def set_dic_yminymax(v_n, dic, yarr, extra=2.):
#
if v_n == "Mej_tot-geo" or v_n == "Mej":
dic['ymin'], dic['ymax'] = 0, 1.5
elif v_n == "Mej_tot-geo_2" or v_n == "Mej2":
dic['ymin'], dic['ymax'] = 0, 2
elif v_n == "Mej_tot-geo_1" or v_n == "Mej1":
dic['ymin'], dic['ymax'] = 0, 0.7
elif v_n == "Mej_tot-geo_3" or v_n == "Mej3":
dic['ymin'], dic['ymax'] = 0, 2
elif v_n == "Mej_tot-geo_4" or v_n == "Mej4":
dic['ymin'], dic['ymax'] = 0, .75
elif v_n == "Mej_tot-geo_5" or v_n == "Mej5":
dic['ymin'], dic['ymax'] = 0, 10.
elif v_n == "Mej_tot-geo_6" or v_n == "Mej6":
dic['ymin'], dic['ymax'] = 0, 0.06
elif v_n == "Mej_tot-geo_entropy_above_10" or v_n == "Mej_shocked":
dic['ymin'], dic['ymax'] = 0, 0.7
elif v_n == "Mej_tot-geo_entropy_below_10" or v_n == "Mej_tidal":
dic['ymin'], dic['ymax'] = 0, 0.5
else:
dic['ymin'], dic['ymax'] = np.array(yarr).min(), np.array(
yarr).max() + (extra *
(np.array(yarr).max() - np.array(yarr).min()))
Printcolor.yellow("xlimits are not set for v_n_x:{}".format(v_n))
return dic
def compute_outflow_new_mask(self, det, sim, mask, rewrite):
# get_tmax60 # ms
print("\tAdding mask:{}".format(mask))
o_outflow = EJECTA_PARS(sim, add_mask=mask)
if not os.path.isdir(Paths.ppr_sims + sim +"/" +"outflow_{:d}/".format(det) + mask + '/'):
os.mkdir(Paths.ppr_sims + sim +"/" +"outflow_{:d}/".format(det) + mask + '/')
Printcolor.blue("Creating new outflow mask dir:{}"
.format(sim +"/" +"outflow_{:d}/".format(det) + mask + '/'))
for task in self.outflow_tasks:
if task == "hist":
# from outflowed import outflowed_historgrams
outflowed_historgrams(o_outflow, [det], [mask], o_outflow.list_hist_v_ns, rewrite=rewrite)
elif task == "corr":
# from outflowed import outflowed_correlations
outflowed_correlations(o_outflow, [det], [mask], o_outflow.list_corr_v_ns, rewrite=rewrite)
elif task == "totflux":
# from outflowed import outflowed_totmass
outflowed_totmass(o_outflow, [det], [mask], rewrite=rewrite)
elif task == "timecorr":
# from outflowed import outflowed_timecorr
outflowed_timecorr(o_outflow, [det], [mask], o_outflow.list_hist_v_ns, rewrite=rewrite)
else:
raise NameError("method for computing outflow with new mask is not setup for task:{}".format(task))
def set_dic_xminxmax(v_n, dic, xarr):
#
if v_n == "Mej_tot-geo" or v_n == "Mej":
dic['xmin'], dic['xmax'] = 0, 1.5
elif v_n == "Lambda":
dic['xmin'], dic['xmax'] = 5, 1500
elif v_n == "Mej_tot-geo_entropy_above_10" or v_n == "Mej_shocked":
dic['xmin'], dic['xmax'] = 0, 0.7
elif v_n == "Mej_tot-geo_entropy_below_10" or v_n == "Mej_tidal":
dic['xmin'], dic['xmax'] = 0, 0.5
else:
dic['xmin'], dic['xmax'] = np.array(xarr).min(), np.array(xarr).max()
Printcolor.yellow("xlimits are not set for v_n_x:{}".format(v_n))
return dic
def get_post_geo_delta_t(self, det):
# o_par1 = ALL_PAR(self.sim1)
# o_par2 = ALL_PAR(self.sim2)
# tmerg1 = self.o_par1.get_par("tmerger")
# tmerg2 = self.o_par2.get_par("tmerger")
t98geomass1 = self.o_par1.get_outflow_par(det, "geo", "t98mass")
t98geomass2 = self.o_par2.get_outflow_par(det, "geo", "t98mass")
t98geomass3 = self.o_par3.get_outflow_par(det, "geo", "t98mass")
tend1 = self.o_par1.get_outflow_par(det, "geo", "tend")
tend2 = self.o_par2.get_outflow_par(det, "geo", "tend")
tend3 = self.o_par3.get_outflow_par(det, "geo", "tend")
if tend1 < t98geomass1:
Printcolor.red("tend1:{} < t98geomass1:{}".format(tend1, t98geomass1))
return np.nan
if tend2 < t98geomass2:
Printcolor.red("tend2:{} < t98geomass2:{}".format(tend2, t98geomass2))
return np.nan
if tend3 < t98geomass3:
Printcolor.red("tend3:{} < t98geomass3:{}".format(tend3, t98geomass3))
return np.nan
Printcolor.yellow("Relaxing the time limits criteria")
# if tend1 < t98geomass2:
# Printcolor.red("Delta t does not overlap tend1:{} < t98geomass2:{}".format(tend1, t98geomass2))
# return np.nan
# if tend2 < t98geomass1:
# Printcolor.red("Delta t does not overlap tend2:{} < t98geomass1:{}".format(tend2, t98geomass1))
# return np.nan
# assert tmerg1 < t98geomass1
# assert tmerg2 < t98geomass2
# tend1 = tend1 - tmerg1
# tend2 = tend2 - tmerg2
# t98geomass1 = t98geomass1 - tmerg1
# t98geomass2 = t98geomass2 - tmerg2
delta_t1 = tend1 - t98geomass1
