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simotonin · 2 years

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Saccharine Deluxe .atn for PS [SFS]

ok here it is, my clay hair action available for the public! took me a while to put this together, but here’s the final result. this was made using Photoshop CS5, so YMMV with older/newer versions. please let me know if there’s any issues with the action file, this is my first time sharing something like this! here’s my hair recolor checklist if anyone wants to take a crack at it to kick off this download :)

before you download, however, you must read the info below in order for this to work properly! a quick visual tutorial is below the cut:

PREREQUISITES:

Photoshop

pastry-box’s editing actions (1/2)

simandy’s hair gradient (you need the gradient labeled ‘10′)

.DDS Plugin

Topaz Clean 3

Topaz DeNoise 5

first, you absolutely need the ‘dirty blonde’ base color from whatever hair you’re recoloring (e.g., miniculesim always uses this for her blonde, whereas spottedonsixam does not)

1. to start, locate the ‘blonde’ .package file for whatever hair you want to recolor, fire it up in SimPE. then use ‘Export’ to create a .png of the hair. save it wherever, then boot up photoshop.

note: depending on your version, you may have to open photoshop via dds64.8bi like I do in order for it to work properly

2. open your .png file, then use ‘Save As’ to save as a .dds file. close the .png, then open the .dds to start recoloring

3a. there’s a [PLAY ALL] action that will take care of the whole recoloring process for you and will create layers of each color for you to individually save. you can either use this one button or go through the whole tutorial below

3b. assuming you’ve already downloaded simandy’s gradients, go to Layer > New Adjustment Layer > Gradient Map, and click ‘Ok’

you should see a square labeled ‘10′ (it doesn’t show up in the pic), this is the one you want...

... and your file should look like this

click ‘Merge Visible’ to merge the layers

4. in the action folder, click [BASE COLOR] to run the base, then click [SHARP & SMOOTH] for photoshop to use topaz clean and denoise. your file should look like this now

note: the base color also doubles for the fam2 gray called ‘milk’, so if you use the shortcut action the [PLAY ALL] action will take care of this, otherwise ignore the action labeled ‘22 // unsweetened milk (gray2)’, that’s there for completionist’s sake

5. now you can start using the numbered color actions. quickest way to go through them all is to save each color one at a time, ctrl+alt+Z to unmerge the colored layers and delete them, then move down to the next number

save your files as .dds, I recommend saving them in folders and naming them something recognizable like ‘fam4 hazelnut’ so you won’t get confused

my setup is like this

and that’s it! for those who know how to make hair packages in bodyshop, this tutorial is done, but for those who don’t...

6. first thing, drop the hair .package labeled ‘black’ into your cc folder, we need to copy from black to get the gray. boot up bodyshop and create a new genetics project. hit ‘Export Selected Textures’

this is the naming system I use for my hairs so I know which one to group up. repeat step 6 for the remaining hair colors of the family you’re using. close bodyshop when you’re finished

7. now boot up cat’s hairbinner. depending on the hair you’re recoloring, there may not be life stages available (like a hair without a toddler stage), so deselect the unavailable ages before you hit ‘Bin’. to link all 4+1 colors together. wait for the hairbinner to bin and close the program after it’s done

note: I deselect ‘make backup files’ but it’s up to you how you do this. my hairs all have gray link to the black for simplicity sake

8. this step, we’re gonna add our recolored .dds files to our binned packages. load SimPE, click ‘New’, then 'Open’ your binned hair file. go to the ‘Texture Image’ tab and right-click ‘Import DDS’ on the hair you’re replacing. locate the new hair color, click ‘Open’. hit ‘Commit’. repeat this step for the other 3 files

9. (this step is optional for those who merge packages, skip if you don’t) now you’re gonna merge these files together. once you’re done with step 8, hit ‘New’ for a new package, then click ‘Add’ for the files you just edited. you can add a tooltip so people can know this is something you made, then hit ‘Save As’ to save your newly merged package

10. to get in the habit, use the Compressorizer to compress your merged/separate package(s), it’ll help cut down the file size. you can now boot bodyshop back up to see the finished product

aaaand you’re done, share your recolored hairs to the world

#s2cc #s2mm #the sims 2 #mydownloads #ok finally it's done #pls pls lmk if anything's wrong with the .atn file so i can fix it right away i'm nervous lol #ts2

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postsofbabel · 5 months

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#posts of babel #screenreader unfriendly

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soufre-de-paris · 1 year

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not me almost telling $FAM2 about the issues i just went through with $FAM1 because it's the exact same fucking situation but thank christ i didn't! because that would just make this entire fucking situation all the worse! haha! oh god!

