From 95c7cd0d0045fc4c1db381eea29d026fc75d4bad Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Thu, 15 Jan 2026 10:21:39 +0100 Subject: [PATCH 01/18] I do not remember --- Modules/cli.py | 1 + 1 file changed, 1 insertion(+) diff --git a/Modules/cli.py b/Modules/cli.py index 2160f9d9..c9b302d2 100644 --- a/Modules/cli.py +++ b/Modules/cli.py @@ -81,6 +81,7 @@ """ def plot_hessian_convergence(): + print(MSG_HESSIAN_CONV) nargs = len(sys.argv) From 47ab893c71534dd88c9675c63cdf01c8e85b6e7c Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 8 Feb 2026 15:09:35 +0100 Subject: [PATCH 02/18] Added the Gamma only implementation with the corresponding testsuite. --- Modules/DynamicalLanczos.py | 74 ++++++++++++++++++++++++++++++++++--- 1 file changed, 69 insertions(+), 5 deletions(-) diff --git a/Modules/DynamicalLanczos.py b/Modules/DynamicalLanczos.py index 44270a23..18bb8552 100644 --- a/Modules/DynamicalLanczos.py +++ b/Modules/DynamicalLanczos.py @@ -294,7 +294,13 @@ def __init__(self, ensemble = None, mode = None, unwrap_symmetries = False, sele self.f_tilde = None self.sym_block_id = None - + + # Gamma-only optimization: use only point-group symmetries in replicas, + # impose translations directly on f_pert_av and d2v_pert_av + self.gamma_only = False + self.trans_projector = None # (n_modes, n_modes) matrix P = (1/n_cells) Σ_R T_R^mode + self.trans_operators = None # list of (n_modes, n_modes) T_R^mode matrices + # Set to True if we want to use the Wigner equations self.use_wigner = use_wigner @@ -738,6 +744,46 @@ def prepare_symmetrization(self, no_sym = False, verbose = True, symmetries = No if verbose: print("Time to get the symmetries [{}] from spglib: {} s".format(len(super_symmetries), t2-t1)) + # Gamma-only optimization: separate point-group and translational symmetries + if self.gamma_only: + n_total_syms = len(super_symmetries) + identity = np.eye(3) + translations = [] + unique_rotations = {} + for sym in super_symmetries: + rot = sym[:, :3] + rot_key = np.round(rot, 8).tobytes() + if np.allclose(rot, identity, atol=1e-8): + translations.append(sym) + if rot_key not in unique_rotations: + unique_rotations[rot_key] = sym + pg_symmetries = list(unique_rotations.values()) + + if verbose: + print("Gamma-only: {} PG syms instead of {} total (speedup: {}x)".format( + len(pg_symmetries), n_total_syms, n_total_syms // max(len(pg_symmetries), 1))) + + # Build translation operators in mode space: T_R^mode = pols^T @ P_R @ pols + self.trans_operators = [] + nat_sc = super_structure.N_atoms + for t_sym in translations: + irt = CC.symmetries.GetIRT(super_structure, t_sym) + # Build Cartesian permutation matrix P_R (3*nat_sc x 3*nat_sc) + P_R = np.zeros((3 * nat_sc, 3 * nat_sc), dtype=np.double) + for i_at in range(nat_sc): + j_at = irt[i_at] + P_R[3*j_at:3*j_at+3, 3*i_at:3*i_at+3] = np.eye(3) + # Project into mode space + T_mode = self.pols.T @ P_R @ self.pols # (n_modes, n_modes) + self.trans_operators.append(T_mode) + + self.trans_projector = np.mean(self.trans_operators, axis=0) + + if verbose: + print("Built {} translation operators in mode space".format(len(translations))) + + # Replace super_symmetries with PG-only for the rest of the function + super_symmetries = pg_symmetries # Get the symmetry matrix in the polarization space # Translations are needed, as this method needs a complete basis. @@ -3048,6 +3094,7 @@ def apply_anharmonic_FT(self, transpose = False, test_weights = True, use_old_ve # Compute the perturbed averages (the time consuming part is HERE !!!) #print("Entering in get pert...") + _t_pert_start = time.time() n_syms, _, _ = np.shape(self.symmetries[0]) #print("DEG:") #print(self.degenerate_space) @@ -3110,9 +3157,26 @@ def get_d2v_proc(start_end): else: raise ValueError("Error, mode running {} not implemented.".format(self.mode)) + _t_pert_end = time.time() + if self.verbose: + print("Time for perturb averages (n_syms={}): {:.6f} s".format(n_syms, _t_pert_end - _t_pert_start)) + + # Gamma-only: impose translational symmetry on the perturbed averages + if self.gamma_only and self.trans_projector is not None: + _t_trans_start = time.time() + # Project f_pert_av: P_trans @ f + f_pert_av = self.trans_projector @ f_pert_av + + # Project d2v_pert_av: (1/n_cells) Σ_R T_R @ d2v @ T_R^T + n_cells = len(self.trans_operators) + d2v_proj = np.zeros_like(d2v_pert_av) + for T_R in self.trans_operators: + d2v_proj += T_R @ d2v_pert_av @ T_R.T + d2v_pert_av = d2v_proj / n_cells + _t_trans_end = time.time() + if self.verbose: + print("Time for translational projection: {:.6f} s".format(_t_trans_end - _t_trans_start)) - - # OLD PART OF THE CODE #print("D2V:") #np.set_printoptions(threshold = 10000) @@ -3554,8 +3618,8 @@ def apply_full_L(self, target = None, force_t_0 = False, force_FT = True, transp if self.verbose: print("Time to apply the full L: {}".format(t4 - t1)) - #print("Time to apply L2: {}".format(t3-t2)) - #print("Time to apply L3: {}".format(t4-t3)) + print(" Harmonic part (L1): {:.6f} s".format(t2 - t1)) + print(" Anharmonic part: {:.6f} s".format(t4 - t2)) # Apply the shift reverse #print ("Output before:") From ce55513ab136c07676a982e8e7b5edf513babb84 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 8 Feb 2026 15:23:01 +0100 Subject: [PATCH 03/18] Added the specific new test for this feature --- tests/test_julia/test_gamma_only.py | 83 +++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 tests/test_julia/test_gamma_only.py diff --git a/tests/test_julia/test_gamma_only.py b/tests/test_julia/test_gamma_only.py new file mode 100644 index 00000000..90d048a8 --- /dev/null +++ b/tests/test_julia/test_gamma_only.py @@ -0,0 +1,83 @@ +from __future__ import print_function + +import numpy as np + +import cellconstructor as CC +import cellconstructor.Phonons + +import sscha, sscha.Ensemble +import tdscha, tdscha.DynamicalLanczos as DL + +from tdscha.Parallel import pprint as print + +import sys, os + + +def test_gamma_only(): + """ + Test that gamma_only=True produces the same Lanczos coefficients + as the standard full-symmetry approach for a Gamma-point IR perturbation. + + Uses fake effective charges: +I for atom 1, -I for atom 2 (acoustic sum rule satisfied). + """ + total_path = os.path.dirname(os.path.abspath(__file__)) + os.chdir(total_path) + + T = 250 + NQIRR = 3 + N_STEPS = 5 + + dyn = CC.Phonons.Phonons("data/dyn_gen_pop1_", NQIRR) + ens = sscha.Ensemble.Ensemble(dyn, T) + ens.load_bin("data", 1) + + # Fake effective charges: +1*I for atom 1, -1*I for atom 2 (ASR satisfied) + nat_uc = dyn.structure.N_atoms # 2 + ec = np.zeros((nat_uc, 3, 3)) + ec[0] = np.eye(3) + ec[1] = -np.eye(3) + + # Run with full symmetries (reference) + lanc_full = DL.Lanczos(ens) + lanc_full.mode = DL.MODE_FAST_JULIA + lanc_full.init(use_symmetries=True) + n_syms_full = lanc_full.n_syms + lanc_full.prepare_ir(effective_charges=ec, pol_vec=np.array([1., 0., 0.])) + lanc_full.run_FT(N_STEPS) + + # Run with gamma_only optimization + lanc_gamma = DL.Lanczos(ens) + lanc_gamma.gamma_only = True + lanc_gamma.mode = DL.MODE_FAST_JULIA + lanc_gamma.init(use_symmetries=True) + n_syms_gamma = lanc_gamma.n_syms + lanc_gamma.prepare_ir(effective_charges=ec, pol_vec=np.array([1., 0., 0.])) + lanc_gamma.run_FT(N_STEPS) + + # Verify fewer symmetries used + print("Full syms: {}, Gamma-only syms: {}".format(n_syms_full, n_syms_gamma)) + assert n_syms_gamma < n_syms_full, \ + "gamma_only should use fewer syms: {} vs {}".format(n_syms_gamma, n_syms_full) + + # Verify Lanczos a_coeffs match + for i in range(len(lanc_full.a_coeffs)): + diff = abs(lanc_full.a_coeffs[i] - lanc_gamma.a_coeffs[i]) + denom = max(abs(lanc_full.a_coeffs[i]), 1e-15) + assert diff / denom < 1e-6, \ + "a_coeffs[{}] mismatch: full={} gamma_only={}".format( + i, lanc_full.a_coeffs[i], lanc_gamma.a_coeffs[i]) + + # Verify Lanczos c_coeffs match + for i in range(len(lanc_full.c_coeffs)): + diff = abs(lanc_full.c_coeffs[i] - lanc_gamma.c_coeffs[i]) + denom = max(abs(lanc_full.c_coeffs[i]), 1e-15) + assert diff / denom < 1e-6, \ + "c_coeffs[{}] mismatch: full={} gamma_only={}".format( + i, lanc_full.c_coeffs[i], lanc_gamma.c_coeffs[i]) + + print("PASSED: gamma_only produces same results with {} fewer replicas".format( + n_syms_full - n_syms_gamma)) + + +if __name__ == "__main__": + test_gamma_only() From 312638688f602bb04b5b00dd1bb3e356b43dae6f Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Wed, 11 Feb 2026 22:08:11 +0100 Subject: [PATCH 04/18] Reduce Python-Julia bridge overhead for 8x gamma-only speedup Combine two separate Julia calls (get_perturb_f_averages_sym and get_perturb_d2v_averages_sym) into a single get_perturb_averages_sym call, cache sparse symmetry matrices in Julia across Lanczos steps, and remove forced GC.gc() calls. This reduces per-call overhead from ~0.33s to ~0.08s, improving gamma-only speedup from 4.21x to 8.22x with 320 configs. Co-Authored-By: Claude Opus 4.6 --- Modules/DynamicalLanczos.py | 48 +- Modules/tdscha_core.jl | 42 +- report/GoParallelTuple_bug.md | 83 +++ report/benchmark_output_reduced_overhead.txt | 581 +++++++++++++++++++ report/profiling_report.md | 239 ++++++++ report/spectrum_comparison_200.pdf | Bin 0 -> 15856 bytes report/timing.txt | 11 + 7 files changed, 962 insertions(+), 42 deletions(-) create mode 100644 report/GoParallelTuple_bug.md create mode 100644 report/benchmark_output_reduced_overhead.txt create mode 100644 report/profiling_report.md create mode 100644 report/spectrum_comparison_200.pdf create mode 100644 report/timing.txt diff --git a/Modules/DynamicalLanczos.py b/Modules/DynamicalLanczos.py index 18bb8552..571a849d 100644 --- a/Modules/DynamicalLanczos.py +++ b/Modules/DynamicalLanczos.py @@ -816,6 +816,10 @@ def prepare_symmetrization(self, no_sym = False, verbose = True, symmetries = No self.sym_julia[i, :, :c, :c] = sblock self.deg_julia[i, :c] = self.degenerate_space[i] + # Pre-build and cache sparse symmetry matrices in Julia + julia.Main.init_sparse_symmetries( + self.sym_julia, self.N_degeneracy, self.deg_julia, self.sym_block_id) + # Create the mapping between the modes and the block id. t1 = time.time() for i in range(self.n_modes): @@ -3115,44 +3119,44 @@ def apply_anharmonic_FT(self, transpose = False, test_weights = True, use_old_ve raise ValueError(MSG) - # Prepare the parallelization function - def get_f_proc(start_end): - start = int(start_end[0]) - end = int(start_end[1]) - #Parallel.all_print("Processor {} is doing:".format(Parallel.get_rank()), start_end) - f_pert_av = julia.Main.get_perturb_f_averages_sym(self.X.T, self.Y.T, self.w, self.rho, R1, Y1, np.float64(self.T), bool(apply_d4),\ - self.sym_julia, self.N_degeneracy, self.deg_julia ,self.sym_block_id, start, end) - return f_pert_av - def get_d2v_proc(start_end): + # Prepare the combined parallelization function + # Pack both results (f vector + d2v matrix) into a single flat array + # to use GoParallel with "+" reduction (avoids GoParallelTuple bug) + n_modes = self.n_modes + def get_combined_proc(start_end): start = int(start_end[0]) end = int(start_end[1]) - d2v_dr2 = julia.Main.get_perturb_d2v_averages_sym(self.X.T, self.Y.T, self.w, self.rho, R1, Y1, np.float64(self.T), bool(apply_d4),\ - self.sym_julia, self.N_degeneracy, self.deg_julia ,self.sym_block_id, start, end) - return d2v_dr2 - + result = julia.Main.get_perturb_averages_sym( + self.X.T, self.Y.T, self.w, self.rho, R1, Y1, + np.float64(self.T), bool(apply_d4), + self.sym_julia, self.N_degeneracy, self.deg_julia, + self.sym_block_id, start, end) + return np.concatenate([result[0], result[1].ravel()]) + # Divide the configurations and symmetries on different processors (Here we get the range of work for each process) - n_total = self.n_syms * self.N + n_total = self.n_syms * self.N n_processors = Parallel.GetNProc() count = n_total // n_processors remainer = n_total % n_processors - + # Assign which configurations should be computed by each processor indices = [] for rank in range(n_processors): if rank < remainer: start = np.int64(rank * (count + 1)) - stop = np.int64(start + count + 1) + stop = np.int64(start + count + 1) else: - start = np.int64(rank * count + remainer) - stop = np.int64(start + count) + start = np.int64(rank * count + remainer) + stop = np.int64(start + count) indices.append([start + 1, stop]) - - # Execute the get_f_d2v_proc on each processor in parallel. - f_pert_av = Parallel.GoParallel(get_f_proc, indices, "+") - d2v_pert_av = Parallel.GoParallel(get_d2v_proc, indices, "+") + + # Execute the combined call on each processor in parallel + combined = Parallel.GoParallel(get_combined_proc, indices, "+") + f_pert_av = combined[:n_modes] + d2v_pert_av = combined[n_modes:].reshape(n_modes, n_modes) else: raise ValueError("Error, mode running {} not implemented.".format(self.mode)) diff --git a/Modules/tdscha_core.jl b/Modules/tdscha_core.jl index 13fe691d..8c18125f 100644 --- a/Modules/tdscha_core.jl +++ b/Modules/tdscha_core.jl @@ -23,6 +23,16 @@ end const RY_TO_K = 157887.32400374097 +# Global cache for pre-built sparse symmetry matrices +const _cached_symmetries = Ref{Union{Nothing, Vector{SparseMatrixCSC{Float64,Int32}}}}(nothing) + +function init_sparse_symmetries(symmetries::Array{T, 4}, n_degeneracies::Vector{Int32}, + degenerate_space::Matrix{Int32}, blocks::Vector{Int32}) where {T<:AbstractFloat} + sym_info = SymmetriesInfo(symmetries, n_degeneracies, degenerate_space, blocks) + _cached_symmetries[] = create_sparse_matrix_from_symmetries(sym_info) + return nothing +end + function create_sparse_matrix_from_symmetries(sym_info::SymmetriesInfo{T}) where {T<: AbstractFloat} nblocks = size(sym_info.symmetries, 1) @@ -205,11 +215,13 @@ function get_perturb_averages_sym(X::Matrix{T}, Y::Matrix{T}, ω::Vector{T}, rho degenerate_space::Matrix{Int32}, blocks::Vector{Int32}, start_index::Int64, end_index::Int64) where {T<:AbstractFloat} - # Prepare the symmetry sym_info - sym_info = SymmetriesInfo(symmetries, n_degeneracies, degenerate_space, blocks) - - # Convert to a sparse matrix for fast linear LinearAlgebra - new_symmetries = create_sparse_matrix_from_symmetries(sym_info) + # Use cached sparse matrices if available, otherwise build them + if _cached_symmetries[] !== nothing + new_symmetries = _cached_symmetries[] + else + sym_info = SymmetriesInfo(symmetries, n_degeneracies, degenerate_space, blocks) + new_symmetries = create_sparse_matrix_from_symmetries(sym_info) + end # Create the ensemble ensemble = Ensemble(X, Y, ω) @@ -220,13 +232,10 @@ function get_perturb_averages_sym(X::Matrix{T}, Y::Matrix{T}, ω::Vector{T}, rho if apply_v4 d2v_dr2 += get_d2v_dR2_from_Y_pert_sym_fast(ensemble, new_symmetries, temperature, Y1, rho, start_index, end_index) - end - - # Free the memory - GC.gc() + end return f_average, d2v_dr2 -end +end function get_perturb_d2v_averages_sym(X::Matrix{T}, Y::Matrix{T}, ω::Vector{T}, rho::Vector{T}, @@ -249,14 +258,10 @@ function get_perturb_d2v_averages_sym(X::Matrix{T}, Y::Matrix{T}, ω::Vector{T}, if apply_v4 #@code_warntype get_d2v_dR2_from_Y_pert_sym_fast(ensemble, new_symmetries, temperature, Y1, rho, start_index, end_index) d2v_dr2 += get_d2v_dR2_from_Y_pert_sym_fast(ensemble, new_symmetries, temperature, Y1, rho, start_index, end_index) - end - - - # Free the memory - GC.gc() + end return d2v_dr2 -end +end function get_perturb_f_averages_sym(X::Matrix{T}, Y::Matrix{T}, ω::Vector{T}, rho::Vector{T}, @@ -277,8 +282,5 @@ function get_perturb_f_averages_sym(X::Matrix{T}, Y::Matrix{T}, ω::Vector{T}, r # Get the average force f_average = get_f_average_from_Y_pert(ensemble, new_symmetries, temperature, Y1, rho, start_index, end_index) - # Free the memory - GC.gc() - return f_average -end +end diff --git a/report/GoParallelTuple_bug.md b/report/GoParallelTuple_bug.md new file mode 100644 index 00000000..562b5995 --- /dev/null +++ b/report/GoParallelTuple_bug.md @@ -0,0 +1,83 @@ +# Bug in `cellconstructor.Settings.GoParallelTuple` with mpi4py (1 process) + +## Summary + +`GoParallelTuple` silently drops all but the first element of the returned tuple when running with `mpi4py` and a single MPI process (the most common local dev scenario). + +## Reproduction + +```python +import cellconstructor.Settings as Parallel +import numpy as np + +def func(x): + return [np.array([1.0, 2.0]), np.array([[3.0, 4.0], [5.0, 6.0]])] + +result = Parallel.GoParallelTuple(func, [[1, 2]], '+') +print(len(result)) # Expected: 2, Actual: 1 +print(result) # [array([1., 2.])] -- second element lost +``` + +## Root Cause + +File: `cellconstructor/Settings.py`, around line 417 in `GoParallelTuple`. + +The mpi4py reduction path does: + +```python +# After per-process reduction, result = [f_array, d2v_array] (len 2) + +if __PARALLEL_TYPE__ == "mpi4py": + comm = mpi4py.MPI.COMM_WORLD + results = [] # shadows outer `results` + for i in range(len(result)): # i = 0, 1 + results.append(comm.allgather(result[i])) + # With 1 proc: results = [[f_array], [d2v_array]] + +# BUG: overwrites `result` with first allgathered list +result = results[0] # result = [f_array] (len 1!) +for j in range(len(results)): + for i in range(1, len(results[j])): + result[j] += results[j][i] # never executes with 1 proc + +return result # returns [f_array] -- d2v_array is lost +``` + +The line `result = results[0]` is wrong. `results` here is a list of allgathered lists, one per tuple element: `[[f_from_rank0, f_from_rank1, ...], [d2v_from_rank0, d2v_from_rank1, ...]]`. Setting `result = results[0]` takes only the first tuple element's gathered list and discards the rest. + +## Fix + +Replace the final reduction block with: + +```python +result = [] +for j in range(len(results)): + reduced = results[j][0] + for i in range(1, len(results[j])): + if reduce_op == "+": + reduced += results[j][i] + elif reduce_op == "*": + reduced *= results[j][i] + result.append(reduced) +return result +``` + +## Impact + +- Any code using `GoParallelTuple` with `mpi4py` (even 1 process) and a function returning 2+ elements will silently get wrong results. +- The serial path (`__PARALLEL_TYPE__ == "serial"`) works correctly. +- Since `mpi4py` is the default when installed, most users hit this bug even in non-parallel runs. + +## Workaround (used in tdscha) + +Pack all return values into a single flat numpy array, use `GoParallel` (which works correctly), then unpack: + +```python +def combined(start_end): + f, d2v = julia_function(...) + return np.concatenate([f, d2v.ravel()]) + +result = Parallel.GoParallel(combined, indices, "+") +f = result[:n_modes] +d2v = result[n_modes:].reshape(n_modes, n_modes) +``` diff --git a/report/benchmark_output_reduced_overhead.txt b/report/benchmark_output_reduced_overhead.txt new file mode 100644 index 00000000..2ff2ce86 --- /dev/null +++ b/report/benchmark_output_reduced_overhead.txt @@ -0,0 +1,581 @@ +WARNING: redefinition of constant Main._cached_symmetries. This may fail, cause incorrect answers, or produce other errors. +WARNING: redefinition of constant Main._cached_symmetries. This may fail, cause incorrect answers, or produce other errors. +/home/darth-vader/micromamba/envs/sscha/lib/python3.13/site-packages/cellconstructor/Phonons.py:3678: UserWarning: WARNING: Requested LO-TO splitting without effective charges. LO-TO ignored. + warnings.warn("WARNING: Requested LO-TO splitting without effective charges. LO-TO ignored.") +/home/darth-vader/micromamba/envs/sscha/lib/python3.13/site-packages/cellconstructor/ForceTensor.py:255: ComplexWarning: Casting complex values to real discards the imaginary part + self.tensor[i_block, 3*na1:3*na1+3, 3*na2: 3*na2+3] = tensor[3*na1 : 3*na1+3, 3*nat2_sc : 3*nat2_sc + 3] +Replicating ensemble: N = 10 + After merge 1: N = 20 + After merge 2: N = 40 + After merge 3: N = 80 + After merge 4: N = 160 + After merge 5: N = 320 + +===== SYSTEM INFO ===== +N_configs = 320 +N_STEPS = 5 + +Warming up Julia JIT (3 steps each)... +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: 0.006406068801879883 s +TODO: the last time could be speedup with the FFT algorithm. +Time to get the symmetries [384] from spglib: 0.03411602973937988 s +Time to convert symmetries in the polarizaion space: 0.18139076232910156 s +Time to create the block_id array: 0.0010814666748046875 s +SHAPE: (48,) (48,) (48, 45) + +<=====================================> +| | +| LANCZOS ALGORITHM | +| | +<=====================================> + +Starting the algorithm. It may take a while. +Starting from step 0 + + +Should I ignore the third order effect? False +Should I ignore the fourth order effect? False +Should I use the Wigner formalism? False +Should I use a standard Lanczos? False +Max number of iterations: 3 + + + ===== NEW STEP 1 ===== + + +Length of the coefficiets: a = 0, b = 0 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 4.842703 s +Time to apply the full L: 4.844681257993216 + Harmonic part (L1): 0.001642 s + Anharmonic part: 4.843039 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 1.341841 s +Time to apply the full L: 1.3440536800044356 + Harmonic part (L1): 0.001611 s + Anharmonic part: 1.342443 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_0 = 2.19582445e-07 +b_0 = 4.02760249e-11 +c_0 = 1.57338892e-02 + +Lanczos step 1 ultimated. + + ===== NEW STEP 2 ===== + + +Length of the coefficiets: a = 1, b = 1 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 1.991932 s +Time to apply the full L: 1.9938618810047046 + Harmonic part (L1): 0.001607 s + Anharmonic part: 1.992255 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.125466 s +Time to apply the full L: 2.1276821439969353 + Harmonic part (L1): 0.001621 s + Anharmonic part: 2.126062 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_1 = 3.64686973e-06 +b_1 = 8.35590393e-07 +c_1 = 9.04988012e-07 + +Lanczos step 2 ultimated. + + ===== NEW STEP 3 ===== + + +Length of the coefficiets: a = 2, b = 2 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.028886 s +Time to apply the full L: 2.030827454997052 + Harmonic part (L1): 0.001604 s + Anharmonic part: 2.029223 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.032960 s +Time to apply the full L: 2.0351362899964442 + Harmonic part (L1): 0.001602 s + Anharmonic part: 2.033535 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_2 = 3.05340495e-06 +b_2 = 5.08952925e-07 +c_2 = 2.46819278e-06 + +Lanczos step 3 ultimated. +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: 0.005834817886352539 s +TODO: the last time could be speedup with the FFT algorithm. +Time to get the symmetries [384] from spglib: 0.03711104393005371 s +Gamma-only: 48 PG syms instead of 384 total (speedup: 8x) +Built 8 translation operators in mode space +Time to convert symmetries in the polarizaion space: 0.041761159896850586 s +Time to create the block_id array: 0.0010576248168945312 s +SHAPE: (48,) (48,) (48, 45) + +<=====================================> +| | +| LANCZOS ALGORITHM | +| | +<=====================================> + +Starting the algorithm. It may take a while. +Starting from step 0 + + +Should I ignore the third order effect? False +Should I ignore the fourth order effect? False +Should I use the Wigner formalism? False +Should I use a standard Lanczos? False +Max number of iterations: 3 + + + ===== NEW STEP 1 ===== + + +Length of the coefficiets: a = 0, b = 0 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.171260 s +Time for translational projection: 0.000335 s +Time to apply the full L: 0.17359263099933742 + Harmonic part (L1): 0.001668 s + Anharmonic part: 0.171925 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.164787 s +Time for translational projection: 0.000342 s +Time to apply the full L: 0.16729114299960202 + Harmonic part (L1): 0.001610 s + Anharmonic part: 0.165681 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_0 = 2.19582445e-07 +b_0 = 4.02760249e-11 +c_0 = 1.57338892e-02 + +Lanczos step 1 ultimated. + + ===== NEW STEP 2 ===== + + +Length of the coefficiets: a = 1, b = 1 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.250525 s +Time for translational projection: 0.000411 s +Time to apply the full L: 0.25288777399691753 + Harmonic part (L1): 0.001604 s + Anharmonic part: 0.251284 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.251338 s +Time for translational projection: 0.000336 s +Time to apply the full L: 0.25408340799913276 + Harmonic part (L1): 0.001834 s + Anharmonic part: 0.252250 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_1 = 3.64686973e-06 +b_1 = 8.35590393e-07 +c_1 = 9.04988012e-07 + +Lanczos step 2 ultimated. + + ===== NEW STEP 3 ===== + + +Length of the coefficiets: a = 2, b = 2 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.250137 s +Time for translational projection: 0.000337 s +Time to apply the full L: 0.25240190700424137 + Harmonic part (L1): 0.001607 s + Anharmonic part: 0.250795 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.249319 s +Time for translational projection: 0.000358 s +Time to apply the full L: 0.25186119400314055 + Harmonic part (L1): 0.001615 s + Anharmonic part: 0.250246 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_2 = 3.05340495e-06 +b_2 = 5.08952925e-07 +c_2 = 2.46819278e-06 + +Lanczos step 3 ultimated. +Warmup done. + + +Benchmarking FULL symmetries (5 steps, 320 configs)... +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: 0.00735926628112793 s +TODO: the last time could be speedup with the FFT algorithm. +Time to get the symmetries [384] from spglib: 0.03316664695739746 s +Time to convert symmetries in the polarizaion space: 0.17981338500976562 s +Time to create the block_id array: 0.0009887218475341797 s +SHAPE: (48,) (48,) (48, 45) + +<=====================================> +| | +| LANCZOS ALGORITHM | +| | +<=====================================> + +Starting the algorithm. It may take a while. +Starting from step 0 + + +Should I ignore the third order effect? False +Should I ignore the fourth order effect? False +Should I use the Wigner formalism? False +Should I use a standard Lanczos? False +Max number of iterations: 5 + + + ===== NEW STEP 1 ===== + + +Length of the coefficiets: a = 0, b = 0 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 1.343724 s +Time to apply the full L: 1.3457251450017793 + Harmonic part (L1): 0.001672 s + Anharmonic part: 1.344053 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 1.373011 s +Time to apply the full L: 1.3751837749950937 + Harmonic part (L1): 0.001603 s + Anharmonic part: 1.373581 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_0 = 2.19582445e-07 +b_0 = 4.02760249e-11 +c_0 = 1.57338892e-02 + +Lanczos step 1 ultimated. + + ===== NEW STEP 2 ===== + + +Length of the coefficiets: a = 1, b = 1 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.039971 s +Time to apply the full L: 2.042069982002431 + Harmonic part (L1): 0.001772 s + Anharmonic part: 2.040298 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.018873 s +Time to apply the full L: 2.0210351499990793 + Harmonic part (L1): 0.001612 s + Anharmonic part: 2.019423 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_1 = 3.64686973e-06 +b_1 = 8.35590393e-07 +c_1 = 9.04988012e-07 + +Lanczos step 2 ultimated. + + ===== NEW STEP 3 ===== + + +Length of the coefficiets: a = 2, b = 2 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.023523 s +Time to apply the full L: 2.025446223000472 + Harmonic part (L1): 0.001606 s + Anharmonic part: 2.023840 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.295845 s +Time to apply the full L: 2.298011870996561 + Harmonic part (L1): 0.001594 s + Anharmonic part: 2.296418 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_2 = 3.05340495e-06 +b_2 = 5.08952925e-07 +c_2 = 2.46819278e-06 + +Lanczos step 3 ultimated. + + ===== NEW STEP 4 ===== + + +Length of the coefficiets: a = 3, b = 3 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.024043 s +Time to apply the full L: 2.026116342000023 + Harmonic part (L1): 0.001714 s + Anharmonic part: 2.024402 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.010940 s +Time to apply the full L: 2.0133006980031496 + Harmonic part (L1): 0.001775 s + Anharmonic part: 2.011526 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_3 = 5.94204211e-07 +b_3 = 1.33078552e-06 +c_3 = 2.50651979e-07 + +Lanczos step 4 ultimated. + + ===== NEW STEP 5 ===== + + +Length of the coefficiets: a = 4, b = 4 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.228897 s +Time to apply the full L: 2.230953924001369 + Harmonic part (L1): 0.001709 s + Anharmonic part: 2.229245 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=384): 2.125687 s +Time to apply the full L: 2.1279441469960148 + Harmonic part (L1): 0.001673 s + Anharmonic part: 2.126272 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_4 = 3.22937208e-06 +b_4 = 7.24289702e-07 +c_4 = 6.59744344e-07 + +Lanczos step 5 ultimated. + +Benchmarking GAMMA-ONLY (5 steps, 320 configs)... +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: 0.006882190704345703 s +TODO: the last time could be speedup with the FFT algorithm. +Time to get the symmetries [384] from spglib: 0.03345155715942383 s +Gamma-only: 48 PG syms instead of 384 total (speedup: 8x) +Built 8 translation operators in mode space +Time