Notebook

Explore MODHID hdf5 Files¶

This notebook is an initial exploration of hdf5 output files produced by MOHID.

MOHID output file types explored:

Lagrangian oil particle tracks

Python libraries explored:

Summary:

PyNIO fails to open the files
h5netcdf successfully opens the files but can't handle groups that contain variables

that don't have associated dimension scales

h5py successfully opens the files and allows access to all of their contents,

but the interface is a really low level, nested-dict one

PyTables successfully opens the files and allows access to al of their contents

via an object interface. The PyTables tutorial also brought the h5ls tool to my attention.

The results are organized as Eulerian snapshots,

in general both by 2D and 3D slabs of the hydrodynamics model (NEMO or FVCOM) grid, and by particle.

Next step:

Write code to transform PyTables tables.File objects into xarray.Dataset objects.

In [2]:

!ls -lh ../../SalishSeaShihan/results/Lagrangian*.hdf5

-rw-r--r-- 1 doug doug 200M Oct 11 07:42 ../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5
-rw-r--r-- 1 doug doug 355M Oct 11 07:47 ../../SalishSeaShihan/results/Lagrangian_7_st_georgia_nested.hdf5

PyNIO¶

In [1]:

import Nio

In [ ]:

h5file = Nio.open_file('../../SalishSeaShihan/results/Lagrangian_7_st_georgia_nested.hdf5', format='hdf5')
h5file

...crashed the kernel :-(

In [ ]:

h5file = Nio.open_file('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5')

...never finished :-(

h5netcdf¶

In [1]:

import h5netcdf

In [69]:

h5file = h5netcdf.File('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5')
h5file

Out[69]:

<h5netcdf.File 'Lagrangian_7_nested_5.hdf5' (mode r+)>
Dimensions:
Groups:
    Grid
    Results
    Time
Variables:
Attributes:

In [11]:

oil['Grid']

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
    700                 type_pprinters=self.type_printers,
    701                 deferred_pprinters=self.deferred_printers)
--> 702             printer.pretty(obj)
    703             printer.flush()
    704             return stream.getvalue()

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    400                         if cls is not object \
    401                                 and callable(cls.__dict__.get('__repr__')):
--> 402                             return _repr_pprint(obj, self, cycle)
    403 
    404             return _default_pprint(obj, self, cycle)

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
    695     """A pprint that just redirects to the normal repr function."""
    696     # Find newlines and replace them with p.break_()
--> 697     output = repr(obj)
    698     for idx,output_line in enumerate(output.splitlines()):
    699         if idx:

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in __repr__(self)
    544         header = ('<%s %r (%s members)>'
    545                   % (self._cls_name, self.name, len(self)))
--> 546         return '\n'.join([header] + self._repr_body())
    547 
    548     def resize_dimension(self, dimension, size):

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in _repr_body(self)
    536             ['    %s: %r %s' % (k, v.dimensions, v.dtype)
    537              for k, v in self.variables.items()] +
--> 538             ['Attributes:'] +
    539             ['    %s: %r' % (k, v) for k, v in self.attrs.items()])
    540 

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in <listcomp>(.0)
    535             ['Variables:'] +
    536             ['    %s: %r %s' % (k, v.dimensions, v.dtype)
--> 537              for k, v in self.variables.items()] +
    538             ['Attributes:'] +
    539             ['    %s: %r' % (k, v) for k, v in self.attrs.items()])

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in dimensions(self)
    110     def dimensions(self):
    111         if self._dimensions is None:
--> 112             self._dimensions = self._lookup_dimensions()
    113         return self._dimensions
    114 

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in _lookup_dimensions(self)
    102                 raise ValueError('variable %r has no dimension scale '
    103                                  'associated with axis %s'
--> 104                                  % (self.name, axis))
    105             name = _name_from_dimension(dim)
    106             dims.append(name)

ValueError: variable '/Grid/Bathymetry' has no dimension scale associated with axis 0

Dimeions scales seem to be optional for HDF5, but required for netCDF4. The h5py docs provide some additional clues.

