[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XE45AFT30ESXE8C83HECC9":3,"skill-same:01M6XE45AFT30ESXE8C83HECC9":64},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":13,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6XE45AFT30ESXE8C83HECC9","lomb-scargle-periodogram",null,"Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Use when analyzing light curves, radial velocity data, or any astronomical time series to detect periodic variations. Works for stellar rotation, pulsation, eclipsing binaries, and general periodic phenomena. Based on lightkurve library.","Waaaalnut\u002Fskillsbench","Waaaalnut","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F103995656?v=4","01M6XCS9K1V51EY3JMB7DGMX24","01M6XCS9Q4WPW88W62C55G1H44",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fexoplanet-detection-period\u002Fenvironment\u002Fskills\u002Flomb-scargle-periodogram\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"lomb scargle periodogram","series","curves","when","radial","for","rotation","stellar","radial velocity","detect","library","signals","time","velocity","general","periodic","use","data","analyzing","time series","2026-11-10 10:12:30",false,"---\nname: lomb-scargle-periodogram\ndescription: Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Use when analyzing light curves, radial velocity data, or any astronomical time series to detect periodic variations. Works for stellar rotation, pulsation, eclipsing binaries, and general periodic phenomena. Based on lightkurve library.\n---\n\n# Lomb-Scargle Periodogram\n\nThe Lomb-Scargle periodogram is the standard tool for finding periods in unevenly sampled astronomical time series data. It's particularly useful for detecting periodic signals in light curves from space missions like Kepler, K2, and TESS.\n\n## Overview\n\nThe Lomb-Scargle periodogram extends the classical periodogram to handle unevenly sampled data, which is common in astronomy due to observing constraints, data gaps, and variable cadences.\n\n## Basic Usage with Lightkurve\n\n```python\nimport lightkurve as lk\nimport numpy as np\n\n# Create a light curve object\nlc = lk.LightCurve(time=time, flux=flux, flux_err=error)\n\n# Create periodogram (specify maximum period to search)\npg = lc.to_periodogram(maximum_period=15)  # Search up to 15 days\n\n# Find strongest period\nstrongest_period = pg.period_at_max_power\nmax_power = pg.max_power\n\nprint(f\"Strongest period: {strongest_period:.5f} days\")\nprint(f\"Power: {max_power:.5f}\")\n```\n\n## Plotting Periodograms\n\n```python\nimport matplotlib.pyplot as plt\n\npg.plot(view='period')  # View vs period (not frequency)\nplt.xlabel('Period [days]')\nplt.ylabel('Power')\nplt.show()\n```\n\n**Important**: Use `view='period'` to see periods directly, not frequencies. The default `view='frequency'` shows frequency (1\u002Fperiod).\n\n## Period Range Selection\n\nChoose appropriate period ranges based on your science case:\n\n- **Stellar rotation**: 0.1 - 100 days\n- **Exoplanet transits**: 0.5 - 50 days (most common)\n- **Eclipsing binaries**: 0.1 - 100 days\n- **Stellar pulsations**: 0.001 - 1 day\n\n```python\n# Search specific period range\npg = lc.to_periodogram(minimum_period=2.0, maximum_period=7.0)\n```\n\n## Interpreting Results\n\n### Power Significance\n\nHigher power indicates stronger periodic signal, but be cautious:\n- **High power**: Likely real periodic signal\n- **Multiple peaks**: Could indicate harmonics (period\u002F2, period*2)\n- **Aliasing**: Very short periods may be aliases of longer periods\n\n### Common Patterns\n\n1. **Single strong peak**: Likely the true period\n2. **Harmonics**: Peaks at period\u002F2, period*2 suggest the fundamental period\n3. **Aliases**: Check if period*2 or period\u002F2 also show signals\n\n## Model Fitting\n\nOnce you find a period, you can fit a model:\n\n```python\n# Get the frequency at maximum power\nfrequency = pg.frequency_at_max_power\n\n# Create a model light curve\nmodel = pg.model(time=lc.time, frequency=frequency)\n\n# Plot data and model\nimport matplotlib.pyplot as plt\nlc.plot(label='Data')\nmodel.plot(label='Model')\nplt.legend()\nplt.show()\n```\n\n## Dependencies\n\n```bash\npip