<jupyter_start><jupyter_text>Computing eigenvector standards<NAME>5.15.2020<jupyter_code>#preamble for dev ipynb

#pylab
%matplotlib inline
import numpy as np, pandas as pd, matplotlib.pyplot as plt
from pylab import imshow, show

#use cuda via numba
from numba import jit, njit, vectorize, cuda, uint32, f8, uint8

#automate the boring stuff
from IPython import utils
import time, os, sys, re
beep = lambda x: os.system("echo -n '\\a';sleep 0.2;" * x)
if not 'nb_dir' in globals():
    nb_dir = os.getcwd()
from lib import *
#%autocall 1
%load_ext autoreload
%autoreload 2


#nota bene: you can increase the ram allocated to the virtual machine running jupyter with 
#$ jupyter notebook --NotebookApp.max_buffer_size=your_value<jupyter_output><empty_output><jupyter_text>import data for northern plains<jupyter_code># state = 'IA'
# data_dir = 'Data'
# filename='STAT_%s.pickle'%state
# command="curl https://mas-dse-open.s3.amazonaws.com/Weather/by_state_2/%s.gz > %s/%s.gz"%(filename,data_dir,filename)
# print(command)
# !$command

# command="gunzip %s/%s.gz"%(data_dir,filename)
# print(command)
# !$command
data_dir = '../DataHW3'
df, keys = pd.read_pickle(f'Data/{filename}')

keys
plt.plot(df['SNWD_s20']['SortedVals'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1360381861575179, 'fraction_numerical': 0.015910898965791568, 'mean_word_length': 5.912087912087912, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 11, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '459482', 'license': 'MIT', 'n_tokens_mistral': 497, 'n_tokens_neox': 484, 'n_words': 118, 'path': 'HW3/Eigenvector Standards.ipynb', 'repo_name': 'timtyree/dsc291team4', 'size': '1260'}