Noe Brucy
1478 lines
44 KiB
Python
1478 lines
44 KiB
Python
# coding: utf-8
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import sys
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import os
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import pymses
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import numpy as np
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import matplotlib as mpl
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if os.environ.get("DISPLAY", "") == "":
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print("No display found. Using non-interactive Agg backend")
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mpl.use("Agg")
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import pylab as P
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import glob as glob
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try:
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import cPickle as pickle
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except:
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print("cPickle not found, using pickle")
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import pickle
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import tables
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from scipy.stats import linregress
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from pymses.sources.ramses import output
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from pymses.analysis import Camera, raytracing, slicing, splatting
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from pymses.filters import CellsToPoints
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from pymses.analysis import ScalarOperator, FractionOperator, MaxLevelOperator
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from scipy.stats import gaussian_kde
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# extension for out files
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out_ext = ".jpeg"
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P.rcParams["image.cmap"] = "plasma"
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P.rcParams["savefig.dpi"] = 400
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def compute_image_data(
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path,
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num,
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radius=0.5,
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path_out=None,
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order="<",
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save_data=True,
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map_size=512,
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tag="",
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axes_los=["x", "y", "z"],
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images=["coldens", "rho", "speed", "Q", "T", "level", "cpu"],
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pos_star=np.array([1.0, 1.0, 1.0]),
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put_title=True,
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force=False,
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fft=False,
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):
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"""
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Make several useful image of an output of a simulation, auxillary function
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Parameters
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----------
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num output number
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path_out path of the pipeline output
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force if set, erase any existing pipeline output files
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tag string to add to the output name
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map_size size of the map
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"""
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ro = pymses.RamsesOutput(path, num, order=order)
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amr = ro.amr_source(["rho", "vel", "P"])
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center = [0.5, 0.5, 0.5]
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lbox = ro.info["boxlen"] # boxlen in codeunits
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G = 1.0 # Gravitational constant
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im_extent = [
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(-radius + center[0]) * lbox,
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(radius + center[0]) * lbox,
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(-radius + center[1]) * lbox,
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(radius + center[1]) * lbox,
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]
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time = ro.info["time"] # time in codeunits
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title = "t=" + str(time)[0:5] + " (code)"
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# Determining output directory
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if path_out is not None:
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directory = path_out
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else:
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directory = path
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# Checking for existing files
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name_save = directory + "/maps_disk" + "_" + tag + "_" + format(num, "05") + ".save"
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if len(glob.glob(name_save)) == 1 and not force:
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return
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# Prepare saving data
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if "T" in images and not "rho" in images:
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images.append("rho")
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if "Q" in images and not "coldens" in images:
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images.append("coldens")
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maps_disk = {
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"time": time,
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"im_extent": im_extent,
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"center": center,
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"radius": radius,
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"lbox": lbox,
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"images": images,
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"axes_los": axes_los,
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}
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# Prepare raytracing
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rho_op = ScalarOperator(lambda dset: dset["rho"], ro.info["unit_density"])
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rt = None
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if fft:
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rt = splatting.SplatterProcessor(amr, ro.info, rho_op)
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else:
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rt = raytracing.RayTracer(amr, ro.info, rho_op)
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# Prepare axes
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ax_nb = {"x": 0, "y": 1, "z": 2} # Associated number of each axes
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axes_h = {"x": "y", "y": "x", "z": "x"} # Associated horizontal axe
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axes_v = {"x": "z", "y": "z", "z": "y"} # Associated vertical axe
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for ax_los in axes_los:
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ax_h = axes_h[ax_los]
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ax_v = axes_v[ax_los]
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cam = Camera(
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center=center,
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line_of_sight_axis=ax_los,
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region_size=[2.0 * radius, 2.0 * radius],
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distance=radius,
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far_cut_depth=radius,
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up_vector=ax_v,
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map_max_size=map_size,
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)
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# Column density
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if "coldens" in images:
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datamap = rt.process(cam, surf_qty=True)
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dmap_col = datamap.map.T * lbox
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maps_disk["coldens_" + ax_los] = dmap_col
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# Rho slice
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if "rho" in images:
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datamap_rho = slicing.SliceMap(amr, cam, rho_op, z=0.0)
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dmap_rho = (datamap_rho.map).T
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maps_disk["rho_" + ax_los] = dmap_rho
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if "speed" in images:
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vh_op = ScalarOperator(
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lambda dset: dset["vel"][:, ax_nb[ax_h]], ro.info["unit_velocity"]
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)
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dmap_vh = slicing.SliceMap(amr, cam, vh_op, z=0.0)
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vv_op = ScalarOperator(
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lambda dset: dset["vel"][:, ax_nb[ax_v]], ro.info["unit_velocity"]
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)
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dmap_vv = slicing.SliceMap(amr, cam, vv_op, z=0.0)
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maps_disk["v" + ax_h + "_" + ax_los] = (dmap_vh.map).T
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maps_disk["v" + ax_v + "_" + ax_los] = (dmap_vv.map).T
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if "T" in images:
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P_op = ScalarOperator(lambda dset: dset["P"], ro.info["unit_pressure"])
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dmap_P = (slicing.SliceMap(amr, cam, P_op, z=0.0)).map.T
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dmap_T = dmap_P / dmap_rho
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maps_disk["T_" + ax_los] = dmap_T
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# Toomre parameter
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if "Q" in images and ax_los == "z":
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def omega_rho_func(dset):
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pos = dset.get_cell_centers()
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pos = pos - (pos_star / lbox)
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xx = pos[:, :, 0]
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yy = pos[:, :, 1]
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rc = np.sqrt(xx ** 2 + yy ** 2) # cylindrical radius
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vx = dset["vel"][:, :, 0]
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vy = dset["vel"][:, :, 1]
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omega_rho = 1.0 / rc ** 2
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omega_rho = omega_rho * dset["rho"]
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vyx = vy * xx
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vxy = vx * yy
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omega_rho = omega_rho * (vyx - vxy)
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return omega_rho
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omega_op = FractionOperator(
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omega_rho_func, lambda dset: dset["rho"], 1.0 / ro.info["unit_time"]
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)
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cs_op = FractionOperator(
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lambda dset: dset["P"],
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lambda dset: dset["rho"],
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ro.info["unit_velocity"],
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)
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rt_omega = raytracing.RayTracer(amr, ro.info, omega_op)
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if fft:
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rt_cs = splatting.SplatterProcessor(amr, ro.info, cs_op, surf_qty=False)
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else:
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rt_cs = raytracing.RayTracer(amr, ro.info, cs_op)
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dmap_omega = rt_omega.process(cam)
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dmap_cs = rt_cs.process(cam)
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map_Q = (lbox * dmap_cs.map.T) * dmap_omega.map.T / (np.pi * G * dmap_col)
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maps_disk["Q_" + ax_los] = map_Q
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# Levels
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if "levels" in images:
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amr.set_read_levelmax(20)
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level_op = MaxLevelOperator()
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rt_level = raytracing.RayTracer(amr, ro.info, level_op)
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datamap = rt_level.process(cam, surf_qty=True)
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map_level = datamap.map.T
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maps_disk["levels_" + ax_los] = map_level
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# Cpus
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if "cpu" in images:
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cpu_op = ScalarOperator(
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lambda dset: dset.icpu * (np.ones(dset["P"].shape)),
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ro.info["unit_pressure"],
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)
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rt_cpu = raytracing.RayTracer(amr, ro.info, cpu_op)
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datamap = rt_cpu.process(cam, surf_qty=True)
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map_cpu = datamap.map.T
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maps_disk["cpu_" + ax_los] = map_cpu
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if save_data:
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f = open(name_save, "w")
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pickle.dump(maps_disk, f)
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f.close()
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return maps_disk
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def plot_maps(
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path,
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num,
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force=False,
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vel_red=40,
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tag="",
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images=None,
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axes_los=None,
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maps_disk=None,
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interactive=False,
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put_title=True,
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):
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"""
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Make several useful image of an output of a simulation, auxillary function
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Parameters
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----------
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amr
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ro pymses.RamsesOutput object
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center 3D array for coordinates center
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num output number
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path path of the pipeline output
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force if set, erase any existing pipeline output files
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tag string to add to the output name
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vel_red Take point each vel_red for velocities
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map_size size of the map
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"""
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path_out = path
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# Load maps file
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print("load maps file")
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name_maps = path + "/maps_disk" + "_" + tag + "_" + format(num, "05") + ".save"
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if maps_disk is None:
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if len(glob.glob(name_maps)) == 0:
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raise IOError("no pickle file for disk maps. Run make_image_disk() first")
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f = open(name_maps, "r")
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maps_disk = pickle.load(f)
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f.close()
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print("maps file loaded")
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time = maps_disk["time"]
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im_extent = maps_disk["im_extent"]
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center = maps_disk["center"]
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radius = maps_disk["radius"]
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lbox = maps_disk["lbox"]
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# Plot parameters
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title = "t=" + str(time)[0:5] + " (code)"
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ntick = 6
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title_ax = {"x": r"$x$ (code)", "y": r"$y$ (code)", "z": r"$z$ (code)"}
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# Determine output directory
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if path_out is not None:
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directory = path_out
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else:
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directory = path
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# Determine what to plot
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if images == None:
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images = maps_disk["images"]
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if axes_los == None:
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axes_los = maps_disk["axes_los"]
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# Prepare axes
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axes_h = {"x": "y", "y": "x", "z": "x"} # Associated horizontal axe
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axes_v = {"x": "z", "y": "z", "z": "y"} # Associated vertical axe
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P.close()
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for ax_los in axes_los:
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ax_h = axes_h[ax_los]
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ax_v = axes_v[ax_los]
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for image in images:
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if (
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(image == "Q" and not ax_los == "z")
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or image == "levels"
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or image == "speed"
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):
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continue
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map_disk = maps_disk[image + "_" + ax_los]
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if image == "Q":
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im = P.imshow(
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map_Q,
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extent=im_extent,
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origin="lower",
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cmap="RdBu",
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norm=mpl.colors.LogNorm(),
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vmin=0.01,
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vmax=100.0,
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)
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else:
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im = P.imshow(
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map_disk,
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extent=im_extent,
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origin="lower",
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norm=mpl.colors.LogNorm(),
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)
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P.locator_params(axis=ax_h, nbins=ntick)
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P.locator_params(axis=ax_v, nbins=ntick)
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if put_title:
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P.title(title)
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P.xlabel(title_ax[ax_h])
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P.ylabel(title_ax[ax_v])
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cbar = P.colorbar(im)
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if image == "coldens":
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cbar.set_label(r"$log(N)$ (code)")
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if "levels" in images:
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map_level = maps_disk["levels_" + ax_los]
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# Computing linewidths
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lw = np.ones(levels_ar.size) * 2
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lvl_th = 8 # Level threeshold for reducing linewidths
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lw[levels_ar >= lvl_th] = lw[levels_ar >= lvl_th] ** (
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lvl_th - levels_ar[levels_ar >= lvl_th]
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)
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lw[levels_ar < lvl_th] = 1.0
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cont = P.contour(
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map_level,
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extent=im_extent,
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origin="lower",
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colors="k",
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linewidths=lw,
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levels=levels_ar,
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)
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cont.levels = cont.levels + 1
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P.clabel(
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cont,
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levels_ar[levels_ar < lvl_th + 2][1::2],
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inline=1,
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fontsize=8.0,
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fmt="%1d",
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)
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elif image == "rho":
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cbar.set_label(r"$log(n)$ (code)")
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if "speed" in images:
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dmap_vh = maps_disk["v" + ax_h + "_" + ax_los]
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dmap_vv = maps_disk["v" + ax_v + "_" + ax_los]
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map_vh_red = dmap_vh[
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::vel_red, ::vel_red
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] # take only a subset of velocities
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map_vv_red = dmap_vv[::vel_red, ::vel_red]
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nh = map_vh_red.shape[0]
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nv = map_vv_red.shape[1]
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vec_h = (
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np.arange(nh) * 2.0 / nh * radius
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- radius
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+ center[0]
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+ radius / nh
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) * lbox
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vec_v = (
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np.arange(nv) * 2.0 / nv * radius
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- radius
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+ center[1]
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+ radius / nv
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) * lbox
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hh, vv = np.meshgrid(vec_h, vec_v)
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max_v = np.max(np.sqrt(map_vh_red ** 2 + map_vv_red ** 2))
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Q = P.quiver(hh, vv, map_vh_red, map_vv_red, units="width")
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P.quiverkey(
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Q,
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0.7,
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0.95,
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max_v,
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r"$" + str(max_v)[0:4] + "$ (code)",
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labelpos="E",
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coordinates="figure",
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)
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elif image == "T":
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cbar.set_label(r"$log(T) \, (K)$")
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elif image == "Q":
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cbar.set_label(r"$Q$")
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else:
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cbar.set_label(image)
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name = (
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directory
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+ "/"
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+ image
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+ "_"
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+ ax_los
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+ "_"
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+ tag
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+ "_"
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+ format(num, "05")
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+ out_ext
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)
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if interactive:
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P.figure()
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else:
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P.tight_layout(pad=1)
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P.savefig(name)
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P.close()
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return maps_disk
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def disk_prop(
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path_in,
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num,
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path_out=None,
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force=False,
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nb_bin=20,
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rad_ext=1.0,
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mass_star=1.0,
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pos_star=np.array([1.0, 1.0, 1.0]),
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binning="log",
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):
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"""
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Compute properties of a disk in the plane (0,x,y)
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with a protostar at the center of the box
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The region of the disk is defined by its scale height
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Parameters
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----------
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path_in path of the input data files (output of ramses)
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num id of the output
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path_out optional path to the output files
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force if set, redo ouptut even if already done
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nb_bin Number of radial bins
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rad_ext Outer radius of the disk
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pos_star position of the central protostar
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mass_star mass of the central protostar
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"""
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# Set the output directory
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if path_out is not None:
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directory_out = path_out
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else:
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directory_out = path_in
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# Check if the output file exists, and exit if it is the case
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name_save = directory_out + "/prop_disk_" + str(num).zfill(5) + ".save"
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if not force and len(glob.glob(name_save)) != 0:
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return
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# Compute the bins array
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if binning == "log":
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lrad = np.log10(rad_ext)
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rad = np.logspace(lrad - 2.0, lrad, num=nb_bin)
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elif binning == "lin":
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rad = np.linspace(0.0, rad_ext, num=nb_bin)
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else:
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raise ValueError(
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"Incorrect binning specification, binnning should be 'lin' or 'log'"
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)
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# Get Ramses data
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ro = pymses.RamsesOutput(path_in, num)
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lbox = ro.info["boxlen"] # boxlen in codeunits (=>pc)
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time = ro.info["time"] # time in codeunits
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# Get array of cell positions
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amr = ro.amr_source(["rho", "vel", "Br", "Bl", "P", "g", "phi"])
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cell_source = CellsToPoints(amr)
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cells = cell_source.flatten()
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dx = cells.get_sizes() * lbox
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pos = cells.points * lbox
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# Get positions in the frame of the protostar
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pos = pos - pos_star
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# Get cylindrical radius
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rc = np.sqrt(pos[:, 0] ** 2 + pos[:, 1] ** 2)
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# Get velocities
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vel = cells["vel"]
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# Get radial component of velocity
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norm_pos = rc
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norm_pos[rc == 0] = 1.0e-10 # Avoid division per 0
|
|
v_rad = (pos[:, 0] * vel[:, 0] + pos[:, 1] * vel[:, 1]) / norm_pos
|
|
# Get azimuthal component of velocity
|
|
v_az = (pos[:, 0] * vel[:, 1] - pos[:, 1] * vel[:, 0]) / norm_pos
|
|
# Gravitational field
|
|
g = cells["g"]
|
|
g_rad = (pos[:, 0] * g[:, 0] + pos[:, 1] * g[:, 1]) / norm_pos
|
|
g_az = (pos[:, 0] * g[:, 1] - pos[:, 1] * g[:, 0]) / norm_pos
|
|
|
|
# Select cells that are actually in the disk, ie within the scale height
|
|
G = 1.0
|
|
cs = np.sqrt(cells["P"] / cells["rho"]) # sound velocity
|
|
height = cs * np.sqrt(rc ** 3 / (G * mass_star))
|
|
mask_pos = np.abs(pos[:, 2]) < height # condition on position
|
|
mask_dens = cells["rho"] > 0.01 * np.mean(cells["rho"]) # condition on density
|
|
mask_vel = abs(v_rad / v_az) < 1.0 # condition on speed
|
|
|
|
mask = mask_pos # & mask_dens & mask_vel
|
|
print("Number of selected cells ", np.sum(mask))
|
|
|
|
pos_disk = pos[mask]
|
|
rc_disk = rc[mask]
|
|
vel_disk = vel[mask]
|
|
rho_disk = cells["rho"][mask] # density
|
|
press_disk = cells["P"][mask] # pressure
|
|
dx_disk = dx[mask]
|
|
dvol_disk = dx_disk ** 3
|
|
v_rad_disk = v_rad[mask]
|
|
v_az_disk = v_az[mask]
|
|
v_kepl = np.sqrt(mass_star * G / rc_disk)
|
|
# height_disk = height[mask]
|
|
g_rad_disk = g_rad[mask]
|
|
g_az_disk = g_az[mask]
|
|
|
|
total_mass_disk = np.sum(rho_disk * dvol_disk)
|
|
total_mass = np.sum(cells["rho"] * dx ** 3)
|
|
|
|
print("Mass disk", total_mass_disk)
|
|
print("Mass box", total_mass)
|
|
|
|
# Initialize binned quantities
|
|
cs_rad = np.zeros(nb_bin - 1)
|
|
temp_rad = np.zeros(nb_bin - 1)
|
|
press_rad = np.zeros(nb_bin - 1)
|
|
rho_rad = np.zeros(nb_bin - 1)
|
|
coldens_rad = np.zeros(nb_bin - 1)
|
|
v_az_rad = np.zeros(nb_bin - 1)
|
|
v_kepl_rad = np.zeros(nb_bin - 1)
|
|
v_rad_rad = np.zeros(nb_bin - 1)
|
|
alpha_rey_rad = np.zeros(nb_bin - 1)
|
|
alpha_rey_rad_bis = np.zeros(nb_bin - 1)
|
|
alpha_grav_rad = np.zeros(nb_bin - 1)
|
|
Q_kepl_rad = np.zeros(nb_bin - 1)
|
|
height_rad = np.zeros(nb_bin - 1)
|
|
vol_rad = np.zeros(nb_bin - 1) # Volume of a bin
|
|
surf_rad = np.zeros(nb_bin - 1) # Surface of a bin
|
|
mass_rad = np.zeros(nb_bin - 1) # Mass of a bin
|
|
|
|
for i in range(nb_bin - 1):
|
|
mask_bin = (rc_disk > rad[i]) & (rc_disk < rad[i + 1])
|
|
|
|
# print("Bin #{} : {} cells between {} and {}".format(i, np.sum(mask_bin), rad[i], rad[i + 1]))
|
|
vol_rad[i] = np.sum(dvol_disk[mask_bin])
|
|
mass_rad[i] = np.sum(rho_disk[mask_bin] * dvol_disk[mask_bin])
|
|
press_rad[i] = np.sum(press_disk[mask_bin] * dvol_disk[mask_bin]) / vol_rad[i]
|
|
rho_rad[i] = np.sum(rho_disk[mask_bin] * dvol_disk[mask_bin]) / vol_rad[i]
|
|
temp_rad[i] = np.sum(press_disk[mask_bin] * dvol_disk[mask_bin]) / mass_rad[i]
|
|
|
|
# Surface of a bin : S = dr * 2 * pi * r with
|
|
# dr = rad[i + 1] - rad[i] and r = (rad[i + 1] + rad[i]) / 2.
|
|
surf_rad[i] = (rad[i + 1] - rad[i]) * (rad[i + 1] + rad[i]) * np.pi
|
|
coldens_rad[i] = mass_rad[i] / surf_rad[i]
|
|
|
|
v_az_rad[i] = (
|
|
np.sum(v_az_disk[mask_bin] * rho_disk[mask_bin] * dvol_disk[mask_bin])
|
|
/ mass_rad[i]
|
|
)
|
|
|
|
v_rad_rad[i] = (
|
|
np.sum(v_rad_disk[mask_bin] * rho_disk[mask_bin] * dvol_disk[mask_bin])
|
|
/ mass_rad[i]
|
|
)
|
|
|
|
try:
|
|
height_rad[i] = (
|
|
np.max(pos_disk[:, 2][mask_bin]) - np.min(pos_disk[:, 2][mask_bin])
|
|
) / 2.0
|
|
except ValueError:
|
|
height_rad[i] = 0
|
|
|
|
alpha_rey_rad[i] = (2.0 / 3) * (
|
|
(
|
|
np.sum(
|
|
v_az_disk[mask_bin]
|
|
* v_rad_disk[mask_bin]
|
|
* rho_disk[mask_bin]
|
|
* dvol_disk[mask_bin]
|
|
)
|
|
/ np.sum(dvol_disk[mask_bin] * press_disk[mask_bin])
|
|
- v_az_rad[i] * v_rad_rad[i] * rho_rad[i] / press_rad[i]
|
|
)
|
|
* v_az_rad[i]
|
|
/ abs(v_az_rad[i])
|
|
)
|
|
|
|
alpha_grav_rad[i] = (2.0 / 3) * (
|
|
np.sum(
|
|
g_az_disk[mask_bin]
|
|
* g_rad_disk[mask_bin]
|
|
* rho_disk[mask_bin]
|
|
* dvol_disk[mask_bin]
|
|
)
|
|
/ (4 * np.pi * G)
|
|
/ np.sum(dvol_disk[mask_bin] * press_disk[mask_bin])
|
|
* coldens_rad[i]
|
|
)
|
|
|
|
v_kepl_rad[i] = (
|
|
np.sum(v_kepl[mask_bin] * rho_disk[mask_bin] * dvol_disk[mask_bin])
|
|
/ mass_rad[i]
|
|
)
|
|
|
|
# Derived quantities
|
|
cs_rad = np.sqrt(temp_rad)
|
|
Q_kepl_rad = cs_rad * v_az_rad / (np.pi * G * coldens_rad * rad[0 : nb_bin - 1])
|
|
|
|
# Means
|
|
mask_mean = (0.1 < rad[0 : nb_bin - 1]) & (rad[0 : nb_bin - 1] < 0.2)
|
|
mass_mean = np.sum(mass_rad[mask_mean])
|
|
alpha_rey_mean = np.sum(alpha_rey_rad[mask_mean] * mass_rad[mask_mean]) / mass_mean
|
|
alpha_grav_mean = (
|
|
np.sum(alpha_grav_rad[mask_mean] * mass_rad[mask_mean]) / mass_mean
|
|
)
|
|
Q_mean = np.sum(Q_kepl_rad[mask_mean] * mass_rad[mask_mean]) / mass_mean
|
|
Q_min = np.nanmin(Q_kepl_rad)
|
|
|
|
# store the results
|
|
prop_disk = {
|
|
"time": time,
|
|
"mass_disk": total_mass_disk,
|
|
"mass_box": total_mass,
|
|
"rad": rad[:-1],
|
|
"center": pos_star,
|
|
"alpha_rey": alpha_rey_rad,
|
|
"alpha_rey_mean": alpha_rey_mean,
|
|
"alpha_grav": alpha_grav_rad,
|
|
"alpha_grav_mean": alpha_grav_mean,
|
|
"v_rad": v_rad_rad,
|
|
"v_az": v_az_rad,
|
|
"v_kepl": v_kepl_rad,
|
|
"coldens": coldens_rad,
|
|
"rho": rho_rad,
|
|
"press": press_rad,
|
|
"temp": temp_rad,
|
|
"cs": cs_rad,
|
|
"Q_kepl": Q_kepl_rad,
|
|
"Q_mean": Q_mean,
|
|
"Q_min": Q_min,
|
|
"height": height_rad,
|
|
}
|
|
f = open(name_save, "w")
|
|
pickle.dump(prop_disk, f)
|
|
f.close()
|
|
|
|
|
|
def plot_disk_prop(
|
|
path,
|
|
num,
|
|
plots=["rho", "T", "V", "coldens", "Q", "alpha", "H"],
|
|
force=False,
|
|
tag="",
|
|
interactive=False,
|
|
put_title=False,
|
|
):
|
|
"""
|
|
Plot properties of a disk
|
|
|
|
num id of the ramses output
|
|
path path to the properties file
|
|
force if set, redo plots even if already done
|
|
"""
|
|
|
|
# Load property file
|
|
name_save = path + "/prop_disk_" + str(num).zfill(5) + ".save"
|
|
|
|
# Check if the properties file exists
|
|
if len(glob.glob(name_save)) == 0:
|
|
raise ("no pickle file for disk properties. Run disk_prop() first")
|
|
f = open(name_save, "r")
|
|
prop_disk = pickle.load(f)
|
|
f.close()
|
|
|
|
# Check if the output file exists, and exit if it is the case
|
|
name_save = path + "/rho_disk_r_" + str(num).zfill(5) + out_ext
|
|
if not force and len(glob.glob(name_save)) != 0:
|
|
return
|
|
|
|
time = prop_disk["time"]
|
|
mass = prop_disk["mass_disk"]
|
|
rad = prop_disk["rad"]
|
|
|
|
for plot in plots:
|
|
title = "t=" + str(time)[0:5] + " (code)"
|
|
P.grid()
|
|
P.xlabel("disk radius")
|
|
if plot == "rho":
|
|
P.xscale("log")
|
|
P.yscale("log")
|
|
P.plot(rad, prop_disk["rho"], color="k", linewidth=2)
|
|
P.ylabel(r"$n \, (code)$")
|
|
elif plot == "T":
|
|
P.xscale("log")
|
|
P.yscale("log")
|
|
P.plot(rad, prop_disk["temp"], color="k", linewidth=2)
|
|
P.ylabel(r"$T \, (K)$")
|
|
elif plot == "V":
|
|
P.xscale("log")
|
|
P.yscale("symlog", linthreshy=0.01)
|
|
P.plot(rad, prop_disk["v_rad"], color="k", linewidth=2)
|
|
P.plot(rad, prop_disk["v_kepl"], color="b", linewidth=2)
|
|
P.plot(rad, abs(prop_disk["v_az"]), color="r", linewidth=2)
|
|
P.plot(rad, prop_disk["cs"], color="c", linewidth=2)
|
|
P.legend(
|
|
(r"$v_r$", r"$v_{kepl}$", r"$v_\phi$", r"$c_s$"), loc="upper right"
|
|
)
|
|
P.ylabel(r"$V \, (km s^{-1})$")
|
|
elif plot == "coldens":
|
|
P.xscale("log")
|
|
P.yscale("log")
|
|
P.plot(rad, prop_disk["coldens"], color="k", linewidth=2)
|
|
P.ylabel(r"$N\, (cm^{-2})$")
|
|
elif plot == "alpha":
|
|
alpha_rey_mean, alpha_grav_mean = (
|
|
prop_disk["alpha_rey_mean"],
|
|
prop_disk["alpha_grav_mean"],
|
|
)
|
|
P.xlim([1e-2, 0.25])
|
|
P.yscale("log")
|
|
P.ylim([1e-7, 1.0])
|
|
P.plot(
|
|
rad,
|
|
abs(prop_disk["alpha_rey"]),
|
|
"b",
|
|
linewidth=2,
|
|
label=r"$\alpha_{Reynolds}$",
|
|
)
|
|
P.plot(rad, abs(alpha_rey_mean * np.ones(len(rad))), "b:", linewidth=1)
|
|
P.plot(
|
|
rad,
|
|
abs(prop_disk["alpha_grav"]),
|
|
"r",
|
|
linewidth=2,
|
|
label=r"$\alpha_{grav}$",
|
|
)
|
|
P.plot(rad, abs(alpha_grav_mean * np.ones(len(rad))), "r:", linewidth=1)
|
|
P.plot(
|
|
rad,
|
|
abs(prop_disk["alpha_rey"]) + abs(prop_disk["alpha_grav"]),
|
|
"g--",
|
|
linewidth=2,
|
|
label=r"$\alpha_{tot}$",
|
|
)
|
|
P.legend()
|
|
P.ylabel(r"$\alpha$")
|
|
title = (
|
|
title
|
|
+ r", $\bar{\alpha}_{Reynolds} = %.1e, \bar{\alpha}_{grav} = %.1e$"
|
|
% (alpha_rey_mean, alpha_grav_mean)
|
|
)
|
|
elif plot == "Q":
|
|
P.ylim([0, 3.0])
|
|
P.xlim([0, 0.25])
|
|
P.yticks(np.arange(0.0, 3, 1.0))
|
|
P.plot(rad, abs(prop_disk["Q_kepl"]), color="b", linewidth=2)
|
|
# P.plot(rad, abs(prop_disk['Q_mean']) * np.ones(len(rad)), 'b:', linewidth=1)
|
|
P.ylabel(r"$Q$")
|
|
P.xlabel("disk radius ")
|
|
title = title + ", mass of disk = {} (code)".format(mass)
|
|
elif plot == "H":
|
|
P.plot(rad, abs(prop_disk["height"] / rad), color="b", linewidth=2)
|
|
P.ylabel(r"H ratio")
|
|
title = title + ", mass of box = {} (code)".format(prop_disk["mass_box"])
|
|
|
|
if put_title:
|
|
P.title(title)
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/" + plot + "_disk_r_" + str(num).zfill(5) + out_ext)
|
|
P.close()
|
|
|
|
|
|
def disk_pdf(
|
|
path,
|
|
num,
|
|
maps_disk,
|
|
pos_star=[1.0, 1.0],
|
|
force=False,
|
|
interactive=False,
|
|
nb_bin_hist=50,
|
|
tag="",
|
|
rad_min=0.075,
|
|
rad_max=0.3,
|
|
put_title=True,
|
|
do_speed=True,
|
|
):
|
|
|
|
# Load property file
|
|
name_prop = path + "/prop_disk_" + str(num).zfill(5) + ".save"
|
|
|
|
# Check if the properties file exists
|
|
if len(glob.glob(name_prop)) == 0:
|
|
raise IOError("no pickle file for disk properties. Run disk_prop() first")
|
|
f = open(name_prop, "r")
|
|
prop_disk = pickle.load(f)
|
|
f.close()
|
|
|
|
# Check if the job has already been done
|
|
if not force:
|
|
try:
|
|
slope = prop_disk["fit"]["slope"]
|
|
print("PDF already computed, slope = {}, exiting ...".format(slope))
|
|
return
|
|
except KeyError:
|
|
pass
|
|
|
|
# Load maps file
|
|
print("load maps file")
|
|
name_maps = path + "/maps_disk" + "_" + tag + "_" + format(num, "05") + ".save"
|
|
|
|
# Check if the maps file exists
|
|
if maps_disk is None:
|
|
if len(glob.glob(name_maps)) == 0:
|
|
raise IOError("no pickle file for disk maps. Run make_image_disk() first")
|
|
f = open(name_maps, "r")
|
|
maps_disk = pickle.load(f)
|
|
f.close()
|
|
print("maps file loaded")
|
|
|
|
# Properties
|
|
time = prop_disk["time"]
|
|
im_extent = maps_disk["im_extent"]
|
|
title = tag.split("_")[1] + " t=" + str(time)[0:5] + " (code)"
|
|
|
|
# Load coldens
|
|
coldens_map = maps_disk["coldens_z"]
|
|
|
|
# radius of the corner of the box plus a margin
|
|
rad_box = (
|
|
np.sqrt((im_extent[1] - pos_star[0]) ** 2 + (im_extent[3] - pos_star[1]) ** 2)
|
|
+ 0.1
|
|
)
|
|
# radial bins
|
|
rad_bins = prop_disk["rad"]
|
|
rad_bins = 0.5 * (rad_bins[0:-1] + rad_bins[1:])
|
|
rad_bins = np.concatenate(([0.0], rad_bins, [rad_box]))
|
|
|
|
x = np.linspace(im_extent[0], im_extent[1], coldens_map.shape[0])
|
|
y = np.linspace(im_extent[2], im_extent[3], coldens_map.shape[1])
|
|
xx, yy = np.meshgrid(x, y)
|
|
rr = np.sqrt((xx - pos_star[0]) ** 2 + (yy - pos_star[1]) ** 2)
|
|
|
|
# Find appropriate bin for each coordinate set
|
|
bins = np.zeros(rr.shape, dtype=int)
|
|
for r in rad_bins[1:]:
|
|
bins = bins + (rr >= r).astype(int)
|
|
bins_flat = bins.flatten()
|
|
rr_flat = rr.flatten()
|
|
|
|
# Mask selecting the zone of interest
|
|
mask_map = (rr > rad_min) & (rr < rad_max)
|
|
mask_flat = mask_map.flatten()
|
|
|
|
# Additionnal maps
|
|
rho_map = maps_disk["rho_z"]
|
|
cs_map = np.sqrt(maps_disk["T_z"])
|
|
|
|
vx_map = maps_disk["vx_z"]
|
|
vy_map = maps_disk["vy_z"]
|
|
v_map = np.sqrt(vx_map ** 2 + vy_map ** 2)
|
|
v_kepl = np.sqrt(1.0 / rr)
|
|
xx_star = xx - pos_star[0]
|
|
yy_star = yy - pos_star[1]
|
|
vrad_map = (vx_map * xx_star + vy_map * yy_star) / rr
|
|
vaz_map = (vy_map * xx_star - vx_map * yy_star) / rr
|
|
|
|
maps = {
|
|
"coldens": coldens_map,
|
|
"rho": rho_map,
|
|
"cs": cs_map,
|
|
"v": v_map,
|
|
"vaz": vaz_map,
|
|
}
|
|
|
|
avg_maps = {}
|
|
fluct_maps = {}
|
|
|
|
for cur_map in maps:
|
|
map_arr = maps[cur_map]
|
|
|
|
# mean of all the cells in the bin
|
|
mean_bin = np.zeros(len(rad_bins) - 1)
|
|
for j in range(len(rad_bins) - 1):
|
|
mean_bin[j] = np.mean(map_arr[bins == j])
|
|
|
|
# Add value for borders
|
|
mean_bin = np.concatenate(([mean_bin[0]], mean_bin))
|
|
|
|
# Compute the map azimuthally averaged
|
|
# use linear interpolation to improve accuracy
|
|
avg_flat = (rad_bins[bins_flat + 1] - rr_flat) * mean_bin[bins_flat]
|
|
avg_flat = avg_flat + (rr_flat - rad_bins[bins_flat]) * mean_bin[bins_flat + 1]
|
|
avg_flat = avg_flat / (rad_bins[bins_flat + 1] - rad_bins[bins_flat])
|
|
avg_maps[cur_map] = np.reshape(avg_flat, rr.shape)
|
|
|
|
# Select zone of interest
|
|
avg_maps[cur_map][np.logical_not(mask_map)] = np.nan
|
|
|
|
# Compute fluctuation
|
|
fluct_maps[cur_map] = map_arr / avg_maps[cur_map]
|
|
|
|
# Histogramm : density fluctuation distribution function
|
|
dcoldens = np.log10(fluct_maps["coldens"]).flatten()
|
|
|
|
nb_cells = np.sum(mask_flat)
|
|
P.grid()
|
|
P.yscale("log")
|
|
P.ylim([0.5 / nb_cells, 1.0])
|
|
P.xlabel(r"$\log(N / \bar{N})$")
|
|
P.ylabel(r"$\mathcal{P}_N$")
|
|
if put_title:
|
|
P.title(title)
|
|
values, edges, _ = P.hist(
|
|
dcoldens[mask_flat],
|
|
nb_bin_hist,
|
|
range=(-1, 3),
|
|
weights=np.ones(nb_cells) / nb_cells,
|
|
)
|
|
centers = 0.5 * (edges[1:] + edges[:-1])
|
|
|
|
# Variance
|
|
var = np.var(dcoldens[mask_flat])
|
|
# Compute the slope of the right part of the histogramm
|
|
mask_fit = (centers > 0.25) & (centers < 1.25) & (values > 0)
|
|
if np.sum(mask_fit > 0):
|
|
(a, b, rho, _, stderr) = linregress(
|
|
centers[mask_fit], np.log10(values[mask_fit])
|
|
)
|
|
P.plot(centers, 10 ** (a * centers + b), "--", linewidth=2)
|
|
print("a=%e, b=%e, rho=%e, var=%e" % (a, b, rho, var))
|
|
try:
|
|
beta = int(tag.split("_")[1][4:])
|
|
except ValueError:
|
|
beta = 0
|
|
fit = {
|
|
"beta": beta,
|
|
"slope": a,
|
|
"origin": b,
|
|
"correlation": rho,
|
|
"stderr": stderr,
|
|
"var": var,
|
|
}
|
|
f = open(name_prop, "w")
|
|
prop_disk["fit"] = fit
|
|
pickle.dump(prop_disk, f)
|
|
f.close()
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/dcol_hist_" + tag + "_" + str(num).zfill(5) + out_ext)
|
|
P.close()
|
|
|
|
# Derived quantities
|
|
drho = fluct_maps["rho"].flatten()
|
|
dcs = fluct_maps["cs"].flatten()
|
|
dv = fluct_maps["v"].flatten()
|
|
dvaz_kepl = abs(maps["vaz"] - v_kepl) / v_kepl
|
|
fluct_maps["vaz_kepl"] = dvaz_kepl
|
|
dmach = abs(maps["v"] - avg_maps["v"]) / maps["cs"]
|
|
fluct_maps["mach"] = dmach
|
|
dmach_mean = np.mean(dmach[mask_map].flatten())
|
|
|
|
# Fluctuations plots
|
|
f = open(name_prop, "w")
|
|
prop_disk["dmach_mean"] = dmach_mean
|
|
print("dmach_mean = {}".format(dmach_mean))
|
|
pickle.dump(prop_disk, f)
|
|
f.close()
|
|
|
|
# dcs = f(drho)
|
|
P.hist2d(
|
|
np.log10(drho[mask_flat]),
|
|
np.log10(dcs[mask_flat]),
|
|
(1000, 1000),
|
|
norm=mpl.colors.LogNorm(),
|
|
)
|
|
P.xlabel(r"$\log(\rho / \bar{\rho})$")
|
|
P.ylabel(r"$\log(c_s / \bar{c_s})$")
|
|
P.ylim([-0.6, 0.6])
|
|
|
|
P.colorbar()
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/dcs_" + tag + "_" + str(num).zfill(5) + out_ext)
|
|
P.close()
|
|
|
|
# dv = f(drho)
|
|
P.hist2d(
|
|
np.log10(drho[mask_flat]),
|
|
dv[mask_flat],
|
|
(1000, 1000),
|
|
norm=mpl.colors.LogNorm(),
|
|
)
|
|
P.xlabel(r"$\log(\rho / \bar{\rho})$")
|
|
P.ylabel(r"$\log(v_\varphi / \bar{v_\varphi})$")
|
|
|
|
P.colorbar()
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/dv_" + tag + "_" + str(num).zfill(5) + out_ext)
|
|
P.close()
|
|
|
|
# Fluctuations maps
|
|
|
|
for cur_map in fluct_maps:
|
|
fluct_map = fluct_maps[cur_map]
|
|
if cur_map == "coldens":
|
|
im = P.imshow(
|
|
fluct_map,
|
|
norm=mpl.colors.LogNorm(),
|
|
extent=im_extent,
|
|
origin="lower",
|
|
cmap="viridis",
|
|
)
|
|
label = r"$log(N/\bar{N})$"
|
|
elif cur_map == "cs":
|
|
im = P.imshow(
|
|
np.log10(fluct_map),
|
|
extent=im_extent,
|
|
origin="lower",
|
|
cmap="RdBu_r",
|
|
vmin=-0.6,
|
|
vmax=0.6,
|
|
)
|
|
label = r"$log(c_s/\bar{c_s})$"
|
|
elif cur_map == "rho":
|
|
im = P.imshow(
|
|
np.log10(fluct_map),
|
|
extent=im_extent,
|
|
origin="lower",
|
|
cmap="RdBu_r",
|
|
vmin=-2.0,
|
|
vmax=2.0,
|
|
)
|
|
label = r"$log(\rho/\bar{\rho})$"
|
|
elif cur_map == "vaz_kepl":
|
|
im = P.imshow(
|
|
fluct_map,
|
|
extent=im_extent,
|
|
origin="lower",
|
|
cmap="RdBu_r",
|
|
norm=mpl.colors.LogNorm(),
|
|
vmax=1.0,
|
|
vmin=0.01,
|
|
)
|
|
label = r"$|v_\varphi - v_{kepl}|/v_{kepl}$"
|
|
elif cur_map == "vaz":
|
|
im = P.imshow(fluct_map, extent=im_extent, origin="lower", cmap="RdBu_r")
|
|
label = r"$v_\varphi / \bar{v_\varphi}$"
|
|
elif cur_map == "mach":
|
|
im = P.imshow(fluct_map, extent=im_extent, origin="lower", cmap="RdBu_r")
|
|
label = r"$|v - \bar{v}| / c_s$"
|
|
|
|
if put_title:
|
|
P.title(title)
|
|
P.xlabel(r"$x$")
|
|
P.ylabel(r"$y$")
|
|
cbar = P.colorbar(im)
|
|
cbar.set_label(label)
|
|
name = path + "/d" + cur_map + "map_" + tag + "_" + format(num, "05")
|
|
name_im = name + out_ext
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(name_im)
|
|
P.close()
|
|
|
|
|
|
def compare(
|
|
path,
|
|
runs,
|
|
nums,
|
|
path_out=None,
|
|
force=False,
|
|
interactive=False,
|
|
Q_in_name=True,
|
|
pdf=False,
|
|
gamma=5.0 / 3.0,
|
|
):
|
|
"""
|
|
Compare time averaged properties of a disk in several simulations
|
|
|
|
nums id or array of ids of the ramses output
|
|
runs list of runs to consider
|
|
path path to the properties file
|
|
force if set, redo plots even if already done
|
|
interactive interactive mode, to use in a %pylab ipython shell
|
|
"""
|
|
|
|
if type(nums) == int:
|
|
nums = [nums]
|
|
|
|
nums_name = "_".join(str(num).zfill(5) for num in [nums[0], nums[-1]])
|
|
|
|
# Initialize arrays
|
|
alpha_rey = np.zeros(len(runs))
|
|
alpha_grav = np.zeros(len(runs))
|
|
Q = np.zeros(len(runs))
|
|
if Q_in_name:
|
|
Q0 = np.zeros(len(runs))
|
|
if pdf:
|
|
beta = np.zeros(len(runs))
|
|
slope = np.zeros(len(runs))
|
|
slope_std = np.zeros(len(runs))
|
|
var = np.zeros(len(runs))
|
|
var_std = np.zeros(len(runs))
|
|
dmach = np.zeros(len(runs))
|
|
dmach_std = np.zeros(len(runs))
|
|
|
|
all_var = []
|
|
all_dmach = []
|
|
|
|
for i, run in enumerate(runs):
|
|
path_run = path + "/" + run
|
|
nb_outputs = 0
|
|
|
|
slope_run = []
|
|
var_run = []
|
|
dmach_run = []
|
|
|
|
for num in nums:
|
|
try:
|
|
# Load property file
|
|
name_save = path_run + "/prop_disk_" + str(num).zfill(5) + ".save"
|
|
|
|
# Check if the properties file exists
|
|
if len(glob.glob(name_save)) == 0:
|
|
raise IOError(
|
|
"no pickle file for disk properties {}. Run disk_prop() first".format(
|
|
name_save
|
|
)
|
|
)
|
|
f = open(name_save, "r")
|
|
prop_disk = pickle.load(f)
|
|
f.close()
|
|
|
|
alpha_rey[i] = alpha_rey[i] + abs(prop_disk["alpha_rey_mean"])
|
|
alpha_grav[i] = alpha_grav[i] + abs(prop_disk["alpha_grav_mean"])
|
|
Q[i] = Q[i] + prop_disk["Q_min"]
|
|
if pdf:
|
|
fit = prop_disk["fit"]
|
|
beta[i] = fit["beta"]
|
|
slope_run.append(fit["slope"])
|
|
var_run.append(fit["var"])
|
|
dmach_run.append(prop_disk["dmach_mean"])
|
|
|
|
all_var = all_var + var_run
|
|
all_dmach = all_dmach + dmach_run
|
|
|
|
nb_outputs = nb_outputs + 1
|
|
print(run, num, nb_outputs)
|
|
|
|
except (IOError, KeyError) as e:
|
|
print(run, num, e, nb_outputs)
|
|
|
|
if nb_outputs > 0:
|
|
alpha_rey[i] = alpha_rey[i] / nb_outputs
|
|
alpha_grav[i] = alpha_grav[i] / nb_outputs
|
|
Q[i] = Q[i] / nb_outputs
|
|
if pdf:
|
|
slope[i] = np.mean(slope_run)
|
|
slope_std[i] = np.std(slope_run)
|
|
var[i] = np.mean(var_run)
|
|
var_std[i] = np.std(var_run)
|
|
dmach[i] = np.mean(dmach_run)
|
|
dmach_std[i] = np.std(dmach_run)
|
|
else:
|
|
for array in [alpha_rey, alpha_grav, Q]:
|
|
array[i] = np.nan
|
|
if pdf:
|
|
slope[i] = np.nan
|
|
var[i] = np.nan
|
|
dmach[i] = np.nan
|
|
|
|
if Q_in_name:
|
|
Q0[i] = float(run.split("_")[2][1:])
|
|
|
|
# Check if the output file exists, and exit if it is the case
|
|
name_save = path + "/alphaQ_" + nums_name + out_ext
|
|
# if (not force and len(glob.glob(name_save)) !=0):
|
|
# return
|
|
|
|
# alpha = f(Qmin)
|
|
P.yscale("log")
|
|
P.ylim([1e-7, 1.0])
|
|
P.grid()
|
|
|
|
P.plot(Q, abs(alpha_rey), "o--", label=r"$\bar{\alpha}_{Reynolds}$")
|
|
P.plot(Q, abs(alpha_grav), "*--", label=r"$\bar{\alpha}_{grav}$")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$\bar{\alpha}$")
|
|
P.xlabel(r"$Q_{min}$")
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path_out + "/alphaQ_" + nums_name + out_ext)
|
|
P.close()
|
|
|
|
if Q_in_name:
|
|
# alpha = f(Q0)
|
|
P.yscale("log")
|
|
P.ylim([1e-7, 1.0])
|
|
P.grid()
|
|
|
|
P.plot(Q0, abs(alpha_rey), "o-.", label=r"$\bar{\alpha}_{Reynolds}$")
|
|
P.plot(Q0, abs(alpha_grav), "*--", label=r"$\bar{\alpha}_{grav}$")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$\bar{\alpha}$")
|
|
P.xlabel(r"$Q_{0}$")
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path_out + "/alphaQ0_" + nums_name + out_ext)
|
|
P.close()
|
|
|
|
if pdf:
|
|
# slope of the pdf = f(beta)
|
|
P.grid()
|
|
P.errorbar(beta, slope, yerr=slope_std, fmt="o")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$d\log\mathcal{P}_N / d\logN$")
|
|
P.xlabel(r"$\beta$")
|
|
|
|
(a, b, rho, _, stderr) = linregress(beta, slope)
|
|
P.plot(beta, a * beta + b, "--", linewidth=1.5)
|
|
print("a=%e, b=%e, rho^2=%e" % (a, b, rho ** 2))
