def load_data(work_dir='./', tmin=0.0, tmax=np.inf):
data_list, times = utilities.select_data_by_time(dir=work_dir,
tmin=tmin,
tmax=tmax)
# pre-load all the data
_all_data = {}
_all_meta_data = {}
for i, k in enumerate(data_list):
try:
_all_data[k] = dd.io.load(k, '/observables')
_all_meta_data[k] = dd.io.load(k, '/meta_data')
except:
print('skipping data entry ', i, k)
# gather all data so it can be readily plotted
all_data = {}
all_meta_data = {}
for k in _all_data[_all_data.keys()[0]].keys():
all_data[k] = utilities.extract_nested_dict_asarray(
_all_data, [k], self_contained=True)
for k in _all_meta_data[_all_meta_data.keys()[0]].keys():
all_meta_data[k] = utilities.extract_nested_dict_asarray(
_all_meta_data, [k], self_contained=True)
return all_data, all_meta_data, times
def plot_metal_retention(workdir = './', outdir = './'):
labels = {'Halo' : 'CGM' , 'Disk' : 'Disk', 'Outside Halo' : 'Outside Halo'}
lstyle = {'Halo' : '-', 'Disk' : '--', 'Outside Halo' : ':'}
gather_keys = {'Disk' : ['gas_meta_data', 'masses', 'Disk', 'Total Tracked Metals'],
'Halo' : ['gas_meta_data', 'masses', 'Halo', 'Total Tracked Metals'],
'FB' : ['gas_meta_data', 'masses', 'FullBox', 'Total Tracked Metals'],
'Outside Box' : ['gas_meta_data', 'masses', 'OutsideBox', 'Total Tracked Metals']}
all_data = {}
data_list, times = utilities.select_data_by_time(dir = workdir,
tmin=0.0,tmax= 650.0)
all_data['times'] = times
for k in gather_keys.keys():
all_data[k] = utilities.extract_nested_dict_asarray(None, gather_keys[k], data_list, False)
fig, ax = plt.subplots()
fig.set_size_inches(8,8)
total = all_data['FB'] + all_data['Outside Box']
disk_frac = all_data['Disk'] / total
halo_frac = all_data['Halo'] / total
outside_halo_frac = (all_data['FB'] - all_data['Halo'] - all_data['Disk'] + all_data['Outside Box']) / total
t = all_data['times'] - all_data['times'][0]
ax.plot(t, halo_frac, lw = line_width, ls = lstyle['Halo'], color = 'black', label = labels['Halo'])
ax.plot(t, disk_frac, lw = line_width, ls = lstyle['Disk'], color = 'black', label = labels['Disk'])
ax.plot(t, outside_halo_frac, lw = line_width, ls = lstyle['Outside Halo'], color = 'black', label = labels['Outside Halo'])
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Fraction of Metals')
ax.set_xlim(0.0, TMAX)
ax.set_ylim(0.0, 1.0)
ax.legend(loc = 'best')
plt.minorticks_on()
plt.tight_layout()
fig.savefig(outdir + 'metal_retention.png')
plt.close()
return
def plot_metal_retention_resolution(work_dir='./',
output_dir=None,
comparison=None,
new_color=False,
colors=None):
if output_dir is None:
output_dir = work_dir
if comparison is None:
labels = {'3pcH2': '3.6 pc', '6pcH2': '7.2 pc', 'Fiducial': 'Fiducial'}
lstyle = {'3pcH2': '--', '6pcH2': '-.', 'Fiducial': '-'}
dirs = {
'3pcH2': '../3pc_H2/',
'6pcH2': '../6pc_H2/',
'Fiducial': work_dir
}
comparison = {}
for k in labels.keys():
comparison[k] = (dirs[k], labels[k], lstyle[k])
else:
dirs = {}
labels = {}
