{ "cells": [ { "cell_type": "markdown", "id": "557cff84", "metadata": {}, "source": [ "\n", " \"image0\"\n", "\n", "\n", "# Numerically Controlled Oscillator\n", "\n", "In this tutorial we will demonstrate how to use the numerically controlled oscillator (NCO) during an experiment by [Changing the modulation frequency](#Frequency-sweeps), e.g. for rapid spectroscopy measurements\n", "\n", "We will show this by using a [QRM](https://docs.qblox.com/en/main/cluster/qrm.html) and directly connecting outputs\n", "$\\text{O}^{[1-2]}$ to inputs $\\text{I}^{[1-2]}$ respectively. We will then use the QRM's sequencers to sequence waveforms on\n", "the outputs and simultaneously acquire the resulting waveforms on the inputs.\n", "\n", "Whilst this tutorial is for the baseband modules, a similar procedure for spectroscopy with RF modules can be found in the [RF Control](https://docs.qblox.com/en/main/tutorials/q1asm_tutorials/QRM-RF/100_rf_control.html) tutorial.\n", "\n", "## Getting Started\n", "\n", "### How the NCO works\n", "\n", "#### Modulation\n", "\n", "The NCO modulates the AWG output with a certain frequency, such that we only need to store the envelope of any waveform to create a wave with the correct frequency and phase. To enable IQ up- and down-conversion to RF frequencies, the device will generate a phase shifted signal on both signal paths.\n", "\n", "If modulation is enabled, the value of the NCO ($e^{i\\omega t}$) will be multiplied by the awg outputs ($z(t) = \\text{awg}_0 + i \\cdot \\text{awg}_1$), and also by a factor of $1 / \\sqrt{2}$ to prevent clipping, and forwarded to path 0/1 as follows:\n", "\n", "\\begin{equation*}\n", "\\text{path}_{0, \\text{out}} = \\frac{1}{\\sqrt{2}}(\\cos(\\omega t)\\text{awg}_0 - \\sin(\\omega t)\\text{awg}_1)\n", "\\end{equation*}\n", "\n", "\\begin{equation*}\n", "\\text{path}_{1, \\text{out}} = \\frac{1}{\\sqrt{2}}(\\sin(\\omega t)\\text{awg}_0 + \\cos(\\omega t)\\text{awg}_1)\n", "\\end{equation*}\n", "\n", "These two outputs can then be used within a QxM-RF module or by an external IQ mixer to generate RF pulses. Each path, 0/1, will have two components (real and imaginary) referred to as I and Q. However, the NCO can also be operated in real mode which will create a direct modulated output.\n", "\n", "#### Demodulation\n", "\n", "When analyzing acquired signals, we are usually interested in their envelope rather than the full oscillating waveform—especially when integrating. To facilitate this, the NCO (Numerically Controlled Oscillator) can be used to demodulate the signal before integration.\n", "\n", "If demodulation is enabled, the signal is multiplied by the NCO (effectively $e^{-i\\omega t}$ during demodulation), introducing a factor of $\\sqrt{2}$.\n", "\n", "This results in a net amplitude factor of 1 for signals that are both modulated and demodulated—making the effect invisible to the user in standard use cases.\n", "\n", "The relevant equations are:\n", "\n", "$$\n", "\\begin{aligned}\n", "\\text{path}_{0,\\text{in}} &= \\sqrt{2}(\\cos(\\omega t)\\, \\text{in}_0 + \\sin(\\omega t)\\, \\text{in}_1) \\\\\n", " &= \\sqrt{2} \\left( \\frac{1}{\\sqrt{2}}\\cos(\\omega t) \\cdot \\cos(\\omega t)\\, \\text{awg}_0\n", " + \\frac{1}{\\sqrt{2}}\\sin(\\omega t) \\cdot \\sin(\\omega t)\\, \\text{awg}_0 \\right) \\\\\n", " &= \\text{awg}_0 \\\\\n", "\\\\\n", "\\text{path}_{1,\\text{in}} &= \\sqrt{2} (-\\sin(\\omega t)\\, \\text{in}_0 + \\cos(\\omega t)\\, \\text{in}_1) \\\\\n", " &= \\sqrt{2} \\left( \\frac{1}{\\sqrt{2}}\\sin(\\omega t) \\cdot \\sin(\\omega t)\\, \\text{awg}_1\n", " + \\frac{1}{\\sqrt{2}}\\cos(\\omega t) \\cdot \\cos(\\omega t)\\, \\text{awg}_1 \\right) \\\\\n", " &= \\text{awg}_1\n", "\\end{aligned}\n", "$$\n", "\n", ".. note:: If only one path is used (for example, measuring a DC voltage with a QRM),\n", " this factor becomes visible. In such cases, the measured signal will appear\n", " larger by a factor of $\\sqrt{2}$ than it truly is." ] }, { "cell_type": "markdown", "id": "9e5e2fb9", "metadata": {}, "source": [ "### Setup\n", "\n", "First, we are going to import the required packages and connect to the instrument." ] }, { "cell_type": "code", "execution_count": 1, "id": "11b7a9c6", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:21.421055Z", "iopub.status.busy": "2025-05-07T16:54:21.420685Z", "iopub.status.idle": "2025-05-07T16:54:22.305110Z", "shell.execute_reply": "2025-05-07T16:54:22.304273Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "from __future__ import annotations\n", "\n", "import json\n", "from typing import TYPE_CHECKING\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from matplotlib import colors\n", "from qcodes.instrument import find_or_create_instrument\n", "from scipy.signal import spectrogram, welch\n", "\n", "from qblox_instruments import Cluster, ClusterType\n", "\n", "if TYPE_CHECKING:\n", " from numpy.typing import NDArray" ] }, { "cell_type": "markdown", "id": "5f01e9de", "metadata": {}, "source": [ "### Scan For Clusters\n", "\n", "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)." ] }, { "cell_type": "markdown", "id": "acf8df59", "metadata": {}, "source": [ "`!qblox-pnp list`" ] }, { "cell_type": "code", "execution_count": 2, "id": "e909daf5", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:22.307790Z", "iopub.status.busy": "2025-05-07T16:54:22.307394Z", "iopub.status.idle": "2025-05-07T16:54:22.310901Z", "shell.execute_reply": "2025-05-07T16:54:22.310343Z" } }, "outputs": [], "source": [ "cluster_ip = \"10.10.200.42\"\n", "cluster_name = \"cluster0\"" ] }, { "cell_type": "markdown", "id": "405cdf5b", "metadata": {}, "source": [ "### Connect to Cluster\n", "\n", "We now make a connection with the Cluster." ] }, { "cell_type": "code", "execution_count": 3, "id": "fab2c72a", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:22.312551Z", "iopub.status.busy": "2025-05-07T16:54:22.312394Z", "iopub.status.idle": "2025-05-07T16:54:23.164105Z", "shell.execute_reply": "2025-05-07T16:54:23.162999Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "cluster = find_or_create_instrument(\n", " Cluster,\n", " recreate=True,\n", " name=cluster_name,\n", " identifier=cluster_ip,\n", " dummy_cfg=(\n", " {\n", " 2: ClusterType.CLUSTER_QCM,\n", " 4: ClusterType.CLUSTER_QRM,\n", " 6: ClusterType.CLUSTER_QCM_RF,\n", " 8: ClusterType.CLUSTER_QRM_RF,\n", " }\n", " if cluster_ip is None\n", " else None\n", " ),\n", ")" ] }, { "cell_type": "markdown", "id": "87b098ec", "metadata": { "lines_to_next_cell": 0 }, "source": [ "#### Get connected modules" ] }, { "cell_type": "code", "execution_count": 4, "id": "c08dafb9", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:23.168087Z", "iopub.status.busy": "2025-05-07T16:54:23.167925Z", "iopub.status.idle": "2025-05-07T16:54:23.337687Z", "shell.execute_reply": "2025-05-07T16:54:23.336150Z" } }, "outputs": [ { "data": { "text/plain": [ "{4: }" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# QRM baseband modules\n", "modules = cluster.get_connected_modules(lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n", "modules" ] }, { "cell_type": "code", "execution_count": 5, "id": "66170fd1", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:23.341814Z", "iopub.status.busy": "2025-05-07T16:54:23.341375Z", "iopub.status.idle": "2025-05-07T16:54:23.347255Z", "shell.execute_reply": "2025-05-07T16:54:23.346145Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "readout_module = modules[4]" ] }, { "cell_type": "markdown", "id": "435ee2fd", "metadata": {}, "source": [ "### Reset the Cluster\n", "\n", "We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters, so resetting between experiments is usually not desirable." ] }, { "cell_type": "code", "execution_count": 6, "id": "61fb1a72", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:23.351056Z", "iopub.status.busy": "2025-05-07T16:54:23.350686Z", "iopub.status.idle": "2025-05-07T16:54:25.936904Z", "shell.execute_reply": "2025-05-07T16:54:25.935777Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status: OKAY, Flags: NONE, Slot flags: NONE\n" ] } ], "source": [ "cluster.reset()\n", "print(cluster.get_system_status())" ] }, { "cell_type": "markdown", "id": "39de61b2", "metadata": {}, "source": [ "Frequency sweeps \n", "------------------------------\n", "One of the most common experiments is to test the frequency response of the system, e.g. to find the resonance frequencies of a qubit or a resonator. For the purpose of this tutorial, we will sweep the full frequency range supported by the QRM. To improve accuracy we can use the maximum integration time and multiple averages. This does not change the overall measurement time much, as most of it is used for the setup." ] }, { "cell_type": "code", "execution_count": 7, "id": "56cdfdcf", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:25.940715Z", "iopub.status.busy": "2025-05-07T16:54:25.940303Z", "iopub.status.idle": "2025-05-07T16:54:25.945945Z", "shell.execute_reply": "2025-05-07T16:54:25.944947Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "start_freq = -500e6\n", "stop_freq = 500e6\n", "\n", "n_averages = 10\n", "MAXIMUM_SCOPE_ACQUISITION_LENGTH = 16384" ] }, { "cell_type": "markdown", "id": "cbcd79d1", "metadata": { "lines_to_next_cell": 2 }, "source": [ "In this tutorial, we will analyze the raw data measured by the scope acquisition of the QRM. For this we will define a simple helper function using [scipy.signal.spectrogram](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html) and [scipy.signal.welch](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.welch.html). The spectrogram shows the frequency spectrum of the QRM output as a function of time, to visualize the frequency sweeps we are doing. Welch's method is used to compute the input power as a function of frequency (power spectral density). This way we obtain the response of the system to find features of interest, e.g. a resonance." ] }, { "cell_type": "code", "execution_count": 8, "id": "f8853529", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:25.949495Z", "iopub.status.busy": "2025-05-07T16:54:25.949106Z", "iopub.status.idle": "2025-05-07T16:54:25.961295Z", "shell.execute_reply": "2025-05-07T16:54:25.960275Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "# Power as function of frequency and time by chunking the data\n", "def plot_spectrogram(time_series: np.ndarray) -> None:\n", " if not np.any(time_series):\n", " return\n", " f_sample = 1e9 # All devices have 1 GSPS sample rate\n", " fig, ax = plt.subplots(1, 2)\n", "\n", " f, t, Sxx = spectrogram(time_series, f_sample, return_onesided=False, detrend=False)\n", "\n", " idx = np.argsort(f)\n", " f = f[idx] / 1e6\n", " Sxx = Sxx[idx]\n", "\n", " cmap = colors.LinearSegmentedColormap.from_list(\"qblox\", [\"#FFFFFF\", \"#00839F\"])\n", " spec = ax[0].pcolormesh(t, f, Sxx, shading=\"auto\", cmap=cmap)\n", " cb = fig.colorbar(spec)\n", " cb.set_label(\"Power Spectral Density [V$^2$/Hz]\")\n", " ax[0].set_ylabel(\"Frequency [MHz]\")\n", " ax[0].set_xlabel(\"Time [s]\")\n", "\n", " f, Pxx = welch(time_series, f_sample, return_onesided=False, detrend=False)\n", "\n", " idx = np.argsort(f)\n", " f = f[idx] / 1e6\n", " Pxx = Pxx[idx]\n", "\n", " ax[1].semilogy(f, Pxx)\n", " ax[1].set_xlabel(\"Frequency [MHz]\")\n", " ax[1].set_ylabel(\"Power Spectral Density [V$^2$/Hz]\")\n", " fig.