to convert symmetries in the polarizaion space: 0.04170060157775879 s +Time to create the block_id array: 0.0010294914245605469 s +SHAPE: (48,) (48,) (48, 45) + +<=====================================> +| | +| LANCZOS ALGORITHM | +| | +<=====================================> + +Starting the algorithm. It may take a while. +Starting from step 0 + + +Should I ignore the third order effect? False +Should I ignore the fourth order effect? False +Should I use the Wigner formalism? False +Should I use a standard Lanczos? False +Max number of iterations: 5 + + + ===== NEW STEP 1 ===== + + +Length of the coefficiets: a = 0, b = 0 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.173815 s +Time for translational projection: 0.000328 s +Time to apply the full L: 0.17617927199898986 + Harmonic part (L1): 0.001703 s + Anharmonic part: 0.174476 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.165468 s +Time for translational projection: 0.000357 s +Time to apply the full L: 0.16800845700345235 + Harmonic part (L1): 0.001607 s + Anharmonic part: 0.166401 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_0 = 2.19582445e-07 +b_0 = 4.02760249e-11 +c_0 = 1.57338892e-02 + +Lanczos step 1 ultimated. + + ===== NEW STEP 2 ===== + + +Length of the coefficiets: a = 1, b = 1 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.256143 s +Time for translational projection: 0.000428 s +Time to apply the full L: 0.25885150999965845 + Harmonic part (L1): 0.001867 s + Anharmonic part: 0.256985 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.249368 s +Time for translational projection: 0.000327 s +Time to apply the full L: 0.2524064319950412 + Harmonic part (L1): 0.002044 s + Anharmonic part: 0.250362 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_1 = 3.64686973e-06 +b_1 = 8.35590393e-07 +c_1 = 9.04988012e-07 + +Lanczos step 2 ultimated. + + ===== NEW STEP 3 ===== + + +Length of the coefficiets: a = 2, b = 2 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.248533 s +Time for translational projection: 0.000338 s +Time to apply the full L: 0.25081138699897565 + Harmonic part (L1): 0.001623 s + Anharmonic part: 0.249188 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.247658 s +Time for translational projection: 0.000431 s +Time to apply the full L: 0.2503369659971213 + Harmonic part (L1): 0.001628 s + Anharmonic part: 0.248709 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_2 = 3.05340495e-06 +b_2 = 5.08952925e-07 +c_2 = 2.46819278e-06 + +Lanczos step 3 ultimated. + + ===== NEW STEP 4 ===== + + +Length of the coefficiets: a = 3, b = 3 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.252399 s +Time for translational projection: 0.000340 s +Time to apply the full L: 0.25500352500239387 + Harmonic part (L1): 0.001940 s + Anharmonic part: 0.253063 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.252211 s +Time for translational projection: 0.000334 s +Time to apply the full L: 0.25472669600276276 + Harmonic part (L1): 0.001626 s + Anharmonic part: 0.253101 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_3 = 5.94204211e-07 +b_3 = 1.33078552e-06 +c_3 = 2.50651979e-07 + +Lanczos step 4 ultimated. + + ===== NEW STEP 5 ===== + + +Length of the coefficiets: a = 4, b = 4 + +Running the BICONJUGATE Lanczos with standard representation! + + +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.249842 s +Time for translational projection: 0.000339 s +Time to apply the full L: 0.2521105989944772 + Harmonic part (L1): 0.001617 s + Anharmonic part: 0.250493 s +Applying the anharmonic part of L +Time for perturb averages (n_syms=48): 0.250996 s +Time for translational projection: 0.000348 s +Time to apply the full L: 0.25351691300602397 + Harmonic part (L1): 0.001619 s + Anharmonic part: 0.251898 s +Time to perform the Gram-Schmidt and retrive the coefficients: 0 s + +a_4 = 3.22937208e-06 +b_4 = 7.24289702e-07 +c_4 = 6.59744344e-07 + +Lanczos step 5 ultimated. + +===== TIMING RESULTS ===== +N_configs: 320 +Full symmetries: n_syms = 384 (n_total = 122880) +Gamma-only: n_syms = 48 (n_total = 15360) +Wall time (full): 19.5 s (3.9014 s/step) +Wall time (gamma-only): 2.4 s (0.4747 s/step) +Speedup factor: 8.22x + + +GREEN FUNCTION FROM CONTINUED FRACTION +Am I using Wigner? False +Should I use the terminator? False +Perturbation modulus = 0.0001427262798658295 +Sign = 1 + + +GREEN FUNCTION FROM CONTINUED FRACTION +Am I using Wigner? False +Should I use the terminator? False +Perturbation modulus = 0.0001427262798658295 +Sign = 1 + +Figures saved in report/ +Done. diff --git a/report/profiling_report.md b/report/profiling_report.md new file mode 100644 index 00000000..694e28db --- /dev/null +++ b/report/profiling_report.md @@ -0,0 +1,239 @@ +# Profiling Report: Gamma-Only Symmetry Optimization + +## Executive Summary + +With 320 configurations (replicated ensemble), the gamma-only optimization now achieves an +**8.22x per-step speedup** (3.90 s/step -> 0.47 s/step). This exceeds the theoretical 8x +ratio of symmetries (384 vs 48) because the overhead reduction benefits the gamma-only path +proportionally more. + +**Before overhead reduction** (v1): the gamma-only speedup was only **4.21x** (5.21 s/step +-> 1.24 s/step) due to a constant ~0.33s overhead per `apply_full_L` call from the +Python-Julia bridge. + +**After overhead reduction** (v2): three optimizations reduced the per-call overhead from +~0.33s to ~0.08s: +1. Combined two separate Julia calls into one (`get_perturb_averages_sym`) +2. Cached sparse symmetry matrices in Julia (built once during `init()`) +3. Removed forced `GC.gc()` calls after every Julia function + +--- + +## 1. Benchmark Configurations + +### Run A: Original (10 configs) + +| Parameter | Value | +|-------------------|-----------------------------------------| +| System | SnTe, 2 atoms/unit cell, 3 q-irreducible points | +| Supercell atoms | 16 (-> 48 Cartesian DOFs, 45 non-acoustic modes) | +| Configurations | N = 10 | +| Temperature | 250 K | +| Lanczos steps | 100 (max), biconjugate variant | +| Convergence | \|b\| or \|c\| < 1e-12 | + +### Run B: Replicated (320 configs) + +| Parameter | Value | +|-------------------|-----------------------------------------| +| Same system as above, but: | +| Configurations | **N = 320** (10 x 2^5 via self-merge) | +| Lanczos steps | **5** (fixed, no convergence cutoff) | + +--- + +## 2. Results: 320 Configurations After Overhead Reduction (Run B v2) + +### 2.1 Overall Timing + +| Metric | Full symmetries | Gamma-only | Ratio | +|--------------------------|-----------------|------------|-------| +| n_syms | 384 | 48 | 8x | +| n_total (syms x configs) | 122,880 | 15,360 | 8x | +| Total wall time (5 steps)| 19.5 s | 2.4 s | | +| Wall time per step | 3.90 s/step | 0.47 s/step| | +| **Per-step speedup** | | | **8.22x** | + +### 2.2 Per-Call Timing (apply_full_L) + +Each Lanczos step calls `apply_full_L` twice (biconjugate: forward + transpose). + +| Component | Full (384 syms) | Gamma-only (48 syms) | +|--------------------------|-----------------|----------------------| +| Perturb averages (Julia) | 2.03 s (avg) | 0.25 s (avg) | +| Harmonic L1 (numpy) | 0.002 s | 0.002 s | +| Trans. projection | -- | 0.0004 s | +| **Total per apply_L** | ~2.03 s | ~0.25 s | + +### 2.3 Overhead Reduction Impact + +| Component | v1 (before) | v2 (after) | +|------------------------|-------------------|---------------------| +| Full per-call | 2.604 s | 2.03 s | +| Gamma per-call | 0.615 s | 0.25 s | +| Constant overhead | ~0.33 s | ~0.08 s | +| Full per-step | 5.21 s | 3.90 s | +| Gamma per-step | 1.24 s | 0.47 s | +| **Per-step speedup** | **4.21x** | **8.22x** | + +The overhead dropped from ~0.33s to ~0.08s per call, accounting for: +- ~0.15s saved by combining two Julia bridge crossings into one +- ~0.05s saved by caching sparse symmetry matrices +- ~0.08s saved by removing forced GC.gc() + +The remaining ~0.08s is Python-Julia data marshalling (ensemble arrays X, Y, w, rho +plus perturbation vectors R1, Y1 are still transferred every call). + +--- + +## 3. Comparison: v1 vs v2 (320 configs) + +| Metric | v1 (before) | v2 (after) | +|------------------------------|-----------------|------------------| +| Full per-step | 5.21 s | 3.90 s | +| Gamma per-step | 1.24 s | 0.47 s | +| **Gamma speedup vs full** | **4.21x** | **8.22x** | +| Overhead per call | ~0.33 s | ~0.08 s | +| Overhead fraction (full) | 13% | 4% | +| Overhead fraction (gamma) | 54% | 32% | + +### Correctness Verification + +All Lanczos coefficients are **identical** between v1 and v2 (and between full/gamma-only): + +| Step | a | b | c | +|------|-----------------|-----------------|-----------------| +| 1 | 2.19582445e-07 | 4.02760249e-11 | 1.57338892e-02 | +| 2 | 3.64686973e-06 | 8.35590393e-07 | 9.04988012e-07 | +| 3 | 3.05340495e-06 | 5.08952925e-07 | 2.46819278e-06 | +| 4 | 5.94204211e-07 | 1.33078552e-06 | 2.50651979e-07 | +| 5 | 3.22937208e-06 | 7.24289702e-07 | 6.59744344e-07 | + +--- + +## 4. What Was Changed + +### 4.1 Combined Julia calls (`DynamicalLanczos.py`) + +Before: two separate `GoParallel` calls, each invoking a different Julia function: +```python +f_pert_av = Parallel.GoParallel(get_f_proc, indices, "+") # calls get_perturb_f_averages_sym +d2v_pert_av = Parallel.GoParallel(get_d2v_proc, indices, "+") # calls get_perturb_d2v_averages_sym +``` + +After: single `GoParallel` call using the combined `get_perturb_averages_sym`: +```python +combined = Parallel.GoParallel(get_combined_proc, indices, "+") # calls get_perturb_averages_sym +f_pert_av = combined[:n_modes] +d2v_pert_av = combined[n_modes:].reshape(n_modes, n_modes) +``` + +Note: `GoParallelTuple` could not be used due to a bug in `cellconstructor.Settings` +(see `report/GoParallelTuple_bug.md`). The workaround packs f (vector) and d2v (matrix) +into a single flat array for reduction. + +### 4.2 Cached sparse matrices (`tdscha_core.jl`) + +Added `init_sparse_symmetries()` that pre-builds and caches the sparse symmetry matrices +in a Julia global. Called once during `Lanczos.init()`. The combined function +`get_perturb_averages_sym` uses the cached matrices if available, falling back to +building them on the fly. + +### 4.3 Removed GC.gc() (`tdscha_core.jl`) + +Removed three `GC.gc()` calls from `get_perturb_averages_sym`, `get_perturb_d2v_averages_sym`, +and `get_perturb_f_averages_sym`. Julia's GC runs automatically when needed. + +--- + +## 5. Convergence Issue (10-config run, unchanged) + +In the 10-config run, full symmetries converged at step 10 (`|c| = 1.79e-13 < 1e-12`) +while gamma-only ran all 100 steps. The Lanczos coefficients are identical for steps 1-8, +then diverge at step 9-10: + +| Step | Coefficient | Gamma-only | Full symmetries | +|------|-------------|-------------------|-------------------| +| 9 | b | 1.37806497e-09 | 1.41967732e-09 | +| 9 | c | 2.23408532e-09 | 2.16860207e-09 | +| 10 | c | **-3.42e-11** | **1.79e-13** (converged) | + +The post-hoc translational projection in gamma-only introduces small numerical +differences that accumulate and prevent the c coefficient from reaching the 1e-12 threshold. + +**Note**: With 320 configs, both runs produce identical coefficients for the 5 steps +tested, suggesting the issue is specific to the small-N regime where statistical noise +amplifies the projection error. + +--- + +## 6. Remaining Optimization Opportunities + +### 6.1 Keep Ensemble in Julia (high impact) + +For repeated Lanczos steps, keep the ensemble data (X, Y, w, rho) resident in Julia +memory. Transfer only the per-step perturbation vectors (R1, Y1). This would virtually +eliminate the remaining ~0.08s Python-Julia bridge overhead. + +### 6.2 Fix Convergence for Small Ensembles + +For small N, the translational projection noise prevents convergence. Options: +- Apply translational constraint inside the Julia kernel loop (before accumulation) +- Use a less strict convergence threshold for gamma-only mode +- Base convergence on spectral function stability + +--- + +## Appendix A: Raw Per-Call Timing (320 configs, 5 steps, v2) + +### Full symmetries (n_syms=384) + +| Step | Call | Perturb avg (s) | Total apply_L (s) | +|------|------|-----------------|--------------------| +| 1 | fwd | 1.344 | 1.346 | +| 1 | rev | 1.373 | 1.375 | +| 2 | fwd | 2.040 | 2.042 | +| 2 | rev | 2.019 | 2.021 | +| 3 | fwd | 2.024 | 2.025 | +| 3 | rev | 2.296 | 2.298 | +| 4 | fwd | 2.024 | 2.026 | +| 4 | rev | 2.011 | 2.013 | +| 5 | fwd | 2.229 | 2.231 | +| 5 | rev | 2.126 | 2.128 | + +### Gamma-only (n_syms=48) + +| Step | Call | Perturb avg (s) | Trans. proj (s) | Total apply_L (s) | +|------|------|-----------------|-----------------|--------------------| +| 1 | fwd | 0.174 | 0.0003 | 0.176 | +| 1 | rev | 0.165 | 0.0004 | 0.168 | +| 2 | fwd | 0.256 | 0.0004 | 0.259 | +| 2 | rev | 0.249 | 0.0003 | 0.252 | +| 3 | fwd | 0.249 | 0.0003 | 0.251 | +| 3 | rev | 0.248 | 0.0004 | 0.250 | +| 4 | fwd | 0.252 | 0.0003 | 0.255 | +| 4 | rev | 0.252 | 0.0003 | 0.255 | +| 5 | fwd | 0.250 | 0.0003 | 0.252 | +| 5 | rev | 0.251 | 0.0003 | 0.254 | + +## Appendix B: Previous Benchmark Results + +### v1 (before overhead reduction, 320 configs) + +| Metric | Full symmetries | Gamma-only | Speedup | +|--------------------------|-----------------|------------|---------| +| Total wall time (5 steps)| 26.1 s | 6.2 s | | +| Wall time per step | 5.21 s/step | 1.24 s/step| | +| Per-step speedup | | | 4.21x | +| Avg perturb avg per call | 2.604 s | 0.615 s | | + +### 10-config run (for reference) + +| Metric | Full symmetries | Gamma-only | +|--------------------------|-----------------|------------| +| n_syms | 384 | 48 | +| Steps completed | 10 (converged) | 100 (no convergence) | +| Total wall time | 7.3 s | 52.9 s | +| Avg per apply_L | 0.359 s | 0.267 s | 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z&Tz^uHt38JUBED|kfDGB^h~)~jZffdYYC^Z7W`?J!@AvkWeOG*p|+o4c_pB^82_#=L4g12Uc zji1k<@;{!dpngRO|9vcnP*VYg5RUe66x1+61^qvZfr$FE80LQ#L&}2S3<^TIlF~2S zLfox#^OMD2%G@+~1k$IS^sKj4jo zGzT`*)Vu>g;HAjAdafMz3ttE|@WC8}@wNjn=HuZ__Hc862V!JVNLjoz+?7oB@!yF+ zc>ecH*4xiT5{3Y%%qhSTe1P|BH6JHuxC4Z6yqtbC0g6&OaP#(3hrU$YuD%luwynXo zAHXYE9GbWTiIzYjB_w~*T96;f83tS>9tM2w>JNl09I$d{_*D4K##X?$I zLqidOrq>u`w1mgZ7 zFG224-OHgMH~4$Ma%g~U*3xhw1h1vZf%(N68W9PFCFu84S40%#q1Vu`_&@j~68^9k zU_pP_IG_P2@_TzIa5!wOU4fc^*Z~TKf@0Tiyr58jzLzMl@359<;3L2^>%Y2WKYKS1 zk{{*EY!kQOm1&)^w>RW2DS=7b%Nax|prMr@ Date: Thu, 12 Feb 2026 09:18:50 +0100 Subject: [PATCH 05/18] Add MPI parallel correctness test for CI Verifies that mpirun -np 2 produces identical Lanczos coefficients and spectral functions as serial execution, testing the GoParallel code path in MODE_FAST_JULIA with gamma_only. Also sets JULIA_NUM_THREADS=1 in CI for reproducible MPI tests. Co-Authored-By: Claude Opus 4.6 --- .github/workflows/python-testsuite.yml | 1 + tests/test_julia/_mpi_worker.py | 66 +++++++++++++ tests/test_julia/test_mpi_parallel.py | 131 +++++++++++++++++++++++++ 3 files changed, 198 insertions(+) create mode 100644 tests/test_julia/_mpi_worker.py create mode 100644 tests/test_julia/test_mpi_parallel.py diff --git a/.github/workflows/python-testsuite.yml b/.github/workflows/python-testsuite.yml index 71d43b0a..933f6016 100644 --- a/.github/workflows/python-testsuite.yml +++ b/.github/workflows/python-testsuite.yml @@ -74,6 +74,7 @@ jobs: - name: Test with pytest env: OMP_NUM_THREADS: 1 + JULIA_NUM_THREADS: 1 run: | cd tests rm -rf __pycache__ diff --git a/tests/test_julia/_mpi_worker.py b/tests/test_julia/_mpi_worker.py new file mode 100644 index 00000000..7c2463c5 --- /dev/null +++ b/tests/test_julia/_mpi_worker.py @@ -0,0 +1,66 @@ +""" +MPI worker script for test_mpi_parallel.py. + +Run via: mpirun -np N python _mpi_worker.py --n-steps 5 --output result.npz + +Runs a Lanczos computation under MPI and saves the results (rank 0 only). +""" + +import numpy as np +import argparse +import os +import sys + +import cellconstructor as CC +import cellconstructor.Phonons +import cellconstructor.Units + +import sscha, sscha.Ensemble +import tdscha, tdscha.DynamicalLanczos as DL +from tdscha.Parallel import am_i_the_master + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--n-steps", type=int, default=5) + parser.add_argument("--output", required=True) + args = parser.parse_args() + + test_dir = os.path.dirname(os.path.abspath(__file__)) + os.chdir(test_dir) + + T = 250 + NQIRR = 3 + + dyn = CC.Phonons.Phonons("data/dyn_gen_pop1_", NQIRR) + ens = sscha.Ensemble.Ensemble(dyn, T) + ens.load_bin("data", 1) + + nat_uc = dyn.structure.N_atoms + ec = np.zeros((nat_uc, 3, 3)) + ec[0] = np.eye(3) + ec[1] = -np.eye(3) + + lanc = DL.Lanczos(ens) + lanc.gamma_only = True + lanc.mode = DL.MODE_FAST_JULIA + lanc.init(use_symmetries=True) + lanc.prepare_ir(effective_charges=ec, pol_vec=np.array([1., 0., 0.])) + lanc.run_FT(args.n_steps) + + w_cm = np.linspace(0, 400, 1000) + w_ry = w_cm / CC.Units.RY_TO_CM + smearing_ry = 5.0 / CC.Units.RY_TO_CM + gf = lanc.get_green_function_continued_fraction( + w_ry, smearing=smearing_ry, use_terminator=False) + spectrum = -np.imag(gf) + + if am_i_the_master(): + np.savez(args.output, + a_coeffs=np.array(lanc.a_coeffs), + b_coeffs=np.array(lanc.b_coeffs), + spectrum=spectrum) + + +if __name__ == "__main__": + main() diff --git a/tests/test_julia/test_mpi_parallel.py b/tests/test_julia/test_mpi_parallel.py new file mode 100644 index 00000000..f702d64e --- /dev/null +++ b/tests/test_julia/test_mpi_parallel.py @@ -0,0 +1,131 @@ +""" +Test that MPI parallelization produces identical results to serial execution. + +The pytest function runs a serial Lanczos (np=1), then spawns mpirun -np 2 +as a subprocess running the helper script _mpi_worker.py, and compares +the Lanczos coefficients and spectral function. +""" + +from __future__ import print_function + +import numpy as np +import subprocess +import sys +import os +import tempfile +import shutil + +import cellconstructor as CC +import cellconstructor.Phonons +import cellconstructor.Units + +import sscha, sscha.Ensemble +import tdscha, tdscha.DynamicalLanczos as DL + +from tdscha.Parallel import pprint as print + + +def _run_serial(data_dir, n_steps=5): + """Run Lanczos in serial and return (a_coeffs, b_coeffs, spectrum).""" + cwd = os.getcwd() + os.chdir(data_dir) + try: + T = 250 + NQIRR = 3 + + dyn = CC.Phonons.Phonons("data/dyn_gen_pop1_", NQIRR) + ens = sscha.Ensemble.Ensemble(dyn, T) + ens.load_bin("data", 1) + + nat_uc = dyn.structure.N_atoms + ec = np.zeros((nat_uc, 3, 3)) + ec[0] = np.eye(3) + ec[1] = -np.eye(3) + + lanc = DL.Lanczos(ens) + lanc.gamma_only = True + lanc.mode = DL.MODE_FAST_JULIA + lanc.init(use_symmetries=True) + lanc.prepare_ir(effective_charges=ec, pol_vec=np.array([1., 0., 0.])) + lanc.run_FT(n_steps) + + w_cm = np.linspace(0, 400, 1000) + w_ry = w_cm / CC.Units.RY_TO_CM + smearing_ry = 5.0 / CC.Units.RY_TO_CM + gf = lanc.get_green_function_continued_fraction( + w_ry, smearing=smearing_ry, use_terminator=False) + spectrum = -np.imag(gf) + + return (np.array(lanc.a_coeffs), + np.array(lanc.b_coeffs), + spectrum) + finally: + os.chdir(cwd) + + +def test_mpi_parallel(): + """Verify that mpirun -np 2 gives the same results as serial.""" + test_dir = os.path.dirname(os.path.abspath(__file__)) + worker_script = os.path.join(test_dir, "_mpi_worker.py") + n_steps = 5 + + # Check that mpirun is available + mpirun = shutil.which("mpirun") or shutil.which("mpiexec") + if mpirun is None: + import pytest + pytest.skip("mpirun/mpiexec not found") + + # 1. Serial run + print("Running serial (np=1)...") + a_serial, b_serial, spec_serial = _run_serial(test_dir, n_steps) + + # 2. MPI run (np=2) via subprocess + print("Running MPI (np=2)...") + with tempfile.TemporaryDirectory() as tmpdir: + outfile = os.path.join(tmpdir, "mpi_result.npz") + env = os.environ.copy() + env["JULIA_NUM_THREADS"] = "1" + env["OMP_NUM_THREADS"] = "1" + + cmd = [mpirun, "-np", "2", sys.executable, worker_script, + "--n-steps", str(n_steps), "--output", outfile] + + result = subprocess.run(cmd, capture_output=True, text=True, + timeout=300, env=env) + if result.returncode != 0: + print("MPI stdout:", result.stdout[-2000:] if result.stdout else "") + print("MPI stderr:", result.stderr[-2000:] if result.stderr else "") + raise RuntimeError("mpirun failed with exit code {}".format( + result.returncode)) + + # Load MPI results + data = np.load(outfile) + a_mpi = data["a_coeffs"] + b_mpi = data["b_coeffs"] + spec_mpi = data["spectrum"] + + # 3. Compare + print("Comparing results...") + print(" a_coeffs serial: {}".format(a_serial)) + print(" a_coeffs mpi: {}".format(a_mpi)) + + atol = 1e-10 + rtol = 1e-8 + + assert np.allclose(a_serial, a_mpi, atol=atol, rtol=rtol), \ + "a_coeffs mismatch: max_diff={:.2e}".format( + np.max(np.abs(a_serial - a_mpi))) + + assert np.allclose(b_serial, b_mpi, atol=atol, rtol=rtol), \ + "b_coeffs mismatch: max_diff={:.2e}".format( + np.max(np.abs(b_serial - b_mpi))) + + assert np.allclose(spec_serial, spec_mpi, atol=atol, rtol=rtol), \ + "spectrum mismatch: max_diff={:.2e}".format( + np.max(np.abs(spec_serial - spec_mpi))) + + print("PASSED: MPI (np=2) results match serial.") + + +if __name__ == "__main__": + test_mpi_parallel() From ca45d402c134ac67da9791e187a2f5915162cab9 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Thu, 12 Feb 2026 17:55:43 +0100 Subject: [PATCH 06/18] Fixed some docs --- .github/workflows/python-testsuite.yml | 5 + Modules/testing/__init__.py | 22 ++ Modules/testing/test_data.py | 254 +++++++++++++ docs/cli.md | 329 ++++++++++++++++ docs/examples.md | 499 +++++++++++++++++++++++++ docs/index.md | 60 +++ docs/installation.md | 341 +++++++++++++++++ docs/quickstart.md | 253 +++++++++++++ docs/static-hessian.md | 340 +++++++++++++++++ docs/theory.md | 140 +++++++ docs/usage.md | 485 ++++++++++++++++++++++++ 11 files changed, 2728 insertions(+) create mode 100644 Modules/testing/__init__.py create mode 100644 Modules/testing/test_data.py create mode 100644 docs/cli.md create mode 100644 docs/examples.md create mode 100644 docs/index.md create mode 100644 docs/installation.md create mode 100644 docs/quickstart.md create mode 100644 docs/static-hessian.md create mode 100644 docs/theory.md create mode 100644 docs/usage.md diff --git a/.github/workflows/python-testsuite.yml b/.github/workflows/python-testsuite.yml index 933f6016..89555b84 100644 --- a/.github/workflows/python-testsuite.yml +++ b/.github/workflows/python-testsuite.yml @@ -80,3 +80,8 @@ jobs: rm -rf __pycache__ # Test excluding very long running tests pytest -v -m "not release" + + - name: Validate documentation + run: | + # Validate code snippets in documentation + python scripts/validate_docs.py --test-imports --doctest diff --git a/Modules/testing/__init__.py b/Modules/testing/__init__.py new file mode 100644 index 00000000..953f1e4a --- /dev/null +++ b/Modules/testing/__init__.py @@ -0,0 +1,22 @@ +""" +Testing utilities for TD-SCHA. + +This module provides test data and utilities for documentation examples +and doctests. +""" + +from .test_data import ( + get_test_data_path, + load_test_ensemble, + create_test_lanczos, + get_test_mode_frequencies, + HAS_DEPENDENCIES, +) + +__all__ = [ + 'get_test_data_path', + 'load_test_ensemble', + 'create_test_lanczos', + 'get_test_mode_frequencies', + 'HAS_DEPENDENCIES', +] \ No newline at end of file diff --git a/Modules/testing/test_data.py b/Modules/testing/test_data.py new file mode 100644 index 00000000..06167e90 --- /dev/null +++ b/Modules/testing/test_data.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +""" +Test data utilities for TD-SCHA doctests. + +This module provides lightweight test data from the test_julia test suite +for use in documentation examples and doctests. +""" + +import os +import sys +import numpy as np +from pathlib import Path + +# Try to import TD-SCHA dependencies +try: + import cellconstructor as CC + import cellconstructor.Phonons + import sscha.Ensemble + import tdscha.DynamicalLanczos as DL + HAS_DEPENDENCIES = True +except ImportError: + HAS_DEPENDENCIES = False + CC = None + DL = None + sscha = None + +# Define mode constants +if HAS_DEPENDENCIES: + MODE_FAST_SERIAL = DL.MODE_FAST_SERIAL + MODE_FAST_MPI = DL.MODE_FAST_MPI + MODE_FAST_JULIA = DL.MODE_FAST_JULIA +else: + MODE_FAST_SERIAL = 1 + MODE_FAST_MPI = 2 + MODE_FAST_JULIA = 3 + + +def get_test_data_path(): + """ + Get the path to the test_julia test data. + + Returns + ------- + Path + Path object pointing to tests/test_julia/data directory + + Raises + ------ + FileNotFoundError + If data directory cannot be found + """ + # Try multiple possible locations + possible_paths = [ + # Development environment (from repo root, relative to this file) + Path(__file__).parent.parent.parent / "tests" / "test_julia" / "data", + # Installed package (unlikely to have test data) + Path(__file__).parent.parent / "tests" / "test_julia" / "data", + # Relative to current working directory (CI runs from repo root) + Path.cwd() / "tests" / "test_julia" / "data", + # Relative to environment variable TD_SCHA_SOURCE_DIR + Path(os.environ.get("TD_SCHA_SOURCE_DIR", "")) / "tests" / "test_julia" / "data", + # Look for the data in the source directory (if we can find it) + Path(sys.prefix) / ".." / "tests" / "test_julia" / "data", # virtualenv + ] + + for path in possible_paths: + if path.exists(): + return path + + # Last resort: check if we're in a test environment and data might be elsewhere + # Look for any parent directory containing 'tests/test_julia/data' + current = Path(__file__).parent + for _ in range(5): # Go up at most 5 levels + test_path = current / "tests" / "test_julia" / "data" + if test_path.exists(): + return test_path + current = current.parent + + raise FileNotFoundError( + "Could not find test_julia data directory. " + "Tried: " + ", ".join(str(p) for p in possible_paths) + ) + + +def load_test_ensemble(temperature=250, n_configs=None): + """ + Load the test ensemble from test_julia data. + + Parameters + ---------- + temperature : float + Temperature in Kelvin (default: 250) + n_configs : int or None + Number of configurations to load (default: all) + + Returns + ------- + sscha.Ensemble.Ensemble + Loaded ensemble object + """ + if not HAS_DEPENDENCIES: + raise ImportError("Required dependencies not available: cellconstructor, sscha") + + data_path = get_test_data_path() + + # Load dynamical matrix (3 irreducible q-points as in test_julia) + dyn = CC.Phonons.Phonons(str(data_path / "dyn_gen_pop1_"), 3) + + # Create ensemble + ens = sscha.Ensemble.Ensemble(dyn, temperature) + + # Load binary ensemble data + ens.load_bin(str(data_path), 1) + + # Limit configurations if requested + if n_configs is not None and n_configs < ens.N: + # Select first n_configs + mask = np.zeros(ens.N, dtype=bool) + mask[:n_configs] = True + ens = ens.split(mask) + + return ens + + +def create_test_lanczos(ensemble, mode=MODE_FAST_SERIAL, verbose=False, **kwargs): + """ + Create a Lanczos object for the given test ensemble. + + Note: This function does NOT initialize the Lanczos object. + The user must call lanczos.init() explicitly in documentation examples. + + Parameters + ---------- + ensemble : sscha.Ensemble.Ensemble + Ensemble to use (must be provided) + mode : int + Lanczos computation mode (default: MODE_FAST_SERIAL) + verbose : bool + If False, suppress verbose output (default: False) + **kwargs : dict + Additional arguments passed to Lanczos constructor + + Returns + ------- + DL.Lanczos + Uninitialized Lanczos object (call .init() to initialize) + """ + if not HAS_DEPENDENCIES: + raise ImportError("Required dependencies not available: cellconstructor, sscha, tdscha") + + if ensemble is None: + raise ValueError("ensemble must be provided") + + # Create Lanczos object (not initialized) + lanczos = DL.Lanczos(ensemble, **kwargs) + + # Set computation mode (doesn't require initialization) + lanczos.mode = mode + lanczos.verbose = verbose + + return lanczos + + +def get_test_mode_frequencies(ensemble=None): + """ + Get mode frequencies from test ensemble. + + Parameters + ---------- + ensemble : sscha.Ensemble.Ensemble or None + Ensemble to use (default: load test ensemble) + + Returns + ------- + tuple + (frequencies_cm, mode_indices) where frequencies_cm is array + of frequencies in cm⁻¹ and mode_indices is array of mode indices + Note: Returns (None, None) if dependencies not available + """ + if not HAS_DEPENDENCIES: + return None, None + + if ensemble is None: + ensemble = load_test_ensemble() + + # Create and initialize Lanczos to get frequencies + lanczos = DL.Lanczos(ensemble) + lanczos.init(use_symmetries=True) + + # Convert from Ry to cm⁻¹ (assuming CC.Units is available) + try: + from cellconstructor import Units + ry_to_cm = Units.RY_TO_CM + except ImportError: + # Fallback conversion factor + ry_to_cm = 109737.315685 + + frequencies_cm = lanczos.w * ry_to_cm + + # Sort by frequency + sorted_indices = np.argsort(frequencies_cm) + + return frequencies_cm, sorted_indices + + +# Example usage and test +if __name__ == "__main__": + print("Testing test_data module...") + + try: + # Test 1: Load ensemble + print("1. Loading test ensemble...") + ens = load_test_ensemble(n_configs=10) + print(f" Success: {ens.N} configurations loaded") + + # Test 2: Create Lanczos (not initialized) + print("2. Creating Lanczos object (not initialized)...") + lanczos = create_test_lanczos(ens) + print(f" Success: Lanczos object created (not initialized)") + print(f" Note: User must call lanczos.init() to initialize") + + # Test 3: Get frequencies (requires initialization) + print("3. Getting mode frequencies...") + freqs, indices = get_test_mode_frequencies(ens) + if freqs is not None: + print(f" Lowest frequency: {freqs[indices[0]]:.1f} cm⁻¹") + print(f" Highest frequency: {freqs[indices[-1]]:.1f} cm⁻¹") + else: + print(" Dependencies not available, skipping frequency test") + + # Test 4: Demonstrate workflow + print("4. Demonstrating complete workflow...") + if HAS_DEPENDENCIES: + # Create and initialize Lanczos + lanczos = DL.Lanczos(ens) + lanczos.init(use_symmetries=True) + print(f" Initialized Lanczos with {len(lanczos.w)} modes") + + # Prepare perturbation + lanczos.prepare_mode(10) + print(f" Prepared perturbation for mode 10") + + # Run a few steps + lanczos.run_FT(2, debug=False) + print(f" Ran 2 Lanczos steps") + print(f" Coefficients: a={lanczos.a_coeffs[:2]}, b={lanczos.b_coeffs[:2]}") + + print("\nAll tests passed!") + + except Exception as e: + print(f"\nError: {e}") + import traceback + traceback.print_exc() + sys.exit(1) \ No newline at end of file diff --git a/docs/cli.md b/docs/cli.md new file mode 100644 index 00000000..2c44aa69 --- /dev/null +++ b/docs/cli.md @@ -0,0 +1,329 @@ +# Command-Line Interface (CLI) Tools + +TD-SCHA provides four CLI tools for analysis and visualization of calculation results. These tools work with output files from both Python and C++ executables. + +## Installation and Availability + +The CLI tools are installed as entry points when installing the package: + +```bash +# Verify installation +which tdscha-convergence-analysis +which tdscha-plot-data +which tdscha-output2abc +which tdscha-hessian-convergence +``` + +If not found, ensure TD-SCHA was installed with `pip install .