lstyle = {}
for k in comparison.keys():
dirs[k] = work_dir + comparison[k][0]
labels[k] = comparison[k][1]
lstyle[k] = comparison[k][2]
gather_keys = {
'Disk TM': ['gas_meta_data', 'masses', 'Disk', 'Total Tracked Metals'],
'Halo TM': ['gas_meta_data', 'masses', 'Halo', 'Total Tracked Metals'],
'FB TM':
['gas_meta_data', 'masses', 'FullBox', 'Total Tracked Metals'],
'Outside Box TM':
['gas_meta_data', 'masses', 'OutsideBox', 'Total Tracked Metals']
}
all_data = {}
if colors is None:
colors = {}
i = 0
for k in comparison.keys():
if not (k in colors.keys()):
colors[k] = 'C%1i' % (i)
i = i + 1
for sim in comparison.keys():
data_list, times = utilities.select_data_by_time(dir=dirs[sim],
tmin=0.0,
tmax=1000.0)
all_data[sim] = {}
all_data[sim]['times'] = times
for k in gather_keys.keys():
all_data[sim][k] = utilities.extract_nested_dict_asarray(
None, gather_keys[k], data_list, False)
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
for sim in all_data.keys():
total = all_data[sim]['FB TM'] + all_data[sim]['Outside Box TM']
disk_frac = all_data[sim]['Disk TM'] / total
halo_frac = all_data[sim]['Halo TM'] / total
outside_halo_frac = (
all_data[sim]['FB TM'] - all_data[sim]['Halo TM'] -
all_data[sim]['Disk TM'] + all_data[sim]['Outside Box TM']) / total
t = all_data[sim]['times'] - all_data[sim]['times'][0]
if new_color:
ax.plot(t, disk_frac, lw=line_width, ls='-', color=colors[sim])
ax.plot(t,
outside_halo_frac,
lw=line_width,
ls=':',
color=colors[sim])
else:
ax.plot(t, disk_frac, lw=line_width, ls=lstyle[sim], color='navy')
ax.plot(t,
outside_halo_frac,
lw=line_width,
ls=lstyle[sim],
color='C1')
# plot this so it shows up on diagram
if new_color:
ax.plot([-10, -1], [0, 0],
lw=line_width,
ls='-',
color='black',
label='Disk')
ax.plot([-10, -1], [0, 0],
lw=line_width,
ls='--',
color='black',
label='Outside Halo')
else:
ax.plot([-10, -1], [0, 0],
lw=line_width,
ls='-',
color='navy',
label='Disk')
ax.plot([-10, -1], [0, 0],
lw=line_width,
ls='-',
color='C1',
label='Outside Halo')
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Fraction of Metals')
ax.set_xlim(0.0, TMAX)
ax.set_ylim(0.0, 1.0)
ax.legend(loc='best')
plt.minorticks_on()
plt.tight_layout()
fig.savefig(work_dir + output_dir + 'metal_retention_resolution.png')
plt.close()
return
def plot_basic_outflow_and_loading(work_dir='./',
t_min=0.0,
t_max=1000.0,
phase=None,
outdir='./'):
data_list, times = utilities.select_data_by_time(dir=work_dir,
tmin=t_min,
tmax=t_max)
xpos = dd.io.load(data_list[0], '/gas_profiles/outflow/sphere')
xpos = xpos['centers_rvir']
temp = dd.io.load(data_list[0], '/gas_meta_data/masses/CNM')
elements = utilities.sort_by_anum([
x for x in temp.keys()
if len(x) <= 2 and (not (x in ['H', 'He', 'H2', 'HI', 'HII', 'HeI']))
])
#
# Mass Outflow Plot
#
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
all_data = obtain_outflow_rates(data_list,
'cell_mass',
elements,
phase=phase)
metal_data = obtain_outflow_rates(data_list,
