diff --git a/doc/conf.py b/doc/conf.py index 9b4c3d02..96be2a10 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -88,7 +88,7 @@ "logo": { "image_light": "_static/logo.png", "image_dark": "_static/logo-dark.png", - "text": "meegkit", + "text": f"meegkit v{version}", }, "show_toc_level": 1, "external_links": [ @@ -126,7 +126,7 @@ "examples_dirs": "../examples", # path to your example scripts "gallery_dirs": "auto_examples", # path to where to save gallery generated output "filename_pattern": "/example_", - "ignore_pattern": "config.py", + "ignore_pattern": r"(config|run_all_notebooks)\.py", "run_stale_examples": False, } diff --git a/doc/index.rst b/doc/index.rst index 34717b6e..69e1cf81 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -15,10 +15,31 @@ in mind that this is mostly development code, and as such is likely to change without any notice. Also, while most of the methods have been fairly robustly tested, bugs can (and should!) be expected. +The package is most useful for readers who want practical reference +implementations of denoising and component-analysis methods, together with +worked examples that show how to interpret the outputs. + The source code of the project is hosted on Github at the following address: https://github.com/nbara/python-meegkit -To get started, follow the installation instructions `in the README `_. +Quick start +----------- + +Install the package with ``pip``: + +.. code-block:: bash + + pip install meegkit + +Some ASR-related functionality requires optional dependencies. To install those +as well, use: + +.. code-block:: bash + + pip install 'meegkit[extra]' + +For development, documentation building, or testing, see the fuller +installation guidance `in the README `_. Available modules ----------------- @@ -49,7 +70,16 @@ Here is a list of the methods and techniques available in ``meegkit``: Examples gallery ---------------- -A number of example scripts and notebooks are available: +A number of example scripts and notebooks are available. + +If you are new to the package, a good starting sequence is: + +1. ``example_asr`` for a full artifact-removal workflow. +2. ``example_dss`` for a simple synthetic component-recovery example. +3. ``example_trca`` or ``example_ress`` for task-oriented spatial filtering. + +Many examples are synthetic sanity checks with known ground truth, which makes +them useful for understanding what each method is expected to recover. .. toctree:: diff --git a/examples/README.rst b/examples/README.rst index 6dc07724..098e7ac3 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -4,5 +4,22 @@ Examples gallery Below is a list of example scripts showing basic usage for most methods in MEEGkit. +The examples are intentionally mixed between two roles: + +1. synthetic sanity checks, where the ground truth is known and the method can + be judged directly, +2. workflow-style demonstrations, where the goal is to show how to interpret + outputs on more realistic data. + +Suggested starting points: + +1. ``example_asr`` for artifact-subspace reconstruction. +2. ``example_dss`` for a compact repeated-signal example. +3. ``example_dss_line`` for line-noise removal. +4. ``example_trca`` for block-wise classification performance. + +Examples such as ``example_trca`` and the notebook-executed gallery pages may +take longer to run than the smaller synthetic demos. + The examples can be browsed directly on `Github `_. diff --git a/examples/example_asr.ipynb b/examples/example_asr.ipynb index 4ed12acd..4b4af566 100644 --- a/examples/example_asr.ipynb +++ b/examples/example_asr.ipynb @@ -1,255 +1,79 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "427a7b19", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:58.338839Z", - "iopub.status.busy": "2024-12-09T08:41:58.338652Z", - "iopub.status.idle": "2024-12-09T08:41:58.625398Z", - "shell.execute_reply": "2024-12-09T08:41:58.625100Z" - }, - "papermill": { - "duration": 0.290853, - "end_time": "2024-12-09T08:41:58.626643", - "exception": false, - "start_time": "2024-12-09T08:41:58.335790", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "19033f8b", - "metadata": { - "papermill": { - "duration": 0.000889, - "end_time": "2024-12-09T08:41:58.628904", - "exception": false, - "start_time": "2024-12-09T08:41:58.628015", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# ASR example\n", - "\n", - "Denoise data using Artifact Subspace Reconstruction.\n", - "\n", - "Uses meegkit.ASR().\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5eef4a2f", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:58.631286Z", - "iopub.status.busy": "2024-12-09T08:41:58.631120Z", - "iopub.status.idle": "2024-12-09T08:41:59.459100Z", - "shell.execute_reply": "2024-12-09T08:41:59.458595Z" - }, - "papermill": { - "duration": 0.830733, - "end_time": "2024-12-09T08:41:59.460463", - "exception": false, - "start_time": "2024-12-09T08:41:58.629730", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from meegkit.asr import ASR\n", - "from meegkit.utils.matrix import sliding_window\n", - "\n", - "# THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))\n", - "raw = np.load(os.path.join(\".\", \"tests\", \"data\", \"eeg_raw.npy\"))\n", - "sfreq = 250" - ] - }, - { - "cell_type": "markdown", - "id": "7107a907", - "metadata": { - "papermill": { - "duration": 0.000892, - "end_time": "2024-12-09T08:41:59.462695", - "exception": false, - "start_time": "2024-12-09T08:41:59.461803", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Calibration and processing\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "19ec9264", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:59.465290Z", - "iopub.status.busy": "2024-12-09T08:41:59.465082Z", - "iopub.status.idle": "2024-12-09T08:41:59.636956Z", - "shell.execute_reply": "2024-12-09T08:41:59.636551Z" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# ASR example\n\nThis example demonstrates a full ASR workflow on short EEG data:\n\n1. Calibrate ASR on a mostly clean segment.\n2. Apply ASR in 1-second windows.\n3. Compare raw and cleaned traces and quantify amplitude reduction.\n\nThis is intended as a first-pass inspection example rather than a benchmark:\nit shows how the calibration choice propagates to the cleaned output.\n\nThe most useful outputs are the calibration retention mask and the channel-wise\nRMS attenuation summary.\n\nUses meegkit.ASR().\n\n## References\n.. [1] Mullen, T., Kothe, C., Chi, Y., Ojeda, A., Kerth, T., Makeig, S.,\n Jung, T. P., & Cauwenberghs, G. (2015). Real-time neuroimaging and\n cognitive monitoring using wearable dry EEG. IEEE Transactions on\n Biomedical Engineering, 62(11), 2553-2567.\n" + ] }, - "papermill": { - "duration": 0.174716, - "end_time": "2024-12-09T08:41:59.638250", - "exception": false, - "start_time": "2024-12-09T08:41:59.463534", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom meegkit.asr import ASR\nfrom meegkit.utils.matrix import sliding_window\n\n# THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))\nraw = np.load(os.path.join(\"..\", \"tests\", \"data\", \"eeg_raw.npy\"))\nsfreq = 250" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Train on a clean portion of data\n", - "asr = ASR(method=\"euclid\")\n", - "train_idx = np.arange(0 * sfreq, 30 * sfreq, dtype=int)\n", - "_, sample_mask = asr.fit(raw[:, train_idx])\n", - "\n", - "# Apply filter using sliding (non-overlapping) windows\n", - "X = sliding_window(raw, window=int(sfreq), step=int(sfreq))\n", - "Y = np.zeros_like(X)\n", - "for i in range(X.shape[1]):\n", - " Y[:, i, :] = asr.transform(X[:, i, :])\n", - "\n", - "raw = X.reshape(8, -1) # reshape to (n_chans, n_times)\n", - "clean = Y.reshape(8, -1)" - ] - }, - { - "cell_type": "markdown", - "id": "ed92ac9d", - "metadata": { - "papermill": { - "duration": 0.0011, - "end_time": "2024-12-09T08:41:59.640823", - "exception": false, - "start_time": "2024-12-09T08:41:59.639723", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calibration and processing\nWe use the first 30 seconds as a calibration segment. In practice, this\nsegment should be as artifact-free as possible because ASR thresholds are\nderived from it.\n\n" + ] }, - "tags": [] - }, - "source": [ - "## Plot the results\n", - "\n", - "Data was trained on a 40s window from 5s to 45s onwards (gray filled area).\n", - "The algorithm then removes portions of this data with high amplitude\n", - "artifacts before running the calibration (hatched area = good).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9f5c981a", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:59.643316Z", - "iopub.status.busy": "2024-12-09T08:41:59.643143Z", - "iopub.status.idle": "2024-12-09T08:42:00.095720Z", - "shell.execute_reply": "2024-12-09T08:42:00.095390Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Train on a clean portion of data\nasr = ASR(method=\"euclid\")\ntrain_idx = np.arange(0 * sfreq, 30 * sfreq, dtype=int)\n_, sample_mask = asr.fit(raw[:, train_idx])\nselected_fraction = np.mean(sample_mask)\n\n# Apply filter using sliding (non-overlapping) windows\nX = sliding_window(raw, window=int(sfreq), step=int(sfreq))\nY = np.zeros_like(X)\nfor i in range(X.shape[1]):\n Y[:, i, :] = asr.transform(X[:, i, :])\n\nraw = X.reshape(8, -1) # reshape to (n_chans, n_times)\nclean = Y.reshape(8, -1)\n\n# A simple quality metric: root-mean-square attenuation per channel.\nrms_before = np.sqrt(np.mean(raw ** 2, axis=1))\nrms_after = np.sqrt(np.mean(clean ** 2, axis=1))\nrms_ratio = rms_after / np.maximum(rms_before, np.finfo(float).eps)" + ] }, - "papermill": { - "duration": 0.45547, - "end_time": "2024-12-09T08:42:00.097194", - "exception": false, - "start_time": "2024-12-09T08:41:59.641724", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the results\n\nThe gray overlay marks the 30-second calibration region actually used by the\ncode. The hatched overlay shows the subset of that region that ASR kept while\nestimating its clean-data statistics.\n\nWhat to look for:\n- After ASR, sharp bursts should be attenuated in many channels.\n- The RMS ratio (after/before) should generally be below 1.\n- Strong attenuation everywhere would suggest over-aggressive calibration.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "times = np.arange(raw.shape[-1]) / sfreq\nf, ax = plt.subplots(8, sharex=True, figsize=(9, 6))\nfor i in range(8):\n ax[i].fill_between(train_idx / sfreq, 0, 1, color=\"grey\", alpha=.3,\n transform=ax[i].get_xaxis_transform(),\n label=\"calibration window\")\n ax[i].fill_between(train_idx / sfreq, 0, 1, where=sample_mask.flat,\n transform=ax[i].get_xaxis_transform(),\n facecolor=\"none\", hatch=\"...\", edgecolor=\"k\",\n label=\"selected window\")\n ax[i].plot(times, raw[i], lw=.5, label=\"before ASR\")\n ax[i].plot(times, clean[i], label=\"after ASR\", lw=.5)\n ax[i].set_ylim([-50, 50])\n ax[i].set_ylabel(f\"ch{i}\")\n ax[i].set_yticks([])\nax[0].set_title(\"Raw and cleaned EEG traces\")\nax[i].set_xlabel(\"Time (s)\")\nax[0].legend(fontsize=\"small\", bbox_to_anchor=(1.04, 1), borderaxespad=0)\nplt.subplots_adjust(hspace=0, right=0.75)\nplt.suptitle(\"Before/after ASR\")\n\nfig, axm = plt.subplots(1, 1, figsize=(7, 3))\naxm.bar(np.arange(raw.shape[0]), rms_ratio)\naxm.axhline(1.0, color=\"k\", ls=\":\", lw=1)\naxm.set_xlabel(\"Channel\")\naxm.set_ylabel(\"RMS ratio (after / before)\")\naxm.set_title(\"Channel-wise attenuation summary\")\naxm.set_xticks(np.arange(raw.shape[0]))\naxm.grid(True, axis=\"y\", ls=\":\", alpha=.4)\nplt.tight_layout()\n\nprint(f\"Median RMS ratio across channels: {np.median(rms_ratio):.3f}\")\nprint(f\"Fraction of calibration samples retained: {selected_fraction:.3f}\")\nprint(\"Interpretation: if only a small fraction of the calibration window is\")\nprint(\"retained, the chosen segment may not be clean enough for stable ASR.\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "times = np.arange(raw.shape[-1]) / sfreq\n", - "f, ax = plt.subplots(8, sharex=True, figsize=(8, 5))\n", - "for i in range(8):\n", - " ax[i].fill_between(train_idx / sfreq, 0, 1, color=\"grey\", alpha=.3,\n", - " transform=ax[i].get_xaxis_transform(),\n", - " label=\"calibration window\")\n", - " ax[i].fill_between(train_idx / sfreq, 0, 1, where=sample_mask.flat,\n", - " transform=ax[i].get_xaxis_transform(),\n", - " facecolor=\"none\", hatch=\"...\", edgecolor=\"k\",\n", - " label=\"selected window\")\n", - " ax[i].plot(times, raw[i], lw=.5, label=\"before ASR\")\n", - " ax[i].plot(times, clean[i], label=\"after ASR\", lw=.5)\n", - " ax[i].set_ylim([-50, 50])\n", - " ax[i].set_ylabel(f\"ch{i}\")\n", - " ax[i].set_yticks([])\n", - "ax[i].set_xlabel(\"Time (s)\")\n", - "ax[0].legend(fontsize=\"small\", bbox_to_anchor=(1.04, 1), borderaxespad=0)\n", - "plt.subplots_adjust(hspace=0, right=0.75)\n", - "plt.suptitle(\"Before/after ASR\")\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py312", - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.936185, - "end_time": "2024-12-09T08:42:00.521003", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_asr.ipynb", - "output_path": "examples/example_asr.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:41:57.584818", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_asr.py b/examples/example_asr.py index 9d59a562..b9277150 100644 --- a/examples/example_asr.py +++ b/examples/example_asr.py @@ -2,9 +2,26 @@ ASR example =========== -Denoise data using Artifact Subspace Reconstruction. +This example demonstrates a full ASR workflow on short EEG data: + +1. Calibrate ASR on a mostly clean segment. +2. Apply ASR in 1-second windows. +3. Compare raw and cleaned traces and quantify amplitude reduction. + +This is intended as a first-pass inspection example rather than a benchmark: +it shows how the calibration choice propagates to the cleaned output. + +The most useful outputs are the calibration retention mask and the channel-wise +RMS attenuation summary. Uses meegkit.ASR(). + +References +---------- +.. [1] Mullen, T., Kothe, C., Chi, Y., Ojeda, A., Kerth, T., Makeig, S., + Jung, T. P., & Cauwenberghs, G. (2015). Real-time neuroimaging and + cognitive monitoring using wearable dry EEG. IEEE Transactions on + Biomedical Engineering, 62(11), 2553-2567. """ import os @@ -21,11 +38,15 @@ ############################################################################### # Calibration and processing # ----------------------------------------------------------------------------- +# We use the first 30 seconds as a calibration segment. In practice, this +# segment should be as artifact-free as possible because ASR thresholds are +# derived from it. # Train on a clean portion of data asr = ASR(method="euclid") train_idx = np.arange(0 * sfreq, 30 * sfreq, dtype=int) _, sample_mask = asr.fit(raw[:, train_idx]) +selected_fraction = np.mean(sample_mask) # Apply filter using sliding (non-overlapping) windows X = sliding_window(raw, window=int(sfreq), step=int(sfreq)) @@ -36,16 +57,26 @@ raw = X.reshape(8, -1) # reshape to (n_chans, n_times) clean = Y.reshape(8, -1) +# A simple quality metric: root-mean-square attenuation per channel. +rms_before = np.sqrt(np.mean(raw ** 2, axis=1)) +rms_after = np.sqrt(np.mean(clean ** 2, axis=1)) +rms_ratio = rms_after / np.maximum(rms_before, np.finfo(float).eps) + ############################################################################### # Plot the results # ----------------------------------------------------------------------------- # -# Data was trained on a 40s window from 5s to 45s onwards (gray filled area). -# The algorithm then removes portions of this data with high amplitude -# artifacts before running the calibration (hatched area = good). +# The gray overlay marks the 30-second calibration region actually used by the +# code. The hatched overlay shows the subset of that region that ASR kept while +# estimating its clean-data statistics. +# +# What to look for: +# - After ASR, sharp bursts should be attenuated in many channels. +# - The RMS ratio (after/before) should generally be below 1. +# - Strong attenuation everywhere would suggest over-aggressive calibration. times = np.arange(raw.shape[-1]) / sfreq -f, ax = plt.subplots(8, sharex=True, figsize=(8, 5)) +f, ax = plt.subplots(8, sharex=True, figsize=(9, 6)) for i in range(8): ax[i].fill_between(train_idx / sfreq, 0, 1, color="grey", alpha=.3, transform=ax[i].get_xaxis_transform(), @@ -59,8 +90,24 @@ ax[i].set_ylim([-50, 50]) ax[i].set_ylabel(f"ch{i}") ax[i].set_yticks([]) +ax[0].set_title("Raw and cleaned EEG traces") ax[i].set_xlabel("Time (s)") ax[0].legend(fontsize="small", bbox_to_anchor=(1.04, 1), borderaxespad=0) plt.subplots_adjust(hspace=0, right=0.75) plt.suptitle("Before/after ASR") + +fig, axm = plt.subplots(1, 1, figsize=(7, 3)) +axm.bar(np.arange(raw.shape[0]), rms_ratio) +axm.axhline(1.0, color="k", ls=":", lw=1) +axm.set_xlabel("Channel") +axm.set_ylabel("RMS ratio (after / before)") +axm.set_title("Channel-wise attenuation summary") +axm.set_xticks(np.arange(raw.shape[0])) +axm.grid(True, axis="y", ls=":", alpha=.4) +plt.tight_layout() + +print(f"Median RMS ratio across channels: {np.median(rms_ratio):.3f}") +print(f"Fraction of calibration samples retained: {selected_fraction:.3f}") +print("Interpretation: if only a small fraction of the calibration window is") +print("retained, the chosen segment may not be clean enough for stable ASR.") plt.show() diff --git a/examples/example_asr_calibration_sensitivity.ipynb b/examples/example_asr_calibration_sensitivity.ipynb new file mode 100644 index 00000000..030fab57 --- /dev/null +++ b/examples/example_asr_calibration_sensitivity.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# ASR calibration sensitivity\n\nThis tutorial-style example illustrates how ASR results depend on the\ncalibration segment used during fitting.\n\nWe fit ASR on three candidate calibration windows and compare the resulting\nchannel-wise RMS attenuation. This helps users choose a calibration strategy in\nreal datasets.\n\nThe main question is whether the cleaning result is stable across plausible\ncalibration windows, or whether one window yields much stronger attenuation.\n\nUses `meegkit.asr.ASR()`.\n\n## References\n.. [1] Mullen, T., Kothe, C., Chi, Y., Ojeda, A., Kerth, T., Makeig, S.,\n Jung, T. P., & Cauwenberghs, G. (2015). Real-time neuroimaging and\n cognitive monitoring using wearable dry EEG. IEEE Transactions on\n Biomedical Engineering, 62(11), 2553-2567.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom meegkit.asr import ASR\nfrom meegkit.utils.matrix import sliding_window\n\nraw = np.load(os.path.join(\"..\", \"tests\", \"data\", \"eeg_raw.npy\"))\nsfreq = 250" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define candidate calibration windows\nIn practice, calibration quality strongly affects how aggressively ASR\nsuppresses artifacts. Here we compare three 20-second windows.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "windows_sec = {\n \"early (0-20s)\": (0, 20),\n \"middle (10-30s)\": (10, 30),\n \"late (20-40s)\": (20, 40),\n}\n\nX = sliding_window(raw, window=int(sfreq), step=int(sfreq))\nraw_win = X.reshape(raw.shape[0], -1)\nrms_before = np.sqrt(np.mean(raw_win ** 2, axis=1))\n\nratios = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit and apply ASR for each candidate window\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for label, (start_s, stop_s) in windows_sec.items():\n train_idx = np.arange(start_s * sfreq, stop_s * sfreq, dtype=int)\n\n asr = ASR(method=\"euclid\")\n asr.fit(raw[:, train_idx])\n\n Y = np.zeros_like(X)\n for i in range(X.shape[1]):\n Y[:, i, :] = asr.transform(X[:, i, :])\n\n clean = Y.reshape(raw.shape[0], -1)\n rms_after = np.sqrt(np.mean(clean ** 2, axis=1))\n ratios[label] = rms_after / np.maximum(rms_before, np.finfo(float).eps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize channel-wise attenuation by calibration choice\nWhat to look for:\n- Ratios below 1 indicate attenuation.\n- Large differences across windows suggest sensitivity to calibration period.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\nchannels = np.arange(raw.shape[0])\n\nfor label, ratio in ratios.items():\n ax.plot(channels, ratio, \"o-\", lw=1.5, label=label)\n\nax.axhline(1.0, color=\"k\", ls=\":\", lw=1)\nax.set_xticks(channels)\nax.set_xlabel(\"Channel\")\nax.set_ylabel(\"RMS ratio (after / before)\")\nax.set_title(\"ASR sensitivity to calibration window\")\nax.grid(True, axis=\"y\", ls=\":\", alpha=.4)\nax.legend(fontsize=\"small\")\nplt.tight_layout()\n\nfor label, ratio in ratios.items():\n print(f\"{label:>16}: median ratio = {np.median(ratio):.3f}\")\n\nbest_label = min(ratios, key=lambda key: np.median(ratios[key]))\nprint(f\"Most aggressive calibration in this example: {best_label}\")\nprint(\"Interpretation: large separation between curves means the ASR result\")\nprint(\"depends strongly on which segment is treated as clean calibration data.\")\n\nplt.show()" + ] + } + ], + "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": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/example_asr_calibration_sensitivity.py b/examples/example_asr_calibration_sensitivity.py new file mode 100644 index 00000000..e2949870 --- /dev/null +++ b/examples/example_asr_calibration_sensitivity.py @@ -0,0 +1,98 @@ +""" +ASR calibration sensitivity +=========================== + +This tutorial-style example illustrates how ASR results depend on the +calibration segment used during fitting. + +We fit ASR on three candidate calibration windows and compare the resulting +channel-wise RMS attenuation. This helps users choose a calibration strategy in +real datasets. + +The main question is whether the cleaning result is stable across plausible +calibration windows, or whether one window yields much stronger attenuation. + +Uses `meegkit.asr.ASR()`. + +References +---------- +.. [1] Mullen, T., Kothe, C., Chi, Y., Ojeda, A., Kerth, T., Makeig, S., + Jung, T. P., & Cauwenberghs, G. (2015). Real-time neuroimaging and + cognitive monitoring using wearable dry EEG. IEEE Transactions on + Biomedical Engineering, 62(11), 2553-2567. +""" +import os + +import matplotlib.pyplot as plt +import numpy as np + +from meegkit.asr import ASR +from meegkit.utils.matrix import sliding_window + +raw = np.load(os.path.join("..", "tests", "data", "eeg_raw.npy")) +sfreq = 250 + +############################################################################### +# Define candidate calibration windows +# ----------------------------------------------------------------------------- +# In practice, calibration quality strongly affects how aggressively ASR +# suppresses artifacts. Here we compare three 20-second windows. +windows_sec = { + "early (0-20s)": (0, 20), + "middle (10-30s)": (10, 30), + "late (20-40s)": (20, 40), +} + +X = sliding_window(raw, window=int(sfreq), step=int(sfreq)) +raw_win = X.reshape(raw.shape[0], -1) +rms_before = np.sqrt(np.mean(raw_win ** 2, axis=1)) + +ratios = {} + +############################################################################### +# Fit and apply ASR for each candidate window +# ----------------------------------------------------------------------------- +for label, (start_s, stop_s) in windows_sec.items(): + train_idx = np.arange(start_s * sfreq, stop_s * sfreq, dtype=int) + + asr = ASR(method="euclid") + asr.fit(raw[:, train_idx]) + + Y = np.zeros_like(X) + for i in range(X.shape[1]): + Y[:, i, :] = asr.transform(X[:, i, :]) + + clean = Y.reshape(raw.shape[0], -1) + rms_after = np.sqrt(np.mean(clean ** 2, axis=1)) + ratios[label] = rms_after / np.maximum(rms_before, np.finfo(float).eps) + +############################################################################### +# Visualize channel-wise attenuation by calibration choice +# ----------------------------------------------------------------------------- +# What to look for: +# - Ratios below 1 indicate attenuation. +# - Large differences across windows suggest sensitivity to calibration period. +fig, ax = plt.subplots(1, 1, figsize=(8, 4)) +channels = np.arange(raw.shape[0]) + +for label, ratio in ratios.items(): + ax.plot(channels, ratio, "o-", lw=1.5, label=label) + +ax.axhline(1.0, color="k", ls=":", lw=1) +ax.set_xticks(channels) +ax.set_xlabel("Channel") +ax.set_ylabel("RMS ratio (after / before)") +ax.set_title("ASR sensitivity to calibration window") +ax.grid(True, axis="y", ls=":", alpha=.4) +ax.legend(fontsize="small") +plt.tight_layout() + +for label, ratio in ratios.items(): + print(f"{label:>16}: median ratio = {np.median(ratio):.3f}") + +best_label = min(ratios, key=lambda key: np.median(ratios[key])) +print(f"Most aggressive calibration in this example: {best_label}") +print("Interpretation: large separation between curves means the ASR result") +print("depends strongly on which segment is treated as clean calibration data.") + +plt.show() diff --git a/examples/example_dering.ipynb b/examples/example_dering.ipynb index 0422ba61..aa5732fa 100644 --- a/examples/example_dering.ipynb +++ b/examples/example_dering.ipynb @@ -1,203 +1,68 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "06ac0a88", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:03.730669Z", - "iopub.status.busy": "2024-12-09T08:42:03.730062Z", - "iopub.status.idle": "2024-12-09T08:42:04.018884Z", - "shell.execute_reply": "2024-12-09T08:42:04.018540Z" - }, - "papermill": { - "duration": 0.294759, - "end_time": "2024-12-09T08:42:04.020209", - "exception": false, - "start_time": "2024-12-09T08:42:03.725450", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "4c949ec3", - "metadata": { - "papermill": { - "duration": 0.000793, - "end_time": "2024-12-09T08:42:04.022365", - "exception": false, - "start_time": "2024-12-09T08:42:04.021572", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Ringing artifact reduction example\n", - "\n", - "This example shows how to subtract the impulse response from a filter to\n", - "reduce ringing artifacts.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cc92a45c", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:04.024537Z", - "iopub.status.busy": "2024-12-09T08:42:04.024367Z", - "iopub.status.idle": "2024-12-09T08:42:04.868852Z", - "shell.execute_reply": "2024-12-09T08:42:04.868437Z" - }, - "papermill": { - "duration": 0.846967, - "end_time": "2024-12-09T08:42:04.870069", - "exception": false, - "start_time": "2024-12-09T08:42:04.023102", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.signal import butter, lfilter\n", - "\n", - "from meegkit.detrend import reduce_ringing\n", - "\n", - "rng = np.random.default_rng(9)" - ] - }, - { - "cell_type": "markdown", - "id": "82dbbe95", - "metadata": { - "papermill": { - "duration": 0.000789, - "end_time": "2024-12-09T08:42:04.872036", - "exception": false, - "start_time": "2024-12-09T08:42:04.871247", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Ringing artifact reduction example\n\nThis example shows how to subtract an estimated filter impulse response to\nreduce ringing artifacts around a sharp event.\n\nThe script reports a simple quantitative proxy: mean absolute amplitude in a\nwindow around the artifact, before versus after correction.\n\nThe goal is not only to see a smaller oscillatory tail, but also to verify\nthat the correction remains local to the ringing event.\n\nUses `meegkit.detrend.reduce_ringing()`.\n\n## References\n.. [1] See the method documentation for\n `meegkit.detrend.reduce_ringing` in meegkit.\n" + ] }, - "tags": [] - }, - "source": [ - "# Detrending\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "df44e205", - "metadata": { - "papermill": { - "duration": 0.000814, - "end_time": "2024-12-09T08:42:04.873598", - "exception": false, - "start_time": "2024-12-09T08:42:04.872784", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.signal import butter, lfilter\n\nfrom meegkit.detrend import reduce_ringing\n\nrng = np.random.default_rng(9)" + ] }, - "tags": [] - }, - "source": [ - "## Basic example with a linear trend\n", - "Simulate the effect of filtering a signal containing a discontinuity, and try\n", - "to remove the resulting ringing artifact by subtracing the opposite of the\n", - "impulse response.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bf7bbd20", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:04.875739Z", - "iopub.status.busy": "2024-12-09T08:42:04.875561Z", - "iopub.status.idle": "2024-12-09T08:42:05.004583Z", - "shell.execute_reply": "2024-12-09T08:42:05.004260Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Detrending\n\n" + ] }, - "papermill": { - "duration": 0.131524, - "end_time": "2024-12-09T08:42:05.005855", - "exception": false, - "start_time": "2024-12-09T08:42:04.874331", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic example with a linear trend\nSimulate the effect of filtering a signal containing a discontinuity, and try\nto remove the resulting ringing artifact by subtracting an estimate of the\nimpulse response.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x = np.arange(1000) < 1\n[b, a] = butter(6, 0.2) # Butterworth filter design\nx = lfilter(b, a, x) * 50 # Filter data using above filter\nx = np.roll(x, 500)\nx = x[:, None] + rng.standard_normal((1000, 2))\ny = reduce_ringing(x, samples=np.array([500]))\n\nroi = slice(450, 560)\nmad_before = np.mean(np.abs(x[roi]))\nmad_after = np.mean(np.abs(y[roi]))\n\nplt.figure()\nplt.plot(x + np.array([-10, 10]), \"C0\", label=\"before\")\nplt.plot(y + np.array([-10, 10]), \"C1:\", label=\"after\")\nplt.xlabel(\"Samples\")\nplt.ylabel(\"Amplitude\")\nplt.title(\"Ringing reduction around the discontinuity\")\nplt.legend()\nplt.tight_layout()\n\nprint(f\"Mean absolute amplitude around event (before): {mad_before:.3f}\")\nprint(f\"Mean absolute amplitude around event (after): {mad_after:.3f}\")\nprint(f\"Relative change (after/before): {mad_after / mad_before:.3f}\")\nprint(\"Interpretation: values below 1 indicate that the ringing energy around\")\nprint(\"the discontinuity was reduced by the correction.\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "x = np.arange(1000) < 1\n", - "[b, a] = butter(6, 0.2) # Butterworth filter design\n", - "x = lfilter(b, a, x) * 50 # Filter data using above filter\n", - "x = np.roll(x, 500)\n", - "x = x[:, None] + rng.standard_normal((1000, 2))\n", - "y = reduce_ringing(x, samples=np.array([500]))\n", - "\n", - "plt.figure()\n", - "plt.plot(x + np.array([-10, 10]), \"C0\", label=\"before\")\n", - "plt.plot(y + np.array([-10, 10]), \"C1:\", label=\"after\")\n", - "plt.legend()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py312", - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.291842, - "end_time": "2024-12-09T08:42:05.326287", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_dering.ipynb", - "output_path": "examples/example_dering.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:03.034445", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_dering.py b/examples/example_dering.py index a5fee882..7c37d77e 100644 --- a/examples/example_dering.py +++ b/examples/example_dering.py @@ -2,8 +2,21 @@ Ringing artifact reduction example ================================== -This example shows how to subtract the impulse response from a filter to -reduce ringing artifacts. +This example shows how to subtract an estimated filter impulse response to +reduce ringing artifacts around a sharp event. + +The script reports a simple quantitative proxy: mean absolute amplitude in a +window around the artifact, before versus after correction. + +The goal is not only to see a smaller oscillatory tail, but also to verify +that the correction remains local to the ringing event. + +Uses `meegkit.detrend.reduce_ringing()`. + +References +---------- +.. [1] See the method documentation for + `meegkit.detrend.reduce_ringing` in meegkit. """ import matplotlib.pyplot as plt @@ -22,7 +35,7 @@ # Basic example with a linear trend # ----------------------------------------------------------------------------- # Simulate the effect of filtering a signal containing a discontinuity, and try -# to remove the resulting ringing artifact by subtracing the opposite of the +# to remove the resulting ringing artifact by subtracting an estimate of the # impulse response. x = np.arange(1000) < 1 @@ -32,8 +45,22 @@ x = x[:, None] + rng.standard_normal((1000, 2)) y = reduce_ringing(x, samples=np.array([500])) +roi = slice(450, 560) +mad_before = np.mean(np.abs(x[roi])) +mad_after = np.mean(np.abs(y[roi])) + plt.figure() plt.plot(x + np.array([-10, 10]), "C0", label="before") plt.plot(y + np.array([-10, 10]), "C1:", label="after") +plt.xlabel("Samples") +plt.ylabel("Amplitude") +plt.title("Ringing reduction around the discontinuity") plt.legend() +plt.tight_layout() + +print(f"Mean absolute amplitude around event (before): {mad_before:.3f}") +print(f"Mean absolute amplitude around event (after): {mad_after:.3f}") +print(f"Relative change (after/before): {mad_after / mad_before:.3f}") +print("Interpretation: values below 1 indicate that the ringing energy around") +print("the discontinuity was reduced by the correction.") plt.show() diff --git a/examples/example_dss.ipynb b/examples/example_dss.ipynb index 708c8da4..b73452e6 100644 --- a/examples/example_dss.ipynb +++ b/examples/example_dss.ipynb @@ -1,301 +1,97 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4e0a8379", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:08.597568Z", - "iopub.status.busy": "2024-12-09T08:42:08.597207Z", - "iopub.status.idle": "2024-12-09T08:42:08.878197Z", - "shell.execute_reply": "2024-12-09T08:42:08.877892Z" - }, - "papermill": { - "duration": 0.287183, - "end_time": "2024-12-09T08:42:08.879491", - "exception": false, - "start_time": "2024-12-09T08:42:08.592308", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "a7b3708c", - "metadata": { - "papermill": { - "duration": 0.001097, - "end_time": "2024-12-09T08:42:08.881966", - "exception": false, - "start_time": "2024-12-09T08:42:08.880869", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# DSS example\n", - "\n", - "Find the linear combinations of multichannel data that maximize repeatability\n", - "over trials.\n", - "\n", - "Uses meegkit.dss0().\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8898d25c", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:08.884486Z", - "iopub.status.busy": "2024-12-09T08:42:08.884339Z", - "iopub.status.idle": "2024-12-09T08:42:09.676980Z", - "shell.execute_reply": "2024-12-09T08:42:09.676580Z" - }, - "papermill": { - "duration": 0.795225, - "end_time": "2024-12-09T08:42:09.678147", - "exception": false, - "start_time": "2024-12-09T08:42:08.882922", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from meegkit import dss\n", - "from meegkit.utils import fold, rms, tscov, unfold\n", - "\n", - "rng = np.random.default_rng(5)" - ] - }, - { - "cell_type": "markdown", - "id": "b4df1571", - "metadata": { - "papermill": { - "duration": 0.000999, - "end_time": "2024-12-09T08:42:09.680718", - "exception": false, - "start_time": "2024-12-09T08:42:09.679719", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Create simulated data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "885bef51", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:09.683235Z", - "iopub.status.busy": "2024-12-09T08:42:09.683074Z", - "iopub.status.idle": "2024-12-09T08:42:09.702464Z", - "shell.execute_reply": "2024-12-09T08:42:09.702130Z" - }, - "papermill": { - "duration": 0.021883, - "end_time": "2024-12-09T08:42:09.703542", - "exception": false, - "start_time": "2024-12-09T08:42:09.681659", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# DSS example\n\nFind the linear combinations of multichannel data that maximize repeatability\nover trials.\n\nThis example uses synthetic data with a known repeated source, so we can check\nwhether the first DSS component better matches the ground truth than the raw\ntrial average.\n\nUses meegkit.dss0().\n\n## References\n.. [1] de Cheveigne, A., & Parra, L. C. (2014). Joint decorrelation, a\n versatile tool for multichannel data analysis. NeuroImage, 98, 487-505.\n" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Data are time * channel * trials.\n", - "n_samples = 100 * 3\n", - "n_chans = 30\n", - "n_trials = 100\n", - "noise_dim = 20 # dimensionality of noise\n", - "\n", - "# Source signal\n", - "source = np.hstack((\n", - " np.zeros((n_samples // 3,)),\n", - " np.sin(2 * np.pi * np.arange(n_samples // 3) / (n_samples / 3)).T,\n", - " np.zeros((n_samples // 3,))))[np.newaxis].T\n", - "s = source * rng.standard_normal((1, n_chans)) # 300 * 30\n", - "s = s[:, :, np.newaxis]\n", - "s = np.tile(s, (1, 1, 100))\n", - "\n", - "# Noise\n", - "noise = np.dot(\n", - " unfold(rng.standard_normal((n_samples, noise_dim, n_trials))),\n", - " rng.standard_normal((noise_dim, n_chans)))\n", - "noise = fold(noise, n_samples)\n", - "\n", - "# Mix signal and noise\n", - "SNR = 0.1\n", - "data = noise / rms(noise.flatten()) + SNR * s / rms(s.flatten())" - ] - }, - { - "cell_type": "markdown", - "id": "26ccd745", - "metadata": { - "papermill": { - "duration": 0.001002, - "end_time": "2024-12-09T08:42:09.705902", - "exception": false, - "start_time": "2024-12-09T08:42:09.704900", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom meegkit import dss\nfrom meegkit.utils import fold, rms, tscov, unfold\n\nrng = np.random.default_rng(5)" + ] }, - "tags": [] - }, - "source": [ - "## Apply DSS to clean them\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2b3f8b1a", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:09.708564Z", - "iopub.status.busy": "2024-12-09T08:42:09.708438Z", - "iopub.status.idle": "2024-12-09T08:42:09.715962Z", - "shell.execute_reply": "2024-12-09T08:42:09.715680Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create simulated data\n\n" + ] }, - "papermill": { - "duration": 0.010016, - "end_time": "2024-12-09T08:42:09.716986", - "exception": false, - "start_time": "2024-12-09T08:42:09.706970", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Data are time * channel * trials.\nn_samples = 100 * 3\nn_chans = 30\nn_trials = 100\nnoise_dim = 20 # dimensionality of noise\n\n# Source signal\nsource = np.hstack((\n np.zeros((n_samples // 3,)),\n np.sin(2 * np.pi * np.arange(n_samples // 3) / (n_samples / 3)).T,\n np.zeros((n_samples // 3,))))[np.newaxis].T\ns = source * rng.standard_normal((1, n_chans)) # 300 * 30\ns = s[:, :, np.newaxis]\ns = np.tile(s, (1, 1, 100))\n\n# Noise\nnoise = np.dot(\n unfold(rng.standard_normal((n_samples, noise_dim, n_trials))),\n rng.standard_normal((noise_dim, n_chans)))\nnoise = fold(noise, n_samples)\n\n# Mix signal and noise\nSNR = 0.1\ndata = noise / rms(noise.flatten()) + SNR * s / rms(s.flatten())" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Compute original and biased covariance matrices\n", - "c0, _ = tscov(data)\n", - "\n", - "# In this case the biased covariance is simply the covariance of the mean over\n", - "# trials\n", - "c1, _ = tscov(np.mean(data, 2))\n", - "\n", - "# Apply DSS\n", - "[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)\n", - "z = fold(np.dot(unfold(data), todss), epoch_size=n_samples)\n", - "\n", - "# Find best components\n", - "best_comp = np.mean(z[:, 0, :], -1)" - ] - }, - { - "cell_type": "markdown", - "id": "492d23e2", - "metadata": { - "papermill": { - "duration": 0.000979, - "end_time": "2024-12-09T08:42:09.719121", - "exception": false, - "start_time": "2024-12-09T08:42:09.718142", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apply DSS to clean them\n\n" + ] }, - "tags": [] - }, - "source": [ - "## Plot results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7a056adc", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:09.721510Z", - "iopub.status.busy": "2024-12-09T08:42:09.721392Z", - "iopub.status.idle": "2024-12-09T08:42:09.888054Z", - "shell.execute_reply": "2024-12-09T08:42:09.887779Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Compute original and biased covariance matrices\nc0, _ = tscov(data)\n\n# In this case the biased covariance is simply the covariance of the mean over\n# trials\nc1, _ = tscov(np.mean(data, 2))\n\n# Apply DSS\n[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)\nz = fold(np.dot(unfold(data), todss), epoch_size=n_samples)\n\n# Find best components\nbest_comp = np.mean(z[:, 0, :], -1)\nraw_average = np.mean(np.mean(data, 2), axis=1)\n\n# Compare the recovered component to the known source waveform.\nsource_corr_raw = np.corrcoef(source[:, 0], raw_average)[0, 1]\nsource_corr_dss = np.corrcoef(source[:, 0], best_comp)[0, 1]" + ] }, - "papermill": { - "duration": 0.169158, - "end_time": "2024-12-09T08:42:09.889197", - "exception": false, - "start_time": "2024-12-09T08:42:09.720039", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot results\nWhat to look for:\n- The raw trial average should still be visibly noisy.\n- The first DSS component should follow the known source more closely.\n- The reported correlation with the source should improve after DSS.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, (ax1, ax2, ax3) = plt.subplots(3, 1)\nax1.plot(source, label=\"source\")\nax1.set_ylabel(\"Amplitude\")\nax1.set_title(\"Ground-truth source\")\nax2.plot(np.mean(data, 2), label=\"data\")\nax2.set_ylabel(\"Amplitude\")\nax2.set_title(f\"Trial-averaged observed data (corr = {source_corr_raw:.2f})\")\nax3.plot(best_comp, label=\"recovered\")\nax3.set_ylabel(\"Amplitude\")\nax3.set_xlabel(\"Samples\")\nax3.set_title(f\"Recovered DSS component (corr = {source_corr_dss:.2f})\")\nax1.legend(loc=\"upper right\", fontsize=\"small\")\nax2.legend(loc=\"upper right\", fontsize=\"small\")\nax3.legend(loc=\"upper right\", fontsize=\"small\")\nplt.tight_layout()\nprint(f\"Correlation with source, raw trial average: {source_corr_raw:.3f}\")\nprint(f\"Correlation with source, first DSS component: {source_corr_dss:.3f}\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "f, (ax1, ax2, ax3) = plt.subplots(3, 1)\n", - "ax1.plot(source, label=\"source\")\n", - "ax2.plot(np.mean(data, 2), label=\"data\")\n", - "ax3.plot(best_comp, label=\"recovered\")\n", - "plt.legend()\n", - "plt.show()" - ] - } - ], - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.290864, - "end_time": "2024-12-09T08:42:10.209409", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_dss.ipynb", - "output_path": "examples/example_dss.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:07.918545", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_dss.py b/examples/example_dss.py index eb4a3b87..4ec922ae 100644 --- a/examples/example_dss.py +++ b/examples/example_dss.py @@ -5,7 +5,16 @@ Find the linear combinations of multichannel data that maximize repeatability over trials. +This example uses synthetic data with a known repeated source, so we can check +whether the first DSS component better matches the ground truth than the raw +trial average. + Uses meegkit.dss0(). + +References +---------- +.. [1] de Cheveigne, A., & Parra, L. C. (2014). Joint decorrelation, a + versatile tool for multichannel data analysis. NeuroImage, 98, 487-505. """ import matplotlib.pyplot as plt import numpy as np @@ -61,13 +70,58 @@ # Find best components best_comp = np.mean(z[:, 0, :], -1) +trial_avg_by_channel = np.mean(data, 2) +raw_average = trial_avg_by_channel.mean(axis=1) + +# DSS components are sign-indeterminate. Flip to align with the known source +# so the comparison is easier to read. +comp_sign = np.sign(np.corrcoef(source[:, 0], best_comp)[0, 1]) +if comp_sign == 0: + comp_sign = 1.0 +best_comp = comp_sign * best_comp + +q10, q90 = np.percentile(trial_avg_by_channel, [10, 90], axis=1) +shown_chans = np.linspace(0, n_chans - 1, min(n_chans, 8), dtype=int) + +# Compare the recovered component to the known source waveform. +source_corr_raw = np.corrcoef(source[:, 0], raw_average)[0, 1] +source_corr_dss = np.corrcoef(source[:, 0], best_comp)[0, 1] ############################################################################### # Plot results # ----------------------------------------------------------------------------- -f, (ax1, ax2, ax3) = plt.subplots(3, 1) +# What to look for: +# - The raw trial average should still be visibly noisy. +# - The first DSS component should follow the known source more closely. +# - The reported correlation with the source should improve after DSS. +f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 7), sharex=True, + constrained_layout=True) +time = np.arange(n_samples) ax1.plot(source, label="source") -ax2.plot(np.mean(data, 2), label="data") -ax3.plot(best_comp, label="recovered") -plt.legend() +ax1.grid(alpha=.2) +ax1.set_ylabel("Amplitude") +ax1.set_title("Ground-truth source") +channel_lines = ax2.plot(time, trial_avg_by_channel[:, shown_chans], color="0.7", + lw=.8, alpha=.5) +channel_lines[0].set_label(f"{len(shown_chans)} channels") +for line in channel_lines[1:]: + line.set_label("_nolegend_") +ax2.plot(time, raw_average, color="C0", lw=2.2, label="channel mean") +ax2.fill_between(time, q10, q90, color="C0", alpha=.18, linewidth=0, + label="10-90% range") +ax2.axhline(0, color="0.5", lw=.8, ls=":", zorder=0) +ax2.grid(alpha=.2) +ax2.set_ylabel("Amplitude") +ax2.set_title(f"Trial-averaged observed data (corr = {source_corr_raw:.2f})") +ax3.plot(time, best_comp, label="recovered") +ax3.axhline(0, color="0.5", lw=.8, ls=":", zorder=0) +ax3.grid(alpha=.2) +ax3.set_ylabel("Amplitude") +ax3.set_xlabel("Samples") +ax3.set_title(f"Recovered DSS component (corr = {source_corr_dss:.2f})") +ax1.legend(loc="upper right", fontsize="small") +ax2.legend(loc="upper right", fontsize="small") +ax3.legend(loc="upper right", fontsize="small") +print(f"Correlation with source, raw trial average: {source_corr_raw:.3f}") +print(f"Correlation with source, first DSS component: {source_corr_dss:.3f}") plt.show() diff --git a/examples/example_dss_line.ipynb b/examples/example_dss_line.ipynb index 22b3d27e..4242208d 100644 --- a/examples/example_dss_line.ipynb +++ b/examples/example_dss_line.ipynb @@ -1,477 +1,140 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "id": "7e40264d", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:26.069160Z", - "iopub.status.busy": "2024-12-09T08:42:26.068902Z", - "iopub.status.idle": "2024-12-09T08:42:26.351850Z", - "shell.execute_reply": "2024-12-09T08:42:26.351405Z" - }, - "papermill": { - "duration": 0.288194, - "end_time": "2024-12-09T08:42:26.353110", - "exception": false, - "start_time": "2024-12-09T08:42:26.064916", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "e2018ce4", - "metadata": { - "papermill": { - "duration": 0.00124, - "end_time": "2024-12-09T08:42:26.356130", - "exception": false, - "start_time": "2024-12-09T08:42:26.354890", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Remove line noise with ZapLine\n", - "\n", - "Find a spatial filter to get rid of line noise [1]_.\n", - "\n", - "Uses meegkit.dss_line().\n", - "\n", - "## References\n", - ".. [1] de Cheveigné, A. (2019). ZapLine: A simple and effective method to remove\n", - " power line artifacts. NeuroImage, 116356.\n", - " https://doi.org/10.1016/j.neuroimage.2019.116356\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f8052eb8", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:26.359254Z", - "iopub.status.busy": "2024-12-09T08:42:26.359076Z", - "iopub.status.idle": "2024-12-09T08:42:27.159022Z", - "shell.execute_reply": "2024-12-09T08:42:27.158711Z" - }, - "papermill": { - "duration": 0.802757, - "end_time": "2024-12-09T08:42:27.160126", - "exception": false, - "start_time": "2024-12-09T08:42:26.357369", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Authors: Maciej Szul \n", - "# Nicolas Barascud \n", - "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy import signal\n", - "\n", - "from meegkit import dss\n", - "from meegkit.utils import create_line_data, unfold" - ] - }, - { - "cell_type": "markdown", - "id": "1e8469f4", - "metadata": { - "papermill": { - "duration": 0.001318, - "end_time": "2024-12-09T08:42:27.163030", - "exception": false, - "start_time": "2024-12-09T08:42:27.161712", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Line noise removal\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "55b091e6", - "metadata": { - "papermill": { - "duration": 0.001181, - "end_time": "2024-12-09T08:42:27.165432", - "exception": false, - "start_time": "2024-12-09T08:42:27.164251", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Remove line noise with dss_line()\n", - "We first generate some noisy data to work with\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9c019060", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:27.168736Z", - "iopub.status.busy": "2024-12-09T08:42:27.168483Z", - "iopub.status.idle": "2024-12-09T08:42:27.199864Z", - "shell.execute_reply": "2024-12-09T08:42:27.199540Z" - }, - "papermill": { - "duration": 0.034327, - "end_time": "2024-12-09T08:42:27.200940", - "exception": false, - "start_time": "2024-12-09T08:42:27.166613", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PCA] Explained variance of selected components : 85.44%\n", - "Power of components removed by DSS: 0.82\n" - ] - } - ], - "source": [ - "sfreq = 250\n", - "fline = 50\n", - "nsamples = 10000\n", - "nchans = 10\n", - "data = create_line_data(n_samples=3 * nsamples, n_chans=nchans,\n", - " n_trials=1, fline=fline / sfreq, SNR=2)[0]\n", - "data = data[..., 0] # only take first trial\n", - "\n", - "# Apply dss_line (ZapLine)\n", - "out, _ = dss.dss_line(data, fline, sfreq, nkeep=1)" - ] - }, - { - "cell_type": "markdown", - "id": "7c593401", - "metadata": { - "papermill": { - "duration": 0.001293, - "end_time": "2024-12-09T08:42:27.203828", - "exception": false, - "start_time": "2024-12-09T08:42:27.202535", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Plot before/after\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4ba3f3af", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:27.207010Z", - "iopub.status.busy": "2024-12-09T08:42:27.206869Z", - "iopub.status.idle": "2024-12-09T08:42:27.622504Z", - "shell.execute_reply": "2024-12-09T08:42:27.622122Z" - }, - "papermill": { - "duration": 0.418532, - "end_time": "2024-12-09T08:42:27.623586", - "exception": false, - "start_time": "2024-12-09T08:42:27.205054", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Remove line noise with ZapLine\n\nFind a spatial filter to get rid of line noise [1]_.\n\nUses meegkit.dss_line().\n\nThe key diagnostic is whether the narrow spectral peak at the line frequency is\nreduced while the surrounding spectrum remains broadly similar.\n\n## References\n.. [1] de Cheveign\u00e9, A. (2019). ZapLine: A simple and effective method to remove\n power line artifacts. NeuroImage, 116356.\n https://doi.org/10.1016/j.neuroimage.2019.116356\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Maciej Szul \n# Nicolas Barascud \nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import signal\n\nfrom meegkit import dss\nfrom meegkit.utils import create_line_data, unfold" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, ax = plt.subplots(1, 2, sharey=True)\n", - "f, Pxx = signal.welch(data, sfreq, nperseg=500, axis=0, return_onesided=True)\n", - "ax[0].semilogy(f, Pxx)\n", - "f, Pxx = signal.welch(out, sfreq, nperseg=500, axis=0, return_onesided=True)\n", - "ax[1].semilogy(f, Pxx)\n", - "ax[0].set_xlabel(\"frequency [Hz]\")\n", - "ax[1].set_xlabel(\"frequency [Hz]\")\n", - "ax[0].set_ylabel(\"PSD [V**2/Hz]\")\n", - "ax[0].set_title(\"before\")\n", - "ax[1].set_title(\"after\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c3b710cf", - "metadata": { - "papermill": { - "duration": 0.001743, - "end_time": "2024-12-09T08:42:27.627545", - "exception": false, - "start_time": "2024-12-09T08:42:27.625802", - "status": "completed" }, - "tags": [] - }, - "source": [ - "## Remove line noise with dss_line_iter()\n", - "We first load some noisy data to work with\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d1ff9998", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:27.631461Z", - "iopub.status.busy": "2024-12-09T08:42:27.631303Z", - "iopub.status.idle": "2024-12-09T08:42:28.524659Z", - "shell.execute_reply": "2024-12-09T08:42:28.524354Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Line noise removal\n\n" + ] }, - "papermill": { - "duration": 0.896547, - "end_time": "2024-12-09T08:42:28.525697", - "exception": false, - "start_time": "2024-12-09T08:42:27.629150", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove line noise with dss_line()\nWe first generate some noisy data to work with. The seeded RNG is intentional:\nit keeps the rendered example visually stable across documentation rebuilds.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(400, 64, 267)\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(42)\nsfreq = 250\nfline = 50\nnsamples = 10000\nnchans = 10\ndata = create_line_data(n_samples=3 * nsamples, n_chans=nchans,\n n_trials=1, fline=fline / sfreq, SNR=2, rng=rng)[0]\ndata = data[..., 0] # only take first trial\n\n# Apply dss_line (ZapLine)\nout, _ = dss.dss_line(data, fline, sfreq, nkeep=1)\nfreq_before, psd_before = signal.welch(\n data, sfreq, nperseg=500, axis=0, return_onesided=True)\nfreq_after, psd_after = signal.welch(\n out, sfreq, nperseg=500, axis=0, return_onesided=True)\nline_bin_before = np.argmin(np.abs(freq_before - fline))\nline_bin_after = np.argmin(np.abs(freq_after - fline))\nmean_before = float(np.mean(psd_before[line_bin_before]))\nmean_after = float(np.mean(psd_after[line_bin_after]))" + ] }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot before/after\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Power of components removed by DSS: 0.00\n" - ] - } - ], - "source": [ - "data = np.load(os.path.join(\"..\", \"tests\", \"data\", \"dss_line_data.npy\"))\n", - "fline = 50\n", - "sfreq = 200\n", - "print(data.shape) # n_samples, n_chans, n_trials\n", - "\n", - "# Apply dss_line(), removing only one component\n", - "out1, _ = dss.dss_line(data, fline, sfreq, nremove=1, nfft=400, show=True)" - ] - }, - { - "cell_type": "markdown", - "id": "698e199e", - "metadata": { - "papermill": { - "duration": 0.001729, - "end_time": "2024-12-09T08:42:28.529867", - "exception": false, - "start_time": "2024-12-09T08:42:28.528138", - "status": "completed" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, ax = plt.subplots(1, 2, sharey=True)\nax[0].semilogy(freq_before, psd_before)\nax[1].semilogy(freq_after, psd_after)\nax[0].set_xlabel(\"frequency [Hz]\")\nax[1].set_xlabel(\"frequency [Hz]\")\nax[0].set_ylabel(\"PSD [V**2/Hz]\")\nax[0].set_title(\"before\")\nax[1].set_title(\"after\")\nprint(f\"Mean line-bin power before ZapLine: {mean_before:.6f}\")\nprint(f\"Mean line-bin power after ZapLine: {mean_after:.6f}\")\nplt.show()" + ] }, - "tags": [] - }, - "source": [ - "Now try dss_line_iter(). This applies dss_line() repeatedly until the\n", - "artifact is gone\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1437b115", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:28.534196Z", - "iopub.status.busy": "2024-12-09T08:42:28.534044Z", - "iopub.status.idle": "2024-12-09T08:42:32.719768Z", - "shell.execute_reply": "2024-12-09T08:42:32.719452Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove line noise with dss_line_iter()\nWe first load some noisy data to work with\n\n" + ] }, - "papermill": { - "duration": 4.18917, - "end_time": "2024-12-09T08:42:32.720789", - "exception": false, - "start_time": "2024-12-09T08:42:28.531619", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = np.load(os.path.join(\"..