Organize in submodules

aggregator.py

@@ -4,7 +4,7 @@ import numpy as np
 from functools import partial
 from baseprocessor import Rule
 import snapshotprocessor
-from mypool import MyPool
+from utils.mypool import MyPool
 
 try:
     from mpi4py.futures import MPIPoolExecutor

baseprocessor.py

@@ -10,11 +10,11 @@ from functools import partial
 import numpy as np
 
 import tables
-from tables import HDF5ExtError
+from tables import HDF5ExtError, NoSuchNodeError
 from tables.registry import class_name_dict
 
-from params import default_params, load_params
+from utils.params import default_params, load_params
-from units import U
+from utils.units import U
 import sys
 import traceback
 
@@ -44,6 +44,7 @@ class Rule:
             return self.process_fn(arg, **kwargs)
         else:
             return self.process_fn(**kwargs)
+
 class BaseProcessor:
     """
     Base class for processors, should not be instanciated
@@ -292,8 +293,17 @@ class HDF5Container(BaseProcessor):
                     )
             else:
                 value = node.read()
+                if isinstance(unit, dict):
+                    name = os.path.basename(node_name)
+                    if name in unit:
+                        unit = unit[name]
+                    else:
+                        unit = None
                 if not (unit is None or unit_old is None or unit_old == U.none):
                     value = value * unit_old.express(unit)
+        except NoSuchNodeError:
+            self.logger.error(f"The value {node_name} is node available", stack_info=True)
+            raise
         finally:
             if not open_before:
                 self.close()

fragdisk/gui.py

@@ -17,7 +17,7 @@ from scipy.stats import linregress
 from skimage.draw import line
 
 from snapshotprocessor import SnapshotProcessor
-from params import default_params
+from utils.params import default_params
 
 
 class DraggablePoint:

ism.py

@@ -1,101 +0,0 @@
-# coding: utf-8
-
-import numpy as np
-import pandas as pd
-from plotter import U
-import snapshotprocessor
-
-mp = 1.4 * 1.66 * 10 ** (-24) * U.g
-z0 = 150 * U.pc
-sink_header = [
-    "Id",
-    "M",
-    "dmf",
-    "x",
-    "y",
-    "z",
-    "vx",
-    "vy",
-    "vz",
-    "rot_period",
-    "lx",
-    "ly",
-    "lz",
-    "acc_rate",
-    "acc_lum",
-    "age",
-    "int_lum",
-    "Teff",
-]
-
-
-def convert_coldens_s(n0):
-    return (np.sqrt(2 * np.pi) * mp * z0 * (n0 * U.cm ** (-3))).express(U.coldens)
-
-
-convert_coldens = np.vectorize(convert_coldens_s)
-
-
-def get_dispersion(dset, name):
-    """
-    Compute dispersion from dset[name]
-    """
-    vel = dset[name]
-    mass = dset["mass"]
-    mass_tot = np.sum(mass)
-    if mass_tot > 0:
-        mean = np.sum(mass * vel) / mass_tot
-        return np.sqrt(np.sum(mass * (vel - mean) ** 2) / mass_tot)
-    else:
-        return 0
-
-def get_sinks(pp):
-    csv_name = f"{pp.path}/output_{pp.num:05}/sink_{pp.num:05}.csv"
-    return pd.read_csv(csv_name, header=None, names=sink_header)
-
-
-def analyze_box(pp):
-    pp.cells["mass"] = snapshotprocessor.mass_func(pp.cells)
-    coldens = pp.get_value("/maps/coldens_z", unit=U.coldens)
-    sinks = get_sinks(pp)
-    sinks["mass"] = sinks.M
-    res = {}
-    dirs = ["x", "y", "z"]
-    res["time"] = pp.info["time"] * pp.info["unit_time"].express(U.Myr)
-    for i, dir in enumerate(dirs):
-        pp.cells[f"v{dir}"] = pp.cells["vel"][:, i]
-        res[f"sigma_{dir}"] = get_dispersion(pp.cells, f"v{dir}") * pp.info[
-            "unit_velocity"
-        ].express(U.km_s)
-        res[f"sigma_sinks_{dir}"] = get_dispersion(sinks, f"v{dir}") * pp.info[
-            "unit_velocity"
-        ].express(U.km_s)
-    res["coldens_mean"] = np.mean(coldens)
-    res["n0"] = pp.get_nml("cloud_params/dens0")
-    res["mass"] = np.sum(
-        pp.cells["mass"]
-        * (pp.info["unit_density"] * pp.info["unit_length"] ** 3).express(U.Msun)
-    )
-    res["coldens_initial"] = convert_coldens_s(res["n0"])
-    res["mass_initial"] = res["coldens_initial"] * 1e6
-    res["coldens_mean"] = np.mean(coldens)
-    res["coldens_beam"] = res["mass"] / (pp.info["unit_length"].express(U.pc)) ** 2
-    res["nsink"] = sinks.M.count()
-    res["mass_sink"] = np.sum(sinks.M)
-
-    return res
-
-
-def load_wrapper(pp, fun):
-    """
-    Wrapper to load_unload data and map function
-    """
-    pp.load_cells(keys=["pos", "vel", "dx", "rho"])
-    pp.coldens("z")
-    res = fun(pp)
-    pp.unload_cells()
-    return res
-
-
-def allinone(pp):
-    return load_wrapper(pp, analyze_box)

pipeline.py

@@ -9,7 +9,7 @@ import numpy as np
 from plotter import Plotter, plt
 from snapshotprocessor import SnapshotProcessor, get_time
 from studyprocessor import StudyProcessor
-from params import default_params, load_params
+from utils.params import default_params, load_params
 
 fake_pp = SnapshotProcessor()
 

plotter.py

@@ -36,12 +36,11 @@ except ModuleNotFoundError:
     print("WARNING: no movie support (missing module moviepy)")
 from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 
-import pspec_read
 from baseprocessor import Rule, BaseProcessor
 from aggregator import Aggregator
 from studyprocessor import StudyProcessor
-from run_selector import RunSelector
+from runselector import RunSelector
-from units import U, unit_str, convert_exp
+from utils.units import U, unit_str, convert_exp
 
 try:
     import lic
@@ -56,7 +55,7 @@ from astrophysix.simdm.experiment import (
     ParameterVisibility,
     Simulation,
 )
-from ramses_astrophysix import ramses
+from galactica.ramses_astrophysix import ramses
 
 
 filetype_from_ext = {ext: ft for ft in FileType for ext in ft.extension_list}
@@ -270,6 +269,7 @@ class Plotter(Aggregator, BaseProcessor):
             self.nums,
             self.path_out,
             self.params,
+            tag=tag,
             unit_time=unit_time,
             selector=self.selector,
         )
@@ -1434,15 +1434,6 @@ class Plotter(Aggregator, BaseProcessor):
         if subname_y:
             hdf5_y.close()
 
-    def _pspec(self, name, **kwargs):
-        """
-        Plot power spectrum (wrapper around pspec_read)
-        """
-        del kwargs["run"]
-        file_pspec = self.current_processor.get_value("/hdf5/pspec")
-        num = self.current_processor.num
-        getattr(pspec_read, "pspec_" + name)(file_pspec, ".", num, **kwargs)
-
     def _overlay_fit(self, x, y, yerr=None, kind="linear", label=None, **kwargs):
         """
         Add an overlay : fit a curve, linear or powerlaw
@@ -1527,7 +1518,7 @@ class Plotter(Aggregator, BaseProcessor):
         This is where rules are defined
         """
         self.rules = {
-            "plot_arrays": PlotRule(lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="comp"
+            "plot_comp": PlotRule(lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="comp"
             ),
             "plot_run": PlotRule(lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="run"
             ),
@@ -1674,7 +1665,6 @@ class Plotter(Aggregator, BaseProcessor):
                 "Density profile",
                 dependencies=["axis", "rho_prof"],
             ),
-            "pspec": PlotRule(self, self._pspec, dependencies={"pspec": None}),
             "sbeta": PlotRule(
                 partial(
                     self._plot,
@@ -1966,6 +1956,8 @@ class Plotter(Aggregator, BaseProcessor):
         for name in ["time", "dt", "a", "mem_cells", "mem_parts"]:
             self._gen_from_log("fine_step_from_log", name_y=name, name_x="fine_step")
         
