def test_read_lnp_data(self):
"""
Read in the lnp data from a cached file and test that selected values
are as expected.
"""
ldata = read_lnp_data(self.lnp_fname_cache)
exp_keys = ["vals", "indxs"]
for ckey in ldata.keys():
assert ckey in exp_keys, f"{ckey} not in lnp data expected keys"
# check an entry for a single model (caching current values 20 Apr 2020)
# fmt: off
exp_vals = [
-56.83604431, -76.34762573, -17.55770874, -18.23323059,
-10.53744507
]
exp_indxs = [14639., 15015., 296., 12636., 1336.]
# fmt: on
np.testing.assert_allclose(
ldata["vals"][0][0:5],
exp_vals,
err_msg="Expected posterior (vals) values not correct",
)
np.testing.assert_allclose(
ldata["indxs"][0][0:5],
exp_indxs,
err_msg="Expected index values not correct",
)
def test_get_lnp_grid_vals(self):
"""
Read in the lnp and sed grid data from cached files and test that
selected values are as expected.
"""
ldata = read_lnp_data(self.lnp_fname_cache)
requested_params = [
"Av", "Rv", "f_A", "M_ini", "logA", "Z", "distance"
]
sdata = read_sed_data(self.seds_trim_fname_cache,
param_list=requested_params)
lgvals_data = get_lnp_grid_vals(sdata, ldata)
# check that otherwise, the requested lgvals data is returned
expected_values = {
"Av": [0.0, 0.0, 0.0, 0.0, 0.0],
"Rv": [2.0, 2.0, 2.0, 2.0, 2.0],
"f_A": [1.0, 1.0, 1.0, 1.0, 1.0],
"M_ini":
[3.89416909, 3.92726111, 3.95603228, 2.04966068, 2.04999995],
"logA": [6.0, 6.0, 6.0, 9.0, 9.0],
"Z": [0.03, 0.03, 0.03, 0.004, 0.004],
"distance": [
783429.64276621,
783429.64276621,
783429.64276621,
783429.64276621,
783429.64276621,
],
}
for cname in requested_params:
assert (cname in lgvals_data.keys()
), f"requsted parameter {cname} not in sed data"
np.testing.assert_allclose(
lgvals_data[cname][0:5, 10],
expected_values[cname],
err_msg=f"expected value of {cname} is not found",
)
def merge_lnp(
subgrid_lnp_fnames,
re_run=False,
output_fname_base=None,
threshold=None,
):
"""
Merge a set of sparsely sampled log likelihood (lnp) files. It is assumed
that they are for each part of a subgrid, such that a given star_# in each
file corresponds to the same star_# in the other file(s). Note that this
should NOT be used to combine files across source density or background bin.
Parameters
----------
subgrid_lnp_fnames: list of string
file names of all the lnp fits files
re_run: boolean (default=False)
If True, re-run the merging, even if the merged files already
exist. If False, will only merge files if they don't exist.
output_fname_base: string (default=None)
If set, this will prepend the output lnp file name
threshold : float (default=None)
If set: for a given star, any lnP values below max(lnP)-threshold will
be deleted
Returns
-------
merged_lnp_fname : string
file name of the resulting lnp fits file (newly created by this function)
"""
# create filename
if output_fname_base is None:
merged_lnp_fname = "combined_lnp.fits"
else:
merged_lnp_fname = output_fname_base + "_lnp.fits"
# check if we need to rerun
if os.path.isfile(merged_lnp_fname) and (re_run is False):
print(str(len(subgrid_lnp_fnames)) + " files already merged, skipping")
return merged_lnp_fname
# dictionaries to compile all the info
merged_lnp = defaultdict(list)
merged_subgrid = defaultdict(list)
merged_idx = defaultdict(list)
for fname in subgrid_lnp_fnames:
# extract subgrid number from filename
subgrid_num = [i for i in fname.split('_') if 'gridsub' in i][0][7:]
# read in the SED indices and lnP values
lnp_data = read_beast_data.read_lnp_data(fname, shift_lnp=False)
n_lnp, n_star = lnp_data['vals'].shape
# save each star's values into the master dictionary
for i in range(n_star):