delta_t2 = tend2 - t98geomass2
delta_t3 = tend3 - t98geomass3
print("\tTime window for bernoulli ")
print("\t{} {:.2f} [ms]".format(self.sim1, delta_t1 * 1e3))
print("\t{} {:.2f} [ms]".format(self.sim2, delta_t2 * 1e3))
print("\t{} {:.2f} [ms]".format(self.sim3, delta_t3 * 1e3))
# exit(1)
delta_t = np.min([delta_t1, delta_t2, delta_t3])
if delta_t < 0.005:
return np.nan# ms
return delta_t
dic['ymin'], dic['ymax'] = 0, 0.5
else:
dic['ymin'], dic['ymax'] = np.array(yarr).min(), np.array(
yarr).max() + (extra *
(np.array(yarr).max() - np.array(yarr).min()))
Printcolor.yellow("xlimits are not set for v_n_x:{}".format(v_n))
return dic
''' --------------------------------------------------------------- '''
total_x = [] # for fits
total_y = [] # for fits
Printcolor.blue("Collecting Data")
o_tbl = GET_PAR_FROM_TABLE()
o_tbl.set_intable = simtable
o_tbl.load_table()
data = {}
all_x = []
all_y = []
all_col = []
all_marker = []
for eos in simlist.keys():
data[eos] = {}
for usim in simlist[eos].keys():
data[eos][usim] = {}
sims = simlist[eos][usim]
print("\t{} [{}] Chrip:{}".format(usim, len(sims),
o_tbl.get_par(sims[0], "mchirp")))
def plot_disk_2d():
# tmp = d3class.get_data(688128, 3, "xy", "ang_mom_flux")
# print(tmp.min(), tmp.max())
# print(tmp)
# exit(1) # dens_unb_geo
""" --- --- --- """
'''sly4 '''
simlist = [
"SLy4_M13641364_M0_SR", "SLy4_M13641364_M0_SR", "SLy4_M13641364_M0_SR",
"SLy4_M13641364_M0_SR"
]
# itlist = [434176, 475136, 516096, 565248]
# itlist = [606208, 647168, 696320, 737280]
# itlist = [434176, 516096, 647168, 737280]
''' ls220 '''
simlist = [
"LS220_M14691268_M0_LK_SR", "LS220_M14691268_M0_LK_SR",
"LS220_M14691268_M0_LK_SR"
] #, "LS220_M14691268_M0_LK_SR"]
itlist = [1515520, 1728512, 1949696] #, 2162688]
''' dd2 '''
simlist = [
"DD2_M13641364_M0_LK_SR_R04", "DD2_M13641364_M0_LK_SR_R04",
"DD2_M13641364_M0_LK_SR_R04"
] #, "DD2_M13641364_M0_LK_SR_R04"]
itlist = [1111116, 1741554, 2213326] #,2611022]
#
simlist = [
"DD2_M13641364_M0_LK_SR_R04", "BLh_M13641364_M0_LK_SR",
"LS220_M14691268_M0_LK_SR", "SLy4_M13641364_M0_SR"
]
itlist = [2611022, 1974272, 1949696, 737280]
#
#
simlist = ["BLh_M13641364_M0_LK_SR"]
itlist = [737280]
v_ns = ["rho", "Ye"]
rl = 3
plane = "xy"
data_dic = {}
Printcolor.blue("Collecting data...")
for sim, it in zip(simlist, itlist):
simit = str(sim) + str(it)
data_dic[simit] = {}
print("sim:{} it:{}".format(sim, it))
d3class = LOAD_PROFILE_XYXZ(sim)
# d1class = ADD_METHODS_ALL_PAR(sim)
x_arr = d3class.get_data(it, rl, plane, "x")
y_arr = d3class.get_data(it, rl, plane, "y")
data_dic[simit]["x_arr"] = x_arr
data_dic[simit]["y_arr"] = y_arr
for v_n in v_ns:
data_arr = d3class.get_data(it, rl, plane, v_n)
data_dic[simit][v_n] = data_arr
Printcolor.green("Data is collected")
#
o_plot = PLOT_MANY_TASKS()
o_plot.gen_set["figdir"] = __outplotdir__
o_plot.gen_set["type"] = "cartesian"
o_plot.gen_set["figsize"] = (
4.2, 3.6
) #(4 * len(simlist), 6.0) # <->, |] # to match hists with (8.5, 2.7)
o_plot.gen_set[
"figname"] = "disk_densmodes.png" # "DD2_1512_slices.png" # LS_1412_slices
o_plot.gen_set["sharex"] = False
o_plot.gen_set["sharey"] = True
o_plot.gen_set["dpi"] = 128
o_plot.gen_set["subplots_adjust_h"] = -0.35
o_plot.gen_set["subplots_adjust_w"] = 0.05
o_plot.set_plot_dics = []
plot_x_i = 1
for sim, it in zip(simlist, itlist):
simit = str(sim) + str(it)
#
mask = "x>0"
v_n = "rho"
cmap = 'Greys'
xmin, xmax, ymin, ymax, zmin, zmax = UTILS.get_xmin_xmax_ymin_ymax_zmin_zmax(
rl)
rho_dic_xy = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': data_dic[simit]["x_arr"],
"yarr": data_dic[simit]["y_arr"],
"zarr": data_dic[simit][v_n],
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'vmin': 1e-9,
'vmax': 1e-5,
'fill_vmin': False, # fills the x < vmin with vmin
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': cmap,
'norm': "log",
'fancyticks': True,
'minorticks': True,
'title': {},
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
rho_dic_xy['cbar'] = {