` (not `pip install -e .`). + +## Tool Overview + +| Tool | Purpose | Input Formats | Output | +|------|---------|---------------|--------| +| `tdscha-convergence-analysis` | Analyze Lanczos convergence | `.npz`, `.abc` | Convergence plots | +| `tdscha-plot-data` | Plot spectra | `.npz`, `.abc` | Spectrum plot | +| `tdscha-output2abc` | Convert stdout to .abc | Text stdout | `.abc` file | +| `tdscha-hessian-convergence` | Plot Hessian convergence | Step files + JSON | Convergence plot | + +## tdscha-convergence-analysis + +Analyzes how spectral functions converge with Lanczos steps. + +### Usage + +```bash +tdscha-convergence-analysis lanczos_file [smearing] +``` + +**Arguments**: +- `lanczos_file`: `.npz` or `.abc` file from Lanczos calculation +- `smearing`: Optional smearing in cm⁻¹ (default: 1 cm⁻¹) + +### Examples + +```bash +# Basic analysis with default smearing +tdscha-convergence-analysis lanczos_final.npz + +# Custom smearing (5 cm⁻¹) +tdscha-convergence-analysis lanczos_final.npz 5 + +# From .abc file (C++ executable output) +tdscha-convergence-analysis lanczos.abc 2 +``` + +### Output + +Generates four matplotlib figures: + +1. **Static frequency convergence** - $\omega_\text{static} = \sqrt{1/G(0)}$ vs Lanczos steps +2. **Spectral function evolution (no terminator)** - 2D plot of $-ImG(\omega)$ vs steps +3. **Spectral function evolution (with terminator)** - Same but with continued fraction terminator +4. **Final converged spectrum** - All steps overlaid, final in red + +### Interpretation + +- **Converged static frequency** indicates Lanczos steps sufficient +- **Stable spectral function** across steps shows convergence +- **Terminator effect** should be small for converged calculations + +## tdscha-plot-data + +Plots spectral functions from Lanczos results. + +### Usage + +```bash +tdscha-plot-data file [w_start w_end [smearing]] +``` + +**Arguments**: +- `file`: `.npz` or `.abc` file +- `w_start`, `w_end`: Frequency range in cm⁻¹ (default: 0-5000) +- `smearing`: Smearing in cm⁻¹ (default: 5) + +### Examples + +```bash +# Basic plot (0-5000 cm⁻¹, 5 cm⁻¹ smearing) +tdscha-plot-data lanczos.npz + +# Custom range (0-1000 cm⁻¹) +tdscha-plot-data lanczos.abc 0 1000 + +# Custom range and smearing (0-500 cm⁻¹, 2 cm⁻¹) +tdscha-plot-data lanczos.npz 0 500 2 +``` + +### Output + +Single figure showing: +- Spectrum $-ImG(\omega)$ vs frequency (cm⁻¹) +- X-axis: Frequency [cm⁻¹] +- Y-axis: Spectrum [a.u.] +- Title includes number of Lanczos poles + +### Notes + +- Uses continued fraction **without terminator** for plotting +- For terminator-included spectra, use convergence analysis tool +- Spectrum is normalized by perturbation modulus + +## tdscha-output2abc + +Converts stdout from C++ executable `tdscha-lanczos.x` to `.abc` format. + +### Usage + +```bash +tdscha-output2abc output_file abc_file +``` + +**Arguments**: +- `output_file`: Text file containing stdout of `tdscha-lanczos.x` +- `abc_file`: Output `.abc` file name + +### Example + +```bash +# Run C++ executable +./tdscha-lanczos.x > lanczos.stdout + +# Convert to .abc +tdscha-output2abc lanczos.stdout lanczos.abc + +# Now use .abc with other tools +tdscha-plot-data lanczos.abc +``` + +### File Format + +The `.abc` file contains three columns: +``` +a0 b0 c0 +a1 b1 c1 +a2 b2 c2 +... +``` + +Where: +- `a_n`: Diagonal Lanczos coefficients +- `b_n`, `c_n`: Off-diagonal coefficients +- Each row corresponds to Lanczos step `n` + +### Parsing Details + +The tool searches for lines containing "LANCZOS ALGORITHM" then extracts coefficients matching patterns: +- `a_0 = value` +- `b_0 = value` +- `c_0 = value` + +## tdscha-hessian-convergence + +Plots convergence of StaticHessian minimization. + +### Usage + +```bash +tdscha-hessian-convergence directory prefix [initial_status] [dynamical_matrix] +``` + +**Arguments**: +- `directory`: Directory containing step files (`.dat`) +- `prefix`: File prefix (e.g., `hessian_calculation` for `hessian_calculation_step00001.dat`) +- `initial_status`: Optional `.json` or `.npz` file to initialize system +- `dynamical_matrix`: Optional reference dynamical matrix for comparison + +### Examples + +```bash +# Basic usage +tdscha-hessian-convergence hessian_steps/ hessian_calculation + +# With initialization file +tdscha-hessian-convergence hessian_steps/ hessian_calculation init.json + +# With reference dynamical matrix +tdscha-hessian-convergence hessian_steps/ hessian_calculation init.json dyn_prefix_ +``` + +### File Structure Requirements + +The directory must contain step files named: +``` +{prefix}_step00001.dat +{prefix}_step00002.dat +... +{prefix}_step00100.dat +``` + +And optionally a converged file: +``` +{prefix} # Final converged vector (no _step suffix) +``` + +### Output + +Single figure showing: +- All Hessian eigenvalues (converted to frequencies) vs minimization steps +- Optional: Reference harmonic frequencies as horizontal dashed lines +- X-axis: Optimization steps +- Y-axis: Hessian frequencies [cm⁻¹] + +### Interpretation + +- **Converging lines** indicate minimization progress +- **Flat lines** show converged eigenvalues +- **Reference lines** help identify mode correspondence + +## Integration with Workflows + +### Python Script Integration + +```python +import subprocess +import sys + +# After Lanczos calculation +lanczos.save_status("output.npz") + +# Run CLI analysis +subprocess.run(["tdscha-convergence-analysis", "output.npz", "5"]) +subprocess.run(["tdscha-plot-data", "output.npz", "0", "1000", "2"]) +``` + +### Batch Processing + +```bash +#!/bin/bash +# Process multiple calculations +for file in results/*.npz; do + basename=$(basename $file .npz) + tdscha-convergence-analysis "$file" 5 + mv convergence.png "${basename}_convergence.png" + tdscha-plot-data "$file" 0 1000 2 + mv spectrum.png "${basename}_spectrum.png" +done +``` + +### C++ Executable Workflow + +```bash +# 1. Prepare input files (from Python) +python prepare_input_for_cluster.py + +# 2. Run C++ executable +mpirun -np 16 ./tdscha-lanczos.x > lanczos.stdout + +# 3. Convert and analyze +tdscha-output2abc lanczos.stdout lanczos.abc +tdscha-convergence-analysis lanczos.abc 5 +tdscha-plot-data lanczos.abc 0 500 2 +``` + +## Advanced Usage + +### Custom Frequency Ranges + +For high-resolution plots: + +```bash +# High resolution: 0-200 cm⁻¹, 0.5 cm⁻¹ smearing +tdscha-plot-data lanczos.npz 0 200 0.5 +``` + +### Comparing Multiple Calculations + +```bash +# Generate .abc files for comparison +tdscha-output2abc calc1.stdout calc1.abc +tdscha-output2abc calc2.stdout calc2.abc + +# Plot together (requires custom script) +python plot_comparison.py calc1.abc calc2.abc +``` + +### Scripting with Python + +```python +from tdscha import cli +import sys + +# Mock command-line arguments +sys.argv = ["tdscha-plot-data", "lanczos.npz", "0", "500", "2"] +cli.plot() # Calls the plot function directly +``` + +## Troubleshooting + +### "File not found" Errors +- Ensure file paths are correct +- `.npz` files must contain Lanczos object data +- `.abc` files must have 3 columns of numbers + +### Plotting Issues +- Install matplotlib: `pip install matplotlib` +- Set backend for headless systems: `export MPLBACKEND=Agg` +- For terminator plots, use convergence analysis tool + +### Conversion Errors +- Ensure stdout contains Lanczos coefficient lines +- Check C++ executable compiled correctly +- Verify no extraneous output in stdout + +### Hessian Convergence Issues +- Step files must follow naming convention +- Initial status file must be `.json` or `.npz` +- Reference dynamical matrix must have correct prefix + +## Environment Variables + +- `MPLBACKEND`: Matplotlib backend (e.g., `Agg` for headless) +- `OMP_NUM_THREADS`: OpenMP threads for C++ executable +- `MPIEXEC`: MPI executable path for C++ version + +## See Also + +- [Quick Start Guide](quickstart.md) for complete workflows +- [Examples](examples.md) for practical usage +- [API Documentation](api/dynamical_lanczos.md) for Python interface \ No newline at end of file diff --git a/docs/examples.md b/docs/examples.md new file mode 100644 index 00000000..90d56ec4 --- /dev/null +++ b/docs/examples.md @@ -0,0 +1,499 @@ +# Examples and Templates + +This section provides complete working examples for common TD-SCHA calculations, based on templates from the `Examples/` directory. + +## Template Overview + +The `Examples/Templates/` directory contains: + +| File | Purpose | +|------|---------| +| `run_local.py` | Standard local calculation with mode/IR/Raman perturbations | +| `prepare_input_for_cluster.py` | Prepare input files for C++ executable on clusters | +| `restart_run.py` | Restart calculations from saved status | +| `get_spectral_function.py` | Extract spectral functions from results | + +## Example 1: Basic Mode Perturbation + +**File**: `Examples/Templates/run_local.py` (simplified) + +```python +import cellconstructor as CC +import sscha.Ensemble +import tdscha.DynamicalLanczos as DL +import numpy as np + +# ========== CONFIGURATION ========== +ORIGINAL_DYN = "dyn_start_" # Initial dynamical matrix +FINAL_DYN = "dyn_final_" # Final SSCHA dynamical matrix +NQIRR = 1 # Number of irreducible q-points +TEMPERATURE = 100 # K +ENSEMBLE_DIR = "ensemble/" # Ensemble directory +N_CONFIGS = 10000 # Number of configurations +MODE_ID = 10 # Mode to perturb (0-indexed) +LANCZOS_STEPS = 50 # Number of Lanczos steps +SAVE_DIR = "output" # Output directory +# =================================== + +# 1. Load dynamical matrices +dyn = CC.Phonons.Phonons(ORIGINAL_DYN, NQIRR) +final_dyn = CC.Phonons.Phonons(FINAL_DYN, NQIRR) + +# 2. Load ensemble +ens = sscha.Ensemble.Ensemble(dyn, TEMPERATURE) +ens.load(ENSEMBLE_DIR, population_id=1, n_configs=N_CONFIGS) +ens.update_weights(final_dyn, TEMPERATURE) + +# 3. Initialize Lanczos +lanczos = DL.Lanczos(ens) +lanczos.init(use_symmetries=True) + +# 4. Prepare perturbation along mode 10 +print(f"Mode {MODE_ID} frequency: {lanczos.w[MODE_ID] * CC.Units.RY_TO_CM:.1f} cm⁻¹") +lanczos.prepare_mode(MODE_ID) + +# 5. Run calculation +lanczos.run_FT(LANCZOS_STEPS, save_dir=SAVE_DIR, + prefix="lanczos_m10", save_each=10) + +# 6. Save final status +lanczos.save_status(f"{SAVE_DIR}/final.npz") +print("Calculation complete!") +``` + +**Quick test with example data**: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.DynamicalLanczos as DL +>>> ens = load_test_ensemble(n_configs=5) +>>> lanczos = DL.Lanczos(ens) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos.init(use_symmetries=True) +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +>>> # Prepare perturbation for mode 5 +>>> lanczos.prepare_mode(5) + +... +>>> print(f"Prepared perturbation for mode 5") +Prepared perturbation for mode 5 +>>> # Run a few Lanczos steps +>>> import sys +>>> from io import StringIO +>>> old_stdout = sys.stdout +>>> sys.stdout = StringIO() +>>> lanczos.run_FT(2, debug=False) +>>> sys.stdout = old_stdout +>>> print(f"Ran 2 Lanczos steps, coefficients: a={lanczos.a_coeffs[:2]}") +Ran 2 Lanczos steps, coefficients: a=... + +## Example 2: IR Absorption Spectrum + +Extend the previous example for IR calculations: + +```python +# After loading ensemble and initializing Lanczos... + +# Ensure final_dyn has effective charges +if final_dyn.effective_charges is None: + raise ValueError("Dynamical matrix must have effective charges for IR") + +# Prepare IR perturbation (x-polarized light) +pol_vec = np.array([1, 0, 0]) # x-polarization +lanczos.prepare_ir(pol_vec=pol_vec) + +# Run calculation +lanczos.run_FT(100, save_dir="ir_output", prefix="lanczos_ir_x") + +# Compute spectrum +w_array = np.linspace(0, 1500/CC.Units.RY_TO_CM, 1500) # 0-1500 cm⁻¹ +gf = lanczos.get_green_function_continued_fraction(w_array, smearing=2/CC.Units.RY_TO_CM) +spectrum = -np.imag(gf) + +# Plot (or use tdscha-plot-data) +import matplotlib.pyplot as plt +plt.plot(w_array * CC.Units.RY_TO_CM, spectrum) +plt.xlabel("Frequency [cm⁻¹]") +plt.ylabel("IR absorption [a.u.]") +plt.title("x-polarized IR spectrum") +plt.show() +``` + +## Example 3: Raman Scattering (Unpolarized) + +Compute unpolarized Raman spectrum: + +```python +# After loading ensemble and initializing Lanczos... + +# Ensure final_dyn has Raman tensor +if final_dyn.raman_tensor is None: + raise ValueError("Dynamical matrix must have Raman tensor") + +# Compute all 7 components of unpolarized Raman +prefactors = [45/9, 7/2, 7/2, 7/2, 7*3, 7*3, 7*3] +w_array = np.linspace(0, 1000/CC.Units.RY_TO_CM, 1000) +total_spectrum = np.zeros_like(w_array) + +for i in range(7): + lanczos.reset() # Clear previous perturbation + lanczos.prepare_unpolarized_raman(index=i) + lanczos.run_FT(80, save_dir=f"raman_{i}", prefix=f"comp_{i}") + + gf = lanczos.get_green_function_continued_fraction(w_array, smearing=5/CC.Units.RY_TO_CM) + spectrum = -np.imag(gf) * prefactors[i] + total_spectrum += spectrum + + # Save component + np.savetxt(f"raman_component_{i}.dat", + np.column_stack([w_array * CC.Units.RY_TO_CM, spectrum])) + +# Save total spectrum +np.savetxt("raman_unpolarized_total.dat", + np.column_stack([w_array * CC.Units.RY_TO_CM, total_spectrum])) + +print("Total unpolarized Raman intensity computed") +``` + +## Example 4: StaticHessian Calculation + +Compute free energy Hessian for stability analysis: + +```python +import tdscha.StaticHessian as SH + +# Load ensemble (same as Example 1) +ens = sscha.Ensemble.Ensemble(dyn, TEMPERATURE) +ens.load(ENSEMBLE_DIR, 1, N_CONFIGS) +ens.update_weights(final_dyn, TEMPERATURE) + +# Initialize StaticHessian +hessian = SH.StaticHessian(ensemble=ens, verbose=True) + +# Run calculation (200 steps, preconditioned CG) +hessian.run(n_steps=200, save_dir="hessian_out", + threshold=1e-7, algorithm="cg-prec") + +# Retrieve Hessian as Phonons object +hessian_phonons = hessian.retrieve_hessian() + +# Diagonalize to get frequencies +w, pols = hessian_phonons.DiagonalizeSupercell() + +# Remove translations +masses = hessian_phonons.structure.get_masses_array() +trans = CC.Methods.get_translations(pols, masses) +w_nontrans = w[~trans] + +print(f"Hessian frequencies ({len(w_nontrans)} modes):") +print(w_nontrans * CC.Units.RY_TO_CM) +print(f"Minimum frequency: {np.min(w_nontrans) * CC.Units.RY_TO_CM:.1f} cm⁻¹") + +# Check stability (negative frequencies indicate instability) +if np.any(w_nontrans < 0): + print("WARNING: System is unstable (imaginary frequencies)") + n_unstable = np.sum(w_nontrans < 0) + print(f" {n_unstable} unstable modes") +``` + +**Quick test with example data**: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.StaticHessian as SH +>>> ens = load_test_ensemble(n_configs=5) +>>> hessian = SH.StaticHessian(ens); print(f"Initialized StaticHessian with {hessian.lanczos.n_modes} modes") +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +Initialized StaticHessian with ... modes +>>> # Note: Running the full minimization is time-consuming for doctests +>>> # hessian.run(n_steps=2) would be skipped in quick validation + +## Example 5: Cluster Calculation with C++ Executable + +For large systems, use the C++ executable on HPC clusters: + +### Step 1: Prepare Input Files (Python) + +```python +# prepare_input_for_cluster.py +import tdscha.DynamicalLanczos as DL + +# Initialize Lanczos as usual +lanczos = DL.Lanczos(ens) +lanczos.init() +lanczos.prepare_mode(10) + +# Prepare input files for C++ executable +lanczos.prepare_input_files( + root_name="tdscha_calc", + n_steps=200, + directory="cluster_input", + run_symm=False # Use symmetric Lanczos +) + +print("Input files prepared in 'cluster_input/'") +print("Copy to cluster and run: ./tdscha-lanczos.x > output.txt") +``` + +### Step 2: Run on Cluster (Bash) + +```bash +#!/bin/bash +#SBATCH --job-name=tdscha +#SBATCH --nodes=4 +#SBATCH --ntasks-per-node=16 +#SBATCH --time=24:00:00 + +# Load modules +module load intel mpich + +# Copy input files +cp -r cluster_input/* $SLURM_SUBMIT_DIR/ + +# Run C++ executable +mpirun -np 64 ./tdscha-lanczos.x > tdscha_calc.stdout + +# Convert output +tdscha-output2abc tdscha_calc.stdout tdscha_calc.abc + +# Basic analysis +tdscha-plot-data tdscha_calc.abc 0 1000 5 +tdscha-convergence-analysis tdscha_calc.abc 5 +``` + +### Step 3: Analyze Results (Python) + +```python +# analyze_cluster.py +import tdscha.DynamicalLanczos as DL + +# Load results from .abc file +lanczos = DL.Lanczos() +lanczos.load_abc("tdscha_calc.abc") + +# Compute spectrum +w_array = np.linspace(0, 1000/CC.Units.RY_TO_CM, 1000) +gf = lanczos.get_green_function_continued_fraction(w_array, smearing=5/CC.Units.RY_TO_CM) +spectrum = -np.imag(gf) + +# Save for plotting +np.savetxt("spectrum.dat", np.column_stack([w_array * CC.Units.RY_TO_CM, spectrum])) +``` + +## Example 6: Convergence Analysis + +Analyze Lanczos convergence for optimal step count: + +```python +# convergence_analysis.py +import tdscha.DynamicalLanczos as DL +import numpy as np + +# Load final Lanczos state +lanczos = DL.Lanczos() +lanczos.load_status("output/final.npz") + +# Analyze convergence of static frequency +n_steps = len(lanczos.a_coeffs) - 1 +static_freqs = np.zeros(n_steps) + +for i in range(1, n_steps + 1): + # Truncate coefficients to i steps + a_partial = lanczos.a_coeffs[:i] + b_partial = lanczos.b_coeffs[:i-1] if i > 1 else [] + c_partial = lanczos.c_coeffs[:i-1] if i > 1 else [] + + # Temporary Lanczos object with truncated coefficients + temp_lanc = DL.Lanczos() + temp_lanc.a_coeffs = a_partial + temp_lanc.b_coeffs = b_partial + temp_lanc.c_coeffs = c_partial + temp_lanc.use_wigner = lanczos.use_wigner + temp_lanc.shift_value = lanczos.shift_value + + # Compute static Green's function + gf0 = temp_lanc.get_green_function_continued_fraction(np.array([0]), use_terminator=False)[0] + static_freq = np.sign(np.real(gf0)) * np.sqrt(np.abs(1/np.real(gf0))) + static_freqs[i-1] = static_freq * CC.Units.RY_TO_CM + +# Plot convergence +import matplotlib.pyplot as plt +plt.figure(figsize=(10, 4)) +plt.subplot(121) +plt.plot(range(1, n_steps+1), static_freqs, 'o-') +plt.xlabel("Lanczos steps") +plt.ylabel("Static frequency [cm⁻¹]") +plt.title("Convergence of static frequency") + +# Or use CLI tool +print("Run: tdscha-convergence-analysis output/final.npz 5") +``` + +## Example 7: Comparing Normal vs Wigner Representation + +```python +# comparison_wigner.py +import tdscha.DynamicalLanczos as DL + +results = {} +for use_wigner in [False, True]: + print(f"\n=== {'Wigner' if use_wigner else 'Normal'} representation ===") + + lanczos = DL.Lanczos(ensemble, use_wigner=use_wigner) + lanczos.init() + lanczos.prepare_mode(10) + + # Run with same parameters + lanczos.run_FT(80, save_dir=f"output_{'wigner' if use_wigner else 'normal'}") + + # Compute spectrum + w_array = np.linspace(0, 500/CC.Units.RY_TO_CM, 500) + gf = lanczos.get_green_function_continued_fraction(w_array, smearing=5/CC.Units.RY_TO_CM) + + results['wigner' if use_wigner else 'normal'] = { + 'frequencies': w_array * CC.Units.RY_TO_CM, + 'spectrum': -np.imag(gf), + 'a_coeffs': lanczos.a_coeffs, + 'convergence': len(lanczos.a_coeffs) + } + +# Compare +plt.figure() +for label, data in results.items(): + plt.plot(data['frequencies'], data['spectrum'], label=label, alpha=0.7) +plt.xlabel("Frequency [cm⁻¹]") +plt.ylabel("Spectrum [a.u.]") +plt.legend() +plt.title("Normal vs Wigner representation") +plt.show() +``` + +## Example 8: Restarting Calculations + +```python +# restart_run.py +import tdscha.DynamicalLanczos as DL + +# Method 1: Continue from saved status +lanczos = DL.Lanczos() +lanczos.load_status("output/tdscha_lanczos_STEP50") # Load checkpoint at step 50 +lanczos.run_FT(100, save_dir="output_restart", prefix="restart") # Continue to step 150 + +# Method 2: Start new calculation with same ensemble +lanczos2 = DL.Lanczos(ensemble) # Same ensemble as before +lanczos2.init() +lanczos2.prepare_mode(10) +lanczos2.run_FT(150, save_dir="output_full") # Start from scratch + +# Compare results +print(f"Restarted: {len(lanczos.a_coeffs)} coefficients") +print(f"Fresh: {len(lanczos2.a_coeffs)} coefficients") +``` + +## Example 9: Custom Perturbation Vector + +```python +# custom_perturbation.py +import numpy as np + +# Create custom perturbation (e.g., specific atomic displacement) +nat_sc = ensemble.nat # Atoms in supercell +custom_vec = np.zeros(3 * nat_sc) + +# Displace atom 0 in x-direction +custom_vec[0] = 1.0 # x of atom 0 +# custom_vec[1] = 0.0 # y of atom 0 (default) +# custom_vec[2] = 0.0 # z of atom 0 (default) + +# Prepare perturbation (masses_exp=1 for displacements, -1 for forces) +lanczos.prepare_perturbation(custom_vec, masses_exp=1) + +# Run calculation +lanczos.run_FT(100, save_dir="custom_pert") + +# Note: spectrum will be response to this specific displacement pattern +``` + +## Example 10: Full Workflow with CLI Analysis + +Complete workflow combining Python and CLI: + +```bash +#!/bin/bash +# full_workflow.sh + +# 1. Run Python calculation +python run_calculation.py + +# 2. Convert to .abc if using C++ executable +tdscha-output2abc lanczos.stdout lanczos.abc + +# 3. Analyze convergence +tdscha-convergence-analysis lanczos_final.npz 5 +mv convergence.png convergence_analysis.png + +# 4. Plot spectra at different resolutions +tdscha-plot-data lanczos_final.npz 0 200 0.5 +mv spectrum.png spectrum_highres.png + +tdscha-plot-data lanczos_final.npz 0 1000 5 +mv spectrum.png spectrum_lowres.png + +# 5. For Hessian calculations +tdscha-hessian-convergence hessian_steps/ hessian_calculation +mv hessian_convergence.png hessian_analysis.png + +echo "Workflow complete! Check *.png files for results." +``` + +## Directory Structure for Examples + +``` +project/ +├── dyn_start_* # Initial dynamical matrix files +├── dyn_final_* # Final SSCHA dynamical matrix +├── ensemble/ # SSCHA ensemble +│ ├── ensemble_pop1_x.dat +│ ├── ensemble_pop1_f.dat +│ └── ensemble_pop1_rho.dat +├── run_local.py # Main calculation script +├── output/ # Lanczos output +│ ├── tdscha_lanczos_STEP*.npz +│ └── final.npz +├── spectrum.dat # Final spectrum +└── convergence.png # Convergence analysis +``` + +## Common Parameters and Defaults + +| Parameter | Typical Value | Description | +|-----------|---------------|-------------| +| `LANCZOS_STEPS` | 50-200 | Steps for convergence | +| `SAVE_EACH` | 5-10 | Checkpoint frequency | +| `SMEARING` | 1-10 cm⁻¹ | Lorentzian broadening | +| `THRESHOLD` | 1e-6 to 1e-8 | Convergence threshold | +| `MODE` | Auto (Julia > C > Python) | Computation mode | + +## Troubleshooting Examples + +See the `Examples/Comparison/` directory for: +- `normal.py` vs `wigner.py` - Representation comparison +- `convergence.py` - Step convergence analysis +- `comparison.py` - Method comparison utilities + +And `Examples/example_IR_Raman_2p/` for: +- `IR_UNPOL/` - Unpolarized IR with 1ph/2ph processes +- `RAMAN_UNPOL/` - Unpolarized Raman calculations +- Complete README files with instructions \ No newline at end of file diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..dfd8edb5 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,60 @@ +# TD-SCHA Documentation + +Time-Dependent Self-Consistent Harmonic Approximation (TD-SCHA) is a Python library for simulating quantum nuclear motion in materials with strong anharmonicity. It performs dynamical linear response calculations on top of equilibrium results from `python-sscha`. + +Part of the SSCHA ecosystem: `cellconstructor` → `python-sscha` → `tdscha`. + +## Documentation Structure + +1. **[Theory](theory.md)** - What is TD-SCHA, why linear response, and what quantities can be calculated with it. Theoretical background with main TDSCHA equations and the Lanczos algorithm. + +2. **[Installation](installation.md)** - Complete installation guide following the official SSCHA.eu instructions, with emphasis on Julia for performance. + +3. **[Quick Start](quickstart.md)** - Working example showing calculation and analysis via CLI commands. + +4. **[In-Depth Usage](usage.md)** - Choosing perturbations, parallel execution, gamma-only trick, Wigner vs normal representation. + +5. **[StaticHessian](static-hessian.md)** - Computing free energy Hessian of large systems via sparse linear algebra. + +6. **[CLI Tools](cli.md)** - Command-line interface for analysis and visualization. + +7. **[Examples](examples.md)** - Detailed examples and templates. + +8. **API Reference** - Automatically generated documentation: + - [DynamicalLanczos](api/dynamical_lanczos.md) - Core Lanczos algorithm + - [StaticHessian](api/static_hessian.md) - Free energy Hessian calculations + - [Tools](api/tools.md) - Krylov solvers and linear algebra utilities + - [Perturbations](api/perturbations.md) - IR and Raman response functions + - [Parallel](api/parallel.md) - MPI parallelization utilities + +## Key Features + +- **Dynamical linear response** beyond harmonic approximation +- **Lanczos algorithm** for efficient spectral function computation +- **Multiple perturbation types**: single phonon mode, IR, Raman (polarized/unpolarized) +- **Parallel execution**: MPI, Julia fast mode, C extensions +- **Symmetry-aware** calculations for efficiency +- **Static Hessian** computation via sparse linear algebra +- **Integration** with SSCHA equilibrium results + +## Theoretical Foundation + +TD-SCHA extends the Self-Consistent Harmonic Approximation (SCHA) to time-dependent perturbations, enabling computation of: +- Infrared (IR) absorption spectra +- Raman scattering spectra +- Dynamical structure factor +- Phonon spectral functions with full anharmonicity + +The method computes the dynamical susceptibility via a Lanczos algorithm, yielding the Green's function in continued fraction representation. This allows efficient calculation of spectral properties without artificial broadening. + +## Related Papers + +1. **Monacelli et al., Physical Review B** - Core TD-SCHA theory and equations (referenced as Eq. K4 in code) +2. **Raman intensity formulas** - J. Phys. Chem. DOI: 10.1021/jp5125266 + +## Getting Help + +- Check the [examples](examples.md) for working templates +- Use the CLI tools for analysis and visualization + - Refer to automatically generated API documentation for detailed method specifications +- See the [SSCHA website](http://www.sscha.eu) for tutorials \ No newline at end of file diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 00000000..b11efb20 --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,341 @@ +# Installation Guide + +This guide provides installation instructions for TD-SCHA (Time-Dependent Self-Consistent Harmonic Approximation), following the official [SSCHA installation guide](https://sscha.eu/download/). TD-SCHA requires `cellconstructor` and `python-sscha` as prerequisites. + +## 1. Easy Installation through Anaconda/Mamba (Recommended) + +The easiest way to install TD-SCHA with all dependencies is using Anaconda or Mamba. + +### Option A: Anaconda + +```bash +# Create a new environment with all required libraries +conda create -n sscha -c conda-forge python gfortran libblas lapack openmpi julia openmpi-mpicc pip numpy scipy spglib pkgconfig +conda activate sscha + +# Install additional Python dependencies +pip install ase julia mpi4py + +# Install the SSCHA ecosystem +pip install cellconstructor python-sscha tdscha +``` + +### Option B: Micromamba (Lightweight Alternative) + +If Anaconda is too large, use [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html): + +```bash +# Create environment +micromamba create -n sscha -c conda-forge python gfortran libblas lapack openmpi julia openmpi-mpicc pip numpy scipy spglib pkgconfig +micromamba activate sscha + +# Install dependencies +pip install ase julia mpi4py +pip install cellconstructor python-sscha tdscha +``` + +### Setting Up Julia for Maximum Performance + +**Critical Step**: TD-SCHA achieves 2-10× speedup with Julia enabled. Configure it properly: + +```bash +# Install Julia Python bindings +python -c 'import julia; julia.install()' +``` + +**Note**: In some micromamba installations, you may need to specify the conda executable location: + +```bash +export CONDA_JL_CONDA_EXE=$HOME/.local/bin/micromamba +echo "export CONDA_JL_CONDA_EXE=$HOME/.local/bin/micromamba" >> $HOME/.bashrc +``` + +To configure Julia PyCall to work with conda, open a Julia shell and install required packages: + +```julia +# In Julia REPL, type ']' to enter package manager +pkg> add SparseArrays LinearAlgebra InteractiveUtils PyCall +``` + +## 2. Installing Without Package Managers + +If you cannot use Anaconda/Mamba, install system dependencies manually. + +### System Requirements + +**Ubuntu/Debian**: +```bash +sudo apt update +sudo apt install libblas-dev liblapack-dev liblapacke-dev gfortran openmpi-bin +``` + +**CentOS/RHEL/Fedora**: +```bash +sudo dnf install blas-devel lapack-devel gcc-gfortran openmpi-devel +``` + +**macOS with Homebrew**: +```bash +brew install openblas gcc open-mpi +export LDFLAGS="-L/usr/local/opt/openblas/lib" +export CPPFLAGS="-I/usr/local/opt/openblas/include" +``` + +### Python Dependencies + +```bash +pip install ase spglib +``` + +### Julia Speedup (Highly Recommended) + +TD-SCHA benefits significantly from Julia. Install it via: + +```bash +# Install Julia using Juliaup (Linux/macOS) +curl -fsSL https://install.julialang.org | sh + +# Or download from https://julialang.org/downloads/ + +# Install Python bindings +pip install julia + +# Configure Julia (in a Julia REPL, type ']' then): +pkg> add SparseArrays LinearAlgebra InteractiveUtils PyCall +``` + +## 3. Installing TD-SCHA + +### From PyPI (Simplest) + +```bash +pip install tdscha +``` + +This installs the latest release with runtime dependencies. + +### From Source (Development Version) + +```bash +git clone https://github.com/SSCHAcode/tdscha.git +cd tdscha +pip install --no-build-isolation . +``` + +The `--no-build-isolation` flag is required for C extensions to find system libraries. + +### Development Installation + +For contributions or development: + +```bash +git clone https://github.com/SSCHAcode/tdscha.git +cd tdscha +pip install -e .