In [70]:

h5file['Results']

Out[70]:

<h5netcdf.Group '/Results' (3 members)>
Dimensions:
Groups:
    Number
    OilSpill
    Percentage Contaminated
Variables:
Attributes:
    Minimum: 9900000000000000.0
    Maximum: -9900000000000000.0

In [71]:

h5file['Time']

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
    700                 type_pprinters=self.type_printers,
    701                 deferred_pprinters=self.deferred_printers)
--> 702             printer.pretty(obj)
    703             printer.flush()
    704             return stream.getvalue()

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    400                         if cls is not object \
    401                                 and callable(cls.__dict__.get('__repr__')):
--> 402                             return _repr_pprint(obj, self, cycle)
    403 
    404             return _default_pprint(obj, self, cycle)

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
    695     """A pprint that just redirects to the normal repr function."""
    696     # Find newlines and replace them with p.break_()
--> 697     output = repr(obj)
    698     for idx,output_line in enumerate(output.splitlines()):
    699         if idx:

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in __repr__(self)
    544         header = ('<%s %r (%s members)>'
    545                   % (self._cls_name, self.name, len(self)))
--> 546         return '\n'.join([header] + self._repr_body())
    547 
    548     def resize_dimension(self, dimension, size):

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in _repr_body(self)
    536             ['    %s: %r %s' % (k, v.dimensions, v.dtype)
    537              for k, v in self.variables.items()] +
--> 538             ['Attributes:'] +
    539             ['    %s: %r' % (k, v) for k, v in self.attrs.items()])
    540 

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in <listcomp>(.0)
    535             ['Variables:'] +
    536             ['    %s: %r %s' % (k, v.dimensions, v.dtype)
--> 537              for k, v in self.variables.items()] +
    538             ['Attributes:'] +
    539             ['    %s: %r' % (k, v) for k, v in self.attrs.items()])

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in dimensions(self)
    110     def dimensions(self):
    111         if self._dimensions is None:
--> 112             self._dimensions = self._lookup_dimensions()
    113         return self._dimensions
    114 

/media/doug/warehouse/conda_envs/mohid-analysis/lib/python3.6/site-packages/h5netcdf/core.py in _lookup_dimensions(self)
    102                 raise ValueError('variable %r has no dimension scale '
    103                                  'associated with axis %s'
--> 104                                  % (self.name, axis))
    105             name = _name_from_dimension(dim)
    106             dims.append(name)

ValueError: variable '/Time/Time_00001' has no dimension scale associated with axis 0

h5py¶

In [8]:

import h5py

In [73]:

h5file = h5py.File('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5', mode='r')
h5file

Out[73]:

<HDF5 file "Lagrangian_7_nested_5.hdf5" (mode r+)>

In [74]:

for k in h5file: print(k)

Grid
Results
Time

In [75]:

h5file['Grid']

Out[75]:

<HDF5 group "/Grid" (9 members)>

In [77]:

for k in h5file['Grid']: print(k)

Bathymetry
ConnectionX
ConnectionY
Define Cells
Latitude
Longitude
OpenPoints
VerticalZ
WaterPoints3D

In [78]:

h5file['Results']

Out[78]:

<HDF5 group "/Results" (3 members)>

In [79]:

for k in h5file['Results']: print(k)

Number
OilSpill
Percentage Contaminated

In [80]:

h5file['Time']

Out[80]:

<HDF5 group "/Time" (167 members)>

In [81]:

for k in h5file['Time']: print(k)

Time_00001
Time_00002
Time_00003
Time_00004
Time_00005
Time_00006
Time_00007
Time_00008
Time_00009
Time_00010
Time_00011
Time_00012
Time_00013
Time_00014
Time_00015
Time_00016
Time_00017
Time_00018
Time_00019
Time_00020
Time_00021
Time_00022
Time_00023
Time_00024
Time_00025
Time_00026
Time_00027
Time_00028
Time_00029
Time_00030
Time_00031
Time_00032
Time_00033
Time_00034
Time_00035
Time_00036
Time_00037
Time_00038
Time_00039
Time_00040
Time_00041
Time_00042
Time_00043
Time_00044
Time_00045
Time_00046
Time_00047
Time_00048
Time_00049
Time_00050
Time_00051
Time_00052
Time_00053
Time_00054
Time_00055
Time_00056
Time_00057
Time_00058
Time_00059
Time_00060
Time_00061
Time_00062
Time_00063
Time_00064
Time_00065
Time_00066
Time_00067
Time_00068
Time_00069
Time_00070
Time_00071
Time_00072
Time_00073
Time_00074
Time_00075
Time_00076
Time_00077
Time_00078
Time_00079
Time_00080
Time_00081
Time_00082
Time_00083
Time_00084
Time_00085
Time_00086
Time_00087
Time_00088
Time_00089
Time_00090
Time_00091
Time_00092
Time_00093
Time_00094
Time_00095
Time_00096
Time_00097
Time_00098
Time_00099
Time_00100
Time_00101
Time_00102
Time_00103
Time_00104
Time_00105
Time_00106
Time_00107
Time_00108
Time_00109
Time_00110
Time_00111
Time_00112
Time_00113
Time_00114
Time_00115
Time_00116
Time_00117
Time_00118
Time_00119
Time_00120
Time_00121
Time_00122
Time_00123
Time_00124
Time_00125
Time_00126
Time_00127
Time_00128
Time_00129
Time_00130
Time_00131
Time_00132
Time_00133
Time_00134
Time_00135
Time_00136
Time_00137
Time_00138
Time_00139
Time_00140
Time_00141
Time_00142
Time_00143
Time_00144
Time_00145
Time_00146
Time_00147
Time_00148
Time_00149
Time_00150
Time_00151
Time_00152
Time_00153
Time_00154
Time_00155
Time_00156
Time_00157
Time_00158
Time_00159
Time_00160
Time_00161
Time_00162
Time_00163
Time_00164
Time_00165
Time_00166
Time_00167

