Role of star in front of numpy array [duplicate]-CodePudding

The question is from this link for using python to fit a curve

https://riptutorial.com/scipy/example/31081/fitting-a-function-to-data-from-a-histogram

# 5.) Fit the function to the histogram data.
popt, pcov = curve_fit(fit_function, xdata=binscenters, ydata=data_entries, p0=[20000, 2.0, 2000, 3.0, 0.3])
print(popt)

# 6.)
# Generate enough x values to make the curves look smooth.
xspace = np.linspace(0, 6, 100000)

# Plot the histogram and the fitted function.
plt.bar(binscenters, data_entries, width=bins[1] - bins[0], color='navy', label=r'Histogram entries')
plt.plot(xspace, fit_function(xspace, *popt), color='darkorange', linewidth=2.5, label=r'Fitted function'

popt is a numpy array, *popt seems not about multiplication. What could it be?

I printed popt and *popt, I got

[ 2.01844780e 03  8.72591456e 03  3.00208158e 00 -2.81683457e 01]
2018.447795213082 8725.914563215998 3.002081584102449 -28.16834574461017

still not sure what is the role of star and how it works in plot (seems has to be there)

CodePudding user response：

According the curve_fit docs, the function has to have form

f(x, …)

which it will call with f(xdata, *p0). p0 is the tuple of fitting parameters, initially [20000, 2.0, 2000, 3.0, 0.3], and at the end the popt list (or tuple).

Used in the plot function

fit_function(xspace, *popt)

it does the same thing.

fit_function(xdata, 20000, 2.0, ....)

Python calls this unpacking.

Looks like popt is an array:

In [152]: np.array([ 2.01844780e 03,  8.72591456e 03,  3.00208158e 00, -2.81683457e 01])
Out[152]: array([ 2.01844780e 03,  8.72591456e 03,  3.00208158e 00, -2.81683457e 01])
In [153]: print(np.array([ 2.01844780e 03,  8.72591456e 03,  3.00208158e 00, -2.81683457e 01]))
[ 2.01844780e 03  8.72591456e 03  3.00208158e 00 -2.81683457e 01]

the unpacked version:

In [154]: print(*np.array([ 2.01844780e 03,  8.72591456e 03,  3.00208158e 00, -2.81683457e 01]))
2018.4478 8725.91456 3.00208158 -28.1683457

A simpler example of this unpacking in the print function:

In [155]: print([1,2])
[1, 2]
In [156]: print(1,2)
1 2
In [157]: print(*[1,2])
1 2