\", \"tests\", \"data\", \"dss_line_data.npy\"))\nfline = 50\nsfreq = 200\nprint(data.shape) # n_samples, n_chans, n_trials\n\n# Apply dss_line(), removing only one component\nout1, _ = dss.dss_line(data, fline, sfreq, nfft=400, nremove=1)" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Power of components removed by DSS: 0.00\n", - "Iteration 0 score: 8.82279465645174e-30\n", - "Power of components removed by DSS: 0.00\n", - "Iteration 1 score: 5.459918440184621e-30\n", - "Power of components removed by DSS: 0.00\n", - "Iteration 2 score: 1.859706167765309e-31\n", - "Power of components removed by DSS: 0.00\n", - "Iteration 3 score: -2.861571467810889e-31\n", - "Removed 3 components\n" - ] - } - ], - "source": [ - "out2, iterations = dss.dss_line_iter(data, fline, sfreq, nfft=400, show=True)\n", - "print(f\"Removed {iterations} components\")" - ] - }, - { - "cell_type": "markdown", - "id": "462c8be0", - "metadata": { - "papermill": { - "duration": 0.001798, - "end_time": "2024-12-09T08:42:32.724832", - "exception": false, - "start_time": "2024-12-09T08:42:32.723034", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now try dss_line_iter(). This applies dss_line() repeatedly until the\nartifact is gone\n\n" + ] }, - "tags": [] - }, - "source": [ - "Plot results with dss_line() vs. dss_line_iter()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0637d3b4", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:32.729050Z", - "iopub.status.busy": "2024-12-09T08:42:32.728893Z", - "iopub.status.idle": "2024-12-09T08:42:33.261002Z", - "shell.execute_reply": "2024-12-09T08:42:33.260727Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "out2, iterations = dss.dss_line_iter(data, fline, sfreq, nfft=400, show=True)\nprint(f\"Removed {iterations} components\")" + ] }, - "papermill": { - "duration": 0.535611, - "end_time": "2024-12-09T08:42:33.262174", - "exception": false, - "start_time": "2024-12-09T08:42:32.726563", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot results with dss_line() vs. dss_line_iter()\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, ax = plt.subplots(1, 2, sharey=True)\nf, Pxx = signal.welch(unfold(out1), sfreq, nperseg=200, axis=0,\n return_onesided=True)\nax[0].semilogy(f, Pxx, lw=.5)\nf, Pxx = signal.welch(unfold(out2), sfreq, nperseg=200, axis=0,\n return_onesided=True)\nax[1].semilogy(f, Pxx, lw=.5)\nax[0].set_xlabel(\"frequency [Hz]\")\nax[1].set_xlabel(\"frequency [Hz]\")\nax[0].set_ylabel(\"PSD [V**2/Hz]\")\nax[0].set_title(\"dss_line\")\nax[1].set_title(\"dss_line_iter\")\nplt.tight_layout()\nprint(\"Interpretation: iterative removal should help when the line artifact\")\nprint(\"spans more than one dominant component.\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "f, ax = plt.subplots(1, 2, sharey=True)\n", - "f, Pxx = signal.welch(unfold(out1), sfreq, nperseg=200, axis=0,\n", - " return_onesided=True)\n", - "ax[0].semilogy(f, Pxx, lw=.5)\n", - "f, Pxx = signal.welch(unfold(out2), sfreq, nperseg=200, axis=0,\n", - " return_onesided=True)\n", - "ax[1].semilogy(f, Pxx, lw=.5)\n", - "ax[0].set_xlabel(\"frequency [Hz]\")\n", - "ax[1].set_xlabel(\"frequency [Hz]\")\n", - "ax[0].set_ylabel(\"PSD [V**2/Hz]\")\n", - "ax[0].set_title(\"dss_line\")\n", - "ax[1].set_title(\"dss_line_iter\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "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": "3.13.2" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 8.254856, - "end_time": "2024-12-09T08:42:33.583487", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_dss_line.ipynb", - "output_path": "examples/example_dss_line.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:25.328631", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/example_dss_line.py b/examples/example_dss_line.py index 677441a5..2fd379d2 100644 --- a/examples/example_dss_line.py +++ b/examples/example_dss_line.py @@ -6,6 +6,9 @@ Uses meegkit.dss_line(). +The key diagnostic is whether the narrow spectral peak at the line frequency is +reduced while the surrounding spectrum remains broadly similar. + References ---------- .. [1] de Cheveigné, A. (2019). ZapLine: A simple and effective method to remove @@ -31,30 +34,40 @@ ############################################################################### # Remove line noise with dss_line() # ----------------------------------------------------------------------------- -# We first generate some noisy data to work with +# We first generate some noisy data to work with. The seeded RNG is intentional: +# it keeps the rendered example visually stable across documentation rebuilds. +rng = np.random.default_rng(42) sfreq = 250 fline = 50 nsamples = 10000 nchans = 10 data = create_line_data(n_samples=3 * nsamples, n_chans=nchans, - n_trials=1, fline=fline / sfreq, SNR=2)[0] + n_trials=1, fline=fline / sfreq, SNR=2, rng=rng)[0] data = data[..., 0] # only take first trial # Apply dss_line (ZapLine) out, _ = dss.dss_line(data, fline, sfreq, nkeep=1) +freq_before, psd_before = signal.welch( + data, sfreq, nperseg=500, axis=0, return_onesided=True) +freq_after, psd_after = signal.welch( + out, sfreq, nperseg=500, axis=0, return_onesided=True) +line_bin_before = np.argmin(np.abs(freq_before - fline)) +line_bin_after = np.argmin(np.abs(freq_after - fline)) +mean_before = float(np.mean(psd_before[line_bin_before])) +mean_after = float(np.mean(psd_after[line_bin_after])) ############################################################################### # Plot before/after f, ax = plt.subplots(1, 2, sharey=True) -f, Pxx = signal.welch(data, sfreq, nperseg=500, axis=0, return_onesided=True) -ax[0].semilogy(f, Pxx) -f, Pxx = signal.welch(out, sfreq, nperseg=500, axis=0, return_onesided=True) -ax[1].semilogy(f, Pxx) +ax[0].semilogy(freq_before, psd_before) +ax[1].semilogy(freq_after, psd_after) ax[0].set_xlabel("frequency [Hz]") ax[1].set_xlabel("frequency [Hz]") ax[0].set_ylabel("PSD [V**2/Hz]") ax[0].set_title("before") ax[1].set_title("after") +print(f"Mean line-bin power before ZapLine: {mean_before:.6f}") +print(f"Mean line-bin power after ZapLine: {mean_after:.6f}") plt.show() @@ -91,4 +104,6 @@ ax[0].set_title("dss_line") ax[1].set_title("dss_line_iter") plt.tight_layout() +print("Interpretation: iterative removal should help when the line artifact") +print("spans more than one dominant component.") plt.show() diff --git a/examples/example_mcca.ipynb b/examples/example_mcca.ipynb index 95ff87e2..d74427b1 100644 --- a/examples/example_mcca.ipynb +++ b/examples/example_mcca.ipynb @@ -1,673 +1,172 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e95aaac1", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:55.526704Z", - "iopub.status.busy": "2024-12-09T08:41:55.526415Z", - "iopub.status.idle": "2024-12-09T08:41:55.822644Z", - "shell.execute_reply": "2024-12-09T08:41:55.822150Z" - }, - "papermill": { - "duration": 0.302473, - "end_time": "2024-12-09T08:41:55.824165", - "exception": false, - "start_time": "2024-12-09T08:41:55.521692", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "2187462c", - "metadata": { - "papermill": { - "duration": 0.001826, - "end_time": "2024-12-09T08:41:55.828305", - "exception": false, - "start_time": "2024-12-09T08:41:55.826479", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Multiway canonical correlation analysis (mCCA)\n", - "\n", - "Find a set of components which are shared between different datasets.\n", - "\n", - "Uses meegkit.cca.mmca()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d79f08ab", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:55.833275Z", - "iopub.status.busy": "2024-12-09T08:41:55.833072Z", - "iopub.status.idle": "2024-12-09T08:41:56.646825Z", - "shell.execute_reply": "2024-12-09T08:41:56.646383Z" - }, - "papermill": { - "duration": 0.817617, - "end_time": "2024-12-09T08:41:56.648040", - "exception": false, - "start_time": "2024-12-09T08:41:55.830423", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from meegkit import cca\n", - "\n", - "rng = np.random.default_rng(5)" - ] - }, - { - "cell_type": "markdown", - "id": "e0b5df67", - "metadata": { - "papermill": { - "duration": 0.001903, - "end_time": "2024-12-09T08:41:56.652128", - "exception": false, - "start_time": "2024-12-09T08:41:56.650225", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## First example\n", - "We create 3 uncorrelated data sets. There should be no common structure\n", - "between them.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "95ede45e", - "metadata": { - "papermill": { - "duration": 0.001752, - "end_time": "2024-12-09T08:41:56.655630", - "exception": false, - "start_time": "2024-12-09T08:41:56.653878", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Build data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1051d11f", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:56.660435Z", - "iopub.status.busy": "2024-12-09T08:41:56.660201Z", - "iopub.status.idle": "2024-12-09T08:41:56.665269Z", - "shell.execute_reply": "2024-12-09T08:41:56.664838Z" - }, - "papermill": { - "duration": 0.008534, - "end_time": "2024-12-09T08:41:56.666194", - "exception": false, - "start_time": "2024-12-09T08:41:56.657660", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aggregated data covariance shape: (30, 30)\n" - ] - } - ], - "source": [ - "x1 = rng.standard_normal((10000, 10))\n", - "x2 = rng.standard_normal((10000, 10))\n", - "x3 = rng.standard_normal((10000, 10))\n", - "x = np.hstack((x1, x2, x3))\n", - "C = np.dot(x.T, x)\n", - "print(f\"Aggregated data covariance shape: {C.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "da62c235", - "metadata": { - "papermill": { - "duration": 0.001816, - "end_time": "2024-12-09T08:41:56.670339", - "exception": false, - "start_time": "2024-12-09T08:41:56.668523", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Apply CCA\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "772e1214", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:56.674602Z", - "iopub.status.busy": "2024-12-09T08:41:56.674475Z", - "iopub.status.idle": "2024-12-09T08:41:56.677557Z", - "shell.execute_reply": "2024-12-09T08:41:56.677305Z" - }, - "papermill": { - "duration": 0.006308, - "end_time": "2024-12-09T08:41:56.678421", - "exception": false, - "start_time": "2024-12-09T08:41:56.672113", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "[A, score, AA] = cca.mcca(C, 10)\n", - "z = x.dot(A)" - ] - }, - { - "cell_type": "markdown", - "id": "b0193bfb", - "metadata": { - "papermill": { - "duration": 0.001875, - "end_time": "2024-12-09T08:41:56.682284", - "exception": false, - "start_time": "2024-12-09T08:41:56.680409", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Plot results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b5b49dab", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:56.686623Z", - "iopub.status.busy": "2024-12-09T08:41:56.686471Z", - "iopub.status.idle": "2024-12-09T08:41:56.882582Z", - "shell.execute_reply": "2024-12-09T08:41:56.882269Z" - }, - "papermill": { - "duration": 0.199467, - "end_time": "2024-12-09T08:41:56.883539", - "exception": false, - "start_time": "2024-12-09T08:41:56.684072", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Multiway canonical correlation analysis (mCCA)\n\nFind a set of components which are shared between different datasets.\n\nThis example walks through three qualitatively different cases:\n\n1. no shared structure across datasets,\n2. some shared structure,\n3. fully shared structure.\n\nThe goal is to see how the transformed covariance changes as shared structure\nbecomes stronger.\n\nUses meegkit.cca.mcca().\n\n## References\n.. [1] de Cheveigne, A., Parra, L. C., & Bialek, W. (2018). Multiset canonical\n correlation analysis. NeuroImage, 186, 728-740.\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom meegkit import cca\n\nrng = np.random.default_rng(5)\n\n\ndef plot_mcca_case(A, C, x, title, interpretation):\n \"\"\"Plot a standard diagnostic panel for one mCCA scenario.\"\"\"\n z = x.dot(A)\n\n fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n axes[0].imshow(A, aspect=\"auto\")\n axes[0].set_title(f\"{title}\\ntransform matrix\")\n axes[0].set_xlabel(\"Components\")\n axes[0].set_ylabel(\"Original channels\")\n\n axes[1].imshow(A.T.dot(C.dot(A)), aspect=\"auto\")\n axes[1].set_title(\"Covariance of\\ntransformed data\")\n axes[1].set_xlabel(\"Components\")\n axes[1].set_ylabel(\"Components\")\n\n axes[2].imshow(x.T.dot(x.dot(A)), aspect=\"auto\")\n axes[2].set_title(\"Cross-correlation between\\nraw & transformed data\")\n axes[2].set_xlabel(\"transformed\")\n axes[2].set_ylabel(\"raw\")\n fig.tight_layout()\n\n fig2, ax = plt.subplots(1, 1, figsize=(6, 3))\n ax.plot(np.mean(z ** 2, axis=0), \"o-\")\n ax.set_title(f\"{title}\\nmean power of transformed components\")\n ax.set_xlabel(\"Component\")\n ax.set_ylabel(\"Mean power\")\n ax.grid(True, ls=\":\", alpha=.4)\n fig2.tight_layout()\n\n print(f\"{title}: {interpretation}\")" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, axes = plt.subplots(1, 3, figsize=(12, 4))\n", - "axes[0].imshow(A, aspect=\"auto\")\n", - "axes[0].set_title(\"mCCA transform matrix\")\n", - "axes[1].imshow(A.T.dot(C.dot(A)), aspect=\"auto\")\n", - "axes[1].set_title(\"Covariance of\\ntransformed data\")\n", - "axes[2].imshow(x.T.dot(x.dot(A)), aspect=\"auto\")\n", - "axes[2].set_title(\"Cross-correlation between\\nraw & transformed data\")\n", - "axes[2].set_xlabel(\"transformed\")\n", - "axes[2].set_ylabel(\"raw\")\n", - "plt.plot(np.mean(z ** 2, axis=0))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0cb59c63", - "metadata": { - "papermill": { - "duration": 0.002037, - "end_time": "2024-12-09T08:41:56.888293", - "exception": false, - "start_time": "2024-12-09T08:41:56.886256", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Second example\n", - "Now Create 3 data sets with some shared parts.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "844e36c7", - "metadata": { - "papermill": { - "duration": 0.001933, - "end_time": "2024-12-09T08:41:56.892258", - "exception": false, - "start_time": "2024-12-09T08:41:56.890325", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Build data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e6ba3659", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:56.897424Z", - "iopub.status.busy": "2024-12-09T08:41:56.897271Z", - "iopub.status.idle": "2024-12-09T08:41:56.902061Z", - "shell.execute_reply": "2024-12-09T08:41:56.901766Z" }, - "papermill": { - "duration": 0.008456, - "end_time": "2024-12-09T08:41:56.903007", - "exception": false, - "start_time": "2024-12-09T08:41:56.894551", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aggregated data covariance shape: (30, 30)\n" - ] - } - ], - "source": [ - "x1 = rng.standard_normal((10000, 5))\n", - "x2 = rng.standard_normal((10000, 5))\n", - "x3 = rng.standard_normal((10000, 5))\n", - "x4 = rng.standard_normal((10000, 5))\n", - "x = np.hstack((x2, x1, x3, x1, x4, x1))\n", - "C = np.dot(x.T, x)\n", - "print(f\"Aggregated data covariance shape: {C.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ab073cfa", - "metadata": { - "papermill": { - "duration": 0.002243, - "end_time": "2024-12-09T08:41:56.907592", - "exception": false, - "start_time": "2024-12-09T08:41:56.905349", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Apply mCCA\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "885f6e30", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:56.912412Z", - "iopub.status.busy": "2024-12-09T08:41:56.912166Z", - "iopub.status.idle": "2024-12-09T08:41:56.914809Z", - "shell.execute_reply": "2024-12-09T08:41:56.914525Z" - }, - "papermill": { - "duration": 0.006033, - "end_time": "2024-12-09T08:41:56.915693", - "exception": false, - "start_time": "2024-12-09T08:41:56.909660", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First example\nWe create 3 uncorrelated data sets. There should be no common structure\nbetween them.\n\n" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "A, score, AA = cca.mcca(C, 10)" - ] - }, - { - "cell_type": "markdown", - "id": "3546cfad", - "metadata": { - "papermill": { - "duration": 0.002078, - "end_time": "2024-12-09T08:41:56.919987", - "exception": false, - "start_time": "2024-12-09T08:41:56.917909", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Build data\n\n" + ] }, - "tags": [] - }, - "source": [ - "Plot results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6c7d6b97", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:56.924614Z", - "iopub.status.busy": "2024-12-09T08:41:56.924496Z", - "iopub.status.idle": "2024-12-09T08:41:57.070784Z", - "shell.execute_reply": "2024-12-09T08:41:57.070462Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x1 = rng.standard_normal((10000, 10))\nx2 = rng.standard_normal((10000, 10))\nx3 = rng.standard_normal((10000, 10))\nx = np.hstack((x1, x2, x3))\nC = np.dot(x.T, x)\nprint(f\"Aggregated data covariance shape: {C.shape}\")" + ] }, - "papermill": { - "duration": 0.149796, - "end_time": "2024-12-09T08:41:57.071853", - "exception": false, - "start_time": "2024-12-09T08:41:56.922057", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply CCA\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "[A, score, AA] = cca.mcca(C, 10)\nplot_mcca_case(\n A, C, x,\n title=\"Case 1: independent datasets\",\n interpretation=\"No component should dominate strongly because nothing is shared.\",\n)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, axes = plt.subplots(1, 3, figsize=(12, 4))\n", - "axes[0].imshow(A, aspect=\"auto\")\n", - "axes[0].set_title(\"mCCA transform matrix\")\n", - "axes[1].imshow(A.T.dot(C.dot(A)), aspect=\"auto\")\n", - "axes[1].set_title(\"Covariance of\\ntransformed data\")\n", - "axes[2].imshow(x.T.dot(x.dot(A)), aspect=\"auto\")\n", - "axes[2].set_title(\"Cross-correlation between\\nraw & transformed data\")\n", - "axes[2].set_xlabel(\"transformed\")\n", - "axes[2].set_ylabel(\"raw\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4253075d", - "metadata": { - "papermill": { - "duration": 0.002208, - "end_time": "2024-12-09T08:41:57.076670", - "exception": false, - "start_time": "2024-12-09T08:41:57.074462", - "status": "completed" }, - "tags": [] - }, - "source": [ - "## Third example\n", - "Finally let's create 3 identical 10-channel data sets. Only 10 worthwhile\n", - "components should be found, and the transformed dataset should perfectly\n", - "explain all the variance (empty last two block-columns in the\n", - "cross-correlation plot).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "da0cd7a8", - "metadata": { - "papermill": { - "duration": 0.002374, - "end_time": "2024-12-09T08:41:57.081193", - "exception": false, - "start_time": "2024-12-09T08:41:57.078819", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Second example\nNow Create 3 data sets with some shared parts.\n\n" + ] }, - "tags": [] - }, - "source": [ - "Build data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6ff2121a", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:57.086647Z", - "iopub.status.busy": "2024-12-09T08:41:57.086490Z", - "iopub.status.idle": "2024-12-09T08:41:57.089774Z", - "shell.execute_reply": "2024-12-09T08:41:57.089464Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Build data\n\n" + ] }, - "papermill": { - "duration": 0.007098, - "end_time": "2024-12-09T08:41:57.090733", - "exception": false, - "start_time": "2024-12-09T08:41:57.083635", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x1 = rng.standard_normal((10000, 5))\nx2 = rng.standard_normal((10000, 5))\nx3 = rng.standard_normal((10000, 5))\nx4 = rng.standard_normal((10000, 5))\nx = np.hstack((x2, x1, x3, x1, x4, x1))\nC = np.dot(x.T, x)\nprint(f\"Aggregated data covariance shape: {C.shape}\")" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aggregated data covariance shape: (30, 30)\n" - ] - } - ], - "source": [ - "x1 = rng.standard_normal((10000, 10))\n", - "x = np.hstack((x1, x1, x1))\n", - "C = np.dot(x.T, x)\n", - "print(f\"Aggregated data covariance shape: {C.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ee6546fe", - "metadata": { - "papermill": { - "duration": 0.002653, - "end_time": "2024-12-09T08:41:57.096353", - "exception": false, - "start_time": "2024-12-09T08:41:57.093700", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply mCCA\n\n" + ] }, - "tags": [] - }, - "source": [ - "Compute mCCA\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "861a44f2", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:57.102269Z", - "iopub.status.busy": "2024-12-09T08:41:57.102139Z", - "iopub.status.idle": "2024-12-09T08:41:57.104653Z", - "shell.execute_reply": "2024-12-09T08:41:57.104410Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "A, score, AA = cca.mcca(C, 10)\nplot_mcca_case(\n A, C, x,\n title=\"Case 2: partially shared datasets\",\n interpretation=\"Shared blocks should produce a clearer low-rank structure.\",\n)" + ] }, - "papermill": { - "duration": 0.00689, - "end_time": "2024-12-09T08:41:57.105920", - "exception": false, - "start_time": "2024-12-09T08:41:57.099030", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Third example\nFinally let's create 3 identical 10-channel data sets. Only 10 worthwhile\ncomponents should be found, and the transformed dataset should perfectly\nexplain all the variance (empty last two block-columns in the\ncross-correlation plot).\n\n" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "A, score, AA = cca.mcca(C, 10)" - ] - }, - { - "cell_type": "markdown", - "id": "c167115b", - "metadata": { - "papermill": { - "duration": 0.002204, - "end_time": "2024-12-09T08:41:57.110420", - "exception": false, - "start_time": "2024-12-09T08:41:57.108216", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Build data\n\n" + ] }, - "tags": [] - }, - "source": [ - "Plot results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9d8c682c", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:57.115417Z", - "iopub.status.busy": "2024-12-09T08:41:57.115286Z", - "iopub.status.idle": "2024-12-09T08:41:57.255585Z", - "shell.execute_reply": "2024-12-09T08:41:57.255229Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x1 = rng.standard_normal((10000, 10))\nx = np.hstack((x1, x1, x1))\nC = np.dot(x.T, x)\nprint(f\"Aggregated data covariance shape: {C.shape}\")" + ] }, - "papermill": { - "duration": 0.14393, - "end_time": "2024-12-09T08:41:57.256537", - "exception": false, - "start_time": "2024-12-09T08:41:57.112607", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute mCCA\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "A, score, AA = cca.mcca(C, 10)\nplot_mcca_case(\n A, C, x,\n title=\"Case 3: identical datasets\",\n interpretation=(\n \"Shared structure is maximal, so the dominant components should be \"\n \"very clear.\"\n ),\n)\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "f, axes = plt.subplots(1, 3, figsize=(12, 4))\n", - "axes[0].imshow(A, aspect=\"auto\")\n", - "axes[0].set_title(\"mCCA transform matrix\")\n", - "axes[1].imshow(A.T.dot(C.dot(A)), aspect=\"auto\")\n", - "axes[1].set_title(\"Covariance of\\ntransformed data\")\n", - "axes[2].imshow(x.T.dot(x.dot(A)), aspect=\"auto\")\n", - "axes[2].set_title(\"Cross-correlation between\\nraw & transformed data\")\n", - "axes[2].set_xlabel(\"transformed\")\n", - "axes[2].set_ylabel(\"raw\")\n", - "plt.show()" - ] - } - ], - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.784917, - "end_time": "2024-12-09T08:41:57.577554", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_mcca.ipynb", - "output_path": "examples/example_mcca.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:41:54.792637", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_mcca.py b/examples/example_mcca.py index d51c6714..a774a693 100644 --- a/examples/example_mcca.py +++ b/examples/example_mcca.py @@ -4,7 +4,21 @@ Find a set of components which are shared between different datasets. -Uses meegkit.cca.mmca() +This example walks through three qualitatively different cases: + +1. no shared structure across datasets, +2. some shared structure, +3. fully shared structure. + +The goal is to see how the transformed covariance changes as shared structure +becomes stronger. + +Uses meegkit.cca.mcca(). + +References +---------- +.. [1] de Cheveigne, A., Parra, L. C., & Bialek, W. (2018). Multiset canonical + correlation analysis. NeuroImage, 186, 728-740. """ import matplotlib.pyplot as plt @@ -14,6 +28,38 @@ rng = np.random.default_rng(5) + +def plot_mcca_case(A, C, x, title, interpretation): + """Plot a standard diagnostic panel for one mCCA scenario.""" + z = x.dot(A) + + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + axes[0].imshow(A, aspect="auto") + axes[0].set_title(f"{title}\ntransform matrix") + axes[0].set_xlabel("Components") + axes[0].set_ylabel("Original channels") + + axes[1].imshow(A.T.dot(C.dot(A)), aspect="auto") + axes[1].set_title("Covariance of\ntransformed data") + axes[1].set_xlabel("Components") + axes[1].set_ylabel("Components") + + axes[2].imshow(x.T.dot(x.dot(A)), aspect="auto") + axes[2].set_title("Cross-correlation between\nraw & transformed data") + axes[2].set_xlabel("transformed") + axes[2].set_ylabel("raw") + fig.tight_layout() + + fig2, ax = plt.subplots(1, 1, figsize=(6, 3)) + ax.plot(np.mean(z ** 2, axis=0), "o-") + ax.set_title(f"{title}\nmean power of transformed components") + ax.set_xlabel("Component") + ax.set_ylabel("Mean power") + ax.grid(True, ls=":", alpha=.4) + fig2.tight_layout() + + print(f"{title}: {interpretation}") + ############################################################################### # First example # ----------------------------------------------------------------------------- @@ -32,21 +78,11 @@ ############################################################################### # Apply CCA [A, score, AA] = cca.mcca(C, 10) -z = x.dot(A) - -############################################################################### -# Plot results -f, axes = plt.subplots(1, 3, figsize=(12, 4)) -axes[0].imshow(A, aspect="auto") -axes[0].set_title("mCCA transform matrix") -axes[1].imshow(A.T.dot(C.dot(A)), aspect="auto") -axes[1].set_title("Covariance of\ntransformed data") -axes[2].imshow(x.T.dot(x.dot(A)), aspect="auto") -axes[2].set_title("Cross-correlation between\nraw & transformed data") -axes[2].set_xlabel("transformed") -axes[2].set_ylabel("raw") -plt.plot(np.mean(z ** 2, axis=0)) -plt.show() +plot_mcca_case( + A, C, x, + title="Case 1: independent datasets", + interpretation="No component should dominate strongly because nothing is shared.", +) ############################################################################### # Second example @@ -66,19 +102,11 @@ ############################################################################### # Apply mCCA A, score, AA = cca.mcca(C, 10) - -############################################################################### -# Plot results -f, axes = plt.subplots(1, 3, figsize=(12, 4)) -axes[0].imshow(A, aspect="auto") -axes[0].set_title("mCCA transform matrix") -axes[1].imshow(A.T.dot(C.dot(A)), aspect="auto") -axes[1].set_title("Covariance of\ntransformed data") -axes[2].imshow(x.T.dot(x.dot(A)), aspect="auto") -axes[2].set_title("Cross-correlation between\nraw & transformed data") -axes[2].set_xlabel("transformed") -axes[2].set_ylabel("raw") -plt.show() +plot_mcca_case( + A, C, x, + title="Case 2: partially shared datasets", + interpretation="Shared blocks should produce a clearer low-rank structure.", +) ############################################################################### # Third example @@ -98,16 +126,12 @@ ############################################################################### # Compute mCCA A, score, AA = cca.mcca(C, 10) - -############################################################################### -# Plot results -f, axes = plt.subplots(1, 3, figsize=(12, 4)) -axes[0].imshow(A, aspect="auto") -axes[0].set_title("mCCA transform matrix") -axes[1].imshow(A.T.dot(C.dot(A)), aspect="auto") -axes[1].set_title("Covariance of\ntransformed data") -axes[2].imshow(x.T.dot(x.dot(A)), aspect="auto") -axes[2].set_title("Cross-correlation between\nraw & transformed data") -axes[2].set_xlabel("transformed") -axes[2].set_ylabel("raw") +plot_mcca_case( + A, C, x, + title="Case 3: identical datasets", + interpretation=( + "Shared structure is maximal, so the dominant components should be " + "very clear." + ), +) plt.show() diff --git a/examples/example_mcca_2.ipynb b/examples/example_mcca_2.ipynb index 871d7059..8eed8607 100644 --- a/examples/example_mcca_2.ipynb +++ b/examples/example_mcca_2.ipynb @@ -1,341 +1,97 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "92b7cf60", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:06.061033Z", - "iopub.status.busy": "2024-12-09T08:42:06.060858Z", - "iopub.status.idle": "2024-12-09T08:42:06.343439Z", - "shell.execute_reply": "2024-12-09T08:42:06.343067Z" - }, - "papermill": { - "duration": 0.28629, - "end_time": "2024-12-09T08:42:06.344597", - "exception": false, - "start_time": "2024-12-09T08:42:06.058307", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "7aa94e5c", - "metadata": { - "papermill": { - "duration": 0.000986, - "end_time": "2024-12-09T08:42:06.346983", - "exception": false, - "start_time": "2024-12-09T08:42:06.345997", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# mCCA example: Sinusoidal target in separable noise\n", - "\n", - "Reproduced from de Cheveigné et al. (2018).\n", - "\n", - "Synthetic data for this example consisted of 10 data matrices, each of\n", - "dimensions 10000 samples x 10 channels. Each was obtained by multiplying 9\n", - "Gaussian noise time series (independent and uncorrelated) by a 9 x 10 mixing\n", - "matrix with random Gaussian coefficients. To this background of noise was added\n", - "a \"target\" consisting of a sinusoidal time series multiplied by a 1 x 10 mixing\n", - "matrix with random coefficients. The target was the same for all data matrices,\n", - "but the mixing matrices differed, as were the noise matrices. The SNR was set\n", - "to 10−20, i.e. a very unfavorable SNR. The noise is of rank 9 and the signal of\n", - "rank 1, so signal and noise are in principle linearly separable.\n", - "\n", - "Uses meegkit.cca.mmca()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eb0a4f87", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:06.349682Z", - "iopub.status.busy": "2024-12-09T08:42:06.349535Z", - "iopub.status.idle": "2024-12-09T08:42:07.158489Z", - "shell.execute_reply": "2024-12-09T08:42:07.158150Z" - }, - "papermill": { - "duration": 0.812008, - "end_time": "2024-12-09T08:42:07.159946", - "exception": false, - "start_time": "2024-12-09T08:42:06.347938", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from meegkit import cca\n", - "\n", - "# Set the seed for the random number generator for reproducibility\n", - "rng = np.random.default_rng(5)" - ] - }, - { - "cell_type": "markdown", - "id": "3bc7b5e7", - "metadata": { - "papermill": { - "duration": 0.001035, - "end_time": "2024-12-09T08:42:07.162638", - "exception": false, - "start_time": "2024-12-09T08:42:07.161603", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Generate toy data\n", - "Constants\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4b33c2e7", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:07.165456Z", - "iopub.status.busy": "2024-12-09T08:42:07.165235Z", - "iopub.status.idle": "2024-12-09T08:42:07.182125Z", - "shell.execute_reply": "2024-12-09T08:42:07.181810Z" - }, - "papermill": { - "duration": 0.019666, - "end_time": "2024-12-09T08:42:07.183259", - "exception": false, - "start_time": "2024-12-09T08:42:07.163593", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# mCCA example: Sinusoidal target in separable noise\n\nReproduced from de Cheveign\u00e9 et al. (2018).\n\nSynthetic data for this example consisted of 10 data matrices, each of\ndimensions 10000 samples x 10 channels. Each was obtained by multiplying 9\nGaussian noise time series (independent and uncorrelated) by a 9 x 10 mixing\nmatrix with random Gaussian coefficients. To this background of noise was added\na \"target\" consisting of a sinusoidal time series multiplied by a 1 x 10 mixing\nmatrix with random coefficients. The target was the same for all data matrices,\nbut the mixing matrices differed, as were the noise matrices. The SNR was set\nto 10\u221220, i.e. a very unfavorable SNR. The noise is of rank 9 and the signal of\nrank 1, so signal and noise are in principle linearly separable.\n\nThe useful question here is whether the first recovered shared component still\nresembles the known sinusoidal target despite the poor SNR.\n\nUses meegkit.cca.mcca().\n\n## References\n.. [1] de Cheveigne, A., Parra, L. C., & Bialek, W. (2018). Multiset canonical\n correlation analysis. NeuroImage, 186, 728-740.\n" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "num_matrices = 10\n", - "num_samples = 10000\n", - "num_channels = 10\n", - "noise_rank = 9\n", - "signal_rank = 1\n", - "unfavorable_SNR_dB = -20 # SNR in decibels\n", - "\n", - "# Generate noise matrices and mixing matrices\n", - "noise_matrices = [rng.normal(size=(num_samples, noise_rank))\n", - " for _ in range(num_matrices)]\n", - "mixing_matrices = [rng.normal(size=(noise_rank, num_channels))\n", - " for _ in range(num_matrices)]\n", - "\n", - "# Generate sinusoidal target\n", - "t = np.linspace(0, 1, num_samples)\n", - "target_signal = np.sin(2 * np.pi * t) # 1 Hz sinusoidal signal\n", - "\n", - "# Generate signal mixing matrix\n", - "signal_mixing_matrix = rng.normal(size=(signal_rank, num_channels))\n", - "\n", - "# Prepare data matrices\n", - "data_matrices = []\n", - "for i in range(num_matrices):\n", - " # Create noise for current data matrix\n", - " noise = np.matmul(noise_matrices[i], mixing_matrices[i])\n", - "\n", - " # Create signal for current data matrix\n", - " signal = np.matmul(target_signal.reshape(-1, 1), signal_mixing_matrix)\n", - "\n", - " # Adjust the power of signal to achieve the desired SNR\n", - " noise_power = np.mean(noise**2)\n", - " signal_power = 10**(unfavorable_SNR_dB / 10) * noise_power\n", - " signal = np.sqrt(signal_power / np.mean(signal**2)) * signal\n", - "\n", - " # Add signal and noise\n", - " data_matrix = signal + noise\n", - "\n", - " data_matrices.append(data_matrix)\n", - "\n", - "# Concatenate data matrices\n", - "x = np.concatenate(data_matrices, axis=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "fb43bfc1", - "metadata": { - "papermill": { - "duration": 0.000977, - "end_time": "2024-12-09T08:42:07.185445", - "exception": false, - "start_time": "2024-12-09T08:42:07.184468", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom meegkit import cca\n\n# Set the seed for the random number generator for reproducibility\nrng = np.random.default_rng(5)" + ] }, - "tags": [] - }, - "source": [ - "## Use mCCA to recover signal in noise\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "90d134df", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:07.188071Z", - "iopub.status.busy": "2024-12-09T08:42:07.187932Z", - "iopub.status.idle": "2024-12-09T08:42:07.199453Z", - "shell.execute_reply": "2024-12-09T08:42:07.199135Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate toy data\nConstants\n\n" + ] }, - "papermill": { - "duration": 0.014044, - "end_time": "2024-12-09T08:42:07.200503", - "exception": false, - "start_time": "2024-12-09T08:42:07.186459", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "num_matrices = 10\nnum_samples = 10000\nnum_channels = 10\nnoise_rank = 9\nsignal_rank = 1\nunfavorable_SNR_dB = -20 # SNR in decibels\n\n# Generate noise matrices and mixing matrices\nnoise_matrices = [rng.normal(size=(num_samples, noise_rank))\n for _ in range(num_matrices)]\nmixing_matrices = [rng.normal(size=(noise_rank, num_channels))\n for _ in range(num_matrices)]\n\n# Generate sinusoidal target\nt = np.linspace(0, 1, num_samples)\ntarget_signal = np.sin(2 * np.pi * t) # 1 Hz sinusoidal signal\n\n# Generate signal mixing matrix\nsignal_mixing_matrix = rng.normal(size=(signal_rank, num_channels))\n\n# Prepare data matrices\ndata_matrices = []\nfor i in range(num_matrices):\n # Create noise for current data matrix\n noise = np.matmul(noise_matrices[i], mixing_matrices[i])\n\n # Create signal for current data matrix\n signal = np.matmul(target_signal.reshape(-1, 1), signal_mixing_matrix)\n\n # Adjust the power of signal to achieve the desired SNR\n noise_power = np.mean(noise**2)\n signal_power = 10**(unfavorable_SNR_dB / 10) * noise_power\n signal = np.sqrt(signal_power / np.mean(signal**2)) * signal\n\n # Add signal and noise\n data_matrix = signal + noise\n\n data_matrices.append(data_matrix)\n\n# Concatenate data matrices\nx = np.concatenate(data_matrices, axis=-1)" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Compute Covariance matrix\n", - "C = np.dot(x.T, x)\n", - "\n", - "# Compute mCCA from covariance\n", - "A, score, AA = cca.mcca(C, 10)\n", - "\n", - "# Compute the recovered signal using first SC\n", - "x_recovered = x.dot(A)[:, 0]\n", - "\n", - "# Normalize the recovered signal\n", - "x_recovered = x_recovered / x_recovered.std()\n", - "\n", - "# Compute variance across SCs\n", - "variance = np.var(x.dot(A), axis=0)" - ] - }, - { - "cell_type": "markdown", - "id": "20abce69", - "metadata": { - "papermill": { - "duration": 0.000994, - "end_time": "2024-12-09T08:42:07.202711", - "exception": false, - "start_time": "2024-12-09T08:42:07.201717", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use mCCA to recover signal in noise\n\n" + ] }, - "tags": [] - }, - "source": [ - "## Plot the results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d1bece33", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:07.205279Z", - "iopub.status.busy": "2024-12-09T08:42:07.205149Z", - "iopub.status.idle": "2024-12-09T08:42:07.593077Z", - "shell.execute_reply": "2024-12-09T08:42:07.592760Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Compute Covariance matrix\nC = np.dot(x.T, x)\n\n# Compute mCCA from covariance\nA, score, AA = cca.mcca(C, 10)\n\n# Compute the recovered signal using first SC\nx_recovered = x.dot(A)[:, 0]\n\n# Normalize the recovered signal\nx_recovered = x_recovered / x_recovered.std()\n\n# Compute variance across SCs\nvariance = np.var(x.dot(A), axis=0)\ntarget_corr = np.corrcoef(target_signal, x_recovered)[0, 1]" + ] }, - "papermill": { - "duration": 0.390642, - "end_time": "2024-12-09T08:42:07.594313", - "exception": false, - "start_time": "2024-12-09T08:42:07.203671", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the results\nWhat to look for:\n- One or a few shared components should dominate the variance plot.\n- The recovered signal should visibly resemble the sinusoidal target.\n- The printed correlation with the target should remain clearly positive.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 4, figsize=(12, 4))\nax[0].plot(target_signal)\nax[0].set_title(\"Target\")\nax[0].set_xlabel(\"Sample\")\nax[0].set_ylabel(\"Amplitude\")\nax[1].plot(data_matrix)\nax[1].set_title(\"Target + Noise\")\nax[1].set_ylabel(\"Amplitude\")\nax[1].set_xlabel(\"Sample\")\nax[2].plot(variance, \"o-k\")\nax[2].set_xlabel(\"SC\")\nax[2].set_ylabel(\"Variance\")\nax[3].plot(x_recovered)\nax[3].set_title(f\"Recovered (corr = {target_corr:.2f})\")\nax[3].set_ylabel(\"Amplitude\")\nax[3].set_xlabel(\"Sample\")\nplt.tight_layout()\nprint(f\"Correlation with target signal: {target_corr:.3f}\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "fig, ax = plt.subplots(1, 4, figsize=(12, 4))\n", - "ax[0].plot(target_signal)\n", - "ax[0].set_title(\"Target\")\n", - "ax[0].set_xlabel(\"Sample\")\n", - "ax[0].set_ylabel(\"Amplitude\")\n", - "ax[1].plot(data_matrix)\n", - "ax[1].set_title(\"Target + Noise\")\n", - "ax[1].set_ylabel(\"Amplitude\")\n", - "ax[1].set_xlabel(\"Sample\")\n", - "ax[2].plot(variance, \"o-k\")\n", - "ax[2].set_xlabel(\"SC\")\n", - "ax[2].set_ylabel(\"Variance\")\n", - "ax[3].plot(x_recovered)\n", - "ax[3].set_title(\"Recovered\")\n", - "ax[3].set_ylabel(\"Amplitude\")\n", - "ax[3].set_xlabel(\"Sample\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.584418, - "end_time": "2024-12-09T08:42:07.914496", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_mcca_2.ipynb", - "output_path": "examples/example_mcca_2.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:05.330078", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_mcca_2.py b/examples/example_mcca_2.py index 696c766e..bc53676e 100644 --- a/examples/example_mcca_2.py +++ b/examples/example_mcca_2.py @@ -14,7 +14,15 @@ to 10−20, i.e. a very unfavorable SNR. The noise is of rank 9 and the signal of rank 1, so signal and noise are in principle linearly separable. -Uses meegkit.cca.mmca() +The useful question here is whether the first recovered shared component still +resembles the known sinusoidal target despite the poor SNR. + +Uses meegkit.cca.mcca(). + +References +---------- +.. [1] de Cheveigne, A., Parra, L. C., & Bialek, W. (2018). Multiset canonical + correlation analysis. NeuroImage, 186, 728-740. """ import matplotlib.pyplot as plt @@ -89,10 +97,15 @@ # Compute variance across SCs variance = np.var(x.dot(A), axis=0) +target_corr = np.corrcoef(target_signal, x_recovered)[0, 1] ############################################################################### # Plot the results # ----------------------------------------------------------------------------- +# What to look for: +# - One or a few shared components should dominate the variance plot. +# - The recovered signal should visibly resemble the sinusoidal target. +# - The printed correlation with the target should remain clearly positive. fig, ax = plt.subplots(1, 4, figsize=(12, 4)) ax[0].plot(target_signal) ax[0].set_title("Target") @@ -106,8 +119,9 @@ ax[2].set_xlabel("SC") ax[2].set_ylabel("Variance") ax[3].plot(x_recovered) -ax[3].set_title("Recovered") +ax[3].set_title(f"Recovered (corr = {target_corr:.2f})") ax[3].set_ylabel("Amplitude") ax[3].set_xlabel("Sample") plt.tight_layout() +print(f"Correlation with target signal: {target_corr:.3f}") plt.show() diff --git a/examples/example_phase_estimation.ipynb b/examples/example_phase_estimation.ipynb index c3089120..f52d4499 100644 --- a/examples/example_phase_estimation.ipynb +++ b/examples/example_phase_estimation.ipynb @@ -1,410 +1,122 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "71b50b3a", - "metadata": { - "papermill": { - "duration": 0.002135, - "end_time": "2024-12-09T08:41:44.253369", - "exception": false, - "start_time": "2024-12-09T08:41:44.251234", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Causal phase estimation example\n", - "\n", - "This example shows how to causally estimate the phase of a signal using two\n", - "oscillator models, as described in [1]_.\n", - "\n", - "Uses `meegkit.phase.ResOscillator()` and `meegkit.phase.NonResOscillator()`.\n", - "\n", - "## References\n", - ".. [1] Rosenblum, M., Pikovsky, A., Kühn, A.A. et al. Real-time estimation\n", - " of phase and amplitude with application to neural data. Sci Rep 11, 18037\n", - " (2021). https://doi.org/10.1038/s41598-021-97560-5\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "46e2f951", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:44.257073Z", - "iopub.status.busy": "2024-12-09T08:41:44.256883Z", - "iopub.status.idle": "2024-12-09T08:41:45.335037Z", - "shell.execute_reply": "2024-12-09T08:41:45.334694Z" - }, - "papermill": { - "duration": 1.081467, - "end_time": "2024-12-09T08:41:45.336317", - "exception": false, - "start_time": "2024-12-09T08:41:44.254850", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.signal import hilbert\n", - "\n", - "from meegkit.phase import NonResOscillator, ResOscillator, locking_based_phase\n", - "\n", - "sys.path.append(os.path.join(\".\", \"tests\"))\n", - "\n", - "from test_filters import generate_multi_comp_data, phase_difference # noqa:E402\n", - "\n", - "rng = np.random.default_rng(5)" - ] - }, - { - "cell_type": "markdown", - "id": "31d917c2", - "metadata": { - "papermill": { - "duration": 0.001334, - "end_time": "2024-12-09T08:41:45.339234", - "exception": false, - "start_time": "2024-12-09T08:41:45.337900", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Build data\n", - "First, we generate a multi-component signal with amplitude and phase\n", - "modulations, as described in the paper [1]_.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f9535d95", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:45.343071Z", - "iopub.status.busy": "2024-12-09T08:41:45.342618Z", - "iopub.status.idle": "2024-12-09T08:41:45.348609Z", - "shell.execute_reply": "2024-12-09T08:41:45.348078Z" - }, - "papermill": { - "duration": 0.009519, - "end_time": "2024-12-09T08:41:45.350001", - "exception": false, - "start_time": "2024-12-09T08:41:45.340482", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "npt = 100000\n", - "fs = 100\n", - "s = generate_multi_comp_data(npt, fs) # Generate test data\n", - "dt = 1 / fs\n", - "time = np.arange(npt) * dt" - ] - }, - { - "cell_type": "markdown", - "id": "43296f71", - "metadata": { - "papermill": { - "duration": 0.001203, - "end_time": "2024-12-09T08:41:45.352618", - "exception": false, - "start_time": "2024-12-09T08:41:45.351415", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Causal phase estimation example\n\nThis example shows how to causally estimate the phase of a signal using two\noscillator models, as described in [1]_.\n\nIt compares three causal estimators against the Hilbert phase reference and\nreports mean absolute phase errors.\n\nUses `meegkit.phase.ResOscillator()` and `meegkit.phase.NonResOscillator()`.\n\nThe comparison is easiest to read through the phase-difference traces and the\nprinted mean absolute phase errors.\n\n## References\n.. [1] Rosenblum, M., Pikovsky, A., K\u00fchn, A.A. et al. Real-time estimation\n of phase and amplitude with application to neural data. Sci Rep 11, 18037\n (2021). https://doi.org/10.1038/s41598-021-97560-5\n" + ] }, - "tags": [] - }, - "source": [ - "### Visualize signal\n", - "Plot the test signal's Fourier spectrum\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0bda5bcf", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:45.355949Z", - "iopub.status.busy": "2024-12-09T08:41:45.355747Z", - "iopub.status.idle": "2024-12-09T08:41:45.544540Z", - "shell.execute_reply": "2024-12-09T08:41:45.544095Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.signal import hilbert\n\nfrom meegkit.phase import NonResOscillator, ResOscillator, locking_based_phase\n\n\ndef phase_difference(phi1, phi2):\n \"\"\"Compute circular phase difference between two phase arrays.\"\"\"\n cos_phi_dif = np.cos(phi1) * np.cos(phi2) + np.sin(phi1) * np.sin(phi2)\n sin_phi_dif = np.sin(phi1) * np.cos(phi2) - np.cos(phi1) * np.sin(phi2)\n return np.arctan2(sin_phi_dif, cos_phi_dif)\n\n\ndef generate_multi_comp_data(npt=40000, fs=100):\n \"\"\"Generate multi-component data with frequency modulation.\"\"\"\n dt = 1 / fs\n t = np.arange(1, npt + 1) * dt\n omega1 = np.sqrt(2) / 30\n omega2 = np.sqrt(5) / 60\n amp = 1 + 0.95 * np.cos(omega1 * t)\n p = t + 5 * np.sin(omega2 * t)\n s = (\n np.cos(p)\n + 0.2 * np.cos(2 * p + np.pi / 6)\n + 0.1 * np.cos(3 * p + np.pi / 3)\n )\n s *= amp\n return s" + ] }, - "papermill": { - "duration": 0.191754, - "end_time": "2024-12-09T08:41:45.545766", - "exception": false, - "start_time": "2024-12-09T08:41:45.354012", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build data\nFirst, we generate a multi-component signal with amplitude and phase\nmodulations, as described in the paper [1]_.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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9AgAwcfFhfzZNEQjBToHYxjJnzyZvmgo1iIRgRw9UFkIw/vcD+GDZUX82zS9IaWkICHM9zBaCsfP34o1FB/H6wgP+ap5foE3t/EuaEWYsBNvTQi/lR6t3V6P9lDXI90BgJ4Rg8b5LeO7n3SgoCR1BvyxuU6E4RtLjApGY+NCfLYTg5NVcXL6VxDjUMFkI66pDiOTYGYqCviuEYKdA1HKSHYDM/BIUGR1D/4ONPedvYuXhdGaZ2Q1fuvTsIszfeQHfbzyDQicpEIKN8zcK7P0pKdgRVitjshBsOWUVav4+eMXnbfQ12QVGPPPTLqw4lM69uNh7gDfPh2qUOADJNCc2LJx7wovz92H5oXT8+O85H7fM92w9fQN/7r3ktmsCTShaNejLQBgTpPPPGbnFuPPzjej84Vq/tM/fOJvoSgVPhLpZ2hmhOyoGMTaVsrMb8kZeCdq8txqdpq7xc6u8z4Pf/otR/9uN8zdKI7joMVnqcaSdhEuC3Odw34UsdP94HYb+YE3vIecwT4i0JivYmbbmBFYduYpnf97N+thZpE2xhDgPNApm6D52dW8zWmwL/VIPfofxx2duw7hf9zHCrbtRj7Z8bp5oPJUOfeZMyh+L82fldEZpEuNQDDYjhPXHtliktbshqMB1iRDsFIjdx87Juj3nswCUZuMPBU5mlDq+M6kKmI+lX6SiwoKRBbsuAAC2p7lORs1o7EJMK0FnzZcywVvXlX42W6TNL8EKfb50KgtXhGogBZ3GxVkaE2e9b7IQNH1nJZq+s9Kja6g0/m9zGh6Z8S9yioyMUMtrqmzQYwKbCiSEbggKPs+p1CQvFE3zrqhwgt3UqVNx2223ITo6GklJSRgwYACOHz8e6GYxqGSiYnWa0HqRAezAQ5+dOxq7YBfstG6qnKymWGlNVrCjYV5cpcv5JMSM/52MX02wIifU8hCJiU+omp7clU9oTd31vOAts/Xe0iPYefamQ5Qv3b9GC53uhAqegHPhL9gwmS04fS3PQagnhDPFyowFQXz6ZabCCXYbNmzA6NGjsW3bNqxevRpGoxF9+vRBfr5yypHY3vXOxmc6OiqYH1gaKVOBVKJRxg8vyC+BmhPs5OQUqZl6KECboJn8bA4DOph1oSfYlX52lZuOXs8HF1Vk6HspmDV2NnIKjaxmkp74SPjYsf64wXsN3lh0EHd8ugF/7LnELOd9jgkhrLk6xMZHT6lwJcVWrFjBfJ8zZw6SkpKwe/dudO/ePUCtYpF7WanACnaukpgqFcLNtuzL6W0kfutOgEWwoOH6Wqo3CUL75U1fBtbcym7HCreh5WNXZDRzTvLy20utD/JHQjIy3l1CRaixUcIl35XSaLMT3tCYBP62y5qD7os1J/FQ2+r25QSOYwF9kZyVG7TdVxWhQkWF09jxZGdnAwDi4+Odri8uLkZOTg7z52vcfVkF88udiW6U1Ng5/y1jijBbsPbYVdwIUpOLu4I5IYS5TkFugXZAI+FDxN/j/AuL13gGK3O2pKHpOyux7lhpuTxXT7fU+mAvqcVOYDz/fTCPi84wWyygp3xSQptUHrtgFuxsaDkXJEIcg8noLfhzNpktGPDtvxg2e6cvm6kYKrRgZ7FYMG7cOHTp0gXNmjVzus3UqVMRGxtr/6tRw/eFtuVmFJ7M6JWMO75EUqYo2sdu3vbzeGrOLvT7crNX26dEXM3CL94sCNo0OGq3BbvSz4QQhzQQFgvBsfScoHuZTVpyBGYLwYvz99qXlVVAC3YlFTs2eH4NWHO9N1oUWMwWXqNN+9o6/8wKfAR/H7gS1PVjrVGwtGWHyI4F/Lhx9kY+9l/IwoYT10IiRZYrKrRgN3r0aBw6dAjz58+X3GbChAnIzs62/124cMHn7ZKoHuOAyWLBvgtZQfkyZ82vrjV2UjPQFbfy4KXnBH+KB0BeqOcTctKcuJqLrh+twwPf/uurpvkU+p5n03iw2/EmfFogNFsIvt90BndN24SPVyorIMpdTMzLywXci82GmRCsPnIV/b/aHFSl1m7kFWPzyevs2FBOwSwE5DqH8ZEdCy3UZ+dC3u97LmL0vD3o+tE63zXSx5gsFgdlAGPB4G4UfmJHjxPFpuB7X3pKhRXsxowZg6VLl2LdunWoXr265HYGgwExMTHMn6/h/a5o6Pt31qY0DPhmC0bP3ePzNnkbSS0d4zzuHPqh1YVwgloaZ6YHmmW3khS7KpquJDJyijDpr8O3hA/PNXZmroyQmRB8uPwYAGDGhtM+abOvYfrVA6mE8U0lwMifduHgpWz8J4iqkjw4/V888X/bse5Yhn2ZqwASVxACnL6WF/TR82weO0qAY4InqAhZ6vPmk9d92jZ/YDJzGjo4ltSj/c/5CSE9YQ723KfuUDHeihSEEIwZMwaLFi3C2rVrUbt27UA3yQHZ4Alq1Q9b0gAAa6iBUMmYzBbM33EeZ67luecHI7EJkx4lNFysALg2O0klJgXkJwNK5T8LD2DOv2fxyHdbmeVymkn6vAlxDCYKJVz72DnXbNHLg6mk1Llbicrnbj/vtX3+feAy7vh0A0b8uMtr+/Q7RNoFRyqPnZkzWwY7fLonEOKQoFvFTfJopIThUKXCRcWOHj0a8+bNw+LFixEdHY30dKspLzY2FuHh4QFunRV339HBpq36Y+8ljP/jIADg0OS+9uXu+NhJZdkP9jGLbr4ruUTOx04ThPkNd5+1JmXO4pJty0VFOpYRgsP3UPCrAjwT9KXdGYLvYkgl2nWGq7Ob/e9ZAMCGE9fkN1Q4KqngCQl/O6nUJ8EMX0JPbjx0qDEdQrlP3SG4JAMvMH36dGRnZ6Nnz56oUqWK/e/XX38NdNPsyGns+DxewcS+C1n2z1IaOylhjkYqPUowQAjBW38etJsM5SK7mN/xtRG5ixOMGjs6olUqnYOjj13pZ2d+NcF4HaRwdW9Lar1lNLvBAJNot5zND4U8hwRy6YBoTRTtb0dfw+C7B5zBV1fhNftygh79tSIIdhVOYxcMN7ncYMTPWoIJusoCoZ4tt6yytBZHIl9TMHA5uwg/b7OamsbeUY9Z5+pcJLWWCM4XmFSqF6kch4BjxKSKC55Qq1Uho6JwdWtbOLO0/TO1TTAKdrS2qfw+dsFz/tdyizHpr8MY1CEVXeol2Jc7i/62QWs3GY2dxL0RrBAQB/My74vLV6WhkYokDlUqnMYuGJB7RwdzKD/9IneWQJKHeUFJvOyD7RoUUOWOCkrMbueb4n1M+OsXjPncaGFUqsg5/2Lmk5K6q/FUIoQQLNh1AcfSnQe8uBL0JRN7B/HzAbAau/K+g4Pp9D9ddRx/H7yCwbO2y24nlYCZ+UzntPNeEwOKw1jAVSBiNf3SptlgnOx4SoXT2AUD7mrsgk1bJZWEVuos+KgnGyaz82twKiMXq49kYHiXWgjTacrfYB9Azxb5ckdmazSAUwjkBycN52sWDBVJpJoo1e/8dzNvfiHB5Sa++shVvCoTteqqqopZQustV2s3GKDN7+WuLBNEp3/xpvNAFwLWx46+y5k8dhLaO1MQmR4JIVh/4hqaV4tFQpSBWi5vijVb5E2xcimUQhEh2CkQuTx2ZU2HoARoB3/m5e1G5QkpzRa9vPdnGwEAWYUlmHB343K21jfQbS82WdjgCRcvMXe1exZCoAkC3ZV00W65AZo1xdLvrGCLdjt6RT7HnCuhTPK5oT8H1yUBwLbfWE7BLphOX9o1AcyET0ozRZuwpYQ8i4Xg7I181E6IVGRprZ1nb2L47J2IMmjZADs41gh3MMXKTGikLD6hijDFKhB5jV3p52C7PZlZJ6NhcG06kqwt6+QH207fKEcrfQvdXD5RpsmFKdHd+o/BYmpgTLH0i4vW2DiYYks/87P0YNNg67TyL1ZPoqTp0YARfoNulODN6+VTrwSTj51WSrADJAvcs5UnnJtl6fHg3aVH0OvTDZi1Ka38DfYBf+2/BADIo1xWAMc8nhZC5L87BE+w2x69koMPlx9DThEbkR8qCMFOgcjNpHiNhY0daZl44Ze9yMgNjgoMcv5izjBLPLTOfmlUsOaGL9pttkgPRjSEm5HyQoyKM3NfyipUfA4zKVOsXFQkL9wyjuQWElSzHb2LdEWuHgspX9MgugTILjBi1eF0xi2BL4clS/lWKwp3gokANqpTqtqEVE67ObfSv3yw/Gi52uortDLmKsdJHf1d/p3CC313f7EJMzacxscrgrNCjSuEYKdA5NyjpPyPBn63FUv2X8ZrQZJpnler25B6mUlFPDnT0ih5MGcFOfmyODQEjoEDUhSUmNHlw7Xo/OFaRYb2X88rdohopbW5Ui8lwFG7S59esGgqbbiKZHalbZI0zXM/M1sI/tx7CRcylVcr9Kkfd+KZ/+3GzE1n7MvYZLIu7l8X1kT6Gv194AoGfLNFsTVT3fWLlRbmLE6XO3sulKrIlLsGspNizi3DMa+d83V7L9wsR2uVixDsFIjcgO8qo/jxdOXWhpTKnu6Og7SU86uznyrZ/MKG3VscBit3tbVyQuCNvBL7Zz75b6D5+8AVtHv/H1lTkIkbsGkYYYYPnrAQJ3U1SdAW/XYZFSsxKeBzQf6+5yLG/boPd36+wfuNLCe7z1lfrPOoahP0WbsU1l2uLt1g9Lw92HchCxNuJUkPFghYjbyU0CZZXiyIJjzSch2XzoR3w3D4zv5aSpunwHmvVxCCnQKR19g5/2xDwTINg7v+YvZtJLZ3JsQp+Rrwka1ms/RgxEA4UwQ3ItHyID2oK01jN3nJYQDAlGVHJdP68DNxGjlTrLP76LPVJ9Bs0krsPpdZnmb7BFe+665uY3fM+ATA+uPWkoNFRmXdCzRSKVpcCSWe5H60cTUnONxV7HDnwGjmJIIkgjW9h1zaJn5Sx0eCu5v/0qtR1wpFCHYKRE5rw9bLc7Fewci9vJ3Ba7psOPupkh3G+VmnXLQW6wTPzzqlj2GUGPiVAP3ykQqekMtjxgszUgK/ja/WnoLZQvDuUmX6FMnh6qUjlXSVfyaUGP3II+Ve4er+LYtCT6nvcqkkxABrcZbSaLPBE86XKx2pyjGEOPpZ81o43gePhr4EZolrFkoIwU6BuOtj50yAUdqg9dnqE+j35SbkFBmZwclzwc793ypZtuXPg59ZS10j/rtD1JeE302JwjR2WokyYjRSeQoBx3xt9GUwWYhk3xcbg88c6zJ4QsIMx/9OKtpSSUgFBrl68bocO5xaNRQ8QNyCdZkk3MTH+fUxSvimBpXGTkqwg6O2nk/7IhchL5VeK5iujSeIPHYKxO10J0EwaH255iQA4MctZ5nlnhapljK5Ofupsq4AC6+hkxuMWC0McTBF0NDXUMmmWPrWlrrLGY2dTPCEmbB+NxYiratVkib7g2VHsfX0DdzVLEV2O5d57KS02PRVIO475QcSqfJo5TXFBlNwFR1A5G46I6lAI5NE5KzSkTLFWsc/+bFTTmMn6cpT3gYrFCHYKRA5wY7P3cOjpBcYTV6JidEcSOVZkn4xl3525VtkIQQjf9qF2HAdPnmkZdkb7SXSs4tw6FI27mic5KBZ4yMbpXzlAHm/RHqdUcKJWgnQLy8pEyFramLXyc3S5cx2Snq5fb/RGgHq6ll1JdRIpQUJRo2dVOoWV6ZYV/0abBp9G3K5PaUCI0wS94PS3DHkkMrXBzh59vlgCpn3gqv3ZqghTLEKRM4lxmXwhPeb4xWMJsK81OnByR0HVnf9iQBraZ7VR65i4e6LKFKACW7Y7B0Y8dMu/LHnEmsiMcuXweEFXlkfElqwk8gJpgSkNHZSqTtkgyeIfG4/GiWO5TfzS2TXu/axK/0s55eoCoIqJKzGzrkm0unvXPSr8wAz5dwM13KLkW2LXJfIGkCI9LPvjpZOSvN7KasQhy5ll7HlvoGZ2HJ9z56To/VKLo+dXNLzUEQIdgrE3XQnzlBqlA8/QEuZYqUGXakXv6uzLTYFXmN17FYKmlVH0h1MsVLnBTgmZ5XX2JV+Zl/yyrofVBJfmPqXMnns+Jk3n9NL6v5RYhkhV4+qSzOkhIaGqULB+WcpFbr1chMYbx8rkOQVm9D5wzXo9el6h/tWLqrTHV86oxtjQJcP1+Lerzbj3I38sp2AD6DffY4TW1YjJzfJ458dqRQnChwWvIIQ7BRIeSwnthtVaQIe/wCxL2/PhC9XCYqZCDIFmSKLTRbZXExmwmo1+ehfOXOClFlOQafvgJqpllG6XKoWML+dxSJfRoj9nbKeB6D8bWKi+8zsveLss6KR0Lz6ooqMUu6Fs9fzYTQT3MgvQS5XQosPkpMqs8Y+986j4V29CvZdyPK06V7lSnYh/j19HYB09K91/Ctd5yzVkdxYIFmSUjFivncRgp0CcZWRXg4C4P2lR9D2/dVIz1ZOvib+ATJKhOa7tS8X5jdfvxjKioU4OvFKRWsBXKQj5J2DCXE+wCvPFEtXmyjFoTTYLeQ0F9YyQqXr5O4jJWY1KK+AIeWe4FhurlyH8TqE07QCvCBTik80dgTYcOIaftt1wev79gQ6qKXIaJb0LyOEvX+l68M61955kjYnEPT/agsGzdyOTSevsdG/jN8ocTAvy/nbylk0+OuRX2zC7C1pii/B6AlCsFMg5ck7RQjBrM1puFlgxNfrTnqxVeVHytG9PJnRnf1UahYbaPh6r471Djnhl9PCyPqQUF8Z/0VCMHXZUby2cH/AfIsIITh0KdvB35G+zZkXl1najMT71LlbuUNpAq6V8klc7DWj741y7dbnzNx0BvXfXIY952/al7HBE759fgkBhv6wA68tPIDDlwPnY0b3WTGXPJpPUyQ1drpTecJ1BRM3G+wjrucVAwA2nbzORUTTkd6OplhZQU/GosFbBD5eeRyTlxzB4Fnby3sqikEIdgqkXKZY6rPRpJwRnncA9loSXSc/lTpOoLHOvFmBVq5ygiezTiktZWGJGd9tPIPfdl3E6Wt55Wp/WVl2MB33frUZY3/ZywhzrCnWuaDPv5PkzC9mC5F8dpRocimvJk0q3Qmj7bEeyem6QPHBsmOwEODdJUfsy6RMh74WyM9eD1zdWNpqUWQ0M5MS3gdZSjMtpb2TqsXtDE9dYXxFsdHMuRdIj38O6U4s/CSP3bfUpJgQYPWRqwCAtOvK8TUsL0KwUyDlMcXKaXUCCT/jYkP2yz6wuHphK0lTwzv7W3iNHTdY8SkspJIQ2/ZlX0dd2yJTqZassCQwA/hPW88CAFYduSopzLgT9WfdjjU58hnlpSJAFfQo2CmvhVTqJciX3KOvuZkQpF3Pt5cZCyTXcovtnyXrn/rg+VXKGMlq2DhtvYnVuDGuChKuFlIaO1coZe5rsrApS0rowDfeYmFxzOvpfkkxdl0w5Hn0FCHYKRB1OXrFlf9ZoCBEbhDyrimWRkkFsHnTocmJX4hUuRuLkxkqu+/Sz/T1pAfHQL3EtBrar855Hjtaa2CU0DwAjmkL2Fm6hZGWLB5oLfyBtycZdHcy14z3sePacPsn6zFs9k7GFBoIpK6Hr10G2OCSwN0YvC+sSmadlEZbSgj2ZNxTijabQL5qDp+M3MHfVqYGLDtusMcNhjyPniIEOwVSLh876rMSzC407CBED1zytV/lcO0/Ip0Cw99YOK2b2WJx8AthXtacFobX7rH7pgRl6nqWKCCnnZQGmnmR0deFc5qmkQs+MVtYNwb25Ubs+wvE/bDl1HU0n7QSC3zksM+YMmX6mb4Hdp8NrGAnJXz4+jalBZlAavSZCjEWC5ecnBdiStdJmmIlEhe7QimvCULY55seu6wWn9JtHUqKOQh60uMjP3YKjZ3AL5THFEtLdoF2orZwL1bWKdq59s5TXL2jt56+gdbvrcYfey6W+RjegjjR2LEmFr6SApt0lnBCDI2UWa6E8WUMjM1FI1EfltZMS72U+EGYNTnyKRAsjEbQWfb+kT/tRv+vN6PY5N/E1a/8th8FJWa8uvCA0/aVF2YSwEWYStcZDewAIXX+vha2WIHAp4eSxSQzBspp7OiJsIkRDssmsCpl4guwplg6BylxsFg4y2tXuid3TbGEkPK9bxVKhRPsNm7ciP79+6Nq1apQqVT4888/A90kB8oXPMHO9AKB7aXJO8GbGcHO+YDkbaYsO4qsAiNe/m2/z47hLrwTtEO6E+KYcJdeJ2eKZTV9zjV2vGnDX2gkUpywlUiktLnsvnjzNOuPJy3E2K7dP0ev4tClHOw+519tlTOfJ2/e9lKO8wB7ndmkxgpxruLwtcuAJ6lAfAmbqoTV3vOCnZwm376PMqY5slgI/j19nfF5DARWd53S744aO3bywlg/iHweO38G5CiBCifY5efno2XLlvjmm28C3RRJypXHjp6NBuAGXn3kKpq9sxK/7jzvJOS89LNRQpOkFH8PX0D4wcjCOf9zGqgSh1l76TqHzOoSvin0rDdQNSOZ3HUq59o7qdx7/D3kaKJiNRmymeup7/4uNefM3ONNTYlctQ5mOwVp7KTwtQZJLp2OP+GtFhaJZ1jOzChXV9ldNp68jkEzt6Pnx+vKtgMvQQjbH44pX0o/O5hiOf9kWY0d74Maego7aAPdAH9z99134+677w50M2TxkiU2IJGAry3cD6OZ4PXfD6Jfi6pcW5ybYqVmoKEGARfxyQ/YhDfF8qYG6cGJMWVKBE8ESkMjVR9Wyllc7n7gs+4TbrDnAwVsWCyEdcz2c6k5Zw7a3rzVpQRjXlPO3DcBNsFJCXC+HgI8KUnoS3gtq3T1GAKN2rmm1Rvj5bpbEdL5JYGtq81r4BmNHXERPEEcBT1m3xJm6lB921Q4jZ2nFBcXIycnh/nzNd5KdxII7ZdUxBb/XaqkmGLcPcrBtdxi3DVtoz3Nhw1+Rmo2EwcNq1RKGD6wwiGZsYRJpkRCYPInailpTiJvl1y6E15jJ5cagy/Jxn/3J84CorypLZLTckqlkFDqo+bryZ1clQ5/QucZNZotrKsKE83ufjqgsqBRiMqKQDp4wpnWkvc5lvLhtv6+9DOfxy4UEYKdC6ZOnYrY2Fj7X40aNXx+TG/UigWsN/N3G05j4IytyOdqEfoKvab0lmJnRtKpPILBPOQJP287h2PpuZi4+DCznNfY8elOTBYiaa428/5kMhq7ErPzF0R58gWWBybFCbVcqsKA3OSANzk6DPbUtvTpEnjHbOVNvPlScXdCxZv4Akmgjs5rcp/8YQcm/HFA5hfexXbf82UVpfwf+ZrIcn1dFgIZPEA4IUvqGhAnwi2voZNK2G39zvZ5qCMEOxdMmDAB2dnZ9r8LF3xfX9BbDxohwNTlx7DjbCZmb0nzyj5dQfsS8U7wUv5j7voHBQsZUk7IxLEEFh8QwUfJlq5zjOySqqtolNHYTfrrMCYuPuRzPyazheCV3/bjh81pknkZLRKzczZClh2gzUzwhONMXCqlDiHEofZuoPGVoM0/QpIpJPx4Ca7lFmP3uUz/HVAG+iV/4moeNp64hl92XPCLq8Kbiw6i5yfrkV1odCiryPQTZ26VCpLwxkTY33JdicmCiYsPYcGuC46TfwntMv9sO+SwJKy/Mq+xk7tmtDY9I7cIA7/bit93Bz6LQnmocD52nmIwGGAwGPx6zPLksWMpvYFvFhi9tE/3YQtXW9jILpPzF7BSI/U8gVJaOpR34qtH8IKJ1KyT9yGx+ZPZFkmaYqnB8UZeMeb8exYA8FLvBqgUqffsxDxg7/mb+H3PRfy+B+jXvIp9OZuUuHR7Ka2tQ0Z+buZNrzWbpZ3MrRq7wOf0o/FVMAt/bvRzV+xn30IbY+btwfa0TCx8tpN9mRLMYLRwUGQ0Q6fxra5j7vbzAIBfdpxn/C5NZoukjx2fx05KyAsWNp28hp+2ngMA9G9Z6ocNApipJ5pOSeRMO++Y7sT55