numpy arrays losing precision when passed to a loop, so comparisons aren't working

Question

I have a function that is passed an array of coordinates in 1D. These are the ends of sections for which piecewise functions have been defined in a dictionary, and also passed in as input. However, when making comparisons, the array values change precision in the loop, and so the comparison is not working anymore. Here is the code:

def parse_mass_data(mass_prop_file, slices, wingline, y_coords):
    data = pd.read_csv(mass_prop_file, delim_whitespace=True,index_col=False, skiprows = 10, skipfooter=(slices+5))
    cogy = []
    mass = []
    y = y_coords.astype(np.float64)
    print(y)
    for i in range(len(data['Name'].values)):
        if data['Name'][i] != 'Totals'and data['Name'][i] != 'Name':
            cogy.append(data['cgY'].values[i])
            mass.append(data['Mass'].values[i])

    cogy = np.array(list(map(float,cogy)))
    mass = np.array(list(map(float,mass)))
    x = np.zeros(len(mass))
    z = np.zeros(len(mass))

    for i in range(len(mass)):
        for j in range(len(y_coords)-1):
            if cogy[i] >= y[j] and cogy[i] < y[j+1]:
                x[i] = wingline['xy'][j][0] * cogy[i] + wingline['xy'][j][1]
                z[i] = wingline['yz'][j][0] * cogy[i] + wingline['yz'][j][1]
            # print(wingline['xy'][j],wingline['yz'][j])
            print(y[j],y[j+1])
        # print(i,x[i],cogy[i],z[i])

The output is as follows:

[ 0.  6. 10.]
0.0 5.999999999999999
5.999999999999999 9.999999999999998
0.0 5.999999999999999
5.999999999999999 9.999999999999998
0.0 5.999999999999999
5.999999999999999 9.999999999999998
0.0 5.999999999999999
5.999999999999999 9.999999999999998

As you can see, the input array, and the values from the loop index don't match, preventing the comparisons from working. How can I make this comparison, or there a better way of implementing this?

I understand that numpy has an interpolate function here, but I already have the equations to fit the values to, and if the input data is not good to begin with, can I use that function?

Answer 1

Due to how computers work, this is a known problem according to the this similar question: Is floating point math broken?

My solution was to use absolute value comparisons, as suggested in the accepted answer to that question. Here is the edited code:

mass = np.array(list(map(float,mass)))
x = np.zeros(len(mass))
z = np.zeros(len(mass))

for i in range(len(mass)):
    for j in range(len(y_coords)-1):
        if cogy[i] >= 0:
            if abs(cogy[i] - y_coords[j] > 0.0001) and abs(cogy[i] - y_coords[j+1] <= 0.0001):
                x[i] = wingline['xy'][j][0] * cogy[i] + wingline['xy'][j][1]
                z[i] = wingline['yz'][j][0] * cogy[i] + wingline['yz'][j][1]
        else:
            if abs(cogy[i]) - abs(y_coords[j]) >= 0.0001 and abs(cogy[i]) - abs(y_coords[j+1]) < 0.0001:
                x[i] = -wingline['xy'][j][0] * cogy[i] + wingline['xy'][j][1]
                z[i] = -wingline['yz'][j][0] * cogy[i] + wingline['yz'][j][1]