Anamitra Saha onnyyonn

## Catalan
#include <stdio.h>

int main()
{
    float n=0.0, cat=1.0;
    while(n<=33)
    {
        cat = 2*(2*n+1)/(n+2)*cat;
        n++;
        printf("Catalan no. %2.0f is %1.0f\n",n,cat);

## Cdoku
#include <stdio.h>
#include <string.h>
#include <ctype.h>

int main()
{
    int prob_sudoku[9][9],empty_cell_not[9][9],prob_len,rowcheck=1,columncheck=1,boxcheck=1,going_back_flag=1,repeat,i,j,k,l;
    char problem[100];

    /*INPUT*/

## mmk_test.py
from __future__ import division
import numpy as np
from scipy import stats

def mmk_test(v, alpha = 0.05):
    """
    Performs the Modified Mann-Kendall test to check if there is any trend present.
    Based on Mann-Kendall test code by Sat Kumar Tomer. Modified to account for autocorrelation (Hamed and Rao 1998)

    Input:

## gist:e7edc44c8c2931e70ff2b9f1e9da51e6
Verifying my Blockstack ID is secured with the address 1KFSD2uC9GE9pVZL97cpDNvzUjTHZXXR3K https://explorer.blockstack.org/address/1KFSD2uC9GE9pVZL97cpDNvzUjTHZXXR3K

## gist:3332914c811523e9e3039c54a7985043
### Keybase proof

I hereby claim:

  * I am onnyyonn on github.
  * I am anamitra (https://keybase.io/anamitra) on keybase.
  * I have a public key ASB16eMLCTYZzbmzoGU96qzJsxj6j7XSR9qJ-njEU3GnWwo

To claim this, I am signing this object:

## firefox.md

      
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                onnyyonn
                / firefox.md
            
            
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                Firefox new tab dark background with no white splash
              
          
Go to about:config, change browser.display.background_color value to #000000 and toolkit.legacyUserProfileCustomizations.stylesheets value to true
Open profile folder in about:profiles under “Root Directory”
go to / create chrome subfolder, open / create userChrome.css and append the following lines:
#browser vbox#appcontent tabbrowser, #content, #tabbrowser-tabpanels, browser[type=content-primary], browser[type=content] > html { background: var(--in-content-page-background)!important  }


## hoon1.hoon
|=  i=@
`@ub`i

## quantile_bias_correction.py
import numpy as np
from statsmodels.distributions.empirical_distribution import ECDF

def bias_correct(obs_data_train, model_data_train, model_data_test):
    """
    Function for bias correcting model_data_test, based on obs_data_train and madel_data_train CDF.
    All three should be numpy array. Can be multi-dimensional.

    obs_data_train = observation / ground truth


## wordle_full.csv


              aahed

            
              aalii

            
              aargh

            
              aarti

            
              abaca

            
              abaci

            
              aback

            
              abacs

            
              abaft

## wordfreqgen.py
# Sample script to generate freqencies of a list of words from Google Ngram Viewer

import pandas as pd, numpy as np
import re, requests
from ast import literal_eval

# Import list of words
df = pd.read_csv("wordle_full.csv")
df.rename( columns={'Unnamed: 0':'word', 'Unnamed: 1':'frequency'}, inplace=True )
	#include <stdio.h>

	int main()
	{
	float n=0.0, cat=1.0;
	while(n<=33)
	{
	cat = 2(2n+1)/(n+2)*cat;
	n++;
	printf("Catalan no. %2.0f is %1.0f\n",n,cat);
	#include <stdio.h>
	#include <string.h>
	#include <ctype.h>

	int main()
	{
	int prob_sudoku[9][9],empty_cell_not[9][9],prob_len,rowcheck=1,columncheck=1,boxcheck=1,going_back_flag=1,repeat,i,j,k,l;
	char problem[100];

	/INPUT/
	from __future__ import division
	import numpy as np
	from scipy import stats

	def mmk_test(v, alpha = 0.05):
	"""
	Performs the Modified Mann-Kendall test to check if there is any trend present.
	Based on Mann-Kendall test code by Sat Kumar Tomer. Modified to account for autocorrelation (Hamed and Rao 1998)

	Input:
	### Keybase proof

	I hereby claim:

	* I am onnyyonn on github.
	* I am anamitra (https://keybase.io/anamitra) on keybase.
	* I have a public key ASB16eMLCTYZzbmzoGU96qzJsxj6j7XSR9qJ-njEU3GnWwo

	To claim this, I am signing this object:
	import numpy as np
	from statsmodels.distributions.empirical_distribution import ECDF

	def bias_correct(obs_data_train, model_data_train, model_data_test):
	"""
	Function for bias correcting model_data_test, based on obs_data_train and madel_data_train CDF.
	All three should be numpy array. Can be multi-dimensional.

	obs_data_train = observation / ground truth
	# Sample script to generate freqencies of a list of words from Google Ngram Viewer

	import pandas as pd, numpy as np
	import re, requests
	from ast import literal_eval

	# Import list of words
	df = pd.read_csv("wordle_full.csv")
	df.rename( columns={'Unnamed: 0':'word', 'Unnamed: 1':'frequency'}, inplace=True )