https://doi.org/10.5281/zenodo.15000786
analyze_cubes_periodic.py
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
import xarray
def get_ind(arr,val) :
return np.argmin(np.abs(arr-val))
t1 = 40
t2 = 49
prefixes = ["/lustre/storm/nwp501/scratch/imn/portUrb/build/cubes_periodic_z0-1.000e-05_",\
"/lustre/storm/nwp501/scratch/imn/portUrb/build/cubes_periodic_z0-1.000e-06_",\
"/lustre/storm/nwp501/scratch/imn/portUrb/build/cubes_periodic_z0-1.000e-07_"]
fnames = [ [f"{prefix}{i:08}.nc" for i in range(t1,t2+1)] for prefix in prefixes]
u0 = [0. for i in range(len(prefixes))]
u1 = [0. for i in range(len(prefixes))]
u2 = [0. for i in range(len(prefixes))]
u3 = [0. for i in range(len(prefixes))]
for k in range(len(prefixes)) :
nc = xarray.open_dataset(fnames[k][0])
x = np.array(nc["x"])
y = np.array(nc["y"])
z = np.array(nc["z"])
nx = len(x)
ny = len(y)
nz = len(z)
dx = x[1]-x[0]
dy = y[1]-y[0]
dz = z[1]-z[0]
xlen = x[-1]+dx/2
ylen = y[-1]+dy/2
zlen = z[-1]+dz/2
k2 = get_ind(z,.02*3)
p0_x = [get_ind(x,2*xlen/8),get_ind(x,6*xlen/8),get_ind(x,2*xlen/8),get_ind(x,6*xlen/8)];
p0_y = [get_ind(y,6*ylen/8),get_ind(y,4*ylen/8),get_ind(y,2*ylen/8),get_ind(y,0*ylen/8)];
p1_x = [get_ind(x,4*ylen/8),get_ind(x,0*ylen/8),get_ind(x,4*ylen/8),get_ind(x,0*ylen/8)];
p1_y = [get_ind(y,6*ylen/8),get_ind(y,4*ylen/8),get_ind(y,2*ylen/8),get_ind(y,0*ylen/8)];
p2_x = [get_ind(x,2*xlen/8),get_ind(x,6*xlen/8),get_ind(x,2*xlen/8),get_ind(x,6*xlen/8)];
p2_y = [get_ind(y,4*ylen/8),get_ind(y,2*ylen/8),get_ind(y,0*ylen/8),get_ind(y,6*ylen/8)];
p3_x = [get_ind(x,4*xlen/8),get_ind(x,0*xlen/8),get_ind(x,4*xlen/8),get_ind(x,0*xlen/8)];
p3_y = [get_ind(y,4*ylen/8),get_ind(y,2*ylen/8),get_ind(y,0*ylen/8),get_ind(y,6*ylen/8)];
for i in range(len(fnames[k])) :
nc = xarray.open_dataset(fnames[k][i])
for j in range(len(p0_x)) :
u = np.array(nc["avg_u"][:,p0_y[j],p0_x[j]])
u0[k] += u
u = np.array(nc["avg_u"][:,p1_y[j],p1_x[j]])
u1[k] += u
u = np.array(nc["avg_u"][:,p2_y[j],p2_x[j]])
u2[k] += u
u = np.array(nc["avg_u"][:,p3_y[j],p3_x[j]])
u3[k] += u
u0[k] /= len(fnames[k])*len(p0_x)
u1[k] /= len(fnames[k])*len(p0_x)
u2[k] /= len(fnames[k])*len(p0_x)
u3[k] /= len(fnames[k])*len(p0_x)
p0_x = [0.390051457975986,\
0.40377358490566,\
0.420926243567753,\
0.439794168096055,\
0.453516295025729,\
0.462092624356775,\
0.472384219554031,\
0.486106346483705,\
0.494682675814751,\
0.503259005145798,\
0.511835334476844,\
0.525557461406518,\
0.534133790737564,\
0.534133790737564,\
0.540994854202402,\
0.549571183533448,\
0.559862778730703,\
0.570154373927959,\
0.577015437392796,\
0.582161234991424,\
0.585591766723842]
p0_y = [1.18628571428571,\
1.23771428571429,\
1.296,\
1.392,\
1.44,\
1.49485714285714,\
1.54971428571429,\
1.59428571428571,\
1.64228571428571,\
1.70057142857143,\
1.752,\
1.78971428571429,\
1.84114285714286,\
1.84114285714286,\
1.89257142857143,\
1.94057142857143,\
1.992,\
2.04,\
2.088,\
2.14285714285714,\
2.19428571428571]
p1_x = [-0.0355640535372849,\
-0.0183556405353729,\
-0.00114722753346083,\
0.0103250478011472,\
0.0122370936902486,\
0.0141491395793499,\
0.0198852772466539,\
0.0217973231357552,\
0.0217973231357552,\
0.0237093690248566,\
0.0237093690248566,\
0.0237093690248566,\
0.0313575525812619,\
0.0523900573613766,\
0.0906309751434034,\
0.12887189292543,\
0.169024856596558,\
0.207265774378585,\
0.253154875717017,\
0.320076481835564,\
0.356405353728489,\
0.392734225621415,\
0.419502868068834,\
0.444359464627151,\
0.444359464627151,\
0.469216061185468,\
0.492160611854684,\
0.503632887189293,\
0.526577437858509,\
0.543785850860421,\
