#Min1

[liroh]

@Liroh

[2802sen]

drdt x dr lapse arei, charles, eden, hu, j and veronika

ace should've been there too but since his talent is the same i decided to not change anything in his design... sorry.

These six, Ace, Teruko, Whit, and Min1 are participants of the 50th Killing Game by XF-ture Tech (Preservation Project).

1Min is one of the two survivors of the 49th Killing Game. She killed the Ultimate Knight and got away with it. However, someone really wanted her to go through this nightmare again.

writing about all of them in one post is too long so i wont do it... but if you want to ask something about this au my askbox is always open, i'll try to answer as quickly as i can

yes, veronika's pose IS a matpat reference (i've never watched a single matpat video in my life)

[peppermintxxx]

You horny level now?

Min1 max10

Honestly pretty comfy right now so the horny level is not that high 😇

[anaguel-bianca-34x]

Cuántos ships y oc tienes?

Aver mejor pongo los nombres para no olvidarme: brisa x Nat, tausi x Pao , Bianca x Paulo, Abby x Eddy, Sarai x mark, mei x julicia, jackson x alma, sara x nicolas, Valentino x akane, lsaac x lily, Freya x Aiden, Annie x Scarlett, Louis x Scarlett x Mila, Ximena x doku,Tábatha x asher , Amelia x Van (algo así se llama), akari x Paul, Yukitox Violet, Ana x jake, Daniela x Jonathan, emily x may, Diego x Andre, Lunaxy, Paula x Leila, lucia x keil, zoe x juny, Isabella x v3x, stefan x İzzy, Youssef x Olivia, Min x Harley, Robert x connor Fabricio x Berenice

Los de rosa aún no son canon Y aqui dejo a los creadores de los ocs :Nat,tausi, bianca @/winlik300 Eddy, Mark, Mei, Jackson, Nicolas, akane, Isaac @/totty-00 Aiden @/felixOwO Doku,Tábatha @/11n-min1 van @/star-dust824 Paul,Yukito @/ayase-yukina12345 Jake @/corderitonegrouwu Jonathan, emily, Diego @/fantasma027 y, Leila,keil, juny @/corrupted-and333 V3x,Stefan @/vampire-bun-bun Youssef @/loststuff min @/the-liminalwitch Connor @/lachingaderita-OwO fabricio @/puffystuff

Y sobre los nombres de mis ocs lo puedes encontrar fácil en mi descripción (aunque aún no lo e actualizado con mis otros ocs quq)

[golmixl]

Аукцион 2 адопта. нач500р. мин60р. авто2000р. Каждый отдельно. Auction 2 adopt. start6$. min1$. auto24$ each separately.

[music-theory-blog101]

Let's apply this idea to c major.  

C Major/Ionian = C-D-E-F-G-A-B-C 

 D Dorian = D-E-F-G-A-B-C-E 

E Phrygian = E-F-G-A-B-C-D-E 

F Lydian= F-G-A-B-C-D-E-F 

G Mixolydian= G-A-B-C-D-E-F-G 

A Aeolian = A-B-C-D-E-F-G 

 B Locrian = B-C-D-E-F-G-A-B 

Thankfully the chord pattern for each works the same way.  

Ionian (Major) 

I: C maj - II: D min - III: E min - IV: F Maj - V: G Maj - VI: A min - VII: B dim 

Dorian 

I: D min - II: E min - III: F maj-IV: G maj - V: A min - VI: B dim - VII: C maj1 

Phrygian 

I: E min - II: F maj - III: G maj - IV:  A min - V: B dim - VI: C maj - VII: D min1 

Lydian 

I: F maj - II: G maj - III: A min - IV: B dim - V: C maj - VI: D min - VII: E min1 

Mixolydian 

I: G maj - II: A min - III: B dim - IV: C maj - V: D min - VI: E min - VII: F maj1 

Aeolian (Natural minor) 

I: A min - II: B dim - III: C maj - IV: D min - V: E min - VI: F maj - VII: G maj1  

Locrian 

I: B dim - II: C maj - III: D min - IV: E min - V: F maj - VI: G maj - VII: A min -  

[fleshlightdoll]

Your horny level now? Min1 max10

about a 5

my horniness comes in waves (pun not intended)

[cuteh0rngirly]

You how horny level now?