In [82]:

h5file['Time']['Time_00001']

Out[82]:

<HDF5 dataset "Time_00001": shape (6,), type "<f4">

In [84]:

h5file['Time']['Time_00001'][:]

Out[84]:

array([2015.,    4.,    8.,    0.,   30.,    0.], dtype=float32)

i.e. the date/time 2015-04-08 00:30:00 stored at 4 32-bit floats

In [85]:

h5file['Results']['Number']

Out[85]:

<HDF5 group "/Results/Number" (167 members)>

In [86]:

h5file.attrs

Out[86]:

<Attributes of HDF5 object at 139899974438056>

In [87]:

for attr in h5file.attrs: print(attr)

In [88]:

for attr, value in h5file['Grid'].attrs.items(): print(attr, value)

Minimum -9900000000000000.0
Maximum -9900000000000000.0

In [89]:

for attr, value in h5file['Grid']['Bathymetry'].attrs.items(): print(attr, value)

Minimum -9900000000000000.0
Maximum -9900000000000000.0
Units b'm'

In [90]:

for attr, value in h5file['Results']['Number'].attrs.items(): print(attr, value)

Minimum -9900000000000000.0
Maximum -9900000000000000.0

In [35]:

oil['Results']['Number']

Out[35]:

<HDF5 group "/Results/Number" (167 members)>

In [91]:

for n in h5file['Results']['Number']: print(n)

Number_00001
Number_00002
Number_00003
Number_00004
Number_00005
Number_00006
Number_00007
Number_00008
Number_00009
Number_00010
Number_00011
Number_00012
Number_00013
Number_00014
Number_00015
Number_00016
Number_00017
Number_00018
Number_00019
Number_00020
Number_00021
Number_00022
Number_00023
Number_00024
Number_00025
Number_00026
Number_00027
Number_00028
Number_00029
Number_00030
Number_00031
Number_00032
Number_00033
Number_00034
Number_00035
Number_00036
Number_00037
Number_00038
Number_00039
Number_00040
Number_00041
Number_00042
Number_00043
Number_00044
Number_00045
Number_00046
Number_00047
Number_00048
Number_00049
Number_00050
Number_00051
Number_00052
Number_00053
Number_00054
Number_00055
Number_00056
Number_00057
Number_00058
Number_00059
Number_00060
Number_00061
Number_00062
Number_00063
Number_00064
Number_00065
Number_00066
Number_00067
Number_00068
Number_00069
Number_00070
Number_00071
Number_00072
Number_00073
Number_00074
Number_00075
Number_00076
Number_00077
Number_00078
Number_00079
Number_00080
Number_00081
Number_00082
Number_00083
Number_00084
Number_00085
Number_00086
Number_00087
Number_00088
Number_00089
Number_00090
Number_00091
Number_00092
Number_00093
Number_00094
Number_00095
Number_00096
Number_00097
Number_00098
Number_00099
Number_00100
Number_00101
Number_00102
Number_00103
Number_00104
Number_00105
Number_00106
Number_00107
Number_00108
Number_00109
Number_00110
Number_00111
Number_00112
Number_00113
Number_00114
Number_00115
Number_00116
Number_00117
Number_00118
Number_00119
Number_00120
Number_00121
Number_00122
Number_00123
Number_00124
Number_00125
Number_00126
Number_00127
Number_00128
Number_00129
Number_00130
Number_00131
Number_00132
Number_00133
Number_00134
Number_00135
Number_00136
Number_00137
Number_00138
Number_00139
Number_00140
Number_00141
Number_00142
Number_00143
Number_00144
Number_00145
Number_00146
Number_00147
Number_00148
Number_00149
Number_00150
Number_00151
Number_00152
Number_00153
Number_00154
Number_00155
Number_00156
Number_00157
Number_00158
Number_00159
Number_00160
Number_00161
Number_00162
Number_00163
Number_00164
Number_00165
Number_00166
Number_00167

In [92]:

h5file['Results']['Number']['Number_00001']

