diff --git a/CSV_RY_sys.ipynb b/CSV_RY_sys.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5b13f39f6374b86fa01b9800be26e6680a00f0e6 --- /dev/null +++ b/CSV_RY_sys.ipynb @@ -0,0 +1,9705 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "40f4a4d0", + "metadata": {}, + "source": [ + "# This script reads RY_rho from results/csv.csv, and plot the difference between RY_rho(with all radiative bgs) and RY(norho),RY_noexc(no exc) RY_nodelta" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1f063539", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy\n", + "import math\n", + "import pandas as pd\n", + "df = pd.read_csv(\"results/csv.csv\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4e70ce8", + "metadata": {}, + "source": [ + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "print(xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f1cfbf34", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Q2</th>\n", + " <th>Q2_corr</th>\n", + " <th>Q2_corr_err</th>\n", + " <th>xbj_set</th>\n", + " <th>xbj</th>\n", + " <th>xbj_corr</th>\n", + " <th>xbj_corr_err</th>\n", + " <th>z_set</th>\n", + " <th>z</th>\n", + " <th>z_corr</th>\n", + " <th>...</th>\n", + " <th>yield_neg_incnorad</th>\n", + " <th>yield_neg_incrad</th>\n", + " <th>yield_pos_incnorad</th>\n", + " <th>yield_pos_incrad</th>\n", + " <th>W2_corr</th>\n", + " <th>Wp2_corr</th>\n", + " <th>xprime_corr</th>\n", + " <th>zprime_corr</th>\n", + " <th>shms_p</th>\n", + " <th>shms_dp</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>4.0</td>\n", + " <td>3.37350</td>\n", + " <td>0.010445</td>\n", + " <td>0.35</td>\n", + " <td>0.275</td>\n", + " <td>0.278530</td>\n", + " <td>0.000075</td>\n", + " <td>0.4</td>\n", + " <td>0.325</td>\n", + " <td>0.344209</td>\n", + " <td>...</td>\n", + " <td>5.60438</td>\n", + " <td>5.37980</td>\n", + " <td>8.1510</td>\n", + " <td>8.7125</td>\n", + " <td>9.62363</td>\n", + " <td>6.52182</td>\n", + " <td>0.273108</td>\n", + " <td>0.337373</td>\n", + " <td>2.22253</td>\n", + " <td>-8.46770</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>4.0</td>\n", + " <td>3.37687</td>\n", + " <td>0.007992</td>\n", + " <td>0.35</td>\n", + " <td>0.275</td>\n", + " <td>0.282184</td>\n", + " <td>0.000071</td>\n", + " <td>0.4</td>\n", + " <td>0.375</td>\n", + " <td>0.373995</td>\n", + " <td>...</td>\n", + " <td>35.40190</td>\n", + " <td>33.96720</td>\n", + " <td>52.2889</td>\n", + " <td>50.8403</td>\n", + " <td>9.47819</td>\n", + " <td>6.17107</td>\n", + " <td>0.276202</td>\n", + " <td>0.365743</td>\n", + " <td>2.38725</td>\n", + " <td>-1.68391</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>4.0</td>\n", + " <td>3.78990</td>\n", + " <td>0.016368</td>\n", + " <td>0.35</td>\n", + " <td>0.325</td>\n", + " <td>0.324561</td>\n", + " <td>0.000101</td>\n", + " <td>0.4</td>\n", + " <td>0.375</td>\n", + " <td>0.376576</td>\n", + " <td>...</td>\n", + " <td>55.36860</td>\n", + " <td>51.16280</td>\n", + " <td>83.3495</td>\n", + " <td>81.0216</td>\n", + " <td>8.77445</td>\n", + " <td>5.71133</td>\n", + " <td>0.316579</td>\n", + " <td>0.366865</td>\n", + " <td>2.34450</td>\n", + " <td>-3.44451</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>4.0</td>\n", + " <td>4.20537</td>\n", + " <td>0.016630</td>\n", + " <td>0.35</td>\n", + " <td>0.375</td>\n", + " <td>0.371173</td>\n", + " <td>0.000095</td>\n", + " <td>0.4</td>\n", + " <td>0.375</td>\n", + " <td>0.380960</td>\n", + " <td>...</td>\n", + " <td>29.19430</td>\n", + " <td>26.20740</td>\n", + " <td>44.6229</td>\n", + " <td>40.6557</td>\n", + " <td>8.01211</td>\n", + " <td>5.19513</td>\n", + " <td>0.360311</td>\n", + " <td>0.369777</td>\n", + " <td>2.30070</td>\n", + " <td>-5.24806</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4.0</td>\n", + " <td>4.59426</td>\n", + " <td>0.011355</td>\n", + " <td>0.35</td>\n", + " <td>0.425</td>\n", + " <td>0.418462</td>\n", + " <td>0.000089</td>\n", + " <td>0.4</td>\n", + " <td>0.375</td>\n", + " <td>0.386497</td>\n", + " <td>...</td>\n", + " <td>7.62266</td>\n", + " <td>6.63342</td>\n", + " <td>11.7916</td>\n", + " <td>10.0885</td>\n", + " <td>7.27071</td>\n", + " <td>4.67881</td>\n", + " <td>0.404638</td>\n", + " <td>0.374082</td>\n", + " <td>2.26198</td>\n", + " <td>-6.84281</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 45 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Q2 Q2_corr Q2_corr_err xbj_set xbj xbj_corr xbj_corr_err z_set \\\n", + "0 4.0 3.37350 0.010445 0.35 0.275 0.278530 0.000075 0.4 \n", + "1 4.0 3.37687 0.007992 0.35 0.275 0.282184 0.000071 0.4 \n", + "2 4.0 3.78990 0.016368 0.35 0.325 0.324561 0.000101 0.4 \n", + "3 4.0 4.20537 0.016630 0.35 0.375 0.371173 0.000095 0.4 \n", + "4 4.0 4.59426 0.011355 0.35 0.425 0.418462 0.000089 0.4 \n", + "\n", + " z z_corr ... yield_neg_incnorad yield_neg_incrad \\\n", + "0 0.325 0.344209 ... 5.60438 5.37980 \n", + "1 0.375 0.373995 ... 35.40190 33.96720 \n", + "2 0.375 0.376576 ... 55.36860 51.16280 \n", + "3 0.375 0.380960 ... 29.19430 26.20740 \n", + "4 0.375 0.386497 ... 7.62266 6.63342 \n", + "\n", + " yield_pos_incnorad yield_pos_incrad W2_corr Wp2_corr xprime_corr \\\n", + "0 8.1510 8.7125 9.62363 6.52182 0.273108 \n", + "1 52.2889 50.8403 9.47819 6.17107 0.276202 \n", + "2 83.3495 81.0216 8.77445 5.71133 0.316579 \n", + "3 44.6229 40.6557 8.01211 5.19513 0.360311 \n", + "4 11.7916 10.0885 7.27071 4.67881 0.404638 \n", + "\n", + " zprime_corr shms_p shms_dp \n", + "0 0.337373 2.22253 -8.46770 \n", + "1 0.365743 2.38725 -1.68391 \n", + "2 0.366865 2.34450 -3.44451 \n", + "3 0.369777 2.30070 -5.24806 \n", + "4 0.374082 2.26198 -6.84281 \n", + "\n", + "[5 rows x 45 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "28177332", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Q2</th>\n", + " <th>Q2_corr</th>\n", + " <th>Q2_corr_err</th>\n", + " <th>xbj_set</th>\n", + " <th>xbj</th>\n", + " <th>xbj_corr</th>\n", + " <th>xbj_corr_err</th>\n", + " <th>z_set</th>\n", + " <th>z</th>\n", + " <th>z_corr</th>\n", + " <th>...</th>\n", + " <th>yield_neg_incnorad</th>\n", + " <th>yield_neg_incrad</th>\n", + " <th>yield_pos_incnorad</th>\n", + " <th>yield_pos_incrad</th>\n", + " <th>W2_corr</th>\n", + " <th>Wp2_corr</th>\n", + " <th>xprime_corr</th>\n", + " <th>zprime_corr</th>\n", + " <th>shms_p</th>\n", + " <th>shms_dp</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>...</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " <td>649.000000</td>\n", + " <td>649.000000</td>\n", + " <td>669.000000</td>\n", + " <td>669.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>4.673767</td>\n", + " <td>4.380915</td>\n", + " <td>0.013070</td>\n", + " <td>0.490284</td>\n", + " <td>0.438378</td>\n", + " <td>0.438830</td>\n", + " <td>0.000082</td>\n", + " <td>0.526831</td>\n", + " <td>0.520590</td>\n", + " <td>0.520255</td>\n", + " <td>...</td>\n", + " <td>10.216065</td>\n", + " <td>8.903928</td>\n", + " <td>16.621890</td>\n", + " <td>14.706117</td>\n", + " <td>6.565718</td>\n", + " <td>3.418747</td>\n", + " <td>0.418692</td>\n", + " <td>0.497507</td>\n", + " <td>2.807791</td>\n", + " <td>3.773031</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>0.596882</td>\n", + " <td>0.700747</td>\n", + " <td>0.007169</td>\n", + " <td>0.080296</td>\n", + " <td>0.080583</td>\n", + " <td>0.076224</td>\n", + " <td>0.000025</td>\n", + " <td>0.099969</td>\n", + " <td>0.097687</td>\n", + " <td>0.094177</td>\n", + " <td>...</td>\n", + " <td>10.097713</td>\n", + " <td>8.974825</td>\n", + " <td>16.098700</td>\n", + " <td>14.496055</td>\n", + " <td>1.160805</td>\n", + " <td>0.680688</td>\n", + " <td>0.068633</td>\n", + " <td>0.093310</td>\n", + " <td>0.661349</td>\n", + " <td>9.963340</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>4.000000</td>\n", + " <td>3.072190</td>\n", + " <td>0.001443</td>\n", + " <td>0.350000</td>\n", + " <td>0.275000</td>\n", + " <td>0.278530</td>\n", + " <td>0.000016</td>\n", + " <td>0.400000</td>\n", + " <td>0.325000</td>\n", + " <td>0.334000</td>\n", + " <td>...</td>\n", + " <td>0.174153</td>\n", + " <td>0.157054</td>\n", + " <td>0.299296</td>\n", + " <td>0.256295</td>\n", + " <td>4.211160</td>\n", + " <td>2.643010</td>\n", + " <td>0.273108</td>\n", + " <td>0.235394</td>\n", + " <td>1.569620</td>\n", + " <td>-9.270250</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>4.000000</td>\n", + " <td>3.845580</td>\n", + " <td>0.007583</td>\n", + " <td>0.450000</td>\n", + " <td>0.375000</td>\n", + " <td>0.377728</td>\n", + " <td>0.000072</td>\n", + " <td>0.450000</td>\n", + " <td>0.425000</td>\n", + " <td>0.439963</td>\n", + " <td>...</td>\n", + " <td>3.076170</td>\n", + " <td>2.552920</td>\n", + " <td>5.160100</td>\n", + " <td>4.422060</td>\n", + " <td>5.705590</td>\n", + " <td>2.857160</td>\n", + " <td>0.364878</td>\n", + " <td>0.422344</td>\n", + " <td>2.300700</td>\n", + " <td>-5.494530</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>4.750000</td>\n", + " <td>4.433180</td>\n", + " <td>0.012377</td>\n", + " <td>0.500000</td>\n", + " <td>0.425000</td>\n", + " <td>0.428279</td>\n", + " <td>0.000094</td>\n", + " <td>0.500000</td>\n", + " <td>0.525000</td>\n", + " <td>0.523391</td>\n", + " <td>...</td>\n", + " <td>6.521680</td>\n", + " <td>5.620500</td>\n", + " <td>10.690800</td>\n", + " <td>9.434030</td>\n", + " <td>6.461430</td>\n", + " <td>3.236060</td>\n", + " <td>0.412930</td>\n", + " <td>0.500448</td>\n", + " <td>2.730540</td>\n", + " <td>1.268850</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>5.500000</td>\n", + " <td>4.885280</td>\n", + " <td>0.017859</td>\n", + " <td>0.550000</td>\n", + " <td>0.475000</td>\n", + " <td>0.479650</td>\n", + " <td>0.000100</td>\n", + " <td>0.600000</td>\n", + " <td>0.575000</td>\n", + " <td>0.580645</td>\n", + " <td>...</td>\n", + " <td>14.073000</td>\n", + " <td>11.851300</td>\n", + " <td>23.062400</td>\n", + " <td>20.101400</td>\n", + " <td>7.372850</td>\n", + " <td>3.786400</td>\n", + " <td>0.458385</td>\n", + " <td>0.560968</td>\n", + " <td>3.248100</td>\n", + " <td>12.169900</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>5.500000</td>\n", + " <td>6.367300</td>\n", + " <td>0.037874</td>\n", + " <td>0.650000</td>\n", + " <td>0.625000</td>\n", + " <td>0.618485</td>\n", + " <td>0.000111</td>\n", + " <td>0.700000</td>\n", + " <td>0.775000</td>\n", + " <td>0.767541</td>\n", + " <td>...</td>\n", + " <td>55.368600</td>\n", + " <td>52.285000</td>\n", + " <td>83.349500</td>\n", + " <td>81.021600</td>\n", + " <td>9.623630</td>\n", + " <td>6.521820</td>\n", + " <td>0.585045</td>\n", + " <td>0.752275</td>\n", + " <td>4.908780</td>\n", + " <td>23.376900</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>8 rows × 45 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Q2 Q2_corr Q2_corr_err xbj_set xbj \\\n", + "count 669.000000 669.000000 669.000000 669.000000 669.000000 \n", + "mean 4.673767 4.380915 0.013070 0.490284 0.438378 \n", + "std 0.596882 0.700747 0.007169 0.080296 0.080583 \n", + "min 4.000000 3.072190 0.001443 0.350000 0.275000 \n", + "25% 4.000000 3.845580 0.007583 0.450000 0.375000 \n", + "50% 4.750000 4.433180 0.012377 0.500000 0.425000 \n", + "75% 5.500000 4.885280 0.017859 0.550000 0.475000 \n", + "max 5.500000 6.367300 0.037874 0.650000 0.625000 \n", + "\n", + " xbj_corr xbj_corr_err z_set z z_corr ... \\\n", + "count 669.000000 669.000000 669.000000 669.000000 669.000000 ... \n", + "mean 0.438830 0.000082 0.526831 0.520590 0.520255 ... \n", + "std 0.076224 0.000025 0.099969 0.097687 0.094177 ... \n", + "min 0.278530 0.000016 0.400000 0.325000 0.334000 ... \n", + "25% 0.377728 0.000072 0.450000 0.425000 0.439963 ... \n", + "50% 0.428279 0.000094 0.500000 0.525000 0.523391 ... \n", + "75% 0.479650 0.000100 0.600000 0.575000 0.580645 ... \n", + "max 0.618485 0.000111 0.700000 0.775000 0.767541 ... \n", + "\n", + " yield_neg_incnorad yield_neg_incrad yield_pos_incnorad \\\n", + "count 669.000000 669.000000 669.000000 \n", + "mean 10.216065 8.903928 16.621890 \n", + "std 10.097713 8.974825 16.098700 \n", + "min 0.174153 0.157054 0.299296 \n", + "25% 3.076170 2.552920 5.160100 \n", + "50% 6.521680 5.620500 10.690800 \n", + "75% 14.073000 11.851300 23.062400 \n", + "max 55.368600 52.285000 83.349500 \n", + "\n", + " yield_pos_incrad W2_corr Wp2_corr xprime_corr zprime_corr \\\n", + "count 669.000000 669.000000 669.000000 649.000000 649.000000 \n", + "mean 14.706117 6.565718 3.418747 0.418692 0.497507 \n", + "std 14.496055 1.160805 0.680688 0.068633 0.093310 \n", + "min 0.256295 4.211160 2.643010 0.273108 0.235394 \n", + "25% 4.422060 5.705590 2.857160 0.364878 0.422344 \n", + "50% 9.434030 6.461430 3.236060 0.412930 0.500448 \n", + "75% 20.101400 7.372850 3.786400 0.458385 0.560968 \n", + "max 81.021600 9.623630 6.521820 0.585045 0.752275 \n", + "\n", + " shms_p shms_dp \n", + "count 669.000000 669.000000 \n", + "mean 2.807791 3.773031 \n", + "std 0.661349 9.963340 \n", + "min 1.569620 -9.270250 \n", + "25% 2.300700 -5.494530 \n", + "50% 2.730540 1.268850 \n", + "75% 3.248100 12.169900 \n", + "max 4.908780 23.376900 \n", + "\n", + "[8 rows x 45 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "030e8884", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Q2</th>\n", + " <th>Q2_corr</th>\n", + " <th>Q2_corr_err</th>\n", + " <th>xbj_set</th>\n", + " <th>xbj</th>\n", + " <th>xbj_corr</th>\n", + " <th>xbj_corr_err</th>\n", + " <th>z_set</th>\n", + " <th>z</th>\n", + " <th>z_corr</th>\n", + " <th>...</th>\n", + " <th>yield_neg_incnorad</th>\n", + " <th>yield_neg_incrad</th>\n", + " <th>yield_pos_incnorad</th>\n", + " <th>yield_pos_incrad</th>\n", + " <th>W2_corr</th>\n", + " <th>Wp2_corr</th>\n", + " <th>xprime_corr</th>\n", + " <th>zprime_corr</th>\n", + " <th>shms_p</th>\n", + " <th>shms_dp</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>...</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " <td>534.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>4.650281</td>\n", + " <td>4.311879</td>\n", + " <td>0.012380</td>\n", + " <td>0.484270</td>\n", + " <td>0.425655</td>\n", + " <td>0.427749</td>\n", + " <td>0.000084</td>\n", + " <td>0.515262</td>\n", + " <td>0.502528</td>\n", + " <td>0.503964</td>\n", + " <td>...</td>\n", + " <td>11.542157</td>\n", + " <td>10.111365</td>\n", + " <td>18.657372</td>\n", + " <td>16.588108</td>\n", + " <td>6.727068</td>\n", + " <td>3.594492</td>\n", + " <td>0.411862</td>\n", + " <td>0.485918</td>\n", + " <td>2.744766</td>\n", + " <td>3.015548</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>0.596861</td>\n", + " <td>0.688576</td>\n", + " <td>0.007033</td>\n", + " <td>0.079549</td>\n", + " <td>0.076843</td>\n", + " <td>0.073219</td>\n", + " <td>0.000025</td>\n", + " <td>0.097657</td>\n", + " <td>0.092143</td>\n", + " <td>0.089724</td>\n", + " <td>...</td>\n", + " <td>10.695048</td>\n", + " <td>9.511840</td>\n", + " <td>17.036808</td>\n", + " <td>15.347672</td>\n", + " <td>1.135261</td>\n", + " <td>0.653363</td>\n", + " <td>0.067641</td>\n", + " <td>0.088526</td>\n", + " <td>0.637703</td>\n", + " <td>10.010175</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>4.000000</td>\n", + " <td>3.072190</td>\n", + " <td>0.001443</td>\n", + " <td>0.350000</td>\n", + " <td>0.275000</td>\n", + " <td>0.278530</td>\n", + " <td>0.000016</td>\n", + " <td>0.400000</td>\n", + " <td>0.325000</td>\n", + " <td>0.334000</td>\n", + " <td>...</td>\n", + " <td>0.416225</td>\n", + " <td>0.344163</td>\n", + " <td>0.713440</td>\n", + " <td>0.583417</td>\n", + " <td>4.254500</td>\n", + " <td>2.801750</td>\n", + " <td>0.273108</td>\n", + " <td>0.306498</td>\n", + " <td>1.569620</td>\n", + " <td>-9.270250</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>4.000000</td>\n", + " <td>3.800867</td>\n", + " <td>0.006728</td>\n", + " <td>0.450000</td>\n", + " <td>0.375000</td>\n", + " <td>0.375571</td>\n", + " <td>0.000075</td>\n", + " <td>0.400000</td>\n", + " <td>0.425000</td>\n", + " <td>0.430317</td>\n", + " <td>...</td>\n", + " <td>3.658075</td>\n", + " <td>3.111682</td>\n", + " <td>6.010357</td>\n", + " <td>5.150313</td>\n", + " <td>5.885103</td>\n", + " <td>3.072730</td>\n", + " <td>0.362735</td>\n", + " <td>0.411899</td>\n", + " <td>2.251145</td>\n", + " <td>-6.172582</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>4.750000</td>\n", + " <td>4.309980</td>\n", + " <td>0.011809</td>\n", + " <td>0.500000</td>\n", + " <td>0.425000</td>\n", + " <td>0.425219</td>\n", + " <td>0.000097</td>\n", + " <td>0.500000</td>\n", + " <td>0.500000</td>\n", + " <td>0.500418</td>\n", + " <td>...</td>\n", + " <td>7.374305</td>\n", + " <td>6.296945</td>\n", + " <td>12.209100</td>\n", + " <td>10.497200</td>\n", + " <td>6.625630</td>\n", + " <td>3.442300</td>\n", + " <td>0.410745</td>\n", + " <td>0.483669</td>\n", + " <td>2.683090</td>\n", + " <td>0.145250</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>5.500000</td>\n", + " <td>4.826403</td>\n", + " <td>0.017191</td>\n", + " <td>0.550000</td>\n", + " <td>0.475000</td>\n", + " <td>0.474645</td>\n", + " <td>0.000101</td>\n", + " <td>0.600000</td>\n", + " <td>0.575000</td>\n", + " <td>0.573636</td>\n", + " <td>...</td>\n", + " <td>16.027350</td>\n", + " <td>14.147500</td>\n", + " <td>25.833350</td>\n", + " <td>22.779950</td>\n", + " <td>7.504227</td>\n", + " <td>3.955335</td>\n", + " <td>0.455816</td>\n", + " <td>0.554418</td>\n", + " <td>3.197442</td>\n", + " <td>11.333800</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>5.500000</td>\n", + " <td>6.061740</td>\n", + " <td>0.036808</td>\n", + " <td>0.650000</td>\n", + " <td>0.625000</td>\n", + " <td>0.618485</td>\n", + " <td>0.000111</td>\n", + " <td>0.700000</td>\n", + " <td>0.725000</td>\n", + " <td>0.724001</td>\n", + " <td>...</td>\n", + " <td>55.368600</td>\n", + " <td>52.285000</td>\n", + " <td>83.349500</td>\n", + " <td>81.021600</td>\n", + " <td>9.623630</td>\n", + " <td>6.521820</td>\n", + " <td>0.585045</td>\n", + " <td>0.709380</td>\n", + " <td>4.623390</td>\n", + " <td>23.376900</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>8 rows × 45 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Q2 Q2_corr Q2_corr_err xbj_set xbj \\\n", + "count 534.000000 534.000000 534.000000 534.000000 534.000000 \n", + "mean 4.650281 4.311879 0.012380 0.484270 0.425655 \n", + "std 0.596861 0.688576 0.007033 0.079549 0.076843 \n", + "min 4.000000 3.072190 0.001443 0.350000 0.275000 \n", + "25% 4.000000 3.800867 0.006728 0.450000 0.375000 \n", + "50% 4.750000 4.309980 0.011809 0.500000 0.425000 \n", + "75% 5.500000 4.826403 0.017191 0.550000 0.475000 \n", + "max 5.500000 6.061740 0.036808 0.650000 0.625000 \n", + "\n", + " xbj_corr xbj_corr_err z_set z z_corr ... \\\n", + "count 534.000000 534.000000 534.000000 534.000000 534.000000 ... \n", + "mean 0.427749 0.000084 0.515262 0.502528 0.503964 ... \n", + "std 0.073219 0.000025 0.097657 0.092143 0.089724 ... \n", + "min 0.278530 0.000016 0.400000 0.325000 0.334000 ... \n", + "25% 0.375571 0.000075 0.400000 0.425000 0.430317 ... \n", + "50% 0.425219 0.000097 0.500000 0.500000 0.500418 ... \n", + "75% 0.474645 0.000101 0.600000 0.575000 0.573636 ... \n", + "max 0.618485 0.000111 0.700000 0.725000 0.724001 ... \n", + "\n", + " yield_neg_incnorad yield_neg_incrad yield_pos_incnorad \\\n", + "count 534.000000 534.000000 534.000000 \n", + "mean 11.542157 10.111365 18.657372 \n", + "std 10.695048 9.511840 17.036808 \n", + "min 0.416225 0.344163 0.713440 \n", + "25% 3.658075 3.111682 6.010357 \n", + "50% 7.374305 6.296945 12.209100 \n", + "75% 16.027350 14.147500 25.833350 \n", + "max 55.368600 52.285000 83.349500 \n", + "\n", + " yield_pos_incrad W2_corr Wp2_corr xprime_corr zprime_corr \\\n", + "count 534.000000 534.000000 534.000000 534.000000 534.000000 \n", + "mean 16.588108 6.727068 3.594492 0.411862 0.485918 \n", + "std 15.347672 1.135261 0.653363 0.067641 0.088526 \n", + "min 0.583417 4.254500 2.801750 0.273108 0.306498 \n", + "25% 5.150313 5.885103 3.072730 0.362735 0.411899 \n", + "50% 10.497200 6.625630 3.442300 0.410745 0.483669 \n", + "75% 22.779950 7.504227 3.955335 0.455816 0.554418 \n", + "max 81.021600 9.623630 6.521820 0.585045 0.709380 \n", + "\n", + " shms_p shms_dp \n", + "count 534.000000 534.000000 \n", + "mean 2.744766 3.015548 \n", + "std 0.637703 10.010175 \n", + "min 1.569620 -9.270250 \n", + "25% 2.251145 -6.172582 \n", + "50% 2.683090 0.145250 \n", + "75% 3.197442 11.333800 \n", + "max 4.623390 23.376900 \n", + "\n", + "[8 rows x 45 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['Wp2_corr']>2.8].