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jonwrightjonwright
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pixel by pixel fitting work in progress
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‎test/pixelmapper/CeO2.par

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‎test/pixelmapper/filter_weak.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "aa2bdb20-738b-4640-a8fb-31c8525e894f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import h5py, numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a89f83e4-8b56-4929-a7a1-d8abbe6ace5c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.1 <class 'h5py._hl.group.Group'>\n",
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"1.1/col (3626536,) uint16\n",
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"1.1/frame (3626536,) uint32\n",
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"1.1/instrument <class 'h5py._hl.group.Group'>\n",
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"1.1/instrument/positioners <class 'h5py._hl.group.Group'>\n",
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"1.1/instrument/positioners/dty () float64\n",
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"1.1/instrument/positioners/px () float64\n",
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"1.1/instrument/positioners/py () float64\n",
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"1.1/instrument/positioners/pz () float64\n",
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"1.1/instrument/positioners/rot (180500,) float64\n",
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"1.1/instrument/positioners/shtx () float64\n",
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"1.1/instrument/positioners/shty () float64\n",
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"1.1/instrument/positioners/shtz () float64\n",
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"1.1/intensity (3626536,) uint16\n",
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"1.1/measurement <class 'h5py._hl.group.Group'>\n",
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"1.1/measurement/rot (180500,) float64\n",
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"1.1/measurement/rot_center (180500,) float64\n",
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"1.1/nnz (180500,) uint32\n",
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"1.1/row (3626536,) uint16\n"
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]
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}
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],
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"source": [
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"with h5py.File('silicon_fullscan_sparse.h5','r') as hin:\n",
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" def v(n,x):\n",
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" if type(x) is h5py.Dataset:\n",
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" print(n,x.shape, x.dtype)\n",
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" else:\n",
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" print(n, type(x))\n",
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" hin.visititems(v)\n",
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" s = hin['1.1']\n",
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" col = s['col'][:]\n",
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" row = s['row'][:]\n",
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" frame = s['frame'][:]\n",
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" nnz=s['nnz'][:]\n",
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" intensity=s['intensity'][:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "de48f01f-ac92-4103-96f3-0f1d0946b046",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#import pylab as pl, numpy as np\n",
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"#pl.hist2d( col, np.log(intensity), bins=(1000,32), norm='log');"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f69df2e7-d0cf-46a7-b3d3-de2acb3e29a3",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(180500,) (180500,)\n"
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]
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}
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],
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"source": [
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"mask = intensity > 9\n",
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"snew = { '1.1/row' : row[mask], \n",
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" '1.1/intensity' : intensity[mask], \n",
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" '1.1/col' : col[mask], \n",
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" '1.1/frame' : frame[mask] } \n",
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"snew['1.1/nnz'] = np.bincount( snew['1.1/frame'], minlength = nnz.shape[0] )\n",
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"print( nnz.shape, snew['1.1/nnz'].shape )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "1f76ee1b-a944-4c7f-b444-d5fb758152b6",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(4, 180465, (180500,))"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"frame.min(), frame.max(), nnz.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "ed82e4d8-265f-467d-847f-1a2632fd1f57",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"skips = \"1.1/instrument/positioners/rot\", \"1.1/measurement/rot\",\n",
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"\n",
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"with h5py.File('silicon_fullscan_sparse.h5','r') as hin:\n",
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" with h5py.File('silicon_fullscan_strong_sparse.h5','w') as hout:\n",
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" def v(n,x):\n",
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" if n in skips:\n",
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" return\n",
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" if type(x) is h5py.Group:\n",
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" hout.require_group( n )\n",
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" hout[n].attrs.update( hin[n].attrs )\n",
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" if type(x) is h5py.Dataset:\n",
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" if n in snew:\n",
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" a = snew[n][()]\n",
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" else:\n",
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" a = hin[n][()]\n",
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" if n.find('rot_center')>0:\n",
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" a = np.round(a*1e5)/1e5\n",
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" if len(a.shape):\n",
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" cmp = { 'compression' : 'gzip', 'compression_opts' : 9 }\n",
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" else:\n",
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" cmp = {}\n",
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" hout.create_dataset( name = n,\n",
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" shape = a.shape,\n",
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" chunks= a.shape,\n",
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" dtype = a.dtype,\n",
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" data = a,\n",
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" ** cmp\n",
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" )\n",
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" hout[n].attrs.update( hin[n].attrs )\n",
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" hin.visititems( v )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a566f203-18bb-46d0-aa2c-4df1bf8c6b91",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (main)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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