import numpy as np

sieve_sizes = np.array([#YOUR DATA])

percent_retained = np.array([#YOUR DATA PERCENTAGES])

sieve_sizes_with_zero = np.append(sieve_sizes, 0) percent_passing_with_zero = np.append(percent_passing, 100)

def find_d_value(percent_passing, sieve_sizes, desired_percent_passing): return np.interp(desired_percent_passing, percent_passing[::-1], sieve_sizes[::-1])

d10 = find_d_value(percent_passing_with_zero, sieve_sizes_with_zero, 10) d50 = find_d_value(percent_passing_with_zero, sieve_sizes_with_zero, 50) d90 = find_d_value(percent_passing_with_zero, sieve_sizes_with_zero, 90)

print(f"D10: {d10} µm") print(f"D50: {d50} µm") print(f"D90: {d90} µm")

try this one I hope you will find it helpful.

import numpy as np

sieve_sizes = np.array(#Data)

percent_retain = np.array(#Data_Percentage)

cumulative_percentage = np.cumsum(percent_retain)

index_d10 = np.argmax(cumulative_percentage >= 10)

index_d50 = np.argmax(cumulative_percentage >= 50)

index_d90 = np.argmax(cumulative_percentageTry >= 90)

# Corresponding sieve sizes

d10 = sieve_sizes[index_d10]

d50 = sieve_sizes[index_d50]

d90 = sieve_sizes[index_d90]

print(f"D10: {d10} µm")

print(f"D50: {d50} µm")

print(f"D90: {d90} µm")

Try this One I hope this is helpful.