diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml index abc712ae..84d99b25 100644 --- a/.github/workflows/nbval.yaml +++ b/.github/workflows/nbval.yaml @@ -29,7 +29,7 @@ jobs: - name: Run notebook and check output run: | # --sanitize-with: pre-process text to remove irrelevant differences (e.g. warning filepaths) - pytest --nbval --sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }} + pytest --nbval --nbval-sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }} - name: Run notebooks again, save files run: | pip install nbconvert[webpdf] diff --git a/docs/TrendAnalysis_example.ipynb b/docs/TrendAnalysis_example.ipynb index 12be8fb2..049e9e1d 100644 --- a/docs/TrendAnalysis_example.ipynb +++ b/docs/TrendAnalysis_example.ipynb @@ -56,6 +56,35 @@ "execution_count": 3, "metadata": {}, "outputs": [], + "source": [ + "empty = pd.Series([])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "empty.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "# Set the random seed for numpy to ensure consistent results\n", "np.random.seed(0)" @@ -77,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -158,7 +187,7 @@ "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n", "#ax.set_ylim(0,1500)\n", "fig.autofmt_xdate()\n", - "ax.set_ylabel('soiling signal');\n", + "ax.set_ylabel('soiling signal')\n", "plt.show()\n", "\n", "df['power'] = df['power_ac'] * df['soiling']" @@ -180,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -193,6 +222,26 @@ " temperature_model=meta['temp_model_params'])" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta.results" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -208,18 +257,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/soiling.py:27: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "ta.sensor_analysis(analyses=['yoy_degradation','srr_soiling'])" ] @@ -233,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -243,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -263,15 +303,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.954\n", - "[0.95 0.957]\n" + "0.953\n", + "[0.949 0.957]\n" ] } ], @@ -291,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -315,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -339,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -368,20 +408,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:172: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -397,20 +429,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:232: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" - ] - }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -426,17 +450,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:272: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" - ] - }, { "data": { "image/png": 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", @@ -462,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -493,6 +509,8 @@ " soiling_rate_high\n", " inferred_start_loss\n", " inferred_end_loss\n", + " inferred_recovery\n", + " inferred_begin_shift\n", " length\n", " valid\n", " \n", @@ -507,6 +525,8 @@ " 0.000000\n", " 1.063788\n", " 1.018062\n", + " -0.018062\n", + " NaN\n", " 29\n", " True\n", " \n", @@ -519,6 +539,8 @@ " -0.000640\n", " 1.024589\n", " 0.964412\n", + " 0.035588\n", + " 0.006526\n", " 63\n", " True\n", " \n", @@ -531,6 +553,8 @@ " 0.000000\n", " 1.072710\n", " 1.056087\n", + " -0.056087\n", + " 0.108299\n", " 28\n", " True\n", " \n", @@ -543,6 +567,8 @@ " -0.001000\n", " 1.057288\n", " 0.932740\n", + " 0.067260\n", + " 0.001202\n", " 109\n", " True\n", " \n", @@ -555,6 +581,8 @@ " -0.001307\n", " 1.020735\n", " 0.961439\n", + " -0.000840\n", + " 0.087995\n", " 31\n", " True\n", " \n", @@ -577,15 +605,15 @@ "12 -0.001301 -0.001000 1.057288 \n", "15 -0.002793 -0.001307 1.020735 \n", "\n", - " inferred_end_loss length valid \n", - "5 1.018062 29 True \n", - "6 0.964412 63 True \n", - "9 1.056087 28 True \n", - "12 0.932740 109 True \n", - "15 0.961439 31 True " + " inferred_end_loss inferred_recovery inferred_begin_shift length valid \n", + "5 1.018062 -0.018062 NaN 29 True \n", + "6 0.964412 0.035588 0.006526 63 True \n", + "9 1.056087 -0.056087 0.108299 28 True \n", + "12 0.932740 0.067260 0.001202 109 True \n", + "15 0.961439 -0.000840 0.087995 31 True " ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -605,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -713,7 +741,7 @@ "2010-02-25 14:20:00-07:00 True " ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -725,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -739,7 +767,7 @@ "Freq: min, dtype: bool" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -751,38 +779,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "application/vnd.plotly.v1+json": { @@ -61308,7 +61307,6 @@ } ], "layout": { - "autosize": true, "legend": { "title": { "text": "mask" @@ -62136,65 +62134,26 @@ }, "xaxis": { "anchor": "y", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - "2012-12-30 15:27:30.8566", - "2013-01-23 08:31:29.1434" - ], "title": { "text": "datetime" - }, - "type": "date" + } }, "yaxis": { "anchor": "x", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - -1.3697493381233599, - 19.223241354790026 - ], "title": { "text": "energy_Wh" - }, - "type": "linear" + } } } - }, - "text/html": [ - "
" - ] + } }, "metadata": {}, "output_type": "display_data" @@ -62211,7 +62170,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -62229,7 +62188,7 @@ "# Visualize the results\n", "ta_new_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n", " scatter_ymin=0.5, scatter_ymax=1.1,\n", - " hist_xmin=-30, hist_xmax=45);\n", + " hist_xmin=-30, hist_xmax=45)\n", "plt.show()" ] }, @@ -62243,7 +62202,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -62262,7 +62221,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -62277,7 +62236,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -62296,7 +62255,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -62316,7 +62275,7 @@ "# Visualize the results\n", "ta_stuck_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n", " scatter_ymin=0.5, scatter_ymax=1.1,\n", - " hist_xmin=-30, hist_xmax=45);\n", + " hist_xmin=-30, hist_xmax=45)\n", "plt.show()" ] }, @@ -62331,7 +62290,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -62340,7 +62299,7 @@ "{'two_way_window_filter': {}, 'ad_hoc_filter': None}" ] }, - "execution_count": 28, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -62358,7 +62317,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -62394,7 +62353,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -62432,7 +62391,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -62450,7 +62409,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -62459,7 +62418,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -62570,7 +62529,7 @@ "2010-03-01 00:00:00-07:00 0.857710 " ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -62590,7 +62549,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -62616,7 +62575,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -62676,7 +62635,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "rdtools3-nb", "language": "python", "name": "python3" }, @@ -62690,7 +62649,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/docs/TrendAnalysis_example_NSRDB.ipynb b/docs/TrendAnalysis_example_NSRDB.ipynb index 8cae4eec..a6863af5 100644 --- a/docs/TrendAnalysis_example_NSRDB.ipynb +++ b/docs/TrendAnalysis_example_NSRDB.ipynb @@ -143,7 +143,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -158,7 +158,7 @@ "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n", "#ax.set_ylim(0,1500)\n", "fig.autofmt_xdate()\n", - "ax.set_ylabel('soiling signal');\n", + "ax.set_ylabel('soiling signal')\n", "df['power'] = df['power_ac'] * df['soiling']\n", "\n", "plt.show()" @@ -293,7 +293,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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GyuWxCXIkEokv+7//+z/69OnDiy++yIMPPsiDDz6IRqNhxowZPProo/GJodPpBOjUMS22vLW1tcNn+fn5ST+vW7euwzmVlJR0EFY9evRIeazY/mJjAnj88ce59tpryczM5JRTTqFXr16YTCYEQWDBggWsX78+yWTg+uuvJycnh6effpp//vOfPPbYYwiCwKRJk3j44YcZNWoUEBUHAF988QVffPFFyvEAeDyepDHtbezdxel0xnuedWdfsfGWl5d3+dzExru3MavV6nh9YCo6O5/W1lZGjx5NZWUlY8aMYd68eWRlZaHRaGhtbeXxxx9Puh/7c93C4TBTp05l5cqVDB06lPPOO4/c3Nz4tbr77ru7NPjoLt159qurq2ltbU0SVvvyPewOXd3vt99+u8ttY/dbrVazePFi7rnnHt555x3+/Oc/A2C1Wrn44ot54IEH4i9dfipmz57Nxx9/zKOPPsoLL7zAs88+C8Bxxx3HAw88wCmnnPKTjkdBQeHgowgrBQWFnwS1Ws25557Lxo0buffee1m8eDGzZs2KT9COPfbY+BvzQ3X8a6+9lmuvvZbGxka+/vpr/vvf//L222+zefNmNm/ejF6vj4/HZrOl3E/MFTCV42Gi6xpEndcSHcE6Y8+ePSmXx8YQO5Yoitx1113k5+fzww8/dJgAx97qt2fevHnMmzeP1tZWvv32W95//31eeOEFTjvtNLZt20Zubm78GI8//jhXX331XsccW39vY+8u6enpOByOlAYSqfYVO/7ZZ5/Ne++9161jxKKbe/bsoU+fPkmfRSIRmpubO+3r1P7exvj3v/9NZWUld955Z4dIynfffcfjjz+ectz7ct0++OADVq5cySWXXNLB8bKhoeGgOcolPvupopBdPfsHk1TXOnbMDz74oNs98DIzM/m///s//u///o+dO3fy1Vdf8eyzz/Lkk0/S2toaNxWJRYvbR35jtLa2dioe95XTTz+d008/Ha/Xy4oVK/j444955plnOOOMM1i7di2DBw8+KMdRUFA4PCh26woKCj8pVqsVIJ7GZLFYGDJkCJs3b8bhcPwkY8jLy2P27Nm89dZbTJ06lYqKCjZt2gREBR5E7ZPbI4oiy5cvB2DkyJEHbTxff/11Slvu2BhiY2pqaqK1tZVx48Z1EFUej2evwjQjI4MZM2bw/PPPc8kll+BwOFi2bBkQTYUD4ue3N6xWK3379qWuro6KiopOx95dRo4ciSRJfP31193a18CBA+POkeFwuFvHiF3HVMf4/vvvO51Yd8XOnTuBqJNde1Kl9CWOIVUkJ9W5xo4xe/bsbh0DiKcZ7ku0qKtnf+fOndTW1lJaWnrQRMa+sK/PZ3v69u3LZZddxldffYXFYuGDDz6If5aZmQlATU1Nh+127tyZFDHeG2q1ulvX3Gw2M3XqVP7xj39w2223EQqF+OSTT7p9HAUFhSMTRVgpKCgcVObPn88XX3yRUijYbDaef/55ACZOnBhffv311xMKhfjNb36TMsWupaXlgKJZwWCQb775psPycDgcF3MmkwmAWbNmkZWVxfz58/n++++T1n/ssceorKzk5JNP7lBfdSCUl5d36Cn1wQcf8NVXX9G3b9+43XpeXh4mk4k1a9YkpbiFw2GuueYampqaOux7yZIlKXsJNTY2Aj+e96hRo5gwYQLvvfceL7zwQspxbty4Mb4dwKWXXookSfz5z39Out+VlZX885//7O7px/cFUVv8xL5MDocjZQ2VRqPhT3/6Ew0NDVx99dX4/f4O6zQ0NCT19po3bx4A9913X9JkORQKcdttt+3TeGPErMzbi5G1a9fywAMPdFi/qKiIU045hcrKSp588smkz2L3vLvH2LVrVzzFrT2xtMbq6upunEWU3/zmNwDce++98Zo6iIqzG2+8EUmSuOyyy7q9v4PJWWedRVlZGU899RSLFi1Kuc53332Hz+cDos/grl27OqzT0tJCMBjEaDTGlw0cOJC0tDQ++OCDpOfb7/d3K3qbSHZ2Nna7PeXzuGzZspTiPRa9jH0XFRQUjl6UVEAFBYWDyooVK3j88cfJz8/nxBNPjPd7qqysZOHChfj9fs466yzmzJkT3+Y3v/kNa9as4emnn6asrIzTTjuNXr164XA4qKysZNmyZVx66aX861//2q8x+f1+TjzxRPr27ctxxx1HSUkJgUCAL774gq1bt3LmmWcyaNAgIBpBe+GFF5g7dy6TJk1i7ty59OrVizVr1vD555+Tn58fr404WEybNo0bbriBTz75hOHDh8f7WBkMBl544YV4qpJKpeLqq6/mwQcfZNiwYZx11lmEQiGWLFmCw+FgypQpLFmyJGnfZ599NhaLhbFjx9K7d29kWWb58uWsWrWK4447jpNPPjm+7htvvMHUqVO57LLL+Oc//8nxxx9PRkYGtbW1bNiwgU2bNvHdd9/F+wrdcMMNLFiwgHfffZeRI0dy2mmn0draGm8w++GHH3b7GvzqV7/izTff5MMPP2To0KGcddZZhMNh3nnnHUaPHp0yKvaXv/yF9evX869//YuPPvqIqVOn0rNnTxobGykvL+ebb77hvvvui6dXTZo0iSuuuILnnnuOIUOGcM4556DVavnoo49IT0+nsLAwpYlIV8ybN4+HH36Ya6+9liVLltCvXz/Ky8v5+OOPmT17dsrm00899RQnnHAC1157LZ9//nn8nr///vvMnDmTjz76KGn9mTNn0rdvX/7xj3+wceNGjj32WKqrq/n44485/fTTU4qnKVOmoFKpuPXWW9m0aVM8KnPHHXd0ei7jxo3j5ptv5u9//ztDhw5lzpw5mM1mPvnkEzZt2sSJJ57ITTfdtE/X52Ch1Wp57733OO200zj99NMZN24cI0aMwGQyUVNTw6pVq9i1axcNDQ2YTCbWr1/P7NmzGT16NIMGDaKwsBC73c4HH3xAOBxOEqRarZZrrrmGv/3tbxx77LGcffbZiKLIF198QWFhIYWFhd0eZ6yv27Rp05g4cSJ6vZ7hw4czc+ZMrr76aurq6hg/fjy9e/dGp9OxZs0aFi9eTElJCeeff/6huHQKCgo/JYfVk1BBQeFnR3V1tfzkk0/Ks2bNkvv37y9brVZZq9XK+fn58vTp0+VXX301pbW4LMvyRx99JJ9++ulybm6urNVq5R49esijR4+Wb7/9dnnr1q1J65Kir1GM9lbSoVBIfuihh+Rp06bJxcXFsl6vl3NycuTjjz9efuaZZ+RgMNhhHytXrpRnzZol5+TkyFqtVi4uLpavuuoqua6ubq/H6y6JNs/ffvutfNJJJ8lWq1W2WCzyKaecIq9cubLDNuFwWH700UflQYMGyQaDQe7Ro4d80UUXybt37045jmeeeUaeNWuWXFpaKhuNRjkzM1MeMWKE/NBDD6Xsh+VyueT77rtPHjlypGw2m2WDwSD37t1bnjFjhvzss8/KHo8naX2n0ylfd911cmFhoazX6+UBAwbIjzzyiFxRUbFPduuyHO0ndffdd8ulpaWyTqeTS0pK5Ntuu00OBAKd3m9JkuRXXnlFnjp1qpyZmSlrtVq5sLBQHj9+vHzffffJ1dXVSetHIhH5H//4hzxgwABZp9PJBQUF8u9//3u5tbVVtlgs8vDhw5PW78xWO5HNmzfLM2fOlHNzc2WTySSPHDlSfv755+XKyspOr0F5ebl8zjnnyOnp6bLJZJLHjh0rf/zxx50er7q6Wr7gggvkwsJC2WAwyIMHD5YfeughORwOd3ptXn311Xj/MdraHsTo6pmdP3++PH78eNlisch6vV4ePHiwfO+99yb1F4vRlb343izf2xOzW++KPXv2yH/+85/lIUOGyEajUTabzXLfvn3lc845R3711Vfj1vs1NTXyrbfeKo8bN07u0aOHrNPp5J49e8rTpk2TFy1a1GG/kiTJDzzwgNynT5/49/2mm26SvV7vPtmtezwe+aqrrpJ79uwpq9XqpPv/5ptvyueff77ct29f2Ww2y1arVR4yZIh82223yY2Njd26RgoKCkc2giynyBFRUFBQUFD4BVFeXk7//v05//zzmT9//uEejoKCgoLCUYhSY6WgoKCg8IvBZrN1qP/z+XxxC/yzzz77MIxKQUFBQeHngFJjpaCgoKDwi+Gxxx5j/vz5TJ48mYKCAmw2G19++SW1tbVMnz6duXPnHu4hKigoKCgcpSjCSkFBQUHhF8Mpp5zC+vXr+fzzz3E4HGg0Gvr378/VV1/Ntdde22m/KgUFBQUFhb2h1FgpKCgoKCgoKCgoKCgcIEqNlYKCgoKCgoKCgoKCwgGiCCsFBQUFBQUFBQUFBYUDRBFWCgoKCgoKCgoKCgoKB4girBQUFBQUFBQUFBQUFA4QRVgpKCgoKCgoKCgoKCgcIIqwUlBQUFBQUFBQUFBQOEAUYaWgoKCgoKCgoKCgoHCAKMJKQUFBQUFBQUFBQUHhAFGElYKCgoKCgoKCgoKCwgGiOdwDOBKRJIn6+nqsViuCIBzu4SgoKCj8YpBlGbfbTWFhISqV8u4vhvJ3SUFBQeHw0d2/TYqwSkF9fT3FxcWHexgKCgoKv1hqamooKio63MM4YlD+LikoKCgcfvb2t0kRVimwWq1A9OKlpaUd5tF0j11NHl7+Zjeb6pzscQXokW5gaGE6F4/vTZ8cy+EenoLCAbOryUNdi5+emUblmf4Z43K5KC4ujv8eVohyNP5dUvhpaWpqoqysLGlZRUUFOTk5h2lECgo/H7r7t0kRVimIpVmkpaUdNX/AWhsC7HbJ1PvAJ+kQPWB1yzhF7VFzDgqHhgq7hxqHj+IsE2W5R6cgqbB7+O/aJpq9IbLNXn47Me2oPReF7qGkuyVzNP5dUvhpCQaDHZZZrVbleVFQOIjs7W+TIqx+JhRnmRAliZAooVMLhCMy4YhEUabxcA9N4TBSYffw/LJdVDt8aNUCl44vZfKAvMM9rH2mxuGj2RtiUL6VrTY3tS1+RVgpKCgoKCgoHFEolcE/ERV2D0u3N1Jh9xyS/ZflWrjg+F5kW/QYtGqyLTouOL6XMvn8hVPj8FHt8OH0hdhS7+bFbyoP2TN4KCnOMqFVC3y9swmtWlBeGCgoKCgoKCgccSgRq5+Anypq8KsxJRSkG9lU5yTboqMg3UiF3aOIq18wMUHS6A6RZtTQ6A6yarfj6HwmZPCHI9hdQWocvqPzHBQUFBQOEenp6SxZsqTDMgUFhZ8ORVj9BCRGDRrdIV78pvKQ1LvEIhFDe6bz6SYbi7fZyTbr+O3EPsokdB/4OdQkxSjLtXDp+FKeWlJOVbMfrVpg2Q47o3tnHVXnVuPw0eoPowJ22j08+vl2gKMyrVFBQUHhUKDT6Zg8efLhHoaCwi8aRVj9BCRGDXKtOsIRea81Ivs6uU+MirkDYbRqFSf2zTki6lE6O5cjUcDErmPUJOHnIUqLs0wMKUzHG4xwXEkmNlfwsD8T+0rsO1Tb4keUJKodvqQXFEfis6SgoKCgoKDwy0IRVoeIxIkewHElmfhCEbRqFb2yTF3WiOzP5D4WFWt0BbA5gxh0Aqur1Hs91qGms3M5UgXMz80kIVFwuwIiq6taKM0xI8syS7c3HjVCpCzXQmmOmRWVDsKihEWlwh0QqW3xAxyRz1IqjkYBeDSOWUFBQUFB4XCgCKtDQKJo0KoEECAckcm16Jk4IHevaVj7M7kvzjIRjkSoaYmme0mywMB8KxeOLQE4bJPozs7lSBUwxVkmss06Vle1oFULyLJ8uId0QMSuc45Zx5Z6JyoBPAGRt1bXEI7IR7wQibF0eyOfbrIRCksAhCUZq0GDLMss3NBAtcNHWY6ZTQ2uA6ohO5Qi4kh9mdAVR+OYFRQUFBQUDheKsDoEJIqG5TubECCelleQbtxrCmCDMyqOttrcZJt13Yo4leVamNAvj/I9XkAmIslkmnXA4X2b3+D009Dqp9kTpH8Pa9K5hMQIq6taDntUrT2FGYa2ejUVn26yxZcfjW/si7NMaFUCX1c0EQhLZFsE7J4gnqB4xKSKdocNtU48ARG1CsISqIRoFPjTTTaqHT6qm33sbPTEa8jy0wzAvt2zQy0iOnuZcCRHhI7UFyAKCkcyvW9ZeLiHcFjY/eDph3sICgqHHUVYHSJCYoTl5U1o1GDSabolkhLTtsKRCBP75zFjWEG3JzK5Vh2SLOMNimhUAqt3O8ix6A/bxGjp9kaeX1ZJiy+EXqNi7qii+ETy0002whEZrVpg2tD8I2KyFrv+62tbaXQFGd83O17Lo9Ooj8o39mW5Fib0z2V3s5eQKOH0i/RIV6NWCUekqO2MXKuOiCxHRRVg1mvY3ezD5gwwqiSTFl+IsChxQlk2FXbvft2zQy0iUkVDj/SIUGzM+/KSR0FB4fAgyxKS3520TGW0IghKZx0FhZ8KRVgdZGKiweUXaXD6KcwwYtZpGFSYRo5F3+V2Czc0sN3mJiRGaHSHMOkczBhW0K3jLt3eyItfV+INiciAIIDdHUQQOGwTow21TtwBkb65ZnY3+2n2hIAfJ7CjSjJZXdXCVzvsbKh1kmvVM6b08LnVxerUwqKEJyjyzc5m+uSaARXDi47eN/ZjSrNYvsNOZbOXHml6ss26I07U7o2CdCMF6UbqW32EIjIuf5htDS4CYYnVVS3kWvT4wxEq7F60bQ2y9/We/RQion00dHhxxj6LuZ8ywlWWa+G3E/uwareDozwrVkHhZ4/kd1P7xIVJy4r+9Dpqk2K5rqDwU6EIq4NMTDQUZxmpbfFTnGnE7gmxercDnUbN+prWDm+lEyNVDU4/IVEiP93QLffA2PZPLd5JdYsPWQIEiMjRt/qje2cxuncWtS1+ijK7TkM82BxTlI7VoGF3sx+rQUO2RcfS7Y0AaFUCX2zdg8MbYpvNhS8oYtBqOKY4nTtnDjksk/2Y85zdHcSkU2HWq5nYP5e6Fj9bbW6C4QhLtjUiy/JBs/n+ySbJ0VI/3EERfzjCuLbIzsY65xGZgtae4iwTxVlGmjxB9BqZiAz98ixUO/xY9GqCooRWrYqLxfU1zn0WSDERcSi+K6mioc3e0D6/+PipI1wVdg8rK5tZXt5Eqy/Mp5saDlkfPgUFBQUFhaOd/RJWDQ0NFBR0L5LySyP21rva4cNq0GD3hPb6Bj0xguMPRRAlCatB2+00rZWVzVQ5vKgRkJHRqSE/3cjskT3jxzkcE+fY5CvWsHh9jZPF2+xoVQLekEhIlAiGpbi5R0QKs7aqhUUbG/jT1H4/+XjLci0cV5LJ97uaESMygVCEZk+QaUPz2Vjn5M1VNWxtcLNshz3p/GLsr0X+oZ4k1zh8tPrChESJ+lY/KmCRL4RRF/3617X4j7gUtPaU5VqYNjSf7TY3/lAESZL5oboVGQiIEVz+MFMH5mFzBSnMMDGmNDtJIO3LvTkUhiWpoqHH98na5xcfP0XNU+xaAXy6ycb2PW7qWnykGbQ4/eIh68OnoKCgoKBwtLNfwqq4uJipU6fy61//mtmzZ2M2mw/2uI5aEt96y7KMIERrKT7dZOv0rXRiCtKAfCvThuYjCMI+vDUX0KpVZJh1qPxhsi16CjOMLN/RBAjdTq87FNGTyQPymDwgj6XbG1m8zZ5k6DGkII1l5XaCYQmpbS4bEiWW7bDvU23ZwcThDSPLoNOoCIQlFm9rJBCWMGjVBMMSvbON7G72s6nOmSSs2jtBTuifu9fr/lMZA8QicTZXAI0gYNKrkWQIhiPkWHQ0e0NHRYpjQbqRPrkWmtwBdjV5afYEMeo09M+z0OgKsKaqhWOKMuLfm9j5dFfAHkqhG7sHTr9IUaYRnUbFxP65+/ziozvpigfyPV66vZEXv6kkHJGjIjAk0i/PQlWzF7snSM8MY7cj6QoKCgoKCr809ktY3XPPPbzxxhtcfPHF/O53v2PWrFlcdNFFnHrqqahUSpFk4qQuRnGWqdO30geagjSmNIu+eRZqW/xkmXVYDVrKcsx8ub0RmyuQMv2wPYc6ehKbWH69swm1CgJhie8rHeg0KnKtenzBCM5AmB5perRq1UGZuO3fBFNGAMSIhCBAYbqBZm+oQ1rj0J7JOeuxiIRBo2J9jYvdzd69pk3ta03P/k6YY5G4tdWtBGUJVyBMrBDv8y176JdniUdpjmSHuuIsEyExQnmjFxkQJYlQJMQP1S3oNCrMek3KmrHuCthDKXTLci1cOr40LloyjFpkOXq99/VedvW74kC+xxV2Dy9+U8mWejdpRg0tvhAC4PKH6ZNrRmh7gXO0GJ4oKCgoKCj81OyXsLrtttu47bbbWLt2La+//jr//e9/eeONN8jLy+NXv/oVF154IaNGjTrYYz1qSDU5TSW2Eol9trKymZWVjn02cTDrNKQZtVj1GgQBvt3VTCQiM6QwDZsryKrdji4nzIdyUhmr07C7AzS5g4Qi0XoYf1hkdEkmarWaokwjq3c7DtrEbX8mmBV2D7uavOg0KkRJJl2vISjKRHxBZBl+O7GUZk+IoT3TU4olmzNAsyeEPywiyzI2ZyApbar9c7EvgvpAJ8xrqlpQqwQyjBpafSIRWcaohmBEwukPx23lP91kSzoGcMQIrbJcCxkmHULbz3Lbf1q1irF9svGHJQQh+mnitU4lYNvfiwq7hw21rbT6QvvklthdIRo1rIBLx5dicwVYtsPO4m2N3Xrpkeo6dLb+gXyPaxw+whGZPKuO2pYAGrXA+L7ZlDd66JVppneOmbw0Pflphniq4OF+JhQUFBQUFI4kDsi84thjj+XYY4/l4YcfZvHixbzxxhu8+OKL/POf/2TAgAFcdNFFXHTRRfTq1etgjfeIJ3ECHAxH6JNrYfKA3L0Wey/d3sgjn2+nxuFDAPKsBi49sTe/GlOy12PWOHyEJZkJfXNYXdVCOBJBp1Gh1aiosHvJMGpZtsPeZUPYQ+WIFkstqnX4aXAFMGpVuPxhTDoN/mCE73Y5KMszo1ULWA3ag+JUF3NYrHb4GFWSyVabe6/CEmBlpYOKRg9pRi3eoEj/Hhaa3EH2uINUN/s5rncmN5w6oNPt89MN5Fp1/FDVSosvjFmnxh0QqW3xA6n7ie1NcMdoP2Huzvkkbuvyi1gNGprcQaS2vEtvWEanFhjR5ky3sc7Z4RjrqluPKCvwkwbl8U25Hb8YPQezVoMgCKypamF4cQayLDN/ZRXLy5uSnvdEAQvJ92La0Hxe+LqSDbWtBMMSRr2aHmmdO3jGSDSd0aqFTqOT7dc7riSLcEROus4rK5vZl7TdzjiQ73FxloleWSa2hyL0SNORbtLR5Anh9ot8taORxdsgz6qjIMN41LYfUFBQUFBQOJQclLw9QRCYMGECM2bMYOzYsciyTHl5OXfddRd9+vRh7ty5NDQ0HIxDHfHEJsBGjYoVlc38d1UVN761nvkrqzrdJubqt73Bjcsv0uoXqbB7ePx/5XEXva5InExp1QJatZpTBvUg16KnIN3AwIK0+EQuVk/Tnlj05KKxJQdtspSYWuQLiYRFiUAogiiBKyAiyhAQJXY3+ahs8pJjidqAx6IO+3vM55ft4vtdzdicgWjPIFW0aezrK6p5ftmuePSgI3I0xSwi4QtF2LHHQ2WTD18ogtMfYlOtk1W7HSm3jE1KQxEZq1FDnlWHWiVgNWgoyjQmCaPO7kFXJN3jbp/Pjzj9YdwBEUEQUKkF1OrocqtBQ5MnKjCG9UxPmpTLMjR7Q+Rb9Wzf4+703H9KxpRmM7Qog1yLjjyrntw0PWP7ZJGXZmBQQRqfbrLxyndVrNjlwKhVJdWPTWqraWp/L77aYWd9bStOv4hflHB4wyzc0MA9H23u8trG0j+dvhBb6t28+E1lyvUT19tQ6+Tj9XWExEj8Xi7a2MATi3fyf19s55Z3N3TrO98ZB/I9jhmEpBk15FgN5Fr0DMi3ohLAExRx+cNUNHlZX9NKfpq+w3NcYfewdHtjt57HI5XNmzczd+5c+vTpg8lkIicnh4kTJ/LRRx91WHfr1q1MmzYNi8VCVlYWv/71r7Hb7R3WkySJv//975SWlmIwGDjmmGOYP3/+T3E6CgoKCgo/MQcsrJYsWcLll19Ojx49OPfcc7HZbDzyyCPU1tbS0NDAgw8+yJdffsmvf/3rgzHeI57YBHhtTSshUUaMyDT7Qjy7dBfzV1Z3OvHyhETUqmhqE4BBoyIkSmyqc+71mImTqWlD8wlHIny8sQG7J0iDM8C2BhdatbDXt9iJk8+DQSy1KN2oodUfBkEmAmhUoNe2PXoy+IIRKu1evi5vwuYMHJArW6LDYrpRi0WvRq9V0+oL71XUjCnNpm+uBV8oggAExQhhSSYckQmIMi3+EMt22FPew9g9OG90MWP7ZNM7x8KwonQuHV9KWa7lgCOCsf2fNCiPvDRDt84nkfx0AxP75ZCfZkCnEuI9iXwhEVcgzLSh+UwekMe0ofkcU5TOtKH5jCnNQqsS+GzLHqqbfXyyseGwT5prHD4yTDqmDckHZJo9Qb7Z2YxGBc2eENtt7iTnvaAYob7VHx93hd1Dg9Of9H3INOk6HCciyezYi5iM1Q02ukPkWnVxU4dU64VFiV1NXgLhCDZXkGZvkKJMIwML0nAHRFTQJuY7F2jd5UC/xzqNmlElmYQlmSyzDldQJCjKSET/YIQjMmuqWpKe49gLjX0R+0ciVVVVuN1uLr74Yh5//HH+8pe/AHDmmWfy3HPPxderra1l4sSJ7Ny5k/vvv58bb7yRhQsXcsoppxAKhZL2efvtt/PnP/+ZU045hSeeeIJevXpxwQUX8N///vcnPTcFBQUFhUPPfqUCrl+/ntdff5358+dTX19Pfn4+l19+OfPmzWPYsGFJ6954440YDAZuvPHGgzLgI53YBDgUkbA564nIMoIMzd4gb6+uSVlTUZxlIteip6IxOhkRAEElkGHSdTBJ6Ir6Vh+fbLJR1ezHH4qmA5blmrG5gpw0KI+CdONP2suqOMtEhlHLzkY3GpWAXqNlYL6FXU0+wpEIgbAUr5MJRSQytVrSjdoDiljFBMzy8iZqHF6qHV7UKgGdRo0/FEGjhvU1rZ2aiEwfVkCTJ0iLL8QeVxAAddtwhrRF/jqrW4ml9cmyzKrdLYzunRlPDTuQRquJ9tfrqlupdviwOQMsL28izahBluUua31i0bRmb4iemUYcvhC+sASAJEGtw8dXbRbysRqrmAV7Xpoef1jEpFWzs9HDqt2Ow5r6Fbu/mxpcRKRok2NvUKSyyYsYId4HLs+qxx8SqW728dH6etbXtDJtaH78/LQqgZMG5TG6dxYAq6scrKl04G27LjIQEmWW7bAzunfq9LyYIcVTS3biCYpkmLSdCmZZkAlFJCQpaufe0Bpoi/wYsBo0VDVHECMyPdL0h9V1LyYW/7d1D2a9hmZPEJUAGgFEGSQZDFoVKpVAYcaP5/pTOVweambMmMGMGTOSlv3xj3/kuOOO4x//+AdXXHEFAPfffz9er5c1a9bEU93HjBnDKaecwksvvRRfr66ujkcffZQ//OEPPPnkkwBcfvnlTJo0iZtuuom5c+eijoWPFRQUFBSOevZLWB177LEYjUZmzZrFvHnzOOWUU7p0AxwyZAgnnHDCfg/yaKMs18IfpvSlxuFlW4MbEDDp1AwpTKPC7mXRxoYkO/GyXAujemexobYVq0ogIsmMKc3mguN7dasRZ4Xdw6OfbWf7HjfNnhAGrQqdRiAiSWyud9G/h7XTyeGhpCzXwoT+uexu9mI1aKhs8hGKyAzMt+LwhQiKHgKhqPueVi3gDojkWKQDiljF0pk21zsJiFEbd6NWhRiRaHQHCIQjVDXvZnm5nT9M6dvh+o4pzWL5Djt1rX5UQrSRsUy02bJKpdprtGnp9kaeWlKBKxBmZaWDgnRj0jFiNUtdmRYkiiQgXp/jDoTRqlWc2DeH5eVNiJJEOCLz1qqaeC+wWM1QIsVZpniNUX2rH3dApDziwhOUiEgygbDEil3N7LJ7cAeiduDVDh+rdjvYXO8kJMrIcgSTbv8F74GSeE1+O7EPizY28PK3u3H6w2hU0T5xvbKi98UbDNPkDeENiLgD0Ugw0KGGrCD9R3F958whrNrtYLvNzQ6bG5srwLiybGyuYJciIfZSRJTkH8PN7ahx+PAGI2jVKoKSRCgiY5B+NJYZ3TuLHLOe8kb3PvWvO1R4AiJ7XAE0KhX+cASIth+Q2pow98wwYnMFWbq9kfrWqACPCd7VVS1o1cIh6QV2uFCr1RQXF7Nq1ar4snfffZczzjgjqX745JNPpn///rz11ltxYfXBBx8QDof5/e9/H19PEAR+97vfccEFF/Ddd99x4okn/nQno6CgoKBwSNkvYfXCCy8wZ84cLJbuTdSnTJnClClT9udQRy1luRYePGc4q3Y7sLuDrN7t4NuKZpz+MIFwpENT1lyrDpNeg0GjIiBKTB2U1y1RBVEnwXW1rUQiEp5AGE8QVIKASa9heHE6F43tfdjeHhekGwiEJRqcHtQCDC9OJ9Ok59NNDRRlGNnV5AUgIstoAFGKCoUGZ4CCdAPAfjnSaVQqtGqBFp/YFqkS0KqF+OQ3lnLVft8xMbh9jxudWqDVF8ag1VCWZ+bMEYV7dURbur0RuzuIQSNgdwdZtsMev4/deavf3v1veHFGvD6n3hlALcAX4QgWnQZQkWPRsbnOBQKMK8umwu6NW3rbnAH0GhUatcAFx/fiV2NKqLB7+GRjAyFRQgAkohGI40oyWb27hbpWPzv2uMkw6mh0BdGq1RRnGrG7Q5RkG+MRngNlX2zdUzkiDuuZTnGmiWZdEIcnhCzDpnoXA3pYGViQxwfr6tCqVXiDIk2eEEWZJob1TKeuxZ8yHTNV3yubK7hXIZ1oHNNVpCYSkZAkGaNWhUqlojjLhM0VRKsS2NrgIhyRKc40MXFA7kF5CbK/tvk1Dh/uoEh6mx28JMnkmHXYpCB5Bg2tfpGddg8Q7bMXS0Wd1D+XaUPz48/ep5tsR4ST5P7i9Xrx+/04nU4+/PBDPvnkE8477zwgGoVqbGxM6Xw7ZswYFi1aFP957dq1mM1mBg0a1GG92OeKsFJQUFD4+bBfwuqSSy45yMP4+RGb2MQmoqsqHTS6AoRECac/RLXDlzQJi9b32Khr9dM317KPE1ghbriAAGqVQJpBS0SS2WX37tN4D8VkKN2oRZQkPAGRTXVOZGhL3Yr2jLIatbj8YSKSTEiUWF3Vwu5mL65A1MmuNMfcpRtfKuzuIE6/CERTKyMRGaNWjTcYNXAoyNDh8ou89n0V/XtYk9zYCtINyDL429LC+uSaiERg5S4HNS2+uCV8qohTllmPWoBQREYtQKb5x/qd9m/161t9LN3emHTN24uvWDSvwRlAI4AvLOENipj1auyeIBV2D0ExgkGrZvG2RjLNOiKSTEG6gSZPkIgkE45IPP6/8nj0rE+uhfW1reTq1DS6Q5h1GirsXuzuQFyE+sMiggC92qJmPTON8XqxA31WOrOObx+pi/07lSAtzjIxIN/Kdhv4giKiJNPqC+MNiRxTlM62Bhfralsx6dSUZJu5dHwpxVkmhhenIwhCSvHSPirWHRv8vdXOVdg9fLrJhlqlwqBVk27SMqwwnbw0PTv2eOiRpscTjKSMou0viU1+O3tOuzqfgjQDdS3RlEqLQcO0oYWsqWqh1uEnIMqUZJlocAWobfHTv4c16Zx1GjXDi47udECAG264gWeffRYAlUrF7Nmz46l8MSOmgoKCDtsVFBTgcDgIBoPo9XoaGhro0aNHh/Tm2Lb19fWdjiEYDBIMBuM/u1yuAzspBQUFBYVDzn4Jq1deeaXLzwVBwGAwUFRUxMiRI9Hr925d/HMiVdQh9ha41R+Ov0FvPwmL/e31h0VWVkaL5rszMRlTmkVJtpkde9wUpBtwBSKEIhI9M4xJBfWdTYYPZXPg4iwTaUYNtS1+8tMNeIIRQqJEtkWPJxB1qnN4wwDIskSD049RqyHNEJ3sh8UILn+409qezib5VqMGi1+NOxhBalvmCYqoBNCpBWSiEaWFGxr4XG1jeHFGknjLMGnRqmBXs4+tNjeSJLOlwQWyTFFbylmqiWOuVUeu1UAoEqFfDyszhiVPvgozDFTYPfhDMs8vqyQ/3dBh8hsSI/FeSqN7ZyHLMpvrnLT4w0gyIMt4g1GDDUmWCYkyeg2o2jo8Of1hmj1BJFnGH45g1ERTujbVOZk8II/JA3L5ZGMDDW01ZEExQn66gT3OAEFRQozIgECuVc+wnul8tcMeN3hIZWW+r89KolBaXdXCoo0NDOuZnlT/hACtvnDcfr+9eInVrC3a2MCijQ0EwxFkmbjz4Q2nDYgbT8ReUiQ+4+1fXKT6Dkzqn7vXc2nfiwxIEssxR0BV2wsPo0ZFKCLx1upaxIiEViVQmGlkW4MLo1bVaf1fd0l04sy1Ru/ZvgicaMQ2hy31TvwhkUZXkG02d9ySPihG2NnooTDTwJSBeUkpzYeqZcPh4Nprr2XOnDnU19fz1ltvEYlE4qYUfn/092mqv2sGgyG+jl6vj/+/q/U644EHHuDuu+8+4HNRUDia+Oyzz5g2bVr8Z41GQ0lJCRdddBG33XYbOl1Hs6EjkWAwyF//+ldeffVVWlpaOOaYY7j33ns55ZRT9rrt5s2bueuuu1izZg02mw2TycTgwYO56aabmDlzZtK6a9as4fbbb+fbb79FlmVOOOEE/v73vzNixIik9erq6rjiiitYvnw5RUVFPPTQQx329d5773HVVVdRXl5Oenrq+n5JkujRowc33XQTN998875dlF8I+x2xir2Ba59Ln7hcEATS0tK49dZbf1E3oP3E0ahTYzVo2OOK1irkWPQdejWtrHSws828YnOdH29wd7ebh8ZqumJvqTUqAX84Eo+syLLcpXDa18LzfYlYxAr8E8fm8IXaal+EaDPeiITcVqKSadLRN89CozuIWgCtRo3USb1GZ4KwOMtEabaZPa4AcjASX1+SQZYhJEs4vSFMeg0WvZqgKLHd5o5P8BucfhrdQZo8wWhUTYhuK0oSagH2uIIphfHS7Y08v6wSVyCMXqNiesI9jo11+x43ja4AA/Kt1Lb4GdZTl+Ts9+kmG+GInNTPq8bhIzctOhHzhSI4AyJpJi1OXwhP2/m5A9HoXG6anpMG5LG6ugWNL4QvGMEXljAKAtmW6B+kyQPyGNc3h8822+iZYaDVJ5Jh1DIg34o7KBKRZIb2TCM/zcBbq2pYXdUSj2RlmXUEwhGmDszba/1RZyRG7mzOAN9VNLN6t4NwRGZUSSbLdzbh8ocJR6JRTog21hUEoYPokGUZtUrAE4ygVQvkpxuS1omlbbb/TraPVB6o+YIsy9Q4fHy6yRbvVxWrdQtHJBrdIbLMOlr9IvaqFgLhCNkmLS2+cDSdsM1174Wvd7HN5trnCG2MxCa/UbdC/T4JnFiErckbIhKREaXodyMckahx+Ai09Q+rdvhYtsNOjkWXFFk0aNUcU5SeJLiORgYOHMjAgQMBmDdvHqeeeiozZ85kxYoVGI3R65kYTYoRCAQA4usYjcZurZeKW2+9leuvvz7+s8vlori4eD/PSEHh6GD9+vUA/OMf/yA3Nxefz8fbb7/N3XffTTAY5IEHHjjMI+wel1xyCe+88w7XXnst/fr146WXXmLGjBksWbJkr+m/ie6khYWF+Hw+3n33Xc4880yeffbZeA3nDz/8wIknnkhxcTF33nknkiTx9NNPM2nSJFauXMmAAQPi+7z44oupq6vjoYce4ptvvmHu3Lls27aN3r17A9HfSTfeeCP33ntvp6IKYOXKlTQ1NXH66acf+EX6mbJfwmrdunVcfPHFZGdn84c//IG+ffsCUF5ezlNPPUVraytPPvkke/bs4YknnuDWW2/FarXyu9/97qAO/kil/cQRIMOo5bQh+Wyqc6JVq1hf42RMqSdh8hHtoSRJMhE5ao+d2INnb0wekEdxlinpzXns33ubNO7Lm+b9iW7FxrZqt4NlO+yIbbUmQ3tmsLGulc11UYe3NKOWP07ty5jSbFbtdvDOmhpavGF6ZqSu7ensvMpyLdxw2gAe/GRr9HiR6DWNIQH+cARBAC8QCEUw6tS8uaqGxVsb8QbDeAIiaqJiT2jbViAaVSzM+DEtLpENtU7cAZG+uWZ2N/tp9vxouxwb69CCNL50BeL1TzsaPZRmm5Pu06iSTFZXtbCxzklxlikuFJ3+MCqVQEnb+jEXydjYzPqogLe5g+RZo2/JI5JMUJTJtugozDDF1z9rRCHra1pp9UXTLSf2z43fI4hGeWocPiqbvXgDYfxtDo4aAQSVKm6Ksj9RicRo03cVzfHzDUckvt7ZhFoVjbq5/GGMWjV2d5CNdc6kyXrMsGVdbStiRCIvzcBpQ/Lj68Q+b3AFKEgzcO7o4vh3srrZx85GT1KksjjLhFYlsHxnEwVphm6fV+L3odUboqbFR0iU4mK9T64Fo1ZN7xwTnqCIAKQbNLT6wjT7wqgFAQmQhajo9wREdjd59zuNLuYAWQ3kWPUpn9POzqPG4aPBGcDlj47T33YeEQncfpFQwpcoJEajqDUOH31yLYTECLubfUiSTIYp2hPtaBZW7ZkzZw5XXnklO3bsiKfxperN2NDQQFZWVjxKVVBQwJIlS+IvGhPXAygsLOz0mHq9/heX7aFwYKh0JnLOuqXDsqOJDRs2YDAYuPrqq+OOmZdccgklJSW8+eabR4WwWrlyJf/97395+OGH447Y8+bNY+jQodx88818++23XW7fXXfSv/zlLxiNRr777juys7MBuOiii+jfvz+33XYb7777LhCNjC9evJilS5cyceJErrrqKr799ls+++wzrrzySgAeeeQR0tPTufzyy7sc26JFiygpKWHIkCH7fmES8Hq9mM3mA9rHkcp+9bH6v//7P3r06MH//vc/zj77bIYNG8awYcOYPXs2//vf/8jNzeU///kPs2bN4osvvmDs2LE8/fTTe92vx+PhzjvvZNq0aWRlZSEIAi+99FK3x9Xa2soVV1xBbm4uZrOZKVOm8MMPP+zPKR4QsYnjCWXZ5Kcb4j1hIDrRLcs1d+g/NKY0mxFFGWSadeRa9QTC0j6n0yT2r0n8996E0740FT2QRreNriCtvjCjSjLJMOmZPCCPh84Zzu+n9GXmMYWccUwhtKWz5acZEBDQa1RYDFH9374BaWfnVWH3sLKymaomLxFJpu3SoyIqjAQBciw6irPMjCrJpHeOmeHFGXiDIs3eIPWtQbzBMJGEsWvVAha9mkEFadw2Y1BKY5FjitKxGjTsbvZjNWiSrPJjY7W5g/TNtTAwP42CDANGrTp2yikjOc8v2wXADacN4OqT+nH9qQP4w5S+1Lb4EKX4piBEa8KyzDpOGpTHpeNLKc02o1arMOvVDMi3Isty/PpNHpDHX2cO5uJxJfx15mAmD8ijLNfC+aN7cf7oXvHnRi0IBCJRUaUSopPtkmwj544uPuCU0RyLjgyTlq02NxlGLUaduk3ICuRa9OSnG4hIMg3OAEu2NSb1R6px+GhwBaL3FAFJkjmmKCM+npihi9MXYl1tKzZXgN9O7MOAfCuCEI0c69QqbM5A/Bn2hqJNcL0hkRqHr1vNbmPfh3yrnh2NbmyuIA5fGG8ogisgkmvRodeqOee4omhaqAB1zkBUpKoECjMM6NWq+DMakUGMyPudRhf7Ll85qYy/zhzSbVfRWA+qTzY2xOsQM00adFoVRp0Kb0hMcj2M/dMfjmDQqNhmc9PqCyMI0OoLdav/3tFELGXP6XTSs2dPcnNzWb16dYf1Vq5cmZSCM2LECHw+H1u3bk1ab8WKFfHPFRQOFoJGi3ngiUn/CRrt4R7WPrF+/XqGDBmS1IZAp9NRWFiI03l0/F555513UKvVcQEE0fTfyy67jO+++46ampp93mfMnbS1tTW+bPny5Zx88slxUQXRlzmTJk3i448/xuOJ/v0KBKL9QTMzM4FoZllGRgY+XzSjo66ujgcffJDHH3+8S4dvgIULF3L66aezZMkSBEHg/fff77DOG2+8gSAIfPfddwDcddddCILAli1buOCCC8jMzPxZm/bsV8RqwYIF3H///Sk/EwSBM888kzvuuIN///vfqFQqzjnnHG6//fa97repqYl77rmHXr16MXz4cJYuXdrtMUmSxOmnn8769eu56aabyMnJ4emnn2by5MmsWbOGfv36dXtfB4OyXAszhhXEXci0KoEKuwebK8AeV4DhxRkdXMluOG0AtS3++NvNA621SEzX21sxfqIrWlfsTx1FLHpQ2eyNp6zFLKVj1+nRz7bzXWUzX2yxUZZnwR+KsLvJR65VR6svWmMVsypPjJS1P6/YJHF9bSvVLf648Mg0aTDrtciSjCcUbXhakGFgQr8cXv++Olo/RTRi0CNdzx6XTCQik2vWYtRpGJhvxWLQ0jvHHDdXaM/kAXk0OP2s3t1CSbYpfu6xaxuL0izbYWerzYXLH+a4kkzKGz289n1VXNgmRnJikbjEhq9LtzeiVaspaYtQysiYdBq8QZFlO5oIihI3nDogqc4oP82QVMM0oX8uY0qzmDwgLy5Y25tGAGRZdJi0agKihFmnoV8PS0qb+n0hdo+qHdF+ZhP75yHL0fMa2taSIE+vx+VX0+oNERQlnL5kw5fiLBNWvYZt3qgjoFajapeWLCAQjQIl2gZE68gieAIiVoOWQYXR6FRiKu76mlYe/Xw7+enGvUZlE/tqiaKU9FlYlKhpM3jITzPQ6Api0WkIiRL+UIR0o4Zsi57++Va+Lm+KpsQSjV3HaiwT78e+1EntqxNgTBx+ub0Ro1aNSacmy6zD4Y2mk2rVAhGNQKgtZRGiQl4Cqhw+dJpohZ87IJJl3rf+e0cSjY2N5OUlP9vhcJhXXnkFo9HI4MGDATjnnHN4+eWXqampiafnffnll+zYsYPrrrsuvu1ZZ53Fddddx9NPPx03v5BlmX/961/07NmTcePG/URnpqBw5BMKhdi+fTu//vWvk5bX19ezZcsWJk2adMDHCIfD3RZoWVlZexUaqVi7di39+/cnLS0taXnMDXTdunXdSuvtyp0UounIqdKJTSYToVCITZs2MXbsWDIzMykrK+P+++/n/vvv59tvv2XdunU88cQTANx8881Mnz6diRMndjkem83G2rVrueeee5g8eTLFxcW8/vrrnH322Unrvf7665SVlXVoszR37lz69evH/fff/7NqydGe/RJWkiSxffv2Tj/ftm0bkvTjJEOv18eLdbuioKCAhoYG8vPzWb16NaNHj+72mN555x2+/fZb3n77bebMmQPAueeeS//+/bnzzjt54403ur2vg0XixL++1c/ibY2cNCCPTQ0uJiZMlBPXPxjpM+3T9WK1Hp1Nzva1Zqq7jmkxYtEDg0aFLyRi0auTasxi0QeDRoUsQ12LH71GlVQnIst0mvaXql4szaAhIskYdBqC4QhZ5mitSTgiU5ZjZlODi0EFaby7ppZtDS4kGfQageIsEzlWA5kmLbubffGIV4s/xKYGFysqmzutgamwe1hf46TBGWBtdSvf73J0MKZY3dYrSa2K9p1atsNOSJRweELscQW44dQBSYI8lXiNpXsBZJq1BMIS1c1+dBoVahVJ9WL5bbVZDc5A0uTZ5grEm+a+taqGBlfUyl0QhHht3vDiDPQaNbNG9OSbXc30zbV0u7daV8QMHRpdARo90SimXqum0RWMv3Q4d1QxH66rp7zRjSTLVLf4Mek18WtRlvtjM2erQYMrIGJzBeLHGFOaxfDiDGzOAPnpBkb3zmJlpQObK8DYPtmUN3o4oU82F44toSzXwsrKZsKRqODxhSPssnspzTEnpeOm+p4kCuY9Tj8+ZzAadQNyrDqmDsxjaJsxR7XDR1CM9m1TCQACFoOGYT0zaHAGqHb4SNdrcPpF3l5dw/Id9qT+ZAfTVCaRRHEo8KNt/7iybLItOt5YUU1di59Msw6HJ4QgQFCM1rapgfy06HfL5RcRJemgPCOHiyuvvBKXy8XEiRPp2bMnNpuN119/nW3btvHoo4/GW4zcdtttvP3220yZMoVrrrkGj8fDww8/zLBhw7j00kvj+ysqKuLaa6/l4YcfJhwOM3r0aBYsWMDy5ct5/fXXlebACgoJbNmyhXA4TGlpKU1NTYTDYTZs2MCf//xn1Go199577wEf45tvvul2+5/Kysp4DdK+0NDQ0KlrKHTtBppIV+6kAAMGDOD7778nEonEf5eEQqF4RLyuri6+7nPPPcecOXP473//C0QNesaPH8+3337L+++/3yGqnopFixZhMBiYOnUqgiBw0UUX8Y9//AOn0xmvy7Lb7Xz++ecpgynDhw8/LHPxn5r9ElZnnnkmTz/9NH379uXyyy+Pi6ZAIMDzzz/Pv/71ryRV/d1338XrsLpCr9eTn5+/1/VS8c4779CjRw9mz54dX5abm8u5557La6+9Fre//Snw+Xw0NTWh1+vJ1uvp2ctKTbqe9TWt2NxBBrQ17E3FwbA9j01ccyw6ttvcNDj96DTqlJOz9tGDIYUZDMhPth9vz74LwGj0IChK+IIiG+ucPLVkJw3OaHrNdpsnalTQ9ma8wBpNAxQjMoMLrXGr7PU1rXuNlMVqZSqbvKgFUAsCxxSnM+e4YmQ52l+nosnLgB5WZBm21Ltoq8cnIEYd9s4cUYgsw0fr68m16NjR6MHhDceFXyx9rP01iIm6HIuO8j2eJGOKslwLCzc0sLnOiTcoIsmgUYHRpMNi0KJVCexu+rF5dFfitb24rXH4eGpJORV2L55A1HwiVi/m9IfJTzeQYdSiVQvxyXOsOe1XO+ysrmpBkiScfhG9Niqq/G11Z1q1QEWTF+Soq+LB6E9UnGWKR3PUgszuJh96rYoeaQZ0GlW83qumTYhIERmVqmP/3Vgz51id1WvfV9HoCnJMUfQX/LmjihGEaLPalZXNfLLJlhQxjomq6L6yKckys8XmwqBWERIllmxvZGSvzGg9215qC+ta/PRIM+L0hwlHZEw6Ncf2ymT6sIL4c1GWY6bFF+L40ix650Zzy7c2uNhY5yTLpCMUjuDwhZHl6P3ZVB+9VyfupUdWV3Tn90lZbrSptlGnZpfdg80VpFeWielt9WoF6cb48yUI0V5zghB9fiMy2FxBssxapg7KO+pNK8477zz+85//8Mwzz9Dc3IzVauW4447joYce4swzz4yvV1xczFdffcX111/PLbfcgk6n4/TTT+fRRx/t8HfmwQcfJDMzk2effZaXXnqJfv368dprr3HBBRf81KenoHBEs2HDBiBaO/SXv/wlvnzy5Ml8/fXXe02dPeOMM7jgggu6/G4NHz6cL774olvj2d/56IG4gSbSlTspwO9//3t+97vfcdlll3HzzTcjSRL33ntvvIYz8ThTp06lurqazZs3U1hYSHFxMZIkcfXVV3PDDTdQUlLCM888w+OPP44sy1x33XVcddVVSeNZtGgRU6ZMiUfJ5s2bxwMPPMA777zDZZddBsCbb76JKIpcdNFFHc6n/f5+ruyXsHr88cepqKjg6quv5sYbb0wq5g2FQowZM4bHH38ciIoto9GY5G50KFi7di0jR47sELYdM2YMzz33HDt27GDYsGGHdAwxFi9e3MHGEkCr1aLR6jAYDbxmMMSLkw1t//ZHVNj9Esdf8hf69y7qMHmrra3lhRdeSNom8b/Ysi17fOzYWMf6iAqNTkd+lpUT+vagxil2mJwlRg+qmr2srW4l3ajj+D5Z++1M1pFoqlqLL0REknF4QjS5g5Q3elAJP5pHFGQY6Z9npabFhxiRSTNq4qJqZWUzhRlGRvTK6LKBatQuOhebK0BRHyPljR6G9cyIp8IlOu41OAPJeWKAWi1QkG6kKNPI+ppo6mFpthm7J0D5nmiqWK/sjo6A8OOb/2qHD6tBg90Tiqc8Lt3eyFuramjxhYnIoFOBWq0i06TFE4rgDUUISzLfVTTHm0fvze47FkqPmYO89n0VK3Y1k59uYGOtC71GwB0QGdZThy8scdKgaBRh2Q57vPmtLMt4giKiKCFKMla1KprapVYRECPkWvUMyLcCJKUmHshzEbtHu5o8CIKAM2ZS4fnxpUONw4coyVj0Glz+MDkWPWkGbYdj56XpMWrVhAWB8j0eah2VWAwaerVF9aYNzefTTba4G+OokkxqWvwdIsZluRb+MLUvj36+PZoSKMiEIzK+UDR1tSsDmLgxSWEada0+DBo1PdIMnDuqOL6OViXw5fbGNlOICDPaBNeGWieD8q18sWUPNlcganUvCGyud1GQZgCB/bIvr7B7WLihnuXlTV32XYPkvlcZRi0TB+QmNcKOprgGeHt1DUWZRjbVuwiGI/hCEUJiBF9IpMkdwh9uIMeij1/Po5Hzzz+f888/v1vrDhkyhM8++2yv66lUKm699VZuvfXWAx2egsLPmpgj4MKFC9HpdOzZs4cHHniANWvWdOlUF2Pr1q0MHTq0y3UyMzM5+eSTD3isoVAIh8ORtCw3Nxe1Wn1AbqCJdOVOKggCV111FTU1NTz88MO8/PLLAIwaNYqbb76Z++67Lx5hj2GxWDj++OPjP7/44ovYbDZuueUW/ve//3HTTTfx2muvIQgCF1xwAQMGDIhH98LhMF988UWSecjAgQMZPXo0r7/+elxYvf7664wdOzZlMKW0tLRb5320s1/CKisri2+++Yb333+fzz77jKqqKgBOPfVUTjvtNGbNmhUXOAaDgeeff/7gjbgTGhoaUuaHJoZeOxNWB7sRY+zL055wOEw4HMbv89LSxfat3mCHBsIAu3fv5s4779zn8WwAPgcu/vt/KcocmvQWuzjLRLClgWUP/B5UGtBoUWm0VBqNrHomg/xMawcR1/7n888/P+UXRhRFnpz/Ma+urMcVBhEVoqBFUGsQ1DoQ9ZiNhrboTdSAoLzRjc0ZJNeqIxxRt0W3yqlq9qNVC5TlWYil5nY2eRtTmsXycjs7Gz24AyLbbW522T1xO++tNjc2VwC7O4BOrcLXVjUiEI1wxQRLrJlsfpqBF76ubNu70CFyEiMxktS+Tm7hhgaCokSPND31ziCiDHqVwBnDC8lLM7Dd5ma7zR1PU+ysbxdEJ8JPLdmJJyhSmmPm3FHRXO1J/XPxhyJxYRcU5STnwZggHd07Kx7tWlnZjEGrQlQJSCERfzhq2eETo0YOLn+Y0b2z6JVlOmj9iSrsHnKtOob0TMfuDqJWCQTDESKijNMXik/onW1No3UaFRlmXVykxvYRi7R6gyKeoIhWLaBWgTsQJtcSjRZurHMmuTHWtvgZ0FbztHR7Y3xMxVkmJg/IY0NtK9ttbsRINOXZ4Q3HGxJ3VluYmEqnVauYNCAXmysYd4FLFPuxSGHiPldXtWBzBfCHJNKMGsSITGmOmd9Pif5hWrXbwb6ko8dqGr+vbMYXjNAzM/qWNFU/u1jfq421Tix6NQGrAbs7yCcbG5IaDI8pzYq/aBjYwxp/0RAMS+wJBdFpBXbu8fLv5bv4dFMDl44vPWrTARUUFA4PGzZsoKSkJMkRb+TIkQwePJinn36ahx9+uNNtA4EAtbW1cSHSGakEUWfEhFIqvv322w4phbHUwYKCgqQ0vBjdcQPtikR30piV+n333ceNN97I5s2bSU9PZ9iwYdx2220A9O/fv9N9uVwubr/9dh555BHMZjPz589nzpw5zJo1K36s119/PX6OX3/9NS6Xq4Nb4bx587jmmmuora0lGAzy/fffJ6UrJtJdQXm0s8/Cyu/3c/vttzNlyhRmz56dlHp3ODmSGjGmelOxL7QEZIrVQocJ7IHud/LgQlZWNndo8HrG4Gzea7UlHwtYXdG9/R5//PFxYZUo2jJUQa6bd/ZetgYEFYJai0arjToYqXWg1jD6/GtxB46nwu5BkmUMGjUbalrxBEQevO1ahhZlkZth6SD2XGHYsrOVRl+EkKQmoygLmyeC2WTAU2Miq0cxy3YI0YgVAml6Nd5QBEmGeqePF76pxKLXxK9RYYaRulZ/PGXPE+gY+YvRWZpkzDGw2RNCq4IeaQbSjFqGF2cyqX9ufDIci2os22FPGZmrsHt4avFO1tW2ohagodWP3R1Eq1bFI3HThxUgyzI2V4BPNjZEDUOEH7dPnFjXOHzxaKIgCAiCgFmrwtmWUqhSRRsFzxhWsE91dZ2RaGSiFgQG5aexq8lD+R43MlDt8PPUkp3MHllEfrqBYT3TqHb4OWlQXjw1DX6MEo0qyaTFG0KtEvAGRSJSNDoUE5PDeqZT1+LH5g4yoigjHo2J1TzFarBiAgIEdGoVshxBlMDhDSHLcpe1hbHPYu0EYtHAWAphjcNHQbqB/j2sSZ8l1me1eEM0yP64+cPM4YVx4RMzbeluX7tYzaJJqyYkSvGG5LIs8+jn2+PnfMOpA6hx+HD5RSRZxuYK4gtF+HyzLf5ywx+KdEhPrW/189bqGrLMOmTA6RcRIzKyHI3wbal38+I3lQecMqqgoLBvRHxOap+4MGlZ0Z9eR206OsxkNmzYEDd4iDFo0CBGjRrFu+++mySsRFHkL3/5C//617/Izs7mtttuo6ysbK8NhFMJos7oqsYqVUphLHVwxIgRLFmyBJfLlWRgcaBuoInupIm0d9n73//+R1FRUZci85577qG0tJQLL4w+L/X19Rx77LHxzwsLC1m3bl3854ULFzJ48OAO1+P888/n+uuvZ/78+fj9frRabVIp0C+RfRZWRqORZ599Nu6OdKRwJDViPFABNKg4K2X/mc4iYd3li+0ObNuijminDukRf3N+TMGB9RJo9kvxt/8x57lss46Z/fZuWAKALCGLQcJi8nULBaNmBpIkR+ufwiIqlUAoFGLDl++zoZvja68PL73pb4St0xhakEZdiw9vMGpQIQiw7ZFfsSXkQ6ONijRJ0KDSaImoNEiCBrVWh9VsxPFeNplWU5KoO+usszq8zYnh3PYdJ6psrHN72dHkx27T4TMa2NYzQFawgEZfBH3QgVH0MrI0lyZ/gOpmb4dnYGWlg91NHiKSTASISGK8J5bdHcIXEnnwnOGU5VpYur0RnUbNiX0z2Gpzp3RWhDaHxgwDWxvcpJu0tPrCGLRqLHoN/dtS8w6WscrKyma+r2zGGxQJizI79rjRqAQCYQmtWkCjFtrsvqPjavaGGJBvTRJVkGxNHxQl0gxaguEIJp2GUESizRuC4rZ0wI11Tob1TGfygDyWbm+k2uFDkiRafSGG9UyL18IdU5SOxaAh6Ilg1KooSDckRZ5SCd2YUD1/dC9G986KR5hiDYMTTWTau33WOHw0e0LkpekxaNUdzB/2p3FxcZaJgjQDdS1+NCoVxVnRvmsNzqhhSTgiUb7HTY80AxeNLSHNqIlGZtMNaFUqIlK0wXB923PVPj21wu5h+Q47e1wBwm1OiDFDRKdfpHe2iXBEPuCUUQUFhV8ONpuNxsbGlKl8p512Gvfddx9bt25l0KBBAPz5z39m69atVFZW4na7GTduXAcXulQcrBqrrlIK58yZwyOPPMJzzz0X72MVDAZ58cUXOf744+PzS5/PR3V1NTk5OeTk5MS37647aSrefPNNVq1axSOPPNKpo+GOHTt48sknWbZsWfzvW48ePdi2bVt8na1btyad/6JFizjjjDM67CsnJ4fp06fz2muvEQgEmDZtWtK5/BLZr1TA4447jk2bNh3ssRwQMUfB9hyORozz5s1j7ty5BAKBeJphMBiM/7yi3ManG2ootGiosjsZW5JG70wdtc0u7K0erj37WPrnd3zDlJeXx9lnn91hf6l+jv070dJyQ4MPY4YJT0Dk24pmijKNyLJ8wEJw4eYmtM3VhMRIUrpdtf3A9lvvCpMZimDQaQiJ4aiTniSzpbarRMq9k5NhQdXWU2pUSRZhSeL7imYiMkhiCFkMExbDhP2pexj5gD3bOi4vKSlhwOiJKc0CbrzxRnbu3Nlhm6tStHdb3Pb/l4jW5V1zzTU8/PDDVNg9vPtDDQ5fOCoEAceXz9HYUk9EpUGt0VGn0/HbT3MYUpyDPyKwZY+fH2QVaWYTwXXp7HIEKcq2UuGT6S2P4pRJ46MNZR0+si169BoVvqCIJuTBGA4xvX8hBWZV3HXoQM1V7O4Qbn+YcNtEPBSRyTBqCIajTbks+qil/ejeWUkpi50ZeMSs6XMsOpo8IQozDFTYffTLs+ALSz9GkZwBtrZZ6m+obY2nEIqRHy3RY8e55uR+vLGimogktzlSps7DS2VoAcTFa0iM4PKLFGcZqXb4EAQhqW5u6fZG7vloC+6AiF6j4rwxxdFeV22fxVJ126cg7u0elOVaOHd0MXZPAE8wQnFmdD8NTj/+cARnW53f+2vrmNQ/l0vHl/LiN5W4/CIaNZh0Glx+kfS23mLta+tirSEWbWzg38t2Eb+ZgFGrIsOUnLapoKCgsDdi9VWpSjZOPfVU7rvvPhYuXMigQYOor6/n+eefZ+fOnWRkZJCRkcG4ceO61bT2YNVYdcXxxx/P3LlzufXWW2lsbKRv3768/PLL7N69m//85z/x9VauXMmUKVO48847ueuuu+LLu+tOumzZMu655x5OPfVUsrOz+f7773nxxReZNm0a11xzTafju+666zjvvPOSooNz5szhrLPOiqcRfvTRR3z88cdANHK3detWnnnmmZT7mzdvXtyN+29/+9v+XbSfEfslrB577DFmzJjB0KFDueSSS9Bo9ms3B5URI0awfPlyJElKUukrVqzAZDJ1mWt6sNFoNFitVqxWa8rP8/p6sJmjE7IBfQWGtvUU2ttEdfTo0bz33nvdHocsy4iiyNbaZu54dw3bHDJyKIJWo0ItCIQjUZe8ucOK+PDDD9nd2IrN4cailUnXCd0SbvXNLgJqE8fkW1ld1YJWLcQngbnmCJmZmfH1Ey34u4Ok1iJJMiC1iaq28xJDXW63N3wRgevaUrdkGQra3tRv2+NmWyS83/t1h+nUOW5/o43h8I/jWVnpoHyPJ9rkWAa1Cvx1W/HXlydts2QdLOnGvtObf8VVc05Lqgv7aoedhRsa2Pnuw6zY+j0fJ6yv0WgQNFpUah1avY5MixmzydAhFXPs2LHcc889KY9ZvW45rhVfE0aNoNEhqDX4NVo0Oj15GRZG9S/gRKtIc2UEvV5PD70elUdPg+jGZDIlFTAn9opLrCtLNA5pdAVZX9OKTq2itsWH3R1ElGTCokT/HhbsnhAj2jkE/mpMCQXpxrihQ2dOiInRpOXlTTy1ZCe9s81Jyxqcfmpbog2j2wu0DbVO3AGR3tlGdjf/2HOt/TOUmIKY6vPOfm9o1Wr65Rlp8kSjcWNKs0nTa3F4w2hU4AuKLNth568zo5OR2PnKMmjUApkmHU5/mNVVLSmFkiyDUafGFRDjFvMje2Vy0Qm9DzhlVEFB4ZdFzBEwVcTqhBNOwGq1smjRIm688Ua+/PJLRo8enRTVsdvtezWu+Cl55ZVX+Mtf/sKrr75KS0sLxxxzDB9//PFee0VB991Je/bsiVqt5uGHH8btdlNaWsq9997L9ddf3+m8fNGiRSxbtowdO3YkLT/jjDO47777eOKJJ5BlmQceeIDp06fHt0lPT2f8+PEp9zlz5kwyMzORJClpfL9U9ksRXXLJJahUKq688kquvvpqevbs2SHVThCE+BuIg01DQwNOp5OysjK02mhX8Tlz5vDOO+/w3nvvxZVzU1MTb7/9NjNnzvzJrNa7Q/uajMXbGpPqJw6G5Tq09STSanGEVFjSs+klBLC7Q/Sw6six6uNvoj0YO7gYdncMFXYPj36+na93NpGfbohbXMcmVolFoqIoJgm17XXNvP5tBbVNLlRSmOOKLXy2voZquwspEsLUow/Ht/UcavGGkMMRRBkkQU3uCbPpYVEzKM+IXpC6jOA5PT5a3D7CoSCSGGZnc5iVlc1xl8AMo5a8NAONLj9yRNzv6+0TBXwJaVurdjvi1/BAooKxOkG7O0AgHEGWo9bjGrUKWdx/IZiTHk0BTUxxa3AG+FxtQwx3FK+iKIIoAn6CXvA47F2ONxUbln1C4+J3Un5WD6wDUgTxgOiLhZUrVyYti32XHnrwQXa+/yY6nQ6tTkeTTk+12YQvIlDVGkKr0xNGjc1oICvNTL07QpVGG0333FrIhWP/3OF4Oo2aXjoP69dvYbHKTnBAQZJpS6YuQoZOYNkOOxV2D1XNXrRqFblWPd6g2JZiZ6Q404jdE4qnXMSI1d3tbo4Kr6E905PE2uqqlnh9UyzStXR7Y7dTA2NROr1GRX2rj0n9c5k8MJf5K6qBNldK84/1CDqNmuFFVpbvbEIAJvTLYXVVC+PKspNSMWPf+d1NXmTAoBEIiFGLea1GpYgqBQWFfeamm27ipptuSvmZVqtNMhVrampKSjez2Wx8++23/Otf/zrk4+wuBoOBhx9+uEvDjcmTJ6fMiOiuO2lZWVm3nEkTmTFjBm63O+Vnt9xyC7fcckuH5QsXLuTUU0/tVKypVCo0Gg0zZ85M+ff/rrvuSorI/dzZb1fA7OzsuCvJweTJJ5+ktbU13kDto48+ora2FoA//elPpKenc+utt/Lyyy8nFRbOmTOHsWPHcumll7JlyxZycnJ4+umniUQiB9WYYn9pL1SiTUkd2JzJTmHQ/TfS3SWxoWzPTCPThuazfEcTy3c2UZBmSHoTXWH3pDS46GoMnmDUQc5i0HQpxDQaDRqNBrPZTIXdwwanB6+xB6ecOJCtNje5RekcY+zD8RoVa2tayTBpCYgSvpBIQIwQkUEtgCHNyll/uh1/WOKisSV7tSUHmL+ymrdX1zCkMI0Kuzfe9FSvEaLpaCYdbn+Ys+55HX8gyMyhuQzuYUoSa//9roLvdthACiOJYaxambIsPXtaPfTLMXDCmJGsC0XTtrSq6IQ7dg179+1PXl4eLW4vrW4fETGMLIYQwyGkvYij2EuBXKuBDKMOUZIIhCWyzDpq5Mhez31v+41RYfcAMoUZBjakEFb7u99EEntwHKz9luVa0Iec1FR2TLVsjwOoabestUcvam++OmUN1yfz32LNu8/w0V72K2h0qDVaZJUWtVaLTq/Hkp7Jidf/K8l2P8b8Dz9l/hvz0ckq0tFQmG3lm3e+wROG3TVu1ofAExaoWmVmyWcWzji2F6U9MmnyRQjbmtgi9SHHaugy3S7dqMUfFnEHRN77oZYxpdn8+oTeVDZ5qWvx0zPTGE89BAiJEVZXtSTZvCf2s4qxstIRjwKGIxJWgw51WKSHVU+rL6zUVikoKBxSBgwYwIMPPkhNTQ0Gg4GLL74YQRC61S9VYd+ZPHkyEyZM6PTzBQsWYLfbmTdv3k84qiOX/RJWS5cuPcjD+JFHHnkkbt8O8N5778XT3y666KJOexmo1WoWLVrETTfdxD//+U/8fj+jR4/mpZdeOiQCcF9oX4sR7aHk79CwNNbsdV+L1fdGexvwBqcfb0iMF/jHiPWzaXQHcfnDTB2YFxd8nY1hZaWDisZoLdLmOieLNjbwp6n9unU9Yq5ssVSjmINbszfEsJ7pDCxIY2NdK75QBBDQqGQyTFp6pBupafF3EIWpjhMTswXpBqwGDRV2L1q1gC8UIShKtPoiqFQCPTMM1LeKVGqyyc7UM3Tk4A520aNP8vDnd9dT1+In26KjV5aZcETmuATxOdruiTunLd7WGL+PD7/0PkWZRu75aDNb6t3kWnXoNWpG9Mqgfw8Lwwst9LRq2F7n4O+LNlLX7CbXKHDZuGJGDYrW7owpzeK43pnYnAEsBg0WvQbnKRfhaGqih1lNvcMVrauLhNESIU0HshhGI4sUpmlQSSJOjw+PLwCRcJJBS+IziiygPoiCLZFwaP8jd35JRYXdk/JZPJCIoE7f0UI+1jT3O033fM5lMYTYlqIaAUJAwOumqtlLSbaZaUPzkyI+L364jC/efKXbY3y/3c+Lt9rolW3ucC3effddLrjgArQ6PSJqIm3mKxVaHWset5KfaUVqM2IRLCZu+8xMSFazyxHEL6k47pzfce7EoRRnmZLq2/bs2cPixYsxGAys3e2keUcjao2OEBqy0kxk6/XYbZCtziRNVdTt81JQUFDYV6ZNm8b06dMZMmQIRUVFTJ06Fbvd3qlZg8KBcfPNN6dcvmLFCjZs2MDf/vY3jj32WCZNmvQTj+zI5PAXR7Vj9+7de13npZde4qWXXuqwPDMzk3//+9/8+9//PvgDOwBWVjrYscfNkMI0NtW5+KHKgT8sERIlju+T1aFhaWf9cg6E2L6fX7Yr3iw1UThBtMZiS72bNGPUanxzvSte1N85MuGIhD8UISBKfLSuPu6+BqlTChOtstunGhVnmVi0sYFlO+w0bGuksslLICwhAyoBMk06ss063AERT0hkZaUj6fxiJAoFrUoAgXhz4ONKMtlucxMIRZCJ1qJVNvnRa9SUZJtQtdmOpxp7nsWANxjBqNEwoV8OhRmmpNSnWDSywu5hfU1r0n2scfgIR5Id16iGTXWt2N15nH5MAd83iGxx6YioMnGEBNb5szirZ8/4vm84dUBSvc3E/lexqM1SPSsQxhOIUJJtxBmICuf8NAMNrgAXj+vN6N5ZSQJ/TpvZQuI9iaWgzbjladSSiFUrM2dED/ItGnbaWqhpbCXDoCLHqOq07q4zi9cKuwdVbhk9hozD5/cTEcNEwlEhSEREJYsghkEKQyRMKBhMqstr9Eo8v2xXygjqgQir/ExryufnrVU17GzoXr+TVKg0Osw6DRFJTkoDrHH4cPk6b/+wN9QaDVMG9kj5WSAQIBQKpYwMVjZCZYptEjl+zlUIgtDBAXHTpk1ccMEFex3bRmDhHSa8Xu9e11VQUFDYH1QqVafzQIWfjmeeeYbXXnuNESNGKPcigf0WVi6Xi6effpolS5bQ2NjIs88+y5gxY3A4HLz00kuceeaZSlgW4tbENleA2hYfoYgcdV1TCUgy7Gz0cExRBqN7ZwHJ0aWDXasQmzwXZxipbvaypqqFY4oyUk76cy06pgzMY0a7NKD2jCnNpiTLzBabC4NGRZMnGO9hA6nTGhOdzlKlGi3bYWfHHjcmnZqQGEGjjjYP1mlUDC3KwB0QGVJg4svtjby9uiZlf58kY4G2mpET++aw1eZGEKKF+S2+MCExavNt0asJSzL1rQGsBg31rT4+2diQNPYah49Wf5iwKLGjxcN7P9TGrc3b09l97JVlohpQqQQikow7EKamxc8uu49tNhd6jZpwpM3COiLxTXlTUpQmleX3sh12mtxBXIGoy115o5eCDD12d4gKuxe1SuCTjQ3IMp1GQ9vfk2lD+yTVylXYPXy7VaQ5pCdbq+O3I/c9RbXG4WPgjEuZfJ6e1dUtZJl0VDt8BMIiLb4wgiDQK8tImkFHmlGDTqMm06BiUA8TizfXMaCHlao2W/T2x77mmms488wzO4i9umYnTa1eDGoJk1omEAhgd3podftwef14fX4G9u8YYY31glKp1Kh0BmQxjCztWxRPUGsJRSTy0ztGVkMH4sSp0rB0e2PKBrwH6vCZm25O6Ty4L/s9kupZFRQUFBQODYq4Tc1+Cava2lomTZpETU0N/fr1Y9u2bXg80XSwrKwsnn32Waqqqnj88ccP6mCPRmocPsKSzEkD8vh2VzO+kEgkokKMyGjVKooyTUlpQtB5k9mDQasvyPpmP2qVgFmvSTp2rywT20MR1AJEZNha70qqwUhFWa6F2cf1xP5VkCZPEL1GoLbFz6K2iXy1w5fSrrkz8biy0kFVs5eIJEeb6apVyLKESoA0o5be2SbWVLWwuroFAZLq01LVyGy1uZNqRrLNOob1TGf1bgf1rX40qqgJRI5Vj0aloldW1GigyRPqIEKKs0yEIxFqWvyoBdixx9tl6mP7+9g+JfPFbypZV+1EqxYwaFXYnAGmDMwj3aCl1R/GrFVjMWi6TMWMCeK+eRYaypswaFX4ghLuQLQXlNWgQa9RxX/uLBq6N0EfE6r51qgoemrxTkaXZnXLzTKRVl/UpQ9kxIiEShDwhyV06mgKR5MnFLf7Ls7SUusMMbI0h549cqlqE7mpIqgjR45k5MiRSctiUUuNN0R6u1rBmNV5JCDSbOgoVIqzTFj1Giwn/pqycRehUasYkGeiT5aBC0cXEAqG2N3YSrZRoIdZQzAYZJethdpmF+m6qHDa0OClYEg+kxKi0RV2D59uspFePIABU+fQN9tAlkFIMl2xOVzUO9zoiODx+dGrJLy+AIFgNIVT0BrYVOc8JMLqyqnRaGP7FyL74mqpCCsFBQUFhV8q+yWsbrrpJtxuN+vWrSMvL69DI7NZs2bF/e9/6cQm+DZ3kAE9rHhCIpvrnIiSiCTL8ZSjA3UA3BuxCZ0nGCEiyYzvm40/LCU1P/3txD689n0Vda3Rif/qKgevfV/FRQlW1Kn2u77GiUoQCIYjBMIRNH6R17+voiDd2Kldc+fiUUajVpFhVOEOilh0Glr8oFdHheCaqhbCkeikXKNWdZqu2F4o1Dh8fLXDTqZJR3GWiUvHl/JUqJwGZxCTVs0pg3tQ3xqg2RtKqvdKFCFluRYm9MujfI+XkBghGI6wbIc9HtXrjpNi+/N+KlROVXM0LSw/3cCMYQXkWHS8+PVuIrKcFO1Itf/Y81Xt8KHXqAiGJXKsOlp8ISRJJiRKqAUt+emGbvWG6uoZbPUFWVPlwBcUqbR7+WZnE8f1zuSGUwfs9dlNfP5CooRRp6La4UenVqFRq1BrQK0SKMk2M6FfDgvW1lNbEXXLy08z7FcUt6t6xfZW5+2FSlmuhVG9syjf40GtAndQpFe2hQZPiI+2uaLNcSMy2WYtv53Yh2NyLYxud77Vy3ZR2+JPsmuPjWnmjGlsHTk+pflKouueSiWQbdbh8ovsbHSjEgSMOjXZFh2pmDt3LqNHj04SarEoXo3dyfzvK6hrdhMJhzCqJEwamZE9LeSZ1YRCIQYUZKR0HjSbzQwdOhSPz0+T00MoGEKOhBEkkXAouV9eV66QCgoKCgoKP2f2S1h9/vnnXHfddQwePJjm5uYOn/fp04eamvbeW79MUk3wH/l8GzsbPfhCEWodPlp9IVbtdhwyYVVh97BwQ0M0etQrky+3N1Kb0BQ1caz9e1j5XG0jKEq4/GG+3LKHPc4AN5yWevJc4/BR7fDhD4uIUrQOSqsS8IcjbdEfdQe75q4YU5rNiKImGlwBCgQBT1DEGxLxhiLgDWHQqLAaNNQ4/GjUUTE3eUAuNQ5f/BwSzycmeN5aVcO62lYEYHWVg1El0dRLf0gkLEpss7k7WMW3L+AHOP2YApaXR1MVcyw6tGrVfrs5Th6QR3GWiVW7o3U8o3v/GP1ZviN6DZB/vIep9p/4fC3Ztoel2+1R4aJVc1xJJuWNHk7ok53Up2lfn7NEURQWZXQaNcFwBFGSsDkD3TJYidfV9cpkoasBpz+MVi2QZtSQbtQytk82/fOtjO6dRY3Dx/e7HAzrqYvblO9PFDdVc90YuVYdahVU2L1kmHQM7ZlsilNh97B6t4NWf4iIFBX75Y0eXAGRGoePkChx6pAeHaKlMfHb4AykFHVdjSmRRleg7ZkWsLuCnDqkB4FwBH84QpZZx/oaJ2NKOxp55OTkdNr1fun2Rj7wbSLDHRWFJp2aPKuBK2cO7hCti41RqxKob/UzZuR4Nm7cyPyVVTyxeCcGjYqAKHH1Sf04b1Qx4XA4LuIikf03PlFQUFBQUDia2S9h5ff7yc3t3OK6M4/8XzKJb3S1ajUGjZpgWCQYkSEgYncfWApPZ7R34AMYUZTBxAG5SRP5GGNKsxhenMHaqhYiMoQiEutqWzsVfrH0OLs7iEw0hdAXipBr1cdtprsrqqDNoOG0AUnpcjZXgPx0AxqVCoc3xO4mH6GIRHGWmSZPiC+27GFjnatTQbOy0sH2PdG3/bIss7nOSV2Ln1ZfCKNOg1YlYHMGEAQhKXqQajJflmth9sievLGimogUNcOQZXm/3RxTHSOWPjqhrSYsdi262v/6mhb+t7URb1BELUBprgV/WGJ4UXLz2/Z0FmVLXJ4oiva4Arj8YSISeAIiVoOmWwYriZHboYXpOHwhGl3RZ753jrnDGHtlmWj2hsgwaqlv9XfqBri3a5sq0hWLshakGxEliQuO79Uhra7G4cMdFMk26whHZCxt57m+xolGLeAOhPm2opmiTGP8u93eMCWxWXbsGsXcBjfWORnWMz3lOa2sbGZXk5eILBORIkhBmTVVLeSl6QlH5A6ptd1lQ62TYFiiJMvEriYfRRlGsiz6eNQ61moBBKYNzcfmCnToswcCAtHmwDE7DkEQ0Ol06HS6TpuiKygoKCgo/BLYL2E1ePBgli1bxpVXXpny8wULFnDsscce0MB+LqSyWi9IM7DLHq1J02sELHoNudZDU5fQlQNfqrHWOHxM6JfDzj1umr0hPIEwZr220/3H0uM21bmISFH3Pp1GxfRhBUwekLdfBhztxcaL31TGHf3cAZGSbIH1NU4a3UGMWjVatapTwREzD3EHwngCIjqNGr1GxZDCNL7f1Yw/JCK31TLVt/pZur0RoNN0vtikXKNSYXf7MWjVfLrJxrSh+Qfk5pgoZDqLaqRaFrPI32X30uQO0i/PTG1rgEyTlqmD8lKK58RjpoqCpXpmY6KoMN1IWJRQqwRkYFQX+08kJnJW7XbEBZUgQK5V32GMsXVj7pAfra9PaVDS3evZPtUu9p2Y0C8qXAszTB22L84yUZBmYI8rgEYtMCDfysD8NDbVRxvuGnRq1IJAOCLHU/3ai+uTBuVRkG7sIOo+3WSj2Ruirq1ur+M5CVFzG0kmJIJGJ8frIdfXOPf7GYs1JLZ7QtEeVFK0fUHMrOLRz7bHo7rDizOY0C+XcERO+m7FXrzYnIF4eqmCgoLC0cKrr77KfffdR0VFBWazmdbWViZPngzsvZXQ0qVLmTJlCkuWLIlvczTyczmP7nDXXXdx9913p2zEfKjYL2F17bXXcvHFF3PMMccwd+5cACRJYufOndx999189913vPvuuwd1oEcr7SdbgiBww2kDGLQxjc8224hIMqXZ5kM2QdmbA1+MxMl0SIxg1mvplWWi0ROkV7apy/EdU5SOXqsmEJZQC0RtxOGguBrGIgkb65zIshyvsRpTmkXfPAv9eli6nGzGoj+nDc5ndXULg/PT8IsR/GGJUSVZDCpMA2Brg4uP1tfHJ4y9skwpJ/JxZ8UsY9TQItNIszeartZZHVCqqFDiMuiYRphqX+2XVdg9cYt8gzZ6zSubfQhETSDWVbd2ed86i7KlemZjx15f08p/V1Vj0KjwBEUc3tA+RZOWbbezrrYVMSJRkm3mD1P6dnpNYu6QOW31RN2N0HQmGGOfNTj9KaNJicQip4lpmgDbGlw0uAJkm/VoNaqk6FHsu7a6qoVwJEKjK9hBNHYnsjmmNIuBBWmsqnSgEmSsBg1iRGbHHg+FGUZG9MpIKZi7E33868zBLNthZ+XuZvwhCU9AjI+rwRXAoFEhy2BzBmjyBONNg2M1kmW5yZb/h7IuVEFBQeFgsm3bNi655BKmTZvGLbfcgsnU8aWaQtfU19fz3HPPMWvWLEaMGHG4h3NEsl/C6qKLLqKqqoo77riD22+/HYg2bJNlGZVKxf3338+sWbMO5jiPalJNTv40tR8zhhUc8gnK3tzeYiTZk5c3IUoSVoOWnplGLh1futfxlWabsWn9OH1hNGqB7TZ3h55D3TF3aE/sDX8slTHdqCXNqOHS8aVx0TWm1NPp+SWmoA0vymhLZ4JVux3IcnQSW+PwsaHWSY5FR/keD8N66mjuxNa7OMtEMBxhY5s4qEmoVUuV1pdqkg/JQmp4cUaHyfak/rmsrGxmwdo6xpRm8qsxJR32n2iR3+gO0beHmcJ0I02eUNxavisx0llkLNXy2LHrW32YdWqCYQmdRp3yPndG3MKcaLrojj3uTq35CzOMcXfIupYAWWZ9tyM0nYmX9ql6J+0lopfqfiamqb61qoblO5vijapjaX5PLYkaktS3VrPN5koy9+hOjVVZroVRJVlsqGlFEASavWF8IQ+7mjwYNGqO653ZQTB3N/r424l96NfDwqebbRg0KnbaPaza7WB07yysBg2Vdg8gkG3VsbXBFY8UJ7qH7k+9m4KCwqFH0BrIOuWqDssUoixduhRJknj88ceT2gF9/vnnh3FUPz0TJ07E7/ej06U2QeqK+vp67r77bnr37q0Iq07Y7z5Wt99+O7/+9a9599132blzJ5IkUVZWxuzZs+nTp8/ed/ALICYKUk1O4KeboCQeJ7GOoiA9+gs3Mf1sdVULTn+YdKOWsBQhy2ze6/6Ls0wUZBgIihF0GjUalUBZrjmpsL+rKEJnJJpu/Ch60vAluBm2P79U554q0rOuupVmb4j1Na1MG5qPViVQ3uhBr1XFa8NSTXprHD6273Hj8IYQgHBE6nBf26/ffpLfvl4qlQ36/JVV3L9wK0FR4n9bbAD8akxJh+se64uVY9Vz6fhSirNMPL9sV7fSxVIZqyzc0MAxRekpxfjS7Y08v6ySFl+0+Wx+umGf6n1i6XVVzV7EiEyPtpqhVNfEqFPH3SF94UhSA+290Zl4ad8EObGusbuiP9EQBaGtzkhIXkeUwKJXxyM/idemuy86cq16zHoNZqDVF4r3NotEJHY3eTtc787EZGJz8tj30e4OEQxLCG1Dt7uDrKyMtoKQAQGZYFii1ReO39/E75uCgsKRiUqrxzryjMM9jCOWxsZoqn9GRkbS8v0RGEczKpXqiHNv9Xq9mLsx3zwaUB3Ixr169eK6667jqaee4plnnuHGG29URFUCsclOWY4ZV0DE5vqxF0yF3cPS7Y3x/yraaq4OJbE6iicW7+T/vtjOHQs28dyyXTy/bBcA04bmk59uIMOkpTjTSEWjl8Xb9nDPR1vitUedIkcN7ALhCO6AyGebbTQ4o6ljsUlrbOIXiwbtbazPL9vF97uasTkD1Dj88fqQ9oIhdi1j17D9z2W5lqReQu3HYnMFQACjVk2fXDNnjijsVPhtqHUSCEmY22ps/KFIl5PORMEaEiPIstxh4j+6dxa/ndiHi8aWxI+7srKFoCiRbdYSFCVW727psO/YJP3KSWX8deYQJg/Iiy9L3FdXxK5NjcPHHQs28e+vd3HHgk3UOHxJ1yx27u6ASN/c6C8/jUpIOq+9EUuvu3R8KcOK0umRZowL2PbXZFL/XEYUZZCbZmBsafZe+6mlui7tr0HivbA5A3xX0czzy3Yxf2UV93y0Of5d2Nt3scLu4cnF5Wysc1KUaYyLw1iaodWgISBKnTYHbv88piJWy9QjzUBpriUqNFUCYQnM+o6GIanEZGJz8sXbGuNGK9saXICMKxAm0xyNTL21upZKuxerQUumSRc3Ztnfei4FBQWFrqirq+Oyyy6jsLAQvV5PaWkpv/vd7wiFQvF1du3axdy5c8nKysJkMjF27FgWLlyYtJ+lS5ciCAJvvfUW9913H0VFRRgMBk466SR27twZX693797ceeedAOTm5iIIAnfddRcAkydP7lBrVFtby6xZszCbzeTl5XHdddd12idwxYoVTJs2jfT0dEwmE5MmTeKbb75JWueuu+5CEAR27tzJJZdcQkZGBunp6Vx66aX4fL4O+3zttdcYM2YMJpOJzMxMJk6c2CGy9sknnzBhwgTMZjNWq5XTTz+dzZs3d33hE65ZYk3Z5MmTGTp0KFu2bGHKlCmYTCZ69uzJ3//+96TtRo+ONha59NJLEQQBQRCSmgTvy7XYsmULF1xwAZmZmZx44ok88sgjCIJAVVVVhzHfeuut6HQ6Wlqic6Hly5czd+5cevXqhV6vp7i4mOuuuw6/v+u5JUBTUxPbtm1Led0PBvsdsYrh8XhoaWlJObHq1avXge7+qKY4y4RWJfDl9kYEojUjsRSeRKe+rmp6DiaJdRTeiIw7ECbXEk17W7XbwbrqVmzOAK2+cNTlT4ZemUZsrlCnDUlj+w1LMv3yLHxX4aAgXU9Fo4eqkJc3V0XToc4dVbxP5g6pTDeG9kxPskOHjk5sAwvS2GZztfUY6jwyFkvPzDBq2dbgptUXjqfPFaR3Hkn40QAgiCCAUafuUlSU5VoYXpzO5nonWrWKTzfZOq2hSjzmmNJM/rfFRrM3jF6jYlTvzE73n8q5cF+fow/W1bPHFUCvUWF3B1m2w97hfsfOfXezn0yTjlMG94jXvCX2auqK9mmwMUfF4rbnP5aiWZxliqfdHQwDlJi4jznyfVfRHH+2Ntc7sTmD5FpT13K1r/26+8PNrKhsJhyRsbuCHN8ni/pWH08uLscTFMm16jltSD7AXgVUexKPFatliqUdVjZ7seg0zB7Zs0N7gVSRsKXbG+PNyTc1uJjYZuLR6g+TadJhcwZp8QTRqVUMLUijrsUXN3MZlJPWof2AgoKCwsGgvr6eMWPG0NrayhVXXMHAgQOpq6vjnXfewefzodPp2LNnD+PGjcPn83H11VeTnZ3Nyy+/zJlnnsk777zD2WefnbTPBx98EJVKxY033ojT6eTvf/87F154IStWrADgscce45VXXuH999/nmWeewWKxcMwxx6Qcn9/v56STTqK6upqrr76awsJCXn31VRYvXtxh3cWLFzN9+nSOO+447rzzTlQqFS+++CJTp05l+fLljBkzJmn9c889l9LSUh544AF++OEH/v3vf5OXl8dDDz0UX+fuu+/mrrvuYty4cdxzzz3odDpWrFjB4sWLOfXUU4GoCcfFF1/MaaedxkMPPYTP5+OZZ57hxBNPZO3atfTu3Xuf70tLSwvTpk1j9uzZnHvuubzzzjv8+c9/ZtiwYUyfPp1BgwZxzz338Ne//pUrrriCCRMmADBu3Lj9uhZz586lX79+3H///ciyzBlnnMHNN9/MW2+9xU033ZS07ltvvcWpp55KZmZ0LvT222/j8/n43e9+R3Z2NitXruSJJ56gtraWt99+u8vzfPLJJ7n77rsPmXnHfgmrQCDA3XffzX/+85+Ufaxi/NL7mZTlWpjQPxebK5CUihNLe+pOTc/BJNHpTEbGatDG095kmSQhU5BmYMVuBzZXCKtB06HPT/v9JjaprXL4CUYkVJKAyxdmU62Tif1z96nJa3dMNxJTBctyzHy5vZHte9wEwhGmDszr0GMotk0sPTMcieANCezY48bmDKRsZNyeyQPy+MPUMj5eX09dix9Rkrts8Bw7XmziXu3wxWuouroGsbS/1btbGNU7s0Ma4MGkwu6hfI+biCTjD0XQqlVkmjumRsSE1qY6Z/x52FjnYnjRvlnMw4+CoL37YGKK5m8n9ung6Le/59f+OLHmz1q1gEalitep5Vr1HaKhsZcgWrXAcSVZ1LX6UasEtCoBCcgx63lvTR0b6pxoVdEeVP5QhAyTrtuCM9U4E88/1lNNlqMidvE2O1qVwIT+uYwpzYoLycTjxL5DFU1e0toaLRdnmdCqhTbDFZkWX5hgRMKgVcfNXFI5Ne7r9d7XWkoFBYVfDrfeeis2m40VK1YwatSo+PJ77rkn/qLywQcfZM+ePSxfvpwTTzwRgN/+9rccc8wxXH/99Zx11lmoVD8mXQUCAdatWxdP68vMzOSaa65h06ZNDB06lFmzZrFu3Tref/995syZ02m/P4DnnnuOHTt28NZbb8UN2n77298yfPjwpPVkWeaqq65iypQpfPLJJ/HslSuvvJIhQ4Zwxx13dIgyHXvssfznP/+J/9zc3Mx//vOfuLDauXMn99xzD2effTbvvPNO0jnGro3H4+Hqq6/m8ssv57nnnot/fvHFFzNgwADuv//+pOXdpb6+nldeeYVf//rXAFx22WWUlJTwn//8h+nTp9OjRw+mT5/OX//6V0444QQuuuiiA7oWw4cP54033khaNnbsWN58880kYbVq1Sp27doVjzACPPTQQxiNP/6tvuKKK+jbty+33XYb1dXVhzWws1/C6ve//z0vv/wys2bNYsKECXEFqdCRMaVZrK9pxeYKdrDO3m5zo1aRZIBwKGnvdCbLMk2eEMN6plOcZWJ9TWtcyPx2Yh9mOnzxSXRn0arYfhNd495eXYM3GCYUkQlFIoQiAT7Z2MDo3lndnijvrRalfX+uFl+05mlIYVpbFMKV8pomRsKW72zCHRA5sW8Oq6taGJhvpV+PrvvwxOzWHb4wje4gVlGivtWPYYk6yeEu8XiJBhPtJ+5d8asxJUmC6lBNWGscPrRqFdlmLe5ghL55lk5T7yYPyIs/CxV2zwFZzLevC9pY59yvXmD7epxEl8OYUKl2+OJ1au1rl6odPpy+EI3uEL6QSKY52ltLlKO1bSU5ZtbWtqISovVVgijhDYqc0Ce7y/Nofz+7cgyM/T9ec2jW8U1FE7ubvZ1a0cfMNF78phKXX+TFbyq5dHwpl44v5f6FW6hs8qJWqwhHZAbmW7vsd9Zd9qeWUkFB4ZeDJEksWLCAmTNnJomqGLEJ+aJFixgzZkxcVAFYLBauuOIKbr31VrZs2cLQoUPjn1166aVJtVKxaMquXbuS1usOixYtoqCggDlz5sSXmUwmrrjiCm6++eb4snXr1lFeXs4dd9zRIchw0kkn8eqrryJJUpI4uuqqZHORCRMm8P777+NyuUhLS2PBggVIksRf//rXpO0Sr80XX3xBa2srv/rVr2hqaop/rlarOf7441myZMk+nW8Mi8WSJJZ0Oh1jxoxh165de932YFwLgPPOO49rr72WiooKysrKAHjzzTfR6/WcddZZ8fUSRZXX68Xv9zNu3DhkWWbt2rVdCqu77rorSaQdbPZLWL333ntcfvnlPPvsswd7PD87OhMI04bm0+D0A0asBk2XBggHezztzSTqWvwp09NqHD6kblr/x/YryzKvfLsbUfrxM51GhTsg7vNEuauUtiSBVN5ErlVH0KrHH5a6bICcGAkrSDOAAFv/v737Dm+qbP8A/j1J9950UKCUvSmUsstQpogiS0CGCCiyFAQBmSKrAiq8oIgCPywgIAgISpmCgAzZAgUKLW1p6d47uX9/9M15myZt0zRtkvb+XBeX9pyT5D4nJ8m5z/M89xObDhOJgLvRqXgYm17qnEmKC20zqQT5MjmSMnJRIAdO3ItBZFIWPuzZQCkJVVdgQpv3uXiXx6ItFbqQmp2PAjngYGmK0QF1NHpeTQsxlETRVVZRWa+ll73YkqTLcT2lVTlUrC+tqqSpVPhvUmyGAhnQpo4DWno5QADE7nX7rkWiQE6QCALqulihjpN1qfuhLgEprWKgYr6yuLRcxKfn4G6BHLkFcpiZ5CG0jCQ0X0bIK5DhTlQm1oWEYnafxujcwBURiVkAAfkFhfPPFe9eqA1tJ8pmjNUM8fHxSEtLKzPZiYiIQEBAgMrypk2biuuLPkfxC2nFDX/FmJzyiIiIQIMGDVTGTzdu3Fjp78ePHwMobCkqSWpqqlLjQ2lx2tnZISwsDBKJBM2aNSvxORWv26tXL7Xr7ezsSnxsaWrXrq2yz46Ojrhz506Zj9XmWPj4+KhsM2zYMHz88cf4+eefsWDBAhAR9u/fj/79+yvt1/Pnz7F48WIcOXJE5T1OTU0tM97KpFViJQgC/Pz8dB1LtVVSgmBmIkW3hg56qbql7gKoaPe0c6FxWH70PtJzCmBrUXialNZqBRReLN6JSv3vpL1A/n97guYWyGFroTrgviIUF6En779EbFoO4jNyYSIADd1tMdy/TomxFk8GgMLS67/8E4XwhKwSx9kUpWghAwTI/pt55uTL8SAmTSwfXnzsS9GxQ9pQvF/utuY4HRqH2LScck+aWxp3ewu09LJDfEae2glzS1LhypZFKut5O1mJY6Baetnr7IK8rASwaFGTon8r/n9CFx+x1Sc1Ox+hRVp1FWOZ6jhboVEtG8Sk5WBMx3rwr+ekdI6dC41TOi9K+vyVVI1xXUgonidmQRCAvAI5CARTqQQJ6bnIyZfjRYr6QbiKxPBFag6ICNHJ2dh+8Rl8XKxhaSaFVFL4vXMvOhVRydkVbmXSpJw8Y6xyyLJS8WLbB0rLPN/bAqlVyV35qwupVKp2eWVODCuXF95BDgoKKrH0uI2N8nepLuJUvO6uXbvg7u6ust7ERLvyCRWJTZtjUbTVScHT0xPdunXDvn37sGDBAvz99994/vy50hg0mUyGV199FUlJSZg3bx6aNGkCa2trREdHY/z48WIs+qLV0R88eDBOnTqFKVOm6DqeGkPfFyBFK6QpqoUVpagAV8/ZEuGJ2aUWrwCUu+YRAHMTKeRyGSxMJXCyNi9xYmJtKYpCnPg3FmlZ+UjOyoeAwoTo8csMTAnMLnFcUvFkQNEVTtPueook5EZECpIyc1FAQJ5MDltzE7FCXPF9LT52SJvWHWdrM9yLSRO7PKobQ6YNRataYmbJZeYrg6KbpKJoiKKAiqIVVZddHktLAMvqvtajsRu8naxw/G6MWPSiaGtM0ePXuvb/Ju+NTMrCrssReJaQATMTqdJzl/T5V1d0Y11IKB7HZYCIIJcDLjamyMonZOcVTu6bWyDHH/di0cHHWW3SOKGLD9aFhCI6ORuO1maITM5GYmYezEykkMkJng4WMJVKdNLKVNFWTMZYxciz0/QdQqlcXV1hZ2eHe/fulbpd3bp1ERoaqrL84cOH4vrKUrduXdy7dw9EpHTTu3g8iq5qdnZ2eOWVV3Ty2r6+vpDL5bh//36JCYridd3c3HT2upoqqRFAl8dixIgRmDp1KkJDQ/Hzzz/DysoKgwYNEtffvXsXjx49ws6dOzF27Fhx+cmTJyv0urqiVbn1RYsW4enTp5g8eTL++ecfxMfHIykpSeUfK5li/EOr2vZV1g1Q3eubSgWxqlvRMtNFK8DZWpjA2cas1LLwRbvm1XG2Quf6znC0NoOJpLDLXGXcNYpPLyzLam5aeBoTgAICIpKysC7kUdkl4v9LcWFsb2WGZp62pXbXU2yblS9Hy9r2aOfjBC9HSzhbmcHDwVJtYlLeUvPqKC5YR/h7o7W3g8qYvYooqTx5ZSueXCgKqBQ9TsVL51dESc+lyfvj61o47qyOk5UY74uULHxz+jEik7JUjp+ixffAP5H4OywRMplM6bkVx7x3Uze09nYoMeZjd2LwOC4D+QVy5BYQzE0l8HCwgrejBYgIBXIgv0CGuPRclbgV+wsArzarBU9HS6TnFCA1Kw/xabnoWN8JdZyt0Ke5u9J+VfSc8nUtu5w8Y6xmkkgkeOONN3D06FFcv35dZb3iWmHAgAG4evUqLl++LK7LzMzE1q1bUa9evVK7ylXUgAED8OLFCxw4cEBclpWVpVIQol27dvD19cWXX36JjAzV36j4+Phyv/Ybb7wBiUSC5cuXq7S8KI5N3759YWdnh5UrVyI/P18nr6spxVxTKSkpSst1eSzeeustSKVS7NmzB/v378drr72mNMeVomWt6HUlEeHrr7/W6PkNstx6w4YNAQA3b95Uqm5SXE2vClgaRbW4yrg7X9LrqSt8kC8juNj8r1qdYp2ider8o3jICbjwOKHUEubFx8t0a+SKqJRsxKfnIjtfVuId9YrsD0CwNjdBTr7yeUYEpGbl4eitF2V2XwTK112vpK6EcWm5cLNTX01NV62TitaMot3MdFFsQHFe6KICX3kUPZZEhJjUbKW5k4hIZ4UQSmuV0vT9KRrvi5QsfH/+mdhVdvGgZkrn2p2oVCRn5cFEIiBTRrgWkYxuDV1VnruslsykzDwQAVZmUuTky9G5vjNGd6qHsw/j8DwpGwAhM1eG7DzlucSKF3dxt7eApYkULjZmaOBmg+sRyYhKzkbjWrZioRJuZWKMVYWVK1ciJCQEgYGBmDx5Mpo2bYqYmBjs378ff/31FxwcHPDpp59iz5496N+/P2bMmAEnJyfs3LkTz549wy+//KJS2EGXJk2ahE2bNmHs2LH4559/4OHhgV27dsHKSvnaQCKRYNu2bejfvz+aN2+OCRMmwMvLC9HR0Th79izs7Oxw9OjRcr12gwYNsHDhQnz++efo1q0bhgwZAnNzc1y7dg2enp5YtWoV7OzssGXLFrzzzjvw8/PDyJEj4erqiufPn+PYsWPo0qULNm3apMtDIvL19YWDgwO+/fZb2NrawtraGgEBAfDx8dHZsXBzc0PPnj2xfv16pKenY8SIEUrrmzRpAl9fX8yZMwfR0dGws7PDL7/8ovF4OoMst7548eIqHxNU3VTlIO/SLipjU3Pw+GUGbC1MVFqVvJ2skJUnQ+jLdMSl5ZRYwlxUZLwMQEjKzENqdj4sTaVaFa/QZH/qu1ijmYctwuIzkZGTj5f/bcWSEfAoLh1h8RkaVWQDNO+uV3xMzvlH8eLFq2KesuLbV2TsUPFYKzyuqcjz6ruCm+L1ihbm6N3UDf71nHT6GSmr4p6m3dcUx/6b049L7SrbqrY9zE0kSMrMg7WZFLVsLdC9WCuOJvvXo7ErTt1/ifScAtSyM0PPpm4gIjR2t4GLjTkSMnJhZiKBpZlUqbS74rmLTukQn5EHV/OSC7xwQsUYqwpeXl64cuUKFi1ahODgYKSlpcHLywv9+/cXk5datWrh0qVLmDdvHjZu3IicnBy0atUKR48excCBAys1PisrK5w+fRrTp0/Hxo0bYWVlhdGjR6N///7o16+f0rY9evTA5cuX8fnnn2PTpk3IyMiAu7s7AgICtB4us3z5cvj4+GDjxo1YuHAhrKys0KpVK7EMOgCMGjUKnp6eWL16NYKCgpCbmwsvLy9069YNEyZMqND+l8bU1BQ7d+7E/Pnz8f7776OgoADbt2+Hj4+PTo/FiBEjcOrUKdja2mLAgAEqMRw9ehQzZszAqlWrYGFhgTfffBPTpk1TKYmvDwJV5sg+I5WWlgZ7e3ukpqZqXV2lLFV5UXsuNA7BV56LF3BjOtZFYCNXnAuNw9bzT+FqU3jRNSXQV6nVQvE4SxMJLoYloJadBVrVdlAbq+K5XGzMkJCRh06+zjjzIA6JmbnIyClAy9r2WDyouU72sfj+9G7qhlvPUxAam45/o1NQQICFiQRNPOww85VGKi0x6o59ZFKW2mOkTtHHp2Tm4XlSFqzMpMiTyTG9d0OM9K9T5utpmuxV5nlS0nlR1dS9n+52FgAgtupWZouVtjEXLe5SvMUKAPZcjcDuK89hKpWgUS1bldcsKybF+RCTmo3EjDw425jhdmSquH1rb3tcD0/Gg5g0NHCzQUKRz7C6Fqs6/y0OYuiT/lbF968x4uNiPOp9ekwvryvLSkXUxtFKy2pPD66y4hXhqys34WFMnzT9DtaudEgxqampsLGxKbGiCFNVvBuULkodl6RoVydTiYAXKYVjV8oqWqDo3nf9eTKkEgHW5qWXhS/e+mVnWXh6+bhYa11mvKz9cbY2g389J/jXc8LxuzFIy8lHWnZ+qZUI1bUUlKe7XtHHn3zwEgVyOQRBipLuUGjSMlHSRfbVZ0l49DJdp8UqFPRdQEVdHKYSAecfxYvdTnWVCOi6qELxyZLVdTl9u0NddPBxLrUaYUkxFZ+YeEKXwrK0Zx7Gi+eRp4MV2tcD/gyNQ3hCJqzMTMTqgMW/X9QdQ57IlzHGGNMtrROr69ev47PPPsP58+eRl5eHkJAQ9OrVCwkJCZg4cSI++uijSum7WJ0U7QZV9AJKk3FB5X0dxRii84/iceZhnNjdrawy1N0auSI2LUe8sC+tC2hhtTwzPE/Kxj8RyUjLLkCBXI5+Ldx1uk8lXZC29LLHoRvRKJATrMykJSaBJc1rpOmFd9HH+zhbw9pciuTMfDT47/gnTV6vOHXJFwBceBSP2LQcRCVnoa6ztU6LgGibbOj6glx57FI2zjyMU5rMV1etaLrqQqlQdLJkbV+zpPXFJyZWTO6rXOyDcPBGFFKy8iEjIE+Wj4M3osWxjOqeW/HeAYVzb8Wk5cDDzgKz+zbWWfdSTtYYY4zVVFolVpcuXUKvXr3g5eWFMWPGYNu2beI6FxcXpKam4rvvvuPESgPqLqAq46JEMe4iX0Zo6m6L6xHJOH43BgNaepR64drBxwm3I1MQFp+ptiy7QtHWLztLE6RlFyCvQIa49DydF65Q7I+659NkPqaSEgpNL7yLtwbsux4JuRywsVD/cdJkjJW65CsyKQv5ckL7Oo64GJaAzNwCpXE0ulDeZKOyuiYq4giLz8DtyJQSpwGoKYpPTJwvK2x1KnreRiZlISkjH3IUVsSUywvHNZbUqqnUhTUrFxGJ2bAxl+JlWg6uhSfppBCKvsfsMcYYY/qkVVmVBQsWoGnTprh//z5Wrlypsr5nz564cuVKhYOrCbydrJBfIEdkSjbsLKTiPEiV9VqKuatiU3Nw5kEclh/9t9Sy5GWVZS+6naLk9IQuPrCzNFG6KNRmn7QptW0qFRCfUfZ8TBUtCa14PABxLqaS9lNRAfJOVKpGx6/4XEeRKdmwMJWiXV1Hrcu1l6S8x7gipeM1eS1Nz7fqzte1cP6pZp62cLAyE8/nouett5MVnGxMIRVQ+E8qwNJMKnb1La7oe5eRK4NMTiACCmRyhMamV/g462JaAcYYY8yYadVide3aNaxatQrm5uZq69V7eXkhNja2wsHVFJbmUphKBGTkytDYyrRSx7q09raHpZkU2Xky5BXIcP9FtkatZCWVZS+qaKtDu7qOyMqTwVQq0WrSWU3ufhftdgQUFjrIlxFMpUKVzg2WVyDD9YjkEvezPNXtirbQFO/Cqcu5q4DytzCExWcolURXjNdTJOalnUOKiW7DEzJhbW6CD3s2UNuNLiw+A3eiUpEvI5WJeGsaxcTERUv7nwuNU6oOOcSvNlKz8pGTL4edpSksTSXYdz0SFx7Fq3TvK9oq6mpjDqkgIDtfBjMTKUJj0/H9+acVamUylDF7jDHGmL5olViZmpqqTFxWVHR0NGxsat6FkDauPktCRk4BujRwQWRytkpJ5ooqOqZCUWHNVCLARCogKrmwRSktu0DsFqhpYYrSXk9xse5qY65S0llTZSUjxZOC1t4O4gTFirE5lU3RElVWMqfJBWdJSY7iny7nrlIoT8JXND5TiYBWte3xICYNR2+/UKo6V9KF+dVnSbgenoSsPBkKZIT/nH2ikogVr2RXWrJaUxS9WVH8/ACAC48SIJVKUMvSDLWdLHH+UTxMpBJEJGbi+N0YTO/VUHyuyKQsWJhKUdvREi/TclAgJwgCYG9pqpMkVtcFQhhjjDFjo1Vi1bFjRxw4cACzZs1SWZeZmYnt27cjMDCworFVe2HxGWJxgpdpOWjt7aC2+EFFnl9xMZZXIFNqBejeyBVWZklIyy5AanY+LoclIjo5u8QLY0VhiviMPJWkpWjLUfGLdQ977S6wykpGir9OQkYu8gpkuPA4ASZS4HZkSqVf3Cli8HWxxvXnyfjzUbzaVhtNLjjLSnK0GQtVVhEBbSshPohNB/C/VkzFPEmJpYzvAQgFcoKcCFIJkJmrOq+Z4jXa13XE9YhkdPZ1Rv9Skn1tGGtxhaLHXzE+kgi4FZUCCxMJEjNyEZeRi7ScApCcIP1vdUXFzZKi5eGlEsDTwRJdG7iIY9l01cqk6wIhjDHGmDHRaozVsmXLcP36dQwcOBC///47AOD27dvYtm0b2rVrh/j4eCxatEingVZHiuIEvRu7wc1OdQJRXTy/4mJM0aqiuIAa0NIDiwc1R6+mbnC3t0D7MsbvmEoFPE/KVikooEjegq88x/fnnwKATroDqRtzVJyiC56pRMCDmDSkZRcgMikTT+Mz8fO151gXElqp43MU5ehP3I9FREImjt+JKfE1yxrTpctuVMXfk5KOgSbHuKT4WnrZw9m6cM4yWwsTxGfklRp3Bx9ntPC0h6WpFDbmpqjnYq22vL/iNeo4Wek8qSpMLv7F1vNPSz0uhqjo+MjniVk4fjcGIf/GQiYj5BbIkZ0ng1xemOiSIMDJ2gymUon4eT4XGo+49BxYmUmQnSdDvkwuHud+LdzRqrZ9lXafZYwxdVJSUjB58mS4urrC2toaPXv2xI0bNzR+/KZNm9C0aVOYm5vDy8sLH3/8MTIzM0t9THBwMARB0Lin1fHjx7F06VKNYyqP6OhoDB8+HA4ODrCzs8PgwYPx9OlTjR4bEhKCiRMnokWLFpBKpahXr16J28rlcqxduxY+Pj6wsLBAq1atsGfPHo1e5/79++jWrRtsbW3Rvn17XL58WWWb9evXo3nz5igoKCjxeTZu3Ah7e3vk5+dr9LrGQqsWq4CAABw/fhwffPABxo4dCwCYPXs2AMDX1xfHjx9Hq1atdBdlNaW4WIpNz0XjWrY6ba0q+vxFL6CKz2czoKUHopOzS7ygV3R3S8suQExqNgTBUqkyXfGWDEXlsmvhSahoQbeS7n4X74LXxMMOd6NT4WhlitCXMliZmcBMKkFsak6ljs9RlKMPfZkOM5PCcXLavqYuu1GVp4tfaS0MRSeojU/PQ2tve3g6/K+IgmL8T0nzJBV/nSWvN8e18CQAUNs9tDK7koXFZ2D7xWe4/yIdrrZmAGBUY7cUx+anvyPwJC4DufkyZOQWwMHSFImZeTAzkYAAOFiaIb9ADitzEzhYFo7XDIvPwL3oFGTnyRCZV1gI5dVmtdDa2xFEJHYRjv7vfG7GckwYY9WLXC7HwIEDcfv2bXzyySdwcXHB5s2b0aNHD/zzzz9o2LBhqY+fN28e1q5di6FDh2LmzJm4f/8+Nm7ciH///RcnTpxQ+5iMjAzMnTsX1tbWGsd5/Phx/Oc//9F5cpWRkYGePXsiNTUVCxYsgKmpKTZs2IDAwEDcunULzs7OpT5+9+7d+Pnnn+Hn5wdPT89St124cCFWr16NSZMmwd/fH4cPH8aoUaMgCAJGjhxZ4uNkMhmGDBkCJycnBAUF4ciRIxg8eDCePHkiTpobFxeH5cuXY9++fTAxKTnNOHbsGPr06QNTU9NSYzU2Ws9j1atXL4SGhuLWrVt4/Pgx5HI5fH190a5duyoZ32LMwuIzcPVZIgBBZxOgqqPJhWpZ2ygmqHW0NkVUcjYcLE0R+jJdLM9cUkvL+UfxiE3NwYXH8ZjdRzdz5CgU7TL2IDYdbnbmMI0RcDsyFXI5ISOnAFJBQFNPi0ofn9PBxwkXHsXjVlQK8mQEd3vtX7O0RLI83dd00fqlaPUKjU3Hk7h0mEolcLAyw+JBzUosT6+o+ldSnJp0E6usrmSKqQbcbM3+W63S3OjGbvm62qBRLVuESGNBBJhKJWhR2wHhCZlo7mmHsPhMNHG3RVh8BtJzCoD/fg1HJmVBRoCLrTly82VwtjFHa29HBDZyxbnQOI2TcMaYYRNMzGHf5W2VZcbiwIEDuHTpEvbv34+hQ4cCAIYPH45GjRphyZIl2L17d4mPjYmJwfr16/HOO+/g//7v/8TljRo1wvTp03H06FEMGjRI5XErVqyAra0tevbsiV9//VXn+1QemzdvxuPHj3H16lX4+/sDAPr3748WLVpg3bp1aqtwF7Vy5Up8//33MDU1xWuvvYZ79+6p3S46Ohrr1q3Dhx9+iE2bNgEA3nvvPQQGBuKTTz7BsGHDIJVK1T728ePHCA0NRUREBOrUqYOxY8fCxcUFly9fRt++fQEUVg7v3r07+vTpU2KsWVlZ+PPPP7Fly5Yyj0tZMjMzy5UYVzatugIW1aZNGwwbNgwjRoxA+/btOakqQ1h8BtadCMXGM0+w6cxj7LseWaljgTQpK+7raiPOi6PoHhUWn4E9VyPw+70YxKbl4F50KiQS4N6LNMSl5eD8o3iExWeodCcDgF2Xw/FPeDKSM/NwOzJFbKXQleKJg389J3Rr5Irajlbo09wdPq7WGNjKQ+cJnTq+rjaY3bcxZvRuiOm9G+r8NTXt1lc8Jk26+JVW/lyRvJqbCMgtkMPFxgzpOQW4F52qszhLok2Z/bIo5lmztzJDM09bTOjiY5QJRAcfJ7T2doDjfwu2vN7aE41q2SI2LRd1nKzQsJYtzEykSuX/vZ2sYGtuggKZHOYmUjR2txWTSq7kx1j1ITGzgEPX0Ur/JGYW+g5LYwcOHECtWrUwZMgQcZmrqyuGDx+Ow4cPIzc3t8THXr58GQUFBSqtLYq/9+7dq/KYx48fY8OGDVi/fn2pLStFjR8/Hv/5z38AAIIgiP8UMjMzMXv2bHh7e8Pc3ByNGzfGl19+qdGcjAcOHIC/v7+YVAFAkyZN0Lt3b+zbt6/Mx3t6emrU+nP48GHk5+dj6tSp4jJBEPDBBx8gKipKbdc+hezswu7ljo6OAAArKytYWloiK6uwSNqNGzcQHByM9evXlxrD6dOnkZubi/79++Pp06cQBAEbNmxQ2e7SpUsQBEHsprh06VIIgoD79+9j1KhRcHR0RNeuXcvc56qkdYsV005kUhZi0nJgYSIBESq9u5omilcc69fCHX/ci0Xoy3TEpeXA29ESz5Oy4ONig5x8WWESlpwttloVr1x2OyoF6Tn5MP1v9yRdK6mV7XZkChIz89C6tgNGd6xbZce0Mgfsl6dbX3liKqvUuuKCOyE9F+YmEiRk5MHBygwtvOx1Gmd549JWdalY5+tqg+HtvcXJphUl2a+FJyEuLRfx6bnILZDhwpMEeNj9r/XUxsIEDlZmkEoEdGvootTqWB2OC2PM+N28eRN+fn6QSJTv+Xfo0AFbt27Fo0eP0LJlS7WPVSRdlpbKN4esrAqnY/nnn39UHjNr1iz07NkTAwYM0ChxAYApU6bgxYsXOHnyJHbt2qW0jojw+uuv4+zZs5g4cSLatGmDEydO4JNPPkF0dLTaxEFBLpfjzp07ePfdd1XWdejQASEhIUhPT4etra1GcZbm5s2bsLa2RtOmTVVeR7G+pGSlUaNGsLe3x9KlSzFjxgzs27cPaWlp8PPzAwDMmDED06ZNQ4MGDUqN4fjx42jXrh1q1aoFAOjSpQuCg4Px0UcfKW0XHBwMW1tbDB48WGn5sGHD0LBhQ6xcuVKjpLUqcWJVxbydrOBhZ4GXaTkQgAp1HdOV4hfFd6NTkZiZhxYedjiWmIlbz1MgCALklIE6Tpa4HpEMAYXd/YqOlYlMysLzpCxx/yxMJGhUCWPHANXEQR8XiFVRYa6yWhQ0qUKoOJ4vUrKQmJGHFv+9kK/MOBXnUFlzpmmjOlSsU4wvLDomCgDOh8bjekQSsvNkEATA1dYCHvaFd6ojk7KQkpUPCxMJ4tLz8Me9WHTwcS6xSydjjOlDTEwMunfvrrLcw8MDAPDixYsSE6vGjRsDAC5evIiePXuKyy9cuACgsPtbUceOHUNISAhu375drhg7deqERo0a4eTJkxgzZozSuiNHjuDMmTNYsWIFFi5cCAD48MMPMWzYMHz99deYNm0afH191T5vUlIScnNzxX0tquj+K/azImJiYlCrVi2VHmZFX6ck1tbW2LJlCyZOnIj169dDKpVizZo1qFu3Lnbv3o0nT57g+PHjZcZw/PhxTJgwQfx77NixmDJlCh4+fIgmTZoAAPLz87Fv3z4MGTJETJAVWrduXWrXUH3ixKqKKbqOlTaIv6qpq/gWnZyN2PRc2JqbIjuvsJUqISMPMjnBwdIU7eo6IjYtV+XCNzY1B+k5BbAyNcHgtl6lzo2la1V5gahpy0pFk6/KShg1SYTKczwVceqiaImmc6bpU9H54YDSJ0fWJXUJMRHhWWImMnMKkJ0vBwGwNitASla+2BUwv0COyJRsuFqbiV0E9f29wxhjRWVnZ8PcXHVMmIWFhbi+JH5+fggICMCaNWvg5eWFnj174sGDB/jggw9gamqq9Ni8vDx89NFHeP/999GsWTOdxX/8+HFIpVLMmDFDafns2bNx4MAB/P7775g2bZraxyri03b/y6MixxkA3n77bfTr1w+hoaHw8fFBrVq1kJWVhXnz5uGLL76AjY0Nli1bhp07d4r//+abb4qPv3fvHp4/f46BAweKy4YPH46ZM2ciODgYn3/+OQDgxIkTSEhIUElgAeD999/Xat+rAidWemBod4jVXbwruhclZ+UiLj0HEYlZMJFKkJMvR06+HGHxmWonby0631Wr2g4GtZ+6pEnXN111a6uM86WyErZbzwu7Y96OTNF6f0ubM01Bn/NRFZ/IuKzJkXVNMc2AiUTA2YdxaOxuAxszE+TJCpNQiQCk5xYgXybDi5TCxMvSvLBqZUauDI2tTPXeSs4Yq5ny8vKQlKQ87trV1RVSqRSWlpZqx1Hl5OQAUO3mV9wvv/yCESNGiN3ppFIpPv74Y/z5558IDQ0Vt9uwYQMSEhKwbNmyiu6OkoiICHh6eqp011N0uYuIiCjxsYp9q8j+a6qixxkoHGPVsWNH8e9Vq1bBzc0NEyZMwI8//ohvv/0WwcHBCA8Px4gRI3D//n2xe+CxY8dQq1YttG/fXny8g4MDBg0ahN27d4uJVXBwMLy8vNCrVy+V1/fx8SnfTlchTqwYAOWLd8VFKxHgaGWOV5vWwrWIJFiZmaBdHUdcf56MJu62KuOYFAUCEjPz1CZd1YkmLT66GndUWXSVsP2vLHtOhfdXk3OossZhaSIsPgPH7sSI3RU1mxy5fM9fUktY0WkG4tNzkJCeh3vRqXCwMsMbbT2RXSBDVFIWpBIBHg6WsDIzwZmHccgrkKFARhjQwgP3YtJ0Pl8eY4xp6tKlS0pd9QDg2bNnqFevHjw8PBATE6PyGMWyskqIe3l54a+//sLjx48RGxuLhg0bwt3dHZ6enmjUqBEAIDU1FStWrMDUqVORlpaGtLQ0AIWlzokI4eHhsLKygpub+m7vlcXJyQnm5uYV2n9NeXh44OzZs+JUKRV9nfDwcKxbtw4hISGQSCTYs2cPpkyZIiZEO3fuxN69e/HZZ58BKGzZ69evn8qN07Fjx2L//v24dOkSWrZsiSNHjmDq1KkqY+4A3SWZlYETK6ak6EWrqUSAqVRAdoEcbb0dkZFXgNOhcSiQyXE3OhWRSVl6H+ekL5rsa02ouKbufKnI/mpyXPWVsBZvqcrOk4mTIxdPArVpUSurJUyx374u1rgTlYKcfDnsLU2QkpUHiSDg65FtxS7GRMCZh3Fo6m5bOIm2VKi0+fIYY4ZBlp2Ol8HzlJbVGr0GUsuKFzzQldatW+PkyZNKy9zd3QEUVpm+cOEC5HK50sX0lStXYGVlJSZHZWnYsKE459X9+/cRExOD8ePHAwCSk5ORkZGBtWvXYu3atSqP9fHxweDBg0stvV5ST4q6devi1KlTKkUmHj58KK4viUQiQcuWLXH9+nWVdVeuXEH9+vV1UrgCKDzO27Ztw4MHD5S6Ql65ckVcXx5z5szB66+/Lha8ePHihVJy5unpKY5xS0lJwaVLl9R2iezXrx9cXV0RHByMgIAAZGVl4Z133inv7umdRomVRCLRqoy6TCYr92OYfhW/aO3d1A0e9pao7WiJq8+SsOtyOPIK5AhPyML2i89ULhwNrZtjZSprX2tColna+aLt/pZ1XPWVsBadP+16RDI6+zqjhZe9yjx02raoKZ6/pJYwxX7fi0mDmYkEAoD0nAI4WRdWayze6nw7MqXUycGZqmvXrmHnzp04e/YswsPD4ezsjI4dO2LFihUqF3UPHjzARx99hL/++gtmZmYYOHAg1q9fD1dXV6Xt5HI5vvzyS2zZsgUxMTFo1KgR5s+fj7ffVp5viLEKIznyE5+rLDMkjo6OeOWVV9SuGzp0KA4cOICDBw+K81glJCRg//79GDRokNK4oLCwMAAosRgEUPjZmzt3LqysrMQxOW5ubjh06JDKtt988w0uX76MPXv2qC0gUZRizqSUlBQ4ODiIywcMGICtW7di06ZNmD9/vrh8w4YNEAQB/fv3L/V5hw4dik8//RTXr18Xu8mFhobizJkzmDNnjtK2Dx8+hJWVFerUqVPqc6ozePBgfPTRR9i8ebM4jxUR4dtvv4WXlxc6d+6s8XOdPXsWx48fF5NHAKhVq5bS3w8ePBDHWIWEhACA2jmuTExM8Pbbb2P37t148OABWrZsiVatWpV7//RNo8Rq8eLFKonVoUOH8O+//6Jv375ilZKHDx8iJCQELVq0wBtvvKHzYFnlUzdHVNELsd1XIvAiNQcu1qY8CF4D1T3RLOt8qQz6SliL7msdJyv0L6Ewi7Ytaornf56UpbYlrGiBkON3YxCVnA0TQcCErvVUqjXWhKS+MqxZswYXL17EsGHD0KpVK8TGxmLTpk3w8/PD33//jRYtWgAAoqKi0L17d9jb22PlypXIyMjAl19+ibt37+Lq1aswMzMTn3PhwoVYvXo1Jk2aBH9/fxw+fBijRo2CIAgqc+4wVpMNHToUHTt2xIQJE3D//n24uLhg8+bNkMlkKuOhevfuDaCwG5rCzJkzkZOTgzZt2iA/Px+7d+/G1atXsXPnTjEBsbKyUnt9+uuvv+Lq1asaXbu2a9cOQGFp8b59+0IqlWLkyJEYNGgQevbsiYULFyI8PBytW7dGSEgIDh8+jFmzZpWaBALA1KlT8f3332PgwIGYM2cOTE1NsX79etSqVQuzZ89W2rZp06YIDAzEuXPnxGV37tzBkSNHAABPnjwRuz0ChS2FigmSa9eujVmzZiEoKAj5+fnw9/fHr7/+igsXLiA4OLjEyYGLk8lkmDVrFj755BOlBG/o0KGYO3cuXF1dERERgbt37yI4OBhA4fiqrl27wt5e/dQtY8eOxTfffIOzZ89izZo1GsVhaDRKrJYuXar099atWxEXF4d79+6plH588OABevXqpbO+oKxqlXZBFpmUhbi0XOQXyBGXngef/04szGquoucLEYnjg6oiudJHstDau7CFqrQEUtsWteLHUl0Lk+L/zz+Kh4OlKdztLdDBx7nE5+OEqnw+/vhj7N69WykxGjFiBFq2bInVq1fjp59+AgCsXLkSmZmZ+Oeff8QLig4dOuDVV1/Fjh07MHnyZACFJZ7XrVuHDz/8ULwz/N577yEwMBCffPIJhg0bpvFFDGPVnVQqxfHjx/HJJ5/gm2++QXZ2Nvz9/bFjxw6Nyoy3bdsWX331FYKDgyGRSNChQwecPn1aZUxXRQ0ZMgTTp0/H3r178dNPP4GIMHLkSEgkEhw5cgSLFy/Gzz//jO3bt6NevXoICgpSSYzUsbW1xblz5/DRRx9hxYoVkMvl6NGjBzZs2KDSEq7OjRs3sGjRIqVlir/HjRsnJlYAsHr1ajg6OuK7777Djh070LBhQ/z0008YNWqUxsfhu+++Q1JSEubNU+5++v777+PZs2dYv349rK2tsX37djRv3hxEhD/++EOl9a2odu3aoXnz5njw4AFGjx6tcSyGRCAt6hk3bNgQEyZMwIIFC9Su/+KLL7Bjxw48fvy4wgHqQ1paGuzt7ZGamgo7Ozt9h2Mwvjn9GLsuR6CWnRkik3LwVjsvLB7UXGkbfVZrY/pzLjQO2y8+Q76MqrRCXlUpb/e+sPiMSmstOhcah+Arz8UWsTEd6yKwUdk/usbCEL9/FXeoFZOM1qpVC4GBgSqTijZu3Bje3t44deoUAGDz5s348MMP8e+//yqNZdizZw9GjRqFCxculDgRZ3GGeFyYevU+PaaX15VlpSJqo/LFaO3pwZBaqW8d0LXw1QPL3ojVWFevXkVAQIDK92Fxbdu2hZOTE06fPl2F0ZVN0+9g1VIbGoiKioKpqWmJ601NTREVFaXNUzMD1qq2PWwtTPAyLQ/ONmboXuxiTnHxGXzlOb4//xRh8Rl6irR6CYvPwLnQOIM9nmHxGdh+8RnuRqUiKikTj14Wdn+rTop271OMeyrpfVHcXKisLng1oSiKISEivHz5Ei4uLgAKW6Hi4uKUSgUrdOjQATdv3hT/vnnzJqytrcVyy0W3U6xnjLGaYuXKlaUmVdevX8etW7cwduzYKoxKt7SqCtiiRQts3rwZo0aNgpeXl9K6qKgobN68ucTZsZlxKdoCpRjHcS86FS287FXGdRh6efHKUNktdPosLa6pq8+SEJWcjXyZHLFpBcjKk+F2ZIo4uW91aL0snswQkdr3Rd37BUCn5wiPn6pawcHBiI6OxvLlywH8rySxugHuHh4eSEpKQm5urlg6uVatWipjlBWPffHiRYmvm5ubqzTXjKIsNGOMGaMOHTqIN5WKu3fvHv755x+sW7cOHh4eGDFiRBVHpztaJVYbNmxA37590ahRI7z55pvipF+PHz/Gr7/+CiIS+6Iz41X8IrFfi8KSqC287MX1xeexqkl30qsi6TH0ZDUsPgMXHsUjOSsPuQWF5b/lBPxxLwa/3oyu8slzK0vxZKak96X48mvhSeKkybo8R3j8VNV4+PAhPvzwQ3Tq1Anjxo0DAGRnF7bGFq1QpmBhYSFuY25uLv63tO1KsmrVKp1PYMoYY4bowIEDWL58ORo3bow9e/aI35HGSKvEqmvXrrhy5QoWLVqEQ4cOiT8OlpaW6Nu3L5YtW8YtVtVA0YvE6xHJ4hgadXPsADXvTromSU9FW7QMPVm9+iwJsWk5aFPbAbejUmAilSCvgGBnYYL49CydTp6rb8WTGXXvi2rLFgw6MWYli42NxcCBA2Fvb48DBw6IRSYUE1MWbU1SyMnJUdrG0tJSo+3UmT9/Pj7++GPx77S0NHh7e2u5N4wxZriWLl2qUijPWGk9QXCLFi1w6NAhyOVyxMfHAwBcXV3VzpDMjFPRi0RTqYB8GZU4x45CTbqTXlbSo4sWLX0lq5okhIrWqti0HLxMy4GPqzVy8uV4kZKNh7HpsDCVqp08tzoo6X0pvhyAOJ+UISbGABecUSc1NRX9+/dHSkoKLly4oFTlVtGNT9ElsKiYmBg4OTmJrVQeHh44e/asWOWx6HYASq2ea25urra1izHGmOHSOrFSkEgksLCwgI2NDSdV1Uzx8s9/3IstcY6dmqispCcyKQvPk7LgYlM4N5G2rRVVnaxqmhBGJmUhX07o3dgN92LS0NLLAaGx6cixkiEuIxcutuZ4vY1nlcxtpQ8lvS/FlxtyK64xjOGrajk5ORg0aBAePXqEU6dOqQy09vLygqurK65fv67y2KtXr6JNmzbi323atMG2bdvw4MEDpee5cuWKuJ4xxlj1oXUmdP36dfTr1w9WVlZwdnbGn3/+CaBwluzBgwcrTVrGjJevqw0CG7miR2M3TOpeH1MCfbF4UDNMCfTlizD87/iUdBxiU3NwOSwJsak50GJmA71QVwFPHW8nK5hKBNyLSYOHnQUCG7nCVCogKTMP1qYSpGblIzQ2vYqjNzxlnSP6pOl7XVPIZDKMGDECly9fxv79+9GpUye127311lv47bffEBkZKS47ffo0Hj16hGHDhonLBg8eDFNTU2zevFlcRkT49ttv4eXlhc6dO1fezjDGGKtyWrVYXbp0Cb169YKXlxfGjBmDbdu2ietcXFyQmpqK7777Dj169NBVnMwA1KRufrribm+Bll5miM/IU6kMZqjKNa5LAIT//tfbyQr9Wrjj3+hUJGTkQSYHDlyPQlh8BpYMas7njgEy9DF8VW327Nk4cuQIBg0ahKSkJJUiTGPGjAEALFiwAPv370fPnj0xc+ZMZGRkICgoCC1btsSECRPE7WvXro1Zs2YhKCgI+fn58Pf3x6+//ooLFy4gODiYJwdmjLFqRqvEasGCBWjatCn+/vtvpKenKyVWANCzZ0/s3LlTJwEyw8FjMcrH28kKdZyskJhZsW6TVX3cNR3XFZmUhZSsfNR2tERCRh6uhSchPj0P9pZmyJMRMnILkFcgw7/RqbgWnsTnjAGqaQVnynLr1i0AwNGjR3H06FGV9YrEytvbG3/++Sc+/vhjfPrppzAzM8PAgQOxbt06lXFRq1evhqOjI7777jvs2LEDDRs2xE8//YRRo0ZV+v4wxhirWlolVteuXcOqVatgbm6OjAzVSUu9vLwQGxtb4eCY4dDlWIyakqDp4qJVX2NgNG2djE3NweOXGTA3keD3uzHIlxFyC+QokMtBBAgCYMJjL/VC088Zt0T/T3m6sDdv3hwnTpwoczuJRIL58+dj/vz5FYiMMcaYMdAqsTI1NYVcLi9xfXR0NGxs+Ie6OlGMxbA0keBaeBK8HC0xvVfDcj9PTRssX5GL1rD4DBy7E4PnSVloX9fRIMt1K7o6PorLQHpOAbo2cEFyZh7yZDIIkMFUKqCJhy386znpO9QapaZ9zhhjgCA1hU3bgSrLGGNVR6vEqmPHjjhw4ABmzZqlsi4zMxPbt29HYGBgRWNjBsTbyQq5+TL89TgeRMDP1yLR0ssePRq7let5DH3CW0OhuDB+npSF2NQcXI9INrgqjEW7Ovo4WwMCcD0iGclZeSiQEdztLWBmIkH/lh78Hlcx/pwxVvNIzK3g3OcDfYfBWI2mVWK1bNkyBAYGYuDAgXj77bcBALdv38bTp0/x5ZdfIj4+HosWLdJpoMakrC44xtgVztfVBvVdbXA7KgV1HC0Rm5aHe9Gp5U6seLC8ZhQXxu3rOuJ6RDI6+zobXIKibs6m43djkJ0nQ16BDHHpeWjmya1V+sCfM8YYY6zqaTX4ISAgAMePH8eTJ08wduxYAIXVlCZPngyZTIbjx4+jVatWWgWUm5uLefPmwdPTE5aWlggICMDJkyc1euypU6fQs2dPuLi4wMHBAR06dMCuXbu0ikNbipaG4CvP8f35pwiLzyjXekPWo7ErnK3NEZuWB1sLE7Twsi/3cyguxsd0rFsjuyeFxWfgXGhcme970QvjOk5WBpdUKRQtJe7raoMBLT3Q2N0WZiZSuNubw8fFGsfuxOBcaBwAzfefVUxN/5wxxhhj+qD1BMG9evVCaGgobt26hcePH0Mul8PX1xft2rWrUFnp8ePHi90MGzZsiB07dmDAgAE4e/YsunbtWuLjjhw5gjfeeAOdOnXC0qVLIQgC9u3bh7FjxyIhIQEfffSR1jGVR1ldcAyxi46mLWiK1ql70alooUU3QIWaOli+PONejLVam6+rDfq1cEdMajbi0nKx73oUJALgYGWGD3v64nZkqtGO+zG2luaa+jljjDHG9EXrxEqhTZs2Ops9/urVq9i7dy+CgoIwZ84cAMDYsWPRokULzJ07F5cuXSrxsZs2bYKHhwfOnDkjlrudMmUKmjRpgh07dlRZYlVWFxxD66JT3kHuPRq7wdvJCpFJWQiLz+ALt3Iob1JtzBfGZiZSmJlIkJ0vg6WJgIT0XJx9GAeCYFA3FTTFxSAYY4wxVhatugJKJBJ4eHjg/PnzatdrO/HhgQMHIJVKMXnyZHGZhYUFJk6ciMuXLyvNcl9cWloaHB0dleYQMTExgYuLCywtqy55Udyxb1XbHv1auKtcfBlaF52iF/uJmXmISs4udXtj7sqob4aWVFcWxX6m5xZAICCvgCCTy2Fjbmq0+1/ez4kh4e6XjDHGWNXQusUqJycHr7zyCoKCgjBz5kydBHPz5k00atQIdnZ2Sss7dOgAoHDyRm9vb7WP7dGjB9asWYNFixZh3LhxEAQBu3fvxvXr17Fv3z6dxKeJsPgM/HEvFomZeYhOzlbbbUhdS4S+uhmV92LfELsyGgJN3j9j7d5XXor99HK0xA9/PUV6dgGkgoDEzFxM6OIDQRCMbv+NKSkuei4C4JY2xmoIeU4G4g6uUFrmNuQzSCz4M89YVdE6sfrqq69w9epVfPTRR7h+/Tq+//57WFhYVCiYmJgYeHh4qCxXLHvx4kWJj120aBGePXuGL774AitWFH6xWFlZ4ZdffsHgwYNLfd3c3Fzk5uaKf6elpWkTPgDtEg99djMq78W+MV1gVpXyjp2qCRe2vq42aOllD2drcwgEmJlIkJ5TAEEQENjIVd/hlZuxJMXFz8XW3g58I4SxGoLkMuRG3lNZxhirOlp1BQQKJwn+z3/+gx07duDgwYPo0qULnj9/XqFgsrOzlbryKSgStuzskrvfmJubo1GjRhg6dCj27NmDn376Ce3bt8eYMWPw999/l/q6q1atgr29vfivpFYxTWiTeOi7m1HRym6abGtIXRkNgb7fP0OWmy9HVr4c6bky2FqYGHUiXp7Pib4UPxcFAXwjhDHGGKsiFS5eMXbsWLRq1QpvvfUW2rVrh71792r9XJaWlkotRwo5OTni+pJMmzYNf//9N27cuAGJpDBfHD58OJo3b46ZM2fiypUrJT52/vz5+Pjjj8W/09LStE6utLmzbWytQDWl1UVTxvb+VaU6zlZoVMsGMWk5BlsyvroIi89ATGo2TKWCeC7613OCfz0ng29pY4wxxqqDCidWQGFlwH/++QejRo1Cv3790K1bN62ex8PDA9HR0SrLY2JiAACenp5qH5eXl4cffvgBc+fOFZMqoLBVrX///ti0aRPy8vJgZmam9vHm5uZqW8q0Vd7EQ91Eq+dC4wyurLOxlZuuKsbSTayqeTtZoY6TFRIz89C6tgNPFFyJinYBNJUI6N3UDf71nMRzkc9JxhhjrPLpJLECAAcHBxw7dgxLly4VxziVV5s2bXD27FmkpaUpFbBQtDaVVNY9MTERBQUFkMlU+xLn5+dDLperXWdIFMmYoZZ1NtS4DEV1b8XTNKkuvl1lJpyc6P9P8bGdHvac4DPGGGNVTasxVs+ePcMbb7yhslwQBCxbtgy3b9/GmTNnyv28Q4cOhUwmw9atW8Vlubm52L59OwICAsTuec+fP8fDhw/Fbdzc3ODg4IBDhw4hLy9PXJ6RkYGjR4+iSZMmVVpyvSLljQ11vI6hxsUqn6Yl9sPiM7AuJBTfnH6MdSGh4jxnlTEuSZOYalKZce6OyhhjjOmfVi1WdevWLXV9ixYttAomICAAw4YNw/z58xEXF4cGDRpg586dCA8Pxw8//CBuN3bsWPz5558gIgCAVCrFnDlz8Nlnn6Fjx44YO3YsZDIZfvjhB0RFReGnn37SKh5tKC4uY1Nz4G5vgdl9GpfrotJQL5AMNa7qwpBbXzStdHn1WRJuR6bATCrBy7QcXAtPqrR9KSsmTVtYDfm4lwd3R2WMMcb0T6PEavny5RAEAQsXLoREIsHy5cvLfIwgCFi0aFG5A/q///s/LFq0CLt27UJycjJatWqF3377Dd27dy/1cQsXLoSPjw++/vprLFu2DLm5uWjVqhUOHDiAt956q9xxaKuiF5f6vkAq7UKztbc9BEFQGrvBKs7Qu1lqnlQTCIAgAKTnmDRJBg39uJeXNt1Rq0tiyRhjjBkCjRKrpUuXQhAEzJs3D2ZmZli6dGmZj9E2sbKwsEBQUBCCgoJK3ObcuXNql48aNQqjRo0q92vqVsUvLvU1XqekC83iy7kIgW4Z+qTLmib7HXyc0aZ2AmLSctDEzqJSz5OyYtIkGTT0415RZSVN1S2xZIwxxvRNo8RKLpeX+jf7n6q8uNS1ki40DfUC1JDutlckFmPoZqlJsu/raoPZfRtXWWtraTFpkgwaw3HXliZJk6F+rhljjDFjpbOqgKxQVV9c6lJJF5rqlus7qTGku+2ltfRpcoz03f1Tl0pKdvRxvpSVDFan416cJklTdU4sGWOMMX3gxKoSGGvp7ZIuNNXNs6XvpMaQ7rariwUo3zEy1nNGE4aUBBdXXY+7JklTdU4sGWOMMX3QKLHy8fGBIAjlemJBEBAWFqZVUDWFvlt91CnpQrPo8nOhcXpNasLiMxCTmg1TqWAQd9vVXcQaUuKnb1efJeHRy3Q097RDbFpujT4WVUXTpKm6JpaMMcaYPmiUWAUGBpY7sWKlM+S7+GXRZxeiosfNVCKgd1M3vVcpLOkitqZ2syp6wwAALjyKR2xaDl6m5aC1t0ONOhb6xEkTY4wxVrU0Sqx27NhRyWHUPMbcoqHPLkTFj5uHvWF0YSp+EVtTu1kVv2HQ2tsBKdn5aFLLFjFpOeheCZMFM8YYAwSpCawad1FZxhirOvyJ0xNjHziur7vhxnTcamKLQWRSFp4nZcHFxgzPk7Lg5WiJ2NQcpOcUwNbCBO52FvoOkTHGqiWJuTVc35iv7zAYq9EqlFjl5+fj4cOHSE1NVVuCvaxJfWuymtqiUVF83AxfbGoOHr/MgK2FCYgAd3sLtPQyQ3xGHncpZowxxli1pVViJZfLMX/+fGzevBlZWVklbieTybQOrLoqXrCCE4Py4+Nm2IomUm525qjjZIXEzDzUcbIy6BZGxhhjjLGK0CqxWrlyJYKCgjBlyhR07doV77zzDtasWQMHBwds3rwZgiBg7dq1uo7V6BlzwQrGNKli6e1kpZRI+ddzgrudBe5Gp6Kllz2f7zWAIVY7ZYwxxqqCVonVjh07MHz4cGzZsgWJiYkAgHbt2qFXr14YN24cOnXqhDNnzuCVV17RabDGzpgLVrCaTZObAooL6n4t3CEIgtg69ce9WCRm5iE6OZsvtqs5vnnEGGOsJpNo86CoqCj06tULAGBubg4AyMnJAQCYmZlhzJgx2LVrl45CrD6MqfACY0UVvSmQmJknToKsoLigDr7yHH/cixXHv5X1OFa98PvNGGOsJtOqxcrZ2RkZGRkAABsbG9jZ2eHp06dK2yQnJ1c8umqmpMIL3HWGGbqybgqU1BrLNxNqFn6/GdMfeW4mEn//RmmZc/8ZkJhb6ykixmoerRKrtm3b4tq1a+LfPXv2xFdffYW2bdtCLpfjm2++QevWrXUWZHVSvPACd51hxqCsaowlXVBzFceahd9vxvSHZAXICr2otMypz1Q9RcNYzaRVYjV58mTs2LEDubm5MDc3xxdffIHu3buje/fuICI4Ojpiz549uo61WuJxV8xYlFaNsbQLasX/RyZlKf3Nqieu2skYY6ym0iqxev311/H666+Lfzdr1gxhYWE4d+4cpFIpOnfuDCcnJ50FWZ1x1xlWXZR0Qc2tsowxxhirCSo0QXBR9vb2GDx4sK6ersbgrjOsuuNWWcYYY4zVBBVKrPLz8xEdHY3k5GQQkcp6Pz+/ijx9jcFdZ1h1xq2yjDHGGKsJtEqsUlJSMGfOHAQHByMvL09lPRFBEATIZLIKB1gTcFVAVp35utqgXwt3niSYMcYYY9WaVonV+PHjcfToUYwcORIBAQGwt7fXdVw1Bo8/YdVdWHwGTxLMGGOMsWpPq8QqJCQEM2bMwIYNG3QdT43D409YdcfnOGOMMcZqAq0nCG7QoIGuY6kRinf74/EnrLrjc5wxxhhjNYHW81jt3bsXH3zwASQSia5jqrZK6vbHVQFZdcbnOGOMMcZqAq0Sq0WLFiE3Nxft27fHO++8g9q1a0MqlapsN2TIkAoHWJ2U1CWKqwKy6o7PccYYY4xVd1olVtHR0Thz5gxu3bqFW7duqd2GqwKq4i5RjDHGGGOMVU9aJVbvvvsubty4gfnz53NVwHLgLlG6weXpjQ+/Z4wxxhir7rRKrP766y/MmzcPy5Yt03U81R53iaoYLk9vfPg9Y4yx6q/ep8f0HYLehK8eqO8QmIHQKrFyd3eHk5OTrmNhrExcutv46Ps949YyxlhNIEikMPduobKMMVZ1tCrpN3v2bGzbtg0ZGRm6joexUvE4NeOjz/dM0VoWfOU5vj//FGHx/J3FSpeRkYElS5agX79+cHJygiAI2LFjh9ptHzx4gH79+sHGxgZOTk545513EB8fr7KdXC7H2rVr4ePjAwsLC7Rq1Qp79uyp5D1hNY3Ewgbuo1Yr/ZNY8M0kxqqSVi1WOTk5MDU1RYMGDTB8+HB4e3urVAUUBAEfffSRToJkTIHHqRmn1t72EAQB/vWcqvQ903drGTM+CQkJWL58OerUqYPWrVvj3LlzareLiopC9+7dYW9vj5UrVyIjIwNffvkl7t69i6tXr8LMzEzcduHChVi9ejUmTZoEf39/HD58GKNGjYIgCBg5cmQV7RljjLHKplViNWfOHPH/N23apHYbTqxYZeFxasaj+Pgq/3pV24WYWzhZeXl4eCAmJgbu7u64fv06/P391W63cuVKZGZm4p9//kGdOnUAAB06dMCrr76KHTt2YPLkyQAKq+iuW7cOH374ofh7+d577yEwMBCffPIJhg0bpna6EmNXk8fbMMZqLq0Sq2fPnuk6DsZYNaTvFiNu4WTlZW5uDnd39zK3++WXX/Daa6+JSRUAvPLKK2jUqBH27dsnJlaHDx9Gfn4+pk6dKm4nCAI++OADjBo1CpcvX0bXrl11vyOMMcaqXLkTq+zsbHz99dfo2bMnBg0aVBkxMVat1OTiCYbQYsQtnEzXoqOjERcXh/bt26us69ChA44fPy7+ffPmTVhbW6Np06Yq2ynWc2LFGGPVQ7kTK0tLS3z33Xdo1qxZZcTDWLVS00uNc4sRq45iYmIAFHYbLM7DwwNJSUnIzc2Fubk5YmJiUKtWLQiCoLIdALx48ULta+Tm5iI3N1f8Oy0tTVfhM8YYqyRadQVs164d7t27p+tYGKt2KtoVrjq0dnGLEatusrOzARR2GyzOwsJC3Mbc3Fz8b2nbqbNq1SqeK5KVizw3C8l/7lRa5hg4DhJzKz1FxFjNo1Vi9dVXX2HAgAFo0aIFxo8fDxMTrZ6GsWqvIl3hanprF9ON6pCcGxpLy8LPcdEWJYWcnBylbSwtLTXarrj58+fj448/Fv9OS0uDt7d3xQJn1RrJ8pFxU7loiEPXUXqKhrGaSauMaPz48ZBIJJgyZQpmzJgBLy8vlR8HQRBw+/ZtnQTJmLGqSFc4fRd+YMaPk/PKoejGp+gSWFRMTAycnJzEVioPDw+cPXsWRKTUHVDxWE9PT7WvYW5urralizHGmOHSKrFycnKCs7MzGjdurOt4GKt2ytMVrmjrgiEUfmDGjZPzyuHl5QVXV1dcv35dZd3Vq1fRpk0b8e82bdpg27ZtePDggdLY5CtXrojrGWOMVQ9aJVYlTZjIGNOeutYFLvygOzWxSxwn55Xnrbfews6dOxEZGSl20Tt9+jQePXqkNIfj4MGD8dFHH2Hz5s3iPFZEhG+//RZeXl7o3LmzXuJnjDGmezw4ijEDoa51IbCRq1EkAYaetNTULnFclVE7mzZtQkpKilix7+jRo4iKigIATJ8+Hfb29liwYAH279+Pnj17YubMmcjIyEBQUBBatmyJCRMmiM9Vu3ZtzJo1C0FBQcjPz4e/vz9+/fVXXLhwAcHBwdVycmDGGKuptE6sZDIZfvrpJxw7dgwREREAgLp16+K1117D6NGj+ceCsXIy1tYFY0haanKXOK7KWH5ffvml+LsGAAcPHsTBgwcBAGPGjIG9vT28vb3x559/4uOPP8ann34KMzMzDBw4EOvWrVMZG7V69Wo4Ojriu+++w44dO9CwYUP89NNPGDWKCwswxlh1olVilZqair59++LatWuwtbVF/fr1AQAnT57EL7/8gi1btuDEiROws7PTabCMVWfG2rpgDEmLsSatTD/Cw8M12q558+Y4ceJEmdtJJBLMnz8f8+fPr2BkjDHGDJlWidXChQvxzz//YOPGjZg0aRJMTU0BAPn5+di2bRtmzJiBhQsXYuPGjToNlrHqzhhbF4whaTHWpJUxxhhjxkOrxOrQoUOYOnUqpk6dqrTc1NQUH3zwAR48eIADBw5wYsVYDWAsSYsxJq2MMcYYMx5aJVaJiYmlllpv0qQJkpKStA7K2Bn6QH7GdI2TFsYYY4zVdBJtHtSgQQMcOXKkxPVHjhyBr6+v1kEZM8VA/uArz/H9+acIi8/Qd0iMMcYYY4yxSqZVYjV16lSEhIRgwIABCAkJQXh4OMLDw3HixAkMHDgQJ0+exLRp03Qdq1EoOpA/MTMPUcnZ+g6JGbiw+AycC43jJJwxxhhjzIhp1RVw6tSpiIuLw+rVq1UqIpmammLx4sX44IMPdBKgsTGGgfzMcBhDqXLGGGOMMVY2reexWrp0KaZNm4ZTp04pzWP1yiuvwMXFRWcBGhtjGcjPDIMxlCpnjDHGGGNl0zqxAgAXFxeMHDlSV7FUGzyQn2mKWzgZY4zphCCBqXMdlWWMsapTocQqPT0dERERSE5OBhGprO/evXtFnp6xao9bOBljjOmC1NIWnu9t1ncYjNVoWpdbnzZtGn755RfIZDIAABFBEASl/1esY4yVjFs4GWOMMcaMn1aJ1aRJk3D06FHMmDED3bp1g6Ojo67jYowZMZ7LjTHGGGM1jVaJVUhICD766COsXbtW1/EwxgyUpskSVzpkjDHGWE2k1ahGKysr1KtXT8ehFMrNzcW8efPg6ekJS0tLBAQE4OTJkxo//ueff0anTp1gbW0NBwcHdO7cGWfOnKmUWBmrKcoz8TXP5cYYY4yxmkirxGrMmDE4dOiQrmMBAIwfPx7r16/H6NGj8fXXX0MqlWLAgAH466+/ynzs0qVL8fbbb8Pb2xvr16/HihUr0KpVK0RHR1dKrIzVFOVJlrjSIWOMMcZqIq26Ag4dOhR//vkn+vXrh8mTJ8Pb2xtSqVRlOz8/v3I979WrV7F3714EBQVhzpw5AICxY8eiRYsWmDt3Li5dulTiY//++28sX74c69atw0cffVS+HWKsGqmM8U3lSZa40iFjjFU9eV4O0q7+orTMrsNbkJhZ6CkixmoerRKrrl27iv+vrpuetlUBDxw4AKlUismTJ4vLLCwsMHHiRCxYsACRkZHw9vZW+9ivvvoK7u7umDlzJogImZmZsLHhCzpWs1TW+KbyJktc6ZAxxqoWFeQi9eIepWW2fq8BnFgxVmW0Sqy2b9+u6zgAADdv3kSjRo1gZ2entLxDhw4AgFu3bpWYWJ0+fRqdO3fGN998gxUrViAxMRHu7u5YuHAhpk2bVinxMmZoinbZexCbjqjkbJ0lOJwsMcYYY4yVTKvEaty4cbqOAwAQExMDDw8PleWKZS9evFD7uOTkZCQkJODixYs4c+YMlixZgjp16mD79u2YPn06TE1NMWXKlBJfNzc3F7m5ueLfaWlpFdwTxvSDxzcxxhhjjOmHVolVUTExMYiLi0ODBg1gbW1doefKzs6Gubm5ynILCwtxvToZGYUVyhITE7F3716MGDECQOFYsJYtW2LFihWlJlarVq3CsmXLKhQ7Y4aAxzcxxhhjjOmHVlUBAeDw4cNo0qQJateuDT8/P1y5cgUAkJCQgLZt22pVNdDS0lKp5UghJydHXF/S4wDA1NQUQ4cOFZdLJBKMGDECUVFReP78eYmvO3/+fKSmpor/IiMjyx07Y4bC19UGgY1cOalijDHGGKtCWiVWR48exZAhQ+Di4oIlS5aAiMR1Li4u8PLywo4dO8r9vB4eHoiJiVFZrljm6emp9nFOTk6wsLCAs7OzSnVCNzc3AIXdBUtibm4OOzs7pX9VKSw+A+dC40qdG4gxxhhjjDFmuLRKrJYvX47u3bvjr7/+wocffqiyvlOnTrh582a5n7dNmzZ49OiRyhgnRWtYmzZt1D5OIpGgTZs2iI+PR15entI6xbgsV1fXcsdTFcoz8SpjjDHGGGPMMGmVWN27dw/Dhw8vcX2tWrUQFxdX7ucdOnQoZDIZtm7dKi7Lzc3F9u3bERAQIFYEfP78OR4+fKj02BEjRkAmk2Hnzp3ispycHAQHB6NZs2YltnbpW3kmXmWMMcYYY4wZJq2KV1hZWSEzM7PE9U+fPoWzs3O5nzcgIADDhg3D/PnzxYIYO3fuRHh4OH744Qdxu7Fjx+LPP/9U6oI4ZcoUbNu2DR9++CEePXqEOnXqYNeuXYiIiMDRo0fLHUtV4SpujDHGGGOMGT+tEquePXti586dmDVrlsq62NhYfP/993jttde0Cuj//u//sGjRIuzatQvJyclo1aoVfvvtN3Tv3r3Ux1laWuLMmTOYO3cufvzxR2RmZqJNmzY4duwY+vbtq1UsVYGruDHGGGOMMWb8tEqsvvjiC3Ts2BH+/v4YNmwYBEHAiRMncObMGXz33XcgIixZskSrgCwsLBAUFISgoKAStzl37pza5W5ubloVzdA3nniVMcYYY4wx46bVGKvGjRvjr7/+grOzMxYtWgQiQlBQEFauXImWLVviwoULqFevno5DZYwxxhhjjDHDpPUEwc2bN8epU6eQnJyMJ0+eQC6Xo379+mL1PSKCIAg6C5QxxhhjjDHGDJXWEwQrODo6wt/fHwEBAXB1dUVeXh62bt2Kxo0b6yI+xhhjjDHGGDN45WqxysvLw5EjRxAWFgZHR0e89tprYhnzrKwsbNq0CV999RViY2Ph6+tbKQEzxhhjjDFVEks7fYfAWI2mcWL14sUL9OjRA2FhYWKZc0tLSxw5cgRmZmYYNWoUoqOj0aFDB2zcuBFDhgyptKAZY4wxxtj/SK3s4T1jt77DYKxG0zixWrhwIZ49e4a5c+eiW7duePbsGZYvX47JkycjISEBzZs3x08//YTAwMDKjJcxxhhjjDHGDI7GidXJkycxYcIErFq1Slzm7u6OYcOGYeDAgTh8+DAkkgoP2WKMMcYYY4wxo6NxJvTy5Ut07NhRaZni73fffZeTKsYYY4wxxliNpXE2JJPJYGFhobRM8be9vb1uo2KMMcYYY4wxI1KuqoDh4eG4ceOG+HdqaioA4PHjx3BwcFDZ3s/Pr2LRMcYYY4wxxpgRKFditWjRIixatEhl+dSpU5X+VkwOLJPJKhYdY4zpWVh8BiKTsuDtZAVfVxt9h8MYY2rJ83ORefek0jLrlq9CYmqup4gYq3k0Tqy2b99emXEwxpjBCYvPwPfnnyIxMw/O1maY1L0+J1eMMYNE+TlIOvmt0jKrJt0ATqwYqzIaJ1bjxo2rzDgYY8zgRCZlITEzD+625rgXk4Zr4UmcWDHGGGNMrXJ1BWSMsZrE28kKphIBp0PjIAA4/yge/vWcOLlijDEmqvfpMX2HoBfhqwfqOwSDwzXSGWOsBL6uNujWyBXudhbo1cQN+TJCVHK2vsNijDHGmAHixIoxxkrRwccJjWrZIjYtF87WZqjtaKnvkBhjjDFmgLgrIGOMlcLX1QaTutdHVHI2ajtacjdAxhhjjKnFiRVjjJXB19WGEyrGGGOMlYoTK6YRfc3lw3MIMVZ+/LmpvlosOQGJuZW+w2CMMaYGJ1asTPqay4fnEGKs/PhzwxhjjOkHF69gZVLM5dPU3RaJmXlVVhVNX6/LmDHjz43hyc3Nxbx58+Dp6QlLS0sEBATg5MmT+g6LMcaYjnFixcrk7WQFZ2szPIhNr9KqaPp6XcaMGX9uDM/48eOxfv16jB49Gl9//TWkUikGDBiAv/76S9+hMcYY0yHuCsjKpK+qaFyNjbHy48+NYbl69Sr27t2LoKAgzJkzBwAwduxYtGjRAnPnzsWlS5f0HCFjjDFd4cSKaURfVdG4Ghtj5cefG8Nx4MABSKVSTJ48WVxmYWGBiRMnYsGCBYiMjIS3t7ceI2SMMaYrnFgxxhhjleTmzZto1KgR7OzslJZ36NABAHDr1i1OrBhjRqnep8f0HUKVkedmabQdJ1ZqEBEAIC0tTc+RMMZYzaL43lV8Dxu7mJgYeHh4qCxXLHvx4oXax+Xm5iI3N1f8OzU1FYDmP+6s5pHnqZ4b8rwsCFJTPUTDWPWi+O4t67eJEys10tPTAYDvIjLGmJ6kp6fD3t5e32FUWHZ2NszNzVWWW1hYiOvVWbVqFZYtW6ayPHrLeJ3Gx6q3F99N0ncIjFUrZf02cWKlhqenJyIjI2FrawtBEFTWp6WlwdvbG5GRkSrdOwwdx64/xhw/x64fxhw7oF38RIT09HR4enpWcnRVw9LSUqnlSSEnJ0dcr878+fPx8ccfi3/L5XIkJSXB2dlZ7e+SITP289hY8XHXDz7u+lHZx13T3yZOrNSQSCSoXbt2mdvZ2dkZ7YeGY9cfY46fY9cPY44dKH/81aGlSsHDwwPR0dEqy2NiYgCgxB9pc3NzlZYuBwcHncdXlYz9PDZWfNz1g4+7flTmcdfkt4nnsWKMMcYqSZs2bfDo0SOVMbtXrlwR1zPGGKseOLFijDHGKsnQoUMhk8mwdetWcVlubi62b9+OgIAAHsvLGGPVCHcF1IK5uTmWLFmidkCyoePY9ceY4+fY9cOYYweMP35dCAgIwLBhwzB//nzExcWhQYMG2LlzJ8LDw/HDDz/oO7wqweeBfvBx1w8+7vphKMddoOpS05YxxhgzQDk5OVi0aBF++uknJCcno1WrVvj888/Rt29ffYfGGGNMhzixYowxxhhjjLEK4jFWjDHGGGOMMVZBnFgxxhhjjDHGWAVxYsUYY4wxxhhjFcSJFWOMsXLj4bmMMcaqglwu13cIGuPEiukdX6CxmiY1NVXfIWjt559/BgAIgqDnSJgh4e/xqpGTk6P0Nx93Vp09fvwYMpkMEonxpCvGE2klunnzJp4/f650sWMsX1ZZWVn6DkFrT58+RVZWlsoPhTG4ffs2Hj9+jKioKHGZsZwzAHD48GFMnToVT58+BWBcd4P27NkDW1tbXLx4Ud+hlNvBgwfRp08fbNiwAeHh4foOp1z27t0LX19fvP322/jrr7/0HQ7To5MnT+LTTz/Fli1bcOnSJQCcaFe2e/fuYdiwYRg5ciTef/99XL16FQAf98r2888/4/3338eaNWuUvveM6ffeGO3atQuNGjVCnz590KxZMyxfvtxobkjW6MTqwYMH6Nq1K3r37o3WrVujQ4cO+OWXX1BQUABBEAz6gxMaGop27drhvffe03co5Xbnzh0MHDgQgwYNgo+PD3r06IGLFy8a9PFWuHPnDl599VW89tpraNeuHVq3bo1vvvlGPGeMwcmTJ/Hmm29i165d+O233wDAKO4G3bx5EwEBAXj33XcxcOBA2NnZ6Tskjb148QIDBw7E2LFjYWZmBisrK1hZWek7LI0ojvu4ceNga2sLCwsL5Obm6jsspgepqakYMWIEBg0ahGPHjmH27Nno27cvvvnmGyQlJQHgC05dUhzLXbt2oVOnToiOjkZ+fj727NmDV199FV9++aWeI6y+Xr58iX79+mHixIm4du0a1qxZg1deeQVLly5FSkqKwV8jGrPvv/8eH3zwAXr16oX33nsPfn5+WLp0KaZOnYqwsDAABn4zmGqoly9fUtu2balz5870448/0o8//kgdO3YkBwcHWrJkCRERyeVy/QaphlwupwMHDlCjRo1IEAQSBIHOnTun77A0UlBQQN988w25urpSYGAgLV68mKZOnUre3t7UpEkTg96PvLw8+uKLL8jBwYECAwNp48aNtGfPHurRowfZ2dnRwYMH9R1imRTn8z///EPOzs5kaWlJAQEBdOvWLSIikslk+gyvRFlZWTRhwgQSBIECAwPp8OHD9PLlS32HVS5Lliyhpk2bUnBwMD1//lzf4WgkNTWVxo4dS4IgUI8ePejw4cN07NgxsrCwoC+//JKICj/TrObYt28fOTo60tatW+n58+f04MEDGjt2LJmbm9Ps2bP1HV611b17d+rXrx+Fh4cTEdGzZ89o9OjRJAgC7dmzh3Jzc/UcYfWzc+dOcnJyouDgYHrx4gUlJibS+PHjydbWlqZOnarv8KqtjIwM6ty5M73yyisUExMjLl+zZg3Z2dnRyJEj9RidZmpsYrV3714yMTGhAwcOiMuioqJoxIgRJAgCnTp1So/RlSwsLIxatGhBzs7OtGLFCmrWrBl17NiR8vPz9R1amf744w+qX78+vfvuu/Tw4UNx+cWLF0kQBJo3b57B7sexY8fIz8+PZs2aRY8ePRIvKB8/fkyCINDatWsNMhFX58CBA9SnTx/69ttvSRAEWrBggbg/hrYPBQUF9MUXX5AgCDRp0iSKj48v8RwxtNgVnj9/TrVq1aIZM2aoLC/KkOLPzMykhg0bUv369WnLli0UERFBRERPnz4lR0dHGjJkiMEm4qzyvP7669SsWTOV5W+88QY5ODjQ3r17iYgTbl26ceMG2djY0Pr165WWR0REUO/evalBgwb0119/6Sm66iswMJA6duyotCwzM5PGjx9PgiDQsWPHiMiwvrerg6SkJHJxcaEVK1YQkfJ3yfvvv08WFhb0ww8/EJHh3gw2/P4/lSQiIgLW1tZ48803AQD5+fnw8vLC3Llz4e/vj1mzZiEuLk7PUaoyMTHB66+/jtOnT2PhwoX48MMPceXKFezcuVPfoZXp/v37MDc3x+rVq9G4cWMAQF5eHjp37oyAgADcuHEDJiYmBtm8bm9vj9GjR2PBggVo2LAhpFIpgMJ+766urqhbt67Bdw1QxObt7Y0rV65gypQp6N27N7Zv346zZ8/qOTr1pFIp+vbti86dO+PChQtwcXGBiYkJjhw5gvHjx2PevHnYvn078vLyDLYrZnh4ONLT0zFt2jQAhd16mjdvjn79+uHNN9/Enj17ABjOWAm5XA4rKyvs3LkTR44cwcSJE1GnTh0AgI+PDxo0aICkpCTk5+cb9PnOdCs3Nxd5eXlwcHAQl+Xl5QEAFi5cCB8fH8yfPx8FBQXi9yOrOHd3d+Tl5cHa2hoAxG64derUwZdffono6Gjs2LEDCQkJ+gyz2pDL5cjNzYWFhQVMTEzE5QUFBbCyssL06dPh5+eHGTNmgIgM5nvbGB07dgx+fn5KY9fS0tIgCAJiYmKQm5sLqVQKmUwGAJg2bRratGmDpUuXIicnx3CHMOg1rasCioy2+F2FDRs2kK2tLZ09e5aISOmO/c8//0zm5ua0cuVKtY+tKiXFnpOTI/5/aGgo9enTh2rXrk0JCQlVGl9pisZeNP7Q0FCl9USFx75Hjx7UtWtXys7OrtpA1SjpuBd34cIFatGiBdnZ2dHSpUvp7t27lJycrPQc+lBW/AcOHKAGDRoQEdHNmzdJEAQaN24cJSUllfq4qlBS7IrWtdmzZ1OfPn1IEARq0KAB2drakiAINGTIELp3757Sc1S1kmK/fv06mZiY0KFDh+jHH38kiURCQ4cOpXHjxpGbmxsJgkDbt2/XQ8T/o8k5L5fLSSaT0Ycffkj29vbiuc53bKuXpKQkevTokfh9UNSwYcOoUaNG4vd4URs2bCALCwv64osviMhw7yYbm7S0NGrdujX17NlTXFb0M/fJJ5+Qra0tnT59Wh/hGbUHDx7QzJkzafr06bRw4UJ69OiRuO6NN96gxo0b0927d4lI+XzeunUrCYJAGzZsUFnHNPPs2TOqW7cuCYJAb775ptK6Hj16UIcOHSgqKkrlcV9//TXZ2trS6tWricgwf3+qbWKlGBOzbds2peWKN+HkyZNkbm5OS5cuFZcpPhyxsbE0fPhwcnV11Uvf5ZJiL8nPP/9MlpaWNHfu3EqOrGzljV2ReLVt25ZGjBghLtMHTWJXnCPz5s0jQRCoZ8+eNG7cOJo4cSI5ODjotf9vWfErjuvVq1fJ1taWXrx4QUREEydOJHNzc9q9ezcRFXZ3qGplfV4jIiJo6NChJAgC9erVi/744w+KiIig6Oho+vzzz0kikdCwYcOqPG6iso/79evXycXFhcaMGUOtW7emRYsWUXp6OhER3blzh/r27UvOzs704MGDqgybiMr/eSUiWrRoEQmCQEeOHKnEyJg+LFiwgBo3bkweHh5kZmZGn376qVISdezYMXFcj4LipmRkZCR17dqVWrduTfHx8VUee3X2ySefkLu7O4WEhBCRcveoJ0+ekIuLC82ZM4eIDPNC09Dk5ubSnDlzyNLSktq3b08NGzYkQRCofv36tH//fiIqvAEpCAL9+OOP4u++4riHh4dT7969ycfHh8e3aSk1NZUcHByoefPmVLt2bfq///s/cd2uXbtIKpUqDdVRHPvnz59T69atqUePHuLNPUNTLROr8+fPU/PmzUkQBOrTpw/dv3+fiFS/cPz8/Kht27biHYmi64ODg8nExIS2bNmi9rH6jr3osri4OHr33XfJwsJCvGuvjy/X8sReVGRkJFlbW9OqVauISD/98zWNXfH3oUOH6Oeff6aEhARx2fz580kikVBQUBARVe1drPIc+3379lGjRo3EAhBpaWlkZWVFPXv2pAkTJtA777wjJl2GFHtwcDCNHz+eLl68qLJu9OjRZG9vL17sG9rntUuXLiSRSMjFxYUuXbqktC4kJIScnJxo5syZRFR15015P6+KuC5cuECCINC+fftK3Z4Zjzt37lBgYCDVrl2bFixYQCtXrqR3332XBEGgiRMniuMaIyMjyd/fn7p06aJ0UaM4B5YuXUq2trZiAsB04+XLl+Tk5ESjRo0Sfx8Vn8f09HQaPXo0eXt76zNEo5Genk4LFiyg+vXr05o1ayg0NJRkMhmdPn2aPD09qVu3bpSVlUUFBQXUunVr6tatm1g0pKhly5aRg4ODONaKaU4ul1NkZCT16NGDvvjiC2rcuDH5+/tTRkYGERWOXff396eAgAClmzSKc37atGnk4eFBT58+1Uv8Zal2idXly5epSZMmVK9ePRo2bBgJgkBr1qxRGvCu+GI6fPgwCYJAK1asELugKdaFhoZS7dq1afLkyVV2oaNJ7CU5ffo0eXl5qTSpVpWKxH7+/HkSBIFOnDhRBZGqKk/spV1EPn78mBo0aECtW7dW6q5Z2TSNXxH7hQsXyMrKiiIjI8V1b7/9NkmlUjI1NaUlS5aIX3CGELsi7tTUVIqLi1N6vGK7v//+mwRBUGqBNoTYFd8nf/zxh1jFU9EypbjTGRcXR/369SNvb+8qO28q8nm9d+8eOTo60vTp04mIEytjl5ycTOPHj6cGDRrQwYMHlVqsBw8eTK6urnThwgUiKvy8ff/99ySRSOg///mPeH7n5eURUeHvpiAIYpVU7iKlO8uXLydXV1dx4H7RG5Dz5s0jNzc3CgsL01d4RuPZs2fk4+NDU6ZMoZSUFKV1U6ZMIVdXV7p+/ToRFbacCIJA69evFz8Xiu/tmzdvkkQioUOHDhERfw+WV1xcHFlYWNCDBw9o9erVZGNjIxasyMnJoZ07d5JUKqVVq1aJx17x+7h//34yNTVV2yXZEFS7xOr+/ftkbm4uNud269aNGjZsSBcvXlS7/YABA8jT05OOHj1KRMpfVs2bN6exY8cSUdV8aMobe9G4MjIyxC46ir7Wf/75Jx0+fFhpO0OKXWHz5s1kYmIido8qKCigsLAw8cvNkGMnUr546NSpE3Xs2LFKE6vi8Xfv3r3U+Pfu3UuNGzemlJQUOnv2LHXt2pWkUinZ2dlRgwYNxIsoQz3ni8amOPbx8fHk4OBQpd1hyxu7ojzylClTiIiUkpihQ4dSs2bNKDU1tfIDp4qd83FxcVS3bl3q3bs3paWlVXaorJIlJSWRv7+/eMFO9L9E6ezZs0q/KUSF1XOHDBlCnp6edPbsWaXvicuXL5O5uTl9++23VbcDNUROTg61aNGCGjRooHKnfurUqeTm5mawXaMMiVwup61btyotU5zv+/btIxMTE/HmV0pKCg0ZMoTc3d3p119/VXrM1atXSRAE2rlzZ9UEXo3IZDKKjo6mxo0b0/nz5yk2NpY6duxIPj4+YrIUGxtLEydOJBsbG9q1a5f4WLlcTu+99x65u7tTZGSkQSa01SqxUiRFRe9qK1pDZsyYIV60FL0QjoiIIBsbG+rYsSPduHFDXP7333+TnZ0dLVu2zKBiV3cSKfbn4cOH5OfnRy1btqRly5aRt7c3OTs7V/qcPxWJnYho0KBB1LlzZyIq7Gry008/Udu2bcnPz48SExMNNvbid2NPnDhBpqamNGvWrEqMWFl54lfsw+nTp8nMzIxee+01kkql1KVLFzp//jzt27dPvPCvin7jujz2mzdvJkEQ6Pvvv6/EiP9Hm++ayMhIsrOzU2md/ffff8nX15fGjBlTJT8SujjuQ4YMoebNm1NGRoZB/rAxzSjezwcPHqgtYBISEkImJib0888/Kz3u7t275OXlRe3atRPP5ZcvX9LcuXPJ09NTbdcpVnGXL18mLy8vatmyJV24cIGeP39Ov//+O/n4+NBHH33En0UNKW5qFR92EBQURFKpVGk6mMjISKpVqxY1b96c/vjjDyIiio6OpmnTplHdunUpNja26gKvRpKSksjKykq8mffdd9+Rk5MTTZw4kYiIEhISKDY2lgICAsje3p4+++wzCgkJoW3btlG9evUMei4xo02s9u7dS1OmTKHVq1fT+fPnxeVFv1gUPxTjxo0jBwcHlTsOig/Vjh07qE6dOuTj40PffPMNbdu2jQYNGkTe3t50584dg4xdnYiICHGOBUEQaPDgwUrdvQwtdrlcTunp6eTh4UEjR46kU6dO0euvv06CIFC/fv3UVoQxlNiLevHiBR09epQCAwOpWbNm4pg9XdNV/BcvXqRWrVpR06ZNadOmTRQZGSl+Frp06UKTJk3SeWJVWcc+NjaWDh06RK1ataLAwMBKqYypy++avXv3koeHBzk5OdGkSZNo5cqV1L9/f3J0dKyUrrCVcdzlcjmtWLGCBEEQ7y7yBV31ong/jxw5QoIgiBeaRd/nc+fOUf369UkQBOrSpQv17t2bzM3N6ZNPPqHc3Fw+JyrJmTNnqH79+mRqakq+vr5kZ2dHfn5+eil+U10ovgNnzpxJ7u7uYguW4nv7xIkT5OfnR4IgUJs2bahTp05kampKy5Yto4KCAj7XtfD06VNq1KiR+HuTm5tLb775Jrm4uNCIESPIz8+P/vnnH3r69ClNmTKFBEEgBwcHsrCwoLfffrvKendow+gSq9jYWOrbty9ZW1uTn58fOTo6krm5OS1ZskRsBi8+2WlUVBTZ2NjQkCFDxERDJpOp/Eh06dKF7O3tydnZmVq1aqXzSfd0GXtxFy5coH79+pFEIqG2bdtq3I1N37E/efKErKysyM/Pj2xsbKhx48Y6LxtbWbGfO3eOJk2aREOHDiVbW1tq3bo1Xbt2Taex6zJ+xV26vLw8On/+PN29e1dMoBSP03W5+8o89u+//z69/fbbZGNjQ35+fnTr1i2Djb3od83Fixepb9++5ODgQG5ubtS2bVulpMfQYldnw4YNJAiCUtUmVv18+umn5OjoSMnJyWrHPT558oSWLl1KI0aMoH79+tFvv/2mr1BrlCdPnlBwcDAtXrxYqZsUq5h27drRW2+9RUSqrVnx8fG0evVqmjRpEo0YMUKlCBErn8TERDI3N1e6zv7kk0/IzMyMpFIpLVy4UKm31YMHD+js2bNigTZDZnSJ1c6dO8nJyYmCg4PpxYsXlJiYSOPHjydbW1u1TYOKH4AvvviCJBIJbd26Vekip+j/Z2dn08uXLyvl4rgyYi/q1KlTZGZmRps2bTKq2M+cOUOCIJCbm5vRxX706FFq0KAB9ejRg3788cdKib2y4q+qO2yVdewPHDhANjY2FBAQUGnd/yrzuyY3N5eSk5Pp9u3bRhG7giLRiomJoR07dlRK7Ez/FO9z3759qVOnThpvz5ixiouLI0tLS7GqL1Hhea1uPjdWcWFhYdSoUSMKCQmhS5cuUbdu3UgqlVLDhg3Jzs5OHKepjyrRFWV0iVVgYCB17NhRaVlmZiaNGzeOBEEQS18W/6LPy8sjX19fCggIECeBCwsLUxpnUNk/DpUZO1HlnoC6jr3onYjvvvtObHo3ttjDwsKM6rx58uSJynlTmSrz2N++fduozvnq8l3D3V6qj9LOw4KCAnJwcKBFixaJyxITE+nMmTOUlZVFRHwusOpDcZP33LlzRFR482jXrl3k7+9fpb+ZNUVUVBSZm5tTmzZtyMTEhDp16kQhISF08eJFat68OXl5eRltUms0iZVMJqOcnBzq27cvdenSRVyu6J7wzz//ULt27ah+/foqX/bFy6vPmzePtm/fTn5+fjRjxoxKnxCVY1cfe2VXFKvM2KuiHHllxq+4MDLG2Cv72PPnVT+xs6ojl8uVkqpDhw7R1atXlba5ceOGWBEwOzubLl26JM5tpZjfkTFjp/geXLNmDTk4ONCjR4/o7Nmz9Oabb5KpqSm1b99eab5KphsFBQX0zjvvUIMGDWjjxo30/Plz8Tdo0aJFNHbsWEpNTTXK426QidWDBw9o5syZNH36dFq4cKF455SI6I033qDGjRuLBQKK/jhs3bqVBEGgDRs2EJFqC05+fj75+/uTVColQRDIw8NDrPLCsXPs+ord2OPn2Dl2ZjyKvt/37t2j3r17kyAItHLlSqWLmK+//pqkUikdOHCAVqxYQc7OzuTu7k67d+/WR9iMVaohQ4aQr68vTZo0iWxtbalhw4Y80XUli4qKonv37qlMT6PJfIqGzKASq9zcXJozZw5ZWlpS+/btqWHDhiQIAtWvX1+cb+XAgQMkCAL9+OOP4sWC4ociPDycevfuTT4+PiqD8m/cuEELFy4kGxsbsrW1pa+++opj59j1Gruxx8+xc+zMeBRNqNLT02ny5MkkCAJ16NBBHItH9L8k/IMPPiBra2uqX78+mZiY0MKFC/USN2OVLTs7m9q0aUOCIJCdnZ1404kxbRhMYpWenk4LFiyg+vXr05o1ayg0NJRkMhmdOnWKPD09qVu3bpSVlUUFBQXUunVr6t69u9q5MpYuXUoODg7iGAKiwouGadOmkSAING7cOHEiWo6dY9dX7MYeP8fOsTPjUHQOO6LCio62trbk5eVFa9eupcePH6sda9WlSxcSBIHGjBnDY0xYtTd37lyaN2+eSusJY+VlMInVs2fPyMfHh6ZMmUIpKSlK66ZMmUKurq50/fp1IiLatWsXCYJA69evF/v9K+683rx5kyQSCR06dIiI/tekePXqVbp//z7HzrEbROzGHj/HzrEz4/LHH39QkyZNyMLCgqZOnUpXr15VO72ComXrypUr4rnEWHXHlS2ZrhhMYiWXy2nr1q1KyxSV4vbt20cmJibiBHgpKSk0ZMgQcnd3V5nM8urVqyQIAu3cubNqAieOnYhj14Yxx8+xc+zMOMhkMvrss89IEAQaNGgQ/f777+JcZowxxnTLYBIrov/dNS0+mDooKIikUqk4+zsRUWRkJNWqVYuaN28uDqyOjo6madOmUd26dSk2NrbqAieOnWPXjjHHz7Fz7Mw4nD17lnbu3ElRUVH6DoUxxqo1g0qsilM0zc6cOZPc3d3FO7OKC4oTJ06Qn58fCYJAbdq0oU6dOpGpqSktW7aMCgoK9FqmkWPn2LVhzPFz7Bw7M0zFx1nxe84YY5VDICKCgWvfvj3q1auHAwcOQCaTQSqViusSEhLwww8/ICwsDGlpaZg5cyY6deqkx2iVcez6YcyxA8YdP8euH8YcO2OMMVYt6DuzK0tcXBxZWlpSUFCQuEwmkxnFjMwcu34Yc+xExh0/x64fxhw7Y4wxVl1I9J3YleXevXvIycmBv78/ACA2Nha7d+9G3759ER8fr+foSsex64cxxw4Yd/wcu34Yc+yMMcZYdWGwiRX9t4fitWvXYG9vD09PT5w7dw5Tp07Fu+++CyKCRCIRtzMkHLt+GHPsgHHHz7HrhzHHzhhjjFU3JvoOoCSCIAAArly5AmdnZwQFBWHv3r1wd3fHsWPH8Oqrr+o5wpJx7PphzLEDxh0/x64fxhw7Y4wxVu1UXa/D8svOzqY2bdqQIAhkZ2dHGzZs0HdIGuPY9cOYYycy7vg5dv0w5tgZY4yx6sTgqwLOmzcPgiBg2bJlMDc313c45cKx64cxxw4Yd/wcu34Yc+yMMcZYdWHwiZVcLodEYrBDwUrFseuHMccOGHf8HLt+GHPsjDHGWHVh8IkVY4wxxhhjjBk6vsXJGGOMMcYYYxXEiRVjjDHGGGOMVRAnVowxxhhjjDFWQZxYMcYYY4wZmR07dkAQBISHh2v1+PHjx6NevXo6jakqVXT/1QkPD4cgCNixY4fOnrO8BgwYgEmTJuns+UaOHInhw4fr7PlY6TixYowxxliNsXnzZgiCgICAAH2HwvRk9+7d+Oqrr/QdhoqLFy8iJCQE8+bNE5elpKRg9OjRcHR0RP369fHDDz+oPO769euwsrLCs2fPVNbNmzcPv/zyC27fvl2psbNCnFgxxhhjrMYIDg5GvXr1cPXqVTx58kTf4TA9KCmxqlu3LrKzs/HOO+9UfVAAgoKC0Lt3bzRo0EBcNmfOHJw7dw7Lli3Da6+9hkmTJuHSpUvieiLCjBkzMGvWLPj4+Kg8Z9u2bdG+fXusW7euSvahpuPEijHGGGM1wrNnz3Dp0iWsX78erq6uCA4O1ndINU5mZqa+QyiRIAiwsLCAVCqt8teOi4vDsWPHVLrt/fbbb1i1ahVmzJiBb775Bt27d8fRo0fF9cHBwYiIiMCCBQtKfO7hw4fj4MGDyMjIqLT4WSFOrBhjjDFWIwQHB8PR0REDBw7E0KFD1SZWinE2X375JbZu3QpfX1+Ym5vD398f165dU9p2/PjxsLGxQXR0NN544w3Y2NjA1dUVc+bMgUwmE7c7d+4cBEHAuXPn1L5W0TE9d+7cwfjx41G/fn1YWFjA3d0d7777LhITE7Xe719//RUtWrSAhYUFWrRogUOHDqndTi6X46uvvkLz5s1hYWGBWrVqYcqUKUhOTlbZbunSpfD09ISVlRV69uyJ+/fvo169ehg/fry4nWIc1J9//ompU6fCzc0NtWvXBgBERERg6tSpaNy4MSwtLeHs7Ixhw4apHTP177//olevXrC0tETt2rWxYsUKyOVyle0OHz6MgQMHwtPTE+bm5vD19cXnn3+u9F706NEDx44dQ0REBARBgCAI4lizksZYnTlzBt26dYO1tTUcHBwwePBgPHjwQGmbpUuXQhAEPHnyBOPHj4eDgwPs7e0xYcIEZGVllfTWiI4dO4aCggK88sorSsuzs7Ph6Ogo/u3k5CQ+X2ZmJj799FOsWrUKNjY2JT73q6++iszMTJw8ebLMOFjFmOg7AMbY/+zYsQMTJkwQ/zY3N4eTkxNatmyJgQMHYsKECbC1tS338166dAkhISGYNWsWHBwcdBgxY4wZj+DgYAwZMgRmZmZ4++23sWXLFly7dg3+/v4q2+7evRvp6emYMmUKBEHA2rVrMWTIEDx9+hSmpqbidjKZDH379kVAQAC+/PJLnDp1CuvWrYOvry8++OCDcsd48uRJPH36FBMmTIC7uzv+/fdfbN26Ff/++y/+/vtvCIJQrucLCQnBW2+9hWbNmmHVqlVITEzEhAkTxASnqClTpoi/QzNmzMCzZ8+wadMm3Lx5ExcvXhT3e/78+Vi7di0GDRqEvn374vbt2+jbty9ycnLUxjB16lS4urpi8eLFYovVtWvXcOnSJYwcORK1a9dGeHg4tmzZgh49euD+/fuwsrICAMTGxqJnz54oKCjAp59+Cmtra2zduhWWlpYqr7Njxw7Y2Njg448/ho2NDc6cOYPFixcjLS0NQUFBAICFCxciNTUVUVFR2LBhAwCUmpScOnUK/fv3R/369bF06VJkZ2dj48aN6NKlC27cuKFSAGT48OHw8fHBqlWrcOPGDWzbtg1ubm5Ys2ZNqe/TpUuX4OzsjLp16yot9/f3x/r169GkSRM8ffoUf/zxB77//nsAwMqVK+Hl5VVm18VmzZrB0tISFy9exJtvvlnqtqyCiDFmMLZv304AaPny5bRr1y768ccfaeXKldSnTx8SBIHq1q1Lt2/fLvfzBgUFEQB69uyZ7oNmjDEjcP36dQJAJ0+eJCIiuVxOtWvXppkzZypt9+zZMwJAzs7OlJSUJC4/fPgwAaCjR4+Ky8aNGyd+ZxfVtm1bateunfj32bNnCQCdPXtW7Wtt375dXJaVlaUS+549ewgAnT9/Xlym+L0o63u9TZs25OHhQSkpKeKykJAQAkB169YVl124cIEAUHBwsNLj//jjD6XlsbGxZGJiQm+88YbSdkuXLiUANG7cOJUYu3btSgUFBUrbq9vPy5cvEwD6v//7P3HZrFmzCABduXJFXBYXF0f29vYq+6/uOadMmUJWVlaUk5MjLhs4cKDSviuoez/atGlDbm5ulJiYKC67ffs2SSQSGjt2rLhsyZIlBIDeffddped88803ydnZWeW1iuvatavSOaNw584dql27NgEgAPTWW2+RTCajp0+fkqWlJV2+fLnM5yYiatSoEfXv31+jbZn2uCsgYwaof//+GDNmDCZMmID58+fjxIkTOHXqFOLi4vD6668jOztb3yEyxphRCQ4ORq1atdCzZ08AheNpRowYgb179yp1FVMYMWKEUhesbt26AQCePn2qsu3777+v9He3bt3UbqeJoi0xOTk5SEhIQMeOHQEAN27cKNdzxcTE4NatWxg3bhzs7e3F5a+++iqaNWumtO3+/fthb2+PV199FQkJCeK/du3awcbGBmfPngUAnD59GgUFBZg6darS46dPn15iHJMmTVIZt1R0P/Pz85GYmIgGDRrAwcFBaT+PHz+Ojh07okOHDuIyV1dXjB49WuV1ij5neno6EhIS0K1bN2RlZeHhw4clxlcSxfEbP348nJycxOWtWrXCq6++iuPHj6s8Rt25kJiYiLS0tFJfKzExUel8U2jZsiUeP36Ma9eu4fHjxzhw4AAkEglmz56Nt956Cx07dsTBgwfRunVr+Pj4YPny5SAiledxdHREQkKCprvOtMSJFWNGolevXli0aBEiIiLw008/AdCsL/7SpUvxySefAAB8fHzEPuVF+7H/9NNPaNeuHSwtLeHk5ISRI0ciMjKySvePMcYqi0wmw969e9GzZ088e/YMT548wZMnTxAQEICXL1/i9OnTKo+pU6eO0t+Ki97i440sLCzg6uqqsm3x7TSVlJSEmTNn6MgxegAACfBJREFUolatWrC0tISrq6tY7S01NbVczxUREQEAaNiwocq6xo0bK/39+PFjpKamws3NDa6urkr/MjIyEBcXp/ScRSvXAYVjf9QlBgDUVqvLzs7G4sWL4e3tDXNzc7i4uMDV1RUpKSlK+xkREaFR/EDhWKw333wT9vb2sLOzg6urK8aMGQOg/MdO8dolvVbTpk2RkJCgUoxD0/NGHXUJEVB4jrVv31485mfOnEFISAhWr16N0NBQjBw5ErNmzcKPP/6IzZs3q52Hi4jK3Y2UlR+PsWLMiLzzzjtYsGABQkJCMGnSJI364g8ZMgSPHj3Cnj17sGHDBri4uACAeCHwxRdfYNGiRRg+fDjee+89xMfHY+PGjejevTtu3rzJY7IYY0bvzJkziImJwd69e7F3716V9cHBwejTp4/SspIqwxW/+NWkglxJF7TqWsqGDx+OS5cu4ZNPPkGbNm1gY2MDuVyOfv36qS3YoCtyuRxubm4lVkosnjyWh7rxUNOnT8f27dsxa9YsdOrUCfb29hAEASNHjtRqP1NSUhAYGAg7OzssX74cvr6+sLCwwI0bNzBv3rxKPXZFaXreFOfs7KxR8iWTyTBz5kx8+umn8PLywueff47OnTuL47OnTJmC4OBgpfHaQGFipy5BZbrFiRVjRqR27dqwt7dHWFgYgMIBwbNnz1bapmPHjnj77bfx119/oVu3bmjVqhX8/PywZ88evPHGG0oDbSMiIrBkyRKsWLFCqVTrkCFD0LZtW2zevLnUEq6MMWYMgoOD4ebmhv/85z8q6w4ePIhDhw7h22+/VZsA6IKi1SIlJUVpuaJFRCE5ORmnT5/GsmXLsHjxYnH548ePtXpdRSEEdY8PDQ1V+tvX1xenTp1Cly5dSj0Oiud88uSJUktUYmJiuVrpDhw4gHHjxinNr5STk6NyjOrWratR/OfOnUNiYiIOHjyI7t27i8vVTZqracuNYl+LvxYAPHz4EC4uLrC2ttboucrSpEkT/PLLL2Vut2XLFqSnp2POnDkAgBcvXsDT01Nc7+npiejoaKXHFBQUIDIyEq+//rpOYmUl466AjBkZGxsbpKenA6h4X/yDBw9CLpdj+PDhSn3q3d3d0bBhQ7FPPWOMGavs7GwcPHgQr732GoYOHaryb9q0aUhPT8eRI0cqLYa6detCKpXi/PnzSss3b96s9LeitaN464a6yWw14eHhgTZt2mDnzp1KXeFOnjyJ+/fvK207fPhwyGQyfP755yrPU1BQICY8vXv3homJCbZs2aK0zaZNm8oVm1QqVdnPjRs3qrTiDRgwAH///TeuXr0qLouPj1dpWVN37PLy8lSOMQBYW1tr1DWw6PErmvDdu3cPISEhGDBgQJnPoalOnTohOTm51LF5SUlJWLJkCYKCgmBhYQEAqFWrltL4sQcPHsDd3V3pcffv30dOTg46d+6ss3iZetxixZiRycjIgJubG4DCL9lly5Zh7969Yv93BU1+NB4/fgwiKrF7QNGSwowxZoyOHDmC9PT0Eu/Wd+zYUZwseMSIEZUSg729PYYNG4aNGzdCEAT4+vrit99+U/netrOzQ/fu3bF27Vrk5+fDy8sLISEhaltdNLVq1SoMHDgQXbt2xbvvvoukpCRs3LgRzZs3V5owNjAwEFOmTMGqVatw69Yt9OnTB6ampnj8+DH279+Pr7/+GkOHDkWtWrUwc+ZMrFu3Dq+//jr69euH27dv4/fff4eLi4vGrUGvvfYadu3aBXt7ezRr1gyXL1/GqVOn4OzsrLTd3LlzsWvXLvTr1w8zZ84Uy63XrVsXd+7cEbfr3LkzHB0dMW7cOMyYMQOCIGDXrl1qu+C1a9cOP//8Mz7++GP4+/vDxsYGgwYNUhtnUFAQ+vfvj06dOmHixIliuXV7e3ssXbpUo33VxMCBA2FiYoJTp05h8uTJardZtGgRWrZsiWHDhonL3nrrLSxfvhwffPAB6tati++++w7r169XetzJkydhZWWFV199VWfxMvU4sWLMiERFRSE1NVUcwFrRvvhyuRyCIOD3339X2y+8tLk9GGPMGAQHB8PCwqLEi0qJRIKBAwciODi4QpPwlmXjxo3Iz8/Ht99+C3NzcwwfPhxBQUFo0aKF0na7d+/G9OnT8Z///AdEhD59+uD3339X6u5VHv369cP+/fvx2WefYf78+fD19cX27dtx+PBhlQmLv/32W7Rr1w7fffcdFixYABMTE9SrVw9jxoxBly5dxO3WrFkDKysrfP/99zh16hQ6deqEkJAQdO3aVWxJKcvXX38NqVSK4OBg5OTkoEuXLjh16hT69u2rtJ2HhwfOnj2L6dOnY/Xq1XB2dsb7778PT09PTJw4UdzO2dkZv/32G2bPno3PPvsMjo6OGDNmDHr37q3ynFOnTsWtW7ewfft2bNiwAXXr1i0xsXrllVfwxx9/YMmSJVi8eDFMTU0RGBiINWvWqC3Koa1atWphwIAB2Ldvn9rE6u7du9i2bRuuXLmitLxly5bYvn07li5divT0dEydOlXl8fv378eQIUO0mgeTlZOeyrwzxtRQzPlx7do1tetXrlxJAGjbtm2UlJREAGjZsmVK2zx69IgA0JIlS8RlX375pdr5TtauXUsAKDQ0VNe7whhjrAZJTk4mALRixQp9h2K0zp8/TxKJhB49eqSz57x58yYJgkA3b97U2XOykvEYK8aMxJkzZ/D555/Dx8cHo0ePLldffMXg2uKDgocMGQKpVIply5apPA8RVerdW8YYY8ZJ3VyKit+eHj16VG0w1Ui3bt3Qp08frF27VmfPuXr1agwdOhRt2rTR2XOyknFXQMYM0O+//46HDx+ioKAAL1++xJkzZ3Dy5EnUrVsXR44cgYWFBSwsLDTui9+uXTsAwMKFCzFy5EiYmppi0KBB8PX1xYoVKzB//nyEh4fjjTfegK2tLZ49e4ZDhw5h8uTJYuUhxhhjDAB+/vln7NixAwMGDICNjQ3++usv7NmzB3369FHqMsjK7/fff9fp86mbXoBVHk6sGDNAijK7ZmZmcHJyQsuWLfHVV19hwoQJSn2kNe2L7+/vj88//xzffvst/vjjD8jlcjx79gzW1tb49NNP0ahRI2zYsAHLli0DAHh7e6NPnz5cmpUxxpiKVq1awcTEBGvXrkVaWppY0GLFihX6Do0xvRKoeP8fxhhjjDHGGGPlwmOsGGOMMcYYY6yCOLFijDHGGGOMsQrixIoxxhhjjDHGKogTK8YYY4wxxhirIE6sGGOMMcYYY6yCOLFijDHGGGOMsQrixIoxxhhjjDHGKogTK8YYY4wxxhirIE6sGGOMMcYYY6yCOLFijDHGGGOMsQrixIoxxhhjjDHGKogTK8YYY4wxxhirIE6sGGOMMcYYY6yC/h8bSczOGr1HUQAAAABJRU5ErkJggg==", 