Update galsec: refector, timeseries, etc.

galsec.py

@@ -4,12 +4,13 @@
     Noé Brucy 2023
 """
 import numpy as np
-from astropy.table import QTable, hstack
+from astropy.table import QTable, hstack, vstack
 from astropy import units as u
 from astropy.units.quantity import Quantity
 from collections import defaultdict
 from numba import jit, prange
 
+from abc import ABC
 
 _atomic_mass = {
     "H+": 1,
@@ -296,15 +297,153 @@ def regroup(
     return result
 
 
-class Galsec:
+class GalsecAnalysis(ABC):
+    
+    def __init__(self):
+        raise NotImplementedError("This method should be implemented in a subclass")
+
+    def analysis(self, bin_deltas: dict, filter_bounds: dict):
+        raise NotImplementedError("This method should be implemented in a subclass")
+    
+    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,
+        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
+        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
+        **kwargs,
+    ):
+        """Compute the aggregation 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
+        """
+
+        bin_deltas = {"r": delta_r, "phi": delta_l}
+        filter_bounds = {"r": [rmin, rmax], "z": [zmin, zmax]}
+        self.sectors = self.analysis(bin_deltas, filter_bounds, **kwargs)
+        return self.sectors
+
+    def cartesian_analysis(
+        self,
+        delta_x: Quantity[u.kpc] = u.kpc,
+        delta_y: Quantity[u.kpc] = u.kpc,
+        delta_z: Quantity[u.kpc] = u.kpc,
+        xmin: Quantity[u.kpc] = -30 * u.kpc,
+        xmax: Quantity[u.kpc] = 30 * u.kpc,
+        ymin: Quantity[u.kpc] = -30 * u.kpc,
+        ymax: Quantity[u.kpc] = 30 * u.kpc,
+        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
+        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
+        **kwargs,
+    ):
+        """Compute the aggregation of quantities in cartesian bins
+
+        Parameters
+        ----------
+        delta_x : Quantity[u.kpc]
+            spacing between two x bins
+        delta_y : Quantity[u.kpc]
+             spacing between two y bins
+        """
+
+        bin_deltas = {"x": delta_x, "y": delta_y, "z": delta_z}
+        filter_bounds = {"x": [xmin, xmax], "y": [ymin, ymax],  "z": [zmin, zmax]}
+        self.grid = self.analysis(bin_deltas, filter_bounds)
+        return self.grid
+
+    def ring_analysis(
+        self,
+        delta_r: Quantity[u.kpc] = u.kpc,
+        rmin: Quantity[u.kpc] = 1 * u.kpc,
+        rmax: Quantity[u.kpc] = 30 * u.kpc,
+        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
+        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
+        **kwargs,
+    ):
+        """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 30*u.kpc
+        """
+
+        bin_deltas = {"r": delta_r}
+        filter_bounds = {"r": [rmin, rmax], "z": [zmin, zmax]}
+        self.rings = self.analysis(bin_deltas, filter_bounds, **kwargs)
+        return self.rings
+
+    def vertical_ring_analysis(
+        self,
+        delta_r: Quantity[u.kpc] = u.kpc,
+        delta_z: Quantity[u.kpc] = u.kpc,
+        rmin: Quantity[u.kpc] = 1 * u.kpc,
+        rmax: Quantity[u.kpc] = 30 * u.kpc,
+        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
+        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
+        **kwargs,
+    ):
+
+        bin_deltas = {"r": delta_r, "z": delta_z}
+        filter_bounds = {"r": [rmin, rmax], "z": [zmin, zmax]}
+        return self.analysis(bin_deltas, filter_bounds, **kwargs)
+    
+    def scale_analysis(
+        self,
+        sub_analysis_method: str = "cartesian_analysis",
+        scales: dict = {
+            "delta_x": np.array([0.01, 0.1, 1, 10, 20]) * u.kpc,
+            "delta_y": np.array([0.01, 0.1, 1, 10, 20]) * u.kpc,
+            "delta_z": np.array([0.01, 0.1, 1, 10, 20]) * u.kpc,
+        },
+        **kwargs):
+        
+        nb_scales = len(list(scales.values())[0])
+        
+        methods = {
+            "cartesian_analysis" : self.cartesian_analysis,
+            "sector_analysis" : self.sector_analysis,
+            "ring_analysis" : self.ring_analysis,
+            "vertical_ring_analysis" : self.vertical_ring_analysis,
+        }
+        
+        for i in range(nb_scales):
+            
+            scale_i = {key: value[i] for key, value in scales.items()}
+            
+            print(f"Doing {sub_analysis_method} for scale {i}/{nb_scales}: {scale_i}")
+            grid = methods[sub_analysis_method](**scale_i, **kwargs)
+            
+            if i==0:
+                all_grids = grid
+            else:
+                all_grids = {fluid: vstack([all_grids[fluid], grid[fluid]]) for fluid in self.fluids}
+        return all_grids
+    
+class Galsec(GalsecAnalysis):
     """
     Galactic sector extractor
     """
 
