From af24256c8bd9f7a9d297f49c4fdd5793913d456c Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Mon, 20 Nov 2023 22:28:11 +0100 Subject: [PATCH] Move code from Run postprocess to UseCase methods The definition of a minute resultion datetimeindex is performed after the days are defined Adapt a jupyter notebook for the example --- docs/notebooks/Plot class.ipynb | 140510 +++++++++++++++------------ ramp/__init__.py | 2 +- ramp/core/core.py | 102 +- ramp/post_process/post_process.py | 64 - 4 files changed, 79130 insertions(+), 61548 deletions(-) diff --git a/docs/notebooks/Plot class.ipynb b/docs/notebooks/Plot class.ipynb index 25cc4e5b..4d36e0c6 100644 --- a/docs/notebooks/Plot class.ipynb +++ b/docs/notebooks/Plot class.ipynb @@ -25,18 +25,9 @@ "execution_count": 1, "id": "eb23109f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.4)\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n" - ] - } - ], + "outputs": [], "source": [ - "from ramp import User,calc_peak_time_range,yearly_pattern" + "from ramp import User,UseCase" ] }, { @@ -124,336 +115,34 @@ " func_time = 4*60,\n", " time_fraction_random_variability = 0.15,\n", " random_var_w = 0.15,\n", - ")\n", - "#%%\n", - "\n", - "peak_time_range = calc_peak_time_range(\n", - " user_list = [Petrol_Station]\n", - ")\n", - "year_behaviour = yearly_pattern()" + ")" ] }, { "cell_type": "code", "execution_count": 3, - "id": "9071d728", - "metadata": {}, - "outputs": [], - "source": [ - "# running the simulation for multiple cases to generate mutlipe simulations for a year = 2022\n", - "\n", - "import pandas as pd\n", - "\n", - "number_of_simulations = 10\n", - "number_of_days = 365\n", - "\n", - "results = {}\n", - "\n", - "idx = pd.date_range(start=f\"2022-01-01\",periods=number_of_days*60*24,freq=\"1min\",name = \"date\")\n", - "\n", - "for profile in range(1,number_of_simulations+1):\n", - " profiles = []\n", - " for prof_i in range(number_of_days): # 365 days of the year\n", - "\n", - " result = Petrol_Station.generate_aggregated_load_profile(\n", - " prof_i = prof_i,\n", - " day_type = 1,\n", - " peak_time_range = peak_time_range\n", - " )\n", - "\n", - " profiles.extend(result.tolist())\n", - "\n", - " results[f\"case {profile}\"] = pd.Series(index=idx,data=profiles)\n", - "\n", - "dataframe = pd.concat(results,axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8844ac40", + "id": "74767bc4-77e0-4481-be8c-8b7d3da4be2f", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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case 1case 2case 3case 4case 5case 6case 7case 8case 9case 10
date
2022-01-01 00:00:0036.036.036.036.036.036.036.036.036.036.0
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2022-01-01 00:04:0036.036.036.036.036.036.036.036.036.036.0
.................................
2022-12-31 23:55:0036.036.036.036.036.036.036.036.036.036.0
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2022-12-31 23:58:0036.036.036.036.036.036.036.036.036.036.0
