{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore MODHID hdf5 Files\n",
"\n",
"This notebook is an initial exploration of hdf5 output files produced by MOHID.\n",
"\n",
"MOHID output file types explored:\n",
"\n",
"* Lagrangian oil particle tracks\n",
"\n",
"Python libraries explored:\n",
"\n",
"* [PyNIO](https://www.pyngl.ucar.edu/Nio.shtml)\n",
"* [h5netcdf](https://github.com/shoyer/h5netcdf)\n",
"* [h5py](http://docs.h5py.org/en/stable/index.html)\n",
"* [PyTables](https://www.pytables.org/index.html)\n",
"\n",
"Summary:\n",
"\n",
"* PyNIO fails to open the files\n",
"* h5netcdf successfully opens the files but can't handle groups that contain variables\n",
"that don't have associated dimension scales\n",
"* h5py successfully opens the files and allows access to all of their contents,\n",
"but the interface is a really low level, nested-dict one\n",
"* PyTables successfully opens the files and allows access to al of their contents\n",
"via an object interface.\n",
"The PyTables tutorial also brought the `h5ls` tool to my attention.\n",
"* The results are organized as Eulerian snapshots,\n",
"in general both by 2D and 3D slabs of the hydrodynamics model (NEMO or FVCOM) grid,\n",
"and by particle.\n",
"\n",
"Next step:\n",
"\n",
"* Write code to transform PyTables `tables.File` objects into `xarray.Dataset` objects."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 doug doug 200M Oct 11 07:42 ../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5\n",
"-rw-r--r-- 1 doug doug 355M Oct 11 07:47 ../../SalishSeaShihan/results/Lagrangian_7_st_georgia_nested.hdf5\n"
]
}
],
"source": [
"!ls -lh ../../SalishSeaShihan/results/Lagrangian*.hdf5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PyNIO"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import Nio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h5file = Nio.open_file('../../SalishSeaShihan/results/Lagrangian_7_st_georgia_nested.hdf5', format='hdf5')\n",
"h5file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...crashed the kernel :-("
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h5file = Nio.open_file('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...never finished :-("
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## h5netcdf"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import h5netcdf"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<h5netcdf.File 'Lagrangian_7_nested_5.hdf5' (mode r+)>\n",
"Dimensions:\n",
"Groups:\n",
" Grid\n",
" Results\n",
" Time\n",
"Variables:\n",
"Attributes:"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file = h5netcdf.File('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5')\n",
"h5file"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "variable '/Grid/Bathymetry' has no dimension scale associated with axis 0",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0mtype_pprinters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_printers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m deferred_pprinters=self.deferred_printers)\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mprinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpretty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py\u001b[0m in \u001b[0;36mpretty\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'__repr__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_repr_pprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_default_pprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py\u001b[0m in \u001b[0;36m_repr_pprint\u001b[0;34m(obj, p, cycle)\u001b[0m\n\u001b[1;32m 695\u001b[0m \u001b[0;34m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 696\u001b[0m \u001b[0;31m# Find newlines and replace them with p.break_()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 698\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_line\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 544\u001b[0m header = ('<%s %r (%s members)>'\n\u001b[1;32m 545\u001b[0m % (self._cls_name, self.name, len(self)))\n\u001b[0;32m--> 546\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mresize_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m_repr_body\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 536\u001b[0m [' %s: %r %s' % (k, v.dimensions, v.dtype)\n\u001b[1;32m 537\u001b[0m for k, v in self.variables.items()] +\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;34m'Attributes:'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 539\u001b[0m [' %s: %r' % (k, v) for k, v in self.attrs.items()])\n\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Variables:'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 536\u001b[0m [' %s: %r %s' % (k, v.dimensions, v.dtype)\n\u001b[0;32m--> 537\u001b[0;31m for k, v in self.variables.items()] +\n\u001b[0m\u001b[1;32m 538\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Attributes:'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 539\u001b[0m [' %s: %r' % (k, v) for k, v in self.attrs.items()])\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36mdimensions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdimensions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensions\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lookup_dimensions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m_lookup_dimensions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 102\u001b[0m raise ValueError('variable %r has no dimension scale '\n\u001b[1;32m 103\u001b[0m \u001b[0;34m'associated with axis %s'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m % (self.name, axis))\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_name_from_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: variable '/Grid/Bathymetry' has no dimension scale associated with axis 0"
