numpy's interp function - how to find a value of x for a given value of y?

Question

So I have an array of values of x (in increasing order) and the corresponding y values. Numpy's interp function takes in the X value, and the x and y arrays. How do I get the value of X for a given value of Y?? Eg if y = 0, x = ?.

Cheers!

code:

j = (((1840/(2*pi))**0.5)*exp(phi)) - 1.0 #y axis, phi is a 1-D array
t = linspace(0, 40, 100) #x axis
a = interp(<x-value>,t,j) # this gives me the corresponding y value! 

So what should I do to get the x value for a given y value!!

Answer 1

y_interp = np.interp(x_interp, x, y) yields an interpolation of the function y_interp = f(x_interp) based on a previous interpolation y = f(x), where x.size = y.size, x_interp.size = y_interp.size.

If you want x as a function of y, you have to construct the inverse function. As @NPE indicated, you have to make sure that x and y are always increasing. An easy way to check this is to use

np.all(np.diff(x) > 0)
np.all(np.diff(y) > 0)

Now finding the inverse function is actually very simple: you have to reverse the roles of x and y (since we deal with interpolations).

With my notations: x_value = np.interp(y_value, x, y).

With your notations: x_value = interp(y_value, j, t)

Answer 2

When using Numpy make sure you're using a Numpy array: np.array([])

#x data array
x_data = np.array([1,2,3,4])
    
#y data array
y_data = np.array([1,3,2,1])

#the known max y value is 3
y_max = 3
            
# sort the arrays
order = y_data.argsort()
y_data = y_data[order]
x_data = x_data[order]
        

# call the interpolation function but reverse the array datasets to find the corresponding x value       
x = np.interp(y_max, y_data, x_data,  left=None, right=None, period=None)

For this example the result is x = 2 when max y = 3. The maximum is (2,3).

Furthermore you can append data to a numpy array: x_data = np.append(x_data, appendedValue)