{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K4CV : 4-circle kappa diffractometer example\n",
"\n",
"The kappa geometry replaces the traditional $\\chi$-ring on a 4-circle\n",
"diffractometer with an alternative kappa stage that holds the phi stage. The kappa stage is tilted at angle $\\alpha$ (typically 50 degrees) from the $\\omega$ stage.\n",
"\n",
"----\n",
"\n",
"Note: This example is available as a\n",
"[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n",
"code website: https://github.com/bluesky/hklpy/tree/main/docs/source/examples/notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup the *K4CV* diffractometer in *hklpy*\n",
"\n",
"In _hkl_ *K4CV* geometry (https://people.debian.org/~picca/hkl/hkl.html#org723c5b9):\n",
"\n",
"![K4CV geometry](resources/k4cv.png)\n",
"\n",
"For this geometry there is a special parameter $\\alpha$, the angle between the kappa rotation axis and the $\\vec{y}$ direction.\n",
"\n",
"axis | moves | rotation axis | vector\n",
"--- | :--- | :---: | :---\n",
"komega | sample | $-\\vec{y}$ | `[0 -1 0]`\n",
"kappa | sample | $\\vec{x}$ | `[0 -0.6428 -0.7660]`\n",
"kphi | sample | $-\\vec{y}$ | `[0 -1 0]`\n",
"tth | detector | $-\\vec{y}$ | `[0 -1 0]`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Define _this_ diffractometer\n",
"\n",
"Create a Python class that specifies the names of the \n",
"real-space positioners. We call it `KappaFourCircle` here but that\n",
"choice is arbitrary. Pick any valid Python name not already in use.\n",
"The convention is to start Python class names with a capital letter\n",
"and use CamelCase to mark word starts.\n",
"\n",
"The argument to the `KappaFourCircle` class tells which *hklpy* base\n",
"class will be used. This sets the geometry. (The class we show\n",
"here could be replaced entirely with `hkl.geometries.SimulatedK4CV`\n",
"but we choose to show here the complete steps to produce that class.)\n",
"The [*hklpy*\n",
"documentation](https://blueskyproject.io/hklpy/geometries.html?highlight=geometries#module-hkl.geometries)\n",
"provides a complete list of diffractometer geometries.\n",
"\n",
"\n",
"In *hklpy*, the reciprocal-space axes\n",
"are known as `pseudo` positioners while the real-space axes\n",
"are known as `real` positioners. For the real positioners,\n",
"it is possible to use different names than the canonical names\n",
"used internally by the *hkl* library. That is not covered here.\n",
"\n",
"Note: The keyword argument `kind=\"hinted\"` is an indication\n",
"that this signal may be plotted.\n",
"\n",
"This `KappaFourCircle()` class example uses simulated motors. See the drop-down\n",
"for an example how to use EPICS motors.\n",
"\n",
"\n",
" KappaFourCircle() class using EPICS motors
\n",
"\n",
"\n",
"```python\n",
"\n",
"from hkl import K4CV\n",
"from ophyd import EpicsMotor, PseudoSingle\n",
"from ophyd import Component as Cpt\n",
"\n",
"class KappaFourCircle(K4CV):\n",
" \"\"\"\n",
" Our kappa 4-circle. Vertical scattering orientation.\n",
" \"\"\"\n",
" # the reciprocal axes are called \"pseudo\" in hklpy\n",
" h = Cpt(PseudoSingle, '', kind=\"hinted\")\n",
" k = Cpt(PseudoSingle, '', kind=\"hinted\")\n",
" l = Cpt(PseudoSingle, '', kind=\"hinted\")\n",
"\n",
" # the motor axes are called \"real\" in hklpy\n",
" komega = Cpt(EpicsMotor, \"pv_prefix:m41\", kind=\"hinted\")\n",
" kappa = Cpt(EpicsMotor, \"pv_prefix:m22\", kind=\"hinted\")\n",
" kphi = Cpt(EpicsMotor, \"pv_prefix:m35\", kind=\"hinted\")\n",
" tth = Cpt(EpicsMotor, \"pv_prefix:m7\", kind=\"hinted\")\n",
"```\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from hkl import K4CV, SimMixin\n",
"from ophyd import SoftPositioner\n",
"from ophyd import Component as Cpt\n",
"\n",
"class KappaFourCircle(SimMixin, K4CV):\n",
" \"\"\"\n",
" Our kappa 4-circle. Vertical scattering orientation.\n",
" \"\"\"\n",
" # the reciprocal axes are defined by SimMixin\n",
"\n",
" komega = Cpt(SoftPositioner, kind=\"hinted\", init_pos=0)\n",
" kappa = Cpt(SoftPositioner, kind=\"hinted\", init_pos=0)\n",
" kphi = Cpt(SoftPositioner, kind=\"hinted\", init_pos=0)\n",
" tth = Cpt(SoftPositioner, kind=\"hinted\", init_pos=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the Python diffractometer object (`sixc`) using the `SixCircle()` class. By convention, the `name` keyword is the same as the object name."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"k4cv = KappaFourCircle(\"\", name=\"k4cv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add a sample with a crystal structure"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"HklSample(name='silicon', lattice=LatticeTuple(a=5.431020511, b=5.431020511, c=5.431020511, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), UB=array([[ 1.15690694e+00, -7.08401189e-17, -7.08401189e-17],\n",
" [ 0.00000000e+00, 1.15690694e+00, -7.08401189e-17],\n",
" [ 0.00000000e+00, 0.00000000e+00, 1.15690694e+00]]), reflections=[])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from hkl import Lattice\n",
"from hkl import SI_LATTICE_PARAMETER\n",
"\n",
"# add the sample to the calculation engine\n",
"a0 = SI_LATTICE_PARAMETER\n",
"k4cv.calc.new_sample(\n",
" \"silicon\",\n",
" lattice=Lattice(a=a0, b=a0, c=a0, alpha=90, beta=90, gamma=90)\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup the UB orientation matrix using *hklpy*\n",
"\n",
"Define the crystal's orientation on the diffractometer using \n",
"the 2-reflection method described by [Busing & Levy, Acta Cryst 22 (1967) 457](https://www.psi.ch/sites/default/files/import/sinq/zebra/PracticalsEN/1967-Busing-Levy-3-4-circle-Acta22.pdf).\n",
"\n",
"### Set the same X-ray wavelength for both reflections, by setting the diffractometer energy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from hkl import A_KEV\n",
"k4cv.energy.put(A_KEV / 1.54) # (8.0509 keV)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the first reflection and identify its Miller indices: (_hkl_)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"r1 = k4cv.calc.sample.add_reflection(\n",
" 4, 0, 0,\n",
" position=k4cv.calc.Position(\n",
" tth=-69.0966,\n",
" komega=55.4507,\n",
" kappa=0,\n",
" kphi=-90,\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the second reflection"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"r2 = k4cv.calc.sample.add_reflection(\n",
" 0, 4, 0,\n",
" position=k4cv.calc.Position(\n",
" tth=-69.0966,\n",
" komega=-1.5950,\n",
" kappa=134.7568,\n",
" kphi=123.3554\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compute the *UB* orientation matrix\n",
"\n",
"The `add_reflection()` method uses the current wavelength at the time it is called. (To add reflections at different wavelengths, change the wavelength _before_ calling `add_reflection()` each time.) The `compute_UB()` method returns the computed **UB** matrix. Ignore it here."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2.01918352e-05, -7.20401174e-06, -1.15690694e+00],\n",
" [ 0.00000000e+00, -1.15690694e+00, 7.20401174e-06],\n",
" [-1.15690694e+00, -1.25733653e-10, -2.01918352e-05]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k4cv.calc.sample.compute_UB(r1, r2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Report what we have setup"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"===================== =================================================================================\n",
"term value \n",
"===================== =================================================================================\n",
"diffractometer k4cv \n",
"geometry K4CV \n",
"class KappaFourCircle \n",
"energy (keV) 8.05092 \n",
"wavelength (angstrom) 1.54000 \n",
"calc engine hkl \n",
"mode bissector \n",
"positions ====== ======= \n",
" name value \n",
" ====== ======= \n",
" komega 0.00000 \n",
" kappa 0.00000 \n",
" kphi 0.00000 \n",
" tth 0.00000 \n",
" ====== ======= \n",
"constraints ====== ========= ========== ===== ==== \n",
" axis low_limit high_limit value fit \n",
" ====== ========= ========== ===== ==== \n",
" komega -180.0 180.0 0.0 True \n",
" kappa -180.0 180.0 0.0 True \n",
" kphi -180.0 180.0 0.0 True \n",
" tth -180.0 180.0 0.0 True \n",
" ====== ========= ========== ===== ==== \n",
"sample: silicon ================= ===============================================================\n",
" term value \n",
" ================= ===============================================================\n",
" unit cell edges a=5.431020511, b=5.431020511, c=5.431020511 \n",
" unit cell angles alpha=90.0, beta=90.0, gamma=90.0 \n",
" ref 1 (hkl) h=4.0, k=0.0, l=0.0 \n",
" ref 1 positioners komega=55.45070, kappa=0.00000, kphi=-90.00000, tth=-69.09660 \n",
" ref 2 (hkl) h=0.0, k=4.0, l=0.0 \n",
" ref 2 positioners komega=-1.59500, kappa=134.75680, kphi=123.35540, tth=-69.09660\n",
" [U] [[ 1.74532925e-05 -6.22695871e-06 -1.00000000e+00] \n",
" [ 0.00000000e+00 -1.00000000e+00 6.22695872e-06] \n",
" [-1.00000000e+00 -1.08680932e-10 -1.74532925e-05]] \n",
" [UB] [[ 2.01918352e-05 -7.20401174e-06 -1.15690694e+00] \n",
" [ 0.00000000e+00 -1.15690694e+00 7.20401174e-06] \n",
" [-1.15690694e+00 -1.25733653e-10 -2.01918352e-05]] \n",
" ================= ===============================================================\n",
"===================== =================================================================================\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k4cv.pa()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the orientation matrix\n",
"\n",
"Perform checks with _forward_ (hkl to angle) and\n",
"_inverse_ (angle to hkl) computations to verify the diffractometer\n",
"will move to the same positions where the reflections were identified.\n",
"\n",
"### Constrain one of the motors to a limited range\n",
"\n",
"* keep `kphi` less than or equal to zero (allowing for small roundoff)\n",
"\n",
"First, we apply constraints directly to the `calc`-level support."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"====== ========= ========== ===== ====\n",
"axis low_limit high_limit value fit \n",
"====== ========= ========== ===== ====\n",
"komega -180.0 180.0 0.0 True\n",
"kappa -180.0 180.0 0.0 True\n",
"kphi -180.0 0.001 0.0 True\n",
"tth -180.0 180.0 0.0 True\n",
"====== ========= ========== ===== ====\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k4cv.calc[\"kphi\"].limits = (-180, 0.001)\n",
"k4cv.show_constraints()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we show how to use additional methods of `Diffractometer()` that support *undo* and *reset* features for applied constraints. The support is based on a *stack* (a Python list). A set of constraints is added (`apply_constraints()`) or removed (`undo_last_constraints()`) from the stack. Or, the stack can be cleared (`reset_constraints()`).\n",
"\n",
"method | what happens\n",
":--- | :---\n",
"`apply_constraints()` | Add a set of constraints and use them\n",
"`undo_last_constraints()` | Remove the most-recent set of constraints and restore the previous one from the stack.\n",
"`reset_constraints()` | Set constraints back to initial settings.\n",
"`show_constraints()` | Print the current constraints in a table.\n",
"\n",
"A set of constraints is a Python dictionary that uses the real positioner names (the motors) as the keys. Only those constraints with changes need be added to the dictionary but it is permissable to describe all the real positioners. Each value in the dictionary is a [hkl.diffract.Constraint](https://blueskyproject.io/hklpy/diffract.html#hkl.diffract.Constraint), where the values are specified in this order: `low_limit, high_limit, value, fit`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply new constraints using the [applyConstraints()](https://blueskyproject.io/hklpy/diffract.html#hkl.diffract.Diffractometer.apply_constraints) method. These *add* to the existing constraints, as shown in the table."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"====== ========= ========== ===== ====\n",
"axis low_limit high_limit value fit \n",
"====== ========= ========== ===== ====\n",
"komega -0.001 180.0 0.0 True\n",
"kappa -90.0 90.0 0.0 True\n",
"kphi -180.0 0.001 0.0 True\n",
"tth -180.0 180.0 0.0 True\n",
"====== ========= ========== ===== ====\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from hkl import Constraint\n",
"k4cv.apply_constraints(\n",
" {\n",
" \"komega\": Constraint(-0.001, 180, 0, True),\n",
" \"kappa\": Constraint(-90, 90, 0, True),\n",
" }\n",
")\n",
"k4cv.show_constraints()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then remove (undo) those new additions."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"====== ========= ========== ===== ====\n",
"axis low_limit high_limit value fit \n",
"====== ========= ========== ===== ====\n",
"komega -180.0 180.0 0.0 True\n",
"kappa -180.0 180.0 0.0 True\n",
"kphi -180.0 0.001 0.0 True\n",
"tth -180.0 180.0 0.0 True\n",
"====== ========= ========== ===== ====\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k4cv.undo_last_constraints()\n",
"k4cv.show_constraints()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use `bissector` mode\n",
"\n",
"where `tth` = 2*`omega`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"k4cv.engine.mode = \"bissector\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (400) reflection test\n",
"\n",
"1. Check the `inverse` (angles -> (_hkl_)) computation.\n",
"1. Check the `forward` ((_hkl_) -> angles) computation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the inverse calculation: (400)\n",
"\n",
"To calculate the (_hkl_) corresponding to a given set of motor angles,\n",
"call `k4cv.inverse((h, k, l))`. Note the second set of parentheses needed by this function.\n",
"\n",
"The values are specified, without names, in the order specified\n",
"by `k4cv.calc.physical_axis_names`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"axis names: ['komega', 'kappa', 'kphi', 'tth']\n"
]
}
],
"source": [
"print(\"axis names:\", k4cv.calc.physical_axis_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, proceed with the inverse calculation."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4 0 0) ? 4.00 -0.00 -0.00\n"
]
}
],
"source": [
"sol = k4cv.inverse((55.4507, 0, -90, -69.0966))\n",
"print(f\"(4 0 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the forward calculation: (400)\n",
"\n",
"Compute the angles necessary to position the diffractometer\n",
"for the given reflection.\n",
"\n",
"Note that for the forward computation, more than one set of angles may be used to reach the same crystal reflection. This test will report the *default* selection. The *default* selection (which may be changed through methods described in the `hkl.calc` module) is the first solution.\n",
"\n",
"function | returns\n",
"--- | ---\n",
"`k4cv.forward()` | The *default* solution\n",
"`k4cv.calc.forward()` | List of all allowed solutions."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400) : tth=-69.0982 komega=55.4509 kappa=0.0000 kphi=-90.0010\n"
]
}
],
"source": [
"sol = k4cv.forward((4, 0, 0))\n",
"print(\n",
" \"(400) :\", \n",
" f\"tth={sol.tth:.4f}\", \n",
" f\"komega={sol.komega:.4f}\", \n",
" f\"kappa={sol.kappa:.4f}\", \n",
" f\"kphi={sol.kphi:.4f}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (040) reflection test\n",
"\n",
"Repeat the `inverse` and `forward` calculations for the\n",
"second orientation reflection."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the inverse calculation: (040)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0 4 0) ? -0.00 4.00 0.00\n"
]
}
],
"source": [
"sol = k4cv.inverse((-1.5950, 134.7568, 123.3554, -69.0966))\n",
"print(f\"(0 4 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the forward calculation: (040)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(040) : tth=-69.0982 komega=-1.5937 kappa=134.7551 kphi=-57.0461\n"
]
}
],
"source": [
"sol = k4cv.forward((0, 4, 0))\n",
"print(\n",
" \"(040) :\", \n",
" f\"tth={sol.tth:.4f}\", \n",
" f\"komega={sol.komega:.4f}\", \n",
" f\"kappa={sol.kappa:.4f}\", \n",
" f\"kphi={sol.kphi:.4f}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scan in reciprocal space using Bluesky\n",
"\n",
"To scan with Bluesky, we need more setup."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from bluesky import RunEngine\n",
"from bluesky import SupplementalData\n",
"from bluesky.callbacks.best_effort import BestEffortCallback\n",
"from bluesky.magics import BlueskyMagics\n",
"import bluesky.plans as bp\n",
"import bluesky.plan_stubs as bps\n",
"import databroker\n",
"from IPython import get_ipython\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.ion()\n",
"\n",
"bec = BestEffortCallback()\n",
"db = databroker.temp().v1\n",
"sd = SupplementalData()\n",
"\n",
"get_ipython().register_magics(BlueskyMagics)\n",
"\n",
"RE = RunEngine({})\n",
"RE.md = {}\n",
"RE.preprocessors.append(sd)\n",
"RE.subscribe(db.insert)\n",
"RE.subscribe(bec)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (_h00_) scan near (400)\n",
"\n",
"In this example, we have no detector. Still, we add the diffractometer\n",
"object in the detector list so that the _hkl_ and motor positions will appear\n",
"as columns in the table."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Transient Scan ID: 1 Time: 2023-11-20 21:59:43\n",
"Persistent Unique Scan ID: '15166a92-8209-484a-af30-ee7ea176a796'\n",
"New stream: 'primary'\n",
"+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n",
"| seq_num | time | k4cv_h | k4cv_k | k4cv_l | k4cv_komega | k4cv_kappa | k4cv_kphi | k4cv_tth |\n",
"+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n",
"| 1 | 21:59:43.4 | 3.900 | -0.000 | -0.000 | 56.431 | -0.000 | -90.001 | -67.137 |\n",
"| 2 | 21:59:44.0 | 3.950 | -0.000 | 0.000 | 55.943 | -0.000 | -90.001 | -68.115 |\n",
"| 3 | 21:59:44.6 | 4.000 | -0.000 | -0.000 | 55.451 | 0.000 | -90.001 | -69.098 |\n",
"| 4 | 21:59:45.2 | 4.050 | -0.000 | 0.000 | 54.956 | -0.000 | -90.001 | -70.087 |\n",
"| 5 | 21:59:45.8 | 4.100 | 0.000 | 0.000 | 54.459 | -0.000 | -90.001 | -71.083 |\n",
"+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n",
"generator scan ['15166a92'] (scan num: 1)\n",
"\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"('15166a92-8209-484a-af30-ee7ea176a796',)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
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