def plot_pdf_and_v_data(t, saveto, PDF, title="{0} - {1}"):
"hard coded to work with v_x, v_y, v_z"
cols_not_in_t = [
col for col in ("v_x", "v_y", "v_z", "group") if col not in t.keys()
]
if cols_not_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.simulated.plot_pdf_and_v_data: The following columns are missing in the table: {0}"
.format(cols_not_in_t))
if PDF.ndim != 3:
raise gen.GFC_Exception(
"\n\ngaia_fc.simulated.plot_pdf_and_v_data: The provided PDF has dimensions {0} -- expected 3 dimensions"
.format(PDF.ndim))
for i, pair in enumerate((("v_y", "v_z"), ("v_x", "v_z"), ("v_x", "v_y"))):
p1, p2 = pair
pair_int = [0, 1, 2]
pair_int.remove(i)
pdfsumi = PDF.sum(axis=i).T
plt.scatter(t[p1], t[p2], c=t["group"], **p)
plt.axis("tight")
plt.contour(pdfsumi,
extent=extent,
linewidths=3,
linestyles="dashed",
levels=levels,
colors='k')
plt.xlabel(t[p1].axis_label + " (" + t[p1].unit.to_string() + ")")
plt.ylabel(t[p2].axis_label + " (" + t[p2].unit.to_string() + ")")
plt.title(title.format(p1, p2))
plt.tight_layout()
plt.savefig(saveto.format(p1, p2))
plt.close()
def add_astrometry(t):
cols_already_in_t = [
col for col in ("phi", "theta", "plx", "muphi*", "mutheta", "v_r")
if col in t.keys()
]
if cols_already_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.simulated.add_astrometry: The following columns are already in the table: {0}"
.format(cols_already_in_t))
as_astrometry = np.array(
gen.toastro(t["x"], t["y"], t["z"], t["v_x"], t["v_y"], t["v_z"]))
t.add_column(table.Column(data=as_astrometry[0], name="phi", unit="rad"))
t["phi"].axis_label = r"$\phi$"
t.add_column(table.Column(data=as_astrometry[1], name="theta", unit="rad"))
t["theta"].axis_label = r"$\theta$"
t.add_column(table.Column(data=as_astrometry[2], name="plx", unit="mas"))
t["plx"].axis_label = r"$\bar\omega$"
t.add_column(
table.Column(data=as_astrometry[3], name="muphi*", unit="mas/yr"))
t["muphi*"].axis_label = r"$\mu_\phi^*$"
t.add_column(
table.Column(data=as_astrometry[4], name="mutheta", unit="mas/yr"))
t["mutheta"].axis_label = r"$\mu_\theta$"
t.add_column(table.Column(data=as_astrometry[5], name="v_r", unit="km/s"))
t["v_r"].axis_label = "$v_r$"
t["plx"] = t["plx"].to("arcsec")
t["plx"].unit = gen.units.arcsec
t["muphi*"] = t["muphi*"].to("arcsec/yr")
t["muphi*"].unit = gen.units.arcsec / gen.units.year
t["mutheta"] = t["mutheta"].to("arcsec/yr")
t["mutheta"].unit = gen.units.arcsec / gen.units.year
def plot_pdf_and_v_data(t, saveto, PDF, title="{0} - {1}"):
"hard coded to work with v_x, v_y, v_z"
cols_not_in_t = [
col for col in ("v_x", "v_y", "v_z") if col not in t.keys()
]
if cols_not_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.hipparcos.plot_pdf_and_v_data: The following columns are missing in the table: {0}"
.format(cols_not_in_t))
if PDF.ndim != 3:
raise gen.GFC_Exception(
"\n\ngaia_fc.hipparcos.plot_pdf_and_v_data: The provided PDF has dimensions {0} -- expected 3 dimensions"
.format(PDF.ndim))
for i, pair, xl, yl in zip([0, 1, 2], [("v_y", "v_z"), ("v_x", "v_z"),
("v_x", "v_y")], [(-130, 120),
(-130, 120),
(-130, 120)],
[(-70, 70), (-70, 70), (-120, 60)]):
p1, p2 = pair
pair_int = [0, 1, 2]
pair_int.remove(i)
pdfsumi = PDF.sum(axis=i).T
plt.scatter(t[p1], t[p2], s=1, c="0.2", edgecolors="face")