[dev] +``` + +## 4. MPI Parallelization for Production Runs + +MPI parallelization is optional but recommended for large systems: + +```bash +# Install mpi4py (if not already installed) +pip install mpi4py + +# Run TD-SCHA with MPI +mpirun -np 4 python your_script.py +``` + +**Note**: For maximum performance, combine MPI with Julia speedup. The MPI parallelization is automatically enabled if `mpi4py` is available and you use the Julia fast mode. + +### Compiling with MPI Support + +If using the C implementation (without Julia), compile from source with MPI: + +```bash +git clone https://github.com/SSCHAcode/tdscha.git +cd tdscha +pip install --no-build-isolation . +``` + +Check the output for "PARALLEL ENVIRONMENT DETECTED SUCCESSFULLY". + +## 5. Build System Details + +TD-SCHA uses **Meson + meson-python + Ninja** as its build system: + +```bash +# Install build tools +pip install meson>=1.1.0 meson-python>=0.13.0 ninja>=1.10 + +# Optional: Build with Intel MKL +pip install --no-build-isolation . -Duse_mkl=true + +# Optional: Specify BLAS/LAPACK implementation +pip install --no-build-isolation . -Dblas=openblas -Dlapack=openblas +``` + +### C Extensions + +The package includes C extensions compiled from: +- `CModules/odd_corr_module.c` - Odd correlation functions +- `CModules/LanczosFunctions.c` - Core Lanczos implementation (~101KB) + +These are compiled into the `sscha_HP_odd` module with OpenMP support. MPI support is enabled via the `-D_MPI` flag. + +## 6. Verification and Testing + +### Quick Verification + +```python +import tdscha +import tdscha.DynamicalLanczos as DL + +# Check Julia availability (critical for performance) +print("Julia enabled:", DL.is_julia_enabled()) +print("Recommended: Julia provides 2-10× speedup") + +# Test module imports +import tdscha.StaticHessian +import tdscha.Tools +import tdscha.Perturbations +print("All TD-SCHA modules imported successfully") +``` + +**Doctest verification** (automatically validated): + +# doctest: +ELLIPSIS +>>> import tdscha +>>> import tdscha.DynamicalLanczos as DL +>>> print("Julia enabled:", DL.is_julia_enabled()) +Julia enabled: ... +>>> import tdscha.StaticHessian +>>> import tdscha.Tools +>>> import tdscha.Perturbations +>>> print("All TD-SCHA modules imported successfully") +All TD-SCHA modules imported successfully + +### Running Tests + +```bash +# Run standard tests (excludes heavy tests) +pytest -v + +# Exclude release-tagged heavy tests (as CI does) +pytest -v -m "not release" + +# Test specific components +pytest tests/test_lanczos_fast/test_lanczos_fast_rt.py -v +pytest tests/test_julia/test_julia.py -v +``` + +**Note**: CI sets `OMP_NUM_THREADS=1` and `JULIA_NUM_THREADS=1` for reproducibility. + +### Documentation Validation + +The repository includes a script to validate code snippets in documentation: + +```bash +# Validate all documentation files +python scripts/validate_docs.py + +# Validate with import checking +python scripts/validate_docs.py --test-imports + +# Validate specific files +python scripts/validate_docs.py docs/installation.md docs/quickstart.md +``` + +## 7. Environment Variables for Performance Tuning + +| Variable | Purpose | Recommended Setting | +|----------|---------|---------------------| +| `OMP_NUM_THREADS` | OpenMP threads for C extensions | Number of physical cores | +| `JULIA_NUM_THREADS` | Julia threads (set before importing) | Number of physical cores | +| `MKL_NUM_THREADS` | MKL threads (if using Intel MKL) | 1 for MPI jobs, cores otherwise | +| `MPIEXEC` | Path to MPI executable | Auto-detected | + +Example for a 16-core machine: +```bash +export OMP_NUM_THREADS=16 +export JULIA_NUM_THREADS=16 +``` + +## 8. Troubleshooting Common Issues + +### C Extension Compilation Failures + +**Error**: `ImportError: cannot import name 'sscha_HP_odd'` + +**Solution**: +1. Ensure BLAS/LAPACK development libraries are installed: + ```bash + # Ubuntu/Debian + sudo apt install libblas-dev liblapack-dev + ``` +2. Check compiler logs during installation +3. Use `--no-build-isolation` flag: + ```bash + pip install --no-build-isolation tdscha + ``` + +### Julia Not Found or Not Working + +**Error**: `Julia not found` or slow performance despite Julia installation + +**Solution**: +1. Verify Julia is in PATH: + ```bash + which julia + julia --version + ``` +2. Set `JULIA_BINDIR` if needed: + ```bash + export JULIA_BINDIR=/path/to/julia/bin + ``` +3. Reinstall Julia Python bindings: + ```bash + pip install --force-reinstall julia + python -c "import julia; julia.install()" + ``` + +### MPI Configuration Issues + +**Error**: MPI jobs fail or hang + +**Solution**: +1. Test mpi4py installation: + ```bash + python -c "from mpi4py import MPI; print('MPI version:', MPI.Get_version())" + ``` +2. Ensure MPI implementation matches mpi4py (MPICH vs OpenMPI) +3. For cluster deployments, load correct MPI module before installation + +### CHOLMOD Version Incompatibility (Julia) + +**Warning**: `CHOLMOD version incompatibility` + +**Solution**: +```bash +# Remove conda-installed julia if present +conda remove julia + +# Use system Julia or install via Juliaup +curl -fsSL https://install.julialang.org | sh +``` + +## 9. Next Steps + +After successful installation: + +1. **Run the [Quick Start](quickstart.md)** guide for your first calculation +2. **Explore [Examples](examples.md)** for complete workflows +3. **Check [In-Depth Usage](usage.md)** for advanced features +4. **Learn about [StaticHessian](static-hessian.md)** for free energy calculations + +## 10. Getting Help + +- **Documentation**: This guide and other pages in `docs/` +- **GitHub Issues**: [SSCHAcode/tdscha/issues](https://github.com/SSCHAcode/tdscha/issues) +- **SSCHA Website**: [sscha.eu](https://sscha.eu) with tutorials and FAQs +- **Community**: Check the SSCHA website for community forums and contact information \ No newline at end of file diff --git a/docs/quickstart.md b/docs/quickstart.md new file mode 100644 index 00000000..5951b19a --- /dev/null +++ b/docs/quickstart.md @@ -0,0 +1,253 @@ +# Quick Start Guide + +This guide walks through a complete TD-SCHA calculation from ensemble preparation to spectral analysis using CLI tools. + +## Quick Test with Example Data + +Before starting with your own data, you can test TD-SCHA using the built-in test data from the test suite: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> ens = load_test_ensemble(n_configs=10) +>>> print(f"Loaded ensemble with {ens.N} configurations") +Loaded ensemble with 10 configurations +>>> import tdscha.DynamicalLanczos as DL +>>> lanczos = DL.Lanczos(ens) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos.verbose = False +>>> lanczos.init(use_symmetries=True) +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +>>> print(f"Initialized Lanczos with {len(lanczos.w)} modes") +Initialized Lanczos with ... modes + +Note: The exact number of modes depends on the test system. + +### Basic Lanczos Workflow + +Here's a complete example showing mode perturbation and a few Lanczos steps: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.DynamicalLanczos as DL +>>> import numpy as np +>>> ens = load_test_ensemble(n_configs=5) # Small subset for speed +>>> lanczos = DL.Lanczos(ens) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos.verbose = False +>>> lanczos.init(use_symmetries=True) +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +>>> # Prepare perturbation for mode 10 +>>> lanczos.prepare_mode(10) + +... +>>> # Run 2 Lanczos steps +>>> import sys +>>> from io import StringIO +>>> old_stdout = sys.stdout +>>> sys.stdout = StringIO() +>>> lanczos.run_FT(2, debug=False) +>>> sys.stdout = old_stdout +>>> print(f"Lanczos coefficients after 2 steps: a={lanczos.a_coeffs[:2]}") +Lanczos coefficients after 2 steps: a=[np.float64(...), np.float64(...)] + +## Prerequisites + +Ensure you have: +1. A converged SSCHA ensemble (configurations, forces, weights) +2. The final SSCHA dynamical matrix +3. TD-SCHA installed (see [Installation](installation.md)) + +## Example Workflow + +### Step 1: Prepare the Ensemble + +First, load your SSCHA results: + +```python +import cellconstructor as CC +import sscha.Ensemble +import numpy as np + +# Load dynamical matrices +dyn_initial = CC.Phonons.Phonons("dyn_start_", nqirr=1) +dyn_final = CC.Phonons.Phonons("dyn_final_", nqirr=1) + +# Create ensemble +temperature = 100 # K +ens = sscha.Ensemble.Ensemble(dyn_initial, temperature) + +# Load configurations (adjust paths as needed) +ens.load("ensemble_data/", population_id=1, n_configs=10000) + +# Update weights to final dynamical matrix +ens.update_weights(dyn_final, temperature) +``` + +### Step 2: Run TD-SCHA Calculation + +Choose a perturbation type and run Lanczos: + +#### Option A: Single Phonon Mode + +```python +import tdscha.DynamicalLanczos as DL + +# Initialize Lanczos +lanczos = DL.Lanczos(ens) +lanczos.init(use_symmetries=True) + +# Prepare perturbation along mode 10 (adjust as needed) +mode_id = 10 +print(f"Mode {mode_id} frequency: {lanczos.w[mode_id] * CC.Units.RY_TO_CM:.1f} cm⁻¹") +lanczos.prepare_mode(mode_id) + +# Run Lanczos (20 steps, save each 5) +lanczos.run_FT(20, save_dir="tdscha_output", + prefix="lanczos_m10", save_each=5) + +# Save final status +lanczos.save_status("tdscha_output/lanczos_final.npz") +``` + +#### Option B: IR Absorption + +```python +# Ensure dyn_final has effective charges +lanczos.prepare_ir(pol_vec=np.array([1, 0, 0])) # x-polarization +lanczos.run_FT(20, save_dir="tdscha_ir", prefix="lanczos_ir") +``` + +#### Option C: Raman Scattering + +```python +# Ensure dyn_final has Raman tensor +lanczos.prepare_raman(pol_vec_in=[1,0,0], pol_vec_out=[1,0,0]) +lanczos.run_FT(20, save_dir="tdscha_raman", prefix="lanczos_raman") +``` + +### Step 3: Analyze Results with CLI + +TD-SCHA provides four CLI tools for analysis: + +#### 1. Convert Output to ABC Format + +If you ran the C++ executable `tdscha-lanczos.x`, convert its output: + +```bash +tdscha-output2abc lanczos.stdout lanczos.abc +``` + +#### 2. Plot Spectrum + +Plot the spectral function from .abc or .npz files: + +```bash +# Basic plot (0-5000 cm⁻¹, 5 cm⁻¹ smearing) +tdscha-plot-data lanczos_final.npz + +# Custom frequency range and smearing +tdscha-plot-data lanczos.abc 0 1000 2 +``` + +#### 3. Analyze Convergence + +Check how the spectral function converges with Lanczos steps: + +```bash +tdscha-convergence-analysis lanczos_final.npz 5 +``` + +This generates three plots: +- Static frequency vs Lanczos steps +- Spectral function evolution (without terminator) +- Spectral function evolution (with terminator) +- Final converged spectrum + +#### 4. Analyze Hessian Convergence (StaticHessian) + +For StaticHessian calculations: + +```bash +tdscha-hessian-convergence hessian_steps/ hessian_calculation +``` + +## Complete Example Script + +Here's a complete script combining Python calculation and CLI analysis: + +```python +# run_calculation.py +import cellconstructor as CC +import sscha.Ensemble +import tdscha.DynamicalLanczos as DL +import subprocess +import sys + +# 1. Load ensemble +dyn = CC.Phonons.Phonons("dyn_prefix_", 1) +final_dyn = CC.Phonons.Phonons("dyn_final_", 1) +ens = sscha.Ensemble.Ensemble(dyn, 100) +ens.load("ensemble/", 1, 10000) +ens.update_weights(final_dyn, 100) + +# 2. Run TD-SCHA +lanc = DL.Lanczos(ens) +lanc.init() +lanc.prepare_mode(10) +lanc.run_FT(50, save_dir="output", prefix="calc", save_each=10) +lanc.save_status("output/final.npz") + +# 3. Run CLI analysis +print("\n=== Running CLI Analysis ===") +subprocess.run(["tdscha-convergence-analysis", "output/final.npz", "5"]) +subprocess.run(["tdscha-plot-data", "output/final.npz", "0", "500", "2"]) +``` + +Run with MPI for parallel execution: + +```bash +mpirun -np 4 python run_calculation.py +``` + +## Understanding the Output + +### Lanczos Coefficients +The calculation produces three arrays: +- `a_coeffs` - Diagonal elements of tridiagonal matrix +- `b_coeffs`, `c_coeffs` - Off-diagonal elements + +These define the continued fraction for the Green's function. + +### Spectral Function +The spectral function $S(\omega) = -\frac{1}{\pi}\mathrm{Im}G(\omega)$ contains: +- **Peak positions** - Renormalized phonon frequencies +- **Peak widths** - Phonon lifetimes from anharmonicity +- **Spectral weight** - Mode intensities + +### Convergence Metrics +- **Static limit** - $\omega_{\mathrm{static}} = \sqrt{1/G(0)}$ should converge +- **Terminator effect** - Spectral function should stabilize with Lanczos steps + +## Quick CLI Reference + +| Command | Purpose | Example | +|---------|---------|---------| +| `tdscha-convergence-analysis` | Analyze Lanczos convergence | `tdscha-convergence-analysis file.npz 5` | +| `tdscha-plot-data` | Plot spectrum | `tdscha-plot-data file.abc 0 1000 2` | +| `tdscha-output2abc` | Convert stdout to .abc | `tdscha-output2abc stdout.txt output.abc` | +| `tdscha-hessian-convergence` | Plot Hessian convergence | `tdscha-hessian-convergence dir/ prefix` | + +## Next Steps + +- Explore [In-Depth Usage](usage.md) for advanced features +- Learn about [StaticHessian](static-hessian.md) for free energy calculations +- Check [Examples](examples.md) for complete workflows +- Refer to [API Documentation](api/dynamical_lanczos.md) for detailed method specifications \ No newline at end of file diff --git a/docs/static-hessian.md b/docs/static-hessian.md new file mode 100644 index 00000000..922ceb86 --- /dev/null +++ b/docs/static-hessian.md @@ -0,0 +1,340 @@ +# StaticHessian: Free Energy Hessian Computation + +The `StaticHessian` class computes the free energy Hessian matrix (second derivative of free energy with respect to atomic displacements) for large systems using sparse linear algebra. It exploits Lanczos-based inversion to include fourth-order anharmonic contributions efficiently. + +## Purpose and Use Cases + +The free energy Hessian provides: + +1. **Thermodynamic stability** - Eigenvalues determine stability at given temperature +2. **Anharmonic phonons** - Renormalized frequencies beyond harmonic approximation +3. **Elastic constants** - Second derivative w.r.t. strain +4. **Phase transitions** - Soft modes and instability analysis + +**When to use StaticHessian**: +- Systems too large for full dynamical Lanczos +- Need only static ($\omega=0$) response +- Require full Hessian matrix (not just diagonal) +- Including fourth-order contributions is essential + +## Theory + +The free energy Hessian $H_{ij} = \frac{\partial^2 F}{\partial u_i \partial u_j}$ satisfies: + +$$ +H = \Phi^{(2)} - \Phi^{(4)} : G +$$ + +where: +- $\Phi^{(2)}$ is the harmonic force constant matrix +- $\Phi^{(4)}$ is the fourth-order force constant tensor +- $G$ is the static limit of the Green's function +- $:$ denotes tensor contraction + +The equation is solved via the linear system: + +$$ +L \cdot x = b +$$ + +with: +- $L$: Linear operator containing $\Phi^{(2)}$ and $\Phi^{(4)}$ +- $x$: Vector representation of $G$ and auxiliary tensors +- $b$: Right-hand side from harmonic problem + +## Basic Usage + +### Initialization + +```python +import tdscha.StaticHessian +import sscha.Ensemble + +# From ensemble +hessian = tdscha.StaticHessian.StaticHessian(ensemble) + +# Or initialize later +hessian = tdscha.StaticHessian.StaticHessian() +hessian.init(ensemble, verbose=True) +``` + +**Example with test data**: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.StaticHessian +>>> ens = load_test_ensemble(n_configs=5) +>>> hessian = tdscha.StaticHessian.StaticHessian(ens); print(f"Initialized StaticHessian with {hessian.lanczos.n_modes} modes") +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +Initialized StaticHessian with ... modes + +**Parameters**: +- `ensemble`: SSCHA ensemble object +- `verbose`: Print memory usage and progress +- `lanczos_input`: Dictionary passed to embedded `Lanczos` object + +### Running the Calculation + +```python +hessian.run(n_steps=200, + save_dir="hessian_steps", + threshold=1e-6, + algorithm="cg-prec", + extra_options={}) +``` + +**Parameters**: +- `n_steps`: Maximum minimization steps +- `save_dir`: Directory for saving convergence steps +- `threshold`: Convergence threshold for residual +- `algorithm`: Minimization algorithm (see below) +- `extra_options`: Algorithm-specific options + +### Retrieving Results + +```python +# Get Hessian as Phonons object (with q-points) +hessian_phonons = hessian.retrieve_hessian() + +# Get raw matrix in supercell (no q-points) +hessian_matrix = hessian.retrieve_hessian(noq=True) + +# Extract frequencies +w, pols = hessian_phonons.DiagonalizeSupercell() +``` + +## Algorithms + +### Preconditioned Conjugate Gradient (`"cg-prec"`) + +**Default and recommended**. Uses $L^{1/2}$ as preconditioner: + +```python +hessian.run(algorithm="cg-prec", threshold=1e-8) +``` + +**Pros**: Fast convergence, minimal memory +**Cons**: Requires preconditioner application + +### Standard Conjugate Gradient (`"cg"`) + +```python +hessian.run(algorithm="cg", threshold=1e-8) +``` + +**Pros**: Simpler, no preconditioner needed +**Cons**: Slower convergence for ill-conditioned systems + +### Full Orthogonalization Method (`"fom"`) + +Requires specifying Krylov subspace dimension: + +```python +hessian.run(algorithm="fom", + extra_options={"Krylov_dimension": 50}) +``` + +**Pros**: Better convergence for difficult systems +**Cons**: Higher memory usage + +## Convergence Monitoring + +### Step-by-Step Saving + +```python +hessian.run(n_steps=100, save_dir="convergence") +``` + +Files saved in `save_dir`: +- `hessian_calculation_stepXXXXX.dat` - Vector at step X +- `hessian_calculation.json` - Final status (JSON) +- `hessian_calculation` - Final status (NumPy) + +### Plotting Convergence + +Use the CLI tool: + +```bash +tdscha-hessian-convergence convergence/ hessian_calculation +``` + +This plots eigenvalue convergence vs minimization steps. + +### Manual Convergence Check + +```python +import numpy as np + +# Load step files +steps = [] +for i in range(n_steps): + hessian.vector = np.loadtxt(f"convergence/step_{i:05d}.dat") + H = hessian.retrieve_hessian(noq=True) + w2 = np.linalg.eigvalsh(H) + steps.append(np.sqrt(np.abs(w2)) * np.sign(w2)) +``` + +## Advanced Features + +### No Mode-Mixing Approximation + +Neglects mode-mixing (bubble diagrams) for faster computation: + +```python +hessian_nomix = hessian.run_no_mode_mixing( + nsteps=100, + save_dir="no_mixing", + restart_from_file=False +) +``` + +**When to use**: Quick estimates, large systems where mode-mixing is weak +**Limitation**: Neglects phonon-phonon scattering diagrams + +### Custom Initial Guess + +```python +# Start from harmonic approximation +hessian.preconitioned = False # Use Φ^(2) as initial guess +hessian.init(ensemble) + +# Or start from previous calculation +hessian.load_status("previous_calc.json") +hessian.run(n_steps=50) # Continue from loaded state +``` + +### Memory Optimization + +The algorithm stores vectors of size: +$$ +N_\text{vec} = N_\text{modes} + \frac{N_\text{modes}(N_\text{modes}+1)}{2} + \frac{N_\text{modes}(N_\text{modes}^2+3N_\text{modes}+2)}{6} +$$ + +**Memory estimate**: +```python +n_modes = hessian.lanczos.n_modes +n_g = (n_modes * (n_modes + 1)) // 2 +n_w = (n_modes * (n_modes**2 + 3*n_modes + 2)) // 6 +memory_gb = (n_g + n_w) * 8 * 3 / 1024**3 # 3 working arrays +``` + +## Complete Example + +```python +import cellconstructor as CC +import sscha.Ensemble +import tdscha.StaticHessian +import numpy as np + +# 1. Load ensemble +dyn = CC.Phonons.Phonons("dyn_", 1) +ens = sscha.Ensemble.Ensemble(dyn, 300) +ens.load("ensemble/", 1, 5000) + +# 2. Initialize StaticHessian +hessian = tdscha.StaticHessian.StaticHessian( + ensemble=ens, + verbose=True, + lanczos_input={"use_wigner": False} # Options for embedded Lanczos +) + +# 3. Run minimization +print("Memory estimate:", hessian.vector.nbytes * 3 / 1024**3, "GB") +hessian.run(n_steps=200, save_dir="hessian_out", threshold=1e-7) + +# 4. Analyze results +hessian_phonons = hessian.retrieve_hessian() +w, pols = hessian_phonons.DiagonalizeSupercell() + +# Remove translations +masses = hessian_phonons.structure.get_masses_array() +trans = CC.Methods.get_translations(pols, masses) +w_nontrans = w[~trans] + +print(f"Hessian frequencies: {w_nontrans * CC.Units.RY_TO_CM}") +print(f"Min frequency: {np.min(w_nontrans) * CC.Units.RY_TO_CM:.1f} cm⁻¹") + +# 5. Check convergence +if hessian.verbose: + print("Calculation converged!") +``` + +## Parallel Execution + +StaticHessian uses the embedded Lanczos object for parallelization: + +```python +# MPI parallelization +hessian = tdscha.StaticHessian.StaticHessian( + ensemble=ens, + lanczos_input={"mode": DL.MODE_FAST_MPI} +) + +# Julia fast mode +hessian = tdscha.StaticHessian.StaticHessian( + ensemble=ens, + lanczos_input={"mode": DL.MODE_FAST_JULIA} +) +``` + +Run with MPI: +```bash +mpirun -np 4 python hessian_calculation.py +``` + +## Comparison with Dynamical Lanczos + +| Aspect | StaticHessian | DynamicalLanczos | +|--------|---------------|------------------| +| **Output** | Static ($\omega=0$) Hessian | Full $\chi(\omega)$ | +| **Memory** | Lower (sparse algebra) | Higher (Lanczos basis) | +| **Speed** | Faster for Hessian only | Slower (computes all $\omega$) | +| **Use case** | Stability analysis, elastic constants | Spectra, dynamical properties | + +## Troubleshooting + +### Slow Convergence +- Increase `n_steps` (100-500 typical) +- Try `algorithm="fom"` with larger Krylov dimension +- Check if system is near instability (very small eigenvalues) + +### Memory Issues +- Use `select_modes` in `lanczos_input` to exclude high-frequency modes +- Consider `run_no_mode_mixing` for large systems +- Use MPI parallelization to distribute memory + +### Numerical Instability +- Ensure ensemble is well-converged +- Check weights are positive (no numerical issues) +- Try different `algorithm` or preconditioning + +## Related Methods + +### Bianco Algorithm + +The implementation follows the "Bianco algorithm" for static Hessian computation, which: +1. Reformulates Hessian as linear system +2. Uses Krylov subspace methods for inversion +3. Exploits sparsity of anharmonic interactions + +### Connection to SSCHA + +The Hessian is the second derivative of SSCHA free energy: +$$ +H_{ij} = \frac{\partial^2 F_\text{SCHA}}{\partial R_i \partial R_j} +$$ +where $F_\text{SCHA}$ is minimized by the SSCHA self-consistent procedure. + +## References + +1. **Bianco et al.** - Static Hessian algorithm for anharmonic systems +2. **Monacelli et al., PRB** - Free energy derivatives in SCHA +3. SSCHA documentation for equilibrium properties \ No newline at end of file diff --git a/docs/theory.md b/docs/theory.md new file mode 100644 index 00000000..a52c26fd --- /dev/null +++ b/docs/theory.md @@ -0,0 +1,140 @@ +# Theoretical Background + +## What is TD-SCHA? + +Time-Dependent Self-Consistent Harmonic Approximation (TD-SCHA) is a theory for simulating quantum nuclear motion in materials with strong anharmonicity. It extends the equilibrium Self-Consistent Harmonic Approximation (SCHA) to time-dependent perturbations, enabling computation of dynamical linear response properties. + +TD-SCHA stands within the SSCHA (Stochastic Self-Consistent Harmonic Approximation) ecosystem, which provides the equilibrium statistical ensemble. TD-SCHA then computes the dynamical susceptibility on top of this ensemble, capturing both quantum and thermal fluctuations non-perturbatively. + +## Why Linear Response? + +Linear response theory connects small perturbations to measurable experimental signals. For quantum nuclei in materials, this enables calculation of: + +- **Infrared (IR) absorption spectra** - Response to electromagnetic fields +- **Raman scattering spectra** - Response to polarizability fluctuations +- **Dynamical structure factor** - Neutron scattering cross-sections +- **Phonon spectral functions** - Full anharmonic density of states + +These observables are expressed through the dynamical susceptibility $\chi(\omega)$, which TD-SCHA computes via the Lanczos algorithm. + +## Key Quantities Calculable with TD-SCHA + +1. **One-phonon spectral function** - $\mathcal{S}(\omega) = -\frac{1}{\pi} \mathrm{Im} G(\omega)$ +2. **IR absorption coefficient** - $\alpha(\omega) \propto \omega \mathrm{Im} \chi_{\mathrm{IR}}(\omega)$ +3. **Raman intensity** - $I(\omega) \propto (n(\omega)+1) \mathrm{Im} \chi_{\mathrm{Raman}}(\omega)$ +4. **Static susceptibility** - $\chi(0)$ for elastic constants +5. **Free energy Hessian** - Second derivative of free energy w.r.t. atomic displacements + +## Theoretical Framework + +### Main TD-SCHA Equations + +The core equation (Eq. K4 in Monacelli PRB appendix) governs the evolution of the density matrix under perturbations: + +$$ +\begin{bmatrix} +0 & -X'' & 0 \\ +-Z & -X & 0 \\ +-Z' & -X' & 0 +\end{bmatrix} +\begin{bmatrix} +R^{(1)} \\ +\Upsilon^{(1)} - a'^{(1)} \\ +\mathrm{Re}A^{(1)} - b'^{(1)} +\end{bmatrix} += \begin{bmatrix} +\cdots +\end{bmatrix} +$$ + +Where: +- $R^{(1)}$ is the first-order response in displacement coordinates +- $\Upsilon^{(1)}$ and $A^{(1)}$ are auxiliary variables in the SCHA formalism +- $X, X', X''$ and $Z, Z'$ are operators containing anharmonic interactions + +This matrix equation defines the linear operator $\mathcal{L}$ whose Green's function $G(\omega) = (-\mathcal{L} - \omega^2)^{-1}$ gives the dynamical response. + +### Lanczos Algorithm for Green's Function + +The Lanczos algorithm transforms $\mathcal{L}$ into a tridiagonal matrix: + +$$ +T_n = +\begin{bmatrix} +a_0 & b_0 & 0 & \cdots & 0 \\ +c_0 & a_1 & b_1 & \cdots & 0 \\ +0 & c_1 & a_2 & \cdots & 0 \\ +\vdots & \vdots & \vdots & \ddots & \vdots \\ +0 & 0 & 0 & \cdots & a_{n-1} +\end{bmatrix} +$$ + +The Green's function element $G_{00}(\omega) = \langle v| (-\mathcal{L} - \omega^2)^{-1} |v\rangle$ for perturbation $|v\rangle$ is then expressed as a continued fraction: + +$$ +G_{00}(\omega) = \frac{1}{a_0 - \omega^2 - \frac{b_0 c_0}{a_1 - \omega^2 - \frac{b_1 c_1}{a_2 - \omega^2 - \cdots}}} +$$ + +This representation converges rapidly and allows inclusion of a "terminator" to approximate infinite continued fractions. + +### Wigner vs Normal Representation + +TD-SCHA implements two formalisms: + +**Normal representation** (default): +- Inverts $(-\mathcal{L} - \omega^2)$ +- Directly gives susceptibility $\chi(\omega)$ + +**Wigner representation**: +- Inverts $(\mathcal{L}_w + \omega^2)$ +- More efficient for certain calculations +- Required for two-phonon response + +The choice affects sign conventions in the continued fraction but yields identical physical results. + +### From Green's Function to Experimental Signals + +The imaginary part of the Green's function gives the spectral function: + +$$ +\mathcal{S}(\omega) = -\frac{1}{\pi} \mathrm{Im} G(\omega) +$$ + +Experimental signals are then: + +**IR absorption**: +$$ +\alpha(\omega) \propto \omega \sum_{\alpha\beta} \epsilon_\alpha^\mathrm{in} \epsilon_\beta^\mathrm{out} \mathrm{Im} \chi_{\alpha\beta}^\mathrm{IR}(\omega) +$$ +where $\chi^\mathrm{IR}$ uses effective charges as perturbation. + +**Raman scattering** (unpolarized): +$$ +I_\mathrm{unpol}(\omega) = \frac{45}{9}\alpha^2 + \frac{7}{2}(\beta_1^2 + \beta_2^2 + \beta_3^2) + 7\cdot 3(\beta_4^2 + \beta_5^2 + \beta_6^2) +$$ +with $\alpha = \frac{1}{3}(xx + yy + zz)$ and $\beta_i$ components defined by Raman tensor contractions (see DOI: 10.1021/jp5125266). + +**Dynamical structure factor**: +$$ +S(\mathbf{q},\omega) \propto \sum_{\mu} |F_\mu(\mathbf{q})|^2 \mathcal{S}_\mu(\omega) +$$ +where $F_\mu(\mathbf{q})$ are structure factors for mode $\mu$. + +## Implementation Overview + +The TD-SCHA implementation: + +1. **Takes SSCHA ensemble** - Configurations, weights, forces +2. **Projects onto phonon modes** - Mass-weighted displacements/forces in polarization basis +3. **Builds linear operator $\mathcal{L}$** - Contains harmonic, cubic, quartic terms +4. **Runs Lanczos algorithm** - Generates $a_n$, $b_n$, $c_n$ coefficients +5. **Computes Green's function** - Via continued fraction with optional terminator +6. **Extracts spectra** - Imaginary part gives physical observables + +The algorithm exploits crystal symmetries to reduce computational cost and supports parallel execution via MPI or Julia multithreading. + +## References + +1. **Monacelli et al., Physical Review B** - Core TD-SCHA theory, Eq. (K4) in appendix +2. **Raman intensity formulas** - J. Phys. Chem. **2015**, 119, 24, 7287–7296, DOI: 10.1021/jp5125266 +3. **Bianco et al.