'Total Tracked Metals',
elements,
phase=phase)
metal_mass = obtain_metal_mass(data_list)
stellar_mass = obtain_stellar_mass(
data_list) # total mass in stars produced at a time, NOT M_* of galaxy
sfr = obtain_sfr(data_list, times)
binned_y = 1.0 * all_data # np.array( [all_data[k] for k in all_data.keys()] )
binned_metal_mass = 1.0 * metal_data
for i, loc in enumerate([0.1, 0.25, 0.5, 1.0]):
loc_bin = np.argmin(np.abs(xpos - loc)) # get the right position bin
y = binned_y[:, loc_bin]
print(i, loc, loc_bin)
bin_edges = utilities.bin_edges(np.round(times))
# rebin with 10 Myr bins, rather than previous 1 Myr bins
newx, rebiny = utilities.simple_rebin(
1.0 * bin_edges, 1.0 * y,
np.arange(np.min(bin_edges),
np.max(bin_edges) + 2, 10), 'average')
plot_histogram(ax,
newx - newx[0],
rebiny,
lw=line_width,
color=plasma(i / 4.0),
label=r"%0.1f R$_{\rm vir}$" % (loc))
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Outflow Rate (M$_{\odot}$ yr$^{-1}$)')
ax.semilogy()
ax.set_xlim(0.0, np.min([TMAX, np.max(newx - newx[0])]))
ax.set_ylim(1.0E-5, 0.03)
plt.tight_layout()
ax.legend(loc='best')
plt.minorticks_on()
outname = outdir + 'total_mass_outflow'
if not (phase is None):
outname += '_' + phase
fig.savefig(outname + '.png')
plt.close()
#
# Mass Loading plot
#
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
# binned_y = np.array( [all_data[k] for k in all_data.keys()] )
for i, loc in enumerate([0.1, 0.25, 0.5, 1.0]):
loc_bin = np.argmin(np.abs(xpos - loc)) # get the right position bin
y = binned_y[:, loc_bin] / sfr
bin_edges = utilities.bin_edges(np.round(times))
# rebin with 10 Myr bins, rather than previous 1 Myr bins
newx, rebiny = utilities.simple_rebin(
1.0 * bin_edges, 1.0 * y,
np.arange(np.min(bin_edges),
np.max(bin_edges) + 2, 10), 'average')
plot_histogram(ax,
newx - newx[0],
rebiny,
lw=line_width,
color=plasma(i / 4.0),
label=r"%0.1f R$_{\rm vir}$" % (loc))
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Mass Loading Factor')
ax.semilogy()
ax.set_xlim(0.0, np.min([TMAX, np.max(newx - newx[0])]))
ax.set_ylim(0.1, 300)
plt.tight_layout()
#ax.legend(loc='best')
plt.minorticks_on()
outname = outdir + 'total_mass_loading'
if not (phase is None):
outname += '_' + phase
fig.savefig(outname + '.png')
plt.close()
#
# Metal Mass Loading
#
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
for i, loc in enumerate([0.1, 0.25, 0.5, 1.0]):
loc_bin = np.argmin(np.abs(xpos - loc)) # get the right position bin
print(np.shape(binned_y), np.shape(binned_metal_mass), np.size(sfr),
np.size(metal_mass), np.size(stellar_mass))
y = binned_metal_mass[:, loc_bin] / (sfr * (metal_mass / stellar_mass)
) # metal mass loading factor
print(loc, np.min(y), np.max(y), np.average(y))
print(np.min(metal_mass), np.max(metal_mass), np.min(stellar_mass),
np.max(stellar_mass))
bin_edges = utilities.bin_edges(np.round(times))
# rebin with 10 Myr bins, rather than previous 1 Myr bins
newx, rebiny = utilities.simple_rebin(
