{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cryogenic Detector Data Augmentation\n",
"\n",
"```{warning}\n",
"Note that this tutorial and the features it describes are currently **not maintained**. If you wish to help out and contribute to it, please reach out to the maintainers.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2021-07-29T09:47:46.276007Z",
"start_time": "2021-07-29T09:47:46.268922Z"
}
},
"source": [
"*Correspondence to: felix.wagner@oeaw.ac.at*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we explore the data augmentation module of the Cait Python package. We are going to simulate a dataset of realistic events, including two different pulse shapes (absorber and carrier recoils), and a set of artifacts."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:10.765346Z",
"start_time": "2021-11-23T12:17:07.439512Z"
}
},
"outputs": [],
"source": [
"from cait.augment import ParameterSampler, plot_events, unfold, L2\n",
"import numpy as np\n",
"import cait as ai # install from the develop branch\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import h5py\n",
"from tqdm.auto import tqdm\n",
"from scipy import signal\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"%config InlineBackend.figure_formats = ['svg']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some definitions for the plots."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:20.886535Z",
"start_time": "2021-11-23T12:17:20.882804Z"
}
},
"outputs": [],
"source": [
"mpl.rcParams['figure.figsize'] = (6, 4)\n",
"mpl.rcParams['savefig.dpi'] = 300"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the global parameters. These define the most important info about the augmented data: Record length, sample frequency and resolution (standard deviation) of the noise.\n",
"\n",
"There is also the possibility to add polynomial drift structures to the baselines, which we observe in measured baselines very often. The rasterization feature introduces a discrete sampling effect, as we would observe it from the 16 bit precision of the digitizer. The saturation applies a saturation curve to all events, as it would happen in any real TES measurement.\n",
"\n",
"The class names list includes the names of all classes that we want to simulate in the dataset. These are all the classes that are currently available within `cait`.\n",
"\n",
"For the whole notebook: Only capitalized parameters need to be set by the user."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:22.130302Z",
"start_time": "2021-11-23T12:17:22.122539Z"
}
},
"outputs": [],
"source": [
"RECORD_LENGTH = 16384\n",
"SAMPLE_FREQUENCY = 25000\n",
"RESOLUTION = 0.01 # 0.003\n",
"\n",
"POLYNOMIAL_DRIFTS = False\n",
"RASTERIZE = True\n",
"SQUARE_WAVES = False\n",
"SATURATION = True\n",
"CLASS_NAMES = ['Event Pulse',\n",
" 'Noise',\n",
" 'Decaying Baseline',\n",
" 'Temperature Rise',\n",
" 'Spike',\n",
" 'Squid Jump',\n",
" 'Reset',\n",
" 'Cosinus Tail',\n",
" 'Decaying Baseline with Event Pulse',\n",
" 'Pile Up',\n",
" 'Early or late Trigger',\n",
" 'Carrier Event',\n",
" 'Decaying Baseline with Tail Event',\n",
" ]\n",
"\n",
"\n",
"# ----------------------------------------- \n",
"# no need to change below parameters\n",
"# ----------------------------------------- \n",
"\n",
"label_names = {\n",
" 'unlabeled': 0,\n",
" 'Event Pulse': 1,\n",
" 'Test/Control Pulse': 2,\n",
" 'Noise': 3,\n",
" 'Squid Jump': 4,\n",
" 'Spike': 5,\n",
" 'Early or late Trigger': 6,\n",
" 'Pile Up': 7,\n",
" 'Carrier Event': 8,\n",
" 'Strongly Saturated Event Pulse': 9,\n",
" 'Strongly Saturated Test/Control Pulse': 10,\n",
" 'Decaying Baseline': 11,\n",
" 'Temperature Rise': 12,\n",
" 'Stick Event': 13,\n",
" 'Square Waves': 14,\n",
" 'Human Disturbance': 15,\n",
" 'Large Sawtooth': 16,\n",
" 'Cosinus Tail': 17,\n",
" 'Light only Event': 18,\n",
" 'Ring & Light Event': 19,\n",
" 'Sharp Light Event': 20,\n",
" 'Reset': 21,\n",
" 'Decaying Baseline with Event Pulse': 22,\n",
" 'Decaying Baseline with Tail Event': 23,\n",
" 'unknown/other': 99,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initializa an instance of the sampler class. This class is responsible for the simulation of all event, but we need to set all the arguments. Otherwise the arguments will be randomly sampled (see notebook `Universal Training Set`)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:26.362520Z",
