mini-pole 0.3

The Python code provided implements the matrix-valued version of the Minimal Pole Method (MPM) as described in Phys. Rev. B 110, 235131 (2024).

Minimal Pole Method (MPM)

The Python code provided implements the matrix-valued version of the Minimal Pole Method (MPM) as described in Phys. Rev. B 110, 235131 (2024), extending the scalar-valued method introduced in Phys. Rev. B 110, 035154 (2024).

The input of the simulation is the Matsubara data $G(i \omega_n)$ sampled on a uniform grid $\lbrace i\omega_{0}, i\omega_{1}, \cdots, i\omega_{n_{\omega}-1} \rbrace$, where $\omega_n=\frac{(2n+1)\pi}{\beta}$ for fermions and $\frac{2n\pi}{\beta}$ for bosons, and $n_{\omega}$ is the total number of sampling points.

1. Installation

Dependencies

• numpy
• scipy
• matplotlib

Installation Commands

1. Via setup.py:

   python3 setup.py install
   

2. Or via pip:

   pip install mini_pole
   

2. Usage

i) The standard MPM is performed using the following command:

p = MiniPole(G_w, w, n0 = "auto", n0_shift = 0, err = None, err_type = "abs", M = None, symmetry = False, G_symmetric = False, compute_const = False, plane = None, include_n0 = True, k_max = 999, ratio_max = 10)

Parameters
----------
1. G_w : ndarray
    An (n_w, n_orb, n_orb) or (n_w,) array containing the Matsubara data.
2. w : ndarray
    An (n_w,) array containing the corresponding real-valued Matsubara grid.
3. n0 : int or str, default="auto"
    If "auto", n0 is automatically selected with an additional shift specified by n0_shift.
    If a non-negative integer is provided, n0 is fixed at that value.
4. n0_shift : int, default=0
    The shift applied to the automatically determined n0.
5. err : float
    Error tolerance for calculations.
6. err_type : str, default="abs"
    Specifies the type of error: "abs" for absolute error or "rel" for relative error.
7. M : int, optional
    The number of poles in the final result. If not specified, the precision from the first ESPRIT is used to extract poles in the second ESPRIT.
8. symmetry : bool, default=False
    Determines whether to preserve up-down symmetry.
9. G_symmetric : bool, default=False
    If True, the Matsubara data will be symmetrized such that G_{ij}(z) = G_{ji}(z).
10. compute_const : bool, default=False
    Determines whether to compute the constant term in G(z) = sum_l Al / (z - xl) + const.
    If False, the constant term is fixed at 0.
11. plane : str, optional
    Specifies whether to use the original z-plane or the mapped w-plane to compute pole weights.
12. include_n0 : bool, default=False
    Determines whether to include the first n0 input points when weights are calculated in the z-plane.
13. k_max : int, default=999
    The maximum number of contour integrals.
14. ratio_max : float, default=10
    The maximum ratio of oscillation when automatically choosing n0.

Returns
-------
Minimal pole representation of the given data.
Pole weights are stored in p.pole_weight, a numpy array of shape (M, n_orb, n_orb).
Shared pole locations are stored in p.pole_location, a numpy array of shape (M,).

ii) The MPM-DLR algorithm is performed using the following command:

p = MiniPoleDLR(Al_dlr, xl_dlr, beta, n0, nmax = None, err = None, err_type = "abs", M = None, symmetry = False, k_max=200, Lfactor = 0.4)

Parameters
----------
1. Al_dlr (numpy.ndarray): DLR coefficients, either of shape (r,) or (r, n_orb, n_orb).
2. xl_dlr (numpy.ndarray): DLR grid for the real frequency, an array of shape (r,).
3. beta (float): Inverse temperature of the system (1/kT).
4. n0 (int): Number of initial points to discard, typically in the range (0, 10).
5. nmax (int): Cutoff for the Matsubara frequency when symmetry is False.
6. err (float): Error tolerance for calculations.
7. err_type (str): Specifies the type of error, "abs" for absolute error or "rel" for relative error.
8. M (int): Specifies the number of poles to be recovered.
9. symmetry (bool): Whether to impose up-down symmetry (True or False).
10. k_max (int): Number of moments to be calculated.
11. Lfactor (float): Ratio of L/N in the ESPRIT algorithm.

Returns
-------
Minimal pole representation of the given data.
Pole weights are stored in p.pole_weight, a numpy array of shape (M, n_orb, n_orb).
Shared pole locations are stored in p.pole_location, a numpy array of shape (M,).

3. Examples

The scripts in the examples folder demonstrate the usage of MPM and MPM-DLR.

i) MPM Algorithm

The examples/MPM folder includes a Jupyter notebook that demonstrates how to use MiniPole to recover synthetic spectral functions. You can modify the lambda expression in the GreenFunc class to recover a different spectrum, but please remember to update the lower and upper bounds (x_min and x_max) of the spectrum accordingly. Additional details will be provided in the future.

ii) MPM-DLR Algorithm

The examples/MPM_DLR folder contains scripts to recover the band structure of Si, as shown in the middle panel of Fig. 9 in Phys. Rev. B 110, 235131 (2024).

Steps:

a) Download the input data file Si_dlr.h5 to the examples/MPM_DLR/ directory.

b) Obtain the recovered poles by running python3 cal_band_dlr.py --obs=<option>, where <option> can be "S" (self-energy), "Gii" (scalar-valued Green's function), or "G" (matrix-valued Green's function).

c) Plot the band structure by running python3 plt_band_dlr.py --obs=<option>.

Note:

a) Reference runtime on a single core of a laptop (using the M1 Max Apple chip as an example): 13 seconds for "Gii" and 160 seconds for both "G" and "S".

b) Parallel computation is supported in cal_band_dlr.py to speed up the process on multiple cores. Use the following command: mpirun -n <num_cores> python3 cal_band_dlr.py --obs=<option>, where <num_cores> is the number of cores and <option> is "S," "Gii," or "G".

c) Full Parameters for cal_band_dlr.py:

• --obs (str): Observation type used in the script. Default is "S".
• --n0 (int): Parameter $n_0$ as described in Phys. Rev. B 110, 235131 (2024).
• --err (float): Error tolerance for computations. Default is 1.e-10.
• --symmetry (bool): Specifies whether to preserve up-down symmetry in calculations.

d) Full Parameters for plt_band_dlr.py:

• --obs (str): Observation type used in the script. Default is "S".
• --w_min (float): Lower bound of the real frequency in eV. Default is -12.
• --w_max (float): Upper bound of the real frequency in eV. Default is 12.
• --n_w (int): Number of frequencies between w_min and w_max. Default is 200.
• --eta (float): Broadening parameter. Default is 0.005.