#paris on paris

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chalisley · 1 year

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15/365

Salah satu hari paling mengecewakan di awal tahun ini. Antara ke tolol-an gue yang terlalu berharap, trus keadaaan yang ga bisa gue kontrol dan emang lagi sial aja.

Kapan ya gue bisa ga sedih. Iya kesel ga sih punya pertanyaan ini mulu. Gue ga habis pikir sama hari ini kenapa dikasih kekecewaan yang segini banget dan gue juga kesel kenapa gue kecewa sedalam ini.

Tapi satu hal yang pasti, gue masih bisa nahan emosi disaat-saat ini. Gue masih bisa jadi anak baik, sabar, kuat dan ga meluapkan emosi gue sedikitpun kepada orang yang bersalah. Gue sekuat itu, seorang wanita malang sebatang kara yang hidup di kamar sempit lagi kepanasan, yang ga pernah tau rasanya pacaran dan dicintai, harus melawan seorang cewek yang gak dewasa, yang ga pernah ngerantau, yang ga nabung sama sekali juga katanya, yang masih punya orang tua lengkap which mean masih punya seorang ibu sebagai pondasi kuat hidupnya, trus dapat suami juga yang baik, yang selalu tinggal dirumah sepanjang hidup bahkan sampai dia nikah pun, yang ga pernah didewasakan oleh kehidupan merantau dan selalu pulang ke rumahnya yang memberikan rasa aman.

Anjir.

Sebenarnya gue gak mau memupuk kebencian kaya gini. Gue yakin ini dominan pengaruh setan juga wkwk. Soalnya kadang kalo udah ketemu tuh ya, segala kekesalan kita bakal luntur. Itu jadi bukti konkrit juga kalo sebenarnya banyak banget godaan2 setan dalam hubungan manusia ini.

Tapi mari kita lanjutkan dulu kekesalan gue diatas.

Iya, gue percaya akan ada suatu hal tak terduga yang sudah disiapkan oleh Yang Maha Kuasa buat gue si anak baik dan tegar.

Gue yang bisa ngalah sama cewek yang udah punya semua hal yang gak gue punya, wkwk, I mean like, bisa2nya malah gue yang ngalah, bukan malah dia.

Ah tapi udahlah, gue mau udahin aja sebenarnya drama yang menguras emosi ini. Gue percaya tomorrow will be better. Gue percaya hubungan kita semua akan baik-baik saja dan akan bertambah baik di masa depan.

Sekarang sih, gue lagi pengen main aman dan main pinter.

Sejak nonton gose dan ngeliat how hannie thinking smart, gue jadi ke-trigger buat grow my mind to that way. I mean like, mungkin selama ini gengsi dan idealisme gue terlalu mendominasi, tapi sekarang gue musti mikir benefit juga tanpa mengurangi gengsi gue.

Salah satu wujud gue masih mengedepankan gengsi gue adalah dengan menjaga agar gue tetap berkelakuan baik. Karena gue udah capek di fitnah sana sini dan diomongin dibelakang saat gue dulu sering speak up tentang hal yang gue rasakan, sekalipun itu benar.

Nah tapi disini, gue mau sedikit ngalah, playing save, dan still gain plus.

Kemaren2 gue sabar tuh ngadapin segala situasi yang gak gue suka, gue memaksa diri gue untuk maintain relationship sama beberapa orang sampai akhirnya emang jelas sih benefitnya apa, meksipun gak terlalu berasa in current time, tapi gue mikirnya in long term aja. Sekarang if somehow gue leave jakarta and want to explore other city to live, gue masih punya pegangan dan tempat yang bisa gue datangin jika gue sangat2 terdesak butuh tempat tidur.

Gitu sih. Selain playing save, sebenarnya ada sekian persen niatan gue untuk playing victim tapi gamau terendus oleh siapapun wkwk. Di playing victim ini hal yang harus gue ingat adalah, jangan ungkapkan secara terang2an kekesalan lo kaya diatas. Cukup limpahkan ke jurnal ini aja atau maksimal ke teman dekat lo. Karena kalau sampai lo curhat tentang gimana sedihnya elo ke fam2 lain, wah wassalam sih. Sekali bocor, akan bocor selamanya wkwk. Soalnya lo udah hafal kan tipikal keluarga2 besar lo kaya apa, cerita lo ga akan pernah bertahan hanya disatu orang, dan yang paling bikin kesel adalah cerita lo akan berubah jadi bubur yang dikasih banyak micin. Intinya... ya gitu, paham lah ya.

