def __init__(self, obs, style="paper", template_name="summary.tex"):
LatexTemplate.__init__(self, template_name, style=style)
self.obs = obs
datetimes = Time(self.obs.time, format='jd', scale='utc').to_datetime()
min_datetime = datetimes.min()
max_datetime = datetimes.max()
obs_duration_hours = (max_datetime - min_datetime).seconds // 3600
obs_duration_mins = ((max_datetime - min_datetime).seconds // 60) % 60
self.obs_duration = f"{min_datetime.strftime('%H:%M')} - {max_datetime.strftime('%H:%M')} " \
f"[{obs_duration_hours}h{obs_duration_mins if obs_duration_mins != 0 else ''}]"
# TODO: adapt to use PSF model block here (se we don't use the plot_... method from Observation)
#self.mean_fwhm = np.mean(self.obs.x.fwhm.values)
#self.obs._compute_psf_model(star=self.obs.target)
#self.mean_target_fwhm = np.mean([self.obs.stack.fwhmx, self.obs.stack.fwhmy])
#self.optimal_aperture = np.mean(self.obs.apertures_radii[self.obs.aperture,:])
self.obstable = [
["Time", self.obs_duration],
["RA - DEC", f"{self.obs.RA} {self.obs.DEC}"],
["Images", len(self.obs.time)],
[
"Mean std · fwhm (epsf)",
f"{self.obs.mean_epsf / (2 * np.sqrt(2 * np.log(2))):.2f} · {self.obs.mean_epsf:.2f} pixels"
],
[
"Fwhm (target)",
f"{self.obs.mean_target_psf:.2f} pixels · {(self.obs.mean_target_psf*self.obs.telescope.pixel_scale.to(u.arcsec)):.2f}"
],
[
"Optimum aperture",
f"{self.obs.optimal_aperture:.2f} pixels · "
f"{(self.obs.optimal_aperture*self.obs.telescope.pixel_scale.to(u.arcsec)):.2f}"
],
["Telescope", self.obs.telescope.name],
["Filter", self.obs.filter],
["Exposure", f"{np.mean(self.obs.exptime)} s"],
]
self.description = f"{self.obs.night_date.strftime('%Y %m %d')} $\cdot$ {self.obs.telescope.name} $\cdot$ {self.obs.filter}"
self._trend = None
self._transit = None
self.dpi = 100
# Some paths
# ----------
self.header = "Observation report"
fig = corner.corner(samples,
labels=para_names,
truths=reals,
quantiles=[.16, .50, .84],
show_titles=True)
fig.savefig("Corner.png")
for k in range(4):
plt.figure()
for i in range(nwalkers):
plt.plot(sampler.chain[i, :, k])
plt.title(para_names[k])
plt.axhline(reals[k], color='k', linewidth=2)
plt.savefig('%s_walk.png' % para_names_file[k])
times_curve = Time(np.linspace(times.min().mjd,
times.max().mjd, 10000),
format='mjd')
eta_fit = orbfits.eta_orb(srce, times_curve, Ecc, a, T0, Pb, Om_peri_dot,
Om_peri, samples[:, 0].mean() * u.deg,
samples[:, 1].mean() * u.deg,
samples[:, 2].mean() * u.deg, dp,
samples[:, 3].mean() * u.kpc, f0, pm_ra, pm_dec)
eta_real = orbfits.eta_orb(srce, times_curve, Ecc, a, T0, Pb, Om_peri_dot,
Om_peri, Om_orb, Om_scr, inc, dp, ds, f0, pm_ra,
pm_dec)
plt.figure()
plt.plot_date(times.plot_date, eta_noisy, label='Data')
plt.plot_date(times_curve.plot_date, eta_real, '-', label='Real')
rcParams['axes.linewidth']=2
#convert mjd to years
time_yr=Time(time,format='mjd').decimalyear
#find new y intercept with these new units.
def find_b_ra(x,b):
return fit_ra[0]*10**6*365.25*x + b
def find_b_de(x,b):
return fit_de[0]*10**6*365.25*x + b
b_ra,c=optimize.curve_fit(find_b_ra,time_yr,(ra-ra[-1])*10**6)
b_de,c=optimize.curve_fit(find_b_de,time_yr,(dec-dec[-1])*10**6)
fig, (ax1,ax2)=plt.subplots(2,sharex=True)
ax1.errorbar(time_yr[0:-1],(ra[0:-1]-ra[-1])*10**6,yerr=e_ra[0:-1]*10**6,fmt='.',mfc='blue',ecolor="black",elinewidth=2,ms=15)
x=np.array(range(int(time_yr.min()),int(time_yr.max())+2))
ax1.plot(x,func(fit_ra[0]*10**6*365.25,b_ra,x),color="red",linewidth=2)
ax1.plot([time_yr[-1]],[0],'o',markersize=15, mfc='none',mec='green',mew=2)
ax1.errorbar([time_yr[-1]],[0],yerr=[e_ra[-1]*10**6],fmt='',ecolor="green",elinewidth=2,capsize=10,capthick=2)
ax1.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
xticks(fontsize='medium')
yticks(fontsize='medium')
ax1.set_title(ivs_i,fontsize=20)
ax1.set_ylabel('$\Delta$ RA ($\mu$as)',fontsize=20)
plt.setp(ax1.get_xticklabels(),visible=False)
#locator_params(axis='x',nbins=4)
ax2.errorbar(time_yr[0:-1],(dec[0:-1]-dec[-1])*10**6,yerr=e_dec[0:-1]*10**6,fmt='.',mfc='blue',ecolor="black",elinewidth=2,ms=15)
x=np.array(range(int(time_yr.min()),int(time_yr.max())+2))
ax2.plot(x,func(fit_de[0]*10**6*365.25,b_de,x),color="red",linewidth=2)
def _init_periods(config, location):
logger.debug("Laying down time period boundries")
period_index = pd.period_range(
config["start_time"],
config["end_time"],
freq=config["time_freq"],
name="period",
)
times = Time(
pd.to_datetime(period_index.to_timestamp()), # pylint: disable=no-member
location=location,
)
logger.debug("Calculating solar ephemeris")
sun_coords = astropy.coordinates.get_sun(times)
logger.debug("Calculating lunar ephemeris")
moon_coords = astropy.coordinates.get_moon(times)
moon_elongation = moon_coords.separation(sun_coords)
logger.debug("Converting solar and lunar coordinates to horizon system")
horizon_coordinate_system = astropy.coordinates.AltAz(location=location)
sun_hzn = sun_coords.transform_to(horizon_coordinate_system)
moon_hzn = moon_coords.transform_to(horizon_coordinate_system)
logger.debug("Building the period DataFrame")
periods = pd.DataFrame(
{
"mjd": times.mjd,
"time": times,
"lst": times.sidereal_time("mean"),
"sun_ra": sun_coords.ra.deg,
"sun_decl": sun_coords.dec.deg,
"sun_alt": sun_hzn.alt.deg,
"moon_ra": moon_coords.ra.deg,
"moon_decl": moon_coords.dec.deg,
"moon_alt": moon_hzn.alt.deg,
"moon_elongation": moon_elongation.deg,
},
index=period_index,
)
periods["moon_waxing"] = (periods["moon_elongation"].shift(-1) >
periods["moon_elongation"])
previous_waxing = periods["moon_waxing"].shift(
1, fill_value=periods.iloc[0]["moon_waxing"])
periods["new_moon"] = periods["moon_waxing"] & ~previous_waxing
periods["lunation"] = periods["new_moon"].cumsum()
mean_local_solar_jd = 2400000.5 + periods["mjd"] + (location.lon.deg /
360.0)
night_local_solar_jd = np.floor(mean_local_solar_jd).astype(int)
periods["night"] = night_local_solar_jd - np.min(night_local_solar_jd) + 1
# Mark daytime periods
periods["observing"] = periods.sun_alt <= config["max_sun_alt_deg"]
# Mark scheduled downtime
periods.reset_index(inplace=True)
periods.set_index("mjd", inplace=True)
for down_time in ScheduledDowntimeData(times.min())():
periods.loc[down_time["start"].mjd:down_time["end"].mjd,
"observing"] = False
periods.reset_index(inplace=True)
periods.set_index("period", drop=False, inplace=True)
return periods
class TestArithmetic:
"""Arithmetic on Time objects, using both doubles."""