merged_lnp['star_'+str(i)] += lnp_data['vals'][:,i].tolist()
merged_idx['star_'+str(i)] += lnp_data['indxs'][:,i].tolist()
merged_subgrid['star_'+str(i)] += np.full(n_lnp, int(subgrid_num)).tolist()
# go through each star and remove values that are too small
if threshold is not None:
# keep track of how long the list of good values is
good_list_len = np.zeros(n_star)
# go through each star
for i in range(n_star):
star_label = "star_"+str(i)
# good indices
keep_ind = np.where(
np.array(merged_lnp[star_label]) >
(max(merged_lnp[star_label]) - threshold)
)[0]
good_list_len[i] = len(keep_ind)
# save just those
merged_lnp[star_label] = np.array(merged_lnp[star_label])[keep_ind].tolist()
merged_idx[star_label] = np.array(merged_idx[star_label])[keep_ind].tolist()
merged_subgrid[star_label] = np.array(merged_subgrid[star_label])[keep_ind].tolist()
# figure out how many padded -inf/nan values need to be appended to make
# each list the same length
n_list_pad = np.max(good_list_len) - good_list_len
else:
# no list padding if there's no trimming for threshold
n_list_pad = np.zeros(n_star)
# write out the things in a new file
with tables.open_file(merged_lnp_fname, "w") as out_table:
for i in range(n_star):
star_label = "star_"+str(i)
star_group = out_table.create_group(star_label)
star_group.create_dataset(
'idx',
data=np.array(merged_idx[star_label] + n_list_pad*[np.nan])
)
star_group.create_dataset(
'lnp',
data=np.array(merged_lnp[star_label] + n_list_pad*[-np.inf])
)
star_group.create_dataset(
'subgrid',
data=np.array(merged_subgrid[star_label] + n_list_pad*[np.nan])
)
return merged_lnp_fname
def megabeast(megabeast_input_file, verbose=True):
"""
Run the MegaBEAST on each of the spatially-reordered BEAST outputs.
Parameters
----------
megabeast_input_file : string
Name of the file that contains settings, filenames, etc
verbose : boolean (default=True)
print extra info
"""
# read in the settings from the file
mb_settings = read_megabeast_input(megabeast_input_file)
# setup the megabeast model including defining the priors
# - dust distribution model
# - stellar populations model (later)
# use nstars image to setup for each pixel
nstars_image, nstars_header = fits.getdata(mb_settings["nstars_filename"],
header=True)
n_x, n_y = nstars_image.shape
# read in the beast data that is needed by all the pixels
beast_data = {}
# - SED data
beast_data.update(
read_beast_data.read_sed_data(
mb_settings["beast_seds_filename"],
param_list=["Av"] # , "Rv", "f_A"]
))
# - max completeness
beast_data.update(
read_beast_data.read_noise_data(
mb_settings["beast_noise_filename"],
param_list=["completeness"],
))
beast_data["completeness"] = np.max(beast_data["completeness"], axis=1)
# setup for output
pixel_fit_status = np.full((n_x, n_y), False, dtype=bool)
n_fit_params = len(mb_settings["fit_param_names"])
best_fit_images = np.zeros((n_x, n_y, n_fit_params), dtype=float) + np.nan
# loop over the pixels with non-zero entries in the nstars image
for i in trange(n_x, desc="x pixels"):
for j in trange(n_y, desc="y pixels", leave=False):
# for i in [6]:
# for j in [6]:
if verbose:
print("working on (%i,%i)" % (i, j))
if nstars_image[i, j] >= mb_settings["min_for_fit"]:
pixel_fit_status[i, j] = True
# get the saved sparse likelihoods
lnp_filename = mb_settings[
"lnp_file_prefix"] + "_{0}_{1}_lnp.hd5".format(j, i)
lnp_data = read_beast_data.read_lnp_data(
lnp_filename,
nstars=nstars_image[i, j],
shift_lnp=True,
)
# get the completeness and BEAST model parameters for the
# same grid points as the sparse likelihoods
lnp_grid_vals = read_beast_data.get_lnp_grid_vals(
