y_list = list(eval(file2.readline().replace('\n','')))
w_list = list(eval(file2.readline().replace('\n','')))
v_list = list(eval(file2.readline().replace('\n','')))
b_list = list(eval(file2.readline().replace('\n','')))
p_list = list(eval(file2.readline().replace('\n','')))
T_list = list(eval(file2.readline().replace('\n','')))
m_list = list(eval(file2.readline().replace('\n','')))
t_list = list(eval(file2.readline().replace('\n','')))
r_list = list(eval(file2.readline().replace('\n','')))
used = list(eval(file2.readline().replace('\n','')))
jumps, start_time, end_time, save_step, R, grid_spacing, g, a, v_0, layers, datafile = eval(file2.readline().replace('\n',''))
print('DONE\n')
# Make an output directory
dirname='Origin_Landing'
psp.mkdir_p(dirname)
print('Extracting Materials...')
# Open the datafile
model=psp.opendatfile(datafile)
model.setScale('km')
step = model.readStep('TrP', 0)
yy = np.append(np.zeros(0), step.ymark)
col = []
for i in range(len(yy)): # find materials based on starting height
if yy[i] > layers[0]:
co = 'black'
elif yy[i] > layers[1]:
co = 'darkgrey'
elif yy[i] > layers[2]:
co = 'y'
import numpy as np
import scipy as sc
import scipy.stats as stats
import pySALEPlot as psp
import matplotlib.pyplot as plt
field1 = 'Pre'
field2 = 'V_y'
field3 = 'Den'
field4 = 'TPS'
field5 = 'Tmp'
dirname = 'shockfronts'
model = psp.opendatfile('./jdata.dat') # Open datafile
psp.mkdir_p('./shockfronts')
model.setScale('mm')
tstart = 1 # Start at t>0 such that there IS a shock present
tfin = 250
tintv = 1
YC = model.yc
XC = model.xc
TME = np.zeros((int((tfin - tstart + 1) / tintv))) # time
YSF = np.zeros((int((tfin - tstart + 1) / tintv))) # Shock Front Y position
PBD = np.zeros((int((tfin - tstart + 1) /
tintv))) # Shock in Particle Bed array. 1 is yes 0 is no
QUP = np.zeros((int(
(tfin - tstart + 1) / tintv))) # Quasi steady state Particle velocity
Us = np.zeros((int((tfin - tstart + 1) / (5 * tintv)) + 1))
Up = np.zeros(
(int((tfin - tstart + 1) / (5 * tintv)) +
import pySALEPlot as psp
from matplotlib.pyplot import figure,colorbar
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
# This example plotting script designed to plot
# damage in the demo2D example simulation
# Make an output directory
#field1 = 'Den'#raw_input('Which field is to be considered? (3 chars) =>')
field2 = 'Den'#raw_input('Which field is to be considered? (3 chars) =>')
dirname='./%s'%field2
psp.mkdir_p(dirname)
# Open the datafile
model=psp.opendatfile('jdata.dat')
# Set the distance units to km
model.setScale('um')
# Set up a pylab figure
fig=figure()
ax=fig.add_subplot(111,aspect='equal')
fig.set_size_inches(4,3)
# Loop over timesteps, in increments of 5
#step_1=model.readStep('%s'%field2,0)
#MAXvalue = abs(np.amax(step_1.data[0]))
plt.tight_layout()
for i in np.arange(0,37,1):
# Set the axis labels
import pySALEPlot as psp
import matplotlib.pyplot as plt
import numpy as np
from math import atan, pi, sqrt, sin
from aux_library import Bin
psp.mkdir_p('Combined/Landing')
psp.mkdir_p('Combined/Material')
psp.mkdir_p('Combined/Pressure')
psp.mkdir_p('Combined/Temperature')
pres_lim = [0, 200]
temp_lim = [0, 4000]
print('Opening data file...')