     fluids = ["gas", "stars", "dm"]
     extensive_fields = defaultdict(
-        lambda: ["mass"],
+        lambda: ["mass", "number"],
-        gas=["mass", "volume"],
+        gas=["mass", "volume", "number"],
     )
     
     weighted_fields = defaultdict(lambda: ["velr", "velphi", "velx", "vely", "velz"])
@@ -322,8 +461,8 @@ class Galsec:
     species = {}
     abundance_He = 0.1
 
-    def __init__(self, data: dict, copy=True) -> None:
+    def __init__(self, data: dict, copy=True, dense_output=True) -> None:
-        """Initiazise a Galaxasec object
+        """Initiazise a Galsec object
 
         Parameters
         ----------
@@ -348,7 +487,7 @@ class Galsec:
                 velocity float array (Ngas, 3) [km/s]
                 mass     float array (Ngas)    [Msun]
         copy : bool, default=True
-            Wheter the data should be copied.
+            Whether the data should be copied.
         """
 
         self.data = {}
@@ -373,6 +512,8 @@ class Galsec:
 
         self.compute_derived_values()
         
+        self.dense_output = dense_output
+
     def compute_derived_values(self) -> None:
         """
         Helper function to computed values derivated from input data
@@ -381,7 +522,7 @@ class Galsec:
             dset = self.data[fluid]
             dset["r"] = np.sqrt(
                 np.sum(dset["position"][:, :2] ** 2, axis=1)
-            )  # galactic radius%run
+            )  # galactic radius
             dset["phi"] = np.angle(
                 dset["position"][:, 0] + dset["position"][:, 1] * 1j
             )  # galactic longitude
@@ -393,6 +534,9 @@ class Galsec:
             )  # azimuthal velocity
             dset["velr"] = vect_r(dset["position"], dset["velocity"])  # radial velocity
             
+            # Number of cells / resolution element per bin (1 when not binned!)
+            dset["number"] = np.ones(len(dset["r"]))
+
             # Making aliases. No copy is done here.
             dset["x"] = dset["position"][:, 0]
             dset["y"] = dset["position"][:, 1]
@@ -401,13 +545,26 @@ class Galsec:
             dset["vely"] = dset["velocity"][:, 1]
             dset["velz"] = dset["velocity"][:, 2]
 
-    def binning(self, bin_deltas):
+    def binning(self, bins, filter_bounds, binning_mode = "delta", dataset=None):
+        """
+        Bin the data
+        """
+        if dataset is None:
+            dataset = self.data
         boxsize = self.box_size
         keys_binning = []
         for fluid in self.fluids:
-            data = self.data[fluid]  # no copy
+            data = dataset[fluid]  # no copy
-            for name in bin_deltas:
+            for name in bins:
-                delta = bin_deltas[name]
+                
+                delta = None
+                if binning_mode == "delta":
+                    delta = bins[name]
+                elif binning_mode == "number":
+                    bin_min =  filter_bounds[name][0].value
+                    bin_max =  filter_bounds[name][1].value 
+                    delta = (bin_max - bin_min) / bins[name]
+
                 if delta is not None:
                     if name in ["x", "y", "z"]:
                         # stay positive
@@ -422,19 +579,83 @@ class Galsec:
                     elif name == "phi":
                         delta_phi = (delta / data["r_bin"]) * u.rad
                         phi_bin = (np.trunc(data["phi"] / delta_phi) + 0.5) * delta_phi
+                        phi_bin[phi_bin >= 2 * np.pi * u.rad] =  0.5 * delta_phi[phi_bin >= 2 * np.pi * u.rad]
                         data["phi_bin"] = phi_bin
                     else:
                         print(f"Unsupported binning variable {name}")
                         break
                     if name not in keys_binning:
                         keys_binning.append(name)
+
+                elif binning_mode == "array":
+                    bin_array = bins[name]
+                    bin = np.digitize(data[name], bin_array, right=False)
+                    bin_array = np.append(bin_array, np.max(data[name]))
+
+                    
+                    # Store the middle value
+                    data[f"{name}_bin"] = (bin_array[bin - 1] + bin_array[bin]) / 2
+
+                    if name not in keys_binning:
+                        keys_binning.append(name)
         return keys_binning
     
-    def analysis(self, bin_deltas: dict, filter_bounds: dict):
+
+    def densify(self, processed_data:dict, bin_deltas:dict, filter_bounds:dict):
+        """
+        Densify the data by adding void bins where no data is present
+        """
+    
+        bins_array = {}
+        for bin_name in bin_deltas:
+            if bin_name == "phi":
+                if "r" in filter_bounds:
+                    rmax = filter_bounds["r"][1].value
+                else:
+                    rmax = np.max([np.max(processed_data[fluid]["r"].value) for fluid in processed_data])
+                delta_phi = bin_deltas["phi"].value / rmax
+                bins_array[bin_name] = np.arange(0, 2 * np.pi, delta_phi*0.9)
+            else:
+                if bin_name in filter_bounds:
+                    bin_min =  filter_bounds[bin_name][0].value + bin_deltas[bin_name].value / 2
+                    bin_max =  filter_bounds[bin_name][1].value 
+                else: 
+                    bin_min = np.min([np.min(processed_data[fluid][bin_name].value) for fluid in processed_data])
+                    bin_max = np.max([np.max(processed_data[fluid][bin_name].value) for fluid in processed_data])
+                bins_array[bin_name] = np.arange(bin_min, bin_max, bin_deltas[bin_name].value*0.9)
+                
+        grids = np.meshgrid(*list(bins_array.values()))
+        bins_array = {key: grid.flatten() for key, grid in zip(bins_array.keys(), grids)}
+
+        for fluid in processed_data:
+            keys = processed_data[fluid].keys()
+            units = {key: processed_data[fluid][key].unit for key in keys}
+            void_array = {key: np.zeros_like(list(bins_array.values())[0]) for key in keys}
+
+            for bin_name in bin_deltas: 
+                void_array[bin_name] = bins_array[bin_name]
+
+            void_array = QTable(void_array, names=keys, units=units)
+            dense_array = vstack([processed_data[fluid], void_array])
+            processed_data[fluid] = dense_array
+            
+        self.binning(dataset=processed_data, bins=bin_deltas, filter_bounds=filter_bounds)
+            
+        for fluid in processed_data:
+            bin_names_with_bin = [bin_name + "_bin" for bin_name in bin_deltas]
+            processed_data[fluid] = processed_data[fluid].group_by(bin_names_with_bin).groups.aggregate(np.add)
+            for bin_name in bin_deltas:
+                    processed_data[fluid].remove_column(bin_name)
+                    processed_data[fluid].rename_column(bin_name + "_bin", bin_name)
+    
+        return processed_data
+                
+
+    def analysis(self, bins: dict, filter_bounds: dict, binning_mode="delta"):
 
         result = {}
 
-        keys = self.binning(bin_deltas)
+        keys = self.binning(bins, filter_bounds, binning_mode=binning_mode)
         keys_bin = [key + "_bin" for key in keys]
 
         for fluid in self.fluids:
@@ -465,94 +686,57 @@ class Galsec:
                 )
                 result["stars"]["sfr"] = sfr * u.Msun / u.year
         
+        if self.dense_output and (binning_mode == "delta" or binning_mode == "number"):
+            result = self.densify(result, bins, filter_bounds)
+
         return result
 
-    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,
-        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
-        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
-    ):
-        """Compute the aggregation 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
-        """
 
-        bin_deltas = {"r": delta_r, "phi": delta_l}
+class GalsecTimeSeries(GalsecAnalysis):
-        filter_bounds = {"r": [rmin, rmax], "z": [zmin, zmax]}
-        self.sectors = self.analysis(bin_deltas, filter_bounds)
-        return self.sectors
 