beast_data, lnp_data)
# initialize the ensemble model with the parameters used
# for the saved BEAST model run results
# currently only dust parameters allowed
# for testing -> only Av
avs = lnp_grid_vals["Av"]
rvs = [3.1] # beast_data['Rv']
fAs = [1.0] # beast_data['f_A']
beast_dust_priors = PriorWeightsDust(
avs,
mb_settings["av_prior_model"],
rvs,
mb_settings["rv_prior_model"],
fAs,
mb_settings["fA_prior_model"],
)
# standard minimization to find initial values
def chi2(args):
return -1.0 * lnprob(*args)
result = op.minimize(
chi2,
[0.25, 2.0, 0.5, 0.5, 1],
args=(beast_dust_priors, lnp_data, lnp_grid_vals),
method="Nelder-Mead",
)
best_fit_images[i, j, :] = result["x"]
# print(result)
# print(result['x'])
# print(result['success'])
# then run through MCMC to fully sample likelihood
# include option not to run MCMC
# output results
# - best fit
# - megabeast parameter 1D pPDFs
# - MCMC chain
master_header = nstars_header
# Now, write the maps to disk
# check that the directory exists
if not os.path.exists("./" + mb_settings["projectname"] + "_megabeast/"):
os.makedirs("./" + mb_settings["projectname"] + "_megabeast/")
for k, cname in enumerate(mb_settings["fit_param_names"]):
hdu = fits.PrimaryHDU(best_fit_images[:, :, k], header=master_header)
# Save to FITS file
hdu.writeto(
"%s_megabeast/%s_%s_bestfit.fits" %
(mb_settings["projectname"], mb_settings["projectname"], cname),
overwrite=True,
)
def fit_ensemble(beast_data,
lnp_filename,
beast_priormodel,
nstars_expected=None):
"""
Run the MegaBEAST on a single set of BEAST results.
Parameters
----------
beast_data : dict
information about the BEAST runs including SED grid and noise model
lnp_filename : string
file with posteriors from BEAST fitting
beast_priormodel : dict
dictionary of the BEAST prior model information
nstars_expected : int
number of stars expected, used as a check
Returns
-------
fit_results : array
set of best fit parameters
"""
# get the saved sparse likelihoods
lnp_data = read_lnp_data(lnp_filename,
nstars=nstars_expected,
shift_lnp=True)
# get the completeness and BEAST model parameters for the
# same grid points as the sparse likelihoods
lnp_grid_vals = get_lnp_grid_vals(beast_data, lnp_data)
# compute the BEAST prior weights
# needed so the BEAST posteriors updated with the MegaBEAST model
# ***currently only AV ensemble model supported***
avs = lnp_grid_vals["Av"]
rvs = [3.1] # beast_data['Rv']
fAs = [1.0] # beast_data['f_A']
beast_dust_priors = PriorWeightsDust(
avs,
beast_priormodel["AV"],
rvs,
beast_priormodel["RV"],
fAs,
beast_priormodel["fA"],
)
# standard minimization to find initial values
def chi2(args):
return -1.0 * lnprob(*args)
result = op.minimize(
chi2,
[0.25, 2.0, 0.5, 0.5, 1],
args=(beast_dust_priors, lnp_data, lnp_grid_vals),
method="Nelder-Mead",
)
# next step would be to
# run through MCMC to fully sample likelihood
# maybe include option not to run MCMC
return result["x"]
def plot_input_data(megabeast_input_file, chi2_plot=[], log_scale=False):
"""
Parameters
----------
megabeast_input_file : string
Name of the file that contains settings, filenames, etc
chi2_plot : list of floats (default=[])
Make A_V histogram(s) with chi2 less than each of the values in this list
log_scale : boolean (default=False)
If True, make the histogram x-axis a log scale (to visualize log-normal
A_V distribution)
"""
# read in the settings from the file
mb_settings = read_input(megabeast_input_file)
# get the project name
projectname = mb_settings["projectname"]
# read in the beast data that is needed by all the pixels
beast_data = {}
# - SED data
beast_data.update(
