Пример #1
0
def tran_quad(f, b, r, D0, D1, D2, fe, be, re, D0e, D1e, D2e):
    result = f + D0 + D1 * (b - r) + D2 * (b - r)**2
    c = b - r
    ce = err_Add([be, re])
    Err_lin = err_Mul([np.ones_like(f) * D1, c], [np.ones_like(f) * D1e, ce])
    Err_quad = err_Mul([np.ones_like(f) * D2, c**2],
                       [np.ones_like(f) * D2e, c * ce * (2**0.5)])
    return result, err_Add([fe, D0e, Err_lin, Err_quad])
Пример #2
0
def drawPCA_simple(ax,
                   F1,
                   F1e,
                   F2,
                   F2e,
                   F3,
                   F3e,
                   F4,
                   F4e,
                   a_star,
                   a_star_err,
                   fsize=17,
                   col='k',
                   alp=1.,
                   err=True):
    F1F2 = F1 - F2
    F1F2e = err_Add([F1e, F2e])
    F2F3 = F2 - F3
    F2F3e = err_Add([F2e, F3e])
    F3F4 = F3 - F4
    F3F4e = err_Add([F3e, F4e])
    plotx = np.array(
        [np.mean(F1F2) - 2 * np.std(F1F2),
         np.mean(F1F2) + 2 * np.std(F1F2)])
    # ax.set_ylabel(FILTERS[2] + '-' + FILTERS[3], fontsize=fsize)
    # ax.set_xlabel('a_star', fontsize=fsize)
    if err is True:
        ax.errorbar(a_star,
                    F3F4,
                    xerr=a_star_err,
                    yerr=F3F4e,
                    fmt='o',
                    markersize=2,
                    ecolor=col,
                    elinewidth=0.5,
                    capsize=0.5,
                    capthick=0.5,
                    c=col,
                    alpha=alp)
    else:
        ax.scatter(a_star, F3F4, s=2, c=col, alpha=alp)
    ax.tick_params(axis='x', labelsize=fsize)
    ax.tick_params(axis='y', labelsize=fsize)
Пример #3
0
    def standardization(self, m_ins, e_ins, c_ins, ce_ins):
        """
        m_std - m_ins = C2 + C1 (b_std - r_std)  --- eq 1
        b_std - r_std = C4 + C3 (b_ins - r_ins)  --- eq 2
        """
        c_std = self.C4 + self.C3 * c_ins
        #try: print(len(m_ins))
        #except: print(c_ins, self.C3err, ce_ins, self.C1, self.C1err, self.C2err)
        ce_std = err_Add([np.ones_like(c_std)*self.C4err, err_Mul([np.ones_like(c_std)*self.C3, c_ins],
                                                                  [np.ones_like(c_std)*self.C3err, ce_ins])])
        
        if type(c_ins) is np.float64:
            if c_ins == 0.:
                ce_std = err_Add([np.ones_like(c_std)*self.C4err, ((self.C3err/self.C3)**2 + ce_ins**2)**0.5])
        

        m_std = m_ins + self.C2 + self.C1 * c_std
        e_std = err_Add([e_ins, np.ones_like(c_std)*self.C2err, err_Mul([np.ones_like(c_std)*self.C1, c_std],
                                                                        [np.ones_like(c_std)*self.C1err, ce_std])])
        return m_std, e_std, c_std, ce_std
Пример #4
0
    def __init__(self,
                 table,
                 FILTERS,
                 constant=True,
                 coe0=np.array([0, 1]),
                 sigmaclip=10,
                 maxiter=2):
        self.FILTERS = FILTERS
        self.HeaderName = ['F1', 'F1e', 'F2', 'F2e', 'F3', 'F3e', 'F4', 'F4e']
        self.AstTab = pd.DataFrame(table, columns=self.HeaderName, dtype=float)

        x = self.AstTab['F1'] - self.AstTab['F2']
        xerr = err_Add([self.AstTab['F1e'], self.AstTab['F2e']])
        y = self.AstTab['F2'] - self.AstTab['F3']
        yerr = err_Add([self.AstTab['F2e'], self.AstTab['F3e']])