]
}
],
"source": [
"oil['Grid']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Dimeions scales](https://www.unidata.ucar.edu/software/netcdf/docs/interoperability_hdf5.html)\n",
"seem to be optional for HDF5, but required for netCDF4.\n",
"The h5py docs provide [some additional clues](http://docs.h5py.org/en/stable/high/dims.html)."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<h5netcdf.Group '/Results' (3 members)>\n",
"Dimensions:\n",
"Groups:\n",
" Number\n",
" OilSpill\n",
" Percentage Contaminated\n",
"Variables:\n",
"Attributes:\n",
" Minimum: 9900000000000000.0\n",
" Maximum: -9900000000000000.0"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Results']"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "variable '/Time/Time_00001' has no dimension scale associated with axis 0",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0mtype_pprinters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_printers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m deferred_pprinters=self.deferred_printers)\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mprinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpretty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py\u001b[0m in \u001b[0;36mpretty\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'__repr__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_repr_pprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_default_pprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py\u001b[0m in \u001b[0;36m_repr_pprint\u001b[0;34m(obj, p, cycle)\u001b[0m\n\u001b[1;32m 695\u001b[0m \u001b[0;34m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 696\u001b[0m \u001b[0;31m# Find newlines and replace them with p.break_()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 698\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_line\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 544\u001b[0m header = ('<%s %r (%s members)>'\n\u001b[1;32m 545\u001b[0m % (self._cls_name, self.name, len(self)))\n\u001b[0;32m--> 546\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mresize_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m_repr_body\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 536\u001b[0m [' %s: %r %s' % (k, v.dimensions, v.dtype)\n\u001b[1;32m 537\u001b[0m for k, v in self.variables.items()] +\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;34m'Attributes:'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 539\u001b[0m [' %s: %r' % (k, v) for k, v in self.attrs.items()])\n\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Variables:'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 536\u001b[0m [' %s: %r %s' % (k, v.dimensions, v.dtype)\n\u001b[0;32m--> 537\u001b[0;31m for k, v in self.variables.items()] +\n\u001b[0m\u001b[1;32m 538\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Attributes:'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 539\u001b[0m [' %s: %r' % (k, v) for k, v in self.attrs.items()])\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36mdimensions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdimensions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensions\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lookup_dimensions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py\u001b[0m in \u001b[0;36m_lookup_dimensions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 102\u001b[0m raise ValueError('variable %r has no dimension scale '\n\u001b[1;32m 103\u001b[0m \u001b[0;34m'associated with axis %s'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m % (self.name, axis))\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_name_from_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: variable '/Time/Time_00001' has no dimension scale associated with axis 0"
]
}
],
"source": [
"h5file['Time']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## h5py"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import h5py"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 file \"Lagrangian_7_nested_5.hdf5\" (mode r+)>"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file = h5py.File('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5', mode='r')\n",
"h5file"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Grid\n",
"Results\n",
"Time\n"
]
}
],
"source": [
"for k in h5file: print(k)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 group \"/Grid\" (9 members)>"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Grid']"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bathymetry\n",
"ConnectionX\n",
"ConnectionY\n",
"Define Cells\n",
"Latitude\n",
"Longitude\n",
"OpenPoints\n",
"VerticalZ\n",
"WaterPoints3D\n"
]
}
],
"source": [
"for k in h5file['Grid']: print(k)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 group \"/Results\" (3 members)>"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Results']"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number\n",
"OilSpill\n",
"Percentage Contaminated\n"
]
}
],
"source": [
"for k in h5file['Results']: print(k)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 group \"/Time\" (167 members)>"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Time']"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time_00001\n",