read_beast_data.read_sed_data(
mb_settings["beast_seds_filename"],
param_list=["Av"] # , "Rv", "f_A"]
))
# - max completeness
beast_data.update(
read_beast_data.read_noise_data(
mb_settings["beast_noise_filename"],
param_list=["completeness"],
))
beast_data["completeness"] = np.max(beast_data["completeness"], axis=1)
# read in the nstars image
nstars_image, nstars_header = fits.getdata(mb_settings["nstars_filename"],
header=True)
# dimensions of images/plotting
y_dimen = nstars_image.shape[0]
x_dimen = nstars_image.shape[1]
# set up multi-page figure
if not log_scale:
pp = PdfPages("{0}_megabeast/plot_input_data.pdf".format(projectname))
if log_scale:
pp = PdfPages(
"{0}_megabeast/plot_input_data_log.pdf".format(projectname))
# save the best-fit A_V
best_av = [[[] for j in range(x_dimen)] for i in range(y_dimen)]
best_av_chi2 = [[[] for j in range(x_dimen)] for i in range(y_dimen)]
# -----------------
# Completeness vs A_V
# -----------------
print("")
print("Making completeness/Av plot")
print("")
# set up figure
plt.figure(figsize=(6, 6))
plt.subplot(1, 1, 1)
for i in tqdm(range(y_dimen), desc="y pixels"):
for j in tqdm(range(x_dimen), desc="x pixels"):
# for i in tqdm(range(int(y_dimen/3)), desc='y pixels'):
# for j in tqdm(range(int(x_dimen/3)), desc='x pixels'):
# for i in [0]:
# for j in [12]:
if nstars_image[i, j] > 20:
# get info about the fits
lnp_filename = mb_settings[
"lnp_file_prefix"] + "_{0}_{1}_lnp.hd5".format(j, i)
lnp_data = read_beast_data.read_lnp_data(
lnp_filename,
nstars=nstars_image[i, j],
shift_lnp=True,
)
# get the completeness and BEAST model parameters for the
# same grid points as the sparse likelihoods
lnp_grid_vals = read_beast_data.get_lnp_grid_vals(
beast_data, lnp_data)
# grab the things we want to plot
plot_av = lnp_grid_vals["Av"]
plot_comp = lnp_grid_vals["completeness"]
for n in range(nstars_image[i, j]):
# plot a random subset of the AVs and completenesses
if (i % 3 == 0) and (j % 3 == 0):
plot_these = np.random.choice(plot_av[:, n].size,
size=20,
replace=False)
plt.plot(
plot_av[plot_these, n] +
np.random.normal(scale=0.02, size=plot_these.size),
plot_comp[plot_these, n],
marker=".",
c="black",
ms=3,
mew=0,
linestyle="None",
alpha=0.05,
)
# also overplot the values for the best fit
max_ind = np.where(lnp_data["vals"][:, n] == np.max(
lnp_data["vals"][:, n]))[0][0]
best_av[i][j].append(plot_av[max_ind, n])
best_av_chi2[i][j].append(-2 *
np.max(lnp_data["vals"][:, n]))
if (i % 3 == 0) and (j % 3 == 0):
plt.plot(
plot_av[max_ind, n] + np.random.normal(scale=0.01),
plot_comp[max_ind, n],
marker=".",
c="magenta",
ms=2,
mew=0,
linestyle="None",
alpha=0.3,
zorder=9999,
)
ax = plt.gca()
ax.set_xlabel(r"$A_V$")
ax.set_ylabel("Completeness")
pp.savefig()
# -----------------
# histograms of AVs
# -----------------
print("")
print("Making Av Histograms")
print("")
# set up figure
plt.figure(figsize=(x_dimen * 2, y_dimen * 2))
# flat list of A_V
# https://stackoverflow.com/questions/952914/making-a-flat-list-out-of-list-of-lists-in-python
flat_av = [i for sublist in best_av for item in sublist for i in item]
# grab the max A_V of all of them
# max_av = max(flat_av)
# define bins
if not log_scale:
uniq_av = np.unique(flat_av)
gap = np.min(np.diff(uniq_av))
bins = np.arange(uniq_av[0], uniq_av[-1], gap)
if log_scale:
uniq_av = np.unique(np.log10(flat_av))
gap = (uniq_av[-1] - uniq_av[0]) / len(uniq_av)
bins = np.arange(uniq_av[0], uniq_av[-1], gap)
for i in tqdm(range(y_dimen), desc="y pixels"):
for j in tqdm(range(x_dimen), desc="x pixels"):
# for i in [0]:
# for j in [12]:
if nstars_image[i, j] > 20:
# set up the subplot
plt.subplot(y_dimen, x_dimen,
(y_dimen - i - 1) * (x_dimen) + j + 1)
# make a histogram
if best_av[i][j] != []:
if not log_scale:
plt.hist(
best_av[i][j],
bins=bins.size,
range=(uniq_av[0] - gap / 2,
uniq_av[-1] + gap / 2),
facecolor="xkcd:azure",
linewidth=0.25,
edgecolor="xkcd:azure",
)
if log_scale:
plt.hist(
np.log10(best_av[i][j]),
bins=bins.size,
range=(uniq_av[0] - gap / 2,
uniq_av[-1] + gap / 2),
facecolor="xkcd:azure",
linewidth=0.25,
edgecolor="xkcd:azure",
)
# plt.xlim(xmax=max_av)
plt.suptitle(r"Best-fit $A_V$ for each pixel", fontsize=40)
pp.savefig()
# -----------------
# histograms of AVs with a chi2 cut
# -----------------
if len(chi2_plot) > 0:
print("")
print("Making Av Histograms with chi^2 cut")
print("")
for chi2_cut in chi2_plot:
# set up figure
plt.figure(figsize=(x_dimen * 2, y_dimen * 2))
for i in tqdm(range(y_dimen), desc="y pixels"):
for j in tqdm(range(x_dimen), desc="x pixels"):
# for i in [0]:
# for j in [12]:
if nstars_image[i, j] > 20:
# set up the subplot
plt.subplot(y_dimen, x_dimen,
(y_dimen - i - 1) * (x_dimen) + j + 1)
# make a histogram
if best_av[i][j] != []:
if not log_scale:
plot_av = np.array(best_av[i][j])[
np.array(best_av_chi2[i][j]) < chi2_cut]
if log_scale:
plot_av = np.log10(
np.array(best_av[i][j])[
np.array(best_av_chi2[i][j]) < chi2_cut])
if len(plot_av) != 0:
plt.hist(
plot_av,
bins=bins.size,
range=(uniq_av[0] - gap / 2,
uniq_av[-1] + gap / 2),
facecolor="xkcd:azure",
linewidth=0.25,
edgecolor="xkcd:azure",
)
plt.suptitle(
r"Best-fit $A_V$ for each pixel, but only using sources with $\chi^2 < $"
+ str(chi2_cut),
fontsize=40,
)
pp.savefig()
# close PDF figure
pp.close()
def simulate_av_plots(
megabeast_input_file, log_scale=False, input_lognormal=None, input_lognormal2=None
):
"""
Plot distributions of simulated AVs, and overplot the best fit lognormals
Parameters
----------
megabeast_input_file : string
Name of the file that contains settings, filenames, etc
log_scale : boolean (default=False)
If True, make the histogram x-axis a log scale (to visualize log-normal
A_V distribution)
input_lognormal, input_lognormal2 : dict (default=None)
Set these to the original values used to create the fake data, and they
will also be plotted
"""
# read in the settings from the file
mb_settings = read_input(megabeast_input_file)
# get the project name
projectname = mb_settings["projectname"]
# read in the beast data that is needed by all the pixels
# *** this likely needs updating - probably will fail - see megabeast.py
beast_data = read_sed_data(
mb_settings["beast_seds_filename"],
mb_settings["beast_noise_filename"],
beast_params=["completeness", "Av"],
) # ,'Rv','f_A'])
av_grid = np.unique(beast_data["Av"])
# also make a more finely sampled A_V grid
if not log_scale:
av_grid_big = np.linspace(np.min(av_grid), np.max(av_grid), 500)
else:
av_grid_big = np.geomspace(np.min(av_grid), np.max(av_grid), 500)
# read in the nstars image
nstars_image, nstars_header = fits.getdata(
mb_settings["nstars_filename"], header=True
)
# dimensions of images/plotting
y_dimen = nstars_image.shape[0]
x_dimen = nstars_image.shape[1]
# read in the best fits
label_list = mb_settings["fit_param_names"]
best_fits = {}
for label in label_list:
with fits.open(
"./"
+ projectname
+ "_megabeast/"
+ projectname
+ "_"
+ label
+ "_bestfit.fits"
) as hdu:
best_fits[label] = hdu[0].data
# set colors for plots
cmap = matplotlib.cm.get_cmap("inferno")
color_data = cmap(0.0)
color_fit = cmap(0.5)
if input_lognormal is not None:
color_input = cmap(0.85)
# -----------------
# plotting
# -----------------
# set up figure
fig = plt.figure(figsize=(x_dimen * 2, y_dimen * 2))