Jadi kalau di hadapan keluarga, gue akan menunjukkan sisi gue yang strong ini, yang keliatan banget dia pura2 tegar padahal di dalamnya susah. Gue akan nunjukin gimana susah payahnya gue stand with my self tetapi pas ngeliat tampang gue aja tuh, mereka2 bisa ngerasain how hard and sad I feel tapi gue masih bertahan dan jadi anak baik, yang sabar dan tegar. Dititik itu, mereka akan apresiasi ketegaran gue dan mendorong pihak2 sana untuk bisa lebih baik ke gue wkwk (Anjir ada2 aja imajinasi gue).

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wandlores · 5 years

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female awesome meme: ladies who deserve better (5/10) - leta lestrange (crimes of grindelwald)

“you're too good, newt. you never met a monster you couldn't love.”

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laezelofkliir · 3 years

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female awesome meme: 3/5 lgbtq+ ladies ♡ korra (the legend of korra)

I'm the old Avatar and my era is not over yet.

#korra #korraedit #tlokedit #lokedit #airbendersource #tlok #*#*gifs #*fam2 #she means so much to me :)#literally the first bi character id ever seen in tv when i was 15 #korra deserves all the love and support in the world

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peppermint-ginger · 3 years

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Hey its the anon that asked about deleting a swatch, thanks so much for the really detailed tutorial - its super easy to do! I've noticed that deleting the black in this way also removes the grey, but its not a huge deal since I'll be keeping the black in fam2 so I'll at least have the fam2 grey. | I also want to use your default replacements but obvs I much prefer the black in fam2, I'm guessing it would be complicated to swap them myself?

Hi! Sorry for late reply.

Switching pre-existing textures is not very complicated, actually. Poppet has a tutorial how to retexture hair with .dds method here. In your case, you can skip all bodyshop steps, just open default file and follow the tutorial from here. The tricky part is editing color. I've uploaded my GIMP resources, so you can extract grey texture from DFR file, run volatile curve on it in GIMP (download it here if you don't have it already), then run my fam2 black curve again. Then import it in SimPE.

I hope it doesn't sound too convoluted.

Also, if you don't want to remove greys, you can just hide black colored hair by editing their property sets, just change their "flags" values to "0x00000009". It will hide them in CAS.

#ask #anon tag #non-sims #gimp tutorial #tutorial #technical

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clementvidalroussel-blog · 4 years

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FAM2

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sharethings · 4 years

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Fam2

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deept268 · 4 years

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Oo na MaBer... Kunin ko mga pusa after ng lockdown... Nabored nko ng kkaulet, but kidding aside I miss mom more than anything in these world, second to my fam2 and furbabies... (at Maricaban, Pasay, Philippines) https://www.instagram.com/p/B-4TAogFbIz4vyY3crS5a097Wtj8oeT8NxSy2E0/?igshid=j58zgb79mm1g

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allstarlistings · 4 years

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HEY SIS ORDERS $75 OR MORE SHIP FOR #FREE!!! https://glamherous.com/product-category/new-arrivals/rep/bconverse #January #like #natural #new #repost #dress #winter #shop #datenight #instastyle #lit #drip #follows #blackowned #fashion #weekdays #holidays #loveit #whattowear (at Edge at Lauberge) https://www.instagram.com/p/B72daC-FAM2/?igshid=v03rzpeglkbf

#free #january #like #natural #new #repost #dress #winter #shop #datenight #instastyle #lit #drip #follows #blackowned #fashion #weekdays #holidays #loveit #whattowear

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kpenlearnspython-blog · 5 years

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Chi Square Test

Primary Research Question: Is there an association of family history of alcoholism to the rate (# drinks/week) of alcohol consumption for people who have never exhibited alcohol abuse or dependence?

Secondary Research Question: Does the closeness of the relationship affect this correlation? (Parent vs more distant relation)

2/11/19 edits: I realized I misunderstood the Bonferroni adjustment upon posting yesterday. Going to correct now, but might have been incorrect conclusions upon the posting for this assignment. Using strikethrough to denote previously wrong assessment.