install lightkurve numpy matplotlib\n```\n\n## References\n\n- [Lightkurve Tutorials](https:\u002F\u002Flightkurve.github.io\u002Flightkurve\u002Ftutorials\u002Findex.html)\n- [Lightkurve Periodogram Documentation](https:\u002F\u002Fdocs.lightkurve.org\u002F)\n\n## When to Use This vs. Other Methods\n\n- **Lomb-Scargle**: General periodic signals (rotation, pulsation, eclipsing binaries)\n- **Transit Least Squares (TLS)**: Specifically for exoplanet transits (more sensitive)\n- **Box Least Squares (BLS)**: Alternative transit detection method\n\nFor exoplanet detection, consider using TLS after Lomb-Scargle for initial period search.\n","# Lomb-Scargle Periodogram\n\nThe Lomb-Scargle periodogram is the standard tool for finding periods in unevenly sampled astronomical time series data. It's particularly useful for detecting periodic signals in light curves from space missions like Kepler, K2, and TESS.\n\n## Overview\n\nThe Lomb-Scargle periodogram extends the classical periodogram to handle unevenly sampled data, which is common in astronomy due to observing constraints, data gaps, and variable cadences.\n\n## Basic Usage with Lightkurve\n\n```python\nimport lightkurve as lk\nimport numpy as np\n\n# Create a light curve object\nlc = lk.LightCurve(time=time, flux=flux, flux_err=error)\n\n# Create periodogram (specify maximum period to search)\npg = lc.to_periodogram(maximum_period=15)  # Search up to 15 days\n\n# Find strongest period\nstrongest_period = pg.period_at_max_power\nmax_power = pg.max_power\n\nprint(f\"Strongest period: {strongest_period:.5f} days\")\nprint(f\"Power: {max_power:.5f}\")\n```\n\n## Plotting Periodograms\n\n```python\nimport matplotlib.pyplot as plt\n\npg.plot(view='period')  # View vs period (not frequency)\nplt.xlabel('Period [days]')\nplt.ylabel('Power')\nplt.show()\n```\n\n**Important**: Use `view='period'` to see periods directly, not frequencies. The default `view='frequency'` shows frequency (1\u002Fperiod).\n\n## Period Range Selection\n\nChoose appropriate period ranges based on your science case:\n\n- **Stellar rotation**: 0.1 - 100 days\n- **Exoplanet transits**: 0.5 - 50 days (most common)\n- **Eclipsing binaries**: 0.1 - 100 days\n- **Stellar pulsations**: 0.001 - 1 day\n\n```python\n# Search specific period range\npg = lc.to_periodogram(minimum_period=2.0, maximum_period=7.0)\n```\n\n## Interpreting Results\n\n### Power Significance\n\nHigher power indicates stronger periodic signal, but be cautious:\n- **High power**: Likely real periodic signal\n- **Multiple peaks**: Could indicate harmonics (period\u002F2, period*2)\n- **Aliasing**: Very short periods may be aliases of longer periods\n\n### Common Patterns\n\n1. **Single strong peak**: Likely the true period\n2. **Harmonics**: Peaks at period\u002F2, period*2 suggest the fundamental period\n3. **Aliases**: Check if period*2 or period\u002F2 also show signals\n\n## Model Fitting\n\nOnce you find a period, you can fit a model:\n\n```python\n# Get the frequency at maximum power\nfrequency = pg.frequency_at_max_power\n\n# Create a model light curve\nmodel = pg.model(time=lc.time, frequency=frequency)\n\n# Plot data and model\nimport matplotlib.pyplot as plt\nlc.plot(label='Data')\nmodel.plot(label='Model')\nplt.legend()\nplt.show()\n```\n\n## Dependencies\n\n```bash\npip install lightkurve numpy matplotlib\n```\n\n## References\n\n- [Lightkurve Tutorials](https:\u002F\u002Flightkurve.github.io\u002Flightkurve\u002Ftutorials\u002Findex.html)\n- [Lightkurve Periodogram Documentation](https:\u002F\u002Fdocs.lightkurve.org\u002F)\n\n## When to Use This vs. Other Methods\n\n- **Lomb-Scargle**: General periodic signals (rotation, pulsation, eclipsing binaries)\n- **Transit Least Squares (TLS)**: Specifically for exoplanet transits (more sensitive)\n- **Box Least Squares (BLS)**: Alternative transit detection method\n\nFor exoplanet detection, consider using TLS after Lomb-Scargle for initial period search.