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dd925180", + "metadata": {}, + "outputs": [], + "source": [ + "def Get_weighted(values,errs):\n", + " sums = 0\n", + " sig = 0\n", + " for i in range(len(values)):\n", + " for j in range(len(errs)):\n", + " sums+=values[i]/(errs[i]*errs[i])\n", + " sig+=1/(errs[i]*errs[i])\n", + " return sums/sig \n", + "def Get_weighted_average(value,error):\n", + " sum_mean = 0\n", + " sum_sigma = 0\n", + " for i in range(len(value)):\n", + " sum_mean += value[i]/(error[i]*error[i])\n", + " sum_sigma += 1/(error[i]*error[i])\n", + " return sum_mean/sum_sigma\n", + "def Get_weighted_sigma(value,error):\n", + " sum_sigma = 0\n", + " for i in range(len(value)):\n", + " sum_sigma += 1/(error[i]*error[i])\n", + " return math.sqrt(1/sum_sigma)\n", + "Q2s = [3.80473,4.56863,5.19412]\n", + "Q2_str = [\"4.000000\",\"4.750000\",\"5.500000\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d0fc9d3d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-6-19117bb531ef>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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61gKvisje7opb0+5oA4Z1auPRmJubG1lZWV1Wf1cJCAiwqiEByMrK4tKlS8THxxMTE8PmzZt7fEPSFbqlMTEtu7semAHkAyeUUrvEuCBWvaVAvIj8Xinlj3EhrdGmz1GAARgJJCmlxolIbXfErmma1pqNGzeaP69fvx5vb287RmM/3XVnMhE4JyJfAiil4oBHgYaNiQD1Y97uA+oHZT8KxIlIJZCrlDpnqi+lOwLXNE2z1ksvvWTvEOymu0ZzuQMN35rJN6U1tAJ4WimVj/GupP7VUmvKopRapJQ6qZQ6WT+KRNM0TesePWlo8ALgjyLiAcwGPlRKWR2fiLwvImEiEjZ06NAuC1LTNE1rqrsecxUADedW8DClNRQDPAIgIilKqXuA+60sq2maptlRd92ZnAB8lFJjlFJOGDvUdzXKcxGYDqCU8gPuAYpN+aKUUv2UUmMAH+B4N8WtaZqmWaFb7kxEpEYp9TKwF+Ow300ikqWUehPjSzK7gH8BNiqlfo6xM/4500s0WUqpeIyd9TXAS3okl6ZpWs/Sbe+ZmN4ZSWyUtqzB58+B77RQdhWwqksD1DRN0zqsJ3XAa5qmab2Ubkw0TdM0m+nGRNO0brVz505eeOEFnnzySfbt22fvcLROohsTa5RegapbUHXT+G9ttb0j0rQe7+OPPyY8PJzg4GDCwsLYu9c4pd5jjz3Gxo0b2bBhAx999FGb9VRUVDBx4kSCg4MxGAwsX768XfnOnDlDSEiIeXN1dSU2NrbF9O60Z88efH198fb2ZvXq1U325+XlERERgb+/PwaDgXXr1pn3rV27FoPBQEBAAAsWLKCioqLV/F2uo9MN9+StU6egb26a7vy/d8o03ZrW2do1BX0X2rp1q0yaNEmKiopERCQnJ0fc3NwkLy/PnGfx4sWSmpraZl11dXVy69YtERGpqqqSiRMnSkpKSofy1dTUiJubm1y4cMGq9K5UU1MjXl5ecv78eamsrJSgoCDJysqyyFNYWGi+Rjdv3hQfHx/JysqS/Px8GT16tHmK/Pnz58vmzZtbzN+czp6CXt+ZtKW5pU3r0zWtt8vdCjtHw5/7GP/N3WpzlaWlpbz++uvEx8czfPhwAHx8fJg6dSrJycmICEuWLGHWrFmEhoa2WZ9SCmdnZ8C46mB1dTVKqQ7lS05OZuzYsTzwwANWpTcWERHB/v37AVi6dCmvvPJKq/lbc/z4cby9vfHy8sLJyYmoqCgSEhIs8owYMcJ8jVxcXPDz8zMv2FVTU0N5eTk1NTWUlZUxcuTIVvN3Nd2YtKWLljbVNLvL3QrHF0HZV4AY/z2+yOYGJS4ujtDQUEaNGmWR3q9fP8rKynj33XdJSkpi+/btbNiwoUn52bNnU1hYaJFWW1tLSEgIw4YNY8aMGYSHhzd77LbyxcXFNbsyYUvpja1cuZJVq1axdetW0tLSrH4s1tw5FRQUWFwjDw+PVn/xX7hwgbS0NMLDw3F3d+e1117D09OTESNGcN999zFz5swW83cH3Zi0pcUlTPWl03q59DegtswyrbbMmG6DzMxMgoODmx4uPZ3x48fz6quvkpqayoYNG3jxxReb5EtMTGTkyJEWaQ4ODpw+fZr8/HyOHz9OZmZms8duLV9VVRW7du1i/vz5FmVaSm/O5MmTERHWrFlDXFwcDg4ObZZp6Zza4/bt20RGRhIbG4urqyvXr18nISGB3NxcCgsLKS0tZcuWLS3m7w76N2JbXDyh2fkm66DgCBQc7faQNK1TlLXwqLaldCu5urpSVVVlkZaSkkJpaSlTpkyxqe6BAwcSERFhXn+9Pfk++eQTQkNDcXNzs8jbUnpzMjIyKCoqwsnJCRcXF3P6woULLfItW7ascdEm3N3dycv7ZkL0/Px83N2bTIhOdXU1kZGRREdHM2/ePACSkpIYM2YMQ4cOpW/fvsybN4+jR4+2mL876MakLQOGwX1jm9nRB+4dCsM7tMKlptlff8/2pVtpzpw5xMfHU78URE5ODgsXLmTz5s306dP+XznFxcWUlJQAUF5ezv79+xk/fny7823btq3ZR1ktpU+fPt3isVNRURHR0dEkJCTg7OxsbqjKy8vJzs5mxYoVREVFcfnyZSor234MPmHCBM6ePUtubi5VVVXExcUxd+5cizwiQkxMDH5+fixevNic7unpybFjxygrK0NESE5Oxs/Pr8X83aKjPfc9eevU0Vz1rnwmUvgPyxFd1891/nE0zQbtGs315RaRuP4iW/lmi+tvTLfRpk2bJCgoSHx9fcXb21uOHj1qddlZs2ZJQUGB+Xt6erqEhIRIYGCgGAwGWblyZbP5W8t3+/ZtGTx4sJSUlFiUbSm9trZWPD09zaOlSktLZdKkSbJv3z4RETl48KBMmjRJRESOHDkia9euFRGRRYsWyZYtWyQ+Pr7Vc6q3e/du8fHxES8vL3nrrbea5D98+LAAEhgYKMHBwRIcHCy7d+8WEZFly5aJr6+vGAwGefrpp6WioqLV/I119mguZSx/ZwkLC5OTJ092bqXFGVBdBkpBHyfo5wK1VTDEr3OPo2k2yM7Oxs+vHT+TuVuNfSRlF413JMGrYEx0p8Vz+fJlpk2bxrZt2wgKCuq0ertaZmYmmzZtsmod+NjYWIKCgpg2bRrPPPMMY8eO5dlnn+3x68A397OilEoVkQ49bum2iR7vCH37f/N5YHOPvjStlxkT3amNR2Nubm5kZWV1Wf1dJSAgwKqGBCArK4tLly4RHx9PTEwMmzdv7vENSVfQjYmmaZoNNm7caP68fv16vL297RiN/ejGRNM0rZO89NJL9g7BbrptNJdS6hGl1Bml1Dml1OvN7F+rlDpt2nKUUiUN9tU22Nd4hUZN0zTNzrrlzkQp5QCsB2YA+cAJpdQuMS6IBYCI/LxB/leABxtUUS4iId0Rq6ZpmtZ+3XVnMhE4JyJfikgVEAc82kr+BcC2bolM0zRNs1l3NSbuQF6D7/mmtCaUUg8AY4BPGyTfo5Q6qZQ6ppR6rIVyi0x5Tta/LKVpmqZ1j574BnwUsF1EahukPWAa+/wUEKuUajIuV0TeF5EwEQkbOnRod8WqaZqm0X2NSQHQcApRD1Nac6Jo9IhLRApM/34JHMCyP0XTNE2zs+5qTE4APkqpMUopJ4wNRpNRWUqp8cAgIKVB2iClVD/T5/uB7wCfNy6raZqm2U+3jOYSkRql1MvAXsAB2CQiWUqpNzHOBVPfsEQBcWI5x4sf8N9KqTqMjd/qhqPANE3TNPvrtpcWRSQRSGyUtqzR9xXNlDsKBHZpcHeCv/3M+G9ErD2j0LQ27dy5k927d3Pz5k1iYmKaLOqk9U49sQNea4+jK+AdBafWGbd3lHE7usLekWl3uY8//pjw8HCCg4MJCwtj7969ADz22GNs3LiRDRs28NFHH7VZT0VFBRMnTiQ4OBiDwcDy5ctbzPv8888zbNgwAgICzGlnzpwhJCTEvLm6uhIbG9tienfas2cPvr6+eHt7s3r16ib78/LyiIiIwN/fH4PBwLp168z71q1bR0BAAAaDwSLu5q5Bt+jodMM9eeuyKegbbj1N3BTjpt3V2jUFfRfaunWrTJo0SYqKikREJCcnR9zc3CQvL8+cZ/HixZKamtpmXXV1dXLr1i0REamqqpKJEydKSkpKs3kPHjwoqampYjAYmt1fU1Mjbm5ucuHCBavSu1JNTY14eXnJ+fPnpbKyUoKCgiQrK8siT2Fhofka3bx5U3x8fCQrK0syMjLEYDBIaWmpVFdXy/Tp0+Xs2bMi0vY1qNfZU9DrOxNNu5tlb4X3R8M7fYz/Ztu2/jtAaWkpr7/+OvHx8QwfPhwAHx8fpk6dSnJyMiLCkiVLmDVrFqGhoW3Wp5TC2dkZMK4iWF1djVKq2byTJ09m8ODBLdaVnJzM2LFjeeCBB6xKbywiIoL9+/cDsHTpUl555ZU242/J8ePH8fb2xsvLCycnJ6KiokhISLDIM2LECPM1cnFxwc/Pj4KCArKzswkPD6d///44OjoyZcoUduzYAbR9DbqKbkw07W6VvRX2LYJbXwFi/HffIpsblLi4OEJDQxk1apRFer9+/SgrK+Pdd98lKSmJ7du3s2HDhiblZ8+eTWFhoUVabW0tISEhDBs2jBkzZhAeHt7h2JpbVbGl9MZWrlzJqlWr2Lp1K2lpaVY/FmvunAoKCiyukYeHh8XKjo1duHCBtLQ0wsPDCQgI4PDhw1y9epWysjISExMtlgC2Bz1rsKbdrQ6/ATVllmk1ZcZ0v46vcZKZmUlwcHCT9PT0dJ577jkiIiJ49dVXWyyfmJjYJM3BwYHTp09TUlLC448/TmZmZrv7BKqqqti1axdvv/22VenNmTx5MiLCmjVrOHDgAA4ODlYdu7lzao/bt28TGRlJbGwsrq6uuLq6smTJEmbOnMmAAQMICQmxOpauou9M7kR/+9k3o7s0rSW3LrYv3Uqurq5UVVVZpKWkpFBaWsqUKVNsqnvgwIFERESY119vj08++YTQ0FDc3NysSm9ORkYGRUVFODk54eLiYk5fuHChRb5ly5Y1LtqEu7u7xd1Efn4+7u5NZ5mqrq4mMjKS6Oho5s2bZ06PiYkhNTWVQ4cOMWjQIMaNG9fmMbuSbkzuJDcu6JFdmvVcPNuXbqU5c+YQHx9P/Rx5OTk5LFy4kM2bN9OnT/t/5RQXF1NSUgJAeXk5+/fvZ/z48e2uZ9u2bc0+ymopffr06RaPnYqKioiOjiYhIQFnZ2dzg1ZeXk52djYrVqwgKiqKy5cvU1lZ2WY8EyZM4OzZs+Tm5lJVVUVcXBxz5861yCMixMTE4Ofnx+LFiy32XblyBYCLFy+yY8cOnnrqqbYvQlfqaM99T97u+tFcemTXXatdo7k+3yIS21/kP/lmi+1vTLfRpk2bJCgoSHx9fcXb21uOHj1qddlZs2ZJQUGB+Xt6erqEhIRIYGCgGAwGWblyZYv5o6KiZPjw4eLo6Cju7u7ywQcfiIjI7du3ZfDgwVJSUmJRtqX02tpa8fT0lLKyMhERKS0tlUmTJsm+fftExDhiatKkSSIicuTIEVm7dq2IiCxatEi2bNki8fHxrZ5Tvd27d4uPj494eXnJW2+91ST/4cOHBZDAwEAJDg6W4OBg2b17t4iIPPzww+Ln5ydBQUGSlJRkLtvSNWiss0dzKWP5O0tYWJicPHmycystzrD8PrSHvUf50VTjv08esPys3VWys7Px8/NrR4Gtxj6SWxeNdyT/b5VN/SWNXb58mWnTprFt2zaCgoI6rd6ulpmZyaZNm6xaBz42NpagoCCmTZvGM888w9ixY3n22Wd7/Drwzf2sKKVSxTipbrvpDnhNu5v5RXdq49GYm5sbWVlZXVZ/VwkICLCqIQHIysri0qVLxMfHExMTw+bNm3t8Q9IVdGNyJ8jeCkXHoLbS+K5A33uhf9udiZqm2W7jxo3mz+vXr8fb29uO0diPbkx6u/p3BWpNHX63vkKPq9A0+3jppZfsHYLd6N86vV1z7wpQBzdy7RKOpml3J92Y9HYtvRNQ2/bQRE3TtM6iG5PerqV3Ahz6dW8cNji+6dcc3/Rre4ehaZoNuq0xUUo9opQ6o5Q6p5R6vZn9a5VSp01bjlKqpMG+Z5VSZ03bs90Vc6/w/1aBY/9GiX3gvp4/muT0R7/jT5FBZO/eSvburfwpMog/RQZx+qPf2Ts0TdPaqVs64JVSDsB6YAaQD5xQSu2SBismisjPG+R/BdM670qpwcByIAwQINVU9np3xN7j1Q/r3BtjfLTl8gA49IXbRVB6CQYMt298rQh58p8IefKf2LPseQAeeXOTnSPSNK2juuvOZCJwTkS+FJEqIA54tJX8C4Btps/fA/aLyDVTA7IfeKRLo+1t/KJhxCTwmAKLLkB1KVTdgJQ37R2Zpml3ie5qTNyBhvMj55vSmlBKPQCMAT5tT1ml1CKl1Eml1Mn6OYHuOvmHjXNxlRYZv6f/3vg99l77xqVp2h2vJ3bARwHbRaS2PYVE5H0RCRORsKFDh3ZRaD3ciIkw/inM/1kd+8P4aHhBDxPuCkf/M4+j/2nfNSQ0raforpcWC4CGK+V4mNKaEwU0fPOnAJjaqOyBToztzuHQD5xcgTqgD9RUQD/XHt1v0hud/O9CTr1fZP6euc04e2voohGE/WSkvcLSNLvqrsbkBOCjlBqDsXGIAprMl6yUGg8MAlIaJO8F/kMpNcj0fSbwi64Nt4GbF+FWM3993rwIrrZN1d0lyi7DgJEwYASMCP/mkZfWacJ+MpKwn4zk/xadAeAH7/vaOaLeZefOnezevZubN28SExPDzJkz7R2S1gm65TGXiNQAL2NsGLKBeBHJUkq9qZRqOIF/FBAnDaYyFpFrwK8wNkgngDdNad3D1RPcv/PN5uRq3HpiQwLw6A4Y5ANOzvDd9cbvPdiXh3ZTnPMZl7NOsv0n3+PLQ7vtHZLWST7++GPCw8MJDg4mLCyMvXv3AvDYY4+xceNGNmzYwEcffdRmPRUVFUycOJHg4GAMBgPLly9vMe/zzz/PsGHDmqzCuHbtWgwGAwEBASxYsICKigrOnDlDSEiIeXN1dbV6Gd7OsmfPHnx9ffH29mb16tVN9ufl5REREYG/vz8Gg4F169aZ961bt46AgAAMBoNF3C1dgy7X0bnre/LWJeuZ3PhKJP/vTbcbX3X+sTqiF65ncv7gX+XDBRPkj/MCzduHCybI+YN/7fRjXdlxSK7sONSpde564QvZ9cIXnVqnrdq1nomIlJ1Jk+L/+Y1c/t2/S/H//EbKzqR1Shxbt26VSZMmSVFRkYiI5OTkiJubm+Tl5ZnzLF68WFJTU9usq66uTm7duiUiIlVVVTJx4kRJSUlpNu/BgwclNTVVDAaDOS0/P19Gjx5tXptk/vz5snnzZotyNTU14ubmJhcuXGjXedqipqZGvLy85Pz581JZWSlBQUGSlZVlkaewsNB8jW7evCk+Pj6SlZUlGRkZYjAYpLS0VKqrq2X69Oly9uxZEWn+GjSns9cz6Ykd8D1T4zuU+q2n3qH0Aqe2/he1lRUWabWVFZza+l+ddoyre/7BuZ+/x41D6dw4lM65n7/HuZ+/x9U9/+i0Y/RW5TmnuXVwJ3W3SwCou13CrYM7Kc85bVO9paWlvP7668THxzN8uLG/zsfHh6lTp5KcnIyIsGTJEmbNmkVoaGib9SmlcHZ2BoxL2FZXV6OUajbv5MmTGTx4cJP0mpoaysvLqampoaysjJEjLfu2kpOTGTt2LA888ECrsURERLB//34Ali5dyiuvvNJm/C05fvw43t7eeHl54eTkRFRUFAkJCRZ5RowYYb5GLi4u+Pn5UVBQQHZ2NuHh4fTv3x9HR0emTJnCjh07Wr0GXU03JprdlF691Hz610Wd9ib8kEfC8V77MveMHck9Y0fivfZlvNe+zJBHwm2uu7cr/cd+qKm2TKypNqbbIC4ujtDQUEaNGmWR3q9fP8rKynj33XdJSkpi+/btbNiwoUn52bNnU1hYaJFWW1tLSEgIw4YNY8aMGYSHW//fz93dnddeew1PT09GjBjBfffd16SfJi4urtmlextbuXIlq1atYuvWraSlpVn9WKy5cyooKLC4Rh4eHhbLBDd24cIF0tLSCA8PJyAggMOHD3P16lXKyspITEy0WE/eHvQU9JrdDBgynNKvmw4QGHD/CJ747712iOjuUn9HYm26tTIzMwkODm6Snp6eznPPPUdERASvvvpqi+UTExObpDk4OHD69GlKSkp4/PHHyczMtLpP4Pr16yQkJJCbm8vAgQOZP38+W7Zs4emnnwagqqqKXbt28fbbb7dZ1+TJkxER1qxZw4EDB3BwcLAqhubOqT1u375NZGQksbGxuLq64urqypIlS5g5cyYDBgwgJCTE6li6ir4z6e2OrjC+mJh/0LjVf755wd6RtSk0+lUc+t1jmagUodEt/6LpKc5+cpXLGaUUpd7mz3MyOPvJVXuH1G59nAe2K91arq6uVFVVWaSlpKRQWlrKlClTbKp74MCBREREsGfPHqvLJCUlMWbMGIYOHUrfvn2ZN28eR48eNe//5JNPCA0Nxc2t7QXlMjIyKCoqwsnJCRcXF3P6woULLfItW7aszbrc3d0t7iby8/Nxd2/6Lnd1dTWRkZFER0czb948c3pMTAypqakcOnSIQYMGMW7cuDaP2ZV0Y9LbfXsF/ItYbh5TwHW0vSNrk9fkOXz7xeX06esEQB9HJ+4bORqvyXPsHFnrzn5ylcNvfUVdlXHQ4e1LVRx+66te16AMCJ8Bjn0tEx37GtNtMGfOHOLj46mfiSInJ4eFCxeyefNm+vRp/6+c4uJiSkpKACgvL2f//v2MHz/e6vKenp4cO3aMsrIyRITk5GSLtc+3bdvW7COu6dOnWzx2KioqIjo6moSEBJydnc0NWnl5OdnZ2axYsYKoqCguX75MZWXbS0BMmDCBs2fPkpubS1VVFXFxccydO9cij4gQExODn58fixcvtth35Yrx/aaLFy+yY8cOnnqqydsW3Uo3Jl0t9WfGrZe49ffd3Pp79w3P9Zo8h6HjgnAzhDHUN4h7Bg7ptmN31In3CqmpEIu0mgrhxHuFLZTome4dF4LLlMfMdyJ9nAfiMuUx7h0XYlO9EydOZOnSpXz3u99l/PjxzJkzhw8++ICHH37YqvKN+xeKioqIiIggKCiICRMmMGPGDL7//e83m3/BggV861vf4syZM3h4ePCHP/yB8PBwnnjiCUJDQwkMDKSuro5FixYBxsEC+/fvt/iLH6Curo5z586ZO7LLysqYN28e77zzDn5+fvzyl79k5cqVAKSlpTF//nxWrFjBfffdR1JSEmFhYa2eE4CjoyPvvfce3/ve9/Dz8+OHP/whBoPBIv+RI0f48MMP+fTTT81DmOsfmUVGRuLv788PfvAD1q9fz8CBA1u8Bt1BiUjbuXqZsLAwOXnypH2D+GwFZK5smh6wHIJWdO2xP5pq/PfJA1YXuX0imbKTnzZJ7x82DecJ0zsnrhbUzxpcrytmD85/zzjSxePleW3kbNv7YanG+asbU7Do5EM212+L7Oxsi7+67e3y5ctMmzaNbdu2ERQUZO9wrJaZmcmmTZtYs2ZNm3ljY2MJCgpi2rRpPPPMM4wdO5Znn32WMWN69jIQzf2sKKVSRSSshSKt0h3wXSVohXFLmmr8/t0D9ovFCs4TpuM8YTrXEz4AYNCjC9so0XvcPHmGiq8uQ00tF978E4NnT8I1rONvrTu7OXH7UlWz6ZolNzc3srKy7B1GuwUEBFjVkABkZWVx6dIl4uPjiYmJYfPmzT2+IekKujG5kxxdASkN7obeMY3F/9ZyY99KD1dbXcWN/C8pv/419w66v1PqvHnyDMXxf4Ma47yhNddvGb9DhxuUCS+P5PBbX1k86nK8RzHhZT0v191o48aN5s/r16/H29vbjtHYj25M7iTfXtErGo2WlBYXUV12m/T/3cCkRUs7pc5riceQ6hqLNKmu4VrisQ43Jj6zjP06B980dsI7D3diwssjzena3eull15qO9MdSjcmmt1d/jwVGvTdndkbz5m98Tj0deLpONv6vmqu32pXurV8Zg3hi798DeiJHjUN9GguzY7q14Cn0SAQ5eDAmP83m8jfW/8uQUscB7m0K13TtI7RjUl36WVDhLtDyJP/xLMff8azH3/GuJnzjYlKIXV1OPV37pR+k8GzJ6H6Wt6Aq76ODJ49yea6NU37hn7M1dVuX4Cyr+DKQeP3M6YppLtjiHAvUnHjGvcOGsq9g4cy1DuQ8pKvO6Xe+n6RKx99CjW1OA5ysXk0V3PqV1z89muj2sipaXemdjcmSqkBQIW0c1ndu5bzaONWrwcPES7POU315YtQW8vXH/6WAeEzbH6BzVoR/7bW/L7JpEVvdGrdrmG+3DxmHJ7aGe+ZNHSrsJL3H0o1f9erLmp3qzYbE6VUH4yLVkUDE4BKoJ9S6mtgN/DfInLOinoeAdYBDsAHItJkJRil1A+BFRhfCUsXkadM6bVAhinbRRGZ27isZpv66cipNf6NUD8dOdBtDUpv5DKyH0/9NUivuqjd9ay5M/kbkIRxqdxMEakDUEoNBiKAXyul/iIiW1qqQCnlAKwHZgD5wAml1C4R+bxBHh/TMb4jIteVUsMaVFEuIiHtOzWtPVqbjlw3JpYarwFff2fiPEK/tKjdvaxpTL4rItWNE8W4dO7HwMdKqb5Ni1mYCJwTkS8BlFJxwKPA5w3yvACsF5HrpvqvWBFbz5a7Fb4+BnWV0KcfOPfct2K7ajryO1H9GvAN1d+ZaNrdqs3RXPUNiVJqsFLqV0qp/1ZK/bNSalDjPK1wBxqu3JJvSmtoHDBOKXVEKXXM9Fis3j1KqZOm9MfairlHyN0KxxcZGxIw/nszx5jeA7U27fiV37/B7RPJ3RdMFyv+y2GK/3LY3mHckS5evMjFixdbzbNz505eeOEFnnzySfbt29dNkWldrT1Dg+OAW8D/Af2BvyulJnZiLI6ADzAVWABsVEoNNO17wDT52FNArFJqbOPCSqlFpgbnZP3U13aV/gbUljVKrDOm90DNTkeOwmX6fIb9dFWXT/bYHaqv3dRL+HaRwsJCTp48yZUrV7hy5QonT57kN7/5DaGhoQQHBxMWFsbevcYFzx577DE2btzIhg0b+Oijj9qsu6KigokTJxIcHIzBYGD58uUt5n3++ecZNmxYk4Wz1q5di8FgICAggAULFlBRYVwues+ePfj6+uLt7c3q1U26cbtcW8fPy8sjIiICf39/DAYD69atM+9bt24dAQEBGAwGixUfW7oGXa09jclQEfmNiPxVRN4GfgBYu1h3AdBwzKSHKa2hfGCXiFSLSC6Qg7FxQUQKTP9+CRwAHmx8ABF5X0TCRCRs6NCh7TitLlLWwl9nLaXbWf105NSv1tbHAYeB9/f6/pL6NeArzhdSe/22Od1xkItewrcTjRw5krCwMFxcXHBxcSEnJ4e//OUvJCYmkp6ezrZt23j22WfJz883l3nrrbesmn6kX79+fPrpp6Snp3P69Gn27NnDsWPHms373HPPNVk4q6CggP/6r//i5MmTZGZmUltbS1xcHLW1tbz00kt88sknfP7552zbto3PP/+82Xq7gjXHd3R05J133uHzzz/n2LFjrF+/ns8//5zMzEw2btzI8ePHSU9P569//Svnzp1r8Rp0h/Y0JteUUoH1X0y/2PtbWfYE4KOUGqOUcsI4OmxXozw7Md6VoJS6H+Njry+VUoOUUv0apH8Hy76Wnqm/Z/vSe4B7x4XQ182TviPH0He4J336O3fLcevfhL+cdZLLWSf5U2RQp68B33C7Z+xIHAd37hvwtdV1fH2mjLKv23ria50LS/dzYalta7E3VnzxBsUXb1ikfXloN9t/8j3+9EQw23/yPb48ZPtaNmVlZbz++uvEx8czfPhwAHx8fJg6dSrJycmICEuWLGHWrFmEhoa2WZ9SCmdn489idXU11dXVKKWazTt58mTzGiQN1dTUUF5eTk1NDWVlZYwcOZLjx4/j7e2Nl5cXTk5OREVFkZCQ0GosERER7N9v/O+ydOlSXnnllTbjb4k1xx8xYoT5Grm4uODn50dBQQHZ2dmEh4fTv39/HB0dmTJlCjt27Gj1GnS19rxn8hKwXSl1GOMwXQNw3pqCIlKjlHoZ2ItxaPAmEclSSr0JnBSRXaZ9M5VSnwO1wL+KyFWl1LeB/1ZK1WFs/FY3HAXWYwWvMvaZWDzq6mNM1yyEPPlPhDz5T/YOwya3C6uovl1H6sZC/t8vHuhwPXm/OUTBf/7d/P3S+ycAcH/tYUb92+QO1Xm18BbXCr+Zi6zkSikAZQUpZHz0a2orjY98Sr8u4ugG46zTtqx2mZiYSGhoKKNGWb7A2a9fP8rKynj33XdJSkrixo0bnDt3jhdffNEi3+zZs/nggw8YOfKbQQ61tbU89NBDnDt3jpdeeonwcOvvJt3d3Xnttdfw9PTk3nvvZebMmcycOZPt27dbxOjh4cE//tH6I8+VK1eybNkyrly5QlpaGrt2Nf6buHnNnVNBQUG7jn/hwgXS0tIIDw+noKCAN954g6tXr3LvvfeSmJjYZEGu7mZ1YyIiXyilQoHHAH/gNPAv7SifCCQ2SlvW4LMAi01bwzxHgUB6mzHRxn//EWPsfFdO0McBhvf+vgftG3/41ilqq76ZWyx7+9dkb/8aBydFTErbf3U3NurfJjPq3yaT9ZhxpL1h59M2xzhkpAtDRrqQf8Y4q4CHr3Gamu0b3zc3JPVqKys4tfW/bGpMzp49S3BwcJP09PR0nnvuOSIiInj11VdbLF+/kmBDDg4OnD59mpKSEh5//HEyMzOt7hO4fv06CQkJ5ObmMnDgQObPn8+WLVu45557rD8pk8mTJyMirFmzhgMHDuBQ/1i4Dc2dU3vcvn2byMhIYmNjcXV1xdXVlSVLljBz5kwGDBhASEiI1bF0FasfcymlpgG/B8KBr4BTNL/enFZvTDTcPwmGTYF+Q6C2HDLetHdUWidasCuQsY8MAtNTF4d7FN6zBrHg/3r+3z+lVy+1K91aAwYMoKrKcvGwlJQUSktLmTJlik11Dxw4kIiIiHb1CSQlJTFmzBiGDh1K3759mTdvHkePHsXd3Z28vG8Gmebn5+Pu3niQqaWMjAyKiopwcnLCxeWbR6ULF1ouJrds2bLGRZuw9vjV1dVERkYSHR1tsbxwTEwMqampHDp0iEGDBjFu3Lg2j9mV2tNnsgnjSK5jgBewDOh9S6h1tyuHjfNyVZhecjv3e/izgrh77RtXG6S2hpqvC6kts22q9jtd/6F9cRrgYPyzSkFtpdB3gAP972/r1Sv7GzBkeLvSrTV16lTi4+OpH1WZk5PDwoUL2bx5M336tH9u2eLiYkpKSgAoLy9n//79jB8/3urynp6eHDt2jLKyMkSE5ORk/Pz8mDBhAmfPniU3N5eqqiri4uKYO/ebyTWmT59OQcE344SKioqIjo4mISEBZ2dnc4NWXl5OdnY2K1asICoqisuXL1NZWdlmXG0dH0BEiImJwc/Pj8WLLR7acOWK8VW8ixcvsmPHDp566imrr0lXaM9/2a9EZKeI/K+I/FJEHhWRu3NJsfYYMhH6DcN8qR36w+hoeDTXrmG1pfZWCVJVSWkz68L3dlJTS2XB19TcLO2U+sqv1dD/fkfuH38v/k/cT/nVmrYL9QCh0a/i0M/yUY9Dv3sIjW75EZQ1goKCWLp0Kd/97ncZP348c+bM4YMPPuDhhx+2qvzs2bMpLCw0fy8qKiIiIoKgoCAmTJjAjBkz+P73v99s/gULFvCtb32LM2fO4OHhwR/+8AfCw8N54oknCA0NJTAwkLq6OhYtWoSjoyPvvfce3/ve9/Dz8+OHP/whBoMBgLq6Os6dO2fuyC4rK2PevHm88847+Pn58ctf/pKVK439S2lpacyfP58VK1Zw3333kZSU1KT/ovE5Aa0evz7/kSNH+PDDD/n0008JCQkhJCTE/MgsMjISf39/fvCDH7B+/XoGDhzY4jXoDkqk9SdVSqn/wfhIawRwWUSsWxjZjsLCwuTkSdsWVeo0SVONLytWFGFuULx/AhNtH6nUmW6fSKastYbDwZFhi1a2vL8XyV2+idqbZbh+J4BhT0ztlDo7e26ujvaZZGdn4+fn1+y+xn0mYBzNdWrrf1F69RIDhgwnNPpVm/pLzpwxXgdfX+N1uHz5MtOmTWPbtm0EBQV1uN7ulpmZyaZNm6xaBz42NpagoCCmTZvGM888w9ixY3n22Wd7/Drwzf2sKKVSTe/0tZs1HfB/BIIBN4yjrf4ZSDdtn4nI/3bkwHeVumq4ZyTcOwLuD4fyorbLdDPnCdNxnjCd2tKb3E75hMqzGYCAY1/6jfHH+duz7B2izc7/6++Rmm8mu755JJObRzJRjg6M/e1P7RiZ/XhNnmNT49EWNzc3srJ639PwgIAAqxoSgKysLC5dukR8fDwxMTFs3ry5xzckXaHNxkREPgXMf7IqpRwBP4wNzARANyZtGWj45vOE9faLwwoOA1xRfe/B3AlQU4Ny6odD/96/MuEDS3/E17uOcDstB8S4SNaAIC/un/sde4dmofjjTG6nFiCVtZwKfY9Rb0xlaGT3vs2sWW/jxo3mz+vXr8fb++58+m/V0GDTNPSvi8h/iEgNxvdMMtoopvVSdeW3Uf1dcBjgguOwUdTdIZ3wjvcNoM89TuZ2Umpq6HOPE46uA+wdmlnxx5nkLk5EKo13UFX5N8ldbHxGrhuUns+aN/rvVFZ1wJumnf9BF8ei9RADH4nGceD9qL79cJ08l4GPRNs7pE5Te6sMB9f+OLkPxfXbAdTebDx/mn3lrTpAXbllB35deQ15qw7YVO/Nq2VU3K6m/FYVuZ9d5ubVnnXeWu/XntFc6Uqp5aa7FE3rlUY8P5u+QwfSp19fhj0xlRHPz7Z3SBaqCm62K90aN6+WceWrG9QPtqmpquXKVzd0g6J1qvY0DIMxzqlVqJRKME1HP7+L4tK0u5KTu2u70q1xteAWUmc5alPqhKsFd8bjS61nsGbZXiVGPzR974dxXq5AjIte/W99nq4NtZf5bAVkNjOU9rMVELSiW0PRuk5Lqy52dA34UW9MJXdxosWjrj73OjLqjakdjrGmqrZd6ZrWEVYt26uU+hhIEJGLIlIJnFJKZQL/Tyn1J4xL+/6xC+PsfYJWWDYaSVO/SdfuGM2tumiL+k72L3+2G6msxcnD1ebRXI5ODs02HI5OnTuX09WrV7l9+zYiwmeffYa7uztDhgzp1GNoPZc1jckjwPPANqXUGKAEuAfj7L/7gFgRSeuyCDXtLjM0MoArH54GOmmiR3cXY59Jg0ddqo9iiHvnDfe+evUqX331lblfpqqqiq+++sp4fN2g3BWsec+kAvgd8DvTWu/3A+UiUtLFsWma1glchxiXHbpywdgJ7+jkwBB3F3N6ZygoKKCurs4ira6ujoKCAt2Y3CXa7IBXSpn/NDKtglikGxJN611ch/TnHue+3OvixJggt05tSIAmswS3la7deax5zPWMUmoCsFhEdI/dHa7xHF1Xfm9cs75/2LRevw781T3/4PreE+bv537+HgCDvjdBL91rIycnp2YbDicnpyZpO3fuZPfu3dy8eZOYmBhmzpzZHSFqXU1EWt0wrW4IHMS4DnybZVqo5xHgDHAO49v0zeX5IcYlebOAPzdIfxY4a9qebetYDz30kPQY6ctFttJ0S19u78i0Hizz0Q8l89EP213u888/b3Ff3hfFkvdFsS1htejrr7+W1NRUOXHihHn7zW9+I6GhoRIUFCQPPfSQ7Nmzx6LMtWvX5Pnnn2+z7vLycpkwYYIEBQWJv7+/LFu2rMW8P/7xj2Xo0KFiMBjMaV988YUEBwebNxcXF1m7dm2L+bvTJ598IuPGjZOxY8fK22+/3WT/xYsXZerUqeLn5yf+/v4SGxtr3hcbGysGg0H8/f3N59Na/saa+1nBuPJtx37HW50R5gFfAIswDgnu346yDhiX+PUCnDBOEunfKI8PkAYMMn0fZvp3MPCl6d9Bps+DWjtej2pMNK0dLv76oKQMXdVku/jrg1aVt1djImJsUE6ePCknTpyQt99+W8LCwqSoqEhERHJycsTNzU3y8vLM+RcvXiypqalt1ltXVye3bt0SEZGqqiqZOHGipKSkNJv34MGDkpqa2mLjUFNTI25ubnLhwgWr8nelmpoa8fLykvPnz0tlZaUEBQVJVlaWRZ7CwkLzNbp586b4+PhIVlaWZGRkiMFgkNLSUqmurpbp06fL2bNnW8zfnM5uTKydm+v7wEKgCggFngYMSqnrYt2aJhOBcyLypam+OOBR011IvReA9SJyHUBErpjSvwfsF5FrprL7Md7lbLMmdk3rTeqX7e0uN0+e4VriMWqu38JxkAuDZ0/CNaxj0+gPGTKEr7/+mrKyMn73u99x5MgRhg83LrTl4+PD1KlTSU5O5kc/+hGvv/46s2bNIjS07aWNlVI4OzsDxlUHq6urUUo1m3fy5MlcuHChxbqSk5MZO3YsDzzwgFX5G4uIiODf//3fmTFjBkuXLuXGjRu8++67Vpdv6Pjx43h7e+Pl5QVAVFQUCQkJ+Pv7m/OMGDGCESNGAODi4oKfnx8FBQWUlJQQHh5O//7Gvq8pU6awY8cO/u3f/q3Z/A3r7CrWdMDnAj8F1opIkIi8KCKTRWQIMNXK47gDeQ2+55vSGhoHjFNKHVFKHVNKPdKOsiilFimlTiqlTtav8KZpWstunjxDcfzfqLlufBO+5votiuP/xs2TZ2yqNzExkdDQUEaNGmWR3q9fP8rKynj33XdJSkpi+/btbNiwoUn55haSqq2tJSQkhGHDhjFjxgzCwzvWxxUXF8eCBQs6VBZg5cqVrFq1iq1bt5KWlkZsbKxV5Zo7p4KCAotr5OHhYbGyY2MXLlwgLS2N8PBwAgICOHz4MFevXqWsrIzExESLJYAb5+8O1tyZzBKRL5rbISL5nRyLD8YGygM4pJSyeiFtEXkfeB+Mi2N1Ylyadke6lngMqbacVFKqa7iWeKzDdycAZ8+eJTg4uEl6eno6zz33HBEREbz6asurOdavJNiQg4MDp0+fpqSkhMcff5zMzEwCAtr3ImdVVRW7du3i7bffble5hiZPnoyIsGbNGg4cOICDg3UvfjZ3Tu1x+/ZtIiMjiY2NxdXVFVdXV5YsWcLMmTMZMGAAISEhFrE0zt8drJmbK0wpVayUyldKPQuglJqklHpLKZVq5XEKgIZ/pniY0hrKB3aJcfhxLpCDsXGxpqymae1Uf0dibbq1BgwY0GRkV0pKCqWlpUyZMsWmugcOHEhERIR5/fX2+OSTTwgNDcXNza3Dx8/IyKCoqAgnJydcXL556XPhwoUW+ZYtW9ZmXe7u7hZ3E/n5+bi7N3noQnV1NZGRkURHRzNv3jxzekxMDKmpqRw6dIhBgwYxbty4VvN3NWsak+XAbOBBYIypz+J/MXak/8zK45wAfJRSY5RSThgnjNzVKM9OTI/NlFL3Y3zs9SWwF+MKj4OUUoOAmaY0TdNs4Dio+TfgW0q31tSpU4mPj6f+cXNOTg4LFy5k8+bN9OnT/knHi4uLKSkpAaC8vJz9+/czfvz4dtezbdu2dj3imj59usVjp6KiIqKjo0lISMDZ2dncoJWXl5Odnc2KFSuIiori8uXLVFZWtln/hAkTOHv2LLm5uVRVVREXF8fcuXMt8ogIMTEx+Pn5sXjxYot9V64Yu5UvXrzIjh07eOqpp1rN3+Xa6qEH0hp8VsAVYGB7e/oxNkg5GEd1vWFKexOY26DuNRg75TOAqAZln8c4pPgc8OO2jqVHc2l3q+ZG6HxdcFNyThQ02Qr2n5Zz//p7Ofuzd83buX/9vdw48UWHj//FF1/IF198IZs2bZKgoCDx9fUVb29vOXr0qNV1zJo1SwoKCszf09PTJSQkRAIDA8VgMMjKlStbzB8VFSXDhw8XR0dHcXd3lw8++EBERG7fvi2DBw+WkpISi7It5a+trRVPT08pKysTEZHS0lKZNGmS7Nu3T0SMo8AmTZokIiJHjhwxD81dtGiRbNmyReLj41s9p3q7d+8WHx8f8fLykrfeeqtJ/sOHDwsggYGB5qHNu3fvFhGRhx9+WPz8/CQoKEiSkpJERFrN31hnj+ZSxvItU0oVYbw7OWPaEkWk7SEYdhQWFiYnT560dxia1u2ys7Px8/OzOn9njuYCOHPG2Hnv62us4/Lly0ybNo1t27YRFBTU4Xq7W2ZmJps2bbJqHfjY2FiCgoKYNm0azzzzDGPHjuXZZ5/t8evAN/ezopRKFZGwjtRnTQf8cozTzUeb/nVRSiVhfCckTUT+3JEDa5pmf65hvjY1Hm1xc3MjKyury+rvKgEBAVY1JABZWVlcunSJ+Ph4YmJi2Lx5c49vSLqCNRM9vt/wu1LKA2OjEgTMAnRjomnaXWvjxo3mz+vXr8fb25pX7+48Vr202JAYhwPnA590fjiapnW14os3ABjqeZ+dI7nzvPTSS/YOwW7a3Zhomta9LizdD8Dot2bYVM/VwltcK/xm2G/JlVIABo90YcjIzlvbRLs76cZE03qovN8couA//27+ful944zH7q893KEpV4aYGo38M18D4OF7f+cEqmnoxkTTeqz6ebqyHtsCdM6qi5rWVdr/BpGmaZqmNaIbE03TNM1m+jGXpmk2KywstJgVt/6l4ZEjRzJy5Eh7haV1I92YaJpmM91oaPoxl6b1cHVVtZRlXqbq8m17h6JpLdKNiab1cFV5N6i9WUn+O39vO3MPcfHiRS5evNjsvp07d/LCCy/w5JNPsm/fvm6OTOsqujHRtB7qH6N+zbFh/0G16Y7kyh9PcWzYf/CPUb+2c2QtKyws5OTJk1y5coW4uDgCAgIYN24cwcHB7N1rXDniscceY+PGjWzYsIGPPvqozTorKiqYOHEiwcHBGAwGli9f3mLe559/nmHDhjVZOGvt2rUYDAYCAgJYsGABFRUV5OXlERERgb+/PwaDgXXr1tl28h2wZ88efH198fb2ZvXq1U32txbjunXrCAgIwGAwWKz42NI16HIdnW64J296CnrtTlB56Zbk/GSnpLj9h6QMXSX/8Py15Ly4Uyov3WqxTHPTijd28fNiOXeqSKqrajozXAu//e1vJTg4WIqKikREJCcnR9zc3CQvL8+cZ/HixZKamtpmXXV1dXLrlvGcq6qqZOLEiZKSktJs3oMHD0pqaqoYDAZzWn5+vowePdo8nfz8+fNl8+bNUlhYaD7+zZs3xcfHR7Kysjp2wh1QU1MjXl5ecv78eamsrJSgoKAmx28pxoyMDDEYDFJaWirV1dUyffp0OXv2rIg0fw2a09lT0Os7E03roZzcnHFwcYI6AQV1lbU4uPTDyc3Zpnprqmqpq63jWuFtzn5ylT/PyeD9sFT+PCeDs59ctTnu0tJS1qxZQ2xsLMOHDwfAx8eHqVOnkpycjIiwZMkSZs2aRWho26tZKKVwdjaec3V1NdXV1Silms07efJkBg8e3CS9pqaG8vJyampqKCsrY+TIkYwYMcJ8fBcXF/z8/Fpdgx0gIiKC/fuN09ssXbqUV155pc34W3L8+HG8vb3x8vLCycmJqKgoEhISLPK0FGN2djbh4eH0798fR0dHpkyZwo4dO1q9Bl2t2xoTpdQjSqkzSqlzSqnXm9n/nGl54NOmbWGDfbUN0huv0Khpd6zq4lL6ujnTP2g4bs8+SPWVjnfCn0st4uzJQmqqawE489diDr55gduXqkDg9qUqDr/1lc0NSlxcHP7+/owYMcIivV+/fpSVlfHuu++SlJTE9u3b2bBhQ5Pys2fPthhmDFBbW0tISAjDhg1jxowZhIeHWx2Pu7s7r732Gp6enowYMYL77ruPmTNnWuS5cOECaWlpbda7cuVKVq1axdatW0lLS7N4vNSa5s6poKCAUaO+WZHcw8Oj1casYYwBAQEcPnyYq1evUlZWRmJiosUSwPbQLUODlVIOwHpgBsYZh08opXaJyOeNsn4kIi83U0W5iIR0cZia1uP4/vEJ83QqY379iE11jQ4cxtf5N7l1rRyAL3feps5yqXZqKoQT7xXiM2tIh4+TmZlpXhyrofT0dJ577jkiIiJ49dVXWyyfmJjYJM3BwYHTp09TUlLC448/TmZmptV9AtevXychIYHc3FwGDhzI/Pnz2bJlC08/bZye5vbt20RGRhIbG4urq2urdU2ePBkRYc2aNRw4cAAHBwerYmjunNqjcYyurq4sWbKEmTNnMmDAAEJCQqyOpat0153JROCciHwpIlVAHPBoNx1b0zTA0cmBPg7f/C9fea222Xy3L1c1m24tV1dXqqurLdJSUlIoLS1lypQpNtU9cOBAIiIizOuvWyMpKYkxY8YwdOhQ+vbty7x58zh69ChgfGwWGRlJdHQ08+bNa7OujIwMioqKcHJywsXlm5mWFy5caJFv2bJlbdbl7u5ucTeRn5+Pu7t7k3wtxRgTE0NqaiqHDh1i0KBBjBs3rs1jdqXuakzcgYb3YPmmtMYilVKfKaW2K6VGNUi/Ryl1Uil1TCn1WHMHUEotMuU5WVxc3HmRa9odpLa6Fse+DvTr35d7hzb/YMLZzcmmY8yZM4c9e/Zw7do1AHJycli4cCGbN2+mT5/2/8opLi6mpKQEgPLycvbv38/48eOtLu/p6cmxY8coKytDREhOTsbPzw8RISYmBj8/PxYvXtyk3PTp0y0eOxUVFREdHU1CQgLOzs7mBq28vJzs7GxWrFhBVFQUly9fprKyss24JkyYwNmzZ8nNzaWqqoq4uDjmzp1rkae1GK9cuQIYh2Hv2LGDp556yupr0iU62nPfng14AvigwfdngPca5RkC9DN9/gnwaYN97qZ/vYALwNjWjqdHc2l3ksxHP5TMRz+0Kq81o7nyviiWvC+KJSfxa/nDt1Plv0NPmrc/fDtVchK/tjVkWbVqlfj6+oqvr694e3vL0aNHrS47a9YsKSgoMH9PT0+XkJAQCQwMFIPBICtXrmwxf1RUlAwfPlwcHR3F3d1dPvjgAxERWbZsmfj6+orBYJCnn35aKioq5PDhwwJIYGCgBAcHS3BwsOzevVtERGpra8XT09M8Aqy0tFQmTZok+/btExHjiKlJkyaJiMiRI0dk7dq1IiKyaNEi2bJli8THx7d6TvV2794tPj4+4uXlJW+99VaT/K3F+PDDD4ufn58EBQVJUlKSuWxL16Cxzh7NpYzlu5ZS6lvAChH5nun7L0wN2dst5HcArolIk6XglFJ/BP4qIttbOl5YWJjUzw2kab1de6agz87Oxs/Pr9U8DdczOfvJVU68V8jty1U4uzkx4eWRNvWX1Dtz5gxgfCw1bdo0tm3bRlBQkM31dpfMzEw2bdpk1TrwsbGxBAUFMW3aNJ555hnGjh3Ls88+2+PXgW/uZ0UplSoiYR2pr7vm5joB+CilxgAFQBRgcU+mlBohIkWmr3OBbFP6IKBMRCqVUvcD3wF+001xa9odzWfWkE5pPFri5uZGVlZWl9XfVQICAqxqSACysrK4dOkS8fHxxMTEsHnz5h7fkHSFbmlMRKRGKfUysBdwADaJSJZS6k2Mt1W7gFeVUnOBGuAa8JypuB/w30qpOox9PKul6SgwTdM0u9i4caP58/r16/H29rZjNPbTbbMGi0gikNgobVmDz78AftFMuaNAYJcHqGk9TONle48N+w+g48v2al3vpZdesncIdqOnoNe0Hqp+2V5N6w30dCqapmmazXRjommaptlMNyaapmmazXSfiabdJa4W3uJa4S3z97MnjRMPDh7pwpCRLi0V65C6ujoqKiqorq6mb9++nVq31jPpxkTT7hJDuqDRaElVVRW1tbUUFhbywAMPdMsxNfvSjYmmaZ0mNTWVhrNqFBcXU1xcjFKKhx56yI6RaV1N95lomtZpAgMDLRZm6tOnD4MHD+5VU6loHaMbE03TOo2Tk5PFuhp1dXU4ODhY9Jvs3LmTF154gSeffJJ9+/bZI0ytC+jGRNO0TlXf6f73v/+dhQsX8sgjjxAWFsbevXsBeOyxx9i4cSMbNmzgo48+arO+iooKJk6cSHBwMAaDgeXLl7eY9/nnn2fYsGFNFs4qKSnhiSeeYPz48fj5+ZGSkgLA2rVrMRgMBAQEsGDBAioqKmw48/bbs2cPvr6+eHt7s3r16ib78/LyiIiIwN/fH4PBwLp168z71q1bR0BAAAaDwWLFx5auQZfr6HTDPXnTU9BrdytrpqDvDr/97W8lODhYioqKREQkJydH3NzcJC8vz5xn8eLFkpqa2mZddXV1cuvWLRERqaqqkokTJ0pKSkqzeQ8ePCipqaliMBgs0n/0ox/Jxo0bRUSksrJSrl+/Lvn5+TJ69GjzNPPz58+XzZs3t/tcO6qmpka8vLzk/PnzUllZKUFBQZKVlWWRp7Cw0HyNbt68KT4+PpKVlSUZGRliMBiktLRUqqurZfr06XL27FkRafkaNNbZU9DrOxNNu4sVf5zJqdD3OOb2H5wKfY/ijzNtrrO0tJQ1a9YQGxvL8OHDAfDx8WHq1KkkJycjIixZsoRZs2YRGhraZn1KKZydnQHjXU91dTVKqWbzTp482aLPBuDGjRscOnSImJgYwPgobuDAgQDU1NRQXl5OTU0NZWVljBw5stVYIiIi2L9/PwBLly7llVdeaTP+lhw/fhxvb2+8vLxwcnIiKiqKhIQEizwjRowwXyMXFxf8/PwoKCggOzub8PBw+vfvj6OjI1OmTGHHjh0tXoPuoBsTTbtLFX+cSe7iRKryb4JAVf5Nchcn2tygxMXF4e/vz4gRIyzS+/XrR1lZGe+++y5JSUls376dDRs2NCk/e/ZsCgsLLdJqa2sJCQlh2LBhzJgxg/DwcKvjyc3NZejQofz4xz/mwQcfZOHChZSWluLu7s5rr72Gp6cnI0aM4L777mPmzJmt1rVy5UpWrVrF1q1bSUtLs3i81JrmzqmgoIBRo75ZUNbDw8NiZcfGLly4QFpaGuHh4QQEBHD48GGuXr1KWVkZiYmJFksA24NuTDTtLpW36gB15TUWaXXlNeStOmBTvZmZmfj6+jZJT09PZ/z48bz66qukpqayYcMGXnzxxSb5EhMTm9whODg4cPr0afLz8zl+/DiZmdY3eDU1NZw6dYqf/vSnpKWlMWDAAFavXs3169dJSEggNzeXwsJCSktL2bJlS6t1TZ48GRFhzZo1xMXFWQw2aE1z59Qet2/fJjIyktjYWFxdXfHz82PJkiXMnDmTRx55hJCQEKtj6Sq6MdG0u1RVwc12pVvL1dWV6upqi7SUlBRKS0uZMmWKTXUPHDiQiIgI8/rr1vDw8MDDw8N8N/PEE09w6tQpkpKSGDNmDEOHDqVv