def test_init_profiles(self):
self.skipTest("Temporary")
# Set the cosinus model
model = pa.CosineModel("output/diff_mc_cyl_s.obj", 6, 10)
# Check if the initialized profiles are corret
self.assertEqual(
np.array_equal(model._diff_bin,
np.array([-1.3937803336775594] * model._bin_num)),
True)
self.assertEqual(
np.array_equal(model._df_bin, np.array([0] * model._bin_num)),
True)
def test_diffusion_mc_model(self):
# self.skipTest("Temporary")
# Check cosine model
model = pa.CosineModel("output/diff_mc_cyl_s.h5", 6, 10)
# Check if the initialized profiles are corret
self.assertTrue(np.array_equal(np.round(model._diff_bin,3), np.array([-3.702] * model._bin_num)))
self.assertTrue(np.array_equal(model._df_bin, np.array([0] * model._bin_num)))
# Check step model
model = pa.StepModel("output/diff_mc_cyl_s.h5", 6, 10)
# Check if the initialized profiles are corret
self.assertTrue(np.array_equal(np.round(model._diff_bin,3), np.array([-3.702] * model._bin_num)))
self.assertEqual(np.array_equal(model._df_bin, np.array([0] * model._bin_num)), True)
def test_diffusion_mc_box(self):
# self.skipTest("Temporary")
# Set Cosine Model for diffusion and energy profile
model = pa.CosineModel("output/diff_mc_box.h5", 6, 10)
# Set the MC class and options
model._len_step = [10, 20, 30, 40, 50]
# Do the MC alogirthm
pa.MC().run(model,"output/diff_test_mc_box.h5", nmc_eq=10000, nmc=3000, is_print=False, is_parallel=False)
# Plot diffusion coefficient over inverse lagtime
plt.figure()
diff = pa.diffusion.mc_fit("output/diff_test_mc_box.h5")
plt.savefig("output/diffusion_fit_box.pdf", format="pdf", dpi=1000)
# Check if diffusion coefficient is in the range
self.assertEqual(abs(diff[0] - (10.5) ) < 0.7, True)
def test_init_likelihood(self):
self.skipTest("Temporary")
# Set the cosinus model
model = pa.CosineModel("output/diff_mc_cyl_s.obj", 6, 10)
# Set the MC class
MC = pa.MC(model)
# Set the variable because this happen in the do_mc_cycles function -> not necessary to call to check the likelihood and Check if the initalize likelihood is correct
MC._len_step = 1
self.assertEqual(MC.log_likelihood_z(model), -128852.33005868513)
# Set the variable because this happen in the do_mc_cycles function -> not necessary to call to check the likelihood and Check if the initalize likelihood is correct
MC._len_step = 2
self.assertEqual(MC.log_likelihood_z(model), -165354.76731180004)
# Set the variable because this happen in the do_mc_cycles function -> not necessary to call to check the likelihood and Check if the initalize likelihood is correct
MC._len_step = 10
self.assertEqual(MC.log_likelihood_z(model), -258946.70553844847)
def test_mc_pore(self):
self.skipTest("Temporary")
# Set Cosine Model for diffusion and energy profile
model = pa.CosineModel("output/diff_mc_cyl_s.obj", 6, 10)
# Set the MC class and options
model._len_step = [10, 20, 30, 40, 50]
MC = pa.MC(model, 5000, 5000)
# Do the MC alogirthm
MC.do_mc_cycles(model, "output/diff_test_mc.obj")
# Plot diffusion coefficient over inverse lagtime
plt.figure()
diff, diff_mean, diff_table = pa.diffusion.diffusion_fit(
"output/diff_test_mc.obj")
plt.savefig("output/diffusion_fit.svg", format="svg", dpi=1000)
# Plot diffusion profile over box length
plt.figure()
pa.diffusion.diff_profile("output/diff_test_mc.obj",
infty_profile=True)
plt.savefig("output/diffusion_profile.svg", format="svg", dpi=1000)
# Plot free energy profile over box length
plt.figure()
pa.diffusion.df_profile("output/diff_test_mc.obj", [10])
plt.savefig("output/energy_profile.svg", format="svg", dpi=1000)
# Plot transition matrix as a heat map
plt.figure()
pa.diffusion.plot_trans_mat("output/diff_test_mc.obj", 10)
plt.savefig("output/transition_heatmap.svg", format="svg", dpi=1000)
# Check if diffusion coefficient is in the range
self.assertEqual(abs(diff - (1.4 * 10**-9)) < 0.3 * 10**-9, True)
def test_mc_box(self):
self.skipTest("Temporary")
# Set Cosine Model for diffusion and energy profile
model = pa.CosineModel("output/diff_mc_box.obj", 6, 10)
# Set the MC class and options
model._len_step = [1, 2, 5, 10, 20, 30, 40, 50]
MC = pa.MC(model, 1000, 1000)
# Do the MC alogirthm
MC.do_mc_cycles(model, "output/diff_test_mc_box.obj")
# Plot diffusion coefficient over inverse lagtime
plt.figure()
diff, diff_mean, diff_table = pa.diffusion.diffusion_fit(
"output/diff_test_mc_box.obj")
plt.savefig("output/diffusion_fit_box.svg", format="svg", dpi=1000)
# Plot diffusion profile over box length
plt.figure()
pa.diffusion.diff_profile("output/diff_test_mc_box.obj",