"Time_00002\n",
"Time_00003\n",
"Time_00004\n",
"Time_00005\n",
"Time_00006\n",
"Time_00007\n",
"Time_00008\n",
"Time_00009\n",
"Time_00010\n",
"Time_00011\n",
"Time_00012\n",
"Time_00013\n",
"Time_00014\n",
"Time_00015\n",
"Time_00016\n",
"Time_00017\n",
"Time_00018\n",
"Time_00019\n",
"Time_00020\n",
"Time_00021\n",
"Time_00022\n",
"Time_00023\n",
"Time_00024\n",
"Time_00025\n",
"Time_00026\n",
"Time_00027\n",
"Time_00028\n",
"Time_00029\n",
"Time_00030\n",
"Time_00031\n",
"Time_00032\n",
"Time_00033\n",
"Time_00034\n",
"Time_00035\n",
"Time_00036\n",
"Time_00037\n",
"Time_00038\n",
"Time_00039\n",
"Time_00040\n",
"Time_00041\n",
"Time_00042\n",
"Time_00043\n",
"Time_00044\n",
"Time_00045\n",
"Time_00046\n",
"Time_00047\n",
"Time_00048\n",
"Time_00049\n",
"Time_00050\n",
"Time_00051\n",
"Time_00052\n",
"Time_00053\n",
"Time_00054\n",
"Time_00055\n",
"Time_00056\n",
"Time_00057\n",
"Time_00058\n",
"Time_00059\n",
"Time_00060\n",
"Time_00061\n",
"Time_00062\n",
"Time_00063\n",
"Time_00064\n",
"Time_00065\n",
"Time_00066\n",
"Time_00067\n",
"Time_00068\n",
"Time_00069\n",
"Time_00070\n",
"Time_00071\n",
"Time_00072\n",
"Time_00073\n",
"Time_00074\n",
"Time_00075\n",
"Time_00076\n",
"Time_00077\n",
"Time_00078\n",
"Time_00079\n",
"Time_00080\n",
"Time_00081\n",
"Time_00082\n",
"Time_00083\n",
"Time_00084\n",
"Time_00085\n",
"Time_00086\n",
"Time_00087\n",
"Time_00088\n",
"Time_00089\n",
"Time_00090\n",
"Time_00091\n",
"Time_00092\n",
"Time_00093\n",
"Time_00094\n",
"Time_00095\n",
"Time_00096\n",
"Time_00097\n",
"Time_00098\n",
"Time_00099\n",
"Time_00100\n",
"Time_00101\n",
"Time_00102\n",
"Time_00103\n",
"Time_00104\n",
"Time_00105\n",
"Time_00106\n",
"Time_00107\n",
"Time_00108\n",
"Time_00109\n",
"Time_00110\n",
"Time_00111\n",
"Time_00112\n",
"Time_00113\n",
"Time_00114\n",
"Time_00115\n",
"Time_00116\n",
"Time_00117\n",
"Time_00118\n",
"Time_00119\n",
"Time_00120\n",
"Time_00121\n",
"Time_00122\n",
"Time_00123\n",
"Time_00124\n",
"Time_00125\n",
"Time_00126\n",
"Time_00127\n",
"Time_00128\n",
"Time_00129\n",
"Time_00130\n",
"Time_00131\n",
"Time_00132\n",
"Time_00133\n",
"Time_00134\n",
"Time_00135\n",
"Time_00136\n",
"Time_00137\n",
"Time_00138\n",
"Time_00139\n",
"Time_00140\n",
"Time_00141\n",
"Time_00142\n",
"Time_00143\n",
"Time_00144\n",
"Time_00145\n",
"Time_00146\n",
"Time_00147\n",
"Time_00148\n",
"Time_00149\n",
"Time_00150\n",
"Time_00151\n",
"Time_00152\n",
"Time_00153\n",
"Time_00154\n",
"Time_00155\n",
"Time_00156\n",
"Time_00157\n",
"Time_00158\n",
"Time_00159\n",
"Time_00160\n",
"Time_00161\n",
"Time_00162\n",
"Time_00163\n",
"Time_00164\n",
"Time_00165\n",
"Time_00166\n",
"Time_00167\n"
]
}
],
"source": [
"for k in h5file['Time']: print(k)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 dataset \"Time_00001\": shape (6,), type \"<f4\">"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Time']['Time_00001']"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2015., 4., 8., 0., 30., 0.], dtype=float32)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Time']['Time_00001'][:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"i.e. the date/time `2015-04-08 00:30:00` stored at 4 32-bit floats"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 group \"/Results/Number\" (167 members)>"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Results']['Number']"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Attributes of HDF5 object at 139899974438056>"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file.attrs"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"for attr in h5file.attrs: print(attr)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum -9900000000000000.0\n",
"Maximum -9900000000000000.0\n"
]
}
],
"source": [
"for attr, value in h5file['Grid'].attrs.items(): print(attr, value)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum -9900000000000000.0\n",
"Maximum -9900000000000000.0\n",
"Units b'm'\n"
]
}
],
"source": [
"for attr, value in h5file['Grid']['Bathymetry'].attrs.items(): print(attr, value)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum -9900000000000000.0\n",
"Maximum -9900000000000000.0\n"
]
}
],
"source": [
"for attr, value in h5file['Results']['Number'].attrs.items(): print(attr, value)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 group \"/Results/Number\" (167 members)>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oil['Results']['Number']"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number_00001\n",
"Number_00002\n",
"Number_00003\n",
"Number_00004\n",
"Number_00005\n",
"Number_00006\n",
"Number_00007\n",
"Number_00008\n",
"Number_00009\n",
"Number_00010\n",
"Number_00011\n",
"Number_00012\n",
"Number_00013\n",
"Number_00014\n",
"Number_00015\n",
"Number_00016\n",
"Number_00017\n",
"Number_00018\n",
"Number_00019\n",
"Number_00020\n",
"Number_00021\n",
"Number_00022\n",
"Number_00023\n",
"Number_00024\n",
"Number_00025\n",
"Number_00026\n",
"Number_00027\n",
"Number_00028\n",
"Number_00029\n",
"Number_00030\n",
"Number_00031\n",
"Number_00032\n",
"Number_00033\n",
"Number_00034\n",
"Number_00035\n",
"Number_00036\n",
"Number_00037\n",
"Number_00038\n",
"Number_00039\n",
"Number_00040\n",
"Number_00041\n",
"Number_00042\n",
"Number_00043\n",
"Number_00044\n",
"Number_00045\n",
"Number_00046\n",
"Number_00047\n",
"Number_00048\n",
"Number_00049\n",
"Number_00050\n",
"Number_00051\n",
"Number_00052\n",
"Number_00053\n",
"Number_00054\n",
"Number_00055\n",
"Number_00056\n",