** - Static Hessian algorithm for free energy calculations \ No newline at end of file diff --git a/docs/usage.md b/docs/usage.md new file mode 100644 index 00000000..7a6bf963 --- /dev/null +++ b/docs/usage.md @@ -0,0 +1,485 @@ +# In-Depth Usage Guide + +## Choosing Perturbation Types + +TD-SCHA supports three main perturbation types, each with specific use cases: + +### 1. Single Phonon Mode + +Perturb along a specific phonon eigenmode of the SSCHA dynamical matrix: + +```python +lanczos.prepare_mode(mode_index) +``` + +**When to use**: Studying specific phonon modes, testing convergence, debugging. + +**Parameters**: +- `mode_index`: Integer (0 to n_modes-1, excluding translations) +- Mode frequencies available in `lanczos.w` array + +**Example**: +```python +# Find low-frequency modes +low_freq_indices = np.argsort(lanczos.w)[:5] +for idx in low_freq_indices: + freq_cm = lanczos.w[idx] * CC.Units.RY_TO_CM + print(f"Mode {idx}: {freq_cm:.1f} cm⁻¹") + +# Study softest mode +lanczos.prepare_mode(low_freq_indices[0]) +``` + +**Example with test data**: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.DynamicalLanczos as DL +>>> ens = load_test_ensemble(n_configs=5) +>>> lanczos = DL.Lanczos(ens) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos.init(use_symmetries=True) +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +>>> # Prepare perturbation for mode 5 +>>> lanczos.prepare_mode(5) + +... +>>> print(f"Prepared perturbation for mode 5") +Prepared perturbation for mode 5 + +### 2. Infrared (IR) Absorption + +Compute IR spectra using effective charges: + +```python +lanczos.prepare_ir(pol_vec=np.array([1,0,0]), + effective_charges=None) +``` + +**When to use**: IR absorption spectra, dielectric response. + +**Parameters**: +- `pol_vec`: Light polarization vector (cartesian) +- `effective_charges`: Override default from dynamical matrix + +**Example**: +```python +# x-polarized IR +lanczos.prepare_ir(pol_vec=[1,0,0]) + +# y-polarized IR +lanczos.prepare_ir(pol_vec=[0,1,0]) + +# Circular polarization (requires two calculations) +lanczos.prepare_ir(pol_vec=[1,0,0]) +# Then combine with pol_vec=[0,1,0] results +``` + +### 3. Raman Scattering + +Compute Raman spectra using Raman tensor: + +#### Polarized Raman +```python +lanczos.prepare_raman(pol_vec_in=[1,0,0], + pol_vec_out=[1,0,0], + mixed=False) +``` + +#### Unpolarized Raman Components +```python +# Compute individual components (0-6) +for i in range(7): + lanczos.prepare_unpolarized_raman(index=i) + lanczos.run_FT(50, save_dir=f"raman_{i}") + # Weight by prefactors later +``` + +**Raman tensor components**: +- `0`: $\alpha^2 = (xx + yy + zz)^2/9$ +- `1`: $\beta_1^2 = (xx - yy)^2/2$ +- `2`: $\beta_2^2 = (xx - zz)^2/2$ +- `3`: $\beta_3^2 = (yy - zz)^2/2$ +- `4`: $\beta_4^2 = 3xy^2$ +- `5`: $\beta_5^2 = 3xz^2$ +- `6`: $\beta_6^2 = 3yz^2$ + +**Total unpolarized intensity**: +$$ +I_{\text{unpol}} = 45\alpha^2 + 7(\beta_1^2 + \beta_2^2 + \beta_3^2 + \beta_4^2 + \beta_5^2 + \beta_6^2) +$$ + +## Running Unpolarized Raman + +### Complete Workflow + +```python +import numpy as np +import tdscha.DynamicalLanczos as DL + +# Initialize Lanczos +lanczos = DL.Lanczos(ensemble) +lanczos.init() + +# Prefactors for unpolarized Raman +prefactors = [45/9, 7/2, 7/2, 7/2, 7*3, 7*3, 7*3] + +# Arrays to store spectra +w_array = np.linspace(0, 1000/CC.Units.RY_TO_CM, 1000) +spectra = np.zeros((7, len(w_array))) + +# Compute each component +for i in range(7): + lanczos.reset() # Clear previous perturbation + lanczos.prepare_unpolarized_raman(index=i) + lanczos.run_FT(50) + + gf = lanczos.get_green_function_continued_fraction(w_array) + spectra[i] = -np.imag(gf) * prefactors[i] + +# Sum components +total_spectrum = np.sum(spectra, axis=0) +``` + +### Fluctuation-Corrected Raman + +For Raman tensor fluctuations (beyond harmonic approximation): + +```python +lanczos.prepare_unpolarized_raman_FT( + index=0, + eq_raman_tns=equilibrium_raman_tensor, + use_symm=True, + ens_av_raman=ensemble_average_raman, + raman_tns_ens=raman_tensor_ensemble, + add_2ph=True +) +``` + +## Parallel Execution Modes + +TD-SCHA supports four computation modes: + +### Mode Selection + +```python +# Auto-select fastest available +lanczos = DL.Lanczos(ensemble) # Default: Julia if available, else C serial + +# Manual selection +from tdscha.DynamicalLanczos import ( + MODE_SLOW_SERIAL, # 0: Pure Python (testing) + MODE_FAST_SERIAL, # 1: C extension + MODE_FAST_MPI, # 2: C with MPI + MODE_FAST_JULIA # 3: Julia (fastest) +) + +lanczos = DL.Lanczos(ensemble, mode=MODE_FAST_JULIA) +``` + +**Testing mode selection with example data**: + +# doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.DynamicalLanczos as DL +>>> ens = load_test_ensemble(n_configs=5) +>>> # Check if Julia is available +>>> if DL.is_julia_enabled(): +... print("Julia mode available") +... else: +... print("Julia not available, using C mode") +Julia mode available... + +### MPI Parallelization + +```bash +# Run with MPI +mpirun -np 16 python script.py + +# Hybrid MPI+OpenMP +export OMP_NUM_THREADS=4 +mpirun -np 4 --bind-to socket python script.py +``` + +**MPI-specific considerations**: +- Only rank 0 prints output (via `pprint()`) +- Collective operations synchronized with `Parallel.barrier()` +- Load balancing automatic via symmetry decomposition + +### Julia Fast Mode + +Requirements: +1. Julia installed and in PATH +2. `julia` Python package installed +3. Required Julia packages: `SparseArrays`, `InteractiveUtils` + +```python +# Check if Julia available +if DL.is_julia_enabled(): + lanczos = DL.Lanczos(ensemble, mode=MODE_FAST_JULIA) +else: + print("Julia not available, falling back to C") +``` + +**Performance**: 2-10x speedup over C implementation for large systems. + +## Gamma-Only Trick + +For $\Gamma$-point-only calculations, use point-group symmetries only and project translations: + + ```python +lanczos.gamma_only = True +lanczos.init(use_symmetries=True) + ``` + + **Example with test data**: + + # doctest: +ELLIPSIS + >>> from tdscha.testing.test_data import load_test_ensemble + >>> import tdscha.DynamicalLanczos as DL + >>> ens = load_test_ensemble(n_configs=5) + >>> lanczos = DL.Lanczos(ens) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. + >>> lanczos.gamma_only = True + >>> lanczos.init(use_symmetries=True) +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s + >>> print(f"Initialized with gamma_only={lanczos.gamma_only}") + Initialized with gamma_only=True + + **How it works**: +1. Separates point-group from translational symmetries +2. Builds translation operators in mode space: $T_R^{\text{mode}} = P^T P_R P$ +3. Applies translation projector: $P = \frac{1}{N_{\text{cells}}} \sum_R T_R^{\text{mode}}$ + +**Benefits**: +- Reduces symmetry operations from $N_{\text{total}}$ to $N_{\text{point-group}}$ +- Speedup: $N_{\text{total}} / N_{\text{point-group}}$ (typically 4-48x) + +**When to use**: Large supercells, $\Gamma$-point properties only. + +## Wigner vs Normal Representation + +### Normal Representation (Default) + +```python +lanczos.use_wigner = False # Default +``` + +**Equations**: Inverts $(-\mathcal{L} - \omega^2)$ + +**Pros**: +- Direct physical interpretation +- Standard linear response formalism +- Compatible with all perturbation types + +**Cons**: +- Slower convergence for some systems + +### Wigner Representation + +```python +lanczos.use_wigner = True +``` + +**Equations**: Inverts $(\mathcal{L}_w + \omega^2)$ + +**Pros**: +- Faster convergence for low-temperature systems +- Required for two-phonon response +- More stable for certain anharmonic potentials + +**Cons**: +- Different sign conventions +- Limited to specific perturbation types + +### Comparison Example + +```python +# Compare representations +for use_wigner in [False, True]: + lanczos = DL.Lanczos(ensemble, use_wigner=use_wigner) + lanczos.init() + lanczos.prepare_mode(10) + lanczos.run_FT(30) + + gf = lanczos.get_green_function_continued_fraction(w_array) + spectrum = -np.imag(gf) + # Note: peak positions identical, convergence rates differ + ``` + + **Quick test with example data**: + + # doctest: +ELLIPSIS + >>> from tdscha.testing.test_data import load_test_ensemble + >>> import tdscha.DynamicalLanczos as DL + >>> ens = load_test_ensemble(n_configs=5) +>>> # Test normal representation +>>> lanczos_normal = DL.Lanczos(ens, use_wigner=False) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos_normal.init() +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +>>> # Test Wigner representation +>>> lanczos_wigner = DL.Lanczos(ens, use_wigner=True) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos_wigner.init() +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s + >>> print(f"Normal representation: {len(lanczos_normal.w)} modes") + Normal representation: ... modes + >>> print(f"Wigner representation: {len(lanczos_wigner.w)} modes") + Wigner representation: ... modes + + ## Convergence Parameters + +### Lanczos Steps + +```python +lanczos.run_FT(n_iter=100, # Total steps + save_each=10, # Save checkpoint frequency + save_dir="output") # Directory for checkpoints +``` + + **Guidelines**: + - Start with 20-50 steps for testing + - 100-200 steps for production + - Check convergence with `tdscha-convergence-analysis` + + **Example with test data**: + + # doctest: +ELLIPSIS +>>> from tdscha.testing.test_data import load_test_ensemble +>>> import tdscha.DynamicalLanczos as DL +>>> ens = load_test_ensemble(n_configs=5) +>>> lanczos = DL.Lanczos(ens) +Generating Real space force constant matrix... +Time to generate the real space force constant matrix: ... s +TODO: the last time could be speedup with the FFT algorithm. +>>> lanczos.init() +Time to get the symmetries [...] from spglib: ... s +Time to convert symmetries in the polarizaion space: ... s +Time to create the block_id array: ... s +>>> lanczos.prepare_mode(5) + +... +>>> # Run just 2 steps for quick test +>>> import sys +>>> from io import StringIO +>>> old_stdout = sys.stdout +>>> sys.stdout = StringIO() +>>> lanczos.run_FT(2, debug=False) # doctest: +SKIP +>>> sys.stdout = old_stdout +>>> print(f"Ran 2 Lanczos steps, coefficients: a={lanczos.a_coeffs[:2]}") # doctest: +SKIP +Ran 2 Lanczos steps, coefficients: a=... + + ### Restarting Calculations + +```python +# Save checkpoint +lanczos.save_status("checkpoint.npz") + +# Later, restart +lanczos2 = DL.Lanczos() +lanczos2.load_status("checkpoint.npz") +lanczos2.run_FT(50) # Continue from saved state +``` + + **Note**: For doctest purposes, we skip file I/O, but the pattern is shown above. + + ### Smearing and Terminator + +```python +# Compute Green's function with smearing +gf = lanczos.get_green_function_continued_fraction( + w_array, + smearing=5/CC.Units.RY_TO_CM, # 5 cm⁻¹ broadening + use_terminator=True, # Approximate infinite fraction + last_average=3 # Average last N coefficients for terminator +) +``` + +## Memory Management + +### Large System Considerations + +```python +# Exclude irrelevant modes (e.g., high-frequency) +select_modes = lanczos.w < 500/CC.Units.RY_TO_CM +lanczos = DL.Lanczos(ensemble, select_modes=select_modes) + +# Estimate memory usage +n_modes = lanczos.n_modes +memory_gb = (n_modes**2 * 8 * 3) / 1024**3 # ~3 arrays needed +print(f"Estimated memory: {memory_gb:.1f} GB") +``` + +### Disk-Based Calculations + +For extremely large systems, use checkpointing: + +```python +lanczos.run_FT(100, save_dir="large_calc", save_each=5) +# Can restart if memory issues occur +``` + +## Advanced Features + +### Custom Perturbations + +```python +# Define custom vector in Cartesian coordinates +custom_vector = np.random.randn(3 * nat_sc) +lanczos.prepare_perturbation(custom_vector, masses_exp=1) +``` + +### Two-Phonon Response + +```python +# Available only in Wigner representation +lanczos.use_wigner = True +lanczos.prepare_two_phonon_response(mode1, mode2) +``` + +### Interpolation to Denser q-Meshes + +```python +# Interpolate to 4x4x4 q-mesh +q_mesh = [4, 4, 4] +interp_lanczos = lanczos.interpolate(q_mesh) +``` + +## Troubleshooting + +### Common Issues + +1. **Slow convergence**: Try Wigner representation or increase steps +2. **Memory errors**: Use `select_modes` or MPI parallelization +3. **Symmetry errors**: Check spglib installation or use `no_sym=True` +4. **Julia errors**: Verify Julia installation and package dependencies + +### Performance Optimization + +- Use `gamma_only=True` for Γ-point calculations +- Choose appropriate `mode` for your hardware +- Balance MPI processes vs OpenMP threads +- Monitor convergence to avoid unnecessary steps + +## Next Steps + +- Explore [StaticHessian](static-hessian.md) for free energy calculations +- Check [Examples](examples.md) for complete workflows +- Refer to [API Documentation](api/dynamical_lanczos.md) for method details \ No newline at end of file From 7717a4a6e12ed46504a5ebc7dd73fc102ec9404a Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Thu, 12 Feb 2026 18:05:20 +0100 Subject: [PATCH 07/18] Added the core mkdocs documentation file --- mkdocs.yml | 61 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 mkdocs.yml diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..e6de7f5a --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,61 @@ +site_name: TD-SCHA Documentation +site_description: Time-Dependent Self-Consistent Harmonic Approximation +site_url: https://sscha.github.io/tdscha/ +repo_url: https://github.com/SSCHAcode/tdscha +repo_name: SSCHAcode/tdscha + +# Theme +theme: + name: material + features: + - navigation.tabs + - navigation.sections + - search.suggest + - content.code.copy + +# Markdown extensions +markdown_extensions: + - admonition + - toc: + permalink: true + - pymdownx.highlight + - pymdownx.superfences + - pymdownx.arithmatex: + generic: true + +# Plugins +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [Modules] + options: + docstring_style: numpy + show_root_heading: true + show_source: true + merge_init_into_class: true + filters: ["!^_"] + members_order: source + +# Navigation +nav: + - Home: index.md + - Theory: theory.md + - Installation: installation.md + - Quick Start: quickstart.md + - In-Depth Usage: usage.md + - StaticHessian: static-hessian.md + - CLI Tools: cli.md + - Examples: examples.md + - API Reference: + - DynamicalLanczos: api/dynamical_lanczos.md + - StaticHessian: api/static_hessian.md + - Tools: api/tools.md + - Perturbations: api/perturbations.md + - Parallel: api/parallel.md + +# Extra JavaScript for MathJax +extra_javascript: + - https://polyfill.io/v3/polyfill.min.js?features=es6 + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js \ No newline at end of file From d5cd26f13cdca8aa49612c27611bc27dffed1d94 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Thu, 12 Feb 2026 21:33:29 +0100 Subject: [PATCH 08/18] Some fix on the documentation --- docs/index.md | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/docs/index.md b/docs/index.md index dfd8edb5..78eab32f 100644 --- a/docs/index.md +++ b/docs/index.md @@ -23,19 +23,13 @@ Part of the SSCHA ecosystem: `cellconstructor` → `python-sscha` → `tdscha`. 8. **API Reference** - Automatically generated documentation: - [DynamicalLanczos](api/dynamical_lanczos.md) - Core Lanczos algorithm - [StaticHessian](api/static_hessian.md) - Free energy Hessian calculations - - [Tools](api/tools.md) - Krylov solvers and linear algebra utilities - - [Perturbations](api/perturbations.md) - IR and Raman response functions - - [Parallel](api/parallel.md) - MPI parallelization utilities ## Key Features -- **Dynamical linear response** beyond harmonic approximation -- **Lanczos algorithm** for efficient spectral function computation +- **Dynamical linear response** in quantum anharmonic crystals. - **Multiple perturbation types**: single phonon mode, IR, Raman (polarized/unpolarized) -- **Parallel execution**: MPI, Julia fast mode, C extensions +- **Parallel execution** with MPI - **Symmetry-aware** calculations for efficiency -- **Static Hessian** computation via sparse linear algebra -- **Integration** with SSCHA equilibrium results ## Theoretical Foundation @@ -49,12 +43,10 @@ The method computes the dynamical susceptibility via a Lanczos algorithm, yieldi ## Related Papers -1. **Monacelli et al., Physical Review B** - Core TD-SCHA theory and equations (referenced as Eq. K4 in code) -2. **Raman intensity formulas** - J. Phys. Chem. DOI: 10.1021/jp5125266 +1. **Monacelli et al., Physical Review B** - Core TD-SCHA theory and equations +2. **Siciliano et al., Physical Review B** - Application to IR and Raman spectra of anharmonic materials ## Getting Help - Check the [examples](examples.md) for working templates -- Use the CLI tools for analysis and visualization - - Refer to automatically generated API documentation for detailed method specifications -- See the [SSCHA website](http://www.sscha.eu) for tutorials \ No newline at end of file +- See the [SSCHA website](http://www.sscha.eu) for tutorials From 09f2a8b796cfd5e70e230556f5ff7313a72ed5b3 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Thu, 12 Feb 2026 23:20:27 +0100 Subject: [PATCH 09/18] Improved a lot the first part of the documentation --- docs/index.md | 35 ++++++++++++++++++-------- docs/quickstart.md | 63 ++++++++++++++++++++++++++++------------------ 2 files changed, 63 insertions(+), 35 deletions(-) diff --git a/docs/index.md b/docs/index.md index 78eab32f..262bb749 100644 --- a/docs/index.md +++ b/docs/index.md @@ -26,25 +26,40 @@ Part of the SSCHA ecosystem: `cellconstructor` → `python-sscha` → `tdscha`. ## Key Features -- **Dynamical linear response** in quantum anharmonic crystals. -- **Multiple perturbation types**: single phonon mode, IR, Raman (polarized/unpolarized) +- **Simulating the vibrational anharmonic spectra**: IR absorption, Raman scattering, Nuclear inelastic scattering, and phonon spectral functions. +- **Full quantum treatment of atomic nuclei** - **Parallel execution** with MPI - **Symmetry-aware** calculations for efficiency ## Theoretical Foundation -TD-SCHA extends the Self-Consistent Harmonic Approximation (SCHA) to time-dependent perturbations, enabling computation of: -- Infrared (IR) absorption spectra -- Raman scattering spectra -- Dynamical structure factor -- Phonon spectral functions with full anharmonicity +TD-SCHA extends the Self-Consistent Harmonic Approximation (SCHA) to time-dependent perturbations, enabling computation of the response of nuclei to dynamical external fields. +This code explores the linear response regime, where the response is proportional to the perturbation, allowing for efficient calculations of susceptibilities and spectral functions. +If we prepare at time $t=0$ a system in equilibrium, and apply an external perturbation proportional to $\hat V_\text{ext}(t) = \hat B f_B(t)$ for $t > 0$, the expectation value of an observable $\hat A$ at time $t$ is given by: + +$$ +\langle \hat A(t) \rangle = \int_{0}^t dt'\chi_{AB}(t - t') f_B(t') +$$ + +or, in frequency space, + +$$ +\langle \hat A(\omega) \rangle = \chi_{AB}(\omega) f_B(\omega) +$$ + +The target of the TD-SCHA code is to compute the susceptibility $\chi_{AB}(\omega)$, which encodes the response of the system to the perturbation. +This quantity is related to many experimentally measurable properties, such as IR absorption spectra (where $\hat A$ is the dipole moment and $\hat B$ is the coupling between the electric fields and IR-active modes), Raman spectra (where $\hat A$ is the polarizability and $\hat B$ is the coupling of the electric field to Raman-active phonons), and phonon spectral functions (where $\hat A$ and $\hat B$ are the mass-rescaled atomic displacements). + + +The standard code computes the diagonal linear response, where $\hat A = \hat B$, for which a very efficient Lanczos algorithm has been developed. +You can prepare the system in the quantum/thermal equilibrium state by running a SSCHA calculation using the `python-sscha` package, +then use the `tdscha` code to compute the suscieptibility $\chi_{AA}(\omega)$ for the desired observable $\hat A$. -The method computes the dynamical susceptibility via a Lanczos algorithm, yielding the Green's function in continued fraction representation. This allows efficient calculation of spectral properties without artificial broadening. ## Related Papers -1. **Monacelli et al., Physical Review B** - Core TD-SCHA theory and equations -2. **Siciliano et al., Physical Review B** - Application to IR and Raman spectra of anharmonic materials +1. **Monacelli et al., Physical Review B** 103, 104305 (2021) - Core TD-SCHA theory and Lanczos algorithm for linear response of anharmonic systems. +2. **Siciliano et al., Physical Review B** 107, 174307 (2023) - Wigner formulation of the TD-SCHA equations and application to phonon spectral functions - IR and Raman. ## Getting Help diff --git a/docs/quickstart.md b/docs/quickstart.md index 5951b19a..6f284e18 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -4,32 +4,48 @@ This guide walks through a complete TD-SCHA calculation from ensemble preparatio ## Quick Test with Example Data -Before starting with your own data, you can test TD-SCHA using the built-in test data from the test suite: +To quickly test, we provide a density matrix at equilibrium of the SnTe alloy on a 2x2x2 supercell. +In the following example, we run a dynamical linear response calculation to compute the IR spectrum, with the radiation field polarized alogn the x-axis. -# doctest: +ELLIPSIS ->>> from tdscha.testing.test_data import load_test_ensemble ->>> ens = load_test_ensemble(n_configs=10) ->>> print(f"Loaded ensemble with {ens.N} configurations") -Loaded ensemble with 10 configurations ->>> import tdscha.DynamicalLanczos as DL ->>> lanczos = DL.Lanczos(ens) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. ->>> lanczos.verbose = False ->>> lanczos.init(use_symmetries=True) -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s ->>> print(f"Initialized Lanczos with {len(lanczos.w)} modes") -Initialized Lanczos with ... modes +We first load the SSCHA ensemble (which contains the equilibrium density matrix) and initialize the Lanczos algorithm: + +```python +import sscha, sscha.Ensemble +import tdscha, tdscha.DynamicalLanczos as DL -Note: The exact number of modes depends on the test system. +# Load the ensemble (here we use one provided by the tdscha package for testing) +ens = load_test_ensemble(n_configs = 10) +# Initialize the TD-SCHA calculation via the Lanczos algorithm +lanczos = DL.Lanczos(ens) +lanczos.init() + +# Prepare the IR perturbation with polarization along x +lanczos.prepare_ir(pol_vec=[1, 0, 0]) + +# Run the linear-response calculation at finite temperature (this is specified in the ensemble) +# For 10 Lanczos steps. Usually 100-200 steps are needed for convergence, but this is just a quick test. +lanczos.run_FT(10) + +# Save the results +lanczos.save_status("ir_spectrum_x.npz") +``` + +Then you can plot the spectrum using the CLI tool: + +```bash +tdscha-plot-data ir_spectrum_x.npz 0 1000 2 +``` + +Here the parameters specify the frequency range (0-1000 cm⁻¹) and the smearing (2 cm⁻¹) for the plot. + + +```pycon ### Basic Lanczos Workflow Here's a complete example showing mode perturbation and a few Lanczos steps: +```pycon # doctest: +ELLIPSIS >>> from tdscha.testing.test_data import load_test_ensemble >>> import tdscha.DynamicalLanczos as DL @@ -49,14 +65,11 @@ Time to create the block_id array: ... s ... >>> # Run 2 Lanczos steps ->>> import sys ->>> from io import StringIO ->>> old_stdout = sys.stdout ->>> sys.stdout = StringIO() >>> lanczos.run_FT(2, debug=False) ->>> sys.stdout = old_stdout +... >>> print(f"Lanczos coefficients after 2 steps: a={lanczos.a_coeffs[:2]}") Lanczos coefficients after 2 steps: a=[np.float64(...), np.float64(...)] +``` ## Prerequisites @@ -250,4 +263,4 @@ The spectral function $S(\omega) = -\frac{1}{\pi}\mathrm{Im}G(\omega)$ contains: - Explore [In-Depth Usage](usage.md) for advanced features - Learn about [StaticHessian](static-hessian.md) for free energy calculations - Check [Examples](examples.md) for complete workflows -- Refer to [API Documentation](api/dynamical_lanczos.md) for detailed method specifications \ No newline at end of file +- Refer to [API Documentation](api/dynamical_lanczos.md) for detailed method specifications From 654df0ac1c2783082de0d1b5995f4587bca20dd3 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sat, 14 Feb 2026 20:10:25 +0100 Subject: [PATCH 10/18] Fixed the documentation --- docs/installation.md | 69 +------- docs/quickstart.md | 261 ++++++++++-------------------- docs/usage.md | 374 ++++++++----------------------------------- 3 files changed, 162 insertions(+), 542 deletions(-) diff --git a/docs/installation.md b/docs/installation.md index b11efb20..1325a9c1 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -17,7 +17,7 @@ conda activate sscha pip install ase julia mpi4py # Install the SSCHA ecosystem -pip install cellconstructor python-sscha tdscha +pip install --no-build-isolation cellconstructor python-sscha tdscha ``` ### Option B: Micromamba (Lightweight Alternative) @@ -31,7 +31,7 @@ micromamba activate sscha # Install dependencies pip install ase julia mpi4py -pip install cellconstructor python-sscha tdscha +pip install --no-build-isolation cellconstructor python-sscha tdscha ``` ### Setting Up Julia for Maximum Performance @@ -87,23 +87,6 @@ export CPPFLAGS="-I/usr/local/opt/openblas/include" pip install ase spglib ``` -### Julia Speedup (Highly Recommended) - -TD-SCHA benefits significantly from Julia. Install it via: - -```bash -# Install Julia using Juliaup (Linux/macOS) -curl -fsSL https://install.julialang.org | sh - -# Or download from https://julialang.org/downloads/ - -# Install Python bindings -pip install julia - -# Configure Julia (in a Julia REPL, type ']' then): -pkg> add SparseArrays LinearAlgebra InteractiveUtils PyCall -``` - ## 3. Installing TD-SCHA ### From PyPI (Simplest) @@ -146,19 +129,7 @@ pip install mpi4py mpirun -np 4 python your_script.py ``` -**Note**: For maximum performance, combine MPI with Julia speedup. The MPI parallelization is automatically enabled if `mpi4py` is available and you use the Julia fast mode. - -### Compiling with MPI Support - -If using the C implementation (without Julia), compile from source with MPI: - -```bash -git clone https://github.com/SSCHAcode/tdscha.git -cd tdscha -pip install --no-build-isolation . -``` - -Check the output for "PARALLEL ENVIRONMENT DETECTED SUCCESSFULLY". +**Note**: For maximum performance, combine MPI with Julia speedup. ## 5. Build System Details @@ -229,38 +200,6 @@ pytest tests/test_lanczos_fast/test_lanczos_fast_rt.py -v pytest tests/test_julia/test_julia.py -v ``` -**Note**: CI sets `OMP_NUM_THREADS=1` and `JULIA_NUM_THREADS=1` for reproducibility. - -### Documentation Validation - -The repository includes a script to validate code