Source code for yt.frontends.chimera.io

"""
Chimera-specific IO functions



"""

import numpy as np
import unyt as un

from yt.frontends.chimera.data_structures import _find_files
from yt.utilities.io_handler import BaseIOHandler
from yt.utilities.logger import ytLogger as mylog
from yt.utilities.on_demand_imports import _h5py as h5py


[docs] class ChimeraIOHandler(BaseIOHandler): _particle_reader = False _dataset_type = "chimera" def __init__(self, ds): super().__init__(ds) self._handle = ds._handle self.filename = ds.filename def _read_particle_coords(self, chunks, ptf): # This needs to *yield* a series of tuples of (ptype, (x, y, z)). # chunks is a list of chunks, and ptf is a dict where the keys are # ptypes and the values are lists of fields. pass def _read_particle_fields(self, chunks, ptf, selector): # This gets called after the arrays have been allocated. It needs to # yield ((ptype, field), data) where data is the masked results of # reading ptype, field and applying the selector to the data read in. # Selector objects have a .select_points(x,y,z) that returns a mask, so # you need to do your masking here. pass def _read_fluid_selection(self, chunks, selector, fields, size): rv = {} nodal_fields = [] for field in fields: finfo = self.ds.field_info[field] nodal_flag = finfo.nodal_flag if np.any(nodal_flag): num_nodes = 2 ** sum(nodal_flag) rv[field] = np.empty((size, num_nodes), dtype="=f8") nodal_fields.append(field) else: rv[field] = np.empty(size, dtype="=f8") ind = 0 for field, mesh, data in self.io_iter(chunks, fields): if data is None: continue else: ind += mesh.select(selector, data.flatten(), rv[field], ind) # caches return rv
[docs] def io_iter(self, chunks, fields): for n, chunk in enumerate(chunks): file = _find_files(self.filename) with h5py.File(file[n], "r") as f: # Generates mask according to the "ongrid_mask" variable m = int(file[n][-5:-3]) - 1 k = f["fluid"]["entropy"].shape[0] mask_0 = f["mesh"]["ongrid_mask"][k * m : k * (m + 1), :] if f["mesh"]["array_dimensions"][2] > 1: nrd = f["mesh"]["array_dimensions"][0] - 2 else: nrd = f["mesh"]["array_dimensions"][0] mask = np.repeat(mask_0[:, :, np.newaxis], nrd, axis=2).transpose() specials = ( "abar", "e_rms_1", "e_rms_2", "e_rms_3", "e_rms_4", "lumin_1", "lumin_2", "lumin_3", "lumin_4", "num_lumin_1", "num_lumin_2", "num_lumin_3", "num_lumin_4", "shock", "nse_c", ) for field in fields: # Reads data by locating subheading ftype, fname = field a_name_2 = [i.decode("utf-8") for i in f["abundance"]["a_name"]] a_name_dict = {name.strip(): name for name in a_name_2} if fname not in specials: if fname in f["fluid"]: ds = f["fluid"][f"{fname}"] elif fname in f["abundance"]: ds = f["abundance"][f"{fname}"] elif fname in a_name_dict: ind_xn = a_name_2.index(a_name_dict[fname]) ds = f["abundance"]["xn_c"][:, :, :, ind_xn] else: mylog.warning("Invalid field name %s", fname) dat_1 = ds[:, :, :].transpose() elif fname == "nse_c": if np.shape(f["abundance"]["nse_c"]) != np.shape( f["fluid"]["rho_c"] ): ds = f["abundance"]["nse_c"][:, :, 1:] else: ds = f["abundance"]["nse_c"] dat_1 = ds[:, :, :].transpose() elif fname == "abar": xn_c = np.array(f["abundance"]["xn_c"]) a_nuc_rep_c = np.array(f["abundance"]["a_nuc_rep_c"]) a_nuc = np.array(f["abundance"]["a_nuc"]) a_nuc_tile = np.tile( a_nuc, (xn_c.shape[0], xn_c.shape[1], xn_c.shape[2], 1) ) yn_c = np.empty(xn_c.shape) yn_c[:, :, :, :-1] = xn_c[:, :, :, :-1] / a_nuc_tile[:, :, :, :] yn_c[:, :, :, -1] = xn_c[:, :, :, -1] / a_nuc_rep_c[:, :, :] ytot = np.sum(yn_c, axis=3) atot = np.sum(xn_c, axis=3) abar = np.divide(atot, ytot) dat_1 = abar[:, :, :].transpose() elif fname in ("e_rms_1", "e_rms_2", "e_rms_3", "e_rms_4"): dims = f["mesh"]["array_dimensions"] n_groups = f["radiation"]["raddim"][0] n_species = f["radiation"]["raddim"][1] n_hyperslabs = f["mesh"]["nz_hyperslabs"][()] energy_edge = f["radiation"]["unubi"][()] energy_center = f["radiation"]["unui"][()] d_energy = [] for i in range(0, n_groups): d_energy.append(energy_edge[i + 1] - energy_edge[i]) d_energy = np.array(d_energy) e3de = energy_center**3 * d_energy e5de = energy_center**5 * d_energy psi0_c = f["radiation"]["psi0_c"][:] row = np.empty( (n_species, int(dims[2] / n_hyperslabs), dims[1], dims[0]) ) for n in range(0, n_species): numerator = np.sum(psi0_c[:, :, :, n] * e5de, axis=3) denominator = np.sum(psi0_c[:, :, :, n] * e3de, axis=3) row[n][:][:][:] = np.sqrt( numerator / (denominator + 1e-100) ) species = int(fname[-1]) - 1 dat_1 = row[species, :, :, :].transpose() elif fname in ( "lumin_1", "lumin_2", "lumin_3", "lumin_4", "num_lumin_1", "num_lumin_2", "num_lumin_3", "num_lumin_4", ): dims = f["mesh"]["array_dimensions"] n_groups = f["radiation"]["raddim"][0] n_hyperslabs = f["mesh"]["nz_hyperslabs"][()] ergmev = float((1 * un.MeV) / (1 * un.erg)) cvel = float(un.c.to("cm/s")) h = float(un.h.to("MeV * s")) ecoef = 4.0 * np.pi * ergmev / (h * cvel) ** 3 radius = f["mesh"]["x_ef"][()] agr_e = f["fluid"]["agr_e"][()] cell_area_GRcorrected = 4 * np.pi * radius**2 / agr_e**4 psi1_e = f["radiation"]["psi1_e"] energy_edge = f["radiation"]["unubi"][()] energy_center = f["radiation"]["unui"][()] d_energy = [] for i in range(0, n_groups): d_energy.append(energy_edge[i + 1] - energy_edge[i]) d_energy = np.array(d_energy) species = int(fname[-1]) - 1 if fname in ("lumin_1", "lumin_2", "lumin_3", "lumin_4"): eNde = energy_center**3 * d_energy else: eNde = energy_center**2 * d_energy lumin = ( np.sum(psi1_e[:, :, :, species] * eNde, axis=3) * np.tile( cell_area_GRcorrected[1 : dims[0] + 1], (int(dims[2] / n_hyperslabs), dims[1], 1), ) * (cvel * ecoef * 1e-51) ) dat_1 = lumin[:, :, :].transpose() if f["mesh"]["array_dimensions"][2] > 1: data = dat_1[:-2, :, :] # Clips off ghost zones for 3D else: data = dat_1[:, :, :] data = np.ma.masked_where(mask == 0.0, data) # Masks data = np.ma.filled( data, fill_value=0.0 ) # Replaces masked value with 0 yield field, chunk.objs[0], data pass
def _read_chunk_data(self, chunk, fields): # This reads the data from a single chunk without doing any selection, # and is only used for caching data that might be used by multiple # different selectors later. For instance, this can speed up ghost zone # computation. pass