| Name | byterat JSON |
| Version |
0.4.3
JSON |
| download |
| home_page | None |
| Summary | Python library to export data from and interact with the Byterat Platform |
| upload_time | 2025-02-12 23:50:05 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | Certain libraries contained in this file are subject to open source license requirements. Specifically:
GQL:
Copyright (c) 2016 GraphQL Python
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Pandas:
Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
All rights reserved.
Copyright (c) 2011-2024, Open source contributors.
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
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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 |
battery
byterat
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## Byterat Library Documentation
**Welcome to the Byterat library documentation!**
This Python library provides tools for interacting with the Byterat API, allowing you to access and analyze your battery data.
### Key Features
- Data Retrieval: Easily fetch battery observation metrics, dataset cycle data, and metadata.
- Filtering: Refine your data retrieval using flexible filter options.
- Asynchronous Operations: Perform efficient data retrieval using asynchronous functions.
- Data Handling: Work with data in a structured format using pandas DataFrames.
### Installation
```bash
pip install byterat
```
### Usage
**1. Initialization:**
```python
from byterat.sync.client import Client as ByteratClientSync
from byterat.async_.client import Client as ByteratClientAsync
# For synchronous operations
sync_client = ByteratClientSync(token="YOUR_API_TOKEN")
# For asynchronous operations
async_client = ByteratClientAsync(token="YOUR_API_TOKEN")
```
**2. Data Retrieval:**
- **Get Battery Observation Metrics:**
```python
# Get all battery observation metrics
observation_data = sync_client.get_observation_metrics()
# Get observation metrics by dataset key
observation_data_by_key = sync_client.get_observation_metrics_by_dataset_key(dataset_key="your_battery_dataset_key")
# Get observation metrics by dataset key and cycle
observation_data_by_key_cycle = sync_client.get_observation_metrics_by_dataset_key_and_dataset_cycle(
dataset_key="your_battery_dataset_key", dataset_cycle=1
)
# Get observation metrics by filename
observation_data_by_filename = sync_client.get_observation_metrics_by_filename(file_name="your_battery_data.csv")
```
- **Get Battery Dataset Cycle Data:**
```python
# Get all battery dataset cycle data
cycle_data = sync_client.get_dataset_cycle_data()
# Get dataset cycle data by dataset key
cycle_data_by_key = sync_client.get_dataset_cycle_data_by_dataset_key(dataset_key="your_battery_dataset_key")
# Get dataset cycle data by dataset key and cycle
cycle_data_by_key_cycle = sync_client.get_dataset_cycle_data_by_dataset_key_and_dataset_cycle(
dataset_key="your_battery_dataset_key", dataset_cycle=1
)
# Get dataset cycle data by filename
cycle_data_by_filename = sync_client.get_dataset_cycle_data_by_filename(file_name="your_battery_data.csv")
```
- **Get Battery Metadata:**
```python
# Get all battery metadata
metadata = sync_client.get_metadata()
# Get metadata by dataset key
metadata_by_key = sync_client.get_metadata_by_dataset_key(dataset_key="your_battery_dataset_key")
```
**3. Filtering:**
```python
from byterat.filter import Filter, FilterOperator, FilterGroup, FilterGroupType
# Create filters
filter1 = Filter(column="voltage", operator=FilterOperator.GT, value=3.5)
filter2 = Filter(column="temperature", operator=FilterOperator.LT, value=30)
# Create a filter group
filter_group = FilterGroup([filter1, filter2], mode=FilterGroupType.AND)
# Retrieve a chunk of filtered observation data -> Returns a ByteratData object
filtered_observation_data = sync_client.get_filtered_observation_data(filters=filter_group)
# Retrieve all filtered observation data (all chunks) -> Returns a DataFrame containing all data matching filters
all_filtered_observation_data = sync_client.get_all_filtered_observation_data(filters=filter_group)
# Retrieve a chunk of filtered dataset cycle data -> Returns a ByteratData object
filtered_cycle_data = sync_client.get_filtered_dataset_cycle_data(filters=filter_group)
# Retrieve all filtered dataset cycle data (all chunks) -> Returns a DataFrame containing all data matching filters
all_filtered_cycle_data = sync_client.get_all_filtered_dataset_cycle_data(filters=filter_group)
# Retrieve a chunk of filtered metadata -> Returns a ByteratData object
filtered_metadata = sync_client.get_filtered_metadata(filters=filter_group)
# Retrieve all filtered metadata (all chunks) -> Returns a DataFrame containing all data matching filters
all_filtered_metadata = sync_client.get_all_filtered_metadata(filters=filter_group)
```
**4. Asynchronous Operations:**
```python
import asyncio
async def main():
# Use async_client for asynchronous operations
observation_data = await async_client.get_observation_metrics()
#... (other asynchronous methods)
asyncio.run(main())
```
**5. Data Handling:**
The retrieved data is returned as a `ByteratData` object, which contains a `pandas` DataFrame (`data`) and a continuation token (`continuation_token`) for handling paginated results.
