{"id":22903112,"url":"https://github.com/space-physics/isr-raw","last_synced_at":"2025-07-26T02:42:43.134Z","repository":{"id":80566364,"uuid":"43934331","full_name":"space-physics/isr-raw","owner":"space-physics","description":"Utilities for working with low-level (raw sample) Incoherent Scatter Radar data, especially from Poker Flat AMISR (PFISR)","archived":false,"fork":false,"pushed_at":"2024-01-14T21:09:40.000Z","size":44985,"stargazers_count":3,"open_issues_count":1,"forks_count":2,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-05-08T08:57:06.453Z","etag":null,"topics":["incoherent-scatter-radar"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/space-physics.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null},"funding":{"github":["scivision"],"ko_fi":"scivision"}},"created_at":"2015-10-09T05:12:50.000Z","updated_at":"2023-07-09T20:11:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"45e67cd9-65a6-4388-aecf-cd5941faa5c3","html_url":"https://github.com/space-physics/isr-raw","commit_stats":{"total_commits":446,"total_committers":6,"mean_commits":74.33333333333333,"dds":"0.32511210762331844","last_synced_commit":"df90f291cbe4bfd59492e69abd384370c9ed71da"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/space-physics%2Fisr-raw","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/space-physics%2Fisr-raw/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/space-physics%2Fisr-raw/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/space-physics%2Fisr-raw/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/space-physics","download_url":"https://codeload.github.com/space-physics/isr-raw/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253033611,"owners_count":21843732,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["incoherent-scatter-radar"],"created_at":"2024-12-14T02:33:51.092Z","updated_at":"2025-07-26T02:42:43.123Z","avatar_url":"https://github.com/space-physics.png","language":"Python","funding_links":["https://github.com/sponsors/scivision","https://ko-fi.com/scivision"],"categories":[],"sub_categories":[],"readme":"# AMISR raw data utilities\n\n[![image](https://zenodo.org/badge/DOI/10.5281/zenodo.164876.svg)](https://doi.org/10.5281/zenodo.164876)\n[![ci](https://github.com/space-physics/isr-raw/actions/workflows/ci.yml/badge.svg)](https://github.com/space-physics/isr-raw/actions/workflows/ci.yml)\n\nUtilities for working with Incoherent Scatter Radar data, especially from Poker Flat AMISR.\n\nWe work with the complex `I + jQ` voltage samples, the lowest level data\navailable from the radar, on a single pulse basis. Depending on the beam\npattern and pulse modulation, the per-beam pulse cadence is perhaps on\nthe 75 milliscond time scale.\n\n```sh\npython -m pip install -e .\n```\n\n## Usage\n\nSeveral types of \"raw\" data exist inside the manually-requsted I+jQ voltage files.\nThey can be loaded with several different functions.\nAll of these examples assume first doing:\n\n```python\nimport isrraw as iu\n```\n\n`P` is a `dict()` with parameters such as altitude range, beam number.\nSee the numerous examples for necessary parameters.\n\n`fn` is the ISR HDF5 .h5 file to process.\n\n* Raw power `hypot(I,Q)`\n\n  ```python\n  snrsamp, azel, isrlla = iu.readpower_samples(fn, P)\n  ```\n\n### Plotting\n\n`singleplot.py` is a main program used to examine raw ISR data. It's\nconfigured via `.ini` files. Some important parameters are:\n\n parameter                         | description\n-----------------------------------|--------------------------------\n scan                              | CFAR detection of turbulent activity (possible association with Alfven waves)\n tlim                              | unless scan=yes, usually you use tlim to only plot over time range of interest (to avoid enormous amount of plots)\n\n## Examples\n\nFrom the Akbari GRL 2012: Anomalous ISR echoes preceding auroral\nbreakup: Evidence for strong Langmuir turbulence\n\u003cdoi:10.1029/2011GL050288\u003e\n\n![Figure 1a Akbari 2012](gfx/Akbari2012_fig1a.png)\n\n![Figure 3a Akbari 2012](gfx/Akbari2012_fig3a.png)\n\n![Figure 3b Akbari 2012](gfx/Akbari2012_fig3b.png)\n\n![Figure 3c Akbari 2012](gfx/Akbari2012_fig3c.png)\n\n## File Types\n\nCurrently, raw ISR data files are *not* currently contained on\n[Madrigal](http://isr.sri.com/madrigal),\nyou will have to email SRI\nstaff to get them manually.\n\nWhen requesting raw AMISR data, please\n[request by experiment name](http://amisr.com/database/61/sched)\nas this is more convenient for\nSRI staff than the date/time.\n\nRaw ISR data files are indexed by date.\nThe four file types indicated by filename suffix are:\n\n* dt0.h5      Ion Line: Alternating Code\n* dt1.h5      Downshifted Plasma line (negative Doppler shift)\n* dt2.h5      Upshifted Plasma line (positive Doppler shift)\n* dt3.h5      Ion Line: Long Pulse (small Doppler )\n\n## Discussion\n\nThe \"ion line\" measurement bandwidth is ~ +/- 100 kHz from the radar\ncenter frequency, and contains the data necessary for volume estimates\nof Electron Density, Ion Temperature, Electron Temperature, and Ion\nVelocity, under certain assumptions for species composition vs.\naltitude.\nSome of the need to make assumptions about atmospheric\ncomposition can be mitigated with combined ion/plasma line inversion,\namong numerous other benefits.\nThe plasma line returns have several MHz\nof bandwidth, but most of the energy is contained in narrower bands\nupshifted and downshifted from the center frequency.\n\nNo one radar waveform is optimal for all conditions, particularly with regard to the spatio-temporal sampling dilemma.\nIncoherent scattering from tiny particles gives exceedingly weak returns, and even with many\nbillions of particles in the scattering volume, it takes well over ten\nthousand radar pulses to build a statistical basis for a usable\nautocorrelation function (ACF).\nThe shape of the ACF is fitted to estimate certain plasma parameters, given assumptions on the particle population that may be violated, causing in some limited sets of cases\neither inaccurate fits or a failure to estimate the parameters.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspace-physics%2Fisr-raw","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspace-physics%2Fisr-raw","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspace-physics%2Fisr-raw/lists"}