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "5438a82a", "metadata": { "lines_to_next_cell": 2 }, "source": [ "And two more helper functions for plotting the amplitude of an array of I, Q values and a scope acquisition:" ] }, { "cell_type": "code", "execution_count": 9, "id": "7967a2ad", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:25.964839Z", "iopub.status.busy": "2025-05-07T16:54:25.964471Z", "iopub.status.idle": "2025-05-07T16:54:25.973158Z", "shell.execute_reply": "2025-05-07T16:54:25.972168Z" } }, "outputs": [], "source": [ "def plot_amplitude(x: NDArray, I_data: NDArray, Q_data: NDArray) -> None:\n", " amplitude = np.abs(I_data + 1j * Q_data)\n", "\n", " plt.plot(x / 1e6, amplitude)\n", " plt.xlabel(\"Frequency [MHz]\")\n", " plt.ylabel(\"Integration [V]\")\n", " plt.show()\n", "\n", "\n", "def plot_scope(trace: NDArray, t_min: int, t_max: int) -> None:\n", " x = np.arange(t_min, t_max)\n", " plt.plot(x, np.real(trace[t_min:t_max]))\n", " plt.plot(x, np.imag(trace[t_min:t_max]))\n", " plt.ylabel(\"Scope [V]\")\n", " plt.xlabel(\"Time [ns]\")\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "80580ef4", "metadata": {}, "source": [ "### Setting up the QRM\n", "We set up a modulated DC offset:" ] }, { "cell_type": "code", "execution_count": 10, "id": "7d5e8a2a", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:25.976721Z", "iopub.status.busy": "2025-05-07T16:54:25.976330Z", "iopub.status.idle": "2025-05-07T16:54:26.080934Z", "shell.execute_reply": "2025-05-07T16:54:26.079775Z" } }, "outputs": [], "source": [ "readout_module.disconnect_outputs()\n", "readout_module.disconnect_inputs()\n", "\n", "# Configure channel map\n", "readout_module.sequencer0.connect_sequencer(\"io0_1\")\n", "\n", "# Set DC Offset\n", "readout_module.sequencer0.offset_awg_path0(1)\n", "readout_module.sequencer0.offset_awg_path1(1)\n", "\n", "# Enable modulation and demodulation. Note that the scope is not demodulated\n", "readout_module.sequencer0.mod_en_awg(True)\n", "readout_module.sequencer0.demod_en_acq(True)\n", "\n", "# Enable hardware averaging for the scope\n", "readout_module.scope_acq_avg_mode_en_path0(True)\n", "readout_module.scope_acq_avg_mode_en_path1(True)\n", "\n", "readout_module.sequencer0.integration_length_acq(MAXIMUM_SCOPE_ACQUISITION_LENGTH)" ] }, { "cell_type": "markdown", "id": "391bbd5c", "metadata": {}, "source": [ "### NCO sweep controlled by the host computer\n", "As a baseline, we will run a simple frequency sweep controlled by the host computer using QCoDeS. We do this by setting a DC offset and modulating it with the NCO. This is quite slow, so we will only measure 200 steps to keep the measurement time reasonable." ] }, { "cell_type": "code", "execution_count": 11, "id": "33fbb9de", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:26.084768Z", "iopub.status.busy": "2025-05-07T16:54:26.084368Z", "iopub.status.idle": "2025-05-07T16:54:26.090978Z", "shell.execute_reply": "2025-05-07T16:54:26.089960Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step size 5.0 MHz\n" ] } ], "source": [ "n_steps = 200\n", "\n", "step_freq = (stop_freq - start_freq) / n_steps\n", "print(f\"Step size {step_freq / 1e6} MHz\")\n", "\n", "nco_sweep_range = np.arange(start_freq, stop_freq, step_freq)" ] }, { "cell_type": "markdown", "id": "aebacf60", "metadata": {}, "source": [ "Now we set up a simple program that acquires data and averages. The frequency will be set later with the QCoDeS interface." ] }, { "cell_type": "code", "execution_count": 12, "id": "0ab3bac6", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:26.094646Z", "iopub.status.busy": "2025-05-07T16:54:26.094257Z", "iopub.status.idle": "2025-05-07T16:54:26.102410Z", "shell.execute_reply": "2025-05-07T16:54:26.101440Z" } }, "outputs": [], "source": [ "acquisitions = {\"acq\": {\"num_bins\": 1, \"index\": 0}}\n", "\n", "# Sequence program.\n", "slow_sweep = f\"\"\"\n", " move {n_averages}, R0 # Average iterator.\n", "\n", "avg_loop:\n", " reset_ph\n", " upd_param 200\n", " acquire 0, 0, {MAXIMUM_SCOPE_ACQUISITION_LENGTH}\n", "\n", " loop R0, @avg_loop\n", "\n", " stop\n", "\"\"\"\n", "\n", "# Add sequence to single dictionary and write to JSON file.\n", "sequence = {\n", " \"waveforms\": {},\n", " \"weights\": {},\n", " \"acquisitions\": acquisitions,\n", " \"program\": slow_sweep,\n", "}\n", "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(sequence, file, indent=4)\n", " file.close()" ] }, { "cell_type": "markdown", "id": "dfc908de", "metadata": {}, "source": [ "Next, we prepare the QRM for the measurement:" ] }, { "cell_type": "code", "execution_count": 13, "id": "f166eb52", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:26.105822Z", "iopub.status.busy": "2025-05-07T16:54:26.105452Z", "iopub.status.idle": "2025-05-07T16:54:26.129076Z", "shell.execute_reply": "2025-05-07T16:54:26.127918Z" } }, "outputs": [], "source": [ "readout_module.sequencer0.sequence(\"sequence.json\")" ] }, { "cell_type": "markdown", "id": "ff326e54", "metadata": {}, "source": [ "Now we can run the frequency sweep. This is simply a loop where we set the frequency with QCoDeS and then run the program defined above. We measure the run time using the `%%time` Jupyter magic command." ] }, { "cell_type": "code", "execution_count": 14, "id": "dfa58814", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:26.132629Z", "iopub.status.busy": "2025-05-07T16:54:26.132238Z", "iopub.status.idle": "2025-05-07T16:54:36.027976Z", "shell.execute_reply": "2025-05-07T16:54:36.026635Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.43 s, sys: 818 ms, total: 2.25 s\n", "Wall time: 9.89 s\n" ] } ], "source": [ "%%time\n", "data = []\n", "for nco_val in nco_sweep_range:\n", " # Set the frequency\n", " readout_module.sequencer0.nco_freq(nco_val)\n", "\n", " # Run the program\n", " readout_module.arm_sequencer(0)\n", " readout_module.start_sequencer()\n", "\n", " # Wait for the sequencer to stop with a timeout period of one minute.\n", " readout_module.get_acquisition_status(0)\n", "\n", " # Move acquisition data from temporary memory to acquisition list.\n", " readout_module.store_scope_acquisition(0, \"acq\")\n", "\n", " # Get acquisition list from instrument.\n", " data.append(readout_module.get_acquisitions(0)[\"acq\"])\n", "\n", " # Clear acquisition data so we do not average the results from different frequencies\n", " readout_module.sequencer0.delete_acquisition_data(\"acq\")" ] }, { "cell_type": "markdown", "id": "b13dbc14", "metadata": {}, "source": [ "Plotting the acquired integration data, we can see the frequency behavior of the QRM." ] }, { "cell_type": "code", "execution_count": 15, "id": "b4451526", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:36.032294Z", "iopub.status.busy": "2025-05-07T16:54:36.031844Z", "iopub.status.idle": "2025-05-07T16:54:36.320095Z", "shell.execute_reply": "2025-05-07T16:54:36.319521Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "I_data = (\n", " np.asarray([d[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"][0] for d in data])\n", " / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n", ")\n", "Q_data = (\n", " np.asarray([d[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"][0] for d in data])\n", " / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n", ")\n", "plot_amplitude(nco_sweep_range, I_data, Q_data)" ] }, { "cell_type": "markdown", "id": "6bec2460", "metadata": {}, "source": [ "We can see that the output amplitude decreases with frequency, this is expected due to the analog filters. We can also analyze the accumulated scope data with a spectrogram. This takes a few seconds, as there are 16384 data points per frequency step. Note that the time axis of the spectrogram refers to measurement time (16.4us * 200 steps $\\approx$ 3.3ms) and not the wall clock time, which is significantly longer." ] }, { "cell_type": "code", "execution_count": 16, "id": "9f36a4a3", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:36.321629Z", "iopub.status.busy": "2025-05-07T16:54:36.321470Z", "iopub.status.idle": "2025-05-07T16:54:38.253699Z", "shell.execute_reply": "2025-05-07T16:54:38.253075Z" }, "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_scope = (\n", " np.asarray([d[\"acquisition\"][\"scope\"][\"path0\"][\"data\"] for d in data]).flatten()\n", " + 1j * np.asarray([d[\"acquisition\"][\"scope\"][\"path1\"][\"data\"] for d in data]).flatten()\n", ")\n", "\n", "plot_spectrogram(data_scope)" ] }, { "cell_type": "markdown", "id": "fb5800da", "metadata": {}, "source": [ "### Spectroscopy using Q1ASM\n", "Now we will run the same spectroscopy experiment using Q1ASM to change the NCO frequency in real time. First, we set up the QRM for continuous wave output and binned acquisition with many bins. This is significantly faster than using QCoDeS. The maximum number of points that can be measured this way is 131072 per sequencer. This corresponds to the number of bins available for acquisition per sequencer.