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QUDA_PATH for pip download.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1 @@ | ||
| __version__ = "0.9.4" | ||
| __version__ = "0.9.5" |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,207 @@ | ||
| from ctypes import Union, Structure, c_char, c_int, sizeof | ||
| from enum import IntEnum | ||
| import io | ||
| from os import path | ||
| from typing import Sequence | ||
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| import numpy | ||
| from numpy.typing import NDArray | ||
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| class _DimensionType(IntEnum): | ||
| dim_other = 0 | ||
| dim_x = 1 | ||
| dim_y = 2 | ||
| dim_z = 3 | ||
| dim_t = 4 | ||
| dim_d = 5 | ||
| dim_c = 6 | ||
| dim_d2 = 7 | ||
| dim_c2 = 8 | ||
| dim_complex = 9 | ||
| dim_mass = 10 | ||
| dim_smear = 11 | ||
| dim_displacement = 12 | ||
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| dim_s_01 = 13 | ||
| dim_s_02 = 14 | ||
| dim_s_03 = 15 | ||
| dim_s_11 = 16 | ||
| dim_s_12 = 17 | ||
| dim_s_13 = 18 | ||
| dim_d_01 = 19 | ||
| dim_d_02 = 20 | ||
| dim_d_03 = 21 | ||
| dim_d_11 = 22 | ||
| dim_d_12 = 23 | ||
| dim_d_13 = 24 | ||
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| dim_conf = 25 | ||
| dim_operator = 26 | ||
| dim_momentum = 27 | ||
| dim_direction = 28 | ||
| dim_t2 = 29 | ||
| dim_mass2 = 30 | ||
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| dim_column = 31 | ||
| dim_row = 32 | ||
| dim_temporary = 33 | ||
| dim_temporary2 = 34 | ||
| dim_temporary3 = 35 | ||
| dim_temporary4 = 36 | ||
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| dim_errorbar = 37 | ||
| """0 means average, 1 means errorbar, ...""" | ||
|
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| dim_operator2 = 38 | ||
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| dim_param = 39 | ||
| dim_fitleft = 40 | ||
| dim_fitright = 41 | ||
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| dim_jackknife = 42 | ||
| dim_jackknife2 = 43 | ||
| dim_jackknife3 = 44 | ||
| dim_jackknife4 = 45 | ||
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| dim_summary = 46 | ||
| """ | ||
| 0 means average, 1 means standard deviation, 2 means minimal value, 3 means maximum value, 4 means standard error, | ||
| 5 means median, ... | ||
| """ | ||
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| dim_channel = 47 | ||
| dim_channel2 = 48 | ||
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| dim_eigen = 49 | ||
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| dim_d_row = 50 | ||
| """on matrix multiplication, row is contracted with the left operand, col is contracted with the right operand.""" | ||
| dim_d_col = 51 | ||
| dim_c_row = 52 | ||
| dim_c_col = 53 | ||
|
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| dim_parity = 54 | ||
| """dimension for different parities. we use 1/-1 for +/- parities for baryons.""" | ||
|
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| dim_noise = 55 | ||
| dim_evenodd = 56 | ||
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| dim_disp_x = 57 | ||
| dim_disp_y = 58 | ||
| dim_disp_z = 59 | ||
| dim_disp_t = 60 | ||
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| dim_t3 = 61 | ||
| dim_t4 = 62 | ||
| dim_t_source = 63 | ||
| dim_t_current = 64 | ||
| dim_t_sink = 65 | ||
|
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| dim_nothing = 66 | ||
| """do not use this unless for unused data.""" | ||
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| dim_bootstrap = 67 | ||
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| # add new dimensions here and add a string name in xqcd_type_dim_desc[] in io_general.c | ||
| # ... | ||
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| dim_last = 68 | ||
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| class _OneDim(Structure): | ||
| # _pack_ = 1 | ||
| _fields_ = [("type", c_int), ("n_indices", c_int), ("indices", c_int * 1024)] | ||
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| type: int | ||
| n_indices: int | ||
| indices: Sequence[int] | ||
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| class _Head(Structure): | ||
| # _pack_ = 1 | ||
| _fields_ = [("n_dimensions", c_int), ("dimensions", _OneDim * 16)] | ||
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| n_dimensions: int | ||
| dimensions: Sequence[_OneDim] | ||
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| @property | ||
| def dimensions_type(self): | ||
| return tuple([_DimensionType(self.dimensions[i].type)._name_[4:] for i in range(self.n_dimensions)]) | ||
