{ "cells": [ { "cell_type": "markdown", "id": "71d00eeb", "metadata": {}, "source": [ "# Tutorial 2. Advanced capabilities of the MeasurementControl\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Tutorial 2. Advanced capabilities of the MeasurementControl.ipynb`\n", "```\n", "\n", "Following this Tutorial requires familiarity with the **core concepts** of Quantify, we **highly recommended** to consult the (short) {ref}`User guide` before proceeding (see Quantify documentation). If you have some difficulties following the tutorial it might be worth reviewing the {ref}`User guide`!\n", "\n", "We **highly recommended** beginning with {ref}`Tutorial 1. Controlling a basic experiment using MeasurementControl` before proceeding.\n", "\n", "In this tutorial, we will explore the more advanced features of Quantify. By the end of this tutorial, we will have covered:\n", "\n", "- Using hardware to drive experiments\n", "- Software averaging\n", "- Interrupting an experiment" ] }, { "cell_type": "code", "execution_count": 1, "id": "f5bfac56", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "import os\n", "import signal\n", "import sys\n", "import time\n", "\n", "import numpy as np\n", "from lmfit import Model\n", "from qcodes import ManualParameter\n", "\n", "import quantify_core.visualization.pyqt_plotmon as pqm\n", "from quantify_core.data.handling import set_datadir, default_datadir\n", "from quantify_core.measurement.control import MeasurementControl\n", "from quantify_core.visualization.instrument_monitor import InstrumentMonitor\n", "\n", "rng = np.random.default_rng(seed=222222) # random number generator" ] }, { "cell_type": "markdown", "id": "75b88a56", "metadata": {}, "source": [ "**Before instantiating any instruments or starting a measurement** we change the\n", "directory in which the experiments are saved using the\n", "{meth}`~quantify_core.data.handling.set_datadir`\n", "\\[{meth}`~quantify_core.data.handling.get_datadir`\\] functions.\n", "\n", "----------------------------------------------------------------------------------------\n", "\n", "⚠️ **Warning!**\n", "\n", "We recommend always setting the directory at the start of the Python kernel and sticking to a single common data directory for all notebooks/experiments within your\n", "measurement setup/PC.\n", "\n", "The cell below sets a default data directory (`~/quantify-data` on Linux/macOS or\n", "`$env:USERPROFILE\\\\quantify-data` on Windows) for tutorial purposes. Change it to your\n", "desired data directory. The utilities to find/search/extract data only work if\n", "all the experiment containers are located within the same directory.\n", "\n", "----------------------------------------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": 2, "id": "21867a1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data will be saved in:\n", "/root/quantify-data\n" ] } ], "source": [ "set_datadir(default_datadir()) # change me!" ] }, { "cell_type": "code", "execution_count": 3, "id": "d236c77d", "metadata": { "mystnb": { "remove-output": true } }, "outputs": [], "source": [ "meas_ctrl = MeasurementControl(\"meas_ctrl\")\n", "plotmon = pqm.PlotMonitor_pyqt(\"plotmon_meas_ctrl\")\n", "meas_ctrl.instr_plotmon(plotmon.name)\n", "insmon = InstrumentMonitor(\"InstrumentMonitor\")" ] }, { "cell_type": "markdown", "id": "75bac5f5", "metadata": {}, "source": [ "## A 1D Batched loop: Resonator Spectroscopy\n", "\n", "### Defining a simple model\n", "\n", "In this example, we want to find the resonance of some devices. We expect to find its resonance somewhere in the low 6 GHz range, but manufacturing imperfections make it impossible to know exactly without inspection.\n", "\n", "We first create `freq`: a {class}`.Settable` with a {class}`~qcodes.parameters.Parameter` to represent the frequency of the signal probing the resonator, followed by a custom {class}`.Gettable` to mock (i.e. emulate) the resonator.\n", "The {class}`!Resonator` will return a Lorentzian shape centered on the resonant frequency. Our {class}`.Gettable` will read the setpoints from `freq`, in this case a 1D array.\n", "\n", "```{note}\n", "The `Resonator` {class}`.Gettable` has a new attribute `.batched` set to `True`. This property informs the {class}`.MeasurementControl` that it will not be in charge of iterating over the setpoints, instead the `Resonator` manages its own data acquisition. Similarly, the `freq` {class}`.Settable` must have a `.batched=True` so that the {class}`.MeasurementControl` hands over the setpoints correctly.\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "id": "5c4c4fe7", "metadata": {}, "outputs": [], "source": [ "# Note that in an actual experimental setup `freq` will be a QCoDeS parameter\n", "# contained in a QCoDeS Instrument\n", "freq = ManualParameter(name=\"frequency\", unit=\"Hz\", label=\"Frequency\")\n", "freq.batched = True # Tells meas_ctrl that the setpoints are to be passed in batches\n", "\n", "\n", "def lorenz(amplitude: float, fwhm: float, x: int, x_0: float):\n", " \"\"\"Model of the frequency response.\"\"\"\n", " return amplitude * ((fwhm / 2.0) ** 2) / ((x - x_0) ** 2 + (fwhm / 2.0) ** 2)\n", "\n", "\n", "class Resonator:\n", " \"\"\"\n", " Note that the Resonator is a valid Gettable not because of inheritance,\n", " but because it has the expected attributes and methods.\n", " \"\"\"\n", "\n", " def __init__(self) -> None:\n", " self.name = \"resonator\"\n", " self.unit = \"V\"\n", " self.label = \"Amplitude\"\n", " self.batched = True\n", " self.delay = 0.0\n", "\n", " # hidden variables specifying the resonance\n", " self._test_resonance = 6.0001048e9 # in Hz\n", " self._test_width = 300 # FWHM in Hz\n", "\n", " def get(self) -> float:\n", " \"\"\"Emulation of the frequency response.\"\"\"\n", " time.sleep(self.delay)\n", " _lorenz = lambda x: lorenz(1, self._test_width, x, self._test_resonance)\n", " return 1 - np.array(list(map(_lorenz, freq())))\n", "\n", " def prepare(self) -> None:\n", " \"\"\"Adding this print statement is not required but added for illustrative\n", " purposes.\"\"\"\n", " print(\"\\nPrepared Resonator...\")\n", "\n", " def finish(self) -> None:\n", " \"\"\"Adding this print statement is not required but added for illustrative\n", " purposes.\"\"\"\n", " print(\"\\nFinished Resonator...\")\n", "\n", "\n", "gettable_res = Resonator()" ] }, { "cell_type": "markdown", "id": "3eba38ef", "metadata": {}, "source": [ "### Running the experiment\n", "\n", "Just like our Iterative 1D loop, our complete experiment is expressed in just four lines of code.\n", "\n", "The main difference is defining the `batched` property of our {class}`.Gettable` to `True`.\n", "The {class}`.MeasurementControl` will detect these settings and run in the appropriate mode.\n", "\n", "At this point the `freq` parameter is empty:" ] }, { "cell_type": "code", "execution_count": 5, "id": "98c44659", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(freq())" ] }, { "cell_type": "code", "execution_count": 6, "id": "fbdd9d47", "metadata": { "mystnb": { "remove-output": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting batched measurement...\n", "Iterative settable(s) [outer loop(s)]:\n", "\t --- (None) --- \n", "Batched settable(s):\n", "\t frequency \n", "Batch size limit: 2000\n", "\n", "\n", "Prepared Resonator...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7155f7b57c2647e7b82ae712409e450b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished Resonator...\n" ] } ], "source": [ "meas_ctrl.settables(freq)\n", "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n", "meas_ctrl.gettables(gettable_res)\n", "dset = meas_ctrl.run()" ] }, { "cell_type": "code", "execution_count": 7, "id": "ecc5e58d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmon.main_QtPlot" ] }, { "cell_type": "markdown", "id": "f04a96a0", "metadata": {}, "source": [ "As expected, we find a Lorentzian spike in the readout at the resonant frequency, finding the peak of which is trivial.\n", "\n", "#### Memory-limited Settables/Gettables\n", "\n", "Instruments (either physical or virtual) operating in `batched` mode have an upper limit on how many datapoints can be processed at once.\n", "When an experiment is comprised of more datapoints than the instrument can handle, the {class}`.MeasurementControl` takes care of fulfilling the measurement of all the requested setpoints by running and an internal loop.\n", "\n", "By default the {class}`.MeasurementControl` assumes no limitations and passes all setpoints to the `batched` settable. However, as a best practice, the instrument limitation must be reflected by the `.batch_size` attribute of the `batched` settables. This is illustrated below." ] }, { "cell_type": "code", "execution_count": 8, "id": "11f48755", "metadata": { "mystnb": { "remove-output": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting batched measurement...\n", "Iterative settable(s) [outer loop(s)]:\n", "\t --- (None) --- \n", "Batched settable(s):\n", "\t frequency \n", "Batch size limit: 256\n", "\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Prepared Resonator...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ce88edba30f34f7d83ad751bc59095a0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished Resonator...\n" ] } ], "source": [ "# Tells meas_ctrl that only 256 datapoints can be processed at once\n", "freq.batch_size = 256\n", "\n", "gettable_res.delay = 0.05 # short delay for plotting\n", "meas_ctrl.settables(freq)\n", "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n", "meas_ctrl.gettables(gettable_res)\n", "dset = meas_ctrl.run()" ] }, { "cell_type": "code", "execution_count": 9, "id": "bd0cbdc5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmon.main_QtPlot" ] }, { "cell_type": "markdown", "id": "a1a6c5b0", "metadata": {}, "source": [ "## Software Averaging: T1 Experiment\n", "\n", "In many cases it is desirable to run an experiment many times and average the result, such as when filtering noise on instruments or measuring probability.\n", "For this purpose, the {meth}`.MeasurementControl.run` provides the `soft_avg` argument.\n", "If set to *x*, the experiment will run *x* times whilst performing a running average over each setpoint.\n", "\n", "In this example, we want to find the relaxation time (aka T1) of a Qubit. As before, we define a {class}`.Settable` and {class}`.Gettable`, representing the varying timescales we will probe through and a mock Qubit emulated in software.\n", "The mock Qubit returns the expected decay sweep but with a small amount of noise (simulating the variable qubit characteristics). We set the qubit's T1 to 60 ms - obviously in a real experiment we would be trying to determine this, but for this illustration purposes in this tutorial we set it to a known value to verify our fit later on.\n", "\n", "Note that in this example meas_ctrl is still running in Batched mode." ] }, { "cell_type": "code", "execution_count": 10, "id": "a8fd9739", "metadata": {}, "outputs": [], "source": [ "def decay(t, tau):\n", " \"\"\"T1 experiment decay model.\"\"\"\n", " return np.exp(-t / tau)\n", "\n", "\n", "time_par = ManualParameter(name=\"time\", unit=\"s\", label=\"Measurement Time\")\n", "# Tells meas_ctrl that the setpoints are to be passed in batches\n", "time_par.batched = True\n", "\n", "\n", "class MockQubit:\n", " \"\"\"A mock qubit.\"\"\"\n", "\n", " def __init__(self):\n", " self.name = \"qubit\"\n", " self.unit = \"%\"\n", " self.label = \"High V\"\n", " self.batched = True\n", "\n", " self.delay = 0.01 # sleep time in secs\n", " self.test_relaxation_time = 60e-6\n", "\n", " def get(self):\n", " \"\"\"Adds a delay to be able to appreciate the data acquisition.\"\"\"\n", " time.sleep(self.delay)\n", " rel_time = self.test_relaxation_time\n", " _func = lambda x: decay(x, rel_time) + rng.uniform(-0.1, 0.1)\n", " return np.array(list(map(_func, time_par())))" ] }, { "cell_type": "markdown", "id": "b0ad9691", "metadata": {}, "source": [ "We will then sweep through 0 to 300 ms, getting our data from the mock Qubit. Let's first observe what a single run looks like:" ] }, { "cell_type": "code", "execution_count": 11, "id": "a04da706", "metadata": { "mystnb": "remove-output:true" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting batched measurement...\n", "Iterative settable(s) [outer loop(s)]:\n", "\t --- (None) --- \n", "Batched settable(s):\n", "\t time \n", "Batch size limit: 300\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fee61464fc874ba7a5d26819b8ec695d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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<xarray.Dataset> Size: 5kB\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 2kB 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 2kB 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20250723-134616-607-6c1508\n",
       "    name:                             noisy\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False
" ], "text/plain": [ " Size: 5kB\n", "Dimensions: (dim_0: 300)\n", "Coordinates:\n", " x0 (dim_0) float64 2kB 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n", "Dimensions without coordinates: dim_0\n", "Data variables:\n", " y0 (dim_0) float64 2kB 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n", "Attributes:\n", " tuid: 20250723-134616-607-6c1508\n", " name: noisy\n", " grid_2d: False\n", " grid_2d_uniformly_spaced: False\n", " 1d_2_settables_uniformly_spaced: False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meas_ctrl.settables(time_par)\n", "meas_ctrl.setpoints(np.linspace(0.0, 300.0e-6, 300))\n", "meas_ctrl.gettables(MockQubit())\n", "meas_ctrl.run(\"noisy\") # by default `.run` uses `soft_avg=1`" ] }, { "cell_type": "code", "execution_count": 12, "id": "5e34df94", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmon.main_QtPlot" ] }, { "cell_type": "markdown", "id": "3cd212ae", "metadata": {}, "source": [ "Alas, the noise in the signal has made this result unusable! Let's set the `soft_avg` argument of the {meth}`.MeasurementControl.run` to 100, averaging the results and hopefully filtering out the noise." ] }, { "cell_type": "code", "execution_count": 13, "id": "cace9ef8", "metadata": { "mystnb": { "remove-output": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting batched measurement...\n", "Iterative settable(s) [outer loop(s)]:\n", "\t --- (None) --- \n", "Batched settable(s):\n", "\t time \n", "Batch size limit: 300\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "52a650e25d1047f38557f38b3bade4d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dset = meas_ctrl.run(\"averaged\", soft_avg=100)" ] }, { "cell_type": "code", "execution_count": 14, "id": "5aa93cbc", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmon.main_QtPlot" ] }, { "cell_type": "markdown", "id": "a7ffa625", "metadata": {}, "source": [ "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1." ] }, { "cell_type": "code", "execution_count": 15, "id": "bd8301b1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'tau': 5.994542423605298e-05}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = Model(decay, independent_vars=[\"t\"])\n", "fit_res = model.fit(dset[\"y0\"].values, t=dset[\"x0\"].values, tau=1)\n", "\n", "fit_res.plot_fit(show_init=True)\n", "fit_res.values" ] }, { "cell_type": "markdown", "id": "a3a36dec", "metadata": {}, "source": [ "## Interrupting\n", "Sometimes experiments, unfortunately, do not go as planned and it is desirable to interrupt and restart them with new parameters. In the following example, we have a long-running experiment where our Gettable is taking a long time to return data (maybe due to misconfiguration).\n", "Rather than waiting for this experiment to complete, instead we can interrupt any {class}`.MeasurementControl` loop using the standard interrupt signal.\n", "In a terminal environment this is usually achieved with a `ctrl` + `c` press on the keyboard or equivalent, whilst in a Jupyter environment interrupting the kernel (stop button) will cause the same result.\n", "\n", "When the {class}`.MeasurementControl` is interrupted, it will wait to obtain the results of the current iteration (or batch) and perform a final save of the data it has gathered, calling the `finish()` method on Settables & Gettables (if it exists) and return the partially completed dataset.\n", "\n", "```{note}\n", "The exact means of triggering an interrupt will differ depending on your platform and environment; the important part is to cause a `KeyboardInterrupt` exception to be raised in the Python process.\n", "```\n", "\n", "In case the current iteration is taking too long to complete (e.g. instruments not responding), you may force the execution of any python code to stop by signaling the same interrupt 5 times (e.g. pressing 5 times `ctrl` + `c`). Mind that performing this too fast might result in the `KeyboardInterrupt` not being properly handled and corrupting the dataset!" ] }, { "cell_type": "code", "execution_count": 16, "id": "e1b46c8b", "metadata": { "tags": [ "raises-exception" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting iterative measurement...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0c7e0899232747b68a7babbf89e36bc3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "[!!!] 1 interruption(s) signaled. Stopping after this iteration/batch.\n", "[Send 4 more interruptions to forcestop (not safe!)].\n", "\n", "\n", "Interrupt signaled, exiting gracefully...\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[16], line 27\u001b[0m\n\u001b[1;32m 25\u001b[0m meas_ctrl\u001b[38;5;241m.\u001b[39mgettables(SlowGettable())\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# Try interrupting me!\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m dset \u001b[38;5;241m=\u001b[39m \u001b[43mmeas_ctrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:561\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_safe_write_dataset() \u001b[38;5;66;03m# Wrap up experiment and store data\u001b[39;00m\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[0;32m--> 561\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[1;32m 563\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset\n", "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:1255\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m 1253\u001b[0m signal\u001b[38;5;241m.\u001b[39msignal(signal\u001b[38;5;241m.\u001b[39mSIGINT, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_previous_handler)\n\u001b[1;32m 1254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_interrupts \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# call outside handler on exit\u001b[39;00m\n\u001b[0;32m-> 1255\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_previous_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43msignal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSIGINT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_interrupts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 1257\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_previous_handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "class SlowGettable:\n", " \"\"\"A mock slow gettables.\"\"\"\n", "\n", " def __init__(self):\n", " self.name = \"slow\"\n", " self.label = \"Amplitude\"\n", " self.unit = \"V\"\n", "\n", " def get(self):\n", " \"\"\"Get method.\"\"\"\n", " time.sleep(1.0)\n", " if time_par() == 4:\n", " # This same exception rises when pressing `ctrl` + `c`\n", " # or the \"Stop kernel\" button is pressed in a Jupyter(Lab) notebook\n", " if sys.platform == \"win32\":\n", " # Emulating the kernel interrupt on windows might have side effects\n", " raise KeyboardInterrupt\n", " os.kill(os.getpid(), signal.SIGINT)\n", " return time_par()\n", "\n", "\n", "time_par.batched = False\n", "meas_ctrl.settables(time_par)\n", "meas_ctrl.setpoints(np.arange(10))\n", "meas_ctrl.gettables(SlowGettable())\n", "# Try interrupting me!\n", "dset = meas_ctrl.run(\"slow\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "a33155c4", "metadata": {}, "outputs": [ { "data": { "image/png": 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QAHA26FQYWK41tP1YWPeXlBiqgwCAE8L5VBhYlqVJctZiUl8AsAnadSrGp4LtvLyzcMP84V58NtVBAMA52a9TVSpVc3NzZ2cnh8Px9vbmcrk9roYeBRvZU1DLYjIWjAqiOggAOC07deqZM2eKiopMDxkMxogRIxITExkMnNAFe2hs6/zkp7Kzf0mmOggAODM7dWpAQMCsWbN8fX25XK5Sqbxw4cKlS5cEAsHIkSPtEwAGuOVpku9wKzcAsDE77SaGh4dHRkYKhUIWiyUSiaZPn85isWpra+2zdRjg3jtw9YnxoVF+AqqDAICTo+YaJZ1Op9freTweJVuHgaC4Rr6vqI4QEuLFvdWsWD9/GNWJAMD52bVT5XJ5V1eXQqEoLi5msVijRo2y59Zh4Mgrv/3KrkuyDg0hhLi4ZKwcR3UiABgQ7NqpBw8ebGtrI4RwudxZs2Z5enrac+swQBTXyF/5/pJMofnlscHw4vbCb1eOjRV7UJoLAJyfXTt13rx5XV1d7e3tly9fPnjw4KxZs8LDw7utk5SU1G1Jfn6+vQKCM9hXVPdroRJCCGlRavYX1qNTAcDW7NqpIpGIEOLt7R0aGrpr167Tp0/f3aloUAAAcFDUDA9lMBg+Pj5tbW16vZ6SAODEFsWLPXi/mSlJxGUvHC2mKg8ADBzUdKpOp7tz5w6Px8OcD2B1sWJRlL+Az/nlGIyIy/oydfRwsYjaVAAwENjp2O+BAwciIyM9PDzYbHZbW9vly5flcnliYqJ9tg4DynZJdZSfYP28mP2F9YSQhaPFKFQAsA/7nU/Nz883HenlcrkPPfQQJlECq6uWKb86WZn3xjRCCC5KAgA7s1Onzp8/X6vVtre3azQaDocjEolcXFzss2kYUJanS75biTkIAYAa9ttPZbFYXl5edtscDEBv/Vjy3OSIMG/MzwUA1MD9U8FJHC5pbFaol40fQXUQABi4aNep6FHoA4W667XdxVfenU11EAAY0DCUBZzBsjTJNtzKDQCohk4Fh/f3I2XJMf6jQnCVLwBQDJ0Kju3cTZnkpuzF6YOpDgIAQL/zqQAWSU2XlGzEaVQAoAXsp4IDW7n1/JfLxrBd8WsMALSAP0bgqLb8fCvUi5cc40d1EACAX9Du2C/Gp0JvVNzp2Capylk7heogAAC/ol2nokehN5anS/aumUh1CgCA38CxX3A8r++5tHbmkEARh+ogAAC/gU4FB5NZXK/W6h5PCKE6CABAd7Q79gtwHzKFZmPmlYvvzKQ6CABAD7CfCo4kNV2SgVu5AQBd9bFTDQaDQqEw3WMcwA42ZV9bMEo8LEhIdRAAgJ5ZcOxXr9efPn06Ly+vuLi4sbFRr9czGAxfX9+RI0c+9NBDkydPZrFYtgsKA9ypiqYr9W0ZmCgfAGisV52q1+szMzO//fZbqVTq7e0dExMzbtw4Pp+vUChkMllhYeGRI0c8PT2XLl26ZMmSfjYrxqdCj1LTJLc+epjqFAAA99OrTn3ppZeqqqrmzZs3e/bsQYMG3b1CbW3tkSNHfvjhh4MHD+7YsaM/gdCjcLfl6ZLvcBoVAGivV506Z86c5ORkLpd7rxWCg4NXrFixfPnyrKws62UDIISQf+fdGBYoTIryoToIAMADuBgMBqoz/CopKSk/P5/qFEAjV+vb1u0pzn4pieogAAAP1qvrftva2nQ6na2jANwNg2cAwIH0qlPz8vIWLVr0xRdf3Lp1y8Z5AH718s7CDfOHe/HZVAcBAOiVXp1PHTx4sL+//44dO3bs2DFs2LCUlJQZM2bw+Xxbh4OBbE9BLYvJWDAqiOogAAC9ZcH51Fu3bmVnZx85cqS5udnNzW3KlCkpKSnx8fEuLi7WSoPzqWDU2Nb56P/7+exfkqkOAgBgAYuvUdLr9RKJJDs7++eff9ZqtQEBAXPnzk1JSQkICOh/mqSkpPfee8/0EONqBqwZ/zj5VeqYwX4CqoMAAFig79f9tre3Hz169NChQ6WlpS4uLvPnz3/99df7mQb7qUAIeffAlVAv/jMTB1EdBADAMn2fQ9/d3X3RokWvv/56QkKCwWC4efOmFWPBgHW89E5VsxKFCgCOqI/3emtpaTly5Eh2dvaNGzcYDMa4ceMee+wx6yaDAUir0//hu4LrH86lOggAQF9Y1qldXV2nT5/Ozs4+e/asTqcTi8WrVq1KSUnx9fW1UT4YUJalSbZhlnwAcFi97dTr169nZ2cfPXpULpdzOJyZM2c+/PDDo0aNsmk4GFA+O14xPtxrXLgX1UEAAPqoV536008/ffDBB4SQESNGPP/889OnT+fxeDYOBgNLUU3rsWu3970wkeogAAB916tO5fF4Tz755MMPPxwaGmrrQDAwpaZJMBoVABxdr8bSaLXaXt4Vtfdr9igpKWnFihXmS5555pk+vxs4itUZBY+MEs8dYYUhzgAAFOrVfurq1aunT5++YMGC+8xH2NnZmZ2d/f3333///ff9CYQSHWi2S6o9+WwUKgA4gV51ampq6ueff56enj5p0qQxY8ZER0d7e3vz+XyVStXc3FxWVlZYWJiXl8fj8Z577jlbJwZnUi1TfnWyMu8NTJgFAM6gt/MoaTSaw4cP//jjjxUVFXc/Gx4evmDBgpSUlPvct7w3MI/SQDP54xMZq8aHeuGSNwBwBhbPTdjY2Hjp0qXGxsaOjg6BQODv7x8XFxcYGGiVNOjUAeUvP14eESR6cjwufAMAJ2HxPEoBAQFWmS4fBrjDJY0yhQaFCgDOpI9zEwL0h0Ld9dru4ivvzqY6CACANfV9Dn2APsMchADglGi3n3rixAnT17h/qlP6+5Gy5Bj/USEeVAcBALAy2nUqetS5nbspk9yU7fpDItVBAACsj3adCs5tWZrk6ns4jQoAzgnnU8F+Vm49/1XqGBYTv3UA4Jz6sp+q1+tLS0vr6+vFYnFMTAwhRKfTMRgMFxcXa8cD57Hl51uhXrzkGD+qgwAA2IrFnXrt2rX33nuvtraWEPL444/HxMTodLpFixYtXbr0ySeftEFCcAYVdzq2Sapy1k6hOggAgA1ZdhROLpe/9tprIpFo8+bNU6dONS5kMpkzZszA/EdwH6lpkgwMngEAZ2fZfmpOTg6Dwfj73//O5/NPnjxpWh4REfHTTz9ZJRDG0jifdbuL182ODhByqA4CAGBblnVqfX19VFTU3Xd84/P57e3tVgmEHnUymcX1mi79Y2OCqQ4CAGBzlnWqUCi8c+fO3csrKiq8vb2tFAmch0yh2Zh55eI7M6kOAgBgD5adT50wYUJ1dfX+/fvNF1ZWVu7Zs2fixIlWDQbOIDVdkrESp1EBYKCw+F5vn3766d69e+Pi4tra2jgcjq+v75kzZ7y8vL7++msvL69+psG93pzJpuxrXgK3P0yOoDoIAICdWDyW5tVXX42KitqzZ09VVZXBYKipqZk5c+Yf/vCH/hcqOJP8601XG9q+w04qAAwkFu+nmmi1Wo1Gw+PxrDjVA/ZTncagP2fd+uhhqlMAANhVr/ZTFQrFvapXoVAYv2AymVwu12q5wJEtT5dgDxUABqBedery5culUun914mNjf3iiy/6HwjjUx3dv/NuDAsUJkX5UB0EAMDeetWpK1asUCqVxq+vX79++PDhhISEmJgYLpdbX1+fm5srFAoXL15slUDoUYd2tb5tf1Fd9ktJVAcBAKBArzp13rx5xi9u3779zTffbNq0adKkSaZnX3jhhRdeeKGmpsYmAcGhpKZLMKkvAAxYlo1PzcnJCQ0NNS9UQohAIPjd7363b98+qwYDx/PSjsKNjwz34rOpDgIAQA3LOrW1tbXHi5UMBkNra6uVIoFD2lNQy3ZlPDIyiOogAACUsaxTBw8eXFpaaj57PiFELpfv3Llz8ODBVg0GjqSxrfOTn8o+eXwk1UEAAKhk2ZwPM2bMyMzMfPvtt0eNGjV06FAOh9PY2JiXl9fV1fWPf/zDRhGB/nArNwAA0oc5H9Rq9fbt2w8fPlxfX08I4fF4CQkJK1asiIyM7H8azPngiN49cCXUi//MxEFUBwEAoFjf51HSaDRardbq8yi99957pocYV0N/x67d2X6uKv3psVQHAQCgnsXz/Zqw2Ww224IrPDUaTVNTk1Kp5PP5Pj4+LBarx9XQow5E06Vfva2g/IO5VAcBAKAFyzpVp9NpNJoen2IwGG5ubvd64eHDh6uqqvR6vfEhl8tNTEyMjo62aOtAN7iVGwCAOcs6NTc3d+PGjT0+df+5CTs6OhITE8PCwng8nkwmy8vLO378uFAoDAwMtCgA0MdnxyvGh3uNC8f9iAAAfmFZp0ZFRa1Zs8Z8SVNT08mTJzkczqJFi+7zwsWLF5tOu/r7+0+fPn3Xrl2VlZXoVAdVVNN67NrtfS/gRvQAAL+yrFNDQ0NDQ0O7LXz22Weff/55uVx+nxd2u45JIBAQQnQ6nUVbB2oV18j3FdURQhbFi5elSc79JZnqRAAA9NL3a5RMOBzOggULtm3b1vtp9CsqKgghwcHB/d862Ede+e1Xdl2SdWgIIRmS6jVTI/huVvjlAQBwJpbNo3QvLBZLJpP1cuXW1tazZ88GBQVFRERYZetga8U18le+/6VQCSHaLt23p6su12E2SgCA37DCrkZVVdXOnTt7WZAKhSIrK4vD4cycObPHga1JSd1vE4ZZICi3r6hOpvjN9d4tSs3+wvpYsQdVkQAAaMiyTj116tTmzZvNl6hUKpVKxeFwPvnkkwe+XKlUZmZm6vX6Rx99lMfj9bgOGhQAAByUZZ3q5+c3depU8yUcDicwMHDatGkikej+r1WpVJmZmVqt9tFHH3V3d7c0KFBoUbz4x8K6VuWvu6oiLnvhaDGFkQAAaMiyTg0JCVm2bJmfn1+35e3t7VKp1NfX914vVKlU+/fvV6vVCxYsEAqFfUkK1IkVi556KOzL3Eptl54QIuKyvkwdPVz8gE9RAAADjWWdevr06YyMjC1btnRbvnfvXolEcp85HzIzM1taWkaOHNnQ0NDQ0GBcKBAIQkJCLE0M9qdQd6Xn39y7OnF/YT0hZOFoMQoVAOBu1hkO0dXVxWDc8xJivV5vvCq4uLjYfHlYWBg61SEsS5NsWzU+VuyBi5IAAO6jt52qUCgMBkNnZ6der+/o6DB/qrW19cKFC3cfEDZhMBirV6/uV0ygzt+PlCXH+I8KQZsCADxAbzt18eLFCoXC+PXcud3vQ8JgMJ566ilr5gJ6OHdTJrkp2/WHRKqDAAA4gN526rPPPqvRaCoqKi5evLhkyRLTchcXFx6PN3LkyLCwMKsEOnHihOlr3PeNcqnpkpKNs6lOAQDgGCzYTyWEXL9+PTo62rxTrQ49Sh8rt57/ctkYtqt1JtsCAHB6Ft+XJioqykZRgFa2/Hwr1IuXHHPP0+QAANBNrzq1vb1dr9eLRCKtVqtUKnt+I1dXPp9v1WxAmYo7HdskVTlrp1AdBADAkfSqU59++unm5uaTJ0/m5eX17Z7k4FhS0yT7/oh7owIAWKZXnfrss892dnYSQqKjo1955ZUe1/Hx8bFmLqDO63suvTZrSICQQ3UQAAAH06tONQ2eCQ4Oxk1PnVtmcb1aq3s8AXNxAABYDLeVhl/JFJqNmVcuvjOT6iAAAA7JgmuUHvBGVrpGCeNTKZSaLslYOZ7qFAAAjqq31yhJpdL7r2Ota5TQo1T56FDpIyODhgXhrkEAAH3U22uU7jWExgTXKDm0UxVNJXXyjFXYSQUA6DvLrlECZ5WaJrn10cNUpwAAcGx9uUZJKpUeO3bsxo0bWq3W19d33LhxCQkJVk8GdrM8XfIdTqMCAPSbxZ168ODBTz/9VKPRcDgcDocjl8t37Ngxbty4999/n8fj2SIi2NR/8m7EBAqTonDoHgCgvyzr1OvXr2/evHncuHFr1qwJDw8nhKhUqgMHDnzxxRefffbZn/70J9uEBFu51tD2Y2HdoZeTqA4CAOAMLLvlSF5enre391//+ldjoRJCuFzukiVLli1bZi1Yj8kAACAASURBVD4GBhzFsjTJNlyXBABgJZbtp2q12vDwcBaL1W35kCFDtFqtVQJhfKrdvLyzcMP84V58NtVBAACchGWdOnr06IMHD7a3t7u7u5svLygoGDNmjFUCoUftY09BLYvJWDAqiOogAADOw7Jjv2PHjp0xY8bLL7+cn58vlUrb29uvX7/+6aefSiSSF154QfM/BoPBRnHBKm63dW7+qeyTx0dSHQQAwKm4WNR/x44du9e93sx9/vnncXFxfUiTlJSUn5/fhxeCRWb+4+QXqWOi/ARUBwEAcCqWHfuNjIxctWrVA1cLCAjoax6wufcOXH1ifCgKFQDA6izr1EGDBg0aNMg2ScAejpfeudWsWD9/GNVBAACcEO71NoB06QzPfXeh4sMUqoMAADgniztVKpXu3r27vLy8paXF/FzskCFD3n77batmAytbli7ZhjkIAQBsxrJObWpqWrVqlVwuj4mJCQr6zTAMPz8/qwTC+FQb+X/HK8YO8hwf4U11EAAAp2VZp+bk5KhUqq1bt4aFhdkoEHrUFoprWo9eu73/hYlUBwEAcGaWdWpra2tUVJTtChVsJDVdcvrPyVSnAABwcpbN+RAXF1dbW2utaQjBPtZsu/i3xXHuHFyPBgBgW5Z1amJi4rhx4z788EOpVGqjQGBd289Ve3BZKbGBVAcBAHB+lu27uLi4LFmyZN26dYsWLeLxeOaT6Q8bNuzjjz+2djzolxqZ8svcyvw3cIoaAMAeLOvU6urqNWvWMJnMadOmeXh4uLi4mJ4Si8XWzgb9lZouycDgGQAAe7H4ul9XV9eMjAxvbwzJoLu395U8mxQR5s2jOggAwEDRl/un2rRQt2zZYv7wmWeesd22nNhPVxql7eoPHh1BdRAAgAHEsk4dM2ZMZmamUqnk8Wy194MS7T+Fpmvt98VX3ptNdRAAgIHFsut+4+PjU1JS/vznP1+5ckWhUGjMYIANfaSmSTJW4TQqAIC9WbafeuLEiZ07dxJCnn/++W5PxcbGfvHFF1bLBX316dHyqdF+o0M9qA4CADDgWO3+qf7+/tbIA/1y/pbsdGXz7ucTqQ4CADAQ4f6pTiU1TXJpI06jAgBQw7Lzqfchk8ms9VbQN6u2Xvh8Wbybq9X+TQEAwCL9nQNWoVDk5ORkZ2e7urp+/vnnVskEfbD19C2xJ3dGDI7AAwBQpo+dajAYCgsLs7KyTp48qVarfX19lyxZYpVAuH9qH9yQKraeuXX8talUBwEAGNAs7tTGxsbs7OxDhw41NjYSQgYPHvzyyy+PHDnSfJ7C/kCP9sGyNMneNbguCQCAYr3tVLVaffLkyaysrMLCQhcXl4SEhOeeey4zM9PPz2/UqFE2jQj398aeS6/OjAoUcakOAgAw0PWqU8+dO7d+/XqFQhEeHv7888/PmjXLx8eHEHL48GEbx4MHOFBcr9LqliSEUB0EAAB616l37txRKBTDhg374x//GBsba+tM0EutSu07+0uK1s+iOggAABDSy7E0CQkJixcvrq2tXbNmze9+97tvvvmmoaHB1snggXArNwAAWnExGAy9XFWr1ebl5WVnZ1+4cMFgMMTFxUml0hEjRrzzzjvWSpOUlJSfn2+td3NufztUKuSxVk+JpDoIAAD8woLrflksVnJycnJyslQqNV76W19fL5VK1Wr1zJkzH3roIRaLZbugYO7niqZLdfJtmCgfAIBOLNhP7cZgMBQXF2dlZeXm5nZ2dsbHx//rX//qZ5qkpKT33nvP9BDjau4l4s3sir/OZVhp/BIAAFhF3zvVRKlUHjt27NatWy+++GI/3wrHfnvjqf+eWzUpfPIQX6qDAADAb/R3bkJCCI/Hmz9/fv/fB3rj6/wb0f7uKFQAABrCfOuOpLSh7YeLdW89HEN1EAAA6AE61ZGkpp/LWDmO6hQAANAzdKrDePX7orcejvERuFEdBAAAeoZOdQw/XKxluLgsHC2mOggAANyTFa5Rsi7c6+1ud9rVfztUeu6tGVQHAQCA+6Fdp6JH75aaJsnA9A4AALSHY7909/7Bq0vHhgzxd6c6CAAAPAA6ldZyy6SV0o6Vk8KpDgIAAA9Gu2O/YNKlN6zaer7irylUBwEAgF7Bfip94TQqAIBjQafS1BcnKuLDPCdEeFMdBAAAegudSkeXauWHrzS+MTua6iAAAGAB2p1PxfhUQkhquuTUGwP0ewcAcFy069QB26MmL2y7uGlRrJCLG7wDADgYex/7bWtra21t1ev1dt6uo9h5vsad4/pwbCDVQQAAwGJ22k9taGg4d+6cVCrVarWEkCeffFIkEtln0w6krkX12fHrP/9pOtVBAACgL+y0n6pQKAghw4YNCw/H9AX3lJouyViJwTMAAI7KTvupgwcPHjx4MCGkoKDg5s2b9tmoQyiuke8rqiOE3GnvXDExPNyHT3UiAADoI9pdozSg5JXffmXXJVmHhhDCcmU+MS6U6kQAANB3GJ9KmeIa+Svf/1KohBBtl+7F7YWX61qpTQUAAH1Gu/3UpKSkbkvy8/MpSWJr+4rqZAqN+ZIWpWZ/YX2s2IOqSAAA0B+061RnbVAAAHB6OPZLmUXxYgHnNxM7iLjshaPFVOUBAIB+QqdSJlYs0nTpPPhs40MRl/Vl6ujhYgzbBQBwVLQ79jtwPPvthc+fjA8Que0vrCeELBwtRqECADg0O3WqWq2+ceMGIUQqlRJCKisruVyum5tbRESEfQLQzbdnqgJFnJnD/AkhuCgJAMA52KlTFQpFbm6u6aFEIiGEeHl5DcxOvdmk2PLzzRPrplIdBAAArMlOnerl5bV69Wr7bIv+lqVJ9jyfSHUKAACwMtqdT3X6+6f+6YdLLydHBXlwqQ4CAABWRrtOdcoeNTl4qUGh7lo6NoTqIAAAYH2061QnJldp3/rxcvGGWVQHAQAAm8D4VPtJTZNkrMKt3AAAnBY61U7+drg0JTYwFiNQAQCcFzrVHk5XNhfXtK6eGkl1EAAAsCGcT7WH5emS8g/mUp0CAABsC/upNvf0f8/99/djmQwXqoMAAIBt0W4/1cnGp6bl34jyd58yxJfqIAAAYHO061Qn6FGT0sb2PQW1h1+ZTHUQAACwB9p1qjNJTZMcfiWJ6hQAAGAnOJ9qK2t3Ff0lJcZH4EZ1EAAAsBN0qk3svVhHCFkUL6Y6CAAA2A+O/VqftF296dC182/NoDoIAADYFfZTrQ9zEAIADEzoVCt7/+DVJWNDov3dqQ4CAAD2Rrtjvw49PvVkubTiTsc784ZRHQQAAChAu051uB410ekNK745X/nXFKqDAAAANXDs12pS0yUZK3EaFQBg4EKnWscXJypGh3omRnpTHQQAACiDTrWCS7Xyw1ca35gdTXUQAACgEu3Opzqi5emSvDcc9TQwAABYC/ZT++uP2y9+uDBWxGVRHQQAACiGTu2XnedrBG6u8+ICqQ4CAADUo92xXwcan1rXovrs+PWf/zSd6iAAAEALtOtUmveoOQyeAQAAczj220fr95c8MzE83IdPdRAAAKALdGpfHL16u0He+VRiGNVBAACARmh37Jf+VFrdSzsKr70/h+ogAABAL9hPtRhu5QYAAD1Cp1rmnznlSVG+Y8I8qQ4CAAC0g061wIWqllPXm16ZEUV1EAAAoCPanU/dsmWL+cNnnnmGqiR3W54mubh+JtUpAACApmjXqbQqUXPPfnvh/54YzWUxqQ4CAAA0hWO/vfLtmapAEWfmMH+qgwAAAH3Rbj+Vhm42Kbb8fPPEuqlUBwEAAFpDpz7YsjTJnucTqU4BAAB0h2O/D/DnHy69lBwV5MGlOggAANAdOvV+Dl5q6FB3/W5sCNVBAADAAeDY7z3JVdq3frxcvGEW1UEAAMAx0K5T6XP/VMxBCAAAFqFdp9Lk/ql/O1yaEhsYKxZRHQQAABwGzqf24Exlc1FN6+qpkVQHAQAAR0K7/VQ6SE2XlH8wl+oUAADgYLCf2t3T/z3339+PZTJcqA4CAAAOBp36G2n5N6L83acM8aU6CAAAOB506q9KG9v3FNS+/XAM1UEAAMAh4Xzqr5anS7JfSqI6BQAAOCradSpV41PX7ip6c26Mr7ub3bYIAABOhnadSsn41L0X6wghi+LF9t80AAA4Ddp1qv1J29WbDl07/9YMqoMAAIBjwzVKmIMQAACsY6B36gdZ1x5PCI72d6c6CAAAOLwB3akny6XXb7evSoqgOggAADiDgXs+Vac3rPjmfOVfU6gOAgAATmLg7qempksyVuI0KgAAWA3t9lPtMz71ixMVo0M9EyO9bfT+AAAwANGuU+0wPvVynfxQSeOBFyfZekMAADCg0K5T7SA1TZL3Bi3ufA4AAM5kwJ1P/eP2ix8ujBVxWVQHAQAAZzOwOnXn+RqBm+u8uECqgwAAgBMaQMd+61pUnx2//vOfplMdBAAAnNMA2k/F4BkAALCpgdKp6/eXPDMxPNyHT3UQAABwWrQ79muL8alHr95ukHe+t2CEVd4NAACgR3bt1M7OzoaGBoPB4OfnJxAIelzH6uNTVVrdSzsKr70/x7pvCwAA0I39OrW0tDQvL0+v17u4uBgMhrFjx44ZM8YO28Wt3AAAwD7s1KlSqTQ3N3fQoEHTp09nMplnzpw5d+6ct7f3oEGDbLrdf+ZcT4ryGRPmadOtAAAAELt16qVLlxgMxrRp09hsNiFk4sSJN2/eLCoqskWnFtfI9xXVEUJiAt3zr0t/WP2Q1TcBAABwNzt1am1tbWBgoJubm/Ghi4tLaGjotWvXtFoti2XNKY3yym+/suuSrENj3Ez60wlWfHMAAID7sMdYGq1Wq1QqRSKR+UIPDw+DwdDW1mbFDRXXyF/5/n+FSggxGNbtKr5c12rFTQAAANyLPfZTNRoNIcR41NfE+FCtVndbOSkpqduS/Pz8Xm5oX1GdTKExX9Ki1OwvrI8Ve1gUGAAAoA8oG59qMBgIIS4uLt2W975BAQAAaMUex36Np1G77ZIaH3I4HCtuaFG82IP3m71hEZe9cLTYipsAAAC4F3t0qqurq7u7e0tLi/nClpYWBoMhFAqtuKFYsej/PTnak/9LrYq4rC9TRw8Xi+7/KgAAAKuw07Ff41W+HR0dxumTurq6qqurg4ODmUymdTc0abDPtyvG7i+sJ4QsHC1GoQIAgN3YqVNHjhxZVlZ29OjRyZMnM5lMiUTS2dkZHx9vi23Fij1wURIAANifi/FaITuoqak5fvy4UqkkhLDZ7KSkpCFDhnRbJykpCdcoAQCAg7Lfdb8hISHLly+XyWQGg8HLy8vqR30BAACoZdexNAwGw8fH5/7r2OJebwAAAHZAu/unokcBAMBB2WMsDQAAwECATgUAALAOdCoAAIB1OGen3j0RP1gLfrY2gh+s7eBnayP4wd7NOTsVAADA/tCp1NuyZQvVEZwWfrY2gh+s7eBnayP2+cHSrlNPmLnPOlbZUD9XsNab9IYDpbVbEqS1URL80touCdLaKIndfmkfCONTAQAArIN2+6kAAAAOyn5z6PcGriIDAACau8+9XujVqQAAAI4Lx34BAACsA50KAABgHehUAAAA66DdWJr+u337dnt7O4/HCwwMdHFxoTrOAyiVSo1Gw+Px2Gw21VnuR6PRNDU1KZVKPp/v4+PDYrGoTnQ/crlcLpdrNBqHSGsil8sNBoO7uzuTyaQ6Sw/0en1bW1u3hQKBwNWV1n9G5HJ5c3MzIcTT09PT05PqOD3o8QdLCGGxWHw+3/55ekOn00ml0o6ODh6P5+PjQ/M/XyqVqrGxUa/X+/r6CoVCm26L1v8ZLNXZ2Xno0KHGxkYmk6nT6Tw9PVNSUmz9E+wblUp14sQJqVSqVCoJIVOnTo2JiaE61D0dPny4qqpKr9cbH3K53MTExOjoaGpT3csPP/xw584d00MOhzNhwgQ6/3iNqqurs7KyCCGPPfaYr68v1XF60Nra+v3333dbOH/+/ODgYEryPJBarT5+/PitW7dMSxISEsaOHUtdop4pFIodO3bcvTwqKmrGjBn2z/NAVVVVeXl5HR0dDAZDr9e7ubk99NBDQ4cOpTpXzwoKCi5cuKDX641phw8fPnnyZNttzqk6NTc3t6mpad68eSEhIU1NTVlZWUeOHHnssceoztWDrq4upVI5aNAgFotVXFxMdZwH6OjoSExMDAsL4/F4MpksLy/v+PHjQqEwMDCQ6mg9iI6OHj9+vKenJ5vNlslk+fn5ubm5Pj4+9CwqI41Gc/LkSU9Pz5aWFqqzPMCECROCgoJMD+m550cIMRgM2dnZMpls2rRp4eHhBoOhubnZ9LmQVng83qJFi8yXVFZWFhcXR0ZGUhXpPjo7O48ePcrn85cuXerl5dXR0XH06NHc3Fx/f38a/jJcv3793LlzQ4YMmTRpkqur69WrV0+dOiUUCkeNGmWjLTrP+dS2trabN2/GxsaGhIQQQnx8fMaOHSuVSmtra6mO1gN3d/fHHntsypQp4eHhVGd5sMWLF8fFxYlEIhaL5e/vP336dEJIZWUl1bl6NmLEiODgYD6fb0xrHPRMz18Dk9OnTwsEAvrvTBNCPDw8/M3Q9qBfWVlZY2PjtGnThg4d6ubmxuFwxGKx8Y8D3TCZTP/famxs5HK5YWFhVEfrgVQq1Wq1I0eO9PLyIoQIBIKxY8caDIaGhgaqo/Xg6tWrHA5nypQpbm5uTCYzNjY2ODi4qKjIdoNInadTjX80zX8LBw0aRGj/x9QhdDstLRAICCE6nY6iOJbp6uoihPB4PKqD3FNtbW15efnUqVPpf/rfiJ57e92UlZUJBIKIiAjiIIFNWltbb9++PWTIEAaDjn+fjf+VjP+tjIxfc7lcyjLdW2trq6enp/kpf19fX5VKJZVKbbRF5zn229raSgjx8PAwLeHxeCwWy7gcrKiiooIQQtuzaIQQvV7f2tqq1+tbWlrOnz/v6+s7ePBgqkP1TKvV5ubmxsfHe3p61tTUUB3nwU6ePKlSqRgMhr+/f0JCAm1/DaRSaWhoaFFRUVFRkUql4vP5w4cPj4+Pp/8Hl2vXrhFCaHvQwtvbOzw8vLCw0MPDw8fHp7W19ezZs35+fvTcq3Z1ddVoNOZLjA9bWlr8/PxsskVbvCkljD+pbkei2Gx2tx8o9JPxv1BQUJBxD4CelEql6WoaX1/fOXPm0PNKWkLI2bNnXV1d4+PjqQ7yYAwGIyQkRCwWc7nctra2K1euHDhwYM6cOTQ8f9HV1aXVamtra2traxMSEgQCQWVl5blz59Rq9UMPPUR1uvvR6/VlZWX0PDdpMnPmzGPHjhkvqSOEBAYGzpkzh5571YGBgeXl5U1NTT4+PoQQrVZ78+ZN8r++sAXn6dQeP34aDAb6fyx1IAqFIisri8PhzJw5k84/WB6Pt3TpUp1OJ5PJLl68+MMPPyxcuJCGV4A3NDRcuXJl4cKF9Px71I2Hh8e8efNMD4cPH75r167Tp0/TsFON1Gq16bLkiIiIzs7Oy5cvx8fHczgcqqPdU1VVlUqlGjduHNVB7kmv12dlZTU3N0+cONHb27utra2goCAzM3PBggVubm5Up+tuzJgxt27dyszMHDZsGJvNLisrM/5fs92fLwf4n9xLxn/Ozs5O84UajYaG/8wOSqlUZmZm6vX6Rx55hM6nJwkhDAbDy8vL19c3Ojp6/vz5KpWqoKCA6lA9yM/PF4vFer2+oaGhoaHBOEixqanp9u3bVEd7MD6fHxER0dbWplAoqM7SnaurK5PJZLFY5oemw8PD9Xp9U1MThcEeqLS01NXVNSoqiuog91ReXl5XVzd16tS4uDixWBwTEzN37tzm5mZ6jl8QiUSLFy8ODw+/detWRUVFeHj4hAkTCCG2G/jrPPupxkMlLS0tph9We3t7V1cXnQ+hOBCVSpWZmanVah999FF3d3eq41hAIBDweDx6nlZXKBTNzc3dLqPLzc1ls9krV66kKlXvGS/9oOd9OLy8vLpNpGA8/k/PtEZKpbK6ujoqKorOs5QYJ9AICAgwLfH29maxWMblNCQSicxvy3369GkXFxd/f38bbc55OjUkJMTFxeXGjRumT6bGwR6hoaGU5nIGKpVq//79arV6wYIFNDyCen/GHSnzIZX0sXjxYvM/8WVlZQUFBXPmzPH29qYwVS91dXVVVVW5ubnRc66fsLCwCxcuyGQy45APQkh9fT0hxPSQhsrKyvR6PW2vTjIyHjmXyWRisdi4pKOjQ6vV0vmIuolSqSwtLQ0NDbXdkTbn6VQejxcTE3P16lVvb+9Bgwbdvn37woULwcHBtvs80k/l5eU6nU4ulxNCTEO7hgwZQsOraTIzM1taWkaOHGk8RGlcKBAIaDjaTyqVFhYWRkREGLu/qampsLCQwWDExsZSHa0H3T6gGP8qCQQCen5w+fnnn3k8nq+vL4fDaWtrKyoqksvlEyZMoOeZ9djY2JKSkpycnKSkJD6fX1lZef369aioKHp+AjAqLS0ViUT0nErFJDIy8sKFC7m5uZMmTTKeTz1z5gwhhJ7HqxUKRUlJifHCOplMdv78eULIpEmTbLdF5+lUQsjEiRO1Wm1eXl5eXh4hJDg4eObMmVSHuqeff/7ZdPa3rKysrKyMEBIeHk63TtXr9TKZjBDS7XxJWFgYDTuVzWa3tLQcPXrUtMQ4RSVtP1o5EBcXl/Pnz5vGJbu5uSUmJtpuPpp+4nA48+bNy8nJ2bdvn3FJVFTUlClTqE11H42Nja2trePHj6c6yAN4eHg8/PDD+fn52dnZxiV8Pn/GjBm0HVV17dq1ixcvGr/29/efM2eOTT+zOuE9yZVKpXEOfcc67QdW1NnZ2d7ertfr+Xy+cYYKsAqdTtfW1qZWq9lstqenJz33ULtpaWnRaDRCoZCekxI4ro6ODoVCwePxBAIBnX8TDAaDXC5Xq9X2+WvghJ0KAABACecZSwMAAEAtdCoAAIB1oFMBAACsA50KAABgHehUAAAA60CnAgAAWIdTzfkAA5lerzfef9THx6fbXDltbW0tLS2EkNDQUDoPpKO5zs7O+0/uHxYW9sknnwwdOtT89jW2duzYsZqamt///vf3Xy0/P7+kpGT16tV2CQUDFzoVnERra2tqaioh5NFHH33ttdfMn9q0adOpU6cIIceOHet2h10wd+rUqczMzA0bNvQ4gV9xcfG6devu8/L8/Pz8/HxCiN06VS6X//3vf1+7du0D14yJidm4ceOoUaMSExPtEAwGLHQqOBUPD4+cnJyXXnrJdGcP403UPTw86HlrGlqpr68/c+ZMV1dXj8+OGDHiq6++Mj188cUXhw0b1m3Pb/Pmzfacv2znzp18Pn/69OkPXNPHx2f27Nn/+c9/0KlgU+hUcCrTpk07cOBAXl5ecnKyccmRI0fc3NwmTpyYlZXVbeX6+vobN264uroOHz68WxN0dnZWVFTIZDKhUBgTE9PtLrxarbasrEwmkwkEgrCwMNNtZFpaWphMpvlsonK5XK/XG284qNVqW1tbRSIRk8ksKSkxTkBv3G9ua2u7du2aVquNjIw0n0JdqVQqFAofHx+FQnH58mU2mx0XF2f8uNDS0mKccj06OrrbHNEGg+H69euNjY1CoXD48OHmNw6TSqU8Ho/P51dUVDQ2NoaGhppu3KRQKIx3Qm1ubtZoNIQQLy8v83fm8/nDhw8335C7u3u3Jd7e3qafldXDd6PVag8cOPDoo492u6O78VtjsVgBAQFhYWGm5XPmzDlw4EBJScmIESPu9Z4A/YROBafi4eExYcKEQ4cOmTr10KFD06ZN63bIV6FQfPjhh/n5+SwWS6fTsVisNWvWLFq0yPjsrl27vvrqK+Ptqzo7O0Ui0TvvvGOa3PzSpUvr16+XyWR8Pl+pVOr1+rfeemvOnDmEkLVr14rF4g8++MC0oY8++qixsXHLli2EkPLy8ueff/7FF1/cu3dvXV0dIWT37t3+/v7p6enbt2/v6upydXXt6upKSUlZt26d8dake/fu/fe//71hw4aPP/5YrVbr9fqQkJDPPvvs9OnTn376qU6n0+v1sbGxn3zyieneVTdv3ty4ceONGzfYbLZGo/Hx8Xn33Xfj4uKMzy5duvSRRx5paGg4ffo0g8Ew3mH+9ddfJ4R8991327ZtI4Q8/fTTxpUzMjLMO6k3VqxYkZSUZDxEbPXw3Vy4cEEul5vvd7a0tKxbt668vJzP52s0Gq1Wm5KS8uabbxqfjY2NdXd3z8nJQaeC7aBTwdmkpKS8/fbbUqnU19f3+vXrFRUVa9euzcnJMV9n/fr1ZWVlH3/88fjx4zUaTVpa2j//+c9BgwbFx8cTQgICAj755JPhw4e7ubk1NjZu2rRpw4YNe/bsMU7AvXnz5uDg4K1bt4pEoq6urqtXr1p058ivv/762WefnT17tk6nEwgEGRkZ33777Zo1axYuXMhisU6cOPHBBx/4+fmtWLHC9JJvvvnm008/jYmJKSoqWrdu3dtvv61Wq9PS0gYNGnTy5Mn169f/+OOPy5YtI4TI5fJXX33V29t769atERERTU1N77///ptvvrljxw7T3vOBAwceeeSRrKwsDodjrPMpU6aMGzdu5cqVQqHwyy+/3L17t0gkIv+791w/WTe8ucLCQldX1yFDhpiWbNmy5c6dO8aXGwyGmpoa42cXIxcXl5iYGNMtSgBsAWNpwNkkJiYKhcKffvqJEJKdnR0cHNzt5qklJSXnzp174YUXEhMTGQwGh8NZs2ZNaGjo3r17jStMnjw5Pj7eeAwzICDgtddeUygUxjsvEkLq6+vj4uKMrePq6hoXF2f+Z/2BpkyZsmTJEpFIZLw59rZt2+bMmfO73/3Ozc2NwWAkJyenpKTs3bvX/OYWzz777PDhwxkMRnx8/Lhx40pKSl5//fWIiAgGgzFt2rSoqChTT2RmZspksvfffz8iIoIQ4uPj884777S3t5vflMX5xgAABiBJREFU/C44OPill14SCoVsNnvVqlUcDsf4chaLZdw55v6PVa6Rtm54c9XV1V5eXuYHh+vq6oKDg40vd3FxCQ0N7Xb2NCgoqLq6GjcOAdvBfio4G1dX15kzZx46dGjp0qVHjx5dsmRJtxUKCwsJIXK5/NChQ6aFPB7v5s2bxq/1ev3x48fPnDnT1NSk1WqNf4Lv3LljfHbMmDE7d+5saWkZP378mDFjLL0kx7grbFReXq5QKJhMpnkSjUYjl8tbWlqMpUsIMf9M4O/vz2KxoqOjzZeY9saKioqEQmFxcbH5zW7NvzXju5nKksVi+fn5SaVSi74Fi1g3vDm5XN7th5+QkPDFF1+88cYbkydPTkhICAgI6PYSd3d3nU6nUChwB0CwEXQqOKGUlJTdu3d//fXX7e3txjOd5trb2wkhR48e7bYfJhaLjV9s2rQpJycnOTl5woQJXC63q6urpKREq9Uan92wYcPWrVuPHz9+8OBBJpM5adKktWvXmvrvgTw8PLolKSoqun79uvk60dHRplt/E0LMb/zJZDLZbLb5VTlMJtO0svHmpj/88IP5uwUHBxsvkrr73bq93BasG96cm5tbW1ub+ZKlS5dyOJwDBw58/PHHBoNh6NChr7322tChQ00rqNVqQgjGU4HtoFPBCQ0ePHjw4ME7d+4cM2aMn59ft2eNJ+c2btwYEhJy92ulUunhw4efe+655cuXG5fU1tb+61//Mq3A5/PXrFmzZs2ahoaG48ePp6enu7q6bty4kRBivOLJ/N2MrXkvxiRPPPHEI4880odvs8c35PP5aWlpVnk3O7M0vLe3d0VFhfkSBoOxcOHChQsXtrW1nTlz5t///vef//znvXv3mlpcLpcLBAJ0KtgOzqeCc3r66aenT5/+xBNP3P1UQkICIcR4wvVuxhY0jTAhhBjnMbhbYGDgsmXLxowZU1ZWZlzi6+tbXV1tWkEul3fbAe1myJAhQqHwyJEj1jrDl5CQ0NzcXFBQ0LeXG8ums7PTKmEsZWn4YcOGyeXypqamu58SCoWzZ89+7LHHmpubZTKZaXlFRUW3wT8A1oVOBec0derUjRs3jhs37u6nhg4dmpycnJGRkZ6eXlVV1draWl5e/t133/3444+EkODgYD6fv2PHjqqqKplMduDAge+//9702vb29o0bN0okkjt37nR0dJw5c+by5cumE4SJiYnV1dX//e9/GxoaSkpK3nzzzW5DJ7thsVjPPvtscXHxhg0bSktL5XL5rVu3srKy/vnPf/btu16wYEFwcPC777576NChxsbGpqam4uLiTz/99NKlS715eWRkJCEkIyPjwoULhYWFdi5XS8Mb/3FLSkpMSzZv3vzTTz/V1tYqFIry8vKcnBxfX1/TYfmOjo6bN2/2+CsBYC049gsD0VtvveXt7b19+/ZvvvnGuCQoKMg4JRCbzX7rrbc2bdpknOnQ19f37bfffvXVV42rMZnMqqqq119/3bhnyWAwkpKSTM/OnTu3qKhoy5YtW7ZsYbFYv//970UiUWNj432SPProo66url9//fWJEyeMS4RC4cKFC/v2ffF4vP/7v//75JNPNm3aZEzo4uIydOhQ09Db+4uNjX3qqaeysrIyMzP1en0fxqf2h6XhQ0JCRo4cmZOTM3XqVOOSzs7Ov/3tb6Yz30OHDt20aZPpY82JEyeYTObs2bNt/p3AAOaCy8phwFKr1VVVVTqdztfX18fHx/ypzs7O6upqJpMZHh5+976mXC6/ffu2wWAICAgwDqoxJ5PJGhsbg4ODexxV2SO9Xl9VVaVQKLy8vPz9/btNLdQHcrm8traWzWYHBATYc7JAq+h9+FOnTq1fv3737t2mqaw6Ozvr6+tVKpWvr2+3U+nPPfdcZGTkn/70JxtGhwEPnQoADuyPf/zj4MGDX3nllfuvdvr06Q0bNuzcudPUvgC2gE4FAAfW2tra3NxsPBN8H42NjV1dXcHBwfZJBQMWOhUAAMA6cN0vAACAdaBTAQAArAOdCgAAYB3oVAAAAOtApwIAAFgHOhUAAMA60KkAAADWgU4FAACwDnQqAACAdaBTAQAArAOdCgAAYB3/H+bwnCgBxOAmAAAAAElFTkSuQmCC", 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