kwargs = ({}, {'axis': None}, {'axis': 0}, {'axis': 1}, {'axis': 2})
functions = ('min', 'max', 'sort')
def setup(self):
mjd = np.arange(50000, 50100, 10).reshape(2, 5, 1)
frac = np.array([0.1, 0.1 + 1.e-15, 0.1 - 1.e-15, 0.9 + 2.e-16, 0.9])
if use_masked_data:
frac = np.ma.array(frac)
frac[1] = np.ma.masked
self.t0 = Time(mjd, frac, format='mjd', scale='utc')
# Define arrays with same ordinal properties
frac = np.array([1, 2, 0, 4, 3])
if use_masked_data:
frac = np.ma.array(frac)
frac[1] = np.ma.masked
self.t1 = Time(mjd + frac, format='mjd', scale='utc')
self.jd = mjd + frac
@pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions))
def test_argfuncs(self, kw, func, masked):
"""
Test that ``np.argfunc(jd, **kw)`` is the same as ``t0.argfunc(**kw)``
where ``jd`` is a similarly shaped array with the same ordinal properties
but all integer values. Also test the same for t1 which has the same
integral values as jd.
"""
t0v = getattr(self.t0, 'arg' + func)(**kw)
t1v = getattr(self.t1, 'arg' + func)(**kw)
jdv = getattr(np, 'arg' + func)(self.jd, **kw)
if self.t0.masked and kw == {'axis': None} and func == 'sort':
t0v = np.ma.array(t0v, mask=self.t0.mask.reshape(t0v.shape)[t0v])
t1v = np.ma.array(t1v, mask=self.t1.mask.reshape(t1v.shape)[t1v])
jdv = np.ma.array(jdv, mask=self.jd.mask.reshape(jdv.shape)[jdv])
assert np.all(t0v == jdv)
assert np.all(t1v == jdv)
assert t0v.shape == jdv.shape
assert t1v.shape == jdv.shape
@pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions))
def test_funcs(self, kw, func, masked):
"""
Test that ``np.func(jd, **kw)`` is the same as ``t1.func(**kw)`` where
``jd`` is a similarly shaped array and the same integral values.
"""
t1v = getattr(self.t1, func)(**kw)
jdv = getattr(np, func)(self.jd, **kw)
assert np.all(t1v.value == jdv)
assert t1v.shape == jdv.shape
def test_argmin(self, masked):
assert self.t0.argmin() == 2
assert np.all(self.t0.argmin(axis=0) == 0)
assert np.all(self.t0.argmin(axis=1) == 0)
assert np.all(self.t0.argmin(axis=2) == 2)
def test_argmax(self, masked):
assert self.t0.argmax() == self.t0.size - 2
if masked:
# The 0 is where all entries are masked in that axis
assert np.all(self.t0.argmax(axis=0) == [1, 0, 1, 1, 1])
assert np.all(self.t0.argmax(axis=1) == [4, 0, 4, 4, 4])
else:
assert np.all(self.t0.argmax(axis=0) == 1)
assert np.all(self.t0.argmax(axis=1) == 4)
assert np.all(self.t0.argmax(axis=2) == 3)
def test_argsort(self, masked):
order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3]
assert np.all(self.t0.argsort() == np.array(order))
assert np.all(self.t0.argsort(axis=0) == np.arange(2).reshape(2, 1, 1))
assert np.all(self.t0.argsort(axis=1) == np.arange(5).reshape(5, 1))
assert np.all(self.t0.argsort(axis=2) == np.array(order))
ravel = np.arange(50).reshape(-1, 5)[:, order].ravel()
if masked:
t0v = self.t0.argsort(axis=None)
# Manually remove elements in ravel that correspond to masked
# entries in self.t0. This removes the 10 entries that are masked
# which show up at the end of the list.
mask = self.t0.mask.ravel()[ravel]
ravel = ravel[~mask]
assert np.all(t0v[:-10] == ravel)
else:
assert np.all(self.t0.argsort(axis=None) == ravel)
@pytest.mark.parametrize('scale', Time.SCALES)
def test_argsort_warning(self, masked, scale):
if scale == 'utc':
pytest.xfail()
with warnings.catch_warnings(record=True) as wlist:
Time([1, 2, 3], format='jd', scale=scale).argsort()
assert len(wlist) == 0
def test_min(self, masked):
assert self.t0.min() == self.t0[0, 0, 2]
assert np.all(self.t0.min(0) == self.t0[0])
assert np.all(self.t0.min(1) == self.t0[:, 0])
assert np.all(self.t0.min(2) == self.t0[:, :, 2])
assert self.t0.min(0).shape == (5, 5)
assert self.t0.min(0, keepdims=True).shape == (1, 5, 5)
assert self.t0.min(1).shape == (2, 5)
assert self.t0.min(1, keepdims=True).shape == (2, 1, 5)
assert self.t0.min(2).shape == (2, 5)
assert self.t0.min(2, keepdims=True).shape == (2, 5, 1)
def test_max(self, masked):
assert self.t0.max() == self.t0[-1, -1, -2]
assert np.all(self.t0.max(0) == self.t0[1])
assert np.all(self.t0.max(1) == self.t0[:, 4])
assert np.all(self.t0.max(2) == self.t0[:, :, 3])
assert self.t0.max(0).shape == (5, 5)
assert self.t0.max(0, keepdims=True).shape == (1, 5, 5)
def test_ptp(self, masked):
assert self.t0.ptp() == self.t0.max() - self.t0.min()
assert np.all(self.t0.ptp(0) == self.t0.max(0) - self.t0.min(0))
assert self.t0.ptp(0).shape == (5, 5)
assert self.t0.ptp(0, keepdims=True).shape == (1, 5, 5)
def test_sort(self, masked):
order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3]
assert np.all(self.t0.sort() == self.t0[:, :, order])
assert np.all(self.t0.sort(0) == self.t0)
assert np.all(self.t0.sort(1) == self.t0)
assert np.all(self.t0.sort(2) == self.t0[:, :, order])
if not masked:
assert np.all(self.t0.sort(None) == self.t0[:, :, order].ravel())
# Bit superfluous, but good to check.
assert np.all(self.t0.sort(-1)[:, :, 0] == self.t0.min(-1))
assert np.all(self.t0.sort(-1)[:, :, -1] == self.t0.max(-1))
def mindatetime(items):
"""Return the minimum datetime from a list of datetime strings in ISO-8601 format."""
time_list = Time(items, format="isot")
return str(time_list.min())
def mindate(items):
"""Return the minimum date from a list of date strings in yyyy-mm-dd format."""
time_list = Time(items, format="iso", in_subfmt="date", out_subfmt="date")
return str(time_list.min())
def mindate(items):
"""Return the minimum time from a list of time strings."""
