![]() ![]() Surface ( z = Z, x = X, y = Y, colorscale = 'RdBu', opacity = 0.999 ) # Package the trace dictionary into a data object data_test1 = go. Scatter3d ( x = data, y = data, z = data, mode = 'markers', marker = dict ( size = 4, color = 'red', line = dict ( color = 'black', width = 0.5 ), opacity = 0.8 ) ) trace2 = go. axis ( 'tight' ) # plot points and fitted surface using Plotly trace1 = go. scatter ( data, data, data, c = 'r', s = 50 ) plt. ![]() ![]() plot_surface ( X, Y, Z, rstride = 1, cstride = 1, alpha = 0.2 ) ax. figure ( figsize = ( 10, 10 )) ax = fig1. # plot points and fitted surface using Matplotlib fig1 = plt. The ability to interactively zoom-in and rotate the Plotly figures is very powerful, and adds a huge amount of value to any 3-dimensional plot displayed online or in a Jupyter Notebook. They are very similar, except of course with the ability to interactively zoom and rotate the Plotly figures! With the Plotly figure, the best-fit plane by default is also color-coded for z-values with a corresponding scale bar. For both the 1st-order and 2nd-order planes, I show the Matplotlib and Plotly figures, with separate code for each. Instead, it is possible to reproduce the same plot by writing the code directly for a Plotly figure. It is possible that this is not a supported plot type for Matplotlylib. Although the Matplotlylib project (Matplotlib + Plotly = Matplotlylib) has tools to convert Matplotlib plots into Plotly interactive plots (using 'tls.mpl_to_plotly' or 'py.iplot_mpl'), these functions fail to successfully convert the fitted planes in my 3-dimensional Matplotlib plots. I also continue with the techniques demonstrated in the previous post, showing the resulting 3-dimensional plots in both Matplotlib and Plotly. The result approximates a slightly warped half-cyclinder surface. Although I recently developed this code to analyze data for the Bridger-Teton Avalanche Center, below I generate a random dataset using a Gaussian function. If you found this article useful, you might be interested in the book NumPy Recipes or other books by the same author.The following code generates best-fit planes for 3-dimensional data using linear regression techniques (1st-order and 2nd-order polynomials). We can then calculate the sum of the squares of the distances: It will be an approximation because the points are scattered around so there is no straight line that exactly represents the data.Ī common way to find a straight line that fits some scatter data is the least squares method.įor a given set of points (xn, yn) and a line L, for each point you calculate the distance, dn, between the point and the line, like this: When we fit a straight line, we try to find a line that best represents the data. The data uses UK shoe sizes, other countries use a totally different system with very different numbers. So in the example data, the first person has height 182 cm and shoe size 8.5, the next person has height 171 cm and shoe size 7, and so on. A marker style with no line style doesn't plot lines, showing just the markers.Įach (x, y) pair of values corresponds to the height and shoe size of one person in the study. The key thing here is that the fmt string declares a style 'bo' that indicates the colour blue and a round marker, but it doesn't specify a line style. We are using the plot function to create the scatter plot. Import matplotlib.pyplot as plt height = shoe = plt. ![]()
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