xr.Dataset({"ival": (["k"], itopo), "ilons": (["k"], grid_x), "ilats": (["k"], grid_y)})
+            idem = xr.Dataset(
+                {
+                    "ival": (["k"], itopo),
+                    "ilons": (["k"], grid_x),
+                    "ilats": (["k"], grid_y),
+                }
+            )
 
         # ---------------------------------------------------------------------
         logger.info("dem done\n")
diff --git a/pyposeidon/grid.py b/pyposeidon/grid.py
index 2c387e65..f210d5ab 100644
--- a/pyposeidon/grid.py
+++ b/pyposeidon/grid.py
@@ -14,22 +14,18 @@
 import pandas as pd
 import sys
 from .jigsaw import *
+from .ugmsh import *
 import logging
 import f90nml
 import os
 import subprocess
 from pyposeidon.utils.verify import *
+import pyposeidon.boundary as pb
 import multiprocessing
 
-try:
-    from .ugmsh import *
-except:
-    pass
-
-NCORES = max(1, multiprocessing.cpu_count() - 1)
-
 logger = logging.getLogger("pyposeidon")
 
+NCORES = max(1, multiprocessing.cpu_count() - 1)
 
 DATA_PATH = os.path.dirname(pyposeidon.__file__) + "/misc/"
 
@@ -78,7 +74,15 @@ def __init__(self, **kwargs):
             y = np.linspace(lat_min, lat_max, nj)
             gx, gy = np.meshgrid(x, y)
 
-            attrs = kwargs.get("attrs", {"Coordinate System": "Spherical", "alfori": 0.0, "xori": 0.0, "yori": 0.0})
+            attrs = kwargs.get(
+                "attrs",
+                {
+                    "Coordinate System": "Spherical",
+                    "alfori": 0.0,
+                    "xori": 0.0,
+                    "yori": 0.0,
+                },
+            )
 
             g = xr.Dataset(
                 {"lons": (["y", "x"], gx), "lats": (["y", "x"], gy)},
@@ -100,7 +104,14 @@ def read_file(filename, **kwargs):
         ni, nj = int(ni), int(nj)
         alfori, xori, yori = header.loc[2, 0].split(" ")
 
-        d = pd.read_csv(filename, header=2, comment="*", delim_whitespace=True, engine="python", na_values="ETA=")
+        d = pd.read_csv(
+            filename,
+            header=2,
+            comment="*",
+            delim_whitespace=True,
+            engine="python",
+            na_values="ETA=",
+        )
         d = d.reset_index()
         data = d.values[~np.isnan(d.values)]
         data = np.array(data)
@@ -116,7 +127,12 @@ def read_file(filename, **kwargs):
             coords={"x": ("x", lons[0, :]), "y": ("y", lats[:, 0])},
         )
 
-        g.attrs = {"Coordinate System": cs, "alfori": alfori, "xori": xori, "yori": yori}
+        g.attrs = {
+            "Coordinate System": cs,
+            "alfori": alfori,
+            "xori": xori,
+            "yori": yori,
+        }
 
         return g
 
@@ -129,7 +145,9 @@ def to_file(self, filename, **kwargs):
             f.write("{} {}\n".format(self.Dataset.lons.shape[1], self.Dataset.lons.shape[0]))
             f.write(
                 "{} {} {}\n".format(
-                    self.Dataset.attrs["xori"], self.Dataset.attrs["yori"], self.Dataset.attrs["alfori"]
+                    self.Dataset.attrs["xori"],
+                    self.Dataset.attrs["yori"],
+                    self.Dataset.attrs["alfori"],
                 )
             )
             for i in range(self.Dataset.lons.shape[0]):
@@ -149,6 +167,12 @@ def __init__(self, **kwargs):
 
         grid_file = kwargs.get("grid_file", None)
         grid_generator = kwargs.get("grid_generator", "jigsaw")
+        geo = kwargs.pop("geometry", None)
+        coasts = kwargs.pop("coastlines", None)
+        boundary = kwargs.pop("boundary", None)
+
+        if geo == "global":
+            kwargs.update({"gglobal": True})
 
         if grid_file:
 
@@ -156,16 +180,30 @@ def __init__(self, **kwargs):
 
         elif grid_generator == "gmsh":
 
-            g = gmsh_(**kwargs)  # create grid with JIGSAW
+            if boundary is None:
+                self.boundary = pb.get_boundaries(geometry=geo, coastlines=coasts, **kwargs)
+            else:
+                self.boundary = boundary
+
+            g, bg = gmsh_(self.boundary, **kwargs)  # create grid with GMSH
 
             self.Dataset = g
 
+            self.bgmesh = bg
+
         elif grid_generator == "jigsaw":
 
-            g = jigsaw(**kwargs)  # create grid with JIGSAW
+            if boundary is None:
+                self.boundary = pb.get_boundaries(geometry=geo, coastlines=coasts, **kwargs)
+            else:
+                self.boundary = boundary
+
+            g, bg = jigsaw(self.boundary, **kwargs)  # create grid with JIGSAW
 
             self.Dataset = g
 
+            self.bgmesh = bg
+
         else:
 
             self.Dataset = None
@@ -230,7 +268,7 @@ def read_file(hgrid, **kwargs):
         nobn = df.loc[n0 + 1, "data"].split("=")[0].strip()
         nobn = int(nobn)
 
-        onodes = []
+        odic = {}
         ottr = []
         idx = n0 + 2
         for nl in range(nob):
@@ -239,26 +277,16 @@ def read_file(hgrid, **kwargs):
             label = df.loc[idx, "data"].split("=")[1]
             label = label[label.index("open") :]
             ottr.append([nn, label])
-            nodes = df.loc[idx + 1 : idx + nn, "data"]
-            onodes.append(nodes.astype(int).values)
+            nodes = df.loc[idx + 1 : idx + nn, "data"].astype("int")
             idx = idx + nn + 1
 
-        oinfo = pd.DataFrame(ottr)
-        try:
-            oinfo.columns = ["nps", "label"]
-            oinfo.label = oinfo.label.str.rstrip()
-            oinfo.label = oinfo.label.str.replace(" ", "_")
-            oinfo.set_index("label", inplace=True, drop=True)
-            oinfo = oinfo.apply(pd.to_numeric)
-        except:
-            pass
+            oi = pd.DataFrame({"node": nodes, "type": "open", "id": int(label[-1])})
+            odic.update({label: oi})
 
-        obnodes = [j for i in onodes for j in i]
-        dfo = pd.DataFrame({"node": obnodes, "type": -1, "id": 0}, index=np.arange(len(obnodes)))
-        idx = 1
-        for l in range(len(onodes)):
-            dfo.loc[dfo.node.isin(onodes[l]), "id"] = idx
-            idx += 1
+        try:
+            dfo = pd.concat(odic).droplevel(0).reset_index(drop=True)
+        except ValueError:
+            dfo = pd.DataFrame()
 
         # Land boundaries
         n1 = df[df.data.str.contains("land boundaries")].index
@@ -270,9 +298,10 @@ def read_file(hgrid, **kwargs):
         nlbn = df.loc[n1 + 1, "data"].split("=")[0].strip()
         nlbn = int(nlbn)
 
-        lnodes = []
+        ldic = {}
         attr = []
         idx = n1 + 2
+        ili = -1
         for nl in range(nlb):
             nn, etype = df.loc[idx, "data"].split("=")[0].strip().split(" ")
             nn = int(nn)
@@ -280,32 +309,30 @@ def read_file(hgrid, **kwargs):
             label = df.loc[idx, "data"].split("=")[1]
             label = label[label.index("land") :]
             attr.append([nn, etype, label])
-            nodes = df.loc[idx + 1 : idx + nn, "data"]
-            lnodes.append(nodes.astype(int).values)
+            nodes = df.loc[idx + 1 : idx + nn, "data"].astype(int)
             idx = idx + nn + 1
 
-        linfo = pd.DataFrame(attr)
-        try:
-            linfo.columns = ["nps", "type", "label"]
-            linfo.label = linfo.label.str.rstrip()
-            linfo.label = linfo.label.str.replace(" ", "_") + "_" + linfo.type.astype(str)
-            linfo.set_index("label", inplace=True, drop=True)
-            linfo = linfo.apply(pd.to_numeric)
-        except:
-            pass
+            li = pd.DataFrame({"node": nodes})
+            tt = ["land" if etype == 0 else "island"]
+            idi = [ili if etype == 1 else 1000 + int(label[-1])]
+            li["type"] = tt[0]
+            li["id"] = idi[0]
+            ldic.update({label: li})
+            ili -= 1
 
-        lbnodes = [j for i in lnodes for j in i]
-        dfl = pd.DataFrame({"node": lbnodes, "type": 0, "id": 0}, index=np.arange(len(lbnodes)))
-        idx = -1
-        for l in range(len(lnodes)):
-            dfl.loc[dfl.node.isin(lnodes[l]), "id"] = idx
-            dfl.loc[dfl.node.isin(lnodes[l]), "type"] = linfo.iloc[l].type
-            idx -= 1
+        try:
+            dfl = pd.concat(ldic).droplevel(0).reset_index(drop=True)
+        except ValueError:
+            dfl = pd.DataFrame()
 
+        # concat boundaries
         bbs = pd.concat([dfo, dfl])
-        bbs.index.name = "bnodes"
 
         bbs.node = bbs.node - 1  # start_index = 0
+        bbs = bbs.reset_index(drop=True)  # reset index
+        bbs = bbs[["type", "node", "id"]]  # set column order
+        bbs = bbs.sort_values(["type", "id", "node"]).reset_index(drop=True)  # sort
+        bbs.index.name = "bnodes"
 
         # merge to one xarray DataSet
         g = xr.merge([grid, depth, els, bbs.to_xarray()])
@@ -352,13 +379,24 @@ def to_file(self, filename, **kwargs):
 
         e.loc[:, ["a", "b", "c"]] = e.loc[:, ["a", "b", "c"]] + 1  # convert to fortran (index starts from 1)
 
-        e.to_csv(filename, index=True, sep="\t", header=None, mode="a", columns=["nv", "a", "b", "c"])
+        e.to_csv(
+            filename,
+            index=True,
+            sep="\t",
+            header=None,
+            mode="a",
+            columns=["nv", "a", "b", "c"],
+        )
 
         bs = self.Dataset[["node", "id", "type"]].to_dataframe()
 
         # open boundaries
-        number_of_open_boundaries = bs.id.max().astype(int)
-        number_of_open_boundaries_nodes = bs.loc[bs.id > 0].shape[0]
+        number_of_open_boundaries = bs.loc[bs.type == "open"].id
+        if not number_of_open_boundaries.empty:
+            number_of_open_boundaries = number_of_open_boundaries.max()
+        else:
+            number_of_open_boundaries = 0
+        number_of_open_boundaries_nodes = bs.loc[bs.type == "open"].shape[0]
 
         if number_of_open_boundaries > 0:
             with open(filename, "a") as f:
@@ -380,31 +418,54 @@ def to_file(self, filename, **kwargs):
 
         # land boundaries
 
-        number_of_land_boundaries = bs.id.min().astype(int)
-        number_of_land_boundaries_nodes = bs.loc[bs.id < 0].shape[0]
+        number_of_land_boundaries = bs.loc[bs.type == "land"].id
+        if not number_of_land_boundaries.empty:
+            number_of_land_boundaries = number_of_land_boundaries.max() - 1000
+        else:
+            number_of_land_boundaries = 0
+        number_of_land_boundaries_nodes = bs.loc[bs.type == "land"].shape[0]
 
-        if number_of_land_boundaries < 0:
-            with open(filename, "a") as f:
+        number_of_island_boundaries = bs.loc[bs.type == "island"].id
+        if not number_of_island_boundaries.empty:
+            number_of_island_boundaries = number_of_island_boundaries.min()
+        else:
+            number_of_island_boundaries = 0
+        number_of_island_boundaries_nodes = bs.loc[bs.type == "island"].shape[0]
 
-                f.write("{} = Number of land boundaries\n".format(-number_of_land_boundaries))
-                f.write("{} = Total number of land boundary nodes\n".format(number_of_land_boundaries_nodes))
+        nlb = number_of_land_boundaries - number_of_island_boundaries
+        nlbn = number_of_land_boundaries_nodes + number_of_island_boundaries_nodes
 
+        if nlb > 0:
+            with open(filename, "a") as f:
+                f.write("{} = Number of land boundaries\n".format(nlb))
+                f.write("{} = Total number of land boundary nodes\n".format(nlbn))
                 ik = 1
-                for i in range(-1, number_of_land_boundaries - 1, -1):
+        else:
+            with open(filename, "a") as f:
+                f.write("{} = Number of land boundaries\n".format(0))
+                f.write("{} = Total number of land boundary nodes\n".format(0))
+
+        if number_of_land_boundaries > 0:
+            with open(filename, "a") as f:
+
+                for i in range(1001, 1000 + number_of_land_boundaries + 1):
                     dat_ = bs.loc[bs.id == i]
                     dat = dat_.node + 1  # fortran
-                    dat_t = dat_.type.iloc[0].astype(int)
 
-                    f.write("{} {} = Number of nodes for land boundary {}\n".format(dat.size, dat_t, ik))
+                    f.write("{} {} = Number of nodes for land boundary {}\n".format(dat.size, 0, ik))
                     dat.to_csv(f, index=None, header=False)
                     ik += 1
 
-        else:
+        if number_of_island_boundaries < 0:
 
             with open(filename, "a") as f:
 
-                f.write("{} = Number of land boundaries\n".format(0))
-                f.write("{} = Total number of land boundary nodes\n".format(0))
+                for i in range(-1, number_of_island_boundaries - 1, -1):
+                    dat_ = bs.loc[bs.id == i]
+                    dat = dat_.node + 1  # fortran
+
+                    f.write("{} {} = Number of nodes for land boundary {}\n".format(dat.size, 1, -i))
+                    dat.to_csv(f, index=None, header=False)
 
     def validate(self, **kwargs):
 
@@ -420,8 +481,8 @@ def validate(self, **kwargs):
         # save bctides.in
         bs = self.Dataset[["node", "id", "type"]].to_dataframe()
         # open boundaries
-        number_of_open_boundaries = bs.id.max().astype(int)
-        number_of_open_boundaries_nodes = bs.loc[bs.id > 0].shape[0]
+        number_of_open_boundaries = np.nan_to_num(bs.loc[bs.type == "open"].id.max()).astype(int)
+        number_of_open_boundaries_nodes = bs.loc[bs.type == "open"].shape[0]
 
         with open(path + "bctides.in", "w") as f:
             f.write("Header\n")
@@ -487,7 +548,11 @@ def validate(self, **kwargs):
 
         # note that cwd is the folder where the executable is
         ex = subprocess.Popen(
-            args=["./launchSchism.sh"], cwd=calc_dir, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE
+            args=["./launchSchism.sh"],
+            cwd=calc_dir,
+            shell=True,
+            stderr=subprocess.PIPE,
+            stdout=subprocess.PIPE,
         )  # , bufsize=1)
 
         out, err = ex.communicate()[:]
diff --git a/pyposeidon/jigsaw.py b/pyposeidon/jigsaw.py
index 09ddfb02..96911eee 100644
--- a/pyposeidon/jigsaw.py
+++ b/pyposeidon/jigsaw.py
@@ -19,6 +19,7 @@
 import sys
 
 from pyposeidon.utils.stereo import to_lat_lon, to_stereo
+from pyposeidon.utils.global_bgmesh import make_bgmesh_global
 from pyposeidon.utils.sort import *
 import pyposeidon.dem as pdem
 from pyposeidon.utils.hfun import *
@@ -33,7 +34,7 @@
 TEST_DATA_PATH = os.path.dirname(pyposeidon.__file__) + "/tests/data/"
 
 
-def geo(df, path=".", tag="jigsaw"):
+def to_geo(df, path=".", tag="jigsaw"):
 
     fgeo = path + tag + "-geo.msh"
     # write header
@@ -41,29 +42,93 @@ def geo(df, path=".", tag="jigsaw"):
         f.write("#{}; created by pyposeidon\n".format(tag + "-geo.msh"))
         f.write("MSHID=2;EUCLIDEAN-MESH\n")
         f.write("NDIMS=2\n")
-        f.write("POINT={}\n".format(df.shape[0]))
+        f.write("POINT={}\n".format(df.nps.sum()))
+
+    # outer contour
+    df_ = df.loc[df.tag != "island"].reset_index(drop=True)  # all external contours
+
+    if not df_.empty:
+
+        # store xy in a DataFrame
+        dic = {}
+        for k, d in df_.iterrows():
+            out = pd.DataFrame(d.geometry.coords[:], columns=["lon", "lat"])
+            out["lindex"] = d.lindex
+            out = out.drop_duplicates(["lon", "lat"])
+            if d.tag == "land":  # drop end points in favor or open tag
+                out = out[1:-1]
+            dic.update({"line{}".format(k): out})
+        o1 = pd.concat(dic, axis=0).droplevel(0).reset_index(drop=True)
+        o1 = o1.drop_duplicates(["lon", "lat"])
+
+        # Do linemerge of outer contours
+        lss = df_.geometry.values
+        merged = shapely.ops.linemerge(lss)
+        o2 = pd.DataFrame({"lon": merged.xy[0], "lat": merged.xy[1]})  # convert to DataFrame
+        o2 = o2.drop_duplicates()
+
+        outer = o2.merge(o1)  # merge to transfer the lindex
+
+        # write lines & compute edges
+        # outer to file
+        with open(fgeo, "a") as f:
+            outer["z"] = 0
+            outer = outer.drop_duplicates(["lon", "lat"])
+            # nodes
+            outer.to_csv(f, index=False, header=0, columns=["lon", "lat", "z"], sep=";")
+            # compute edges
+
+        edges = [
+            list(a)
+            for a in zip(
+                np.arange(outer.shape[0]),
+                np.arange(outer.shape[0]) + 1,
+                outer.lindex.values,
+            )
+        ]  # outer
+        edges[-1][1] = 0
+
+        # sort (duplicated bounds)
+        edges = pd.DataFrame(edges, columns=["index", "ie", "lindex"])
+
+        edges["lindex1"] = (
+            pd.concat([outer.loc[1:, "lindex"], outer.loc[0:0, "lindex"]]).reset_index(drop=True).astype(int)
+        )
+        edges["que"] = np.where(
+            ((edges["lindex"] != edges["lindex1"]) & (edges["lindex"] > 0) & (edges["lindex"] < 1000)),
+            edges["lindex1"],
+            edges["lindex"],
+        )
+
+        edges = edges.reset_index().loc[:, ["index", "ie", "que"]]
+
+        edges = edges.values.tolist()
+
+    else:
 
-    # write lines
+        edges = []
+
+    # the rest to file
     with open(fgeo, "a") as f:
-        for line in df.index.levels[0]:
-            df.loc[line].to_csv(f, index=False, header=0, columns=["lon", "lat", "z"], sep=";")
-
-    edges = pd.DataFrame([])  # initiate
-
-    # create edges
-    for line in df.index.levels[0]:
-        i0 = edges.shape[0]
-        ie = df.loc[line].shape[0] + edges.shape[0]
-        dline = df.loc[line].copy()
-        dline.index = range(i0, ie)
-        dline.loc[:, "ie"] = dline.index.values + 1
-        dline.loc[dline.index[-1], "ie"] = i0
-        dout = dline.reset_index().loc[:, ["index", "ie", "tag"]]
-
-        dout["tag1"] = dline.loc[dline.ie.values, "tag"].values.astype(int)
-        dout["que"] = np.where(((dout["tag"] != dout["tag1"]) & (dout["tag"] > 0)), dout["tag1"], dout["tag"])
-        dout = dout.reset_index().loc[:, ["index", "ie", "que"]]
-        edges = edges.append(dout)
+        for k, d in df.loc[df.tag == "island"].iterrows():
+            out = pd.DataFrame(d.geometry.coords[:], columns=["lon", "lat"])
+            out["z"] = 0
+            out = out.drop_duplicates(["lon", "lat"])
+
+            # nodes
+            out.to_csv(f, index=False, header=0, columns=["lon", "lat", "z"], sep=";")
+            # compute edges
+            i0 = len(edges)
+            ie = out.shape[0] + len(edges)
+
+            lindex = d.lindex
+
+            for m in range(i0, ie):
+                edges.append([m, m + 1, lindex])
+
+            edges[-1][1] = i0
+
+    edges = pd.DataFrame(edges)  # convert to pandas
 
     # write header
     with open(fgeo, "a") as f:
@@ -79,288 +144,135 @@ def read_msh(fmsh):
     npoints = int(grid.loc[2].str.split("=")[0][1])
 
     nodes = pd.DataFrame(
-        grid.loc[3 : 3 + npoints - 1, "data"].str.split(";").values.tolist(), columns=["lon", "lat", "z"]
+        grid.loc[3 : 3 + npoints - 1, "data"].str.split(";").values.tolist(),
+        columns=["lon", "lat", "z"],
     )
 
     ie = grid[grid.data.str.contains("EDGE")].index.values[0]
     nedges = int(grid.loc[ie].str.split("=")[0][1])
     edges = pd.DataFrame(
-        grid.loc[ie + 1 : ie + nedges, "data"].str.split(";").values.tolist(), columns=["e1", "e2", "e3"]
+        grid.loc[ie + 1 : ie + nedges, "data"].str.split(";").values.tolist(),
+        columns=["e1", "e2", "e3"],
     )
 
     i3 = grid[grid.data.str.contains("TRIA")].index.values[0]
     ntria = int(grid.loc[i3].str.split("=")[0][1])
     tria = pd.DataFrame(
-        grid.loc[i3 + 1 : i3 + ntria + 1, "data"].str.split(";").values.tolist(), columns=["a", "b", "c", "d"]
+        grid.loc[i3 + 1 : i3 + ntria + 1, "data"].str.split(";").values.tolist(),
+        columns=["a", "b", "c", "d"],
     )
 
     return [nodes, edges, tria]
 
