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Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
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The tutorial below imports NumPy, Pandas, SciPy and PeakUtils.
importplotly.plotlyaspyimportplotly.graph_objsasgofromplotly.toolsimportFigureFactoryasFFimportnumpyasnpimportpandasaspdimportscipyimportpeakutilsfromscipyimportsignalLet us import some stock data for our fitting:
stock_data=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/stockdata.csv') df=stock_data[0:15] table=FF.create_table(df) py.iplot(table, filename='stockdata-peak-fitting')Let us plot the SBUX column of the data and highlight a section we will fit to:
left_endpt=1857right_endpt=1940original_trace=go.Scatter( x= [jforjinrange(len(stock_data['SBUX']))], y=stock_data['SBUX'][0:left_endpt].tolist() + [Noneforkinrange(right_endpt-left_endpt)] +stock_data['SBUX'][right_endpt+1:len(stock_data['SBUX'])].tolist(), mode='lines', name='Full Data', marker=dict(color='rgb(160,200,250)') ) highlighted_trace=go.Scatter( x= [jforjinrange(left_endpt, right_endpt)], y=stock_data['SBUX'][left_endpt:right_endpt], mode='lines', name='Highlighted Section', marker=dict(color='rgb(0,56,210)') ) data= [original_trace, highlighted_trace,] py.iplot(data, filename='stock-data-SBUX')Before we are able to apply Peak Fitting we need to detect the peaks in this waveform to properly specify a peak to fit to.
x= [jforjinrange(len(stock_data))][left_endpt:right_endpt] y=stock_data['SBUX'][left_endpt:right_endpt] y=y.tolist() cb=np.array(y) indices=peakutils.indexes(cb, thres=0.75, min_dist=0.1) trace=go.Scatter( x=x, y=y, mode='lines', marker=dict( color='rgb(0,56,210)' ), name='Highlighted Plot' ) trace2=go.Scatter( x=indices+left_endpt, y=[y[j] forjinindices], mode='markers', marker=dict( size=8, color='rgb(255,0,0)', symbol='cross' ), name='Detected Peaks' ) data= [trace, trace2] py.iplot(data, filename='stock-data-with-peaks')Since we have detected all the local maximum points on the data, we can now isolate a few peaks and superimpose a fitted gaussian over one.
defgaussian(x, mu, sig): returnnp.exp(-np.power(x-mu, 2.) / (2*np.power(sig, 2.))) first_index=indices[6] left_gauss_bound=1894right_gauss_bound=1910x_values_1=np.asarray(x[left_gauss_bound-left_endpt:right_gauss_bound-left_endpt]) y_values_1=np.asarray(y[left_gauss_bound-left_endpt:right_gauss_bound-left_endpt]) gaussian_params_1=peakutils.gaussian_fit(x_values_1, y_values_1, center_only=False) gaussian_y_1= [gaussian(x_dummy, gaussian_params_1[1], 1.5) forx_dummyinx_values_1] trace=go.Scatter( x=x, y=y, mode='lines', marker=dict( color='rgb(0,56,210)' ), name='Highlighted Plot' ) trace2=go.Scatter( x=indices+left_endpt, y=[y[j] forjinindices], mode='markers', marker=dict( size=8, color='rgb(255,0,0)', symbol='cross' ), name='Detected Peaks' ) trace3=go.Scatter( #x=x_values_1,x=[item_x+1.5foritem_xinx_values_1], y=[item_y+38.2foritem_yingaussian_y_1], mode='lines', marker=dict( size=2, color='rgb(200,0,250)', ), name='Gaussian Fit' ) data= [trace, trace2, trace3] py.iplot(data, filename='stock-data-with-peaks-and-fit')fromIPython.displayimportdisplay, HTMLdisplay(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">')) ! pipinstallgit+https://github.com/plotly/publisher.git--upgradeimportpublisherpublisher.publish( 'python-Peak-Fitting.ipynb', 'python/peak-fitting/', 'Peak Fitting | plotly', 'Learn how to fit to peaks in Python', title='Peak Fitting in Python | plotly', name='Peak Fitting', language='python', page_type='example_index', has_thumbnail='false', display_as='peak-analysis', order=5, ipynb='~notebook_demo/119')