suryadutta

import sys

import requests
from bs4 import BeautifulSoup
from dataclasses import dataclass
from typing import List

SECTIONS_TO_SCRAPE = [
    "general",
    "security",

suryadutta

[
  {
    "State": "Alabama",
    "Abbreviation": "AL",
    "State Tax Rate": 0.04,
    "State Tax Rank": 40,
    "Local Tax Rate": 0.051,
    "Combined Tax Rate": 0.091,
    "Combined Rank": 5
  },

suryadutta

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(temp,mag,yerr=magstdev,fmt='o')
ax1.set_xscale("log")
ax1.set_yscale("log")
plt.show()

suryadutta

def calculate_and_save_values(Msamp,Esamp,spin,num_analysis,index,temp,data_filename,corr_filename):
    try:

        M_mean = np.average(Msamp[-num_analysis:])
        E_mean = np.average(Esamp[-num_analysis:])
        M_std = np.std(Msamp[-num_analysis:])
        E_std = np.std(Esamp[-num_analysis:])

        M_std_array = []

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correlation_lengths = []
correlation_uncertainties = []

for temp in temp_range:
  
   # read corr file, extract values for each temp
    
   # perform fit for values vs distance, extract correlation length and uncertainty
  
   # append correlation length and uncertainty to arrays

suryadutta

async.series({
    function(callback) {
        //add function here
        callback();
    },
}, function(err, results) {
    response.redirect('');
});

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router.get('/', function(req, res, next) {
    if (req.query.id) {
        asyncStuff.series([
            function(callback) {
                moduleModel.getPageItems(req.query.id, function(Data) {
                    pageData = Data[0];
                    console.log('task 1');
                    callback();
                });
            },

suryadutta

router.get('/', function(req, res, next) {
    if (req.query.id) {
        asyncStuff.series([
            function(callback) {
                moduleModel.getPageItems(req.query.id, function(Data) {
                    pageData = Data[0];
                    console.log('task 1');
                    callback();
                });
            },

suryadutta

reduceDTM <- function(dtm){
 term_tfidf <- 
    tapply(dtm$v/row_sums(dtm)[dtm$i], dtm$j, mean) *
    log2(nDocs(dtm)/col_sums(dtm > 0))
 
  dtm <- dtm[,term_tfidf >= median(term_tfidf)]
  dtm <- dtm[row_sums(dtm) > 0,]
  
  return(dtm)
}

suryadutta

#install.packages('tm')
library(tm)

#install.packages('slam')
library("slam")

#import data
alldata <- read.csv('stackexchange/20161215StatsPostsMerged.csv', header = TRUE, stringsAsFactors = FALSE)

#make corpus