\n", "\n", "The sequencer program can fundamentally only support integer values. However, the NCO has a frequency resolution of 0.25 Hz and supports $10^9$ phase values. Therefore, frequencies in the sequencer program must be given as integer multiple of $1/4$ Hz, and phases as integer multiple of $360/10^9$ degree." ] }, { "cell_type": "code", "execution_count": 17, "id": "d9d89b24", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.255644Z", "iopub.status.busy": "2025-05-07T16:54:38.255485Z", "iopub.status.idle": "2025-05-07T16:54:38.259132Z", "shell.execute_reply": "2025-05-07T16:54:38.258611Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200 steps with step size 5.0 MHz\n" ] } ], "source": [ "n_steps = 200\n", "\n", "step_freq = (stop_freq - start_freq) / n_steps\n", "print(f\"{n_steps} steps with step size {step_freq / 1e6} MHz\")\n", "\n", "# Convert frequencies to multiples of 0.25 Hz\n", "nco_int_start_freq = int(4 * start_freq)\n", "nco_int_step_freq = int(4 * step_freq)" ] }, { "cell_type": "code", "execution_count": 18, "id": "9b84a033", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.260743Z", "iopub.status.busy": "2025-05-07T16:54:38.260593Z", "iopub.status.idle": "2025-05-07T16:54:38.264986Z", "shell.execute_reply": "2025-05-07T16:54:38.264476Z" } }, "outputs": [], "source": [ "acquisitions = {\"acq\": {\"num_bins\": n_steps, \"index\": 0}}\n", "\n", "setup = f\"\"\"\n", " move {n_averages}, R2\n", "\n", "avg_loop:\n", " move 0, R0 # frequency\n", " move 0, R1 # step counter\n", "\"\"\"\n", "\n", "# To get a negative starting frequency, we subtract a positive number from 0\n", "if start_freq <= 0:\n", " setup += f\"\"\"\n", " sub R0, {-nco_int_start_freq}, R0\n", " \"\"\"\n", "else:\n", " setup += f\"\"\"\n", " add R0, {nco_int_start_freq}, R0\n", " \"\"\"\n", "\n", "spectroscopy = (\n", " setup\n", " + f\"\"\"\n", " reset_ph\n", " set_freq R0\n", " upd_param 200\n", "\n", "nco_set:\n", " set_freq R0 # Set the frequency\n", " add R0, {nco_int_step_freq}, R0 # Update the frequency register\n", " upd_param 200 # Wait for time of flight\n", " acquire 0, R1, {MAXIMUM_SCOPE_ACQUISITION_LENGTH}\n", " add R1, 1, R1\n", " nop\n", " jlt R1, {n_steps}, @nco_set # Loop over all frequencies\n", "\n", " loop R2, @avg_loop\n", "\n", " stop # Stop\n", "\"\"\"\n", ")\n", "\n", "# Add sequence to single dictionary and write to JSON file.\n", "sequence = {\n", " \"waveforms\": {},\n", " \"weights\": {},\n", " \"acquisitions\": acquisitions,\n", " \"program\": spectroscopy,\n", "}\n", "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(sequence, file, indent=4)" ] }, { "cell_type": "markdown", "id": "83dcc266", "metadata": {}, "source": [ "Now we prepare the QRM for measurement." ] }, { "cell_type": "code", "execution_count": 19, "id": "f5d0a954", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.266617Z", "iopub.status.busy": "2025-05-07T16:54:38.266464Z", "iopub.status.idle": "2025-05-07T16:54:38.288372Z", "shell.execute_reply": "2025-05-07T16:54:38.287551Z" } }, "outputs": [], "source": [ "readout_module.sequencer0.sequence(\"sequence.json\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "74fafbf0", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.290757Z", "iopub.status.busy": "2025-05-07T16:54:38.290414Z", "iopub.status.idle": "2025-05-07T16:54:38.397730Z", "shell.execute_reply": "2025-05-07T16:54:38.396977Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status:\n", "Status: WARNING, State: RUNNING, Info Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_OVERWRITTEN_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_SCOPE_OVERWRITTEN_PATH_1, Warning Flags: OUTPUT_OVERFLOW, Error Flags: NONE, Log: []\n", "CPU times: user 49.9 ms, sys: 38 ms, total: 88 ms\n", "Wall time: 102 ms\n" ] } ], "source": [ "%%time\n", "readout_module.arm_sequencer(0)\n", "readout_module.start_sequencer()\n", "\n", "print(\"Status:\")\n", "print(readout_module.get_sequencer_status(0))\n", "\n", "# Wait for the sequencer to stop with a timeout period of one minute.\n", "readout_module.get_acquisition_status(0)\n", "\n", "data = readout_module.get_acquisitions(0)[\"acq\"]" ] }, { "cell_type": "markdown", "id": "95db8fa5", "metadata": {}, "source": [ "Note that the same measurement as before is now two orders of magnitude faster. If we plot the integrated data, we get the same results as before." ] }, { "cell_type": "code", "execution_count": 21, "id": "67464dc5", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.399636Z", "iopub.status.busy": "2025-05-07T16:54:38.399453Z", "iopub.status.idle": "2025-05-07T16:54:38.482545Z", "shell.execute_reply": "2025-05-07T16:54:38.481939Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For plotting, convert the NCO integer values back to frequencies\n", "nco_sweep_range = (np.arange(n_steps) * nco_int_step_freq + nco_int_start_freq) / 4.0\n", "\n", "I_data = (\n", " np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"])\n", " / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n", ")\n", "Q_data = (\n", " np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"])\n", " / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n", ")\n", "plot_amplitude(nco_sweep_range, I_data, Q_data)" ] }, { "cell_type": "markdown", "id": "71422bd7", "metadata": {}, "source": [ "#### NCO input delay compensation\n", "By default, the input and output of the QRM are multiplied with the same NCO value. As the output path has a time of flight of about 149 ns between the NCO and playback (provided no RTP filters are activated), this means that there is a short time window after frequency/phase updates where demodulation is updated, but playback is still using the old value. There is also always a (fixed) relative phase between playback and demodulation. We can showcase this by using a similar program as before, but with less points, so that the frequency steps are more clearly visible." ] }, { "cell_type": "code", "execution_count": 22, "id": "1a5d51e8", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.484244Z", "iopub.status.busy": "2025-05-07T16:54:38.484091Z", "iopub.status.idle": "2025-05-07T16:54:38.487809Z", "shell.execute_reply": "2025-05-07T16:54:38.487267Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20 steps with step size 50.0 MHz\n" ] } ], "source": [ "n_steps = 20\n", "n_averages = 1000\n", "\n", "step_freq = (stop_freq - start_freq) / n_steps\n", "print(f\"{n_steps} steps with step size {step_freq / 1e6} MHz\")\n", "\n", "# Convert frequencies to multiples of 0.25 Hz\n", "nco_int_start_freq = int(4 * start_freq)\n", "nco_int_step_freq = int(4 * step_freq)\n", "\n", "# For plotting, convert the NCO integer values back to frequencies\n", "nco_sweep_range = np.arange(nco_int_start_freq, 4 * stop_freq, nco_int_step_freq) / 4.0" ] }, { "cell_type": "markdown", "id": "c71e11b3", "metadata": {}, "source": [ "To make the effect of NCO delay compensation more apparent, we modify the spectroscopy program for short integration time and acquire immediately after the frequency update, without waiting for time of flight. This means that the output at the new frequency only arrives at the input AFTER integration in the current loop iteration has finished. Without further modifications of the program this leads to an off-by-one error. Therefore, we increase the frequency as the first step in the loop." ] }, { "cell_type": "code", "execution_count": 23, "id": "8baf2e33", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.489542Z", "iopub.status.busy": "2025-05-07T16:54:38.489381Z", "iopub.status.idle": "2025-05-07T16:54:38.493765Z", "shell.execute_reply": "2025-05-07T16:54:38.493233Z" } }, "outputs": [], "source": [ "acquisitions = {\"acq\": {\"num_bins\": n_steps, \"index\": 0}}\n", "\n", "setup = f\"\"\"\n", " move {n_averages}, R2\n", "\n", "avg_loop:\n", " move 0, R0 # frequency\n", " move 0, R1 # step counter\n", "\"\"\"\n", "\n", "# To get a negative starting frequency, we subtract a positive number from 0\n", "if start_freq <= 0:\n", " setup += f\"\"\"\n", " sub R0, {-nco_int_start_freq}, R0\n", " \"\"\"\n", "else:\n", " setup += f\"\"\"\n", " add R0, {nco_int_start_freq}, R0\n", " \"\"\"\n", "\n", "spectroscopy = (\n", " setup\n", " + f\"\"\"\n", " reset_ph\n", " set_freq R0\n", " upd_param 200\n", "\n", "nco_set:\n", " # Due to time of flight, the new frequency will only arrive at the input AFTER integration is done\n", " # Therefore, we already increase the frequency before the first measurement.\n", " add R0, {nco_int_step_freq}, R0\n", " nop\n", " set_freq R0\n", "\n", " # we removed upd_param, so that acquisition starts the moment the frequency is updated\n", " acquire 0, R1, 1200 # Update the NCO and immediately acquire\n", " add R1, 1, R1\n", " nop\n", " jlt R1, {n_steps}, @nco_set # Loop over all frequencies\n", "\n", " loop R2, @avg_loop\n", "\n", " stop # Stop\n", "\"\"\"\n", ")\n", "\n", "# Add sequence to single dictionary and write to JSON file.\n", "sequence = {\n", " \"waveforms\": {},\n", " \"weights\": {},\n", " \"acquisitions\": acquisitions,\n", " \"program\": spectroscopy,\n", "}\n", "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(sequence, file, indent=4)" ] }, { "cell_type": "markdown", "id": "fd706998", "metadata": {}, "source": [ "As a baseline, we will do a measurement with delay compensation disabled." ] }, { "cell_type": "code", "execution_count": 24, "id": "0d058e20", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.495451Z", "iopub.status.busy": "2025-05-07T16:54:38.495293Z", "iopub.status.idle": "2025-05-07T16:54:38.534357Z", "shell.execute_reply": "2025-05-07T16:54:38.533441Z" } }, "outputs": [], "source": [ "readout_module.sequencer0.sequence(\"sequence.json\")\n", "readout_module.sequencer0.integration_length_acq(140)\n", "readout_module.sequencer0.nco_prop_delay_comp_en(False)" ] }, { "cell_type": "code", "execution_count": 25, "id": "d5025ff1", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.536754Z", "iopub.status.busy": "2025-05-07T16:54:38.536480Z", "iopub.status.idle": "2025-05-07T16:54:38.651242Z", "shell.execute_reply": "2025-05-07T16:54:38.650545Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status:\n", "Status: OKAY, State: RUNNING, Info Flags: ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "readout_module.arm_sequencer(0)\n", "readout_module.start_sequencer()\n", "\n", "print(\"Status:\")\n", "print(readout_module.get_sequencer_status(0))\n", "\n", "# Wait for the sequencer to stop with a timeout period of one minute.