+        self._gen_from_log("SN_momentum_from_log", name_x="time", name_y="SN_momentum")
+
         # Dict of overlays
         self.overlays = {
             "g": partial(self._overlay_vector, "g"),

pspec_read.py

@@ -1,374 +0,0 @@
-from builtins import str
-
-import matplotlib.pyplot as P
-import numpy as np
-import tables as T
-
-################################################################
-# 3D
-################################################################
-
-
-def pspec_rho(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/rho")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 2) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^2 \mathcal{P}(\rho)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_rho_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_rho_" + format(num, "05") + ".jpeg")
-
-
-def pspec_B(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/B")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 2 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^2 \mathcal{P}(B)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_B_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_B_" + format(num, "05") + ".jpeg")
-
-
-def pspec_v(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/v")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 4 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^4 \mathcal{P}(v)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_v_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_v_" + format(num, "05") + ".jpeg")
-
-
-def pspec_vz(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/vz")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 4 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^4 \mathcal{P}(vz)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_vz_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_vz_" + format(num, "05") + ".jpeg")
-
-
-def pspec_cos(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/cos_vB")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 2 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^2 \mathcal{P}(\cos(\vec{v}.\vec{B}))$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_cos_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_cos_" + format(num, "05") + ".jpeg")
-
-
-def pspec_logrho(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/logrho")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 2 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^2 \mathcal{P}(\log(\rho))$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_logrho_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_logrho_" + format(num, "05") + ".jpeg")
-
-
-def pspec_kr(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/kr")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 4) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^4 \mathcal{P}(\rho^{1/3}v)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_kr_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_kr_" + format(num, "05") + ".jpeg")
-
-
-def pspec_vc(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/vc")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 4) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^4 \mathcal{P}(v_c)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_vc_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_vc_" + format(num, "05") + ".jpeg")
-
-
-def pspec_vs(file, path_out, num, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d3/vs")
-    kbins = h5f.get_node(group, "kbins").read()
-    pspec = h5f.get_node(group, "pspec").read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 4) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^4 \mathcal{P}(v_s)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_vs_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_vs_" + format(num, "05") + ".jpeg")
-
-
-###################################################################
-# 2D
-###################################################################
-
-
-def pspec_rho_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/rho")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 1) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k \mathcal{P}(\rho)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_rho_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_rho_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_B_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/B")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 1 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k \mathcal{P}(B)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_B_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_B_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_v_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/v")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 3 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^3 \mathcal{P}(v)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_v_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_v_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_vz_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/vz")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k ** 3 * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^3 \mathcal{P}(v_z)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_vz_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_vz_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_cos_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/cos_vB")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k \mathcal{P}(\cos(\vec{v}.\vec{B}))$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_cos_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_cos_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_logrho_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/logrho")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = k * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k \mathcal{P}(\log(\rho))$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_logrho_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_logrho_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_kr_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/kr")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 3) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^3 \mathcal{P}(\rho^{1/3}v)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_kr_2D_" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_kr_2D_" + format(num, "05") + ".jpeg")
-
-
-def pspec_vc_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/vc")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 3) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^3 \mathcal{P}(v_c)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_vc_2D" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_vc_2D" + format(num, "05") + ".jpeg")
-
-
-def pspec_vs_2D(file, path_out, num, plan, color="r", save=False, **kwargs):
-
-    h5f = T.open_file(file, mode="r")
-    group = h5f.get_node("/out_" + format(num, "05") + "/d2/vs")
-    kbins = h5f.get_node(group, "kbins_" + str(plan)).read()
-    pspec = h5f.get_node(group, "pspec_" + str(plan)).read()
-    h5f.close()
-
-    k = np.sqrt(kbins[1:] * kbins[:-1])
-    pspec = (k ** 3) * pspec
-    if save:
-        P.figure(figsize=(8, 8))
-    P.xlabel(r"$k$", fontsize=12)
-    P.ylabel(r"$k^3 \mathcal{P}(v_s)$", fontsize=12)
-    P.loglog(k, pspec, "-", color=color, **kwargs)
-    if save:
-        P.savefig(path_out + "pspec_vs_2D" + format(num, "05") + ".ps")
-        P.savefig(path_out + "pspec_vs_2D" + format(num, "05") + ".jpeg")

select_snapshot.py

@@ -5,7 +5,7 @@
   Select snaphots with a criterion
 """
 
