Baseline detection

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Imports

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

import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as ff

import numpy as np
import pandas as pd
import scipy
import peakutils

Import Data

For our baseline detection example, we will import some data on milk production by month:

milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
time_series = milk_data['Monthly milk production (pounds per cow)']
time_series = np.asarray(time_series)

df = milk_data[0:15]

table = ff.create_table(df)
py.iplot(table, filename='milk-production-dataframe')

Plot with Baseline

# calculate baseline y values
baseline_values = peakutils.baseline(time_series)

trace = go.Scatter(
    x=[j for j in range(len(time_series))],
    y=time_series,
    mode='lines',
    marker=dict(
        color='#B292EA',
    ),
    name='Original Plot'
)

trace2 = go.Scatter(
    x=[j for j in range(len(time_series))],
    y=baseline_values,
    mode='markers',
    marker=dict(
        size=3,
        color='#EB55BF',
        symbol='circle-open'
    ),
    name='Baseline'
)

data = [trace, trace2]
py.iplot(data, filename='milk-production-plot-with-baseline')

from IPython.display import display, HTML

display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

! pip install git+https://github.com/plotly/publisher.git --upgrade
import publisher
publisher.publish(
    'python-Baseline-Detection.ipynb', 'python/baseline-detection/', 'Baseline Detection | plotly',
    'Learn how to detect baselines on data in Python.',
    title='Baseline Detection in Python | plotly',
    name='Baseline Detection',
    language='python',
    page_type='example_index', has_thumbnail='false', display_as='peak-analysis', order=1,
    ipynb= '~notebook_demo/117')
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