diff --git a/.gitignore b/.gitignore index 9e7adf3d..c22a7d53 100644 --- a/.gitignore +++ b/.gitignore @@ -40,4 +40,4 @@ tmp/ .idea # qcodes -experiments.db +*.db diff --git a/examples/dataset/Example of doND wrappers.ipynb b/examples/dataset/Example of doND wrappers.ipynb index 3d977ab3..1973d586 100644 --- a/examples/dataset/Example of doND wrappers.ipynb +++ b/examples/dataset/Example of doND wrappers.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,10 @@ "from qcodes.dataset.database import initialise_database\n", "from qcodes.dataset.experiment_container import new_experiment\n", "from qcodes.dataset.plotting import plot_by_id\n", - "import plottr\n", + "try:\n", + " import plottr\n", + "except ModuleNotFoundError:\n", + " pass\n", "import qcodes.config\n", "from qcodes.tests.instrument_mocks import DummyChannelInstrument" ] @@ -46,17 +49,25 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Upgrading database: : 0it [00:00, ?it/s]\n", + "Upgrading database: : 0it [00:00, ?it/s]\n" + ] + }, { "data": { "text/plain": [ - "doNd-tutorial#no sample#10@C:\\Users\\jenielse/experiments.db\n", - "-----------------------------------------------------------" + "doNd-tutorial#no sample#1@/Users/natalie/experiments.db\n", + "-------------------------------------------------------" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -68,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -120,15 +131,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 27\n", - "plot by id took 0.19638419151306152\n" + "Starting experimental run with id: 1\n", + "plot by id took 0.037342071533203125\n" ] } ], @@ -138,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -147,8 +158,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 28\n", - "plot by id took 1.528921365737915\n" + "Starting experimental run with id: 2\n", + "plot by id took 0.3784291744232178\n" ] } ], @@ -175,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -205,7 +216,7 @@ "output_type": "stream", "text": [ "called before the loop\n", - "Starting experimental run with id: 29\n", + "Starting experimental run with id: 6\n", "called at each step\n", "called at each step\n", "called at each step\n", @@ -217,7 +228,7 @@ "called at each step\n", "called at each step\n", "called after the loop\n", - "plot by id took 0.1400294303894043\n" + "plot by id took 0.03285670280456543\n" ] } ], @@ -228,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -236,7 +247,7 @@ "output_type": "stream", "text": [ "called before the loop\n", - "Starting experimental run with id: 30\n", + "Starting experimental run with id: 7\n", "called before inner loop\n", "called at each step\n", "called at each step\n", @@ -250,7 +261,7 @@ "called at each step\n", "called after inner loop\n", "called after the loop\n", - "plot by id took 0.18652963638305664\n" + "plot by id took 0.06169295310974121\n" ] } ], @@ -272,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -281,15 +292,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 31\n", - "plot by id took 0.15599322319030762\n" + "Starting experimental run with id: 8\n", + "plot by id took 0.03528785705566406\n" ] } ], @@ -328,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -337,15 +348,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 35\n", - "plot by id took 0.07499814033508301\n" + "Starting experimental run with id: 9\n", + "plot by id took 0.045063018798828125\n" ] } ], diff --git a/examples/fitting/DIY Fitting.ipynb b/examples/fitting/DIY Fitting.ipynb new file mode 100644 index 00000000..7f85033b --- /dev/null +++ b/examples/fitting/DIY Fitting.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/logger.py:16: UserWarning: The logger.py of qdev-wrappers is deprecated and will be removed soon. Please use the logger of QCoDeS instead.\n", + " warnings.warn('The logger.py of qdev-wrappers is deprecated and will be '\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from qcodes.dataset.plotting import plot_by_id\n", + "from qcodes.dataset.measurements import Measurement\n", + "from qdev_wrappers.fitting.fitters import CosFitter\n", + "from qdev_wrappers.fitting.plotting import plot_fit_by_id\n", + "from qdev_wrappers.fitting.helpers import make_json_metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To give an example of some code that would create a 'fit' dataset of the type that could be plotted with plot_fit_by_id. However of course you are free to use the fitter in whatever way pleases you and your regular fave plot_by_id will work it's magic as usual.\n", + "\n", + "The main take home is that it's \"nice\" to have 1 dataset for your data and 1 for your fit results. Thats 2 datasets in total (1 + 1 = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Least Squares Fitter Example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "fitter = CosFitter()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create 'data' measurement and register parameters\n", + "meas = Measurement()\n", + "\n", + "meas.register_custom_parameter(name='power',\n", + " label='Power',\n", + " unit='dBm')\n", + "\n", + "meas.register_custom_parameter(name='pulse_duration',\n", + " label='Pulse Duration',\n", + " unit='s')\n", + "\n", + "meas.register_custom_parameter(name='cavity_response',\n", + " label='Cavity Response',\n", + " unit='V', \n", + " setpoints=('pulse_duration', 'power'))\n", + "\n", + "# create 'fit' measurement and register parameters\n", + "analysis = Measurement()\n", + "\n", + "analysis.register_custom_parameter(name='power',\n", + " label='Power',\n", + " unit='dBm')\n", + "for param in fitter.all_parameters:\n", + " analysis.register_parameter(param, setpoints=('power', ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we run both measurements simultaneously and fill up the datasets :) The main thing to note is that the analysis dataset needs metadata about which fitter was used and which data was used. Helpfully there's a helper funciton for this. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# make some fake data and check it looks readonable\n", + "powers = np.arange(20) * -0.5\n", + "pulse_durations = np.arange(500) * 1e-9\n", + "cavity_responses = []\n", + "for p in powers:\n", + " w = np.sqrt(10**((p - 30) / 10)) * 1e9\n", + " cavity_response = fitter.evaluate(pulse_durations, 1., w, 0., 1e-3) + np.random.random(500) + 0.6\n", + " cavity_responses.append(cavity_response)\n", + "plt.plot(pulse_durations, cavity_responses[0])\n", + "plt.plot(pulse_durations, cavity_responses[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting experimental run with id: 9\n", + "Starting experimental run with id: 10\n" + ] + } + ], + "source": [ + "dependent_parameter_name = 'cavity_response'\n", + "independent_parameter_name = 'pulse_duration'\n", + "\n", + "with meas.run() as datasaver, analysis.run() as fitsaver:\n", + "\n", + " # create and add metadata\n", + " metadata = make_json_metadata(datasaver._dataset,\n", + " fitter,\n", + " dependent_parameter_name,\n", + " independent_parameter_name)\n", + " fitsaver.dataset.add_metadata(*metadata)\n", + " \n", + " \n", + " # measurement as normal, plus fitting\n", + " for i, power in enumerate(powers):\n", + " for j, pulse_dur in enumerate(pulse_durations):\n", + " datasaver.add_result(('power', power),\n", + " ('pulse_duration', pulse_dur),\n", + " ('cavity_response', cavity_responses[i][j]))\n", + " fitter.fit(cavity_responses[i], pulse_durations)\n", + " fit_result = [('power', power)]\n", + " for fit_param in fitter.all_parameters:\n", + " fit_result.append((fit_param.name, fit_param()))\n", + " fitsaver.add_result(*fit_result)\n", + "\n", + " data_run_id = datasaver.run_id\n", + " fit_run_id = fitsaver.run_id" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_by_id(data_run_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_fit_by_id(fit_run_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/fitting/Fit_by_id.ipynb b/examples/fitting/Fit_by_id.ipynb new file mode 100644 index 00000000..b08e45eb --- /dev/null +++ b/examples/fitting/Fit_by_id.ipynb @@ -0,0 +1,1072 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/logger.py:16: UserWarning: The logger.py of qdev-wrappers is deprecated and will be removed soon. Please use the logger of QCoDeS instead.\n", + " warnings.warn('The logger.py of qdev-wrappers is deprecated and will be '\n" + ] + } + ], + "source": [ + "import qcodes as qc\n", + "import numpy as np\n", + "from qcodes.dataset.plotting import plot_by_id\n", + "from qcodes.dataset.data_export import load_by_id\n", + "from qcodes.dataset.sqlite.database import connect\n", + "from qdev_wrappers.fitting.fitters import CosFitter, ExpDecayFitter, MinimumFitter\n", + "from qdev_wrappers.fitting.fit_by_id import fit_by_id\n", + "from qdev_wrappers.fitting.plotting import plot_fit_by_id\n", + "from qcodes import new_experiment" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "transmon_db_path = qc.config.user['transmon_db_location']\n", + "transmon_db_connection = connect(transmon_db_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data in the transmon database (for reference)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
descriptionrun_id
rabis_frequency1
t12
ramsey_frequency3
t2*4
readout_fidelity5
spectroscopy6
resonator_power7
readout_fidelity_cavity_power_frequency8
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit by ID\n", + "\n", + "Now that you know what a fitter is (if you don't go through the 'Introducing the Fitter' notebook) you might want to use the helper functions to fit to a competed dataset:\n", + "\n", + "- **fit_by_id** \n", + " - *Args*:\n", + " - 'data_run_id'\n", + " - 'fitter'\n", + " - 'dependent_parameter_name' (the name of the measured parameter)\n", + " - 'experiment_parameters' (then name(s) of the independent parameter(s)). \n", + " - *Kwargs*:\n", + " - 'plot'(whether to plot results, default True)\n", + " - 'save_plots' (if plot, whether to save plots, default True)\n", + " - 'show_variance' (if plot, whether show variance, default True)\n", + " - 'show_initial_values' (if plot, whether show initial values, default True)\n", + " - Any other kwargs are passed to the fit function of the fitter\n", + "\n", + "Let's start by fitting without generating any plots" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/natalie/Documents/PhD/Qdev/Qcodes/qcodes/utils/deprecate.py:59: QCoDeSDeprecationWarning: The function <_save_val> is deprecated. Use \"`cache.set`\" as an alternative.\n", + " issue_deprecation_warning(f'{t} <{n}>', reason, alternative)\n" + ] + } + ], + "source": [ + "rabi_fitter = CosFitter()\n", + "t1_fitter = ExpDecayFitter()\n", + "minimum_fitter = MinimumFitter()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting to a 1d\n", + "\n", + "Start by choosing a 1d T1 dataset where we want to fit an exponential decay to it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results #2@/Users/natalie/Documents/PhD/Qdev/QdevWrappers/examples/fitting/../transmons_data/transmons.db\n", + "---------------------------------------------------------------------------------------------------------\n", + "alazar_controller_ch_0_m_records_drive_readout_delay - numeric\n", + "alazar_controller_ch_0_m_records_data - numeric\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(load_by_id(2, conn=transmon_db_connection))\n", + "ax, clb = plot_by_id(2, conn=transmon_db_connection)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting experimental run with id: 7. \n" + ] + } + ], + "source": [ + "t1_fit_run_id, ax, clb = fit_by_id(\n", + " 2,\n", + " t1_fitter,\n", + " 'alazar_controller_ch_0_m_records_data',\n", + " 'alazar_controller_ch_0_m_records_drive_readout_delay',\n", + " source_conn=transmon_db_connection,\n", + " plot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "results #7@/Users/natalie/Documents/PhD/Qdev/QdevWrappers/examples/fitting/../fitting.db\n", + "----------------------------------------------------------------------------------------\n", + "T_initial_value - numeric\n", + "success - numeric\n", + "a - numeric\n", + "c - numeric\n", + "c_initial_value - numeric\n", + "T - numeric\n", + "a_initial_value - numeric\n", + "T_variance - numeric\n", + "a_variance - numeric\n", + "c_variance - numeric" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_by_id(t1_fit_run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we can see this gives us a dataset with all the fitter parameters saved. Yay." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting to a 2d\n", + "\n", + "What about if there was some other variable in the dataset...? Let's choose a Rabi measuement where we want to fit a cosine at each frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results #1@/Users/natalie/Documents/PhD/Qdev/QdevWrappers/examples/fitting/../transmons_data/transmons.db\n", + "---------------------------------------------------------------------------------------------------------\n", + "rs_qubit_frequency - numeric\n", + "alazar_controller_pulse_duration - numeric\n", + "alazar_controller_ch_0_m_records_data - numeric\n", + "alazar_controller_ch_0_p_records_data - numeric\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(load_by_id(1, conn=transmon_db_connection))\n", + "ax, clb = plot_by_id(1, conn=transmon_db_connection)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting experimental run with id: 8. \n" + ] + } + ], + "source": [ + "rabi_fit_run_id, ax, clb = fit_by_id(\n", + " 1,\n", + " rabi_fitter,\n", + " 'alazar_controller_ch_0_m_records_data',\n", + " 'alazar_controller_pulse_duration',\n", + " source_conn=transmon_db_connection,\n", + " plot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "results #8@/Users/natalie/Documents/PhD/Qdev/QdevWrappers/examples/fitting/../fitting.db\n", + "----------------------------------------------------------------------------------------\n", + "rs_qubit_frequency - numeric\n", + "success - numeric\n", + "a - numeric\n", + "w - numeric\n", + "p - numeric\n", + "c - numeric\n", + "a_variance - numeric\n", + "w_variance - numeric\n", + "p_variance - numeric\n", + "c_variance - numeric\n", + "a_initial_value - numeric\n", + "w_initial_value - numeric\n", + "p_initial_value - numeric\n", + "c_initial_value - numeric" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_by_id(rabi_fit_run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So this looks like it does the expected thing and add a column for the frequency. Let's for completeness also try out a different kind of fitter. We use the simple minimum fitter to track a cavity as a funciton of power." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results #7@/Users/natalie/Documents/PhD/Qdev/QdevWrappers/examples/fitting/../transmons_data/transmons.db\n", + "---------------------------------------------------------------------------------------------------------\n", + "rs_vna_S21_power - numeric\n", + "rs_vna_S21_S21_frequency - numeric\n", + "rs_vna_S21_trace - numeric\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(load_by_id(7, conn=transmon_db_connection))\n", + "ax, clb = plot_by_id(7, conn=transmon_db_connection)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting experimental run with id: 9. \n" + ] + }, + { + "data": { + "text/plain": [ + "results #9@/Users/natalie/Documents/PhD/Qdev/QdevWrappers/examples/fitting/../fitting.db\n", + "----------------------------------------------------------------------------------------\n", + "rs_vna_S21_power - numeric\n", + "success - numeric\n", + "location - numeric\n", + "value - numeric" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_fit_run_id, ax, clb = fit_by_id(\n", + " 7,\n", + " minimum_fitter,\n", + " 'rs_vna_S21_trace',\n", + " 'rs_vna_S21_S21_frequency',\n", + " source_conn=transmon_db_connection,\n", + " plot=False)\n", + "load_by_id(min_fit_run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That also works :) Now lets check out plotting (if we had set the plot kwarg of fit_by_id to be True we would get exactly the same plots as below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot by ID\n", + "\n", + "For 1d datasets a 1d plot will be generated with the data against the fit to the data. For 2d datasets a 2d heatmap of the fit result will be generated and a 1d plot for each of the fit parameters as a function of the 'setpoint' variable.\n", + "\n", + "- **plot_fit_by_id** assumes a saved analysis dataset. Either make this with fit_by_id or check out the 'DIY fitting' notebook\n", + " - *Args*:\n", + " - 'fit_run_id'\n", + " - *Kwargs*:\n", + " - 'show_variance' (whether to plot variance, default True)\n", + " - 'show_initial_values' (whether to plot the initial values, default True)\n", + " - 'save_plots' (whether to save the plots generated, default True)\n", + " - Any other kwargs are taken to be values of the setpoints" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting a 1d \n", + "\n", + "Play with the kwargs to see how variance and initial_values are displayed and change plot_save to True to save the figure" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_fit_by_id(t1_fit_run_id,\n", + " show_initial_values=False,\n", + " show_variance=False,\n", + " source_conn=transmon_db_connection,\n", + " save_plots=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting a 2d\n", + "\n", + "Again have aplay with the kwargs, things look a bit different here." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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hfrHjFwAIwlHxo+hp6+HO5+8kl89RF63j6rddzepDV9MQbaAx2khjXePsX/u2hmgDEYkEong27dzEdG4agOncNJt2bvJVtqXM/ZS9rmcd0UiUbD5LY7SRa86+hmMTx5JXeXL5HHmVn/3kVMHvfI4fPf4jNj69kTx53++ZG2jlodE45ItnfLGs49Z2rz3oZdDS0MKbVryJN6140+y2gfQAW/Zu4eG9D/PQnoe4e+fdzORnAONleMlvLym7bFGJklM5BKGprsm33qz9RRqVKOt61nku0+LNK948+70h2uCb7LXda3nnUe/klmduqcjSisfibHx6I4L4Wm630MqjgGQyyfvf/362bNnCu9/9bpYsWcLSpUu55JL5G/CaNWv4wQ9+wHHHHedDSd3liiuuKPv/DAO1fK8L6Yp1cVbvWZzVexYAv9/1e8744RnM5Gaoi9Txn2f/J31dfUxlp5jKTTGdm579Xrjt7p13c88L96BQvvZm13av5bRlp7H5xc389Wv+2tce9AlLTwCgo6mDW95/i6+y66P1dC/urkjmW1a+hYZoA2uWr+Hqt11dU1YHaOVxEF/60pfo6+vj9ttvJ5lMcvLJJ9Pf31/WuZ/61Ke48sor+cUvfuFxKd0lmUxyww03lP1/+s3HP/5xli1bxmc/+9nZbbV6r8vh9Ye/nrsuvKsi19Pbd7+d068/nWw+63tvdiAzAECsPuabTIBkOglANp/1/QWcnEiSaKk4qzlLW5ZyZMeRNac4QM8wP4g77riD9773vQBcf/31nHPOOTQ3N5d17vnnn8/dd9/Nvn0HR9yEGaf/Z7Vs2LCBDRs2lH38Aw88wBvf+MYDtpV7r53KCgtru9dyxZuucPxSWdu9litPvxKAb5/7bd9eStl8lueHngcglUn5ItMiOWEoj7HpMaayU/7KTieJx+IVn59oSZBK+3u/3CJ0ykNEmkTkIRHZKiJPiMjn/JA7PT1NW1sb27Zt47zzzuOEE07gtttu481vfsWf+k//9E+8613vmv196aWXcsYZZzAzY/inm5qaOOWUU/jd735XdXn279/P+eefz9KlS1m8eDHnnXceo6OjAOzevZt3v/vdJBIJurq6uPjii2fP27FjB+vWraO9vZ3jjjuOjRs3zu77t3/7N5YvX86iRYs46qijuPPOOwEO+j8BxsbGWL58Offcc88B23fv3o2IMDAwUNY9qZR8Ps/VV19NT08P27ZtY/369Xz1q1+d3e/mvQa46aabOOWUU2hra+PII49k06ZNrlw3CP5s1Z8B/loAu0d2z47TWC9zv7C/fC0rxE/ZiVjllkc8FtfKw0WmgLcqpU4CTgbOFpHXeS20oaGBzZs3s2TJEsbHx9m2bRvbtm3jqKOOmj3msssu4+677+axxx7jW9/6Fr/5zW/45S9/SX19/ewxxxxzDFu3bj3o+ueeey7t7e1FP+eee+5Bx4+OjvJ3f/d37Nq1i507d5JKpfj2t79NLpfj3HPPnU2TsWfPHtavXw/AzMwM5513Hm9/+9vZv38/3/jGN7jgggt46qmneOqpp7j22mt5+OGHGRsb47e//S3WCoyF/yfAV77yFY4//viDlEp3dzctLS1s27at7HtSCV/60pe45ZZb+PrXv86qVau4+eabufTSS9m9e/e899opX/nKV/jCF77Ad77zHYaGhvjVr35FLa9O2dfZB0D/oH9uyGcGnwGgpb7F95ehXWH4KVspRXKiOssjHov7rvDcInRjHsrIGzJu/qw3P77kEnnsscc46aSTZn8PDw+zaNGi2d9dXV1ccsklXHjhhYyMjHD//ffT1tZ2wDUWLVpU1JXy61//2lFZent76e3tBaCxsZEzzzyToaEhHnroIfbu3cu///u/U1dnPD7LpfOHP/yB8fFxLr/8ciKRCG9961s599xzuemmm/jABz7A1NQUf/rTn0gkEge8HAv/z1wux3//939z7bXXAsaYyNjYGEcccQQAdXV1ZDKZsu+JU8bGxrjqqqt44oknuOWWW1i9ejVr1qyhu7ubJ598ku7ubmDue+2EZDLJ5z73Oe67777ZZ3/CCSdUdc2gWdS4iKUtS2df6H5gKarTlp/GrpFdvsmFAsvDR6tnbHqMmfxMdZZHs7Y8XEVEoiLyGLAfuF0p9WDB/o+JyBYR2ZJMuldZCpVHR0cHY2NjBxyzevVqtm3bxpe+9KXZl5idsbEx2tvbqy7Lz372M97whjewZMkS2tvb+fKXv8yqVavYvXs3K1asmFUcdvbu3Ut3dzeRyCuPdcWKFezZs4fe3l6uueYaNmzYwJIlS1i/fj179+4t+n9u376d/fv3c9ZZRvTPV7/6Va677joAMpkMY2NjLFmypOx7AgdaXl/+8pf58pe/PKflddddd7Fq1Sp6enrYunUrq1evJp/PMzQ0dIDcue61E1l33HEHJ5xwwgHP/dVAb2ev78qjua6ZE5ec6L/lYVMYfvbiLbnVDJgnWhKMTo3OzpGpJUKpPJRSOaXUycBhwBoROb5g/3VKqVOVUqcmEpU/uEK2bt16wEvkxBNP5Omnn579vW3bNj7+8Y/zoQ99iO9///tFr7Fjx46iL6J3vOMdtLa2Fv284x3vOODYu+66i8suu4xrrrmGvXv3kkqlWLJkCSeffDLd3d3s2rWLbDZ7kIxly5axe/du8vn87LZdu3axfPlyAN7//vdz//3388ILLyAiXHbZZUX/zz179tDR0cHixcZs59/85jezL+177rmHjo4OVq9eXfY9AcPyGh4eZnh4mMsvv5zLL7989nehVfbSSy/R2WlMvHvsscdYvXo19957L21tbZx44onz3msnsgYHB11R9mGjr6uPZwb8VR69nb0saVnC6NSorwPXqXSK5rrm2e9+YSmqat1WYMz1qTVCqTwslFLDwCbgbD/kFSqPc845Z3bAeM+ePZx33nl861vf4r/+67/Ytm3bQYOqU1NTPPLII5x55pkHXfu2225jfHy86Oe22247qBzd3d0cffTRDA0N8ZGPfIT9+/dz7LHHsmbNGg499FAuv/xyJiYmmJyc5IEHHgDgta99LS0tLVx99dXMzMywadMmbr75ZtavX89TTz3FXXfdxdTUFE1NTTQ3NxONRg/6PwE6OzsZHR3l+eef56abbmJ6epo//elPDA8Ps2HDBi655BIikUhZ96QSjj76aB555BGee+45tm/fTmdnJxdffDFXX331bFbbUvfaCatXr+b+++9n69atKKV45pln2LFjR9X/Q9D0dfaxb3wf49Pj8x/sApbysHrhVtiuHyTTSXo7e4lIxFe3laWoqh0wB/8H+t0gdMpDRBIi0m5+bwbeBjzptdyXXnqJoaEhjj766NltF154IbfeeisjIyOcc845/MM//APnn38+sViMSy+9lE9/+tMHXGPjxo2sW7eOZcuWVVWWCy64gJmZGQ455BDOPfdc+vr6OPbYY2loaCAajXLzzTfT39/P4YcfzmGHHcZPfvITwBj037hxI7fddhvxeJxPfOIT3HDDDRx99NFMTU1x+eWXE4/HOeSQQ9i/fz9XXXXVAf+nNY5x2mmnsX79ek4++WS+973vsXHjRn7/+9/T19fHa1/7Wi677DJGR0fLuieVcPrpp3PRRRdxyimnMDk5yQUXXMBll102GxgA7t3r17/+9XzmM5/h3HPPZdGiRbzrXe+avQ+1jJ+D5rl8jmeHnqW3s/eVl6HPL/ElLUvoau4KxG3lhuVRk+MeSqlQfYATgT8CjwPbgStLHX/KKacopxj/dnlcccUV6mtf+1pZx65Zs0Zt27bNcXnCgJP/0y9uuukmddZZZxXdF9S9dlJ3guSP+/6o2ID66fafei7rheEXFBtQ397ybXXvznsVG1B3PHuH53Iter/eq9b/fL065tpj1F/85C98k3v1/VcrNqDGpsYqvsb2l7crNqB+sv0nLpasPIAtqop3dRijrR4HVgddDgurd14ODz744PwHhRQn/6dfPP300weFEFvU8r32g95OI1LPD8vDknGA5eGjBWDNtUi0JPy1PNJJmuqaaKlvqfgatWx5hM5tpdFYPPXUU6xatSroYtQkrQ2tHNJ6iC8RV3blYY15+PUynMnNMDw5TDwW933CnTW73MnqkoVYGZn9nljpBqGzPDQaixtvvDHoItQ0fZ19vimPxmgjhy0+DKUUgvj2MrQPWidiCe6buM8XuZbsagbLwUis2N7Uri0PjUYTHvo6/QnX7R/s54iOI2ZXzeuKdfn2MrTkxGNxErEEA5mB2cW2vKba2eUWiVjC93xgbqCVh0bzKqWvq4+XJ15mdGrUUzlWmK6Fnyk3LDmJlgTxWJy8yjOUGfJFdiqdqmqCoEWt5rfSykOjeZXiR7iuUuog5ZGI+Zcp9gDLw3yR+6m44s3VWx7xWLwmxzy08tBoXqX0dXmvPPaN7yOTzQRneVgpQswxD/s2L5nKTjE6NbqgLY8FOWC+YsWKqiIkNAuXFStWBF2Esjmy40gAT8c97JFWFvFYnM0vbvZMph3rpdvZ3Olr2Ks1g77aAXPrGql0ygg2qKH30oK0PHbu3FnxxJhzbjyH13z7NSil+MydnyHyuQi5fM7zyZOf3/R52ADT2WmUUhzxn0fw/l+8P/BJnfN9zvrhWaz5zprAy+HWZ+fOnUFX37JpaWhh2aJlnkZcFVMe9peh1yTTSTqaOqiP1vvqtnJjdrlFPBZnKjfFxMxE1dfykwWpPKohlU7NVhhrgG54ctgXuW2NbdRH62dl14Kpa79fGv/xOly3f7Cfukgdh7cdPrstHouTzWcZmRrxTK5FYXsEf9xWsyHCLrmtoPbmemjl4ZDAKmvmwJdwrSiPZDrpimmvqQyvw3X7B/tZ2b6SusgrHvBZC8CHdpFMv7KGeFNdE60Nrb60Czcy6lrU6ixzrTwckkqnZiMs/JxNW9iDrxXloS2PYOnr6iOZTjIy6Y0VUBhpBf6+DAvrVyLmT4oSNzLqWvg9K98ttPJwwHRumtGp0YMsj0CURw2sQJaeSZOeSWvLI0C8DNdV6uAwXXjlherX2IO9fiVa/AkTTk4kEWQ2vUg1aMtjAWAt2BIK5RGLz76cw4o9Bl8TDNaL3Ytxj/0T+xmbHgvM8lBKHZQixK8w4WQ6SWdzJ9FItOpr1eqaHlp5OKDwZejnQz/IPLcW3QnxCmRuLNOpqY4jO70L17WsGcu6sfBrzGN0apSZ/MzBbiufBszdqtdtjW3UReq05fFqplB5xOpjxOpjnj90y8IotDzsZQojbg4qaiojVh/jsMWHeWJ5FAvTtWQ21zV7XjftqUks/BoLtDLquoGI1MwYph2tPBxQzA3jx0MvdJfZv4e5wrk5qKipHK/CdfsH+4lKlBXtB0+c9GNtjWLtMRFLkMlmmJj2ds6EGxl17Wjl8SonKOUxl1z7vjDi5kQqTeV4Fa7bP9TPivYVNEQbDtrnR7uwpyax8GuioFsZdS38TOniFlp5OMBqDF2xrtltfjz0UsojzBUulU4RlSjtTe1BF2VB09fVx0BmwPVss8UirSz8CJkNqlOVV3lteaCVhyNS6RSLGxcf0NPyI4PorNJqfkVpdTR1IEioK5wbK61pqseLJWmVUjwz8Ay9HcWVhy+WR5ExDz+SI45MjpBTOVcDQfzMROwWWnk4oHCWNwTntopGonQ2d4a6wrkZkaKpHCsays1xj8HMICNTI6UtD4+jnlLpFI3RxgPWEPfDbeVFIEg8FmcwM0gun3Ptml6jlYcDis2WjsfijE6NMp2b9lSuIHQ0dxwkO8zKw82IFE3lHNl5JIK4Ou4xV6SVRTwWZ2x6jKnslGsyC7FSk9gtWz/cVl4EgswuZDXpz0JWbuCb8hCRbhG5W0R2iMgTIvLJIsesE5EREXnM/FzpV/nKoZif04/KOpAZoKO544D8QZbsMCsPt/3Cmspoqmuiu63bVctjPuXhR8qNYp25tsY26iP1nlo9XgSC1EIATCF+Wh5Z4B+VUscArwP+VkSOLXLcfUqpk83P530s37wUq6zWy9HvRgLhVx5uR6RoKsftcN3+wX4EYWXHyqL7/XgZFqYmgVfmTPjhtnJ7zAO08iiKUmqfUupR8/sYsANY7pd8N5jLbWXt81MuhHutq5RbAAAgAElEQVSQLZfPMZgZ1JZHSOjr7HN1wLx/qJ/utm6a6pqK7vcjv9VcY2ped6q8SLtTi2nZAxnzEJEeYDXwYJHda0Vkq4jcJiLHzXH+x0Rki4hsSSb9udnFZnlDsMrDaiR+LLrjlMHMIAqlLY+Q0NvZy2BmkMHMoCvXKxWmCz5ZHnOsIe71BMXkRHI2u4RbaLdVGYhIK/AL4BKl1GjB7keBFUqpk4BvAL8qdg2l1HVKqVOVUqcmEv70bIvN8rb/9rLHYE8DXyh7Jj/D2PSYZ7Irxc3FcjTVY61n7tagef9g/5xhuuB9fqtSa4h7bZGnMu6P5WnlMQ8iUo+hOG5USv2ycL9SalQpNW5+vxWoF5FQdF3nMlWtCYNePXQrc+hcloeXsqth1i+s3VahwM1w3eHJYVLpVEnLw+t5SNYa4nO1C68HzN22qJvrm2mpbwllW54LP6OtBPgesEMp9dU5jjnEPA4RWWOWLxRpY+dSHnWROjqaOjx76BMzE0zlpkoqjzD6SXU69nBxRMcRRCTiiuXx7OCzwNyRVmDMQ+qKdXnmPiqWmsQiEUswNDnETG7GG9m21QvdpNZSlNTNf4hrvAH4ILBNRB4zt/0zcDiAUupbwHuAj4tIFsgA61VIHPqlXobxWJxUxhvlMZ9c+zFhQqdjDxeNdY0c3na4K5bHfGG6Fl4OXJfTLgYzgyxtXeqJ7GPix7h+3bBHTxbim/JQSt0PlMxToZS6FrjWnxI5Y77K6lXvv1aVR7GUKppgcSviyrrGER1HlDzOy/xWpcJl7bPMvVAeXoWg+7UKolvoGeZlMjvLu6njoH1ePvRaVR7JdJLFjYtprGsMuigak97OXp4ZfKbq6Lz+oX6WLVpGS0NLyeOCsjy8zG+VmckwMTPhyVherVkeWnmUSSqdmnPZSS/XEy/VSBY3Lg7tCmQ6NUn46OvsY3hyeHawuVLmC9O18DK/Vak1xL3sVHk5lhdvrq0xD608yqRYUkQLL+dblKqsYV6BTKcmCR9uhev2D/YftPRsMbxuF8VS9oC3yRG9DEGPx+KMT48zmZ10/dpeoJVHmcwVLgvGQ5/KTTE+Pe6J3KhEaWtqK7o/EUt4NlhfDTo1SfhwI1x3bGqMl8ZfKs/yaEmQUzmGJ4crljcXyfTBqUksrHE2L6weL5dWthSSNacs7GjlUSallIeXSeBS6RRdsS4iUvxRhdry0JFWoWJlx8qqw3WfHZo/TNfCa/fRXPWrPlpPe1O7J3JLhQhXS5jHMIuhlUeZzGd5WMf4KdeSHbbKppSaM3WEJjgaog30tPfQP1R5xFW5YbrgbX6r+cbUvIr08tptBeFeHdSOVh5lUGqWN2jlUUh6Js1kdlJbHiGk2vXMLeVxZMeR8x7rZbsollHXjlcRkMl00rOllbXl8SpkfHqc6dz0vMrDq57OfMojbCuQeekX1lRHteG6/YP9LG1ZyqLGRfMe61V+q/k6c+DdbO353MjVUGtp2bXyKIP5wvO8fOhzJUW0COMKZF6stKZxh77OPkanRit+sZYbpgve9aSHJ4eNNcRLWR4ehQl7GYLe0extPjC30cqjDOZTHl7Nt7B6WFbyxWKE0dT1YqU1jTtUG67rRHlYacvdtgDKmWvhVZjwfO6yaqiL1NHR3BHKXHXF0MqjDOarrF7NtxiZGiGncvM2EnsZw4BOxx5eqgnXTc+k2TO2p2zlAd6MyZWzkl8ilmAmP8PoVOGqD9XhdRShl3ny3EYrjzIot6cTVA/LfmwY0GMe4aWnvYeoRCvKcfXc0HNAeZFWFl5EPZXTLryaKOh1FGGYVwctRCuPMiirsnrw0MuVaz82DKTSKeoidbQ1Fp/YqAmO+mg9KztWVmR5OAnTtfDE8ihjroUXnarZpZW9tjxC1JZLoZVHGczO8i7xMvTioc+1eqEdrxejqgRrdrm5NIsmZPR29lY05uEkTNci0eL+wLWTTpWbsocmh8irvKcWtdcLWbmJVh5lYIUFlnoZeqE8ymkk1qBkmJSHF8t0atyjr7OvonDd/sF+upq76Gg+OLP0XHiRNDSZTtJc11wyq68XbisvZ5dbeJkPzG208iiDUkkRLeKxOAPpAVfnW5SbwTNspq7OaxVu+jr7GJ8e5+WJlx2d5yTSyiLRkmBseoyp7JSj80pRzqC1F24rP1bHtAb6x6bHPJPhFlp5lMF8E5LAeOgK5ep8i1Q6RX2knkUNpSdkhU55eLRMp8YdKg3XrUR5ePESL2euRUt9C011Ta66gMqJ8qqWMAbAzIVWHmVQjvLwqqdTzthB2JTHfBMbNcFSSbjuVHaKXSO7nFseHuS3Kifdv4i4nnHaj8mvs9kqamDcQyuPMghMeZThLrNkhyWZWjaf9TwiRVMdK9pXUBepcxSu+/zw8yhUOCyPMt2ibg8++zH5VVseryLyKs9AeqBs5eFmZS1HaYG3Kxk6ZTAzCOjUJGGmLlLHynZn4bqVhOmCN/mtSq3lUSjbbYtnUcMiT5dW9nJ5B7fRymMeRibnn+UN3jz0spVHLM7o1CjTuWnXZFeKTk1SG/R1OcuuaymPclYQtON2T3oyO8n49HhZ9cvtuVd+LK2sLY9XEeVGWFirlwWlPCAcK5Dp1CS1QV9nH/2D/WWHhPYP9tPe1F50zfBSdDR1EJGIaxaAk/rlutvKh0CQRQ2LqI/Uh8YNXYrQKQ8R6RaRu0Vkh4g8ISKfDLI85SqP5vpmWupbXFMe1mzWsnpYITJ1dWqS2qCvs4+JmQn2je8r63gr0srpxM9oJEpnc6drddNJuGwi5m6YcDkD9dXiVZ48Lwid8gCywD8qpY4BXgf8rYgcG1RhnFRWNweuhyeHy57NGiZTV6djrw2chus+M/iM4/EOCzfzWzmZqOd2u/Br/pJXC1m5TeiUh1Jqn1LqUfP7GLADWB5UeRz1dFx86E6Vlv2cILEad6k08prgscYuyom4ms5Ns3N4J70dlSkPN3vSTtsjuBcm7IflAeELvZ+L0CkPOyLSA6wGHizY/jER2SIiW5JJb32DTl/iC115pNIp2hrbaIg2BF0UTQm627qpj9SXFXH1wvAL5FW+csvDxfxWTibquZnfamJ6gkw244vlEabQ+1KEVnmISCvwC+ASpdQBSfmVUtcppU5VSp2aSHjbE0ilUzRGG2mpnzuPjkVQysOLwfpK8SMiRVM9dZE6jug4oizlUWmYroWboeSpdIqIROhomj+/lpudKj9ml1uEKfS+FKFUHiJSj6E4blRK/TLIspQ7yxuMh+52VEk5L+L6aD1tjW2hqHBeL5ajcY9yw3WrVR6WOzev8hWdbyc5kaSzuZNoJFqWXHDHbeXnWF6iJcFQZohsPuu5rGoInfIQ4y39PWCHUuqrQZen3FneYDz08elxJrOT1ct1mIQtLKautjxqBytcd76Xev9gP60NrSxpWVKRnHgsTk7lGJkcqeh8O04yNs+GCbvgtvJz/lI8Fjfy5GXcy5PnBaFTHsAbgA8CbxWRx8zPOUEVpty5FuDufItUOkVzXTOx+ljZskNjeehIq5qgr7OPTDbD3rG9JY/rH6osTNfCzfxWTiKe3AwT9nP+0my2ihB0BksROuWhlLpfKSVKqROVUiebn1uDKk8lysOVyppJOYpYCoPyUErpdOw1hBWuO1/EVSXZdO24Pfbg5AXuVpiwn/OXwhQAU4rQKY+wUYnycMvH6qSihkF5TMxMMJWb0pZHjWAphFLjHtl8lueHnq84TBfczfvmNGOzW+HzyYmkb0srh3Fp6WJo5VGCbD7LUGao/DEPFx+6U+XhxRrqTtF5rWqL7sXdNEQbSkZc7R7ZzUx+pirLw60MCFaSUieWh1tjgU4CZ6qlVtKya+VRgqHMEAoVjNuqAssjk82QnklXLbtS/Axn1FRPNBLlyI4jSyqPaiOtwD2LfHhyuKwkpXYSMXfmmJSbydcNLHd10J3B+dDKowROI546mjsQxJWHPpAecGSeh8FP6scynRp3mS9c1w3lEauPEauPVV03K1lDPBFLMJAZqDpM2M8Q9Ka6JlobWrXyqGWcvgzrInV0NHdU3dPJ5rMMTZbvLoNwKI9KGrcmWPo6+3h26Nk5X679g/001zVz6KJDq5Ljhvuoks5JPBYnr/JVh736HYLu9iqIXqCVRwkqqaxuPHRrQaVaUx7a8qg9+jr7mMxOsmd0T9H9/UP9HNl5JBGp7lXhxphcJW5RtyYKJif8c1uB++nkvUArjxJU2tOptpFUKtd+bhAk00nqI/UsblwcWBk0zpiNuJpj3KPaMF0LN16GlczydqNdVOIJqJYwRE/Oh1YeJbAenpU7qhyCVh5B9lb8jEjRuEOp1Ox5lefZwWcdrx5YDDdCZiuJ5nMjOaI16ddvy0MrjxomlU7RUt9Cc31z2ee42cNy0kjam9qJSCRwy0NHWtUWhy0+jKa6pqKWx57RPUzlptyxPFzI+1ZJe3TDbRXE6phhCL2fD608SuAkr5WF9dDLXd6zqNwKlIfbK7ZVgk5NUntEJDJnuK4bkVYWbuR9q2TQ2g23VRCrY8ZjcSZmJsjMZHyT6RStPErgdK4FGA99Jj/D2PRYVXLB+YJK8Vg80AgNnZqkNpkrXNdN5eHWS9xp798Ke63GGxDE6phhGMOcD608SlCp8rDOrUZua0MrTXVNjs4L2tTVlkdtYoXr5vK5A7b3D/bTGG3ksMWHVS3DjewLlbRHS3Y1naogMido5VHjVKM8qu3pVFJRgxxkm8nN+B6RonGH3s5epnPTvDj64gHbnxl8hiM6jqg6TBfcaReVhstWOw4ZhNvKrZQuXqKVRwkqeYm78dBrUXlYc1P0gHntYUVTFY57uBWmC8G2i0RLdZl1raWV66P1FV/DKbWQll0rjzmYzk0zOjUamNuqGuVRzWB9pQTRO9O4Q7FwXaWUq8qj2pdhZibDxMxERZZHte7cIKIItduqhrFiu2tNeWTzWUanRuc/2GWCGFTUuMOyRctorms+wPLYN76PTDbjmvKwVvWrtF1Uk72gWrdVEGN51d4vP9DKYw4qrayLGhZRH6mv2kx2khTRIsjeik7HXrtEJEJvZ+8BysPNSCt4JZS80pd4NRmbE7EEmWyGiemJymQHEEUYhtD7+dDKYw4qVR4iUtVs2qnsFGPTYxX3sCAYP2kQE6k07tHX1XfAioJuKw+oLuqpGsu22k6Vn+nY7bi1FolXaOUxB9WayZVW1IFMZe4y+zmBWB5mJXeSykUTHno7enlu6LnZcN3+wX7qInUc3na4azKqcR9VY9lWM8tcKVWxG7lawp6iRCuPOQhKeVQr134NP0lOJGlvavc1IkXjHn1dfUznptk1sgswlMfK9pXURepck1GNRV6NZVtNfqux6TGmc9OBWNRBz9uaD0fKQ0QaRKT8xDI1TCVJES2qMTcrnV1uybVfw08qSeWiCQ+F4bpuRlpZVJPfKplOEpUo7U3tzuVW0S6CDAR51VgeIvJJYB/QLyI7RORiJ4JE5Psisl9Ets+xf52IjIjIY+bnSifXd5tqYrur6TFUY3ksalhEQ7QhMMtDR1rVLvZwXbfDdC0SLQkG0pWt6pdKp+iKdVU0YbEat1WQgSBBht6Xw7xPQkSuEZELgU8CxyillgOnA8eKyL86kHU9cPY8x9ynlDrZ/HzewbVdp5qedDwWZygzRDafdS63CuUhIoH1VoLyC2vc4dDWQ2mpb+GZwWdIppOMTY+5b3nE4uRUjuHJYcfnVrOSX1tjG3WRuorcVtVEeVWLFXo/MjXiu+xyKEeN3wP0AnHg9yLyKPDvwLPAehEpy45USt0LDFZaUL+p5mUYj8VRqIqWvqzGXWbJDmrAXFsetYuI0NvZS/9gvyeRVlBdfqtqLNtqOlVBro7pRj4wL5lXeSil/q9S6krgD8A7gbcB/wNkgU5gk4j0l7iEE9aKyFYRuU1EjpvrIBH5mIhsEZEtyaQ3oWzVKg+ozEyuNhVCEMojyIgUjXtYcz28Uh7V5Leqtn4lYpWlKLHKGtSYB9Sw8rDxt8CPgK8Aq4HjgW1KqZOBY10oy6PACqXUScA3gF/NdaBS6jql1KlKqVMTCW8eajWVtZoew0BmoKpGEoTyCDIiReMefZ19PDf0HE+mniQiEXrae1y9fjX5raq1bCuN9EqlUzRGG2ltaK1YdqWEYXXQUpStPJRSzwCvBX4ONAOPA+8y901XWxCl1KhSatz8fitQLyKBdWXdsDwqraxVKY9m/5VHkKa9xj36uvrI5rPc+fyd9LT30BBtcPX6lVrkuXyOwcxg1Z2qiiwPc6wliKWVw255OAriNpXELebHVUTkEOBlpZQSkTUYim3AbTnlkJ5Jk55JB6Y8Dm09tCK5luzBzCC5fI5oJFrxdZwQpGmvcQ8rXPfhPQ9z5pFnun79StvF0OQQeZWvyrJNxBIVu8uCsqjDnpbdvRlA8yAiNwHrgLiIvAj8C1APoJT6FvAe4OMikgUywHoVUIxapUkRLar17Z6w5ISK5FqyFYrBzKBvlV5bHq8OrHBdhaK3w93xDoBYfYxYfcxxu3BjrkUilmBo0oiAdDLxsZoor2ppqW+hMdqolYdS6q/m2X8tcK1PxSlJtS/DxrpGFjUsCsZtZevd+aU8ggxn1LjH0paltDa0Mj497vpguUUl+a3cmGthnTuQHmBp61JHsle2r6xYbjVYUWJhzW+l05MUwY2edCXriVfrLrPkgr+mrrY8Xh2IyKzryivlUUl+KzeSblY6UTDoKMIwzzLXyqMIrikPhw+9WncZBOMnTU4kaYg2sKhhkW8yNd7Q0dQBwPj0uCfXryTqyY2FxirpVE3nphmZGgl0LK+afGBeo5VHEdxSHpX2sGrR8kjEEoFEpGjcY/Puzdy7614ALtp4EZt3b3ZdRiVuGDfaRSXJEa3OXJDuWG151BipdApBZnthlVBJj8GNRmLNTPfV8ghwUFHjHpt2bprNozSdm2bTzk2uy6gk71tyIklrQytNdU2Vy63AbRWGpZWrSSbpNVp5FCGVTtHZ3FlVqGsl8y3cUB7N9c201Lf4b3nowfKaZ13POhqiDUQlSkO0gXU961yXEY/FGZ8eZzI7WfY5bqS+qaRTFYYQ9HgszvDkMDO5mcDKMBe+RVvVEm6kF4/H4kzMTJCZydBcX14We7cGnisZrK+GZDrJivYVvsnTeMPa7rXceeGdbNq5iXU961jbvdZ1GfbsC4ctPqysc9wYtK6P1tPe1O7IbRWGQBCrUzaYGXQUJeYHWnkUwY3Kah976G7rLltute4yS7bfA+Z6guCrg7Xdaz1RGhb2OVDlKo9kOskhrYdULdtpmHAYQtDt75GwKQ/ttiqCG8qjkqgnN9xlUN1yn06Zyc0wMjWixzw0ZVFpu3CjfjltF1ZnrrO5s2rZlVJNklWv0cqjCG5bHmXLdWk1Pj8tjyBXWtPUHpW8DN2ybBMtzjLrJieSdDR3uLoUr1PCnN9KK48C3EovXpHycLGH5bfy0JaHphycZpxOz6TJZDPuKA+HkV5hWKMmzGt6aOVRwPj0ONO5adeUh5OejpvKY2x6jKnsVNXXmo8w+IU1tUNHcwcRiZTtPnJzGVjLbVVuyrygZ5cDdMWMKLEwpmXXyqMAt3rSHU1GI3FqeVS6gqAdq7cykPE+KbG2PDROiEiEruaustuFG6lJLBKxBDP5GUanRss6PplOBt4paog2sLhxsbY8agG3XobRSJTO5s6yH7qbq/H56ScNQyy8prZwMsvczYl6TgfrU+kU8ebgO0V+h96Xi1YeBbjZk3Yy9uCWu8ySC/4oD0tGkBEpmtrCSfYFNwMynLiSrc5c0JYHVDYr3w+08ijAbeVRbg/Lbbn2a3pJMp2ko6mj4jXXNQsPR5aHi2MeTvJbDU8Ok81nQ2FR+xl67wStPApw8yXupMdQq8ojDIOKmtrCSbtIppNEJUp7U3vVcp20izCN5YU1OaJWHgWk0imiEqWtsa3qazl56NbgthuV1XIh+WV5hMG019QO8VicgfQAeZWf91irc+JGxmYnyRHDFEWolUeN4GZltR56OaGBbvZ0KsnjUyna8tA4JRFLkFM5hieH5z3Wzc5JS30LTXVNZbWLMFkeiViCTDZDeiYddFEOQCuPAtya5Q1Gxcvms4xMjcwv1+XK6leEhs5rpXGKPb/VfLjZObGWdS2nXYQpitDJ/fITrTwKcLOyOvWxRiVKW1P17jJLttemrpvhxZqFg5OQWbc7J4lYwpHlERa3FYRvlrlWHgW4+TJ0kloglU7RFesiIu48Ej+Ux+jUKDP5mVD0zjS1g5OQWWuVSrcoN0w4mU7SXNdMrD7mmuxK0cqjTETk+yKyX0S2ByE/SMvDzR68H7HhYfILa2qHcjtVuXyOwcygq/Wr3DDhMAWCVJKJ2A9CpzyA64GzgxCcV3kG0gOuKw+/fbuW7HIH6yslTBEpmtqh3HYxmBlEoVytX07cVmHpFIU1LXvolIdS6l5gMAjZI5Mj5FTuVWF5xGNxJrOTnkZouDmBS7NwKHepZC/WEE/EEmUlDQ1TIEh7U7vjPHl+EDrlUQ4i8jER2SIiW5JJ97Sx226Y1oZWGqON5SsPF/Po+OEn1Wt5aCqlHPeRF/Wr3HYRJsvDaTJJv6hJ5aGUuk4pdapS6tREwr2K5bbymA0NnOehexG15Ify0G4rTaWU0y68sGzLnSgYhrU87DjJB+YXNak8vMKLAeByelgjU+66yyy54L3l0RhtpKW+xTMZmlcn5azq50XnpJx2MZmdZHx6PFSdIif5wPxCKw8bXimPckxkL+Tar+0FVkSKG7PxNQsLJ+3CjTVuLMpJjhjGKMIwpigJnfIQkZuAzcBRIvKiiFzkl2wvKk055qaXysPL3kqY/MKa2qKcqKfkRJLFjYtprGt0T24ZbqswzS63iDeHT3kEt7L7HCil/ioo2V64Ycp56F4oDz8iNMIUkaKpLeKxOBMzE2RmMjTXNxc9xs1UQRYdTR0IUrJdhNHySLQkZpNJujWRuFrCUYqQ4GZSRIt4LM7Q5BAzuZmScq1j3cKPCA1teWgqpZyJgl50TqKRKF2xrpJWTxgDQeKxeNnJJP1CKw8bXvR0rOsNZuaeuuJVT8frCI2wRaRoaodyxuS8WskvEUuUTI4YSrdVCFOUaOVhw4uedDmpBVLpFPWRelobWl2V7eUg23RumtGpUW15aCqirLGHdNKT+jXfynypdIqIROho7nBddqU4yZPnF1p52PBCeZTbw3LbXWbJ9qqyhSnrqKb2mK9dzK4h7kHvf74w4WQ6SVeze0lK3UBbHiHHS+VRqrJ6NXbgZYRGGAcVNbXDfCGzEzMTTGYnPalf8yUNDeNYXhjX9NDKwySbzzKUGQrU8nAbL5MjhtEvrKkdOpo7SkYDepn6Zr5lcMOUUddCWx4hZigzhEK96pRHTuXKWsnQKdry0FSDFQ04l0XuZdJNaxncoczQnLLD1ilqaWihua5ZK48w4tXLsCHawOLGxYEpD+v6bhPGcEZNbVFqTM7LMbX5gljC6LYC/5aWLhetPEy87EmXykuTy+cYmnTfXWbJBW/8pNb96mzudP3amoVBqYFrL9KxW5Qah8yrPAOZgdBZHjB/lJjfaOVh4rXymKuXMzw5TF7la8/ymEjS2dxJXSR0SQo0NUKpduHlmFqpwfqhzJBn7bFawpbfSisPEy+VR6noDq+Vll2Gm3gVg69ZOJTKb2XNfVrcuNh1uaXaRZjdsWFLy66Vh4kXGTwtyvHteqK0PFz72KsYfM3CIR6LM5ApHvVkdU68yNhcaoJimANBwpYcUSsPk1Q6RUt9y5xJ2qqhlK/Sy8raUt9S9kqGTtGWh6ZaErEEeZUvGvXkVWoSgKa6JlobWotbHiEOQY/H4oxMjTCdmw66KIBWHrN4kdfKIh6Lk8lmiq4n7qXyKHclw0rQloemWuZzH3nZOZkriCXMbivrfgykBwIuiYFWHiZehueVykvjtZnsRXifF8vmahYe87mPvOyczDXeEma3lZdu6ErQysPEy5dhqR5WKp2iua6ZWH3MM9luV7aRqRGy+Wwoe2ea2qGk5THhreUx1+BzciJJa0MrTXVNnsmulLDNMtfKw8QP5VG0p+Ohu8yS7XZlC7NfWFM7zBUym81nGZoc8rR+zeW28ro9VoMfq4M6QSsPkyAtj1pTHmE27TW1w1ztwvLpe2p5zOG2CmNqEgtteYQQr9emKOWr9EN5DGYGyeazrl0zzIOKmtqhub6ZlvqWg3rSfqT7T8QSRYNYwpgU0cKaRqCVR4jwuqdTaj1xP5QHlF7J0Cna8tC4RTHL2MvUJHa5cLDLLMyBIPXRetqb2kOTokQrD7x/GZbKIOqX8nCzt6LHPDRuUSy/lR/1a65IrzC7rSBcyRF9VR4icraIPCUi/SJyeZH9HxaRpIg8Zn4+6ke5/OhJF+thzeRmGJ4c9ty3C+4qj1Q6RVNdk2cRYpqFQ7F24Ud7LNYu0jNpMtlMaC0PCFd+K9+Uh4hEgW8C7wCOBf5KRI4tcuhPlFInm5/v+lE2XyprkdBAy5VUc5ZH2uideZE6QrOwKDZwHZTbqhYs6vlWQfQTPy2PNUC/Uuo5pdQ08GPgnT7Kn5OgLA8v82nZ5dpluUGY/cKa2mKudtHe1E59tN4zucXcVn4orWoJU1p2P5XHcmC37feL5rZC/kJEHheRn4tId7ELicjHRGSLiGxJJqu/kb68xJsPjiv3Q2l1xdyP0AhzRIqmtkjEEkzMTJCZycxu8yNvWltjG3WRugPahR9RXtXi5dLSTvFTeRTzcRTegZuBHqXUicAdwP8Uu5BS6jql1KlKqVMTieofdCqdoq2xzdOeTrF1k/1QHqWSwFWKtjw0blHMMvYjb5qV963W3FbxWJyp3BQTMxNBF8VX5fEiYLckDgP22g9QSg0opabMn98BTvGjYH7MKk20GOsmj0y+sp64XyGvbg+yhRSoSHYAABDXSURBVD0iRVM7FHUfeZyaZFZ2LHFA5FIthKB7EQBTKX4qj4eBPhFZKSINwHpgo/0AETnU9vN8YIcfBfOjJz1XDwtecS15KdutyjaVnWJseizUDUxTOwRleViyD7A80kmiEqW9qd1z2ZXi5dLSTvFNeSilssDFwG8xlMJPlVJPiMjnReR887C/F5EnRGQr8PfAh/0om5/Kw97DSqVTviRhK7WGulNm/cLa8tC4QGF+K6WUb2vFFM4xsd4DYY4iDFOKEl8XoFZK3QrcWrDtStv3K4Ar/CwTGA/ihCUneCqjaA/LpyRs8VicJ1NPunKtWohI0dQOhe1ifHqc6dy0L4PWhWGvtRAIEibloWeY44/lUcxXOZAe8Ed5uLh8ZS1EpGhqh47mDiISme2U+Nk5Kcz7VgtjeWFa02PBK4/0TJr0TDqwMQ+/zPPx6XEms5NVX8tyL2jLQ+MGVuoeq134GfFkybBy29VCFGFbYxtRiYYiLfuCVx5+pH8GiNXHaKprOmCgy6/K6ubylXrMQ+M29rEHPyOeCnvxVuaEMOPl0tJOWfDKw6/KOvvQC0ID483+KQ83KlwynUQQOps7q76WRgMHRgP6me7fHsSSzWcZygyF3vKA8OS30srDz56ObYDOz5BXN5VHKp2is7mTaCRa9bU0Gjgwv5Wflq090mswM4hC1cRY3lxL6PqNVh4+Kg97j2Eg44+7zC7DLcujFnpnmtrhAMtjIklDtIHWhlZf5ILRLmphdrmFm6H31aCVh8/Ko7CHVWvKI5VO1UTvTFM7JGIJBjJG6h5rgqAfcy3sbqtamF1u4Wb0ZDVo5ZFOIQgdTR2ey7L3sPysrNb4hCuWh0+pIzQLh3gsTl7lGcoM+WrZWivzpdKpmlpa2QoxzuVzgZZDKw8fffiJWIKRqRFmcjO+Ko+6SB0dTR2umLp+pY7QLBzs+a38tmwtF1AtWR6JlgR5lWd4cjjQcmjl4dMsb7CFzGYGfK+sbkRo2N0KGo1bHDD24POYmjVYX0vzl4qlOgoCrTx8nBhkT2pmvcj9Cnl1Q3mMTI6QU7maaGCa2sEe9eR358SKXEqmkyxuXExDtME32ZUSlhQlWnkEoDxS6ZQvq6XZcSO8r5b8wprawWoX+8b3MTw57GvnxFqkrZYsaq08QoKfysM+o9XvVAhuRGjUkl9YUztY9clK3um35ZGcSNZEUkSLsKzpsaCVh1IqUMvDV+XhwvKVtRQLr6kdmuubaalvYUfKWL7H7zGPmfwMzw09VzOdImv9n6DX9FjQysNK/+xXpbHWSLfMZC/XTC/EjeUrdTp2jVckWhLsSO6Y/e4XVl1+fuj5mukUxepjxOpj2vIIEr/dMPXRetoa2wKzPKA6U1enY9d4RTwWZ8/YHsB/txWAQtVUp6gwT14QaOWBz2ayOXBdi8ojOZGkua6ZWH3MrWJpNMCBCsNvt1Wx72GncCGrINDKA38razwWZ9fILjLZTM0pj1RGpybReIO9LVg+fb/l1lLdLlx/PQi08sB/5RHEwKB9jkml6NQkGq+wev0dTR3URfxbHduuMGqpbochLbtWHvj/Eh/MDAYiF6of86gl015TO1j10+/ef0t9C43RRkN2DdVt7bYKmFQ6RVSitDW2+SYzKN9uW5OxfGVVYx46HbvGI6x65Xf9EpFZhVVLdTseizM2PcZUdiqwMix45RGPxX1J/2xhr6B+VtaIRKo2dbXlofEK6wUeRP2yZNbamAcEO1EwlMpDRM4WkadEpF9ELvdKjp9JES2CUh6WvErD+yazk4xPj9dU70xTOwRleYCRdToiEba/vN132ZWilUcRRCQKfBN4B3As8FcicqwXsvwOl4VXHrpfa4gUyq60slnnbdm3hc27N7tZLI1mtvffP9jva/3avHszj+57lLzK87Yfvq1m6rY91VFQhE55AGuAfqXUc0qpaeDHwDu9EBSE8rAaSRDrgDtVHumZNDuSO7j1mVu58u4rAdj45EbOuOGMmmlkmtpg18guAO594V5f69emnZtQGCl7pnPTbNq5yRe51RIGy8O/mLjyWQ7stv1+EXit/QAR+RjwMYDDDz+8YkFBWh5BmOfZfJYXhl9g8+7NrO1ey3Ruml0ju3h+6Hl2Du/k+eHneX7Y/D70PC9PvHzQNfLkZxvZ2u61vv8PmlcnW/ZuISIR8srf+rWuZx2N0Uamc9M0RBtY17POc5luYL0/btx2I4e3HR5IWwyj8ig2en1ANj+l1HXAdQCnnnpqRZn+8irPQHpgwSiPzbs3c8szt5DNZ3njD95IV3OXkSjRdmvrInUc3nY4K9tXct6q8+hp72Flx0pWtq8klU7xvp+/r+YamaY2COolvrZ7LXdeeCebdm5iXc+6mukQPTXwFAC/fvrX3PHcHdx54Z2+lz2MyuNFoNv2+zBgr9tCglrYqK2pjQgRBjODsxaAH2zauYl8Pg8YivOwxYfxidM+wcr2lbNKYvmi5SVdabXYyDS1QZAv8bXda2uuPt//wv2AkZMrKE9AGJXHw0CfiKwE9gDrgfe7LSSotSkefPFBFIonU09yxg1n+NZjWNezjsa6V3p23zznm47l1mIj09QOun6Vz7qedTTXNQfqCQid8lBKZUXkYuC3QBT4vlLqCbfl3L3zbsD/nPibdm5CRHz37daqea7RaA4mDO1ZqlkcKAyceuqpasuWLY7O2bx7M+v+Zx3TuWkao43c/aG7fbv5m3dv5owbzpjtMQThq9RoNBoReUQpdWql54fO8vCDTTs3kc1nASMCyU9/YRh6DBqNRlMtC1J5BB2ep327Go2m1lmQykP3/jUajaY6FqTyAN3712g0mmoIY3oSjUaj0YQcrTw0Go1G4xitPDQajUbjGK08NBqNRuMYrTw0Go1G4xitPDQajUbjmJpPTyIiSeAFH0TFgeBWXpkbXS5n6HI5Q5fLGbVUrhVKqYoXbq955eEXIrKlmjwwXqHL5QxdLmfocjljIZVLu600Go1G4xitPDQajUbjGK08yue6oAswB7pcztDlcoYulzMWTLn0mIdGo9FoHKMtD41Go9E4RisPjUaj0ThmQSoPEWkXkZ+LyJMiskNE1hbsP1pENovIlIh8qmDfJ0Vku4g8ISKX2LZvEJE9IvKY+TnH5zJfICKPm5/fi8hJYZEvImeLyFMi0i8il9u2Xy8iz9vu2ckBlvH7IrJfRLZXUwYvy+J2Hau0jCLSLSJ3m+c8ISKfrKYcXpUlRPerSUQeEpGtZhk/V005vCqL4/aolFpwH+B/gI+a3xuA9oL9S4DTgC8Cn7JtPx7YDsQw1kK5A+gz922wHxtAmV8PdJjf3wE8GAb5QBR4FjjCPG8rcKy573rgPUGX0fx9OvAaYHtYy+J2HavimR4KvMb8vgh42nqmYSpLiO6XAK3m93rgQeB1YSuL0/a44BaDEpHFGI3zwwBKqWlg2n6MUmo/sF9E/qzg9GOAPyil0ua17gHeBVwdgjL/3vbzD8BhIZG/BuhXSj1nXuvHwDuBP7lVPhfKiFLqXhHpebWVxYsyKqX2AfvM72MisgNYToXPNExl8aiMChg3t9ebn4ojlcJSloXotjoCSAI/EJE/ish3RaSlzHO3A6eLSJeIxIBzgG7b/otNM/H7ItIRYJkvAm4LifzlwG7bvhfNbRZfNO/Z10SkMaAyuo2XZXGrjrlSRlPJrcbowYaxLKG4XyISFZHHgP3A7UqpwO7XPGUpvz26ZdLVygc4FcgCrzV//yfwr3Mcu4ECs9d8EI8C9wLfAr5mbl+K4aKJYLi7vh9Qmd8C7AC6wiAfeC/wXdv+DwLfML8fimFGN2KY4VcGeY+AHlxwW3lVFjfrmEtlbAUeAd4dgvt1UFnCdr/Mfe3A3cDxYSuL0/ZYVSOpxQ9wCLDT9vtNwC1zHLuBEj5T4CrgE0W2u/ISclpm4ESM8YVVQdyzYvKBtcBvbb+vAK4ocu464NdB3iO3npsfZam2rNWWEcPd8VvgH4K+X+WUJej7VXDMv5R6r4ShLOW0xwXntlJKvQTsFpGjzE1n4MA/KiJLzL+HA+8GbjJ/H2o77F0YLi5XKKfMZnl+CXxQKfW0W7JdkP8w0CciK0WkAVgPbDTPOdT8K8CfU8U9C/oe+VEWN+tYNWU0n9f3gB1Kqa9WWgavyxKi+5UQkXbzezPwNuDJsJXFcXusttdQix/gZGAL8DjwK6AD+Bvgb2ya/UVgFBg2vy82991nPqitwBm2a/4Q2GZecyNwqM9l/i4wBDxmfraERT7G2NDTGL2gT9u232Xes+3AjzCjQAIq400YA68z5vO+KGxlcbuOVVpG4I0Yg6yP2/adE7ayhOh+nQj80TxvO1W4Z70sCw7bo05PotFoNBrHLDi3lUaj0WiqRysPjUaj0ThGKw+NRqPROEYrD41Go9E4RisPjUaj0ThGKw9NzSIiOVsG0Me8zgflNyKyWkS+a/t9tpkR9Unz//2JGc9vZUR9T8H544XXtO1rEJF7RWTB5bfTuIOuOJpaJqOUmjNttIjUKaWyfhbIZf4Z+AKAiBwPfAM4Xym1w9x2PsbM6V1OL6yUmhaRO4H3ATe6VWDNwkFbHppXFSLyYRH5mYjcDPzO3HapiDxsJnyzr1/waTHWGblDRG4Sc+0WEdkkIqea3+MistP8HhWRf7dd6/81t68zz7HWV7jRnKWLiJwmxnoKW02rYZGI3Ce2tRJE5AERObHg/1gEnKiU2mpuugy4ylIcAEqpjUqpe8u4J5+3WWd7ROQH5q5fARc4usEajYm2PDS1TLOZHRTgeaXUu8zvazFevIMi8nagDyM1vAAbReR0YAIjVcpqjHbwKEZivVJcBIwopU4zM44+ICK/M/etBo4D9gIPAG8QkYeAnwDvU0o9LEYq7QzG7N8PA5eIyCqgUSn1eIGsUzkwPcRxwH/MU75/F5HPFG5USl0JXCkibRgZEq41d23HWLdGo3GMVh6aWmYut9XtSqlB8/vbzc8fzd+tGMpkEfB/1Strs2wsQ97bgRNtYwtt5rWmgYeUUi+a13oMw500AuxTSj0MoJQaNff/DPisiFwKfARjEZ5CDsVIu30QItIF3ImxKNl1SilLqVyqlPq57bhx23fBcE99TSn1iFmenIhMi8gipdRYGf+/RjOLVh6aVyMTtu8CfEkp9W37AWIsITxXbp4sr7h0mwqu9XdKqd8WXGsdMGXblMNoW1JMhlIqLSK3YyyK9ZcYVkYhmQLZT2CsLrhVKTUAnGy62Vrn+B8K2QC8qJT6QcH2RmCyzGtoNLPoMQ/Nq53fAh8RkVYAEVkuRmbke4F3iUizOb5wnu2cncAp5vf3FFzr4yJSb15rlZRehOdJYJmInGYev8gW3fRd4OvAwzYryc4OoNf2+2rg0yJyjG1brITsWUTkXOBM4O8LtncBSaXUTDnX0WjsaMtD86pGKfU784W72RzDHgc+oJR6VER+gpFx9AWMsQCL/wB+KiIfxMg0avFdDHfUo6YbKImRunou2dMi8j7gG2b66wxGCuxxpdQjIjIKFFoC1rlPikib5VJSSm0TkU8CN5jKbgAjyupfyrgN/wgsAx4y78FGcxzkLcCtZZyv0RyEzqqr0QAisgHjpT7foLRb8pYBm4CjlVL5OY75P8CYUuq7xfa7UIZfYizM9ZQX19e8utFuK43GZ0TkQox1tj89l+Iw+W8OHEtxswwNwK+04tBUirY8NP9/+3VMAwAAACCof2sz+EMJJ8DmPADYxAOATTwA2MQDgE08ANgCRVJ8Du7MQfsAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_fit_by_id(rabi_fit_run_id,\n", + " show_initial_values=False,\n", + " show_variance=False,\n", + " source_conn=transmon_db_connection,\n", + " save_plots=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And for the simple minimum 2d plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_fit_by_id(min_fit_run_id,\n", + " source_conn=transmon_db_connection,\n", + " save_plots=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to see a 1d plot of the fit result vs the data at one of these points you can specify a setpoint value and it will plot the cut at the nearest point. Saving this will not overwrite the other plots as the setpoint value is included in the saved name." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_fit_by_id(rabi_fit_run_id,\n", + " rs_qubit_frequency=6.215e9,\n", + " show_initial_values=False,\n", + " show_variance=False,\n", + " source_conn=transmon_db_connection,\n", + " save_plots=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Just for fun comparison..