I tried to blockquote all Python. Written code is italicized. Printed code is not.

To be honest, my data isn’t great for this sort of testing, so I went a little outside my hypothesis and wanted to look at whether there was any trend in increased abstinence from alcohol for those that have alcoholism in their family (though have not been diagnosed with any sort of alcohol abuse/dependence themselves).

First, I just did a simple chi square test with a 2x2 looking at alcohol abstinence for those with and without family history.

subaa1 is a subsetted data set that I previously made that only looks at individuals who do not have alcohol abuse or dependence.

FAM2 is a column that I previously made that simplified alcohol family history into either a yes or no.

subaa1["S2AQ1"]=subaa1["S2AQ1"].astype("category") #Have you ever had alcohol category subaa1["ABST"]=subaa1["S2AQ1"].cat.rename_categories(["Drinks", "Abstains"]) ct1=pd.crosstab(subaa1["ABST"], subaa1["FAM2"]) #categorical variables print(ct1) #get counts colsum=ct1.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct=ct1/colsum print(colpct)

print("chi-square value, p value, expected counts") cs1=sst.chi2_contingency(ct1) print(cs1)

FAM2 Family History No Family History ABST Abstains 2380 5886 Drinks 9494 13490 FAM2 Family History No Family History ABST Abstains 0.200438 0.303778 Drinks 0.799562 0.696222 chi-square value, p value, expected counts (403.6040870866473, 9.04438165422264e-90, 1, array([[ 3140.815488, 5125.184512], [ 8733.184512, 14250.815488]]))

The chi-square value (403.6) is much greater than 3.84, and the p-value (9.0e-90) is much less that 0.05, so I can reject the null hypothesis that there is no correlation between family history of alcoholism and whether a person drinks or abstains. From the table, it appears that those with a family history of alcoholism are more likely to drink than expected (79.9% obtained vs 73.5% expected).

To do a post hoc test, I decided to look at my categories for family history with alcoholism (1 parent, 2 parents, 1 extended relative, >1 extended relative, 1 parent+extended relatives, 2 parents + extended relative, no alcoholic family known) in relation to drinking vs abstaining.

ct2=pd.crosstab(subaa1["ABST"], subaa1["AAFAM2"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) #7 degrees of freedom

print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2) print("Expected chi-square for 7 degrees of freedom is 14.07.") print("Corrected p-value for 20 comparisons") 0.05/20

AAFAM2 1 Par 2 Par 1 ExtRel >1 ExtRel 1 Par, ExtRel 2 Par, ExtRel \ ABST Abstains 434 36 896 467 479 68 Drinks 1437 132 3285 1953 2345 342 AAFAM2 None known ABST Abstains 5886 Drinks 13490 AAFAM2 1 Par 2 Par 1 ExtRel >1 ExtRel 1 Par, ExtRel 2 Par, ExtRel \ ABST Abstains 0.231962 0.214286 0.214303 0.192975 0.169618 0.165854 Drinks 0.768038 0.785714 0.785697 0.807025 0.830382 0.834146 AAFAM2 None known ABST Abstains 0.303778 Drinks 0.696222 chi-square value, p value, expected counts (434.99112479242615, 8.331856012499931e-91, 6, array([[ 494.901952, 44.438016, 1105.924672, 640.11904 , 746.981888, 108.44992 , 5125.184512], [ 1376.098048, 123.561984, 3075.075328, 1779.88096 , 2077.018112, 301.55008 , 14250.815488]])) Expected chi-square for 7 degrees of freedom is 14.07. Corrected p-value for 20 comparisons Out[39]: 0.0025

Considering my expected chi-square (according to google) is 14.07, and I got 434.99, I can safely reject the null hypothesis that there is no correlation between specific family with alcoholism and alcohol abstinence.

I further did post-hoc analysis to find the groups with significant differences. Am looking for the Bonferroni adjusted p-value of 0.0025.

This is about 20 comparisons, so I will summarize the ones with significant differences here. For those numbered below, I can reject the null hypothesis that there is no difference in alcohol abstinence for their family history.