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" - ] + } }, "metadata": {}, "output_type": "display_data" @@ -93194,7 +93045,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -93231,7 +93082,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -93250,7 +93101,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": { "tags": [ "nbsphinx-thumbnail" @@ -93296,7 +93147,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -93313,7 +93164,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -93343,20 +93194,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/soiling.py:27: UserWarning:\n", - "\n", - "The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Calculate the daily insolation, required for the SRR calculation\n", "daily_insolation = filtered['insolation'].resample('D').sum()\n", @@ -93373,14 +93213,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The P50 insolation-weighted soiling ratio is 0.953\n" + "The P50 insolation-weighted soiling ratio is 0.952\n" ] } ], @@ -93390,14 +93230,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The 68.2% confidence interval for the insolation-weighted soiling ratio is 0.949–0.956\n" + "The 68.2% confidence interval for the insolation-weighted soiling ratio is 0.948–0.956\n" ] } ], @@ -93408,22 +93248,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 39, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:172: UserWarning:\n", - "\n", - "The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - "\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -93440,22 +93270,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 40, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:232: UserWarning:\n", - "\n", - "The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - "\n" - ] - }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -93473,7 +93293,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -93504,6 +93324,8 @@ " soiling_rate_high\n", " inferred_start_loss\n", " inferred_end_loss\n", + " inferred_recovery\n", + " inferred_begin_shift\n", " length\n", " valid\n", " \n", @@ -93516,8 +93338,10 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 0.756022\n", - " 0.756022\n", + " 0.757953\n", + " 0.757953\n", + " NaN\n", + " NaN\n", " 6\n", " False\n", " \n", @@ -93530,6 +93354,8 @@ " 0.0\n", " 0.793434\n", " 0.793434\n", + " NaN\n", + " NaN\n", " 0\n", " False\n", " \n", @@ -93542,6 +93368,8 @@ " 0.0\n", " 0.819566\n", " 0.819566\n", + " NaN\n", + " NaN\n", " 0\n", " False\n", " \n", @@ -93554,6 +93382,8 @@ " 0.0\n", " 1.053380\n", " 1.053380\n", + " NaN\n", + " NaN\n", " 1\n", " False\n", " \n", @@ -93566,6 +93396,8 @@ " 0.0\n", " 1.033119\n", " 1.033119\n", + " NaN\n", + " NaN\n", " 1\n", " False\n", " \n", @@ -93582,21 +93414,21 @@ "4 2010-03-08 00:00:00-07:00 2010-03-09 00:00:00-07:00 0.0 \n", "\n", " soiling_rate_low soiling_rate_high inferred_start_loss \\\n", - "0 0.0 0.0 0.756022 \n", + "0 0.0 0.0 0.757953 \n", "1 0.0 0.0 0.793434 \n", "2 0.0 0.0 0.819566 \n", "3 0.0 0.0 1.053380 \n", "4 0.0 0.0 1.033119 \n", "\n", - " inferred_end_loss length valid \n", - "0 0.756022 6 False \n", - "1 0.793434 0 False \n", - "2 0.819566 0 False \n", - "3 1.053380 1 False \n", - "4 1.033119 1 False " + " inferred_end_loss inferred_recovery inferred_begin_shift length valid \n", + "0 0.757953 NaN NaN 6 False \n", + "1 0.793434 NaN NaN 0 False \n", + "2 0.819566 NaN NaN 0 False \n", + "3 1.053380 NaN NaN 1 False \n", + "4 1.033119 NaN NaN 1 False " ] }, - "execution_count": 22, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -93609,19 +93441,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 42, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:272: UserWarning:\n", - "\n", - "The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - "\n" - ] - }, { "data": { "image/png": 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", @@ -93648,7 +93470,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -93810,7 +93632,7 @@ "11 8 " ] }, - "execution_count": 24, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -93824,7 +93646,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -93872,9 +93694,9 @@ " \n", " 2\n", " 2012\n", - " 0.950871\n", - " 0.943630\n", - " 0.956604\n", + " 0.947953\n", + " 0.938482\n", + " 0.954513\n", " \n", " \n", " 3\n", @@ -93912,14 +93734,14 @@ " year soiling_ratio_median soiling_ratio_low soiling_ratio_high\n", "0 2010 0.962952 0.954986 0.969507\n", "1 2011 0.957442 0.951042 0.962425\n", - "2 2012 0.950871 0.943630 0.956604\n", + "2 2012 0.947953 0.938482 0.954513\n", "3 2013 0.948067 0.938270 0.956403\n", "4 2014 0.934236 0.915437 0.947448\n", "5 2015 0.959483 0.945367 0.967091\n", "6 2016 0.966123 0.961269 0.970014" ] }, - "execution_count": 25, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -93955,7 +93777,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -93973,7 +93795,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -93982,7 +93804,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -94093,7 +93915,7 @@ "2010-03-01 00:00:00-07:00 0.857710 " ] }, - "execution_count": 28, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -94113,7 +93935,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -94139,7 +93961,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -94199,7 +94021,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "rdtools3-nb", "language": "python", "name": "python3" }, @@ -94213,7 +94035,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/docs/sphinx/source/changelog/v2.2.0-beta.2.rst b/docs/sphinx/source/changelog/v2.2.0-beta.2.rst index 32fa3da2..6f0dab31 100644 --- a/docs/sphinx/source/changelog/v2.2.0-beta.2.rst +++ b/docs/sphinx/source/changelog/v2.2.0-beta.2.rst @@ -20,4 +20,4 @@ Requirements Contributors ------------ * Martin Springer (:ghuser:`martin-springer`) -* Michael Deceglie (:ghuser:`mdeceglie`) \ No newline at end of file +* Michael Deceglie (:ghuser:`mdeceglie`) diff --git a/docs/sphinx/source/changelog/v3.0.0-beta.0.rst b/docs/sphinx/source/changelog/v3.0.0-beta.0.rst index f69ffff4..831b0834 100644 --- a/docs/sphinx/source/changelog/v3.0.0-beta.0.rst +++ b/docs/sphinx/source/changelog/v3.0.0-beta.0.rst @@ -15,14 +15,17 @@ when compared with older versions of RdTools * Upgrade pvlib 0.9.0 to 0.11.0 (:pull:`428`) +* Upgrade soiling algorithms SRR and CODS. Remove experimental warning label. (:pull:`426`) + Enhancements ------------ * Added a new wrapper function for clearsky filters (:pull:`412`) * Improve test coverage, especially for the newly added filter capabilities (:pull:`413`) * Added codecov.yml configuration file (:pull:`420`) +* Added new methods perfect_clean_complex and inferred_clean_complex which detects negative shifts and piecewise changes in the slope for soiling detection in :py:func:`~rdtools.soiling.soiling_srr`(:pull:`426`) * Availability module no longer considered experimental (:pull:`429`) * Add capability to seed the CircularBlockBootstrap (:pull:`429`) -* Allow sub-daily aggregation in :py:func:`~rdtools.degradation.degradation_year_on_year` (:pull:`390`) +* Allow sub-daily aggregation in :py:func:`~rdtools.degradation.degradation_year_on_year` (:pull:`390`) Bug fixes --------- @@ -31,11 +34,15 @@ Bug fixes * Deploy workflow was replaced with trusted publisher workflow for pypi (:pull:`427`) * Fix pandas 2.0.0 deprications and update syntax changes (:pull:`428`) * Fix numpy 2.0.0 deprications and update syntax changes (:pull:`428`) +* Fixed pylint bare except error for :py:func:`~rdtools.soiling.segmented_soiling_period` +in ``soiling.py`` (:pull:`432`) Tests ----- * Testing matrix was updated to include python = [3.9, 3.10, 3.11, 3.12] (:pull:`428`) * nbval sanitization rules were added for date and time stamp (:pull:`428`) +* Added pytests to cover invalid segementations for +:py:func:`~rdtools.soiling.segmented_soiling_period` in ``soiling_cods_test.py`` (:pull:`432`) Documentation ------------- @@ -184,5 +191,8 @@ Contributors * Martin Springer (:ghuser:`martin-springer`) * Michael Deceglie (:ghuser:`mdeceglie`) * Kirsten Perry (:ghuser:`kperrynrel`) +* Matthew Muller (:ghuser:`mmuller`) +* Noah Moyer (:ghuser:`noromo01`) +* Quyen Nguyen (:ghuser:`qnguyen345`) * Dirk Jordan (:ghuser:`dirkjordan`) * Chris Deline (:ghuser:`cdeline`) diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb index 7a44ee09..be5d0cc0 100644 --- a/docs/system_availability_example.ipynb +++ b/docs/system_availability_example.ipynb @@ -139,7 +139,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -222,13 +222,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ls/3vjh_45x2nd120557szqfcm8g4gf61/T/ipykernel_18955/689292658.py:3: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_65036\\689292658.py:3: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " fig.axes[1].legend(loc='upper left')\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -608,7 +608,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -618,7 +618,7 @@ } ], "source": [ - "aa2.plot();" + "fig = aa2.plot()" ] }, { @@ -631,7 +631,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "rdtools3-nb", "language": "python", "name": "python3" }, diff --git a/rdtools/plotting.py b/rdtools/plotting.py index b589dab7..50fd0548 100644 --- a/rdtools/plotting.py +++ b/rdtools/plotting.py @@ -4,7 +4,6 @@ import pandas as pd import plotly.express as px import numpy as np -import warnings def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield, @@ -133,15 +132,10 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, profile_alpha=0.05, ymin=None, ymax=None, profiles=None, point_color=None, profile_color='C1'): - ''' + """ Create figure to visualize Monte Carlo of soiling profiles used in the SRR analysis. - .. warning:: - The soiling module is currently experimental. The API, results, - and default behaviors may change in future releases (including MINOR - and PATCH releases) as the code matures. - Parameters ---------- soiling_info : dict @@ -168,13 +162,7 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, Returns ------- fig : matplotlib.figure.Figure - ''' - warnings.warn( - 'The soiling module is currently experimental. The API, results, ' - 'and default behaviors may change in future releases (including MINOR ' - 'and PATCH releases) as the code matures.' - ) - + """ fig, ax = plt.subplots() renormalized = normalized_yield / soiling_info['renormalizing_factor'] ax.plot(renormalized.index, renormalized, 'o', alpha=point_alpha, @@ -197,14 +185,9 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, profile_alpha=1, ymin=None, ymax=None, point_color=None, profile_color=None): - ''' + """ Create figure to visualize valid soiling profiles used in the SRR analysis. - .. warning:: - The soiling module is currently experimental. The API, results, - and default behaviors may change in future releases (including MINOR - and PATCH releases) as the code matures. - Parameters ---------- soiling_info : dict @@ -228,13 +211,7 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, Returns ------- fig : matplotlib.figure.Figure - ''' - warnings.warn( - 'The soiling module is currently experimental. The API, results, ' - 'and default behaviors may change in future releases (including MINOR ' - 'and PATCH releases) as the code matures.' - ) - + """ sratio = soiling_info['soiling_ratio_perfect_clean'] fig, ax = plt.subplots() renormalized = normalized_yield / soiling_info['renormalizing_factor'] @@ -249,14 +226,9 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, def soiling_rate_histogram(soiling_info, bins=None): - ''' + """ Create histogram of soiling rates found in the SRR analysis. - .. warning:: - The soiling module is currently experimental. The API, results, - and default behaviors may change in future releases (including MINOR - and PATCH releases) as the code matures. - Parameters ---------- soiling_info : dict @@ -268,12 +240,7 @@ def soiling_rate_histogram(soiling_info, bins=None): Returns ------- fig : matplotlib.figure.Figure - ''' - warnings.warn( - 'The soiling module is currently experimental. The API, results, ' - 'and default behaviors may change in future releases (including MINOR ' - 'and PATCH releases) as the code matures.' - ) + """ soiling_summary = soiling_info['soiling_interval_summary'] fig, ax = plt.subplots() diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 92a3bfb8..600fdd03 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,44 +1,39 @@ -''' +""" Functions for calculating soiling metrics from photovoltaic system data. +""" -The soiling module is currently experimental. The API, results, -and default behaviors may change in future releases (including MINOR -and PATCH releases) as the code matures. -''' from rdtools import degradation as RdToolsDeg from rdtools.bootstrap import _make_time_series_bootstrap_samples +import bisect +import itertools +import sys +import time import warnings -import pandas as pd import numpy as np -from scipy.stats.mstats import theilslopes -from filterpy.kalman import KalmanFilter +import pandas as pd +import scipy.stats as st +import statsmodels.api as sm from filterpy.common import Q_discrete_white_noise -import itertools -import bisect -import time -import sys +from filterpy.kalman import KalmanFilter +from scipy.optimize import curve_fit +from scipy.stats.mstats import theilslopes from statsmodels.tsa.seasonal import STL from statsmodels.tsa.stattools import adfuller -import statsmodels.api as sm -lowess = sm.nonparametric.lowess -warnings.warn( - 'The soiling module is currently experimental. The API, results, ' - 'and default behaviors may change in future releases (including MINOR ' - 'and PATCH releases) as the code matures.' -) +lowess = sm.nonparametric.lowess # Used in CODSAnalysis/Matt # Custom exception class NoValidIntervalError(Exception): - '''raised when no valid rows appear in the result dataframe''' + """raised when no valid rows appear in the result dataframe""" + pass -class SRRAnalysis(): - ''' +class SRRAnalysis: + """ Class for running the stochastic rate and recovery (SRR) photovoltaic soiling loss analysis presented in Deceglie et al. JPV 8(2) p547 2018 @@ -55,10 +50,9 @@ class SRRAnalysis(): precipitation_daily : pandas.Series, default None Daily total precipitation. (Ignored if ``clean_criterion='shift'`` in subsequent calculations.) - ''' + """ - def __init__(self, energy_normalized_daily, insolation_daily, - precipitation_daily=None): + def __init__(self, energy_normalized_daily, insolation_daily, precipitation_daily=None): self.pm = energy_normalized_daily # daily performance metric self.insolation_daily = insolation_daily self.precipitation_daily = precipitation_daily # daily precipitation @@ -66,23 +60,22 @@ def __init__(self, energy_normalized_daily, insolation_daily, # insolation-weighted soiling ratios in _calc_monte: self.monte_losses = [] - if pd.infer_freq(self.pm.index) != 'D': - raise ValueError('Daily performance metric series must have ' - 'daily frequency') + if pd.infer_freq(self.pm.index) != "D": + raise ValueError("Daily performance metric series must have " "daily frequency") - if pd.infer_freq(self.insolation_daily.index) != 'D': - raise ValueError('Daily insolation series must have ' - 'daily frequency') + if pd.infer_freq(self.insolation_daily.index) != "D": + raise ValueError("Daily insolation series must have " "daily frequency") if self.precipitation_daily is not None: - if pd.infer_freq(self.precipitation_daily.index) != 'D': - raise ValueError('Precipitation series must have ' - 'daily frequency') - - def _calc_daily_df(self, day_scale=13, clean_threshold='infer', - recenter=True, clean_criterion='shift', precip_threshold=0.01, - outlier_factor=1.5): - ''' + if pd.infer_freq(self.precipitation_daily.index) != "D": + raise ValueError("Precipitation series must have " "daily frequency") + + ############################################################################### + # add neg_shift and piecewise into parameters/Matt + def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, + clean_criterion="shift", precip_threshold=0.01, outlier_factor=1.5, + neg_shift=False, piecewise=False): + """ Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily performance @@ -118,26 +111,39 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. - ''' - if (day_scale % 2 == 0) and ('shift' in clean_criterion): - warnings.warn('An even value of day_scale was passed. An odd value is ' - 'recommended, otherwise, consecutive days may be erroneously ' - 'flagged as cleaning events. ' - 'See https://github.com/NREL/rdtools/issues/189') + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. + """ + if (day_scale % 2 == 0) and ("shift" in clean_criterion): + warnings.warn("An even value of day_scale was passed. An odd value is " + "recommended, otherwise, consecutive days may be erroneously " + "flagged as cleaning events. " + "See https://github.com/NREL/rdtools/issues/189") df = self.pm.to_frame() - df.columns = ['pi'] + df.columns = ["pi"] df_insol = self.insolation_daily.to_frame() - df_insol.columns = ['insol'] + df_insol.columns = ["insol"] df = df.join(df_insol) precip = self.precipitation_daily + if precip is not None: df_precip = precip.to_frame() - df_precip.columns = ['precip'] + df_precip.columns = ["precip"] df = df.join(df_precip) else: - df['precip'] = 0 + df["precip"] = 0 # find first and last valid data point start = df[~df.pi.isnull()].index[0] @@ -145,16 +151,16 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', df = df[start:end] # create a day count column - df['day'] = range(len(df)) + df["day"] = range(len(df)) # Recenter to median of first year, as in YoY degradation if recenter: - oneyear = start + pd.Timedelta('364d') - renorm = df.loc[start:oneyear, 'pi'].median() + oneyear = start + pd.Timedelta("364d") + renorm = df.loc[start:oneyear, "pi"].median() else: renorm = 1 - df['pi_norm'] = df['pi'] / renorm + df["pi_norm"] = df["pi"] / renorm # Find the beginning and ends of outages longer than dayscale bfill = df['pi_norm'].bfill(limit=day_scale) @@ -171,50 +177,110 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', df_ffill = df.ffill(limit=day_scale).copy() # Calculate rolling median - df['pi_roll_med'] = \ - df_ffill.pi_norm.rolling(day_scale, center=True).median() + df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median() # Detect steps in rolling median - df['delta'] = df.pi_roll_med.diff() - if clean_threshold == 'infer': + df["delta"] = df.pi_roll_med.diff() + if clean_threshold == "infer": deltas = abs(df.delta) - clean_threshold = deltas.quantile(0.75) + \ - outlier_factor * (deltas.quantile(0.75) - deltas.quantile(0.25)) + clean_threshold = deltas.quantile(0.75) + outlier_factor * ( + deltas.quantile(0.75) - deltas.quantile(0.25) + ) - df['clean_event_detected'] = (df.delta > clean_threshold) - precip_event = (df['precip'] > precip_threshold) + df["clean_event_detected"] = df.delta > clean_threshold - if clean_criterion == 'precip_and_shift': + ########################################################################## + # Matt added these lines but the function "_collapse_cleaning_events" + # was written by Asmund, it reduces multiple days of cleaning events + # in a row to a single event + if piecewise is True: + reduced_cleaning_events = _collapse_cleaning_events( + df.clean_event_detected, df.delta.values, 5 + ) + df["clean_event_detected"] = reduced_cleaning_events + + ########################################################################## + precip_event = df["precip"] > precip_threshold + + if clean_criterion == "precip_and_shift": # Detect which cleaning events are associated with rain # within a 3 day window - precip_event = precip_event.rolling( - 3, center=True, min_periods=1).apply(any).astype(bool) - df['clean_event'] = (df['clean_event_detected'] & precip_event) - elif clean_criterion == 'precip_or_shift': - df['clean_event'] = (df['clean_event_detected'] | precip_event) - elif clean_criterion == 'precip': - df['clean_event'] = precip_event - elif clean_criterion == 'shift': - df['clean_event'] = df['clean_event_detected'] + precip_event = ( + precip_event.rolling(3, center=True, min_periods=1).apply(any).astype(bool)) + df["clean_event"] = df["clean_event_detected"] & precip_event + elif clean_criterion == "precip_or_shift": + df["clean_event"] = df["clean_event_detected"] | precip_event + elif clean_criterion == "precip": + df["clean_event"] = precip_event + elif clean_criterion == "shift": + df["clean_event"] = df["clean_event_detected"] else: - raise ValueError('clean_criterion must be one of ' - '{"precip_and_shift", "precip_or_shift", ' - '"precip", "shift"}') + raise ValueError("clean_criterion must be one of" + '{"precip_and_shift", "precip_or_shift", "precip", "shift"}') - df['clean_event'] = df.clean_event | out_start | out_end + df["clean_event"] = df.clean_event | out_start | out_end + + ####################################################################### + # add negative shifts which allows further segmentation of the soiling + # intervals and handles correction for data outages/Matt + df.delta = df.delta.fillna(0) # to avoid NA corrupting calculation + if neg_shift is True: + df["drop_event"] = df.delta < -2.5 * clean_threshold + df["break_event"] = df.clean_event | df.drop_event + else: + df["break_event"] = df.clean_event.copy() - df = df.fillna(0) + ####################################################################### + # This happens earlier than in the original code but is necessary + # for adding piecewise breakpoints/Matt # Give an index to each soiling interval/run - df['run'] = df.clean_event.cumsum() - df.index.name = 'date' # this gets used by name + df["run"] = df.break_event.cumsum() + df.index.name = "date" # this gets used by name + + ####################################################################### + # df.fillna(0) /remove as the zeros introduced in pi_norm negatively + # impact various fits in the code, I havent yet found the original purpose + # or a failure due to removing/Matt + + ##################################################################### + # piecewise=True enables adding a single breakpoint per soiling intervals + # if statistical criteria are met with the piecewise linear fit + # compared to a single linear fit. Intervals <45 days reqire more + # stringent statistical improvements/Matt + if piecewise is True: + warnings.warn("Piecewise = True was passed, for both Piecewise=True" + "and neg_shift=True cleaning_method choices should" + "be perfect_clean_complex or inferred_clean_complex") + min_soil_length = 27 # min threshold of days necessary for piecewise fit + piecewise_loop = sorted(list(set(df["run"]))) + cp_dates = [] + for r in piecewise_loop: + run = df[df["run"] == r] + pr = run.pi_norm.copy() + pr = pr.ffill() # linear fitting cant handle nans + pr = pr.bfill() # catch first position nan + if len(run) > min_soil_length and run.pi_norm.sum() > 0: + sr, cp_date = segmented_soiling_period(pr, days_clean_vs_cp=13) + if cp_date is not None: + cp_dates.append(pr.index[cp_date]) + # save changes to df, note I would like to rename "clean_event" from + # original code to something like "break_event + df["slope_change_event"] = df.index.isin(cp_dates) + df["break_event"] = df.break_event | df.slope_change_event + df["run"] = df.break_event.cumsum() + else: + df["slope_change_event"] = False + ###################################################################### self.renorm_factor = renorm self.daily_df = df - def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, - max_negative_step=0.05, min_interval_length=7): - ''' + ###################################################################### + # added neg_shift into parameters in the following def/Matt + def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negative_step=0.05, + min_interval_length=7, neg_shift=False, piecewise=False): + """ Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of self.daily_df with additional columns calculated during analysis. @@ -234,13 +300,19 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, min_interval_length : int, default 7 The minimum duration for an interval to be considered valid. Cannot be less than 2 (days). - ''' + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + """ daily_df = self.daily_df result_list = [] if trim: # ignore first and last interval - res_loop = sorted(list(set(daily_df['run'])))[1:-1] + res_loop = sorted(list(set(daily_df["run"])))[1:-1] else: res_loop = sorted(list(set(daily_df['run']))) @@ -257,98 +329,273 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, # valid=False row if not run_filtered.empty: run = run_filtered + #################################################################### + # see commented changes result_dict = { - 'start': start, - 'end': end, - 'length': length, - 'run': r, - 'run_slope': 0, - 'run_slope_low': 0, - 'run_slope_high': 0, - 'max_neg_step': min(run.delta), - 'start_loss': 1, - 'inferred_start_loss': run.pi_norm.mean(), - 'inferred_end_loss': run.pi_norm.mean(), - 'valid': False + "start": start, + "end": end, + "length": length, + "run": r, + "run_slope": 0, + "run_slope_low": 0, + "run_slope_high": 0, + "max_neg_step": min(run.delta), + "start_loss": 1, + "inferred_start_loss": ( + np.nan if run.pi_norm.isna().any() else run.pi_norm.median() + ), + "inferred_end_loss": ( + np.nan if run.pi_norm.isna().any() else run.pi_norm.median() + ), + "slope_err": 10000, # added high dummy start value for later logic/Matt + "valid": False, + "clean_event": run.clean_event.iloc[0], # record of clean events to distiguisih + # from other breaks/Matt + "run_loss_baseline": 0.0, # loss from the polyfit over the soiling intercal/Matt + ############################################################## } if len(run) > min_interval_length and run.pi_norm.sum() > 0: fit = theilslopes(run.pi_norm, run.day) fit_poly = np.poly1d(fit[0:2]) - result_dict['run_slope'] = fit[0] - result_dict['run_slope_low'] = fit[2] - result_dict['run_slope_high'] = min([0.0, fit[3]]) - result_dict['inferred_start_loss'] = fit_poly(start_day) - result_dict['inferred_end_loss'] = fit_poly(end_day) - result_dict['valid'] = True + result_dict["run_slope"] = fit[0] + result_dict["run_slope_low"] = fit[2] + result_dict["run_slope_high"] = min([0.0, fit[3]]) + result_dict["valid"] = True + ######################################################## + # moved the following 2 line to the next section within conditional statement/Matt + # result_dict['inferred_start_loss'] = fit_poly(start_day) + # result_dict['inferred_end_loss'] = fit_poly(end_day) + + #################################################### + # the following is moved here so median values are retained/Matt + # for soiling inferrences when rejected fits occur + + result_dict["slope_err"] = ( + result_dict["run_slope_high"] - result_dict["run_slope_low"] + ) / abs(result_dict["run_slope"]) + + if (result_dict["slope_err"] <= (max_relative_slope_error / 100.0)) & ( + result_dict["run_slope"] < 0): + result_dict["inferred_start_loss"] = fit_poly(start_day) + result_dict["inferred_end_loss"] = fit_poly(end_day) + ############################################# + # calculate loss over soiling interval per polyfit/matt + result_dict["run_loss_baseline"] = ( + result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"]) + + ############################################### + result_list.append(result_dict) results = pd.DataFrame(result_list) if results.empty: - raise NoValidIntervalError('No valid soiling intervals were found') + raise NoValidIntervalError("No valid soiling intervals were found") + """ # Filter results for each interval, # setting invalid interval to slope of 0 + #moved above to line 356/Matt results['slope_err'] = ( results.run_slope_high - results.run_slope_low)\ / abs(results.run_slope) - # critera for exclusions - filt = ( - (results.run_slope > 0) | - (results.slope_err >= max_relative_slope_error / 100.0) | - (results.max_neg_step <= -1.0 * max_negative_step) - ) - - results.loc[filt, 'run_slope'] = 0 - results.loc[filt, 'run_slope_low'] = 0 - results.loc[filt, 'run_slope_high'] = 0 - results.loc[filt, 'valid'] = False + """ + ############################################################### + # negative shifts are now used as breaks for soiling intervals/Matt + # so new criteria for final filter to modify dataframe + if neg_shift is True: + warnings.warn("neg_shift = True was passed, for both Piecewise=True" + "and neg_shift=True cleaning_method choices should" + "be perfect_clean_complex or inferred_clean_complex") + filt = ((results.run_slope > 0) + | (results.slope_err >= max_relative_slope_error / 100.0) + # |(results.max_neg_step <= -1.0 * max_negative_step) + ) + + results.loc[filt, "run_slope"] = 0 + results.loc[filt, "run_slope_low"] = 0 + results.loc[filt, "run_slope_high"] = 0 + # only intervals that are now not valid are those that dont meet + # the minimum inteval length or have no data + # results.loc[filt, 'valid'] = False + ################################################################## + # original code below setting soiling intervals with extreme negative + # shift to zero slopes, /Matt + if neg_shift is False: + filt = ((results.run_slope > 0) + | (results.slope_err >= max_relative_slope_error / 100.0) + | (results.max_neg_step <= -1.0 * max_negative_step) + # remove line 389, want to store data for inferred values + # for calculations below + # |results.loc[filt, 'valid'] = False + ) + results.loc[filt, "run_slope"] = 0 + results.loc[filt, "run_slope_low"] = 0 + results.loc[filt, "run_slope_high"] = 0 + # results.loc[filt, "valid"] = False # Calculate the next inferred start loss from next valid interval - results['next_inferred_start_loss'] = np.clip( - results[results.valid].inferred_start_loss.shift(-1), - 0, 1) + results["next_inferred_start_loss"] = np.clip( + results[results.valid].inferred_start_loss.shift(-1), 0, 1) + # Calculate the inferred recovery at the end of each interval - results['inferred_recovery'] = np.clip( - results.next_inferred_start_loss - results.inferred_end_loss, - 0, 1) + ######################################################################## + # remove clipping on 'inferred_recovery' so absolute recovery can be + # used in later step where clipping can be considered/Matt + results["inferred_recovery"] = results.next_inferred_start_loss - results.inferred_end_loss + + ######################################################################## + # calculate beginning inferred shift (end of previous soiling period + # to start of current period)/Matt + results["prev_end"] = results[results.valid].inferred_end_loss.shift(1) + # if the current interval starts with a clean event, the previous end + # is a nan, and the current interval is valid then set prev_end=1 + results.loc[ + (results.clean_event is True) & (np.isnan(results.prev_end) & (results.valid is True)), + "prev_end"] = 1 # clean_event or clean_event_detected + results["inferred_begin_shift"] = results.inferred_start_loss - results.prev_end + # if orginal shift detection was positive the shift should not be + # negative due to fitting results + results.loc[results.clean_event, "inferred_begin_shift"] = np.clip( + results.inferred_begin_shift, 0, 1) + ####################################################################### + + if neg_shift is False: + results.loc[filt, "valid"] = False if len(results[results.valid]) == 0: - raise NoValidIntervalError('No valid soiling intervals were found') + raise NoValidIntervalError("No valid soiling intervals were found") new_start = results.start.iloc[0] new_end = results.end.iloc[-1] pm_frame_out = daily_df[new_start:new_end] - pm_frame_out = pm_frame_out.reset_index() \ - .merge(results, how='left', on='run') \ - .set_index('date') - - pm_frame_out['loss_perfect_clean'] = np.nan - pm_frame_out['loss_inferred_clean'] = np.nan - pm_frame_out['days_since_clean'] = \ - (pm_frame_out.index - pm_frame_out.start).dt.days - - # Calculate the daily derate - pm_frame_out['loss_perfect_clean'] = \ - pm_frame_out.start_loss + \ - pm_frame_out.days_since_clean * pm_frame_out.run_slope - # filling the flat intervals may need to be recalculated - # for different assumptions - pm_frame_out.loss_perfect_clean = \ - pm_frame_out.loss_perfect_clean.fillna(1) - - pm_frame_out['loss_inferred_clean'] = \ - pm_frame_out.inferred_start_loss + \ - pm_frame_out.days_since_clean * pm_frame_out.run_slope - # filling the flat intervals may need to be recalculated - # for different assumptions - pm_frame_out.loss_inferred_clean = \ - pm_frame_out.loss_inferred_clean.fillna(1) + pm_frame_out = ( + pm_frame_out.reset_index().merge(results, how="left", on="run").set_index("date")) + + pm_frame_out["loss_perfect_clean"] = np.nan + pm_frame_out["loss_inferred_clean"] = np.nan + pm_frame_out["days_since_clean"] = (pm_frame_out.index - pm_frame_out.start).dt.days + ####################################################################### + # new code for perfect and inferred clean with handling of/Matt + # negative shifts and changepoints within soiling intervals + # goes to line 563 + if (piecewise) | (neg_shift): + ################################################################### + pm_frame_out.inferred_begin_shift.bfill(inplace=True) + pm_frame_out["forward_median"] = ( + pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median()) + prev_shift = 1 + soil_inferred_clean = [] + soil_perfect_clean = [] + day_start = -1 + start_infer = 1 + start_perfect = 1 + soil_infer = 1 + soil_perfect = 1 + total_down = 0 + shift = 0 + shift_perfect = 0 + begin_perfect_shifts = [0] + begin_infer_shifts = [0] + + for date, rs, d, start_shift, changepoint, forward_median in zip( + pm_frame_out.index, pm_frame_out.run_slope, pm_frame_out.days_since_clean, + pm_frame_out.inferred_begin_shift, pm_frame_out.slope_change_event, + pm_frame_out.forward_median): + new_soil = d - day_start + day_start = d + + if new_soil <= 0: # begin new soil period + if (start_shift == prev_shift) | (changepoint): # no shift at + # a slope changepoint + shift = 0 + shift_perfect = 0 + else: + if (start_shift < 0) & (prev_shift < 0): # (both negative) or + # downward shifts to start last 2 intervals + shift = 0 + shift_perfect = 0 + total_down = total_down + start_shift # adding total downshifts + # to subtract from an eventual cleaning event + elif (start_shift > 0) & (prev_shift >= 0): # (both positive) or + # cleanings start the last 2 intervals + shift = start_shift + shift_perfect = 1 + total_down = 0 + # add #####################3/27/24 + elif (start_shift == 0) & (prev_shift >= 0): + shift = start_shift + shift_perfect = start_shift + total_down = 0 + ############################################################# + elif (start_shift >= 0) & (prev_shift < 0): # cleaning starts the current + # interval but there was a previous downshift + shift = start_shift + total_down # correct for the negative shifts + shift_perfect = shift # dont set to one 1 if correcting for a + # downshift (debateable alternative set to 1) + total_down = 0 + elif (start_shift < 0) & (prev_shift >= 0): + # negative shift starts the interval, previous shift was cleaning + shift = 0 + shift_perfect = 0 + total_down = start_shift + # check that shifts results in being at or above the median of + # the next 10 days of data + # this catches places where start points of polyfits were + # skewed below where data start + if (soil_infer + shift) < forward_median: + shift = forward_median - soil_infer + if (soil_perfect + shift_perfect) < forward_median: + shift_perfect = forward_median - soil_perfect + + # append the daily soiling ratio to each modeled fit + begin_perfect_shifts.append(shift_perfect) + begin_infer_shifts.append(shift) + # clip to last value in case shift ends up negative + soil_infer = np.clip((soil_infer + shift), soil_infer, 1) + start_infer = soil_infer # make next start value the last inferred value + soil_inferred_clean.append(soil_infer) + # clip to last value in case shift ends up negative + soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1) + start_perfect = soil_perfect + soil_perfect_clean.append(soil_perfect) + if changepoint is False: + prev_shift = start_shift # assigned at new soil period + + elif new_soil > 0: # within soiling period + # append the daily soiling ratio to each modeled fit + soil_infer = start_infer + rs * d + soil_inferred_clean.append(soil_infer) + + soil_perfect = start_perfect + rs * d + soil_perfect_clean.append(soil_perfect) + + pm_frame_out["loss_inferred_clean"] = pd.Series( + soil_inferred_clean, index=pm_frame_out.index) + pm_frame_out["loss_perfect_clean"] = pd.Series( + soil_perfect_clean, index=pm_frame_out.index) + + results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts) + results["begin_infer_shift"] = pd.Series(begin_infer_shifts) + else: + pm_frame_out['loss_perfect_clean'] = pm_frame_out.start_loss + \ + pm_frame_out.days_since_clean * pm_frame_out.run_slope + # filling the flat intervals may need to be recalculated + # for different assumptions + pm_frame_out.loss_perfect_clean = pm_frame_out.loss_perfect_clean.fillna(1) + # inferred_start_loss was set to the value from poly fit at the beginning of the + # soiling interval + pm_frame_out['loss_inferred_clean'] = pm_frame_out.inferred_start_loss + \ + pm_frame_out.days_since_clean * pm_frame_out.run_slope + # filling the flat intervals may need to be recalculated + # for different assumptions + pm_frame_out.loss_inferred_clean = pm_frame_out.loss_inferred_clean.fillna(1) + ####################################################################### self.result_df = results self.analyzed_daily_df = pm_frame_out - def _calc_monte(self, monte, method='half_norm_clean'): - ''' + def _calc_monte(self, monte, method="half_norm_clean"): + """ Runs the Monte Carlo step of the SRR method. Calculates self.random_profiles, a list of the random soiling profiles realized in the calculation, and self.monte_losses, a list of the @@ -358,47 +605,60 @@ def _calc_monte(self, monte, method='half_norm_clean'): ---------- monte : int number of Monte Carlo simulations to run - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ - default 'half_norm_clean' + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', + perfect_clean_complex,inferred_clean_complex} \ + default 'half_norm_clean' + How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and - its sigma equal to 1/3 * (1-b) where b is the intercept - of the fit to the interval. - ''' + its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True + """ # Raise a warning if there is >20% invalid data - if (method == 'half_norm_clean') or (method == 'random_clean'): - valid_fraction = self.analyzed_daily_df['valid'].mean() + if ((method == "half_norm_clean") or (method == "random_clean") + or (method == "perfect_clean_complex") or (method == "inferred_clean_complex")): + valid_fraction = self.analyzed_daily_df["valid"].mean() if valid_fraction <= 0.8: warnings.warn('20% or more of the daily data is assigned to invalid soiling ' 'intervals. This can be problematic with the "half_norm_clean" ' 'and "random_clean" cleaning assumptions. Consider more permissive ' 'validity criteria such as increasing "max_relative_slope_error" ' - 'and/or "max_negative_step" and/or decreasing "min_interval_length".' - ' Alternatively, consider using method="perfect_clean". For more' - ' info see https://github.com/NREL/rdtools/issues/272' - ) + 'and/or "max_negative_step" and/or decreasing ' + '"min_interval_length". Alternatively, consider using ' + 'method="perfect_clean". For more info see ' + 'https://github.com/NREL/rdtools/issues/272') monte_losses = [] random_profiles = [] for _ in range(monte): results_rand = self.result_df.copy() df_rand = self.analyzed_daily_df.copy() # only really need this column from the original frame: - df_rand = df_rand[['insol', 'run']] - results_rand['run_slope'] = \ - np.random.uniform(results_rand.run_slope_low, - results_rand.run_slope_high) - results_rand['run_loss'] = \ - results_rand.run_slope * results_rand.length + df_rand = df_rand[["insol", "run"]] + results_rand["run_slope"] = np.random.uniform( + results_rand.run_slope_low, results_rand.run_slope_high) + results_rand["run_loss"] = results_rand.run_slope * results_rand.length - results_rand['end_loss'] = np.nan - results_rand['start_loss'] = np.nan + results_rand["end_loss"] = np.nan + results_rand["start_loss"] = np.nan # Make groups that start with a valid interval and contain # subsequent invalid intervals @@ -409,16 +669,19 @@ def _calc_monte(self, monte, method='half_norm_clean'): group += 1 group_list.append(group) - results_rand['group'] = group_list + results_rand["group"] = group_list # randomize the extent of the cleaning inter_start = 1.0 + delta_previous_run_loss = 0 start_list = [] - if (method == 'half_norm_clean') or (method == 'random_clean'): + if (method == "half_norm_clean") or (method == "random_clean"): # Randomize recovery of valid intervals only valid_intervals = results_rand[results_rand.valid].copy() - valid_intervals['inferred_recovery'] = \ - valid_intervals.inferred_recovery.fillna(1.0) + valid_intervals["inferred_recovery"] = np.clip( + valid_intervals.inferred_recovery, 0, 1) + valid_intervals["inferred_recovery"] = valid_intervals.inferred_recovery.fillna( + 1.0) end_list = [] for i, row in valid_intervals.iterrows(): @@ -426,18 +689,18 @@ def _calc_monte(self, monte, method='half_norm_clean'): end = inter_start + row.run_loss end_list.append(end) - if method == 'half_norm_clean': + if method == "half_norm_clean": # Use a half normal with the inferred clean at the # 3sigma point x = np.clip(end + row.inferred_recovery, 0, 1) inter_start = 1 - abs(np.random.normal(0.0, (1 - x) / 3)) - elif method == 'random_clean': + elif method == "random_clean": inter_start = np.random.uniform(end, 1) # Update the valid rows in results_rand valid_update = pd.DataFrame() - valid_update['start_loss'] = start_list - valid_update['end_loss'] = end_list + valid_update["start_loss"] = start_list + valid_update["end_loss"] = end_list valid_update.index = valid_intervals.index results_rand.update(valid_update) @@ -471,49 +734,83 @@ def _calc_monte(self, monte, method='half_norm_clean'): # Update results rand with the invalid rows replace_levels = np.concatenate(replace_levels) invalid_update = pd.DataFrame() - invalid_update['start_loss'] = replace_levels + invalid_update["start_loss"] = replace_levels invalid_update.index = invalid_intervals.index results_rand.update(invalid_update) - elif method == 'perfect_clean': + elif method == "perfect_clean": for i, row in results_rand.iterrows(): start_list.append(inter_start) end = inter_start + row.run_loss inter_start = 1 - results_rand['start_loss'] = start_list + results_rand["start_loss"] = start_list + ################################################################## + # matt additions + + elif method == "perfect_clean_complex": + for i, row in results_rand.iterrows(): + if row.begin_perfect_shift > 0: + inter_start = np.clip( + (inter_start + row.begin_perfect_shift + delta_previous_run_loss), + end, 1) + delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline + else: + delta_previous_run_loss = ( + delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline) + # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) + start_list.append(inter_start) + end = inter_start + row.run_loss + + inter_start = end + results_rand["start_loss"] = start_list + + elif method == "inferred_clean_complex": + for i, row in results_rand.iterrows(): + if row.begin_infer_shift > 0: + inter_start = np.clip( + (inter_start + row.begin_infer_shift + delta_previous_run_loss), + end, 1) + delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline + else: + delta_previous_run_loss = ( + delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline) + # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) + start_list.append(inter_start) + end = inter_start + row.run_loss + + inter_start = end + results_rand["start_loss"] = start_list + ############################################### else: raise ValueError("Invalid method specification") - df_rand = df_rand.reset_index() \ - .merge(results_rand, how='left', on='run') \ - .set_index('date') - df_rand['loss'] = np.nan - df_rand['days_since_clean'] = \ - (df_rand.index - df_rand.start).dt.days - df_rand['loss'] = df_rand.start_loss + \ - df_rand.days_since_clean * df_rand.run_slope + df_rand = ( + df_rand.reset_index().merge(results_rand, how="left", on="run").set_index("date")) + df_rand["loss"] = np.nan + df_rand["days_since_clean"] = (df_rand.index - df_rand.start).dt.days + df_rand["loss"] = df_rand.start_loss + df_rand.days_since_clean * df_rand.run_slope - df_rand['soil_insol'] = df_rand.loss * df_rand.insol + df_rand["soil_insol"] = df_rand.loss * df_rand.insol soiling_ratio = ( - df_rand.soil_insol.sum() / df_rand.insol[ - ~df_rand.soil_insol.isnull()].sum() - ) + df_rand.soil_insol.sum() / df_rand.insol[~df_rand.soil_insol.isnull()].sum()) monte_losses.append(soiling_ratio) - random_profile = df_rand['loss'].copy() - random_profile.name = 'stochastic_soiling_profile' + random_profile = df_rand["loss"].copy() + random_profile.name = "stochastic_soiling_profile" random_profiles.append(random_profile) self.random_profiles = random_profiles self.monte_losses = monte_losses - def run(self, reps=1000, day_scale=13, clean_threshold='infer', - trim=False, method='half_norm_clean', - clean_criterion='shift', precip_threshold=0.01, min_interval_length=7, - exceedance_prob=95.0, confidence_level=68.2, recenter=True, - max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5): - ''' + ####################################################################### + # add neg_shift and piecewise to the following def/Matt + def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, + method="half_norm_clean", clean_criterion="shift", precip_threshold=0.01, + min_interval_length=7, exceedance_prob=95.0, confidence_level=68.2, recenter=True, + max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5, + neg_shift=False, piecewise=False): + """ Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented in Deceglie et al. "Quantifying Soiling Loss Directly From PV Yield" @@ -534,17 +831,31 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ - default 'half_norm_clean' + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', + perfect_clean_complex,inferred_clean_complex} \ + default 'perfect_clean_complex' + How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval. + the interval, + pair with piecewise=False and neg_shift=False + * 'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + * 'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -581,6 +892,18 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. Returns ------- @@ -634,59 +957,58 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', | | be treated as a valid soiling interval | +------------------------+----------------------------------------------+ - ''' - self._calc_daily_df(day_scale=day_scale, - clean_threshold=clean_threshold, - recenter=recenter, - clean_criterion=clean_criterion, - precip_threshold=precip_threshold, - outlier_factor=outlier_factor) - self._calc_result_df(trim=trim, - max_relative_slope_error=max_relative_slope_error, + """ + self._calc_daily_df(day_scale=day_scale, clean_threshold=clean_threshold, + recenter=recenter, clean_criterion=clean_criterion, + precip_threshold=precip_threshold, outlier_factor=outlier_factor, + neg_shift=neg_shift, piecewise=piecewise) + + self._calc_result_df(trim=trim, max_relative_slope_error=max_relative_slope_error, max_negative_step=max_negative_step, - min_interval_length=min_interval_length) + min_interval_length=min_interval_length, neg_shift=neg_shift, + piecewise=piecewise) + self._calc_monte(reps, method=method) # Calculate the P50 and confidence interval half_ci = confidence_level / 2.0 result = np.percentile(self.monte_losses, - [50, - 50.0 - half_ci, - 50.0 + half_ci, - 100 - exceedance_prob]) + [50, 50.0 - half_ci, 50.0 + half_ci, 100 - exceedance_prob]) P_level = result[3] # Construct calc_info output - + ############################################### + # add inferred_recovery, inferred_begin_shift /Matt + ############################################### intervals_out = self.result_df[ - ['start', 'end', 'run_slope', 'run_slope_low', - 'run_slope_high', 'inferred_start_loss', 'inferred_end_loss', - 'length', 'valid']].copy() - intervals_out.rename(columns={'run_slope': 'soiling_rate', - 'run_slope_high': 'soiling_rate_high', - 'run_slope_low': 'soiling_rate_low', - }, inplace=True) + ["start", "end", "run_slope", "run_slope_low", "run_slope_high", "inferred_start_loss", + "inferred_end_loss", "inferred_recovery", "inferred_begin_shift", "length", "valid"] + ].copy() + intervals_out.rename(columns={"run_slope": "soiling_rate", + "run_slope_high": "soiling_rate_high", + "run_slope_low": "soiling_rate_low"}, inplace=True) df_d = self.analyzed_daily_df - sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] - calc_info = { - 'exceedance_level': P_level, - 'renormalizing_factor': self.renorm_factor, - 'stochastic_soiling_profiles': self.random_profiles, - 'soiling_interval_summary': intervals_out, - 'soiling_ratio_perfect_clean': sr_perfect - } + # sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] + sr_perfect = df_d.loss_perfect_clean + + calc_info = {"exceedance_level": P_level, "renormalizing_factor": self.renorm_factor, + "stochastic_soiling_profiles": self.random_profiles, + "soiling_interval_summary": intervals_out, + "soiling_ratio_perfect_clean": sr_perfect} return (result[0], result[1:3], calc_info) -def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, - precipitation_daily=None, day_scale=13, clean_threshold='infer', - trim=False, method='half_norm_clean', - clean_criterion='shift', precip_threshold=0.01, min_interval_length=7, +# more updates are needed for documentation but added additional inputs +# that are in srr.run /Matt +def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitation_daily=None, + day_scale=13, clean_threshold="infer", trim=False, method="half_norm_clean", + clean_criterion="shift", precip_threshold=0.01, min_interval_length=7, exceedance_prob=95.0, confidence_level=68.2, recenter=True, - max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5): - ''' + max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5, + neg_shift=False, piecewise=False): + """ Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented in Deceglie et al. JPV 8(2) p547 2018. @@ -718,17 +1040,31 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', + perfect_clean_complex,inferred_clean_complex} \ default 'half_norm_clean' + How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval. + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -764,6 +1100,18 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. Returns ------- @@ -816,34 +1164,24 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, | 'valid' | Whether the interval meets the criteria to | | | be treated as a valid soiling interval | +------------------------+----------------------------------------------+ - ''' + """ - srr = SRRAnalysis(energy_normalized_daily, - insolation_daily, + srr = SRRAnalysis(energy_normalized_daily, insolation_daily, precipitation_daily=precipitation_daily) sr, sr_ci, soiling_info = srr.run( - reps=reps, - day_scale=day_scale, - clean_threshold=clean_threshold, - trim=trim, - method=method, - clean_criterion=clean_criterion, - precip_threshold=precip_threshold, - min_interval_length=min_interval_length, - exceedance_prob=exceedance_prob, - confidence_level=confidence_level, - recenter=recenter, - max_relative_slope_error=max_relative_slope_error, - max_negative_step=max_negative_step, - outlier_factor=outlier_factor) - + reps=reps, day_scale=day_scale, clean_threshold=clean_threshold, trim=trim, + method=method, clean_criterion=clean_criterion, precip_threshold=precip_threshold, + min_interval_length=min_interval_length, exceedance_prob=exceedance_prob, + confidence_level=confidence_level, recenter=recenter, + max_relative_slope_error=max_relative_slope_error, max_negative_step=max_negative_step, + outlier_factor=outlier_factor, neg_shift=neg_shift, piecewise=piecewise) return sr, sr_ci, soiling_info def _count_month_days(start, end): - '''Return a dict of number of days between start and end - (inclusive) in each month''' + """Return a dict of number of days between start and end + (inclusive) in each month""" days = pd.date_range(start, end) months = [x.month for x in days] out_dict = {} @@ -853,9 +1191,8 @@ def _count_month_days(start, end): return out_dict -def annual_soiling_ratios(stochastic_soiling_profiles, - insolation_daily, confidence_level=68.2): - ''' +def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confidence_level=68.2): + """ Return annualized soiling ratios and associated confidence intervals based on stochastic soiling profiles from SRR. Note that each year may be affected by previous years' profiles for all SRR cleaning @@ -895,18 +1232,17 @@ def annual_soiling_ratios(stochastic_soiling_profiles, | | for insolation-weighted soiling ratio for | | | the year | +------------------------+-------------------------------------------+ - ''' + """ # Create a df with each realization as a column all_profiles = pd.concat(stochastic_soiling_profiles, axis=1) all_profiles = all_profiles.dropna() if not all_profiles.index.isin(insolation_daily.index).all(): - warnings.warn( - 'The indexes of stochastic_soiling_profiles are not entirely ' - 'contained within the index of insolation_daily. Every day in ' - 'stochastic_soiling_profiles should be represented in ' - 'insolation_daily. This may cause erroneous results.') + warnings.warn("The indexes of stochastic_soiling_profiles are not entirely " + "contained within the index of insolation_daily. Every day in " + "stochastic_soiling_profiles should be represented in " + "insolation_daily. This may cause erroneous results.") insolation_daily = insolation_daily.reindex(all_profiles.index) @@ -914,30 +1250,26 @@ def annual_soiling_ratios(stochastic_soiling_profiles, all_profiles_weighted = all_profiles.multiply(insolation_daily, axis=0) # Compute the insolation-weighted soiling ratio (IWSR) for each realization - annual_insolation = insolation_daily.groupby( - insolation_daily.index.year).sum() + annual_insolation = insolation_daily.groupby(insolation_daily.index.year).sum() all_annual_weighted_sums = all_profiles_weighted.groupby( - all_profiles_weighted.index.year).sum() - all_annual_iwsr = all_annual_weighted_sums.multiply( - 1/annual_insolation, axis=0) - - annual_soiling = pd.DataFrame({ - 'soiling_ratio_median': all_annual_iwsr.quantile(0.5, axis=1), - 'soiling_ratio_low': all_annual_iwsr.quantile( - 0.5 - confidence_level/2/100, axis=1), - 'soiling_ratio_high': all_annual_iwsr.quantile( - 0.5 + confidence_level/2/100, axis=1), - }) - annual_soiling.index.name = 'year' + all_profiles_weighted.index.year + ).sum() + all_annual_iwsr = all_annual_weighted_sums.multiply(1 / annual_insolation, axis=0) + + annual_soiling = pd.DataFrame( + {"soiling_ratio_median": all_annual_iwsr.quantile(0.5, axis=1), + "soiling_ratio_low": all_annual_iwsr.quantile(0.5 - confidence_level / 2 / 100, axis=1), + "soiling_ratio_high": all_annual_iwsr.quantile(0.5 + confidence_level / 2 / 100, axis=1), + }) + annual_soiling.index.name = "year" annual_soiling = annual_soiling.reset_index() return annual_soiling def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, - max_relative_slope_error=500.0, reps=100000, - confidence_level=68.2): - ''' + max_relative_slope_error=500.0, reps=100000, confidence_level=68.2): + """ Use Monte Carlo to calculate typical monthly soiling rates. Samples possible soiling rates from soiling rate confidence intervals associated with soiling intervals assuming a uniform @@ -1002,75 +1334,67 @@ def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, | | intervals contribute, the confidence interval | | | is likely to underestimate the true uncertainty. | +-----------------------+--------------------------------------------------+ - ''' + """ # filter to intervals of interest - high = soiling_interval_summary['soiling_rate_high'] - low = soiling_interval_summary['soiling_rate_low'] - rate = soiling_interval_summary['soiling_rate'] + high = soiling_interval_summary["soiling_rate_high"] + low = soiling_interval_summary["soiling_rate_low"] + rate = soiling_interval_summary["soiling_rate"] rel_error = 100 * abs((high - low) / rate) intervals = soiling_interval_summary[ - (soiling_interval_summary['length'] >= min_interval_length) & - (soiling_interval_summary['valid']) & - (rel_error <= max_relative_slope_error) - ].copy() + (soiling_interval_summary["length"] >= min_interval_length) + & (soiling_interval_summary["valid"]) & (rel_error <= max_relative_slope_error)].copy() # count the overlap of each interval with each month month_counts = [] for _, row in intervals.iterrows(): - month_counts.append(_count_month_days(row['start'], row['end'])) + month_counts.append(_count_month_days(row["start"], row["end"])) # divy up the monte carlo reps based on overlap for month in range(1, 13): days_in_month = np.array([x[month] for x in month_counts]) - sample_col = f'samples_for_month_{month}' + sample_col = f"samples_for_month_{month}" if days_in_month.sum() > 0: - intervals[sample_col] = np.ceil( - days_in_month / days_in_month.sum() * reps) + intervals[sample_col] = np.ceil(days_in_month / days_in_month.sum() * reps) else: intervals[sample_col] = 0 intervals[sample_col] = intervals[sample_col].astype(int) # perform the monte carlo month by month - ci_quantiles = [0.5 - confidence_level/2/100, 0.5 + confidence_level/2/100] + ci_quantiles = [0.5 - confidence_level / 2 / 100, 0.5 + confidence_level / 2 / 100] monthly_rate_data = [] relevant_interval_count = [] for month in range(1, 13): rates = [] - sample_col = f'samples_for_month_{month}' + sample_col = f"samples_for_month_{month}" relevant_intervals = intervals[intervals[sample_col] > 0] for _, row in relevant_intervals.iterrows(): rates.append(np.random.uniform( - row['soiling_rate_low'], - row['soiling_rate_high'], - row[sample_col])) + row["soiling_rate_low"], row["soiling_rate_high"], row[sample_col])) rates = [x for sublist in rates for x in sublist] if rates: - monthly_rate_data.append(np.quantile(rates, - [0.5, ci_quantiles[0], - ci_quantiles[1]])) + monthly_rate_data.append(np.quantile(rates, [0.5, ci_quantiles[0], ci_quantiles[1]])) else: - monthly_rate_data.append(np.array([np.nan]*3)) + monthly_rate_data.append(np.array([np.nan] * 3)) relevant_interval_count.append(len(relevant_intervals)) monthly_rate_data = np.array(monthly_rate_data) # make a dataframe out of the results - monthly_soiling_df = pd.DataFrame(data=monthly_rate_data, - columns=['soiling_rate_median', - 'soiling_rate_low', - 'soiling_rate_high']) - monthly_soiling_df.insert(0, 'month', range(1, 13)) - monthly_soiling_df['interval_count'] = relevant_interval_count + monthly_soiling_df = pd.DataFrame( + data=monthly_rate_data, + columns=["soiling_rate_median", "soiling_rate_low", "soiling_rate_high"]) + monthly_soiling_df.insert(0, "month", range(1, 13)) + monthly_soiling_df["interval_count"] = relevant_interval_count return monthly_soiling_df -class CODSAnalysis(): - ''' +class CODSAnalysis: + """ Container for the Combined Degradation and Soiling (CODS) algorithm for degradation and soiling loss analysis. Based on the method presented in [1]_. @@ -1166,7 +1490,7 @@ class CODSAnalysis(): ---------- .