"start_time": "2021-11-23T12:17:26.359746Z"
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"parsam = ParameterSampler(record_length=RECORD_LENGTH,\n",
" sample_frequency=SAMPLE_FREQUENCY)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define the detector resolution. The measured resolution will deviate from the set resolution, depending on the noise power spectrum and the method for reconstructing the resolution."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:27.468151Z",
"start_time": "2021-11-23T12:17:27.464346Z"
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"parsam.set_args(resolution=np.array([RESOLUTION]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define the pulse shapes. You can either ask the ParameterSampler instance to generate some shapes randomly (you can always resample, in case you don't like them), or you put your own parameters. The pulses follow the pulse shape model for cryogenic TES detectors, introduced by Franz Pröbst. In the cell below, we commented to option to randomly sample pulse shapes out, but you can always uncomment it (and comment the \"define your own\" section instead)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:30.030025Z",
"start_time": "2021-11-23T12:17:28.792112Z"
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# sample pulse shapes\n",
"\n",
"# ps1, _ = parsam.sample_pulse_par(size=1, t0=np.array([0.]))\n",
"# ps2, _ = parsam.sample_pulse_par(size=1, t0=np.array([0.]))\n",
"# print(ps1)\n",
"# print(ps2)\n",
"\n",
"# or define your own pulse shapes ...\n",
"ps1 = {'t0': np.array([0.]), \n",
" 'tau_t': np.array([0.05815608]), \n",
" 'tau_in': np.array([0.02059209]), \n",
" 'tau_n': np.array([0.01395427]), \n",
" 'An': np.array([-0.02508469])/0.0715207609981429, \n",
" 'At': np.array([0.14102789])/0.0715207609981429}\n",
"ps2 = {'t0': np.array([0.]), \n",
" 'tau_t': np.array([0.01730027]), \n",
" 'tau_in': np.array([0.00128385]), \n",
" 'tau_n': np.array([0.00815371]), \n",
" 'An': np.array([2.59789859])/1.553262177826752,\n",
" 'At': np.array([0.02694577])/1.553262177826752}\n",
"\n",
"for i,p in enumerate([ps1, ps2]):\n",
"\n",
" event = ai.fit.pulse_template(parsam.t, **unfold(p, 1))\n",
" plot_events(event.reshape(1,-1), t=parsam.t, show=True, text=['Standard Event'])\n",
" \n",
"parsam.set_args(pulse_shapes=[[ps1['t0'], ps1['An'], ps1['At'], ps1['tau_n'], ps1['tau_in'], ps1['tau_t']],\n",
" [ps2['t0'], ps2['An'], ps2['At'], ps2['tau_n'], ps2['tau_in'], ps2['tau_t']],\n",
" ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define the noise power spectrum, with randomly sampled parameters. You can always resample, in case you don't like it.\n",
"\n",
"The noise power spectrum follows a parametric shape: Two $1/f^a$ functions are superposed, with $a$ sampled in between 1 and 2. The constant component is forced to zero. A low pass filter starts above 1e4 Hz. The characteristic 50 Hz Peak, including its first and second harmonic, are sampled with heights relative to the overall NPS height."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:34.533972Z",
"start_time": "2021-11-23T12:17:33.345526Z"
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# sample nps\n",
"\n",
"parsam.set_args(nps=None)\n",
"\n",
"noise_par, info = parsam.sample_noise(size=1)\n",
"\n",
"# or define your own nps ...\n",
"# noise_par['nps'] = nps[:, 1].reshape(1,-1)\n",
"# info['fq'] = np.fft.rfftfreq(n=RECORD_LENGTH, d=1 / SAMPLE_FREQUENCY)\n",
"\n",
"# plot begin -------\n",
"\n",
"plt.close()\n",
"\n",
"fig, ax = plt.subplots(constrained_layout=True, figsize=(6,2.5))\n",
"\n",
"ax.loglog(info['fq'], noise_par['nps'][0], color='black', linewidth=2.5, zorder=50) #ab9c73 #6a7d8e\n",
"\n",
"# eye guidelines\n",
"\n",
"ax.set_xlabel('Frequency (Hz)')\n",
"ax.set_ylabel('Amplitude (V^2 / Hz)')\n",
"# ax.text(x=.6,y=.5,s='Noise Power Spectrum', \n",
"# transform=ax.transAxes,\n",
"# bbox=dict(boxstyle='square', fc='white', alpha=0.8, ec='k'))\n",
"plt.savefig('test_data/nps.pdf')\n",
"plt.show()\n",
"\n",
"parsam.set_args(nps=noise_par['nps'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define the saturation curve. It is modeled with a generalized logistics function, we put some parameters explicitely, but you can also randomy sample new ones."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:38.670070Z",