```python
# Access the DataFrame
df = observation_data.data
# Use pandas functionalities for data analysis and manipulation
print(df.head())
#...
```
**6. Handling Paginated Results**
The Byterat API uses a continuation token for pagination. When you request data, and the result set is large, the API will only return a portion of the data along with a continuation token. This token acts as a pointer to the next chunk of data.
After making a request to the Byterat API (e.g., `get_observation_metrics`), the response will include:
- `data`: A `pandas` DataFrame containing the current chunk of data.
- `continuation_token`: A string that can be used to fetch the next chunk of data. If this is `None`, then there is no more data to retrieve.
To get the next chunk of data, simply pass this `continuation_token` back to the same API function as an argument.
```python
from byterat.sync.client import Client as ByteratClientSync
# Initialize the client
client = ByteratClientSync(token="YOUR_API_TOKEN")
# Fetch the first batch of data
response = client.get_observation_metrics()
# Process the first batch
if not response.data.empty:
print(response.data.head())
# Keep fetching and processing data until no more is available
while response.continuation_token is not None: # Correct termination condition
# Fetch the next batch using the token
response = client.get_observation_metrics(continuation_token=response.continuation_token)
# Process the next batch
if not response.data.empty:
print(response.data.head())
```
**Important Considerations**
- Efficiency: Retrieving data in chunks can be more efficient than trying to download a massive dataset all at once.
- Large Datasets: For very large datasets, consider using asynchronous operations to avoid blocking your application while waiting for data.
- State: The continuation token represents a specific point in the data. If the underlying data changes significantly, the token may become invalid.
### Class and Function Explanations
- `Client` (sync and async): This class provides the main interface for interacting with the Byterat API. It handles authentication, data retrieval, and filtering.
- `get_observation_metrics`: Retrieves battery observation metrics data. You can filter by dataset key, dataset cycle, or filename.
- `get_dataset_cycle_data`: Retrieves battery dataset cycle data. You can filter by dataset key, dataset cycle, or filename.
- `get_metadata`: Retrieves battery metadata. You can filter by dataset key.
- `get_filtered_observation_data`: Retrieves a single chunk of observation data filtered by the provided filter group.
- `get_all_filtered_observation_data`: Retrieves all observation data that matches the filter by retrieving data in chunks until no continuation token is returned.
- `get_filtered_dataset_cycle_data`: Retrieves a single chunk of dataset cycle data filtered by the provided filter group.
- `get_all_filtered_dataset_cycle_data`: Retrieves all dataset cycle data that matches the filter by retrieving data in chunks until no continuation token is returned.
- `get_filtered_metadata`: Retrieves a single chunk of metadata filtered by the provided filter group.
- `get_all_filtered_metadata`: Retrieves all metadata that matches the filter by retrieving data in chunks until no continuation token is returned.
- `Filter`: Represents a single filter condition.
- `FilterOperator`: Defines the available filter operators (e.g., equals, greater than, contains).
- `FilterGroup`: Combines multiple filters using logical operators (AND, OR). FilterGroups can be nested within each other to create complex filter conditions.
- `ByteratData`: A container class that holds the retrieved data as a pandas DataFrame and a continuation token for pagination.
### Building Complex Filters with FilterGroup
The `FilterGroup` class allows you to create complex filter expressions by combining multiple `Filter` objects or even other `FilterGroup` objects. This recursive structure enables you to build sophisticated filtering logic.
