nipunbatra

Before PR	After PR

nipunbatra

(base) ➜  prog-ml.github.io git:(main) ✗ pip install black[jupyter]
zsh: no matches found: black[jupyter]
(base) ➜  prog-ml.github.io git:(main) ✗ black notebooks/introduction/simpson.ipynb 
Skipping .ipynb files as Jupyter dependencies are not installed.
You can fix this by running ``pip install black[jupyter]``
No Python files are present to be formatted. Nothing to do 😴
(base) ➜  prog-ml.github.io git:(main) ✗ which black
/Users/nipun/miniconda3/bin/black
(base) ➜  prog-ml.github.io git:(main) ✗ pip install 'black[jupyter]'              

nipunbatra

nipunbatra

nipunbatra

from nilmtk import DataSet
ds = DataSet("/home/nipunbatra-pc/Downloads/iawe.h5")

elec = ds.buildings[1].elec

elec.meters[0].load(chunksize=10)
next(elec.meters[0].load(chunksize=10))

nipunbatra

ps aux|grep sparse-transfer-cv-static-matrix.py|awk '{print $2}'|xargs kill -9

nipunbatra

We've all seen those weird looking mathematics equations that pop up when we hear about Fourier transforms. In this blog post, we'll programatically try and develop an intuitive understanding into the whole process. While I'll be using the scientific Python stack in this blog post, code in Matlab, R should not be that different.

Goal: Given a time series signal, we'll try and decompose it into various contituent freqencies.

First, let us assume that we are doing some signal acquisition and we can sample at 100 Hz frequency (100 times per second). We collect data for 10 seconds. So, we have a total of 1000 samples.

Samples collection duration (T) = 10s
Sampling frequency (Fs) =100Hz

nipunbatra

We've all seen those weird looking mathematics equations that pop up when we hear about Fourier transforms. In this blog post, we'll programatically try and develop an intuitive understanding into the whole process. While I'll be using the scientific Python stack in this blog post, code in Matlab, R should not be that different.

Goal: Given a time series signal, we'll try and decompose it into various contituent freqencies.

First, let us assume that we are doing some signal acquisition and we can sample at 100 Hz frequency (100 times per second). We collect data for 10 seconds. So, we have a total of 1000 samples.

Samples collection duration (T) = 10s
Sampling frequency (Fs) =100Hz

nipunbatra

nipunbatra