377MmzePo0ePtlpXRkYGRUVFODk54eLyzQugCxcutMi3bNmyxkWbcHd3t7ibyM/Px93dvUm+6upqIiMjiY6OZt68eeb0mJgYUlNTOXToEIMGDWLcuHFtHrMrdVtjopR6RCl1Ril1Tin1ejP7n1NKFSulTpu2hQ32PauUOmvanu2umDXtTubk7tqudGvNmTOHPXv2cO3aNQBycnJYuHAhmzdvpk+f9v/KKS4upqSkBIDy8nL279/P+PHjrS4/fPhwRo0aZV5KODk5GX9/fzw9PTl27BhlZWWICMnJyRbL2E6fPt3isVNRURHR0dEkJCTg7OxsbtDKy8vJzs5mxYoVREVFcfnyZSorK9uMa8KECZw9e5bc3FyqqqqIi4tj7ty5FnlEhJiYGPz8/Fi8eLHFvitXrgBw8eJFduzYwVNPWSxe2/062nPfng3j6ornAS/ACUgH/BvleQ54r5myg4EvTf8OMn0e1Nrx9Ggu7W7VntFcV7ZnyD88fy0pQ1eZt394/lqubM+wOY5Vq1aJr6+v+Pr6ire3txw9etTqsrNmzZKCggLz9/T0dAkJCZHAwEAxGAyycuXKFvNHRUXJ8OHDxdHRUdzd3eWDDz4QEZG0tDR56KGHJDAwUB599FG5du2aiIgsW7ZMfH19xWAwyNNPPy0VFRUiIlJbWyuenp7mkV6lpaUyadIk2bdvn4gYR0xNmjRJRESOHDkia9euFRGRRYsWyZYtWyQ+Pr7Vc6q3e/du8fHxES8vL3nrrbea5D98+LAAEhgYKMHBwRIcHCy7d+8WEZGHH35Y/Pz8JCgoSJKSksxlW7oGjXX2aC4lDaY+6CpKqW8BK0Tke6bvvzA1ZG83yPMcECYiLzcquwCYKiI/MX3/b+CAiGxr6XhhYWFy8uTJTj8PTevpsrOzLf66bkvxx5nkrTpAVcFNnNxdGfXGVIZG2v5+Qv1dwMCBA5k2bRrbtm3rVW/BZ2ZmsmnTJqvWgY+NjSUoKIhp06bxzDPPMHbsWJ599tkevw58cz8rSqlUEQnrSH3dNTeXO9BwqEE+0NxwjEil1GQgB/i5iOS1ULbJg0Wl1CJgEYCnp2cnha1pd7ahkQGd0ni0xM3NjaysrC6rv6sEBARY1ZAAZGVlcenSJeLj44mJiWHz5s09viHpCj1posf/A7aJSKVS6ifAn4Bp1hYWkfeB98F4Z9I1IWqaplnauHGj+fP69evx9va2YzT2012NSQEwqsF3D1OamYhcbfD1A+A3DcpObVT2QKdHqGmaZqOXXnrJ3iHYTXeN5joB+CilxiilnIAoYFfDDEqphm84zQWyTZ/3AjOVUoOUUoOAmaY0TdM0rYfoljsTEalRSr2MsRFwADaJSJZS6k2Mowd2Aa8qpeYCNcA1jKO7EJFrSqlfYWyQAN4UkWvdEbemaZpmnW7rMxGRRCCxUdqyBp9/AfyihbKbgE1dGqCmaZrWYfoNeE3TNM1mPWk0l6ZpvVxhYaHFhIb173uNHDnSprmptJ5PNyaapnUa3WjcvfRjLk3TNM1mujHRNE3TbKYbE03TNM1mujHRNK1b7dy5kxdeeIEnn3ySffv22TscrZPoxkTTtC7x8ccfEx4eTnBwMGFhYezda5y44rHHHmPjxo1s2LCBjz76qM16KioqmDhxIsHBwRgMBpYvX95i3ueff55hw4YREGA5eeW6desICAjAYDBYLLVbUlLCE088wfjx4/Hz8yMlJaVjJ9tBe/bswdfXF29vb1avXt1kf15eHhEREfj7+2MwGFi3bp15X0vn1NI16HIdnbu+J296PRPtbtWe9UxERD7d8pk898A6+b56U557YJ18uuWzTolj69atMmnSJCkqKhIRkZycHHFzc5O8vDxznsWLF0tqamqbddXV1cmtW7dERKSqqkomTpwoKSkpzeY9ePCgpKamisFgMKdlZGSIwWCQ0tJSqa6ulunTp8vZs2dFRORHP/qRbNy4UUREKisr5fr16x06346oqakRLy8vOX/+vFRWVkpQUJBkZWVZ5CksLDRfo5s3b4qPj49kZWW1ek7NXYPmdPZ6JvrORNPuUn/bmsF7i3ZT/NUNRKD4qxu8t2g3f9uaYVO9paWlvP7668THxzN8+HAAfHx8mDp1KsnJyYgIS5YsYdasWYSGhrZZn1IKZ2dnwLiEbXV1NUqpZvNOnjyZwYMHW6RlZ2cTHh5O//79cXR0ZMqUKezYsYMbN25w6NAhYmJiAHBycmLgwIGtxhIREcH+/fsBWLp0Ka+88kqb8bfk+PHjeHt74+XlhZOTE1FRUSQkJFjkGTFihPkaubi44OfnR0FBQYvn1NI16A66MdG0u9T/vPE3Ksss12qvLKvmf974m031xsXFERoayqhRoyzS+/XrR1lZGe+++y5JSUls376dDRs2NCk/e/ZsixcfAWprawkJCWHYsGHMmDHDvJ67NQICAjh8+DBXr16lrKyMxMRE8vLyyM3NZejQofz4xz/mwQcfZOHChZSWlrZa18qVK1m1ahVbt24lLS3N4vFSa5o7p4KCAotr5OHhYbFMcGMXLlwgLS2N8PDwFs/JnvRLi5p2l/r64o12pVsrMzOT4ODgJunp6ek899xzRERE8Oqrr7ZYPjExsUmag4MDp0+fpqSkhMcff5zMzEyr+wT8/PxYsmQJM2fOZMCAAYSEhODg4EBNTQ2nTp3i3XffJTw8nH/+539m9erV/OpXv2qxrsmTJyMirFmzhgMHDuDg4GBVDM2dU3vcvn2byMhIYmNjcXV1xdXVtdlzsid9Z6Jpd6n7Pe9rV7q1XF1dqaqqskhLSUmhtLSUKVOm2FT3wIEDiYiIYM+ePe0qFxMTQ2pqKocOHWLQoEGMGzcODw8PPDw8zHc5TzzxBKdOnWq1noyMDIqKinBycsLFxcWcvnDhQot8y5Yta1y0CXd3d4u7ifz8fNzdmywiS3V1NZGRkURHRzNv3rxWz8medGOiaXepH62KoF//vhZp/fr35UerImyqd86cOcTHx1NcXAxATk4OCxcuZPPmzfTp0/5fOcXFxZSUlABQXl7O/v37GT9+fLvquHLlCgAXL15kx44dPPXUUwwfPpxRo0aZ16tPTk7G39/fXGb69OkWj52KioqIjo4mISEBZ2dnc4NWXl5OdnY2K1asICoqisuXL1NZWdlmTBMmTODs2bPk5uZSVVVFXFwcc+fOtcgjIsTExODn58fixYvbPCe76mjPfU/e9Ggu7W7VU0Zzbdq0SYKCgsTX11e8vb3l6NGjVpedNWuWFBQUmL+np6dLSEiIBAYGisFgkJUrV7aYPyoqSoYPHy6Ojo7i7u4uH3zwgYiIPPzww+Ln5ydBQUGSlJRkLpuWliYPPfSQBAYGyqOPPirXrl0TEZHa2lrx9PSUsrIyEREpLS2VSZMmyb59+0TEOGJq0qRJIiJy5MgRWbt2rYiILFq0SLZs2SLx8fGtnlO93bt3i4+Pj3h5eclbb73VJP/hw4cFkMDAQAkODpbg4GDZvXt3q+fU0jVorLNHc3XbL3jgEeAMcA54vZV8kYAAYabvo4Fy4LRp29DWsXRjot2t2tuYdLVLly6Jv7+/pKen2zuUdsnIyJCf//znVuVdu3atJCcni4jI008/LcuXL5cvv/yyK8PrFJ3dmHRLB7xSygFYD8wA8oETSqldIvJ5o3wuwD8D/2hUxXkRCemOWDVN6zxubm5kZWXZO4x2CwgIYM2aNVblzcrK4tKlS8THxxMTE8PmzZsZM2ZMF0fY83TXaK6JwDkR+RJAKRUHPAp83ijfr4BfA//aTXFpmqbZZOPGjebP69evx9vb247R2E93NSbuQMNB0PmAxUBxpVQoMEpEdiulGjcmY5RSacBNYKmIHG58AKXUImARgKenZ2fGrmmaZpWXXnrJ3iHYTY8YzaWU6gOsAf6lmd1FgKeIPAgsBv6slHJtnElE3heRMBEJGzp0aNcGrGmaplnorsakAGj4OqyHKa2eCxAAHFBKXQAmAbuUUmEiUikiVwFEJBU4D9h3QLWmaZpmobsakxOAj1JqjFLKCYgCdtXvFJEbInK/iIwWkdHAMWCuiJxUSg01deCjlPICfIAvuyluTdM0zQrd0mciIjVKqZeBvYADsElEspRSb2IcirarleKTgTeVUtVAHfCiiFzr+qg1TdM0a3Xb3FwikggkNkprds4BEZna4PPHwMddGpymaZpmkx7RAa9pmqb1brox0TRN02ymGxNN0zTNZrox0TStW+3cuZMXXniBJ598kn379tk7HK2T6MZE07Qu8fHHHxMeHk5wcDBhYWHs3bsXgMcee4yNGzeyYcMGPvroozbrqaioYOLEiQQHB2MwGFi+fHm785WUlPDEE08wfvx4/Pz8SElJMe+rra3lwQcf5Pvf/76NZ9x+e/bswdfXF29vb1avXt1kf15eHhEREfj7+2MwGFi3bp1539q1azEYDAQEBLBgwQIqKirM+55//nmGDRtm9QJinaKjM0T25E3PGqzdrXrKrMFbt26VSZMmSVFRkYiI5OTkiJubm+Tl5ZnzLF68WFJTU9usq66uTm7duiUiIlVVVTJx4kRJSUlpV74f/ehHsnHjRhERqayslOvXr5vLvfPOO7JgwQKZM2dOx062g2pqasTLy0vOnz8vlZWVEhQUJFlZWRZ5CgsLzdfo5s2b4uPjI1lZWZKfny+jR482T5E/f/582bx5s7ncwYMHJTU1VQwGQ4vH7+xZg/WdiabdxbZu3cro0aPp06cPo0ePZuvWrTbXWVpayuuvv058fDzDhw8HwMfHh6lTp5KcnIyIsGTJEmbNmkVoaGib9SmlcHZ2BoyrDlZXV6OUsjrfjRs3OHToEDExMQA4OTkxcOBAwLi64e7du5uslNiSiIgI9u/fD8DSpUt55ZVXrCrXnOPHj+Pt7Y2XlxdOTk5ERUWRkJBgkWfEiBHma+Ti4oKfn595wa6amhrKy8upqamhrKyMkSNHmstNnjyZwYMHdzi2jtCNiabdpbZu3cqiRYv46quvEBG++uorFi1aZHODEhcXR2hoKKNGjbJI79evH2VlZbz77rskJSWxfft2NmzY0KT87NmzKSwstEirra0lJCSEYcOGMWPGDPNSu401ly83N5ehQ4fy4x//mAcffJCFCxdSWloKwM9+9jN+85vfWL0C5MqVK1m1ahVbt24lLS2N2NhYq8o1d04FBQUW18jDw8NiZcfGLly4QFpaGuHh4bi7u/Paa6/h6enJiBEjuO+++5g5c6ZVsXQV3Zho2l3qjTfeoKyszCKtrKyMN954w6Z6MzMzCQ4ObpKenp7O+PHjefXVV0lNTWXDhg28+OKLTfIlJiZa/JUN4ODgwOnTp8nPz+f48eNkZmY2e+zm8tXU1HDq1Cl++tOfkpaWxoABA1i9ejV//etfGTZsGA899JDV5zZ58mREhDVr1hAXF4eDg4NV5Zo7p/a4ffs2kZGRxMbG4urqyvXr10lISCA3N5fCwkJKS0vZsmVLh+vvDLox0bS71MWLF9uVbi1XV1eqqqos0lJSUigtLWXKlCk21T1w4EAiIiLM669bk8/DwwMPDw/z3cwTTzzBqVOnOHLkCLt27WL06NFERUXx6aef8vTTT7dab0ZGBkVFRTg5OeHi4mJOb/yYbNmyZif3sODu7k5e3jcrc+Tn5+Pu7t4kX3V1NZGRkURHRzNv3jwAkpKSGDNmDEOHDqVv377MmzePo0ePtnnMrqQbE027S7W07o+t6wHNmTOH+Ph4iouLAcjJyWHhwoVs3rzZ6sdJDRUXF1NSUgJAeXk5+/fvZ/z48VbnGz58OKNGjeLMmTMAJCcn4+/vz9tvv01+fj4XLlwgLi6OadOmWfx1P336dIvHTkVFRURHR5OQkICzs7O5QSsvLyc7O5sVK1YQFRXF5cuXqaysbPO8JkyYwNmzZ8nNzaWqqoq4uDjmzp1rkUdEiImJwc/Pj8WLF5vTPT09OXbsGGVlZYgIycnJ+Pn5WXdBu0pHe+578qZHc2l3q/aM5tqyZYv0799fAPPWv39/2bJli81xbNq0SYKCgsTX11e8vb3l6NGjVpedNWuWFBQUmL+np6dLSEiIBAYGisFgkJUrVzabv7V8aWlp8tBDD0lgYKA8+uijcu3aNYs6/va3v1mM5qqtrRVPT0/zaKnS0lKZNGmS7Nu3T0SMo6UmTZokIiJHjhyRtWvXiojIokWLZMuWLRIfH9/qOdXbvXu3+Pj4iJeXl7z11ltN8h8+fFgACQwMlODgYAkODpbdu3eLiMiyZcvE19dXDAaDPP3001JRUWEuHxUVJcOHDxdHR0dxd3eXDz74oMmxO3s0l91/8XfFphsT7W7V3qHBW7ZskQceeECUUvLAAw90SkPS0KVLl8Tf31/S09M7td6ulpGRIT//+c+tyrt27VpJTk4WEZGnn35ali9fLl9++WVXhtcpOrsxUcbyd5awsDA5efKkvcPQtG6XnZ1t/8cdd5kXXniBIUOGUFJSQlRUFJs3b+ZPf/qTvcNqU3M/K0qpVBEJ60h93TYFvaZp2p1o48aN5s/r16/H29vbjtHYT7d1wCulHlFKnVFKnVNKvd5KvkillCilwhqk/cJU7oxS6nvdE7GmaVr7vPTSS/zyl7+0dxh20S13JqZld9cDM4B84IRSapeIfN4onwvwz8A/GqT5Y1zm1wCMBJKUUuNEpLY7Ytc0TdPa1l13JhOBcyLypYhUAXHAo83k+xXwa6CiQdqjQJyIVIpILnDOVJ+maZrWQ3RXn4k7kNfgez5gMR+CUioUGCUiu5VS/9qo7LFGZZu82aOUWgQsMn2tVEo1/4ps73A/8LW9g7CBjt9O9u/fH1hVVYWDg0ONvWPpqNraWsfeGn9viv3SpUuO/v7+GY2SfTtaX4/ogFdK9QHWAM91tA4ReR9431TfyY6OSOgJdPz21ZvjT09P/7JPnz5uAQEB2faOpaMyMzP9emv8vSX2uro6VVtbO6jxz7lSqsPDYLurMSkAGs765mFKq+cCBAAHTLOBDgd2KaXmWlFW07RvZFZXV4+sq6tTffr0ufPG/Ws2q6urU8XFxfcBnfr0prsakxOAj1JqDMaGIAp4qn6niNzA+GgBAKXUAeA1ETmplCoH/qyUWoOxA94HON5NcWtar1JTU7Pw1KlTOQ4ODoPopdMlFRcXO9bW1t7fds6ep5fEXgdk1tTUWDfvvpW6pTERkRql1MvAXsAB2CQiWUqpNzG+cbmrlbJZSql44HOgBnjJipFc73dW7Hai47evXhv/Qw89dCUsLOzfTI99eyWl1KLeGn9vjt2kw7HfkW/Aa5qmad2rV94Ga5qmaT2Lbkw0TdM0m/XqxqStKVqUUi8qpTKUUqeVUn83vU3fY9gyxUxPYMX1f04pVWy6/qeVUp3a4Wcra66/UuqHSqnPlVJZSqk/d3eMLbHi2q9tcN1zlFIldgizRVbE76mU+ptSKk0p9ZlSarY94myJFfE/oJRKNsV+QCnlYY84m6OU2qSUutLSu3jK6L9M5/aZ6R3AtnV0umF7bxg78s8DXoATkA74N8rj2uDzXGCPveNuT/ymfC7AIYwvbobZO+52Xv/ngPfsHasN8fsAacAg0/dh9o67PT87DfK/gnHQi91jb8e1fx/4qemzP3DB3nG3M/7/BZ41fZ4GfGjvuBvENhkIBTJb2D8b+ARQwCTgH9bU25vvTNqcokVEbjb4OgDjAkA9hS1TzPQE1sbfU1kT/wvAehG5DiAiV7o5xpa099ovALZ1S2TWsSZ+AVxNn+8DCrsxvrZYE78/8Knp89+a2W83InIIuNZKlkeB/xGjY8BApdSIturtzY1Jc1O0NDfNyktKqfPAb4BXuyk2a7QZf8MpZrozMCtZdf2BSNOt8nal1Khm9tuLNfGPA8YppY4opY4ppR7ptuhaZ+21Ryn1ADCGb36x9QTWxL8CeFoplQ8kYry76imsiT8dmGf6/DjgopQa0g2xdQarf74a6s2NiVVEZL2IjAWWAEvtHY+1Gkwx8y/2jsUG/weMFpEgYD/Q81cMsuSI8VHXVIx/3W9USg20Z0AdEAVsl943y/YC4I8i4oHxscuHpv8neovXgClKqTRgCsaXtXvbf4N26U3/cRpr7zQrccBjXRlQO7VnipkLGJ9d7upBnfBtXn8RuSoilaavHwAPdVNs1rDm5ycf2CUi1WKcsToHY+Nib+352Y+iZz3iAuvijwHiAUQkBbiHBrNk2Jk1P/uFIjJPRB4E3jCllXRbhLbp2BRW9u4MsqETyRH4EuMtfH0nmKFRHp8Gn3+ADesb2yP+RvkP0LM64K25/iMafH4cOGbvuNsZ/yPAn0yf78d46z+kN8RuyjceuIDp5eSesll57T8BnjN99sPYZ9IjzsPK+O8H+pg+rwLetHfcjeIbTcsd8HOw7IA/blWd9j4pGy/IbIx/LZ4H3jClvQnMNX1eB2QBpzF2grX4y7onxt8ob49qTKy8/m+brn+66fqPt3fM7YxfYXzU+DmQAUTZO+b2/Oxg7HdYbe9YO3jt/YEjpp+d08BMe8fczvifAM6a8nwA9LN3zA1i3wYUAdUY775jgBeBF037FcbFDM+bfu6t+r2jp1PRNE3TbNab+0w0TdO0HkI3JpqmaZrNdGOiaZqm2Uw3JpqmaZrNdGOiaZqm2Uw3JpqmaZrNdGOiaZqm2Uw3JprWTkqpTxusFVKhlPqhvWPSNHvTLy1qWgcppX4KRAALpPdNpKhpncrR3gFoWm+klPoRMAuItLUhUUop0X/Vab2cbkw0rZ2UUvOBaOBREak2pa0ABgFXgWLgCxH5m1JqE/DPwC+A/hgn/3tVKTUc+AuwE/BSSlUAV0XkTaVUPyAWuA58B/ihqQ5z+e46V02zlu4z0bR2UEp9H/gnYJ6IVJjS3DH+YVaC8Zd/BuCvlJoMnAB+BNxr2n+fqaoQjBPubcHY+NSXBfgpxrU8/h3jinhPNFNe03oUfWeiae3zJ4y/4I8opQDexdgI/DMwFOM6EJkYF3eaACwENgAvyTdru4CxMUnAuCxzw7L1+zYopZyBS8CDzZTXtB5FNyaa1g4i0mTpVdPqi68BQ4A0ESkx3ZUsF5EapVQC8EelVB7wqYjswbjI1hmMU/Sby5qq3Av8Dqg0pRU1U17TehQ9mkvTehhT534gxnUlltY/TtO0nkw3JpqmaZrNdAe8pmmaZjPdmGiapmk2042JpmmaZjPdmGiapmk2042JpmmaZjPdmGiapmk2042JpmmaZjPdmGiapmk2+/85cyp9xZA9HgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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ZtGgR3/3udwE4//zzOeaYY/jSl77E5MmT6dixY+rzmQ9lngb7dGnu0/bu4iHorTEtW7aMfv36pa6/bs4c/vLYY2xft459DjqIQy+8MK/+kurWrFnDyJEjeeSRRxg4cGCj7bepLV68mKlTp3LvvffWW/e+++5j4MCBjBw5kssuu4wjjzySyy+/vFFH420qNf28SJofEaW1NKlTwTrgJY0BfkRmcqwHI+KuatsPA34GdEzq3BiZOVCQ9B0y0/ruAK6NiOcKFbfZp9VBJ5zQqMmjuoMPPpglS5Y02f6byoABA1IlEoAlS5bwwQcfMH36dMaPH89DDz3UIhJJUyhIMkmm3Z0MjALKgXmSZkRmQqxdbgKmR8R/SjqGzERavZLP44D+wKHAC5KOiogdhYjdzKw2DzzwQNXnyZMn06dPnyJGU1yFujIZBqyIiHcAJE0Dzgayk0kA+yefDwB23WQ8G5gWEduAdyWtSPY3txCBm5mlcdVVVxU7hKIqVAd8dyD7zZnypCzbROBSSeVkrkp2vV6api2SrpRUJqls15MkZmZWGM3paa6LgZ9GRA/gTODnklLHFxFTIqI0Ikq7ZM2zbmZmTa9Qt7lWAdnjK/RIyrKNB8YARMRcSe2AzinbmplZERXqymQe0FdSb0klZDrUZ1Sr8z5wKoCkfkA7YG1Sb5yktpJ6A32B1woUt5mZpVCQK5OIqJR0NfAcmcd+p0bEEkm3kRnyeAbwbeABSf9MpjP+imRI5CWSppPprK8ErvKTXGZmzUvB3jNJ3hl5plrZzVmflwI1PvQeEXcAd9S0zczMiq85dcCbmVkL5WRiZmZ583wmBbJuzhw2rVhBVFaiNm0o8ePLthd68sknefrpp/nkk08YP348o0ePLnZI1kh8ZVIAu2a1i8pKAKKykm0ffNAo06SaNUdPPPEEw4cPZ9CgQZSWlvLcc5nh9M455xweeOAB7r//fh599NHU+9uxYwdDhgypGtm3Jr169eLYY49l8ODBu43c++yzz/LZz36WPn36cNddmSEBt27dyrBhwxg0aBD9+/fnlltuaeCRNlxNcVVX2zEBrF+/ngsuuICjjz6afv36MXfu3wcFSXO+GpuvTAqgplntiMh7Vjuz5uiXv/wlkyZN4qmnnqJbt2689dZbfOELX6CsrIwePXoA8P3vfz+n4Ud+9KMf0a9fPz755JM667300kt07ty5an3Hjh1cddVVzJw5kx49enD88cdz1lln0a9fP2bNmkX79u3Zvn07J554ImeccQaf+9znGnbQOaotrl1zktR1TLtcd911jBkzhscff5yKigo2b95ctS3t+WpMvjIpgKaa1c4sHxWbl/HJmilsWH0Pn6yZQsXmZXnvc9OmTdx4441Mnz6dbt26AdC3b19OOeUUXnzxRSKCG264gTPOOIPjjjsu1T7Ly8t5+umn95jRMI3XXnuNPn36cMQRR1BSUsK4ceN46qmnkET79u0B2L59O9u3b0dSnfsaMWIEM2fOBOCmm27immuuqbN+Q+JKa8OGDbz88suMHz8egJKSkqp5S/I5X/lwMimApprVzqyhKjYvY8uG54mdmZn8YudGtmx4Pu+EMm3aNI477jh69uy5W3nbtm3ZvHkzkyZN4oUXXuDxxx/n/vvv36N9TRNJXX/99dx99931ztQoidGjRzN06FCmTJkCwKpVq3aLpUePHlUzKO7YsYPBgwfTtWtXRo0aVTUFbm1uvfVW7rjjDn7xi1+wcOFC7rvvvjrr13VMdcVV3zEBvPvuu3Tp0oWvfe1rDBkyhAkTJrBp0yYg/flqbL7NVQCHXnhhps+k2q2ug8eOLVJEtrfbuvEVMu8AZ6tk68ZXKNk3/QRb1S1evJhBgwbtUf76669zxRVXMGLECK699tpa2z/zzG6vovHb3/6Wrl27MnToUGbPnl3nd//+97+ne/fufPjhh4waNWq3aXhr0rp1axYtWsT69es599xzWbx4cdW0uDU56aSTiAjuvfdeZs+eTevWrevcf23HlIuajumkk06isrKSBQsWMGnSJIYPH851113HXXfdxfDhw1Ofr8bmK5MCOOiEEzjs619HbZLcnVxOby0vL2JUtjfbdUWStjyt/fffn4pqfzTNnTuXTZs2cfLJJ+e8vzlz5jBjxgx69erFuHHjmDVrFpdeemmNdbt3zwwm3rVrV84991xee+01unfvzsqVfx90vLy8vKreLh07dmTEiBFV87rX5o033mD16tWUlJTQoUOHqvLqt5Nuvvnm6k1rjLW+uGo7JshcyfTo0aPqauqCCy5gwYIFOZ2vxuZkUiAHnXACsSMZBSaZKvmvs2ax4LLLWPj1rxcxMtsbqVWHnMrTGjt2LNOnT2fXNBDLly9nwoQJPPTQQw267XLnnXdSXl7Oe++9x7Rp0xg5ciQPP/zwHvU2bdrExo0bqz4///zzDBgwgOOPP5633nqLd999l4qKCqZNm8ZZZ53F2rVrWb9+PQBbtmxh5syZu13JnHrqqbvddlq9ejWXXHIJTz31FO3bt69KPFu2bGHZsmVMnDiRcePGsWbNGrZt21bvcdUWV5pjAujWrRs9e/bkzTffBODFF1/kmGOOSX2+moKTSQHte+SRtOnQoerKRCUldPr85xmQcopQs8bSrsMX2PMud5ukvOGGDRvGTTfdxGmnncbRRx/N2LFjefDBBznxxBNTta+pfyFN/TVr1nDiiScyaNAghg0bxtixYxkzZgxt2rThxz/+Maeffjr9+vXjoosuon///qxevZoRI0YwcOBAjj/+eEaNGlX1GO3OnTtZsWIFBx54IACbN2/mvPPO45577qFfv35873vf49ZbbwVg4cKFXHjhhUycOJEDDjiAF154YY9HeGs6ptriSnNMu0yaNIlLLrmEgQMHsmjRIr773e+mPm9NQZH8lfxpUlpaGmVlZcUOYw/L77iDrR98QOX69VUJpfOIERz2ta8VNzD7VFi2bBn9+qXv76jYvIytG18hdm5ErTrQrsMX8uovqW7NmjWMHDmSRx55hIEDBzbafpva4sWLmTp1aqp54O+77z4GDhzIyJEjueyyyzjyyCO5/PLLW8Q88DX9vEiaHxGltTSpkzvgCywqK2nTsSP7dOzIfkceyfYNG4odku2lSvbt16jJo7qDDz6YJUuWNNn+m8qAAQNSJRKAJUuW8MEHHzB9+nTGjx/PQw891CISSVNwMimwzyQvbQEcdsUVxQvEzPL2wAMPVH2ePHkyffr0KWI0xeVkYmbWCHJ5o//TqGAd8JLGSHpT0gpJN9aw/YeSFiXLcknrs7btyNpWfYZGMzMrsoJcmUhqDUwGRgHlwDxJM5IJsQCIiH/Oqn8NMCRrF1siYnAhYjUzs9wV6spkGLAiIt6JiApgGnB2HfUvBh4pSGRmZpa3QiWT7sDKrPXypGwPkg4HegOzsorbSSqT9Kqkc2ppd2VSp2zXC1Nme5tP46P+1via4uekOb60OA54PCJ2ZJUdnjz7/BXgPklHVm8UEVMiojQiSrt44inbC7Vr145169Y5oVidIoJ169bRrl27Rt1voZ7mWgVkDyPaIymryThgt8ciImJV8t93JM0m05/yduOHadZy9ejRg/LycnxlbvVp165d1dwyjaVQyWQe0FdSbzJJZByZq4zdSDoa6ATMzSrrBGyOiG2SOgMnAHcXJGqzFmSfffbZa1+Ys+IrSDKJiEpJVwPPAa2BqRGxRNJtQFlE7HrcdxwwLXa/Tu8H/JeknWRuy92V/RSYmZkVX8FeWoyIZ4BnqpXdXG19Yg3t/gAc26TBmZlZXppjB7yZmbUwTiZmZpY3JxMzM8ubk4mZmeXNycTMzPLmZGJmZnlzMjEzs7w5mZiZWd6cTMzMLG9OJmZmljcnEzMzy5uTiZmZ5c3JxMzM8uZkYmZmeXMyMTOzvBUsmUgaI+lNSSsk3VjD9h9KWpQsyyWtz9p2uaS3kuXyQsVsZmbpFGRyLEmtgcnAKKAcmCdpRvaMiRHxz1n1ryEzzzuSDgRuAUqBAOYnbT8uROxmZla/Ql2ZDANWRMQ7EVEBTAPOrqP+xcAjyefTgZkR8VGSQGYCY5o0WjMzy0mhpu3tDqzMWi8HhtdUUdLhQG9gVh1tu9fQ7krgSoDDDjss/4gb0V9+9Ss++PWvayw/9LzzihCRmVnjKtgc8DkYBzweETtyaRQRU4ApAKWlpdEUgTXUoeedt1vSWH7HHVXlZmafBoW6zbUK6Jm13iMpq8k4/n6LK9e2ZmZWBIVKJvOAvpJ6SyohkzBmVK8k6WigEzA3q/g5YLSkTpI6AaOTMjMzayYKcpsrIiolXU0mCbQGpkbEEkm3AWURsSuxjAOmRURktf1I0u1kEhLAbRHxUSHibs62bHgJgM8cMKLIkZiZgbJ+b39qlJaWRllZWbHD2ENtHfHdzj03df/J1o1/YNvf5u5R3rb9P9Cuw+fzjtHM9l6S5kdEaYPaOpm0TH9b9ygA7Q/6cpEjMbNPi3ySiYdTMTOzvDmZmJlZ3pxMzMwsb04mZmaWNyeTFm7LhpeqHhM2MyuW5jiciqWws3IDsXMjOyrKAajYvADwI8JmVhxOJi1UqzYHAAdUrfsRYTMrJt/mMjOzvDmZmJlZ3pxMzMwsb04mZmaWNycTMzPLm5OJmZnlLedkImk/Sa2bIhgzM2uZ6k0mklpJ+oqkpyV9CPwZWC1pqaR/l9QnzRdJGiPpTUkrJN1YS52Lkv0ukfTLrPIdkhYlyx4zNJqZWXGleWnxJeAF4DvA4ojYCSDpQGAE8ANJv46Ih2vbQXIlMxkYBZQD8yTNiIilWXX6Jt9xQkR8LKlr1i62RMTg3A7NzMwKJU0yOS0itlcvTKbOfQJ4QtI+9exjGLAiIt4BkDQNOBtYmlXnG8DkiPg42f+HKWIzM7NmoN7bXLsSiaQDJd0u6b8kXSepU/U6degOrMxaL0/Ksh0FHCVpjqRXJY3J2tZOUllSfk59MZuZWWHl0gE/DdgI/AbYF/i9pGGNGEsboC9wCnAx8ICkjsm2w5OpJL8C3CfpyOqNJV2ZJJyytWvXNmJYZmZWn1ySSZeIuDsifhsRdwJfAv4jZdtVQM+s9R5JWbZyYEZEbI+Id4HlZJILEbEq+e87wGxgSPUviIgpEVEaEaVdunTJ4bBanorNy9hRsZodFeXsqFhN7Nhc7JDMbC+XSzL5SNKxu1aSX+z7pmw7D+grqbekEmAcUP2prCfJXJUgqTOZ217vSOokqW1W+Qns3teyV6nYvIwtG54HdiQlO9i54yMqNi8rZlhmtpfLZQj6q4DHJb0CvAH0B95O0zAiKiVdDTwHtAamRsQSSbcBZRExI9k2WtJSMr8p/zUi1kn6PPBfknaSSX53ZT8FtrfZuvEVoLLG8pJ9+xU+IDMzQBGRvnLmquIc4Bgyt6l+HhFbmya0histLY2ysrJih9EkNqy+p9ZtBxzy7QJGYmafNpLmJ/3TOUt9ZSJpJHAJsB5YDPwJSJ+JrFGoVQdi58Yay83MiiWXPpOpZJ7kehU4ArgZWNIUQVnt2nX4AjX9DZApNzMrjlz6TP4nIp5MPj/WBLFYCrv6RbZseI5M11IroBVt2h5WzLDMbC+XZmyu/5Z0PfCqpG81fUhWn5J9+9G65BBal/SAVp8BKtn2t7nFDsvM9mJprkx+CgwCDibztNV1wOvJ8qeI8FVKEeyoKN9tvWLz61Rsfh1ozQGHXF+UmMxs71VvMomIWcCsXeuS2gD9yCSY4/Etr6Jotc8h7KxcD7ElKWnDPu360G7/U4oYlZntrVL1mUhqBdwYEf87IirJvGfyRpNGZnWSWoNaZT1PV4lataVV6/2KGZaZ7aVSPc2VDDv/pSaOxXIVO6DVfrRq05WSfQexc8emYkdkZnupXJ7mel3SLcDtu+Y0seJqvU/nqs+fOeC0IkZiZnu7XJLJgcDJwD9K+iOZlxbdAW9mZvUnE0mKjIuS9bZkxuU6lsykV4/tqtO0oZqZWXOVps/kJUnXSDoMICK2RcQC4BHgWUk/Ay5vyiDNzKx5S3ObawzwdeARSb3JjM3Vjszov88D90XEwiaL0MzMmr0075lsBX4C/CSZ670zsCUi1jdxbGZm1kKkGU7l0l2fk1kQVzuRmJlZtjR9JpdJ+pGk1k0ejZmZtUhpkskZwBZglqQGT64uaYykNyWtkHRjLXUukrRU0hJJv8wqv1zSW8nizn4zs2YmTZ/JTuBGSecBr0i6F1gELI6IzWm+JLmqmQyMAsqBeZJmZE+/K6kv8B3ghIj4WFLXpPxA4BaglMzgIfOTth/ncJxmZtaEUg2nIumLwASgAjgO+D/ASkkrUn7PMGBFRLwTERXANODsanW+AUzelSQi4sOk/HRgZkR8lGybSeYJMzMzaybSvLT4LrAU+GFEzKy2rUfK7+kOrMxaLweGV6tzVLLPOWQeO54YEc/W0rZ7DXFeCVwJcNhhnijKzKyQ0rxnckZE/LmmDRFRXlN5HrH0BU4BegAvSzo2beOImAJMASgtLfXb+GZmBZTmNleppLWSynd1fkv6nKTvS5qf8ntWAT2z1nskZdnKgRnJ48fvAsvJJJc0bc3MrIjSJJNbgDOBIUBvSTPJTIhVAlyf8nvmAX0l9ZZUAowDZlSr8ySZqxIkdSZz2+sd4DkyMzx2ktQJGJ2U7ZW2bvwDG1bfw46K8t2WrRv/UOzQzGwvluY2198iYh6ApFuBNcBRuby4GBGVkq4mkwRaA1MjYomk24CyiJjB35PGUmAH8K8RsS753tvJJCSA2yLio7Tf/WnTrsPnadfh81Xrf1v3aFW5mVmxpEkm3ZLO7TeTpbwhb8BHxDPAM9XKbs76HMC3kqV626nA1Fy/08zMCiNNMrmFzHDzlyT/7SDpBWAhsDAifllXYzMz+/RL89LilOz15HHgY4GBZN6OdzIxM9vL5TLTIlD1OHA58LvGD8fMzFqiVG/Am5mZ1SXnKxNrHrZu/APb/ja3an3D6nsAaNv+H/xkl5kVnJNJC1X9EWEzs2LybS4zM8ubk4mZmeXNycTMzPLmZGJmZnlzMjEzs7w5mZiZWd6cTMzMLG9OJmZmljcnEzMzy1vBkomkMZLelLRC0o01bL8imR54UbJMyNq2I6u8+gyNZmZWZAUZTkVSa2AyMIrMiMPzJM2IiKXVqj4aEVfXsIstETG4icM0M7MGKtSVyTBgRUS8ExEVwDTg7AJ9t5mZNbFCJZPuwMqs9fKkrLrzJf1J0uOSemaVt5NUJulVSefU9AWSrkzqlK1du7bxIjczs3o1pw743wC9ImIgMBP4Wda2wyOiFPgKcJ+kI6s3jogpEVEaEaVdunQpTMRmZgYULpmsArKvNHokZVUiYl1EbEtWHwSGZm1blfz3HWA2MKQpgzUzs9wUKpnMA/pK6i2pBBgH7PZUlqRDslbPApYl5Z0ktU0+dwZOAKp33JuZWREV5GmuiKiUdDXwHNAamBoRSyTdBpRFxAzgWklnAZXAR8AVSfN+wH9J2kkm+d1Vw1NgZmZWRIqIYsfQ6EpLS6OsrKzYYZiZtSiS5if90zlrTh3wZmbWQjmZmJlZ3pxMzMwsb04mZmaWNycTMzPLm5OJmZnlzcnEzMzy5mRiZmZ5czIxM7O8OZmYmVnenEzMzCxvTiZmZpY3JxMzM8ubk4mZmeXNycTMzPJWsGQiaYykNyWtkHRjDduvkLRW0qJkmZC17XJJbyXL5YWK2czM0inITIuSWgOTgVFAOTBP0owaZkx8NCKurtb2QOAWoBQIYH7S9uMChG5mZikU6spkGLAiIt6JiApgGnB2yranAzMj4qMkgcwExjRRnGZm1gCFSibdgZVZ6+VJWXXnS/qTpMcl9cylraQrJZVJKlu7dm1jxW1mZik0pw743wC9ImIgmauPn+XSOCKmRERpRJR26dKlSQI0M7OaFSqZrAJ6Zq33SMqqRMS6iNiWrD4IDE3b1szMiqtQyWQe0FdSb0klwDhgRnYFSYdkrZ4FLEs+PweMltRJUidgdFJmZmbNREGe5oqISklXk0kCrYGpEbFE0m1AWUTMAK6VdBZQCXwEXJG0/UjS7WQSEsBtEfFRIeI2M7N0FBHFjqHRlZaWRllZWbHDMDNrUSTNj4jShrRtTh3wZmbWQjmZmJlZ3pxMzMwsb04mZmaWNycTMzPLm5OJmZnlzcnEzMzy5mRiZmZ5czIxM7O8OZmYmVnenEzMzCxvTiZmZpY3JxMzM8ubk4mZmeXNycTMzPJWsGQiaYykNyWtkHRjHfXOlxSSSpP1XpK2SFqULPcXKmYzM0unIDMtSmoNTAZGAeXAPEkzImJptXodgOuAP1bbxdsRMbgQsZqZWe4KdWUyDFgREe9ERAUwDTi7hnq3Az8AthYoLjMzawSFSibdgZVZ6+VJWRVJxwE9I+LpGtr3lrRQ0v+T9IWavkDSlZLKJJWtXbu20QI3M7P6NYsOeEmtgHuBb9eweTVwWEQMAb4F/FLS/tUrRcSUiCiNiNIuXbo0bcBmZrabQiWTVUDPrPUeSdkuHYABwGxJ7wGfA2ZIKo2IbRGxDiAi5gNvA0cVJGozM0ulUMlkHtBXUm9JJcA4YMaujRGxISI6R0SviOgFvAqcFRFlkrokHfhIOgLoC7xToLjNzCyFgjzNFRGVkq4GngNaA1MjYomk24CyiJhRR/OTgNskbQd2At+MiI+aPmozM0tLEVHsGBpdaWlplJWVFTsMM7MWRdL8iChtSNtm0QFvZmYtm5OJmZnlzcnEzMzy5mRiZmZ5czIxM7O8OZmYmVnenEzMzCxvTiZmZpY3JxMzM8ubk4mZmeXNycTMzPLmZGJmZnlzMjEzs7w5mZiZWd6cTMzMLG8FSyaSxkh6U9IKSTfWUe98SSGpNKvsO0m7NyWdXpiIzcwsrYLMtJhMuzsZGAWUA/MkzYiIpdXqdQCuA/6YVXYMmWl++wOHAi9IOioidhQidjMzq1+hrkyGASsi4p2IqACmAWfXUO924AfA1qyys4FpEbEtIt4FViT7MzOzZqIgVyZAd2Bl1no5MDy7gqTjgJ4R8bSkf63W9tVqbbtX/wJJVwJXJqvbJC1ujMCLpDPw12IHkQfHX1yOv3hacuwAn21ow0IlkzpJagXcC1zR0H1ExBRgSrK/sobOY9wcOP7icvzF1ZLjb8mxQyb+hrYtVDJZBfTMWu+RlO3SARgAzJYE0A2YIemsFG3NzKzICtVnMg/oK6m3pBIyHeozdm2MiA0R0TkiekVELzK3tc6KiLKk3jhJbSX1BvoCrxUobjMzS6EgVyYRUSnpauA5oDUwNSKWSLoNKIuIGXW0XSJpOrAUqASuSvEk15TGir1IHH9xOf7iasnxt+TYIY/4FRGNGYiZme2F/Aa8mZnlzcnEzMzy1qKTSX1DtEj6pqQ3JC2S9PvkbfpmI58hZpqDFOf/Cklrk/O/SNKEYsRZmzTnX9JFkpZKWiLpl4WOsTYpzv0Ps877cknrixBmrVLEf5iklyQtlPQnSWcWI87apIj/cEkvJrHPltSjGHHWRNJUSR/W9i6eMv4jObY/Je8A1i8iWuRCpiP/beAIoAR4HTimWp39sz6fBTxb7LhziT+p1wF4mcwTbqXFjjvH838F8ONix5pH/H2BhUCnZL1rsePO5Wcnq/41ZB56KXrsOZz7KcA/Jp+PAd4rdtw5xv8YcHnyeSTw82LHnRXbScBxwOJatp8J/A4Q8Dngj2n225KvTOodoiUiPsla3Q9oTk8b5DPETHOQNv7mKk383wAmR8THABHxYYFjrE2u5/5i4JGCRJZOmvgD2D/5fADwlwLGV5808R8DzEo+v1TD9qKJiJeBj+qocjbw35HxKtBR0iH17bclJ5OahmipaZiVqyS9DdwNXFug2NKoN/7sIWYKGVhKqc4/cH5yqfy4pJ41bC+WNPEfBRwlaY6kVyWNKVh0dUt77pF0ONCbv/9iaw7SxD8RuFRSOfAMmaur5iJN/K8D5yWfzwU6SDqoALE1htQ/X9lacjJJJSImR8SRwA3ATcWOJ62sIWa+XexY8vAboFdEDARmAj8rcjy5akPmVtcpZP66f0BSx2IG1ADjgMej5Y2yfTHw04joQea2y8+T/ydain8BTpa0EDiZzKgdLe3fICct6R+nulyHWZkGnNOUAeUolyFm3iNz73JGM+qEr/f8R8S6iNiWrD4IDC1QbGmk+fkpB2ZExPbIjFi9nExyKbZcfvbH0bxucUG6+McD0wEiYi7Qjswgis1Bmp/9v0TEeRExBPi3pGx9wSLMT8OGsCp2Z1AenUhtgHfIXMLv6gTrX61O36zPXyLztn3RY08bf7X6s2leHfBpzv8hWZ/PBV4tdtw5xj8G+FnyuTOZS/+DWkLsSb2jgfdIXk5uLkvKc/874Irkcz8yfSbN4jhSxt8ZaJV8vgO4rdhxV4uvF7V3wI9l9w7411Lts9gHlecJOZPMX4tvA/+WlN1GZlwvgB8BS4BFZDrBav1l3Rzjr1a3WSWTlOf/zuT8v56c/6OLHXOO8YvMrcalwBvAuGLHnMvPDpl+h7uKHWsDz/0xwJzkZ2cRMLrYMecY/wXAW0mdB4G2xY45K/ZHgNXAdjJX3+OBbwLfTLaLzGSGbyc/96l+73g4FTMzy1tL7jMxM7NmwsnEzMzy5mRiZmZ5czIxM7O8OZmYmVnenEzMzCxvTiZmZpY3JxOzHEmalTVXyFZJFxU7JrNi80uLZg0k6R+BEcDF0fIGUjRrVG2KHYBZSyTpq8AZwPn5JhJJCv9VZy2ck4lZjiRdCFwCnB0R25OyiUAnYB2wFvhzRLwkaSpwHfAdYF8yg/9dK6kb8GvgSeAISVuBdRFxm6S2wH3Ax8AJwEXJPqraF+pYzdJyn4lZDiR9Efgn4LyI2JqUdSfzh9l6Mr/83wCOkXQSMA/4KvCZZPsBya4Gkxlw72EyyWdXW4B/JDOXx3fJzIh3QQ3tzZoVX5mY5eZnZH7Bz5EEMIlMErgO6EJmHojFZCZ3Oh6YANwPXBV/n9sFMsnkKTLTMme33bXtfkntgQ+AITW0N2tWnEzMchARe0y9msy++C/AQcDCiFifXJXcEhGVkp4CfippJTArIp4lM8nWm2SG6K9qm+zyOeAnwLakbHUN7c2aFT/NZdbMJJ37x5KZV+KmXbfTzJozJxMzM8ubO+DNzCxvTiZmZpY3JxMzM8ubk4mZmeXNycTMzPLmZGJmZnlzMjEzs7w5mZiZWd7+P47fg3NePGt4AAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot()\n", + " xbj_one_corr = []\n", + " xbj_one_err_corr = []\n", + " CSV_one = []\n", + " CSV_one_err = []\n", + " #RY_err = []\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " #print(zs)\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs = []\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_error = row['error']\n", + " RYs.append(RYi)\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYs_rho.append(RYi_rho)\n", + " RYs_error.append(RYi_error)\n", + " #print('RY_error ',RY_error)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " #print('RYs ',RYs)\n", + " #print('RYs err ',RYs_error)\n", + " RY = Get_weighted_average(RYs,RYs_error)\n", + " RY_err = Get_weighted_sigma(RYs,RYs_error)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " ax.plot([z_corr+0.005,z_corr+0.005],[RY_rho+RY_err,RY_rho-RY_err],marker = \"_\",color = colors_all[i_col])\n", + " plt.plot(z_corr+0.005,RY_rho,\"*\",color = colors_all[i_col])\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$RY(rho)$')\n", + " plt.xlim(0.3,1)\n", + " plt.ylim(0.4,0.85)\n", + " plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + " \n", + " xbj_one = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " xbj_ones_plot.append(xbj_one)\n", + " xbj_one_err = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3ba81fd0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-7-5d492cf5185f>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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S1WrghIgctde3tzMCNwzDcMfjjz9OeXm5v8Pwm87qTMKBYof3JUBykzwZwHYRmQdcDdzqUPbjJmWbjaETkbnAXID+/fuze/dub8TtF5cvXzbx+1F3jT8sLIzy8nLq6+u79Zdad46/O8VeVVXl1d/zrrRs7wzgNVVdLiI3A6+LSKy7hVV1DbAGYNSoUZqSkuKbKDvB7t27MfH7T3eN/9ChQ4SGhnaLpWNd6c7xd6fYQ0JCGDt2rNfq66zO5BTgOL/CEHuao9nAHQCquldEQoDr3CxrGIZh+FFnjeb6BBgpIsNFJBjbDfXNTfJ8AUwBEJFoIAQ4Z8+XLiI9RWQ4MBLY10lxG4ZhGG7olDMTVa0TkSeAbUAg8KqqForIc9jGNW8G/hNYKyJPY7sZ/7B93HOhiGRju1lfBzxuRnIZhmF0LZ12z0RVt2Ab7uuYttjh9WfAd1spuwzw7NFcwzAMw2fMdCqGYRiGx0xnYhiGYXjMdCaGYXSaTZs28eijj/Lggw+yfft2f4djeJHpTAzD8Lq3336b5ORk4uPjGTduHNu2bQPg3nvvZe3atWRmZvLmm2+2WU9VVRVJSUnEx8cTExPDkiVLWsy3cuVKYmJiiI2NZcaMGVRVVQFQXFxMamoqo0ePJiYmhhdffLGxzNatWxk1ahQjRozghRde8EKr26et47tqe2vtbWufT3V0hsiuvJlZg/3LxO8f7Z012Fc2btyoEyZM0NLSUlVVLSoq0oEDB2pxcXFjnvnz52teXl6L5R3jt1qtWl5erqqqNTU1mpSUpHv37nXKX1JSohEREWqxWFRVdfr06bp+/XpVVT19+nTjcS5duqQjR47UwsJCraur08jISD127JhWV1drXFycFhYWetx2dz97d47fWttdtdfVvqa67azBhmF0Lef37KHgqaf49KGHKHjqKc7v2eNxnRUVFSxcuJDs7GwGDRoEwMiRI0lJSSE3NxdVZcGCBdx5550kJia2WZ+I0Lt3bwBqa2upra1FRJrlq6uro7Kykrq6OiwWS+MyuYMHD248TmhoKNHR0Zw6dYp9+/YxYsQIIiMjCQ4OJj09nZycHJexpKamsmPHDgAWLVrEvHnz3P9gmnDn+K7a3lp729rnS6YzMYwr0Pk9e/ji1VepPX8egNrz5/ni1Vc97lCysrJITExk6NChTuk9e/bEYrHw0ksvsXPnTt566y0yMzOblW+6OBbY5rtKSEhgwIABTJ06leRk52n9wsPDeeaZZxg2bBiDBw8mLCyM2267rVndJ0+eJD8/n+TkZE6dOuUU45AhQ5xWVmzJ0qVLWbZsGRs3biQ/P59Vq1a19XE0tqnp4ljuHr+ltrtqr7ufhS+YzsQwrkCn//hHtMla7VpTw+k//tGjeg8ePEh8fHyz9AMHDhAVFcWTTz5JXl4emZmZPPbYY83ybdmyhcGDBzulBQYGsn//fkpKSti3bx8HDx502n/hwgVycnI4ceIEp0+fpqKigg0bNjjluXz5Mvfddx+rVq2iT58+dMTEiRNRVVasWEFWVhaBgYFulduyZUuHzw5aarur9rrzWfiK6UwM4wrUcEbibrq7+vTpQ02TTmrv3r1UVFQwadIkj+ru27cvqampjeuvN9i5cyfDhw+nf//+9OjRg7S0ND766KPG/bW1tdx3333MnDmTtLQ0wPYXfHHxNxOZl5SUEB7ebDJyJwUFBZSWlhIcHOw0meOcOXOc8i1evLhp0Wbae3zHtrtqb1ufhS+ZzsQwrkA9+vVrV7q7pk2bRnZ2NufOnQOgqKiIOXPmsH79egIC2v91c+7cOS5evAhAZWUlO3bsICoqyinPsGHD+Pjjj7FYLKgqubm5REdHA7YBRrNnzyY6Opr58+c3lhk/fjxHjhzhxIkT1NTUkJWVxd133924f8qUKU6XnUpLS5k5cyY5OTn07t27sUOrrKzk0KFDZGRkkJ6eztmzZ6murm6zXW0d31XbXbXX1T5fM52JYVyBrp8+HQkOdkqT4GCunz7do3qTkpJYtGgRt956K1FRUUybNo1169Zxyy23uFW+6T2T0tJSUlNTiYuLY/z48UydOpXvfe97TvmHDh3K/fffT2JiImPGjMFqtTJ37lwA9uzZw+uvv877779PQkICCQkJbNmyhaCgIF5++WVuv/12oqOjeeCBB4iJiQHAarVy9OhRrr32WgAsFgtpaWksX76c6Ohonn32WZYuXQpAfn4+06dPJyMjg7CwMHbt2sW4ceOatanpPRNXx2/I31rbk5OTW22vq30+19FhYF15M0OD/cvE7x/tHRr81V//qv/4yU80b9Ys/cdPfqJf/fWvXo3nzJkzOnr0aD1w4EC7yvl7aHNBQYE+/fTTbuVduXKl5ubmqqrqrFmzdOHChXr8+HFfhuc13h4a3JUWxzIMoxP1++536ffdFudW9YqBAwdSWFjos/p9JTY2lhUrVriVt7CwkDNnzpCdnc3s2bNZs2YNw4cP93GEXZPpTAzDMDpo7dq1ja9Xr15NZGSkH6PxL9OZGIZheMHjjz/ebdZ/94VOuwEvIneIyGEROSoiC1vYv1JE9tu3IhG56LCv3mFf0xUaDcMwDD/rlDMTEQkEVgNTgRLgExHZrLYFsQBQ1acd8s8DHFe6r1TVhM6I1TAMw2i/zjozSQKOqupxVa0BsoB7XOSfAbzRKZG14amnnuKpp57ydxiGYRhdmthGg/n4ICL3A3eo6hz7+4eAZFV9ooW8NwAfA0PUvta7iNQB+7GtAf+Cqm5qodxcYC5A//79b8rOzvYo5tdee43f/e53zdJ/+MMf8vDDD3tUd1suX77cOMFbd2Ti94+wsDBGjBhBfX2921N9dEXdOf7uFPvRo0cpKytzSktNTc1T1XGtFHGpK96ATwfeauhI7G5Q1VMiEgm8LyIFqnrMsZCqrgHWAIwaNUpTUlI8CiIlJYXXXnuNhnp2797deIbiad1t2b17t8+P4Usmfv84dOgQoaGhlJeXO0330d105/i7U+whISGMHTu27Yxu6qzLXKcAx2lEh9jTWpJOk0tcqnrK/u9xYDfO91N87uTJk4gIL774Ii+++CIigoiQkZHRmWEYhmF0WZ11ZvIJMFJEhmPrRNKBf22aSUSigGuAvQ5p1wAWVa0WkeuA7wK/7JSo7SIiIjh58qTTWYphGIbxjU7pTFS1TkSeALYBgcCrqlooIs9he3y/YbhvOpClzjdyooHfiogV25nUC46jwAzDMAz/67R7Jqq6BdjSJG1xk/cZLZT7CBjj0+AMw+gUmzZt4t133+XSpUvMnj270xZuMnzPzBpsGIbXvf322yQnJxMfH8+4cePYtm0bAPfeey9r164lMzOTN998s816qqqqSEpKIj4+npiYGJYsWdJivpUrVxITE0NsbCwzZsygqqqqcd8jjzzCgAEDiI2NbXe9vrR161ZGjRrFiBEjeOGFF5rtdxWjq/a++OKLxMbGEhMT4/ZqkF7R0Rkiu/LmzVmDJ02apJMmTWr22pe666y1DUz8/tHeWYN9ZePGjTphwgQtLS1VVdWioiIdOHCgFhcXN+aZP3++5uXltVjeMX6r1arl5eWqqlpTU6NJSUm6d+9ep/wlJSUaERGhFotFVVWnT5+u69evb9z/wQcfaF5ensbExLSr3o5w97Ovq6vTyMhIPXbsmFZXV2tcXJwWFhY65WktRlftLSgo0JiYGK2oqNDa2lqdMmWKHjlypMUYvD1rsDkzMYwrVI3lEJe+XENZ6XIufbmGGsshj+usqKhg4cKFZGdnM2jQIABGjhxJSkoKubm5qCoLFizgzjvvJDExsc36RKTxmZ/a2lpqa2sRkWb56urqqKyspK6uDovF4rRM7sSJExvXJmlvvY5SU1PZsWMHAIsWLWLevHltxt+affv2MWLECCIjIwkODiY9PZ2cnBy3Y2ytvYcOHSI5OZlevXoRFBTEpEmTeOeddzocZ3uYzsQwrkA1lkNUlm1HrbaJCdVaTmXZdo87lKysLBITExk6dKhTes+ePbFYLLz00kvs3LmTt956i8zMzGblmy6OBbYHARMSEhgwYABTp04lOTnZaX94eDjPPPMMw4YNY/DgwYSFhbl1L6ateptaunQpy5YtY+PGjeTn57t9CamlxbFOnTrl9BkNGTLEaWVHVzG6am9sbCx/+ctfOH/+PBaLhS1btjgtD+xLpjMxjCtQVflfsE0o4ajOnt5xBw8eJD4+vln6gQMHiIqK4sknnyQvL4/MzEwee+yxZvm2bNnC4MGDndICAwPZv38/JSUl7Nu3j4MHDzrtv3DhAjk5OZw4cYLTp09TUVHBhg0b2oy1rXqbmjhxIqrKihUryMrKcvtJ9y1btjidKbVHSzG6am90dDQLFizgtttu44477iAhIaHTnsg3nYlhXIEazkjcTXdXnz59qKmpcUrbu3cvFRUVTJo0yaO6+/btS2pqauP66w127tzJ8OHD6d+/Pz169CAtLY2PPvrI43qbKigooLS0lODgYKen3OfMmeOUb/HixU2LNhMeHu50xlBSUkJ4eLhbMbbV3tmzZ5OXl8eHH37INddcw4033thmPN5gOhPDuAJJQMtTfrSW7q5p06aRnZ3NuXPnACgqKmLOnDmsX7+egID2f92cO3eOixcvAlBZWcmOHTuIiopyyjNs2DA+/vhjLBYLqkpubi7R0dEe1TtlyhSny06lpaXMnDmTnJwcevfu3djxVFZWcujQITIyMkhPT+fs2bNUV1e32a7x48dz5MgRTpw4QU1NDVlZWdx9991uxdhWe8+ePQvAF198wTvvvMO//muz58N9wnQmLmzcuJGPP/6YDz74gIiICL788kt/h2QYXhES+r9o/phZkD2945KSkli0aBG33norUVFRTJs2jXXr1nHLLbe4Vb7pPZPS0lJSU1OJi4tj/PjxTJ06le9973tO+YcOHcr9999PYmIiY8aMwWq1Mnfu3MY8M2bM4Oabb+bw4cMMGTKEV155xWW9VquVo0ePNt60t1gspKWlsXz5cqKjo3n22WdZunQpAPn5+UyfPp2MjAzCwsLYtWsX48Y5z5PY0j2ToKAgXn75ZW6//Xaio6N54IEHiImJccrfWozJycku23vfffcxevRovv/977N69Wr69u3r1mfvsY4OA+vKmzeGBm/YsEF79eqlQOMWEBCgUVFRHtfdlu46NLWBid8/2js0uLriMy0781u9ePpXWnbmt1pd0XyoqCfOnDmjo0eP1gMHDrSrnL+HNhcUFOjTTz/tVt6VK1dqbm6uqqrOmjVLFy5cqMePH/dleF7j7aHBXXHW4C7h5z//ORaLxSnNarVy+PBhzpw50zjs0TC6q+Be0QT3cn05yBMDBw6ksLDQZ/X7SmxsLCtWrHArb2FhIWfOnCE7O5vZs2ezZs0ahg8f7uMIuybTmbTiiy++aDFdVXnuuef49a9/3ckRGYbR1axdu7bx9erVq4mMjPRjNP5l7pm0YtiwYa3u+81vfoOIcNVVV3ViRIZhdGWPP/44CxYs8HcYfmM6k1YsW7aMXr16tbivV69ezJw5kxMnTnRyVIZhGF2T6UxaMXPmTNasWUPPnj0BGqc1CAgIoKqqij59+pj7JoZhGHbmnokLM2fObLwmeu211/K3v/2NwYMHk5yc3GzKB8MwjCtZp52ZiMgdInJYRI6KyMIW9q8Ukf32rUhELjrs+6GIHLFvP+ysmB298847jBw5kt69e7N69epOmzzNMAyjO+iUMxMRCQRWA1OBEuATEdmsDismqurTDvnnYV/nXUSuBZYA47A975FnL3uhM2I3DMMw2tZZZyZJwFFVPa6qNUAWcI+L/DOAN+yvbwd2qOrX9g5kB3CHT6M1DMMw2qWz7pmEA47zIJcALc73LCI3AMOB912UbTYjmojMBeYC9O/fn927d3scNNA4N87u3budXvvS5cuXfX4MXzLx+0dYWBjl5eXU19dTXu7ZhI3+1J3j706xV1VVefX3vCvegE8H3lLV+vYUUtU1wBqAUaNGaUpKileCaZjXJiUlxem1L+3evdvnx/AlE79/HDp0iNDQUMrLy51mte1uunP83Sn2kJAQxo4d67X6Ousy1ynAcbWcIfa0lqTzzSWu9pY1DMMw/KCzOpNPgJEiMlxEgrF1GJubZhKRKOAaYK9D8jbgNhG5RkSuAW6zpxmG0c1s2rSJRx99lAcffJDt27f7OxzDizqlM1HVOuAJbJ3AISBbVQtF5DkRcZzEPx3Iss9e2VD2a+AX2DqkT4Dn7GmGYXRRb7/9NsnJycTHxzNu3Di2bbP9/Xfvvfeydu1aMjMzefPNN9usp6qqiqSkJOLj44mJiWHJkiUt5lu5ciUxMTHExsYyY8YMqqqqnPbX19czduzYxmnmi4uLSU1NZfTo0cTExPDiiy962OL227p1K6NGjWLEiBG88MILzfa7arur9rb1WfhMR6cb7sqbN6agbzBp0iSdNGlSs9e+1F2nQG9g4veP9k5BX1FRp2fO1Ojp0zV65kyNVlTUeSWOjRs36oQJE7S0tFRVVYuKinTgwIFaXFzcmGf+/Pmal5fXYnnH+K1Wq5aXl6uqak1NjSYlJenevXud8peUlGhERIRaLBZVVZ0+fbquX7/eKc/y5ct1xowZOm3aNFVVPX36dOPxL126pCNHjtTCwkIPWt08dlfq6uo0MjJSjx07ptXV1RoXF9fs+K213VV73fksGnh7CnoznYphXIEslnrKyqxYrbb3ViuUlVmxWNo17qWZiooKFi5cSHZ2duN0QyNHjiQlJYXc3FxUlQULFnDnnXeSmJjYZn0i0jiVUW1tLbW1tYhIs3x1dXVUVlZSV1eHxWJxWnO9pKSEd99912l53cGDBzcePzQ0lOjoaKeVFVuSmprKjh07AFi0aBHz5s1rM/7W7Nu3jxEjRhAZGUlwcDDp6enk5OQ45XHVdlftdbXPl0xn0oqMjAxEhA8++IAPPvig8fXJkyf9HZphuK/iom1rorzc2mL21tLdlZWVRWJiIkOHDnVK79mzJxaLhZdeeomdO3fy1ltvkZmZ2ax805UWwXaJKiEhgQEDBjB16lSSk52fKggPD+eZZ55h2LBhDB48mLCwMG677bbG/U899RS//OUvW102+OTJk+Tn5zert6mlS5eybNkyNm7cSH5+PqtWrXKZ37FNTVdaPHXqlNNnNGTIkBY7s5ba7qq9bX0WvmQ6k1ZkZGQ0O42bNGkSERER/g7NMNpmuQRfnYLKCtv21SnbZrkE0HhG0lRr6e46ePAg8fHxzdIPHDhAVFQUTz75JHl5eWRmZvLYY481y7dlyxYGDx7slBYYGMj+/fspKSlh3759HDx40Gn/hQsXyMnJ4cSJE5w+fZqKigo2bNgAwJ///GcGDBjATTfd1GK8ly9f5r777mPVqlX06dPHZdsmTpyIqrJixQqysrIIDAx0md+xTR09O2ip7a7a62qfr5nOxDC+jXr1gevCoUewbbsu3Lb1sn1htvJHeqvp7urTpw81NTVOaXv37qWiooJJkyZ5VHffvn1JTU1l69atTuk7d+5k+PDh9O/fnx49epCWlsZHH30EwJ49e9i8eTMRERGkp6fz/vvvM2vWLMB26ei+++5j5syZpKWltXn8goICSktLCQ4OdnqWxPHyGcDixYvbrCs8PJzi4m+exS4pKSE8vNmz2C223VV7Xe3zNdOZuKGlS14iQkZGhr9DM4wOCQ1t+b9+a+numjZtGtnZ2Zw7dw6AoqIi5syZw/r161u9zOTKuXPnGmeeqKysZMeOHURFRTnlGTZsGB9//DEWiwVVJTc3l+ho23LEzz//PCUlJZw8eZKsrCwmT57Mhg0bUFVmz55NdHQ08+fPb3bcKVOmOF12Ki0tZebMmeTk5NC7d+/GDq2yspJDhw6RkZFBeno6Z8+epbq6us12jR8/niNHjnDixAlqamrIysri7rvvdsrTWttdtdfVPl8znYkbWrrkpaqmMzG6rV69AgkLC2g8EwkIgLCwAHr1cu/STWuSkpJYtGgRt956K1FRUUybNo1169Zxyy23uFW+6T2T0tJSUlNTiYuLY/z48UydOrVxeG9D/qFDh3L//feTmJjImDFjsFqtzJ071+Vx9uzZw+uvv877779PQkICCQkJbNmyBQCr1crRo0e59tprAbBYLKSlpbF8+XKio6N59tlnWbp0KQD5+flMnz6djIwMwsLC2LVrF+PGjWvWpqb3TIKCgnj55Ze5/fbbiY6O5oEHHiAmJsYpf2ttT05ObrW9rvb5XEeHgXXlzZtDg/2huw5NbWDi948WhwZfPGvb/ODMmTM6evRoPXDgQLvKuTu81lcKCgr06aefdivvypUrNTc3V1VVZ82apQsXLtTjx4/7Mjyv8fbQ4K44N9e3SrH95tdQ+3Vaw7hSDBw4kMLCQn+H0W6xsbGsWLHCrbyFhYWcOXOG7OxsZs+ezZo1axg+fLiPI+yaTGfiI6ffeYcz//M/je/P2Z8AHvSDH3C9Gzf7OqKybBcAV4Wl+qR+wzCcNazECrB69WoiIyP9GI1/mc7ER65PS+P6tDSKli0D4Maf/9xnx6oq/4jqy99MZ1Zj+RSAnr1vJiT0X3x23G6v3j6aJnCo63yG4YbHH3+820w/7wumM/kWCAn9F0JC/4XL521zHfXu96CfI+ri6k+D9cw37622kUcEDILAznla2DC+bUxnYlx5Aq+3bXVFtvdBN/o3HsP4FjBDgztJ8YYNjTfjfaHGcoj6mlLqa0q49OUaaiyHfHYswzCMpsyZiQ+d37OHy4cPgyqX//lPwDc34mssh6gs2w7YJulTazmVZe9RU/kZvfvd55VjGIZhuNLuzkRErgaqtJ3L6l5pzu/Zwxevvgqq3ySKcMO//zv9vvtdrx6rqvwvQF2TVKW+5iRlpcsBczO+mfrzoBWAQm0BBFwPgf38HZVhdFttXuYSkQAR+VcReVdEzgL/BEpF5DMR+W8RGeHOgUTkDhE5LCJHRWRhK3kesNdbKCJ/cEivF5H99q3ZCo1d0ek//hFtMkcRqpz+4x+9fiy1tj6CJGzwfxI2+D9NR+Ko/jxYvwAaOvpa2/v68/6MqpnKrVupbDIPVbtUW6CuBmpr4OsztveG4SPunJnsAnYCPwUOqqoVQESuBVKB/yci/6Oqrd4QEJFAYDUwFSgBPhGRzar6mUOekfZjfFdVL4jIAIcqKlU1oX1N86/a8y1/MbWW7gkJCG2lQ/FsaoxvLetpvulIGqgtvQucnVTt3k31Bx80vq/5298A6DlpEiEpKe5VUm2Byxe/aaa13vYeoGcvb4VqGI3c6UxuVdXapolqWzr3beBtEenRRh1JwFFVPQ4gIlnAPcBnDnkeBVar6gV7/WfdiK3L6tGvX4sdR9A113j9WCGh/8t+z8T5UldAYJjXj/Xt0OzXuY30zhWSkkJISgqXX3sNgN4PP9z+SiouOV9iBdv7ikumMzF8os3LXA0diYhcKyK/EJHfishPROSapnlcCAeKHd6X2NMc3QjcKCJ7RORjEbnDYV+IiPzdnn5vWzF3BddPn44EBzdLD2myVoM3BPeK5qqw22g4E5GAUCSgL1brZaz1FV4/XvfX2t8+bf1N1I1YW7ml2Vp6J9m0aROPPvooDz74INu3b/drLIZ3iTb966W1jCLbsV3u+gwYA8wCfqSq+9woez9wh6rOsb9/CEhW1Scc8vwZ25+GDwBDgA+BMap6UUTCVfWUiEQC7wNTVPVYk2PMBeYC9O/f/6bs7Gy32uVLQUePctUHH9B8kVHQwEDKW/mL8/Lly43LdbbHiKGHAThaPIqYyAP0CKrjq4vXUXL2hnbX5Yn2xF8U+TIANx5/oo2c3jPguiBGjbiKwMBvfjL19crho5Wc/aquw5+/t43Yvx+AowkJbuUPCwtjxIgR1NfX06feQkAL/7etIlQEh7ZQ2rtycnJYtWoV1dXV9OjRg2effZZbb721cf+FCxdYtGgRq1evbla2vr6+ceGpqqoq7rjjDmpqaqirq+Oee+7h5y3MJvHyyy/z+9//HhFh9OjR/OY3vyEkJASAixcvMm/ePD777DNEhNWrV5OcnMx//Md/sHXrVvr378/f7JcSPeUYe1t27NjBggULqK+v54c//GGzqfBdtb219h45coSHHb5XTp48yc9+9jMef/zxZsc/evQoZWVlTmmpqal5qjquWWZ3uDsjJJDf5H0k8LGbZW8Gtjm8/ynw0yZ5MrF1Tg3vc4HxLdT1GnC/q+N1pVmDD2Vk6IEf/1jzHnpI82bN0k8feUSP//rXWnPhQqtlOjprbflXWXrx9K9a2VZ2qM6OcCf+3PoluqiOZltu/RIfR2dX95VqzaeqNXmqNf+wvbfrKrMGl69fr+Xr17ud32nW4KoK1a9OqZ4r+Wb76pQt3cc2btyoEyZM0NLSUlVVLSoq0oEDB2pxcXFjnvnz52teXl6L5R1nDbZarVpeXq6qqjU1NZqUlKR79+51yl9SUqIRERFqsVhUVXX69Om63uFz+7d/+zddu3atqqpWV1frBfv/vQ8++EDz8vI0JibGswa3ErsrdXV1GhkZqceOHdPq6mqNi4vTwsJCpzyttb2t9joeY+DAgXry5MkWY/D2rMHteWjxaxEZ49AJHQfcvfj6CTBSRIaLSDCQDjQdlbUJSAEQkeuwXfY6LiLXiEhPh/Tv4nyvpUsLCAqCwEDb9WoRtLaWwJAQevTt67VjVJV/RFnpcuprSlrYG0SPkChCBzzqteN5w+SADH4RqEQwiQgm8YtA5ReByuSAjM4JILAfyNUgvSGgL9C1RjrVFBRQX1JC/eefc2nVKmoKCtpXQc9e0LsvjafFAYG29w73Sw5YN/Kr+gierQ/gV/URHLBu9DjuiooKFi5cSHZ2NoMGDQJg5MiRpKSkkJubi6qyYMEC7rzzThITE9usT0QazxJra2upra1FpPm5fl1dHZWVldTV1WGxWBqXyS0rK+PDDz9k9uzZAAQHB9PX/n9v4sSJjWuWuCM1NZUdO3YAsGjRIubNm+d22ab27dvHiBEjiIyMJDg4mPT0dHJycpzyuGp7a+11lJuby3e+8x1uuKFzrky0pzN5HHhDRH4jIv8hIquBY20VAlDVOuAJYBtwCMhW1UIReU5EGpYX2wacF5HPsI0g+9+qeh6IBv4uIgfs6S+owyiw7kDr6gjq25erbriB6yZPpvbSJa/WHxL6L41DgMMG/yfBveIc9tYhAT0JCLzaq8f8VtBq0Mu2ubms56D2U6j9lIihPf0aVk1BAZV/+hPU2x9CLSuj8k9/6liHEmRftvfaQc06khydSxmfA0oZn5Ojcz3uULKyskhMTGToUOfJM3v27InFYuGll15i586dvPXWW2RmZjYr33RxLLBdOkpISGDAgAFMnTqV5ORkp/3h4eE888wzDBs2jMGDBxMWFsZtt90GwIkTJ+jfvz8/+tGPGDt2LHPmzKGiomP3EZcuXcqyZcvYuHEj+fn5rFq1yq1yLS2OderUKafPaMiQIU4rOzZoqe2u2usoKyuLGTNmtK+RHnC7M1HVfwKJ2L7QBwD7AbcjVdUtqnqjqn5HVZfZ0xar6mb7a1XV+ao6WlXHqGqWPf0j+/t4+7+vuN88/zn9zjt8+tBDXP7nP6m/fJm6ixepPHmSoD59+M5PfuLTY1vrLRBwNQFBAwjuFd9lb8IfsG6kmI85yQde+8vYLfWnbR1H09FbAYOgRyIni9tedtWXqnJzobZJbLW1tnQv2aE/p7bJ2VgtFnaoZ7NbHzx4kPj4+GbpBw4cICoqiieffJK8vDwyMzN57LHHmuXbsmULg5sMUgkMDGT//v2UlJSwb98+Dh486LT/woUL5OTkcOLECU6fPk1FRQUb7FMX1dXV8emnn/LjH/+Y/Px8rr76al544YUOtW3ixImoKitWrCArK8vteyNbtmxp8czBHS213VV7G9TU1LB582amT5/eoeN2hNudiYhMBn4DJAOfA5/SfLC+YXd9WhqJr7/euPWOiqJ3VJTP1jJxdPW19xAYdA0SEMxVYbdy9bX3+PyY7dXwl3E9ti9ub/1l7JbA66FH4jeb9LZtHZgx+MDZMg6cLWs7YztoWcv1tZbeEWV80a50d/Xp04eaJg/r7t27l4qKCiZNmuRR3X379iU1NbVx/fUGO3fuZPjw4fTv358ePXqQlpbGRx99BNj+4h8yZEjj2cz999/Pp59+2qHjFxQUUFpaSnBwMKGh3wximDNnjlO+xYsXt1lXeHg4xcXfDHAtKSkhPLzpANdvOLbdVXsbvPfeeyQmJjJw4EB3m+ex9lzmehX4E/Axtpvvi4Hut4ya0W5lZfWUlXl3SKmv/jLuLJ99Vc47h0s5dsHCsQsW3jlcyjuHS/nsK8/Xs5Cwlp8Pai29I8IY1q50d02bNo3s7GzOnbNN619UVMScOXNYv349AQHt+bqxOXfuHBcvXgSgsrKSHTt2EBUV5ZRn2LBhfPzxx1gsFlSV3NxcoqOjARg0aBBDhw7l8GHbSMfc3FxGjx7d5nGnTJnidNmptLSUmTNnkpOTQ+/evRs7tMrKSg4dOkRGRgbp6emcPXuW6uq2z2zHjx/PkSNHOHHiBDU1NWRlZXH33Xc75Wmt7a7a2+CNN97o1Etc0L7O5HNV3aSqf1TVZ1X1HlV1ayoVo3sqL6+ntLQWi8WKxWKltLSW0tJayss971h89Zdxh6gV1AJtPi71jdHXhZI2ajDXXRXMdVcFkzZqMGmjBjP6Os+H3YZMmQI9mjzz0qOHLd0TFRdtGzBVltGjyfiZHvRiqizz6BBJSUksWrSIW2+9laioKKZNm8a6deu45ZZb3Crf9J5JaWkpqampxMXFMX78eKZOncr3vvc9p/xDhw7l/vvvJzExkTFjxmC1Wpk7d25jnpdeeomZM2cSFxfH/v37+dnPfgbAjBkzuPnmmzl8+DBDhgzhlVdsV9CtVitHjx5tvDlvsVhIS0tj+fLlREdH8+yzz7J06VIA8vPzmT59OhkZGYSFhbFr1y7GjXMeWdvSPZOgoCBefvllbr/9dqKjo3nggQeIiYlxyt9a25OTk122t6Kigh07dpDWCVdBHLX5nImI/B7bJa3BwJeq6t7iyH40atQobfhLxN+aLt/bwNWswbt37ybF3Wkzmmi66mIDTyZ6PH/e9mR9v37uzQvqTvy/qo+w3/x1FsYNPBN4sr0heqb2H0AdyHUQNKxdn/+HX9hmOZg4zLvTsNQUFFCZkwP19UhYGCFTphA8ZozLMocOHSI6Opry8vJvLsOUnbPdyG/hYcUDvd9jR4//oowvCGMYU2UZ8QEzvdaGL7/8ksmTJ/PGG28QFxfXdgE7p/j94ODBg7z66qturQO/atUq4uLimDx5Mg899BBDhgxh7ty53WId+IbfF0ci0uHnTNz5dngNiAcGAreJyE+AA/btH6rq/ZkLv0Ualu/tLA2rLnZ1U2UZOTrX6VKXN/4ybpfafJxu++lXUPsV/+tm/32RNQgeM4aavDygg9OpOAoMtI3mKrOvKBnWH4B45hDPHBcFPTNw4EAKC7vflfDY2Fi3OhKAwsJCzpw5Q3Z2NrNnz2bNmjXdoiPxhTY7E1V9H9tT5wCISBC24brxwHjAdCbfYhZLPTU1ti/cL7+sJTQ0gF69PJ9AMj5gJljhf3Q29VQTxg30J4pTfEI83vvr2KWgWKgvAdt0cICA9OVvf/+Cf3FjlYAvyix8XVWDVeG9Y18Sc10ow8LMvFdXkrVr1za+Xr16NZGRkX6Mxr/cum4hIgHAQlX9v/ZnRgrsm/EtZrHUU1ZmbXxvtdL43lsdyt/r13KBk5TxOWV8zlHdxt76FwFIlSW+fYhReuA8s7ICgdTUtj1I8YsyC/lflmG1Z62ss5L/pW20lelQrkyPP/445eWeD8DortzqTFTVKiLfB/6vj+MxupDycmur6d7oTBpcQwTPBJ7klfoUAGYH7vZa3W2rxfbfoIftiXg3Zw4u/Kqc+iZ9Tr3a0k1nYlyJ2rPS4gERWQL8Qu1rmhjfbtZWfsqtpbfH+9YMdunSxvfP1tumiehL505KSdB3oK7I/rphWGxxq9kbVNa1/CG0lm4Y33bt6UyuBSYBPxaRvwH/wNyA/1YLCPBOx9GSyQEZTCbDKa3hzKQ7uCoooMWO46qg9j9L0VTTxbHK7MNQ27U4lmF0sjY7ExER+1QnD9jf9wRisE1DnwT8sSGPb0M1OltoaIDTPRNHpaW2y0G9ewcQGuqdS151VHOOQ5TrGUJlkFfq9JWY60LJ/7LM6VJXoNjSPdWwOJZHLJfA4nD9/iv7A3gBZvVNwzfcWrZXRN4GclT1C1WtBj4VkYPA/xKR32Gbr+s1H8Zp+EHDfZGGDiUgAEQgMFDcfuakPS7yOdWUscv6HHcH/trr9TdTfxqsZ755X2ubZsOdiR4b7ot8ar8Jf1VQQNcazdWrj21z1DA02DB8wJ1vhDuAR7DNGDwcuAiEYBsGsx1Ypar5PovQ8KtevQKprLT9+d2vX1DjA4zetLT+Kuqoanz/Cb/hk/rfEEQISwIrvX68RoHXtzgf18ni3UR8p+3iw8J6cbLMFp+3H1o0jO7GnedMqoBfA7+2r/V+HVCpqhd9HJvRBakqdXW2lQkdVyr0xPyA42y1PkMBWShWetCLaH7AHQG/8kr9hmH4Xpt3C0VkVsNrVa1V1VLTkVy56utt63xdvuy9iR9DZTA96YNiBQKoo4qe9Ony900Mw/iGO5e5HhKR8cB8VfXu1LFGl1ZeXs/ly9/cgG+46Q5gsSgWi+394ME9mpVtr8t8SW+upw+DGUIy5ZS2XciPPvuqnH+ev9z4/p3Dtnij+vX2ymSP31abNm3i3Xff5dKlS8yePbvFRZ2M7smdcYx3ApXA+yLSv6MHEpE7ROSwiBwVkYWt5HlARD4TkUIR+YND+g9F5Ih9+2FHYzDaJzQ0kMGDezB4cA8GDAgiJMT5slZIiDBggHduxP9r4Dtcx0iC6c33A1fzr4HveKVeX2mYNbjp5ouOpHLrViqbrOHRIapQV9vipI/e9vbbb5OcnEx8fDzjxo1j27ZtANx7772sXbuWzMxM3nzzzTbrqaqqIikpifj4eGJiYliyZEmL+VauXElMTAyxsbHMmDGDqqoqp/319fWMHTvWacbhiIgIxowZQ0JCQrOZfjvD1q1bGTVqFCNGjGhxwS5XbXfV3rY+C59xd7F4IA34JzAX25DgXu0oG4htid9IIBjbJJGjm+QZCeQD19jfD7D/ey1w3P7vNfbX17g63o033qjd2a5du/wdQosuXqzV06drGreLF2tbzNfR+NfVTdJ1dZM6Gp7XdJXPv3LXLr2YkdFsq2wlvs8++0xVVS9dutRyhedPq54rUS2/4JuA7TZu3KgTJkzQ0tJSVVUtKirSgQMHanFxcWOe+fPna15eXovlHeO3Wq1aXl6uqqo1NTWalJSke/fudcpfUlKiERERarFYVFV1+vTpun79eqc8y5cv1xkzZui0adMa02644QY9d+5cxxvaRuyu1NXVaWRkpB47dkyrq6s1Li5OCwsLnfK01nZX7XXns2jQ8PviCPi7uvm93nRz6wkrEfkeMAeowbZ076+AYhE56maflQQcVdXjqloDZAFNl/97FFitapt1T1XP2tNvB3ao6tf2fTuwjTAzOll9vW14cFAQ9OolDUuUGz4SkpJC2JIlBN5wA4E33EDYkiWELVnS/mdQzp+yPWfS8ARqVYXtfdlRqC2wDYmuLYD68x7HXFFRwcKFC8nOzmbQINs9r5EjR5KSkkJubi6qyoIFC7jzzjtJTExssz4RoXfv3gDU1tZSW1uLSPOBH3V1dVRWVlJXV4fFYnFaJrekpIR333232YqI7ZWamsqOHTsAWLRoEfPmzetwXfv27WPEiBFERkYSHBxMeno6OTk5Tnlctd1Ve13t8yV3Hlo8AXwGrFTVHU32DXHzOOE4z1FRgm35X0c32uvcg+1MJkNVt7ZSttn6liIyF9tZE/3792f37t1uhtb1XL58ucvGP3RoLADFxQdbzdPe+I8Pe40TN/yu8X3D1CrDP/8hkV883KE4PdHVPv8R9tX2/t5GTGFhYZSXl1NfX+804aD06E3PuiqCrHUItuks64OrCQwpRxqn4K9F67+gqqqKOmuflqp3y+9//3vi4uLo27evUwwBAQF8/fXX/Pd//zfbt2/nq6++4uDBg8yePdup/H333ceLL77IkCHffLXU19czceJEjh8/zqOPPsro0aOd6u7Tpw9PPPEEw4YNIyQkhMmTJ3PzzTc35nniiSdYsmQJly9fpq6uzqnsrbfeiojwox/9iB/96Ecu27ZgwQKee+45Pv/8cz755BPefPPNZhM7Nv3sG9r08ssvO61tf/ToUQYNGtSYt1+/fvz9739vsb6mbW9oU0vtbeuzcFRVVeXd3/O2Tl2AqI6e9jjUcT+wzuH9Q8DLTfL8GfgfoAcwHFsH0hd4BljkkO9Z4BlXxzOXuXznq69q9auvWr681aArx++OjsS//8uLuv/Li94PRlXL16/X8lYuVThyeZmr/ILtElfDVn1AtSavhe0fHsX61FNP6eLFi5ulx8fH6/vvv+9WHa1dKrpw4YKmpKRoQUGBU/rXX3+tqampevbsWa2pqdF77rlHX3/9dVVV/dOf/qQ//vGPVdX2c3W8zFVSUqKqql9++aXGxcXpBx980GZsEydO1MTExFZjdPcy1x//+EedPXt24/vf//73+vjjj7ea37Htrtrral9T/rjMNU5EzolIScPNbxGZICL/JSJ5bvZZp4ChDu+H2NMclQCb1Tb8+ARQhO0+ijtlDcMvfLkWvFdZG65R9oCQq4HWHj51f+nilvTp04eamhqntL1791JRUcGkSZM8qrtv376kpqY2rr/eYOfOnQwfPpz+/fvTo0cP0tLS+OijjwDYs2cPmzdvJiIigvT0dN5//31mzbI97RAebrvAMWDAAH7wgx+wb98+l8cvKCigtLSU4OBgp5Ugm14+W7x4cZttCQ8Pp7j4mwsuJSUljfG01XZX7XW1z9fc6UyWAHcBY4HhIrID24JYwcBTbh7nE2CkiAwXkWAgHdjcJM8mIAVARK7DdtnrOLAN2wqP14jINcBt9jTD8DtfrgXfQOvqqD9zBuvly21nbk2ffhAYZJsPp3df+1ouLfFsmPe0adPIzs7m3Dnb1C1FRUXMmTOH9evXExDQ/kkwz507x0X7Zb7Kykp27NhBVFSUU55hw4bx8ccfY7FYUFVyc3Mbl6N9/vnnKSkp4eTJk2RlZTF58mQ2bNhARUVF46WfiooKtm/fTmxsbGOdU6ZM4dSpb/5mLS0tZebMmeTk5NC7d+/GDq2yspJDhw6RkZFBeno6Z8+epbq6us12jR8/niNHjnDixAlqamrIysri7rvvdqvtrtrrap+vufPTvayqn6jqOWApthUWx6jq/1HVv7hzELUtqPUEtk7gEJCtqoUi8pyINHyC24DzIvIZtrm+/reqnlfVr4FfYOuQPgGes6cZnai8vJ7S0lpqapSaGqW0tJbS0lrKy81deF+zlpVBdbXTTMIeC7geaHojW+zpHZeUlMSiRYu49dZbiYqKYtq0aaxbt45bbrnFrfJ33XUXpaXfPGNUWlpKamoqcXFxjB8/nqlTpzoN773rrrsYOnQo999/P4mJiYwZMwar1crcuXNdHufLL7/klltuIT4+nqSkJKZNm8Ydd9jG9VitVo4ePcq1114LgMViIS0tjeXLlxMdHc2zzz7LUvtMzvn5+UyfPp2MjAzCwsLYtWtXs2HGd911F6dPn3ZKCwoK4uWXX+b2228nOjqaBx54gJiYGKf8rbU9OTm51fa62udrom1M9isipdjOTg7bty2q2vYwDD8aNWqUHj582N9hdNju3btJ6cZTjV+J8X/4hW0klDfn6CpbtgzqWrgcFRRE2M9/3iz50KFDREdHU15e7nQZ5psKndeAp/48WE9ju7TVw9aRBHov/i+//JLJkyfzxhtvEBcX53a5VuPvJAcPHuTVV191ax34VatWERcXx+TJk3nooYcYMmQIc+fO7RbrwDf8vjgSkTxV7dBDN+48cbYE23TzM+3/horITmzPhOSr6h9cFTaMK4FVlUvVdVTV1RMS5J1p3kOffJKq7dupLSy0PXAYFESP6GhCvPXUeGA/r3YeTQ0cOJDCwkKf1e8rsbGxbnUkAIWFhZw5c4bs7Gxmz57NmjVrukVH4gvuTPS4xvG9fTjwGCAO29PxpjMxrngVtfXUqXLoq8uMHRTmlToDQkORnj1tHQlAfT3SsycB9mcPDP9bu3Zt4+vVq1cTGRnpx2j8q91zYahqCbaRV+95PxzD6F42FZVidbhSfKLMwokyCwEC9944uPWCbrJWVEDv3gSEhhIUHu7ZTXjDpx5//PEWn+e4Unh/hSPDuILcHjmAgrOXKCm3zX8UKHB97xDGDOj4g3+Orn7wQS6/9hoAV02b5pU6DcMXPF+w2jCuYFcFBdLDYchrvUJQQIDX7psYRndhzkwMw0PV9fX0DAzgqqAArgkJptpMWmZcgUxnYhgemhB+bePQYG/dfDeM7sZc5jIMwzA8Zs5MDKOLqtq92+mp9zL7U9c9J01q/zT0huFjpjMxjC4qJCXFdBpGt2E6E8PwQLdYC95yCSwOzz98ZZ/AsFco9PLOEGbDMJ2JYXhg9HWhXafTaE2vPl2m09i0aRPvvvsuly5dYvbs2dzmralhDL8zN+ANw/C6t99+m+TkZOLj4xk3bhzbttlWjbj33ntZu3YtmZmZvPnmm23WU1VVRVJSEvHx8cTExLBkyZIW861cuZKYmBhiY2OZMWMGVVVVjfseeeQRBgwY4DTFPEBERARjxowhISGh2Uy/nWHr1q2MGjWKESNG8MILLzTb76rtrtr74osvEhsbS0xMDKtWreqMpth0dFWtrryZlRb9y8TvHy5XWuxEGzdu1AkTJmhpaamqqhYVFenAgQO1uLi4Mc/8+fM1Ly+vxfKO8VutVi0vL1dV1ZqaGk1KStK9e/c65S8pKdGIiAi1WCyqqjp9+nRd77Ay5QcffKB5eXkaExPjVO6GG27Qc+fOdbyhbcTuSl1dnUZGRuqxY8e0urpa4+LitLCw0ClPa2131d6CggKNiYnRiooKra2t1SlTpuiRI0dajMEfKy0ahvEt9EWZhfeOfck7h0t579iXfFFm8bjOiooKFi5cSHZ2NoMGDQJg5MiRpKSkkJubi6qyYMEC7rzzThIT217JQkTobZ/Ysra2ltraWkSarsMCdXV1VFZWUldXh8Vi4frrv1mXZeLEiY1rk3giNTWVHTt2ALBo0SLmzZvX4br27dvHiBEjiIyMJDg4mPT0dHJycpzyuGp7a+09dOgQycnJ9OrVi6CgICZNmsQ777zT4Tjbo9M6ExG5Q0QOi8hREVnYwv6H7csD77dvcxz21TukN12h0TCMdvqizEL+l2VU1lkBqKyzkv9lmccdSlZWFomJiQwdOtQpvWfPnlgsFl566SV27tzJW2+9RWZmZrPyTRfHAqivrychIYEBAwYwdepUkpOTnfaHh4fzzDPPMGzYMAYPHkxYWJhb92JEhNtuu42bbrqJNWvWtJl/6dKlLFu2jI0bN5Kfn+/2JaSWFsc6deqU02c0ZMgQp5UdG7TUdlftjY2N5S9/+Qvnz5/HYrGwZcsWp+WBfalTOhMRCQRWY5uyfjQwQ0RGt5D1TVVNsG/rHNIrHdLvbqGcYRjtUPhVOfVN1sWrV1u6Jw4ePEh8fHyz9AMHDhAVFcWTTz5JXl4emZmZPPbYY83ybdmyhcGDnWdbDgwMZP/+/ZSUlLBv3z4OHjzotP/ChQvk5ORw4sQJTp8+TUVFBRs2bGgz1r/+9a98+umnvPfee6xevZoPP/zQZf6JEyeiqqxYsYKsrCwCA92bf23Lli1OZ0rt0VLbXbU3OjqaBQsWcNttt3HHHXeQkJDgdpye6qwzkyTgqKoeV9UaIAu4p5OObRhGEw1nJO6mu6tPnz7U1NQ4pe3du5eKigomTZrkUd19+/YlNTW1cf31Bjt37mT48OH079+fHj16kJaWxkcffdRmfeHh4QAMGDCAH/zgB+zbt89l/oKCAkpLSwkODnZaCXLOnDlO+RYvXuzWsR3PGEpKShrjaYlj29tq7+zZs8nLy+PDDz/kmmuu4cYbb2wzHm/orKHB4YDjuVYJkNxCvvtEZCJQBDytqg1lQkTk70Ad8IKqbmpaUETmAnMB+vfvz+7du70XfSe7fPmyid+Pumv8YWFhlJeXU19f3+a6GiGBQlXTUxN7uidrcqSkpPDII48wd+5crrvuOo4cOcIjjzzC6tWrqaiocKsOx/i/+uorgoKC6Nu3L5WVlWzdupWnnnrKKcZ+/frx0Ucf8eWXX3LVVVexdetWxo4d65Tn8uXLWK3WxrSKigqsViuhoaFUVFTw3nvvsWDBgsb93//+9/ntb3/beEZx5swZZsyYwR/+8Af+z//5P7zzzjtMnTqVyspKDh48yE9/+lOKiop4/vnnKS8vb/MzjIqKoqioiIKCAq6//nr+8Ic/8MorrziVa63tbbX33Llz9O/fn+LiYt566y1yc3NbjKeqqsq7v+cdvXPfng24H1jn8P4h4OUmefoBPe2v/x1432FfuP3fSOAk8B1XxzOjufzLxO8f7RnN9fnFCt10+LS+/c9vtk2HT+vnFys8juPVV1/VuLg4HTVqlI4YMUI/+ugjt8veeeedevjw4cb3Bw4c0ISEBB0zZozGxMTo0qVLm+U/deqULl68WEeNGqUxMTE6a9YsraqqasyTnp6ugwYN0qCgIA0PD9d169bpsWPHNC4uTuPi4nT06NH6X//1X4356+vrddiwYY2jpSoqKnTChAm6fft2VbWNDpswYYKqqu7Zs0dXrlypqqpz587VtWvXanZ2dosxNvXuu+/qyJEjNTIy0un4Dfldtd1Ve2+55RaNjo7WuLg43blzZ6uftbdHc3VWZ3IzsM3h/U+Bn7rIHwiUtbLvNeB+V8cznYl/mfj9o71Dgz+/WKFbjp7Rt/95WrccPeOVjsTRmTNndPTo0XrgwIF2lfP30OaCggJ9+umn3cq7cuVKzc3NVVXVWbNm6cKFC/X48eO+DM9rvN2ZdNZlrk+AkSIyHDgFpAP/6phBRAarasMwjruBQ/b0awCLqlaLyHXAd4FfdlLchvGtNSysF8PCevms/oEDB1JYWOiz+n0lNjaWFStWuJW3sLCQM2fOkJ2dzezZs1mzZg3Dhw/3cYRdU6d0JqpaJyJPANuwnXW8qqqFIvIctp5wM/CkiNyN7b7I18DD9uLRwG9FxIptwMALqvpZZ8RtGIbhytq1axtfr169msjISD9G41+dNjeXqm4BtjRJW+zw+qfYLn81LfcRMMbnARqGYXjg8ccf92jwQndnnoA3DMMwPGY6E8MwDMNjpjMxDMMwPGY6E8MwDMNjpjMxDMMwPGY6E8MwDMNjpjMxDMMwPGY6E8MwDMNjpjMxDKPTbNq0iUcffZQHH3yQ7du3+zscw4tMZ2IYhte9/fbbJCcnEx8fz7hx49i2bRsA9957L2vXriUzM5M333zT7frq6+sZO3Ys3/ve91rN88gjjzBgwABiY2Mb06qqqkhKSiI+Pp6YmBiWLFniMn9n2rp1K6NGjWLEiBG88MILzfa7ih1g5cqVxMTEEBsby4wZM6iqqqK4uJjU1FRGjx5NTEwML774Ymc1p3NmDe7szcwa7F8mfv9o76zBvrJx40adMGGClpaWqqpqUVGRDhw4UIuLixvzzJ8/X/Py8los31L8y5cv1xkzZui0adNaPe4HH3ygeXl5GhMT05hmtVq1vLxcVVVramo0KSlJ9+7d22p+T7n72dfV1WlkZKQeO3ZMq6urNS4uTgsLC53yuIq9pKREIyIiGqfJnz59uq5fv15Pnz7d+LleunRJR44c2azeBt6eNdicmRjGFaqmoIBLq1ZRtnQpl1atoqagwOM6KyoqWLhwIdnZ2QwaNAiAkSNHkpKSQm5uLqrKggULuPPOO0lMTHSrzpKSEt59991mKxo2NXHiRK699lqnNBGhd+/eANTW1lJbW4uItJrfldTUVHbs2AHAokWLmDdvnttlm9q3bx8jRowgMjKS4OBg0tPTycnJcTt2gLq6OiorK6mrq8NisXD99dczePDgxs81NDSU6OjoFteW9wXTmRjGFaimoIDKP/0JLSsDQMvKqPzTnzzuULKyskhMTGTo0KFO6T179sRisfDSSy+xc+dO3nrrLTIzM5uVv+uuuygtLXVKe+qpp/jlL39JQEDHvq7q6+tJSEhgwIABTJ06leTklhZ5bdvSpUtZtmwZGzduJD8/n1WrVrlV7q677uL06dNOaadOnXL6jIYMGdLil35rsYeHh/PMM88wbNgwBg8eTFhYGLfddptT2ZMnT5Kfn9/h9raX6UwM4wpUlZsLtbXOibW1tnQPHDx4kPj4+GbpBw4cICoqiieffJK8vDwyMzN57LHHmuXbsmULgwcPbnz/5z//mQEDBnDTTTd1OKbAwED2799PSUkJ+/bt4+DBgx2qZ+LEiagqK1asICsri8DAQLfKbdmypXH53/ZqLfYLFy6Qk5PDiRMnOH36NBUVFWzYsKGx3OXLl7nvvvtYtWoVffr06dCx28t0JoZxBWo4I3E33V19+vShpqbGKW3v3r1UVFQwadKkdte3Z88eNm/eTEREBOnp6bz//vvMmjWrQ7H17duX1NRUtm7d2qHyBQUFlJaWEhwcTGhoaGN608tvixcvblq0mfDwcIqLixvfl5SUEB4e3mr+prHv3LmT4cOH079/f3r06EFaWhofffQRYLskdt999zFz5kzS0tLa1UZPdFpnIiJ3iMhhETkqIgtb2P+wiJwTkf32bY7Dvh+KyBH79sPOitkwvq0kLKxd6e6aNm0a2dnZnDt3DoCioiLmzJnD+vXrO3SZ6vnnn6ekpISTJ0+SlZXF5MmTnf4Cb8u5c+e4ePEiAJWVlezYsYOoqKg2y02ZMsXpslNpaSkzZ84kJyeH3r17N36pV1ZWcujQITIyMkhPT+fs2bNUV1e3Wf/48eM5cuQIJ06coKamhqysLO6++263Yx82bBgff/wxFosFVSU3N5fo6GhUldmzZxMdHc38+fPd+Yi8plM6ExEJBFYDdwKjgRkiMrqFrG+qaoJ9W2cvey2wBEgGkoAl9qV8DcPooJApU6BHD+fEHj1s6R5ISkpi0aJF3HrrrURFRTFt2jTWrVvHLbfc4lb5lu6ZtJW/4X7EjBkzuPnmmzl8+DBDhgzhlVdeobS0lNTUVOLi4hg/fjxTp05tHF7cUn4Aq9XK0aNHG2/OWywW0tLSWL58OdHR0Tz77LMsXboUgPz8fKZPn05GRgZhYWHs2rWLcePGtRpjg6CgIF5++WVuv/12oqOjeeCBB4iJiXHK7yr25ORk7r//fhITExkzZgxWq5W5c+eyZ88eXn/9dd5//30SEhJISEhgyxanNQl9p6PDwNqzATcD2xze/xT4aZM8DwMvt1B2BvBbh/e/BWa4Op4ZGuxfJn7/aO/Q4Op//EPLVq7UixkZWrZypVb/4x9ejefMmTM6evRoPXDgQLvK+Xtoc0FBgT799NNu5V25cqXm5uaqquqsWbN04cKFevz4cV+G5zXeHhostvK+JSL3A3eo6hz7+4eAZFV9wiHPw8DzwDmgCHhaVYtF5BkgRFX/y57vWaBSVX/V5BhzgbkA/fv3vyk7O9vn7fKVy5cvNw4J7I5M/P4RFhbGiBEjqK+vd/vmcFfUneKfN28e1157LWVlZdx33328/vrrrFmzxt9hueXo0aOUNblHlpqamqeq41op4lKnrQHvhj8Bb6hqtYj8O/A7YLK7hVV1DbAGYNSoUZqSkuKTIDvD7t27MfH7T3eN/9ChQ4SGhlJeXu50g7i76U7xv/baa42vV69ezXe+851uE3tISAhjx471Wn2d1ZmcAhwHng+xpzVS1fMOb9cBv3Qom9Kk7G6vR2gYhuGBxx9/nPLycn+H4TedNZrrE2CkiAwXkWAgHdjsmEFEBju8vRs4ZH+9DbhNRK6x33i/zZ5mGIZhdBGdcmaiqnUi8gS2TiAQeFVVC0XkOWw3fDYDT4rI3UAd8DW2G/Ko6tci8gtsHRLAc6r6dWfEbRiGYbin0+6ZqOoWYEuTtMUOr3+KbZRXS2VfBV71aYCGYRhGh5kn4A3DMAyPmc7EMAzD8JjpTAzDMAyPmc7EMAzD8JjpTAzDMAyPmc7EMAzD8JjpTAzD6DSbNm3i0Ucf5cEHH2T79u3+DsfwItOZGIbhdW+//TbJycnEx8czbtw4tm2zTVpx7733snbtWjIzM3nzzTfdrq++vp6xY8c2TsHekkceeYQBAwYQGxvrlL5161ZGjRrFiBEjeOGFF9rM31lai6tBVVUVSUlJxMfHExMTw5IlSxr3HT58uHGK+YSEBPr06cOqVataTe8UHZ1uuCtvZgp6/zLx+0d7p6DXY/mqf/yl6ms/s/17LN8rcWzcuFEnTJigpaWlqqpaVFSkAwcO1OLi4sY88+fP17y8vBbLtxT/8uXLdcaMGTpt2rRWj/vBBx9oXl6exsTENKbV1dVpZGSkHjt2TKurqzUuLk4LCwtbze8pdz97V3E1sFqtWl5erqqqNTU1mpSUpHv37m2xroEDB+rJkyfdSm/g7SnozZmJYVyJju+HjzZBxUXb+4qLtvfH93tUbUVFBQsXLiQ7O5tBgwYBMHLkSFJSUsjNzUVVWbBgAXfeeSeJiYlu1VlSUsK7777bbHncpiZOnNi4oFWDffv2MWLECCIjIwkODiY9PZ2cnJxW87uSmprKjh07AFi0aBHz5s1zu2xTruJqICKNSyHU1tZSW1uLiDSrKzc3l+985zvccMMNbqX7iulMDONK9OkOqK91TquvtaV7ICsri8TERIYOHeqU3rNnTywWCy+99BI7d+7krbfeIjMzs1n5llZafOqpp/jlL3/ZoWV/T5065RTLkCFDnJbjbY+lS5eybNkyNm7cSH5+vtuXj1paadHduOrr60lISGDAgAFMnTqV5OTkZnmysrKYMWOG2+m+0pXWMzEMo7M0nJG4m+6mgwcPEh8f3yz9wIEDPPzww6SmpvLkk0+2Wn7Lli1O07j/+c9/ZsCAAdx0003s3r3bo9g8NXHiRFSVFStWsHv3brcX8PJk2dzAwED279/PxYsX+cEPfsDBgwed7vHU1NSwefNmnn/+eadyraX7kjkzMYwr0dV925fupj59+lBTU+OUtnfvXioqKpg0aVK769uzZw+bN28mIiKC9PR03n//fWbNmuV2+fDwcIqLixvfl5SUEB4e3u44AAoKCigtLSU4ONhpAayml98WL17ctKjHcfXt25fU1FS2bt3qlP7ee++RmJjIwIED3Ur3JdOZGMaVKHEqBPZwTgvsYUv3wLRp08jOzubcuXMAFBUVMWfOHNavX9+hy1TPP/88JSUlnDx5kqysLCZPnsyGDRvcLj9+/HiOHDnCiRMnqKmpISsri7vvvrvNclOmTHG67FRaWsrMmTPJycmhd+/ejV/qlZWVHDp0iIyMDNLT0zl79izV1dVeievcuXNcvHix8Tg7duwgKirKKc8bb7zR4qWs1tJ9yXQmhnElikyAf7n3mzORq/va3kcmeFRtUlISixYt4tZbbyUqKopp06axbt06brnlFrfKt3TPpK38DfcjZsyYwc0338zhw4cZMmQIr7zyCkFBQbz88svcfvvtREdH88ADDxATE9NqfgCr1crRo0cbb85bLBbS0tJYvnw50dHRPPvssyxduhSA/Px8pk+fTkZGBmFhYezatYtx48a1GmMDV3E15C8tLSU1NZW4uDjGjx/P1KlTnYZGV1RUsGPHDtLS0pzqbi3d5zo6DKy9G3AHcBg4Cix0ke8+QIFx9vcRQCWw375ltnUsMzTYv0z8/tHuocE+dubMGR09erQeOHCgXeX8HX9BQYE+/fTTbuVduXKl5ubmqqrqrFmzdOHChXr8+HFfhuc13h4a3Ck34EUkEFgNTAVKgE9EZLOqftYkXyjwE+BvTao4pqoJnRGrYRjeMXDgQAoLC/0dRrvFxsayYsUKt/IWFhZy5swZsrOzmT17NmvWrGH48OE+jrBr6qzRXEnAUVU9DiAiWcA9wGdN8v0C+H/A/+6kuAzDMDps7dq1ja9Xr15NZGSkH6Pxr87qTMKBYof3JYDTgGkRSQSGquq7ItK0MxkuIvnAJWCRqv6l6QFEZC4wF6B///5+H0boicuXL5v4/ai7xh8WFkZ5eTn19fVOw2u7m+4a/7/92791q9irqqq8+nveJZ4zEZEAYAXwcAu7S4FhqnpeRG4CNolIjKpecsykqmuANQCjRo3SlJQU3wbtQ7t378bE7z/dNf5Dhw4RGhpKeXm509DV7qY7x9+dYg8JCWHs2LFeq6+zRnOdAhwfiR1iT2sQCsQCu0XkJDAB2Cwi41S1WlXPA6hqHnAMuLFTojYMwzDc0lmdySfASBEZLiLBQDqwuWGnqpap6nWqGqGqEcDHwN2q+ncR6W+/gY+IRAIjgeOdFLdhGIbhhk65zKWqdSLyBLANCAReVdVCEXkO21C0zS6KTwSeE5FawAo8pqpf+z5qw+h+bKM7DcM1X/yedNo9E1XdAmxpktbivAOqmuLw+m3gbZ8GZxjfAiEhIZw/f57g4GB/h2J0YarK+fPnCQkJ8Wq9XeIGvGEYnhsyZAglJSVcvHjR618Unamqqqrbxt9dYg8JCWHIkCFerdN0JobxLdGjRw+GDx/O7t27vTpKp7N15/i7c+yeMnNzGYZhGB4znYlhGIbhMdOZGIZhGB4znYlhGIbhMdOZGIZhGB4znYlhGIbhMdOZGIZhGB4znYlhGIbhMdOZGIZhGB4znYlhGIbhMdOZGIZhGB4znYlhGIbhMdOZGIZhGB7rtM5ERO4QkcMiclREFrrId5+IqIiMc0j7qb3cYRG5vXMiNgzDMNzVKVPQ25fdXQ1MBUqAT0Rks6p+1iRfKPAT4G8OaaOxLfMbA1wP7BSRG1W1vjNiNwzDMNrWWWcmScBRVT2uqjVAFnBPC/l+Afw/oMoh7R4gS1WrVfUEcNRen2EYhtFFdNbiWOFAscP7EiDZMYOIJAJDVfVdEfnfTcp+3KRseNMDiMhcYK79bbWIHPRG4H5yHfCVv4PwgInfv0z8/tOdYwcY1dGCXWKlRREJAFYAD3e0DlVdA6yx1/d3VR3XRpEuy8TvXyZ+/+rO8Xfn2MEWf0fLdlZncgoY6vB+iD2tQSgQC+wWEYBBwGYRuduNsoZhGIafddY9k0+AkSIyXESCsd1Q39ywU1XLVPU6VY1Q1Qhsl7XuVtW/2/Oli0hPERkOjAT2dVLchmEYhhs65cxEVetE5AlgGxAIvKqqhSLyHPB3Vd3somyhiGQDnwF1wONujORa463Y/cTE718mfv/qzvF359jBg/hFVb0ZiGEYhnEFMk/AG4ZhGB4znYlhGIbhsW7dmbQ1RYuIPCYiBSKyX0T+an+avsvwZIqZrsCNz/9hETln//z3i8gcf8TZGnc+fxF5QEQ+E5FCEflDZ8fYGjc++5UOn3uRiFz0Q5itciP+YSKyS0TyReQfInKXP+JsjRvx3yAiufbYd4vIEH/E2RIReVVEzrb2LJ7Y/H/2tv3D/gxg21S1W27YbuQfAyKBYOAAMLpJnj4Or+8Gtvo77vbEb88XCnyIbYTbOH/H3c7P/2HgZX/H6kH8I4F84Br7+wH+jrs9vzsO+edhG/Ti99jb8dmvAX5sfz0aOOnvuNsZ/x+BH9pfTwZe93fcDrFNBBKBg63svwt4DxBgAvA3d+rtzmcmbU7RoqqXHN5eDXSl0QaeTDHTFbgbf1flTvyPAqtV9QKAqp7t5Bhb097PfgbwRqdE5h534legj/11GHC6E+Nrizvxjwbet7/e1cJ+v1HVD4GvXWS5B/i92nwM9BWRwW3V2507k5amaGlpmpXHReQY8EvgyU6KzR1txu84xUxnBuYmtz5/4D77qfJbIjK0hf3+4k78NwI3isgeEflYRO7otOhcc/ezR0RuAIbzzRdbV+BO/BnALBEpAbZgO7vqKtyJ/wCQZn/9AyBURPp1Qmze4Pbvl6Pu3Jm4RVVXq+p3gAXAIn/H4y6HKWb+09+xeOBPQISqxgE7gN/5OZ72CsJ2qSsF21/3a0Wkrz8D6oB04C3tfrNszwBeU9Uh2C67vG7/P9FdPANMEpF8YBK2WTu628+gXbrTD6ep9k6zkgXc68uA2qk9U8ycxHbtcnMXugnf5uevqudVtdr+dh1wUyfF5g53fn9KgM2qWqu2GauLsHUu/tae3/10utYlLnAv/tlANoCq7gVCsE2i2BW487t/WlXTVHUs8HN72sVOi9AzHZvCyt83gzy4iRQEHMd2Ct9wEyymSZ6RDq+/j+1pe7/H7m78TfLvpmvdgHfn8x/s8PoHwMf+jrud8d8B/M7++jpsp/79ukPs9nxRwEnsDyd3lc3Nz/494GH762hs90y6RDvcjP86IMD+ehnwnL/jbhJfBK3fgJ+G8w34fW7V6e9GefiB3IXtr8VjwM/tac9hm9cL4EWgENiP7SZYq1/WXTH+Jnm7VGfi5uf/vP3zP2D//KP8HXM74xdslxo/AwqAdH/H3J7fHWz3HV7wd6wd/OxHA3vsvzv7gdv8HXM7478fOGLPsw7o6e+YHWJ/AygFarGdfc8GHgMes+8XbIsZHrP/3rv1vWOmUzEMwzA81p3vmRiGYRhdhOlMDMMwDI+ZzsQwDMPwmOlMDMMwDI+ZzsQwDMPwmOlMDMMwDI+ZzsQwDMPwmOlMDKOdROR9h7VCqkTkAX/HZBj+Zh5aNIwOEpEfA6nADO1+EykahlcF+TsAw+iOROTfgDuB+zztSERE1PxVZ3RzpjMxjHYSkenATOAeVa21p2UA1wDngXPAP1V1l4i8CvwE+CnQC9vkf0+KyCDgf4BNQKSIVAHnVfU5EekJrAIuAN8FHrDX0Vi+s9pqGO4y90wMox1E5HvAfwBpqlplTwvH9ofZRWxf/gXAaBGZCHwC/BtwlX1/mL2qBGwT7m3A1vk0lAX4Mba1PH6GbUW8+1sobxhdijkzMYz2+R22L/g9IgLwErZO4CdAf2zrQBzEtrjTeGAOkAk8rt+s7QK2ziQH27LMjmUb9mWKSG/gDDC2hfKG0aWYzsQw2kFVmy29al998RmgH5CvqhftZyVLVLVORHKA10SkGHhfVbdiW2TrMLYp+hvL2qvcBvwaqLanlbZQ3jC6FDOayzC6GPvN/THY1pVY1HA5zTC6MtOZGIZhGB4zN+ANwzAMj5nOxDAMw/CY6UwMwzAMj5nOxDAMw/CY6UwMwzAMj5nOxDAMw/CY6UwMwzAMj5nOxDAMw/DY/w/jGMG0j5menAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "\n", + "xs = df[df['Q2']==4.75].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot()\n", + " xbj_one_corr = []\n", + " xbj_one_err_corr = []\n", + " CSV_one = []\n", + " CSV_one_err = []\n", + " #RY_err = []\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " #print(zs)\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs = []\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_error = row['error']\n", + " RYs.append(RYi)\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYs_rho.append(RYi_rho)\n", + " RYs_error.append(RYi_error)\n", + " #print('RY_error ',RY_error)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " #print('RYs ',RYs)\n", + " #print('RYs err ',RYs_error)\n", + " RY = Get_weighted_average(RYs,RYs_error)\n", + " RY_err = Get_weighted_sigma(RYs,RYs_error)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " ax.plot([z_corr+0.005,z_corr+0.005],[RY_rho+RY_err,RY_rho-RY_err],marker = \"_\",color = colors_all[i_col])\n", + " plt.plot(z_corr+0.005,RY_rho,\"*\",color = colors_all[i_col])\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$RY(rho)$')\n", + " plt.xlim(0.3,1)\n", + " plt.ylim(0.4,0.85)\n", + " plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + " \n", + " xbj_one = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " xbj_ones_plot.append(xbj_one)\n", + " xbj_one_err = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9244d357", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-8-09a5266367fc>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-8-09a5266367fc>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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3lJCQEFauXGlT3qysLK5cuUJSUhKzZ88mISGhWad1b0sc0piYl91dA8QABuCIiGxTpgWxKi0CkpRSb4nIIEwLafmbX8cDwUAvYI+IDFBKGR0Ru6ZpWl3WrVtX9XrNmjX07dvXidE4l6OuTCKAc0qp8wAikgg8BFRvTBRQuaKPJ1B5k/EhIFEpdRu4ICLnzPUdckTgmqZptpg3b17VIIA7kaMaE1/gUrX3BiCyRp6lwC4ReRroDDxQrezhGmVrjaETkbnAXABvb2/S0tKaI26nuHXrlo7fidpq/J6enhQUFGA0Gtv0l1pbjr8txV5SUtKsv+etadne6cDflFIrROR+4J8iYvMMbEqpBCABYODAgSoqKqplonSAtLQ0dPzO01bjz87OpkuXLm1i6dj6tOX421Ls7u7uDB06tNnqc1RjkgNUn1/Bz5xW3WxgHIBS6pCIuAP32FhW0zRNcyJHPWdyBOgvIgEi4oapQ31bjTxfAWMBRCQIcAfyzfniRaSDiAQA/YF0B8WtaZqm2cAhVyZKqXIReQrYiWnY79tKqSwReQHTuOZtwH8B60TkWUyd8bPM456zRCQJU2d9OTBPj+TSNE1rXRzWZ2J+ZiSlRtriaq9PAT+oo+xyYHmLBqhpmqY1mZ5ORdM0TbObbkw0TdM0u+nGRNM0h9m6dStPPvkkjz76KLt27XJ2OFoz0o2JpmnNbsuWLURGRhIaGsrw4cPZudM0nd7DDz/MunXrWLt2Le+++67N9RmNRoYOHVo1a641/v7+DB48mLCwsFoz91or/8QTT+Dj40NIiM2PszWrHTt2MHDgQPr168fLL79ca39JSQkRERGEhoYSHBzMkiVLLPavWrWK4OBgQkJCmD59OiUlJYBpvfuQkBCCg4NtXg2yOejGRNO0ZrVp0yZeffVVkpOTycjIYPPmzTz++OMYDIaqPC+++CLz5s2zuc7XX3/dpnU59u3bx4kTJzh69GiD5WfNmlW1LomjGY1G5s2bx4cffsipU6fYvHkzp06dssjToUMH9u7dS0ZGBidOnGDHjh0cPmyaDCQnJ4c33niDo0ePkpmZidFoJDExkczMTNatW0d6ejoZGRl88MEHnDt3ziHnpBsTTbtTXdgIW/1hUzvTvxc22l1lYWEhCxcuJCkpiR49egDQv39/oqKiSE1NRSnFggULGD9+POHh4TbVaTAY2L59e60VDW1VV/lRo0ZVrVlii+joaHbv3g3AokWLePrpp5sUD0B6ejr9+vWjb9++uLm5ER8fT3JyskUeEcHDwwOAsrIyysrKEJGq/eXl5RQXF1NeXk5RURG9evUiOzubyMhIOnXqhKurK6NHj+b9999vcpyNoRsTTbsTXdgI6XOh6EtAmf5Nn2t3g5KYmEh4eDi9e/e2SO/QoQNFRUW8+eab7Nmzh/fee4+1a9fWKm9tcaz58+fzyiuvNLhSo4gQGxvLsGHDSEhIaHT5hixbtozly5ezceNGjh8/bvMtJGuLY+Xk5Fh8Rn5+fhYrO1YyGo2EhYXh4+NDTEwMkZGmKQ19fX157rnn6NOnDz179sTT05PY2FhCQkL4+OOPuXbtGkVFRaSkpFgsD9ySWtPcXJqmOUrGH8BYZJlmLDKlB8xocrWZmZmEhobWPlxGBrNmzSI6OppnnnmmzvIpKSkWEyV+8MEH+Pj4MGzYsAYnJfzkk0/w9fUlLy+PmJgYAgMDuXnzps3lGzJq1CiUUqxcuZK0tDRcXFxsKpeSktJwpjq4uLhw4sQJbty4waRJk8jMzCQkJITr16+TnJzMhQsX8PLyYurUqWzYsIGZM2eyYMECYmNj6dy5M2FhYTbHaS99ZaJpd6KirxqXbqOuXbtSWlpqkXbo0CEKCwsZPXp0o+s7cOAA27Ztw9/fn/j4ePbu3cvMmTOt5vX1NU0m7uPjw6RJk0hPT29U+YacPHmS3Nxc3NzcLCZzrHn7bPHixTWLWo21+hWDwWCoit8aLy8voqOjq/p49uzZQ0BAAN7e3rRv357Jkydz8OBBAGbPns2xY8fYv38/d911FwMGDGjUeTaVbkw07U7UqY613utKt1FcXBxJSUnk5+cDcObMGebMmcM777zTpNtML730EgaDgYsXL5KYmMiYMWPYsGFDrXyFhYVVVzSFhYXs2rWLkJAQm8vXNHbsWIvbTrm5ucyYMYPk5GQ8PDyqvtSLi4vJzs5m6dKlxMfHk5eXx+3btxusf8SIEZw9e5YLFy5QWlpKYmIiEydOtMiTn5/PjRs3qo6ze/duAgMDAejTpw+HDx+mqKgIpRSpqalVAwzy8vIA+Oqrr3j//fd57LHHGoynOejGRNPuRKHLwaWTZZpLJ1O6HSIiIli0aBEPPPAAgYGBxMXFsX79ekaOHGlTeWt9Jg3lv3z5MlevXmXkyJGEhoYSERFBXFwc48aNq7fs9OnTuf/++zl9+jR+fn789a9/BaCiooJz585Vdc4XFRUxefJkVqxYQVBQEM8//zzLli0D4Pjx40ydOpWlS5fi6enJvn37ag1LttZn4urqyurVq3nwwQcJCgpi2rRpBAcHW+TPzc0lOjqaIUOGMGLECGJiYqqGNkdGRjJlyhTCw8MZPHgwFRUVzJ07F4BHHnmEQYMG8eMf/5g1a9bg5eVl8+dpl6YuHt+atwEDBqi2bN++fc4OwS46fuc4deqUUkqpmzdv2lbg/Aal/vdepTaK6d/zG5o1nitXrqhBgwapjIyMRpWzOf4WcvLkSfXss8/alHfVqlUqNTVVKaXUzJkz1cKFC9X58+dbMrxmU/n7Uh2miXeb9L2rO+A17U4VMMOuzvaGdO/enaysrBarv6WEhISwcuVKm/JmZWVx5coVkpKSmD17NgkJCQQEBLRwhK2Tbkw0TdOaaN26dVWv16xZQ9++fZ0YjXPpxkTTNK0ZzJs3r82s/94SHNYBLyLjROS0iJwTkYVW9q8SkRPm7YyI3Ki2z1htX80VGjVN0zQnc8iViYi4AGuAGMAAHBGRbcq0IBYASqlnq+V/Gqi+0n2xUirMEbFqmqZpjeeoK5MI4JxS6rxSqhRIBB6qJ/90YLNDItM0TdPs5qg+E1+g+gQxBiDSWkYRuRcIAPZWS3YXkaOY1oB/WSm11Uq5ucBcAG9vb7unTnCmW7du6fidqK3G7+npSUFBAUajsU3fu2/L8bel2EtKSpr197w1dsDHA+8ppYzV0u5VSuWISF9gr4icVEp9Ub2QUioBSAAYOHCgioqKcljAzS0tLQ0dv/O01fizs7Pp0qULBQUFFtN9tDVtOf62FLu7uztDhw5tOKONHHWbKweoPo2onznNmnhq3OJSSuWY/z0PpGHZn6JpmqY5maMakyNAfxEJEBE3TA1GrVFZIhII3AUcqpZ2l4h0ML++B/gBcKpmWU3TNM15HHKbSylVLiJPATsBF+BtpVSWiLyA6fH9yoYlHkg0P9ZfKQj4i4hUYGr8Xq4+CkzTNE1zPof1mSilUoCUGmmLa7xfaqXcQWBwiwbXkBvnTf963blPt2pac9i6dSvbt2/n5s2bzJ49m9jYWGeHpDUTPWtwfW5+BTkHoDDXtOUcMG15Gc6OTNNatS1bthAZGUloaCjDhw9n586dADz88MOsW7eOtWvX8u6779pcn9FoZOjQoVWz5lrj7+/P4MGDCQsLq5q5t6SkhIiICEJDQwkODmbJkiVV+V9//XVCQkIIDg62edXE5rRjxw4GDhxIv379ePnll2vtry92gFWrVhEcHExISAjTp0+npKQEcOJ5NXWGyNa8NfuswXmfK3U5XSnDJ7W3b79s3mOptjtrbSUdv3M0etbgFrJx40Z13333qdzcXKWUUmfOnFHdu3dXly5dqsrz61//Wh07dsxqeWvxr1ixQk2fPl3FxcXVedx7771X5efnW6RVVFSogoICpZRSpaWlKiIiQh06dEidPHlSBQcHq8LCQlVWVqbGjh2rzp492+hztSV2a8rLy1Xfvn3VF198oW7fvq2GDBmisrKybIpdKaUMBoPy9/dXRUVFSimlpk6dqt55551GnVdzzxqsr0xs5eoOvj8At66mzfcHpq2rfYsJaZrTZG+EBH9Y0c70b7Z967+DaWGqhQsXkpSURI8ePQDo378/UVFRpKamopRiwYIFjB8/nvDwcJvqNBgMbN++vdaKhrYQETw8PAAoKyujrKwMESE7O5vIyEg6deqEq6sro0eP5v3336+3rujoaHbv3g3AokWLePrppxsdT6X09HT69etH3759cXNzIz4+nuTkZJtir1ReXk5xcTHl5eUUFRXRq1evJp1Xc9GNiabdibI3wq65UPAloEz/7pprd4OSmJhIeHg4vXv3tkjv0KEDRUVFvPnmm+zZs4f33nuPtWvX1ipvbXGs+fPn88orrzS4UqOIEBsby7Bhw0hISKhKNxqNhIWF4ePjQ0xMDJGRkYSEhPDxxx9z7do1ioqKSElJsVhG15ply5axfPlyNm7cyPHjx22+hWRtcaycnByLz8jPz89iZcf6YgfTsr/PPfccffr0oWfPnnh6ehIbG9uk82ourfGhRU3TWtrHf4DyIsu08iJTelDT1zjJzMwkNDS0VnpGRgazZs0iOjqaZ555ps7yKSkpFk+Qf/DBB/j4+DBs2LAGn9b+5JNP8PX1JS8vj5iYGAIDAxk1ahQuLi6cOHGCGzduMGnSJDIzMwkJCWHBggXExsbSuXNnwsLCcHFxqbf+UaNGoZRi5cqVpKWlNZi/+jk1VV2xX79+neTkZC5cuICXlxdTp05lw4YNzJw5s9Hn1Vz0lYmm3YkKvmpcuo26du1KaWmpRdqhQ4coLCxk9OjRja7vwIEDbNu2DX9/f+Lj49m7dy8zZ860mtfX1xcAHx8fJk2aRHp6usV+Ly8voqOjq9Zvnz17NseOHWP//v3cddddDBgwoN5YTp48SW5uLm5ubhZPude8/bZ48eKaRa3GWv2KwWAwVMVvTc3Y9+zZQ0BAAN7e3rRv357Jkydz8ODBJp1Xc9GNiabdibrU0ddXV7qN4uLiSEpKIj8/H4AzZ84wZ84c3nnnnQZvU1nz0ksvYTAYuHjxIomJiYwZM4YNGzbUyldYWFh1RVNYWMiuXbsICQkhPz+fGzduAFBcXMzu3bsJDAwEIC8vD4CvvvqK999/n8cee6yqvrFjx1rcdsrNzWXGjBkkJyfj4eFR9aVeXFxMdnY2S5cuJT4+nry8PG7fvt3geY0YMYKzZ89y4cIFSktLSUxMZOLEiRZ56ou9T58+HD58mKKiIpRSpKamEhQU1OB5tSTdmGjaneiHy8G1k2WaaydTuh0iIiJYtGgRDzzwAIGBgcTFxbF+/XpGjhxpU3lrfSYN5b98+TJXr15l5MiRhIaGEhERQVxcHOPGjSM3N5fo6GiGDBnCiBEjiImJqRpe/MgjjzBo0CB+/OMfs2bNGry8vACoqKjg3LlzdOvWDYCioiImT57MihUrCAoK4vnnn2fZsmUAHD9+nKlTp7J06VI8PT3Zt29f1bDkmjFW5+rqyurVq3nwwQcJCgpi2rRpBAcHW+SvL/bIyEimTJlCeHg4gwcPpqKigrlz59Z7Xi2uqcPAWvPWIkOD8z6v/bqFtNWhqZV0/M7R6KHBpzYo9Zd7lXpVTP+e2tCs8Vy5ckUNGjRIZWRkNKqcs4c2nzx5Uj377LM25V21apVKTU1VSik1c+ZMtXDhQnX+/PmWDK/ZNPfQYN0Br2l3qqAZdnW2N6R79+5kZWW1WP0tJSQkhJUrV9qUNysriytXrpCUlMTs2bNJSEggICCghSNsnXRjomma1kTr1q2rer1mzRr69r1zp1zSjYmmaVozmDdvXptZGKsl6A74lnZsvmnTNE37HtNXJi3l86WQuey796dfN/0bsgSGLHVCQJqmaS1HNyYtZchS07YnyvT+gTTnxaJpmtbCHHabS0TGichpETknIgut7F8lIifM2xkRuVFt3+Micta8Pe6omJuVvt2ladr3mEOuTETEBVgDxAAG4IiIbFPVVkxUSj1bLf/TmNd5F5FuwBJgOKCAY+ay1x0Ru91uXYSiLyHvI9P7Vni7q+CT7QB0GRnn5Eg0TWurHHWbKwI4p5Q6DyAiicBD1L2W+3RMDQjAg8BupdQ35rK7gXHA5haNuLl4+Ju2Sq3odtetI6kUHd1b9b74pGlun07Dx+AxYqyzwtI0rQ1yVGPiC1SfB9kARFrLKCL3AgFA5bectbK1ZkQTkbnAXABvb+8GZxhtjDBf06ybJ7LSLF7bVNY8t06lEzbEdevWrWaNv24uEBTDvV8eBeDLe83TQBQCdhzfcfG3jLYav6enJwUFBRiNxjY9RLUtx9+WYi8pKWnW3/PW2AEfD7ynlDI2ppBSKgFIABg4cKCKiopqvojyTwIQ1X+w5Wtb7PGyeGtLXGlpaTblay7Xk88BENBMx3R0/M2trcafnZ1Nly5dKCgosJjVtq1py/G3pdjd3d0ZOnRos9XnqA74HKD6ajl+5jRr4rG8hdWYspqmaZoTOKoxOQL0F5EAEXHD1GBsq5lJRAKBu4BD1ZJ3ArEicpeI3AXEmtM0TWtjtm7dypNPPsmjjz7Krl27nB2O1owc0pgopcqBpzA1AtlAklIqS0ReEJHqk/jHA4nm2Ssry34D/DemBukI8EJlZ7ymaa3Tli1biIyMJDQ0lOHDh7Nzp+nvv4cffph169axdu1a3n33XZvrMxqNDB06tGoKdmv8/f0ZPHgwYWFhtaaBt1b+iSeewMfHh5CQkEaeXfPYsWMHAwcOpF+/frz88su19peUlBAREUFoaCjBwcEsWbLEYv+qVasIDg4mJCSE6dOnU1JSAsDrr79OSEgIwcHBNi8t3Bwc1meilEoBUmqkLa7xfmkdZd8G3m6x4DTtDlR85gSFn+6m4tYN2nl40Tkyho4Dwuyud9OmTbz55pskJyfTo0cPzp49yw9/+EOOHj2Kn58fAC+++CLz5s2zuc7XX3+doKAgbt68WW++ffv2cc8999hUftasWTz11FP89Kc/tTmO5mI0Gpk3bx67d+/Gz8+PESNGMHHiRAYNGlSVp0OHDuzduxcPDw/KysoYOXIk48eP57777iMnJ4c33niDU6dO0bFjR6ZNm0ZiYiLDhw9n3bp1pKen4+bmxrhx4/jRj35Ev379Wvyc9NxcmnYHKj5zgoKPtlJx6wYAFbduUPDRVorPnLCr3sLCQhYuXEhSUhI9evQAoH///kRFRZGamopSigULFjB+/HjCw8NtqtNgMLB9+/Zay+Paqq7yo0aNqloAyxbR0dHs3r0bgEWLFvH00083KR6A9PR0+vXrR9++fXFzcyM+Pp7k5GSLPCKCh4cHAGVlZZSVlSEiVfvLy8spLi6mvLycoqIievXqRXZ2NpGRkXTq1AlXV1dGjx7N+++/3+Q4G0M3Jpp2Byr8dDeUl1kmlpeZ0u2QmJhIeHg4vXv3tkjv0KEDRUVFvPnmm+zZs4f33nuPtWvX1ipvbaXF+fPn88orrzS47K+IEBsby7Bhw0hISGh0+YYsW7aM5cuXs3HjRo4fP27zLSRrKy3m5ORYfEZ+fn4WywRXMhqNhIWF4ePjQ0xMDJGRpicqfH19ee655+jTpw89e/bE09OT2NhYQkJC+Pjjj7l27RpFRUWkpKRYrDXfklrj0GDNSfST8HeOyisSW9NtlZmZSWhoaK30jIwMZs2aRXR0NM8880yd5VNSUiye0/jggw/w8fFh2LBhDT4T8cknn+Dr60teXh4xMTEEBgZy8+ZNm8s3ZNSoUSilWLlyJWlpabi4uNhULiUlpeFMdXBxceHEiRPcuHGDSZMmkZmZSUhICNevXyc5OZkLFy7g5eXF1KlT2bBhAzNnzmTBggXExsbSuXNnwsLCbI7TXvrKRMN48zplly9QfPIgxScPkvfWH8h76w/cOpLq7NC0FtLOw6tR6bbq2rUrpaWlFmmHDh2isLCQ0aNHN7q+AwcOsG3bNvz9/YmPj2fv3r3MnDnTal5fX9OzzD4+PkyaNIn09PRGlW/IyZMnyc3Nxc3NzeJZkpq3zxYvXlyzqNVYq18xGAyGqvit8fLyIjo6mh07dgCwZ88eAgIC8Pb2pn379kyePJmDB00zWMyePZtjx46xf/9+7rrrLgYMGNCo82wq3ZhouHS9i/a9Aqo2n18ux+eXy++IKVXy//dj8v/34yaVPfjqJQ6+6phbCM2tc2QMuLa3THRtb0q3Q1xcHElJSeTn5wNw5swZ5syZwzvvvNOk20wvvfQSBoOBixcvkpiYyJgxY9iwYUOtfIWFhVVXNIWFhezatYuQkBCby9c0duxYi9tOubm5zJgxg+TkZDw8PKq+1IuLi8nOzmbp0qXEx8eTl5fH7du3G6x/xIgRnD17lgsXLlBaWkpiYiITJ060yJOfn88N8wwaxcXF7N69m8DAQAD69OnD4cOHKSoqQilFamoqQUFBAOTl5QHw1Vdf8f777/PYY481GE9z0Le5tDvStR2fcn3nkar33+7PAOCuB0eAe/1lj/7lMp8lfHdfP3Oz6T9v+NyeDP95r+YPtgVUjtpq7tFcERERLFq0iAceeIDbt29jNBr5xz/+wf33329T+QkTJvDaa6/Z/BT5hAkTWL9+PSUlJUyaNAkwdUw/9thjjBs3rt6y06dPJy0tja+//ho/Pz+WLVvG7Nmzqaio4Ny5c1Wd80VFRUyePJkVK1YQFBTE888/z4IFCxg3bhzHjx9n6tSpzJ8/n5///Ofs27ev1rDkyhh79frud8PV1ZXVq1fz4IMPYjQaeeKJJwgODrbI//XXX/P4449jNBqpqKhg2rRpVUObIyMjmTJlCuHh4bi6ujJ06FDmzp0LwCOPPMK1a9do3749a9aswcvLy6bP0l5S7ZGO742BAweq06dPN1+F5ilU8B5s+doWleuZVLJhokfHT6ey3uL9XQ81bdRMpbY0HYlhtWmki99Tk6vSbI3/33NNv2M/ThjYIrE1VnZ2NkFBQa1mSo+rV68yZswYNm/ezJAhQ2wu5+z4MzMzefvtt1m5cmWDeV977TWGDBnCmDFj+MlPfoKfnx9z584lICDAAZHap/L3pToROaaUGl5HkXrpKxNN01pE9+7dycrKcnYYjRYSEmJTQwKQlZXFlStXSEpKYvbs2SQkJLSJhqQl6MZE0+4g+V99C4B3H08nR/L9sG7duqrXa9asoW/fvk6Mxrl0Y9KSLmyErw9DxW1o1wE87sy/WDTnu3a5gG8ufzfk9kZeIQDdenXh7l7OvyX2fTBv3rw2M/18S9CNSUu5sBHS55oaEjD9e/OMKT1ghnNj0+44d5sbDcPprwHwG1h7yhFNs4ceGtxSMv4AxqIaiRWm9Fak+MwJyq5+RdnlC5Rd+YqKolvODknTtDZINyYtpeirOtK/dGwc9aicnwmjeR2yCiPGG1/bPT+Tpml3Ht2YtJROfaynu3g4No56WJ2fCWX3/Eyapt15Gt2YiEhnEXHMZC9tWehycOlUO914CzYJJHZ0fEw1tNT8TJqm3XkabExEpJ2IPCYi20UkD/g/IFdETonI/4iITRPli8g4ETktIudEZGEdeaaZ680SkU3V0o0icsK81VqhsVUKmAERCSBulukuncB/Bjx0wTlxVdNS8zNpmnbnseXKZB/wH8DvgB5Kqd5KKR9gJHAY+JOI1DtzmvlKZg0wHhgETBeRQTXy9Dcf4wdKqWBgfrXdxUqpMPNmOYFNaxYwA7zvB/ee5oR2YCwB167QsYdTQ4M65mdC7J6fqa24efQ0JV9epeSLy1x84e/cPNqMsyZo2h3GlqHBDyilat5Yr1xOdwuwRURqfiPVFAGcU0qdBxCRROAh4FS1PE8Ca5RS183159kQW8srzIPSAkDBlaOgBFQZGEvBxa3B4gBUlIF7L+jYE+6JhOLchss4QOU8TAVp75s64du54NK1W7Osttfa3Tx6mvykfVBuGnxQfr3A9N5GZz+8xtWThVSUKjbFnWTEU73oP/7ulgpX01q9BhuTyoZERLoBzwI+mBqBf1T74q/V2NTgC1SfXtUARNbIM8B8nAOAC7BUKbXDvM9dRI4C5cDLSqmtDcXdLArz4NsvAPP8ZcZqs4EWXAKv/7CtHq/g716PWNNs4TWHjgPCKMk+6uwwHO6blMOosnKLNFVWTv57aTCu/rm2zn54jY9f/JKKUtPvxa0rpXz8ommUnm5Q6rd161a2b9/OzZs3mT17NrGxsc4OSWsmjXloMRHYA3wKDAY+EZGfKaXSmzGW/kAU4AfsF5HBSqkbwL1KqRwR6QvsFZGTSqkvqhcWkbnAXABvb2+7F8IBuM/fFff2Yn1n4RUovIKxQvHxF+XW8wBh5imkK52wIa5bt241S/y2urdGjBl2HtvR8TeF7/UCrP1k1e0y/JIzOZecyc2B3twM7F4rz/kVnSkvsbxDXF6i+HjFeXI6nmyhiBvm6elJQUEBRqOxziexjeYrsZZ+Ujs5OZnXXnuN27dv0759e55//nkeeOABxo4dy9ixY7l+/TqLFi2yOpuwtfiNRiOjR4+mZ8+e/Otf/7J6zJCQEDw8PHBxccHV1ZWPPvqo3vK/+tWv2LFjB97e3nz66afNct71ffY17d69mwULFmA0Gnn88cf59a9/bbG/pKSEcePGUVpaSnl5OQ899BB/+MN3z6mtXr2af/zjH4gIgwYN4q233sLd3Z0///nP/P3vf0cpxeOPP868efOsHr+kpKRZ/582pjHxVkq9Yn79gYi8C2wC7rOhbA5QfR1PP3NadQbgU/NVzgUROYOpcTmilMoBUEqdF5E0YChg0ZgopRKABDDNGtwss9bmHKhnZzvoeDcunv5E9a7ndtceL4u3tsTl+FmDz1m8D7Dz2G1h1uCL+7+k/LqV//SuLhjiguqN/8xzx6yml99o59Tzzs7OpkuXLvXOuvutq+nquiVn5d20aRNr1qzhgw8+oEePHpw9e5Yf/vCHHD16FD8/PwCWLl3K/PnzrcZhLf6VK1cSHBzMzZs364xdRPjoo4+4557aT/dbK//kk0/y7LPP8tOf/rTZPg9bZzw2Go385je/Yffu3fj5+TFixAimTp3KoEHfdSV7eHjw0Ucf4eHhQVlZGSNHjuThhx/mvvvuIycnh4SEBE6dOkXHjh2ZNm0a27dvZ/jw4fzzn//k6NGjuLm5MW7cOKZMmUK/frXHSbm7uzN06NBmOW9o3NDgb0Skat51c/+HlbGvVh0B+otIgIi4AfFAzVFZWzFdlSAi92C67XVeRO4SkQ7V0n+AZV9Ly3HpUM/OCmjnYnu/SRugjOWUf30ZY9H3f36hbhPuQ9rX+FtKoH23rg2W9ehu/WdeV3prdX7/dt77+YP8fUoo7/38Qc7v3253nYWFhSxcuJCkpCR69DANMunfvz9RUVGkpqailGLBggWMHz+e8PBwm+o0GAxs37691oqGtqqr/KhRo6rWLLFFdHQ0u3ebnsFatGgRTz/9dJPiAUhPT6dfv3707dsXNzc34uPjSU5OtsgjInh4mJ5LKysro6ysDJHvrqfLy8spLi6mvLycoqIievXqRXZ2NpGRkXTq1AlXV1dGjx7N+++/3+Q4G6Mxjck8YLOIvCUivxKRNdS4OqiLUqoceArYCWQDSUqpLBF5QUQqR2ftBK6JyClMI8h+o5S6BgQBR0Ukw5z+slLKMY1Jlz4gVj6idh2gcw9TJ/z3iLHgBqr0NoVH9zrsmOlv/4n0t//ksONV6jp8IN7TosHV9MiU611daO99Fy5dGn7+Z8RTvXB1t7xJ5uoujHiqbSyMBaaG5ODaZRR+nQtKUfh1LgfXLrO7QUlMTCQ8PJzevXtbpHfo0IGioiLefPNN9uzZw3vvvcfatWtrlZ8wYQK5uZYDVObPn88rr7zS4EqNIkJsbCzDhg0jISGh0eUbsmzZMpYvX87GjRs5fvw4r732mk3lJkyYwOXLly3ScnJyLD4jPz8/i5UdKxmNRsLCwvDx8SEmJobISFNXs6+vL8899xx9+vShZ8+eeHp6EhsbS0hICB9//DHXrl2jqKiIlJQUi+WBW5LNt7mUUv8nIuHAw5iG954A/qsR5VOAlBppi6u9VsCvzVv1PAcx9dE4Xmcf0783zgHKfKXSDlza29753gaUXb5I1SADoCQrnZKsdHBxxWfushY55ol3/0xG0ndfJtnbNwIQOu0XhD36qxY5Zk1dhw/k5mHTeht+T02uWiirIZWd7B+9YOqE9+jh1uZGc3228Q2Mt0ss0oy3S/hs4xv0HRXX5HozMzMJDQ2tlZ6RkcGsWbOIjo7mmWeeqbN8SkqKRZ/DBx98gI+PD8OGDWvw/v4nn3yCr68veXl5xMTEEBgYyM2bN20u35BRo0ahlGLlypWkpaXh4mLbs9spKSkNZ6qDi4sLJ06c4MaNG0yaNInMzExCQkK4fv06ycnJXLhwAS8vL6ZOncqGDRuYOXMmCxYsIDY2ls6dOxMWFmZznPayuakWkTHAW5hGYX0JfEb1b6Dvq84+4NYF3LpCj+GmhuR7xrW7H9KxM1R2Sbu2p0P/UO6e+VyLHTPs0V/x+JbP6R48nO7Bw3l8y+c8vuVzhzUk9uo//m66D+5Mz2EePLZ9MPlZRW1qPfjCa1calW6rrl27UlpqecV+6NAhCgsLGT16dKPrO3DgANu2bcPf35/4+Hj27t3LzJnWH2vz9fUFwMfHh0mTJpGent6o8g05efIkubm5uLm5WfSL1Lx9tnjx4ppFrcZa/YrBYDBUxW+Nl5cX0dHRVWvP79mzh4CAALy9vWnfvj2TJ0/m4MGDAMyePZtjx46xf/9+7rrrLgYMGNCo82yqxlz3vQ38G9ODin2BxUDbW0ZNq0VcXM238xQgUF6OuHXApZNe56IhBZdvkzDsGJmb88jcnEfCsGMkDDvG0b9cbriwE3W+2/pDs3Wl2youLo6kpCTy8/MBOHPmDHPmzOGdd95p0m2ml156CYPBwMWLF0lMTGTMmDFs2LChVr7CwsKqK5rCwkJ27dpFSEiIzeVrGjt2rMVtp9zcXGbMmEFycjIeHh5VX+rFxcVkZ2ezdOlS4uPjycvL4/bt23VVW2XEiBGcPXuWCxcuUFpaSmJiIhMnWj6PnZ+fzw3zSMvi4mJ2795NYGAgAH369OHw4cMUFRWhlCI1NbVqCd68PNMjel999RXvv/8+jz32WIPxNIfG/HS/VEptVUr9Syn1vFLqIaWUTVOpaG1AhRHp1AVX7564B0foqeht1KVXB+YeG0bPYR70HObB3GPDmHtsGMN/3rr7T8JnPINLB3eLNJcO7oTPqPsWlC0iIiJYtGgRDzzwAIGBgcTFxbF+/XpGjhxpU3lrfSYN5b98+TJXr15l5MiRhIaGEhERQVxcHOPGjau