1.0 * bin_edges, 1.0 * y,
np.arange(np.min(bin_edges),
np.max(bin_edges) + 2, 10), 'average')
plot_histogram(ax,
newx - newx[0],
rebiny,
lw=line_width,
color=plasma(i / 4.0),
label=r"%0.1f R$_{\rm vir}$" % (loc))
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Metal Mass Loading Factor')
ax.semilogy()
ax.set_xlim(0.0, np.min([TMAX, np.max(newx - newx[0])]))
ax.set_ylim(0.04, 20)
plt.tight_layout()
ax.legend(loc='upper right')
plt.minorticks_on()
outname = outdir + 'metal_mass_loading'
if not (phase is None):
outname += '_' + phase
fig.savefig(outname + '.png')
plt.close()
#
# Metal Mass Loading
#
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
for i, loc in enumerate([0.1, 0.25, 0.5, 1.0]):
loc_bin = np.argmin(np.abs(xpos - loc)) # get the right position bin
print(np.shape(binned_y), np.shape(binned_metal_mass), np.size(sfr),
np.size(metal_mass), np.size(stellar_mass))
y = binned_metal_mass[:, loc_bin] / (sfr) # metal mass loading factor
print(loc, np.min(y), np.max(y), np.average(y))
print(np.min(metal_mass), np.max(metal_mass), np.min(stellar_mass),
np.max(stellar_mass))
bin_edges = utilities.bin_edges(np.round(times))
# rebin with 10 Myr bins, rather than previous 1 Myr bins
newx, rebiny = utilities.simple_rebin(
1.0 * bin_edges, 1.0 * y,
np.arange(np.min(bin_edges),
np.max(bin_edges) + 2, 10), 'average')
print(np.sum(rebiny) / (1.0 * np.size(rebiny)), '-------------')
plot_histogram(ax,
newx - newx[0],
rebiny,
lw=line_width,
color=plasma(i / 4.0),
label=r"%0.1f R$_{\rm vir}$" % (loc))
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Metal Mass Loading Factor')
ax.semilogy()
ax.set_xlim(0.0, np.min([TMAX, np.max(newx - newx[0])]))
ax.set_ylim(1.0E-6, 1.0)
plt.tight_layout()
ax.legend(loc='best')
plt.minorticks_on()
outname = outdir + 'metal_mass_loading_sfr'
if not (phase is None):
outname += '_' + phase
fig.savefig(outname + '.png')
plt.close()
return
def plot_species_outflow_panel(work_dir='./',
t_min=0.0,
t_max=1000.0,
phase=None,
method='fraction',
outdir='./'):
"""
Default to plotting the following:
For each species, x, plots (dM_x/dt / M_x_prod) / SFR
or the fractional mass outflow rate per unit SFR.
"""
data_list, times = utilities.select_data_by_time(dir=work_dir,
tmin=0.0,
tmax=1000)
xpos = dd.io.load(data_list[0], '/gas_profiles/outflow/sphere')
xpos = xpos['centers_rvir']
temp = dd.io.load(data_list[0], '/gas_meta_data/masses/CNM')
elements = utilities.sort_by_anum([
x for x in temp.keys()
if len(x) <= 2 and (not (x in ['H', 'He', 'H2', 'HI', 'HII', 'HeI']))
])
fig, ax = plt.subplots(4, 4, sharex=True, sharey=True)
fig.set_size_inches(16, 16)
fig.subplots_adjust(hspace=0.0, wspace=0.0)
SFR = obtain_sfr(data_list, times)
axi, axj = 0, 0
for e in elements:
index = (axi, axj)
field_name = e + '_Mass'
all_data = obtain_outflow_rates(data_list,
field_name,
elements,
phase=phase)
M_prod = obtain_mprod(data_list, e)
