Sergey Gulbin - 1 year ago 93

Python Question

I'm ananlyzing a dataset, and I know that the data should follow a power model:

`y = a*x**b`

I transformed it to linear by taking logarithms:

`ln(y) = ln(a) + b* ln(x)`

However, the problems arised on adding a trend line to the plot

`slope, intercept, r_value, p_value, std_err = scipy.stats.mstats.linregress(x_ln, y_ln)`

yy = np.exp(intercept)*wetarea_x**slope

plt.scatter(wetarea_x, arcgis_wtrshd_x, color = 'blue')

plt.plot(wetarea_x, yy, color = 'green')

This is what I get with this code.

How to modify the code, so that the trend line on the plot would be correct?

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Answer Source

Your green strange plot is what you get when you do a line plot in `matplotlib`

, with the `x`

values unsorted. It's a line plot, but it connects by lines *(x, y)* pairs jumping right and left (in your specific case, it looks like back to near the x-origin). That gives these strange patterns.

You don't have this problem with the blue plot, because it's a scatter plot.

Try calling the plot after sorting both arrays according to the indices of the first using `numpy.argsort`

, say

```
wetarea_x[np.argsort(wetarea_x)]
```

and

```
yy[np.argsort(wetarea_x)]
```

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