 
-def jigsaw(**kwargs):
-
-    logger.info("Creating grid with JIGSAW\n")
-
-    geometry = kwargs.get("geometry", None)
-
-    # Set bgmesh
-    bgmesh = kwargs.get("bgmesh", None)
-
-    if bgmesh == "auto":
-
-        try:
-
-            logger.info("Read DEM")
-            dem = pdem.dem(**kwargs)
-
-            res_min = kwargs.get("resolution_min", 0.01)
-            res_max = kwargs.get("resolution_max", 0.5)
-            dhdx = kwargs.get("dhdx", 0.15)
-
-            logger.info("Evaluate bgmesh")
-            w = hfun(
-                dem.Dataset.elevation, resolution_min=res_min, resolution_max=res_max, dhdx=dhdx
-            )  # resolution in lat/lon degrees
-
-            path = kwargs.get("rpath", "./bgmesh/")
-
-            if not os.path.exists(path):  # check if run folder exists
-                os.makedirs(path)
-
-            logger.info("Save bgmesh to {}bgmesh.nc".format(path))
-            w.to_netcdf(path + "bgmesh.nc")  # save bgmesh
-
-            kwargs.update({"bgmesh": path + "bgmesh.nc"})
-
-        except:
+def make_bgmesh(boundary, **kwargs):
 
-            logger.warning("bgmesh failed... continuing without background mesh size")
+    logger.info("Read DEM")
+    gglobal = kwargs.get("gglobal", False)
 
-    if isinstance(geometry, dict):
+    contours = boundary.contours
 
-        df, bmindx = tag_(**kwargs)
-
-        gr = jigsaw_(df, bmindx, **kwargs)
-
-    elif isinstance(geometry, str):
-
-        if geometry == "global":
-
-            bgmesh = hfun_(kwargs.get("coastlines", None), kwargs.get("res", 0.1), kwargs.get("R", 1.0))
-
-            kwargs.update({"bgmesh": bgmesh})
-
-            df = sgl(**kwargs)
+    if gglobal:
+        lon_min = -180.0
+        lon_max = 180.0
+        lat_min = -90.0
+        lat_max = 90.0
+    else:
+        lon_min = contours.bounds.minx.min()
+        lon_max = contours.bounds.maxx.max()
+        lat_min = contours.bounds.miny.min()
+        lat_max = contours.bounds.maxy.max()
 
-            bmindx = df.tag.min()
+    dem = pdem.dem(lon_min=lon_min, lon_max=lon_max, lat_min=lat_min, lat_max=lat_max, **kwargs)
 
-            gr = jigsaw_(df, bmindx, **kwargs)
+    kwargs_ = kwargs.copy()
+    res_min = kwargs_.pop("resolution_min", 0.01)
+    res_max = kwargs_.pop("resolution_max", 0.5)
+    dhdx = kwargs_.pop("dhdx", 0.15)
 
-            convert = kwargs.get("to_lat_lon", True)
+    rpath = kwargs.get("rpath", ".")
+    tag = kwargs.get("tag", "jigsaw")
+    fpos = rpath + "/jigsaw/" + tag + "-hfun.msh"
 
-            if convert:
-                # convert to lat/lon
-                u, v = gr.SCHISM_hgrid_node_x.values, gr.SCHISM_hgrid_node_y.values
+    if gglobal:
 
-                rlon, rlat = to_lat_lon(u, v, R=kwargs.get("R", 1.0))
+        logger.info("Evaluate global bgmesh")
+        nds, lms = make_bgmesh_global(boundary, fpos, dem, **kwargs)
+        logger.info("Saving global background scale file")
+        dh = to_global_hfun(nds, lms, fpos, **kwargs)
 
-                gr["SCHISM_hgrid_node_x"].values = rlon
-                gr["SCHISM_hgrid_node_y"].values = rlat
+    else:
 
-        else:
+        logger.info("Evaluate bgmesh")
+        dh = get_hfun(
+            dem, resolution_min=res_min, resolution_max=res_max, dhdx=dhdx, **kwargs_
+        )  # resolution in lat/lon degrees
 
-            df = jcustom(**kwargs)
+        logger.info("Saving background scale file")
+        to_hfun_grid(dh, fpos)  # write bgmesh file
 
-            bmindx = df.tag.min()
+    return dh
 
-            gr = jigsaw_(df, bmindx, **kwargs)
 
-    return gr
+def jigsaw(boundary, **kwargs):
 
+    logger.info("Creating JIGSAW files\n")
 
-def sgl(**kwargs):
+    contours = boundary.contours
 
-    try:
-        geo = gp.GeoDataFrame.from_file(kwargs.get("coastlines", None))
-    except:
-        logger.warning("coastlines is not a file, trying with geopandas Dataset")
-        try:
-            geo = kwargs.get("coastlines", None)
-        except:
-            logger.error("coastlines argument not valid ")
-            sys.exit(1)
-
-        # Manage coastlines
-        logger.info("preparing coastlines")
-
-        # ANTARTICA
-        anta_mask = geo.bounds.miny < geo.bounds.miny.min() + 0.1  # indentify antartica
-        anta = geo.loc[anta_mask]
-        indx = anta.index  # keep index
-
-        try:
-            anta = pd.DataFrame(anta.boundary.values[0].coords[:], columns=["lon", "lat"])
-        except:
-            anta = pd.DataFrame(
-                anta.boundary.explode()[0].coords[:], columns=["lon", "lat"]
-            )  # convert boundary values to pandas
-        d1 = anta.where(anta.lon == anta.lon.max()).dropna().index[1:]  # get artificial boundaries as -180/180
-        d2 = anta.where(anta.lon == anta.lon.min()).dropna().index[1:]
-        anta = anta.drop(d1).drop(d2)  # drop the points
-        d3 = anta.where(anta.lat == anta.lat.min()).dropna().index  # drop lat=-90 line
-        anta = anta.drop(d3)
-        an = gp.GeoDataFrame(
-            {
-                "geometry": [shapely.geometry.LineString(anta.values)],
-                "length": shapely.geometry.LineString(anta.values).length,
-            },
-            index=indx,
-        )  # put together a LineString
-        geo.loc[indx] = shapely.geometry.LineString(anta.values)  # put it back to geo
-
-        # International Meridian
-        m1 = geo[geo.bounds.minx == -180.0].index
-        m2 = geo[geo.bounds.maxx == 180.0].index
-        mm = np.concatenate((m1, m2))  # join them
-        mm = [j for j in mm if j != indx]  # subtract antartica
-
-        # convert to u,v (stereographic coordinates)
-        for idx, poly in geo.iterrows():
-            geo.loc[idx, "geometry"] = shapely.ops.transform(
-                lambda x, y, z=None: to_stereo(x, y, R=kwargs.get("R", 1.0)), poly.geometry
-            )
-
-        w = geo.drop(indx)  # get all polygons
-        ww = w.loc[mm]  # join the split polygons
-        gw = gp.GeoDataFrame(
-            geometry=list(ww.buffer(0.0001).unary_union)
-        )  # merge the polygons that are split (around -180/180)
+    tag = kwargs.get("tag", "jigsaw")
+    rpath = kwargs.get("rpath", ".")
 
-        w = w.drop(mm)
-        # Check antartica LineString
-        if not geo.iloc[indx].geometry.values[0].is_ring:
-            ca = gp.GeoDataFrame(geometry=[shapely.geometry.LinearRing(geo.loc[indx].geometry.values[0])], index=indx)
-            ca["geometry"] = shapely.geometry.LineString(ca.geometry.values[0])
-        else:
-            ca = geo.loc[indx]
+    if not os.path.exists(rpath):
+        os.makedirs(rpath)
 
-        # PUT ALL TOGETHER
-        geo = pd.concat([w, gw, ca], ignore_index=True).reset_index(drop=True)
+    path = rpath + "/jigsaw/"
+    if not os.path.exists(path):
+        os.makedirs(path)
 
-    logger.info("storing boundaries")
+    # HFUN FILE
+    bgmesh = kwargs.get("bgmesh", None)
 
-    geo["tag"] = -(geo.index + 1)
+    if bgmesh is None:
+        dem_source = kwargs.get("dem_source", None)
+        if dem_source:
+            bgmesh = "auto"
+            kwargs.update({"bgmesh": "auto"})
 
-    idx = 0
-    dic = {}
-    for i, line in geo.iloc[:-1].iterrows():
-        lon = []
-        lat = []
-        try:
-            for x, y in line.geometry.boundary.coords[:]:
-                lon.append(x)
-                lat.append(y)
-            dic.update({"line{}".format(idx): {"lon": lon, "lat": lat, "tag": line.tag}})
-            idx += 1
-        except:
-            for x, y in line.geometry.boundary[0].coords[:]:
-                lon.append(x)
-                lat.append(y)
-            dic.update({"line{}".format(idx): {"lon": lon, "lat": lat, "tag": line.tag}})
-            idx += 1
+    if bgmesh is not None:
 
-    for i, line in geo.iloc[-1:].iterrows():
-        lon = []
-        lat = []
-        for x, y in line.geometry.coords[:]:
-            lon.append(x)
-            lat.append(y)
-            dic.update({"line{}".format(idx): {"lon": lon, "lat": lat, "tag": line.tag}})
+        logger.info("Set background scale")
 
-    dict_of_df = {k: pd.DataFrame(v) for k, v in dic.items()}
+        if bgmesh.endswith(".nc"):
 
-    df = pd.concat(dict_of_df, axis=0)
+            try:
+                dh = xr.open_dataset(bgmesh)
 
-    df["z"] = 0
-    df = df.drop_duplicates()  # drop the repeat value on closed boundaries
+                if "longitude" in dh.coords:
+                    to_hfun_grid(dh, path + tag + "-hfun.msh")  # write bgmesh file
+                else:
+                    to_hfun_mesh(dh, path + tag + "-hfun.msh")
+            except:
+                logger.warning("bgmesh failed... continuing without background mesh size")
+                bgmesh = None
 
-    return df
+        elif bgmesh == "auto":
 
+            dh = make_bgmesh(boundary, **kwargs)
 
-def jcustom(**kwargs):
+        elif bgmesh.endswith(".msh"):
 
-    geometry = kwargs.get("geometry", None)
+            pass
 
     try:
-        geo = gp.GeoDataFrame.from_file(geometry)
+        bg = dh
     except:
-        logger.error("geometry argument not a valid file")
-        sys.exit(1)
-
-    idx = 0
-    dic = {}
-    for idx, line in geo.iterrows():
-        lon = []
-        lat = []
-        for x, y in line.geometry.coords[:]:
-            lon.append(x)
-            lat.append(y)
-        dic.update({"line{}".format(idx): {"lon": lon, "lat": lat, "tag": line.tag}})
-        idx += 1
+        bg = None
 
-    dict_of_df = {k: pd.DataFrame(v) for k, v in dic.items()}
+    # GEO FILE
+    to_geo(contours, path=path, tag=tag)
 
-    df = pd.concat(dict_of_df, axis=0)
-
-    df["z"] = 0
-    df = df.drop_duplicates()  # drop the repeat value on closed boundaries
-
-    out_b = []
-    for line in df.index.levels[0]:
-        out_b.append(shapely.geometry.LineString(df.loc[line, ["lon", "lat"]].values))
-
-    merged = shapely.ops.linemerge(out_b)
-    merged = pd.DataFrame(merged.coords[:], columns=["lon", "lat"])
-    merged = merged.drop_duplicates()
-    match = df.drop_duplicates(["lon", "lat"]).droplevel(0)
-    match = match.reset_index(drop=True)
-
-    df1 = merged.sort_values(["lon", "lat"])
-    df2 = match.sort_values(["lon", "lat"])
-    df2.index = df1.index
-    final = df2.sort_index()
-    final = pd.concat([final], keys=["line0"])
-
-    return final
-
-
-def jigsaw_(df, bmindx, **kwargs):
-
-    logger.info("Creating JIGSAW files\n")
-
-    tag = kwargs.get("tag", "jigsaw")
-    rpath = kwargs.get("rpath", ".")
-
-    if not os.path.exists(rpath):
-        os.makedirs(rpath)
-
-    path = rpath + "/jigsaw/"
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-    geo(df, path=path, tag=tag)
-
-    bgmesh = kwargs.get("bgmesh", None)
-
-    if bgmesh is not None:
-
-        if isinstance(bgmesh, str):
-            dh = xr.open_dataset(bgmesh)
-            to_hfun_grid(dh, path + tag + "-hfun.msh")  # write bgmesh file
-        else:
-            to_hfun_mesh(bgmesh, path + tag + "-hfun.msh")
-
-    # write jig file
+    # JIG FILE
     fjig = path + "/" + tag + ".jig"
 
     with open(fjig, "w") as f:
@@ -381,9 +293,10 @@ def jigsaw_(df, bmindx, **kwargs):
 
     calc_dir = rpath + "/jigsaw/"
 
-    execute = kwargs.get("execute_jigsaw", True)
+    # EXECUTE
+    setup_only = kwargs.get("setup_only", False)
 
-    if execute:
+    if not setup_only:
 
         # ---------------------------------
         logger.info("executing jigsaw\n")
@@ -414,14 +327,20 @@ def jigsaw_(df, bmindx, **kwargs):
         logger.info("Jigsaw FINISHED\n")
         # ---------------------------------
 
-        gr = to_dataset(bmindx, **kwargs)
+        gr = to_dataset(**kwargs)
 
         logger.info("..done creating mesh\n")
 
-        return gr
+        return gr, bg
 
+    else:
 
-def to_dataset(bmindx, **kwargs):
+        gr = None
+
+        return gr, bg
+
+
+def to_dataset(**kwargs):
 
     logger.info("..reading mesh\n")
 
@@ -473,64 +392,56 @@ def to_dataset(bmindx, **kwargs):
 
     # Boundaries
 
-    # LAND Boundaries (negative tag)
-    ib = 1
-    ib_ = 1
+    # LAND Boundaries (1000+ tag)
+    lnd = []
+    for ik in range(1001, edges.e3.max() + 1):
+        bb = np.unique(edges.loc[edges.e3 == ik, ["e1", "e2"]].values.flatten())
+        bf = pd.concat([nodes.loc[bb]], keys=["land_boundary_{}".format(ik - 1000)])
+        bf["id"] = ik
+
+        if not bf.empty:
+            lnd.append(bf)
+
+    if lnd:
+        landb = pd.concat(lnd)
+    else:
+        landb = pd.DataFrame([])
+
+    # ISLAND Boundaries (negative tag)
     isl = []
+
     for ik in range(edges.e3.min(), 0):
         bb = np.unique(edges.loc[edges.e3 == ik, ["e1", "e2"]].values.flatten())
-        bf = pd.concat([nodes.loc[bb]])
-        bf["idx"] = bb
-        if ik >= bmindx:
-            flag = 0
-            bf["indx"] = "land_boundary_{}_{}".format(ib, flag)
-            c1 = True
-            c2 = False
-        else:
-            flag = 1
-            bf["indx"] = "land_boundary_{}_{}".format(ib_, flag)
-            c1 = False
-            c2 = True
-
-        bf["flag"] = flag
-        bf = bf.reset_index().set_index(["indx", "index"])
-        bf.index.names = ["", ""]
+        bf = pd.concat([nodes.loc[bb]], keys=["island_boundary_{}".format(-ik)])
+        bf["id"] = ik
 
         if not bf.empty:
             isl.append(bf)
-            if c1 is True:
-                ib += 1
-            if c2 is True:
-                ib_ += 1
 
     if isl:
-
-        landb = pd.concat(isl)
-
-        land_i = sorted(landb.index.levels[0], key=lambda x: [int(x.split("_")[3]), int(x.split("_")[2])])
-
+        islandb = pd.concat(isl)
     else:
+        islandb = pd.DataFrame([])
 
-        land_i = []
-
-    # WATER Boundaries (positive tag)
+    # Open Boundaries (positive tag < 1000)
+    nr_open = edges.loc[(edges.e3 > 0) & (edges.e3 < 1000)].e3
+    if not nr_open.empty:
+        nr_open = nr_open.max()
+    else:
+        nr_open = 0
 
-    wb = 1
     wbs = []
-    for ik in range(1, edges.e3.max() + 1):
+    for ik in range(0, nr_open + 1):
         bb = np.unique(edges.loc[edges.e3 == ik, ["e1", "e2"]].values.flatten())
-        bf = pd.concat([nodes.loc[bb]], keys=["open_boundary_{}".format(wb)])
-        bf["idx"] = bb
+        bf = pd.concat([nodes.loc[bb]], keys=["open_boundary_{}".format(ik)])
+        bf["id"] = ik
 
         wbs.append(bf)
-        wb += 1
 
     if wbs:
 
         openb = pd.concat(wbs)
 
-        open_i = sorted(openb.index.levels[0], key=lambda x: int(x.split("_")[2]))
-
         if openb.index.levels[0].shape[0] == 1:  # sort the nodes if open box
 
             pts = openb[["lon", "lat"]].values
@@ -554,19 +465,7 @@ def to_dataset(bmindx, **kwargs):
 
     else:
 
-        open_i = []
-
-    # Fix an issue with jumping identifiers
-
-    nns = np.array([int(x.split("_")[-1]) for x in open_i])
-    try:
-        if nns.max() != nns.size:
-            nns = np.arange(nns.size) + 1
-            open_i = ["open_boundary_{}".format(x) for x in nns]
-            lidx = dict(zip(openb.index.levels[0].to_list(), open_i))
-            openb.rename(index=lidx, level=0, inplace=True)
-    except:
-        pass  # continue is nns=[]
+        openb = pd.DataFrame([])
 
     # MAKE Dataset
 
@@ -576,69 +475,55 @@ def to_dataset(bmindx, **kwargs):
         name="SCHISM_hgrid_face_nodes",
     )
 
+    # convert if global
+    gglobal = kwargs.get("gglobal", False)
+    if gglobal:
+        R = kwargs.get("R", 1.0)
+        rlon, rlat = to_lat_lon(nodes.lon, nodes.lat, R=R)
+        nodes["lon"] = rlon
+        nodes["lat"] = rlat
+
     nod = (
         nodes.loc[:, ["lon", "lat"]]
         .to_xarray()
-        .rename({"index": "nSCHISM_hgrid_node", "lon": "SCHISM_hgrid_node_x", "lat": "SCHISM_hgrid_node_y"})
+        .rename(
+            {
+                "index": "nSCHISM_hgrid_node",
+                "lon": "SCHISM_hgrid_node_x",
+                "lat": "SCHISM_hgrid_node_y",
+            }
+        )
     )
     nod = nod.drop_vars("nSCHISM_hgrid_node")
 
     dep = xr.Dataset({"depth": (["nSCHISM_hgrid_node"], np.zeros(nod.nSCHISM_hgrid_node.shape[0]))})
 
     # open boundaries
-    op = []
-    nop = []
-    o_label = []
-    for line in open_i:
-        op.append(openb.loc[line, "idx"].values)
-        nop.append(openb.loc[line, "idx"].size)
-        o_label.append(line)
-
-    oattr = pd.DataFrame({"label": o_label, "nps": nop})
-    oattr["type"] = np.nan
-    oattr.set_index("label", inplace=True, drop=True)
-
-    obns = [j for i in op for j in i]
-    odf = pd.DataFrame({"node": obns, "type": 0, "id": 0}, index=np.arange(len(obns)))
-    idx = 1
-    for l in range(len(op)):
-        odf.loc[odf.node.isin(op[l]), "id"] = idx
-        odf.loc[odf.node.isin(op[l]), "type"] = oattr.iloc[l].type
-        idx += 1
+    if not openb.empty:
+        odf = openb.reset_index()[["level_0", "level_1", "id"]]
+        odf.columns = ["type", "node", "id"]
+        odf.type = [x.split("_")[0] for x in odf.type]
+    else:
+        odf = None
 
     # land boundaries
+    if not landb.empty:
+        ldf = landb.reset_index()[["level_0", "level_1", "id"]]
+        ldf.columns = ["type", "node", "id"]
+        ldf.type = [x.split("_")[0] for x in ldf.type]
+    else:
+        ldf = None
+
+    # island boundaries
+    if not islandb.empty:
+        idf = islandb.reset_index()[["level_0", "level_1", "id"]]
+        idf.columns = ["type", "node", "id"]
+        idf.type = [x.split("_")[0] for x in idf.type]
+    else:
+        idf = None
 