binned_y = 1.0 * all_data # np.array( [all_data[k] for k in all_data.keys()] )
for i, loc in enumerate([0.1, 0.25, 0.5, 1.0]):
loc_bin = np.argmin(np.abs(xpos -
loc)) # get the right position bin
y = binned_y[:, loc_bin]
bin_edges = utilities.bin_edges(np.round(times))
# rebin with 10 Myr bins, rather than previous 1 Myr bins
# convert y from outflow rate to outflow rate / mass produced / sfr
if method == 'fraction':
y = (y / M_prod) * 1.0E6 # convert to 1/Myr
else:
y = (y / M_prod) / SFR
newx, rebiny = utilities.simple_rebin(
1.0 * bin_edges, 1.0 * y,
np.arange(np.min(bin_edges),
np.max(bin_edges) + 2, 5.0), 'average')
plot_histogram(ax[index],
newx - newx[0],
rebiny,
lw=line_width,
color=plasma(i / 4.0),
label=r"%0.1f R$_{\rm vir}$" % (loc))
if i == 0:
xy = (np.max(newx - newx[0]) * 0.8, np.max(rebiny) * 10.0)
ax[index].annotate(e, xy=xy, xytext=xy)
axj = axj + 1
if axj >= 4:
axj = 0
axi = axi + 1
for i in np.arange(4):
ax[(3, i)].set_xlabel(r'Time (Myr)')
# ax[(i,0)].set_ylabel(r'log(X Fractional Outflow per SFR [M$_\odot$ yr$^{-1}$]$^{-1}$)')
ax[(i, 0)].set_xlim(newx[0] - newx[0],
np.min([TMAX, newx[-1] - newx[0]]))
if method == 'fraction':
ax[(0, i)].set_ylim(1.0E-5, 1.0E-1)
else:
ax[(0, i)].set_ylim(1.0E-7, 1.0E-3)
ax[(0, i)].semilogy()
if method == 'fraction':
ax[(2, 0)].set_ylabel(r'log(X Fractional Outflow Rate [Myr$^{-1}$])')
else:
ax[(2, 0)].set_ylabel(
r'log(X Fractional Outflow per SFR [M$_\odot$ yr$^{-1}$]$^{-1}$)')
plt.minorticks_on()
if method == 'fraction':
fig.savefig(outdir + 'X_Fractional_Outflow_panel.png')
else:
fig.savefig(outdir + 'X_Fractional_Outflow_loading_panel.png')
plt.close()
return
def plot_time_average_PD(wdir,
t_min,
t_max,
nbin=100,
plots=['nT', 'G_o', 'Q_o'],
outdir=None):
x = utilities.select_data_by_time(wdir, tmin=0.0, tmax=np.inf)
sim_files = {'files': x[0], 'Time': x[1]}
# 'DD_files' : np.sort(glob.glob(wdir + '/DD????/DD????'))}
#iremove = len(sim_files['files'])
#sim_files['DD_files'] = sim_files['DD_files'][-iremove:]
# --- get data sets associated with output files (this is gross - I am sorry)
sim_files['DD_files'] = np.array([
x.split('_galaxy_data')[0] + '/' + x.split('_galaxy_data')[0][-6:]
for x in sim_files['files']
])
# --- make sure they exist
sim_files['Time'] = np.array([
sim_files['Time'][i] for i in np.arange(np.size(sim_files['Time']))
if os.path.isfile(sim_files['DD_files'][i])
])
sim_files['DD_files'] = np.array(
[x for x in sim_files['DD_files'] if os.path.isfile(x)])
# --- select the ones in the time range we want
sim_files['phase_files'] = sim_files['DD_files'][ (sim_files['Time'] >= t_min) *\
(sim_files['Time'] <= t_max)]
gal = Galaxy(sim_files['DD_files'][0].split('/')[-1])
if 'G_o' in plots:
pd = tapd.time_average_phase_diagram(
t_min,
t_max,
ds_list=sim_files['phase_files'],
wdir=wdir,
x_bins=np.linspace(0.0, 600.0, nbin) * yt.units.pc,
xlog=False,
xfield='cylindrical_radius',
ylabel=r'G$_{\rm o}$',
y_bins=np.logspace(np.log10(0.00324), 3, nbin) * yt.units.pc /
