diff --git a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
index 2cbf2883903625f971734edb684e66e5c8011853..a5b646c7c26529cc5f7f4c8213059917515cb46a 100644
--- a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
@@ -834,12 +834,12 @@
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
"raw_mean = np.mean(raw, axis=1)\n",
+ "vmin, vmax = np.percentile(raw_mean, [5, 95])\n",
"geom.plot_data_fast(\n",
" raw_mean,\n",
" ax=ax,\n",
- " vmin=min(0.75*np.median(raw_mean[raw_mean > 0]), 2000),\n",
- " vmax=max(1.5*np.median(raw_mean[raw_mean > 0]), 16000),\n",
- " cmap=\"jet\",\n",
+ " vmin=vmin,\n",
+ " vmax=vmax,\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
")\n",
"ax.set_title(f'{karabo_id} - Mean RAW', size=18)\n",
@@ -863,12 +863,9 @@
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
"corrected_mean = np.mean(corrected, axis=1)\n",
- "_corrected_vmin = min(0.75*np.median(corrected_mean[corrected_mean > 0]), -0.5)\n",
- "_corrected_vmax = max(2.*np.median(corrected_mean[corrected_mean > 0]), 100)\n",
+ "vmin, vmax = np.percentile(corrected_mean, [5, 95])\n",
"\n",
- "mean_plot_kwargs = dict(\n",
- " vmin=_corrected_vmin, vmax=_corrected_vmax, cmap=\"jet\"\n",
- ")\n",
+ "mean_plot_kwargs = dict(vmin=vmin, vmax=vmax)\n",
"\n",
"if strixel_sensor:\n",
" if strixel_sensor == \"A1256\":\n",
@@ -929,11 +926,11 @@
"display(Markdown((f\"#### A single image from train {tid}\")))\n",
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
+ "vmin, vmax = np.percentile(corrected_train, [5, 95])\n",
"\n",
"single_plot_kwargs = dict(\n",
- " vmin=min(0.75 * np.median(corrected_train[corrected_train > 0]), -0.5),\n",
- " vmax=max(2.0 * np.median(corrected_train[corrected_train > 0]), 100),\n",
- " cmap=\"jet\"\n",
+ " vmin=vmin,\n",
+ " vmax=vmax,\n",
")\n",
"\n",
"if not strixel_sensor:\n",
@@ -1060,7 +1057,6 @@
"geom.plot_data_fast(\n",
" gain_max,\n",
" ax=ax,\n",
- " cmap=\"jet\",\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
")\n",
"plt.show()"
@@ -1081,7 +1077,14 @@
"outputs": [],
"source": [
"table = []\n",
- "for item in BadPixels:\n",
+ "badpixels = [\n",
+ " BadPixels.OFFSET_OUT_OF_THRESHOLD,\n",
+ " BadPixels.NOISE_OUT_OF_THRESHOLD,\n",
+ " BadPixels.OFFSET_NOISE_EVAL_ERROR,\n",
+ " BadPixels.NO_DARK_DATA,\n",
+ " BadPixels.WRONG_GAIN_VALUE,\n",
+ " ]\n",
+ "for item in badpixels:\n",
" table.append(\n",
" (item.name, f\"{item.value:016b}\"))\n",
"md = display(Latex(tabulate.tabulate(\n",
@@ -1107,16 +1110,17 @@
"display(Markdown(f\"#### Bad pixels image for train {tid}\"))\n",
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
+ "vmin, vmax = (0, sorted([bp.value for bp in badpixels])[-2])\n",
"if not strixel_sensor:\n",
" geom.plot_data_fast(\n",
" np.log2(mask_train),\n",
" ax=ax,\n",
- " vmin=0, vmax=32, cmap=\"jet\",\n",
+ " vmin=vmin, vmax=vmax,\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
" )\n",
"else:\n",
" mask = ax.imshow(\n",
- " mask_train.squeeze(), vmin=0, vmax=32, cmap='jet', aspect=aspect)\n",
+ " mask_train.squeeze(), vmin=vmin, vmax=vmax, aspect=aspect)\n",
" plt.colorbar(mask)\n",
"\n",
"plt.show()"
diff --git a/notebooks/Jungfrau/Jungfrau_darks_Summary_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_darks_Summary_NBC.ipynb
index 6da1df8b914372e6bcfb7489438fd9cba056c0fd..08494dc42112fba9e6135c7291795ff1d4c4e048 100644
--- a/notebooks/Jungfrau/Jungfrau_darks_Summary_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_darks_Summary_NBC.ipynb
@@ -56,9 +56,13 @@
"matplotlib.use(\"agg\")\n",
"%matplotlib inline\n",
"\n",
+ "import tabulate\n",
+ "from IPython.display import Latex, Markdown, display\n",
+ "from XFELDetAna.plotting.simpleplot import simplePlot\n",
+ "\n",
+ "from cal_tools.enums import BadPixels\n",
"from cal_tools.plotting import init_jungfrau_geom, show_processed_modules_jungfrau\n",
- "from cal_tools.tools import CalibrationMetadata\n",
- "from XFELDetAna.plotting.simpleplot import simplePlot"
+ "from cal_tools.tools import CalibrationMetadata"
]
},
{
@@ -188,55 +192,60 @@
"source": [
"gainstages = 3\n",
"gain_names = [\"High Gain\", \"Medium Gain\", \"Low Gain\" ]\n",
- "const_range = {\n",
- " \"Offset\": [(0, 8000), (8000, 16000), (8000, 16000)],\n",
- " \"Noise\": [(0., 50.), (0., 50.), (0., 50.)],\n",
- " \"BadPixelsDark\": [(0., 5.), (0., 5.), (0., 5.)],\n",
- "}\n",
- "# vmin and vmax are different for Offset for fixed gain constants. \n",
- "if fixed_gain:\n",
- " const_range[\"Offset\"] = [(0, 8000), (0, 8000), (0, 8000)]\n",
- "\n",
- "diff_const_range = {\n",
- " \"Offset\": [(0, 500), (0, 500), (0, 500)],\n",
- " \"Noise\": [(0., 5.), (0., 5.), (0., 5.)],\n",
- " \"BadPixelsDark\": [(0., 5.), (0., 5.), (0., 5.)],\n",
- "}\n",
- "percentage_range = (0, 100)\n",
- "perc_const_range = {c: [percentage_range]*3 for c in dark_constants}\n",
+ "\n",
"gs = gridspec.GridSpec(2, 4)\n",
"\n",
"axes = {\n",
- " \"ax0\": {\n",
+ " \"map\": {\n",
" \"gs\": gs[0, 1:3],\n",
" \"shrink\": 0.7,\n",
" \"pad\": 0.05,\n",
" \"label\": \"ADCu\",\n",
" \"title\": \"{}\",\n",
" \"location\": \"right\",\n",
- " \"range\": const_range,\n",
" },\n",
- " \"ax1\": {\n",
+ " \"diff\": {\n",
" \"gs\": gs[1, :2],\n",
" \"shrink\": 0.7,\n",
" \"pad\": 0.02,\n",
" \"label\": \"ADCu\",\n",
" \"location\": \"left\",\n",
" \"title\": \"Difference with previous {}\",\n",
- " \"range\": diff_const_range,\n",
" },\n",
- " \"ax2\": {\n",
+ " \"diff_frac\": {\n",
" \"gs\": gs[1, 2:],\n",
" \"shrink\": 0.7,\n",
" \"pad\": 0.02,\n",
" \"label\": \"%\",\n",
" \"location\": \"right\",\n",
" \"title\": \"Difference with previous {} %\",\n",
- " \"range\": perc_const_range,\n",
" },\n",
" }"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def badpx(constant_name):\n",
+ " return \"bad\" in constant_name.lower()\n",
+ "\n",
+ "\n",
+ "def bp_entry(bp):\n",
+ " return [f\"{bp.name:<30s}\", f\"{bp.value:032b}\", f\"{int(bp.value)}\"]\n",
+ "\n",
+ "\n",
+ "badpixels = [\n",
+ " BadPixels.OFFSET_OUT_OF_THRESHOLD,\n",
+ " BadPixels.NOISE_OUT_OF_THRESHOLD,\n",
+ " BadPixels.OFFSET_NOISE_EVAL_ERROR,\n",
+ " BadPixels.NO_DARK_DATA,\n",
+ " BadPixels.WRONG_GAIN_VALUE,\n",
+ " ]"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -253,19 +262,38 @@
"outputs": [],
"source": [
"for cname, const in curr_constants.items():\n",
+ " if badpx(cname):\n",
+ " table = [bp_entry(bp) for bp in badpixels]\n",
+ " display(Markdown(\"\"\"**The bad pixel** mask is encoded as a bit mask.\"\"\"))\n",
+ " display(Latex(\n",
+ " tabulate.tabulate(\n",
+ " table,\n",
+ " tablefmt='latex',\n",
+ " headers=[\"Name\", \"bit value\", \"integer value\"]\n",
+ " )))\n",
"\n",
" # Prepare the stacked mean of constant,\n",
" # the difference with the previous constant\n",
" # and the fraction of that difference.\n",
+ "\n",
" mean_const = np.nanmean(const, axis=3)\n",
- " mean_diff = np.abs(np.nanmean(const, axis=3) - np.nanmean(prev_constants[cname], axis=3)) # noqa\n",
- " mean_frac = np.abs(mean_diff / mean_const) * 100\n",
- " \n",
+ " mean_diff = np.abs(\n",
+ " np.nanmean(const, axis=3) - np.nanmean(\n",
+ " prev_constants[cname],\n",
+ " axis=3)\n",
+ " )\n",
+ " mean_frac = np.divide(\n",
+ " mean_diff,\n",
+ " mean_const,\n",
+ " out=np.zeros_like(mean_const),\n",
+ " where=(mean_const != 0)\n",
+ " ) * 100\n",
+ "\n",
" for gain in range(gainstages):\n",
" data_to_plot = {\n",
- " f'ax0': mean_const[..., gain],\n",
- " f'ax1': mean_diff[..., gain],\n",
- " f'ax2': mean_frac[..., gain],\n",
+ " f'map': mean_const[..., gain],\n",
+ " f'diff': mean_diff[..., gain],\n",
+ " f'diff_frac': mean_frac[..., gain],\n",
" }\n",
"\n",
" # Plotting constant overall modules.\n",
@@ -279,18 +307,25 @@
"\n",
" # Avoid difference plots if previous constants\n",
" # are missing for the detector.\n",
- " if cname in exculded_constants and axname != \"ax0\":\n",
+ " if cname in exculded_constants and axname != \"map\":\n",
" break\n",
" ax = fig.add_subplot(axv[\"gs\"])\n",
- " vmin, vmax = axv[\"range\"][cname][gain]\n",
+ "\n",
+ " if badpx(cname):\n",
+ " vmin, vmax = (0, sorted([bp.value for bp in badpixels])[-2])\n",
+ " else:\n",
+ " vmin, vmax = np.percentile(data_to_plot[axname], [5, 95])\n",
+ "\n",
" geom.plot_data(\n",
" data_to_plot[axname],\n",
- " vmin=vmin, vmax=vmax, ax=ax, \n",
+ " vmin=vmin,\n",
+ " vmax=vmax,\n",
+ " ax=ax, \n",
" colorbar={\n",
" \"shrink\": axv[\"shrink\"],\n",
" \"pad\": axv[\"pad\"],\n",
" \"location\": axv[\"location\"],\n",
- " }\n",
+ " },\n",
" )\n",
"\n",
" colorbar = ax.images[0].colorbar\n",
@@ -300,7 +335,7 @@
" ax.set_title(axv[\"title\"].format(\n",
" f\"{cname} {gain_names[gain]}\"), fontsize=15)\n",
" \n",
- " if axname == \"ax0\":\n",
+ " if axname == \"map\":\n",
" ax.set_xlabel('Columns', fontsize=15)\n",
" ax.set_ylabel('Rows', fontsize=15)\n",
" ax.tick_params(labelsize=15)\n",