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This commit is contained in:
Noe Brucy
2022-11-28 18:01:18 +01:00
parent 7548ef7e0a
commit fa396178c6
14 changed files with 371 additions and 304 deletions
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turbox/turbox.py
+65 -42
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@@ -7,7 +7,7 @@
"""
import numpy as np
from plotter import U
from plotter import U
from baseprocessor import Rule
from matplotlib import pyplot as plt
import pandas as pd
@@ -15,8 +15,8 @@ import tables
from scipy.stats import linregress
def get_pspec(pp, field:str, dim:int=3):
"""Read power spectruù
def get_pspec(pp, field: str, dim: int = 3):
"""Read power spectruù
Parameters
----------
@@ -30,7 +30,7 @@ def get_pspec(pp, field:str, dim:int=3):
-------
tupple (np.array, np.array)
wave number and corresponding powers
"""
"""
h5file = tables.File(pp.pspec_filename)
path = f"/out_{pp.num:05}/d{dim}/{field}"
node = h5file.get_node(path)
@@ -38,17 +38,13 @@ def get_pspec(pp, field:str, dim:int=3):
pspec = node.pspec.read()
h5file.close()
k = (kbins[:-1] + kbins[1:]) / 2
return k, pspec
return k, pspec
span_resolution = {
256: (0.8, 1.1),
512: (0.5, 1.7),
1024: (0.4, 1.7)
}
span_resolution = {256: (0.8, 1.1), 512: (0.5, 1.7), 1024: (0.4, 1.7)}
def get_pspec_slope(pp, field:str, resol:int, plotdebug:bool=False):
def get_pspec_slope(pp, field: str, resol: int, plotdebug: bool = False):
"""Get the slope of the Power specturm using linear regression in the selected range
Parameters
@@ -65,23 +61,35 @@ def get_pspec_slope(pp, field:str, resol:int, plotdebug:bool=False):
Slope, square value of the correlation coefficient
"""
# Trustworthy span od the power spectrum in log10(k) as a function of the resolution
# Trustworthy span od the power spectrum in log10(k) as a function of the resolution
logkmin, logkmax = span_resolution[resol]
k, power = get_pspec(pp, field)
logk, logpower = np.log10(k), np.log10(power)
logk, logpower = np.log10(k), np.log10(power)
mask = (logk >= logkmin) & (logk < logkmax)
results = linregress(logk[mask], logpower[mask])
if plotdebug:
plt.figure()
plt.plot(logk, logpower)
plt.plot(logk[mask], results.slope*logk[mask]+ results.intercept, lw=3, ls=":", color="k")
pp.logger.info(f"Fit results in get_slope({field}, {resol}): slope:{results.slope:.2f}, b:{results.intercept:.2f}, R2:{results.rvalue**2:.2f}")
if results.rvalue**2 < 0.8:
pp.logger.warning(f"Bad fit in get_slope({field}, {resol}) with {logkmin} <= logk < {logkmax}")
plt.plot(
logk[mask],
results.slope * logk[mask] + results.intercept,
lw=3,
ls=":",
color="k",
)
pp.logger.info(
f"Fit results in get_slope({field}, {resol}): slope:{results.slope:.2f}"
+ f", b:{results.intercept:.2f}, R2:{results.rvalue**2:.2f}"
)
if results.rvalue ** 2 < 0.8:
pp.logger.warning(
f"Bad fit in get_slope({field}, {resol}) with {logkmin} <= logk < {logkmax}"
)
pp.logger.warning(f"log(k) is \n {logk[mask]}")
pp.logger.warning(f"log(power) is \n {logpower[mask]}")
return results.slope, results.intercept, results.rvalue**2
return results.slope, results.intercept, results.rvalue ** 2
