nipunbatra

nipunbatra

nipunbatra

nipunbatra

cols_to_use = []

for degree in range(1, 360, 2):
  cols_to_use.append("Latitude."+str(degree))
  cols_to_use.append("Longitude."+str(degree))

nipunbatra

import os

appliances = ["hvac","fridge","wm","mw","dw","oven"]
appliances = ['hvac','fridge']
features = ["Static","Monthly","Monthly+Static"]
features=["Monthly+Static","Static","Monthly"]

SLURM_OUT = "../slurm_out"
if not os.path.exists(SLURM_OUT):
    os.makedirs(SLURM_OUT)

nipunbatra

import json
f = open("/Users/nipunbatra/Downloads/testHP.drw",'r')
stream = f.read()
stream = stream.replace("\n",'')
stream = stream.replace("\xef\xbb\xbf","")
stream = stream.replace("HP := ","")
stream = stream.replace(":","")
three_d_array = json.loads(stream)

#shape

nipunbatra

In [16]: e = json.load(open("/Users/nipunbatra/Downloads/test_dk.txt",'r'))

In [17]: e
Out[17]: [[[2, 3], [4, 5]], [[1, 2], []]]

nipunbatra

Some of these questions have been asked on various forums (mail/github) and I thought of addressing them here:

Question 1. When I looked into the appliance level data (3.csv, 4.csv, etc.) and compare with mains, I observed that each appliance starting time period is different for example, fridge starts from 7th June and Washing machine starts from 10th June, whereas mains have data from 22nd May.

This is correct. We used jPlugs for collecting our appliance level data. Our smart meter deployment to collect mains data started a few days before the data collection for different appliances. jPlugs would collect data only when the appliance was ON. Thus, washing machine data starts from 10th June, the day it was first used during the data collecion.

Question 2. What is the best start and end time for using electricity data?

The nilmtk HDF5 I created uses data between 7-13-2013 and 8-4-2013. This period has the maximum amount of sensor data available.

nipunbatra

Pandas

1. Efficiently iterating over rows of a DataFrame
2. Changing specific column name in Pandas
3. Python pandas / matplotlib annotating labels above bar chart columns
4. Annotate bars with values on Pandas bar plots

Matplotlib

nipunbatra

import pandas as pd
df = pd.read_csv("server_idle.csv")
ax = df.throughput.plot('ko-')
plt.xticks(range(len(df)), df["# threads"])