"start_time": "2021-11-23T12:17:37.771490Z"
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# sat = parsam.sample_saturation(size=1)\n",
"# print(sat)\n",
"\n",
"# or define your own saturation ...\n",
"sat = {'K': np.array([8.8786987]),\n",
" 'C': np.array([1.23606841]),\n",
" 'Q': np.array([2.12402435]),\n",
" 'B': np.array([0.57876924]),\n",
" 'nu': np.array([1.61744008]),\n",
" 'A': np.array([-7.95906284])}\n",
"\n",
"plt.close()\n",
"\n",
"fig, ax = plt.subplots(constrained_layout=True, figsize=(6,2.5))\n",
"\n",
"ax.plot(np.arange(0, 10, 0.1), ai.fit.scaled_logistic_curve(np.arange(0, 10, 0.1), **sat), color='black', linewidth=2.5)\n",
"ax.set_xlabel('True pulse height (V)')\n",
"ax.set_ylabel('Saturated pulse height (V)')\n",
"\n",
"# ax.text(x=.6,y=.5,s='Saturation Curve', transform=ax.transAxes,\n",
"# bbox=dict(boxstyle='square', fc='white', alpha=0.8, ec='k'))\n",
"\n",
"plt.savefig('test_data/sat.pdf')\n",
"plt.show()\n",
"\n",
"parsam.set_args(saturation_pars=sat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are ready to plot some augmented events!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:17:44.605631Z",
"start_time": "2021-11-23T12:17:40.901837Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Event Pulse\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Resolution (only meaningful without Saturation): 0.09678070047987046\n",
"Noise\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Carrier Event\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decaying Baseline\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Carrier Event\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cosinus Tail\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"NMBR_PLOTS = 6\n",
"\n",
"classes = np.random.choice(CLASS_NAMES, size=NMBR_PLOTS)\n",
"classes[0] = CLASS_NAMES[0]\n",
"classes[1] = CLASS_NAMES[1]\n",
"classes[2] = CLASS_NAMES[11]\n",
"# classes = [CLASS_NAMES[0] for i in range(NMBR_PLOTS)]\n",
"\n",
"for i in range(NMBR_PLOTS):\n",
"\n",
" event, info = parsam.get_event(label=classes[i],\n",
" size=4,\n",
" rasterize=RASTERIZE,\n",
" poly=POLYNOMIAL_DRIFTS,\n",
" square=SQUARE_WAVES,\n",
" saturation=SATURATION,\n",
" verb=False,\n",
" )\n",
"\n",
" print(classes[i])\n",
" plot_events(event, t=parsam.t)\n",
" if classes[i] == 'Event Pulse':\n",
" print('Resolution (only meaningful without Saturation): ', np.std(np.abs(np.max(event, axis=1) - info['pulse_height'])))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot for the paper\n",
"classes = ['Event Pulse',\n",
" 'Noise',\n",
" 'Spike',\n",
" 'Reset',\n",
" 'Decaying Baseline with Event Pulse',\n",
" 'Pile Up',\n",
" #'Early or late Trigger',\n",
" ]\n",
"text = ['(i) Target recoil', '(ii) Noise trigger', '(iii) Spike from electronics', \n",
" '(iv) Flux quantum loss', '(v) Decaying baseline', '(vi) Pile-up', \n",
" #'Early trigger',\n",
" ]\n",
"nmbr_x = 3\n",
"nmbr_y = 2\n",
"\n",
"fig, axs = plt.subplots(nmbr_y, nmbr_x, constrained_layout=True, figsize=(12,4))\n",
"\n",
"clwheel = ['black', 'black']\n",
"\n",
"for i,c in enumerate(classes):\n",
"\n",
" event, info = parsam.get_event(label=c,\n",
" size=1,\n",
" rasterize=RASTERIZE,\n",
" poly=POLYNOMIAL_DRIFTS,\n",
" square=SQUARE_WAVES,\n",
" saturation=SATURATION,\n",
" verb=False,\n",
" )\n",
" \n",
" axs[int(i / nmbr_x), int(i % nmbr_x)].plot(parsam.t, event[0], color=clwheel[int(i%2)], linewidth=2) \n",
" \n",
" axs[int(i / nmbr_x), int(i % nmbr_x)].set_title(text[i])\n",
" \n",
"# axs[int(i / nmbr_x), int(i % nmbr_x)].text(x=.6, y=.5, s=text[i],\n",
"# transform=axs[int(i / nmbr_x), int(i % nmbr_x)].transAxes,\n",
"# bbox=dict(boxstyle='square', fc='white', alpha=0.8, ec='k'))\n",
" \n",
"fig.supxlabel('Time (ms)')\n",
"fig.supylabel('Amplitude (V)')\n",
"plt.savefig('test_data/events_plot.pdf')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are ready to create an HDF5 data set with these simulated events!\n",
"\n",
"To change the number of simulated events, change the EVENTS_PER_CLASS dictionary. Please notice, that the number of events per class always has to be a multiple of the BATCHSIZE, otherwise the simulation will be spoiled."