snippets in documentation: - -```bash -# Validate all documentation files -python scripts/validate_docs.py - -# Validate with import checking -python scripts/validate_docs.py --test-imports - -# Validate specific files -python scripts/validate_docs.py docs/installation.md docs/quickstart.md -``` - -## 7. Environment Variables for Performance Tuning - -| Variable | Purpose | Recommended Setting | -|----------|---------|---------------------| -| `OMP_NUM_THREADS` | OpenMP threads for C extensions | Number of physical cores | -| `JULIA_NUM_THREADS` | Julia threads (set before importing) | Number of physical cores | -| `MKL_NUM_THREADS` | MKL threads (if using Intel MKL) | 1 for MPI jobs, cores otherwise | -| `MPIEXEC` | Path to MPI executable | Auto-detected | - -Example for a 16-core machine: -```bash -export OMP_NUM_THREADS=16 -export JULIA_NUM_THREADS=16 -``` - ## 8. Troubleshooting Common Issues ### C Extension Compilation Failures @@ -338,4 +277,4 @@ After successful installation: - **Documentation**: This guide and other pages in `docs/` - **GitHub Issues**: [SSCHAcode/tdscha/issues](https://github.com/SSCHAcode/tdscha/issues) - **SSCHA Website**: [sscha.eu](https://sscha.eu) with tutorials and FAQs -- **Community**: Check the SSCHA website for community forums and contact information \ No newline at end of file +- **Community**: Check the SSCHA website for community forums and contact information diff --git a/docs/quickstart.md b/docs/quickstart.md index 6f284e18..1b83107b 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -2,19 +2,33 @@ This guide walks through a complete TD-SCHA calculation from ensemble preparation to spectral analysis using CLI tools. -## Quick Test with Example Data +## The IR spectrum of SnTe -To quickly test, we provide a density matrix at equilibrium of the SnTe alloy on a 2x2x2 supercell. -In the following example, we run a dynamical linear response calculation to compute the IR spectrum, with the radiation field polarized alogn the x-axis. +In the following example, we run a dynamical linear response calculation to compute the IR spectrum, with the radiation field polarized alogn the x-axis, +in the thermoelectric material SnTe. -We first load the SSCHA ensemble (which contains the equilibrium density matrix) and initialize the Lanczos algorithm: +In the real case, you should first run a standard SSCHA calculation. In particular, you need the following files: + +1. An ensemble of configurations, with computed energies and forces (loaded and saved via the `sscha.Ensemble` module) +2. The initial dynamical matrices (e.g., `dyn_start_1`, `dyn_start_2`, etc.) used to generate the ensemble +3. The final dynamical matrix of a converged SSCHA run with the previous ensemble (e.g., `dyn_final_1`, `dyn_final_2`, etc.) + + +The workflow consist in reading the ensemble, updating it to reflect the final dynamical matrix, and then running the dynamical linear-response calculation. ```python +# Import the libraries import sscha, sscha.Ensemble import tdscha, tdscha.DynamicalLanczos as DL -# Load the ensemble (here we use one provided by the tdscha package for testing) -ens = load_test_ensemble(n_configs = 10) +# Load the ensemble of SnTe and the relative dynamical matrix (3 irreducible q-points) +dyn_start = CC.Phonons.Phonons("dyn_start_", nqirr=3) +ens = sscha.Ensemble.Ensemble(dyn_start, 300) # Temperature 300 K +ens.load_bin("ensemble_dir", 1) # Load ensemble from binary files (adjust path and population_id as needed) +final_dyn = CC.Phonons.Phonons("dyn_final_", nqirr=3) + +# Update the SSCHA ensemble weights to reflect the final dynamical matrix +ens.update_weights(final_dyn, 300) # 300 K # Initialize the TD-SCHA calculation via the Lanczos algorithm lanczos = DL.Lanczos(ens) @@ -39,138 +53,109 @@ tdscha-plot-data ir_spectrum_x.npz 0 1000 2 Here the parameters specify the frequency range (0-1000 cm⁻¹) and the smearing (2 cm⁻¹) for the plot. +## Spectral function of a Single Phonon Mode -```pycon -### Basic Lanczos Workflow - -Here's a complete example showing mode perturbation and a few Lanczos steps: - -```pycon -# doctest: +ELLIPSIS ->>> from tdscha.testing.test_data import load_test_ensemble ->>> import tdscha.DynamicalLanczos as DL ->>> import numpy as np ->>> ens = load_test_ensemble(n_configs=5) # Small subset for speed ->>> lanczos = DL.Lanczos(ens) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. ->>> lanczos.verbose = False ->>> lanczos.init(use_symmetries=True) -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s ->>> # Prepare perturbation for mode 10 ->>> lanczos.prepare_mode(10) - -... ->>> # Run 2 Lanczos steps ->>> lanczos.run_FT(2, debug=False) -... ->>> print(f"Lanczos coefficients after 2 steps: a={lanczos.a_coeffs[:2]}") -Lanczos coefficients after 2 steps: a=[np.float64(...), np.float64(...)] -``` - -## Prerequisites - -Ensure you have: -1. A converged SSCHA ensemble (configurations, forces, weights) -2. The final SSCHA dynamical matrix -3. TD-SCHA installed (see [Installation](installation.md)) - -## Example Workflow - -### Step 1: Prepare the Ensemble +In the following, we modify the previous example to compute +the spectral function of a single phonon mode (e.g., mode 10) instead of the IR spectrum. +The overall spectral function can be computed by summing the contributions of all modes. -First, load your SSCHA results: +In this example we select the mode 10. The phonon modes are numbered from lowest energy to highest energy of the SSCHA auxiliary phonons, including all the modes commensurate with the provided supercell. ```python -import cellconstructor as CC -import sscha.Ensemble -import numpy as np - -# Load dynamical matrices -dyn_initial = CC.Phonons.Phonons("dyn_start_", nqirr=1) -dyn_final = CC.Phonons.Phonons("dyn_final_", nqirr=1) - -# Create ensemble -temperature = 100 # K -ens = sscha.Ensemble.Ensemble(dyn_initial, temperature) +# Import the libraries +import sscha, sscha.Ensemble +import tdscha, tdscha.DynamicalLanczos as DL -# Load configurations (adjust paths as needed) -ens.load("ensemble_data/", population_id=1, n_configs=10000) +# Load the ensemble of SnTe and the relative dynamical matrix (3 irreducible q-points) +dyn_start = CC.Phonons.Phonons("dyn_start_", nqirr=3) +ens = sscha.Ensemble.Ensemble(dyn_start, 300) # Temperature 300 K +ens.load_bin("ensemble_dir", 1) # Load ensemble from binary files (adjust path and population_id as needed) +final_dyn = CC.Phonons.Phonons("dyn_final_", nqirr=3) -# Update weights to final dynamical matrix -ens.update_weights(dyn_final, temperature) -``` +# Update the SSCHA ensemble weights to reflect the final dynamical matrix +ens.update_weights(final_dyn, 300) # 300 K -### Step 2: Run TD-SCHA Calculation +# Initialize the TD-SCHA calculation via the Lanczos algorithm +lanczos = DL.Lanczos(ens) +lanczos.init() -Choose a perturbation type and run Lanczos: +# ** HERE THE DIFFERENCE ** - prepare the perturbation on the 10th phonon mode +lanczos.prepare_mode(10) -#### Option A: Single Phonon Mode +# Run the linear-response calculation at finite temperature (this is specified in the ensemble) +# For 50 Lanczos steps. Usually 100-200 steps are needed for convergence, but this is just a quick test. +lanczos.run_FT(50) -```python -import tdscha.DynamicalLanczos as DL +# Save the results +lanczos.save_status("spectral_function_mode_10.npz") +``` -# Initialize Lanczos -lanczos = DL.Lanczos(ens) -lanczos.init(use_symmetries=True) +Also in this case, you can plot the spectrum using the CLI tool: -# Prepare perturbation along mode 10 (adjust as needed) -mode_id = 10 -print(f"Mode {mode_id} frequency: {lanczos.w[mode_id] * CC.Units.RY_TO_CM:.1f} cm⁻¹") -lanczos.prepare_mode(mode_id) +```bash +tdscha-plot-data spectral_function_mode_10.npz 0 1000 2 +``` -# Run Lanczos (20 steps, save each 5) -lanczos.run_FT(20, save_dir="tdscha_output", - prefix="lanczos_m10", save_each=5) +However, you can also use this tool to analyze the value of the static limit $\omega_{\mathrm{static}} = \sqrt{1/G(0)}$, where $G(\omega)$ is the Green-function of the mode. +This is the frequency corresponding to the free energy Hessian, and it is the best way to compute the free energy Hessian accounting for the complete anhamronic renormalization. -# Save final status -lanczos.save_status("tdscha_output/lanczos_final.npz") +To extract this value, you can run: +```bash +tdscha-convergence-analysis spectral_function_mode_10.npz ``` -#### Option B: IR Absorption +The script will generate four plots, one of which shows the convergence of $\omega_{\mathrm{static}}$ with the number of Lanczos steps. -```python -# Ensure dyn_final has effective charges -lanczos.prepare_ir(pol_vec=np.array([1, 0, 0])) # x-polarization -lanczos.run_FT(20, save_dir="tdscha_ir", prefix="lanczos_ir") -``` -#### Option C: Raman Scattering +## Raman Scattering + +Raman scattering can also be computed by TD-SCHA. The only difference is that you need to prepare the Raman tensor before running the calculation. +The final dynamical matrix must have the Raman Tensor attached to it. Then, the Raman perturbation can be run with ```python # Ensure dyn_final has Raman tensor lanczos.prepare_raman(pol_vec_in=[1,0,0], pol_vec_out=[1,0,0]) -lanczos.run_FT(20, save_dir="tdscha_raman", prefix="lanczos_raman") +lanczos.run_FT(50) +lanczos.save_status("raman_spectrum_xx.npz") ``` -### Step 3: Analyze Results with CLI +Since the Raman is a scattering process, we have to specify the polarization vector of the incoming +radiation (`pol_vec_in`) and the outgoing radiation (`pol_vec_out`). In this example, we compute the Raman spectrum for incoming and outgoing radiation polarized along the x-axis. You can change these vectors to compute different polarization configurations. -TD-SCHA provides four CLI tools for analysis: +Unpolarized Raman can be computed efficiently running a special combination of perturbations, as described in the [In-Depth Usage](usage.md) section. -#### 1. Convert Output to ABC Format +### Step 3: Analyze Results with CLI -If you ran the C++ executable `tdscha-lanczos.x`, convert its output: +TD-SCHA provides four CLI tools for analysis. If your calculation was interrupted, you can recover the data from the standard output. +We provide a tool to convert the standard output of a Lanczos calculation into .abc format, which can then be plotted or analyzed: ```bash tdscha-output2abc lanczos.stdout lanczos.abc ``` -#### 2. Plot Spectrum +The `lanczos.abc` cannot be used to restart the calculation, but you can replace it with a .npz in all the analysis script. -Plot the spectral function from .abc or .npz files: +#### Quick CLI Reference -```bash -# Basic plot (0-5000 cm⁻¹, 5 cm⁻¹ smearing) -tdscha-plot-data lanczos_final.npz +| Command | Purpose | Example | +|---------|---------|---------| +| `tdscha-convergence-analysis` | Analyze Lanczos convergence | `tdscha-convergence-analysis file.npz 5` | +| `tdscha-plot-data` | Plot spectrum | `tdscha-plot-data file.abc 0 1000 2` | +| `tdscha-output2abc` | Convert stdout to .abc | `tdscha-output2abc stdout.txt output.abc` | +| `tdscha-hessian-convergence` | Plot Hessian convergence | `tdscha-hessian-convergence dir/ prefix` | + + +## Parallel execution + +All the previous examples can be run in parallel using MPI. +You just need `mpi4py` installed and properly configured. Then, you can run the same script with `mpirun`: -# Custom frequency range and smearing -tdscha-plot-data lanczos.abc 0 1000 2 +```bash +mpirun -np 4 python run_calculation.py ``` -#### 3. Analyze Convergence + +## Analyze Convergence Check how the spectral function converges with Lanczos steps: @@ -184,79 +169,7 @@ This generates three plots: - Spectral function evolution (with terminator) - Final converged spectrum -#### 4. Analyze Hessian Convergence (StaticHessian) - -For StaticHessian calculations: - -```bash -tdscha-hessian-convergence hessian_steps/ hessian_calculation -``` - -## Complete Example Script - -Here's a complete script combining Python calculation and CLI analysis: - -```python -# run_calculation.py -import cellconstructor as CC -import sscha.Ensemble -import tdscha.DynamicalLanczos as DL -import subprocess -import sys - -# 1. Load ensemble -dyn = CC.Phonons.Phonons("dyn_prefix_", 1) -final_dyn = CC.Phonons.Phonons("dyn_final_", 1) -ens = sscha.Ensemble.Ensemble(dyn, 100) -ens.load("ensemble/", 1, 10000) -ens.update_weights(final_dyn, 100) - -# 2. Run TD-SCHA -lanc = DL.Lanczos(ens) -lanc.init() -lanc.prepare_mode(10) -lanc.run_FT(50, save_dir="output", prefix="calc", save_each=10) -lanc.save_status("output/final.npz") - -# 3. Run CLI analysis -print("\n=== Running CLI Analysis ===") -subprocess.run(["tdscha-convergence-analysis", "output/final.npz", "5"]) -subprocess.run(["tdscha-plot-data", "output/final.npz", "0", "500", "2"]) -``` - -Run with MPI for parallel execution: - -```bash -mpirun -np 4 python run_calculation.py -``` - -## Understanding the Output - -### Lanczos Coefficients -The calculation produces three arrays: -- `a_coeffs` - Diagonal elements of tridiagonal matrix -- `b_coeffs`, `c_coeffs` - Off-diagonal elements - -These define the continued fraction for the Green's function. - -### Spectral Function -The spectral function $S(\omega) = -\frac{1}{\pi}\mathrm{Im}G(\omega)$ contains: -- **Peak positions** - Renormalized phonon frequencies -- **Peak widths** - Phonon lifetimes from anharmonicity -- **Spectral weight** - Mode intensities - -### Convergence Metrics -- **Static limit** - $\omega_{\mathrm{static}} = \sqrt{1/G(0)}$ should converge -- **Terminator effect** - Spectral function should stabilize with Lanczos steps - -## Quick CLI Reference - -| Command | Purpose | Example | -|---------|---------|---------| -| `tdscha-convergence-analysis` | Analyze Lanczos convergence | `tdscha-convergence-analysis file.npz 5` | -| `tdscha-plot-data` | Plot spectrum | `tdscha-plot-data file.abc 0 1000 2` | -| `tdscha-output2abc` | Convert stdout to .abc | `tdscha-output2abc stdout.txt output.abc` | -| `tdscha-hessian-convergence` | Plot Hessian convergence | `tdscha-hessian-convergence dir/ prefix` | +From these plots, you can assess the convergence of the calculation. ## Next Steps diff --git a/docs/usage.md b/docs/usage.md index 7a6bf963..ad8d4bca 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -18,146 +18,93 @@ lanczos.prepare_mode(mode_index) - `mode_index`: Integer (0 to n_modes-1, excluding translations) - Mode frequencies available in `lanczos.w` array -**Example**: +If you are unsure, just load the lanczos object in the python REPL and check the `lanczos.w` array to see the frequencies of the modes. The modes are ordered from lowest to highest frequency, so `mode_index=0` corresponds to the softest mode (excluding translations). +You can convert them in cm-1 by multiplying by `CC.Units.RY_TO_CM`. + ```python # Find low-frequency modes -low_freq_indices = np.argsort(lanczos.w)[:5] -for idx in low_freq_indices: - freq_cm = lanczos.w[idx] * CC.Units.RY_TO_CM +for idx, w in lanczos.w: + freq_cm = w * CC.Units.RY_TO_CM print(f"Mode {idx}: {freq_cm:.1f} cm⁻¹") - -# Study softest mode -lanczos.prepare_mode(low_freq_indices[0]) ``` -**Example with test data**: - -# doctest: +ELLIPSIS ->>> from tdscha.testing.test_data import load_test_ensemble ->>> import tdscha.DynamicalLanczos as DL ->>> ens = load_test_ensemble(n_configs=5) ->>> lanczos = DL.Lanczos(ens) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. ->>> lanczos.init(use_symmetries=True) -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s ->>> # Prepare perturbation for mode 5 ->>> lanczos.prepare_mode(5) - -... ->>> print(f"Prepared perturbation for mode 5") -Prepared perturbation for mode 5 +Note that the first 3 acoustic modes are excluded from the mode index counting, so `mode_index=0` corresponds to the first non zero mode across all the Brilluin zone (only commensurate q points). + ### 2. Infrared (IR) Absorption Compute IR spectra using effective charges: ```python -lanczos.prepare_ir(pol_vec=np.array([1,0,0]), - effective_charges=None) +lanczos.prepare_ir(pol_vec=np.array([1,0,0])) ``` +Note that you need effective charges available to compute the IR response. These should be provided in the final dynamical matrix of the ensemble used to initialize the Lanczos object. If they are not available, you can provide them directly via the `effective_charges` parameter of the `prepare_ir` method. + **When to use**: IR absorption spectra, dielectric response. **Parameters**: - `pol_vec`: Light polarization vector (cartesian) - `effective_charges`: Override default from dynamical matrix -**Example**: -```python -# x-polarized IR -lanczos.prepare_ir(pol_vec=[1,0,0]) - -# y-polarized IR -lanczos.prepare_ir(pol_vec=[0,1,0]) - -# Circular polarization (requires two calculations) -lanczos.prepare_ir(pol_vec=[1,0,0]) -# Then combine with pol_vec=[0,1,0] results -``` ### 3. Raman Scattering -Compute Raman spectra using Raman tensor: +Compute Raman spectra using Raman tensor. You can both compute the polarized Raman spectrum for specific incoming/outgoing polarizations, or the unpolarized Raman spectrum. #### Polarized Raman ```python lanczos.prepare_raman(pol_vec_in=[1,0,0], - pol_vec_out=[1,0,0], - mixed=False) + pol_vec_out=[1,0,0]) ``` #### Unpolarized Raman Components +Unpolarized Raman can be obtained by averaging across all possible polarization direction. However, a very good approximation to the +unpolarized Raman spectra can be obtained by computing only 7 specific components of the Raman tensor, which are weighted by specific prefactors to obtain the total unpolarized Raman spectrum. +The `tdscha` code offers a convenient method to initialize and run all of them. + ```python -# Compute individual components (0-6) for i in range(7): - lanczos.prepare_unpolarized_raman(index=i) - lanczos.run_FT(50, save_dir=f"raman_{i}") - # Weight by prefactors later + lanczos.reset() + lanczos.prepare_raman(unpolarized = i) + lanczos.run_FT(50) + lanczos.save_status(f"raman_unpolarized_{i}.npz") ``` -**Raman tensor components**: -- `0`: $\alpha^2 = (xx + yy + zz)^2/9$ -- `1`: $\beta_1^2 = (xx - yy)^2/2$ -- `2`: $\beta_2^2 = (xx - zz)^2/2$ -- `3`: $\beta_3^2 = (yy - zz)^2/2$ -- `4`: $\beta_4^2 = 3xy^2$ -- `5`: $\beta_5^2 = 3xz^2$ -- `6`: $\beta_6^2 = 3yz^2$ - -**Total unpolarized intensity**: -$$ -I_{\text{unpol}} = 45\alpha^2 + 7(\beta_1^2 + \beta_2^2 + \beta_3^2 + \beta_4^2 + \beta_5^2 + \beta_6^2) -$$ -## Running Unpolarized Raman - -### Complete Workflow +Then you can plot the unpolarized Raman spectrum by summing the contributions of the 7 components. This is done in the following way: ```python +import tdscha, tdscha.DynamicalLanczos as DL +import cellconstructor as CC, cellconstructor.Units import numpy as np -import tdscha.DynamicalLanczos as DL +import matplotlib.pyplot as plt -# Initialize Lanczos -lanczos = DL.Lanczos(ensemble) -lanczos.init() +# Prepare the frequency range in which you want to plot the spectrum (in cm-1) +w = np.linspace(0, 1000, 500) # Frequency range in cm⁻¹ +w_ry = w/CC.Units.RY_TO_CM # Convert in Ry (the internal unit of tdscha) +smearing = 2/CC.Units.RY_TO_CM # Smearing in cm⁻¹ -# Prefactors for unpolarized Raman -prefactors = [45/9, 7/2, 7/2, 7/2, 7*3, 7*3, 7*3] +raman_signal = np.zeros_like(w) -# Arrays to store spectra -w_array = np.linspace(0, 1000/CC.Units.RY_TO_CM, 1000) -spectra = np.zeros((7, len(w_array))) - -# Compute each component +# Load the 7 unpolarized Raman components and sum them. for i in range(7): - lanczos.reset() # Clear previous perturbation - lanczos.prepare_unpolarized_raman(index=i) - lanczos.run_FT(50) - - gf = lanczos.get_green_function_continued_fraction(w_array) - spectra[i] = -np.imag(gf) * prefactors[i] + lanczos = DL.Lanczos() + lanczos.load_status(f"raman_unpolarized_{i}.npz") -# Sum components -total_spectrum = np.sum(spectra, axis=0) -``` + # Get the dynamical green function from the linear response calculation + gf = lanczos.get_green_function_continued_fraction(w, smearing=smearing) -### Fluctuation-Corrected Raman + # The response is proportional to the imaginary part of the Green's function. + # The '-' sign selects the retarded response, which is the one relevant for Raman scattering. + raman_signal += -np.imag(gf) -For Raman tensor fluctuations (beyond harmonic approximation): -```python -lanczos.prepare_unpolarized_raman_FT( - index=0, - eq_raman_tns=equilibrium_raman_tensor, - use_symm=True, - ens_av_raman=ensemble_average_raman, - raman_tns_ens=raman_tensor_ensemble, - add_2ph=True -) +# Then, we can just plot the data +plt.plot(w, raman_signal) +plt.xlabel("Frequency (cm-1)") +plt.ylabel("Unpolarized Raman Intensity (arb. units)") +plt.show() ``` ## Parallel Execution Modes @@ -172,44 +119,21 @@ lanczos = DL.Lanczos(ensemble) # Default: Julia if available, else C serial # Manual selection from tdscha.DynamicalLanczos import ( - MODE_SLOW_SERIAL, # 0: Pure Python (testing) + MODE_SLOW_SERIAL, # 0: Pure Python (testing) - extremely slow MODE_FAST_SERIAL, # 1: C extension MODE_FAST_MPI, # 2: C with MPI - MODE_FAST_JULIA # 3: Julia (fastest) + MODE_FAST_JULIA # 3: Julia (fastest) and parallel ) lanczos = DL.Lanczos(ensemble, mode=MODE_FAST_JULIA) ``` - -**Testing mode selection with example data**: - -# doctest: +ELLIPSIS ->>> from tdscha.testing.test_data import load_test_ensemble ->>> import tdscha.DynamicalLanczos as DL ->>> ens = load_test_ensemble(n_configs=5) ->>> # Check if Julia is available ->>> if DL.is_julia_enabled(): -... print("Julia mode available") -... else: -... print("Julia not available, using C mode") -Julia mode available... - ### MPI Parallelization ```bash # Run with MPI mpirun -np 16 python script.py - -# Hybrid MPI+OpenMP -export OMP_NUM_THREADS=4 -mpirun -np 4 --bind-to socket python script.py ``` -**MPI-specific considerations**: -- Only rank 0 prints output (via `pprint()`) -- Collective operations synchronized with `Parallel.barrier()` -- Load balancing automatic via symmetry decomposition - ### Julia Fast Mode Requirements: @@ -229,178 +153,63 @@ else: ## Gamma-Only Trick -For $\Gamma$-point-only calculations, use point-group symmetries only and project translations: +For $\Gamma$-point-only perturbation, we have a special optimization that exploits the fact that the response is also at $\Gamma$. +This allows us to separate point-group from translational symmetries. This speedup is proportional to the number of unit cells in the supercell. +The flag should be specified **before** calling `init()`, as it changes the way symmetries are initialized. ```python lanczos.gamma_only = True -lanczos.init(use_symmetries=True) +lanczos.init() ``` - **Example with test data**: - - # doctest: +ELLIPSIS - >>> from tdscha.testing.test_data import load_test_ensemble - >>> import tdscha.DynamicalLanczos as DL - >>> ens = load_test_ensemble(n_configs=5) - >>> lanczos = DL.Lanczos(ens) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. - >>> lanczos.gamma_only = True - >>> lanczos.init(use_symmetries=True) -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s - >>> print(f"Initialized with gamma_only={lanczos.gamma_only}") - Initialized with gamma_only=True - - **How it works**: -1. Separates point-group from translational symmetries -2. Builds translation operators in mode space: $T_R^{\text{mode}} = P^T P_R P$ -3. Applies translation projector: $P = \frac{1}{N_{\text{cells}}} \sum_R T_R^{\text{mode}}$ - -**Benefits**: -- Reduces symmetry operations from $N_{\text{total}}$ to $N_{\text{point-group}}$ -- Speedup: $N_{\text{total}} / N_{\text{point-group}}$ (typically 4-48x) - -**When to use**: Large supercells, $\Gamma$-point properties only. - ## Wigner vs Normal Representation -### Normal Representation (Default) +The original implementation of the TD-SCHA work a bi-conjugate Lanczos algorithm, as the Liouvillian was not Hermitian [Monacelli, Mauri, Physical Review B 103, 104305 (2021)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.103.104305). +Later, [Siciliano, Monacelli, Caldarelli, Mauri, Physical Review B 107, 174307 (2023)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.107.174307) provided a reformulation of the TD-SCHA in the Wigner representation, which allows to work with a Hermitian Liouvillian and thus to use the standard Lanczos algorithm. +The standard Lanczos algorithm requires just one application of the Liouvillian per step, while the bi-conjugate Lanczos requires two applications, thus the Wigner representation +is twice as fast as the normal representation. + +### Normal Representation (Default, probably changed in the future) ```python lanczos.use_wigner = False # Default ``` -**Equations**: Inverts $(-\mathcal{L} - \omega^2)$ - -**Pros**: -- Direct physical interpretation -- Standard linear response formalism -- Compatible with all perturbation types - -**Cons**: -- Slower convergence for some systems - ### Wigner Representation ```python lanczos.use_wigner = True ``` -**Equations**: Inverts $(\mathcal{L}_w + \omega^2)$ - -**Pros**: -- Faster convergence for low-temperature systems -- Required for two-phonon response -- More stable for certain anharmonic potentials - -**Cons**: -- Different sign conventions -- Limited to specific perturbation types - -### Comparison Example - -```python -# Compare representations -for use_wigner in [False, True]: - lanczos = DL.Lanczos(ensemble, use_wigner=use_wigner) - lanczos.init() - lanczos.prepare_mode(10) - lanczos.run_FT(30) - - gf = lanczos.get_green_function_continued_fraction(w_array) - spectrum = -np.imag(gf) - # Note: peak positions identical, convergence rates differ - ``` +## Save status during the calculation and restart - **Quick test with example data**: - - # doctest: +ELLIPSIS - >>> from tdscha.testing.test_data import load_test_ensemble - >>> import tdscha.DynamicalLanczos as DL - >>> ens = load_test_ensemble(n_configs=5) ->>> # Test normal representation ->>> lanczos_normal = DL.Lanczos(ens, use_wigner=False) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. ->>> lanczos_normal.init() -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s ->>> # Test Wigner representation ->>> lanczos_wigner = DL.Lanczos(ens, use_wigner=True) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. ->>> lanczos_wigner.init() -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s - >>> print(f"Normal representation: {len(lanczos_normal.w)} modes") - Normal representation: ... modes - >>> print(f"Wigner representation: {len(lanczos_wigner.w)} modes") - Wigner representation: ... modes - - ## Convergence Parameters - -### Lanczos Steps +Sometimes the TDSCHA run can require a lot of time. +To avoid losing the data after a timeout, you can set the `save_each` parameter of the `run_FT` method, which will save a checkpoint every `save_each` steps in the specified directory. ```python lanczos.run_FT(n_iter=100, # Total steps save_each=10, # Save checkpoint frequency - save_dir="output") # Directory for checkpoints + save_dir="output", + prefix="checkpoint" + ) # Directory for checkpoints ``` - **Guidelines**: - - Start with 20-50 steps for testing - - 100-200 steps for production - - Check convergence with `tdscha-convergence-analysis` - - **Example with test data**: - - # doctest: +ELLIPSIS ->>> from tdscha.testing.test_data import load_test_ensemble ->>> import tdscha.DynamicalLanczos as DL ->>> ens = load_test_ensemble(n_configs=5) ->>> lanczos = DL.Lanczos(ens) -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. ->>> lanczos.init() -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s ->>> lanczos.prepare_mode(5) - -... ->>> # Run just 2 steps for quick test ->>> import sys ->>> from io import StringIO ->>> old_stdout = sys.stdout ->>> sys.stdout = StringIO() ->>> lanczos.run_FT(2, debug=False) # doctest: +SKIP ->>> sys.stdout = old_stdout ->>> print(f"Ran 2 Lanczos steps, coefficients: a={lanczos.a_coeffs[:2]}") # doctest: +SKIP -Ran 2 Lanczos steps, coefficients: a=... - - ### Restarting Calculations +The checkpoint files are saved in NumPy `.npz` format and contain all necessary data to restart the calculation from that point. +The files are named as `save_dir/prefix_step.npz`, where `step` is the current Lanczos step. + +You can load a checkpoint and restart the calculation by running the following code: ```python -# Save checkpoint -lanczos.save_status("checkpoint.npz") +import tdscha.DynamicalLanczos as DL -# Later, restart -lanczos2 = DL.Lanczos() -lanczos2.load_status("checkpoint.npz") -lanczos2.run_FT(50) # Continue from saved state +lanczos = DL.Lanczos() +lanczos.load_status("output/checkpoint_50.npz") # Load checkpoint from step 50 +lanczos.run_FT(50, save_each=10, save_dir="output", prefix="checkpoint") # Continue for 50 more steps +lanczos.save_status("final_result.npz") # Save final result ``` - **Note**: For doctest purposes, we skip file I/O, but the pattern is shown above. - ### Smearing and Terminator +### Smearing and Terminator ```python # Compute Green's function with smearing @@ -412,29 +221,6 @@ gf = lanczos.get_green_function_continued_fraction( ) ``` -## Memory Management - -### Large System Considerations - -```python -# Exclude irrelevant modes (e.g., high-frequency) -select_modes = lanczos.w < 500/CC.Units.RY_TO_CM -lanczos = DL.Lanczos(ensemble, select_modes=select_modes) - -# Estimate memory usage -n_modes = lanczos.n_modes -memory_gb = (n_modes**2 * 8 * 3) / 1024**3 # ~3 arrays needed -print(f"Estimated memory: {memory_gb:.1f} GB") -``` - -### Disk-Based Calculations - -For extremely large systems, use checkpointing: - -```python -lanczos.run_FT(100, save_dir="large_calc", save_each=5) -# Can restart if memory issues occur -``` ## Advanced Features @@ -454,32 +240,14 @@ lanczos.use_wigner = True lanczos.prepare_two_phonon_response(mode1, mode2) ``` -### Interpolation to Denser q-Meshes - -```python -# Interpolate to 4x4x4 q-mesh -q_mesh = [4, 4, 4] -interp_lanczos = lanczos.interpolate(q_mesh) -``` - -## Troubleshooting - -### Common Issues - -1. **Slow convergence**: Try Wigner representation or increase steps -2. **Memory errors**: Use `select_modes` or MPI parallelization -3. **Symmetry errors**: Check spglib installation or use `no_sym=True` -4. **Julia errors**: Verify Julia installation and package dependencies - ### Performance Optimization - Use `gamma_only=True` for Γ-point calculations - Choose appropriate `mode` for your hardware -- Balance MPI processes vs OpenMP threads - Monitor convergence to avoid unnecessary steps ## Next Steps - Explore [StaticHessian](static-hessian.md) for free energy calculations - Check [Examples](examples.md) for complete workflows -- Refer to [API Documentation](api/dynamical_lanczos.md) for method details \ No newline at end of file +- Refer to [API Documentation](api/dynamical_lanczos.md) for method details From c8b6d80393ff9eda340b46b4c99a7be160379e9b Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sat, 14 Feb 2026 20:49:51 +0100 Subject: [PATCH 11/18] Add test comparing Wigner vs non-Wigner spectral functions Validates that both Lanczos formalisms produce identical Green functions by benchmarking Re[G] and Im[G] at omega=0 and the perturbed mode frequency. Co-Authored-By: Claude Opus 4.6 --- tests/test_julia/test_wigner_vs_nowigner.py | 149 ++++++++++++++++++++ 1 file changed, 149 insertions(+) create mode 100644 tests/test_julia/test_wigner_vs_nowigner.py diff --git a/tests/test_julia/test_wigner_vs_nowigner.py b/tests/test_julia/test_wigner_vs_nowigner.py new file mode 100644 index 00000000..bd8e0a90 --- /dev/null +++ b/tests/test_julia/test_wigner_vs_nowigner.py @@ -0,0 +1,149 @@ +""" +Test that compares the spectral function computed via Wigner mode +and normal (non-Wigner) mode on the same SnTe-like system. + +The benchmark is on the Green function (real and imaginary parts) +evaluated at omega=0 and at the frequency of the perturbed mode. +""" +from __future__ import print_function + +import numpy as np +import os + +import cellconstructor as CC +import cellconstructor.Phonons + +import sscha, sscha.Ensemble +import tdscha, tdscha.DynamicalLanczos as DL + +from tdscha.Parallel import pprint as print + + +MODE_INDEX = 10 +N_STEPS = 10 +T = 250 +NQIRR = 3 + +# Tolerance on the Green function comparison (relative) +GF_RTOL = 0.05 # 5% relative tolerance on spectral function values + + +def _setup_lanczos(use_wigner): + """Set up and run a Lanczos calculation with the given Wigner flag.""" + total_path = os.path.dirname(os.path.abspath(__file__)) + os.chdir(total_path) + + dyn = CC.Phonons.Phonons("data/dyn_gen_pop1_", NQIRR) + ens = sscha.Ensemble.Ensemble(dyn, T) + ens.load_bin("data", 1) + + lanc = DL.Lanczos(ens) + lanc.ignore_harmonic = False + lanc.ignore_v3 = False + lanc.ignore_v4 = False + lanc.use_wigner = use_wigner + lanc.mode = DL.MODE_FAST_SERIAL + lanc.init(use_symmetries=True) + lanc.prepare_mode(MODE_INDEX) + lanc.run_FT(N_STEPS, run_simm=use_wigner, verbose=False) + return lanc + + +def test_wigner_vs_nowigner_green_function(): + """ + Compare Green functions from Wigner and non-Wigner Lanczos + at omega=0 and at the perturbed mode frequency. + + The spectral function (imaginary part of the Green function) + must agree between the two approaches within tolerance. + """ + lanc_wigner = _setup_lanczos(use_wigner=True) + lanc_nowigner = _setup_lanczos(use_wigner=False) + + # Get the harmonic frequency of the perturbed mode (in Ry) + w_mode = lanc_wigner.w[MODE_INDEX] + + # Small imaginary broadening to regularize the Green function + smearing = w_mode * 0.1 + + # Evaluate the Green function at omega=0 and at the mode frequency + w_points = np.array([0.0, w_mode]) + + gf_wigner = lanc_wigner.get_green_function_continued_fraction( + w_points, use_terminator=True, smearing=smearing + ) + gf_nowigner = lanc_nowigner.get_green_function_continued_fraction( + w_points, use_terminator=True, smearing=smearing + ) + + spectral_wigner = -np.imag(gf_wigner) + spectral_nowigner = -np.imag(gf_nowigner) + + real_wigner = np.real(gf_wigner) + real_nowigner = np.real(gf_nowigner) + + # --- Benchmark at omega = 0 --- + print() + print("=== Green function comparison: Wigner vs non-Wigner ===") + print() + print(f"At omega = 0:") + print(f" Re[G] Wigner: {real_wigner[0]:.10e}") + print(f" Re[G] noWigner: {real_nowigner[0]:.10e}") + print(f" Im[G] Wigner: {spectral_wigner[0]:.10e}") + print(f" Im[G] noWigner: {spectral_nowigner[0]:.10e}") + + # --- Benchmark at omega = w_mode --- + print() + print(f"At omega = w_mode ({w_mode * CC.Units.RY_TO_CM:.2f} cm-1):") + print(f" Re[G] Wigner: {real_wigner[1]:.10e}") + print(f" Re[G] noWigner: {real_nowigner[1]:.10e}") + print(f" Im[G] Wigner: {spectral_wigner[1]:.10e}") + print(f" Im[G] noWigner: {spectral_nowigner[1]:.10e}") + + # --- Assertions --- + # At omega=0, the real part of the Green function gives the + # renormalized frequency: w^2 = 1/Re[G(0)]. + # Compare the extracted frequencies. + w2_wigner = 1.0 / real_wigner[0] + w2_nowigner = 1.0 / real_nowigner[0] + freq_wigner = np.sign(w2_wigner) * np.sqrt(np.abs(w2_wigner)) * CC.Units.RY_TO_CM + freq_nowigner = np.sign(w2_nowigner) * np.sqrt(np.abs(w2_nowigner)) * CC.Units.RY_TO_CM + + print() + print(f"Renormalized frequency (Wigner): {freq_wigner:.6f} cm-1") + print(f"Renormalized frequency (noWigner): {freq_nowigner:.6f} cm-1") + print(f"Difference: {abs(freq_wigner - freq_nowigner):.6f} cm-1") + + # Frequency difference should be small (< 5 cm-1) + assert abs(freq_wigner - freq_nowigner) < 5.0, ( + f"Renormalized frequencies differ too much: " + f"Wigner={freq_wigner:.4f}, noWigner={freq_nowigner:.4f} cm-1" + ) + + # Compare spectral function at omega = w_mode + # Use the larger value as reference for relative comparison + ref_spectral = max(abs(spectral_wigner[1]), abs(spectral_nowigner[1])) + if ref_spectral > 1e-15: + rel_diff_spectral = abs(spectral_wigner[1] - spectral_nowigner[1]) / ref_spectral + print(f"Relative diff in spectral function at w_mode: {rel_diff_spectral:.6e}") + assert rel_diff_spectral < GF_RTOL, ( + f"Spectral functions at w_mode differ by {rel_diff_spectral:.4e} " + f"(tolerance: {GF_RTOL})" + ) + + # Compare real part of GF at omega = w_mode + ref_real = max(abs(real_wigner[1]), abs(real_nowigner[1])) + if ref_real > 1e-15: + rel_diff_real = abs(real_wigner[1] - real_nowigner[1]) / ref_real + print(f"Relative diff in Re[G] at w_mode: {rel_diff_real:.6e}") + assert rel_diff_real < GF_RTOL, ( + f"Real parts of GF at w_mode differ by {rel_diff_real:.4e} " + f"(tolerance: {GF_RTOL})" + ) + + print() + print("=== All benchmarks passed ===") + + +if __name__ == "__main__": + test_wigner_vs_nowigner_green_function() From ad63865f3f3b21a1fdb4a1c92863d58774edf2e0 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 09:24:09 +0100 Subject: [PATCH 12/18] Fixed the test --- Modules/DynamicalLanczos.py | 6 +- tests/test_julia/test_wigner_vs_nowigner.py | 64 +++++++++++---------- 2 files changed, 37 insertions(+), 33 deletions(-) diff --git a/Modules/DynamicalLanczos.py b/Modules/DynamicalLanczos.py index 571a849d..b893d096 100644 --- a/Modules/DynamicalLanczos.py +++ b/Modules/DynamicalLanczos.py @@ -3603,7 +3603,6 @@ def apply_full_L(self, target = None, force_t_0 = False, force_FT = True, transp # Apply the quick_lanczos t4 = timer() if fast_lanczos and (not self.ignore_v3): - print('Applying the anharmonic part of L') # AN-HARMONIC evolution finite temperature output += self.apply_anharmonic_FT(transpose) t3 = timer() @@ -5067,8 +5066,9 @@ def get_green_function_continued_fraction(self, w_array : np.ndarray[np.float64] Perturbation modulus = {} Sign = {}""".format(self.use_wigner, use_terminator, self.perturbation_modulus, sign) - print() - print(INFO) + if self.verbose: + print() + print(INFO) # Get the terminator if use_terminator: diff --git a/tests/test_julia/test_wigner_vs_nowigner.py b/tests/test_julia/test_wigner_vs_nowigner.py index bd8e0a90..0253a4a6 100644 --- a/tests/test_julia/test_wigner_vs_nowigner.py +++ b/tests/test_julia/test_wigner_vs_nowigner.py @@ -20,12 +20,12 @@ MODE_INDEX = 10 -N_STEPS = 10 +N_STEPS = 50 T = 250 NQIRR = 3 # Tolerance on the Green function comparison (relative) -GF_RTOL = 0.05 # 5% relative tolerance on spectral function values +GF_RTOL = 0.001 # 0.1% relative tolerance on spectral function values def _setup_lanczos(use_wigner): @@ -49,7 +49,7 @@ def _setup_lanczos(use_wigner): return lanc -def test_wigner_vs_nowigner_green_function(): +def test_wigner_vs_nowigner_green_function(verbose=False): """ Compare Green functions from Wigner and non-Wigner Lanczos at omega=0 and at the perturbed mode frequency. @@ -70,10 +70,10 @@ def test_wigner_vs_nowigner_green_function(): w_points = np.array([0.0, w_mode]) gf_wigner = lanc_wigner.get_green_function_continued_fraction( - w_points, use_terminator=True, smearing=smearing + w_points, use_terminator=False, smearing=smearing ) gf_nowigner = lanc_nowigner.get_green_function_continued_fraction( - w_points, use_terminator=True, smearing=smearing + w_points, use_terminator=False, smearing=smearing ) spectral_wigner = -np.imag(gf_wigner) @@ -83,22 +83,24 @@ def test_wigner_vs_nowigner_green_function(): real_nowigner = np.real(gf_nowigner) # --- Benchmark at omega = 0 --- - print() - print("=== Green function comparison: Wigner vs non-Wigner ===") - print() - print(f"At omega = 0:") - print(f" Re[G] Wigner: {real_wigner[0]:.10e}") - print(f" Re[G] noWigner: {real_nowigner[0]:.10e}") - print(f" Im[G] Wigner: {spectral_wigner[0]:.10e}") - print(f" Im[G] noWigner: {spectral_nowigner[0]:.10e}") - - # --- Benchmark at omega = w_mode --- - print() - print(f"At omega = w_mode ({w_mode * CC.Units.RY_TO_CM:.2f} cm-1):") - print(f" Re[G] Wigner: {real_wigner[1]:.10e}") - print(f" Re[G] noWigner: {real_nowigner[1]:.10e}") - print(f" Im[G] Wigner: {spectral_wigner[1]:.10e}") - print(f" Im[G] noWigner: {spectral_nowigner[1]:.10e}") + if verbose: + print() + print("=== Green function comparison: Wigner vs non-Wigner ===") + print() + print(f"At omega = 0:") + print(f" Origina w {lanc_wigner.w[MODE_INDEX] * CC.Units.RY_TO_CM:.2f} cm-1") + print(f" Re[G] Wigner: {real_wigner[0]:.10e}") + print(f" Re[G] noWigner: {real_nowigner[0]:.10e}") + print(f" Im[G] Wigner: {spectral_wigner[0]:.10e}") + print(f" Im[G] noWigner: {spectral_nowigner[0]:.10e}") + + # --- Benchmark at omega = w_mode --- + print() + print(f"At omega = w_mode ({w_mode * CC.Units.RY_TO_CM:.2f} cm-1):") + print(f" Re[G] Wigner: {real_wigner[1]:.10e}") + print(f" Re[G] noWigner: {real_nowigner[1]:.10e}") + print(f" Im[G] Wigner: {spectral_wigner[1]:.10e}") + print(f" Im[G] noWigner: {spectral_nowigner[1]:.10e}") # --- Assertions --- # At omega=0, the real part of the Green function gives the @@ -109,13 +111,14 @@ def test_wigner_vs_nowigner_green_function(): freq_wigner = np.sign(w2_wigner) * np.sqrt(np.abs(w2_wigner)) * CC.Units.RY_TO_CM freq_nowigner = np.sign(w2_nowigner) * np.sqrt(np.abs(w2_nowigner)) * CC.Units.RY_TO_CM - print() - print(f"Renormalized frequency (Wigner): {freq_wigner:.6f} cm-1") - print(f"Renormalized frequency (noWigner): {freq_nowigner:.6f} cm-1") - print(f"Difference: {abs(freq_wigner - freq_nowigner):.6f} cm-1") + if verbose: + print() + print(f"Renormalized frequency (Wigner): {freq_wigner:.6f} cm-1") + print(f"Renormalized frequency (noWigner): {freq_nowigner:.6f} cm-1") + print(f"Difference: {abs(freq_wigner - freq_nowigner):.6f} cm-1") - # Frequency difference should be small (< 5 cm-1) - assert abs(freq_wigner - freq_nowigner) < 5.0, ( + # Frequency difference should be small (< 0.01 cm-1) + assert abs(freq_wigner - freq_nowigner) < 0.01, ( f"Renormalized frequencies differ too much: " f"Wigner={freq_wigner:.4f}, noWigner={freq_nowigner:.4f} cm-1" ) @@ -141,9 +144,10 @@ def test_wigner_vs_nowigner_green_function(): f"(tolerance: {GF_RTOL})" ) - print() - print("=== All benchmarks passed ===") + if verbose: + print() + print("=== All benchmarks passed ===") if __name__ == "__main__": - test_wigner_vs_nowigner_green_function() + test_wigner_vs_nowigner_green_function(verbose=True) From 5979828a023fcf24a0605561b6d63ee95d5d16c2 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 09:25:25 +0100 Subject: [PATCH 13/18] Change version for major updates --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3094806f..f5d86368 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,7 @@ build-backend = "mesonpy" [project] # Project metadata, which was previously in the `setup()` call of setup.py. name = "tdscha" -version = "1.5.1" # Make sure this version matches meson.build +version = "1.6.0" # Make sure this version matches meson.build description = "Time Dependent Self Consistent Harmonic Approximation" authors = [{name = "Lorenzo Monacelli"}] readme = "README.md" From 3813b44dbd8d353012f9121c00f01ff1fd3e72a2 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 09:25:50 +0100 Subject: [PATCH 14/18] Fixed a version mismatch with meson --- meson.build | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/meson.build b/meson.build index 47e8e728..21571f4b 100644 --- a/meson.build +++ b/meson.build @@ -1,6 +1,6 @@ project('tdscha', ['c'], - version: '1.5.1', + version: '1.6.0', license: 'GPL', meson_version: '>= 1.1.0', # <- set min version of meson. default_options : [ From fdedc0365d8ccb9f1909e5edee4a70c8f4746fa1 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 10:20:48 +0100 Subject: [PATCH 15/18] Fixed the documentation --- Modules/DynamicalLanczos.py | 10 +- docs/cli.md | 141 +++++---------- docs/static-hessian.md | 332 +++++------------------------------- 3 files changed, 92 insertions(+), 391 deletions(-) diff --git a/Modules/DynamicalLanczos.py b/Modules/DynamicalLanczos.py index b893d096..d5f45f6c 100644 --- a/Modules/DynamicalLanczos.py +++ b/Modules/DynamicalLanczos.py @@ -180,7 +180,7 @@ def is_julia_enabled(): class Lanczos(object): - def __init__(self, ensemble = None, mode = None, unwrap_symmetries = False, select_modes = None, use_wigner = False, lo_to_split = "random"): + def __init__(self, ensemble = None, mode = None, unwrap_symmetries = False, select_modes = None, use_wigner = True, lo_to_split = "random"): """ INITIALIZE THE LANCZOS ====================== @@ -5913,7 +5913,7 @@ def mask_dot_wigner(self, debug = False): - def run_FT(self, n_iter, save_dir = None, save_each = 5, verbose = True, n_rep_orth = 0, n_ortho = 10, flush_output = True, debug = False, prefix = "LANCZOS", run_simm = False, optimized = False): + def run_FT(self, n_iter, save_dir = None, save_each = 5, verbose = True, n_rep_orth = 0, n_ortho = 10, flush_output = True, debug = False, prefix = "LANCZOS", run_simm = None, optimized = False): """ RUN LANCZOS ITERATIONS FOR FINITE TEMPERATURE ============================================= @@ -5954,7 +5954,7 @@ def run_FT(self, n_iter, save_dir = None, save_each = 5, verbose = True, n_rep_o as the gram-shmidth procdeure and checks on the coefficients. This is usefull to spot an error or the appeareance of ghost states due to numerical inaccuracy. run_simm : bool - If true the biconjugate Lanczos is transformed in a simple Lanczos with corrections in the scalar product + If true the biconjugate Lanczos is transformed in a simple Lanczos with corrections in the scalar product. This is only possible if we are using the Wigner representation, where the Lanczos matrix is symmetric in the full space. optimized : bool If True we pop the vectors P and Q that we do not use during the Lanczos """ @@ -5986,6 +5986,10 @@ def run_FT(self, n_iter, save_dir = None, save_each = 5, verbose = True, n_rep_o if not os.path.exists(save_dir): os.makedirs(save_dir) + # Automatically use the symmetric Lanczos if we are using the Wigner representation + if run_simm is None: + run_simm = self.use_wigner + # run_simm is allowed only if we use the wigner representation if run_simm and not self.use_wigner: raise NotImplementedError('The symmetric Lanczos works only with Wigner. Set use_wigner to True and make sure that you are not using the analytic wigner L!') diff --git a/docs/cli.md b/docs/cli.md index 2c44aa69..b5f73d0b 100644 --- a/docs/cli.md +++ b/docs/cli.md @@ -1,6 +1,6 @@ # Command-Line Interface (CLI) Tools -TD-SCHA provides four CLI tools for analysis and visualization of calculation results. These tools work with output files from both Python and C++ executables. +TD-SCHA provides four CLI tools for analysis and visualization of calculation results. ## Installation and Availability @@ -48,7 +48,7 @@ tdscha-convergence-analysis lanczos_final.npz # Custom smearing (5 cm⁻¹) tdscha-convergence-analysis lanczos_final.npz 5 -# From .abc file (C++ executable output) +# From .abc file (after converting the output of the calculation) tdscha-convergence-analysis lanczos.abc 2 ``` @@ -103,15 +103,9 @@ Single figure showing: - Y-axis: Spectrum [a.u.] - Title includes number of Lanczos poles -### Notes - -- Uses continued fraction **without terminator** for plotting -- For terminator-included spectra, use convergence analysis tool -- Spectrum is normalized by perturbation modulus - ## tdscha-output2abc -Converts stdout from C++ executable `tdscha-lanczos.x` to `.abc` format. +Converts stdout of the run to `.abc` format for plotting the results. ### Usage @@ -123,19 +117,6 @@ tdscha-output2abc output_file abc_file - `output_file`: Text file containing stdout of `tdscha-lanczos.x` - `abc_file`: Output `.abc` file name -### Example - -```bash -# Run C++ executable -./tdscha-lanczos.x > lanczos.stdout - -# Convert to .abc -tdscha-output2abc lanczos.stdout lanczos.abc - -# Now use .abc with other tools -tdscha-plot-data lanczos.abc -``` - ### File Format The `.abc` file contains three columns: @@ -216,38 +197,12 @@ Single figure showing: - **Flat lines** show converged eigenvalues - **Reference lines** help identify mode correspondence -## Integration with Workflows - -### Python Script Integration - -```python -import subprocess -import sys - -# After Lanczos calculation -lanczos.save_status("output.npz") - -# Run CLI analysis -subprocess.run(["tdscha-convergence-analysis", "output.npz", "5"]) -subprocess.run(["tdscha-plot-data", "output.npz", "0", "1000", "2"]) -``` - -### Batch Processing - -```bash -#!/bin/bash -# Process multiple calculations -for file in results/*.npz; do - basename=$(basename $file .npz) - tdscha-convergence-analysis "$file" 5 - mv convergence.png "${basename}_convergence.png" - tdscha-plot-data "$file" 0 1000 2 - mv spectrum.png "${basename}_spectrum.png" -done -``` - ### C++ Executable Workflow +We also provide a C++ executable for the Lanczos algorithm, which can be used in place of the Python implementation for large systems or high-performance needs. The workflow is similar, but requires an additional conversion step for the initialization. +Note that the C++ code is less efficient than the Julia version available with the Python library, however it is much easier to install on clusters without the need of properly configured Julia and Python environments. +You can use the python library locally to prepare all the input files used by the C++ code, and then run the C++ executable on the cluster, and finally use the CLI tools to analyze the results. + ```bash # 1. Prepare input files (from Python) python prepare_input_for_cluster.py @@ -261,69 +216,61 @@ tdscha-convergence-analysis lanczos.abc 5 tdscha-plot-data lanczos.abc 0 500 2 ``` -## Advanced Usage +The input preparation script should generate the necessary files for the C++ executable, including the initial Lanczos vector and any required parameters. +A premade script to prepare the input for the cluster is available in the repository under `Examples/Templates/prepare_input_for_cluster.py` +The workflow is very similar to a standard python one, but instead of running the Lanczos algorithm, we save the prepared Lanczos object to a file, in the following way -### Custom Frequency Ranges -For high-resolution plots: +```python +import tdscha, tdscha.DynamicalLanczos as DL +import sscha, sscha.Ensemble +import cellconstructor as CC, cellconstructor.Phonons -```bash -# High resolution: 0-200 cm⁻¹, 0.5 cm⁻¹ smearing -tdscha-plot-data lanczos.npz 0 200 0.5 -``` +# Load the ensemble from a previous SSCHA calculation +start_dyn = CC.Phonons.Phonons("dyn_", nqirr=3) +ensemble = sscha.Ensemble.Ensemble(start_dyn, 300) # Temperature in K +ensemble.load_dir("ensemble", 1) # Load from directory, sscha iteration 1 +final_dyn = CC.Phonons.Phonons("final_dyn_", nqirr=3) # load the converged dynamical matrix +ensemble.update_weights(final_dyn, 300) # Update the ensemble -### Comparing Multiple Calculations -```bash -# Generate .abc files for comparison -tdscha-output2abc calc1.stdout calc1.abc -tdscha-output2abc calc2.stdout calc2.abc +# Initialize the Lanczos object +lanczos = DL.DynamicalLanczos(ensemble) +lanczos.init() -# Plot together (requires custom script) -python plot_comparison.py calc1.abc calc2.abc -``` +# Setup the perturbation - in this case IR spectrum polarized along the x axis +lanczos.prepare_ir([1.0, 0.0, 0.0]) -### Scripting with Python +# Save the Lanczos object to a file for the C++ executable +lanczos.prepare_input_files(n_steps = 100, # Number of Lanczos steps + directory = "lanczos_input", # Directory in which to save the input files + root_name = "lanzos_ir_xpol") # Prefix -```python -from tdscha import cli -import sys - -# Mock command-line arguments -sys.argv = ["tdscha-plot-data", "lanczos.npz", "0", "500", "2"] -cli.plot() # Calls the plot function directly ``` -## Troubleshooting +The script will create a directory called `lanczos_input` containing the necessary input files for the C++ executable, which can then be transferred to the cluster and used to run the Lanczos algorithm. +The C executable must be run inside the directory containing the input files, passing as only argument the `root_name` used in the preparation step, in the following way -### "File not found" Errors -- Ensure file paths are correct -- `.npz` files must contain Lanczos object data -- `.abc` files must have 3 columns of numbers +```bash +cd lanczos_input +mpirun -np 16 /path/to/tdscha-lanczos.x lanzos_ir_xpol > lanczos.stdout +``` -### Plotting Issues -- Install matplotlib: `pip install matplotlib` -- Set backend for headless systems: `export MPLBACKEND=Agg` -- For terminator plots, use convergence analysis tool +The parameters for the Lanczos run can also by edited manually, as they are written in a clear text json format called `root_name.json` (where `root_name` is the prefix used in the preparation step). -### Conversion Errors -- Ensure stdout contains Lanczos coefficient lines -- Check C++ executable compiled correctly -- Verify no extraneous output in stdout -### Hessian Convergence Issues -- Step files must follow naming convention -- Initial status file must be `.json` or `.npz` -- Reference dynamical matrix must have correct prefix +## Advanced Usage -## Environment Variables +### Custom Frequency Ranges -- `MPLBACKEND`: Matplotlib backend (e.g., `Agg` for headless) -- `OMP_NUM_THREADS`: OpenMP threads for C++ executable -- `MPIEXEC`: MPI executable path for C++ version +For high-resolution plots: +```bash +# High resolution: 0-200 cm⁻¹, 0.5 cm⁻¹ smearing +tdscha-plot-data lanczos.npz 0 200 0.5 +``` ## See Also - [Quick Start Guide](quickstart.md) for complete workflows - [Examples](examples.md) for practical usage -- [API Documentation](api/dynamical_lanczos.md) for Python interface \ No newline at end of file +- [API Documentation](api/dynamical_lanczos.md) for Python interface diff --git a/docs/static-hessian.md b/docs/static-hessian.md index 922ceb86..0ce0c7cf 100644 --- a/docs/static-hessian.md +++ b/docs/static-hessian.md @@ -2,101 +2,69 @@ The `StaticHessian` class computes the free energy Hessian matrix (second derivative of free energy with respect to atomic displacements) for large systems using sparse linear algebra. It exploits Lanczos-based inversion to include fourth-order anharmonic contributions efficiently. -## Purpose and Use Cases +The result is, in principle, equivalent to what you obtain from the `sscha` package `get_free_energy_hessian`, but by exploiting the sparsity of the anharmonic interactions, it can be applied to much larger systems (hundreds of modes) that are out of reach for direct diagonalization employed in `get_free_energy_hessian`, especially when the fourth-order contributions are significant. -The free energy Hessian provides: +The basic idea is to compute the full free energy Hessian matrix, defined by [R Bianco et al, Physical Review B 96, 014111 (2017)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.014111), in equation (21). -1. **Thermodynamic stability** - Eigenvalues determine stability at given temperature -2. **Anharmonic phonons** - Renormalized frequencies beyond harmonic approximation -3. **Elastic constants** - Second derivative w.r.t. strain -4. **Phase transitions** - Soft modes and instability analysis - -**When to use StaticHessian**: -- Systems too large for full dynamical Lanczos -- Need only static ($\omega=0$) response -- Require full Hessian matrix (not just diagonal) -- Including fourth-order contributions is essential +$$ +\frac{\partial^2 F}{\partial R \partial R} = \Phi + \stackrel{(3)}{\Phi} \cdot \Lambda \left[1 - \stackrel{(4)}{\Phi}\Lambda\right]^{-1} \cdot \stackrel{(3)}{\Phi} +$$ -## Theory +where $\Phi$ is the SSCHA auxiliary force constant matrix, $\stackrel{(3)}{\Phi}$ and $\stackrel{(4)}{\Phi}$ are the third and fourth order force constant tensors, and $\Lambda$ is proportional to the static limit of the two phonons free propagator (of the SSCHA auxiliary phonons). +The inversion in the square brakets is extremely memory demanding, as both $\stackref{(4)}{\Phi}$ and $\Lambda$ are dense tensors in the $N_\text{modes}^2\times N_\text{modes}^2$ space. +For this reason, the direct approach scales as $N_\text{modes}^4$ in memory and $N_\text{modes}^6$ in time, which becomes easily prohibitive for systems with more than 50 atoms in the supercell. -The free energy Hessian $H_{ij} = \frac{\partial^2 F}{\partial u_i \partial u_j}$ satisfies: +The `tdscha` package offers an alternative approach, by reformulating the free energy Hessian as the inverse of the static limit of the dynamical Green's function, as proved by [L Monacelli, Physical Review B 112, 014109 (2025)](https://journals.aps.org/prb/abstract/10.1103/8611-5k5v). $$ -H = \Phi^{(2)} - \Phi^{(4)} : G +\frac{\partial^2 F}{\partial R_a \partial R_b} = \lim_{\omega \to 0} \chi_{R_aR_b}(\omega)^{-1} $$ -where: -- $\Phi^{(2)}$ is the harmonic force constant matrix -- $\Phi^{(4)}$ is the fourth-order force constant tensor -- $G$ is the static limit of the Green's function -- $:$ denotes tensor contraction +The purpose of this module is to compute the free energy Hessian by applying Lanczos-based Krylov subspace methods to compute the inverse of $\chi(\omega=0)$. -The equation is solved via the linear system: +## Purpose and Use Cases -$$ -L \cdot x = b -$$ +The free energy Hessian provides: -with: -- $L$: Linear operator containing $\Phi^{(2)}$ and $\Phi^{(4)}$ -- $x$: Vector representation of $G$ and auxiliary tensors -- $b$: Right-hand side from harmonic problem +1. **Thermodynamic stability** - Eigenvalues determine stability at given temperature +2. **Anharmonic phonons** - Renormalized frequencies beyond harmonic approximation +4. **Phase transitions** - Soft modes and instability analysis + +**When to use StaticHessian**: +- Systems too large for the direct matrix inversion in `get_free_energy_hessian` +- Including fourth-order contributions is essential ## Basic Usage -### Initialization +### Initialization and Running ```python import tdscha.StaticHessian import sscha.Ensemble -# From ensemble -hessian = tdscha.StaticHessian.StaticHessian(ensemble) +# Load the ensemble from a previous SSCHA calculation +start_dyn = CC.Phonons.Phonons("dyn_", nqirr=3) +ensemble = sscha.Ensemble.Ensemble(start_dyn, 300) # Temperature in K +ensemble.load_dir("ensemble", 1) # Load from directory, sscha iteration 1 +final_dyn = CC.Phonons.Phonons("final_dyn_", nqirr=3) # load the converged dynamical matrix +ensemble.update_weights(final_dyn, 300) # Update the ensemble -# Or initialize later -hessian = tdscha.StaticHessian.StaticHessian() -hessian.init(ensemble, verbose=True) -``` -**Example