def neg_params(parsed_parameters, parameter_names, dates, subpop_names):
    # If any values in the array are below zero: 
    if ((parsed_parameters)< 0).any():

        # Create an array with the indices of each negative param
        # (parameter, day, subpop)
        negative_index_parameters = np.argwhere(parsed_parameters<0)  

        # This loop will record redundant rows in negative_index array
        unique_param_sp_combinations = []
        row_index = -1
        redundant_rows = []
        for row in negative_index_parameters:
            row_index += 1
            # If the (param, subpop) combination already exists in the list, count that row as one that needs to be removed
            if (row[0],row[2]) in unique_param_sp_combinations: 
                redundant_rows.append(row_index)
            # If the (param, sbupop) combination does not exist in the list, add it to the list
            if (row[0],row[2]) not in unique_param_sp_combinations: 
                unique_param_sp_combinations.append((row[0],row[2]))

        # Remove the redundant rows 
        non_redundant_negative_parameters = np.delete(negative_index_parameters, (redundant_rows), axis=0)

    
        # If there are any negative params, error will be raised and negative param locales will be cited
        print("The earliest date negative for each subpop and unique parameter are:")
        for param_idx, day_idx, sp_idx in non_redundant_negative_parameters:
            print("subpop: ", subpop_names[sp_idx], ", parameter ", parameter_names[param_idx], ": ", dates[day_idx].date(), sep="")
        raise ValueError("There are negative parsed-parameters, which is likely to result in incorrect integration.")