'location': 'left -0.6 .00',
'label': r'$\rho$ [GEO]', # 'fmt': '%.1e',
'labelsize': 14,
'fontsize': 14
}
o_plot.set_plot_dics.append(rho_dic_xy)
#
contour_dic_xy = {
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'task': 'contour',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': data_dic[simit]["x_arr"],
"yarr": data_dic[simit]["y_arr"],
"zarr": data_dic[simit][v_n],
'levels': [1.e13 / 6.176e+17],
'colors': ['white'],
'lss': ["-"],
'lws': [1.],
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': 'rho',
'xscale': None,
'yscale': None,
'fancyticks': True,
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
o_plot.set_plot_dics.append(contour_dic_xy)
# ---
mask = "x<0"
v_n = "Ye"
cmap = "bwr_r"
ye_dic_xy = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': data_dic[simit]["x_arr"],
"yarr": data_dic[simit]["y_arr"],
"zarr": data_dic[simit][v_n],
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'fill_vmin': False, # fills the x < vmin with vmin
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'vmin': 0.01,
'vmax': 0.5,
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': cmap,
'norm': None,
'fancyticks': True,
'minorticks': True,
'title': {},
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
ye_dic_xy['cbar'] = {
'location': 'right -.02 .00',
'label': r'$Y_e$',
'fmt': '%.1f',
'labelsize': 14,
'fontsize': 14
}
o_plot.set_plot_dics.append(ye_dic_xy)
plot_x_i += 1
o_plot.main()
exit(1)
o_plot = PLOT_MANY_TASKS()
o_plot.gen_set["figdir"] = __outplotdir__
o_plot.gen_set["type"] = "cartesian"
o_plot.gen_set["figsize"] = (4 * len(simlist), 6.0
) # <->, |] # to match hists with (8.5, 2.7)
o_plot.gen_set["figname"] = "disk_structure_last.png".format(
simlist[0]) #"DD2_1512_slices.png" # LS_1412_slices
o_plot.gen_set["sharex"] = False
o_plot.gen_set["sharey"] = True
o_plot.gen_set["dpi"] = 128
o_plot.gen_set["subplots_adjust_h"] = -0.35
o_plot.gen_set["subplots_adjust_w"] = 0.05
o_plot.set_plot_dics = []
#
rl = 3
#
o_plot.gen_set["figsize"] = (4.2 * len(simlist), 8.0
) # <->, |] # to match hists with (8.5, 2.7)
plot_x_i = 1
for sim, it in zip(simlist, itlist):
print("sim:{} it:{}".format(sim, it))
d3class = LOAD_PROFILE_XYXZ(sim)
d1class = ADD_METHODS_ALL_PAR(sim)
t = d3class.get_time_for_it(it, d1d2d3prof="prof")
tmerg = d1class.get_par("tmerg")
xmin, xmax, ymin, ymax, zmin, zmax = UTILS.get_xmin_xmax_ymin_ymax_zmin_zmax(
rl)
# --------------------------------------------------------------------------
# --------------------------------------------------------------------------
mask = "x>0"
#
v_n = "rho"
data_arr = d3class.get_data(it, rl, "xz", v_n)
x_arr = d3class.get_data(it, rl, "xz", "x")
z_arr = d3class.get_data(it, rl, "xz", "z")
# print(data_arr); exit(1)
contour_dic_xz = {
'task': 'contour',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": z_arr,
"zarr": data_arr,
'levels': [1.e13 / 6.176e+17],
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'colors': ['white'],
'lss': ["-"],
'lws': [1.],
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': 'rho',
'xscale': None,
'yscale': None,
'fancyticks': True,
'sharey': False,
'sharex': True, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
o_plot.set_plot_dics.append(contour_dic_xz)
rho_dic_xz = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": z_arr,
"zarr": data_arr,
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'v_n_x': 'x',
'v_n_y': 'z',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': zmin,
'ymax': zmax,
'vmin': 1e-9,
'vmax': 1e-5,
'fill_vmin': False, # fills the x < vmin with vmin
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': 'Greys',
'norm': "log",
'fancyticks': True,
'minorticks': True,
'title': {
"text": sim.replace('_', '\_'),
'fontsize': 12
},
#'title': {"text": r'$t-t_{merg}:$' + r'${:.1f}$ [ms]'.format((t - tmerg) * 1e3), 'fontsize': 14},
'sharey': False,
'sharex': True, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
data_arr = d3class.get_data(it, rl, "xy", v_n)
x_arr = d3class.get_data(it, rl, "xy", "x")
y_arr = d3class.get_data(it, rl, "xy", "y")
