Ejemplo n.º 1
0
    def check_distance(self):
        cat = self.cat0
        cat["D_Z"] = cat["Redshift_Cosmological"].map(lambda x: z2d(x, h=1.0, wm=self.wm))
        cat["colour"] = cat["Johnson_B_Apparent"] - cat["Johnson_R_Apparent"]
        cat["K Correction"] = cat.apply(apply_kcor, axis=1)
        cat["DM"] = cat["Johnson_B_Apparent"] - cat["Johnson_B_Absolute"] - cat["K Correction"]
        cat["D Lum"] = cat["DM"].map(lambda x: math.pow(10, 0.2 * x + 1) / 1.0e6)
        cat["D_M"] = cat.apply(self.make_d_m(), axis=1)
        cat["Diff"] = cat.apply(calc_diff, axis=1)

        stats = cat["Diff"].describe()

        logger.info("Stats:\n{0}".format(stats))
        logger.debug("First 10:\n{0}".format(cat.head(10)))
        if abs(stats["mean"]) > self.job_params.MAX_MEAN_DIFFERENCE:
            logger.error("Mean difference {0} exceeds {1}".format(stats["mean"], self.job_params.MAX_MEAN_DIFFERENCE))

        if abs(stats["max"]) > self.job_params.MAX_MAX_DIFFERENCE:
            logger.error("Max difference {0} exceeds {1}".format(stats["max"], self.job_params.MAX_MAX_DIFFERENCE))

        if abs(stats["mean"]) < self.job_params.MIN_MEAN_DIFFERENCE:
            logger.error(
                "Mean difference {0} is less than {1}".format(stats["mean"], self.job_params.MIN_MEAN_DIFFERENCE)
            )

        if abs(stats["max"]) < self.job_params.MIN_MAX_DIFFERENCE:
            logger.error("Max difference {0} is less than {1}".format(stats["max"], self.job_params.MIN_MAX_DIFFERENCE))

        return
Ejemplo n.º 2
0
header = data.readline()
headers = header.split(csv_delimiter)

ids = {}
for field in required_fields:
    if required_fields[field] in headers:
        ids[field] = headers.index(required_fields[field])

if len(ids) != len(required_fields):
   print "can't find all required fields in the catalogue."
   sys.exit()

print "   z(TAO)          d(TAO)           d(z)            d(DM)      diff %"
for i in range(0,n):
    line = data.readline()
    values = line.split(csv_delimiter)
    z      = float(values[ids['z']])
    d      = float(values[ids['d']])
    b_abs  = float(values[ids['b_abs']])
    b_app  = float(values[ids['b_app']])
    r_app  = float(values[ids['r_app']])

    d_z    = z2d(z, 1)  # [Mpc/h]
    color  = b_app - r_app
    k_corr = calc_kcor('B', z, 'B - Rc', color)
    dm     = b_app - b_abs - k_corr # distance modulus
    d_lum  = math.pow(10, 0.2*dm + 1)/1e6  # luminosity distance [Mpc]
    d_m    = h*d_lum/(1+z)  # co-moving distance [Mpc/h]
    diff   = 100*d_m/d - 100; # %
    print "%e\t%e\t%e\t%e\t%.2f" % (z, d, d_z, d_m, diff)