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-10T11:26:56.815727Z",
"start_time": "2021-11-10T11:25:59.551036Z"
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0b40c68fb35f4e89bb6f347ab9c98f58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/3250 [00:00 0.006)\n",
"resolution = np.sqrt(np.mean((dh.get('events', 'pulse_height')[0, clean_unsat] - dh.get('events', 'true_ph')[0, clean_unsat])**2))\n",
"print('resolution', resolution)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'clean_unsat_surv' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m plt.scatter(dh.get('events', 'true_ph')[0, clean_unsat], \n\u001b[1;32m 2\u001b[0m dh.get('events', 'pulse_height')[0, clean_unsat], marker='o', s=10, rasterized=True)\n\u001b[0;32m----> 3\u001b[0;31m plt.scatter(dh.get('events', 'true_ph')[0, clean_unsat_surv], \n\u001b[0m\u001b[1;32m 4\u001b[0m dh.get('events', 'pulse_height')[0, clean_unsat_surv], marker='o', s=10, rasterized=True)\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'True PH'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'clean_unsat_surv' is not defined"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(dh.get('events', 'true_ph')[0, clean_unsat], \n",
" dh.get('events', 'pulse_height')[0, clean_unsat], marker='o', s=10, rasterized=True)\n",
"plt.scatter(dh.get('events', 'true_ph')[0, clean_unsat_surv], \n",
" dh.get('events', 'pulse_height')[0, clean_unsat_surv], marker='o', s=10, rasterized=True)\n",
"plt.xlabel('True PH')\n",
"plt.ylabel('PH')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"confusion_matrix = np.zeros((24, 2), dtype=int)\n",
"for i,j in zip(dh.get('events', 'labels')[0], dh.get('events', 'cnn_cut')[0]):\n",
" confusion_matrix[i,int(j)] += 1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nmbr used classes: 13\n"
]
}
],
"source": [
"not_zero = np.sum(confusion_matrix, axis=1) > 0\n",
"nmbr_not_zero = np.sum(not_zero)\n",
"print('Nmbr used classes: ', nmbr_not_zero)\n",
"confusion_matrix_squeeze = confusion_matrix[not_zero]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"labels = np.unique(dh.get('events', 'labels')[0])\n",
"\n",
"ax = sns.heatmap(confusion_matrix_squeeze, # /np.sum(confusion_matrix_squeeze, axis=1)\n",
" annot=True, fmt='f', linewidths=.5, xticklabels=['Cut','Survived'], yticklabels=labels)\n",
"plt.ylabel(\"Label\") \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" # 'unlabeled': 0,\n",
" # 'Event Pulse': 1,\n",
" # 'Test/Control Pulse': 2,\n",
" # 'Noise': 3,\n",
" # 'Squid Jump': 4,\n",
" # 'Spike': 5,\n",
" # 'Early or late Trigger': 6,\n",
" # 'Pile Up': 7,\n",
" # 'Carrier Event': 8,\n",
" # 'Strongly Saturated Event Pulse': 9,\n",
" # 'Strongly Saturated Test/Control Pulse': 10,\n",
" # 'Decaying Baseline': 11,\n",
" # 'Temperature Rise': 12,\n",
" # 'Stick Event': 13,\n",
" # 'Square Waves': 14,\n",
" # 'Human Disturbance': 15,\n",
" # 'Large Sawtooth': 16,\n",
" # 'Cosinus Tail': 17,\n",
" # 'Light only Event': 18,\n",
" # 'Ring & Light Event': 19,\n",
" # 'Sharp Light Event': 20,\n",
" # 'Reset': 21,\n",
" # 'Decaying Baseline with Event Pulse': 22,\n",
" # 'Decaying Baseline with Tail Event': 23,\n",
" # 'unknown/other': 99,"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[8000, 3000, 500, 500, 500, 9000, 8000, 500, 500, 500, 500,\n",
" 500, 500]])"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"with h5py.File('test_data/test_v0_2.h5', 'r') as f:\n",
" \n",
" fig, axs = plt.subplots(3, 4, constrained_layout=True, figsize=(12,5))\n",
" \n",
" # pile ups\n",
" idx = np.extract(np.logical_and(dh.get('events', 'labels') == 7, dh.get('events', 'cnn_cut') == 0), np.arange(f['events']['event'].shape[1]))\n",
" i = 0\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[0,0].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[0,0].text(-0.1,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='red')\n",
" \n",
" i = 1\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[1,0].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[1,0].text(-0.1,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='red')\n",
" \n",
" i = 2\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[2,0].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[2,0].text(-0.1,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='red')\n",
" \n",
" \n",
" # \n",
" idx = np.extract(np.logical_and(dh.get('events', 'labels') == 7, dh.get('events', 'cnn_cut') == 1), np.arange(f['events']['event'].shape[1]))\n",
" i = 3\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[0,1].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[0,1].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='green')\n",