time_list = Time(items)
return time_list.min()
def save_report(self, destination, fields, remove_temp=True):
def draw_table(table, table_start, marg=5, table_cell=(20,4)):
pdf.set_draw_color(200,200,200)
for i, datum in enumerate(table):
pdf.set_font("helvetica", size=6)
pdf.set_fill_color(249,249,249)
pdf.rect(table_start[0] + 5, table_start[1] + 1.2 + i*table_cell[1],
table_cell[0]*3, table_cell[1], "FD" if i%2 == 0 else "D")
pdf.set_text_color(100,100,100)
value = datum[1]
if value is None:
value = "--"
else:
value = str(value)
pdf.text(
table_start[0] + marg + 2,
table_start[1] + marg + i*table_cell[1] - 1.2, datum[0])
pdf.set_text_color(50,50,50)
pdf.text(
table_start[0] + marg + 2 + table_cell[0],
table_start[1] + marg + i*table_cell[1] - 1.2, value)
if path.isdir(destination):
file_name = "{}_report.pdf".format(self.products_denominator)
else:
file_name = path.basename(destination.strip(".html").strip(".pdf"))
temp_folder = path.join(path.dirname(destination), "temp")
if path.isdir("temp"):
shutil.rmtree(temp_folder)
if os.path.exists(temp_folder):
shutil.rmtree(temp_folder)
os.mkdir(temp_folder)
self.show_stars(10, view="reference")
star_plot = path.join(temp_folder, "starplot.png")
fig = plt.gcf()
fig.patch.set_alpha(0)
plt.savefig(star_plot)
plt.close()
plt.figure(figsize=(6,10))
self.plot_raw_diff()
lc_report_plot = path.join(temp_folder, "lcreport.png")
fig = plt.gcf()
fig.patch.set_alpha(0)
plt.savefig(lc_report_plot)
plt.close()
plt.figure(figsize=(6,10))
self.plot_systematics(fields=fields)
syst_plot = path.join(temp_folder, "systplot.png")
fig = plt.gcf()
fig.patch.set_alpha(0)
plt.savefig(syst_plot)
plt.close()
if self.comparison_stars is not None:
plt.figure(figsize=(6,10))
self.plot_comps_lcs()
lc_comps_plot = path.join(temp_folder, "lccompreport.png")
fig = plt.gcf()
fig.patch.set_alpha(0)
plt.savefig(lc_comps_plot)
plt.close()
plt.figure(figsize=(10,3.5))
psf_p = self.plot_psf_fit()
psf_fit = path.join(temp_folder, "psf_fit.png")
plt.savefig(psf_fit, dpi=60)
plt.close()
theta = psf_p["theta"]
std_x = psf_p["std_x"]
std_y = psf_p["std_y"]
marg_x = 10
marg_y = 8
pdf = prose_FPDF(orientation='L', unit='mm', format='A4')
pdf.add_page()
pdf.set_draw_color(200,200,200)
pdf.set_font("helvetica", size=12)
pdf.set_text_color(50,50,50)
pdf.text(marg_x, 10, txt="{}".format(self.target["name"]))
pdf.set_font("helvetica", size=6)
pdf.set_text_color(74,144,255)
pdf.text(marg_x, 17, txt="simbad")
pdf.link(marg_x, 15, 8, 3, self.simbad)
pdf.set_text_color(150,150,150)
pdf.set_font("Helvetica", size=7)
pdf.text(marg_x, 14, txt="{} · {} · {}".format(
self.observation_date, self.telescope.name, self.filter))
pdf.image(star_plot, x=78, y=17, h=93.5)
pdf.image(lc_report_plot, x=172, y=17, h=95)
pdf.image(syst_plot, x=227, y=17, h=95)
if self.comparison_stars is not None:
pdf.image(lc_comps_plot, x=227, y=110, h=95)
datetimes = Time(self.jd, format='jd', scale='utc').to_datetime()
min_datetime = datetimes.min()
max_datetime = datetimes.max()
obs_duration = "{} - {} [{}h{}]".format(
min_datetime.strftime("%H:%M"),
max_datetime.strftime("%H:%M"),
(max_datetime-min_datetime).seconds//3600,
((max_datetime-min_datetime).seconds//60)%60)
max_psf = np.max([std_x, std_y])
min_psf = np.min([std_x, std_y])
ellipticity = (max_psf**2 - min_psf**2)/max_psf**2
draw_table([
["Time", obs_duration],
["RA - DEC", "{} - {}".format(*self.target["radec"])],
["images", len(self.time)],
["GAIA id", None],
["mean std · fwhm", "{:.2f} · {:.2f} pixels".format(np.mean(self.fwhm)/(2*np.sqrt(2*np.log(2))), np.mean(self.fwhm))],
["Telescope", self.telescope.name],
["Filter", self.filter],
["exposure", "{} s".format(np.mean(self.data.exptime))],
], (5, 20))
draw_table([
["PSF std · fwhm (x)", "{:.2f} · {:.2f} pixels".format(std_x, 2*np.sqrt(2*np.log(2))*std_x)],
["PSF std · fwhm (y)", "{:.2f} · {:.2f} pixels".format(std_y, 2*np.sqrt(2*np.log(2))*std_y)],
["PSF ellipicity", "{:.2f}".format(ellipticity)],
], (5, 78))
pdf.image(psf_fit, x=5.5, y=55, w=65)
pdf_path = path.join(destination, "{}.pdf".format(file_name.strip(".html").strip(".pdf")))
pdf.output(pdf_path)
if path.isdir("temp") and remove_temp:
shutil.rmtree(temp_folder)
print("report saved at {}".format(pdf_path))
def do_compare_file_types(TIME_WINDOW):
# This will only execute on qmaster
# Count the number of raw files staged at /mnt/sn1 that are like zen.(\d+).(\d+).uvh5
# Compare with the number of processed files that match zen.(\d+).(\d+).HH.uvh5
# only compare files that have a JD newer than the oldest raw file
if sys.version_info[0] < 3:
# py2
computer_hostname = os.uname()[1]
else:
# py3
computer_hostname = os.uname().nodename
if computer_hostname != "qmaster":
return
timesteps = np.linspace(-1 * TIME_WINDOW, 0, 24 * 14 * 6, endpoint=True)
time_array = Time.now() + TimeDelta(timesteps, format="jd")
_data = []
raw_regex = r"zen.(\d+.\d+).uvh5"
processed_regex = r"zen.(\d+.\d+).HH.uvh5"
data_dir = "/mnt/sn1/"
try:
raw_names = [f for f in os.listdir(data_dir) if re.search(raw_regex, f)]
except OSError as err:
print(
"Experienced OSError while " "attempting to find files: {err}".format(err)
)
return
try:
processed_names = [
f for f in os.listdir(data_dir) if re.search(processed_regex, f)
]
except OSError as err:
print(
"Experienced OSError while " "attempting to find files: {err}".format(err)
)
return
raw_jd = Time([float(re.findall(raw_regex, f)[0]) for f in raw_names], format="jd")
proc_jd = Time(
[float(re.findall(processed_regex, f)[0]) for f in processed_names], format="jd"
)
# try to find the times they were created
raw_times = Time(
[creation_date(os.path.join(data_dir, n)) for n in raw_names], format="unix"
)
hh_times = Time(
[creation_date(os.path.join(data_dir, n)) for n in processed_names],
format="unix",
)
# Only consider processed files if their JD is equal to or newer than
# the oldest raw file
hh_times = hh_times[proc_jd >= raw_jd.min()]
n_files_raw = []
n_files_processed = []
for _t in time_array:
n_files_raw.append(int(sum(list(_t >= raw_times))))
n_files_processed.append(int(sum(list(_t >= hh_times))))
__data = {
"x": time_array.isot.tolist(),
"y": n_files_raw,
"name": "Raw files".replace(" ", "\t"),
"type": "scatter",
}
_data.append(__data)
__data = {
"x": time_array.isot.tolist(),
"y": n_files_processed,
"name": "Processed files".replace(" ", "\t"),
"type": "scatter",
}
_data.append(__data)
return _data
class TestArithmetic():
"""Arithmetic on Time objects, using both doubles."""
