Before I ask my question, I provide you with the code:
from scipy import *
x = randn(10)
cum_x = cumsum(x)
# The objective is to recover x using cum_x and the diff function.
y = append(cum_x[0], diff(cum_x))
# Now, y should be equal to x but this is not confirmed by the function in1d
test = in1d(x,y)
The variable test does not return an array of "True" boolean values even if y and x are clearly the same. What is the problem here?
if you use set_printoptions to increase precision you will see some differences:
import numpy as np
from scipy import *
np.set_printoptions(30)
x = randn(10)
cum_x = cumsum(x)
#The objective is to recover x using cum_x and the diff function.
y = append(cum_x[0], diff(cum_x))
print(x)
print("\n")
print(y)
#Now, y should be equal to x but this is not confirmed by the function in1d
test = in1d(x, y)
print(test)
Output:
[ 0.54816314147543721002620031868 0.14319052613251953554041051575
0.489110961092741158839913850898 -0.093011827554544138085823590245
-0.58370623188476589149331630324 -0.40395493550429123486011917521
0.387387395892057895263604905267 1.001637373359834937147638811439
-1.486778459872974744726548124163 1.446772274227251076084144187917]
[ 0.54816314147543721002620031868 0.143190526132519591051561747008
0.48911096109274110332876261964 -0.093011827554544179719187013688
-0.58370623188476589149331630324 -0.40395493550429123486011917521
0.387387395892057895263604905267 1.001637373359834937147638811439
-1.486778459872974744726548124163 1.446772274227251076084144187917]
[ True False False False True True True True True True]
What you probably want is allclose but interestingly setting the dtype to np.float128 or np.longdouble on my ubuntu system does not lose precision and in1d returns True.
cum_x = cumsum(x,dtype=np.longdouble)