| Name | h5io-browser JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Easy navigation and data storage for HDF5 |
| upload_time | 2024-10-17 04:10:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | BSD 3-Clause License Copyright (c) 2023, Jan Janssen All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
hdf5
h5io
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Easy navigation and data storage for HDF5
[](https://github.com/h5io/h5io_browser/actions)
[](https://coveralls.io/github/h5io/h5io_browser?branch=main)
[](https://mybinder.org/v2/gh/h5io/h5io_browser/HEAD?labpath=notebooks%2Fexample.ipynb)
The [hierarchical data format (HDF)](https://www.hdfgroup.org) is aimed to ensure efficient and equitable access to
science and engineering data across platforms and environments. The [h5py](https://www.h5py.org) package provides a
pythonic interface to the HDF5 binary data format and the [h5io](https://github.com/h5io/h5io) package simplifies this
interface by introducing the `read_hdf5()` and `write_hdf5()` functions for loading and storing python objects in HDF5.
The [h5io](https://github.com/h5io/h5io) package also provides a `list_file_contents()` function to print the internal,
structure of an HDF5 file.
The `h5io_browser` package extends this interface by providing a pointer `h5io_browser.Pointer` to a specific path
inside the hierarchical structure of the HDF5 file. With this pointer, data can be read, stored, copied and deleted from
the HDF5 file, while at the same time simplifying the navigation inside the hierarchy of the file. The `h5io_browser`
package is developed with three constraints and goals:
* Simplify navigating HDF5 files created by the [h5io](https://github.com/h5io/h5io) package. This includes interactive
navigation inside an interactive Python shell or a Jupyter Notebook environment.
* Integrate standard functionality to interact with the data stored in the HDF5 file like read, write, copy and delete
using the interface defined by the [h5py](https://www.h5py.org) package and the [h5io](https://github.com/h5io/h5io)
package.
* Finally, balance flexibility and performance. Just like the [h5io](https://github.com/h5io/h5io) package, the
`h5io_browser` only opens the HDF5 file when accessing the data and does not maintain an open file handle while
waiting for user input. At the same time the interface defined by the [h5io](https://github.com/h5io/h5io) package
is extended to store multiple python objects at the same time for improved performance.
## Installation
The `h5io_browser` package can be installed either via the [Python Package Index](https://pypi.org):
```
pip install h5io_browser
```
Or alternatively, via the community channel on the [conda package manager](https://anaconda.org/conda-forge/h5io_browser)
maintained by the [conda-forge community](https://conda-forge.org):
```
conda install -c conda-forge h5io_browser
```
## Example
Demonstration of the basic functionality of the `h5io_browser` module.
### Import Module
Start by importing the `h5io_browser` module:
```python
import h5io_browser as hb
```
From the `h5io_browser` module the `Pointer()` object is created to access a new HDF5 file named `new.h5`:
```python
hp = hb.Pointer(file_name="new.h5")
```
### Write Data
For demonstration three different objects are written to the HDF5 file:
* a list with the numbers one and two is stored in the HDF5 path `data/a_list`
* an integer number is stored in the HDF5 path `data/an_integer_number`
* a dictionary is stored in the HDF5 path `data/sub_path/a_dictionary`
This can either be done using the edge notation, known from accessing python dictionaries, or alternatively using the
`write_dict()` function which can store multiple objects in the HDF5 file, while opening it only once.
```python
hp["data/a_list"] = [1, 2]
hp.write_dict(data_dict={
"data/an_integer_number": 3,
"data/sub_path/a_dictionary": {"d": 4, "e": 5},
})
```
### Read Data
One strength of the `h5io_browser` package is the support for interactive python environments like, Jupyter notebooks.
