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| wxWidgets | https://www.wxwidgets.org/downloads | WXMSW3 |
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| WEX | https://github.com/NREL/wex | WEXDIR |
| Google Test | https://github.com/google/googletest | GTEST |
| SSC | https://github.com/mjwagner2/ssc, -b solarpilot-develop | SSCDIR |
| SolTrace | https://github.com/NREL/SolTrace | CORETRACEDIR |
| SolarPILOT | https://github.com/NREL/SolarPILOT |
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|\r\n|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\r\n\r\n## Features ##\r\nThe use of extended Open Inventor files to represent the ""scene"" (i.e. the solar concentrating system, the sunlight model, etc.)\r\nAn advance and easy-to-use GUI providing:\r\n * 3D and tree views of the ""scene"" to simulate.\r\n * Handlers and manipulators to modify and query scene objects using 3D views.\r\n * Interface elements to manage the undo and redo of user actions.\r\n * Interface elements to define de type of Monte Carlo ray tracing to execute.\r\nA pervasive plugin architecture which allos the user to:\r\n * Add new sunlight models.\r\n * Add new geometric surfaces.\r\n * Add new reflective materials.\r\n * (planned) Add new refractive materials.\r\n * (planned) Add new photon map and other results analyzers, and post-processors.\r\n * (planned) Add new spectrum models.\r\n## Requirements ##\r\nAs any other ambitious open source program, Tonatiuh uses and leverages on several existing open source libraries, and tools. The principal open source resources used by Tonatiuh are:\r\n * Digia Qt for the Graphic User Interface (GUI).\r\n * Coin3D Toolkit for 3D Graphics Visualization.\r\n * Marble generic geographical map widget and framework.\r\n * CPPUnit for testing the code.\r\nAll these tools are used for developing Tonatiuh within the Eclipse IDE in a standard development environment used by the entire developing team.\r\n\r\n## Tonatiuh\'s output files format ##\r\n\r\nFrom version 2.0.1 the format of the outputs of simulations has been changed to be more flexible for post-processing. You can find a description of the format [Tonatiuh\'s output files format](https://github.com/iat-cener/tonatiuh/wiki/Output-files-format) .\r\n\r\n## Citing Tonatiuh ##\r\n\r\nThese are some of the most relevant references:\r\n * Les, I., Mutuberria, A., Sch\xc3\xb6ttl, P., Nitz, P. (2017). New Functionalities for the Tonatiuh Ray-tracing Software. Proceedings of the 23rd SolarPACES Conference.\r\n * J. Cardoso, J., Mutuberria, A., Marakko, C., Schoettl, P., Os\xc3\xb3rio, T., Les, I.,(2017). New Functionalities for the Tonatiuh Ray-tracing Software. Proceedings of the 23rd SolarPACES Conference.\r\n * Blanco, M., Mutuberria, A., Monreal, A., & Albert, R. (2011). Results of the empirical validation of Tonatiuh at Mini-Pegase CNRS-PROMES facility. Proc SolarPACES.\r\n * Blanco, M. J., Mutuberria, A., & Martinez, D. (2010). Experimental validation of Tonatiuh using the Plataforma Solar de Almer\xc3\xada secondary concentrator test campaign data. In 16th annual SolarPACES symposium.\r\n * Blanco, M. J., Mutuberria, A., Garcia, P., Gastesi, R., & Martin, V. (2009). Preliminary validation of Tonatiuh SOLARPACES Symposium. Berlin, Germany.\r\n * Blanco, M. J., Amieva, J. M., & Mancillas, A. (2005, January). The Tonatiuh Software Development Project: An open source approach to the simulation of solar concentrating systems. In ASME 2005 International Mechanical Engineering Congress and Exposition (pp. 157-164). American Society of Mechanical Engineers.\r\n \r\n \r\n