kwargs = ({}, {'axis': None}, {'axis': 0}, {'axis': 1}, {'axis': 2})
functions = ('min', 'max', 'sort')
def setup(self):
mjd = np.arange(50000, 50100, 10).reshape(2, 5, 1)
frac = np.array([0.1, 0.1+1.e-15, 0.1-1.e-15, 0.9+2.e-16, 0.9])
if use_masked_data:
frac = np.ma.array(frac)
frac[1] = np.ma.masked
self.t0 = Time(mjd, frac, format='mjd', scale='utc')
# Define arrays with same ordinal properties
frac = np.array([1, 2, 0, 4, 3])
if use_masked_data:
frac = np.ma.array(frac)
frac[1] = np.ma.masked
self.t1 = Time(mjd + frac, format='mjd', scale='utc')
self.jd = mjd + frac
@pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions))
def test_argfuncs(self, kw, func, masked):
"""
Test that np.argfunc(jd, **kw) is the same as t0.argfunc(**kw) where
jd is a similarly shaped array with the same ordinal properties but
all integer values. Also test the same for t1 which has the same
integral values as jd.
"""
t0v = getattr(self.t0, 'arg' + func)(**kw)
t1v = getattr(self.t1, 'arg' + func)(**kw)
jdv = getattr(np, 'arg' + func)(self.jd, **kw)
if self.t0.masked and kw == {'axis': None} and func == 'sort':
t0v = np.ma.array(t0v, mask=self.t0.mask.reshape(t0v.shape)[t0v])
t1v = np.ma.array(t1v, mask=self.t1.mask.reshape(t1v.shape)[t1v])
jdv = np.ma.array(jdv, mask=self.jd.mask.reshape(jdv.shape)[jdv])
assert np.all(t0v == jdv)
assert np.all(t1v == jdv)
assert t0v.shape == jdv.shape
assert t1v.shape == jdv.shape
@pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions))
def test_funcs(self, kw, func, masked):
"""
Test that np.func(jd, **kw) is the same as t1.func(**kw) where
jd is a similarly shaped array and the same integral values.
"""
t1v = getattr(self.t1, func)(**kw)
jdv = getattr(np, func)(self.jd, **kw)
assert np.all(t1v.value == jdv)
assert t1v.shape == jdv.shape
def test_argmin(self, masked):
assert self.t0.argmin() == 2
assert np.all(self.t0.argmin(axis=0) == 0)
assert np.all(self.t0.argmin(axis=1) == 0)
assert np.all(self.t0.argmin(axis=2) == 2)
def test_argmax(self, masked):
assert self.t0.argmax() == self.t0.size - 2
if masked:
# The 0 is where all entries are masked in that axis
assert np.all(self.t0.argmax(axis=0) == [1, 0, 1, 1, 1])
assert np.all(self.t0.argmax(axis=1) == [4, 0, 4, 4, 4])
else:
assert np.all(self.t0.argmax(axis=0) == 1)
assert np.all(self.t0.argmax(axis=1) == 4)
assert np.all(self.t0.argmax(axis=2) == 3)
def test_argsort(self, masked):
order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3]
assert np.all(self.t0.argsort() == np.array(order))
assert np.all(self.t0.argsort(axis=0) == np.arange(2).reshape(2, 1, 1))
assert np.all(self.t0.argsort(axis=1) == np.arange(5).reshape(5, 1))
assert np.all(self.t0.argsort(axis=2) == np.array(order))
ravel = np.arange(50).reshape(-1, 5)[:, order].ravel()
if masked:
t0v = self.t0.argsort(axis=None)
# Manually remove elements in ravel that correspond to masked
# entries in self.t0. This removes the 10 entries that are masked
# which show up at the end of the list.
mask = self.t0.mask.ravel()[ravel]
ravel = ravel[~mask]
assert np.all(t0v[:-10] == ravel)
else:
assert np.all(self.t0.argsort(axis=None) == ravel)
def test_min(self, masked):
assert self.t0.min() == self.t0[0, 0, 2]
assert np.all(self.t0.min(0) == self.t0[0])
assert np.all(self.t0.min(1) == self.t0[:, 0])
assert np.all(self.t0.min(2) == self.t0[:, :, 2])
assert self.t0.min(0).shape == (5, 5)
assert self.t0.min(0, keepdims=True).shape == (1, 5, 5)
assert self.t0.min(1).shape == (2, 5)
assert self.t0.min(1, keepdims=True).shape == (2, 1, 5)
assert self.t0.min(2).shape == (2, 5)
assert self.t0.min(2, keepdims=True).shape == (2, 5, 1)
def test_max(self, masked):
assert self.t0.max() == self.t0[-1, -1, -2]
assert np.all(self.t0.max(0) == self.t0[1])
assert np.all(self.t0.max(1) == self.t0[:, 4])
assert np.all(self.t0.max(2) == self.t0[:, :, 3])
assert self.t0.max(0).shape == (5, 5)
assert self.t0.max(0, keepdims=True).shape == (1, 5, 5)
def test_ptp(self, masked):
assert self.t0.ptp() == self.t0.max() - self.t0.min()
assert np.all(self.t0.ptp(0) == self.t0.max(0) - self.t0.min(0))
assert self.t0.ptp(0).shape == (5, 5)
assert self.t0.ptp(0, keepdims=True).shape == (1, 5, 5)
def test_sort(self, masked):
order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3]
assert np.all(self.t0.sort() == self.t0[:, :, order])
assert np.all(self.t0.sort(0) == self.t0)
assert np.all(self.t0.sort(1) == self.t0)
assert np.all(self.t0.sort(2) == self.t0[:, :, order])
if not masked:
assert np.all(self.t0.sort(None) ==
self.t0[:, :, order].ravel())