        mask, fit, Niter = linfit_sigclip(x,
                                          y,
                                          xerr,
                                          yerr,
                                          coe0=coe0,
                                          sigmaclip=sigmaclip,
                                          maxiter=maxiter)
        theta = np.arctan(fit[0][1])
        coserr = (np.cos(np.arctan(fit[0][1] - fit[1][1, 1]**0.5)) -
                  np.cos(np.arctan(fit[0][1])) +
                  np.cos(np.arctan(fit[0][1] + fit[1][1, 1]**0.5)) -
                  np.cos(np.arctan(fit[0][1]))) / 2
        sinerr = (np.sin(np.arctan(fit[0][1] - fit[1][1, 1]**0.5)) -
                  np.sin(np.arctan(fit[0][1])) +
                  np.sin(np.arctan(fit[0][1] + fit[1][1, 1]**0.5)) -
                  np.sin(np.arctan(fit[0][1]))) / 2
        a_star = np.cos(theta) * x + np.sin(theta) * y
        a_star_err = err_Add([
            err_Mul([
                np.full(len(x), np.cos(np.arctan(fit[0][1])), dtype=np.float),
                x
            ], [np.full(len(x), coserr, dtype=np.float), xerr]),
            err_Mul([
                np.full(len(y), np.sin(np.arctan(fit[0][1])), dtype=np.float),
                y
            ], [np.full(len(y), sinerr, dtype=np.float), yerr])
        ])

        if constant is False:
            print('a_star = %.3f * (%s - %s) + %.3f * (%s - %s)' %
                  (np.cos(theta), self.FILTERS[0], self.FILTERS[1],
                   np.sin(theta), self.FILTERS[1], self.FILTERS[2]))

        elif constant is True:
            print('a_star = %.3f * (%s - %s) + %.3f * (%s - %s) - %.3f' %
                  (np.cos(theta), self.FILTERS[0], self.FILTERS[1],
                   np.sin(theta), self.FILTERS[1], self.FILTERS[2],
                   np.mean(a_star)))
            a_star = a_star - np.mean(a_star)

