I am using logging package to write some error values and a matrix inside a log file.
logging.info("Error = %.4f"%err)
logging.info(error_mat) #error_mat is a NxN matrix
However, the matrix gets written in a different manner:
2014-09-08 14:10:20,107 - root - INFO - [[ 8.30857546 0.69993454 0.20645551
77.01797674 13.76705776]
[ 8.35205432 0.53417203 0.19969048 76.78598173 14.12810144]
[ 8.37066492 0.64428449 0.18623849 76.4181809 14.3806312 ]
[ 8.50493296 0.5110043 0.19731849 76.45838604 14.32835821]
[ 8.18900791 0.4955451 0.22524777 76.96966663 14.12053259]]
or
2014-09-08 14:12:22,211 - root - INFO - [[ 3.25142253e+01 1.11788106e+00 1.51065008e-02 6.16496299e+01
4.70315726e+00]
[ 3.31685887e+01 9.53522041e-01 1.49767860e-02 6.13449154e+01
4.51799710e+00]
[ 3.31101827e+01 1.09729703e+00 5.03347259e-03 6.11818594e+01
4.60562742e+00]
[ 3.32506957e+01 1.13837592e+00 1.51783456e-02 6.08651657e+01
4.73058437e+00]
[ 3.26809490e+01 1.06617279e+00 1.00110121e-02 6.17429172e+01
4.49994994e+00]]
How can we specify the precision for the float values inside the error_mat, e.g. %.3f for a 3 decimal point precision? Also this will ensure the matrix values to be written in a single without line breaks.
Floating point number formatting is supported now:
you can use %.nf where n is the number of floating point numbers after the dot.
example :- _logger.info("Client started in %.4f seconds", end-start)
If error_mat is a NumPy array, then you could use np.set_printoptions (as Padraic Cunningham commented):
import numpy as np
arr = np.random.random((3, 4))-0.5
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
print(arr)
prints something like
[[ 0.019 0.351 -0.138 -0.183]
[-0.087 0.322 0.490 -0.407]
[ 0.357 0.353 -0.077 0.411]]
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