diff --git a/galsec.py b/galsec.py new file mode 100644 index 0000000..5e0e538 --- /dev/null +++ b/galsec.py @@ -0,0 +1,355 @@ +# coding: utf-8 +""" + Galaxy kiloparsec extractor + +""" +import numpy as np +from astropy.table import QTable, hstack +from astropy import units as u +from astropy.units.quantity import Quantity + + +def vect_r(position: np.array, vector: np.array) -> np.array: + """Radial component of a vector + + Parameters + ---------- + position : np.array (N, 3) + (only reads x and y) + vector : np.array (N, 3) + (only reads x and y components) + + Returns + ------- + np.array (N) + Radial component + """ + r = position[:, :2] + ur = np.transpose((np.transpose(r) / np.sqrt(np.sum(r**2, axis=1)))) + return np.einsum("ij, ij -> i", vector[:, :2], ur) + + +def vect_phi(position: np.array, vector: np.array): + """Azimuthal component of a vecto + + Parameters + ---------- + position : np.array (N, 3) + (only reads x and y) + vector : np.array (N, 3) + (only reads x and y components) + + Returns + ------- + np.array (N) + Azimuthal component + """ + r = position[:, :2] + r_norm = np.sqrt(np.sum(r**2, axis=1)) + rot = np.array([[0, -1], [1, 0]]) + uphi = np.transpose(np.einsum("ij, kj -> ik", rot, r) / r_norm) + return np.einsum("ij,ij -> i", vector[:, :2], uphi) + + +def get_sfr( + stars: QTable, time: Quantity[u.Myr], average_on: Quantity[u.Myr] = 30 * u.Myr +) -> Quantity[u.Msun / u.year]: + """_summary_ + + Parameters + ---------- + stars : QTable + _description_ + time : Quantity[u.Myr], + time at wich the SFR should be computed + average_on : Quantity, optional + compute the sfr on the last `average_on` years, by default 30*u.Myr + + Returns + ------- + float + SFR in Msun/yr + """ + try: + mask = (stars["birth_time"] > max(time - average_on, 0 * u.Myr)) & ( + stars["birth_time"] < time + ) + dtime = min(average_on, time).to(u.year) + if dtime == 0: + sfr = 0.0 * u.Msun / u.year + else: + sfr = np.sum(stars["mass"][mask]) / dtime + except KeyError: + sfr = 0.0 * u.Msun / u.year + return sfr + + +def aggregate( + grouped_data: QTable, + weight_field: str = "mass", + extensive_fields: str = ["mass", "ek"], + weighted_fields: list = ["velr", "velphi", "velz"], +) -> QTable: + """Aggregate wisely from grouped data + + Parameters + ---------- + grouped_data : QTable + should already have group information + weight_field : str, optional + the field used for the weighting of the non extensive quantities, by default "mass" + extensive_fields : str, optional + these will be summed. Should include weight_field, by default ["mass", "ek"] + weighted_fields : list, optional + the field that will be weighted, by default ["velr", "velphi", "velz"] + + Returns + ------- + QTable + a tabble with the aggregated value for each group + """ + assert weight_field in extensive_fields + + for field in weighted_fields: + grouped_data[field] *= grouped_data[weight_field] + + binned_data = grouped_data[extensive_fields + weighted_fields].groups.aggregate( + np.add + ) + + for field in weighted_fields: + binned_data[field] /= binned_data[weight_field] # weighted mean + + # Veocity dispersion + grouped_data[field] /= grouped_data[weight_field] + for i in range(len(grouped_data.groups)): + slice = grouped_data.groups.indices[i], grouped_data.groups.indices[i + 1] + grouped_data[slice[0] : slice[1]][field] -= binned_data[i][field] + grouped_data[field] = grouped_data[weight_field] * grouped_data[field] ** 2 + binned_data[f"sigma_{field}"] = grouped_data[field,].groups.aggregate( + np.add + )[field] + binned_data[f"sigma_{field}"] = np.sqrt( + binned_data[f"sigma_{field}"] / binned_data[weight_field] + ) + + return binned_data + + +class Galsec: + """ + Galactic sector extractor + """ + + fluids = ["gas", "stars", "dm"] + units = { + "gas": { + "position": u.kpc, + "volume": u.kpc**3, + "velocity": u.km / u.s, + "mass": u.Msun, + }, + "stars": { + "position": u.kpc, + "velocity": u.km / u.s, + "mass": u.Msun, + "birth_time": u.Myr, + }, + "dm": {"position": u.kpc, "velocity": u.km / u.s, "mass": u.Msun}, + } + + def __init__(self, data: dict, copy=True) -> None: + """Initiazise a Galaxasec object + + Parameters + ---------- + cell_data : dict + A dataset of cells in the following format: + header: + time float [Myr] time since the start of the simulation + gas: + position float array (Ngas, 3) [kpc], centered + volume float array (Ngas) [pc^3] + velocity float array (Ngas, 3) [km/s] + mass float array (Ngas) [Msun] + stars: + position float array (Nstar, 3) [kpc], centered + velocity float array (Nstar, 3) [km/s] + mass float array (Nstar) [Msun] + birth_time float array (Nstar) [Myr] + dm: + position float array (Ngas, 3) [kpc], centered + velocity float array (Ngas, 3) [km/s] + mass float array (Ngas) [Msun] + maps: + extent float list [xmin, xmax, ymin, ymax] Coordinates of the edges of the map, centered + gas_coldens float array (Nx, Ny) [Msun/pc^2] Map of column density + copy : bool, default=True + Wheter the data should be copied. + """ + + self.data = {} + + for fluid in self.fluids: + self.data[fluid] = QTable(data[fluid], units=self.units[fluid], copy=copy) + + self.time = data["header"]["time"] * u.Myr + + self.compute_derived_values() + + def compute_derived_values(self) -> None: + """ + Helper function to computed values derivated from input data + """ + for fluid in self.fluids: + dset = self.data[fluid] + dset["r"] = np.sqrt( + np.sum(dset["position"][:, :2] ** 2, axis=1) + ) # galactic radius + dset["phi"] = np.angle( + dset["position"][:, 0] + dset["position"][:, 1] * 1j + ) # galactic longitude + dset["phi"][dset["phi"] < 0] += ( + 2 * np.pi * u.rad + ) # rescaling to get only positive values + dset["velphi"] = vect_phi( + dset["position"], dset["velocity"] + ) # azimuthal velocity + dset["velr"] = vect_r(dset["position"], dset["velocity"]) # radial velocity + dset["velz"] = dset["velocity"][:, 2] # vertical velocity + dset["ek"] = (dset["mass"] * np.sum(dset["velocity"] ** 2, axis=1)).to( + u.erg + ) # kinetic energy + + def ring_binning(self, delta_r: Quantity[u.kpc]): + """Add radial bin informations to the dataset + + Parameters + ---------- + delta_r : Quantity[u.kpc] + spacing between two radial bins + """ + for fluid in self.fluids: + r_bin = np.trunc(self.data[fluid]["r"] / delta_r) + r_bin = (r_bin + 0.5) * delta_r # Store the middle value + self.data[fluid]["r_bin"] = r_bin + + def sector_binning(self, delta_r: Quantity[u.kpc], delta_l: Quantity[u.kpc]): + """Add sector bin information to the dataset + + Parameters + ---------- + delta_r : Quantity[u.kpc] + spacing between two radial bins + delta_l : Quantity[u.kpc] + spacing (in spatial units) between two azimuthal bins + """ + + self.ring_binning(delta_r) + + for fluid in self.fluids: + delta_phi = (delta_l / self.data[fluid]["r_bin"]) * u.rad + phi_bin = (np.trunc(self.data[fluid]["phi"] / delta_phi) + 0.5) * delta_phi + self.data[fluid]["phi_bin"] = phi_bin + + def sector_analysis( + self, + delta_r: Quantity[u.kpc] = u.kpc, + delta_l: Quantity[u.kpc] = u.kpc, + rmin: Quantity[u.kpc] = 1 * u.kpc, + rmax: Quantity[u.kpc] = 12 * u.kpc, + ): + """Compute the aggration of quantities in sectors bins + + Parameters + ---------- + delta_r : Quantity[u.kpc], optional + spacing between two radial bins, by default u.kpc + delta_l : Quantity[u.kpc], optional + spacing (in spatial units) between two azimuthal bins, by default u.kpc + rmin : Quantity[u.kpc], optional + filter out bin below that radius, by default 1*u.kpc + rmax : Quantity[u.kpc], optional + filter out bin beyond that radius, by default 