37PTp07n//vs5ffo0fn5+/PWvfwWgoqKCc+fOVXXOFxUVMXnyZFasWEFQUBDPP/88y5aZbv8eP36cqVOnsnTpUjw9Pdm3bx/Dh1suoW6tz8TV1ZXVq1fz4IMPEhQUxLRp0wgODrbIn5ubS3R0NEOGDGHEiBHExMTwox/9CIDIyEimTJlCeHg4gwcPpqKigrlz5wLwyCOPMGjQIH784x+zZs0avLy8bP487dFgn4mI/APTLa3DIvJrpZRtiyNrzrNvvunf6NdsLuLa7bvnKbqOajsz1mhNU9kv8tnGNyi8doXOd/cgfMYzdvWXVPrZz37Gz372M65evcqYMWPo3LmzzWVr9plUFxUVVWvodfX+iIyMjHrrrll+8+bNVvOdOnWKRx55hI4dTYMxOnXqxKFDh6r2jxo1qup9eno6Q4YMAUyNzhdffFH1pW4txuomTJjAhAkTaqVX5u/VqxfHjx+v83yWLVtW1ahV9/HHH9dZpiXZ0gH/NyAU6A7Eish/Ahnm7XOllPUniDTHO7gUDlX75frsddO/9y+B/7fUGRE1SuWorognFjg5kjtD31FxzdJ41KV79+5kZbW9O+EhISGsXGnb38xZWVlcuXKFpKQkZs+eTUJCAgEBd+by3LZMp7IXqBorKiKumIbrhgIjAN2YtBb/b6lpezfK9P7RNOfF0gi38nL4+yNDqt47Y2SXpjXFunXrql6vWbOGvn37OjEa57JpaLCItAMWKqX+aH5m5KR50zS7efj4MmXtTnYsfgKAcS+87ZQ4VLmR0qvXaddPDzzQGm/evHktPk1Na2ZTB7xSqgL4cQvHomlOVX69AFVSStfT+c4ORdPanMaM5soQkSXmqxStLdg3/7vOeK1OJecvc+7Z1RhvFgHgcfEbzj27mi9+85aTI9O0tqMxEz12A0YDvxSRT4HP0R3wrdO3F6HgSzCYZ01tpR3x5/dvJ//M51SUlfLezx/EtYM77l6OeYr82o5Pub7ziNV9FS5C17D+3DPxBw6JRdO+D2wZGizKZJr5fQcgGNMUJxHAvyrztGyobcznSyHTylQkny+FIUtb9tie/qatUivsiK+cG6qizPS0dOHXuSDSQKnmc/e4SO4e992SOnn/SuPmwUwQEKOinbsbrl2tD2k9+pfLfJbw3bMQCcNMswh79GxbEz1qWnOy5cpkn4hsAZKVUl8ppW4Dn4lIJvBDEfk7pgkY/9aCcbY9Q5ZaNhp7or5LbyVuHUmlyMqkjreOpOIxYmyLHtva3FAoRcHV2pPdOYKxoAiXrp1w6dqZb1yNeJhveVkz/Oe9aj2U+O+5eslf7c5mS2MyDngC04zBAcANwB3Taoi7gNeUUnU/WaO1Wh4jxlo0GteT11elt7S65oCqKHfOTMw9n5hQNdHjjZDuhDVyXRJjWQXXz5dQ9HUZne75/s3fpmkNabAzXSlVopT6s1LqB8C9wFggXCl1r1LqSd2QaE1R1xxQ7Vzb5q2iW5dLKbtVwbF1rXdOrpvXiii5VUZxQSkXPr/KzWt1X31pWmM12JiISNUUm0qpMqVUrnkpXc0Wny+FTQJ5H5m2TWLaPl/q3LiczNrcUIjQpXvdM6e2Rn+9/zMShh2j6GvT0s3Z731NwrBj/PX+z5wcmaWb14rI+/JbKrs2y0uN5H35rW5QtGZjy22un4jICODXSiljSwf0vVOz70QDvpsb6sCfl1BRVkrne3rSztWVouv5FF//mo531V56tTWavm0wh167xBc7r5uWvHEXAqK9uG9+74YLO9C1nAJUheUYGVWhuJZTQNe7bV0w1X5bt25l+/bt3Lx5k9mzZxMbG+uwY2sty5ZnRsYDxcBeEfFu6oFEZJyInBaRcyKysI4800TklIhkicimaumPi8hZ8/Z4U2PQWpe+o+LwHjCE7sHDmfKXnZTfLqGs6BYZ/6q9Al9r1cm7PW6dXapm7zfeVrTv7NLq+k3KS63/HVhXur22bNlCZGQkoaGhDB8+nJ07dwLw8MMPs27dOtauXcu7775rc31Go5GhQ4dWzZprjb+/P4MHDyYsLKzWzL01y5eUlBAREUFoaCjBwcEsWbKkCWdpnx07djBw4ED69evHyy+/XGt/QzGuWrWK4OBgQkJCmD59OiUlJfWmtzRb5uaqABaKyGTgYxFZiWmVxUyllE3XyCLiAqwBYgADcEREtlVffldE+gO/A36glLouIj7m9G7AEmA4pv+yx8xlrzfiPO8c2Rsh9zAYb5tWhvRs/ZPOXT11zGJurtM7kzi9MwmX9m7MTDzqxMhsU/xNOZ3ucaWTd3t8QjpX3fJqTVzdXKw2HK5uzb8K36ZNm3jzzTdJTk6mR48enD17lh/+8IccPXoUPz8/AF588UXmzZtnc52vv/46QUFB3Lx5s958+/bt4557al/V1izfoUMH9u7di4eHB2VlZYwcOZLx48dz3333NeJMm85oNDJv3jx2796Nn58fI0aMYOLEiQwaNKgqT30x5uTk8MYbb3Dq1Ck6duzItGnTSExMJCYmxmr6rFmzWvycbHqaXUR+BMwBSoFw4FXgkoics/E4EcA5pdR5pVQpkAg8VCPPk8CaykZCKZVnTn8Q2K2U+sa8bzemEWZaTdkbYddcU0MCpn+/OWNKb8Xu6T+YgB9OqHrOxMXNnYAfTuCRt3Y4OTLbxL76H3je6077Ti6MXHgvsa+2viWd7/btgrSzfI5HLl5CvZ/CuWdXc/GFv3PzqP3DmwsLC1m4cCFJSUn06GEaZNG/f3+ioqJITU1FKcWCBQsYP3484eHhNtVpMBjYvn17rRUNbWWtvIjg4eEBQFlZGWVlZUgDzzlFR0eze/duABYtWsTTTz/dpHjANHV9v3796Nu3L25ubsTHx5OcnGyRp6EYy8vLKS4upry8nKKiInr16lVvekuz5aHFC8ApYJVSaneNfX42HscXqL6mqQHT8r/VDTDXeQDTsOOlSqkddZSt1UsrInOBuQDe3t52r/dcXZiv6a+3E1lpFq9byq1bt5oU/30n/wv38poXixWU7PkvDl9tuGP7XvOqbhl2fnaNib9yJbmS69+CMt0rMpbeJv/6t3yakWlXHI3lbY7l1i23Rn/+N26Y1r5ozt+7xvL09KSgoACj0VhrwkFxg67dO3DzSglKgXxlgE8/o6LcdLVSfr2AvKS9lJSU0DG06TPf/uMf/2DIkCF4eXlZxNCuXTu++eYb/ud//oddu3bx9ddfk5mZyezZsy3KP/LII7z++utVVzAATz31FEuWLOHWrVuUl5fXO5niAw88gIhUralSX3mj0cioUaM4f/48Tz75JIMGDaq37gULFvDCCy/w5ZdfcuTIEd59991a+a199o888girV6+mZ8+eVWnnzp2jR48eVXnvvvtujh49arU+azF27dqVp556ij59+uDu7s6YMWO4//77q863Zrq18yopKWnW31dbOuDHK6X+z9oOpZSh2SIxxdIfiAL8gP0iMtjWwkqpBCABYODAgarmIjp2yTdNkBzVf7Dl6xaSlpZWaxEgmxzLs5rsXppnU33Xk00XmgF2fnaNiX/H3n8A0KGzO8a7vOnYzRvvfoMpvvF10z4DOxgyvwHAw8Oj0cf+9ybTX/VRUTX/RnKc7OxsunTpQkFBgcUa5ZW6dIHSW18DUJ6ZTXl5jdteZUaK9p7AZ2Rok2M4d+4cw4YNq3X8U6dO8eSTTxIdHc1vf/vbOsvv2rXLIv4PPviAXr16MWrUKNLS0nB1dbV6bmBaL97X15e8vDxiYmIICwvj5s2b9Zb//PPPuXHjBpMmTeLLL78kJCSkztjGjRvHSy+9xFtvvUVaWprVOKx99rt27aqVr2PHjrRv374qb8eOHWutLV9fjNevX2fnzp1cuHABLy8vpk6dSnJyMnFxcVbTra177+7uztChQ+s838ay5TbXcBHJFxFDZee3iNwnIi+KyDEbj5MDVB/e4mdOq84AbDMPP74AnMHUuNhSVgPo0qdx6Wa3jqSS99YfKLt8gbLLF8h76w/kvfUHbh1JbYEgrYv+7Sq69rqX9u6duG/uH4j+7SqHHftOVH7d+l/gdaXbqmvXrpSWWj54eujQIQoLCxk9enSj6ztw4ADbtm3D39+f+Ph49u7da/WLEcDX13T17ePjw6RJk0hPT7epvJeXF9HR0VXrutfl5MmT5Obm1vrSr3n7bfHixQ2el6+vL5cufXfDxWAwVMVvTc0Y9+zZQ0BAAN7e3rRv357Jkydz8ODBOtMdwZbGZAkwARgKBIjIbkwLYrkB8208zhGgv4gEiIgbEA9sq5FnK6arEkTkHky3vc4DOzGt8HiXiNwFxJrTtJp+uBxcrQzzjPhNvcU8RozF55fLa22OeBJec7yy20ZUp45W97neZd9aLnFxcSQlJZGfb5rG/8yZM8yZM4d33nmHdu0aP+H4Sy+9hMFg4OLFiyQmJjJmzBg2bNhQK19hYWHVrZzCwkJ27dpFSEhIneXz8/OrbrEWFxeze/duAgMDq+obO3YsOTnf/c2am5vLjBkzSE5OxsPDo+pLvbi4mOzsbJYuXUp8fDx5eXncvn27wfMaMWIEZ8+e5cKFC5SWlpKYmMjEiZbLZdcXY58+fTh8+DBFRUUopUhNTSUoKKjOdEew5ad7Syl1RCmVDyzDtMLiYKXUb5VSNi02bF5Q6ylMjUA2kKSUyhKRF0Sk8hPcCVwTkVOY5vr6jVLqmlLqG+C/MTVIR4AXzGlaTUEzIDbBNIoLqPrxft32lk51pGs7PuXcs6sp+eIyJV9cxi85k3PPrubajk+dHVqLaN/Bhe6TRoKr5Uguae9Ktwn2jWaKiIhg0aJFPPDAAwQGBhIXF8f69esZOXKkTeUnTJhAbm5uwxmr5b98+TJXr15l5MiRhIaGEhERQVxcHOPG1T1OJzc3l+joaIYMGcKIESOIiYmpGjZcUVHBuXPn6NatG2Ba233y5MmsWLGCoKAgnn/++aq1148fP87UqVNZunQpnp6e7Nu3r9aw5MoYq3N1dWX16tU8+OCDBAUFMW3aNIKDgy3y1xdjZGQkU6ZMITw8nMGDB1NRUcHcuXPrTHcEaWiyXxHJxXR1ctq8pSilbBuG4SQDBw5Up08348R75n4SvAdbvm4hTe4zqbTCBaione7iDvOLm16vjRrVZ1JtdUVnr7RYqSmff+VEjz9OGNgCEdkmOzuboKCgOvtMAAynTX0mfgPv4eIHRyn/9HO4VYTrXV3oNuE+ug5vvvivXr3KmDFj2Lx5M0OGDGm4gFl98TtCZmYmb7/9tk3rwL/22msMGTKEMWPG8JOf/AQ/Pz/mzp3bJtaBr/x9qU5EjimlhtdRpF62dMAvwTTd/Azzv11EZA9wHDiulNpUX2HNCXpGwI3zUPw1UGG69dVvEkS96uzItFbEtb8/rv398RvYMrMNdO/enaystndVHBISYlNDApCVlcWVK1dISkpi9uzZJCQktImGpCXY8tBiQvX35uHAg4EhmJ6O141Ja+PSAdpVXp20g/IS6NAVOlufXNEZTrz7ZzKSvnvSvfKhxc7ePesqommtzrp166per1mzhr59mz6suq1rzEqLQNVwYAPwYfOHozUbYxl07gWde0LPSCi0/T60I4Q9+ivCHv2VRVrlbS5Na4vmzZtX73Mq33eNbky0NuKe4O9eP7DGeXFomnZH0I2JptmhriV8w+f2rLUao6Z9n+nGRNPsYG0JX027EzX+KSJN0zRNq0FfmWhOV9fIrtBpv6jVSa9pWuukGxPN6ayN7NI0rW3Rt7k0TdM0u+nGRNM0TbObbkw0rZW6uGg3FxftbjhjG7J161aefPJJHn30UavrfGhtl25Mvk8OLoUVAoaPLLeDS50dmdYIl17Zz2GfP3Il4QhXEo5w2OePHPb5I5de2e/s0Gy2ZcsWIiMjCQ0NZfjw4ezcaVo14uGHH2bdunWsXbuWd9991+b6jEYjQ4cOrZo11xp/f38GDx5MWFhYrZl7rZV/4okn8PHxqXdBrJa0Y8cOBg4cSL9+/Xj55Zdr7S8pKSEiIoLQ0FCCg4NZsmSJxf5Vq1YRHBxMSEgI06dPp6SkpN70FqeU+t5tAwYMUM0q73PTVvN1C9m3b1/zVJQ42rQ5WLPF7yStJf7Mh/6pMh/6p835T506pZRS6ubNm3XmufR/+erS/+XXet2cNm7cqO677z6Vm5urlFLqzJkzqnv37urSpUtVeX7961+rY8eOWS1vLf4VK1ao6dOnq7i4uDqPe++996r8fOvnY638Rx99pI4dO6aCg4NtOi9b1PfZV1deXq769u2rvvjiC3X79m01ZMgQlZWVZZGnoqJCFRQUKKWUKi0tVREREerQoUNKKaUMBoPy9/dXRUVFSimlpk6dqt555506062p/H2pDjiqmvi9q69M6nPzK8g5AKU3TVvla2PDi99oWmuX88kt9j5jIGH4MTbFneTsh9fsrrOwsJCFCxeSlJREjx6miUX79+9PVFQUqampKKVYsGAB48ePJzzctpUsDAYD27dvr7Wioa3qKj9q1KiqNUtsER0dze7dptuOixYt4umnn25SPADp6en069ePvn374ubmRnx8PMnJyRZ5RAQPDw8AysrKKCsrQ0Sq9peXl1NcXEx5eTlFRUX06tWr3vSW5rDGRETGichpETknIgut7J9lXh74hHmbU22fsVp6zRUaW07XPuD7A9PWpdrKwcbbpoYl54CpwdG0Nubsh9c4uf4bSr42goJbV0r5+MUv7W5QEhMTCQ8Pp3fv3hbpHTp0oKioiDfffJM9e/bw3nvvsXbt2lrlrS2ONX/+fF555ZUGV2oUEWJjYxk2bBgJCd9Ndm5r+YYsW7aM5cuXs3HjRo4fP85rr71mUzlri2Pl5ORYfEZ+fn4WKztWMhqNhIWF4ePjQ0xMDJGRkYBp2d/nnnuOPn360LNnTzw9PYmNja0z3REc8pyJiLgAa4AYTDMOHxGRbUqpUzWyvquUespKFcVKqbAWDrN+XfuYNk37Hjiy+jIVpZYL45WXKI6svkz/8Xc3ud7MzExCQ0NrpWdkZDBr1iyio6N55pln6iyfkpJiMfPuBx98gI+PD8OGDSMtLa3eY3/yySf4+vqSl5dHTEwMgYGB3Lx50+byDRk1ahRKKVauXElaWhouLi4NF8J0Tk3l4uLCiRMnuHHjBpMmTSIzM5OQkBCuX79OcnIyFy5cwMvLi6lTp7Jhwwbi4uKsptdc974lOOrKJAI4p5Q6r5QqBRKBhxx07DtPzY74FWLadEe8Znbrammj0m3VtWtXSkst6zh06BCFhYWMHj260fUdOHCAbdu24e/vT3x8PHv37q3zi9HX1xcAHx8fJk2aRHp6eqPKN+TkyZPk5ubi5uZmsRJkzdtnixcvbrAuX19fLl26VPXeYDBUxW+Nl5cX0dHRVWvP79mzh4CAALy9vWnfvj2TJ0/m4MGDdaY7gqOegPcFLlV7bwAireR7RERGAWeAZ5VSlWXcReQoUA68rJTaWrOgiMwF5gJ4e3vb/VeIM926dcvO+KNgWFTt5FLAAZ+L/fE7V2uJ3+3GDQCbY/H09KSgoACj0VjnuhrGciMAnXxcKbpaXmt/Jx9Xu9bkiIqK4oknnmDu3Lncc889nD17lieeeII1a9ZQWFhoUx3V4//973/P73//ewA+/vhj3njjDd56661aMRYWFlJRUUGXLl0oLCzkww8/ZMGCBfz85z+vt/ytW7eoqKioVd+Pf/xj/vKXv1T1N1y5coXp06ezadMmfvvb3/L+++8TExNDcXExmZmZ/O53v+PMmTO89NJLFBQUNPgZBgYGcubMGU6ePEmvXr3YtGkTf/3rXy3Kff3117i6uuLl5UVxcTE7duxg/vz5FBQUcPfdd3Pw4EGuXr1Kx44d2bFjB0OHDq0z3Vo8JSUlzft73tSe+8ZswBRgfbX3PwFW18hzN9DB/PrnwN5q+3zN//YFLgL/Ud/xmn00l4O1ltFETaXjbx4tOZrrTMrXat19x9Rfwo9WbX/9f8fUmZSv7Y777bffVkOGDFEDBw5U/fr1UwcPHrS57Pjx49Xp06et7tu3b1+t0Vzjx49XOTk56osvvlBDhgxRQ4YMUYMGDVIvvvhig+Xj4+NVjx49lKurq/L19VXr169XSillNBpVnz59qkZEFRYWqvvuu0/t2rVLKWUaBXbfffcppZQ6cOCAWrVqlVJKqblz56p169appKQkqzHWtH37dtW/f3/Vt29fi3gr82dkZKiwsDA1ePBgFRwcrJYtW2ZRfvHixWrgwIEqODhYzZw5U5WUlNSbXlNzj+ZyVGNyP7Cz2vvfAb+rJ78L8G0d+/4GTKnveLoxcS4df/No6aHBh9dfUH+PPa7+Muyo2jjh82ZpSKq7cuWKGjRokMrIyGhUOVuH17aUkydPqmeffdamvKtWrVKpqalKKaVmzpypFi5cqM6fP9+S4TWb5m5MHHWb6wjQX0QCgBwgHnisegYR6amUqhzGMRHINqffBRQppW6LyD3AD4BXHBS3pn0vXLtcwDeXv7vVcfboZbqFujFqRE/8h3RvkWN2796drKysFqm7JYWEhLBy5Uqb8mZlZXHlyhWSkpKYPXs2CQkJBAQEtHCErZNDGhOlVLmIPAXsxHTV8bZSKktEXsDUEm4DnhGRiZj6Rb4BZpmLBwF/EZEKTAMGXla1R4Fp2vdK/pZMbh3LQd028ln4anr/IQrvR5r+pPbdvbpwd68uFmmG01/bG+Ydb926dVWv16xZQ9++fZ0YjXM5bAp6pVQKkFIjbXG117/DdPurZrmDwOAWD1DTWon8LZlc+HUK6raps7zUcJMLvzb917GnQdFa1rx58+wavNDW6SfgNa2VubQ8jYpiy5FWFcXlXFqe1qzHURVwu6ic8jJjs9ar3Zl0Y6JprUxpzs1GpTdVeamRCmMF31y+1az1ancmvdKiprUybr5dKTXUbjjcfLs2S/3njuVWjowE4Nv8Qr7NL0RE6DesZ7McQ7vz6CsTTWtlev8hinYdLf/Oa9fRld5/iGqW+v0H+9ClW8eq99JO6NKtI/5DfJqlfu3OpK9MNK2VqexkPz9/O+q2ETe/rnaP5qrO1c2Fdi7f/R2pKhTtXNrh2t62uaY0zRrdmGhaK+T9SAh5/zwBQPDW5p+kz1hmxLW9Cy7t2+He2Q2j7oTX7KQbE027A/Xs163qOROfez2dHI32faD7TDRN0zS76cZE0zSH2bp1K08++SSPPvoou3btcnY4WjPSjYmmac1uy5YtREZGEhoayvDhw9m5cycADz/8MOvWrWPt2rW8++67NtdnNBoZOnQoP/rRj+rM4+/vz+DBgwkLC2P48OH1li8pKSEiIoLQ0FCCg4NZsmRJE87SPjt27GDgwIH069ePl19+udb+hmJctWoVwcHBhISEMH36dEpKSupNb2m6MdE0rVlt2rSJV199leTkZDIyMti8eTOPP/44BoOhKs+LL77IvHnzbK7z9ddfJygoqMF8+/bt48SJExw9erTe8h06dGDv3r1kZGRw4sQJduzYweHDh22Ox15Go5F58+bx4YcfcurUKTZv3sypU5ZTDtYXY05ODm+88QZHjx4lMzMTo9FIYmJinemOoBsTTbtDFe46S+6UTRzu/kc+C19N/pZM++ssLGThwoUkJSXRo0cPAPr3709UVBSpqakopViwYAHjx48nPDzcpjoNBgPbt2+vtaKhrayVFxE8PDwAKCsro6ysDBGpt57o6Gh2794NwKJFi3j66aebFA9Aeno6/fr1o2/fvri5uREfH09ycrJFnoZiLC8vp7i4mPLycoqKiqoW8qorvaXpxkTT7kD5WzK58crHGK/eAvXdZJL2NiiJiYmEh4fTu3dvi/QOHTpQVFTEm2++yZ49e3jvvfdYu3ZtrfITJkwgNzfXIm3+/Pm88sortGtX/9eViBAbG8uwYcNISEhosLzRaCQsLAwfHx9iYmKIjLS2+Ot3li1bxvLly9m4cSPHjx/ntddeqzd/9XO6fPmyRVpOTo7FZ+Tn50dOTk6tsnXF6Ovry3PPPUefPn3o2bMnnp6exMbG1pnuCLox0bQ70KXlaajbzT+ZZGZmJqGhobXSMzIyCAwM5JlnnuHYsWOsXbuWX/ziF7XypaSk0LPnd1O6fPDBB/j4+DBs2LAGj/3JJ5/w2Wef8eGHH7JmzRr2799fb3kXFxdOnDiBwWAgPT2dzMz6G9JRo0ahlGLlypUkJibi4mLbQ54pKSlNvjqoK8br16+TnJzMhQsXuHz5MoWFhWzYsKHOdEfQjYmmtTKXXtnPYZ8/UnDwKwoOfsVhnz9y2OePXHplf7Mdo6Umk+zatSulpaUWaYcOHaKwsJDRo0c3ur4DBw6wbds2/P39iY+PZ+/evcycaf0hTl9fXwB8fHyYNGkS6enpNpX38vIiOjqaHTt21BvLyZMnyc3Nxc3NjS5dvlsbpubtt8WLF9csajXWS5cuVb03GAxV8VtTM8Y9e/YQEBCAt7c37du3Z/LkyRw8eLDOdEdwWGMiIuNE5LSInBORhVb2zxKRfBE5Yd7mVNv3uIicNW+POypmTXOG3r8dxX15v6+19f7tqGY7Rl2TRto7mWRcXBxJSUnk5+cDcObMGebMmcM777zT4G0qa1566SUMBgMXL14kMTGRMWPGWP1Lu7CwsGotkcLCQnbt2kVISEid5fPz87lx4wYAxcXF7N69m8DAwKr6xo4da3HbKTc3lxkzZpCcnIyHh0fVl3pxcTHZ2dksXbqU+Ph48vLyuH37doPnNWLECM6ePcuFCxcoLS0lMTGRiRMnWuSpL8Y+ffpw+PBhioqKUEqRmppKUFBQnemO4JDGRERcgDXAeGAQMF1EBlnJ+q5SKsy8rTeX7QYsASKBCGCJeSlfTdOaqPcfopAOzT+ZZEREBIsWLeKBBx4gMDCQuLg41q9fz8iRI20qb63PpKH8ly9f5urVq4wcOZLQ0FAiIiKIi4tj3LhxdZbLzc0lOjqaIUOGMGLECGJiYqqGDVdUVHDu3Dm6desGQFFREZMnT2bFihUEBQXx/PPPs2zZMgCOHz/O1KlTWbp0KZ6enuzbt6/WsGRrfSaurq6sXr2aBx98kKCgIKZNm0ZwcLBF/vpijIyMZMqUKYSHhzN48GAqKiqYO3dunekO0dTF4xuzAfcDO6u9/x3wuxp5ZgGrrZSdDvyl2vu/ANPrO96AAQNUW7Zv3z5nh2AXHb9znDp1Siml1M2bN23K/39vHFTpIa+rQz7L1bGhb6q89042azxXrlxRgwYNUhkZGY0qZ2v8LeXkyZPq2WeftSnvqlWrVGpqqlJKqZkzZ6qFCxeq8+fPt2R4zaby96U6TMuoN+l7XlS1dQ1aiohMAcYppeaY3/8EiFRKPVUtzyzgJSAfOAM8q5S6JCLPAe5KqRfN+Z4HipVSr9Y4xlxgLoC3t/ewpKSkFj+vlnLr1q2qIYFtkY7fOTw9PenXrx9Go9GmzuFvvioCoFufTi0dWqPYGn9r8PTTT9OtWze+/fZbHnnkEf75z39ajCRrzc6dO8e3335rkRYdHX1MKTW8jiL1ak0TPf4b2KyUui0iPwf+DoyxtbBSKgFIABg4cKCKiopqkSAdIS0tDR2/87TV+LOzs+nSpQsFBQUWHcR1+dbVdG/flryOZGv8rcHf/va3qtdr1qzhP/7jP9pM7O7u7gwdOrTZ6nNUY5IDVB947mdOq6KUulbt7XrglWplo2qUTWv2CDVN0+wwb968qkEAdyJHjeY6AvQXkQARcQPigW3VM4hI9fVCJwLZ5tc7gVgRucvc8R5rTtM0TdNaCYdcmSilykXkKUyNgAvwtlIqS0RewNThsw14RkQmAuXAN5g65FFKfSMi/42pQQJ4QSn1jSPi1jRN02zjsD4TpVQKkFIjbXG117/DNMrLWtm3gbdbNEBN0zStyfQT8JrWyl1ctJuLi3Y7OwxNq1drGs2laVo1l17ZT86rn1S9v5JgutPr+9zIZn0aXtOag25MNK2V6v3bUfT+7SiyHjZNHxK81fqcVJrWGujbXJqmaZrddGOiaZqm2U03JpqmaZrddGOiaZrDbN26lSeffJJHH32UXbt2OTscrRnpxkTTtGa3ZcsWIiMjCQ0NZfjw4ezcaZq04uGHH2bdunWsXbuWd9991+b6jEYjQ4cOrZqC3Rp/f38GDx5MWFhY1TTwJSUlREREEBoaSnBwMEuWLKnKv2PHDgYOHEi/fv14+eWXm3imTdfQ8euLHWDVqlUEBwcTEhLC9OnTKSkpAeDGjRtMmTKFwMBAgoKCOHTokEPOxyFT0Dt601PQO5eOv3llPvRPlfnQPxvM19gp6N/7nwPqJ71Wqh/JC2rWva+rvRs+tyvOShs3blT33Xefys3NVUopdebMGdW9e3d16dKlqjy//vWv1bFjx6yWtxb/ihUr1PTp01VcXFydx7333ntVfn6+RVpFRYUqKChQSilVWlqqIiIi1KFDh1R5ebnq27ev+uKLL9Tt27fVkCFDVFZWVqPP1ZbYrbHl+HXFrpRSBoNB+fv7q6KiIqWUUlOnTlXvvPOOUkqpn/70p2rdunVKKaVu376trl+/bjWG5p6CXl+ZaNodaN/Gk2x8/iO+uXwLpSD/y29ZPXc7+zaetKvewsJCFi5cSFJSEj169ACgf//+REVFkZqailKKBQsWMH78eMLDw22q02AwsH379lrL49pCRKqWEygrK6OsrAwRIT09nX79+tG3b1/c3NyIj48nOTm53rqio6PZvdv08OiiRYt4+umnGx1PJVuOX1fslcrLyykuLqa8vJyioiJ69erFt99+y/79+5k9ezYAbm5ueHl5NTnOxtCNiabdYa5dLuDt53ZTWlJukX67qIx//GGfXXUnJiYSHh5O7969LdI7dOhAUVERb775Jnv27OG9995j7dq1tcpbW2lx/vz5vPLKKw0u+ysixMbGMmzYMIs1RYxGI2FhYfj4+BATE0NkZCQ5OTkWMfr5+Vks02vNsmXLWL58ORs3buT48eO89tpr9eavfk41V1q09fjWYgfTGvLPPfccffr0oWfPnnh6ehIbG8uFCxfw9vbmZz/7GUOHDmXOnDkUFhbaFKe9dGOiaa1cRamRosyrlF691Sz13d2rCzeuWv+C+fqrb62m2yozM5PQ0NBa6RkZGQQGBvLMM89w7Ngx1q5dyy9+8Yta+VJSUujZ87sJxD/44AN8fHwYNmxYg8f+5JNP+Oyzz/jwww9Zs2YN+/fvB8DFxYUTJ05gMBhIT08nMzOzSec2atQolFKsXLmSxMREmxfwSklJoVevXk06Zl2xX79+neTkZC5cuMDly5cpLCxkw4YNlJeX89lnn/HLX/6S48eP07lzZ4f1B+nGRNNaudJL32K8eRvDik8azmyje/p4NirdVl27dqW0tNQi7dChQxQWFjJ69OhG13fgwAG2bduGv78/8fHx7N27l5kzrc8E4OvrC4CPjw+TJk0iPT3dYr+XlxfR0dHs2LEDX19fLl26VLXPYDBUla/LyZMnyc3Nxc3NzWIBrJq33xYvXlyzqNVYG3P86rED7Nmzh4CAALy9vWnfvj2TJ0/m4MGD+Pn54efnV3UFM2XKFD777LMG42kOujHRtFbq095/4rDPHykzX5Hk/e0zDvv8kU97/8nuun+6PJoOndpbpHXo1J6fLo+2q964uDiSkpLIz88H4MyZM8yZM4d33nmnwdtU1rz00ksYDAYuXrxIYmIiY8aMYcOGDbXyFRYWVi1MVVhYyK5duwgJCSE/P58bN24AUFxczO7duwkMDGTEiBGcPXuWCxcuUFpaSmJiIhMnTqyqb+zYsRa3nXJzc5kxYwbJycl4eHhUfakXFxeTnZ3N0qVLiY+PJy8vj9u3bzd4Xg0dH6gzdoA+ffpw+PBhioqKUEqRmppKUFAQPXr0oHfv3pw+fRqA1NRUBg0aZOOnbR/dmGhaKzX06DzunhwM7Uydru06unL3I8EMPTrP7rqjZwzmqYQ4vO/1RAS87/XkqYQ4omcMtqveiIgIFi1axAMPPEBgYCBxcXGsX7+ekSNH2lTeWp9JQ/kvX77M1atXGTlyJKGhoURERBAXF8e4cePIzc0lOjqaIUOGMGLECGJiYvjRj36Eq6srq1ev5sEHHyQoKIhp06YRHBwMQEVFBefOnaNbt24AFBUVMXnyZFasWEFQUBDPP/88y5YtA+D48eNMnTqVpUuX4unpyb59+6qGJdeMsbr6jl+Zv67YASIjI5kyZQrh4eEMHjyYiooK5s6dC8Cbb77JjBkzGDJkCCdOnOD3v/+9zZ+nXZo6DKyxGzAOOA2cAxbWk+8RQAHDze/9gWLghHlb29Cx9NBg59LxN58vnktRh7yXq0M+y9Wh7n9UX/zmwzrzNnZocEu7cuWKGjRokMrIyGhUOWfHf/LkSfXss8/alHfVqlUqNTVVKaXUzJkz1cKFC9X58+dbMrxm09xDgx0ya7CIuABrgBjAABwRkW1KqVM18nUB/hP4tEYVXyilwhwRq6a1JmX5hbTv7kH7Hh50Ce/VbJ3wjtC9e3eysrKcHUajhYSEsHLlSpvyZmVlceXKFZKSkpg9ezYJCQkEBAS0cIStk6OmoI8AzimlzgOISCLwEHCqRr7/Bv4E/MZBcWlaqzbwb1OqpqAP+NM4J0ej1bRu3bqq12vWrKFv375OjMa5HNWY+AKXqr03AJHVM4hIONBbKbVdRGo2JgEichy4CSxSSn1c8wAiMheYC+Dt7U1aWlozhu9Yt27d0vE7UWuL383cCdtQTJ6enhQUFGA0Gqs6o9uithr/T3/60zYVe0lJSbP+nreKxbFEpB2wEphlZXcu0EcpdU1EhgFbRSRYKXWzeialVAKQADBw4EAVFRXVskG3oLS0NHT8ztPa4s/yMgAQ3EBM2dnZdOnShYKCAouhq21NW46/LcXu7u7O0KFDm60+R43mygGqPxLrZ06r1AUIAdJE5CJwH7BNRIYrpW4rpa4BKKWOAV8AAxwStaZpmmYTRzUmR4D+IhIgIm5APLCtcqdS6lul1D1KKX+llD9wGJiolDoqIt7mDnxEpC/QHzjvoLg1TdM0GzjkNpdSqlxEngJ2Ai7A20qpLBF5AdNQtG31FB8FvCAiZUAF8Aul1DctH7WmtT2m0Z2aVr+W+D1xWJ+JUioFSKmRZnXeAaVUVLXXW4AtLRqcpn0PuLu7c+3aNdzc3JwditaKKaW4du0a7u7uzVpvq+iA1zTNfn5+fhgMBm7cuNHsXxSOVFJS0mbjbyuxu7u74+fn16x16sZE074n2rdvT0BAAGlpac06SsfR2nL8bTl2e+m5uTRN0zS76SsTTWulLr2yn5xXv5t2/rDPHwHwfW4kvX87yllhaZpVujHRtFaq929H6UZDazP0bS5N0zTNbrox0TRN0+ymGxNN0zTNbrox0TRN0+ymGxNN0zTNbrox0TRN0+ymGxNN0zTNbrox0TRN0+ymGxNN0zTNbrox0TRN0+zmsMZERMaJyGkROSciC+vJ94iIKBEZXi3td+Zyp0XkQcdErGmaptnKIXNzmZfdXQPEAAbgiIhsU0qdqpGvC/CfwKfV0gZhWuY3GOgF7BGRAUopoyNi1zRN0xrmqCuTCOCcUuq8UqoUSAQespLvv4E/ASXV0h4CEpVSt5VSF4Bz5vo0TdO0VsJRswb7ApeqvTcAkdUziEg40FsptV1EflOj7OEaZX1rHkBE5gJzzW9vi0hmcwTuJPcAXzs7CDvo+J1Lx+88bTl2gIFNLdgqpqAXkXbASmBWU+tQSiUACeb6jiqlhjdQpNXS8TuXjt+52nL8bTl2MMXf1LKOakxygN7V3vuZ0yp1AUKANBEB6AFsE5GJNpTVNE3TnMxRfSZHgP4iEiAibpg61LdV7lRKfauUukcp5a+U8sd0W2uiUuqoOV+8iHQQkQCgP5DuoLg1TdM0GzjkykQpVS4iTwE7ARfgbaVUloi8ABxVSm2rp2yWiCQBp4ByYJ4NI7kSmit2J9HxO5eO37nacvxtOXawI35RSjVnIJqmadodSD8Br2maptlNNyaapmma3dp0Y9LQFC0i8gsROSkiJ0TkE/PT9K2GPVPMtAY2fP6zRCTf/PmfEJE5zoizLrZ8/iIyTUROiUiWiGxydIx1seGzX1Xtcz8jIjecEGadbIi/j4jsE5HjIvK5iExwRpx1sSH+e0Uk1Rx7moj4OSNOa0TkbRHJq+tZPDF5w3xun5ufAWyYUqpNbpg68r8A+gJuQAYwqEaertVeTwR2ODvuxsRvztcF2I9phNtwZ8fdyM9/FrDa2bHaEX9/4Dhwl/m9j7PjbszvTrX8T2Ma9OL02Bvx2ScAvzS/HgRcdHbcjYz/X8Dj5tdjgH86O+5qsY0CwoHMOvZPAD4EBLgP+NSWetvylUmDU7QopW5We9sZaE2jDeyZYqY1sDX+1sqW+J8E1iilrgMopfIcHGNdGvvZTwc2OyQy29gSvwK6ml97ApcdGF9DbIl/ELDX/Hqflf1Oo5TaD3xTT5aHgH8ok8OAl4j0bKjettyYWJuixdo0K/NE5AvgFeAZB8Vmiwbjrz7FjCMDs5FNnz/wiPlS+T0R6W1lv7PYEv8AYICIHBCRwyIyzmHR1c/Wzx4RuRcI4LsvttbAlviXAjNFxACkYLq6ai1siT8DmGx+PQnoIiJ3OyC25mDz71d1bbkxsYlSao1S6j+ABcAiZ8djq2pTzPyXs2Oxw78Bf6XUEGA38Hcnx9NYrphudUVh+ut+nYh4OTOgJogH3lNtb5bt6cDflFJ+mG67/NP8f6KteA4YLSLHgdGYZu1oaz+DRmlLP5yaGjvNSiLwcEsG1EiNmWLmIqZ7l9taUSd8g5+/UuqaUuq2+e16YJiDYrOFLb8/BmCbUqpMmWasPoOpcXG2xvzux9O6bnGBbfHPBpIAlFKHAHdMkyi2Brb87l9WSk1WSg0F/mBOu+GwCO3TtCmsnN0ZZEcnkitwHtMlfGUnWHCNPP2rvf4xpqftnR67rfHXyJ9G6+qAt+Xz71nt9STgsLPjbmT844C/m1/fg+nS/+62ELs5XyBwEfPDya1ls/Gz/xCYZX4dhKnPpFWch43x3wO0M79eDrzg7LhrxOdP3R3wcVh2wKfbVKezT8rOD2QCpr8WvwD+YE57AdO8XgCvA1nACUydYHV+WbfG+GvkbVWNiY2f/0vmzz/D/PkHOjvmRsYvmG41ngJOAvHOjrkxvzuY+h1ednasTfzsBwEHzL87J4BYZ8fcyPinAGfNedYDHZwdc7XYNwO5QBmmq+/ZwC+AX5j3C6bFDL8w/97b9L2jp1PRNE3T7NaW+0w0TdO0VkI3JpqmaZrddGOiaZqm2U03JpqmaZrddGOiaZqm2U03JpqmaZrddGOiaZqm2U03JprWSCKyt9paISUiMs3ZMWmas+mHFjWtiUTkl0A0MF21vYkUNa1ZuTo7AE1ri0Tkp8B44BF7GxIREaX/qtPaON2YaFojichUYAbwkFKqzJy2FLgLuAbkA/+nlNonIm8D/wn8DuiEafK/Z0SkB/C/wFagr4iUANeUUi+ISAfgNeA68ANgmrmOqvKOOldNs5XuM9G0RhCRHwG/AiYrpUrMab6Y/jC7genL/yQwSERGAUeAnwIdzfs9zVWFYZpwbwOmxqeyLMAvMa3l8XtMK+JNsVJe01oVfWWiaY3zd0xf8AdEBOBNTI3AfwLemNaByMS0uNMIYA6wFpinvlvbBUyNSTKmZZmrl63ct1ZEPIArwFAr5TWtVdGNiaY1glKq1tKr5tUXnwPuBo4rpW6Yr0qWKKXKRSQZ+JuIXAL2KqV2YFpk6zSmKfqrypqr3An8GbhtTsu1Ul7TWhU9mkvTWhlz5/5gTOtKLKq8naZprZluTDRN0zS76Q54TdM0zW66MdE0TdPsphsTTdM0zW66MdE0TdPsphsTTdM0zW66MdE0TdPsphsTTdM0zW66MdE0TdPs9v8BjFyw2IogQTUAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "\n", + "xs = df[df['Q2']==5.5].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot()\n", + " xbj_one_corr = []\n", + " xbj_one_err_corr = []\n", + " CSV_one = []\n", + " CSV_one_err = []\n", + " #RY_err = []\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " #print(zs)\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs = []\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_error = row['error']\n", + " RYs.append(RYi)\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYs_rho.append(RYi_rho)\n", + " RYs_error.append(RYi_error)\n", + " #print('RY_error ',RY_error)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " #print('RYs ',RYs)\n", + " #print('RYs err ',RYs_error)\n", + " RY = Get_weighted_average(RYs,RYs_error)\n", + " RY_err = Get_weighted_sigma(RYs,RYs_error)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " ax.plot([z_corr+0.005,z_corr+0.005],[RY_rho+RY_err,RY_rho-RY_err],marker = \"_\",color = colors_all[i_col])\n", + " plt.plot(z_corr+0.005,RY_rho,\"*\",color = colors_all[i_col])\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$RY(rho)$')\n", + " plt.xlim(0.3,1)\n", + " plt.ylim(0.4,0.85)\n", + " plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + " \n", + " xbj_one = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " xbj_ones_plot.append(xbj_one)\n", + " xbj_one_err = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6520493c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-9-363018597acc>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)#/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(rho)$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e42f34c7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-10-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1f650168", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-11-aaf8d37d97f0>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e3c22689", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-12-4f308ee58a58>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4.75].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_2ndQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c648127d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-13-c492904fd9f8>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-13-c492904fd9f8>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==5.5].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_3rdQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f4a255f9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-14-cb66ea7c6ba5>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.34647\n", + "-0.294727\n", + "-0.2506799294500985\n", + "-0.20257687877502\n", + "-0.342816\n", + "-0.28409597125827424\n", + "-0.2504318462643136\n", + "-0.2032344800466055\n", + "-0.1526289422163012\n", + "-0.10354322856050224\n", + "-0.05574199999999985\n", + "-0.3338903873263857\n", + "-0.28404702050018993\n", + "-0.25256807019110583\n", + "-0.2033404711000174\n", + "-0.15405467999072664\n", + "-0.10505681264046762\n", + "-0.06059134116141762\n", + "-0.3340540664190014\n", + "-0.28538802383842665\n", + "-0.24730359071195934\n", + "-0.20222745912784706\n", + "-0.15459255386578424\n", + "-0.1058530108550576\n", + "-0.3341247734284914\n", + "-0.28519156333280576\n", + "-0.25118427841028373\n", + "-0.2015141432153626\n", + "-0.1546220302435538\n", + "-0.11186252400226182\n", + "-0.33311802965587295\n", + "-0.2856064437511988\n", + "-0.2495836629977108\n", + "-0.2026166609665433\n", + "-0.15737665782969507\n", + "-0.11420799999999998\n", + "-0.3341003968069907\n", + "-0.2859935647766409\n", + "-0.24813545970158052\n", + "-0.20497959044921205\n", + "-0.1596199167102348\n", + "-0.3335179944342012\n", + "-0.2950521832499023\n", + "-0.2525162132561221\n", + "-0.2083293377670144\n", + "-0.33414803134732274\n", + "-0.2964916367203637\n", + "-0.2568394314279863\n", + "-0.2155083268014879\n", + "-0.34405\n", + "-0.305543\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==4.0].