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ def __init__(self, energy_normalized_daily): self.pm = energy_normalized_daily # daily performance metric @@ -1175,18 +1499,17 @@ def __init__(self, energy_normalized_daily): first_keeper = self.pm.isna().idxmin() self.pm = self.pm.loc[first_keeper:] - if self.pm.index.freq != 'D': - raise ValueError('Daily performance metric series must have ' - 'daily frequency (missing dates should be ' - 'represented by NaNs)') + if self.pm.index.freq != "D": + raise ValueError("Daily performance metric series must have " + "daily frequency (missing dates should be " + "represented by NaNs)") def iterative_signal_decomposition( - self, order=('SR', 'SC', 'Rd'), degradation_method='YoY', - max_iterations=18, cleaning_sensitivity=.5, convergence_criterion=5e-3, - pruning_iterations=1, clean_pruning_sensitivity=.6, soiling_significance=.75, - process_noise=1e-4, renormalize_SR=None, ffill=True, clip_soiling=True, - verbose=False): - ''' + self, order=("SR", "SC", "Rd"), degradation_method="YoY", max_iterations=18, + cleaning_sensitivity=0.5, convergence_criterion=5e-3, pruning_iterations=1, + clean_pruning_sensitivity=0.6, soiling_significance=0.75, process_noise=1e-4, + renormalize_SR=None, ffill=True, clip_soiling=True, verbose=False): + """ Estimates the soiling losses and the degradation rate of a PV system based on its daily normalized energy, or daily Performance Index (PI). The underlying assumption is that the PI @@ -1325,14 +1648,14 @@ def iterative_signal_decomposition( .. [3] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ pi = self.pm.copy() - if degradation_method == 'STL' and 'Rd' in order: - order = tuple([c for c in order if c != 'Rd']) + if degradation_method == "STL" and "Rd" in order: + order = tuple([c for c in order if c != "Rd"]) - if 'SR' not in order: - raise ValueError('\'SR\' must be in argument \'order\' ' + - '(e.g. order=[\'SR\', \'SC\', \'Rd\']') + if "SR" not in order: + raise ValueError( + "'SR' must be in argument 'order' " + "(e.g. order=['SR', 'SC', 'Rd']") n_steps = len(order) day = np.arange(len(pi)) degradation_trend = [1] @@ -1345,143 +1668,125 @@ def iterative_signal_decomposition( convergence_metric = [_RMSE(pi, np.ones((len(pi),)))] # Find possible cleaning events based on the performance index - ce, rm9 = _rolling_median_ce_detection(pi.index, pi, ffill=ffill, - tuner=cleaning_sensitivity) + ce, rm9 = _rolling_median_ce_detection( + pi.index, pi, ffill=ffill, tuner=cleaning_sensitivity) pce = _collapse_cleaning_events(ce, rm9.diff().values, 5) small_soiling_signal, perfect_cleaning = False, True ic = 0 # iteration counter if verbose: - print('It. nr\tstep\tRMSE\ttimer') + print("It. nr\tstep\tRMSE\ttimer") if verbose: - print('{:}\t- \t{:.5f}'.format(ic, convergence_metric[ic])) + print("{:}\t- \t{:.5f}".format(ic, convergence_metric[ic])) while ic < max_iterations: t0 = time.time() ic += 1 # Find soiling component - if order[(ic-1) % n_steps] == 'SR': + if order[(ic - 1) % n_steps] == "SR": if ic > 2: # Add possible cleaning events found by considering # the residuals pce = soiling_dfs[-1].cleaning_events.copy() cleaning_sensitivity *= 1.2 # decrease sensitivity ce, rm9 = _rolling_median_ce_detection( - pi.index, residuals, ffill=ffill, - tuner=cleaning_sensitivity) + pi.index, residuals, ffill=ffill, tuner=cleaning_sensitivity) ce = _collapse_cleaning_events(ce, rm9.diff().values, 5) pce[ce] = True clean_pruning_sensitivity /= 1.1 # increase pruning sensitivity # Decompose input signal - soiling_dummy = (pi / - degradation_trend[-1] / - seasonal_component[-1] / - residual_shift) + soiling_dummy = ( + pi / degradation_trend[-1] / seasonal_component[-1] / residual_shift) # Run Kalman Filter for obtaining soiling component kdf, Ps = self._Kalman_filter_for_SR( - zs_series=soiling_dummy, - clip_soiling=clip_soiling, - prescient_cleaning_events=pce, - pruning_iterations=pruning_iterations, + zs_series=soiling_dummy, clip_soiling=clip_soiling, + prescient_cleaning_events=pce, pruning_iterations=pruning_iterations, clean_pruning_sensitivity=clean_pruning_sensitivity, - perfect_cleaning=perfect_cleaning, - process_noise=process_noise, + perfect_cleaning=perfect_cleaning, process_noise=process_noise, renormalize_SR=renormalize_SR) soiling_ratio.append(kdf.soiling_ratio) soiling_dfs.append(kdf) # Find seasonal component - if order[(ic-1) % n_steps] == 'SC': + if order[(ic - 1) % n_steps] == "SC": season_dummy = pi / soiling_ratio[-1] # Decompose signal if season_dummy.isna().sum() > 0: - season_dummy.interpolate('linear', inplace=True) + season_dummy.interpolate("linear", inplace=True) season_dummy = season_dummy.apply(np.log) # Log transform # Run STL model - STL_res = STL(season_dummy, period=365, seasonal=999999, - seasonal_deg=0, trend_deg=0, - robust=True, low_pass_jump=30, seasonal_jump=30, - trend_jump=365).fit() + STL_res = STL( + season_dummy, period=365, seasonal=999999, seasonal_deg=0, trend_deg=0, + robust=True, low_pass_jump=30, seasonal_jump=30, trend_jump=365).fit() # Smooth result - smooth_season = lowess(STL_res.seasonal.apply(np.exp), - pi.index, is_sorted=True, delta=30, - frac=180/len(pi), return_sorted=False) + smooth_season = lowess( + STL_res.seasonal.apply(np.exp), pi.index, is_sorted=True, delta=30, + frac=180 / len(pi), return_sorted=False) # Ensure periodic seaonal component - seasonal_comp = _force_periodicity(smooth_season, - season_dummy.index, - pi.index) + seasonal_comp = _force_periodicity(smooth_season, season_dummy.index, pi.index) seasonal_component.append(seasonal_comp) - if degradation_method == 'STL': # If not YoY - deg_trend = pd.Series(index=pi.index, - data=STL_res.trend.apply(np.exp)) + if degradation_method == "STL": # If not YoY + deg_trend = pd.Series(index=pi.index, data=STL_res.trend.apply(np.exp)) degradation_trend.append(deg_trend / deg_trend.iloc[0]) yoy_save.append(RdToolsDeg.degradation_year_on_year( - degradation_trend[-1], uncertainty_method=None)) + degradation_trend[-1], uncertainty_method=None)) # Find degradation component - if order[(ic-1) % n_steps] == 'Rd': + if order[(ic - 1) % n_steps] == "Rd": # Decompose signal - trend_dummy = (pi / - seasonal_component[-1] / - soiling_ratio[-1]) + trend_dummy = pi / seasonal_component[-1] / soiling_ratio[-1] # Run YoY - yoy = RdToolsDeg.degradation_year_on_year( - trend_dummy, uncertainty_method=None) + yoy = RdToolsDeg.degradation_year_on_year(trend_dummy, uncertainty_method=None) # Convert degradation rate to trend - degradation_trend.append(pd.Series( - index=pi.index, data=(1 + day * yoy / 100 / 365.0))) + degradation_trend.append( + pd.Series(index=pi.index, data=(1 + day * yoy / 100 / 365.0))) yoy_save.append(yoy) # Combine and calculate residual flatness - total_model = (degradation_trend[-1] * - seasonal_component[-1] * - soiling_ratio[-1]) + total_model = degradation_trend[-1] * seasonal_component[-1] * soiling_ratio[-1] residuals = pi / total_model residual_shift = residuals.mean() total_model *= residual_shift convergence_metric.append(_RMSE(pi, total_model)) if verbose: - print('{:}\t{:}\t{:.5f}\t\t\t{:.1f} s'.format( - ic, order[(ic-1) % n_steps], convergence_metric[-1], - time.time()-t0)) + print("{:}\t{:}\t{:.5f}\t\t\t{:.1f} s".format( + ic, order[(ic - 1) % n_steps], convergence_metric[-1], time.time() - t0)) # Convergence happens if there is no improvement in RMSE from one # step to the next if ic >= n_steps: - relative_improvement = ((convergence_metric[-n_steps-1] - - convergence_metric[-1]) / - convergence_metric[-n_steps-1]) - if perfect_cleaning and ( - ic >= max_iterations / 2 or - relative_improvement < convergence_criterion): + relative_improvement = (convergence_metric[-n_steps - 1] - convergence_metric[-1] + ) / convergence_metric[-n_steps - 1] + if perfect_cleaning and (ic >= max_iterations / 2 or + relative_improvement < convergence_criterion): # From now on, do not assume perfect cleaning perfect_cleaning = False # Reorder to ensure SR first - order = tuple([order[(i+n_steps-1-(ic-1) % n_steps) % n_steps] - for i in range(n_steps)]) + order = tuple( + [order[(i + n_steps - 1 - (ic - 1) % n_steps) % n_steps] + for i in range(n_steps)]) change_point = ic if verbose: - print('Now not assuming perfect cleaning') - elif (not perfect_cleaning and - (ic >= max_iterations or - (ic >= change_point + n_steps and - relative_improvement < - convergence_criterion))): + print("Now not assuming perfect cleaning") + elif not perfect_cleaning and ( + ic >= max_iterations + or (ic >= change_point + n_steps + and relative_improvement < convergence_criterion)): if verbose: if relative_improvement < convergence_criterion: - print('Convergence reached.') + print("Convergence reached.") else: - print('Max iterations reached.') + print("Max iterations reached.") ic = max_iterations # Initialize output DataFrame - df_out = pd.DataFrame(index=pi.index, - columns=['soiling_ratio', 'soiling_rates', - 'cleaning_events', 'seasonal_component', - 'degradation_trend', 'total_model', - 'residuals']) + df_out = pd.DataFrame( + index=pi.index, + columns=[ + "soiling_ratio", "soiling_rates", "cleaning_events", "seasonal_component", + "degradation_trend", "total_model", "residuals"]) # Save values df_out.seasonal_component = seasonal_component[-1] @@ -1496,39 +1801,33 @@ def iterative_signal_decomposition( soiling_loss = (1 - df_out.soiling_ratio).mean() * 100 # Total model - df_out.total_model = (df_out.soiling_ratio * - df_out.seasonal_component * - df_out.degradation_trend) + df_out.total_model = ( + df_out.soiling_ratio * df_out.seasonal_component * df_out.degradation_trend) df_out.residuals = pi / df_out.total_model residual_shift = df_out.residuals.mean() df_out.total_model *= residual_shift RMSE = _RMSE(pi, df_out.total_model) - adf_res = adfuller(df_out.residuals.dropna(), regression='ctt', autolag=None) + adf_res = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None) if verbose: - print('p-value for the H0 that there is a unit root in the' + - 'residuals (using the Augmented Dickey-fuller test):' + - '{:.3e}'.format(adf_res[1])) + print("p-value for the H0 that there is a unit root in the" + + "residuals (using the Augmented Dickey-fuller test):" + + "{:.3e}".format(adf_res[1])) # Check size of soiling signal vs residuals - SR_amp = float(np.diff(df_out.soiling_ratio.quantile([.1, .9]))) - residuals_amp = float(np.diff(df_out.residuals.quantile([.1, .9]))) + SR_amp = float(np.diff(df_out.soiling_ratio.quantile([0.1, 0.9]))) + residuals_amp = float(np.diff(df_out.residuals.quantile([0.1, 0.9]))) soiling_signal_strength = SR_amp / residuals_amp if soiling_signal_strength < soiling_significance: if verbose: - print('Soiling signal is small relative to the noise') + print("Soiling signal is small relative to the noise") small_soiling_signal = True df_out.SR_high = 1.0 df_out.SR_low = 1.0 - SR_amp # Set up results dictionary - results_dict = dict( - degradation=degradation, - soiling_loss=soiling_loss, - residual_shift=residual_shift, - RMSE=RMSE, - small_soiling_signal=small_soiling_signal, - adf_res=adf_res - ) + results_dict = dict(degradation=degradation, soiling_loss=soiling_loss, + residual_shift=residual_shift, RMSE=RMSE, + small_soiling_signal=small_soiling_signal, adf_res=adf_res) return df_out, results_dict @@ -1545,7 +1844,7 @@ def run_bootstrap(self, verbose=False, bootstrap_seed=None, **kwargs): - ''' + """ Bootstrapping of CODS algorithm for uncertainty analysis, inherently accounting for model and parameter choices. @@ -1668,7 +1967,7 @@ def run_bootstrap(self, ---------- .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ pi = self.pm.copy() # ###################### # @@ -1676,14 +1975,13 @@ def run_bootstrap(self, # ###################### # # Generate combinations of model parameter alternatives - parameter_alternatives = [order_alternatives, - cleaning_sensitivity_alternatives, - clean_pruning_sensitivity_alternatives, - forward_fill_alternatives] + parameter_alternatives = [ + order_alternatives, cleaning_sensitivity_alternatives, + clean_pruning_sensitivity_alternatives, forward_fill_alternatives] index_list = list(itertools.product([0, 1], repeat=len(parameter_alternatives))) - combination_of_parameters = [[parameter_alternatives[j][indexes[j]] - for j in range(len(parameter_alternatives))] - for indexes in index_list] + combination_of_parameters = [ + [parameter_alternatives[j][indexes[j]] for j in range(len(parameter_alternatives))] + for indexes in index_list] nr_models = len(index_list) bootstrap_samples_list, list_of_df_out, results = [], [], [] @@ -1692,24 +1990,24 @@ def run_bootstrap(self, reps += nr_models - reps % nr_models if verbose: - print('Initially fitting {:} models'.format(nr_models)) + print("Initially fitting {:} models".format(nr_models)) t00 = time.time() # For each combination of model parameter alternatives, fit one model: for c, (order, dt, pt, ff) in enumerate(combination_of_parameters): try: df_out, result_dict = self.iterative_signal_decomposition( - max_iterations=18, order=order, clip_soiling=True, - cleaning_sensitivity=dt, pruning_iterations=1, - clean_pruning_sensitivity=pt, process_noise=process_noise, ffill=ff, - degradation_method=degradation_method, **kwargs) + max_iterations=18, order=order, clip_soiling=True, cleaning_sensitivity=dt, + pruning_iterations=1, clean_pruning_sensitivity=pt, + process_noise=process_noise, ffill=ff, degradation_method=degradation_method, + **kwargs) # Save results list_of_df_out.append(df_out) results.append(result_dict) - adf = result_dict['adf_res'] + adf = result_dict["adf_res"] # If we can reject the null-hypothesis that there is a unit # root in the residuals: - if adf[1] < .05: + if adf[1] < 0.05: # ... generate bootstrap samples based on the fit: bootstrap_samples_list.append( _make_time_series_bootstrap_samples( @@ -1719,42 +2017,38 @@ def run_bootstrap(self, # Print progress if verbose: - _progressBarWithETA(c+1, nr_models, time.time()-t00, - bar_length=30) + _progressBarWithETA(c + 1, nr_models, time.time() - t00, bar_length=30) except ValueError as ex: print(ex) # Revive results - adfs = np.array([(r['adf_res'][0] if r['adf_res'][1] < 0.05 else 0) for r in results]) - RMSEs = np.array([r['RMSE'] for r in results]) - SR_is_one_fraction = np.array( - [(df.soiling_ratio == 1).mean() for df in list_of_df_out]) - small_soiling_signal = [r['small_soiling_signal'] for r in results] + adfs = np.array([(r["adf_res"][0] if r["adf_res"][1] < 0.05 else 0) for r in results]) + RMSEs = np.array([r["RMSE"] for r in results]) + SR_is_one_fraction = np.array([(df.soiling_ratio == 1).mean() for df in list_of_df_out]) + small_soiling_signal = [r["small_soiling_signal"] for r in results] # Calculate weights weights = 1 / RMSEs / (1 + SR_is_one_fraction) weights /= np.sum(weights) # Save sensitivities and weights for initial model fits - _parameters_n_weights = pd.concat([pd.DataFrame(combination_of_parameters), - pd.Series(RMSEs), - pd.Series(SR_is_one_fraction), - pd.Series(weights), - pd.Series(small_soiling_signal)], - axis=1, ignore_index=True) + _parameters_n_weights = pd.concat( + [pd.DataFrame(combination_of_parameters), pd.Series(RMSEs), + pd.Series(SR_is_one_fraction), pd.Series(weights), pd.Series(small_soiling_signal)], + axis=1, ignore_index=True) if verbose: # Print summary - _parameters_n_weights.columns = ['order', 'dt', 'pt', 'ff', 'RMSE', - 'SR==1', 'weights', 'small_soiling_signal'] + _parameters_n_weights.columns = [ + "order", "dt", "pt", "ff", "RMSE", "SR==1", "weights", "small_soiling_signal"] if verbose: - print('\n', _parameters_n_weights) + print("\n", _parameters_n_weights) # Check if data is decomposable if np.sum(adfs == 0) > nr_models / 2: raise RuntimeError( - 'Test for stationary residuals (Augmented Dickey-Fuller' - + ' test) not passed in half of the instances:\nData not' - + ' decomposable.') + "Test for stationary residuals (Augmented Dickey-Fuller" + + " test) not passed in half of the instances:\nData not" + + " decomposable.") # Save best model self.initial_fits = [df for df in list_of_df_out] @@ -1764,83 +2058,76 @@ def run_bootstrap(self, # don't do bootstrapping if np.sum(small_soiling_signal) > nr_models / 2: self.result_df = result_df - self.residual_shift = results[np.argmax(weights)]['residual_shift'] + self.residual_shift = results[np.argmax(weights)]["residual_shift"] YOY = RdToolsDeg.degradation_year_on_year(pi) self.degradation = [YOY[0], YOY[1][0], YOY[1][1]] self.soiling_loss = [0, 0, (1 - result_df.soiling_ratio).mean()] self.small_soiling_signal = True self.errors = ( - 'Soiling signal is small relative to the noise. ' - 'Iterative decomposition not possible. ' - 'Degradation found by RdTools YoY.') + "Soiling signal is small relative to the noise. " + "Iterative decomposition not possible. " + "Degradation found by RdTools YoY." + ) warnings.warn(self.errors) return self.result_df, self.degradation, self.soiling_loss self.small_soiling_signal = False # Aggregate all bootstrap samples - all_bootstrap_samples = pd.concat(bootstrap_samples_list, axis=1, - ignore_index=True) + all_bootstrap_samples = pd.concat(bootstrap_samples_list, axis=1, ignore_index=True) # Seasonal samples are generated from previously fitted seasonal # components, by perturbing amplitude and phase shift # Number of samples per fit: sample_nr = int(reps / nr_models) - list_of_SCs = [list_of_df_out[m].seasonal_component - for m in range(nr_models) if weights[m] > 0] - seasonal_samples = _make_seasonal_samples(list_of_SCs, - sample_nr=sample_nr, - min_multiplier=.8, - max_multiplier=1.75, - max_shift=30) + list_of_SCs = [ + list_of_df_out[m].seasonal_component for m in range(nr_models) if weights[m] > 0] + seasonal_samples = _make_seasonal_samples( + list_of_SCs, sample_nr=sample_nr, min_multiplier=0.8, max_multiplier=1.75, + max_shift=30) # ###################### # # ###### STAGE 2 ####### # # ###################### # if verbose and reps > 0: - print('\nBootstrapping for uncertainty analysis', - '({:} realizations):'.format(reps)) - order = ('SR', 'SC' if degradation_method == 'STL' else 'Rd') + print("\nBootstrapping for uncertainty analysis", + "({:} realizations):".format(reps)) + order = ("SR", "SC" if degradation_method == "STL" else "Rd") t0 = time.time() - bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = \ - [], [], [], [], [], [], [], [], ['Bootstrapping errors'] + bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = ( + [], [], [], [], [], [], [], [], ["Bootstrapping errors"]) for b in range(reps): try: # randomly choose model sensitivities - dt = np.random.uniform(parameter_alternatives[1][0], - parameter_alternatives[1][-1]) - pt = np.random.uniform(parameter_alternatives[2][0], - parameter_alternatives[2][-1]) + dt = np.random.uniform(parameter_alternatives[1][0], parameter_alternatives[1][-1]) + pt = np.random.uniform(parameter_alternatives[2][0], parameter_alternatives[2][-1]) pn = np.random.uniform(process_noise / 1.5, process_noise * 1.5) - renormalize_SR = np.random.choice([None, - np.random.uniform(.5, .95)]) + renormalize_SR = np.random.choice([None, np.random.uniform(0.5, 0.95)]) ffill = np.random.choice([True, False]) parameters.append([dt, pt, pn, renormalize_SR, ffill]) # Sample to infer soiling from - bootstrap_sample = \ - all_bootstrap_samples[b] / seasonal_samples[b] + bootstrap_sample = all_bootstrap_samples[b] / seasonal_samples[b] # Set up a temprary instance of the CODSAnalysis object temporary_cods_instance = CODSAnalysis(bootstrap_sample) # Do Signal decomposition for soiling and degradation component kdf, results_dict = temporary_cods_instance.iterative_signal_decomposition( - max_iterations=4, order=order, clip_soiling=True, - cleaning_sensitivity=dt, pruning_iterations=1, - clean_pruning_sensitivity=pt, process_noise=pn, - renormalize_SR=renormalize_SR, ffill=ffill, - degradation_method=degradation_method, **kwargs) + max_iterations=4, order=order, clip_soiling=True, cleaning_sensitivity=dt, + pruning_iterations=1, clean_pruning_sensitivity=pt, process_noise=pn, + renormalize_SR=renormalize_SR, ffill=ffill, + degradation_method=degradation_method, **kwargs) # If we can reject the null-hypothesis that there is a unit # root in the residuals: - if results_dict['adf_res'][1] < .05: # Save the results + if results_dict["adf_res"][1] < 0.05: # Save the results bt_kdfs.append(kdf) - adfs.append(results_dict['adf_res'][0]) - RMSEs.append(results_dict['RMSE']) - bt_deg.append(results_dict['degradation']) - bt_SL.append(results_dict['soiling_loss']) - rss.append(results_dict['residual_shift']) + adfs.append(results_dict["adf_res"][0]) + RMSEs.append(results_dict["RMSE"]) + bt_deg.append(results_dict["degradation"]) + bt_SL.append(results_dict["soiling_loss"]) + rss.append(results_dict["residual_shift"]) SR_is_1.append((kdf.soiling_ratio == 1).mean()) else: seasonal_samples.drop(columns=[b], inplace=True) @@ -1851,20 +2138,16 @@ def run_bootstrap(self, # Print progress if verbose: - _progressBarWithETA(b+1, reps, time.time()-t0, bar_length=30) + _progressBarWithETA(b + 1, reps, time.time() - t0, bar_length=30) # Reweight and save weights weights = 1 / np.array(RMSEs) / (1 + np.array(SR_is_1)) weights /= np.sum(weights) self._parameters_n_weights = pd.concat( - [pd.DataFrame(parameters), - pd.Series(RMSEs), - pd.Series(adfs), - pd.Series(SR_is_1), - pd.Series(weights)], - axis=1, ignore_index=True) - self._parameters_n_weights.columns = ['dt', 'pt', 'pn', 'RSR', 'ffill', - 'RMSE', 'ADF', 'SR==1', 'weights'] + [pd.DataFrame(parameters), pd.Series(RMSEs), pd.Series(adfs), + pd.Series(SR_is_1), pd.Series(weights)], axis=1, ignore_index=True) + self._parameters_n_weights.columns = [ + "dt", "pt", "pn", "RSR", "ffill", "RMSE", "ADF", "SR==1", "weights"] # ###################### # # ###### STAGE 3 ####### # @@ -1881,68 +2164,64 @@ def run_bootstrap(self, concat_ce = pd.concat([kdf.cleaning_events for kdf in bt_kdfs], axis=1) # Find confidence intervals for SR and soiling rates - df_out['SR_low'] = concat_SR.quantile(ci_low_edge, 1) - df_out['SR_high'] = concat_SR.quantile(ci_high_edge, 1) - df_out['rates_low'] = concat_r_s.quantile(ci_low_edge, 1) - df_out['rates_high'] = concat_r_s.quantile(ci_high_edge, 1) + df_out["SR_low"] = concat_SR.quantile(ci_low_edge, 1) + df_out["SR_high"] = concat_SR.quantile(ci_high_edge, 1) + df_out["rates_low"] = concat_r_s.quantile(ci_low_edge, 1) + df_out["rates_high"] = concat_r_s.quantile(ci_high_edge, 1) # Save best estimate and bootstrapped estimates of SR and soiling rates df_out.soiling_ratio = df_out.soiling_ratio.clip(lower=0, upper=1) - df_out.loc[df_out.soiling_ratio.diff() == 0, 'soiling_rates'] = 0 - df_out['bt_soiling_ratio'] = (concat_SR * weights).sum(1) - df_out['bt_soiling_rates'] = (concat_r_s * weights).sum(1) + df_out.loc[df_out.soiling_ratio.diff() == 0, "soiling_rates"] = 0 + df_out["bt_soiling_ratio"] = (concat_SR * weights).sum(1) + df_out["bt_soiling_rates"] = (concat_r_s * weights).sum(1) # Set probability of cleaning events df_out.cleaning_events = (concat_ce * weights).sum(1) # Find degradation rates - self.degradation = [np.dot(bt_deg, weights), - np.quantile(bt_deg, ci_low_edge), - np.quantile(bt_deg, ci_high_edge)] - df_out.degradation_trend = 1 + np.arange(len(pi)) * \ - self.degradation[0] / 100 / 365.0 + self.degradation = [ + np.dot(bt_deg, weights), np.quantile(bt_deg, ci_low_edge), + np.quantile(bt_deg, ci_high_edge)] + df_out.degradation_trend = 1 + np.arange(len(pi)) * self.degradation[0] / 100 / 365.0 # Soiling losses - self.soiling_loss = [np.dot(bt_SL, weights), - np.quantile(bt_SL, ci_low_edge), - np.quantile(bt_SL, ci_high_edge)] + self.soiling_loss = [ + np.dot(bt_SL, weights), np.quantile(bt_SL, ci_low_edge), + np.quantile(bt_SL, ci_high_edge)] # Save "confidence intervals" for seasonal component df_out.seasonal_component = (seasonal_samples * weights).sum(1) - df_out['seasonal_low'] = seasonal_samples.quantile(ci_low_edge, 1) - df_out['seasonal_high'] = seasonal_samples.quantile(ci_high_edge, 1) + df_out["seasonal_low"] = seasonal_samples.quantile(ci_low_edge, 1) + df_out["seasonal_high"] = seasonal_samples.quantile(ci_high_edge, 1) # Total model with confidence intervals - df_out.total_model = (df_out.degradation_trend * - df_out.seasonal_component * - df_out.soiling_ratio) - df_out['model_low'] = concat_tot_mod.quantile(ci_low_edge, 1) - df_out['model_high'] = concat_tot_mod.quantile(ci_high_edge, 1) + df_out.total_model = ( + df_out.degradation_trend * df_out.seasonal_component * df_out.soiling_ratio) + df_out["model_low"] = concat_tot_mod.quantile(ci_low_edge, 1) + df_out["model_high"] = concat_tot_mod.quantile(ci_high_edge, 1) # Residuals and residual shift df_out.residuals = pi / df_out.total_model self.residual_shift = df_out.residuals.mean() df_out.total_model *= self.residual_shift self.RMSE = _RMSE(pi, df_out.total_model) - self.adf_results = adfuller(df_out.residuals.dropna(), - regression='ctt', autolag=None) + self.adf_results = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None) self.result_df = df_out self.errors = errors if verbose: - print('\nFinal RMSE: {:.5f}'.format(self.RMSE)) + print("\nFinal RMSE: {:.5f}".format(self.RMSE)) if len(self.errors) > 1: print(self.errors) return self.result_df, self.degradation, self.soiling_loss - def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, - rate_std=.005, max_soiling_rates=.0005, - pruning_iterations=1, clean_pruning_sensitivity=.6, - renormalize_SR=None, perfect_cleaning=False, - prescient_cleaning_events=None, - clip_soiling=True, ffill=True): - ''' + def _Kalman_filter_for_SR( + self, zs_series, process_noise=1e-4, zs_std=0.05, rate_std=0.005, + max_soiling_rates=0.0005, pruning_iterations=1, clean_pruning_sensitivity=0.6, + renormalize_SR=None, perfect_cleaning=False, prescient_cleaning_events=None, + clip_soiling=True, ffill=True): + """ A function for estimating the underlying Soiling Ratio (SR) and the rate of change of the SR (the soiling rate), based on a noisy time series of daily (corrected) normalized energy using a Kalman Filter (KF). See @@ -2013,39 +2292,36 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, References ---------- .. [1] R. R. Labbe, Kalman and Bayesian Filters in Python. 