-    lp = []
-    nlp = []
-    l_label = []
-    itype = []
-    for line in land_i:
-        lp.append(landb.loc[line, "idx"].values)
-        nlp.append(landb.loc[line, "idx"].size)
-        itype.append(landb.loc[line, "flag"].values[0])
-        l_label.append(line)
-
-    lattr = pd.DataFrame({"label": l_label, "nps": nlp})
-    lattr["type"] = itype
-    lattr.set_index("label", inplace=True, drop=True)
-
-    lbns = [j for i in lp for j in i]
-    ldf = pd.DataFrame({"node": lbns, "type": 1, "id": 0}, index=np.arange(len(lbns)))
-
-    idx = -1
-    for l in range(len(lp)):
-        ldf.loc[ldf.node.isin(lp[l]), "id"] = idx
-        ldf.loc[ldf.node.isin(lp[l]), "type"] = lattr.iloc[l].type
-        idx -= 1
-
-    if not ldf.empty:
-        lk = -1
-        for k in range(-1, ldf.id.min() - 1, -1):
-            if not ldf.loc[ldf.id == k].empty:
-                ldf.loc[ldf.id == k, "id"] = lk
-                lk -= 1
-
-    tbf = pd.concat([odf, ldf])
+    tbf = pd.concat([odf, ldf, idf])
+    tbf = tbf.reset_index(drop=True)
     tbf.index.name = "bnodes"
 
     gr = xr.merge([nod, dep, els, tbf.to_xarray()])  # total
diff --git a/pyposeidon/meteo.py b/pyposeidon/meteo.py
index d6a0a004..d3a7c299 100644
--- a/pyposeidon/meteo.py
+++ b/pyposeidon/meteo.py
@@ -224,7 +224,11 @@ def cfgrib(
     # ---------------------------------------------------------------------
 
     data = xr.open_mfdataset(
-        filenames, combine=meteo_combine_by, engine="cfgrib", backend_kwargs=backend_kwargs, **xr_kwargs
+        filenames,
+        combine=meteo_combine_by,
+        engine="cfgrib",
+        backend_kwargs=backend_kwargs,
+        **xr_kwargs,
     )
 
     data = data.squeeze(drop=True)
@@ -344,7 +348,10 @@ def cfgrib(
 
         sh = (
             data[["msl", "u10", "v10"]]
-            .isel(longitude=slice(lon_0, data.longitude.size), latitude=slice(lat_0, lat_1))
+            .isel(
+                longitude=slice(lon_0, data.longitude.size),
+                latitude=slice(lat_0, lat_1),
+            )
             .sel(time=tslice)
         )
         sh = sh.assign_coords({"longitude": sh.longitude.values - 360.0})
@@ -427,7 +434,11 @@ def pynio(
     # ---------------------------------------------------------------------
 
     data = xr.open_mfdataset(
-        filenames, combine=meteo_combine_by, engine="pynio", backend_kwargs=backend_kwargs, **xr_kwargs
+        filenames,
+        combine=meteo_combine_by,
+        engine="pynio",
+        backend_kwargs=backend_kwargs,
+        **xr_kwargs,
     )
 
     data = data.squeeze(drop=True)
@@ -448,7 +459,13 @@ def pynio(
     ]
 
     data = data.rename(
-        {msl_[0]: "msl", u10_[0]: "u10", v10_[0]: "v10", lon_coord[0]: "longitude", lat_coord[0]: "latitude"}
+        {
+            msl_[0]: "msl",
+            u10_[0]: "u10",
+            v10_[0]: "v10",
+            lon_coord[0]: "longitude",
+            lat_coord[0]: "latitude",
+        }
     )
 
     if time_coord:
@@ -544,7 +561,10 @@ def pynio(
 
         sh = (
             data[["msl", "u10", "v10"]]
-            .isel(longitude=slice(lon_0, data.longitude.size), latitude=slice(lat_0, lat_1))
+            .isel(
+                longitude=slice(lon_0, data.longitude.size),
+                latitude=slice(lat_0, lat_1),
+            )
             .sel(time=tslice)
         )
         sh = sh.assign_coords({"longitude": sh.longitude.values - 360.0})
@@ -727,7 +747,13 @@ def netcdf(
     v10_ = [x for (x, v) in sn.items() if ("v wind" in v) | ("v-component" in v)]
 
     data = data.rename(
-        {msl_[0]: "msl", u10_[0]: "u10", v10_[0]: "v10", lon_coord[0]: "longitude", lat_coord[0]: "latitude"}
+        {
+            msl_[0]: "msl",
+            u10_[0]: "u10",
+            v10_[0]: "v10",
+            lon_coord[0]: "longitude",
+            lat_coord[0]: "latitude",
+        }
     )
 
     data = data.sel(longitude=slice(lon_min, lon_max)).sel(latitude=slice(lat_min, lat_max))
diff --git a/pyposeidon/model.py b/pyposeidon/model.py
index e6cc0cc7..d99fd5f7 100644
--- a/pyposeidon/model.py
+++ b/pyposeidon/model.py
@@ -56,7 +56,11 @@
             "formatter": "verbose",
             "filename": "pyposeidon.log",
         },
-        "console": {"level": "DEBUG", "class": "colorlog.StreamHandler", "formatter": "color"},
+        "console": {
+            "level": "DEBUG",
+            "class": "colorlog.StreamHandler",
+            "formatter": "color",
+        },
     },
     "loggers": {
         "pyposeidon": {
diff --git a/pyposeidon/schism.py b/pyposeidon/schism.py
index 680186c9..9a52b905 100644
--- a/pyposeidon/schism.py
+++ b/pyposeidon/schism.py
@@ -36,9 +36,8 @@
 import pyposeidon.dem as pdem
 from pyposeidon.utils.get_value import get_value
 from pyposeidon.utils.converter import myconverter
-from pyposeidon.utils import obs
+from pyposeidon.utils.vals import obs
 from pyposeidon.utils.cpoint import closest_node
-from pyposeidon.utils.hfun import hfun
 from pyposeidon.utils.unml import unml
 from pyposeidon.utils.data import data
 
@@ -54,6 +53,7 @@
 # DATA_PATH = pkg_resources.resource_filename('pyposeidon', 'misc')
 DATA_PATH = os.path.dirname(pyposeidon.__file__) + "/misc/"
 TEST_DATA_PATH = os.path.dirname(pyposeidon.__file__) + "/tests/data/"
+
 # add conda path to PATH
 cpath = pyposeidon.__path__[0].split("/lib/")[0]
 os.environ["PATH"] += os.pathsep + cpath + "/bin"
@@ -118,7 +118,12 @@ def __init__(self, **kwargs):
                     logger.error("geometry argument not a valid geopandas file")
                     sys.exit(1)
 
-                self.lon_min, self.lat_min, self.lon_max, self.lat_max = geo.total_bounds
+                (
+                    self.lon_min,
+                    self.lat_min,
+                    self.lon_max,
+                    self.lat_max,
+                ) = geo.total_bounds
 
         # coastlines
         coastlines = kwargs.get("coastlines", None)
@@ -128,7 +133,9 @@ def __init__(self, **kwargs):
 
             # world polygons - user input
             coast = cf.NaturalEarthFeature(
-                category="physical", name="land", scale="{}m".format({"l": 110, "i": 50, "h": 10}[cr])
+                category="physical",
+                name="land",
+                scale="{}m".format({"l": 110, "i": 50, "h": 10}[cr]),
             )
 
             self.coastlines = gp.GeoDataFrame(geometry=[x for x in coast.geometries()])
@@ -224,7 +231,8 @@ def config(self, config_file=None, output=False, **kwargs):
             logger.warning("rnday larger than simulation range\n")
             logger.warning(
                 "rnday={} while simulation time is {}\n".format(
-                    params["core"]["rnday"], (self.end_date - self.start_date).total_seconds() / (3600 * 24.0)
+                    params["core"]["rnday"],
+                    (self.end_date - self.start_date).total_seconds() / (3600 * 24.0),
                 )
             )
             # ---------------------------------------------------------------------
@@ -309,11 +317,24 @@ def to_force(ar0, **kwargs):
             coords={"time": tlist},
         )
 
-        sout.attrs = {"description": "Schism meteo data", "history": "pyposeidon", "source": "netCDF4 python module"}
+        sout.attrs = {
+            "description": "Schism meteo data",
+            "history": "pyposeidon",
+            "source": "netCDF4 python module",
+        }
 
-        sout.time.attrs = {"long_name": "Time", "standard_name": "time", "base_date": bdate, "units": udate}
+        sout.time.attrs = {
+            "long_name": "Time",
+            "standard_name": "time",
+            "base_date": bdate,
+            "units": udate,
+        }
 
-        sout.lat.attrs = {"units": "degrees_north", "long_name": "Latitude", "standard_name": "latitude"}
+        sout.lat.attrs = {
+            "units": "degrees_north",
+            "long_name": "Latitude",
+            "standard_name": "latitude",
+        }
 
         sout.prmsl.attrs = {
             "units": "Pa",
@@ -445,8 +466,9 @@ def output(self, **kwargs):
         # save bctides.in
         bs = self.grid.Dataset[["node", "id", "type"]].to_dataframe()
         # open boundaries
-        number_of_open_boundaries = bs.id.max()
-        number_of_open_boundaries_nodes = bs.loc[bs.id > 0].shape[0]
+        number_of_open_boundaries = bs.loc[bs.type == "open"].id.max()
+        number_of_open_boundaries_nodes = bs.loc[bs.type == "open"].shape[0]
+        print(number_of_open_boundaries)
 
         with open(path + "bctides.in", "w") as f:
             f.write("Header\n")
@@ -633,7 +655,11 @@ def run(self, **kwargs):
 
         # note that cwd is the folder where the executable is
         ex = subprocess.Popen(
-            args=["./launchSchism.sh"], cwd=calc_dir, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE
+            args=["./launchSchism.sh"],
+            cwd=calc_dir,
+            shell=True,
+            stderr=subprocess.PIPE,
+            stdout=subprocess.PIPE,
         )  # , bufsize=1)
 
         with open(calc_dir + "err.log", "w") as f:
@@ -881,7 +907,12 @@ def global2local(self, **kwargs):
         for i in range(len(gfiles)):
             with open(gfiles[i], "r") as f:
                 frames[i] = pd.read_csv(
-                    f, skiprows=3, header=None, nrows=nels[i], names=["local", "global_n"], delim_whitespace=True
+                    f,
+                    skiprows=3,
+                    header=None,
+                    nrows=nels[i],
+                    names=["local", "global_n"],
+                    delim_whitespace=True,
                 )
 
         elems = pd.concat(frames, keys=keys)
@@ -938,7 +969,13 @@ def global2local(self, **kwargs):
                 header=None,
                 nrows=1,
                 delim_whitespace=True,
-                names=["start_year", "start_month", "start_day", "start_hour", "utc_start"],
+                names=[
+                    "start_year",
+                    "start_month",
+                    "start_day",
+                    "start_hour",
+                    "utc_start",
+                ],
             )
         with open(gfiles[0], "r") as f:
             h1 = pd.read_csv(
@@ -947,7 +984,19 @@ def global2local(self, **kwargs):
                 header=None,
                 nrows=1,
                 delim_whitespace=True,
-                names=["nrec", "dtout", "nspool", "nvrt", "kz", "h0", "h_s", "h_c", "theta_b", "theta_f", "ics"],
+                names=[
+                    "nrec",
+                    "dtout",
+                    "nspool",
+                    "nvrt",
+                    "kz",
+                    "h0",
+                    "h_s",
+                    "h_c",
+                    "theta_b",
+                    "theta_f",
+                    "ics",
+                ],
             )
 
         ztots = ["ztot_" + str(i) for i in range(1, h1.loc[:, "kz"].values[0] - 1)]
@@ -1175,13 +1224,13 @@ def xcombine(self, tfs):
         for key in tfs[0].variables:
             if "nSCHISM_hgrid_face" in tfs[0][key].dims:
                 r = self.combine_(key, tfs, self.misc["melems"], "nSCHISM_hgrid_face")
-                side.append(r)
+                el.append(r)
             elif "nSCHISM_hgrid_node" in tfs[0][key].dims:
                 r = self.combine_(key, tfs, self.misc["mnodes"], "nSCHISM_hgrid_node")
                 node.append(r)
             elif "nSCHISM_hgrid_edge" in tfs[0][key].dims:
                 r = self.combine_(key, tfs, self.misc["msides"], "nSCHISM_hgrid_edge")
-                node.append(r)
+                side.append(r)
             elif len(tfs[0][key].dims) == 1:
                 single.append(tfs[0][key])
 
@@ -1229,7 +1278,13 @@ def read_vgrid(self, **kwargs):
 
         path = get_value(self, kwargs, "rpath", "./schism/")
 
-        vgrid = pd.read_csv(path + "vgrid.in", header=None, index_col=False, engine="python", delimiter="!")
+        vgrid = pd.read_csv(
+            path + "vgrid.in",
+            header=None,
+            index_col=False,
+            engine="python",
+            delimiter="!",
+        )
 
         try:
             vgrid = vgrid.drop(1, axis=1)
@@ -1300,8 +1355,14 @@ def results(self, **kwargs):
                     ["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"],
                     gt34 - 1,
                 ),  # -> start index = 0
-                "SCHISM_hgrid_face_x": (["nSCHISM_hgrid_face"], gt3.loc[:, "xc"].values),
-                "SCHISM_hgrid_face_y": (["nSCHISM_hgrid_face"], gt3.loc[:, "yc"].values),
+                "SCHISM_hgrid_face_x": (
+                    ["nSCHISM_hgrid_face"],
+                    gt3.loc[:, "xc"].values,
+                ),
+                "SCHISM_hgrid_face_y": (
+                    ["nSCHISM_hgrid_face"],
+                    gt3.loc[:, "yc"].values,
+                ),
                 "ele_bottom_index": (["nSCHISM_hgrid_face"], gt3.kbe.values),
             }
         )
@@ -1375,7 +1436,12 @@ def results(self, **kwargs):
         sigms = header2.loc[:, sigmas].values.flatten()  # get sigmas
         iwet_dry = 0  # defined by the user
         ihgrid_id = -2147483647  # defined by user - 0,dummy_dim,ihgrid_id
-        one = xr.Dataset({"dry_value_flag": (("one"), [iwet_dry]), "SCHISM_hgrid": (("one"), [ihgrid_id])})
+        one = xr.Dataset(
+            {
+                "dry_value_flag": (("one"), [iwet_dry]),
+                "SCHISM_hgrid": (("one"), [ihgrid_id]),
+            }
+        )
 
         # compute cs
         klev = np.arange(header2.kz.values[0], header2.nvrt.values[0] + 1)
@@ -1511,7 +1577,10 @@ def results(self, **kwargs):
                 "positive": "down",
             }
 
-            xc.Cs.attrs = {"long_name": "Function C(s) at whole levels", "positive": "up"}
+            xc.Cs.attrs = {
+                "long_name": "Function C(s) at whole levels",
+                "positive": "up",
+            }
 
             xc.dry_value_flag.attrs = {"values": "0: use last-wet value; 1: use junk"}
 
@@ -1591,7 +1660,11 @@ def results(self, **kwargs):
             }
 
             base_date = " ".join([str(x) for x in date.T.values.flatten()])
-            xc.time.attrs = {"long_name": "Time", "base_date": base_date, "standard_name": "time"}
+            xc.time.attrs = {
+                "long_name": "Time",
+                "base_date": base_date,
+                "standard_name": "time",
+            }
 
             xc.sigma.attrs = {
                 "long_name": "S coordinates at whole levels",
@@ -1626,7 +1699,7 @@ def set_obs(self, **kwargs):
 
         path = get_value(self, kwargs, "rpath", "./schism/")
         nspool_sta = get_value(self, kwargs, "nspool_sta", 1)
-        tg_database = get_value(self, kwargs, "tide_gauges", None)  # TODO
+        tg_database = get_value(self, kwargs, "obs", DATA_PATH + "critech.csv")
         coastal_monitoring = get_value(self, kwargs, "coastal_monitoring", False)
         flags = get_value(self, kwargs, "station_flags", [1] + [0] * 8)
 
@@ -1645,23 +1718,32 @@ def set_obs(self, **kwargs):
             index=[0],
         )
 
-        z = self.__dict__.copy()
         ## FOR TIDE GAUGE MONITORING
-        tg = obs.obs(**z)
+        z = self.__dict__.copy()
+        tg = obs(**z)
+
         logger.info("get in-situ measurements locations \n")
 
         gpoints = np.array(
-            list(zip(self.grid.Dataset.SCHISM_hgrid_node_x.values, self.grid.Dataset.SCHISM_hgrid_node_y.values))
+            list(
+                zip(
+                    self.grid.Dataset.SCHISM_hgrid_node_x.values,
+                    self.grid.Dataset.SCHISM_hgrid_node_y.values,
+                )
+            )
         )
 
         stations = []
         grid_index = []
         for l in range(tg.locations.shape[0]):
-            plat, plon = tg.locations.loc[l, ["lat", "lon"]]
+            plat, plon = tg.locations.loc[l, ["latitude", "longitude"]]
             cp = closest_node([plon, plat], gpoints)
             grid_index.append(
                 list(
-                    zip(self.grid.Dataset.SCHISM_hgrid_node_x.values, self.grid.Dataset.SCHISM_hgrid_node_y.values)
+                    zip(
+                        self.grid.Dataset.SCHISM_hgrid_node_x.values,
+                        self.grid.Dataset.SCHISM_hgrid_node_y.values,
+                    )
                 ).index(tuple(cp))
             )
             stations.append(cp)
@@ -1727,7 +1809,17 @@ def get_station_data(self, **kwargs):
             # get the station flags
             flags = pd.read_csv(path + "station.in", header=None, nrows=1, delim_whitespace=True).T
             flags.columns = ["flag"]
-            flags["variable"] = ["elev", "air_pressure", "windx", "windy", "T", "S", "u", "v", "w"]
+            flags["variable"] = [
+                "elev",
+                "air_pressure",
+                "windx",
+                "windy",
+                "T",
+                "S",
+                "u",
+                "v",
+                "w",
+            ]
 
             vals = flags[flags.values == 1]  # get the active ones
         except OSError as e:
@@ -1748,7 +1840,7 @@ def get_station_data(self, **kwargs):
             pindex = pd.MultiIndex.from_product([df.T.columns, df.T.index])
 
             r = pd.DataFrame(df.values.flatten(), index=pindex, columns=[vals.loc[idx, "variable"]])
-            r.index.names = ["time", "point"]
+            r.index.names = ["time", "index"]
 
             dfs.append(r.to_xarray())
 
diff --git a/pyposeidon/ugmsh.py b/pyposeidon/ugmsh.py
index ae316fd4..4dd03a38 100644
--- a/pyposeidon/ugmsh.py
+++ b/pyposeidon/ugmsh.py
@@ -16,13 +16,27 @@
 from tqdm import tqdm
 import sys
 import gmsh
-
-from pyposeidon.utils.tag import *
-from pyposeidon.utils.spline import *
-from pyposeidon.utils.stereo import to_lat_lon
-from pyposeidon.utils.pos import *
+import subprocess
+import shapely
+
+from pyposeidon.utils.spline import use_spline
+from pyposeidon.utils.global_bgmesh import make_bgmesh_global
+from pyposeidon.utils.stereo import to_stereo, to_3d, to_lat_lon
+from pyposeidon.utils.pos import to_st, to_global_pos
+from pyposeidon.utils.scale import scale_dem
+from pyposeidon.utils.topology import (
+    MakeTriangleFaces_periodic,
+    MakeQuadFaces,
+    tria_to_df,
+    tria_to_df_3d,
+    quads_to_df,
+)
 import pyposeidon.dem as pdem
 
+import multiprocessing
+
+NCORES = max(1, multiprocessing.cpu_count() - 1)
+
 import logging
 
 logger = logging.getLogger("pyposeidon")
@@ -66,7 +80,7 @@ def read_gmsh(mesh, **kwargs):
         onodes, xyz = gmsh.model.mesh.getNodesForPhysicalGroup(dim=getattr(row, "dim"), tag=getattr(row, "tag"))
 
         db = pd.DataFrame({"node": onodes - 1})
-        db["type"] = np.nan
+        db["type"] = "open"
         db["id"] = getattr(row, "Index") + 1
 
         bounds.append(db)
@@ -81,28 +95,22 @@ def read_gmsh(mesh, **kwargs):
         lnodes, xyz = gmsh.model.mesh.getNodesForPhysicalGroup(dim=getattr(row, "dim"), tag=getattr(row, "tag"))
 
         db = pd.DataFrame({"node": lnodes - 1})
-        db["type"] = 0
-        db["id"] = -(getattr(row, "Index") + 1)
+        db["type"] = "land"
+        db["id"] = 1000 + (getattr(row, "Index") + 1)
 
         bounds.append(db)
 
-    if lbs.empty:  # Store max index from land boundaries
-        itag = 0
-    else:
-        itag = lbs.tag.max() - 1000
-
     # islands
     logger.info("islands")
 
     ibs = bgs.loc[bgs.tag > 2000]
     ibs.reset_index(inplace=True, drop=True)
-    ibs.index = ibs.index + itag  # set index
 
     for row in ibs.itertuples(index=True, name="Pandas"):
 
         inodes, xyz = gmsh.model.mesh.getNodesForPhysicalGroup(dim=getattr(row, "dim"), tag=getattr(row, "tag"))
         db = pd.DataFrame({"node": inodes - 1})
-        db["type"] = -1
+        db["type"] = "island"
         db["id"] = -(getattr(row, "Index") + 1)
 
         bounds.append(db)
@@ -122,9 +130,14 @@ def read_gmsh(mesh, **kwargs):
     # check if global and reproject
     sproj = kwargs.get("gglobal", False)
     if sproj:  # convert to lat/lon
-        xd, yd = to_lat_lon(nodes.x, nodes.y)
-        nodes["x"] = xd
-        nodes["y"] = yd
+        if nodes.z.any() != 0:
+            xd, yd = to_lat_lon(nodes.x, nodes.y, nodes.z)
+            nodes["x"] = xd
+            nodes["y"] = yd
+        else:
+            xd, yd = to_lat_lon(nodes.x, nodes.y)
+            nodes["x"] = xd
+            nodes["y"] = yd
 
     grid = pd.DataFrame({"lon": nodes.x, "lat": nodes.y})
 
@@ -134,13 +147,21 @@ def read_gmsh(mesh, **kwargs):
 
     ## make dataset
     els = xr.DataArray(
-        tri3, dims=["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"], name="SCHISM_hgrid_face_nodes"
+        tri3,
+        dims=["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"],
+        name="SCHISM_hgrid_face_nodes",
     )
 
     nod = (
         grid.loc[:, ["lon", "lat"]]
         .to_xarray()
-        .rename({"index": "nSCHISM_hgrid_node", "lon": "SCHISM_hgrid_node_x", "lat": "SCHISM_hgrid_node_y"})
+        .rename(
+            {
+                "index": "nSCHISM_hgrid_node",
+                "lon": "SCHISM_hgrid_node_x",
+                "lat": "SCHISM_hgrid_node_y",
+            }
+        )
     )
     nod = nod.drop_vars("nSCHISM_hgrid_node")
 
@@ -153,30 +174,76 @@ def read_gmsh(mesh, **kwargs):
     return gr
 
 
-def gmsh_(**kwargs):
+def gmsh_(boundary, **kwargs):
 
     logger.info("Creating grid with GMSH\n")
 
-    geometry = kwargs.get("geometry", None)
+    rpath = kwargs.get("rpath", ".")
+
+    if not os.path.exists(rpath):
+        os.makedirs(rpath)
+
+    gpath = os.path.join(rpath, "gmsh")
+    if not os.path.exists(gpath):
+        os.makedirs(gpath)
+
+    use_bindings = kwargs.get("use_bindings", True)
+    setup_only = kwargs.get("setup_only", False)
+    bgmesh = kwargs.get("bgmesh", None)
+
+    if bgmesh is None:
+        dem_source = kwargs.get("dem_source", None)
+        if dem_source:
+            bgmesh = "auto"
+            kwargs.update({"bgmesh": "auto"})
+
+    if bgmesh == "auto":
 
-    if isinstance(geometry, dict):
+        try:
 
-        rpath = kwargs.get("rpath", ".")
+            rpath = kwargs.get("rpath", ".")
 
