oztalha

oztalha

oztalha

oztalha

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import pandas as pd
import time

driver = webdriver.Chrome()
#driver.get('http://stats.nba.com/teams/gamelogs/#!?Season=ALLTIME&SeasonType=Regular%20Season')
for year in range(2016,1945,-1):
    url = 'http://stats.nba.com/teams/gamelogs/#!?Season='+str(year)+'-'+format((year+1)%100,'02d')+'&SeasonType=Regular%20Season'
    driver.get(url)

oztalha

oztalha

#!/bin/bash
echo '---------------------------------------------------------------'
echo 'Grant SELECT access to a user for all tables in a PostgreSql DB'
echo '[USAGE]:    ./scriptname username dbname'
echo '---------------------------------------------------------------'
for table in `echo "SELECT schemaname || '.' || relname FROM pg_stat_all_tables;" | psql $2 | grep -v "pg_" | grep "^ " | grep -v "\?column\?"`;
do
    echo "GRANT SELECT ON TABLE $table TO $1;"
    echo "GRANT SELECT ON TABLE $table TO $1;" | psql $2
done

oztalha

Okay so here's the setup:

[-] The primary server API is exposed via Flask (Python) and all static files, including all html, css, js is served by nginx. 
[-] Python is exposing an API at url http://domain.com/api/download/<file_id>, where file_id is a database id for the file that we're interested in downloading. 

1. User wants to download a file, so we spawn a new window with the url '/api/download/<file_id>'
2. Nginx intercepts the request, sees that it starts with /api/, and then forwards the request to Flask, which is being served on port 5000. 
3. Flask routes the request to its download method, retrieves the pertinent data from the file_id, and constructs additional header settings to make nginx happy and to force the browser to see the file stream as a download request instead of the browser just trying to open the file in a new window. Flask then returns the modified header stream to nginx
4. Nginx is finally ready to do some work. While parsing the headers for the incoming request, it encounters "X

oztalha

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np