2022-12-31 23:59:000.00.00.00.00.00.00.00.00.00.0
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525600 rows × 10 columns

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" - ], - "text/plain": [ - " case 1 case 2 case 3 case 4 case 5 case 6 case 7 \\\n", - "date \n", - "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "... ... ... ... ... ... ... ... \n", - "2022-12-31 23:55:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-12-31 23:56:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-12-31 23:57:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-12-31 23:58:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", - "2022-12-31 23:59:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - " case 8 case 9 case 10 \n", - "date \n", - "2022-01-01 00:00:00 36.0 36.0 36.0 \n", - "2022-01-01 00:01:00 36.0 36.0 36.0 \n", - "2022-01-01 00:02:00 36.0 36.0 36.0 \n", - "2022-01-01 00:03:00 36.0 36.0 36.0 \n", - "2022-01-01 00:04:00 36.0 36.0 36.0 \n", - "... ... ... ... \n", - "2022-12-31 23:55:00 36.0 36.0 36.0 \n", - "2022-12-31 23:56:00 36.0 36.0 36.0 \n", - "2022-12-31 23:57:00 36.0 36.0 36.0 \n", - "2022-12-31 23:58:00 36.0 36.0 36.0 \n", - "2022-12-31 23:59:00 0.0 0.0 0.0 \n", - "\n", - "[525600 rows x 10 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "You will simulate 365 days from 2022-01-01 until 2022-12-31 00:00:00\n" + ] } ], "source": [ - "dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f40014b0", - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a Plot class\n", - "from ramp import Plot\n", + "# running the simulation for multiple cases to generate mutlipe simulations for a year = 2022\n", "\n", - "plot = Plot(dataframe)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "003bedb5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10\n", - "date \n", - "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - " ......" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot" + "number_of_simulations = 11\n", + "number_of_days = 365\n", + "\n", + "uc = UseCase(users=[Petrol_Station], date_start=\"2022-01-01\")\n", + "uc.initialize(num_days=number_of_days)\n", + "\n", + "plot = uc.generate_daily_load_profiles(cases=[profile for profile in range(1,number_of_simulations+1)])\n", + "\n" ] }, { @@ -476,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c651b69f", "metadata": {}, "outputs": [], @@ -503,11 +192,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "0e8797dd", "metadata": {}, "outputs": [], "source": [ + "import pandas as pd \n", "second_day = pd.date_range(\n", " start=\"2022-01-01 00:00:00\",\n", " freq = \"1min\",\n", @@ -527,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "9f7d1159", "metadata": {}, "outputs": [ @@ -548,7 +238,7 @@ " ......" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -561,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "b4e4b23a", "metadata": {}, "outputs": [ @@ -569,7 +259,6 @@ "data": { "text/plain": [ " case 1\n", - "date \n", "2022-01-01 00:00:00 36.0\n", "2022-01-01 00:01:00 36.0\n", "2022-01-01 00:02:00 36.0\n", @@ -583,7 +272,7 @@ " ......" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -604,24 +293,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "13191cdd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10\n", - "date \n", - "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:10:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 case 11\n", + "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:10:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", " ......" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -642,29 +330,28 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "e205e410", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 new column\n", - "date \n", - "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", - "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 case 11 new column\n", + "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", " ......" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -697,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "4d5b93f3", "metadata": {}, "outputs": [ @@ -707,7 +394,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -726,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "id": "1f41870c", "metadata": {}, "outputs": [], @@ -748,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "e53de489", "metadata": {}, "outputs": [ @@ -758,7 +445,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -777,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "id": "de50f6a4", "metadata": {}, "outputs": [], @@ -790,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "ee3a07e0", "metadata": {}, "outputs": [ @@ -800,7 +487,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -821,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "id": "5064b978", "metadata": {}, "outputs": [ @@ -829,21 +516,20 @@ "data": { "text/plain": [ " Mean\n", - "date \n", "2022-01-01 00:00:00 36.000000\n", "2022-01-01 01:00:00 36.000000\n", "2022-01-01 02:00:00 36.000000\n", "2022-01-01 03:00:00 36.000000\n", "2022-01-01 04:00:00 36.000000\n", - "2022-01-01 05:00:00 36.333950\n", - "2022-01-01 06:00:00 36.826410\n", - "2022-01-01 07:00:00 4.776980\n", - "2022-01-01 08:00:00 23.261020\n", - "2022-01-01 09:00:00 17.369903\n", + "2022-01-01 05:00:00 36.000258\n", + "2022-01-01 06:00:00 36.622398\n", + "2022-01-01 07:00:00 8.479811\n", + "2022-01-01 08:00:00 9.919655\n", + "2022-01-01 09:00:00 19.313958\n", " ......" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -855,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "id": "9628df92", "metadata": {}, "outputs": [ @@ -863,21 +549,20 @@ "data": { "text/plain": [ " Sum\n", - "date \n", - "2022-01-01 00:00:00 360.000000\n", - "2022-01-01 01:00:00 360.000000\n", - "2022-01-01 02:00:00 360.000000\n", - "2022-01-01 03:00:00 360.000000\n", - "2022-01-01 04:00:00 360.000000\n", - "2022-01-01 05:00:00 363.339500\n", - "2022-01-01 06:00:00 368.264100\n", - "2022-01-01 