| Name | dnv-bladed-results JSON |
| Version |
2.0.0
JSON |
| download |
| home_page | None |
| Summary | An API for working with Bladed results. |
| upload_time | 2024-11-07 11:46:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2023 DNV 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. |
| keywords |
bladed
results
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Bladed Results API 2.0
The Bladed Results API is an easy, fast, and robust way to access Bladed results using Python.
It provides features for:
- Discovering Bladed runs
- Finding variables in a set of runs
- Getting data from variables
- Reporting run and variable metadata
- Exploring and writing output groups.
The API is able to read results from any Bladed version.
The API depends on the [`numpy`](https://numpy.org) package.
> Currently only Windows is supported.
> Bladed Results API 2.0 replaces Results API 1.x which is being discontinued.
## Pre-requisites
- Requires a _32- or 64-bit Windows_ installation of:
- Python 3.9
- or Python 3.10
- or Python 3.11
- or Python 3.12
> 64-bit Python is recommended.
- The Results API has been tested on Windows 10.
### Quick Start
```shell
pip install dnv-bladed-results
```
```python
from dnv_bladed_results import *
run = ResultsApi.get_run(run_dir, run_name)
var_1d = run.get_variable_1d(variable_name)
print(var_1d.get_data())
```
### Code completion
We recommend enabling code completion in the IDE for the best user experience. Code completion displays a popup listing the available functions as the user types. Together with inline documentation, code completion makes it easy to explore and understand the API.
For guidance on enabling and customising code completion, please refer to the following resources:
[`IntelliSense in Visual Studio Code`](https://code.visualstudio.com/docs/editor/intellisense)
[`Code completion in PyCharm`](https://www.jetbrains.com/help/pycharm/auto-completing-code.html)
> If using Visual Studio Code, a type hint is needed for code completion to work with API classes inside a loop. This issue does not affect PyCharm.
> ```python
> # Note the following type hint declared on the run loop variable
> run: Run
> for run in runs:
> # Do something with run - easy now code completion works!
> ```
> If using PyCharm, we recommend enabling the options "Show suggestions as you type" and "Show the documentation popup in...", available in Settings > Editor > General > Code Completion.
## Usage Examples
Usage examples demonstrating core functionality are distributed with the package. A brief description of each example follows.
The `UsageExamples` installation folder and list of available examples may be enquired as follows:
```python
import os
from dnv_bladed_results import UsageExamples
print(UsageExamples.__path__[0])
os.listdir(UsageExamples.__path__[0])
```
The examples below show how each script may be launched from within a Python environment.
### Basic Operations
Load a Bladed run, request groups and variables, and get data for tower members and blade stations:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_BasicOperations
ResultsApi_BasicOperations.run_script()
```
### Variable Data
Load a Bladed run, request 1D and 2D variables* from both the run and from a specific output group, and obtain data from the returned variables:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ReadBasic
ResultsApi_VariableData_ReadBasic.run_script()
```
Obtain data from a 2D variable* for specific independent variable values, and specify the precision of the data to read:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ReadExtended
ResultsApi_VariableData_ReadExtended.run_script()
```
> *1D and 2D variables are dependent variables with one and two independent variables respectively.
### Runs
Use filters and regular expressions to find a subset of runs in a directory tree:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_FindRuns
ResultsApi_FindRuns.run_script()
```
Find and process runs asynchronously using a Python generator:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_FindRunsUsingGenerator
ResultsApi_FindRunsUsingGenerator.run_script()
```
### Metadata
Get metadata for runs, groups, and variables:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_RunMetadata
ResultsApi_RunMetadata.run_script()
```
```python
from dnv_bladed_results.UsageExamples import ResultsApi_GroupMetadata
ResultsApi_GroupMetadata.run_script()
```
```python
from dnv_bladed_results.UsageExamples import ResultsApi_VariableMetadata
ResultsApi_VariableMetadata.run_script()
```
```python
from dnv_bladed_results.UsageExamples import ResultsApi_VariableStats
ResultsApi_VariableStats.run_script()
```
### Output
Export 1D and 2D Bladed output groups, as well as an entire run, using the HDF5 file format:
> Requires the `h5py` library, available via pip: `pip install h5py`. The example has been tested with h5py 3.11.0.
