Is it possible for me to integrate but set my integration limits in a loop? So, for instance, integrate my function from 8 to 8.9 until it reaches a value of 2.5? Thank you!
f1 = lambda x,c : c/x
integrate1=quad(f, 8,8.97, args=c1)
print(integrate1)
Maybe?
for index in range(8,9):
f1 = lambda x,c: c/x
integrate1 = quad(f, index, index+0.1, args=c1)
print(integrate1)
for a non-integer loop with a fixed step you may do something like that:
for val in xrange(80, 90):
val /= 10
........
or
val = 8.0
while val<8.9:
<do your worst>
val += step
Rounding on the way may be good idea - to get required precision
Well, obviously you can do that:
import numpy as np
import scipy.integrate as si
def test_fn(x, c):
return c / x
def main():
lower_limit = 8.0
target_area = 2.5
c_val = 42.0
for upper_limit in np.arange(8., 10., 0.1):
area = si.quad(test_fn, lower_limit, upper_limit, args=(c_val,))
if area >= target_area:
print("Area is {} at ul={}".format(area, upper_limit))
break
if __name__=="__main__":
main()
but your step interval limits your result accuracy, and you're doing an awful lot of unnecessary calculations (== slow).
As @Jakob_Weisblat says, you can switch to a binary search algorithm. That's faster, but you have to do some extra bookkeeping. Why not delegate?
I suggest turning it into a metaproblem: solve for the upper limit such that integrating results in your desired target value:
import functools
import scipy.integrate as si
import scipy.optimize as so
def test_fn(x, c):
return c / x
def integrate_fn(ul, fn, ll, args, target):
return si.quad(fn, ll, ul, args=args) - target
def main():
lower_limit = 8.0
target_area = 2.5
c_val = 42.0
sol = so.brentq(
integrate_fn, lower_limit, 20.0, args=(
test_fn, lower_limit, (c_val,), target_area
)
)
print(sol)
if __name__=="__main__":
main()
(Do note that this code is untested, as this machine does not have scipy installed.)
for ...there should be a:and the following inner part of the loop should be indented. Also your namefandf1should probably be the same.