07:00:00 47.769800\n", - "2022-01-01 08:00:00 232.610200\n", - "2022-01-01 09:00:00 173.699033\n", + "2022-01-01 00:00:00 396.000000\n", + "2022-01-01 01:00:00 396.000000\n", + "2022-01-01 02:00:00 396.000000\n", + "2022-01-01 03:00:00 396.000000\n", + "2022-01-01 04:00:00 396.000000\n", + "2022-01-01 05:00:00 396.002833\n", + "2022-01-01 06:00:00 402.846383\n", + "2022-01-01 07:00:00 93.277917\n", + "2022-01-01 08:00:00 109.116200\n", + "2022-01-01 09:00:00 212.453533\n", " ......" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -897,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "id": "c0297a1f", "metadata": {}, "outputs": [], @@ -907,46 +592,38 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "id": "00e89c9b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'case 1': date\n", - " 2022-03-16 21:00:00 153.917183\n", + "{'case 1': 2022-06-01 16:00:00 162.917617\n", " Freq: H, Name: case 1, dtype: float64,\n", - " 'case 2': date\n", - " 2022-12-22 06:00:00 156.001167\n", + " 'case 2': 2022-04-30 06:00:00 162.6675\n", " Freq: H, Name: case 2, dtype: float64,\n", - " 'case 3': date\n", - " 2022-05-25 05:00:00 166.001067\n", + " 'case 3': 2022-09-27 21:00:00 159.335717\n", " Freq: H, Name: case 3, dtype: float64,\n", - " 'case 4': date\n", - " 2022-02-11 21:00:00 165.0009\n", + " 'case 4': 2022-09-14 09:00:00 161.167733\n", " Freq: H, Name: case 4, dtype: float64,\n", - " 'case 5': date\n", - " 2022-10-16 06:00:00 165.251917\n", + " 'case 5': 2022-07-11 21:00:00 163.501033\n", " Freq: H, Name: case 5, dtype: float64,\n", - " 'case 6': date\n", - " 2022-11-25 08:00:00 158.334717\n", + " 'case 6': 2022-06-22 06:00:00 162.670033\n", " Freq: H, Name: case 6, dtype: float64,\n", - " 'case 7': date\n", - " 2022-08-24 06:00:00 162.668033\n", + " 'case 7': 2022-09-21 06:00:00 166.00235\n", " Freq: H, Name: case 7, dtype: float64,\n", - " 'case 8': date\n", - " 2022-06-04 06:00:00 185.585083\n", + " 'case 8': 2022-05-11 13:00:00 157.501983\n", " Freq: H, Name: case 8, dtype: float64,\n", - " 'case 9': date\n", - " 2022-03-25 10:00:00 177.918433\n", + " 'case 9': 2022-02-08 06:00:00 159.335117\n", " Freq: H, Name: case 9, dtype: float64,\n", - " 'case 10': date\n", - " 2022-06-16 19:00:00 182.918433\n", - " Freq: H, Name: case 10, dtype: float64}" + " 'case 10': 2022-07-03 06:00:00 170.1698\n", + " Freq: H, Name: case 10, dtype: float64,\n", + " 'case 11': 2022-11-08 15:00:00 150.83635\n", + " Freq: H, Name: case 11, dtype: float64}" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -976,23 +653,23 