Organize in submodules

2022-10-07 11:20:04 +02:00
parent 1f3b0762c9
commit 0d90179292
32 changed files with 231 additions and 510 deletions
@@ -0,0 +1,130 @@
+from scipy.integrate import solve_ivp
+from plotter import U
+import select_snapshot
+import numpy as np
+import pandas as pd
+
+ssfr_base = 2.5e-10
+
+
+def get_sinks(self, overwrite=False, stellar=True, convert_units=False):
+    
+    self.sinks_from_log(overwrite=overwrite)
+    self.coarse_step_from_log(overwrite=overwrite)
+    self.sinks = self.get_value("/series/sinks_from_log")
+    
+    if stellar:
+        self.stellar_from_log(overwrite=overwrite)
+        self.stellar = self.get_value("/dataset/stellar_from_log")
+        self.stellar["sn_time"] = {}
+        for run in self.runs:
+            self.stellar["sn_time"][run] =  (self.stellar["time"][run]  + self.stellar["lifetime"][run] )*self.info["unit_time"].express(U.year)
+            ind_sort = self.stellar["sn_time"][run].argsort()
+            for key in self.stellar:
+                self.stellar[key][run] = self.stellar[key][run][ind_sort]
+        self.stellar["cum_mass"] = {}
+        sn = {}
+        sn["time"] = {}
+        sn["cum_mass"] = {}
+    
+    self.sinks["cum_mass"] = {}
+
+    for run in self.runs:
+        
+        if convert_units:
+            self.sinks["time"][run] *= self.info["unit_time"].express(U.year)
+        
+        self.sinks["cum_mass"][run] = self.sinks["mass_sink"][run].copy()
+        
+        if stellar:
+            self.stellar["cum_mass"][run] = np.cumsum(self.stellar["mass"][run]) * self.info["unit_mass"].express(U.Msun)
+            sn["time"][run], idx, count = np.unique(self.stellar["sn_time"][run], return_index=True, return_counts=True) 
+            idx += count - 1
+            sn["cum_mass"][run] = self.stellar["cum_mass"][run][idx]
+            ind_sn = np.searchsorted(self.sinks["time"][run], sn["time"][run])
+            for i, indi in enumerate(ind_sn[ind_sn < ind_sn[-1]]):
+                self.sinks["cum_mass"][run][indi:ind_sn[i+1]] +=  sn["cum_mass"][run][i]
+            
+
+    time_gas, total_mass, mass_gas = self.total_mass()
+    
+    sk_ssfr = {}
+    for run in self.runs:
+        time_gas[run] *= self.info["unit_time"].express(U.Myr)
+        sk_ssfr[run] = ssfr_base * 1e6 * (mass_gas[run]/1e6)**1.4
+
+    def dermass(t, sm, run, fact_sfr=1):
+        ind = min(np.searchsorted(time_gas[run], t), sk_ssfr[run].size - 1)
+        return sk_ssfr[run][ind]*fact_sfr
+
+    tspan = np.linspace(0, 200, 100)
+
+    self.time_esm = {}
+    self.expected_stellar_mass = {}
+    self.expected_stellar_mass_max = {}
+    self.expected_stellar_mass_min = {}
+
+    for run in self.runs:
+        sol = solve_ivp(dermass, (tspan[0], tspan[-1]), [0], t_eval=tspan, args=[run])
+        self.time_esm[run], self.expected_stellar_mass[run] = sol["t"], sol["y"][0]
+        sol_max = solve_ivp(dermass, (tspan[0], tspan[-1]), [0], t_eval=tspan, args=[run, 3])
+        self.expected_stellar_mass_max[run] = sol_max["y"][0]
+        sol_min = solve_ivp(dermass, (tspan[0], tspan[-1]), [0], t_eval=tspan, args=[run, 1./3])
+        self.expected_stellar_mass_min[run] = sol_min["y"][0]
+ 
+def compute_sfr(self, target_start=0.05, target_end=0.3):
+    mass = self.sinks["cum_mass"]
+    time = self.sinks["time"]
+    sfr = {}
+    sfr_err = {}
+    tend = {}
+    tstart = {}
+    for run in self.runs:
+        tend[run] = select_runs.time_mcons(self, run, target=target_end) * 1e6
+        tstart[run] = select_runs.time_mcons(self, run, target=target_start) * 1e6
+        if tend[run] < tstart[run]:
+            tend[run] = time[run][-1]
+
+
+    for run in self.runs:
+        idx1 = time[run].searchsorted(tstart[run])
+        idx2 = min(time[run].searchsorted(tend[run]), len(time[run]) - 1)
+
+        sfr[run] = (mass[run][idx2] - mass[run][idx1]) /  (time[run][idx2] - time[run][idx1])
+        sfr_other = []
+        for i in range(idx1, idx2-10):
+            sfr_other.append((mass[run][i+10:idx2+1] - mass[run][i]) /  (time[run][i+10:idx2+1] - time[run][i]))
+
+        sfr_err[run] = np.std(np.concatenate(sfr_other))
+    self.sfr = np.array(list(sfr.values()))
+    self.sfr_err = np.array(list(sfr_err.values()))
+    return self.sfr, self.sfr_err
+
+
+def compute_power(self, target_start=0.05, target_end=0.3):
+    mass = self.sinks["cum_mass"]
+    time = self.sinks["time"]
+    sfr = {}
+    sfr_err = {}
+    tend = {}
+    tstart = {}
+    for run in self.runs:
+        tend[run] = select_runs.time_mcons(self, run, target=target_end) * 1e6
+        tstart[run] = select_runs.time_mcons(self, run, target=target_start) * 1e6
+        if tend[run] < tstart[run]:
+            tend[run] = time[run][-1]
+
+
+    for run in self.runs:
+        idx1 = time[run].searchsorted(tstart[run])
+        idx2 = min(time[run].searchsorted(tend[run]), len(time[run]) - 1)
+
+        sfr[run] = (mass[run][idx2] - mass[run][idx1]) /  (time[run][idx2] - time[run][idx1])
+        sfr_other = []
+        for i in range(idx1, idx2-10):
+            sfr_other.append((mass[run][i+10:idx2+1] - mass[run][i]) /  (time[run][i+10:idx2+1] - time[run][i]))
+
+        sfr_err[run] = np.std(np.concatenate(sfr_other))
+    self.sfr = np.array(list(sfr.values()))
+    self.sfr_err = np.array(list(sfr_err.values()))
+    return self.sfr, self.sfr_err