Commit d10eb6a8 authored by Zhang Xin's avatar Zhang Xin
Browse files

pep8 formatting

parent 4c36363e

observation_sim/mock_objects/SpecDisperser/SpecDisperser.py

+35 −19
Original line number Diff line number Diff line
@@ -173,9 +173,11 @@ class SpecDisperser(object): 
        lam_index = argsort(lam_beam)

        conf_sens = self.grating_conf.sens[beam]



        lam_intep = np.linspace(self.band_start, self.band_end, int((self.band_end - self.band_start) / 0.1))

        lam_intep = np.linspace(self.band_start, self.band_end, int(

            (self.band_end - self.band_start) / 0.1))



        thri = interpolate.interp1d(conf_sens["WAVELENGTH"], conf_sens["SENSITIVITY"])

        thri = interpolate.interp1d(

            conf_sens["WAVELENGTH"], conf_sens["SENSITIVITY"])

        spci = interpolate.interp1d(self.spec["WAVELENGTH"], self.spec["FLUX"])



        beam_thr = thri(lam_intep)
@@ -201,7 +203,8 @@ class SpecDisperser(object): 
        sensitivity_beam = ysens



        len_spec_x = len(dx)

        len_spec_y = int(abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)

        len_spec_y = int(

            abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)



        beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x)

        modelf = zeros(product(beam_sh), dtype=float)
@@ -246,7 +249,8 @@ class SpecDisperser(object): 
        else:

            beam_flat = zeros([len(modelf), len(self.flat_cube)])



            sub_flat_cube = zeros([len(self.flat_cube), beam_sh[0], beam_sh[1]])

            sub_flat_cube = zeros(

                [len(self.flat_cube), beam_sh[0], beam_sh[1]])

            sub_flat_cube[0] = sub_flat_cube[0] + 1.0



            overlap_flag = 1
@@ -330,9 +334,11 @@ class SpecDisperser(object): 


    def writerSensitivityFile(self, conffile="", beam="", w=None, sens=None):

        orders = {"A": "1st", "B": "0st", "C": "2st", "D": "-1st", "E": "-2st"}

        sens_file_name = conffile[0:-5] + "_sensitivity_" + orders[beam] + ".fits"

        sens_file_name = conffile[0:-5] + \

            "_sensitivity_" + orders[beam] + ".fits"

        if not os.path.exists(sens_file_name) == True:

            senstivity_out = Table(array([w, sens]).T, names=("WAVELENGTH", "SENSITIVITY"))

            senstivity_out = Table(

                array([w, sens]).T, names=("WAVELENGTH", "SENSITIVITY"))

            senstivity_out.write(sens_file_name, format="fits")
@@ -431,9 +437,11 @@ class aXeConf: 
                self.dxlam[beam] = np.arange(

                    self.conf["BEAM{0}".format(beam)].min(), self.conf["BEAM{0}".format(beam)].max(), dtype=int

                )

                self.nx[beam] = int(self.dxlam[beam].max() - self.dxlam[beam].min()) + 1

                self.nx[beam] = int(self.dxlam[beam].max() -

                                    self.dxlam[beam].min()) + 1

                self.sens[beam] = Table.read(

                    "{0}/{1}".format(os.path.dirname(self.conf_file), self.conf["SENSITIVITY_{0}".format(beam)])

                    "{0}/{1}".format(os.path.dirname(self.conf_file),

                                     self.conf["SENSITIVITY_{0}".format(beam)])

                )

                # self.sens[beam].wave = np.cast[np.double](self.sens[beam]['WAVELENGTH'])

                # self.sens[beam].sens = np.cast[np.double](self.sens[beam]['SENSITIVITY'])
@@ -548,7 +556,8 @@ class aXeConf: 
            dpfull = xfull * 0.0

            lt0 = xfull < 0

            if lt0.sum() > 1:

                dpfull[lt0] = np.cumsum(np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1]

                dpfull[lt0] = np.cumsum(

                    np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1]

                dpfull[lt0] *= -1



            #
@@ -592,8 +601,10 @@ class aXeConf: 
        NORDER = self.orders[beam] + 1



        xi, yi = x - self.xoff, y - self.yoff

        xoff_beam = self.field_dependent(xi, yi, self.conf["XOFF_{0}".format(beam)])

        yoff_beam = self.field_dependent(xi, yi, self.conf["YOFF_{0}".format(beam)])

        xoff_beam = self.field_dependent(

            xi, yi, self.conf["XOFF_{0}".format(beam)])

        yoff_beam = self.field_dependent(

            xi, yi, self.conf["YOFF_{0}".format(beam)])



        # y offset of trace (DYDX)

        dydx = np.zeros(NORDER)  # 0 #+1.e-80
@@ -690,19 +701,24 @@ class aXeConf: 


        s = 200  # marker size

        fig = plt.figure(figsize=[10, 3])

        plt.scatter(0, 0, marker="s", s=s, color="black", edgecolor="0.8", label="Direct")

        plt.scatter(0, 0, marker="s", s=s, color="black",

                    edgecolor="0.8", label="Direct")



        for beam in beams:

            if "XOFF_{0}".format(beam) not in self.conf.keys():

                continue



            xoff = self.field_dependent(x0, x1, self.conf["XOFF_{0}".format(beam)])

            xoff = self.field_dependent(

                x0, x1, self.conf["XOFF_{0}".format(beam)])

            dy, lam = self.get_beam_trace(x0, x1, dx=dx, beam=beam)

            xlim = self.conf["BEAM{0}".format(beam)]

            ok = (dx >= xlim[0]) & (dx <= xlim[1])

            plt.scatter(dx[ok] + xoff, dy[ok], c=lam[ok] / 1.0e4, marker="s", s=s, alpha=0.5, edgecolor="None")

            plt.text(np.median(dx[ok]), np.median(dy[ok]) + 1, beam, ha="center", va="center", fontsize=14)

            print("Beam {0}, lambda=({1:.1f} - {2:.1f})".format(beam, lam[ok].min(), lam[ok].max()))

            plt.scatter(dx[ok] + xoff, dy[ok], c=lam[ok] / 1.0e4,

                        marker="s", s=s, alpha=0.5, edgecolor="None")

            plt.text(np.median(dx[ok]), np.median(

                dy[ok]) + 1, beam, ha="center", va="center", fontsize=14)

            print("Beam {0}, lambda=({1:.1f} - {2:.1f})".format(beam,

                  lam[ok].min(), lam[ok].max()))



        plt.grid()

        plt.xlabel(r"$\Delta x$")