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\n Supported Products •\n Documentation •\n Features •\n Supported Databases •\n Examples\n
\n\n## Supported Products\n* **Outback MATEs** (FX Inverter, MX/FM Charge Controller)\n* **Renogy Rover** (And other Renogy products) over modbus serial.\n * Includes Rover, Rover Elite, Wanderer, Adventurer, Dual Input DCDC Charger, Rover Boost and possibly others\n * Compatible with all [SRNE Solar](https://www.srnesolar.com) Charge Controllers (And rebranded products)\n * Compatible with **Zenith Grape** Solar Charge Controller, **PowMr** MPPT Charge Controller, **RICH** SOLAR MPPT, **WindyNations TrakMax** MPPT\n* **EPEver Tracer**\n * Includes the AN series and the TRIRON N series\n * Possibly includes the BN series (untested)\n* DS18B20 Temperature Sensors and PZEM-003 and PZEM-017 Shunts\n\n# Quickstart\nReady to install? Use the [Quickstart](https://solarthing.readthedocs.io/en/latest/installation.html)!\n\n\n# Features\n* Supports **multiple types of solar products**.\n* Runs reliably **24-7**. Recovers from connection errors and has verbose logging features.\n* Fully customizable through JSON (**No programming experience required**).\n* Supports CouchDB, InfluxDB, local JSON file, and PVOutput exporting.\n * Multiple databases can even be used at the same time!\n * Packets are uploaded in parallel to multiple databases at the same time\n* Can [report Raspberry Pi CPU temperature](https://solarthing.readthedocs.io/en/latest/config/rpi-cpu-temp.html).\n* Easy setup on Linux. Runs *without* root.\n\n## Supported Databases\n* CouchDB\n * Allows for [SolarThing Android](https://github.com/wildmountainfarms/solarthing-android) and [SolarThing Web](https://github.com/wildmountainfarms/solarthing-web) to function\n * Used for PVOutput data collection\n* GraphQL\n * Allows use of CouchDB SolarThing data with Grafana\n * Supplements the CouchDB database\n* InfluxDB\n * Simplest to set up with Grafana\n* [PVOutput.org](https://pvoutput.org)\n * Allows for viewing of data on [pvoutput.org](https://pvoutput.org)\n * Requires CouchDB to be set up\n * Enables usage of the [PVOutput Mycroft skill](https://github.com/wildmountainfarms/pvoutput-mycroft)\n* REST API\n * With the ""post"" database, all packets can be posted to a URL endpoint, useful for REST APIs\n\n\n### Examples\nPVOutput Wild Mountain Farms: [PVOutput System](https://pvoutput.org/intraday.jsp?sid=72206) and \n[PVOutput SolarThing Teams](https://pvoutput.org/listteam.jsp?tid=1528)\n\n---\n\nSolarThing Android: [Github](https://github.com/wildmountainfarms/solarthing-android)\n|\n[Google Play](https://play.google.com/store/apps/details?id=me.retrodaredevil.solarthing.android)\n\nSolarThing Android displays data in a persistent notification that updates at a configurable rate\n![alt text](other/docs/solarthing-android-notification-screenshot-1.jpg ""SolarThing Android Notification"")\nSend a coffee to woheller69@t-online.de \n![]()
\n\n\n| **RadarWeather** | **Gas Prices** | **Smart Eggtimer** |\n|:---:|:---:|:---:|\n| [\n \n
\n \n
\n\nAn open source project from Data to AI Lab at MIT.\n
\n\n
\nAutoML for Time Series.\n
