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# Smoothing in Python

Learn how to perform smoothing using various methods in Python.

#### Imports¶

The tutorial below imports NumPy, Pandas, SciPy and Plotly.

In [1]:
import plotly.graph_objects as go

import numpy as np
import pandas as pd
import scipy

from scipy import signal


#### Savitzky-Golay Filter¶

Smoothing is a technique that is used to eliminate noise from a dataset. There are many algorithms and methods to accomplish this but all have the same general purpose of 'roughing out the edges' or 'smoothing' some data.

There is reason to smooth data if there is little to no small-scale structure in the data. The danger to this thinking is that one may skew the representation of the data enough to change its percieved meaning, so for the sake of scientific honesty it is an imperative to at the very minimum explain one's reason's for using a smoothing algorithm to their dataset.

In this example we use the Savitzky-Golay Filter, which fits subsequents windows of adjacent data with a low-order polynomial.

In [2]:
import plotly.graph_objects as go

import numpy as np
import pandas as pd
import scipy

from scipy import signal

x = np.linspace(0, 10, 100)
y = np.sin(x)
noise = 2 * np.random.random(len(x)) - 1 # uniformly distributed between -1 and 1
y_noise = y + noise

fig = go.Figure()
x=x,
y=y,
mode='markers',
marker=dict(size=2, color='black'),
name='Sine'
))

x=x,
y=y_noise,
mode='markers',
marker=dict(
size=6,
color='royalblue',
symbol='circle-open'
),
name='Noisy Sine'
))

x=x,
y=signal.savgol_filter(y,
53, # window size used for filtering
3), # order of fitted polynomial
mode='markers',
marker=dict(
size=6,
color='mediumpurple',
symbol='triangle-up'
),
name='Savitzky-Golay'
))

fig.show()

/home/circleci/repo/venv/lib/python3.6/site-packages/scipy/signal/_arraytools.py:45: FutureWarning:

Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.



#### Triangular Moving Average¶

Another method for smoothing is a moving average. There are various forms of this, but the idea is to take a window of points in your dataset, compute an average of the points, then shift the window over by one point and repeat. This will generate a bunch of points which will result in the smoothed data.

Let us look at the common Simple Moving Average first. In the 1D case we have a data set of $N$ points with y-values $y_1, y_2, ..., y_N$. Setting our window size to $n < N$, the new $i^{th}$ y-value after smoothing is computed as:

\begin{align*} SMA_i = \frac{y_i + ... + y_{i+n}}{n} \end{align*}

In the Triangular Moving Average, two simple moving averages are computed on top of each other, in order to give more weight to closer (adjacent) points. This means that our $SMA_i$ are computed then a Triangular Moving Average $TMA_i$ is computed as:

\begin{align*} TMA_i = \frac{SMA_i + ... + SMA_{i+n}}{n} \end{align*}
In [3]:
def smoothTriangle(data, degree):
triangle=np.concatenate((np.arange(degree + 1), np.arange(degree)[::-1])) # up then down
smoothed=[]

for i in range(degree, len(data) - degree * 2):
point=data[i:i + len(triangle)] * triangle
smoothed.append(np.sum(point)/np.sum(triangle))
# Handle boundaries
smoothed=[smoothed[0]]*int(degree + degree/2) + smoothed
while len(smoothed) < len(data):
smoothed.append(smoothed[-1])
return smoothed

fig = go.Figure()
x=x,
y=y,
mode='markers',
marker=dict(
size=2,
color='rgb(0, 0, 0)',
),
name='Sine'
))

x=x,
y=y_noise,
mode='markers',
marker=dict(
size=6,
color='#5E88FC',
symbol='circle-open'
),
name='Noisy Sine'
))