change background estimation to gaussian mean+factor*sigma

src/FOC_reduction.py

@@ -33,13 +33,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
 
     # Background estimation
     error_sub_type = 'freedman-diaconis'   # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
-    subtract_error = 1.20
+    subtract_error = 1.00
     display_bkg = False
-    display_error = False
 
     # Data binning
     rebin = True
-    pxsize = 0.10
+    pxsize = 0.05
     px_scale = 'arcsec'         # pixel, arcsec or full
     rebin_operation = 'sum'     # sum or average
 
@@ -50,7 +49,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
 
     # Smoothing
     smoothing_function = 'combine'  # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
-    smoothing_FWHM = 0.2           # If None, no smoothing is done
+    smoothing_FWHM = 0.10          # If None, no smoothing is done
     smoothing_scale = 'arcsec'      # pixel or arcsec
 
     # Rotation
@@ -90,7 +89,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
         plots_folder = "."
     if not path_exists(plots_folder):
         system("mkdir -p {0:s} ".format(plots_folder))
-    infiles = [p[1] for p in prod]  # if p[1] not in ['x2rp0202t_c0f.fits', 'x2rp0302t_c0f.fits']]
+    infiles = [p[1] for p in prod]
     data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
 
     figname = "_".join([target, "FOC"])
@@ -117,13 +116,9 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
 
     #  Estimate error from data background, estimated from sub-image of desired sub_shape.
     background = None
-    data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_type=error_sub_type, subtract_error=subtract_error, display=display_error, savename="_".join([
+    data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_type=error_sub_type, subtract_error=subtract_error, display=display_bkg, savename="_".join([
                                                                       figname, "errors"]), plots_folder=plots_folder, return_background=True)
 
-    if display_bkg:
-        proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min(
-        )*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "bkg"]), plots_folder=plots_folder)
-
     #  Align and rescale images with oversampling.
     data_array, error_array, headers, data_mask = proj_red.align_data(
         data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)

src/lib/background.py

@@ -157,9 +157,9 @@ def bkg_estimate(img, bins=None, chi2=None, coeff=None):
     hist, bin_edges = np.histogram(img[img > 0], bins=bins[-1])
     binning = bin_centers(bin_edges)
     peak = binning[np.argmax(hist)]
-    bins_fwhm = binning[hist > hist.max()/2.]
+    bins_stdev = binning[hist > hist.max()/2.]
-    fwhm = bins_fwhm[-1]-bins_fwhm[0]
+    stdev = bins_stdev[-1]-bins_stdev[0]
-    p0 = [hist.max(), peak, fwhm, 1e-3, 1e-3, 1e-3, 1e-3]
+    p0 = [hist.max(), peak, stdev, 1e-3, 1e-3, 1e-3, 1e-3]
     try:
         popt, pcov = curve_fit(gausspol, binning, hist, p0=p0)
     except RuntimeError:
@@ -231,7 +231,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
         weights = 1/chi2**2
         weights /= weights.sum()
 
-        bkg = np.sum(weights*coeff[:, 1])*subtract_error if subtract_error > 0 else np.sum(weights*coeff[:, 1])
+        bkg = np.sum(weights*(coeff[:, 1]+np.abs(coeff[:, 2])*subtract_error))
 
         error_bkg[i] *= bkg
 
@@ -332,12 +332,12 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
         # bkg = np.sqrt(np.sum(image[np.abs(image-hist_max)/hist_max<0.5]**2)/image[np.abs(image-hist_max)/hist_max<0.5].size)
 
         # Fit a gaussian to the log-intensity histogram
-        bins_fwhm = binning[-1][hist > hist.max()/2.]
+        bins_stdev = binning[-1][hist > hist.max()/2.]
-        fwhm = bins_fwhm[-1]-bins_fwhm[0]
+        stdev = bins_stdev[-1]-bins_stdev[0]
-        p0 = [hist.max(), binning[-1][np.argmax(hist)], fwhm, 1e-3, 1e-3, 1e-3, 1e-3]
+        p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev, 1e-3, 1e-3, 1e-3, 1e-3]
         popt, pcov = curve_fit(gausspol, binning[-1], hist, p0=p0)
         coeff.append(popt)
-        bkg = popt[1]*subtract_error if subtract_error > 0 else popt[1]
+        bkg = popt[1]+np.abs(popt[2])*subtract_error
 
         error_bkg[i] *= bkg
 

src/lib/plots.py

@@ -1584,7 +1584,7 @@ class slit(object):
         self.angle = angle
 
         self.rect_center = (self.x0, self.y0)-np.dot(rot2D(self.angle), (self.width/2, self.height/2))
-        self.rect = Rectangle(self.rect_center, self.width, self.height, alpha=0.8, ec='grey', fc='none')
+        self.rect = Rectangle(self.rect_center, self.width, self.height, angle=self.angle, alpha=0.8, ec='grey', fc='none')
         self.ax.add_patch(self.rect)
 
         self.fig.canvas.mpl_connect('button_press_event', self.on_press)