2016. - ''' + """ # Ensure numeric index zs_series = zs_series.copy() # Make copy, so as not to change input zs_series = zs_series.astype(float) original_index = zs_series.index.copy() - if (original_index.dtype not in [int, 'int64']): + if original_index.dtype not in [int, "int64"]: zs_series.index = range(len(zs_series)) # Check prescient_cleaning_events. If not present, find cleaning events if isinstance(prescient_cleaning_events, list): cleaning_events = prescient_cleaning_events else: - if (isinstance(prescient_cleaning_events, type(zs_series)) and - (prescient_cleaning_events.sum() > 4)): + if isinstance(prescient_cleaning_events, type(zs_series)) and ( + prescient_cleaning_events.sum() > 4): if len(prescient_cleaning_events) == len(zs_series): prescient_cleaning_events = prescient_cleaning_events.copy() prescient_cleaning_events.index = zs_series.index else: raise ValueError( - "The indices of prescient_cleaning_events must correspond to the" + - " indices of zs_series; they must be of the same length") + "The indices of prescient_cleaning_events must correspond to the" + + " indices of zs_series; they must be of the same length") else: # If no prescient cleaning events, detect cleaning events - ce, rm9 = _rolling_median_ce_detection( - zs_series.index, zs_series, tuner=0.5) - prescient_cleaning_events = \ - _collapse_cleaning_events(ce, rm9.diff().values, 5) + ce, rm9 = _rolling_median_ce_detection(zs_series.index, zs_series, tuner=0.5) + prescient_cleaning_events = _collapse_cleaning_events(ce, rm9.diff().values, 5) cleaning_events = prescient_cleaning_events[prescient_cleaning_events].index.tolist() # Find soiling events (e.g. dust storms) - soiling_events = _soiling_event_detection( - zs_series.index, zs_series, ffill=ffill, tuner=5) + soiling_events = _soiling_event_detection(zs_series.index, zs_series, ffill=ffill, tuner=5) soiling_events = soiling_events[soiling_events].index.tolist() # Initialize various parameters @@ -2066,17 +2342,17 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, dt = 1 # All time stemps are one day # Initialize Kalman filter - f = self._initialize_univariate_model(zs_series, dt, process_noise, - measurement_noise, rate_std, - zs_std, initial_slope) + f = self._initialize_univariate_model( + zs_series, dt, process_noise, measurement_noise, rate_std, zs_std, initial_slope) # Initialize miscallenous variables - dfk = pd.DataFrame(index=zs_series.index, dtype=float, - columns=['raw_pi', 'raw_rates', 'smooth_pi', - 'smooth_rates', 'soiling_ratio', - 'soiling_rates', 'cleaning_events', - 'days_since_ce']) - dfk['cleaning_events'] = False + dfk = pd.DataFrame( + index=zs_series.index, + dtype=float, + columns=[ + "raw_pi", "raw_rates", "smooth_pi", "smooth_rates", "soiling_ratio", + "soiling_rates", "cleaning_events", "days_since_ce"]) + dfk["cleaning_events"] = False # Kalman Filter part: ####################################################################### @@ -2086,8 +2362,7 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, # Save results and smooth with rts smoother dfk, Xs, Ps = self._smooth_results( - dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, - perfect_cleaning) + dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning) ####################################################################### # Some steps to clean up the soiling data: @@ -2100,34 +2375,28 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, rm_smooth_pi = dfk.smooth_pi.rolling(7).median().shift(-6) pi_after_cleaning = rm_smooth_pi.loc[cleaning_events] # Detect outiers/false positives - false_positives = _find_numeric_outliers(pi_after_cleaning, - clean_pruning_sensitivity, 'lower') - cleaning_events = \ - false_positives[~false_positives].index.tolist() + false_positives = _find_numeric_outliers( + pi_after_cleaning, clean_pruning_sensitivity, "lower") + cleaning_events = false_positives[~false_positives].index.tolist() # 2: Remove longer periods with positive (soiling) rates if (dfk.smooth_rates > max_soiling_rates).sum() > 1: exceeding_rates = dfk.smooth_rates > max_soiling_rates new_cleaning_events = _collapse_cleaning_events( exceeding_rates, dfk.smooth_rates, 4) - cleaning_events.extend( - new_cleaning_events[new_cleaning_events].index) + cleaning_events.extend(new_cleaning_events[new_cleaning_events].index) cleaning_events.sort() # 3: If the list of cleaning events has changed, run the Kalman # Filter and smoother again if not ce_0 == cleaning_events: - f = self._initialize_univariate_model(zs_series, dt, - process_noise, - measurement_noise, - rate_std, zs_std, - initial_slope) + f = self._initialize_univariate_model( + zs_series, dt, process_noise, measurement_noise, rate_std, zs_std, + initial_slope) Xs, Ps, rate_std, zs_std = self._forward_pass( - f, zs_series, rolling_median_7, cleaning_events, - soiling_events) + f, zs_series, rolling_median_7, cleaning_events, soiling_events) dfk, Xs, Ps = self._smooth_results( - dfk, f, Xs, Ps, zs_series, cleaning_events, - soiling_events, perfect_cleaning) + dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning) else: counter = 100 # Make sure the while loop stops @@ -2136,14 +2405,13 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, if perfect_cleaning: # SR = 1 after cleaning events if len(cleaning_events) > 0: pi_dummy = pd.Series(index=dfk.index, data=np.nan) - pi_dummy.loc[cleaning_events] = \ - dfk.smooth_pi.loc[cleaning_events] + pi_dummy.loc[cleaning_events] = dfk.smooth_pi.loc[cleaning_events] dfk.soiling_ratio = 1 / pi_dummy.ffill() * dfk.smooth_pi # Set the SR in the first soiling period based on the mean # ratio of the Kalman estimate (smooth_pi) and the SR - dfk.loc[:cleaning_events[0], 'soiling_ratio'] = \ - dfk.loc[:cleaning_events[0], 'smooth_pi'] \ - * (dfk.soiling_ratio / dfk.smooth_pi).mean() + dfk.loc[: cleaning_events[0], "soiling_ratio"] = ( + dfk.loc[: cleaning_events[0], "smooth_pi"] + * (dfk.soiling_ratio / dfk.smooth_pi).mean()) else: # If no cleaning events dfk.soiling_ratio = 1 else: # Otherwise, if the inut signal has been decomposed, and @@ -2151,40 +2419,37 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, dfk.soiling_ratio = dfk.smooth_pi # 5: Renormalize Soiling Ratio if renormalize_SR is not None: - dfk.soiling_ratio /= dfk.loc[cleaning_events, 'soiling_ratio' - ].quantile(renormalize_SR) + dfk.soiling_ratio /= dfk.loc[cleaning_events, "soiling_ratio"].quantile( + renormalize_SR) # 6: Force soiling ratio to not exceed 1: if clip_soiling: dfk['soiling_ratio'] = dfk['soiling_ratio'].clip(upper=1) dfk.soiling_rates = dfk.smooth_rates - dfk.loc[dfk.soiling_ratio.diff() == 0, 'soiling_rates'] = 0 + dfk.loc[dfk.soiling_ratio.diff() == 0, "soiling_rates"] = 0 # Set number of days since cleaning event nr_days_dummy = pd.Series(index=dfk.index, data=np.nan) - nr_days_dummy.loc[cleaning_events] = [int(date-dfk.index[0]) - for date in cleaning_events] + nr_days_dummy.loc[cleaning_events] = [int(date - dfk.index[0]) for date in cleaning_events] nr_days_dummy.iloc[0] = 0 dfk.days_since_ce = range(len(zs_series)) - nr_days_dummy.ffill() # Save cleaning events and soiling events - dfk.loc[cleaning_events, 'cleaning_events'] = True + dfk.loc[cleaning_events, "cleaning_events"] = True dfk.index = original_index # Set index back to orignial index return dfk, Ps - def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, - soiling_events): - ''' Run the forward pass of the Kalman Filter algortihm ''' + def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, soiling_events): + """Run the forward pass of the Kalman Filter algortihm""" zs = zs_series.values N = len(zs) Xs, Ps = np.zeros((N, 2)), np.zeros((N, 2, 2)) # Enter forward pass of filtering algorithm for i, z in enumerate(zs): - if 7 < i < N-7 and (i in cleaning_events or i in soiling_events): - rolling_median_local = rolling_median_7.loc[i-5:i+5].values - u = self._set_control_input(f, rolling_median_local, i, - cleaning_events) + if 7 < i < N - 7 and (i in cleaning_events or i in soiling_events): + rolling_median_local = rolling_median_7.loc[i - 5 : i + 5].values + u = self._set_control_input(f, rolling_median_local, i, cleaning_events) f.predict(u=u) # Predict wth control input u else: # If no cleaning detection, predict without control input f.predict() @@ -2196,49 +2461,47 @@ def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, rate_std, zs_std = Ps[-1, 1, 1], Ps[-1, 0, 0] return Xs, Ps, rate_std, zs_std # Convert to numpy and return - def _set_control_input(self, f, rolling_median_local, index, - cleaning_events): - ''' + def _set_control_input(self, f, rolling_median_local, index, cleaning_events): + """ For each cleaning event, sets control input u based on current Kalman Filter state estimate (f.x), and the median value for the following week. If the cleaning event seems to be misplaced, moves the cleaning event to a more sensible location. If the cleaning event seems to be correct, removes other cleaning events in the 10 days surrounding this day - ''' + """ u = np.zeros(f.x.shape) # u is the control input window_size = 11 # len of rolling_median_local HW = 5 # Half window moving_diff = np.diff(rolling_median_local) # Index of maximum change in rolling median max_diff_index = moving_diff.argmax() - if max_diff_index == HW-1 or index not in cleaning_events: + if max_diff_index == HW - 1 or index not in cleaning_events: # The median zs of the week after the cleaning event - z_med = rolling_median_local[HW+3] + z_med = rolling_median_local[HW + 3] # Set control input this future median u[0] = z_med - np.dot(f.H, np.dot(f.F, f.x)) # If the change is bigger than the measurement noise: - if np.abs(u[0]) > np.sqrt(f.R)/2: - index_dummy = [n+3 for n in range(window_size-HW-1) - if n+3 != HW] - cleaning_events = [ce for ce in cleaning_events - if ce-index+HW not in index_dummy] + if np.abs(u[0]) > np.sqrt(f.R) / 2: + index_dummy = [n + 3 for n in range(window_size - HW - 1) if n + 3 != HW] + cleaning_events = [ + ce for ce in cleaning_events if ce - index + HW not in index_dummy] else: # If the cleaning event is insignificant u[0] = 0 if index in cleaning_events: cleaning_events.remove(index) else: # If the index with the maximum difference is not today... cleaning_events.remove(index) # ...remove today from the list - if moving_diff[max_diff_index] > 0 \ - and index+max_diff_index-HW+1 not in cleaning_events: + if (moving_diff[max_diff_index] > 0 + and index + max_diff_index - HW + 1 not in cleaning_events): # ...and add the missing day - bisect.insort(cleaning_events, index+max_diff_index-HW+1) + bisect.insort(cleaning_events, index + max_diff_index - HW + 1) return u - def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events, - soiling_events, perfect_cleaning): - ''' Smoother for Kalman Filter estimates. Smooths the Kalaman estimate - between given cleaning events and saves all in DataFrame dfk''' + def _smooth_results( + self, dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning): + """Smoother for Kalman Filter estimates. Smooths the Kalaman estimate + between given cleaning events and saves all in DataFrame dfk""" # Save unsmoothed estimates dfk.raw_pi = Xs[:, 0] dfk.raw_rates = Xs[:, 1] @@ -2253,8 +2516,7 @@ def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events, # Smooth between cleaning events for start, end in zip(ce_dummy[:-1], ce_dummy[1:]): num_ind = df_num_ind.loc[start:end].iloc[:-1] - Xs[num_ind], Ps[num_ind], _, _ = f.rts_smoother(Xs[num_ind], - Ps[num_ind]) + Xs[num_ind], Ps[num_ind], _, _ = f.rts_smoother(Xs[num_ind], Ps[num_ind]) # Save smoothed estimates dfk.smooth_pi = Xs[:, 0] @@ -2262,17 +2524,15 @@ def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events, return dfk, Xs, Ps - def _initialize_univariate_model(self, zs_series, dt, process_noise, - measurement_noise, rate_std, zs_std, - initial_slope): - ''' Initializes the univariate Kalman Filter model, using the filterpy - package ''' + def _initialize_univariate_model( + self, zs_series, dt, process_noise, measurement_noise, + rate_std, zs_std, initial_slope): + """Initializes the univariate Kalman Filter model, using the filterpy + package""" f = KalmanFilter(dim_x=2, dim_z=1) - f.F = np.array([[1., dt], - [0., 1.]]) - f.H = np.array([[1., 0.]]) - f.P = np.array([[zs_std**2, 0], - [0, rate_std**2]]) + f.F = np.array([[1.0, dt], [0.0, 1.0]]) + f.H = np.array([[1.0, 0.0]]) + f.P = np.array([[zs_std**2, 0], [0, rate_std**2]]) f.Q = Q_discrete_white_noise(dim=2, dt=dt, var=process_noise**2) f.x = np.array([initial_slope[1], initial_slope[0]]) f.B = np.zeros(f.F.shape) @@ -2281,19 +2541,14 @@ def _initialize_univariate_model(self, zs_series, dt, process_noise, return f -def soiling_cods(energy_normalized_daily, - reps=512, - confidence_level=68.2, - degradation_method='YoY', - process_noise=1e-4, - order_alternatives=(('SR', 'SC', 'Rd'), - ('SC', 'SR', 'Rd')), - cleaning_sensitivity_alternatives=(.25, .75), - clean_pruning_sensitivity_alternatives=(1/1.5, 1.5), - forward_fill_alternatives=(True, False), - verbose=False, - **kwargs): - ''' +def soiling_cods( + energy_normalized_daily, reps=512, confidence_level=68.2, degradation_method="YoY", + process_noise=1e-4, order_alternatives=( + ("SR", "SC", "Rd"), ("SC", "SR", "Rd")), + cleaning_sensitivity_alternatives=(0.25, 0.75), + clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5), + forward_fill_alternatives=(True, False), verbose=False, **kwargs): + """ Functional wrapper for :py:class:`~rdtools.soiling.CODSAnalysis` and its subroutine :py:func:`~rdtools.soiling.CODSAnalysis.run_bootstrap`. Runs the combined degradation and soiling (CODS) algorithm with bootstrapping. @@ -2406,31 +2661,26 @@ def soiling_cods(energy_normalized_daily, ---------- .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ CODS = CODSAnalysis(energy_normalized_daily) CODS.run_bootstrap( - reps=reps, - confidence_level=confidence_level, - verbose=verbose, - degradation_method=degradation_method, - process_noise=process_noise, + reps=reps, confidence_level=confidence_level, verbose=verbose, + degradation_method=degradation_method, process_noise=process_noise, order_alternatives=order_alternatives, cleaning_sensitivity_alternatives=cleaning_sensitivity_alternatives, clean_pruning_sensitivity_alternatives=clean_pruning_sensitivity_alternatives, - forward_fill_alternatives=forward_fill_alternatives, - **kwargs) + forward_fill_alternatives=forward_fill_alternatives, **kwargs) sr = 1 - CODS.soiling_loss[0] / 100 sr_ci = 1 - np.array(CODS.soiling_loss[1:3]) / 100 - return sr, sr_ci, CODS.degradation[0], np.array(CODS.degradation[1:3]), \ - CODS.result_df + return (sr, sr_ci, CODS.degradation[0], np.array(CODS.degradation[1:3]), CODS.result_df) def _collapse_cleaning_events(inferred_ce_in, metric, f=4): - ''' A function for replacing quick successive cleaning events with one + """A function for replacing quick successive cleaning events with one (most probable) cleaning event. Parameters @@ -2447,10 +2697,9 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): ------- inferred_ce : pandas.Series boolean values for cleaning events - ''' + """ # Ensure numeric index - if isinstance(inferred_ce_in.index, - pd.core.indexes.datetimes.DatetimeIndex): + if isinstance(inferred_ce_in.index, pd.core.indexes.datetimes.DatetimeIndex): saveindex = inferred_ce_in.copy().index inferred_ce_in.index = range(len(saveindex)) else: @@ -2470,11 +2719,10 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): end_true_vals = collapsed_ce_dummy.loc[start_true_vals:].idxmin() - 1 if end_true_vals >= start_true_vals: # If the island ends # Find the day with mac probability of being a cleaning event - max_diff_day = \ - metric.loc[start_true_vals-f:end_true_vals+f].idxmax() + max_diff_day = metric.loc[start_true_vals - f : end_true_vals + f].idxmax() # Set all days in this period as false - collapsed_ce.loc[start_true_vals-f:end_true_vals+f] = False - collapsed_ce_dummy.loc[start_true_vals-f:end_true_vals+f] = False + collapsed_ce.loc[start_true_vals - f : end_true_vals + f] = False + collapsed_ce_dummy.loc[start_true_vals - f : end_true_vals + f] = False # Set the max probability day as True (cleaning event) collapsed_ce.loc[max_diff_day] = True # Find the next island of true values @@ -2488,51 +2736,52 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): def _rolling_median_ce_detection(x, y, ffill=True, rolling_window=9, tuner=1.5): - ''' Finds cleaning events in a time series of performance index (y) ''' + """Finds cleaning events in a time series of performance index (y)""" y = pd.Series(index=x, data=y) y = y.astype(float) if ffill: # forward fill NaNs in y before running mean rm = y.ffill().rolling(rolling_window, center=True).median() else: # ... or backfill instead rm = y.bfill().rolling(rolling_window, center=True).median() - Q3 = rm.diff().abs().quantile(.75) - Q1 = rm.diff().abs().quantile(.25) + Q3 = rm.diff().abs().quantile(0.75) + Q1 = rm.diff().abs().quantile(0.25) limit = Q3 + tuner * (Q3 - Q1) cleaning_events = rm.diff() > limit return cleaning_events, rm def _soiling_event_detection(x, y, ffill=True, tuner=5): - ''' Finds cleaning events in a time series of performance index (y) ''' + """Finds cleaning events in a time series of performance index (y)""" y = pd.Series(index=x, data=y) y = y.astype(float) if ffill: # forward fill NaNs in y before running mean rm = y.ffill().rolling(9, center=True).median() else: # ... or backfill instead rm = y.bfill().rolling(9, center=True).median() - Q3 = rm.diff().abs().quantile(.99) - Q1 = rm.diff().abs().quantile(.01) + Q3 = rm.diff().abs().quantile(0.99) + Q1 = rm.diff().abs().quantile(0.01) limit = Q1 - tuner * (Q3 - Q1) soiling_events = rm.diff() < limit return soiling_events -def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5, - max_multiplier=2, max_shift=20): - ''' Generate seasonal samples by perturbing the amplitude and the phase of - a seasonal components found with the fitted CODS model ''' - samples = pd.DataFrame(index=list_of_SCs[0].index, - columns=range(int(sample_nr*len(list_of_SCs))), - dtype=float) +def _make_seasonal_samples( + list_of_SCs, sample_nr=10, min_multiplier=0.5, max_multiplier=2, max_shift=20): + """Generate seasonal samples by perturbing the amplitude and the phase of + a seasonal components found with the fitted CODS model""" + samples = pd.DataFrame( + index=list_of_SCs[0].index, + columns=range(int(sample_nr * len(list_of_SCs))), + dtype=float) # From each fitted signal, we will generate new seaonal components for i, signal in enumerate(list_of_SCs): # Remove beginning and end of signal signal_mean = signal.mean() # Make a signal matrix where each column is a year and each row a date - year_matrix = signal.rename('values').to_frame().assign( - doy=signal.index.dayofyear, - year=signal.index.year - ).pivot(index='doy', columns='year', values='values') + year_matrix = ( + signal.rename("values").to_frame() + .assign(doy=signal.index.dayofyear, year=signal.index.year) + .pivot(index="doy", columns="year", values="values")) # We will use the median signal through all the years... median_signal = year_matrix.median(1) for j in range(sample_nr): @@ -2543,25 +2792,21 @@ def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5, # constructing the new signal based on median_signal shifted_signal = pd.Series( index=signal.index, - data=median_signal.reindex( - (signal.index.dayofyear-shift) % 365 + 1).values) + data=median_signal.reindex((signal.index.dayofyear - shift) % 365 + 1).values) # Perturb amplitude by recentering to 0 multiplying by multiplier - samples.loc[:, i*sample_nr + j] = \ - multiplier * (shifted_signal - signal_mean) + 1 + samples.loc[:, i * sample_nr + j] = multiplier * (shifted_signal - signal_mean) + 1 return samples def _force_periodicity(in_signal, signal_index, out_index): - ''' Function for forcing periodicity in a seasonal component signal ''' + """Function for forcing periodicity in a seasonal component signal""" # Make sure the in_signal is a Series if isinstance(in_signal, np.ndarray): - signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), - data=in_signal) + signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal) elif isinstance(in_signal, pd.Series): - signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), - data=in_signal.values) + signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal.values) else: - raise ValueError('in_signal must be numpy array or pandas Series') + raise ValueError("in_signal must be numpy array or pandas Series") # Make sure that we don't remove too much of the data: remove_length = np.min([180, int((len(signal) - 365) / 2)]) @@ -2573,66 +2818,157 @@ def _force_periodicity(in_signal, signal_index, out_index): # Make a signal matrix where each column is a year and each row is a date year_matrix = pd.DataFrame(index=np.arange(0, 365), columns=unique_years) for year in unique_years: - dates_in_year = pd.date_range(str(year)+'-01-01', str(year)+'-12-31') + dates_in_year = pd.date_range(str(year) + "-01-01", str(year) + "-12-31") # We cut off the extra day(s) of leap years - year_matrix[year] = \ - signal.loc[str(year)].reindex(dates_in_year).values[:365] + year_matrix[year] = signal.loc[str(year)].reindex(dates_in_year).values[:365] # We will use the median signal through all the years... median_signal = year_matrix.median(1) # The output is the median signal broadcasted to the whole time series - output = pd.Series( - index=out_index, - data=median_signal.reindex(out_index.dayofyear - 1).values) + output = pd.Series(index=out_index, data=median_signal.reindex(out_index.dayofyear - 1).values) return output -def _find_numeric_outliers(x, multiplier=1.5, where='both', verbose=False): - ''' Function for finding numeric outliers ''' +def _find_numeric_outliers(x, multiplier=1.5, where="both", verbose=False): + """Function for finding numeric outliers""" try: # Calulate third and first quartile - Q3 = np.quantile(x, .75) - Q1 = np.quantile(x, .25) + Q3 = np.quantile(x, 0.75) + Q1 = np.quantile(x, 0.25) except IndexError as ie: print(ie, x) except RuntimeWarning as rw: print(rw, x) IQR = Q3 - Q1 # Interquartile range - if where == 'upper': # If detecting upper outliers + if where == "upper": # If detecting upper outliers if verbose: - print('Upper limit', Q3 + multiplier * IQR) - return (x > Q3 + multiplier * IQR) - elif where == 'lower': # If detecting lower outliers + print("Upper limit", Q3 + multiplier * IQR) + return x > Q3 + multiplier * IQR + elif where == "lower": # If detecting lower outliers if verbose: - print('Lower limit', Q1 - multiplier * IQR) - return (x < Q1 - multiplier * IQR) - elif where == 'both': # If detecting both lower and upper outliers + print("Lower limit", Q1 - multiplier * IQR) + return x < Q1 - multiplier * IQR + elif where == "both": # If detecting both lower and upper outliers if verbose: - print('Upper, lower limit', - Q3 + multiplier * IQR, - Q1 - multiplier * IQR) + print("Upper, lower limit", Q3 + multiplier * IQR, Q1 - multiplier * IQR) return (x > Q3 + multiplier * IQR), (x < Q1 - multiplier * IQR) def _RMSE(y_true, y_pred): - '''Calculates the Root Mean Squared Error for y_true and y_pred, where - y_pred is the "prediction", and y_true is the truth.''' - mask = ~np.isnan(y_pred) - return np.sqrt(np.mean((y_pred[mask]-y_true[mask])**2)) + """Calculates the Root Mean Squared Error for y_true and y_pred, where + y_pred is the "prediction", and y_true is the truth.""" + mask = ~pd.isnull(y_pred) + return np.sqrt(np.mean((y_pred[mask] - y_true[mask]) ** 2)) def _MSD(y_true, y_pred): - '''Calculates the Mean Signed Deviation for y_true and y_pred, where y_pred - is the "prediction", and y_true is the truth.''' + """Calculates the Mean Signed Deviation for y_true and y_pred, where y_pred + is the "prediction", and y_true is the truth.""" return np.mean(y_pred - y_true) def _progressBarWithETA(value, endvalue, time, bar_length=20): - ''' Prints a progressbar with an estimated time of "arrival" ''' + """Prints a progressbar with an estimated time of "arrival" """ percent = float(value) / endvalue * 100 - arrow = '-' * int(round(percent/100 * bar_length)-1) + '>' - spaces = ' ' * (bar_length - len(arrow)) + arrow = "-" * int(round(percent / 100 * bar_length) - 1) + ">" + spaces = " " * (bar_length - len(arrow)) used = time / 60 # Time Used - left = used / percent*(100-percent) # Estimated time left + left = used / percent * (100 - percent) # Estimated time left sys.stdout.write( - "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) + - " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent)) + "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) + + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent)) sys.stdout.flush() + + +############################################################################### +# all code below for new piecewise fitting in soiling intervals within srr/Matt +############################################################################### +def piecewise_linear(x, x0, b, k1, k2): + cond_list = [x < x0, x >= x0] + func_list = [lambda x: k1 * x + b, lambda x: k1 * x + b + k2 * (x - x0)] + return np.piecewise(x, cond_list, func_list) + + +def segmented_soiling_period( + pr, fill_method="bfill", days_clean_vs_cp=7, initial_guesses=[13, 1, 0, 0], + bounds=None, min_r2=0.15): + # note min_r2 was 0.6 and it could be worth testing 10 day forward median as b guess + """ + Applies segmented regression to a single deposition period + (data points in between two cleaning events). + Segmentation is neglected if change point occurs within a number of days + (days_clean_vs_cp) of the cleanings. + + Parameters + ---------- + pr : + Series of daily performance ratios measured during the given deposition period. + fill_method : str (default='bfill') + Method to employ to fill any missing day. + days_clean_vs_cp : numeric (default=7) + Minimum number of days accepted between cleanings and change points. + bounds : numeric (default=None) + List of bounds for fitting function. If not specified, they are + defined in the function. + initial_guesses : numeric (default=0.1) + List of initial guesses for fitting function + min_r2 : numeric (default=0.1) + Minimum R2 to consider valid the extracted soiling profile. + + Returns + ------- + sr: numeric + Series containing the daily soiling ratio values after segmentation. + List of nan if segmentation was not possible. + cp_date: datetime + Datetime in which continuous change points occurred. + None if segmentation was not possible. + """ + # Check if PR dataframe has datetime index + if not isinstance(pr.index, pd.DatetimeIndex): + raise ValueError("The time series does not have DatetimeIndex") + + # Define bounds if not provided + if bounds is None: + # bounds are neg in first 4 and pos in second 4 + # ordered as x0,b,k1,k2 where x0 is the breakpoint k1 and k2 are slopes + bounds = [(13, -5, -np.inf, -np.inf), ((len(pr) - 13), 5, +np.inf, +np.inf)] + y = pr.values + x = np.arange(0.0, len(y)) + + try: + # Fit soiling profile with segmentation + p, e = curve_fit(piecewise_linear, x, y, p0=initial_guesses, bounds=bounds) + + # Ignore change point if too close to a cleaning + # Change point p[0] converted to integer to extract a date. + # None if no change point is found. + if p[0] > days_clean_vs_cp and p[0] < len(y) - days_clean_vs_cp: + z = piecewise_linear(x, *p) + cp_date = int(p[0]) + else: + z = [np.nan] * len(x) + cp_date = None + R2_original = st.linregress(y, x)[2] ** 2 + R2_piecewise = st.linregress(y, z)[2] ** 2 + + R2_improve = R2_piecewise - R2_original + R2_percent_improve = (R2_piecewise / R2_original) - 1 + R2_percent_of_possible_improve = R2_improve / (1 - R2_original) + # improvement relative to possible improvement + + if len(y) < 45: # tighter requirements for shorter soiling periods + if (R2_piecewise < min_r2) | ( + (R2_percent_of_possible_improve < 0.5) & (R2_percent_improve < 0.5)): + z = [np.nan] * len(x) + cp_date = None + else: + if (R2_percent_improve < 0.01) | (R2_piecewise < 0.4): + z = [np.nan] * len(x) + cp_date = None + except ValueError as ex: + print(f"Segmentation was not possible. Error: {ex}") + z = [np.nan] * len(x) + cp_date = None + # Create Series from modelled profile + sr = pd.Series(z, index=pr.index) + + return sr, cp_date diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py index b940f2df..4085b997 100644 --- a/rdtools/test/conftest.py +++ b/rdtools/test/conftest.py @@ -54,6 +54,41 @@ def soiling_normalized_daily(soiling_times): return normalized_daily +@pytest.fixture() +def soiling_normalized_daily_with_neg_shifts(soiling_times): + interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1) + interval_1_v2 = (0.9 - 0.005 * 15) - 0.005 * np.arange(0, 10, 1) + interval_2 = 1 - 0.002 * np.arange(0, 25, 1) + interval_3_v1 = 1 - 0.001 * np.arange(0, 10, 1) + interval_3_v2 = (0.95 - 0.001 * 10) - 0.001 * np.arange(0, 15, 1) + profile = np.concatenate( + (interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2) + ) + np.random.seed(1977) + noise = 0.01 * np.random.rand(75) + normalized_daily = pd.Series(data=profile, index=soiling_times) + normalized_daily = normalized_daily + noise + + return normalized_daily + + +@pytest.fixture() +def soiling_normalized_daily_with_piecewise_slope(soiling_times): + interval_1_v1 = 1 - 0.002 * np.arange(0, 20, 1) + interval_1_v2 = (1 - 0.002 * 20) - 0.007 * np.arange(0, 20, 1) + interval_2_v1 = 1 - 0.01 * np.arange(0, 20, 1) + interval_2_v2 = (1 - 0.01 * 20) - 0.001 * np.arange(0, 15, 1) + profile = np.concatenate( + (interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2) + ) + np.random.seed(1977) + noise = 0.01 * np.random.rand(75) + normalized_daily = pd.Series(data=profile, index=soiling_times) + normalized_daily = normalized_daily + noise + + return normalized_daily + + @pytest.fixture() def soiling_insolation(soiling_times): insolation = np.empty((75,)) diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index a1a67837..7f87cf2d 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -5,6 +5,7 @@ from rdtools.soiling import annual_soiling_ratios from rdtools.soiling import monthly_soiling_rates from rdtools.soiling import NoValidIntervalError +from rdtools.soiling import segmented_soiling_period import pytest @@ -13,86 +14,94 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times reps = 10 np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=reps) + assert 0.964369 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected value' + "Soiling ratio different from expected value" assert np.array([0.962540, 0.965295]) == pytest.approx(sr_ci, abs=1e-6), \ - 'Confidence interval different from expected value' - assert 0.960205 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6), \ - 'Exceedance level different from expected value' - assert 0.984079 == pytest.approx(soiling_info['renormalizing_factor'], abs=1e-6), \ - 'Renormalizing factor different from expected value' - assert len(soiling_info['stochastic_soiling_profiles']) == reps, \ + "Confidence interval different from expected value" + assert 0.960205 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6), \ + "Exceedance level different from expected value" + assert 0.984079 == pytest.approx(soiling_info["renormalizing_factor"], abs=1e-6), \ + "Renormalizing factor different from expected value" + assert (len(soiling_info["stochastic_soiling_profiles"]) == reps), \ 'Length of soiling_info["stochastic_soiling_profiles"] different than expected' - assert isinstance(soiling_info['stochastic_soiling_profiles'], list), \ + assert isinstance(soiling_info["stochastic_soiling_profiles"], list), \ 'soiling_info["stochastic_soiling_profiles"] is not a list' # Check soiling_info['soiling_interval_summary'] - expected_summary_columns = ['start', 'end', 'soiling_rate', 'soiling_rate_low', - 'soiling_rate_high', 'inferred_start_loss', 'inferred_end_loss', - 'length', 'valid'] - actual_summary_columns = soiling_info['soiling_interval_summary'].columns.values + expected_summary_columns = [ + "start", "end", "soiling_rate", "soiling_rate_low", "soiling_rate_high", + "inferred_start_loss", "inferred_end_loss", "inferred_recovery", + "inferred_begin_shift", "length", "valid"] + actual_summary_columns = soiling_info["soiling_interval_summary"].columns.values for x in actual_summary_columns: - assert x in expected_summary_columns, \ + assert (x in expected_summary_columns), \ f"'{x}' not an expected column in soiling_info['soiling_interval_summary']" for x in expected_summary_columns: - assert x in actual_summary_columns, \ + assert (x in actual_summary_columns), \ f"'{x}' was expected as a column, but not in soiling_info['soiling_interval_summary']" - assert isinstance(soiling_info['soiling_interval_summary'], pd.DataFrame), \ + + assert isinstance(soiling_info["soiling_interval_summary"], pd.DataFrame), \ 'soiling_info["soiling_interval_summary"] not a dataframe' - expected_means = pd.Series({'soiling_rate': -0.002644544, - 'soiling_rate_low': -0.002847504, - 'soiling_rate_high': -0.002455915, - 'inferred_start_loss': 1.020124, - 'inferred_end_loss': 0.9566552, - 'length': 24.0, - 'valid': 1.0}) - expected_means = expected_means[['soiling_rate', 'soiling_rate_low', 'soiling_rate_high', - 'inferred_start_loss', 'inferred_end_loss', - 'length', 'valid']] - actual_means = soiling_info['soiling_interval_summary'][expected_means.index].mean() + + expected_means = pd.Series( + {"soiling_rate": -0.002644544, "soiling_rate_low": -0.002847504, + "soiling_rate_high": -0.002455915, "inferred_start_loss": 1.020124, + "inferred_end_loss": 0.9566552, "inferred_recovery": 0.065416, + "inferred_begin_shift": 0.084814, "length": 24.0, "valid": 1.0}) + expected_means = expected_means[ + ["soiling_rate", "soiling_rate_low", "soiling_rate_high", "inferred_start_loss", + "inferred_end_loss", "inferred_recovery", "inferred_begin_shift", "length", "valid"]] + actual_means = soiling_info["soiling_interval_summary"][expected_means.index].mean() pd.testing.assert_series_equal(expected_means, actual_means, check_exact=False) # Check soiling_info['soiling_ratio_perfect_clean'] - pd.testing.assert_index_equal(soiling_info['soiling_ratio_perfect_clean'].index, soiling_times, - check_names=False) - sr_mean = soiling_info['soiling_ratio_perfect_clean'].mean() - assert 0.968265 == pytest.approx(sr_mean, abs=1e-6), \ - "The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected" - assert isinstance(soiling_info['soiling_ratio_perfect_clean'], pd.Series), \ - 'soiling_info["soiling_ratio_perfect_clean"] not a pandas series' + pd.testing.assert_index_equal( + soiling_info["soiling_ratio_perfect_clean"].index, soiling_times, check_names=False) + sr_mean = soiling_info["soiling_ratio_perfect_clean"].mean() + assert 0.968265 == pytest.approx( + sr_mean, abs=1e-6 + ), "The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected" + assert isinstance( + soiling_info["soiling_ratio_perfect_clean"], pd.Series + ), 'soiling_info["soiling_ratio_perfect_clean"] not a pandas series' @pytest.mark.filterwarnings("ignore:.