\n", "readout_module.get_acquisition_status(0)\n", "\n", "data = readout_module.get_acquisitions(0)[\"acq\"]\n", "I_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) / 140\n", "Q_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) / 140\n", "plot_amplitude(nco_sweep_range, I_data, Q_data)" ] }, { "cell_type": "markdown", "id": "fcbe2942", "metadata": {}, "source": [ "Even though we only measured a small number of points, we can see that this is not compatible with the previous spectroscopy measurements. What happened is that `set_freq` updates the NCO frequency immediately. However, there is a time of flight of about 149 ns between the NCO and the output of the device. Thus, the signal will be demodulated at $f_0 + 100$ MHz immediately after `set_freq`, but the incoming signal is still modulated at $f_0$ for another 149 ns - longer than the integration time chosen above. The integrated signal will therefore be approximately zero.\n", "\n", "Now we run the same experiment again, with delay compensation enabled." ] }, { "cell_type": "code", "execution_count": 26, "id": "9722922e", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.653108Z", "iopub.status.busy": "2025-05-07T16:54:38.652957Z", "iopub.status.idle": "2025-05-07T16:54:38.683343Z", "shell.execute_reply": "2025-05-07T16:54:38.682495Z" } }, "outputs": [], "source": [ "readout_module.sequencer0.sequence(\"sequence.json\")\n", "readout_module.sequencer0.nco_prop_delay_comp_en(True)" ] }, { "cell_type": "markdown", "id": "03f25727", "metadata": {}, "source": [ "This ensures that, for demodulation, the NCO only updates after the time of flight, i.e. that frequency and phase of modulation and demodulation always match." ] }, { "cell_type": "code", "execution_count": 27, "id": "88f84d0c", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.686059Z", "iopub.status.busy": "2025-05-07T16:54:38.685678Z", "iopub.status.idle": "2025-05-07T16:54:38.824679Z", "shell.execute_reply": "2025-05-07T16:54:38.824176Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status:\n", "Status: OKAY, State: RUNNING, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n" ] }, { "data": { "image/png": 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WxeHd2BN8VhgRWQwWMCIialTJOfkIW7oRhzKy4WZvix0PDsXYwNaiY1ETYqNQ4McxEXi5bzAA4LWYY5i+aR90fFYYEVkAFjAiImo0+9Kuou8PG3EhrwBtmjlj3yMjEObdUnQsaoLkMhnmD+qBL+7sDRmARUeTcdfvO1FUXiE6GhGRUVjAiIioUaxOuoxBy7cgp6QMvbzcsXfycLRr7iI6FjVxT/XsgD/vGQg7pQLrzqZi0PItuFbEZ4URkfliASMiogb35eHTuOv3nSjV6TGqnQ92PjgULR3tRcciMzEuqDWiHxiK5va2OJiRjb4/bMD5XK3oWERE9cICRkREDWrx0WQ8vfkAJABPdA/EX/cOhKONSnQsMjN9fVpi7+Th8FM74dz1AvRdthEXrvOBzURkfljAiIiowey8dAVPbt4HAHg1vAu+GtYHSjl/9VD9BLqpse+R4ejq0QxXi0ox8tftyC8tFx2LiKhO+FuQiIgaxNlcLe7+Yyd0BgkTOwXg7QHdIOMzvshIGicHbJxwB1o5O+B0dj7G/xXD2RGJyKywgBERkcldLynDyF+243ppOfq0aoElo/qyfJHJeDk7YO19g+GgUmLLhQw8t+2Q6EhERLXGAkZERCZVoTfg3j9jkJyrhY+LI1bfOwh2SqXoWGRhunu6YfmYCADAwkOn8dXhM4ITERHVjlkWsLy8PMyePRvBwcHw9PSERqOBRqNBx44dMX36dGRlZVW77fTp0yvXv9XL0dERZWVljfhpiIgshyRJmLn1AKIvXYGjSon14wfDw4mzHVLDGBfUGvMHdgcAzNhyANsuZAhORER0e2ZXwPR6PQYPHgxHR0fExMQgIyMDmZmZyMzMxL59+xAUFITw8HAUFxffcvuvv/66cv1bvZydnWFjY9PIn4qIyDJ8efgMvj6SBBmAn8f1RxeP5qIjkYV7qW8wHg5uA70k4d4/d+JMdp7oSERENTK7ArZq1Sr4+flh7ty5cHNzq3JPgVqtxsyZMzF8+HAsWrSozvsuKCiAg4MD71MgIqqHLefTMXPrQQDA+4N7YnR7X8GJyBrIZDJ8O6Iv+vm0RH5ZBUb+Go2cYj6omYiaLrMrYAkJCRg4cGCN60RFRSEhIaHO+16zZg2GDBlS32hERFbrdHYe7vsrBgZJwiNd2+KFPp1ERyIrYqtU4K97BsHf1Qnnrxfgrj92olyvFx2LiOiWzK6ApaWlwdPTs8Z1vLy8kJKSUqf9pqen44033sDzzz9vTDwiIquTXVyKkb9EQ1tWgQgfD3w9LIwjCajRtXC0w7rxg+Fiq8LulCw8sXEfJEkSHYuI6D/MroDp9XooFIoa11EqldDpdLXe59GjRzFgwAC8//77aNeuXbXrlZWVQavVVnkREVmzcr0ed/+xExfyCuDv6oS/7h0IW2XNP6OJGkqnFs3w612RkMtkWHriHD7af1J0JCKi/zC7AmZqmzdvxn333YcVK1bg7rvvrnHd+fPnQ61WV758fHwaKSURUdMjSRKmb9yP3SlZcLFVYf34KLg72ImORVbuzjbe+HRIKADgpejDWJtctxExREQNzewLWFJSEgICApCcnFznbS9evIjp06dj+/btCA0Nve36c+bMQX5+fuUrNTW1PpGJiCzCxwdO4vsTZyGXyfDrXZHo2MJVdCQiAMDTPYMwvUcgJAATV+3G8cwc0ZGIiCqZfQELDAzEhQsX0L59+zpv+8orr+Cdd96Bn59frda3tbWFi4tLlRcRkTVal5yKF7cfBgB8ckcv3NnGW3Aiov8nk8nw2ZDeiPL3RFGFDqN/24ErBbd+PA0RUWMzuwJWm/u7dDodlErlbdfZsWMH7rvvPlPGIyKyePFZuZi4ehckAI93b49nenUQHYnoP1QKOX6/ewCC3NRI1RZh7O87UFJR+/vDiYgaitkVMG9vb2RmZta4TkZGBnx9a37+zMmTJxEUFASVSmXKeEREFi2rsASjfo1GYbkOg/w8sXBoH854SE2Wq50t1o0fjOb2tjiYkY1H1sVyZkQiEs7sClhwcDCio6NrXCc6OhrBwcE1rpOeno5WrVqZMhoRkUUr1ekw7o8dSNEWoV1zF/x+9wCoFGb3a4SsTNvmLvjrnoFQyeX49dQlvLn7uOhIRGTlzO4359ixY5GWloa5c+ciJyenyl+ytFotFi5ciA0bNmDatGk17qewsBCOjo4NHZeIyCJIkoSp6/diX9o1uNrZYP3NqwpE5iCytQbfDA8DALy55wR+OXlBcCIismZmV8AUCgW2b9+OkpISREZGwsvLCxqNBhqNBqGhoUhMTERsbGxluYqJiUHbtm3/88wulUoFjUYj4iMQEZmd+XEJWJF4AQqZDH/cPQDt3dSiIxHVyaMh7fBCn04AgMlrY7E/7argRERkrWQSB0PXm1arhVqtRn5+PmdEJCKL9efpS7jnzxgAwDfDwvB4j0CxgYjqSW8w4K4/dmJtcio8HO1w8NGR8FU7iY5FRBaitt3A7K6AERFR4zl6JQcPrdkDAJjRqwPLF5k1hVyOFWP7o6tHM2QVlWLUr9EoKKsQHYuIrAwLGBER3VJGQTFG/RaNEp0ed7ZphQV39BIdichoTjYqrL1vMDwc7RB/9ToeWL0beoNBdCwisiIsYERE9B/FFTqM+S0aGQXF6Ojuil/GRUIp568Msgy+aiesuW8wbBVyrDubipd3HBEdiYisCH+bEhFRFQZJwuS1sTh8JQdu9jeeo6S2sxEdi8ikerdqgR9G9wMAfLT/JJYcSxaciIisBQsYERFV8ebu4/j99CWo5HKsuncQApo5i45E1CAmdArA3IiuAIAnNu1DzKUrghMRkTVgASMiokorEy/grT0nAADfjghDhK+H4EREDWtu/xBM6OgPnUHC3X/G4Fyu9vYbEREZgQWMiIgAAAfSr+GRdbEAgBfDOmNy13aCExE1PJlMhu9HhaN3K3fklpRh5K/bcb2kTHQsIrJgLGBERISU/EKM+S0aZXoDRrf3wfyB3UVHImo09iolVt87CD4ujkjK0eK+v2JQoefMiETUMFjAiIisXGF5BUb/Fo2solJ0adkMK8b2h4IzHpKV0Tg5YN34wXBUKbH94hXM2HIAkiSJjkVEFoi/YYmIrJhBkvDg6t04kXUdLR3tsG78YDjZqETHIhKiq0dz/DyuP2QAvjmahIWHTouOREQWiAWMiMiKvbLzCNYkp8JWIcfqewfBV+0kOhKRUKPb++L9wT0BALO2HcKmc2mCExGRpWEBIyKyUj+cOIv39yYCAJaMDEeYd0vBiYiahhf6dMKjXdvBIEkY/9cunLx2XXQkIrIgLGBERFYoNiUL0zbsAwC81q8LHghuIzgRUdMhk8nw9fA+iPT1QEF5BUb9Go1rRaWiYxGRhWABIyKyMheuF2DcHztQYTDgng6t8WZkN9GRiJocG4UCf94zEG2aOeNiXiHG/b4DZTq96FhEZAFYwIiIrIi2rByjfo1GdnEZeni6YdnoCMhlMtGxiJokNwc7rB8/GGpbFeLSruKxDXs5MyIRGY0FjIjISugMBkz4axdOZefBy9kBa+4dBAeVUnQsoiYtyN0Vv989EAqZDD8lnMd7exNERyIiM8cCRkRkJV7cfhibzqfDXqnAmnsHoZWLo+hIRGbhjgAvLLyzNwDglZ1H8deZy4ITEZE5YwEjIrIC3x5NwqcHTwEAfhwTgZ5e7oITEZmX6T2C8EyvDgCAh9bswdErOYITEZG5YgEjIrJwOy5ewVOb9wMA3o7shns6+IkNRGSmPr6jF4YGeKG4QodRv0UjXVskOhIRmSEWMCIiC5ack497/twJnUHCxE4BeLVfF9GRiMyWUi7Hr3cNQEd3V2QUFGPM7ztQXKETHYuIzAwLGBGRhbpeUoZRv0bjemk5erdyx5JRfSHjjIdERlHb2WDd+MFwd7DFkSs5eHjNHhg4MyIR1QELGBGRBarQG3DvnzFIztXCx8URq+8dBDslZzwkMoWAZs74655BUMnl+PPMZbwec0x0JCIyIyxgREQWRpIkzNhyANGXrsBRpcS68YOhcXIQHYvIokT4emDxiL4AgHfj4rE84bzgRERkLljAiIgszBeHz+Cbo0mQAfh5XH909WguOhKRRZrUtS1e7hsMAJiyPg5xqVmCExGROWABIyKyIDGXruDZrQcBAO8P7onR7X0FJyKybO8O7I5xgb4o1xsw7vedSOPMiER0GyxgREQWIrekDA/enBDg4eA2eKFPJ9GRiCyeXCbDT2MiEOLRHNeKSzFpbSwn5SCiGrGAERFZAEmS8PjGvUgvKEa75i74clgfznhI1EgcbVT49a5IOKiU2HHpChbsPyk6EhE1YSxgREQW4IcT5/DH6ctQymX4eWx/ONmoREcisirt3dT4bEgoAODVnUdx9EqO4ERE1FSxgBERmblzuVo8s+UAAODtyG7o6eUuOBGRdZoS0g53BbVGhcGAiat3oai8QnQkImqCWMCIiMxYhd6Aiat3o6hCh0hfD7wY1ll0JCKrJZPJ8O3wMLRydkBSjhbPbTskOhIRNUEsYEREZuzN3cdxKCMbrnY2+GlMfyjk/LFOJJKbgx1+HB0BGYBvjyVjddJl0ZGIqInhb2oiIjO1+3Im5sXFAwC+Hd4XPmpHwYmICAAG+XvihZtXo6eu34uMgmLBiYioKWEBIyIyQ3mlN6aclwA80rUt7u3oJzoSEf3DOwO6oZumOXJKyjBp7R5OTU9ElVjAiIjMjCRJeGLjPqRqi9CmmTM+G9JbdCQi+hcbhQI/j+0Pe6UC2y9ewacHTomORERNBAsYEZGZ+SnhPH49dQkKmQwrxvaHsy2nnCdqioLcXfHJHTempp+z8wiOZ3JqeiJiASMiMivnc7V4avN+AMCbkSHo3aqF4EREVJNp3dtjTHsflN+csbS4Qic6EhEJxgJGRGQmdAYDHlyzB4XlOkT4eODlvsGiIxHRbchkMnw3MhyeTvY4nZ2PF7Zzanoia8cCRkRkJt7ecwL7069BbavCT2MiOOU8kZlwd7DDstERAICvjyRhXXKq4EREJBJ/exMRmYG41Cy8E3tjyvlvhoehtauT4EREVBd3BHjhud6dAACPro/FFU5NT2S1WMCIiJq4/NJyPLB6NwyShIeC22BCpwDRkYioHuYN7I6uHs2QXVyGyetiOTU9kZViASMiauKe2rwfl/OL4O/qhC/u5JTzRObKVqnAz2MjYadUYOuFDCw8dFp0JCISgAWMiKgJW5FwHisSL1ROOe9iayM6EhEZoWMLVyyI6gUAmB19GPFZuYITEVFjYwEjImqiLl4vwJM3p5x/PaIrwrxbCk5ERKYwvUcgRrbzrpyavoRT0xNZFRYwIqImSGcw4KE1e6Atq0Bf75Z4pV8X0ZGIyERkMhmWjAyHh6MdTl7Lw+zow6IjEVEjYgEjImqC5sXGIy7tKlxsVVg+JgJKTjlPZFFaOtrjh9H9AABfHD6DjefSBCciosbC3+hERE3MvrSreGvPCQDAV3f2gX8zZ8GJiKgh3NnGGzNDOwAAHlkXi6zCEsGJiKgxsIARETUh2rIbU87rJQkTOwXggeA2oiMRUQN6b1APBLdshqtFpXhkXSwkTk1PZPFYwIiImpBnNh/AxbxCtFY74qthfUTHIaIGZqdU4uex/WGrkGPT+XR8efiM6EhE1MDMpoDl5eVh9uzZCA4OhqenJzQaDTQaDTp27Ijp06cjKyurxu3LysrwzjvvIDAwsHLbv19t27bFa6+9htLS0kb6NERE//XLyQv4MeE85DIZlo/pD7Udp5wnsgadWzbDRzenpn9h+yEkXr0uOBERNSSzKGB6vR6DBw+Go6MjYmJikJGRgczMTGRmZmLfvn0ICgpCeHg4iouLq93H+++/jxMnTiAuLq5y279fhw4dwsWLF/HOO+804qciIvp/KfmFeGLjPgDAq+Fd0M/XQ3AiImpMT/UMwvC23ii7OTV9qY5T0xNZKplkBoON//jjD6xcuRJ//vlntevMmDED/v7+mDVr1i2Xt2/fHjExMfDy8rrl8tTUVIwYMQLx8fG1zqXVaqFWq5Gfnw8XF5dab0dE9E96gwEDf9qCPalZ6NOqBfZMGsZZD4msUFZhCbosXoOrRaWYGdoBnw7pLToSEdVBbbuBWfyGT0hIwMCBA2tcJyoqCgkJCdUuz8rKgqenZ7XLW7Vqhd69+YOOiBrf+3sTsSc1C042Sk45T2TFPJzssXTUjanpPzt4GpvPc2p6IktkFr/l09LSaixPAODl5YWUlJRql8tkMshksmqXy+VyLF68uN4ZiYjq42D6NczdfQwA8MXQPmjTnFfTiazZ8LbeeLpnEABg8tpYXC3i1PRElsYsCpher4dCoahxHaVSCd1txkvHxcVh7NixaNOmTeUEHH9f+fr6669vu31ZWRm0Wm2VFxFRfRWWV2Di6t3QGSSM7+iHh7twynkiAj4Y3BOdWrgiq6gUU9bHcWp6IgtjFgXMFEpKSvDJJ5/gvffeQ3JycuUEHCkpKfjpp5+wbt06LFiwoMZ9zJ8/H2q1uvLl4+PTSOmJyBLN3HIQ568XwMfFEV8PC6vxKj0RWQ971f9PTb/+bBq+PpIkOhIRmZBZFrCkpCQEBAQgOTm51tuUl5fjww8/RFBQUJWraQqFAu3bt8fSpUvx1Vdf1biPOXPmID8/v/KVmppa789ARNbtj9OX8P2Js5ABWD4mAs3sbUVHIqImpItHc7w/uCcA4Pnth3DqWp7YQERkMmZZwAIDA3HhwgW0b9++1tvY2trCz8+v2uUeHh4oKytDWVlZjftwcXGp8iIiqqvU/CI8tmEvAGBOeBf0b60RnIiImqJnenXA0AAvlOr0mLh6F8p0etGRiMgEzKKA1eb+Lp1OB6VSWe1yOzu72w7vsbOzQ0kJb3YlooajNxjw8No9yCstRy8vd7zRP0R0JCJqouQyGX4Y3Q/uDrY4kXUdr+w8KjoSEZmAWRQwb29vZGZm1rhORkYGfH19q12uVCpvexNraWkp7Ozs6pWRiKg2Ptp/EjGXM+GoUmLF2P5QKczixzARCaJxcqicmv7jAyex7UKG4EREZCyz+M0fHByM6OjoGteJjo5GcHBwtcs9PT2Rllb98zRycnKgUqlYwIiowRy5ko3XYm78Bfvzob3RjlPOE1EtjGzngyd73JiaftLaPcguLhWciIiMYRYFbOzYsUhLS8PcuXORk5NT5UqWVqvFwoULsWHDBkybNq3afUyfPh1PPvkk0tPT/7MsMzMT06ZNw9SpUxskPxFRUXkFJq66MeX8PR1a45GubUVHIiIz8mFUT3RwV+NKYQmmcmp6IrNmFgVMoVBg+/btKCkpQWRkJLy8vCqf4xUaGorExETExsbC0dERABATE4O2bdtWeU7X448/jmHDhuHOO++ssr2npycGDhyI0NBQzJkzR9RHJCILN2vbISTnatHK2QGLhvfllPNEVCcON6emt1HIsSY5Fd8erf1M0ETUtMgk/gml3rRaLdRqNfLz8zkjIhFVa9WZy7jrj52QAYh+cCgG+nmKjkREZurj/Sfx/PZDsFcqcHTqKAS5u4qOREQ31bYbmMUVMCIic5WuLcLUm1POz+7bmeWLiIzybO+OuMPfCyU6PSau3o1yPaemJzI3LGBERA3EIEmYtDYWuSVl6OHphrciu4mORERm7u+p6d3sbXEsMxevxRwTHYmI6ogFjIiogXy8/ySiL12Bw80p520UCtGRiMgCeDk7YMnIcADAh/sSEX2RU9MTmRMWMCKiBnAsM6fyoamf3hGKQDe14EREZEnGBPri8e7tAQAPr41FDqemJzIbLGBERCZWXKHDxFW7UWEwYFygL6Z2ayc6EhFZoAVRvRDo5oKMgmI8tmEvp6YnMhMsYEREJvb8tkM4k5MPL2cHLB7BKeeJqGE42qjw89hIqORyrEpKwZLjZ0VHIqJaYAEjIjKhtckp+OZoEgBg2ah+cHOwE5yIiCxZd083vDuwOwBg5taDSM7JF5yIiG6HBYyIyESuFBRjyvo4AMDzfTohKsBLcCIisgbP9+mEQX6eN4Y/c2p6oiaPBYyIyAQMkoTJ62KRXVyGEI/meHdAd9GRiMhKyGUy/Di6H5rZ2eDIlRzM3XVcdCQiqgELGBGRCXx+8BS2XsiAvVKBn8f1h62SU84TUeNp5eKI725OTf/+3gTEXLoiOBERVYcFjIjISCeycvHSjiMAgI/v6IUO7q5iAxGRVborqDWmhrSDBOChNXuQW1ImOhIR3QILGBGREUpuTjlfrjdgVDsfPN49UHQkIrJinwwJRbvmLkgrKMbjGzk1PVFTVKsCptVqoVaroVAoTPqys7PDoUOHGvozEhE1mNnRh3EqOw8aJ3ssGRnOKeeJSCgnGxV+HtsfSrkMf5y+jB9OnBMdiYj+RVmblQoKClBQUIB58+YhLCzMJAe+cuUKJk6ciGvXrplkf0REjW3TuTR8cfgMAOCHUf3QwpFTzhOReD293PF2ZDfM2XkUz2w5gP6+HmjT3EV0LCK6qVYF7O+/6Hbt2hWRkZEmOXBKSkqVfRMRmZPrJWWYumEvAGBmaAcMbdNKcCIiov/3YlhnbD6fjl0pWZi8Lha7Hh4GOb9zETUJtRqC6OXlhSVLlqB///4mO7Cvry++++47k+6TiKixzNx6EBkFxWjf3AXzB/YQHYeIqAqFXI4fRveDk40SsalX8dnBU6IjEdFNtZ6Ew9XVFZMmTcLWrVtNdvBHH30Ujo6OJtsfEVFjWJOUgp8SzkMuk2HZ6H6wV9VqMAERUaPyc3XGgqheAIBXdh5FUk6+4EREBNShgLVu3RpXrlzBnXfeiaCgIHz11VcoLCxsyGxERE1OTnEpHt94Y+jhi2Gd0Me7peBERETVe6xbewwJ8EKpTo/Ja2OhNxhERyKyerUuYN27d0dcXBwOHTqE3r1747nnnoO3tzeef/55nD9/viEzEhE1GU9vPoCsolJ0dHfFG/1DRMchIqqRTCbDdyPC4WKrwv70a1iw/6ToSERWr87PAevRoweWLVuGlJQUPP/88/jll1/Qvn17jBkzBtHR0Q2RkYioSfjj9CX8cuoiFDeHHtopOfSQiJo+H7UjPr0jFADwv13HcOpanthARFau3g9ibtmyJf73v//h8uXLWLFiBbKzs3HHHXegU6dOWLRoEYqLi02Zk4hIqKtFJZi+aR8AYE54MHp6uQtORERUe5O7tsXwtt4o1xswae0e6DgUkUiYehewvymVSkyYMAFxcXE4cOAAevTogRkzZqBVq1Z48cUXcfHiRVPkJCISRpIkPLlpP7KLy9ClZTP8L6Kr6EhERHUik8mweERfuNrZ4PCVHLy/N0F0JCKrZXQB+6devXrhxx9/RGpqKmbNmoUVK1agXbt2GDduHGJiYkx5KCKiRvPrqYv488xlKOU3hh7aKBSiIxER1ZmXswMWDu0NAHhz9wmcyMoVnIjIOpm0gP2tZcuWeP3113H58mX89NNPSElJweDBgxEREdEQhyMiajCZhcV4avMBAMD/+nVFiMZNcCIiovp7oHMAxgb6osJgwOS1sSjX60VHIrI6DVLA/paamopDhw7h3LlzUCgU6NSpU0MejojIpCRJwrQN+5BbUoZumuaYE95FdCQiIqPIZDJ8MywMbva2OJ6Vi3mx8aIjEVmdBilg0dHRGDNmDNq3b48VK1Zg5syZuHTpEr755puGOBwRUYP4KeE81p1NhUoux7LREVApGvRvVkREjcLDyR5fDesDAHg3Lh5Hr+QITkRkXUz2baKkpATffvstOnfujCFDhiAtLQ1LlixBSkoK3nrrLXh5eZnqUEREDS5dW4QZW24MPXwzMgTBLZsJTkREZDr3dfTHvR38oDNImLR2D8p0HIpI1FiMLmApKSl46aWX4O3tjaeeegodO3bErl27cOTIEUyaNAm2tramyElE1GgkScJjG/Yiv6wCoV7ueDGss+hIREQm9+WdfdDCwQ6J1/Lw5u7jouMQWY16F7Ddu3fjnnvuQZs2bbBkyRJMmzYNFy5cwG+//YZ+/fqZMiMRUaP6/vhZbDqfDluFHD+M7gelnEMPicjytHC0w6LhYQCA9/cl4mD6NcGJiKxDnb5VlJWVYenSpejWrRsGDhyI5ORkfPPNN0hNTcX8+fPh4+PTUDmJiBpFSn4hZm07BAB4Z0B3dHB3FRuIiKgBjQtqjYmdAmCQJExaG4uSCp3oSEQWr9YFbOPGjfD29sbUqVPh7++P6OhoxMfHY8qUKbC3t2/IjEREjUKSJExZH4eC8gr09W6JWb07io5ERNTgFt7ZGxone5zJycfru46JjkNk8WpdwJo3b44nn3wS58+fx19//YUBAwY0YCwiosa36GgStl+8AnulAktHhUPBoYdEZAWa29ti8Yi+AIAF+09ib+pVwYmILFutvl2Ul5fjm2++wdSpU+Hn52eSA5eVlWHSpElITU01yf6IiIxx8XoBXth+GAAwf1APtHdTC05ERNR4RrbzweQubSEBmLwuFsUcikjUYGpVwK5du4affvoJiYmJJjtwZmYmli9fjoSEBJPtk4ioPgyShEfWxaKoQof+vh54plcH0ZGIiBrdJ0N6oZWzA87mavHKziOi4xBZLGVtV5QkCStWrMChQ4dMcuC8vDyT7IeIyFhfHj6DXSlZcFQpsXRUP8hlMtGRiIganaudLb4b2RfDVm7HZwdPY1xga0S21oiORWRxalXA3N3dMWLECKSnpyM9Pd1kBx88eDCCgoJMtj8ioro6l6vFS9E3hh5+MLgnApo5C05ERCTOnW288Vi39lh8LBmPrItF/LQxcLJRiY5FZFFqVcBsbW2xbt26hs5CRNSo9AYDJq+NRYlOj8F+nniiR6DoSEREwn0U1RNbzqfjYl4hZkcfxlfDwkRHIrIonOKLiKzWZwdPIy7tKpxslFgyMpxDD4mIALjY2uD7UeEAgK+PJGH7hQzBiYgsCwsYEVmlM9l5eDXmKADg46hQtHZ1EpyIiKjpGOzvhSd73LhNZMr6OGjLygUnIrIcLGBEZHV0BgMmr4tFqU6PoQFemNqtnehIRERNzvuDeyDA1Rkp2iI8v800k7AREQsYEVmhBftP4kB6NtS2Knw3MhwyDj0kIvoPJxsVlt4civjd8bPYfD5NcCIiy8ACRkRWJfHqdby+6xgA4NMhofB2cRSciIio6erfWoOZoTeejTh1/V7klZYJTkRk/ljAiMhqVOhvDD0s1xswsp03JnVpKzoSEVGTN29gD7Rr7oL0gmI8u/Wg6DhEZo8FjIisxvt7E3DkSg6a2dlg0fC+HHpIRFQLDiolfrj5kPpl8eexNjlFdCQis8YCRkRW4URWLt7acwIAsHBob3g5OwhORERkPvr6tMTzfToBAB7fuA85xaWCExGZL5MXMIPBUO2LiEiEcr0ek9buQYXBgHGBvpjYOUB0JCIis/NWZAg6uKuRWViCZ7YcEB2HyGwZXcCys7MxY8YMdOnSBc7OzlCpVNW+2rbl/RZE1PjejY3HiazrcLO3xdfDwjj0kIioHuyUSiwbHQGFTIaVJy/iz9OXREciMktKYza+du0aOnW6cTl60qRJaN26NZydnatd38vLy5jDERHV2ZEr2Xg3Nh4A8PWwMHg42QtORERkvnp5ueOlvsGYFxeP6Zv2o7+vBi0c7UTHIjIrRhWw119/HZIk4cyZM3BzczNVplvKy8vDvHnzsGnTJmRnZ0OSJABA8+bNERkZiTfeeAMeHh7Vbj9p0iSsW7cONjY21a4zePBgrFixwuTZiUiMMp0ek9bGQi9JuK+jH+7t6Cc6EhGR2Xs9oivWnU1FwtXreHLzPvx21wCOLCCqA6OGIMbFxeH+++9v8PKl1+sxePBgODo6IiYmBhkZGcjMzERmZib27duHoKAghIeHo7i4uNp95Ofn46+//qrc7lYvli8iy/LG7uM4eS0PLR3t8OWdfUTHISKyCLZKBX4Y1Q9KuQx/nL6M305dEh2JyKwYVcDOnz+P1q1bmypLtVatWgU/Pz/MnTsXbm5uVf7KolarMXPmTAwfPhyLFi1q8CxEZB4OpF/DB/sSAQDfDAuDuwOHyBARmUp3Tze8Gt4FAPDk5v3ILKz+j+BEVJVRBay0tLTGIX2mkpCQgIEDB9a4TlRUFBISEho8CxE1fSUVOkxeGwuDJOGBzgEYF9TwfygiIrI2r/brihCP5sgtKcMTG/dV3h5CRDUzqoC5u7vj2rVrpspSrbS0NHh6eta4jpeXF1JS+GBAIgL+t+sYzuTkw9PJHp8P7S06DhGRRVIp5PhxTARUcjnWJKdiecIF0ZGIzIJRBaxLly44e/asqbJUS6/XQ6FQ1LiOUqmETqercZ1Vq1ZhyJAhCAgIgIeHBzQaDXx8fNCvXz+sXLnytn+5KSsrg1arrfIioqYlLjULH+8/CQD4dkRfNLe3FZyIiMhyBbdshjf6hwAAZmw9gHRtkdhARGbAqAL298yCBQUFpsrTYDp27IhLly7hjTfewPHjx5GVlYXMzExcvHgRX331FT7//HP88ssvNe5j/vz5UKvVlS8fH59GSk9EtVF8c+ihBGByl7YY2Y7/jxIRNbTZfTujp6cb8krLMY1DEYluy6gC9uCDD2LcuHEYO3Zsow7/S0pKQkBAAJKTk2u9zbx587BmzRr07dsXLi4ule8rlUp06dIFX3zxBb766qsa9zFnzhzk5+dXvlJTU+v9GYjI9ObsOIJz1wvg7eyAT4b0Eh2HiMgqKOVyLBsdAVuFHBvPpWHpiXOiIxE1aUY9BywyMhLnzp3DlStX4O/vD41GU+NQwYCAAMTExBhzSABAYGAgLlww7TjjDh064Pz58zWuY2trC1tbDmciaop2Xc7E54dOAwC+GxkOVzv+v0pE1Fg6tnDF2wO6Y3b0YczadhBR/p7wVTuJjkXUJBlVwCZPnlynK1/1HbJXm/u7dDodlMr6fxwHB4canyNGRE1XYXkFHlkXCwB4rFt7DG3TSnAiIiLr81zvjliVdBn70q5h6vq92DLxDj6gmegWjCpgjzzyiKly1Mjb2xuZmZk1rpORkQFfX99qlz/77LP49NNPq11eUlICe3v7+kYkIoFmRx/GxbxCtFY7YkEUhx4SEYmgkMvxw6h+6Lp4LbZdzMC3R5PxeI9A0bGImhyj7gFrLMHBwYiOjq5xnejoaAQHB1e7/M8//6xx1sLk5GQEBATUOyMRibH9Qga+PpIEAFgyMhzOtirBiYiIrFd7NzXmD+wOAHh++yFcvN70J2ojamxmUcDGjh2LtLQ0zJ07Fzk5OVVm19FqtVi4cCE2bNiAadOmVbuPcePG4dlnn8X169ervC9JEi5evIhnn322xu2JqOnRlpVjyvo4AMCTPYIw2N9LcCIiIpoR2hERPh4oqtDh0fVxMHBWRKIqzKKAKRQKbN++HSUlJYiMjISXlxc0Gg00Gg1CQ0ORmJiI2NhYODo6AgBiYmLQtm3bKle83nvvPWg0GvTs2bNyW41Gg1atWmHcuHF44IEH8PDDD4v6iERUD89vO4QUbRECXJ3x/uAeouMQEREAuUyGpaPC4aBSIuZyJr46fEZ0JKImRSYZ+bCGsrIyfPHFF9i1axcuXryI/Pz8ymVqtRr+/v6IjIzE008/bXEzCGq1WqjVauTn51eZ2p6IGt7m82kYtnI7AGDXQ3eif2uN4ERERPRPXx4+jac3H4CDSokTj41G2+b8rkSWrbbdwKgCVlBQgB49euDy5csYOXIk/P394ezsXGX5xYsXsX79erRu3RpHjhypstzcsYARiZFXWobOi9YgvaAYM0M74NMhvUVHIiKifzFIEu5YsRU7Ll1BuHdL7Hr4TijkZjH4iqheatsNjJoF8Z133kFGRgbi4+MRGFj9LDdJSUno3r073nnnHbz//vvGHJKICM9uPYj0gmK0a+6CeQM59JCIqCmSy2RYMrIvgr9dg7i0q/js4Gk816eT6FhEwhn1Z4ht27bh/vvvr7F8ATcenPzAAw9g27ZtxhyOiAhrk1OwLP485DIZfhjVDw4qo/6OREREDcjP1RkfR4UCAF6NOYoz2XliAxE1AUYVsPPnz6Nt27a1WrdNmza4cOGCMYcjIiuXU1yKxzfuAwA836cT+vq0FJyIiIhuZ2q3dhga4IVSnR6T18VCZzCIjkQklFEFrKCgoNb3dDk5OaGggM+CIKL6m7HlIDILSxDkpsZbkSGi4xARUS3IZDJ8NzIcalsVDqRnY8H+k6IjEQnFOyGJyCysOnMZP5+8ALlMhmWj+8FOyaGHRETmwtvFEZ8OuTEU8fVdx3Dy2vXbbEFkuYwqYM7OzrW+qlVYWGhRMyASUePJLSnD9E03hh6+FNYZoa1aCE5ERER1NalLW4xs541yvQFT1sdBz6GIZKWMKmBt2rTBuXPnarXu+fPnERAQYMzhiMhKPbftILKKShHkpsbr/buKjkNERPUgk8nwzbAwuNwcivjZwdOiIxEJYVQBi4qKws8//4ykpKQa10tKSsKKFSsQFRVlzOGIyAptPp+GZfHnIQPw/ahwDj0kIjJjrVwcsSCqFwDgtZijOJerFZyIqPGZ7EHMw4cPh5+fX5VhhoWFhbh06RI2btwIHx8fHD161KKGIfJBzEQNq6CsAp0XrUaKtogPXCYishDSzQc0R1+6ggGtNYh+cCjkMpnoWERGq203MKqAAUBZWRm++OIL7N69G5cvX0Z+fn7lMmdnZ/j5+SEiIgLPPPMM7OzsjDlUk8MCRtSwntq0H18dOQN/VyckTBsDRxuV6EhERGQCF68XoPO3a1BcocM3w8LweI+anylLZA4arYBZMxYwooaz+3ImIn/aDADY/sAQDPb3EpyIiIhM6bODp/Ds1oNwtlHh5ONj4aN2FB2JyCi17Qachp6ImpySCh2mbtgLAJga0o7li4jIAj3dMwhh3i1QUF6BJzbtA68JkLVgASOiJmfu7uM4m6uFl7MDPrp5szYREVkWhVyOJSPDYaOQY+O5NKxIvCA6ElGjYAEjoiblUEY2Fuw/CQD4ZlgY1HY2ghMREVFD6eDuitcjbjxeZObWg8gqLBGciKjh1Xo+58jISMTFxeHRRx/Ft99+CwBYsmQJLl++XOuDtW7dGlOmTKl7SiKyCuV6Paasj4NBknB/J3+Mau8jOhIRETWw2WHB+OP0ZRzPysUzWw7gt7sHiI5E1KBqXcCOHz8Og8GAhISEyvd+/vlnXLhQ+8vFAQEBLGBEVK35cQlIuHod7g62+IxTzhMRWQWVQo7vR4Wj15L1+P30Jaw6cxnjglqLjkXUYGpdwGJjY3H8+HFERERUvhcdHd0goYjI+iRevY53Y+MBAAuH9kYLR8t6bAUREVWvm8YNs8M6Y/7eBDy5eT8GtNagmb2t6FhEDaLW94AFBwfjoYcegp+fXwPGISJrpDcY8Oj6OFQYDBjd3gfjO/qLjkRERI3s9f5dEeSmRmZhCZ7bdkh0HKIGw0k4iEi4Tw+ewqGMbKhtVfh6WBhkMpnoSERE1MjslEosGRkOGYAf4s9hy/l00ZGIGoRRBWzFihW4cuVKrdbNyMjA8uXLjTkcEVmgc7lavBZzDACwIKoXvJwdBCciIiJR+vq0xDO9OgAApm3Yi4KyCsGJiEzPqAL28MMPY/Xq1bVa99dff8X06dONORwRWRiDJGHq+jiU6vSI8vfEoyHtREciIiLB3h3YHX5qJ6RoizBn5xHRcYhMzqgCJklSrZ9anpKSgtatOaMNEf2/b48mYVdKFhxUSnw7vC+HHhIREZxsVFg8oi8A4MvDZ7AnJUtwIiLTapR7wPLz87Fy5Ur06dOnMQ5HRGYgNb8Is6Nv/GVz/sDu8G/mLDgRERE1FVEBXphyc1TElPVxKKnQCU5EZDq1noYeALKysrBx48YqV7327t0LO7tbTxddXFyM1NRULF++HOXl5Xj77beNS0tEFkGSJDy+cS8KyivQ17slnuoZJDoSERE1MR9F9cSm8+k4m6vFG7uP4/3BPUVHIjIJmVTbMYQAli1bhkceeeT/N5bJahyC6ODgAD8/P4SHh+ONN96Ap6encWmbGK1WC7Vajfz8fLi4uIiOQ2Q2foo/j4fX7oGNQo7jj41GB3dX0ZGIiKgJWpucgjG/7YBcJsOBR0agp5e76EhE1aptN6jTEMRJkybBYDBUviRJwhdffFHlvX++CgsLkZiYiEWLFllc+SKi+skqLMGz2w4CAOZGhLB8ERFRtUa398WEjv4wSBIeXR+Hcr1edCQioxl1D5itrW21ww+JiG7l6S37kVtShhCP5ngxrLPoOERE1MR9PrQ33B1skXD1Ot7fmyg6DpHRjCpg8fHxeOCBB0yVhYgs3F9nLuOP05ehkMnw/ahwqBR8FjwREdWshaMdPh/SGwDw9p4TOHntuuBERMYx6ttPu3btYGtra6osRGTBckvK8OSmfQCAl/oGo5vGTXAiIiIyFxM6+WNUOx9UGAyYsj4OeoNBdCSieuOfn4moUTy37SCyikoR5KbG/yK6iI5DRERmRCaT4ethfeBiq8KB9Gx8dvC06EhE9VanaehvpaysDF9++SX27NmDS5cuIT8/v9p1/f39ER0dbewhicjMbD6fhmXx5yED8P2ocNgpjf7RQ0REVqaViyMWRPXCYxv24rWYoxjd3gdtm3MWajI/Rl0BKygoQHBwMF5++WXo9Xp07doVly5dQmhoKB544AGMHz8ebm5uuHTpEiIiIjBhwgRT5SYiM1FQVoHHN9wYejgjtAPCvFsKTkREROZqSkg7DPbzRIlOj8c27IWh9k9TImoy6vQcsH+bPXs2vvrqKxw6dAgdOnRAWloafH19sX79egwfPrxyvfnz5+Orr75CQkICXF1dTZG7SeBzwIhu76lN+/HVkTPwd3VCwrQxcLRRiY5ERERm7OL1AnT+dg2KK3T4ZlgYHu8RKDoSEYAGeg7Yv23fvh0TJ05Ehw4dANwYn3srL7/8MlQqFT766CNjDkdEZmb35Ux8deQMAGDxiL4sX0REZDT/Zs6YN7A7AODF6MNIzS8SnIiobowqYOfPn0ebNm1uu55MJsOIESOwdu1aYw5HRGakpEKHqRv2AgCmhrTDYH8vwYmIiMhSPN0zCGHeLVBQXoEnNu2DEQO6iBqd0feAOTs712pdLy8vXL582ZjDEZEZmbv7OM7mauHl7ICPonqJjkNERBZEIZdjychw2Cjk2HguDSsSL4iORFRrRhUwuVwOvV5fq3WdnZ1RWFhozOGIyEwcysjGgv0nAQDfDAuD2s5GcCIiIrI0HdxdMTciBAAwc+tBZBWWiA1EVEtGFbCWLVsiLy/v/3cmv7G7W5UySZKqvUeMiCxHuV6PKevjYJAk3N/JH6Pa+4iOREREFurFsM4I8WiO3JIyPLPlgOg4RLViVAFr06YNDh8+XPnfHh4eUKlUyMrK+s+6WVlZaNWqlTGHIyIzMD8uAQlXr8PdwRafDektOg4REVkwlUKO70eFQyGT4ffTl7DqDG93oabPqAJ2//33Y/369Vi4cCGKi4shl8vRo0cPLF68GFqttnK9rKws/Pzzz+jdm1/GiCxZ4tXreDc2HgCwcGhvtHC0E5yIiIgsXTeNG17qGwwAeHLzflwvKROciKhmRhWwxx9/HA8++CBmzpyJO++8EwDwySefIDk5Gd7e3ujevTtCQkLg7++PkpISzJs3zyShiajp0RsMeHR9HCoMBoxu74PxHf1FRyIiIivxv4guCHJTI7OwBM9tOyQ6DlGNjHoQ89/S09NRVFSE9u3bAwCys7OxYsUKXLx4EQqFAm3btsUDDzxgcQ8r5oOYif7fgv2JeGH7YahtVTj1xDh4OTuIjkRERFZkb+pV9Fu2ERKAzfffgaFteOsLNa7adgOTFDBrxQJGdMO5XC2Cv12DUp0e343oiynd2ouOREREVmjmlgP4/NBp+Lo4IvHxsXC2VYmORFaktt3AqCGIREQGScLU9XEo1ekR5e+JR0PaiY5ERERW6t2B3eGndkKKtghzdh4RHYfolowqYGFhYdi6daupshCRGfr2aBJ2pWTBQaXEt8P78nETREQkjJONCotH9AUAfHn4DPak/HdmbiLRjCpg58+fx+7du02VhYjMTGp+EWZH3/gL4/yB3eHfzFlwIiIisnZRAV6YcnM0xpT1cSip0AlORFSVUQUsMDAQ58+fN1UWIjIjkiTh8Y17UVBegb7eLfFUzyDRkYiIiAAAH0X1hJezA87mavHG7uOi4xBVYVQBe+ihh7Bx40ZkZ2ebKg8RmYnlCRew6Xw6bBRyfDeyLxRy3lJKRERNg6udLb4e1gcA8NH+kzicwe+q1HQY9Y1pypQp6NGjB5566imUlpaaKlO18vLyMHv2bAQHB8PT0xMajQYajQYdO3bE9OnTkZVVv3G+p06dQrt2nDiAqLayCkvw7LaDAIC5ESHo4O4qNhAREdG/jG7viwkd/WGQJDy6Pg7ler3oSEQAAKUxG+fk5GD+/Pl47bXX0KNHD0yaNAkdO3asdtpFDw8PBAYG1utYer0egwcPxujRoxETE4PmzZtX3uyfn5+PH374AeHh4YiPj4eDQ+2fP6TVanHvvffiwoUL9cpFZI2e3rIfuSVlCPFojhfDOouOQ0REdEufD+2N7ZcykHD1Ot7fm4j/RXQVHYnIuOeAeXh4IDs7G//exa1mQZMkCe7u7rh69Wq9jvXHH39g5cqV+PPPP6tdZ8aMGfD398esWbNqtU9JknDPPfegV69eeO+995CXl1enTHwOGFmjv85cxt1/7IRCJsOhKSPRTeMmOhIREVG1ViZewMTVu6GSy3HssVHo1KKZ6EhkoWrbDYy6ArZr1646DfvTaDT1PlZCQgIGDhxY4zpRUVFYvXp1rff50UcfoaKiArNnz8Z7771X72xE1iK3pAxPbtoHAHipbzDLFxERNXkTOvlj5cmLWHc2FVPWxyFu0nDet0xCGVXAgoKCEBTUODOfpaWloUuXLjWu4+XlhZSUlFrtb+fOnfjuu+9w4MAByPk/IVGtPLftILKKShHkpsb/Imr+/5GIiKgpkMlk+HpYH+xKycSB9Gx8dvA0nuvTSXQssmJm0zz0ej0UCkWN6yiVSuh0t3/WQ1paGiZPnoxffvkFrq6utc5QVlYGrVZb5UVkLTafT8Oy+POQAfh+VDjslEb9/YaIiKjRtHJxxIKoXgCA12KO4lwuv8OROGZTwEylvLwc9957L+bOnYtu3brVadv58+dDrVZXvnx8fBooJVHTUlBWgcc33Bh6OCO0A8K8WwpOREREVDdTQtphsJ8nSnR6PLZhLwz1nwaByChGFzCDwVDnlxHzflRKSkpCQEAAkpOT67TdrFmz0LlzZzz66KN1PuacOXOQn59f+UpNTa3zPojM0cs7jiBFWwR/Vye8O6C76DhERER1JpPJsHhEXziolIi5nInFR+v2HZLIVIwqYG3atIFKparzS6lUonXr1nj22WdRVFRUr2MHBgbiwoULaN++fa23+fHHH7Fv3z4sXLiwXse0tbWFi4tLlReRpdt9ORNfHTkDAFg8oi8cbVSCExEREdWPfzNnzBt44w+JL0YfRmp+/b6HEhnDqJs4Pv/8c0yZMgUuLi548MEH4ePjU2VCC0mSEB8fjyVLlmD06NGIiooCcOOq2aVLl/DJJ59AJpPhk08+uX3QWtzfpdPpoKzmvpQTJ05gzpw52LNnD+zs7OrwKYmsV0mFDlM37AUATA1ph8H+XoITERERGefpnkH49dRF7Eu7hic27cP68YNv+QglooZiVAE7ePAgFAoFjh07BkdHx2rXGzlyJIYPH45XX321yqyJxcXF+P3332tVwLy9vZGZmVnjOhkZGfD19b3lsq+++gpFRUXo27fvLZfn5+dDo9Hgww8/xEMPPXTbPETWYO7u4zibq4WXswM+unnzMhERkTlTyOVYMjIcIYvXYuO5NKxIvIAHg9uIjkVWxKghiFu3bsW9995bY/kCgEGDBqFr16746KOPqrzv6+uLtLS0Wh0rODgY0dHRNa4THR2N4ODgWy5btGgR8vLykJmZecuXWq1GZmYmyxfRTYcysrFg/0kAwDfDwqC2sxGciIiIyDQ6uLtibkQIAGDm1oPIKiwRG4isilEF7OTJk7WeCTAiIgI7d+6senC5vNYTcowdOxZpaWmYO3cucnJyqmyn1WqxcOFCbNiwAdOmTav9ByCiWyrX6zFlfRwMkoT7O/ljVHvO+ElERJblxbDOCPFojtySMjyz5YDoOGRFjCpghYWFsLe3r9W6LVu2vO0QwpooFAps374dJSUliIyMhJeXFzQaDTQaDUJDQ5GYmIjY2NjKq3ExMTFo27ZtrZ/V1apVq3pnI7I08+MSkHD1OtwdbPHZkN6i4xAREZmcSiHH96PCoZDJ8PvpS/jrzGXRkchKGP0k1dpewXJwcEBpaalRx1Kr1fjggw/wwQcf3HbdAQMG4Ny5c7Xe98mTJ42JRmQxTmTl4p3YEwCAhUN7o4UjJ60hIiLL1E3jhpf6BmNeXDymb9qHSF8PuDnw9x41LKOugDVv3hz5+fm1Wler1aJZs2bGHI6IGliF3oDJa2OhM0gYF+iL8R39RUciIiJqUK9HdEVHd1dcLSrFjC0HRcchK2BUAWvbti2SkpJqte7fD07+p+Li4mqnjSeixjcvLh7Hs3LhZm+Lr4eFcVpeIiKyeLZKBX4Y3Q8KmQw/n7yA1UkcikgNy6gCNm7cOPzyyy/Ys2dPjevFxsZi5cqVuOuuu6q8f+zYsWqnjSeixvXPoYdf3NkbHk61u7+TiIjI3PXycsfssM4AgCc27kNOsXG3zRDVxKjLT8899xy2bNmCAQMGICQkBL6+vnB2dq5cXlRUhJSUFBw9ehT9+vXD888/DwDIzs7G5MmTsWnTJsybN8+4T0BERuPQQyIisnZz+4dgTXIqTmXnYcaWg1gxrr/oSGShjLoCplKpsGPHDvzxxx+IiIiAJElITU2tfFVUVKBv37749ddfsWvXLqhUKgBAWVkZnJ2d8d133+Gll14yyQchovrj0EMiIrJ2fw9FlHMoIjUwmVTbaQzpP7RaLdRqNfLz8+Hi4iI6DlG9nMjKRc8l66AzSFg5rj8mdAq4/UZEREQWas6OI3hvbwI8HO1w6olxaG5vKzoSmYnadgOjroARkXnj0EMiIqKq5va/MStiVlEpZvABzdQAWMCIrNh8Dj0kIiKqwk6pxNJR4ZDLZFiReAFrklJERyILY/ICZjAYav1wZiIS50RWLt7mrIdERET/EdqqBV4M6wQAeGLTPuSWlAlORJbE6AKWn5+POXPmoEePHnBzc4NKpYKDgwOOHj0KAOjWrRsGDRqErVu3Gh2WiEyDQw+JiIhq9kb/EHRwVyOzsAQzORSRTMioApaXl4dOnTrh66+/Rt++ffHaa6/hww8/RFlZGTIzMwEAb7/9NmQyGUaOHInY2FiThCYi4/w99LA5hx4SERHd0o2hiDdmRVyeeAFrkzkUkUzDqAI2d+5clJSUICkpCQsXLsSsWbMwfvz4KuuMHDkS27ZtQ+fOnfHpp58aczgiMoEqQw+HcughERFRdXq3aoEX+twYivj4Rg5FJNMwqoDt2rULEydOhIeHR80HkcsxfPhwHDlyxJjDEZGR/j30cEInDj0kIiKqyZuRIQhyuzEU8dmtB0XHIQtgVAE7d+4cWrduXat13d3dK4clEpEYHHpIRERUN3ZKZeUDmn9KOI91yamiI5GZM6qAFRcXw8HBoVbrqlQqlJeXG3M4IjIChx4SERHVzz+HIk7buJdDEckoRs+CWNsp5zk1PZE4HHpIRERknH8ORZzFoYhkBKMKWPPmzZGXl1erdQsKCtCsWTNjDkdE9cShh0RERMb55wOaf+RQRDKCUQWsbdu2OHXqVK3WTU5ORps2bYw5HBHVA4ceEhERmUYf75Z4vnJWxL24zqGIVA9GFbBx48bht99+w+bNm2tc79ChQ1i5ciXuvvtuYw5HRHXEoYdERESm9Wb/EAS6ueAKZ0WkepJJRtycVVFRgaFDh2LXrl3o0KEDAgICoFKpsGrVKgwYMADu7u5ITU3FoUOH0K9fP2zbtg0qlcqU+YXSarVQq9XIz8+Hi4uL6DhE//HW7uOYu/s4mtvb4uTjY6Bxqt2kOURERFS9/WlXEb5sEwyShHXjB2NkOx/RkagJqG03MOoKmEqlwo4dO/DHH38gKioKcrkcubm5iIyMhCRJKC0tRWhoKH799VfExMRYVPkiaur+PfSQ5YuIiMg0+ni3xHO9OwK48YBmDkWkujDqCpi14xUwaqoq9AaEfr8ex7NyMS7QF3/eM5ATbxAREZlQSYUO3b5bi6QcLSZ1aYMfRkeIjkSCNcoVMCJqmv456+FXw/qwfBEREZmYvUqJpaP6QQZgWfx5bDjLWRGpdljAiCwMhx4SERE1jjDvlniu8gHNHIpItWNUAQsMDMS6detqte6aNWsQGBhozOGI6DYq9AY8so6zHhIRETWWtyO7oX1zF2QUFOO5bYdExyEzYFQBO3v2LFJTa3e59dixYygsLDTmcER0G/Pj4nEsk0MPiYiIGss/hyL+EH8OG8+liY5ETVyjDUE8fPgwAgICGutwRFaHQw+JiIjE6OvTErN63xiK+NiGvcgr5VBEqp7RBex2f2EvKSnBhx9+iE2bNuGpp54y9nBEdAscekhERCTWOwM4FJFqp07T0P/111+YNWsW/t4kLS0Nrq6ucHJyuuX6xcXFuH79OhQKBWbNmoX333/fNKmbCE5DT00FH7hMREQkXlxqFiKWbYIEYMOEKAxv6y06EjWi2nYDZV12GhwcjClTpkCSJEiShLfeegthYWEIDQ295fqOjo7w8/NDr1690Lp167p9AiKqFQ49JCIiahrCfTzwbO+O+OTAKUzbsBeJj4+Bq52t6FjUxBj1IGa5XI4vvvgCTz75pCkzmQ1eASPRKvQG9F66HscyczE20Bd/8YHLREREQhVX6BCyeC3O5mrxaNd2WDIqXHQkaiSN8iDmRx99FL179zZmF0RkhPf2JlTOevg1Zz0kIiISzkGlxNJR4ZAB+P7EWWzirIj0L0YVsO+++w49evQwVRYiqoP4rFy8vYdDD4mIiJqav4ciAjdmRcwvLReciJqSRpuGnohMp0JvwOR1sagwGDCWsx4SERE1Oe8M6I62zZyRzlkR6V/qNAlHdUpKSnD69GlcvHgR+fn51a7n7e2NIUOGmOKQRFaNQw+JiIiaNoebD2ju/+MmfH/iLO7t2Bp3tuGsiGSCArZ06VLMmTMH165dw+3m82jTpg3Onj1r7CGJrBqHHhIREZmHfr4emBnaEZ8ePIXH1u9F4uNjobazER2LBDNqCOKuXbswZcoUjBgxAvHx8dBqtTAYDNW+WL6IjMOhh0RERObl3YE3hiKmFRTj+e0cikhGFrD58+ejd+/eWLJkCTp16lTtA5mJyDQ49JCIiMi8OKiU+H5UP8gALDl+FlvOp4uORIIZVcCSkpIwaNAgU2Uhohr8c+jhQg49JCIiMhsRvh6YEdoBADB1fRxnRbRyRhWw1NRUaDQaU2Uhomr8e+jh/Rx6SEREZFbeHdAdbW4ORXyBQxGtmlEFTKVSobycDZ6ooXHoIRERkXlztFFh6c2hiN8dP4utHIpotYwqYBqNBpmZmabKQkS3wKGHREREliHC1wPP9Lo5FJEPaLZaRhWwAQMGYO/evabKQkT/wqGHRERElmXewBtDEVO1RXgx+rDoOCSAUQXshRdewOHDhxEbG2uqPET0Dxx6SEREZFkcbVT4fmQ4AGDxsWQORbRCRj2IWSaT4emnn8aoUaPw3HPPoU+fPlAoFNWur9Fo0LFjR2MOSWQ1OPSQiIjIMvVvrcEzvTpg4aHTmLphLxIfHwMXWz6g2VrIJEmS6ruxRqPB1atXa72+h4cHrly5Ut/DNTlarRZqtRr5+flwcXERHYcsSIXegN5L1+NYZi7GBvrir3sG8uoXERGRBSkqr0CXb9fiQl4BpnVrj0Uj+oqOREaqbTcw6gpYUlIS8vLyar2+q6urMYcjshocekhERGTZHG1U+H5UOAb8tBnfHkvGPR38cEeAl+hY1AiMKmBqtRpqtdpUWYgIHHpIRERkLSJba/B0zyB8cfgMpq6PQwKHIloFoybhaEx5eXmYPXs2goOD4enpCY1GU3lP2fTp05GVlVXj9jqdDm+++SbatWtXua1Go4G3tzciIyMRExPTOB+EqAac9ZCIiMi6vDeoBwJcnZGiLcKL2zkrojUwiwKm1+sxePBgODo6IiYmBhkZGcjMzERmZib27duHoKAghIeHo7i4uNp9fPzxxzh27Bj27t1buW1mZiZSU1Px/vvvY/Lkybh48WIjfiqi/+LQQyIiIuviaKPCkpE37v/69lgytl/IEJyIGppZFLBVq1bBz88Pc+fOhZubW5UvpWq1GjNnzsTw4cOxaNGiavexZcsWvPbaa2jRokWV92UyGfr06YOHHnoI69evb7DPQHQ7HHpIRERknQb4eeLpnkEAgCnr41BQViE4ETWkWt8DNmjQIJw7d86og7Vp0wY7d+6s83YJCQkYOHBgjetERUVh9erV1S6fNWsWunTpUu3yVq1aIS0trc7ZiEyBQw+JiIis2/xBPbDhXBou5hXixehD+GY4Z0W0VLUuYKYYote6det6bZeWllZjeQIALy8vpKSkVLt85MiRtz1G9+7d65WPyFgcekhERGTdnG4+oHng8i1YdDQZ9wT5IYqzIlqkWhewhx9+uCFz1Eiv19f4gGcAUCqV0Ol0dd63TqfDgQMHsGXLFjz//PM1rltWVoaysrLK/9ZqtXU+HtG/ceghERERATeGIj7VMwhfHj6DqRvikDBtLJxtVaJjkYmZxT1gpvbZZ59VzoLo6OiIAQMGYP78+WjWrFmN282fP79y6n21Wg0fH59GSkyWqlyv59BDIiIiqvTeoB7wd3XC5fwivLD9kOg41ADMsoAlJSUhICAAycnJ9dp+5syZlbMgFhQUYO/evXj11Vexb9++GrebM2cO8vPzK1+pqan1Oj7R397ZE49jmblw49BDIiIiwv8PRQRuzIq4+TznKLA0ZlnAAgMDceHCBbRv397ofdnY2KBXr1744IMP8O6779a4rq2tLVxcXKq8iOrrUEY25sXFAwC+HhbGoYdEREQE4MZQxJmhHQAAU9bvxfWSsttsQebELApYbe7v0ul0UCprfUvbf3Tt2hXx8fH13p6oLkoqdJi0dg/0koQJHf1xb0c/0ZGIiIioCZk3sAfaN3dBRkExntlyQHQcMiGzKGDe3t7IzMyscZ2MjAz4+vrecll8fDweffTRGreXy+WQy83in4MswGsxx3A6Ox8aJ3t8cWdv0XGIiIioiXFQKfHjmAjIZTKsSLyAP09fEh2JTMQsGkdwcDCio6NrXCc6OhrBwcG3XNasWTMcOHAAkiRVu/3JkyfRpk0bo3IS1cbuy5n45MBJAMB3I/rCzcFOcCIiIiJqinq3aoE5fW98v3184z5kFZYITkSmYBYFbOzYsUhLS8PcuXORk5NTpUhptVosXLgQGzZswLRp0265vbe3N9zc3PDRRx9VmUYeACRJwtmzZzFr1iw888wzDfo5iArLKzB5XSwkAFNC2mFEO86kSURERNV7vX9XdPVohpySMkzbuLfGCwpkHsyigCkUCmzfvh0lJSWIjIyEl5dX5TTyoaGhSExMRGxsLBwdHQEAMTExaNu2beVzumQyGX799VecOXMGgYGBldtqNBq0atUKEyZMwIwZMzBmzBiRH5OswIvbD+NiXiF8XRzx8R29RMchIiKiJs5GocCPoyOgksuxNjkVP8afFx2JjCSTWKPrTavVQq1WIz8/nzMi0m1tOZ+OO1duAwDseHAoBvp5Ck5ERERE5mJ+XDxe2XkULrYqJE4bCx+1o+hI9C+17QZmcQWMyNxdLynDlPVxAIAZvTqwfBEREVGdvBjWGX1atYC2rAKPro+FgddQzBYLGFEjmLn1INILitGuuQvmD+ohOg4RERGZGaVcjmWj+8FeqcD2i1fw9ZEzoiNRPbGAETWwVWcu46eE85DLZPhxdD84qOr/vDoiIiKyXu3d1Hh/cE8AwOzoIzibqxWciOqDBYyoAV0tKsHjG/cBAF4K64w+3i0FJyIiIiJz9lTPIAzy80RxhQ6T18ZCbzCIjkR1xAJG1EAkScL0TftxrbgUwS2bYW7/ENGRiIiIyMzJZTIsHRUOZxsV9qZdxYL9J0VHojpiASNqID8nXsBfZy5DKb8x9NBWqRAdiYiIiCyAr9oJnw0JBQD8b9cxJFy9LjgR1QULGFEDSNcW4ektBwAAcyNCEKJxE5yIiIiILMnkrm0xqp0PyvUGPLxmD8r1etGRqJZYwIhMTJIkTFkfh7zScvTycsfL4cGiIxEREZGFkclk+HZEGNzsbXE8Kxfv7IkXHYlqiQWMyMQWH0vGlgsZsFMq8OPoflDK+b8ZERERmZ7GyQFfDwsDAMyLi8ehjGzBiag2+M2QyIQuXC/Ac9sOAQDmDeyOIHdXsYGIiIjIot3b0Q/3d/KHXpLw8Jo9KKnQiY5Et8ECRmQiBknCI+tiUVShQ6SvB2aGdhQdiYiIiKzAF3f2gaeTPc7k5OPVmKOi49BtsIARmchnB09hd0oWHFVKLB3VD3KZTHQkIiIisgLN7W3x3chwAMCnB05h1+VMwYmoJixgRCZwOjsPc3YcAQB8fEcv+DdzFpyIiIiIrMnwtt6YGtIOEoDJa2NRUFYhOhJVgwWMyEg6gwGT1saiTG/AnW1a4bFu7UVHIiIiIiv08R2h8FM74VJ+IZ7ffkh0HKoGCxiRkd6LS8ChjGy42tnguxF9IePQQyIiIhLA2VaFH0b3A3BjVuaN59IEJ6JbYQEjMsLxzBy8uec4AOCLob3RysVRbCAiIiKyapGtNXj25kRgU9fHIbekTHAi+jcWMKJ6KtPp8fDaWOgMEu4Kao2JnQNERyIiIiK68SgcNzWuFJbg6c37Rcehf2EBI6qnN3YfR8LV62jhYIdvhoVx6CERERE1CfYqJZaN7geFTIaVJy/i91OXREeif2ABI6qHfWlX8cG+RADAtyPC0MLRTnAiIiIiov8X2qoF5oQHAwCmb9qHzMJiwYnobyxgRHVUXKHDpLWxMEgSHgpug7GBrUVHIiIiIvqP/0V0RYhHc+SUlGHahn2QJEl0JAILGFGdzdlxBGdztWjl7IDPh4aKjkNERER0SzYKBX4cEwEbhRzrzqZiWfw50ZEILGBEdbLj4hV8fug0AOD7keFwtbMVnIiIiIioesEtm+GtyG4AgJlbDyIlv1BwImIBI6olbVk5HlkXCwB4onsghrRpJTgRERER0e290KcTwrxbQFtWgUfWxcHAoYhCsYAR1dJz2w4hRVsEf1cnfBjVU3QcIiIiolpRyOVYNjoCDioldly6gq8OnxEdyaqxgBHVwoazqVhy/CxkAJaNjoCTjUp0JCIiIqJaa9fcBR8M7gEAmB19GMk5+YITWS8WMKLbyCkuxdQNewEAz/XphAhfD8GJiIiIiOpueo8gDPbzRIlOj0lrY6E3GERHskosYES38fTmA8gsLEEHdzXeGdBNdBwiIiKiepHLZFg6qh9cbFXYn34NH958pik1LhYwohr8duoifjl1EQqZDMtGR8BOqRQdiYiIiKjefNSO+HxIbwDA67uOIz4rV3Ai68MCRlSNzMJiPLlpPwDglfAu6OXlLjgRERERkfEe7tIGo9v7oMJgwMNr96BcrxcdyaqwgBHdgiRJeGzDXuSUlKGbpjlei+giOhIRERGRSchkMnw7vC/c7G1xIus63tp9QnQkq8ICRnQLy+LPYf3ZNNgobkzbaqNQiI5EREREZDIeTvb4ZngYAGD+3gQcSL8mOJH1YAEj+peU/ELM3HoQAPBWZDcEt2wmOBERERGR6d3TwQ8TOwXAIEmYtHYPSip0oiNZBRYwon8wSBIeWRcHbVkFwrxb4IU+nURHIiIiImowX9zZG17ODkjK0WLOziOi41gFFjCif/j6yBnsuHQF9koFlo2OgELO/0WIiIjIcjWzt8V3I/oCAD47eBo7L10RnMjy8dsl0U1nc7WYHX3jLz8fDO6Jds1dBCciIiIianjD2npjWrf2AIBH1sVCW1YuOJFlYwEjAqA3GDB5bSyKK3QY5OeJJ3sGiY5ERERE1Gg+iuoFf1cnXM4vwnPbDomOY9FYwIgALNh/EnvTrsLZRoWlo8Ihl8lERyIiIiJqNM62Kvwwqh9kAJYcP4sNZ1NFR7JYLGBk9RKvXsf/dh0DAHw2JBS+aifBiYiIiIgaX//WGszqfWMCsqkb9iKnuFRwIsvEAkZWrUL/9xPgDRjZzhuTu7YVHYmIiIhImHcGdEMHdzUyC0vw9OYDouNYJBYwsmrvxJ7AscxcNLe3xeIRfSHj0EMiIiKyYvYqJX4cHQGFTIZfTl3Eb6cuio5kcVjAyGodzsjGu7HxAICvh/WBxslBcCIiIiIi8Xp6uePVfl0AANM37ceVgmLBiSwLCxhZpVKdDg+v3QO9JGF8Rz/c19FfdCQiIiKiJuPVfl3QTdMcuSVlmLZxLyRJEh3JYrCAkVV6LeYYTmfnQ+Nkjy/v7CM6DhEREVGTYqNQ4MfREbBRyLH+bBqWnjgnOpLFYAEjq7MnJQsf7z8JAFg8oi/cHOwEJyIiIiJqejq3bIa3I7sBAJ7dehCX8woFJ7IMLGBkVQrLKzB5bSwkAI92bYeR7XxERyIiIiJqsp7v0wnh3i1RUF6BR9bFwsChiEZjASOr8sL2Q7iQVwBfF0d8MqSX6DhERERETZpCLscPo/vBQaXEzsuZWHjotOhIZo8FjKzGX2cuY9HRZADA0lH94GJrIzgRERERUdPXtrkLPorqCQCYHX0YJ7JyBScybyxgZBVS8gsxZX0cAOClvp0xyN9TcCIiIiIi8/FE90CMaueDcr0BE/7ahaLyCtGRzJbZFLC8vDzMnj0bwcHB8PT0hEajgUajQceOHTF9+nRkZWXddvunn34a/v7+ldtqNBp4e3tj0KBB2Lt3byN9EmpsOoMBD6zejbzScoR6uePtyO6iIxERERGZFZlMhu9HhcPL2QFncvIxc+tB0ZHMllkUML1ej8GDB8PR0RExMTHIyMhAZmYmMjMzsW/fPgQFBSE8PBzFxdU/JG7WrFlQqVRITEys3DYzMxMpKSl466238MADDyAjI6MRPxU1lnf2nEBs6lU426iwclwkVAqzOO2JiIiImhR3BzssHxMBGYAlx8/i15MXRUcyS2bxTXTVqlXw8/PD3Llz4ebmBplMVrlMrVZj5syZGD58OBYtWlTtPjZv3ox58+bB0dGxyvtyuRz9+vXDhAkTsGbNmgb7DCTG7suZeDs2HgCwaHgYApo5C05EREREZL4G+nni1X5dAADTNu7FxesFghOZH7MoYAkJCRg4cGCN60RFRSEhIaHa5bNnz4a9vX21y318fJCamlrvjNT05JaU4YHVu2GQJEzu0hb3dw4QHYmIiIjI7M3tH4Iw7xbQllVg4urdqNAbREcyK2ZRwNLS0uDpWfOkCV5eXkhJSal2+axZs2rcPjExEW3btq1XPmp6JEnClPVxSCsoRrvmLlh4Z2/RkYiIiIgsglIux89j+0Ntq8L+9Gt4c/dx0ZHMilkUML1eD4VCUeM6SqUSOp2uXvtfu3YtNm/ejHHjxtW4XllZGbRabZUXNU3fHE3C6qQUqORy/DIuEk42KtGRiIiIiCyGn6szFo8IBwDMi4vHzktXBCcyH2ZRwBrK9evXMXnyZMyaNQurV69Gs2bNalx//vz5UKvVlS8fH59GSkp1kXD1OmbdnJnng8E90N3TTXAiIiIiIstzb0c/TA1pBwnAg2v2ILu4VHQks2CWBSwpKQkBAQFITk6u1/aSJOGXX35B165d0axZMxw9ehRdunS57XZz5sxBfn5+5Yv3jDU9xRU6TPhrF8r0Bgxv642ZoR1FRyIiIiKyWJ8OCUWQmxoZBcV4dF0cJEkSHanJU4oOUB+BgYG4cOFCvbZNTU3F5MmTIUkSNm3ahE6dOtV6W1tbW9ja2tbruNQ4ntt2EKey86BxssfSUeFVZswkIiIiItNytFHhl7siEfr9eqw7m4ovDp/BM706iI7VpJnFFbDa3N+l0+mgVNbcJw8ePIj+/ftj/PjxiI6OrlP5oqbvz9OXsOhoMmQAfhodgZaO1c96SURERESm0dWjOT6K6gUAeGH7IZzIyhWcqGkziwLm7e2NzMzMGtfJyMiAr69vtcvz8vJw11134aeffsK0adN4ZcTCpOQXYuqGvQCAl/oGIyrAS3AiIiIiIuvxdM8gjGrng3K9ARP+2oWi8grRkZossyhgwcHBiI6OrnGd6OhoBAcHV7t8wYIFmDhxIvr162fqeCSYzmDAxFW7kVdajlAvd7wV2U10JCIiIiKrIpPJ8P2ocHg5O+BMTj6evTkhGv2XWRSwsWPHIi0tDXPnzkVOTk6Vm/u0Wi0WLlyIDRs2YNq0adXu48iRIxgxYkRjxKVG9vaeE4hLuwpnGxVWjouESmEWpzURERGRRXF3sMPyMRGQAfju+Fn8duqi6EhNkll8U1UoFNi+fTtKSkoQGRkJLy8vaDQaaDQahIaGIjExEbGxsXB0dAQAxMTEoG3btlWe05Wbm4t77rmncrtbvR544AFRH5HqadflTLwTGw8AWDQ8DAHNnAUnIiIiIrJeA/088Ur4jdnFp23Yi0t5BYITNT0yiXNF1ptWq4VarUZ+fj5cXFxEx7E6OcWlCFm8FmkFxXika1t8P4rDS4mIiIhEq9AbEPnTJuxLu4Yw7xbY/fAwKOVmcd3HKLXtBpb/L0EWSZIkTFkfh7SCYrRv7oLPh/YWHYmIiIiIAKgUcvw8tj9cbFXYl3YNb+w6LjpSk8ICRmbp6yNJWJOcChuFHL/cFQknG5XoSERERER0k5+rMxaP6AsAmBcXj52XrghO1HSwgJHZSbh6Hc9tuzGzzvuDeqCbxk1wIiIiIiL6t/s6+mNqSDtIAB5cswfZxaWiIzUJLGBkVoordJjw1y6U6Q0Y0dYbM0M7io5ERERERNX4dEgogtzUyCgoxqPr4sDpJ1jAyMzM2noQp7LzoHGyx9JR/fhAbSIiIqImzNFGhV/uioSNQo51Z1Px5eEzoiMJxwJGZuOP05fw7bFkyAAsHxOBFo52oiMRERER0W109WiOj6J6AQBe2H4IJ7JyBScSiwWMzMLlvEI8tmEvAOClvsEY7O8lOBERERER1dbTPYMwsp03yvQGTPhrF4rKK0RHEoYFjJo8ncGAB1bvRl5pOXq3csdbkd1ERyIiIiKiOpDJZFg6qh88nexxJicfz249KDqSMCxg1OS9tfsE4tKuwsVWhZVjI6FS8LQlIiIiMjfuDnZYPqY/ZAC+O34Wv526KDqSEPwmS01azKUreCf2BADgm2Fh8G/mLDgREREREdXXIH9PzAnvAgCYtmEvLuUVCE7U+FjAqMnKKS7Fg2v2QALwSNe2uL9zgOhIRERERGSkN/qHIMy7BfLLKjBx9W7oDAbRkRoVCxg1SZIk4dH1cUgvKEagmws+H9pbdCQiIiIiMgGVQo6fx/aHi60K+9Ku4c3dx0VHalQsYNQkfXXkDNYmp8JGIcfKcZFwslGJjkREREREJuLn6oxvh/cFALwbG4+YS1cEJ2o8LGDU5MRn5eL5bYcAAB8M7oluGjfBiYiIiIjI1MZ38seUkHaQADywZg+yi0tFR2oULGDUpBRX6DBh1S6U6Q0Y0dYbM3p1EB2JiIiIiBrIZ0NCEejmgoyCYjy6Lg6SJImO1OBYwKhJeXbrQZzOzoenkz2WjuoHmUwmOhIRERERNRBHGxV+GRcJG4Uc686m4svDZ0RHanAsYNRk/H7qEhYfS4YMwE9jItDC0U50JCIiIiJqYCEaN3w4uCcA4IXth3AiK1dwoobFAkZNwuW8Qjy2IQ4A8HLfYAz29xKciIiIiIgayzO9OmBkO2+U6Q2Y8NcuFJVXiI7UYFjASDidwYCJq3cjv6wCvVu5483IbqIjEREREVEjkslkWDqqHzyd7HEmJx+zbk7IZolYwEi4N3cfx960q3CxVWHl2EioFDwtiYiIiKyNu4Mdlo/pDxmAxceS8fupS6IjNQh+0yWhYi5dwbux8QCAb4f3hX8zZ8GJiIiIiEiUQf6emBPeBQDw2IY4XM4rFJzI9FjASJjs4lI8sGYPJACPdm2H8Z38RUciIiIiIsHe6B+CPq1aIL+sAhNX74bOYBAdyaRYwEgISZIwZX0cMgqKEejmgs+HhoqORERERERNgEohx89j+8PFVoW9aVfx5u7joiOZFAsYCfHl4TNYm5wKG4Ucv4yLhKONSnQkIiIiImoi/Js549vhfQEA78bGI+bSFcGJTIcFjBrdiaxcvLD9xsw2Hw7uiRCNm+BERERERNTUjO/kj0e7toME4IE1e5BdXCo6kkmwgFGjKiqvwIS/dqFMb8CItt54plcH0ZGIiIiIqIn6fGgoAt1ckFFQjCnr4yBJkuhIRmMBo0Y1a9shnMnJh6eTPZaO6geZTCY6EhERERE1UY42KvwyLhI2CjnWJqfiqyNnREcyGgsYNZrfT13C4mPJkAFYPqY/WjjaiY5ERERERE1ciMYNHw7uCQB4ftshxGflCk5kHBYwahSX8grw2IY4AMCc8C4Y5O8pOBERERERmYtnenXAiLbeKNMbMGHVLhRX6ERHqjcWMGpwOoMBE1fvRn5ZBfq0aoE3+oeIjkREREREZkQmk2HpqH7wdLLH6ex8PLv1oOhI9cYCRg3uzd3HsS/tGlxsVfh5bH+oFDztiIiIiKhuWjja4acxEZABWHwsGb+fuiQ6Ur3wmzA1qJ2XruDd2HgAwLfD+8K/mbPgRERERERkrgb7e+HlvsEAgMc2xOFyXqHgRHXHAkYNJru4FA+u2QMJwJSQdhjfyV90JCIiIiIyc29GdkOfVi2QX1aBiat3Q2cwiI5UJyxg1CAuXC/A6N+ikVFQjEA3F3w2JFR0JCIiIiKyACqFHD+P7Q8XWxX2pl3FW7tPiI5UJyxgZFKSJOGbI2fQ5ds12Jd2DU42SvwyLhKONirR0YiIiIjIQvg3c8a3w/sCALZeTEeF3nyugilFByDLcTmvEFM3xGH7xSsAgP6+Hlg6qh8CeN8XEREREZnY+E7+kMtkGBPoY1aTvLGAkdEkScKS42fx3LZDKCivgL1SgfcG9cDTvTpALpOJjkdEREREFurejn6iI9QZCxgZJU1bhMc27MXm8+kAgL7eLbF0VDjau6kFJyMiIiIianpYwKheJEnCj/HnMXPrAeSXVcBWIcc7A7pjVu+OUMjN5xIwEREREVFjYgGjOrtSUIzHN+7DurOpAIBQL3f8MLofOri7ig1GRERERNTEsYBRrUmShJUnL+LpzftxvbQcNgo53uwfghfCOkPJq15ERERERLfFAka1crWoBNM37cdfZy4DALpr3LBsdD90btlMcDIiIiIiIvPBAka39fupS3hy8z5kF5dBKZfhf/26Yk54F7Oa7pOIiIiIqClgAaNqZReX4unN+/HrqUsAgC4tm2HZ6H4I0biJDUZEREREZKZYwOiWViddxuMb9+FqUSkUMhleCe+C1yK6wEahEB2NiIiIiMhssYBRFbklZZix5QBWJF4AAHR0d8Wy0f3Q08tdcDIiIiIiIvPHAkaVNpxNxWMb9uJKYQnkMhlmh3XG3P5dYafkaUJEREREZAr8Zk3IKy3Dc9sOYemJcwCAQDcX/DCqH/p4txScjIiIiIjIsrCAWbkt59MxdX0c0gqKIQMwq3cnvDOgG+xVPDWIiIiIiEzNrOcRz8vLw+zZsxEcHAxPT09oNBpoNBp07NgR06dPR1ZWVq32s2/fPrRu3RpFRUUNnLjpKCirwLQNe3Hnym1IKyhG22bO2P3wMCy4oxfLFxERERFRAzHbAqbX6zF48GA4OjoiJiYGGRkZyMzMRGZmJvbt24egoCCEh4ejuLi4xv3s3r0bEyZMwPXr11FRUdFI6cWKvpiB4G9XY/GxZADAjF4dcPyx0ejn6yE4GRERERGRZTPbArZq1Sr4+flh7ty5cHNzg0wmq1ymVqsxc+ZMDB8+HIsWLap2H3FxcXjwwQexevVqNG/evDFiC1VYXoGnNu1H1IqtuJxfBD+1E3Y+OBSfDe0NRxuV6HhERERERBbPbMeaJSQkYODAgTWuExUVhdWrV1e73MbGBps2bUKnTp1MnK7p2X05E4+si8OFvAIAwPQegfhgcE84sXgRERERETUasy1gaWlp6NKlS43reHl5ISUlpdrlvXr1MnWsJqe4QodXdh7B5wdPQwLg6+KIJSPDERXgJToaEREREZHVMdsCptfroVAoalxHqVRCp9OZ7JhlZWUoKyur/G+tVmuyfTeEvalXMXldLM7m3sg5NaQdFtzRCy62NoKTERERERFZJ7MtYCLMnz8fb775pugYt1Wq0+F/McewYP9JSAC8nB3w3Yi+GNbWW3Q0IiIiIiKrZraTcPxbUlISAgICkJyc3GDHmDNnDvLz8ytfqampDXas+jqYfg3dFq/DRzfL16QubZA4bQzLFxERERFRE2AxV8ACAwNx4cKFBj2Gra0tbG1tG/QY9VWm0+PN3cfx/r5EGCQJGid7LBoehtHtfUVHIyIiIiKim8y2gNXm/i6dTgel0mw/Yq1lFBRj6M9bkXgtDwAwsVMAPh8aCjcHO7HBiIiIiIioCrNtJ97e3sjMzKxxnYyMDPj6Wv4VIA9HOzjbqtDCwQ7fDA/DXUGtRUciIiIiIqJbMNt7wIKDgxEdHV3jOtHR0QgODm6kROIo5HL8PLY/Tj4+luWLiIiIiKgJM9sCNnbsWKSlpWHu3LnIycmBJEmVy7RaLRYuXIgNGzZg2rRpAlM2Hj9XZ7Rw5JBDIiIiIqKmzGwLmEKhwPbt21FSUoLIyEh4eXlBo9FAo9EgNDQUiYmJiI2NhaOjIwAgJiYGbdu2rfbZXS1btoSNDZ+PRUREREREDUcm/fPSEdWJVquFWq1Gfn4+XFxcRMchIiIiIiJBatsNzPYKGBERERERkblhASMiIiIiImokLGBERERERESNhAWMiIiIiIiokbCAERERERERNRIWMCIiIiIiokbCAkZERERERNRIWMCIiIiIiIgaCQsYERERERFRI2EBIyIiIiIiaiQsYERERERERI2EBYyIiIiIiKiRsIARERERERE1EhYwIiIiIiKiRsICRkRERERE1EhYwIiIiIiIiBqJUnQAcyZJEgBAq9UKTkJERERERCL93Qn+7gjVYQEzQkFBAQDAx8dHcBIiIiIiImoKCgoKoFarq10uk25X0ahaBoMBGRkZcHZ2hkwmEx2HakGr1cLHxwepqalwcXERHYcsAM8pMiWeT2RKPJ/I1HhO1UySJBQUFMDLywtyefV3evEKmBHkcjm8vb1Fx6B6cHFx4Q8OMimeU2RKPJ/IlHg+kanxnKpeTVe+/sZJOIiIiIiIiBoJCxgREREREVEjYQEjq2Jra4u5c+fC1tZWdBSyEDynyJR4PpEp8XwiU+M5ZRqchIOIiIiIiKiR8AoYERERERFRI2EBIyIiIiIiaiQsYERERERERI2EBYyIiIiIiKiRsICRxZIkCTNnzsRHH330n2VHjhzB6NGjERAQAI1GA41Gg1atWiE0NBTfffcd9Hr9f7Y5e/YsJk6ciHbt2lVu4+Xlhe7du+P9999HWVlZY3wsagSbNm1CWFgYvLy84OHhAY1GA09PT7Rv3x6vvvoqiouL/7MNzymqr7y8PMyePRvBwcHw9PSsPBc6duyI6dOnIysrS3REEmT58uUICQmpcl54enqiY8eO+Pjjj6HT6aqsHx0djaioKPj5+VWu7+Pjg4iICPz111+41bxr9fnZRZZFr9fjrrvuwu+//17lfZ5PDUgislDffvutZGNjI82dO7fK+4cPH5b8/f2lDRs2SKWlpZXv6/V66cyZM9KYMWOk559/vso2qampko+Pj7RixQqpqKio8n2DwSClpKRIjz32mHT33Xc36OehxpGUlCT5+vpKR44ckQwGQ5Vl+fn50mOPPSY999xzVd7nOUX1pdPppO7du0tvvPGGlJ2dXeWcy8vLkz799FOpTZs2Vc4Rsg47d+6UOnXqJCUlJf1nWWZmpjRq1Cjp888/r3xv1apVUnBwsLRnzx6pvLy88v2KigrpyJEjUr9+/aSFCxdW2U99fnaR5XnllVckGxsbaenSpZXv8XxqWCxgZJEOHjwotWnTRnrllVf+U8BGjBghrV27ttptCwsLJV9fXykzM7PyvaeeeqrKL7p/0+v1UteuXaWjR48anZ3E+uqrr6SXX3652uWFhYWSp6dnlfd4TlF9/f7779Jdd91V4zrPPPOM9PHHHzdSImoqZs+eLX3zzTfVLj9z5owUGhpa+d+dOnWSTpw4Ue36aWlpkpeXl6TT6Srfq8/PLrIsq1evlkJCQqRp06ZVKWA8nxoWhyCSxcnOzsbEiRPx008/oUWLFv9ZnpCQgIEDB1a7vaOjI3r06IGkpKRabyOXyzFo0CAkJCQYF56E69SpE+6+++5qlzs6OkKv11cZHshziurrducBAERFRfE8sEJhYWG44447ql3u4+OD1NRUAEBFRQWys7PRpUuXatdv1aoVPDw8kJ6eXvlefX52keU4e/YsZs6ciT/++AP29vaV7/N8angsYGRR9Ho97r//fsyYMQNhYWG3XCc/Px9OTk417sfLywspKSmV/52WlgZPT886bUPmqX///ujZs2e1y7OysmBnZwdbW9vK93hOUX3xPKDqjB07FgEBAdUuT0xMRNu2bQEAmZmZt/yD47/9+1yqz88usgxFRUW4++678fnnn6NNmzZVlvF8angsYGRRXn/9dbi7u+Ppp582aj9KpbLKzc16vR4KhaJO25DlycvLw0MPPYRnnnmmztvynKJb4XlA9ZGeno7HH38cM2bMAFC78wio37nE88/ySJKEadOmYcSIERg9evR/lvN8angsYNQkLV++HK6urjW+li9fXmWbNWvWYM2aNVi8eDFkMpmg5NQU1ed8+rf169ejc+fOCAkJwXPPPddIyYmI/p8kSfjhhx8QEhKCyZMn45577hEdiczQF198gStXruDtt98WHcVqsYBRk/Tggw8iLy+vxteDDz5Yuf7Zs2fx1FNP4ffff7/t5e9/e++99275F6CaLF++HN26davTNiROXc+nf8rIyMD999+P2bNnY9myZfjggw8gl9f8o5PnFNVXUlISAgICkJycLDoKNTHJyckYMmQIFi1ahC1btmDmzJk1rv/UU0/hqaeeqtMx6vOzi8xLXFwcFixYgJUrV0KpVNZ6O55PplX7f3miJurvccwLFixAhw4d6rz9yy+/XOdtHnzwwWq/sJNlkCQJixYtwjvvvINZs2bhxx9/hEqlqtW2PKeovgIDA3HhwgXRMagJ0ev1ePfdd/Hdd9/hrbfewsMPP3zbPwIBwJdfflnnY9XnZxeZj8zMTEycOBE///wzPDw86rQtzyfTYgEjs/b3OOZBgwZh/PjxJtuvTqer8peh2oxZ/vc2ZL4qKirw9NNP49SpUzhw4ABatWpl9D55TtGt8DygmhQWFmLixIlQKpWIj4+Hq6vrLder7X019TmXeP5ZBp1Oh/Hjx2PWrFkIDw+vcV2eTw2P/wJk1i5evIh169bBzs4Ov/zyy3+WFxYWAgBWrVqF48ePQyaTwdXVFYWFhTUOVczIyICvr2/lf3t7eyMzMxPu7u41bhMSElL/D0NNxoIFC5CRkYEdO3bU6qoXzymqr7/Pg5r8+9wh6/HCCy/A29sbX375ZY33Nms0GmRnZ992f/8+l+rzs4vMU1xcHI4cOYIzZ87gvffe+8/y/Px82NjY4I8//sDq1at5PjUw3gNGZi0gIABarRZXr15FZmbmf14vvPAC3nnnHZw4caLyl1dwcDCio6Or3WdRURGOHDmCwMDAyvdut43BYMDOnTsRHBxsug9HQmi1WixYsADff/99rYcc8pyi+rrdeQAA0dHRPA+s0NmzZ7F582YsWLDgthNLKZVKtGjRAsePH692nYyMDGRlZVW5ol+fn11kniIjI1FYWIisrKxbfl8aP348li1bhvXr1/N8agQsYGR13nrrLTz77LPYsGFDlYfpGgwGnDt3Dg8++CDuueeeKuOjX375ZSxYsAArVqxAcXFx5fuSJCE9PR1PPPEEAgICOImCBThz5gw6d+5cq2eg/I3nFNXX2LFjkZaWhrlz5yInJweSJFUu02q1WLhwITZs2IBp06YJTEkiHDt2DAMHDqzygNyavPvuu3jooYcQGxuLioqKyvf1ej2OHz+O8ePH4+WXX64yvXh9fnaRdeD51LBk0j9/2hNZmI8//hhqtRpTpkyp8v6xY8cwd+5cJCYmVn75lcvlaNWqFR577DFMmTLlP8/AOHfuHP73v//h8OHDKCgoAADIZDJ4eHhgwoQJePbZZ2FnZ9c4H4wazKZNm3DXXXdBrVZXu45MJsP69evRo0ePyvd4TlF95efn491338XGjRurlDBXV1dERkbizTffhEajEZySGtvXX3+NF198scbhXCqVCgcOHICXlxcAYMeOHZg3bx7Onj1b+QVYpVKhdevWmDVrFu66667/XE2rz88usjwzZ87EqFGjEBUVVfkez6eGwwJGRERERETUSDgEkYiIiIj+r737j4m6/uMA/rzDQ2EBCoiI3HGA5E/E1K0FccA31DBN0KELMQ0zssQmKA7LQMQ1oDKGSRKCmKYyxhRXAlNGabWZkLp0Cic/BDWNuPgRx4/gvn84mefdcYfCceTzsfnP5/3jXh9g8157v9+vNxEZCRMwIiIiIiIiI2ECRkREREREZCRMwIiIiIiIiIyECRgREREREZGRMAEjIiIiIiIyEiZgRERERERERsIEjIiIiIiIyEhGDXcARERENPQ6OjrQ2NgIABg/fjxGjx49zBENDYVCgX/++QdmZmZwdHSEQCAY7pCIiNRwBYyIaATKycmBQCAw6N+hQ4eGO1wyAaGhoRCLxRCLxVi/fn3f85ycHEyePLkvOTNUQ0MDJBIJTp8+rdGWmJiIdevWGTyXSqXC1KlTUVpaOqAYHqdUKuHo6AixWAwnJycUFBQ81XxEREOBK2BERCNQXV0dbGxskJSU1G8/gUCA//3vf0aKikxZa2sr3njjDYSFhcHT07PveV1dHW7evInY2FhkZ2cbPF9UVBTq6+tx+/Ztjbbq6mrI5XKD5+rp6cGNGzdw69Ytg8doY2FhgdLSUigUCkRERKC1tfWp5iMiGgpMwIiIRqjnnnsOGzduHO4waAR5/vnnsXjxYq1tOTk5WLNmDfz8/PTOU1hYiBMnTgxydIPDx8cHAGBpaTnMkRARacctiERERM+4iRMn4uWXX0ZkZCQ6Ozv77dvW1oaNGzdiyZIlRoqOiOi/hQkYERHRM04oFGL//v2orq5GcnJyv30TEhLQ1NSEvXv3Gik6IqL/FiZgRETPEA8PD5w8eRIAUFxcjKVLl8LZ2Rk2Nja4f/++Wt8rV64gJCQEHh4eEIlEfUU9xGIxXnnlFXz//ff9ftalS5cQHByMyZMnQyQSQSgUwt7eHj4+PkhLS0N3dzcCAgKQmZmpMXby5MkoKirS+z4BAQH4+uuvtba1tbUhLi4Oc+bMgbW1dV/8NjY2mDdvHnbs2AGlUql1bHZ2NmQyGVQqFQ4fPozAwEA4ODhAKBTC0tISnp6eePfddzV+Zo/q7e1FVlYWvL29+6rxCQQCiEQiiMVirFy5EleuXNEYFx8fj9WrV+t9d0P7GWr69OnYtm0bdu/ejcrKSq19Ll++jC+++AJJSUmQSCSD9tm6dHV1YevWrZBIJDAzM9NZaCY2NnbIYyEiGiw8A0ZE9AyRy+Wor6/Htm3bkJqaigULFiA0NBQzZsyAnZ1dX7+zZ89i4cKFcHNzw9KlSzFp0iSIRCL09vbi3r17+Pnnn/Haa68hOTlZ65ffkpISBAUFYcqUKQgJCYGTkxOEQiEaGxshl8vx0UcfobCwEJWVlVoLL9y8eRMNDQ1636empgZ1dXUazzs7OzFv3jzU1tYiLCwMoaGhsLKyAvCgGIVcLsenn36KkydPory8HCKRSG38rVu3IJfLsXr1ahQUFGDJkiV45513MH78eHR1daGurg5Hjx5Ffn4+rl27BgcHB40YIiIikJubi5CQECxYsAB2dnYQCARQKpW4ceMGysrK4O/vj8uXL0MsFveNq6urQ01Njd53N7TfQGzfvh3Hjh3Dhg0bcObMGbUS7r29vYiMjISXl5fRzh7u3LkTaWlpCAsLw6xZs2Bubq7WfuLECZw9e7bv3BcR0UjABIyI6Blz4sQJ3Lt3D5cuXcKsWbO09omOjsaLL76I0tJSrfdFqVQqxMXFYdeuXVi7dq1aAqJSqRATE4OAgAAUFRVh1CjN/2ra29uxatUq3LlzZ/Be7BH79u1DVVUVKioq4OXlpbXP5s2b4eXlhaysLGzYsEGj/e7du2hoaMCdO3cwduxYjfaEhARMmzYNiYmJGtvxysvLkZubi4MHD2LNmjVaP7+lpQUSiQR5eXmIiYkZ+EsOAQsLC2RkZGD+/Pk4fPiw2grb/v378euvv+LChQtaf6dDoaCgAO+//z727Nmj0fbbb79h69at2LRpE5YuXWqUeIiIBgMTMCKiEerff//VuUrk6Oio80vyTz/9hIqKCkybNk1ru0KhwJUrV3D8+HGdl/UKBAJs2bIFycnJ+OWXX9S+ACsUCvz+++/YvXu3zhgsLS1x4MCBvu2Qg62srAwLFy7UmXwBwMyZMxEUFISSkhKtCRjwoDKgtuQLAOzt7bFmzRqtWyV//PFH2NnZ9btF0NraGpmZmWqrX6YgMDAQ4eHhiI6OxqJFi2BnZ4e7d+8iLi4OH3zwAebOnWvQPJ2dnQatYgIP/pa1iYuLg0wm03je1NSEZcuWYc6cOUhNTTXoM4iITAUTMCKiEerevXs6v7xHRkbiq6++0tomk8l0Jl8AUFtbCwBwc3Pr9/Pt7e1hZWXV1//x8frOCNna2g5Z8lFbWwtfX1+9/dzc3HDu3DmtbY6OjnB1de13vIuLC2pqaqBSqdS269XU1MDZ2RlCYf9HrVesWKE3xuHw2Wef4bvvvkNsbCwOHDiAzZs3w9raGomJiQbPcfHixaf+/b755psaz3p7exEeHo729nbk5eVpbEskIjJ1TMCIiEYoW1tb5Obmam2bPXu2znEzZszod97m5mYAgFKp1LuCYW1tjT///FPtWUtLC4AH95Tp82jSMpiam5shFAr1xm9mZqYR/6Nt+lhZWaG9vR09PT1qq30tLS0Gvb+pcnBwQEpKCtavXw8nJyccP34chYWFA3qnadOmISUlxaC+PT09CA4ONqjvrl27UFJSgjNnzmDSpEkGx0NEZCqYgBERjVAWFhY6L9Xtj7W1tUH9tG390mbMmDEDjsEY0tPTkZ6errefu7u7EaIZeR4WEUlKSsLy5csHfO+Xra2twX+furYgPu706dPYuXMnUlJS4O/vP6B4iIhMBRMwIiLSav/+/XBycuq3j0AgMNkKdCtWrDCoTLu+bYbPKqFQiNzcXKSmpiIhIWG4w0F1dTVWrVqFkJAQkylaQkT0JJiAERGRmofl2r29vTFz5swBj7exsQHw4B6up4nBkPG6Vk6srKwgFoufaIVwMFhbWz/x+1taWuq8n+xRCoXiieYfCDc3N2RkZAz55+ijVCqxfPlyODg4ICcnZ8i2rhIRGQMvYiYiIjUPV4Tq6+v77adSqSCTyfDtt9+qPZdKpQCg9X4vQ0mlUr3ja2pqcPv2ba1trq6ueuMHgIyMjCFJ0lxdXXHr1i309vb22y8/Px8XLlxQeyaVStHQ0ACVSqVzXHd3N8rLywclVlOnUqnw3nvvoaqqCgUFBVq30J47dw7nz58fhuiIiAaOCRgREakZN24cZsyYgX379vWbQPzwww84d+6cxjbFsWPHwtPTE+np6Qaf7Xmcn58fDh06hMbGRq3tzc3NWLdunc7xMpkMp06d0qjQ+Kj29nakp6fD3t7+iWLsj0wmg0Kh0FkkBXhQqOPtt9/WqML40ksv4f79+ygoKNA6rqOjA1FRUTqTz/+azMxMHDx4EFlZWZg+fbpG+8ME7dixY8MQHRHRwHELIhERqREIBNizZw9effVVvPDCC1i0aBEmTJiAUaNGQaVSoa2tDdevX0d+fj5kMpnGGTCBQIDPP/+87x6uxYsXw9HRESKRSOOzHlZMfNyOHTtw9OhRTJ06FWFhYRCLxTA3N8dff/2FqqoqnDp1Cq+//rrOMucbN25EZmYmZs2ahVWrVsHFxaWvgl9nZyf++OMPnDx5Enfu3MHhw4ef8iemae7cuXjrrbcQERGBU6dOYfbs2Rg3bhwEAgGUSiUqKytRVlYGgUCA0NBQtbG+vr4IDg7GihUrsHLlSkyfPh02NjZQKBSor69HYWEhRo8eDT8/vydOcEeKq1evYtOmTZgyZQoaGxs1LrxuaWlBaWkprl69in379g1TlEREA8MEjIhoBJJIJH1b/QbC3d0dLi4uevvNnz8f5eXliI+PR15eHurq6tDT0wPgwQqZWCzG9u3bERUVpTWxCgwMREVFBeLj45Gfn6823hAODg64du0aPv74Y5SWlqK6uhodHR2wtbWFl5cXMjIyEB4ejoCAAK3vM2bMGJSXlyMpKQnFxcU4cuQIWltb+9omTpwIHx8fxMTEaC3ZLxaL9d6DBgBOTk5wdXXVet9XVlYWvL29kZ2djS+//BL3798H8KC8/YQJE+Dj44MPP/xQ631peXl5SE9Px9GjR1FcXIympiY4OTlh6tSpWLduHaKjo7FlyxbI5XK9MeojkUieuBKku7s7nJ2dNZ5LpVJ0d3cbPI9QKIRUKtVIqC9evIiuri7cuHEDUVFRGuPMzc3h4eGBI0eOGHTvGxGRKRCo+ttkTkRENISkUinCw8ORlJQ03KGMOGvXroVcLjf47JO/vz/8/f1NoqKhMUilUiQkJGDt2rXDHQoRkRqeASMiIjIhKSkpiIyM1Nuvp6fHoMuiiYjItDABIyIiMiF///03ioqK9FZQrK2t1bp9Ud/ctbW16OjoeJoQTVpjYyNqa2v/8+fjiGjk4hkwIiIiExIUFIRPPvkEy5Ytg6+vL0aPHq3W3t3djUuXLuH8+fOIjo42eF6RSIS0tDSkpaUhPDwc33zzzWCHPuyUSiUmTpzYl3xpO59IRDTcmIAREdGwkUgkBhUFeZb4+voiNzcXKSkpKC4u1litMjc3h6urK/bu3Yvg4GCD583IyMD169cBAJ6enoMZssmwsLBAWVkZFAoFzMzM4OfnN9whERFpYBEOIiIiIiIiI+EZMCIiIiIiIiNhAkZERERERGQkTMCIiIiIiIiMhAkYERERERGRkTABIyIiIiIiMhImYEREREREREbCBIyIiIiIiMhImIAREREREREZCRMwIiIiIiIiI/k/wB77kmutIEEAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "readout_module.arm_sequencer(0)\n", "readout_module.start_sequencer()\n", "\n", "print(\"Status:\")\n", "print(readout_module.get_sequencer_status(0))\n", "\n", "# Wait for the sequencer to stop with a timeout period of one minute.\n", "readout_module.get_acquisition_status(0)\n", "\n", "data = readout_module.get_acquisitions(0)[\"acq\"]\n", "I_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) / 140\n", "Q_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) / 140\n", "amplitude = np.abs(I_data + 1j * Q_data)\n", "\n", "plot_amplitude(nco_sweep_range, I_data, Q_data)" ] }, { "cell_type": "markdown", "id": "447a82e7", "metadata": {}, "source": [ "We can see that modulation and demodulation frequency now match, producing similar results as the spectroscopy measurements before." ] }, { "cell_type": "markdown", "id": "653ae160", "metadata": {}, "source": [ "## Stop\n", "\n", "Finally, let's stop the playback and close the instrument connection. One can also display a detailed snapshot containing the instrument parameters before\n", "closing the connection by uncommenting the corresponding lines." ] }, { "cell_type": "code", "execution_count": 28, "id": "8db9ca0d", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:54:38.826762Z", "iopub.status.busy": "2025-05-07T16:54:38.826480Z", "iopub.status.idle": "2025-05-07T16:54:44.923880Z", "shell.execute_reply": "2025-05-07T16:54:44.922409Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n", "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n", "\n", "Snapshot:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connected :\tTrue \n", "in0_gain :\t-6 (dB)\n", "in0_offset :\t0 (V)\n", "in1_gain :\t-6 (dB)\n", "in1_offset :\t0 (V)\n", "marker0_exp0_config :\tbypassed \n", "marker0_exp1_config :\tbypassed \n", "marker0_exp2_config :\tbypassed \n", "marker0_exp3_config :\tbypassed \n", "marker0_fir_config :\tbypassed \n", "marker0_inv_en :\tFalse \n", "marker1_exp0_config :\tbypassed \n", "marker1_exp1_config :\tbypassed \n", "marker1_exp2_config :\tbypassed \n", "marker1_exp3_config :\tbypassed \n", "marker1_fir_config :\tbypassed \n", "marker1_inv_en :\tFalse \n", "marker2_exp0_config :\tbypassed \n", "marker2_exp1_config :\tbypassed \n", "marker2_exp2_config :\tbypassed \n", "marker2_exp3_config :\tbypassed \n", "marker2_fir_config :\tbypassed \n", "marker2_inv_en :\tFalse \n", "marker3_exp0_config :\tbypassed \n", "marker3_exp1_config :\tbypassed \n", "marker3_exp2_config :\tbypassed \n", "marker3_exp3_config :\tbypassed \n", "marker3_fir_config :\tbypassed \n", "marker3_inv_en :\tFalse \n", "out0_exp0_config :\tbypassed \n", "out0_exp1_config :\tbypassed \n", "out0_exp2_config :\tbypassed \n", "out0_exp3_config :\tbypassed \n", "out0_fir_config :\tbypassed \n", "out0_latency :\t0 (s)\n", "out0_offset :\t0 (V)\n", "out1_exp0_config :\tbypassed \n", "out1_exp1_config :\tbypassed \n", "out1_exp2_config :\tbypassed \n", "out1_exp3_config :\tbypassed \n", "out1_fir_config :\tbypassed \n", "out1_latency :\t0 (s)\n", "out1_offset :\t0 (V)\n", "present :\tTrue \n", "scope_acq_avg_mode_en_path0 :\tTrue \n", "scope_acq_avg_mode_en_path1 :\tTrue \n", "scope_acq_sequencer_select :\t0 \n", "scope_acq_trigger_level_path0 :\t0 \n", "scope_acq_trigger_level_path1 :\t0 \n", "scope_acq_trigger_mode_path0 :\tsequencer \n", "scope_acq_trigger_mode_path1 :\tsequencer \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer0:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\tin0 \n", "connect_acq_Q :\tin1 \n", "connect_out0 :\tI \n", "connect_out1 :\tQ \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tTrue \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t140 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tTrue \n", "nco_freq :\t4.95e+08 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tTrue (ns)\n", "offset_awg_path0 :\t1 \n", "offset_awg_path1 :\t1 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer1:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer2:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer3:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer4:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer5:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Status: OKAY, Flags: NONE, Slot flags: NONE\n" ] } ], "source": [ "# Stop both sequencers.\n", "readout_module.stop_sequencer()\n", "\n", "# Print status of both sequencers (should now say it is stopped).\n", "print(readout_module.get_sequencer_status(0))\n", "print(readout_module.get_sequencer_status(1))\n", "print()\n", "\n", "# Print an overview of the instrument parameters.\n", "print(\"Snapshot:\")\n", "readout_module.print_readable_snapshot(update=True)\n", "\n", "# Reset the cluster\n", "cluster.reset()\n", "print(cluster.get_system_status())" ] } ], "metadata": { "files_to_bundle_in_zip_file": [ "Banner.jpeg" ], "jupytext": { "notebook_metadata_filter": "variants,files_to_bundle_in_zip_file" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.20" }, "variants": [ "QRM" ] }, "nbformat": 4, "nbformat_minor": 5 }