1 Parent vs. >1 Extended Relative

1 Parent vs. 1 Parent + Extended Relatives

1 Parent vs 2 Parents + Extended Relatives

1 Parent vs None known

2 Parents vs. None known

1 Extended Relative vs. >1 Extended Relative

1 Extended Relative vs. 1 Parent + Extended Relatives

1 Extended Relative vs. 2 Parents + Extended Relatives

1 Extended Relative vs. None known

1 Parent + Extended Relatives vs. >1 Extended Relative

>1 Extended Relative vs. None known

1 Parent + Extended Relatives vs. None known

2 Parents + Extended Relatives vs. None known

FAMCOMPv1 1 Par 2 Par ABST Abstains 434 36 Drinks 1437 132 FAMCOMPv1 1 Par 2 Par ABST Abstains 0.231962 0.214286 Drinks 0.768038 0.785714 chi-square value, p value, expected counts (0.18103193164993958, 0.6704879106105417, 1, array([[ 431.27513487, 38.72486513], [1439.72486513, 129.27513487]])) FAMCOMPv2 1 ExtRel 1 Par ABST Abstains 896 434 Drinks 3285 1437 FAMCOMPv2 1 ExtRel 1 Par ABST Abstains 0.214303 0.231962 Drinks 0.785697 0.768038 chi-square value, p value, expected counts (2.2488225420879204, 0.13371611345920698, 1, array([[ 918.82518176, 411.17481824], [3262.17481824, 1459.82518176]])) FAMCOMPv3 1 Par >1 ExtRel ABST Abstains 434 467 Drinks 1437 1953 FAMCOMPv3 1 Par >1 ExtRel ABST Abstains 0.231962 0.192975 Drinks 0.768038 0.807025 chi-square value, p value, expected counts (9.434649397276742, 0.002129237910827844, 1, array([[ 392.86203682, 508.13796318], [1478.13796318, 1911.86203682]])) FAMCOMPv4 1 Par 1 Par, ExtRel ABST Abstains 434 479 Drinks 1437 2345 FAMCOMPv4 1 Par 1 Par, ExtRel ABST Abstains 0.231962 0.169618 Drinks 0.768038 0.830382 chi-square value, p value, expected counts (27.52696632852945, 1.5491937328656087e-07, 1, array([[ 363.83876464, 549.16123536], [1507.16123536, 2274.83876464]])) FAMCOMPv5 1 Par 2 Par, ExtRel ABST Abstains 434 68 Drinks 1437 342 FAMCOMPv5 1 Par 2 Par, ExtRel ABST Abstains 0.231962 0.165854 Drinks 0.768038 0.834146 chi-square value, p value, expected counts (8.181860997484213, 0.004231133032437819, 1, array([[ 411.76764577, 90.23235423], [1459.23235423, 319.76764577]])) FAMCOMPv6 1 Par None known ABST Abstains 434 5886 Drinks 1437 13490 FAMCOMPv6 1 Par None known ABST Abstains 0.231962 0.303778 Drinks 0.768038 0.696222 chi-square value, p value, expected counts (41.76775439560775, 1.027846499574022e-10, 1, array([[ 556.53598155, 5763.46401845], [ 1314.46401845, 13612.53598155]])) FAMCOMPv7 2 Par None known ABST Abstains 36 5886 Drinks 132 13490 FAMCOMPv7 2 Par None known ABST Abstains 0.214286 0.303778 Drinks 0.785714 0.696222 chi-square value, p value, expected counts (5.899442601003834, 0.015145677174912012, 1, array([[ 50.90544413, 5871.09455587], [ 117.09455587, 13504.90544413]])) FAMCOMPv8 2 Par 2 Par, ExtRel ABST Abstains 36 68 Drinks 132 342 FAMCOMPv8 2 Par 2 Par, ExtRel ABST Abstains 0.214286 0.165854 Drinks 0.785714 0.834146 chi-square value, p value, expected counts (1.5804032985363066, 0.20870261116515335, 1, array([[ 30.2283737, 73.7716263], [137.7716263, 336.2283737]])) FAMCOMPv9 1 Par, ExtRel 2 Par ABST Abstains 479 36 Drinks 2345 132 FAMCOMPv9 1 Par, ExtRel 2 Par ABST Abstains 0.169618 0.214286 Drinks 0.830382 0.785714 chi-square value, p value, expected counts (1.9178315136173767, 0.16609591015709457, 1, array([[ 486.0828877, 28.9171123], [2337.9171123, 139.0828877]])) FAMCOMPv10 2 Par >1 ExtRel ABST Abstains 36 467 Drinks 132 1953 FAMCOMPv10 2 Par >1 ExtRel ABST Abstains 0.214286 0.192975 Drinks 0.785714 0.807025 chi-square value, p value, expected counts (0.32968603816180103, 0.5658440186807167, 1, array([[ 32.65224111, 470.34775889], [ 