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Exponential Fit in Python

Create a polynomial fit / regression in Python and add a line of best fit to your chart

# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api

import plotly.plotly as py
import plotly.graph_objs as go

# Scientific libraries
import numpy as np
from scipy.optimize import curve_fit



x = np.array([399.75, 989.25, 1578.75, 2168.25, 2757.75, 3347.25, 3936.75, 4526.25, 5115.75, 5705.25])
y = np.array([109,62,39,13,10,4,2,0,1,2])

def exponenial_func(x, a, b, c):
    return a*np.exp(-b*x)+c


popt, pcov = curve_fit(exponenial_func, x, y, p0=(1, 1e-6, 1))

xx = np.linspace(300, 6000, 1000)
yy = exponenial_func(xx, *popt)

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=x, 
                  y=y, 
                  mode='markers',
                  marker=go.Marker(color='rgb(255, 127, 14)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xx, 
                  y=yy, 
                  mode='lines',
                  marker=go.Marker(color='rgb(31, 119, 180)'),
                  name='Fit'
                  )

annotation = go.Annotation(
                  x=2000,
                  y=100,
                  text='$\textbf{Fit}: 163.56e^{-0.00097x} - 1.16$',
                  showarrow=False
                  )
layout = go.Layout(
                title='Exponential Fit in Python',
                plot_bgcolor='rgb(229, 229, 229)',
                  xaxis=go.XAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),
                  yaxis=go.YAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),
                  annotations=[annotation]
                )

data = [trace1, trace2]
fig = go.Figure(data=data, layout=layout)

py.plot(fig, filename='Exponential-Fit-in-python')
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