0.55717017208413,\
0.574378585086042]
p1_y = [0.252229299363057,\
0.301910828025478,\
0.351592356687898,\
0.401273885350319,\
0.439490445859873,\
0.489171974522293,\
0.538853503184713,\
0.6,\
0.653503184713376,\
0.69171974522293,\
0.74140127388535,\
0.78343949044586,\
0.844585987261147,\
0.898089171974522,\
0.943949044585987,\
0.970700636942675,\
0.997452229299363,\
1.02420382165605,\
1.04713375796178,\
1.0968152866242,\
1.15031847133758,\
1.24203821656051,\
1.34522292993631,\
1.44458598726115,\
1.44458598726115,\
1.54777070063694,\
1.64713375796178,\
1.74649681528662,\
1.84203821656051,\
1.94904458598726,\
2.05222929936306,\
2.14777070063694]
p2_x = [0.148659003831418,\
0.162068965517241,\
0.169731800766283,\
0.181226053639847,\
0.19272030651341,\
0.204214559386973,\
0.238697318007663,\
0.273180076628353,\
0.294252873563218,\
0.319157088122605,\
0.361302681992337,\
0.40536398467433,\
0.436015325670498,\
0.468582375478927,\
0.493486590038314,\
0.501149425287356,\
0.531800766283525,\
0.552873563218391,\
0.564367816091954,\
0.579693486590038,\
0.602681992337165]
p2_y = [0.340996168582375,\
0.409961685823755,\
0.494252873563218,\
0.597701149425287,\
0.685823754789272,\
0.781609195402299,\
0.896551724137931,\
1,\
1.03831417624521,\
1.09195402298851,\
1.14176245210728,\
1.24521072796935,\
1.34865900383142,\
1.44444444444444,\
1.54406130268199,\
1.64367816091954,\
1.73563218390805,\
1.83908045977012,\
1.9272030651341,\
2.03831417624521,\
2.13409961685824]
p3_x = [0.0178387650085763,\
0.0590051457975986,\
0.0864493996569468,\
0.115608919382504,\
0.0950257289879931,\
0.124185248713551,\
0.143053173241852,\
0.14819897084048,\
0.153344768439108,\
0.161921097770154,\
0.179073756432247,\
0.191080617495712,\
0.232246998284734,\
0.251114922813036,\
0.269982847341338,\
0.290566037735849,\
0.307718696397942,\
0.306003430531732,\
0.328301886792453,\
0.343739279588336,\
0.376329331046312,\
0.410634648370497,\
0.436363636363636,\
0.467238421955403,\
0.475814751286449,\
0.49811320754717,\
0.52041166380789,\
0.534133790737564,\
0.556432246998285,\
0.570154373927959]
p3_y = [0.257142857142857,\
0.308571428571429,\
0.325714285714286,\
0.401142857142857,\
0.353142857142857,\
0.449142857142857,\
0.504,\
0.558857142857143,\
0.6,\
0.641142857142857,\
0.699428571428571,\
0.754285714285714,\
0.850285714285714,\
0.891428571428571,\
0.949714285714286,\
1.00114285714286,\
1.07314285714286,\
1.04228571428571,\
1.09714285714286,\
1.152,\
1.25485714285714,\
1.34742857142857,\
1.45028571428571,\
1.55657142857143,\
1.64914285714286,\
1.752,\
1.85142857142857,\
1.95428571428571,\
2.05714285714286,\
2.15657142857143]
for k in range(len(prefixes)) :
plt.plot(u0[k]/10,z/.02,label=f"${k}$")
plt.scatter(p0_x,p0_y,label="Experiment",facecolors='none',edgecolors='r',s=35)
plt.xlim(-0.2,0.8)
plt.ylim(0,3)
plt.grid()
plt.legend()
plt.show()
plt.close()
for k in range(len(prefixes)) :
plt.plot(u1[k]/10,z/.02,label=f"${k}$")
plt.scatter(p1_x,p1_y,label="Experiment",facecolors='none',edgecolors='r',s=35)
plt.xlim(-0.2,0.8)
plt.ylim(0,3)
plt.grid()
plt.legend()
plt.show()
plt.close()
for k in range(len(prefixes)) :
plt.plot(u2[k]/10,z/.02,label=f"${k}$")
plt.scatter(p2_x,p2_y,label="Experiment",facecolors='none',edgecolors='r',s=35)
plt.xlim(-0.2,0.8)
plt.ylim(0,3)
plt.grid()
plt.legend()
plt.show()
plt.close()
for k in range(len(prefixes)) :
plt.plot(u3[k]/10,z/.02,label=f"${k}$")
plt.scatter(p3_x,p3_y,label="Experiment",facecolors='none',edgecolors='r',s=35)
plt.xlim(-0.2,0.8)
plt.ylim(0,3)
plt.grid()
plt.legend()
plt.show()
plt.close()