Jb/buGESZpv1p7CjrRM7EjLxENtq3t6aopBaOwUiLfyJTKqaj+Nomy6CW7WKZXuJMQ0dkxkJm9qYAZoi0OtU3o9P7jz2zK1JekgCQs9OJYuL6QiQQt8HBWamV9S+kWqfquUKVbGOdxk5oVddtZO3+a8L5VUUIkvuXizwF7knMdXbgeyptgA+dhtT7Nq6xbvu+zHo3pGoR8jpQtLzFxyYWnBhPcnk8pZWFb8bZLPLSp1C5KLXC/htW6cKwXjc0xcCXbOJ46EsMNTIBQgvqDCaezy8vJw6tQp+/e0tDTs27cP8fHxSE1NDWDLSvGeKdb/fgVszqzS5SYzYRPScjmcbPhSY5d2PR9nr+fj9kZJPjsGAJmUG4551xz8RmR8THgfO/ouYa6hybnGrshIpUHx8UuM1nyY3QiM4M1SNvj7gfcvUnHXWsq8a+Gunz8CSTJyi9D1o3WokxDp82PROKQZUoAp1kZBgCMveYqZZ8J/2q8SswV6S+kzYrLwUbHy2nr777xwH7OB0sSLFiPn6LWl500L09YE7qXb8ZMvI1dFhy8ZSF8juYTPcu+YUKlCUeE0drt27ULr1q3RunVrAMDLL7+M1q1bY+LEiQFuWSm+0NgFok4oHyDBag6cD06+1Njd/sl6DJ+zE1tOXffZMQDez5DX2NHnapE1L/CDEy/UWCRm8VL1FuncbT4XbLjC86Vtc+7rwvsU2bdx8JXhTFSy14SNnjDK+O75grRr+QCAM9fz/aoV4TWBrBak9B6waT/zik1+M+kpp8qBlSLK3OfPwC0HvzmLhZkIM5MSmcAAb0yE+Wo4voZ+vxVSgr6FSEdwA86qcZSuczTFSo+dvNaalmOFYBek9OzZ014vkv6bM2dOoJtmx1szJvrW9teYRTedH4xYnyrng5Mvq1DY2Hbmhk/3Tw8OvIbEMSq29He8L43RIj2wOWwrMSNlNXZmp9v4Aql8cmYJc6hUHj7H6Db6hccLyuw14dMlSJkkfYWW0lr607eN71v6vGmtlMlCkHY9Hx2m/IMXf93nl7bRT7cShDzmevjZX43XIEsFT/DjhLejYuluWHbwCh6Z8S/O3cgv936lKR0daI0dHyBBT0IAzr/WSfoT9hpJj520iVcBt6BPqHCCXTAQ3KbYUvgBqCzpLXyBr49BXwN64HKI5LI4+s3JaexkNVkSQkuJhI+dP4NUJIuWM8K985eaQ/AE51/Ep4uhKeZm5lJJsX0FPfn3Z6ULIydESmXzN5ot+OfIVeSXmPH3gSt+aZsShDka2kHf1/cEX6OUrwvNCHomduyWipb3tobtxfn7sPPsTby6wHfpX+jzpjV2Zl64dWWKdfCvlXbhkPLhBaSVEcGMEOwUiLe0wWxOO//fsLzPCPvQ+t8U669j0BpXZuDi/EKc5bHjB3saVjDhNFlSGjvqM90WX7/EWC2Rc3OX1PnIRa8y5aAsfIoD9pqw2lJHjd25G/no+/lG/I9LJO0t6OP5VbDjTbHUV7odVr9X/5qeiMTnQMFqMH072TEy6Xfkc1o6+ti5dmfwJpeyCn2yX4A9t0JmbLBwPsb8pI63WLDCr6wpVmLiyN+FgSq76G2EYKdAvDXYsqpqr+zSJbwzO/1Z0jnYj74tgH99aXjBju4Hi1M/kdLv/AtaLheTlMDEmGIlTJ++gG5PscRxmUheCVOsw8ybCyCRi4Qr5oInjFzAzv+2nsPxq7l4m0sk7S3YZMA+OYRT+L6VMkEbLRboNCrJ3/kCZn6pgHdokR81dvz++SwB0uOjbytP+Bv6OhRyfr9S5mjAmY9x6TqrGZfalp/kSUx8rVGxzt9ZwYwQ7BSIt0yxTHmxW6rqnCLvhnMXm8x4dcF+/LXfMY0BL2ywqSj868hO4yvtpc0UYJKYkRq5YAl+lu5YK1banMC/JKRqwkr52JWYLT4xi6VnF+FYeo6ktkq6CkLpPqTM9IATjZ2EFpjfv3U9q8mUSkPiLQI1++ffTfQ1KjKyAjQ90vDXyxfQTQtEQBcPrbEzmi2YuvwoFu+75JNjGTnXAEZ7b2YnvsWcf6icNsoX+NJkTj/DRSWsxk7OD7aEM8XybixyJcXYVEeUpo87z0AqHLyJEOwUiLesI2ymfWDSX4fRYtIq7L+Q5Z0DAPhjzyUs2H0RY3/Z67BOVmNn8u9AReMLf8Pp60+j84drcSojlxFGCjnTl2O9Q0h+l4vs4oU+SR87ajvaJJqZX4IuH671eimle7/ahH5fbsa5GwWlxzU5F0ilNKdy58lmnweTHoGfIPAvBiZ4xESY4AZfEKgSbjz0OMAnfaUdjHyV7oPx9eXu/0BDa+y2nr6B7zacwYvz9/nkWLzQwJQP5CZ9jnnswHz3Nb48hFFifDTK5PLjv1sjaEF9l/+tVLAe9wgwY09WQQmmLj/q1felvxCCnQLxlsaO90n48Va2709WHffK/gHgMueLQTedj8CTjPryczSaLwatj1Ycw5XsIkxddoz1ISkpTcZpNDtJcSAzy3T0o5MZuGgBzo3giWUH03E5uwi/7PBuibzreSUwWwiTUobXFNqQekHJOYfzyazlHKblBLsSs9lrEyie3CIjzlzL8/uERQqjxL1h5LS23o7czSs2odhklrwOSnBUp8coOqm2LyZ/JVw/0OXxeI0dXylBTmjxBd7um2u5xXh+7m5sO3OD1dgZ2fFAdozjFQWyUbG8RcM9NyD6d99tPIPvNpzB/d9scXl+SkMIdgrEWy8c3sfIhjcHBi2VddiaMLZ0Haut4h1jaV8f/w7wZkLw4fJjWHHI+5GA1pxgEho7F3nr5AQ52+9Lt5UWeKSCJ+hB1NdmtwJKoPVUYJDytwO4dCcuTFQlJjZggT5/o4n4rPj56Hl70fuzDTh4Mdsn+/cUXqCwYbIQRvgtNnkvwKPIaEbPj9fjzs82OuQTtKE0jZ07yz3FbCG4eUtg5J9RJn+c2SJZdcZV9Lcv8PYhPl55DMsOpuOx77dJj49mC+daIWOKdQiegNvaPj5Qi7Usla47dEkZz29ZqHCVJ4IBb+Wx4/00bHjT90ctIcgBjr5VUm3wt8ZuR1omTmXkAQDOftjPq/s2WwgzcNCZ9vlZuYmL6nTwqXPIxeS5qcHICHZslnf6uN4wS7K+hbTW0rOXJOObyeex4zR2TFk1GYdpa5vYdC8Gncb+3WLxXoToxhPXAAC/7fKuNrSsMMIbdw2MjJnWe89henaR3YfxRl6pJowVzL12uDLDPBOU1FlQYkaEvvyvxy/+OYGv1p3CnOHtUSU2zL68xGRhqrMYLXwON1arRD8G/hCICSHYdyELWrUKzarFlnt/x6/m2T/TzzSfNF2jlr4feS0mn/WBdWOR/m0JN4b4O7+lPxAaOwXiLU0C/dAw2jIvDgz0nvgXOB84YJZ48Pw9wJ+5lud6ozJiJoT1IXFwDqa25U2JDoEC7HdeKJTalhEAJUyxNN6qGysleHqq/XDXVMLXz/XMFGthJiW+SCCcXaiMupOscM+a6Vmhz3vXgBbgrlFBKkoxT9ugr0dROSYjUny59hQIAf674phDfkk2eILPY8dOyv2d/y+/xIRHv9uKe7/a7JVUPQZaiDXJjI8ywplJRovJJzeWq1hTwvn8SkXwK8BToMwIwU6BeMsUK1VCyuxFp24+6pJ+gBiNnZmrgxpAx3Jay+PtAdPqJ+J8RmoyO0tIXPpbPrGsXPZ0+Rmpc5Mt3xZny8uDVGSup5dY1hTLO1DTgh0fFesg2LHtU/k4gbA/q03IwZpieY2dr0yxpfulI/H9mc/PU1j3Ae+2M6fI6PD8mjkLhlQiaX6c8AdFRov9/vXKBIUJUJC2aLhbUozPA+jgh8j75sqUYWOeASM9vgSvZCcEOwXiLZOQlE+VN02x9ABYUMI6SjMJcfkHzxS4h8aXmhoLIcw1YAYuzuFXLimp9bu0tkpuW9Z/0bmQR2vpvKWpKaZKAJXnBS5XUowPnjDLTBaKZU2x7MtSKUKYL2D96Nj7gdfm5RWbsHD3xXKnRaL7P7eoVGCS0horAfpZLSzx8rhgYe/dEt7nmL+XuQlMIFPDFHhBe0m/0STz2Fn4XH7cRM2FBo8xxcoIhQ457iSyCASxXCcEOyXiLZ9uZnYu4W9XXmjhsaDExLyICzkfO6mBy9/QCSm9MWjRWCyQvAaAYy4mRmPnEAEqNzhJBxXQ3SvV1QXFpS/b8mpRsguNWLL/MvKoF3h+cTkEOxmTCp9CR86vhp/x8/5lvLb5QmYBdp3NLHO7lYqU1tZotjho82ZvTsN/FuzHpyvLFzlPjwu5lJDoLROnL6DHAm8FT9gghHDaUYtDWh+5iW8gA0280WdMShEJi4bZRboTuTHP7CJ4QsoUC7CTHcZ9yc+RyN5ECHYKxHsJiks/+yrxIi08FhktkhFPRj7iKYAPSonEwOINCNhrzQ+KTOF73seON8XK+InIpQFxB/oltvXMDXSausZpkml3mLnxDF74ZS/+s2C/fVkeJTiWB8fKE+wEhTdn0fBaOPp+LDaxZsgikxlDf9iBh2dsxZHLOWVur79qMnuClMauhDPFFhkt+HLtSQCwp0YqK/S4QGvslGyKLWQ0dmZMXHwIP2xO88q+zYQ4pjvhIrqlgies6wM3XhYavfMs26DHLtr87RBAIqN1A9ggCN4U6zg+Og+e4I9DPx90/+QUGrHuWAbyvTSu+Roh2CkQX+TX4gW7/Rey8MC3W7CznBoKxuHYaJIUahzyDCnEiTq70IhtZ26Uyadm1qYz+GbdKWYGSjhTLK+xYyKVXZpi5X3uykM+NaDO2pSGK9lFTpNMu8PX604BAPacz/JG0xjkgkT4dCcOL0O5BMWcxq6wxIwz1/MBgMnB5y6XsgoxddlRXLzpuxqbZUVqImM0WxjNUJHR7MXArdJj5gWNKba0nQcvZeOnrefw7tIjXhGqLIS9H/ngCbmSYkBgx8vymKXP3yiA2cKmFqLvwUKmEgor7PITM0dTLCuE0avlXFXkEpnz7ywbs7ecxfA5OzHhj4MIBoRgp0B8kV+LSQRJCCYvOYy957Pw0q/7PNoPIQRD/m87npqzExYLYQSiwhKLpGBnTe2hPNX29PWn8dj32/DlmpMe/a7EZMH7fx/FxyuP48iVUg0PIVzaDxcaO7mkmnJCTXkpoEyl5zMLZLYMLHIlxcwWcOli5H1y+OAJui8YM1wZhI/3lx7BdxvP4P2/j3j8W18jmaDYxGqRikxmaL3m30tr7ILPFJtVUJqi5Sb12RP48ndsahmzk2Tlpb/ltVWBrGJys6AEv+++6HEJvkV7L6L7x+vwxZqTjCmW1ubTCdz54AmXplhOY0dktPd0Xzjkx5OI4Kfv1R+2WDW3ZbVq+Bsh2CkQb+Wxo6EfJoulVLviqYbhWl4xNp28jrXHMnAus4B5UeSXmJjBiU8+yQcOKAHbg/rNutMe/Y5WyV/OKrJ/thA2fN5BY8cIJtJlhADXee3KQyBN4Z7geA1kNHYOPnUyGjtOqKFf3mW5NZcfSgcArDpy1fMf+xjaD5GvPMEnsNaUQ7DbduYG7v5iE7aevsG8IFlTrHLvO1awKxVGb+aXLZCkiPNr5qMv+eTachq7QKaJ+XnbObyyYD/e8FBb9f1GqzD05ZqTzD1Iv4sKmAA7i2R1IkDeLYNPfyJntpVTKtDtpNvmbV9sXyMEOwXiC1MsbWYwWSxlPkY2NeBdzSmSjH4DeEdU5ZhfvQE9OF3NKRXsSrikr7yGgnbgNzk4/8v7zZlD6Pq5Cy9ksRo7V4O5tI8dr7G7SZWTUoo22RfwufyMnON4eQS7j1cex9ErOZj012FGqKYjbJU8oaDvD7q8mCfpPvKKTfi/zWk4f6PAwWLhWFKMDZ7gfe6UwvY0q7uOp5MWWutJP1NSpnmrxq7093KRrbbtbTgEosm4sbh7bb3lJxwIhGCnQHxhiqWjFPmM/Z6QRQ1yGbnFjAYghxsAeW1VKL0waSE2I7dUsCssMTPCBz/Tc8xPJaOx41N9KETLaWPtsas4lZHr12MyCYpdDOZyGjw+cCCT0soE84DuCr6OMKtFMkPDlQj0hN3nbgIAjl/NldTYKRn6fsksKJtgN3/Heby39AheXbifEaILSsxcgmIzGxXLuaooWQB2F/o9Rt939PNVxKeDkkl3IldLmxDIB0+UIZ1RsARKOEMIdgrEFxo73uRS1mPQJorMvGLmZckP4A7aqhDKFUYPThk5pb4nhUazZBkvgBUu+KSkrgYyf5Rem/L3EbR+d5W95JoUx9Jz8NScXej92Uaft4nGIXhCLu+VTFQsb4bMzC/tw7xiE8wWgnM38r3WbqUgV1KsyGSBTlM6MNh848ric8gmKA6+F+RNJxo7dwTdf45atVrb0zId0j3RgkKxyYkpNojTa7iCfhbp9wRf9UYq0Tq/jv/Opz7irUNl0YBKuQudu5GPP/ZcVEStYymEYKdAfOFjR1NoNDO53AixFqrOdSMpKT17zcwvYR4+PqlpoVFeg6I0pq8/jcGztjFmGJ4Vh65g25kbyCtmNZc2io2cKZbPY8ckHuUjhd03PfiKmZvScLPAiJkbz8hud/hS2VOClAeTjF8Nr+HkExQzZkiunFYmNWHJKzLh523n0OPj9ZixQdr38uLNAtw1bSPmbi9fahB/wtaPZq9JETcpySk04Z3Fh9Dq3VXYfS7Ts+NQ++U1+cEAb4od+sMOdP1orcvEzXI5MmlrB2+KNZotjGCi1LHyQmYBVh5Ol6zYU2Q047ddF5CRU8QETEhp7PhrJDf5d7RoUKZY4iJdjBf9k99efBgv/7Yf8xT83AvBToH4whRLY+YqIFzNKUaPj9fh/q+3OJ2FFJaYcTnLGmRB+03cyC9homL5AZyf6SvdvPDRimPYcuoGFu295HT9lexCPPvzHjz2/TZczy29Dtcowa7EbGEGJ4eBy8HHzv0oMH+WYbuUJR9U4+NbVBLWFMvnapTX2BUx5bTYKEVaQ5NTZMQnq6wJej9cfkyyLYv2XMKx9Fy8ueiQZycRQPgXZx6XqJq+X7MLjfhx6zkUGS2YveWsR8ehJzTlrWIRCGgt4/W8Ymw4cQ1Xsouw4fg12d/Rz0V6NvsMZXHRtXzdXhqlauye+d9ujPrfbsno0N/3XMRrCw/gubl7mGtBKw0Yn0uHZMHS2mFHC4Z0Hjseb1aR2HjCeg/M33nBezv1MkKwUyC+MMXy0ALcrnOZyCky4cz1fKRddzQ/vTh/L7p+tBY7z2Y6auwkVOyAoylWyaprmrNOroF1eWlakL0XsuyfaY0dAEabJ6exc3T45dOd+F9jZ6PQaMb64xnYe/4ms3z1kat4f+kRXM3xLPWBt6AHaDNniuUp4V4SfIk9RmNHCXZZBUa3/GtOuDBXBwNMGS2jhY0MLSy9Js5S4hQZzXjs+60Y+8teBw0OnRojWHzspDhN9fO1XMf7/lJWoX3iSwst/DWj3VgAdnzkBRolBU/QHL2V2mn5wXSn61fcig7ffe4m86xezyu9l+SELLnzljXFciXF/EFukQnFJrMiU/hUWMHum2++Qa1atRAWFoYOHTpgx44dgW6SHV9r7HjoTPsnruaioMSE9cczUGS05lpadeQqLMSakJcenG5wgp2jKVZ5N7w75BYZ8drC/Xjlt/2wWAh+2noWSw9cxhVqBn7oUrb9M5/fSW4mzuSx48sIuUhx4M8UMacy8jBs9k48NP1f5BWbsO3MDRQZzXhj0UHM2pwma6L0F3zwCY9DHrsS1r+sWEKwyy40IlKvtX+/nFWIsb/sxT9HriK3yIixv+zFor0XESClpVeho+V5jRItmNgEvoMXs7H+eAYAqx/ZtjOZ+Gv/ZRy4mM389hKVAijYOXWtVLA7dyMfpzJy8eHyY0jPLkJhiRn9vtyE3p9twPW8YmRSAsyFTF5jJz0+Bpv/cYnZgs9Xn8DsLWkwmi34aMUxbDhxjRnDPAk6cQdZU6yLscAX5BYZcfe0Tej2X9cmen+jdb1J6PHrr7/i5ZdfxowZM9ChQwdMmzYNffv2xfHjx5GUlBTo5vndzEUPysfTc7Hl1HXM3X4ej91WAyO61bGvy8wvgUGrYb4Xy2nsglSw23jyuv1F37hKNN7/+ygA4MlONe3bHLqc7fS3rqBlMweHX5lkvP7GNihbCPDWooP4c99l9GmSbNdYeHvQLityxdHlgicI519GR0FmFZRAry2d83666gT+2n8ZKw6nY8LdjfDX/sv4a/9ldK5b2RunEFDoaHk+Ee/Jq6UCzcWbBcgpMuLR77eioMSMWU+2wwkqInrTSdZEecWFKT+YOHOtVIN/5no+3l16FBtPXMO5G/l4slMtu8C25uhV3KAmCBdusho7/vrS2lE+56LS2XchC2uPWQX8ghIzpq8/jenrT6N1apx9G29rah1cU2Sq+PiDmwVG3LzV9/+euoG7mqX49fhyVEiN3WeffYaRI0di+PDhaNKkCWbMmIGIiAj88MMPgW4aAP9r7PZfzLJ/Ppaeg7nbzwOw+hDY/AkAq2aPHpwcTbHsy16pDsCuoLU3tI/V/B2lPhXeGEP49CUOeZsUYrr+c5/Vn0aJyXflzNOO2fvZbWn/Mnrb/BKzfcAGrH5Dtm1sGegB4N/TN8rWaAXB5G7LYwWPE1dLBbciowW/7rhgF0ambzjNaPo3nmTLsPHuCaHC6Yw8+5i4/FA61hwtfSZWH7nKTHQvcKZYfjJEa0vLUtIwkNBj5Mcrj9s/7/VBWUEbcgERZi4Qzd88+/Nu3PvVJiw7eCVwjaBQEanwlhClpKQEERERWLhwIQYMGGBfPnToUGRlZWHx4sXM9sXFxSguLh2kcnJyUKNGDWRnZyMmJsYnbVy09yJe+nW/6w0DQJRBK5nnS6NWBY0fnRJQqVgB0aBVMy8Gfr3AMyL0mqDLGB9I9Bo18/KMi9Ax5sNKETpG4BXIw1/PMJ1a0ZU3lI5axVo8tGqVfbKm06gU4Zf45eOtcV/Lqj7Zd05ODmJjY92SPSqcxu769eswm81ITk5mlicnJyM93dEhdOrUqYiNjbX/1ahRw+dt9LfGzhPkkrcKoc4zeKGN1ygJoa58lCX/WkWG14jwPmE2oS4mrEJ68HiMXK1igec4VKGRyWEZKFpVjwt0EwBUQMHOUyZMmIDs7Gz734ULvg9x9nUeOynqJkbaP898sh2qxYUDACL1GvT30SxEybSvFW//H6nXuNi6/ChZMG5ZPTbQTfAYb17OdjUroWlV6yy5oj0LHWrH2z9XjtRj7oiO0N4K3e/dOBnVK4UHqmkBY8LdjeznfXvDRHSrnxDgFgUG27jQrJpvrFfBgOGWP27fpslIrRwR4NZYqXBTr4SEBGg0Gly9yvoLXb16FSkpjs6PBoMBBoPBX80D4J90J874/NFWeP33g7izcRLubJKM2gkR+GvfZdzTogqOXcnFEip3UUKUwSEaNJRoUT0Wc0d2wIGL2WhRPRYfLT+GWZvTcGeTZOw9nxXS507zz8s9cPRKDu5uloLF+y7DQghWHErHmluO06FMh9rxOHQpG/klZozsXgcd61TG+RsFaFYtBhdvFmDv+SwH83kowJu87mleBcUmCw5czMJ7A5qhefVYLHyuM9Ku5+HuZlUw/vcDuHgzdIIlpHi4bXUs3H0R1eLCMbRzLTzQphpOZeShfa14zNyUhk23/AyrxYW7zAMZzNjccfo0ScaXj7fG8fRcNKsWiwen/4v9F7Jwe8NErHOR7y+U2DK+F45eyUGH2soJpqpwgp1er0fbtm2xZs0au4+dxWLBmjVrMGbMmMA27hb+MsXSfgl1EiLRonoclr/Yzb6+XlI0Xu7T8Na2pcrduAgdalaOCEnh5t37myI2XIeOdSpDp1Gjbc1KAIA37mmMB9pUQ8PkaGuC4hA8dxuNUqJxPrMAI7rVQb2kKNRLigIAPNS2OgCrE3hFEOwaJEfj7Xub4FpeMW5vaI2Wb35LQ/H5wFbYeuYG6idF4eEZWwPZTK9TLykKJ6iI2PpJUVjwbCfkFpkQH6kHALSqEYdWNeIAAG1rxdsDbBqlRONYun/rB/uLp7vWxhMda6J6pXCE6TQI02mQFB0GAOjRIBEfrbAGWo3oVhuTlxwJZFN9yoR7GqFjncqoXikcBq0GLW/dB3NHdMD5GwWICdei60frAttIH/Nw2+owmS24s0kKEqIM6FY/MdBNYqhwgh0AvPzyyxg6dCjatWuH9u3bY9q0acjPz8fw4cMD3TQA/tPYNa0ai323Eu02d2Fqq105EjFhWuQUmdCzQSLyQ9QpvVWNOLRw4iehVqvQtKr1GiXFlGpwQ1Fz+UTHmni8fSo0EjfikE41kRofgZTYMNz39RY/t8631E2MxOlb6S2aVo1Bs2rOn4taCZGolRCJ7BAMJqhZOZIR7JpXj4VOo7YLdTw9GyQiTKdGpQg9hnWuhfF/HPRXU/1K/aQoaDXOvZeaVI3B90PaIsqgRbPqsYxglxhtcJrYOFhpXaMS6iZGOSyPMmjRpGqMol1KvEX72vEY2M73/vZlpUIKdo8++iiuXbuGiRMnIj09Ha1atcKKFSscAioChb987JJjDLitViXsPHsTj92WKrutWq3CJ4+0xNYzNzDujgaYuvyofV1KTBjSc4I3IWmkXoNCoxmx4To0ruLaV+S2WvFYdjAdLWvEQaNyTFAc7NRJjJQU6gDAoNWgT9MUh4S2oUDdxCi7YNfmlrZWjphwLWLDdcguNAZ11CMdwRnNBUdEh+lkf1sjPgKbX+8Fg1aN4yGmrauTGIkz1/JRT0aos9GnqdWVhxDC3AvV4sKDXrAL12nQqW5lhOs1aFwlWnZbjVqFVjXisP9iFga2rYFfdym39FZZaeLGeyKQVEjBDgDGjBmjGNMrj79MsXHhenzxWGtczSlCzcqRLrfv0zTFPnjRs/fKUXpcyysO2pla1/oJGNGtDipH6hmTsxSDOqQiNlyH7g0S8eoCZaal8ZTKkXoUGc2wEDjVWDojJkznkNIh2OBTetzVLAX7L2YhNT4C9ZMctRI8KpUKHz7YHAcvZUOrUePLNSd92VyfUSlSZy8TF6HX4PH2qfhlx3mM7VXPrd8nRBlu7YfV6gX7/fGfPg3x3YbTeOnOBm7/RqVSoWpcuD2xcZ3ESLtlBFBOag5PiA3X4Ydht7m9/Q/DbkNGbhGuZBeFjGDXJjUOCVEGWIgQ7ARlwJemWDr3T2K0AWE6jVtCHQ8t2MWE6RCp1zCFs4OJuHA9bqsV73rDWxi0GjzYxupvFhteqs0I12mCttpG3cQofPBgM2jVakQZ3BsW1GoVJvZvgvXHr6FypD4oB/BwnQYmA7Gn8WmUEoPNr/eCVq1yW3N+d/MquLt5Fcy7ldg72NCqVYgNLxXs4iMNGNW9Du5uloJOHlbXSI4JY/ZLC4zBSN+mKbineRWPf5cQZbALdq1qxOGPPZfs62LD9UGn5Q/3MCtAfKQe8ZH6kMojWSUuHN8MahPoZriFSHeiQHypsaMH3vKEZleOKhXsqsSGMcKAP1KDeBNPBy0aWrCrEhcms6WyCddrUC8pGrUSPBPyn+hYE7OGtvP4d4EkgupvvVbNfE+Itmpty+IOQT8TwYReq2bMrSkxYYg0aNG9QaJbGmwaehwI12mYmrvBiJxLghyNU6zmyoQog4OfZmx48F2TBsmutdfOoMfHpGj/ZpfwNsnRwTO+C8FOgfjSEptMOf6nxpddsIuPLN1PzcqRiKQGdFc+OUqjnhsmNymiKH+kxinKVs/LYdCWbyiIiwiePo+h7k+DVs1oWStHlv3lU5nSYgdTbjedRs341dFjRFlocSsQ69HbajDjQkViRLc6uKd5Cj5+pAVzvwGssKN0utZLQKReg2e61y3T72lhLpieCRv0RCXKEDwKCyHYKRBfauyqxpU+XHXKoWVJjCp9YG+rVYkT7IJjMH/5zgbo3iAR97Uqe8JZWohtlFLqVByuC55BAABSYss3G6VfVuUVDLwNr3SJoTQmeq2aKVZeVg0NYI2U1ahViDJo0TrVdeBFIInktJb0C4zW6peFyfc1xbDOtfBcz7qIDKKXIU95Jis14iPw7eC2uL1hkkOljkoRrGY3QsEWjqkPNseeiXfa0z55SnSYzp68+BEFR5FKQfdd3XIoAPxNcLyBKxi+FOza1463py5IKscA3jAlGv1bVoXFQtC+djwjzAWLYDf2jvrl3seAVtWw9lgGejdOgkZdOk+Kj9QHRZLSO5skY92xDDx8K0ddWaFfVg2SoxXlVxWmY2vG0hqUKIMWyTEGr7Q3IcqAJWO6wqBT48d/z5Z7f74k0qC1pyzSa9QwaEuFi/IK+a1TK9kFW1pgDJao4QdbV8Mfey/hRS+MDwAQw2nokrnrG6HXKtYXLTZCx9wbZWH2sPbIzC+BTlP6XosyaFFisig+sCYmXIfBHWviwMUs9G6sjKwZ7hAcb+AKhi+DJ5KiDfj80Vbl3o9GrcJXj7e2f6d9afh8V6FczD4lNgy/jeoEAIzzfKVIHSPYKTU68OtBrZFdaLQnWi0r7WpVwrM96qJuYiT+OXrV9Q+8CH9/adQqJkKbF+zYSYgOw7vUxofLj+GNexqVuy1NbpUd03vom+ZvogxaZNxKwREdpkWRiTZHe89XkNbkx0focTlb+WmR3hvQDA+0qYYudb1TJox3c+B9taIMGlzPgyKJ9oIpPTHagMRoA65kl46H4XoNVHCspws4Pr+BJNKgxejb3YsMVxLKHn0qKN7OY0er+uMifOPgTQ/giZyTbESQmSXLSpiu9HHizS0GHfuo0bPXQGLQasot1AFWP63xdzfCI+1q+F2I5x38w7gXKW8WpzUo0WFaPNOtDtb9pydGdqvjtTYpvcxYJOM7pEX/FtbIz671Erw6/tDHKY+FwJ9EGrToVj8Rai/NsPnrGR/JavAiFBxg4s17gdbeGrRqGKjnknaBKK+/b3mhza+BbktZCc5WhzhaL7/0af8nXuDwFrRjaUIUK9iFK3jg8ib0S4y/zmGccEELG0oR8rxFkZ+FGo1KxbwY+GvN+zDRGruYMB3UahVqJ0R69SWmVNOaDdr3LSpMi75NU/Dz0x28os2noSd1tRRSID3Q8Pcn3RdKGAtsQXVtUuO8ul/WLK9hJsK8z2cgoQVtvq+ChYrxxg0yvO1jF8UIHL6JyKKFGt6UE65XzvzBVqB7SMeaXt93HCVAV40LZ4qph3Eau3Aq71+YVgOjOThzADojr8i/ZbbUKqtwZ4b1YjsIdpw5ifax85U/qMItscxkL8qghUqlQtf63jE90tARxzXKEYUfStDplbRqFePDpoSxoH/LKqiTEOVRbk93oCdO/HgYG6Gzj4e8lszfplmdtrSdSnepkCI4Wx3ilCcyz9X+/GGKrclF20bolDN/+E/fBvjkkZZ47a6GXt93LCU0V4kNY0yEUQZWoKY1dvwMNVgHExu5fk5UrVbzGjv2+vGuAHQks1T90/LycFtrBOCgDvKl+vwFf4/FhvteuAVYtwwlmxx9TZd61kTPj7evwWnr1Uzf8C4bgSAmTIeH2lYvV55TV9SoFME8p9HU+GjNI1m6Le9awd/LtJZTW8Z3J90W+n0VTGmcaAJ/Fwkc0HhZY0enOPGVmpvWCqbGRzABIOVJAOxtKkca8HDb6j7JtRcXziZtpq81//IM4wZ3Gld9VF5zzYBb6V3+08f9Mkme0LNhok/2K4XGQbCTNnUB7GDNuw14i/a147HjzTswZUAzn+zfU8K0auaZpAU7Ps+aN3moTXW0rB6LKQ80cxC4lUjdRN8k2v5Pn4YY1CEV43o3YO7PCL2GmcgpwfTnyzY82q4GNGoVhnWuxWgq6RREGrWKEdD49hj48ZL67mlCbRv0xDsqTIs7GiVBrbLmYgxGKu4USsGovTz+2W7OHg1898K1UB7z1eLCmUhE2lmdNk8GAl8mTK0cpUdsuA65RUY0rx4Lg1YNW0l0PpcVLexq1CqmfqReqwao7Bt8bUmdRg2juew+XG/d2wR3NauCOxonlXkfcgzpWAuXs4owsnsdDPhmi0+OQZtnNCoVCPXMOPrYsdeeFuZ8WS3CG0Ep3sKmGbKlG6E1977SWgJW8+viMV0BsFHjEXqNovwQ37u/KT5YdgyT7/ONIE6ngLl4szQ6NMKgYSZyShDsfOnjNuWBZnjtroaoHGVgBH16cmGbqNnGPP6a6LjxUa9V21P36DQqFFKeIHw2AjqCnl4XHaa1l3mL0Gvw5eOtcT2vuEzlNpWAEOwUiLdNsVVjwz0q4FwW6iZakzfWrByBMJ0GBq26VLBjoowCW0/V3TqoZUGnUWPR851RaDSjSmw4M3vktSIRnGCnVZcKa7yPiZ4T5Kz7Lfs1jAvX4a5mKWX+vStSK0fgm8G+ramo16hRaLFeA5VKxfi08ZohWmOnUrF+pok+0tgpDa1GBZ2mVLCjJ1uV/XQN+KjxghLl5Hkc0qkWhnSq5Zdj0abYCJ2WNcUqIArTl5NfrUZtv9/CKI0dbUHRqlXQqdUogvVe5c3TDuOjVlpjp9eygp2BmtyE6zUoKbR+pscIg1aNSIM2qKumBG/LQxhvmGLp2Yg/sr93q5+AGU+0QdOq1nJC1lmWdepEv0QMOnVABTtfJ0+uk1ianZwecPgkpfTgruVMDw4+d9SMFCi/KVYb5D58gPWa2O4jjRpQkdJrwqc3Cad8ZiL1WlSrVBrYUlEc+rVqNQxaYtcg06ZYb+atk4M2vSVEG4IigbcvYPy5DBpGGOHv3UDkv6zhp9JftMCWQGnO9Vo1NNQYxydIdjY+2nDq1kJp9wxajV2wi9RrkH1LvUfnYQ10VK43EIKdAvFG/qSYcJ1dtexLLZUNlUqFu5pVsX83SPiXWWdp/o2apPFnoXZGY8cJdnQOJ7VaxaS44Qcn/rvWTVt9oM3eNN5+QdHXRKNSgX5iDDI+dpEGDarEhuOnpzogXK9WhOnLH+g0KlgoezUdwe0rP0MeWqCpKJpSZ9BuGFEGLTNWOviTaf0n2H30UHNcyipCqxpxfjke7V5C5zjUa9TMRNeZBUPqO2/tctiW2hfdD7QFpax+ekpCCHYKxBsaO/phCIRKWcoMGYior9T4CHSpl4CkaEO5y+N4Aj3G0NGBOo0KOmqlRqVitGhypgbAMRxfyockQq9FXrEy0qhEGDQoKSj7C4r3M9RTgrBarYKKEmDDtNI+draZuS9Se0hRJyESZ67n++14ztBq1KBl/DqJkUiNj0CR0Yx6fqqBST979POg16pRovCEzt6E1srFhuuY55u3rvAaJ1/y6G3+jeAuZMr8se469OSVd63g3yF6LevWQk9o5TIO0O9F+n0lBDuBT/CGjx2tAQpEkWk67xAdhcibGvyBXqvG1Aeb+/24dF3MREpTqFWrWY2TG6ZYGh016PE+JGGUD2OYTqMYwS46TIusAkdNrVatgskNtSIfMEJfE7VKBVplp9eygzttdgxEhPbE/k3w595LaF49Du8tPeK34xq0ansFDJ1GjWKqbFhchB4rxnWD2UL8dk0MjMau9HkIq2CCHa2Viw7TMsKGQathAoOU4HPnKwpKSscm+nnWa9WyUe5yWjhb4IWFDkSjoK8n/S6K5GoaBzvBfwYhiDdMsXSUqrdLlLkDHfHGlJLRabweHOKKQM3AaOGWNg9qNayGTsOZYuVMDQB7PvTApeX2Qw9itoTMCX40RdNtuKd5qZlezswihVxKGF4wtl5P564AgbgXejZMwrTHWqNqrH+jZNmUOioUUxONCJ0GEXqtT9L+SEH3UfVKpb6N/nAVURKMNUXvGDxB+9CGgr+XFPQ7Qs9ZLOhxjNfAO2rhSrdVqdgE//zYyaeasUFPbmoFaSQsTejeNUFMWZMs0nSvb01tEqgZHz0bo9tg0Kj9XhdQH6AyPSYLFY1FC2MatYMpkdbC8T5irkyxNqxpU5xfz9G318Mz3evgt1GdPDyL8jFraDs81KY6RnWva19m0DoXTHnox0DO71CtYidDWrWauS60f6M3nq2y4m9/Pj5Ah9bseqsOqic0rhKDdjUr4ZG21Zm8Zbz/qb947FYaqOd71nWxpXehJ9qVIvUOCYp5jXyoMrCd9frf36qqg8aOzWMnbXoFWBM/X16Qv36sXx3toqHBW/0ao1v9BDzSNjhz19FUrKlSkOCNkmL3t6qGtjUroaWfHGF56MhXXsOkU6tQcus7bS7yFYHS2FkHDquDjI5z2qU1Slouwa4rjZ1WYuDXcU7HhNLaJscY8MY9jct4JmWnW/1EdKufyLQlKkxrj/I1yATT0KlxeOGcFezY66dWqRitB1+jMlD427+UKV2lCby5M0ynwcLnOgMA1hy9al/uywTJcjzfsx7ua1UVbW7ll/MnTavG4MTVXPRtmoKNJ67Zlxu0Gk7rTgVZKSgYyhsM6pCK2HAdbm+UhMOXs+3LrYIda56mkTPFqlUqxkedVxxIaeki9FqM6FYHI7rVKePZKIvQnQ4EMWU1VYZzauYH21S355fzN3xCXRtWNbvzyCRfwdcK9RfDu9QCAIzqUYcZjCL07ODtafCEXsIUy2vs6HdAIMzxNPTxIxnTvPR50+vk/A6tDtOUxo4zddMz/kAKdv44Nj10GBihX+XzCZQnsKmA/Pd88hVxOtdNCMg9MW9ER6x+qQfqJUU5McWyY4UNX2jvat8q/ziyW22v79sVYToNHmpbHfGReuacI/Va2RKBDsET9CRPrWK00fw1o/s6Uk/72IVWdLzQ2CmQskbFRodpKcf5wMrstSpH4OyNAtRLinLU2NH+E34YVFtWj/X5MZwxpGNNtKgeh6ZVY3Dyap59ebiedZjWaVXQmdwPnpDyx9Np2GLZ/vZldJdoWrDjXmq08EGv43PvGbhZOn1N1CqVg0O6jUCWt9NzWlp3gkY8xRokUZqA1UaYVuPXQuqukEve7UtozWUg74XYCJ29tjR/r+okJr5hOg0TkOUNPh3YEkcu5+CB1tW8ul9P4csvyr0jHEqKMWMBZF19aOVHOGWKDbU6xhVOYzdlyhR07twZERERiIuLC3RznFLWkmJMvrgA5+f6T9+GiNBr8FrfhtBrWaGFCWX3YfqRP0d3wct3NsALver77BhyqFQqtKoR51DoO0LHhvO7ytsk51/Ga65oYUGpYfu0xi5SxkxKC2Ry14TX2Ok0Ku7F4DyQwt/QJuFYH/mV0UIC/RILU1C9ZkA+x6MvoSfNfHH5QEE/w1FhWsmMBt7yR6bvkYRIA57oWDPgVRboNkWH6RjhjG8br7FjrB/cWODgYyehsQtE5ghfoow724+UlJTgkUcewXPPPRfopkhSVk2L3EvS39zboioOT+6LPk1ToNewg5NW4qXrbVpWj8XYO+orwgGZN8XqZIRdl+lOJKLmtGo1E7BBR0YrCfo+jZIwywLsi4y/n+n7Ru2kaDg7a9fghV71kBxjwNNd/W9yslGzcgTevb8pvh3cxmfBC0wCVlo7obBEzExwCyVs+/pZpS+7UiqwOGqr6D5k87t5gwgDrQVU5jWgx0NesKOviYYLPFPz5QWpa6ZSsWMMPaEQGrsgZ/LkyXjppZfQvLn/85q5S1mDJ+hfBVIzYcPmV0ULMZEGLedz53xG6g2XsED7ldHwGc/ZyDfW506rVjPnz5se6GsWwTjIq2CifBvzFZLDjoeuekCX8uFfXHL+d/SArdewfjUGrRq0TBumU+OVPg2xbcIdAfM5Baz345OdauGe5lXgK5mbKSjPaSRsPp9PdQmccGuDHhPiIkpT8ET6WHNiVuBkR8+ZpelJSrjeua8oUPYIbyk3hUDCauxYrWU0L9hR10SjUjHlx9QqNriMfgZ0anYCTft2hpqPXYUT7DyluLgYOTk5zJ+vKavGTs1pKZQCLchFG1jHWCkHeaWaEcsKrWWL1LM+JHoNG1Ci07IaKP5aSCXZtJpiSzV2T9zKXdcmNa78J+AFutZLgF6jxgDKnyeK1tbICLCOgRVsCSA+wTPtq2cTHpUk6LOhLd6Dvldo4Tdcp8GrfRviy8db4/W7G/rk2J5A93VSTGkVCl+bBBumxPh0/2WBCSThNHa0JslZuTH7Pso4XipFoGGvgbwplh7zVCpWwNVq1IwrE5Pyh3PRoN0h/JnP0R8EXq2jcKZOnYrJkyf79ZhlDZ7o0yQFadfz0TYA4fty0INOVJjWwWxmw6DVIBcm+28CnZ7BmzA+dgY23Qmft8mgsc4sbZUWeCdvqaLhWjWrsXu+Zz1UjQ1Hn6bJ3juRcvDDsNuQW2RkUjbQplgtl9LEXVOsTqNmzM58fc1A5GxzhTfjGOiycvSLi9X2WBMS39eyqvcOXA5S4yPQqU5lhOs1SIouTdwc6WOT2BMdUtGtXgK61PNfSTlXsBHCOuY5CNdJT270WrU9bZBWowKV71cWOohGiebouAhWaxnFWZ/osUDP5UXVadh0J/z4SF/bhCgD6iVFIbfI6Leyev5CGb1aTsaPHw+VSiX7d+zYsTLte8KECcjOzrb/Xbhwwcutd6SsL6LqlcKxbcIdmDGkrZdbVD5oQSTKoGO1ChIDF/+SD3b4FAb0C9jACXa8BoofyOj7I0zPCsZ08IReq8bA22owpq5AoteqUTnKIBnxxvc4HzHLrmOrKvA+dcVGN99yAcLoxUkL/axIaeyU5hyu1ajxyzMd8cOw29hgAUqD5At/uwi9Fv/p2xCd6lb2+r7LipyPXQQXFSv1O08sHLbcpkrxrwPYCV6N+AhG4OQrk9ATXZ2TsVPNmLJ5zT6rDf1rTBf883KPkKt+EhJn88orr2DYsGGy29SpU6dM+zYYDDAYDK43DBB00XeDVh3woAlnMGZITvVP+9QYdGUbqGg61onHtjOZqJOorLIwTO45DZvSQM/lrtJp1YzfCG9OoC8NE/mooIFaDqmcfY4mZ+mXWjhniiWETZNCa+yUSJHJe4Kn9brdSuTMRVjaCGRqD1fQL2ZaY0cnMvcWSq+9mhQdxvnYyWvsbNDPDv1OcEbrGnEY2a0Oqsb5t8SdHGE6DX5/rjNMZgsSogzMNeD9xdlnn81ZqVWr2chnzmzLarQ1IRc0YSMkzioxMRGJiYmBbkZAMGjV9txGSoj+dAafgNNIvXSZqEgtq5kqC4+3T8XwLrXRsnpcmX7vK+hZZFKMgTXFOpgT1JIO1Fq1ipl1hnOm7GBAK+Fj6SDYyQVPUOu0GjVXl9f31UzKyzPd6+CbdacxrHMtzPn3bLn2JaUBZypuKPjeoO99WkPlizyMShwjG6fEIDU+AvWSohCuZ/MN0jn+5Cow0AKLTi0/sVGrVYrSWNpoW7PUhchdHzt+rOS19/Q4oVGrGD9bpUWKe5OQEOw84fz588jMzMT58+dhNpuxb98+AEC9evUQFRV8dna9plSwU+qNyptiacEuXCKzOp900l2fJINWjb5NU8rRWt8x7dFW2HE2EwNaVcOGExn25VafOzqYghXewriBjI6aDueiYoMBKXOhnI+dwWHmzb7UTGb6+ml8FnXqLV7q3QAtq8eha/0Eu2BH+8q5gk5wrJPQ7tKaDjoKVWnQ9zrt0O4bU6zyxshwvQYbXu1pFzro8TFaJhWMXsuOCzZcuWgHsl6yu9A+s7zGjnlPcJNiLT8+Mj52asYHWYn3greocILdxIkT8eOPP9q/t27dGgCwbt069OzZM0CtKjvWh9sacBDoJJNSGLRqJEUbkF9sQpOqMUy5MaYmIJeZ34ae0kq6wht1dn3FgNbV7BGh9HlHGbRM+hMdN1iFuWmKDYYBG2Bn49EyUbGsAEhdH7UafPWNEjVlig0Ck7RWo0YfbgKi98CErNWUCnb0/RHOaOx0TpcrDbrbacHOF5HxStVq05ok+h7QSYyJ/HdauHel6VRqRRqaQioSJIozlzIZBFxo7CL0rNmWzhqgdLN8eQjdM5Ngzpw5IIQ4/AWjUAewN6dSZyAqlQpLXuiKFeO6Iz5SDxM1cEk5Tus4U6Wzz85QoqnFGWx2eS3jU6fT8Emc2VmnlHNwMAzYPLQvJK+xY/Idcho7dpbOB0+o0atREgCgtUJSvbiDzgONaxjjW+m8ZBotNCs5nQMd8BMX4X3Bjh4zkmOU6y9tg84GwExguLGNd9+w4Wxy6+8yjuWlkAp+4oMJtdyzzwh63PhoYCwCbKYFZaU/8i7KVPEI3CYhSo9ikwWFJSZUjQsPdHMkSY4pddQ1StQzjWRq99FBFRqgyKqV1LkI6w+GQQvgUsAYNNDRgzQX6cU7C0uH86sxqnsdfLfxDF6/q5Gvmu4Vvnq8NS7eLEQbKjUP/yKXq/kop+E0aDWY8kAzzNqUhme6ly1oKhBE6LW4WWCUXK/TqOzabqsJ27ot7UfE+NiFadGvRRWcu5HPXGelYQsYMFkIUw3AW64FBq0a/zesHYqMFiTFKCdgQApGsJNwyQDY54MW6J3JKxp16b2jhOT1rpCz0Mj5I2s1KtC3Da/xzC9RZtJ2b6P8HhbIotOo8c/L3WG2kKARahiNnYTanPG9o2ejLrRS/iwoXh7oWWaEXssFU0gHSGgdhBg2RcwrfRri9kZJuK1WvK+a7hX6O8mnxmtj2SALaY2dTsNGwkUaNKgSG463723izSb7HFca9zCtBkaz9cXECHMSaU3CtBp8M6iNl1vpfeIj9fjlmY6IDddh6+kb9uW6MhTNdhYRqlar0K1+8ATX0a4qUoEA/Dp6jHCmsdOq1bijUQIOXspGzwZJ3myuTyiSSVfEJ3d3GAs4QY9e17VeIn7edh6p8RFebrGyEIJdkKNWqRSTp8xdpAauSKkIWa7QPY9eo8Yj7aojq9CIhinR3m6uT2BTwDgmbWZn4+ysk17HXxu9Vo2OdZQX8eYOvIaG8ZVh8t2xiUb5Yu7BmsKgfnIUTmbkSa436DTIvVUmjk0F43wSECzpbwDYJyKbTl63LyuLxk6jUsHESXbB4ntqg47olgqMAdi8j3ywGY9GrcLMJ9tBpQoOEyStueXR0Np6hyo9Kkaw5f2T72qWgllPtkPjqsqrQOJNgnMEFNhRcrSbFEYPNXa0VspZVQ61GpjygHJr/zqDNhFEclGxVg2e88GJ104xZtoyaDiUBG+K1UoI/Vo+alivYRzOleprKsW0R1vhWHouqlUKx7KD6ZLbsZU4nJvowmUS2gYDZsq5vSyBUM5+Eix+tzYKKHOhVBAVwEaO0mOCc42dSpEVWKQYc3s9PJm2w8GVgq/So1WrGUGPt2gwFWpubde7iTIq8fgSIdgFOUqN8pKDnpHSDyX98g6nHsgIxn/EcXAqawm2QEIPvpUi9FxGdI1kviUdX/CerhUbJOlOpHD0saM0UXRaF05ryWsygi3azRYp/dPWs7LbMdVKJIIn2ALvwXUdANa/zOSlumvBJtj1apSEpQeuoEeDRMkAIoC9PvSY4EyAC7ba290bJGLrhF5IjmZ9IvUOgWXcd61GcmwIlnRQ3kAIdkFOMA7etnxdes7xldHY6Wi/odLb1Nmkkx/wggE6+i8+Uu+QEZ32Q6QrCPAzUrlaksEG79QtZW7V8gmcdRpGUA4GU5MzXNVGlkpELGVyVWr6Izlop3mjk9QvdP4+V1SK0OFmgRG9GweXhuaNexojPlKPp7vWZs6VdzmwUOvoO97Z3a/kyiNSVIl1DAbUaVQOzz6fzF3qOVFKXVx/UHHONMRof8sn5clOtQLbkDLwwYPN7f/plzcdFRsmYZZ15mMXjAJN1bhw/PhUe/z+XOdbDr+sxo5OpElXEOCjYumXuq8LqPsa3jdQygylU6sY7WSYTuP0ZRZsuKqWQWueGEGXM0vbCMbnIjm2VENjciLYeZLSZ8GznTC2Vz282Lu+V9rmL6rGhePd+5uhZuVIyfJiAGCmTLF0tQqL0rNzlwMtV+81XK9xGCfoW4QfNyoKwf0mqMDMHNoOFzIL0KxabKCb4jEPt62Ou5qlIFKvwaK9l+zL6Tx2tCZKKuIrMdqAa7nFuLdFFR+32Df0aOA8Ui9cp4HRIpWklM3TFHZrxmqyEHStn+C7xvqQJWO64sTVXHSpx7ZfqvSYVqNm/AnDdBpUD4EoN1caO62EWY5+X93RKAkf6jVoWi02KDWXj7argXPX89G1fgLeXHTIYb1WrUKxzO9pZV69pGi83Keh9xvpR+g+p6OfNWoVaLmXFvLo4LTxdzfCJyuP48MHg8sHWQo+CjZcp2UnNloNcw/QE9+IINRgl5WKc6YhRmy4DrFBKNTZsGmhaFMDrXFiTbHUS4xSQvw2qhMW7b2E53rU9WFL/YOJK7NGm6FoDR2fxDZcp8HKl7oj7Vp+0EbDNq8ei+bV2XtZo+ai27QyPnZ6DQa2q45lB6/gsdtq+L7BPuKB1tXw3cbTuLNJCpbsv+ywXi9hmqYFuOSYMGx94w4mijiY0GvVeOtWmhpnVThcaezMXvLLUwp8tLwNq2BXen3o86Y/P9ujLoZ1rhWUgTTO0GtZn7pwPedjp9cwGkva57RpiEfC0gjBThBQaJOjVESflH9I7YRIvHxnA981zo/Qgpxew9Y0pDV01nqHpduG6TSomxiFuonBV+dYDo2aTWnCauzYdeE6DapXisA/L/fwaxu9Ta2ESGyf0Bvheo1zwY6pnevczKpRq4Iml6MrnJligy0IoLwwPsgGdnJDC3D0eMELxKEi1AG3MghwGjpG+NWqmTyGKpUKc4bfhn+OXsWwzrX82NLAUrGeEoHioEP76UFbSmNnkbdWBS1FXKkbZ47jgFWoodcFox+VO/ApS1iNnXRKg2AnNkInGcVJv9BoLW6o+lQ58zl0FdnY/JYVIyla+aXD3IHu82jKlKhRqxiTIy3kSY0dwUyLWxr9wR1qOrho8Np7/nno2TAJ7w9oHlICritCZ0SsALx3f1OoVMCb9zQOdFO8Bm9esC9nijc7z98VSvD+VbSfDI1Oo2YiB4MpN5UnxEfoGS0Em8tPxfjYKbnAvTdh6oFS/R5q5kcbj7dPBQDUTyrVRrt6OT/SrjqmPdoK85/p6NO2+YuoMC3qJESiWlw4alB+pFa/Wjo1TOnn2gmRCDW+H9IOnz/aEo+2q8EI91o1G0xRkYQ3OULzLRlC0P7PdzZJwaFJfTGiW+3ANcjL9GteBe1rxeO1uxpK1kelfa1s/jdDO9X0XyP9wMNtq0OnUeHhttUBODdDAVbhl06VEmq8N6AZDFo1Pn6kBfOyMnDpTujnIlYmS32w0vVWIMnTXUufdVqLSSsl7rtVnq1GvHJrRZeFsb3q4817GmPBs53sy1xpqA1aNQa0roY6IeKaoFGrsHRsV6x+ubtD6g7aekFrNz96qAVqVo7AzCfb+bOpPiUlNgwPtK4OtZpNTq7Tctp7rcZldHlFQPjYKRydWm33mQjXaYIyN5UclSL1+O3WwH3kco59eYxEoeqeDRKx663eqBwZXGXUXNGsWiy2v9HbnstNWmNnFf4W7LqIO0Mwg/qQjjUxpKNVaKfrhtIaKo1axTwHsSEo6H4zuA1WHLqC+1tVw/9tTgMgrZnsUKcyFj7bCbVCTFMTG6HDSK7ygKtqFLHhoTUuAM5L5IXrNCik6qnSwsxtteKx4dXb/dK2QMDXytZxwRShONHzlNCSEkKQSpE6XM2xBvhHGkJbzUz7hkRLOICrVCokRIWG/wxPPCWsOosIBKzh/XEReqx8qbu/mhUwCkqcFwIP12lQOyESb/VrjLgIfVBWX3FFbLgOj96WyizTM5Gw7PbtbuW1DHWqxoXjWHquw/L+LavieHoOugVpyh9PiY/U48y10rrCrlLlhBK0IBcTpmUmfRF6LV7t2xCHL2Xj+dvrBaJ5ikCYYhVO3cQo/DaqE/4a0yXkM2fbfEgi9BroqRq4dzVLQbRBW2EGbQBomBwNAKgWx5rXEkPEKdwdmlZ1ns7HNsEZ0a2O3XQdytj6vHPd0nQ2tmchMggrCpSFLx5rhf4tqzL9bdPMhOs0+Orx1lj1Uo+Qs2jwvHlPY+g1aky+rymjpZt8f1MA1hqroY5KpcIdjZJQNTYMneokMEJtbLgODZKj8e+EO/BEx9By1/GE0H4Kgpgxt9fDt+tPYcLdjR1yfIUq8ZF6bHz1dhh0ams1AZW1jmq9xChsfO32oCyLU1amPdYK7/99BG/cCpQZ0bU21h7PwCPtQl+QsXFP8xR8+GBztEqNA2D1Q/xjz0U8GwJ5Cz1h+YvdcCOvxK7RDddpcHvDJMwefhvqhYgvmSvub1UN97eqhoycIgDWAIEpA5rhPwv248OHWgS4df5jZPc6dvP0mNvr4dPVJ/Bg62q4vWESdr7ZGwlRoWeKdsb/DbsNhBCoVCrUTYyCVm215PDR9BUVFSEhGivvI3JychAbG4vs7GzExPgu4aHFQlBsslQoYYbnxNVcGLRq1KwcWr5DgrJRZDTjZkGJ0xqSFYVTGbkwaDVMhGRF41JWIaIM2grvS2U0W7D9TCba1Ixz6odXkTh/owBhejWSosNcbxykeCJ7CMHOQ/wl2AkEAoFAIBAAnskeoe20JRAIBAKBQFCBEIKdQCAQCAQCQYggBDuBQCAQCASCEKFie1yWAZtLYk5OjostBQKBQCAQCMqPTeZwJyxCCHYekptrTY5Zo0aNALdEIBAIBAJBRSI3NxexsfIp0ERUrIdYLBZcvnwZ0dHRULkob1NWcnJyUKNGDVy4cEFE3gYY0RfKQPSDchB9oRxEXygDf/QDIQS5ubmoWrUq1Gp5LzqhsfMQtVqN6tX9kyQ2JiZGPKwKQfSFMhD9oBxEXygH0RfKwNf94EpTZ0METwgEAoFAIBCECEKwEwgEAoFAIAgRhGCnQAwGA9555x0YDBWn4LtSEX2hDEQ/KAfRF8pB9IUyUFo/iOAJgUAgEAgEghBBaOwEAoFAIBAIQgQh2AkEAoFAIBCECEKwEwgEAoFAIAgRhGAnEAgEAoFAECIIwU4gEAgEAoEgRBCCnQL55ptvUKtWLYSFhaFDhw7YsWNHoJsUUkydOhW33XYboqOjkZSUhAEDBuD48ePMNkVFRRg9ejQqV66MqKgoPPTQQ7h69Sqzzfnz59GvXz9EREQgKSkJr776Kkwmkz9PJaT48MMPoVKpMG7cOPsy0Q/+49KlS3jiiSdQuXJlhIeHo3nz5ti1a5d9PSEEEydORJUqVRAeHo7evXvj5MmTzD4yMzMxePBgxMTEIC4uDk8//TTy8vL8fSpBi9lsxttvv43atWsjPDwcdevWxXvvvccUfhf94Bs2btyI/v37o2rVqlCpVPjzzz+Z9d667gcOHEC3bt0QFhaGGjVq4L///a/3T4YIFMX8+fOJXq8nP/zwAzl8+DAZOXIkiYuLI1evXg1000KGvn37ktmzZ5NDhw6Rffv2kXvuuYekpqaSvLw8+zbPPvssqVGjBlmzZg3ZtWsX6dixI+ncubN9vclkIs2aNSO9e/cme/fuJcuWLSMJCQlkwoQJgTiloGfHjh2kVq1apEWLFuTFF1+0Lxf94B8yMzNJzZo1ybBhw8j27dvJmTNnyMqVK8mpU6fs23z44YckNjaW/Pnnn2T//v3kvvvuI7Vr1yaFhYX2be666y7SsmVLsm3bNrJp0yZSr1498vjjjwfilIKSKVOmkMqVK5OlS5eStLQ0smDBAhIVFUW++OIL+zaiH3zDsmXLyJtvvkn++OMPAoAsWrSIWe+N656dnU2Sk5PJ4MGDyaFDh8gvv/xCwsPDyXfffefVcxGCncJo3749GT16tP272WwmVatWJVOnTg1gq0KbjIwMAoBs2LCBEEJIVlYW0el0ZMGCBfZtjh49SgCQrVu3EkKsg4BarSbp6en2baZPn05iYmJIcXGxf08gyMnNzSX169cnq1evJj169LALdqIf/Mfrr79OunbtKrneYrGQlJQU8vHHH9uXZWVlEYPBQH755RdCCCFHjhwhAMjOnTvt2yxfvpyoVCpy6dIl3zU+hOjXrx956qmnmGUPPvggGTx4MCFE9IO/4AU7b133b7/9llSqVIkZm15//XXSsGFDr7ZfmGIVRElJCXbv3o3evXvbl6nVavTu3Rtbt24NYMtCm+zsbABAfHw8AGD37t0wGo1MPzRq1Aipqan2fti6dSuaN2+O5ORk+zZ9+/ZFTk4ODh8+7MfWBz+jR49Gv379mOsNiH7wJ3/99RfatWuHRx55BElJSWjdujVmzpxpX5+Wlob09HSmL2JjY9GhQwemL+Li4tCuXTv7Nr1794Zarcb27dv9dzJBTOfOnbFmzRqcOHECALB//35s3rwZd999NwDRD4HCW9d969at6N69O/R6vX2bvn374vjx47h586bX2qv12p4E5eb69eswm83MSwoAkpOTcezYsQC1KrSxWCwYN24cunTpgmbNmgEA0tPTodfrERcXx2ybnJyM9PR0+zbO+sm2TuAe8+fPx549e7Bz506HdaIf/MeZM2cwffp0vPzyy3jjjTewc+dOjB07Fnq9HkOHDrVfS2fXmu6LpKQkZr1Wq0V8fLzoCzcZP348cnJy0KhRI2g0GpjNZkyZMgWDBw8GANEPAcJb1z09PR21a9d22IdtXaVKlbzSXiHYCSo0o0ePxqFDh7B58+ZAN6XCceHCBbz44otYvXo1wsLCAt2cCo3FYkG7du3wwQcfAABat26NQ4cOYcaMGRg6dGiAW1dx+O233zB37lzMmzcPTZs2xb59+zBu3DhUrVpV9IPAbYQpVkEkJCRAo9E4RP1dvXoVKSkpAWpV6DJmzBgsXboU69atQ/Xq1e3LU1JSUFJSgqysLGZ7uh9SUlKc9pNtncA1u3fvRkZGBtq0aQOtVgutVosNGzbgyy+/hFarRXJysugHP1GlShU0adKEWda4cWOcP38eQOm1lBubUlJSkJGRwaw3mUzIzMwUfeEmr776KsaPH4/HHnsMzZs3x5AhQ/DSSy9h6tSpAEQ/BApvXXd/jVdCsFMQer0ebdu2xZo1a+zLLBYL1qxZg06dOgWwZaEFIQRjxozBokWLsHbtWgfVeNu2baHT6Zh+OH78OM6fP2/vh06dOuHgwYPMg7x69WrExMQ4vCAFzrnjjjtw8OBB7Nu3z/7Xrl07DB482P5Z9IN/6NKli0PKnxMnTqBmzZoAgNq1ayMlJYXpi5ycHGzfvp3pi6ysLOzevdu+zdq1a2GxWNChQwc/nEXwU1BQALWafS1rNBpYLBYAoh8Chbeue6dOnbBx40YYjUb7NqtXr0bDhg29ZoYFINKdKI358+cTg8FA5syZQ44cOUKeeeYZEhcXx0T9CcrHc889R2JjY8n69evJlStX7H8FBQX2bZ599lmSmppK1q5dS3bt2kU6depEOnXqZF9vS7PRp08fsm/fPrJixQqSmJgo0myUEzoqlhDRD/5ix44dRKvVkilTppCTJ0+SuXPnkoiICPLzzz/bt/nwww9JXFwcWbx4MTlw4AC5//77naZ7aN26Ndm+fTvZvHkzqV+/vkiz4QFDhw4l1apVs6c7+eOPP0hCQgJ57bXX7NuIfvANubm5ZO/evWTv3r0EAPnss8/I3r17yblz5wgh3rnuWVlZJDk5mQwZMoQcOnSIzJ8/n0RERIh0JxWBr776iqSmphK9Xk/at29Ptm3bFugmhRQAnP7Nnj3bvk1hYSF5/vnnSaVKlUhERAR54IEHyJUrV5j9nD17ltx9990kPDycJCQkkFdeeYUYjUY/n01owQt2oh/8x5IlS0izZs2IwWAgjRo1It9//z2z3mKxkLfffpskJycTg8FA7rjjDnL8+HFmmxs3bpDHH3+cREVFkZiYGDJ8+HCSm5vrz9MIanJycsiLL75IUlNTSVhYGKlTpw558803mfQYoh98w7p165y+F4YOHUoI8d51379/P+natSsxGAykWrVq5MMPP/T6uagIoVJaCwQCgUAgEAiCFuFjJxAIBAKBQBAiCMFOIBAIBAKBIEQQgp1AIBAIBAJBiCAEO4FAIBAIBIIQQQh2AoFAIBAIBCGCEOwEAoFAIBAIQgQh2AkEAoFAIBCECEKwEwgEAoFAIAgRhGAnEAgELhg2bBgGDBgQsOMPGTIEH3zwgVvbPvbYY/j000993CKBQKBUROUJgUBQoVGpVLLr33nnHbz00ksghCAuLs4/jaLYv38/evXqhXPnziEqKsrl9ocOHUL37t2RlpaG2NhYP7RQIBAoCSHYCQSCCk16err986+//oqJEyfi+PHj9mVRUVFuCVS+YsSIEdBqtZgxY4bbv7ntttswbNgwjB492octEwgESkSYYgUCQYUmJSXF/hcbGwuVSsUsi4qKcjDF9uzZEy+88ALGjRuHSpUqITk5GTNnzkR+fj6GDx+O6Oho1KtXD8uXL2eOdejQIdx9992IiopCcnIyhgwZguvXr0u2zWw2Y+HChejfvz+z/Ntvv0X9+vURFhaG5ORkPPzww8z6/v37Y/78+eW/OAKBIOgQgp1AIBCUgR9//BEJCQnYsWMHXnjhBTz33HN45JFH0LlzZ+zZswd9+vTBkCFDUFBQAADIyspCr1690Lp1a+zatQsrVqzA1atXMXDgQMljHDhwANnZ2WjXrp192a5duzB27Fi8++67OH78OFasWIHu3bszv2vfvj127NiB4uJi35y8QCBQLEKwEwgEgjLQsmVLvPXWW6hfvz4mTJiAsLAwJCQkYOTIkahfvz4mTpyIGzdu4MCBAwCAr7/+Gq1bt8YHH3yARo0aoXXr1vjhhx+wbt06nDhxwukxzp07B41Gg6SkJPuy8+fPIzIyEvfeey9q1qyJ1q1bY+zYsczvqlatipKSEsbMLBAIKgZCsBMIBIIy0KJFC/tnjUaDypUro3nz5vZlycnJAICMjAwA1iCIdevW2X32oqKi0KhRIwDA6dOnnR6jsLAQBoOBCfC48847UbNmTdSpUwdDhgzB3Llz7VpBG+Hh4QDgsFwgEIQ+QrATCASCMqDT6ZjvKpWKWWYTxiwWCwAgLy8P/fv3x759+5i/kydPOphSbSQkJKCgoAAlJSX2ZdHR0dizZw9++eUXVKlSBRMnTkTLli2RlZVl3yYzMxMAkJiY6JVzFQgEwYMQ7AQCgcAPtGnTBocPH0atWrVQr1495i8yMtLpb1q1agUAOHLkCLNcq9Wid+/e+O9//4sDBw7g7NmzWLt2rX39oUOHUL16dSQkJPjsfAQCgTIRgp1AIBD4gdGjRyMzMxOPP/44du7cidOnT2PlypUYPnw4zGaz098kJiaiTZs22Lx5s33Z0qVL8eWXX2Lfvn04d+4cfvrpJ1gsFjRs2NC+zaZNm9CnTx+fn5NAIFAeQrATCAQCP1C1alVs2bIFZrMZffr0QfPmzTFu3DjExcVBrZYeikeMGIG5c+fav8fFxeGPP/5Ar1690LhxY8yYMQO//PILmjZtCgAoKirCn3/+iZEjR/r8nAQCgfIQCYoFAoFAwRQWFqJhw4b49ddf0alTJ5fbT58+HYsWLcKqVav80DqBQKA0hMZOIBAIFEx4eDh++ukn2UTGNDqdDl999ZWPWyUQCJSK0NgJBAKBQCAQhAhCYycQCAQCgUAQIgjBTiAQCAQCgSBEEIKdQCAQCAQCQYggBDuBQCAQCASCEEEIdgKBQCAQCAQhghDsBAKBQCAQCEIErScbHz16FPPnz8emTZtw7tw5FBQUIDExEa1bt0bfvn3x0EMPwWAw+KqtAoFAIBAIBAIZ3Mpjt2fPHrz22mvYvHkzunTpgvbt26Nq1aoIDw9HZmYmDh06hE2bNiEnJwevvfYaxo0bJwQ8gUAgEAgEAj/jlmBXu3ZtvPrqqxg0aBDi4uIkt9u6dSu++OILtGjRAm+88YY32ykQCAQCgUAgcIFbgp3RaIROp3N7p55uLxAIBAKBQCAoP24FT9BC2vnz51FcXOywjcViwfnz5x22FwgEAoFAIBD4B4+jYmvVqoU2bdrg9OnTzPJr166hdu3aXmuYQCAQCAQCgcAzypTupHHjxmjfvj3WrFnDLHfDqisQCAQCgUAg8BEeC3YqlQrffvst3nrrLfTr1w9ffvkls04gEAgEAoFAEBg8FuxsWrmXXnoJixYtwsSJEzFy5EiUlJR4vXECgUD5TJo0SVGTup49e6Jnz56BboZfOHv2LFQqFebMmRPopggEAoVQrsoTd999N/7991+sW7cO9957r7faJBBUKFQqlVt/69evL/exCgoKMGnSJK/sK1iwCT/eOGebEOvsb8aMGeVvrKBMLFu2DJMmTQp0MwQCReBR5QkA6NGjB/R6vf17kyZNsH37djz44IPCx04gKAP/+9//mO8//fQTVq9e7bC8cePG5T5WQUEBJk+eDABe02q99dZbGD9+vFf2FSxMnz4dUVFRzLIOHTr4vR01a9ZEYWFhhc9EsGzZMnzzzTdCuBMIUAbBbt26dQ7LKleujA0bNnilQQJBReOJJ55gvm/btg2rV692WK5UtFottFqPh5Kg5uGHH0ZCQkLAjm8ymWCxWKDX6xEWFua1/ebn5yMyMtJr+1Mi9LUTCEIRt02xOTk5bv0JBALvY7FYMG3aNDRt2hRhYWFITk7GqFGjcPPmTWa7Xbt2oW/fvkhISEB4eDhq166Np556CoDVJJmYmAgAmDx5st2EKKflMBqNmDx5MurXr4+wsDBUrlwZXbt2xerVq+3bOPOxKywsxNixY5GQkIDo6Gjcd999uHTpksPxbL89deoUhg0bhri4OMTGxmL48OEoKChg9jl79mz06tULSUlJMBgMaNKkCaZPn16Wy4n09HQMHz4c1atXh8FgQJUqVXD//ffj7NmzZdofz4IFC9C2bVuEh4cjISEBTzzxBC5dusRsI+ULOGzYMNSqVcv+3WZK/uSTTzBt2jTUrVsXBoMBR44ckfSxO3bsGB5++GHEx8cjLCwM7dq1w19//cVsM2fOHKhUKmzYsAHPP/88kpKSUL16ddnz+uqrr9C0aVNERESgUqVKaNeuHebNm2dfb+vPY8eOYeDAgYiJiUHlypXx4osvoqioyGF/P//8s/06xcfH47HHHsOFCxccttu+fTvuueceVKpUCZGRkWjRogW++OIL+/X65ptvALBuDa6une38+T5fv369g+m+Z8+eaNasGQ4cOIAePXogIiIC9erVw8KFCwEAGzZsQIcOHRAeHo6GDRvin3/+kb2OAoEvcXuaHRcXJ+sgTQiBSqWC2Wz2SsMEAkEpo0aNwpw5czB8+HCMHTsWaWlp+Prrr7F3715s2bIFOp0OGRkZ6NOnDxITEzF+/HjExcXh7Nmz+OOPPwAAiYmJmD59Op577jk88MADePDBBwEALVq0kDzupEmTMHXqVIwYMQLt27dHTk4Odu3ahT179uDOO++U/N2wYcPw22+/YciQIejYsSM2bNiAfv36SW4/cOBA1K5dG1OnTsWePXswa9YsJCUl4aOPPrJvM336dDRt2hT33XcftFotlixZgueffx4WiwWjR4/26Ho+9NBDOHz4MF544QXUqlULGRkZWL16Nc6fP88IVVJkZmYy3zUaDSpVqgQA9n667bbbMHXqVFy9ehVffPEFtmzZgr1798qWZZRj9uzZKCoqwjPPPAODwYD4+HhYLBaH7Q4fPowuXbqgWrVqGD9+PCIjI/Hbb79hwIAB+P333/HAAw8w2z///PNITEzExIkTkZ+fL3n8mTNnYuzYsXj44YftgtqBAwewfft2DBo0iNl24MCBqFWrFqZOnYpt27bhyy+/xM2bN/HTTz/Zt5kyZQrefvttDBw4ECNGjMC1a9fw1VdfoXv37sx1Wr16Ne69915UqVIFL774IlJSUnD06FEsXboUL774IkaNGoXLly87dV+Qu3aecvPmTdx777147LHH8Mgjj2D69Ol47LHHMHfuXIwbNw7PPvssBg0ahI8//hgPP/wwLly4gOjoaI+PIxCUG+Im69evt/+tW7eOhIeHk7lz5zLL169f7+7uBAKBBKNHjyb0o7lp0yYCgMydO5fZbsWKFczyRYsWEQBk586dkvu+du0aAUDeeecdt9rSsmVL0q9fP9lt3nnnHaa9u3fvJgDIuHHjmO2GDRvmcGzbb5966ilm2wceeIBUrlyZWVZQUOBw7L59+5I6deowy3r06EF69Ogh2d6bN28SAOTjjz+WPS9n2NrL/9WsWZMQQkhJSQlJSkoizZo1I4WFhfbfLV26lAAgEydOdNnOoUOH2vdHCCFpaWkEAImJiSEZGRnMtrZ1s2fPti+74447SPPmzUlRUZF9mcViIZ07dyb169e3L5s9ezYBQLp27UpMJpPLc7///vtJ06ZNZbexXZ/77ruPWf78888TAGT//v2EEELOnj1LNBoNmTJlCrPdwYMHiVartS83mUykdu3apGbNmuTmzZvMthaLxf6Zf2ZsyF072/mnpaUxy9etW0cAkHXr1tmX9ejRgwAg8+bNsy87duwYAUDUajXZtm2bffnKlSsd+kQg8Cdum2J79Ohh/+vZsyc0Gg06duzILO/Ro0d55UyBQMCxYMECxMbG4s4778T169ftf23btkVUVJTd79Wm4Vi6dCmMRqNXjh0XF4fDhw/j5MmTbv9mxYoVAKyaIJoXXnhB8jfPPvss871bt264ceMG494RHh5u/5ydnY3r16+jR48eOHPmDLKzs91uX3h4OPR6PdavX+9gynaX33//HatXr7b/zZ07F4DVFJ6RkYHnn3+e8X3r168fGjVqhL///rtMxwOsWkabKV2KzMxMrF27FgMHDkRubq79Xrlx4wb69u2LkydPOpiER44cCY1G4/L4cXFxuHjxInbu3OlyW16Dauv7ZcuWAQD++OMPWCwWDBw4kLmnU1JSUL9+ffs9vXfvXqSlpWHcuHEOmk5PUuy4c+1cERUVhccee8z+vWHDhoiLi0Pjxo2ZwBnb5zNnzpTreAJBWalYHs8CQRBy8uRJZGdnIykpyen6jIwMANbJ10MPPYTJkyfj888/R8+ePTFgwAAMGjQIBoOhTMd+9913cf/996NBgwZo1qwZ7rrrLgwZMkTWfHvu3Dmo1WqHEoP16tWT/E1qairz3WbWvHnzJmJiYgAAW7ZswTvvvIOtW7c6+N9lZ2cjNjbWrXMyGAz46KOP8MorryA5ORkdO3bEvffeiyeffBIpKSlu7aN79+5OgyfOnTsHwPrS52nUqBE2b97s1v6d4U7JxlOnToEQgrfffhtvv/22020yMjJQrVo1j/YLAK+//jr++ecftG/fHvXq1UOfPn0waNAgdOnSxWHb+vXrM9/r1q0LtVpt92c7efIkCCEO29mwRfnaSlc2a9bMrTZK4Y1yl9WrV3cQJmNjY1GjRg2HZQDKPGkQCMqLEOwEAoVjsViQlJRk1wrx2DQRKpUKCxcuxLZt27BkyRKsXLkSTz31FD799FNs27bNIT2HO3Tv3h2nT5/G4sWLsWrVKsyaNQuff/45ZsyYgREjRpTrvGikNEbkVgql06dP44477kCjRo3w2WefoUaNGtDr9Vi2bBk+//xzp75mcowbNw79+/fHn3/+iZUrV+Ltt9/G1KlTsXbtWrRu3brc5+MOKpXKaYooKT9lWmMphe06/Oc//0Hfvn2dbsML2O7sF7Cm2zl+/DiWLl2KFStW4Pfff8e3336LiRMn2lPoSMELRBaLBSqVCsuXL3fa92W5V+Vwdo5SGj+p6y91j7q6dwUCf1MuwU5J2eYFglClbt26+Oeff9ClSxe3XsIdO3ZEx44dMWXKFMybNw+DBw/G/PnzMWLEiDI9s/Hx8Rg+fDiGDx+OvLw8dO/eHZMmTZIU7GrWrAmLxYK0tDRGI3Pq1CmPj21jyZIlKC4uxl9//cVo95ylX3KXunXr4pVXXsErr7yCkydPolWrVvj000/x888/l3mfNWvWBAAcP34cvXr1YtYdP37cvh6waiWdmetsWr+yUKdOHQBWjVfv3r3LvB8pIiMj8eijj+LRRx9FSUkJHnzwQUyZMgUTJkxgTM8nT55ktGSnTp2CxWKxB6bUrVsXhBDUrl0bDRo0kDxe3bp1AQCHDh2SPZ+y3Nc2rXBWVhazvDzXXyBQAm772D344IPMX1FREZ599lmH5QKBwLsMHDgQZrMZ7733nsM6k8lkfzHdvHnTQUvQqlUrAEBxcTEAICIiAoDjy0yKGzduMN+joqJQr149+/6cYdMUffvtt8zyr776yq1jOsOmFaHPLzs7G7Nnz/Z4XwUFBQ6pN+rWrYvo6GjZ83KHdu3aISkpCTNmzGD2tXz5chw9epSJDK5bty6OHTuGa9eu2Zft378fW7ZsKfPxk5KS0LNnT3z33Xe4cuWKw3r6WJ7C3wt6vR5NmjQBIcTBp9OWfsSGre/vvvtuANb3iUajweTJkx3uWUKI/Vht2rRB7dq1MW3aNId7lv6dLfeeu/c1UCo0bty40b7MbDbj+++/d3sfAoEScVtjFxMTw8yKgiV5qkAQ7PTo0QOjRo3C1KlTsW/fPvTp0wc6nQ4nT57EggUL8MUXX+Dhhx/Gjz/+iG+//RYPPPAA6tati9zcXMycORMxMTG45557AFhNUk2aNMGvv/6KBg0aID4+Hs2aNZP0YWrSpAl69uyJtm3bIj4+Hrt27cLChQsxZswYyfa2bdsWDz30EKZNm4YbN27Y052cOHECQNm0K3369IFer0f//v0xatQo5OXlYebMmUhKSnIqwMhx4sQJ3HHHHRg4cCCaNGkCrVaLRYsW4erVq4xzfFnQ6XT46KOPMHz4cPTo0QOPP/64Pd1JrVq18NJLL9m3feqpp/DZZ5+hb9++ePrpp5GRkYEZM2agadOm5coJ+s0336Br165o3rw5Ro4ciTp16uDq1avYunUrLl68iP3795dpv3369EFKSgq6dOmC5ORkHD16FF9//TX69evnkNYjLS0N9913H+666y5s3boVP//8MwYNGoSWLVsCsApV77//PiZMmICzZ89iwIABiI6ORlpaGhYtWoRnnnkG//nPf6BWqzF9+nT0798frVq1wvDhw1GlShUcO3YMhw8fxsqVKwFY7zkAGDt2LPr27QuNRuOyL5s2bYqOHTtiwoQJyMzMRHx8PObPnw+TyVSm6yMQKIbABOMKBAIppFI3fP/996Rt27YkPDycREdHk+bNm5PXXnuNXL58mRBCyJ49e8jjjz9OUlNTicFgIElJSeTee+8lu3btYvbz77//krZt2xK9Xu8y9cn7779P2rdvT+Li4kh4eDhp1KgRmTJlCikpKbFvw6c7IYSQ/Px8Mnr0aBIfH0+ioqLIgAEDyPHjxwkA8uGHHzr89tq1a8zvnaWi+Ouvv0iLFi1IWFgYqVWrFvnoo4/IDz/84LCdq3Qn169fJ6NHjyaNGjUikZGRJDY2lnTo0IH89ttvkr9x1V6eX3/9lbRu3ZoYDAYSHx9PBg8eTC5evOiw3c8//0zq1KlD9Ho9adWqFVm5cqVkuhNn6VmcpTshhJDTp0+TJ598kqSkpBCdTkeqVatG7r33XrJw4UL7NrZrLJceh+a7774j3bt3J5UrVyYGg4HUrVuXvPrqqyQ7O9vh+hw5coQ8/PDDJDo6mlSqVImMGTOGSf9i4/fffyddu3YlkZGRJDIykjRq1IiMHj2aHD9+nNlu8+bN5M477yTR0dEkMjKStGjRgnz11Vf29SaTibzwwgskMTGRqFQq+/0od+1s16l3797EYDCQ5ORk8sYbb5DVq1c7TXfiLNVLzZo1naYDAkBGjx4tf0EFAh+hIsQ9D8/U1FTcd999uP/++3H77bdXuBJCAoGgfOzbtw+tW7fGzz//jMGDBwe6OQIfMGnSJEyePBnXrl0LaMk1gaAi47aP3f/+9z8YDAY8//zzSEhIwKOPPoq5c+d65NMgEAgqBoWFhQ7Lpk2bBrVaje7duwegRQKBQFAxcFvtZktA/Omnn+Lw4cP466+/8NVXX+Hpp59G586dcd999+G+++6zR2UJBIKKy3//+1/s3r3brt1fvnw5li9fjmeeecYh75dAIBAIvIfbGjuapk2bYsKECdi2bRvS0tLw+OOPY82aNXYn7PJkVxcIBMFP586dkZmZiffeew+vvPIKTpw4gUmTJjlESwoEAoHAu7jtY+cOBQUFWLlyJaKjo32SQ0kgEAgEAoFAIE25BDtCCNatW4fCwkJ07tzZnvBRIBAIBAKBQOB/3DbFZmVlYejQofbcSDk5OejWrRt69+6N/v37o3Hjxjhw4IAv2yoQCAQCgUAgkMFtjd2IESOwceNGDB06FEuWLIFarQYhxB7p9tprryEqKgpLlizxdZslmTp1Kv744w8cO3YM4eHh6Ny5Mz766COmIHdRURFeeeUVzJ8/H8XFxejbty++/fZbJCcnu3UMi8WCy5cvIzo6WpRUEwgEAoFA4HMIIcjNzUXVqlWhVrvQybmb8K5q1apk/fr1hBBCLl68SFQqFZPAcfv27SQ5Odk72fXKSN++fcns2bPJoUOHyL59+8g999xDUlNTSV5enn2bZ599ltSoUYOsWbOG7Nq1i3Ts2JF07tzZ7WNcuHCBABB/4k/8iT/xJ/7En/jz69+FCxdcyilua+y0Wi0uXLiAKlWqALDWnDx48KC93l56ejqqVasGs9nszu78wrVr15CUlIQNGzage/fuyM7ORmJiIubNm4eHH34YAHDs2DE0btwYW7duRceOHR32UVxczNR8zM7ORmpqKtLS0hzK6HgLo9GIdevW4fbbb4dOp/PJMQTuIfpCGYh+UA6iL5SD6Atl4I9+yM3NRe3atZGVlYXY2FjZbd3OY2exWOyFuAFrUW7aFKlEs2R2djYAID4+HgCwe/duGI1GJmK3UaNGSE1NlRTspk6dismTJzss37p1q72gui+IiIjA9u3bfbZ/gfuIvlAGoh+Ug+gL5SD6Qhn4uh8KCgoAuCdreVQXbNasWYiKigIAmEwmzJkzx142Jjc319N2+hSLxYJx48ahS5cu9gLn6enp0Ov1iIuLY7ZNTk5Genq60/1MmDABL7/8sv17Tk4OatSogT59+iAmJsYnbTcajVi9ejXuvPNOMQsLMKIvlIHoB+Ug+kI5iL5QBv7oh5ycHLe3dVuwS01NxcyZM+3fU1JS8L///c9hG6UwevRoHDp0CJs3by7XfgwGAwwGg8NynU7n8wfJH8cQuIfoC2Ug+kE5iL5QDqIvlIEv+8GT/bot2J09e7YsbQkIY8aMwdKlS7Fx40ZUr17dvjwlJQUlJSXIyspitHZXr15FSkpKAFoqjdEC7D2fhXa1E6BWK8/MLRAIBAKBQHmUqaSYUiGEYMyYMVi0aBHWrl2L2rVrM+vbtm0LnU6HNWvW2JcdP34c58+fR6dOnfzdXFkWnFFj4Mwd6PnJ+kA3RSAQCAQCQZDglsbuyy+/dHuHY8eOLXNjysvo0aMxb948LF68GNHR0Xa/udjYWISHhyM2NhZPP/00Xn75ZcTHxyMmJgYvvPACOnXq5DRwIpBsv2aVuc9nFgS4JQKBQCAQCIIFtwS7zz//nPl+7do1FBQU2M2ZWVlZiIiIQFJSUkAFu+nTpwMAevbsySyfPXs2hg0bBsB6Lmq1Gg899BCToFhphGsICs1WEywhRJFRxwKBQCAQCJSFW6bYtLQ0+9+UKVPQqlUrHD16FJmZmcjMzMTRo0fRpk0bvPfee75uryyEEKd/NqEOAMLCwvDNN9/gvffeQ2JiIpYtW4b7778fO3bsCFzDnRCjL/1cbLIEriECgUAgEAiCBo997N5++2189dVXTJmuhg0b4vPPP8dbb73l1cb5il9//RUvv/wy3nnnHezZswctW7ZE3759kZGREeim2aE7psQsBDuBQCAQCASu8Viwu3LlCkwmk8Nys9mMq1eveqVRvuazzz7DyJEjMXz4cDRp0gQzZsxAREQEfvjhh0A3rRTK8moyu1UcRCAQCAQCQQXHowTFAHDHHXdg1KhRmDVrFtq0aQPAWtHhueeeYyo6KJWSkhLs3r0bEyZMsC9Tq9Xo3bs3tm7d6rA9X1LMliTQaDTCaDT6pI1GoxF0obeComJE64WPXSCw9bGv+lrgHqIflIPoC+Ug+kIZ+KMfPNm3x4LdDz/8gKFDh6Jdu3b2hHkmkwl9+/bFrFmzPN2d37l+/TrMZjOSk5OZ5cnJyTh27JjD9lIlxVatWuXTkmIWUlq+bdU/a1E5zGeHErjB6tWrA90EAUQ/KAnRF8pB9IUy8GU/2EqKuYPHgp0t4ODkyZM4evQoAGu91QYNGni6q6AgUCXF3t2z1v793b1arH+lG6rFhfvkeAJpRMkeZSD6QTmIvlAOoi+UQdCWFOOpX78+6tevX9afB4yEhARoNBoHf0Cp6hOBKinGu9UN/r9d2DK+l8+OJ5BHlOxRBqIflIPoC+Ug+kIZKKWkmFvBEx9++CEKCwvd2uH27dvx999/u90Af6PX69G2bVum+oTFYsGaNWsUVX3Cwgl2l7Lcu/4CgUAgEAgqLm4JdkeOHEFqaiqef/55LF++HNeuXbOvM5lMOHDgAL799lt07twZjz76KKKjo33WYG/w8ssvY+bMmfjxxx9x9OhRPPfcc8jPz8fw4cMD3TQ7zgJhCRHRsQKBQCAQCKRxyxT7008/Yf/+/fj6668xaNAg5OTkQKPRwGAw2B36WrdujREjRmDYsGEIC1O2p/+jjz6Ka9euYeLEiUhPT0erVq2wYsUKh4CKQMJr7ADg+41n8Fj7VMSGC5W7QCAQCAQCR9z2sWvZsiVmzpyJ7777DgcOHMC5c+dQWFiIhIQEtGrVCgkJCb5sp9cZM2YMxowZE+hmSOJMYzd1+THsOX8T3w1p5/8GCQQCgUAgUDweJyhWq9Vo1aoV7r//fjz22GPo3bu3IoS6s2fP4umnn0bt2rURHh6OunXr4p133kFJSQmz3YEDB9CtWzeEhYWhRo0a+O9//xugFsvjTGMHACsPB0cSaIFAIBAIBP6nzFGxSuPYsWOwWCz47rvvUK9ePRw6dAgjR45Efn4+PvnkEwDWcOE+ffqgd+/emDFjBg4ePIinnnoKcXFxeOaZZwJ8BixSgl2rGnF+bYdAIBAIBILgIWQEu7vuugt33XWX/XudOnVw/PhxTJ8+3S7YzZ07FyUlJfjhhx+g1+vRtGlT7Nu3D5999pmkYBeIyhMlJSWwUDXFmlWNwciutfDibweg06hElnE/IjK7KwPRD8pB9IVyEH2hDIK+8kQwkZ2djfj4ePv3rVu3onv37tDr9fZlffv2xUcffYSbN2+iUqVKDvsIROUJswWguyY7OxuHDuwFoMHVa5lYtmyZT44rkEZkdlcGoh+Ug+gL5SD6QhkEbeWJYOHUqVP46quv7No6AEhPT0ft2rWZ7WyRsOnp6U4Fu0BUnsgtKAK2b7R/rxQXi26d6mPmsd0wREbhnnu6+OS4AkdEZndlIPpBOYi+UA6iL5RB0FeemD17Nh599FGf1kmlGT9+PD766CPZbY4ePYpGjRrZv1+6dAl33XUXHnnkEYwcObJcxw9I5Qm1ifl6V/MqiAyztqHIZBEPcAAQmd2VgegH5SD6QjmIvlAGSqk84bFgN378eLz44ot45JFH8PTTT6Nz586e7sIjXnnlFQwbNkx2mzp16tg/X758Gbfffjs6d+6M77//ntkuJSXFaSkx2zqlYKYiJ758vDXubpaCE1dzAQDFRkugmiUQCAQCgUDheCzYXbp0CUuWLMGcOXPQs2dP1KlTB8OHD8fQoUN9IhwlJiYiMTHR7bbdfvvtaNu2LWbPng21ms3m0qlTJ7z55pswGo126Xf16tVo2LChUzNsoDBbSoW3/i2qQKVSQa+xnovRLAQ7gUAgEAgEzvE4j51Wq8UDDzyAxYsX48KFCxg5ciTmzp2L1NRU3HfffVi8eDEsFv8LH5cuXULPnj2RmpqKTz75BNeuXUN6ejrS09Pt2wwaNAh6vR5PP/00Dh8+jF9//RVffPEF40OnBIy3NHZatQoqlTU6VmcX7ERZMYFAIBAIBM4pV/BEcnIyunbtihMnTuDEiRM4ePAghg4dikqVKmH27Nno2bOnl5rpmtWrV+PUqVM4deoUqlevzqyz1ViNjY3FqlWrMHr0aLRt2xYJCQmYOHGi4nLY2UyxGnVpyhOd1irYlQiNnUAgEAgEAgk81tgBVr+0Tz75BE2bNkXPnj2Rk5ODpUuXIi0tDZcuXcLAgQMxdOhQb7dVlmHDhoEQ4vSPpkWLFti0aROKiopw8eJFvP76635tpzvYzK02LR0AxhTLn5NAIBAIBAIBUAbBrn///qhRowbmzJmDkSNH4tKlS/jll1/Qu3dvAEBkZCReeeUVXLhwweuNdZfi4mK0atUKKpUK+/btY9YFQ0mx/GIzACBSr7Evswl2hLDBFQKBQCAQCAQ2PDbFJiUlYcOGDejUqZPkNomJiUhLSytXw8rDa6+9hqpVq2L//v3M8mApKZZXbE13EmkoFex02lKzrNFMoNU4/EwgEAgEAkEFx2PBrkePHmjTpo3D8pKSEsyfPx9PPvkkVCoVatas6ZUGesry5cuxatUq/P7771i+fDmzLlhKiuUUWI8XodeUHoMKSMkvKoZWJXIW+QNRskcZiH5QDqIvlIPoC2WgtJJiKuKhw5ZGo8GVK1eQlJTELL9x4waSkpJgNps92Z1XuXr1Ktq2bYs///wTCQkJqF27Nvbu3YtWrVoBAJ588knk5OTgzz//tP9m3bp16NWrFzIzM52mPJk0aZLTkmLz5s3zWZLmPddV+PGkBvVjLBjT1CrQEQKM22aVw99ra0KMXm4PAoFAIBAIQoWCggIMGjQI2dnZLqteeayxI4TYU3DQXLx4EbGxsZ7uzmsQQjBs2DA8++yzaNeuHc6ePeuwTbCUFMvafg44eRzVUxJxzz1t7ctf2/UPSkwW9Li9F6rEhvnk2AIWUbJHGYh+UA6iL5SD6AtlELQlxVq3bg2VyppX7Y477oBWW/pTs9mMtLQ03HXXXZ611A3cLSm2atUq5ObmYsKECV49fiBKihWZrErUqDD2GHqNGiUmC4hKLR5iPyNK9igD0Q/KQfSFchB9oQyCrqTYgAEDAAD79u1D3759ERUVZV+n1+tRq1YtPPTQQ+630k3cLSm2du1abN261UEIa9euHQYPHowff/wxaEqK2aNiDWz36DRWTamoPiEQCAQCgcAZbgt277zzDgCgVq1aePTRRxEW5h9ToLslxb788ku8//779u+XL19G37598euvv6JDhw4AgqekWH7JrahYPRv6astrV2wSgp1AIBAIBAJHPPax83fiYXdJTU1lvts0inXr1rVXohg0aBAmT56Mp59+Gq+//joOHTqEL774Ap9//rnf2ytHnqTGTpQVEwgEAoFAII1bgl18fDxOnDiBhIQEVKpUyWnwhI3MzEyvNc7bBEtJsfxbeeyiOMHOoC2tPiEQCAQCgUDA45Zg9/nnnyM6Otr+WU6wCzR///033n33XRw4cABxcXGYNGkSk94kLi4OMTExUKvVKCkpwfXr12EymZhgkEDjyhRrFKZYgUAgEAgETnBLmqHNr64CGQLJ77//jpEjR+KDDz5Ar169YDKZcOjQIft6s9mMfv36ISUlBf/++y+uXLmCJ598EjqdDh988EEAW84iGTxxq/pEidDYCQQCgUAgcILHtWL37NmDgwcP2r8vXrwYAwYMwBtvvIGSkhKvNs4TTCYTXnzxRXz88cd49tln0aBBAzRp0gQDBw60b7Nq1SocOXIEP//8M1q1aoW7774b7733Hr755puAtp3HrrEzSGjshI+dQCAQCAQCJ3hsfxw1ahTGjx+P5s2b48yZM3j00Ufx4IMPYsGCBSgoKMC0adN80EzX7NmzB5cuXYJarUbr1q2Rnp6OVq1a4eOPP0azZs0AAFu3bkXz5s3tSYkBoG/fvnjuuedw+PBhtG7d2mG/gSgplldkFewMaraMiFZt1dgVFpeIEjJ+QpTsUQaiH5SD6AvlIPpCGSitpJjHgt2JEyfsJboWLFiAHj16YN68ediyZQsee+yxgAl2Z86cAWAtAfbZZ5+hVq1a+PTTT9GzZ0+cOHEC8fHxSE9PZ4Q6gK084YypU6c6LSm2atUqn5UUu5mrAaDCwb07cfNE6fKcm2oAauzcvRfkvNDa+ZPVq1cHugkCiH5QEqIvlIPoC2Xgy34oKChwe9sylRSz3CpI/88//+Dee+8FANSoUQPXr1/3dHcucbfyhK1Nb775pj1R8uzZs1G9enUsWLAAo0aNKtPxA1FSbMLuNQDM6NW9C+oll5ZpW3RjD45nX0eT5i1wT5tqPjm2gEWU7FEGoh+Ug+gL5SD6QhkEbUkxG+3atcP777+P3r17Y8OGDZg+fToAIC0tzUEb5g3crTxx5coVAECTJk3syw0GA+rUqYPz588DsFaX2LFjB/NbV5Un/F1SzGIhKCixBk/ERYYxxzDorD53FqjEQ+xnRMkeZSD6QTmIvlAOoi+UQdCVFLMxbdo0DB48GH/++SfefPNN1KtXDwCwcOFCdO7c2dPducTdyhNt27aFwWDA8ePH0bVrVwBWKfrs2bOoWbMmAGvliSlTpiAjIwNJSUkArKrTmJgYRiAMJCoVsP6Vblj+zzpUitAz60S6E4FAIBAIBHJ4LNi1aNGCiYq18fHHH0Oj0Tj5hX+IiYnBs88+i3feeQc1atRAzZo18fHHHwMAHnnkEQBAnz590KRJEwwZMgT//e9/kZ6ejrfeegujR492qpULBCqVCtXiwlE1AtCo2XyBeq2IihUIBAKBQCBNmbPylpSUICMjw+7bZoMv7eVPPv74Y2i1WgwZMgSFhYXo0KED1q5da68Dq9FosHTpUjz33HPo1KkTIiMjMXToULz77rsBa7Mn6G9p7EQeO4FAIBAIBM4oU1Ts008/jX///ZdZTgiBSqWC2Wz2WuM8RafT4ZNPPsEnn3wiuU3NmjWxbNkyP7bKe4TfqkSRd6vkmEAgEAgEAgGNxwmKhw8fDrVajaVLl2L37t3Ys2cP9uzZg71792LPnj2+aKPbnDhxAvfffz8SEhIQExODrl27Yt26dcw258+fR79+/RAREYGkpCS8+uqrMJmCQ1CKv+VzdzNfOcmUBQKBQCAQKAePNXb79u3D7t270ahRI1+0p1zce++9qF+/PtauXYvw8HBMmzYN9957L06fPo2UlJSgKSkmRaVIq2CXKQQ7gUAgEAgETvBYsGvSpIlP8tWVl+vXr+PkyZP4v//7P7Ro0QIA8OGHH+Lbb7/FoUOHkJKSYi8p9s8//yA5ORmtWrXCe++9h9dffx2TJk2CXq932G8gKk9IZbGOMVgVrJn5xSLTuJ8Qmd2VgegH5SD6QjmIvlAGSqs8oSKEeBRiuXbtWrz11lv44IMP0Lx5c4fcKr5K2usKQggaN26Mbt26Ydq0aTAYDJg2bRo+/vhjHDt2DJUqVcLEiRPx119/Yd++ffbfpaWloU6dOtizZ4/TkmKTJk1yWnli3rx5Pqs8IcXJbBW+PqJBcjjBG60C58soEAgEAoHAfxQUFGDQoEHIzs52KWd5LNip1VatkUrFpuJQQvDExYsXMWDAAOzZswdqtRpJSUn4+++/7QLbM888g3PnzmHlypX23xQUFCAyMhLLli3D3Xff7bBPZxo7W5UNXwmxUlmsT1zNRb+vtyI+Uoft42/3ybEFLCKzuzIQ/aAcRF8oB9EXysBflScSEhLcEuw8NsXywQi+xt2SYg0bNsTo0aORlJSETZs2ITw8HLNmzUL//v2xc+dOVKlSpUzH93flCbljpMRFAgBuFhhhUalh0AYub2BFQ2R2VwaiH5SD6AvlIPpCGQRt5YkePXp4+pNy4W5JsbVr12Lp0qW4efOmXZr99ttvsXr1avz4448YP358mUqKKYn4SD2iDFrkFZtwIbMQ9ZKiAt0kgUAgEAgECqJMCYo3bdqE7777DmfOnMGCBQtQrVo1/O9//0Pt2rXt5by8hbslxQoKCgCUmoptqNVqexLlYCgpJodKpUJqfASOXMnBuRv5QrATCAQCgUDA4HEeu99//x19+/ZFeHg49uzZY/c/y87ODmjKkE6dOqFSpUoYOnQo9u/fjxMnTuDVV19FWloa+vXrB4AtKbZ//36sXLlScSXFXFErwRqwceZafoBbIhAIBAKBQGl4LNi9//77mDFjBmbOnMnYfLt06RLQBMUJCQlYsWIF8vLy0KtXL7Rr1w6bN2/G4sWL0bJlSwClJcU0Gg06deqEJ554Ak8++WTQlBQDgKZVYwEA+y5kBbYhAoFAIBAIFIfHptjjx4+je/fuDstjY2ORlZXljTaVmXbt2jERr84I5pJiANC2prXu7a5zmfZIZIFAIBAIBAKgDBq7lJQUnDp1ymH55s2bUadOHa80yhlTpkxB586dERERgbi4OKfbuFMubP369WjTpg0MBgPq1auHOXPm+KzNvqBl9TjoNCpczSnGyYy8QDdHIBAIBAKBgvBYsBs5ciRefPFF/H97dx7W1Jm2AfwOkISQhLDviwiIS9VaOlW0goprbbWb088uarG2jmhRR63buNRaa611dGy1TluxrdWq1bbW1sqIuFIrKiIuKLiAKIvsISQhyfv9EYhEUBJM4ADP77pyQc45SV58LvTxPee898mTJ8Hj8XD79m1s3boVs2bNwj/+8Q9rjBEAoFarMWbMmAd+Rm1cmFqtxokTJ7BlyxbEx8dj0aJFhmNqr7cbOHAgUlNTMX36dLz11luNzvJxiUhgi8hQ/c0kv6bdaeHREEIIIYRLzD4VO3fuXOh0OkRHR0OhUCAyMhJCoRCzZs3CtGnTrDFGADCkPzxohs2UuLCNGzciKCgIq1evBgB06dIFx44dw5o1azBs2LAG35dLkWK1RnTzwMHLBdiVkoPJ/QPBtzW7PycmosgebqA6cAfVgjuoFtzQ6iPFaqnVamRmZkIul6Nr166QSJpn6Y34+HhMnz693vV8psSFRUZG4oknnsC///1vwzGbN2/G9OnTUVZW1uDncSlSrJZaCyw9Ywu5hoc3QrR40r1JJSSEEEJIK2BOpFiT1rFjjKG8vByenp6cWf8tLy8Pnp6eRttqn+fl5T30mPLyclRVVUEkEtV733nz5mHmzJmG57WRYkOHDm32SLG6cqXX8O+DmTheKsXc1/pCYEezdtZAkT3cQHXgDqoFd1AtuKG5IsVMZVZjl5eXhzlz5uCXX35BRUUFAMDR0REvvPACVqxYUa9paoypcWGdO3c2630tiUuRYnXF9O+I705m49pdBbal5OKt/ta7cYVQZA9XUB24g2rBHVQLbmh1kWLl5eXo27cv5HI53nzzTXTu3BmMMVy8eBHbtm3DsWPHcObMGbNOyZoaF2YKU+LCvLy8DNvqHuPo6NjgbB2XOdrzMWdYZ8z5MQ2fJlzBkK6eCHQVt/SwCCGEENKCTG7s1q5dC1tbW1y4cKFexNfChQvRr18/rFu3DvPnzzf5w02NCzOFKXFhERER9dawS0hIQEREhEXG0NxeDvfDrjO38Nf1Ykz/IRU734mAHd1IQQghhLRbJncB+/btw/z58xtsxDw8PDBv3jzs3bvXooOrKzs7G6mpqcjOzoZWq0VqaipSU1Mhl+vXcjMlLmzy5Mm4du0a5syZg8uXL+Pzzz/Hjh07MGPGDKuN25psbHhY88rjkNrb4Wx2KZb/dqmlh0QIIYSQFmRyY3flyhX07dv3gfv79u2LjIwMiwyqIYsWLUKvXr2wePFiyOVy9OrVC7169UJKSgoA0+LCgoKCsG/fPiQkJKBnz55YvXo1vvzyywcuddIa+DqJsOrlHgCAzcdv4Ls/b7bwiAghhBDSUkxu7MrLyx+Y+AAATk5OZt21Ya7Q0FBERERAJBJBJpOBMQbGGAYMGAAAOHfuHObOnYvz58+DMQY3Nzf4+/vDzq7+2ebaGK62Esc1/DFvzBraCQCw6Od0/Jp2u4VHRAghhJCWYHJjxxiDjc2DD+fxeGjikngmaSx54vTp0/Dw8MB3332HCxcuYMGCBZg3bx7Wr19vOKYtJE88SOzAELzypD90DIjbnorfz1MqBSGEENLemHzzBGMMnTp1euAslzWbOqDx5ImYmBij5x07dkRycjJ2796NqVOnAkCTkidaCx6Phw9f7I5qnQ67z+Qi9vszeH/0Y3i9T2BLD40QQgghzcTkxm7z5s3WHIdVlJWVwcXFxfA8OTkZgwcPNjpm2LBhmD59+gPfg4uRYg/z4eiusOUBO0/nYuFP6bh5V45/DgmFrU3bOO3c3CiyhxuoDtxBteAOqgU3cC1SzOTGbvz48U0aTEs5ceIEfvjhB+zbt8+wrSnJEytWrGgwUuzAgQNWjxRLSEho0uv68YFKfx5+y7HFf4/dwJHz1/BGqA5SWr+yyZpaC2JZVAfuoFpwB9WCG6xZB4VCYfKxTYoUsxRrJU+kp6dj9OjRWLx4MYYOHfooQ+RspFhjRgIYdO4OFv58ARllwLoMEVa/3B19Oro0+lpyD0X2cAPVgTuoFtxBteCGVh0pZmmWTJ6odfHiRURHR+Ptt9/GwoULjfY1JXmCq5Fipnj5yQD08HfGlK1nkFkgxxubU/Ba7wDMHdEZUnv6S8AcFNnDDVQH7qBacAfVghtaXaSYNVgyeQIALly4gEGDBmH8+PFYvnx5vf1tLXnCFJ08pfhlaj8s+/UStv2Vja0ns3HocgGWjOqGIV0928ySL4QQQggxY7mTltZY8kR6ejoGDhyIoUOHYubMmcjLy0NeXh4KCwsN79HWkidM5SCww4oXu+P7Sb0R4OKA22VKvP3tabz+1UlcumO9tQcJIYQQ0rxaTWPXWPLErl27UFhYiO+++w7e3t6Gx9/+9jfDe7TF5Alz9A12w/7p/TFlQDAEdjY4nlmEkeuOYs6uc8gpNv3CTEIIIYRwk0mnYuvePNCYTz/9tMmDeZj4+PgHrmEHAEuWLMGSJUsafZ8BAwbg7NmzlhtYK+MgsMOc4Z0x9qkAfLT/Mval3cGOlFvYfSYXLz3hh9iBIQhwte7dvoQQQgixDpMaO1MbIWter7V8+XLs27cPqampEAgEKC0tfeCxRUVF6NmzJ3Jzc1FSUmIUhZaUlISZM2fiwoUL8Pf3x8KFCxu9gaMt8ndxwGevPoGYfiX49/+u4OjVu/ghJQe7ztzCyO7eiHk6CI/7O7X0MAkhhBBiBpMau0OHDll7HI2qjRSLiIjAV1999dBjJ06ciB49eiA3N9doe22k2OTJk7F161YcPHgQb731Fry9vdvN6dj7hQc649uJvXH6ZgnWHryKI1cK8cu52/jl3G30CnBCTL8gDH/MC3zbVnPWnhBCCGm3WvSuWHM0FilWa8OGDSgtLcWiRYvw+++/G+1ry5Fijyo80BnfxDyF9NwybD5+A3vP3cbZ7FJMyz4LN4kAL/Tyxd+f9Eeop7Slh0oIIYSQB2hSY5eSkoIdO3YgOzsbarXaaN/u3bstMrCmuHjxIt5//32cPHkS165dq7e/PUSKPaowDwd89EJX/HNwMLadysG2U7dwV67Gf49ex3+PXsfj/jK81MsXw7t5wsmh7a+bRJE93EB14A6qBXdQLbih1UaK1dq+fTvGjRuHYcOG4cCBAxg6dCiuXLmC/Px8vPDCC+a+ncWoVCqMHTsWq1atQkBAQIONXXuJFLOUEADzuwEXS3k4WcDDhRIeUnPKkJpThsV7LyBMxtDLlaG7C4NDq5n7bZqWrgXRozpwB9WCO6gW3NBqI8U+/PBDrFmzBrGxsZBKpVi7di2CgoLwzjvvwNvb26z3smSk2Lx589ClSxe8/vrrZo3BlPdtjZFilvRczdfCChV+Oncbv5zLw+W8Clwq5eFSKcC/wUO/YFcM6eKBqE5u8HS0b8nhWhTXatFeUR24g2rBHVQLbmj1kWJZWVkYOXIkAEAgEKCyshI8Hg8zZszAoEGDGpzdehBLRoolJibi/Pnz2LVrFwCAMQYAcHNzw4IFC7B06dJ2FylmaT4ufEwZ2AlTBnZCVqEc+9LuYF/aHWTkVyDpyl0kXbkLAOjuK8Ogzh6I7uKBx3xksLFp/ekWXKtFe0V14A6qBXdQLbih1UaKOTs7o6KiAgDg6+uL9PR0dO/eHaWlpWZNFQKWjRT78ccfUVVVZXh+6tQpxMTE4OjRowgODgbQPiPFrCXYXYJ3o0PxbnQoruZX4Pf0PBy8XIBzOaU4n1uG87llWHvwKtylQjwd4oaIYFf0C3GDr1PDDTQhhBBCHp3ZjV1kZCQSEhLQvXt3jBkzBnFxcUhMTERCQgKio6OtMUYA+kix4uJio0gxAAgJCYFEIjE0b7Xu3tXPHnXp0sWwjt3kyZOxfv16zJkzBzExMUhMTMSOHTuwb98+q427PQj1lCLUU4p3o0NRUKFEUkYhDl0uwJErhSisUGHP2VzsOatfeqaDqwP6hrihX7C+2XMRC1p49IQQQkjbYXZjt379eiiVSgDAggULwOfzceLECbz00ktYuHChxQdYa9GiRdiyZYvhea9evQDo19gbMGCASe9RGyk2Y8YMrF27Fn5+fu0qUqw5eEjt8fcn/fH3J/2h0mhx+kYJjmfdxfHMIqTdKsWNIgVuFGXj+5PZAIBgdzGeDHRBeAdnPBnojCA3sVUXuiaEEELaMrMaO41Gg19//dXQCNnY2GDu3LlWGdj9GosUu9+AAQMM19ndv709R4o1J6GdLfqGuKFviBtmDwPKldX461oxjmfdxYnMImTkVyCrsBJZhZX4ISUHAOAqFuCJQH2T1yvAGd18HCEWtvFbbgkhhBALMetfTDs7O0yePBmXLl2y1ngeyNRIsfj4eHz66ae4cuUKHB0dMWbMGHz22WeG/WlpaYiNjcWpU6fg7u6OadOmYc6cOc30U7RvjvZ8DO7qicFd9UvOFFeqceZmCVJuluD0zWKcu1WGoko1Ei7mI+Gi/iYXGx4Q4iFBd18n9PCTobufDF29HWHPt23JH4UQQgjhJLOnQp566imkpqYiMDDQGuN5IFMixT799FOsXr0aq1atQu/evVFZWYkbN24Y9peXl2Po0KEYPHgwNm7ciPPnzyMmJgZOTk54++23m+knIbVcxAKjRk+l0SI9txynbxYj5UYJ0m6VIa9ciSv5clzJl+PHM7cAALY2PHTylKK7ryO6ejuis7cjOntJ4eRA1+sRQghp38xu7KZMmYKZM2ciJycH4eHhEIvFRvt79OhhscHV1VikWElJCRYuXIi9e/ca3cRRdzxbt26FWq3G119/DYFAgG7duiE1NRWffvrpAxu79pY80ZJsAPTwkaCHjwRvRgQAAAoqVEi/XY703DKczy3H+dxyFFWqcelOOS7dMV7Xx8tRiDBPKcK8JAjzlKKzlwRBbuJHyrltr7XgGqoDd1AtuINqwQ1cS57gsYYuRHsIG5v6/0jyeDwwxsDj8aDVas15O7PFx8dj+vTp9U7F7tixA+PGjcOmTZuwYsUKVFRUoG/fvli9ejX8/f0BAOPGjUN5eTl++uknw+sOHTqEQYMGobi4GM7OzvU+b8mSJQ2uzff9999bPXmC1McYUKYGbsp5uFXJw20FcFvBQ7Gq4RsubHkMHiLAS8TgKQI8RQyeIgZ3e0BAZ3MJIYS0AgqFAq+++irKysoaDUcwe8bu+vXrTR6YNV27dg06nQ4ffvgh1q5dC5lMhoULF2LIkCFIS0uDQCBAXl4egoKCjF5XGzGWl5fXYGNHyROtQ4WyGlfy5cjIlyMjvwIZeXJczq9ApUqLOwrgjsK48ePxAD8nETq6ixHiLkZHNzGC3cXo6C6Gk4hvuDOXasENVAfuoFpwB9WCG1p98sTNmzfRt29f2NkZv1Sj0eDEiRNmXXtnyUgxnU6H6upqrFu3DkOHDgUAbNu2DV5eXjh06FCTlzSh5InWwYXPRx+pA/qEeBi2McZwq6QKV/IrkFUoR2bBvUe5UoOckirklFThcE1iRi1Hezt0cBMj0FUMfyd7lBXw4HlbjmBPGdwkAlqOpQXR7wR3UC24g2rBDa02eWLgwIG4c+cOPDw8jLaXlZVh4MCBZp2KtWSkWG1ObdeuXQ3b3N3d4ebmhuxs/ZppD4oUq91H2hYejwd/Fwf4uzgguounYTtjDHflaqNmL6tQjqwCOW6XKVGu1CDtVhnSbpXVvMIWW7NOAQAcBLYIdBWjg6uD4au/iwP8nEXwlokgsGv69XyEEELIozK7sau9lu5+RUVF9W6kaIwlI8X69esHAMjIyICfnx8AoLi4GHfv3jXMIkZERGDBggWorq42dL8JCQkICwtr8DQsaZt4PB7cpUK4S4Xo09HVaF+VWovsYgVuFFXiZlElrhXKceZKNip5DrhdpoRCrW3wxg39+wJejvbwcxbB10kEP2d9w+fn7ABfZxF8nOwhtKML+wghhFiPyY3diy++CED/j+KECROMTk9qtVqkpaWhb9++lh9hjcYixTp16oTRo0cjLi4OmzZtgqOjI+bNm4fOnTtj4MCBAIBXX30VS5cuxcSJE/Hee+8hPT0da9euxZo1a6w2btK6iAS2CPOSIsxLCkB/7cRvv93AM89EQsezwa2SKtwsqsSNuwr91yIFbpUocKukCiqNDnfKlLhTpsQplNR7bx4P8JAK9Y2ekwi+ziJ4y+zh5WgPb5kIXjJ7uIoFsLGhU72EEEKaxuTGTiaTAdDP2EmlUohE98LcBQIB+vTpg0mTJll+hDVMiRT75ptvMGPGDIwcORI2NjaIiorC/v37DbNzMpkMBw4cQGxsLMLDw+Hm5oZFixbRGnbEJEI7WwS7SxDsLqm3r/b0bm5plaHRu1WiQG5JVc33Vaiq1iK/XIX8chVO36zf+AEA35YHT0d7eMv0zZ63zB5eMvuar/rnbhIhbKn5I4QQ0gCTG7vNmzcDADp06IDZs2c3+1IfoaGhiIiIeGjyREZGBm7cuAHGGHQ6HSorK1FcXGxY7uR+PB6PLoQnFlH39O7j/k719jPGUKKoNmr6bpcqkVemxJ1yJfLKqlBQoUK1lhkaQTQw6wfoF2j2lArhKbOHh1QID6n+q7tUCA/He89dqQEkhJB2x+xr7MaNG4fc3FyEhoYabb969Sr4fD46dOhgqbEZaSx5Qi6XY/jw4Rg1ahQ+//xzaDQaLF68GMOGDUNOTg74fD4lT5AWw+Px4CIWwEUsQA8/pwaPqdbqUFihqjmdW6Vv+spqmr+a5/kVKmh1DLfLlLhdpnzoZ9rwABexUN/8OQrvNYGOQrhL7jWB7lIhRbQRQkgbYXZjN2HCBMTExNRr7E6ePIkvv/wSSUlJlhqbkcaSJy5fvozi4mK8//77hhm6xYsXo0ePHrh58yZCQkKalDxBSHPh29rAx0kEHycRgIZv5tHqGO7KVYaGr7BCiYIKFQrKVSio/b5ChSK5CjoG3JWrcFeuwsU7D/9sqdAOrhIBXCVCuIr1X90kAsP3rhIB3Gr2OTvQdYCEEMJVZjd2Z8+eNdyBWlefPn0wdepUiwyqKcLCwuDq6oqvvvoK8+fPh1arxVdffYUuXboYZhGTk5MRGRkJgeBepuiwYcOwcuVKlJSUNHhnLEWKtW9crIWLyBYuIjG6eT34LnStjqGoUo3CChUK5SoUVqhQUKGu+Vp3m/70b4VKgwqVBjeKFI1+vn4msKbpq5mFdJXce+4qFsBFIoCLgwDODnxI7e0e+ZIHLtahvaJacAfVghu4FilmdmPH4/FQUVFRb3tZWZnV48QeRiqVIikpCc8//zyWLVsGQH9d3h9//GFYTLkpyRMrVqxoMFLswIEDVr/OMCEhwarvT0zX2mshBhAEIMgWgFPNA/qItiotUFENyKuBimpezVdAbvieB7lGv02h4dXMBKpxV6426bNtwODAB8R2gMQOEPMZxHb65w/6XmSnbyDv19rr0JZQLbiDasEN1qyDQtH4f7prmd3YRUZGYsWKFdi2bRtsbfXX5Wi1WqxYsQJPP/20We9lyeSJqqoqTJw4Ef369cO2bdug1WrxySefYOTIkTh16pTRXbzmoEix9o1qYaxaq0OJohpFcjWKKvWP4kp1necq/Ta5GiWKalSqtdBB3yDKqwH9cuCNz97Z8ACZiA/nmlk/mcgOipICdAvpAFepEE4iAZxEfMgc7CCz50PmwIfMng97vg3dEGVl9DvBHVQLbmj1kWIrV65EZGQkwsLC0L9/fwDA0aNHUV5ejsTERLPey5LJE99//z1u3LiB5ORk2NjYGLY5Ozvj559/xv/93/81KXmCIsUIQLWoxecDDvZC+LqYdrxKo0WpohrFlWqUKNQoqayu+apGsUJt2Feq0D8vqayGXKWBjgElimqUKOqefrDBnwXZD/08gZ0NZCI+ZCK+vvET1TR9Ij6cRALIRHZwchDct50PRxEffFtKDTEH/U5wB9WCG1ptpFjXrl2RlpaG9evX49y5cxCJRBg3bhymTp0KFxcT/7avYcnkCYVCARsb4/+t1z7X6XQAKHmCkOYmtLOFp6MtPB3tTX6NWqNDqUI/41fbEN6tqMLJs+nw8O+IsioNihVqlFVVo0xRjbKqapRWVUOrY1Br9HcWF1aoGv+g+0iEdoamUCbiw8nBuDGU2vPhaG8HR1HNV3t+zfc0U0gI4Q6zGzsA8PHxwYcffmjpsTxUY8kTQ4YMwezZsxEbG4tp06ZBp9Pho48+gp2dHSVPENKKCOxs4OFoD486zWB1dTVkhefxzIiwBv/nyhhDpVqL0gYavrKqapTWPC+rUhs/V1SjQqUBAMhVGshVGuSWVpk9ZjsbnqHhk9rz4SjSN37SOg1gw9/rG0WJwI7uNCaEWESTGrujR4/iiy++wLVr17Bz5074+vri22+/RVBQkNnX2ZmqseSJzp07Y+/evVi6dCkiIiJgY2ODXr16Yf/+/fD29gZAyROEtFU8Hg8SoR0kQjv4mTn5rtHqUK7U1DR8NY3hfQ1hqaIaFcpqVCg1KFdW6x9VGlQoq6FjgEbHUFxzvWHTxq+fMTQ0gzUzgXVnCKU1+yQ130uEdvrnNV/F1BwSQtCExu7HH3/EG2+8gddeew1nzpwxLAVSVlaGDz/8EL/99pvFBwno16970Bp2tYYMGYIhQ4Y89JgePXrg6NGjFhwZIaQ1s7O1MSwerb9/2HS1M4XlVXWavgd+X9sUalBRpf9aXlUNtVYHxoAKpQYVSs0j/Sx1mz1JzVfHmiZQUqcJ1B/Dr2kS7SCts58aREJaN7Mbuw8++AAbN27EuHHjsH37dsP2fv364YMPPrDo4GrduHEDy5YtQ2JiIvLy8uDj44PXX38dCxYsMFqTLi0tDbGxsTh16hTc3d0xbdo0zJkzx+i9du7ciX/961+4ceMGQkNDsXLlSjzzzDNWGTchpG2rO1PYVMpqrVEjWK7UzwTWNoJ1v5cr9esNVig1kKtqnis10OgYgHunkx/tZwIkAuNGUGLP1zd/QjuIBTa4ncND/ombcHIQGo6T2NvBsU7D6MC3pQaRkBZg9t9GGRkZiIyMrLddJpM1mN9qCZcvX4ZOp8MXX3yBkJAQpKenY9KkSaisrMQnn3wCACbFhZ04cQJjx47FihUr8Oyzz+L777/H888/jzNnzuCxxx6zytgJIeRh7Pm2sOfbwl1a/+57UzDGoNLoapo9TU3zp58plNduU91rDA3H1DSJ9zeIjMGwYPWD2WL/rYyHjuv+BtFoxrCm+WtohrHubGJt00yZx4SYzuzGzsvLC5mZmfUyYY8dO2by0iTmGj58OIYPH2543rFjR2RkZGDDhg2Gxs6UuLC1a9di+PDhmD17NgBg2bJlSEhIwPr167Fx40arjJ0QQqyJx+M9cnMIPKBBVOpvLqltCCuU1ShTqHHx6jW4ePqgUq0zzCI2rUE0jYPA1tD8SevMHjbaHN63j5a0Ie2B2Y3dpEmTEBcXh6+//ho8Hg+3b99GcnIyZs2ahX/961/WGGODysrKjJZXMSUuLDk52Wix4dpjfvrppwd+DkWKtW9UC26gOjQPWwBO9jZwshcAMkGDx1RXVyNBm4khQ7o88A5llUZnmCmUK7X3vlfdaxrlKm1Ns6ipv1+l36/W6JeqUqi1UKi1KGjCMjZ1Ce1sjJs9oa1hVrDuDGFj24V820cah6XQ7wU3tPpIsblz50Kn0yE6OhoKhQKRkZEQCoWYNWsWpk2bZu7bNUlmZib+85//GGbrANPiwvLy8gzb6h6Tl5f3wM+iSDECUC24gurAHU2phQCAS80DgL6TFOOB96xodIBSe/+DB6Xmvue132vubVPVeY1apz+Vq9LooNLoU1IehS2Pwd4Wxg+7+7fdey60BeztjLeJbAG+jf6U9aOi3wtuaLWRYjweDwsWLMDs2bORmZkJuVyOrl27QiKRmPtWTYoUy83NxfDhwzFmzBhMmjTJ7M80F0WKtW9UC26gOnBHa6yFRqtDpVpbf3awzszhw2YUK2u2V6r1eehaxkOlBqg0Ostsfodma8Mznh2s+7DXbxcL75sxrDODKLIF/jpxBCOHDYZQ0PAMK7G+Vh8pVksgEEAqlUIqlTapqQPMjxS7ffs2Bg4ciL59+2LTpk1Gx5kSF/agYx4UJwZQpBjRo1pwA9WBO1pTLfh8QGQPuD3i/8W1OoZKdd1rDus2gtX3XZ9Yc4xKA7myus6NLfrtjOnfr6xKg7KqR7kO0Q5zTyUZ3ajiWBupVydOz8lBn6ZSu8/JQWCI06ObUyyj1UaKaTQaLF26FOvWrYNcLgcASCQSTJs2DYsXLzbrw82JFMvNzcXAgQMRHh6OzZs3G/Jga5kSFxYREYGDBw9i+vTphtclJCQgIiLC5DETQghpn2xteDULRz/aP96MMSjU2nrN4f03qxjvr653TWKFUgOtBW5UkdrbGSL0nEQCyBzuZS07OdzbVve5kwMf9hy51pAYM7uxmzZtGnbv3o2PP/7Y0BAlJydjyZIlKCoqwoYNGyw+yNzcXAwYMACBgYH45JNPUFhYaNhXO9tmSlxYXFwcoqKisHr1aowcORLbt29HSkpKvdk/QgghxFp4PB7ENadZPR9hFlGtVuPnX39HvwHRUGphaPbK68Xp3YvSuxevV21Y87B2cewcmBen5yCwhatEAFexEG41X10lArhK7j13EQvgJhHAWSygu5KbidmN3ffff4/t27djxIgRhm09evSAv78/xo4da5XGLiEhAZmZmcjMzISfn5/RPsb0C3OaEhfWt29ffP/991i4cCHmz5+P0NBQ/PTTT7SGHSGEkFaHx+NBYAu4S4VNOgVYrdXdF5/XcANYqlAbGsUyhb5p1Or0s46K4irkFJvWEDo58OEqFsBNIoSnoz28ZPbwkArhJbOHp6M9PKX28HAU0kzgIzK7sRMKhfXWsAOAoKAgo6VGLGnChAmNXosHmBYXNmbMGIwZM8ZCIyOEEEJaJ76tDdwkQrhJzFv/kDGGCpUGxXI1iipVuCtXo0iuRpFchaJK/V3HRXKVflulCsWVaugYDA1jVmHlQ9/fyYEPL0d7eMvs4e/iAD9nEfycHeDvrP/eyYEPniVuJ26jzG7spk6dimXLlmHz5s2GmwpUKhWWL1+OqVOnWnyAgGmRYklJSVizZg3++usvlJeXIzQ0FLNnz8Zrr71m9F4UKUYIIYQ0HY9371rDDm6N5ytrdQylitqGT42CCiUKylXIL1cir1z/fV65EvnlSqg0OkMDeDmvosH3kwjtDM2en7MIHVwdEOwhQYiHBF6O9u2+6TO7sTt79iwOHjwIPz8/9OzZEwBw7tw5qNVqREdH48UXXzQcu3v3bosM0pRIsRMnTqBHjx5477334OnpiV9//RXjxo2DTCbDs88+aziGIsUIIYSQ5mNrw4OrRAhXiRDwfPBxjDGUVVUjv6bRu11ahZxiBW6VVCGnRP+1sEIFuUqDy3kVDTZ+YoEtgj0kCHbXN3pdvR3xmK/skVJZWhuzGzsnJye89NJLRtv8/f0tNqCGmBIpNn/+fKPXxMXF4cCBA9i9e7ehsWtKpBglT7RvVAtuoDpwB9WCO9piLcR8Hjq62qOjqz0Ap3r7ldVa5JYqkVtahZySKtwqqcLNIgWyCiuRXaxApVqLtFtlSLtVZvQ6T6kQ3Xwc0c1Hih5+MoQHOEH6iHc312r1yRObN2829yVWcX+k2IOO6dKli+F5UyLFKHmCAFQLrqA6cAfVgjvaay2cax7dnQA46ZNKilRAnoKHAiVwR8HDrUoeCqqA/AoV8jMKkZihX1WDBwZfMRDiyBDiyBAq06eCPIpWmzxxv8OHD6OyshIRERGG9eKsraFIsfvt2LEDp06dwhdffGHY1pRIMUqeaN+oFtxAdeAOqgV3UC1MU1lz6vbCnQqk3y7HmZuluFmswK1K4FYlD0l3AL4tDxEdXRDd2QNDuniYdeq21SZPrFy5EnK5HMuWLQOgPxc+YsQIHDhwAADg4eGBgwcPolu3biZ/uLUixQ4dOoQ333wT//3vf80aT0MoeYIAVAuuoDpwB9WCO6gWD+fE56NPiAh9QjwM2/LKlDh5vQgnrxfjeOZd3CxS4MjVIhy5WoT3913GwDB3vBzuj+guHiavv9fqkid++OEHvPfee4bnu3btwpEjR3D06FF06dIF48aNw9KlS7Fjxw6TP9ySkWK1Dh8+jOeeew5r1qzBuHHjjPY1JVKMEEIIIW2Ll8weox/3xejHfcEYQ1ahHAcu5uOPC/k4l1OK/10qwP8uFcDPWYTJUcF4Odyv1ayvZ3Jjd/36dfTo0cPw/LfffsPLL7+Mfv36AQAWLlxo9vpwlowUA/RLnjz77LNYuXKl0cLEtShSjBBCCCF18Xg8hHhIEeIhxZQBIcgskGPn6RzsSrmFWyVVWPhTOj4/lImFz3bFiMe8OL+cisn5HhqNxuiUZHJyMvr27Wt47uPjg7t371p2dDVqI8UCAgIMkWJ5eXlG18YdOnQII0eOxLvvvouXXnrJsL+4uNhwTFxcHPbv34/Vq1fj8uXLWLJkCVJSUqy2/h4hhBBCWpcQDwnmjeiCY+8NwpLnusJbZo/bZUpM2XoGb397GmUKbt+FbHJjFxwcjCNHjgAAsrOzceXKFURGRhr237p1C66urpYfIe5FitWun+ft7W141NqyZQsUCgVWrFhhtL/uunq1kWKbNm1Cz549sWvXLooUI4QQQkg9IoEtJvQLwqFZA/BudCgEtjZIuJiPkf85iowHLJ7MBSY3drGxsZg6dSomTpyIESNGICIiAl27djXsT0xMRK9evawyyAkTJoAx1uCjVnx8fIP7k5KSjN5rzJgxyMjIgEqlQnp6OqVOEEIIIeSB7Pm2mDmkE3ZP6YsAFwfcKqnC/21KxoXbZY2/uAWY3NhNmjQJ69atQ3FxMSIjI/Hjjz8a7b99+zZiYmIsPkBAHyk2ceJEBAUFQSQSITg4GIsXL4ZarW7w+MzMTEilUjg5OdXbt3PnTnTu3Bn29vbo3r07fvvtN6uMmRBCCCFtx2O+MvwytR96+juhRFGNmPhTyC9XtvSw6jG5sQOAmJgY7NmzBxs2bKh3J+nnn3+OF154waKDq1U3UuzChQtYs2YNNm7cWC9tAtCvJzN27Fj079+/3r7aSLGJEyfi7NmzeP755/H8888jPT3dKuMmhBBCSNvh5CDAtxOfQqiHBPnlKsRuPQOtjjX+wmb0yAsUNwdTIsVqLVy4EJ07d0Z0dDROnDhhtI8ixYi5qBbcQHXgDqoFd1AtWobIFtjw2uMY/XkyUm6W4Ktj1+CHVhwpxhUNRYolJiZi586dSE1Nxe7du+u9hiLFSFNRLbiB6sAdVAvuoFq0jOd8edh+zRZr/peJGd3bUKRYS2goUqyoqAgTJkzAd99998CoL4oUI+aiWnAD1YE7qBbcQbVoWSMYQ8HWVCRmFOKLS7Y4MKM/XKXWmfCxSqSYNVgyUmzSpEl49dVXjZZgsQSKFCMA1YIrqA7cQbXgDqpFy/n0lcfx+PsJKK/mIe12JYY+JrPK51glUgzQ/+9AJBIhNTXVImu/WTJSLDExEb/88othFo8xBp1OBzs7O2zatAkxMTEUKUYIIYQQi3FyEKBfsCuOZxWhlCMLF5vV2PH5fAQEBECr1Vrkwy0ZKZacnGw0rp9//hkrV67EiRMn4OvrC4AixQghhBBiWeEBTigrLoSbRNDSQwHQhFOxCxYswPz58/Htt9/Wu3nBWmojxQIDAw2RYrVqZ9u6dOli9JqUlBTY2NgYzSzGxcUhKioKq1evxsiRI7F9+3akpKTUm/0jhBBCCDHFtEHBCFZmoH+oW0sPBUATGrv169cjMzMTPj4+CAwMhFgsNtp/5swZiw2uVm2kWGZmJvz8/Iz21U2faExtpNjChQsxf/58hIaGUqQYIYQQQtoMsxu7559/3grDeLgBAwYgJiYGiYmJyMvLg4+PD15//XUsWLDA6DjGGFavXo1Nmzbh5s2bcHNzw/Lly42Oc3d3NzSjSqUSBQUFzfqzEEIIIYRYi9mN3eLFi60xjoeqmzwREhKC9PR0TJo0CZWVlUZLnsTFxeHAgQP45JNP0L17dxQXF6O4uNiw//r16xg5ciQmT56MrVu34uDBg3jrrbfg7e2NYcOGNfvPRQghhBBiSU1a7qS0tBS7du1CVlYWZs+eDRcXF5w5cwaenp6GGxUsyZTkiUuXLmHDhg1IT09HWFgYACAoKMjofTZu3IigoCCsXr0agP66vGPHjmHNmjXU2BFCCCGk1TO7sUtLS8PgwYMhk8lw48YNTJo0CS4uLti9ezeys7PxzTffWGOc9dyfPLF371507NgRv/76K4YPHw7GGAYPHoyPP/7YcFxycjIGDx5s9D7Dhg0zukv2fvdHipWVlQEAiouLrRopplAoUFRURGsTtTCqBTdQHbiDasEdVAtuaI46VFRUADDxvgJmpujoaDZ79mzGGGMSiYRlZWUxxhg7fvw4CwwMNPftmuTq1avM0dGRbdq0ybDtnXfeYUKhkPXu3ZsdOXKEHTp0iD3++ONs4MCBhmNCQ0PZhx9+aPRe+/btYwCYQqFo8LMWL17MANCDHvSgBz3oQQ96tOgjJyen0R7J7Bm7U6dO4Ysvvqi33dfX96HRXA2xZPKETqeDSqXCN998g06dOgEAvvrqK4SHhyMjI8NwetZc90eK6XQ6FBcXw9XVFTwer0nv2Zja2LKcnByrxZYR01AtuIHqwB1UC+6gWnBDc9SBMYaKigr4+Pg0eqzZjZ1QKGwws+zKlSsmLzZcy5LJE97e3rCzszM0dcC9te2ys7MRFhb2wOQJR0dHiESiBj+/oUgxJyenxn40i3B0dKRfVo6gWnAD1YE7qBbcQbXgBmvXQSaTmXSc2Y3dqFGj8P7772PHjh0AAB6Ph+zsbLz33nt46aWXzHovSyZP9OvXDxqNBllZWQgODgagbzYBIDAwEIA+eeK3334zeh0lTxBCCCGkrbBp/BBjq1evhlwuh4eHB6qqqhAVFYWQkBBIpVIsX77cGmM0JE8EBAQYkify8vKMTv0OHjwYTzzxBGJiYnD27FmcPn0a77zzDoYMGWKYxZs8eTKuXbuGOXPm4PLly/j888+xY8cOzJgxwyrjJoQQQghpTmbP2MlkMiQkJODYsWNIS0uDXC7HE088Ue9uU0syJXnCxsYGe/fuxbRp0xAZGQmxWIwRI0YYljYB9Muf7Nu3DzNmzMDatWvh5+eHL7/8knNLnQiFQixevLjeKWDS/KgW3EB14A6qBXdQLbiBa3XgMWZGJhf0aQ329vbWGg8hhBBCCGkisxs7e3t7PPXUU4iKisLAgQMRERHxwBsPCCGEEEJI8zG7sTt27BiOHDmCpKQknDhxAhqNBk8++SSioqIwYMAADBkyxFpjJYQQQgghD2F2Y1eXRqMxrGu3detW6HQ6aLVaS46PEEIIIYSYqElZsVeuXEFSUpLhoVKp8Oyzz2LAgAEWHh4hhBBCCDGV2cud+Pr6ok+fPti/fz/69OmD33//HXfv3sWePXsQFxdnjTG2O5999hk6dOgAe3t79O7dG3/99VdLD6lNO3LkCJ577jn4+PiAx+Php59+MtrPGMOiRYvg7e0NkUiEwYMH4+rVqy0z2DZuxYoV+Nvf/gapVAoPDw88//zzyMjIMDpGqVQiNjYWrq6ukEgkeOmll+otPE4ezYYNG9CjRw/DgqsRERH4/fffDfupBi3no48+Ao/HM8o4p3o0jyVLloDH4xk96iZjcaUOZjd27u7uUCgUhnXk8vPzUVVVZY2xtUs//PADZs6cicWLF+PMmTPo2bMnhg0bhoKCgpYeWptVWVmJnj174rPPPmtw/8cff4x169Zh48aNOHnyJMRiMYYNGwalUtnMI237Dh8+jNjYWPz5559ISEhAdXU1hg4disrKSsMxM2bMwN69e7Fz504cPnwYt2/fxosvvtiCo257/Pz88NFHH+H06dNISUnBoEGDMHr0aFy4cAEA1aCl1F761KNHD6PtVI/m061bN9y5c8fwOHbsmGEfZ+rQaJpsA0pKStjPP//MZs6cycLDw5lIJGIRERFs/vz5TXk7UsdTTz3FYmNjDc+1Wi3z8fFhK1asaMFRtR8A2J49ewzPdTod8/LyYqtWrTJsKy0tZUKhkG3btq0FRti+FBQUMADs8OHDjDH9nz2fz2c7d+40HHPp0iUGgCUnJ7fUMNsFZ2dn9uWXX1INWkhFRQULDQ1lCQkJLCoqisXFxTHG6HeiOS1evJj17NmzwX1cqoPZM3aAPit11KhRmD9/PubNm4eXX34Zp06dwkcffWS5jrMdUqvVOH36tNFizzY2Nhg8eDCSk5NbcGTt1/Xr15GXl2dUE5lMht69e1NNmkFZWRkAwMXFBQBw+vRpVFdXG9Wjc+fOCAgIoHpYiVarxfbt21FZWYmIiAiqQQuJjY3FyJEj64UBUD2a19WrV+Hj44OOHTvitddeQ3Z2NgBu1cHsmyd2795tuGni4sWLcHFxwdNPP43Vq1cjKirKGmNsN+7evQutVgtPT0+j7Z6enrh8+XILjap9q42ta6gmdSPtiOXpdDpMnz4d/fr1w2OPPQZAXw+BQAAnJyejY6kelnf+/HlERERAqVRCIpFgz5496Nq1K1JTU6kGzWz79u04c+YMTp06VW8f/U40n969eyM+Ph5hYWG4c+cOli5div79+yM9PZ1TdTC7sZs8eTIiIyPx9ttvIyoqCt27d7fGuAgh7VxsbCzS09ONrmEhzScsLAypqakoKyvDrl27MH78eBw+fLilh9Xu5OTkIC4uDgkJCZT61MJGjBhh+L5Hjx7o3bs3AgMDsWPHDk4FNZjd2NFF/Nbj5uYGW1vbenfR5Ofnw8vLq4VG1b7V/rnn5+fD29vbsD0/Px+PP/54C42q7Zs6dSp+/fVXHDlyxCgf2svLC2q1GqWlpUb/M6bfEcsTCAQICQkBAISHh+PUqVNYu3YtXnnlFapBMzp9+jQKCgrwxBNPGLZptVocOXIE69evxx9//EH1aCFOTk7o1KkTMjMzMWTIEM7UoUnX2Gm1Wvz444/44IMP8MEHH2D37t20MLEFCAQChIeH4+DBg4ZtOp0OBw8eRERERAuOrP0KCgqCl5eXUU3Ky8tx8uRJqokVMMYwdepU7NmzB4mJiQgKCjLaHx4eDj6fb1SPjIwMZGdnUz2sTKfTQaVSUQ2aWXR0NM6fP4/U1FTD48knn8Rrr71m+J7q0TLkcjmysrLg7e3Nrd8Lc++2uHr1KgsNDWUODg6sV69erFevXszBwYGFhYWxzMxMK9zf0b5s376dCYVCFh8fzy5evMjefvtt5uTkxPLy8lp6aG1WRUUFO3v2LDt79iwDwD799FN29uxZdvPmTcYYYx999BFzcnJiP//8M0tLS2OjR49mQUFBrKqqqoVH3vb84x//YDKZjCUlJbE7d+4YHgqFwnDM5MmTWUBAAEtMTGQpKSksIiKCRUREtOCo2565c+eyw4cPs+vXr7O0tDQ2d+5cxuPx2IEDBxhjVIOWVveuWMaoHs3ln//8J0tKSmLXr19nx48fZ4MHD2Zubm6soKCAMcadOpjd2I0YMYINHz6cFRUVGbbdvXuXDR8+nD3zzDMWHVx79Z///IcFBAQwgUDAnnrqKfbnn3+29JDatEOHDjEA9R7jx49njOmXPPnXv/7FPD09mVAoZNHR0SwjI6NlB91GNVQHAGzz5s2GY6qqqtiUKVOYs7Mzc3BwYC+88AK7c+dOyw26DYqJiWGBgYFMIBAwd3d3Fh0dbWjqGKMatLT7GzuqR/N45ZVXmLe3NxMIBMzX15e98sorRhNaXKmD2VmxYrEYf/75Z72bJs6dO4d+/fpBLpc/8iwiIYQQQggxn9nX2AmFQlRUVNTbLpfLIRAILDIoQgghhBBiPrMbu2effRZvv/02Tp48CaY/lYs///wTkydPxqhRo6wxRkIIIYQQYgKzT8WWlpZi/Pjx2Lt3L/h8PgBAo9Fg1KhRiI+Ph0wms8pACSGEEELIw5nd2NXKzMzEpUuXAABdunQxrHdECCGEEEJahskLFOt0OqxatQq//PIL1Go1oqOjsXjxYk6ttkwIIYQQ0p6ZfI3d8uXLMX/+fEgkEvj6+mLt2rWIjY215tgIIYQQQogZTD4VGxoailmzZuGdd94BAPzvf//DyJEjUVVVBRubJgVYEEIIIYQQCzK5sRMKhcjMzIS/v79hm729PTIzM42yHAkhhBBCSMsweapNo9HA3t7eaBufz0d1dbXFB0UIIYQQQsxn8s0TjDFMmDABQqHQsE2pVGLy5MkQi8WGbbt377bsCAkh5BFNmDABW7Zsqbf96tWrdEc/IaRNMbmxGz9+fL1tr7/+ukUHQwgh1jJ8+HBs3rzZaJu7u7vRc7VaTQk6hJDWrdnTaQkhpJmNHz+ejR49ut72qKgoFhsby+Li4pirqysbMGAAY4yx8+fPs+HDhzOxWMw8PDzY66+/zgoLCw2vk8vl7I033mBisZh5eXmxTz75pF4wOwC2Z88eo8+TyWRs8+bNhufZ2dlszJgxTCaTMWdnZzZq1Ch2/fr1euNetWoV8/LyYi4uLmzKlClMrVYbjlEqlWzOnDnMz8+PCQQCFhwczL788kum0+lYcHAwW7VqldEYzp49ywCwq1evmv8HSQjhPLqdlRDSrm3ZsgUCgQDHjx/Hxo0bUVpaikGDBqFXr15ISUnB/v37kZ+fj7///e+G18yePRuHDx/Gzz//jAMHDiApKQlnzpwx63Orq6sxbNgwSKVSHD16FMePH4dEIsHw4cOhVqsNxx06dAhZWVk4dOgQtmzZgvj4eMTHxxv2jxs3Dtu2bcO6detw6dIlfPHFF5BIJODxeIiJiak3S7l582ZERkbSKWhC2qqW7iwJIcTaxo8fz2xtbZlYLDY8Xn75ZRYVFcV69epldOyyZcvY0KFDjbbl5OQwACwjI4NVVFQwgUDAduzYYdhfVFTERCKRWTN23377LQsLC2M6nc6wX6VSMZFIxP744w/DuAMDA5lGozEcM2bMGPbKK68wxhjLyMhgAFhCQkKDP3dubi6ztbVlJ0+eZIwxplarmZubG4uPjzfhT40Q0hqZfI0dIYS0ZgMHDsSGDRsMz8ViMcaOHYvw8HCj486dO4dDhw5BIpHUe4+srCxUVVVBrVajd+/ehu0uLi4ICwszazznzp1DZmYmpFKp0XalUomsrCzD827dusHW1tbw3NvbG+fPnwcApKamwtbWFlFRUQ1+ho+PD0aOHImvv/4aTz31FPbu3QuVSoUxY8aYNVZCSOtBjR0hpF0Qi8UNnn6se1c/AMjlcjz33HNYuXJlvWO9vb2RmZlp0ufxeDyw+5YJrbs8lFwuR3h4OLZu3VrvtXVv6uDz+fXeV6fTAYBJkY5vvfUW3njjDaxZswabN2/GK6+8AgcHB5N+BkJI60ONHSGE1PHEE0/gxx9/RIcOHWBnV/+vyODgYPD5fJw8eRIBAQEAgJKSEly5csVo5szd3R137twxPL969SoUCoXR5/zwww/w8PCAo6Njk8bavXt36HQ6HD58GIMHD27wmGeeeQZisRgbNmzA/v37ceTIkSZ9FiGkdaCbJwghpI7Y2FgUFxdj7NixOHXqFLKysvDHH3/gzTffhFarhUQiwcSJEzF79mwkJiYiPT0dEyZMqBetOGjQIKxfvx5nz55FSkoKJk+ebDT79tprr8HNzQ2jR4/G0aNHcf36dSQlJeHdd9/FrVu3TBprhw4dMH78eMTExOCnn34yvMeOHTsMx9ja2mLChAmYN28eQkNDERERYZk/KEIIJ1FjRwghdfj4+OD48ePQarUYOnQounfvjunTp8PJycnQvK1atQr9+/fHc889h8GDB+Ppp5+ud63e6tWr4e/vj/79++PVV1/FrFmzjE6BOjg44MiRIwgICMCLL76ILl26YOLEiVAqlWbN4G3YsAEvv/wypkyZgs6dO2PSpEmorKw0OmbixIlQq9V48803H+FPhhDSGpicFUsIIeTBBgwYgMcffxz//ve/W3oo9Rw9ehTR0dHIycmBp6dnSw+HEGJFdI0dIYS0USqVCoWFhViyZAnGjBlDTR0h7QCdiiWEkDZq27ZtCAwMRGlpKT7++OOWHg4hpBnQqVhCCCGEkDaCZuwIIYQQQtoIauwIIYQQQtoIauwIIYQQQtoIauwIIYQQQtoIauwIIYQQQtoIauwIIYQQQtoIauwIIYQQQtoIauwIIYQQQtqI/wdIA17W3+XQOAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "npt = 40000\nfs = 100\ns = generate_multi_comp_data(npt, fs) # Generate test data\ndt = 1 / fs\ntime = np.arange(npt) * dt" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, ax = plt.subplots(2, 1)\n", - "ax[0].plot(time, s)\n", - "ax[0].set_xlabel(\"Time (s)\")\n", - "ax[0].set_title(\"Test signal\")\n", - "ax[1].psd(s, Fs=fs, NFFT=2048*4, noverlap=fs)\n", - "ax[1].set_title(\"Test signal's Fourier spectrum\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "e1ce7a7c", - "metadata": { - "papermill": { - "duration": 0.001593, - "end_time": "2024-12-09T08:41:45.549291", - "exception": false, - "start_time": "2024-12-09T08:41:45.547698", - "status": "completed" }, - "tags": [] - }, - "source": [ - "### Compute phase and amplitude\n", - "We compute the Hilbert phase and amplitude, as well as the phase and\n", - "amplitude obtained by the locking-based technique, non-resonant and\n", - "resonant oscillator.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "de804e62", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:45.553386Z", - "iopub.status.busy": "2024-12-09T08:41:45.553215Z", - "iopub.status.idle": "2024-12-09T08:41:50.844846Z", - "shell.execute_reply": "2024-12-09T08:41:50.844508Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize signal\nPlot the test signal's Fourier spectrum\n\n" + ] }, - "papermill": { - "duration": 5.295098, - "end_time": "2024-12-09T08:41:50.846101", - "exception": false, - "start_time": "2024-12-09T08:41:45.551003", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, ax = plt.subplots(2, 1)\nax[0].plot(time, s)\nax[0].set_xlabel(\"Time (s)\")\nax[0].set_title(\"Test signal\")\nax[1].psd(s, Fs=fs, NFFT=2048*4, noverlap=fs)\nax[1].set_title(\"Test signal's Fourier spectrum\")\nplt.tight_layout()" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/nbarascud/Github/python-meegkit/meegkit/utils/buffer.py:68: UserWarning: Buffer overflow: some old data has been discarded\n", - " warnings.warn(\"Buffer overflow: some old data has been discarded\")\n" - ] - } - ], - "source": [ - "ht_ampl = np.abs(hilbert(s)) # Hilbert amplitude\n", - "ht_phase = np.angle(hilbert(s)) # Hilbert phase\n", - "\n", - "lb_phase = locking_based_phase(s, dt, npt)\n", - "lb_phi_dif = phase_difference(ht_phase, lb_phase)\n", - "\n", - "osc = NonResOscillator(fs, 1.1)\n", - "nr_phase, nr_ampl = osc.transform(s)\n", - "nr_phase = nr_phase[:, 0]\n", - "nr_phi_dif = phase_difference(ht_phase, nr_phase)\n", - "\n", - "osc = ResOscillator(fs, 1.1)\n", - "r_phase, r_ampl = osc.transform(s)\n", - "r_phase = r_phase[:, 0]\n", - "r_phi_dif = phase_difference(ht_phase, r_phase)" - ] - }, - { - "cell_type": "markdown", - "id": "3a11f783", - "metadata": { - "papermill": { - "duration": 0.001473, - "end_time": "2024-12-09T08:41:50.849747", - "exception": false, - "start_time": "2024-12-09T08:41:50.848274", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute phase and amplitude\nWe compute the Hilbert phase and amplitude, as well as the phase and\namplitude obtained by the locking-based technique, non-resonant and\nresonant oscillator.\n\n" + ] }, - "tags": [] - }, - "source": [ - "## Results\n", - "Here we reproduce figure 1 from the original paper [1]_.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "77d498c0", - "metadata": { - "papermill": { - "duration": 0.00147, - "end_time": "2024-12-09T08:41:50.852634", - "exception": false, - "start_time": "2024-12-09T08:41:50.851164", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ht_ampl = np.abs(hilbert(s)) # Hilbert amplitude\nht_phase = np.angle(hilbert(s)) # Hilbert phase\n\nlb_phase = locking_based_phase(s, dt, npt)\nlb_phi_dif = phase_difference(ht_phase, lb_phase)\n\nosc = NonResOscillator(fs, 1.1)\nnr_phase, nr_ampl = osc.transform(s)\nnr_phase = nr_phase[:, 0]\nnr_phi_dif = phase_difference(ht_phase, nr_phase)\n\nosc = ResOscillator(fs, 1.1)\nr_phase, r_ampl = osc.transform(s)\nr_phase = r_phase[:, 0]\nr_phi_dif = phase_difference(ht_phase, r_phase)\n\nprint(f\"Mean absolute phase error, locking-based: {np.mean(np.abs(lb_phi_dif)):.3f}\")\nprint(f\"Mean absolute phase error, nonresonant: {np.mean(np.abs(nr_phi_dif)):.3f}\")\nprint(f\"Mean absolute phase error, resonant: {np.mean(np.abs(r_phi_dif)):.3f}\")\nprint(\"Interpretation: smaller mean absolute phase error indicates a closer\")\nprint(\"match to the Hilbert phase reference.\")" + ] }, - "tags": [] - }, - "source": [ - "The first row shows the test signal $s$ and its Hilbert amplitude\n", - "$a_H$ ; one can see that ah does not represent a good envelope for\n", - "$s$. On the contrary, the Hilbert-based phase estimation yields good\n", - "results, and therefore we take it for the ground truth. Rows 2-4 show the\n", - "difference between the Hilbert phase and causally estimated phases\n", - "($\\phi_L$, $\\phi_N$, $\\phi_R$) are obtained by means of the\n", - "locking-based technique, non-resonant and resonant oscillator, respectively).\n", - "These panels demonstrate that the output of the developed causal algorithms\n", - "is very close to the HT-phase. Notice that we show $\\phi_H - \\phi_N$\n", - "modulo :math:`2\\pi, since the phase difference is not bounded.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e69fc87b", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:50.856624Z", - "iopub.status.busy": "2024-12-09T08:41:50.856450Z", - "iopub.status.idle": "2024-12-09T08:41:51.530770Z", - "shell.execute_reply": "2024-12-09T08:41:51.530474Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results\nHere we reproduce figure 1 from the original paper [1]_.\n\n" + ] }, - "papermill": { - "duration": 0.678906, - "end_time": "2024-12-09T08:41:51.532897", - "exception": false, - "start_time": "2024-12-09T08:41:50.853991", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first row shows the test signal $s$ and its Hilbert amplitude\n$a_H$ ; one can see that ah does not represent a good envelope for\n$s$. On the contrary, the Hilbert-based phase estimation yields good\nresults, and therefore we take it for the ground truth. Rows 2-4 show the\ndifference between the Hilbert phase and causally estimated phases\n($\\phi_L$, $\\phi_N$, $\\phi_R$) are obtained by means of the\nlocking-based technique, non-resonant and resonant oscillator, respectively).\nThese panels demonstrate that the output of the developed causal algorithms\nis very close to the HT-phase. Notice that we show $\\phi_H - \\phi_N$\nmodulo $2\\pi$, since the phase difference is not bounded.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, ax = plt.subplots(4, 2, sharex=True, sharey=True, figsize=(12, 8))\nax[0, 0].plot(time, s, time, ht_phase, lw=.75)\nax[0, 0].set_ylabel(r\"$s,\\phi_H$\")\nax[0, 0].set_title(\"Signal and its Hilbert phase\")\n\nax[1, 0].plot(time, lb_phi_dif, lw=.75)\nax[1, 0].axhline(0, color=\"k\", ls=\":\", zorder=-1)\nax[1, 0].set_ylabel(r\"$\\phi_H - \\phi_L$\")\nax[1, 0].set_ylim([-np.pi, np.pi])\nax[1, 0].set_title(\"Phase locking approach\")\n\nax[2, 0].plot(time, nr_phi_dif, lw=.75)\nax[2, 0].axhline(0, color=\"k\", ls=\":\", zorder=-1)\nax[2, 0].set_ylabel(r\"$\\phi_H - \\phi_N$\")\nax[2, 0].set_ylim([-np.pi, np.pi])\nax[2, 0].set_title(\"Nonresonant oscillator\")\n\nax[3, 0].plot(time, r_phi_dif, lw=.75)\nax[3, 0].axhline(0, color=\"k\", ls=\":\", zorder=-1)\nax[3, 0].set_ylim([-np.pi, np.pi])\nax[3, 0].set_ylabel(r\"$\\phi_H - \\phi_R$\")\nax[3, 0].set_xlabel(\"Time\")\nax[3, 0].set_title(\"Resonant oscillator\")\n\nax[0, 1].plot(time, s, time, ht_ampl, lw=.75)\nax[0, 1].set_ylabel(r\"$s,a_H$\")\nax[0, 1].set_title(\"Signal and its Hilbert amplitude\")\n\nax[1, 1].axis(\"off\")\n\nax[2, 1].plot(time, s, time, nr_ampl, lw=.75)\nax[2, 1].set_ylabel(r\"$s,a_N$\")\nax[2, 1].set_title(\"Amplitudes\")\nax[2, 1].set_title(\"Nonresonant oscillator\")\n\nax[3, 1].plot(time, s, time, r_ampl, lw=.75)\nax[3, 1].set_xlabel(\"Time\")\nax[3, 1].set_ylabel(r\"$s,a_R$\")\nax[3, 1].set_title(\"Resonant oscillator\")\nplt.suptitle(\"Amplitude (right) and phase (left) estimation algorithms\")\nplt.tight_layout()\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "f, ax = plt.subplots(4, 2, sharex=True, sharey=True, figsize=(12, 8))\n", - "ax[0, 0].plot(time, s, time, ht_phase, lw=.75)\n", - "ax[0, 0].set_ylabel(r\"$s,\\phi_H$\")\n", - "ax[0, 0].set_title(\"Signal and its Hilbert phase\")\n", - "\n", - "ax[1, 0].plot(time, lb_phi_dif, lw=.75)\n", - "ax[1, 0].axhline(0, color=\"k\", ls=\":\", zorder=-1)\n", - "ax[1, 0].set_ylabel(r\"$\\phi_H - \\phi_L$\")\n", - "ax[1, 0].set_ylim([-np.pi, np.pi])\n", - "ax[1, 0].set_title(\"Phase locking approach\")\n", - "\n", - "ax[2, 0].plot(time, nr_phi_dif, lw=.75)\n", - "ax[2, 0].axhline(0, color=\"k\", ls=\":\", zorder=-1)\n", - "ax[2, 0].set_ylabel(r\"$\\phi_H - \\phi_N$\")\n", - "ax[2, 0].set_ylim([-np.pi, np.pi])\n", - "ax[2, 0].set_title(\"Nonresonant oscillator\")\n", - "\n", - "ax[3, 0].plot(time, r_phi_dif, lw=.75)\n", - "ax[3, 0].axhline(0, color=\"k\", ls=\":\", zorder=-1)\n", - "ax[3, 0].set_ylim([-np.pi, np.pi])\n", - "ax[3, 0].set_ylabel(r\"$\\phi_H - \\phi_R$\")\n", - "ax[3, 0].set_xlabel(\"Time\")\n", - "ax[3, 0].set_title(\"Resonant oscillator\")\n", - "\n", - "ax[0, 1].plot(time, s, time, ht_ampl, lw=.75)\n", - "ax[0, 1].set_ylabel(r\"$s,a_H$\")\n", - "ax[0, 1].set_title(\"Signal and its Hilbert amplitude\")\n", - "\n", - "ax[1, 1].axis(\"off\")\n", - "\n", - "ax[2, 1].plot(time, s, time, nr_ampl, lw=.75)\n", - "ax[2, 1].set_ylabel(r\"$s,a_N$\")\n", - "ax[2, 1].set_title(\"Amplitudes\")\n", - "ax[2, 1].set_title(\"Nonresonant oscillator\")\n", - "\n", - "ax[3, 1].plot(time, s, time, r_ampl, lw=.75)\n", - "ax[3, 1].set_xlabel(\"Time\")\n", - "ax[3, 1].set_ylabel(r\"$s,a_R$\")\n", - "ax[3, 1].set_title(\"Resonant oscillator\")\n", - "plt.suptitle(\"Amplitude (right) and phase (left) estimation algorithms\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py312", - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 8.36615, - "end_time": "2024-12-09T08:41:51.854572", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_phase_estimation.ipynb", - "output_path": "examples/example_phase_estimation.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:41:43.488422", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_phase_estimation.py b/examples/example_phase_estimation.py index b7ac9230..ec2600bf 100644 --- a/examples/example_phase_estimation.py +++ b/examples/example_phase_estimation.py @@ -5,8 +5,14 @@ This example shows how to causally estimate the phase of a signal using two oscillator models, as described in [1]_. +It compares three causal estimators against the Hilbert phase reference and +reports mean absolute phase errors. + Uses `meegkit.phase.ResOscillator()` and `meegkit.phase.NonResOscillator()`. +The comparison is easiest to read through the phase-difference traces and the +printed mean absolute phase errors. + References ---------- .. [1] Rosenblum, M., Pikovsky, A., Kühn, A.A. et al. Real-time estimation @@ -14,20 +20,35 @@ (2021). https://doi.org/10.1038/s41598-021-97560-5 """ -import os -import sys - import matplotlib.pyplot as plt import numpy as np from scipy.signal import hilbert from meegkit.phase import NonResOscillator, ResOscillator, locking_based_phase -sys.path.append(os.path.join("..", "tests")) -from test_filters import generate_multi_comp_data, phase_difference # noqa:E402 - -rng = np.random.default_rng(5) +def phase_difference(phi1, phi2): + """Compute circular phase difference between two phase arrays.""" + cos_phi_dif = np.cos(phi1) * np.cos(phi2) + np.sin(phi1) * np.sin(phi2) + sin_phi_dif = np.sin(phi1) * np.cos(phi2) - np.cos(phi1) * np.sin(phi2) + return np.arctan2(sin_phi_dif, cos_phi_dif) + + +def generate_multi_comp_data(npt=40000, fs=100): + """Generate multi-component data with frequency modulation.""" + dt = 1 / fs + t = np.arange(1, npt + 1) * dt + omega1 = np.sqrt(2) / 30 + omega2 = np.sqrt(5) / 60 + amp = 1 + 0.95 * np.cos(omega1 * t) + p = t + 5 * np.sin(omega2 * t) + s = ( + np.cos(p) + + 0.2 * np.cos(2 * p + np.pi / 6) + + 0.1 * np.cos(3 * p + np.pi / 3) + ) + s *= amp + return s ############################################################################### # Build data @@ -36,7 +57,7 @@ # modulations, as described in the paper [1]_. ############################################################################### -npt = 100000 +npt = 40000 fs = 100 s = generate_multi_comp_data(npt, fs) # Generate test data dt = 1 / fs @@ -76,6 +97,12 @@ r_phase = r_phase[:, 0] r_phi_dif = phase_difference(ht_phase, r_phase) +print(f"Mean absolute phase error, locking-based: {np.mean(np.abs(lb_phi_dif)):.3f}") +print(f"Mean absolute phase error, nonresonant: {np.mean(np.abs(nr_phi_dif)):.3f}") +print(f"Mean absolute phase error, resonant: {np.mean(np.abs(r_phi_dif)):.3f}") +print("Interpretation: smaller mean absolute phase error indicates a closer") +print("match to the Hilbert phase reference.") + ############################################################################### # Results @@ -134,4 +161,4 @@ ax[3, 1].set_title("Resonant oscillator") plt.suptitle("Amplitude (right) and phase (left) estimation algorithms") plt.tight_layout() -plt.show() \ No newline at end of file +plt.show() diff --git a/examples/example_ress.ipynb b/examples/example_ress.ipynb index 40510a63..0b30fbb9 100644 --- a/examples/example_ress.ipynb +++ b/examples/example_ress.ipynb @@ -1,376 +1,97 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "aa07b6fc", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:41.365815Z", - "iopub.status.busy": "2024-12-09T08:41:41.365620Z", - "iopub.status.idle": "2024-12-09T08:41:41.654309Z", - "shell.execute_reply": "2024-12-09T08:41:41.653884Z" - }, - "papermill": { - "duration": 0.292807, - "end_time": "2024-12-09T08:41:41.655580", - "exception": false, - "start_time": "2024-12-09T08:41:41.362773", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "18bd5fc8", - "metadata": { - "papermill": { - "duration": 0.001419, - "end_time": "2024-12-09T08:41:41.658591", - "exception": false, - "start_time": "2024-12-09T08:41:41.657172", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Rhythmic Entrainment Source Separation (RESS) example\n", - "\n", - "Find the linear combinations of multichannel data that maximize the\n", - "signal-to-noise ratio of the narrow-band steady-state response in the frequency\n", - "domain.\n", - "\n", - "Uses `meegkit.RESS()`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "08e243fa", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:41.661478Z", - "iopub.status.busy": "2024-12-09T08:41:41.661305Z", - "iopub.status.idle": "2024-12-09T08:41:42.477698Z", - "shell.execute_reply": "2024-12-09T08:41:42.477360Z" - }, - "papermill": { - "duration": 0.819313, - "end_time": "2024-12-09T08:41:42.479040", - "exception": false, - "start_time": "2024-12-09T08:41:41.659727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import scipy.signal as ss\n", - "\n", - "from meegkit import ress\n", - "from meegkit.utils import fold, matmul3d, snr_spectrum, unfold\n", - "\n", - "# import config\n", - "\n", - "rng = np.random.default_rng(9)" - ] - }, - { - "cell_type": "markdown", - "id": "ccbfaf31", - "metadata": { - "papermill": { - "duration": 0.001409, - "end_time": "2024-12-09T08:41:42.483003", - "exception": false, - "start_time": "2024-12-09T08:41:42.481594", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Create synthetic data\n", - "Create synthetic data containing a single oscillatory component at 12 hz.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dc8d0dd1", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:42.486172Z", - "iopub.status.busy": "2024-12-09T08:41:42.485943Z", - "iopub.status.idle": "2024-12-09T08:41:42.697114Z", - "shell.execute_reply": "2024-12-09T08:41:42.696775Z" - }, - "papermill": { - "duration": 0.213924, - "end_time": "2024-12-09T08:41:42.698171", - "exception": false, - "start_time": "2024-12-09T08:41:42.484247", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Rhythmic Entrainment Source Separation (RESS) example\n\nFind the linear combinations of multichannel data that maximize the\nsignal-to-noise ratio of the narrow-band steady-state response in the frequency\ndomain.\n\nUses `meegkit.RESS()`.\n\nBecause the simulation contains a known oscillatory target at 12 Hz, the main\nsanity check is whether the target-frequency SNR stands out more clearly after\nRESS than the surrounding spectrum.\n\n## References\n.. [1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source\n separation: Optimizing analyses of neural responses to rhythmic sensory\n stimulation. NeuroImage, 147, 43-56.\n" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.signal as ss\n\nfrom meegkit import ress\nfrom meegkit.utils import fold, matmul3d, snr_spectrum, unfold\n\n# import config\n\nrng = np.random.default_rng(9)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_times = 1000\n", - "n_chans = 10\n", - "n_trials = 30\n", - "target = 12\n", - "sfreq = 250\n", - "noise_dim = 8\n", - "SNR = .2\n", - "t0 = 100\n", - "\n", - "# source\n", - "source = np.sin(2 * np.pi * target * np.arange(n_times - t0) / sfreq)[None].T\n", - "s = source * rng.standard_normal((1, n_chans))\n", - "s = s[:, :, np.newaxis]\n", - "s = np.tile(s, (1, 1, n_trials))\n", - "signal = np.zeros((n_times, n_chans, n_trials))\n", - "signal[t0:, :, :] = s\n", - "\n", - "# noise\n", - "noise = np.dot(\n", - " unfold(rng.standard_normal((n_times, noise_dim, n_trials))),\n", - " rng.standard_normal((noise_dim, n_chans)))\n", - "noise = fold(noise, n_times)\n", - "\n", - "# mix signal and noise\n", - "signal = SNR * signal / np.sqrt(np.mean(signal ** 2))\n", - "noise = noise / np.sqrt(np.mean(noise ** 2))\n", - "data = signal + noise\n", - "\n", - "# Plot\n", - "f, ax = plt.subplots(3)\n", - "ax[0].plot(signal[:, 0, 0], c=\"C0\", label=\"source\")\n", - "ax[1].plot(noise[:, 1, 0], c=\"C1\", label=\"noise\")\n", - "ax[2].plot(data[:, 1, 0], c=\"C2\", label=\"mixture\")\n", - "ax[0].legend()\n", - "ax[1].legend()\n", - "ax[2].legend()" - ] - }, - { - "cell_type": "markdown", - "id": "6f42a209", - "metadata": { - "papermill": { - "duration": 0.001389, - "end_time": "2024-12-09T08:41:42.701316", - "exception": false, - "start_time": "2024-12-09T08:41:42.699927", - "status": "completed" }, - "tags": [] - }, - "source": [ - "## Enhance oscillatory activity using RESS\n", - "Apply RESS\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a218b27", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:42.704630Z", - "iopub.status.busy": "2024-12-09T08:41:42.704486Z", - "iopub.status.idle": "2024-12-09T08:41:42.795187Z", - "shell.execute_reply": "2024-12-09T08:41:42.794858Z" - }, - "papermill": { - "duration": 0.093554, - "end_time": "2024-12-09T08:41:42.796198", - "exception": false, - "start_time": "2024-12-09T08:41:42.702644", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create synthetic data\nCreate synthetic data containing a single oscillatory component at 12 hz.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "text/plain": [ - "(0.0, 40.0)" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_times = 1000\nn_chans = 10\nn_trials = 30\ntarget = 12\nsfreq = 250\nnoise_dim = 8\nSNR = .2\nt0 = 100\n\n# source\nsource = np.sin(2 * np.pi * target * np.arange(n_times - t0) / sfreq)[None].T\ns = source * rng.standard_normal((1, n_chans))\ns = s[:, :, np.newaxis]\ns = np.tile(s, (1, 1, n_trials))\nsignal = np.zeros((n_times, n_chans, n_trials))\nsignal[t0:, :, :] = s\n\n# noise\nnoise = np.dot(\n unfold(rng.standard_normal((n_times, noise_dim, n_trials))),\n rng.standard_normal((noise_dim, n_chans)))\nnoise = fold(noise, n_times)\n\n# mix signal and noise\nsignal = SNR * signal / np.sqrt(np.mean(signal ** 2))\nnoise = noise / np.sqrt(np.mean(noise ** 2))\ndata = signal + noise\n\n# Plot\nf, ax = plt.subplots(3)\nax[0].plot(signal[:, 0, 0], c=\"C0\", label=\"source\")\nax[1].plot(noise[:, 1, 0], c=\"C1\", label=\"noise\")\nax[2].plot(data[:, 1, 0], c=\"C2\", label=\"mixture\")\nax[0].set_ylabel(\"Amplitude\")\nax[1].set_ylabel(\"Amplitude\")\nax[2].set_ylabel(\"Amplitude\")\nax[2].set_xlabel(\"Samples\")\nax[0].legend()\nax[1].legend()\nax[2].legend()" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Enhance oscillatory activity using RESS\nApply RESS\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "r = ress.RESS(sfreq=sfreq, peak_freq=target, compute_unmixing=True)\n", - "out = r.fit_transform(data)\n", - "\n", - "# Compute PSD\n", - "nfft = 250\n", - "df = sfreq / nfft # frequency resolution\n", - "bins, psd = ss.welch(np.squeeze(out), sfreq, window=\"hamming\", nperseg=nfft,\n", - " noverlap=125, axis=0)\n", - "psd = psd.mean(axis=1, keepdims=True) # average over trials\n", - "snr = snr_spectrum(psd, bins, skipbins=2, n_avg=2)\n", - "\n", - "f, ax = plt.subplots(1)\n", - "ax.plot(bins, snr, \"o\", label=\"SNR\")\n", - "ax.plot(bins[bins == target], snr[bins == target], \"ro\", label=\"Target SNR\")\n", - "ax.axhline(1, ls=\":\", c=\"grey\", zorder=0)\n", - "ax.axvline(target, ls=\":\", c=\"grey\", zorder=0)\n", - "ax.set_ylabel(\"SNR (a.u.)\")\n", - "ax.set_xlabel(\"Frequency (Hz)\")\n", - "ax.set_xlim([0, 40])" - ] - }, - { - "cell_type": "markdown", - "id": "54bf48f0", - "metadata": { - "papermill": { - "duration": 0.001926, - "end_time": "2024-12-09T08:41:42.800072", - "exception": false, - "start_time": "2024-12-09T08:41:42.798146", - "status": "completed" }, - "tags": [] - }, - "source": [ - "Project components back into sensor space to see the effects of RESS on the\n", - "average SSVEP.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "de26e40d", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:41:42.804531Z", - "iopub.status.busy": "2024-12-09T08:41:42.804255Z", - "iopub.status.idle": "2024-12-09T08:41:43.159551Z", - "shell.execute_reply": "2024-12-09T08:41:43.159212Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "r = ress.RESS(sfreq=sfreq, peak_freq=target, compute_unmixing=True)\nout = r.fit_transform(data)\n\n# Compute PSD\nnfft = 250\ndf = sfreq / nfft # frequency resolution\nbins, psd = ss.welch(np.squeeze(out), sfreq, window=\"hamming\", nperseg=nfft,\n noverlap=125, axis=0)\npsd = psd.mean(axis=1, keepdims=True) # average over trials\nsnr = snr_spectrum(psd, bins, skipbins=2, n_avg=2)\ntarget_idx = np.argmin(np.abs(bins - target))\nneighbor_mask = np.logical_and(bins >= target - 4, bins <= target + 4)\nneighbor_mask[target_idx] = False\ntarget_snr = float(snr[target_idx])\nneighbor_snr = float(np.mean(snr[neighbor_mask]))\n\nf, ax = plt.subplots(1)\nax.plot(bins, snr, \"o\", label=\"SNR\")\nax.plot(bins[bins == target], snr[bins == target], \"ro\", label=\"Target SNR\")\nax.axhline(1, ls=\":\", c=\"grey\", zorder=0)\nax.axvline(target, ls=\":\", c=\"grey\", zorder=0)\nax.set_ylabel(\"SNR (a.u.)\")\nax.set_xlabel(\"Frequency (Hz)\")\nax.set_xlim([0, 40])\nax.set_title(\"SNR spectrum after RESS\")" + ] }, - "papermill": { - "duration": 0.359059, - "end_time": "2024-12-09T08:41:43.160932", - "exception": false, - "start_time": "2024-12-09T08:41:42.801873", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Project components back into sensor space to see the effects of RESS on the\naverage SSVEP.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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vkZWV5Tcy0ev1WLNmDR577DH07dsXq1evxsqVK/H666/TsNKgQYPwzDPPYObMmUhPT8dNN90EjUaD06dPY9myZfjss89w991319t+165d8dhjj2HBggWoqanBoEGDsG/fPnz77bcYM2YMhgwZcl75AWDSpEn4+++/ccstt+Dxxx9Hz549YbPZcOzYMfz22284d+6cxJW8fv163HrrrddEzFzGxUHWK1ePXgkODsbAgQPx4YcfwuPxICEhAevWravXU1wXevbsiS+//BLvvvsuUlJSEB0djaFDhzZaB9QHj8eDrVu3+iVTX7W4/BOu/v+gvqmXHTt2rPP82lMvWZYlM2bMIElJSUSn05Hu3buTFStWkMcee4wkJSVJyqIBUy8bU59wfmJiIgFA3n333TrrdLvd5IMPPiAdO3YkOp2OhIWFkZ49e5K3336bmEwmiXzPPfdcnXUsXLiQtG3bluh0OtKhQweyePFiMm3aNL++s9ls5LnnniPh4eEkMDCQjBkzhmRmZhIA5P3335ecW1paSp577jmSmJhINBoNiY2NJcOGDSMLFiw4bx8RQojL5SKRkZFk+vTpkv3C9Ovs7Gxy0003EaPRSGJiYsi0adMIwzB+9SxYsID07NmTGAwGEhQURDp37kxeffVVUlRU5FdnbXg8HvL222+TVq1aEY1GQxITE8mUKVOI0+mUnJeUlERGjx5d53VYLBYyZcoUkpKSQrRaLYmMjCT9+/cns2fPJm63m56XkZFBAJANGzZcsG9kXHnIeuXfpVcKCgrIHXfcQUJDQ0lISAi55557SFFRkV/f1zelu6SkhIwePZoEBQURAJJ73RAdIEzpnjVrVp1yr169mgAgp0+fvuA1Xg1QECIvISrj2kV6ejq6d++OH374AQ899FCz1Tt9+nQsXrwYp0+fvuTff7rSeOmll7Bt2zYcPHhQ9tTIkAFZr4gxZswYKBQKv0U/r1bIOTUyrhk4HA6/fZ9++imUSiUGDhzYrG29/PLLsFqt+Pnnn5u13qsNlZWV+Oabb/Duu+/KhEbG/0vIeqV+ZGRkYMWKFXXmB16tkHNqZFwz+PDDD3Hw4EEMGTIEarUaq1evxurVq/H000/7TfO8WAQGBjZo3YlrHREREU3Ko5Ah498CWa/Uj9TU1EYlFF8NkMNPMq4ZrF+/Hm+//TZOnjwJq9WKli1b4pFHHsEbb7wBtVrm5zJkyGg8ZL3y74JMamTIkCFDhgwZ/wpcUzk127Ztw6233or4+HgoFAq/NQdkyJAhQ4YMGf9/cU2RGpvNhq5du2LevHlXWhQZMmTIkCFDxlWGaypgePPNN+Pmm29ucnmWZVFUVISgoCB5pocMGVcAhBBYLBbEx8dDqbw2xlSy3pAh48qjwbrjyi2Rc3EAQP7444/znuN0OonJZKK/kydPEgDyT/7Jvyv8y8/Pv6j3f+7cuXSxtz59+pC9e/fWe+7ixYv92tfpdA1uKz8//4r3l/yTf/KP+11Id1xTnprGYubMmXj77bf99ufn5yM4OPgKSCRDxv9vmM1mJCYmIigoqMl1/PLLL5g4cSLmz5+Pvn374tNPP8WIESOQmZmJ6OjoOssEBwcjMzOTbjfG4yLIKusNGTKuHBqqO/7VpGbKlCmYOHEi3RY6JTg4WFZOMmRcQVxMGOfjjz/GuHHjMHbsWADA/PnzsXLlSixatAivvfZave3FxsZelKyy3pAh48rjQrrj2ghqNxE6nY4qIlkhNQ92ZVfA4vRcaTFgc3nxxZYzV1oMGZcZbrcbBw8exPDhw+k+pVKJ4cOHn/cr01arFUlJSUhMTMTtt9+OEydOXA5xZciQcZnxryY1MqT4fk8ufj9YcFF1PPj1XuzKrmwmiZqO/Go7PlyTeeETZTQrCqrtkm1CCHadqbhs7VdUVIBhGMTExEj2x8TEoKSkpM4y7du3x6JFi/DXX3/hhx9+AMuy6N+/PwoK6n4XXC4XzGaz5CdDhoxrA9cUqbFarUhPT0d6ejoAICcnB+np6cjLy7uygl0l+O0ChGXl0SJszSq/6HYuxXKN/WZu9DOY54PLwza/EE3ELXO2o6jG//sxdaHY5MCkZUca3Uap2Ylf9+c3utzF4reDBUh+bSXdvv6DzSCiB8DDEDz4zd7LLldj0K9fPzz66KPo1q0bBg0ahOXLlyMqKgpfffVVnefPnDkTISEh9NfcS+XLkCHj0qHJpMZkMiEzMxOZmZkwmUzNKVO9OHDgALp3747u3bsDACZOnIju3btj6tSpl6X9SwW3lwXLNo4pJL+2UlLGw7DnNXp/pRdiz9kqqJUXPyWVnIfVFNU48PO+xpFMQgiKTU6wPE9Jz6/BsI+2oNziAgBsyyrH098dQMrrq/ATX3e13S2po9TsxDv/nGxUu82F44VmFJsaRmryqxxYdrAAK48Wn/e8Gz7cBIeboduH86rx6u9H6XaF1YVBszaft473Vp7EmTJLg+SqD/+tg4C5vD5C6Wa4//OrGk5ILwaRkZFQqVQoLS2V7C8tLW1wzoxGo0H37t1x5kzd4cspU6bAZDLRX37+5SeTMmTIaBoaTWq++eYbpKWlITw8HGlpaZL/Fy5ceClkpBg8eDAIIX6/JUuWXNJ2LwUYESG556vdWLLr3HnP9zAsPlhzSrIvu9z3IUK7m4HV5fvw2Ft/S3MGvtudCwBQikjNM98fgIeRejxu+HCTZFtsWAWcj3/9faQI767MqP+EOuDwMHy9XMU/78tDdrkNjy3aB4C7znUnS+FlCbZmcp6mxxfvl9SRX2XHop05kn1OD4OZq3yymJshF2jP2Uqcq7D57bfX0U91Qchxe27pIbqPEIKJv6ZLzsuvciBPRBRUtdZlqLC6kFvpO75gWzYqrS7JOV9vz0FBdf1ka8XRIpwsqj+0UmVz17lfIJsAUM2fY3Nfno/eabVa9OzZExs3bqT7WJbFxo0b0a9fvwbVwTAMjh07hri4uDqPy7l4MmRcu2gUqZk1axZefPFF3H777di4cSOOHz+O48ePY+PGjRgzZgxefPFFzJ49+1LJ+q/Bvpwq9HlvAwDOoB3Jr4FBq6r3/IJqO9q+sRpfbsmW7L/xk230f7vbixq7G16epCzZdQ57zlaixOQEAAztwE11FXtq1p4opYQCAPaerUR+lUPiiUmdugYVtYwlex5Pjd3NIEgvnVS3JfP8X6U9W86RBC/vqlHxMgrtfL8n97zlAcDilBpVoV+/2nYWRwtqkFtpQ5e31kmO766VG7TnbCXsFzDOT317AGtO+OduCOGwcxU2CdkEgBGfbEOp2VlvnS4vi+WHCuH2Sgmm2BuVUyGt89XfjtLrAIAZq05hX04VJRkCxLdq0ympd+OzDaex7KDPC8GyRHLvv62HaI/77gAAwOL04IYPOW+R1Xn5vuQ7ceJEfP311/j222+RkZGB8ePHw2az0dlQjz76KKZMmULPf+edd7Bu3TqcPXsWhw4dwsMPP4zc3Fw89dRTl03m5oCXYf2eo1MlUlKanl8jIe82l9fvmTA5pOS+0uqS3PczZVbJAKna5sZf6YWSMrXrLLe44BTpktqyehkWi2sNOnacrpC0m1Nhk0xCcHoYZJZIPY21B2FlZqekjtxKGwpFoWCHm8G8zVKPXG6ldFCy60wF1ZMAp2/F7XoZFnmVUk+kmNgDXH/YXOd/B2rfu3UnSiT3ihAiGewSQiT3AeB09Pk85dU2t98zURvHC02Se3WqxCzRWYQQyX1gWOJ3786WWyV1lFtckkFQicmJ8T8clJTJKpXey305VSg7j15sKhpFaubOnYvFixdj5syZGDx4MFJTU5GamorBgwdjxowZWLhwIT7//PNmF/JqQm6lDVOWH2ty+QqrC4fyqlHJPwBWlxfhAVq4RA+Iy8tIPCSC0RdQ18tjdzMoMjkx8VdfuOD+BXuw7iRngL0M7wWpFaIS56bct2APV38tr0OlVarAzkdqHG4vDBopQXt88X7MXM15TGrsbj+Pwi1zdnAy8i+08F6f4hVL7euvC7W9MGcrbPR6Jv9+DIt2cC+lkNRabffgga/3YPUxXxjo/gV7sOuMj+g4PQxe/+MYJQOCkhETQyEcJhCywbO3YNhHWyWyZJZaKOkS7oMYguL6bGOWZL/4GZixyuelO1ZgotMazQ7fs/Da8mPUAyQodrEReGLJARzKq6bbQXq1hIy0fn0Vxn13gBLjzzae9pMVAHRqTm2IFfCZMmud514K3HfffZg9ezamTp2Kbt26IT09HWvWrKHJw3l5eSgu9t3X6upqjBs3DqmpqRg1ahTMZjN27dqFtLS0yyZzc2D76Qr0nbGRGjW724uRn26XGMsx83ZiuSi3bt7mMxi7xOfV3HWmAr3eXU+3GZag57sbsCXTl2s37rsD+Hid71lce6IEL/6cTrcrrS50n74eNSLS3f/9jVh/0kea524+g7u+3EW3jxeZ8fY/J6nshBA8vHAvMkWG7v4Fu/GLSD8t3ZuHu0V1mJ0etH1jtWTg0WfGRpwQeRvHLt6Pr7b6Bn87zlRg1lrfhIIauxuDZm1BmcXXZw9+sxfbT/uu/8st2Xj1tyOSOgbO2kzD/SxL0Pu9DRJiM+rz7RJv+9oTJRghGnSWmJzoO2MjfbdYluDp7w9i52lfkv3k34/inX98HvZVx0oweNYWuu3yMrhvwR6cFXmKb5+7AyuOFtHtzzedxkuie3WiyIQB72+i+pFhCW6ZswPp+TW+dn87ijmid/2Pw4W4SST72XIr3v7npERvD/1oK3aIZJ+y/Kikzw7lVWP1cd/gr8rmxk2fbJM8M48t2udnj5oDjSI1ZWVl6Ny5c73HO3fujIqKSzsTYt68eUhOToZer0ffvn2xb9++S9oeIM0hyauyU0MmHKu2uZH82kqa6LovpwrrTpRQo7T+ZCnNOfhoXRbeX+0zUC4vi5SoQGSW+ozCvfN3Y/yPB2m7jy7yXSMhRBJ2ECAkqmYUmyW5NoE6Nd9O3eGRuvYLIznhARQ8NYJnQzxKO1NmweZTPk+M1eXF2Qqb3whrA6/wZq/LxCMLfdcjGMZQo4Ya/PpkBbjwjfh+nOVHGMUmJxJCDfTFE4/GCCE4WsjlfQlJrUIb4388JJH/qe8O4IstZ0AIwRdbsrF0bx7S82oAcISldVSAJNQ0gw9veXjZH+uXRNsEfH2YUcwpXmcd1yaQVJ1aSgYFL1rt0cziXTk4wislt4i0mBweqryEdgWiKJCbB7/eQ89Xq5RYdrBAQp42ZJQh5Y3VfjIKaBcTiCMFJszbfIZeM8ARqsuJCRMmIDc3Fy6XC3v37kXfvn3psS1btkhC0p988gk9t6SkBCtXrqR5edcS9p+rQo+WoZSwnimzokWYAXvOcu8lIQTtY4KQJSKYZ8ttVAcAwMliM1qEGak+KjY50DY6ECeLfcQgIdQgSXw/VWJBh9gg+gydKrEgLkRPQ5cuL4PwAK0kLJueX4N2Mb5F0nIrbQjSq2k4tMLqhkqpQI5owBIbrJeM5rNKLWgZYaTbmzLKkBBqoATa7PQgPkSPTaL3NyFMKvvRghokhBqo7Adzq9EmKoDKzrIE0UE6SZi20upGoMjbfLbchjZRAcjn9XthjQORgToJiYsN0UuI/aaMMug0PvOanl+NIJ0auXy/l1tdaB0VQLcBToeJvUwni00INWqoPj5ZZEZkoI56jRxuBi4vi0O5NbRMYbUDMcF60fWbEKBT0ftdWM3JXii63iC9RpIndzivBiEGjaSO1Lhg5PD31+FmEBeix/EiXy4tV9432MuttCPMqKF69lSJGUkRRhzM9Q2qOieE4PQlGAw1itT07t0b77//Prxef08BwzD44IMP0Lt372YTrjaElUSnTZuGQ4cOoWvXrhgxYgTKys4f3rgYnC61SMIWtQnGjZ9sQ/fp3MjnSL4JhBDc+9VuPP39QaROXQOAG/ns4D0EUYFaSf0uL4sOcUEoMTmo++5cpR1bMssl7rw3R6cC4EiAeITs9rJweRlKFEwOD6av9CXMqlXcLXZ6WMTyD7t49F5XzsxNH3OehoJqB5IijHjom72osrlpns5booTc4R9vk4wEK3ivzsli7oH/+wg3ikiLDwEAJEcESNyjQogjLS4Yt8zZAaeHQahB2ke1UWP3eWWKebfx8UITbu4Ui0l8WOaJJQckZWoTQXGoZzKfgKtRcS/ln4cLUVjjwOf86EXD92FupR0pUYHIKrWg2zvrYHZ6fB4Y3lPjZlj0aBlKyUS3d7hn4/mfDgOoe9aW+HoA4ON13MjS4WZACEGfGRslx+0u7p6lRAfC6WH8wglWl5c+O8K9FgiS08OCEIKiGgcyeMXe0FwjQggSwzgjU1DtwO6zV35q//8n5FbaMbBdFIpquGc+u9yKUZ3jkM0TA7PTi7T4YEkoxcOwCNSpqXEpNTtxU8cYHOdJfrHJiQEpkRKjrlMrJWS5oNqOPq3CKVnIq7LjhraROMe/U3mVdgxsG0W3AUCpUEABH7kvqHbgprRYOtgpqLajT3I4NeJOD4O4EAPVHwAX0kiODKChkJwKG4Z2iKYG+VSxBXf3bCEZQGlVSknOX2GNA9enRFKDnFdlx9AO0ZSAlZid6NMqXDII8jAsgnQ+g1xU4+Cvn+vX9Pwa3NUjgZIcm8uLhFADzKL3sNzqQnJEAH23MootuLVbPE7zpK3Y5MSANpGU1LEs8Ze92oH+bSIk/X59SgQdPBeZHLiudQSVA+Dsg0aloP2eXWbF9SlR9JkorHGgb2tfv3sZFgatSqIPS8xOtAw30vCSr12uTHa5FSM7xVJCQoggu3Twf2NaDE7zg/XiGu45E54Rs9OD2BA97BcI2TUFjQ4/rVu3DrGxsbjzzjsxfvx4jB8/HnfeeSdiYmKwfv36S/oFbfFKomlpaZg/fz6MRiMWLVrU7G25vSyOF5pQUO2Ahe/4VceKaY6Cl2FRUO2QsPPnlh6SuO0AzkOhVPi8H1q1tMvdXhY6tRJhAVosO8C54oIN3CjhpV/S6XmhRs7QuxkWlTafG9Dm8kpybRweBot3nqPbwkPj9DJ4745O3P8eBmPm7QQgzcuhdboZFJscuGXODqTFcUmSZRYnSi31xz8F743A+F/+5QjOlFnw9bazXHne2+BhCFjim70leClK+OPT/johSfhlWYI+rcLptkIBlFlcGD+4Dd8eAy/Dwu5m0D42SDJqE0CIeAzBQRxnDzVqUGN3U8+DlyXUG/P2bR2xJasc//mRiw+nRAfC5PCgxu6h7uf7eiXCwxDkV9nx0758BOjUktlNyRFGJIQaqLwCHG4G87dm45MNnKtf8Dp9vomTze5hJCMo7loITcrtFB8Ml5fBuG99BE6lVOLGj7diIR9uE7xfJp44RQbqYHMz+P1gAX2uPQwrMYS1cUPbSADAzNWnUM57wjQqBV7giVpMsI7KdrnQWI/tsmXL0KFDB+j1enTu3BmrVq26TJI2H1xeBq0iA+hMu5wKO65rHY5S/t6VmJyIDdH7Peuc94I7p9jkRN9W4TQJvajGga6JISjn322nh4Feo4ICoB5fQoCW4UbkV3HtFtc40K9NBM7xRKCgxoEuLUJoDpiXYaFSKhBi8HkZ8qvs6J0cRnVIQTVnXAVDWWJyIp5/R8RoFx1ESVuxyYFeyWHUIBdU29E2JogSdovTgwCdGgaNihpkk92Dbi1DKYkpNbvQp1WEj5BV2dEpIQTmWjlhsSF6lJm5Z73Y7ETPJB+p4+QQb9cte3yoAcV8v5+rtGFg2yjJ9XZOCKFe8EqbG5GBOujUSiq7xelF+9gg2k5upR39RQS01OREizCDJKSlUCgQHqCl6Q35AiEVnhGzA72TwigxKrO4EBOs4z6qRHzhtRZhRjpgLDE50Ss5nHp7zlXa0Ck+hA6IzQ4vgg0aaFVKqt/KLU70FLVTYnaiZ8swei3ZZVa0iQr067PmQKNITZcuXZCVlYXp06cjKCgIZ8+exdmzZxEUFIR3330Xp06dQqdOnS6JoE1ZSbSpi2gdLzSh3ZurccucHdQLQQjBf370zVhJeWO1JC4poHaS6FPfHUBUkI6GnAS9P7RDNFiWS8jSqpXYcboCM1efgtPDgBDOUGw/XYH956oA+MiQ28vi8cX7qaGxub2SsEWQyNWsVHCuRAAwOzyIDOSMj9PDSmSva32b7DJOCTzQpyUAoNrmoYa+Q2wQ2r25GixL0DUxFACnpDjG7lOpwz/ehmP8iDBIr+YMsstLy4z/UZpIBgBVtaZq29xehIpcoV6G4FSJGdFB3LUs2ZWLCqsbsSF6mmwtjrknRRhBQKjnJCKAI4e/HvBdc1aplXpUAG60JyjjlrzLdNWxEvRKCsO9vRLpy33wHOdKvTEtBgxLqBIoNTslhHRIh2i0j+Vc8S4Pi8/u74bxg9vgWKEJ+3OqsCWzHNsmDUElH8YEgOeHpsDp9pEanVqJ4akxOFdpx3Y+lh1s0MDpYSWempggHUwOD82REDxIQkJvZKAW4384SEeu9/VKxPCPt+K6mRvRo2Wo3/0Q+gMAFmw7i2yexKtFs7G+faIPAGko7FKisR7bXbt24YEHHsCTTz6Jw4cPY8yYMRgzZgyOHz9+WeRtLhDChWgEAlpmdqJjfAgd5BSbHIgL0UOhUIBhCdxeFmqVAgmhBmpcHG4GbaN9hrKoxokWYUbqISiotiMhzIDYED2K+SRchYIPSZl8RrxPK5/HoNzsQrQo5FFhdSMqSIe4UD0lU+UWFzrEBaPC4ubbcaBPcjglaIU1DsSH6mHQqGB3eymhEoeTKq1udGnhC7+Vml0SEldsciIuVI/oYJ2EpMcE61DGD0BKzU50TQyheUglJifiQnyyOz0MdBold/18HU43gzZRARIS0y4mkOajlZqdiA3WQ8MbdQ/DQq1UID5UT/vM4vQiNS5IRGociBG1W2p2IiZEj7gQg0T2eFEo8FylDf3bRKCA3y4x8yRWwXlmqu1uhBk1SAg1Um8WwxK0DDeixOS7350SQmieZFGNA/GhBkpABWITH6r39bvNhe6JoZQIC/0uoMTsREywHnGhekriCIHkWopqHOiR5CM1+dUOtIwwQKFAo5czuRAaPaU7KCgI48ePx7fffou1a9di7dq1+Pbbb/Hss89e0qmPTVlJtKmLaNU1U6XVFP+R3a5s//yhuu5PsJ4zyoQQOL0M3hiViiC9GiM+3Ybb5u7E8kOFuLkTt8ZG3xkbUVDtwMLHuDCesE6ITq3EA31a0jDKLV3iMHZAMiYtO4oP1pyCRqXAg31bIi3edw/S4oNRbnVhwPub4PQw6JQQgpEdY+H0MDQUJbRxxxc7JTL/vJ/LG0rlPTUbM0qpi/LmTnFwe1kM+WgLWkUY8eFdXVBYY4fLy0KnqXsWl1Grht3NYO7mM/jioR4AuNlXwohJ8CgIse4NEwciNS4YFqcXEaKQXZHJgRd/TkeoUYNB7aKwLasc87dmIyZIDxWfPDtDNI2by7Nxw+TwYNULN6DS5sbB3Ko6ZRTAEkIThpPCfTH9EIMG8aEGKvPaEyV4eXg7qFUKTFl+jIZ8BBd6Oz43pVVkAFW8Ti/D1ROih93tpbH7xHADJSsAMDw1BnY3g65vr6MybcgoxZDZW+g5SoUCt8zZIQknrjtZSsnn8NQYZBRbJLO8AnRqbD9dQcOC5yptcPIhseTIAHqemJyfKrFQ97xSqcCEISmSWXPtojnC5rxMCyI21mP72WefYeTIkZg0aRJSU1Mxffp09OjRA3Pnzr0s8jYUXtF9rGsmDMAZCsHYVli50b0wUCoxccYlKkiHCqsLZRZuu0WYQRJeEhvsohqeCPFt5Fc7kBhmQGI4ZxjNTi+C9BrEhvjIVJXNjfgQPb3fpWYnooN00KgUcHtZjlwF8wbazLVLACoXwJGnVlEB9L0vrHEgIdSAFmEGFFY7UGlzIzxAKyFxAGdsBcMpkAmhf4pqHIgPMSA2WI9Ss5P3XADRQXrqKTY5PIgK1FFdVsLXEaBTw+L00DriQvSStafiQgwoFhGhmGA9hLtTzHvI4kI5WcstLkQH6xAf4iMkCnrvBELiQlyIHkolR0BLzU7EBOsQH6pHYY2Del0SQg0o5K+3xu5BQqgBTv79FshEBO+ZKRPa5evg+kXaZyUmJ+JEXiWh3+P4Z8Lk8CDEqKH3AeBsWlSQjnp/yizc/VYqfLLHBut4WR3w8J46gRhzZVxIDDPQQVop34eRgTpU2KQTRy4WTV58j2VZZGVlYceOHdi2bZvkd7WgqYtoxYX4uxLrwk/7GlZfUgRnLFxeFk4Pi+FpMQgzamlMsl/rCLx8YzsAvjBVx/hg3JgWAz3vhbkpLQYGjYp6PpIjAsCyhOY1fPdEX0we2YEquFCjBoRw3hrhAVcpFUiKMMLlZdA/JUIio+DRmf9wTwDACj58EhWkw/TbO+KbHb6QkDCyya20I9igQUIY9+Ld99VuBGhVuLdXC3ruDW0jEWLQoFVkAJ15I3hLAOD3Q5zHRFAygqxRQXokhhlQY/cg1KjFK3z/5FZwhKJ/m0j0a8Ndw5Jd5xBq1NA1eH7YwxGyO3skYHhqDH0Z28YE4rau8bjrS59nb+1LA+n/d/dsgXPvj0ZWqRWfbMjCo/2SkCgiNXY3IwkfKhTAC8NSqPERvF8CyXAzLLq3DMWj/ZLh8rL47WABNmSUQadWwaBVw+Fm6Eyx2h9pM2hVNCzF1enPloVZWELc/sen+kqOKxVc3zz9PReeSokOROcELrfJyxJ8eHcXqmT6JIdj+u0+L+uwj7aiT3I4Qo0aFNY4aC7Z5JEd8Gi/JEmyvFKpQL/WEZdlBlRTPLa7d++WnA8AI0aMaHYPL8BN073pk614Ysl+fLP9LKYsP4aP12dh6OwteP2PY1i6Nw8v/nwYTy7Zj+TXViL5tZV48Os9+Gb7WaS8sRqPLtqHF38+jCeW7Eeb11fh/gW7YXV5YXFxBDgmxOeFYAmhyx8AvKciRE8NlGB8hdG+YOQ1KiV9ZgXDKIQsCqodaBFmpEa93OJCVJCOC6Xw7RJIn9dSixPRwXrEBOtRZnHSMFhcCOepYVgCpUKBiAAtJTWlZheig3wDq6IaBxLCDIgO1qPM4qLbsSE6lIqmbevUKvp+lZqdiArSIUivhtnpRVENFwaKCdajxOxEhZWTPVrkqVHUlp33dnDJ0U5ah0AeBQMdGahFBV+Hy8tCr1FRo15ictA6CmsclChJPBfgvO0CiSsxOXijrkWl1UXLtAjj6hC8Llwf+siVQqGgZKrM7OLLcInf5RYXogI5clFscsLLsFDyYcAa3q4IBFTwkBTVOBHHk7gSk5O7/6EGJIQaqUdIaBeidqOD9Xy/Oukz1CLMiIJqO+3TmGA9DY2yLIFapaSDO+F6uXOuAlKzZ88epKSkIDU1FQMHDsTgwYPpb8iQIc0qoICmrCTa1EW0BG+HRqWQZIFTWUSeg+eHpgAA5jzgm03xQJ/aHiGC+3snosP/1qDE7IRBo5IYy4/u7Qqj1hc2GtohGgqFAknhRiSGG/Dm6FQoFArEBOvw5p+cy7xv6wjJgmwtwjgXoodXVo9el4QXh7XFhgzOJX+kgCNDXpbgf3+ewPJDhZj7oP8MEJ3IaC8dxxlJIdkYAD68uwvN+QGArVnliA81YPqKkzhSYMKvBwpoyIqrT4Uj025Cr+Qwmu+i16jQtUUIPScmWCeZbQAAAVoV4kMNGPX5dq6fh7UFAFhcXjzWLwkxwXp4RPkmwQY1+reJkMz0+PjebggXESiNSolbu8bTbZVSgXYxvrhu7ZSQJwa0gkalROsojpTWXmBuc2Y5FAoFJQa5lTZEBeng8rK4vzf3DAhk8USRCf9ddgTbssqh0yhh1KpgczNgWIJ9bwxDbYinxfdMCpM8XwIGtosC4PMMDUiJlBxX86FAIZl5xh2d0V0UYvIyhJLJUKMGATo1Tr93Mx6+riXdlz71JsQE66iH8OHrkhAm6tP2/AyXVlEBkmfnUqEpHtuSkpLL4uEFuKnLgTo1bmgbCYebQVpcEFYcKUK3xFD0aBkGpYIbDHRvGYrfx/fHpBHtYXMziArS4cuHemB051iEGbVoEWbEA31a4s7uLfDT3jyUmV2ICdZLjLoAnYbLwxCMSWyIHsU1DkpyYnjPRZXdLXkfACGxVMkNTKodNPwUzxv5MosTUYE6RAbqUG6Rrmdj1KpgdXmpMY3hvSpcuwbqmeDCJDroNb6EVMITMqNODavLS8MgMcEciSniPQjRPEERBjdiuHlyEROsR7nFScNvwvUW8l6XiAAdfUcE6RUKBVjqIdHTUJEgh2DkBYKmFhFBwTBHBmpRafMREq7fnSgzcyQvPoQL2dlcXhhrrUNmcXoRrFdTAlbK39/4UO4+CNtBeg1sLi8lhgCno11e7n5HB+uQGM554jhPjZ73qjlQzpM6MSER7ndUkI88xofqqRzC9QvhJ7vbS8P6Qi1mhwfBejXiedJaauJCZ4J3R5A9WK/2Wz9MqVTAy7A0dFbbI9YcaJIWevbZZ9GrVy8cP34cVVVVqK6upr+qqvO79puK5lhJtDE4NX0klAoFXh7OGdOxA5LpsX2vD8ebo1Oh1yjxyk3tkfnuSLSLCaKhmpl3dsHix3vjvTs6ITGc8/oE8+Ro5dFiGDQq9G/j85QoFAqJB+ANfqbT/txqbMgow4iOHGlrF+ubIglwI3FfHdxfhs+h6JEUhps6+siekNSaUWym3p0b02Iw98HuEkPXLTGUelqEsJmQ1b7/jeG4t1ciDCICZtSqJTHp8YPboHvLMHRK8BFDAOiSEIp1J0oxaUR7AMCn9/uM9NJx1+G/N7Wn2z+Nuw5qlRK3duVWfBX0aNtojoAICmpw+2ja5yEGDYL0GvRtFY7hqdFIn3ojAEhIDiAlbZteGQSFQoEOfL/e3ZO77t+e5Z4nIewltCGsJzM8NYbva+kU9OxyK35/tj9SogIpERIgJsc2F5cn9N9lR7DsYAEiA3Si/uQUiFqUmxQVqMOtXePxAk/sBAikBgDi+XsQFeSra/LIDpLzE8IMEsPAsCwSwqReSY1KSZXn6C5xfH/09ztHwN/PDwDAEaZOCSH4N+BiPpMw8cZ2WP6fARg7oBWeH9YWj/RLxqb/DsbH93XD3T1b4P4+LfHncwMwYWhb9EwKw3NDUvDXcwNwe7cE3Nw5Dvf1bom3buuI6WM6YeadnTGgbSTOVdqo10WAkIwLcOGVUt4zERmg40I2ZiclOREBWlTZPLyB5u63QCYEcKNsB4pquKURuLCPg4ZSVEoub8Ps8NJnuUUYF6LyMgRatZIjAmYnNVjxoZzHKL/KTmfNAdKEci404kCl1Y2IAC1PSFw0LBKk49ZSKjJxxhfgvJjitbqignS0THwolw9UanZRr4sgu4k3xgBoyMbl4YhRXAiX1FtIyZROQtAECGEhrt91KDO7UGbmPUI8USgxcSQn1KiBye7hyAbvlVLzITqA0/uCV00IxwjT6Tnvl+9dFjxm4nY9DAudWkXvnRAWihV56sRpBuJ+5zxCdkrqhPCi0IfhAVpU29xcuIqvQ69V0XxFTnYRaeVlL6hxcKRORKYcboZOb48K0qHc6oLTzcCoVXPtNvMCfE0iNadPn8aMGTOQmpqK0NBQyagmJOTSKbYLrSTanNBrVHj5xnbomRSOA28Ox6sjOuDkOyMAcGwzNkSPR67j1iTRqVVoHxuEF4el0PJDOkTjob5J2PTKYHxyXzfoRcZUp1GiVaTU6IkRxhueqbek8vVzZTvUJjW8Uru5Uyxa8EpjJ58LImSm/z6eM9BdeM+InvcAdIgNglalxC1d4vHz09cBAP74T3+EBWjx4d1dAfgMsRDaFwiK4EXQqpT467kB0GtUWPAIF7a6tQvnCREYujArIIQPY0TxycpicqFVKTGyk4+ACWElg4ZTQAJ5++f56xGsV9Pk184tQnBn9wSJrCqlAl6WUOMdwJOa+3pxo+1gEbkQyvzydD/0aBnqa5cnFgIhmvtAd7wwrC1VpKFGqfdOwY9hskqtSAgzYNWLN1AiKuBe3nPz3JA2ktBZRICW3sfxg9vQpOiYID3+exMXchNI5UQ+BCfG/27hFpDbPGkwAGD67R3pMSHsKYTuEkINCBPJ7mUJvnq4J0Z1jqWzmsQQSK3QH2feu5keW/cyF7arvbbOpUZTPLaxsbGXxcN7KSDkhwieC4DTHwX8eiMAqEFiiU83lZpd1CAreaMuNnLxoXpkFJvps8yFTuxwuL0I0Pk8CIIXRkAR7w0BwOfq2Kn3I1bkqYkN0XPJp3YP8qvsVD8B3HRnQfYWfMhGCGlFB3EhDYGQCIZRCJMIsuZX2+lATkhINdk9CDNqEBOsk3gdAM5DU1jtoCRe8AgJsgseAyFhWfCIFYvIlFKp4GXndEs0H24TvB8xfDim1MJ5KgTZhXwZgM/vsfjaFbxKlTaO1IUHaFFlcyO/yo6WvDdfqVQgv9pOBy5CuwKE+1DGh/SMfGhbnHMUZtSixOyk5ELIsxJk554ZJw37CWEugaBysnNkivYZH17jQok6KYkTkalikwOxwVy/C94dAeIcseZCk0hN37596/0Y3KXEhVYSbW48O6gNOrcIQWSgDgatShIiuqVLPN4YLV2RtF1MEJ4d1EayT6NSIkivwd4cnwdLp1ZSciHGMwNbA/AZW8EwCyNjgUwM4z95IDywLcL8c4AEoynU9feE62nbAPDncwPoSycYJvEIHPAZNcGrJIwMuyaGIC0uGIPaR1EPk/CgC6E7YT2W2rIJow3xpxS0IpnEqP3pCL1GhT6tIiQJlENToyWyRgXpJCt99kgKxe4pQ/HB3V0AcJ6ow/+7UdJukF6N3sm+aeNCfwj9o1AoEKhT0fVhan8T9JYucVjx/PWSPgoL0NLkbwAYzz8X0UF6qJQKKBQKbJs0RJLYrVerMKg9dz1KpQIdYrljozrX/Y0iABjULlIis0DihGnwa166AS3CffcgTOSpIbwR7JQQQmdDiSGEKgL4514chhQvrHY50RSPbb9+/STnA8D69esviYe3uaFSKsASX0InwBmCIwU1dAag4JkREMMnxtYeqZeYfTN9EkIN2JdTRcmGEDoQBvNC/kd+lZ2GyvUaFc6UWSkxaBFmQFapleolgVzZXF4E6tT0/cmvdlCPdUSgFkfzTVQvxPOkRgCX1OviDKHIAyyESQDO43i0wISIAB+pE0IYCoUCRq0aTjcj6bMAnRqZpWYkhHLXEhOiR26lHXreyAvhtmqbL0RHAOq5AIAwowbHCkyUXHHhNp+RF2QvrdXvQgKvIGtWqYXqZV8CNoGS1wsEwLkKO5L5QUlssB7peTW0P2KCBWKgoNulZi4fRugjgE9o5svEhfB1BPvunTh5PJIP0RXVOBHPX59SwZFJcbsni800J1IIr7GEz5fhZS8yOej1hxg0yCq1SO6dQGKFazvfchJNQYNJzdGjR+nv+eefxyuvvIIlS5bg4MGDkmNHjx69cGUXgfOtJHo5MJw3onWhdVQgXru5Q53HFj7eG2teugGAf1KogCmjUvFovyRqGAXiI3gXhO23buNG42MHJGPyyA4Y0sFfJoEQiYkY1zb390L5D5GBWprnIqwnIJAenVqFFc9fj3kP9qDnhwdo0UdEDFa9eAP0GqXfZwGEkWGQXoNz748G4Asv1f68grD9MO8RA7jkWHHSbJuoQNzXK5HmeQguYAE6tcov8Vs4V5iWrFQqMGVUqqiMf994GAIV76lS1rp/apVS4l4HOJL1JZ90DfjuuV60ymiLMIPkeXlhWApm3NGJhpXaxgRi+u0dMYb3RgHAyheul7RT+/4K9+jncZz3rUNssCRXSOj/fW8Mw4N9udyZIL1G8rkE4fyOPOESyyzG3xMG1Ln/UqOx33568cUXsWbNGnz00Uc4deoU3nrrLRw4cAATJky4IvI3FgqA93b4iMDhvBoajooL0eNUiYUOFIINapgcHkk+hE6jRG6ljZaJD+FWIhbqjA3hZszU1k2FNT4iFBuix8HcahERMmJXdgUlPeLZLmKIw09JEQHYfrqclokPNeB4oZkSCYNWBYeHofkyAOflPVlkpuQiIdSAXWcqkBQp1KFHYY0TtdVqUY2PGCWFG7HrTCUlZPEhBuw4U0G9mWH8WlWA711VKxXIq7TTdltFBmJrVjklaHEhehwrNFHPjYEPz9Q4PDTvMFCvxskiM9VBcSF67DxTSQlLXDDnqRC/owpw68skiO73wdxqKkeLMG5bGCAKxJdhWTrwUCkVOF1mpbM340K5e+cjtUacLrVAzz8fgjfP6WHoMxMZqEN6fjUlhjHBOuw9W0lnSYpnxAlQKhTILrMiOdJHHg+JCFlCqBFZJRY6+BKe1eZEg0lNt27d0L17d3Tr1g133XUXMjIy8MQTT6B3796SY9fi8uONwTePNW3F5ECdGh1ig3HwTd8sjOGpMdj+qjSx+h3RDJToIB0WPtaLkhzB2AoKQaFQYPxgLpxRH2ob6MkjO6B9TFCdxEr8Yh1480bJzAoAkm2lUpoH1Ds5HL8+6xv5hgdoMfuerhgmIoHfPdGHrtciYMPEgdQ1mxhukCgm8ShKwBujUympE/DeHZ187uwwg9/H5urChokDaTitNoINGkkOFcB5PoQQ1vDUGEkeEgAE6OoOwwg5RAAwpH0UOsb7wrNKpUKyrVBwI7Xv+HVfkiIC8Eg/qRzC+Qsf68W1W4vUCPdI/DV2lVJBCaIQUosO0lOjcVvXeCz/jy9v5tF+Sfj1mX5UQSoUCmS96ws9CejSItRv3+VAY7/91L9/fyxduhQLFixA165d8dtvv+HPP/+8ZGtqNTdiQ/Q4UWSmZDwxzIANGaVow+dttY0Jwt/pRXShTIVCAYYQyTueHBGATafK6GcHEsIM2JtThRb8u6VRKVFUI83j0GmUsLu99DmIC9FjW1Y5DYu0COOWIUjm6zRq1SgzOyU6JyGM8yoJsndOCMFP+/KRwufHJYQZsPNMBQ2tCGBEyiglOhCbM8soAUsIM2BrVjlaR3J1RAXqcLLI57kR+kA8W7FlhBG7siupge7SIgR/Hi6kaQAKhQImh0fyaYPYED3S82sQw5OHtPhgrDtZQvVvYrgRWzLL0CpSuogcEfV9r+Rw/Lgnl3o228UE4e8jRWgfy5UJMWpwqsSCEFFYODKQC40JA5T4UD0O5FZRQpISHYi1J0ro/Qe4adbifLruLcPwx+FCn6xhRuw4U0H7MDpIh13ZlUgRLYBHIP2uX4swA9adKEVbXvb4EAO2ZJXT3Ea9RoXTZVaJdyg2RIe8Kjv1HLeODMD3u3PRldcVyRFGrDtZIun3urVw06G+8CkccnJyLnySjAsiQhSf/oY3TPVBo1JiWKovtKZQKPDC0JTzlPBHiEGD9+/0fa8rKSIAa18e6Hfe4sd7o21MoN9+MdS14y4XwC1d4iXb4sRWASnRPpJj1KqRM3M03Q41anFk6k2S88WzxqhcorDI6C5x6BB34dCIuN3aCDFoMO1WKXHqnRxOQ1TD02IwPC1GssifWqXEk9e38qvruSG++7V4bJ8LytUQfHZ/N/pcGGuRqVaRAZgwRPqMjO4chxvacn2vUChofpWAEINGQlDa1hFaqr0S9pXGhAkT6vW0bNmyxW/fPffcg3vuuecSS3Vp0DkhBAfO+b6Z071lGAgBXcQyPECL9rFBGJ7m0xUGjUpCbIXv7AiktmW4EW4vi46i5G6tWkm9cwCnK/KrfGsnpUQH4myFjRokgRQL3g6AW11WPAuvT6twrBV91f661hFwMyxNpA/Wa5BXZUf3lmH0nJhgnSQU3j4mCGUWF/VKtooMQLXdQ59jtUqJMosLw1N9g58WYQacEH2bqE1UAAprHFT2iEAdWEIkeYqnSix46gbfO9wpIQTf7c6l+iU1LgilZhddlynEoEGxySmZBMASSJLvr0+JxP/4jxYDHKkpt7jQQ3S92WVW3NHdpyvbxgQit8oXDm4VGQCGJbSfIwK0KDY50Sbap6/tLkYyRX5gu0h8sOYUHeR0jA/GqRIL+vP3RqlUoMzikoSRA3Vquko0ANzaNR5fbMmmz0yHuGDkVtrRjX/uAMDq9GJIe9/ANSUqULKw6fDUGHy3O5fq7ehgPbJKrXh1hO+5+/i+bmhWkCZgxowZZOHChX77Fy5cSN5///2mVHlBvPvuu6Rfv37EYDCQkJCQJtVhMpkIAGIymZpXuKsISZNXkBVHii5JvTJkXCwu9h2srKwkDz74IAkKCiIhISHkiSeeIBaL5bxlBg0aRMANROnvmWeeuWwyXywYhiV2l/e857AsK9musbtJpdVFt+0uLzlXYZWck55XLdl2exlJPS4PQ2psbsnxvEqbpIyXkbZbVGMnVqdHIrvJ4Zack1shraOw2k4YUT1ehpXI4fEyfmWcHml/nCmzkAqLk27bXV6J7CzLkipRfwjXJ4bF6ZG0yzAsya+StrvyqFS3lpodxOH2yVJucRKL6PqFfWJ4vNJ2/04vJMcLa+i2l2H9zqmNvEqbpA9KzQ5SY5f2s7WWHDaXdPt4YY3k3hzOqyb7cyrP225t2yK+dmH7dKn5vHVUWJx+z01D0ND3UEFI4z/YkpycjKVLl6J/f+lUz7179+L++++/JF6dadOmITQ0FAUFBVi4cCFqamoaXYfZbEZISAhMJtMVndFwKZH82kp8/Wgv3JjWvMnTya+tpDkwMmQ0FRf7Dt58880oLi7GV199BY/Hg7Fjx6J3795YunRpvWUGDx6Mdu3a4Z133qH7jEZjg9v//6A3ZMi42tHQ97DB4ScxSkpKEBfnPyMjKipKEstuTrz99tsAgCVLllyS+v9NaGSUSIaMawIZGRlYs2YN9u/fj169uNDtnDlzMGrUKMyePRvx8fH1ljUajfVO4ZYhQ8a/B00KlCcmJmLnzp1++3fu3HlexXK5cTHLnV+reLBvS7S+RF8/lSHjSmL37t0IDQ2lhAYAhg8fDqVSib1795637I8//ojIyEh06tQJU6ZMgd1ur/fc/496Q4aMfwua5KkZN24cXnrpJXg8HgwdOhQAsHHjRrz66qt45ZVXmlXAi8HMmTOph+f/C2bc0fnCJ8mQcQ2ipKQE0dHS5QvUajXCw8Pr/eQBADz44INISkpCfHw8jh49ismTJyMzMxPLly+v8/z/j3pDhox/C5pEaiZNmoTKykr85z//gdvNze3X6/WYPHmyZH2IC+G1117DBx98cN5zMjIy0KFD3Wu/XAhTpkzBxIkT6bbZbG7Ud1xk+PBKHavZypDRHGioHmgqnn76afp/586dERcXh2HDhiE7Oxtt2rTxO1/WGzJkXLtoEqlRKBT44IMP8L///Q8ZGRkwGAxo27YtdDrdhQuL8Morr+Dxxx8/7zmtW7duiogAuOXOGyuTjLrxfK3vDsmQ0VxoqB6IjY1FWVmZZL/X60VVVVWj8mWEBTvPnDlTJ6mR9YYMGdcumkRqBAQGBqJ376YtRgdwicVRUf5rl8iQIeP/DxqqB/r164eamhocPHgQPXtyqzVv2rQJLMs2amXx9PR0AKhzsoMMGTKubVwUqbmcyMvLQ1VVFfLy8sAwDFVMKSkpCAxsWGKsMHtdTvyTIePKQHj3mrCSBFJTUzFy5EiMGzcO8+fPh8fjwYQJE3D//ffTCQqFhYUYNmwYvvvuO/Tp0wfZ2dlYunQpRo0ahYiICBw9ehQvv/wyBg4ciC5dujSoXVlvyJBx5dFg3dHoFXCuEB577DG/BbQAkM2bNze4jvz8/DrrkH/yT/5d3l9+fn6T9EBlZSV54IEHSGBgIAkODiZjx46VLL6Xk5NDAJ9eyMvLIwMHDiTh4eFEp9ORlJQUMmnSpEYtpCfrDfkn/66e34V0R5MW37tWwbIsioqKEBRU97ePZMiQcWlBCIHFYkF8fDyUyqvr0wv1QdYbMmRceTRUd1xTpGbbtm2YNWsWDh48iOLiYvzxxx8YM2bMlRZLhgwZMmTIkHEV4NoYKvGw2Wzo2rUr5s2bd6VFkSFDhgwZMmRcZbhmEoUB7rsvN99885UWQ4YMGTJkyJBxFeKaIjUXCzk2LkPGlYWcUyNDhoymoMG6o1FTD64iACB//PHHec9xOp3EZDLR38mTJ6945rb8k3/yr+mznwTMnTuXJCUlEZ1OR/r06UP27t1b77mLFy/2a1+n0zW4LXn2k/yTf1fP70K641/tqanvGy75+fnn/XS5DBkyLg2ETw4EBQU1uY5ffvkFEydOxPz589G3b198+umnGDFiBDIzM/2+DSUgODgYmZmZdLsxHhdBVllvyJBx5dBQ3fGvJjX1fcMlODhYVk4yZFxBXEwY5+OPP8a4ceMwduxYAMD8+fOxcuVKLFq0CK+99lq97TXmUwp1ySrrDRkyrjwupDuujaB2E6HT6agikhWSDBnXPtxuNw4ePIjhw4fTfUqlEsOHD8fu3bvrLWe1WpGUlITExETcfvvtOHHiRL3nulwumM1myU+GDBnXBq4pUmO1WpGenk4/kZCTk4P09HTk5eVdWcFkyJBxWVBRUQGGYRATEyPZHxMTg5KSkjrLtG/fHosWLcJff/2FH374ASzLon///igoKKjz/JkzZyIkJIT+5C90y5Bx7eCaIjUHDhxA9+7d0b17dwDAxIkT0b17d0ydOvUKS3Z1Ib/KfqVFkCHjqkG/fv3w6KOPolu3bhg0aBCWL1+OqKgofPXVV3WeP2XKFJhMJvrLz8+/zBLLkCGjqWhWUnPkyBGoVKrmrFKCwYMHgxDi91uyZMkla/NaxA0fbj7vcavLi7/SC5tcv9XlbXLZ86HK5sbKo8WXpO7z4cst2Ze9zSuJFUeLrrQITUZkZCRUKhVKS0sl+0tLSxucM6PRaNC9e3ecOXOmzuNy2FqGjGsXze6pIdfOVxeuCtTur3vn78aJItMlbfN0qQUv/px+3nNcXqbeY52mrb0k93lfTiWeW3qoSWWPF3J95mVYyX6L03PB/vxgzakmtXk54PayYFhfX9fY3Vh/svQ8JaQghOBMmVWyb8LSw379dD4czK3CIwv3Nvj8SwmtVouePXti48aNdB/Lsti4cSP69evXoDoYhsGxY8cQFxd3qcSUIUPGFUKjSM2dd9553t/EiRP/NYtTCV6g+lBQbUe3d9ZdVBsmuwetpqyi2zV2N/adq0Kp2Un3OT0MqmzuOsszLMHOMxVgWYIlO3MAAJ56jJXYu6IU3aPscp/B++dIEdafLMXxQhPav7lGUn53dqXEELrraKcpRMfDsDA5PPz/DSvPskRi6AHgljk7AAApb6yW9NfRAhNGf76jSXI1FflVdnT43+omlxfjsUX78M32s3S72zvrMe67A3T7cF51neHGPWcrAQA2N4PhH2/1O177/v2VXoi1J6Q5Ke3eXA2z0wOTw4Ptpysu6jqaExMnTsTXX3+Nb7/9FhkZGRg/fjxsNhudDfXoo49iypQp9Px33nkH69atw9mzZ3Ho0CE8/PDDyM3NxVNPPXWlLkGGDBmXCI0iNf/88w+cTqckiU78CwwMvFRyUsybNw/JycnQ6/Xo27cv9u3bd0nambvpDF76Jb3e43Y3gxq7R7Ivt9JG/y8xOTHqs+3nbaPK7jO+hBB0e2c9AEABjnScKjHjs42ncfu8uo1yjd2Nh77Zi9NlVqw8xoVtnB6G1gcAW7PKcbrUgk7T1tJyYlIz7CPO4M3fmo3nfzqMv48UUYIg1JNdbsUDX+/BGREBcro5o/jW3ydgd3vh8jISglYXHlvku1cLtmVjyvKjaPvGatw+l2vPy3J1zttcd1hAwEfrM/HfZUcAcJ6MGruU9OVU2PzKCMTJ4Waw6lgxkl9bifT8Gsl1Cnhk4V60fcOflGSXW6msAOfNYlmCLZllOJJfA5YlqLC64PAwcHoaR4o2ZtTtfcmpsCGPJy11ec9e+fUIvt+TK9m39kQJ7l+wBwD8+kaA2+uTz+L04LvduVi6l0u4X3O8GK/9fhRuLwuPl0WQXlNnHSxPLE0OD+zuSxOSrAv33XcfZs+ejalTp6Jbt25IT0/HmjVraPJwXl4eiot9Yczq6mqMGzcOqampGDVqFMxmM3bt2oW0tLTLJrMMGTIuDxpFalJTU3HXXXdh8eLFdf7qWuiuOSEsujVt2jQcOnQIXbt2xYgRI1BWVtbsbX20PosaE6eHQZnIewIAhTUOybaXYTFo1hYAnKGptLlwstg3FdThZvDhmlNYsI3L3yiqcWDIbO78TadK4RIZGZ7TYPTnO/Dllux6DaSXNyp/HylEpdXNy8qdm11uw9oTJXhs0T78uFc6O8zikpKx5386jF3Z3MheKXK0/bI/D48v3k+Jz8hPfSRNuP4lu86h2OTEuO8O+slXbHLA5WVw4FwVik0ObM0qpx6WGatO4ad9XAJmjeCp8XLHZq3NhMnuof3DsgRmpwf3zt+NM2UWzNucjUqbGya7B7/sz6NkUMB/lx1B8msrsS2rnHpcur69ju8XK/7zIxfiOldhoyQwt9Ln7RC8EsmvrYTF6eurYR9txZECEzXmd325C31nbsSXW7Jx+7yd2H22Er3e3QCBH/24lyMbvx0swHsrT9J6CCHoOJXzhLm9LBxuBk9+ewAehsXAWvlQZqeH3r9Xfj1C93+0LhMMS3C2woYF285KnsdnvufuhZdhKfGu/byKn7cnlxzAwdxqbM0qBwDsOVuFn/dz98bNSMNfDEuQW2mDxelB69dXIaPYjKe/O4C0qWtRbnHhcmHChAnIzc2Fy+XC3r170bdvX3psy5Ytkjy7Tz75hJ5bUlKClStX0skGMmTI+HehUaSmZ8+eOHSo/pwHnU6Hli1bXrRQ9UG86FZaWhrmz58Po9GIRYsWXZL21LyFX7o3DyM+3YbrZmzE0YIaJL+2EmMX7wcAfLbhNJweBnbeOP6wJxe/HyrA+6uleRqpU9fgUF41tmRyhsMmCgc9seQAXLWIy71f7abGxOr0ghCCvEq7xPsjhB3mbc5GhZUzKD/t4wxgmcVJR/9Ldp2T1P3g11x+hGDQ/zlShCobV14cQZr8+zFq6LRq6aNy71e7kfzaSgDAvpwqmGp5BPIq7eg3cxPmbTqDu+fvxucbOe9Lm9f9vTk1dg+cHga/HvDNMim3uqjH5a8jhRgwcxP2navC8I+3AQCKaxzo+s46iINQhBColQpartLmwuP8fRKw44wvjBJi0OBsOXfu4Nlb8MQS6bkA0PktjgydE3l/CqodSH5tJTKKLSi3uBBm1AIAXvjpMABfvx7Oq8Ez3x/Af5cdwdfbc3DXl7uQ/NpKdHtnPWxuBoQQ9H9/I1J5gpNXZUdelR3/HCmi99Pu5upiWYISk49YrztRimpRnwuhwYU7cui+lDdWUw/VgPc3YeKv6Rj9Off89J2xkfPEMCz2nauiZfaerUSZxdeOy8PCy4cFK6wunC23YtCsLXjjj+MAgJs/2469OVz5/aJ6LjUa67FdtmwZOnToAL1ej86dO2PVqvN7FWXIkHFtolGkZv78+Zg1a1a9x1NTU5GTk1Pv8YtBUxfdagrWHOdc1/vPVSM9vwarjhWj2u5BidmJ2+bulJz7yYYs5FbaYXdxxufNPzllL4z2K60u7OD/33O2CruyK+H2srjxk22SenbzORAAsDWzHPtyfAbC4WHQ7s3VGDhrM04Wm3G80IRnvz+Iu+f7rtvs9MLtZfHx+iz0TApDlc2NUvP5R87i8NnxQs6r9PeRumfGCOEKgciIc3SmLD+GQe2iEGrU4La5O/Dr/nwMnMV5HIp5Qyw2eG/97b/wWadpa3Egt5puC8Z47OJ9UEABS60ZV04+FDP1L19dh/NrEMoTDEBK0ACObIjJJksI7p6/i25vOlW3x2/l0WIM5r1GgC9fRSCdeg33GlXyuTxFvFfkt4MFWHvCF1Y6yF+fcG03fbINFVYfMRFI6vM/Hca0v09gyvKj9FhulV3S54U1DloPwHn1fj9YgMN5vj4EINlefqgQJ4p83kO72wuHRxrSWnWsGKuO+XJrThabsXQf53Hq9e4GmjNX13PiZRufU9UUNNZju2vXLjzwwAN48skncfjwYYwZMwZjxozB8ePHL4u8Fwsvw0o8hoCPONe3bXV5JQMnD8Oi0irVB8Umh18Zcd6c08PALGqXEELJtgCL0yMJ3drdXoksDEtgqhWmr+31rrG7qfdTaLd2HaW1yphr9UeZxSkJgTo9jOR9IYT4hWJtLq9EdrPTI5GDYf2v91yt0LbJ4ZHk35ns/qHY6lo5kbWPF5sckuvxMpz39nxlyi0uiawmh0dShhDiV6audsWyV1pdft7W2s9VXqU0f8/q8krk8DLsBT22NXa3X05kc6JRpEan08FoNF4qWc6Lpiy61dSVQWNDDPT/MfN2UmNr1NY9XX3HmQo8+M2eOo/1fHcDHq41c6Tdm758jft6cQt7PfuDL3yz/JD/omBCEq1Bo8Itc3ZgzQn/axYSZLu2CMWEpYf9cksIIVgnKldpa1q4YHiq//d1Pt90Bq0iA3C0wIRXf/cZ49XHufbEM3Bqe44Af4N4utQCANicWV5nblN+lcNv3/gfDqJtdCAWPNITcx7ojq+2npUc7/3uBvr/oHZRNOQjOee9DaiNaX9LjZ/4+gBArZK+RuN/9PdmavlzhnXw9d3pWrOSxF6llUeLaXgO4BSSOAnf6vJi9tpMPNYviW6/suyINIwJYPa6LD9ZBDy39BA+EJG89jFB+Ha3ND/nPz8ekpCc+nJ/AGBQ26h6jzUnGuux/eyzzzBy5EhMmjQJqampmD59Onr06IG5c+deFnkvFh+uzcQDX++hhONoQQ36ztiIYwXcrL4KqwujP99OvbQA8PzSQ3hZ9N4s2HYWjy7aRwnG2XIr+s3chKMFNQA43fDg13skMwH/9+dxPLlkPzX8a0+U4voPNlGCkVdpR+e31uGvdI7gsizB098dxKy1vm9sfbw+E3fN30Vl35dThT4zNlLdZHJ4MOqz7RIP4ws/HcbzvNcTAL7ddQ63zd1Bid25ChsGzNyEbbwX2eVl8Pii/ZJn+c0/j+PFnw5T2VceK8YNH25GQTVnlMvMTgz8cDP+ES0hcf9XezBV9K7PWpuJW+fsoIb9wLkqDJ69hc60tLu9uG7GRizZec7X7z8fph5bgBvYDJy1mZKjrFILhs7eil3Z3EDXy7C484tdksHWjFWn8NA3e6js60+Wos97G2kIucLqwo2fbMVS0f1+csl+vL86g25/tzsXN3ywmcp+MLcawz/ail28p9rlZXDL5zvwrUgXT1l+TCL7T/vycP0HmygZOlFkwsBZm7GTr4MQgru+2IWP1ovvdxbunr9LIvvwj7dSMuhwMxj+8TYsEt3vt/+pf3XvpqDJU7pZlkVWVhZ27NiBbdu2SX5XC5q6Mmi3xNA696fF1b1exbzNZ2gY40KYMCSF/v9YvyS0jPAnieJZQAmhBsmxz+7vVm/djy7iyFOPpFAAvjyKG9pGAgBaTVmFp7/3kaemzAqqC4nhnIwRATq/Y+LR0ndP9LlgXWFGLin1teXH6jw+sqP/WiRzHuDyI9RKJQJ0atzUMRaBejUyeWL03JA2MGhU1NszuH0URnfmpvN6GIJfnr4OKdFckntdowzBm/L8UN+9G9kxFhEBnFfonzq8Fh/c1VmyLcw2CgvQ+p0rxtRb6k5eHf35DmQUS0n56uMl6BgfItnXmOneO89USvKt2sVe+COT+dV2DEiJqPOYWnXpZz42xWO7e/duyfkAMGLEiGb38F4KuL0sMorNuKVLPHbyeW/LDhTgg7s6Y/lhbvCz8mgxJo3ogFX8ZIHcShtiQwwI1Kup13DP2Uo8PbA1VvFe6L+PFOH9OzvjFz536kiBCf3bRCKj2AKWJXB6GFTa3OiRFIajPHladawYH93TjZKnf44WYeFjvajXLr2gBt1bhiKnwgabywuGJThaYMI9PVtgO28IF+/MwXdP9MGPfHL7uhMlePnGdth2miMoJSYnAvVqBOnV1JO0ObMME29shw08of4zvRAf3t0Fv/Dh6p1nKnBnjwScrbCBYTkPhcnhQduYICr7n4eLMOvurpSALTtYgJl3dsYf/AAyu9yKHkmhqLRyuXqEEBwvNOHpga0pefr9UAG+frQXfjtYwPdHCd6+rSO2ZpWDEC48HBWoQ1SQnk4a+Su9ELPu7kLX3/rtYAFm3dOFJuUfyK3GA31aoqiG85p4GRanyywYkBJJvfUrjhZh6q1pWM3f37/Si/D+nV1of1RYXYgN0aPM4kKZ2QmWJdiQUYr/jmiPdbw++ONwAT66txt+4693S2Y5xg9ug82ZZSCEoNrmhlatREKYgdqN1cdLMHlkB6rfVh0rxuKxvfEDf++OFJgwPC0axwvNcHkZMCzBsUITbu+WgP3nOEfA7wcLMO3WNCw7yN2rdSdLMPHGdvR5yK20+Q3ELhZNIjV79uxBSkoKUlNTMXDgQAwePJj+hgwZ0qwCCmjKolvNvTLoQ9e1xAu8Yfvw7i50v+AhWTy2d71lw4waRAZq0TbGN0MsPtSAqEB/IiAOCbSLkc4ou6mWUdeJcl2ySrmRf5Beg8kjO1AXX/82kZIyPZPCJNtJEUa8OToVm14ZBAB4745O+P5JKQH55enr6P8lZic2vjIIx966CTemxeDT+7pj4yuDEBviu5Y//tPf77oCdNLvp654/nrJ9teP9sLuKcP8ygm4v3ci5j/Sk8opQPC2FNY46AyhQW2jKCHUq1XQ8SEigbxUilzCsSF6pNYirNPHdEKnBG6fEF4K5OWPDtJh/iM9sfBx7n67vCzGD24jKa/X1O3VE9/bX56+Dgsf64WXh7ej+4RE7Q48wbizewL6JIcDAD6+tysAX65XgFaFW7rG0eP1Yf7DPfDisLbnPQcAZt3dhZK9+lBY7aiX3F8OUtMUj21JScll8fACwNS/jmPkp9vw4Nd78MjCvXhk4V48+PUe3D53Bx78eg/GLt6Hu77chQe/3oNRn23HyE+34dY5O/DEkv24/oNNeGDBHjy5ZD9GfbYd7d5YjccW7cPdPVtgdOc4rDleDJeXQW6VHSM6xlLv5/bTFRjULgqtIwNwpsyK9SdLcVNaDEZ2jMW6EyU4W25FckQAbkyLwcaMUhBCcOBcNe7tlYiCagfcXhZ/Hi7EHd0T0DMpDPvPVWHH6Qrc0DYSN6XFYtOpMlhdXKhyZKdYGkbdl1OFge2iEKhTo9LqwroTpRjRMRbDUqOxNasc+89VoU9yOO7onoA/DhXC6WHgYVjc0DYSp0o48rTuZClGdopFq8gAnCmzYHNmGYZ2iMatXeKx4kgxCqrtiA8xYGTHOKw7wcm+/1wVRnSMhdPNhak2ZJThprRY9EkOx74cTvbrUyJxW9d4/HOkCCa7BzqNEjemxdD8rwPnqjCkQzTCjFqUmZ1YdbQYozvHY2SnWKw9UYITRWZ0jA/GqM5xWH28BF6GRUG1AzemxSC73ApCCLZmlePGtBi0jw1CZqkFG0+VYlhqNG5Ki8GGjDKcq7AhMdyIG9pGYeeZChBCcLLIjOtTIuHiJwisP8n1We/kcOw/x6Un9GsTgTHdE7DqWDHsbi9sLgZjuiXQfMBdZyowuH0UJ7vFib/Ti3B7twTc2jUeGzLKcKSgBl1ahGB4agw2ZZTyyf129GsTgSqbG24vi3+OFGFM9wR0bRGKIwUmbDpVhmGp0biV77PscitahhtwS5d4GkI/WmDCoLZRYAmBzeXFqmPFuLlTHK5PicSes1XYnV2J61pH4Cb+ObO5vGAJwYA2kTiSbwIhBCuOFuOWrnGICtSh2OTA2hMlGFHHQPVi0KSvdD/77LPo1asXVq5cibi4uMuyNo140a0xY8YA8C26NWHChDrL6HQ66HT+pKGhmHpLGt5ZcRK3d4vHX+lF0KlVmHhTezzYNwkRgVpYnF5MX3ESPVqG4lBeDXomhdGHAuDCVUKiJ8MStI4KxA0iF71KqcA9vVrA4WHw5ZZslJidWPZsP9wjypVZ9HhvOlW6e8tQAMBNaTFYd7IUA1Ii4GUIOsQGYUtWOXIr7Xjr1jTckBKJjGIztColHuzbkhpBAPjzuQHolhiKlNdX4YE+LfHmLanQqTkDLMRkH+rLhTQ2vjKIznzq29o3Oj9eaEabKI4cfP1orzr7LtjAeVw0KgX1PKmUCozuEkdHLZ0SOC/DplcGYehHW3FjGmd40qfeiG7vrMdrN3dA54QQPPTNXix9qi/1SiRHBAAA3hiVivdWZSDEoMH4wW3w5ZZsfPFQDwCAUqmgZMfDx6gjA7VoGx0Iq8sLD8MiLS4YJ4vN0GtU+Oy+bhKPyyPXJSEuWI+nvjsAo1aN9jEGaPgQkvA3QBSOnHhjO4QbtXhvFecC1qlViAnW4e6eLTBpRAdkl1uxL6cKMcE6ev2J4UbEhxowLDUGn2zgwkRGnRp9W4VTj1Gb6EC8PjoVvd7dgDHdEjDx1yO4v08iftiTh5hgPYxaNR7ulyRJ9q2NFmFGnC611ntcuCd6jQrzHuqB7W+thdnJebWUCk7O3Eo7fe4GtovC7+P7YdJvRzG4XTT+d0sqWk1ZBY3ymvrqSr2YOXNmk2dyvnN7JwCca/58epFbB4v7X8m/n04PA51aCYVCAYZfi0mtVNDjBdUObM/iCIxCoUByRAAO51VDq1bAoFXhpo6cQT5wrgqP9EsCIcBzPx5CjcOD0V3iYNSqEazXYHNmGVLjgqBUKnBD20hszSpHdrkV7WODoNco8eWWbNjdDN4YnYqIAC0+3ZCF9SdLcGNaDFRKBVqGG7HrTAWig3TQqJQY3D4KmzPLcaywBpNHtkd0sA4zV51CsF6NR/olITpYD5eXwZbMcnRvGQaFQoFuiaFYn1EKg0aFIL2G15vFOFFkwif3dYNeo8LiXeegUADD02IQYtSAEG69rE7xIVAqFejXJgK7syuRX2VHywgjRneJw8IdOXB7Wbx0YzvEh+hxqoQjSoPaRkGlVCAuWI/DedUIMWigUSkxLDUGa0+WYvfZSvxnSAqcHgYv/ZKOvCo7RnSKRUywHiaHB1syy3EdrwM7JYRgX04VHG4GYQFa3JQWg7XHS3GssAafP9AdSoUC3+/JhdPD4JbOcQjQqUEA7M2pQlp8MBQKBQa2i8KmU2XILLHQgesv+/NhcXrwwrC2aBFmQHa5jQvfpEZDq1YiIkCHQ3nVCDFqoNeoMLRDNDZllGFzZhkWPd4bTg+DV387iuxyK+7u2QJRQTqYHB7szq6kA9kBbSKx+ngxPAyLyEAdRnWOw99HipBXaccHd3VBgE6Fb7afhcPN4LauCTBoVQgP0GJrVjlaRQZAqVTg+pRIbMsqx8kiM6bc3AEBOjUW7ciB08PgxeFtkRBqwMzVGdh4qgyD2kdBqVQgJToQO89UIkCrQrBeg5s6xmD1sRLsyq7E4/1bNeldqw9N0kKnT5/GjBkzkJqaitDQUL/1ai4VLrToVnPj1q7xAIC7e7bAwHZRGJDCeTxiQ/TQqJR4qC8302vZs/3xxIBWCNZrMKYbVyYuRI+7e7bA80NTMP/hHuiaGIpZd3dBeIAWTw9sDZ1aCZVSAYVCgcf6JyMhzIA7eySgR0ufF+XMezdTxRhi0OCP/wwAACzgicSXD/fEp/d3g1GnBstryM4tQqFUKmB3eRERqMVbt3WESkRqhNAaARduEAgNABi0Kom3KYQnJuG1Qib1hUjcXhbRQRyJFAxc1rs3S0+qlR/Wv00EbUdAqFGLAK0KKoWC9nlCmAEhfGhKUPJjByQj452RGJYag8kjO6BluFGypkpMsB5RQToM4EdGk0a0h5bv96cHtsbPz3DeJ6NWRevMmTmKemiGp8Xg1ZHtQQjBXxOuR5voQIQHaOlMMLE3RqNSYtzA1hjVWRh1cCOUDrFcXW2iAvFAn5YY2iEGT13fCgoFEBHo69dbu8bjs/u7YWiHaPzyTD+4vCyGp8bg/t6J1EMkyHhH9xZoGe4LW97WNR7H3roJAOf92TF5CM69PxqrXriBl1NJvWR7Xx/GPY8tfO/pvAd7YN/rPg/Z0bdGAABOTR+Jna8NRZBejQf7tsSCR3vhhWFtEahTo2dSOFa9cAPeGJ1Kn1Gl8tIPbprisY2Njb3sHt4LDfQUCo6siPtMr1HRciqlAlq1UnK8d3I4PlhzCjd34uS+r3ciHl+8H7d3SwAA9GkVjt8PFiAx3AidWgW9RoVAvRqbM8upN+/WrvF4fulhPMgPXEZ3iaOJ/gCQFBGA02VWMIQgJlgPtUqJLi1CMGtNJh1Rj+megPE/HsK9vblw/o1pMZi9NhMDUiKhUCgQHaSH1eVFeoEJKdGcx/G2rgl4bukh3NIljrY7+fejdLtnyzCsO1mK6GA9gvQc4UiOMGLpvjwMac/JdnfPFhj/4yF6vbd3S8Dk34/S8HrrqEDkVztgdzNICDVAoVDgutbhmLv5DEby7+Xt3eIxYelh3MPnMg5LjcZH6zLRt1UEVEoFAnRq6DUq7D5biW4tQgEAQzpE439/HcdtInvwwNd7cDuv63slh2NLVhmigrhBhl6jQoswA/48XIg+rbh+v69XIp5Ysh/38X02Ii0G76w4gcHtOYLaLiYQp8ssqLa7kRhuhEKhQO/kcHy24TRG8vf7nl4t8OjCfRjDX/+g9lFYtDMHafHB0KiUCNJroFAA6fk11NN7c+c4TPw1HaN4D+zNnWPx+vJjdLtjfDAOnKuCSqVAiFEDtUqJ1LhgrDxWjF48EbqjewL1GAr38t2VGbiudTgUCgVaRQbgVIkZFVYXWoRxsg9uF41Xfk2nnt9bu8bhv8uO+Pq9QzR+3p+HuBC938zaiwZpAoYMGUJWr17dlKIXjTlz5pCWLVsSrVZL+vTpQ/bs2dPgsiaTiQAgJpPpguf+diCfEELI2MX7yMmi+s+fteaUZLuoxk4+25BFbC4PYRi23nIpr68k3+3Kodtv/nGMfL0tmxBCSNLkFeTp7/bTY08u2U8sTo+kfNLkFfT/zzdkkZ7T15HxPxwgRTV2Qggh0/85QYbO3kwIIaTa5iJfbT1D7v5yJy3z2u9HyL6cynrlE/DV1jOk34wNhBBCzA43+W5XDikxOeo81+7ykgPnqkjS5BWkxu4mk387Qggh5Ott2SRp8gri9jLkPz8cJPd8uYtsySyj5axOj+R6CCGk3RuryMLtZ2m7tVH7/LpQY3eTGpubnr/hZAkxOXz7atdTY/dv58stZ0jnaWvo9rS/jpObPt5KCCGk1OQgSZNXSOootzhJ0uQV5M/DBReU73y46eOt5MklvmdA6IvXfuf69L2VJ8ngWZvpcafHS5Imr/C7N0mTV5C8ShvZlFFKkiavIA63lx7rN2NDvf0ofnZPl5pJQbX9vPI25H4Q0rh3sD706dOHTJgwgW4zDEMSEhLIzJkz6zz/3nvvJbfccotkX79+/cgzzzxz2WRuDni8DCmsdR9yyq2S7YJqO7G5fLrC7HCTcouTbrMs6/eMlJocxONl6HZhrTrsLq+fDqwtR36Vjbg8vjqcHi+pELXrZVhyvLBGUuZYQQ1hWd9zlldpk+g5p8dL8iptkjJiuQghpMzslNRRYXGSKquLbru9jF+7tWU/V2ElJpGOKbc4SX6Vr12PlyFna/Vz7T6ssDiJVSS7l2ElchBCSG6F9FqO5FdL3ke7y0vsLt+228uQUrO0nawSs+R6zQ635N6ZHW5SXOMrw7Isqawlx+lSs6RMiclBSkXX42WkZViWJScKpfc/r1J6v2vsbsn12lwecuBclaRM7Wf1XIVV0mcXQkPfwwaTmiNHjtDf8uXLSVpaGlm8eDE5cOCA5NiRI0caLOTlRlOU0zPfHfB7sJoD7d9cRZbuza3zWNLkFZIHpi5sOFlC/192IJ+M+GSr5HiJyUFOl1ouWk6HW6qcLgSB1NSH8T8cIA9/IyWiXob1K5NTfv4HvrbCuBC+2nrG7+UmhPgpzdrYlFFKXv7lMN1+7fejZPTn2wghnNzf7z5Hqm3SepMmr6CkuKl45dd0cucXO+s9Xm5xkjNlvvvLMCxp98YqPyKdNHkFya+yEZb17+PiGkeDyciFcDlJzc8//0x0Oh1ZsmQJOXnyJHn66adJaGgoKSnh3olHHnmEvPbaa/T8nTt3ErVaTWbPnk0yMjLItGnTiEajIceOHbtsMsuQIePi0ND3sME5Nd26dYNCoZDM63/iiSfo/8IxhUIBhqn/Y4jXGh4fkFznrJ6LxZb/DkFYQN3Lz3dOCLmgS25Yqi/x8a4eCdQ1KiAmWI+YZvi4sF6jqjfptS4khBrw5PX1x0jbRAX6feVbpVRIwh8AkBwZcN52LjSLqDaeHtimzv2J4edfomBIh2gMEU3D9jAsnZ6tUirw8HVJfmW2vzoEUUEX98wEaFWSdUZqIzJQh0hRkrlSqaAxbzEGpEQgUKeGQqHAufdHS47pmtHtG6hrUnpek3DfffehvLwcU6dORUlJCbp16+b3mQSlKL+nf//+WLp0Kd588028/vrraNu2Lf7880906tTpssksQ4aMywMFEbOU8yA3N/fCJ/FISvJX9FcDzGYzQkJCYDKZEBzcDBZfxv87fLwuE+VWN2be2fnCJ18E8irtqLC5JDlWF8Jf6YU036Ah8DAsFu7IwbOD6iZ8lwLX4jt4LcosQ8a/DQ19Dxs8VEtKSqK/pUuXYuPGjZJ9SUlJ2LhxI37++edmuYDaeO+999C/f38YjUaEhoZekjZkyLgQJt7U/pITGgBoGWFsFKEB0ChCA3DJzZeT0DQHqqqq8NBDDyE4OBihoaF48sknYbXWP7MLAAYPHgyFQiH5Pfvss5dJYhkyZFxONMn//NVXX6FDhw5++zt27Ij58+dftFB1we1245577sH48eMvSf0yZMi4+vHQQw/hxIkTWL9+PVasWIFt27bh6aefvmC5cePGobi4mP4+/PDDyyCtDBkyLjeaFAgvKSlBXJz/Il1RUVEoLi6uo8TFQ1g3Qvz1XRkyZPz/QUZGBtasWYP9+/ejVy9uWYM5c+Zg1KhRmD17NuLj4+stazQa653CLUOGjH8PmuSpSUxMxM6dO/3279y587yKRYYMGTKait27dyM0NJQSGgAYPnw4lEol9u7de56SwI8//ojIyEh06tQJU6ZMgd1uP+/5MmTIuDbRJE/NuHHj8NJLL8Hj8WDo0KEAgI0bN+LVV1/FK6+80qwCXgxcLhdcLt+3fBqz3LkMGTKuLpSUlCA6WvoxVbVajfDw8Ho/eQAADz74IJKSkhAfH4+jR49i8uTJyMzMxPLly+s8X9YbMmRcu2gSqZk0aRIqKyvxn//8B2439w0dvV6PyZMnY8qUKQ2u57XXXsMHH3xw3nMyMjLqzN9pCC5muXMZMmRcHjRUDzQV4pybzp07Iy4uDsOGDUN2djbatPFPlJb1hgwZ1y4aPKW7LlitVmRkZMBgMKBt27aN/s5SeXk5Kisrz3tO69atodX61iRZsmQJXnrpJdTU1Fyw/rpGXImJifLUTBkyrhDqmpbZUD3www8/4JVXXkF1dTXd7/V6odfrsWzZMtxxxx0NksFmsyEwMBBr1qzBiBEj/I7LekOGjKsPDZ3SfVErZgUGBqJ37/q/TH0hREVFISoq6sInNhEX+0FLGTJkXHo0VA/069cPNTU1OHjwIHr27AkA2LRpE1iWRd++fRvcXnp6OgDUOdkBkPWGDBnXMi7fMqAXiby8PFRVVSEvLw8Mw1DFlJKSgsDAwAbVITil5Bi5DBlXBsK71xQHcWpqKkaOHIlx48Zh/vz58Hg8mDBhAu6//346QaGwsBDDhg3Dd999hz59+iA7OxtLly7FqFGjEBERgaNHj+Lll1/GwIED0aVLlwa1K+sNGTKuPBqsOy7pxxqaEY899hgB941nyW/z5s0NriM/P7/OOuSf/JN/l/eXn9+0b2NVVlaSBx54gAQGBpLg4GAyduxYYrH4voGVk5NDAJ9eyMvLIwMHDiTh4eFEp9ORlJQUMmnSpEZ9x0nWG/JP/l09vwvpjovKqbnWwLIsioqKEBQUBIVCceECMmTIaFYQQmCxWBAfHy/5PtPVDFlvyJBx5dFQ3fH/itTIkCFDhgwZMv69uDaGSjy2bduGW2+9FfHx8VAoFPjzzz+vtEgyZMiQIUOGjKsE1xSpsdls6Nq1K+bNm3elRZEhQ4YMGTJkXGW4ZmY/AcDNN9+Mm2++ucnl5di4DBlXFnJOjQwZMpqChuqOa4rUNBa1F9EqLCxEWlraFZRIhgwZAJCfn48WLVpcaTEahKKiIiQmJl5pMWTIkIEL645/Nampb7nz/Pz8f93KoAdzqxCoU6N9bPNeV1GNA/Ghhmat898Oh5uBQau60mJclRBW5w0KCrqoeubNm4dZs2ahpKQEXbt2xZw5c9CnT586z12yZAnGjh0r2afT6eB0OhvUliDrv1FvNBSEkIv2UjEsgUp5+T1ddrcXRu2/2tRdcjT23jk9DLZklmFkp7oXuGwKGqw7GrNGxNUEAOSPP/447zlOp5OYTCb6E9abaMwaFdcK7vpiJ3n5l8PNWifLsiRp8grJvtOllnrOrhsrjxaRp7/b35xiNStsLs95jzs93kbXWbvPLgSH29vofr1WYTKZLvod/Pnnn4lWqyWLFi0iJ06cIOPGjSOhoaGktLS0zvMXL15MgoODSXFxMf2VlJRcVpmbAzaXhxwrqJHs+3xDlmR74i/pJKPYJ2epyUHyKm10m2VZUlhtl5TJKjETj5eh2wu2ZpOsEjPdzig2kZ7T10vKnKuwSrZPl1pIjc1Nt61ODzmSX023LU6P33vx4k+HiJdh6faqo0XkbLmv3lKTg3y9LVtSxuGWvo/7cioldSzecZb8ebiAbhfV2P3a/WVfnmT7ySX7yJbMMrq9KaOULNpxVnIt4uOEEJKeVy3ZPppfI+nnunDgXKVku+f09ZJ+dnkYyX3wMiypsDglZe78YidhRNdbG8cKasi3u3LOK8e8zadJmdlX79xNp8li0fV6GZaUmBx02+H2+vXhqqNFpFwk24FzVeSwqE8O5Vb5lZm99pRk++nv9pNNp+p+Z+tCQ9/DayOo3UTodDoEBwdLftcCbC4vzE6PZN/4Hw5Ktnu9uwEnikx0W6dRwuVlJedklVrO286cjaex4mhRvcctLq/fvuEfb5VsMyw57wqPuZV27DlbJdlX+3yTwwOnhzmvrA43A5f3/OdcCMcLTZK2y8xOpE1dS7eLahyYuUr64cT2b66RlHls0b7zXu/5jonb9TC+e3W0wOTXr7Wx/mQpLLWeCTFYlqDa5pbsO5xXDZb1yfPuipN46+8TF5RPDGsdz8CVxscff4xx48Zh7NixSEtLw/z582E0GrFo0aJ6yygUCsTGxtJfTEzMZZS4eXAotwa3zNlBt70Mi4/WZ8Fk9z0Xvx8qwOG8Grq9eNc5PPXtAbp9OL8G/d/fJKn3xk+2YVe279tbi3bm4PdDhXT7ZJEZFVZfGN/u9mLQrC2S93H4x1ux/Uw53V5+qAC3zd1Jt3PKbX7X82d6Ec5V+vaP//EQtmX56tiQUYZ3V/reRw/DosP/1kie6Xvm70auqI7vduciPd93/dll0nadHgav/n4UDrdP9g0ZZZJ3Z31GKZYdKKDbh/Nq8NiifZJ6bp+3U6KzHvpmD3476CtzosiEG0XvdI3djbu+3C3RDxVWF/Kq7HR76l/HMVX0fm7IKMXADzfTbUIIDuZWI7/aV+a+r3ZL+uzPw4X4evtZup1Xace9X+2W3KsP12RK+n3TqTIcLfDZkvUnS9B3xka6XWLiPJpiXTD+x0M4Vugrs3DHWby/2nevSs2+5wUA3F4WczadkfTZlsxyZJdZ0dz4V5OaawUmhwenRQTknX9O4oWfDkvOWX28RLJdYXVh9Oc+BadTq+DySEnNTZ9sO2+7H63PwoSlvna+3nYWz3zvU4D1EQ3xizlp2RF8sCaz3jaUCu761hwvpvtaTVmFohoH3b51zg78uDdPUq62gR4zbyfeEym4Lm+thbsWibsQbpmzA/vPVdPtylptVFhd+Grb2drFUCMyGluzytH7vQ31tlGbWNaFPjM2ote7vjoach3jvjuAAyLZAeCvdJ/h+e1ggUQBAsAdX+zCrXN9z0helV2iRP1lZ3Aw19eG2elBp2lr/c7bf67Kb9/lgtvtxsGDBzF8+HC6T6lUYvjw4di9e3e95axWK5KSkpCYmIjbb78dJ07UT+5cLhfMZrPkdzVAeGcEQlzOEw2xkQszalAleq5NDo8kbCAYEeEdFox7mcVnhKKCdJL3r9jkRJhRQ7cLqjk58ms9SyaH7z3Zk1OFnklhdLvaztXH8IREuIYykfFLDDegxOwLCRZU25EgCn0L7RWbpWFD8eBNr1FJ3sHscivUousv5PvwjMiY6tRKKh8AMAyBVu0zjZU2TkahrwSCkFHsey50GhXEw5nVx0okA9PcSk52G1+H8M5X26XnmEV9WFTjkFyLcL/FZTKKzRJyYXJ4EBHg+25ZZqkF6Xk1qLBy12d3c8REPEByeRnoNL7rzSyxIjLQV0exyQmFAiit1e/lomem3OJCeIDvw9Mmh1S3Zpdz/S08OwCQEGrwq7M5cE2RGqvVivT0dPrdp5ycHKSnpyMvL+/8Ba9yLN2bh7u+3EW3bW4vSkxOeBmpsRMUkfBi3twplh7TqZUSNi6cezhPagjPh+NFJqw9UUrbrc/YekUjpRCjBkt25dRbpxCG3yoaTQDAGhFJs7sZPy9E9+nrJd6MzFILHTEAgNnplShvq8t7Xm+PoEzFfVSbOClQd8zYUateQUHUBYvT37Px+cbTqLRKRy5iAyAoGtqem8HSvXn477Ijkv21vXcv/pxO/z9dZkFEoE+pCP19srh+g/y/P49j9TEf2cyvckieQy/j73X6dEMW7pnvIw/T/jqOr+sggpcKFRUVYBjGz9MSExODkpKSOsu0b98eixYtwl9//YUffvgBLMuif//+KCgoqPP8mTNnIiQkhP6uliThghoHuiWGUmNSaXUjPkQvIRcxwXqUiQxFldUteS4sTi+SIox0JF1td6N9TJDEQIUYNJJnrdLqRmSgjnpIKq1uRAfp6HvAsAQRAVoJ+Xd5GAkRMjk8CNSpKWkRjLe4nTCjFjUiclHj8CAswFfHsUITEsMN9L11uBnEBuuRLfICxQTrJESp0sbJLujDgmoHWkcGSEhMmFErIQoWlwdBel8OTrXNjdZRAdRbVW3zIDJQi1MlPjKVGGZAochgV9rciA7S0+1yiwsGjQoVfD/XONxIjjBKdJBBq4JTNDCtsrmRHBlAdWOJyYmIAC3VHW4vi9gQvV+fhYr6vcrmQlKEkdZRaXVL6gCAUIN0u8rmQqTomSk1O9E+Joie42FYRAfpJM9ZkF4jsRcmB9eHwjNjcnjQIswgIeBRQToJmW4uXFOk5sCBA+jevTu6d+8OAJg4cSK6d++OqVOnXnZZWJZQ9tlYnC614L6vfIbhl/150Kh8t4JhCU6VWCReBQBYzruEi3lSI450cKTG91AJL4fYrTztr+OS+q5rHY5OCb6QnMDwBWVVm9QIiqHtG6vpPi9D0DrS90HRb3edQ8epa7DzTAUAH1EQe5G0aiXeWXGSbidHGPHphtOoDYGkCC/GupOlkuNil/j9C3bj3ZUnUR8EgiTiY3jwm72Sc9yMtL3actSFORtP48/DPo9J7XDN5xtP4+P1WdQlzrD+RKE2acqrsuP1P45J3Nmc7L6yz34vDUcW1TjRIsxItyf+kg5A+oysO1kqqWNzZhn+TC+k1yvc7wXbsiXbYnJZ+z5lFFtwXBQGvRrRr18/PProo+jWrRsGDRqE5cuXIyoqCl999VWd50+ZMgUmk4n+8vPzL7PEdaPM7ES7mEBKBKpsbnRKCEERT/YZlnCkRmQoPAwLncjrYHV50S4mCEUmToeYnR4khhsk5EKrUkruOWcYA2g4usbuRqvIAGpMTQ4PkkXbdcHk8CAlOpCSJ8HIiT0ToUYtqm2+bZPdg1CDz7hWWt1IifJdf5nFic4tQiTGVaVUSDzJFv76BINcZnaibUwgJTVOD4OYWsTA7WWhFeljs9OLFmFG2m5BtR2dEkIoiSOEIMyolXgnqm1uhAVoqSzlVhdS44Ko18fi9KJlhJRccZrSJ7vZ4UHLcCPMTqHfPWgZYaTXwhGjAAmZZFkClSihu8rm4UmNl78Wod99ekqnVvoRkmCDjxhVWF1oFRlA2622udEhLlgSYlIqpLqmxu5Bm6hA6g23OL1oHRWISt62EEIQoFPD5rq4lIK60GhSs2rVKjz11FN49dVXcerUKcmx6upqDB06tNmEq43BgweDEOL3W7JkySVrsz6cLDZj2Efnz4OoDzd+sg17czgXvtPD4FylHV0TQ+nxugwfAOzjywhHvazvQfwzvUhCagqq7ejRMpR6SpweBt/uzpXUF6hTIzbYN5oQRlaCS9Bdy1MkHkUIhrDG4UGI6AWY9vcJ2NwM7Lyb9T0+R0Xw7jjcjB9ZCjVq0S7G/0vrgrE/LIqRAz5yJR5dHi80S17mL7acQfJrK3GI91QJfSMOe9WGcI6T9+YI90FMOoanRmNYh2i6/dH6LLzEEwiAy4cS/92cWQbA58ERyM2Q9lG0jNXlhXhiQX25M+J+W3NC6pVwM6yE5NU3U2FLps9jplEpsfZEKXXnu7wM7u+diPwqro8EMlc7rCWGUilVZpcakZGRUKlUKC2VEtzS0lLExsbWU0oKjUaD7t2748yZM3Uev1pz8SwuL+JCDPRZqrZLR/I2txfhAVoJIQG4Z0HYZ3F60DoqgHozLLzBFo/UAek95c4xSLwryREB1LtRZXPx3o/6c75MDs7ICe+s2elFYpjPYLu9LIwalUSnubwMQo0aUciEJxe8QS41u5AWF3xez6nZ4UXryED6bthcXrQMN1IiUGP31CG7AhoRsbM4PUgI9fV7scmJ9rFBlOTY3QyMOunsKi/LItSgoeGmMrMLqXHBKLdwslqdXrQMN1BSU1cuXm3iV213IyncSLetTi8Swgx+904MSkh5WX196CNktSe1WV1ehBo09PqtLun9r+DJZbWExCok4bcahwftYgKpLRH6UKjD7mYQoFMDaH7l0ShSs3TpUtx2220oKSnB7t270b17d/z444/0uNvtxtatTTP01xoEI107rnwh1CYsLg+Lbomh2HSqDGfKOHemMJoWQiWCYU4M5+LLtT0JwgtxRJwgV25DalwwPF7umJAgWmwSG3Xp08wSYHD7KPogWmuFUsSjOYHw2FxeBOh805eF+LVBI53SLLwg4li2WPkG6NR+L6fTzR0Xj+YYkYds7JL9NJ7eITYI3+7OpWGeD/k8n4O8t8vDsOjXOgKZIpfx4PZREoLi4cMtAiFr8/oqAJAkHl4IFqcXWrWSJuK1DOe8JwLxEbiGmHRU29wIM/pGpFW1wmKsiBAK19IqMkByDiGcd08gIuIYeX0QlFmVyDMXHaSj3iaB5BWb/OPegox6jcrP03QpodVq0bNnT2zc6EtkZFkWGzduRL9+/RpUB8MwOHbsGOLimm+66eWAy8MgMkhH38tKqxuJ4Ua6bXF6Eajzn7ocrNf4DKHLi6Rwn1eltrGpDyEGDX0/OS+DkRq1ajvnDaj9/ioVChrKNvMGWiD7tQ22xelBsMFf9shAHR3dm50eJIT5vEo1djdiQ/T0mRcTA+F/i9ODpEgjJT5WlxeJ4T7ZaxxuxIXoYZd4WAkC9WpJv0pl9aJFqIESBaur7n4P0qvpORVWF9qIvExWF0fqhPfI5WWhr7UMhLUWiTU5PGgZ4fOYcORDK/G+ApwnXLAdtT01tYmQjZIL0dUTINSokZSJFz0jlTYXWoQZaL8zLIFSIbUmJrvgvfPJmhCqp3VW2bgw2KVAo0jNrFmz8PHHH2PFihXYvn07vv32WzzzzDNYuHDhJRHuaoBgUFpNWUmN8LID+WD4EcXaE3XH8etD7fwJN8OifYx03r1AmATPyE98Eq2NGjXpQ2x3M+iTHC4x0CeKTIgM1FGZhQTQfjPFMx+k9XyyIQs9W4bRF/5uPndCCJWJFZ9g8JQKqfK6tWs8XhreVkJYhqfGICqIC21p1NyjP7h9FCUPAMHhvBq8y4ek5m3mRtDP8jO+xGTK6WEkHqk9ZytBCKHu0vxqqSdGzxt3t5dFqFEjCSWplQrJKEXwhLz9jy+MFahT440/pGE7po5RVR6fCPj5xtNwe1maxB0TrMeEISn0vLrW+pi9LguVNje+3XUOgD+pySy1oFtiKN7i5aq2u9E5IURyjiDSbj7cODw1BvMf7oFbu8b7tecrxP2p4F3i3+3JhZcl1PAIijFYlF8wrEM0urcMpcRSpVCgzOyUzMC51Jg4cSK+/vprfPvtt8jIyMD48eNhs9noWjSPPvoopkyZQs9/5513sG7dOpw9exaHDh3Cww8/jNzcXDz11FOXTebmQrBe7TPqDg8SwwyUhFqdXkkuiAAuR8ZnoOND9bQOi9OL6GAdJcxODwM9PyARk4QQg4YaKDNv5IV7bnV6EWLQUF3J/VVwxEBEYuJD9bDx+s/s8CAx3BfSMTu9CNb7PL4CIgK0ohCGlIBZXdLrtbkZGLVqSbIwSwjCjVpRGQYtwgwST404B0VAoE4tCdlwfcb3s8uDeJHnxlJPvwcbNNSrxIX5jBJiFBagpXlrFqcXQTo11EpfKIgQ/t45fLImifrM6vQiUK+m775wv4L1GonsYmJkcfF9KPS7w1O37CIiXNubZ6qVt2N1cXKoVQoqu8vLIDJQB6vLV0e8qN3Kq4XUnD59Grfeeivdvvfee/HPP//gpZdewvz585tduLowb948JCcnQ6/Xo2/fvti3b9+FCzUSdreXvrCtX1+FHtPXgxCg3ZurcbLIjEm/HaVZ3GKWbHJ4JIpA8LwAQPJrKwEAR/Kl+QcehqWZ9ip+6ecTRWYE6tTUqOg1KrSOCqB5KcLIeENGGTwMC4vTi5SYQMlDNWfTGUQGauHhydft3eIl2ekMyy2mpVAowLCEluNGF1LiJYTKTBJSw/BeJwV0GhWcQujGwyAqSEdlH54ajQ/v7uKbOeBhMfHGdogPNcDhZkAIESXxcnXMWst5WYQkV7PTi8Vje+PeXi3g9DAY/fkO9EkOB8C99DY3gxg+jFbFG+ihPMHT8Qraw7AI1mvw835pfkRWqZUq43HfcTO/AkQjpnkP9cDwVK4u4d5GBepQZpF6L77YwhGx6GAdfh/fDx1iOaLqcDO4r3ci7RuXRxhBuSkRjAzU4tau8bQfas/Ksji9EgXg8rAID9Di+pRIKrvAlfac5UiN28uidVSgZObH8NRo3JQWQ9sV8riEkfDKo8U4VWKhz7Rwz+JCpIsvjuwYC0K4Pt14qgxHCkx4f410KvylxH333YfZs2dj6tSp6NatG9LT07FmzRqaPJyXl4fiYl8CdHV1NcaNG4fU1FSMGjUKZrMZu3btugZXF1dI3k+r04vYED3NdbG6PAisx7gK767DzSA6SC8ytl5J/gSXT6GGQauigw6FgqvDF8LwSLwdglETnkGb24tAnUpiXE0OD2JDDLDzORSCp8Yk8tQE1UFqQoxS48p5aoTrlXpIBG+PmJAAnBdYIFNWl9TIW2uRKR8xUMPi8iXkRgbqJCGcmGA9Jf8WpwdBF/DU2FwM4kL0Es+UuIxwLcEGXxkA3LbLR2paRoiIkYsjQkK/u7ws9BoVgg1qSXhJ7HWh5EJEtoL1GmoHfLJrJF4l8b2y1vIIUtn1vmeEECCoFjEUE9JqmxuhVwOpCQ4O9otlDxkyBCtWrMCkSZMwZ86cZhWuNn755RdMnDgR06ZNw6FDh9C1a1eMGDECZWVlzdrOmTIr3lt1kj7gwsiZEF++hTDzRHBIfLbhNLq+vY4m5n6z/SyGf7wNBdXS8NTB3Grc3i2eelW2ny6n7n2GZUEIQbnFhQ/u6kK9CjUODxY80osSBYHUdEoIht3FwMpn62tUSknCW8uIABp+Ygnw5UM9MKozl3dQVONAYbUDgfwL73Az6Nc6Aj2TwumDNzxVOsPE7PRgys0dAHDhrBq7G2FGDZLCjdQT5GFYBOk1kvwbvUZJZXZ5ucTFAK0KdrcXu7MrsZnP86gvIdfs4F48Ax/qUCqAqGAd3rm9IwwaFap51j/nge7UzaxUAF881AN7+TVy3F6WurdZlsDk8MDDEEQF6ZBXZafk4Nau8WjHe86Gp0ZjUDtf7ku13QOdWoXEcCOKapwghKAXP21VkN3hZtC1RShahBnovRLcuwXVDjg8DP4zuA0O5dVgFT8DrGuLUNzbqwUNTXEJoL48DrvbC49oFCxMwdxxpgI/7ssDIQQbMsowPDWGjowFEid4BlkRAXUzLCqtLmTyoTvxzKyYYN9UTmGmSlwoRxhNdg+sLi/NN7C7GLTmw2CXM68GACZMmIDc3Fy4XC7s3bsXffv2pce2bNkiybP75JNP6LklJSVYuXIlnWxwrSFQp6H32ObyIiJAR4my2enliQGXLOthWKiUCsloH4DEcHLvls8wmvkkUW5GkE/viYmCEH4RDKPN5UWAtpaR06sRJPIqeRgWYUYNJRcmhzRPxezwItighk6jooMd7nrVkmc6KlBXr6fG7OCuX+wh8tXB8LJyAyDBg2B1eSXhF5eXhU6jQmCtwR3nufBdf4hBQ5954Xo1Ki7s4+X7PUhE6gAi8ZgJZQTYeDmC9b5zAJ5c8O3WONySMJ/gqRH3ewBPJqV1qCWELDxAS3OXLDypFXtmAPDEyOexjQ7SSUJJElLjFAiZv+xWl5RM+TxGXuoBbsjaXo1Bo0hNnz59sHr1ar/9gwYNwj///INPP/20ueSqE01ZdKsp6NIiFL8eKJAswCTgbK1FpP5KL8QLPx3GJxuyAHBkYc7G03TRqJs/207zPgjhYrX39fJNEZ38+zFsyOCI4rzN2TTEFB6gpQ+A3cU9iDSR1c1gxh2dEaTT4I4vdnLKTKfGmTIrfj/EzWbpmRSGlOhA+vC6vRzZELw9X2w5g5PFnEfI6vTi5V/TsftsJUKNGizdJ3gzCCaP7IAb2kYC4JRGNG/0MootqLC6ERmkQ1p8sCRp16hRUeUFAHq1ihr9n/flweTwwKBVw+5mwBCCt27lRsyCt6dDbBC2ThpM16iwOLkXIDpYj1KzEw/0aYk3R6ciUKeGhyGotrsRatQgLkQvSZZVKrjFyACOGIbzs7vsHgYjP92GrVnluL93InacqaB9e2uXOL8EaQEbTpaiRbgBBg13PZ9sOI0DPJkL5XNiWEKgFs2ccHgYGDQqjOkWD4DA6WGRHBGAsQOSEaxX00Q9o1ZN732VzY0fn7qOkkqnh8GNqdFoHRkAN8PC6WGhU6swqnMsTUbskxyOWXd3oWTS7WVh1Kmoe9vp5T7doFYqYHV5UW3npgMvHtubKslB7aLwzu2dIETHi2qc+OXp66DmZ5R8vD4Te3OqoFEr4WZYuLwM+rYOp+1dTjTWY7ts2TJ06NABer0enTt3xqpVqy6TpM0LsYGyuqVG3crrgQCdCjY3w4ejNJyB4ssIXhdxOCpI7zPQJj7xP8zIhZvcXhZqlYL3XPg8E2Eiw+jvMfEiUKfx8/oa+Xce4AZI4QFaOpgw856aMKMG1XY3JRcBOrUkty9Y7EFwSgkJ5+1R87NqRKRGlB9jdXGEhIZ9RF4mliV0ABWk10ja5YiSjxgEijwk1KjzJMbmYniC4ut3QAhH+WQXe6aEEJb4HIWC9xhRAupFZICOvmucR0wNrVoJp4ehxChIr5YQFDG5EnSpcL+F8BNHdL2i+y2d1i/OqRKuT4DgIaxNnsXEsDaZEvrMoFU3e05eo0jNyy+/DL1eX+exwYMH459//sGjjz7aLILVRlMW3brYRbRe/f2o377aa4dklVqwN8c3bZoQbkaMAIvTixGfcovguRmWsuvaqRUPX9cSfxwuRLXNjcf6JSEyUIsjolUe9aIVgx0eLi68+2wlzlbYqGL63y1pdGp3bIgeGpVv1oPLy0CvUdJ8nZbhARjcPgobMkrx+OJ9dFG2IL0aFVYXVTbjB7ehU0JNImKQHGFEhdWFyEAdQo0aiYcoutZaEUqlgk6lXn64EPtyqhAZqEWF1QWXh4VWrYJeo4SBz39JCDUgKSIAqXGcx8Ts5EaPCaEG6u0waFTQqpVwMwyq+ETbsAAtTXoFpPkrxSYnYkM42a1OL/WO3dYtHutPltIXK9igkcS0xXAxLK5rFQG9hlMiFVYXZtzRGZ/e180vHEXLeBjo1Eq0DDfC5WVRYnIgPECLtLhgONwMzQMI0Kkk7mxuFONLXA4L0KJrYihcXhZFNQ5olArc0iUeWaUWmB0etIsN5FeV9uVdaVVKX0jAxSBAq8Ifhwsx7ruDqLZ78Ei/ZLSLCaKKR6NS8CEpYT0SF6KCdLz7nkGryAB890QfaFUKeLw+cnVTWoxksbJLjcZ6bHft2oUHHngATz75JA4fPowxY8ZgzJgxOH78eJ3nX70gEqIgPFsCBGPLeVU81JMhNkgAEKgVGxtfTgUhnAdTMOoWpxd2t2AofaEFhiWSJSgEI6dRcfkggoGWeiogecbNtWZNCs+8UEbwIAXp1JKVzcWGsi4yFazX0LCH4AEI0PpIDiHSbxgJRNCoVcPuYfjcHo4YCGEfgNOLVtH7KfaQCPpXuDcWFxda8vOYiK5FkF2hUIBlCd0WvFuC7EGSOgiUItktvC0RyIQvhCUlJLVlr02maJ+5PPx90EjIFQCoVUoalhZIjBCyEvKBgvXcZA/BUyX2sjncXhi1KggDJi66oKGD6uZEozTRoEGDJAl4tTFkyBAsXrz4ooWqC01ZdOtyLKLFEumS0FuyOMUqHkEJxtHpZvHJhiy6KJPY7Sasx8AlYWkREajDP0eKYHd7wRLOQP1zhPukwdECk+SDiQLrDTNqUWVzwykYfZUSHoab9r79dAV0GhU0Km7UvSu7ApNHdkCxyYmsUitu7RKP+Q/3oEqiLvY87e8T1GVo4A1kZKAW4QFaSXJrLO9R4cA9xEoRwWgTFYiYYD1KTE64+XU0tk0agvAALcxOj182vjB6El4Sl4eLHWtUSni8BDV2buRo1Ipn4igwomMsDfPtz6lCTLAeA9tF4bqZGxEfwv2vU3PeC8GTFKRX1+t12HWmAkatCnreU6MAMKRDFMZ0T4Dby/JLDEjLEMKROi2/FsRb/5xEqJFTVON/PMS7+9XQq6UroYoJ2dfbcxARoIOWNxpPf38QfxwuRKXVhTmbzlBDpBPVYXd7eYPH1TN302maYH623IpyC0dYxCP/2qi0uRERyI0M/zhUQI2Vhn+u5mw6DaVCgXkP9fBLbr6UaKzH9rPPPsPIkSMxadIkpKamYvr06ejRowfmzp172WSuPWMR8F8wcvmhAnovjhWYsCu7gpbziEIaYmPDPSdiQ+EjBnT0LwphCM+jQFwFgywk15qdHNkQngubm+GIUh25dgJsbs64Cu+nQJRqP1tGrZrmadVeC4WGjvg6hFwPsZcFkK5DwxESDZ2ybubbFcJPwswesXH19ZtwDzw0VGZxeugAqnZeTpDOR9BqkzqLS+plEYffxNevFMlu5u9NoI7zagtyBFMyydD8GF8d0pGwlc+pEUiMnR8giUNWAEdixWEg8aBa0mdOL82xqu1lkrbLPRNBPCERrleQQ6ijtqdOrNN83i21hPg1B5r86VKWZXHmzBmUlZWBZaVGYODAgRctWHNgypQpmDhxIt0WvvLZGAxsFyX5tsaFbsKqYxzBqms1VrvHi2EdohEbogchvtlMgG9Gzd6cKmjVSoTyL/ySXeewObOchjTKzE78fqgAY7rH4/H+yViy6xwsvCs3PECLKrsbNrcXBo0KRi33Qri8LE6VWKBXK6nh06iUSI0LxuLHe2PHmQp4WRaJ4Ubajt0tJjW+l1GrVuL2bvH4K50jWHd0T6ArcgrnRATqRGtHEMnfO3sk4PVRqcgoMSO30oZqO5cwyCkiBuUWF2JDBNLHlXR6WOg1Ss5jUOHlckrUSmjVXK6O1eVF25hAGDVq/Lg3Dw/1TZL0OyEE3+/JxaP9kuDgw2IjOsVibP9W9ByBDOnUKhp+Et7BnAobik0OrD5egmcGteFJDUvJhIBKfsEtAIgN0eN0qYXWIZCaASkR6JkUhn38pwb25lTC5PBCp+GOV9nc2JDBEWNhVllCqAF9W4Vj1bFiagTbRAfinl6JWHawAFmllv9j77vDq6jy99/be0luekgPJfSOoAIKCmJDXdcVXbHhV112VVwVXLur2Muqu9gA0XVti7gKoogg0ntPQnpvNze5vd/5/TFzzszcFCAkFH/3fZ55YG6mnGnn8573Uw7yk/SQSdlRX7snAK1SLupEPtpWhXf/OAb/3VsLl5+tWD0g2cB1eGF4AiGa9UJARs/7a9rx/eFG3DU5Fwa1AgqZFKFIBF/uqcUfz8uCQjCK62sQxVY4uDqeYrtt2zZRPwAAM2bMwKpVqzrd3u/3w+/nByono/BWt3pw1Tub4fAG8fRVQ3Cw1o5rRqdjzvs78MfzsjBzaAr+u7cWh2rtKOHKG1w6OBk3js/Egi8OoF+cBkPSjJDLpFh9kA12LnnuMt61IIgP4cEbed6YhhAKRyghcfmDndYk8QRC0Cn54FK7J4jcRD0kEnCulBBnKLseUVOFSM0bOUKuqlrdNClBq+Td0mRgwh+DD/J1+0PUTcYSkq6MKxtDQvZxcLE+UokELn+Qulb0arE7SnQMQdtFChFHLgKhCORSKRsXGBDfd6JUkGOQ+x4MS6FXKbjg6i7O6xfcM3+Iy8pSQiVn0Orywy0IvhUSlA5tFyhx7DFlMKjlqLC6KBEWkinyvAGWaJOYGhKrpREpNV2Q2ChVye0PIV6nggTsdBuE1HZGJimZ9oe62ebU0CPNePv27cjPz0dBQQEmT56MqVOn0uWiiy7q1QYS9KTo1qkU0fruzxcAAO68gDV8L/9uOG4/P0c0uojGXy7mU3f7xWnw/DXDRH93+8O0U1ErZbRE9tJbx9IR2bPfHYU3EKYyI0kVBkBJDACMzYrHHVzbyGjLrFWgzR3AvBV7UNvmgVIuRSjMUIJiUCsQYRg0O/zUCGXEa6CQSam6AwA3jM3gAkzZNhCX1vn5FgxJM+H+6QNomyx6JXU/rTnUCJlUAplUggqrSzQKPVBrx7EmJ+crl9LgvRfXFqHJ4YNGIcO3B+pRY/N0qHEDsB0I2Yd0km5/CE/SgGUl1Er2dS5scNDaOVKpBDd/uAOT8izon2ygI6xgOELTyyUSPtBXq5TB5Q+JVLT7pg+g0zpEGAYahQzLtlTAFwxzkiqL9YVNVKHLSWALjZEBulLGxqFoFDJIJBLcPTkPF/ZPwPKtVeifpIeSCzK0uvz43Zh+ANi4KqsrgJ8KmyCVSqBWSPHC92zBy7duHAW1QoaDtXaUtbhpNeH1Rc2wugI0hZ5gekESZgxJwc6/TcPk/mwtohSTio7aSfl0IRiGve8kXoiQHOJmACAYsXZe7K+30RPFtrGx8bQpvFe9sxntniBGZ8bh8W+OoLTFhTnvs5Wrd1S04r7P9mPl3jqUNLtw//T+ANhKz59sr8Lvx/ZDnFaJH440UUIz78IcrC9sokZQaKCEYBiGpkWTOAyWXPASPxnJR0MikdBRtt1LRtkKegw9Rxx4d4yE7hfhSgAQwsW6MMQxNcTIKWTSDnW6CDEgbdcRpYYjF8To+4LhDi7OcISNXxOex6iR0wGSkx5T1sFwklvIZhApqEJC+lJj1DE7K8VAkh14ZYq9h9FKDemvhCDuGD2nABE3GFFIyPOOnv5G+LxJkLNRzZIaYUwNIaR6VUd7RfvSQMf7TAK2SbYbSTAQwsO1XagICt1eZF2rlNFBZDTEZLJ3y0H0SKm5++67MXbsWKxevRqpqamdPvDehrDo1uzZswHwRbfmz5/f6+cjE3qNzorDDWMzcP3YDKw+2IAIw+Cpq4bQFG2Cyhcux65KG/7xM5vau+a+C6GQSfHo14cAsLJps9NH3TtH6uy4kKvUOjTdROuL3DA2A3MnZQMA7rggRxT4urnUSovXaZQyqhIQ1qtWyBAIRVDY4IBaYab7kQwYpVyK7w83orTZRSebIwaKjblhDXSSUYV91e206J9OJYPDF4RGwbZd6MdP0KtYdSMUwZ8+3YtRmex5y1rcovmdWpx+XPr6JkwvSIZSJmVHbdTfGqbv0K3LdmEhl2G1vqgZaw830uAynUqGLaVWmml1+bBUfDGgFjYPq5CQ0ubCOIFIhMGW0laalk2MsMsXEknIj686jL9eOgCpJjU+3VGNgckGqpjkJepQ0uTEpDwLRmWY4Q6E8T/ONSh89x/57yGM5q5fo5ChxeWnnXiEIZMJcu44qQQquRQpRhXumZoHfygCPxePQArrWfRK1LZ5uCBj4I4LcvHC94VINKhEo9ziRgeuGsEWkrtkcDKmv/YL7p6Sx/1VbESSDGrIpRI0OXxI4tLgfypsxu/GZFDy8lNhs2iyzOvH9MMz3x1FYYMTerUcSrmEqlmy0xdKc9pwKgrvjw9MhlwqpeUViBvDpFFQo1xj83Axb1LcP32AKDaEuDAZsH3G3uo2bC21IidB38EtS94lEoDLx2WwxiYcYRMTDCoFnP4QdT10BmIYifspwrCjeA8XL0P6FiFI7IonEIZWwRs5F/cOG1RyjiiJ42fE5yVuD+JilsHBtd2gVlC3j5NzNXUGYpCFQc/0mBrWLRtdZZnGsvhYtUfofko1qam6RY7Z3XkJidFz1xthGE4xYdUOEtAbDYmET9En7ieGYV1xJE6J9C+kwB0AqhS7uKwzotSwtbiU4nvWxfMmcTgkY86glqOuzQuFTEpJncMXgotLz++67XzslkImhUNArjrjBcRV6PLzsVpnNKaGoKSkBM8//zwKCgpgNptFoxqTyXT8A/QQxyu61ZtIMalR+cLl0KvkePF3wwEAlw9PxVNXDQEAzJ2YhRW3jxftMyrDjOH92OtXRPX2t0zMQkO7jxqOK4bzRdFkEgnvauGyVACwmS2CEUZ05XuTVoHpBUkiI76+iDXE145KB8CSH+FcMLefn4OSZhcdsZH0XJs7QOvYaJVyPLbqMI2TKEg14mi9A8RACg2qOaqzSubihUwahWgiPYJgOAIpF0Tm5j6G2VxbCYSXefcne+g59Co59lS14aYJmex2EgkUUgnaPEHEa5X0IyIjNAAd3CLXj2VVkFX766mRCYYZ7K1uR2GjExKJBJPyLHjyf0fw7Oyh9B61eQLQKlmVRaNga3h05nGRc7WGtEoZ6tq9lKCkmNRYxqlsBAzD++dVcin8XEdFnqVFp0Jtm5feb6VcilX76zE4Vaw4lre4aUVi8qyJ2hUMM8heuJoSNIAlVCRjjqCq1S3qADcWt9B3+PYLcjC9IAn7a9qhUcgQr+OrvPKxUqfH/dQTxTYlJeW0KbxJBjXidUoY1Qok6FnymWhQiVSGjHitqH8QGjyJhHUXkGBWsWuhcwPFG/WgOLaFe5dIgK7Lz9dGia5LQtwNNBtGYLCF6dr+UBhKTuHkA1LZAFbifnL6giJy1S2pEY7u1XLoVQrOlcTFmnFB+cKKw9FtJ64T0nYd57KKLiwXDEfo90liWUi2Io3l8RIjzwUs+zovTgeAZlkRtYf0aS5OqdJy/UR01XWh0kbOK06L7vi8henbZB8S9Gzinp2bxLpQotT1O0NqF1GVSSV2v+mUgnapO7/vQhKr42rs2L1BuAN8VhrDQKT2kOcb5GZC10cFgvcGekRqJkyY0OW8KX2J4xXdOp14+uqhmDwgEZsf4d1tcpkU903r32HbnY9OQ2a8Fg9+eYB+3H+dMZBOe6BTyTE+h02NdftDUHMdoEYpw0+FzVh26zgAfHG+aLQ4/aLRREGqEX+cmA2AVYF+OtpE2zWFm3OIqB8KmRQ7KlpR3OSkxpO4YIi6kWJUo6bNS2MAhEoNicFRyKQY3s+El69nCeDDMwfi81011Jjedj7bHjJ7tE4lR127F04fW7ZcCDIiJDViyD0jH0p+kp5uG2EYbCxqFgVOkxEa+3eI/p05hDdkSoErSrhOCvcR37NcKsEn26vptURPmgewMwiz94ObJkIpQ6PdR9s+a1gqV9Olc+Mvl0qw9kgj5i7dSTsii16Junae1JD7Lpzx/PlrhqHc6qZkmUyVoVF23JYgQa9Ci9NPSeDjVwzGsSaXKD7o6311ItcaCTKWSCTItmjxGpfhdzpUWiF6Mk3CxIkTRdsDwLp16054WoUzCUJqhO80AC7Wg733xCARgkxiSEiBNznn9hFOoxAdI0PcHsLMJQfnwhCnTfPH4LOqiMHiyZRRkA0UTWoiET62h5yXGGidSsaqLN4QLQrHMMIaPB3bbqBkKsQnFAjWCUhQPsATQQJ+H1Yx0ipkcHMuLGHfGuIGZQBoPJovEOZi/gh55F2FAEONPsAWAxUmBJC2uwSERBhsTNsuKNYXHYdCCAp5VkRlij6GsM8isTreABt2QPYh7xlxc7oELixS00zYdqdfkHXFvTMuf1g0YGJJjozfxxcC6QeN6q4D0HuKEyY1Bw8epMuf//xnPPjgg1i+fDn27Nkj+tvBgx3ToHsT3RXdOhPoF6elLgeADy4VIsmopsZfWF46EIrgvmn9oVbIcOmQFFw5Ig1uf5huS9SUNK5ei5IzmKRaLcC6Cr472EA7mlsnZdOCaAAQr1XinxvLMIdTN4jhJkFvSpkUx5pcovo7xHguvpYlKPE6Jb7ZV4cqLr5H1Un6bijCsNIn1wGo5TLs5dxEADB7pFiN0SplggwpHjdNyMRtXKwQUb1IZ06MrFApOtbkEgVcA2y2EIlXIkrNFcNZ94wwnZOMlsm3TozEnRfmsvdGLhVtp+5mPiWSMkuqBxvUcqzYViV63nXtXlp7B2DdhuTcQnJAnmWCXoWXfyim7rbO0qbjdUqEI0yHCSyjg36FSDerUSeY2HN8djy2lVk7jEiF9MsbCOMaTlEza5UYl826L8lpfyps5tS8vsfJTpNw3333Ye3atXj11VdRVFSEp556Crt37+4Tt3Vvg5IaL0nzBxfEy2cJRhs54vKKroUiVAGNagVaXD5acZu4I0IRNk2fpIV7AmKVQWjk+bRfkn7M7kNUFxJTEU1qhCP5Dpk6XKxHx9TjoKjtDl+Q9rUkYJWP9WBH/9FEUDgVg14VVYdGmHWl4QmJ8LwSCZvcQQy2sIYOiVMhbj5R/SB/CHquLxdW2eXPG4TLzz5PQpTauMKmnd336Do8VM3jgoBJ7JKw8q9aQSpE8woguYc0xqYTIii8fkI4CQhBIWqWVimDxy8mwiTgnBxDWJARENcQ6i2cMKkZOXIkRo0ahZEjR+K6665DYWEhbr/9dowbN070t3O1UuepYOW959P/S6KCqipfuBwA8HvO7RFNaoSGSiWXiupJECNO2PY/bx4DgK0dEw1ynPwkPa1mC4BOk5DEBY4S6Zhk+5BgWSGIqkHdIHoVAuEIJUTyTgIpxufE4fJhvAqi5irXrn9wCm2XEBKJhFaoJKh84XI8d80w+iHOGsYSEfKREcOvFJzf5g7g8Sv4cvfLbmNVLRJETT7+a0f3Ex1jdKaZEgESc5QdNUkkmfWbqC8keBxgiY5wOgNS9O4qbq4l4nYSduatUQG8OytssAnq+7xwLRtYTq6fkFmqAnUy83Z0QPBLnKuUyOw7Hp3WYZ+MeK0oBduglqPR4aOd5sMzB3bYp8rmFhElQriFqfpdzS7f2zjZaRImTZqETz/9FO+99x5GjBiBr776CqtWrcLQoUNPS3tPBcRACacz0KvkaHL4aaxDtGGgLo2okTpLNjhCopajxual31Z0NViWkITZ4nSCUbewvkx0/RtCJogxJd9aNKkRrgsLAwI8UXAI3E2k7cK2tnkC9Pi8O4b9vonRj3Y/CdPI9SpZh/o3wrgcer2C88qlbMV2Siaj1R5OIXNH3TO3PyxyHTmjjLpQZSNkqt0TgIkr86GUS9Hq9ouIgZAYmagrKaqgn19IJhRodPgoQSWuIvrsuLgroZpF7jtPasRkSpghpuYSICSSjpMci4hwVO0iYf2j3sIJBwpXVFT06ol/q1ArZB1iXwA2VRgALV4HsGX3hVJkSbOLznfE7sMpNpwxSeUCO8mxOoNOJUM/gTuHGBrSARDVgWT7dJZpNHtUGqwuPz2/RafEwVo7nr16SJfnvWuymGhplFJYBRk1OpUcu/42HXdy8ysBEMX6dIYCLnYkevZeIRGMdgXlJ7LkiaRWdxXkJ8TFg5JxoNaOPwkmngT4uagIQbAInt3oLDP+sZ53wU7Ks2BrWSu9z2S+JGFnzlY+5uOMdv5tGj02APxhfCYWrjxEO8A0bnqCr+5m3STk2BcN5KduGJZuolNXAEBWlCuPzIklvt4kvD2HH3xolTJEGJ7E3nFBDp3lnKDJ4RdN+UEIN0m/B4Bh/founi4a8+fP71Jp2bhxY4ffrr/+elx//fV93Kq+A+sWYRVa1kB5o2IsOo5+A+GIWDEQECOjRoHaNg9PLtRigkIJiWDCR6lEgjZPgCpGRrUcVpeQXLDqRygiHqzZvUE6aa9OJUd9u48nKFF1dwgxiDDooBiQuDGjmj2GXqD2kAQKgCeCzihC0ury0+slapaIGBF3m9CV7Q2iHze40HIuZerCijLyrNEPIxRmoiruhkREyOoK0AGCQTDthRBs7S0F3aa+3dulUmPkXFYyqYTuA7DPm0x7YlTL0dDuE6ldtW1eqhQTFYZhxH2mMC5Hr2TJNLmWrmrZCPdRK2RceAS3j0Zc7fiMpnRnZWXR5dNPP8X69etFv2VlZWH9+vX47LPPerWBBM899xwmTZoErVYLs9ncJ+foDZyXG49DT83o8u8JUcGzF+Qn0P9fNjQFL3MjbYAdUd98XiY10MSXG+1qEGLmkFRcN5p39dw0IQt7H7+ErhOSw09WJ8GAZLGKolXK8Zdp/ekHTzqCFteJF1jjOyD+A0k0qPDNn84XbffQjI6qQDSEH9nLvxuOCVz8EcBWwRVOa0DUDULe3r9lLMqfn9XhmEJR4bzceNw4PrPDNiSVUsEpNXGClGcFl6JN8Om880SuJQCYNSyFziNFzikcwZCg0miQ4xDy2j9JPIv73y7nlSmlXIr/m8ITymjlhmD1X3iVyaBWiALV1UoyelOIzivE9IIkkUKmVcoxOtNMg7Y3/nVqp+ftbdhsNtx0000wGo0wm82444474HK5ut1n6tSpdPJWstx9992npb29AZIRQ74lg1qOBrtP4MLpxKUR6BjkKx65y1Hb5qUGOtrYEAiJuJ4jE0aBUlPb5hG1q7P4CKEyY9awtWtMAnIljMvR0bpJ4nIJdi/vjiKEzCBSPzqe1+HjXXZmrQI1No8gpoYQBQFBCfCuJHrPBCqDViVDvd0bRYwEKpNSHKBNICIGKjka7ILzduF+afMISJxGjjoBIdGr5LB5AnRARGKmAqGI6Nt1+YOiGKr6di+Nj4lW5kjFYFIdGGBJbLsnKCJTNW0e2q7oitG07VyFd3peu5ceM5o8E3WvN9GjQOF3330XgwYN6vD7kCFD+my27kAggOuvvx733HNPnxy/t0Dkz86w4a9T6YgFAIqenUkDhAHg7il5uF4wL9TQdBP+Pltc6+b/JufStGkAmJhrEfleNUqZqBaFTCoRGU7iJnl0VgH9be6k7E7VJQKpVIJRmWZ4ol4+4urpDMS4Srs58NJbx+JKgXHtDDeMzRCpDdePzaCpyABr1MmknQB7vUefmSH6e3QbnrpyMF6/YSRdn5BrweJrxfe5X5xG1GlEIxiOQBF13C0LLxat//OmMR2CoDurEyJE0bMzYdHzxGTHo9NgEjzfyhcu7+DKEyI3UY/7p/fHyAyz6HddN+clal2HmBqBAnZh/0RcOoQPyNepWB89MQDRrru+wk033YQjR45g3bp1+O6777Bp0ybcddddx91v3rx5aGhooMtLL710Glrbe3D4xEa9QaBU6FUK2NwBSuTZTLqwaEoAiUQCu5c3cmSkLlRqOht1CwmJQSVHXTtPYkxaBapsHtr/RLtjaNuFpEarRLWAXBjUcjTaebeITCrh4uD42i4apQzNDr9IZahq9dB+zdDVeQWExKRVoFrgbtOr5Ki28Qa6q4rJwuwni06FihaekBFySf5OVGPh9BXEZSUkF9WtHpHR75wIBgTnUaCujSdCJg27LnQtdhbLaXMHRYp1vV18DJvb38kAmZ+KQc8RISEBq7F5qHLXVdutrgAtiWJQKziFiH93ra4AVfLIu9qb6FGdmsbGRqSmpnb4PTExUeTL7k08/fTTACCaffdcQ05Ux99dMGdXWCQgIwDw1FVDUNR44gGaIzPM+OL/JmJoOu8qYBhxbERnWHbruA7GPVHfuSoAAGkmTQflIhoXDzp+1tqLAuWqMwxMMSJeILkCxycOt56f0+3fAbbeCBn1dBbLkmxU05ifE8WvD18kctd0huh3ojP30fEgLI5I0N3cTMQYRpOaTAEhI7WTCIammaKqTvc9CgsLsXbtWuzatQtjx44FALz11luYNWsWXnnlFaSldU2QtVptlyncZzukUgmaHX7eHaGWo6jBQcmtWcu6X0gJBZIxJPykDSo5GgVGzahRoKbNA6OavSdqhUyU2UQQCjOC94NVSKYMYLMDjVzF4AIucUGn7IxcSGBzBygxj9MqsLPShuH9zPQYde1eagQ7QxxHhIRxOJWtbqowG9RytHsCHQy0LyAsj6HEniobLtOw15toUKKkyYlRmWzAu4abHVx4zzRKOZqdfqpMJRlV2FxixeA04hZnyVX0N01ie9jzqHC03oGJeRYAgEmjxNayVozgrj86cBZg+2IyTQJ7j+Qi91O8TonSFhfSBOeNLi+hV8lRaXVT4mfUyHGozo4RJPlCrUCNzUtjLTuDUa1AhdWNaznl36iWo6bNi7FcjbPOiuup5DJuwmIZbUdtmwfD+hm561egwe6lg6y+yJ7skVKTkZGBLVu2dPh9y5Yt3XYspxunOqHluYCBKQZcHZVZ1B00SplIHQLY3IXjvVtmrbJDhydMo46GVCrpoFz0BZbfOg43n5d1/A1PElqlnHaS0TWHAODqkel4+foRJ3XM6PokpxNd1QkRQihdH/v7ZXjgko7kiOD34zLwykle/6li27ZtMJvNlNAAwPTp0yGVSrFjx45u9/33v/+NhIQEDB06FIsWLYLH4+ly27Ot3zBrFGhx+enzMWkUKGtxUyKQoFehsNEhIgbR33O8ToniJpdI3RAqNYA4KJQ9hoRO3wKwI/W6dt5lZeKMuolTHWRSdloPISw6JRrsPtr2JKMah+scAmPLkhrReSE20ha9EmUtLj4eSK1AZaubqhB6Lk6nq8KCAEv8hMQo3azF4ToHVR0kEgmYqPNSFx133CSDGiXNLlE8UGWru4MLWXjvsyxa7Kluo88m2ciSHEJAVXK2fIaQkEW7dQwc8SPtsOhZQiZUdKOfXZZFh/017VQRMlGXHU+MK6xuJAhITTQxMpJt9CQuRyEiShKJBK1uPqAZ6Bj4bdYqsK+6nQ6QTBqWKMV14nbvLfSoh503bx7uv/9+LFu2DFVVVaiqqsLSpUvxwAMPYN68eb3dxh7jdExo+VuASaPooCKdCDoLMj7dkEolfV4rRSaV4NYopeJcwp0X5HTpEiW4NqoAolIuPe01aI6HxsZGJCUliX6Ty+WIj4/vcsoDAJgzZw4++eQTbNiwAYsWLcLHH3+Mm2++ucvtz7Z+w6JXwebmg+qTDGocqrMjyUhIjRLlLW7RNxxdmj/FpEazgy/+aVSzLithNiap6ktg1oqDeA1qOQ7XOWjcmlbJqjtC93f0FB3pcRrUtfHlA4alm1Bt89DYQrOGVZmE7ZBKJQgJgt4S9CpUWN3UqJs0rMpAjKucK44pbLtaIc5uMnPGlBTyTDSo0OjwUaMPdAzkN6jksHsCVKFOMqhQ2syTK4tOidJml8hAB8Pi8gqZ8VqRqyzZyBIjocpd3OgUhSVEZyYRVY0QsnidEiXNLhGZsrr8IlKbk6BDo8MnKOKoxOE6B9K5zFgpV+FeuI9cJkFAMGdhqlmDokanKOusuMmJVIFCVGl1IyeBV3RNGiXaBBmd+Yl6lDS7kGXR0es/WGsXXf/EPD6utDfQI1Lz0EMP4Y477sC9996L3Nxc5Obm4s9//jP+8pe/dDuLdzQWLlzYIYAveikqKupJEwGw5c7tdjtdampqenys3zKuHJ6K1X+58KT303aj1PzWQCpJn4t4TJDy3hVeE8QYnW70dT9w1113YcaMGRg2bBhuuukmrFixAl9//TXKyso63f5s6zcS9Eo02XlSQ0a9WRb2Xzk3Oa0woJxMA0CQbFSjv8BwEqVA6GKsb/eKCIlBLTauSQY1vMFwhxILwukToo1rulkjKn1A3KlEZZBKWVdLRhzfDl8wLIrxS9CrEBG4dMgxSIYhALRHzfrN19BhYdYqEQwztG3E2AvJ1L7qdgxN56tHJxpUqBTMvUfuDSF1hHwJY+s83GTCBMSYE4WWHCNLQEDbPUGR6qJRyETlLgxqdvoEsq9KLgPDQDRXmzcYFrmuybtBkBGvgcsfwgDBO+ILRkSJK+w8VPx5x2XH0+tkj6FFIBQRudvaPEFRPF2SQYVmB/+uDuBck+T6lVzZEuExSPmN3kKPYmokEglefPFFPP744ygsLIRGo0H//v2hUnXtn+sMDz74IG699dZut8nNze1JEwGw5c5Ptk3/P0IikdAMn5NBT2KCYoghGifaD6SkpKC5uVn0eygUgs1mO6l4GVKws7S0FHl5HWs+nW39xtSBiSJXb4pJjY1/nSpyFz52eQH6C7IYR2eaaRVogFVMhqTxBpu4IYTKxDtzRouy9S4pSBbFxeUn6TGJiw0heGjGQFw5gg85mJSXgNxE3sgN72fCgwI3plIuhUImEaVND0oxYLggsJ0UJCXITdSJCpwq5VI8fdUQZAsMd4pRLVIQRmWYRQpSfpIe/zc5V5Q08PLvhmNURhxdn3dhDi4ZzMf5XTYsFcu2VNJ1QoiECsmlg5NFLrwR/cyi0gZZFq2oBINCJsUTVwwW3VeNUjzh5uxR6aKs2OH9TPjuzxeIlLe8RJ2IgI7NjhcRtLwkvUhdTjKocc/UPFHSgUmjEB0jEI7Q2lzkHF/fO0m0PSCO83vl+hGYJFBachJ0InKZoFeh6NmZEOKqEWk98gycKCRMZ1O+nsVYvnw57r//frS3t5/0vg6HAyaTCXa7/aTmc4mhI55bfRSPzio461wUMZzdOJVvsLCwEIMHD8bu3bsxZgxbiPLHH3/EzJkzUVtbe8LxfFu2bMEFF1yAAwcOYPjw7gPRT7XNZxNC4Ygo2N8rCKQ91xEMRyCVSLotd9EbKG12Ij+qxMKp4vtDDciy6GgAck9AzPjJ9Mf+UBgKKZ8dWtTogAQSDEzp+vpO5Pqji8r2Fk70O+yRUnMmUF1dDZvNhurqaoTDYezfvx8AkJ+fD72+6xRXIciDP9OBf78F/PnCfnA6nWe6GTGcYyDfXk/GUgUFBZg5cybmzZuHJUuWIBgMYv78+fjDH/5ACU1dXR2mTZuGFStWYPz48SgrK8Onn36KWbNmwWKx4ODBg3jggQcwefLkEyI0wrb+FvuNYMeZSmLoBknq3n8Pzs9iVYsz8X4Jy5+maQGA6bYdJ3r9ffFanXDfwZwjmDt3Llu8IGrZsGHDCR+jpqam02PEltgSW07vUlNT06N+oLW1lbnxxhsZvV7PGI1G5rbbbmOcTif9e0VFBQPw/UJ1dTUzefJkJj4+nlGpVEx+fj7z0EMPMXa7PdZvxJbYcg4ux+s7zjn306kgEomgvr4eBoMh5jaJIYYzAIZh4HQ6kZaWBmkXs86fbYj1GzHEcOZxon3HOUVqNm3ahJdffhl79uxBQ0MDvv76a8yePftMNyuGGGKIIYYYYjgLcG4MlTi43W6MGDEC77zzzpluSgwxxBBDDDHEcJbhnAkUBoDLLrsMl1122ZluRgwxxBBDDDHEcBbinCI1p4qYbzyGGM4sYjE1McQQQ09wwn3HSaUenEUAwHz99dfdbuPz+Ri73U6Xo0ePnvHI7dgSW2JLz7OfCN5++20mKyuLUalUzPjx45kdO3Z0ue2yZcs6nF+lUp3wuWLZT7Eltpw9y/H6jt+0UrN48WI6u7cQNTU153QRrRhiOFfhcDiQkZEBg6HnBcw+//xzLFiwAEuWLMGECRPwxhtvYMaMGSguLu4wNxSB0WhEcXExXT8ZxYW0NdZvxBDDmcOJ9h2/aVKzaNEiLFiwgK6Tm2I0GmOdUwwxnEGcihvntddew7x583DbbbcBAJYsWYLVq1dj6dKlWLhwYZfnO5mpFDpra6zfiCGGM4/j9R3nhlO7h1CpVLQjinVIMcRw7iMQCGDPnj2YPn06/U0qlWL69OnYtm1bl/u5XC5kZWUhIyMDV199NY4cOXI6mhtDDDGcZpxTSo3L5UJpaSldr6iowP79+xEfH4/MzMwz2LIYYjh74PQFoVXK+3wenDMBq9WKcDiM5ORk0e/JycldzuQ9cOBALF26FMOHD4fdbscrr7yCSZMm4ciRI+jXr1+H7f1+P/x+voD8b3F6hBhi+K2iV5QahmEQDod741DdYvfu3Rg1ahRGjRoFAFiwYAFGjRqFJ554os/PHUMM5wqmvrwR3x6oP9PNOGswceJE3HLLLRg5ciSmTJmClStXIjExEe+++26n2y9evBgmk4kuGRkZp7nFMcQQQ09xUqQmFArhsccew5QpU/Dkk08CAF5++WXo9XpotVrMnTsXgUCgTxoKAFOnTgXDMB2W5cuX99k5TwVf7K7p83P8dLSp148ZiTCobvX0+nFPFV/srsHX+2rPdDO6RDjCnOkmAABa3QEEwpEu//6fndV4fk3haWxR7yEhIQEymQxNTeL3vqmp6YRjZhQKBUaNGiVSfYVYtGgR7HY7XWpq+v47jiGGGHoHJ0Vqnn76aXzwwQcYO3YsvvrqK9xzzz1466238N577+H999/H+vXr8cYbb/RRU889PPzVwV4/5ifbq2Bz88TxzhW7UWM7OQLi9oe6NcC1bV5MfnmD6LeyFtdJGW2GYVDX7j2pdh0P3x1swMtri4+/YTcIhSMIdWPwAaCu3Yv/HUfpcPlDeG71UdFveY+u6XYfuzd4Yo3sBUi7CabbV92GnRW2Uz5HdaunR7NtnwqUSiXGjBmD9evX098ikQjWr1+PiRMnntAxwuEwDh06hNTU1E7/HovFiyGGcxcnRWo+/fRTfPDBB3j11VfxzTff4L333sPLL7+Mm266Cbfccgtee+01fPzxx33V1nMeB2vbT3ofh09sCB9bdRhzl+4U/fblHrF6UdXqFq1Pe3WjiAjd/OEOPP1t14GS/lBHV+J7v5SjWkCedlfasGjlIdE2bn+I/r/B7sP5L/yMQKh7AhGNZocPDXYvbYfdw19/JMKg3u4TkSuXPwRfsHvXp/Dvi78vwl+/PNDt9nur2vCX/+zrdpsmhw/v/1pB1zsz7o12n+j3EU//KFoPhCJY/D2vmLy0tgg1Ng9aXXw8xx8/3CE6JsMwcAnu856qtk5JmqybLzsQikAl5zf4Zn8dSptddN0bCOPLKJUxGI6I7vurPxZj8ssbYHX1nTLbFRYsWID3338fH330EQoLC3HPPffA7XbTbKhbbrkFixYtots/88wz+PHHH1FeXo69e/fi5ptvRlVVFe68887T3vYYYoihb3FSpKa+vh4jRowAAOTn50OpVNJ1ABg3bhyqqqp6t4VnGJFO1Amry39c1SLayIUjDK56e4vot8N1dkoMGIbB+S/83OG4w5/6EVtKraLf6jkFhBAVhmFE+015eSNKmpx0vazFLSIqxxqdWLGNf04Mw2DmG5vo8bydkARfKAybmze29XYf/rOzmq7//t1tGPLkD9hX3QYAaHGy2xJS1uz0od1zfAM4/vn1VOH64NcK/P5dPqMlwt3TNsFxrnp7MwY9vrZbxWDQ42vR5PABYNWSmjavyGhnL1yN7IWr6XooEqFtFuJATTv9P7nf5Jo6c/ect3g95q3YI/qtXUDS7N4g3v2lnK7/c2MZrvnnVoz5+0/0t19LxM/+QK0dQ5/8ga5f96+tePirgyhqZINZyX0gSo3dG8QxwbsAAP5QBDsESs19n+3H2z+X0PUGuxcPRamMV761WaRErePcnp4AS7D6wg3aFW644Qa88soreOKJJzBy5Ejs378fa9eupcHD1dXVaGhooNu3tbVh3rx5KCgowKxZs+BwOLB161YMHjz4tLU5ht8G+kKZDIYjndqZ041whDluO063MtsTnBSpMZlMaG9vp+ujR48WFcLx+/19Xkb8nXfeQXZ2NtRqNSZMmICdO3cef6dTQO6ja6ihJpj0ws9Yc6iBKgqdwR+lUBCi4OSM/D83luKKtzZTYhAMs+6aS1//hboGSEzOLZwyQ0hHqzuAy978Fcu3sErBV3tq8bslW9Fg91KF4e5PWGNKjiVUHtQKGQD+Bf3Tp3tR1Oik11PVSTyNLxgWjcqJ+vEZ135ynhD3UfxyrAUpRjWcPtbo3fvJXlzzz610f4cviAa7V/SRkA+K/OQLhlHc5IQ3wJ7Lzf079u8/YVcle74haSYAwKcCguULhul9JihpYpUIlVyKPVVtHYw2AHpMXzCCC/snoLCBJQOtLj8mPP8Trn6HJ6X/WM+SAHJ9RJHaUNQMgCc9PxWKjX2zkyeG28tbAUBE9qwClYZAeI8cnAtLqD6t3FeHmW/8CgBUxbnvs/0AgP/uqcWlr28SxSKRbe7/jH0nzs+34IcjTbTN72wo69CGokZnp+vk+u9csbvDPn2J+fPno6qqCn6/Hzt27MCECRPo3zZu3CiKs3v99dfpto2NjVi9ejVNNjgXsLvShtfXHaPr3kAY/1hfIjJAH2+rRJtAjf3g13J88CtPmPfXtOO1H3nXbSgcwZAn1oqU1H9tLMPuSp7sfnewXqQkljY78X8fi5/z+S/8LHJ/P/3tEXx/iCeUh+vseOZb3k0biTC4+JWNInXxzo92iQZh3+yvw+qD/DEKGxy4ffku0TEWfLFf9I3f99k+fLO/jq6v2ldHv1EAqLF5cNeK3aJv6Zlvj4r68Ce+OYxiwXu+r7pNpJR6AiHkLFojGiD+6d97sVkw8HjzpxJ8vovvi9o9Ady6jLdRkQiDe/+9hw4GAOCaf27BZ7v4QdaWUis+2lpJ1+vbvXjh+yJR23/3r604Ws9n5T397RFRckCz04cHPt9P133BMP7v490ICu779Uu2ip73Kz8W40GBiu3wBfHiWj6jMBCKIGfRGpEi/9LaIpQ28/ds07EW3PkR/474Qx1V3/s+20cHYUDvE6WTIjWDBw/G3r176fqWLVuQnp5O1w8dOoT+/fv3XuuiQCqJPvnkk9i7dy9GjBiBGTNmoLm5uVfPU9LkxNi/r6M3+5p/bsWgx7/HopUHcazJiUAogl2VNkxc/DMqrG48vuowDta24waBqkDUlM0lVoQjDB1dD3vqRwDAS1GxIaSzKGtxU6JDFAtibBZ8sZ9uX9jggIyb/6LB7kObO4ANRS00FqSV6+B2VrCGc1clS8w8gRAlNQ4v+3KuOdQIgCdNf+YI0JF6O3cMG7aWtYpcWB7uxV648hBVQQBeiWh2+jApz4JKzhUWijCosLppXMmCz/dj4uKfkbNoDT7cXIFwhMHaI430el3+EN76mQ3kLOY6PKFScv0S9l4TV83W0lYwDANPIISnvz2Kez7h31MANDBWJZfR33440ij6oA7XsdfrC4aRbdHR9TF//wlNDvY8xY1OhCMMvuM6XXI9xDjcxnW+l7z+Cz2uPxSmqo9Q/SH3ubat+9ijshb2Hh6pt1OCW97i7nRbQmajIXzfiPqzn7ufGoUM3mAY+2vYd+S/e1kC9If3tuGLXTUdAt6FLlGHIE7oeG7A3sTJDm6+/PJLDBo0CGq1GsOGDcOaNd3HP51N+GZ/Pf53oJ6+q9vKrfjg13Ls455XmzuAJb+U0+cGsIMKocK7vrAJ3x1qoIS2tMUFdyCMvYIB29f7arFKQAzWHm7E3qo2wTGt2FvdTpMIfMEw6tq92Cw4z+7KNqwWkJr/7q0VfWfbK1pRbnXTb9rpC2J/TTvtg9h21OEzATH45VgL9lW3USJQ1uLCmkMN2F7ODaTCERQ3OukgAQBWH2rApmMtdH1nhQ27q9rogM3mDmDZ1gpsLW0VbfPOBj54fNW+OlS2uikR2FPVBpNGQfsFhy+I9UVNOMz1kwCwu8qGtYf5a/n+cCN+LbHSgUtxk5P+BnDuZF9I1Ld9d7Aen2znlfQ9VW1YtqWCuv9t7gAqrG7sqODbvrnEKrr+b/bV45v9dZQ87qlqw6FaOzYWs/ckHGFQ2uzCFsH1H2t0igZVn+6oxrItFfSdOVDbDotOKTrPqn11+GQ7/6zWFzaJSM7aw4146KuDNL6y1eXHtrJW/HCYH+x11Wf1FCdFapYsWYLJkyd3+fdgMIiHH374lBvVFYSVRAcPHowlS5ZAq9Vi6dKlvXoehy8IqyuAbYKH5wtG8J+dNbj09U0AeGN20Ssb8fH2Klz19hbsqLBRo7Wvuh0AG78idNNolbJOmemG4hbIuboiQ9LYwMRpg/iS70s3V1DDScqPxOkU9O9D0k149Gs+xqV/kr7Ta2uw+3BeroX9v8OLHzki8fgVg2F1+UUjCPKy/v7dbXD6QpTUrDvahKcEo68fBa4H4hr6ZHs10uM0uG3ZLjTaffR+zV26E80OH34q5Ino3qo25D26Bvf+myUinkBINAq7ffkuUbsIIhGG1mJZfagBm0utGPzED/jPzmpsL28V3eejDQ4wDINEg4r+VtXqhsPHH3fx90Vocfrx9LdHkWhQ4eUfijsoJ8+vKRSNCG3uAL47WN/B/WRQK/DWjawa4PCGMP45NrD1juW7KUlNMaoxPjsejXafaIRI8M+NbAc7/bVfUNrspCQEAFoFrkC9ii031e4J4FCtHTsenQaAHeET4VSrZMnc1jIr+sVp2PvHiIPGr/uXuHjd9nIbjjU5RcYhEIpQ1+JfpvXHhuJm6oo62nB66rmc7OBm69atuPHGG3HHHXdg3759mD17NmbPno3Dhw+flvaeKuravTgvNx4NdrZvKWp0Yu6kbBysZY3poTo75kzIxB6OgITCEUreyTdQ1OjE5cNSqSJS2uwSrbv9IfRPMqChnSfdnkAYiQYV/fYqrC7cMDYDhdwou6zFhcuHp9J3t90TQP8kPTwBntxWt3oweUACGrmBz7ayVvz54nza9rIWN64ZlY6yFhdtr1QigVImpW0/VGfHtaP7oayZJfIlzS5cPyaDxiiy98dCBx4A2zckG9XUIBc1OjB7ZDpKuNixHeWt+L/JeZSQhMIRZMRrRSpEXbsP0wYlUzKxo9yGWyZmobiRPUZJkxNzJ2bjSD3v+lXIpJBKJJRMbDrWggWXDKDf7v6adjwwfQAliy1OPybkWNAi6GeaHH4MSDbQtpe1uHDliDQ6kDlY246bzsvCoTqeTPWL04gGRzsqWnHnhbn0eo81OfH7cRko5p5djc2Dy4alolDwzTIAFIL7XtzoxB0X5OAQ96zKW1y4cXwmJWB2bxBjs+NFsZa1bV4MSTfR/v5grR0LLhmAHZwtPdrgwG3n56C4yUHv+3HyNk4aJ0VqBgwYgJycnC7/PmfOHPz+978/5UZ1hp5UEvX7/XA4HKLlREA+yjnv7+hyG9LBRGP8c+vhD4VFMt5jq9jOc2KuBVMHJlKFhJAYIiOPzY7DitvHY3OpFc1OHwLhCDb+dSoA4JnvjmJrGftiEBdfg92HD+eOxYdzx8Kk4QnOLw9Nxa7KNlzy2i945UdetgYAly+EzHgtnp09FMWNTtohDU41wuoMiD6M6ADUT3dUw+kLUqOeoGcJwuPc9fVP0uPhrw7STjDNzBrPeruXxsPsr2mnH9qdF7DvktBAA2zciC8YodvY3AHUt3uRm6ADAFw7ilUH395Qil9LrHjpd8MBAG2CeJVQhIE7EIY/FKZBsTmL1oABgy/vZrNk/rWxDCOe/pH+PRCK0LboOKIQTWo8gRAWfME/2/p2L+Z/ug83f8C/K/d9tg8JOiWuHJGGC/snUFJrUMkRCEfw3cF6HK6zIz1Og3suykOVzYMZb7Bk+bHLCwAAdyzfJVJXpr+2CRLwrl2bO4BLOTVoXHYcAODzXTVw+EJINqoBgN5nAIjTKgGw7zR5xtU2D4Y8+QM2FPOkhRCcv1ycD4B1m+qUfI3OhSsP4oZ3twMAsuK1eP/XCjoyzYjT4nTgZAc3b775JmbOnImHHnoIBQUFePbZZzF69Gi8/fbbp6W9pwJiYPonGWgwd1mzG1MHJqHGxj7HylY3Bqcaqcu7we5DulmNFJOaujvDEQb5SXqqnJY1u3HZsBQc49yyZS0u5CfpQYYBkQgDCXdesk1Duw8X9k+g7ShpcmF6QRI1apWtHmRZdJBLJSK31sBk/hjHmpy4YngajfMqbXZhRIaZGvAWlx9JBhUS9Cpq6H2BMIb3M1HiU9rswqxhqXS93OpGbqKOni8YjkAmlSAvSY9ybpsKqweXDE5GCaciVLS6cdHARKrc1LR5kRGnhVoho+5uhmHQP1lPyUSF1Y1Zw1Jp2yutHgzrZ6IDsBYn2/Z+cRpqH/yhCAanGfl7ZHVjWkESKqxues9yuLYLB2EFqQYUNRDy6Mb0giSUc/uUt7gxLjuOJlHYvUEYNQrE65TUBRlhgEEpBnqPKqxuTC9IpscoaXZhSJqRqlBOXxB6lRzJRjUlhw5vEKMy4lBuZY9RbWPvIVHZKrj7LhG0nQEwNM1E215j8+DC/gn0ekua2PP6uf69vt1HB1m9hR4X34tEIjh27Bg2b96MTZs2iZa+QHeVRBsbGzvdp6dFtHQqOR6aMbDTv13YPwEAuk2JHfjY2k5/v3hQEmzuAEY8w7qglt02DnqVHC9wfkuFTIpkoxobi1sw/dVfsLnUCo1SJjrG0WdmUMPzr41lMGsVGJ0ZR18SAB2MGiEPJHNGr5YjxajGfZ/th5PrTBL0SlhdfqpE/feeSShsdCB74WpcNDAR47LjUNfuxbEmFwxqlkANTRenuhKSc97zrCqhVrCvV327F2Oz4vH9fRcCAB1hLJrFGnDyEU3MteC5a4ZiYLKBdiyErH17oAHlVjc+vmM8LhnMvgPbOJI3op8ZADpkLNW2eTDwsbWYf1E+/U2Y+UNI0NUj0+jfW5x+XDE8FZcUsOeotPJunjSTGiaNEoNS+DiyhVygd5nAHfTN/nqqkOyubMNrXDzEkHQjrh2Vjvs+248r3tqMPVVtSDdr6GjpTxfl4c4LcxGnVWB9Ea865HOq24ptlQCAj+8YjzZ3gBqKF64bjvum9Ud5ixuPzhpEn8WilYfwvwP1UMgkkMskogDjaJD32hsM4/JhqVhwKfv++4IRfC5wP63cWwery4+pAxPpexbmAquFKlhfoSeDm23btom2B4AZM2Z0O63C2YImhx/JRjXyk/SUTNjcfgxLN6G2jRhKD7ITdJBKWPJS2epGpkWHbIsO5S1uhDmCkpOgQ6WV3aesxYUL+ydSN3lpM0tqNEoZ3P4QGhw+pJrVyE7Q0mxKBkD/ZN5QHmtyYmAy3wdUWt3ITtAiI16LmjYP7B7W2A5IMeAYN3gKhVlyRQgZIVNSCV8fK9OiRV6SDmXNbmoscxN4glLa7MKQdCMd+FS0uJFt0cGkUaDdE0BdmxfpcRrkJepECtCgFANKuW+mutWDnAQdHSaUt7iQm6hDLrePPxSGQiYVndcbDCM/SU9dKVU2D7LidVDIpAiGI6i2sW3PsuhQ2cq3PTNeS1121TYP8hL1NPaw0upGtkWLeJ0Sre4AbO4AzFoFClKNKGzkVbTh/cy0HdXceQkFKmlyYkCyAflJepRbXfAGwtAoZMiy6Chpq2/3YnCqkYYHlDQ70T/JAJlUgmA4gqpWD7ItWvqeMQwDBkB2go72gdU2L/KS9AiF2TNXWF3ISdAhiSNCdk8QJo0CmfFa1HADp1CEwYBkA0/IrC4RAa1oZa+/N9GjaRK2b9+OOXPmoKqqqoMrRSKRnJbqwieCria0PB5GZ8ZhdGYcXv6BHyn/+84JuOmDHfhg7tguSUt3iNcpMW9yLj4W+ErPz0vAZ3edhyve2gyAzVjJiGdZq8MXwuq/XABlVG6uVil+ZKMz4+ALRkSxGiRmhuCRywbhg80VuPOj3ThYZ8dDlw6kZMHuCeK9P45Bgl4FqyuAUZlmpBjVGJMVRz+ADcUtqFg8CzmL1uBIvZ0qD+fnJVAfLQB8dPt4DHjsezh8IcikEqSZNLiwfwLWHm7Edwcb8MSVbLbJC9+zJE4mleDXhy/CH95jR/7NTh9umpCFHeU2vP9rOeZMyMScCZnQq+V4jVOcLuyfSM9H3IMDUwxIN2tQ1+7FQzMG4p4pech9dA2eX8OeRyqVYEQ/Ew7U2vHGTyWiYwBAqom954NSDNhVYcOIfmboVOw9vPuTvZBKgPE58XjzD6Mw6YWfMTLDjF1/m46qVjd+t4Q3jEeenoEhUcTBrFXAa2e/hwvyEzBnQhZW7mOVrgcvGYB+cRrqo89LZMmLUHECeNWoqNGJL++eCI1Chl2VbRicasTbc0Yh2ahGepwGm0utmM2pWC9fPxy3LduFfdXtWHv/hShudNLgYblUgqJnZyL/b98DAPY9fgk8wTDmvL+dEp93uHOTOI0v/m8ifMEwjel58brhsOhY9ScYPn0ZET2ZJqGxsfGkBkOnMk3CHct3YX1RM6YXJMHhDSEYiVBX9Hm58ZBLpdhcasXwfibIpBIUNzrhCYQxbVASdlbYMDjNiAqrGxGGgdUVwPSCJFw+PBU5CTqsL2yi/a1SLqWGsbbNg3SzBqkmDerbvajkDFSIEhwtUkxqamwBdmRu0ih4w9jswtUj01DSxBp1uzeInAQ9MuK02FFhgy/IKp7xOiXtF8pb2JE6IUIVnArh8IVQaXXD4Q0iJ0GHAckGrNxbR9suk0qocksISapJg3q7lyUG8VpolTKUtbjQP1kPi16J3EQdNYwufwhGbmDFMAyqbR5MK0hCHreNwxtEVjxroL8/1AhfMAylXIo4nRLtXl5VSTSoYFDLYfcGUd7ixpB0IwxqOcpaXFArpCy5StRh1T7e3ayQSemgssbmQUa8hrp+qlrZtmsUMlS1etA/yY9kowrpZt415A9FoFbIRAT0yhFpyLZoUdXqQTjCIC9RjyyLDjsqbFQxSzGq0cipP/XtXqSZ1dCp5HD6gjjW5EL/JD28wTBqbF5olXJkWbTItmhpIkeEYftBgtImF24cl8kSLpsHla1uZCfoEK9TorTZidxEHVJMavp3gCVXepWctfERBuUtbswYkoImhw+VrW4oZBLkJ+mRGa/FukI28UAqYfsvMkhttPuQZtJQAlrR4kJ2Ak9yegM9IjV33303xo4di9WrVyM1NbXPM56AnlUSValUUKlObfS4+i8X4Ks9tRiQzI7OVXIZzsuNx/ZyGyoWz8K/finDS2uLkRmvRaPdh58WTMFdH+/G53dNpIrMzken0RfDoGZvefHfZ0IqlWBQigH3T++PN34qgVGjgFYpx6o/nY/Z72xBvzgtOpu+h5wfYEmkSi6lgWcXDRQb7BevGwYFR4zI6D/NrKHtqGh1Y3g/M0waBTVglS9cDgCiSHmJRIKlt46lRAEA/jgxC89xAbh3T8mDUlD7RK+SY0KuBRNyLbjmn2zWkEHV8XXLiNciFIlg3oU5mHdhLgBAp5LhPztrMD47Hha9Cr8b0w9PfysudPfpvAki9yBRhf4kUGVILIg/FMEtE7Px4JcHcPmwVKowLbhkAF5bd4ymcA9KMeAfP5fi7TmjYNGrcGH/BPxaYsWn887DebkW+IJhhCMM9lS1QaOUYXRmHMxaBe3kCfkY3s8Et5/9iEksy4h+Jsy/uD8Nps2yaPHnaWxQfVGjE1eNSMOVI9Lo/gdr7TBrFchL1CMYjsColsPhC0EpYw0LCcbM5YhQP7MGpc0uquyRTh9gO8RBKUZKakIRBnKZFKMzzXhk5iDE6ZSIAxswDIBXwhZdjImLf8Zdk3MxPidedP+1Shnk3Hu1+lAD3p5z7mQTHQ+LFy/G008/3aN9/3XzGJRbXQiFGWTEaeHwBZFoUKG02QWzVoEEvQoufwgN7T5sLbNiwSUDkG3RwahW4M31Jbh+bD8Y1HK0e4LYUWHDiH4mjM2ORygcQb3dB6srQBVRgDXqoQgDpVzKjqpb3ahoceOC/ASEwhHsrWpDZryWKhlOX4g1lFyfbdYqYHMHUN7Cjrr7c64iTyCE3AQdMuK1+O/eWlRxriVyToZhEAizBjqTU2aIYXT6QihscHDESIcEvQqtLj9s7gDiOSJMyIQ/FIZaIeNUFTeqbR5cPCgJJo0Cm45ZUdXqRpZFB51KDk8gTA0lQNTlAGfkNchN1KOs2QVvMIysBB2rslhdqG3zdnBxMGD7tCyLDtWtHhq3YlQr8OPRJmiVcuQk6JBoYN1gRIUA2MFnOMLA7g3CrFVyCpgb5VYXLh+WBqVciu3lrShvcSEvUQ+1QoYAl7ZNuvM0M0tAq2weZFm0yLToUNXqhi8YQX6SHv3iNKixeVBvZ69NKpVQAkq+X0KEjjU5MSkvGzZPAFtKrPRdiNcpYXMHKCElz7vNHUCbJ4A4nRJ5Sew9q2r14LxcC5KNKvxU2ITcZhfyE/UseY4atKSZ1ahv96Lc6kZOgo4jdG4wDKsqZyVoUWF10+cifFcZjlyxBNuDylYPLhLEjvYGekRqSkpK8NVXXyE/P//4G/cShJVEZ8+eDYCvJDp//vw+OWfF4lmQSCQYkmYSGfiFlxVg9jtbIJFIcO/UfLy0thh3Tc7Fk/87gkyLFmvvZ4Opty+ahvMWr0eSUY0kTqoflWnGkXoHDeSTy6S4f/oApJs1sOjZD74glSVQOs5w7Hv8Erz0QxGmDGAJy2d3TUR5iwsXv8rGVAgZ+Pn5rBvh6auGYGi6CWOy4jpc15A0I/Vh76ywwaJXio5BQF7mRZexLo0kg5pK4E9dOVhUwE3HGdO/TOuPf6wvQQJ3LQAoS+/sHADw9pzRSNCr6D0iMRzEJaLvhAwNS2dTuX99+CIAwOJrh4tq2qz5y4WY9Q82zfn/JueCAfDglwdwz9Q8qOQy/GFcBuZflI9gOIKRGWZYdEq4OCJCzvfWjaOgUcrosxIqYGq5FFKpBPufuBTf7K/Dc6v51FcSBAnw6ekHuN/IPRPeu0EpBtwwLoOSzwHJBhjUcjw6qwBD0thYgo3FLXj2u6NQKaRINak73I/sBB3s3iA9rknD30Ozln8Ww9JN9HmuvPf8DscBgLunsOSSKFgkcF0Iohg+c/UQPPHNEYzMMHd6rN5GTwY3KSkpJ7V9TxVegFVQBqXw98ukJa5aE/1NrZAhQa/CsH4m0b5EyQSAfnHifeScQlBhddMYjAS9Ei1OPzXyOQlaVFrdqG3zoF+cBuEIg6pWD0qanOifzCcO1LV7kc4Zm7xE1r0SDDNQyWWcqlILdyCEiwYmIVGvQrPDz543gXUTxOtUaLD76LtG3CtOH6ugZCfo8P3hBti9QcwYwt5jiUSCGs4tBLDv+JF6O33n85L0KGlyobTZhTsvzIVGIUNtmxeVVtboA4AErDrSj4vdyuKIQDjCBujmJeqxan8dAqEILuyfCI1SBl8wghpO/QFYMtXi9FMFPDtBi8pWN5ocPiQbWeWmvMUFvUqGYelmSv4qWt20HelmNera+HIU2RYdSptd3D3SQSoFamxelLWIXS21guvP5lQzbyAMrVKObIsW6wub4fKHMCE3E2qFDP5QBKXNLuRxxzBpFKhv99LBR5ZFh2qbB7VtHmTEa6FTyVHT5gED4IL+CbTthGwCQG4Cq8SRv+Ul6rGnqg1lXBCwWaNAfbuPuiPJs2t2+mhcXma8FjU2D7yBMHQqObITtPjf/nb4QxHcNCETRrUCLl+Qc2mx5002qlFt89CBbzZ9V/l3sbfQo5iaCRMmdDlvSl/ieJVEextCBUouleC8XHa0Gu0SAlhJNbpwXopJjWN/v0z027NXD8XDMzvG61w/NgMXD2JHyCq5DMV/n0lHwnE6JRZfOxwzh/Jl3XMT9fj92I4zDN/JqR1zJ2WLCM37t4wFANw3rT/idEqkmNR44dphuCA/gXYs0Vh66zjaNkDcyd56fg69Pwl6Je6ZmgeAVT8AYMoAnn3nJepx/Ri2rUQF+vvsofTv47LjkSOQIEnQG1EyyHmEHQSJ6yGKBAmWJSDGRCaVQKeSQ6+S48CTl9JreOG64ZBKJXjw0oGYVpCMPY9fggcuYc9HSI1ZqxSlgAPsB33P1Dz6bADgyuFp2MAFdB986lLR9iQ2a/VfLhBdy4JL+Hdg7f2TKRkFgBeuHYYP546jNXjyEvVI4QifQa3oVBlNNanx2OUFtCMi92fF7eNF2y24dAAmCc4lhFGtwFUj0jAmi1dlphckdYiVyU/S06yz3AROKTpNQcI9mSZh4sSJou0BYN26dV1uf7ZOkyABG8dAAuazE3TYUmal5DPbokOF1YMQZ+SJQlDS7KJKs1mrwO4qGzWUeYk6FDY6aZ+Wk6BDBWds+sWxCkGYYTgDzT7r3EQdfjjSSDMss+K1KLe6KblKNarR0O6jI3mA/W52lLfSkXv/JD1+PNJECVpeoh6lLS54AmHoVXLqoipuYmM/AFbd2FTSgjxyXouWBqCS9UqrGzU2DzWUUokEpc0uZHLGNcuiw8biZmRxBC0nQY+SJiekEgkkEglVhEgQLMCSib1VbdQ9nJ2gw85KG/0uchJ4gkIGQf5QGGUtbvp9JOiV2FZupfcsO0GLkiYXLZKZFc8SNOLCIs+7pMlFr7cg1YDvDtbT9WwLS8jCXBYoIblCQqJVyXGw1k7fmZwEPTaXWgWkliU57Z4g4nXs4JZhGJS28MdIM7NxniSWMNvCESPuvmfGa1Fl81DXIcCS++ImJ7K5+5yXqMP6wmb6PgxKMaKwwYEIw4j60t7ACR/t4MGDdPnzn/+MBx98EMuXL8eePXtEfzt4sPfnOyI4XiXRvoREIsFnd7Gd4OA0I4r/PpP+7YNbxuKqEWn4aUHHdHehS4Yc596px1e4oo1pZ3jpdyOOuw0BcSkQ9UMhk+IP4zPxyZ180bLowN8R3OjbLMisAthAVSEenjlI9GLeOilbNOp8+frheFZAYh6dNQg3Tcjssq35SXpMjXKjAcAr14uv96+XDqCxLxKJBGXPz6J/S+Y6nK/u5g2XKeo6ojEkzQSNQtatO3XTwxfhkZmDRL9JOeIEsMRgUp6F/u2yYan02ASLrx2GmUO7nnxRzhkkIaYOTMS38y8QjWrGCkirRCLBnRfmUoKaoFfhv/dM6ngt3YS/vD1nFB67okD02wdzx2FSnpgEERcCANGUDacLJztNwn333Ye1a9fi1VdfRVFREZ566ins3r27zxTevkKySY1fS6x01D083Ywvd9dS45MRr8WRersouUAqYZUZou7lWHT47546+m3nJurx45FGSi6Ucin8oQhknJEHgHgtW5uEEKO8RD2+2V+Pwdw7PSjViNUHGyjpkXKEhIzkAaB/sh5f76vDQO4YA5INWHu4EfncPilGNcqaXVSFAFh37r7qNkoE8hJ1+O5AA91neLoZq/bXIYMzpGqFDL5gGP5QhPa7mfFafHewHsO5wUxOghb/O1CPoVzbByYb8OPRJupaI/ests2LJK4PyU3Q4cejjZTUDO9nxue7qun1ppk1KG9xi/pArVKOY01O6vYalRmH5VurMJhTPbMt7DEHcAqaSatAqzuAUCRCv+GMeC02Hmum5ylINWLVvnoBMdJhRzlPrsjzcviCdLA3JM2Ibw/UU4KWk6DD2sONVLmL14nVPoDNlCxqcNB3JjdBh+8PNdB9hvcz4X8H6ul91yrZmJlgOELvQf8kA77cXYNh6Wb+vEca6buan6TH0QYHzQDuTZwwqRk5ciRGjRqFkSNH4rrrrkNhYSFuv/12jBs3TvS3vq7U2V0l0dMJIemYPjgZOpUc+UmGbvY4O9AdKx4rGKET7Hv8EpHbaPMjF4kCbX99+CJcFmWgn7pqiGhdq5SLjPRdk/O6JQ6zhqXitd+PFP22/LZxKEgRk675F/cXXY9M0E65TIqNf5160m6Rg09d2qnL7mSw7LZx+HRe1+/ljeO7JnRdQaeSi9wVlS9cjq/umdTl9jKppNPrYLphNUlGNZIMHV1bQvzpojwsuXkMXR+fE4/P7zqv2316Gyc7TcKkSZPw6aef4r333sOIESPw1VdfYdWqVRg6dGhXpzgrMXVAIr47KDDqGSZsLWvFxVxMgkImxdF6B8Zk8s89P8kApy9Ev7fhGWbsq26jGYPZFh1+LbGK9rG5A6LgzdFZcdhcaqWDglGZZuyvacfoLPYY8TolyltcIjflkDQTzdYB2JF5UaOTKqUZ8Vo0OnzUyEulEjQ6fKJ3fEQ/E2psXtr/zBqeip2VNrpPpkWLLaWtuGI4r2C3uPwi1XdMVhwO1NoRxxHx8/PZODnybWiUMhQ1OjEole+7U00a+IJhes+GpBuxo8JGDfKIfibsqmzDLE45l0klqGv3YlSmmR5j+mA205X0TxfkJ6CwwUGvPzNei+3lNpH6nWJUU3cNue9lzW6kmUjoQhyONjhoeEKCXoV2T0Ck8mZZdKKpWM7LteBYkxP5iew++Ul6FDU66TskkUjg9oeoa4ns0+4N0usfn2PBhuIWet+TjGrsrW6nnguAJYLC0g+DUg0oanRSwjUs3YSdFTZcwA2qZVIJihqdosFeb0HCnGCN4pOZ0ykrK6vHDepLOBwOmEwm2O32s0ZS7i1kL1yN128YgWtGdXRJCbdZee8kjM7s2mhvKG7GRQN7N3Dr/3cIYxjOJC557Re8cv0IOko/EzgXv8Gzpc2RCAOnPyRSHNn4Gd71V2F1I9WkpoMIX5AdQRsEgeO+YFg0yHD4gjBwWS0AO/dXnFZJt3H7Qzja4MC4bN6IubhMGAK7JwitSkZVBoZh4PDxbY1EGOypbhMd40i9XWTUGu0+mLUKet5gOIImh090fSGBGgCwNVqE96PZ6YNMIoGFC6YOhCLYXWkTuVybnT4ReT/W5ESiXkWJT5s7AE8wTL/ZSISB1eWnMX+dndflD0EulXRQWIVo9wRE8W37qtswKMVIlTVSr4yQOIZhaLYUARsf1fXg2eUPoc0doCpKZ6hqdSPNrKHPqoaLdSElGhiGgS8Yoe1iGAa7q8TPrtHuQ7xOSRUxhy8ICXi3t90TxP8O1OGPE7PpPg12L3WVAuy0G4l6NQ0VOB5O9Ds8YVIjxOLFi5GcnIzbb79d9PvSpUvR0tKCRx555GQPeVpwtnROfYGLX92Inx+c2u022QtX44v/m9ghkyWGGE4XzsVv8Fxscwwx/NZwot9hjyJ03n33XQwaNKjD70OGDMGSJUt6csjj4rnnnsOkSZOg1WphNpv75BznMo5HaABg/YNTMFogkcYQw7kGm82Gm266CUajEWazGXfccQdcLle3+0ydOhUSLkaELHffffdpanEMMcRwOtEjUtPY2IjU1NQOvycmJop82b2JQCCA66+/Hvfcc0+fHP//B+Ql6ns90jyGGE4nbrrpJhw5cgTr1q3Dd999h02bNuGuu+467n7z5s1DQ0MDXV566aXT0NoYYojhdKNHdWoyMjKwZcuWDvNAbdmyBWlpaV3sdWogxbCWL1/eJ8ePIYYYzm4UFhZi7dq12LVrF8aOZUsUvPXWW5g1axZeeeWVbvserVbbZV2aGGKI4beDHg3b582bh/vvvx/Lli1DVVUVqqqqsHTpUjzwwAOYN29eb7exx+jphJYxxBDD2Ydt27bBbDZTQgMA06dPh1QqxY4dXU8+CwD//ve/kZCQgKFDh2LRokXweDxdbhvrN2KI4dxFj5Sahx56CK2trbj33nsRCLCzgqrVajzyyCOi+hBnGqdS7jyGGGI4u9DY2IikJHFmnlwuR3x8fJfzOAHAnDlzkJWVhbS0NBw8eBCPPPIIiouLsXLlyk63j/UbMcRw7qJHSo1EIsGLL76IlpYWbN++HQcOHIDNZsMTTzxxUsdZuHBhhwC+6KWrSepOBIsWLYLdbqdLTU3N8XeKIYYYTiv6uh+46667MGPGDAwbNgw33XQTVqxYga+//hplZWWdbh/rN2KI4dxFj5QaAr1ej3HjxvV4/wcffBC33nprt9vk5ub2+Pi9MaFlDDHE0Lc40X4gJSUFzc3Not9DoRBsNttJxcuQgp2lpaXIy8vr8PdYvxFDDOcuTonUnCoSExORmNixHH5fgZTkifnIY4jhzIB8e8LyWCfaD0ycOBHt7e3Ys2cPxoxhqxr//PPPiEQiJ1VZfP/+/QDQaQZnZ4j1GzHEcObRWd/RGc4oqTkZVFdXw2azobq6GuFwmHZM+fn50Ov13e/Mwel0AsAJz7gbQwwx9A2cTidMppMrkV5QUICZM2di3rx5WLJkCYLBIObPn48//OEPNPOprq4O06ZNw4oVKzB+/HiUlZXh008/xaxZs2CxWHDw4EE88MADmDx5MoYPH37CbQVi/UYMMZwNOF7f0aOKwmcCt956Kz766KMOv2/YsAFTp049oWNEIhHU19fDYDB0O/dQDDHE0DdgGAZOpxNpaWmQSk8+pM9ms2H+/Pn49ttvIZVKcd111+Ef//gHHdhUVlYiJyeH9gs1NTW4+eabcfjwYbjdbmRkZOCaa67BY489dsLVgWP9RgwxnHmcaN9xzpCaGGKIIYYYYoghhu5wTpWX3bRpE6688kqkpaVBIpFg1apVZ7pJMcQQQwwxxBDDWYJzitS43W6MGDEC77zzzpluSgwxxBBDDDHEcJbhnAkUBoDLLrsMl1122ZluRgwxxBBDDDHEcBbinCI1Jwu/3w+/30/XI5EIbDYbLBZLLOAvhhjOAE41UPhMIBYoHEMMZx4n3Hcw5ygAMF9//XW32zz55JMMgNgSW2LLWbbU1NSc0vf/9ttvM1lZWYxKpWLGjx/P7Nixo8ttly1b1uH8KpXqhM9VU1Nzxu9XbIktsYVdjtd3/KaVmkWLFmHBggV03W63IzMzEzU1NSeczhlDDDH0HhwOBzIyMmAwGHp8jM8//xwLFizAkiVLMGHCBLzxxhuYMWMGiouLO8wNRWA0GlFcXEzXT0ZxIW2N9RsxxHDmcKJ9x2+a1HRV7txoNMY6pxhiOIM4FTfOa6+9hnnz5uG2224DACxZsgSrV6/G0qVLsXDhwi7PdzJTKXTW1li/EUMMZx7H6zvODad2DDHEcML4+3dHcbC2/Uw3o08QCASwZ88eTJ8+nf4mlUoxffp0bNu2rcv9XC4XsrKykJGRgauvvhpHjhzpclu/3w+HwyFaYoghhnMD5xSpcblc2L9/P50ioaKiAvv370d1dfWZbVgP0erywx8Kn+lm9AjtngAe+epgt9tkL1zd5+0IhCIIR5hTOobdE4QnEOqlFp15fLC5AoUNv01DbLVaEQ6HkZycLPo9OTkZjY2Nne4zcOBALF26FN988w0++eQTRCIRTJo0CbW1tZ1uv3jxYphMJrrEpkeIIYZzB71CapYvXw673d4bh+oWu3fvxqhRozBq1CgAwIIFCzBq1Cg88cQTfX7u3kCNzQOXnzeeM9/8FZ/vqjmpYwz42/cIhCKi37wBMTF64PP9ovWn/ndEdN7jweEL4pPtVd1u0+Tw4/PdJ9f2U8W/d1ThjuW7RL/d/MEOLNtS0e1+f/3yAJYLttlaZsWilTwhu3PFLrz64zHRPtH3+NUfi3GyqG71HHfytWg0O3zH3cYXPD4RlgmyA06E9A16/HvRdifb7rMZEydOxC233IKRI0diypQpWLlyJRITE/Huu+92uv2iRYtgt9vpUlNzet/zaEQizHGfefTzanMH0Ozk3yWnL4hjTU7RNj8dbRKth8IR0XGC4Qjs3iBdD4QiqLS6O7RNiBqbB60uPuOUYRg4fEHRNqXNrg77CI8TiTCidzEQinRoeygs/j6P1NvRaOev1+0PweYOiNph94jbEf1duPwh0fWHIwzKW/i2MgyDN34S9xP17V7RgKi2zQNn1PW2OP2i9ejzvrW+BL+WtND1SITpcF+jUd7iEr0T9e1e0fUC7D0QIvod2lNlQ7uH32ft4UZ8d7C+2/O+vk58/cGo5+Dyh3Cgpr3bYzQ7fR2eX2+iV0jNXXfdhfr67m9Gb2Dq1KlgGKbDsnz58j4/d2/gwpc24IlvDtN1hzeIk7UdgXAEZYIPzekLouCJtaJtvt5XJ1pfvrVS1DnN/3Qvdlfa6Ho4woheskqrG4+tOiw6xhPfHBZ9eDPf3CT6O8MwHT6i4+Hj7VXYU9Um+u2Rrw6KOrB/biylH+O6o01YX9Qs+uArWt3Yf5yPaG9VG+oFHd6c93fg56Jmuu4NhuEVfPBlLS4MeOx7USfw1s+lx+1oojH55Q2oa/ee1D7jn18v6linvLxBdN5DtXYMenxth/2ijZpM8GXnPbpG9LePtlbiudVH6bovGIYvGEG1zQMAsLr8GPT4Wry3qazbtlZa3bRD31/T3oFc9wUSEhIgk8nQ1CQ2yE1NTSccM6NQKDBq1CiUlpZ2+neVSkXjZ86GOJqV++pw5Vub6brDF8Slr/8iMo4PfnFA9K498t+DeGIV72LbXm7DWz/z1+sPhXHnit2iZ3btv7Zi5V6+73jzpxLc9MF2ur6r0oapr2wUtS330TXYJehLbl22E59s55XzH4824WLBPuEIg+mv/SJSqC98aQO2lbfS9ce+OYzn1xTS9a1lVlz6+ibRMfL/9r2IPM15fwe+2sOTz5d/KMYdH/EDoAO1dox45kfRdzL0yR9EiubYv6/Ddwcb6PqaQw24+NVf6Hq7J4g3fioRkZhJL/yML3fzit8j/z2Il9byA6DaNg/GPfcTXY9EGOQ9ukZEsN7aUIriRr7PW7GtEnOX7aTrhQ0OXPzKRlHbL371F+yo4O/79Uu24b1N5XS9tNmJIU/+QNdd/hAGPb5W1Kdd969tomP8eKQR7wuOYXX5MV7Qdl8wjDfXl4ju+/kv/IytpVa6/vXeWlz9zha67gmEMG/Fbggx/rn12CSwJb2NkyI18fHxnS6hUAgTJ06k6zF0BGG0QsMfijAQxjztKG8VGXlvIIzF3xeKOg0AqGvjOy/hxw50HDmRj8cjOO93BxvwuyV8/EHeo2uQ/7fv6Xo00WIYBiu2VaG8xd3lNp/vqsGMN/i2/HKs40s75tl12CL4AB5fdRjX/WsrXd9X3YbPd9fgv3v5TuKltcWUkEm5m+X08dfS5g6IOiIA+ODXctF6udUNpUz8qhvUCvp/mUQi6jD2V7cDAFq5kQ/5m5AYfrGrpsOoTQgyaqqx8c8qGDUSdvlDoudV3cqSCrfA0FS1emAVdCLRo96j9Q5kL1zd4R5Io4Lp9lXz71VRo1NkRO79914AvErkDYThD0Xw/Joius3C/x4UjYSPNTkx9ZWNWLqZVcBmv7MFvxzjiWJfQalUYsyYMVi/fj39LRKJYP369Zg4ceIJHSMcDuPQoUNITU3tq2b2KkqanPALlMOj9Q4ca3KhpJk1hP5QGOsKm7BD8EwD4QhCEX6fCqsLeypt9P2rb2efpXAA4fKFcKSeN/INdp/ou6lsdUOnlNF3m7y79QIypVHKUNfuoevRbtCqVrYPaeDOHwxHoFXKcLiOV/qbHX6RmlNj80AulXQ4X5GACCQaVLC6eNWhxemHQqBW1rd7oZRJ0cypJsFwBN5gWNSnDUoxigY7h+rsyIzXiq5fIgHdJxiOQCKBqA+XSaVoEqit5LoIiau3e+mxCeK1SlHbK1s9ontabfOg3OqmbfcFw5BLJWi089uoFFKRunygRuw5KW50QK+SYx/XtwGARiFDjY1/Vu5ACEYN3y/+cKQRzU4/Jc8lTS6o5FLRswlFGOwW2Kzadi8y4jV0fXdlG9YdbaLk2RsIQ6OQoaqVP29PVPDucFKkJhgMYtKkSXj99dfp8tprr0EqleLhhx+mv8XQEWsPs/5+j8BghSMMnviGH00t21IpMpTfHazHu7+UY3cl+9I4fEGkmtT0g/AEQmiwi90VPu7jIR/RM9+xI/KTcT+9xkmM5CPxBdl/I1FMJjdRR/+/fGslagVkazPHxEknGokwaHUHcNMHO+g20wuSMb2AT8H9y2f7AADpZvajIB8cUZ5I39Hq5o18KIrEeQIh/H11YYfO1KAWJ/qZBB+v0xdCi9NPO01CQMmIxMndO+Ho4uH/HsQbP5XQ9Ytf3ShS0GzuAAxquWhU1/9v36NS8DGP+/tPyBWoKJNf3gCAVfCEEBKZR78+BIC/r4QkRj+baFzzz62CNQZNDv4e8goN27EKiSbByn11+PYAr8aS+6vgjN7AZAOyE3Qd9usLLFiwAO+//z4++ugjFBYW4p577oHb7abZULfccgsWLVpEt3/mmWfw448/ory8HHv37sXNN9+Mqqoq3HnnnaelvaeKersP6WYNfT9r27yYPCCRGoa6Ni/GZMWJ3nmpRALhp1Fh9WBwmomqOa0uP7ItWpEBtuiVsAm+Lbs3ALNWSddbXQFMzLPQ77LNE0C/OA2auXeJYRgkGdQiA13S7MKAZANt+84KG8Znx9N2tDj9GJ0ZRw02wL7LcinPFGravBiVaUYL9z02O30Y3s9EDb8nEEJmvFZE/n3BMHQqGV1vtPswIsNEr7fC6sbwfiY0CIiBRacUuY5aXQFkWbS076xq9WBcdjx1JzU5fBifHU8JIgAopBKRgnaozo4JOfF0IFphdWNirgU1bew9DEcYDEkzokRALq0uP5IMalE7hqQZ6Xnr2r0Ylx0vGtymmzUiMnmwth0j+pnoILrJ4cekPAuqbSwh8wbC6J+sF5GaUJgRkdjqVg8m5lroPWtx+TA+Jx6VHDFlGAaDU42i967Z4UeKkW/70QYHpgxIpDarps2D6YOTRX1lb8f/nRSp2bdvH5qbm/Hzzz/juuuuw9y5c3HrrbdCIpFg9uzZmDt3LubOndurDYzGO++8g+zsbKjVakyYMAE7d+48/k6ngGaHD+uifM8M073Pk/jAfy7i91PK2VttjzJYQiQaVPi1xEqNKRk1vbiWHTFXtLhxyeBk+jG7/TxBIqPowU/8IPobMYhCUjNtUBLOz7cAYIlLRrwGBalGaih/OdYCmVSCz3axMjIhWq0usc9WOIoRjpoAINmohkWnpB+iq4tAXJs7QFk8UTWIRHrhS6yRf+F79vqD3D0XKgjR+PEIe88ve/NXeqw4rQIyrpMkHY6Q5CToVfipsJmO0lz+EGYNS6FKDVG77vtsPwCIiAp5nuUtbiwWSOZ2bwDpZo1IdQEgykoiLi+GYSiBlEklotEjAOziSK3dE6SGLMARr1e4UQ65rvOeZxWMYJh/P7VKvnMH2A6txemn10FGXqQDbI6KAQCA3AQdNUTrC5vovVAr2GNnxGswKOX0uGluuOEGvPLKK3jiiScwcuRI7N+/H2vXrqXBw9XV1Who4JWrtrY2zJs3DwUFBZg1axYcDge2bt2KwYMHn5b2niq8gRCyE7TUqLe6/BiWbqTGpsHuY40c93wYhoEE/CCA7FOQaqDfsNXlx6AUIz2mLxhGgl4lUkEBQC6V0Hez1eXHgGQD/aZbXH4MS+eJgtMfglEtF53XFwizhIMjSw12H8Zmx9G2Wl1+DEwxiFwa0ahv92J4PzMlLS3OAIalmyiZaHUFkBGn6RAzolHK6G9NTh+GpJlo28tb3JiYZ6H9pjcQhibqO7F7g8hL1NO2Vba6MS47jsYqtTj9GJFhFikmBKQvLW9xY+rAJHq99e1eTMiNFxFDi14p2tcbCMOsVdC2W11+DEw20P6ors2LsdlxIpe6UiYVffO1bV6cl2sRkcdBKfwxrC4/hqabRIObaFhdAYzIMNPBqtUVwNB0Ex1IewJhxOmUIoXI6QsiyaCmfbrNHcDIDDMlj1auHbYoFbw3cVKkJj8/H1u3bkVKSgpGjhyJLVu2HH+nXgQpuvXkk09i7969GDFiBGbMmIHm5r6TvQ/U2jFvxW74gmEca3LiUK0dOYvWiEbYtW0eZC9cjepWD/ZUtWHC4vWotnlw+3LWl2h1+elLrFexxjQ6wAoAVu2vQ16ijrpuKlvdeObqIfTvV7+zBakmDdo5QxoIR3Dd6H6Yf1E+DtfZRS8IMVCkgznWxDPjCMNAwxkjbyCMcdnxyE3QiSTumUNTqIr0AedieHsD65MPRxhcMjgZsk7qBRDfsNUVwIyhKSjk1qMJEdtWBnur2/HPjaX0w/jglrHwBzsPIiMf+U+FTdSIqxXsK0z2/3JPDZ6+agjGZ7NuUJc/hCSDmipkFVY3chN02FjcQjtjQnDu5Hy/JU0uZFt0tAPcV9OOC/snAOCDJ8dlxwFgOylyzT8VNtNRx77qdozOiqNuP0J+7vtsfwf3mNUVoATj33dOoKMaEve0aOUh2NwBlFn5Z+gNhBEKR+gzI9ffyF3TX788AIANBiTXTt4PF0d4CdGN1ylxz9Q8PPzfzrPZdpS3wuYO4IcjjXD7Q/i1hHchkvfodGP+/PmoqqqC3+/Hjh07MGHCBPq3jRs3iuLsXn/9dbptY2MjVq9eTZMNzhUk6FX0fWx1B5CfpEebO0jX080a+g44/SEY1HIo5VL6zUQYBkkGFWxcYGiLK4CCVH70b3X5kaAX1/RiGHZwQsmUO4CBKQYBuWANI3nnbK4A4nUd64IlGVRUzWlysCqL8LzZCTr6LvqCYagVUsikEtpH+oIsMRISsmHpJjQ6WENpc3d+XuE9a3awbSVtt7kDGJJmQgNpuyeAeJ2YXDAMI3JrNdp9GN7PTK/F5maVHEIEfcEwVAopDGo5VXeD4QjSzGpqxG3uIIak8WSCtD26K002qul5Wl1+DBAQP6vLj/7JBrRzgy2GYcMYpBJx+IHoGO4A+ifzpLbF5UeaSU1dlOQYwneGJXU6es/aPew7Q/pNmzsAS9Q9A4D0OF41anUF0D9ZT+9hi8uPfnEa2sc7/SFqE3sLJx0oLJfL8eKLL+K9997DnDlz8Oijj562+VCERbcGDx6MJUuWQKvVYunSpX1yvkAoQknBx9uqcOnrm/DCWuFoPIh5K3bjWk7ar233oMHuRYvTL2KiY//+E/6+uhCr/nQ+dNwDrLZ5RL5HbyCMvEQ9Hp45iH7McqkE+Yl6UZvcggh9P6dC5Cfp4QmGKQs3aRR4c30JbSPAu3JKm53YUNwCQne8wTDUChlWH2rAJ9urEIkwmJhrgVGgZBACMSSNHYm7/CEo5dIOHyIAGldjc/sxONWIbzjX0cbiZjw0YyAuHsS6mz7ZUY2fCnllhJCw9DgNdaERXDo4WWTAAZacAMD5eQm4+bxMOiLZUtqKzHgtHf1c/o9fkWRUURfL098eQTm371GOgEgkwL1T8+g117Z7cMvEbGq8//KffVQOdvtD8AbCGJlhBgA8+91R/OU/rNvsv/dMotlstW1ejMwwUyNS1uLCHRfkAADe50jNRQMT8ddLB6CwwUE79XSzho782j1BPHZ5AcZmxcHm9qO2zYs3bhgJgFXTnL4Qrh2djmmDkrBw5aGODwNsp/L8NcMA8OoOwGBcdhxV80ZlmHFerqXT/QOhCG54bzuanX402H04Uu8Qxet0RYT6Gier2H755ZcYNGgQ1Go1hg0bhjVr1nS7/dkEhmENNDEuVqcf/ZMMaOPerTY3a5DJmKbdHUScTol4nZJuAwBxOiVshBg4WeWGJxcBJBp440r6mBSTmqoZhFxQpcbpR5ZFS0flre4A4nUKESEBgESjWnQeMZkKIFEvaLsnCLNWCYteJRoIWfRK6na2ckaeEgUPe16CUDgCqVSCBL2K9okObxC5ifxApc3DEkEf1/Y2N+tqUytkdIAhkbAuKUIm7N4g8pP0ImIUL3DPkbbH6ZRoE2QixWn5dZvbj5wELe2XbW5x2wmSjWpKFq2uAKvUuAJ0nwSdEuysAWy7jBoF+44I3IcWvZL2P0RlI/esxelHooEngoRcsPeZt13Ce2hzB9E/SY8WJ08uE7h+Vjigtuj4GCG7N4icBB11a7Y4/UjU8+9ZV0T4VNDj7KeLL74Ye/fuRVFREXQ6HWSyvh2x9bToVk+x6VgLBjz2PR29P8e5FraU8sF4n++qxrqjTVSun/P+Dsz/lDVwxGWUs4jvPPsn6UWy5N1T8mhMSWGjAwWpBijl4oAv4YsHsCSEvD+BcAQqhRRapQzeQIga+qevGkLjH3QqOYqenYnlWyvxvwP1CIT4YDs264UN3HriisGI1ykRCEcQr1Pi4RmDMCiFLUc9PjseL143DClGNRiGwR/e247VBxsgkbD+40iEEcXGtLr8+GJ3LQpSDVjJkRpPIIyJeRboVHK4/SGEBZ2eNxCGm+tIlHIp3tlQRkccFp2S7RQ8QZg0vBvptuU7acbWryVWXCTIDkg1q2mn2uTwIxRmaKdCfn/umqFgGAbBcATBMIN5F+aiXxzrTlPLZUgxqUXB1XqVDDeOz4TTF8Kq/fVo9wQx78Ic5CToaPxSfpKedlbLt1bi4kFJWLq5EgzD4FijE9kW9vhkMCWTSjAmeWG5kQABAABJREFUKx6FDQ54AmHMuzCHk8zZNt65Yje0SjkuGpQEuzcEjz9ESfHiNUXwBMPQKeWYMyETAB+c/O87edWCyNvXjEqHPxTBz0VN+KmwGZPyEuDyhejoMhwRq2N3XpCDSXkWkasty6KFXCYBCXf4wzi+fkt0bFNf4mQV261bt+LGG2/EHXfcgX379mH27NmYPXs2Dh8+3On2ZxM8gRDUShlHajgD7QsiI15L03Fb3WKVweZhjS1rkHnjGq9T0neEuJ+EqgtRahiGoWpPnFYpSvsVkqsWpzj2o41THeI5o07IRaJAMWEYhlV/ulCI2jwBxGkVSNQr6XkYBrDoVLAKjGmqSU0HW8QwSiUShMIRtHuDiNMqYNErYRW4UhMF97At+p65A4jXcsTAKb5eYtQDoQhSTWrYOIWEkBhioEnb47VKUXo16b/Y8wRh0aloX0WUGpWcdZUFwxHIZRKkmFSU1PiCYaTHaWg72j0saSV2gBAUi5593uTYcVolbC6xqkTi9aJJDbmH7PXz9yxeQITbub6EHL+V20enksPNKcdSiQRxOuE7wyBRQFCtrgASBOdtdXd0v50qTiml22KxYOXKlWhra8PAgQN7q02doidFt3paGbS02Ylblu7sVIkQQpj+mBMVJCmMMifQqeT0RaywupCToMNPhc1Ysa0STl8IBalGqGRS+EN8lkyamVdzLuyfgEdmDoIvFMbhOjs8gTCUMhm0Sjnc/rDIxXVBfgL1rZO4h21lVvhCYfz10gE42uDAl3tqOaVGyo64gmzWi1IuRZxOiX5x7LkNajluGJeJNk8Aj359iJ5Hq2RHNc1OP5IEwWFEjk02ioPdzBoFkg3sx6pSyPD+LWPp3y9+9RfcPSUP2RYd+ifpabzJxoem4lCdHX/8cAfyEnX48YHJAIBwmMHNH+7A+qJmLLhkADLjtfAEwrhqRBqN7SDxRDcIjG+CXoVBKQZ4A2Hcvnw3vtpTi+kFSdAoZTja4GCJDtdZSgRybk6CDhadEi5/CP9YX4KtZa14aMYgtLoCVEEyqOT02ifmWpCgV2Fy/wTYvUEsXHlIFEBY1MiqM/3iNGiw++Dyh2DSKKCSS0Xprm4uTsHhC8LLEdArhqfiokFJ8PhD3DNgt199iI0jEQZw29xBxOuU7HGDEXy9jyW7w/uZsKOilXbMUwYkYXg/ExiGJaiPXTEYUomEqloLLhmAWyZmw8dlRgHAQI707q60YWNx36VoRuNkFds333wTM2fOxEMPPYSCggI8++yzGD16NN5+++3T1uaegiUOKiREGXmjWk7VvTZ3AHE6JaRS1qiT9XgdO+oORxhIJBJYdCrByD2AZJMKIS4Ow+pijZxRo4DDG0KrKwCLXgWzVkHdHABEI/kOhpEjCuS8bZ4g4rVKJBlVNA5FImH7I/KOW50BJOhVkEjY7KY2dwBxWiUSDCx5Iv1ggl5JVQirMwCLruN52UDnAL1+lpAISI2BJ1eE+BG0edh9Eg2s2uEJhKBSyETEiAHrbvVyRL+Nc1kpZOw3y5IaTqnx8OSCvYe8cmHSKHilglNq4nQKtHkC9L4n6nniB4gVIxt3HoJmjlyaNUq0e4Jw+FhCGq9TUgIWCEWoHSDPLkGvou4monYR0hrhMnOFal+7JwizhidxrW4/LHolJT7EhSckcQzDKYQCpaoDmezEhXUq6DGpiUQiOHbsGDZv3oxNmzaJlrMFPa0MSjI6jhfDRIJj/3HjKNw/vX+32147Oh0AsL6oGRuKm/H8miLkJeqRoFfiiW+OoLjRAY1Cxio1nHqgkEmhU8kxvSAZkQgDlVwKpVyKLaWtuOKtzbj2n1vhCYSgU8lELhzy8vqC/Iv8/DXDEK9T4oNfy0UxFuuONqGkyQWtUg5vIIxAKNIh/ZkgxaTGf3bWwBMI4fDTMyCTSPDXLw+gstWNrHgtVtw+HgCrJt00IRPpZg2GprMByMVNDqTHaTAwxYCiBicWrTwEuUyCLQsvxrqjTQiEInD5g5BJJciyaFHa7MJdk3NhUCvQ7PShqNGJZVsqYdEpccvELAxJN9HO9uqR6RiQrIfLH4Keus0kcPlC+ON5WZg9Kp1egy8YwYo7xlNFpdLqxvB+Zqi4QO6dFTZs4mKajBoFHL4gphck4dbzc6BXy+k5pVJWVQpFeLeYVCoBwBYOI+1Qc8rL9IJk/HFiNgA2vf76Jduwq7INiQYVmhw+ePxhaJVyqOQykZtt8oBEztAE0eYOQKOU4rbzs9EvToNLXt8EfyhCSQzJGEk1aahyRjpavUoOpy8IuVSCOK0Cg9OM+GhbJfZWt9FA6iSDmpJaANhcaqWu1TsvzGE7dC6g9IaxGVQ1srkDWHTZoG7f/95CTxTbbdu2ibYHgBkzZnS5/alMk/DtgXq8t6kMO8pbsfZwAw7X2fHIVwfx5e4aFDY4cKTejld+KMbWMivc/hBqbB68t6kMNTYPHL4gihod+PFII77YXYNfS1pQ1+5FkkENi17s0pBIJLR/IgY6TqtAuzdIlZt4vQo2tx/tHnZAEadT0FF3MByBSi42cokGdqTe4vJT42PWsMaWJfcS2k8AbHxEokEFGUemyKibKELE6CUZVJ0GnwOcUmNQIU7LnqfNE0ScVskqM64A3IEw9Gq5yB0VivDvKMCP9sk+JNaDqFskXkSt4AOHHV4+7sgfCnOqA6tuWZ1+llxQYsSeVwLxvENUmdERMsW2nV0n5ELBGnVKDBlIpYJnR9QtTt0hypWQCAGAWUAU2j0BmLUKyGWsm488O7IPPWaUGyz6vicaVCzRdQfESo3LT11aQhLb2X1PIM/bE6DtIM+SQCGT0gEdS4wUVJmKJjm9gR5F6Gzfvh1z5sxBVVVVh+hliUSCcLj3i3D1pOhW9CzdZJbP4yHLosPfZhVg3uRcDH/qBziiMgKuGZWOcqsbL/9uOH73r624akQaVkUVvLt+TD98uYettzJlQCJe+d0IAMAlg5Ox6VgLjGo5kgwqLLysAH/98gCeX1OEJ64YDKVcipfWFuOqEWk0CFMll2JLmRWH6zp2rnE6JbItOny8rQrKFAM+nDsW5+db8Mn2KjQ7fbTuwMQ8C/693YU1hxoxe2Qa/nXTaFTbPGh2+jC9IBlapQwlTS58uLkCMhn74TIMKGNnr7sf1hxqRIRh1SB/OIIfjjRhfI4FOQk6TB6QiMuGpmBvdRvnn5fgcJ0DOyps0ChkUMllyIzX0mJ5yQY19Eo5/WiIR2ojF/Nz6RBWlVtx+wR8sqMKTXYfzFolHpg+AM+uPgqdSo5jf7+Mf77eIAycod1f0wanLwQtl9apUcpg9wTxU2ET/qUdjZlDU/DWz6V4d1M5bhiXQTurekEmg1GtwJpDjTT250BNO83EuqQghd4jV9T7UdzopMF028paselYCwYmG6jrzB0I47zceDx91VCoFTJ8f7gRF/RPgF4lh0ImweF6O3ZX2jAiw4yBKQbUt3txG1dJecbQFEQifGCwPxTGkDQTANYwXT5cXHulwe5Dol6FrAQdKlvdcPqC2Pv4JQiEI6ixefH6umP4/dgM+p7Z3AHolOJuYc6ETGiVcmiVMtS3e8GAwZwJmRjez4SHvzqIuz7eg2dnD+3wbvYFulNsi4o6z4prbGw8KYV38eLFePrpp3vUvu3lrfj3DjZrcFCKgQ58Pt9dA71Kjol5Fqw72kSD7gnWHW2iWW4Am81CYqB+ffgiaJQyOvqPVpD9QTZzJ54YKLcf/eLMCEcYFDY4qKLAjqA5Ay04BsMwND6CuIocviAS9EqYtUrYvUE4fEEYNeL3wuULQaeUscbUG4TN7WcJCWcIJWBH+iRglyhGQngC7DEsHHlo8wSQZlZDp5Rjf007VW7MGrGRZyFWd0jcjdsfglmrRIJeyV1LiNalEsYMSaXEVRKkioE/GEEzFxNJ1B+hYiREmzsIE0dqWjmlop9ZA7VChro2O1q5e6pTykSubIAPyCVxOWwcThDBSASJel7tIe4oGTdgYp83O1glKgoJ2Hb5Q7ByhNSiZ593dJVh4fO26HiXFbneRIMSZS0ulvToVSJlKrrtxP1EyBOJYzJr2XvqD4VFJAjg0vVlUgHxDSAvKm70VNEjUnP33Xdj7NixWL16NVJTU09LoLCw6Nbs2bMB8EW35s+f3+k+Xc3SfSKYNzkXAPDcNcPw5//sQ7ZFiy/unogETvYkD+fgUzMA8MG0t07KxlNXsRlLX+6pxdyJWTg/P4EbxbMy/pP/O4J7puZDIpHgd2P6od0TwGvrjuG287Np5ssFL27AgGT2Ye+qtFHXAsDLqBNy4nH3lDwArKtl2ZYKfDh3HCQSCfrFaTDl5Y24aGAiAECnlNH4FqlEgrwkPQ7W2aFXKTAy0wyzVkGnPXjs8gIA7Cf07x1VdJRFMo0ISMDoe5vKsOnhiwAA3x9uxPeHG/HQjIH0WMLKyakmDb4/3IjpBUkYnGZEJMKwUfsM7+oJRRj8VNhE1a0EgxJuPxtbALBy7tf76sAwfKp8g92HS17fhJvPY+NLpg5MQkmzE3rOQI/NisO019jqoAqZFEPSTJhekISfCpuh5bZ5ZOYgVFg9ePJKNtXXoJbjhyO84RuTFYfvDzdi5pAUPHIZe33eYFgQgMtmQFldARoQHY4wqG3zon8S+yx3PDoNE55fj+3lNsQJAgSb7D4MTDFCIpGgxubF82sKablxoTHRKeXwhyJUVr/jglz6twqrG3M5NUghk2JXJVsG3aRVIMmgQmmzC25/GBKJhKpxJc0uGs+kkrP79Ofeu3/dNBr3/HsvTd2fmGfBJa/9gqtGpiGBI60E1wrUsHMdPR0MAWx/8ffZQ+ELRkQpwu2eAOd24IitP4TiJiey4rWwcDElX+yqwYyhKTCq5ThSz5KRwalGWPQqNlbEE6Qj6M5AYmhaXewIOhhmWMPhYl0rwhEzAcnUIQpBkpFVatz+kEAxYNWfzjJdJBIJjbth40WUiNepUMHVIUnQq6BWyBAIReD0BWmmoTggV8K6l5ysqjQ4zQiTRgGry09VJ6G6QWDilFSimBA1xxMII9mooioLUTaAjup7vJa7Z27WDRYMs0TQ5mbdQESZ8gTC0HIDJoYBjcdTyXly0eryY2Q/M5RyKWyeAKcgsd8JT6a483L72NwBxAvUDj+nhMZzygwhDsJ9ybEIaWlx+ZFkVEHhlqCk2QkrpzqRlPZQOEIHVHrueYfCDJRyKSycMmPzBJCdoKMElKhwndl2Qo5bXX6RMscAnPrDEsE2zvXdGYhLysa9m72JHrmfSkpK8Pzzz6OgoABms1nk4jGZTL3aQCGOV3SrL3DliDRUvnA5Nj50EZIMakilEkilEsijXDRh7o0jhAYAZg1LweNXDMalQ3glyahR4EidHclGleg3T4A1Nj5BOjMx2MQdduN4tmPdtvBiABCVuA5H2IJqpCYJ6SiJ312rklPW/vdrhnIjnyDnvpIjkYs1STdrcOeFrKE0axR4bd2xLgv3qbn2NTn8VMpOM7FxND9yZCBBr8KnO6sxeQBLrpJNKlFlWqlUgnFc+rUqijSRVD+1QoZv9tfTY0skEtw2KUe0LamloObaMSLDjA83V1A30AX9E2F1+fGsIEWeYdiiceRZWPRKVFjdMHKjOqNagV+OtWAdF8dDspfWHmmkpCC6qF+qSY39Ne0YkMzGm5DnsYGLORHGGRFF5M8X5+NYkwtpZv5vewUxWQWpfP2XzHgtlHIpDtc5cNfkXORzZOnhmQNhdfmpumdUK3D9km20cKNZo8Dza4polpOwsyLv164qG/69vRpZFtadNawf+y2TgOxkoxqjs+LwyfZqJAqCOw1qOXVF9TV6otimpKSc1PanOk2CRCLpUPOEDSjl77lOJcfozDj6nQLA78dlUOIzNN2EC/sn0r/LZVJEGIYNtKS/ibOMhKPfeB1rXFiXRkD0vHzBMP1eLXoVbK4AHf0TpabVxbmfOFeCMG2axL8Ir62NI1wmjYKelxg9AvJ3gKsA7AxQQ51oYMlUK6dcJHCuJBIfw56XC5zm3vF4nQI2d4AOMEncEYmP0ank8PhD1KVFjiFUXeIoueBUJs4gR5M4YfoyCYwljzNex7uOLHolGx9Drl9HsoPYhAgyMCME1O1nlSqSIUUCaTVKGXyBMKeY8O0Qtp2ct9nho+4nuyfYIfhamK5u0amoCxIAErjrJUoNcT8J37Po87JtDcLpC8GgklOSI3Sd2T1BtApcSyTuiJI6rYJTFTsny6eCHpGaCRMmdDlvSl/ieEW3ziRSTWpMyBFPEfHPm8Z0ID9ahQzuQJgaTgAiuX9MVhz+ddNoAMCyW9kYFYVMzJajjwnwRjPVxAb36lUySCTA8tvG0/MCrBqhVcph1Cjwn53VsHuD0CllkEgkuHJEmmgOpEyLFgyAj7hjkA6UfMxzJ2V3aMfPf53K7csaxnCEwcbiFozJYuu6qOSyDjV6CDlbdFmB6HdiKImhFg4yn7hyMBZfO4yu94vT4MbxGXh0FnuMa0elY191OyURJJB7Yh6futzMyezE2CTolaiwumjHO2t4Kj66fTz6cwRFIpFQckb2mTVM7O7ZtmiaqM1kZPy3WeJrA3jCo5BJsfZII20jIU/8dnLcOikby24dB6lUAqVciv017RidGUe3MaoVaHb4qZo2Yyj7TZDnSQI6B6d1NNBE7auxebGz0kbfTWL4AoKiXr+WWPG3WQUiWTkU7ijN9xV6Mk3CxIkTRdsDwLp16054WoWzBxJRYKVZQ9xJ7LsYz1UEJnELZDTc4vSJAnpbnDzZIO4VgkQDG9Tb7PQj2ci6H4irgRgfs1aBZqefkuE4LWvEiUuHBApHB4GSQFNynnq7lyoIFh2rsjQ7/Eg2qmHUyOHwBjsYPRJYDPCEhIAoMza3OJBWSIwMagXaPEHIZeS87D0iygUbKBzoEOshzDATZgQJ207icoiRb4kiBsSlQ9pOBpkSiUREjGgWGrcPIbYm7r6T7zKaXJgJuXAGaKq1RCIOxhWmxgPgArI5QsYRQW8gDKtTTIxaBfeU3Hd2igi+7SSmhvSNwvPG65VosvPvTLxehTZPgHVrqjtXHnuKEx5eHTzI16P485//jAcffBCNjY0YNmwYFApxo4YPH957LYzC/Pnzu3Q3nUlc2D8RF/ZPPO52ZARHRvIARAZCJpXQcvOkI/rD+Ey8+VOJaLQFgLo4CMZlx9HjhyIMGIYnBlKpBNkWLe6ZyhowMtrZXGKlRlwlKLwEsCm87Z4gfblJ+uZbN7KFy4amm7Drb9NFE7aRgNs3uXoqROVJEihTde3eDlVuP7/rPNr2nx+cgotf/QVJ3PUrZFLMGpaCiVG1VG4cn0n//+J1w6FTyambj1w3uYcyqQSVL1wu2l84/wrAjjgP1zmovJ9u1tApGwj+fHE+DSQG2CDlkRlmkWpG2gwAeYl6lDS7cM1o3j3z3h/HoNHhox8/qehLStI/dnkBPtxcgSU3j6b7CBVAHXefiJsIYNW+4iYn7bwvHpSMOy7IwYdc4cRczm+tErxrr/1+BBZ8cYAalodmDMTLPxTTjoioUcISA+EIg/Q4/p5s+OvUDpOS9jUWLFiAuXPnYuzYsRg/fjzeeOONDtMkpKenY/HixQCA++67D1OmTMGrr76Kyy+/HJ999hl2796N995777S2uzdAFBQAMOsUqLF5qVpo0SlxsMZOyYUUbMmFFpcfE7hvRyaVoMnho980UUTIQCXJwGbduLmilUIDRb5Hi16JokYH/bZIDAUBMXIRrr4OwKp5tW1emDhXaqJeheJGJzXYCQYltpb52RITgr7B5g5gqJlVDHUqOSpb3fS8bHCtIDOLC/INRxia2SSR8AG9bFsVKGtxiQy0cOZxtUIGfxSJk0gkaBbcM4tOiUaHjzfQ3PUGw3xiRijMoNXlxwhO7VTKpaht81Ky0VkNoTZPAHZvkCckAOdKZM+bbFTjcJ2dv369kirUEokEJk59V8nFhMTqDND7bNEpUW3zUAWbKDMOb5C66ACWTE3iqs7rVXKUNrtoH04CgYnqQjK3PIEwbTt5duRdTdApcazJScl0gl6Jo/UOMAxon91bOGFSM3LkSC7inh+V3X777fT/5G99FSj8WwExKpkWfooB4csNdJzH5+4pebjzghxR0bOCVKMoq+eaUemiuUDaPEGakk2w8aGLOrSn1R0QyeLCU+cmsIaQdDJD09kP9IrhaXQbkjpIQI5FXtRBKQbcOzVPVM+i0eHD0wIjvetv00UfQ26iHgWpRpGr5p83jenQdiFI26IRTZ6E+OH+yaJJOAnRFH7c0eisgm6WRUddNgQknXzJHzu2W+iOBFjC8rxAdZJIJKhYPKvLWDWzVom7JueKpqkgrkDhqDY3UUc7cwIhgb52dD+RcvWni/JRYXXTVG29Wo5ko6qDuiN8P3MSdB3KGfQ1brjhBrS0tOCJJ55AY2MjRo4c2WGaBKlgMsNJkybh008/xWOPPYZHH30U/fv3x6pVqzB06OkJbu5NtLoDSOXcsIQYiBQEd8csI2HqdZxWiZJmF2+gOZerirgtOYVEGK/GMOx8S0PT2ffAolPiSL0DKVw74nVscCkhx2oFq8YSlw7ADi5Km13ItGjo+trDjZQoEeMqfOUlEnYANIP7Xiw69npJXxHHXS95HfkMIYYSPalEgiaHHyMy2P4hXqtEUaOTlqBI1KuwK2pAQu4zf18VKGl2UVdvvF5soIky1eEYAkISr1OiWEAELToVjtTbqWJEsp+8gTB1u0skEjQ7/RiVaQYApBjZef+EpO6gYOJKEjNFMpsAVsGtsLpovxCvU2JPdRsdICfo+VRxYX8jVIzidSoca3LS/jhBrxJNaklie0JhhrZdpZCiwe6jBVvjdUoUNznpMdPNGjqlTW/jhElNRUVFnzTg/zdIJBL68RNcNSJNpEJkW3R4Z85o0TbRLqfv77tQfNyo8/zpojzMuzAH3SG60J9eJReN/oelm/DTgsmifZbeOla0rlbIsPfxS0S/EfcZAEzItdBRIsGmhy+iAbxAxwKDQMfr6ymiM3mESDGqRZWT9So5bj8/p9to/M6mt4jGjkennVSaYmfbHi/4/tEodxZxcwn3+/3YDMwaKnaPRRNm4q4keOX6EfT/MqkEOx4Vp0KfLehOsd24cWOH366//npcf/31fdyqvoVexc6qTAxFulmDTSVWOniJ1ylh7cS42tx8TEm8XolDdXZcOpglgBY9Sy6IkevsvZNJJahr89KBiUWnws9FzVQpTjGqcajWLiqqBoC6dABWAdpZacPoLDMA9ps/2uDA4LR+AIhhjJ4XT4K6di8lTxa9EoWNDlzBZfilmTTYVtZKlVWZlB1YM+AHVfE6JQ7WttN94jhCNpxTUFJMahQ1OkQTdwJ8YT1yj441OXFebjy9lp+Lmikh6yx1WiJhiQH5tjPjtdhV2Ybfje1H9ylscNDJHzWCelPkGbBkyolLuGeVbGTnBiTPLl6nRH27V1R/RiIB/IKaNGlmDQ7V2XHliDR6LUUNTjqY0avkHWImlXIpmhw8IWWVOScuzGenikmP0+B/B+qpwq+QSakLmrQ9yaDGvuo22naLXoV9hU0Yy8VPppo0oslEexMnHFOTlZVFl08//RTr168X/ZaVlYX169fjs88+65OGPvfcc5g0aRK0Wi3MZnOfnON0oez5WaJ1tUKGDMGoW6eSd0jNPR4Cggh3gI2HMBzHV/n32UOx9n6ePNwwLgNf3s3HGUilEuQnGUT7XDzo+PFLlw3rvu1GtaLXJceu0J1SY9IqaPYawRNXDu5AOoUYlGKk9Xi6QrJRTaXp04Vh/Uwd4nYUMmmHzIJTjX/ZvmgaHTmfCdhsNtx0000wGo0wm82444474HK5ut1n6tSpXAYKv9x9992nqcW9hxSTBvtq2vnRbpwG28tbKanRKuWoaHGLCmHKZRJR32DRKbGrwkZVWIuOJQrCgUUgzFAFAWDjBQ/XO6hCkmJS40BtOyUbySYV9te0I1Vw3mgkGVXYXWlDVjzvWj9a76BkSiaVoFWg7ACsitDk8FFilGJkg/DJ9WcnaPFzUTOyBKp3hGEg/HwT9CocqrNTdSvFyBpb0vY4rQKFDU76dwCQS9mgVnLPEjgiQM47KMWATcda6D1TyqVodftFwfLxOiUa7D5KLrIsWmwpsyKDq1pu0StxuN6BFMGgIsIVSxW2XXiPot1PCXoVDtfbRYo2IJ4lnCU1Dt4NpGfJlJDEBsIM5AJlM0HP1hUi159kUOFgbTs9b7qZJZNCm8VAXCYg2ajG7so2/np1LJkmJE4pl8Lh5TNaexM96nnfffddDBrUsdjWkCFDsGTJklNuVGcIBAK4/vrrcc899/TJ8c91mLUKjIsKVD4efj82QzSzskQiERXk+i1A1Ym76FSgUcposPDZhH5xWlqGoCukmzUdsrVOFimm00/YhLjppptw5MgRrFu3Dt999x02bdqEu+6667j7zZs3Dw0NDXR56aWXTkNrexcFqQbsrLDRKuPpZg0qrG6q3ABsWn9OAm9sUoxq1LZ56HqSUY2SZheddy7JqMLhOofIVd3s8Inm4+kXp0WF1UXV4nSzBrVtXur+VMllaHT4kCwgBgl6FUpbeLKZqFeJXOIWnQqhCCNSd2psXlEF9RSTmsaMAGyQf3mLm8a5GdQKVNs8GCUImK9t84rUx8x4LWrbvLTtA7naQRlcOyQSVg0Stj26inKqSY3iJiclQikmNeravaLEkMIGJyWKAOtCP9bkpOs5CXq0i65fidJmccajPygubtcvTouiRidMnAs5L0mPRgcf9K1RylDS7BLFuCUalKJKxOlxGhGJSTGp0RxVCdrq9Itc7pnxWpQI2p6bqMfhOp74qhUy1LV7RW7naLUn2cgGbZPrSY/ToLzFTafeAdj5D7MExKi30KMerrGxEampHUfjiYmJaGho6GSPUwcphiWcfTcGHs9ePfS0TSx6rmDv45dQiTQGYAtXCuBcRWFhIdauXYtdu3Zh7FjWDfrWW29h1qxZeOWVV5CWltblvlqttssU7nMFY7PjMXdiFq+66FXITdAhR2BM85J0ooHKxQXJGCQoCTClfyIeu7yA9hUquQx/vjgfE3J4F/HAFIMoCWFYP5No9J9l0eKOC3JEROhPF+VhRD8zXZ87KVvkViXxKEShlUklMKjlonIFlw5JFhGFiwYm4bAgmD/VpMYL1w4TpcFfMyodY7N4UjM2O04Ua0bcXQT94jQY0c8kcjFnxmupKwlgY+uEfemwdBNuGJtBB3wKmRSXD0+l1wQA916Uh8GCa7l1UjZGZfDnzk/S45pR6VS5ISRLqHYsmiUWCiYPSMCrAncwyRISkokv/m8iJWgAMDEvAUGBGjsx14Iv755I1TuVXIaCVKPoHo3MNNOipQCb9SkM1ibXKdzniuGpNMYKYJ+DX5BkkhaVYJFl0aFfnEb0zhjUchq/16tgeoD8/Hzm448/7vD7ihUrmJycnJ4c8oSxbNkyxmQy9Whfu93OAGDsdnvvNiqGGGI4IZzKN/jhhx8yZrNZ9FswGGRkMhmzcuXKLvebMmUKk5CQwFgsFmbIkCHMwoULGbfb3eX2Pp+PsdvtdKmpqTln+o1IJNInx3X6gqd8DIc30AstOT6E9yASiTDNDl+32wdDYSYc7pv71tvoi+cbCkeOe9yTfXbeQIhZX9jY7TaBUPikrudE+44eKTXz5s3D/fffj2AwiIsvZkd/69evx8MPP4wHH3yw1wjXqcLv98Pv56W4k5nDJYYYYji70NjYiKQkcRkDuVyO+Pj4Lqc8AIA5c+YgKysLaWlpOHjwIB555BEUFxdj5cqVnW5/KtMknGn0lVqr74XiiseL8estCO+BRCLpNBFBiM7qfp2t6Ivn210MIcHJPju1Qnbc+Mu+cmP36E196KGH0NrainvvvReBABv1rVar8cgjj2DRokUnfJyFCxfixRdf7HabwsLCTuN3TgTncucUQwz/v+BE+4GeQhhzM2zYMKSmpmLatGkoKytDXl5eh+1PZZqEGGKI4cxCwjDHm4u6a7hcLhQWFkKj0aB///4nPc9SS0sLWltbu90mNzcXSiXvm12+fDnuv/9+tLe3H/f4nSk1GRkZsNvtJ136PIYYYjh1OBwOmEwm0Td4ov3AJ598ggcffBBtbXyxv1AoBLVajS+//BLXXHPNCbXB7XZDr9dj7dq1mDFjxnG376zNMcQQw+nFiX6Hp6Qp6vV6jBs3rsf7JyYmIjGx7zJJTmVCyxhiiOH04ET7gYkTJ6K9vR179uzBmDFsUcOff/4ZkUgEEyZMOOHz7d+/HwA6TXaIIYYYzm2cnlnoegHV1dWw2Wyorq5GOBymHVN+fj70+hObupyIUrHYmhhiODMg315PBOKCggLMnDkT8+bNw5IlSxAMBjF//nz84Q9/oJlPdXV1mDZtGlasWIHx48ejrKwMn376KWbNmgWLxYKDBw/igQcewOTJk094OpdYvxFDDGceJ9x3nHDo8RnG3LlzGbA1fkTLhg0bTvgYJIshtsSW2HJml5qamh71A62trcyNN97I6PV6xmg0MrfddhvjdDrp3ysqKhiA7xeqq6uZyZMnM/Hx8YxKpWLy8/OZhx566KQymWL9RmyJLWfPcry+45Rias41RCIR1NfXw2AwxGq6xBDDGQDDMHA6nUhLSxPNz3Q2I9ZvxBDDmceJ9h3/X5GaGGKIIYYYYojht4tzY6jEYdOmTbjyyiuRlpYGiUSCVatWnekmxRBDDDHEEEMMZwnOKVLjdrsxYsQIvPPOO2e6KTHEEEMMMcQQw1mGcyb7CQAuu+wyXHbZZT3eP+YbjyGGM4tYTE0MMcTQE5xw33FSqQdnEQAwX3/9dbfbRM/hcvTo0TMeuR1bYkts6Xn2E8Hbb7/NZGVlMSqVihk/fjyzY8eOLrddtmxZh/OrVKoTPlcs+ym2xJazZzle33FOKTUni66mSaipqYlVBo0hhjMAUtXbYOj57Lyff/45FixYgCVLlmDChAl44403MGPGDBQXF3eYG4rAaDSiuLiYrp+M4kLaGus3YojhzOFE+47fNKnpag4Xo9EY65xiiOEM4lTcOK+99hrmzZuH2267DQCwZMkSrF69GkuXLsXChQu7PF9KSsoptTXWb8QQw5nH8fqOc8Op3UOoVCraEcU6pBhiOPcRCASwZ88eTJ8+nf4mlUoxffp0bNu2rcv9XC4XsrKykJGRgauvvhpHjhw5Hc2NIYYYTjN+06Tmt4wfjjSe6SbEEMNph9VqRTgcRnJysuj35ORkNDZ2/k0MHDgQS5cuxTfffINPPvkEkUgEkyZNQm1tbafb+/1+OBwO0RJDDDGcGzinSI3L5cL+/fvpvE8VFRXYv38/qqurz2zDzgD+7+M9Z7oJMZwg3P4QfMHwmW4Gqls9CIYjZ7oZpx0TJ07ELbfcgpEjR2LKlClYuXIlEhMT8e6773a6/eLFi2EymeiSkZFxmlscQwwx9BQnRWp27tyJcJjvnL/77jtMmTIF6enpGDt2LFasWNHrDRRi9+7dGDVqFEaNGgUAWLBgAUaNGoUnnniiT88bQ9/jo62VsHuDvXrM5VsqEOpjI84wDEqbnd1uM2/Fbrz6Y3G325wOTH55A34taTnTzTglJCQkQCaToampSfR7U1PTCcfMKBQKjBo1CqWlpZ3+fdGiRbDb7XSpqak55XbHEEMMpwcnRWomTpyI1tZWAMC3336Lq6++GtnZ2fjb3/6GUaNG4Y477sDXX3/dJw0FgKlTp4JhmA7L8uXL++ycJ4JDtfZTPsaT3xw+pf1P1Hjb3AF8sr2qy2NEIgxd9wbCeG9TGQKhvh/dP/m/I8clByeLp749CnegbxUSXzCC6a9tEv3GRM084vAF+7wdJwoJzu06K0qlEmPGjMH69evpb5FIBOvXr8fEiRNP6BjhcBiHDh1Campqp3+PxeLFEMO5i5MiNcLO+qWXXsLDDz+Mjz76CPfeey/ef/99PP3003jppZd6vZFnO658e/NJbW/3dFQkPtrWOdE4UeT/7XtUWN1d/v3WZTsBAEUNDjy2iidQnkCI/n/RykN4+ls+gPKtn0vw/JoiNNi9XR63M7fK6+uOdetuOVzXOQn0d0Oelm2pOK6S8/H2Ksx681fRb9Wtnm73EeLDzRVYvqXiuNttOsarHf5Qx+vMWbRG9K2cVUSim6aEI8xJ3a8zhQULFuD999/HRx99hMLCQtxzzz1wu900G+qWW27BokWL6PbPPPMMfvzxR5SXl2Pv3r24+eabUVVVhTvvvPNMXUIMMcTQR+hxTM2xY8fwu9/9TvTbddddh6KiolNu1LmE37/bdcZFZ/CHwhjxzI8iox/m1BGn78TcL+9sYGXz1QcbRL97BWqAUHEBgI3FLbj2n1tQZRMbrcFP/ED/b3X58dG2Kny9jw2gtLkDAACHlyU+bn+InmPl3lr8dLQJl74uVikKGxx4c30J/vTvvaLfhWrPFW9t7tA+gCdI5S0u3PjedtHfnv72KMpbXHS93RPAC9+L37XHVx3G0QZxUGd3hLPF6Rc9h1+OteDXEislXbcs3YnPdorjtV5bdwy3LN1JSYsv2DkRe3FtMXZX2uh6VWvXhLM7tLkD8AbCqGp14631Jdw5w/h4W2W3+zEMg+JGJ2a+sYnuczw8+MV+TH55A10PhiP4w3vbRATt/Bd+xi/HxC6sWW/+irr2rolvb+OGG27AK6+8gieeeAIjR47E/v37sXbtWho8XF1djYYG/ttoa2vDvHnzUFBQgFmzZsHhcGDr1q0YPHjwaWtzDDHEcHpw0qTm6NGjOHjwIDQaDSKRjh16KBTqZK/ewzvvvIPs7Gyo1WpMmDABO3fu7JPzdGYE/vKffQBYdcPuCWL4Uz9gZ4VNtM17m8rQ7PDR9UiEEcUx7K9uBwCUNrtQ2+bBF7trsO4oGx9w8au/dNmeFqcfdyzfBQB4+Qc2PuOlH4pw14rdlCDM+sev1BWW97c12FDUTAkTAOytbu+UOEW7S7490ABfMIwfuXa1uv0AgJs/3IE315fg4+1VWPDFASxceRDVNg8+2V6F7eWsW/IyTikRGrlQOIIBj30vOoebU4gYhkFJE+t2un35bnoftnHHE0JYn+AP723Hkl/K6Hq0YhKtBn22sxoPfnFAtM9lb/6KT3fwpCUciWBfTTuueIslQpVWN976uRSbS6x0m39wxCLAufuiz0vUpH/vqKLG/1CdHVtKxdeTvXA1Nh1rQSTC4N1fyvBzUROauPdm5DM/InvhatjcAdz4/nb8a2Mppry8Ea+uO4Z91W2wuvx4/BtxSvJfvzyAuUv5b6He7sOMNzahqJG9t+R+CoWadzaUInvharq+an89AGBrqRX+UBj9//Y9tpfbMO453tVT1+7F3KU7RQHHRxscaOxGzesLzJ8/H1VVVfD7/dixYwcmTJhA/7Zx40aRS/r111+n2zY2NmL16tU0Li+GGGL4beGkSc20adMwcuRIVFdXY8uWLaK/7du3D5mZmb3WuGiQSqJPPvkk9u7dixEjRmDGjBlobm7u9XMNenwtAGBDUTN2cSPu/x2ox7ItFfhiVw1m/3MLHD6ewAVCETQ7fHh+TREqBRK+0xfCHz/cSY28j1Msrv3nVqzYVoWHvzpIg0itLj+C4QjKW1wIRxiqbhypt+OyN3/F+iLxdVa1evDj0SZ4BQSs1e1HWYsLDAPctnwX8h5dI9rn+TW8ukGuq51zhzU6WPIilbCuJ6LUtLrYf5vsPiz5pQyfcK4yhYx9fR5bdRj/2siSBZmUNZvCNgn/TwiU28/+1uYJ4pIotWd6QTKkEmDG65sQCEWw9rA4VdfmDlBjTXDr0l2idUJMAJZoLFx5CP/dW4stpSxBaXH6YXX58cx3RykBCkcYes02dwAKmQR17V7c/OEOAGIljTwbQkjbuP3IsZy+EGzuAGpsHd05G4rZ57jgiwNwBUJY/H0Rbl++G4vXFALgn8foZ9ehqNGJejtPkl2CTCqh+vPVnlr8cqwFyzj3WbsnILp+0t5bl+2iRIyQYwAikjLngx30eQLsexmNxdx7RPa77l8np1jGcPIIRxi0Rj2L6PerutUjGpD5gmG4/Hw/xTAMHFEDmzZ3QKScNjl8IrLuDYRxpF48SIg+RqvLLzpGJMLQb4Ks/z/2zjs8imr949/dTbLpvXeSQAgEQigJRSkSDWBDvRbEriB6ufcqXgQUQUXFgooCChelqSgWilSBUEIKgfTee9m0zdZk+/z+mNmzOymQhND8zed55klm58yZd9o53/Oe95zpHntY3qJgNahEUhXL26w3UD26vrs3NsVKDSuPVrmadVyd3oDceglrH/NGJwBUtCpYXdsdSg0azRplhl7sEJsdA6CvR3fbuo807P4e5dVLWftodAaWR9tgoHo0QivMvNVAzwapXKUljaO+qGpTsmxrknahRc5uiJuHJVBUz3vXLFOxGszSLi3rmVCqdaxuegCsawrQHvnuz9FQMCBRU1VVhcrKSlRVVaGqqgpPP/00a7tGo8GKFSuG1EBzzGcSHTVqFLZu3QpbW1vs2LFjSI9jfFD+yKjH87su49GtqaTr473DhejSGno81CNWH8dsxtPy2LZULPjfRfzr5ywczaPd4BUt9P7P7riEe8f4QKM3QM6IIuPDQVHA8LeP467PzyP0rWP4x9YUAMC9Xyf1WrEYeZqpdAG60rrYzcvR/cE3suL3XPg526BFroZap4ezjSUAukvF/CVvVaiRUSMmlWsJ41kxfwHPl7ZCb6BgxQgdY+FU3qLAtI/PkHTDVtEia/L6BMR9cb5HIU1fCwoGij5Om0KNJT/Sw9cNzHmcKe4pYs09O8bzdbSmJ8w276br0uixKaEM93xp8oody2vCXwUiXKw0ed3u+/oCKlpNokGnNyDeTHxdrBSDoih8cJQWItHrTiGrtgMLvzPdC72Bwp2fmrpzypln4PmdtABrU6ihNKtwkspNHiFzzAspPo9HBOGMz85hyQ8Z+D7JFAf03uFCLNqTjnu/Nom6qPdOIqdOQtZ/vVyHYhG7m66jU4MFMQFws7MCAFS2srvLdHoDq2DbkVwFsVKD4W+bPHBDHeh9JQbqsf3tt98wcuRIWFtbY8yYMTh27NgV09+KHMxqwLRPzpAKp0Wmwp2fnmV5JR/+NoX1PHx0rAjPmXnwTuSLMO1jUx5KtQ4xH53G4dxGkmbRnnR8cKSIrP94sQaPb7tI3quqNiXGvnuSiGqDgcKMz87h9wzTvD+rD+XjSbN34WJVO+7fnESOq9LqEffFeWTWdpA0C79je1+/PVeOh7akkPVikQwj3zlBRIlOb8D4dadwushUHrz8Qzo+OWFquO3PasADm5OJ7ZWtCsR8lMB6VuduvIA/c0zn/+lfxXjtl2zTNSsQYdaGc0RgdGp0GM+870bmb0kmYQEA8N2FSsRvNJUXRU0yTPzgNClXVVo97t+chOP5prJp2a/ZWH0wj6zvS6/D3V8kEgHSIldh9ufnWY2we75MxO6UarLPR8eKsHSvqes/pbwNI1YfJ9dModZh1oZzrLLmX3uz8MlxUwNnR3IVHthsclik13Tg/s1JREAbDBRmfnYOB7MaTHn8nIVVf5hsP54vYnXTV7UpMfXjMyxR9tLudHxz1nS/h4oBiZqgoCDW4ubmxtr+zDPP4JlnnhlSA40MZibRwU6iZRyp8sZvOeQ3866hT04U9xq0KjeroFIr23E4pxFvHciDj5M1KlsVSKmgHyRjy0nMdOtU9hHgm9vLqCrzFriRTKZLy0j3wNSOXgKTVVo9KtuUeHZqEL67UImYDxPwwDhfYvuPF03dMhtPl+Lp73tWGuYtQAD44lQJ8dSIlRqIlRrEfXGeeLS6q/LyFgVLOAA9u3PMK/SHv0nBz5dq8fYB08vz/uFCAMDskZ7Y/+pUAEBRE11gHX9tOgDgLbP0ufVSXKxqZ12Tb85V4NMT7PgcbbeYnxa5muUxWbQnHbIuHaL8nVjnY06O2f3j8YC4L873iK35xCwuqE2h6TXW6IJZ91degxQPbknGWOa4JwpEWHekkJXe6D0yxzyI3EABB7NMhfiK33PRodTCxdYKZ96YCQCo62B7AOQqHcTdnr3x604BAKaG0uVA93t5vRioxzYlJQULFizAiy++iKysLMyfPx/z589Hfv61jTi80SQUN2Px9FBkMwL1XEkrFk8PIZ5MsVKDqaFurFiuOnEnPB2F5F29VC3Gk7GBSGCEQG69FE9NDiIeTIqi4GJrxYq9u1jZjgfH+ZJyKrm8DY9PDCDdqyXNcsyJ9CYNC4qi0CTpgp+zNRmVebmqAw9H+5FnOa9BimlhbrhcTQsDuUqLEV4OLK9KVq0EE4JdSIPuaG4TnpkShEvM+V2u7sDLM0JI5Wow0LY3SlWkMk0pb8MTkwJIrN2Z4hYsnh6Cs8Wt5PrcPdoLl83CCBolKrjaWZEGx6UqMR4Z70+u+6nCZrx4xzAcZ667tFOL0b5OyDMTl8nlbRjt60TK6yO5jVgQE0hCEQoapXh6chCSyuhrptEZ0KXRo8nc9op23DPaC8VMeZZY2oaHo/2Ih72iVYHJIW7k3gFAfUcXnGwsiRD6q0CEZyYHEdtz6iS4b6wPsmro667S6uFmb8VqNF+sFCPYzY48M+eZ5yyNuUa5DVI8NtEfZxiPs05vgCWfB7laR2xPq2zHPyb4kzrscpUYC2MDcYZ57toUaozxd0KJaOgnthx0oLDBYEBpaSmSkpKQmJjIWq4Hg5lJdLCTaBljPIYKG0sB3j1ciCe30y2XuFH0OZiLlgUx7G47Y/hI99bvuPdPXfV43StOYyE3wssed42kP/h356dn8dzUYDwZG4TfMuoh7dJifKALAJPn6IEoXzw1ORAqrQGdGj1ejxuB/z09geTbfbTSlrMVsLESAAB0BooUtvPG0POHZHcTXwCIF8bIvst1rJbXQ9+ksLav2p/HOu7eSzXIrpOgo1OD8YEueHCcL578jg4ytrbgI8jNlrW/Rm8g8S2WApP4M1bIVevnAaDd2OZMNfM2GWlTquFuL8RIb/oDa927xIqYgrT643thdJbN+OwcK40xjsWItEsLP2ebHscCgK+eGIf9mXRr2NHaEmvvv3qgq4eDEABd4VkwglOrN7C8dweyG9Ag6YSLrRW5fx3dPJEZNR2smB1z1jB22FgKrmrPUDBQj+1XX32FOXPmYPny5YiIiMC6deswfvx4bN68+YbYO1R0afSYPMyVtNSz6jrwVGwQSpnyKq9BinEBzhDw+dDqDVBp9bCy4CM6wIW8ezXtnXhsYgAymEotp16Ce0Z5o5npeq5oVSLEww6+TtZolHTBYKBAAZga6o4MRoDk1kuwaPowUsnl1Utx10hP4r1ulqnh5WiNME8HlDMt87wGKR6bFIAS5h3JqOnA81OHERGTV0/bbm0pgEKtI2LozjB3IjiKmuR4MjYQmTUSYvvMEZ7E+1PZpkCIhx2GudmiTkx3dUi7tJgd4UUq9cImGZ42q+QzazsQF+EJmUoLiqKgVOtgayXAGH8nFDQy72+7EvOj/Yi3s6BRhscmBpBGTH6jFGP8HGFtIUCXRg+1Tg9LAR/RAc6kYVPcJMeLdwST657fIMNdIz1JvGJpsxwjfRwQ4m6HWkZQylVaTB/ugew6ep+Lle14dVYoacQWNEoxMdgFOgNFus497IWI8HEkQqhW3IkHx/kRwZVTL8ETkwKJyCtolGK0rxOcbCwh7dTCYKCgNxgwKdgFucz5ljTL8VRsELkPWbUduCvCi4i+yjYlQj3tEexmS2xvkatx7xgfcp1zGyR4ekoQspn7fbGyHdPC3CHg84Z8LrFBiZqLFy8iLCwMERERmD59OmbOnEmWWbNmDamB18JgJ9EK87RnVYb8XobB+jpZAwDeuHsE6/cA154Vkq3QVNgLLfh4KpYWME1My3/FnJF4ZLwfa5//zB4OAHjlR/YoIiPWln3fum2JlQCAfzN5LGZmH+bzeHhrXgQCXG3QKlfDzc4K9kLTN00DXW1x5F93kPWXZ4Tgg/ljkPfuPQBocWLdS8U1fYQH+d+B6fL5+OExxB37ZvxIvDknHEv3ZiLM0x7H/3MnFk8PAQBMC3PDzHAP3DuWnjPkbEn/JofbxogrldaA+VuSyYveIlNDwXiGbKwEOL98FoK7CRsjvZ0Lj8fD2/MiyHX5c+k0PDbRv9f9m2UqUADeuW8UHpvoj++TqhAd6Nwv+835edFkrJgzEgDwyLcpaJB0YYbZNQVoAeZoY4nSZrogXTc/kpXm4Wg/jPCy75G3lyMtavIapMhYfTcAYE9qDXlGALqV+MKudFS0KojQM1ZyRl7aQwdxr3twNADAh3n+AWCEJy3qJgS5DPTUB8xgPLapqams9AAQHx8/5B5egI5tWvlHLlYfzMN3Fyrx+ckSfHOuHCt+z8XHx4vxzblyrDmUjzWH8jFnYyKe23kJy/ZlY1NCGcJXH8e7fxbg85MlWLU/D6FvHcOq/blolqmYytYCkf5OpDEkkqoQ4GpDWuX5DVKM8XdCuLc9SpvlKBHJEe7tiPFBzsis7SBewBB3O1QzDZ88Zh9HG0tIOjXIqBFjYpArxge5IKtWglpxJwJdbTE+yBlZTOXaIlcjzNMBchXdMi9skiHCxxHejtZokamQ1yBFpJ8Txvo7IbdOSs9/RVEY5etIhEBOnQRTw9ygVOtBURSy6iSIDnShxUSDFKXNCgz3ckCkHy0uKIqCzmDACE8HVLXReRQ2yjDK1xG2VhZQqHXIqpUgKsAZo32dUNAohVipgYutFSJ8HEgDQ9KpRYCrLRmoUNAow2hfJwS72aGqTUmuxxg/J+TWS6DRGWDB52G0ryMRApWtSoR50u+a3kCR8430c0R+oxRlzQqEezsgKsAZOXUSUBQFA0Uh1MMe1Yy3K79BitF+jnC1s0K7Qo3CJtqO6ED6uks7tXCyscS4QGdkMcKgTUFfdxnTS1DQIMNoX0eEezugWCSju+d8HBAdSN8rrd4AAZ/PbJeTfcYFOkPPCKHsOimiApwx1t8J+Y1SlLcqEOZpT99/xnat3oBAN1sSd1PYKEOEjwNc7WgPDy2MHDE+0AWZtR1okang6SBEpJ8TEeD1HV0I93JAJyOE8htkGOPnhDBPB5R183BfK4MSNUuWLMHEiRORn58PsViMjo4OsojF4qtnMAgGM5PoYCfRcrC2hJeDqdB2srGEq50V3po3Et8/OxEA8Mk/xjJp2R865/N4mBLihoL34gEAvy+Zgp9emky2U1TPr4y+MjMUE4Ndyfri6SF4LY4WS8Yb/vsS9sRivs42eHpyEFkPcbeDtSUfO5+b1Od58Xk8hHnak1aMLVNxG82xsRKwAteMFbuDtSUOvDoVw9ztEDPMFW/OCafTWwqwZEYo9rwQQ/YxxuU421qhQdKFbxeOR7C7HV6YNgwylQ7lLQpE+Dji8Um01yy5vB3u9kIsY8RhHqPk/3ptOvEy9Eb8aG9898xEsj4tjO4CKWiU4vW7R2CUjyOEFrRo6e51MbJkRihrfT7T/eZqZwWFWoe4CE9E+DhiuGfPT93fM8oLT25Pw5niFkwLc0dcBO19e3teBKo/vpekM7fRHDsrk6Aa5euIuAjag2Z08e9+IQYr59JCZ9/iySj7cB6JVwIAV1sr2JkJ0onBrvjnrDCsvjeC/Hb2vzPx68v0c9Op0cPJ1hInXruTtKaM9kb6OYLHA96+N4I8m119DAH3dqJFu3lMGZ/Pw5k3ZrDsuV4MxmMrEoluiIcXADwd6JbyQ9F+8HexwfhAFzjbWCE2xBXTwtww1s8Zd4S5Y2a4BzYtiMYL04ZhcqgbJg1zxa7nYzA7whMjvR0xJdQNXz4+DveM8sb3SVUoFskR7u0AR2tLKNQ66A0U+DweeDwePByEaJGp6ErexxFj/JyQVy+lK2g/J7rlLpKhql2JYe524PF4sBLwodLqoVTrYC+0wHim8syskWBCkAstSBokKGmmj+vjZIMmqQqdGh3xyAW42KC+owt14k4Eudoi0s8ReQ2m4471d0JOvQTFIjkifEy2UxSFLq0etlYW8GfyyDeztahJhuw6CaIDnTHcixZo1e2dCHazA5/Pg6WAD7VOD5mKrvjDvR1QIpIhp16CKH9njPJ1REGjDNl1HYgKcIafsw0aJSrac8W8Q/ZCC8hUWlS2KhDibodxAc7IrZciu06CcQHOGMNUyKXNcoz0poVTl4YWYHqDAQI+D8PcaSFkFEaj/WhBZqzkR/s6orBRhmaZGt5O1uDxeMQz0apQw9PBmhFgMhQ20gIlOtAZWbUdKBLJMNLbEe72QoiVGiJyANr72ipXo6pNiWHu9pgY5IKMmg4iYiOZ+1/RqkCopx2sLPjQMd5ZpYa+3+HejigRyZFbL8E4f1oI5jdIkVXbgehAF0T60nk0y9SkAWO87m2M7VH+zsitlxBxNSHIBZk1EhQ0yhDp5wQPByHaFGpGGPLB4/HgZGOJDqUG5S20eBrjx+62GwoGVQqVlZXh999/R1hY2JAacyXMZxKdP38+ANNMokuXLh3y4wkt+fjnrFDIunRokqrw3bOmymmYux1R6jZWAlR/fC90egPC3j6OiUGuCPGwIwW8uVgBTEOBf1syBY9u7dlSTFoxC/4uPT0LE4NdUf7hXIQxgZkOQgvykAOAu70Q4wKcMYvpXtrzQgwmBrvggSgfxH2RCBtLAcujYi+0IN1C5R/Ow+T19LDdEA+6i+pMcQurkopmuqYsBMCrM8OgUOnw86VaUvECdIFOe4JsSQzH3DG0B6a7V8TfxQb2QrqF9dFDY2Ap4EFowSexLuHeDgh2s0WrXI2z/52JWRvO9bgmxm48APjxRXpI7+p7R2HacHf8c5bp2fR0tMZjEwPwyYlicm4AMDnEDSvmjCSBhRufoIf5ujLBsrRdfMyJ9EZugxSHcxrx9YJoPBDli3MlLWTIu/k+3b0V9ozozXv3Hjy27SJpMQ7zsMMzk4NxobwNTjaWcLKxxOLpIfifmQfluanB+McEf7jb0+LOYNZlZCcUgGfWJBFa8PHgOD9odAZ8cLQIVevn9RDPAKDTs2N2npsWDCdbS7z5ey4crC17pK/8aB4O5zbiQlkb3O2FGO3riPTVcUgqa8OJfBEWMR63EI+eXqLblVWrVmHZsmVkXSaT9VvYTB/hwXrPrsZwr56C2RyDgcKe1Gp4OVpjXIAzAMDNTojM2g4EMh7ISD+6la3U6GAntMBoXydsv1AJjc6AuAgvUiHnN0gR6Uc37Eb7OuGvAhECmLJmfKALEoqa0SxXwdvJGu72VqhoUUAo4OMuRrDbCS2QVilGpB8dzzUx2AVpVWJQoIVtpJ8Tzpe2okQkw6szQyG04KNB0oXL1WJSDloJ+ChskiHYzQ4AXa5k10mg0uphYyXAKB9HnMgTwUBRWB4fDqGFAFq9ATmM2DDafrmqg4grWoDIUN/RBX8XG3g6CvF1ghx8Hl1G8Hg88Hk8ZNR0YAwTizbK1xH59VIYKMBCwEdUgDP2pFajRabGU5ODYC+0gKRLi+w62vsD0I3AgkYZghjbI3xo7420ixYcRtuFlny8dEcIrC0FUOv0KGqiBQoAhHrYo6hJDqEF/fJG+joik/GIBbjYgscDasSduFwlxmQmVs3R2hJJ5W0Yw1z3UcxxDRQFAZ+HCUEuJND5/ihfuNsL0apQI79Bhkhfeh9/F1vk1kvhamvF3G9nZNR2QNalhZOtJUb5OuLnS7WwEwrw79nD6QauTk+8UADdg5HfICWNxbH+TjhX0opKRlzxeXQsXmGTDJOM99uCj7wGCSJ8HJh9nJHbIIXeYIClgI8xfk74LqkSj00cuu+rDcpTExsb2+d3U64nV5tJdCh5LW44lsePxLr5kSxBA9AtYB8nG1p1My5dC6YF8MyUIFKhlnwwp8/8JwW7ImftPT1+93K07iU1yDGO/+dOTAp2wcYnorH0LlPFracoUokd+dcdmBjsAlsrC4R5OuD5acG4vDqOJUA+fywKPkyrW8Dn4fLbtHve1c4KOxhvj51V35rX2lJAhnQbufR2HCYGu8LL0RouzMtjzsszQsj/QgsBfniR9vBYWdAqPp/xbhn5ZfEU5lh8fPhQJPndPP4o/714zB/nS879sUkBPWJSzrwxA6/MpL0ytkzBuWruSEwIcsErM0NR+sFcHPrnNJLeKESM9zTA1Raf/WMsLPg8PBBFe3NmhtPi0djdNC7AGamr7uohJIy2OFhb4vh/7sS+xbTX7uCr0/DoRH9sWmCaL6X7iDprSwERNAAt9ADg0tuzYSHgw9HaEpnv3A1LAQ8dTECiFVNYmtsRYyasR3o7kK6+f90VBksBv9duuLmRtODl83l4cJwfNjwahZVzR8LX2Qbu9kLMj/bD1qcn3JAuJ3MG47H19va+IR7e6wGfz4OVBR9JZa2kch0X4IRdKdWkworyd8bBrEbiXfZ3sUFVmxINki7S/ehsa4VThc2kYhwX6Izvk6rI/Rvl64iM2g7YMh5ECwEfBooOdh/tS5//OH9nbL9QSZ75iUGu2JFURfKM8HFEVq0EKq0B1pYC4hE6V9JKjjMx2AWf/VWC2GGuzLk441heE/EAejoI0SjtQotcDU+mLPR2ssHhnEZy/uODnLEtsYIIpTH+TrhY2Q4hU44ILQTQ6A3IqZcSMRHl74SdyVUkbnB8oAu2JlaSgPtgN1tUtCohU2mJh9pOaIHE0lYyGGBCoAs2nSnDxGA6j7H+Tjic04hAV5PtTTIVqtqUJAzB28kGv6bXEUEWFeCMrecrSCNxlK8jLlW1k3tN28/Hxap2RPk7k+P8cLGanH90oDN+uVRLxJWzrRWaJCooVDpSXjjbWOJQdgO57lEBztidWk3Od3yQC04WiOBqR6d3srGEtEuLJqmK1Av+Ljb4PaOOXLPRvk7YkVRNzj/CxxEZNR0Q8GkPFI/Hg62VAImlreQ44d6O+D6pipzL+CAXHM5pJMcNcLXpddqLa6HfoiY3N5cs//rXv/DGG29g165dyMjIYG3Lzc0dUgPNudpMokPJhCDXq6YR8HmssfpH/30HInxMBaBR0QL03CtPTAoglSJAP0ilH8xl5WkuFA7+cxomh7DtiPBxxG9L6K4ga0sBvl04HvGjvRjVTqeJ9HOCrZkgWXv/aFbsDNBzDoXu/Gf28CvG7Wh0Bpat5l0uAOBi17PVv/yecCSvvIusd+9eMuZn7NIzjqQS8HhYGBuEmeF0C3j9w2PIPvZCC+Jh6QtjBW8cHeViZ4mXzbqerCz4pMAATAGv5oHE1pYClH80j5Xv7hdi8PsSOk8LAZ8UBgB9PX59eQp8uwmsCUEuOL1sBiwE/B4CqEujx3BPezwZ2/tcT54O1rhrpCc8zbpGXe2s8Pa8iCu2+ClQeJiJ2bIQ8BHgYotAV1u8cQ/djdh9xJWbnRW+fWoCiRu7lRjMt5+mTJnCSg8Ap06d6ve3om42cyK9YSHgE89s3CgvNEq6iKdyjJ8TNDoDnpsWDIB+3oPcbBHCdDUZ8+jU6BHKeNSiA+nuljuHuwOg3z0nG0vcNdJUlk4IcoHQgk/ey0nDXJHXICUVpb+LDQqbZJjBvJfWlgJE+DiSPAHg4fF+6NLo4ch4AR+bFIBzJa24g0kT6mGHnDoJ5jAimsfjYay/ExFSAC2wCxplJDZufKALqtuVuIc5f3d7IZplKuIdAOgReaN8HYnInxHugdx6KRFkMcGuECvVpNuYx+NBpdHjUTOPwd0RXshrkBJxde9YH/xV0IzJIbQHZbinPbJqJZgS4k7ycLe3wigfR3LdX7wjGCkV7UT4zQz3wIWyVtzHNCwcrOk4OfNyfnaEF7wcrYntk0PcIOnUkjyi/J2RVNZGbAfouM1pYabr/tKdIajv6CLlz53D3XGpSkw85+72QpQ2y3HPaFMevs424JuVSQtiAlEn7iIDIWJDXHE0rwnzzLzvPB7wxCTTNbtnlDdaFWrSUHp8UgCsLQXkGRnr54TL1WIy0pbH4+GFO4ZhSKH6CY/Ho/h8PsXj8XpdjNv4fH5/s7zhSKVSCgAllUqHJL9PTxRR6dXiIcmLoigqaMWRHr/p9QaqQ6m+6r5ple1UYWP/zutAZj3VJOkasH3mfHS0kJr52dkrplFpdVfcrtXpqTNFzazf0irbWeu17UrKYDBQFEVRx/Maqf+drxi4sWZsOVtGfXy86IppDAYDFbTiCNWlubL9Q01yeSuVVNY65PnO35JE/ffXbLL+/uECavbn58h6QpGImrXh7JAftzeG4h385ZdfKKFQSO3atYsqLCykFi9eTDk7O1MikYiiKIp6+umnqZUrV5L0ycnJlIWFBbVhwwaqqKiIWrt2LWVpaUnl5eXdMJtvNAaDgdLrDax1rU7PSqPW6rvv1q98r7a9P2kGkidFUaxz6Q2tTs/Kpz929Ifux5WrtFfcPpjjSjo1Vy0re7PL/Djd1/uDptvzoFRrqU71le1QqrVX3E5RVI/nrDuDvS/9fQ/7HVNTVXX1D/39f2N5/MirJxoA5i0cI3w+D869dOV0J2bY1T1LRuZH+1090VWwsRKwRsD0hrmnqjcsBHwSA2Sk+3kEuJrii+ZE9v5V5YHw6syrx4HxeDxYCnhkCPSNYmpoz/s/FAxzt0Owux1Zj/BxZJ3brHBPzBzh2duutySPP/44WltbsWbNGohEIowbN67Ht5/4fJMXcerUqdi7dy9Wr16Nt956C8OHD8fBgwcRGRnZ1yFue3g8HswdgTweDxYC9vNs9AQMNN9r2d5bmv7sw7/Ku2jRrSu8P3n2h+7H7e7x7r59MMc1j40crF1Xuz690T18wPYK4QYDSdP9XnRnqO5Nn/lTVB/TzV6B9evXw8vLCy+88ALr9x07dqC1tfW6zip8LchkMjg5OUEqld7UfvK/A1q9AXoD1Ws8BgdHX9yO7+DtaDMHx9+N/r6HgwoU3rZtG0aO7OmlGD16NLZu3TqYLK/Khx9+iKlTp8LW1hbOzs7X5Rgc/aevAFMOjuuJWCzGwoUL4ejoCGdnZ7z44otQKK48z8XMmTMZz4VpWbJkyQ2ymIOD40YyKFEjEong49OzK8DDwwNNTU297HHtaDQaPProo3jllVeuS/4cHBy3PgsXLkRBQQFOnTqFI0eOIDExEYsXL77qfosWLUJTUxNZPv300xtgLQcHx41mUPPUBAQEIDk5GcOGsaOWk5OT4evr28de18Z7770HANi1a9d1yZ+Dg+PWpqioCCdOnMDly5cxcSI9zcKmTZswb948bNiw4Yplj62tbZ9DuDk4OP4+DMpTs2jRIrz22mvYuXMnampqUFNTgx07duD111/HokWLhtrGQXMt051zcHDcWqSmpsLZ2ZkIGgCIi4sDn89HWlraFfYEfvrpJ7i7uyMyMhKrVq1CZ2ffc2Nw5QYHx+3LoDw1y5cvR3t7O1599VVoNPSkX9bW1lixYgVWrVo1pAZeC+vXryceHg4OjtsbkUgET0/2KC0LCwu4urr2+ckDAHjyyScRFBQEX19f5ObmYsWKFSgpKcH+/ft7Tc+VGxwcty+D8tTweDx88sknaG1txcWLF5GTkwOxWIw1a9YMKJ+VK1f2CODrvhQXFw/GRACD/6AlBwfHjeN6lwOLFy9GfHw8xowZg4ULF2LPnj04cOAAKioqek3PlRscHLcv1/QFOnt7e0ya1PcHFK/GG2+8geeee+6KaUJCQq64/UoIhUIIhX1/FJGDg+Pm099ywNvbGy0tLazfdTodxGLxgOJlYmPp74SVl5cjNDS0x3au3ODguH25/p/VvQIeHh7w8Oj/x9+uFeOUPFwfOQfHzcH47plPj9XfcmDKlCmQSCTIyMjAhAkTAABnzpyBwWAgQqU/ZGdnA0CvIzh7gys3ODhuPr2VHb1xU0XNQKitrYVYLEZtbS30ej0pmMLCwmBv378vBMvlcgDo9xd3OTg4rg9yuRxOTk4D2iciIgJz5szBokWLsHXrVmi1WixduhRPPPEEGfnU0NCA2bNnY8+ePYiJiUFFRQX27t2LefPmwc3NDbm5uXj99dcxffp0jB07tt+2Aly5wcFxK3C1smNQMwrfDJ577jns3r27x+9nz57FzJkz+5WHwWBAY2MjHBwcrvtUzRwcHD2hKApyuRy+vr6sTxn0F7FYjKVLl+Lw4cPg8/l45JFH8PXXX5OGTXV1NYYNG0bKhbq6Ojz11FPIz8+HUqlEQEAAHnroIaxevbrfswNz5QYHx82nv2XHbSNqODg4ODg4ODiuxKBGP90sEhMTcf/998PX1xc8Hg8HDx682SZxcHBwcHBw3CLcVqJGqVQiKioKW7ZsudmmcHBwcHBwcNxi3DaBwgAwd+5czJ0792abwcHBwcHBwXELcluJmmuFC/jj4Li5XGug8M2AKzc4OG4+/S47qNsUANSBAweumEalUlFSqZQshYWFFABu4RZuuclLXV3dNb3/mzdvpoKCgiihUEjFxMRQaWlpfabduXNnj+MLhcJ+H6uuru6mXy9u4RZuoZerlR1/a09NX99wqaur6/dwTg4OjqFDJpMhICAADg4Og85j3759WLZsGbZu3YrY2Fhs3LgR8fHxKCkp6fFtKCOOjo4oKSkh6wPxuBht5coNDo6bR3/Ljr+1qFm1ahWWLVtG1o0XxdHRkSucODhuItfSjfPFF19g0aJFeP755wEAW7duxdGjR7Fjxw6sXLmyz+MN5FMKvdnKlRscHDefq5Udt0en9iARCoWkIOIKJA6O2x+NRoOMjAzExcWR3/h8PuLi4pCamtrnfgqFAkFBQQgICMCDDz6IgoKCPtOq1WrIZDLWwsHBcXtwW4kahUKB7Oxs8omEqqoqZGdno7a29uYaxnFbUdGquNkmcAyStrY26PV6eHl5sX738vKCSCTqdZ/w8HDs2LEDhw4dwo8//giDwYCpU6eivr6+1/Tr16+Hk5MTWbjPI3Bw3D5cs6gpKytDQkICysvLh8KeK5Keno7o6GhER0cDAJYtW4bo6GisWbPmuh+b4+/D7M/Psz6KptUb0K5Q30SLOK4nU6ZMwTPPPINx48ZhxowZ2L9/Pzw8PLBt27Ze069atQpSqZQsdXV1N9hiDg6OwTIgUbN+/XokJCQAADo6OhAXF4fw8HDcfffdCA8Px9y5cyGRSK6HnQCAmTNngqKoHsuuXbuu2zE5bi6STs11yVdvMIma39Lrcc+XidflOBxDi7u7OwQCAZqbm1m/Nzc39ztmxtLSEtHR0X02xLhuaw6O25cBiZpvvvkGrq6uAIA333wTYrEYGRkZ6OzsRGZmJiQSCf773/9eF0P/P1DaLEflNXaNfHehEk3SLrLepdH3SPP5yRKs+D33mo5zOKcRX5wsYf0WvPLoNeXZHZVWj3HvnxrSPI1o9SZR897hAlBXSNtfzIVSd3R6A1pkKtZvpwqb0anRQaMz9LpPl0aPORvZYuu39DrsSq66Jjs3nylDUdPtGSdiZWWFCRMmkMYVQM8jk5CQgClTpvQrD71ej7y8PPj4+FwvMzk4OG4SAxI1ra2tRNScPn0aGzduRHR0NKytrREVFYXNmzfj2LFj18XQ/w98cLQId31+nvWbTKWFVt97pVfWLMfZkpYeeWTWSEgFG7HmBJq7VaYVrQo0SEzCp0Wm6uERufPTM3j3z76DKTNrO/BXgam1/FcBHc9guELFPlB6E2Td0RsonMhvumKaD48W4lB2A+s3jdk1VesMsLUSkHWjB3Ag/HypFk9/n8b6bcXvueR6nC5qRsxHCaztXyWU4qnv0vC/xAoAwNPfp6FYZBIbGxNKUSySs/ZZ/nsujuX3HjtipLpNyTqX7s/PF6dK8dGxoivmsezXbJwvbSXrwSuPYuy7f11xnxvFsmXLsH37duzevRtFRUV45ZVXoFQqyWioZ555BqtWrSLp33//fZw8eRKVlZXIzMzEU089hZqaGrz00ks36xQ4ODiuEwMSNUFBQcjPzwdAD6uysGCPCBcIBFAqlb3tOmRs2bIFwcHBsLa2RmxsLC5dunRdj3ctnCpsxhenSgHQlcv9m5JY2zs1Oqz4PRcKtQ4A7ZkA2MJg4fY0bDxN5yHt0iKprI1sO5jdgDWH8nsc9597M/GmmSemXWESLCKpCsfyREgqbyO2fXSsCK/tyzZLr0aduAsJxbRo0ekNWGa2HQB44KGkWY5WuZrkAQAKja7P6/HyD+kkSFdvoKDS6nGpSgy9gYJYqcEvl2qhM6uAjZVxd09G8MqjpPIPfesYlvyYydr+yo8ZePfPAnRp9NDqDdh+oQqpFe2sNN0renMN83VCOf7zC/t81x8rglKt67aPaaeMmg7kN0hZ2/el10HO7NNd6ynUOoikajTL1FBpaVsulLWx7Nx2vhK9YX7c3SnV+Op0GUoY8bNqfx5mbjhHtu/PbMD9m5LQoTQ9AxZ8Pi6YPUfd86wTd2J/ZgNq2k3vspONJSgKECs1aFOo+/Qu3Qgef/xxbNiwAWvWrMG4ceOQnZ2NEydOkODh2tpaNDWZhG5HRwcWLVqEiIgIzJs3DzKZDCkpKRg1atTNOgUODo7rxIBEzaJFi7B8+XKUl5dj6dKl+O9//4uKCrqVWVVVhddffx333HPPdTEUME26tXbtWmRmZiIqKgrx8fFoaWm5+s4DIKu2AynlpkI/taKdVLb1HZ3YctbUF59W2Y7vk6qQUdOBsmZTq/qdg/nYmVyFrxPK0KXRQ6HWIY+p9E4VNuP3jHpM+/gM9qXX4fsLVShvUeBSlRgAWxhYW/LR0EF7VTJqxHjq+zS0yFWYteEc+Dwe6sRd+PBoYY9zECtNga+SLlOFllMvIf9/nVAGlVYPhVoPvYEiwbITPjgNAKgT08d9+vtL2J/VQEQXRVHgM1MFfHKiGCKpCjXtnQCAytbeRe228xX4q6AZufUSKNU6bD5TjpHvnMBj21LRKlfjTHELVu7Pw+u/5pB9ntlBC9Zvzpmu9+qDeQCA4iY56sSd5PeUCvp+GQwUjueLsCulGvdvTsK28/TzebKwmYgcgPYCtchUOJ5HV34Nki5SiTdIOlEikqPNLHj4VGEzRq+lPRWSTg3+80sWhq06BrFSA5VWj98z6iFT6VDeQj8DxnvSydxL4/VKZp4rkVQFlVYPncHA8hq9d7jnvcyo6UCTtIuIXXMBVt2uxJenS7E7tRqfnCjGz5fokYBGsSmSqVAskmP2F+d75Gv01pW3KMi5ASZxbZwPgqIoSLu0kKt1GL/uFCZ+cBr70utY3h5zz9+NYOnSpaipqYFarUZaWhpiY2PJtnPnzrHi7L788kuSViQS4ejRo2SwAQcHx9+LAYma//73v4iLi8OoUaOwfft2ZGVlYcSIERAKhQgLC4NCocCmTZuul62sSbdGjRqFrVu3wtbWFjt27BjS46h1BuQ3StGmUIOiKCzYfhFtjLcjsbQNn/1Fx5Lk1Uux52IN9mfWY+PpUsz7+gJkKi0A4IeLNUhhWt2FTVJS2ZwvbcVfBSL897ccdHTSafk8kAp6xggPPLn9IrHFgs/HwexG+vwZz0p6dQeq2pSki2n7hSocyKrHWjOvjY1Zd0pCUQtOFzZjxmdn8fIPGaxz7dLoodbpcaGsDXd9fp4cw4hObyBekZHvnIBMpcWwVcegZ2rW3zPq8e9fskj6iha6a+vfP2dhf2Y96ULal06PIHl9Xw5e25eNRrNKkIKpi+RwTqNJuDDeh42ny6A3UAheeRQ/XqSv42v7svHglmSSRxXT5RLylqn7s6GjC1sZb4dYqUFZi5wIxzs/PYuYjxJYwqXdzJtR0izHs4yo0ukNqGXuT7NMhfqOLhxi7kl1uxKyLi3Z76uEclyqEmNHcjUAQKmmz98Yw7PwO7qLKu6L81CodZB0avG/RLZHprsXJKdOginrz6Cug7YhvaYDJSI5ThU2I6GIFvR702rx7bkKss+kD08Tb5jx/AH6OTOKqOeY83vk2xR0avREmLz+azYAWpgDIOdqzjsH81l2T/v4TI8015OBemx/++03jBw5EtbW1hgzZsxt1U0uV2l7dCmaC3qAFpXmHt46cSdLaEq7tChsZMdRJZezvXU17UrIVaZnuVmmYk1/oNLqkV0nYe1T0ChleT0bJV2s7m6lWsdq7AHAxUq21zS3XkKeU+NxjaLcmEdGjZi1T1GTjOVdzKmTQCQ1HbdNoUZ9h+kaqXV6ZNR0sPKobFWwPMOVrQqWHZ0aHfLqTd5XiurZ1V3eooC0U2u2LmeVbQYD1cODa2z4GLlQ1sq6v3KVlnUuGp2BtR0ALleLWfeqslWBFrlpH53egNJu1717HF1aZTukZmVXUZMMOWb3l6KoHsc9VdjMuu417UrWNVOo2dcMAOv6AECJSE4ae9eDAQ/p/vrrr5GTk4MXXngBzz33HF566SWsWrUKJ06cwKVLlwY9a+fVGOykW4PBw0GIVrkaEz84jYmM10Kh1qKiVYG3DuRhuKc9AOD+zUk4mtuEgkYZXO2soNVTePr7Sxi2ih0w+8i3qfjoWDEA4Lmdl2DBZ8+I+PmpUlhZ8PH4xAC42wuR3yCDpFODjadLkcoUABRFIb+BfiiNosrYbQHQYmF3ag1ZP5Znirv4PqkKaw7lE2+KOSv+yCVdEXwesD+TPXdHp1aPkd6m0R9j3z0JANjJVNoAiFAAgLMlLZj28Rn8mdOIZb/m4KFvaOHhYG1J0kg7teyYFq0BmxLKyLpRuJhjLMQ3LYjGkhmhAADjVXwo2g9vH8hnedcAuotLYdZl9OLu9B6B2JYC0ytg9Ij9mk5fA3sh3b3aptBAx1QYsR8lsAr6kwXNkDAFw92jvHA4pxGbzpQRwTn3q0Qo1DqkVLTB3V4IAKxuHbXOgLgIT7Qp1AjztMc9o7wg6dLgvk0X6HPkAfmNdCGx4g9Tl2JaVTu2nq8gYqs3mqRdUHcTSHd+epb8b29tgcM5jaRgm/bxGegNpufMyGvduh6NzB7piZ3JVfjkBP1sm1dE15OBemxTUlKwYMECvPjii8jKysL8+fMxf/580pV+q7P5TDle3H2ZVCZpVWLM/eoCqaRkKi2e+F8qfjd7d986kEdEKUCL3tf2ZZHKpLpNiYXfpbEq3MV7MvC12Xv4yfFivGbWDXuupBXztySTClel1ePer5NwJJcWvRRFYeX+PHxuNoBg6/kKPLvjErE9p06CJ/53kbxDnRodnvjfRfxgVnatPpiPlWbP+u8Z9Vj4XRqpPOvEnbj36wtILqfLRr2Bwn9+ycK3Zh7dj44Vsbyepwqb8eT2i2QQhbRTi/iNiThuFp/27M5LWHfEtM9XCWX4x9YUInyy6iRY8mMmCpj3UanWIe6L89iXbiqv3v2zkBUScCSvCfdtSiKN3eo2JeK+SMSFslZyzf75Uyb+d8HUQPj8ZCle2HWZrJ8tacGsz8+R6y7t0uLRrak4kmsSWC/tSceWM6bz/y2DHtFptD2ztgOPfJtCBKXeQOGZHZew77LJ9k9OFOPdw6Y4yn2X63D3l+dJIyu/QYpFe9JxsdJU3i/8Lo3Vc7HpTBme/M7UKD9b3IK5XyWS667RGTD3q0T8ZFbGm1/zoWBQ89RERERg+fLl+Pbbb7Ft2za8++67uPvuu6/rF2wHM+nWYGcG9XAQYvsFeoSJsfUe90UiZjNBvB4OQijUOvg4WZN9jAVMTp0EV4oxpShAwIgaZ1tTRd+mUGNsgBN5cce9fwobT5sKmN8yTAWW0SuRVG4K5OwN42ikCUEu8HAQsrZNCHIBQHfLGLGxFPSINdl3qY4Iq6vxetwI1osG0N6WRkkX7Mw8Rxq9gdXSLGtRoNGsZRIzzBWTmaBaVzsrzB7piXamO21upDdWzh2JuAhPdDHXalqYOwDgjd/oritP5lz13W5Eq1yNdw6xg5+NoubLx6Pwa3odCXgGADuhBSiKwh/dhJ4x7sXR2gJbz1fgkW9TAAD/mT0cAFjxKlo9haSyVjTL1Jg9kv4u0YzPzgEAnp8WbLp2+7JR3qJAmKc93vg1hwiLc/+dif2ZdJBzlVmrac2hgh4tz+40SlSsyuKntBrW9qxaCf71cxbrt9C32B6M7nFE5rQpNXjvcCF+Y0Rgf5+Ta2WgHtuvvvoKc+bMwfLlyxEREYF169Zh/Pjx2Lx58w2x91opFskxfYQHyltoIXChrBWvxQ0ngwRSytvx4rRhOMkE7ivVOtgLLcDn0R4KAEivFmN+tB+yayUA6IbI05ODyLPaIldhtJ8jOQYAiDs1GO5pTzwAl6vF+NddYaSrN6dOgmemBBFx0SRVwc/ZGs0yNRExeQ1SxEd6ExFzIKsB7z0wGmcYD2NWrQQvTw9FOuOJUev0oChazBsr0+TyNiyeHkq8RKmV7fjP7BE4XUSfb4lIjjmRPqhhyhSKomP0HKwtyIjDpLI2/Hv2cFKpnypqxvL4cHIN5Sotxvo5szxeZc0KvHjHMOIxvlDahtfihpP3P7O2A2/cPQKXq+n3UKXVw9pSAAs+n7w3f2Y3YO39o5DMXOeE4hasmx+JE4yYqmnvxEPRfizvTlWbEhE+jkTEpFeL8dIdw8g1Siprw+p7I0h8pd5AIdDVFuVmnqe/CkT4z+zhyDJes4p2vBkfToL/i5pkeGZKENIYgaI3UODzeHC3F5I66HxpKxZPDyHlzOVqMVbfG0HKSJFUhWmh7sg188yUiOSYG+lNvGQnC5uxZGYoTjP3O7degn/OCsOlavq4SrWONVp3KBj05HsGgwGlpaVISkpCYmIia7lVGOzMoA5MCz0qwBkAXXmZk1LRjsi1f6HJrCIubTYVBq/FDccnj4zpM/+f0miVKrTg44P5kQCA//ySjWapCjqDSVS42lmR/809I+bHXPfgaNZvj08MwKq5I1m/TQ5xxayRpg/9XX47Dl89MY6V5snYQDRKVWiW0eLhyL/uAAB8eKwI3RxL8DUTc+ZMDXPr/fePz5CuOADIrpMgvaYDo31pD9CiPelkW6CrLeyFFhAxhZGNpQAJxS34/GQp1s2PhAUjQigK6GS6toLcbAGA3I8WxmNg9Jb8Y4I//F1serXNKOIifBxhJeCzuuf4PGDE6uPEM2bkl8t1uHeMD5bMpD1GcpUOc0Z7k/MxEj+aFuCFjTLwAKxj7rWReWNMQ4o97IUI8bDDzHBPlihyNPNwGe9N93u36M5h5P9npwSR/x/blkqEHwC8fcDUgvzvPSNYeUT69T4Xi/loLHOsLPhQMK1PY/zW+EDnXtMOJYPx2KamprLSA0B8fHyf6a/lMwk6vYFU6AYDBYOBgt7AHk1HV9ym9StNBdCuUMPF1hKxw9xIRVDUJMejEwJQwHQn5dRLEDPMDQaKPl5BowyRfk6I9HNCYaMMBgMFCsCEQBdSyV2uFmPx9BBk1TIVVlUHJg9zg5UFHyqtHq1yNdzshIgZ5opMplIrb1HgiZhAXGbsSKsS48FxvsRDl10nwbgAZ/i52KBB0gWFWgdbKwGmhboTIVDdrsSjE/1ZFeWdI9yh0RlgMFAoEckxyscBkX5OyG+UgqIoaPQG3BHmbtqnSownYgKIxzOrrgPRgc6ws7KAQq1Dk1QFHydrxA5zRTqzj0imwkPRfqQSz62XYM5oHzKIIqdOinEBzgj1sEdlmwJavQF8Hg+Tgl2RzpxvQaMUz04JJnbk1ksRG+IGiqKg0xuI7WP8nZDfQNuuN1CYFOxK4ilz6iR4YKwvETE59RJEB7rAwdoS0i4tZCot7K0tMH2EO5IYz3NZiwKPTwpAZo2E2D59hAeUjNetqEmG0b6OiA5wQWGTDCqtHlYCPmKHuZL7m1cvxT8mBqCUEWiXqsSYyjQGdXoDKlvpBlWUvxPyGNu7tHrMGe1DvEr5DTLMj/YjjavM2g6MD3KGq50VxEoNud+xw9yQyYjnJmkX5o3xIXakVrTjzuEe0DLvSV6DFGP8nPt8/gfDoETNxYsXERYWhoiICEyfPh0zZ84ky6xZs4bUQCODmXRrsDOD8ng8XHhzFg79cxoA4NGJtBiyF1rg5RkheJWpzAC6cO/Ov+8aDnOHh9GBtfHxcTj5+nTyu7WlAE9NNlVCSo0er8WNwPL4cADAueUziQ3Gfmnj0GNj18iCmEAUvh9P8nhlZihenhGKZ8wqt9fiRrAEkoeDkOxvxFwIFa+bg0g/J+LdGePnBCsLPgJcaWHQKFXh2L/vZO2fuHwWrC0ErN+M5wEA7vZWuPAm+9n445Wp6E7im7PgZmar8dqlVLSzxKW1pelYk4JdWXlMH+HBWg/1sIevE1vU7H4hBuMDnbFyPx2/wwMPclVPr4QxFubnRZNZ12zLwvHwdDCJO38XG/B4PKS9NZv89tUTdDDqd0lVKGqS9XhWIn2d8OaccJwuakGTVIUDr0yDlyPboya0ZO/z8vQQuNmZ0iStmIW376VH8ex9KRZr7x+Ni6tmoy/uZYTU7Ai2x9NcPJmz8XQZ3rh7BBZPD8FTkwPJ7xHeDuTaGCjgs3+Mhb+LbZ/HHSoG47EViUQDSn8tn0l4+0A+Rr5zAvO3JGPB9ot4+NsUxG9MRNR7J/HglmQ8/X0a7v06CbO/OI/Ra05gzNq/ELn2Lzz9fRqCVx7FnI2JiP8yEbEfnUbwyqN4YddlPDzeH+MDnZFbR8ev8AA42VoSb0CpSI4RXvYI9bBDRasC2XUdGBfgjHEBzsiqlaCyTYlh7naksgVo4R/gagudga6QL1W1I2aYK6ICnJFdJ0EWU2GN9qWFkUKtg51QAD9nGzRK6MZDbr0EY/yc4WZvhXaFmhE1LohmjptbJ0GUvzNiQlxxsUoMjc4AKwEftlYWUOv0TFenFJG+TgjztEdFqwJ5DVJE+jkRMVHf0QV/FxuM9XdCLjPIoUWuhpejNSwEfGh0BuQ3SBHlT59vdq0EufUSjPV3xsRgV1yuFkPapYWjtSV8nW1IY6lO3IkAVxt4OQrRLFMho6YD44NcMCnYBZerO1DcRAuU6EBnZNZKyNQILnZW6NLSorSgUYpRvo4Y7uWA0mYFchukGOPvjCh/Z+TWS1HVpkSQmx1GMNsBuqvQydYS1pYCdGnoGKWoAGeM9XNCXr0UmTUdmBDogvGBLsipk6BLo4fQgo8Qd1psAbTICfWwh7u9EC1yFS5WtmNyiBsi/ZxQ0CgziVp/J+TW0wJFpdOT8kunNyCjpgMTg1wQ4eOIoiY5sph7NTHYFZeqxKhoVSDE3R6jfB1JL4RYqYa7vRB2QgE6NTra1iAXRPk7IadegsyaDowPdMEYfyfk1dO221gK4G4vJDF9OfUSRAU4wcfJGiKZClm1tBAeSgb1le4lS5Zg4sSJOHr0KHx8fK5rt5MR80m35s+fD8A06dbSpUt73UcoFEIoFPa67WoEuJoKaA8HIRytLeDjZAOVRo/3HozEN0xQZtH7c3ChrBXP7TT1gfL5POJx+d/TE6A3UJg+wgN2zEO18fFxeG1fNoTdKrm5kd6I8HGEWmfAZ3+VwEFogWB3OwCAzkDB2dYS2WvuQfDKo/htyRSIpCpYCPiwEPDh62SNKaHuJL0xfgOgu1h4PB4s+DyUfzQPAOBsa4XnpwUTD5DQTJAYBYO90AIqDd1qW3b3CKRWtJMRUaN8HTHGz4m0QGyFAnRqdXCyoVsco30d8c9ZYfghtQYimQov3hHCuqY/vRQLa0sBflsyBY9uTUX66jgSmxPKxCwBwNJZYXhsYgB+Sqsh3UwAXajTdpuu4dxIbxzPF2H385Pw2r5sHMpuxNRQNyyZEYJh7rakpVv98b0AQAKBAcBCwCN95QDt8TIGNwPAlFA3/PX6dFZA7D8m+COhqBnH80VYxng+HBjhNTPcg1zHTo2eeJUAwILPg85AwcZKgFdnhoGigA0nS+BkawknW0ssjw9HlL8zfJ2tYWvFfkX/PXs4a04d4zFy1t4DJxv6mnibedJejxuB/8QNh1ylxZh3T+LZqcE4mtcEO7N8F08PYcVWvHv/KMyO8MKdn57FhbI2fPzIWPg526BdocbJgma0yNX4adFkPLrV5OkwPnd/B1atWoVly5aRdZlM1m9h88k/xuKTf4zt8TtFUaxyknhzzLqjFWod7KwE4PF40OoN0BsolnhvkatQ1CTDKMYj6GhtCUmnBgaKgoWAj3EBtCcmp06KJ2ODoNMbsD+zAbZWAkwIcoGtlQU6NXq0yFWkfIjwcUCxSI4acSeC3GwxOcQNiaWt6NTo8fB4PwS72WHzWTmpsAA6Fkus1IDH48HKgo8xfk7Ib5ShrFmOME97CPjAz5fqaA9TiBscrWkBVtGqQIiHPXNcR+TW0131VhZ8RoB0IKtWgjfjw2FvbYEfL9bA19kGUf7OsLYUQKMzQCRVke7lkd4OKBHJIZKq4OUoxIRgFySWtkKtM+C+sT4IcbdDZasSBQ1S4om0tRJA2qWFgE+XiVEBzsipo4XQkpkhkHXZYv3xImj1BkQHusDZ1grSLi3KGSEBAD5ONmiUqqBU00JhHCMEM2s68M59oyDg8bAvvQ7OtpaYGOwCKws+dAZ68k3jdY9kys7qNiWC3Wwx1t8JGbUdUKp1mBvpA38XG9R1dCKHEWh8Pg82lgK0K9QQ8HkQ8Gnbc+ukyKjpwJOxgWhXaPC/xErIVVqMD3SBo7UlFGodqts7EexGv5/h3vT9Vmp0cLC2xIRgF6TXiFHarMA/Z4XC3V6IHUlVcLG1QswwFwj49D1ukatgy9Rfo3xooWMUPnKVDudLW6HTU4gf7Y0wD3uUNCuQ30gLVABwsbUiAeRCCwGi/GkBmt8gZTXAh4JBeWrKysrw0UcfISIiAs7OzqxWjZOT05AaaM7VJt26Hrx7/yhY8Hng8Xh0xc1UTruen4TYYa4Q8HmYGU537Zx6fTqW3U1Xbgtjg1D6wVzcM9obc8f4EEEDAPOj/QCAVELVH9+LqvXzMJHxOBgrah6PBycbS6SsvAt7XojBwVdpr03lR/MQ4ePI6lLycbaBuba8dyzdIi/9YC4Auiule4wJn9lh76JYWAro/w8vvYNsP/jPaUhedRf+eHUqlswIxWOMx2rf4skAgDH+9L2+9NZsuNsLMdLbEVnv3A0ApJvo15fpWV6NrUrjduPLPdLbgawbu2OsBHzYWApQvG4OnogJBJ/Pw9NTgllCTcv0tx9kPFkfzI/Et09NINdt4+PjMDnEFbtfiAGPx8OcSB8Ur5vToysRAHLfvQehHvZYEEN7In58MRaf/GMsvl7AHvbr52yDio/m4fQyk7ft40foCswoPmyYSqj7CKbE5bSXqvD9+B6zFztaW5B7AQD/nBWGO4a7kwrgxGt3YvcLMfh9yRTYCS3A4/GIN8vo/TE+S0aqP74XK+aMRMww+plysLbE5bfjyLq92XUY7esICwEfDzHPpau9EAGutnhuajA5bwBwsxcieeVdsBTwYGclwK8v08/B3EhvUuBfbwbjsfX29h5Q+uvxmYTuDT8ejy5TBGZ9u/bMvQXohoi5oDH+llYpJuIiOtAZ+y7XkWs/LpD2kMiZmBpjhXypSkzuu6+zNQ5mNSBmGJ3HhCAXnC5qhpONJXg8Ht1QqZeiqEmGcC8HWFsKoNIakFLRjthhdPfyOH9nfHehEtFMd+NYf2cklbXC2lIAAZ9HexUYr8sYplIL93LA/sx6RPjQ73tsiBu+T6oild7EIBekV4vRIlfD09GaCLCksjZy3FAPe/x8qRaTmHOJ8ndGckUbLJhGW6SvE/IbZChukmGElwNdZlsJcL6sFdHMNRvt64SfL9USkTMhyAXnS1sh4PMgtBDAw0GIdoUGmTUdJPzAx8kaf+Y0mh3XCYeyGxDINNJoz1QHOjo1cLWzgpMtLTbTqztI7GKwmx1+Sqsl92FcgDMulLXC1oq+55F+Tsitk6KgUYaR3rTtQgs+ksraiCd6Sqgbvj1XQTwbUf5OyKztYCYPtYC/iw1qxZ3IqpWQe+NhL8Tx/CayHh3ogn2X68hgl/GBLsio6UCDpAv+LrawZuIq06raSX0UHeiCrecqEcvYPj7QBRfK6OvO5/MYb48M+Y1SRPg4gM+n663Thc2IDjAe1xk/pNaQrqapoe5ILGuDUqNj1Y1DwaBETWxs7A35gGV3rjbp1vXguWnDsDA2CMf/cyc2Pj4OrzOiZWa4J/a9bJqWPXXVXQjztMe/mWBRo8Lti/2vTsX3z00i6+aFXvc+dl9nG0wf4UFaw/zuQS4ADGZzxwB0AVD98b3EBj6P1yOA2Zh+tI8TeDweEpfPIkIFoCtKR2tL+DBdN3MjvZH5zt2IDaELmY8eGoOUlXexgpD5fB5+eimWxHk42tAPrBfjPXCxs0KIhx3CGTFjL7TAKbMuOYDuchHweT0KdXN4PDr+JsKHLpyM3XiVjCeKx+Phl8VTWKObrC0FyH3X1FV3/D934o27R5Cul9X3jULh+/GYxsQGjWUK3L2LTHOgCPg8hHk6sK6RUagZjwuAjDo6v3wmHp3gT7rubK0sEOJuh4fH+5nO10IAK0Hfz8pIb0fMGOFBChkAyHjnbuS9e0+f3UYA3RU5JdQU52R+n5xtLFG1fh5KPpiDB8f54avHx+HdB+j4LKMXYfW9Edjy5HhWnpYCPso+nAcejwcHa0ssiAnEt09NYHVvXk8G85mEKVOmsNIDwKlTp/r9WYVbhTuHu+PrhDKMZyrKuAgvrD9ejDmRtDjzdbLGqcJmRJm9w96O1qhqV5IGwaRgV2w5W4H40fQ+EwJd8c25CkxlnhNLAR+tCrp7x/gsTwhywc+XaomH6M4R7vjmXAXimC7MKH8n/HypjuTB5/PgaGOJBkkXeYenhLph+4UqzBxBN8QmBrngSG4TYkPoZ9rNXoiceglG+ZgE5GhfR5wraUUgEzM3b6wPvkoowxSm/LljuDs+Pl6MeWPoc7Gy4KNNoYazrRV576eEumHb+UoiBGeGe+Dj48XE9nAvB/yaXkfKNIBuYNV1dJFnenKIG3YkVSGGef/mRHrj0xMl5Lp7OlrjcrUYYWbCPjrQBZeqxaTsfGCcL75KKMNdTEM0KsAZm86UY/oI2vtsJ7SAzkDBycaSNAhjh7nh94x6jGXu56xwT3yXVIXJjK0jvR1xLK8JE4Ppc+PxeHCzt6IHZjBCIX60NzaeKiPiamKQC364WIMYRig62ViiUdIFF7NBK+MDXZDXICXPzJQQN+xIriL3d9IwV/wvsRIzmG5+a0sBZF06WDK9BgAwNdQN2xIriYiLHeaGzWfLMTuCPv9AN1ukVbYTD9KQQvWTnJwcsuzfv58aNWoUtXPnTio9PZ21LScnp79Z3nCkUikFgJJKpTfblCvS0NFJPb/z0oD2efTbFOqt/bl9bt93qZYKWnGE9dtHxwqpmA9PDcrG/tKl0fU47tXoVOuosmb5FdNsSiilXtx1+VpMuyrN0q4B205RFHUwq5765mx5n9v1egNlMBjIukqro5okXYOycTDsSKrsc1vQiiPUwaz663bsoXgHf/nlF0ooFFK7du2iCgsLqcWLF1POzs6USCSiKIqinn76aWrlypUkfXJyMmVhYUFt2LCBKioqotauXUtZWlpSeXl5N8zmoUCj01O17UrWb0VNbJtKRDJKrtKSdWmXhmqRqci6Xm/o8aw1S7sorU5P1mvalJRSbcpD1qWhLle1s/Zp6Ohkrde2Kym11pSHWqtnpdHpDVRaJTuPrNoO1ntQ0SKnpF0aVh4VLexyoE2uYq3XiZWsPOrESqpZajo/lVZHXaxoY+3TPc+8egnVrlCTdZG0i5VGo9NTZc2yHudrTotMRcnMbDcYDJREqWGlqWxVsNZTK9pY90qu0rLWNTo961woiqJy6ySs8+3S6Ci93rQu7dL0OD/zPI15dGl0ZL1OrKTqxKbz0ej0rOtsMBiolHL2NaxsVbDud6tcRbWa7SPt0lDH85pY+5SK2NewoEHKuu5Xo7/vYb9FDY/Ho/h8PsXj8XpdjNv4fH6/jbzR3CqF0/VArFD3eHjNOV/SQj2wOYn1W1Wrgkrt9sLfLhgMBlZBfD1QqrXU8LeOXddj3GpcrmqnJJ2aqyccJEP1Dm7atIkKDAykrKysqJiYGOrixYtk24wZM6hnn32Wlf7XX3+lRowYQVlZWVGjR4+mjh49esNt5uDgGDz9fQ95FNW/L/fV1NRcPRFDUNDQBv4MFTKZDE5OTpBKpUPST87BwTEwbsd38Ha0mYPj70Z/38N+x9QEBQWRZe/evUhISGD9FhQUhISEBPzyyy9DcgLd+fDDDzF16lTY2trC2dn5uhyDg4Pj1kYsFmPhwoVwdHSEs7MzXnzxRSgUiivuM3PmTBKYa1yWLFlygyzm4OC4kQwqUHjbtm0YOXJkj99Hjx6NrVu3XrNRvaHRaPDoo4/ilVdeuS75c3Bw3PosXLgQBQUFOHXqFI4cOYLExEQsXrz4qvstWrQITU1NZPn0009vgLUcHBw3mkGNpRKJRPDx8enxu4eHB5qamnrZ49p57733AID19V0ODo7/PxQVFeHEiRO4fPkyJk6cCADYtGkT5s2bhw0bNsDX17fPfW1tba/bd+k4ODhuHQblqQkICEBycnKP35OTk69YsHBwcHAMltTUVDg7OxNBAwBxcXHg8/lIS0u74r4//fQT3N3dERkZiVWrVqGzs+8PgXJwcNy+DMpTs2jRIrz22mvQarW46667AAAJCQl488038cYbbwypgdeCWq2GWm36cvBAvuHCwcFxayESieDp6cn6zcLCAq6urn1+8gAAnnzySQQFBcHX1xe5ublYsWIFSkpKsH///l7Tc+UGB8fty6BEzfLly9He3o5XX30VGg39TQdra2usWLECq1at6nc+K1euxCeffHLFNEVFRb3G7/SH9evXk24rDg6OW5P+lgODxTzmZsyYMfDx8cHs2bNRUVGB0NDQHum5coOD4/al30O6e0OhUKCoqAg2NjYYPnz4gL+z1Nraivb29iumCQkJgZWVabbSXbt24bXXXoNEIrlq/r21uAICArihmRwcN4nehmX2txz48ccf8cYbb6Cjo4P8rtPpYG1tjd9++w0PPfRQv2xQKpWwt7fHiRMnEB8f32M7V25wcNx69HdI9zV9dMHe3h6TJk26esI+8PDwgIeHx9UTDpJr+aAlBwfHjaG/5cCUKVMgkUiQkZGBCRPo73ydOXMGBoMBsbGxV9nbRHZ2NgD0OtgB4MoNDo7bmaH9ktR1pLa2FmKxGLW1tdDr9aRgCgsLg719/z6mZ3RKcX3kHBw3B+O7NxgHcUREBObMmYNFixZh69at0Gq1WLp0KZ544gkyQKGhoQGzZ8/Gnj17EBMTg4qKCuzduxfz5s2Dm5sbcnNz8frrr2P69OkYO7bn17R7gys3ODhuPv0uO67zzMZDxrPPPksB6LGcPXu233nU1dX1mge3cAu33Nilrq5uUOVAe3s7tWDBAsre3p5ydHSknn/+eUouN33rpqqqigJM5UJtbS01ffp0ytXVlRIKhVRYWBi1fPnyAX3ygCs3uIVbbp3lamXHNcXU3G4YDAY0NjbCwcGB9VVsDg6OGwNFUZDL5fD19QWfP6gZJW44XLnBwXHz6W/Z8f9K1HBwcHBwcHD8fbk9mkoMiYmJuP/+++Hr6wsej4eDBw/ebJM4ODg4ODg4bhFuK1GjVCoRFRWFLVu23GxTODg4ODg4OG4xbpvRTwAwd+5czJ07d9D7c33jHBw3Fy6mhoODYzD0u+wY0NCDWwgA1IEDB66YRqVSUVKplCyFhYU3PXKbW7iFWwY/+snI5s2bqaCgIEooFFIxMTFUWlpan2l37tzZ4/hCobDfx+JGP3ELt9w6y9XKjtvKUzNQ+pruvK6ujpsZlIPjJmCcndfBwWHQeezbtw/Lli3D1q1bERsbi40bNyI+Ph4lJSU9vg1lxNHRESUlJWR9IB4Xo61cucHBcfPob9nxtxY1q1atwrJly8i68aI4OjpyhRMHx03kWrpxvvjiCyxatAjPP/88AGDr1q04evQoduzYgZUrV/Z5PG9v72uylSs3ODhuPlcrO26PTu1BIhQKSUHEFUgcHLc/Go0GGRkZiIuLI7/x+XzExcUhNTW1z/0UCgWCgoIQEBCABx98EAUFBTfCXA4OjhvM31rUcHBw/L1oa2uDXq+Hl5cX63cvLy+IRKJe9wkPD8eOHTtw6NAh/PjjjzAYDJg6dSrq6+t7Ta9WqyGTyVgLBwfH7cFt1f2kUChQXl5O1quqqpCdnQ1XV1cEBgbeRMs4ODhuVaZMmYIpU6aQ9alTpyIiIgLbtm3DunXreqTvKxaPg4Pj1ue28tSkp6cjOjoa0dHRAIBly5YhOjoaa9asucmWcXDcGDafKbvZJtxU3N3dIRAI0NzczPq9ubm53zEzlpaWiI6OZjWQzFm1ahWkUilZ6urqrtluDg6OG8OARI2DgwNefPFFpKSkXC97rsjMmTNBUVSPZdeuXTfFnlsBg4FCRk3HzTaj32TVmmzV6Q24UNZK1hd+d/FmmHRDWf5bzjXtv+FkKQwGqt/p9QYK494/SdbLW+SYsO7UNdlwM7GyssKECROQkJBAfjMYDEhISGB5Y66EXq9HXl4efHx8et3OxeJxcNy+DEjUKJVKpKWl4Y477kBERAQ+//xztLa2Xn3HvxnSTu3NNoGg0OjwyLdDIzJ1egMUat2Q5NUbBgOFh74x2dqm0OCH1BqyLbm8HS0yVb/zW7YvGyqtfkhsO13YfPVEZpwtacF3FyoHfJzfMnqP4xgIKl3/z1mrN0DSqcW6I4UAgI5OLdqVGqi75bH2UD6+OFnC+k2jMwAAqtqU+F9iRY+86XvWNlDzr5lly5Zh+/bt2L17N4qKivDKK69AqVSS0VDPPPMMVq1aRdK///77OHnyJCorK5GZmYmnnnoKNTU1eOmll2647RwcHNeXAXc/nTlzBllZWYiLi8NHH30Ef39/PPLIIzh+/Dio/yffxowya/nqDRQMBgpavaHXtO0KNQBA0qlBRo14wMdS6/RYti8bADD783M9tuv0/bvmLTIVSpvlV0zz5h+5iFz7F8qa5Si7SloAKBGZ0hQ1yVDTrmRt79Lo0aZQ4+PjxdidUo33mYrViFipISJKzVSgMzecu+IxJZ0a/JpOdwfsz2rAyHdOsLZvSihjeSaSytpIhV7eougz35f2pJP/2xVqKNU6cpzk8jYsMtsOAOeKW/C7mUBRafWobO07fwA4nNMIgH5mAOBEvgir9udecZ/e6NToyTH/8W0KLpS19hApRnTMsb5PqgIA4uUJX32CJQj/KmhGVXsna98Rq4/DYKBQ3CTDR8eKcSK/CZJODdkuU2mx8Lu0Adt/rTz++OPYsGED1qxZg3HjxiE7OxsnTpwgwcO1tbVoamoi6Ts6OrBo0SJERERg3rx5kMlkSElJwahRo2647RwcHNeXQcXUREVFYdOmTWhsbMSuXbsglUpx3333ITAw8LrHt2zZsgXBwcGwtrZGbGwsLl26dN2O1aHUoL6js9dtMpUW0i4tHt2agqe+T8MDm5PJthKRnFQYEz44DQA4ktuER75NZbWGVVo9qeCMVLWZhIFGZ8Brv2Rjf1YDVFo9KlrZogEAxjNdCRM/OI0/cxpxskCE4JVHyXaKotCuUGPTmXIs/91Ugaq0etaxAKCZ8ZLc/WUi3j6YT66BTm9A8MqjqDWr9O7bdAHxGxMxbNVRdCg1WPlHLmZ8do6V3/8SK/HY1lSUtyhwOKcRu1KqWdsf2JyEdgVdSRor5U6NniWO8xukMBgoHMpuQGJpK/IapHjzd7YQSCxtxfM76efg81OlkHRqIVfR3rSLle04nteE5b/lIO6L89DpDaAoinghzDFW+Hd8chY7k6vIccpbFDhfwvZIavQUikVyfHOuHJsSyrBg+0Xc9fl5VLcp8cqPGVi0J72HF+mPTFoEfXKiGABQLJLhbDE730yz7jmVVo9//ZyFF3ddZqXp0ujRodTgzk/PIr2mA09/fwkiae8eLp2Z2NbpDdCbXds9qdXkf1EfHjKFRgcHa0sAwJIfM5HfYBoJ9OR2WtD86+esAXnYhoKlS5eipqYGarUaaWlpiI2NJdvOnTvH6pL+8ssvSVqRSISjR4+SuDwODo6/FwMSNd0nvREKhViwYAFOnz6NiooKPPfcc9c1vsU4k+jatWuRmZmJqKgoxMfHo6Wl5bocL3rdKTy/8zL+KhBhyQ8ZECtNrdSx755E1HsnkVkrQYtcjaImGWI+PI2zJS2I35iI1Mp2rD9eBABokHShi2ldf32mHLuZyn3kOycQ+tYxPLA5CQBd6cwy81SMWH0cx/PpYapLfswAAJwsEKFVTnt/zEVJm0KNf/+chR/TagHQAoWiKAxbdQwTPjiNHy7WoKGjE3oDhaImGUa+cwKzNpzDm7/nIKmsDRqdgSWw1EyFHL3uFA5kNQAApn92FgAtlIyVG0UBCrUOMpWp20okVWHL2XI0SDqJBya9l7gfnYFCjViJx7alknQAUNqswM7kKvzr5yzctykJIW8dw/LfcvHMjks9hBhAC4HEMnY3SBNTyau0ejRKVaTbJ+ajBAxbdQwjVps8i0YBpNTQ59Cl1eNipcmrptUboNEbWMc2euaO5jbhbEkLsmolAGhP0/F8EU4VNmPK+gS0KdTEgxPibg+AFntqnR4GAwW+2StVJ+7Ew9+k4N0/6TlUZCotDuc0IqG4BZm1HXhsGz0PS1mLHPmNUvIcAOhVpNF2mu6pQq1jrX90rJiV1kBR0OkNWGzmldLoDDD/zIqNFR9avQGfnChGYRP9DBzOaUTMRwng4ODguNkMSNRcqXspODgY69atQ01NzTUb1RfmM4mOGjUKW7duha2tLXbs2DHkxzK22gV8HgoapDhRIML4daeQUyfpkdbYrdEiV2MF07rX6ylsO0/HXNzzxXkkV5gq3eTyNqSYrefWSwEAEWvorhS9gUKDpIt1jHOMp2DxDxmY9CHt/ZnVS1dNFiMe0qs7MPerC6xtbQoNdiZX4aXdpkrr1/R6PPV9GtYcysflapPwyKmXorCRrrSMHh4+j34GVv6Rx8q3U6NHk7SLXIvk8jZ89lcJfk2vB0VRMHR7braer8AHTJeQSmvApSoxy6vxfVIl3jtcSLprAEDDiIg1h0wVvpGNp8vQfY7JOnEnlv+Wg++Ybhcj5sK0XUl3CY55l+6uulgpJh6HJCZWZMK6U+TYz+28hFa5GvUdnaTrydpSACuL3l+jjk4tHt2airs+P48T+SJ0mHXdhK8+geSKdvB4PIS+dQwA0CJnezskZrFb50pacamKFlr//CkL/+0WcNyl1ePj48VolHRBJFUhs7YD+Q1SbDIbLSXr0vXw+gCm9/pobhM+PFaEk4XNWLo3EwAQ98V5fHLC5F1sU2gw/O3j+PZczxibG8lAPba//fYbRo4cCWtra4wZMwbHjh27QZZycHDcSAYkatauXQt7e/srprleX7EdzEyi1zKJljFWY06kN8sL8eCW5L52AUALG4Ado6HU6IkoAegK6Fge3ecfP5qOAzhd2Exa0b9n1GHax2eueJzL1b3H58iZGBWZSotiUc+4mA+OFvUQTADwy+W6Hl1h3SvZYHc7FDXJsS+9Dj5O1uT3+I2JGOlNjxCJ++I8uswESlcvXWwfHy8mYmNamBtJZ+TX9KsH04599+QVtx/PF101KDe9ugOPfGt6dhbtSe/hcWhXaqBkrmlNeyeW7s3EHZ+cJdv1BgqWgr5fI6N3Z8mPGcTjZcQ4ak1voFAn7kSbQoMHx/liV0o1ksracM+XiQAAB2sLtClMXhn6mrKP0ypXY+v5Cjz1XRomr0/Aw9+k4L5NSdiTWgPjKzn9s7MkxsaIQq3DsFWmCn5ncjUAursUoIWVuZB/+YeMPs+1+32+XgzUY5uSkoIFCxbgxRdfRFZWFubPn4/58+cjPz//hth7rRSLZKS8AGjv4w8Xa1hxfEdzm9BhJtjPl7ayRhZWtipYMWBavQGf/VXMisU6kttIGjIAcKlKjONmx22Vq7Er2dRIoCgK/0usYJUTSWVtyGMaacbjHso2PfdavQFfJ5SxnpWfL9WiTmzq2k6pYDf66sSdrKB8vYHCntRq1qCGfZdrWTGLGTUdSDLz3rbK1dieWMkaOXggq571Xh3La2LF3ZWI5Pj5Ui1ZV2n1WHson9UA25tWy4qlO1XYzDqutEuLnWbXTG+g8POlWpZndXdKNdIq28l6QaOUNXBBrNTgh4s1xHaKorDtfAWrHD9ZIGJd93aFGgey2DF/u5KrSI8BAGxPrES1mff5rwIR9mfWs/L4w+yZUev0ePfPAlYeh7IbWLGUZc1yEosIAJ0aHf4qELEcInvT2Pe7eyzmtTJgUWNrazukBvSXwcwkun79ejg5OZElICCgX8fKrO0grfMfL9b0iAXpi5dnhPQr3YWyNvx4kX5ZXO2sALBF0ArGExIV4Iz40V6IGebaI49Ht/Y9JfzEIBes2p/X53ZzHIR9z7/43E5Tq770g7mobFVi3te09ye2m03ZZhXf6oOmyqKjUwsLft9CN7mcfplLm68cZGvEKAIB2nMU4UOLKZ2BwvlSUyFuXoAvjw/vNa/Esv6N3Nty1uSVSKtii8nsOgkulA1+BJCxYHp8Wype/iEDAS70+2VeIdlYCqDSsONzzAtjAHiR8b55OAh7HGNSsCtW3xvR6/Ej1/7VLzufjO19cks35vkF6NimG8FAPbZfffUV5syZg+XLlyMiIgLr1q3D+PHjsXnz5hti77Xy+clS/JBaQ8TDkdwmHM1txIFMWixk1IhxKLsBHx6ju7s1OgO2nC3H5jPlJKbqq4QynMgXIb+BrvjOl7TiRL4Ih7Job6hCrcOelBp8nWDy7G05W46dKSbx8FtGHfal15P7fKa4Bb+l15MgdGmXFpvOlOHzUybP3sfHi7HHzPbtFyrxVwHdPQvQAwwO5zSS4+oNFL46XYavTpuEz+Yz5UgoaiEDHc6XtuBIThOJCWuUdOFIbhPxjAPAxtOl+PZ8ObF9d0o1zhS34ALjgU0ub8Oe1Bp8d4G2Xa3TY8vZcqw5lE8q4K8SSrHvch2pgH/PqEdRkxzH82mhV9osxw8Xa/AN47XU6Q3YnliJzWfLiGhZf6wIp4uakc40Qo/kNuJUYTO2MyKtXaHG/qwGbDHzfG48XYZtiRXEG73vch1+S6/DRUb4pNd0ILmiHVuZfSiKwsbTZVh/vIgIn3cPF2J3Sg0RXAeyGnC5ugM/XqwxXbO8JnzNeHIpisKu5Grsu1xHnpkNJ0vwU1oNylvo636qsBnFIhl2MCJNptJid0o1GYhh3GdXcjUZULDlbDl+S6/DXwV0HV3VpsQfmfX48lQpAPpZXfvn0H6yZNCT7xkMBpSWliIpKQmJiYms5VZhsJNoWTEtbxtLAdoUml7TxI/2wuyRpi8C71s8GWP9nAEA3ywc3+s+f7wytcdv9lcQFSqNHtuenoiYYJOAKPtw7lXtF3f2bnNvbHtmAkLc7a6aztjF8tIdwwAAM8I9WNu9HNmV6ffPTkT6atqrllBMt6B3Pj8J+e/FkzRudla4byw9V8g5Js3L03sKw6gAZ/L/89OG4R8T/AEAI7wccOif08i2Z3dcgoPQAgfNfgOAh6L9MCnYpUe+e9Nqe/x2zyivHr8BQNH7czA+0LnXbQCQuHxWj9/+PXt4n+m708jEAAW60qJmWyJd6LnbCzFvjA/LW2jOsrtHsNbNRZcv402Tq3SwsRKQ34PcejZMdjw3kfxvayXA6WXTWdst+Dysf3hMj/30FIUQD/r5uWO4ex9nN3QMxmObmprKSg8A8fHx18XDe760Fb9n0BX/kdxGJJa24nBOI47mNuFUYTOO5zXhRH4TEoqakVLehuN5Tfgjox4nmYr+THEz/sxpxO8Z9dh3uRanC5vhZGOJF+8YhsM5dGV6LK8JXz8RjTPMO3Mktwn/jQ9Hh1IDlVaPi5XtmD3SE9NHeOBSNd21q1Dp8Prdw4m38M+cRux4bhJOMJXN8bwmPD4pAAI+Dx1KDerEnfBxssY/JvgjoagZFEUhpbwdO5+bRPL4q0CEnc9PQkGDDAYDhYSiZjw2MQCBrrYob5GjVa6GtaUAS+8Kw35GgKVWtGPvosnEi3AwqwGr5kagTaGGSqtHZm0HpoW5487h7rhY2Q6t3oBmuQr/jQ/Hn9m0ADuU3YhNT0YjtYKu5I/ni/D8tGAILQUkhi3IzRYPRfvjr3zaS5BTL8EXj0fhoPH8sxvx9RPRKGiUQqc3ILG0DY+M98cYPyfkN8ggV2mh01NYdvcI4rU8W9yCb54aj2N59DU7ktOID+ZHQtJJe3MzayWYFuaOWeGeuFjZDrVOj2aZCmvuG032+atAhA2PRhGBcrqoGS/dMQzudlZolqnQLFPBzkqAZ6cG4xBzvikVbfjisXE4zNhxJKcRa+4bhRoxHSN5uboDcaO8MDHYFek1HZB2aqE3GLBy7kgcZfZJKGrGRw+PId3qB7IasPyecHSq9ZB2aZHfIENUgDNmhnsiuaIdKq0eTVIVPpg/Bj8x5eRfBc3Y/OR4pFS0gaIonCpoxsLYIDjbWqFB0oUWuQpWFgK8PCMEpwrpZyanToqPHxlLruGv6XV4a14E2pkBKMkVbZgWOrTlxqBEzcWLFxEWFoaIiAhMnz4dM2fOJMusWT0L96FgMDOJDnYSrVAPuovt9BszemwztoTvCHNHmJc9qSDc7K1I0Oe8MexJvYzemN4ETPAVBMXXC+gRGubdG927OpxtLREXwa6Iu0/Otv0ZusK6/HYcESVGvBytcea/M3s9/ldPjAMAuNjSo188HIR4YJwvXrpjGOluMqLSGlC8bg5ZtxdawN2eLXRsLAWsazDazwmbnxyPnxdNxn6msPlPXE8hsPv5SfjoIbpC9XexwVmmMB/p7QArCz6xD6C738aZiSAA4PFA4oXiR3uh+uN7UbV+HivN5BBaOD4+KQClH8xF5jt341FGPJ1eNh02VgL8tsQkSkd6O+Dy23RF6WZnBTd7K1Z+m5+MxsPRfmT9qcm0p2NBTAC5PgBwcdVskma4pz1CPdndu0/GBGBamDtOFzVj61PjkfbWbFhbmp6BKzjBMJd5DhVqLeu5uX+sL8s2AAh2Mz2HE4NdEephz4oV0hsoePbiBZJ0avHYRPqcrtQNN1QMxmMrEoluiIcXALQ6AySdGhQ2yaBU61AikkOh1qFB0olGSRdkKi3qO7pQ1abEudJWVLQqIFPRcwedK2lBRYsSegOdR269FGUtCrwZH447hrsjtaINNe1KeNgL4eloDZ2BQqdGh/IWBYZ72mNGuAfOl7bieH4T5kb6YG6kN47niXCupBV3DnfHKB9HlDbLIenUoEurR5CbHTzshagTd+KvAhHmRHrj/igf/JnTiD9zGnHvWB/MDPfA+ZJWFDXJEe7tAG8na0i76NGFzTI1/F1sER3ojJx6CU4VNiMuwgvzxvjgWJ4IJ/KbMDfSG9OHe+BCWSvqOzrh52wDJxtLWAj4aFeoUdAoQ6SfI+6K8MKZ4hacyBchLsILD47zw6HsBlysbMeUEDeMD3RGdp0E0i4tujR6eDlaw9/FFjXtSqRWtGFqqDvuHeODY3lNOFnYjPjR3ogf7YUTBSIUNMow2tcJPk42kHTSwq9JpkKAqy1iGCFgPN/50X44mN2AhKIWxEV4YWqoG1Iq2iCSquBoYwl3eyGsLPiQdGqQVSfBuABn3DXSC+dKWnEsrwlzIr0RP9obfxWIkFjahukjPDDCyx5lLXLIGKHkameFUA97lDXLcaa4BbNGeuLuUV44VdiMwzmNeGCcL+4a6YnE0lYUi2QI9bBHmKc9GiRdZBRsmKc9xgc6I7O2A79n1OHRCf6YM9obpwpF+KtAhHtGeWNSsCsu13SgVa6GrZUFnGws4eVI3+/UinZMCXVDfKQX/ioQ4Rhzrx4c54tDWQ04U9yC2SM9McrXEeUt9DPapdHB3V6IEV4OKGtR4GShCHeP9sKc0d44WSDCn9mNmD/OFzPDPXG6qBllLQoM97KHu70QKq0BMpUWufUSjA90RswwV9K9OSeyfzOB95dBlUJLlizBxIkTkZ+fD7FYjI6ODrKIxQOfi6U/DMVMov3FxkqADx+KZFUYq++NwNRQN1x6i66E3OyFeGZKMDYtiMaFN2ch1MMe5lLiwpsmcXfgVboypJgUxkrp80ej8GRMIHLW3kPS/vceU8s73NsBAF1xudpZEdFgnrcFn48vHo8i68Futpgc4sY6n7H+TgBoUfLQeLoyy3v3HjhYW7BiYxyt6Yp2OFOxzo7wwot3DEPWGtq+y2/HYay/M1bfNwoRPo54g/ESvHPfKDhYW8DaUoDzy2cCAISWJs/A0X/fAV8n6x5dIwrGvervYkN+s7Yw7TeSOX9nWyuM9HFAXIQX/F1siSdq4xO06HtwnKmCvpPxFuS/Fw8/ZxvcH+ULb0drPDsliHVs89ivio/mwagDR/s6wcqCD1c7K0T6MdfNnr5GArMH4sRr0+HhIISTjSVTSNPbXo8bAU8HIe4b69urYH3/wUgAQAIjmJ3NBNmCmEAEuNig/MO5RPTYCS0gZMSFk40VvBytcfw/Ji8Kj8cjggkAtj09gfxvFNNylQ6WjH0utpaYNdIDXzw+jmWXr7PpHnz/7ETweDxMMXuOlswIxWxGPM8M98CZN2YgjXkXgt1sUf3xvT3O9XblWj6TEDfKCy/dGYIlM0Lx+KRALJoeggUxgVg8PRTPTg3G45MC8dKdIXjpzhC8NS8CS+8ajuenDcOCmEB8+NAYLJoegoei/fHSnSH48KExeGVmKDwdrWFtKYDQQoAdSVW4P8oXADA7whPv/lmAMX5O4PF4mDPaGz9fqkWjRIVAN1uEeNijul2J39Lr8MA4P/B4PEwLc8fSvVnEQ3p/lC8+PlEMZ1sr2AktcNdILxzLa0JyOS0UPB2sIVNpsS2xAvOZ92xmuAdW/pFH3rX40d749lwFLAV8ONlaYlKwK9Kq2nE8X4SZ4Z4Q8HkYH+iC9w4XYmY47d2eM9obaw4VYEqoG3g8Hu4d44PfM+pR2ixHhI8DAlxt0abQYEdSFe6L8gWPx8NYfycs/y0H9zK23zfWBxtPl8HaUgBrSwFmhnvgVGEzzpW0YHKIGxysLWFjKcC35ytwLyPwp4a64/0jhZgaSj/b9471wbbzFTBQFLwcrRHBCL8/cxpxz2gvWAj4GOZuh09PFOOBcfR1v2eUF1btz8NwTwcI+DzcM9oLB7IaUNGqQLi3A4Ld7VDX0YV9l+tw71gf8Hg8jPZ1wso/cont88b4YOv5SthaWcBeaIEZ4R44wYyavHO4B2yt6PJ0y9kKzGcaIFND3fDWgTzMZLzkcyK9sSu5GmKlFgGutojwcUBhkwzH85twz2gvCPg8BLna4pMTxZjLCId7x/ri/SOFiApwgoDPQ1yEF47mNiG3XoKx/k7wdbaBTKXFntRq3DeWPt9Z4Z7476855N7dN9YXm8+Uw0LAh6O1Je4Y7k66FKeP8ICTjSXshZb4/GQJ61lde6gA08LcwePx8OA4X+xMqUazTI0A16ENaRnUBy3Lysrw+++/IywsbEiNuRrLli3Ds88+i4kTJyImJgYbN25kzSQ6lCyMDSJBdF6OQtwf5Yt/TPAHj8fDm3PCMWe0N/h8HvzMKgPzQT7mN8rWygIvTw+Bt6M17hzujuTyNvz7rjDcz7ysdkzXwOll0xHm6YANJ0tZFf2skZ7kxaXtMQkRSwEPjtaminHTgvGkr3mktwP4PB5szboeLAV8+DpZw8HaEnnvmrqCMpiuommfnEFUgDN+XzIV9kILvHNf3xOU/Wv2cLxwxzBYWwrw2ETaqxHEtPiN5zR7pCf8XWyRYuaRqFo/D0nlbWjo6CLn42pnBbFSAz4jHCL9HPH2vFFYsJ3+dML4QBd89yztcQrzsEeZWUDfuw+MRoSPA1b8kQcXW5NXTG+gEOphBx6Ph/cejIS1lYB0LRqZHOJKxMrD0X7wNhN5T08OwpOxgSwPxNvzInAoxxT4+OfSafj3L9mwNBv33Fu4rPHZMOZlbSEgQqDio3nQGQwQmgk6Z1tLKNQ6PD9tGPm0hNFzYpRWL94xDLPCPeHpKCQxWjNGmLoFbSwFSF8dhzpxJ5nHJn313eR81z04GjuTqzHM3Q7WlgIkrZiF9ceKiY27X4jBh0cLsf1CFet5fni8P0I8TB4lT7Pn8XozGI+tt7f3gD28QmFPr9TN5rW44fgjswFTmAr5oWg/5NZLsZjpsvV0tMb4QBeyHQCenxaMmvZOInAfGe8PpVqH+5kKa0qoG9Kq2vEgU2FbWfBxz2hvhLjbkefkP7NHYO+lWoxhGkcPj/dHbXsnnppMNxQi/ZwQ5mlPKjABn4cXpg1Dg6SLdHs+GRuID44Uke7duAgvnC5qxqNMueFqZ4VxAc6YGORCGhyvzgxFUnkbKWOXzAzFTxdrET+avm9TQ92QViUmHlVrSwEeHOcHZxtL8gy/OisUv6fXE9sXxAZi46lSLIihGwIhHvYIcrPD/VEm7/rjkwLQJFHBmSlLnpsajN0p1bgzjBZx94/1RVatBEvvous/d3shZoR7kEYYACy/Jxy5DRJ4OtDvxiszQvH5qRIiriYGueDHizV4dVYoALqOeHpKECwFPGL7KzNCcbakBVGM7c9OCcbbB/Lw8Hj6fMO9HDAp2AUTgmgvM4/Hw9OTg1ErVsLWyoKc/08XazGLCZW4I8wdf2Y34olJ9Pk7WFti+ggPuNlZkev+WtwIZNR0wIV5Zh4e74eUijbikZ0Q5IKThSI8HG267v+YEAArCz6x/flpwdh4ugzjA+lu//vG+qC4SYanmWfG38UWEd4OPRrgQwI1CGbNmkUdP358MLteM5s2baICAwMpKysrKiYmhrp48WK/95VKpRQASiqV9nufoBVHKLFC3a+0pSIZtfZQPmvfg1n1lFanZ6Xbm1ZDFTfJWL+JFWrKYDBQFEVRS35Iv+qxSkQy6tMTRdS0jxMoiqKosmY5FbTiCJVV20EdzmmgglYcoR7YnETSa7rZ0BcqrY7q0uj6lbYvRrx9jFJpB57Hgcx6iqIoqkujI9fiqe963l+d3kDp9YYevwetOMKy/UhOI1UqkvVIZ56+Q0nf29OFIiq1om3ANkuUGmJj0Ioj1NpD+dR9X18g29VaPTV1fQL11v5c6uFvknu1s698jc9ddm0HFbTiCLkm7Qo19cqPpmdEqdZSQSuOUBk1YoqiKOrBzUnUusMFVKOkk6TR6vRUk6RrwOdHURTVJleR/43PtJESkazH830lBvMOdicmJoZaunQpWdfr9ZSfnx+1fv36XtM/9thj1H333cf6bcqUKdTLL798w2zm4OC4Nvr7HvZb1OTk5JBl//791KhRo6idO3dS6enprG05OTnXbPz1YjCFU0aNuNcKtD/oBrlff2mUdFJ59RKyHrTiCJVe3U5RFEWt/COXun/Thb52/VvSX/FpJKFIdM33SK83UGeKmymKoqj7vr5Aybo0RCgZ2XK2jDqU3UDWzQVKf2iWdlFBK45c0YYrbR9KHtqSNCjxZ2QoBMIvv/xCCYVCateuXVRhYSG1ePFiytnZmRKJRBRFUdTTTz9NrVy5kqRPTk6mLCwsqA0bNlBFRUXU2rVrKUtLSyovL++G2czBwXFt9Pc97Hf307hx48Dj8VjjzV944QXyv3Ebj8eDXj80Hxm8FTC6zwaD4EpRnEOAj5MNfJxsWL8Z3X8O1hawMYtr+f+Ai53V1ROZcdfI3kc6DQQ+n4dZTF/z4X/d0WuaV2eyu2kHGn/i4SBkjfLqzYai9+f0uX0o2f9q33bcKB5//HG0trZizZo1EIlEGDduXI9vP/HNugOnTp2KvXv3YvXq1XjrrbcwfPhwHDx4EJGRkTfrFDg4OK4TPIrq31coBzJTcFBQ0NUT3QRkMhmcnJwglUr7PRLqdqJLoyd92CqtHlq9gXy3h4PjVuB2fAdvR5s5OP5u9Pc97Pfop6CgILLs3bsXCQkJrN+CgoKQkJCAX375ZUhOoDsffvghpk6dCltbWzg7O1+XY9zumM9FYm0p4AQNx98OsViMhQsXwtHREc7OznjxxRehUFx54saZM2eCx+OxliVLltwgizk4OG4kgxrSvW3bNowcObLH76NHj8bWrVuv2aje0Gg0ePTRR/HKK69cl/w5ODhufRYuXIiCggKcOnUKR44cQWJiIhYvXnzV/RYtWoSmpiayfPrppzfAWg4OjhvNoIZ0i0Qi+Pj49Pjdw8MDTU1Nvexx7bz33nsAcF2/As7BwXHrUlRUhBMnTuDy5cuYOJEe3r9p0ybMmzcPGzZsgK+vb5/72tra9jmEm4OD4+/DoDw1AQEBSE7u+WHH5OTkKxYsN5prme6cg4Pj1iI1NRXOzs5E0ABAXFwc+Hw+0tLSrrjvTz/9BHd3d0RGRmLVqlXo7OzsMy1XbnBw3L4MylOzaNEivPbaa9BqtbjrrrsAAAkJCXjzzTfxxhtvDKmB18L69euJh4eDg+P2RiQSwdPTk/WbhYUFXF1d+/zkAQA8+eSTCAoKgq+vL3Jzc7FixQqUlJRg//79vabnyg0OjtuXQYma5cuXo729Ha+++io0GnrKemtra6xYsQKrVq3qdz4rV67EJ598csU0RUVFvcbv9IdVq1Zh2bJlZF0mkw3oOy4cHBzXn/6WA4PFPOZmzJgx8PHxwezZs1FRUYHQ0NAe6blyg4Pj9mVQoobH4+GTTz7BO++8g6KiItjY2GD48OEDnlr8jTfewHPPPXfFNCEhPb/a3F9u1enOOTg4TPS3HPD29kZLSwvrd51OB7FYPKB4mdjYWABAeXl5r6KGKzc4OG5fBiVqjNjb22PSpEmD3t/DwwMeHh5XT8jBwfG3pb/lwJQpUyCRSJCRkYEJE+gPd545cwYGg4EIlf6QnZ0NAL0OduDg4Li9uSZRcyOpra2FWCxGbW0t9Ho9KZjCwsJgb29/5Z0ZjPMMcoF/HBw3B+O71885P1lERERgzpw5WLRoEbZu3QqtVoulS5fiiSeeIAMUGhoaMHv2bOzZswcxMTGoqKjA3r17MW/ePLi5uSE3Nxevv/46pk+fjrFjx/bruFy5wcFx8+l32XGdP9cwZDz77LMU6A8gs5azZ8/2O4+6urpe8+AWbuGWG7vU1dUNqhxob2+nFixYQNnb21OOjo7U888/T8nlcrK9qqqKAkzlQm1tLTV9+nTK1dWVEgqFVFhYGLV8+fIBfceJKze4hVtuneVqZUe/P5Pwd8BgMKCxsREODg7kM+scHBw3DoqiIJfL4evry/o+060MV25wcNx8+lt23FaiJjExEZ999hkyMjLQ1NSEAwcOYP78+TfbLA4ODg4ODo5bgNujqcSgVCoRFRWFLVu23GxTODg4ODg4OG4xbptAYQCYO3cu5s6de7PN4ODg4ODg4LgFua1EzbXC9Y1zcNxcuJgaDg6OwdDvsmNAQw9uIQBQBw4cuGIalUpFSaVSshQWFt70yG1u4RZuGfzoJyObN2+mgoKCKKFQSMXExFBpaWl9pt25c2eP4wuFwn4fixv9xC3ccussVys7/taemr6+4VJXVwdHR8ebYNH1Q63Tg8/jwVJwe7R+Of5/YvzkgIODw6Dz2LdvH5YtW4atW7ciNjYWGzduRHx8PEpKSnp8G8qIo6MjSkpKyPpAPC5GW/+O5QYHx+1Cf8uOv7Wo6esbLo6Ojn+7wmnRnnSEuNth1byIm23KoFCodbAX/q0fRw4zrqUb54svvsCiRYvw/PPPAwC2bt2Ko0ePYseOHVi5cmWfxxvIpxR6s/XvWG5wcNxuXK3s+Fs364VCISmI/u4FUodSgzaF5mabQdDoDDhf2tqvtAYDhci1f11XeyiKGtQsthy3FhqNBhkZGYiLiyO/8fl8xMXFITU1tc/9FAoFgoKCEBAQgAcffBAFBQV9plWr1ZDJZKyFg4Pj9uC2EjUKhQLZ2dnkEwlVVVXIzs5GbW3tzTXsFkDA50FvMNxsM6DTG1DTrkR9Ryee3XGpX/tob4Dd+zMbcLqo5eoJr0BVmxJVbcohsohjMLS1tUGv18PLy4v1u5eXF0QiUa/7hIeHY8eOHTh06BB+/PFHGAwGTJ06FfX19b2mX79+PZycnMjCfaGbg+P2YVCiRq/Xs9YvXbqEixcvQq1WD4lRfZGeno7o6GhER0cDAJYtW4bo6GisWbPmuh73eiOSqpBU1jbo/U/ki5BWJYZKe2VxoDdQMBj6762Qq7R46ru0AdlyqUqMGZ+dG1Bsj1Z//T0ocpUWcpUWWbUdSKtsH1QeHxwpxDM70pBeLR5i64CJH5xCWbOcrLcr1FDr9H2m1xsoaHTXJgZ1egM2nykb0D6tcjV0+psvngfClClT8Mwzz2DcuHGYMWMG9u/fDw8PD2zbtq3X9KtWrYJUKiVLXV3dDbaYg4NjsAxI1NTU1GDixIkQCoWYO3cuZDIZ7r77bkyePBlTp07FqFGjUFpaer1sxcyZM0k3gvmya9eu63bM68Wh7AbUiTsBAEfzmrBoTzoAuqKp7uYN0BsodGp0ZN3oLThb0oJvzpWjvoPO50SBCB3Kvrug3j6Qh3VHCwEAaw/lI7O244o2Sjq1SCofoNi6SqhEeYsCJSI567feKslXf8rA2eJr86yYo9IZ0KXV47ukKnw9wIrcCI8H1Im78OYfuX2mSa8WD0qgtik06NLSIqZLo8e9Xyfh57S+PZDbL1TiuZ1sT1jwyqOs9X/9nIUDWSZvRJO0i7VdqdFjw8mBva8xH53GqcLmAe0zlLi7u0MgEKC5mW1Dc3Nzv2NmLC0tER0djfLy8l63/3/qtubg+LsxIFHzxhtvwN7eHgcPHoSjoyPmzZsHnU6Huro6NDQ0YPjw4VixYsX1svWGomIqmMM5jdDpDdD2o3XaLFPh50v96wp7fV82K+bEGPuUWtmOmRvO4bd0U+vwYFYD5my8QNZnbTgHiqJwobQNB7MaoDPzvrQraW/ZiXwRtp2vYB2zVa4mQmp3ag3LM2DE3CbDAGNQVFo9BMyJmNvUKOnCmkP5aJWr8fnJEvznlyzk1UvJdk0v1/ZYnghFooHFMoxYffyKtn1zln09ujR67M/svQsCADo1OrTIVT1+l6tMAnN7YiXOlZjE1/dJVdiWWDGo+B0+j4fCRhki1pyASqdH1xU8b61yNVrkJs9o9+ONWH0ch3MaUdVKC+AujR5T1p9hpXnvz97jSk7kNwGgxXT3fG92WJKVlRUmTJiAhIQE8pvBYEBCQgKmTJnSrzz0ej3y8vLg4+Nzvczk4OC4SQxI1CQmJmLjxo2477778L///Q8pKSlYt24d/Pz84OPjg48++uiKwXq3EyPfOQGKovCvn7PQrtRg3HsnkVFz5W6HsmYFVu3PI+sxH57uMy0dA2OqITo1ehzNbSLry3/PxfbESgB0pV/LiBEjap0BO5KrYG0pYOXTpaErwouV7TiQ1UB+b5J2IaG4BRqzrp4Vf+SxPEAURbHiYIzCxNyuKzH23ZPIrJUAACu+Z+rHZ7AntQYNki5QFFAskmN3arXpON26nxIZYdWlMXW/rNqfiy9OXdmrcKXuGJXWgAZJF3gwVcwRa05g2a85ZnbQ+495lw5a/iG1BvdvSjLLpacbau+lWjy387LZcfS4UNY2qPgdtc4ACrRxAt6VY6R0egMs+CZ79jJiul1BCx3jtTA+GubPAgDEf5mI/d1+M7Lkx0wAwHM7L2FHcjXESg3kKi3ZLuD3vA7iK3gIh5ply5Zh+/bt2L17N4qKivDKK69AqVSS0VDPPPMMVq1aRdK///77OHnyJCorK5GZmYmnnnoKNTU1eOmll26YzRwcHDeGAYkalUoFJycnAPTcDQKBgDVm3NHREZ2dnX3tPiRs2bIFwcHBsLa2RmxsLC5d6l8w6mDoZCpVpVoHpUaPBokK2xMr0Szr2XoHAHtr9pDkFrka2xMrIenUoLCR7XXg80yixtga/vxkCfhmw9V+SqsBAHx6orjHsYweF6EFn+QzK9yDiBQeD2iQ0N0Nl6rEeHEX3b2l6RanUd9h6pIwnu+h7AYmLV0xLv/dVPEfz2vCmkP5rDyaZSpcKGuFRm/AJ4ytvcXJmLf6zcVUdy/YM4ywMp7Xsn3ZKBbJUdGqANCzm6WvY6w9lI8zxc3QGyjiebuSpyHs7eNolHQRT4y1pQAdndo+03dp9FCodSxbjXFNkk5TJX/HJ2fIvbgSaq0ePJg8XV+cKiVdWQe7CRCdgUJlq5Kc79sH6HtS2MR+zholXaAoCm8doMW2wUChQdKFEjMvHUVRuFDWc6RaR6cGh3MasfC7NHx8vJiIvsvVYszfkgylWgexUgOKojB+3amrnt9Q8fjjj2PDhg1Ys2YNxo0bh+zsbJw4cYIED9fW1qKpySTEOzo6sGjRIkRERGDevHmQyWRISUnBqFGjbpjNHBwcN4YBiZrRo0djx44dAIDdu3fDzc0Nv/zyC9n+888/Y8SIEUNroRnGSbfWrl2LzMxMREVFIT4+Hi0tQxd7Yc7kj2gXt7FioygKHx4rQhFTcaSUt+HbcxWI/zIR0i4tKzZkzsZEAMCHx4rwQ2oN5n19gZW3Wmcg3TtKNV3hVrYpIesyVaIypnI1Hr9J2gUp8/8RxnsitBAQj4q/iy2Jy0gub4NcpUN9RycKGqWkshMrNShlBaSaKl9jZf6fX7KJjYBJ7LTIVHjlp0zsSa0h16NB0oXjeU14aXc66/yMLXejmACMU0JSrDwBIMHMq9G9u0OnN2B/VgOyaiVQm3XHDFt1FFq9AQu/uwgAJODVaPPbB/Kw52INXtiVjv8lVpKgW5VWD4pi2yU1u+bmHg1bKwE0OgO+PVfR41wqWxWIWHMCSkbUtCnU2J1STY7zY1otDAYK1W1K1Hd0sTwd0k4tLleLewRtV7d3kvgoaZcWBgrIqZdAozPgtX3ZrGuiUOug0Rt6iCWxUoNWs26p/VkNqGk3NTSq2pXYfIYdS1Ld3omnv78EvYEix9+dUg1PB2tk10lQ1CQDBfo5tLLgY/uFKmTXSfBTWg3GrzvVw4t4I1i6dClqamqgVquRlpaG2NhYsu3cuXOsOLsvv/ySpBWJRDh69CgZbMDBwfH3YkCi5t1338WGDRsgFArx6quv4tdff8X+/fsRGxuLKVOm4L333sObb755vWxlTbo1atQobN26Fba2tkRoDTVypsLKrZcAALLr6L8/XqzBf37JwgdHi7DlbDlKmuWIeu8kmqS0B+c/v2Sh2CwY9vNu3SZGT4PeQOG5nZdQ1mJK+8pPmeR/sVKDl3abujamrD+Ddw/TcRBfJdCVuLUln1SOjjYW+PBoEQCgtJn2atzxyVmWd6K0WYF7vkwk6wq1jnhXjF4HAMipk0CtZXt1YhiRNzHIBQCQViXGtI/P4N3Dheg+H5IxmHTkOyfIbxRl8miIpCq8fSAPGp0B7x+hg5czajpY8TUUaO+JkWKRDDXtSpLX2wfykFzeDplKSwJejaLmp7Ract7SLi3xoCQUt4AChS1n6YrdXmgBtU5P1j/7i551dt/lWqxkuhK/SqDzNt4noQUfzTJaOHRq9HC0tsCGv0qw9s8CcpycOgm+OFWKmRvOAQDu/ZruxvrlUi2i3j+JR7emEkFi9Iy9dSAPHzD3z4gFn4d0ptvzYFYDIt45gdf2ZeNQdiOAnt0+MpUOKRXsQGWVmXdu9ufne8R9Xa6i80+rascdn5wFAKz9s4Alfvk8YNKHp1ldfJVMvM7FQY4muxYG6rH97bffMHLkSFhbW2PMmDE4duzYDbJ06DAYKMhUbM9h95ivJmkX6x5pdAaWGAfY3bqAUeibxeUp1CzvqUqrR3mL4op5SLu0rDwoimIJeYqiesTwGQW0kbZuI/4MBgptCvaI2u5dzPJu10PSqWFdI72BYj3HAHoMpqgTd7LKPplKy2oYUBRFunX7Om6nRtfDtu6NFvPGEwCUt8hZ56s3UKyGMUVRPa5z92vWvRHYpdH3sLU7jZIuVsiCpFPDuiYURbHsoigK5S3sayhWaljnp1TrWN53lVaPjBr2QJTu97K+o5M0CoeSAYma+Ph4FBUVYe/evSgqKsL06dORmJiI+Ph4TJs2DadOncJjjz025EYCg590azA0dmv9vneYrnR3JlcDAE4XteBQdiMKm2Ssl+FfP2cBAKlwuvPKjxnIrO2Am50VAEBPUThX0tpndxYA5DewuxPMg1SNthiDex+bGIAgNzscz2PHwFwptnPRnnTiiahsNRVc5S0KljfF/OWxZWb+feJ/F8lvKq0B4wOd4WRjCQDYlVLd41hdGj3xShWL5PgprZYV3PvItylQaUwvdffzqO/owuqDpq6vX9PpIN+x755knU9Bo5S1X1J5K2ukl4EyBScL+Dxo9VSPkVaVrUpWl1JteycRMnS3lKkQmBTsit8yaFvMu382nzV5RIx5rTSLuRLJVPjlUi3xjAGAA9OF+Y8J/gCA9ceL8eR2elj9a/uy0cXE7JA8pCqsP24SQglFzaz8ABDvXndenhGCGSM8yGgu43GMmHdNni2mn7FHGbsA4JfLdDD7ij/ycCMZqMc2JSUFCxYswIsvvoisrCzMnz8f8+fPR35+fq/pb1UOZDVg2vozpCJokakQ82EC63l/+JsUfJdUSdY/OlZEunMBOgh86scJJA+FWofo90/hsFnc3Et70knjCKAbcY98m0LKgKo2JUatPUEaGAYDhemfnsXvGaag+9UH8/HU96bjXqwU4+4vE8lxVVo97vjkLOu9XLg9jZRFAPDt+Qo8uDmZrBeL6CB6Y3mp0xsw7v1TOG02Gm/RnnSsP2ayfX9mPe75MpHYXtmqQPS6U6SSpigK8RsTWd27648V418/mxqXJ/JFmPHZOSJklGodxrx7Ellmts/fksyaHmFHUhXiN5oaj0VNMkS9d5IImy6NHnFfJOJ4nmlupdf3ZbPKt32X6xD3xXkiMFtkKtzxyVnkN9D3W2+gMPerC/jBLD5x3dFCvGrWME6paEPk2r/QwlwzuUqLqR+fYY1qffmHDHxkds12JFezrntJsxxxXySSkAeDgcKMT8+yvNr/3JuJN383jQo9ktuER75NIcKnuk2JiR+cZonj53ZeZpWRQ8WA56kJDg7GI488guDgYAD0pFfvv/8+NmzYgFmzZg21fYTBTLo12JlBzd31fdHfWd4fn2iauOt4vggPf5OCZfeMQJS/E+lOuVzd99BqUTfB06ZQ44lJ7MnA8piHXMDnoUur6yGq1jGekO48NzWY/F/eosDiHzLIut5A4fldJi/R62bdHxRF4XheE7wdrVn5iZWaK44Se+r7NKRepVXfqaULvVAPO1T3ch/qrtLV0STtwvcXqli/5TfIWPf0UpUYWTUSAHTrKaOmA+ndWhXbEitZ69M/O0v+L29RsAqO/vJut9FGG/4qYYkcAChgYq8crS37zMc8QLhYJMdvjLj7911hOFfSMzamu/fHyANRvtj+zMR+2W70KsWGuPWZ5kbN2DxQj+1XX32FOXPmYPny5YiIiMC6deswfvx4bN68+YbYO1ScKmzG4ukhyGKC8U8XtWDprDAczqEFSYtchSkhbkirNA1oqG5XwsvRmgjblIp2PD05iHhSs2sleGZKEM4zz43eQMHZxhIVrQpyP1Mq2vFQtB/x/iaWtuLpyUEkGL6wSYZ7x/ogkRHbFEWhvqMLHvZC4r1IqWjDgpgAMgggq1aCWeEeSK2gywNppxZhXvbk3AB6eoSJwS4QMR7wQ9mNeH5qMNkntbIdr84MxR/MCEad3gAHa0s0SFSkEZFY1oanJgcilxltebqoGUtnheFUIW17VZsS8aO9WeVSo6QLbvZC4vFJrWzHoxP9SRnxV4EIr8wMJSEAYqUGo3wckWM2ovN8aSsi/ZyIx+dgVgOemxpMRkrmNUjxwrRhZF2l1aNTo0OjVEWEwIXyNsSP9iYi5mxJC56YFEA8oyUiOaaEupFGrfG6O9pYEu/c0dwmPDMliHhNMmo68PB4PzJXV6dGB1c7KzSbeaZSK9oQ7GZHBFhKeTsenxiAZEYIZdV14ImYAJwuop8hjY4etCBT6YjtKRVtWBgbiKy6DnINn5sajJOFdF0tkqoQ5e9MQjmGkkHPKGwwGFBaWoqkpCQkJiaylluFwc4MSl3Rt8GkYZL4OdtcMV10oDMA4M7h7uS3+8b44vPHxpH4FwDwcTIJBEtB34pJpdXD36X3Y1rw+Ugub8eJgt5FXnc8HITk/7gvzrO2dZ+L5aCZUGqS0rE1IpkKIe525HetnmJ5dwDg5ekhGOPndFVbHBkPhXHY8c+LJveazih0jNfd/LoWvBePsX7OpAvqSlxiJtCzF1rg34yHDQB2PT/pqvt2x4K5X91FXne6e6/SqvoeTVcrZs9VZGclIP8LLejX9pHx/vjiVCnpghrr79wjnw2PRhHR2x1Ha0tYWfBZz97ViPLv+17qDddf1AzGY5uamspKD9Be51ttpKa5O5+iek6UqdbpMSXUjXg30mvEeGZKEOnWya6VYHyQC6wt+SSI3c7KApOCXXCZed5rxZ14PCaQPHsZNR2Ij/QmU0EUNckQ4eOIEHc7VLUpodUbwOfxcEeYO9Kq6Iowq7YDr8wMJd2W2XUS3BXuCRnTBVUr7kSQmy1G+ToSz2VBowxPTAok62lV7Vg0PYR06WfWdmBSkAucbS0hVmqg0uphIeBjZrgHOW6JSI6npwSRdzerVoKZ4Z6kHC0WyRHh44BwL3tUMqJM1qVFXIQXuWZ5DTI8OzUYOcxx02s6EBfhBaVaB53eAOn/tXfd8VFVafuZXjItk0ySmVQCaUBCSwhBOihN14JdPxEUy4qKsIi4a1fQtax90bWv8tkWdRXFQhFFOoQaQk3PpE7P9LnfH/feM/emEUIA8bvP7ze/5M7ccu655573Oc/7nve0BaFXyTAs1YC91fR7U9nShlnDU8j97qm245rCVKJs76m2Y1haLLRKKRzeIDz+EGIUEhRmxJLrHm5w4ZYx/Ui976m2Y3JeAuxMnR2sdyI/2YCcRA2OMmV3+0KYkGMihOTXoy24e3IW2d5bY8fIDCMAEXzBMBqcflj0SgxJ0RMSV2v34rJhyaQcu6vsuKE4jZCJ0io7hqYakKBVoNHpIyR0VKaRzPbdWWnDXRMHEDK1vcKGSbmJ8DJuy8MNLuQkaZGbpMWRRrrsze4AZuabSVl3V9kw94J+KGVI66ajzRiXHQ+5RNzBPXq66BWp2bJlCwYMGIC8vDyMGzcOEyZMIJ8zpdb0JulWbzODdmcUbx+fydtmR7ArbhzBIzh3TewPAJgykFaWuIs16lRSqOQSvMlRA/59SzTQMUHLNzJf3nUBDj85HQD94rZ4AjixfEaHsrWffbXpgUld3gcQnZq7dtH4bvdrD66E2J7EAHwFKJZxtZ0MCRxCkB6nho5xY2WaaNL0zKx8PHxxdLaKSi6BVCzClLyocqeWS+ALhrt8SaYP5reT568agn4cUgYAE3I6X+W5JyjpT6sYaUY1+a6T2c8nxcx8c4c2+BDn3uuYkesd7drihBwTDjw2lfddAYeEvHNzIa4ujLqPWDWIJUmd4e9XFvC2sxI7rpD7xKWDAPBzE50p9EaxtVqtZ0XhBWhVdPgTP2LWP3/DvA924Lo3t+C6N7dg3N/X4+o3NuPW93fgstc2YfpLv2DwI99j0MNrUPTUT7jpnW3IfPBbXPrqr7ji9U248B8bkfngt7jyn79hd5UNrZ4AYtVyDE7Wk5G7zRNAgk4JCjQhKq2mDVRBigH76xzYW2NHfooeRRlG7Ki0wRcMQy4RI9mgIokY99U6MMiig1mvRJ3diy3HWzAqMw5F/YzYWWnDsSY3BiRoUJgRix2Momz3BmHWqxAIRxCOUNhf60B+ih79GCLElmN4mgG7Km1oC4Qgl4iRk6TFEUbt2VfjQFGGESHmHDsqW1GYYcSwVAP21Nixr9aBgmQ9hqQYUFpNB8uLRSKkGdWoZdyiB+ucGGTRIS5GgWa3HzsrbRiRHov8FAP21DhQY/MiJVaF/GQ9IfZuXxAmrQL+EG2Q99bYUZCixyCLDoesLuysasWI9FgMSzNgd5WNJoYKCfLMOhInyZI2uZQ2yLuZ+x2WFovdVTbsr3VgkEWPEemx2FlpQzhCQSwSIdWoJqENe5k6SzaoUGv3Ym81XY6iDCO2nWhFg9MPs17JlMMOiqLg8AaRbFDBFwwjEqGwp8aBglQDhqUZsKfajgN19LMckW7EjspWtAVCUMkkGGDSkD6bJU8yCU18t1fYUJhB328pOYceRf2M2HbCRpMrfwhpcWqi3Byoc2JQsg5pRjWqWtuwp8aOghQDRmYYse1ECypa2pAZH4N8DrlqdPmRFqdGMBxBKBzBnho7hqXGYnCynqjTfYVekZo77rgDhYWF2L9/P1pbW2Gz2cintbXvU8gDvUu61dvMoCZNVMFY/5cJkHNS/vc3aTo9ZtrgJIzLplWDX+6fiD8NSabLwBgMs54mPH8aYoFIJIJZp8QgS7Q8SZzR8uOXDsK/bxlJtoemGiCXigkJaXT5IRKJsP4vE3hl4I7mV98zplMV6T93luA/d44GAMy9oB+5p4HmjnVj0ipQ9vg0xHPqg4uPbi2GJxAi7hDWJcf+Le5nxMx8M64qTME7N0fdHN/dO5Z3nvQ4NZ6+Ih8AMC7bhO8XjCP1dtvYTMilYlxTlIZZw6MGuc0fwpGnpmM2h0Cxq7eWd5JUcN2i8R1cY5mmGPKdVCzqkH+FJZIpsSosvyKfxLtwcXGBmYxu7po4AGOz4vEsQwSyEzXY9yifZLCkwsI8b5b8fn5HCY48NR1HnpoOuVQMfyiCRF203osz4zooQSzxA4BtD06GVCJGDIc8n1g+AypZtE2kx8Xwlq9g70fazZIWXCWMLSsXgyw6SMT08WeD1JwNnM7aT/dMysLG+yfik9tG4dkrC/DW7EK8eO1QfHnXBfhg7ki8ev0wvDW7EKvuHI19j16E/Y9NxY/3jcO7Nxfh+LIZ+OT2EnxwSzE+vb0Eh5+cjlevH46VW6uwp5omKEqZBIFwBG2BEBRS+tn2i4/B8WYPDje4kJukpYlAlR27q+wYlmpAVoIGRxtdKKt3YiDT5xhUMjS6fKAoCgqpBEUZRmyvaMWuKhuGp8dieFosdlXZCXEwqOVweOkA2rgYul3mmbU4WOeE1elDglZBG9ca+rpDUw0YlhqL0mo7djCGUymTwB8Kwx8KQyQSQSYRM2TBibJ6uuz5KXrsr3FgR4UNRf2MyIiLQWVLGw7W0+UQiUQwqGWweQLwhcJQyiTIT9ZhX62DLntaLIak6LGvxo4dla0oyjAiTqNAqyeAZrefDLKS9EpYnT5Ut9LEhyUgrJEfZNFjf52DVmFSYyER08kW2gIhSCViiEQi5Jl1KKt3EnWLvq4De2scGJJiQFaCFkcb3Tje5CaDM7VcArc/BLcvCJ1ShiGpBuypdmBvrQMFKXoUZtBkspR53lqlDG5/CDU2L9KMdH+eHheDytY21Du8sOiVGJVpxJbjrThQ58RAix4FKTTxPVDnxOBkPcRiESRiMfyhMELhCORSMU3Aqm3YV+vAYIY87qmxY3tFK0b2MyI3SYdyqxNHGmlSCwBapQyONnq5GZ1SxrQRG/ZWOzA01UDI875aB/KT6bJ7/CE4vEFomQEUS2KqW9uQalRhaKqBqHV9hV6RmiNHjmDZsmXIy8uDwWDgdQBsHpszgZMl3eorDE7WY3JuAgqY0cfiqTlQyuiqGpZqwKVDLQCiisSjl9Cj6OVXFGBkhhEWgwrZiRrseuhCaJUyHFs2AzIp32CKxSLcMT5qJDQKKW4clQYAiFFIMTbL1KFcn95Ok7cXrh4CACR7LwvWqGsVUgyydP4cuHZdzhmhswZ9aKqBfLdx8USo5BJ8dGtUReJioFkHbyCMB6bnMuemjVoOM5q/cGAiUo1q3FSSgUm50ZFynlmHo09NJyrGR7cWozDDiHHZJkQiFJQyCbmXy4cn49f7afVPKY+WNzZGTvZZdnk+OReb4I9bXwCQadLgwRl5vPKzBgKgXTnb/8p3UcilYmQnavDrkkm4bmRaB4LyzKx8LJ2RR2Y8ZcSp8e9biqFgiMThBjePZPz2wCTEaxS4cGAiLhlCt6EJOQmIkUswIEEDmUQMmUQMuYQmNW/dFHWFyaVirPsLX1FjlZbZJek8pYuFSCSCkkNqYuRSXiyYmKk0LtG5Yngy7xxSsZgQ7MVTc8n3rIt09T1jCak9G2tC9UaxTUpKOisKLwDo1TJoFFJIJWIY1HLEKKRI1ClhjJFDKZNAKZMgXqOASk63cdpIyyERiyAW089Lo5DCGCOHXCpGkl6JRpcf2ypoAw0AWQkafL2nDnnMQGRIKq2IRCiaoOYn67G3NqrUSCV0LqvdVXYMYd7vERlGvLepAvmMkjeyH20Y2wJhaBRSmPVK1Du82F7RSlzoqUYVVu2qQVEGPftxVGYc1hyoh04pg0gkImSqosWDfvEx0KtlcPqC2MyoPwCQqFPi2331GJpKX7cow4jfjrZALKLLPtBMG7091XYMSTFALBZBLAK2Hm/BCGbWZVGGEV+W1pKB4tC0WJRW2eHwBmFQy4mCsO0ETVAAIFYtx3f76lHcj67DghQDNpQ3QauUQiQSYVgqbaDLrS7kJunoviEUwY4KmuQBQK5Zi4+3VZM+siBFT5QYuVSMgRYdDtQ5aRUmWQ+JWASKorC72o7BjAs+P9mAtWUNiGMGirRRp5W4OI0Cxhg5Wj0B/HKkCWMG0AOKjDg1vthdi+FpseS6W4+3QM6Qq/xkA/bW2LGj0obByTrEKKTw+MPYVWnDEMYlPdCiw9d76pHJDMqLM43YcqwFoUgECqkEOUlalFtdhJBKxDTp/KmsAWOYgc3gZB2+P2Alg6sR6bHYVWmH1elDok4JvVoGe1sQOytaMZRpM3EaBdaWNZA6K8mMw69HmyFm2j5N6uw9fr96gl6RmuLi4i7XTTmTOFnSrb5EVqKWzFLKiI/B+GwT+f7Fa4YCAB5hyMz0/Gi69YUXZdOsXiSCkTleIhZ1qvCwrg/WmLIvPqtSSMUiXDQwem+sYsSO0CRdxN4kt4u5YRWbeydnISNe3dkhhLRx43VUjPKTkxR1OTzOuBrY30MRihhX1mVy+fBkKGXiTjPPspBKxHjo4oEYMyCejPwy4tQdpl8qpBJisLmKGffcAy063DyaVp0OWV1E4RjJdGAsMk0aHnFRySS4sTgdAL3+Fvu87pk0gOzzw318IvEb49L78b5xuLowFckGFZn+yCoebF1ycU1hKiwGFf5yUQ7euHEEdCoZijJiUZRhxIHHp8Ggjrrp7rswGy9cPQTxWvq7+6flINmgglrOV4pUcglyErWE3HUGFUe9U8kl6Ix3ZCdG2+YTlw4GQBswgG6DnS2U+peLcsj/E3Npl93a01wFvSfojWJbUlLC2x8Afvzxxz5XeM8U0uPU+P6AlZCYkv5xePb7w7hgAN1fjO4fh9c2HMVoxv2pV8vQ6PShLRAmbWZwsh6fbK8mhrEoIxavbziG0f1pg5USq8aOilby/otEIsQopNhT7UBKrJo5xojl3x0ibtYR6bF4bf0xsp0ep8bhBje0DMkB6D7ui121RAm+ZIgF932yh7h5h6fH4uW1R0g8mEougTcYhicQIm23MMOIV9cdJeRqVKYRz6w5hIk5dJ880KzD13vqiLEViURQy6WotXtJ33fBgHg8+305ivvRZZ2Um4BH/3uA9OuxMXLsqLBBq5SSviXNqMYbG49hcLKOqed4PP7NQYzKpPuVEWlGPLm6jBAHtVwKW1sA9rYA9Gq6T5yUl4gl/9mLCxiCMj7bhHs/LsWUPPr++5s0+O+eOp53wKxXYkeFDelxtH0Ywdx/YTp93bFZJjz69QFiL+RSMY43e2DSKIhtGJUZh6fXHEJRP/p5F/cz4unvDqGEOSY/WY+3fj1Byi6TiBEM01Po2YFYYQb9bEYxdTY8LRb3/2cvLmRsUkqsCuvLG3ku/Iw4Nb7aU0fs3diseLzw42HyrIanx+LFnw4TMq1XyXo86aan6Kind4G9e6OBo3fffTcWLVoEq9WK/Px8yGT8mRoFBQXtD+8zzJ8/H/Pnzz9j52ex6KJsojxcODARFw5MJPllRCIRfrl/IkQiEe6ZnAWDOnr/o7qYHXLViBR83C4/yECzDusWjScBu2w2WbZhyqVinmFur/a0V2oA2qU0KZcfF3LjqHQ8s+YQ7ruQTozInar7IRPLs+LGEfh0Rw0StAp8s7eeZ+i4uKkkAw9/dQAFKXpCvhQyMa4uTMGW461o9QQgl4gRiYCXHbkzzLmgH+YwLjCAVnDYxH6dgWu8uWcemmrgKUw3lWRgV1UpANoVOPbv0ZlL3OBopUyCuWP64fFvDmIZ4/4CgDsm9O9ADFlYDCq8dVMhL7Zkcl4izw3GPr8FU7IAAMPTDOTZserI7eMyccuY6L1zwboi2bwO3GUkji2bgf4PfktclxSobutZJZMgO1EDsUgEtVxCgk+5cU8vXjMU9XYftlW0IkZBqzm3jc3EQ18dAAV+9mcAeHdOEUb3jyPuL7ZO5d3E5vQlFi5ciNmzZ6OwsBAjR47Eiy++2GGZhOTkZCxfvhwAcO+992L8+PF4/vnnMXPmTHz88cfYsWMH3nzzzbNS3tPFnycMwKVDLcTYTshOwKvXD0Mho9zEaxR4+dphhPQAdAwWt1n8z6h0jOxnJCkXchK1ePSSgUR1AYCnZxXwXNYLJmfBzsmtMjE3AStuHEGMrVouxUvXDiVxbSKRCI/8aSCk4mg7uGviAFw4MJG0+wsG0O5Ztv3qVTL845qhRFEBgAdn5PEGLVeNSEFqrJq4MAYkaPHqdcMxgTGUcqkYf52Zx4tlWzw1B22BMOkzpg5KhEIqRgZjgBN1Srw3ZySGpEYV7RevHYrshOh7fdekARiXbeIRhXsmDSDEUK+W4YO5I5HN6Qv+MjUHMZzBxzWFqUjUKogLf6BFh3fnFGE8o8RLxLQNGcxR1hdMyebln5qUm4D35hQhLY6+P5NWgaevKCAKCkDHvnHDBOZckIHByTpS9pLMOCy7fDAmM2RKJhFj+RX5PHs1b2wmPJx3fe4F/ZCfbCDkckR6LO6dnEUIqUgkwkvXDuUN2O+aOIAQOIAOtxhk0ROFSCmT4O3ZRbzJIy9d28eJMKkeQiQSUWKxmBKJRJ1+2N/EYnFPT3nW4XA4KACUw+Ho1fGBUPi0rn/dm5upu1fu6vL3eruXKnj0e6qi2U1RFEWtO9RAVbV4yO9uX5D6x4/lZLvB6aXSl3xDhcKRLs9510c7qUAoTLl8QfLdzspWKn3JN10eU1bfsX64+6cv+YbyBkLk/0+2VVF3/HsHdeNbW8h+y749SO2qbO1wjiv/uanL6/5nZzXvOuw12t9P+pJvqD+9+mun57C3BagjDU5yngaHt8O9vrruCJW+5BvK7gl0uLe+QK2tjUpf8g316rojFEVRVLPLRzm9gVM+TyAUptKXfEM9/0M573tueSc/v4F64usDHX5vf0+Xv0bX16JPS6krXu/4DL7ZU0eOyXjgG2rl1krqSIOTikQilNXhpb7ZU9dtWSc+u57aV2M/6T2d7jvI4pVXXqHS0tIouVxOjRw5ktqyZQv5bfz48dTs2bN5+3/66adUdnY2JZfLqUGDBlGrV6/u8bX6qswCBAjoPXr6HvZYqTlx4sTJd/qDQ9ZNQGVPMDhZT0ZKnSFJr8TOv00hboyJ7WbixCikWDAlugwFO0Lvzs3z6vXDAZxa2XOTTi63syrN5qWToFPKsOaAFQqpBLseuhAAsHR6XqfHfXbH6G7OKeFtc+NBWFw2NBl7axxI1HYevKxXyaBXyXDoiWmdnhOIZvZUdOIm6gvEaWi3Eesui+si0PpkkEnESIlVdRurMjTVQEZBLJ66fDBZC4oFq6bkmXWdBn7PLDDjrpX0/8svz0dxZhyRlRN1Ssws6H5F63XtgtbPNLpTbDds2NDhu6uuugpXXXXVGS6VAAECzjV6TGrS09PJ/8uXL0diYiLmzp3L2+edd95BU1MTlixZ0ncl/AOhfaBqZ+huJkp7dJforjukxKo6JPA7GSqenkn+/59R6UTWZYP1/KEwYtVyEpfSGU42dbwgRU9cNl1hysBETMgx4WQTbVhC1BlxuakkHQlaBSFmXNdVX0AhlWDDXyYgvgvidSrIT9bzZsYBIIQNoPPQtMcNxem4agT/+b5yHU1uu3J5AcC2v04GAFw7Mq3X5T3TaG1txd13342vv/4aYrEYs2bNwksvvQSNpnN3KQBMmDABP//Mz8N0++23Y8WKFWe6uAIECDjLEFHUqacBzcjIwMqVKzF6NH/UvXXrVlx77bVnRNV56qmnsHr1apSWlkIul8Nut5/yOZxOJ/R6PRwOxzkP/usLBMMRbDzchMl5fR8ofapY9OkeWAxKLOIEkP5e4PaHeHmCBJw7nO47OH36dNTX1+ONN95AMBjEnDlzUFRUhJUrV3Z5zIQJE5CdnY3HH3+cfKdWq3t8/T9avyFAwPmInr6HverprVYrzOaOcrTJZEJ9fX0nR5w+AoEArrrqKpSUlODtt98+I9c43yCTiH8XhAYAnrvqzAWHny4EQvPHQFlZGdasWYPt27ejsJDOe/TKK69gxowZeO6552CxWLo8Vq1WdzmFW4AAAX8c9CqoIDU1FZs2berw/aZNm7rtWE4Hjz32GO677z7k5+effGcBZx1szg0BAs4UNm/eDIPBQAgNAEyZMgVisRhbt27t5kjgo48+Qnx8PAYPHoylS5eire3k67sJECDg/EOvhrDz5s3DggULEAwGMWkSnbdj7dq1uP/++7Fo0aI+LeDpwO/3w++PLtR1KunOBQgQ8PuC1WpFQgI/eF4qlcJoNHa55AEAXH/99UhPT4fFYsHevXuxZMkSlJeXY9WqVZ3uL/QbAgScv+gVqVm8eDFaWlrw5z//GYEAPZ9eqVRiyZIlWLp0aZ8W8HSwfPlyPPbYY+e6GAIECOgGDzzwAJ555plu9ykr63yl8Z7gtttuI//n5+fDbDZj8uTJOHbsGPr377j0g9BvCBBw/qJXgcIs3G43ysrKoFKpkJWVBYXi1GZ79LQzy82Npmd/7733sGDBgh4FCnc24kpNTRUC/gQIOEfoLNivqakJLS0t3R6XmZmJDz/8EIsWLYLNZiPfh0IhKJVKfPbZZ7j88st7VAaPxwONRoM1a9Zg6tSpHX4X+g0BAn5/OKOBwiw0Gg2KiopOvmMXWLRoEW6++eZu98nMzOz29+6gUChOmWgJECDg7MJkMsFk6rjWWXuUlJTAbrdj586dGDFiBABg3bp1iEQiKC7ufH2yzlBaWgoAnU52AIR+Q4CA8xnndFpITzuzvgIrSgk+cgECzg3Yd683AnFeXh6mTZuGefPmYcWKFQgGg5g/fz6uvfZaMkGhtrYWkydPxgcffICRI0fi2LFjWLlyJWbMmIG4uDjs3bsX9913H8aNG9fj5VyEfkOAgHOPnvYd581c16qqKrS2tqKqqgrhcJiMtgYMGNBt4i0uXC56scTU1FNLPCdAgIC+hcvlgl7f+Ury3eGjjz7C/PnzMXnyZJJ87+WXXya/B4NBlJeXk9lNcrkcP/30E1kfKjU1FbNmzcLf/va3UyorIPQbAgT8HnCyvuO0YmrOJm6++Wa8//77Hb5fv349JkyY0KNzRCIR1NXVQavtfmVjAQIEnBlQFAWXywWLxQKx+OwsgHm6EPoNAQLOPXrad5w3pEaAAAECBAgQIKA7nB9DJQYbN27EJZdcAovFApFIhC+//PJcF0mAAAECBAgQ8DvBeUVqPB4PhgwZgtdee+1cF0WAAAECBAgQ8DvDeRMoDNCL2U2fPr3Xxwu+cQECzi2EmBoBAgT0Bj3tO84rUnOqaJ9Eq7a2FgMHDjyHJRIgQAAAVFdXIyUl5VwXo0eoq6sTZj4JEPA7wcn6jj80qekq3Xl1dbWQGVSAgHMANjuvVqs9rfO89tprePbZZ2G1WjFkyBC88sorGDlyZKf7vvfee5gzZw7vO4VCAZ/P16NrsWUV+g0BAs4detp3/KFJzdKlS7Fw4UKyzVaKTqcTOicBfQZ/KAyFVHKui3Fe4XTcOJ988gkWLlyIFStWoLi4GC+++CKmTp2K8vLyDgtestDpdCgvL+/V9dl9hX5DgIBzj5O9u+eHU7uXUCgUpCMSOiQBZwL+UBg5f1tzrovx/wovvPAC5s2bhzlz5mDgwIFYsWIF1Go13nnnnS6PEYlESEpKIp/ExMSzWGIBAgScLfyhSc35DF8wfK6L0CfwBcPIeGD1Obm2wxs849cIhCJn/BoCoggEAti5cyemTJlCvhOLxZgyZQo2b97c5XFutxvp6elITU3FpZdeigMHDnS5r9/vh9Pp5H0ECBBwfuC8IjVutxulpaVkiYQTJ06gtLQUVVVV57ZgpwiXL4hIpOuch+VWF3IfOr3Rv9MXhNXRfcxAWyCEFre/231OF6Fu7vNMY8hjP6AtEOry98e+PoBv99Wf1jXOJ1KztqzhpERv9d7Tq48zjebmZoTD4Q5KS2JiIqxWa6fH5OTk4J133sFXX32FDz/8EJFIBKNHj0ZNTU2n+y9fvhx6vZ58hCBhAQLOH/Sa1FRVVWHr1q3Yvn07Wlpa+rJMXWLHjh0YNmwYhg0bBgBYuHAhhg0bhocffvisXL+vMOLJn/Dz4aYuf4/0QZLnN34+hln//K3bfV5ffwzXvrnltK5TVu/sQNBKq+3k/1C4d0a/O9IH9HxBRH+w6+vvqrJjQ3ljt8d7/KFuiYu/D0jN2Urqfcv7O7CzspVsh8IRrC1r4O3z7qYTCHKe2cbDTbzt8xElJSW46aabMHToUIwfPx6rVq2CyWTCG2+80en+S5cuhcPhIJ/q6uqzXGIBAgT0FqdMal5//XWkp6ejX79+GD16NEaNGoWEhASMGTMGO3fuPBNlJJgwYQIoiurwee+9987odfsagVAEUknXwU7hHqgbd37YfV0bVHLY2wLd7uMPhXnXsnkCPCMXCkfw8Ff78ePBhs4OBwBMf+kXNLVTey57bRO+Kq0FAATDp26wjzW5kfW377rd5/p/bcWqXZ2PtAFg63GaaHNJx3f76nmusEiEgqRdvgMuCQtHKAx65Hs89OV+3j65D0XLdqTR3eHa+2sdcPqiikgwHOmWpBUvW8sjgu2x6WgzZv3zN55L8mSkj0V7N6YI0XZX7/Dhlvd3oNbuJd85fUG4fFF166Z3tqHe3rNZQmcD8fHxkEgkaGjgt8mGhgYkJSX16BwymQzDhg3D0aNHO/1diMUTIOD0ca5WYDolUvPcc8/hqaeewuLFi/HGG28gJycHjz76KFavXo3MzEyMGzcOO3bsOFNl/UOgqoVePVjcLoL7qdUHyf/+UMd4mnkf7ECjM2pcvtvfUWq/75NSYqAiFNXhGu0RjgDHmz1EifjlaDNueT/6/N785Tg+2FyJA3UOALTBtzp8HVxW3JE8a2zf/62CPibScZTf7Pbjk+1duwzdvhCPbFEUhaWr9vL2afH4sacbInDXyt0AonVJURTu/GgXb59AKAIJ5w349UgzLnhmHdl2McTkkx38kbqPo/7Mfmdbh2tf8fpv+HJ3Ldle8Ekpln1bRrbf23SCRx4bXX5c9tomsh2JUBjDKUet3YudlTY89nU0DiTzwW87XPeSV37lbTu8wQ5uzAWflJL/rUx7Yp8VALh8ITjbuag6a49c7Kqy4T7Oec8k5HI5RowYgbVr15LvIpEI1q5di5KSkh6dIxwOY9++fTCbzWeqmGcEbn8I20608r77dHs1z3i8u+kEKpo9ZLu6tQ0H66IxQaFwBPtqHLxz7Km288jvmv31vHNUtbRh6ap9vGN2Vto6bHP7BYc3iF1V0X3aAiHc1O5deXntEd4gYuPhJlS3tpHtRpevg3uY+zsA7Kho5fUV3x+w8uqo1RPA1Sv4sVZr9vPP+craI9hfG62TnZU2fH8g2r+6fMEOyjo7aGJRVu9EvSM6OKAoCjZPgLe9sd05HvxiH29A4WgL8o7xBsI42ujiHbP8uzLe/Tragjwludzqwg8H+LbB7ee74P93WxXvOt/uq8dPnIGrLxjGkYbodf2hMG54i6/orzvUwHve5VYXr50daXCh4NEfOlyXi39uOMar977CKZGa1157DW+99Rbmz5+PW2+9FV9++SVeeuklTJkyBR988AFuvfVWPPjgg31eyHONX4404cEvoi/1F7tr8OGWyl6d65JXacPjYRpak8uPfTUOfLG7juzja+cy8QXD+PFgAxqcdCNiOzFu495VZcMXu2tJQ/MGw3BxXCcefwj1Di/+tfE4OYZ1c7FxFu1H9WxMjlRMk6NDVhf+5+2tGPHkT7z9xzyznrw4rLFlSxZilBpvIHruq1dsxrJvDwGgX5iMB1bzOqKVW6uYY+myB8IR/O+2ap76oZRJ8P7m6DOoamnD6xuOkroxaRW8uuxMMQpGIvhwSxU55sa3t6LB6SfPpjfxMvUOLwLhCCEGSz7fi9V763GYo+h8tLWKRx5j1TIA0ee64XAjamxeUr8Shpw6vXS5XL7O42L2tesg2ON9wTCJLXJ4o/Fc7H2yz3dPtR31Dh+vngHAxekUjzW5kfHAalLWZd+W4a9f7McXHBJ3prFw4UL861//wvvvv4+ysjLceeed8Hg8JBfNTTfdhKVLl5L9H3/8cfzwww84fvw4du3ahRtvvBGVlZW49dZbz1qZ+wIbDzfh6jc2k/fe4Q3i/v/sRXVr1DA+uboMP3EI83u/VfAI57pDjbjk1V/J8wuGI7j0tU1Ydyjqhn1mTTne/vUE55gGfMwZhNTZvZj1z994g6xZ//wNG8qjRvuFH8px+7+javL+Wic2Hm4ibS8cofDCj4exn2MI71q5iyi8APDZjhpe2Ztcfoz9+3o42qLt88oVm7GjItp3PLPmEP67J3qOjYebsI3ze4PThzs+3MUjR8//eJj37ny6vRov/nSEbG8+1oLZ72wjg7dQOIJr3txCBqjs/X+0JVpH/7utGhf+YyPZrmhpw03vbCN9SjhCYeXWKuzikMPFn+/BE5zB7Td76zD9pV/Itscfwhs/H0e5NUo4pr20EZ/vjCrW72+uwNNrDpHtrcdbUPDo92hlSEwgFMHSVftwsD5a76+uO8obJH+yvZo3yDpsdWPT0RYeaZv73g5s5hC7v325D8u/iw7cDtY7ef1GvcOLpav28drMv345jh+68QL0FqdEahobG5GXl0e2s7Ky4HA40NREN+a5c+d2OwOhL/Daa68hIyMDSqUSxcXF2Lat40i5r1FaZSfs99JXf8XqvVasO9SIbI6LZF+NA0cbXch4YHW3KoTDG4RFr8Rt/94JXzCMT7ZX4ZJXf4WtLUA6Gq66ANDKBgDsYOIhWEP9wo/RvBtXvE7HzyhldL4UL2PQWLIx6JHvUbJ8HZ5iFINAKILvmBELSzhqWtsg47jF2pjvJWIxwhEKzW4/6jnBx43OKFN3+YK8Ef3uKjsA4H/e3goAqGyNjvyON3uQm6Rl7pW+l6vfiLYbVhmpaPHw7vcYQwyC4QjKmJeSdbGNf249/r6mHEMeo0cHajldD7e8v51XH1zIGZmGJXWpRhWpK+51uWCJFtuRtCckza4As82/lyCHIPmYemI7yeFpsRCJonVRa6M7D1ZlYT1kdQ4vwhGKuIfu/Xg3OSfb4XBHYMXLaDWjyeXnjaJYo3cXo1xJmXr4x0+HAQB/enUTKIrCk9/QHaybuV6jy4dH/0urRWwA+Jsbj5NncbZwzTXX4LnnnsPDDz+MoUOHorS0FGvWrCHBw1VVVaivj47GbTYb5s2bh7y8PMyYMQNOpxO//fbbeZddvLTajgsGxKHGRhvT401uZCVosJ0x2hRFIc+sxWHOKLu6tQ2JeiXZrmxpQ26SFicYJabe7sOQFD15twAg1ahGA8f4HGvyYLBFT97vI41upMepcYgxrr5gGMkGFaptUSNfbfOiIFlPtmvtbTBpFeSdbnb7oZCKyb0AwIAEDY5zFKIKTj8BAOvLG5GVoMHxZrqs9rYAMk0x2HS0meyTZlTz3KWHG1zIiFOTspdW2zHIosMRRgEJRyikGlWo4pAcuzeAuBg52a5qbUOeWYca5r2sam1DmlGNDYejRHCwRU/uDaDVnpRYFekXDtY5Ea+Rk30aXT7kJml51/WFIjzCdqzJg+xELXmnD1mdsOiVqGPUHbc/hAStgve8G50+pBvV0ftvdGOQRU+OqbV7kWxQkW0AiNPI4fZHr1ve4EJGfAzZPljvwJBUAyHPHn8I/eJjeORKo5BCIY3SiRqbF4k6BRlYHWv0ICdRi12MXQCA7ERNByWqL3BKpCY7Oxs//vgj2V6/fj3kcjnxZSuVyjO6NgqbdOuRRx7Brl27MGTIEEydOhWNjd0He/YWdXYv2gIh7Kt1QCWX4B8/HsaeGge8wRBaPAEEQhFc/cZm+IJhXPLqr8TIL/lPVNU53ODiMXoAkDEPv9buJfUVjlCERDi9IaTEqrCDYfFsvMVjX9NGxsOMum1tQRJXxIJVRnzMuYY/EX1eXDQ4fUT5cftDuP/zPVi9r56naNiZF0wqFqH/g9+ixR3gSZk2TsxOMETxYjFYVDD3Pu1FesTBNvKtJ1oRCkd46lD7GKApL9Ajnf/uoVUsdpRztNFNynklIy2zVeBkysCSM65aAQDJBpq4RCIUIYDsPeUk8jNVfrQ1qgRRFIX9tQ5Mev5nAMB/GFIzIj0W0wcnIcAQlDIrbeDFYv57wCVVbPDy9f/agkiEgtsfwqzhKZjMnPuhr/jTjVk34u4qOw43uAj5+aq0Dit+PgYAKFlOu6uGMc/7aLtYH38ogr/NpAckbN15mDbCKjV2TofqC0bwFjNaZ+un1ubFL0eamXOc2+Dh+fPno7KyEn6/H1u3bkVxcTH5bcOGDbw4u3/84x9kX6vVitWrV5PJBucTam1ejMyIIwrqiWYPJuUloJIxjE5vCNkJWjS7o+9ROEIhRi4h7b/B6cOk3ARCSKxOH0akG1HHGYXLxCKeClzv8GJ4moGQhVqbFyWZcYTEVLa0YcyAeJ6BZls/2zfV2ryYkG3CsSbaqNfavSjMiCXn9AXDSNIpeW2w1RNAWlwMUSZrbV6M7h+9/3KrC5cUWHC0KdrWJSIRuJqs1eFDSf84VDL9UK3NizED4sm21elDUbqRV/ZgmIJGISV1ZnX4MCzNQAYO++ucmJFvRh1Tdrc/hES9kqdG29oCSI9TE7WivMGF6YPNhDw2OP0o7mdEJUNyIhEKMrGIN0mk3uFFUYaRDCSrW70YlRlHymF1+DA8PZZHUCIUIBGLSL1XNHswKtNI6qze4WWOobdD4QiUMgmxGwA9WE02qMj919p9KO5nJNc50ezB5NwEQkApJtSBG+ZXY/NiYk4CjjS4o/XcL5aQWKcviAStstOB4+nilEjN0qVLcdddd+Gaa67B7Nmzcemll+Kee+4hhnnDhg0YPHhwnxeSRW+SbvUWm4+1YPTT6zDw4e/xw8EGVLd68dJaWpLcdLSFxHNsO9FKRtTXv7WVd443fj6G2e9swwOr9iKfGf0DwMUFtC+/xuYFlwOy5z/R7MENxelkBLav1kEM0lu/HCcv4MqtVfh6bz22HOfMaInQQalHGt1IiVV1eX8ezlTnm9/dhk931JAOJ+qqoDvHMPOC/HKEVuSSdPTIr8VDk6Lbx2fCHwrD5Qt1e02AbzgbXH7eyKQrl94z39FyKju65JIn9sWblJuAAQka8r1Fr8K4bBOmDaYJ93FOZ/ry2iPIfPBbQhY9/jD21ThwuIFPBN7YeBwmrQIz883whyKobm0jdR/mdD4qmQQtjCG5//O9KMqI5Rn9ZIMKSln0VVMw/59o9sAdCMGsV0ImEfP86yxC4Qju/biUbNvaAjy32DNrDhFJX6uIJgif8sLPvDq64a2tZJQoEvGD+FjVhRuszB1Bs+3QyyOgZz4HUHc4VcX2s88+Q25uLpRKJfLz8/Httx1jkn7v8AXDSI9To8FFv3eVLW0oSjeiyUUbqAaXDwk6JdoPK5P0SmLUGlx+jEiPJWpgvcOLwck6NDEKoy8YJu2TbSMURas3bPtscPpQmGEkg7U6hxeDknWkTYQjFEQiEXQqGXGZ1tp9GJYWiyam7HV2L49MNTh9SOIoSiz6m2LIu0uTi1jUMfdS5/Cif4KGDBI8/hDUCimUMjHpFxzeIPKTDYTENLn9GJ4eS97jmtY2ZCVqySCQRaJOQcpa7/RhaKohWocOH4anGYia1eD0IUmn6FB2br1XtXgwun8crw4HWnSk32htC8AYI4dcKiaqktsXwoAEDaxO+pjq1jYUZxpRyxCSRpcPyQYVeX8jEQoiALFqOXE31dq8GJEeiwamjTQ6/RiWGiVoze4AcdWzoCgKybEqcn+NTvp+2bJXtbYh16yDn6ljpy8ErVIKmURE+qYmlx9DUw28+x2SEq3DE00eZJpicCZwSqTm6quvxldffQWpVAqPx4MXXngBy5cvJ79feeWV+Prrr/u8kEDvkm6dThKt6/51elOdl39bhuXfHUK9w4ffjrXw/IvBMIV/3VSIWpuXjML/fmUBUhlCsLPShqsLU3CYGU01OHy4uojOlfHk6jJc8fpvuGJYMgDahcItazAcwc4qG3471gIjI6G2nykz8bkNJJ7ikUsGYkKOCQAwJIWWix9nFCFWyXiaIRVfltZBKhYhUa9Ek8uPZncAK24cDqVUAn+IjiOJ10RfkGNNHWcGzX5nG6YzRMPq8OEqjtspJZaWTbmSMxB1xz2wah+qWto6BCqHI/RI4fM7SkgdHKx34olLB8HpDYGiKFJHozKNhNDdMzkLt4/LRL3Dixd+LEdVaxshnGydKWViqOUStAXCRI0BaLmZhUgkwqUcH/Tw9Fj4ghFiFOZPGoAIRQf1vb7hKJFxQxEKHn8IGqWUXI9LNrQKKdqYjuOqEfQCbjYP7ea7Y3x/JOmUoCi60wWA/JRombhgfd9jBsTj6sIUePwh0ullxsfApFWQkfm7c4oAAK+so2cGfXhLMd7dVIFGp48YKIBuQ8//UE7OcTZxqortb7/9huuuuw633HILdu/ejcsuuwyXXXYZ9u/f3+n+v2ck6BQkLqHR5UeeRUeUmQanD4k6BUQiuv2GwhGIxSLa3cAYMW8ghP4mDY9MWAxRN0m9wwezXoUEjlEXiQCzXhU16k4fCtNjUcMYrGaXHybOe9/i8SNeI0eiTkmMaZPLj5wkLVo9XFITSwxnvcMHi14FuYQmJGx5LHoVUSpaPH4MTtajnrluk8uPBI5BtjLkIkGrJGUHgAStgrjwG51+5Cfriare4PIjSR89hz8UhlwiRoJOScrmC4SRGR9DytHg9KF/gobEzTU6/UjUKSGViBAMRxCOUJCIRTDroqTG5QshJ0lLztHopAkoi0anHwk6BZJ0Sp5b36xXRpUaWxuK+8URxaTJ5SeEhKIoOLxB6NUymA3ROgtFIkiJVaOB2bYyZIqtjzqHF2adEjEKKS9Wz6JXETWn2R1AQYqBDHQanT5evTcxZDqJU2cAxdiJaJ0NTTWQctXYvKS/7+tZUqc8pXv69On46KOP8Pnnn2PevHm83+Li4hAXF9dnheOiN0m3eptEq720/u9bOl8oT9bNtOzOpvqyEfQuXwjGGDlsbQEyqspO1KLVE8SKn4/h16PNMMbIiSLh9oehkfOX6bp8OE1q2ue0OdzgJu6Cz+8YDYAOgOXiRLMHNzCqUkn/OKgY8pIWRxsnlvmzPlILZwQ1sp8RcokIRU/9hGe/L0dKrBrHmz14/odycl8sWHcKi301DpRz3CeLP9tDRnf3TcnG8u/K8NPBBiKNA2yelOg9tnj8aHb7MSmXXuMnEIrgzg93QquUQquk41vKrS4csrqgV8mwu9qGlUzU/Td3j8HCC3PIyxqrlsGsV+Lmd7fD46fJA6s6vvjTYQxLM+Dt2UWIUUjh8YdwgIlL+c+dJYjTyMnLeOeE/mhy+UFRFMZnm3BjcTrWHWogL3A4QuHCvERsPNKEdzdVAKAJ1fhsEzz+EGIUUqL8BMMUpg9OQsXTMzGzwExGoTMYstXaFoA/FIFBLcPKebTLpbTKjnsnZ2HOBf0A0HFfADD3gn64b0o2GS2Nz07ApUOT4fAGsb/OiTvG98eS6bkIhSNEnUtlOhp2W6ei253dG+QFEPtDEZTV08/pm3vG4GziVBXbl156CdOmTcPixYuRl5eHJ554AsOHD8err756VsvdF0jkGMoWN23UWULcwBjXWDXdt7R4AojXKGAxRA0UwFcQ6h0+mhwzv9XZvbAYVEiJVaPa5oXHH4JKLkWiTkEISrM7gFSjmrhbmty0cRWLaLdVg8PPGDkFuQ5FUTBpFISA1dl9yIyPISP7eocXSXolLIxBdniD0KtkMOkUxDBSFK16RolBO1Lj8CFJr4JJq0Cjy0e75kFPGmBJjsMbgFmvJAMl2kAriULS6PQjSc8a6Ci5SNQpCZlscPmJWg3QikmCTokErRKNLj9a3H7Exch59QzwiWGjy49ErZKopo0uuhxmJt6FoiiIRPxn1eKmXVrsgJS+fyX0jCLW7KbJJZcIURTTZjiqklmvJO56q8MHs0EFM3MdN9MfJemVRM2hKApJuihRZJ83+xv7HNiy0+1RhERttA4bnX6kx8UQtbfRRRNwg1rW56pvr5PvRSIRHD58GL/++is2btzI+/xe0NskWo3Mw4thgk3HZtFKxuBkfr4KKRPBed3ItA7n4M4mYOM4fjxIky+5RASDWoZnvy8nBGRAggZHGl34mZlBIBKJsPFIE5Z9S89maB+jkRKrhliEDlmDl67aRyL75Qwp4ZIEFr5gBFMHJSJBq4SbMegKqRjfLxgHty/ETHcWYePiibwRxXtzRmJ7BR3rk2ZUY3CyHgdqHVhf3gSXL4hJuQn47/wLeNdaPDUHQFS50SqluHFUGvHJKqRiFKTo0eD0Y+0hOoh1LmOg2Wmg3y8YR+6p2R3AfVOyAdAuETaCXsLUEftCyyRiVLd68dcv9iNeI8eABA3Ucgm+3hOdaRbDuGxiY2hCdNfE/jBpFXh53VEcaXAjO1ELtVyCww0uvLnxOL69ZyykYjFCYQp1Dh8vQO4o4/LTKqU43ODG6KfpOBeHN4gEnQJ3/+9u0jH8aYgZCVoFIaxuQmBDRCFbd6gRd63chSl5CZiYQ5O4h77cj5/LmyCXiMl+d63cBZVcgil59D57ahwoyojFw5cMhF4lJWqgQiqGVinFk6vL0OTyoyBFD4NKhse+PojPdtTg+uI0WAz0s7YxHQ1LFL/dV0/aGtsUDzJT/ZXMYp6NrjOfz6Y3iu3mzZt5+wPA1KlTz4jC6/aH8PWeOqzeW48DdQ78dLABdXYvPtpaiZ8PN6HO7sX2ilZ8VVqL8c+uxyNf7cfGw02otXuR8cBqfLG7BjsqWvHp9mpMem4DPthcAYqi4AuGoZRJkMQxUOEIBRknJwGr1LBGjN1O0itRzxhKiqIVWJZMsG4frVIKpy9IAkkTtLQi1MwY6ESOgkBRFHnXgKhiYNLK0eL2M+4YJW2QnT5C/uM0UbdIvcOLRE6/QitESqIQ1Tt8MBuUPMMIACpOfFCjiyZPSrkEbYEQIWgmrQKNTj/sbUEY1DIeqaEo/oKITS4/qbNGJ33dJL2S1GEkQpMLmijR52hjDD9bFw1OHxK1CoaAeOlyaRVI0qvIs2pf9ganDwk6BfQqOextQXKMmakzlz8ErVIGM0eposAvO02mFLAYlKi1e9Hk9iNeEy0HS4ziYuRo9QQ5bURJys7WWYKOJmRWhxdmgxIWQ5QYAXSMIEuE2OfNurkamW2WkDZ7/DBp5bSqyPQJwXAEcqmYDOIbGELGVfP6Cr1apXvLli24/vrrUVlZ2UE6EolECIf7ft2i3iTdUigUUCg6+jpPBqvDi6cuH4yL8y34Ynd0utw3d4/F0lX7yHx7qUSERROyMaPATL5LiVWRKPnFU3Pw7PflJNHeh8yUv4cuHkjcCq+sO4q/zyqARiFFqyfAmyYXDFN4kzMFm4sknRKP/mkQHmaCSvPMOjILhZ0SDdDxLusPNaLVE8AVw5KxijP1dmyWiXRmADDQrENOkhZNbj9mvvwLRvYzIi1OzYu1kEvFmJlvxup99cS4LbwoG/NX7saCT0rxwdyRKEgxYM2CsZj24i/ITdLizvH90d+kwe5qG8ZmxeOZWQU43uQh9fHTwvHEx50Sq8Ygiw5XDE/GO5ui00qTGdfcrkobfiprwDWMO84bCMOiV2Lhhdlk33kf0NOluR3+pNwEKGUSngqXlaAlkmuEAqYNSkJukg4j0mKx5oCVEM4YhZT49eM1cjS5/aixtWH6ixtx/7RcEoNw4T82YvHUHBjUcoxIj8XOShuGpxkwLM2ANj//nZBJxPjXLyfwr19O4OGLB+KCAXH4/qAVjS4fib9pdPlJR8pFncOLVKOKN9tALhHTizYynXE2E/Q8OFmPR5lgarFYRAhOja0NJZlxpHN+df1RJOqUUMulmDYoCWuY2X4a5nfuFFepRIxAKEJiG1jC3eoJIEHbMS6iL9GdYnvo0KFOj7Faraes8D722GO9Kt+Nb21FabUd/eJjYFDLIBaJSE6XCTkmyCVi/HCwARa9Ev0TNHh/cyXe31yJ8dkmWPRKvLepAntqHJBL6Tr+5Ugz9CoZhqfFIl4jR4xCygtIBej6D0coojok6mkDHYpQSNQpaYLi8sPNuDqBaMoFf5AOFDXrVai3+1Bn9yIvVwe7V4KjjW4kMCQhQafgzYgC6ADzQCiCJhdtTE0MAWlw0YbSGCPHkYZm2NqCiI2RM25c+p0KhSnIpWJIxfR9WhkykcAQEI8/hCTGDdbo8qEtEIKSGWSyaAuEoFFIaQXIFUCD04dRmXFw+YKobGkjRClOIyfulvZocPpg0irJ/dU7vDDrlUjSK/DLkSa0eAKIi1HwiCALnUoGpy9EFLIkHU0E1HIJTJxtdoDIRasnAKOaNfx+NLn86G+KQThChx9EiYMMNk+AZ2dFjCLGEiELo5C0BcOI18qRpFNiZ4UNtrYgDGo5Q0go3vNm1R2rw4vx2SbY2gJodPkQjlAw65QMmfIiGO6s7EHEquWEtDa6fMhPNkAmEWFbRSsanX6YtEoYObE9XESVKQVRxHJ7ljezR+gVqbnjjjtQWFiI1atXw2w2n9EZTyy4Sbcuu+wyANGkW/Pnz+/Ta41IN2JEuhEAcDOrGJSkAwAe+9Mg/O+2KozNiseeajvkUjF0zGgWAN65uQgXMfkJbh+XibsmDsCiT/eQQDWANgo6iRiXD0vGz4ebcMkQCwDgt2OdLzfBKkQrbhyOOz6kp+EqZWJcNzKNkJrFU7OxcmsVfipr5Ckziy/KwZe7a1GUEYubL8jgkRqANq5snoe5Y+h7ZTvhGfm0y+ODuSNx0zvbSLDyY5cOwup99eS5X1xgwfyVu+EPRZDOuLBYYzh3TD+IxSLoVFK88fNxTMmjycVAS1T14sqjGoUUf5mag8Gc6aAAoGQMODsziHVzhSK0YpLKmcYI0NMFue5BBaMmcIOJx2TFk7wYDm8Qz181hK4TKV/AVMslhGAZY+Swe4P4jJn9lKBVEMUEoFUoAJhzQQZ2VtqwYEo2RvePx7pDfDIu51xDo5Di6qJUVLW2YfFne4myJ5eIeXE8LFbtqsWNo9Kh4FyXvdfsJC2sTh/+yjwrs4EfuD2IqfcXfzqCixeaMSBBi3fnFOFvX+zH45cOAgAsmZ6LNQesMKhl5DmyGJysw2Fr1LU6bRDdGy2Zltvp7LfzEUuXLsXChQvJttPp7LHretWdtMuX7RJFIhEzYo62xfbb3eFoowuf7qhBSqyaF9DJNXLxGlYhoeMyErUKWJ20gbIYlESpYI0vQKuawXCEkBuLQYk6hxf1dppcKGVi/Hq0GRY3HR+jkNJG3RcMk7Zr1tMxFN5AGDEK2kXV6KJnVQ5JMcAYI4fV6UOtjXZp0XXBv7/oMTQhM2kVONbohlwqgVnHGkZ6HbskTtm5SftMWgWa3D7iwlLJJNhe0Qqr0wuzXgWFlB7McDO5q+VSuP0huHwh6JRSoghZHXTsBxtT0+D08abEcxGtV1oxMWkVON7khlImQYJWgXiGTLV4/Ihnnh3rbqIomowmamkyRccH0dexOrxodtH1zrYTp5cuJ8AqLwE6Hk8hRbKBnpIeClPIStTQ6gdDNrjuOS4sBhVq7V5YGXdbqyeAXVU2BEMUTSa1CjQ4aVc/2+7ETL2zSh25jtOPhDy6H6xjFCOTVkHUHTZwHAD0Khkc3iAcjIqW2AlZPl30yv105MgRLFu2DHl5eTAYDLy4Fb2+82DFvsDJkm6dSTx+KT2rSy4Vw6CW4d+3FOPpWQW4aFASiTuoeHomshO1OL5sBjb8ZQLJ//H81UPIedhATIBOrtTqCUDVbgSyiFEdRmbQxGp8tqlDeUQiEVEi/nNnCSblJiLP3DGdu1QiRoPTj3FZJhSkGHi/sepHV0nm+jEBoP0ZInDr2EwAIMHAnaXqZ+/FoKZJx9WFtDEoyYyDWi7pMNUYoOtUwkkAp2Duq7ifkXcfXChlEvKSc8EOKl6/YQREIhHen0vHQ7GqhpZDQAEgJ0mLWLUMtTYvISftY6XkUjEJ7pVKxGQKNADEaxU8xYR1SV5cQBNVlny1r2PucgWsImRQy7Gv1oHbxtP1HKPgt4sTy2eQ/3OTtNAopOT8rK/68mH0ddVMDFa8hv79sT/RhIVrTONi6OeoU0pRa/eSYE/2ua+5dxyUMjG+nh+Nmfnm7rF4YHou2V7xPyMAABqltMukgH2J3ii2SUlJp6zw9naZBLFYBLFYBJFIROq6PYE5lUEgMT5MvAh9PG3kWALNKiTBcAQKqQQJTPwDq9ywRp1r5BJ1CtTavKSts0pNi4dxNzFqTzPj0mDBDc5lFQK2F2CJQSPHyDe5/Ki1e5HCIdfeQJioLkmMeyXIKDc0QaHdIEl6JaMCUkwQMG30WXWDJUisu4mN7eC5sPSsq4WNJ6HLwbrXWJdOAhO7U++gA6e1CilcvhDvHABIzAkAmDT0dbyBMNRyKSk7q6BIJWJEIhSaXQHybrHuJhas24dVZtgBXp3DS543BTrAlyVXbL2yrrRkpo2wzypWLYOtLcC4eOjryqV0ADbb9MwGOmamjSFGCUwd1jm8sBiUkErEjPoXJcLxjPuQfd5JOiWsDj8aXPQ+iQwRamofOO6m3VHs/bLuRLreo7FKfYVekZri4uIu1005kzhZ0q2zhdKHLwJAKxn94mOgkEpQ8fRM8rtYLOIlLwLoqYkAMHZAPPmuq3WR7p6cBQD49I4SWPTKbpc7OPTENKIqLbooB0efmt7pfhOZwFoAeO6qIfh1yUQSp6GSS8gInkWyQUVmAqlkfOPKorMVuNmkd+1H+CKRCENTDSRvTXvEMEZ41Q1406EAABEXSURBVO5aMhJ85Xo6lwgbK8LOmmKx+p6xHc6z5xH62SQwUywHM/flCXTuEs1N0uHWsZmotXsJIbtoYCJvBhZLQPYwz50lYJnxMTBpFCSJHxANrGXBGoTCDCNmMvV518T+MMbI8er1wzCzwAydiiZabD0PT4sFwCfDQNQY5iZpCWlhFSw2Jueyocm8YxRSCQxqGcmBxIWeuW6akW6b6XFRtau/KQaxMTKIRCKiFP7zhuEAoooeFwXJel6MxJlCb5ZJKCkp4e0PAD/++GOPl1U4l1DLpfAFwqh3eEnAflyMAoesTjKCTuTELgDoVJkB2Bk29LZZr8Luahsx8mYmqRurIGiZ2TANDh9REEQiEWrtXqL+sYSL7Z3YkXuzm3bZyBjDWGv3kgGUTCLGiWYPuRc2qJU9B0sUWHLBgnVPsWU/0ewhgxB21g0bY8SqO6zqxJa9urWNEBTa3eSPXpdx0VkdNCFj3zUumVLJJaho9hCiwJIYinOOJpefBA6zaHL7yLNK0ClQa/cSxYhVKvwhOmZKLhUjGKZQ3epFGqM+65RSHG10k3Kw12VhZgLB6+xeWPRRRYw7wypRp0RZvROxzGDTYlAR9zGpD5ePV88iROO0ALovq7V7SX/Hup9YYsSSOJqg0deRSkSotnlJG2IVQRaJ7QKy+wI9dj/t3Rtde+fuu+/GokWLYLVakZ+fD5mMP/otKCjouxK2w/z58/vc3XQ2sHbRBEx+fgNPcdi4eCIMMbJujmKCw5j/2ch+VsEBwHN9AFFFo/zJaeS7f98ykrhzuOSLhVwixmQO6QGAl68bSs7FzbHC4vritE7X7eDGsfxy/0Tebx/MHcl7GSfkmLCVybGTk6QlkfAsqdEpZRiSasBbs4t452YJmLYTpYZVYticLVEXTdfTBlk/P0sQpg02w+kL4f7P+etNsfEI7Oyt64vTYNLSHeCDM3Kx7NtDGMMhrQAdHAnQHcIlBWas3luPxVNppePiAgtm5kfdt+3zNkzKTcS394zFjJejqdJvHJXGi3G6Z3IWHli1jyQd7EwFiJFLyT2yyErQkFgYk1aB48tm8I5du2gC+V8kEnXabrgYkmro9ve+xMKFCzF79mwUFhZi5MiRePHFFzssk5CcnEzSTdx7770YP348nn/+ecycORMff/wxduzYgTfffPOslfl0Ue/wkRl/iXol9tU6ooZSyxAD5vGxxCAUifAy41qdPgxjnlOSTomdlTakM4SWnfbNdZsBdGZgNtO2MUaGA3VOQgwsBhX+W1rLM9iNzCxAlviLRHSuFLbsKbEqbK9oJYQlSU+XgzXysWo2qJUi2brFIhFqbV6M6k/PrDUzx7DlSNQpea57Vt1hp6fT+yiwu9pO3NQJWiUO1jt5ddjoiqpdpM4cXhSmx5J9dlfZSNkTtArsqXEQ5ZYNnJWKxUSpEIlEaHBG1a5ErQJ7axzEyLeffg7Q/T2btZiuIxVKq21ksJOgVaCyxUPULo1CijZ/CBQAvTpqTxqcPpT0jyd1tLfGQQiLRa8ifS8QJc/+YBjxMVF1p6q1Df1NtFJv0iqwu8pOcpHRM/E65taqbm3DlUwKCpNWgQN10esm6ZR0KhO279Eo+nyCQY+VmqFDh2LYsGEYOnQoZs2ahbKyMsydOxdFRUW8387HTJ1nC1xDAQBpcWpePM7WByd3OIai9VEAwNBUA7b/dQpeY0bMXWHFjcN5LyY7e6sryKRi+Dk+6iXTckkOAYCe3dLeVkpEIl4WypeuHdrhvO3jXKQSMelkAODdm4tw8PGpZJtVpFhSo5RJ8NVd0ZlUT8/Kx8pbi8lLJZOIySiCi7LHp5FOOeoait5AdqKGt//s0RmkfCwSdUrcNi6Tf8/Mi5gRF4OPbxuFW8dmElKpkEoY16ScdwyX5EnFHV83LpGYkJOA/Y9N5f3OjQECwOTAiJ7n2pFp+OquC0jMV2dQMXl2uPdxE3PPLNrPrjsZbh+Xib/OyDv5jmcAp7pMwujRo7Fy5Uq8+eabGDJkCD7//HN8+eWXZzRRaF9CLhXjeJObGFOzXoldVTaiwlgMSmw70UreLbmUjsWKUNHnalDLcajeyVFqlNhZaSfnNKhlsDp8vH6DouhYM7ZNJ+r4Rj3ZoMLWE63kPY+LkaPJ5eMNHyRiESpaPGRWXVqcGr8caSIG26xXorTaTu5FzGTV5c5SMuuV2FllI0qFWa/Cb8eaiRqepFOiurWN964BQKvHT4hRqlGN7RWtSOaUY/2hRqQx6iQbkMtFjIKvkGQlarChvImoTmYDTdDYvlIiprPq+kLRuKOUWBV+Lm8iKqjFoMLGw01kAJOgU/DWVALoPqPB6SNlJ3XEEIP0uBj8eqSZxN4BtGrOfYUNajm2VdiIK9lioJ8def4GJfbW2HmuRQBEqQNowrnleCspe4JWie0V0e24GDkaXX5e36GUS1DRElWzErVKbK+w8Yjw/loHjEybkkvFPDvSF+gxqTlx4gSOHz+OEydOdPphfzt+vPPZOgJOjkSdEoef5LuP/vPn0Zg3tqPc3x2mDT611Ycn5iQgOyHqbrlzQn+ebC0Wi3BiOX+kPrPAjFs55bp0aDLKHp+GUwE37gAANi+dhOuL03gvKxdquRRpcWoyyolRSPHrkkmYksd3P3JjlGQSMfKT9TxStubecVj/lwlku7MZO+OzTXiQMdojmJEaC7lUjFGZcR2+ax830z4p3ZiseF58Smdo77aTS8U8lSQUptOpczEk1YBME5/8cHFtUSqKMqL3QK/g3m0xToqlM/Iwrx3pO5s4lWUSAOCqq65CeXk5/H4/9u/fjxkzZuB8QZpRjSONbkKgM+Ji8O0+K3IYF2l/kwYbjzTx1LIwk12WRXaiBtsrbJzRP+2OYAcIIpEI9Q4f773XM7EZLBJ1SqwtaySG0qRV4JDVRQiLVCJGrd3HU4dyk3R0nBxDlkZlxuGnskbSXs16VQcDLQIQ5Li2ByRosPFwEzGMKbEqbDneigxmUoJOJUWZ1cnLSKyQSuD2h0j/khqrxqajzYSADDTr8OvRZqJUiUQiuP0h3jmS9LS6YWDIRXaiFmsPNZKyWgxKrDvUyHPbBsMRHrkq7mfEmgNWUmd5Zh3WHLCSwYpCKkFptZ33/ibpaTWDLXuSniYG7HX7xcdgfXkjr3+ptrXxBqKjMo3YeLiJEKOUWBV+ONhAwiB0Shl2VdmQxRngSSUiokIDdLv6qayBkFaLQYkfDzaQmZVisYispcUiJVaF6tZo2fNT9Ph6Tx2KGO9CpikGX5bWkrYLAAMSu+67eoMek5r09HTyWblyJdauXcv7Lj09HWvXrsXHH3/cpwVk8dRTT2H06NFQq9UwGAxn5Bq/B8jbzbxJNqg6BLf2NR790yDMYuTCnmJUZhyuGM4/pn3A86lCIZVg2eX53d5vSqwaf7s4qhDIJGK8Nbuw2/NS4HfwYrGIdDIs/n5l1y7TnCTtSd0vCmnHV2kdhzgBtPLUVdbfnkItlyA2Rt7tPu3LeuvYTB7RfWjmQF4Q9vmE1tZW3HDDDdDpdDAYDLjlllvgdncMPudiwoQJhECznzvuuOMslfj0ccGAeGIUAFqxvaE4jaxVppRJMG9sJnHxALTxSOMY28HJeozLNhG1kSU33MkF47NNvHOMSI/lkY38ZD0MahkZ3bPnyk82kH0SdQqekRubFc9r81mMMWezp8ulYsQopLiA47YdPSAeFw6MDlQGWvRMxt6o+jE4WYchqfR5RSIRVDIJ750emmrgKcW5Zi36xccQ0qaUSTApNwEFnLIVZRhx46io4jkhxwSXL0qMWIWXNeoKqQTZiRpCegB6gDSUQy5H9jMiLkZOCGlKrAqZphgUpkefp8sXIvFtAN23TuPED+Yl6TBreAopu0ougUwi5qm4KbFq3rMakxWP3CQtKfsgpg657Ugm5p9Do5DxFiW+dKgFfxpiISQtz6xDKELxZq5e0J//rAZZ+P1bSf84/HVGHgmuVsulsBhUPAK+dHrfKr4iqhc5ijMyMrBy5UqMHj2a9/3WrVtx7bXX4sSJE10c2Xs88sgjMBgMqKmpwdtvvw273X7K53A6ndDr9XA4HKc0o0HA+Y2ZL/+CYWkGPHlZ/hm7hr0tgP21TozJij/5zqcBOoX86RPIc4XTfQenT5+O+vp6vPHGGwgGg5gzZw6KioqwcuXKLo+ZMGECsrOz8fjjj5Pv1Gp1j69/PvYb3GnD7HagXbyIhzOT51TO293srTDjBulun0iE4rksejLFvf0x7eELhiERi3gqyalMne/pddmMy9zfRZz77c01jze5EauWn3SwwkX7emaVuVNxI3uYRJ8sOW10+SAWiTq4pLig8/h0vb4fRVHwhyIdYj3b79Ob59LT97BXeWqsVivM5o4uDpPJxPNl9yXYZFjtZWUBAk4GiuJPoT4TMKjlZ5zQAB0Dw/8/oaysDGvWrMH27dtRWEirc6+88gpmzJiB5557DhaLpctj1Wp1l1O4/4igFSn+NpfQADhlQsOepzu0T9TWGdob3p4YuJMZ687ei77In9b+upZ2rvHe3Et7dOc67grt67kn9d4e7Z9/TxJndkdoAPr+T9ZHnem8dr2a0p2amopNmzZ1+H7Tpk3ddiwCBJwLGNSyXnXgAn5f2Lx5MwwGAyE0ADBlyhSIxWJs3bq1myOBjz76CPHx8Rg8eDCWLl2KtrbOUwsAp7dMggABAs4tetXTz5s3DwsWLEAwGMSkSZMAAGvXrsX999+PRYsW9WkBTwd+vx9+f3S6nNA5/f/EW7MLezWSEfD7gtVqRUICP/WAVCqF0WjscskDALj++uuRnp4Oi8WCvXv3YsmSJSgvL8eqVas63f90lkkQIEDAuUWvSM3ixYvR0tKCP//5zwgE6Oh4pVKJJUuWYOnSpT0+zwMPPIBnnnmm233KysqQm5vb7T5dQeicBADR7LoCfp/oaT/QW9x2223k//z8fJjNZkyePBnHjh1D//79O+x/OsskCBAg4NyiV4HCLNxuN8rKyqBSqZCVlXXKi0c2NTWhpaXz9Y5YZGZmQi6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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fromress = r.from_ress\nproj = matmul3d(out, fromress)\nf, ax = plt.subplots(n_chans, 2, sharey=\"col\")\nfor c in range(n_chans):\n ax[c, 0].plot(data[:, c].mean(-1), lw=.5, label=\"data\")\n ax[c, 1].plot(proj[:, c].mean(-1), lw=.5, label=\"projection\")\n ax[c, 0].set_ylabel(f\"ch{c}\")\n if c < n_chans:\n ax[c, 0].set_xticks([])\n ax[c, 1].set_xticks([])\n\nax[0, 0].set_title(\"Trial average (before)\")\nax[0, 1].set_title(\"Trial average (after)\")\nprint(f\"Target-bin SNR after RESS: {target_snr:.3f}\")\nprint(f\"Mean neighboring SNR: {neighbor_snr:.3f}\")\nprint(\"Interpretation: the target-bin SNR should exceed the nearby spectral\")\nprint(\"background if RESS successfully isolates the rhythmic response.\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "fromress = r.from_ress\n", - "proj = matmul3d(out, fromress)\n", - "f, ax = plt.subplots(n_chans, 2, sharey=\"col\")\n", - "for c in range(n_chans):\n", - " ax[c, 0].plot(data[:, c].mean(-1), lw=.5, label=\"data\")\n", - " ax[c, 1].plot(proj[:, c].mean(-1), lw=.5, label=\"projection\")\n", - " ax[c, 0].set_ylabel(f\"ch{c}\")\n", - " if c < n_chans:\n", - " ax[c, 0].set_xticks([])\n", - " ax[c, 1].set_xticks([])\n", - "\n", - "ax[0, 0].set_title(\"Trial average (before)\")\n", - "ax[0, 1].set_title(\"Trial average (after)\")\n", - "plt.show()" - ] - } - ], - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.986147, - "end_time": "2024-12-09T08:41:43.482350", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_ress.ipynb", - "output_path": "examples/example_ress.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:41:40.496203", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_ress.py b/examples/example_ress.py index b5a202d0..d34f2081 100644 --- a/examples/example_ress.py +++ b/examples/example_ress.py @@ -7,6 +7,16 @@ domain. Uses `meegkit.RESS()`. + +Because the simulation contains a known oscillatory target at 12 Hz, the main +sanity check is whether the target-frequency SNR stands out more clearly after +RESS than the surrounding spectrum. + +References +---------- +.. [1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source + separation: Optimizing analyses of neural responses to rhythmic sensory + stimulation. NeuroImage, 147, 43-56. """ import matplotlib.pyplot as plt @@ -57,6 +67,10 @@ ax[0].plot(signal[:, 0, 0], c="C0", label="source") ax[1].plot(noise[:, 1, 0], c="C1", label="noise") ax[2].plot(data[:, 1, 0], c="C2", label="mixture") +ax[0].set_ylabel("Amplitude") +ax[1].set_ylabel("Amplitude") +ax[2].set_ylabel("Amplitude") +ax[2].set_xlabel("Samples") ax[0].legend() ax[1].legend() ax[2].legend() @@ -75,6 +89,14 @@ noverlap=125, axis=0) psd = psd.mean(axis=1, keepdims=True) # average over trials snr = snr_spectrum(psd, bins, skipbins=2, n_avg=2) +snr = np.squeeze(np.asarray(snr)) +if snr.ndim != 1: + snr = snr.reshape(len(bins), -1).mean(axis=1) +target_idx = np.argmin(np.abs(bins - target)) +neighbor_mask = np.logical_and(bins >= target - 4, bins <= target + 4) +neighbor_mask[target_idx] = False +target_snr = float(np.asarray(snr[target_idx]).squeeze()) +neighbor_snr = float(np.mean(snr[neighbor_mask])) f, ax = plt.subplots(1) ax.plot(bins, snr, "o", label="SNR") @@ -84,6 +106,7 @@ ax.set_ylabel("SNR (a.u.)") ax.set_xlabel("Frequency (Hz)") ax.set_xlim([0, 40]) +ax.set_title("SNR spectrum after RESS") ############################################################################### # Project components back into sensor space to see the effects of RESS on the @@ -101,5 +124,9 @@ ax[0, 0].set_title("Trial average (before)") ax[0, 1].set_title("Trial average (after)") +print(f"Target-bin SNR after RESS: {target_snr:.3f}") +print(f"Mean neighboring SNR: {neighbor_snr:.3f}") +print("Interpretation: the target-bin SNR should exceed the nearby spectral") +print("background if RESS successfully isolates the rhythmic response.") plt.show() diff --git a/examples/example_star.ipynb b/examples/example_star.ipynb index c533220c..99f6625b 100644 --- a/examples/example_star.ipynb +++ b/examples/example_star.ipynb @@ -1,306 +1,97 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b19d7745", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:10.971754Z", - "iopub.status.busy": "2024-12-09T08:42:10.971369Z", - "iopub.status.idle": "2024-12-09T08:42:11.261129Z", - "shell.execute_reply": "2024-12-09T08:42:11.260815Z" - }, - "papermill": { - "duration": 0.296287, - "end_time": "2024-12-09T08:42:11.262330", - "exception": false, - "start_time": "2024-12-09T08:42:10.966043", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "85f7a7ca", - "metadata": { - "papermill": { - "duration": 0.000992, - "end_time": "2024-12-09T08:42:11.264697", - "exception": false, - "start_time": "2024-12-09T08:42:11.263705", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Sparse time artifact removal on simulated data\n", - "\n", - "This example is similar to test_nt_star.m in Noisetools. Results are equivalent\n", - "(within numerical precision) to the Matlab code.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "258a8721", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:11.267426Z", - "iopub.status.busy": "2024-12-09T08:42:11.267255Z", - "iopub.status.idle": "2024-12-09T08:42:12.099311Z", - "shell.execute_reply": "2024-12-09T08:42:12.098887Z" - }, - "papermill": { - "duration": 0.834834, - "end_time": "2024-12-09T08:42:12.100595", - "exception": false, - "start_time": "2024-12-09T08:42:11.265761", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from meegkit import star\n", - "from meegkit.utils import demean, normcol\n", - "\n", - "rng = np.random.default_rng(9)" - ] - }, - { - "cell_type": "markdown", - "id": "c5a2650a", - "metadata": { - "papermill": { - "duration": 0.001026, - "end_time": "2024-12-09T08:42:12.103040", - "exception": false, - "start_time": "2024-12-09T08:42:12.102014", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Create simulated data\n", - "Simulated data consist of N channels, 1 sinusoidal target, N-3 noise sources,\n", - "with temporally local artifacts on each channel.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c544156f", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:12.106157Z", - "iopub.status.busy": "2024-12-09T08:42:12.105881Z", - "iopub.status.idle": "2024-12-09T08:42:12.109960Z", - "shell.execute_reply": "2024-12-09T08:42:12.109594Z" - }, - "papermill": { - "duration": 0.006521, - "end_time": "2024-12-09T08:42:12.110951", - "exception": false, - "start_time": "2024-12-09T08:42:12.104430", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Sparse time artifact removal on simulated data\n\nThis example is similar to test_nt_star.m in Noisetools. Results are equivalent\n(within numerical precision) to the Matlab code.\n\nUses `meegkit.star.star()`.\n\nThe important comparison is between the contaminated signal and the residual\nafter STAR. Because the ground-truth clean signal is known here, we can also\nquantify how much the denoised output approaches it.\n\n## References\n.. [1] de Cheveigne, A. (2016). Sparse time artifact removal. Journal of\n Neuroscience Methods, 262, 14-20.\n" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Create simulated data\n", - "nchans = 10\n", - "n_samples = 1000\n", - "f = 2\n", - "target = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)\n", - "target = target[:, np.newaxis]\n", - "noise = rng.standard_normal((n_samples, nchans - 3))\n", - "\n", - "# Create artifact signal\n", - "SNR = np.sqrt(1)\n", - "x0 = normcol(np.dot(noise, rng.standard_normal((noise.shape[1], nchans)))) + \\\n", - " SNR * target * rng.standard_normal((1, nchans))\n", - "x0 = demean(x0)\n", - "artifact = np.zeros(x0.shape)\n", - "for k in np.arange(nchans):\n", - " artifact[k * 100 + np.arange(20), k] = 1\n", - "x = x0 + 10 * artifact\n", - "\n", - "# This is to compare with matlab numerically\n", - "# from scipy.io import loadmat\n", - "# mat = loadmat('/Users/nicolas/Toolboxes/NoiseTools/TEST/X.mat')\n", - "# x = mat['x']\n", - "# x0 = mat['x0']" - ] - }, - { - "cell_type": "markdown", - "id": "be6aae2d", - "metadata": { - "papermill": { - "duration": 0.000995, - "end_time": "2024-12-09T08:42:12.113078", - "exception": false, - "start_time": "2024-12-09T08:42:12.112083", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom meegkit import star\nfrom meegkit.utils import demean, normcol\n\nrng = np.random.default_rng(9)" + ] }, - "tags": [] - }, - "source": [ - "## Apply STAR\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f6672f94", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:12.115934Z", - "iopub.status.busy": "2024-12-09T08:42:12.115779Z", - "iopub.status.idle": "2024-12-09T08:42:12.128449Z", - "shell.execute_reply": "2024-12-09T08:42:12.128196Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create simulated data\nSimulated data consist of N channels, 1 sinusoidal target, N-3 noise sources,\nwith temporally local artifacts on each channel.\n\n" + ] }, - "papermill": { - "duration": 0.015249, - "end_time": "2024-12-09T08:42:12.129392", - "exception": false, - "start_time": "2024-12-09T08:42:12.114143", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create simulated data\nnchans = 10\nn_samples = 1000\nf = 2\ntarget = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)\ntarget = target[:, np.newaxis]\nnoise = rng.standard_normal((n_samples, nchans - 3))\n\n# Create artifact signal\nSNR = np.sqrt(1)\nx0 = normcol(np.dot(noise, rng.standard_normal((noise.shape[1], nchans)))) + \\\n SNR * target * rng.standard_normal((1, nchans))\nx0 = demean(x0)\nartifact = np.zeros(x0.shape)\nfor k in np.arange(nchans):\n artifact[k * 100 + np.arange(20), k] = 1\nx = x0 + 10 * artifact\n\n# This is to compare with matlab numerically\n# from scipy.io import loadmat\n# mat = loadmat('/Users/nicolas/Toolboxes/NoiseTools/TEST/X.mat')\n# x = mat['x']\n# x0 = mat['x0']" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "proportion artifact free: 0.70\n", - "proportion artifact free: 0.70\n", - "proportion artifact free: 0.70\n", - "depth: 1\n", - "fixed channels: 10\n", - "fixed samples: 299\n", - "ratio: 1.01\n", - "power ratio: 0.40\n" - ] - } - ], - "source": [ - "y, w, _ = star.star(x, 2)" - ] - }, - { - "cell_type": "markdown", - "id": "9e26dcce", - "metadata": { - "papermill": { - "duration": 0.001076, - "end_time": "2024-12-09T08:42:12.131717", - "exception": false, - "start_time": "2024-12-09T08:42:12.130641", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apply STAR\n\n" + ] }, - "tags": [] - }, - "source": [ - "## Plot results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "aa3d5cc4", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:12.134354Z", - "iopub.status.busy": "2024-12-09T08:42:12.134226Z", - "iopub.status.idle": "2024-12-09T08:42:12.384528Z", - "shell.execute_reply": "2024-12-09T08:42:12.384182Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y, w, _ = star.star(x, 2)\nresidual = demean(y) - x0\nsource_corr_before = np.corrcoef(x[:, 0], x0[:, 0])[0, 1]\nsource_corr_after = np.corrcoef(y[:, 0], x0[:, 0])[0, 1]" + ] }, - "papermill": { - "duration": 0.253175, - "end_time": "2024-12-09T08:42:12.385931", - "exception": false, - "start_time": "2024-12-09T08:42:12.132756", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot results\nWhat to look for:\n- Local spikes should be strongly reduced after STAR.\n- The residual should be dominated by artifact remnants, not the target.\n- Correlation with the clean reference should improve after denoising.\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, (ax1, ax2, ax3) = plt.subplots(3, 1)\nax1.plot(x, lw=.5)\nax1.set_title(f\"Signal + Artifacts (SNR = {SNR}, corr = {source_corr_before:.2f})\")\nax1.set_ylabel(\"Amplitude\")\nax2.plot(y, lw=.5)\nax2.set_title(f\"Denoised (corr = {source_corr_after:.2f})\")\nax2.set_ylabel(\"Amplitude\")\nax3.plot(residual, lw=.5)\nax3.set_title(\"Residual\")\nax3.set_ylabel(\"Amplitude\")\nax3.set_xlabel(\"Samples\")\nf.set_tight_layout(True)\nprint(f\"Correlation with clean reference before STAR: {source_corr_before:.3f}\")\nprint(f\"Correlation with clean reference after STAR: {source_corr_after:.3f}\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "f, (ax1, ax2, ax3) = plt.subplots(3, 1)\n", - "ax1.plot(x, lw=.5)\n", - "ax1.set_title(f\"Signal + Artifacts (SNR = {SNR})\")\n", - "ax2.plot(y, lw=.5)\n", - "ax2.set_title(\"Denoised\")\n", - "ax3.plot(demean(y) - x0, lw=.5)\n", - "ax3.set_title(\"Residual\")\n", - "f.set_tight_layout(True)\n", - "plt.show()" - ] - } - ], - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.494461, - "end_time": "2024-12-09T08:42:12.708600", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_star.ipynb", - "output_path": "examples/example_star.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:10.214139", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_star.py b/examples/example_star.py index 9255630a..40258f40 100644 --- a/examples/example_star.py +++ b/examples/example_star.py @@ -5,6 +5,17 @@ This example is similar to test_nt_star.m in Noisetools. Results are equivalent (within numerical precision) to the Matlab code. +Uses `meegkit.star.star()`. + +The important comparison is between the contaminated signal and the residual +after STAR. Because the ground-truth clean signal is known here, we can also +quantify how much the denoised output approaches it. + +References +---------- +.. [1] de Cheveigne, A. (2016). Sparse time artifact removal. Journal of + Neuroscience Methods, 262, 14-20. + """ import matplotlib.pyplot as plt import numpy as np @@ -48,16 +59,29 @@ # Apply STAR # ----------------------------------------------------------------------------- y, w, _ = star.star(x, 2) +residual = demean(y) - x0 +source_corr_before = np.corrcoef(x[:, 0], x0[:, 0])[0, 1] +source_corr_after = np.corrcoef(y[:, 0], x0[:, 0])[0, 1] ############################################################################### # Plot results # ----------------------------------------------------------------------------- +# What to look for: +# - Local spikes should be strongly reduced after STAR. +# - The residual should be dominated by artifact remnants, not the target. +# - Correlation with the clean reference should improve after denoising. f, (ax1, ax2, ax3) = plt.subplots(3, 1) ax1.plot(x, lw=.5) -ax1.set_title(f"Signal + Artifacts (SNR = {SNR})") +ax1.set_title(f"Signal + Artifacts (SNR = {SNR}, corr = {source_corr_before:.2f})") +ax1.set_ylabel("Amplitude") ax2.plot(y, lw=.5) -ax2.set_title("Denoised") -ax3.plot(demean(y) - x0, lw=.5) +ax2.set_title(f"Denoised (corr = {source_corr_after:.2f})") +ax2.set_ylabel("Amplitude") +ax3.plot(residual, lw=.5) ax3.set_title("Residual") +ax3.set_ylabel("Amplitude") +ax3.set_xlabel("Samples") f.set_tight_layout(True) +print(f"Correlation with clean reference before STAR: {source_corr_before:.3f}") +print(f"Correlation with clean reference after STAR: {source_corr_after:.3f}") plt.show() diff --git a/examples/example_star_dss.ipynb b/examples/example_star_dss.ipynb index 1f89538a..c9c3002f 100644 --- a/examples/example_star_dss.ipynb +++ b/examples/example_star_dss.ipynb @@ -1,430 +1,133 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "bd916e98", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:01.263180Z", - "iopub.status.busy": "2024-12-09T08:42:01.263011Z", - "iopub.status.idle": "2024-12-09T08:42:01.549350Z", - "shell.execute_reply": "2024-12-09T08:42:01.549020Z" - }, - "papermill": { - "duration": 0.290045, - "end_time": "2024-12-09T08:42:01.550470", - "exception": false, - "start_time": "2024-12-09T08:42:01.260425", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "49fe2eff", - "metadata": { - "papermill": { - "duration": 0.001207, - "end_time": "2024-12-09T08:42:01.553309", - "exception": false, - "start_time": "2024-12-09T08:42:01.552102", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Example demonstrating STAR + DSS\n", - "\n", - "This example shows how one can effectively combine STAR and DSS to recover\n", - "signal components which would not have been discoverable with either these\n", - "two techniques alone, due to the presence of strong artifacts.\n", - "\n", - "This example replicates figure 1 in [1]_.\n", - "\n", - "## References\n", - ".. [1] de Cheveigné A (2016) Sparse Time Artifact Removal, Journal of\n", - " Neuroscience Methods, 262, 14-20, doi:10.1016/j.jneumeth.2016.01.005\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "665bff0a", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:01.556559Z", - "iopub.status.busy": "2024-12-09T08:42:01.556281Z", - "iopub.status.idle": "2024-12-09T08:42:02.345219Z", - "shell.execute_reply": "2024-12-09T08:42:02.344827Z" - }, - "papermill": { - "duration": 0.791926, - "end_time": "2024-12-09T08:42:02.346416", - "exception": false, - "start_time": "2024-12-09T08:42:01.554490", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.optimize import leastsq\n", - "\n", - "from meegkit import dss, star\n", - "from meegkit.utils import demean, normcol, tscov\n", - "\n", - "# import config # noqa\n", - "\n", - "rng = np.random.default_rng(9)" - ] - }, - { - "cell_type": "markdown", - "id": "6819daee", - "metadata": { - "papermill": { - "duration": 0.001294, - "end_time": "2024-12-09T08:42:02.349663", - "exception": false, - "start_time": "2024-12-09T08:42:02.348369", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Create simulated data\n", - "Simulated data consist of N channels, 1 sinusoidal target, N-3 noise sources,\n", - "with temporally local artifacts on each channel.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "26d9da0a", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:02.352790Z", - "iopub.status.busy": "2024-12-09T08:42:02.352610Z", - "iopub.status.idle": "2024-12-09T08:42:02.357682Z", - "shell.execute_reply": "2024-12-09T08:42:02.357297Z" - }, - "papermill": { - "duration": 0.007881, - "end_time": "2024-12-09T08:42:02.358745", - "exception": false, - "start_time": "2024-12-09T08:42:02.350864", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Source\n", - "n_chans, n_samples = 10, 1000\n", - "f = 2\n", - "target = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)\n", - "target = target[:, np.newaxis]\n", - "noise = rng.standard_normal((n_samples, n_chans - 3))\n", - "\n", - "# Create artifact signal\n", - "SNR = np.sqrt(1)\n", - "x0 = normcol(np.dot(noise, rng.standard_normal((noise.shape[1], n_chans)))) + \\\n", - " SNR * target * rng.standard_normal((1, n_chans))\n", - "x0 = demean(x0)\n", - "artifact = np.zeros(x0.shape)\n", - "for k in np.arange(n_chans):\n", - " artifact[k * 100 + np.arange(20), k] = 1\n", - "x = x0 + 10 * artifact\n", - "\n", - "\n", - "def _sine_fit(x):\n", - " \"\"\"Fit a sinusoidal trend.\"\"\"\n", - " guess_mean = np.mean(x)\n", - " guess_std = np.std(x)\n", - " guess_phase = 0\n", - " t = np.linspace(0, 4 * np.pi, x.shape[0])\n", - "\n", - " # Optimization function, in this case, we want to minimize the difference\n", - " # between the actual data and our \"guessed\" parameters\n", - " def func(y):\n", - " return np.mean(x - (y[0] * np.sin(t + y[1]) + y[2])[:, None], 1)\n", - "\n", - " est_std, est_phase, est_mean = leastsq(\n", - " func, [guess_std, guess_phase, guess_mean])[0]\n", - " data_fit = est_std * np.sin(t + est_phase) + est_mean\n", - " return np.tile(data_fit, (x.shape[1], 1)).T" - ] - }, - { - "cell_type": "markdown", - "id": "5358eff1", - "metadata": { - "papermill": { - "duration": 0.001191, - "end_time": "2024-12-09T08:42:02.361380", - "exception": false, - "start_time": "2024-12-09T08:42:02.360189", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## 1) Apply STAR\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fe64bbfc", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:02.364417Z", - "iopub.status.busy": "2024-12-09T08:42:02.364269Z", - "iopub.status.idle": "2024-12-09T08:42:02.377294Z", - "shell.execute_reply": "2024-12-09T08:42:02.377051Z" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Example demonstrating STAR + DSS\n\nThis example shows how one can effectively combine STAR and DSS to recover\nsignal components which would not have been discoverable with either these\ntwo techniques alone, due to the presence of strong artifacts.\n\nThe key didactic point is to compare DSS alone versus STAR followed by DSS,\nand verify that STAR + DSS recovers a component that is closer to the known\ntarget signal.