generator for integrals

inte = sum(f(xvec) * xweight)

is much faster than

inte = 0
for i1, xval in enumerate(xvec):
  inte += f(xval) * xweight[i1]

generator for nested sum

cast a nested sum into one big iterator:

  • simple case:

N1 = 5
N2 = 8
p1_arr = np.linspace(0.8, 1.2, N1)
p2_arr = np.linspace(3.8, 4.5, N2)

grid = (
        (i1, i2)
        for i1 in range(N1) for i2 in range(N2)
        )

ans = (
        _f(p1_arr[i1], p2_arr[i2])
        for i1, i2 in grid
        )

ans = sum(ans)
  • further processing:
N1 = 10
N2 = 80
para1_arr = np.linspace(0.8, 1.2, N1)
para2_arr = np.linspace(3.6, 4.0, N2)

grid = (
        (i1, i2)
        for i1 in range(N1) for i2 in range(N2)
        )

pool = [
        [fitfunc([para1_arr[i1], para2_arr[i2]]), i1, i2]
        for i1, i2 in grid
        ]
pool = np.array(pool).T

_ii = np.argmin(pool[0])
i1, i2 = int(pool[1][_ii]), int(pool[2][_ii])

print(para1_arr[i1], para2_arr[i2])

passing tuple as index for array

lat[tuple([*xvec, d])]
lat[xvec[0], xvec[1], xvec[2], xvec[3], d]

data stacking

data = vstack((data, new_col))

filename versioning

filename=f'file_{int(num):03}'

watch out for side effects

import numpy as np

# test 1
xarr = [1, 2, 3]
yarr = [2, 4, 6]
a = xarr
a += yarr
# Guess what xarr becomes now?

# test 2
xarr = np.array([1, 2, 3])
yarr = np.array([2, 4, 6])
a = xarr
a += yarr
# Guess what xarr becomes now?

This can be a hard bug to track down!

use a dictionary to assess and update global observables

adding NaN to signal discontinuities

# some condition to decide
iloc = np.argmax(data[2])
# tag: adding nan for discontinuity
data = np.insert(data, iloc, np.nan, axis=1)