Min1 max10

Max 10 for sure always horny heh

[datenanalystxyz]

week_4

here Basic scatter plot for Co2 emissions and breastcanserper100th.

this plot is over all data without subset.. we can see an outlier in CO2 emissions

outlier is with red colour marked below in picture.

the same Plot with regression line

I want to filterout this outlier to see the other measuring points more clearly.Because of that, I create a subset of data

scatter plot with data from sub2 dataframe

I create a new subset (sub4) to see the range until CO2 emission value 15.e9

scatterplot for data sub4 (co2 emissions <15e9) and breastcanserper100 is below.

we can see a positiv but weaker relationsheep.

distribution is very big

I create co2emissionsgroup in 10 Levels..

class 10 is the level for NaNs in rows.

bivariate bar graph and plot bar graph to see

I changed x Axis Labeling

the evaluation above is done with all datarows(without subsets)

if we are interested in specific areas, we need to filter and define data as a subset.

We can see a corolation between beastcanser and CO2 emissions.

other way1

we have to change labeling of x axis..

We can see in 9 groups of CO2 emissions , mean Value of breastcanser per 10000 women

other way2

We can see in 4 quartilies groups of CO2 emissions , mean Value of breastcanser per 10000 women

Corrolation shows, that with increased CO2 emissions, the number of women who get breast cancer also increases ...

print ('co2emissions') print ('---------------------') print ('mode') mode1 = data['co2emissions'].mode() print (mode1)

print ('mean') mean1 = data['co2emissions'].mean() print (mean1)

print ('std') std1 = data['co2emissions'].std() print (std1)

print ('min') min1 = data['co2emissions'].min() print (min1)

print ('max') max1 = data['co2emissions'].max() print (max1)

print ('median') median1 = data['co2emissions'].median() print (median1)

print ('breastcancerper100th') print ('---------------------')

print ('mode') mode2 = data['breastcancerper100th'].mode() print (mode2)

print ('mean') mean2 = data['breastcancerper100th'].mean() print (mean2)

print ('std') std2 = data['breastcancerper100th'].std() print (std2)

print ('min') min2 = data['breastcancerper100th'].min() print (min2)

print ('max') max2 = data['breastcancerper100th'].max() print (max2)

print ('median') median2 = data['breastcancerper100th'].median() print (median2)

[adooooora]

Tametsi Effect

I see them in my sleep. Patterns, Mines, One-Two-Ones, Two out of Threes, Lines connecting squares indicating that YES, there is a EXACTLY one across all these fields. The patterns have texture, sometimes they are so familiar that even the logical connections of connections have become routine. Today I found a series of wild jumps in a corner and the whole mess of entanglements fell down into a singular, solid state of free and taken space.

Tametsi is a minesweeper game of 100 Puzzles and 60 bonus puzzles. It came out in 2017, and costs 2.35€ on Steam. Its also one of the greatest games I have ever played. I am not kidding.

The puzzles of Tametsi are basicaly just Minesweeper. Find the mines, mark them with a right click, find the safe spots and left click to reveal more numbers. The numbers say how many mines are next to them. There are a few different variations of shapes the board can be made out of, although mostly its just squares or hexagons. Oh and one more thing - these levels are all made by hand, crafted up to the smallest details

---by a mad architect who is forcing me to look at all possible futures all at once.---

There is never a point in Tametsi where you have to guess. Let me say that again - THERE IS ALWAYS EXACTLY ENOUGH INFORMATION YOU CAN SEE TO FIND OUT WHERE THE NEXT MINE IS AND IS NOT. no matter how obscure that information may be.

Tametsi teached me tricks. The most basic one I quickly had to put to constant use - marking tiles as connected by having an exact number of mines. If the mine is on the right, it can't be on the left, and vice verca.

---Each possibility nessecitates others to be false, in order to be true. For now, the cat is both dead and alive---

In order to compress information, Tametsi boasts an innovative feature: Painting Mode! Using a wide variety of colors and brush sizes you can make your screen look like you are trying to solve the great murder of 1978! My most simple tool is a simple connecting line ---one mine--- and if im fancy the double line ===two mines=== And soon after, I can start to combine the lines! A shorter line that exactly matches a longer one means all those extra tiles can't be mines! A single three long right next to a double three long means one mine here, and no mine there.