```python
from dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ExportHDF5
ResultsApi_VariableData_ExportHDF5.run_script()
```
Export Bladed output groups using the Matlab file format:
> Requires the `scipy` library, available via pip: `pip install scipy`. The example has been tested with scipy 1.13.1.
```python
from dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ExportMatlab
ResultsApi_VariableData_ExportMatlab.run_script()
```
Write 1D and 2D output groups using the Bladed file format:
```python
from dnv_bladed_results.UsageExamples import ResultsApi_WriteGroup
ResultsApi_WriteGroup.run_script()
```
### Charting
Create 2D and 3D plots of blade loads:
> Requires the `matplotlib` library, available via pip: `pip install matplotlib`. The examples have been tested with matplotlib 3.9.1.
```python
from dnv_bladed_results.UsageExamples import ResultsApi_Charting2D
ResultsApi_Charting2D.run_script()
```
```python
from dnv_bladed_results.UsageExamples import ResultsApi_Charting3D
ResultsApi_Charting3D.run_script()
```
### Post-Processing
Post-process two-dimensional variable data into bespoke data structures and into a Pandas DataFrame.
Plot the data choosing specific points of the DataFrame.
> Requires the `matplotlib` library, available via pip: `pip install matplotlib`. The example has been tested with matplotlib 3.9.1.
> Requires the `pandas` library, available via pip: `pip install pandas`. The example has been tested with pandas 2.2.3.
```python
from dnv_bladed_results.UsageExamples import ResultsApi_PostProcessing
ResultsApi_PostProcessing.run_script()
```
## Results Viewer example
The following images demonstrate how Bladed data shown in Results Viewer may be accessed using the Results API 2.0.
Results Viewer is a standalone package providing enhanced results viewing functionality. Bladed and the Results Viewer application are both available from the [`Downloads`](https://mysoftware.dnv.com/knowledge-centre/bladed/help-library/getting-started/installation#downloading-installers) page.
One-dimensional variables:
Two-dimensional variables:
## Data types
The API comprises a generated Python wrapper dispatching to a C++ DLL. The DLL performs the work of fetching and storing data, validation, and memory management.
### NumPy support
All arrays returned or accepted by API functions are of type NumPy `ndarray`. These functions wrap the underlying data without copying*.
For every function returning a NumPy array, the API provides counterpart functions returning C-style native array denoted with the suffix `_native_array`. These functions slightly improve performance by avoiding the (generally small) cost of wrapping a native array as NumPy.
> For most purposes, the functions returning NumPy array should be preferred as they offer several useful functions and improved memory safety.
*Functions returning a two-dimensional NumPy array, for example the 2D variable function `get_data_for_all_independent_variable_values`, perform a deep copy of the underlying data. In performance-sensitive code, the counterpart function returning native array should be preferred.
### One- and two-dimensional variable return types
The API has separate functions for getting 1D and 2D variables* primarily due to differences in the shape of the data and hence differences in the functions required to operate on the data.
> *1D and 2D variables are dependent variables with one and two independent variables respectively.
## Glossary
### Run
The output from running a Bladed calculation. Typically, this comprises several output _groups_, with each group containing variables that relate to a specific part of the model.
### Variable
In the context of the Results API, the term _variable_ is synonymous with _dependent variable_.
### Dependent variable
A variable calculated as the result of changing one or more independent variables. Dependent variables are listed next to the `VARIAB` key of an output group header file.
Dependent variables may be one-dimensional (1D) or two-dimensional (2D).
- The value of a one-dimensional variable is determined by one independent variable, known as the _primary_ independent variable.
