problem on generating more data points and plotting-CodePudding

The #####################################################

Now I need to generate more data points of x and y using interpolation function and want to generate same plot as above code produced(depicted above). I tried the below mentioned code but it produces different plot(depicted below), But definition says after interpolation, we should have the same plot, we just have more data points.

import numpy as np from scipy.interpolate import interp1d import matplotlib.pyplot as plt x = np.array([3.45, 3.88, 3.99, 4.33]) y = np.array([14.0, 11.0, 14.0, 31.0]) y = np.cumsum(y) y0 = np.array([0]) x0 = np.interp([0], y, x) x = np.concatenate([x0, x]) y = np.concatenate([y0, y]) # I need to generate more data points NUM = 100 # modify the number of points funct = interp1d(x, y, kind='next') x_cont = np.linspace(x[0], x[-1], NUM) y_cont = funct(x_cont) # Plot fig, ax1 = plt.subplots() ax1.step(x_cont, y_cont, color='r', where='post') ax1.set_xlim(2, 5) ax1.set_ylim(0, y.max()) ax1.invert_yaxis() plt.show()

CodePudding user response：

This line

funct = interp1d(x, y, kind='next')

needs to be changed in

funct = interp1d(x, y, kind='previous')

This because you want values in [3.99, 4.33] (e.g.) to refer to the cumsum-value of 3.99, which is ~40 (previous) instead of values related to 4.33 which is ~70 (next).

CodePudding user response：

You want to increase the number of points for each step. You can use a custom function to interpolate values from (x1, y1) to (x2, y1) then from (x2, y1) to (x2, y2). It means you compute x points first then y. The function take 3 couple of parameters and the step size:

x, y: your intermediate step points (your two arrays x and y)

x0, y0: start coords (here you have to specify y=0 because it does not appear in your data)

xn, yn: end coords (here you can reuse the last value of x and y arrays)

step: the step size (we approximate the value to have points evenly spaced)

def interpolate(x, y, x0, y0, xn, yn, step): # create arrays with all points x = np.hstack([x0, x, xn]) y = np.hstack([y0, y, yn]) x_cont = [] y_cont = [] # get points pairwise for x1, x2, y1, y2 in zip(x, x[1:], y, y[1:]): # approximate the number of samples per segment numx = int(np.abs(x2 - x1) / step) numy = int(np.abs(y2 - y1) / step) # x-first (left or right move) a = np.linspace(x1, x2, numx) b = np.repeat(y1, numx) # y-second (top or down move) c = np.repeat(x2, numy) d = np.linspace(y1, y2, numy) # save points x_cont.append([*a, *c]) y_cont.append([*b, *d]) # flat all computed values return np.hstack(x_cont), np.hstack(y_cont) # Original data x = np.array([3.45, 3.88, 3.99, 4.33]) y = np.array([14.0, 11.0, 14.0, 31.0]) y = np.cumsum(y) # It seems you want the cumulative sum of y x_cont, y_cont = interpolate(x, y, x[0], 0, x[-1], y[-1], step=0.1)

Plot the values:

fig, ax = plt.subplots() ax.step(x_cont, y_cont, color='red', where='post') ax.set_xlim(2, 5) ax.set_ylim(0, y.max()) ax.invert_yaxis() plt.show()

Output: