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Noe Brucy
80
loaders/load_data_arepo.py
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80
loaders/load_data_arepo.py
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# coding: utf-8
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import numpy as np
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import h5py
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from astropy import units as u
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def load_fields_arepo(path):
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"""
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Parameters
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----------
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path : _type_
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_description_
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output : _type_
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_description_
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Returns
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-------
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dict
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A dataset of cells in the following format:
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gas:
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position (Ngas, 3) [kpc], centered
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volume (Ngas) [pc^3]
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velocity (Ngas, 3) [km/s]
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mass (Ngas) [Msun]
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stars:
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position (Nstar, 3) [kpc], centered
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velocity (Nstar, 3) [km/s]
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mass (Nstar) [Msun]
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birth_time (Nstar) [Myr]
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dm:
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position (Ngas, 3) [kpc], centered
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velocity (Ngas, 3) [km/s]
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mass (Ngas) [Msun]
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maps:
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extent (xmin, xmax, ymin, ymax) Coordinates of the edges of the map, centered
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gas_coldens (Nx, Ny) [Msun/pc^2], map of column density
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"""
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snap = h5py.File(path)
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info = snap["Header"].attrs
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gas = snap["PartType0"]
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stars = snap["PartType4"]
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time = info["Time"]
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box_size = info["BoxSize"]
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UnitLength_in_kpc = info["UnitLength_in_cm"] * u.cm.to(u.kpc)
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UnitVelocity_in_km_per_s = info["UnitVelocity_in_cm_per_s"] * u.cm.to(u.km)
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UnitTime_in_Myr = (
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info["UnitLength_in_cm"] / info["UnitVelocity_in_cm_per_s"]
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) * u.s.to(u.Myr)
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UnitMass_in_Msun = info["UnitMass_in_g"] * u.g.to(u.Msun)
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# Create dataset
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data = {
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"header": {"time": time * UnitTime_in_Myr,
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"fluids": ["gas", "stars"],
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"box_size": box_size * UnitLength_in_kpc },
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"gas": {
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"position": (
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np.asarray(gas["CenterOfMass"]) - np.array([0.5, 0.5, 0.5]) * box_size
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)
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* UnitLength_in_kpc,
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"volume": (np.asarray(gas["Masses"]) / np.asarray(gas["Density"]))
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* UnitLength_in_kpc**3,
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"velocity": np.asarray(gas["Velocities"]) * UnitVelocity_in_km_per_s,
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"mass": np.asarray(gas["Masses"]) * UnitMass_in_Msun,
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},
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"stars": {
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"position": (
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np.asarray(stars["Coordinates"]) - np.array([0.5, 0.5, 0.5]) * box_size
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)
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* UnitLength_in_kpc,
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"velocity": np.asarray(stars["Velocities"]) * UnitVelocity_in_km_per_s,
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"mass": np.asarray(stars["Masses"]) * UnitMass_in_Msun,
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"birth_time": np.asarray(stars["StellarFormationTime"]) * UnitTime_in_Myr,
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},
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}
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snap.close()
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return data
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110
loaders/load_data_suez.py
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110
loaders/load_data_suez.py
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# coding: utf-8
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import numpy as np
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from snapshotprocessor import U
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from tables import NoSuchNodeError
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def load_fields(pp):
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"""
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Parameters
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----------
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path : _type_
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_description_
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output : _type_
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_description_
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Returns
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-------
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dict
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A dataset of cells in the following format:
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gas:
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position (Ngas, 3) [kpc], centered
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volume (Ngas) [pc^3]
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velocity (Ngas, 3) [km/s]
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mass (Ngas) [Msun]
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stars:
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position (Nstar, 3) [kpc], centered
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velocity (Nstar, 3) [km/s]
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mass (Nstar) [Msun]
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birth_time (Nstar) [Myr]
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dm:
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position (Ngas, 3) [kpc], centered
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velocity (Ngas, 3) [km/s]
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mass (Ngas) [Msun]
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maps:
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extent (xmin, xmax, ymin, ymax) Coordinates of the edges of the map, centered
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gas_coldens (Nx, Ny) [Msun/pc^2], map of column density
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"""
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# Load arrays
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pp.load_cells(keys=["pos", "vel", "dx", "rho"])
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try:
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pp.load_parts(keys=["pos", "vel", "mass", "epoch"])
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except (KeyError, NoSuchNodeError):
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pp.load_parts(keys=["pos", "vel", "mass"])
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cells = pp.cells
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parts = pp.parts
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if "epoch" not in parts:
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parts["epoch"] = np.zeros(len(pp.parts["mass"]))
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# Compute extra fields and convert units
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for dset in (cells, parts):
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dset["position"] = dset["pos"] - np.array([0.5, 0.5, 0.5])
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cells["mass"] = (
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cells["rho"]
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* cells["dx"] ** 3
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* (pp.info["unit_density"] * pp.info["unit_length"] ** 3).express(U.Msun)
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)
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cells["volume"] = cells["dx"] ** 3 * (pp.info["unit_length"] ** 3).express(
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U.kpc**3
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)
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# Separate DM from stars
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mass_dm = np.max(parts["mass"])
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mask_dm = parts["mass"] == mass_dm
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mask_star = parts["mass"] < mass_dm
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# Create dataset
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data = {
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"header": {"time": pp.time * pp.info["unit_time"].express(U.Myr),
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"fluids": ["gas", "stars", "dm"],
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"box_size": pp.info["unit_length"].express(U.kpc)},
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"gas": {
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"position": cells["position"] * pp.info["unit_length"].express(U.kpc),
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"volume": cells["volume"],
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"velocity": cells["vel"] * pp.info["unit_velocity"].express(U.km_s),
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"mass": cells["mass"],
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},
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"stars": {
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"position": parts["position"][mask_star]
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* pp.info["unit_length"].express(U.kpc),
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"velocity": parts["vel"][mask_star]
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* pp.info["unit_velocity"].express(U.km_s),
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"mass": parts["mass"][mask_star] * pp.info["unit_mass"].express(U.Msun),
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"birth_time": parts["epoch"][mask_star]
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* pp.info["unit_time"].express(U.Myr),
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},
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"dm": {
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"position": parts["position"][mask_dm]
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* pp.info["unit_length"].express(U.kpc),
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"velocity": parts["vel"][mask_dm]
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* pp.info["unit_velocity"].express(U.km_s),
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"mass": parts["mass"][mask_dm] * pp.info["unit_mass"].express(U.Msun),
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},
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}
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return data
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if __name__ == "__main__" and False:
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from snapshotprocessor import SnapshotProcessor
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pp = SnapshotProcessor(
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"/home/nbrucy/ecogal/galturb/F20H_15_4pc_frig_from_relax", num=50, params="params_gal.yml"
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)
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data = load_fields(pp)
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96
loaders/params_gal.yml
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96
loaders/params_gal.yml
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plot : # Plot parameters
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put_title : False # Add a title to plot
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# Maps
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ntick : 6 # Number of ticks for maps
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# Overlays
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vel_red : 40 # Take point each vel_red for velocities
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time_fmt : "time = {:.3g} {}" # Time format string, 1st field is time and 2nd is unit
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disk: # Disk specific parameters
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enable : True # Enable specific disk analysis
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center : [0.5, 0.5, 0.5] # Position of the center
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binning : "log" # Kind of binning (lin : linear, log : logarithmic)
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mass_star : 1. # Mass of the central star
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nb_bin : 256 # Number of bins for averaged quantities
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bin_in : 1e-3 # Outer radius of the inner bin
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bin_out : 18 # Inner radius of the outer bin
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rmin_pdf : 1 # Inner radius for PDF computation
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rmax_pdf : 18 # Outer radius for PDF computation
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pdf: # parameters for probability density functions
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nb_bin : 100 # Number of bins for the PDF
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range : [-1.5, 2.5] # Range of the PDF (log of fluctuation)
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xmin_fit : 0.3 # Lower boundary of the fit (log of fluctuation)
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xmax_fit : 1.5 # Upper boundary of the fit (log of fluctuation)
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fit_cut : 1e-4 # Exclude value that are < fit_cut * maximum
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pymses: # Parameters for Pymses reader
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# Source settings
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variables : ["rho", "vel", "P"] # Read these variables
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part_variables : ["vel","mass","id","level","epoch"] # Read these variables
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order : '<' # Bit order
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# Processing options
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levelmax : 20 # Maximal AMR level visited when looking levels
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fft : False # Quick and dirty rendering using FFT
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verbose : True # Let pymses write on standart output
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multiprocessing : True # Whether to use multiprocessing
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# Camera settings
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center : [0.5, 0.5, 0.5] # Center of the image
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zoom : 4. # Zoom of the image
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map_size : 2048 # Size of the computed maps in pixel
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# Filter parameters
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filter : False # Enable filtering
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min_coords : [0.35, 0.35, 0.45]
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max_coords : [0.65, 0.65, 0.55]
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input: # Parameters on how to look for input files (= output from Ramses)
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log_prefix : "run.log" # Prefix of the log file
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label_filename : "label.txt" # Name of the label file
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nml_filename : "galaxy.nml" # name of the namelist file
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ramses_ism : False # If ramses-ism is used
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out: # Parameters for post processing
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tag : "" # Tag for the image
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interactive : True # Interactive mode (keep figures open)
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save : True # Save the plots on the disk
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ext : '.jpeg' # extension for plots
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fmt : "" # Format of the output filename for plots
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# The following keys are accepted
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# {out} : The output directory (where hdf5 files are also stored)
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# {run} : Name of the relevant run
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# {num} : Name of the input file (from Ramses)
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# {ext} : Extension defined above
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# {name} : Name of the rule
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# {tag} : Tag defined above
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# {nml[nml_key]} : The value of nml_key in the namelist (ex: {nml[amr_params/levelmin]})
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process: # General setting of the post-processor module
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verbose : True # Give more infos on what is going on
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num_process : 1 # Number of forks
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save_cells : True # Save cells structure on disk
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save_parts : True # Save particles on disk
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unload_cells : True # Save memory usage
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rules: # Specific rules parameters
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turb_energy_threshold : -1 # Remove invalid data (<0 = no threshold)
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astrophysix: # Parameters for astrophysix and galactica
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simu_fmt : "{tag}_{run}" # Format of the name of simulation
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descr_fmt : "{tag}_{run}" # Format of the default description
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# The following keys are accepted
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# {run} : Name of the relevant run
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# {tag} : Tag defined above
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# {nml[nml_key]} : The value of nml_key in the namelist (ex: {nml[amr_params/levelmin]})
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