# open tracer file
file2 = open('Results.dat', 'r')
x_list = list(eval(file2.readline().replace('\n', '')))
y_list = list(eval(file2.readline().replace('\n', '')))
w_list = list(eval(file2.readline().replace('\n', '')))
v_list = list(eval(file2.readline().replace('\n', '')))
b_list = list(eval(file2.readline().replace('\n', '')))
p_list = list(eval(file2.readline().replace('\n', '')))
T_list = list(eval(file2.readline().replace('\n', '')))
m_list = list(eval(file2.readline().replace('\n', '')))
t_list = list(eval(file2.readline().replace('\n', '')))
r_list = list(eval(file2.readline().replace('\n', '')))
used = list(eval(file2.readline().replace('\n', '')))
jumps, start_time, end_time, save_step, R, grid_spacing, g, a, v_0, layers, datafile = eval(
file2.readline().replace('\n', ''))
print('DONE\n')
def hugoniot_point_general_ND(AAA,YSF,TME,N):
global t1,t2
t1 = .0
t2 = 0.8
D = -3.2e-2 # in mm and -ve as up is +ve
AAA = np.ma.masked_invalid(AAA)
time = TME[(YSF<(N*D))*(YSF>-1.)*(TME<t2)] # Only use times and particle velocities for results WITHIN the particle bed
a = AAA[(YSF<(N*D))*(YSF>-1.)*(TME<t2)] # .8mm into the particle bed is given, for the shock to stabilise
sf = YSF[(YSF<(N*D))*(YSF>-1.)*(TME<t2)] # Shock front positions
A = np.ma.mean(a[1:])
ASD = np.ma.std(a[1:])
return A,ASD
dir_0 = './hugoniot_data' # Directory name for the output files
dirs = ['A-1.1201_K-28.673']
psp.mkdir_p(dir_0)
psp.mkdir_p(dir_0+'/cmaps')
N = 11
UP_2 = np.zeros((N))
US_2 = np.zeros((N))
UPSD_2 = np.zeros((N))
SGY_ = np.zeros((N))
SGYSD = np.zeros((N))
TPS_ = np.zeros((N))
TPSSD = np.zeros((N))
PRE_ = np.zeros((N))
import numpy as np
import scipy as sc
import scipy.stats as stats
import pySALEPlot as psp
import matplotlib.pyplot as plt
field1 = 'Pre'
field2 = 'V_y'
field3 = 'Den'
field4 = 'TPS'
field5 = 'Tmp'
dirname= 'shockfronts'
model = psp.opendatfile('./jdata.dat') # Open datafile
psp.mkdir_p('./shockfronts')
model.setScale('mm')
tstart = 1 # Start at t>0 such that there IS a shock present
tfin = 250
tintv = 1
YC = model.yc
XC = model.xc
TME = np.zeros(( int((tfin-tstart+1)/tintv) )) # time
YSF = np.zeros(( int((tfin-tstart+1)/tintv) )) # Shock Front Y position
PBD = np.zeros(( int((tfin-tstart+1)/tintv) )) # Shock in Particle Bed array. 1 is yes 0 is no
QUP = np.zeros(( int((tfin-tstart+1)/tintv) )) # Quasi steady state Particle velocity
Us = np.zeros(( int((tfin-tstart+1)/(5*tintv))+1 ))
Up = np.zeros(( int((tfin-tstart+1)/(5*tintv))+1 )) # Shock and particle velocities calculated every 5 timesteps
Time = np.zeros(( int((tfin-tstart+1)/(5*tintv))+1 )) # Equivalent time every 5 timesteps (=.1 us)
print "**** ERROR ****"
# Print output
print "The selected Lagrangian pseudo-cell is made of tracers:"
print "# {} --- {} #".format(tl, tr)
print "# --------------- #"
print "# {} --- {} #".format(bl, br)
print "==================================="
# Calculate Time Resolution
T_res = (step_final.time - step_initial.time) / model.nsteps
####################################################################################################################################### USER
# Create output directory
dirname = 'StressStrain_{}'.format(TR)
psp.mkdir_p(namemodel + '/' + dirname)
####################################################################################################################################### USER
# ---------------------------------------------------------------------------------- #
# LOOP OVER TIMESTEPS TO FIND LOCATION DATA AND STRESS DATA, THEN ORGANISE THAT DATA #
# ---------------------------------------------------------------------------------- #
# ------------------- NOTE ------------------- #
# DEVIATORIC STRESS TENSOR in 3D has the form #
# ( 'TrX' 'TrR' 0 )
# ( 'TrR' 'TrY' 0 )
# ( 0 0 'TrH' )
# FULL STRESS TENSOR in 3D has the form #
# ( 'TrX'-'TrP' 'TrR' 0 )
# ( 'TrR' 'TrY'-'TrP' 0 )