"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"dp.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calibrating the Diffraction Pattern\n",
"\n",
"It is important that we keep track of the center of the diffraction pattern as well as the calibration. Lets consider the diffraction pattern above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dp.set_diffraction_calibration(1e-2) # known from simulation\n",
"dp.beam_energy = 300 #kEv\n",
"dp.unit = \"k_A^-1\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[########################################] | 100% Completed | 106.20 ms\n",
"[########################################] | 100% Completed | 110.89 ms\n"
]
}
],
"source": [
"shifts, aligned = dp.center_direct_beam(method=\"blur\",half_square_width=20,sigma=5, return_shifts=True, inplace=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"< Axes manager, axes: (10, 10|128, 128) >
\n",
"\n",
"\n",
" \n",
"Navigation axis name | \n",
"size | \n",
"index | \n",
"offset | \n",
"scale | \n",
"units |
\n",
" \n",
" | \n",
"10 | \n",
"0 | \n",
"0.0 | \n",
"1.0 | \n",
" |
\n",
" \n",
" | \n",
"10 | \n",
"0 | \n",
"0.0 | \n",
"1.0 | \n",
" |
\n",
"\n",
"\n",
" \n",
"Signal axis name | \n",
"size | \n",
" | \n",
"offset | \n",
"scale | \n",
"units |
\n",
" \n",
"kx | \n",
"128 | \n",
" | \n",
"-0.64 | \n",
"0.01 | \n",
"k_A^-1 |
\n",
" \n",
"ky | \n",
"128 | \n",
" | \n",
"-0.64 | \n",
"0.01 | \n",
"k_A^-1 |
\n"
],
"text/plain": [
"\n",
" Name | size | index | offset | scale | units \n",
"================ | ====== | ====== | ======= | ======= | ====== \n",
" | 10 | 0 | 0 | 1 | \n",
" | 10 | 0 | 0 | 1 | \n",
"---------------- | ------ | ------ | ------- | ------- | ------ \n",
" kx | 128 | 0 | -0.64 | 0.01 | k_A^-1 \n",
" ky | 128 | 0 | -0.64 | 0.01 | k_A^-1 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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