import pytest
from random import randint

# Test the negative_parameters() function
def test_negative_parameters():
   parameter_names = ['1*1*1*1', 'alpha*1*1*1', 'sigma_OMICRON*1*1*1', '3*gamma*1*1*1', 'epsilon+omegaph4*1*1*1', '1*zeta*1*1', 'r0*gamma*theta10*1*chi_OMICRON*1', 'r0*gamma*theta9*1*chi_OMICRON*1', 'r0*gamma*theta8*1*chi_OMICRON*1', 'r0*gamma*theta7*1*chi_OMICRON*1', 'r0*gamma*theta6*1*chi_OMICRON*1', 'r0*gamma*theta5*1*chi_OMICRON*1', 'r0*gamma*theta4*1*chi_OMICRON*1', 'r0*gamma*theta3*1*chi_OMICRON*1', 'r0*gamma*theta2*1*chi_OMICRON*1', 'r0*gamma*theta1*1*chi_OMICRON*1', 'r0*gamma*theta0*1*chi_OMICRON*1', 'eta_X0toX3_highIE*1*1*nuage0to17', 'eta_X0toX3_highIE*1*1*nuage18to64LR', 'eta_X0toX3_highIE*1*1*nuage18to64HR', 'eta_X0toX3_highIE*1*1*nuage65to100', 'eta_X0toX4_highIE*1*1*nuage0to17', 'eta_X0toX4_highIE*1*1*nuage18to64LR', 'eta_X0toX4_highIE*1*1*nuage18to64HR', 'eta_X0toX4_highIE*1*1*nuage65to100', 'eta_X0toX5_highIE*1*1*nuage0to17', 'eta_X0toX5_highIE*1*1*nuage18to64LR', 'eta_X0toX5_highIE*1*1*nuage18to64HR', 'eta_X0toX5_highIE*1*1*nuage65to100', 'eta_X1toX4_highIE*1*1*nuage0to17', 'eta_X1toX4_highIE*1*1*nuage18to64LR', 'eta_X1toX4_highIE*1*1*nuage18to64HR', 'eta_X1toX4_highIE*1*1*nuage65to100', 'eta_X1toX5_highIE*1*1*nuage0to17', 'eta_X1toX5_highIE*1*1*nuage18to64LR', 'eta_X1toX5_highIE*1*1*nuage18to64HR', 'eta_X1toX5_highIE*1*1*nuage65to100', 'eta_X2toX4_highIE*1*1*nuage0to17', 'eta_X2toX4_highIE*1*1*nuage18to64LR', 'eta_X2toX4_highIE*1*1*nuage18to64HR', 'eta_X2toX4_highIE*1*1*nuage65to100', 'eta_X2toX5_highIE*1*1*nuage0to17', 'eta_X2toX5_highIE*1*1*nuage18to64LR', 'eta_X2toX5_highIE*1*1*nuage18to64HR', 'eta_X2toX5_highIE*1*1*nuage65to100', 'eta_X2toX6_highIE*1*1*nuage0to17', 'eta_X2toX6_highIE*1*1*nuage18to64LR', 'eta_X2toX6_highIE*1*1*nuage18to64HR', 'eta_X2toX6_highIE*1*1*nuage65to100', 'eta_X3toX5_highIE*1*1*nuage0to17', 'eta_X3toX5_highIE*1*1*nuage18to64LR', 'eta_X3toX5_highIE*1*1*nuage18to64HR', 'eta_X3toX5_highIE*1*1*nuage65to100', 'eta_X3toX6_highIE*1*1*nuage0to17', 'eta_X3toX6_highIE*1*1*nuage18to64LR', 'eta_X3toX6_highIE*1*1*nuage18to64HR', 'eta_X3toX6_highIE*1*1*nuage65to100', 'eta_X4toX6_highIE*1*1*nuage0to17', 'eta_X4toX6_highIE*1*1*nuage18to64LR', 'eta_X4toX6_highIE*1*1*nuage18to64HR', 'eta_X4toX6_highIE*1*1*nuage65to100', 'eta_X4toX7_highIE*1*1*nuage0to17', 'eta_X4toX7_highIE*1*1*nuage18to64LR', 'eta_X4toX7_highIE*1*1*nuage18to64HR', 'eta_X4toX7_highIE*1*1*nuage65to100', 'eta_X5toX7_highIE*1*1*nuage0to17', 'eta_X5toX7_highIE*1*1*nuage18to64LR', 'eta_X5toX7_highIE*1*1*nuage18to64HR', 'eta_X5toX7_highIE*1*1*nuage65to100', 'eta_X6toX7_highIE*1*1*nuage0to17', 'eta_X6toX7_highIE*1*1*nuage18to64LR', 'eta_X6toX7_highIE*1*1*nuage18to64HR', 'eta_X6toX7_highIE*1*1*nuage65to100', 'eta_X6toX8_highIE*1*1*nuage0to17', 'eta_X6toX8_highIE*1*1*nuage18to64LR', 'eta_X6toX8_highIE*1*1*nuage18to64HR', 'eta_X6toX8_highIE*1*1*nuage65to100', 'eta_X7toX8_highIE*1*1*nuage0to17', 'eta_X7toX8_highIE*1*1*nuage18to64LR', 'eta_X7toX8_highIE*1*1*nuage18to64HR', 'eta_X7toX8_highIE*1*1*nuage65to100', 'eta_X8toX9_highIE*1*1*nuage0to17', 'eta_X8toX9_highIE*1*1*nuage18to64LR', 'eta_X8toX9_highIE*1*1*nuage18to64HR', 'eta_X8toX9_highIE*1*1*nuage65to100', 'eta_X9toX9_highIE*1*1*nuage0to17', 'eta_X9toX9_highIE*1*1*nuage18to64LR', 'eta_X9toX9_highIE*1*1*nuage18to64HR', 'eta_X9toX9_highIE*1*1*nuage65to100', 'eta_X10toX10_highIE*1*1*nuage0to17', 'eta_X10toX10_highIE*1*1*nuage18to64LR', 'eta_X10toX10_highIE*1*1*nuage18to64HR', 'eta_X10toX10_highIE*1*1*nuage65to100']
   dates = pd.date_range('2023-03-19', '2025-08-31', freq="D")
   subpop_names = ['56000', '50000', '11000', '02000', '38000', '46000', '10000', '30000', '44000', '23000', '33000', '15000', '16000', '54000', '31000', '35000', '20000', '32000', '28000', '05000', '49000', '19000', '09000', '40000', '41000', '21000', '22000', '01000', '45000', '27000', '08000', '55000', '24000', '29000', '18000', '47000', '25000', '04000', '53000', '51000', '34000', '26000', '37000', '13000', '39000', '17000', '42000', '36000', '12000', '48000', '06000']

   # Test case 1: no negative params
   test_array1 = np.zeros((93,897,51))

   # Test case 2: randomized negative params
   test_array2 = np.zeros((93,897,51))
   for num in range(5):
       test_array2[randint(0,93)][randint(0,897)][randint(0,51)] = -1

   # Test case 3: set negative params with redundancy
   test_array3 = np.zeros((93,897,51))
   test_array3[0][0][0] = -1
   test_array3[50,400:,2] =-1
   test_array3[6][7][15] = -1
   test_array3[6][8][15] = -1

   neg_params(test_array1, parameter_names, dates, subpop_names) # NoError

   with pytest.raises(ValueError):
        assert neg_params(test_array2, parameter_names, dates, subpop_names) # ValueError
        assert neg_params(test_array3, parameter_names, dates, subpop_names) # ValueError