yt.units.pc,
yfield='G_o',
region_type='Disk',
region_kwargs={
'normal': np.array([0.0, 0.0, 1.0]),
'radius': 600 * yt.units.pc,
'height': (1.5 * yt.units.pc * 20),
'center': np.array([0.5, 0.5, 0.5])
},
zlog=True,
zlabel=r'Mass (M$_{\odot}$)',
# region_type = 'FullBox', zlog = True,
ylog=True,
zlim=[5.0E-2, 1.0E5],
zunit='Msun',
cmap='cubehelix',
# outname = 'G_o_r_cell_mass_2D_phase.png',
outdir=outdir)
if ('Q_o' in plots) or ('Q0' in plots) or ('Q_0' in plots):
pd = tapd.time_average_phase_diagram(
t_min,
t_max,
ds_list=sim_files['phase_files'],
wdir=wdir,
x_bins=np.linspace(0.0, 600.0, nbin) * yt.units.pc,
xlog=False,
xfield='cylindrical_radius',
ylabel=r'HI Ionizing Flux (erg s$^{-1}$ cm$^{-2}$)',
y_bins=np.logspace(-8, 1, nbin) * yt.units.erg / yt.units.s /
yt.units.cm**2,
yfield='Q0_flux',
region_type='Disk',
region_kwargs={
'normal': np.array([0.0, 0.0, 1.0]),
'radius': 600 * yt.units.pc,
'height': (1.5 * yt.units.pc) * 20,
'center': np.array([0.5, 0.5, 0.5])
},
zlog=True,
zlabel=r'Mass (M$_{\odot}$)',
# region_type = 'FullBox', zlog = True,
ylog=True,
zlim=[5.0E-2, 1.0E5],
zunit='Msun',
cmap='cubehelix',
# outname = 'Q_o_r_cell_mass_2D_phase.png',
outdir=outdir)
if 'nT' in plots:
pd = tapd.time_average_phase_diagram(
t_min,
t_max,
ds_list=sim_files['phase_files'],
wdir=wdir,
x_bins=np.logspace(-6, 3, 512) * yt.units.cm**(-3),
xlog=True,
xfield='number_density',
y_bins=np.logspace(0, 7.5, 512) * yt.units.K,
yfield='Temperature',
region_type='sphere',
region_kwargs={
'radius': 3.6 * yt.units.kpc,
'center': np.array([0.5, 0.5, 0.5])
},
zlog=True,
# region_type = 'FullBox', zlog = True,
ylog=True,
zlim=[1.0E-2, 6.0E4],
zunit='Msun',
cmap='cubehelix',
# outname = 'T_n_cell_mass_2D_phase.png',
outdir=outdir)
if 'nT_special' in plots:
pd = tapd.time_average_phase_diagram(
t_min,
t_max,
ds_list=sim_files['phase_files'],
wdir=wdir,
x_bins=np.logspace(-6, 3, 512) * yt.units.cm**(-3),
xlog=True,
xfield='number_density',
y_bins=np.logspace(0, 7.5, 512) * yt.units.K,
yfield='Temperature',
region_type='sphere_exclude_disk',
region_kwargs={
'sphere_radius': 3.6 * yt.units.kpc,
'disk_radius': 600 * yt.units.pc,
'disk_height': 200 * yt.units.pc,
'center': np.array([0.5, 0.5, 0.5])
},
zlog=True,
# region_type = 'FullBox', zlog = True,
ylog=True,
zlim=[1.0E-2, 6.0E4],
zunit='Msun',
cmap='cubehelix',
outname='nT_outside_disk.png',
outdir=outdir)
pd = tapd.time_average_phase_diagram(
t_min,
t_max,
ds_list=sim_files['phase_files'],
wdir=wdir,
x_bins=np.logspace(-6, 3, 512) * yt.units.cm**(-3),
xlog=True,
xfield='number_density',
y_bins=np.logspace(0, 7.5, 512) * yt.units.K,
yfield='Temperature',
region_type='disk',
region_kwargs={
'radius': 600 * yt.units.pc,
'height': 200 * yt.units.pc,
'center': np.array([0.5, 0.5, 0.5])
},
zlog=True,
# region_type = 'FullBox', zlog = True,
ylog=True,
zlim=[1.0E-2, 6.0E4],
zunit='Msun',
cmap='cubehelix',
outname='nT_disk.png',