Example: in a time series turbine simulation, 1D variable `Rotor speed` depends on primary independent variable `Time`. The data for `Rotor speed` is a one-dimensional array indexed on time.
- The value of a two-dimensional variable is determined by two independent variables, known as _primary_ and _secondary_ independent variables.
Example: In a time series turbine simulation with a multi-member tower, 2D variable `Tower Mx` depends on primary independent variable `Time`, and secondary independent variable `Location`. The data for `Tower Mx` is a two-dimensional array indexed on member location and time.
### Independent variable
A variable whose value does not depend on other variables in the calculation. Independent variables are denoted by the `AXISLAB` key of an output group header file.
In a time series calculation, a _primary_ independent variable typically represents time. A _secondary_ independent variable typically represents an measurement point, such as a blade station.
### Header file
A file containing metadata describing an output group. A header files extension takes the form `.%n`, where `n` is a number uniquely identifying the group within the run.
### Data file
A file containing an output group’s data (binary or ASCII). A data file extension takes the form `.$n`, where `n` matches the corresponding header file number.
### (Output) group
A collection of variables that relate to a specific part of the model. For example, the variables `Rotor speed` and `Generator speed` belong to the `Drive train variables` group.
A Bladed group is represented by two files: a header file containing metadata, and a data file containing data for all dependent variables in the group.
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"description": "# Bladed Results API 2.0\r\n\r\nThe Bladed Results API is an easy, fast, and robust way to access Bladed results using Python.\r\n\r\nIt provides features for:\r\n\r\n- Discovering Bladed runs\r\n- Finding variables in a set of runs\r\n- Getting data from variables\r\n- Reporting run and variable metadata\r\n- Exploring and writing output groups.\r\n\r\nThe API is able to read results from any Bladed version.\r\n\r\nThe API depends on the [`numpy`](https://numpy.org) package.\r\n\r\n> Currently only Windows is supported.\r\n\r\n> Bladed Results API 2.0 replaces Results API 1.x which is being discontinued.\r\n\r\n## Pre-requisites\r\n\r\n- Requires a _32- or 64-bit Windows_ installation of:\r\n - Python 3.9\r\n - or Python 3.10\r\n - or Python 3.11\r\n - or Python 3.12\r\n\r\n> 64-bit Python is recommended.\r\n\r\n- The Results API has been tested on Windows 10.\r\n\r\n### Quick Start\r\n\r\n```shell\r\npip install dnv-bladed-results\r\n```\r\n\r\n```python\r\nfrom dnv_bladed_results import *\r\n\r\nrun = ResultsApi.get_run(run_dir, run_name)\r\nvar_1d = run.get_variable_1d(variable_name)\r\nprint(var_1d.get_data())\r\n```\r\n\r\n### Code completion\r\n\r\nWe recommend enabling code completion in the IDE for the best user experience. Code completion displays a popup listing the available functions as the user types. Together with inline documentation, code completion makes it easy to explore and understand the API.\r\n\r\n\r\n\r\nFor guidance on enabling and customising code completion, please refer to the following resources:\r\n\r\n[`IntelliSense in Visual Studio Code`](https://code.visualstudio.com/docs/editor/intellisense)\r\n\r\n[`Code completion in PyCharm`](https://www.jetbrains.com/help/pycharm/auto-completing-code.html)\r\n\r\n> If using Visual Studio Code, a type hint is needed for code completion to work with API classes inside a loop. This issue does not affect PyCharm.\r\n> ```python\r\n> # Note the following type hint declared on the run loop variable\r\n> run: Run\r\n> for run in runs:\r\n> # Do something with run - easy now code completion works!\r\n> ```\r\n\r\n> If using PyCharm, we recommend enabling the options \"Show suggestions as you type\" and \"Show the documentation popup in...\", available in Settings > Editor > General > Code Completion.\r\n\r\n## Usage Examples\r\n\r\nUsage examples demonstrating core functionality are distributed with the package. A brief description of each example follows.