" \n",
" i = 4\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[1,1].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[1,1].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='green')\n",
" \n",
" i = 5\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[2,1].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[2,1].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='green')\n",
" \n",
" \n",
" # \n",
" idx = np.extract(np.logical_and(dh.get('events', 'labels') == 23, dh.get('events', 'cnn_cut') == 0), np.arange(f['events']['event'].shape[1]))\n",
" i = 0\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[0,2].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[0,2].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='red')\n",
" \n",
" i = 1\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[1,2].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[1,2].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='red')\n",
" \n",
" i = 2\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[2,2].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[2,2].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='red')\n",
" \n",
" \n",
" # decaying baseline with tail event\n",
" idx = np.extract(np.logical_and(dh.get('events', 'labels') == 23, dh.get('events', 'cnn_cut') == 1), np.arange(f['events']['event'].shape[1]))\n",
" i = 3\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[0,3].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[0,3].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='green')\n",
" \n",
" i = 4\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[1,3].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[1,3].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='green')\n",
" \n",
" i = 5\n",
" ev = f['events']['event'][0, idx[i]]\n",
" axs[2,3].plot(parsam.t, ev, color='black', linewidth=2.5)\n",
" true_ph = dh.get('events', 'true_ph')[0, idx[i]]\n",
" axs[2,3].text(0.2,0.85*np.max(ev),f\"CNN out: {np.exp(dh.get('events', 'cnn_prob')[0, idx[i], 1]):.2f}\", color='green')\n",
" \n",
" \n",
" fig.supxlabel('Time (ms)')\n",
" fig.supylabel('Amplitude (V)')\n",
" plt.savefig('test_data/false_positive.pdf')\n",
" plt.show()\n",
" \n",
" #ab9c73 #6a7d8e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":24: RuntimeWarning:\n",
"\n",
"invalid value encountered in true_divide\n",
"\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# calc energy dependent efficiency\n",
"\n",
"clean_unsat = dh.get('events', 'labels')[0] == 1 # np.logical_or(dh.get('events', 'labels')[0] == 1, dh.get('events', 'labels')[0] == 8)\n",
"clean_unsat = np.logical_and(clean_unsat, dh.get('events', 'pulse_height')[0] < 1)\n",
"correct = np.logical_and(clean_unsat, dh.get('events', 'cnn_cut')[0])\n",
"\n",
"clean_unsat_2 = dh.get('events', 'labels')[0] == 8 # np.logical_or(dh.get('events', 'labels')[0] == 1, dh.get('events', 'labels')[0] == 8)\n",
"clean_unsat_2 = np.logical_and(clean_unsat_2, dh.get('events', 'pulse_height')[0] < 1)\n",
"correct_2 = np.logical_and(clean_unsat_2, dh.get('events', 'cnn_cut')[0])\n",
"\n",
"bins = np.linspace(0.001, 0.05, 50)\n",
"all_ , _ = np.histogram(dh.get('events', 'pulse_height')[0, clean_unsat], bins=bins)\n",
"surv_ , _ = np.histogram(dh.get('events', 'pulse_height')[0, correct], bins=bins)\n",
"\n",
"all_2 , _ = np.histogram(dh.get('events', 'pulse_height')[0, clean_unsat_2], bins=bins)\n",
"surv_2 , _ = np.histogram(dh.get('events', 'pulse_height')[0, correct_2], bins=bins)\n",
"\n",
"all_noise, _ = np.histogram(dh.get('events', 'pulse_height')[0, dh.get('events', 'labels')[0] == 3], bins=bins)\n",
"surv_noise, _ = np.histogram(dh.get('events', 'pulse_height')[0, np.logical_and(dh.get('events', 'labels')[0] == 3, dh.get('events', 'cnn_cut')[0] == 1)], bins=bins)\n",
"\n",
"fig, ax = plt.subplots(1,1,figsize=(7.2, 4.45))\n",
"ax.hist(bins[:-1], bins, weights=surv_2/all_2, histtype='step', color='#deebf7', linewidth=2.5, label='Survival rate short pulse shape')\n",
"ax.hist(bins[:-1], bins, weights=surv_/all_, histtype='step', color='#9ecae1', linewidth=2.5, label='Survival rate long pulse shape')\n",
"ax.hist(bins[:-1], bins, weights=surv_noise/all_noise, histtype='step', color='#3182bd', linewidth=2.5, label='Survival rate noise')\n",
"ax.axvline(x=5*resolution, color='black', linestyle='dashed', linewidth=2, label='5 sigma threshold')\n",
"ax.legend(loc='lower right')\n",
"ax.set_xlim(0,0.05)\n",
"ax.set_ylim(-0.05,1.05)\n",
"fig.supylabel('Survival rate')\n",
"fig.supxlabel('Pulse height (V)')\n",
"plt.tight_layout()\n",
