Differences
This shows you the differences between two versions of the page.
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talksposters:spsearch [2024/04/29 10:55] lspitler |
talksposters:spsearch [2024/04/29 11:18] (current) lspitler |
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| Example command: | Example command: | ||
| - | ''waterfaller.py %%--%%show-ts %%--%%show-spec %%--%%colour-map='viridis' -d 57 -T 100 -t 5 %%--%%downsamp=8 %%--%%scaleindep MY_DATA.fil'' | + | ''> waterfaller.py %%--%%show-ts %%--%%show-spec %%--%%colour-map='viridis' -d 57 -T 100 -t 5 %%--%%downsamp=8 %%--%%scaleindep MY_DATA.fil'' |
| Parameters to play around with: | Parameters to play around with: | ||
| Line 38: | Line 38: | ||
| Example command: | Example command: | ||
| - | ''rfifind -o mask_file_name -time 1.0 -zapchan N:N MY_DATA.fil'' | + | ''> rfifind -o mask_file_name -time 1.0 -zapchan N:N MY_DATA.fil'' |
| Parameters to play around with: | Parameters to play around with: | ||
| Line 51: | Line 51: | ||
| Original mask: mask_file_name_rfifind.mask | Original mask: mask_file_name_rfifind.mask | ||
| - | ''rfifind -o mask_file_name -nocompute -freqsig 8 -mask mask_file_name_rfifind.mask MY_DATA.fil'' | + | ''> rfifind -o mask_file_name -nocompute -freqsig 8 -mask mask_file_name_rfifind.mask MY_DATA.fil'' |
| === Step 3: generate dedispersed time series === | === Step 3: generate dedispersed time series === | ||
| + | |||
| + | This command assumes the data are for B0355+54 - change the DM range accordingly if using a different test pulsar. | ||
| This command can be used to generate a single dedipsersed time series by setting -numdms 1. Having dedispered time series over a range around the true value is useful for in Step 4. | This command can be used to generate a single dedipsersed time series by setting -numdms 1. Having dedispered time series over a range around the true value is useful for in Step 4. | ||
| - | ''prepsubband -nobary -lodm 52.4 -dmstep 0.5 -numdms 20 -clip 0 -downsamp 1 -mask mask_file_name_rfifind.mask -o my_ts MY_DATA.fil'' | + | ''> prepsubband -nobary -lodm 52.4 -dmstep 0.5 -numdms 20 -clip 0 -downsamp 1 -mask mask_file_name_rfifind.mask -o my_ts MY_DATA.fil'' |
| Variations: | Variations: | ||
| Line 65: | Line 67: | ||
| * Run with clipping to see if this changes the time series | * Run with clipping to see if this changes the time series | ||
| - | Manually inspect the time series with ''exploredat''. | + | Manually inspect the time series: |
| - | + | ||
| + | ''> exploredat my_ts.dat'' | ||
| * Can you see bright pulses? | * Can you see bright pulses? | ||
| * Note the slowly varying | * Note the slowly varying | ||
| * If you've applied an RFIfind mask, what impact does this have on the time series? | * If you've applied an RFIfind mask, what impact does this have on the time series? | ||
| + | |||
| + | === Step 4: generate single pulse candidates === | ||
| + | |||
| + | ''> single_pulse_search.py -m 30 -t 6 -b *.dat'' | ||
| + | |||
| + | Parameters to vary: | ||
| + | * Turn on bad block flagging | ||
| + | * Use different detrend chunksizes | ||
| + | |||
| + | Look at SP diagnostic plot: my_ts_singlepulse.ps | ||
| + | |||
| + | Recreate this plot with a different threshold or time range: | ||
| + | |||
| + | ''> single_pulse_search.py -m 30 -t 8 -s 0 -e 120 -b *.singlepulse'' | ||
| + | |||
| + | === Step 5: machine learning classifier === | ||
| + | |||
| + | To be done... | ||