\n", + "...here is how plot_by_id would handle these" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multi-dimensional data encountered. parameter T depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter T_initial_value depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter T_variance depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter a depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter a_initial_value depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter a_variance depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter c depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter c_initial_value depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter c_variance depends on 0 parameters, cannot plot that.\n", + "Multi-dimensional data encountered. parameter success depends on 0 parameters, cannot plot that.\n" + ] + }, + { + "data": { + "image/png": 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MzIo4SMzMrEhTQSLpIEljcvfJki6RtGO9pZmZ2VDQ7BnJZyNiiaTXAm8BzgPOrK8sMzMbKpoNkpX571uBMyPi18CoekoyM7OhpNkgeVjS94GDgSskje7HvGZmthprNgwOBq4E9omIRcBLgeNrq8rMzIaMEU1ONwH4TUQ8LWlvYDvg/NqqMjOzIaPZM5JfAislvQr4IbA58NPaqjIzsyGj2SB5NiJWAAcC34qIT5LOUszMbJhrNkiekXQYcARweR42sp6SzMxsKGk2SI4CdgdOi4gHJG0OXFBfWWZmNlQ0dbE9Iu6WdAKwae5/APhynYWZmdnQ0OwtUt4OzAL+J/dvL2lGnYWZmdnQ0GzT1jRgF2ARQETMIn1yy8zMhrlmg2RFRCxuGBatLqZK0rmSFkiaXed6zMysTLNBMlvSfwBrStpC0neAG2usC2A6sE/N6zAzs0LNfrP9Y8BJwNOkLyJeCZxaV1EAEXG9pMl1rqPL5y+7i7vnP7EqVmVm1nJTNl6PqW/fpm3rb/ZTW8tIQXJSveX0j6RjgGMANt100zZXY2Y2PCmi70sdkq4GDso3bETSBsBFEfGWWotLZySXR8S2fU3b0dERnZ2ddZZjZrbakTQzIjpKltHsNZJxXSECEBGPAy8rWbGZma0emr7XlqTn2o4kbUbNn9oyM7OhodmL7ScBf5T0+9y/J/naRF0kXQjsDYyTNA+YGhE/rHOdZmbWf81ebP8fSTsCuwECPhkRj9VZWEQcVufyzcysNZq9RcoBwDMRcXlEXAaskPTOekszM7OhoNlrJFOr32zPF96n1lOSmZkNJc0GSXfTNXt9xczMVmPNBkmnpG9KeqWkV0g6HZhZZ2FmZjY0NBskHwOWAxcDPwf+BXykrqLMzGzoaPZTW08Cn665FjMzG4KaChJJ19LNFxAj4vUtr8jMzIaUZi+YH1fpXgt4F7Ci9eWYmdlQ02zTVuOF9Rsq33I3M7NhrNmmrZdWetcAOoCX11KRmZkNKc02bc0kXSMR8AzwIHB0TTWZmdkQ0uzHf08Ato+IzYEfA08Cy2qryszMhoxmg+TkiHhC0muBN5F+T/3M2qoyM7Mho9kgWZn/vhU4KyJ+DYyqpyQzMxtKmg2ShyV9HzgYuELS6H7Ma2Zmq7Fmw+Bg4Epgn3zn35cCx9dWlZmZDRnNfo9kGXBJpf8R4JG6ijIzs6HDzVNmZlbEQWJmZkUcJGZmVsRBYmZmRRwkZmZWxEFiZmZFHCRmZlbEQWJmZkUcJGZmVsRBYmZmRRwkZmZWxEFiZmZFHCRmZlak1iCRtI+keyXNlfTpbsZvJukaSXdKuk7SxMq4r0q6S9IcSWdIUp21mpnZwNQWJJLWBL4H7AtMAQ6TNKVhsq8D50fEdsApwJfyvP8G/DuwHbAtsDOwV121mpnZwNV5RrILMDci7o+I5cBFwP4N00wBrsnd11bGB7AW6ed8RwMjgb/XWKuZmQ1QnUGyCfBQpX9eHlZ1B/Cu3H0AMEbShhFxEylYun5A68qImFNjrWZmNkB1Bkl31zSiof84YC9Jt5Oarh4GVkh6FbA1MJEUPq+XtOeLViAdI6lTUufChQtbW72ZmTWlziCZB0yq9E8E5lcniIj5EXFgROwAnJSHLSadndwcEUsjYinwW2C3xhVExNkR0RERHePHj6/r/zAzs17UGSS3AltI2lzSKOBQYEZ1AknjJHXVcCJwbu7+G+lMZYSkkaSzFTdtmZkNQrUFSUSsAD4KXEkKgZ9FxF2STpH0jjzZ3sC9ku4DNgJOy8N/AfwF+DPpOsodEXFZXbWamdnAKaLxssXQ1NHREZ2dne0uw8xsSJE0MyI6Spbhb7abmVkRB4mZmRVxkJiZWREHiZmZFXGQmJlZEQeJmZkVcZCYmVkRB4mZmRVxkJiZWREHiZmZFXGQmJlZEQeJmZkVcZCYmVkRB4mZmRVxkJiZWREHiZmZFXGQmJlZEQeJmZkVcZCYmVkRB4mZmRVxkJiZWREHiZmZFXGQmJlZEQeJmZkVcZCYmVkRB4mZmRVxkJiZWREHiZmZFXGQmJlZEQeJmZkVcZCYmVmRWoNE0j6S7pU0V9Knuxm/maRrJN0p6TpJEyvjNpV0laQ5ku6WNLnOWs3MbGBqCxJJawLfA/YFpgCHSZrSMNnXgfMjYjvgFOBLlXHnA1+LiK2BXYAFddVqZmYDV+cZyS7A3Ii4PyKWAxcB+zdMMwW4Jndf2zU+B86IiLgaICKWRsSyGms1M7MBqjNINgEeqvTPy8Oq7gDelbsPAMZI2hDYElgk6RJJt0v6Wj7DeQFJx0jqlNS5cOHCGv4FMzPrS51Bom6GRUP/ccBekm4H9gIeBlYAI4A98vidgVcAR75oYRFnR0RHRHSMHz++haWbmVmz6gySecCkSv9EYH51goiYHxEHRsQOwEl52OI87+25WWwFcCmwY421mpnZANUZJLcCW0jaXNIo4FBgRnUCSeMkddVwInBuZd4NJHWdZrweuLvGWs3MbIBqC5J8JvFR4EpgDvCziLhL0imS3pEn2xu4V9J9wEbAaXnelaRmrWsk/ZnUTPaDumo1M7OBU0TjZYuhqaOjIzo7O9tdhpnZkCJpZkR0lCzD32w3M7MiDhIzMyviIDEzsyIOEjMzK+IgMTOzIg4SMzMr4iAxM7MiDhIzMyviIDEzsyIOEjMzK+IgMTOzIg4SMzMr4iAxM7MiDhIzMyviIDEzsyIOEjMzK+IgMTOzIg4SMzMr4iAxM7MiDhIzMyviIDEzsyIOEjMzK+IgMTOzIg4SMzMroohodw0tIWkh8NdVsKpxwGOrYD395br6Z7DWBYO3NtfVP0Olrs0iYnzJAlebIFlVJHVGREe762jkuvpnsNYFg7c219U/w6kuN22ZmVkRB4mZmRVxkPTf2e0uoAeuq38Ga10weGtzXf0zbOryNRIzMyviMxIzMyviIDEzsyLDPkgkjZX0C0n3SJojafeG8VtJuknS05KOaxj3CUmzJd0l6djK8GmSHpY0Kz/2W4X1vkfSnflxo6TXtGrdpeuXtI+keyXNlfTpyvDpkh6obK/t21jjuZIWSJpdUkOdtbT6+TXQGiVNknRtnucuSZ8oqaOuWgbR9lpL0i2S7sg1fr6kjrpqGdDrMSKG9QM4D3h/7h4FjG0Y/zJgZ+A04LjK8G2B2cA6wAjgf4Et8rhp1WlXcb3/BmyQu/cF/jQY1g+sCfwFeEWe7w5gSh43HXh3u2vM/XsCOwKzB2strX5+FezTCcCOuXsMcF/XPh1MtQyi7SVg3dw9EvgTsNtgq2Ugr8cRDGOS1iO9WI8EiIjlwPLqNBGxAFgg6a0Ns28N3BwRy/Kyfg8cAHy1zfXeWOm9GZg4SNa/CzA3Iu7Py7oI2B+4u1X1taBGIuJ6SZNXt1rqqDEiHgEeyd1LJM0BNmGA+3Qw1VJTjQEszcNH5seAP+00mGoZ7k1brwAWAj+SdLukcyS9pMl5ZwN7StpQ0jrAfsCkyviP5tPJcyVt0KZ6jwZ+26J1l65/E+Chyrh5eViX0/L2Ol3S6DbV2Gp11tKq51dLasyBtwPpyHYw1jIotpekNSXNAhYAV0dE27ZXH7X07/XYqtO9ofgAOoAVwK65/9vAF3qYdhoNp8d5x9wGXA+cBZyeh29EaspZg9Qkdm4b6n0dMAfYsE3b6wXrBw4CzqmMPxz4Tu6eQDrVHk06Vf9cO7cRMJkWNG3VVUsrn18tqnFdYCZw4CDYXi+qZbBtrzxuLHAtsO1gq2Ugr8eiF8pQfwAvBx6s9O8B/KaHaafRSzsr8EXgw90Mb8mbUn/qBbYjXY/Ysh3bq7v1A7sDV1b6TwRO7GbevYHL27mNWrXPVkUtpbWW1khqErkS+K92b69mamn39mqYZmpv7ymDoZZmX4/DumkrIh4FHpL06jzoDfSjTVXSy/LfTYEDgQtz/4TKZAeQmsFWSb25lkuAwyPivlast0XrvxXYQtLmkkYBhwIz8jwT8l8B76Rge7V7G62KWlr5/CqpMe+vHwJzIuKbA62h7loG0fYaL2ls7l4beCNwz2CrZUCvx9KjiKH+ALYHOoE7gUuBDYAPAh+spP484AlgUe5eL4/7Q95xdwBvqCzzx8Cf8zJnABNWYb3nAI8Ds/KjcxVvrx7XT7qOdB/p6OikyvDf5e01G7iA/GmSNtV4Iemi7TN5Xx892Gpp9fNroDUCryVdoL2zMm6/wVbLINpe2wG35/lmU9CEW2ctDOD16FukmJlZkWHdtGVmZuUcJGZmVsRBYmZmRRwkZmZWxEFiZmZFHCQ25ElaWblT6ay670+1qknaQdI5lf598p1b78n/78X5+wJdd259d8P8SxuXWRk3StL1kob1ffesjJ88tjp4KiJ6vNW1pBERsWJVFtRinwFOBZC0LfAd4B0RMScPewfpG9t/6++CI2K5pGuAQ4CftKpgG158RmKrJUlHSvq5pMuAq/Kw4yXdmm9GV/39hZOUfiflfyVdqPy7M5Kuk9SRu8dJejB3rynpa5VlfSAP3zvP0/X7ED/J3w5G0s5KvwdxRz6bGCPpD6r81oOkGyRt1/B/jAG2i4g78qATgC92hQhARMyIiOub2CanVM7aHpb0ozzqUuA9/drAZhU+I7HVwdr5LqYAD0TEAbl7d9Kb8D8lvRnYgnQ7ewEzJO0JPEm6XcsOpNfDbaSb/vXmaGBxROyc74x6g6Sr8rgdgG2A+cANwL9LugW4GDgkIm5Vuv33U6RvHR8JHCtpS2B0RNzZsK4OXniLim2Ar/dR39ckndw4MCI+B3xO0vqkuzJ8N4+aTfrNHbMBcZDY6qCnpq2rI+KfufvN+XF77l+XFCxjgF/F878rM6OJ9b0Z2K5yLWL9vKzlwC0RMS8vaxapyWkx8EhE3AoQEU/k8T8HPivpeOB9pB8UajSBdKvwF5G0IXAN6cfVzo6IroA5PiJ+UZluaaVbpCas0yNiZq5npaTlksZExJIm/n+zF3CQ2OrsyUq3gC9FxPerEyj9RHJP9wlawfPNv2s1LOtjEXFlw7L2Bp6uDFpJeo2pu3VExDJJV5N+4Otg0tlHo6ca1n0X6VcT74iIfwDb56a4dXv4HxpNA+ZFxI8aho8G/tXkMsxewNdIbLi4EnifpHUBJG2idPfm64EDJK2dr0e8vTLPg8BOufvdDcv6kKSReVlbqvcfFLoH2FjSznn6MZVPSZ0DnAHcWjl7qpoDvKrS/1XgJElbV4at08u6nyPpbcCbgI83DN8QWBgRzzSzHLNGPiOxYSEirspvvjfl699Lgf+MiNskXUy6M+pfSdcOunwd+Jmkw0l3RO1yDqnJ6rbcVLSQdLvtnta9XNIhwHfyLbufIt22e2lEzJT0BNB4htA17z2S1u9qdoqIP0v6BHB+Dr5/kD6tNbWJzfApYGPglrwNZuTrJq8DrmhifrNu+e6/ZhWSppHe4Pu6oN2q9W0MXAdsFRHP9jDNJ4ElEXFOd+NbUMMlpB8Zu7eO5dvqz01bZm0i6QjS74qf1FOIZGfywmsvraxhFHCpQ8RK+IzEzMyK+IzEzMyKOEjMzKyIg8TMzIo4SMzMrIiDxMzMivx/qCDZF5mL/tIAAAAASUVORK5CYII=\n", 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sfwzwFuAee/0a4EJ7+d32d+ztZ4tb4TiKMgqHO3q45o4NlOWm8+v3LQ+rtIsbqINfiTdiekeISKKIbAbqgSeAPUCLMabfbrIPKLOXy4BaAHt7K5A/zHGvFpH1IrK+oaHBzX9BmaR868FttHX184crVsTNFMXq4FfiiZgqF2PMgDFmKVAOnAQEKxvrv0OCWSlB7x5jzC3GmBXGmBWFhYXOCKsoNv/ZWsfDW+r47NlzmF8SWWkXp1EHvxIvxEW0mDGmBXgaOAXIFRF/5YBy4IC9vA+oALC35wDqvVSiSou3l6/fv43jSrP5uAsZ+E6gDn4lHohltFihiOTay+nAW4EdwFPAJXazK4EH7OUH7e/Y2580avcrUeY7D22nxdvLTy5dTHKM/SzD4XfwV0zJUAe/EjNieXeUAk+JyBZgHfCEMeYh4MvA50WkEsuncpvd/jYg317/eeCGGMisTGKe2lnPfRv384kzZ3P81JxYizMiOenJ3HLFiergV2KGTPSX/xUrVpj169fHWgxlnNPW3cc5Nz2DJy2Jf3/mdFKT3C3v4hT/2VrHNXds5L0rKvjhe05wrd6ZMrEQkQ3GmBWRHCM+7XpFiTN+8MhODrV18+NLlowbxQLq4FdihyoXRRmF5ysPc+crNXzsjFksrciNtThhow5+JRaoclGUEejs6eeG+7YwsyCTz71tXqzFGRPq4FdigSoXRRmBnzy2i33NXfz4ksWul9F3k6EOfp9vYvtaldijykVRhmFdVRNrXqziylNnTIjZHucWe/jC2+ezubaFmiZvrMVRJjiqXBQlCN19A3z5ni2U5aZz/TnzYy2OYywotSoK7G/pirEkykQnafQmijL5uOm/r7P3cCd/u+pkMlMnzm1SnpsBwP5mVS6Ku6jloihDeLW2hT8+s5f3nVTBqjkFsRbHUUpy0hCBfWq5KC6jykVRAujpH+D6e16lyJPGV84PVkd1fJOSlEBJdhr7mtXnorjLxLH3FcUBfvPUHl4/1MGfPryC7LT4KKXvNGW56TospriOWi6KYrP9QBu/faqSi5eV8ZYFxbEWxzXK8tLVoa+4jioXRQH6Bnxcf8+r5Gak8M13HhdrcVylLDedg63dDGiui+IiqlwUBbjlmb1sO9DGdy88ntyMlFiL4ypleen0+wyH2jRTX3EPVS7KpGf3oXZ++d/dXHBCKecuKo21OK5TnmeFI+9Tv4viIqpclEnNgM9w/T1byExN5MZ3HR9rcaJCWW46APtbNGJMcQ+NFlMmNX9+/g0217bwy8uXUuhJjbU4UWFQuajloriIWi7KpGXXwXZ++vgu3rqwmHctmRprcaJGekoi+ZkpGjGmuIoqF2VSUtPo5YrbXiY7LZnvXbRo0s3QWJaXrj4XxVVUuSiTjoOt3bz/1pfoHfBxx1UnU5ydFmuRok55niZSKu6iykWZVDR29PDB216mxdvHmo+cxLxiT6xFiglluVYipTGa66K4gyoXZdLQ1t3HlX9+hdomL7dduYIl43DKYqcoy02np9/H4Y7eqPRnjOFjf1nPY9sORqU/JfaoclEmBV29A6y+fR0769r5/QdP5ORZ+bEWKaaU2bku0XLqN3v7eGL7IZ55vSEq/SmxR5WLMuHp7fdxzR0b2FDdzC8uX8pZC4piLVLMiXY4clVjJwCH2nqi0p8SezTPRZnQ9A/4uO6uTfzv9QZ+9J4TeMfiyRNyPBJleZZyiVbp/WpbudS3a8mZyYJaLsqExecz3HDfazzy2kG+fsFC3rtyWqxFihty0pPxpCVFbVis6rClxOrVcpk0qHJRJiTGGL7z0Hbu2bCPa8+ey1VnzIq1SHFHNOd18VsuDR09Wo15kqDKRZmQ3PTE69z+QhUfXTWT6946N9bixCXlUZzXparRslwGfIamzuhEqCmxRZWLMuH44zN7ufnJSi5bUc433rFw0mXfh0q0LZeCLGsqAy31PzlQ5aJMKO58pYbvPbKDC04o5QcXL1bFMgJleem09/TT2tXnaj+t3j6avX2cNHMKoE79yULMlIuIVIjIUyKyQ0S2ici19vopIvKEiOy2/+bZ60VEbhaRShHZIiLLYyW7Ep88+OoBvvqv1zhzfiE3vXcpiQmqWEbiyLwu7kaMVTdZ/paVM2zlok79SUEsLZd+4AvGmIXAKcCnROQ44AZgrTFmLrDW/g5wHjDX/lwN/C76IivxypM7D/H5uzazcvoUfveBE0lJUqN8NKKV6+L3t/iVi+a6TA5idgcaY+qMMRvt5XZgB1AGvBtYYzdbA1xoL78b+IuxeAnIFZGJP22gMiov7mnkE3dsZGFpNrd9eAXpKYmxFmlc4M91cdupX33YslxmF2YxJTNFh8UmCXHxeiciM4BlwMtAsTGmDiwFBPjTqcuA2oDd9tnrgh3vahFZLyLrGxq03MREZuv+Vq5as45pUzJY89GT8KQlx1qkcUN+ZgppyQlRsVxKstNIT0mkyJOqlsskIebKRUSygHuB64wxbSM1DbIuaMC8MeYWY8wKY8yKwsJCJ8RU4hBvbz+fuXMTOenJ/HX1yUzJTIm1SOMKEWFqrvvhyNWNnUzPt/w7xdlparlMEmKqXEQkGUux/M0Yc5+9+pB/uMv+W2+v3wdUBOxeDhyIlqxK/PHDR3fyxuFOfnbZUkpyJt+cLE5Qnpfh+qRhVY1eZuRnAlDkSVWH/iQhltFiAtwG7DDG/Dxg04PAlfbylcADAes/ZEeNnQK0+ofPlMnHM6838JcXq1l9+kxOnT25KxxHQpnLlktHTz+HO3qYXnDEctEs/clBLAtXrgKuAF4Tkc32uq8CPwTuFpHVQA1wqb3tEeB8oBLwAh+JrrhKvNDq7eNL92xhTlEW158zP9bijGvK89Jp6uzF29tPRorzjwN/2ZdByyU7lQGfobGzhyKPWpsTmZgpF2PMcwT3owCcHaS9AT7lqlDKuOBbD27lcEcPf/zQCtKSNTIsEvzhyAdauphT5PysnNV2GLLf5+JXKPVtqlwmOjF36CtKODy8pY77Nx/gM2+ZywnlObEWZ9xzpPS+O0Nj/nlcptuWS3F2KqBZ+pMBVS7KuKG+rZuv3/8aS8pz+ORZs2MtzoTAb7m4pVyqD3spyEolK9UaJCnKPmK5KBMbVS7KuMAYa24Wb+8AP7tsKcmJ+tN1guLsNJISxDWnflVjJzPsITGAwizLctFcl4mP3qHKuOCudbU8ubOeG85bwJyirFiLM2FITBBKc9NcS6SsbvQODokBpCQlkJ+ZwiEdFpvwqHJR4p6aRi//99B2Tpudz5Wnzoi1OBMOt8KRu3oHONjWfZTlAlCouS6TAlUuSlwz4DN88Z+vkiDCTy5dQoJWOnacstwMVyyXmiY7Uqwg86j1mqU/OVDlosQ1tz23l1eqmrjxXccPOp8VZynLS+dQeze9/T5Hj1s1mONytOWiWfqTA1UuStyy62A7P33sdc45vpiLlwetUao4QHleOsZAXauz1os/gXL6lGMtF83Sn/ioclHikt5+H5+7azPZ6Ul8/6ITdEZJFyl3aV6XqkYveRnJ5GQcXam6OCBLX5m4qHJR4pKb1+5me10b37/oBPLt8FXFHQYTKR126lvVkDOPWV/o0VyXyYAqFyXu2FjTzG+fruTSE8t5+/ElsRZnwlOak46IC5bLYe8x/hbQLP3JgioXJa7w9vbzhbtfpTQnnW++87hYizMpSElKoMiT6miWfk//AAdau4JaLsV2lr4mUk5sYlkVWVGO4YeP7qSqsZM7P3aKzioZRcrzMtjf4nXseLVNXRgDMwuOVS4F9jCnDotNbNRyUeKGwTlaVs3klFk6R0s0cTqRcjBSLMiwmGbpTw5UuShxgX+OlrlFWXxR52iJOmV56dS1dDsWHlxll9qfEWRYDDRLfzKgykWJC/xztPz8sqU6R0sMKMtNp99nHHOyVzd2kp2WRG5G8KFNzdKf+IxJuYhIpojoE0BxhEdes+Zo+ezZOkdLrPCHIzsVMVbV6GVGQeaw+UnF2akcalPlMpEJSbmISIKIvF9EHhaRemAnUCci20TkJyIy110xlYnMn557g7lFWXzyTJ2jJVaUOzyvy3A5Ln6KPGkc7ujVLP0JTKiWy1PAbOArQIkxpsIYUwScAbwE/FBEPuiSjMoExhjD7voOVsyYQpLO0RIzBi0XB5z6fQM+9jV3Bc1x8aNZ+hOfUEOR32qM6Ru60hjTBNwL3CsiGjeqhE1jZy+tXX06R0uMyUhJYkpmiiOWy/7mLgZ8ZkTLJTBLv8heViYWIb0qBlMsY2mjKEOprO8AUOUSBzgVjjxcNeRAopmlX9Po5bFtB13vRzkaHYdQYsqeBku5zC4c/i1XiQ5luensb448kbLaDkMeyXKJZpb+H5/dyyf/tpHuvgHX+1KOoMpFiSmV9R2kJycyNUfnaok1ZXmW5WJMZE72qsZOMlMSKchKGbZNNLP0qxo7GfCZwRcZJTqoclFiSmV9B7OLMnWGyTigPC+d7j4fjZ29ER2nutHL9Pzhw5Ahuln6tfaMmK8fane9L+UIoyoXEblCRM4SkX+KyJ0i8oloCKZMDvbUdzC7UP0t8UCZQ/O6VDV2MqNgeH+Ln6LsNOpdznUZ8JnBIIVdB9VyiSahWC4rgQuMMZcaY94HLHBZJmWS0NnTz4HWbuaocokLnAhH7h/wUdvkHdHf4qfIk0p9u7vDYnWtXfTbuTRquUSXUEKR24AyEfkY0Ayo51VxhL0NVlSRRorFB+W5lrURieVS19pN34AZMVLMT3F2KjsPto25r1CosYMLCj2pqlyiTCiWyzeAB4BiIAX4jKsSKZOGygbrZlflEh9kpyeRlZrEvggixqoGqyGHYrm4n6VfY/tb3rqwiH3NXXT09LvWl3I0oyoXY3E/cJIx5u/GGGenq1MmLXvqO0lMkJAeRIr7iAjleZHluoxWDTmQaGTp1zR5SUoQ3jyvEIDdar1EjXCixRwP5xGRP4lIvYhsDVg3RUSeEJHd9t88e72IyM0iUikiW0RkudPyKNGlsr6D6VMySEnSoMV4oSw3PaIs/erDnaQlWzNbjkZR9pEsfbeoafJSlpfOwtJsQP0u0SScu9oN2/V24Nwh624A1hpj5gJr7e8A5wFz7c/VwO9ckEeJIpUNHczWIbG4oswBy2X6lNBCy/0KyM0s/ZomL9OmZFCRl0FacoJGjEWRmFouxphngKYhq98NrLGX1wAXBqz/iz1M9xKQKyKlTsukRIe+AR/VjZ0ahhxnlOWm097dT2vX2Ko5WdWQR3fmQ3Sy9P3KJSFBmFfsUcslioSjXL7imhRHU2yMqQOw/xbZ68uA2oB2++x1xyAiV4vIehFZ39DQ4KqwytioafLSN2DUmR9nRDKvi89nqG6y5nEJhULbcnFrXpfWrj5avH1Mm2Ipu3nFHnapcokaISsXY8zW0Vu5SjDLKehQnTHmFmPMCmPMisLCQpfFUsaCFqyMTwYTKccwNHawrZvefl/IlktyopWl71auiz8z3y/P/GIPDe09NEVYgUAJjVBL7iMiC7CGpsqwHuoHgAeNMTsclumQiJQaY+rsYa96e/0+oCKgXbktgzIO8SuXWVqwMq4oz/PnuoQfjnykGnLo19TNLH1/GHKF33Ip8QCWU/+UWfmu9KkcIdSZKL8M/APLengFWGcv3ykiN4y07xh4ELjSXr4SK8fGv/5DdtTYKUCrf/hMGX/saeigODuV7DSdBiieKMhKITUpYUyWy5FqyKFZLuBulv5Q5TK/2FIuGo4cHUK1XFYDxw+ds0VEfg5sA344ls5F5E7gTKBARPYB37KPdbeIrAZqgEvt5o8A5wOVgBf4yFj6VOKDPfUdOiQWh4jImOd1qWrsJCUxgdIwKly7maVf0+QlLyN58AXGeplJUr9LlAhVufiAqUD1kPWl9rYxYdcqC8bZQdoa4FNj7UuJH4wx7Gno5D3Lg8ZjKDGmLG9suS7Vh71UTEknMYwK18XZaTS09zDgM2HtFwo1jV6mBQzRidgRYxqOHBVCVS7XAWtFZDdHIramAXPQcjBKmBxq66Gjp19zXOKUstx0th8I35qoauwMy98C1rCYz0Bjp/PTHdc0eVlSkXvUunklHh7eUocxZsQpAZTICXWa4/8A84D1xbJsAAAgAElEQVRvA48BjwM3AvONMY+6Jp0yIRmMFNMcl7ikPC+dxs5eunpDn7nRGDM4j0s4uJWl3z/gY39LF9OmHD1EN7/YQ2tXn+vVmJXwQpF9xpiXjDH3GmPusZcHRER9H0pYVNZrwcp4Ziyl9xvae+jqGwhpHpdA3MrSr2vtZsBnBnNc/Myznfq7DrrvdxnwGX78n508sHk/fQNj9h6MW5wo6vRtB46hTCL2NHTiSU0aTKJT4osyf+n9MJRL1WCkWHiWi1tZ+v7ItWlTjpZnXrH1QhONTP0t+1r47dN7uPYfmznjR0/x+//todU7tsoH45GQfC4ismW4TVil+BUlZKypjbN0zDtOGUuW/pEcl/AsF7ey9P1hyNOGyJOflUpBVmpULJeNNS0A/OSSxfxr035++OhObl67m0tPLOcjq2aGXMlgvBKqQ78YOAdrsrBABHjBUYmUMbOjro2bnnidX16+jPSUxFiLMyyVDR2DJdCV+KPYk0pigoQ1r0t1YydJCTKY4R8qbmXp1zR5SU4USrKPDRKYX5IVFctlU00zU3PSuHRFBZeuqGDbgVb+9FwVf3+lhr+8VM3bFhZz1RmzWDkjb0K+aIU6LPYQkGWMqR7yqQKedk06JSxuf76Kx7cf4qW9jbEWZVhau/poaO9Rf0sck5SYQGlOWtjDYuV56SQlhj/S7kaWfk1TJxV5GUHDm+cVe9hd34HPxUnKADbVtLBset7g9+On5vCzy5bw/JffwqfPmsO6qiYu+8OLvOvXz09Iv0yo0WKrjTHPDbPt/c6KpIyFvgEf/9l2EIDnKw/HWJrh2dNgRYppNeT4piw3PaxhMasa8tiGeYqzUx33udQ0eQcz84cyv9iDt3cgoqkFRuNQWzf7W7pYPi3vmG1F2Wl84e3zeeGGs/neRYvw9vYP+mV+9/TE8cuEWv5lVJstlDaKezxfeZjWrj4yUxJ5YU/8Wi5asHJ8EM68LsYYqg97w/a3+LFKwDhsuTR6j4kU8zM3ChFjm2osD8KyabnDtklPSeQDJ0/nic+9mT9/eCWzizL50X92csoP1vKtB7ZSdbjTNfmiQag27FMi8hkRmRa4UkRSROQtIrKGI/XAlBjw8JY6PKlJfPT0mWyva4vbyq976jtISUygIi+8sXklupTnpnPIrnI8Gk2dvbT39EdguRzJ0neCVm8fbd39wyoXf8SYm2VgNtW0kJKYwPFTs0dtm5AgnLWgiL9ddQqPfPYMLlhcyp2v1HLWz57m9uffcE1GtwlVuZwLDGAVqjwgIttFZC+wG3gfcJMx5naXZFRGobffx2PbDvK244o5c741/U28+l32NHQwoyBjTGPzSvQoy0vHZ+Bg6+gWhT8MOdwcFz+BWfpOUN1kvfEPjRTz40lLpiw33VWn/saaZo4vyyY1KbzAmuOmZvPTS5fw3A1nsWJ6Hr//317HlG60CdXn0m2M+a0xZhUwHav213JjzHRjzMeMMZtdlVIZkecrD9PW3c8Fi0tZUp5DVmpS3PpdKrVg5bjAX3p/X8voEWPVdhjyWC0Xp7P0B8OQh7FcwLJe3BoW6xvwsWVfa1B/S6gUedK48rQZHGzrjtsXxdEI+/XRGNNnjKkzxrS4IZASPg9tqcOTlsTpcwtISkzgpJlTeDEO/S7dfQPUNHm17Ms4YHDSsBCc+lWNXhLEKhszFo4kUjrjdxlaaj8Y80o87G3odCVCa0ddGz39vhH9LaHw1oXFeFKTuHfjPockiy46NjHO6e338fj2g7z9uJJBE/y02fnsPdxJXat70TBjoaqxE59BC1aOA0pzrQd+KE796sZOpuamhz0E5OdICRhnLJfaJi/5mSlkpQ6fxje/2EPvgG/Q6nKSjdWWMz8SywUgLTmR808o5T9bD+Lt7XdCtKiiymWc81xlA+3d/bxjcengutNmFwDwQmV8WS976q0bWcOQ45/UpESKPKkhWy7hVkMOxOks/epG77D+Fj9Haow5X35/U20LxdmplOZEXuX5ouVleHsHeHzbIQckiy5hKRcRyReRT4jIR0TkJBHRkJ8Y89CWOrLTklg1p2Bw3YISD1MyU3h+T3z5XSrrOxBR5TJeCHVeFyvHZWzOfLCy9AuynMvSr2kaPgzZz5yiLBLEnRpjG2uaWT7Nmaz7k2ZMoSw3nfs27XdAsugSruXyL6AQ+D7wE6BVRHY6LtU45yv3vcZvnqp0vZ+e/gGe2HaIc44vISXpyKVMSBBOnZXPi3saseZYiw8qGzooy02P69I0yhFCmZGyxdtLi7cvIssFoNDjTJZ+34CPAy1doyqXtOREZuRnOq5cGtp7qG3qitjf4ichQbhoWRnP7W5wvIqB24SrXDzGmO8Ah4wxb8YKQ/6z82KNX56vPMydr9Twqyd30+xyrsmzrx+mvaef8wOGxPycNiefutZu3oijRKw99R1qtYwjyvMyqGvtGrFMSvVgNeSxWy7gXJb+gZYufGZkZ76fucVZjue6+JMnI/W3BHLR8jJ8Bh589YBjx4wG4SoXv+rsEZF0Y8y9WPPaK4DPZ/jhozvJz0yhu8/H31+pcbW/h1+rIyc9mVWzC47ZNuh3iZOoMZ/PsPewhiGPJ8ry0ukbMCMOVw1WQ46wwq9TWfqDyi4E5TK/2EPV4U66+0KfFG00NtW2kJwoLCrLceyYswuzWFKew30bx9fQWLjK5aciMgW4C/iTiHwG0InQbR56rY7X9rfytQsWcsbcAta8UBVShvNY6O4b4Inthzjn+OKjhsT8zMjPYGpOGi/Eid9lf0sX3X0+VS7jiHJ/OPIIuS7VjV5ERs4pCQWnsvSHK7UfjHklHnzmSL07J9hY3cxxpdmkJTs79HvRsjK217Wx82D400/HirCUiz0LZZMx5ufAI0AF8G5XJBtn9Pb7+Olju1hQ4uHdS8tYffpM6tt7ePg1d0zZZ15voKOnnwsWTw26XUQ4dXYBL+5pdL36ayhUasHKcYd/XpeRnPpVjZ2UZqdF/DAtyk6zsvQ7Ihsaq23ykpKUQLFn9Eit+XbEmFN+l347eXKZg0Nift65ZCpJCcK/xpH1MuZQZGPMX40xXzLGbHNSoPHKna/UUNPk5YbzFpCYILx5XiFzirK49dk3XHGqP/xaHbkZyZw2O3/YNqvm5NPs7WNHHLzt7NGCleMOfyLlSMqlutE75sz8QJzKdalu9FKRl05CkFL7Q5lRkElyojgWjrzzYDtdfQOOOfMDyc9K5c3zCrl/8/5xUw5G81wcoL27j5vX7ubUWfmDk2CJCB9dNZNtB9p4+Y0mR/vr7hvgv9sPce7xJSSPUKPL73eJh2z9yvoOpmSmMCUzJdaiKCGSmZpEXkbyiBFj1Y2dY64pFohTWfqhhCH7SU5MYHahcxOHbaq1ipY46cwP5KLlZRxq64mL+zkUVLk4wB+ffYPGzl5uOG/BUbHtFy8vIy8jmduec7ay6dO7GujsHeCCIFFigZTkpDGrMDMu6oxV1ncwu3BiT+s6ESnLG35el/buPg539MaN5WKMoTYM5QJWMqVjyqW6mYKs1DGXwRkNfzmY+zaNj3IwqlwipL69m1uf3WsVjaw42hxOS07kg6dM5787Djk6N8PDr9WRl5HMqbOGHxLzc9rsfF55oynms9ztadBIsfHISLku/sissc7jEogTWfot3j7ae/pDCkP2M7/Ew77mLjp6Ii+vYiVP5ro2ZXFaciIXLB4/5WBUuUTIzWt309vv4/q3zw+6/YpTppOUINz+QpUj/XX3DbB2xyHOXVQaUtn602YX0Nk7wJZ9sasz2tjRQ7O3T53545Cy3Az2N3cF9RseyXGJ3HLxZ+lHkutS3RS+PP4yMLsjtF6aOnupavS64swP5KJl46ccjCqXCNjb0MGdr9Ty/pOnDRvnX5SdxjuXTOXu9bW0dkU+fenTu+rx9g4cVUtsJPzWTSzrjOnsk+OXsrx0uvoGgk4+VzVYaj9yywWsLP2GCHJdQim1PxT/xGGRDo0dSZ503pkfyMpxVA5GlUsE/PTxXaQlJfDZs+eO2G716TPx9g7wDweSKh/aUkd+Zgonz5wSUvu8zBSOK82OaZ2xPQ1asHK84vcfBBsaq27spMiTSkbK8NWHwyHSLP3awVL7ofs8KvIySEtOiDhibFNNC4kJwgnlziVPBmM8lYNR5TJGNtU088hrB/nYm2ZRkJU6Ytvjp+ZwyqwprHmhiv4IfB9dvQOs3VHPuYtKwprJcdWcfDZWtziaiRwOlfUdpCcnDoa2KuOHkeZ1ibQa8lCKPWkRZenXNHopyApP2SUkiCNO/Y01zSws9TimaEfCXw7mgc3xXQ5m3CkXETlXRHaJSKWI3BALGYwx/ODRnRRkpfCxM2aFtM9Vp8/iQGs3j249OOZ+n9pVT1ff6FFiQzltdgG9Az7WVzWPue9IqGzoYFZhZki5B0p8MZrl4tSQGEBRdmpEWfrVTWOTZ16xJ6IaYwM+w6u1LSyrcNff4mewHEycD42NK+UiIonAb4DzgOOA94nIcdGW46ld9bzyRhPXnj2XzBEmJArkLQuKmFmQya3PjT2p8uEtdRRkpXDyzNGjxAJZOXMKSQkSs1IwWrBy/JKTnkxmSuIxiZTe3n4OtfVEXFMskEiz9GubRq+GHIz5xR4a2nuC+pVC4fVD7XT2DrB8urv+lkAuWlbGjjgvBzOulAtwElBpjNlrjOkF/kGUy88M+Aw/enQXM/IzuPykaSHvl5AgfGTVDF6tbWFjTfgWhLe3n7U7D3HeolISw7QAslKTWFKRG5Milt7efva3dKkzf5wiIkHndalpcqYaciCR5Lr09vs40NoVVhiyn3klkZWB2VTjbvJkMMZDOZjxplzKgNqA7/sIUjhTRK4WkfUisr6hocFRAe7buI9dh9q5/pwFI2bHB+M9y8vJTksaU1Llkzvr6e7zcf4J4Q2J+Vk1O58t+1po6448Yi0c9trOfFUu45dguS5Vh/05Lg76XCLI0t/f0oUxYyugOT/CcOSNNc1MyUyJuHhnOORnpXLm/PguBzPelEuwV/Zjzqwx5hZjzApjzIrCwkLHOu/uG+DnT7zOkopczj+hJOz9M1OTeP/J0/nP1oODkS2hYg2JpXJSiFFiQzl1dgE+A6/sdbYUzWhoGPL4pzwvg/3NR/9e/XPPh1J9OFSKs/2JlOFbLtURhEUXZ6eSnZY0Zr+L28mTw3HRsvK4Lgcz3pTLPqxKzH7KgaiFTKx5oYq61m5uOHfBmH9IV542nQQJL6mys6efJ3fWc/4JJWEPiflZPj2X1KSEqIck72noIEGcHT5RoktZXjpt3f20B1i9VY1e8jNTyE5LdqyfgqxURBhTxFjtGHJc/IjYEWNjCEdu8fayt6HT9eTJYJy9sAhPWvyWgxlvymUdMFdEZopICnA58GA0Om719vGbpyo5a34hp45QiXg0SnPSOf+EUu5aV3vUzToSa3fW09Pv44IxDokBpCYlsnLGlKi/5VTWdzA9P5PUJJ3aeLwyGI4cMDTmdKQYWFn6+Zljy9KvafKSmpRA4ShpAcMxr8SKGAs32MZfrNKNSsijkZacyAUnxG85mHGlXIwx/cCngceAHcDd0Sr5/9unK2nv6edL5y6I+FirT59JR08/d68P7Y3j4S0HKPKksmLG2IbE/Jw2J5+dB9s5HOGcGeFQqZFi457BeV2aApWLszkuforGmKVf3WgVrBxruPv8Yg+tXX1hBxNsqmkhQWBJefSVCxwpB/PYtrGnOLjFuFIuAMaYR4wx84wxs40x34tGnwdauvjzC1VcvKychaXZER9vSUUuK2fkcfsLb4zqjOvo6eepXQ2cf0L4UWJDiXYJ/v4BH1WNncwu0mrI45nyIZZLd98AB1q7HKkpNpSiMWbph1NqPxj+GmO7Dobnd9lU08z8kuyQUxKcZrAcTBxGjY075RILbnridQA+//Z5jh1z9ekzqW3q4ontI79xrN1xiN5+X9iJk8FYNDUbT1pS1PJdapq89A0Y5qjlMq4pyEolJSlhULnsa/ZiDI7M4zKUYk9a2NFi/lL7YwlD9jOWGmM+n2FzTUtMhsT8+MvBPF95OO7KwahyGYVdB9u5d+M+rjx1uqPlS952XAkVU9K59dmRw5If3lJHcXYqJzrgMExKTODkmflRy3fRSLGJQUKCWOHIdq6LPwzZLcvlcEd4WfpNnb109g5EZLnkZ6VSkJUaluVS2dBBe09/VPNbghGv5WBUuYzCj/+zk8zUJD511hxHj5uYIHz4tJmsr27m1drg5fDbu/t4+nVrSMyp0imr5uRT3ehlX3N4odBjobLBUi6zVbmMe8py09lnWy7+ashOzOMylLFk6Vc7lNA5vyS8WSn9lZBjablA/JaDUeUyAi/vbWTtzno+eeYccjOcn573shXleFKHT6pcu6Oe3n5fyOX1Q8Hvd4mG9bKn3qqa62S4qhIbLMvFeohXN3rJSU925Z4oHkOWfiRhyIHMK/awu74DX4hW08bqFnIzkpnlYAmcsXLx8vK4KwejymUY/MUpS7LT+MiqGa704UlL5r0rK3jktToOBCkM+NCWOkpz0hwtiDevOIuCrBReiMLUx5U6++SEoSwvncMdvXT3DVDV2OmK1QKW5QLhZenX2JOWledFaLkUe/D2Dgw78+ZQNtU2s6wi+smTwYjHcjCqXIbhP1sPsrm2hc+/bR5pye7laFx52gx8xrDmxaqj1rd19/GMw0NiYCWMnTq7gBf2NI65gGYoGGPYU6/KZaIQWB25utHrir8FxpalX9PkpciTSnpKZPfp3DAixtq6+9hd3xGT5MlgTMlMibtyMKpcgtA34OMnj+1iXnEW7zmx3NW+KqZkcO6iEu58uYbOgHm8/7v9EL0DzkSJDWXV7Hzq23vY0xDZBEkjUd/eQ0dPv+a4TBD8wSzVjZ3sa/a6ZrmMJUu/usnrSEKnP2IslDIwm2taMCa6xSpHw18OJlbVz4eiyiUILd4+SnLS+NI5CyLOLQmF1afPpK27n3s3HkmqfHhLHWW56SyrcN5ZGA2/i0aKTSz8iZQv723CZ9yJFIOxZelHGobsx5OWTFluekhO/U01LYjAkgp3Z54MB385mHgZGlPlEoRCTyp/u+pkzl5YFJX+lk/LY2lFLn967g18PkNrVx/P7G7gvEUlroznTsvPoDwvnedd9LuocplYlGSnkZggPGf/ZtzIcfFT5EkLOWeju2+Ag23djlUknlecFdKw2MaaZuYVefDEUbDKYDmYbfFRDkaVyzCISNQcdSLC6tNnUtXoZe3Oep7Yfoi+AePKkJif02bn89LeJtfGZ/c0dJCVmjQ4R4cyvklKTKAkO43tdVY0kluWC1i5LqFGi/lL7TtV52xeiYe9DZ30jTAduc9n2Fwb2+TJ4YincjCqXOKE8xaVMDUnjdue28vDWw5QlpvOUheGxPysmlNAa1cf2w+4E7pYWd/B7KKsuIikUZyhLDcdY6zJ5/IznQ9D9hNOlr4/Uswpy2V+sYfeAd9gCf9g7D3cSWtXX1z5W/zEUzkYVS5xQlJiAleeNoOX9jbxv9cbuGBxqasP5lNnWZWd3XL+VdZ3aNmXCYY/Ymx6foarv83iMLL0/TNiOuFzgcAaY8MHu8RL8mQwEhKEi5fHRzkYVS5xxOUnTSMjJRGfIaLy+qFQlJ3GnKIsnnfBqd/WbVWX1YKVEwu/U9+NasiBFIaRpV/T5CU9OXHMpfaHMqcoiwQZucbYxpoWPGlJcRsJedGy+CgHo8oljshJT2b16TNZUp7D4nL3o1BWzc5n3RtN9PYPP748Fvb4nflxevMpY8Mfjuz2xG/+LP1QIsb8pfadsqTSkhOZkZ85onLZVNPM0opcR/PPnGRWYRZLKnJjXg5GlUuc8YW3z+eBT58eFV/FqbML6OobYPMwtc3GikaKTUyiZbn4s/RDyXVxKgw5kLnFWcPmunT09PP6ofa49LcEct3Zc/nsW+a4mig9GqpcJjGnzspHxHm/S2VDB8mJ4piTVYkPlk3L491Lp3Lm/EJX+wk1S98YE/E8LsGYX+yh6nAn3X0Dx2x7tbYFn4Hl0+NbuZy1oIjzTnDXbzsaqlwmMTkZySyamsMLlc76XfbUdzIjP5OkRP15TSSyUpP45eXLBi0Ltwg1S/9wRy9dfQNMm+LcVBhghSP7DEErWPid+UtjNPPkeELv/knOaXPy2VTb7GjS1R4tWKlEQKhZ+jVNVriw0zk38+2IsWB+l401LcwpyiInI36SJ+MVVS6TnNNmF9A3YFhX1ezI8Xr6B6hu7FTlokREKFn6Toch+5lRkElyohwTjmyMYVNNsyslmSYiqlwmOStn5JGcKI75XaoOe/EZ4jZMUxkfFIeQpV/TaJXG9+ffOEVyYgKzC4+dOKyq0Uuzty/u/S3xgiqXSU5GShLLKvIc87v4x6nVclEioSiELP2aJi8l2WmuTIkxr9hzTI2xeE6ejEdUuSicNiefrQdaafX2RXwsfxjyrEJNoFTGTihZ+jVNnUxzKedmfomH/S1ddARMg7Gxppms1CTmFnlc6XOiocpF4bTZBRgDL+6N3HqprO+gLDedjJQkByRTJiuhZOm7EYbsx18GZnfA0NimmhaWVOREZRqOiYAqF4WlFbmkJyfyogN+lz0NVsFKRYmE0bL0u/sGONTW46JysX7Dfr+Lt7efnQfjP3kynlDlopCSlMDKmVMirjPm8xkrDFmd+UqEFI+Spb+v2YoUc6sUTUVeBmnJCYMRY1v2tTLgM+pvCQNVLgpg1RmrrO+IqJLq/pYuuvt86sxXIqZolCz96kZ3wpD9JCQI84o9g5bLRr8zv0Itl1BR5aIAcMZcq6THtf/YzP6WrjEdwx8pNlud+UqE+LP0h4sY8+e4uFliaF6xZ7DG2MbqFmYVZJLn4jw2Ew1VLgoAx03N5sfvWcyWfS2ce9Mz3LthX9hF77RgpeIUVpb+8LkuNU1eMlISXZ20bH6xh4b2Hpo6e9lc28xSHRILC1UuyiCXrazg0WvfxMLSbL7wz1e55o4NIc2p4WdPQwd5GcnkOzS3hjK5KfKkDjtMW+Nwqf1gzCuxIsbW7jjE4Y5edeaHSUyUi4gsEJEXRaRHRL44ZNu5IrJLRCpF5IaA9TNF5GUR2S0id4mI2qcuMC0/gzuvPoWvnr+Ap3Y2cM4vnuGJ7YdC2reyvkMz8xXHKM5O5dAwDn03w5D9+GuM3bWuFtDkyXCJleXSBHwW+GngShFJBH4DnAccB7xPRI6zN/8IuMkYMxdoBlZHT9zJRWKCcPWbZvPvz5xOkSeNj/1lPV+651Xau0dOstzToDXFFOew6osdazm7VWp/KMXZqWSnJbG+upmMlMRBZaOERkyUizGm3hizDhj6tDoJqDTG7DXG9AL/AN4tlu37FuAeu90a4MKoCTxJmV/i4f5PreLTZ83hng37OPcXz/LSMImWTZ29NHX2qnJRHGO4LP2G9h56+n2uZef7ERHm20Nji8tzdAqJMIm3s1UG1AZ832evywdajDH9Q9YHRUSuFpH1IrK+oaHBNWEnAylJCXzxnPn885rTSE4U3vfHl/juQ9uPmUjJ78zXBErFKYqGydKvjkKkmJ+5trWi/pbwiTflEsw7Z0ZYHxRjzC3GmBXGmBWFhe7OmjdZOHF6Ho9cewYfPHk6tz73Bu/81XNs3d86uH2wYKX6XBSHKBomS7+mMXrKxT8UtkyVS9hETbmIyKdEZLP9mTpMs31ARcD3cuAAcBjIFZGkIeuVKJKRksT/XbiINR89ibbuPi78zfP8au1u+gd8VNZ3kJacQFmus+XPlcmLP0t/aK5LTZMXEShzuNR+MM45voRLTixn1Zx81/uaaEStuqAx5jdYzvqRWAfMFZGZwH7gcuD9xhgjIk8Bl2D5Ya4EHnBTXmV43jyvkMeuexPffGAbP3vidf67s54Bn49ZBVkkaFE/xSH8WfpDc11qm7xMzUknNcn5UvtDKclJ46eXLnG9n4lIrEKRS0RkH/B54Osisk9Esm2fyqeBx4AdwN3GmG32bl8GPi8ilVg+mNtiIbtikZuRws3vW8av3reMqsOdbN3fpv4WxVGGy9KvbvJSMUUt5HgnJnXRjTEHsYa2gm17BHgkyPq9WNFkShzxziVTOWnmFH7x39d5x+LhRjsVJXyGy9KvafJy1nz1pcY7OumGEjHF2Wn84OLFsRZDmYAMzdLv6h2god29UvuKc8RbtJiiKMogQ7P0a+1S+9PytThqvKPKRVGUuKU4++gs/eoohiErkaHKRVGUuKXIY2Xp9w/4gOiU2lecQZWLoihxy2CWfmcvYIUhZ6UmkZeRHGPJlNFQ5aIoStziz9L3D41VN3a6XmpfcQZVLoqixC1Ds/SjUQ1ZcQZVLoqixC1+5VLf3oPPZ6ht7nK9GrLiDKpcFEWJWwqyUgaz9Ovbe+jt91Ghlsu4QJWLoihxS9Jgln431Y2dAExX5TIuUOWiKEpcU5ydSn1bj4YhjzNUuSiKEtcUeaws/domLwkCU3Vah3GBKhdFUeIaf5Z+TZOXqbnppCTpY2s8oFdJUZS4xp+l/8bhTh0SG0eoclEUJa7xZ+lvr2tT5TKOUOWiKEpc48916RswGoY8jlDloihKXOMvAQMwXRMoxw2qXBRFiWv8lgtoGPJ4QpWLoihxjT9LH1S5jCd0mmNFUeIaf5Z+T/8AOelaan+8oMpFUZS4pzg7FRG01P44QpWLoihxz6fPmqOKZZyhykVRlLjnvBNKYy2CEibq0FcURVEcR5WLoiiK4jiqXBRFURTHUeWiKIqiOI4qF0VRFMVxVLkoiqIojqPKRVEURXEcVS6KoiiK44gxJtYyuIqINADVLndTABx2uY+xoHKFT7zKpnKFh8oVHkPlmm6MKYzkgBNeuUQDEVlvjFkRazmGonKFT7zKpnKFh8oVHm7IpcNiiqIoiuOoclEURVEcR5WLM9wSawGGQeUKn3iVTcQr14UAAAgCSURBVOUKD5UrPByXS30uiqIoiuOo5aIoiqI4jioXRVEUxXFUuQRBRHJF5B4R2SkiO0Tk1CHbF4jIiyLSIyJfHLLtWhHZKiLbROS6gPU3ish+Edlsf86PorwfEJEt9ucFEVniVN+R9i8i54rILhGpFJEbAtbfLiJvBJyvpTGU8U8iUi8iWyORwU1ZnP59jVVGEakQkafsfbaJyLWRyOGWLHF0vtJE5BURedWW8duRyOGWLGO6H40x+hnyAdYAV9nLKUDukO1FwErge8AXA9YvArYCGVizfP4XmGtvuzGwbZTlPQ3Is5fPA16Oh/6BRGAPMMve71XgOHvb7cAlsZbR/v4mYDmwNV5lcfr3FcE1LQWW28se4HX/NY0nWeLofAmQZS8nAy8Dp8SbLGO5H3Wa4yGISDbWDfxhAGNML9Ab2MYYUw/Ui8gFQ3ZfCLxkjPHax/ofcBHw4xjL+0LA15eA8jjp/ySg0hiz1z7WP4B3A9udks8BGTHGPCMiMyaaLG7IaIypA+rs5XYR2QGUMcZrGk+yuCSjATrs9cn2Z8xRVvEkiw6LHcssoAH4s4hsEpFbRSQzxH23Am8SkXwRyQDOByoCtn/aNkX/JCJ5MZJ3NfCoQ31H2n8ZUBuwbZ+9zs/37PN1k4ikxkhGp3FTFqd+X47IaCvBZVhvwPEoS1ycLxFJFJHNQD3whDEmZudrFFnCux+dMgsnygdYAfQDJ9vffwn83zBtb2SIaW1frI3AM8DvgZvs9cVYw0AJWMNpf4qBvGcBO4D8GJ2vo/oHLgVuDdh+BfAre7kUy0xPxTLzvxnLcwTMwIFhMbdkcfL35ZCMWcAG4OI4OF/HyBJv58velgs8BSyKN1nGcj9GdKNMxA9QAlQFfD8DeHiYtjcywrgt8H3gk0HWO/KgCkdeYDGWf2NeLM5XsP6BU4HHAr5/BfhKkH3PBB6K5Tly6ppFQ5ZIZY1URqzhlMeAz8f6fIUiS6zP15A23xrpmRIPsoR6P+qw2BCMMQeBWhGZb686mzDGaEWkyP47DbgYuNP+XhrQ7CKsIbSoyGvLch9whTHmdSf6daj/dcBcEZkpIinA5cCD9j6l9l8BLiSC8xXrcxQNWZz8fUUio329bgN2GGN+PlYZ3JYljs5XoYjk2svpwFuBnfEmy5jux0jfLCbiB1gKrAe2APcDecA1wDUBbwf7gDagxV7Otrc9a1/MV4GzA475V+A1+5gPAqVRlPdWoBnYbH/WR/l8Dds/ll/qday3qK8FrH/SPl9bgTuwo1hiJOOdWI7hPvtar443WZz+fY1VRuB0LCfwloBt58ebLHF0vhYDm+z9thLB8K+bsjCG+1HLvyiKoiiOo8NiiqIoiuOoclEURVEcR5WLoiiK4jiqXBRFURTHUeWiKIqiOI4qF2VCIiIDARVcN7tdjyvaiMgyEbk14Pu5dkXbnfb/e5edz+CvaHvJkP07hh4zYFuKiDwjIlp7UBkz+uNRJipdxphhy4KLSJIxpj+aAjnMV4HvAojIIuBXwLuMMTvsde/CyjyvCffAxpheEVkLvBf4m1MCK5MLtVyUSYOIfFhE/iki/wYet9ddLyLr7IJ8gfNXfE2seWb+KyJ3ij1vj4g8LSIr7OUCEamylxNF5CcBx/q4vf5Mex///Bp/s7OcEZGVYs2n8aptdXhE5FkJmCtDRJ4XkcVD/g8PsNgY86q96svA9/2KBcAY86Ax5pkQzsl3Aqy7/SLyZ3vT/cAHwjrBihKAWi7KRCXdru4K8IYx5iJ7+VSsB3OTiLwdmItV+l+AB0XkTUAnVimaZVj3yEaswocjsRpoNcastCvGPi8ij9vblgHHAweA54FVIvIKcBfwXmPMOrFKpXdhZU9/GLhOROYBqcaYLUP6WsHR5TeOB346inw/EZGvD11pjPkm8E0RycGqLvFre9NWrDmLFGVMqHJRJirDDYs9YYxpspffbn822d+zsJSNB/iXOTIvz4Mh9Pd2YHGAbyPHPlYv8IoxZp99rM1Yw1WtQJ0xZh2AMabN3v5P4Bsicj3wUaxJmoZSilVW/RhEJB9YizVh3S3GGL/Sud4Yc09Au46AZcEa/rrJGLPBlmdARHpFxGOMaQ/h/1eUo1Dlokw2OgOWBfiBMeYPgQ3Emp56uLpI/RwZTk4bcqzPGGMeG3KsM4GegFUDWPedBOvDGOMVkSewJk27DMtKGUrXkL63Yc1O+aoxphFYag/jZQ3zPwzlRmCfMebPQ9anAt0hHkNRjkJ9Lspk5jHgoyKSBSAiZWJVtX4GuEhE0m3/xjsD9qkCTrSXLxlyrE+ISLJ9rHky8iRNO4GpIrLSbu8JiM66FbgZWBdgZQWyA5gT8P3HwNdEZGHAuowR+h5ERN4BvA347JD1+UCDMaYvlOMoylDUclEmLcaYx+0H8ou2j70D+KAxZqOI3IVVMbYayxfh56fA3SJyBValWD+3Yg13bbSHmRqwSpMP13eviLwX+JVd3rwLq8R5hzFmg4i0AUMtCf++O0Ukxz9kZYx5TUSuBf5iK8NGrCixb4VwGr4ATAVesc/Bg7Yf5izgkRD2V5SgaFVkRRkFEbkR66E/mtPcqf6mAk8DC4wxvmHafA5oN8bcGmy7AzLchzVx2y43jq9MfHRYTFHiCBH5ENY8718bTrHY/I6jfTlOypAC3K+KRYkEtVwURVEUx1HLRVEURXEcVS6KoiiK46hyURRFURxHlYuiKIriOKpcFEVRFMf5fxyUkqP4FMVaAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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X9oQi6VhJX5e0RdJ1kl6Vx49KuljST/PfkTxekt4v6UZJ10h6aLtjNus25dL1hY3r+uhzgUhbQieOUGaA10TEicAjgVdIeiDweuCSiDgeuCQ/B/gt4Pj8OAP4UPtDNusu5cKQBUkMDw7MTzOr1vaEEhG3RcQP8vAOYAtwNHAqcF6e7Tzg6Xn4VODjkVwGDEs6ss1hm3WV8akKB5cKQxZGh1wg0hbX0T4USZuAhwDfB46IiNsgJR3g8Dzb0cAtpcW25nHV6zpD0pWSrty2bVsrwzY74I1NVhZcg1IYdj0vW0LHEoqkg4DPAK+OiO1LzVpj3D61HyLirIjYHBGbDzvssGaFadaVUh2vfROKC0TaUjqSUCT1k5LJJyLis3n0HUVTVv57Zx6/FTi2tPgxwK3titWsG1WXri+kEvbuQ7HaOnGWl4CPAlsi4j2lSRcCp+fh04EvlMa/IJ/t9Ujg7qJpzMxaY3xyuvYRylA/E1MuEGm19XVgm78OPB/4kaSr87g3Au8EPiXpJcAvgGfnaRcBTwNuBKaAF7U3XLPuk+6F0r/P+JHBvQUiD16/73Trbm1PKBHxbWr3iwA8ocb8AbyipUGZ2bzd07Psmp6t2SlfLr/ihGLVfKW8mS1QdLrX6kNx+RVbihOKmS1QJItafSjFhY4TrjhsNTihmNkCe0vX79uk5SMUW4oTipktUKswZMEFIm0pTihmtsB86foaCaUoEOkjFKvFCcXMFiiSxfCGfZu8igKRvmuj1eKEYmYLTExNc/D6Pvp6a389uECkLcYJxcwWGJusXXalMDI4wJj7UKwGJxQzW2B8qnal4cLo0ICPUKwmJxQzWyCVXVk8obgPxRbjhGJmC0xM1S4MWRgd6mfcBSKthmUlFElDknqbHYyZdV7qQ1m8TtfI4ACzc8H23TNtjMrWgroSiqQeSc+T9GVJdwI3ALdJuk7S30s6vrVhmlk7LFUYslAcvUy4Y96q1HuE8nXgvsAbgHtGxLERcTjwGOAy4J2S/qhFMZpZm8xf1Lhkk5fLr1ht9Zavf2JE7NMLFxFjpDsvfibfhdHM1rClCkMWXH7FFlPXEUqtZLKcecxsdRvPt/dd6jqU0cHiCMUfeVvIZ3mZ2byx+SavxRschoeKEvY+QrGFnFDMbN7EEoUhCy4QaYtxQjGzeUsVhixIYmRowH0oto/9JhRJL5f0EUmnSfqSpD9uR2Bm1n7jkxUO2dC/aGHIwshgv49QbB/1nOX1m8BzgW9FxKMlfbjFMZlZh4xNTS/Zf1IYcfkVq6GeJq+7ItVYeFd+vqeF8ZhZB03spzBkwQUirZZ6Esr7ACLii/n5Z1sXjpl10thkZf604KW4D8Vq2W9CiYgbACQdmp9/s9VBmVlnjE/Wd4QyMtjP+NS0C0TaAo2c5XVOy6Iws1VhbKpSdx+KC0RatUYSiloWhZl13K7KLLun5+ruQwHcj2ILNJJQfGxrdgAr+kTq7UMBfCtgW8BHKGYGlC5qrCehuIS91dBIQnlDy6Iws46bmNp/YciCC0RaLfWWrweYkfQ64GhS89etwIURsaUlkZlZWxXNV0vdrbEwkudxH4qV1XvHxtcBF5CavS4HrsjD50t6fevCM7N2Ga/jXiiFg4oCkW7yspJ6m7xeApwSEe+MiH/Nj3cCD8/T6ibpHEl3Srq2NO5MSb+UdHV+PK007Q2SbpT0Y0lPaWRbZla/og/lkCUKQxaKApHuQ7GyehPKHHBUjfFH5mmNOBd4ao3x742Ik/PjIgBJDwROAx6Ul/lnSb0Nbs/M6jAxVV9hyMLo4IALRNoC9fahvBq4RNJPgVvyuOOA+wGvbGSDEXGppE11zn4qcEFE7AF+LulG0lHR9xrZppnt39jUdF0d8oWRof75OzyaQZ0JJSL+Q9IJpC/zo0n9J1uBKyJitkmxvFLSC4ArgddExHje1mWlebbmcfuQdAZwBsBxxx3XpJDMusf4ZIXhOq6SL4wMDnDjnTtbGJGtNXWfNhwRcxFxWUR8JiI+nYeblUw+BNwXOBm4DXh3Hl/r2peaF1hGxFkRsTkiNh922GFNCsuse9RbGLLgApFWbcV3bJT0opWuIyLuiIjZiJgDPkI6EoJ0RHJsadZjSKcrm1mT1Vu6vjCa74kyN+ciGpY04xbAf7XSFUg6svT0GUBxBtiFwGmS1km6N3A86bRlM2uysalKg30oqUDkDheItKyuPhRJ1yw2CTiikQ1KOh94HHCopK3AW4HHSTqZ1Jx1E/AygIi4TtKngOuBGeAVTWxmM7OsKAzZWB9KvrhxqsIhDSxnB656z/I6AngKMF41XsB3G9lgRPxBjdEfXWL+dwDvaGQbZtaYsQYKQxbKBSI3MdSSuGxtqTehfAk4KCKurp4g6RtNjcjM2m7+KvkG+1DKy5rVe9rwolfDR8TzmheOmXXCfOn6RvpQ5gtEOqFYUm8tr/2Wrq9nHjNbncbm63g10IeSC0QWVYrN6j3L6+uS/lTSgisGJQ1I+k1J5wGnNz88M2uHIinUUxiycNC6Pvp7XSDS9qq3D+WpwItJ1YXvDUwA64Fe4GukOlz79K+Y2dowNllBqq8wZEESI4MD7kOxefX2oewG/plUnLEfOBTYFRETrQzOzNpjfKrCwevrLwxZGBn01fK2VyM32AIgIqZJ5VHM7AAxNtnYRY0FF4i0smZcKW9ma9zE1HRDHfKF0aEB96HYPCcUM1v2Ecqw+1CspKGEouSPJL0lPz9O0sP3t5yZrW7jUxWGGzjDqzA6OMDELheItKTRI5R/Bh4FFOVTdgAfbGpEZtZ2y+9DcYFI26vRhPKIiHgFsBsg3wSr8Xehma0auyqz7JmZa+galMJovrjR/SgGjSeU6XxP9wCQdBiN31PezFaR+cKQQ413yhfNZD512KDxhPJ+4HPA4ZLeAXwb+NumR2VmbVN0qi+3D6W8DutuDV2HEhGfkHQV8ARS6fqnR8SWlkRmZm2xnMKQhWIZF4g0aPwsr/OA2yPigxHxAeB2See0JjQza4e9hSGX1ykPbvKypNEmr5PK5VZyp/xDmhuSmbXT+DIqDReGBnrp7xXjrjhsNJ5QeiSNFE8kjbKM8i1mtnqMTU03XBiy4AKRVtZoMng38F1Jnyad6fUcfHteszVtYqrCIRsaLwxZGB0acB+KAY13yn9c0pXAb5I65Z8ZEde3JDIza4uxyUpD95KvNjzY7z4UA5ZXbfh6wEnE7ACRyq403txVGB0a4Cd37GxiRLZWNZRQJK0Dfh/YVF42It7W3LDMrF3GJqc5enj9spd3H4oVGm00/QJwKjADTJYeZrZGTUxVlnXKcGF0KN1kywUirdEmr2Mi4qkticTM2i4ill0YsjA8OMBcwI7dMxyygqYzW/saPUL5rqT/1ZJIzKztdk2nwpDLKbtScIFIKzSaUB4NXCXpx5KukfQjSde0IjAza73idN/lFIYsFM1lPnXYGm3y+q2WRGFmHTGRr3BfSR/KiAtEWtbodSg35yvljwfKp4Xc3NSozKwt5ut4raAPZdT1vCxr9LThlwKvAo4BrgYeCXyPdKGjma0xRRJY0RGKE4pljfahvAo4Bbg5Ih5PKgy5relRmVlbjE8uv3R9YWigl4HeHsYmXSCy2zWaUHZHxG5IFzlGxA3A/Zsflpm1w0oKQxYkpfIr7kPpeo0mlK2ShoHPAxdL+gJwayMrkHSOpDslXVsaNyrpYkk/zX9H8nhJer+kG/NZZQ9tMF4zW8L4ZCoM2dujFa2nuLjRultDCSUinhERExFxJvBm4KPA0xvc5rlA9cWRrwcuiYjjgUvyc0hnlR2fH2cAH2pwW2a2hLGplRWGLIwMOqFY40co8yLimxFxYUQ09C6KiEuBsarRpwLn5eHz2JukTgU+HsllwLCkI5cbs5ktNDFVWdEZXgWXsDeoM6FI+nb+u0PS9tJjh6TtTYjjiIi4DSD/PTyPPxq4pTTf1jyuVoxnSLpS0pXbtvk8AbN6jE1Or+gMr8LwYP/8NS3WvepKKBHxaEkCHhQRB5ceGyPi4BbGV6tht2YFuog4KyI2R8Tmww47rIUhmR04xicry7r1bzUXiDRooMkrIgL4XIviuKNoysp/78zjtwLHluY7hgZPAjCz2iIi9aE0oclrJBeI3L7bRyndrNE+lMskndKCOC4ETs/Dp5PK5BfjX5DP9nokcHfRNGZmK7NrepbKzFxT+lBGigKR7kfpao3W8no88DJJN5PugyLSwctJ9a5A0vnA44BDJW0F3gq8E/iUpJcAvwCenWe/CHgacCMwBbyowXjNbBHzZVea0OQ1X8/L/Shdre3FISPiDxaZ9IQa8wbwipVu08z2NT658sKQhfl6Xj5C6WouDmnWpYrrRprVhwK+J0q3c3FIsy41XxiyKX0oPkIxF4c061p7+1BWnlCKApHuQ+luLg5p1qXGJysrLgxZkMTIkAtEdrtGO+Wri0OO4+tCzNak8alphptQGLIwMjjgPpQu12in/DPy4JmSvg4cAvxH06Mys5Ybm6o0pbmrMDI4wIQTSlert5bXByT97/K45RaHNLPVYXyyOYUhCy4QafX2ofwUeLekmyS9S9LJrQzKzFpvbLLJRyhD/e6U73L1Fod8X0Q8CvgNUun5j0naIuktkk5oaYRm1hITU9OMDq28Q75QNHm5QGT3avQGWzdHxLsi4iHA84BnAFtaEpmZtUxRGLLZfSguENndGkookvol/a6kTwBfAX4C/H5LIjOzlpmqNK8wZKG44t79KN2rrrO8JD0J+APgt4HLgQuAMyJisoWxmVmLFF/6zbj9b2H+anmf6dW16j1t+I3AvwGvjYjq2/ea2RpT3F2xmUcoRdXisUk3eXWruhJKLrNiZgeI4gLEZpSuL+wtYe8jlG7VaOkVMzsAFCVSWtGH4vIr3avhhCJpQNKGVgRjZu0xX7q+iX0og7lApMuvdK9Gz/J6FXAbcGO+DuWVrQnLzFqpKAx5cBMKQxaKApET7kPpWvWWXvlHSS8gla8/MSKOBh4LPFDSX7cyQDNrvrGpSlMLQxZcILK71XuE8k3gfsChwHcl/QD4e+B/gNNyBWIzWyPGJ6eb2n9SGB0acB9KF6u39MrnIuItwGXAqcATgfOAGWAU+IakG1sWpZk11fhUpan9JwUfoXS3Ru+H8grgU6Tb//4IOBH4UUQ8TlLz351m1hJjkxWOGRls+npHhvrnr3Gx7tNoLa+fAo8APg1sAK4h1fPCZezN1o7xqUpTC0MWRnOByFkXiOxKjR6hFInjy/lhZmtMRLSsD2VkKBeI3NWa9dvq5gsbzbrMVGWWyuxcy/pQAPejdCknFLMuUxSGbGbp+kJxVOJbAXcnJxSzLlNcJd+S04aLIxRf3NiVnFDMusx86foWdMoP52KTvhalOzmhmHWZ4rTe4RY0eY36nihdzQnFrMu04uZahcGBXgb6XCCyWzmhmHWZ8akKPU0uDFmQxOigy690KycUsy4zPlVheHCg6YUhC8OD/e6U71INX9jYSpJuAnYAs8BMRGyWNAp8EtgE3AQ8JyLGOxWj2Vo3Pjk933neCqNDAz5tuEutxiOUx0fEyRGxOT9/PXBJRBwPXJKfm9kyjU22pjBkYWTIBSK71WpMKNVOJVU2Jv99egdjMVvzxqcqLS2LMjLY7z6ULrXaEkoAX5N0laQz8rgjIuI2gPz38FoLSjpD0pWSrty2bVubwjVbe1pVur4wOjjA3bumXSCyC62qPhTg1yPiVkmHAxdLuqHeBSPiLOAsgM2bN/udbFZDURhyuAUXNRZcILJ7raojlIi4Nf+9E/gc8HDgDklHAuS/d3YuQrO1bbKFhSELxcWN7kfpPqsmoUgakrSxGAaeDFwLXAicnmc7HfhCZyI0W/uKvo1WHjkUV+C7H6X7rKYmryOAz0mCFNe/RcR/SLoC+JSklwC/AJ7dwRjN1rT5wpAt7kNJ2/K1KN1m1SSUiPgZ8Gs1xt8FPKH9EZkdeFpZGLIwMuQCkd1q1TR5mVnrteUIxX0oXcsJxayLjOeSKKMt7EPZ0J8KRPoIpfs4oZh1kfnCkOtb1+Q1XyDSRyhdxwnFrIuMTabCkD0tKgxZGBkacIHILuSEYtZFxqcqjLSwMGRhZLDfRyhdyAliFYBdAAAPRElEQVTFrIuMT063tP+kMDLkJq9u5IRi1kWKe6G0mm+y1Z2cUMy6SKtL1xdGhgaYcIHIruOEYtYlIqLlpesLI4P9RMDdu9wx302cUMy6xGRllunZaEunfNFP436U7uKEYtYl2lEYsjDiApFdyQnFrEvM1/FqR6d8UX7FCaWrOKGYdYmitlY7jlCGc7Oam7y6ixOKWZeYmC8M2c4+FHfKdxMnFLMuMdaGwpCFDf29rHOByK7jhGLWJcYnW18YsiCJkcEB96F0GScUsy5RXCXf6sKQhVR+xU1e3cQJxaxLtKswZGF0yAUiu40TilmXGJustKX/pDDiel5dxwnFrEuMT0639Na/1UYGB3wb4C7jhGLWJVKTVxsTytAAd7tAZFdxQjHrAu0sDFkYdYHIruOEYtYFdu6ZYXo2GB1qX6f8iMuvdB0nFLMuMJ4vamx3Hwq4/Eo3cUIx6wLj82VX2tjkNeSKw93GCcWsC7SzMGRhxPdE6TpOKGZdoDhKaO91KKm/pqghZgc+JxSzLlCUQGnnlfJFgcgJH6F0jb5OB7CaXHXzGNffup11fb2s6+8p/e1hff5wrOvrZX1p2vq+Xvp7hdSe+khmy9HOwpAFSYwOuUBkN3FCKfnadXfwL5f+rOHlJFjX18PQQB9Hj2zg2JFBjhndwHGjgxw7Mshxo4McNbyBgT4fEFpnjOWLGttVGLIwMjjgPpQu4oRS8udPOoH/89j7sHt6lj0zc/N/90zPsXtmlj3Tc+wp/y3PMzPHjt0z/HJiF9fftp2Lr7+Dyuzc/Lp7BEcesoFjRnKiGR3Mf1MCOmzjOh/lWMuMT7b3osbCyFC/j1AaEBFUZueYmQ1mZoPpuTlm54LpYtxcMDOXhqdni2l53Fxa5qRjDuGIg9d3JP41kVAkPRV4H9ALnB0R72zFdtb397K+v7cp65qdC+7Yvptbxqa4ZXwXvxibYuvYFL8Ym+LSn27jju17Fsy/rq+HQzb0M7Suj8GBXoYG+hha18vguj6GBnoZzM+H1vUxNJDnWbfw7+BAmndDHu5t869RW73aXWm4MDI4wG0T29u+3XbaMzPLzt0z7Nwzw47d6bFzzwxTlfx3z2zp+SxTlRkm98wyWZ6nMjv/d6Wlaj74vIfy2ycd2aT/rjGrPqFI6gU+CDwJ2ApcIenCiLi+s5EtrbdHHDW8gaOGN/CIGtN3T8+ydXwXt4ynRHPL+C527J5msvSGu2uywi/GppiqpDffZINvtoG+nvlktGGgd0GySQkoDa/r6wGBED1KTXjFMBICeqQ8Pk/Pz3uU5kvT03Bvz97hYrokeiV6ehbO29fTw0Cf6Ovpoa9XDPT20NfbQ1+PGOhLf/t7e+jvTdP783z9vT2l7eKju0XMzQVzEYxNVth0j6G2b390aIC7JitUZuaYixTL7FwwF+nXeDFcnhZBHp8eM3Np/Ozc3uGZ2eL53MLxc8Fs/gU/OxcEEAFpqBjOIuaHI8cDzC+TWh2m2blnhp27Z9i+e4ade6bnE8fO3TPs2DNDZWaOemzo753/UTg40MdB63oZHhzgmJHB+R+GQ+vSZ7KvR/T19tDfK3p79r7vi89G8bno7dH856GY77jRwWa+hA1Z9QkFeDhwY0T8DEDSBcCpwKpOKPuzvr+X+x1+EPc7/KC6l4kI9szMzSeYqcosk5WZ+eGpSvq7qzKbEtP0zPzwrumUpHZVZrlzx26m9szOL7NnZi59sPIHr/iwFx+staJIcEUCROxNOgvGlZNjSkRFooSqxEmNeUtJl/nl9l1feaA8rdi3RPHlVfXFRuQvOBbMH/NfvPnLOIK54gs4Yn7a3Nze4bKH3Wt05Tu5QaO5QOQJf/mVtm+7GXp7xMb1fRy0Lj0OXt/P4RvXc59D+9L49X1sXNfHxvX9aZ71ffPzF60JRZLohhaDtZBQjgZuKT3fCvv+6Jd0BnAGwHHHHdeeyNpM0nyzXDuvJ4iI+S+z8i+++XHFF93c3l+axZfeXPFlN1eaL/b+Ki3ahqdn5+bbgueH58en9uG949O4uaAqroVxzuWfo3vjKeJdmCznv8Dnx+39Qq+eFjkLRGnfpCXK69t3GqVtKR0OzieZvckrKcaxT0LLR3w95aO/9OgtHfktGC6OFHvE73SgGeS0U46jv7eHiMhxp5iUj2TL/0+vqp7nI92+nvQrfe/fvb/Ma47Pz4v1U7V/qfoxsM9+pzjRJp3R6aPf+q2FhFLr1dznd3NEnAWcBbB58+Y19Lt69Ztv3qr5Upgt7p6HrOcVj79fp8OwNlkL57FuBY4tPT8GuLVDsZiZ2SLWQkK5Ajhe0r0lDQCnARd2OCYzM6uy6pu8ImJG0iuBr5JOGz4nIq7rcFhmZlZl1ScUgIi4CLio03GYmdni1kKTl5mZrQFOKGZm1hROKGZm1hROKGZm1hQqruY9kEjaBtzchk0dCvyqDdtplONqjONqjONqzFqK614RcdhyV3hAJpR2kXRlRGzudBzVHFdjHFdjHFdjuikuN3mZmVlTOKGYmVlTOKGszFmdDmARjqsxjqsxjqsxXROX+1DMzKwpfIRiZmZN4YRiZmZN4YSSSRqW9GlJN0jaIulRVdMfIOl7kvZIem3VtFdJulbSdZJeXRp/pqRfSro6P57W5pj/UNI1+fFdSb+2GrYt6amSfizpRkmvL40/V9LPS/vr5A7HeY6kOyVduxrjaMX7a7lxSjpW0tfzMtdJetVqjKXZ+2wFMa6XdLmkH+YY/2olcbQqloY/k1HcErXLH8B5wEvz8AAwXDX9cOAU4B3Aa0vjHwxcCwySqjf/J3B8nnZmed4OxPy/gZE8/FvA9zu9bdItCP4HuE9e7ofAA/O0c4FnrZZ9BDwWeChw7WqMoxXvrxW8rkcCD83DG4GfFK/raoql2ftsBTEKOCgP9wPfBx652mJp9DO5JsrXt5qkg0kf2hcCREQFqJTniYg7gTsl/XbV4icCl0XEVF7XN4FnAH+3CmL+bunpZaS7XXZ62w8HboyIn+V1XQCcClzfjNiaGCcRcamkTQdKHK2MMyJuA27LwzskbQGOZpmv62qKpUUxBrAzj+/Pj2WfIbVaYnGTV3IfYBvwMUn/LelsSUN1Lnst8FhJ95A0CDyNhbcsfmU+xDxH0kgHY34J8JVVsO2jgVtK07bmcYV35P31XknrOhhnM7Uyjma+v5oSZ05+DyH90l2NsTRrn60oRkm9kq4G7gQujoiO7a/9xFL/Z7JZh35r+QFsBmaAR+Tn7wP+epF5z6TqkDm/OD8ALgU+DLw3jz+C1MTTQ2oqO6dDMT8e2ALco9PbBp4NnF2a/nzgn/LwkaTD73Wkw/e3dHofAZtYYZNXq+Jo9vurSXEeBFwFPHM1xtLMfdaszyAwDHwdePBqi6XRz+SyX/AD6QHcE7ip9PwxwJcXmfdMlmiDBf4G+JMa41f8xbScmIGTSH0WJ6yGbQOPAr5aev4G4A01ln0c8KVO76NmvG7tiGM1xElqKvkq8H87+R6rN5aV7rNmfgaBty71vbIaYqnnM+kmLyAibgdukXT/POoJNNDeKunw/Pc44JnA+fn5kaXZnkFqHmuKemLO8XwWeH5E/GSVbPsK4HhJ95Y0AJwGXJiXOTL/FfB0Vri/OrmP2hFHs99fK4kzv2YfBbZExHtWEkcrY2nmPlthjIdJGs7DG4AnAjestlga/kyu9JfEgfIATgauBK4BPg+MAC8HXl76BbAV2A5M5OGD87Rv5Rfvh8ATSuv8f8CP8jovBI5sc8xnA+PA1flx5WrYNqmf6SekX0pvKo3/r7y/rgX+lXzmSQfjPJ/UuTudX++XrKY4WvH+Wm6cwKNJHbnXlKY9bbXF0ux9toIYTwL+Oy93LSts3m1VLDT4mXTpFTMzawo3eZmZWVM4oZiZWVM4oZiZWVM4oZiZWVM4oZiZWVM4odgBQ9JsqSrq1e2of9VOkh4i6ezS86fmKrE35P/3k/lag6JK7LOqlt9Zvc7StAFJl0pyfT9bNr957ECyKyIWLa8tqS8iZtoZUJO9EXg7gKQHA/8E/F5EbMnjfo909fcvGl1xRFQkXQI8F/hEswK27uIjFDugSXqhpH+X9EXga3ncX0i6Ihe8K9/74U1K92n5T0nnK9/3RtI3JG3Ow4dKuikP90r6+9K6XpbHPy4vU9yb4hP5SmMknaJ0L4of5qOLjZK+pdJ9JiR9R9JJVf/HRuCkiPhhHvU64G+KZAIQERdGxKV17JO3lY7ifinpY3nS54E/bGgHm5X4CMUOJBtyxVSAn0fEM/Lwo0hfxmOSngwcTyqjL+BCSY8FJkllYB5C+lz8gFRYcCkvAe6OiFNyFdbvSPpanvYQ4EHArcB3gF+XdDnwSeC5EXGFUsnxXaQrmF8IvFrSCcC6iLimalubWVj24kHAP+wnvr+X9JfVIyPiLcBbJB1CqvLwgTzpWtI9f8yWxQnFDiSLNXldHBFjefjJ+fHf+flBpASzEfhc7L2vzYV1bO/JwEmlvopD8roqwOURsTWv62pSU9TdwG0RcQVARGzP0/8deLOkvwBeTLqpUbUjSeXJ9yHpHsAlpJu8nRURRaL5i4j4dGm+naVhkZq23hsRV+V4ZiVVJG2MiB11/P9mCzihWDeYLA0L+NuI+JfyDEq3bl6sDtEMe5uH11et608j4qtV63ocsKc0apb0WVOtbUTElKSLSTcaew7paKTarqptX0e6i+MPI+Iu4OTcRHfQIv9DtTOBrRHxsarx64Ddda7DbAH3oVi3+SrwYkkHAUg6Wqla9KXAMyRtyP0Vv1ta5ibgYXn4WVXr+mNJ/XldJ2jpmxrdABwl6ZQ8/8bSWVVnA+8HrigdTZVtAe5Xev53wJsknVgaN7jEtudJ+h3gScCfVY2/B7AtIqbrWY9ZNR+hWFeJiK/lL+Hv5X7yncAfRcQPJH2SVIX1ZlLfQuEfgE9Jej6p+mrhbFJT1g9yE9I2UonvxbZdkfRc4J9ymfBdpFLhOyPiKknbgeojhmLZGyQdUjRHRcSPJL0K+HhOgHeRzu56ax274TXAUcDleR9cmPtVHg9cVMfyZjW52rBZDZLOJH3R76/ju1nbOwr4BvCAiJhbZJ4/B3ZExNm1pjchhs+Sbnb241as3w58bvIy6zBJLyDd8/xNiyWT7EMs7JtpZgwDwOedTGwlfIRiZmZN4SMUMzNrCicUMzNrCicUMzNrCicUMzNrCicUMzNriv8P5IhigwqpN6QAAAAASUVORK5CYII=\n", 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\n", 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\n", 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WsbfYqvwaY6KDNSJBNGpIKjuKipk+7xevoxhjTI2wRiSIBnVsSveWDZlkh/saY6KENSJBJCKMHNKBJRt3krNmu9dxjDEm5KwRCbJhA9rSML4OmbNsB7sxpvazRiTIEuLqcH56Oz5auJEtO626rzGmdrNGJARGHNGBElVe+XGt11GMMSakrBEJgdTmiRzbLZlXZ69l/4FSr+MYY0zIWCMSIiOHpJK3ax8fL97kdRRjjAmZGmlERGSiiGwRkUUVPHejiKiINHcfi4g8ISIrRGSBiBxeExmD7ZiuyaQ2SyBzZq7XUYwxJmRqaktkEjC0/EARaQecBPjuPDgV6OrexgHP1EC+oIuJEUYMTiVnzXYWbSjwOo4xxoREjTQiqjoD2FbBU48C/wf4npl3JjBZHT8AjUWkdQ3EDLpz01JIiIu1rRFjTK3l2T4RETkD2KCq88s91RZY5/N4vTss4iTVr8uwAW15d/4vbC/c73UcY4wJOk8aERFJAG4H7qjo6QqGVVhDRETGiUi2iGTn5eUFM2LQjBycyv4DpUzNXlf5yMYYE2G82hLpDHQE5otILpACzBGRVjhbHu18xk0BKqxoqKrPq2q6qqYnJyeHOHLVdG/VkMGdmvHSrDWUlFo9LWNM7eJJI6KqC1W1haqmqmoqTsNxuKpuAqYDI92jtI4AClR1oxc5g2XUkA5s2LGHL5Zu9jqKMcYEVU0d4jsFmAV0F5H1IjL2EKN/CKwCVgAvAFfWQMSQOrFnS9okxdvlc40xtU6dmpiJqv65kudTfe4rcFWoM9WkOrExXHREBx765CdWbNlFlxYNvY5kjDFBYWes15DhGe2IqxPDZKvua4ypRawRqSHNGtTj9H5teCtnPbv2FnsdxxhjgsIakRo0akgHCveX8FbOeq+jGGNMUFgjUoP6pTSmf7vGTJ61hlI73NcYUwtYI1LDRg9JZVV+Id+tyPc6ijHGVFvAjYiIJIpIbCjCRINT+7aieYM4Js/K9TqKMcZUW6WNiIjEiMiFIvKBiGwBlgEbRWSxiDwkIl1DH7P2qFcnlgsHtueLZVtYu7XI6zjGGFMt/myJfIVTpuRWoJWqtlPVFsDRwA/Av0Tk4hBmrHUuHNSBGBFe/tEO9zXGRDZ/TjY8UVV/d0yqqm4D3gLeEpG6QU9Wi7VKimdo71ZMzVrH9Sd2o36c9Q4aYyJTpVsiFTUgVRnH/NaoIakU7Cnm7bl2uK8xpmpUlX9MW8QPq7Z6lsHvHesi8oWInFZu2PPBjxQdMlKbcHj7xtz3wVKWbtzpdRxjTAR6/IvlvPTDGuas3e5ZhkCOzuoI3Cwid/oMSw9ynqghIjx9URoN4uswdlIWW3bu9TqSMSaCvDtvA499vpxzDk/himM6e5YjkEZkB3AC0FJE3hORpBBlihqtkuKZMCqD7UXFXDo5m73FJV5HMsZEgJw127jpzQUM7NiU+8/ui0hF1/KrGYE0IqKqB1T1Spwd6t8BLUITK3r0aZvE48P7s2BDATe8Pt/OZDfGHNK6bUWMm5xD66R4nrs4jbg63p4zHsjcny27o6qTgFHAp8EOFI1O7t2KW0/twQcLN/LIZz97HccYE6Z27i1mzKQsiktKmTg6gyaJcV5HqvwQXxF5Evca5yLyRLmnd4ciVDS69OhOrMor5KmvVtCxeSLnpKV4HckYE0YOlJRy9atzWZ1fyOQxA+mc3MDrSIB/54lk+9y/C7jzYCOaqhMR7j6zD2u2FnHL2wto1zSBgR2beh3LGBMm7n5/CTN+zuNfZ/dlSJfmXsf5H3EuJOjnyCJzVXVACPNUWXp6umZnZ1c+YpgrKCpm2NPfs71oP9OuOpIOzRK9jmSM8dik71cz/r0ljPtDJ247rWdQX1tEclS1ykfaBrpHxvb6hlhSQl0mjs5AgTGTsijYY+dxGhPNvlq2hbvfX8JJvVpy89AeXsf5HSsFH4ZSmyfy3MVprN1WxJWv5FBcUup1JGOMB5Zt2sk1U+bSo1UjHrugP7Ex3h3KezD+VPHdJSI7RWQn0K/sftnwGsgYlQZ1asZ9w/ry/Yqt3PHuYgLpdjTGRL68XfsYOymbxHqxTBidTmI9f3Zh17xKU6lqw5oIYn7vvPR2rMov5JmvV9I5OZFLju7kdSRjTA3YW1zCpZOz2Va4n9cvG0zrpPpeRzqo8GzazP/cdHJ3cvMLuffDpaQ2S+TEXi29jmSMCaHSUuXGN+Yzf/0Onrkojb4p4V0cxJ/zRKYf6nlVPSN4cUx5MTHCI+f3Z8Pzs7j2tbm8cflgercJ7zeVMabqHvv8Z95fsJFbTu3B0D6tvI5TKX+2RAYD64ApwI9A+O3ZqeXqx8Xy4sh0zvzP91ySmc27Vx1Ji0bxXscyxgTZO3PX88SXKzg/PYXL/hAZ3df+HJ3VCrgN6AM8DpwE5KvqN6r6TSjDmV+1aBTPi6PSKdhTzCWTs9mz34o1GlObZOVu4+Y3F3JEp6bcc5a3RRUD4c9FqUpU9WNVHQUcAawAvhaRa0KezvxG7zZJPDF8AAs3FHD91HlWrNGYWmLt1iIueymHtk3q82wYFFUMhF9JRaSeiJwNvAxcBTwBvB3KYKZiJ/Zqye2n9eTjxZt47HMr1mhMpCvYU8yYzCxKSpWJozNonOB9UcVA+LNjPROnK+sj4C5VXRTyVOaQxh7VkYUbCnh2xipGH9mRpmFQydMYE7jiklKufnUOufmFvDR2EB2bR16ZI3+2REYA3YC/AjPtZEPviQhXHdeF/QdKeS1rrddxjDFVoKqMn76Yb5fnc9+wvgzu3MzrSFXizz6RGFVt6N4a+dwaqmqjmghpfq9by4YM6dyMV35YywEri2JMxJn4fS6v/LiWy47pxPkZ7byOU2X+lD2p9BABf8YxwTdycCobduzhi2VbvI5ijAnAF0s3c88HSzild0tuPiX8iioGwp/urK9E5BoRae87UETiROR4d5/JqNDEM4dyYs8WtEmKJ3NmrtdRjDF+WrpxJ9dOmUvvNo149IL+xIRhUcVA+NOIDAVKgCki8ouILBGRVcBy4M/Ao+7lck0NqxMbw8WDOzBz5VaWb97ldRxjTCW27NzL2ElZNIivw4sjM0iIi/zKU/7sE9mrqk+r6pFAB+AE4HBV7aCql6rqvJCnNAc1PKM9cXViyJyV63UUY8wh7NnvFFXcXlTMhFEZtEqqHVUnAjqjRVWLVXWjqu4IVSATmKaJcZxxWBvenrOBnXvtAlbGhKPSUuWGN+axYEMBjw/vT5+2taf+XeScFmkOatTgVIr2l/BWznqvoxhjKvDIZz/z4cJN3HpqD07uHf5FFQNRI42IiEwUkS0isshn2EMiskxEFojIOyLS2Oe5W0VkhYj8JCKn1ETGSNY3JYkB7RszedYaK4ViTJh5K2c9T321guEZ7bi0Fl4TyO9GRBwXi8gd7uP2IjLQz8kn4eyg9/UZ0EdV+wE/A7e6r9sLGA70dqd5WkRi/c0ZrUYPSWV1fiHfrsj3OooxxjV79TZueXsBgzs14+4z+0RMUcVABLIl8jROWfg/u493Af/xZ0JVnQFsKzfsU1U94D78AUhx758JvKaq+1R1NU7BR38bq6h1ap/WNG9Qzw73NSZM5OYXctlL2bRrkhBxRRUDEch/NUhVrwL2AqjqdiBYRZvG4NTmAmiLc/2SMuvdYb8jIuNEJFtEsvPy8oIUJTLF1YnhwkHt+eqnLazZWuh1HGOiWkGRU1RRgYmjM0hKqOt1pJAJpBEpdruVFEBEkoFq19sQkduBA8ArZYMqGK3Cjn5VfV5V01U1PTk5ubpRIt5Fg9oTK8LLP6zxOooxUau4pJQrX81h3bYinrs4jdQILKoYiEAakSeAd4CWInIv8B1wX3VmLiKjgD8BF6lqWUOxHvAtJJMC/FKd+USLlo3iGdqnFVOz1lG0/0DlExhjgkpVuePdxXy/Yiv3DevLoE6RWVQxEH43Iqr6CvB/OA3HL8BZqvpGVWcsIkOBm4EzVLXI56npwHD3GiYdga7A7KrOJ9qMGpLKzr0HeHeetbvG1LQJ361myuy1XHFsZ85Lj9yiioEI5OisO4DzgWbu7byyI7X8mHYKMAvoLiLrRWQs8BTQEPhMROaJyLMAqroYeB1YAnwMXKWqdi1YP6V3aELP1o3InJnLrxt3xphQ+3zJZu79cCmn9mnFTSd39zpOjQmkcIvv3tp4nG6opf5MqKp/rmDwhEOMfy9wbwDZjEtEGD2kAze/tZDZq7dFxea0MV5bvnkX1742l75tk3jk/MgvqhiIQLqz/u1zuxc4loMcNWW8dcZhbUmqX9fqaRlTQ576agWxIrw4Mp36cdF1Wlt1DlxOAGrf6Ze1QP24WIZntOOTxZvZWLDH6zjG1Gpbdu3lw4UbOTc9hRaNakdRxUAEsk9koVuiZIGILAZ+Ah4PXTRTHRcf0YFSVV790S6fa0wovTZ7HcUlyogjOngdxROB7BP5k8/9A8BmnzPOTZhp1zSBE3q0YMrstVx9fBfq1YmuTWxjakJxSSmv/LiGP3RLplNyA6/jeCKQfSJrfG4brAEJf6OGpJK/ez8fLtzodRRjaqVPFm9i8859jB4SnVsh4N811neJyM4KbrtEZGdNhDRVc2Tn5nRKTmTSTDuD3ZhQyJyZS/umCRzTrYXXUTzjz5UNG6pqowpuDVW1UU2ENFUTEyOMGpzK/HU7mLfOriNmTDAt/qWArNztjBzcgdgoOqS3vICOzhKRJiIyUET+UHYLVTATHGcf3pbEuFgmz8r1OooxtcpLs9YQXzeG89Ki48z0gwnk6KxLgBnAJ8Bd7t/xoYllgqVhfF3OSUvh/fkbyd+9z+s4xtQKO4r2M23eBoYNaFurK/T6I5Atkb8CGcAaVT0OGABEd/31CDFycCr7S0qZmrWu8pGNMZV6PXsde4tLGTk41esongukEdmrqnsBRKSeqi4DoqdATATr0qIBR3Vpzss/rOFASbWr9xsT1UpKlcmz1jCwY1N6trbdwoE0Iuvd66BPwyma+C5Woj1ijBqSysaCvXy2ZLPXUYyJaF8t28L67XsYPSTV6yhhwe+TDVV1mHt3vIh8BSThVNk1EeD4Hi1o27g+mbNyObVva6/jGBOxMmfl0qpRPCf1aul1lLDgz3kiT4nIEN9hqvqNqk5X1f2hi2aCKTZGGDG4Az+s2sayTXZ6jzFVsTJvN98uz+eiQe2pG1s7r5keKH+WwnLg3yKSKyIPiEj/UIcyoXFBejvq1Ylh8iw7+dCYqnhp1hriYmP486D2XkcJG/6cbPi4qg4GjgG2Af8VkaUicoeIdAt5QhM0TRLjOLN/G96Zs4GComKv4xgTUXbvO8CbOev5Y7/WNG9Qz+s4YSPQ2lkPqOoA4EJgGH5elMqEj5GDU9lTXMIbOXa4rzGBeHvOenbvO8Ao26H+G4GcbFhXRE4XkVeAj4CfgXNClsyERJ+2SaR3aMLkWWvYW2xXHTbGH6pK5sxcDktJon+7xl7HCSv+7Fg/SUQmAuuBccCHQGdVvUBVp4U6oAm+q47vwtptRdz69kK7Drsxfvh+xVZW5hXaVkgF/NkSuQ2YBfRU1dNV9RVVLaxsIhO+juveghtP7sY7czfw5JcrvI5jTNjLnJVLs8Q4TrPD43+n0vNE3BInppa56rgurMor5JHPfqZj80ROP6yN15GMCUvrthXxxdLNXHFsZ+Lr2sXdyrMDnaOUiHD/OX3JSG3CDW/MZ87a7V5HMiYsvfzjGkSEiwZF74WnDsUakShWr04sz41Ip1WjeMZNzmbdtiKvIxkTVvYWlzA1ax0n92pJm8b1vY4TlqwRiXJNE+OYODqDfQdKuSQzm1177fwRY8pMn/cLO4qKbYf6IdjlcQ1dWjTgmYvSWJG3m2umzLVKv8bgHNY7aWYu3Vs2ZFDHpl7HCVt2eVwDwFFdm/PPM/vw9U953POBnUNqTM6a7SzZuJORQzogEr2Xv62M31V8wbk8LtAViC8bpqozgh3KeOPCQe1ZmbebCd+tplNyol1wx0S1zFlraBhfh2ED2nodJaz53Yi4l8f9K5ACzAOOwDl/5PjQRDNeuO20nqzZWsj46Ytp3zSBY7u38DqSMTVu8869fLRwI6OGpJIQF9Bv7ahjl8c1vxGW9eptAAAZUUlEQVQbIzw+fADdWzXi6lfn8tOmXV5HMqbGvfrjWkpUGXGEHdZbGbs8rvmdxHp1mDAqnYS4WMZMyiJ/9z6vIxlTY/YfKOXV2Ws5tlsyqc0TvY4T9uzyuKZCbRrX58VR6Wwt3Me4ydlWrNFEjY8WbSRv1z5G2mG9fgmkFPwwVd2hquOBfwATgLNCFcx4r19KYx45vz9z1u7g/95cYMUaTVSYPGsNqc0SOKZrstdRIkKVTja0y+NGj9P6tuamU7ozff4vPPb5cq/jGBNSizYUkLNmOyMGpxITY4f1+qPSww5E5DtVPUpEdgG+P0UFUDtXpPa78tjOrMor5PEvltMpOZEz+9shj6Z2ypyZS0JcLOempXgdJWL4U8X3KPdvw9DHMeFIRLj/7L6s217ETW8sIKVJfdI62Bm8pnbZXrifd+f/wnlpKSTVr+t1nIgRyJUNH/BnmKmd4urE8NzFabRuHM+4yTlWrNHUOpNnrWH/gVKrkxWgQPaJnFTBsFODFcSEvyZuscbiklLGTMpipxVrNLXEjJ/zeOLL5Qzt3YpuLa3TJRD+FGC8QkQWAt1FZIHPbTWwwJ+ZiMhEEdkiIot8hjUVkc9EZLn7t4k7XETkCRFZ4c7n8Kr+cyb4Oic34NmL01idX8hVr8yxYo0m4i3fvIurXplD1xYNePj8w7yOE3H82RJ5FTgdmO7+LbulqerFfs5nEjC03LBbgC9UtSvwhfsYnK2bru5tHPCMn/MwNWRIl+bcc1Yfvl2ez13vLbFDf03E2rp7H2Mys6hXN5YJozNoUM9KnATKnx3rBUAB8OeqzkRVZ4hIarnBZwLHuvczga+Bm93hk9X5ZvpBRBqLSGtV3VjV+ZvgGz6wPavyC3l+xio6JSfylyM7eh3JmIDsO1DCZS/lsGXnPqZeNpi2dtGpKvGnO+s792/ZdUV2+dyqcz2RlmUNg/u3rNJfW2Cdz3jr3WEVZRsnItkikp2XZ2W8atrNQ3twYs+W/PP9JXy1bIvXcYzxm6pyy1sLyV6znUfO70//do29jhSx/LmeyP8O8fW5jkjZLRTniFR0hk+F/SWq+ryqpqtqenKynV1a05xijf3p0aoR10yZy7JNdo0yExme/HIF78zdwI0nd+OP/Vp7HSeiBXKIbz0RuVBEbhORO8pu1Zj3ZhFp7b52a6Dsp+x6oJ3PeClYja6wlVivDhNGp5NYL5axk7LZsmuv15GMOaT35v/CI5/9zNkD2nLVcV28jhPxAjnE912c/RUHgEKfW1VNB0a590e5r182fKR7lNYRQIHtDwlvrZPq8+LIDLdYY44VazRha+7a7dz4xnwyUptw/zl97YqFQRDIoQgpqlr+CCu/iMgUnJ3ozUVkPXAn8C/gdREZC6wFznNH/xA4DVgBFAF/qco8Tc3qm5LEYxf05/KX53DDG/N5cvgAqz1kwsr67UVcOjmblo3ieW5EOvXqxHodqVYIpBGZKSJ9VXVhoDNR1YMd2XVCBeMqcFWg8zDeG9qnNTcP7cEDHy+jc/NE/nayXW7GhIdde4sZOymbfQdKeW1cBk0T47yOVGsE0ogcBYx2TzLcx68FGPuFJJmJSJcf04nV+bt54ssVdExOZNgAK2RnvHWgpJRrpsxlRd5uMv8ykC4tGngdqVYJpBGxEiemUiLCPWf1Ze22Im5+cyEpTRLISLVijcY793ywlK9/yuPeYX04qmtzr+PUOoFclGpNRbdQhjORKa5ODM9enEbbJvW57KUc1m61Yo3GGy/NymXSzFzGHtWRiwbZ9dJDwZ+TDctOMix/q+7JhqYWa5wQx4RR6ZSUKmMysyjYY8UaTc365uc8xr+3hBN7tuC203p6HafW8udkw7KTDMvfQnWyoaklOiU34JmLDyc3v5CrX51DsRVrNDXk5827uPqVOXRr2ZDHhw8g1o4UDJkqXR7XGH8N6dyc+4b15dvl+YyfvtiKNZqQy9+9jzGTsoiPi2XCqHQSrahiSNnSNSF3fkY7Vubv5rlvVtEpuQFjj7JijSY09haXMG5yNvm79zF13GDaWFHFkLNGxNSIm0/pweq8Qu75YAmpzRI4oWdLryOZWkZVufmtBcxZu4OnLzqcw6yoYo0IpHbW4yLyqYh8IiIPikj/UAYztUtMjPDY8P70btOIa6fMZelGOybDBNfjXyzn3Xm/cNMp3TmtrxVVrCmB7BNZCjwEPI5TLPFlEbk6JKlMrZQQV4cXR2bQIL4OYydlsWWnFWs0wfHuvA089vlyzjk8hSuP7ex1nKgSyHkiz6rqZ6r6oao+DKQDl4UumqmNWiXFM2FUBtuLirl0cjZ79luxRlM9OWu2c9ObCxiY2pT7zu5jRRVrWMBHZ4nI5SLyMM4WifVJmID1aZvE48P7s2BDATe8MY/SUjtiy1TNum1FjJucTeukeJ4dkWZFFT1QlUN8P8Tp2koB7g9uHBMtTu7diltP7cGHCzfxyGc/ex3HRKCde4sZm5lFcUkpE0dbUUWvBLJj/XUR6amqa1V1AnA6cG/oopna7tKjO3FBejue+moFb+Ws9zqOiSAHSkq5+tW5rMor5JmL0+icbEUVvRLIIb4vA1PF6XDMARoAdgqyqTIR4Z9n9WHttiJueXsB7ZomMLCjFWs0lbv7/SXM+DmP+8/uy5FdrKiilwLZsT7dLft+EfAl8D5wSqiCmehQVqyxXZMELnspm9z86lws00SDzJm5TJ61hkuP7sifB7b3Ok7UC3ifiKouUNXJqjpJVbdUPoUxh5aUUJeJozNQcIo1FlmxRlOxr37awl3vLebEni255VQrqhgOrHaWCQupzRN57uI01m0r4spXc6xYo/mdnzbt4ppX59KjVSMeH97fiiqGCWtETNgY1KkZ95/dj+9XbOWOd61Yo/lV3i6nqGJCXCwTRltRxXBia8KElXPTUliVt5unv15J5+RELjm6k9eRjMf2Fpdw6eRsthbu443LhtA6yYoqhhNrREzYufHk7qzOL+TeD5eS2iyRE3tZscZoparc+MZ85q3bwbMXH07flCSvI5lyrDvLhJ2YGOGR8/vTt20S1742l8W/FHgdyXjk0c+X8/6Cjdw8tAdD+1hRxXBkjYgJS/XjYnlxZDpJ9etySWa2FWuMQtPmbuCJL5ZzXloKlx9j3ZrhyhoRE7ZaNHKKNRbsKeYSK9YYVbJzt/F/by5gUMem3DusrxVVDGPWiJiw1qtNI54YPoCFGwq4fqoVa4wGa7cWMe6lHNo2qc+zF6cRV8e+psKZrR0T9k7s1ZLbT+vJx4s38dCnP3kdx4RQwZ5ixmRmUVKqTBiVThMrqhj27OgsExHGHtWRVfmFPPP1Sjo1T+S89HZeRzJB5hRVnENufiGTxw6kkxVVjAjWiJiIICLcdUZv1m4t4rZ3FtKuaQJHdGrmdSwTJKrK+PcW8+3yfB44py9DOltRxUhh3VkmYtSNjeE/Fx1O+6YJXP5yDqutWGOt8d/vc3n5h7Vc9odOXJBhRRUjiTUiJqIk1XeKNQowdpIVa6wNvly2mXs+WMLJvVpy89AeXscxAbJGxEScDs0SeX5kOuu37+GKV6xYYyRbunEn17w6l15tGvHY8P7EWFHFiGONiIlIGalNuf/svsxcuZV/TFtkxRoj0JZdexk7KYsG8XV4cWQGCXG2izYS2VozEeuctBRW5xfy1Fcr6JScyLg/dPY6kvGTU1Qxh+1Fxbxx+WBaJcV7HclUkTUiJqL97aRurM4v5P6PlpHaLJGTe7fyOpKpRGmpcsPr81mwfgfPXpxGn7ZWVDGSWXeWiWgxMcK/zz+MfimN+etr81i0wYo1hrtHPvuZDxZu5JahPTjFGv2IZ42IiXjxdWN5YWQaTRKcYo2bCqxYY7h6e856nvpqBRekt2PcH6yoYm1gjYipFVo0jGfC6Ax27S3mkslZFO0/4HUkU05W7jZueWshgzs1459n9bGiirWE542IiFwvIotFZJGITBGReBHpKCI/ishyEZkqIlZAx1SqZ+tGPHnhAJb8stOKNYaZNVsLGTc5mxQrqljreLomRaQtcC2Qrqp9gFhgOPAA8KiqdgW2A2O9S2kiyfE9WvL3P/bik8WbefATK9YYDgr2FDNmUhYKTBydQVJCXa8jmSAKh58DdYD6IlIHSAA2AscDb7rPZwJneZTNRKC/HJnKRYPa8+w3K3k9e53XcaJacUkpV76Sw9ptRTx3cRqpzRO9jmSCzNNGRFU3AA8Da3EajwIgB9ihqmWd2uuBthVNLyLjRCRbRLLz8vJqIrKJACLC+DN6c3TX5tz29kJmrdzqdaSopKrc8e5ivl+xlfuG9WWQFcyslbzuzmoCnAl0BNoAicCpFYxaYee2qj6vqumqmp6cnBy6oCbi1I2N4akLDye1eaIVa/TIhO9WM2X2Wq44trOV7q/FvO7OOhFYrap5qloMvA0MARq73VsAKcAvXgU0kSupfl0mjsogNkYYMymLHUX7vY4UNT5fspl7P1zKqX1acdPJ3b2OY0LI60ZkLXCEiCSIc7zfCcAS4CvgXHecUcC7HuUzEa59swSeH5HGhu17uPzlHPYfsGKNobbkl51c+9pc+rZN4pHzrahibef1PpEfcXagzwEWunmeB24G/iYiK4BmwATPQpqIl57alAfP7ccPq7bx92kLrVhjCG3ZuZexmVkk1a/LCyPTqR8X63UkE2Ke185S1TuBO8sNXgUM9CCOqaXOGtCWVXm7eeLLFXRKbsDlx1ixxmDbs7+ESyZnU7DHKarYspEVVYwGnjcixtSU60/qxqr8Qh742CnWOLSP1W0KltJS5W+vz2PhhgJeGJFO7zZWVDFaeL1PxJgaIyI8fN5hHJbSmOunWrHGYHr405/4aNEmbj+tJyf2aul1HFODrBExUcUp1phO08Q4xmZmWbHGIHgjex1Pf72SPw9sz9ijOnodx9Qwa0RM1EluWI8Jo9Mp3FfC2MwsCvdZscaq+nHVVm57ZyFHdmnG3Wf2tqKKUcgaEROVerRyijUu3biT66bOo8SKNQYsN7+Qy17OoV3TBJ6+MI26sfZ1Eo1srZuodVz3Ftzxp158tmQzD3y8zOs4EaWgyCmqKMB/rahiVLOjs0xUGzUklZV5hTw/YxWdmicyfGB7ryOFveKSUq54JYd124t45ZIj6NDMiipGM2tETFQTEe48vRdrthXx92mLaN80gSFdmnsdK2ypKv+YtoiZK7fy7/MOY2DHpl5HMh6z7iwT9erExvDUhQPo6BZrXJm32+tIYevFb1fzWtY6rjquM+ekpXgdx4QBa0SMARrF12Xi6AzqxsYwZlIW2wutWGN5ny7exH0fLeW0vq244SQrqmgc1ogY42rXNIHnR6axsWAvl72cw74DJV5HChuLNhTw19fm0a9tEv8+z4oqml9ZI2KMj7QOTXno3H7MXr2N295eZMUagU0Fe7kkM5smCXV5YZQVVTS/ZTvWjSnnzP5tWZVXyONfLKdzi0SuPLaL15E8U7T/AJdMzmLX3mLeuHwILRpaUUXzW9aIGFOB607syur8Qh78+Cc6Nkvk1L6tvY5U40pLleunzmPJLzt5YWQ6vdo08jqSCUPWnWVMBUSEB8/tx+HtG3P96/NYsH6H15Fq3IOf/MQnizdz+x97cUJPK6poKmZbIsYcRHzdWJ4bkc5Z//meC1/4kdZJ0dOVU6rKyrxCLhrUnjFHpnodx4Qxa0SMOYTkhvXIHDOQ/3y1IuqO1jqpVytuOLmbFVU0h2SNiDGV6NKiAY9e0N/rGMaEJdsnYowxpsqsETHGGFNl1ogYY4ypMmtEjDHGVJk1IsYYY6rMGhFjjDFVZo2IMcaYKrNGxBhjTJVJbSl1LSJ5wJoamFVzIL8G5hMoyxUYyxUYyxWYSMrVQVWTq/qCtaYRqSkikq2q6V7nKM9yBcZyBcZyBSaacll3ljHGmCqzRsQYY0yVWSMSuOe9DnAQliswliswliswUZPL9okYY4ypMtsSMcYYU2XWiBhjjKmyqG5ERKSxiLwpIstEZKmIDC73fA8RmSUi+0TkxnLP/VVEFonIYhG5zmf4eBHZICLz3NtpNZz5IhFZ4N5mishh4TJ/ERkqIj+JyAoRucVn+CQRWe2zzKp1BahqZpwoIltEZFF1MoQyS7DfY1XNKCLtROQrd5rFIvLX6uQIVZYwWl7xIjJbROa7Ge+qTo5QZQn486iqUXsDMoFL3PtxQONyz7cAMoB7gRt9hvcBFgEJOFeH/Bzo6j433ndcDzIPAZq4908FfgyH+QOxwEqgkzvdfKCX+9wk4FyvM7qP/wAcDiwK1yzBfo9VY522Bg537zcEfi5bp+GUJYyWlwAN3Pt1gR+BI8ItS6Cfx6i9PK6INML5kI4GUNX9wH7fcVR1C7BFRP5YbvKewA+qWuS+1jfAMODBMMg80+fhD0BKmMx/ILBCVVe5r/UacCawJFj5gpARVZ0hIqm1LUsoMqrqRmCje3+XiCwF2lLFdRpOWUKUUYHd7vC67q3KRzaFS5Zo7s7qBOQB/xWRuSLyoogk+jntIuAPItJMRBKA04B2Ps9f7W4+ThSRJh5mHgt8FCbzbwus83luvTuszL3uMntUROp5lDHYQpklWO+xoGR0G7sBOL9owzFLWCwvEYkVkXnAFuAzVfVseVWSxf/PY7A28SLtBqQDB4BB7uPHgX8eZNzxlNscdlfIHGAG8CzwqDu8JU7XTQxON9hEjzIfBywFmoXD/IHzgBd9nh8BPOneb42zeV0PZ/P8Di+XEZBKELqzQpUlmO+xIGVsAOQAZ4fB8vpdlnBbXu5zjYGvgD7hliXQz2O1PiSRfANaAbk+j48GPjjIuOM5RJ8qcB9wZQXDg/JlFGhmoB/O/oduXiyziuYPDAY+8Xl8K3BrBdMeC7zv5TIK1nqriSzVzVrdjDjdIJ8Af/N6efmTxevlVW6cOw/1vRIOWfz5PEZtd5aqbgLWiUh3d9AJBNB/KiIt3L/tgbOBKe7j1j6jDcPp+goKfzK7ed4GRqjqz8GadxDmnwV0FZGOIhIHDAemu9O0dv8KcBbVWGZeL6OayBLM91h1MrrrawKwVFUfqWqGUGcJo+WVLCKN3fv1gROBZeGWJeDPY3V/PUTyDegPZAMLgGlAE+By4HKfln49sBPY4d5v5D73rbvC5gMn+LzmS8BC9zWnA61rOPOLwHZgnnvLDpf54+w7+hnnV9HtPsO/dJfZIuBl3KNGPMo4BWcHbbG7vseGW5Zgv8eqmhE4Cmdn7AKf504LtyxhtLz6AXPd6RZRjW7bUGYhwM+jlT0xxhhTZVHbnWWMMab6rBExxhhTZdaIGGOMqTJrRIwxxlSZNSLGGGOqzBoRE/FEpMSn4ui8UNebqmkiMkBEXvR5PNStwLrM/X+nuucDlFVgPbfc9LvLv6bPc3EiMkNEoraOnqkee+OY2mCPqh60XLWI1FHVAzUZKMhuA+4BEJE+wJPAGaq61B12Bs6Z2GsDfWFV3S8iXwAXAK8EK7CJHrYlYmolERktIm+IyHvAp+6wm0Qkyy0s53v9hNvFuc7J5yIyRdxrx4jI1yKS7t5vLiK57v1YEXnI57Uuc4cf605Tdn2HV9yzfhGRDHGu5zDf3YpoKCLfis+1GkTkexHpV+7/aAj0U9X57qCbgfvKGhAAVZ2uqjP8WCZ3+2ytbRCR/7pPTQMuCmgBG+OyLRFTG9R3q5ECrFbVYe79wThfwNtE5GSgK05JegGmi8gfgEKcEiwDcD4Pc3AK+B3KWKBAVTPcCqffi8in7nMDgN7AL8D3wJEiMhuYClygqlnilPDeg3M28WjgOhHpBtRT1QXl5pXOb8tO9AYeriTfQyLy9/IDVfUO4A4RScKpuPCU+9QinOvmGBMwa0RMbXCw7qzPVHWbe/9k9zbXfdwAp1FpCLyjv14bZrof8zsZ6Oez7yHJfa39wGxVXe++1jycbqYCYKOqZgGo6k73+TeAf4jITcAYnIsBldcap9z374hIM+ALnIujPa+qZY3LTar6ps94u33uC0631aOqmuPmKRGR/SLSUFV3+fH/G/M/1oiY2qzQ574A96vqc74jiHNp44PV/jnAr12+8eVe6xpV/aTcax0L7PMZVILzGZOK5qGqRSLyGc7Fuc7H2eoob0+5eS/GudrhfFXdCvR3u98aHOR/KG88sF5V/1tueD1gr5+vYcz/2D4REy0+AcaISAMAEWkrTiXmGcAwEanv7n843WeaXCDNvX9uude6QkTquq/VTQ59MaBlQBsRyXDHb+hzNNSLwBNAls9Wk6+lQBefxw8Ct4tIT59hCYeY9/+IyJ+Ak4Bryw1vBuSparE/r2OML9sSMVFBVT91v3hnufu6dwMXq+ocEZmKU+F0Dc6+gjIPA6+LyAicyqZlXsTppprjdg/l4ZTMPti894vIBcCTbtntPTilt3erao6I7ATKbxmUTbtMRJLKuppUdaGI/BWY7DZ6W3GOyrrTj8VwA9AGmO0ug+nufpLjgA/9mN6Y37Eqvsb4EJHxOF/ule28Dtb82gBfAz1UtfQg41wP7FLVFyt6PggZ3sa5QNhPoXh9U7tZd5YxHhGRkTjXAb/9YA2I6xl+u68lmBnigGnWgJiqsi0RY4wxVWZbIsYYY6rMGhFjjDFVZo2IMcaYKrNGxBhjTJVZI2KMMabK/h/bVkay4i/xmwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_by_id(rabi_fit_run_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax, clb = plot_by_id(min_fit_run_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.7" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/fitting/Introducing the Fitter.ipynb b/examples/fitting/Introducing the Fitter.ipynb new file mode 100644 index 00000000..969ec0b9 --- /dev/null +++ b/examples/fitting/Introducing the Fitter.ipynb @@ -0,0 +1,634 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/logger.py:16: UserWarning: The logger.py of qdev-wrappers is deprecated and will be removed soon. Please use the logger of QCoDeS instead.\n", + " warnings.warn('The logger.py of qdev-wrappers is deprecated and will be '\n" + ] + } + ], + "source": [ + "from qdev_wrappers.fitting.base import Fitter\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import scipy.fftpack as fftpack" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is a Fitter?\n", + "It's an instrument that can perform fits for you. It includes\n", + "- **fit_parameters channel** for parameters which are learned by performing the fit\n", + "- **success** parameter to indicate if the fit was successful (0 or 1)\n", + "- **fit function** which given data should perfom the fit and (minimally) update the fit_parameters and success\n", + "- **metadata dictionary** about the fitter with keys (minimally) 'method', 'name', 'function', and 'fit_parameters'. 'method' and 'name' should have string values, 'fit_parameters' is a list of the names of the fit_parameters and 'function' is a dictionary with (minimally) 'str' which should have a string value describing the fit function\n", + "\n", + "On initialisation it requires a name, fit_parameter specifications in a dictionary and function metadata dictionary\n", + "\n", + "### Example...\n", + "...of writing your own fitter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class MinimumFitter(Fitter):\n", + " def __init__(self, name='MinimumFitter'):\n", + " fit_parameters = {'location': {'label': 'location of minimum'},\n", + " 'value': {'label': 'minimum value'}}\n", + " function_metadata = {'str': 'find minimum (simple)'}\n", + " super().__init__(name, fit_parameters,\n", + " method_description='SimpleMinimum',\n", + " function_description=function_metadata)\n", + "\n", + " def fit(self, measured_values, experiment_values):\n", + " self._check_fit(measured_values, experiment_values)\n", + " min_index = np.argmin(measured_values)\n", + " self.fit_parameters.location._save_val(experiment_values[min_index])\n", + " self.fit_parameters.value._save_val(measured_values[min_index])\n", + " self.success._save_val(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "minimum_fitter = MinimumFitter()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check it has the fit_parameter channel with the specified parameters on it and a success parameter initialised in 0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fit_parameters: ['location', 'value']\n", + "success = 0\n" + ] + } + ], + "source": [ + "print('fit_parameters: ', list(minimum_fitter.fit_parameters.parameters.keys()))\n", + "print('success = ', minimum_fitter.success())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try finding the minimum of some 'data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "location: 7\n", + "value: 1\n", + "\n", + "success: 1\n" + ] + } + ], + "source": [ + "data = np.array([10, 9, 8, 3, 6, 3, 2, 1, 3, 4, 6, 7, 8])\n", + "x = np.arange(len(data))\n", + "minimum_fitter.fit(data, x)\n", + "print('\\n'.join(f'{k}: {v()}' for k, v in minimum_fitter.fit_parameters.parameters.items()))\n", + "print('\\nsuccess: ', minimum_fitter.success())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'some measured value')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "plt.plot(x, data, label='data')\n", + "x = minimum_fitter.fit_parameters.location()\n", + "y = minimum_fitter.fit_parameters.value()\n", + "plt.plot(x, y, 'r.', label=f'minimum @ ({x}, {y})')\n", + "plt.legend()\n", + "plt.xlabel('some variable')\n", + "plt.ylabel('some measured value')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Handily this fitter is already in the module so you can just import it as below for the same effect :)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from qdev_wrappers.fitting.fitters import MinimumFitter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Least Squares fitting\n", + "\n", + "If we want to do a least squares fit it gets a little bit more complicated so we wrote a subclass which has the additional features:\n", + "- **variance_parameters channel** to store the variance on each fit parameter\n", + "- **initial_value_parameters channel** to store the initial values used in the least squares optimization\n", + "- **evaluate function** function to be fit to. This will just evaluate the metadata['function']['np'] string with the fit parameters and experiment parameter input\n", + "- optionally a **guess function** to generate the initial_values for the least squares fit if none provided\n", + "- the addition of variance_parameters, initial_value_parameters, and the 'np' description of the function to the **metadata**\n", + "\n", + "You need again need subclass this, specifying the fit parameters (as before) and function metadata. Note that the 'np' function metadata string is evaluated and optimised over so it must be runnable once all the fit_parameters and 'x' are specified. The order of the fit_parameters must match the order of appearance of the parameters in this funciton.\n", + "\n", + "### Example" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from qdev_wrappers.fitting.base import LeastSquaresFitter\n", + "\n", + "class CosFitter(LeastSquaresFitter):\n", + " def __init__(self, name='CosFitter'):\n", + " fit_parameters = {'a': {'label': '$a$'},\n", + " 'w': {'label': r'$\\omega$', 'unit': 'Hz'},\n", + " 'p': {'label': r'$\\phi$'},\n", + " 'c': {'label': '$c$', 'unit': ''}}\n", + " function_metadata = {'str': r'$f(x) = a\\cos(\\omega x + \\phi)+c$',\n", + " 'np': 'a * np.cos(w * x + p) + c'}\n", + " super().__init__(name, fit_parameters, function_metadata)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "cos_fitter = CosFitter()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again check out the channels and parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fit_parameters: ['a', 'w', 'p', 'c']\n", + "variance_parameters: ['a_variance', 'w_variance', 'p_variance', 'c_variance']\n", + "initial_value_parameters: ['a_initial_value', 'w_initial_value', 'p_initial_value', 'c_initial_value']\n", + "success = 0\n" + ] + } + ], + "source": [ + "print('fit_parameters: ', list(cos_fitter.fit_parameters.parameters.keys()))\n", + "print('variance_parameters: ', list(cos_fitter.variance_parameters.parameters.keys()))\n", + "print('initial_value_parameters: ', list(cos_fitter.initial_value_parameters.parameters.keys()))\n", + "print('success = ', cos_fitter.success())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try finding parameter values of some 'data'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "a, w, p, c = 1., 0.5, np.pi/4, 0.3\n", + "x = np.arange(30)\n", + "data = a * np.cos(w * x + p) + c + (np.random.random(len(x)) - 0.5) / 3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual_values:\n", + "a: 1.0\n", + "w: 0.5\n", + "p: 0.785\n", + "c: 0.3\n", + "\n", + "fit_values:\n", + "a: 0.958\n", + "w: 0.499\n", + "p: 0.764\n", + "c: 0.308\n", + "\n", + "fit_variances:\n", + "a_variance: 0.000622\n", + "w_variance: 7.75e-06\n", + "p_variance: 0.00221\n", + "c_variance: 0.000304\n", + "\n", + "success: 1\n" + ] + } + ], + "source": [ + "cos_fitter.fit(data, x, initial_values=(1, 0.4, np.pi/2, 0.1))\n", + "print('actual_values:')\n", + "print(f'a: {a:.3}\\nw: {w:.3}\\np: {p:.3}\\nc: {c:.3}\\n')\n", + "print('fit_values:')\n", + "print('\\n'.join(f'{k}: {v():.3}' for k, v in cos_fitter.fit_parameters.parameters.items()))\n", + "print('\\nfit_variances:')\n", + "print('\\n'.join(f'{k}: {v():.3}' for k, v in cos_fitter.variance_parameters.parameters.items()))\n", + "print('\\nsuccess: ', cos_fitter.success())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'some measured value')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "plt.plot(x, data, label='data')\n", + "fit_values = [v() for v in cos_fitter.fit_parameters.parameters.values()]\n", + "plt.plot(x, cos_fitter.evaluate(x, *fit_values), label='fit')\n", + "plt.legend()\n", + "plt.xlabel('some variable')\n", + "plt.ylabel('some measured value')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that I could equally have generated the pretend data by using the 'evaluate' function on the cosine fitter and adding noise to it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fit failure\n", + "As you can see I used the initial_values kwarg to provide a guess for the fitter (if None provided then all will be 1). If I make my guess a lot worse the fit will be much worse. The fit is still called a 'success' unless the least-squares minimization fails. However looking at the variance parameters tells us it's not very good! Have a play with the initial_values and see if you can get it to fail. It is possible to condition the fit success on whether any of the variances are infinite using the variance_limited kwarg.\n", + "\n", + "If the fit fails the fit_values and variance values are stored as nan" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual_values:\n", + "a: 1.0\n", + "w: 0.5\n", + "p: 0.785\n", + "c: 0.3\n", + "\n", + "fit_values:\n", + "a: 0.00908\n", + "w: 1e+04\n", + "p: 1e+07\n", + "c: 0.291\n", + "\n", + "standard deviation:\n", + "a: 0.19\n", + "w: 0.000622\n", + "p: 0.0113\n", + "c: 0.131\n", + "\n", + "success: 1\n" + ] + } + ], + "source": [ + "initial_values = (1e10, 1e4, 1e7, 1e10)\n", + "\n", + "cos_fitter.fit(data, x, initial_values=initial_values, variance_limited=True)\n", + "print('actual_values:')\n", + "print(f'a: {a:.3}\\nw: {w:.3}\\np: {p:.3}\\nc: {c:.3}\\n')\n", + "print('fit_values:')\n", + "print('\\n'.join(f'{k}: {v():.3}' for k, v in cos_fitter.fit_parameters.parameters.items()))\n", + "print('\\nstandard deviation:')\n", + "print('\\n'.join(\n", + " f'{k.split(\"_\")[0]}: {np.sqrt(v()):.3}' for k, v in cos_fitter.variance_parameters.parameters.items()))\n", + "print('\\nsuccess: ', cos_fitter.success())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to limit based on the r^{2} value which measures the distance of the fit from the estimate and is 1 if the fit matches the data exactly and is < 1 otherwise. Try playing with the r2 limit to make the fit fail" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual_values:\n", + "a: 1.0\n", + "w: 0.5\n", + "p: 0.785\n", + "c: 0.3\n", + "\n", + "fit_values:\n", + "a: nan\n", + "w: nan\n", + "p: nan\n", + "c: nan\n", + "\n", + "standard deviation:\n", + "a: nan\n", + "w: nan\n", + "p: nan\n", + "c: nan\n", + "\n", + "success: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/fitting/base.py:230: UserWarning: Fit failed due to: r2 0.98 exceeds limit 0.99\n", + " warnings.warn('Fit failed due to: ' + message)\n" + ] + } + ], + "source": [ + "r2_limit = 0.99\n", + "\n", + "cos_fitter.fit(data, x, initial_values=(1, 0.4, np.pi/2, 0.1), r2_limit=r2_limit)\n", + "print('actual_values:')\n", + "print(f'a: {a:.3}\\nw: {w:.3}\\np: {p:.3}\\nc: {c:.3}\\n')\n", + "print('fit_values:')\n", + "print('\\n'.join(f'{k}: {v():.3}' for k, v in cos_fitter.fit_parameters.parameters.items()))\n", + "print('\\nstandard deviation:')\n", + "print('\\n'.join(\n", + " f'{k.split(\"_\")[0]}: {np.sqrt(v()):.3}' for k, v in cos_fitter.variance_parameters.parameters.items()))\n", + "print('\\nsuccess: ', cos_fitter.success())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding a guess function\n", + "Instead of always having to provide initial_values or hoping that 1s are good enough I can use a function to give good initial guesses. Adding this to the fitter means that if I don't provide initial_values this function will be used to generate initial values." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a_initial_value: 1.0\n", + "w_initial_value: 1.0\n", + "p_initial_value: 1.0\n", + "c_initial_value: 1.0\n" + ] + } + ], + "source": [ + "cos_fitter.fit(data, x)\n", + "print('\\n'.join(f'{k}: {v():.3}' for k, v in cos_fitter.initial_value_parameters.parameters.items()))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def guess_cosine(y, x):\n", + " \"\"\"Guess for y = a * cos(wx + p) + c\"\"\"\n", + " c = y.mean()\n", + " a = (y.max() - y.min()) / 2\n", + " # Get initial guess for frequency from a fourier transform\n", + " yhat = fftpack.rfft(y - y.mean())\n", + " idx = (yhat ** 2).argmax()\n", + " freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi))\n", + " w = freqs[idx]\n", + " if (y[0] - c) / a > 1:\n", + " p = 0\n", + " elif (y[0] - c) / a < -1:\n", + " p = np.pi\n", + " else:\n", + " p = np.arccos((y[0] - c) / a)\n", + " return [a, w, p, c]\n", + "\n", + "cos_fitter.guess = guess_cosine" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a_initial_value: 1.07\n", + "w_initial_value: 0.419\n", + "p_initial_value: 0.971\n", + "c_initial_value: 0.29\n" + ] + } + ], + "source": [ + "cos_fitter.fit(data, x)\n", + "print('\\n'.join(f'{k}: {v():.3}' for k, v in cos_fitter.initial_value_parameters.parameters.items()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again you will be pleased to hear that a selection of fitters and a library of guess functions already exist in qdev_wrappers.fitting.fitters and qdev_wrappers.fitting.guess respectively so I can just import the CosineFitter (which uses the guess function above) from there. Also there:\n", + "- T1Fitter\n", + "- BenchmarkingFitter\n", + "- CosineFitter\n", + "- DecayingRabisFitter\n", + "\n", + "Feel free to add more if you make up some useful ones :)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from qdev_wrappers.fitting.fitters import CosFitter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.7" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/fitting/qcodesrc.json b/examples/fitting/qcodesrc.json new file mode 100644 index 00000000..82183945 --- /dev/null +++ b/examples/fitting/qcodesrc.json @@ -0,0 +1,79 @@ +{ + "core": { + "default_fmt": "data/{date}/#{counter}_{name}_{time}", + "register_magic": true, + "db_location": "../fitting.db", + "db_debug": false, + "loglevel": "WARNING", + "file_loglevel": "INFO" + }, + "logger": { + "console_level": "WARNING", + "file_level": "INFO", + "logger_levels": { + "pyvisa": "INFO" + } + }, + "subscription": { + "subscribers": { + "QCPlot": { + "factory": "qcplotutils.qcodes_dataset.QCPlotDatasetSubscriber", + "factory_kwargs": { + "log": false + }, + "subscription_kwargs": { + "min_wait": 0, + "min_count": 1, + "callback_kwargs": {} + } + }, + "Plottr": { + "factory": "plottr.qcodes_dataset.QcodesDatasetSubscriber", + "factory_kwargs": { + "log": false + }, + "subscription_kwargs": { + "min_wait": 0, + "min_count": 1, + "callback_kwargs": {} + } + } + }, + "default_subscribers": [] + }, + "gui": { + "notebook": true, + "plotlib": "all", + "pyqtmaxplots": 100, + "defaultcolormap": "hot" + }, + "plotting": { + "default_color_map": "magma", + "rasterize_threshold": 5000, + "auto_color_scale": { + "enabled": false, + "cutoff_percentile": [ + 0.5, + 0.5 + ], + "color_over": "#a1c4fc", + "color_under": "#017000" + } + }, + "user": { + "scriptfolder": ".", + "mainfolder": ".", + "transmon_db_location": "../transmons_data/transmons.db", + "experiment_db_location": "../experiment_data/test_experiments.db" + }, + "station_configurator": { + "enable_forced_reconnect": false, + "default_folder": ".", + "default_file": "instrument_config.yml" + }, + "GUID_components": { + "location": 0, + "work_station": 0, + "sample": 0 + } +} \ No newline at end of file diff --git a/examples/fitting/qcodesrc_schema.json b/examples/fitting/qcodesrc_schema.json new file mode 100644 index 00000000..417fdea9 --- /dev/null +++ b/examples/fitting/qcodesrc_schema.json @@ -0,0 +1,312 @@ +{ + "$schema": "http://json-schema.org/draft-04/schema#", + "type": "object", + "description": "schema for a qcodes config file", + "properties": { + "core": { + "description": "controls core settings of qcodes", + "type": "object", + "properties": { + "default_fmt": { + "type": "string", + "description": "default location formatter", + "default": "data/{date}/#{counter}_{name}_{time}" + }, + "loglevel": { + "type": "string", + "description": "deprecated - use logger.console_level", + "default": "DEBUG", + "enum": [ + "CRITICAL", + "ERROR", + "WARNING", + "INFO", + "DEBUG" + ] + }, + "file_loglevel": { + "type": "string", + "description": "deprecated - use logger.file_level", + "default": "INFO", + "enum": [ + "CRITICAL", + "ERROR", + "WARNING", + "INFO", + "DEBUG" + ] + }, + "register_magic": { + "description": "Register QCoDeS magic when in iPython. Can be set to True, False, or a list of magic commands to be registered", + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "array" + } + ], + "default": true + }, + "db_debug": { + "description": "Use debugging mode in sqlite ", + "type": "boolean", + "default": false + }, + "db_location": { + "type": "string", + "description": "location of the database", + "default": "./experiments.db" + } + }, + "required": [ + "db_location" + ] + }, + "logger": { + "description": "controls all settings related to the logging module `qcodes.utils.logger`.", + "type": "object", + "properties": { + "console_level": { + "type": "string", + "description": "control logging level of console output", + "default": "DEBUG", + "enum": [ + "CRITICAL", + "ERROR", + "WARNING", + "INFO", + "DEBUG" + ] + }, + "file_level": { + "type": "string", + "description": "control logging level of file output.", + "default": "INFO", + "enum": [ + "CRITICAL", + "ERROR", + "WARNING", + "INFO", + "DEBUG" + ] + }, + "logger_levels": { + "type": "object", + "additionalProperties": { + "type": "string" + }, + "default": { + "pyvisa": "INFO" + } + } + }, + "required": [ + "console_level", + "file_level" + ] + }, + "subscription": { + "type": "object", + "description": "Controls how subscriptions to the dataset, for e.g. live plotting, are handled. Here subscribers can be registered so that they will be available through `DataSet.subscribe_from_config`. Additionally default subscribers for every DataSet that is generated by the `Measurement` object can be defined.", + "properties": { + "subscribers": { + "type": "object", + "description": "List of available pre-configured subscribers. The names of the subscriber entries are available as arguments of the `DataSet.subscribe_from_config` method and can be used in this conifg in the `subscription.default_subscribers` property.", + "additional_properties": { + "type": "object", + "properties": { + "factory": { + "type": "string", + "description": "Full location of a factory class in the form '..'. The factory class has the following footprint:\n class Subscriber:\n def __init__(self, dataset, **factory_kwargs):\n def __call__(self, results, length, state):\n" + }, + "factory_kwargs": { + "type": "object", + "additionnal_properties": {}, + "description": "kwargs that get passed to the factory when creating a new subscriber." + }, + "subscription_kwargs": { + "type": "object", + "description": "kwargs with which subscribe will be called.", + "properties": { + "min_wait": { + "type": "integer", + "default": 0 + }, + "min_count": { + "type": "integer", + "default": 1 + }, + "callback_kwargs": { + "description": "kwargs passed to the callback.", + "type": "object", + "additionnal_properties": {}, + "default": {} + } + }, + "default_subscribers": { + "description": "List of default subscribers as defined in 'subscription.subscribers'. All subscribers in the list will be added to any DataSet created by the `Measurement.run()` call.", + "type": "array", + "items": { + "type": "string" + }, + "default": [] + } + } + } + } + } + } + }, + "gui": { + "type": "object", + "description": "controls legacy gui of qcodes", + "properties": { + "notebook": { + "description": "Use notebook frontend", + "type": "boolean", + "default": true + }, + "plotlib": { + "description": "Plotting library set to null to run without plotting", + "type": [ + "string", + "null" + ], + "enum": [ + "QT", + "matplotlib", + "all", + null + ], + "default": "all" + }, + "pyqtmaxplots": { + "description": "Maximum number of PyQtPlots to automatically keep in memory", + "type": "integer", + "default": 100 + } + }, + "required": [ + "notebook", + "plotlib", + "pyqtmaxplots" + ] + }, + "plotting": { + "type": "object", + "description": "controls plotting functions i.e. `plot_by_id`", + "properties": { + "default_color_map": { + "description": "Default colormap to use for `plot_by_id`.", + "type": "string", + "default": "viridis" + }, + "rasterize_threshold": { + "description": "Scatter plots and heatmaps with more than this number of points will have their data rasterized by default when saved in vector format", + "type": "integer", + "default": 5000 + }, + "auto_color_scale": { + "type": "object", + "description": "Control of a auto color scale, that scales such that potential outliers of the data will not be included in the min/max range.", + "properties": { + "enabled": { + "description": "Enable automatic color scaling", + "type": "boolean", + "default": false + }, + "cutoff_percentile": { + "description": "Upper and lower percentage of datapoints that may maximally be discarded by the auto color scale. For example for [1,1] the auto color scale will in a set of 10 0000 points not clip more than the 100 points of lowest and highest value.", + "type": "array", + "items": { + "type": "number" + }, + "default": [ + 0.5, + 0.5 + ] + }, + "color_over": { + "description": "Matplotlib color representing the datapoints clipped by the upper limit", + "default": "white" + }, + "color_under": { + "description": "Matplotlib color representing the datapoints clipped by the lower limit", + "default": "grey" + } + } + } + } + }, + "user": { + "type": "object", + "properties": { + "scriptfolder": { + "type": "string", + "default": "/", + "description": "Location of scripts for general experiments" + }, + "mainfolder": { + "type": "string", + "default": "", + "description": "Location of experiments" + } + }, + "description": "Optional feature for qdev-wrappers package: controls user settings of qcodes" + }, + "station_configurator": { + "type": "object", + "properties": { + "enable_forced_reconnect": { + "type": "boolean", + "default": false, + "description": "if set to true, on instantiation of an existing instrument, the existing will be disconnected." + }, + "default_folder": { + "type": [ + "string", + "null" + ], + "default": null, + "description": "default folder where to look for a station configurator config file" + }, + "default_file": { + "type": [ + "string", + "null" + ], + "default": null, + "description": "default file name, specifying the file to load, when none is specified. Can be a relative or absolute path" + } + }, + "description": "Optional feature for qdev-wrappers package: Setting for the StationConfigurator." + }, + "GUID_components": { + "type": "object", + "properties": { + "location": { + "type": "integer", + "default": 0, + "description": "Geographical location code" + }, + "work_station": { + "type": "integer", + "default": 0, + "description": "Work station identification code" + }, + "sample": { + "type": "integer", + "default": 0, + "description": "Sample identification code" + } + }, + "description": "Identifiers for creating a GUID per run in the dataset database." + } + }, + "required": [ + "gui", + "core", + "GUID_components" + ] +} \ No newline at end of file diff --git a/examples/transmons_data/1_0.png b/examples/transmons_data/1_0.png new file mode 100644 index 00000000..d58ab4d9 Binary files /dev/null and b/examples/transmons_data/1_0.png differ diff --git a/examples/transmons_data/1_1.png b/examples/transmons_data/1_1.png new file mode 100644 index 00000000..d0df619e Binary files /dev/null and b/examples/transmons_data/1_1.png differ diff --git a/examples/transmons_data/2_0.png b/examples/transmons_data/2_0.png new file mode 100644 index 00000000..8077070e Binary files /dev/null and b/examples/transmons_data/2_0.png differ diff --git a/examples/transmons_data/3_0.png b/examples/transmons_data/3_0.png new file mode 100644 index 00000000..53816d5a Binary files /dev/null and b/examples/transmons_data/3_0.png differ diff --git a/examples/transmons_data/3_1.png b/examples/transmons_data/3_1.png new file mode 100644 index 00000000..ba478b39 Binary files /dev/null and b/examples/transmons_data/3_1.png differ diff --git a/examples/transmons_data/4_0.png b/examples/transmons_data/4_0.png new file mode 100644 index 00000000..06eb3d3e Binary files /dev/null and b/examples/transmons_data/4_0.png differ diff --git a/examples/transmons_data/5_0.png b/examples/transmons_data/5_0.png new file mode 100644 index 00000000..32419b2f Binary files /dev/null and b/examples/transmons_data/5_0.png differ diff --git a/examples/transmons_data/5_1.png b/examples/transmons_data/5_1.png new file mode 100644 index 00000000..3e9deb36 Binary files /dev/null and b/examples/transmons_data/5_1.png differ diff --git a/examples/transmons_data/6_0.png b/examples/transmons_data/6_0.png new file mode 100644 index 00000000..0211ddb1 Binary files /dev/null and b/examples/transmons_data/6_0.png differ diff --git a/examples/transmons_data/6_1.png b/examples/transmons_data/6_1.png new file mode 100644 index 00000000..71c2f298 Binary files /dev/null and b/examples/transmons_data/6_1.png differ diff --git a/examples/transmons_data/7_0.png b/examples/transmons_data/7_0.png new file mode 100644 index 00000000..7e19f7db Binary files /dev/null and b/examples/transmons_data/7_0.png differ diff --git a/examples/transmons_data/8_0.png b/examples/transmons_data/8_0.png new file mode 100644 index 00000000..70e4233f Binary files /dev/null and b/examples/transmons_data/8_0.png differ diff --git a/examples/transmons_data/transmons.db b/examples/transmons_data/transmons.db new file mode 100644 index 00000000..a5c60caa Binary files /dev/null and b/examples/transmons_data/transmons.db differ diff --git a/examples/transmons_data/transmons_db_look_up.html b/examples/transmons_data/transmons_db_look_up.html new file mode 100644 index 00000000..0e35417c --- /dev/null +++ b/examples/transmons_data/transmons_db_look_up.html @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
descriptionrun_id
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readout_fidelity_cavity_power_frequency8
\ No newline at end of file diff --git a/qdev_wrappers/dataset/doNd.py b/qdev_wrappers/dataset/doNd.py index 2d60c35a..b68e3a07 100644 --- a/qdev_wrappers/dataset/doNd.py +++ b/qdev_wrappers/dataset/doNd.py @@ -54,12 +54,21 @@ def do0d(*param_meas: Union[_BaseParameter, Callable[[], None]], dataid = datasaver.run_id if do_plot is True: - ax, cbs = _save_image(datasaver) + plt.ioff() + start = time.time() + axes, cbs = plot_by_id(dataid) + stop = time.time() + print(f"plot by id took {stop-start}") + plt.ion() + exp_name = datasaver._dataset.exp_name + sample_name = datasaver._dataset.sample_name + save_image(axes, exp_name=exp_name, + sample_name=sample_name, run_id=dataid) else: - ax = None, + axes = None, cbs = None - return dataid, ax, cbs + return dataid, axes, cbs def do1d(param_set: _BaseParameter, start: number, stop: number, @@ -136,14 +145,23 @@ def do1d(param_set: _BaseParameter, start: number, stop: number, dataid = datasaver.run_id # convenient to have for plotting if do_plot is True: - ax, cbs = _save_image(datasaver) + plt.ioff() + start = time.time() + axes, cbs = plot_by_id(dataid) + stop = time.time() + print(f"plot by id took {stop-start}") + plt.ion() + exp_name = datasaver._dataset.exp_name + sample_name = datasaver._dataset.sample_name + save_image(axes, exp_name=exp_name, + sample_name=sample_name, run_id=dataid) else: - ax = None, + axes = None, cbs = None if interrupted: raise KeyboardInterrupt - return dataid, ax, cbs + return dataid, axes, cbs def do2d(param_set1: _BaseParameter, start1: number, stop1: number, @@ -235,17 +253,27 @@ def do2d(param_set1: _BaseParameter, start1: number, stop1: number, dataid = datasaver.run_id if do_plot is True: - ax, cbs = _save_image(datasaver) + plt.ioff() + start = time.time() + axes, cbs = plot_by_id(dataid) + stop = time.time() + print(f"plot by id took {stop-start}") + plt.ion() + exp_name = datasaver._dataset.exp_name + sample_name = datasaver._dataset.sample_name + save_image(axes, exp_name=exp_name, + sample_name=sample_name, run_id=dataid) else: - ax = None, + axes = None, cbs = None if interrupted: raise KeyboardInterrupt - return dataid, ax, cbs + return dataid, axes, cbs -def _save_image(datasaver) -> AxesTupleList: +def save_image(axes, exp_name=None, sample_name=None, + run_id=None, name_extension=None, **kwargs) -> AxesTupleList: """ Save the plots created by datasaver as pdf and png @@ -254,35 +282,32 @@ def _save_image(datasaver) -> AxesTupleList: as plot. """ - plt.ioff() - dataid = datasaver.run_id - start = time.time() - axes, cbs = plot_by_id(dataid) - stop = time.time() - print(f"plot by id took {stop-start}") - + run_id = run_id or 0 mainfolder = config.user.mainfolder - experiment_name = datasaver._dataset.exp_name - sample_name = datasaver._dataset.sample_name - storage_dir = os.path.join(mainfolder, experiment_name, sample_name) - os.makedirs(storage_dir, exist_ok=True) - - png_dir = os.path.join(storage_dir, 'png') - pdf_dif = os.path.join(storage_dir, 'pdf') + if exp_name and sample_name: + storage_dir = os.path.join(mainfolder, exp_name, sample_name) + os.makedirs(storage_dir, exist_ok=True) + png_dir = os.path.join(storage_dir, 'png') + pdf_dir = os.path.join(storage_dir, 'pdf') + else: + png_dir = 'png' + pdf_dir = 'pdf' os.makedirs(png_dir, exist_ok=True) - os.makedirs(pdf_dif, exist_ok=True) + os.makedirs(pdf_dir, exist_ok=True) - save_pdf = True - save_png = True + save_pdf = config.user.get('save_pdf', True) + save_png = config.user.get('save_png', True) for i, ax in enumerate(axes): + filename = f'{run_id}' + if name_extension is not None: + filename += '_' + name_extension + filename += f'_{i}' if save_pdf: - full_path = os.path.join(pdf_dif, f'{dataid}_{i}.pdf') + full_path = os.path.join(pdf_dir, filename + '.pdf') ax.figure.savefig(full_path, dpi=500) if save_png: - full_path = os.path.join(png_dir, f'{dataid}_{i}.png') + full_path = os.path.join(png_dir, filename + '.png') ax.figure.savefig(full_path, dpi=500) - plt.ion() - return axes, cbs diff --git a/qdev_wrappers/fitting/base.py b/qdev_wrappers/fitting/base.py new file mode 100644 index 00000000..1de7ee45 --- /dev/null +++ b/qdev_wrappers/fitting/base.py @@ -0,0 +1,231 @@ +import numpy as np +import warnings +from scipy.optimize import curve_fit +from qcodes.instrument.base import Instrument +from qcodes.instrument.channel import InstrumentChannel +from qcodes import validators as vals +from qcodes.instrument.parameter import Parameter + +# TODO: remove r2 limit or make it a saveable parameter (nataliejpg) +# TODO: add fitter for >1 independent and dependent variable (nataliejpg) + + +class FitParameter(Parameter): + """ + Parameter to be used in the fitter which is represented by its name rather + than its full name as this facilitates it being used both before and after + saving in the same way (especially useful when evaluating functions stored + as strings as in the LeastSquaredFitter where the parameter names are + important) + """ + + def __str__(self): + return self.name + + +class Fitter(Instrument): + """ + Instrument which can perform fits on data and has parameters to store the + fit parameter values. Can be used with the fit_by_id and saved datasets + generated can be plotted using plot_fit_by_id helpers. + + Args: + name (str) + fit_parameters (dict): dictionary describing the parameters generated + in the fitting procedure. Keys are used as parameter names and + values are a dict with at keys 'unit' and/or 'label' + method_description (str) (optional): identifier for fitting method + function_description (dict) (optional): dictionary with more detail + about the function. Keys should include 'str' + """ + + def __init__(self, name, + fit_parameters, + method_description=None, + function_description=None): + super().__init__(name) + fit_parameters_ch = InstrumentChannel(self, 'fit_parameters') + self.add_submodule('fit_parameters', fit_parameters_ch) + for paramname, paraminfo in fit_parameters.items(): + self.fit_parameters.add_parameter(name=paramname, + set_cmd=False, + parameter_class=FitParameter, + **paraminfo) + self.add_parameter(name='success', + set_cmd=False, + parameter_class=FitParameter, + vals=vals.Enum(0, 1)) + self.success._save_val(0) + self.metadata = { + 'method': method_description, + 'name': name, + 'function': function_description, + 'fit_parameters': list(self.fit_parameters.parameters.keys())} + + @property + def all_parameters(self): + """ + Gathers all parameters on instrument and on it's submodules + and returns them in a list. + """ + params = [] + params += [p for n, p in self.parameters.items() if n != 'IDN'] + for s in self.submodules.values(): + params += list(s.parameters.values()) + return params + + def fit(self, *args, **kwargs): + """ + Function which is expected to take data and any other information, + perform the fit and (minimally) update the fit_parameters and success + parameter + """ + raise NotImplementedError( + 'Fit function must be implemented in Children') + + def _check_fit(self, *args): + """ + Checks shape of arguments provided to check for consistancy. + """ + for a in args[1:]: + if a.shape != args[0].shape: + raise RuntimeError( + f"experiment_parameter data does not have the same shape " + f"as measured data.\nMeasured data shape: {args[0].shape}," + f"experiment_parameter data shape: {a.shape}") + + +class LeastSquaresFitter(Fitter): + """ + An extension of the Fitter which uses the least squares method to fit + an array of data to a function and learn the most likely parameters of + this function and the variance on this knowledge. Also stored the initial + guesses used. + + Args: + name (str) + fit_parameters (dict): dictionary describing the parameters in the + function which is being fit to, keys are taken as parameter names + as they appear in the function, values are dictionary with keys + 'unit' and 'label' to be used in plotting. + function_metadata (dict): description of the function to be fit to + including the exact code to be evaluated (key 'np') and the + label to be printed (key 'str') + """ + + def __init__(self, name, fit_parameters, function_metadata): + super().__init__(name, fit_parameters, + method_description='LeastSquares', + function_description=function_metadata) + variance_parameters_ch = InstrumentChannel( + self, 'variance_parameters') + self.add_submodule('variance_parameters', variance_parameters_ch) + for paramname, paraminfo in fit_parameters.items(): + self.variance_parameters.add_parameter( + name=paramname + '_variance', + label=paraminfo.get('label', paramname) + ' Variance', + unit=paraminfo['unit'] + '^2' if 'unit' in paraminfo else None, + set_cmd=False, + parameter_class=FitParameter) + initial_value_parameters_ch = InstrumentChannel( + self, 'initial_value_parameters') + self.add_submodule('initial_value_parameters', + initial_value_parameters_ch) + for paramname, paraminfo in fit_parameters.items(): + self.initial_value_parameters.add_parameter( + name=paramname + '_initial_value', + label=paraminfo.get('label', paramname) + ' Initial Value', + unit=paraminfo.get('unit', None), + set_cmd=False, + parameter_class=FitParameter) + self.metadata.update( + {'variance_parameters': list( + self.variance_parameters.parameters.keys()), + 'initial_value_parameters': list( + self.initial_value_parameters.parameters.keys())}) + + def guess(self, measured_values, experiment_values): + """ + Function to generate initial values of the parameters for fitting, + if not implemented in children then values must be provided + at time of fitting. + """ + raise NotImplementedError('Optionally implemented in children') + + def evaluate(self, experiment_values, *fit_values): + """ + Evaluates the function to be fit to (stored in the metadata) for + the values of the independent variable (experiment value) and the + fit parameters provided. + """ + fit_params = list(self.fit_parameters.parameters.keys()) + if len(fit_values) == 0: + fit_values = [v() for v in self.fit_parameters.parameters.values()] + kwargs = {'x': experiment_values, + 'np': np, + **dict(zip(fit_params, fit_values))} + return eval(self.metadata['function']['np'], kwargs) + + def _get_r2(self, estimate, measured_values): + """ + Finds residual and total sum of squares, calculates the R^2 value + """ + meas = np.array(measured_values).flatten() + est = np.array(estimate).flatten() + ss_res = np.sum((meas - est) ** 2) + ss_tot = np.sum((meas - np.mean(meas)) ** 2) + r2 = 1 - (ss_res / ss_tot) + return r2 + + def fit(self, measured_values, experiment_values, + initial_values=None, r2_limit=None, variance_limited=False): + """ + Performs a fit based on a measurement and using the evaluate function. + Updates the instrument parameters to record the success and results + of the fit. + """ + self._check_fit(measured_values, experiment_values) + if initial_values is None: + try: + initial_values = self.guess(measured_values, experiment_values) + except NotImplementedError: + initial_values = [ + 1. for _ in self.initial_value_parameters.parameters] + for i, initial_param in enumerate(self.initial_value_parameters.parameters.values()): + initial_param._save_val(initial_values[i]) + try: + popt, pcov = curve_fit( + self.evaluate, experiment_values, measured_values, + p0=initial_values) + variance = np.diag(pcov) + estimate = self.evaluate(experiment_values, *popt) + r2 = self._get_r2(estimate, measured_values) + if r2_limit is not None and r2 < r2_limit: + success = 0 + message = 'r2 {:.2} exceeds limit {:.2}'.format(r2, r2_limit) + elif variance_limited and any(variance == np.inf): + success = 0 + message = 'infinite variance' + else: + success = 1 + except (RuntimeError, ValueError) as e: + success = 0 + message = str(e) + self.success._save_val(success) + fit_params = list(self.fit_parameters.parameters.values()) + variance_params = list( + self.variance_parameters.parameters.values()) + initial_params = list( + self.initial_value_parameters.parameters.values()) + if success: + for i, val in enumerate(popt): + fit_params[i]._save_val(val) + if variance[i] == np.inf: + variance_params[i]._save_val(float('nan')) + else: + variance_params[i]._save_val(variance[i]) + else: + for i, param in enumerate(fit_params): + param._save_val(float('nan')) + variance_params[i]._save_val(float('nan')) + warnings.warn('Fit failed due to: ' + message) diff --git a/qdev_wrappers/fitting/fit_by_id.py b/qdev_wrappers/fitting/fit_by_id.py new file mode 100644 index 00000000..a11d509f --- /dev/null +++ b/qdev_wrappers/fitting/fit_by_id.py @@ -0,0 +1,112 @@ +import numpy as np +import json +from itertools import product +from qcodes.dataset.measurements import Measurement +from qcodes.dataset.data_export import load_by_id +from qcodes.instrument.parameter import Parameter +from qdev_wrappers.fitting.fitters import LeastSquaresFitter +from qdev_wrappers.fitting.plotting import plot_fit_by_id +from qdev_wrappers.fitting.helpers import organize_exp_data, make_json_metadata + + +def fit_by_id(data_run_id, fitter, + dependent_parameter_name: str, + *independent_parameter_names: str, + plot=True, + save_plots=True, + show_variance=True, + show_initial_values=True, + source_conn=None, + **kwargs): + """ + Given the run_id of a dataset, a fitter and the parameters to fit to + performs a fit on the data and saves the fit results in a separate dataset. + + Args: + data_run_id (int) + fitter (qdev_wrappers fitter) + dependent_parameter_name (str): name of the dependent parameter to fit + to in the data + independent_parameter_names (list of strings): name of the independent + parameters to fit to in the data + plot (bool) (default True): whether to generate plots of the fit + save_plots (bool) (default True): whether to save the plots + show_variance (bool) (default True): if plot then whether to show the + variance on the fit parameter values (if relevant) + show_initial_values (bool) (default True): if plot then whether to show + the initial values of the fit parameters (if relevant) + **kwargs: passed to the fitter.fit function + + Returns: + fit_run_id (int): run id of the generated fit dataset + axes (list of matplotlib axes): list of plots generated + colorbar (matplotlib colorbar): colorbar of 2d heatmap plot if + generated, otherwise None + """ + exp_data = load_by_id(data_run_id, conn=source_conn) + dependent, independent, setpoints = organize_exp_data( + exp_data, dependent_parameter_name, *independent_parameter_names) + setpoint_paramnames = list(setpoints.keys()) + + # register setpoints + meas = Measurement() + setpoint_params = [] + for setpoint in setpoints.values(): + setpoint_param = Parameter(name=setpoint['name'], + label=setpoint['label'], + unit=setpoint['unit']) + meas.register_parameter(setpoint_param) + setpoint_params.append(setpoint_param) + + # register fit parameters + for param in fitter.all_parameters: + meas.register_parameter(param, + setpoints=setpoint_params or None) + + # set up dataset metadata + metadata = make_json_metadata( + exp_data, fitter, dependent_parameter_name, + *independent_parameter_names) + + # run fit for data + with meas.run() as datasaver: + datasaver._dataset.add_metadata(*metadata) + fit_run_id = datasaver.run_id + if len(setpoints) > 0: + # find all possible combinations of setpoint values + setpoint_combinations = product( + *[set(v['data']) for v in setpoints.values()]) + for setpoint_combination in setpoint_combinations: + # find indices where where setpoint combination is satisfied + indices = [] + for i, setpoint in enumerate(setpoints.values()): + indices.append( + set(np.argwhere(setpoint['data'] == + setpoint_combination[i]).flatten())) + u = None + if len(indices) > 0: + u = list(set.intersection(*indices)) + dependent_data = dependent['data'][u].flatten() + independent_data = [d['data'][u].flatten() + for d in independent.values()] + fitter.fit(dependent_data, *independent_data, **kwargs) + result = list(zip(setpoint_paramnames, setpoint_combination)) + for fit_param in fitter.all_parameters: + result.append((fit_param.name, fit_param())) + datasaver.add_result(*result) + else: + dependent_data = dependent['data'] + independent_data = [d['data'] for d in independent.values()] + fitter.fit(dependent_data, *independent_data, **kwargs) + result = [(p.name, p()) for p in fitter.all_parameters] + datasaver.add_result(*result) + # plot + if plot: + axes, colorbar = plot_fit_by_id(fit_run_id, + show_variance=show_variance, + show_initial_values=show_initial_values, + save_plots=save_plots) + else: + axes, colorbar = [], None + + return fit_run_id, axes, colorbar diff --git a/qdev_wrappers/fitting/fitters.py b/qdev_wrappers/fitting/fitters.py new file mode 100644 index 00000000..3ebc2e6c --- /dev/null +++ b/qdev_wrappers/fitting/fitters.py @@ -0,0 +1,102 @@ +from qdev_wrappers.fitting.base import Fitter, LeastSquaresFitter +from qdev_wrappers.fitting import guess +import numpy as np + +# TODO: add 2D MinimumFitter (nataliejpg) +# TODO: add fitter for resonators (nataliejpg) + + +class MinimumFitter(Fitter): + """ + Fitter instrument which finds the minimum of a 1d array and. + """ + def __init__(self, name='MinimumFitter'): + fit_parameters = {'location': {'label': 'location of minimum'}, + 'value': {'label': 'minimum value'}} + function_metadata = {'str': 'find minimum (simple)'} + super().__init__(name, fit_parameters, + method_description='SimpleMinimum', + function_description=function_metadata) + + def fit(self, measured_values, experiment_values): + """ + Find minimum value and location, update fit_parameters and + save succcess as yay. + """ + self._check_fit(measured_values, experiment_values) + min_index = np.argmin(measured_values) + self.fit_parameters.location._save_val(experiment_values[min_index]) + self.fit_parameters.value._save_val(measured_values[min_index]) + self.success._save_val(1) + + +class ExpDecayFitter(LeastSquaresFitter): + """ + Least Squares Fitter which fits to an exponential decay using the equation + a * np.exp(-x / T) + c + given the measured results and the values of x. Useful for T1 fitting. + """ + def __init__(self, name='ExpDecayFitter'): + fit_parameters = {'a': {'label': '$a$'}, + 'T': {'label': '$T$', 'unit': 's'}, + 'c': {'label': '$c$'}} + function_metadata = {'str': r'$f(x) = a \exp(-x/T) + c$', + 'np': 'a * np.exp(-x / T) + c'} + super().__init__(name, fit_parameters, function_metadata) + self.guess = guess.exp_decay + + +class ExpDecayBaseFitter(LeastSquaresFitter): + """ + Least Squares Fitter which fits to an exponential using the equation + a * p**x + b + and given the measured results and the values of x. Useful for fitting + benchmarking results. + """ + def __init__(self, name='ExpDecayBaseFitter'): + fit_parameters = {'a': {'label': '$a$', 'unit': 'V'}, + 'p': {'label': '$p$'}, + 'b': {'label': '$b$', 'unit': 'V'}} + function_metadata = {'str': r'$f(x) = A p^x + B$', + 'np': 'a * p**x + b'} + super().__init__(name, fit_parameters, function_metadata) + self.guess = guess.power_decay + + +class CosFitter(LeastSquaresFitter): + """ + Least Squares Fitter which fits to a cosine using the equation + a * np.cos(w * x + p) + c + and given the measured results and the values of x. Useful for fitting + Rabi oscillations. + """ + def __init__(self, name='CosFitter'): + fit_parameters = {'a': {'label': '$a$'}, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'p': {'label': r'$\phi$'}, + 'c': {'label': '$c$', 'unit': ''}} + function_metadata = {'str': r'$f(x) = a\cos(\omega x + \phi)+c$', + 'np': 'a * np.cos(w * x + p) + c'} + super().__init__(name, fit_parameters, function_metadata) + self.guess = guess.cosine + + +class ExpDecaySinFitter(LeastSquaresFitter): + """ + Least Squares Fitter which fits to an exponentially decaying sine using the + equation + a * np.exp(-x / T) * np.sin(w * x + p) + c + and given the measured results and the values of x. Useful for fitting + Ramsey oscillations to find T2*. + """ + def __init__(self, name='ExpDecaySinFitter'): + fit_parameters = {'a': {'label': '$a$', 'unit': ''}, + 'T': {'label': '$T$', 'unit': 's'}, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'p': {'label': r'$\phi$'}, + 'c': {'label': '$c$'}} + function_metadata = { + 'str': r'$f(x) = a \exp(-x / T) \sin(\omega x + \phi) + c$', + 'np': 'a * np.exp(-x / T) * np.sin(w * x + p) + c'} + super().__init__(name, fit_parameters, function_metadata) + self.guess = guess.exp_decay_sin diff --git a/qdev_wrappers/fitting/guess.py b/qdev_wrappers/fitting/guess.py new file mode 100644 index 00000000..2aa75051 --- /dev/null +++ b/qdev_wrappers/fitting/guess.py @@ -0,0 +1,79 @@ +import numpy as np +import scipy.fftpack as fftpack + + +def exp_decay(y, x): + """Guess for f(x) = a * e^(-x / b) + c""" + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + + a = val_init - val_fin + + c = val_fin + + # guess b as point where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - c)).argmin() + b = x[idx] + + return [a, b, c] + + +def exp_decay_sin(y, x): + """Guess for f(x) = a * e^(-x / b) sin(w*x + p) + c""" + a = y.max() - y.min() + + c = y.mean() + + # guess b as point half way point in data + b = x[round(len(x) / 2)] + + # Get initial guess for frequency from a fourier transform + yhat = fftpack.rfft(y - y.mean()) + idx = (yhat**2).argmax() + freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) + w = freqs[idx] + + p = 0 + + return [a, b, w, p, c] + + +def power_decay(y, x): + """Guess for f(x) = a * b^x + c""" + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + + a = val_init - val_fin + + c = val_fin + + # find index where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - c)).argmin() + # guess b as e^(-1/x[idx]): + b = np.e ** (-1 / x[idx]) + + return [a, b, c] + + +def cosine(y, x): + """Guess for f(x) = a * cos(wx + p) + c""" + c = y.mean() + + a = (y.max() - y.min()) / 2 + + # Get initial guess for frequency from a fourier transform + yhat = fftpack.rfft(y - y.mean()) + idx = (yhat ** 2).argmax() + freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) + w = freqs[idx] + + if (y[0] - c) / a > 1: + p = 0 + elif (y[0] - c) / a < -1: + p = np.pi + else: + p = np.arccos((y[0] - c) / a) + + return [a, w, p, c] diff --git a/qdev_wrappers/fitting/helpers.py b/qdev_wrappers/fitting/helpers.py new file mode 100644 index 00000000..c176704e --- /dev/null +++ b/qdev_wrappers/fitting/helpers.py @@ -0,0 +1,262 @@ +import numpy as np +import json + + +def strip_none(arr, u): + return np.array(arr).flatten()[u].flatten() + # return arr[arr != np.array(None)] + + +def make_json_metadata(dataset, fitter, dependent_parameter_name, + *independent_parameter_names): + """ + Given a dataset, fitter and the names of the dependent and independent + variables for fitting produces a dictionary of metadata about the fit + (used in plotting), converts this to json and returns the arguments + of the add_metadata function of a dataset. + + Args: + dataset (qcodes dataset): dataset containing the data to be used + for fitting + fitter (qdev_wrappers fitter): fitter used to fit dataset + dependent_parameter_name (str): name of the parameter in the dataset + to be fit to (think y axis) + independent_parameter_names (list of strings): names of the + parameters in the dataset which you measure the dependent + parameter as a function of and are to be used in the fit + (think x axis) + + Returns: + metadata_key: the key which fitting metadata is to be saved under + in the