"plt.savefig('test_data/efficiency_hist.pdf')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
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"needs_background": "light"
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}
],
"source": [
"fig, axes = plt.subplots(1,2,figsize=(8,4), sharex=True, sharey=True)\n",
"\n",
"axes[0].scatter(dh.get('events', 'pulse_height')[0, np.logical_and(dh.get('events', 'labels')[0] != 1, dh.get('events', 'labels')[0] != 8)], \n",
" dh.get('events', 'decay_time')[0, np.logical_and(dh.get('events', 'labels')[0] != 1, dh.get('events', 'labels')[0] != 8)], \n",
" rasterized=True, marker='o', s=3, color='grey', label='artifacts')\n",
"axes[0].scatter(dh.get('events', 'pulse_height')[0, np.logical_or(dh.get('events', 'labels')[0] == 1, dh.get('events', 'labels')[0] == 8)], \n",
" dh.get('events', 'decay_time')[0, np.logical_or(dh.get('events', 'labels')[0] == 1, dh.get('events', 'labels')[0] == 8)], \n",
" rasterized=True, marker='o', s=3, color='green', label='events')\n",
"axes[0].legend()\n",
"\n",
"axes[1].scatter(dh.get('events', 'pulse_height')[0, dh.get('events', 'cnn_cut')[0] != 1], \n",
" dh.get('events', 'decay_time')[0, dh.get('events', 'cnn_cut')[0] != 1], \n",
" rasterized=True, marker='o', s=3, color='grey', label='rejected')\n",
"axes[1].scatter(dh.get('events', 'pulse_height')[0, dh.get('events', 'cnn_cut')[0] == 1], \n",
" dh.get('events', 'decay_time')[0, dh.get('events', 'cnn_cut')[0] == 1], \n",
" rasterized=True, marker='o', s=3, color='red', label='accepted')\n",
"axes[1].legend()\n",
"\n",
"fig.supxlabel('Pulse height (V)')\n",
"fig.supylabel('Decay time (ms)')\n",
"plt.tight_layout()\n",
"plt.savefig('test_data/efficiency_scatter.pdf')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-08T11:25:49.164844Z",
"start_time": "2021-11-08T11:25:49.159320Z"
}
},
"source": [
"Now lets see the VizTool!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:18:01.277997Z",
"start_time": "2021-11-23T12:17:59.459990Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataHandler Instance created.\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03f6bebe57324312aa0446e499b34ebb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HBox(children=(Dropdown(description='xaxis', options=('Pulse Height Phonon (V)', 'Rise Time Pho…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"datasets = {\n",
" 'Pulse Height Phonon (V)': ['pulse_height', 0, None],\n",
" 'Rise Time Phonon (ms)': ['rise_time', 0, None],\n",
" 'Decay Time Phonon (ms)': ['decay_time', 0, None],\n",
" 'Onset Phonon (ms)': ['onset', 0, None],\n",
" 'Slope Phonon (V)': ['slope', 0, None],\n",
" 'Variance Phonon (V^2)': ['var', 0, None],\n",
" 'Mean Phonon (V)': ['mean', 0, None],\n",
" 'Skewness Phonon': ['skewness', 0, None],\n",
"}\n",
"\n",
"viz = ai.VizTool(path_h5='test_data/', \n",
" fname='test_v0_2',\n",
" group='events', \n",
" datasets=datasets, \n",
" nmbr_channels=1, \n",
" batch_size=1000,\n",
" sample_frequency=25000,\n",
" record_length=16384)\n",
"viz.set_colors(dh.get('events', 'cnn_cut')[0])\n",
"viz.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And lets see a t-SNE plot of the events, and try to classify them with a random forest classifier."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"ExecuteTime": {
"end_time": "2021-11-23T12:19:07.852423Z",
"start_time": "2021-11-23T12:18:58.330059Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/felix/.pyenv/versions/3.8.6/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning:\n",
"\n",
"The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
"\n",
"/Users/felix/.pyenv/versions/3.8.6/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning:\n",
"\n",
"The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
"\n"
]
},
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"data": {
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" \n",
" "
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"source": [
"et = ai.EvaluationTools()\n",
"\n",
"et.add_events_from_file(file='test_data/test_v0_1.h5',\n",
" channel=0,\n",
" which_data='add_mainpar',\n",
" )\n",
"\n",
"et.split_test_train(test_size=0.60)\n",
"\n",
"_, _, X_train, _, y_train = et.get_train()\n",
"\n",
"clf_rf = RandomForestClassifier(criterion='entropy', max_depth=7)\n",
"\n",
"clf_rf.fit(X_train, \n",
" y_train)\n",
"\n",
"et.add_prediction(pred_method='RFC', \n",
" pred=clf_rf.predict(et.features), \n",
" true_labels=True)\n",
"\n",
"et.plt_pred_with_tsne_plotly(pred_methods=['RFC'], what='all', verb=True, inline=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Done."