-        if not os.path.exists(rpath):
-            os.makedirs(rpath)
+            if not os.path.exists(rpath + "/gmsh/"):  # check if run folder exists
+                os.makedirs(rpath + "/gmsh/")
 
-        gpath = os.path.join(rpath, "gmsh")
-        if not os.path.exists(gpath):
-            os.makedirs(gpath)
+            fpos = rpath + "/gmsh/bgmesh.pos"
 
-        df, bmindx = tag_(**kwargs)
+            gglobal = kwargs.get("gglobal", False)
+            if gglobal:
 
-        make_gmsh(df, **kwargs)
+                dem = pdem.dem(**kwargs)
 
-        gr = read_gmsh(rpath + "/gmsh/mymesh.msh")
+                nds, lms = make_bgmesh_global(boundary, fpos, dem, **kwargs)
+                dh = to_global_pos(nds, lms, fpos, **kwargs)
+            else:
+                dh = make_bgmesh(boundary.contours, fpos, **kwargs)
 
-    return gr
+            kwargs.update({"bgmesh": fpos})
+
+        except OSError as e:
+
+            logger.warning("bgmesh failed... continuing without background mesh size")
+            dh = None
+            kwargs.update({"bgmesh": None})
+
+    if use_bindings:
+        logger.info("using python bindings")
+        make_gmsh(boundary, **kwargs)
+    else:
+        to_geo(boundary, **kwargs)
+        if not setup_only:
+            logger.info("using GMSH binary")
+            gmsh_execute(**kwargs)
+
+    if not setup_only:
+        gr = read_gmsh(rpath + "/gmsh/mymesh.msh", **kwargs)
+
+    try:
+        bg = dh
+    except:
+        bg = None
+
+    return gr, bg
 
 
 def gset(df, **kwargs):
@@ -200,7 +267,7 @@ def gset(df, **kwargs):
         contour = conts[ic]
         curve = df.loc[contour, ["lon", "lat"]]
         curve = pd.concat([curve, curve.loc[0:0]]).reset_index(drop=True)
-        di = spline(curve, ds=0.01, method="slinear")
+        di = use_spline(curve, ds=0.01, method="slinear")
         di["z"] = df.loc[contour].z.values[0]
         di["tag"] = df.loc[contour].tag.values[0].astype(int)
         di["lc"] = df.loc[contour].lc.values[0]
@@ -224,7 +291,7 @@ def gset(df, **kwargs):
         contour = df0.tag == ic
         curve = df0.loc[contour, ["lon", "lat"]].reset_index(drop=True)
         #    curve = pd.concat([curve,curve.loc[0:0]]).reset_index(drop=True)
-        di = spline(curve, ds=0.01, method="slinear")
+        di = use_spline(curve, ds=0.01, method="slinear")
         di["z"] = df0.loc[contour].z.values[0]
         di["tag"] = df0.loc[contour].tag.values[0].astype(int)
         di["lc"] = df0.loc[contour].lc.values[0]
@@ -259,118 +326,419 @@ def gset(df, **kwargs):
     return ddf
 
 
-def make_bgmesh(dem, res_min, res_max):
+def to_geo(boundary, **kwargs):
+
+    df = boundary.contours
+
+    gglobal = kwargs.get("gglobal", False)
+
+    lc = kwargs.get("lc", 0.5)
+    df["lc"] = lc
+
+    bspline = kwargs.get("bspline", False)
+
+    # save boundary configuration for Line0
+    df_ = df.loc[df.tag != "island"].reset_index(drop=True)  # all external contours
+
+    if not df_.empty:
+        rb0, land_lines, open_lines = outer_boundary(df, **kwargs)
+    else:
+        rb0 = pd.DataFrame({})
+        open_lines = {}
+        land_lines = {}
+
+    ptag = 0
+    ptag0 = 1
+    ltag = 0
+    loops = []
+    pl = 100
+
+    # ... and save it to disk
+    rpath = kwargs.get("rpath", ".")
+
+    logger.info("save geo file")
+    filename = rpath + "/gmsh/mymesh.geo"
+
+    with open(filename, "w") as f:
+
+        if gglobal:
+
+            f.write("Point(1) = {{0,0,0,{}}};\n".format(lc))
+            f.write("Point(2) = {{1,0,0,{}}};\n".format(lc))
+            f.write("PolarSphere (1) = {1,2};\n")
+            ptag = 2
+            ptag0 = 3
+            ltag = 1
+            loops = []
+            pl = 0
+
+        # outer contour
+        points = []
+        for row in rb0.itertuples(index=True, name="Pandas"):
+            ptag += 1
+            f.write(
+                "Point({}) = {{{},{},{},{}}};\n".format(
+                    ptag,
+                    getattr(row, "lon"),
+                    getattr(row, "lat"),
+                    getattr(row, "z"),
+                    getattr(row, "lc"),
+                )
+            )
+            points.append(ptag)
+
+        try:
+            points = points + [points[0]]
+
+            if not bspline:
+
+                t2 = [[points[l], points[l + 1]] for l in range(0, len(points) - 1)]
+                lines = []
+                for [a, b] in t2:
+                    ltag += 1
+                    f.write("Line({}) = {{{}, {}}};\n".format(ltag, a, b))
+                    lines.append(ltag)
+                ltag += 1
+
+            else:
+
+                ltag += 1
+                f.write("BSpline ({}) = {}".format(ltag, "{"))
+                f.write(",".join(map(str, points)))
+                f.write("{};\n".format("}"))
+                lines = [ltag]
+                ltag += 1
+
+            f.write("Line Loop ({}) = {}".format(ltag, "{"))
+            f.write(",".join(map(str, lines)))
+            f.write("{};\n".format("}"))
+
+            loops.append(ltag)
+
+            pl += 1
+            f.write("Physical Line ({}) = {{{}}};\n".format(pl, ltag))
+
+        except:
+
+            pass
+
+        # The rest
+        for k, d in df.loc[df.tag == "island"].iterrows():
+
+            points = []
+
+            rb = pd.DataFrame(d.geometry.coords[:], columns=["lon", "lat"])
+            rb["z"] = 0
+            rb["lc"] = lc
+            rb = rb.drop_duplicates(["lon", "lat"])
+
+            if not shapely.geometry.LinearRing(rb[["lon", "lat"]].values).is_ccw:  # check for clockwise orientation
+                rb_ = rb.iloc[::-1].reset_index(drop=True)
+                rb = rb_
+
+            for row in rb.itertuples(index=True, name="Pandas"):
+                ptag += 1
+                f.write(
+                    "Point({}) = {{{},{},{},{}}};\n".format(
+                        ptag,
+                        getattr(row, "lon"),
+                        getattr(row, "lat"),
+                        getattr(row, "z"),
+                        getattr(row, "lc"),
+                    )
+                )
+                points.append(ptag)
+
+            points = points + [points[0]]
+
+            if not bspline:
+
+                t2 = [[points[l], points[l + 1]] for l in range(0, len(points) - 1)]
+                lines = []
+                for [a, b] in t2:
+                    ltag += 1
+                    f.write("Line({}) = {{{}, {}}};\n".format(ltag, a, b))
+                    lines.append(ltag)
+                ltag += 1
+
+            else:
+
+                ltag += 1
+                f.write("BSpline ({}) = {}".format(ltag, "{"))
+                f.write(",".join(map(str, points)))
+                f.write("{};\n".format("}"))
+                lines = [ltag]
+                ltag += 1
+
+            f.write("Line Loop ({}) = {}".format(ltag, "{"))
+            f.write(",".join(map(str, lines)))
+            f.write("{};\n".format("}"))
+
+            loops.append(ltag)
+
+            pl += 1
+            f.write("Physical Line ({}) = {{{}}};\n".format(pl, ltag))
+
+        f.write("Plane Surface (2) = {")
+        f.write(",".join(map(str, loops)))
+        f.write("{};\n".format("}"))
+
+        f.write("Field[1] = Distance;\n")
+
+        if not bspline:
+            f.write("Field[1].CurvesList = {{{}}};\n".format(",".join(map(str, np.arange(1, ltag + 1)))))
+        else:
+            f.write("Field[1].NodesList =\n{")
+            f.write(",".join(map(str, np.arange(ptag0, ptag + 1))))
+            f.write("};\n")
+
+        SizeMin = kwargs.get("SizeMin", 0.1)
+        SizeMax = kwargs.get("SizeMax", 0.5)
+        DistMin = kwargs.get("DistMin", 0.01)
+        DistMax = kwargs.get("DistMax", 0.2)
+
+        f.write("Field[2] = Threshold;\n")
+        f.write("Field[2].IField = 1;\n")
+        f.write("Field[2].DistMin = {};\n".format(DistMin))
+        f.write("Field[2].DistMax = {};\n".format(DistMax))
+        f.write("Field[2].SizeMin = {};\n".format(SizeMin))
+        f.write("Field[2].SizeMax = {};\n".format(SizeMax))
+
+        bgmesh = kwargs.get("bgmesh", None)
+
+        if bgmesh:
+
+            f.write('Merge "{}";\n'.format(bgmesh))
+
+            f.write("Field[2].StopAtDistMax = 1;\n")
+
+            f.write("Field[3] = PostView;\n")
+            f.write("Field[3].ViewIndex = 0;\n")
+
+            f.write("Field[4] = Min;\n")
+            f.write("Field[4].FieldsList = {2, 3};\n")
+
+            f.write("Background Field = 4;\n")
+
+        else:
+
+            f.write("Background Field = 2;\n")
+
+        MeshSizeMin = kwargs.get("MeshSizeMin", None)
+        MeshSizeMax = kwargs.get("MeshSizeMax", None)
+
+        if MeshSizeMin is not None:
+            f.write("Mesh.MeshSizeMin = {};\n".format(MeshSizeMin))
+        if MeshSizeMax is not None:
+            f.write("Mesh.MeshSizeMax = {};\n".format(MeshSizeMax))
+
+        if bgmesh:
+            f.write("Mesh.MeshSizeFromPoints = 0;\n")
+            f.write("Mesh.MeshSizeFromCurvature = 0;\n")
+            f.write("Mesh.MeshSizeExtendFromBoundary = 0;\n")
+
+    return
+
+
+def gmsh_execute(**kwargs):
+
+    rpath = kwargs.get("rpath", ".")
+    setup_only = kwargs.get("setup_only", False)
+    calc_dir = rpath + "/gmsh/"
+    ncores = kwargs.get("ncores", NCORES)
 
-    # scale bathymetry
     try:
-        b = dem.adjusted.to_dataframe()
+        bin_path = os.environ["GMSH"]
     except:
-        b = dem.elevation.to_dataframe()
+        bin_path = kwargs.get("gpath", None)
 
-    b.columns = ["z"]
+    if bin_path is None:
+        # ------------------------------------------------------------------------------
+        logger.warning("gmsh executable path (gpath) not given -> using default \n")
+        # ------------------------------------------------------------------------------
+        bin_path = "gmsh"
 
-    b[b.z >= -10] = -1.0e-4  # normalize to only negative values
+    if not setup_only:
 
-    b.z = np.sqrt(-b.z) / 0.5  # scale
+        # ---------------------------------
+        logger.info("executing gmsh\n")
+        # ---------------------------------
 
-    # adjust scale
+        myoutput = open(calc_dir + "myoutput.txt", "w+")
 
-    bg = b.z.values
+        # execute jigsaw
+        ex = subprocess.Popen(
+            ["{} -nt {} {} -2".format(bin_path, NCORES, "mymesh.geo")],
+            cwd=calc_dir,
+            shell=True,
+            stderr=myoutput,
+            stdout=myoutput,
+            universal_newlines=True,
+        )  # , bufsize=1)
 
-    a2 = (bg - bg.min()) / (bg.max() - bg.min())
+        output, errors = ex.communicate()
 
-    d2 = res_min + a2 * (res_max - res_min)
+        # ---------------------------------
+        logger.info("Gmsh FINISHED\n")
+        # ---------------------------------
 
-    b["d2"] = d2
+    return
 
-    nodes = b.reset_index()
 
-    nodes["z"] = 0
+def outer_boundary(df, **kwargs):
 
-    x = dem.longitude.values
-    y = dem.latitude.values
+    lc = kwargs.get("lc", 0.5)
 
-    quad = MakeFacesVectorized(y.shape[0], x.shape[0])  # get element structure from array
-    elems = pd.DataFrame(quad, columns=["a", "b", "c", "d"])
+    df_ = df.loc[df.tag != "island"].reset_index(drop=True)  # all external contours
 
-    df = to_df(elems, nodes)
+    # store xy in a DataFrame
+    dic = {}
+    for k, d in df_.iterrows():
+        out = pd.DataFrame(d.geometry.coords[:], columns=["lon", "lat"])
+        out["lindex"] = d.lindex
+        out = out.drop_duplicates(["lon", "lat"])
+        if d.tag == "land":  # drop end points in favor or open tag
+            out = out[1:-1]
+        dic.update({"line{}".format(k): out})
+    o1 = pd.concat(dic, axis=0).droplevel(0).reset_index(drop=True)
+    o1 = o1.drop_duplicates(["lon", "lat"])
 
-    return df
+    # Do linemerge of outer contours
+    lss = df_.geometry.values
+    merged = shapely.ops.linemerge(lss)
+    o2 = pd.DataFrame({"lon": merged.xy[0], "lat": merged.xy[1]})  # convert to DataFrame
+    o2 = o2.drop_duplicates()
 
+    rb0 = o2.merge(o1)  # merge to transfer the lindex
 
-def make_gmsh(df, **kwargs):
+    rb0["z"] = 0
 
-    logger.info("create grid")
+    # check orientation
+    if not shapely.geometry.LinearRing(rb0[["lon", "lat"]].values).is_ccw:
 
-    model = gmsh.model
-    factory = model.geo
+        rb0_ = rb0.iloc[::-1].reset_index(drop=True)
+        rb0 = rb0_
 
-    gmsh.initialize()
-    model.add("schism")
+    rb0["lc"] = lc
 
-    #    gmsh.option.setNumber("General.Terminal", 1)
+    logger.info("Compute edges")
+    edges = [list(a) for a in zip(np.arange(rb0.shape[0]), np.arange(rb0.shape[0]) + 1, rb0.lindex.values)]  # outer
+    edges[-1][1] = 0
 
-    interpolate = kwargs.get("interpolate", False)
-    if interpolate:
-        ddf = gset(df, **kwargs)
-    else:
-        ddf = df
-    lc = kwargs.get("lc", 0.5)
+    # sort (duplicated bounds)
+    edges = pd.DataFrame(edges, columns=["index", "ie", "lindex"])
 
-    ddf["lc"] = lc
-    ddf = ddf.apply(pd.to_numeric)
+    edges["lindex1"] = pd.concat([rb0.loc[1:, "lindex"], rb0.loc[0:0, "lindex"]]).reset_index(drop=True).astype(int)
+    edges["que"] = np.where(
+        ((edges["lindex"] != edges["lindex1"]) & (edges["lindex"] > 0) & (edges["lindex"] < 1000)),
+        edges["lindex1"],
+        edges["lindex"],
+    )
+
+    edges = edges.reset_index().loc[:, ["index", "ie", "que"]]
+
+    rb0["bounds"] = edges.loc[:, ["index", "ie"]].values.tolist()
+
+    # get boundary types
+    land_lines = {
+        your_key: edges.loc[edges.que == your_key].index.values
+        for your_key in [x for x in edges.que.unique() if x > 1000]
+    }
+    open_lines = {
+        your_key: edges.loc[edges.que == your_key].index.values
+        for your_key in [x for x in edges.que.unique() if x < 1000]
+    }
+
+    return rb0, land_lines, open_lines
 
-    # save boundary configuration for Line0
-    rb0 = ddf.loc["line0"].copy()
 
-    if not shapely.geometry.LinearRing(rb0[["lon", "lat"]].values).is_ccw:  # check for clockwise orientation
-        rb0 = ddf.loc["line0"].iloc[::-1].reset_index(drop=True)
+def make_bgmesh(df, fpos, **kwargs):
 
-    rb0.index = rb0.index + 1  # fix index
-    rb0["bounds"] = [[i, i + 1] for i in rb0.index]
-    rb0["bounds"] = rb0.bounds.values.tolist()[:-1] + [[rb0.index[-1], 1]]  # fix last one
+    logger.info("Read DEM")
 
-    # store blines
-    blines = {}
+    lon_min = df.bounds.minx.min()
+    lon_max = df.bounds.maxx.max()
+    lat_min = df.bounds.miny.min()
+    lat_max = df.bounds.maxy.max()
 
-    for tag_ in rb0.tag.unique():
+    dem = pdem.dem(lon_min=lon_min, lon_max=lon_max, lat_min=lat_min, lat_max=lat_max, **kwargs)
 
-        ibs = rb0.loc[rb0.tag == tag_].index.values
-        # ibs
+    res_min = kwargs.get("resolution_min", 0.01)
+    res_max = kwargs.get("resolution_max", 0.5)
 
-        lbs = rb0.loc[rb0.tag == tag_].bounds.values.tolist()
-        # lbs
+    logger.info("Evaluate bgmesh")
 
-        ai = np.unique(np.concatenate(lbs))
-        # ai
+    # scale bathymetry
+    try:
+        b = dem.adjusted.to_dataframe()
+    except:
+        b = dem.elevation.to_dataframe()
+
+    nodes = scale_dem(b, res_min, res_max, **kwargs)
 
-        itags = [i for i in ai if i in ibs]
-        # itags
+    x = dem.longitude.values
+    y = dem.latitude.values
 
-        if tag_ > 0:
-            items = set(itags)
+    quad = MakeQuadFaces(y.shape[0], x.shape[0])
+    elems = pd.DataFrame(quad, columns=["a", "b", "c", "d"])
+    df = quads_to_df(elems, nodes)
 
-            imask = [set(x).issubset(items) for x in rb0.loc[rb0.tag == tag_].bounds]
-            # imask
+    dh = xr.Dataset(
+        {
+            "h": (
+                ["longitude", "latitude"],
+                nodes.d2.values.flatten().reshape((x.shape[0], y.shape[0])),
+            )
+        },
+        coords={"longitude": ("longitude", x), "latitude": ("latitude", y)},
+    )
 
-            bi = rb0.loc[rb0.tag == tag_][imask].index.values.tolist()
+    logger.info("Save bgmesh to {}".format(fpos))
 
-        else:
+    to_st(df, fpos)  # save bgmesh
 
-            bi = rb0.loc[rb0.tag == tag_].index.values.tolist()
+    kwargs.update({"bgmesh": fpos})
 
-        blines.update({tag_: bi})
+    return dh
 
-    al = [j for i in list(blines.values()) for j in i]
-    lover = [x for x in rb0.index if x not in al]
 
-    for i, v in rb0.loc[lover].iterrows():
-        nns = rb0.loc[v[5], ["tag"]].values
-        itag = [x for x in nns if x < 0][0]
-        blines.update({itag[0]: blines[itag[0]] + [i]})
+def make_gmsh(boundary, **kwargs):
 