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "id": "02e17fdf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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Wuv8e6k4XERkgIk0iMkdETi0mE+u37+GsO99ms9t9ajmGYauEV2c5t19bdsVr/Sp90QQ47ZbRnPL76Iy8dMcbSxi7uPYGA29PpNi+x//DxpmrtnGbjxfMIH8neLWomJL7IOCcHtP6A6NV9XhgtPsZ4FyccVOPB64AHiwmE7va/VU/ZCuplavCJbh0ol67WVic2nH7tXzzLnZ3JEKv8nvC7f61mK6CB09cEerdTBgX9V+8MNtX66xa4LV6pmBwV9VxQM977X7AYPfvwcD5GdOfVMdk4BARCW1ctnSXs3EQgcKv8SA99GCm618Jv073DnfAlrYinkndNHQ+1/19TthZqhrVWgjx+zKm3zr3I1U1PVryBuBI9++jgdUZ861xp1WEvaEaf38avpBbX19YcL6RAXQ8lxkbzr2ndzvyNdt2V7zVTE/b9+Quuf/k6ek5B5HPFgeXbGzl7LveDihn5RPkLinlArF0Yytn3TnW993UBQ9O7BqHoRglP1BV5z7U8yqLyBUi0igijYlE6f1qZz6Q9eqGV+dxyu/f6DU9YuepyWLguGU8/PaygvNd+fSMMuQml2geSMPnbujVgiTfMf/nEYtYuSX4t7PLKajCu5/YcN+YJpZt2uX7Wcy8tTu45rniuyn2G9w3pqtb3H/Tl5O1wLEZ8x3jTutFVQeqal9V7dvQUHrnlGfd6ZQo8m3zXFfdpyavpGV3Zd84m7GqhR8OnlbRPITJLpTVram5lTcXVm//7RCHp1ve+A3uQ4HL3L8vA4ZkTP+u22rms8D2jOqbwPkNGJW4dVbVgs8IKnHyVOKA/8ULs6uyDb//oyaYrbxtVwe7PL6I09qW4JrnZnaNA+vX4g3Rb0aYi+T4O+6KaQr5LDAJ+KiIrBGRy4HbgH8TkaXAV9zPAMOBZUAT8Ajwk1ByXaJiWzQE+QAm19tslXrIU84L3NTlvdu+L90Y/WCxqz3Bo+MLV/mUy8IN3gePX7ShlSGz1vHoBG/r8csXo/UgdtSCjcxds72oeb28udpTqedjrmcYmda27OGFxtUF5ytVwfoQVb04x1dnZ5lXgatKzVRYKlk10BxiXx9Rv9389sOTeOi/C7/y0J5Isl9DfRlyVJzbXl/EU0WcrCZ8P3qy+MHnvzNwMtd+5fgQc5P7IvDbV+fx5ROO4NjDDsz527+4LZ76nfL+UI/3SLyhWg2SKfXV9W3QKtV1QqmKyfZ5A6I1stEOjz3/VemuKUGVVXIUsYOyFQC9rmUiAnECbJi9vDJ39OduHR1Yx0PdXikO4PyoslMsp6XN0a+qqVbFXHieeGd5WfvVB5izpoXLHp/qq+BU7MW01CpIzfF3GHou/0O/Ge57WVVdci9nUGtubae1fW+TzWotQfcUk9UwAfhrlm6iV23Zza720psq5/LAmHfZtruzq/8XL94tpvmzaijnajHXi+YdbUxYupk+/YcxrQJ9LsWu5J55lfbbm1o5lSO4Nre2ceiB+7JP/d5rua/CTI1dCaphdbft6iipL5d8xixq5vuDpvHJYw7mx1/6cK/vK9289ZHxy739IEuGpy7fymnHHRZQjro77U+ju/4etWBjUb8JcpNWdcndq6icrOU8KdoTSU67ZTS/emkOuzsSpEKoDxSBj98wghuHRKNL1Z0hljT9CPO4O+OOsd26VQ6yz/LvD3Leu5hTZCuVSMo82bLsiG8/XL6BPsot1sE9V/cDUXtFPEzpUZSGzlrHiTeO5JbhhV/V92NPZ5InJxXfsmTr7g769B/GpHf9N1vLZvbqFk66aWSgy4yynqX2j9+YvZvoariLDUstne+Zqjq41+JOyyx8tHUmnQEJsnRi1VP6Cf5L7hievkqTOba3n2XNcke7Gjgu2B4A56wNrpQZ5XBYe0e+8aqqg7vfByWFflfMcr2mHEaJeUeButZLH5vCV/86rtu0ZFIDvXUP0/SV2+jTfxhLNkZjuMMoBfsg89Kn/zDmr/N+Ucx3gUmllJdnrCm5+fC3H55U1GDfX7jtLd9pDJkVbD/wcwMsYJSiqoN7MTKrZrwW9Ku9dDR+6WbWtuzpNq21PcHHbxxR8YdhxRg2x+m5YtyS6hn0olpbUY1e2Jz3guG1Wue5aav5xQuzGTxxRUn5mrp8K/PWFr4z7XmcZ5NrDQZ5yGMx+/dnzxbfuVeYYh/cS6lrLMdpWp2hIDi1vv7lEsY1J98it7qjjRUadawczwLCKMdUQdmouoO71zr3xhXbQllupVRzYAxrEwe5WD+l8Go5dryK89gIM1e18Py0VZ5+Uw3nXlW2c29qbuWZKav5xsm9B3nKdwhOydKBVT5j3H6Xv/+F4zylY8IzfeVWxi/dnPP7qJ506SqmYt31xuJA0y90zfFzPJd6DrwxfwPvNAXbWiqffNfqX700l/cfckDZ8lIOkQ7uU5ZtYdXW3oMDXPb4NNa27OHUDx4Savrjlmxi3JJNWYN7KUHEa4FwXUZ9YtDBq1xN5HIFl3zbIlveLniwcu2SSwlmVz3jbbCQAW81lZBadbjiqellSafYm6lLH5vKj7/0oXAzU0aRDu7fGTg57/dh3SpG7aHYyzOC7+/Dz5abs6aFEfNKH67Oa/oTmjbzwy+W76Tb2Z7ggH3qY1XF0vOQDmLYwWpU6Mx+eFzxXSN7OTqK7mbcwzILiXRwLyTbuVfMxgnipK30yE2VMHbxJt9DhJWabrFK3bNtnUlOumkk3/t8H88X+WgVCfL7cY9Ss59ToiqufQEU1HKtppclq8KCdd774y9FVT5QLabZE/g7+CYs3cyWXf4GsPWj0LHXmUwxfG72UtZnMvquKNW6Irdp3KXfAXg14LbPlfbA2He55NH8d8JhSaWU1+asC6Xri1zeXNi7L5fV23azu0LveDwxcUXZez2tyuBeqlwlslRK+e/HpvCax4dffr04fU3Bvi3uHb20qDdQS/Xlv4zx/dtSSnDBP0OoXl7r5b0q58PLTE9NXslPn5lZltGH0rK1jfd7t+2ny9+eMabYl7kKnUqZy2lqzv9yX0nBXUR+LiLzRWSeiDwrIvuLyHEiMkVEmkTkeRHZt5Q0yqlcgSEdDIsZwm3d9rZQ85I+BtN90ISdTk/57hii1vzOb3VesVu2UIua9s6Ur/SLEfTzhcz93dzqHMObdxY/GtmyTTt5Zoq35olheS3LEJmFttb8kKpg0pt1xLz1fOWucXnn9R3cReRo4GdAX1U9CagHLgJuB/6qqh8BtgGX+02jYB4KTPNa3XbBgxNLyU7RvHSW1XPwhMAe9kakwrQpz62qn5Y8gbZzD3BZQbj2+Vldf0dh74V58T3rzrf5zStzS19QAMf5naOWeO5ptDMZzoVY1WkmO3phc8F5S32g2gAcICKdwIHAeuAs4L/c7wcDNwMPlphOVkHHp1mrW4JdYA43DJnPQfs3sGiD/z5Totaix6tcrRLGLGrmxPe/jyPft3+Zc9Rdts0b1Db3299KWP22FyPXhTaRTLGns3c9dkTKDoFJ9dj3KYWnJ5f/zmJPZ7LoZrK+S+6quha4A1iFE9S3A9OBFlVNX+bWAEf7TaNwHsJacvh+/vzskn7/7qZdAeWkMnKVhL4/aBrfvP+dMudmr3xBqdDhVmw8u2lo7n7vj79+eOWCuI/z6f9enMO/3PxG8HkJUFh3GIsj0qFdLqVUyxwK9AOOA94PvAc4x8PvrxCRRhFpTCT8Da5QxbE9ACWufYBXxqAvsmE9Z1i0YQcLQ3w4XexmeGVG7pY4nUnlP+4rPFB4FI59EXhlZrxaFcVJKQ9UvwIsV9VNqtoJvAx8AThERNLVPccAWfe+qg5U1b6q2rehIbzm9tmu2nF6OaVW3PDqPDoSpdVjnnP3eM69p3D3sV16RNAhs3o/WCvGT5721gpm5Zbeb2VXWtQebsdB2DUPpQT3VcBnReRAcaLl2cACYAxwoTvPZcCQ0rKYW8/DbeOOtm6DWJs8quACl3nwPzV5Zc2+VRlV1Vwt6kcqpb3q3qPMd5FZVaeIyIvADCABzAQGAsOA50Tkj+60x4LIaDGuztKPctyGFwt6bZ6essrXyPNxF9ZAMAC7qmSwlEJGVMnFNohyjAAf+s1wzvtk784KfS8zR76CKsSUVB+iqjcBN/WYvAw4zduCSsnFXrs7ugep3J1VxSvgl+ofWdrxBu1Kj1UTpgKifzNXcWG/4KjAr18OoAkoMexbxsRHJfs78ftcphqe52zL0r1GWNnesL29a9zeiqjhglyVdz8Q/ROp1qyo8MPAcvZfUq0+9YdR2b8IcNOlY+rUFZXp8iBo1XhUVXXJvTo3eTRUw2XRT6HrhiHzfac3c9U2Fq7f23Z5aYG+O3qqhuq+nzxdnj7UZ6wqbtQzE54qD+61K0pxpApqIoryzQe6dz+xZGN5e/Erh1w9jAbd1HFMBbqGDpPXreO7Ws/Xr7Kr8mqZ2hOloB6mUQs2sqOtgq/b18qGdpW7VVm6M7G4isLxE42Su+/LVfweer25oHc/1NlEeBUC8aMnG3tNK+c6V/7UzK/cuz/o4H/aLcGNRZBX3E+UPKqq5L5xR/erfaH9tnbbHja1Ft/NaFCWbdrJdp99R/8wS1DrtuzN8asqMJUX1zdQl2xozdqxWVSs2BxeH1FVFdwLjTzU8wCdtGwLp9/ufxAKv866822+NsDDa+4enHN3/uVOW7G1qOVUY4EmzDvdXj01+kgryneExYj63YofI+Zv4Mq/RfcdizPuGBvasqNRLRPQUVXsrWO6PmxPR7Ko3wzxMeRasUMBBu0/H8o/spPJ7slJKyqdhUjz201xFHgZJCSXMC7cuQorQdXXRyO4e7AlgB2V9ombRlDMMXvNc7MCSzNMU5YXV2qHYEvBEXh2VLLf/WNBt89+VikKD9H8KhS7ft9j+xSrijdJSaJwF1dV1TLQvYe9ngdOtnET86niwkhWc9e0VDoLpkzKfeiWc9D4KArjwp0r/gd1Yai64B7ELVYcFBo/sZDfvpp7wAivJnoYNtBEVxhlzdYa7ZSumIvBH19b2Pt3Aeah6oK7yS0Kt4JxUQ1VLKOKbDZbjLCOnEoODRh1T01eGeryqy64ZwYwv7Gs2oPgE+8szzq9uteqsCrfbYEbOO7dSmfBRFjVBffMkkCtnus9H/51ifkG+ekzvfvrD4vfcns1lPhzqd6ch68aC4RVF9wr8VKSX3e/uaTSWTA+VUOMrrYXj+54o3bOhyhcDKouuEfdpIyHi3e/ubSCOTGl8PO6vRKNk9oE77Rb3vQ0/8ot/t48jUzHYSJyiIi8KCKLRGShiHxORA4TkVEistT999BCy2lLlPf14DBvnf9qpfVYaOssbTDusEnX/0w57PY4NOJLM7y/+Bi0Ukvu9wAjVPVjwMnAQqA/MFpVjwdGu59DYaUkY4Jhp1KwSilABlX49B3cReRg4Eu4A2CraoeqtgD9gMHubIOB80vLYvBenrE2tBF7Zq9uCWW5Jvpmrmph7bbyjURl8djkU0rJ/ThgE/CEiMwUkUdF5D3AkaqaHkV2A3Bkth+LyBUi0igi+btBDMGyzbt4oXF1KMtuT1Tudr7aHrDF0YxVLZXOgm/V3NLH9FZKcG8ATgUeVNVPAbvoUQWjztGS9YhR1YGq2ldV+/rNwOvz/I9EvnV3bb9ObapbU/NOFq731t1GPu2dKXZ5rFc24YhC9wNrgDWqOsX9/CJOsN8oIkcBuP82l5bF3F6OwEMLYyqh3/3vsCPAV/vvHGUNAeLGd3BX1Q3AahH5qDvpbGABMBS4zJ12GTCkpByGJI53oNbvjjEmrdQuf68GnhaRfYFlwPdxLhgviMjlwErg2yWmYYr09pJ4DUpsTLXyW3gMssxZUnBX1VlAtjrzs0tZrjHGVLOpHsZWCEvNvqFqLQOMMWHpSFb+JbiaDe7GGBNnFtyNMSZCanYM1aA8/PaySmfBGGO6eXH6Gto6g3nfoGaDe2t7bQ7/ZYyJLq8dlOVj1TLGGBNDFtyNMSaGLLgbY0wMWXA3xpgYsuBujDExZMHdGGNiyIK7McbEkAV3Y4yJIQvuxhgTQxbcjTEmhiy4G2NMDJUc3EWkXkRmishr7ufjRGSKiDSJyPPuKE3GGGPKKIiS+zXAwozPtwN/VdWPANuAywNIwxhjjAclBXcROQb4OvCo+1mAs4AX3VkGA+eXkoYxxhjvSi253w38EkiPKfVPQIuqpvvTXQMcXWIaxhhjPPId3EXkPKBZVaf7/P0VItIoIo1+