def build_suite(pl, redo=False, cs0=0.28834810480560674):
"""Compute an array of parameter for each run in the Plotter pl
@@ -99,9 +107,9 @@ def build_suite(pl, redo=False, cs0=0.28834810480560674):
pd.Dataframe
dataframe with the properties of the simulation
"""
df = dict()
df["snapshots"] = pl.nums.values()
df = {}
df["snapshots"] = pl.nums.values()
df["n0"] = pl.study.get_nml("galbox_params/dens0").values()
df["turbinit"] = pl.study.get_nml("galbox_params/turb").values()
df["solver"] = pl.study.get_nml("hydro_params/riemann").values()
@@ -113,33 +121,46 @@ def build_suite(pl, redo=False, cs0=0.28834810480560674):
df["comp"] = pl.study.get_nml("turb_params/comp_frac").values()
df["L"] = pl.study.get_nml("amr_params/boxlen").values()
df["T_turb"] = (np.array(list(pl.study.get_nml("turb_params/turb_T").values()))
* pl.study.info["unit_time"].express(U.Myr))
df["T_turb"] = np.array(
list(pl.study.get_nml("turb_params/turb_T").values())
) * pl.study.info["unit_time"].express(U.Myr)
df = pd.DataFrame(df, index=pl.runs)
if redo:
pl.study.avg_time_sigma("x", overwrite_dep=False)
pl.study.avg_time_sigma("y", overwrite_dep=False)
pl.study.avg_time_sigma("z", overwrite_dep=False)
pl.study.time(overwrite=True)
for ax in ["x", "y", "z"]:
df[f"sigma_{ax}"] = np.array(list(map(
lambda x : x.T[0],
[pl.study.get_value(f"/series/time_sigma_{ax}",
unit=U.km_s)[run] for run in pl.runs])))
df["sigma_all"] = df[f"sigma_x"]**2 + df[f"sigma_y"]**2 + df[f"sigma_z"]**2
for ax in ["x", "y", "z"]:
df[f"sigma_{ax}"] = np.array(
list(
map(
lambda x: x.T[0],
[
pl.study.get_value(f"/series/time_sigma_{ax}", unit=U.km_s)[run]
for run in pl.runs
],
)
)
)
df["sigma_all"] = df["sigma_x"] ** 2 + df["sigma_y"] ** 2 + df["sigma_z"] ** 2
df["sigma_all"] = list(map(np.sqrt, df["sigma_all"].values))
df["Mach_all"] = list(map(lambda v: v/cs0, df["sigma_all"].values))
df["time"] = list(map(lambda x : x.T[0],
[pl.study.get_value(f"/series/time", unit=U.Myr)[run]
for run in pl.runs]))
df["Mach_all"] = list(map(lambda v: v / cs0, df["sigma_all"].values))
df["time"] = list(
map(
lambda x: x.T[0],
[pl.study.get_value("/series/time", unit=U.Myr)[run] for run in pl.runs],
)
)
df["sigma"] = list(map(lambda l: np.mean(l), df["sigma_all"].values))
df["Mach"] = df["sigma"] / cs0
df["turnover"] = (df["L"] * U.pc.express(U.km) / (2 * df["sigma"]))* U.s.express(U.Myr)
df["turnover"] = (df["L"] * U.pc.express(U.km) / (2 * df["sigma"])) * U.s.express(
U.Myr
)
return df
@@ -148,13 +169,15 @@ def rho_pdf(pp):
rho = pp.cells["rho"] * pp.info["unit_density"].express(U.H_cc)
rho_0 = np.mean(rho)
print(rho_0)
s = np.log(rho/rho_0)
values, edges = np.histogram(s, bins=300, range=(-15, 11),
density=True)
s = np.log(rho / rho_0)
values, edges = np.histogram(s, bins=300, range=(-15, 11), density=True)
pp.unload_cells()
centers = 0.5 * (edges[1:] + edges[:-1])
return (np.stack([values, centers]), {"logbins": True})
rule_pdf=Rule(rho_pdf, "Density PDF", name="rho_pdf", group="/hist")
rule_pdf = Rule(rho_pdf, "Density PDF", name="rho_pdf", group="/hist")
def apply_rule_pdf(pp):
return pp.process(rule_pdf, pp, overwrite=True)