\n\n\n[](https://pypi.python.org/pypi/draco-ml)\n[](https://github.com/sintel-dev/Draco/actions?query=workflow%3A%22Run+Tests%22+branch%3Amaster)\n[](https://pepy.tech/project/draco-ml)\n[](https://mybinder.org/v2/gh/sintel-dev/Draco/master?filepath=tutorials)\n\n\n# Draco\n\n- License: [MIT](https://github.com/sintel-dev/Draco/blob/master/LICENSE)\n- Documentation: https://sintel-dev.github.io/Draco\n- Homepage: https://github.com/sintel-dev/Draco\n\n## Overview\n\nThe Draco project is a collection of end-to-end solutions for machine learning problems\ncommonly found in time series monitoring systems. Most tasks utilize sensor data\nemanating from monitoring systems. We utilize the foundational innovations developed for\nautomation of machine Learning at Data to AI Lab at MIT.\n\nThe salient aspects of this customized project are:\n\n* A set of ready to use, well tested pipelines for different machine learning tasks. These are\n vetted through testing across multiple publicly available datasets for the same task.\n* An easy interface to specify the task, pipeline, and generate results and summarize them.\n* A production ready, deployable pipeline.\n* An easy interface to ``tune`` pipelines using Bayesian Tuning and Bandits library.\n* A community oriented infrastructure to incorporate new pipelines.\n* A robust continuous integration and testing infrastructure.\n* A ``learning database`` recording all past outcomes --> tasks, pipelines, outcomes.\n\n## Resources\n\n* [Data Format](DATA_FORMAT.md).\n* [Draco folder structure](DATA_FORMAT.md#folder-structure).\n\n# Install\n\n## Requirements\n\n**Draco** has been developed and runs on Python 3.6, 3.7 and 3.8.\n\nAlso, although it is not strictly required, the usage of a [virtualenv](\nhttps://virtualenv.pypa.io/en/latest/) is highly recommended in order to avoid interfering\nwith other software installed in the system where you are trying to run **Draco**.\n\n## Download and Install\n\n**Draco** can be installed locally using [pip](https://pip.pypa.io/en/stable/) with\nthe following command:\n\n```bash\npip install draco-ml\n```\n\nThis will pull and install the latest stable release from [PyPi](https://pypi.org/).\n\nIf you want to install from source or contribute to the project please read the\n[Contributing Guide](https://sintel-dev.github.io/Draco/contributing.html#get-started).\n\n# Data Format\n\nThe minimum input expected by the **Draco** system consists of the following two elements,\nwhich need to be passed as `pandas.DataFrame` objects:\n\n## Target Times\n\nA table containing the specification of the problem that we are solving, which has three\ncolumns:\n\n* `turbine_id`: Unique identifier of the turbine which this label corresponds to.\n* `cutoff_time`: Time associated with this target\n* `target`: The value that we want to predict. This can either be a numerical value or a\n categorical label. This column can also be skipped when preparing data that will be used\n only to make predictions and not to fit any pipeline.\n\n| | turbine_id | cutoff_time | target |\n|----|--------------|---------------------|----------|\n| 0 | T1 | 2001-01-02 00:00:00 | 0 |\n| 1 | T1 | 2001-01-03 00:00:00 | 1 |\n| 2 | T2 | 2001-01-04 00:00:00 | 0 |\n\n## Readings\n\nA table containing the signal data from the different sensors, with the following columns:\n\n * `turbine_id`: Unique identifier of the turbine which this reading comes from.\n * `signal_id`: Unique identifier of the signal which this reading comes from.\n * `timestamp (datetime)`: Time where the reading took place, as a datetime.\n * `value (float)`: Numeric value of this reading.\n\n| | turbine_id | signal_id | timestamp | value |\n|----|--------------|-------------|---------------------|---------|\n| 0 | T1 | S1 | 2001-01-01 00:00:00 | 1 |\n| 1 | T1 | S1 | 2001-01-01 12:00:00 | 2 |\n| 2 | T1 | S1 | 2001-01-02 00:00:00 | 3 |\n| 3 | T1 | S1 | 2001-01-02 12:00:00 | 4 |\n| 4 | T1 | S1 | 2001-01-03 00:00:00 | 5 |\n| 5 | T1 | S1 | 2001-01-03 12:00:00 | 6 |\n| 6 | T1 | S2 | 2001-01-01 00:00:00 | 7 |\n| 7 | T1 | S2 | 2001-01-01 12:00:00 | 8 |\n| 8 | T1 | S2 | 2001-01-02 00:00:00 | 9 |\n| 9 | T1 | S2 | 2001-01-02 12:00:00 | 10 |\n| 10 | T1 | S2 | 2001-01-03 00:00:00 | 11 |\n| 11 | T1 | S2 | 2001-01-03 12:00:00 | 12 |\n\n## Turbines\n\nOptionally, a third table can be added containing metadata about the turbines.