\n\nThis example replicates figure 1 in [1]_.\n\n## References\n.. [1] de Cheveign\u00e9 A (2016) Sparse Time Artifact Removal, Journal of\n Neuroscience Methods, 262, 14-20, doi:10.1016/j.jneumeth.2016.01.005\n" + ] }, - "papermill": { - "duration": 0.015628, - "end_time": "2024-12-09T08:42:02.378176", - "exception": false, - "start_time": "2024-12-09T08:42:02.362548", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.optimize import leastsq\n\nfrom meegkit import dss, star\nfrom meegkit.utils import demean, normcol, tscov\n\n# import config # noqa\n\nrng = np.random.default_rng(9)" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "proportion artifact free: 0.70\n", - "proportion artifact free: 0.70\n", - "proportion artifact free: 0.70\n", - "depth: 1\n", - "fixed channels: 10\n", - "fixed samples: 299\n", - "ratio: 1.01\n", - "power ratio: 0.40\n" - ] - } - ], - "source": [ - "y, w, _ = star.star(x, 2)" - ] - }, - { - "cell_type": "markdown", - "id": "3a73312d", - "metadata": { - "papermill": { - "duration": 0.001244, - "end_time": "2024-12-09T08:42:02.380921", - "exception": false, - "start_time": "2024-12-09T08:42:02.379677", - "status": "completed" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create simulated data\nSimulated data consist of N channels, 1 sinusoidal target, N-3 noise sources,\nwith temporally local artifacts on each channel.\n\n" + ] }, - "tags": [] - }, - "source": [ - "## 2) Apply DSS on raw data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "945ba3c9", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:02.383932Z", - "iopub.status.busy": "2024-12-09T08:42:02.383809Z", - "iopub.status.idle": "2024-12-09T08:42:02.387583Z", - "shell.execute_reply": "2024-12-09T08:42:02.387292Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Source\nn_chans, n_samples = 10, 1000\nf = 2\ntarget = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)\ntarget = target[:, np.newaxis]\nnoise = rng.standard_normal((n_samples, n_chans - 3))\n\n# Create artifact signal\nSNR = np.sqrt(1)\nx0 = normcol(np.dot(noise, rng.standard_normal((noise.shape[1], n_chans)))) + \\\n SNR * target * rng.standard_normal((1, n_chans))\nx0 = demean(x0)\nartifact = np.zeros(x0.shape)\nfor k in np.arange(n_chans):\n artifact[k * 100 + np.arange(20), k] = 1\nx = x0 + 10 * artifact\n\n\ndef _sine_fit(x):\n \"\"\"Fit a sinusoidal trend.\"\"\"\n guess_mean = np.mean(x)\n guess_std = np.std(x)\n guess_phase = 0\n t = np.linspace(0, 4 * np.pi, x.shape[0])\n\n # Optimization function, in this case, we want to minimize the difference\n # between the actual data and our \"guessed\" parameters\n def func(y):\n return np.mean(x - (y[0] * np.sin(t + y[1]) + y[2])[:, None], 1)\n\n est_std, est_phase, est_mean = leastsq(\n func, [guess_std, guess_phase, guess_mean])[0]\n data_fit = est_std * np.sin(t + est_phase) + est_mean\n return np.tile(data_fit, (x.shape[1], 1)).T" + ] }, - "papermill": { - "duration": 0.006323, - "end_time": "2024-12-09T08:42:02.388457", - "exception": false, - "start_time": "2024-12-09T08:42:02.382134", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Apply STAR\n\n" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "c0, _ = tscov(x)\n", - "c1, _ = tscov(x - _sine_fit(x))\n", - "[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)\n", - "z1 = normcol(np.dot(x, todss))" - ] - }, - { - "cell_type": "markdown", - "id": "ce6eafb2", - "metadata": { - "papermill": { - "duration": 0.00131, - "end_time": "2024-12-09T08:42:02.391194", - "exception": false, - "start_time": "2024-12-09T08:42:02.389884", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y, w, _ = star.star(x, 2)" + ] }, - "tags": [] - }, - "source": [ - "## 3) Apply DSS on STAR-ed data\n", - "Here the bias function is the original signal minus the sinusoidal trend.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "33834718", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:02.394409Z", - "iopub.status.busy": "2024-12-09T08:42:02.394205Z", - "iopub.status.idle": "2024-12-09T08:42:02.397907Z", - "shell.execute_reply": "2024-12-09T08:42:02.397650Z" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Apply DSS on raw data\n\n" + ] }, - "papermill": { - "duration": 0.006337, - "end_time": "2024-12-09T08:42:02.398762", - "exception": false, - "start_time": "2024-12-09T08:42:02.392425", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "c0, _ = tscov(x)\nc1, _ = tscov(x - _sine_fit(x))\n[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)\nz1 = normcol(np.dot(x, todss))" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "c0, _ = tscov(y)\n", - "c1, _ = tscov(y - _sine_fit(y))\n", - "[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)\n", - "z2 = normcol(np.dot(y, todss))" - ] - }, - { - "cell_type": "markdown", - "id": "da409275", - "metadata": { - "papermill": { - "duration": 0.00122, - "end_time": "2024-12-09T08:42:02.401342", - "exception": false, - "start_time": "2024-12-09T08:42:02.400122", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Apply DSS on STAR-ed data\nHere the bias function is the original signal minus the sinusoidal trend.\n\n" + ] }, - "tags": [] - }, - "source": [ - "## Plots\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e107b482", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:02.404518Z", - "iopub.status.busy": "2024-12-09T08:42:02.404349Z", - "iopub.status.idle": "2024-12-09T08:42:02.705030Z", - "shell.execute_reply": "2024-12-09T08:42:02.704738Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "c0, _ = tscov(y)\nc1, _ = tscov(y - _sine_fit(y))\n[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)\nz2 = normcol(np.dot(y, todss))\n\n# Compare how well the best recovered component matches the known target.\nr_dss = np.corrcoef(z1[:, 0], target[:, 0])[0, 1]\nr_star_dss = np.corrcoef(z2[:, 0], target[:, 0])[0, 1]" + ] }, - "papermill": { - "duration": 0.30402, - "end_time": "2024-12-09T08:42:02.706668", - "exception": false, - "start_time": "2024-12-09T08:42:02.402648", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(7, 9))\nax0.plot(target, lw=.5)\nax0.set_title(\"Target\")\n\nax1.plot(x, lw=.5)\nax1.set_title(f\"Signal + Artifacts (SNR = {SNR})\")\n\nax2.plot(z1[:, 0], lw=.5, label=\"Best DSS component\")\nax2.set_title(f\"DSS (corr with target = {r_dss:.2f})\")\nax2.legend(loc=\"lower right\")\nax2.set_ylabel(\"Amplitude\")\n\nax3.plot(z2[:, 0], lw=.5, label=\"Best DSS component\")\nax3.set_title(f\"STAR + DSS (corr with target = {r_star_dss:.2f})\")\nax3.legend(loc=\"lower right\")\nax3.set_ylabel(\"Amplitude\")\nax3.set_xlabel(\"Samples\")\n\nf.set_tight_layout(True)\nprint(f\"Correlation with target, DSS only: {r_dss:.3f}\")\nprint(f\"Correlation with target, STAR + DSS: {r_star_dss:.3f}\")\nplt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "f, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(7, 9))\n", - "ax0.plot(target, lw=.5)\n", - "ax0.set_title(\"Target\")\n", - "\n", - "ax1.plot(x, lw=.5)\n", - "ax1.set_title(f\"Signal + Artifacts (SNR = {SNR})\")\n", - "\n", - "ax2.plot(z1[:, 0], lw=.5, label=\"Best DSS component\")\n", - "ax2.set_title(\"DSS\")\n", - "ax2.legend(loc=\"lower right\")\n", - "\n", - "ax3.plot(z2[:, 0], lw=.5, label=\"Best DSS component\")\n", - "ax3.set_title(\"STAR + DSS\")\n", - "ax3.legend(loc=\"lower right\")\n", - "\n", - "f.set_tight_layout(True)\n", - "plt.show()" - ] - } - ], - "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": "3.12.7" + ], + "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": "3.13.11" + } }, - "papermill": { - "default_parameters": {}, - "duration": 2.501712, - "end_time": "2024-12-09T08:42:03.027598", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_star_dss.ipynb", - "output_path": "examples/example_star_dss.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:00.525886", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_star_dss.py b/examples/example_star_dss.py index 66eb7956..5aaca989 100644 --- a/examples/example_star_dss.py +++ b/examples/example_star_dss.py @@ -6,6 +6,10 @@ signal components which would not have been discoverable with either these two techniques alone, due to the presence of strong artifacts. +The key didactic point is to compare DSS alone versus STAR followed by DSS, +and verify that STAR + DSS recovers a component that is closer to the known +target signal. + This example replicates figure 1 in [1]_. References @@ -75,6 +79,9 @@ def func(y): ############################################################################### # 2) Apply DSS on raw data # ----------------------------------------------------------------------------- +# DSS alone uses a bias covariance built from the signal minus a fitted +# sinusoidal trend. This encourages components that resemble the oscillatory +# target, but the strong sparse artifacts still dominate the decomposition. c0, _ = tscov(x) c1, _ = tscov(x - _sine_fit(x)) [todss, _, pwr0, pwr1] = dss.dss0(c0, c1) @@ -83,15 +90,22 @@ def func(y): ############################################################################### # 3) Apply DSS on STAR-ed data # ----------------------------------------------------------------------------- -# Here the bias function is the original signal minus the sinusoidal trend. +# After STAR, the same DSS bias becomes much more informative because the large +# transient artifacts have already been suppressed. c0, _ = tscov(y) c1, _ = tscov(y - _sine_fit(y)) [todss, _, pwr0, pwr1] = dss.dss0(c0, c1) z2 = normcol(np.dot(y, todss)) +# Compare how well the best recovered component matches the known target. +r_dss = np.corrcoef(z1[:, 0], target[:, 0])[0, 1] +r_star_dss = np.corrcoef(z2[:, 0], target[:, 0])[0, 1] + ############################################################################### # Plots # ----------------------------------------------------------------------------- +# The key comparison is not just visual denoising, but whether the recovered +# component aligns more strongly with the known target after STAR preprocessing. f, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(7, 9)) ax0.plot(target, lw=.5) ax0.set_title("Target") @@ -100,12 +114,17 @@ def func(y): ax1.set_title(f"Signal + Artifacts (SNR = {SNR})") ax2.plot(z1[:, 0], lw=.5, label="Best DSS component") -ax2.set_title("DSS") +ax2.set_title(f"DSS (corr with target = {r_dss:.2f})") ax2.legend(loc="lower right") +ax2.set_ylabel("Amplitude") ax3.plot(z2[:, 0], lw=.5, label="Best DSS component") -ax3.set_title("STAR + DSS") +ax3.set_title(f"STAR + DSS (corr with target = {r_star_dss:.2f})") ax3.legend(loc="lower right") +ax3.set_ylabel("Amplitude") +ax3.set_xlabel("Samples") f.set_tight_layout(True) +print(f"Correlation with target, DSS only: {r_dss:.3f}") +print(f"Correlation with target, STAR + DSS: {r_star_dss:.3f}") plt.show() diff --git a/examples/example_trca.ipynb b/examples/example_trca.ipynb index 08ca8d6b..736a0006 100644 --- a/examples/example_trca.ipynb +++ b/examples/example_trca.ipynb @@ -1,492 +1,115 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "34e94f0c", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:13.455615Z", - "iopub.status.busy": "2024-12-09T08:42:13.455235Z", - "iopub.status.idle": "2024-12-09T08:42:13.750939Z", - "shell.execute_reply": "2024-12-09T08:42:13.750589Z" - }, - "papermill": { - "duration": 0.3024, - "end_time": "2024-12-09T08:42:13.752463", - "exception": false, - "start_time": "2024-12-09T08:42:13.450063", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "26a47fcc", - "metadata": { - "papermill": { - "duration": 0.001139, - "end_time": "2024-12-09T08:42:13.755375", - "exception": false, - "start_time": "2024-12-09T08:42:13.754236", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "# Task-related component analysis for SSVEP detection\n", - "\n", - "Sample code for the task-related component analysis (TRCA)-based steady\n", - "-state visual evoked potential (SSVEP) detection method [1]_. The filter\n", - "bank analysis can also be combined to the TRCA-based algorithm [2]_ [3]_.\n", - "\n", - "This code is based on the Matlab implementation from:\n", - "https://github.com/mnakanishi/TRCA-SSVEP\n", - "\n", - "Uses `meegkit.trca.TRCA()`.\n", - "\n", - "## References\n", - "```\n", - ".. [1] M. Nakanishi, Y. Wang, X. Chen, Y.-T. Wang, X. Gao, and T.-P. Jung,\n", - " \"Enhancing detection of SSVEPs for a high-speed brain speller using\n", - " task-related component analysis\", IEEE Trans. Biomed. Eng, 65(1): 104-112,\n", - " 2018.\n", - ".. [2] X. Chen, Y. Wang, S. Gao, T. -P. Jung and X. Gao, \"Filter bank\n", - " canonical correlation analysis for implementing a high-speed SSVEP-based\n", - " brain-computer interface\", J. Neural Eng., 12: 046008, 2015.\n", - ".. [3] X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. -P. Jung, S. Gao,\n", - " \"High-speed spelling with a noninvasive brain-computer interface\",\n", - " Proc. Int. Natl. Acad. Sci. U. S. A, 112(44): E6058-6067, 2015.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b9c7f499", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:13.758573Z", - "iopub.status.busy": "2024-12-09T08:42:13.758392Z", - "iopub.status.idle": "2024-12-09T08:42:14.581157Z", - "shell.execute_reply": "2024-12-09T08:42:14.580677Z" - }, - "papermill": { - "duration": 0.825934, - "end_time": "2024-12-09T08:42:14.582424", - "exception": false, - "start_time": "2024-12-09T08:42:13.756490", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Authors: Giuseppe Ferraro \n", - "# Nicolas Barascud \n", - "import os\n", - "import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import scipy.io\n", - "\n", - "from meegkit.trca import TRCA\n", - "from meegkit.utils.trca import itr, normfit, round_half_up\n", - "\n", - "t = time.time()" - ] - }, - { - "cell_type": "markdown", - "id": "edb377c3", - "metadata": { - "papermill": { - "duration": 0.001148, - "end_time": "2024-12-09T08:42:14.585244", - "exception": false, - "start_time": "2024-12-09T08:42:14.584096", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Parameters\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46bfe7e1", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:14.588119Z", - "iopub.status.busy": "2024-12-09T08:42:14.587924Z", - "iopub.status.idle": "2024-12-09T08:42:14.590983Z", - "shell.execute_reply": "2024-12-09T08:42:14.590692Z" - }, - "papermill": { - "duration": 0.005842, - "end_time": "2024-12-09T08:42:14.592216", - "exception": false, - "start_time": "2024-12-09T08:42:14.586374", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "dur_gaze = 0.5 # data length for target identification [s]\n", - "delay = 0.13 # visual latency being considered in the analysis [s]\n", - "n_bands = 5 # number of sub-bands in filter bank analysis\n", - "is_ensemble = True # True = ensemble TRCA method; False = TRCA method\n", - "alpha_ci = 0.05 # 100*(1-alpha_ci): confidence interval for accuracy\n", - "sfreq = 250 # sampling rate [Hz]\n", - "dur_shift = 0.5 # duration for gaze shifting [s]\n", - "list_freqs = np.array(\n", - " [[x + 8.0 for x in range(8)],\n", - " [x + 8.2 for x in range(8)],\n", - " [x + 8.4 for x in range(8)],\n", - " [x + 8.6 for x in range(8)],\n", - " [x + 8.8 for x in range(8)]]).T # list of stimulus frequencies\n", - "n_targets = list_freqs.size # The number of stimuli\n", - "\n", - "# Useful variables (no need to modify)\n", - "dur_gaze_s = round_half_up(dur_gaze * sfreq) # data length [samples]\n", - "delay_s = round_half_up(delay * sfreq) # visual latency [samples]\n", - "dur_sel_s = dur_gaze + dur_shift # selection time [s]\n", - "ci = 100 * (1 - alpha_ci) # confidence interval" - ] - }, - { - "cell_type": "markdown", - "id": "0fdd935c", - "metadata": { - "papermill": { - "duration": 0.00112, - "end_time": "2024-12-09T08:42:14.594739", - "exception": false, - "start_time": "2024-12-09T08:42:14.593619", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Load data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "026e4375", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:14.597643Z", - "iopub.status.busy": "2024-12-09T08:42:14.597489Z", - "iopub.status.idle": "2024-12-09T08:42:14.715246Z", - "shell.execute_reply": "2024-12-09T08:42:14.714904Z" - }, - "papermill": { - "duration": 0.120665, - "end_time": "2024-12-09T08:42:14.716501", - "exception": false, - "start_time": "2024-12-09T08:42:14.595836", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "path = os.path.join(\".\", \"tests\", \"data\", \"trcadata.mat\")\n", - "eeg = scipy.io.loadmat(path)[\"eeg\"]\n", - "\n", - "n_trials, n_chans, n_samples, n_blocks = eeg.shape\n", - "\n", - "# Convert dummy Matlab format to (sample, channels, trials) and construct\n", - "# vector of labels\n", - "eeg = np.reshape(eeg.transpose([2, 1, 3, 0]),\n", - " (n_samples, n_chans, n_trials * n_blocks))\n", - "labels = np.array([x for x in range(n_targets)] * n_blocks)\n", - "crop_data = np.arange(delay_s, delay_s + dur_gaze_s)\n", - "eeg = eeg[crop_data]" - ] - }, - { - "cell_type": "markdown", - "id": "156e20ba", - "metadata": { - "papermill": { - "duration": 0.001126, - "end_time": "2024-12-09T08:42:14.719468", - "exception": false, - "start_time": "2024-12-09T08:42:14.718342", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## TRCA classification\n", - "Estimate classification performance with a Leave-One-Block-Out\n", - "cross-validation approach.\n", - "\n", - "To get a sense of the filterbank specification in relation to the stimuli\n", - "we can plot the individual filterbank sub-bands as well as the target\n", - "frequencies (with their expected harmonics in the EEG spectrum). We use the\n", - "filterbank specification described in [2]_.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "23d04cfe", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:14.722262Z", - "iopub.status.busy": "2024-12-09T08:42:14.722122Z", - "iopub.status.idle": "2024-12-09T08:42:14.913316Z", - "shell.execute_reply": "2024-12-09T08:42:14.913009Z" - }, - "papermill": { - "duration": 0.193961, - "end_time": "2024-12-09T08:42:14.914502", - "exception": false, - "start_time": "2024-12-09T08:42:14.720541", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Task-related component analysis for SSVEP detection\n\nSample code for the task-related component analysis (TRCA)-based steady\n-state visual evoked potential (SSVEP) detection method [1]_. The filter\nbank analysis can also be combined to the TRCA-based algorithm [2]_ [3]_.\n\nThis code is based on the Matlab implementation from:\nhttps://github.com/mnakanishi/TRCA-SSVEP\n\nUses `meegkit.trca.TRCA()`.\n\nThis example focuses on interpretation rather than implementation details:\nthe main outputs are classification accuracy and information transfer rate\n(ITR) across leave-one-block-out folds.\n\n## References\n.. [1] M. Nakanishi, Y. Wang, X. Chen, Y.-T. Wang, X. Gao, and T.-P. Jung,\n \"Enhancing detection of SSVEPs for a high-speed brain speller using\n task-related component analysis\", IEEE Trans. Biomed. Eng, 65(1): 104-112,\n 2018.\n.. [2] X. Chen, Y. Wang, S. Gao, T. -P. Jung and X. Gao, \"Filter bank\n canonical correlation analysis for implementing a high-speed SSVEP-based\n brain-computer interface\", J. Neural Eng., 12: 046008, 2015.\n.. [3] X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. -P. Jung, S. Gao,\n \"High-speed spelling with a noninvasive brain-computer interface\",\n Proc. Int. Natl. Acad. Sci. U. S. A, 112(44): E6058-6067, 2015.\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "filterbank = [[(6, 90), (4, 100)], # passband, stopband freqs [(Wp), (Ws)]\n", - " [(14, 90), (10, 100)],\n", - " [(22, 90), (16, 100)],\n", - " [(30, 90), (24, 100)],\n", - " [(38, 90), (32, 100)],\n", - " [(46, 90), (40, 100)],\n", - " [(54, 90), (48, 100)]]\n", - "\n", - "f, ax = plt.subplots(1, figsize=(7, 4))\n", - "for i, _band in enumerate(filterbank):\n", - " ax.axvspan(ymin=i / len(filterbank) + .02,\n", - " ymax=(i + 1) / len(filterbank) - .02,\n", - " xmin=filterbank[i][1][0], xmax=filterbank[i][1][1],\n", - " alpha=0.2, facecolor=f\"C{i}\")\n", - " ax.axvspan(ymin=i / len(filterbank) + .02,\n", - " ymax=(i + 1) / len(filterbank) - .02,\n", - " xmin=filterbank[i][0][0], xmax=filterbank[i][0][1],\n", - " alpha=0.5, label=f\"sub-band{i}\", facecolor=f\"C{i}\")\n", - "\n", - "for f in list_freqs.flat:\n", - " colors = np.ones((9, 4))\n", - " colors[:, :3] = np.linspace(0, .5, 9)[:, None]\n", - " ax.scatter(f * np.arange(1, 10), [f] * 9, c=colors, s=8, zorder=100)\n", - "\n", - "ax.set_ylabel(\"Stimulus frequency (Hz)\")\n", - "ax.set_xlabel(\"EEG response frequency (Hz)\")\n", - "ax.set_xlim([0, 102])\n", - "ax.set_xticks(np.arange(0, 100, 10))\n", - "ax.grid(True, ls=\":\", axis=\"x\")\n", - "ax.legend(bbox_to_anchor=(1.05, .5), fontsize=\"small\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "49634a82", - "metadata": { - "papermill": { - "duration": 0.001464, - "end_time": "2024-12-09T08:42:14.918005", - "exception": false, - "start_time": "2024-12-09T08:42:14.916541", - "status": "completed" }, - "tags": [] - }, - "source": [ - "Now perform the TRCA-based SSVEP detection algorithm\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7964c7d0", - "metadata": { - "collapsed": false, - "execution": { - "iopub.execute_input": "2024-12-09T08:42:14.921558Z", - "iopub.status.busy": "2024-12-09T08:42:14.921422Z", - "iopub.status.idle": "2024-12-09T08:42:22.620702Z", - "shell.execute_reply": "2024-12-09T08:42:22.620382Z" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Giuseppe Ferraro \n# Nicolas Barascud \nimport os\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.io\n\nfrom meegkit.trca import TRCA\nfrom meegkit.utils.trca import itr, normfit, round_half_up\n\nt = time.time()" + ] }, - "papermill": { - "duration": 7.702219, - "end_time": "2024-12-09T08:42:22.621698", - "exception": false, - "start_time": "2024-12-09T08:42:14.919479", - "status": "completed" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameters\n\n" + ] }, - "tags": [] - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results of the ensemble TRCA-based method:\n", - "\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dur_gaze = 0.5 # data length for target identification [s]\ndelay = 0.13 # visual latency being considered in the analysis [s]\nn_bands = 5 # number of sub-bands in filter bank analysis\nis_ensemble = True # True = ensemble TRCA method; False = TRCA method\nalpha_ci = 0.05 # 100*(1-alpha_ci): confidence interval for accuracy\nsfreq = 250 # sampling rate [Hz]\ndur_shift = 0.5 # duration for gaze shifting [s]\nlist_freqs = np.array(\n [[x + 8.0 for x in range(8)],\n [x + 8.2 for x in range(8)],\n [x + 8.4 for x in range(8)],\n [x + 8.6 for x in range(8)],\n [x + 8.8 for x in range(8)]]).T # list of stimulus frequencies\nn_targets = list_freqs.size # The number of stimuli\n\n# Useful variables (no need to modify)\ndur_gaze_s = round_half_up(dur_gaze * sfreq) # data length [samples]\ndelay_s = round_half_up(delay * sfreq) # visual latency [samples]\ndur_sel_s = dur_gaze + dur_shift # selection time [s]\nci = 100 * (1 - alpha_ci) # confidence interval" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 0: accuracy = 97.5, \tITR = 301.3\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data\n\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 1: accuracy = 100.0, \tITR = 319.3\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "path = os.path.join(\"..\", \"tests\", \"data\", \"trcadata.mat\")\neeg = scipy.io.loadmat(path)[\"eeg\"]\n\nn_trials, n_chans, n_samples, n_blocks = eeg.shape\n\n# Convert dummy Matlab format to (sample, channels, trials) and construct\n# vector of labels\neeg = np.reshape(eeg.transpose([2, 1, 3, 0]),\n (n_samples, n_chans, n_trials * n_blocks))\nlabels = np.array([x for x in range(n_targets)] * n_blocks)\ncrop_data = np.arange(delay_s, delay_s + dur_gaze_s)\neeg = eeg[crop_data]" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 2: accuracy = 95.0, \tITR = 286.3\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TRCA classification\nEstimate classification performance with a Leave-One-Block-Out\ncross-validation approach.\n\nTo get a sense of the filterbank specification in relation to the stimuli\nwe can plot the individual filterbank sub-bands as well as the target\nfrequencies (with their expected harmonics in the EEG spectrum). We use the\nfilterbank specification described in [2]_.\n\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 3: accuracy = 95.0, \tITR = 286.3\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filterbank = [[(6, 90), (4, 100)], # passband, stopband freqs [(Wp), (Ws)]\n [(14, 90), (10, 100)],\n [(22, 90), (16, 100)],\n [(30, 90), (24, 100)],\n [(38, 90), (32, 100)],\n [(46, 90), (40, 100)],\n [(54, 90), (48, 100)]]\n\nf, ax = plt.subplots(1, figsize=(7, 4))\nfor i, _band in enumerate(filterbank):\n ax.axvspan(ymin=i / len(filterbank) + .02,\n ymax=(i + 1) / len(filterbank) - .02,\n xmin=filterbank[i][1][0], xmax=filterbank[i][1][1],\n alpha=0.2, facecolor=f\"C{i}\")\n ax.axvspan(ymin=i / len(filterbank) + .02,\n ymax=(i + 1) / len(filterbank) - .02,\n xmin=filterbank[i][0][0], xmax=filterbank[i][0][1],\n alpha=0.5, label=f\"sub-band{i}\", facecolor=f\"C{i}\")\n\nfor f in list_freqs.flat:\n colors = np.ones((9, 4))\n colors[:, :3] = np.linspace(0, .5, 9)[:, None]\n ax.scatter(f * np.arange(1, 10), [f] * 9, c=colors, s=8, zorder=100)\n\nax.set_ylabel(\"Stimulus frequency (Hz)\")\nax.set_xlabel(\"EEG response frequency (Hz)\")\nax.set_xlim([0, 102])\nax.set_xticks(np.arange(0, 100, 10))\nax.grid(True, ls=\":\", axis=\"x\")\nax.legend(bbox_to_anchor=(1.05, .5), fontsize=\"small\")\nplt.tight_layout()\nplt.show()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 4: accuracy = 95.0, \tITR = 286.3\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now perform the TRCA-based SSVEP detection algorithm\n\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 5: accuracy = 100.0, \tITR = 319.3\n", - "\n", - "Mean accuracy = 97.1%\t(95% CI: 97.0-97.1%)\n", - "Mean ITR = 299.8\t(95% CI: 299.4-300.2)\n", - "\n", - "Elapsed time: 8.0 seconds\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trca = TRCA(sfreq, filterbank, is_ensemble)\n\nprint(\"Results of the ensemble TRCA-based method:\\n\")\naccs = np.zeros(n_blocks)\nitrs = np.zeros(n_blocks)\nfor i in range(n_blocks):\n\n # Select all folds except one for training\n traindata = np.concatenate(\n (eeg[..., :i * n_trials],\n eeg[..., (i + 1) * n_trials:]), 2)\n y_train = np.concatenate(\n (labels[:i * n_trials], labels[(i + 1) * n_trials:]), 0)\n\n # Construction of the spatial filter and the reference signals\n trca.fit(traindata, y_train)\n\n # Test stage\n testdata = eeg[..., i * n_trials:(i + 1) * n_trials]\n y_test = labels[i * n_trials:(i + 1) * n_trials]\n estimated = trca.predict(testdata)\n\n # Evaluation of the performance for this fold (accuracy and ITR)\n is_correct = estimated == y_test\n accs[i] = np.mean(is_correct) * 100\n itrs[i] = itr(n_targets, np.mean(is_correct), dur_sel_s)\n print(f\"Block {i}: accuracy = {accs[i]:.1f}, \\tITR = {itrs[i]:.1f}\")\n\n# Mean accuracy and ITR computation\nmu, _, muci, _ = normfit(accs, alpha_ci)\nprint(f\"\\nMean accuracy = {mu:.1f}%\\t({ci:.0f}% CI: {muci[0]:.1f}-{muci[1]:.1f}%)\") # noqa\n\nmu, _, muci, _ = normfit(itrs, alpha_ci)\nprint(f\"Mean ITR = {mu:.1f}\\t({ci:.0f}% CI: {muci[0]:.1f}-{muci[1]:.1f})\")\nif is_ensemble:\n ensemble = \"ensemble TRCA-based method\"\nelse:\n ensemble = \"TRCA-based method\"\n\nfig, axes = plt.subplots(1, 2, figsize=(8, 3.5))\naxes[0].bar(np.arange(n_blocks), accs)\naxes[0].axhline(np.mean(accs), color=\"k\", ls=\":\", lw=1)\naxes[0].set_title(\"Accuracy by block\")\naxes[0].set_xlabel(\"Block\")\naxes[0].set_ylabel(\"Accuracy (%)\")\n\naxes[1].bar(np.arange(n_blocks), itrs)\naxes[1].axhline(np.mean(itrs), color=\"k\", ls=\":\", lw=1)\naxes[1].set_title(\"ITR by block\")\naxes[1].set_xlabel(\"Block\")\naxes[1].set_ylabel(\"ITR\")\n\nplt.suptitle(f\"TRCA summary: {ensemble}\")\nplt.tight_layout()\n\nprint(f\"\\nElapsed time: {time.time()-t:.1f} seconds\")\nprint(\"Interpretation: stable performance across blocks suggests the learned\")\nprint(\"spatial filters generalize well across repetitions.\")\nplt.show()" + ] + } + ], + "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": "3.13.11" } - ], - "source": [ - "trca = TRCA(sfreq, filterbank, is_ensemble)\n", - "\n", - "print(\"Results of the ensemble TRCA-based method:\\n\")\n", - "accs = np.zeros(n_blocks)\n", - "itrs = np.zeros(n_blocks)\n", - "for i in range(n_blocks):\n", - "\n", - " # Select all folds except one for training\n", - " traindata = np.concatenate(\n", - " (eeg[..., :i * n_trials],\n", - " eeg[..., (i + 1) * n_trials:]), 2)\n", - " y_train = np.concatenate(\n", - " (labels[:i * n_trials], labels[(i + 1) * n_trials:]), 0)\n", - "\n", - " # Construction of the spatial filter and the reference signals\n", - " trca.fit(traindata, y_train)\n", - "\n", - " # Test stage\n", - " testdata = eeg[..., i * n_trials:(i + 1) * n_trials]\n", - " y_test = labels[i * n_trials:(i + 1) * n_trials]\n", - " estimated = trca.predict(testdata)\n", - "\n", - " # Evaluation of the performance for this fold (accuracy and ITR)\n", - " is_correct = estimated == y_test\n", - " accs[i] = np.mean(is_correct) * 100\n", - " itrs[i] = itr(n_targets, np.mean(is_correct), dur_sel_s)\n", - " print(f\"Block {i}: accuracy = {accs[i]:.1f}, \\tITR = {itrs[i]:.1f}\")\n", - "\n", - "# Mean accuracy and ITR computation\n", - "mu, _, muci, _ = normfit(accs, alpha_ci)\n", - "print(f\"\\nMean accuracy = {mu:.1f}%\\t({ci:.0f}% CI: {muci[0]:.1f}-{muci[1]:.1f}%)\") # noqa\n", - "\n", - "mu, _, muci, _ = normfit(itrs, alpha_ci)\n", - "print(f\"Mean ITR = {mu:.1f}\\t({ci:.0f}% CI: {muci[0]:.1f}-{muci[1]:.1f})\")\n", - "if is_ensemble:\n", - " ensemble = \"ensemble TRCA-based method\"\n", - "else:\n", - " ensemble = \"TRCA-based method\"\n", - "\n", - "print(f\"\\nElapsed time: {time.time()-t:.1f} seconds\")" - ] - } - ], - "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": "3.12.7" }, - "papermill": { - "default_parameters": {}, - "duration": 10.229303, - "end_time": "2024-12-09T08:42:22.942463", - "environment_variables": {}, - "exception": null, - "input_path": "examples/example_trca.ipynb", - "output_path": "examples/example_trca.ipynb", - "parameters": {}, - "start_time": "2024-12-09T08:42:12.713160", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/example_trca.py b/examples/example_trca.py index b36f2bb6..1f43d726 100644 --- a/examples/example_trca.py +++ b/examples/example_trca.py @@ -11,6 +11,10 @@ Uses `meegkit.trca.TRCA()`. +This example focuses on interpretation rather than implementation details: +the main outputs are classification accuracy and information transfer rate +(ITR) across leave-one-block-out folds. + References ---------- .. [1] M. Nakanishi, Y. Wang, X. Chen, Y.-T. Wang, X. Gao, and T.-P. Jung, @@ -164,4 +168,23 @@ else: ensemble = "TRCA-based method" +fig, axes = plt.subplots(1, 2, figsize=(8, 3.5)) +axes[0].bar(np.arange(n_blocks), accs) +axes[0].axhline(np.mean(accs), color="k", ls=":", lw=1) +axes[0].set_title("Accuracy by block") +axes[0].set_xlabel("Block") +axes[0].set_ylabel("Accuracy (%)") + +axes[1].bar(np.arange(n_blocks), itrs) +axes[1].axhline(np.mean(itrs), color="k", ls=":", lw=1) +axes[1].set_title("ITR by block") +axes[1].set_xlabel("Block") +axes[1].set_ylabel("ITR") + +plt.suptitle(f"TRCA summary: {ensemble}") +plt.tight_layout() + print(f"\nElapsed time: {time.time()-t:.1f} seconds") +print("Interpretation: stable performance across blocks suggests the learned") +print("spatial filters generalize well across repetitions.") +plt.show() diff --git a/meegkit/asr.py b/meegkit/asr.py index 05914263..2009b90a 100755 --- a/meegkit/asr.py +++ b/meegkit/asr.py @@ -314,6 +314,8 @@ def clean_windows(X, sfreq, max_bad_chans=0.2, zthresholds=[-3.5, 5], X : array, shape=(n_channels, n_samples) Continuous data set, assumed to be appropriately high-passed (e.g. > 1Hz or 0.5Hz - 2.0Hz transition band) + sfreq : float + Sampling frequency in Hz. max_bad_chans : float The maximum number or fraction of bad channels that a retained window may still contain (more than this and it is removed). Reasonable range @@ -341,6 +343,8 @@ def clean_windows(X, sfreq, max_bad_chans=0.2, zthresholds=[-3.5, 5], Maximum fraction that can have dropouts. This is the maximum fraction of time windows that may have arbitrarily low amplitude (e.g., due to the sensors being unplugged) (default=0.1). + show : bool + If True, plot the retained windows overlaid on the data. Returns ------- @@ -628,13 +632,15 @@ def asr_process(X, X_filt, state, cov=None, detrend=False, method="riemann", If True, detrend filtered data (default=False). method : {'euclid', 'riemann'} Metric to compute the covariance matrix average. + sample_weight : array-like | None + Optional weights used when averaging multiple covariance matrices. Returns ------- clean : array, shape=([n_trials, ]n_channels, n_samples) Clean data. - state : 3-tuple - Output ASR parameters. + state : dict + Updated ASR state dictionary. """ M, T, R = state["M"], state["T"], state["R"] diff --git a/meegkit/cca.py b/meegkit/cca.py index f73b1a65..6e6fb0c2 100644 --- a/meegkit/cca.py +++ b/meegkit/cca.py @@ -218,8 +218,10 @@ def nt_cca(X=None, Y=None, lags=None, C=None, m=None, thresh=1e-12, sfreq=1): Parameters ---------- - X, Y : arrays, shape=(n_times, n_chans[, n_trials]) - Data. + X : array, shape=(n_times, n_chans[, n_trials]) + First data array. + Y : array, shape=(n_times, n_chans[, n_trials]) + Second data array. lags : array, shape=(n_lags,) Array of lags. A positive lag means Y delayed relative to X. If :attr:`sfreq` is > 1, lags are interpreted as times in seconds. @@ -328,9 +330,22 @@ def nt_cca(X=None, Y=None, lags=None, C=None, m=None, thresh=1e-12, sfreq=1): def whiten(C, fudge=1e-18): - """Whiten covariance matrix C of X. + """Compute a whitening matrix from a covariance matrix. - If X has shape=(observations, components), X_white = np.dot(X, W). + If ``X`` has shape ``(observations, components)``, then + ``X_white = X @ W``. + + Parameters + ---------- + C : ndarray, shape=(n_features, n_features) + Covariance matrix. + fudge : float + Small regularization term added to eigenvalues. + + Returns + ------- + W : ndarray, shape=(n_features, n_features) + Whitening matrix. References ---------- @@ -360,6 +375,11 @@ def whiten_nt(C, thresh=1e-12, keep=False): If True, infrathreshold components are set to zero. If False (default), infrathreshold components are truncated. + Returns + ------- + W : ndarray + Whitening matrix in the Noisetools convention. + """ d, V = linalg.eigh(C) # eigh if matrix symmetric, eig otherwise d = np.real(d) @@ -394,7 +414,23 @@ def whiten_nt(C, thresh=1e-12, keep=False): def whiten_svd(X): - """SVD whitening.""" + """Whiten data matrix with singular value decomposition. + + Parameters + ---------- + X : ndarray, shape=(n_samples, n_features) + Input data matrix. + + Returns + ------- + X_white : ndarray, shape=(n_samples, n_features) + Whitened matrix. + + Notes + ----- + This computes ``U @ Vt`` after SVD of ``X = U @ S @ Vt``. + + """ U, S, Vt = linalg.svd(X, full_matrices=False) # U and Vt are the singular matrices, and s contains the singular values. diff --git a/meegkit/detrend.py b/meegkit/detrend.py index 33d911a2..1f20982d 100644 --- a/meegkit/detrend.py +++ b/meegkit/detrend.py @@ -32,8 +32,10 @@ def detrend(x, order, w=None, basis="polynomials", threshold=3, n_iter=4, Basis for regression. threshold : int Threshold for outliers, in number of standard deviations (default=3). - niter : int - Number of iterations (default=5). + n_iter : int + Number of iterations (default=4). + show : bool + If True, plot the original data, detrended data, and rejected samples. Returns ------- @@ -222,13 +224,15 @@ def reduce_ringing(X, samples, order=10, n_samples=100, extra=50, threshold=3, Sample indices where to find ringing artifacts. order : int Order of polynomial trend (default=10). - n_samples = 100 + n_samples : int Number of samples over which to estimate impulse response (default=100). extra : int Samples before stimulus to anchor trend (default=50). - threshold: float + threshold : float Threshold for robust detrending (default=3). + show : bool + If True, plot the detected ringing segments and the corrected output. Returns ------- diff --git a/meegkit/sns.py b/meegkit/sns.py index cd068c87..41b625fd 100644 --- a/meegkit/sns.py +++ b/meegkit/sns.py @@ -135,6 +135,22 @@ def sns1(X, n_neighbors=None, skip=0): """Sensor Noise Suppression 1. This version of SNS first regresses out major shared components. + + Parameters + ---------- + X : ndarray, shape=(n_samples, n_chans) + Input data matrix. + n_neighbors : int | None + Number of nearest channels to include in each channel projection. + If ``None``, all channels except skipped/self channels are used. + skip : int + Number of top-correlated neighbors to skip before regression. + + Returns + ------- + y : ndarray, shape=(n_samples, n_chans) + Denoised signal. + """ if X.ndim > 2: raise Exception("SNS1 works only with 2D matrices") diff --git a/meegkit/trca.py b/meegkit/trca.py index 6983aa1b..80580d4e 100644 --- a/meegkit/trca.py +++ b/meegkit/trca.py @@ -266,6 +266,8 @@ def trca_regul(X, method): ---------- X : array, shape=(n_samples, n_chans[, n_trials]) Training data. + method : str + Covariance estimator name passed to pyRiemann. Returns ------- diff --git a/meegkit/utils/asr.py b/meegkit/utils/asr.py index d2524259..105248af 100755 --- a/meegkit/utils/asr.py +++ b/meegkit/utils/asr.py @@ -213,6 +213,13 @@ def yulewalk(order, F, M): Magnitude breakpoints for the filter such that PLOT(F,M) would show a plot of the desired frequency response. + Returns + ------- + B : ndarray + Numerator coefficients of the designed IIR filter. + A : ndarray + Denominator coefficients of the designed IIR filter. + References ---------- .. [1] B. Friedlander and B. Porat, "The Modified Yule-Walker Method of @@ -300,7 +307,7 @@ def yulewalk_filter(X, sfreq, zi=None, ab=None, axis=-1): Sampling frequency. zi : array, shape=(n_channels, filter_order) Initial conditions. - a, b : 2-tuple | None + ab : 2-tuple | None Coefficients of an IIR filter that is used to shape the spectrum of the signal when calculating artifact statistics. The output signal does not go through this filter. This is an optional way to tune the sensitivity @@ -410,7 +417,17 @@ def geometric_median(X, tol=1e-5, max_iter=500, sample_weight=None): def polystab(a): - """Polynomial stabilization. + """Reflect unstable polynomial roots inside the unit circle. + + Parameters + ---------- + a : ndarray + Polynomial coefficients. + + Returns + ------- + b : ndarray + Stabilized polynomial coefficients. POLYSTAB(A), where A is a vector of polynomial coefficients, stabilizes the polynomial with respect to the unit circle; @@ -443,9 +460,26 @@ def polystab(a): def numf(h, a, nb): - """Find numerator B given impulse-response h of B/A and denominator A. + """Estimate IIR numerator coefficients from an impulse response. + + Parameters + ---------- + h : ndarray + Target impulse response. + a : ndarray + Denominator coefficients. + nb : int + Numerator order. + + Returns + ------- + b : ndarray + Estimated numerator coefficients. + + Notes + ----- + This function is used by ``yulewalk``. - NB is the numerator order. This function is used by YULEWALK. """ nh = np.max(h.size) xn = np.concatenate((1, np.zeros((1, nh - 1))), axis=None) @@ -460,11 +494,23 @@ def numf(h, a, nb): def denf(R, na): - """Compute denominator from covariances. + """Estimate IIR denominator coefficients from autocovariances. + + Parameters + ---------- + R : ndarray + Autocovariance sequence ``R[0], ..., R[n]``. + na : int + Denominator order. + + Returns + ------- + A : ndarray + Estimated denominator coefficients. - A = DENF(R,NA) computes order NA denominator A from covariances - R(0)...R(nr) using the Modified Yule-Walker method. This function is used - by YULEWALK. + Notes + ----- + Uses the modified Yule-Walker method and is used by ``yulewalk``. """ nr = np.max(np.size(R)) diff --git a/meegkit/utils/auditory.py b/meegkit/utils/auditory.py index 26d1b58b..5e74888a 100644 --- a/meegkit/utils/auditory.py +++ b/meegkit/utils/auditory.py @@ -22,7 +22,19 @@ def erb_bandwidth(fc): def erb2hz(erb): - """Convert ERB-rate values to the corresponding frequencies in Hz.""" + """Convert ERB-rate values to frequencies in Hz. + + Parameters + ---------- + erb : array_like + Values on the ERB-rate scale. + + Returns + ------- + ndarray + Frequencies in Hz. + + """ f = (1. / 0.00437) * np.sign(erb) * (np.exp(np.abs(erb) / 9.2645) - 1) return f @@ -30,6 +42,16 @@ def erb2hz(erb): def hz2erb(f): """Convert frequencies to the corresponding ERB-rates. + Parameters + ---------- + f : array_like + Frequencies in Hz. + + Returns + ------- + ndarray + Values on the ERB-rate scale. + Notes ----- There is a round-off error in the Glasberg & Moore paper, as 1000 / (24.7 * @@ -42,7 +64,25 @@ def hz2erb(f): def erbspace(flow, fhigh, n): - """Generate n equidistantly spaced points on ERB scale.""" + """Generate points uniformly spaced on the ERB-rate scale. + + Parameters + ---------- + flow : float + Lower frequency bound in Hz. + fhigh : float + Upper frequency bound in Hz. + n : int + Number of frequencies. + + Returns + ------- + y : ndarray + Frequencies in Hz. + bw : float + Step size in ERB-rate units. + + """ audlimits = hz2erb([flow, fhigh]) y = erb2hz(np.linspace(audlimits[0], audlimits[1], n)) @@ -216,7 +256,22 @@ def filter(self, X): class AuditoryFilterbank(GammatoneFilterbank): - """Special case of Gammatone filterbank with preset center frequencies.""" + """Gammatone filterbank with standard audiometric center frequencies. + + Parameters + ---------- + sfreq : float + Sampling frequency of the signals to filter. + b : float + Beta value of the gammatone filters. + order : int + Filter order. + q : float + ERB Q factor. + min_bw : float + Minimum equivalent rectangular bandwidth. + + """ def __init__(self, sfreq, b=1.019, order=1, q=9.26449, min_bw=24.7): diff --git a/meegkit/utils/base.py b/meegkit/utils/base.py index be4ff314..0890ac7d 100644 --- a/meegkit/utils/base.py +++ b/meegkit/utils/base.py @@ -15,6 +15,18 @@ def mrdivide(A, B): >> mldivide(B.T, A.T).T + Parameters + ---------- + A : ndarray + Right-hand side matrix. + B : ndarray + Coefficient matrix. + + Returns + ------- + ndarray + Solution matrix ``X``. + References ---------- https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html @@ -31,6 +43,18 @@ def mldivide(A, B): - linalg.solve(A, B) if A is square - linalg.lstsq(A, B) otherwise + Parameters + ---------- + A : ndarray + Coefficient matrix. + B : ndarray + Right-hand side matrix. + + Returns + ------- + ndarray | None + Solution matrix, or ``None`` if no stable solution is found. + References ---------- https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html diff --git a/meegkit/utils/coherence.py b/meegkit/utils/coherence.py index 0d13ab98..d15cf077 100644 --- a/meegkit/utils/coherence.py +++ b/meegkit/utils/coherence.py @@ -244,6 +244,9 @@ def polycoherence_2d(X, sfreq, ofreqs=None, norm=2, flim1=None, flim2=None, Frequency limits for f1. If None, it is set to (0, nyquist / 2) flim2: tuple | None Frequency limits for f2. + synthetic: tuple(float, float, float) | None + Synthetic signal specification ``(freq, amplitude, phase)`` for the + first fixed frequency. Returns ------- @@ -290,6 +293,16 @@ def compute_spectrogram(X, sfreq, **kwargs): Simple wrapper around scipy.signal.spectrogram. + Parameters + ---------- + X : ndarray + Input signal. + sfreq : float + Sampling frequency in Hz. + **kwargs + Additional keyword arguments passed to + :func:`scipy.signal.spectrogram`. + Returns ------- f: ndarray, shape=(n_freqs,) @@ -349,7 +362,26 @@ def norm_spectrum(spec, P1, P2, P12, time_axis=-2): def plot_polycoherence(freq1, freq2, bicoh, ax=None): - """Plot polycoherence.""" + """Plot a 2D polycoherence map. + + Parameters + ---------- + freq1 : ndarray + Frequency grid for the first axis. + freq2 : ndarray + Frequency grid for the second axis. + bicoh : ndarray + Polycoherence values to display. + ax : matplotlib.axes.Axes | None + Existing axes to draw on. If ``None``, a new figure and axes are + created. + + Returns + ------- + matplotlib.axes.Axes + Axes containing the pseudocolor plot. + + """ df1 = freq1[1] - freq1[0] # resolution df2 = freq2[1] - freq2[0] freq1 = np.append(freq1, freq1[-1] + df1) - 0.5 * df1 @@ -365,14 +397,45 @@ def plot_polycoherence(freq1, freq2, bicoh, ax=None): return ax def plot_polycoherence_1d(freq, coh): - """Plot polycoherence for fixed frequencies.""" + """Plot 1D polycoherence values. + + Parameters + ---------- + freq : ndarray + Frequency axis. + coh : ndarray + Coherence values evaluated along ``freq``. + + Returns + ------- + None + The function creates a matplotlib figure as a side effect. + + """ plt.figure() plt.plot(freq, coh) plt.xlabel("freq (Hz)") def plot_signal(t, signal, ax=None): - """Plot signal and spectrum.""" + """Plot a time-domain signal together with its magnitude spectrum. + + Parameters + ---------- + t : ndarray + Time vector. + signal : ndarray + Signal samples. + ax : array-like of matplotlib.axes.Axes | None + Two axes for time and frequency plots. If ``None``, a new figure is + created. + + Returns + ------- + array-like of matplotlib.axes.Axes + Axes used for the time-domain and spectral plots. + + """ if ax is None: f, ax = plt.subplots(2, 1) @@ -417,7 +480,8 @@ def synthetic_signal(t, synthetic): Returns ------- - Complex signal. + ndarray + Complex synthetic signal. """ return np.prod([amp * np.exp(2j * np.pi * freq * t + phase) diff --git a/meegkit/utils/covariances.py b/meegkit/utils/covariances.py index 2f05bae5..f7e5f66d 100644 --- a/meegkit/utils/covariances.py +++ b/meegkit/utils/covariances.py @@ -25,6 +25,11 @@ def block_covariance(data, window=128, overlap=0.5, padding=True, estimator="cov Window size. overlap : float Fraction of overlap between successive windows (higher = more overlap). + padding : bool + If True, zero-pad the signal at both ends before extracting blocks. + estimator : str + Covariance estimator passed to :func:`pyriemann.geometry.covariance.covariances`. + Use ``"scm"`` for the uncentered second-moment path implemented here. Returns ------- @@ -124,8 +129,10 @@ def tsxcov(X, Y, shifts=None, weights=None, assume_centered=True): Parameters ---------- - X, Y : arrays, shape=(n_times, n_chans[, n_trials]) - Data to cross correlate. X can be 1D, 2D or 3D. + X : array, shape=(n_times, n_chans[, n_trials]) + First input data array. + Y : array, shape=(n_times, n_chans[, n_trials]) + Second input data array. shifts : array Time shifts. weights : array diff --git a/meegkit/utils/denoise.py b/meegkit/utils/denoise.py index c896a998..0eaa5419 100644 --- a/meegkit/utils/denoise.py +++ b/meegkit/utils/denoise.py @@ -66,7 +66,23 @@ def demean(X, weights=None, return_mean=False, inplace=False): def mean_over_trials(X, weights=None): - """Compute weighted mean over trials (last dimension).""" + """Compute the mean across trials, optionally with weights. + + Parameters + ---------- + X : ndarray, shape=(n_samples, n_chans, n_trials) + Input data. + weights : ndarray | None + Optional weights with the same trial structure as ``X``. + + Returns + ------- + y : ndarray, shape=(n_samples, n_chans) + Mean signal across trials. + tw : ndarray + Average effective weights per sample and channel. + + """ if weights is None: weights = np.array([]) else: @@ -99,7 +115,23 @@ def mean_over_trials(X, weights=None): def wpwr(X, weights=None): - """Weighted power.""" + """Compute weighted signal power. + + Parameters + ---------- + X : ndarray + Input data. + weights : ndarray | None + Optional weights broadcastable to ``X`` after unfolding. + + Returns + ------- + y : float + Sum of squared weighted samples. + tweight : float + Total effective weight. + + """ if weights is None: weights = np.array([]) @@ -118,7 +150,24 @@ def wpwr(X, weights=None): def find_outlier_samples(X, toobig1, toobig2=[]): - """Find outlier trials using an absolute threshold.""" + """Detect outlier samples by absolute and relative thresholds. + + Parameters + ---------- + X : ndarray, shape=(n_samples, n_chans[, n_trials]) + Input data. + toobig1 : float | None + Absolute threshold. Samples exceeding this value are iteratively + masked. + toobig2 : float | list | ndarray + Relative threshold applied after re-centering. + + Returns + ------- + weights : ndarray, shape=(n_samples, n_chans, n_trials) + Binary weights equal to 0 for outlier samples and 1 elsewhere. + + """ n_samples, n_chans, n_trials = theshapeof(X) X = unfold(X) diff --git a/meegkit/utils/matrix.py b/meegkit/utils/matrix.py index 5b73746b..b5d259a3 100644 --- a/meegkit/utils/matrix.py +++ b/meegkit/utils/matrix.py @@ -470,7 +470,28 @@ def shiftnd(X, shift, fill_value=0, axis=None): def theshapeof(X): - """Return the shape of X.""" + """Return a normalized 3D view of an array shape. + + Parameters + ---------- + X : ndarray + Input array with up to 3 dimensions. + + Returns + ------- + n_samples : int + Number of samples. + n_chans : int + Number of channels. + n_trials : int + Number of trials. + + Notes + ----- + One- and two-dimensional inputs are promoted logically to + ``(n_samples, n_chans, n_trials)`` with singleton trailing dimensions. + + """ X = _check_data(X) # if not isinstance(X, np.ndarray): # raise AttributeError('X must be a numpy array') @@ -486,7 +507,19 @@ def theshapeof(X): def unsqueeze(X): - """Append singleton dimensions to an array.""" + """Reshape an array to its normalized 3D form. + + Parameters + ---------- + X : ndarray + Input array. + + Returns + ------- + ndarray + Reshaped array with explicit sample, channel, and trial dimensions. + + """ X = _check_data(X) if X.shape != theshapeof(X): return X.reshape(theshapeof(X)) @@ -495,7 +528,22 @@ def unsqueeze(X): def fold(X, epoch_size): - """Fold 2D (n_times, n_channels) X into 3D (n_times, n_chans, n_trials).""" + """Fold a 2D array into epochs. + + Parameters + ---------- + X : ndarray, shape=(n_times, n_chans) or (n_times,) + Input data. + epoch_size : int + Number of samples per epoch. + + Returns + ------- + ndarray + Folded data with shape ``(epoch_size, n_chans, n_trials)`` when at + least one full epoch is available. + + """ if X.ndim == 1: X = X[:, np.newaxis] if X.ndim > 2: @@ -510,7 +558,20 @@ def fold(X, epoch_size): def unfold(X): - """Unfold 3D X into 2D (concatenate trials).""" + """Concatenate trials of a 3D array into a 2D matrix. + + Parameters + ---------- + X : ndarray + Input array with up to 3 dimensions. + + Returns + ------- + ndarray + Unfolded array with shape ``(n_samples * n_trials, n_chans)`` when + trials are present. + + """ n_samples, n_chans, n_trials = theshapeof(X) if X.size == 0: return X @@ -615,7 +676,9 @@ def matmul3d(X, mixin): Parameters ---------- X : array, shape=(n_samples, n_chans, n_trials) - mixing : array, shape=(n_chans, n_components) + Input data. + mixin : array, shape=(n_chans, n_components) + Mixing matrix applied to each trial. Returns ------- diff --git a/meegkit/utils/sig.py b/meegkit/utils/sig.py index 600fc654..267c5b0b 100644 --- a/meegkit/utils/sig.py +++ b/meegkit/utils/sig.py @@ -252,6 +252,20 @@ def next_pow_2(x): def spectral_envelope(x, sfreq, lowpass=32): """Compute envelope with convolution. + Parameters + ---------- + x : array_like + One-dimensional input signal. + sfreq : float + Sampling frequency in Hz. + lowpass : float | None + Low-pass cutoff used to define the smoothing window. + + Returns + ------- + ndarray + Smoothed envelope with the same length as ``x``. + Notes ----- The signal is first padded to avoid edge effects. To align the envelope @@ -290,7 +304,7 @@ def gaussfilt(data, sfreq, f, fwhm, n_harm=1, shift=0, return_empvals=False, sho Sampling rate in Hz. f : float Break frequency of filter. - fhwm : float + fwhm : float Standard deviation of filter, defined as full-width at half-maximum in Hz. n_harm : int @@ -435,6 +449,13 @@ def slope_sum(x, w: int, axis=0): Input data. w : int Window. + axis : int + Axis along which to compute the slope-sum transform. + + Returns + ------- + ndarray + Slope-sum-transformed signal. References ---------- @@ -473,7 +494,7 @@ def stmcb(x, u_in=None, q=None, p=None, niter=5, a_in=None): u_in : array q : int p : int - n_iter : int + niter : int a_in : array Returns @@ -572,6 +593,13 @@ def prony(h, nb, na): na : int Denominator order. + Returns + ------- + b : ndarray + Numerator coefficients. + a : ndarray + Denominator coefficients. + References ---------- T.W. Parks and C.S. Burrus, Digital Filter Design, John Wiley and Sons, diff --git a/meegkit/utils/stats.py b/meegkit/utils/stats.py index b5686c47..cbdf8bb2 100644 --- a/meegkit/utils/stats.py +++ b/meegkit/utils/stats.py @@ -13,12 +13,46 @@ def rms(X, axis=0): - """Root-mean-square along given axis.""" + """Compute root-mean-square values along an axis. + + Parameters + ---------- + X : ndarray + Input array. + axis : int | tuple of int + Axis (or axes) over which to compute RMS. + + Returns + ------- + ndarray + RMS values with dimensions preserved along reduced axes. + + """ return np.sqrt(np.mean(X ** 2, axis=axis, keepdims=True)) def robust_mean(X, axis=0, percentile=[5, 95]): - """Do robust mean based on JR Kings implementation.""" + """Compute a percentile-trimmed mean. + + Values below the lower percentile or above the upper percentile are masked + as NaN before averaging. + + Parameters + ---------- + X : array_like + Input data. + axis : int | None + Axis over which to compute the robust mean. If ``None``, the input is + flattened. + percentile : list[float] | tuple[float, float] + Lower and upper percentile used for trimming. + + Returns + ------- + ndarray + Robust mean values. + + """ X = np.array(X) axis_ = axis # force axis to be 0 for facilitation @@ -49,6 +83,9 @@ def rolling_corr(X, y, window=None, sfreq=1, step=1, axis=0): Sampling frequency (default=1). step : int If > 1, only compute correlations every `step` samples. + axis : int + Currently unused. It is retained for API compatibility with older + calling code. Returns ------- diff --git a/meegkit/utils/testing.py b/meegkit/utils/testing.py index 86f7ad97..d18e4aad 100644 --- a/meegkit/utils/testing.py +++ b/meegkit/utils/testing.py @@ -6,7 +6,8 @@ def create_line_data(n_samples=100 * 3, n_chans=30, n_trials=100, noise_dim=20, - n_bad_chans=1, SNR=.1, fline=0.1, t0=None, show=False): + n_bad_chans=1, SNR=.1, fline=0.1, t0=None, show=False, + rng=None): """Create synthetic data. Parameters @@ -21,17 +22,31 @@ def create_line_data(n_samples=100 * 3, n_chans=30, n_trials=100, noise_dim=20, Dimensionality of noise (default=20). n_bad_chans : int Number of bad channels (default=1). + SNR : float + Signal-to-noise ratio scaling applied to the synthetic source. t0 : int Onset sample of artifact. fline : float Normalized frequency of artifact (freq/samplerate), (default=0.1). + show : bool + If True, plot source, noise, and mixture summaries. + rng : numpy.random.Generator | numpy.random.RandomState | None + Random number generator used to synthesize the example. If ``None``, a + deterministic internal generator is used. Returns ------- data : ndarray, shape=(n_samples, n_chans, n_trials) - source : ndarray, shape=(n_samples,) + Synthetic data array. + source : ndarray, shape=(n_samples, 1) + Underlying line-noise source waveform stored as a column vector. """ - rng = np.random.RandomState(2022) + if rng is None: + rng = np.random.RandomState(2022) + + sample_normal = getattr(rng, "standard_normal", None) + if sample_normal is None: + sample_normal = rng.randn if t0 is None: t0 = n_samples // 3 @@ -45,7 +60,7 @@ def create_line_data(n_samples=100 * 3, n_chans=30, n_trials=100, noise_dim=20, source = source[:, None] # mix source in channels - s = source * rng.randn(1, n_chans) + s = source * sample_normal((1, n_chans)) s = s[:, :, np.newaxis] s = np.tile(s, (1, 1, n_trials)) # create trials @@ -54,8 +69,8 @@ def create_line_data(n_samples=100 * 3, n_chans=30, n_trials=100, noise_dim=20, # noise noise = np.dot( - unfold(rng.randn(n_samples, noise_dim, n_trials)), - rng.randn(noise_dim, n_chans)) + unfold(sample_normal((n_samples, noise_dim, n_trials))), + sample_normal((noise_dim, n_chans))) noise = fold(noise, n_samples) # mix signal and noise