for i in tqdm(range(y_dimen), desc="y pixels"):
for j in tqdm(range(x_dimen), desc="x pixels"):
# for i in [0]:
# for j in [12]:
if nstars_image[i, j] > 20:
# -------- data
# read in the original lnp data
lnp_filename = mb_settings["lnp_file_prefix"] + "_%i_%i_lnp.hd5" % (
j,
i,
)
lnp_data = read_lnp_data(lnp_filename, nstars_image[i, j])
lnp_vals = np.array(lnp_data["vals"])
# completeness for each of the values
lnp_comp = beast_data["completeness"][lnp_data["indxs"]]
# best A_V for each star
best_av = []
for k in range(lnp_vals.shape[1]):
vals = lnp_vals[:, k]
lnp_vals[:, k] = np.log(np.exp(vals) / np.sum(np.exp(vals)))
inds = lnp_data["indxs"][:, k]
best_val_ind = np.where(vals == np.max(vals))[0][0]
best_av.append(beast_data["Av"][inds[best_val_ind]])
best_av = np.array(best_av)
# stack up some representation of what's being maximized in ensemble_model.py
prob_stack = np.sum(lnp_comp * np.exp(lnp_vals), axis=1)
# normalize it (since it's not clear what the numbers mean anyway)
# prob_stack = prob_stack / np.sum(prob_stack)
prob_stack = prob_stack / np.trapz(prob_stack, av_grid)
# stack up the probabilities at each A_V
# prob_stack = np.sum(np.exp(lnp_vals), axis=1)
# set up the subplot
plt.subplot(y_dimen, x_dimen, (y_dimen - i - 1) * (x_dimen) + j + 1)
# make a histogram
if not log_scale:
plt.plot(
av_grid,
prob_stack,
marker=".",
ms=0,
mew=0,
linestyle="-",
color=color_data,
linewidth=4,
)
if log_scale:
plt.plot(
np.log10(av_grid),
prob_stack,
marker=".",
ms=0,
mew=0,
linestyle="-",
color=color_data,
linewidth=4,
)
ax = plt.gca()
# -------- input lognormal(s)
if input_lognormal is not None:
# create lognormal
lognorm = _lognorm(
av_grid_big,
input_lognormal["max_pos"],
input_lognormal["sigma"],
input_lognormal["N"],
)
# if there's a second lognormal
if input_lognormal2 is not None:
lognorm += _lognorm(
av_grid_big,
input_lognormal2["max_pos"],
input_lognormal2["sigma"],
input_lognormal2["N"],
)
# normalize it
# lognorm = lognorm / np.sum(lognorm)
lognorm = lognorm / np.trapz(lognorm, av_grid_big)
# plot it
# yrange_before = ax.get_ylim()
if not log_scale:
plt.plot(
av_grid_big,
lognorm,
marker=".",
ms=0,
mew=0,
linestyle="-",
color=color_input,
linewidth=2,
alpha=0.85,
)
if log_scale:
plt.plot(
np.log10(av_grid_big),
lognorm,
marker=".",
ms=0,
mew=0,
linestyle="-",
color=color_input,
linewidth=2,
alpha=0.85,
)
# ax.set_ylim(yrange_before)
# -------- best fit
# generate best fit
lognorm = _two_lognorm(
av_grid_big,
best_fits["Av1"][i, j],
best_fits["Av2"][i, j],
sigma1=best_fits["sigma1"][i, j],
sigma2=best_fits["sigma2"][i, j],
N1=nstars_image[i, j]
* (1 - 1 / (best_fits["N12_ratio"][i, j] + 1)),
N2=nstars_image[i, j] / (best_fits["N12_ratio"][i, j] + 1),
)
# normalize it
# lognorm = lognorm / nstars_image[i,j]
# lognorm = lognorm / np.sum(lognorm)
lognorm = lognorm / np.trapz(lognorm, av_grid_big)
# plot it
yrange_before = ax.get_ylim()
if not log_scale:
plt.plot(
av_grid_big,
lognorm,
marker=".",
ms=0,
mew=0,
dashes=[3, 1.5],
color=color_fit,
linewidth=2,
)
if log_scale:
plt.plot(
np.log10(av_grid_big),
lognorm,
marker=".",
ms=0,
mew=0,
dashes=[3, 1.5],
color=color_fit,
linewidth=2,
)
ax.set_ylim(yrange_before)
fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor="none", top="off", bottom="off", left="off", right="off")
plt.grid(False)
if not log_scale:
plt.xlabel(r"$A_V$", size=15)
else:
plt.xlabel(r"Log $A_V$", size=15)
plt.ylabel("PDF", size=15)
plt.tight_layout()
# save figure
plt.savefig("./" + projectname + "_megabeast/" + projectname + "_bestfit_plot.pdf")
plt.close()