\nThe only requirement for this table is to have a `turbine_id` field, and it can have\nan arbitraty number of additional fields.\n\n| | turbine_id | manufacturer | ... | ... | ... |\n|----|--------------|----------------|-------|-------|-------|\n| 0 | T1 | Siemens | ... | ... | ... |\n| 1 | T2 | Siemens | ... | ... | ... |\n\n## CSV Format\n\nA part from the in-memory data format explained above, which is limited by the memory\nallocation capabilities of the system where it is run, **Draco** is also prepared to\nload and work with data stored as a collection of CSV files, drastically increasing the amount\nof data which it can work with. Further details about this format can be found in the\n[project documentation site](DATA_FORMAT.md#csv-format).\n\n# Quickstart\n\nIn this example we will load some demo data and classify it using a **Draco Pipeline**.\n\n## 1. Load and split the demo data\n\nThe first step is to load the demo data.\n\nFor this, we will import and call the `draco.demo.load_demo` function without any arguments:\n\n```python3\nfrom draco.demo import load_demo\n\ntarget_times, readings = load_demo()\n```\n\nThe returned objects are:\n\n* ``target_times``: A ``pandas.DataFrame`` with the ``target_times`` table data:\n\n ```\n turbine_id cutoff_time target\n 0 T001 2013-01-12 0\n 1 T001 2013-01-13 0\n 2 T001 2013-01-14 0\n 3 T001 2013-01-15 1\n 4 T001 2013-01-16 0\n ```\n\n* ``readings``: A ``pandas.DataFrame`` containing the time series data in the format explained above.\n\n ```\n turbine_id signal_id timestamp value\n 0 T001 S01 2013-01-10 323.0\n 1 T001 S02 2013-01-10 320.0\n 2 T001 S03 2013-01-10 284.0\n 3 T001 S04 2013-01-10 348.0\n 4 T001 S05 2013-01-10 273.0\n ```\n\nOnce we have loaded the `target_times` and before proceeding to training any Machine Learning\nPipeline, we will have split them in 2 partitions for training and testing.\n\nIn this case, we will split them using the [train_test_split function from scikit-learn](\nhttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html),\nbut it can be done with any other suitable tool.\n\n```python3\nfrom sklearn.model_selection import train_test_split\n\ntrain, test = train_test_split(target_times, test_size=0.25, random_state=0)\n```\n\nNotice how we are only splitting the `target_times` data and not the `readings`.\nThis is because the pipelines will later on take care of selecting the parts of the\n`readings` table needed for the training based on the information found inside\nthe `train` and `test` inputs.\n\nAdditionally, if we want to calculate a goodness-of-fit score later on, we can separate the\ntesting target values from the `test` table by popping them from it:\n\n```python3\ntest_targets = test.pop(\'target\')\n```\n\n## 2. Exploring the available Pipelines\n\nOnce we have the data ready, we need to find a suitable pipeline.\n\nThe list of available Draco Pipelines can be obtained using the `draco.get_pipelines`\nfunction.\n\n```python3\nfrom draco import get_pipelines\n\npipelines = get_pipelines()\n```\n\nThe returned `pipeline` variable will be `list` containing the names of all the pipelines\navailable in the Draco system:\n\n```\n[\'lstm\',\n \'lstm_with_unstack\',\n \'double_lstm\',\n \'double_lstm_with_unstack\']\n```\n\nFor the rest of this tutorial, we will select and use the pipeline\n`lstm_with_unstack` as our template.