**Example:**
```python
from byterat.filter import Filter, FilterOperator, FilterGroup, FilterGroupType
# Create individual filters
filter1 = Filter(column="cycle_count", operator=FilterOperator.GT, value=100)
filter2 = Filter(column="state_of_charge", operator=FilterOperator.EQ, value=1.0)
filter3 = Filter(column="manufacturer", operator=FilterOperator.EQ, value="Tesla")
# Create a nested FilterGroup
battery_filter = FilterGroup([filter2, filter3], mode=FilterGroupType.AND)
# Combine with the cycle count filter
combined_filter = FilterGroup([filter1, battery_filter], mode=FilterGroupType.AND)
# Use the combined filter to retrieve data
filtered_data = sync_client.get_all_filtered_observation_data(filters=combined_filter)
```
In this example, `battery_filter` combines the state of charge and manufacturer filters with an AND condition. Then, `combined_filter` further combines the `battery_filter` with the cycle count filter, again using an AND condition. This results in a filter that selects data where the cycle count is greater than 100 AND the state of charge is 1.0 AND the manufacturer is Tesla.
By nesting and chaining `FilterGroup` objects, you can express arbitrarily complex filter conditions to precisely target the data you need.
### Additional Notes
- Error Handling: The library may raise exceptions for invalid input or API errors. Make sure to handle exceptions appropriately in your code.
- Asynchronous vs. Synchronous: Choose the appropriate client (ByteratClientSync or ByteratClientAsync) based on your application's needs.
- Filtering: The library offers a variety of filter operators for precise data selection.
- Pagination: Use the continuation token to retrieve subsequent pages of data when dealing with large datasets.
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"description": "## Byterat Library Documentation\n\n**Welcome to the Byterat library documentation!**\n\nThis Python library provides tools for interacting with the Byterat API, allowing you to access and analyze your battery data.\n\n### Key Features\n\n- Data Retrieval: Easily fetch battery observation metrics, dataset cycle data, and metadata.\n- Filtering: Refine your data retrieval using flexible filter options.\n- Asynchronous Operations: Perform efficient data retrieval using asynchronous functions.\n- Data Handling: Work with data in a structured format using pandas DataFrames.\n\n### Installation\n\n```bash\npip install byterat\n```\n\n### Usage\n\n**1. Initialization:**\n\n```python\nfrom byterat.sync.client import Client as ByteratClientSync\nfrom byterat.async_.client import Client as ByteratClientAsync\n\n# For synchronous operations\nsync_client = ByteratClientSync(token=\"YOUR_API_TOKEN\")\n\n# For asynchronous operations\nasync_client = ByteratClientAsync(token=\"YOUR_API_TOKEN\")\n```\n\n**2. Data Retrieval:**\n\n- **Get Battery Observation Metrics:**\n\n```python\n# Get all battery observation metrics\nobservation_data = sync_client.get_observation_metrics()\n\n# Get observation metrics by dataset key\nobservation_data_by_key = sync_client.get_observation_metrics_by_dataset_key(dataset_key=\"your_battery_dataset_key\")\n\n# Get observation metrics by dataset key and cycle\nobservation_data_by_key_cycle = sync_client.get_observation_metrics_by_dataset_key_and_dataset_cycle(\n dataset_key=\"your_battery_dataset_key\", dataset_cycle=1\n)\n\n# Get observation metrics by filename\nobservation_data_by_filename = sync_client.get_observation_metrics_by_filename(file_name=\"your_battery_data.csv\")\n```\n\n- **Get Battery Dataset Cycle Data:**\n\n```python\n# Get all battery dataset cycle data\ncycle_data = sync_client.get_dataset_cycle_data()\n\n# Get dataset cycle data by dataset key\ncycle_data_by_key = sync_client.get_dataset_cycle_data_by_dataset_key(dataset_key=\"your_battery_dataset_key\")\n\n# Get dataset cycle data by dataset key and cycle\ncycle_data_by_key_cycle = sync_client.get_dataset_cycle_data_by_dataset_key_and_dataset_cycle(\n dataset_key=\"your_battery_dataset_key\", dataset_cycle=1\n)\n\n# Get dataset cycle data by filename\ncycle_data_by_filename = sync_client.get_dataset_cycle_data_by_filename(file_name=\"your_battery_data.csv\")\n```\n\n- **Get Battery Metadata:**\n\n```python\n# Get all battery metadata\nmetadata = sync_client.get_metadata()\n\n# Get metadata