diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py
index 24f87bc7aad902187693b27e73a9971a8957f6f0..6e505f29ab9d68fe505cbce53a1219934134cdef 100644
--- a/src/toolbox_scs/detectors/hrixs.py
+++ b/src/toolbox_scs/detectors/hrixs.py
@@ -185,33 +185,34 @@ class hRIXS:
     Attributes
     ----------
 
-    PROPOSAL: int
+    PROPOSAL : int
         the number of the proposal
-    X_RANGE: slice
+    X_RANGE : slice
         the slice to take in the dispersive direction, in pixels. Defaults
         to the entire width.
-    Y_RANGE: slice
+    Y_RANGE : slice
         the slice to take in the energy direction
-    THRESHOLD: float
+    THRESHOLD : float
         pixel counts above which a hit candidate is assumed, for centroiding.
         use None if you want to give it in standard deviations instead.
-    STD_THRESHOLD:
+    STD_THRESHOLD :
         same as THRESHOLD, in standard deviations.
-    BINS: int
+    BINS : int
         the number of bins used in centroiding
-    CURVE_A, CURVE_B: float
+    CURVE_A, CURVE_B : float
         the coefficients of the parabola for the curvature correction
-    USE_DARK: bool
+    USE_DARK : bool
         whether to do dark subtraction. Is initially `False`, magically
         switches to `True` if a dark has been loaded, but may be reset.
-    ENERGY_INTERCEPT, ENERGY_SLOPE:
+    ENERGY_INTERCEPT, ENERGY_SLOPE :
         The calibration from pixel to energy
-    FIELDS:
+    FIELDS :
         the fields to be loaded from the data. Add additional fields if so
         desired.
 
     Example
     -------
+    A usual setup for the hRIXS class looks like this::
 
         h = hRIXS()
         h.PROPOSAL = 3145
@@ -264,12 +265,15 @@ class hRIXS:
 
         Example
         -------
+        Load data from a run::
 
             data = h.from_run(145)  # load run 145
 
+        Combine data from two runs::
+
             data1 = h.from_run(145)  # load run 145
             data2 = h.from_run(155)  # load run 155
-            data = xarray.concat([data1, data2], 'trainId')  # combine both
+            data = xarray.concat([data1, data2], 'trainId')
         """
         if proposal is None:
             proposal = self.PROPOSAL
@@ -287,8 +291,9 @@ class hRIXS:
 
         Example
         -------
+        Load a dark run into the hRIXS object::
 
-            h.load_dark(166)  # load dark run 166
+            h.load_dark(166)
         """
         data = self.from_run(runNB, proposal)
         self.dark_image = data['hRIXS_det'].mean(dim='trainId')
@@ -308,19 +313,21 @@ class hRIXS:
         Parameters
         ----------
 
-        runNB: int
+        runNB : int
             the run number to use
-        proposal: int
+        proposal : int
             the proposal to use, default to the current proposal
-        plot: bool
+        plot : bool
             whether to plot the found curvature onto the data
-        args: pair of float, optional
+        args : pair of float, optional
             a starting value to prime the fitting routine
 
         Example
         -------
 
-            h.find_curvature(155)  # use run 155 to fit the curvature
+        Use run 155 to fit the curvature::
+
+            h.find_curvature(155)
         """
         data = self.from_run(runNB, proposal)
 
@@ -380,9 +387,11 @@ class hRIXS:
 
         Example
         -------
+        Find photons in all images of run `data`, then plot the spectrum of
+        the first image::
 
-            h.centroid(data)  # find photons in all images of the run
-            data.spectrum[0, :].plot()  # plot the spectrum of the first image
+            h.centroid(data)
+            data.spectrum[0, :].plot()
         """
         if bins is None:
             bins = self.BINS
@@ -424,9 +433,11 @@ class hRIXS:
 
         Example
         -------
+        Create a spectrum by summing pixels with same energy in each image of
+        a run, then plot the spectrum of the first image::
 
-            h.integrate(data)  # create spectrum by summing pixels
-            data.spectrum[0, :].plot()  # plot the spectrum of the first image
+            h.integrate(data)
+            data.spectrum[0, :].plot()
         """
         bins = self.Y_RANGE.stop - self.Y_RANGE.start
         margin = 10
@@ -479,14 +490,19 @@ class hRIXS:
 
         Example
         -------
+        Create spectra by finding photons, then sum all spectra, and
+        plot the result::
+
+            h.centroid(data)
+            agg = h.aggregate(data)
+            agg.spectrum.plot()
 
-            h.centroid(data)  # create spectra from finding photons
-            agg = h.aggregate(data)  # sum all spectra
-            agg.spectrum.plot()  # plot the resulting spectrum
+        Group data by a variable, sum the corresponding spectra, and
+        plot the spectrum for the first value of the variable::
 
-            groups = data.groupby('hRIXS_index')  # group data by a variable
-            agg = groups.map(h.aggregate)  # sum corresponding spectra
-            agg.spectrum[0, :].plot()  # plot the spectrum for first value
+            groups = data.groupby('hRIXS_index')
+            agg = groups.map(h.aggregate)
+            agg.spectrum[0, :].plot()
         """
         ret = ds.map(self.aggregator, dim=dim)
         ret = ret.drop_vars([n for n in ret if n not in self.aggregators])