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_xbj_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eafbc554", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-15-2a0b93d9965e>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.286494\n", + "-0.23720565530103332\n", + "-0.19895784912318026\n", + "-0.15331866926861015\n", + "-0.10344574214478575\n", + "-0.05527000000000004\n", + "-0.28550099999999995\n", 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==4.75].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_xbj_2ndQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e46f031d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-16-779ffd92e0b4>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.23674099999999998\n", + "-0.20164700000000002\n", + "-0.14852919376935414\n", + "-0.10121399999999992\n", + "-0.05289100000000002\n", + "-0.23600300000000002\n", + "-0.1963258328142316\n", + "-0.14782817281848615\n", + "-0.10254902486615369\n", + "-0.05367885136939221\n", + "-0.23619870584981761\n", + "-0.18659343593465838\n", + "-0.14683735156701594\n", + "-0.10060633965245691\n", + "-0.05214329952932839\n", + "-0.23569911668297439\n", + "-0.1856402448150109\n", + "-0.14753983588835556\n", + "-0.10070922171794905\n", + "-0.05436761327771411\n", + "-0.2364431358783346\n", + "-0.18579052874273144\n", + "-0.147394009092445\n", + "-0.10111060847205278\n", + "-0.06099601303695501\n", + "-0.23662407985799366\n", + "-0.18648510874009278\n", + "-0.14606827799116545\n", + 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==5.5].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_rho = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY']\n", + " RYi_rho=row[\"RY_rho\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_rho = (RYi-RYi_rho)/RYi\n", + " RYs_rho.append(dRY_rho)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_rho = Get_weighted_average(RYs_rho,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_rho,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(rho)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_rho_xbj_3rdQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "243f91d7", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-17-511776ff5b12>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot()\n", + " xbj_one_corr = []\n", + " xbj_one_err_corr = []\n", + " CSV_one = []\n", + " CSV_one_err = []\n", + " #RY_err = []\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " #print(zs)\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs = []\n", + " RYs_noexc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_error = row['error']\n", + " RYs.append(RYi)\n", + " RYi_noexc=row[\"RY_noexc\"]\n", + " RYs_noexc.append(RYi_noexc)\n", + " RYs_error.append(RYi_error)\n", + " #print('RY_error ',RY_error)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " #print('RYs ',RYs)\n", + " #print('RYs err ',RYs_error)\n", + " RY = Get_weighted_average(RYs,RYs_error)\n", + " RY_err = Get_weighted_sigma(RYs,RYs_error)\n", + " RY_noexc = Get_weighted_average(RYs_noexc,RYs_error)\n", + " ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " ax.plot([z_corr+0.005,z_corr+0.005],[RY_noexc+RY_err,RY_noexc-RY_err],marker = \"_\",color = colors_all[i_col])\n", + " plt.plot(z_corr+0.005,RY_noexc,\"*\",color = colors_all[i_col])\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$RY(exc)$')\n", + " plt.xlim(0.3,1)\n", + " plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + " \n", + " xbj_one = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " xbj_ones_plot.append(xbj_one)\n", + " xbj_one_err = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6d7fd458", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-18-53c92fcc7000>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_exc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_exc=row[\"RY_noexc\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_exc = (RYi-RYi_exc)/RYi\n", + " RYs_exc.append(dRY_exc)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_exc = Get_weighted_average(RYs_exc,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_exc,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(exc)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_exc_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "43a35b5b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-19-54216644e54c>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4.75].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_exc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_exc=row[\"RY_noexc\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_exc = (RYi-RYi_exc)/RYi\n", + " RYs_exc.append(dRY_exc)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_exc = Get_weighted_average(RYs_exc,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_exc,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(exc)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_exc_2ndQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4d5f9dfc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-20-ca282c0f32f7>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-20-ca282c0f32f7>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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/BD4RERskfQi4NCLeV4McDwCHSppBUlTOAj7Zr89i4Fzgl8AZwF0REelrnk+H1g4C3gr8tgaZRoRG2Vum0t0933hqGV0//T70JhMgt3W/lhxDXQtOOXkXQbMiq2YY7W8kfRL4qaTNQDew0xTloUgLxYXAHUATcGNEPC7pCmBpRCwGbgBulrQCWEtSkCC5ufRiSVuAbcCfpDPlLNUIe8vMnndR2TOw/rt79tx/5/ZCs13vFnruvzPXX+xFLoJmRVDNMNqJwB+R3NS5H/CHEbG8VkHKrbUWEZeWPN5IsvBn/9fdDNxcqxyWj0rPwLZ1v1b29QO110tRi6BZUVQzjHYJybDZzyQdAdwq6XMRcVdG2WyUqeQMbMzEPcoWljET98gmVIWKWgTNiqKaLQY+EBE/Sx8/SnID5pezCmZWTusxJ0Fzv9WRmscm7TkaqNjlXQTNiqLiYiPpUEk3SroWICJeAk7MLJlZGRMOO5JJx5++/Zf4mIl7MOn403MfqipbBJuaaX3rIfDCvfDyUuhZnU84swKoZhjtZuBy0mViJL0d+ALJttFmdTPhsCNzLy799eXZPhutdRKt7Qcz4S1Tkw5bN8H69B7o1qn5hDTLUTXFZkxE/EjS3wNExGNpwTEz+hXBl5cmBaZUbIOu51xsbFSqZm20F9N7WgJAkki2HDCz/voXmsHazUa4as5s/hy4HthX0h+QrLD8WBahzBpeU0v5wtLUUv8sZgVQzWy035IUmIuAmcBPgXOyiWXW4CYdCOr3v5fGJO1mo9CgZzbpMv4ByZ3+JMv73zZQHzPjzesyXc8lZzhNLUmhGc71micXws8uga5nobkJWrfC3gfBO6+EGfNqk9ssI5UMo90t6XvADyLiub5GSeNIloo5F7gb+FYmCc0aVevU2k0GeHIh/L/50LshOe7dCusBnoX/mp+0ueBYgVUyjDaHZDvo70p6UdITkp4BfgOcDXw9Ir6VYUYz+9klbxaaUl3A1g3wyCV1j2RWjUrObM6IiG8C35Q0FtgbeCMiXss0mVktbR+Cei4Zznr/lfC2BjoT6HqufPu29OuGAZ43K4hKis05ko4CPhcRW4Cd14E3K6g3nlpGz8//g20btzAmPkxr091M6HoiGZKCxik4kw5MrtX01zc2sZsnHlixVTKMdgqwkeTaje9Gs4bRt+z/tk29ILFtzGS6xp3KG02zkiGpnzXQ0NP7r4Tm3XZunwQ07ZZMEjArsEGLTURsi4iLgX8G7pE0X9LRksr85JsVR9ll/zWOnrEnJI8HGpoqorfNg5MXwKSDkuPmpmSv2r0PgqMXeHKAFV5FN3VK+jBwPrAZmA18Cjhc0rqIOCTDfDZMo3n3yAGX/dfuyYNGu+flbfMaZ9jPrJ9Bz2zSmWefBa6OiHdExAURcVxETAE6ahVE0hxJyyWtkLTTDqCSWiTdmj5/v6TpaftJkh6U9Gj69QO1ytTotg8jpb90+3aPfOOpZbnmqpcBl/2P15Mhqfd76MmsXiq6ZhMRp0XEnf2fiIhVtQghqQm4luT60CzgbEmz+nU7D+g7k7qadPVp4FXgv0fEEST3/HjXztSudo8cDcou+x+baW16NBmS8lmCWd0MOowWEb+uQ46jgRURsRJA0i3AXOCJkj5zgS+lj28DvpGuXPBwSZ/HgQmSWiJi1K94OCp2j9zFlOadlv3fPoz4jzkGNhudKlmu5hzga8Am4JKIuEnSscCHSc563l2DHNOA50uOVwHHDNQnInolrQemkJzZ9PkY8JALTSLTLZSfWZjcSLjhuWTabR5LpvS/q77r2Z2mNBdx7xuz0UiDLWkm6TfAJ4FngAuB9wFvBb4L/LBvq+hhhZDOAOZExPnp8TnAMRFxYUmfx9I+q9Ljp9M+r6bHhwOLgZMj4ukB/p75wHyAtra2dy9atGi40XPT3d3NxIkTd9ln9/Uv8ZaXnmBMbNvetk1jeHG/Wbw+eb8h/91TN/yY9vX/RFNJTd+qFpZP/jyrd/tgTbJX4thHz2L85t/t1L5x3D7cd8Qtw37/gZTmn7rmx8x88XpaNq9m07iprHzL+ayeMvhnkKdaff55cf58nXDCCQ9GxFHVvq6SYvNwRLwrfSzgd8BhtVxBQNLvAV+KiA+lx18EiIh/KOlzR9rnl5KagZeBtogISfsDdwF/EBH3VvJ3tre3x/Lly2v1LdRdZ2cnHR0dg/bLZDba96fDhjI3GO52EJz+20FfXmn2Qf2vMaTbK/Uj+MttZdprY3v+/mdWkEw8KPj1oJp9/jlx/nxJGlKxqWTq877pGcHy9M+qDJaqeQA4NN2c7QXgLJKzqVKLSSYA/BI4A7grLTR7ALcDF1daaBrByntu56GF19Cz5mVap+zL7HkXMfO406p+n0yGkQZaGqXeS6YMdFd9vaY0l1uvrO9m0QIXG7M8VFJsLgOOAOalXydJ+jHwMPBwRHxnuCHSazAXAncATcCNEfG4pCuApRGxGLgBuFnSCmAtSUGCZGjvEOBSSZembSdHxOrh5srLyntu5xfXXc7WTRsB6Hn1JX5x3eUAQyo4NbfbgQOc2bz5S74u9/e8/8ryZxb1mtI80E2hjXSzqFmdVFJsVgH/NyJeBEiHrI4A3gGcCgy72ABExBJgSb+2S0sebwTOLPO6LwNfrkWGonho4TXbC02frZs28tDCa4pRbN55ZbKs/daSX/IlS6b03d/TN+267/4eoLYFp+/sIcsFNstNhGBa8lzeZ1ZmDaSSYvP7wBWS9gF+DTwCLCMpDP+UXbQG0rO6pptk9ax5uar2uuubdTbAbLRd3d9T87ObLO+qf2bhjkV1Q7J3zNRJfwF05H9mZdZAKrnP5o8AJP01yT/pVgInAAtIhrP2zzJg4fWshqe+CSuvg02roWUqzLwADvuTIRec1in70vPqzotrt07Zd7hpa2fGvAGnOo+Y+3seuWTHszeArRuY2XU98OX6nFllpQhT121UqWhttNQnIuKdfQeSvgn8z9pHajC/+RdY/hXYlk4D3vS75FhNcOTfDuktZ8+7aIdrNgBNLeOZPe+i4eetw74umd7fU08DTHho2VpyObAB1yubuuHH8F9X73TGBrjgWGYqWa6mz+uStt/AGREPAofVPlKDefraNwtNn22bkvYhmnncabz3gsto3Xs/kGjdez/ee8Flw79e0zdVt+tZIN68CfLJhcN7337KLhPTPDZpbyQD7BGzqamxd9qY2XV92TM27/ZpWarmzOY84P9IegB4kGSSwJZdv2QU2DTApLeB2is087jTaj8ZoE5TdSccdiS8eC89Tz7FtmhljHpoPfSwbO7kz3I4aICJECsnnU//hfsayQ5nZqW826dlqOJiExFPSZoNnE5SaJ4E/jqjXI1jwjR4o8x6pBOm1T/LYOo1VffJhUx45AtMKC1sj+wG++xR+5liZS7gA7UpOANMhFj97LSGLjabmqYyfuvOKy94t0/LUjXDaETE5ohYFBF/GxFfj4g1WQVrGEd+BZom7NjWNCFpL5qBpuTWeqrurs6gammAC/g1HQ6aMS9ZFeGT25KvI+CaxspJ5ydT1Ut5t0/LWFXFxsqYMQ+O/tdkqRaUfD36X4v5S6nc1sJZTNWt1xlUUVYyaDCrd/tgsrvnDj+z3u3TslXNNRsbyC6mARdKvabq1utmxwpWMrABNMrPrI0YLjajTbVTdYdyw2q9bnYcZCUDMysOFxsbWM9qWP809G1RsHVTcgy7Ljj1OoMaZCWD/mq1uKmZVc/FxgbW9dybhaZPbEvaBzu7qdfNjhUOBxV+cVOzEc4TBGxgWwfY8HSg9gLb1eKmZpY9FxsbWFNLde0FVvjFTc1GOBcbG9ikA0H9fkQ0piGX0B9oEdNhL276zMJk59LvjEm+PlPbpX/MRgoXGxtY61SYfPCbZzJNLcnxMLZPyMvseRfR1DJ+h7ZhL27at4LBhnStub4VDFxwzHZSmGIjaY6k5ZJWSLq4zPMtkm5Nn79f0vS0fYqkuyV1S/pG3YOPdK1TYd+jYNr7kq8NWGggo8VN67GCgdkIUYjZaJKagGuBk0h2Bn1A0uKIeKKk23nAuog4RNJZwFXAJ4CNwN8Cb0//2K6M4n1Mar64qVcwMKtYUc5sjgZWRMTKiNgM3ALM7ddnLnBT+vg24ERJioieiPg5SdGxXfGwT20NtFKBVzAw20lRis004PmS41Vs3+h95z4R0QusB6bUJd1I4WGf2nrnlV7Q0qxChRhGqxdJ84H5AG1tbXR2duYbaBi6u7urzn/8hudQmfbY8Bw/reNnMZTsRfJm/mlMnfQXzOy6npatq9nUNJWVk85n9bPT4NnOnFMObOR8/o2p0fMPVVGKzQvAASXH+6dt5fqsktQMTAaq2uIgIhYACwDa29ujo6NjqHmzVcHWzZ2dnVSd//vlF67UbgdW/17DMKTsBbJj/g7gywCMB2alf4psZH3+jafR8w9VUYbRHgAOlTRD0jjgLGBxvz6LgXPTx2cAd0VE1DFjfWS5dbOHfcwsJ4UoNuk1mAuBO0h2AF0UEY9LukLSR9JuNwBTJK0APgdsnx4t6bfA14DPSFolqej/uBxYlhuPzZjnfUzMLBdFGUYjIpYAS/q1XVryeCNw5gCvnZ5puHrKeuMx72NiZjkoxJmNlajX1s1mZnVUmDObRvPGU8vouf9OtnW/xpiJe9B6zElMOOzI4b/x+6+EO/4Qtm5+s61pXO03HjMzqyMXmyF446lldP30+9C7BYBt3a8lx1B1wXl96XLWLrmP3nVdNO85ib2O3sruuwdvdM2ip/kEtml3xuh1Wte+xoTafhtmZnXjYbQh6Ln/zu2FZrveLUl7FV5fupxXFt1N77qu5C3WdfHKnet59bVT6Bp3KtvGTAaJbUym61fP8cZTy2r0HZiZ1ZeLzRBs636tqvaBrF1yH7Gld4e22DaW9S91AOP69W6uupiZmRWFi80QjJm4R1XtA+k7o+kvesuPblZbzMzMisLFZghajzkJmsfu2Ng8NmmvQvOek8q2q7m3bHu1xczMrChcbIZgwmFHMun407f/8h8zcQ8mHX961ZMD9jpxFmre8T+BxjYzefZYoF/BGUIxMzMrCs9GG6IJhx05vKnOPavZfUYvnHwAa3/+Ir2vb6F593HsdfK72P1978luarWZWQ5cbPLS9RzENnaftRe7z9rrzfamZF3mYRczM7MC8TBaXrZuqq7dzKyBudjkpamlunYzswbmYpOXSQeC+n38GuM10MxsRPI1m7y0Tk2+dj2XDJ01tSSFpq/dzGwEcbGphQp21iyrdaqLi5mNCi42w9W3s2bfhmd9O2tCZQXHzGwUKMw1G0lzJC2XtELSxWWeb5F0a/r8/ZKmlzz3xbR9uaQP1TV4ljtrmpmNEIUoNpKagGuBU4BZwNlltnY+D1gXEYcAVwNXpa+dBZwFHA7MAb6Zvl99ZL2zppnZCFCIYgMcDayIiJURsRm4BZjbr89c4Kb08W3AiZKUtt8SEZsi4hlgRfp+9eGdNc3MBlWUYjMNeL7keFXaVrZPRPQC64EpFb42O++/Epp327GteTfvrGlmVmJUTRCQNB+YD9DW1kZnZ2cN3nUaU/f/C2a+eD0tm1ezadxUVr7lfFb/bhr8rhbvX153d/eQ8k+dKGbu3URLM2zqhZWvbmV1d9Q+4C4MNXtROH++nL8xFaXYvAAcUHK8f9pWrs8qSc3AZGBNha8FICIWAAsA2tvbo6OjoxbZgQ7gywCMJ7no1P+CU611dnZSdf6e1bD+aYhtAIwfC7PeMo5Zkw+u6xTsIWUvEOfPl/M3pqIMoz0AHCpphqRxJBf8F/frsxg4N318BnBXRETaflY6W20GcCjwX3XK3VjSxT93ENs8mcHMMleIM5uI6JV0IXAH0ATcGBGPS7oCWBoRi4EbgJslrQDWkhQk0n6LgCdINoH504jYmss3UnRe/NPMclKIYgMQEUuAJf3aLi15vBE4c4DXXgn4ivxgmlrKF5aiLv7Zs9rL+ZiNEEUZRrN6aKTFP/uuL/UVx62bkuOe1fnmMrMhcbEZTVqnwuSD3zyTaWpJjot4tuDrS2YjSmGG0axOGmXxT19fMhtRfGZjxeTN5cxGFBcbK6ZGur5kZoPyMNoQrbzndh5aeA09a16mdcq+zJ53ETOPOy3vWCOHN5czG1FcbIZg5T2384vrLmfrpo0A9Lz6Er+47nIAF5xayvv6kqdem9WMh9GG4KGF12wvNH22btrIQwuvySmR1ZynXpvVlIvNEPSsebmqdmtAnnptVlMuNkPQOmXfqtqtAXnqtVlNudgMwex5F9HUMn6HtqaW8cyed1FOiazmPPXarKZcbIZg5nGn8d4LLqN17/1AonXv/XjvBZd5csBI4qnXZjXl2WhDNPO401xcRrJdTr1+ItdoZo3IxcZsIHlPvTYbQTyMZmZmmXOxMTOzzOVebCTtJelOSb9Jv+45QL9z0z6/kXRuSfuVkp6X1F2/1GZmVo3ciw1wMfCTiDgU+El6vANJewGXAccARwOXlRSlH6ZtZmZWUEUoNnOBm9LHNwGnl+nzIeDOiFgbEeuAO4E5ABFxX0S8VI+gZmY2NEUoNvuUFIuXgX3K9JkGPF9yvCptMzOzBlCXqc+SfgyUW8vlktKDiAhJkWGO+cB8gLa2Njo7O7P6qzLX3d3dsPkbOTsUI//UiWLm3k20NMOmXlj56lZWd1f2v04R8g+H8zemuhSbiPjgQM9J+p2k/SLiJUn7AeWW1X0B6Cg53h/oHEKOBcACgPb29ujo6Nj1Cwqss7OTRs3fyNmhAPn7VqROFwodPxZmvWUcsyYfXNF9QbnnHybnb0xFGEZbDPTNLjsX+EGZPncAJ0vaM50YcHLaZjb6eEVqa0BFKDZfAU6S9Bvgg+kxko6SdD1ARKwF/g54IP1zRdqGpK9KWgXsJmmVpC/l8D2Y1Y9XpLYGlPtyNRGxBjixTPtS4PyS4xuBG8v0+wLwhSwzmhVKU0v5wuIVqa3AinBmY2bV8IrU1oByP7MxsyrtckVqs2JysTFrRF6R2hqMh9HMzCxzLjZmZpY5FxszM8uci42ZmWVOEZktRVZokrqA5XnnGIa9gVfzDjFEjZwdnD9vzp+v9oiYVO2LRvNstOURcVTeIYZK0tJGzd/I2cH58+b8+ZK0dCiv8zCamZllzsXGzMwyN5qLzYK8AwxTI+dv5Ozg/Hlz/nwNKf+onSBgZmb1M5rPbMzMrE5cbMzMLHMjuthImiNpuaQVki4u8/wFkh6VtEzSzyXNyiPnQAbLX9LvY5JCUqGmU1bw+X9G0ivp579M0vnl3icvlXz+kj4u6QlJj0v6Tr0z7koFn//VJZ/9U5JeyyHmgCrIf6CkuyU9LOlXkk7NI2c5FWQ/SNJP0tydkvbPI+dAJN0oabWkxwZ4XpKuSb+/X0maPeibRsSI/AM0AU8DM4FxwCPArH59di95/BHgP/POXU3+tN8k4B7gPuCovHNX+fl/BvhG3lmHkf9Q4GFgz/R4at65q/35Ken/P4Ab885d5ee/APhs+ngW8Nu8c1eR/d+Bc9PHHwBuzjt3v3zHAbOBxwZ4/lTgR4CAY4H7B3vPkXxmczSwIiJWRsRm4BZgbmmHiHi95LAVKNJsiUHzp/4OuArYWM9wFag0f1FVkv+PgGsjYh1ARKyuc8ZdqfbzPxv4bl2SVaaS/AHsnj6eDLxYx3y7Ukn2WcBd6eO7yzyfq4i4B1i7iy5zgX+LxH3AHpL229V7juRiMw14vuR4Vdq2A0l/Kulp4KvARXXKVolB86enrgdExO31DFahij5/4GPpafhtkg6oT7SKVJL/MOAwSfdKuk/SnLqlG1ylnz+SDgJm8OYvvyKoJP+XgE9JWgUsITk7K4JKsj8CfDR9/PvAJElT6pCtVir++eozkotNRSLi2og4GPgr4G/yzlMpSWOArwF/mXeWYfghMD0i3gHcCdyUc55qNZMMpXWQnBn8q6Q98gw0RGcBt0XE1ryDVOls4FsRsT/JsM7N6f8XjeDzwPGSHgaOB14AGu3zr0qj/IcZiheA0n8p75+2DeQW4PQsA1VpsPyTgLcDnZJ+SzJuurhAkwQG/fwjYk1EbEoPrwfeXadslajk52cVsDgitkTEM8BTJMWnCKr5+T+LYg2hQWX5zwMWAUTEL4HxJItc5q2Sn/0XI+KjEfEu4JK07bW6JRy+an+/juhi8wBwqKQZksaR/A+1uLSDpNJfDKcBv6ljvsHsMn9ErI+IvSNiekRMJ5kg8JGIGNIieRmo5PMvHeP9CPBkHfMNZtD8wPdJzmqQtDfJsNrKOmbclUryI+mtwJ7AL+ucbzCV5H8OOBFA0ttIis0rdU1ZXiU/+3uXnIV9EbixzhmHazHw6XRW2rHA+oh4aZevyHvWQ8YzKk4l+dfm08AladsVJL+UAf4ZeBxYRnKR7vC8M1eTv1/fTgo0G63Cz/8f0s//kfTzf2vemavML5KhzCeAR4Gz8s5c7c8PyXWPr+SddYif/yzg3vTnZxlwct6Zq8h+Bsk/bp8iOatvyTtzv/zfBV4CtpCcwZ8HXABckD4v4Nr0+3u0kt89Xq7GzMwyN5KH0czMrCBcbMzMLHMuNmZmljkXGzMzy5yLjZmZZc7FxszMMudiY1Zjku4qWbp/o6SP553JLG++z8YsI5I+C5wAnB2Nt+6YWU015x3AbCSS9GngFOBjwy00khT+V6E1OBcbsxqTdCYwD5gbEVvSti+RrEG2hmT9rl9HxN2SbgT+jGR9rN2AMRFxkaR9gf8gWX9tpqSNwJqIuEJSC/B1YB3wPuDj6Xtsf329vlezSvmajVkNSfow8CfARyNiY9o2jeQfdq+RFIdHgVmSjiNZtPHTwIT0+cnpWx1Jsj7Vt0mKU99rAT5LsrT+X5NscHVGmdebFYrPbMxq6yaSAnCvJID/TVIk/gxoI1mW/TGSvVjeA5wPXAf8aby53QIkxeYHJDuxlr6277nrJE0EXgbeVeb1ZoXiYmNWQxGx026L6YZqnwemAA9HxGvpWc1lEdEr6QfAtyQ9D9wVEf9Jsi/OcpJVsbe/Nn3LO4BvApvStpfKvN6sUDwbzazBpJMPjiBZ5v1v+obrzIrMxcbMzDLnCQJmZpY5FxszM8uci42ZmWXOxcbMzDLnYmNmZplzsTEzs8y52JiZWeZcbMzMLHMuNmZmlrn/D9sbZnramajLAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==5.5].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_exc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_exc=row[\"RY_noexc\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_exc = (RYi-RYi_exc)/RYi\n", + " RYs_exc.append(dRY_exc)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_exc = Get_weighted_average(RYs_exc,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_exc,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(exc)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_exc_3rdQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9cd41c82", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-21-347546c66573>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.34647\n", + "-0.294727\n", + "-0.2506799294500985\n", + "-0.20257687877502\n", + "-0.342816\n", + "-0.28409597125827424\n", + "-0.2504318462643136\n", + "-0.2032344800466055\n", + "-0.1526289422163012\n", + "-0.10354322856050224\n", + "-0.05574199999999985\n", + "-0.3338903873263857\n", + "-0.28404702050018993\n", + "-0.25256807019110583\n", + "-0.2033404711000174\n", + "-0.15405467999072664\n", + "-0.10505681264046762\n", + "-0.06059134116141762\n", + "-0.3340540664190014\n", + "-0.28538802383842665\n", + "-0.24730359071195934\n", + "-0.20222745912784706\n", + "-0.15459255386578424\n", + "-0.1058530108550576\n", + "-0.3341247734284914\n", + "-0.28519156333280576\n", + "-0.25118427841028373\n", + "-0.2015141432153626\n", + "-0.1546220302435538\n", + "-0.11186252400226182\n", + "-0.33311802965587295\n", + "-0.2856064437511988\n", + "-0.2495836629977108\n", + "-0.2026166609665433\n", + "-0.15737665782969507\n", + "-0.11420799999999998\n", + "-0.3341003968069907\n", + "-0.2859935647766409\n", + "-0.24813545970158052\n", + "-0.20497959044921205\n", + "-0.1596199167102348\n", + "-0.3335179944342012\n", + "-0.2950521832499023\n", + "-0.2525162132561221\n", + "-0.2083293377670144\n", + "-0.33414803134732274\n", + "-0.2964916367203637\n", + "-0.2568394314279863\n", + "-0.2155083268014879\n", + "-0.34405\n", + "-0.305543\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==4.0].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_exc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_exc=row[\"RY_noexc\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_exc = (RYi-RYi_exc)/RYi\n", + " RYs_exc.append(dRY_exc)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_exc = Get_weighted_average(RYs_exc,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_exc,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(exc)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_exc_xbj_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "358a4e9f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-22-c9bb8d24d5db>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.286494\n", + "-0.23720565530103332\n", + "-0.19895784912318026\n", + "-0.15331866926861015\n", + "-0.10344574214478575\n", + "-0.05527000000000004\n", + "-0.28550099999999995\n", 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==4.75].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_exc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_exc=row[\"RY_noexc\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_exc = (RYi-RYi_exc)/RYi\n", + " RYs_exc.append(dRY_exc)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_exc = Get_weighted_average(RYs_exc,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_exc,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(exc)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_exc_xbj_2ndQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5a6358a7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-23-92f2aded1b2f>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.23674099999999998\n", + "-0.20164700000000002\n", + "-0.14852919376935414\n", + "-0.10121399999999992\n", + "-0.05289100000000002\n", + "-0.23600300000000002\n", + "-0.1963258328142316\n", + "-0.14782817281848615\n", + "-0.10254902486615369\n", + "-0.05367885136939221\n", + "-0.23619870584981761\n", + "-0.18659343593465838\n", + "-0.14683735156701594\n", + "-0.10060633965245691\n", + "-0.05214329952932839\n", + "-0.23569911668297439\n", + "-0.1856402448150109\n", + "-0.14753983588835556\n", + "-0.10070922171794905\n", + "-0.05436761327771411\n", + "-0.2364431358783346\n", + "-0.18579052874273144\n", + "-0.147394009092445\n", + "-0.10111060847205278\n", + "-0.06099601303695501\n", + "-0.23662407985799366\n", + "-0.18648510874009278\n", + "-0.14606827799116545\n", + 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==5.5].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_exc = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_exc=row[\"RY_noexc\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_exc = (RYi-RYi_exc)/RYi\n", + " RYs_exc.append(dRY_exc)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_exc = Get_weighted_average(RYs_exc,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_exc,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(exc)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_exc_xbj_3rdQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f56fa5d4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-24-52471ca32e40>:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot()\n", + " xbj_one_corr = []\n", + " xbj_one_err_corr = []\n", + " CSV_one = []\n", + " CSV_one_err = []\n", + " #RY_err = []\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " #print(zs)\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs = []\n", + " RYs_nodelta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_error = row['error']\n", + " RYs.append(RYi)\n", + " RYi_nodelta=row[\"RY_nodelta\"]\n", + " RYs_nodelta.append(RYi_nodelta)\n", + " RYs_error.append(RYi_error)\n", + " #print('RY_error ',RY_error)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " #print('RYs ',RYs)\n", + " #print('RYs err ',RYs_error)\n", + " RY = Get_weighted_average(RYs,RYs_error)\n", + " RY_err = Get_weighted_sigma(RYs,RYs_error)\n", + " RY_nodelta = Get_weighted_average(RYs_nodelta,RYs_error)\n", + " ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " ax.plot([z_corr+0.005,z_corr+0.005],[RY_nodelta+RY_err,RY_nodelta-RY_err],marker = \"_\",color = colors_all[i_col])\n", + " plt.plot(z_corr+0.005,RY_nodelta,\"*\",color = colors_all[i_col])\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$RY(delta)$')\n", + " plt.xlim(0.3,1)\n", + " plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + " \n", + " xbj_one = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " xbj_ones_plot.append(xbj_one)\n", + " xbj_one_err = Get_weighted_average(xbj_one_corr,xbj_one_err_corr)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "0b6ad081", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-25-5ac1b676f134>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_delta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_delta=row[\"RY_nodelta\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_delta = (RYi-RYi_delta)/RYi\n", + " RYs_delta.append(dRY_delta)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_delta = Get_weighted_average(RYs_delta,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_delta,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " \n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.xlabel(r'$z_{average}$')\n", + "plt.ylabel(r'$dRY(delta)/RY$')\n", + "plt.xlim(0.3,1) \n", + "plt.savefig(\"notebook_results/dRY_delta_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "87fd6106", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-26-a44f777c4064>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==4.75].