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Documentation updates #1085
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Documentation updates #1085
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- update apparent mag notebook by running it with an install of numpy 2.0 to quit the errors in the online generation - update lightcurve notebook so that the url to add-ons shows (pandoc generation changed the parsing) - remove the confusing notebooks for developing scripts to run multiple runs
add in instruction to install add-ons package to compile the docs
address sqlite error when running multiple versions at the same time
documentation updates
documentation updates
documentation updates
documentation updates
doc updates
documentation updates
update docs
add in color notebook so it generates links - start comet activity notebook
add a reminder about the colors demo notebook
Add complete demo for cometary activity
updated cometary activity notebook
make cometary activity notebook
update docs
``sorcha`` or sorcha/Sorcha --> ``Sorcha``
doc updates
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Looks good. I've mainly commented on spelling and grammar. Maybe also put in a comment that LSSTCometActivity assumes an aperture radius of 1".
"id": "c6a7190d", | ||
"metadata": {}, | ||
"source": [ | ||
"The goal of this notebook is to demonstrate the apply cometary activity within `Sorcha`.\n", |
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Something is wrong with this sentence.
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Thanks!
"\n", | ||
"We will use the community tools part of the `Sorcha-addons`(https://github.com/dirac-institute/sorcha-addons) package\n", | ||
"\n", | ||
"The idea is that the user can, in principle, implement their own method for cometary activity, and incorporate them in their simulation. The goal of `Sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom coemtary activity models. " |
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"and incorporate [it] in their simulation."
"custom [cometary] activity models."
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thanks!
"id": "191c5e0f", | ||
"metadata": {}, | ||
"source": [ | ||
"Now we calculate the magnitude of the nuceleus assuming no phase curve model in PPCalculateApparentMagnitudeInFilter." |
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nuceleus --> nucleus
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thanks!
"\n", | ||
"Let's use the LSSTCometActivity class from `sorcha_addons`. We need the following columns in our dataframe:\n", | ||
"\n", | ||
" * ``afrho1\"`` = V-band Afρ value of the comet at 1 au\n", |
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Is this supposed to be formatted as a code block? Regardless, the units on Afρ (cm in this case) should be stated here or somewhere near here.
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done
"outputs": [ | ||
{ | ||
"data": { | ||
"image/png": 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nucelus --> nucleus
"observed to [be] much brighter"
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thanks!
big overview of inputs in particular
remove comment from trailing losses notebook
documentation updates
Fixes #974
Fixes #1065
Fixs #966
Documentation updates to make a complete documentation