contour_dic_xy = {
'task': 'contour',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": y_arr,
"zarr": data_arr,
'levels': [1.e13 / 6.176e+17],
'position': (2, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'colors': ['white'],
'lss': ["-"],
'lws': [1.],
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': 'rho',
'xscale': None,
'yscale': None,
'fancyticks': True,
'sharey': False,
'sharex': True, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
o_plot.set_plot_dics.append(contour_dic_xy)
rho_dic_xy = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": y_arr,
"zarr": data_arr,
'position': (2, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'vmin': 1e-9,
'vmax': 1e-5,
'fill_vmin': False, # fills the x < vmin with vmin
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': 'Greys',
'norm': "log",
'fancyticks': True,
'minorticks': True,
'title': {},
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
if plot_x_i == 1:
rho_dic_xy['cbar'] = {
'location': 'bottom -.05 .00',
'label': r'$\rho$ [GEO]', # 'fmt': '%.1e',
'labelsize': 14,
'fontsize': 14
}
if plot_x_i > 1:
rho_dic_xz['sharey'] = True
rho_dic_xy['sharey'] = True
o_plot.set_plot_dics.append(rho_dic_xz)
o_plot.set_plot_dics.append(rho_dic_xy)
# ----------------------------------------------------------------------
v_n = "dens_unb_bern"
#
data_arr = d3class.get_data(it, rl, "xz", v_n)
x_arr = d3class.get_data(it, rl, "xz", "x")
z_arr = d3class.get_data(it, rl, "xz", "z")
dunb_dic_xz = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": z_arr,
"zarr": data_arr,
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'v_n_x': 'x',
'v_n_y': 'z',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': zmin,
'ymax': zmax,
'vmin': 1e-10,
'vmax': 1e-7,
'fill_vmin': False, # fills the x < vmin with vmin
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': 'Blues',
'norm': "log",
'fancyticks': True,
'minorticks': True,
'title':
{}, #{"text": r'$t-t_{merg}:$' + r'${:.1f}$ [ms]'.format((t - tmerg) * 1e3), 'fontsize': 14},
'sharex': True, # removes angular citkscitks
'sharey': False,
'fontsize': 14,
'labelsize': 14
}
#
data_arr = d3class.get_data(it, rl, "xy", v_n)
x_arr = d3class.get_data(it, rl, "xy", "x")
y_arr = d3class.get_data(it, rl, "xy", "y")
dunb_dic_xy = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": y_arr,
"zarr": data_arr,
'position': (2, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'fill_vmin': False, # fills the x < vmin with vmin
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'vmin': 1e-10,
'vmax': 1e-7,
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': 'Blues',
'norm': "log",
'fancyticks': True,
'minorticks': True,
'title': {},
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
if plot_x_i == 2:
dunb_dic_xy['cbar'] = {
'location': 'bottom -.05 .00',
'label': r'$D_{\rm{unb}}$ [GEO]', # 'fmt': '%.1e',
'labelsize': 14,
'fontsize': 14
}
if plot_x_i > 1:
dunb_dic_xz['sharey'] = True
dunb_dic_xy['sharey'] = True
o_plot.set_plot_dics.append(dunb_dic_xz)
o_plot.set_plot_dics.append(dunb_dic_xy)
# ----------------------------------------------------------------------
mask = "x<0"
#
v_n = "Ye"
cmap = "bwr_r"
#
data_arr = d3class.get_data(it, rl, "xz", v_n)
x_arr = d3class.get_data(it, rl, "xz", "x")
z_arr = d3class.get_data(it, rl, "xz", "z")
ye_dic_xz = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": z_arr,
"zarr": data_arr,
'position': (1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'fill_vmin': False, # fills the x < vmin with vmin
'v_n_x': 'x',
'v_n_y': 'z',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': zmin,
'ymax': zmax,
'vmin': 0.05,
'vmax': 0.5,
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': cmap,
'norm': None,
'fancyticks': True,
'minorticks': True,
'title':
{}, #{"text": r'$t-t_{merg}:$' + r'${:.1f}$ [ms]'.format((t - tmerg) * 1e3), 'fontsize': 14},
'sharey': False,
'sharex': True, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
data_arr = d3class.get_data(it, rl, "xy", v_n)
x_arr = d3class.get_data(it, rl, "xy", "x")