]
}
],
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",
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",
"encoding": "base64",
"path": [
"_data",
0,
"x",
"value"
]
},
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FIUDD9Shcj8IdQEfhehSuRyNAH4XrUbgeI0BI4XoUrkcZQPYoXI/C9RhAAAAAAAAAUUBwPQrXo3AjQNejcD0K1yNAMzMzMzMzI0AzMzMzMzMjQFyPwvUoXCNAhetRuB6FI0AzMzMzMzMjQGZmZmZmZiJAXI/C9ShcI0BwPQrXo3AjQNijcD0K1yFAcT0K16NwGUCF61G4HoUjQHA9CtejcCNAo3A9CtejIEDXo3A9CtcjQLkehetRuB5Aj8L1KFyPGkBcj8L1KFwjQFyPwvUoXCFAR+F6FK5HI0DrUbgehesdQOtRuB6F6yFAXI/C9ShcH0DhehSuR+EgQFyPwvUoXCNAcD0K16NwI0Bcj8L1KFwjQFyPwvUoXCNAcT0K16NwHUDiehSuR+EiQAvXo3A9CiNAXI/C9ShcI0ApXI/C9SggQMP1KFyPwiFAH4XrUbgeI0AfhetRuB4jQHA9CtejcCNAj8L1KFyPIED2KFyPwvUiQOxRuB6F6yNAcT0K16NwHUBH4XoUrkcjQFyPwvUoXCNASOF6FK5HHUAzMzMzMzMjQEfhehSuRyNAXI/C9ShcH0CamZmZmZkZQFyPwvUoXCNAZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/ZmZmZmZm9j/D9Shcj8L1P65H4XoUrvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z9mZmZmZmb2P2ZmZmZmZvY/w/UoXI/C9T8L16NwPQr3Px+F61G4HvU/C9ejcD0K9z8L16NwPQr3P65H4XoUrvc/61G4HoXr8T8L16NwPQr3P8P1KFyPwvU/ZmZmZmZm9j9mZmZmZmb2PwvXo3A9Cvc/ZmZmZmZm9j9mZmZmZmb2PwvXo3A9Cvc/w/UoXI/C9T9mZmZmZmb2PwvXo3A9Cvc/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/rkfhehSu9z+uR+F6FK73PwvXo3A9Cvc/rkfhehSu9z/D9Shcj8L1PwvXo3A9Cvc/C9ejcD0K9z8fhetRuB71PwvXo3A9Cvc/rkfhehSu9z9mZmZmZmb2PwvXo3A9Cvc/mpmZmZmZ+T+uR+F6FK73PwvXo3A9Cvc/rkfhehSu9z8L16NwPQr3P65H4XoUrvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/ZmZmZmZm9j8L16NwPQr3P65H4XoUrvc/C9ejcD0K9z8L16NwPQr3P2ZmZmZmZvY/w/UoXI/C9T8L16NwPQr3PwvXo3A9Cvc/H4XrUbge9T+uR+F6FK73PwvXo3A9Cvc/ZmZmZmZm9j8L16NwPQr3P65H4XoUrvc/rkfhehSu9z8L16NwPQr3PwvXo3A9Cvc/w/UoXI/C9T8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P65H4XoUrvc/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/rkfhehSu9z9mZmZmZmb2P65H4XoUrvc/C9ejcD0K9z8fhetRuB71P2ZmZmZmZvY/C9ejcD0K9z8L16NwPQr3P5qZmZmZmfk/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z9mZmZmZmb2PwvXo3A9Cvc/w/UoXI/C9T/D9Shcj8L1P3sUrkfhevQ/w/UoXI/C9T9mZmZmZmb2PwvXo3A9Cvc/w/UoXI/C9T/D9Shcj8L1P65H4XoUrvc/C9ejcD0K9z/D9Shcj8L1PwvXo3A9Cvc/ZmZmZmZm9j8L16NwPQr3Px+F61G4HvU/C9ejcD0K9z/D9Shcj8L1P2ZmZmZmZvY/rkfhehSu9z8L16NwPQr3P1K4HoXrUfg/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P2ZmZmZmZvY/C9ejcD0K9z8L16NwPQr3P65H4XoUrvc/rkfhehSu9z8L16NwPQr3PwvXo3A9Cvc/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z9mZmZmZmb2PwvXo3A9Cvc/w/UoXI/C9T+uR+F6FK73P2ZmZmZmZvY/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/ZmZmZmZm9j89CtejcD36P2ZmZmZmZvY/w/UoXI/C9T8L16NwPQr3Px+F61G4HvU/H4XrUbge9T+uR+F6FK4pQGZmZmZmZvY/UrgehetR+D9mZmZmZmb2P2ZmZmZmZvY/w/UoXI/C9T9mZmZmZmb2Px+F61G4HvU/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P2ZmZmZmZvY/