\r\n\r\nThe `UsageExamples` installation folder and list of available examples may be enquired as follows:\r\n\r\n```python\r\nimport os\r\nfrom dnv_bladed_results import UsageExamples\r\nprint(UsageExamples.__path__[0])\r\nos.listdir(UsageExamples.__path__[0])\r\n```\r\n\r\nThe examples below show how each script may be launched from within a Python environment.\r\n\r\n### Basic Operations\r\n\r\nLoad a Bladed run, request groups and variables, and get data for tower members and blade stations:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_BasicOperations\r\nResultsApi_BasicOperations.run_script()\r\n```\r\n\r\n### Variable Data\r\n\r\nLoad a Bladed run, request 1D and 2D variables* from both the run and from a specific output group, and obtain data from the returned variables:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ReadBasic\r\nResultsApi_VariableData_ReadBasic.run_script()\r\n```\r\n\r\nObtain data from a 2D variable* for specific independent variable values, and specify the precision of the data to read:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ReadExtended\r\nResultsApi_VariableData_ReadExtended.run_script()\r\n```\r\n\r\n > *1D and 2D variables are dependent variables with one and two independent variables respectively.\r\n\r\n### Runs\r\n\r\nUse filters and regular expressions to find a subset of runs in a directory tree:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_FindRuns\r\nResultsApi_FindRuns.run_script()\r\n```\r\n\r\nFind and process runs asynchronously using a Python generator:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_FindRunsUsingGenerator\r\nResultsApi_FindRunsUsingGenerator.run_script()\r\n```\r\n\r\n### Metadata\r\n\r\nGet metadata for runs, groups, and variables:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_RunMetadata\r\nResultsApi_RunMetadata.run_script()\r\n```\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_GroupMetadata\r\nResultsApi_GroupMetadata.run_script()\r\n```\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_VariableMetadata\r\nResultsApi_VariableMetadata.run_script()\r\n```\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_VariableStats\r\nResultsApi_VariableStats.run_script()\r\n```\r\n\r\n### Output\r\n\r\nExport 1D and 2D Bladed output groups, as well as an entire run, using the HDF5 file format:\r\n\r\n > Requires the `h5py` library, available via pip: `pip install h5py`. The example has been tested with h5py 3.11.0.\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ExportHDF5\r\nResultsApi_VariableData_ExportHDF5.run_script()\r\n```\r\n\r\nExport Bladed output groups using the Matlab file format:\r\n\r\n > Requires the `scipy` library, available via pip: `pip install scipy`. The example has been tested with scipy 1.13.1.\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_VariableData_ExportMatlab\r\nResultsApi_VariableData_ExportMatlab.run_script()\r\n```\r\n\r\nWrite 1D and 2D output groups using the Bladed file format:\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_WriteGroup\r\nResultsApi_WriteGroup.run_script()\r\n```\r\n\r\n### Charting\r\n\r\nCreate 2D and 3D plots of blade loads:\r\n\r\n > Requires the `matplotlib` library, available via pip: `pip install matplotlib`. The examples have been tested with matplotlib 3.9.1.\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_Charting2D\r\nResultsApi_Charting2D.run_script()\r\n```\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_Charting3D\r\nResultsApi_Charting3D.run_script()\r\n```\r\n\r\n### Post-Processing\r\n\r\nPost-process two-dimensional variable data into bespoke data structures and into a Pandas DataFrame.\r\nPlot the data choosing specific points of the DataFrame.\r\n\r\n > Requires the `matplotlib` library, available via pip: `pip install matplotlib`. The example has been tested with matplotlib 3.9.1.\r\n \r\n > Requires the `pandas` library, available via pip: `pip install pandas`. The example has been tested with pandas 2.2.3.\r\n\r\n```python\r\nfrom dnv_bladed_results.UsageExamples import ResultsApi_PostProcessing\r\nResultsApi_PostProcessing.run_script()\r\n```\r\n\r\n## Results Viewer example\r\n\r\nThe following images demonstrate how Bladed data shown in Results Viewer may be accessed using the Results API 2.0.