[Python] How to "squeeze" bounds to some number on both sides of a calculation [Python]

So I have this piece of code where I want to squeeze both bounds (b1 and b2) so that eventually y1 < 0.01 and y2 > -0.01, then takes the average from both x-intercept values, putting that in a dictionary.

Right now I am just using -1 and 1 for testing purposes to speed up the calculation.

Currently the code does get close to the result I'm looking for, but for accuracy's I want it to adjust both bounds instead of just one.

#...More code above this line...
b1 = 0.005 # lower bound
b2 = 0.8 # upper bound

beta = {}

for i in purposes:
# Beta value for work
if ( i == 'work' ):
ideal = staline_euclidean_odin[0]
print('Ideal for '+i+':', ideal)

# To determine the distances using b1
first_avg_dist = Distance_array(i,n_zones,trip_prod,trip_attr,dist_matrix,b1,balance_constant)
y1 = first_avg_dist - ideal # Should always be above 0

# To determine the distances using b2
second_avg_dist = Distance_array(i,n_zones,trip_prod,trip_attr,dist_matrix,b2,balance_constant)
y2 = second_avg_dist - ideal # Should always be below 0

print('y1 is:', y1)
print('y2 is:', y2)

print('\nUpper bound')

while( y2 < -1):
x_intercept = ((b1 * y2)-(b2 * y1))/(y2-y1)
x_int_float = sum(x_intercept)
print('\nX-intercept:',x_int_float)

b2 = x_int_float

second_avg_dist = Distance_array(i,n_zones,trip_prod,trip_attr,dist_matrix,b2,balance_constant)
y2 = second_avg_dist - ideal

print('\nThe',i,'value is',x_int_float)
beta = x_int_float

print('\nFinal beta values are:', beta)


As it stands, I removed the second while loop (while( y > 1)) because it basically did nothing lol. Here is an example of the output though of the above code:

Ideal for work: 12.544276968293396
Average Trip Distance: [82.47777808]
Average Trip Distance: [2.43391276]
y1 is: [69.93350111]
y2 is: [-10.11036421]

Upper bound

X-intercept: 0.6995833157953546
Average Trip Distance: [2.6939947]

X-intercept: 0.6138285241716822
Average Trip Distance: [2.98388599]

X-intercept: 0.5406073171738418
Average Trip Distance: [3.30435637]

X-intercept: 0.478099357080947
Average Trip Distance: [3.65585204]

X-intercept: 0.4247498852729269
Average Trip Distance: [4.03831264]

X-intercept: 0.3792322681820317
Average Trip Distance: [4.45095828]

X-intercept: 0.34041508943238846
Average Trip Distance: [4.89207298]

X-intercept: 0.30733341952072446
Average Trip Distance: [5.35880098]

X-intercept: 0.27916383522587485
Average Trip Distance: [5.84697726]

X-intercept: 0.25520274705938095
Average Trip Distance: [6.35105819]

X-intercept: 0.23484773433432096
Average Trip Distance: [6.86423559]

X-intercept: 0.2175817182463294
Average Trip Distance: [7.37877097]

X-intercept: 0.20295979202234204
Average Trip Distance: [7.88651161]

X-intercept: 0.19059842177765768
Average Trip Distance: [8.37949335]

X-intercept: 0.18016663825325702
Average Trip Distance: [8.8505177]

X-intercept: 0.17137881442565908
Average Trip Distance: [9.29361024]

X-intercept: 0.16398866309095483
Average Trip Distance: [9.7043061]

X-intercept: 0.1577841608300955
Average Trip Distance: [10.0797501]

X-intercept: 0.15258318119652844
Average Trip Distance: [10.41863338]

X-intercept: 0.14822968457439337
Average Trip Distance: [10.72100868]

X-intercept: 0.14459036044782916
Average Trip Distance: [10.98803384]

X-intercept: 0.14155165130996347
Average Trip Distance: [11.22168922]

X-intercept: 0.13901710944153153
Average Trip Distance: [11.42450479]

X-intercept: 0.13690505149520227
Average Trip Distance: [11.59932024]

The work value is 0.13690505149520227

Final beta values are: {'work': 0.13690505149520227}


Also, if there is a more efficient/better way to do this, let me know. This has so far been the fastest method I've used.

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