y_arr = d3class.get_data(it, rl, "xy", "y")
ye_dic_xy = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": y_arr,
"zarr": data_arr,
'position': (2, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'fill_vmin': False, # fills the x < vmin with vmin
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'vmin': 0.01,
'vmax': 0.5,
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': cmap,
'norm': None,
'fancyticks': True,
'minorticks': True,
'title': {},
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
if plot_x_i == 3:
ye_dic_xy['cbar'] = {
'location': 'bottom -.05 .00',
'label': r'$Y_e$',
'fmt': '%.1f',
'labelsize': 14,
'fontsize': 14
}
if plot_x_i > 1:
ye_dic_xz['sharey'] = True
ye_dic_xy['sharey'] = True
o_plot.set_plot_dics.append(ye_dic_xz)
o_plot.set_plot_dics.append(ye_dic_xy)
# ----------------------------------------------------------
tcoll = d1class.get_par("tcoll_gw")
if not np.isnan(tcoll) and t >= tcoll:
print(tcoll, t)
v_n = "lapse"
mask = "z>0.15"
data_arr = d3class.get_data(it, rl, "xz", v_n)
x_arr = d3class.get_data(it, rl, "xz", "x")
z_arr = d3class.get_data(it, rl, "xz", "z")
lapse_dic_xz = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": z_arr,
"zarr": data_arr,
'position':
(1, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'v_n_x': 'x',
'v_n_y': 'z',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': zmin,
'ymax': zmax,
'vmin': 0.,
'vmax': 0.15,
'fill_vmin': False, # fills the x < vmin with vmin
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': 'Greys',
'norm': None,
'fancyticks': True,
'minorticks': True,
'title':
{}, #,{"text": r'$t-t_{merg}:$' + r'${:.1f}$ [ms]'.format((t - tmerg) * 1e3),
#'fontsize': 14},
'sharey': False,
'sharex': True, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
data_arr = d3class.get_data(it, rl, "xy", v_n)
# print(data_arr.min(), data_arr.max()); exit(1)
x_arr = d3class.get_data(it, rl, "xy", "x")
y_arr = d3class.get_data(it, rl, "xy", "y")
lapse_dic_xy = {
'task': 'colormesh',
'ptype': 'cartesian',
'aspect': 1.,
'xarr': x_arr,
"yarr": y_arr,
"zarr": data_arr,
'position':
(2, plot_x_i), # 'title': '[{:.1f} ms]'.format(time_),
'cbar': {},
'v_n_x': 'x',
'v_n_y': 'y',
'v_n': v_n,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'vmin': 0,
'vmax': 0.15,
'fill_vmin': False, # fills the x < vmin with vmin
'xscale': None,
'yscale': None,
'mask': mask,
'cmap': 'Greys',
'norm': None,
'fancyticks': True,
'minorticks': True,
'title': {},
'sharey': False,
'sharex': False, # removes angular citkscitks
'fontsize': 14,
'labelsize': 14
}
#
# if plot_x_i == 1:
# rho_dic_xy['cbar'] = {'location': 'bottom -.05 .00', 'label': r'$\rho$ [GEO]', # 'fmt': '%.1e',
# 'labelsize': 14,
# 'fontsize': 14}
if plot_x_i > 1:
lapse_dic_xz['sharey'] = True
lapse_dic_xy['sharey'] = True
o_plot.set_plot_dics.append(lapse_dic_xz)
o_plot.set_plot_dics.append(lapse_dic_xy)
plot_x_i += 1
o_plot.main()
exit(0)
def get_tmax_d3_data(self):
isd3_1, itd3_1, td3_1 = self.o_par1.get_ittime("profiles", "prof")
isd3_2, itd3_2, td3_2 = self.o_par2.get_ittime("profiles", "prof")
isd3_3, itd3_3, td3_3 = self.o_par3.get_ittime("profiles", "prof")
if len(td3_1) == 0:
Printcolor.red("D3 data not found for sim1:{}".format(self.sim1))
return np.nan
if len(td3_2) == 0:
Printcolor.red("D3 data not found for sim2:{}".format(self.sim2))
return np.nan
if len(td3_3) == 0:
Printcolor.red("D3 data not found for sim3:{}".format(self.sim3))
return np.nan
tmerg1 = self.o_par1.get_par("tmerger")
tmerg2 = self.o_par2.get_par("tmerger")
tmerg3 = self.o_par3.get_par("tmerger")
Printcolor.blue("\ttd3_1[-1]:{} tmerg1:{} -> {}".format(td3_1[-1], tmerg1, td3_1[-1] - tmerg1))
Printcolor.blue("\ttd3_2[-1]:{} tmerg2:{} -> {}".format(td3_2[-1], tmerg2, td3_2[-1] - tmerg2))
Printcolor.blue("\ttd3_3[-1]:{} tmerg3:{} -> {}".format(td3_3[-1], tmerg3, td3_3[-1] - tmerg3))
td3_1 = np.array(td3_1 - tmerg1)
td3_2 = np.array(td3_2 - tmerg2)
td3_3 = np.array(td3_3 - tmerg3)
#
if td3_1.min() > td3_2.max():
Printcolor.red("D3 data does not overlap. sim1 has min:{} that is > than sim2 max: {}"