82CMMSa7Ugbr+ALwHyLyNWB/4H3APcAhItLglt6PAdZm+7GqDgQGAux31PE2WrUxxgTId8ldVX+tqseoah/gIuAtVb0EGANc6M52GTCk5FwaY4zxJIx27r8CfiEiTTh18I+FkIYxxpg8AhlDVVXHAmPdv5cBpwWxXGOMMf7YG6rGGBNDFtyNMSaGLLgbY0wMWXA3xpgYsuBujDExZMHdGGNiyIK7McbEkAV3Y4yJIQvuxhgTQxbcjTEmhiy4G2NMDFlwN8aYGLLgbowxMWTB3RhjYsiCuzHGxJAFd2OMiSEL7sYYE0O+g7uIHCsiY0RkgYjMF5Fr3OmHicgoEVnq/ntocNk1xhhTjFJK7gngf1X1ROCzwFUiciLQHxitqscDo93Pxhhjysh3cFfV9ao6w/27FVgIHA30Awa7sw0Gzi8xj8YYYzwKpM5dRPoAnwKmAEeq6nr3qw3AkUGkYYwxpnglB3cROQh4CbhWVXdkfqeqCmiO310hIo0i0lhqHowxxnRXUnAXkX1wAvvTqvqyO3mjiBzlfn8U0Jztt6o6UFX7qmrfUvJgjDGmt1JaywjwGLBQVe/K+GoocJn792XAEP/ZM8YY40dDCb/9AnApMFdEZrnTfgPcBrwgIpcDK4Fvl5RDY4wxnvkO7qo6AZAcX5/td7nGGGNKZ2+oGmNMDFlwN8aYGLLgbowxMWTB3RhjYsiCuzHGxJAFd2OMiSEL7sYYE0MW3I0xJoYsuBtjTAxZcDfGmBiy4G6MMTFkwd0YY2LIgrsxxsSQBXdjjIkhC+7GGBNDFtyNMSaGLLgbY0wMhRbcReQcEVksIk0i0j+sdIwxxvQWSnAXkXrgfuBc4ETgYhE5MYy0jDHG9BZWyf00oElVl6lqB/Ac0C+ktIwxxvQQVnA/Glid8XmNO62LiFwhIo0i0hhSHowxpmY1VCphVR0IDAT4dN++OuWWc2mo33utSaaU+jqhrTPJfg11JFKKAA31dXQkUuxTLyRTCu60zN8JkFJFRKivc+ZT1a55Ugr7NtTRmUwBsE/G73d3JNivoZ46gYS7/HoRJ31x5u1MppCMdUmnn3CX15FMceC+DSSSKRS65lWgwc1PQ30dqooq1NVJ1+/rRLo+u9uJREqpE0F17+8SKSWRVPapl5z5UKBOhEQqxb71dYh0X25n0tnGqs6/ItK1TVQhPfs+WfZLWnpfpBTq66Qr7ToRkqrUi5DK2PaZ+yqd1/S0VEpJqnZLL5FMkUgp+zXU0ZFMsV9Dfa/fp/cz0JX/OjfdlJsHoGsd08dDQ4992VBfR3siyT51dai7jRp65AWc46ehTrrtt57zpdd4n/o6EskU9XXONhKc/d3WmaShTrqOiY5kin3q6tx9vTevKfcYrKsTdrYn2K+hrlt+M/MlItTJ3n2UTGlXPhrqpOt4VndfpedJb7v2RJKGOmeZ6XMovU32ra8jpVAndB1HqkpHMkW9SLf1Tc+fnq+t0/mc3qaJlLrL06zHQ3pdBOcYTLrHRSpF1/HeUF/XLe/pvKRSzm8a3O1dJ868mfuo0z3P6jP2X+b2TEultCuPiWSq137PTL/T3Q51GedA+vjaJ+PfzHyoate61Yt025/pOCdCt/3dmUzRkHEcN9xOTpIZ9IIiIp8DblbVr7qff+2uzK3Z5u/bt682NloB3hhjvBCR6araN9t3YVXLTAOOF5HjRGRf4CJgaEhpGWOM6SGUahlVTYjIT4GRQD3wuKrODyMtY4wxvYVW566qw4HhYS3fGGNMbvaGqjHGxJAFd2OMiSEL7sYYE0MW3I0xJoZCaefuORMircBi4GBge5ZZck3P912h3+wDbA4xnVxp+FmWl98cnpGmnzx7Sf9woNPjb0pNP3P9iv2Nn3SgtG3p9zc91y+MdHKto59lef1N0MdMod8EdQ4GecwEkU7aR1X1vVnndt6SrOx/QKP778Ac32edXspv0mmGlU6uNMJez8w0/eTZS/pAY9DbrNCyem7TMI6NILZlCb/xdMyUup2L3Z5BpR/0MVPoN0Gdg0EeM0GuZ671U9XIVcv8w+P0KP+m0un7+U2tp+/nN0Gn73VZftKJ2zar9TxnFZVqmUbN8QptNacZ1/WqRFqVSDPu62fpxTu9qJTcB8Y0zbiuVyXSqkSacV8/Sy/G6UWi5G6MMSZYUSm5G2OMCZAFd2OMiaGyBncR2VnGtJIiMivjvz555h0rIp4fgoiIisjfMj43iMgmEXnNZ7aLTfd8N+2PhZhGRdbNTatsx4mXdP0eJz2WEfq+65He9SIyX0TmuOfBZ8qQ5jEiMkRElorIuyJyj9v1d675rxWRA32koyJyZ8bn60TkZp/ZLia9dEyZLyKzReR/RSSyBeTIZiwAe1T1lIz/VoSQxi7gJBE5wP38b8BaLwsQET89c14MTHD/9ZJWfeG5upS8biYrX/vOD3fQnPOAU1X1k8BX6D78ZRhpCvAy8KqqHg+cABwE3JLnZ9cCnoM70A58S0QO9/FbP9Ix5RM458O5wE1lStuzsgd3ETlIREaLyAwRmSsi/dzpfURkoYg84l4Z38gILEGl/WkReVtEpovISBE5KuPrS92r8jwROc3DYocDX3f/vhh4NiO900RkkojMFJGJIvJRd/r3RGSoiLwFjPa4DgcBpwOX4wyCgoicISLjRGSYiCwWkYfSJQoR2Skid4rIbOBzXtLyuW7jROSUjPkmiMjJHtNNr9NrGZ/vE5HvuX+vEJHfZRxDgZWC86UbwLJz7btc6/k1EVnkHq8DfNw1HQVsVtV2AFXdrKrrcp0H7p3JPT7Pg7SzgDZVfcJNMwn8HPiBiLxHRO5wlz1HRK4WkZ8B7wfGiMgYj2klcFqL/LznF248ectNZ7SIfEBEDhaRlRnnxntEZLWI7ON1JVW1GbgC+Kk46kXkLyIyzU3zxxl5+ZV7nM4Wkdu8puVXJUrubcA3VfVU4EzgTvdqD3A8cL97ZWwBLighnQNkb5XMK+