Update and import tutorials
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Jakob Lass committed
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@@ -8,4 +8,5 @@ maxdepth: 1
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caption: Tutorials:
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---
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simulated_data.ipynb
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Simulate_MnF2_data.ipynb
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```
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@@ -74,7 +74,7 @@
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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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"execution_count": 3,
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"id": "b1e107a4-306c-49dd-95d9-732ba4284c1e",
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"metadata": {},
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"outputs": [],
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@@ -112,7 +112,7 @@
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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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"execution_count": 4,
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"id": "9180b624-f76b-4d62-b87c-bda5388ef3f9",
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"metadata": {},
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"outputs": [],
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@@ -140,7 +140,7 @@
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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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"execution_count": 5,
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"id": "99d07fc8-250c-4870-bfa8-a68dd212ad95",
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"metadata": {},
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"outputs": [
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@@ -198,7 +198,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 6,
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"id": "456b2e68-9d32-4e78-9575-e6a2bcf7e9e5",
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"metadata": {},
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"outputs": [
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@@ -208,13 +208,13 @@
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"Text(0, 0.5, 'L [r.l.u.]')"
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]
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},
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"execution_count": 27,
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"execution_count": 6,
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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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"data": {
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
|
||||
@@ -231,6 +231,7 @@
|
||||
"EIdx = np.argmin(np.abs(E-energy))\n",
|
||||
"ax.pcolormesh(H[:, :, EIdx], L[:, :, EIdx], I[:, :, EIdx], vmin=0, vmax = 50)\n",
|
||||
"\n",
|
||||
"ax.axis('equal')\n",
|
||||
"ax.set_title('E = {:.2f} meV'.format(e[EIdx]))\n",
|
||||
"ax.set_xlabel('H [r.l.u.]')\n",
|
||||
"ax.set_ylabel('L [r.l.u.]')"
|
||||
@@ -246,7 +247,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 7,
|
||||
"id": "5484ba62-d027-408b-9e34-e0f0e0ab4528",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -256,13 +257,13 @@
|
||||
"Text(0, 0.5, 'E [meV]')"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
@@ -292,7 +293,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"execution_count": 8,
|
||||
"id": "3a58ab49",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -330,7 +331,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 9,
|
||||
"id": "60e2fbf2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -339,21 +340,21 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Test - ( 0.1 )\n",
|
||||
"RMSE - ( 4.4478 0.1 55.50635141061015 ) : 3.4844415\n",
|
||||
"RMSE - ( 4.4478 0.1 55.50269024150275 ) : 3.486001\n",
|
||||
"Test - ( 1.0 )\n",
|
||||
"RMSE - ( 4.4478 1.0 55.50635141061015 ) : 3.4835355\n",
|
||||
"RMSE - ( 4.4478 1.0 55.50269024150275 ) : 3.4850485\n",
|
||||
"Test - ( 10.0 )\n",
|
||||
"RMSE - ( 4.4478 10.0 55.50635141061015 ) : 3.4762373\n",
|
||||
"RMSE - ( 4.4478 10.0 55.50269024150275 ) : 3.47741\n",
|
||||
"Test - ( 100.0 )\n",
|
||||
"RMSE - ( 4.4478 100.0 55.50635141061015 ) : 3.4504814\n",
|
||||
"RMSE - ( 4.4478 100.0 55.50269024150275 ) : 3.4501917\n",
|
||||
"Test - ( 200.0 )\n",
|
||||
"RMSE - ( 4.4478 200.0 55.50635141061015 ) : 3.443771\n",
|
||||
"RMSE - ( 4.4478 200.0 55.50269024150275 ) : 3.4432225\n",
|
||||
"Test - ( 300.0 )\n",
|
||||
"RMSE - ( 4.4478 300.0 55.50635141061015 ) : 3.4432797\n",
|
||||
"RMSE - ( 4.4478 300.0 55.50269024150275 ) : 3.4426384\n",
|
