Added cutoff_error parameter to enable Automatic Linestrength Cutoff #451
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radis/lbl/base.py
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| cutoff -= 0.0001 | ||
| b = df.S <= cutoff | ||
| error = df.S[b].sum() / df.S.sum() * 100 |
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what about :
1 . we sort (a copy of) the lines by intensity intensity
2. we compute the cumulated intensity on the sorted array [last value is the total sum]
3. we find the index in the sorted array so that cumulated-intensity is ERROR_fraction * the total
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Yes, this seems to be a much better option. I'll work on this.
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Please let me know. It will be important to also add tests.
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Hello @sagarchotalia ,did you try the approach I suggested above ?
Codecov Report
@@ Coverage Diff @@
## develop #451 +/- ##
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- Coverage 77.11% 77.03% -0.09%
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Files 161 161
Lines 18676 18689 +13
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- Hits 14402 14397 -5
- Misses 4274 4292 +18 |
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Also, see the error raised : https://app.travis-ci.com/github/radis/radis/jobs/567095871#L1858 |
Yes, the error was resolved in the next commit. |
Had accidentally changed the docstring in my previous commit; changed it back to raw string
radis/lbl/base.py
Outdated
| cutoff -= 0.0001 | ||
| b = df.S <= cutoff | ||
| error = df.S[b].sum() / df.S.sum() * 100 |
There was a problem hiding this comment.
Hello @sagarchotalia ,did you try the approach I suggested above ?
Description
This PR is to address #268.
Added
cutoff_errorparameter that will allow users to input a cutoff error, which will then be used to keep the internally calculated error below the inputted cutoff error.The way I am doing this currently is, decrementing the
cutoffby0.001(arbitrary value assumed for now, will change it if needed) and then recalculating the bool array 'b' and then calculating the internal error againThe size of the boolean array 'b' shall decrease with each iteration until the internal error is less than the cutoff error.
Further Changes:
I am not sure that the approach I am using now fixes #268. If there is a more efficient way to decrease the internal error, kindly suggest the same in the comments.