Out[92]:

<HDF5 dataset "Number_00001": shape (20, 380, 210), type "<f4">

In [93]:

for attr in h5file['Results']['OilSpill'].attrs: print(attr)

Minimum
Maximum

In [94]:

for foo in h5file['Results']['OilSpill']: print(foo)

Data_2D
Data_3D

In [95]:

for attr, value in h5file['Results']['OilSpill']['Data_2D'].attrs.items(): print(attr, value)

Minimum 9900000000000000.0
Maximum -9900000000000000.0

In [96]:

for foo in h5file['Results']['OilSpill']['Data_2D']: print(foo)

Beaching Time
Oil Arrival Time
OilConcentration_2D
Thickness_2D

In [97]:

h5file['Results']['OilSpill']['Data_2D']['OilConcentration_2D']

Out[97]:

<HDF5 group "/Results/OilSpill/Data_2D/OilConcentration_2D" (167 members)>

In [98]:

h5file['Results']['OilSpill']['Data_2D']['OilConcentration_2D']['OilConcentration_2D_00001']

Out[98]:

<HDF5 dataset "OilConcentration_2D_00001": shape (380, 210), type "<f4">

In [99]:

for attr, value in h5file['Results']['OilSpill']['Data_2D']['OilConcentration_2D']['OilConcentration_2D_00001'].attrs.items(): print (attr, value)

Minimum -9900000000000000.0
Maximum -9900000000000000.0
Units b'ppm'

In [100]:

for attr, value in h5file['Results']['OilSpill']['Data_3D'].attrs.items(): print(attr, value)

Minimum 9900000000000000.0
Maximum -9900000000000000.0

In [101]:

for foo in h5file['Results']['OilSpill']['Data_3D']: print(foo)

Dissolution_3D
OilConcentration_3D

PyTables¶

In [54]:

import tables

In [102]:

h5file = tables.open_file('../../SalishSeaShihan/results/Lagrangian_7_nested_5.hdf5', mode='r')
for group in h5file.walk_groups(): print(group)

/ (RootGroup) ''
/Grid (Group) ''
/Results (Group) ''
/Time (Group) ''
/Results/Number (Group) ''
/Results/OilSpill (Group) ''
/Results/Percentage Contaminated (Group) ''
/Results/OilSpill/Data_2D (Group) ''
/Results/OilSpill/Data_3D (Group) ''
/Results/OilSpill/Data_3D/Dissolution_3D (Group) ''
/Results/OilSpill/Data_3D/OilConcentration_3D (Group) ''
/Results/OilSpill/Data_2D/Beaching Time (Group) ''
/Results/OilSpill/Data_2D/Oil Arrival Time (Group) ''
/Results/OilSpill/Data_2D/OilConcentration_2D (Group) ''
/Results/OilSpill/Data_2D/Thickness_2D (Group) ''
/Grid/OpenPoints (Group) ''
/Grid/VerticalZ (Group) ''

In [103]:

for node in h5file.walk_nodes('/Results/Number'): print(node)

/Results/Number (Group) ''
/Results/Number/Number_00001 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00002 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00003 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00004 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00005 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00006 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00007 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00008 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00009 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00010 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00011 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00012 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00013 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00014 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00015 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00016 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00017 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00018 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00019 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00020 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00021 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00022 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00023 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00024 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00025 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00026 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00027 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00028 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00029 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00030 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00031 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00032 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00033 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00034 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00035 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00036 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00037 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00038 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00039 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00040 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00041 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00042 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00043 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00044 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00045 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00046 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00047 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00048 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00049 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00050 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00051 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00052 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00053 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00054 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00055 (CArray(20, 380, 210), zlib(6)) ''
/Results/Number/Number_00056 (CArray(20, 380, 210), zlib(6)) ''
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In [104]:

h5file.root.Results.Number.Number_00001.attrs

Out[104]:

/Results/Number/Number_00001._v_attrs (AttributeSet), 3 attributes:
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In [105]:

h5file.root.Results.OilSpill.Data_3D.OilConcentration_3D.OilConcentration_3D_00001.attrs

Out[105]:

/Results/OilSpill/Data_3D/OilConcentration_3D/OilConcentration_3D_00001._v_attrs (AttributeSet), 3 attributes:
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In [106]:

h5file.root.Results.OilSpill.Data_3D.OilConcentration_3D.OilConcentration_3D_00001

Out[106]:

/Results/OilSpill/Data_3D/OilConcentration_3D/OilConcentration_3D_00001 (CArray(20, 380, 210), zlib(6)) ''
  atom := Float32Atom(shape=(), dflt=0.0)
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In [ ]: