{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# E4CV : 4-circle diffractometer example\n",
"\n",
"The [IUCr provides a schematic of the 4-circle diffractometer](http://ww1.iucr.org/iucr-top/comm/cteach/pamphlets/2/node14.html) (in horizontal geometry typical of a laboratory instrument).\n",
"\n",
"\n",
"![E4CH geometry](resources/img69.gif)\n",
"\n",
"At X-ray synchrotrons, the vertical geometry is more common\n",
"due to the polarization of the X-rays.\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 *E4CV* diffractometer in *hklpy*\n",
"\n",
"In _hkl_ *E4CV* geometry (https://people.debian.org/~picca/hkl/hkl.html#org7ef08ba):\n",
"\n",
"![E4CV geometry](resources/3S+1D.png)\n",
"\n",
"* xrays incident on the $\\vec{x}$ direction (1, 0, 0)\n",
"\n",
"axis | moves | rotation axis | vector\n",
"--- | --- | --- | ---\n",
"omega | sample | $-\\vec{y}$ | `[0 -1 0]`\n",
"chi | sample | $\\vec{x}$ | `[1 0 0]`\n",
"phi | 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 `FourCircle` 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 `FourCircle()` 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.SimulatedE4CV`\n",
"but we choose to show here the complete steps to produce that class.)\n",
"The [*hklpy*\n",
"documentation](https://blueskyproject.io/hklpy/master/geometries.html)\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 `FourCircle()` class example uses simulated motors. See the drop-down\n",
"for an example how to use EPICS motors.\n",
"\n",
"\n",
" FourCircle() class using EPICS motors
\n",
"\n",
"\n",
"```python\n",
"\n",
"from hkl import E4CV\n",
"from ophyd import EpicsMotor, PseudoSingle\n",
"from ophyd import Component as Cpt\n",
"\n",
"class FourCircle(E4CV):\n",
" \"\"\"\n",
" Our 4-circle. Eulerian. 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",
" omega = Cpt(EpicsMotor, \"pv_prefix:m41\", kind=\"hinted\")\n",
" chi = Cpt(EpicsMotor, \"pv_prefix:m22\", kind=\"hinted\")\n",
" phi = 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 E4CV, SimMixin\n",
"from ophyd import SoftPositioner\n",
"from ophyd import Component as Cpt\n",
"\n",
"class FourCircle(SimMixin, E4CV):\n",
" \"\"\"\n",
" Our 4-circle. Eulerian, vertical scattering orientation.\n",
" \"\"\"\n",
" # the reciprocal axes are defined by SimMixin\n",
"\n",
" omega = Cpt(SoftPositioner, kind=\"hinted\", init_pos=0)\n",
" chi = Cpt(SoftPositioner, kind=\"hinted\", init_pos=0)\n",
" phi = 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 (`fourc`) using the `FourCircle()` class. By convention, the `name` keyword is the same as the object name."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fourc = FourCircle(\"\", name=\"fourc\")"
]
},
{
"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",
"fourc.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",
"fourc.energy.put(A_KEV / 1.54) # (8.0509 keV)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specify the first reflection and identify its Miller indices: (_hkl_)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"r1 = fourc.calc.sample.add_reflection(\n",
" 4, 0, 0,\n",
" position=fourc.calc.Position(\n",
" tth=69.0966,\n",
" omega=-145.451,\n",
" chi=0,\n",
" phi=0,\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specify the second reflection"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"r2 = fourc.calc.sample.add_reflection(\n",
" 0, 4, 0,\n",
" position=fourc.calc.Position(\n",
" tth=69.0966,\n",
" omega=-145.451,\n",
" chi=90,\n",
" phi=0,\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([[-1.41342846e-05, -1.41342846e-05, -1.15690694e+00],\n",
" [ 0.00000000e+00, -1.15690694e+00, 1.41342846e-05],\n",
" [-1.15690694e+00, 1.72682934e-10, 1.41342846e-05]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fourc.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 fourc \n",
"geometry E4CV \n",
"class FourCircle \n",
"energy (keV) 8.05092 \n",
"wavelength (angstrom) 1.54000 \n",
"calc engine hkl \n",
"mode bissector \n",
"positions ===== ======= \n",
" name value \n",
" ===== ======= \n",
" omega 0.00000 \n",
" chi 0.00000 \n",
" phi 0.00000 \n",
" tth 0.00000 \n",
" ===== ======= \n",
"constraints ===== ========= ========== ===== ==== \n",
" axis low_limit high_limit value fit \n",
" ===== ========= ========== ===== ==== \n",
" omega -180.0 180.0 0.0 True \n",
" chi -180.0 180.0 0.0 True \n",
" phi -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 omega=-145.45100, chi=0.00000, phi=0.00000, tth=69.09660 \n",
" ref 2 (hkl) h=0.0, k=4.0, l=0.0 \n",
" ref 2 positioners omega=-145.45100, chi=90.00000, phi=0.00000, tth=69.09660\n",
" [U] [[-1.22173048e-05 -1.22173048e-05 -1.00000000e+00] \n",
" [ 0.00000000e+00 -1.00000000e+00 1.22173048e-05] \n",
" [-1.00000000e+00 1.49262536e-10 1.22173048e-05]] \n",
" [UB] [[-1.41342846e-05 -1.41342846e-05 -1.15690694e+00] \n",
" [ 0.00000000e+00 -1.15690694e+00 1.41342846e-05] \n",
" [-1.15690694e+00 1.72682934e-10 1.41342846e-05]] \n",
" ================= =========================================================\n",
"===================== ===========================================================================\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fourc.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 the motors to limited ranges\n",
"\n",
"* allow for slight roundoff errors\n",
"* keep `tth` in the positive range\n",
"* keep `omega` in the negative range\n",
"* keep `phi` fixed at zero\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",
"omega -180.0 0.001 0.0 True\n",
"chi -180.0 180.0 0.0 True\n",
"phi -180.0 180.0 0.0 True\n",
"tth -0.001 180.0 0.0 True\n",
"===== ========= ========== ===== ====\n",
"\n"
]
}
],
"source": [
"fourc.calc[\"tth\"].limits = (-0.001, 180)\n",
"fourc.calc[\"omega\"].limits = (-180, 0.001)\n",
"fourc.show_constraints()\n",
"\n",
"fourc.phi.move(0)\n",
"fourc.engine.mode = \"constant_phi\""
]
},
{
"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",
"omega -180.0 0.001 0.0 True\n",
"chi -90.0 90.0 0.0 True\n",
"phi -180.0 180.0 0.0 True\n",
"tth -0.001 90.0 0.0 True\n",
"===== ========= ========== ===== ====\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from hkl import Constraint\n",
"fourc.apply_constraints(\n",
" {\n",
" \"tth\": Constraint(-0.001, 90, 0, True),\n",
" \"chi\": Constraint(-90, 90, 0, True),\n",
" }\n",
")\n",
"fourc.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",
"omega -180.0 0.001 0.0 True\n",
"chi -180.0 180.0 0.0 True\n",
"phi -180.0 180.0 0.0 True\n",
"tth -0.001 180.0 0.0 True\n",
"===== ========= ========== ===== ====\n",
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fourc.undo_last_constraints()\n",
"fourc.show_constraints()"
]
},
{
"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 `fourc.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 `fourc.calc.physical_axis_names`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"axis names: ['omega', 'chi', 'phi', 'tth']\n"
]
}
],
"source": [
"print(\"axis names:\", fourc.calc.physical_axis_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, proceed with the inverse calculation."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4 0 0) ? 4.00 0.00 0.00\n"
]
}
],
"source": [
"sol = fourc.inverse((-145.451, 0, 0, 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",
"`fourc.forward()` | The *default* solution\n",
"`fourc.calc.forward()` | List of all allowed solutions."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400) : tth=69.0982 omega=-145.4502 chi=-0.0000 phi=0.0000\n"
]
}
],
"source": [
"sol = fourc.forward((4, 0, 0))\n",
"print(\n",
" \"(400) :\", \n",
" f\"tth={sol.tth:.4f}\", \n",
" f\"omega={sol.omega:.4f}\", \n",
" f\"chi={sol.chi:.4f}\", \n",
" f\"phi={sol.phi:.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": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0 4 0) ? 0.00 4.00 0.00\n"
]
}
],
"source": [
"sol = fourc.inverse((-145.451, 90, 0, 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": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(040) : tth=69.0982 omega=-145.4502 chi=90.0000 phi=0.0000\n"
]
}
],
"source": [
"sol = fourc.forward((0, 4, 0))\n",
"print(\n",
" \"(040) :\", \n",
" f\"tth={sol.tth:.4f}\", \n",
" f\"omega={sol.omega:.4f}\", \n",
" f\"chi={sol.chi:.4f}\", \n",
" f\"phi={sol.phi:.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": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 17,
"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": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Transient Scan ID: 1 Time: 2023-11-20 21:54:23\n",
"Persistent Unique Scan ID: '09df1bc7-c0f4-4602-838a-d951148e9bbc'\n",
"New stream: 'primary'\n",
"+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n",
"| seq_num | time | fourc_h | fourc_k | fourc_l | fourc_omega | fourc_chi | fourc_phi | fourc_tth |\n",
"+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n",
"| 1 | 21:54:24.1 | 3.900 | -0.000 | -0.000 | -146.431 | -0.000 | 0.000 | 67.137 |\n",
"| 2 | 21:54:24.7 | 3.950 | -0.000 | -0.000 | -145.942 | 0.000 | 0.000 | 68.115 |\n",
"| 3 | 21:54:25.3 | 4.000 | -0.000 | -0.000 | -145.450 | -0.000 | 0.000 | 69.098 |\n",
"| 4 | 21:54:25.9 | 4.050 | -0.000 | 0.000 | -144.956 | -0.000 | 0.000 | 70.087 |\n",
"| 5 | 21:54:26.5 | 4.100 | -0.000 | -0.000 | -144.458 | -0.000 | 0.000 | 71.083 |\n",
"+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n",
"generator scan ['09df1bc7'] (scan num: 1)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/prjemian/.conda/envs/bluesky_2023_3/lib/python3.11/site-packages/bluesky/callbacks/fitting.py:167: RuntimeWarning: invalid value encountered in scalar divide\n",
" np.sum(input * grids[dir].astype(float), labels, index) / normalizer\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"('09df1bc7-c0f4-4602-838a-d951148e9bbc',)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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