||||
"Test - ( 400.0 )\n",
|
||||
"RMSE - ( 4.4478 400.0 55.50635141061015 ) : 3.4455476\n",
|
||||
"RMSE - ( 4.4478 400.0 55.50269024150275 ) : 3.4448829\n",
|
||||
"Test - ( 500.0 )\n",
|
||||
"RMSE - ( 4.4478 500.0 55.50635141061015 ) : 3.44926\n"
|
||||
"RMSE - ( 4.4478 500.0 55.50269024150275 ) : 3.4486284\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -380,7 +381,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 10,
|
||||
"id": "1f9a8679",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -389,41 +390,41 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" Iteration 1\n",
|
||||
" Loss function: 24608190.0\n",
|
||||
" Loss function: 24616092.0\n",
|
||||
" Iteration 2\n",
|
||||
" Loss function: 18320822.0\n",
|
||||
" Loss function: 18311960.0\n",
|
||||
" Iteration 3\n",
|
||||
" Loss function: 15589522.0\n",
|
||||
" Loss function: 15576615.0\n",
|
||||
" Iteration 4\n",
|
||||
" Loss function: 14332520.0\n",
|
||||
" Loss function: 14319358.0\n",
|
||||
" Iteration 5\n",
|
||||
" Loss function: 13731277.0\n",
|
||||
" Loss function: 13718133.0\n",
|
||||
" Iteration 6\n",
|
||||
" Loss function: 13459373.0\n",
|
||||
" Loss function: 13445899.0\n",
|
||||
" Iteration 7\n",
|
||||
" Loss function: 13346343.0\n",
|
||||
" Loss function: 13333092.0\n",
|
||||
" Iteration 8\n",
|
||||
" Loss function: 13302241.0\n",
|
||||
" Loss function: 13288917.0\n",
|
||||
" Iteration 9\n",
|
||||
" Loss function: 13285422.0\n",
|
||||
" Loss function: 13271952.0\n",
|
||||
" Iteration 10\n",
|
||||
" Loss function: 13278990.0\n",
|
||||
" Loss function: 13265445.0\n",
|
||||
" Iteration 11\n",
|
||||
" Loss function: 13276512.0\n",
|
||||
" Loss function: 13262938.0\n",
|
||||
" Iteration 12\n",
|
||||
" Loss function: 13275553.0\n",
|
||||
" Loss function: 13261957.0\n",
|
||||
" Iteration 13\n",
|
||||
" Loss function: 13275184.0\n",
|
||||
" Loss function: 13261567.0\n",
|
||||
" Iteration 14\n",
|
||||
" Loss function: 13275036.0\n",
|
||||
" Loss function: 13261418.0\n",
|
||||
" Iteration 15\n",
|
||||
" Loss function: 13274984.0\n",
|
||||
" Loss function: 13261355.0\n",
|
||||
" Iteration 16\n",
|
||||
" Loss function: 13274959.0\n",
|
||||
" Loss function: 13261332.0\n",
|
||||
" Iteration 17\n",
|
||||
" Loss function: 13274950.0\n",
|
||||
" Loss function: 13261326.0\n",
|
||||
" Iteration 18\n",
|
||||
" Loss function: 13274948.0\n"
|
||||
" Loss function: 13261322.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -444,7 +445,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 11,
|
||||
"id": "15d23f11",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -472,13 +473,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"execution_count": 12,
|
||||
"id": "e9e74383",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 2000x900 with 3 Axes>"
|
||||
]
|
||||
@@ -501,6 +502,7 @@
|
||||
"EIdx = np.argmin(np.abs(E-energy))\n",
|
||||
"ax0.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_obs[:,:,EIdx],vmin=-2,vmax=50)\n",
|
||||
"\n",
|
||||
"ax0.axis('equal')\n",
|
||||
"ax0.set_xlabel('H [r.l.u.]')\n",
|
||||
"ax0.set_ylabel('L [r.l.u.]')\n",
|
||||
"ax0.set_title('Observations')\n",
|
||||
@@ -512,6 +514,7 @@
|
||||
"EIdx = np.argmin(np.abs(E-energy))\n",
|
||||
"p = ax1.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_sub[:,:,EIdx],vmin=-2,vmax=50)\n",
|
||||
"\n",
|
||||
"ax1.axis('equal')\n",
|
||||
"ax1.set_xlabel('H [r.l.u.]')\n",
|
||||
"ax1.set_ylabel('L [r.l.u.]')\n",
|
||||
"ax1.set_title('Subtracted signal')\n",
|
||||
@@ -523,13 +526,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 21,
|
||||
"id": "da0f7346",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 2000x900 with 3 Axes>"
|
||||
]
|
||||
@@ -539,10 +542,11 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig0 = plt.figure(figsize=(20, 9))\n",
|
||||
"fig0,axx = plt.subplots(figsize=(20, 9),ncols=2,nrows=1)\n",
|
||||
"\n",
|
||||
"# Plot 1: Observations Y\n",
|
||||
"ax0 = fig0.add_subplot(1, 2, 1)\n",
|
||||
"\n",
|
||||
"ax0 = axx[0]\n",
|
||||
"\n",
|
||||
"h_value = 0.25\n",
|
||||
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
||||
@@ -553,7 +557,7 @@
|
||||
"ax0.set_title('Observations')\n",
|
||||
"\n",
|
||||
"# Plot 2: Subtracted Y\n",
|
||||
"ax1 = fig0.add_subplot(1, 2, 2)\n",
|
||||
"ax1 = axx[1]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
||||
@@ -563,9 +567,10 @@
|
||||
"ax1.set_ylabel('E [meV]')\n",
|
||||
"ax1.set_title('Subtracted signal')\n",
|
||||
"\n",
|
||||
"fig0.colorbar(p)\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"fig0.colorbar(p,ax =axx, shrink=0.2, location='bottom')\n",
|
||||
"\n",
|
||||
"#plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
@@ -579,13 +584,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"execution_count": 14,
|
||||
"id": "12829c7d-fbfd-4e4e-b3a1-c759d83f834e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
|
||||
Reference in new issue
Block a user