outdir=outdir)
return
from galaxy_analysis.plot.plot_styles import *
from galaxy_analysis.analysis.compute_time_average import compute_time_average
from galaxy_analysis.utilities import utilities
import deepdish as dd
import numpy as np
import matplotlib.pyplot as plt
import glob
TMAX = 500.0
# filepath = '/mnt/ceph/users/emerick/enzo_runs/pleiades/starIC/run11_30km/final_sndriving'
work_dir = '/mnt/ceph/users/emerick/enzo_runs/pleiades/starIC/run11_30km/final_sndriving/'
#work_dir = '/mnt/ceph/users/emerick/enzo_runs/pleiades/starIC/run11/corrected_sndriving/'
data_list, times = utilities.select_data_by_time(dir=work_dir,
tmin=0.0,
tmax=1000)
print(data_list[0], times[0])
print(data_list[-1], times[-1])
print(times[-1] - times[0])
all_data = {}
# gather metallicities for:
# 1) ISM
# 2) Halo
# 3) Outflowing gas (at all radii)
# 4) Inflowing gas (at all radii)
#data_list = data_list[:60]
#times = times[:60]
for k in ['ISM', 'Halo', 'outflow', 'inflow']:
def plot_mass_resolution(work_dir='./',
output_dir=None,
comparison=None,
new_color=False,
colors=None):
if output_dir is None:
output_dir = work_dir
if comparison is None:
labels = {'3pcH2': '3.6 pc', '6pcH2': '7.2 pc', 'Fiducial': 'Fiducial'}
lstyle = {'3pcH2': '--', '6pcH2': '-.', 'Fiducial': '-'}
dirs = {
'3pcH2': '../3pc_H2/',
'6pcH2': '../6pc_H2/',
'Fiducial': work_dir
}
comparison = {}
for k in labels.keys():
comparison[k] = (dirs[k], labels[k], lstyle[k])
else:
dirs = {}
labels = {}
lstyle = {}
for k in comparison.keys():
dirs[k] = work_dir + comparison[k][0]
labels[k] = comparison[k][1]
lstyle[k] = comparison[k][2]
# labels = {'3pc_hsn' : '3.6 pc - SNx2', '3pc' : '3.6 pc', 'final_sndriving' : 'Fiducial', '6pc_hsn' : '7.2 pc'}
# lstyle = {'3pc_hsn' : '--', '3pc' : ':', 'final_sndriving' : '-', '6pc_hsn' : '-.'}
all_data = {}
for k in comparison.keys():
all_data[k] = {}
# if k == 'final_sndriving':
# all_data[k]['times'] = times
# all_data[k]['M_HI'] = M_HI
# all_data[k]['M_star'] = M_star
# all_data[k]['M_total'] = M_total
# all_data[k]['M_H2I'] = M_H2
#
if True:
dl, t = utilities.select_data_by_time(dir=dirs[k],
tmin=0.0,
tmax=1000.0)
all_data[k]['times'] = t
all_data[k]['M_HI'] = np.zeros(np.size(dl))
all_data[k]['M_star'] = np.zeros(np.size(dl))
all_data[k]['M_total'] = np.zeros(np.size(dl))
all_data[k]['M_H2I'] = np.zeros(np.size(dl))
for i, d in enumerate(dl):
all_data[k]['M_HI'][i] = dd.io.load(d, '/meta_data/M_HI')
all_data[k]['M_star'][i] = dd.io.load(d, '/meta_data/M_star')
all_data[k]['M_total'][i] = dd.io.load(
d, '/meta_data/M_H_total') + dd.io.load(
d, '/meta_data/M_He_total')
all_data[k]['M_H2I'][i] = dd.io.load(d, '/meta_data/M_H2I')
# done collecting data rom all data sets
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
if colors is None:
colors = {}
i = 0
for k in comparison.keys():
if not (k in colors.keys()):
colors[k] = 'C%1i' % (i)
i = i + 1
lstyle['M_total'] = '-'
lstyle['M_HI'] = '--'
lstyle['M_H2I'] = '-.'