        self.AstTab['a_star'] = a_star
        self.AstTab['a_star_err'] = a_star_err
        self.a_star = a_star
        self.a_star_err = a_star_err
        self.PCAfit = fit
Пример #5
0
def tran_lin(f, b, r, C0, C1, fe, be, re, C0e, C1e):
    result = f + C0 + C1 * (b - r)
    c = b - r
    ce = err_Add([be, re])
    Err_lin = err_Mul([np.ones_like(f) * C1, c], [np.ones_like(f) * C1e, ce])
    return result, err_Add([fe, C0e, Err_lin])
Пример #6
0
 def Figure_Fit(self, dpi=150, save=False, dir_save='', AddSaveName=''):
     TabF1, TabF2, TabF3, TabF4 = self.MatTabSet
     AstFSet = [0, TabF1[0], TabF2[0], TabF3[0], TabF4[0]]
     F1, F2, F3, F4 = self.FILTERS
     eq1_Xlabels = [
         '%s_{std} - %s_{std}' % (F1, F2),
         '%s_{std} - %s_{std}' % (F1, F2),
         '%s_{std} - %s_{std}' % (F2, F3),
         '%s_{std} - %s_{std}' % (F3, F4)
     ]
     eq1_Ylabels = [
         '%s_{std} - %s_{ins}' % (F1, F1),
         '%s_{std} - %s_{ins}' % (F2, F2),
         '%s_{std} - %s_{ins}' % (F3, F3),
         '%s_{std} - %s_{ins}' % (F4, F4)
     ]
     eq2_Xlabels = [
         '%s_{ins} - %s_{ins}' % (F1, F2),
         '%s_{ins} - %s_{ins}' % (F1, F2),
         '%s_{ins} - %s_{ins}' % (F2, F3),
         '%s_{ins} - %s_{ins}' % (F3, F4)
     ]
     eq2_Ylabels = [
         '%s_{std} - %s_{std}' % (F1, F2),
         '%s_{std} - %s_{std}' % (F1, F2),
         '%s_{std} - %s_{std}' % (F2, F3),
         '%s_{std} - %s_{std}' % (F3, F4)
     ]
     fig = plt.figure(figsize=(19, 9))
     fig.subplots_adjust(top=0.90, bottom=0.10, left=0.05, right=0.97)
     fig.suptitle('Std. Fit. %s #%s' % (self.MODE, self.AstNum))
     axs = []
     for k in range(8):
         axs.append(fig.add_subplot(2, 4, k + 1))
     for k in range(4):
         axs[k].set_title(self.FILTERS[k])
         self.FitSet[k].plot_fitting(axs[k],
                                     axs[k + 4],
                                     eq1_Xlabel=eq1_Xlabels[k],
                                     eq1_Ylabel=eq1_Ylabels[k],
                                     eq2_Xlabel=eq2_Xlabels[k],
                                     eq2_Ylabel=eq2_Ylabels[k])
     # ast g #
     axs[0].errorbar(self.AstF1_cal[0] - self.AstF2_cal[0],
                     self.AstF1_cal[0] - AstFSet[1][5],
                     xerr=err_Add([self.AstF1_cal[1], self.AstF2_cal[1]]),
                     yerr=err_Add([self.AstF1_cal[1], AstFSet[1][6]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     axs[4].errorbar(AstFSet[1][5] - AstFSet[2][5],
                     self.AstF1_cal[0] - self.AstF2_cal[0],
                     xerr=err_Add([AstFSet[1][6], AstFSet[2][6]]),
                     yerr=err_Add([self.AstF1_cal[1], self.AstF2_cal[1]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     # ast r #
     axs[1].errorbar(self.AstF1_cal[0] - self.AstF2_cal[0],
                     self.AstF2_cal[0] - AstFSet[2][5],
                     xerr=err_Add([self.AstF1_cal[1], self.AstF2_cal[1]]),
                     yerr=err_Add([self.AstF2_cal[1], AstFSet[2][6]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     axs[5].errorbar(AstFSet[1][5] - AstFSet[2][5],
                     self.AstF1_cal[0] - self.AstF2_cal[0],
                     xerr=err_Add([AstFSet[1][6], AstFSet[2][6]]),
                     yerr=err_Add([self.AstF1_cal[1], self.AstF2_cal[1]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     # ast i #
     axs[2].errorbar(self.AstF2_cal[0] - self.AstF3_cal[0],
                     self.AstF3_cal[0] - AstFSet[3][5],
                     xerr=err_Add([self.AstF2_cal[1], self.AstF3_cal[1]]),
                     yerr=err_Add([self.AstF3_cal[1], AstFSet[3][6]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     axs[6].errorbar(AstFSet[2][5] - AstFSet[3][5],
                     self.AstF2_cal[0] - self.AstF3_cal[0],
                     xerr=err_Add([AstFSet[2][6], AstFSet[3][6]]),
                     yerr=err_Add([self.AstF2_cal[1], self.AstF3_cal[1]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     # ast z #
     axs[3].errorbar(self.AstF3_cal[0] - self.AstF4_cal[0],
                     self.AstF4_cal[0] - AstFSet[4][5],
                     xerr=err_Add([self.AstF3_cal[1], self.AstF4_cal[1]]),
                     yerr=err_Add([self.AstF4_cal[1], AstFSet[4][6]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     axs[7].errorbar(AstFSet[3][5] - AstFSet[4][5],
                     self.AstF3_cal[0] - self.AstF4_cal[0],
                     xerr=err_Add([AstFSet[3][6], AstFSet[4][6]]),
                     yerr=err_Add([self.AstF3_cal[1], self.AstF4_cal[1]]),
                     fmt='o',
                     markersize=4,
                     ecolor='r',
                     elinewidth=1,
                     capsize=1.2,
                     capthick=0.8,
                     c='r')
     #plt.show()
     if save is True:
         fig.savefig(dir_save + 'StdFit' + AddSaveName + '_%s_%s.png' %
                     (self.MODE, self.AstNum),
                     dpi=dpi)
         plt.close('all')
Пример #7
0
    def StdAsteroid(self,
                    sig_err=[1, 1, 1, 1],
                    sig_eq1=[5, 5, 5, 5],
                    sig_eq2=[2, 2, 2, 2],
                    maxiter=[10, 10],
                    fit_losstype=['basic', 'basic'],
                    fit_cliptype=['fit', 'fit'],
                    fit_clip_err=True,
                    Mconsist=2,
                    fit_Nlim_min=34):
        """
        fittype=['basic', 'basic']
        !!! Recent tables from GetMatTabSet are used !!!
        :output: self.FitSet, self.AstF_cal, self.TabF_cal
        """
        TabF1, TabF2, TabF3, TabF4 = self.MatTabSet
        AstF1 = TabF1[0]
        AstF2 = TabF2[0]
        AstF3 = TabF3[0]
        AstF4 = TabF4[0]
        TabF1 = TabF1[1:]
        TabF2 = TabF2[1:]
        TabF3 = TabF3[1:]
        TabF4 = TabF4[1:]
        # [0] newID, [1] X_Image, [2] Y_Image,
        # [3] MagStd, [4] MerrStd, [5] MagIns, [6] MerrIns, [7] ra_obs, [8] dec_obs
        # standardization_fit(m_ins, e_ins, m_std, e_std, r_ins, re_ins, r_std, re_std, b_ins, be_ins, b_std, be_std)
        ### FLAGING ###
        # FLAG_edge
        if AstF1[1] < 100 or AstF1[1] > 9116 or AstF1[2] < 100 or AstF1[2] > 9132 or \
                AstF2[1] < 100 or AstF2[1] > 9116 or AstF2[2] < 100 or AstF2[2] > 9132 or \
                AstF3[1] < 100 or AstF3[1] > 9116 or AstF3[2] < 100 or AstF3[2] > 9132 or \
                AstF4[1] < 100 or AstF4[1] > 9116 or AstF4[2] < 100 or AstF4[2] > 9132:
            self.FLAG += 1
        # FLAG_extrapolation_bright
        if AstF1[5] < np.min(TabF1[:, 5]) or AstF2[5] < np.min(TabF2[:, 5]) or \
                AstF3[5] < np.min(TabF3[:, 5]) or AstF4[5] < np.max(TabF4[:, 5]):
            self.FLAG += 2
        # FALG_extrapolation_faint
        if AstF1[5] > np.max(TabF1[:, 5]) or AstF2[5] > np.min(TabF2[:, 5]) or \
                AstF3[5] > np.max(TabF3[:, 5]) or AstF4[5] > np.max(TabF4[:, 5]):
            self.FLAG += 4
        # 9 mag is the brightest in 60s EXPTIME img.
        #if AstF1[5] > 11 : mag Flaging

