| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pylab | |
| import datetime as dt | |
| ## | |
| # Response time analysis | |
| df_tbi = pd.DataFrame(np.arange(1,8)) | |
| df_tbi['response_cal_time'] = np.array([850,4345,19000,5827,3904,2756,8550]) |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pylab | |
| plt.rcParams['font.size'] = 8.0 | |
| plt.rcParams['figure.figsize'] = 6.0, 4.0 | |
| df = pd.DataFrame.from_csv('/Users/gsc//Dropbox/datasets/pseudo_facebook.tsv', sep='\t') |
| require 'linalg' | |
| include Linalg | |
| # Linear equation to solve: | |
| # a * x = b | |
| # | |
| # Example | |
| # a = DMatrix[[1,0,0],[0,2,0],[0,0,1]] | |
| # b = DMatrix[[1,1],[2,2],[3,3]] | |
| # |
| import numpy as np | |
| from scipy import stats | |
| np.random.seed(1) | |
| F_true = 1000 # random range | |
| N = 50 # number of measurements | |
| F = stats.poisson(F_true).rvs(N) | |
| e = np.sqrt(F) # errors on Poisson counts estimated via square root | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <title>dc.js - Demo</title> | |
| <meta charset="UTF-8"> | |
| <link rel="stylesheet" type="text/css" href="http://nickqizhu.github.io/dc.js/css/bootstrap.min.css"> | |
| <link rel="stylesheet" type="text/css" href="http://nickqizhu.github.io/dc.js/css/dc.css"/> |
| // Aggregate users and count location updates | |
| db.locations.aggregate([ | |
| { $group: {_id: "$locatable_id", number: {$sum: 1} }}, | |
| { $sort: { "number": -1}} | |
| ]) | |
| // Get all updates from a user, ordered by time | |
| db.locations.find( | |
| { "locatable_id": ObjectId("51ac7bccee4340adef00000d") }, | |
| {'_id': 0, 'coordinates': 1, 'created_at': 1} |
| def solution(a) | |
| radii = {} | |
| a.count.times do |c| | |
| (c-a[c]..c+a[c]).to_a.each do |r| | |
| radii[r] = [] if !radii.key?(r) | |
| radii[r] << c | |
| end | |
| end | |
| combs = [] |
| def solution(s, p, q) | |
| v = {"A" => 1, "C" => 2, "G" => 3, "T" => 4} | |
| min_nuc = [] | |
| if s!= nil and p != nil and q != nil | |
| if p.length > 0 and q.length > 0 | |
| if p.length == q.length | |
| p.length.times.each do |i| | |
| max = 5 | |
| s[p[i]..q[i]].each_char do |c| | |
| max = v[c] if v[c] < max |
| def solution(x, a) | |
| counter = 0 | |
| path = {} | |
| if x != nil and a != nil | |
| if x > 0 and a.length > 0 | |
| pos = 0 | |
| a.each do |i| | |
| if !path.key?(i) and i <= x | |
| counter += 1 | |
| end |
The file schema.py defines the schema used for PyTables, and therefore should be run first.
We'll be creating three tables, according to the data already available in the PostgreSQL server:
SensorData - This table will have data from each MAC address, per day
minified_raw_data - This sub-table has raw data from the routers