# Bit superfluous, but good to check.
assert np.all(self.t0.sort(-1)[:, :, 0] == self.t0.min(-1))
assert np.all(self.t0.sort(-1)[:, :, -1] == self.t0.max(-1))
def save_report(self, destination=None, remove_temp=True):
if destination is None:
destination = self.phot.folder
def draw_table(table, table_start, marg=5, table_cell=(20, 4)):
pdf.set_draw_color(200, 200, 200)
for i, datum in enumerate(table):
pdf.set_font("helvetica", size=6)
pdf.set_fill_color(249, 249, 249)
pdf.rect(table_start[0] + 5,
table_start[1] + 1.2 + i * table_cell[1],
table_cell[0] * 3, table_cell[1],
"FD" if i % 2 == 0 else "D")
pdf.set_text_color(100, 100, 100)
value = datum[1]
if value is None:
value = "--"
else:
value = str(value)
pdf.text(table_start[0] + marg + 2,
table_start[1] + marg + i * table_cell[1] - 1.2,
datum[0])
pdf.set_text_color(50, 50, 50)
pdf.text(table_start[0] + marg + 2 + table_cell[0] * 1.2,
table_start[1] + marg + i * table_cell[1] - 1.2,
value)
if path.isdir(destination):
file_name = "{}_NEB_{}arcmin.pdf".format(
self.phot.products_denominator, self.radius)
else:
file_name = path.basename(destination.strip(".html").strip(".pdf"))
temp_folder = path.join(path.dirname(destination), "temp")
if path.isdir("temp"):
shutil.rmtree(temp_folder)
if os.path.exists(temp_folder):
shutil.rmtree(temp_folder)
os.mkdir(temp_folder)
star_plot = path.join(temp_folder, "starplot.png")
self.show(10)
fig = plt.gcf()
fig.patch.set_alpha(0)
plt.savefig(star_plot)
plt.close()
lcs = []
a = np.arange(len(self.nearby_ids))
if len(self.nearby_ids) > 30:
split = [
np.arange(0, 30),
*np.array([a[i:i + 7 * 8] for i in range(30, len(a), 7 * 8)])
]
else:
split = [np.arange(0, len(self.nearby_ids))]
for i, idxs in enumerate(split):
lcs_path = path.join(temp_folder, "lcs{}.png".format(i))
lcs.append(lcs_path)
if i == 0:
self.plot(np.arange(0, np.min([30, len(self.nearby_ids)])),
W=5)
else:
self.plot(idxs, W=8)
viz.paper_style()
fig = plt.gcf()
fig.patch.set_alpha(0)
plt.savefig(lcs_path)
plt.close()
lcs = np.array(lcs)
plt.figure(figsize=(10, 3.5))
psf_p = self.phot.plot_psf_fit(cmap="viridis", c="C0")
psf_fit = path.join(temp_folder, "psf_fit.png")
plt.savefig(psf_fit, dpi=60)
plt.close()
theta = psf_p["theta"]
std_x = psf_p["std_x"]
std_y = psf_p["std_y"]
marg_x = 10
marg_y = 8
pdf = viz.prose_FPDF(orientation='L', unit='mm', format='A4')
pdf.add_page()
pdf.set_draw_color(200, 200, 200)
pdf.set_font("helvetica", size=12)
pdf.set_text_color(50, 50, 50)
pdf.text(marg_x, 10, txt="{}".format(self.phot.target["name"]))
pdf.set_font("helvetica", size=6)
pdf.set_text_color(50, 50, 50)
pdf.text(240, 15, txt="Nearby Eclipsing Binary diagnostic")
pdf.set_font("helvetica", size=6)
pdf.set_text_color(74, 144, 255)
pdf.text(marg_x, 17, txt="simbad")
pdf.link(marg_x, 15, 8, 3, self.phot.simbad)
pdf.set_text_color(150, 150, 150)
pdf.set_font("Helvetica", size=7)
pdf.text(marg_x,
14,
txt="{} · {} · {}".format(self.phot.observation_date,
self.phot.telescope.name,
self.phot.filter))
datetimes = Time(self.phot.jd, format='jd', scale='utc').to_datetime()
min_datetime = datetimes.min()
max_datetime = datetimes.max()
obs_duration = "{} - {} [{}h{}]".format(
min_datetime.strftime("%H:%M"), max_datetime.strftime("%H:%M"),
(max_datetime - min_datetime).seconds // 3600,
((max_datetime - min_datetime).seconds // 60) % 60)
max_psf = np.max([std_x, std_y])
min_psf = np.min([std_x, std_y])
ellipticity = (max_psf**2 - min_psf**2) / max_psf**2
draw_table([
["Time", obs_duration],
[
"RA Dec", "{:.4f} {:.4f}".format(self.phot.skycoord.ra,
self.phot.skycoord.dec)
],
["images", len(self.time)],
["GAIA id", None],
[
"mean fwhm", "{:.2f} pixels ({:.2f}\")".format(
np.mean(self.phot.fwhm),
np.mean(self.phot.fwhm) * self.phot.telescope.pixel_scale)
],
["Telescope", self.phot.telescope.name],
["Filter", self.phot.filter],
["exposure", "{} s".format(np.mean(self.phot.data.exptime))],
], (5 + 12, 20 + 100))
draw_table(
[[
"stack PSF fwhm (x)", "{:.2f} pixels ({:.2f}\")".format(
psf_p["fwhm_x"],
psf_p["fwhm_x"] * self.phot.telescope.pixel_scale)
],
[
"stack PSF fwhm (y)", "{:.2f} pixels ({:.2f}\")".format(
psf_p["fwhm_y"],
psf_p["fwhm_y"] * self.phot.telescope.pixel_scale)
], ["stack PSF model", "Moffat2D"],
["stack PSF ellipicity", "{:.2f}".format(ellipticity)],
[
"diff. flux std", "{:.3f} ppt (5 min bins)".format(
np.mean(
utils.binning(self.phot.time,
self.phot.lc.flux,
5 / (24 * 60),
std=True)[2]) * 1e3)
]], (5 + 12, 78 + 100))
pdf.image(psf_fit, x=5.5 + 12, y=55 + 100, w=65)
pdf.image(star_plot, x=5, y=20, h=93.5)
pdf.image(lcs[0], x=100, y=22, w=185)
for lcs_path in lcs[1::]:
pdf.add_page()
pdf.image(lcs_path, x=5, y=22, w=280)
pdf_path = path.join(
destination,
"{}.pdf".format(file_name.strip(".html").strip(".pdf")))
pdf.output(pdf_path)
if path.isdir("temp") and remove_temp:
shutil.rmtree(temp_folder)
print("report saved at {}".format(pdf_path))
def iod(observations,
observation_selection_method="combinations",
chi2_threshold=10**3,
contamination_percentage=20.0,
iterate=True,
light_time=True,
backend="THOR",
backend_kwargs=None):
"""
Run initial orbit determination on a set of observations believed to belong to a single
object.
Parameters
----------
observations : `~pandas.DataFrame`
Data frame containing the observations of a possible linkage.
observation_selection_method : {'first+middle+last', 'thirds', 'combinations'}, optional
Which method to use to select observations.
[Default = 'combinations']
chi2_threshold : float, optional
Minimum chi2 required for an initial orbit to be accepted. Note that chi2 here needs to be
interpreted carefully, residuals of order a few arcseconds easily contribute significantly
to the total chi2.
contamination_percentage : float, optional
Maximum percent of observations that can flagged as outliers.
iterate : bool, optional
Iterate the preliminary orbit solution using the state transition iterator.
light_time : bool, optional
Correct preliminary orbit for light travel time.
backend : {'THOR', 'PYOORB'}, optional
Which backend to use for ephemeris generation.
backend_kwargs : dict, optional
Settings and additional parameters to pass to selected
backend.
Returns
-------
orbit : `~pandas.DataFrame` (7)
Preliminary orbit with epoch_mjd (in UTC) and the the cartesian state vector. Also
has a column for number of observations after outliers have been removed, arc length
of the remaining observations and the value of chi2 for the solution.
orbit_members : `~pandas.DataFrame` (3)
Data frame with two columns: orbit_id and observation IDs.
outliers : `~numpy.ndarray`, None
Observation IDs of potential outlier detections.