12*u.kpc + """ + + self.sector_binning(delta_r, delta_l) + grouped_data = {} + self.sectors = {} + + for fluid in self.fluids: + if fluid == "gas": + extensive_fields = ["mass", "ek", "volume"] + else: + extensive_fields = ["mass", "ek"] + filtered_data = self.data[fluid][ + np.logical_and( + self.data[fluid]["r"] > rmin, self.data[fluid]["r"] < rmax + ) + ] + grouped_data[fluid] = filtered_data.group_by(["r_bin", "phi_bin"]) + self.sectors[fluid] = hstack( + [ + grouped_data[fluid]["r_bin", "phi_bin"].groups.aggregate(np.fmin), + aggregate(grouped_data[fluid], extensive_fields=extensive_fields), + ] + ) + self.sectors[fluid].rename_column("r_bin", "r") + self.sectors[fluid].rename_column("phi_bin", "phi") + + self.sectors["stars"]["sfr"] = ( + np.zeros(len(self.sectors["stars"]["mass"])) * u.Msun / u.year + ) + for i, group in enumerate(grouped_data["stars"].groups): + self.sectors["stars"]["sfr"][i] = get_sfr(group, self.time) + + self.sectors["stars"]["sfr"][i] = get_sfr(group, self.time) + + def ring_analysis( + self, + delta_r: Quantity[u.kpc] = u.kpc, + rmin: Quantity[u.kpc] = 1 * u.kpc, + rmax: Quantity[u.kpc] = 12 * u.kpc, + ): + """Compute the aggration of quantities in radial bins + + Parameters + ---------- + delta_r : Quantity[u.kpc], optional + spacing between two radial bins, by default u.kpc + rmin : Quantity[u.kpc], optional + filter out bin below that radius, by default 1*u.kpc + rmax : Quantity[u.kpc], optional + filter out bin beyond that radius, by default 12*u.kpc + """ + + self.ring_binning(delta_r) + grouped_data = {} + self.rings = {} + + for fluid in self.fluids: + if fluid == "gas": + extensive_fields = ["mass", "ek", "volume"] + else: + extensive_fields = ["mass", "ek"] + filtered_data = self.data[fluid][ + np.logical_and( + self.data[fluid]["r"] > rmin, self.data[fluid]["r"] < rmax + ) + ] + grouped_data[fluid] = filtered_data.group_by(["r_bin"]) + self.rings[fluid] = hstack( + [ + grouped_data[fluid][ + "r_bin", + ].groups.aggregate(np.fmin), + aggregate(grouped_data[fluid], extensive_fields=extensive_fields), + ] + ) + self.rings[fluid].rename_column("r_bin", "r") + + self.rings["stars"]["sfr"] = ( + np.zeros(len(self.rings["stars"]["mass"])) * u.Msun / u.year + ) + for i, group in enumerate(grouped_data["stars"].groups): + self.rings["stars"]["sfr"][i] = get_sfr(group, self.time) diff --git a/galsec_plot.py b/galsec_plot.py new file mode 100644 index 0000000..886c74a --- /dev/null +++ b/galsec_plot.py @@ -0,0 +1,55 @@ +# coding: utf-8 +""" + Galaxy kiloparsec plotter +""" +from astropy.table import QTable +import matplotlib.pyplot as plt +import numpy as np + + +def plot_radial(table: QTable): + + fig = plt.figure(figsize=(6, 5)) + # setting the axis limits in [left, bottom, width, height] + rect = [0.1, 0.1, 0.8, 0.8] + + # the polar axis: + ax_polar = fig.add_axes(rect, polar=True, frameon=False) + rmax = 10 + ax_polar.set_rmax(rmax) + + ax_polar.set_xticklabels([]) + + sc = ax_polar.scatter(table["phi"], table["r"], c=table["mass"], s=100, alpha=1) + plt.colorbar(sc) + + fig, ax = plt.subplots(subplot_kw=dict(projection="polar"), figsize=(6, 5)) + + rbins = np.unique(table["r"]) + delta_r = rbins[1] - rbins[0] + + for r in rbins: + + mask = table["r"] == r + phibins = table["phi"][mask].value + + C = np.log10(table["mass"][mask].value) + N = len(C) + C = np.arange(N) + r.value + np.random.shuffle(C) + C = C.reshape(1, N) + P = np.zeros(shape=(2, N + 1)) + + R = np.ones(shape=(2, N + 1)) * (r + delta_r / 2.0) + R[0, :] -= delta_r + R = R.value + + deltaphi = phibins[1] - phibins[0] + P[0, :-1] = phibins - deltaphi / 2 + P[1, :-1] = phibins - deltaphi / 2 + P[0, -1] = P[0, 0] + P[1, -1] = P[1, 0] + + pc = ax.pcolormesh(P, R, C, cmap="tab20c") #, vmin=6, vmax=9) + print(P, R, C) + #fig.colorbar(pc)