To browse the HDF5 file by executing the `Pointer()` object:
```python
hp
```
In comparison the string representation lists the `file_name`, `h5_path` as well as the `nodes` and `groups` at this
`h5_path`:
```python
str(hp)
>>> 'Pointer(file_name="/Users/jan/test/new.h5", h5_path="/") {"groups": ["data"], "nodes": []}'
```
List content of the HDF5 file at the current `h5_path` using the `list_all()` function:
```python
hp.list_all()
>>> ['data']
```
In analogy the `groups` and `nodes` of any `h5_path` either relative to the current `h5_path` or as absolute `h5_path`
can be analysed using the `list_h5_path()`:
```python
hp.list_h5_path(h5_path="data")
>>> {'groups': ['sub_path'], 'nodes': ['a_list', 'an_integer_number']}
```
To continue browsing the HDF5 file the edge bracket notation can be used, just like it s commonly used for python
dictionaries to browse the HDF5 file:
```python
hp["data"].list_all()
>>> ['a_list', 'an_integer_number', 'sub_path']
```
The object which is returned is again a Pointer with the updated `h5_path`, which changed from `/` to `/data`:
```python
hp.h5_path, hp["data"].h5_path
>>> ('/', '/data')
```
Finally, individual nodes of the HDF5 file can be loaded with the same syntax using the `/` notation known from the
file system, or by combining multiple edge brackets:
```python
hp["data/a_list"], hp["data"]["a_list"]
>>> ([1, 2], [1, 2])
```
### Convert to Dictionary
To computationally browse through the contents of an HDF5 file, the `to_dict()` method extends the interactive browsing
capabilities. By default it returns a flat dictionary with the keys representing the `h5_path` of the individual nodes
and the values being the data stored in these nodes. Internally, this loads the whole tree structure, starting from the
current `h5_path`, so depending on the size of the HDF5 file this can take quite some time:
```python
hp.to_dict()
>>> {'data/a_list': [1, 2],
>>> 'data/an_integer_number': 3,
>>> 'data/sub_path/a_dictionary': {'d': 4, 'e': 5}}
```
An alternative representation, is the hierarchical representation which can be enabled by the `hierarchical` being set
to `True`. Then the data is represented as a nested dictionary:
```python
hp.to_dict(hierarchical=True)
>>> {'data': {'a_list': [1, 2],
>>> 'an_integer_number': 3,
>>> 'sub_path': {'a_dictionary': {'d': 4, 'e': 5}}}}
```
### With Statement
For compatibility with other file access methods, the `h5io_browser` package also supports the with statement notation.
Still technically this does not change the behavior, even when opened with a with statement the HDF5 file is closed
between individual function calls.
```python
with hb.Pointer(file_name="new.h5") as hp:
print(hp["data/a_list"])
>>> [1, 2]
```
### Delete Data
To delete data from an HDF5 file using the `h5io_browser` the standard python `del` function can be used in analogy to
deleting items from a python dictionary. To demonstrate the deletion a new node is added named `data/new/entry/test`:
```python
hp["data/new/entry/test"] = 4
```
To list the node, the `to_dict()` function is used with the `hierarchical` parameter to highlight the nested structure:
```python
hp["data/new"].to_dict(hierarchical=True)
>>> {'entry': {'test': 4}}
```
The node is then deleted using the `del` function. While this removes the node from the index the file size remains the
same, which is one of the limitations of the HDF5 format. Consequently, it is not recommended to create and remove nodes
in the HDF5 files frequently:
```python
print(hp.file_size())
del hp["data/new/entry/test"]
print(hp.file_size())
>>> (18484, 18484)
```
Even after the deletion of the last node the groups are still included in the HDF5 file. They are not listed by the
`to_dict()` function, as it recursively iterates over all nodes below the current `h5_path`:
```python
hp["data/new"].to_dict(hierarchical=True)
>>> {}
```
Still with the `list_all()` function lists all nodes and groups at a current `h5_path` including empty groups, like the