.format(td3_1.min(), td3_2.max()))
return np.nan
if td3_1.min() > td3_3.max():
Printcolor.red("D3 data does not overlap. sim1 has min:{} that is > than sim3 max: {}"
.format(td3_1.min(), td3_3.max()))
return np.nan
if td3_2.min() > td3_3.max():
Printcolor.red("D3 data does not overlap. sim2 has min:{} that is > than sim3 max: {}"
.format(td3_2.min(), td3_3.max()))
return np.nan
# ---
if td3_1.max() < td3_2.min():
Printcolor.red("D3 data does not overlap. sim1 has max:{} that is < than sim2 min: {}"
.format(td3_1.max(), td3_2.min()))
return np.nan
if td3_1.max() < td3_3.min():
Printcolor.red("D3 data does not overlap. sim1 has max:{} that is < than sim3 min: {}"
.format(td3_1.max(), td3_3.min()))
return np.nan
if td3_2.max() < td3_3.min():
Printcolor.red("D3 data does not overlap. sim2 has max:{} that is < than sim3 min: {}"
.format(td3_2.max(), td3_3.min()))
return np.nan
tmax = np.min([td3_1.max(), td3_2.max(), td3_3.max()])
Printcolor.blue("\ttmax for D3 data: {}".format(tmax))
return float(tmax)
def plot_correlation():
sims = ['DD2_M13641364_M0_SR_R04', 'DD2_M13641364_M0_LK_SR_R04']
v_ns = ["Y_e", "Y_e"]
det = 0
masks = ["geo", "geo"]
#
dic_data = {}
for sim, v_n, mask in zip(sims, v_ns, masks):
o = ADD_METHODS_ALL_PAR(sim)
corr_table = o.get_outflow_corr(det, mask,
"{}_{}".format(v_n, "theta"))
dic_data[sim] = {}
dic_data[sim]["data"] = corr_table.T
hist = o.get_outflow_hist(det, mask, "theta")
dic_data[sim]['hist'] = hist.T
print(hist.T.shape)
Printcolor.green("data in collected")
#
# for sim in sims:
# dic_data[sim]['data'] /= np.sum(dic_data[sim]['data'][1:, 1:])
o_plot = PLOT_MANY_TASKS()
o_plot.gen_set["figdir"] = __outplotdir__
o_plot.gen_set["type"] = "cartesian"
o_plot.gen_set["figsize"] = (8., 5.6) # <->, |]
o_plot.gen_set["figname"] = "comparison.png".format("Ye")
o_plot.gen_set["sharex"] = False
o_plot.gen_set["sharey"] = False
o_plot.gen_set["dpi"] = 128
o_plot.gen_set["subplots_adjust_h"] = 0.0
o_plot.gen_set["subplots_adjust_w"] = 0.0
o_plot.set_plot_dics = []
i = 1
for sim in sims:
# HISTOGRAMS
plot_dic = {
'task': 'hist1d',
'ptype': 'cartesian',
'position': (1, i),
'data': dic_data[sim]["hist"],
'normalize': True,
'v_n_x': "theta",
'v_n_y': "mass",
'color': "black",
'ls': '-',
'lw': 1.,
'ds': 'steps',
'alpha': 1.0,
'xmin': 0.,
'xamx': 90.,
'ymin': 1e-4,
'ymax': 1e0,
'xlabel': Labels.labels("theta"),
'ylabel': r"$M_{\rm{ej}}/M$",
'label': None,
'yscale': 'log',
'fancyticks': True,
'minorticks': True,
'fontsize': 14,
'labelsize': 14,
'sharex': True, # removes angular citkscitks
'sharey': False,
'title': {
'text': sim.replace('_', '\_'),
'fontsize': 12
},
# 'textold': {'coords': (0.1, 0.1), 'text':sim.replace('_', '\_'), 'color': 'black', 'fs': 10},
'legend': {} # 'loc': 'best', 'ncol': 2, 'fontsize': 18
}
# if v_n == "tempterature":
if i != 1:
plot_dic['sharey'] = True
o_plot.set_plot_dics.append(plot_dic)
# CORRELATIONS
corr_dic2 = { # relies on the "get_res_corr(self, it, v_n): " method of data object
'task': 'corr2d', 'dtype': 'corr', 'ptype': 'cartesian',
'data': dic_data[sim]['data'],
'position': (2, i),
'v_n_x': 'theta', 'v_n_y': 'ye', 'v_n': 'mass', 'normalize': True,
'cbar': {},
'cmap': 'viridis', #'set_under': 'white', #'set_over': 'black',
'xlabel': r"Angle from binary plane", 'ylabel': r"$Y_e$",
'xmin': 0., 'xmax': 90., 'ymin': 0.05, 'ymax': 0.5, 'vmin': 1e-4, 'vmax': 1e-2,
'xscale': "linear", 'yscale': "linear", 'norm': 'log',
'mask_below': None, 'mask_above': None,
'title': {}, # {"text": o_corr_data.sim.replace('_', '\_'), 'fontsize': 14},
'fancyticks': True,
'minorticks': True,
'sharex': False, # removes angular citkscitks
'sharey': False,
'fontsize': 14,
'labelsize': 14,
}
corr_dic2["axhline"] = {
"y": 0.25,
"linestyle": "-",
"linewidth": 0.5,
"color": "black"
}
# corr_dic2["axvline"] = {"x": 0.25, "linestyle": "-", "linewidth": 0.5, "color": "black"}
if i != 1:
corr_dic2['sharey'] = True
if sim == sims[-1]:
corr_dic2['cbar'] = \
{'location': 'right .03 .0', 'label': r"$M_{\rm{ej}}/M$", # 'fmt': '%.1f',