____ When one line meets a number in part, there can be only a maximum of one mine there, but also maybe none at all____ Max 1

But Max 1 on a 3 means Min2 on the rest of the spaces and oh look there is another 3 right next to it andthenmin1thereandthatgivesmemax1herebecauseofthe2ANDOHMYGODTHERENEEDSTOBEATLEAST1HERETHATMEANS

Min1 & Max1 => line Line & 2 => line Line & 3 => double Double & 3 => line

Uhh this line is shorter now!

What the fuck am I doing here?

Recently I have been obsessed with LOGIC. In first-order LOGIC there are TWO STATES a preposition can be in: MINE and NOT A MINE. These prepositions are interlinked/interlinked through logical connecters such as AND, OR, EXISTS, FOR ALL, TWO, THREE, LINE, DOUBLE LINE, MIN, MAX, FIFTEEN GREENS, FOUR IN COLUMN SIX

---Consider all possibilities to be in a possibility space---If either A or B or C have to be true and they all share that THERE IS A MINE ON THE OTHER FUCKING SIDE OF THE PUZZLE YOU ASSHOLE GENIUS TORTURE DEMON I HAVE SAT HERE FOR 20 MINUTES NOT ONE MINE TO MY NAME---Then all possibilities where there is no mine there can be discarded. The cat is dead.

Have fun!

[learner0010]

Number of Cigarettes per month vs count is the univariate graph and the proportion of Nicotine dependents vs Packs per month is the Bi Variate graph.

Univariate graph - It displays withonly one variable. Here it displays how the young adults smoke cigarttes per month. we can see that the Max cigarettes smoked is 400 per month and it can be as less as 10 for some months which indicate there is a huge standard deviation

Bi Variate graph - It displays Nicotine dependents vs Packs per month. we can see that Max packs of 147 per month is used by 75% people. Also less smokers use 5 packs which amount to 25% people. The trend we can see is that the high smokers are in the Maximum range of 75%.

Below is the programming code used.

import pandas import numpy import seaborn import matplotlib.pyplot as plt

any additional libraries would be imported here

Set PANDAS to show all columns in DataFrame

pandas.set_option('display.max_columns', None)

Set PANDAS to show all rows in DataFrame

pandas.set_option('display.max_rows', None)

bug fix for display formats to avoid run time errors

pandas.set_option('display.float_format', lambda x:'%f'%x)

data = pandas.read_csv('nesarc_pds.csv', low_memory=False)

print(len(data)) #number of observations (rows) print(len(data.columns)) # number of variables (columns)

checking the format of your variables

data['ETHRACE2A'].dtype

setting variables you will be working with to numeric (updated)

data['TAB12MDX'] = pandas.to_numeric(data['TAB12MDX']) data['CHECK321'] = pandas.to_numeric(data['CHECK321']) data['S3AQ3B1'] = pandas.to_numeric(data['S3AQ3B1']) data['S3AQ3C1'] = pandas.to_numeric(data['S3AQ3C1']) data['AGE'] = pandas.to_numeric(data['AGE'])

subset data to young adults age 18 to 25 who have smoked in the past 12 months

sub1=data[(data['AGE']>=18) & (data['AGE']<=25) & (data['CHECK321']==1)]

make a copy of my new subsetted data

sub2 = sub1.copy()

SETTING MISSING DATA

recode missing values to python missing (NaN)

sub2['S3AQ3B1']=sub2['S3AQ3B1'].replace(9, numpy.nan)

recode missing values to python missing (NaN)

sub2['S3AQ3C1']=sub2['S3AQ3C1'].replace(99, numpy.nan)

recode1 = {1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1} sub2['USFREQ']= sub2['S3AQ3B1'].map(recode1)

recode2 = {1: 30, 2: 22, 3: 14, 4: 5, 5: 2.5, 6: 1} sub2['USFREQMO']= sub2['S3AQ3B1'].map(recode2)

A secondary variable multiplying the number of days smoked/month and the approx number of cig smoked/day

sub2['NUMCIGMO_EST']=sub2['USFREQMO'] * sub2['S3AQ3C1']

univariate bar graph for categorical variables

First hange format from numeric to categorical

sub2["TAB12MDX"] = sub2["TAB12MDX"].astype('category')

seaborn.countplot(x="TAB12MDX", data=sub2) plt.xlabel('Nicotine Dependence past 12 months') plt.title('Nicotine Dependence in the Past 12 Months Among Young Adult Smokers in the NESARC Study')