"Number_00057\n",
"Number_00058\n",
"Number_00059\n",
"Number_00060\n",
"Number_00061\n",
"Number_00062\n",
"Number_00063\n",
"Number_00064\n",
"Number_00065\n",
"Number_00066\n",
"Number_00067\n",
"Number_00068\n",
"Number_00069\n",
"Number_00070\n",
"Number_00071\n",
"Number_00072\n",
"Number_00073\n",
"Number_00074\n",
"Number_00075\n",
"Number_00076\n",
"Number_00077\n",
"Number_00078\n",
"Number_00079\n",
"Number_00080\n",
"Number_00081\n",
"Number_00082\n",
"Number_00083\n",
"Number_00084\n",
"Number_00085\n",
"Number_00086\n",
"Number_00087\n",
"Number_00088\n",
"Number_00089\n",
"Number_00090\n",
"Number_00091\n",
"Number_00092\n",
"Number_00093\n",
"Number_00094\n",
"Number_00095\n",
"Number_00096\n",
"Number_00097\n",
"Number_00098\n",
"Number_00099\n",
"Number_00100\n",
"Number_00101\n",
"Number_00102\n",
"Number_00103\n",
"Number_00104\n",
"Number_00105\n",
"Number_00106\n",
"Number_00107\n",
"Number_00108\n",
"Number_00109\n",
"Number_00110\n",
"Number_00111\n",
"Number_00112\n",
"Number_00113\n",
"Number_00114\n",
"Number_00115\n",
"Number_00116\n",
"Number_00117\n",
"Number_00118\n",
"Number_00119\n",
"Number_00120\n",
"Number_00121\n",
"Number_00122\n",
"Number_00123\n",
"Number_00124\n",
"Number_00125\n",
"Number_00126\n",
"Number_00127\n",
"Number_00128\n",
"Number_00129\n",
"Number_00130\n",
"Number_00131\n",
"Number_00132\n",
"Number_00133\n",
"Number_00134\n",
"Number_00135\n",
"Number_00136\n",
"Number_00137\n",
"Number_00138\n",
"Number_00139\n",
"Number_00140\n",
"Number_00141\n",
"Number_00142\n",
"Number_00143\n",
"Number_00144\n",
"Number_00145\n",
"Number_00146\n",
"Number_00147\n",
"Number_00148\n",
"Number_00149\n",
"Number_00150\n",
"Number_00151\n",
"Number_00152\n",
"Number_00153\n",
"Number_00154\n",
"Number_00155\n",
"Number_00156\n",
"Number_00157\n",
"Number_00158\n",
"Number_00159\n",
"Number_00160\n",
"Number_00161\n",
"Number_00162\n",
"Number_00163\n",
"Number_00164\n",
"Number_00165\n",
"Number_00166\n",
"Number_00167\n"
]
}
],
"source": [
"for n in h5file['Results']['Number']: print(n)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 dataset \"Number_00001\": shape (20, 380, 210), type \"<f4\">"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Results']['Number']['Number_00001']"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum\n",
"Maximum\n"
]
}
],
"source": [
"for attr in h5file['Results']['OilSpill'].attrs: print(attr)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data_2D\n",
"Data_3D\n"
]
}
],
"source": [
"for foo in h5file['Results']['OilSpill']: print(foo)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum 9900000000000000.0\n",
"Maximum -9900000000000000.0\n"
]
}
],
"source": [
"for attr, value in h5file['Results']['OilSpill']['Data_2D'].attrs.items(): print(attr, value)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Beaching Time\n",
"Oil Arrival Time\n",
"OilConcentration_2D\n",
"Thickness_2D\n"
]
}
],
"source": [
"for foo in h5file['Results']['OilSpill']['Data_2D']: print(foo)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 group \"/Results/OilSpill/Data_2D/OilConcentration_2D\" (167 members)>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Results']['OilSpill']['Data_2D']['OilConcentration_2D']"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 dataset \"OilConcentration_2D_00001\": shape (380, 210), type \"<f4\">"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file['Results']['OilSpill']['Data_2D']['OilConcentration_2D']['OilConcentration_2D_00001']"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum -9900000000000000.0\n",
"Maximum -9900000000000000.0\n",
"Units b'ppm'\n"
]
}
],
"source": [
"for attr, value in h5file['Results']['OilSpill']['Data_2D']['OilConcentration_2D']['OilConcentration_2D_00001'].attrs.items(): print (attr, value)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum 9900000000000000.0\n",
"Maximum -9900000000000000.0\n"
]
}
],
"source": [
"for attr, value in h5file['Results']['OilSpill']['Data_3D'].attrs.items(): print(attr, value)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dissolution_3D\n",
"OilConcentration_3D\n"
]
}
],
"source": [
"for foo in h5file['Results']['OilSpill']['Data_3D']: print(foo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PyTables"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"import tables"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/ (RootGroup) ''\n",
"/Grid (Group) ''\n",
"/Results (Group) ''\n",
"/Time (Group) ''\n",
"/Results/Number (Group) ''\n",
"/Results/OilSpill (Group) ''\n",
"/Results/Percentage Contaminated (Group) ''\n",
"/Results/OilSpill/Data_2D (Group) ''\n",
"/Results/OilSpill/Data_3D (Group) ''\n",
"/Results/OilSpill/Data_3D/Dissolution_3D (Group) ''\n",
"/Results/OilSpill/Data_3D/OilConcentration_3D (Group) ''\n",
"/Results/OilSpill/Data_2D/Beaching Time (Group) ''\n",
"/Results/OilSpill/Data_2D/Oil Arrival Time (Group) ''\n",
"/Results/OilSpill/Data_2D/OilConcentration_2D (Group) ''\n",
"/Results/OilSpill/Data_2D/Thickness_2D (Group) ''\n",
"/Grid/OpenPoints (Group) ''\n",
"/Grid/VerticalZ (Group) ''\n"
]
}
],
"source": [
"h5file = tables.open_file('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5', mode='r')\n",
"for group in h5file.walk_groups(): print(group)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Results/Number (Group) ''\n",