lstyle['M_star'] = ':'
for k in comparison.keys():
for field, color in [('M_total', 'black'), ('M_HI', 'C0'),
('M_H2I', 'C1'), ('M_star', 'C3')]:
# for field, ls in [('M_total','-'), ('M_HI', '--'), ('M_H2I', ':')]: # , ('M_star' , '-.')]:
if field == 'M_total':
label = labels[k]
else:
label = None
# print k, field, np.size(all_data[k]['times']), np.size(all_data[k][field])
if new_color:
ax.plot(all_data[k]['times'] - all_data[k]['times'][0],
all_data[k][field],
ls=lstyle[field],
lw=line_width,
color=colors[k])
else:
plot_histogram(ax,
all_data[k]['times'] - all_data[k]['times'][0],
all_data[k][field],
ls=lstyle[k],
lw=line_width,
color=color)
if new_color:
ax.plot((-1, -1), (-2, -2),
ls='-',
lw=3,
color='black',
label=r'M$_{\rm total}$')
ax.plot((-1, -1), (-2, -2),
ls='--',
lw=3,
color='black',
label=r'M$_{\rm HI}$')
ax.plot((-1, -1), (-2, -2),
ls='-.',
lw=3,
color='black',
label=r'M$_{\rm H_2}$')
ax.plot((-1, -1), (-2, -2),
ls=':',
lw=3,
color='black',
label=r'M$_{\rm *}$')
else:
ax.plot((-1, -1), (-2, -2),
ls='-',
lw=3,
color='black',
label=r'M$_{\rm total}$')
ax.plot((-1, -1), (-2, -2),
ls='-',
lw=3,
color='C0',
label=r'M$_{\rm HI}$')
ax.plot((-1, -1), (-2, -2),
ls='-',
lw=3,
color='C1',
label=r'M$_{\rm H_2}$')
ax.plot((-1, -1), (-2, -2),
ls='-',
lw=3,
color='C3',
label=r'M$_{\rm *}$')
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Mass in Disk (M$_{\odot}$)')
ax.semilogy()
ax.set_xlim(0.0, 500.0)
ax.legend(loc='lower right')
# ax.set_ylim(6E4, 3E6)
plt.tight_layout()
plt.minorticks_on()
fig.savefig(work_dir + output_dir + 'mass_evolution_resolution.png')
plt.close()
return
def plot_mass_evolution(work_dir,
t_f=None,
image_num=0,
outdir='./',
TMAX=500.0):
data_list, times = utilities.select_data_by_time(dir=work_dir,
tmin=0.0,
tmax=650.0)
M_HI = np.ones(np.size(data_list))
M_star = np.ones(np.size(data_list))
M_total = np.ones(np.size(data_list))
M_H2 = np.ones(np.size(data_list))
for i, k in enumerate(data_list):
M_HI[i] = dd.io.load(k, '/meta_data/M_HI')
M_star[i] = dd.io.load(k, '/meta_data/M_star')
M_total[i] = dd.io.load(k, '/meta_data/M_H_total') + dd.io.load(
k, '/meta_data/M_He_total')
M_H2[i] = dd.io.load(k, '/meta_data/M_H2I')
selection = (times == times) # all vals
plot_times = times
if not (t_f is None):
selection = times <= t_f
plot_times = times[selection]
plot_times = plot_times - plot_times[0]
fig, ax = plt.subplots()
fig.set_size_inches(8, 8)
plot_histogram(ax,
plot_times,
M_total[selection],
lw=line_width,
color='black',
label=r'M$_{\rm total}$')
plot_histogram(ax,
plot_times,
M_HI[selection],
lw=line_width,
color='C0',
label=r'M$_{\rm HI}$')
plot_histogram(ax,
plot_times,
M_H2[selection],
lw=line_width,
color='C1',
label=r'M$_{\rm H_2 }$')
plot_histogram(ax,
plot_times,
M_star[selection],
lw=line_width,
color='C3',
label=r'M$_*$')
ax.set_xlabel(r'Time (Myr)')
ax.set_ylabel(r'Mass in Disk (M$_{\odot}$)')
ax.semilogy()
ax.set_xlim(np.min(times - times[0]),
np.min([TMAX, np.max(times - times[0])]))
ax.legend(loc='lower right')
plt.tight_layout()
plt.minorticks_on()
if t_f is None:
fig.savefig(outdir + 'mass_evolution.png')
else:
fig.savefig('./mass_evolution_movie/mass_evolution_%0004i.png' %
(image_num))
plt.close()
return