def map(
imbin_file,
som_file,
map_file,
trans_file,
som_width,
som_height,
cpu_only=False,
nrot=360,
numthreads=10,
overwrite=False,
log=True,
):
# /// Map through SOM \\\
imgs = pu.ImageReader(imbin_file)
if not os.path.exists(map_file) or overwrite:
map_imbin(
imbin_file,
som_file,
map_file,
trans_file,
som_width,
som_height,
numthreads=numthreads,
cpu=cpu_only,
nrot=nrot,
log=log,
)
som = pu.SOM(som_file)
mapping = pu.Mapping(map_file)
transform = pu.Transform(trans_file)
somset = pu.SOMSet(som, mapping, transform)
return somset
def init_somset(som_file, map_file=None, trans_file=None):
"""Initialize the SOMSet, which contains the SOM, mapping,
and transform files.
Args:
som_file (str): Name of the SOM file.
map_file (str, optional): Name of the mapping file. Defaults to None,
in which case it follows the default naming scheme.
trans_file (str, optional): Name of the transform file. Defaults to None.
Returns:
pu.SOMSet: A container holding the SOM, mapping, and transform files.
"""
som = pu.SOM(som_file)
if map_file is None:
map_file = som_file.replace("SOM", "MAP")
mapping = pu.Mapping(map_file)
if trans_file is None:
trans_file = map_file.replace("MAP", "TRANSFORM")
transform = pu.Transform(trans_file)
somset = pu.SOMSet(som=som, mapping=mapping, transform=transform)
return somset
file=outfile)
print(f" \includegraphics[width=0.75\\textwidth]{{{base}_img0.png}}",
file=outfile)
print(f" \includegraphics[width=0.75\\textwidth]{{{base}_img1.png}}",
file=outfile)
print(f" \caption{{{caption}}}", file=outfile)
print(f" \label{{fig:{label}}}", file=outfile)
print(f"\end{{figure*}}", file=outfile)
if len(sys.argv) < 5:
print(f"USAGE: {sys.argv[0]} IMG SOM MAP TRANSFORM")
imgs = pu.ImageReader(sys.argv[1])
som = pu.SOM(sys.argv[2])
mapping = pu.Mapping(sys.argv[3])
trans = pu.Transform(sys.argv[4])
somset = pu.SOMSet(som, mapping, trans)
path = pu.PathHelper("Inspection")
outfile = open("latex_imgs.tex", "w")
### SOM 2D histogram ###
som_counts(somset, True)
plt.savefig(f"som_stats.png")
plt.clf()
caption = f"The total number of images within the {imgs.data.shape[0]} input images mapped to each neuron in the SOM."
latex_figure("som_stats.png", caption, "som_stats", outfile)
print("", file=outfile)
########################
if len(sys.argv) != 3:
print("USAGE: {} sample_file som_file".format(sys.argv[0]))
sys.exit(-1)
sample_file = sys.argv[1]
df = pd.read_csv(sample_file)
imbin = pu.ImageReader(sample_file.replace(".csv", "_imgs.bin"))
som_file = sys.argv[2]
som = pu.SOM(som_file)
map_file = som_file.replace(".bin", "_Similarity.bin")
mapping = pu.Mapping(map_file)
trans_file = som_file.replace(".bin", "_Transform.bin")
transform = pu.Transform(trans_file)
somset = pu.SOMSet(som=som, mapping=mapping, transform=transform)
### Update the component catalogue ###
bmus = mapping.bmu()
df["bmu_x"] = bmus[:, 0]
df["bmu_y"] = bmus[:, 1]
df["bmu_tup"] = mapping.bmu(return_tuples=True)
df["bmu_ed"] = mapping.bmu_ed()
trans = transform.data[np.arange(transform.data.shape[0]), bmus[:, 0], bmus[:, 1]]
def source_from_catalogue(
src_cat,
radio_cat,
imbin_path,
som=None,
show_nearby=False,
show_bmu=False,
idx=None,
):
if idx is None:
# idx = np.random.randint(len(src_cat))
idx = src_cat[src_cat.N_components > 1].sample(1).index[0]
src = src_cat.loc[idx]
comp_names = src.Component_names.split(";")
tile_id = radio_cat[radio_cat.Component_name == comp_names[0]].Tile.iloc[0]
radio_tile = radio_cat[radio_cat.Tile == tile_id].reset_index(drop=True)
comps = radio_tile[radio_tile["Component_name"].isin(comp_names)]
radio_ra = np.array(src.RA_components.split(";"), dtype=float)
radio_dec = np.array(src.DEC_components.split(";"), dtype=float)
radio_pos = SkyCoord(radio_ra, radio_dec, unit=u.deg)
if src.N_host_candidates > 0:
ir_ra = np.array(src.RA_host_candidates.split(";"), dtype=float)
ir_dec = np.array(src.DEC_host_candidates.split(";"), dtype=float)
ir_pos = SkyCoord(ir_ra, ir_dec, unit=u.deg)
img_file, map_file, trans_file = binary_names(tile_id, imbin_path)
imgs = pu.ImageReader(img_file)
mapping = pu.Mapping(map_file)
transform = pu.Transform(trans_file)
if som is not None:
somset = pu.SOMSet(som, mapping, transform)
img_idx = comps.index[0]
comp = comps.loc[img_idx]
npix = imgs.data.shape[-1]
wcs = create_wcs(comp.RA, comp.DEC, npix, 3 * u.arcmin / npix)
if show_bmu:
plot_image(
imgs,
img_idx,
somset=somset,
wcs=wcs,
transform_neuron=True,
show_bmu=show_bmu,
)
else:
plot_image(imgs, img_idx, wcs=wcs, show_bmu=False)
axes = plt.gcf().axes
if show_nearby:
posn = SkyCoord(comp.RA, comp.DEC, unit=u.deg)
coords = SkyCoord(radio_cat.RA, radio_cat.DEC, unit=u.deg)
nearby = coords[posn.separation(coords) < 1.5 * u.arcmin]
for ax in plt.gcf().axes:
ax.scatter(
nearby.RA, nearby.DEC, c="w", transform=ax.get_transform("world")
)
# for ax in plt.gcf().axes:
# ax.scatter(comps.RA, comps.DEC, c="r", transform=ax.get_transform("world"))
axes[0].scatter(
radio_pos.ra, radio_pos.dec, c="r", transform=axes[0].get_transform("world")
)
if src.N_host_candidates > 0:
axes[1].scatter(
ir_pos.ra, ir_pos.dec, c="w", transform=axes[1].get_transform("world")
)
plt.suptitle(f"Source ID: {idx}")