135.34775889, 1949.65224111]])) FAMCOMPv11 1 ExtRel 2 Par ABST Abstains 896 36 Drinks 3285 132 FAMCOMPv11 1 ExtRel 2 Par ABST Abstains 0.214303 0.214286 Drinks 0.785697 0.785714 chi-square value, p value, expected counts (0.009091829850638621, 0.9240359656620418, 1, array([[ 895.99724074, 36.00275926], [3285.00275926, 131.99724074]])) FAMCOMPv12 1 ExtRel >1 ExtRel ABST Abstains 896 467 Drinks 3285 1953 FAMCOMPv12 1 ExtRel >1 ExtRel ABST Abstains 0.214303 0.192975 Drinks 0.785697 0.807025 chi-square value, p value, expected counts (4.126102965534052, 0.04222648604697947, 1, array([[ 863.30904408, 499.69095592], [3317.69095592, 1920.30904408]])) FAMCOMPv13 1 ExtRel 1 Par, ExtRel ABST Abstains 896 479 Drinks 3285 2345 FAMCOMPv13 1 ExtRel 1 Par, ExtRel ABST Abstains 0.214303 0.169618 Drinks 0.785697 0.830382 chi-square value, p value, expected counts (21.051577696848984, 4.470845718926547e-06, 1, array([[ 820.68165596, 554.31834404], [3360.31834404, 2269.68165596]])) FAMCOMPv14 1 ExtRel 2 Par, ExtRel ABST Abstains 896 68 Drinks 3285 342 FAMCOMPv14 1 ExtRel 2 Par, ExtRel ABST Abstains 0.214303 0.165854 Drinks 0.785697 0.834146 chi-square value, p value, expected counts (4.995438947932991, 0.02541420669226183, 1, array([[ 877.90982357, 86.09017643], [3303.09017643, 323.90982357]])) FAMCOMPv15 1 ExtRel None known ABST Abstains 896 5886 Drinks 3285 13490 FAMCOMPv15 1 ExtRel None known ABST Abstains 0.214303 0.303778 Drinks 0.785697 0.696222 chi-square value, p value, expected counts (133.85527554859885, 5.876686334055551e-31, 1, array([[ 1203.69919769, 5578.30080231], [ 2977.30080231, 13797.69919769]])) FAMCOMPv15 1 Par, ExtRel >1 ExtRel ABST Abstains 479 467 Drinks 2345 1953 FAMCOMPv15 1 Par, ExtRel >1 ExtRel ABST Abstains 0.169618 0.192975 Drinks 0.830382 0.807025 chi-square value, p value, expected counts (4.652191417597543, 0.03101393014512997, 1, array([[ 509.44012204, 436.55987796], [2314.55987796, 1983.44012204]])) FAMCOMPv16 2 Par, ExtRel >1 ExtRel ABST Abstains 68 467 Drinks 342 1953 FAMCOMPv16 2 Par, ExtRel >1 ExtRel ABST Abstains 0.165854 0.192975 Drinks 0.834146 0.807025 chi-square value, p value, expected counts (1.5099437664564455, 0.2191476673124011, 1, array([[ 77.50883392, 457.49116608], [ 332.49116608, 1962.50883392]])) FAMCOMPv17 >1 ExtRel None known ABST Abstains 467 5886 Drinks 1953 13490 FAMCOMPv17 >1 ExtRel None known ABST Abstains 0.192975 0.303778 Drinks 0.807025 0.696222 chi-square value, p value, expected counts (127.35685258994046, 1.5520087618161547e-29, 1, array([[ 705.37071022, 5647.62928978], [ 1714.62928978, 13728.37071022]])) FAMCOMPv18 1 Par, ExtRel None known ABST Abstains 479 5886 Drinks 2345 13490 FAMCOMPv18 1 Par, ExtRel None known ABST Abstains 0.169618 0.303778 Drinks 0.830382 0.696222 chi-square value, p value, expected counts (216.27137431045233, 5.884680839320336e-49, 1, array([[ 809.67387387, 5555.32612613], [ 2014.32612613, 13820.67387387]])) FAMCOMPv19 1 Par, ExtRel 2 Par, ExtRel ABST Abstains 479 68 Drinks 2345 342 FAMCOMPv19 1 Par, ExtRel 2 Par, ExtRel ABST Abstains 0.169618 0.165854 Drinks 0.830382 0.834146 chi-square value, p value, expected counts (0.014277578109831067, 0.9048880969104677, 1, array([[ 477.6524428, 69.3475572], [2346.3475572, 340.6524428]])) FAMCOMPv20 2 Par, ExtRel None known ABST Abstains 68 5886 Drinks 342 13490 FAMCOMPv20 2 Par, ExtRel None known ABST Abstains 0.165854 0.303778 Drinks 0.834146 0.696222 chi-square value, p value, expected counts (35.65456040933068, 2.355957096167399e-09, 1, array([[ 123.37713535, 5830.62286465], [ 286.62286465, 13545.37713535]]))