dataset + json: the string form of the fitting metadata dictionary as it is to be + saved + + """ + exp_metadata = {'run_id': dataset.run_id, + 'exp_id': dataset.exp_id, + 'exp_name': dataset.exp_name, + 'sample_name': dataset.sample_name} + metadata = {'fitter': fitter.metadata, + 'inferred_from': {'dept_var': dependent_parameter_name, + 'indept_vars': independent_parameter_names, + **exp_metadata}} + metadata_key = 'fitting_metadata' + return metadata_key, json.dumps(metadata) + + +def load_json_metadata(dataset): + """ + Given a dataset attempts to load and return the fitting metadata + + Args: + dataset (qcodes dataset) + + Returns: + metadata dictionary of the form generated by make_json_metadata + """ + try: + return json.loads(dataset.metadata['fitting_metadata']) + except KeyError: + raise RuntimeError( + "'fitting_metadata' not found in dataset metadata, are you sure " + "this is a fitted dataset?") + + +def organize_exp_data(data, dependent_parameter_name, + *independent_parameter_names, **setpoint_values): + """ + Given a dataset and the names of the parameters tp be used in the fit + organizes the data into dictionaries. Filters the data based on + specified setpoint_values. + + Args: + data (qcodes dataset) + dependent_parameter_name (str): name of the parameter in the dataset + to be fit to (think y axis) + independent_parameter_names (list of strings): names of the + parameters in the dataset at which you measure the dependent + parameter and are to be used in the fit (think x axis) + setpoint_values (dict) (optional): keys should be names of parameters + in the dataset (not dependent or independent parameters). values + are the values of these parameters for which you want the data + extracted. + + Returns: + dependent (dict): data dictionary of the dependent variable with keys + 'name', 'label', 'unit', 'data'. + independent (dict): dictionary of independent variables data with keys + being the names of the independent variables and values being the + corresponding data dictionaries. + setpoints (dict): dictionariy of setpoint variables where present + (ie all parameters in the dataset outside of the independent and + dependent parameters). This excludes any specified in + setpoint_values. Same format as independent dict so keys are + parameter names and values are data dictionaries. + """ + parameters = data.parameters.split(',') + if not all(v in parameters for v in setpoint_values.keys()): + raise RuntimeError(f'{list(setpoint_values.keys())} not all found ' + f'in dataset. Parameters present are {parameters}') + + indices = [] + for setpoint, value in setpoint_values.items(): + d = np.array(data.get_data(setpoint)).flatten() + nearest_val = value + np.amin(d - value) + indices.append(set(np.argwhere(d == nearest_val).flatten())) + if len(indices) > 0: + u = list(set.intersection(*indices)) + else: + u = None + + setpoints = {} + for p in parameters: + depends_on = data.paramspecs[p].depends_on.split(', ') + for d in depends_on: + non_vals = list(setpoint_values.keys()) + \ + [''] + list(independent_parameter_names) + if d not in non_vals: + setpoints[d] = None + dependent = None + independent = {} + for name in parameters: + if name == dependent_parameter_name: + dependent = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': np.array(data.get_data(name)).flatten()[u].flatten()} + elif name in independent_parameter_names: + independent[name] = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': np.array(data.get_data(name)).flatten()[u].flatten()} + elif name in setpoints: + setpoints[name] = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': np.array(data.get_data(name)).flatten()[u].flatten()} + if dependent is None: + raise RuntimeError(f'{dependent_parameter_name} not found in dataset. ' + f'Parameters present are {parameters}') + if len(independent) != len(independent_parameter_names): + raise RuntimeError(f'{independent_parameter_names} not all found ' + f'in dataset. Parameters present are {parameters}') + return dependent, independent, setpoints + + +def organize_fit_data(data, **setpoint_values): + """ + Given a fit dataset works out the categories from the metadata and sorts + the data into corresponding dictionaries. Filters the data based on + specified setpoint_values. + + Args: + data (qcodes dataset) + setpoint_values (dict) (optional): keys should be names of parameters + in the dataset. values are the values of these parameters for + which you want the data extracted. + + Returns: + success (dict): data dictionary of the success variable of the fitter + with keys 'name', 'label', 'unit', 'data' + fit (dict): dictionary of fit_parameter data with keys + being the names of the fit_parameters and values being the + corresponding data dictionaries + variance (dict): dictionary of variance_parameter data where present. + Same format as fit. Otherwise empty dict. + initial_values (dict): dictionary of initial_value_parameter data + where present. Same format as fit. Otherwise empty dict. + setpoints (dict): dictionariy of setpoint variables where present + (ie all parameters in the dataset outside of the independent and + dependent parameters). This excludes any specified in + setpoint_values. Same format as independent dict so keys are + parameter names and values are data dictionaries. + point_values (list): actual value for each specified setpoint_value + in the same order where the data returned in other dictionaries + will be for these setpoint values as they were the nearest in the + dataset to those specified. + """ + + # extract metadata and parameters present + metadata = load_json_metadata(data) + parameters = data.parameters.split(',') + + # check fit parameters and setpoint parameters are present + if 'success' not in parameters: + raise RuntimeError( + f"'success' parameter found " + "in dataset. Parameters present are {parameters}") + if not all(v in parameters for v in metadata['fitter']['fit_parameters']): + raise RuntimeError( + f"{metadata['fitter']['fit_parameters']} not all found " + "in dataset. Parameters present are {parameters}") + if not all(v in parameters for v in setpoint_values.keys()): + raise RuntimeError( + f"{list(setpoint_values.keys())} not all found " + "in dataset. Parameters present are {parameters}") + + # find indices for specified setpoint_values + setpoints = {} + indices = [] + point_values = [] + try: + success_setpoints = data.get_setpoints('success') + for setpoint, value in setpoint_values.items(): + d = np.array(success_setpoints[setpoint]).flatten() + nearest_val = value + np.amin(d - value) + indices.append(set(np.argwhere(d == nearest_val).flatten())) + point_values.append(nearest_val) + if len(indices) > 0: + u = list(set.intersection(*indices)) + else: + u = None + # populate dictionaries + for p in parameters: + depends_on = data.paramspecs[p].depends_on.split(', ') + for d in depends_on: + non_vals = list(setpoint_values.keys()) + [''] + if d not in non_vals: + setpoints[d] = None + except ValueError: + u = None + + fit = {} + variance = {} + initial_values = {} + for name in parameters: + none_indices = data.get_data(name) + if name == 'success': + success = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': strip_none(data.get_values(name), u)} + elif name in metadata['fitter']['fit_parameters']: + fit[name] = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': strip_none(data.get_values(name), u)} + elif name in metadata['fitter'].get('variance_parameters', []): + variance[name] = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': strip_none(data.get_values(name), u)} + elif name in metadata['fitter'].get('initial_value_parameters', []): + initial_values[name] = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': strip_none(data.get_values(name), u)} + elif name in setpoints: + setpoints[name] = { + 'name': name, + 'label': data.paramspecs[name].label, + 'unit': data.paramspecs[name].unit, + 'data': strip_none(success_setpoints[name], u)} + + return success, fit, variance, initial_values, setpoints, point_values diff --git a/qdev_wrappers/fitting/plotting.py b/qdev_wrappers/fitting/plotting.py new file mode 100644 index 00000000..a03c8300 --- /dev/null +++ b/qdev_wrappers/fitting/plotting.py @@ -0,0 +1,354 @@ +import qcodes as qc +from qcodes.dataset.data_export import load_by_id +from qdev_wrappers.fitting.helpers import organize_exp_data, organize_fit_data, load_json_metadata +from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_on_a_plain_grid +import json +import warnings +import matplotlib.pyplot as plt +import numpy as np +from qdev_wrappers.dataset.doNd import save_image + + +def plot_least_squares_1d(indept, dept, metadata, title, + fit=None, variance=None, + initial_values=None, + text=None): + """ + Plots a 1d plot of the data against the fit result (if provided) with the + initial fit values result also plotted (optionally) and text about the + fit values, fit function and (optionally) the variance. + + Args: + indept (dict): data dictionary with keys 'data', 'label', 'unit', + 'name' for x axis + dept (dict): data dictionary with keys 'data', 'label', 'unit', 'name' + for y axis + metadata (dict): fit metadata dictionary + title (str) + fit (dict) (optional): dictionary with a key for each fit_parameter + and with data dictionaries as values + variance (dict) (optional): dictionary with a key for each + variance_parameter which if fit then also prints standard deviation + on fit parameters + initial_values (dict) (optional): dictionary with a key for each + initial_value_parameter which if fit then also plots the fit result + using the initial values used in fitting + text (str) (optional): string to be prepended to the fit parameter + values information text box + + Returns: + matplotlib ax + """ + plt.figure(figsize=(10, 4)) + ax = plt.subplot(111) + box = ax.get_position() + ax.set_position([box.x0, box.y0, box.width * 0.7, box.height]) + + # plot data + ax.plot(indept['data'], dept['data'], + marker='.', markersize=5, linestyle='', color='C0', + label='data') + + # plot fit + if fit is not None: + fit_paramnames = metadata['fitter']['fit_parameters'] + variance_paramnames = metadata['fitter'].get('variance_parameters') + initial_value_paramnames = metadata['fitter'].get( + 'initial_value_parameters') + x = np.linspace(indept['data'].min(), indept['data'].max(), + num=len(indept['data']) * 10) + finalkwargs = {'x': x, + 'np': np, + **{f['name']: f['data'][0] for f in fit.values()}} + finaly = eval(metadata['fitter']['function']['np'], + finalkwargs) + ax.plot(x, finaly, color='C1', label='fit') + if initial_values is not None: + initialkwargs = {} + for i, name in enumerate(initial_value_paramnames): + initialkwargs[fit_paramnames[i] + ] = initial_values[name]['data'][0] + initialkwargs.update( + {'x': x, + 'np': np}) + intialy = eval(metadata['fitter']['function']['np'], + initialkwargs) + ax.plot(x, intialy, color='grey', label='initial fit values') + plt.legend() + p_label_list = [text] if text else [] + p_label_list.append(metadata['fitter']['function']['str']) + for i, fpn in enumerate(fit_paramnames): + fit_val = fit[fpn]['data'][0] + if variance is not None: + variance_val = variance[variance_paramnames[i]]['data'][0] + standard_dev = np.sqrt(variance_val) + p_label_list.append('{} = {:.3g} ± {:.3g} {}'.format( + fit[fpn]['label'], fit_val, + standard_dev, fit[fpn]['unit'])) + else: + p_label_list.append('{} = {:.3g} {}'.format( + fit[fpn]['label'], fit_val, fit[fpn]['unit'])) + textstr = '\n'.join(p_label_list) + else: + textstr = '{} \n Unsuccessful fit'.format( + fitter.metadata['fitter']['function']['str']) + + # add text, axes, labels, title and rescale + ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) + ax.set_xlabel(f"{indept['label']} ({indept['unit']})") + ax.set_ylabel(f"{dept['label']} ({dept['unit']})") + ax.set_title(title) + data_lst = [indept, dept] + _rescale_ticks_and_units(ax, data_lst) + + return ax + + +def plot_heat_map(x, y, z, title): + """ + Plots a 2d heatmap of the x, y and z data provided + + Args: + x (dict): data dictionary with keys 'data', 'label', 'unit', 'name' + for x axis (data should be 1d - length n) + y (dict): data dictionary with keys 'data', 'label', 'unit', 'name' + for y axis (data should be 1d - length m) + z (dict): data dictionary with keys 'data', 'label', 'unit', 'name' + for z axis (data should be 2d - shape n * m) + title (str) + + Returns: + matplotlib ax + matplotlib colorbar + """ + fig, ax = plt.subplots(1, 1) + ax, colorbar = plot_on_a_plain_grid( + x['data'], y['data'], z['data'], ax, + cmap=qc.config.plotting.default_color_map) + ax.set_xlabel(f"{x['label']} ({x['unit']})") + ax.set_ylabel(f"{y['label']} ({y['unit']})") + colorbar.set_label(f"{z['label']} ({z['unit']})") + ax.set_title(title) + data_lst = [x, y, z] + _rescale_ticks_and_units(ax, data_lst) + return ax, colorbar + + +def plot_fit_param_1ds(setpoint, fit, metadata, title, + variance=None, initial_values=None): + """ + Plots a 1d plot for each fit parameter against the setpoint. + + Args: + setpoint (dict): data dictionary with keys 'data', 'label', 'unit', + 'name' for x axes + fit (dict): dictionary with a key for each fit_parameter and with data + dictionaries as values + metadata (dict): fit metadata dictionary + title (str) + variance (dict) (optional): dictionary with a key for each + variance_parameter which if fit then also adds error bars + initial_values (dict) (optional): dictionary with a key for each + initial_value_parameter which if fit then also plots these against + the setpoint + + Returns: + list of matplotlib axes + """ + axes = [] + order = setpoint['data'].argsort() + xpoints = setpoint['data'][order] + fit_paramnames = metadata['fitter']['fit_parameters'] + variance_paramnames = metadata['fitter'].get('variance_parameters') + intial_value_paramnames = metadata['fitter'].get( + 'initial_value_parameters') + for i, fpn in enumerate(fit_paramnames): + fig, ax = plt.subplots(1, 1) + ypoints = fit[fpn]['data'][order] + if variance is not None: + var = variance[variance_paramnames[i]]['data'][order] + standard_dev = np.sqrt(var) + ax.errorbar(xpoints, ypoints, + standard_dev, None, 'g.-', label='fitted value') + else: + ax.plot(xpoints, ypoints, 'g.-', label='fitted value') + ax.text(0.05, 0.95, metadata['fitter']['function']['str'], + transform=ax.transAxes, fontsize=12, + verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) + if initial_values is not None: + ini = initial_values[intial_value_paramnames[i]]['data'][order] + ax.plot(xpoints, ini, '.-', color='grey', label='initial guess') + plt.legend() + + # add axes, labels, title and rescale + xlabel = setpoint['label'] or setpoint['name'] + xlabel += f" ({setpoint['unit']})" + ax.set_xlabel(xlabel) + ylabel = fit[fpn]['label'] or fit[fpn]['name'] + ylabel += f" ({fit[fpn]['unit']})" + ax.set_ylabel(ylabel) + ax.set_title(title) + data_lst = [setpoint, fit[fpn]] + _rescale_ticks_and_units(ax, data_lst) + axes.append(ax) + return axes + + +def plot_fit_by_id(fit_run_id, + show_variance=True, + show_initial_values=False, + save_plots=True, + source_conn=None, + **setpoint_values): + """ + Plots the result of a fit (against the data where possible) and + optionallt saves the plots + + For a 1d plots the fit result against the data if the fitter method + is 'LeastSquares'. For a 2d plots a 1d for each fit parameter against the + setpoint variable unless a specific value of the setpoint variable + is provided (in which case it plots a 1d of the fit result against the + data at this point if the fitter method is 'LeastSquares'). In the case + of a 'LeastSquares' fitter also plots the 2d fit result as a heatmap + (if setpoint_values not provided). + + Args: + fit_run_id (int): run id of fit dataset + show_variance (bool) (default True) + show_initial_values (bool) (default False) + save_plots (bool) (default True) + **setpoint_values: key, value pairs of names of the setpoints and the + value at which the cut should be taken. This is only relevant if + the fit has been performed at multiple values of some 'setpoint' + variable + + Returns + list of matplotlib axes + matplotlib colorbar of the 2d heatmap (None if not generated) + """ + + # load data and metadata and sort into dictionaries based on metadata and + # setpoint_values + fit_data = load_by_id(fit_run_id) + metadata = load_json_metadata(fit_data) + dependent_parameter_name = metadata['inferred_from']['dept_var'] + independent_parameter_names = metadata['inferred_from']['indept_vars'] + exp_run_id = metadata['inferred_from']['run_id'] + exp_data = load_by_id(exp_run_id, source_conn) + + dept, independent, exp_setpoints = organize_exp_data( + exp_data, dependent_parameter_name, *independent_parameter_names, + **setpoint_values) + success, fit, variance, initial_values, fit_setpoints, point_vals = organize_fit_data( + fit_data, **setpoint_values) + + # implement variance and setpoint plotting choices + if not show_variance or not variance: + variance = None + if not show_initial_values or not initial_values: + initial_values = None + + # check for unplottable fit options + if len(independent) > 1: + raise NotImplementedError("Plotting only currently works for " + "fitters with 1 independent variable") + else: + indept = independent[independent_parameter_names[0]] + if len(exp_setpoints) != len(fit_setpoints): + raise RuntimeError(f'Different number of data and fit setpoints: ' + f'{len(exp_setpoints)} != {len(fit_setpoints)}') + + # generate additional label text and filename text + text_list = [] + extra_save_text_list = [] + for i, s in enumerate(setpoint_values.keys()): + pspec = fit_data.paramspecs[s] + val = point_vals[i] + text_list.append( + '{} = {:.3g} {}'.format(pspec.label, val, pspec.unit)) + extra_save_text_list.append( + '{}_{:.3g}'.format(pspec.name, val).replace('.', 'p')) + text = '\n'.join(text_list) + extra_save_text = '_'.join(extra_save_text_list) + + # generate title + axes = [] + colorbar = None + title = "Run #{} (#{} fitted), Experiment {} ({})".format( + fit_run_id, + metadata['inferred_from']['run_id'], + metadata['inferred_from']['exp_id'], + metadata['inferred_from']['sample_name']) + + # calculate data dimension + dimension = len(fit_setpoints) + 1 + if dimension == 2: + fit_setpoint = next(f for f in fit_setpoints.values()) + exp_setpoint = next(e for e in exp_setpoints.values()) + if dimension > 2: + raise RuntimeError('Cannot make 3D plot. Try specifying a cut') + + # make the plots + # 1D PLOTTING - data + fit 1d plot + if dimension == 1: + if metadata['fitter']['method'] == 'LeastSquares': + if not success['data'][0]: + fit = None + variance = None + initial_values = None + ax = plot_least_squares_1d(indept, dept, metadata, title, + fit=fit, variance=variance, + initial_values=initial_values, + text=text) + axes.append(ax) + else: + warnings.warn( + 'Attempt to plot a 1d plot of a non LeastSquares fit') + # 2D PLOTTING: 2d fit heat map plot + fit param plots + elif dimension == 2: + xpoints, ypoints, zpoints = [], [], [] + if metadata['fitter']['method'] == 'LeastSquares': + for i, val in enumerate(fit_setpoint['data']): + indices = np.argwhere(exp_setpoint['data'] == val).flatten() + xpoints.append(indept['data'][indices].flatten()) + ypoints.append(exp_setpoint['data'][indices].flatten()) + success_point = success['data'][i] + if success_point: + fit_vals = {f['name']: f['data'][i] for f in fit.values()} + kwargs = { + 'np': np, + 'x': indept['data'][indices].flatten(), + **fit_vals} + zpoints.append(eval(metadata['fitter']['function']['np'], + kwargs)) + else: + zpoints.append([None] * len(indept['data'][indices])) + xpoints = np.array(xpoints).flatten() + ypoints = np.array(ypoints).flatten() + zpoints = np.array(zpoints).flatten() + # 2D simulated heatmap plot + x = {**indept, 'data': xpoints} + y = {**exp_setpoint, 'data': ypoints} + z = {**dept, 'data': zpoints, 'label': 'Simulated ' + dept['label']} + ax, colorbar = plot_heat_map(x, y, z, title) + axes.append(ax) + + # 1D fit parameter vs setpoint plots + axes += plot_fit_param_1ds(fit_setpoint, fit, metadata, title, + variance=variance, + initial_values=initial_values) + # 3D PLOTTING: nope + else: + warnings.warn( + "Plotting for more than one setpoint not yet implemented") + + # saving + if save_plots: + kwargs = {'run_id': fit_data.run_id, + 'exp_id': fit_data.exp_id, + 'exp_name': fit_data.exp_name, + 'sample_name': fit_data.sample_name} + name_extension = '_'.join(['fit'] + [extra_save_text]) + save_image(axes, name_extension=name_extension, **kwargs) + return axes, colorbar