4OvBe4UFU/DTxO99LEgap6CvAT97tiPQdcJCL7A58EpmR8twj4oqp+CrgR+FPGd6e6efmyx/XqB4xQ1SXAFhH5tDv9NOBq4ETgw8C33OnvAaao6smqOsFjWn7W7THgewAicgKwv6rO9phuMTa7x9CDwHUhLD8MufZdL+42fxg41z1ej/CR3hvAsSKyREQeEJEvh3gepH0CmJ45QVV3AKuAHwJ9gFPcO4mnVXUAsA44U1XP9JHe/cAlInJwj+n3AoPT6QADVHU7MAtIn3PnASNVtdNHuqjqMpzBiP4Z54K9XVX/FfhX4EfijER3Ls5+/4yqngz82U9aflRigGwB/iQiXwJSwNHAke53y1V1lvv3dJwDwa897kHqJCpyEnASMMq9ltQD6zPmfxZAVceJyPtE5BBVbSmUiKrOEac+/2J6D05yMDBYRI7HGa86s4QwSlW3el0pN5173L+fcz+/Bkx1DzZE5FmcEuKLQBJ4yUc6ftft78ANIvJ/wA+AQX7SLsLL7r/T2Xshi7pc+y6bjwHLVHW5+/lZnJJi0VR1p3sB+SJOQep54I+EcB4U6QzgAVVNuGn4Of67UdUdIvIk8DNgT8ZXn2PvcfEUe4Pq88B3gDE4d08PlJoH178DnxSRC93PB+MUVr8CPKGqu938lrzOxapEcL8EpxTyaVXtFJEVwP7ud+0Z8yWBIKtlBJivqrmqJno2+PfyAsBQ4A6cg/efMqb/ARijqt90g+TYjO92eVg+ACJyGM5t77+IiOKcmAoMy5Lf9Oc299bYL0/rpqq7RWQUTmnl20DO0mkBCbrfWe7f4/v0sZIk2OO4ULq+5Nl3Q8JIL83d92OBsSIyF7iK8M4DgAXAhZkTROR9wAeAFR6XVay7gRnAE0XMOxSncHkYzrH5lt9EReRDOMdfM058uVpVR/aY56t+l1+qSlTLHAw0u4H9TOCDZUp3MXCEOA+ZEJF9ROQTGd9/x51+Os7tVa6e2bJ5HPidqs7tMf1g9j6E/J6vXHd3IfCUqn5QVfuo6rHAcpyS2WnubWAdzrp4rYLJxc+6PQoMAKap6jaf6a4EThSR/UTkEOBsn8uJSrq59l1djvQWAx+Sva28vuM1QRH5qHtnlXYKsJDwzgNwniEdKCLfdZdTD9yJcwc3EvixuI0I3AAL0Apk79mwCG5p+AWcqpG0ibjPNXAKlOPdeXcC03DuoF7zW/ARkSOAh4D71HkTdCRwZbr+XkROEJH3AKOA74vbGihjnUNXtpK7u0Pbceq//uGWIhpx6m5Dp6od7i3TALd+rgHnip8euLtNRGbiVC/8wOOy1+AEs57+jFN18Vuc0nWpLgZu7zHtJeBKnAP2PuAjOLecrwSQnq91U9XpIrKD4kpS3aSPE1VdLSIvAPNwguBMz5mPVrq59t1FOIGpW3qqukdEfgKMEJFdOPvXq4OAe92LRgJowqnaGUgI54GbbxWRbwIPiMgNOBev4cBvcEq5JwBzRKQTeATnmB3oruc6n/Xu4FxAfprx+WrgCbd6cBPw/YzvnsepPjzDYxoHiMgsnG2TwKnuucv97lGcauQZ7jPETcD5qjpCnAYGjSLSwd5tEbqydT8gTouJR1TVzxN4k4eInAFcp6rnVTgrAIjI+3GqAj6mqimPv63IcRLF41NEDnLrzQXnweFSVf1riOmNxTmOGsNKw5RPWaplROR/cB7U/LYc6ZnKcW/HpwDX+wjsFTlOInx8/sgtKc7HqQZ7uLLZMdXEOg4zxpgYCqXkLiLHisgYEVkgzgtJ17jTDxORUeK8ljxKRA51p18iTsP/ueK8EHNyvuUYY4zJL5SSuzhvvB2lqjNE5L04bZHPx2lVsVVVbxOR/sChqvorEfk8sFBVt4nT6P9mVf1MruWo6oLAM22MMTESSsldVder6gz371ac5ldH47R9HuzONhgn4KOqEzOazE0GjimwHGOMMXmE/kDVbaf7KZyHbEeqavptuA3sfTM10+XA6wWWY4wxJo9Q27mL01HSS8C17mvCXd+57WG1x/xn4gT30/MtJ8w8G2NMHIRWcnff1HoJp3OgdD8gG2VvD3RH4by2m57/kzgvAvRT1S0FlmOMMSaPsFrLCE7vgAtV9a6Mr4YCl7l/X4bTrwYi8gGcjqAudXvMK7QcY4wxeYTVWuZ0nL4c5uL0/AjOK7dTcF61/gBOHx7fVtWtIvIoTve+K915E6raN9dyVLVnD4XGGGMy2EtMxhgTQ3EeZs8YY2qWBXdjjIkhC+7GGBNDFtyNMSaGLLgbY0wMWXA3BhCRm0Xkujzfny8iJ5YzT8aUwoK7McU5H7DgbqqGtXM3NUtErsd5U7oZWI3TpfR2nHFG98UZc/RSnIGlX3O/247zwh04Q98dAewGfqSqZRkP2JhiWHA3NUlEPg0MAj6D04HeDJzR7J9I920kIn8ENqrqvSIyCHhNVV90vxsN/I+qLhWRzwC3qupZ5V8TY7ILtVdIYyLsi8ArqrobQESGutNPcoP6IcBBwMieP3R7Kf088PeMnk73CzvDxnhhwd2Y7gbhjPY1W0S+B5yRZZ46oEVVTylftozxxh6omlo1DjhfRA5wh3D8hjv9vcB6t6vpSzLmb3W/wx1TYLmI/Cc4vZemx/01JiosuJua5A7f+DwwG2fkr2nuVzfg9F76DpD5gPQ54P9EZKaIfBgn8F8uIrOB+ThDSBoTGfZA1RhjYshK7sYYE0MW3I0xJoYsuBtjTAxZcDfGmBiy4G6MMTFkwd0YY2LIgrsxxsSQBXdjjImh/w+SCcvoMmdpDAAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1010,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "id": "9bef8400", "metadata": {}, "outputs": [ @@ -1019,75 +696,18 @@ "text/html": [ "