'labelsize': 14, 'fontsize': 14}
o_plot.set_plot_dics.append(corr_dic2)
i = i + 1
o_plot.main()
def plot_correlation_from_two_datadirs_vertical():
sims = ['SFHo_M135135_LK', 'SFHo_M135135_M0', "SFHo_M13641364_M0_LK_SR"]
colors = ["blue", "red", "green"] #, "orange"]
lss = ["-", "--", "-."] #, ":"]
rowdata = [
"/data1/numrel/WhiskyTHC/Backup/2017/",
"/data1/numrel/WhiskyTHC/Backup/2017/",
"/data1/numrel/WhiskyTHC/Backup/2018/GW170817/"
]
datapaths = [
"/data01/numrel/vsevolod.nedora/postprocessed_radice2/",
"/data01/numrel/vsevolod.nedora/postprocessed_radice2/",
"/data01/numrel/vsevolod.nedora/postprocessed4/"
]
v_ns = ["Y_e", "Y_e", "Y_e"]
det = 0
masks = ["geo", "geo", "geo"]
#
dic_data = {}
for sim, v_n, mask, path in zip(sims, v_ns, masks, datapaths):
Paths.ppr_sims = path
o = ADD_METHODS_ALL_PAR(sim)
corr_table = o.get_outflow_corr(det, mask,
"{}_{}".format(v_n, "theta"))
dic_data[sim] = {}
dic_data[sim]["data"] = corr_table.T
hist = o.get_outflow_hist(det, mask, "theta")
dic_data[sim]['hist'] = hist.T
print(hist.T.shape)
Printcolor.green("data in collected")
#
# for sim in sims:
# dic_data[sim]['data'] /= np.sum(dic_data[sim]['data'][1:, 1:])
o_plot = PLOT_MANY_TASKS()
o_plot.gen_set["figdir"] = __outplotdir__
o_plot.gen_set["type"] = "cartesian"
o_plot.gen_set["figsize"] = (4.2, 3.6 * len(sims)) # <->, |]
o_plot.gen_set["figname"] = "comparison.png".format("Ye")
o_plot.gen_set["sharex"] = True
o_plot.gen_set["sharey"] = False
o_plot.gen_set["dpi"] = 128
o_plot.gen_set["subplots_adjust_h"] = 0.0
o_plot.gen_set["subplots_adjust_w"] = 0.0
o_plot.set_plot_dics = []
i = 1
for sim, ls, color in zip(sims, lss, colors):
# HISTOGRAMS
plot_dic = {
'task': 'hist1d',
'ptype': 'cartesian',
'position': (1, 1),
'data': dic_data[sim]["hist"],
'normalize': True,
'v_n_x': "theta",
'v_n_y': "mass",
'color': color,
'ls': ls,
'lw': 1.,
'ds': 'steps',
'alpha': 1.0,
'xmin': 50.,
'xamx': 90.,
'ymin': 1e-4,
'ymax': 1e0,
'xlabel': Labels.labels("theta"),
'ylabel': r"$M_{\rm{ej}}/M$",
'label': sim.replace('_', '\_'),
'yscale': 'log',
'fancyticks': True,
'minorticks': True,
'fontsize': 14,
'labelsize': 14,
'sharex': True, # removes angular citkscitks
'sharey': False,
'title': {}, #{'text':sim.replace('_', '\_'), 'fontsize':12},
# 'textold': {'coords': (0.1, 0.1), 'text':sim.replace('_', '\_'), 'color': 'black', 'fs': 10},
'legend': {
'loc': 'best',
'ncol': 1,
'fontsize': 10
} # 'loc': 'best', 'ncol': 2, 'fontsize': 18
}
# if v_n == "tempterature":
# if i!=1:
# plot_dic['sharey'] = True
o_plot.set_plot_dics.append(plot_dic)
i = 2
for sim in sims:
# CORRELATIONS
corr_dic2 = { # relies on the "get_res_corr(self, it, v_n): " method of data object
'task': 'corr2d', 'dtype': 'corr', 'ptype': 'cartesian',
'data': dic_data[sim]['data'],
'position': (i, 1),
'v_n_x': 'theta', 'v_n_y': 'ye', 'v_n': 'mass', 'normalize': True,
'cbar': {},
'cmap': 'viridis', #'set_under': 'white', #'set_over': 'black',
'xlabel': r"Angle from binary plane", 'ylabel': r"$Y_e$",
'xmin': 0., 'xmax': 90., 'ymin': 0.05, 'ymax': 0.5, 'vmin': 1e-4, 'vmax': 1e-2,
'xscale': "linear", 'yscale': "linear", 'norm': 'log',
'mask_below': None, 'mask_above': None,
'title': {}, # {"text": o_corr_data.sim.replace('_', '\_'), 'fontsize': 14},
'fancyticks': True,
'minorticks': True,
'sharex': True, # removes angular citkscitks
'sharey': False,
'fontsize': 14,
'labelsize': 14,
}
corr_dic2["axhline"] = {
"y": 0.25,
"linestyle": "-",
"linewidth": 0.5,
"color": "black"
}
# corr_dic2["axvline"] = {"x": 0.25, "linestyle": "-", "linewidth": 0.5, "color": "black"}
if sim == sims[-1]:
corr_dic2['sharex'] = False
if sim == sims[-1]:
corr_dic2['cbar'] = \
{'location': 'right .03 .0', 'label': r"$M_{\rm{ej}}/M$", # 'fmt': '%.1f',
'labelsize': 14, 'fontsize': 14}
o_plot.set_plot_dics.append(corr_dic2)
i = i + 1
o_plot.main()
def plot_hists_from_different_dirs():
