2. Analyzing One-Body Reduced Density Matrix Data

This example covers analyzing the one-rdm stochastic data output by SAFIRE when back-propagation or mixed estimators are used.

This example assumes that you have already run an AFQMC calculation using using SAFIRE. If not, see Basic Back-propagation with CPU build for an example on running AFQMC with back-propagation. While the AFQMC executable runs, it prints scalar data samples to a text-based file. The name of the file depends on the parameters set in the “project” block of the AFQMC input file and follows the pattern, “[id].s[###].scalar.dat” where “[id]” is replaced by the “id” string and “[###]” is replaced by the value of “series” but at a fixed width of 3. Similarly, if back-propagation is used, the one-rdm data is written to a file called, “[id].s[###].stat.h5”.

The one-rdm data can be analyzed via the afqmctools Python package as shown below.

from afqmctools.analysis.rdm import average_afqmc_rdm

rho_avg, delta_rho = average_afqmc_rdm(
    rdm_file="qmc.s000.stat.h5"
)

# Note: the AFQMC input file may already include
#         an equilibration time for back-propagation.
#         If not, or if you want to increase the size
#         of the equilibration phase ...

# ... you can specify the Number of equilibration blocks
rho_avg, delta_rho = average_afqmc_rdm(
    rdm_file="qmc.s000.stat.h5",
    Neq=5
)

# ... OR you can specify the equilibration time
rho_avg, delta_rho = average_afqmc_rdm(
    rdm_file="qmc.s000.stat.h5",
    Teq=10.0
)

# ... if both are given, Neq is used.
rho_avg, delta_rho = average_afqmc_rdm(
    rdm_file="qmc.s000.stat.h5",
    Neq=5,
    Teq=10.0
)