-from run_selector import RunSelector
+from runselector import RunSelector
 from plotter import Plotter, U
 import os
 

snapshotprocessor.py

@@ -34,10 +34,10 @@ from pymses.sources.hop.hop import HOP
 
 from fil_finder import FilFinder2D
 from scipy import fft
-import pspec_new
+from scripts import pspec as pspec
 
 
-from units import U
+from utils.units import U
 from baseprocessor import (
     HDF5Container,
     Rule,
@@ -48,7 +48,7 @@ from baseprocessor import (
     oct_vect_getter,
 )
 
-from run_selector import RunSelector
+from runselector import RunSelector
 
 
 # Getters
@@ -212,10 +212,10 @@ def pspec(map2D):
     # Compute fft
     fmap = fft.fft2(map2D)
     # Compute the power map from the fft
-    pmap = pspec_new.pcube(fmap)
+    pmap = pspec.pcube(fmap)
     # Use the power map and the fft to compute the powerspectrum
     # This is typically an histogram of k weighted by the fourier transform value
-    pspec, kbins, pspec2, fbins = pspec_new.pspectrum(pmap, kmap, kbins, 1, 0)
+    pspec, kbins, pspec2, fbins = pspec.pspectrum(pmap, kmap, kbins, 1, 0)
     # Return bin center and power spectrum
     return 0.5 * (kbins[1:] + kbins[:-1]), pspec
 
@@ -1434,10 +1434,10 @@ class SnapshotProcessor(HDF5Container):
 
         return {key: df[key].values for key in df}
 
-    def _pspec(self, overwrite_file=False, **kwargs):
+    def pspec(self, overwrite_file=False, **kwargs):
         outfile = self.pspec_filename
         if overwrite_file or not os.path.exists(self.pspec_filename):
-          pspec_new.pspec(repo=self.path, iouts=[self.num], outfile=outfile, **kwargs)
+          pspec.pspec(repo=self.path, iouts=[self.num], outfile=outfile, **kwargs)
         return np.array([self.pspec_filename])
 
     def _write_particles(self):
@@ -1754,7 +1754,7 @@ class SnapshotProcessor(HDF5Container):
                     "vz_gas": "unit_velocity",
                 },
             ),
-            "pspec": Rule(self._pspec, "Power spectrum", "/hdf5"),
+            "pspec": Rule(self.pspec, "Power spectrum", "/hdf5"),
             "write_particles": Rule(self._write_particles, "Particles file", "/hdf5"
             ),
             "write_cells": Rule(self._write_cells, "Cells file", "/hdf5"),

studyprocessor.py

@@ -12,9 +12,9 @@ from scipy.stats import linregress
 from baseprocessor import Rule, HDF5Container
 from aggregator import Aggregator
 from snapshotprocessor import SnapshotProcessor
-from run_selector import RunSelector
+from runselector import RunSelector
-from params import default_params
+from utils.params import default_params
-from units import U
+from utils.units import U
 
 
 class StudyProcessor(Aggregator, HDF5Container):
@@ -452,6 +452,31 @@ class StudyProcessor(Aggregator, HDF5Container):
                 series["turb_energy"][run].append(np.nan)
         return series
 
