diff --git a/notebooks/AGIPD/Characterize_AGIPD_Gain_FlatFields_NBC.ipynb b/notebooks/AGIPD/Characterize_AGIPD_Gain_FlatFields_NBC.ipynb
index b3a8fc651b6720d0946be226cd0a7d2689297b7b..6ef38779844fcbbb19adaee9122642876b0a99fa 100644
--- a/notebooks/AGIPD/Characterize_AGIPD_Gain_FlatFields_NBC.ipynb
+++ b/notebooks/AGIPD/Characterize_AGIPD_Gain_FlatFields_NBC.ipynb
@@ -14,8 +14,8 @@
"metadata": {},
"outputs": [],
"source": [
- " in_folder = \"/gpfs/exfel/exp/SPB/202030/p900138/scratch/karnem/r0203_r0204_v01/\" # the folder to read histograms from, required\n",
- "out_folder = \"/gpfs/exfel/exp/SPB/202030/p900138/scratch/karnem/r0203_r0204_v01/\" # the folder to output to, required\n",
+ "in_folder = \"/gpfs/exfel/exp/SPB/202030/p900138/scratch/karnem/r0203_r0204_v01/\" # the folder to read histograms from, required\n",
+ "out_folder = \"\" # the folder to output to, required\n",
"hist_file_template = \"hists_m{:02d}_sum.h5\" # the template to use to access histograms\n",
"modules = [10] # modules to correct, set to -1 for all, range allowed\n",
"\n",
@@ -376,38 +376,35 @@
"metadata": {},
"outputs": [],
"source": [
- "fig = plt.figure(figsize=(10,5))\n",
+ "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
+ "fig.set_size_inches(16, 7)\n",
"for i, shape in enumerate(shapes):\n",
" idx = shape[3]\n",
- " plt.errorbar(x[idx], hist[idx], np.sqrt(hist[idx]),\n",
- " marker='+', ls=''\n",
- " )\n",
+ " ax1.errorbar(\n",
+ " x[idx], hist[idx],\n",
+ " np.sqrt(hist[idx]),\n",
+ " marker='+', ls='',\n",
+ " )\n",
" yg = gaussian(x[idx], *shape[:3])\n",
" l = f'Peak {i}: {shape[1]:0.1f} $ \\pm $ {shape[2]:0.2f} ADU'\n",
- " plt.plot(x[idx], yg, label=l)\n",
- "plt.grid(True)\n",
- "plt.xlabel(\"Signal [ADU]\")\n",
- "plt.ylabel(\"Counts\")\n",
- "plt.legend(ncol=2)\n",
+ " ax1.plot(x[idx], yg, label=l)\n",
+ "ax1.grid(True)\n",
+ "ax1.set_xlabel(\"Signal [ADU]\")\n",
+ "ax1.set_ylabel(\"Counts\")\n",
+ "ax1.legend(ncol=2)\n",
"\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
"_ = xana.simplePlot(\n",
- " d, \n",
- " x_label='Signal [ADU]', \n",
+ " d,\n",
+ " use_axis=ax2,\n",
+ " x_label='Signal [ADU]',\n",
" y_label='Counts',\n",
- " secondpanel=True, y_log=False, \n",
- " x_range=(frange[0], frange[1]), \n",
- " y_range=(1., np.max(hist)*1.6), \n",
- " legend='top-left-frame-ncol2'\n",
- ")"
+ " secondpanel=True, y_log=False,\n",
+ " x_range=(frange[0], frange[1]),\n",
+ " y_range=(1., np.max(hist)*1.6),\n",
+ " legend='top-left-frame-ncol2',\n",
+ ")\n",
+ "\n",
+ "plt.show()"
]
},
{
@@ -500,33 +497,39 @@
"metadata": {},
"outputs": [],
"source": [
- "labels = ['Noise peak [ADU]',\n",
- " 'First photon peak [ADU]',\n",
- " f\"gain [ADU/keV], $\\gamma$={photon_energy} [keV]\",\n",
- " \"$\\chi^2$/nDOF\",\n",
- " 'Fraction of bad pixels']\n",
- "for i, key in enumerate(['g0mean', 'g1mean', 'gain', 'chi2_ndof', 'mask']):\n",
+ "labels = [\n",
+ " \"Noise peak [ADU]\",\n",
+ " \"First photon peak [ADU]\",\n",
+ " f\"gain [ADU/keV] $\\gamma$={photon_energy} [keV]\",\n",
+ " \"$\\chi^2$/nDOF\",\n",
+ " \"Fraction of bad pixels\",\n",
+ "]\n",
"\n",
+ "for i, key in enumerate(['g0mean', 'g1mean', 'gain', 'chi2_ndof', 'mask']):\n",
" fig = plt.figure(figsize=(20,5))\n",
" ax = fig.add_subplot(121)\n",
" data = fit_result[key]\n",
" if key == 'mask':\n",
- " data = data>0\n",
+ " data = data > 0\n",
" vmin, vmax = [0, 1]\n",
" else:\n",
" vmin, vmax = get_range(data, 5)\n",
- " _ = heatmapPlot(np.mean(data, axis=0).T,\n",
- " add_panels=False, cmap='viridis', use_axis=ax,\n",
- " vmin=vmin, vmax=vmax, lut_label=labels[i] )\n",
- " \n",
+ " _ = heatmapPlot(\n",
+ " np.mean(data, axis=0).T,\n",
+ " add_panels=False, cmap='viridis', use_axis=ax,\n",
+ " vmin=vmin, vmax=vmax, lut_label=labels[i]\n",
+ " )\n",
+ "\n",
" if key != 'mask':\n",
" vmin, vmax = get_range(data, 7)\n",
- " ax1 = fig.add_subplot(122)\n",
- " _ = xana.histPlot(ax1,data.flatten(), \n",
- " bins=45,range=[vmin, vmax],\n",
- " log=True,color='red',histtype='stepfilled')\n",
- " plt.xlabel(labels[i])\n",
- " plt.ylabel(\"Counts\") "
+ " ax = fig.add_subplot(122)\n",
+ " _ = xana.histPlot(\n",
+ " ax, data.flatten(),\n",
+ " bins=45,range=[vmin, vmax],\n",
+ " log=True,color='red',histtype='stepfilled'\n",
+ " )\n",
+ " ax.set_xlabel(labels[i])\n",
+ " ax.set_ylabel(\"Counts\")"
]
},
{
@@ -551,7 +554,7 @@
"ax.set_title(f'{hist_std[hist_std<intensity_lim].shape[0]} histograms below threshold in {intensity_lim} ADU',\n",
" fontsize=14, fontweight='bold')\n",
"ax.grid()\n",
- "plt.yscale('log')"
+ "ax.set_yscale('log')"
]
},
{
@@ -561,7 +564,7 @@
"outputs": [],
"source": [
"def plot_par_distr(par):\n",
- " fig = plt.figure(figsize=(16,5))\n",
+ " fig = plt.figure(figsize=(16, 5))\n",
" sel = fit_result['mask'] == 0\n",
" \n",
" for i in range(n_peaks_fit) :\n",
@@ -582,7 +585,7 @@
" log=True,color='g',\n",
" label='good fits only',\n",
" )\n",
- " plt.xlabel(f\"g{i} {par} [ADU]\")\n",
+ " ax.set_xlabel(f\"g{i} {par} [ADU]\")\n",
" ax.legend()\n",
" \n",
"plot_par_distr('mean')\n",
@@ -603,7 +606,7 @@
" }\n",
"fig = plt.figure(figsize=(16,5))\n",
"for i, (par, data) in enumerate(dsets.items()):\n",
- " ax = fig.add_subplot(1,3,i+1)\n",
+ " ax = fig.add_subplot(1, 3, i+1)\n",
" plt_range=get_range(data, 10)\n",
" num_bins = 100\n",
" _ = xana.histPlot(ax,data.flatten(), \n",
@@ -616,14 +619,14 @@
" log=True,color='g',\n",
" label='good fits only',\n",
" )\n",
- " plt.xlabel(f\"{par}\")\n",
+ " ax.set_xlabel(f\"{par}\")\n",
" ax.legend()\n",
" if 'd01' in par :\n",
- " plt.axvline(d0_lim[0])\n",
- " plt.axvline(d0_lim[1])\n",
+ " ax.axvline(d0_lim[0])\n",
+ " ax.axvline(d0_lim[1])\n",
" if 'rel' in par :\n",
- " plt.axvline(gain_lim[0])\n",
- " plt.axvline(gain_lim[1])"
+ " ax.axvline(gain_lim[0])\n",
+ " ax.axvline(gain_lim[1])"
]
},
{
@@ -701,7 +704,8 @@
"metadata": {},
"outputs": [],
"source": [
- "fig = plt.figure(figsize=(10, 5))\n",
+ "fig, ax = plt.subplots()\n",
+ "fig.set_size_inches(10, 5)\n",
"\n",
"n_bars = 8\n",
"x = np.arange(n_bars)\n",
@@ -753,12 +757,13 @@
" 'No Entry',\n",
" 'Gain deviation']\n",
"\n",
- "plt.bar(x, y2, width, label='Only this cut')\n",
- "plt.bar(x, y, width, label='Cut flow')\n",
- "plt.xticks(x, labels, rotation=90)\n",
- "plt.ylim(y[5]-0.5, 100)\n",
- "plt.grid(True)\n",
- "plt.legend()\n",
+ "ax.bar(x, y2, width, label='Only this cut')\n",
+ "ax.bar(x, y, width, label='Cut flow')\n",
+ "ax.set_xticks(x)\n",
+ "ax.set_xticklabels(labels, rotation=90)\n",
+ "ax.set_ylim(y[5]-0.5, 100)\n",
+ "ax.grid(True)\n",
+ "ax.legend()\n",
"plt.show()"
]
}