"""
# Extract column names
obs_id_col = "obs_id"
time_col = "mjd_utc"
ra_col = "RA_deg"
dec_col = "Dec_deg"
ra_err_col = "RA_sigma_deg"
dec_err_col = "Dec_sigma_deg"
obs_code_col = "observatory_code"
obs_x_col = "obs_x"
obs_y_col = "obs_y"
obs_z_col = "obs_z"
# Extract observation IDs, sky-plane positions, sky-plane position uncertainties, times of observation,
# and the location of the observer at each time
obs_ids_all = observations[obs_id_col].values
coords_all = observations[[ra_col, dec_col]].values
ra = observations[ra_col].values
dec = observations[dec_col].values
sigma_ra = observations[ra_err_col].values
sigma_dec = observations[dec_err_col].values
coords_obs_all = observations[[obs_x_col, obs_y_col, obs_z_col]].values
times_all = observations[time_col].values
times_all = Time(times_all, scale="utc", format="mjd")
backend_kwargs = _backendHandler(backend, "ephemeris")
if backend == "THOR":
backend_kwargs["light_time"] = light_time
observers = observations[[
obs_code_col, time_col, obs_x_col, obs_y_col, obs_z_col
]]
elif backend == "PYOORB":
if light_time == False:
err = (
"PYOORB does not support turning light time correction off.")
raise ValueError(err)
observers = {}
for code in observations[obs_code_col].unique():
observers[code] = Time(observations[observations[obs_code_col] ==
code][time_col].values,
scale="utc",
format="mjd")
else:
err = ("backend should be one of 'THOR' or 'PYOORB'")
raise ValueError(err)
chi2_sol = 1e10
orbit_sol = None
obs_ids_sol = None
remaining_ids = None
arc_length = None
outliers = np.array([])
converged = False
num_obs = len(observations)
num_outliers = int(num_obs * contamination_percentage / 100.)
orbit = pd.DataFrame(columns=[
"orbit_id", "epoch_mjd_utc", "obj_x", "obj_y", "obj_z", "obj_vx",
"obj_vy", "obj_vz", "arc_length", "num_obs", "chi2"
])
orbit_members = pd.DataFrame(columns=["orbit_id", "obs_id"])
# Select observation IDs to use for IOD
obs_ids = selectObservations(
observations,
method=observation_selection_method,
)
if len(obs_ids) == 0:
return orbit, orbit_members, outliers
j = 0
while not converged:
if j == len(obs_ids):
break
ids = obs_ids[j]
mask = np.isin(obs_ids_all, ids)
# Grab sky-plane positions of the selected observations, the heliocentric ecliptic position of the observer,
# and the times at which the observations occur
coords = coords_all[mask, :]
coords_obs = coords_obs_all[mask, :]
times = times_all[mask]
# Run IOD
orbits_iod = gaussIOD(coords,
times.utc.mjd,
coords_obs,
light_time=light_time,
iterate=iterate,
max_iter=100,
tol=1e-15)
if len(orbits_iod) == 0:
j += 1
continue
# Propagate initial orbit to all observation times
ephemeris = generateEphemeris(orbits_iod[:, 1:],
Time(orbits_iod[:, 0],
scale="utc",
format="mjd"),
observers,
backend=backend,
backend_kwargs=backend_kwargs)
ephemeris = ephemeris[[
'orbit_id', 'mjd_utc', 'RA_deg', 'Dec_deg', 'obj_x', 'obj_y',
'obj_z', 'obj_vx', 'obj_vy', 'obj_vz'
]].values
# For each unique initial orbit calculate residuals and chi-squared
# Find the orbit which yields the lowest chi-squared
orbit_ids = np.unique(ephemeris[:, 0])
for i, orbit_id in enumerate(orbit_ids):
orbit_name = np.array(
["{}_v{}".format("_".join(ids.astype(str)), i + 1)])
# Select unique orbit solution
orbit_i = ephemeris[np.where(ephemeris[:, 0] == orbit_id)]
# Calculate residuals in RA, make sure to fix any potential wrap around errors
pred_dec = np.radians(orbit_i[:, 3])
residual_ra = (ra - orbit_i[:, 2]) * np.cos(pred_dec)
residual_ra = np.where(residual_ra > 180., 360. - residual_ra,
residual_ra)
# Calculate residuals in Dec
residual_dec = dec - orbit_i[:, 3]
# Calculate chi2
chi2 = ((residual_ra**2 / sigma_ra**2) +
(residual_dec**2 / sigma_dec**2))
chi2_total = np.sum(chi2)
# All chi2 values are above the threshold, continue loop
if np.all(chi2 >= chi2_threshold):
continue
# If the total chi2 is less than the threshold accept the orbit
elif chi2_total <= chi2_threshold:
orbit_sol = orbits_iod[i:i + 1, :]
obs_ids_sol = ids
chi2_sol = chi2_total
remaining_ids = obs_ids_all
arc_length = times_all.max() - times_all.min()
converged = True
# Let's now test to see if we can remove some outliers, we
# anticipate we get to this stage if the three selected observations
# belonging to one object yield a good initial orbit but the presence of outlier
# observations is skewing the sum total of the residuals and chi2
elif num_outliers > 0:
for o in range(num_outliers):
# Select i highest observations that contribute to
# chi2 (and thereby the residuals)
remove = chi2[~mask].argsort()[-(o + 1):]
# Grab the obs_ids for these outliers
obs_id_outlier = obs_ids_all[~mask][remove]
# Subtract the outlier's chi2 contribution
# from the total chi2
chi2_new = chi2_total - np.sum(chi2[~mask][remove])
# If the updated chi2 total is lower than our desired
# threshold, accept the soluton. If not, keep going.
if chi2_new <= chi2_threshold:
orbit_sol = orbits_iod[i:i + 1, :]
obs_ids_sol = ids
chi2_sol = chi2_new
outliers = obs_id_outlier
num_obs = len(observations) - len(remove)
ids_mask = np.isin(obs_ids_all, outliers, invert=True)
arc_length = times_all[ids_mask].max(
) - times_all[ids_mask].min()
remaining_ids = obs_ids_all[ids_mask]
converged = True
break
else:
continue
j += 1
if converged:
orbit = pd.DataFrame(orbit_sol,
columns=[
"epoch_mjd_utc", "obj_x", "obj_y", "obj_z",
"obj_vx", "obj_vy", "obj_vz"
])
orbit["arc_length"] = arc_length
orbit["num_obs"] = num_obs
orbit["chi2"] = chi2_sol
orbit.insert(0, "orbit_id", orbit_name)
orbit_members = pd.DataFrame(np.vstack(
[[orbit_name[0] for i in range(num_obs)], remaining_ids]).T,
columns=[
"orbit_id",
"obs_id",
])
return orbit, orbit_members, outliers
def correlate(sources, tles, propagator, **kwargs):
logger = sorts.profiling.get_logger('correlate')
if propagator.lower() == 'orekit':
from sorts.propagator import Orekit
prop_opts = dict(
orekit_data = kwargs['orekit_data'],
settings=dict(
in_frame='TEME',
out_frame='ITRS',
drag_force = kwargs.get('drag_force',False),
radiation_pressure = False,
),
logger = logger,
)
pop = tle_catalog(
tles,
propagator = Orekit,
propagator_options = prop_opts,
sgp4_propagation = False,
)
elif propagator.lower() == 'sgp4':
prop_opts = dict(
logger = logger,
)
pop = tle_catalog(
tles,
propagator_options = prop_opts,
sgp4_propagation = True,
)
else:
raise ValueError(f'Propagator "{propagator}" not recognized.')
pop.out_frame = 'ITRS'
if 'oids' in kwargs:
pop.filter('oid', lambda oid: oid in kwargs['oids'])
measurements = []
for sc in sources:
t = Time(sc.data['date'], format='datetime64', scale='utc')
epoch = t.min()
t = (t - epoch).sec
dat = {
'az': sc.data['az'],
'el': sc.data['el'],
't': t,
'epoch': epoch,
'tx': alis4d.stations[sc.kwargs['station']]['st'],
'rx': alis4d.stations[sc.kwargs['station']]['st'],
}
measurements.append(dat)
indecies, metric, cdat = sorts.correlate(
measurements = measurements,
population = pop,
metric=residual_distribution_metric,
metric_reduce=lambda x,y: x+y,
forward_model=generate_measurements,
variables=['az','el'],
n_closest=kwargs.get('n_closest', 1),
profiler=None,
logger=logger,
MPI=kwargs.get('MPI', False),
)
return measurements, indecies, metric, cdat, pop
def plot_airmass(targets,
observer,
time,
ax=None,
style_kwargs=None,
style_sheet=None,
brightness_shading=False,
altitude_yaxis=False,
min_airmass=1.0,
min_region=None,
max_airmass=3.0,
max_region=None):
r"""
Plots airmass as a function of time for a given target.
