For this, you can use ks.test: require (vcd) require (MASS) data generation ex <- rexp (10000, rate 1.85) generate some exponential distribution control <- abs (rnorm (10000)) generate some other distribution estimate the parameters fit1. XAxis ( zerolinecolor = 'rgb(255,255,255)', gridcolor = 'rgb(255,255,255)' ), yaxis = go. This won't tell you if the distribution fits or not, so you must then use goodness of fit test. Layout ( title = 'Exponential Fit in Python', plot_bgcolor = 'rgb(229, 229, 229)', xaxis = go. Annotation ( x = 2000, y = 100, text = '$ \t extbf - 1.16$', showarrow = False ) layout = go. Our example data is air pressure versus altitude. Scatter ( x = xx, y = yy, mode = 'lines', marker = go. In this video we use R's linear model to fit data to an exponential function. Scatter ( x = x, y = y, mode = 'markers', marker = go. linspace ( 300, 6000, 1000 ) yy = exponenial_func ( xx, * popt ) # Creating the dataset, and generating the plot trace1 = go. exp ( - b * x ) + c popt, pcov = curve_fit ( exponenial_func, x, y, p0 = ( 1, 1e-6, 1 )) xx = np. array () def exponenial_func ( x, a, b, c ): return a * np. In order to fit properly, the y data (or x, if fitting as a function of x) must have a properly defined, preferably symmetric error. # Learn about API authentication here: # Find your api_key here: import otly as py import aph_objs as go # Scientific libraries import numpy as np from scipy.optimize import curve_fit x = np.
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