*20% or more of the daily data.*:UserWarning") -@pytest.mark.parametrize('method,expected_sr', - [('random_clean', 0.936177), - ('half_norm_clean', 0.915093), - ('perfect_clean', 0.977116)]) -def test_soiling_srr_consecutive_invalid(soiling_normalized_daily, soiling_insolation, - soiling_times, method, expected_sr): +@pytest.mark.parametrize( + "method,neg_shift,piecewise,expected_sr", + [("random_clean", False, False, 0.936177), + ("half_norm_clean", False, False, 0.915093), + ("perfect_clean", False, False, 0.977116), + ("perfect_clean_complex", True, True, 0.977116), + ("inferred_clean_complex", True, True, 0.975805)]) +def test_soiling_srr_consecutive_invalid( + soiling_normalized_daily, soiling_insolation, soiling_times, + method, neg_shift, piecewise, expected_sr): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=reps, - max_relative_slope_error=20.0, method=method) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, + reps=reps, max_relative_slope_error=20.0, + method=method, piecewise=piecewise, + neg_shift=neg_shift) assert expected_sr == pytest.approx(sr, abs=1e-6), \ - f'Soiling ratio different from expected value for {method} with consecutive invalid intervals' # noqa: E501 + f"Soiling ratio different from expected value for {method} \ + with consecutive invalid intervals" -@pytest.mark.parametrize('clean_criterion,expected_sr', - [('precip_and_shift', 0.982546), - ('precip_or_shift', 0.973433), - ('precip', 0.976196), - ('shift', 0.964369)]) -def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, soiling_times, - clean_criterion, expected_sr): +@pytest.mark.parametrize("clean_criterion,expected_sr", + [("precip_and_shift", 0.982546), + ("precip_or_shift", 0.973433), + ("precip", 0.976196), + ("shift", 0.964369)]) +def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, + soiling_times, clean_criterion, expected_sr): precip = pd.Series(index=soiling_times, data=0) - precip['2019-01-18 00:00:00-07:00'] = 1 - precip['2019-02-20 00:00:00-07:00'] = 1 + precip["2019-01-18 00:00:00-07:00"] = 1 + precip["2019-02-20 00:00:00-07:00"] = 1 - kwargs = { - 'reps': 10, - 'precipitation_daily': precip - } + kwargs = {"reps": 10, "precipitation_daily": precip} np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, clean_criterion=clean_criterion, **kwargs) @@ -101,18 +110,18 @@ def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, s def test_soiling_srr_confidence_levels(soiling_normalized_daily, soiling_insolation): - 'Tests SRR with different confidence level settingsf from above' + "Tests SRR with different confidence level settings from above" np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=95, reps=10, exceedance_prob=80.0) assert np.array([0.959322, 0.966066]) == pytest.approx(sr_ci, abs=1e-6), \ - 'Confidence interval with confidence_level=95 different than expected' - assert 0.962691 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6), \ + "Confidence interval with confidence_level=95 different than expected" + assert 0.962691 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6), \ 'soiling_info["exceedance_level"] different than expected when exceedance_prob=80' def test_soiling_srr_dayscale(soiling_normalized_daily, soiling_insolation): - 'Test that a long dayscale can prevent valid intervals from being found' + "Test that a long dayscale can prevent valid intervals from being found" with pytest.raises(NoValidIntervalError): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, @@ -120,45 +129,51 @@ def test_soiling_srr_dayscale(soiling_normalized_daily, soiling_insolation): def test_soiling_srr_clean_threshold(soiling_normalized_daily, soiling_insolation): - '''Test that clean test_soiling_srr_clean_threshold works with a float and - can cause no soiling intervals to be found''' + """Test that clean test_soiling_srr_clean_threshold works with a float and + can cause no soiling intervals to be found""" np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, - clean_threshold=0.01) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.01) assert 0.964369 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio with specified clean_threshold different from expected value' + "Soiling ratio with specified clean_threshold different from expected value" with pytest.raises(NoValidIntervalError): np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, - reps=10, clean_threshold=0.1) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.1) def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, - trim=True) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, trim=True) assert 0.978093 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio with trim=True different from expected value' - assert len(soiling_info['soiling_interval_summary']) == 1, \ - 'Wrong number of soiling intervals found with trim=True' - - -@pytest.mark.parametrize('method,expected_sr', - [('random_clean', 0.920444), - ('perfect_clean', 0.966912) - ]) -def test_soiling_srr_method(soiling_normalized_daily, soiling_insolation, method, expected_sr): + "Soiling ratio with trim=True different from expected value" + assert (len(soiling_info["soiling_interval_summary"]) == 1), \ + "Wrong number of soiling intervals found with trim=True" + + +@pytest.mark.parametrize( + "method,neg_shift,piecewise,expected_sr", + [("random_clean", False, False, 0.920444), + ("perfect_clean", False, False, 0.966912), + ("perfect_clean_complex", True, True, 0.966912), + ("inferred_clean_complex", True, True, 0.965565)]) +def test_soiling_srr_method( + soiling_normalized_daily, soiling_insolation, method, neg_shift, piecewise, expected_sr +): np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, - method=method) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, method=method, + neg_shift=neg_shift, piecewise=piecewise + ) assert expected_sr == pytest.approx(sr, abs=1e-6), \ f'Soiling ratio with method="{method}" different from expected value' def test_soiling_srr_min_interval_length(soiling_normalized_daily, soiling_insolation): - 'Test that a long minimum interval length prevents finding shorter intervals' + "Test that a long minimum interval length prevents finding shorter intervals" with pytest.raises(NoValidIntervalError): np.random.seed(1977) # normalized_daily intervals are 25 days long, so min=26 should fail: @@ -172,12 +187,12 @@ def test_soiling_srr_min_interval_length(soiling_normalized_daily, soiling_insol def test_soiling_srr_recenter_false(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, - recenter=False) - assert 1 == soiling_info['renormalizing_factor'], \ - 'Renormalizing factor != 1 with recenter=False' + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, recenter=False) + assert (1 == soiling_info["renormalizing_factor"]), \ + "Renormalizing factor != 1 with recenter=False" assert 0.966387 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different than expected when recenter=False' + "Soiling ratio different than expected when recenter=False" def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation): @@ -185,97 +200,107 @@ def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation) stepped_daily.iloc[37:] = stepped_daily.iloc[37:] - 0.1 np.random.seed(1977) - with pytest.warns(UserWarning, match='20% or more of the daily data'): + with pytest.warns(UserWarning, match="20% or more of the daily data"): sr, sr_ci, soiling_info = soiling_srr(stepped_daily, soiling_insolation, reps=10) - assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, False, True], \ - 'Soiling interval validity differs from expected when a large negative step\ - is incorporated into the data' + assert list(soiling_info["soiling_interval_summary"]["valid"].values) == [ + True, False, True], \ + "Soiling interval validity differs from expected when a large negative step\ + is incorporated into the data" assert 0.936932 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected when a large negative step is incorporated into the data' # noqa: E501 + "Soiling ratio different from expected when a large negative step is\ + incorporated into the data" def test_soiling_srr_max_negative_slope_error(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) - with pytest.warns(UserWarning, match='20% or more of the daily data'): + with pytest.warns(UserWarning, match="20% or more of the daily data"): sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, max_relative_slope_error=45.0) - assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, True, False], \ - 'Soiling interval validity differs from expected when max_relative_slope_error=45.0' + assert list(soiling_info["soiling_interval_summary"]["valid"].values) == [ + True, True, False], \ + "Soiling interval validity differs from expected when max_relative_slope_error=45.0" assert 0.958761 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected when max_relative_slope_error=45.0' + "Soiling ratio different from expected when max_relative_slope_error=45.0" def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolation): - ''' + """ Previous versions had a bug which would have raised an error when an entire interval was NaN. See https://github.com/NREL/rdtools/issues/129 - ''' + """ reps = 10 normalized_corrupt = soiling_normalized_daily.copy() normalized_corrupt[26:50] = np.nan np.random.seed(1977) - with pytest.warns(UserWarning, match='20% or more of the daily data'): + with pytest.warns(UserWarning, match="20% or more of the daily data"): sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, soiling_insolation, reps=reps) assert 0.948792 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected value when an entire interval was NaN' + "Soiling ratio different from expected value when an entire interval was NaN" + + with pytest.warns(UserWarning, match="20% or more of the daily data"): + sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, soiling_insolation, + reps=reps, method="perfect_clean_complex", + piecewise=True, neg_shift=True) + assert 0.974225 == pytest.approx(sr, abs=1e-6), \ + "Soiling ratio different from expected value when an entire interval was NaN" def test_soiling_srr_outlier_factor(soiling_normalized_daily, soiling_insolation): - _, _, info = soiling_srr(soiling_normalized_daily, soiling_insolation, - reps=1, outlier_factor=8) - assert len(info['soiling_interval_summary']) == 2, \ - 'Increasing the outlier_factor did not result in the expected number of soiling intervals' + _, _, info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=1, outlier_factor=8 + ) + assert (len(info["soiling_interval_summary"]) == 2), \ + "Increasing the outlier_factor did not result in the expected number of soiling intervals" def test_soiling_srr_kwargs(monkeypatch, soiling_normalized_daily, soiling_insolation): - ''' + """ Make sure that all soiling_srr parameters get passed on to SRRAnalysis and SRRAnalysis.run(), i.e. all necessary inputs to SRRAnalysis are provided by soiling_srr. Done by removing the SRRAnalysis default param values and making sure everything still runs. - ''' + """ # the __defaults__ attr is the tuple of default values in py3 monkeypatch.delattr(SRRAnalysis.__init__, "__defaults__") monkeypatch.delattr(SRRAnalysis.run, "__defaults__") _ = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10) -@pytest.mark.parametrize(('start,expected_sr'), - [(18, 0.984779), (17, 0.981258)]) -def test_soiling_srr_min_interval_length_default(soiling_normalized_daily, soiling_insolation, - start, expected_sr): - ''' +@pytest.mark.parametrize(("start,expected_sr"), [(18, 0.984779), (17, 0.981258)]) +def test_soiling_srr_min_interval_length_default( + soiling_normalized_daily, soiling_insolation, start, expected_sr): + """ Make sure that the default value of min_interval_length is 7 days by testing on a cropped version of the example data - ''' + """ reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily[start:], - soiling_insolation[start:], reps=reps) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily[start:], soiling_insolation[start:], reps=reps + ) assert expected_sr == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected value' + "Soiling ratio different from expected value" -@pytest.mark.parametrize('test_param', ['energy_normalized_daily', - 'insolation_daily', - 'precipitation_daily']) +@pytest.mark.parametrize( + "test_param", ["energy_normalized_daily", "insolation_daily", "precipitation_daily"]) def test_soiling_srr_non_daily_inputs(test_param): - ''' + """ Validate the frequency check for input time series - ''' - dummy_daily_explicit = pd.Series(0, index=pd.date_range('2019-01-01', periods=10, freq='d')) - dummy_daily_implicit = pd.Series(0, index=pd.date_range('2019-01-01', periods=10, freq='d')) + """ + dummy_daily_explicit = pd.Series(0, index=pd.date_range("2019-01-01", periods=10, freq="d")) + dummy_daily_implicit = pd.Series(0, index=pd.date_range("2019-01-01", periods=10, freq="d")) dummy_daily_implicit.index.freq = None dummy_nondaily = pd.Series(0, index=dummy_daily_explicit.index[::2]) kwargs = { - 'energy_normalized_daily': dummy_daily_explicit, - 'insolation_daily': dummy_daily_explicit, - 'precipitation_daily': dummy_daily_explicit, + "energy_normalized_daily": dummy_daily_explicit, + "insolation_daily": dummy_daily_explicit, + "precipitation_daily": dummy_daily_explicit, } # no error for implicit daily inputs kwargs[test_param] = dummy_daily_implicit @@ -283,27 +308,104 @@ def test_soiling_srr_non_daily_inputs(test_param): # yes error for non-daily inputs kwargs[test_param] = dummy_nondaily - with pytest.raises(ValueError, match='must have daily frequency'): + with pytest.raises(ValueError, match="must have daily frequency"): _ = SRRAnalysis(**kwargs) def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolation): - ''' + """ Make sure various argument validation warnings and errors are raised - ''' + """ kwargs = { - 'energy_normalized_daily': soiling_normalized_daily, - 'insolation_daily': soiling_insolation, - 'reps': 10 + "energy_normalized_daily": soiling_normalized_daily, + "insolation_daily": soiling_insolation, + "reps": 10, } - with pytest.warns(UserWarning, match='An even value of day_scale was passed'): + with pytest.warns(UserWarning, match="An even value of day_scale was passed"): _ = soiling_srr(day_scale=12, **kwargs) - with pytest.raises(ValueError, match='clean_criterion must be one of'): - _ = soiling_srr(clean_criterion='bad', **kwargs) + with pytest.raises(ValueError, match="clean_criterion must be one of"): + _ = soiling_srr(clean_criterion="bad", **kwargs) - with pytest.raises(ValueError, match='Invalid method specification'): - _ = soiling_srr(method='bad', **kwargs) + with pytest.raises(ValueError, match="Invalid method specification"): + _ = soiling_srr(method="bad", **kwargs) + + +# ########################### +# negetive shift and piecewise tests +# ########################### +@pytest.mark.parametrize( + "method,neg_shift,expected_sr", + [("half_norm_clean", False, 0.980143), + ("half_norm_clean", True, 0.975057), + ("perfect_clean_complex", True, 0.964117), + ("inferred_clean_complex", True, 0.963585)]) +def test_negative_shifts( + soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, + method, neg_shift, expected_sr): + reps = 10 + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=reps, + method=method, neg_shift=neg_shift) + assert expected_sr == pytest.approx(sr, abs=1e-6), \ + f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" \ + different from expected value' + + +@pytest.mark.parametrize( + "method,piecewise,expected_sr", + [("half_norm_clean", False, 0.8670264), + ("half_norm_clean", True, 0.927017), + ("perfect_clean_complex", True, 0.896936), + ("inferred_clean_complex", True, 0.896214)]) +def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, + soiling_times, method, piecewise, expected_sr): + reps = 10 + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, reps=reps, method=method, + piecewise=piecewise) + assert expected_sr == pytest.approx(sr, abs=1e-6), \ + f'Soiling ratio with method="{method}" and piecewise="{piecewise}" \ + different from expected value' + + +def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope, + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, soiling_times): + reps = 10 + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, reps=reps, + method="perfect_clean_complex", piecewise=True, + neg_shift=True) + assert 0.896936 == pytest.approx(sr, abs=1e-6), \ + "Soiling ratio different from expected value for data with piecewise slopes" + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=reps, + method="perfect_clean_complex", piecewise=True, + neg_shift=True) + assert 0.964117 == pytest.approx(sr, abs=1e-6), \ + "Soiling ratio different from expected value for data with negative shifts" + + +def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, soiling_insolation): + """Test that clean test_soiling_srr_clean_threshold works with a float and + can cause no soiling intervals to be found""" + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=10, clean_threshold=0.1, + method="perfect_clean_complex", piecewise=True, + neg_shift=True) + assert 0.934926 == pytest.approx(sr, abs=1e-6), \ + "Soiling ratio with specified clean_threshold different from expected value" + + with pytest.raises(NoValidIntervalError): + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=10, clean_threshold=1) # ########################### @@ -313,25 +415,24 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio @pytest.fixture() def multi_year_profiles(): - times = pd.date_range('01-01-2018', '11-30-2019', freq='D') - data = np.array([0]*365 + [10]*334) + times = pd.date_range("01-01-2018", "11-30-2019", freq="D") + data = np.array([0] * 365 + [10] * 334) profiles = [pd.Series(x + data, times) for x in range(10)] # make insolation slighly longer to test for proper normalization - times = pd.date_range('01-01-2018', '12-31-2019', freq='D') - insolation = 350*[0.8] + (len(times)-350)*[1] + times = pd.date_range("01-01-2018", "12-31-2019", freq="D") + insolation = 350 * [0.8] + (len(times) - 350) * [1] insolation = pd.Series(insolation, index=times) return profiles, insolation def test_annual_soiling_ratios(multi_year_profiles): - expected_data = np.array([[2018, 4.5, 1.431, 7.569], - [2019, 14.5, 11.431, 17.569]]) - expected = pd.DataFrame(data=expected_data, - columns=['year', 'soiling_ratio_median', 'soiling_ratio_low', - 'soiling_ratio_high']) - expected['year'] = expected['year'].astype(int) + expected_data = np.array([[2018, 4.5, 1.431, 7.569], [2019, 14.5, 11.431, 17.569]]) + expected = pd.DataFrame( + data=expected_data, + columns=["year", "soiling_ratio_median", "soiling_ratio_low", "soiling_ratio_high"]) + expected["year"] = expected["year"].astype(int) srr_profiles, insolation = multi_year_profiles result = annual_soiling_ratios(srr_profiles, insolation) @@ -340,12 +441,11 @@ def test_annual_soiling_ratios(multi_year_profiles): def test_annual_soiling_ratios_confidence_interval(multi_year_profiles): - expected_data = np.array([[2018, 4.5, 0.225, 8.775], - [2019, 14.5, 10.225, 18.775]]) - expected = pd.DataFrame(data=expected_data, - columns=['year', 'soiling_ratio_median', 'soiling_ratio_low', - 'soiling_ratio_high']) - expected['year'] = expected['year'].astype(int) + expected_data = np.array([[2018, 4.5, 0.225, 8.775], [2019, 14.5, 10.225, 18.775]]) + expected = pd.DataFrame( + data=expected_data, + columns=["year", "soiling_ratio_median", "soiling_ratio_low", "soiling_ratio_high"]) + expected["year"] = expected["year"].astype(int) srr_profiles, insolation = multi_year_profiles result = annual_soiling_ratios(srr_profiles, insolation, confidence_level=95) @@ -356,9 +456,10 @@ def test_annual_soiling_ratios_confidence_interval(multi_year_profiles): def test_annual_soiling_ratios_warning(multi_year_profiles): srr_profiles, insolation = multi_year_profiles insolation = insolation.iloc[:-200] - match = ('The indexes of stochastic_soiling_profiles are not entirely contained ' - 'within the index of insolation_daily. Every day in stochastic_soiling_profiles ' - 'should be represented in insolation_daily. This may cause erroneous results.') + match = ( + "The indexes of stochastic_soiling_profiles are not entirely contained " + "within the index of insolation_daily. Every day in stochastic_soiling_profiles " + "should be represented in insolation_daily. This may cause erroneous results.") with pytest.warns(UserWarning, match=match): _ = annual_soiling_ratios(srr_profiles, insolation) @@ -370,41 +471,42 @@ def test_annual_soiling_ratios_warning(multi_year_profiles): @pytest.fixture() def soiling_interval_summary(): - starts = ['2019/01/01', '2019/01/16', '2019/02/08', '2019/03/06'] - starts = pd.to_datetime(starts).tz_localize('America/Denver') - ends = ['2019/01/15', '2019/02/07', '2019/03/05', '2019/04/07'] - ends = pd.to_datetime(ends).tz_localize('America/Denver') + starts = ["2019/01/01", "2019/01/16", "2019/02/08", "2019/03/06"] + starts = pd.to_datetime(starts).tz_localize("America/Denver") + ends = ["2019/01/15", "2019/02/07", "2019/03/05", "2019/04/07"] + ends = pd.to_datetime(ends).tz_localize("America/Denver") slopes = [-0.005, -0.002, -0.001, -0.002] slopes_low = [-0.0055, -0.0025, -0.0015, -0.003] slopes_high = [-0.004, 0, 0, -0.001] valids = [True, True, False, True] soiling_interval_summary = pd.DataFrame() - soiling_interval_summary['start'] = starts - soiling_interval_summary['end'] = ends - soiling_interval_summary['soiling_rate'] = slopes - soiling_interval_summary['soiling_rate_low'] = slopes_low - soiling_interval_summary['soiling_rate_high'] = slopes_high - soiling_interval_summary['inferred_start_loss'] = np.nan - soiling_interval_summary['inferred_end_loss'] = np.nan - soiling_interval_summary['length'] = (ends - starts).days - soiling_interval_summary['valid'] = valids + soiling_interval_summary["start"] = starts + soiling_interval_summary["end"] = ends + soiling_interval_summary["soiling_rate"] = slopes + soiling_interval_summary["soiling_rate_low"] = slopes_low + soiling_interval_summary["soiling_rate_high"] = slopes_high + soiling_interval_summary["inferred_start_loss"] = np.nan + soiling_interval_summary["inferred_end_loss"] = np.nan + soiling_interval_summary["length"] = (ends - starts).days + soiling_interval_summary["valid"] = valids return soiling_interval_summary def _build_monthly_summary(top_rows): - ''' + """ Convienience function to build a full monthly soiling summary dataframe from the expected_top_rows which summarize Jan-April - ''' + """ - all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]]*8)) + all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]] * 8)) - df = pd.DataFrame(data=all_rows, - columns=['month', 'soiling_rate_median', 'soiling_rate_low', - 'soiling_rate_high', 'interval_count']) - df['month'] = range(1, 13) + df = pd.DataFrame( + data=all_rows, + columns=["month", "soiling_rate_median", "soiling_rate_low", + "soiling_rate_high", "interval_count"]) + df["month"] = range(1, 13) return df @@ -413,11 +515,11 @@ def test_monthly_soiling_rates(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary) - expected = np.array([ - [1.00000000e+00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e+00], - [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e+00], - [3.00000000e+00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00], - [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]]) + expected = np.array( + [[1.00000000e00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e00], + [2.00000000e00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e00], + [3.00000000e00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00], + [4.00000000e00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -427,11 +529,11 @@ def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary, min_interval_length=20) - expected = np.array([ - [1.00000000e+00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e+00], - [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e+00], - [3.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00], - [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]]) + expected = np.array( + [[1.00000000e00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e00], + [2.00000000e00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e00], + [3.00000000e00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00], + [4.00000000e00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -439,13 +541,15 @@ def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): def test_monthly_soiling_rates_max_slope_err(soiling_interval_summary): np.random.seed(1977) - result = monthly_soiling_rates(soiling_interval_summary, max_relative_slope_error=120) - - expected = np.array([ - [1.00000000e+00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e+00], - [2.00000000e+00, np.nan, np.nan, np.nan, 0.00000000e+00], - [3.00000000e+00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e+00], - [4.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00]]) + result = monthly_soiling_rates( + soiling_interval_summary, max_relative_slope_error=120 + ) + + expected = np.array( + [[1.00000000e00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e00], + [2.00000000e00, np.nan, np.nan, np.nan, 0.00000000e00], + [3.00000000e00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e00], + [4.00000000e00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -455,11 +559,11 @@ def test_monthly_soiling_rates_confidence_level(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary, confidence_level=95) - expected = np.array([ - [1.00000000e+00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e+00], - [2.00000000e+00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e+00], - [3.00000000e+00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e+00], - [4.00000000e+00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e+00]]) + expected = np.array( + [[1.00000000e00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e00], + [2.00000000e00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e00], + [3.00000000e00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e00], + [4.00000000e00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) @@ -470,12 +574,95 @@ def test_monthly_soiling_rates_reps(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary, reps=3) - expected = np.array([ - [1.00000000e+00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e+00], - [2.00000000e+00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e+00], - [3.00000000e+00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e+00], - [4.00000000e+00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e+00]]) + expected = np.array( + [[1.00000000e00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e00], + [2.00000000e00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e00], + [3.00000000e00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e00], + [4.00000000e00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) + + +# ###################################### +# invalid segmented_soiling_period tests +# ###################################### + + +@pytest.fixture +def pr_series(): + """ + Panda series of daily performance ratios measured during the given deposition period + with datetime index and is length 10. + """ + pr_idx = pd.date_range(start="2022-01-01", periods=10, freq="D") + pr_series = pd.Series(np.random.rand(10), index=pr_idx) + return pr_series + + +def test_no_datetime_index_pr(pr_series): + """ + Tests if ValueError is raised when pr_series does not have datetime index. + """ + pr = pr_series.reset_index() + with pytest.raises(ValueError, match="The time series does not have DatetimeIndex"): + _ = segmented_soiling_period(pr) + + +def test_no_change_point(pr_series): + """ + Tests if no change point was found when fitting soiling profile with segmentation. + """ + days_clean_vs_cp = 7 + result_sr, result_cp_date = segmented_soiling_period(pr_series, + days_clean_vs_cp=days_clean_vs_cp) + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date + + +def test_except_block(): + """ + Tests except block for when all segementation methods did not work. + """ + pr_idx = pd.date_range(start="2022-01-01", periods=5, freq="D") + pr_series = pd.Series(np.array([1, 2, 3, 4, 5]), index=pr_idx) + result_sr, result_cp_date = segmented_soiling_period(pr_series) + + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date + + +def test_short_segmentation_periods(): + """ + Tests if segmentation fails for short soiling periods. + """ + pr_idx = pd.date_range(start="2022-01-01", periods=35, freq="D") + pr_series = pd.Series(np.random.normal(loc=5, scale=2, size=35), index=pr_idx) + result_sr, result_cp_date = segmented_soiling_period(pr_series) + + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date + + +def test_long_segmentation_periods(): + "Tests if segmentation fails for longer soiling periods." + pr_idx = pd.date_range(start="2022-01-01", periods=47, freq="D") + testing_list = list(np.arange(46)) + [50] + pr_series = pd.Series(testing_list, index=pr_idx) + result_sr, result_cp_date = segmented_soiling_period(pr_series) + + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date diff --git a/setup.py b/setup.py index 441b16c0..f38a2bec 100755 --- a/setup.py +++ b/setup.py @@ -36,9 +36,7 @@ "pytest-cov", "coverage", "flake8", - # nbval greater than 0.9.6 has a bug with semicolon - # https://github.com/computationalmodelling/nbval/issues/194 - "nbval<=0.9.6", + "nbval", "pytest-mock", ]