        sigset = np.array([sig_eq1, sig_eq2, sig_err]).T
        ### Fitting ###
        # FitF1 : g, g-r [F1, F1-F2]
        FitF1 = standardization_fit(TabF1[:, 5], TabF1[:, 6], TabF1[:, 3],
                                    TabF1[:, 4], TabF1[:, 5], TabF1[:, 6],
                                    TabF1[:, 3], TabF1[:, 4], TabF2[:, 5],
                                    TabF2[:, 6], TabF2[:, 3], TabF2[:, 4])
        FitF1.fit(coe1=np.array([np.mean(TabF1[:, 3] - TabF1[:, 5]), 0.15]),
                  coe2=np.array([0.1, 1]),
                  sigmaclip=sigset[0],
                  maxiter=maxiter,
                  losstype=fit_losstype,
                  cliptype=fit_cliptype,
                  clip_err=fit_clip_err,
                  Nlim_min=fit_Nlim_min)
        # FitF2 : r, g-r [F2, F1-F2]
        FitF2 = standardization_fit(TabF2[:, 5], TabF2[:, 6], TabF2[:, 3],
                                    TabF2[:, 4], TabF1[:, 5], TabF1[:, 6],
                                    TabF1[:, 3], TabF1[:, 4], TabF2[:, 5],
                                    TabF2[:, 6], TabF2[:, 3], TabF2[:, 4])
        FitF2.fit(coe1=np.array([np.mean(TabF2[:, 3] - TabF2[:, 5]), 0.1]),
                  coe2=np.array([0.1, 1]),
                  sigmaclip=sigset[1],
                  maxiter=maxiter,
                  losstype=fit_losstype,
                  cliptype=fit_cliptype,
                  clip_err=fit_clip_err,
                  Nlim_min=fit_Nlim_min)
        # FitF3 : i, r-i [F3, F2-F3]
        FitF3 = standardization_fit(TabF3[:, 5], TabF3[:, 6], TabF3[:, 3],
                                    TabF3[:, 4], TabF2[:, 5], TabF2[:, 6],
                                    TabF2[:, 3], TabF2[:, 4], TabF3[:, 5],
                                    TabF3[:, 6], TabF3[:, 3], TabF3[:, 4])
        FitF3.fit(coe1=np.array([np.mean(TabF3[:, 3] - TabF3[:, 5]), 0.1]),
                  coe2=np.array([0.1, 1]),
                  sigmaclip=sigset[2],
                  maxiter=maxiter,
                  losstype=fit_losstype,
                  cliptype=fit_cliptype,
                  clip_err=fit_clip_err,
                  Nlim_min=fit_Nlim_min)
        # FitF4 : z, i-z [F4, F3-F4]
        FitF4 = standardization_fit(TabF4[:, 5], TabF4[:, 6], TabF4[:, 3],
                                    TabF4[:, 4], TabF3[:, 5], TabF3[:, 6],
                                    TabF3[:, 3], TabF3[:, 4], TabF4[:, 5],
                                    TabF4[:, 6], TabF4[:, 3], TabF4[:, 4])
        FitF4.fit(coe1=np.array([np.mean(TabF4[:, 3] - TabF4[:, 5]), 0.1]),
                  coe2=np.array([0.1, 1]),
                  sigmaclip=sigset[3],
                  maxiter=maxiter,
                  losstype=fit_losstype,
                  cliptype=fit_cliptype,
                  clip_err=fit_clip_err,
                  Nlim_min=fit_Nlim_min)
        if (FitF1.FitFailureFlag == True) or (FitF2.FitFailureFlag == True) \
                or (FitF3.FitFailureFlag == True) or (FitF4.FitFailureFlag == True):
            return -1
        self.sig_eq1 = sig_eq1
        self.FitSet = [FitF1, FitF2, FitF3, FitF4]