by dataset key\nmetadata_by_key = sync_client.get_metadata_by_dataset_key(dataset_key=\"your_battery_dataset_key\")\n```\n\n**3. Filtering:**\n\n```python\nfrom byterat.filter import Filter, FilterOperator, FilterGroup, FilterGroupType\n\n# Create filters\nfilter1 = Filter(column=\"voltage\", operator=FilterOperator.GT, value=3.5)\nfilter2 = Filter(column=\"temperature\", operator=FilterOperator.LT, value=30)\n\n# Create a filter group\nfilter_group = FilterGroup([filter1, filter2], mode=FilterGroupType.AND)\n\n# Retrieve a chunk of filtered observation data -> Returns a ByteratData object\nfiltered_observation_data = sync_client.get_filtered_observation_data(filters=filter_group)\n\n# Retrieve all filtered observation data (all chunks) -> Returns a DataFrame containing all data matching filters\nall_filtered_observation_data = sync_client.get_all_filtered_observation_data(filters=filter_group)\n\n# Retrieve a chunk of filtered dataset cycle data -> Returns a ByteratData object\nfiltered_cycle_data = sync_client.get_filtered_dataset_cycle_data(filters=filter_group)\n\n# Retrieve all filtered dataset cycle data (all chunks) -> Returns a DataFrame containing all data matching filters\nall_filtered_cycle_data = sync_client.get_all_filtered_dataset_cycle_data(filters=filter_group)\n\n# Retrieve a chunk of filtered metadata -> Returns a ByteratData object\nfiltered_metadata = sync_client.get_filtered_metadata(filters=filter_group)\n\n# Retrieve all filtered metadata (all chunks) -> Returns a DataFrame containing all data matching filters\nall_filtered_metadata = sync_client.get_all_filtered_metadata(filters=filter_group)\n```\n\n**4. Asynchronous Operations:**\n\n```python\nimport asyncio\n\nasync def main():\n # Use async_client for asynchronous operations\n observation_data = await async_client.get_observation_metrics()\n #... (other asynchronous methods)\n\nasyncio.run(main())\n```\n\n**5. Data Handling:**\n\nThe retrieved data is returned as a `ByteratData` object, which contains a `pandas` DataFrame (`data`) and a continuation token (`continuation_token`) for handling paginated results.\n\n```python\n# Access the DataFrame\ndf = observation_data.data\n\n# Use pandas functionalities for data analysis and manipulation\nprint(df.head())\n#...\n```\n\n**6. Handling Paginated Results**\n\nThe Byterat API uses a continuation token for pagination. When you request data, and the result set is large, the API will only return a portion of the data along with a continuation token. This token acts as a pointer to the next chunk of data.\n\nAfter making a request to the Byterat API (e.g., `get_observation_metrics`), the response will include:\n\n- `data`: A `pandas` DataFrame containing the current chunk of data.\n- `continuation_token`: A string that can be used to fetch the next chunk of data. If this is `None`, then there is no more data to retrieve.\n\nTo get the next chunk of data, simply pass this `continuation_token` back to the same API function as an argument.\n\n```python\nfrom byterat.sync.client import Client as ByteratClientSync\n\n# Initialize the client\nclient = ByteratClientSync(token=\"YOUR_API_TOKEN\")\n\n# Fetch the first batch of data\nresponse = client.get_observation_metrics()\n\n# Process the first batch\nif not response.data.empty:\n print(response.data.head())\n\n # Keep fetching and processing data until no more is available\n while response.continuation_token is not None: # Correct termination condition\n # Fetch the next batch using the token\n response = client.get_observation_metrics(continuation_token=response.continuation_token)\n\n # Process the next batch\n if not response.data.empty:\n print(response.data.head())\n```\n\n**Important Considerations**\n\n- Efficiency: Retrieving data in chunks can be more efficient than trying to download a massive dataset all at once.\n- Large Datasets: For very large datasets, consider using asynchronous operations to avoid blocking your application while waiting for data.\n- State: The continuation token represents a specific point in the data. If the underlying data changes significantly, the token may become invalid.\n\n### Class and Function Explanations\n\n- `Client` (sync and async): This class provides the main interface for interacting with the Byterat API. It handles authentication, data retrieval, and filtering.