#loop = TLM.loops['u302']
loop = new_looper
tracks_read_write.save_loop_only_trackage(loop, 'TRACKAGE_TEST.h5')
if 0:
import tracks_read_write
reload(tracks_read_write)
test_file = 'TRACKAGE_TEST_CORE14.h5'
savefile = 'TRACKAGE_TEST.h5'
savefile = 'u302_tracks_only_c0-20.h5'
savefile = 'u302_zero_test_tracks_only.h5'
savefile = "u302_zero_test_tracks_only.h5"
#ktracks_read_write.load_loop_only_trackage( loop, )
loop3 = looper.core_looper(directory=dl.sims['u302'],
savefile_only_trackage=savefile)
if 1:
mt1 = mass_tools.mass_tool(new_looper)
mt1.run()
if 1:
reload(mass_tools)
fig, ax = plt.subplots(1, 1)
mass_tools.plot_mass_tracks(mt1, ax)
ax.plot([0, 1], [0, 1])
fig.savefig('plots_to_sort/%s_mass_test_read_test.png' % "U302_c0014")
plt.close(fig)
if 0:
import tracks_read_write
reload(tracks_read_write)
savefile = 'TRACKAGE_TEST.h5'
loop = looper.core_looper(directory=dl.sims['u302'],
savefile_only_trackage=savefile)
mt1 = mass_tools.mass_tool(loop)
mt1.run(core_list=[14])
if 0:
reload(mass_tools)
fig, ax = plt.subplots(1, 1)
mass_tools.plot_mass_tracks(mt1, ax)
fig.savefig('plots_to_sort/%s_mass_test_read_test.png' %
mt1.this_looper.out_prefix)
plt.close(fig)
if 1:
import three_loopers_mountain_top as TLM
reload(TLM)
if 0:
mt1 = mass_tools.mass_tool(TLM.loops['u301'])
mt1.run()
if 1:
fig, ax = plt.subplots(1, 1)
mass_tools.plot_mass_tracks(mt1, ax)
fig.savefig('plots_to_sort/u301_mass_time.png')
if 0:
density_cmap = rainbow_map(np.log10(density)[:, 0].max())
mass_ratio = 1
big_core = 0
core_list = []
for nc, core_id in enumerate(mt.cores_used):
ax[0][0].plot(mt.times, mt.volume[core_id], c=[0.5] * 4)
#c=density_cmap( np.log10(mt.mean_rho[core_id][0]))
c = color_map.to_rgba(np.log10(density[nc, n0]))
Y2 = mt.mean_rho[core_id] #/nar(mt.mean_rho[core_id])[0:2].mean()
ax[0][1].plot(mt.times, Y2, c=c) #[0.5]*4)
M = mt.unique_mass[
core_id] #/nar(mt.unique_mass[core_id])[:6].mean()
Mbar = mt.unique_mass[core_id] / nar(
mt.unique_mass[core_id])[:6].mean()
this_ratio = Mbar.max() / Mbar.min()
if Mbar.min() < 0.1:
core_list.append(nc)
ax[1][0].plot(mt.times, M)
mass_tools.plot_mass_tracks(mt, ax[1][1], core_list=core_list)
#ax[1].plot([mt.times[0], mt.times[-1]], [Y2[0],Y2[-1]],c=c)#[0.5]*4)
#ax[1].plot(mt.times, mt.mean_rho_w[core_id],c=[0.5]*4)
axbonk(ax[1][0], xlabel='t', ylabel='M', yscale='log')
axbonk(ax[0][1], xlabel='t', ylabel='rho', yscale='log')
axbonk(ax[0][0], xlabel='t', ylabel='V', yscale='log')
fig.savefig('plots_to_sort/%s_meansies.pdf' % this_simname)
plt.close(fig)
if 0:
import tracks_read_write
reload(tracks_read_write)
savefile = 'TRACKAGE_TEST.h5'
loop = looper.core_looper(directory= dl.sims['u302'],savefile_only_trackage=savefile)
mt1 = mass_tools.mass_tool(loop)
mt1.run(core_list = [14])
if 0:
reload(mass_tools)
fig,ax=plt.subplots(1,1)
mass_tools.plot_mass_tracks(mt1,ax)
fig.savefig('plots_to_sort/%s_mass_test_read_test.png'%mt1.this_looper.out_prefix)
plt.close(fig)
if 0:
import three_loopers_mountain_top as TLM
reload(TLM)
if 0:
mt_dict={}
for this_simname in ['u301','u302','u303']:
mt_dict[this_simname] = mass_tools.mass_tool(TLM.loops[this_simname])
mt_dict[this_simname].run()
if 0:
for this_simname in ['u301','u302','u303']:
fig,ax = plt.subplots(1,1)
import data_locations as dl
from collections import defaultdict
import davetools
reload(davetools)
import mass_tools
reload(mass_tools)
import looper2
plt.close('all')
sim_list = ['u501']
if 'u501' not in these_loops:
these_loops = {}
print('read u501')
these_loops['u501'] = looper2.load_looper('u501_all_frame_all_prim.h5')
if 'mtd' not in dir():
mtd = {}
for this_simname in sim_list:
if this_simname not in mtd:
mtd[this_simname] = mass_tools.mass_tool(these_loops[this_simname])
mtd[this_simname].run()
for this_simname in sim_list:
fig, ax = plt.subplots(1, 1)
mass_tools.plot_mass_tracks(mtd[this_simname], ax)
fig.savefig('plots_to_sort/thing.pdf')