plt.contour(pdfsumi,
extent=extent,
linewidths=3,
levels=levels,
colors='k')
plt.xlabel(t[p1].axis_label + " (" + t[p1].unit.to_string() + ")")
plt.ylabel(t[p2].axis_label + " (" + t[p2].unit.to_string() + ")")
plt.title(title.format(p1, p2))
plt.xlim(xl)
plt.ylim(yl)
plt.tight_layout()
plt.savefig(saveto.format(p1, p2))
plt.close()
def plot_columns(t, saveto, *cols):
cols_not_in_t = [col for col in cols if col not in t.keys()]
if cols_not_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.simulated.plot_columns: The following columns are missing in the table: {0}"
.format(cols_not_in_t))
for j, p1 in enumerate(cols):
for p2 in cols[j + 1:]:
plt.scatter(t[p1], t[p2], c=t["group"], **p)
plt.xlabel(t[p1].axis_label + " (" + t[p1].unit.to_string() + ")")
plt.ylabel(t[p2].axis_label + " (" + t[p2].unit.to_string() + ")")
plt.title("Simulated stars distribution: {0} vs {1}".format(
p1, p2))
plt.axis("tight")
plt.tight_layout()
plt.savefig(saveto.format(p1, p2))
plt.close()
def plot_columns(t, saveto, *cols, **kw):
cols_not_in_t = [col for col in cols if col not in t.keys()]
if cols_not_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.hipparcos.plot_columns: The following columns are missing in the table: {0}"
.format(cols_not_in_t))
for j, p1 in enumerate(cols):
for p2 in cols[j + 1:]:
plt.scatter(t[p1], t[p2], **p)
plt.xlabel(t[p1].axis_label + " (" + t[p1].unit.to_string() + ")")
plt.ylabel(t[p2].axis_label + " (" + t[p2].unit.to_string() + ")")
plt.title("Hipparcos: {0} vs {1}".format(p1, p2))
plt.axis("tight")
if kw:
plt.setp(plt.gca(), **kw)
plt.tight_layout()
plt.savefig(saveto.format(p1, p2))
plt.close()
def convert_back_from_astrometry_without_v_r(t):
cols_already_in_t = [
col for col in ("v_x_b", "v_y_b", "v_z_b") if col in t.keys()
]
if cols_already_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.simulated.convert_back_from_astrometry_without_v_r: The following columns are already in the table: {0}"
.format(cols_already_in_t))
as_phase = np.array(
gen.tophase(t["phi"], t["theta"], 1000. * t["plx"],
1000. * t["muphi*"], 1000. * t["mutheta"],
np.zeros_like(t["v_r"])))
t.add_column(table.Column(data=as_phase[3], name="v_x_B", unit="km/s"))
t["v_x_B"].axis_label = r"$v_x (B)$"
t.add_column(table.Column(data=as_phase[4], name="v_y_B", unit="km/s"))
t["v_y_B"].axis_label = r"$v_y (B)$"
t.add_column(table.Column(data=as_phase[5], name="v_z_B", unit="km/s"))
t["v_z_B"].axis_label = r"$v_z (B)$"
def reconstruct_v_from_astrometry(t):
cols_already_in_t = [
col for col in ("v_x_R", "v_y_R", "v_z_R") if col in t.keys()
]
if cols_already_in_t:
raise gen.GFC_Exception(
"\n\ngaia_fc.simulated.reconstruct_v_from_astrometry: The following columns are already in the table: {0}"
.format(cols_already_in_t))
w = np.vstack((t["w1"], t["w2"], t["w3"])).T
reconstr = gen.map_np(lambda A, w: A.dot(w), t["A"], w)
t.add_column(table.Column(data=reconstr[:, 0], name="v_x_R",
unit="km / s"))
t["v_x_R"].axis_label = r"$v_x (R)$"
t.add_column(table.Column(data=reconstr[:, 1], name="v_y_R",
unit="km / s"))
t["v_y_R"].axis_label = r"$v_y (R)$"
t.add_column(table.Column(data=reconstr[:, 2], name="v_z_R",
unit="km / s"))
t["v_z_R"].axis_label = r"$v_z (R)$"