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| @property | ||
| def dimensions_n_indices(self): | ||
| return tuple([self.dimensions[i].n_indices for i in range(self.n_dimensions)]) | ||
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| class _FileType(Union): | ||
| _fields_ = [("head", _Head), ("blank", c_char * 102400)] | ||
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| head: _Head | ||
| blank: bytes | ||
|
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| @property | ||
| def n_dimensions(self): | ||
| return self.head.n_dimensions | ||
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| @n_dimensions.setter | ||
| def n_dimensions(self, value): | ||
| self.head.n_dimensions = value | ||
|
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| @property | ||
| def dimensions(self): | ||
| return self.head.dimensions | ||
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| @property | ||
| def dimensions_type(self): | ||
| return self.head.dimensions_type | ||
|
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| @property | ||
| def dimensions_n_indices(self): | ||
| return self.head.dimensions_n_indices | ||
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| def __repr__(self): | ||
| retval = "" | ||
| for i in range(self.n_dimensions): | ||
| retval += f"{_DimensionType(self.dimensions[i].type)._name_:18s}{self.dimensions[i].n_indices:<6d}( " | ||
| for j in range(self.dimensions[i].n_indices): | ||
| retval += f"{self.dimensions[i].indices[j]} " | ||
| retval += ")\n" | ||
| return retval | ||
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| def read(filename: str): | ||
| filename = path.expanduser(path.expandvars(filename)) | ||
| with open(filename, "rb") as f: | ||
| head = _FileType.from_buffer_copy(f.read(sizeof(_FileType))) | ||
| data = numpy.frombuffer(f.read(), "<f8").reshape(head.dimensions_n_indices) | ||
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| return head, data | ||
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| def write(filename: str, head: _FileType, data: NDArray[numpy.float64]): | ||
| assert head.dimensions_n_indices == data.shape | ||
| assert data.dtype == numpy.float64 | ||
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| filename = path.expanduser(path.expandvars(filename)) | ||
| with open(filename, "wb") as f: | ||
| f.write(bytes(head)) | ||
| f.write(data.tobytes()) | ||
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| class IOGeneral: | ||
| def __init__(self, filename: str): | ||
| self.filename = path.expanduser(path.expandvars(filename)) | ||
| with open(self.filename, "rb") as f: | ||
| self.head = _FileType.from_buffer_copy(f.read(sizeof(_FileType))) | ||
| self.data = None | ||
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| def read(self): | ||
| with open(self.filename, "rb") as f: | ||
| f.seek(sizeof(_FileType), io.SEEK_SET) | ||
| self.data = numpy.frombuffer(f.read(), "<f8").reshape(self.head.dimensions_n_indices) | ||
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| def write(self): | ||
| with open(self.filename, "wb") as f: | ||
| f.write(bytes(self.head)) | ||
| f.write(self.data.reshape(self.head.dimensions_n_indices).tobytes()) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,29 @@ | ||
| import cupy as cp | ||
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| from check_pyquda import weak_field | ||
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| from pyquda import init | ||
| from pyquda_utils import core, io, source | ||
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| xi_0, nu = 2.464, 0.95 | ||
| kappa = 0.115 | ||
| mass = 1 / (2 * kappa) - 4 | ||
| coeff = 1.17 | ||
| coeff_r, coeff_t = 0.91, 1.07 | ||
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| init(None, [4, 4, 4, 8], -1, xi_0 / nu, resource_path=".cache") | ||
| latt_info = core.getDefaultLattice() | ||
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| dslash = core.getDefaultDirac(mass, 1e-12, 1000, xi_0, coeff_t, coeff_r) | ||
| gauge = io.readQIOGauge(weak_field) | ||
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| dslash.loadGauge(gauge) | ||
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| point_source = source.multiFermion(latt_info, "point", [0, 0, 0, 0]) | ||
| propagator = dslash.invertMultiSrc(point_source).toPropagator() | ||
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| dslash.destroy() | ||
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| propagator_chroma = io.readQIOPropagator("pt_prop_1") | ||
| propagator_chroma.toDevice() | ||
| print(cp.linalg.norm(propagator.data - propagator_chroma.data)) |