`entry` group in this case:
```python
hp["data/new"].list_all()
>>> ['entry']
```
To remove the group from the HDF5 file the same `del` command is used:
```python
del hp["data/new"]
```
After deleting both the newly created groups and their nodes the original hierarchy of the HDF5 file is restored:
```python
hp.to_dict(hierarchical=True)
>>> {'data': {'a_list': [1, 2],
>>> 'an_integer_number': 3,
>>> 'sub_path': {'a_dictionary': {'d': 4, 'e': 5}}}}
```
Still even after deleting the nodes from the HDF5 file, the file size remains the same:
```python
hp.file_size()
>>> 18484
```
### Loop over Nodes
To simplify iterating recursively over all nodes contained in the selected `h5_path` the `Pointer()` object can be used
as iterator:
```python
hp_data = hp["data"]
{h5_path: hp_data[h5_path] for h5_path in hp_data}
>>> {'a_list': [1, 2],
>>> 'an_integer_number': 3,
>>> 'sub_path/a_dictionary': {'d': 4, 'e': 5}}
```
### Copy Data
In addition to adding, browsing and removing data from an existing HDF5 file, the `Pointer()` object can also be used to
copy data inside a given HDF5 file or copy data from one HDF5 file to another. A new HDF5 file is created, named
`copy.h5`:
```python
hp_copy = hb.Pointer(file_name="copy.h5")
```
The data is transferred from the existing `Pointer()` object to the new HDF5 file using the `copy_to()` functions:
```python
hp["data"].copy_to(hp_copy)
hp_copy
```
## Disclaimer
While we try to develop a stable and reliable software library, the development remains a opensource project under the
BSD 3-Clause License without any warranties:
```
BSD 3-Clause License
Copyright (c) 2023, Jan Janssen
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
```
Raw data
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"description": "# Easy navigation and data storage for HDF5\n\n[](https://github.com/h5io/h5io_browser/actions)\n[](https://coveralls.io/github/h5io/h5io_browser?branch=main)\n[](https://mybinder.org/v2/gh/h5io/h5io_browser/HEAD?labpath=notebooks%2Fexample.ipynb)\n\nThe [hierarchical data format (HDF)](https://www.hdfgroup.org) is aimed to ensure efficient and equitable access to \nscience and engineering data across platforms and environments. The [h5py](https://www.h5py.org) package provides a \npythonic interface to the HDF5 binary data format and the [h5io](https://github.com/h5io/h5io) package simplifies this\ninterface by introducing the `read_hdf5()` and `write_hdf5()` functions for loading and storing python objects in HDF5.\nThe [h5io](https://github.com/h5io/h5io) package also provides a `list_file_contents()` function to print the internal,\nstructure of an HDF5 file. \n\n\n\nThe `h5io_browser` package extends this interface by providing a pointer `h5io_browser.Pointer` to a specific path \ninside the hierarchical structure of the HDF5 file. With this pointer, data can be read, stored, copied and deleted from \nthe HDF5 file, while at the same time simplifying the navigation inside the hierarchy of the file. The `h5io_browser`\npackage is developed with three constraints and goals: \n\n* Simplify navigating HDF5 files created by the [h5io](https://github.com/h5io/h5io) package. This includes interactive\n navigation inside an interactive Python shell or a Jupyter Notebook environment. \n* Integrate standard functionality to interact with the data stored in the HDF5 file like read, write, copy and delete\n using the interface defined by the [h5py](https://www.h5py.org) package and the [h5io](https://github.com/h5io/h5io) \n package.