\n\n```python3\npipeline_name = \'lstm_with_unstack\'\n```\n\n## 3. Fitting the Pipeline\n\nOnce we have loaded the data and selected the pipeline that we will use, we have to\nfit it.\n\nFor this, we will create an instance of a `DracoPipeline` object passing the name\nof the pipeline that we want to use:\n\n```python3\nfrom draco.pipeline import DracoPipeline\n\npipeline = DracoPipeline(pipeline_name)\n```\n\nAnd then we can directly fit it to our data by calling its `fit` method and passing in the\ntraining `target_times` and the complete `readings` table:\n\n```python3\npipeline.fit(train, readings)\n```\n\n## 4. Make predictions\n\nAfter fitting the pipeline, we are ready to make predictions on new data by calling the\n`pipeline.predict` method passing the testing `target_times` and, again, the complete\n`readings` table.\n\n```python3\npredictions = pipeline.predict(test, readings)\n```\n\n## 5. Evaluate the goodness-of-fit\n\nFinally, after making predictions we can evaluate how good the prediction was\nusing any suitable metric.\n\n```python3\nfrom sklearn.metrics import f1_score\n\nf1_score(test_targets, predictions)\n```\n\n## What\'s next?\n\nFor more details about **Draco** and all its possibilities and features, please check the\n[project documentation site](https://sintel-dev.github.io/Draco/)\nAlso do not forget to have a look at the [tutorials](\nhttps://github.com/sintel-dev/Draco/tree/master/tutorials)!\n'",,"2018/09/27, 14:50:42",1854,MIT,11,294,"2023/07/24, 12:12:04",6,54,71,7,93,0,0.8,0.5914893617021277,"2023/07/31, 16:02:44",v0.3.0,0,5,false,,false,true,,,https://github.com/sintel-dev,https://dai.lids.mit.edu/,,,,https://avatars.githubusercontent.com/u/13336772?v=4,,, pyconturb,Constrained Stochastic Turbulence for Wind Energy Applications.,pyconturb,,custom,,Wind Energy,,,,,,,,,,https://gitlab.windenergy.dtu.dk/pyconturb/pyconturb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ORBIT,Offshore Renewable Balance-of-system Installation Tool computes capital costs and activity times for offshore wind plant balance-of-system (everything besides the turbine) costs.,WISDEM,https://github.com/WISDEM/ORBIT.git,github,,Wind Energy,"2023/04/21, 15:30:22",14,0,5,true,Python,WISDEM,WISDEM,Python,https://wisdem.github.io/ORBIT/,"b'ORBIT\n=====\n\nOffshore Renewables Balance of system and Installation Tool\n\n\n:Version: 1.0.8\n:Authors: `Jake Nunemaker\n\n\n\n
\n\n\n\n\n\n \n Interactional aerodynamics solver for multirotor aircraft and wind energy\n \n
\n\n\n\n---\n\nFLOWUnsteady is an open-source variable-fidelity framework for unsteady\naerodynamics and aeroacoustics based on the\n[reformulated vortex particle method](https://scholarsarchive.byu.edu/etd/9589/)\n(rVPM).\nThis suite brings together various tools developed by the\n[FLOW Lab](http://flow.byu.edu/) at Brigham Young University: Vortex lattice\nmethod, strip theory, blade elements, 3D panel method, and rVPM.\nThe suite also integrates an FW-H solver and a BPM code for tonal\nand broadband prediction of aeroacoustic noise.\nIn the low end of fidelity, simulations are similar to a free-wake method,\nwhile in the high end simulations become meshless large eddy simulations.\n\n\n* *Documentation:* [flow.byu.edu/FLOWUnsteady](https://flow.byu.edu/FLOWUnsteady)\n* *Code:* [github.com/byuflowlab/FLOWUnsteady](https://github.com/byuflowlab/FLOWUnsteady)\n\n### What is the Reformulated VPM?\n\nThe [reformulated VPM](https://scholarsarchive.byu.edu/etd/9589/) is a meshless\nCFD method solving the LES-filtered incompressible Navier-Stokes equations in\ntheir vorticity form,\n\n \n
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