# sims = ['SFHo_M135135_LK', 'SFHo_M135135_M0', 'SFHo_M13641364_M0_SR', "SFHo_M13641364_M0_LK_SR"]
# sims = ['LS220_M135135_LK', 'LS220_M135135_M0', 'LS220_M13641364_M0_SR', "LS220_M13641364_M0_LK_SR"]
# sims = ['DD2_M135135_LK', 'DD2_M135135_M0', 'DD2_M13641364_M0_SR_R04', "DD2_M13641364_M0_LK_SR_R04"]
# sims = ['BHBlp_M130130_LK', 'BHBlp_M135135_M0']
# sims = ['BLh_M13651365_M0_SR', 'BLh_M13641364_M0_LK_SR']
sims = ['SLy4_M13641364_M0_SR', 'SLy4_M13641364_M0_LK_SR']
colors = ["blue", "red"]#, "green", "purple"]
lss = ["-", "--"]#, "-.", ":"]
# datapaths = ["/data01/numrel/vsevolod.nedora/postprocessed_radice2/",
# "/data01/numrel/vsevolod.nedora/postprocessed_radice2/"]
datapaths = ["/data01/numrel/vsevolod.nedora/postprocessed4/",
"/data01/numrel/vsevolod.nedora/postprocessed4/"]
#
v_ns = ["Y_e", 'theta', 'entropy']
det = 0
masks = ['geo', 'geo', 'geo']
#
dic_data = {}
for v_n, mask in zip(v_ns, masks):
dic_data[v_n+mask] = {}
for sim, path in zip(sims, datapaths):
dic_data[v_n+mask][sim] = {}
Paths.ppr_sims = path
o = ADD_METHODS_ALL_PAR(sim)
# corr_table = o.get_outflow_corr(det, mask, "{}_{}".format(v_n, "theta"))
# dic_data[v_n+mask][sim]["data"] = corr_table.T
hist = o.get_outflow_hist(det, mask, v_n)
dic_data[v_n+mask][sim]['hist'] = hist.T
dic_data[v_n + mask][sim]['mej'] = sum(hist.T[:,1])
print(hist.T.shape)
Printcolor.green("data in collected")
#
# for sim in sims:
# dic_data[sim]['data'] /= np.sum(dic_data[sim]['data'][1:, 1:])
o_plot = PLOT_MANY_TASKS()
o_plot.gen_set["figdir"] = __outplotdir__
o_plot.gen_set["type"] = "cartesian"
o_plot.gen_set["figsize"] = (4.2*len(v_ns), 3.6) # <->, |]
o_plot.gen_set["figname"] = "comparison_sly4.png"
o_plot.gen_set["sharex"] = False
o_plot.gen_set["sharey"] = False
o_plot.gen_set["dpi"] = 128
o_plot.gen_set["subplots_adjust_h"] = 0.0
o_plot.gen_set["subplots_adjust_w"] = 0.0
o_plot.set_plot_dics = []
i = 1
for v_n, mask in zip(v_ns, masks):
# textdic = {'task': 'text', 'ptype': 'cartesian',
# 'position': (1, i), 'x': 0.5, 'y': 0.5,
# 'text': r"{}".format(dic_data[v_n + mask][sim]['mej'] * 1e2) + "$[10^2M_{\odot}]$", 'fs': 12,
# 'color': 'black', 'horal': True, 'transform': True}
for sim, ls, color in zip(sims, lss, colors):
# HISTOGRAMS
mej = np.sum(dic_data[v_n + mask][sim]["hist"][:, 1])
plot_dic = {
'task': 'hist1d', 'ptype': 'cartesian',
'position': (1, i),
'data': dic_data[v_n+mask][sim]["hist"], 'normalize': False,
'v_n_x': v_n, 'v_n_y': "mass",
'color': color, 'ls': ls, 'lw': 1., 'ds': 'steps', 'alpha': 1.0,
'xmin': 0., 'xamx': 90., 'ymin': 1e-5, 'ymax': 1e-3,
'xlabel': Labels.labels(v_n), 'ylabel': r"$M_{\rm{ej}}$",
'label': None, 'yscale': 'log',
'fancyticks': True, 'minorticks': True,
'fontsize': 14,
'labelsize': 14,
'sharex': False, # removes angular citkscitks
'sharey': False,
# 'yticks': [],
'title':{},#{'text':sim.replace('_', '\_'), 'fontsize':12},
# 'textold': {'coords': (0.1, 0.1), 'text':sim.replace('_', '\_'), 'color': 'black', 'fs': 10},
'legend': {'loc': 'best', 'ncol': 1, 'fontsize': 10} # 'loc': 'best', 'ncol': 2, 'fontsize': 18
}
# textdic = {'task': 'text', 'ptype': 'cartesian',
# 'position': (1, i), 'x': 0.5, 'y': 0.5,
# 'text': r"{}".format(mej * 1e2) + "$[10^2M_{\odot}]$", 'fs': 12,
# 'color': 'black', 'horal': True, 'transform': True}
# if v_n == "tempterature":
# if i!=1:
# plot_dic['sharey'] = True
if v_n != v_ns[0]:
plot_dic['sharey'] = False
plot_dic['yticks'] = []
plot_dic['rmylbls'] = True
plot_dic['ylabel'] = None
if v_n == v_ns[0]:
# mej = np.sum(dic_data[v_n+mask][sim]["hist"][:,1])
# o_plot.set_plot_dics.append(textdic)
plot_dic['label'] = sim.replace('_', '\_')
elif v_n == v_ns[-1]:
mej = np.sum(dic_data[v_n + mask][sim]["hist"][:, 1])
plot_dic['label'] = r"$M_{\rm{ej}}$ "+"{:.2f}".format(mej * 1e2) + "$ [10^2M_{\odot}]$"
if v_n == "theta":
plot_dic['xmin'], plot_dic['xmax'] = 0., 90.
plot_dic['xmajorticks'] = np.arange(5) * 90. / 4.
plot_dic['xminorticks'] = np.arange(17) * 90. / 16
plot_dic['xmajorlabels'] = [r"$0^\circ$", r"$22.5^\circ$", r"$45^\circ$",
r"$67.5^\circ$", r"$90^\circ$"]
elif v_n == "entropy":
plot_dic['xmin'], plot_dic['xmax'] = 5, 95.
elif v_n == "Y_e":
plot_dic['xmin'], plot_dic['xmax'] = 0.05, 0.45
o_plot.set_plot_dics.append(plot_dic)
i = i + 1
o_plot.main()
exit(1)