\n* Finally, balance flexibility and performance. Just like the [h5io](https://github.com/h5io/h5io) package, the \n `h5io_browser` only opens the HDF5 file when accessing the data and does not maintain an open file handle while \n waiting for user input. At the same time the interface defined by the [h5io](https://github.com/h5io/h5io) package\n is extended to store multiple python objects at the same time for improved performance. \n\n## Installation \nThe `h5io_browser` package can be installed either via the [Python Package Index](https://pypi.org):\n```\npip install h5io_browser\n```\n\nOr alternatively, via the community channel on the [conda package manager](https://anaconda.org/conda-forge/h5io_browser) \nmaintained by the [conda-forge community](https://conda-forge.org): \n```\nconda install -c conda-forge h5io_browser\n```\n\n## Example\nDemonstration of the basic functionality of the `h5io_browser` module.\n\n### Import Module\nStart by importing the `h5io_browser` module:\n\n```python\nimport h5io_browser as hb\n```\n\nFrom the `h5io_browser` module the `Pointer()` object is created to access a new HDF5 file named `new.h5`:\n\n```python\nhp = hb.Pointer(file_name=\"new.h5\")\n```\n\n### Write Data \nFor demonstration three different objects are written to the HDF5 file: \n\n* a list with the numbers one and two is stored in the HDF5 path `data/a_list`\n* an integer number is stored in the HDF5 path `data/an_integer_number`\n* a dictionary is stored in the HDF5 path `data/sub_path/a_dictionary`\n\nThis can either be done using the edge notation, known from accessing python dictionaries, or alternatively using the \n`write_dict()` function which can store multiple objects in the HDF5 file, while opening it only once.\n\n```python\nhp[\"data/a_list\"] = [1, 2]\nhp.write_dict(data_dict={\n \"data/an_integer_number\": 3,\n \"data/sub_path/a_dictionary\": {\"d\": 4, \"e\": 5},\n})\n```\n\n### Read Data \nOne strength of the `h5io_browser` package is the support for interactive python environments like, Jupyter notebooks. \nTo browse the HDF5 file by executing the `Pointer()` object:\n\n```python\nhp\n```\n\nIn comparison the string representation lists the `file_name`, `h5_path` as well as the `nodes` and `groups` at this \n`h5_path`:\n\n```python\nstr(hp)\n>>> 'Pointer(file_name=\"/Users/jan/test/new.h5\", h5_path=\"/\") {\"groups\": [\"data\"], \"nodes\": []}'\n```\n\nList content of the HDF5 file at the current `h5_path` using the `list_all()` function: \n\n```python\nhp.list_all()\n>>> ['data']\n```\n\nIn analogy the `groups` and `nodes` of any `h5_path` either relative to the current `h5_path` or as absolute `h5_path` \ncan be analysed using the `list_h5_path()`:\n\n```python\nhp.list_h5_path(h5_path=\"data\")\n>>> {'groups': ['sub_path'], 'nodes': ['a_list', 'an_integer_number']}\n```\n\nTo continue browsing the HDF5 file the edge bracket notation can be used, just like it s commonly used for python \ndictionaries to browse the HDF5 file:\n\n```python\nhp[\"data\"].list_all()\n>>> ['a_list', 'an_integer_number', 'sub_path']\n```\n\nThe object which is returned is again a Pointer with the updated `h5_path`, which changed from `/` to `/data`:\n\n```python\nhp.h5_path, hp[\"data\"].h5_path\n>>> ('/', '/data')\n```\n\nFinally, individual nodes of the HDF5 file can be loaded with the same syntax using the `/` notation known from the \nfile system, or by combining multiple edge brackets:\n\n```python\nhp[\"data/a_list\"], hp[\"data\"][\"a_list\"]\n>>> ([1, 2], [1, 2])\n```\n\n### Convert to Dictionary \nTo computationally browse through the contents of an HDF5 file, the `to_dict()` method extends the interactive browsing \ncapabilities. By default it returns a flat dictionary with the keys representing the `h5_path` of the individual nodes \nand the values being the data stored in these nodes. Internally, this loads the whole tree structure, starting from the \ncurrent `h5_path`, so depending on the size of the HDF5 file this can take quite some time:\n\n```python\nhp.to_dict()\n>>> {'data/a_list': [1, 2],\n>>> 'data/an_integer_number': 3,\n>>> 'data/sub_path/a_dictionary': {'d': 4, 'e': 5}}\n```\n\nAn alternative representation, is the hierarchical representation which can be enabled by the `hierarchical` being set \nto `True`. Then the data is represented as a nested dictionary: \n\n```python\nhp.to_dict(hierarchical=True)\n>>> {'data': {'a_list': [1, 2],\n>>> 'an_integer_number': 3,\n>>> 'sub_path': {'a_dictionary': {'d': 4, 'e': 5}}}}\n```\n\n### With Statement\nFor compatibility with other file access methods, the `h5io_browser` package also supports the with statement notation. \nStill technically this does not change the behavior, even when opened with a with statement the HDF5 file is closed \nbetween individual function calls.