Minor fixes

ess-dmsc-dram · Sep 30, 2021 · 0473653 · 0473653
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diff --git a/notebooks/6_scipp/1_exploring-data.ipynb b/notebooks/6_scipp/1_exploring-data.ipynb
@@ -241,7 +241,7 @@
     "\n",
     "- Change the magic parameter `400` in the cell above until pixels fall cleanly into layers, either 4 layers of tubes or 12 layers of straws.\n",
     "- Store `layer` as a new coord in `data`.\n",
-    "- Use `sc.groupby(data, group='layer').sum('spectrum')` to group spectra into layers.\n",
+    "- Use `data.groupby(group='layer').sum('spectrum')` to group spectra into layers.\n",
     "- Inspect and understand the HTML view of the result.\n",
     "- Plot the result.\n",
     "  There are two options:\n",

diff --git a/notebooks/6_scipp/2_working-with-masks.ipynb b/notebooks/6_scipp/2_working-with-masks.ipynb
@@ -91,7 +91,7 @@
     "   - If something goes wrong, masks can be removed with Python's `del`, e.g., `del data.masks['wrong']`.\n",
     "   - If you run into an error regarding a length mismatch when inserting the coordinate, remember that `'tof'` is a bin-edge coordinate, i.e., it is by 1 longer than the number of bins.\n",
     "     Use, e.g., only the left bin edges, i.e., all but the last, to create the masks.\n",
-    "     This can be achieved using slicing, e.g., `array[dim_name, integer_index]`.\n",
+    "     This can be achieved using slicing, e.g., `array[dim_name, start_index:end_index]`.\n",
     "2. Use the HTML view and plot the data after masking to explore the effect.\n",
     "3. Pass a `dict` containing `counts` (computed above as `counts = data.sum('tof')`) and the equivalent counts computed *after* masking to `sc.plot`.\n",
     "   Use this to verify that the prompt-pulse mask results in removal of counts.\n",
@@ -201,7 +201,7 @@
    "source": [
     "theta_edges = sc.array(dims=['theta'], unit='rad', values=np.linspace(0, 0.1, num=100))\n",
     "data.coords['theta'] = 0.5 * scn.two_theta(data)\n",
-    "sc.groupby(data, group='theta', bins=theta_edges).sum('spectrum').plot()"
+    "data.groupby(group='theta', bins=theta_edges).sum('spectrum').plot()"
    ]
   },
   {

diff --git a/solutions/6_scipp/1_exploring-data.ipynb b/solutions/6_scipp/1_exploring-data.ipynb
@@ -481,7 +481,7 @@
     "\n",
     "- Change the magic parameter `400` in the cell above until pixels fall cleanly into layers, either 4 layers of tubes or 12 layers of straws.\n",
     "- Store `layer` as a new coord in `data`.\n",
-    "- Use `sc.groupby(data, group='layer').sum('spectrum')` to group spectra into layers.\n",
+    "- Use `data.groupby(group='layer').sum('spectrum')` to group spectra into layers.\n",
     "- Inspect and understand the HTML view of the result.\n",
     "- Plot the result.\n",
     "  There are two options:\n",

diff --git a/solutions/6_scipp/2_working-with-masks.ipynb b/solutions/6_scipp/2_working-with-masks.ipynb
@@ -91,7 +91,7 @@
     "   - If something goes wrong, masks can be removed with Python's `del`, e.g., `del data.masks['wrong']`.\n",
     "   - If you run into an error regarding a length mismatch when inserting the coordinate, remember that `'tof'` is a bin-edge coordinate, i.e., it is by 1 longer than the number of bins.\n",
     "     Use, e.g., only the left bin edges, i.e., all but the last, to create the masks.\n",
-    "     This can be achieved using slicing, e.g., `array[dim_name, integer_index]`.\n",
+    "     This can be achieved using slicing, e.g., `array[dim_name, start_index:end_index]`.\n",
     "2. Use the HTML view and plot the data after masking to explore the effect.\n",
     "3. Pass a `dict` containing `counts` (computed above as `counts = data.sum('tof')`) and the equivalent counts computed *after* masking to `sc.plot`.\n",
     "   Use this to verify that the prompt-pulse mask results in removal of counts.\n",
@@ -305,7 +305,7 @@
    "source": [
     "theta_edges = sc.array(dims=['theta'], unit='rad', values=np.linspace(0, 0.1, num=100))\n",
     "data.coords['theta'] = 0.5 * scn.two_theta(data)\n",
-    "sc.groupby(data, group='theta', bins=theta_edges).sum('spectrum').plot()"
+    "data.groupby(group='theta', bins=theta_edges).sum('spectrum').plot()"
    ]
   },
   {