pep8 formatting

observation_sim/mock_objects/SpecDisperser/SpecDisperser.py

@@ -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
@@ -276,9 +280,9 @@ class SpecDisperser(object):
             if overlap_flag == 1:
                 sub_flat_cube[
                     :,
-                    beam_y_s - originOut_y : beam_y_e - originOut_y + 1,
-                    beam_x_s - originOut_x : beam_x_e - originOut_x + 1,
-                ] = self.flat_cube[:, beam_y_s : beam_y_e + 1, beam_x_s : beam_x_e + 1]
+                    beam_y_s - originOut_y: beam_y_e - originOut_y + 1,
+                    beam_x_s - originOut_x: beam_x_e - originOut_x + 1,
+                ] = self.flat_cube[:, beam_y_s: beam_y_e + 1, beam_x_s: beam_x_e + 1]
 
             for i in arange(0, len(self.flat_cube), 1):
                 beam_flat[:, i] = sub_flat_cube[i].flatten()
@@ -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"$\Delta x$")