diff --git a/tests/test_base.py b/tests/test_base.py
index d9873c9d78fbf7eb6c4047b2e215e4cafce3cd84..10a4ed76a623a1e6b0163f8c429030c26d9227b5 100755
--- a/tests/test_base.py
+++ b/tests/test_base.py
@@ -106,7 +106,10 @@ def launch(
# smoothness = opt.smoothness,
# fields_name = 'regular')
for info in [(2, 'Heun'),
- (4, 'cRK4')]:
+ (2, 'AdamsBashforth'),
+ (4, 'cRK4'),
+ (4, 'AdamsBashforth'),
+ (6, 'AdamsBashforth')]:
c.add_particles(
integration_steps = info[0],
integration_method = info[1],
@@ -142,7 +145,7 @@ def launch(
return c
-def acceleration_test(c, m = 3, species = 9):
+def acceleration_test(c, m = 3, species = 0):
import numpy as np
import matplotlib.pyplot as plt
from bfps.tools import get_fornberg_coeffs
@@ -158,35 +161,35 @@ def acceleration_test(c, m = 3, species = 9):
fc = get_fornberg_coeffs(0, range(-n, n+1))
dt = d['parameters/dt'].value*d['parameters/niter_part'].value
- num_acc1 = sum(fc[1, n-i]*vel[n-i:vel.shape[0]-i-n] for i in range(-n, n+1)) / dt
- num_acc2 = sum(fc[2, n-i]*pos[n-i:pos.shape[0]-i-n] for i in range(-n, n+1)) / dt**2
- num_vel1 = sum(fc[1, n-i]*pos[n-i:pos.shape[0]-i-n] for i in range(-n, n+1)) / dt
+ num_acc1 = sum(fc[1, n-i]*vel[1+n-i:vel.shape[0]-i-n-1] for i in range(-n, n+1)) / dt
+ num_acc2 = sum(fc[2, n-i]*pos[1+n-i:pos.shape[0]-i-n-1] for i in range(-n, n+1)) / dt**2
+ num_vel1 = sum(fc[1, n-i]*pos[1+n-i:pos.shape[0]-i-n-1] for i in range(-n, n+1)) / dt
- pid = np.unravel_index(np.argmax(np.abs(num_acc1[:] - acc[n:-n])), dims = num_acc1.shape)
def SNR(a, b):
- return -20*np.log10(np.average((a - b)**2)**.5 / np.average(a**2)**.5)
+ return -10*np.log10(np.mean((a - b)**2, axis = (0, 2)) / np.mean(a**2, axis = (0, 2)))
+ pid = np.argmax(SNR(num_acc1, acc[n+1:-n-1]))
print('SNR diffpos vs vel {0}, SNR diffvel vs acc {1}, SNR diffpos vs acc {2}'.format(
- SNR(num_vel1, vel[n:-n]),
- SNR(num_acc1, acc[n:-n]),
- SNR(num_acc2, acc[n:-n])))
- #for cc in range(3):
- # a.plot(num_acc1[:, pid[1], cc], color = col[cc])
- # #a.plot(num_acc2[:, pid[1], cc], color = col[cc], dashes = (2, 2))
- # a.plot(acc[m:, pid[1], cc], color = col[cc], dashes = (1, 1))
-
- #for n in range(1, m):
- # fc = get_fornberg_coeffs(0, range(-n, n+1))
- # dt = d['parameters/dt'].value*d['parameters/niter_part'].value
-
- # num_acc1 = sum(fc[1, n-i]*vel[n-i:vel.shape[0]-i-n] for i in range(-n, n+1)) / dt
- # num_acc2 = sum(fc[2, n-i]*pos[n-i:pos.shape[0]-i-n] for i in range(-n, n+1)) / dt**2
-
- # for cc in range(3):
- # a.plot(num_acc1[m-n:, pid[1], cc], color = col[cc])
- # #a.plot(num_acc2[m-n:, pid[1], cc], color = col[cc], dashes = (2, 2))
- #fig.tight_layout()
- #fig.savefig('acc_test_{0}_{1}.pdf'.format(c.simname, species))
- return None
+ np.mean(SNR(num_vel1, vel[n+1:-n-1])),
+ np.mean(SNR(num_acc1, acc[n+1:-n-1])),
+ np.mean(SNR(num_acc2, acc[n+1:-n-1]))))
+ for cc in range(3):
+ a.plot(num_acc1[:, pid, cc], color = col[cc])
+ a.plot(num_acc2[:, pid, cc], color = col[cc], dashes = (2, 2))
+ a.plot(acc[m+1:, pid, cc], color = col[cc], dashes = (1, 1))
+
+ for n in range(1, m):
+ fc = get_fornberg_coeffs(0, range(-n, n+1))
+ dt = d['parameters/dt'].value*d['parameters/niter_part'].value
+
+ num_acc1 = sum(fc[1, n-i]*vel[n-i:vel.shape[0]-i-n] for i in range(-n, n+1)) / dt
+ num_acc2 = sum(fc[2, n-i]*pos[n-i:pos.shape[0]-i-n] for i in range(-n, n+1)) / dt**2
+
+ for cc in range(3):
+ a.plot(num_acc1[m-n:, pid, cc], color = col[cc])
+ a.plot(num_acc2[m-n:, pid, cc], color = col[cc], dashes = (2, 2))
+ fig.tight_layout()
+ fig.savefig('acc_test_{0}_{1}.pdf'.format(c.simname, species))
+ return pid
if __name__ == '__main__':
print('this file doesn\'t do anything')
diff --git a/tox_plain.ini b/tox_plain.ini
index e15e6296ee701e654fdd91a2d0f1c1a0057ca1f3..f01ef6218ab4ae9937535a55134439dffb4975ee 100644
--- a/tox_plain.ini
+++ b/tox_plain.ini
@@ -13,8 +13,8 @@ commands =
python tests/test_plain.py \
-n 32 --run --initialize --ncpu 4 \
--nparticles 10 --niter_todo 24 \
- --precision single --wd "data/single" \
- --multiplejob
+ --precision single --wd "data/single" #\
+ #--multiplejob
#python tests/test_plain.py -n 32 --run --initialize --multiplejob --ncpu 2 --nparticles 16 --niter_todo 64 --precision single --wd "data/single"
#python tests/test_plain.py -n 32 --run --initialize --multiplejob --ncpu 2 --nparticles 16 --niter_todo 64 --precision double --wd "data/double"