Selection of second columns data based on match of first column with another text file in python - numpy

Question

I have little knowledge of numpy arrays and iterations. I have two input
files. First columns of both files represents time in milliseconds.
Input file 1 is reference or simulated value. Input file 2 is obtained from test value. I want to compare(plot second vs first ) second column of input-2
file with second column of first file if and only if when there is match of time in first column in corresponding files.
I am trying it through iterations but could not find proper results yet.How
to find index when there is a match?
import numpy as np
my_file=np.genfromtxt('path/input1.txt')
Sim_val=np.genfromtxt('path/input2.txt')
inp1=my_file[:,0]
inp12=my_file[:,1]
inpt2=Sim_val[:,0]
inpt21=Sim_val[:,1]
xarray=np.array(inp1)
yarray=np.array(inp12)
data=np.array([xarray,yarray])
ldata=data.T
zarray=np.array(inpt2)
tarray=np.array(inpt21)
mdata=np.array([zarray,tarray])
kdata=mdata.T
i=np.searchsorted(kdata[:,0],ldata[:,0])
print i
My inputfile-2 & Inputfile-1 is
0 5 0 5
100 6 50 6
200 10 200 15
300 12 350 12
400 15 # Obtained 400 15 #Simulated Value
500 20 #Value 500 25
600 0 650 0
700 11 700 11
800 12 850 8
900 19 900 19
1000 10 1000 3
Having really a hard time with numpy arrays and iterations.
Please anybody suggest how can I solve above problem. In-fact I
have other columns too but all manipulation is depend on match of first column(Time match).
Once again very much thanks in Advance.

Answer 1

Did you mean something like
import numpy as np
simulated = np.array([
(0, 5),
(100, 6),
(200, 10),
(300, 12),
(400, 15),
(500, 20),
(600, 0),
(700, 11),
(800, 12),
(900, 19),
(1000, 10)
])
actual = np.array([
(0, 5),
(50, 6),
(200, 15),
(350, 12),
(400, 15),
(500, 25),
(650, 0),
(700, 11),
(850, 8),
(900, 19),
(1000, 3)
])
def indexes_where_match(A, B):
""" an iterator that goes over the indexes of wherever the entries in A's first-col and B's first-col match """
return (i for i, (a, b) in enumerate(zip(A, B)) if a[0] == b[0])
def main():
for i in indexes_where_match(simulated, actual):
print(simulated[i][1], 'should be compared to', actual[i][1])
if __name__ == '__main__':
main()
You could also use column-slicing, like this:
simulated_time, simulated_values = simulated[..., 0], simulated[..., 1:]
actual_time, actual_values = actual[..., 0], actual[..., 1:]
indexes_where_match = (i for i, (a, b) in enumerate(zip(simulated_time, actual_time)) if a == b)
for i in indexes_where_match:
print(simulated_values[i], 'should be compared to', actual_values[i])
# outputs:
# [5] should be compared to [5]
# [10] should be compared to [15]
# [15] should be compared to [15]
# [20] should be compared to [25]
# [11] should be compared to [11]
# [19] should be compared to [19]
# [10] should be compared to [3]