Univariate histogram for quantitative variable:

seaborn.distplot(sub2["NUMCIGMO_EST"].dropna(), kde=False); plt.xlabel('Number of Cigarettes per Month') plt.title('Estimated Number of Cigarettes per Month among Young Adult Smokers in the NESARC Study')

Code for Week 4 Python Lesson 3 - Measures of Center & Spread

standard deviation and other descriptive statistics for quantitative variables

print ('describe number of cigarettes smoked per month') desc1 = sub2['NUMCIGMO_EST'].describe() print (desc1)

c1= sub2.groupby('NUMCIGMO_EST').size() print (c1)

print ('describe nicotine dependence') desc2 = sub2['TAB12MDX'].describe() print (desc2)

c1= sub2.groupby('TAB12MDX').size() print (c1)

print ('mode') mode1 = sub2['TAB12MDX'].mode() print (mode1)

print ('mean') mean1 = sub2['NUMCIGMO_EST'].mean() print (mean1)

print ('std') std1 = sub2['NUMCIGMO_EST'].std() print (std1)

print ('min') min1 = sub2['NUMCIGMO_EST'].min() print (min1)

print ('max') max1 = sub2['NUMCIGMO_EST'].max() print (max1)

print ('median') median1 = sub2['NUMCIGMO_EST'].median() print (median1)

print ('mode') mode1 = sub2['NUMCIGMO_EST'].mode() print (mode1)

c1= sub2.groupby('TAB12MDX').size() print (c1)

p1 = sub2.groupby('TAB12MDX').size() * 100 / len(data) print (p1)

c2 = sub2.groupby('NUMCIGMO_EST').size() print (c2)

p2 = sub2.groupby('NUMCIGMO_EST').size() * 100 / len(data) print (p2)

A secondary variable multiplying the number of days smoked per month and the approx number of cig smoked per day

A secondary variable multiplying the number of days smoked per month and the approx number of cig smoked per day

sub2['PACKSPERMONTH']=sub2['NUMCIGMO_EST'] / 20

c2= sub2.groupby('PACKSPERMONTH').size() print (c2)

sub2['PACKCATEGORY'] = pandas.cut(sub2.PACKSPERMONTH, [0, 5, 10, 20, 30, 147])

change format from numeric to categorical

sub2['PACKCATEGORY'] = sub2['PACKCATEGORY'].astype('category')

print ('pack category counts') c7 = sub2['PACKCATEGORY'].value_counts(sort=False, dropna=True) print(c7)

print ('describe PACKCATEGORY') desc3 = sub2['PACKCATEGORY'].describe() print (desc3)

sub2['TAB12MDX'] = pandas.to_numeric(sub2['TAB12MDX'])

bivariate bar graph C->Q

seaborn.catplot(x="PACKCATEGORY", y="TAB12MDX", data=sub2, kind="bar", ci=None) plt.xlabel('Packs per Month') plt.ylabel('Proportion Nicotine Dependent')

creating 3 level smokegroup variable

def SMOKEGRP (row): if row['TAB12MDX'] == 1 : return 1 elif row['USFREQMO'] == 30 : return 2 else : return 3

sub2['SMOKEGRP'] = sub2.apply (lambda row: SMOKEGRP (row),axis=1)

c3= sub2.groupby('SMOKEGRP').size() print (c3)

creating daily smoking variable

def DAILY (row): if row['USFREQMO'] == 30 : return 1 elif row['USFREQMO'] != 30 : return 0

sub2['DAILY'] = sub2.apply (lambda row: DAILY (row),axis=1)

c4= sub2.groupby('DAILY').size() print (c4)

seaborn.catplot(x='ETHRACE2A', y='DAILY', data=sub2, kind="bar", ci=None) plt.xlabel('Ethnic Group') plt.ylabel('Proportion Daily Smokers')

you can rename categorical variable values for graphing if original values are not informative

first change the variable format to categorical if you haven’t already done so

sub2['ETHRACE2A'] = sub2['ETHRACE2A'].astype('category')

second create a new variable (PACKCAT) that has the new variable value labels

sub2['ETHRACE2A']=sub2['ETHRACE2A'].cat.rename_categories(["White", "Black", "NatAm", "Asian", "Hispanic"])

bivariate bar graph C->C

seaborn.catplot(x='ETHRACE2A', y='DAILY', data=sub2, kind="bar", ci=None) plt.xlabel('Ethnic Group') plt.ylabel('Proportion Daily Smokers')

check to see if missing data were set to NaN

print ('counts for S3AQ3C1 with 99 set to NAN and number of missing requested') c4 = sub2['S3AQ3C1'].value_counts(sort=False, dropna=False) print(c4)

print ('counts for TAB12MDX - past 12 month nicotine dependence') c5 = sub2['TAB12MDX'].value_counts(sort=False) print(c5)