"/Results/Number/Number_00001 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00002 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00003 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00004 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00005 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00006 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00007 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00008 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00009 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00010 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00011 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00012 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00013 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00014 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00015 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00016 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00017 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00018 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00019 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00020 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00021 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00022 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00023 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00024 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00025 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00026 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00027 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00028 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00029 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00030 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00031 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00032 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00033 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00034 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00035 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00036 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00037 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00038 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00039 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00040 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00041 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00042 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00043 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00044 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00045 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00046 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00047 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00048 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00049 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00050 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00051 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00052 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00053 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00054 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00055 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00056 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00057 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00058 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00059 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00060 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00061 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00062 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00063 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00064 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00065 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00066 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00067 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00068 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00069 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00070 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00071 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00072 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00073 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00074 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00075 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00076 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00077 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00078 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00079 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00080 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00081 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00082 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00083 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00084 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00085 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00086 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00087 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00088 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00089 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00090 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00091 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00092 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00093 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00094 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00095 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00096 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00097 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00098 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00099 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00100 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00101 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00102 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00103 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00104 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00105 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00106 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00107 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00108 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00109 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00110 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00111 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00112 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00113 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00114 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00115 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00116 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00117 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00118 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00119 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00120 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00121 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00122 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00123 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00124 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00125 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00126 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00127 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00128 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00129 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00130 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00131 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00132 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00133 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00134 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00135 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00136 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00137 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00138 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00139 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00140 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00141 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00142 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00143 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00144 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00145 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00146 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00147 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00148 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00149 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00150 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00151 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00152 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00153 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00154 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00155 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00156 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00157 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00158 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00159 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00160 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00161 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00162 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00163 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00164 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00165 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00166 (CArray(20, 380, 210), zlib(6)) ''\n",
"/Results/Number/Number_00167 (CArray(20, 380, 210), zlib(6)) ''\n"
]
}
],
"source": [
"for node in h5file.walk_nodes('/Results/Number'): print(node)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"/Results/Number/Number_00001._v_attrs (AttributeSet), 3 attributes:\n",
" [Maximum := -9900000000000000.0,\n",
" Minimum := -9900000000000000.0,\n",
" Units := b'a']"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file.root.Results.Number.Number_00001.attrs"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"/Results/OilSpill/Data_3D/OilConcentration_3D/OilConcentration_3D_00001._v_attrs (AttributeSet), 3 attributes:\n",
" [Maximum := -9900000000000000.0,\n",
" Minimum := -9900000000000000.0,\n",
" Units := b'Kg/m3']"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file.root.Results.OilSpill.Data_3D.OilConcentration_3D.OilConcentration_3D_00001.attrs"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"/Results/OilSpill/Data_3D/OilConcentration_3D/OilConcentration_3D_00001 (CArray(20, 380, 210), zlib(6)) ''\n",
" atom := Float32Atom(shape=(), dflt=0.0)\n",
" maindim := 0\n",
" flavor := 'numpy'\n",
" byteorder := 'little'\n",
" chunkshape := (20, 380, 210)"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5file.root.Results.OilSpill.Data_3D.OilConcentration_3D.OilConcentration_3D_00001"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}