\r\n\r\nResults Viewer is a standalone package providing enhanced results viewing functionality. Bladed and the Results Viewer application are both available from the [`Downloads`](https://mysoftware.dnv.com/knowledge-centre/bladed/help-library/getting-started/installation#downloading-installers) page.\r\n\r\nOne-dimensional variables:\r\n\r\n\r\n\r\nTwo-dimensional variables:\r\n\r\n\r\n\r\n## Data types\r\n\r\nThe API comprises a generated Python wrapper dispatching to a C++ DLL. The DLL performs the work of fetching and storing data, validation, and memory management.\r\n\r\n### NumPy support\r\n\r\nAll arrays returned or accepted by API functions are of type NumPy `ndarray`. These functions wrap the underlying data without copying*.\r\n\r\nFor every function returning a NumPy array, the API provides counterpart functions returning C-style native array denoted with the suffix `_native_array`. These functions slightly improve performance by avoiding the (generally small) cost of wrapping a native array as NumPy.\r\n\r\n> For most purposes, the functions returning NumPy array should be preferred as they offer several useful functions and improved memory safety.\r\n\r\n *Functions returning a two-dimensional NumPy array, for example the 2D variable function `get_data_for_all_independent_variable_values`, perform a deep copy of the underlying data. In performance-sensitive code, the counterpart function returning native array should be preferred.\r\n\r\n### One- and two-dimensional variable return types\r\n\r\nThe API has separate functions for getting 1D and 2D variables* primarily due to differences in the shape of the data and hence differences in the functions required to operate on the data.\r\n\r\n > *1D and 2D variables are dependent variables with one and two independent variables respectively. \r\n\r\n## Glossary\r\n\r\n### Run\r\n\r\nThe output from running a Bladed calculation. Typically, this comprises several output _groups_, with each group containing variables that relate to a specific part of the model.\r\n\r\n### Variable\r\n\r\nIn the context of the Results API, the term _variable_ is synonymous with _dependent variable_.\r\n\r\n### Dependent variable\r\n\r\nA variable calculated as the result of changing one or more independent variables. Dependent variables are listed next to the `VARIAB` key of an output group header file.\r\n\r\n Dependent variables may be one-dimensional (1D) or two-dimensional (2D).\r\n\r\n- The value of a one-dimensional variable is determined by one independent variable, known as the _primary_ independent variable.\r\n\r\n Example: in a time series turbine simulation, 1D variable `Rotor speed` depends on primary independent variable `Time`. The data for `Rotor speed` is a one-dimensional array indexed on time.\r\n\r\n- The value of a two-dimensional variable is determined by two independent variables, known as _primary_ and _secondary_ independent variables.\r\n\r\n Example: In a time series turbine simulation with a multi-member tower, 2D variable `Tower Mx` depends on primary independent variable `Time`, and secondary independent variable `Location`. The data for `Tower Mx` is a two-dimensional array indexed on member location and time.\r\n\r\n### Independent variable\r\n\r\nA variable whose value does not depend on other variables in the calculation. Independent variables are denoted by the `AXISLAB` key of an output group header file.\r\n\r\nIn a time series calculation, a _primary_ independent variable typically represents time. A _secondary_ independent variable typically represents an measurement point, such as a blade station.\r\n\r\n### Header file\r\n\r\nA file containing metadata describing an output group. A header files extension takes the form `.%n`, where `n` is a number uniquely identifying the group within the run.\r\n\r\n### Data file\r\n\r\nA file containing an output group\u2019s data (binary or ASCII). A data file extension takes the form `.$n`, where `n` matches the corresponding header file number.\r\n\r\n### (Output) group\r\n\r\nA collection of variables that relate to a specific part of the model. For example, the variables `Rotor speed` and `Generator speed` belong to the `Drive train variables` group.\r\n\r\nA Bladed group is represented by two files: a header file containing metadata, and a data file containing data for all dependent variables in the group.\r\n",
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