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/rkfhehSu9z8L16NwPQr3P2ZmZmZmZvY/H4XrUbge9T8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z/D9Shcj8L1PwvXo3A9Cvc/rkfhehSu9z8L16NwPQr3PwvXo3A9Cvc/rkfhehSu9z9mZmZmZmb2P8P1KFyPwvU/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/H4XrUbge9T8L16NwPQr3Px+F61G4HvU/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/C9ejcD0K9z8fhetRuB71P65H4XoUrvc/rkfhehSu9z8L16NwPQr3PwvXo3A9Cvc/rkfhehSu9z+uR+F6FK73P2ZmZmZmZvY/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/rkfhehSu9z+uR+F6FK4HQMP1KFyPwvU/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/w/UoXI/C9T8L16NwPQr3PwvXo3A9Cvc/w/UoXI/C9T8L16NwPQr3PwvXo3A9Cvc/ZmZmZmZm9j+uR+F6FK73PwvXo3A9Cvc/mpmZmZmZ+T+uR+F6FK73P2ZmZmZmZvY/w/UoXI/C9T8L16NwPQr3P65H4XoUrvc/H4XrUbge9T8L16NwPQr3P65H4XoUrvc/ZmZmZmZm9j8L16NwPQr3P65H4XoUrvc/C9ejcD0K9z/D9Shcj8L1P8P1KFyPwvU/rkfhehSu9z/D9Shcj8L1PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P65H4XoUrvc/ZmZmZmZm9j8L16NwPQr3P8P1KFyPwvU/ZmZmZmZm9j+uR+F6FK73P2ZmZmZmZvY/C9ejcD0K9z8L16NwPQr3P8P1KFyPwvU/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/C9ejcD0K9z9mZmZmZmb2PwvXo3A9Cvc/C9ejcD0K9z+uR+F6FK73P2ZmZmZmZvY/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/mpmZmZmZ+T8L16NwPQr3PwvXo3A9Cvc/w/UoXI/C9T/D9Shcj8L1PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P2ZmZmZmZvY/C9ejcD0K9z8L16NwPQr3P2ZmZmZmZvY/rkfhehSu9z8fhetRuB71PwvXo3A9Cvc/C9ejcD0K9z9SuB6F61H4PwvXo3A9Cvc/4noUrkfhAkDD9Shcj8L1PwvXo3A9Cvc/ZmZmZmZm9j+uR+F6FK73P2ZmZmZmZvY/w/UoXI/C9T8L16NwPQr3P8P1KFyPwvU/C9ejcD0K9z8L16NwPQr3Px+F61G4HvU/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3Px+F61G4HvU/ZmZmZmZm9j8L16NwPQr3Px+F61G4HvU/rkfhehSu9z/D9Shcj8L1P8P1KFyPwvU/ZmZmZmZm9j8L16NwPQr3P65H4XoUrvc/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/rkfhehSu9z9mZmZmZmb2P65H4XoUrvc/rkfhehSu9z8L16NwPQr3P8P1KFyPwvU/C9ejcD0K9z/D9Shcj8L1Px+F61G4HvU/ZmZmZmZm9j9mZmZmZmb2P65H4XoUrvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P65H4XoUrvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z+uR+F6FK73PwvXo3A9Cvc/ZmZmZmZm9j8L16NwPQr3P65H4XoUrvc/C9ejcD0K9z8fhetRuB71P8P1KFyPwvU/ZmZmZmZm9j8L16NwPQr3Px+F61G4HvU/C9ejcD0K9z/D9Shcj8L1P65H4XoUrvc/ZmZmZmZm9j+uR+F6FK73PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3P8P1KFyPwvU/C9ejcD0K9z8L16NwPQr3P2ZmZmZmZvY/ZmZmZmZm9j9mZmZmZmb2PwvXo3A9Cvc/C9ejcD0K9z9mZmZmZmb2P65H4XoUrvc/rkfhehSu9z8L16NwPQr3P2ZmZmZmZvY/C9ejcD0K9z/D9Shcj8L1P2ZmZmZmZvY/H4XrUbge9T9SuB6F61H4PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/w/UoXI/C9T8L16NwPQr3P8P1KFyPwvU/w/UoXI/C9T8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PwvXo3A9Cvc/C9ejcD0K9z8L16NwPQr3PxSuR+F6FP4/ZmZmZmZm9j8L16NwPQr3PwvXo3A9Cvc/H4XrUbge9T9mZmZ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