If a `~matplotlib.axes.Axes` object already exists, an additional
airmass plot will be "stacked" on it. Otherwise, creates a new
`~matplotlib.axes.Axes` object and plots airmass on top of that.
When a scalar `~astropy.time.Time` object is passed in (e.g.,
``Time('2000-1-1')``), the resulting plot will use a 24-hour window
centered on the time indicated, with airmass sampled at regular
intervals throughout.
However, the user can control the exact number and frequency of airmass
calculations used by passing in a non-scalar `~astropy.time.Time`
object. For instance, ``Time(['2000-1-1 23:00:00', '2000-1-1
23:30:00'])`` will result in a plot with only two airmass measurements.
For examples with plots, visit the astroplan Read the Docs
documentation [1]_.
Parameters
----------
targets : list of `~astroplan.FixedTarget` objects
The celestial bodies of interest.
If a single object is passed it will be converted to a list.
observer : `~astroplan.Observer`
The person, telescope, observatory, etc. doing the observing.
time : `~astropy.time.Time`
If scalar (e.g., ``Time('2000-1-1')``), will result in plotting target
airmasses once an hour over a 24-hour window.
If non-scalar (e.g., ``Time(['2000-1-1'])``, ``[Time('2000-1-1')]``,
``Time(['2000-1-1', '2000-1-2'])``),
will result in plotting data at the exact times specified.
ax : `~matplotlib.axes.Axes` or None, optional.
The `~matplotlib.axes.Axes` object to be drawn on.
If None, uses the current ``Axes``.
style_kwargs : dict or None, optional.
A dictionary of keywords passed into `~matplotlib.pyplot.plot_date`
to set plotting styles.
style_sheet : dict or `None` (optional)
matplotlib style sheet to use. To see available style sheets in
astroplan, print *astroplan.plots.available_style_sheets*. Defaults
to the light theme.
brightness_shading : bool
Shade background of plot to scale roughly with sky brightness. Dark
shading signifies times when the sun is below the horizon. Default
is `False`.
altitude_yaxis : bool
Add alternative y-axis on the right side of the figure with target
altitude. Default is `False`.
min_airmass : float
Lower limit of y-axis airmass range in the plot. Default is ``1.0``.
max_airmass : float
Upper limit of y-axis airmass range in the plot. Default is ``3.0``.
min_region : float
If set, defines an interval between ``min_airmass`` and ``min_region``
that will be shaded. Default is `None`.
max_region : float
If set, defines an interval between ``max_airmass`` and ``max_region``
that will be shaded. Default is `None`.
Returns
-------
ax : `~matplotlib.axes.Axes`
An ``Axes`` object with added airmass vs. time plot.
Notes
-----
y-axis is inverted and shows airmasses between 1.0 and 3.0 by default.
If user wishes to change these, use ``ax.<set attribute>`` before drawing
or saving plot:
References
----------
.. [1] astroplan plotting tutorial:
https://astroplan.readthedocs.io/en/latest/tutorials/plots.html#time-dependent-plots
"""
# Import matplotlib, set style sheet
if style_sheet is not None:
_set_mpl_style_sheet(style_sheet)
import matplotlib.pyplot as plt
from matplotlib import dates
# Set up plot axes and style if needed.
if ax is None:
ax = plt.gca()
if style_kwargs is None:
style_kwargs = {}
style_kwargs = dict(style_kwargs)
style_kwargs.setdefault('linestyle', '-')
style_kwargs.setdefault('linewidth', 1.5)
style_kwargs.setdefault('fmt', '-')
# Populate time window if needed.
time = Time(time)
if time.isscalar:
time = time + np.linspace(-12, 12, 100) * u.hour
elif len(time) == 1:
warnings.warn(
'You used a Time array of length 1. You probably meant '
'to use a scalar. (Or maybe a list with length > 1?).',
PlotWarning)
if not isinstance(targets, Sequence):
targets = [targets]
for target in targets:
# Calculate airmass
airmass = observer.altaz(time, target).secz
# Mask out nonsense airmasses
masked_airmass = np.ma.array(airmass, mask=airmass < 1)
# Some checks & info for labels.
try:
target_name = target.name
except AttributeError:
target_name = ''
# Plot data
ax.plot_date(time.plot_date,
masked_airmass,
label=target_name,
**style_kwargs)
# Format the time axis
if not np.all(masked_airmass.mask):
date_formatter = dates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(date_formatter)
plt.setp(ax.get_xticklabels(), rotation=30, ha='right')
# Shade background during night time
if brightness_shading:
starttime = time[0]
#Figure out how many days are in observation to do multiple shadings
ndays = time.max() - time.min()
if ndays.jd > 1:
for i in range(int(ndays.jd)):
start = (starttime + i * u.day).datetime
twilights = [
(observer.sun_set_time(Time(start),
which='next').datetime, 0.0),
(observer.twilight_evening_civil(Time(start),
which='next').datetime,
0.1),
(observer.twilight_evening_nautical(Time(start),
which='next').datetime,
0.2),
(observer.twilight_evening_astronomical(
Time(start), which='next').datetime, 0.3),
(observer.twilight_morning_astronomical(
Time(start), which='next').datetime, 0.4),
(observer.twilight_morning_nautical(Time(start),
which='next').datetime,
0.3),
(observer.twilight_morning_civil(Time(start),
which='next').datetime,
0.2),
(observer.sun_rise_time(Time(start),
which='next').datetime, 0.1),
]
twilights.sort(key=operator.itemgetter(0))
for i, twi in enumerate(twilights[1:], 1):
ax.axvspan(twilights[i - 1][0],
twilights[i][0],
ymin=0,
ymax=1,
color='grey',
alpha=twi[1])
# Calculate and order twilights and set plotting alpha for each
else:
start = starttime.datetime
twilights = [
(observer.sun_set_time(Time(start),
which='next').datetime, 0.0),
(observer.twilight_evening_civil(Time(start),
which='next').datetime, 0.1),
(observer.twilight_evening_nautical(Time(start),
which='next').datetime,
0.2),
(observer.twilight_evening_astronomical(Time(start),
which='next').datetime,
0.3),
(observer.twilight_morning_astronomical(Time(start),
which='next').datetime,
0.4),
(observer.twilight_morning_nautical(Time(start),
which='next').datetime,
0.3),
(observer.twilight_morning_civil(Time(start),
which='next').datetime, 0.2),
(observer.sun_rise_time(Time(start),
which='next').datetime, 0.1),
]
twilights.sort(key=operator.itemgetter(0))
for i, twi in enumerate(twilights[1:], 1):
ax.axvspan(twilights[i - 1][0],
twilights[i][0],
ymin=0,
ymax=1,
color='grey',
alpha=twi[1])