-    def cartesian_analysis(
+    def __init__(self, galsecs: list, loader=None):
-        self,
+        self.galsecs = galsecs 
-        delta_x: Quantity[u.kpc] = u.kpc,
+        self.loader = loader
-        delta_y: Quantity[u.kpc] = u.kpc,
+        self.loaded = loader is None
-        delta_z: Quantity[u.kpc] = u.kpc,
-        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
-        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
-    ):
-        """Compute the aggregation of quantities in cartesian bins
 
-        Parameters
-        ----------
-        delta_x : Quantity[u.kpc]
-            spacing between two x bins
-        delta_y : Quantity[u.kpc]
-             spacing between two y bins
-        """
         
-        bin_deltas = {"x": delta_x, "y": delta_y, "z": delta_z}
+    def _analysis_single(self, galsec, bins, filter_bounds, binning_mode="delta"):
-        filter_bounds = {"z": [zmin, zmax]}
+        if not self.loaded:
-        self.grid = self.analysis(bin_deltas, filter_bounds)
+            galsec = self.loader(galsec)
-        return self.grid
+        analysis_result = galsec.analysis(bins, filter_bounds, binning_mode=binning_mode)
+        for fluid in analysis_result:
+            analysis_result[fluid]["time"] = galsec.time
+        return analysis_result
 
-    def ring_analysis(
-        self,
-        delta_r: Quantity[u.kpc] = u.kpc,
-        rmin: Quantity[u.kpc] = 1 * u.kpc,
-        rmax: Quantity[u.kpc] = 30 * u.kpc,
-        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
-        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
-    ):
-        """Compute the aggration of quantities in radial bins
         
-        Parameters
+    def analysis(self, bins: dict, filter_bounds: dict, 
-        ----------
+                 binning_mode="delta", 
-        delta_r : Quantity[u.kpc], optional
+                 aggregate=True, std=True, percentiles=[],
-            spacing between two radial bins, by default u.kpc
+                 num_processes=1):
-        rmin : Quantity[u.kpc], optional
+        timed_data = []
-            filter out bin below that radius, by default 1*u.kpc
+        if num_processes == 1:
-        rmax : Quantity[u.kpc], optional
+            for galsec in self.galsecs:
-            filter out bin beyond that radius, by default 30*u.kpc
+                timed_data.append(self._analysis_single(galsec, bins, filter_bounds, binning_mode))
-        """
+        else:
+            from multiprocessing import Pool
+            with Pool(num_processes) as p:
+                timed_data = p.starmap(
+                    self._analysis_single,
+                    [(galsec, bins, filter_bounds, binning_mode) for galsec in self.galsecs],
+                )
         
-        bin_deltas = {"r": delta_r}
+        averaged_data = {}
-        filter_bounds = {"r": [rmin, rmax], "z": [zmin, zmax]}
+        for fluid in timed_data[0]:
-        self.rings = self.analysis(bin_deltas, filter_bounds)
+                averaged_data[fluid] = vstack([result[fluid] for result in timed_data])
-        return self.rings
+                if aggregate:
+                    grouped_data = averaged_data[fluid].group_by(list(bins.keys())).groups
+                    if std:
+                        averaged_data[fluid + "_std"] =  grouped_data.aggregate(lambda x: np.std(x.value))
+                    for q in percentiles:
+                        averaged_data[fluid + f"_q{q}"] = grouped_data.aggregate(lambda x: np.percentile(x.value, q))
+                    averaged_data[fluid] =  grouped_data.aggregate(np.mean)
                     
-    def vertical_ring_analysis(
-        self,
-        delta_r: Quantity[u.kpc] = u.kpc,
-        delta_z: Quantity[u.kpc] = u.kpc,
-        rmin: Quantity[u.kpc] = 1 * u.kpc,
-        rmax: Quantity[u.kpc] = 30 * u.kpc,
-        zmin: Quantity[u.kpc] = -0.5 * u.kpc,
-        zmax: Quantity[u.kpc] = 0.5 * u.kpc,
-    ):
                 
-        bin_deltas = {"r": delta_r, "z": delta_z}
+        return averaged_data
-        filter_bounds = {"r": [rmin, rmax], "z": [zmin, zmax]}
+                
-        return self.analysis(bin_deltas, filter_bounds)