infty_profile=True)
plt.savefig("output/diffusion_profile_box.svg", format="svg", dpi=1000)
# Plot free energy profile over box length
plt.figure()
pa.diffusion.df_profile("output/diff_test_mc_box.obj", [10])
plt.savefig("output/energy_profile_box.svg", format="svg", dpi=1000)
# Plot transition matrix as a heat map
plt.figure()
pa.diffusion.plot_trans_mat("output/diff_test_mc_box.obj", 10)
plt.savefig("output/transition_heatmap_box.svg",
format="svg",
dpi=1000)
def test_diffusion_mc_mc(self):
# self.skipTest("Temporary")
# Set Cosine Model for diffusion and energy profile
model = pa.CosineModel("output/diff_mc_cyl_s.h5", 6, 10)
# Set the variable because this happen in the do_mc_cycles function -> not necessary to call to check the likelihood and Check if the initalize likelihood is correct
pa.MC._len_step = 1
self.assertEqual(round(pa.MC()._log_likelihood_z(model),2),-149411.12)
# Set the variable because this happen in the do_mc_cycles function -> not necessary to call to check the likelihood and Check if the initalize likelihood is correct
pa.MC._len_step = 2
self.assertEqual(round(pa.MC()._log_likelihood_z(model),2),-169616.56)
# Set the variable because this happen in the do_mc_cycles function -> not necessary to call to check the likelihood and Check if the initalize likelihood is correct
pa.MC._len_step = 10
self.assertEqual(round(pa.MC()._log_likelihood_z(model),2), -234093.97)
# Set len_step for MC run test
model._len_step = [10,20,40]
#### Test Single ####
# Do the MC alogirthm
pa.MC().run(model,"output/diff_test_mc.yml", nmc_eq=1000, nmc=2000, is_print=False, is_parallel=False)
# Plot diffusion coefficient over inverse lagtime
diff = pa.diffusion.mc_fit("output/diff_test_mc.yml")
plt.savefig("output/mc_fit.pdf", format="pdf", dpi=1000)
# Plot pore diffusion coefficient over inverse lagtime
plt.figure()
diff_pore = pa.diffusion.mc_fit("output/diff_test_mc.yml", section="pore")
plt.savefig("output/mc_fit_pore.pdf", format="pdf", dpi=1000)
# Check if diffusion coefficient is in the range
self.assertEqual(abs(diff[0] - (1.6) ) < 0.3, True)
self.assertEqual(abs(diff_pore[0] - (1.2) ) < 0.3, True)
#### Test Parallel ####
# Do the MC alogirthm
pa.MC().run(model,"output/diff_test_mc.h5", nmc_eq=8000, nmc=2000, is_print=False, is_parallel=True)
# Plot diffusion coefficient over inverse lagtime
plt.figure()
diff = pa.diffusion.mc_fit("output/diff_test_mc.h5")
plt.savefig("output/mc_fit.pdf", format="pdf", dpi=1000)
# Plot pore diffusion coefficient over inverse lagtime
plt.figure()
diff_pore = pa.diffusion.mc_fit("output/diff_test_mc.h5", section="pore")
plt.savefig("output/mc_fit_pore.pdf", format="pdf", dpi=1000)
# Check if diffusion coefficient is in the range
self.assertEqual(abs(diff[0]-(1.6) ) < 0.3, True)
self.assertEqual(abs(diff_pore[0]-(1.2) ) < 0.3, True)
# Test MC output
# Set Step Model for diffusion and energy profile
model = pa.StepModel("output/diff_mc_cyl_s.h5", 6, 10, is_print=True)
# Set Cosine Model for diffusion and energy profile
model = pa.CosineModel("output/diff_mc_cyl_s.h5", 6, 10, is_print=True)
## Set the MC class and options
model._len_step = [10]
# Do the MC alogirthm
pa.MC().run(model,"output/diff_test_mc_spline.h5", nmc_eq=100, nmc=2000, is_print=True, is_parallel=False)
if ana_props["dens"] or ana_props["diff"]:
sample = pa.Sample("../_gro/pore.yml", ana_props["traj"], ana_props["mol"], ana_props["atoms"], [1 for x in ana_props["atoms"]])
if ana_props["dens"]:
sample.init_density("dens_"+ana_name+".obj", remove_pore_from_res=True)
if ana_props["diff"]:
sample.init_diffusion_bin("diff_"+ana_name+".obj", bin_num=35)
sample.sample(is_parallel=True)
if ana_props["mc_trans"]:
sample = pa.Sample(pore["dimensions"], ana_props["traj"], ana_props["mol"], ana_props["atoms"], [1 for x in ana_props["atoms"]])
sample.init_diffusion_mc("diff_"+ana_name+"_trans.obj", [1, 2, 5, 10, 20, 30, 40, 50, 60, 70])
sample.sample(is_parallel=True)
if ana_props["mc"]:
model = pa.CosineModel("diff_"+ana_name+"_trans.obj", 6, 10)
pa.MC().run(model, "diff_"+ana_name+"_mc_cos.obj", nmc_eq=1000000, nmc=1000000)
# Automation
is_auto = True
if is_auto:
# Calculate density - area is given in bins
dens = pa.density.bins("dens_MOLSHORT.obj", target_dens=TARGETDENS, area=[[10, 90], [10, 50]])
# Fill and rerun
num_diff = dens["diff"]
if num_diff > 10:
ps.utils.copy("../_fill/fillBackup.sh", "../_fill/fill.sh")
ps.utils.replace("../_fill/fill.sh", "FILLDENS", str(int(num_diff)))
os.system("cd ../_fill;sh fill.sh;cd ../min;SUBMITCOMMAND")