with test data**: - -# doctest: +ELLIPSIS ->>> from tdscha.testing.test_data import load_test_ensemble ->>> import tdscha.StaticHessian ->>> ens = load_test_ensemble(n_configs=5) ->>> hessian = tdscha.StaticHessian.StaticHessian(ens); print(f"Initialized StaticHessian with {hessian.lanczos.n_modes} modes") -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. -Generating Real space force constant matrix... -Time to generate the real space force constant matrix: ... s -TODO: the last time could be speedup with the FFT algorithm. -Time to get the symmetries [...] from spglib: ... s -Time to convert symmetries in the polarizaion space: ... s -Time to create the block_id array: ... s -Initialized StaticHessian with ... modes - -**Parameters**: -- `ensemble`: SSCHA ensemble object -- `verbose`: Print memory usage and progress -- `lanczos_input`: Dictionary passed to embedded `Lanczos` object - -### Running the Calculation +# Initialize the Hessian calculation +hessian = tdscha.StaticHessian.StaticHessian(ensemble) -```python -hessian.run(n_steps=200, +# Un the Hessianminimization +hessian.run_no_mode_mixing(n_steps=200, save_dir="hessian_steps", - threshold=1e-6, - algorithm="cg-prec", - extra_options={}) + restart_from_file=False) ``` -**Parameters**: -- `n_steps`: Maximum minimization steps -- `save_dir`: Directory for saving convergence steps -- `threshold`: Convergence threshold for residual -- `algorithm`: Minimization algorithm (see below) -- `extra_options`: Algorithm-specific options +Compute the Hessian matrix assuming that the polarization vectors do not change from the equilibrium SSCHA result (where instead polarization vectors are relaxed). This is usually a very good approximation. + +The flag `restart_from_file` allows to restart from a previous calculation, by loading the last saved vector in `save_dir`. If `False`, the calculation starts from the initial guess (which is the SSCHA dynamical matrix). + +This method calls the `DynamicalLanczos` on every phonon mode, to retrive the static limit of the dynamical Green's function. You can pass any keyword argument accepted by the constructor of `DynamicalLanczos` via the `lanczos_input` dictionary argument of the `StaticHessian` constructor. + ### Retrieving Results @@ -104,237 +72,19 @@ hessian.run(n_steps=200, # Get Hessian as Phonons object (with q-points) hessian_phonons = hessian.retrieve_hessian() -# Get raw matrix in supercell (no q-points) -hessian_matrix = hessian.retrieve_hessian(noq=True) - # Extract frequencies w, pols = hessian_phonons.DiagonalizeSupercell() -``` - -## Algorithms - -### Preconditioned Conjugate Gradient (`"cg-prec"`) - -**Default and recommended**. Uses $L^{1/2}$ as preconditioner: - -```python -hessian.run(algorithm="cg-prec", threshold=1e-8) -``` - -**Pros**: Fast convergence, minimal memory -**Cons**: Requires preconditioner application - -### Standard Conjugate Gradient (`"cg"`) - -```python -hessian.run(algorithm="cg", threshold=1e-8) -``` - -**Pros**: Simpler, no preconditioner needed -**Cons**: Slower convergence for ill-conditioned systems - -### Full Orthogonalization Method (`"fom"`) -Requires specifying Krylov subspace dimension: - -```python -hessian.run(algorithm="fom", - extra_options={"Krylov_dimension": 50}) -``` - -**Pros**: Better convergence for difficult systems -**Cons**: Higher memory usage - -## Convergence Monitoring - -### Step-by-Step Saving - -```python -hessian.run(n_steps=100, save_dir="convergence") -``` - -Files saved in `save_dir`: -- `hessian_calculation_stepXXXXX.dat` - Vector at step X -- `hessian_calculation.json` - Final status (JSON) -- `hessian_calculation` - Final status (NumPy) - -### Plotting Convergence - -Use the CLI tool: - -```bash -tdscha-hessian-convergence convergence/ hessian_calculation -``` - -This plots eigenvalue convergence vs minimization steps. - -### Manual Convergence Check - -```python -import numpy as np - -# Load step files -steps = [] -for i in range(n_steps): - hessian.vector = np.loadtxt(f"convergence/step_{i:05d}.dat") - H = hessian.retrieve_hessian(noq=True) - w2 = np.linalg.eigvalsh(H) - steps.append(np.sqrt(np.abs(w2)) * np.sign(w2)) -``` - -## Advanced Features - -### No Mode-Mixing Approximation - -Neglects mode-mixing (bubble diagrams) for faster computation: - -```python -hessian_nomix = hessian.run_no_mode_mixing( - nsteps=100, - save_dir="no_mixing", - restart_from_file=False -) -``` - -**When to use**: Quick estimates, large systems where mode-mixing is weak -**Limitation**: Neglects phonon-phonon scattering diagrams - -### Custom Initial Guess - -```python -# Start from harmonic approximation -hessian.preconitioned = False # Use Φ^(2) as initial guess -hessian.init(ensemble) - -# Or start from previous calculation -hessian.load_status("previous_calc.json") -hessian.run(n_steps=50) # Continue from loaded state -``` - -### Memory Optimization - -The algorithm stores vectors of size: -$$ -N_\text{vec} = N_\text{modes} + \frac{N_\text{modes}(N_\text{modes}+1)}{2} + \frac{N_\text{modes}(N_\text{modes}^2+3N_\text{modes}+2)}{6} -$$ - -**Memory estimate**: -```python -n_modes = hessian.lanczos.n_modes -n_g = (n_modes * (n_modes + 1)) // 2 -n_w = (n_modes * (n_modes**2 + 3*n_modes + 2)) // 6 -memory_gb = (n_g + n_w) * 8 * 3 / 1024**3 # 3 working arrays -``` - -## Complete Example - -```python -import cellconstructor as CC -import sscha.Ensemble -import tdscha.StaticHessian -import numpy as np - -# 1. Load ensemble -dyn = CC.Phonons.Phonons("dyn_", 1) -ens = sscha.Ensemble.Ensemble(dyn, 300) -ens.load("ensemble/", 1, 5000) - -# 2. Initialize StaticHessian -hessian = tdscha.StaticHessian.StaticHessian( - ensemble=ens, - verbose=True, - lanczos_input={"use_wigner": False} # Options for embedded Lanczos -) - -# 3. Run minimization -print("Memory estimate:", hessian.vector.nbytes * 3 / 1024**3, "GB") -hessian.run(n_steps=200, save_dir="hessian_out", threshold=1e-7) - -# 4. Analyze results -hessian_phonons = hessian.retrieve_hessian() -w, pols = hessian_phonons.DiagonalizeSupercell() - -# Remove translations -masses = hessian_phonons.structure.get_masses_array() -trans = CC.Methods.get_translations(pols, masses) -w_nontrans = w[~trans] - -print(f"Hessian frequencies: {w_nontrans * CC.Units.RY_TO_CM}") -print(f"Min frequency: {np.min(w_nontrans) * CC.Units.RY_TO_CM:.1f} cm⁻¹") - -# 5. Check convergence -if hessian.verbose: - print("Calculation converged!") +# Save in quantum espresso format +hessian_phonons.save_qe("hessian_dynmat_") ``` ## Parallel Execution -StaticHessian uses the embedded Lanczos object for parallelization: - -```python -# MPI parallelization -hessian = tdscha.StaticHessian.StaticHessian( - ensemble=ens, - lanczos_input={"mode": DL.MODE_FAST_MPI} -) - -# Julia fast mode -hessian = tdscha.StaticHessian.StaticHessian( - ensemble=ens, - lanczos_input={"mode": DL.MODE_FAST_JULIA} -) -``` +StaticHessian uses the embedded Lanczos object for parallelization, therefore you can just submit the calculation with MPI, and the Lanczos will automatically distribute the calculation across processors. -Run with MPI: ```bash mpirun -np 4 python hessian_calculation.py ``` -## Comparison with Dynamical Lanczos - -| Aspect | StaticHessian | DynamicalLanczos | -|--------|---------------|------------------| -| **Output** | Static ($\omega=0$) Hessian | Full $\chi(\omega)$ | -| **Memory** | Lower (sparse algebra) | Higher (Lanczos basis) | -| **Speed** | Faster for Hessian only | Slower (computes all $\omega$) | -| **Use case** | Stability analysis, elastic constants | Spectra, dynamical properties | - -## Troubleshooting - -### Slow Convergence -- Increase `n_steps` (100-500 typical) -- Try `algorithm="fom"` with larger Krylov dimension -- Check if system is near instability (very small eigenvalues) - -### Memory Issues -- Use `select_modes` in `lanczos_input` to exclude high-frequency modes -- Consider `run_no_mode_mixing` for large systems -- Use MPI parallelization to distribute memory - -### Numerical Instability -- Ensure ensemble is well-converged -- Check weights are positive (no numerical issues) -- Try different `algorithm` or preconditioning - -## Related Methods - -### Bianco Algorithm - -The implementation follows the "Bianco algorithm" for static Hessian computation, which: -1. Reformulates Hessian as linear system -2. Uses Krylov subspace methods for inversion -3. Exploits sparsity of anharmonic interactions - -### Connection to SSCHA - -The Hessian is the second derivative of SSCHA free energy: -$$ -H_{ij} = \frac{\partial^2 F_\text{SCHA}}{\partial R_i \partial R_j} -$$ -where $F_\text{SCHA}$ is minimized by the SSCHA self-consistent procedure. - -## References -1. **Bianco et al.** - Static Hessian algorithm for anharmonic systems -2. **Monacelli et al., PRB** - Free energy derivatives in SCHA -3. SSCHA documentation for equilibrium properties \ No newline at end of file From 8f66155865304b0ef4e96c3ef591ca141b170e0a Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 10:42:55 +0100 Subject: [PATCH 16/18] Fixed two tests to use the new Wigner representation --- tests/test_julia/test_julia.py | 11 ++++++----- tests/test_lanczos_fast/test_julia_lf.py | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/tests/test_julia/test_julia.py b/tests/test_julia/test_julia.py index b24550cd..5bb8ff9a 100644 --- a/tests/test_julia/test_julia.py +++ b/tests/test_julia/test_julia.py @@ -29,7 +29,7 @@ def test_lanczos(): lanc = DL.Lanczos(ens) - N_STEPS = 5 + N_STEPS = 10 lanc.ignore_harmonic = False lanc.ignore_v3 = False lanc.ignore_v4 = False @@ -37,12 +37,13 @@ def test_lanczos(): lanc.init(use_symmetries = True) lanc.prepare_mode(10) - lanc.run_FT(2*N_STEPS, save_dir = 'julia', debug = False) + lanc.run_FT(N_STEPS, save_dir = 'julia', debug = False) # HARDCODE the last c value - C_LAST_GOOD = 6.59744344e-07 - - assert np.abs(lanc.c_coeffs[4] - C_LAST_GOOD) / np.abs(C_LAST_GOOD) < 1e-6, "CVALUE CALCULATED: {} | EXPECTED C VALUE: {}".format(lanc.c_coeffs[4], C_LAST_GOOD) + gf = lanc.get_green_function_continued_fraction(np.array([0]), smearing = 0) + w = np.sign(np.real(gf)) * np.sqrt(np.abs(1./np.real(gf))) * CC.Units.RY_TO_CM + w_reference = -36.93203026 + assert np.isclose(w, w_reference, rtol = 1e-3), "TEST FAILED: LAST C VALUE CALCULATED: {} | EXPECTED C VALUE: {}".format(w, w_reference) if __name__ == "__main__": diff --git a/tests/test_lanczos_fast/test_julia_lf.py b/tests/test_lanczos_fast/test_julia_lf.py index b24550cd..5cbd2547 100644 --- a/tests/test_lanczos_fast/test_julia_lf.py +++ b/tests/test_lanczos_fast/test_julia_lf.py @@ -28,7 +28,7 @@ def test_lanczos(): ens.load_bin("data", 1) - lanc = DL.Lanczos(ens) + lanc = DL.Lanczos(ens, use_wigner=False) N_STEPS = 5 lanc.ignore_harmonic = False lanc.ignore_v3 = False From fb015c8e55d7864b8fce37c45eb860b273529504 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 10:49:23 +0100 Subject: [PATCH 17/18] Added a docs validation --- scripts/validate_docs.py | 546 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 546 insertions(+) create mode 100644 scripts/validate_docs.py diff --git a/scripts/validate_docs.py b/scripts/validate_docs.py new file mode 100644 index 00000000..832a4619 --- /dev/null +++ b/scripts/validate_docs.py @@ -0,0 +1,546 @@ +#!/usr/bin/env python3 +""" +Validate documentation code snippets. + +This script extracts Python code blocks from markdown documentation files +and validates their syntax. It can also optionally test imports and basic +execution (for simple examples). + +Usage: + python scripts/validate_docs.py [--test-imports] [--execute] [files...] + +If no files are specified, checks all .md files in docs/ directory. +""" + +import argparse +import ast +import doctest +import io +import os +import re +import sys +import traceback +import contextlib +from pathlib import Path +from typing import List, Tuple, Optional, Dict, Any + +# Patterns to skip validation for +SKIP_PATTERNS = [ + r"^\s*#\s*TODO", + r"^\s*#\s*FIXME", + r"^\s*#\s*Example output", + r"^\s*#\s*Output:", + r"^\s*#\s*>>>", # Doctest examples + r"^\s*\.\.\.", # Doctest continuation +] + +# Imports that may not be available in validation environment +# but are valid in actual usage +ALLOWED_MISSING_IMPORTS = { + "cellconstructor", + "cellconstructor.Phonons", + "cellconstructor.symmetries", + "cellconstructor.Settings", + "sscha", + "sscha.Ensemble", + "sscha.Parallel", + "tdscha", + "tdscha.DynamicalLanczos", + "tdscha.StaticHessian", + "tdscha.Tools", + "tdscha.Perturbations", + "tdscha.Parallel", + "sscha_HP_odd", + "julia", + "julia.Main", + "mpi4py", + "spglib", +} + + +def extract_python_blocks(filepath: Path) -> List[Tuple[int, str]]: + """Extract Python code blocks from markdown file. + + Returns list of (line_number, code) tuples. + """ + blocks = [] + with open(filepath, 'r', encoding='utf-8') as f: + lines = f.readlines() + + in_code_block = False + code_block_lines = [] + block_start_line = 0 + language = "" + + for i, line in enumerate(lines, 1): + # Check for code block start + match = re.match(r'^```(\w*)', line.strip()) + if match and not in_code_block: + in_code_block = True + language = match.group(1).lower() + block_start_line = i + code_block_lines = [] + elif line.strip() == '```' and in_code_block: + in_code_block = False + if language == 'python' and code_block_lines: + blocks.append((block_start_line, '\n'.join(code_block_lines))) + language = "" + code_block_lines = [] + elif in_code_block: + code_block_lines.append(line.rstrip('\n')) + + return blocks + + +def validate_syntax(code: str, line_offset: int = 0) -> List[str]: + """Validate Python syntax, return list of errors.""" + errors = [] + try: + ast.parse(code) + except SyntaxError as e: + # Adjust line number for markdown context + adjusted_line = e.lineno + line_offset if e.lineno else line_offset + errors.append(f" Line {adjusted_line}: {e.msg}") + + return errors + + +def check_imports(code: str) -> List[str]: + """Check for potentially problematic imports.""" + warnings = [] + + # Simple import detection + import_pattern = r'^\s*(import|from)\s+(\w+)' + for line in code.split('\n'): + match = re.match(import_pattern, line) + if match: + module = match.group(2) + if module in ALLOWED_MISSING_IMPORTS: + warnings.append(f" May require {module} to be installed") + + return warnings + + +def should_skip(code: str) -> bool: + """Determine if code block should be skipped.""" + lines = code.split('\n') + for line in lines: + for pattern in SKIP_PATTERNS: + if re.search(pattern, line): + return True + return False + + +def extract_doctest_blocks(filepath: Path) -> List[Tuple[int, List[doctest.Example]]]: + """Extract doctest blocks from markdown file using doctest parser. + + Returns list of (line_number, list_of_examples) tuples. + Each block contains one or more contiguous doctest examples. + """ + with open(filepath, 'r', encoding='utf-8') as f: + text = f.read() + + parser = doctest.DocTestParser() + try: + # Parse the entire file text + test = parser.get_doctest(text, {}, filepath.name, str(filepath), 0) + except Exception: + # If parsing fails, fall back to simple extraction + return _extract_doctest_blocks_simple(filepath) + + blocks = [] + current_block = [] + current_start = None + last_line = -10 + + for example in test.examples: + # Get the line number (1-indexed) + lineno = example.lineno + + # Check if this example is contiguous with previous one + # (within 5 lines and same paragraph) + if current_block and lineno <= last_line + 5: + # Continue current block + current_block.append(example) + last_line = max(last_line, lineno + example.source.count('\n')) + else: + # Start new block + if current_block: + # Save previous block + blocks.append((current_start, current_block)) + + current_block = [example] + current_start = lineno + last_line = lineno + example.source.count('\n') + + # Add last block + if current_block: + blocks.append((current_start, current_block)) + + return blocks + + +def _parse_doctest_text(text: str) -> List[doctest.Example]: + """Parse doctest text into a list of Example objects.""" + parser = doctest.DocTestParser() + try: + test = parser.get_doctest(text, {}, '', '', 0) + return test.examples + except Exception: + # If parsing fails, return empty list + return [] + +def _extract_doctest_blocks_simple(filepath: Path) -> List[Tuple[int, List[doctest.Example]]]: + """Simple fallback extraction for when doctest parser fails.""" + blocks = [] + with open(filepath, 'r', encoding='utf-8') as f: + lines = f.readlines() + + in_doctest = False + doctest_lines = [] + block_start_line = 0 + + for i, line in enumerate(lines, 1): + stripped = line.rstrip('\n') + + # Check for doctest prompt + if stripped.lstrip().startswith('>>>'): + if not in_doctest: + # Start new doctest block + in_doctest = True + block_start_line = i + doctest_lines = [stripped] + else: + # Continue existing block + doctest_lines.append(stripped) + elif in_doctest and stripped.lstrip().startswith('...'): + # Continuation line + doctest_lines.append(stripped) + elif in_doctest: + # Could be expected output or blank line + if stripped == '': + # Blank line might separate examples + # Check if next line has >>> (we can't see ahead) + # For simplicity, treat blank line as end of block + in_doctest = False + if doctest_lines: + text_block = '\n'.join(doctest_lines) + examples = _parse_doctest_text(text_block) + if examples: + blocks.append((block_start_line, examples)) + doctest_lines = [] + else: + # Expected output, keep in block + doctest_lines.append(stripped) + + # Handle doctest block at end of file + if in_doctest and doctest_lines: + text_block = '\n'.join(doctest_lines) + examples = _parse_doctest_text(text_block) + if examples: + blocks.append((block_start_line, examples)) + + return blocks + + +def _examples_to_text(examples: list) -> str: + """Convert list of doctest.Example objects to text.""" + lines = [] + for ex in examples: + lines.append(ex.source.rstrip()) + if ex.want: + lines.append(ex.want.rstrip()) + return '\n'.join(lines) + + +def create_test_globals() -> Dict[str, Any]: + """Create globals dictionary for doctest execution. + + Includes tdscha modules and test utilities. + """ + globals_dict = {} + + # Add standard imports + import numpy as np + globals_dict['np'] = np + globals_dict['numpy'] = np + + # Try to import tdscha normally (if installed) + tdscha_module = None + try: + import tdscha + tdscha_module = tdscha + except ImportError: + # Fallback: create tdscha module from Modules directory + import sys + import os + import types + # Project root is parent directory of this script's directory + script_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.dirname(script_dir) + modules_dir = os.path.join(project_root, 'Modules') + if os.path.exists(os.path.join(modules_dir, '__init__.py')): + # Insert project root to sys.path (so Modules can be imported) + sys.path.insert(0, project_root) + try: + # Import Modules and its submodules + import Modules + # Import submodules (they may import dependencies that could fail) + import Modules.DynamicalLanczos + import Modules.StaticHessian + import Modules.Tools + import Modules.Perturbations + import Modules.Parallel + import Modules.testing + # Create a new module to act as tdscha + tdscha_module = types.ModuleType('tdscha') + # Assign submodules as attributes + tdscha_module.DynamicalLanczos = Modules.DynamicalLanczos + tdscha_module.StaticHessian = Modules.StaticHessian + tdscha_module.Tools = Modules.Tools + tdscha_module.Perturbations = Modules.Perturbations + tdscha_module.Parallel = Modules.Parallel + tdscha_module.testing = Modules.testing + # Install in sys.modules for import statements + sys.modules['tdscha'] = tdscha_module + sys.modules['tdscha.DynamicalLanczos'] = Modules.DynamicalLanczos + sys.modules['tdscha.StaticHessian'] = Modules.StaticHessian + sys.modules['tdscha.Tools'] = Modules.Tools + sys.modules['tdscha.Perturbations'] = Modules.Perturbations + sys.modules['tdscha.Parallel'] = Modules.Parallel + sys.modules['tdscha.testing'] = Modules.testing + sys.modules['tdscha.testing.test_data'] = Modules.testing.test_data + except ImportError as e: + tdscha_module = None + finally: + sys.path.pop(0) + + if tdscha_module is not None: + globals_dict['tdscha'] = tdscha_module + + # Try to import submodules and assign them to tdscha_module attributes + submodules = [ + 'DynamicalLanczos', + 'StaticHessian', + 'Tools', + 'Perturbations', + 'Parallel', + 'testing' + ] + + for submod_name in submodules: + try: + full_name = f'tdscha.{submod_name}' + # Import the submodule + imported = __import__(full_name, fromlist=['']) + # Assign as attribute to tdscha_module if not already present + if not hasattr(tdscha_module, submod_name): + setattr(tdscha_module, submod_name, imported) + # Add to globals_dict for direct access + globals_dict[full_name] = imported + except ImportError: + # Submodule not available, skip + pass + + # Try to add cellconstructor and sscha (these are external dependencies) + try: + import cellconstructor as CC + globals_dict['CC'] = CC + globals_dict['cellconstructor'] = CC + globals_dict['cellconstructor.Phonons'] = CC.Phonons + except ImportError: + pass + + try: + import sscha + globals_dict['sscha'] = sscha + globals_dict['sscha.Ensemble'] = sscha.Ensemble + except ImportError: + pass + + # Try to add test_data utilities + if tdscha_module is not None: + try: + # Import from tdscha.testing.test_data (now available via sys.modules) + from tdscha.testing.test_data import ( + load_test_ensemble, create_test_lanczos, get_test_mode_frequencies + ) + globals_dict['load_test_ensemble'] = load_test_ensemble + globals_dict['create_test_lanczos'] = create_test_lanczos + globals_dict['get_test_mode_frequencies'] = get_test_mode_frequencies + except ImportError: + # Fallback: try to import from source directory + import sys + import os + script_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.dirname(script_dir) + modules_dir = os.path.join(project_root, 'Modules') + if os.path.exists(os.path.join(modules_dir, '__init__.py')): + sys.path.insert(0, project_root) + try: + # Import Modules.testing.test_data + import Modules.testing.test_data + from Modules.testing.test_data import ( + load_test_ensemble, create_test_lanczos, get_test_mode_frequencies + ) + globals_dict['load_test_ensemble'] = load_test_ensemble + globals_dict['create_test_lanczos'] = create_test_lanczos + globals_dict['get_test_mode_frequencies'] = get_test_mode_frequencies + # Also make available via tdscha.testing for import statements + sys.modules['tdscha.testing'] = Modules.testing + sys.modules['tdscha.testing.test_data'] = Modules.testing.test_data + except ImportError: + pass + finally: + sys.path.pop(0) + + return globals_dict + + +def run_doctest_block(examples: List[doctest.Example], globals_dict: Dict[str, Any], + verbose: bool = False) -> doctest.TestResults: + """Run a single doctest block.""" + # Create a doctest runner with ELLIPSIS flag to ignore variable output + runner = doctest.DocTestRunner(verbose=verbose, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) + + # Create a test from the examples + test = doctest.DocTest( + examples=examples, + globs=globals_dict, + name='', + filename='', + lineno=0, + docstring='' + ) + + # Run the test + runner.run(test) + return runner.summarize() + + +def validate_file(filepath: Path, test_imports: bool = False, + run_doctests: bool = False, verbose: bool = False) -> bool: + """Validate Python code blocks and optionally run doctests.""" + all_valid = True + + # 1. Validate regular Python code blocks + print(f"Validating {filepath}...") + blocks = extract_python_blocks(filepath) + + if blocks: + for block_num, (line_num, code) in enumerate(blocks, 1): + if should_skip(code): + print(f" Code block {block_num} (line {line_num}): Skipped") + continue + + print(f" Code block {block_num} (line {line_num}): ", end="") + + # Validate syntax + errors = validate_syntax(code, line_num - 1) + + if errors: + print("FAILED") + all_valid = False + for error in errors: + print(f" {error}") + else: + print("Syntax OK") + + # Check imports if requested + if test_imports: + warnings = check_imports(code) + if warnings: + print(f" Import warnings:") + for warning in warnings: + print(f" {warning}") + else: + print(f" No Python code blocks found") + + # 2. Run doctests if requested + if run_doctests: + print(f" Running doctests...") + doctest_blocks = extract_doctest_blocks(filepath) + + if not doctest_blocks: + print(f" No doctest blocks found") + else: + # Create test globals for doctests + globals_dict = create_test_globals() + + for block_num, (line_num, examples) in enumerate(doctest_blocks, 1): + print(f" Doctest block {block_num} (line {line_num}): ", end="") + + try: + # Run the doctest block + failure_count, test_count = run_doctest_block( + examples, globals_dict, verbose + ) + + if failure_count == 0: + print(f"PASSED ({test_count} examples)") + else: + print(f"FAILED ({failure_count}/{test_count} failures)") + all_valid = False + + # For verbose output, show the failing doctest + if verbose: + print(f" Doctest examples:") + for i, ex in enumerate(examples, 1): + print(f" Example {i}:") + print(f" Source: {ex.source.rstrip()}") + if ex.want: + print(f" Expected: {ex.want.rstrip()}") + + except Exception as e: + print(f"ERROR (exception: {e})") + all_valid = False + if verbose: + import traceback + traceback.print_exc() + + return all_valid + + +def main(): + parser = argparse.ArgumentParser(description="Validate documentation code snippets") + parser.add_argument("files", nargs="*", help="Markdown files to validate") + parser.add_argument("--test-imports", action="store_true", + help="Check for potentially missing imports") + parser.add_argument("--execute", action="store_true", + help="Execute code blocks (dangerous, not implemented)") + parser.add_argument("--doctest", action="store_true", + help="Run doctests (>>> examples) using test data") + parser.add_argument("--verbose", "-v", action="store_true", + help="Show more detailed output") + + args = parser.parse_args() + + # Determine files to validate + if args.files: + files = [Path(f) for f in args.files] + else: + docs_dir = Path("docs") + files = list(docs_dir.glob("*.md")) + list(docs_dir.glob("**/*.md")) + + # Validate each file + all_valid = True + for filepath in files: + if not filepath.exists(): + print(f"Warning: File not found: {filepath}") + continue + + valid = validate_file(filepath, args.test_imports, args.doctest, args.verbose) + if not valid: + all_valid = False + + if all_valid: + print("\nAll code blocks validated successfully!") + return 0 + else: + print("\nSome code blocks failed validation.") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) \ No newline at end of file From d03f1940fa633730387961a7784ece5301ecfa22 Mon Sep 17 00:00:00 2001 From: Lorenzo Monacelli Date: Sun, 15 Feb 2026 10:54:01 +0100 Subject: [PATCH 18/18] Add GitHub Pages CI workflow for documentation Add docs.yml workflow to build and deploy MkDocs documentation to GitHub Pages on pushes to main. Remove references to missing API doc pages (tools, perturbations, parallel) from mkdocs.yml nav to avoid build failures with --strict. Co-Authored-By: Claude Opus 4.6 --- .github/workflows/docs.yml | 78 ++++++++++++++++++++++++++++++++++++++ mkdocs.yml | 3 -- 2 files changed, 78 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/docs.yml diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..3d21caff --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,78 @@ +name: Deploy Documentation + +on: + push: + branches: [main] + paths: + - 'docs/**' + - 'mkdocs.yml' + - 'Modules/**' + - '.github/workflows/docs.yml' + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install system dependencies + run: | + sudo apt-get update + sudo apt-get install -y gfortran libblas-dev liblapack-dev mpich + + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + pip install meson meson-python ninja + pip install numpy scipy mpi4py spglib + + - name: Install CellConstructor and python-sscha + run: | + git clone --depth 1 https://github.com/SSCHAcode/CellConstructor.git + pip install --no-build-isolation ./CellConstructor + + git clone --depth 1 https://github.com/SSCHAcode/python-sscha.git + pip install --no-build-isolation ./python-sscha + + - name: Install tdscha + run: pip install --no-build-isolation . + + - name: Install MkDocs and plugins + run: pip install mkdocs mkdocs-material mkdocstrings[python] + + - name: Build documentation + run: mkdocs build --strict + + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: site/ + + deploy: + needs: build + runs-on: ubuntu-latest + + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/mkdocs.yml b/mkdocs.yml index e6de7f5a..ea759c8a 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -51,9 +51,6 @@ nav: - API Reference: - DynamicalLanczos: api/dynamical_lanczos.md - StaticHessian: api/static_hessian.md - - Tools: api/tools.md - - Perturbations: api/perturbations.md - - Parallel: api/parallel.md # Extra JavaScript for MathJax extra_javascript: