from Manipulate import FindSteps from Manipulate import IbcLayer from Manipulate import LowerMantle from Files import fFileColD from Files import HzFileColD Route = "/home/p300/Desktop/SpaImpactOverturn/I420deg60ibc50v1e-3R65c192" CaseName = "a" InputFile = "%s/input_solidus_i/in_65moon" % (Route) ppFile = "%s/post_process" % (Route) plogfile = '%s/plog' % (Route) MaxStep = 100001 oRoute = './test' # read input # inD = Mread.read_input(InputFile) # get step list # Steps = FindSteps(Route, CaseName, MaxStep) TD = TimeDat(inD, fFileColD, Steps) ToF = TD.Check(Route) TD.Read(Route) timeArray = TD.GetTime() TD.PlotMachineTime(oRoute) # horiz_avg file # IbcCol = HzFileColD['chemical'][2] VrCol = HzFileColD['vr'] VthCol = HzFileColD['vth'] VCol = [VrCol, VthCol] rIbc = IbcLayer(inD) rLM = LowerMantle(inD) HA = HorizAvg(inD, HzFileColD, Steps, timeArray)
def remove_extra_file1(route, case_name, in_dict, time_array, step_tuple,
step_start, p_file, plogfile):
caps = 12
steps_inter = 30
nprocz = readf.get_variable(in_dict, 'nprocz', 'int')
nprocx = readf.get_variable(in_dict, 'nprocx', 'int')
nprocy = readf.get_variable(in_dict, 'nprocy', 'int')
nproc = caps * nprocx * nprocy * nprocz
p_dict = readf.read_input(p_file)
e_steps = readf.get_variable(p_dict, 'episode_steps', 'int_list')
steps_in = readf.get_variable(p_dict, 'steps_remain', 'int_list')
length = len(step_tuple)
time_at_steps = [time_array[step] for step in step_tuple]
step_end = step_tuple[len(step_tuple) - 1]
stime = time_array[step_start]
ftime = time_array[step_end]
plog = open(plogfile, 'a')
#----get time of steps that are worth keeping----#
time_query = []
for time in np.linspace(stime, ftime, steps_inter):
time_query.append(time)
#----get steps that are worth keeping----#
for time in time_query:
n = np.argmin(abs(time_at_steps - time))
step = step_tuple[n]
if step not in steps_in:
steps_in.append(step)
is_see = input('deleting steps: see steps to keep?(y/n)')
if is_see:
print(steps_in)
input()
#----delete extra files----#
for n in range(len(step_tuple)):
step = step_tuple[n]
if step < step_start:
continue
# bash_command = []
# bash_command.append('rm %s/%s.proc0.%d.vts'%(route,case_name,step))
# bash_command.append('rm %s/%s.%d.vtm'%(route,case_name,step))
if step not in steps_in:
for proc in range(nproc):
filename = '%s/%s.proc%d.%d.vts' % (route, case_name, proc,
step)
if os.path.isfile(filename):
os.remove(filename)
if proc % nprocz is nprocz - 1:
filename = '%s/%s.surf.%d.%d' % (route, case_name, proc,
step)
if os.path.isfile(filename):
os.remove(filename)
filename = '%s/%s.surf_ori.%d.%d' % (route, case_name,
proc, step)
if os.path.isfile(filename):
os.remove(filename)
filename = '%s/%s.surf_sph.%d' % (route, case_name, step)
if os.path.isfile(filename):
os.remove(filename)
filename = '%s/%s.%d.vtm' % (route, case_name, step)
if os.path.isfile(filename):
os.remove(filename)
# readf.run_bash(bash_command)
plog.write('remove file at step %d\n' % (step))
plog.close()
#----write remain steps to p_file----#
readf.overwrite_to_file(p_file,
'steps_remain',
steps_in,
vtype='int',
vvtype='array')
input()
import os
import sys
sys.path.append('../public')
import imgPP #self writen module for dealing images
import dataPP #self writen module dealing with CitcomS output files
import readfiles as Mread
route = "/home/p300/Documents/Research/work/CitcomS2/MgH30V5e20E100L"
#dataRoute = "/home/p300/Desktop/lunar_project/m1t_1"
dataRoute = "/media/p300/Seagate0/mg_project/MgH30V5e20E100L"
caseName = "MgH30V5e20E100L"
pp_file = '%s/post_process' % (dataRoute)
use_eps = 1
pp_dict = Mread.read_input(pp_file)
PVarray = []
MLarray = []
SGarray = []
steparray = []
#image types from paraview goes here
#PVarray.append('volume')
#PVarray.append('slice')
#PVarray.append('composition')
#PVarray.append('cslice0')
PVarray.append('cslice2')
PVarray.append('melting')
#image types from matlab goes here
MLarray.append('temp')
MLarray.append('rho')
MLarray.append('visc')
MLarray.append('velo')
include_field = 1
def chemical(in_dict, time_array, step_tuple, column_dict, route, o_dir,
case_name, pp_file):
get_end = 1
split = 6
init_lower_percent = 0.01
pi = np.pi
tiny = 1e-8
year = 365 * 24 * 3600
case_dir = Mpf.assign_output_dir("%s/%s" % (o_dir, case_name))
o_dir = Mpf.assign_output_dir("%s/chemical" % (case_dir))
if os.path.isfile(pp_file):
pp_dict = readf.read_input(pp_file)
try:
pp_dict['end_time']
except KeyError:
get_end = 1
else:
get_end = 0
else:
get_end = 1
#----read parameters----#
cols = column_dict['cols']
nprocz = readf.get_variable(in_dict, 'nprocz', 'int')
noz = readf.get_variable(in_dict, 'nodez', 'int')
Ro = readf.get_variable(in_dict, 'radius', 'float')
interface = readf.get_variable(in_dict, 'z_interface', 'float_list')
r_inter = interface[-1]
r_o = readf.get_variable(in_dict, 'radius_outer', 'float')
r_in = readf.get_variable(in_dict, 'radius_inner', 'float')
r_lith = r_o - readf.get_variable(in_dict, 'z_lith', 'float')
Kappa = readf.get_variable(in_dict, 'thermdiff', 'float')
r_half = (r_o + r_in) / 2.0
lnoz = int((noz - 1) / nprocz) + 1
time_scaling = pow(Ro, 2.0) / Kappa / (1e6 * year)
total_ibc = 4 * pi / 3 * (pow(r_lith, 3.0) - pow(r_lith - 20e3 / Ro, 3.0))
#----prepare chemical data----#
chemical = np.zeros((len(step_tuple), 3))
time1 = np.zeros(len(step_tuple))
n = 0
for step in step_tuple:
time1[n] = time_array[step]
print(step, end='\r')
data_in = readf.read_horig_output(route, case_name, step, nprocz, lnoz,
cols)
rr = data_in[:, column_dict['radius']]
C = data_in[:, column_dict['ic']]
for i in range(noz - 1):
volume = 4.0 / 3.0 * pi * (pow(rr[i + 1], 3.0) - pow(rr[i], 3.0))
if rr[i] > r_inter - tiny and rr[i] < r_lith + tiny:
chemical[n,
0] = chemical[n, 0] + (C[i] + C[i + 1]) / 2.0 * volume
if rr[i] > r_half - tiny:
chemical[n,
1] = chemical[n, 1] + (C[i] + C[i + 1]) / 2.0 * volume
if rr[i] < r_half + tiny:
chemical[n,
2] = chemical[n, 2] + (C[i] + C[i + 1]) / 2.0 * volume
n = n + 1
total_chemical = chemical[0, 1] + chemical[0, 2]
chemical_low = chemical[:, 2] #chemical_low is lower chemical percent
#print(chemical[0,1]+chemical[0,2])
#print(chemical[:,0]) #debug
#print(chemical[:,1])
#print(chemical[:,2])
#----plot figures----#
#----figure 1 : retained chemical----#
fig, ax = plt.subplots()
line1, = ax.plot(time1 * time_scaling, chemical[:, 0] / chemical[0, 0],
'r-')
ax.set(xlabel='Time [ma]',
ylabel='Chemical Percent[1]',
title='Retained Chemicals in Initial Layer')
fig.savefig("%s/retained_ibc.eps" % (o_dir))
#plt.show()
plt.close(fig)
#----figure 2 : overturned chemical----#
time2 = time1 * time_scaling
fig, ax = plt.subplots()
line1, = ax.plot(time2, chemical_low / total_chemical, 'b-')
ax.set(xlabel='Time [ma]',
ylabel='Chemical Percent[1]',
title='Overturned Chemical')
fig.savefig("%s/overturned_chemical.eps" % (o_dir))
#plt.show()
plt.close(fig)
#----figure 3 : overturned ibc volume----#
fig, ax = plt.subplots()
line1, = ax.plot(time2, chemical_low / total_chemical * total_ibc, 'c-')
ax.set(xlabel='Time [ma]', ylabel='IBC Volume[1]', title='Overturned IBC')
fig.savefig("%s/overturned_IBC.eps" % (o_dir))
if get_end is 1:
temp = input(
"input scheme to continue getting end for overturn: 'a(auto)/m(manual):"
)
if temp is 'a':
cid = fig.canvas.mpl_connect('button_press_event', on_press)
plt.show()
end_time = get_end_time(Gmc[0], Gmc[1], Gmc[2])
diff_t = abs(time2 - end_time)
end_n = np.argmin(diff_t)
end_c = chemical_low[end_n]
episode_c = np.linspace(0.0, 1.0, split) * end_c
episode_c[0] = init_lower_percent * end_c
episode_steps = []
for cc in episode_c:
diff_c = abs(chemical_low - cc)
tempn = np.argmin(diff_c)
episode_steps.append(step_tuple[tempn])
end_c_percent = end_c / total_chemical
episode_completness = episode_c / end_c
fig.canvas.mpl_disconnect(cid)
elif temp is 'm':
plt.show()
temp = input('endstep for episode:')
end_step = int(temp)
end_time = time_array[end_step] * time_scaling
diff_t = abs(time2 - end_time)
end_n = np.argmin(diff_t)
episode_t = np.linspace(0.0, 1.0, split) * end_time
end_c = chemical_low[end_n]
end_c_percent = end_c / total_chemical
episode_completness = episode_t / end_time
episode_steps = []
for tt in episode_t:
diff_t = abs(time2 - tt)
tempn = np.argmin(diff_t)
episode_steps.append(step_tuple[tempn])
readf.write_to_file(pp_file, 'end_time', end_time, 'float')
readf.write_to_file(pp_file, 'end_c_percent', end_c_percent, 'float')
readf.write_to_file(pp_file, 'episode_completness',
episode_completness, 'float', 'array')
readf.write_to_file(pp_file, 'episode_steps', episode_steps, 'int',
'array')
else:
plt.show()
plt.close(fig)