-    land_lines = {your_key: blines[your_key] for your_key in [x for x in blines.keys() if x < 0]}
-    open_lines = {your_key: blines[your_key] for your_key in [x for x in blines.keys() if x > 0]}
+    logger.info("create grid")
+
+    df = boundary.contours
+
+    model = gmsh.model
+    factory = model.geo
+
+    gmsh.initialize()
+    model.add("schism")
+
+    #    gmsh.option.setNumber("General.Terminal", 1)
+
+    interpolate = kwargs.get("interpolate", False)
+    if interpolate:
+        df = gset(df, **kwargs)
+    else:
+        df = df
+    lc = kwargs.get("lc", 0.5)
+
+    df["lc"] = lc
+
+    # save boundary configuration for Line0
+    df_ = df.loc[df.tag != "island"].reset_index(drop=True)  # all external contours
+
+    if not df_.empty:
+
+        rb0, land_lines, open_lines = outer_boundary(df, **kwargs)
+        # join
+        ols = [j for i in list(open_lines.values()) for j in i]
+        lls = [j for i in list(land_lines.values()) for j in i]
+
+    else:
+        rb0 = pd.DataFrame({})
+        open_lines = {}
+        land_lines = {}
 
     logger.info("Define geometry")
 
@@ -379,31 +747,48 @@ def make_gmsh(df, **kwargs):
     all_lines = []
 
     ltag = 1
+    tag = 0
 
-    for row in rb0.itertuples(index=True, name="Pandas"):
-        factory.addPoint(
-            getattr(row, "lon"), getattr(row, "lat"), getattr(row, "z"), getattr(row, "lc"), getattr(row, "Index")
-        )
-    for row in rb0.itertuples(index=True, name="Pandas"):
-        factory.addLine(getattr(row, "bounds")[0], getattr(row, "bounds")[1], getattr(row, "Index"))
+    if not df_.empty:
 
-    lines = rb0.index.values
-    all_lines.append(lines)
+        for row in rb0.itertuples(index=True, name="Pandas"):
+            factory.addPoint(
+                getattr(row, "lon"),
+                getattr(row, "lat"),
+                getattr(row, "z"),
+                getattr(row, "lc"),
+                getattr(row, "Index"),
+            )
+        for row in rb0.itertuples(index=True, name="Pandas"):
+            factory.addLine(
+                getattr(row, "bounds")[0],
+                getattr(row, "bounds")[1],
+                getattr(row, "Index"),
+            )
+
+        lines = rb0.index.values
+        all_lines.append(lines)
+
+        tag = rb0.index.values[-1]
 
-    tag = rb0.index.values[-1]
+        factory.addCurveLoop(lines, tag=ltag)
+        # print(loop)
+        loops.append(ltag)
+        all_lines.append(lines)
+
+        tag += 1
+        ltag += 1
 
-    factory.addCurveLoop(lines, tag=ltag)
-    # print(loop)
-    loops.append(ltag)
-    all_lines.append(lines)
+    for k, d in df.loc[df.tag == "island"].iterrows():
 
-    tag += 1
-    ltag += 1
+        rb = pd.DataFrame(d.geometry.coords[:], columns=["lon", "lat"])
+        rb["z"] = 0
+        rb["lc"] = lc
+        rb = rb.drop_duplicates(["lon", "lat"])
 
-    for contour in tqdm(ddf.index.levels[0][1:]):
-        rb = ddf.loc[contour].copy()
         if not shapely.geometry.LinearRing(rb[["lon", "lat"]].values).is_ccw:  # check for clockwise orientation
-            rb = ddf.loc[contour].iloc[::-1].reset_index(drop=True)
+            rb_ = rb.iloc[::-1].reset_index(drop=True)
+            rb = rb_
 
         rb.index = rb.index + tag
         rb["bounds"] = [[i, i + 1] for i in rb.index]
@@ -411,10 +796,18 @@ def make_gmsh(df, **kwargs):
 
         for row in rb.itertuples(index=True, name="Pandas"):
             factory.addPoint(
-                getattr(row, "lon"), getattr(row, "lat"), getattr(row, "z"), getattr(row, "lc"), getattr(row, "Index")
+                getattr(row, "lon"),
+                getattr(row, "lat"),
+                getattr(row, "z"),
+                getattr(row, "lc"),
+                getattr(row, "Index"),
             )
         for row in rb.itertuples(index=True, name="Pandas"):
-            factory.addLine(getattr(row, "bounds")[0], getattr(row, "bounds")[1], getattr(row, "Index"))
+            factory.addLine(
+                getattr(row, "bounds")[0],
+                getattr(row, "bounds")[1],
+                getattr(row, "Index"),
+            )
 
         lines = rb.index.values
         all_lines.append(lines)
@@ -441,7 +834,7 @@ def make_gmsh(df, **kwargs):
 
     ## Group land boundaries lines
     for key, values in land_lines.items():
-        gmsh.model.addPhysicalGroup(1, values, 1000 - int(key))
+        gmsh.model.addPhysicalGroup(1, values, int(key))
 
     ntag = 1
     for k in tqdm(range(len(islands))):
@@ -473,49 +866,19 @@ def make_gmsh(df, **kwargs):
     # Set bgmesh
     bgmesh = kwargs.get("bgmesh", None)
 
-    if bgmesh == "auto":
-
-        try:
-
-            logger.info("Read DEM")
-            dem = pdem.dem(**kwargs)
+    if bgmesh:
 
-            res_min = kwargs.get("resolution_min", 0.01)
-            res_max = kwargs.get("resolution_max", 0.5)
+        gmsh.merge(bgmesh)
 
-            logger.info("Evaluate bgmesh")
-            w = make_bgmesh(dem.Dataset, res_min, res_max)
-
-            path = kwargs.get("rpath", ".")
-
-            if not os.path.exists(path):  # check if run folder exists
-                os.makedirs(path)
-
-            logger.info("Save bgmesh to {}/bgmesh/bgmesh.pos".format(path))
-
-            fpos = path + "/bgmesh/bgmesh.pos"
-            to_sq(w, fpos)  # save bgmesh
-
-            kwargs.update({"bgmesh": fpos})
+        model.mesh.field.setNumber(2, "StopAtDistMax", 1)
 
-            model.mesh.field.setNumber(2, "StopAtDistMax", 1)
+        model.mesh.field.add("PostView", 3)
+        model.mesh.field.setNumber(3, "ViewIndex", 0)
 
-            # Merge a post-processing view containing the target anisotropic mesh sizes
-            gmsh.merge(fpos)
+        model.mesh.field.add("Min", 4)
+        model.mesh.field.setNumbers(4, "FieldsList", [2, 3])
 
-            model.mesh.field.add("PostView", 3)
-            model.mesh.field.setNumber(3, "ViewIndex", 0)
-
-            model.mesh.field.add("Min", 4)
-            model.mesh.field.setNumbers(4, "FieldsList", [2, 3])
-
-            model.mesh.field.setAsBackgroundMesh(4)
-
-        except:
-
-            logger.warning("bgmesh failed... continuing without background mesh size")
-
-            model.mesh.field.setAsBackgroundMesh(2)
+        model.mesh.field.setAsBackgroundMesh(4)
 
     else:
 
@@ -525,7 +888,17 @@ def make_gmsh(df, **kwargs):
     gmsh.option.setNumber("Mesh.MeshSizeFromPoints", 0)
     gmsh.option.setNumber("Mesh.MeshSizeFromCurvature", 0)
 
-    logger.info("execute")
+    MeshSizeMin = kwargs.get("MeshSizeMin", None)
+    MeshSizeMax = kwargs.get("MeshSizeMax", None)
+
+    if MeshSizeMin is not None:
+        gmsh.option.setNumber("Mesh.MeshSizeMin", MeshSizeMin)
+    if MeshSizeMax is not None:
+        gmsh.option.setNumber("Mesh.MeshSizeMax", MeshSizeMax)
+
+    logger.info("execute gmsh")
+
+    gmsh.option.set_number("General.Verbosity", 0)
 
     gmsh.model.mesh.generate(2)
 
@@ -539,3 +912,5 @@ def make_gmsh(df, **kwargs):
     #    gmsh.write('mymesh.vtk')
 
     gmsh.finalize()
+
+    return
diff --git a/pyposeidon/utils/bfs.py b/pyposeidon/utils/bfs.py
index 59962a9a..dd7e44b2 100644
--- a/pyposeidon/utils/bfs.py
+++ b/pyposeidon/utils/bfs.py
@@ -23,7 +23,7 @@ def append_if(self, queue, x, y, island_counter):
                 #                queue.append((x, y))
                 for [i, j], [k, l] in b:
                     try:
-                        if self.grid[x + i][y + j] in ["1", island_counter] and self.grid[x + k][y + l] in [
+                        if self.grid[x + i][y + j] in ["1", island_counter,] and self.grid[x + k][y + l] in [
                             "1",
                             island_counter,
                         ]:
diff --git a/pyposeidon/utils/cast.py b/pyposeidon/utils/cast.py
index 7dc673ef..86dc9a54 100644
--- a/pyposeidon/utils/cast.py
+++ b/pyposeidon/utils/cast.py
@@ -175,7 +175,11 @@ def set(self, **kwargs):
             logger.info("meteo files present\n")
 
         # modify mdf file
-        m.config(config_file=ppath + m.tag + ".mdf", config={"Restid": outresfile}, output=True)
+        m.config(
+            config_file=ppath + m.tag + ".mdf",
+            config={"Restid": outresfile},
+            output=True,
+        )
 
         m.config_file = rpath + m.tag + ".mdf"
 
@@ -217,7 +221,12 @@ def set(self, **kwargs):
 
         pwd = os.getcwd()
 
-        files = ["bctides.in", "launchSchism.sh", "/sflux/sflux_inputs.txt", "/outputs/flux.out"]
+        files = [
+            "bctides.in",
+            "launchSchism.sh",
+            "/sflux/sflux_inputs.txt",
+            "/outputs/flux.out",
+        ]
         files_sym = [
             "hgrid.gr3",
             "hgrid.ll",
@@ -405,9 +414,20 @@ def set(self, **kwargs):
                 times, datasets = zip(*m.meteo.Dataset.groupby("time.{}".format(self.meteo_split_by)))
                 mpaths = ["sflux/sflux_air_1.{:04d}.nc".format(t + 1) for t in np.arange(len(times))]
                 for das, mpath in list(zip(datasets, mpaths)):
-                    m.to_force(das, vars=["msl", "u10", "v10"], rpath=rpath, filename=mpath, date=self.date0)
+                    m.to_force(
+                        das,
+                        vars=["msl", "u10", "v10"],
+                        rpath=rpath,
+                        filename=mpath,
+                        date=self.date0,
+                    )
             else:
-                m.to_force(m.meteo.Dataset, vars=["msl", "u10", "v10"], rpath=rpath, date=self.date0)
+                m.to_force(
+                    m.meteo.Dataset,
+                    vars=["msl", "u10", "v10"],
+                    rpath=rpath,
+                    date=self.date0,
+                )
 
         else:
             logger.warning("meteo files present\n")
diff --git a/pyposeidon/utils/data.py b/pyposeidon/utils/data.py
index 58c23ba9..0a37c8a5 100644
--- a/pyposeidon/utils/data.py
+++ b/pyposeidon/utils/data.py
@@ -10,7 +10,7 @@
 
 import numpy as np
 import os
-from pyposeidon.utils.obs import obs
+from pyposeidon.utils.vals import obs
 from pyposeidon.grid import *
 import pyposeidon.model as pm
 import datetime
@@ -87,7 +87,10 @@ def __init__(self, **kwargs):
 
         self.dem = xr.Dataset(
             {"bathymetry": (["latitude", "longitude"], -b)},
-            coords={"longitude": ("longitude", grid.lons[0, :]), "latitude": ("latitude", grid.lats[:, 0])},
+            coords={
+                "longitude": ("longitude", grid.lons[0, :]),
+                "latitude": ("latitude", grid.lats[:, 0]),
+            },
         )
 
         self.grid = grid
@@ -210,70 +213,71 @@ def __init__(self, **kwargs):
         try:
             p.get_station_data(dstamp=dstamp)
             self.time_series = p.time_series
-        except:
-            logger.info("no station data loaded")
 
-        dic = {}
+            dic = {}
 
-        try:
+            try:
 
-            with open(self.folders[0] + "/" + tag + "_model.json", "r") as f:
-                info = pd.read_json(f, lines=True).T
-                info[info.isnull().values] = None
-                self.info = info.to_dict()[0]
+                with open(self.folders[0] + "/" + tag + "_model.json", "r") as f:
+                    info = pd.read_json(f, lines=True).T
+                    info[info.isnull().values] = None
+                    self.info = info.to_dict()[0]
 
-            dic = self.info.copy()  # start with x's keys and values
-            dic.update(kwargs)  # modifies z with y's keys and values & returns None
+                dic = self.info.copy()  # start with x's keys and values
+                dic.update(kwargs)  # modifies z with y's keys and values & returns None
 
-        except:
-            pass
+            except:
+                pass
 
-        if "sa_date" not in dic.keys():
-            dic.update({"sa_date": self.Dataset.time.values[0]})
+            if "sa_date" not in dic.keys():
+                dic.update({"sa_date": self.time_series.time.values[0]})
 
-        if "se_date" not in dic.keys():
-            dic.update({"se_date": self.Dataset.time.values[-1]})
+            if "se_date" not in dic.keys():
+                dic.update({"se_date": self.Dataset.time.values[-1]})
 
-        if "lon_min" not in dic.keys():
-            dic.update({"lon_min": self.Dataset.SCHISM_hgrid_node_x.values.min()})
+            if "lon_min" not in dic.keys():
+                dic.update({"lon_min": self.Dataset.SCHISM_hgrid_node_x.values.min()})
 
-        if "lon_max" not in dic.keys():
-            dic.update({"lon_max": self.Dataset.SCHISM_hgrid_node_x.values.max()})
+            if "lon_max" not in dic.keys():
+                dic.update({"lon_max": self.Dataset.SCHISM_hgrid_node_x.values.max()})
 
-        if "lat_min" not in dic.keys():
-            dic.update({"lat_min": self.Dataset.SCHISM_hgrid_node_y.values.min()})
+            if "lat_min" not in dic.keys():
+                dic.update({"lat_min": self.Dataset.SCHISM_hgrid_node_y.values.min()})
 
-        if "lat_max" not in dic.keys():
-            dic.update({"lat_max": self.Dataset.SCHISM_hgrid_node_y.values.max()})
+            if "lat_max" not in dic.keys():
+                dic.update({"lat_max": self.Dataset.SCHISM_hgrid_node_y.values.max()})
 
-        logger.info("Retrieve observations info\n")
+            logger.info("Retrieve observations info\n")
 
-        self.obs = obs(**dic)
+            self.obs = obs(**dic)
 
-        ret = kwargs.get("online", False)
+            ret = kwargs.get("online", False)
 
-        if ret is True:
-            logger.info("collect observational data")
-            tgs = self.obs.locations.loc[self.obs.locations.Group == "TD UNESCO"]
+            if ret is True:
+                logger.info("collect observational data")
+                tgs = self.obs.locations.loc[self.obs.locations.Group == "TD UNESCO"]
 
-            dic = {}
-            for i in tgs.index:
-                #            print(i, tgs.loc[i].Name.strip())
-                while True:
-                    p = self.obs.iloc(i)
-                    if p is not None:
-                        if p.shape[0] > 1:
-                            p = p.dropna()
-                        break
-                dic.update({tgs.loc[i].Name.strip(): p})
-
-            tg = pd.concat(dic, axis=0, sort=True)
+                dic = {}
+                for i in tgs.index:
+                    #            print(i, tgs.loc[i].Name.strip())
+                    while True:
+                        p = self.obs.iloc(i)
+                        if p is not None:
+                            if p.shape[0] > 1:
+                                p = p.dropna()
+                            break
+                    dic.update({tgs.loc[i].Name.strip(): p})
 
-            try:
-                tg = tg.drop("TimeUTC", axis=1)
-            except:
-                pass
+                tg = pd.concat(dic, axis=0, sort=True)
+
+                try:
+                    tg = tg.drop("TimeUTC", axis=1)
+                except:
+                    pass
+
+                tg.to_csv(self.folders[0] + "/" + "obs.csv")
 
-            tg.to_csv(self.folders[0] + "/" + "obs.csv")
+                self.obs.dataframe = tg
 
-            self.obs.data = tg
+        except:
+            logger.info("no station data loaded")
diff --git a/pyposeidon/utils/features.py b/pyposeidon/utils/features.py
index 31414f41..2fcdcdf9 100644
--- a/pyposeidon/utils/features.py
+++ b/pyposeidon/utils/features.py
@@ -23,7 +23,10 @@ def create_geojson_features(gf, time):
                 "type": "Polygon",
                 "coordinates": [[row["ap"].tolist(), row["bp"].tolist(), row["cp"].tolist()]],
             },
-            "properties": {"times": [time], "style": {"fillColor": row["color"], "weight": 0, "fill-opacity": 1.0}},
+            "properties": {
+                "times": [time],
+                "style": {"fillColor": row["color"], "weight": 0, "fill-opacity": 1.0},
+            },
         }
         features.append(feature)
     return features
diff --git a/pyposeidon/utils/point.py b/pyposeidon/utils/get_ts.py
similarity index 81%
rename from pyposeidon/utils/point.py
rename to pyposeidon/utils/get_ts.py
index d74bdc8e..a0b0c281 100644
--- a/pyposeidon/utils/point.py
+++ b/pyposeidon/utils/get_ts.py
@@ -11,7 +11,7 @@
 
 import numpy as np
 from pyposeidon.utils.pplot import *
-from pyposeidon.utils.obs import obs
+from pyposeidon.utils.vals import obs
 from pyposeidon.grid import *
 import pyposeidon.model as pmodel
 import pyresample
@@ -28,7 +28,7 @@
 import scipy.interpolate
 
 
-class point:
+class spot:
     def __init__(self, solver=None, lon=None, lat=None, dataset=None, **kwargs):
 
         self.lon = lon
@@ -51,7 +51,10 @@ def tseries(self, **kwargs):
         plat = float(self.lat)
         plon = float(self.lon)
 
-        X, Y = self.data.Dataset.XZ.values[1:-1, 1:-1].T, self.data.Dataset.YZ.values[1:-1, 1:-1].T
+        X, Y = (
+            self.data.Dataset.XZ.values[1:-1, 1:-1].T,
+            self.data.Dataset.YZ.values[1:-1, 1:-1].T,
+        )
         xh = np.ma.masked_array(X, self.data.w)  # mask land
         yh = np.ma.masked_array(Y, self.data.w)
 
@@ -71,13 +74,22 @@ def tseries(self, **kwargs):
         if method == "nearest":
             for k in range(vals.shape[0]):
                 s = pyresample.kd_tree.resample_nearest(
-                    orig, vals[k, :, :], targ, radius_of_influence=100000, fill_value=np.nan
+                    orig,
+                    vals[k, :, :],
+                    targ,
+                    radius_of_influence=100000,
+                    fill_value=np.nan,
                 )
                 svals.append(s[0])
         elif method == "gauss":
             for k in range(vals.shape[0]):
                 s = pyresample.kd_tree.resample_gauss(
-                    orig, vals[k, :, :], targ, radius_of_influence=100000, fill_value=np.nan, sigmas=25000
+                    orig,
+                    vals[k, :, :],
+                    targ,
+                    radius_of_influence=100000,
+                    fill_value=np.nan,
+                    sigmas=25000,
                 )
                 svals.append(s[0])
 