        ### Asteroid ###
        # standardization(self, m_ins, e_ins, c_ins, ce_ins)
        self.AstF1_cal = FitF1.standardization(AstF1[5], AstF1[6],
                                               AstF1[5] - AstF2[5],
                                               err_Add([AstF1[6], AstF2[6]]))
        self.AstF2_cal = FitF2.standardization(AstF2[5], AstF2[6],
                                               AstF1[5] - AstF2[5],
                                               err_Add([AstF1[6], AstF2[6]]))
        self.AstF3_cal = FitF3.standardization(AstF3[5], AstF3[6],
                                               AstF2[5] - AstF3[5],
                                               err_Add([AstF2[6], AstF3[6]]))
        self.AstF4_cal = FitF4.standardization(AstF4[5], AstF4[6],
                                               AstF3[5] - AstF4[5],
                                               err_Add([AstF3[6], AstF4[6]]))
        print('AstMag\t%.3f\t%.3f\t%.3f\t%.3f' %
              (self.AstF1_cal[0], self.AstF2_cal[0], self.AstF3_cal[0],
               self.AstF4_cal[0]))
        print('AstMerr\t%.4f\t%.4f\t%.4f\t%.4f' %
              (self.AstF1_cal[1], self.AstF2_cal[1], self.AstF3_cal[1],
               self.AstF4_cal[1]))

        ### Stars ###
        self.TabF1_cal = FitF1.standardization(
            TabF1[:, 5], TabF1[:, 6], TabF1[:, 5] - TabF2[:, 5],
            err_Add([TabF1[:, 6], TabF2[:, 6]]))
        self.TabF2_cal = FitF2.standardization(
            TabF2[:, 5], TabF2[:, 6], TabF1[:, 5] - TabF2[:, 5],
            err_Add([TabF1[:, 6], TabF2[:, 6]]))
        self.TabF3_cal = FitF3.standardization(
            TabF3[:, 5], TabF3[:, 6], TabF2[:, 5] - TabF3[:, 5],
            err_Add([TabF2[:, 6], TabF3[:, 6]]))
        self.TabF4_cal = FitF4.standardization(
            TabF4[:, 5], TabF4[:, 6], TabF3[:, 5] - TabF4[:, 5],
            err_Add([TabF3[:, 6], TabF4[:, 6]]))

        # consistency
        mag_diff = np.max([self.AstF1_cal[0], self.AstF2_cal[0], self.AstF3_cal[0], self.AstF4_cal[0]])-\
                np.min([self.AstF1_cal[0], self.AstF2_cal[0], self.AstF3_cal[0], self.AstF4_cal[0]])
        if mag_diff > Mconsist:
            print('StdAsteroid - consistency error %.3f' % mag_diff)
            return -1
        else:
            return 0