+    def _extract_SN_Mom_from_log(self, series, log_filename, run):
+        cmd_grep = f"grep -e 'SN momentum' -e 'Fine step' {log_filename}"
+        content = os.popen(cmd_grep).readlines()
+        block_err = []
+        time = 0
+        for i in range(0, len(content)):
+            try:
+                if content[i][1:5] == "Fine":
+                    data = content[i].replace("=", " ").split()
+                    time = np.float(data[4])
+                elif content[i][1:3] == "SN"    :
+                    series["time"][run].append(time)
+                    series["SN_momentum"][run].append(np.float(content[i].split()[-1]))
+                else:
+                    raise ValueError("Wrong start of line")
+            except (AssertionError, ValueError, IndexError):
+                block_err.append(i)
+        
+        if len(block_err) > 0:
+            self.logger.warning(
+                    f"Error encountered in parsing {log_filename} (grepped blocks {block_err})"
+                )  
+        return series
+        
+
     def get_logs(self, run):
         glob_str = f"{self.path}/{run}/{self.params.input.log_prefix}*"
         logs = glob.glob(glob_str)
@@ -815,6 +840,15 @@ class StudyProcessor(Aggregator, HDF5Container):
                     "id": U.none,
                 },
             ),
+            "SN_momentum_from_log": Rule(
+                partial(self._from_log, ["time", "SN_momentum"], self._extract_SN_Mom_from_log),
+                group="/series",
+                unit={"time": "unit_time", "SN_momentum" : {"unit_mass" : 1, "unit_velocity" : 1}},
+                description={
+                    "time": "Time",
+                    "SN_momentum": "Injected momentum",
+                },
+            ),
             "turb_power": Rule(
                 self._turb_power,
                 group="/series/rms_from_log",

turbox/turbox.py

@@ -0,0 +1,160 @@
+"""
+    Toolbox for the TURBOX project (simulation part).
+
+    By Noé Brucy and Corentin Le Yuehlic, 2022
+
+    Compute and plot eta_d, from the slope of the logdensity power spectrum
+"""
+
+import numpy as np
+from plotter import  U
+from baseprocessor import Rule
+from matplotlib import pyplot as plt
+import pandas as pd
+import tables
+from scipy.stats import linregress
+
+
+def get_pspec(pp, field:str, dim:int=3):
+    """Read power spectruù 
+
+    Parameters
+    ----------
+    pp : SnapshotProcessor
+    field : str
+        field to get
+    dim : int, optional
+        dimension (2 or 3), by default 3
+
+    Returns
+    -------
+    tupple (np.array, np.array)
+        wave number and correponding powers
+    """    
+    h5file = tables.File(pp.pspec_filename)
+    path = f"/out_{pp.num:05}/d{dim}/{field}"
+    node = h5file.get_node(path)
+    kbins = node.kbins.read()
+    pspec = node.pspec.read()
+    h5file.close()
+    k = (kbins[:-1] + kbins[1:]) / 2
+    return  k, pspec
+
+
+span_resolution = {
+    256: (0.8, 1.1),
+    512: (0.5, 1.7),
+    1024: (0.4, 1.7)
+}
+
+
+def get_slope(pp, field:str, resol:int, plotdebug:bool=False):
+    """Get the slope of the Power specturm using linear regression in the selected range
+
+    Parameters
+    ----------
+    pp : Snapshotprocessor
+    field : str
+        field name
+    resol : int
+        resolution (number of cells on 1 side of the simulation cube)
+
+    Returns
+    -------
+    tuple (float, float)
+        Slope, square value of the correlation coefficient
+    """
+    # 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)
+    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}")
+        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
+
+def build_suite(pl, redo=False, cs0=0.28834810480560674):
+    """Compute an array of parameter for each run in the Plotter pl
+
+    Parameters
+    ----------
+    pl : Plotter
+    redo : bool, optional
+        redo the comptutation of the velocity dispersion, by default False
+    cs0 : float, optional
+        Sound speed in the simulations, by default 0.28834810480560674
+
+    Returns
+    -------
+    pd.Dataframe
+        dataframe with the properties of the simulation
+    """
+    
+    df = dict()
+    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()
+    df["slope"] = pl.study.get_nml("hydro_params/slope_type").values()
+    df["res"] = pl.study.get_nml("amr_params/levelmin").values()
+    df["frms"] = pl.study.get_nml("turb_params/turb_rms").values()
+    df["seed"] = pl.study.get_nml("turb_params/turb_seed").values()
+
+    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 = 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
+    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["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)
+    
+    return df
+
+
+def rho_pdf(pp):
+    pp.load_cells()
+    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)
+    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")
+def apply_rule_pdf(pp):
+    return pp.process(rule_pdf, pp, overwrite=True)

utils/params.py

@@ -33,7 +33,7 @@ def params_from_file(filename):
 
 
 def default_params():
-    return params_from_file(_dir_path + "/params.yml")
+    return params_from_file(_dir_path + "/../params.yml")
 
 
 def load_params(filename):