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path_out + "/dcolslopebeta_" + nums_name + out_ext)
|
|
P.close()
|
|
|
|
# var of the pdf = f(beta)
|
|
P.grid()
|
|
P.errorbar(beta, var, yerr=var_std, fmt="o")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$Var(\log(N / \bar(N))$")
|
|
P.xlabel(r"$\beta$")
|
|
|
|
(a, b, rho, _, stderr) = linregress(beta, var)
|
|
P.plot(beta, a * beta + b, "--", linewidth=1.5)
|
|
print("a=%e, b=%e, rho^2=%e" % (a, b, rho ** 2))
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path_out + "/varbeta_" + nums_name + out_ext)
|
|
P.close()
|
|
|
|
# var = f(log(dmach)
|
|
P.grid()
|
|
P.plot(all_dmach, all_var, "o")
|
|
|
|
P.legend()
|
|
P.xlabel(r"$<(v - \bar{v}) / c_s>$")
|
|
P.ylabel(r"$Var(\log(N / \bar(N))$")
|
|
P.yscale("log")
|
|
|
|
# (a, b, rho, _, stderr) = linregress(var, log(dmach)
|
|
# P.plot(var, a*var + b, '--', linewidth=1.5)
|
|
# print("a=%e, b=%e, rho^2=%e"% (a,b,rho**2))
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path_out + "/dmachvar_" + nums_name + out_ext)
|
|
P.close()
|
|
|
|
# alpha = f(beta)
|
|
P.yscale("log")
|
|
P.ylim([1e-7, 1.0])
|
|
P.grid()
|
|
|
|
# theoritical alpha (Gammie 2001)
|
|
alpha_th = (4.0 / 9.0) / (gamma * (gamma - 1.0) * beta)
|
|
|
|
P.plot(beta, abs(alpha_rey), "o-.", label=r"$\bar{\alpha}_{Reynolds}$")
|
|
P.plot(beta, abs(alpha_grav), "*--", label=r"$\bar{\alpha}_{grav}$")
|
|
P.plot(beta, abs(alpha_th), ":", label=r"$\bar{\alpha}_{th}$")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$\bar{\alpha}$")
|
|
P.xlabel(r"$\beta_{cool}$")
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path_out + "/alphabeta_" + nums_name + out_ext)
|
|
P.close()
|
|
|
|
|
|
def evolution(path, nums, force=False, interactive=False, pdf=False):
|
|
"""
|
|
Plot properties over time
|
|
|
|
path path to the properties file
|
|
nums list of id of the ramses output
|
|
force if set, redo plots even if already done
|
|
interactive interactive mode, to use in a %pylab ipython shell
|
|
"""
|
|
|
|
# Initialize arrays
|
|
time = np.zeros(len(nums))
|
|
alpha_rey = np.zeros(len(nums))
|
|
alpha_grav = np.zeros(len(nums))
|
|
Qmin = np.zeros(len(nums))
|
|
Qmean = np.zeros(len(nums))
|
|
mass_disk = np.zeros(len(nums))
|
|
mass_box = np.zeros(len(nums))
|
|
if pdf:
|
|
slope = np.zeros(len(nums))
|
|
|
|
for i, num in enumerate(nums):
|
|
|
|
# Load property file
|
|
name_prop = path + "/prop_disk_" + str(num).zfill(5) + ".save"
|
|
|
|
# Check if the properties file exists
|
|
if len(glob.glob(name_prop)) == 0:
|
|
raise ("no pickle file for disk properties. Run disk_prop() first")
|
|
f = open(name_prop, "r")
|
|
prop_disk = pickle.load(f)
|
|
f.close()
|
|
|
|
time[i] = prop_disk["time"]
|
|
|
|
try:
|
|
alpha_rey[i] = prop_disk["alpha_rey_mean"]
|
|
alpha_grav[i] = prop_disk["alpha_grav_mean"]
|
|
Qmin[i] = prop_disk["Q_min"]
|
|
Qmean[i] = prop_disk["Q_mean"]
|
|
mass_disk[i] = prop_disk["mass_disk"]
|
|
mass_box[i] = prop_disk["mass_box"]
|
|
except:
|
|
for array in [alpha_rey, alpha_grav, Qmin, Qmean, mass_disk, mass_box]:
|
|
array[i] = np.nan
|
|
|
|
if pdf:
|
|
slope[i] = prop_disk["fit"]["slope"]
|
|
|
|
# Check if the output file exists, and exit if it is the case
|
|
name_save = path + "/alpha_time" + out_ext
|
|
if not force and len(glob.glob(name_save)) != 0:
|
|
return
|
|
|
|
# Alpha
|
|
P.yscale("log")
|
|
P.ylim([1e-7, 1.0])
|
|
P.grid()
|
|
|
|
P.plot(time, abs(alpha_rey), "o-.", label=r"$\bar{\alpha}_{Reynolds}$")
|
|
P.plot(time, abs(alpha_grav), "*--", label=r"$\bar{\alpha}_{grav}$")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$\bar{\alpha}$")
|
|
P.xlabel(r"time (code)")
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/alpha_time" + out_ext)
|
|
P.close()
|
|
|
|
# Q
|
|
P.grid()
|
|
P.plot(time, Qmin, "o-.", label=r"$Q_{min}$")
|
|
P.plot(time, Qmean, "*--", label=r"$\bar{Q}$")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$Q$")
|
|
P.xlabel(r"time (code)")
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/Q_time" + out_ext)
|
|
P.close()
|
|
|
|
# M
|
|
P.grid()
|
|
P.plot(time, mass_disk, "o-.", label=r"$M_{disk}$")
|
|
P.plot(time, mass_box, "*--", label=r"$M_{box}$")
|
|
|
|
P.legend()
|
|
P.ylabel(r"$M / M_{*}$")
|
|
P.xlabel(r"time (code)")
|
|
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/mass_time" + out_ext)
|
|
P.close()
|
|
|
|
# PDF
|
|
if pdf:
|
|
P.grid()
|
|
print(time, slope)
|
|
P.plot(time, slope, "o-.")
|
|
P.legend()
|
|
P.ylabel(r"$d\log\mathcal{P}_{N} / d\logN$")
|
|
P.xlabel(r"time (code)")
|
|
if interactive:
|
|
P.figure()
|
|
else:
|
|
P.tight_layout(pad=1)
|
|
P.savefig(path + "/dcolslope_time" + out_ext)
|
|
P.close()
|