\n\n```python\nwith hb.Pointer(file_name=\"new.h5\") as hp:\n print(hp[\"data/a_list\"])\n>>> [1, 2]\n```\n\n### Delete Data\nTo delete data from an HDF5 file using the `h5io_browser` the standard python `del` function can be used in analogy to \ndeleting items from a python dictionary. To demonstrate the deletion a new node is added named `data/new/entry/test`:\n\n```python\nhp[\"data/new/entry/test\"] = 4\n```\n\nTo list the node, the `to_dict()` function is used with the `hierarchical` parameter to highlight the nested structure:\n\n```python\nhp[\"data/new\"].to_dict(hierarchical=True)\n>>> {'entry': {'test': 4}}\n```\n\nThe node is then deleted using the `del` function. While this removes the node from the index the file size remains the \nsame, which is one of the limitations of the HDF5 format. Consequently, it is not recommended to create and remove nodes\nin the HDF5 files frequently: \n\n```python\nprint(hp.file_size())\ndel hp[\"data/new/entry/test\"]\nprint(hp.file_size())\n>>> (18484, 18484)\n```\n\nEven after the deletion of the last node the groups are still included in the HDF5 file. They are not listed by the \n`to_dict()` function, as it recursively iterates over all nodes below the current `h5_path`:\n\n```python\nhp[\"data/new\"].to_dict(hierarchical=True)\n>>> {}\n```\n\nStill with the `list_all()` function lists all nodes and groups at a current `h5_path` including empty groups, like the \n`entry` group in this case: \n\n```python\nhp[\"data/new\"].list_all()\n>>> ['entry']\n```\n\nTo remove the group from the HDF5 file the same `del` command is used:\n\n```python\ndel hp[\"data/new\"]\n```\n\nAfter deleting both the newly created groups and their nodes the original hierarchy of the HDF5 file is restored:\n\n```python\nhp.to_dict(hierarchical=True)\n>>> {'data': {'a_list': [1, 2],\n>>> 'an_integer_number': 3,\n>>> 'sub_path': {'a_dictionary': {'d': 4, 'e': 5}}}}\n```\n\nStill even after deleting the nodes from the HDF5 file, the file size remains the same: \n\n```python\nhp.file_size()\n>>> 18484\n```\n\n### Loop over Nodes\nTo simplify iterating recursively over all nodes contained in the selected `h5_path` the `Pointer()` object can be used \nas iterator:\n\n```python\nhp_data = hp[\"data\"]\n{h5_path: hp_data[h5_path] for h5_path in hp_data}\n>>> {'a_list': [1, 2],\n>>> 'an_integer_number': 3,\n>>> 'sub_path/a_dictionary': {'d': 4, 'e': 5}}\n```\n\n### Copy Data\nIn addition to adding, browsing and removing data from an existing HDF5 file, the `Pointer()` object can also be used to\ncopy data inside a given HDF5 file or copy data from one HDF5 file to another. A new HDF5 file is created, named \n`copy.h5`:\n\n```python\nhp_copy = hb.Pointer(file_name=\"copy.h5\")\n```\n\nThe data is transferred from the existing `Pointer()` object to the new HDF5 file using the `copy_to()` functions:\n\n```python\nhp[\"data\"].copy_to(hp_copy)\nhp_copy\n```\n\n## Disclaimer\nWhile we try to develop a stable and reliable software library, the development remains a opensource project under the \nBSD 3-Clause License without any warranties:\n```\nBSD 3-Clause License\n\nCopyright (c) 2023, Jan Janssen\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n```\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2023, Jan Janssen All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ",
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