\n",{"name":5,"description":7},"main","7eeb258ada72b7f58119b0e7a2c00ea89d9beedd","tasks\u002Fexoplanet-detection-period\u002Fenvironment\u002Fskills\u002Flomb-scargle-periodogram","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench","https:\u002F\u002Fgithub.com\u002FWaaaalnut","student of Zhejiang University","2026-04-21 23:16:23",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add Waaaalnut\u002Fskillsbench --skill lomb-scargle-periodogram",{"tool":56,"command":57},"bunx","bunx skills add Waaaalnut\u002Fskillsbench --skill lomb-scargle-periodogram",{"tool":59,"command":60},"pnpm","pnpm dlx skills add Waaaalnut\u002Fskillsbench --skill lomb-scargle-periodogram","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fexoplanet-detection-period\u002Fenvironment\u002Fskills\u002Flomb-scargle-periodogram\u002FSKILL.md",true,[],[65,78,93,119,139,157],{"skillNo":66,"name":67,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":75,"publishedAt":38},"01M6XEWMJ6VY3ZC53MPDCPG3GH","data-reconciliation","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fxlsx-recover-data\u002Fenvironment\u002Fskills\u002Fdata-reconciliation\u002FSKILL.md",[70],{"categoryNo":71,"categoryName":72,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[76,77,35],"data reconciliation","reconciliation",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":38},"01M6XEW8MNPMF1KRXV6ATGFRQF","speech-to-text","Transcribe video to timestamped text using Whisper tiny model (pre-installed).","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-tutorial-indexer\u002Fenvironment\u002Fskills\u002Fspeech-to-text\u002FSKILL.md",[],[85,86,87,88,89,90,91,92],"speech","text","speech to text","transcribe video","whisper","model","Video","transcribe",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":104,"publishedAt":38},"01M6XEW3FQZP0T1RKQQJFXVY9T","video-processor","Process videos by removing segments and concatenating remaining parts. Use when you need to remove detected pauses\u002Fopenings from videos, create highlight reels, or batch process segment removals using ffmpeg filter_complex.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fvideo-processor\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[91,105,106,107,108,109,21,110,111,112,113,114,115,116,117,34,118],"processor","video processor","batch","create","reels","parts","segments","process","videos","you","segment","filter","ffmpeg","remove",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":126,"publishedAt":38},"01M6XEVXJ5KZ0NTY3XZKRY4AS1","silence-detector","Detect initial silence segments in audio\u002Fvideo using energy-based analysis. Use when you need to find low-energy periods at the start of recordings (title slides, setup time, pre-roll silence).","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fsilence-detector\u002FSKILL.md",[125],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[127,128,129,130,21,114,30,131,132,133,27,111,134,135,91,136,137,138,34],"detector","silence detector","title","slides","energy","at","recordings","find","Audio","analysis","start","setup",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":145,"publishedAt":38},"01M6XEVT3NGRN1FT7JPMAPXS6M","segment-combiner","Combine multiple segment detection results into a unified list. Use when you need to merge segments from different detectors, prepare removal lists for video processing, or consolidate detection outputs.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fsegment-combiner\u002FSKILL.md",[],[115,146,91,147,148,149,150,23,21,151,152,111,114,153,154,34,155,156],"segment combiner","detection","prepare","merge","video processing","outputs","combine","processing","consolidate","results","list",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":166,"publishedAt":38},"01M6XEVMPVASGS2KX86QK4FMT7","report-generator","Generate compression reports for video processing. Use when you need to create structured JSON reports with duration statistics, compression ratios, and segment details after video processing.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Freport-generator\u002FSKILL.md",[163],{"categoryNo":164,"categoryName":165,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000034","行政与办公人员",[167,168,169,115,21,23,114,170,153,171,172,173,34,150,174,91,175,176,108],"generator","report generator","report","with","statistics","structured","reports","json","compression","generate"]