kees-study/cds-monte-carlo-methods
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

09: Move stand-alone functionality to function main

Browse Source
This commit is contained in:
Kees van Kempen
2022-11-17 11:46:54 +01:00
parent 37e1c0ee77
commit 46c794c1cb
1 changed files with 38 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

72
Exercise sheet 9/latticescalar.py
View File

@ -48,38 +48,42 @@ def batch_estimate(data,observable,k):
values = np.apply_along_axis(observable, 1, batches)
return np.mean(values), np.std(values)/np.sqrt(k-1)
lamb = 1.5
kappas = np.linspace(0.08,0.18,11)
width = 3
num_sites = width**4
delta = 1.5 # chosen to have ~ 50% acceptance
equil_sweeps = 10
measure_sweeps = 2
measurements = 20
def main():
lamb = 1.5
kappas = np.linspace(0.08,0.18,11)
width = 3
num_sites = width**4
delta = 1.5 # chosen to have ~ 50% acceptance
equil_sweeps = 10
measure_sweeps = 2
measurements = 20
mean_magn = []
for kappa in kappas:
phi_state = np.zeros((width,width,width,width))
run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites)
magnetizations = np.empty(measurements)
for i in range(measurements):
run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites)
magnetizations[i] = np.mean(phi_state)
mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10)
mean_magn.append([mean,err])
output_filename = 'preliminary_simulation.h5'
with h5py.File(output_filename,'a') as f:
if not "mean-magn" in f:
dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn)
# store some information as metadata for the data set
dataset.attrs["lamb"] = lamb
dataset.attrs["kappas"] = kappas
dataset.attrs["width"] = width
dataset.attrs["num_sites"] = num_sites
dataset.attrs["delta"] = delta
dataset.attrs["equil_sweeps"] = equil_sweeps
dataset.attrs["measure_sweeps"] = measure_sweeps
dataset.attrs["measurements"] = measurements
dataset.attrs["start time"] = starttime
dataset.attrs["stop time"] = time.asctime()
mean_magn = []
for kappa in kappas:
phi_state = np.zeros((width,width,width,width))
run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites)
magnetizations = np.empty(measurements)
for i in range(measurements):
run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites)
magnetizations[i] = np.mean(phi_state)
mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10)
mean_magn.append([mean,err])
output_filename = 'preliminary_simulation.h5'
with h5py.File(output_filename,'a') as f:
if not "mean-magn" in f:
dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn)
# store some information as metadata for the data set
dataset.attrs["lamb"] = lamb
dataset.attrs["kappas"] = kappas
dataset.attrs["width"] = width
dataset.attrs["num_sites"] = num_sites
dataset.attrs["delta"] = delta
dataset.attrs["equil_sweeps"] = equil_sweeps
dataset.attrs["measure_sweeps"] = measure_sweeps
dataset.attrs["measurements"] = measurements
dataset.attrs["start time"] = starttime
dataset.attrs["stop time"] = time.asctime()
if __name__ == "__main__":
main()