@@ -93,7 +105,11 @@ def stseries(self, **kwargs):
         plon = float(self.lon)
 
         points = pd.concat(
-            [self.data.SCHISM_hgrid_node_x[:].to_dataframe(), self.data.SCHISM_hgrid_node_y[:].to_dataframe()], axis=1
+            [
+                self.data.SCHISM_hgrid_node_x[:].to_dataframe(),
+                self.data.SCHISM_hgrid_node_y[:].to_dataframe(),
+            ],
+            axis=1,
         )
         values = self.data[var].values
 
diff --git a/pyposeidon/utils/global_bgmesh.py b/pyposeidon/utils/global_bgmesh.py
new file mode 100644
index 00000000..a9bcef6f
--- /dev/null
+++ b/pyposeidon/utils/global_bgmesh.py
@@ -0,0 +1,133 @@
+import numpy as np
+import pandas as pd
+import pyresample
+import xarray as xr
+import os
+
+from pyposeidon.utils.stereo import to_3d, to_lat_lon, stereo_to_3d
+from pyposeidon.utils.pos import to_sq, to_st
+from pyposeidon.utils.hfun import to_hfun_mesh, to_hfun_grid
+from pyposeidon.utils.scale import scale_dem
+from pyposeidon.utils.topology import (
+    MakeQuadFaces,
+    quads_to_df,
+    tria_to_df_3d,
+    tria_to_df,
+)
+import pyposeidon.dem as pdem
+import pyposeidon.grid as pg
+import logging
+
+logger = logging.getLogger("pyposeidon")
+
+
+def make_bgmesh_global(b, fpos, dem, **kwargs):
+
+    grid_generator = kwargs.get("grid_generator", None)
+
+    bk = b.contours.copy()
+
+    b.contours = b.contours.loc[b.contours.length == b.contours.length.max()]
+
+    out = b.contours.buffer(0.2).exterior
+
+    R = kwargs.get("R", 1.0)
+
+    bgm_res = kwargs.get("bgm_res", 0.01)
+
+    # create simple grid
+    npu = kwargs.get("npu", 1000)
+
+    d1 = np.linspace(out.bounds.minx, out.bounds.maxx, npu)
+    d2 = np.linspace(out.bounds.miny, out.bounds.maxy, npu)
+
+    ui, vi = np.meshgrid(d1, d2)
+
+    # stereo->2D scale
+    ci = 4 * R ** 2 / (ui ** 2 + vi ** 2 + 4 * R ** 2)
+
+    # set weight field
+    scale = bgm_res / ci.flatten()
+
+    # save as bgmesh
+    nodes = pd.DataFrame({"longitude": ui.flatten(), "latitude": vi.flatten(), "z": 0, "d2": scale})
+
+    # tesselation
+    quad = MakeQuadFaces(npu, npu)
+    elems = pd.DataFrame(quad, columns=["a", "b", "c", "d"])
+
+    if grid_generator == "gmsh":
+
+        dn = quads_to_df(elems, nodes)
+
+        logger.info("Save pre-process bgmesh to {}".format(fpos))
+
+        to_sq(dn, fpos)  # save bgmesh
+
+    elif grid_generator == "jigsaw":
+
+        dh = xr.Dataset(
+            {"z": (["longitude", "latitude"], nodes.d2.values.reshape(ui.shape))},
+            coords={
+                "longitude": ("longitude", d1.flatten()),
+                "latitude": ("latitude", d2.flatten()),
+            },
+        )
+
+        logger.info("Save pre-process bgmesh to {}".format(fpos))
+
+        to_hfun_grid(dh, fpos)  # write bgmesh file
+
+    rpath = kwargs.get("rpath", ".")
+
+    logger.info("Create interim global scale mesh")
+
+    mesh = pg.grid(
+        type="tri2d",
+        boundary=b,
+        grid_generator=grid_generator,
+        rpath=rpath,
+        bgmesh=fpos,
+    )
+
+    x0 = mesh.Dataset.SCHISM_hgrid_node_x.values
+    y0 = mesh.Dataset.SCHISM_hgrid_node_y.values
+    trii0 = mesh.Dataset.SCHISM_hgrid_face_nodes.values[:, :3]
+
+    # Select DEM
+    try:
+        dm = dem.Dataset.adjusted.to_dataframe()
+    except:
+        dm = dem.Dataset.elevation.to_dataframe()
+
+    lon = dem.Dataset.longitude.values
+    lat = dem.Dataset.latitude.values
+
+    X, Y = np.meshgrid(lon, lat)
+    # Stereo -> lat/lon
+    clon, clat = to_lat_lon(x0, y0)
+
+    # resample bathymetry
+    gdem = dm.values.flatten()
+
+    orig = pyresample.geometry.SwathDefinition(lons=X.flatten(), lats=Y.flatten())  # original bathymetry points
+    targ = pyresample.geometry.SwathDefinition(lons=clon, lats=clat)  # wet points
+
+    bw = pyresample.kd_tree.resample_nearest(orig, gdem, targ, radius_of_influence=50000, fill_value=0)
+
+    bz = pd.DataFrame({"z": bw.flatten()})
+
+    # scale
+    res_min = kwargs.get("resolution_min", 0.01)
+    res_max = kwargs.get("resolution_max", 0.5)
+
+    nodes = scale_dem(bz, res_min, res_max, **kwargs)
+
+    nodes["u"] = x0
+    nodes["v"] = y0
+
+    elems = pd.DataFrame(trii0, columns=["a", "b", "c"])
+
+    b.contours = bk
+
+    return nodes, elems
diff --git a/pyposeidon/utils/hfun.py b/pyposeidon/utils/hfun.py
index 59181b9d..e3982bc1 100644
--- a/pyposeidon/utils/hfun.py
+++ b/pyposeidon/utils/hfun.py
@@ -6,84 +6,100 @@
 from .limgrad import *
 import matplotlib
 from pyposeidon.utils.stereo import to_lat_lon, to_stereo
+from pyposeidon.utils.topology import MakeTriangleFaces, MakeTriangleFaces_periodic
+from pyposeidon.utils.scale import scale_dem
 import pyposeidon
 import math
 
-# https://stackoverflow.com/questions/44934631/making-grid-triangular-mesh-quickly-with-numpy
-def MakeFacesVectorized1(Nr, Nc):
 
-    out = np.empty((Nr - 1, Nc - 1, 2, 3), dtype=int)
+def get_hfun(dem, path=".", tag="jigsaw", resolution_min=0.01, resolution_max=0.5, dhdx=0.15, imax=100, **kwargs):
 
-    r = np.arange(Nr * Nc).reshape(Nr, Nc)
+    # scale bathymetry
+    try:
+        b = dem.adjusted.to_dataframe()
+    except:
+        b = dem.elevation.to_dataframe()
 
-    out[:, :, 0, 0] = r[:-1, :-1]
-    out[:, :, 1, 0] = r[:-1, 1:]
-    out[:, :, 0, 1] = r[:-1, 1:]
+    nodes = scale_dem(b, resolution_min, resolution_max, **kwargs)
 
-    out[:, :, 1, 1] = r[1:, 1:]
-    out[:, :, :, 2] = r[1:, :-1, None]
+    x = dem.longitude.values
+    y = dem.latitude.values
 
-    out.shape = (-1, 3)
-    return out
+    tria = MakeTriangleFaces(y.shape[0], x.shape[0])
 
+    points = np.column_stack([nodes.longitude, nodes.latitude])
+    tria3 = pd.DataFrame(tria, columns=["a", "b", "c"])
 
-def hfun(data, path=".", tag="jigsaw", resolution_min=0.01, resolution_max=0.5, dhdx=0.15, imax=100, **kwargs):
+    # Use Matplotlib for triangulation
+    triang = matplotlib.tri.Triangulation(points[:, 0], points[:, 1], tria)
+    #    tri3 = triang.triangles
+    edges = triang.edges
+    # edge lengths
+    ptdiff = lambda p: (p[0][0] - p[1][0], p[0][1] - p[1][1])
+    diffs = map(ptdiff, points[edges])
+    elen = [math.hypot(d1, d2) for d1, d2 in diffs]
 
-    X, Y = np.meshgrid(data.longitude.values, data.latitude.values)
-    V = data.values
+    hfun = nodes.d2.values
+    hfun = hfun.reshape(hfun.shape[0], -1)
 
-    # scale
-    hmin = resolution_min  # min. H(X) [deg.]
-    hmax = resolution_max  # max. H(X)
+    [fun, flag] = limgrad2(edges, elen, hfun, dhdx, imax)
 
-    V[V > 0] = 0  # normalize to only negative values
+    cfun = fun.flatten().reshape((y.shape[0], x.shape[0]))
 
-    hfun = np.sqrt(-V) / 0.5  # scale with sqrt(H)
+    dh = xr.Dataset(
+        {"h": (["longitude", "latitude"], cfun)},
+        coords={"longitude": ("longitude", x), "latitude": ("latitude", y)},
+    )
 
-    # adjust scale
-    a2 = (hfun - hfun.min()) / (hfun.max() - hfun.min())
-    hfun = hmin + a2 * (hmax - hmin)
+    return dh
 
-    #    subspace = kwargs.get('subspace', None)
 
-    #    if subspace is not None:
-    #        mask = ...
-    #        hfun[mask] =
+def to_global_hfun(nodes, elems, fpos, **kwargs):
 
-    hfun = hfun.flatten()  # make it 1-d
+    elems["d"] = 0
+    nodes["z"] = 0
 
-    hfun = hfun.reshape(hfun.shape[0], -1)  # convert it to the appropriate format for LIMHFN2 below
+    R = kwargs.get("R", 1.0)
 
-    # triangulate
-    points = np.column_stack([X.flatten(), Y.flatten()])
-    tria = MakeFacesVectorized1(V.shape[0], V.shape[1])
-    # Use Matplotlib for triangulation
-    triang = matplotlib.tri.Triangulation(points[:, 0], points[:, 1], tria)
-    #    tri3 = triang.triangles
-    edges = triang.edges
-    # edge lengths
-    ptdiff = lambda p: (p[0][0] - p[1][0], p[0][1] - p[1][1])
-    diffs = map(ptdiff, points[edges])
-    elen = [math.hypot(d1, d2) for d1, d2 in diffs]
+    sv = 4 * R ** 2 / (nodes.u ** 2 + nodes.v ** 2 + 4 * R ** 2)
+    nodes["h"] = nodes.d2 / sv
 
-    [fun, flag] = limgrad2(edges, elen, hfun, dhdx, imax)
+    out = xr.merge([nodes.to_xarray(), elems.to_xarray()])
 
-    cfun = fun.flatten().reshape(X.shape).T
+    to_hfun_mesh(out, fpos)
 
-    ##OUTPUT
+    ## make dataset
+    els = xr.DataArray(
+        elems.loc[:, ["a", "b", "c"]],
+        dims=["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"],
+        name="SCHISM_hgrid_face_nodes",
+    )
 
-    dh = xr.Dataset(
-        {"z": (["longitude", "latitude"], cfun)},
-        coords={"longitude": ("longitude", data.longitude.values), "latitude": ("latitude", data.latitude.values)},
+    nod = (
+        nodes.loc[:, ["u", "v"]]
+        .to_xarray()
+        .rename(
+            {
+                "index": "nSCHISM_hgrid_node",
+                "u": "SCHISM_hgrid_node_x",
+                "v": "SCHISM_hgrid_node_y",
+            }
+        )
     )
+    nod = nod.drop_vars("nSCHISM_hgrid_node")
+
+    bg = xr.Dataset({"h": (["nSCHISM_hgrid_node"], nodes.h.values)})
+
+    dh = xr.merge([nod, els, bg])
 
     return dh
 
 
 def to_hfun_mesh(dh, fhfun):
 
-    dps = dh[["u", "v", "z", "h"]].to_dataframe()
-    tria3 = dh.tria.to_pandas()
+    dps = dh[["u", "v", "z"]].to_dataframe().dropna()
+    hs = dh[["h"]].to_dataframe().dropna()
+    trii = dh[["a", "b", "c", "d"]].to_dataframe().dropna()
 
     with open(fhfun, "w") as f:
         f.write("#{}; created by pyposeidon\n".format(pyposeidon.__version__))
@@ -92,15 +108,15 @@ def to_hfun_mesh(dh, fhfun):
         f.write("POINT={}\n".format(dps.shape[0]))
 
     with open(fhfun, "a") as f:
-        dps[["u", "v", "z"]].to_csv(f, index=False, header=0, sep=";")
+        dps.to_csv(f, index=False, header=0, sep=";")
 
     with open(fhfun, "a") as f:
         f.write("VALUE={};1\n".format(dps.shape[0]))
-        dps[["h"]].to_csv(f, index=False, header=0)
+        hs.to_csv(f, index=False, header=0)
 
     with open(fhfun, "a") as f:
-        f.write("TRIA3={}\n".format(tria3.shape[0]))
-        tria3.to_csv(f, index=False, header=0, sep=";")
+        f.write("TRIA3={}\n".format(trii.shape[0]))
+        trii.to_csv(f, index=False, header=0, sep=";")
 
 
 def to_hfun_grid(dh, fhfun):
@@ -127,62 +143,3 @@ def to_hfun_grid(dh, fhfun):
 
     with open(fhfun, "a") as f:
         np.savetxt(f, dh.z.values.flatten())
-
-
-def hfun_(coastlines, res=0.1, R=1.0):
-
-    amask = coastlines.bounds.miny < coastlines.bounds.miny.min() + 0.1
-    anta = coastlines[amask]
-    anta = anta.reset_index(drop=True)
-
-    ### convert to stereo
-    try:
-        ant = pd.DataFrame(anta.boundary.values[0].coords[:], columns=["lon", "lat"])
-    except:
-        ant = pd.DataFrame(
-            anta.boundary.explode().values[0].coords[:], columns=["lon", "lat"]
-        )  # convert boundary values to pandas
-
-    d1 = ant.where(ant.lon == ant.lon.max()).dropna().index[1:]  # get artificial boundaries as -180/180
-    d2 = ant.where(ant.lon == ant.lon.min()).dropna().index[1:]
-    ant = ant.drop(d1).drop(d2)  # drop the points
-    d3 = ant.where(ant.lat == ant.lat.min()).dropna().index  # drop lat=-90 line
-    ant = ant.drop(d3)
-    ub, vb = to_stereo(ant.lon.values, ant.lat.values, R)
-    ant.lon = ub
-    ant.lat = vb
-
-    an = gp.GeoDataFrame(
-        {
-            "geometry": [shapely.geometry.LineString(ant.values)],
-            "length": shapely.geometry.LineString(ant.values).length,
-        }
-    )  # put together a LineString
-
-    # create simple grid
-    d1 = np.linspace(an.bounds.minx, an.bounds.maxx, 100)
-    d2 = np.linspace(an.bounds.miny, an.bounds.maxy, 100)
-
-    ui, vi = np.meshgrid(d1, d2)
-    # Use Matplotlib for triangulation
-    triang = matplotlib.tri.Triangulation(ui.flatten(), vi.flatten())
-    tri = triang.triangles
-
-    # stereo->2D scale
-    ci = 4 * R ** 2 / (ui ** 2 + vi ** 2 + 4 * R ** 2)
-    ci
-
-    # create weight field
-    points = np.column_stack([ui.flatten(), vi.flatten()])
-    dps = pd.DataFrame(points, columns=["u", "v"])
-    dps["z"] = 0
-    dps["h"] = res / ci.flatten()
-
-    tria3 = pd.DataFrame(tri, columns=["a", "b", "c"])
-    tria3["w"] = 0
-
-    p1 = dps.to_xarray()
-    p1 = p1.rename({"index": "nodes"})
-    p1 = p1.assign({"tria": (["elem", "n"], tria3.values)})
-
-    return p1
diff --git a/pyposeidon/utils/mplot.py b/pyposeidon/utils/mplot.py
index d99ec50a..b77ee68f 100644
--- a/pyposeidon/utils/mplot.py
+++ b/pyposeidon/utils/mplot.py
@@ -269,7 +269,7 @@ def to_file(self, **kwargs):
         # ANIMATE THE FIGURE WITH MOVIEPY, WRITE AN ANIMATED GIF
 
         def make_frame(t):
-            """ Generates and returns the frame for time t. """
+            """Generates and returns the frame for time t."""
             dt = 1.0 / fps
             i = int(t / dt)
             scalars = z[i, :]
diff --git a/pyposeidon/utils/norm.py b/pyposeidon/utils/norm.py
new file mode 100644
index 00000000..5267ca0d
--- /dev/null
+++ b/pyposeidon/utils/norm.py
@@ -0,0 +1,48 @@
+import xarray as xr
+import pandas as pd
+
+# to be used for name normalization
+LONGITUDE_NAMES = {"longitude", "lon", "x", "Lon", "LONGITUDE", "LON", "X"}
+LATITUDE_NAMES = {"latitude", "lat", "y", "Lat", "LATITUDE", "LAT", "Y"}
+
+
+def normalize_coord_names(dataset: xr.Dataset) -> xr.Dataset:
+    """Return a dataset with coords containing "longitude" and "latitude" """
+    coords = set(dataset.coords.keys())
+    # longitude
+    for lon_name in LONGITUDE_NAMES:
+        if lon_name in coords:
+            break
+    else:
+        raise ValueError(f"Couldn't normalize longitude: {coords}")
+    # latitude
+    for lat_name in LATITUDE_NAMES:
+        if lat_name in coords:
+            break
+    else:
+        raise ValueError(f"Couldn't normalize latitude: {coords}")
+    dataset = dataset.rename({lon_name: "longitude", lat_name: "latitude"})
+    return dataset
+
+
+def normalize_column_names(dataframe: pd.DataFrame) -> pd.DataFrame:
+    """Return a dataframe with columns containing "longitude" and "latitude" """
+    cols = dataframe.columns.tolist()
+    # longitude
+    for lon_name in LONGITUDE_NAMES:
+        if lon_name in cols:
+            break
+    else:
+        raise ValueError(f"Couldn't normalize longitude: {cols}")
+    # latitude
+    for lat_name in LATITUDE_NAMES:
+        if lat_name in cols:
+            break
+    else:
+        raise ValueError(f"Couldn't normalize latitude: {cols}")
+
+    dic = {lon_name: "longitude", lat_name: "latitude"}
+    new_cols = [dic.get(n, n) for n in cols]
+
+    dataframe.columns = new_cols
+    return dataframe
diff --git a/pyposeidon/utils/pos.py b/pyposeidon/utils/pos.py
index f8611961..11c9ed97 100644
--- a/pyposeidon/utils/pos.py
+++ b/pyposeidon/utils/pos.py
@@ -1,110 +1,8 @@
-import numpy as np
 import pandas as pd
+import xarray as xr
 
-
-def MakeFacesVectorized(Nr, Nc):
-
-    out = np.empty((Nr - 1, Nc - 1, 4), dtype=int)
-
-    r = np.arange(Nr * Nc).reshape(Nr, Nc)
-
-    out[:, :, 0] = r[:-1, :-1]
-    out[:, :, 1] = r[:-1, 1:]
-    out[:, :, 2] = r[1:, 1:]
-    out[:, :, 3] = r[1:, :-1]
-
-    out.shape = (-1, 4)
-    return out
-
-
-def MakeFacesVectorized_periodic(Nr, Nc):
-
-    out = np.empty((Nr - 1, Nc, 4), dtype=int)
-
-    r = np.arange(Nr * Nc).reshape(Nr, Nc)
-
-    out[:, :-1, 0] = r[:-1, :-1]
-    out[:, :-1, 1] = r[:-1, 1:]
-    out[:, :-1, 2] = r[1:, 1:]
-    out[:, :-1, 3] = r[1:, :-1]
-
-    out[:, -1, 0] = r[:-1, -1]
-    out[:, -1, 1] = r[:-1, 0]
-    out[:, -1, 2] = r[1:, 0]
-    out[:, -1, 3] = r[1:, -1]
-
-    out.shape = (-1, 4)
-    return out
-
-
-def to_df(elems, nodes):
-    # cells to polygons
-    ap = nodes.loc[elems.a, ["longitude", "latitude"]]
-    bp = nodes.loc[elems.b, ["longitude", "latitude"]]
-    cp = nodes.loc[elems.c, ["longitude", "latitude"]]
-    dp = nodes.loc[elems.d, ["longitude", "latitude"]]
-
-    ap["z"] = 0
-    bp["z"] = 0
-    cp["z"] = 0
-    dp["z"] = 0
-
-    elems["ap"] = ap.values.tolist()
-    elems["bp"] = bp.values.tolist()
-    elems["cp"] = cp.values.tolist()
-    elems["dp"] = dp.values.tolist()
-
-    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
-    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
-    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
-    elems["vd"] = nodes.loc[elems.d, "d2"].values.tolist()
-
-    return elems.drop(["a", "b", "c", "d"], axis=1)
-
-
-def to_df_uv(elems, nodes):
-    # cells to polygons
-    ap = nodes.loc[elems.a, ["u", "v"]]
-    bp = nodes.loc[elems.b, ["u", "v"]]
-    cp = nodes.loc[elems.c, ["u", "v"]]
-    dp = nodes.loc[elems.d, ["u", "v"]]
-
-    ap["z"] = 0
-    bp["z"] = 0
-    cp["z"] = 0
-    dp["z"] = 0
-
-    elems["ap"] = ap.values.tolist()
-    elems["bp"] = bp.values.tolist()
-    elems["cp"] = cp.values.tolist()
-    elems["dp"] = dp.values.tolist()
-
-    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
-    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
-    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
-    elems["vd"] = nodes.loc[elems.d, "d2"].values.tolist()
-
-    return elems.drop(["a", "b", "c", "d"], axis=1)
-
-
-def to_df_3d(elems, nodes):
-    # cells to polygons
-    ap = nodes.loc[elems.a, ["x", "y", "z"]]
-    bp = nodes.loc[elems.b, ["x", "y", "z"]]
-    cp = nodes.loc[elems.c, ["x", "y", "z"]]
-    dp = nodes.loc[elems.d, ["x", "y", "z"]]
-
-    elems["ap"] = ap.values.tolist()
-    elems["bp"] = bp.values.tolist()
-    elems["cp"] = cp.values.tolist()
-    elems["dp"] = dp.values.tolist()
-
-    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
-    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
-    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
-    elems["vd"] = nodes.loc[elems.d, "d2"].values.tolist()
-
-    return elems.drop(["a", "b", "c", "d"], axis=1)
+from pyposeidon.utils.topology import tria_to_df, tria_to_df_3d
+from pyposeidon.utils.stereo import stereo_to_3d
 
 
 def to_sq(df, fpos):
@@ -144,3 +42,95 @@ def to_sq(df, fpos):
             )
 