# Invert y-axis and set limits.
y_lim = ax.get_ylim()
if y_lim[1] > y_lim[0]:
ax.invert_yaxis()
ax.set_ylim([max_airmass, min_airmass])
# Draw lo/hi limit regions, if present
ymax, ymin = ax.get_ylim() # should be (hi_limit, lo_limit)
if max_region is not None:
ax.axhspan(ymax, max_region, facecolor='#F9EB4E', alpha=0.10)
if min_region is not None:
ax.axhspan(min_region, ymin, facecolor='#F9EB4E', alpha=0.10)
# Set labels.
ax.set_ylabel("Airmass")
ax.set_xlabel("Time from {0} [UTC]".format(min(time).datetime.date()))
if altitude_yaxis and not _has_twin(ax):
altitude_ticks = np.array([90, 60, 50, 40, 30, 20])
airmass_ticks = 1. / np.cos(np.radians(90 - altitude_ticks))
ax2 = ax.twinx()
ax2.invert_yaxis()
ax2.set_yticks(airmass_ticks)
ax2.set_yticklabels(altitude_ticks)
ax2.set_ylim(ax.get_ylim())
ax2.set_ylabel('Altitude [degrees]')
# Redraw figure for interactive sessions.
ax.figure.canvas.draw()
# Output.
return ax
def deltac_vs_time_BH15_targets(target, t_obs_prop=None, dtmax=None):
"""
target : 'ob120169', 'ob140613', 'ob150029', 'ob150211'
"""
target_name = copy.deepcopy(target)
target_name = target_name.replace('OB', 'ob')
print(target_name)
mod_fit, data = lu_2019_lens.get_data_and_fitter(tdir[target_name])
mod_all = mod_fit.get_best_fit_modes_model(def_best = 'maxL')
mod = mod_all[0]
# Sample time
tmax = np.max(np.append(data['t_phot1'], data['t_phot2'])) + 90.0
if dtmax is not None:
tmax += dtmax
t_mod_ast = np.arange(data['t_ast'].min() - 180.0, tmax, 2)
t_mod_pho = np.arange(data['t_phot1'].min(), tmax, 2)
# Get the linear motion curves for the source (includes parallax)
p_unlens_mod = mod.get_astrometry_unlensed(t_mod_ast)
p_unlens_mod_at_ast = mod.get_astrometry_unlensed(data['t_ast'])
# Get the lensed motion curves for the source
p_lens_mod = mod.get_astrometry(t_mod_ast)
p_lens_mod_at_ast = mod.get_astrometry(data['t_ast'])
x = (data['xpos'] - p_unlens_mod_at_ast[:,0]) * -1e3
xe = data['xpos_err'] * 1e3
y = (data['ypos'] - p_unlens_mod_at_ast[:, 1]) * 1e3
ye = data['ypos_err'] * 1e3
r2 = x**2 + y**2
r = np.sqrt(r2)
xterm = (x * xe)**2/r2
yterm = (y * ye)**2/r2
re = np.sqrt(xterm + yterm)
xmod = (p_lens_mod[:, 0] - p_unlens_mod[:, 0])*-1e3
ymod = (p_lens_mod[:, 1] - p_unlens_mod[:, 1])*1e3
# Convert to decimal dates
t_ast_dec = Time(data['t_ast'], format='mjd', scale='utc')
t_mod_ast_dec = Time(t_mod_ast, format='mjd', scale='utc')
t_ast_dec.format='decimalyear'
t_mod_ast_dec.format='decimalyear'
t_ast_dec = t_ast_dec.value
t_mod_ast_dec = t_mod_ast_dec.value
if t_obs_prop is not None:
# Turn the epoch YYYY-MM-DD into a decimal.
t_obs_prop_dec = np.zeros(len(t_obs_prop))
for idx, tt in enumerate(t_obs_prop):
t_strp = dt.strptime(tt, '%Y-%m-%d')
t_dec = dtUtil.toYearFraction(t_strp)
t_obs_prop_dec[idx] = t_dec
plt.figure(1)
plt.clf()
plt.errorbar(t_ast_dec, x, yerr=xe, fmt='k.', alpha=1, zorder = 1000, label='Data')
plt.scatter(t_mod_ast_dec, xmod, s = 1)
plt.title(target)
plt.xlabel('Time (Year)')
plt.ylabel('$\delta$RA (mas)')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
plt.axvline(x = obs, color='red', ls=':')
plt.axvline(x = 0, color='red', ls=':', label='Proposed')
plt.xlim(t_mod_ast_dec.min() - 0.1, t_mod_ast_dec.max() + 0.1)
plt.axhline(y=0, color='black', alpha=0.5)
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_RA_vs_time.png')
plt.show()
plt.figure(2)
plt.clf()
plt.errorbar(t_ast_dec, y, yerr=ye, fmt='k.', alpha=1, zorder = 1000, label='Data')
plt.scatter(t_mod_ast_dec, ymod, s = 1)
plt.title(target)
plt.xlabel('Time (Year)')
plt.ylabel('$\delta$Dec (mas)')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
plt.axvline(x = obs, color='red', ls=':')
plt.axvline(x = 0, color='red', ls=':', label='Proposed')
plt.xlim(t_mod_ast_dec.min() - 0.1, t_mod_ast_dec.max() + 0.1)
plt.axhline(y=0, color='black', alpha=0.5)
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_Dec_vs_time.png')
plt.show()
plt.figure(3)
plt.clf()
plt.errorbar(t_ast_dec, r, yerr=re, fmt='k.', alpha=1, zorder = 1000, label='Data')
plt.scatter(t_mod_ast_dec, np.sqrt(xmod**2 + ymod**2), s = 1)
plt.xlabel('Time (Year)')
plt.ylabel('$\delta_c$ (mas)')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
plt.axvline(x = obs, color='red', ls=':')
plt.axvline(x = 0, color='red', ls=':', label='Proposed')
plt.xlim(t_mod_ast_dec.min() - 0.1, t_mod_ast_dec.max() + 0.1)
plt.gca().set_ylim(bottom=0)
plt.axhline(y=0.15, color='gray', ls='--')
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_vs_time.png')
plt.show()
for author_name, profile in tqdm(any_author_profiles.items()):
# If they haven't published a first author paper in 3 years, and their total career span is less than 5 years,
# then let's say dead.
if len(profile) > 1 and (Time("2021-04-15") - Time(profile[-1][1])).value >= 3 * 365 \
and (Time(profile[-1][1]) - Time(profile[0][1])).value <= (5 * 365):
dead_author_names.append(author_name)
# We need to calculate summary statistics for every author so that we can test predictors.
Ns = (3, 5, 10) # years
metrics = {}
for author_name, profile in tqdm(any_author_profiles.items()):
times = Time([date for arxiv_id, date in profile])
# career longevity
t_s, t_e = (times.min(), times.max())
metrics[author_name] = dict(longevity=(t_e - t_s).value)
# Calculate number of papers within N years.
for N in Ns:
key = f"first_author_paper_within_{N}_years"
metrics[author_name][key] = 0
for arxiv_id, date in profile:
first_author_name = unique_ify(
records[arxiv_id]["all_authors"].split("; ")[0])
if first_author_name == author_name and (
(Time(date) - t_s).value / 365) <= N:
metrics[author_name][key] += 1
