def f_observing_log(title="Observing log for each tile, for each band"):
"""
Makes a graphical observing log.
"""
tile_tables = filter_by_tile()[0]
fig = plt.figure()
for i, tile_table in zip(range(len(tile_tables)), tile_tables):
# How do we J slice? BAND CUT
j_tile_table = band_cut(tile_table, 'j')
h_tile_table = band_cut(tile_table, 'h')
k_tile_table = band_cut(tile_table, 'k')
j_dates = list(set(j_tile_table.MEANMJDOBS))
h_dates = list(set(h_tile_table.MEANMJDOBS))
k_dates = list(set(k_tile_table.MEANMJDOBS))
plt.plot(j_dates, 5/4+i*np.ones(len(j_dates)), 'b.')
plt.plot(h_dates, 1+i*np.ones(len(h_dates)), 'g.')
plt.plot(k_dates, 3/4+i*np.ones(len(k_dates)), 'r.')
plt.xlabel("Modified Julian Date")
plt.ylabel("Tile #", rotation='horizontal')
plt.title(title)
plt.ylim(1-1/3, 16+1/3)
return fig
def f_observing_map():
"""
Makes a map of observations and how many J, H, K band datapoints
each tile has.
"""
max_ra = maxvars.RA.max()
min_ra = maxvars.RA.min()
max_dec = maxvars.DEC.max()
min_dec = maxvars.DEC.min()
tile_size_ra = (max_ra - min_ra) / 4
tile_size_dec = (max_dec - min_dec) / 4
tile_spreadsheets = filter_by_tile()[1]
fig = plt.figure(figsize=(6,6))
ij_list = [(x, y) for x in range(4) for y in range(4)]
text_params = {'horizontalalignment':'center',
'verticalalignment':'center'}
# print the nice text and stuff
for k, ij, tile_spreadsheet in zip(range(len(tile_spreadsheets)),
ij_list, tile_spreadsheets):
ra_i, dec_j = ij
tile_ra = min_ra + tile_size_ra*ra_i + tile_size_ra/2
tile_dec = min_dec + tile_size_dec*dec_j + tile_size_dec/2
plt.text(np.degrees(tile_ra), np.degrees(tile_dec+tile_size_dec/4),
"Tile #%d" % (k+1), **text_params)
plt.text(np.degrees(tile_ra), np.degrees(tile_dec+tile_size_dec/12),
"J: %3d" % tile_spreadsheet.N_j.max(), color='b',
**text_params)
plt.text(np.degrees(tile_ra), np.degrees(tile_dec-tile_size_dec/12),
"H: %3d" % tile_spreadsheet.N_h.max(), color='g',
**text_params)
plt.text(np.degrees(tile_ra), np.degrees(tile_dec-tile_size_dec/4),
"K: %3d" % tile_spreadsheet.N_k.max(), color='r',
**text_params)
physical_tile_size_ra = (max_ra - min_ra) / 3.88
physical_tile_size_dec = (max_dec - min_dec) / 3.88
# make the overlapping rectangles
for k, ij in zip(range(len(ij_list)), ij_list):
ra_i, dec_j = ij
southeast_corner_ra = min_ra + 0.94*physical_tile_size_ra*ra_i
southeast_corner_dec = min_dec + 0.94*physical_tile_size_dec*dec_j
if ij == (0,0) or ij == (0,2) or ij == (2,0) or ij == (2,2):
rectangle_params = {'color': '0.85',
'zorder':-10}
else:
rectangle_params = {'fill': False}
plt.gca().add_patch(
plt.Rectangle((np.degrees(southeast_corner_ra),
np.degrees(southeast_corner_dec)),
np.degrees(physical_tile_size_ra),
np.degrees(physical_tile_size_dec),
ec='k', **rectangle_params))
northeast_corner = (np.degrees(maxvars.RA.max() + 0.001),
np.degrees(maxvars.DEC.max() + 0.001))
southwest_corner = (np.degrees(maxvars.RA.min() - 0.001),
np.degrees(maxvars.DEC.min() - 0.001))
plt.xlim(northeast_corner[0], southwest_corner[0])
plt.ylim(southwest_corner[1], northeast_corner[1])
plt.xlabel("RA (deg)")
plt.ylabel("Dec (deg)")
return fig