Full code below if you click “Read More”. It’s a lot and it’s repetitive.

recode2={"1 Par":"1 Par", "2 Par":"2 Par"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv1']=subaa1['AAFAM2'].map(recode2) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv1"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode3={"1 Par":"1 Par", "1 ExtRel":"1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv2']=subaa1['AAFAM2'].map(recode3) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv2"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode4={"1 Par":"1 Par", ">1 ExtRel":">1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv3']=subaa1['AAFAM2'].map(recode4) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv3"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode5={"1 Par":"1 Par", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv4']=subaa1['AAFAM2'].map(recode5) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv4"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode6={"1 Par":"1 Par", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv5']=subaa1['AAFAM2'].map(recode6) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv5"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode7={"1 Par":"1 Par", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv6']=subaa1['AAFAM2'].map(recode7) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv6"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode8={"2 Par":"2 Par", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv7']=subaa1['AAFAM2'].map(recode8) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv7"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode9={"2 Par":"2 Par", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv8']=subaa1['AAFAM2'].map(recode9) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv8"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode10={"2 Par":"2 Par", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv9']=subaa1['AAFAM2'].map(recode10) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv9"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode11={"2 Par":"2 Par", ">1 ExtRel":">1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv10']=subaa1['AAFAM2'].map(recode11) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv10"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode12={"2 Par":"2 Par", "1 ExtRel":"1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv11']=subaa1['AAFAM2'].map(recode12) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv11"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode13={"1 ExtRel":"1 ExtRel", ">1 ExtRel":">1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv12']=subaa1['AAFAM2'].map(recode13) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv12"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode14={"1 ExtRel":"1 ExtRel", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv13']=subaa1['AAFAM2'].map(recode14) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv13"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode15={"1 ExtRel":"1 ExtRel", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv14']=subaa1['AAFAM2'].map(recode15) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv14"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode16={"1 ExtRel":"1 ExtRel", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv15']=subaa1['AAFAM2'].map(recode16) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv15"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode16={">1 ExtRel":">1 ExtRel", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv15']=subaa1['AAFAM2'].map(recode16) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv15"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode17={">1 ExtRel":">1 ExtRel", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv16']=subaa1['AAFAM2'].map(recode17) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv16"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode18={">1 ExtRel":">1 ExtRel", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv17']=subaa1['AAFAM2'].map(recode18) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv17"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode19={"1 Par, ExtRel":"1 Par, ExtRel", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv18']=subaa1['AAFAM2'].map(recode19) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv18"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode20={"1 Par, ExtRel":"1 Par, ExtRel", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv19']=subaa1['AAFAM2'].map(recode20) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv19"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode21={"None known":"None known", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv20']=subaa1['AAFAM2'].map(recode21) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv20"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

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chariflare · 7 years

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media update

dante & ari: actually read this in march but didn’t make a post about it? it was really different to anything i’ve ever read. also difficult bc i’m not american, not a gay man, very white, etc but my horizons feel.... expanded. you should read it

p&p: i loved it. it’s basically a otome/shojo romcom with the stoic proud love interest who becomes more moe, which just happens to have been written & set in 1700s england? interesting as a historical piece & the other chars are all great (EXTREME VICE) but i don’t think you should bother unless you’re into the rom premise

kh: decided to keep going on proud mode. i hate how in wonderland all the rooms are boxes? what’s the deal?

eccentric fam2: it’s good. surprise i like the top hat guy

#pfssws #this boat runs on happy faces.............

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5axismachiningchina · 7 years

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