[codingforcoursera-blog]

Week Four

import seaborn import matplotlib.pyplot as plt

Set PANDAS to show all columns in DataFrame

pandas.set_option('display.max_columns', None)

Set PANDAS to show all rows in DataFrame

pandas.set_option('display.max_rows', None)

bug fix for display formats to avoid run time errors

pandas.set_option('display.float_format', lambda x:'%f'%x)

data = pandas.read_csv('data.csv', low_memory=False)

print(len(data)) #number of observations (rows) print(len(data.columns)) # number of variables (columns)

Convert data to categorical varibale

data["S2AQ4B"] = data["S2AQ4B"].astype('category') data["S7Q1"] = data["S7Q1"].astype('category') data["S2AQ16A"] = data["S2AQ16A"].astype('category')

generate basic graph for univariable varible

seaborn.countplot(x="S2AQ4B", data=data) plt.xlabel('How often drank coolers in the past 12 months') plt.title('Alcohol Dependence in the Past 12 Months in the NESARC Study')

seaborn.countplot(x="S7Q1", data=data) plt.xlabel('Strong fear or avoidance of social settings') plt.title('Avoidance or Fear of social setting amongst sample in the NESARC Study')

describing data notes

we describe through shape:

stating its symmetry of skewness, its peakness or modality

modes how many high tops it has, peakness number of times the data peaks ?

skewed right or skewejed left , it can be this and bimodal ! q

modes is values that occurs most often

mode is not always at the center, center if the midpoint

range is aprox minimum and maximum, aprox range is 40 points if max 90 - min 45

measure of the center of distribtution

mode is the total of all scored and divide by number obsercations

if we want mode, its the most common score

median is the value of the middle item

measire of variablitily or spread of distibution

standard deviation is ussed to quantify the spread of the distribtuion

as it measures how far away the variables are from there means

standard deviation and other descriptive statistics for quantitative variables

print ('Strong fear of avoidance of social setting') desc1 = data['S7Q1'].describe() print (desc1)

print ('How often drank coolers in the past 12 months') desc1 = data['S2AQ4B'].describe() print (desc1)

finding mean, median, standard deviation and mode

print ('mode') mode1 = data['S2AQ4B'].mode() print (mode1)

print ('mean') mean1 = data['S2AQ4B'].mean() print (mean1)

print ('std') std1 = data['S2AQ4B'].std() print (std1)

print ('min') min1 = data['S2AQ4B'].min() print (min1)

print ('max') max1 = data['S2AQ4B'].max() print (max1)

print ('median') median1 = data['S2AQ4B'].median() print (median1)

for categorical just use the describe code but for above the 7 one use qunaititve

use appropriaote discreptie statics for coding

for qunaitivate use the historgram code and the code right aboce

for qualitivates use the frequnecy distibtiion code the describe one

and the bar chart code

two variables

imposing causal model: # xi is anxiety, y is amount they drink each week w

when graphin x is on x axis and y is on y axis

both variables are categorical so data management is not required

set pandas to show all columns and rows in DataFrame

pandas.set_option('display.max_columns', None)

pandas.set_option('display.max_rows', None)

bivariate bar graph C->C

seaborn.catplot(x='S2AQ4B', y='S7Q1', data=data, kind="bar", ci=None) plt.xlabel('Level of fear or avoidance of social setting') plt.ylabel('Amount of Alcohol Consumed')

[luridsims]

[transcript]

alex: look at you: all grown up, flying the nest.

hae-won: haha, thanks mom.

alex: i mean it, i’m actually really proud of you.

hae-won: this is crazy, isn’t it?

alex: oh god, absolutely.

hae-won: she could be an axe murderer.

alex: oh yeah. big time. but for someone who never used to leave the house, you did good. and if anyone had to live in the wilderness with a hot girl... well, i’m glad it’s you.

hae-won: hey, thanks. i’ll remember that in my final moments. then i’ll come back to haunt the shit out of you.