         f.write("{};\n".format("}"))
+
+
+def to_st(df, fpos):
+
+    with open(fpos, "w") as f:
+        f.write("//*********************************************************************\n")
+        f.write("// *\n")
+        f.write("// *  pyposeidon\n")
+        f.write("// *\n")
+        f.write("// *  Scalar 2D post-processing view\n")
+        f.write("// *\n")
+        f.write("// *********************************************************************/\n\n")
+
+        f.write("// This view contains a scalar field defined on triangles.\n")
+        f.write("\n")
+        f.write('View "{}" {}\n'.format("bgmesh", "{"))
+        for idx, vals in df.iterrows():
+            f.write(
+                "ST({},{},{},{},{},{},{},{},{}){{{},{},{}}};\n".format(
+                    vals.ap[0],
+                    vals.ap[1],
+                    vals.ap[2],
+                    vals.bp[0],
+                    vals.bp[1],
+                    vals.bp[2],
+                    vals.cp[0],
+                    vals.cp[1],
+                    vals.cp[2],
+                    vals.va,
+                    vals.vb,
+                    vals.vc,
+                )
+            )
+
+        f.write("{};\n".format("}"))
+
+
+def to_global_pos(nodes, elems, fpos, **kwargs):
+
+    use_bindings = kwargs.get("use_bindings", True)
+    R = kwargs.get("R", 1.0)
+
+    if use_bindings:
+        # Keep stereographic
+
+        elems["d"] = 0
+
+        sv = 4 * R ** 2 / (nodes.u ** 2 + nodes.v ** 2 + 4 * R ** 2)
+        nodes["d2"] = nodes.d2 / sv
+
+        dout = tria_to_df(elems, nodes, x="u", y="v")
+        # save bgmesh
+        to_st(dout, fpos)
+
+    else:
+        # use 3D
+        x, y, z = stereo_to_3d(nodes.u.values, nodes.v.values)
+
+        nodes["x"] = x
+        nodes["y"] = y
+        nodes["z"] = z
+
+        # create output dataframe
+        dout = tria_to_df_3d(elems, nodes)
+        # save bgmesh
+        to_st(dout, fpos)
+
+    ## make dataset
+    els = xr.DataArray(
+        elems.loc[:, ["a", "b", "c"]],
+        dims=["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"],
+        name="SCHISM_hgrid_face_nodes",
+    )
+
+    nod = (
+        nodes.loc[:, ["u", "v"]]
+        .to_xarray()
+        .rename(
+            {
+                "index": "nSCHISM_hgrid_node",
+                "u": "SCHISM_hgrid_node_x",
+                "v": "SCHISM_hgrid_node_y",
+            }
+        )
+    )
+    nod = nod.drop_vars("nSCHISM_hgrid_node")
+
+    bg = xr.Dataset({"h": (["nSCHISM_hgrid_node"], nodes.d2.values)})
+
+    dh = xr.merge([nod, els, bg])
+
+    return dh
diff --git a/pyposeidon/utils/postgrid.py b/pyposeidon/utils/postgrid.py
index c924f3fb..242d8d65 100755
--- a/pyposeidon/utils/postgrid.py
+++ b/pyposeidon/utils/postgrid.py
@@ -136,7 +136,10 @@ def check(g, shp, bad):
             if ids_.shape[0] > 1:
                 itype = bnodes.loc[bnodes.id == ids_[-1], ["type"]].values[0]
                 bnodes.loc[bnodes.id.isin(ids_[:-1]), "id"] = ids_[-1]
-                ibs = pd.DataFrame({"node": ibs, "type": itype, "id": ids_[-1]}, index=np.arange(len(ibs)))
+                ibs = pd.DataFrame(
+                    {"node": ibs, "type": itype, "id": ids_[-1]},
+                    index=np.arange(len(ibs)),
+                )
             else:
                 itype, iid = bnodes.loc[bnodes.node.isin(ibs), ["type", "id"]].values[0]
                 ibs = pd.DataFrame({"node": ibs, "type": itype, "id": iid}, index=np.arange(len(ibs)))
@@ -221,7 +224,7 @@ def check(g, shp, bad):
     gover = gp.GeoDataFrame(fd, geometry="overlap")
 
     # #### Reject small injuctions
-    ipols = gover.explode().loc[0]
+    ipols = gover.explode(index_parts=True).loc[0]
 
     ipols.columns = ["geometry"]
 
@@ -287,10 +290,16 @@ def check(g, shp, bad):
                 if ids_.shape[0] > 1:
                     itype = bnodes.loc[bnodes.id == ids_[-1], ["type"]].values[0]
                     bnodes.loc[bnodes.id.isin(ids_[:-1]), "id"] = ids_[-1]
-                    ibs = pd.DataFrame({"node": ibs, "type": itype, "id": ids_[-1]}, index=np.arange(len(ibs)))
+                    ibs = pd.DataFrame(
+                        {"node": ibs, "type": itype, "id": ids_[-1]},
+                        index=np.arange(len(ibs)),
+                    )
                 else:
                     itype, iid = bnodes.loc[bnodes.node.isin(ibs), ["type", "id"]].values[0]
-                    ibs = pd.DataFrame({"node": ibs, "type": itype, "id": iid}, index=np.arange(len(ibs)))
+                    ibs = pd.DataFrame(
+                        {"node": ibs, "type": itype, "id": iid},
+                        index=np.arange(len(ibs)),
+                    )
             else:
                 maxb -= 1
                 ibs = pd.DataFrame({"node": ibs, "type": 1, "id": maxb}, index=np.arange(len(ibs)))
@@ -332,7 +341,7 @@ def check(g, shp, bad):
     w["overlap"] = w["overlap"].apply(shapely.wkt.loads)
     gw = gp.GeoDataFrame(w, geometry="overlap")
 
-    gw = gw.explode()
+    gw = gw.explode(index_parts=True)
     gw = gw.droplevel(0)
     gw.columns = ["geometry"]
     gw["length"] = gw["geometry"][:].length
@@ -412,7 +421,13 @@ def check(g, shp, bad):
     nod = (
         nodes.loc[:, ["lon", "lat"]]
         .to_xarray()
-        .rename({"index": "nSCHISM_hgrid_node", "lon": "SCHISM_hgrid_node_x", "lat": "SCHISM_hgrid_node_y"})
+        .rename(
+            {
+                "index": "nSCHISM_hgrid_node",
+                "lon": "SCHISM_hgrid_node_x",
+                "lat": "SCHISM_hgrid_node_y",
+            }
+        )
     )
     nod = nod.drop_vars("nSCHISM_hgrid_node")
 
diff --git a/pyposeidon/utils/scale.py b/pyposeidon/utils/scale.py
new file mode 100644
index 00000000..48f9de3a
--- /dev/null
+++ b/pyposeidon/utils/scale.py
@@ -0,0 +1,33 @@
+import numpy as np
+import xarray as xr
+
+
+def scale_dem(b, res_min, res_max, **kwargs):
+
+    b.columns = ["z"]
+
+    b[b.z >= -10] = -1.0e-4  # normalize to only negative values
+
+    b.z = np.sqrt(-b.z) / 0.5  # scale
+
+    # adjust scale
+
+    bg = b.z.values
+
+    a2 = (bg - bg.min()) / (bg.max() - bg.min())
+
+    d2 = res_min + a2 * (res_max - res_min)
+
+    b["d2"] = d2
+
+    nodes = b.reset_index(drop=True)
+
+    nodes["z"] = 0
+
+    #    subspace = kwargs.get('subspace', None)
+
+    #    if subspace is not None:
+    #        mask = ...
+    #        hfun[mask] =
+
+    return nodes
diff --git a/pyposeidon/utils/seam.py b/pyposeidon/utils/seam.py
index 68fa67fa..e68c4945 100644
--- a/pyposeidon/utils/seam.py
+++ b/pyposeidon/utils/seam.py
@@ -186,7 +186,10 @@ def to_2d(files=None, var=None, grid=None, **kwargs):
             var: (["time", "nSCHISM_hgrid_node"], xelev),
             "SCHISM_hgrid_node_x": (["nSCHISM_hgrid_node"], xn),
             "SCHISM_hgrid_node_y": (["nSCHISM_hgrid_node"], yn),
-            "SCHISM_hgrid_face_nodes": (["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"], tri3n),
+            "SCHISM_hgrid_face_nodes": (
+                ["nSCHISM_hgrid_face", "nMaxSCHISM_hgrid_face_nodes"],
+                tri3n,
+            ),
         },
         coords={"time": ("time", out.time.values)},
     )
diff --git a/pyposeidon/utils/spline.py b/pyposeidon/utils/spline.py
index 71baec68..68b88b8c 100644
--- a/pyposeidon/utils/spline.py
+++ b/pyposeidon/utils/spline.py
@@ -8,7 +8,7 @@
 # based on https://stackoverflow.com/questions/52014197/how-to-interpolate-a-2d-curve-in-python
 
 
-def spline(df, ds=0.001, method="slinear"):
+def use_spline(df, ds=0.001, method="slinear"):
 
     points = df.values  # a (nbre_points x nbre_dim) array
 
diff --git a/pyposeidon/utils/statistics.py b/pyposeidon/utils/statistics.py
index f8da7a51..0658cc34 100755
--- a/pyposeidon/utils/statistics.py
+++ b/pyposeidon/utils/statistics.py
@@ -5,6 +5,20 @@
 import warnings
 
 
+def get_stats(sim_, obs_):
+
+    # match time frames
+    try:
+        end = min(obs_.index[-1], sim_.index[-1])
+        obs_ = obs_.loc[:end]
+        sim_ = sim_.loc[:end]
+        obs_ = obs_.reindex(sim_.index, method="nearest")  # sample on simulation times
+    except:
+        pass
+
+    return vtable(obs_.values, sim_.values)
+
+
 def vtable(obsrv, model):
 
     if len(obsrv) != len(model):
diff --git a/pyposeidon/utils/stereo.py b/pyposeidon/utils/stereo.py
index 8c7a2c00..140a25f3 100644
--- a/pyposeidon/utils/stereo.py
+++ b/pyposeidon/utils/stereo.py
@@ -44,15 +44,17 @@ def stereo_to_3d(u, v, R=1):
     return x, y, z
 
 
-def to_lat_lon(x, y, z=None):
+def to_lat_lon(x, y, z=None, R=1):
 
     if z is None:
-        x, y, z = to_3d(x, y)
+        x, y, z = stereo_to_3d(x, y, R=R)
 
     # to lat/lon
     rad = x ** 2 + y ** 2 + z ** 2
     rad = np.sqrt(rad)
 
+    rad[rad == 0] = rad.max()
+
     rlat = np.arcsin(z / rad)
     rlon = np.arctan2(y, x)
 
diff --git a/pyposeidon/utils/tag.py b/pyposeidon/utils/tag.py
index e1e7569f..ce5b4568 100644
--- a/pyposeidon/utils/tag.py
+++ b/pyposeidon/utils/tag.py
@@ -16,7 +16,7 @@ def tag_(**kwargs):
         logger.error("coastlines not given")
         sys.exit(1)
 
-    world = world.explode()
+    world = world.explode(index_parts=True)
 
     geometry = kwargs.get("geometry", None)
 
@@ -34,7 +34,13 @@ def tag_(**kwargs):
 
     # create a LineString of the grid
     grl = shapely.geometry.LineString(
-        [(lon_min, lat_min), (lon_min, lat_max), (lon_max, lat_max), (lon_max, lat_min), (lon_min, lat_min)]
+        [
+            (lon_min, lat_min),
+            (lon_min, lat_max),
+            (lon_max, lat_max),
+            (lon_max, lat_min),
+            (lon_min, lat_min),
+        ]
     )
 
     # check -180/180 trespass
diff --git a/pyposeidon/utils/topology.py b/pyposeidon/utils/topology.py
new file mode 100644
index 00000000..be49f974
--- /dev/null
+++ b/pyposeidon/utils/topology.py
@@ -0,0 +1,185 @@
+import numpy as np
+
+
+def MakeQuadFaces(Nr, Nc):
+
+    out = np.empty((Nr - 1, Nc - 1, 4), dtype=int)
+
+    r = np.arange(Nr * Nc).reshape(Nr, Nc)
+
+    out[:, :, 0] = r[:-1, :-1]
+    out[:, :, 1] = r[:-1, 1:]
+    out[:, :, 2] = r[1:, 1:]
+    out[:, :, 3] = r[1:, :-1]
+
+    out.shape = (-1, 4)
+    return out
+
+
+def MakeQuadFaces_periodic(Nr, Nc):
+
+    out = np.empty((Nr - 1, Nc, 4), dtype=int)
+
+    r = np.arange(Nr * Nc).reshape(Nr, Nc)
+
+    out[:, :-1, 0] = r[:-1, :-1]
+    out[:, :-1, 1] = r[:-1, 1:]
+    out[:, :-1, 2] = r[1:, 1:]
+    out[:, :-1, 3] = r[1:, :-1]
+
+    out[:, -1, 0] = r[:-1, -1]
+    out[:, -1, 1] = r[:-1, 0]
+    out[:, -1, 2] = r[1:, 0]
+    out[:, -1, 3] = r[1:, -1]
+
+    out.shape = (-1, 4)
+    return out
+
+
+# https://stackoverflow.com/questions/44934631/making-grid-triangular-mesh-quickly-with-numpy
+def MakeTriangleFaces(Nr, Nc):
+
+    out = np.empty((Nr - 1, Nc - 1, 2, 3), dtype=int)
+
+    r = np.arange(Nr * Nc).reshape(Nr, Nc)
+
+    out[:, :, 0, 0] = r[:-1, :-1]
+    out[:, :, 1, 0] = r[:-1, 1:]
+    out[:, :, 0, 1] = r[:-1, 1:]
+
+    out[:, :, 1, 1] = r[1:, 1:]
+    out[:, :, :, 2] = r[1:, :-1, None]
+
+    out.shape = (-1, 3)
+    return out
+
+
+def MakeTriangleFaces_periodic(Nr, Nc):
+
+    out = np.empty((Nr - 1, Nc, 2, 3), dtype=int)
+
+    r = np.arange(Nr * Nc).reshape(Nr, Nc)
+
+    out[:, :-1, 0, 0] = r[:-1, :-1]
+    out[:, :-1, 1, 0] = r[:-1, 1:]
+    out[:, :-1, 0, 1] = r[:-1, 1:]
+
+    out[:, :-1, 1, 1] = r[1:, 1:]
+    out[:, :-1, :, 2] = r[1:, :-1, None]
+
+    out[:, -1, 0, 0] = r[:-1, -1]
+    out[:, -1, 1, 0] = r[:-1, 0]
+    out[:, -1, 0, 1] = r[:-1, 0]
+    out[:, -1, 1, 1] = r[1:, 0]
+    out[:, -1, :, 2] = r[1:, -1, None]
+
+    out.shape = (-1, 3)
+    return out
+
+
+def quads_to_df(elems, nodes, x="longitude", y="latitude"):
+    # cells to polygons
+    ap = nodes.loc[elems.a, [x, y]]
+    bp = nodes.loc[elems.b, [x, y]]
+    cp = nodes.loc[elems.c, [x, y]]
+    dp = nodes.loc[elems.d, [x, y]]
+
+    ap["z"] = 0
+    bp["z"] = 0
+    cp["z"] = 0
+    dp["z"] = 0
+
+    elems["ap"] = ap.values.tolist()
+    elems["bp"] = bp.values.tolist()
+    elems["cp"] = cp.values.tolist()
+    elems["dp"] = dp.values.tolist()
+
+    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
+    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
+    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
+    elems["vd"] = nodes.loc[elems.d, "d2"].values.tolist()
+
+    return elems.drop(["a", "b", "c", "d"], axis=1)
+
+
+def quads_to_df_uv(elems, nodes, x="longitude", y="latitude"):
+    # cells to polygons
+    ap = nodes.loc[elems.a, [x, y]]
+    bp = nodes.loc[elems.b, [x, y]]
+    cp = nodes.loc[elems.c, [x, y]]
+    dp = nodes.loc[elems.d, [x, y]]
+
+    ap["z"] = 0
+    bp["z"] = 0
+    cp["z"] = 0
+    dp["z"] = 0
+
+    elems["ap"] = ap.values.tolist()
+    elems["bp"] = bp.values.tolist()
+    elems["cp"] = cp.values.tolist()
+    elems["dp"] = dp.values.tolist()
+
+    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
+    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
+    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
+    elems["vd"] = nodes.loc[elems.d, "d2"].values.tolist()
+
+    return elems.drop(["a", "b", "c", "d"], axis=1)
+
+
+def quads_to_df_3d(elems, nodes, x="x", y="y", z="z"):
+    # cells to polygons
+    ap = nodes.loc[elems.a, [x, y, z]]
+    bp = nodes.loc[elems.b, [x, y, z]]
+    cp = nodes.loc[elems.c, [x, y, z]]
+    dp = nodes.loc[elems.d, [x, y, z]]
+
+    elems["ap"] = ap.values.tolist()
+    elems["bp"] = bp.values.tolist()
+    elems["cp"] = cp.values.tolist()
+    elems["dp"] = dp.values.tolist()
+
+    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
+    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
+    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
+    elems["vd"] = nodes.loc[elems.d, "d2"].values.tolist()
+
+    return elems.drop(["a", "b", "c", "d"], axis=1)
+
+
+def tria_to_df_3d(elems, nodes, x="x", y="y", z="z"):
+    # cells to polygons
+    ap = nodes.loc[elems.a, [x, y, z]]
+    bp = nodes.loc[elems.b, [x, y, z]]
+    cp = nodes.loc[elems.c, [x, y, z]]
+
+    elems["ap"] = ap.values.tolist()
+    elems["bp"] = bp.values.tolist()
+    elems["cp"] = cp.values.tolist()
+
+    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
+    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
+    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
+
+    return elems.drop(["a", "b", "c"], axis=1)
+
+
+def tria_to_df(elems, nodes, x="longitude", y="latitude"):
+    # cells to polygons
+    ap = nodes.loc[elems.a, [x, y]]
+    bp = nodes.loc[elems.b, [x, y]]
+    cp = nodes.loc[elems.c, [x, y]]
+
+    ap["z"] = 0
+    bp["z"] = 0
+    cp["z"] = 0
+
+    elems["ap"] = ap.values.tolist()
+    elems["bp"] = bp.values.tolist()
+    elems["cp"] = cp.values.tolist()
+
+    elems["va"] = nodes.loc[elems.a, "d2"].values.tolist()
+    elems["vb"] = nodes.loc[elems.b, "d2"].values.tolist()
+    elems["vc"] = nodes.loc[elems.c, "d2"].values.tolist()
+
+    return elems.drop(["a", "b", "c"], axis=1)
diff --git a/pyposeidon/utils/obs.py b/pyposeidon/utils/vals.py
similarity index 82%
rename from pyposeidon/utils/obs.py
rename to pyposeidon/utils/vals.py
index 79015b6d..22dfa15b 100644
--- a/pyposeidon/utils/obs.py
+++ b/pyposeidon/utils/vals.py
@@ -15,6 +15,7 @@
 import pandas as pd
 import pkg_resources
 import pyposeidon
+from pyposeidon.utils.norm import normalize_column_names
 import os
 import sys
 import logging
@@ -44,21 +45,24 @@ def __init__(self, **kwargs):
         lat_min = kwargs.get("lat_min", None)
         lat_max = kwargs.get("lat_max", None)
 
-        #        db = kwargs.get('filename', DATA_PATH+'ioc.csv')
+        db = kwargs.get("tg_database", DATA_PATH + "critech.csv")
+        tg = pd.read_csv(db)
+        tg = normalize_column_names(tg)
 
-        #        ioc = pd.read_csv(db)
-        critech = pd.read_csv(DATA_PATH + "critech.csv")
-
-        critech.loc[:, ["lon", "lat"]] = critech.loc[:, ["lon", "lat"]].apply(pd.to_numeric)
-
-        w = critech.loc[
-            (critech["lon"] > lon_min)
-            & (critech["lon"] < lon_max)
-            & (critech["lat"] > lat_min)
-            & (critech["lat"] < lat_max)
+        tg.loc[:, ["longitude", "latitude"]] = tg.loc[:, ["longitude", "latitude"]].apply(pd.to_numeric)
+        try:
+            tg.loc[:, "Name"] = tg.Name.str.replace(" ", "").values
+        except:
+            pass
+
+        w = tg.loc[
+            (tg["longitude"] > lon_min)
+            & (tg["longitude"] < lon_max)
+            & (tg["latitude"] > lat_min)
+            & (tg["latitude"] < lat_max)
             & (
-                (pd.to_datetime(critech["Min. Time"]).dt.round("D") < self.sdate)
-                | (pd.to_datetime(critech["Min. Time"]).dt.round("D") < self.edate)
+                (pd.to_datetime(tg["Min. Time"]).dt.round("D") < self.sdate)
+                | (pd.to_datetime(tg["Min. Time"]).dt.round("D") < self.edate)
             )
         ]
 
@@ -85,7 +89,17 @@ def webcritech(self, **kwargs):
         pdate = min([self.edate, datetime.datetime.now()])
 
         url = "http://webcritech.jrc.ec.europa.eu/SeaLevelsDb/Home/ShowBuoyData?id={}&dateMin={}%2F{:02d}%2F{:02d}+{:02d}%3A{:02d}&dateMax={}%2F{:02d}%2F{:02d}+{:02d}%3A{:02d}&field=&options=".format(
-            point, sdate.year, sdate.month, sdate.day, sdate.hour, 0, pdate.year, pdate.month, pdate.day, pdate.hour, 0
+            point,
+            sdate.year,
+            sdate.month,
+            sdate.day,
+            sdate.hour,
+            0,
+            pdate.year,
+            pdate.month,
+            pdate.day,
+            pdate.hour,
+            0,
         )
 