\n- `get_observation_metrics`: Retrieves battery observation metrics data. You can filter by dataset key, dataset cycle, or filename.\n- `get_dataset_cycle_data`: Retrieves battery dataset cycle data. You can filter by dataset key, dataset cycle, or filename.\n- `get_metadata`: Retrieves battery metadata. You can filter by dataset key.\n- `get_filtered_observation_data`: Retrieves a single chunk of observation data filtered by the provided filter group.\n- `get_all_filtered_observation_data`: Retrieves all observation data that matches the filter by retrieving data in chunks until no continuation token is returned.\n- `get_filtered_dataset_cycle_data`: Retrieves a single chunk of dataset cycle data filtered by the provided filter group.\n- `get_all_filtered_dataset_cycle_data`: Retrieves all dataset cycle data that matches the filter by retrieving data in chunks until no continuation token is returned.\n- `get_filtered_metadata`: Retrieves a single chunk of metadata filtered by the provided filter group.\n- `get_all_filtered_metadata`: Retrieves all metadata that matches the filter by retrieving data in chunks until no continuation token is returned.\n- `Filter`: Represents a single filter condition.\n- `FilterOperator`: Defines the available filter operators (e.g., equals, greater than, contains).\n- `FilterGroup`: Combines multiple filters using logical operators (AND, OR). FilterGroups can be nested within each other to create complex filter conditions.\n- `ByteratData`: A container class that holds the retrieved data as a pandas DataFrame and a continuation token for pagination.\n\n### Building Complex Filters with FilterGroup\n\nThe `FilterGroup` class allows you to create complex filter expressions by combining multiple `Filter` objects or even other `FilterGroup` objects. This recursive structure enables you to build sophisticated filtering logic.\n\n**Example:**\n\n```python\nfrom byterat.filter import Filter, FilterOperator, FilterGroup, FilterGroupType\n\n# Create individual filters\nfilter1 = Filter(column=\"cycle_count\", operator=FilterOperator.GT, value=100)\nfilter2 = Filter(column=\"state_of_charge\", operator=FilterOperator.EQ, value=1.0)\nfilter3 = Filter(column=\"manufacturer\", operator=FilterOperator.EQ, value=\"Tesla\")\n\n# Create a nested FilterGroup\nbattery_filter = FilterGroup([filter2, filter3], mode=FilterGroupType.AND)\n\n# Combine with the cycle count filter\ncombined_filter = FilterGroup([filter1, battery_filter], mode=FilterGroupType.AND)\n\n# Use the combined filter to retrieve data\nfiltered_data = sync_client.get_all_filtered_observation_data(filters=combined_filter)\n```\n\nIn this example, `battery_filter` combines the state of charge and manufacturer filters with an AND condition. Then, `combined_filter` further combines the `battery_filter` with the cycle count filter, again using an AND condition. This results in a filter that selects data where the cycle count is greater than 100 AND the state of charge is 1.0 AND the manufacturer is Tesla.\n\nBy nesting and chaining `FilterGroup` objects, you can express arbitrarily complex filter conditions to precisely target the data you need.\n\n### Additional Notes\n\n- Error Handling: The library may raise exceptions for invalid input or API errors. Make sure to handle exceptions appropriately in your code.\n- Asynchronous vs. Synchronous: Choose the appropriate client (ByteratClientSync or ByteratClientAsync) based on your application's needs.\n- Filtering: The library offers a variety of filter operators for precise data selection.\n- Pagination: Use the continuation token to retrieve subsequent pages of data when dealing with large datasets.\n",
"bugtrack_url": null,
"license": "Certain libraries contained in this file are subject to open source license requirements. Specifically:\n GQL:\n Copyright (c) 2016 GraphQL Python\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n \n Pandas:\n Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team\n All rights reserved.\n \n Copyright (c) 2011-2024, Open source contributors.\n \n Redistribution and use in source and binary forms, with or without\n modification, 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 \n THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.",
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