         # print(url)
@@ -177,8 +191,16 @@ def webcritech(self, **kwargs):
                 tg = tg.apply(pd.to_numeric)
             except:
                 tg = pd.DataFrame(
-                    {"TimeUTC": np.nan, "Level m": np.nan, "Tide m": np.nan, "Level-Tide m": np.nan}, index=[0]
+                    {
+                        "TimeUTC": np.nan,
+                        "Level m": np.nan,
+                        "Tide m": np.nan,
+                        "Level-Tide m": np.nan,
+                    },
+                    index=[0],
                 )
+            tg.index.name = "time"
+            tg.columns = ["Total", "Tide", "Surge"]
             return tg
         except Exception as e:
             print(e)
diff --git a/pyposeidon/utils/verify.py b/pyposeidon/utils/verify.py
index 80780b90..c1f206aa 100755
--- a/pyposeidon/utils/verify.py
+++ b/pyposeidon/utils/verify.py
@@ -101,7 +101,7 @@ def verify(g, shp, thorough=False):
         gover = gp.GeoDataFrame(fd, geometry="overlap")
 
         # #### Reject small injuctions
-        ipols = gover.explode().loc[0]
+        ipols = gover.explode(index_parts=True).loc[0]
 
         ipols.columns = ["geometry"]
 
diff --git a/pyproject.toml b/pyproject.toml
index 58ffed31..58d265e1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,7 +38,7 @@ rasterio = "^1.2"
 matplotlib = "^3.3"
 dask = "^2021.2"
 xarray = ">=0.16.0,<1.0"
-geopandas = "^0.8.2"
+geopandas = "^0.10.1"
 tqdm = "^4.58"
 pyresample = "^1.17"
 pygeos = "^0.9"


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
index adb01978..8c069c35 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -33,7 +33,7 @@ def pytest_collection_modifyitems(config, items):
 
 
 def download_file_in_chunks(url: str, chunk_size: int = 1024):
-    """ Download file from `url` in chunks of size `chunk_size`.  """
+    """Download file from `url` in chunks of size `chunk_size`."""
     response = requests.get(url, stream=True)
     response.raise_for_status()
     for chunk in response.iter_content(chunk_size=chunk_size):
@@ -48,6 +48,8 @@ def save_as(url: str, path: pathlib.Path) -> None:
 
 
 TEST_DATA = {
+    DATA_DIR / "ocean.zip": "https://www.dropbox.com/sh/cgyk6ychwimjlzr/AADlueXH1I1F01mlgerMUSb6a?dl=1",
+    DATA_DIR / "bl.zip": "https://www.dropbox.com/sh/9tfdl67sll1ax8c/AACntQeIavCzRfTZZ9Tp8uFda?dl=1",
     DATA_DIR / "dem.nc": "https://www.dropbox.com/s/l16crheqc9d89gy/dem.nc?dl=1",
     DATA_DIR / "dem.tif": "https://www.dropbox.com/s/dgdlnr2p5q66np7/dem.tif?dl=1",
     DATA_DIR / "era5.grib": "https://www.dropbox.com/s/mo8z8mv8k9kqj91/era5.grib?dl=1",
diff --git a/tests/test_boundary.py b/tests/test_boundary.py
new file mode 100644
index 00000000..dcd07097
--- /dev/null
+++ b/tests/test_boundary.py
@@ -0,0 +1,70 @@
+import pyposeidon.boundary as pb
+import pytest
+import geopandas as gp
+import cartopy.feature as cf
+
+from . import DATA_DIR
+
+noaa = DATA_DIR / "bl.zip"
+
+land = cf.NaturalEarthFeature(category="physical", name="land", scale="50m")
+nland = gp.GeoDataFrame(geometry=[x for x in land.geometries()])
+
+coast = cf.NaturalEarthFeature(category="physical", name="coastline", scale="50m")
+ncoast = gp.GeoDataFrame(geometry=[x for x in coast.geometries()])
+
+# gh = cf.GSHHSFeature(scale="intermediate", levels=[1])
+# iGSHHS = gp.GeoDataFrame(geometry=[x for x in gh.geometries()])
+
+INPUTS = pytest.mark.parametrize("input", [nland, ncoast])  # , iGSHHS])
+CUSTOM = pytest.mark.parametrize("boundary", [noaa])
+WINDOWS = pytest.mark.parametrize(
+    "window",
+    [
+        {"lon_min": -30, "lon_max": -10.0, "lat_min": 60.0, "lat_max": 70.0},
+        {"lon_min": 175.0, "lon_max": 184.0, "lat_min": 14.5, "lat_max": 16.5},
+    ],
+)
+
+
+@INPUTS
+@WINDOWS
+def test_window(tmpdir, window, input):
+
+    df = pb.get_boundaries(geometry=window, coastlines=input, rpath=str(tmpdir) + "/")
+
+    assert isinstance(df.contours, gp.GeoDataFrame)
+
+
+@INPUTS
+def test_global(tmpdir, input):
+
+    df = pb.get_boundaries(geometry="global", coastlines=input, rpath=str(tmpdir) + "/")
+
+    assert isinstance(df.contours, gp.GeoDataFrame)
+
+
+@INPUTS
+@WINDOWS
+def test_buffer(tmpdir, window, input):
+
+    df = pb.get_boundaries(geometry=window, coastlines=input, cbuffer=0.01, rpath=str(tmpdir) + "/")
+
+    assert isinstance(df.contours, gp.GeoDataFrame)
+
+
+@INPUTS
+@WINDOWS
+def test_isodem(tmpdir, window, input):
+
+    df = pb.get_boundaries(geometry=window, blevels=[-100], rpath=str(tmpdir) + "/")
+
+    assert isinstance(df.contours, gp.GeoDataFrame)
+
+
+@CUSTOM
+def test_custom(tmpdir, boundary):
+
+    df = pb.get_boundaries(geometry=boundary, rpath=str(tmpdir) + "/")
+
+    assert isinstance(df.contours, gp.GeoDataFrame)
diff --git a/tests/test_d3d_cast.py b/tests/test_d3d_cast.py
index b58a6629..fff49e88 100644
--- a/tests/test_d3d_cast.py
+++ b/tests/test_d3d_cast.py
@@ -15,7 +15,12 @@
 GRIB_FILES_1 = [(DATA_DIR / filename).as_posix() for filename in ("uvp_2018100100.grib", "uvp_2018100112.grib")]
 GRIB_FILES_2 = [
     (DATA_DIR / filename).as_posix()
-    for filename in ("uvp_2018100100.grib", "uvp_2018100112.grib", "uvp_2018100200.grib", "uvp_2018100212.grib")
+    for filename in (
+        "uvp_2018100100.grib",
+        "uvp_2018100112.grib",
+        "uvp_2018100200.grib",
+        "uvp_2018100212.grib",
+    )
 ]
 DEM_FILE = (DATA_DIR / "dem.nc").as_posix()
 
@@ -87,7 +92,12 @@ def d3d(tmpdir):
     # set cast
     for l in range(len(rpaths) - 1):
         h = cast.cast(
-            solver="d3d", model=b, ppath=rpaths[l], cpath=rpaths[l + 1], meteo=meteo[l + 1], date=date_list[l + 1]
+            solver="d3d",
+            model=b,
+            ppath=rpaths[l],
+            cpath=rpaths[l + 1],
+            meteo=meteo[l + 1],
+            date=date_list[l + 1],
         )
         h.set(execute=True)  # execute
 
diff --git a/tests/test_dem.py b/tests/test_dem.py
index 658c9114..d5fafed3 100644
--- a/tests/test_dem.py
+++ b/tests/test_dem.py
@@ -21,9 +21,38 @@
     [
         {"lon_min": -30, "lon_max": -10.0, "lat_min": 60.0, "lat_max": 70.0},
         {"lon_min": 175.0, "lon_max": 184.0, "lat_min": 14.5, "lat_max": 16.5},
-        {"lon_min": 175.0 - 360.0, "lon_max": 184.0 - 360.0, "lat_min": 14.5, "lat_max": 16.5},
+        {
+            "lon_min": 175.0 - 360.0,
+            "lon_max": 184.0 - 360.0,
+            "lat_min": 14.5,
+            "lat_max": 16.5,
+        },
     ],
 )
 def test_answer(dem_source, kwargs):
     df = pdem.dem(dem_source=dem_source, **kwargs)
     assert np.isnan(df.Dataset.elevation.values).sum() == 0
+
+
+@pytest.mark.parametrize(
+    "full",
+    [
+        {"lon_min": -180.0, "lon_max": 180.0, "lat_min": -90.0, "lat_max": 90.0},
+        {
+            "lon_min": -180.0 - 180.0,
+            "lon_max": 180.0 - 180.0,
+            "lat_min": -90.0,
+            "lat_max": 90.0,
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "local_dem_source",
+    [
+        pytest.param(DATA_DIR / "dem.nc", id="local netcdf"),
+        pytest.param(DATA_DIR / "dem.tif", id="local geotiff"),
+    ],
+)
+def test_answer_global(local_dem_source, full):
+    df = pdem.dem(dem_source=local_dem_source, **full)
+    assert np.isnan(df.Dataset.elevation.values).sum() == 0
diff --git a/tests/test_dem_io.py b/tests/test_dem_io.py
index fbf96b44..936f43fc 100644
--- a/tests/test_dem_io.py
+++ b/tests/test_dem_io.py
@@ -30,7 +30,7 @@
 }
 
 
-def schism(tmpdir, kwargs):
+def schism(tmpdir, dem_source, kwargs):
 
     grid = pgrid.grid(type="tri2d", grid_file=(DATA_DIR / "hgrid.gr3").as_posix())
 
@@ -41,7 +41,7 @@ def schism(tmpdir, kwargs):
 
     kwargs.update({"grid_x": xp, "grid_y": yp})
     # get dem
-    df = pdem.dem(**kwargs)
+    df = pdem.dem(dem_source=dem_source, **kwargs)
 
     grid.Dataset["depth"].loc[:] = -df.Dataset.ival.values
 
@@ -56,7 +56,7 @@ def schism(tmpdir, kwargs):
     return grid.Dataset.equals(grid_.Dataset)
 
 
-def d3d(tmpdir, kwargs):
+def d3d(tmpdir, dem_source, kwargs):
 
     ## lat,lon grid
     resolution = 0.1
@@ -67,7 +67,7 @@ def d3d(tmpdir, kwargs):
     kwargs.update({"grid_x": xp, "grid_y": yp})
 
     # get dem
-    df = pdem.dem(**kwargs)
+    df = pdem.dem(dem_source=dem_source, **kwargs)
 
     rpath = str(tmpdir) + "/"
     # output
@@ -88,4 +88,4 @@ def d3d(tmpdir, kwargs):
 @pytest.mark.parametrize("kwargs", [window1])
 @pytest.mark.parametrize("solver", [schism, d3d])
 def test_answer(tmpdir, dem_source, kwargs, solver):
-    assert solver(tmpdir, kwargs) == True
+    assert solver(tmpdir, dem_source, kwargs) == True
diff --git a/tests/test_grid.py b/tests/test_grid.py
index 94b3c618..9c42897a 100644
--- a/tests/test_grid.py
+++ b/tests/test_grid.py
@@ -7,7 +7,11 @@
 
 
 cr = "i"
-coast = cf.NaturalEarthFeature(category="physical", name="land", scale="{}m".format({"l": 110, "i": 50, "h": 10}[cr]))
+coast = cf.NaturalEarthFeature(
+    category="physical",
+    name="land",
+    scale="{}m".format({"l": 110, "i": 50, "h": 10}[cr]),
+)
 
 
 natural_earth = gp.GeoDataFrame(geometry=[x for x in coast.geometries()])
@@ -20,20 +24,37 @@
 # define the lat/lon window and time frame of interest
 window0 = {"lon_min": -30, "lon_max": -10.0, "lat_min": 60.0, "lat_max": 70.0}
 
-window1 = {"lon_min": 175.0, "lon_max": 184.0, "lat_min": -21.5, "lat_max": -14.5}  # lat/lon window
+window1 = {
+    "lon_min": 175.0,
+    "lon_max": 184.0,
+    "lat_min": -21.5,
+    "lat_max": -14.5,
+}  # lat/lon window
 
 window2 = {"lon_min": -20.0, "lon_max": -10.0, "lat_min": 63.0, "lat_max": 67.0}
 
-window3 = {"lon_min": 175.0 - 360.0, "lon_max": 184.0 - 360.0, "lat_min": -21.5, "lat_max": -14.5}  # lat/lon window
+window3 = {
+    "lon_min": 175.0 - 360.0,
+    "lon_max": 184.0 - 360.0,
+    "lat_min": -21.5,
+    "lat_max": -14.5,
+}  # lat/lon window
 
 window4 = {"lon_min": -25.0, "lon_max": -10.0, "lat_min": 60.0, "lat_max": 68.0}
 
 
 @pytest.mark.parametrize("window", [window0, window1, window2, window3, window4])
 @pytest.mark.parametrize("coast", [natural_earth, GSHHS])
-def test_answer(tmpdir, window, coast):
-
-    df = pg.grid(type="tri2d", geometry=window, coastlines=coast, rpath=str(tmpdir) + "/")
+@pytest.mark.parametrize("ggor", ["jigsaw", "gmsh"])
+def test_answer(tmpdir, window, coast, ggor):
+
+    df = pg.grid(
+        type="tri2d",
+        geometry=window,
+        coastlines=coast,
+        rpath=str(tmpdir) + "/",
+        grid_generator=ggor,
+    )
 
     check = np.isnan(df.Dataset.depth.values).sum() == 0
     assert check == True
diff --git a/tests/test_grid_global.py b/tests/test_grid_global.py
index 7c191298..5f6831f1 100644
--- a/tests/test_grid_global.py
+++ b/tests/test_grid_global.py
@@ -5,28 +5,28 @@
 import geopandas as gp
 import cartopy.feature as cf
 
+from . import DATA_DIR
 
-ne = []
-for cr in ["l", "i", "h"]:
-    coast = cf.NaturalEarthFeature(
-        category="physical", name="land", scale="{}m".format({"l": 110, "i": 50, "h": 10}[cr])
-    )
-
-    gdf = gp.GeoDataFrame(geometry=[x for x in coast.geometries()])
-
-    w = gdf.explode().reset_index(drop=True)
-    ne.append(w)
+DEM_FILE = (DATA_DIR / "dem.nc").as_posix()
 
-coast = cf.GSHHSFeature(scale="auto", levels=[1])
-
-GSHHS = gp.GeoDataFrame(geometry=[x for x in coast.geometries()])
+COAST_FILE = (DATA_DIR / "ocean.zip").as_posix()
 
 
 @pytest.mark.slow
-@pytest.mark.parametrize("coast", ne[0:1])
-def test_answer(tmpdir, coast):
-
-    df = pg.grid(type="tri2d", geometry="global", coastlines=coast, rpath=str(tmpdir) + "/")
+@pytest.mark.parametrize("ggor", ["jigsaw", "gmsh"])
+@pytest.mark.parametrize("bgmesh", [None, DEM_FILE])
+@pytest.mark.parametrize("bindings", [True, False])
+def test_answer(tmpdir, ggor, bgmesh, bindings):
+
+    df = pg.grid(
+        type="tri2d",
+        geometry="global",
+        coastlines=COAST_FILE,
+        rpath=str(tmpdir) + "/",
+        grid_generator=ggor,
+        dem_source=bgmesh,
+        use_bindings=bindings,
+    )
 
     check = np.isnan(df.Dataset.depth.values).sum() == 0
 
diff --git a/tests/test_schism.py b/tests/test_schism.py
index 2a5b6a27..0e449c03 100644
--- a/tests/test_schism.py
+++ b/tests/test_schism.py
@@ -25,7 +25,15 @@
     "dem_source": DEM_FILE,
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 0.3, "nhot": 0, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 0.3,
+        "nhot": 0,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 case2 = {
@@ -41,7 +49,15 @@
     "meteo_engine": "cfgrib",
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 0.3, "nhot": 0, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 0.3,
+        "nhot": 0,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 
@@ -58,7 +74,15 @@
     "dem_source": DEM_FILE,
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 0.3, "nhot": 0, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 0.3,
+        "nhot": 0,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 
diff --git a/tests/test_schism_cast.py b/tests/test_schism_cast.py
index 7de7ad66..2fa77e1e 100644
--- a/tests/test_schism_cast.py
+++ b/tests/test_schism_cast.py
@@ -18,7 +18,12 @@
 METEO_FILES_1 = [(DATA_DIR / name).as_posix() for name in ("uvp_2018100100.grib", "uvp_2018100112.grib")]
 METEO_FILES_2 = [
     (DATA_DIR / name).as_posix()
-    for name in ("uvp_2018100100.grib", "uvp_2018100112.grib", "uvp_2018100200.grib", "uvp_2018100212.grib")
+    for name in (
+        "uvp_2018100100.grib",
+        "uvp_2018100112.grib",
+        "uvp_2018100200.grib",
+        "uvp_2018100212.grib",
+    )
 ]
 
 
@@ -39,7 +44,15 @@
     "meteo_xr_kwargs": {"concat_dim": "step"},
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 0.5, "nhot": 1, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 0.5,
+        "nhot": 1,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 # define in a dictionary the properties of the model..
@@ -59,7 +72,15 @@
     "meteo_xr_kwargs": {"concat_dim": "step"},
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 1.5, "nhot": 0, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 1.5,
+        "nhot": 0,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 
@@ -94,7 +115,12 @@ def schism(tmpdir):
     # set cast
     for l in range(len(rpaths) - 1):
         h = cast.cast(
-            solver="schism", model=b, ppath=rpaths[l], cpath=rpaths[l + 1], meteo=meteo[l + 1], date=date_list[l + 1]
+            solver="schism",
+            model=b,
+            ppath=rpaths[l],
+            cpath=rpaths[l + 1],
+            meteo=meteo[l + 1],
+            date=date_list[l + 1],
         )
         h.set(execute=True)  # execute
 
diff --git a/tests/test_schism_reforecast.py b/tests/test_schism_reforecast.py
index cfdaed63..f4f06623 100644
--- a/tests/test_schism_reforecast.py
+++ b/tests/test_schism_reforecast.py
@@ -20,7 +20,12 @@
 METEO_FILES_1 = [(DATA_DIR / "uvp_2018100100.grib").as_posix()]
 METEO_FILES_2 = [
     (DATA_DIR / name).as_posix()
-    for name in ("uvp_2018100100.grib", "uvp_2018100112.grib", "uvp_2018100200.grib", "uvp_2018100212.grib")
+    for name in (
+        "uvp_2018100100.grib",
+        "uvp_2018100112.grib",
+        "uvp_2018100200.grib",
+        "uvp_2018100212.grib",
+    )
 ]
 
 
@@ -41,7 +46,15 @@
     "meteo_xr_kwargs": {"concat_dim": "step"},
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 0.5, "nhot": 1, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 0.5,
+        "nhot": 1,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 
@@ -58,7 +71,15 @@
     "meteo_engine": "passthrough",
     "ncores": NCORES,  # number of cores
     "update": ["all"],  # update only meteo, keep dem
-    "parameters": {"dt": 400, "rnday": 1.5, "nhot": 0, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+    "parameters": {
+        "dt": 400,
+        "rnday": 1.5,
+        "nhot": 0,
+        "ihot": 0,
+        "nspool": 9,
+        "ihfskip": 36,
+        "nhot_write": 108,
+    },
 }
 
 
@@ -93,7 +114,12 @@ def schism(tmpdir):
     # set cast
     for l in range(len(rpaths) - 1):
         h = cast.cast(
-            solver="schism", model=b, ppath=rpaths[l], cpath=rpaths[l + 1], meteo=meteo[l + 1], date=date_list[l + 1]
+            solver="schism",
+            model=b,
+            ppath=rpaths[l],
+            cpath=rpaths[l + 1],
+            meteo=meteo[l + 1],
+            date=date_list[l + 1],
         )
         h.set(execute=True)  # execute
 
diff --git a/tests/test_schism_total.py b/tests/test_schism_total.py
index 7bf4691b..4edde5b9 100644
--- a/tests/test_schism_total.py
+++ b/tests/test_schism_total.py
@@ -11,11 +11,21 @@
 
 case0 = {"lon_min": -30, "lon_max": -10.0, "lat_min": 60.0, "lat_max": 70.0}
 
-case1 = {"lon_min": 175.0, "lon_max": 184.0, "lat_min": -21.5, "lat_max": -14.5}  # lat/lon window
+case1 = {
+    "lon_min": 175.0,
+    "lon_max": 184.0,
+    "lat_min": -21.5,
+    "lat_max": -14.5,
+}  # lat/lon window
 
 case2 = {"lon_min": -20.0, "lon_max": -10.0, "lat_min": 63.0, "lat_max": 67.0}
 
-case3 = {"lon_min": 175.0 - 360.0, "lon_max": 184.0 - 360.0, "lat_min": -21.5, "lat_max": -14.5}  # lat/lon window
+case3 = {
+    "lon_min": 175.0 - 360.0,
+    "lon_max": 184.0 - 360.0,
+    "lat_min": -21.5,
+    "lat_max": -14.5,
+}  # lat/lon window
 
 case4 = {"lon_min": -25.0, "lon_max": -10.0, "lat_min": 60.0, "lat_max": 68.0}
 
@@ -35,7 +45,15 @@ def schism(tmpdir, case):
         "dem_source": DEM_FILE,
         "ncores": NCORES,  # number of cores
         "update": ["all"],  # update only meteo, keep dem
-        "parameters": {"dt": 400, "rnday": 0.3, "nhot": 0, "ihot": 0, "nspool": 9, "ihfskip": 36, "nhot_write": 108},
+        "parameters": {
+            "dt": 400,
+            "rnday": 0.3,
+            "nhot": 0,
+            "ihot": 0,
+            "nspool": 9,
+            "ihfskip": 36,
+            "nhot_write": 108,
+        },
     }
 
     rpath = str(tmpdir) + "/"