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==4.75][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_delta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_delta=row[\"RY_nodelta\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_delta = (RYi-RYi_delta)/RYi\n", + " RYs_delta.append(dRY_delta)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_delta = Get_weighted_average(RYs_delta,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_delta,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(delta)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "#plt.legend()\n", + "plt.savefig(\"notebook_results/dRY_delta_2ndQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "1abc3921", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-27-69d5f0ee7854>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-27-69d5f0ee7854>:24: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "xs = df[df['Q2']==5.5].sort_values('xbj')['xbj'].unique()\n", + "#print(xs)\n", + "#ax.errorbar(xs,zs,RYs,RY_err,marker = \"_\")\n", + "for ix in range(0,len(xs)):\n", + " zs = df[df['Q2']==5.5][df['xbj']==xs[ix]].sort_values('z')['z'].unique()\n", + " xbj = xs[ix]\n", + " for iz in zs:\n", + " #print(iRunGroup)\n", + " RYs_delta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_delta=row[\"RY_nodelta\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_delta = (RYi-RYi_delta)/RYi\n", + " RYs_delta.append(dRY_delta)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_delta = Get_weighted_average(RYs_delta,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(z_corr,RY_delta,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$z_{average}$')\n", + " plt.ylabel(r'$dRY(delta)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_delta_3rdQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9642f470", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-28-376206d10ab9>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.34647\n", + "-0.294727\n", + "-0.2506799294500985\n", + "-0.20257687877502\n", + "-0.342816\n", + "-0.28409597125827424\n", + "-0.2504318462643136\n", + "-0.2032344800466055\n", + "-0.1526289422163012\n", + "-0.10354322856050224\n", + "-0.05574199999999985\n", + "-0.3338903873263857\n", + "-0.28404702050018993\n", + "-0.25256807019110583\n", + "-0.2033404711000174\n", + "-0.15405467999072664\n", + "-0.10505681264046762\n", + "-0.06059134116141762\n", + "-0.3340540664190014\n", + "-0.28538802383842665\n", + "-0.24730359071195934\n", + "-0.20222745912784706\n", + "-0.15459255386578424\n", + "-0.1058530108550576\n", + "-0.3341247734284914\n", + "-0.28519156333280576\n", + "-0.25118427841028373\n", + "-0.2015141432153626\n", + "-0.1546220302435538\n", + "-0.11186252400226182\n", + "-0.33311802965587295\n", + "-0.2856064437511988\n", + "-0.2495836629977108\n", + "-0.2026166609665433\n", + "-0.15737665782969507\n", + "-0.11420799999999998\n", + "-0.3341003968069907\n", + "-0.2859935647766409\n", + "-0.24813545970158052\n", + "-0.20497959044921205\n", + "-0.1596199167102348\n", + "-0.3335179944342012\n", + "-0.2950521832499023\n", + "-0.2525162132561221\n", + "-0.2083293377670144\n", + "-0.33414803134732274\n", + "-0.2964916367203637\n", + "-0.2568394314279863\n", + "-0.2155083268014879\n", + "-0.34405\n", + "-0.305543\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==4.0].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==4.0][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_delta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.0][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_delta=row[\"RY_nodelta\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_delta = (RYi-RYi_delta)/RYi\n", + " RYs_delta.append(dRY_delta)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_delta = Get_weighted_average(RYs_delta,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_delta,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(delta)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_delta_xbj_1stQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d89f8116", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-29-99c2a5c33add>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.286494\n", + "-0.23720565530103332\n", + "-0.19895784912318026\n", + "-0.15331866926861015\n", + "-0.10344574214478575\n", + "-0.05527000000000004\n", + "-0.28550099999999995\n", + "-0.23655150352598014\n", + "-0.19689948716778638\n", + "-0.15112474144311233\n", + "-0.1027701304970462\n", + "-0.05371065639276429\n", + "-0.2857092386765205\n", + "-0.236448733364999\n", + "-0.1970665942380374\n", + "-0.1521481929698827\n", + "-0.10295357171817454\n", + "-0.05487642191282527\n", + "-0.28461447394486133\n", + "-0.2365772343734655\n", + "-0.19633560394112065\n", + "-0.14983300293593443\n", + "-0.10213565505014388\n", + "-0.05807871853914692\n", + "-0.285537390656003\n", + "-0.23757031881989288\n", + "-0.19590062659165258\n", + "-0.15108913706367622\n", + "-0.10411489951594799\n", + "-0.059888510212737334\n", + "-0.2853843833857007\n", + "-0.23731941204129375\n", + "-0.1946321429947817\n", + "-0.15307425600633157\n", + "-0.10903613572621096\n", + "-0.28549399114792195\n", + "-0.23627726593196846\n", + "-0.1970424804793558\n", + "-0.15602316694879254\n", + "-0.11416470678948909\n", + "-0.2853343594759052\n", + "-0.24322550966814283\n", + 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==4.75].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==4.75][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_delta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==4.75][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_delta=row[\"RY_nodelta\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_delta = (RYi-RYi_delta)/RYi\n", + " RYs_delta.append(dRY_delta)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_delta = Get_weighted_average(RYs_delta,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_delta,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(delta)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_delta_xbj_2ndQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6c574830", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + "<ipython-input-30-f5e03a946b94>:29: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.23674099999999998\n", + "-0.20164700000000002\n", + "-0.14852919376935414\n", + "-0.10121399999999992\n", + "-0.05289100000000002\n", + "-0.23600300000000002\n", + "-0.1963258328142316\n", + "-0.14782817281848615\n", + "-0.10254902486615369\n", + "-0.05367885136939221\n", + "-0.23619870584981761\n", + "-0.18659343593465838\n", + "-0.14683735156701594\n", + "-0.10060633965245691\n", + "-0.05214329952932839\n", + "-0.23569911668297439\n", + "-0.1856402448150109\n", + "-0.14753983588835556\n", + "-0.10070922171794905\n", + "-0.05436761327771411\n", + "-0.2364431358783346\n", + "-0.18579052874273144\n", + "-0.147394009092445\n", + "-0.10111060847205278\n", + "-0.06099601303695501\n", + "-0.23662407985799366\n", + 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xbj_ones_corr = []\n", + "xbj_all_corr=[]\n", + "xbj_ones_plot = []\n", + "i_col = 0\n", + "colors_all = ['bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','olive','orange','orangered','palevioletred','pink','red','royalblue','seagreen','sienna','slateblue','springgreen','tan','teal','thistle','tomato','wheat','yellow','bisque','orange','darkorange','darksalmon','sienna','palevioletred','darkorchid','mediumvioletred','indigo','black','indianred','khaki','lavender','lawngreen','lemonchiffon','lightblue','lightcoral','lightsalmon','lightsteelblue','mediumorchid','mediumturquoise','navy','bisque','orange','darkorange','darksalmon','sienna','bisque','orange','darkorange','darksalmon','sienna']\n", + "fig = plt.figure()\n", + "\n", + "zs = df[df['Q2']==5.5].sort_values('z')['z'].unique()\n", + "#print(zs)\n", + "\n", + "\n", + "for iz in zs:\n", + " #print(iz)\n", + " xs = df[df['Q2']==5.5][df['z']==iz].sort_values('xbj')['xbj'].unique()\n", + " z = iz\n", + " #xbj = xs[ix]\n", + " for ix in range(0,len(xs)):\n", + " \n", + " #print(iRunGroup)\n", + " RYs_delta = []\n", + " RYs_error = []\n", + " zs_corr = []\n", + " zs_corr_err = []\n", + " xbjs_corr = []\n", + " xbjs_corr_err = []\n", + " Q2s_corr = []\n", + " Q2s_corr_err = []\n", + " \n", + " for i,row in df[df['Q2']==5.5][df['xbj']==xs[ix]][df['z']==iz].iterrows():\n", + " zi_corr = row['z_corr']\n", + " #print(z_corr)\n", + " RYi = row['RY_rho']\n", + " RYi_delta=row[\"RY_nodelta\"]\n", + " RYi_error = row['error']\n", + " RYs_error.append(RYi_error)\n", + " dRY_delta = (RYi-RYi_delta)/RYi\n", + " RYs_delta.append(dRY_delta)\n", + " zs_corr.append(zi_corr)\n", + " zi_corr_err = row[\"z_corr_err\"]\n", + " zs_corr_err.append(zi_corr_err)\n", + " xbji_corr = row[\"xbj_corr\"]\n", + " xbji_corr_err = row[\"xbj_corr_err\"]\n", + " xbjs_corr.append(xbji_corr)\n", + " xbjs_corr_err.append(xbji_corr_err)\n", + " Q2i_corr = row[\"Q2_corr\"]\n", + " Q2i_corr_err = row[\"Q2_corr_err\"]\n", + " Q2s_corr.append(Q2i_corr)\n", + " Q2s_corr_err.append(Q2i_corr_err)\n", + " if Q2i_corr<3:\n", + " print(Q2i_corr)\n", + " \n", + " xbj_ones_corr.append(xbji_corr)\n", + " #print(xbji_corr)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_error,RY-RY_error],marker=\"_\",color = colors[RunGroup//10])\n", + " xbj_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " #print(xbj_corr)\n", + " if abs(xbj_corr-xbj)>0.025:\n", + " print(xbj_corr-xbj)\n", + " z_corr = Get_weighted_average(zs_corr,zs_corr_err)\n", + " Q2_corr = Get_weighted_average(Q2s_corr,Q2s_corr_err)\n", + " if Q2_corr<3:\n", + " print(\"corr \",Q2_corr)\n", + " RY_delta = Get_weighted_average(RYs_delta,RYs_error)\n", + " #ax.plot([z_corr,z_corr],[RY+RY_err,RY-RY_err],color = colors_all[i_col],marker = \"_\")\n", + " plt.plot(xbj_corr,RY_delta,\"o\",color = colors_all[i_col],label = f\"$Q^2:{Q2_corr:.3f},x_{{bj}}:{xbj_corr:.3f}$\")\n", + " xbj_all_corr.append(xbj_corr)\n", + " xbj_one_corr.append(xbj_corr)\n", + " xbj_onei_err_corr = Get_weighted_average(xbjs_corr,xbjs_corr_err)\n", + " xbj_one_err_corr.append(xbj_onei_err_corr)\n", + " plt.xlabel(r'$x_{average}$')\n", + " plt.ylabel(r'$dRY(delta)/RY$')\n", + " plt.xlim(0.3,1)\n", + " #plt.ylim(0.4,0.85)\n", + " #plt.legend()\n", + " plt.grid()\n", + " #ax.set_title(f\"$Q^2:{Q2_str[0][0:4]},x_{{bj}}:{(xbj-0.025):.3f}-{(xbj+0.025):.3f}$\")#str(xbj-0.025)[0:5]+\"-\"+str(xbj+0.025)[0:5])\n", + " i_col = i_col+1\n", + "plt.savefig(\"notebook_results/dRY_delta_xbj_3rdQ2.pdf\")\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c3cacd1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/CSV_sys.ipynb b/CSV_sys.ipynb index 38a5e333d5ed6f7e877bed51b4aa39bf32df0a9e..9f3d452eb142d8e35991ade6a09b6e6612b4aa39 100644 --- a/CSV_sys.ipynb +++ b/CSV_sys.ipynb @@ -8,6 +8,14 @@ "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/3697949924.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " df = df[df['CSV_err']<0.1][df['rho_coe']!=1.8][df['rho_coe']!=-1.8]\n" + ] + }, { "data": { "text/html": [ @@ -40,39 +48,39 @@ " <tbody>\n", " <tr>\n", " <th>count</th>\n", - " <td>360.000000</td>\n", - " <td>360.000000</td>\n", - " <td>360.000000</td>\n", - " <td>360.000000</td>\n", - " <td>360.000000</td>\n", - " <td>360.000000</td>\n", + " <td>300.000000</td>\n", + " <td>300.000000</td>\n", + " <td>300.000000</td>\n", + " <td>300.000000</td>\n", + " <td>300.000000</td>\n", + " <td>300.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>4.712500</td>\n", " <td>0.465000</td>\n", " <td>0.464719</td>\n", - " <td>-0.727778</td>\n", - " <td>0.014711</td>\n", - " <td>0.024950</td>\n", + " <td>-0.633333</td>\n", + " <td>0.015595</td>\n", + " <td>0.023657</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.604345</td>\n", - " <td>0.100887</td>\n", - " <td>0.093487</td>\n", - " <td>1.317889</td>\n", - " <td>0.065242</td>\n", - " <td>0.019203</td>\n", + " <td>0.604514</td>\n", + " <td>0.100916</td>\n", + " <td>0.093513</td>\n", + " <td>1.205709</td>\n", + " <td>0.064918</td>\n", + " <td>0.019066</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>4.000000</td>\n", " <td>0.275000</td>\n", " <td>0.287822</td>\n", - " <td>-1.800000</td>\n", - " <td>-0.242627</td>\n", - " <td>0.003589</td>\n", + " <td>-1.500000</td>\n", + " <td>-0.199142</td>\n", + " <td>0.003425</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", @@ -80,8 +88,8 @@ " <td>0.375000</td>\n", " <td>0.387709</td>\n", " <td>-1.500000</td>\n", - " <td>-0.030034</td>\n", - " <td>0.012880</td>\n", + " <td>-0.031798</td>\n", + " <td>0.010779</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", @@ -89,8 +97,8 @@ " <td>0.475000</td>\n", " <td>0.470421</td>\n", " <td>-1.500000</td>\n", - " <td>0.015014</td>\n", - " <td>0.019938</td>\n", + " <td>0.014927</td>\n", + " <td>0.019279</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", @@ -98,15 +106,15 @@ " <td>0.537500</td>\n", " <td>0.532629</td>\n", " <td>1.000000</td>\n", - " <td>0.064073</td>\n", - " <td>0.028527</td>\n", + " <td>0.066308</td>\n", + " <td>0.026372</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>5.500000</td>\n", " <td>0.625000</td>\n", " <td>0.614131</td>\n", - " <td>1.800000</td>\n", + " <td>1.500000</td>\n", " <td>0.157690</td>\n", " <td>0.079212</td>\n", " </tr>\n", @@ -116,14 +124,14 @@ ], "text/plain": [ " Q2 xbj xbj_corr rho_coe CSV CSV_err\n", - "count 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000\n", - "mean 4.712500 0.465000 0.464719 -0.727778 0.014711 0.024950\n", - "std 0.604345 0.100887 0.093487 1.317889 0.065242 0.019203\n", - "min 4.000000 0.275000 0.287822 -1.800000 -0.242627 0.003589\n", - "25% 4.000000 0.375000 0.387709 -1.500000 -0.030034 0.012880\n", - "50% 4.750000 0.475000 0.470421 -1.500000 0.015014 0.019938\n", - "75% 5.500000 0.537500 0.532629 1.000000 0.064073 0.028527\n", - "max 5.500000 0.625000 0.614131 1.800000 0.157690 0.079212" + "count 300.000000 300.000000 300.000000 300.000000 300.000000 300.000000\n", + "mean 4.712500 0.465000 0.464719 -0.633333 0.015595 0.023657\n", + "std 0.604514 0.100916 0.093513 1.205709 0.064918 0.019066\n", + "min 4.000000 0.275000 0.287822 -1.500000 -0.199142 0.003425\n", + "25% 4.000000 0.375000 0.387709 -1.500000 -0.031798 0.010779\n", + "50% 4.750000 0.475000 0.470421 -1.500000 0.014927 0.019279\n", + "75% 5.500000 0.537500 0.532629 1.000000 0.066308 0.026372\n", + "max 5.500000 0.625000 0.614131 1.500000 0.157690 0.079212" ] }, "execution_count": 1, @@ -137,7 +145,7 @@ "\n", "import pandas as pd\n", "df = pd.read_csv(\"results/csv_systematic.txt\")\n", - "df = df[df['CSV_err']<0.1]\n", + "df = df[df['CSV_err']<0.1][df['rho_coe']!=1.8][df['rho_coe']!=-1.8]\n", "#df = df[df['rho_coe']!=1.8][df['rho_coe']!=-1]#[df['PDF_model']=='JAM20']\n", "#print(df['PDF_model'].unique())\n", "df.describe()" @@ -155,18 +163,18 @@ "text": [ "[0.28782192 0.33444932 0.37382584 0.42002944 0.46941981 0.51694167\n", " 0.56676958]\n", - "[-0.026958002140542538, 0.03287816093054649, 0.06077350590649397, 0.06059100313417237, 0.045399547047715164, 0.009573851881902823, -0.008198016769935089]\n", - "[0.04837687932542548, 0.023301972554631033, 0.017664115637918712, 0.01694446086718141, 0.019762120590087605, 0.03258449408390359, 0.05955389127416828]\n", - "[0.06974773233930014, 0.06841332246460034, 0.0786778036662892, 0.06548785441247734, 0.05480036432124183, 0.04491073165764466, 0.017357022160135075]\n" + "[-0.01587065400509233, 0.03580057443957649, 0.05915536342507383, 0.058309679331349305, 0.04306639982946169, 0.0070135978843704865, -0.007830103962199027]\n", + "[0.04560024260079093, 0.022004638229826907, 0.016690447979338052, 0.01604071310817034, 0.018709786310816948, 0.030704446515275426, 0.05649741489804487]\n", + "[0.06425837661406325, 0.07585923316398581, 0.08825893920998762, 0.07290970067709725, 0.05997300120559509, 0.04843003663297368, 0.0179298394472416]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_28464/1888431322.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/1888431322.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " CSVs = df[df['Q2']==4][df['xbj_corr']==ix]['CSV']\n", - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_28464/1888431322.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/1888431322.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " CSVs_errs = df[df['Q2']==4][df['xbj_corr']==ix]['CSV_err']\n" ] } @@ -229,7 +237,7 @@ { "data": { "text/plain": [ - "<matplotlib.collections.PolyCollection at 0x11e2f8d90>" + "<matplotlib.collections.PolyCollection at 0x11e0af430>" ] }, "execution_count": 4, @@ -238,7 +246,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -272,18 +280,18 @@ "text": [ "[0.339491 0.3861515 0.42582662 0.47142311 0.51930108 0.56819041\n", " 0.61413082]\n", - "[-0.11504369842255159, 0.038662615532960215, 0.04852028146336274, 0.052907283679518774, 0.04915891812202681, 0.03020609452852679, -0.036695069820708505]\n", - "[0.058113549017002504, 0.021086363103139327, 0.01316502655782451, 0.011228679320554671, 0.012114773181326489, 0.018370545190885466, 0.03483024699251875]\n", - "[0.08093480944003661, 0.05705110052038263, 0.056386005684029024, 0.05705270104341463, 0.05280989825456127, 0.04289627968624421, 0.028356089260740156]\n" + "[-0.09996629216101623, 0.03842994689751441, 0.047098140642856015, 0.050273991499957496, 0.045660857388841646, 0.027000851502615594, -0.03299572043682688]\n", + "[0.055262116869378944, 0.019990283116018068, 0.01247476722400182, 0.010634260382571988, 0.011464155337326273, 0.017326951015240934, 0.03352703917606186]\n", + "[0.06890660796848445, 0.06367591556837612, 0.06335836714520847, 0.06299373861996443, 0.05717747172618631, 0.04627906746235526, 0.028212398584495858]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_28464/2448108693.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/2448108693.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " CSVs = df[df['Q2']==4.75][df['xbj_corr']==ix]['CSV']\n", - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_28464/2448108693.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/2448108693.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " CSVs_errs = df[df['Q2']==4.75][df['xbj_corr']==ix]['CSV_err']\n" ] } @@ -324,7 +332,7 @@ { "data": { "text/plain": [ - "<matplotlib.collections.PolyCollection at 0x11ea38f10>" + "<matplotlib.collections.PolyCollection at 0x11e29fa00>" ] }, "execution_count": 6, @@ -333,7 +341,7 @@ }, { "data": { - "image/png": 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\n", 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IatbSr4YqCAaVWZid8U+Rv/J8YkBaG0mSS5TLc1Qqc1Qq5+rLtXvllkqvcOnSd6hWL7/S1CzO/gfeRRzPZNMUzBBF6XNtW9on0h3d7siV9iRJoaEJKSCKpkiSIoGp+agfKQi2IDOr9zHAgSvumyRlKpWzWUC8lj3OUqmky0tLR5if/2aLPg/LwmKmIRxmiOPd2foe4nhnx84Eu9mRu9X6Bpp1+/e5J5TL50gSSChw4cI3VzRfaqBE7ykIhlwQxPU7X63G3alWF7KQOJ09n6FcPk2pdIZC4SUuXnyCJFls/vSsuWk3udxu4nh3Fhx76s8baYbayh25W13j0N/v/GCRvZ94len3vQpAGI439GlNqRmpBxQEsiYzI4omiKIJRkdvXHW/anWBUulMFhanVywvLh6mXP6ry/ouzHL1YEgDY0/9uRYea82vL/1traG/tUEScDyrzU42DHyY1BDVLlAQSMeE4Tijo+OrhkVas7hAqXSKcvlM0/NpLl48RKVyluYRUlG0PQuKPRT/xW6CC3s4f353vaaRTrGsg0W/2sjQX3evdzrDMcyC+pDpNBwmVWPYBAoC6Zq0ZrGdKNoOfF/LfZKk3NAEdboeEqXSaYrF41Te8i0YWeTYscbPjRpqFLsbnvfUlzWcsXfaGfrrntRHxKXBYIThRMO1NFO6fqEDFATSV4IgJp/fRz6/r+XrR94Nnl9g/x+cplw+Ral0uh4U5fJpFhaeplw+QzpjSePnjjV0Ys9kQdHYHDWjTstN0smhv2mNIR1GXZv9Pgy3NdQapgjDbe0VeAgpCGTgWLHWBHVTy9fdq1Qqc5RKp+vNT2lopM9LS89lZ5grLTdB7W4Ki9q2XTr7vAqbPfQ3nRdpkVIp7XxOr5epXYU/QRhO6u+2BgWBbDnp1AfpAXxs7I0t90mSYtY/cTprhmrss3iZhYWnSJLLp2NOhz3OrHikNY3lay6CYGSzf+JA6fYcTu4VyuU5yuXl+SvDcJQwnMyalSYIw3H1NTRQEMhQCoI8+fx+8vn9q+5TrS5mtYlaYJzJHq9lF+Q9nU3/sVIYTjRcrb38iKLG5eGasK3XQ3/TifOWgOU7soXhGGGYhkIUTRAEYwTBcB4Sh/NXy8Dq5kEkbXu+npGR61fdp1pdqofD8vUVZ+rLS0tHqVTmuHyuqLA+L08aDDvr643L6fxQwxMY3VStXrrsJjxpzWHlxI/DMHxZQSDShvTAcYCRkdWv4HavZlN8vNYQGK/Vp/soFo+zsPBUy9rFcmDU5u3ZuWIiwcZndXa3b7nmsHw7XLMoqz2MZ89jW672sHV+iUifMgvJ5dK+hCtJ+y3OZlN+zDUs1+aHepXFxWezMfaXz0YbBGP1UFiewmF6xay0te3qx1g/90rDtQ3LgiCfzV48ltUex7J7iwxex7SCQKRPpP0W15DPX3PF/dID0/ksKOay2sa5FcuFwvNcvDjXssM7/a6R+pQO6fN2wnB7w5Tmtde2Z/e92PrNIxuVTi1fBM6t2B4EcXYTquVp7tMbUI32bTOfgkBkwKQX0KWdzmtJklIWEuezoKgtn6NavUC5fI5S6QyLi9/LhtRWW35OEIxkF3BN1i/kSsfsT67Y1ngDpWG5aVKzJEnvbgjzl72W/nccrd+tMH2MZI/eHY4VBCJbWBDksnmb9qy5b2368sb7W9SaRKrVxuULLC6+TKVyYdUaB6TTmaejciaIokkK/2YSK0xw8uR4Fhbj9SGdy52zE1kH7eA1r6xHenOqAs21CKjVJGqhUAuIfH3bZg53VRCICLBy+vIrDattlDZTzVOtXmi4aVLt+SLV6nz9LnvxW16hWj3M3Nx8djBcXXrwG2/opF25nLbLLz8a12tt9oN2ncByTaLVoIFap/U4ExNv7vh3KwhE5KqlzVTpyKWNSJJyFhALWVgsNKwvNK1folI5R7F4omGm0tZNWCvLtrIzd7nNfqRl+/1yu36rZpvRjt/OdqPS+6E3T/XeGR0JAjO7FfhNIAQ+5+73Nb2eB34f+CHgLPBT7v5i9tovAXeR/mU/6u4Pd6JMItK/0maQjQcI1O4HXsymllggSS7VrwlI15eoVi81PC/W10ul0yTJYv1+4ZffcOmKpW4Kh+ZHvv5slm/Yls9uW5tveC3XsFx7Pdfw3N3aTNtBYGmJPwu8EzgBPG5mB9392Ybd7gLOufvrzOwO4DPAT5nZDwB3AG8ErgG+bmavd/e1415EhlJ6P/CR+n2R2+FeJUmWsmBYqi+vXC80PDcvF0mSApXKeZKkgHuxvq3VfcPXL2wIhrghIPK86U1fWXWeravViRrBW4Ej7n4UwMweAm4HGoPgduA/Z8tfBn7L0jrW7cBDnsbyC2Z2JPu8b3agXCIiV2QW1vsd4g73T6chU2oIiBJJUqyHhXupYVu6XNtveb3ctFzZlKG8nQiCa6nNB5s6AfzIavu4e8XMLgA7s+2PNb235eS0ZnY3cDfAgQNXvg+viMiRd6fPvbqtaRoyo4ThaMc+Mwhy5PNXMX/3Wp/b8U/cJO5+v7vPuvvszMyVr9AUEZH160QQnASua1jfn21ruY+ZRcAUaafxet4rIiKbqBNB8Dhws5ndaGY50s7fg037HATuzJbfC3zD3T3bfoeZ5c3sRuBm4O86UCYREVmntvsIsjb/jwAPkw4ffcDdnzGze4FD7n4Q+F3gD7LO4DnSsCDb74ukHcsV4Gc1YkhEpLs6ch2Bu38F+ErTtk82LBeAlvcjcvdPA5/uRDlERLYKs/Cyaww62fHcSFcWi4h0WfMFZa0vNuveRWUKAhGRDjKzy646vvxK4/4asKkgEBHZoPSsvTZL6GjD9NIjmOUGbvptBYGISAvpfEjbCILR7CC/vDxoM5uuRUEgIkMrbcZpvpvYtuygPzyHx+H5pSIytNID/raGexbU7jE8OnDNOJtBQSAiW0raXj9GECxgFjI5+UaCYJsO+FegIBCRgWQWNN3BLL1zWa1JJwieBCAMx3pZzIGgIBCRvtc4XXR6D+QJneV3kIJARPpKeqZfO+CnN7dXW/7mUhCISE+F4Vj9gJ8+xnXQ7zIFgYh0jVmUHfQn68+bMUzz1OdPMf/YPF50vnnDN7np0zex5wN7Ov49W4WCQNrmVSAAncRJs/QOXVNE0RRRNNmVdv1Tnz/Fc3c/hxcdgOKxIs/d/RyAwmAVCgJpizssPQNeBovB8hDkIRhJly0Ei4Aoe11hsaWF4ThRtD078E8RBLmul+Hox4+SLCYrtiWLCUc/flRBsAoFgbTHwUsQbQevpI+kCNVz4MnyPgBYFga5NCysFhhRQ1hE0GfzcckqzCzr0E0P/GE41RdX4xZfKm5ouygIpIPqB/Qr8Gr6SBbAzzeFhWXPEQS5LCRGlsOCsOE71BTVdemBf5wo2lE/+PfjnDv5A3mKxy4/6OcP5HtQmsGgIJCusjB9XEk9LC6BzwOV5UpF+iEsB0fjtkYNtYoVNQxrCpArLTeuN35e02u2yn5X/IymWo/lIYhZrhXF/VEzSkf07CCOd/TNGf9abvr0TTx393MrmoeCbQE3ffqmHpaqv/X/X1WGznrCopl784Z1LF/2IU2f17hvtem11T5yle+6Yvmyz/fGWpEBYVobqj0s3xAS66h9XY0gyNcP/FG0oydt/O2q9QN8967v4kUnf31eo4bWoCCQLeGyZqI2m436odWpXjOaX9nnUssKrKGvZTR7zm2sc94syJp5ponjacJw2+b+qC7Z84E9vPw7LwNwy6O39Lg0/U9BINKn6jWjVU7K3UlrEgWoLLCyCS0LAIuzfpZaWOQgym8jNzpNvG2aOLe97+6WJd2nIBAZUGbU+xRW41XwimHlKYJLk0TBBEGYp+pQMac0vki8PSYcDwlGAoKcQmEYtRUEZjYNfAG4AXgReL+7n2ux353AL2er/9XdH8y2PwrsA5ay197l7qfbKZN01/O3QXUBbvhcr0sijYJwJG3yCbcTRuMtz/rdHS85xZeLeDWtS1hsRJMR0faIcDQNBwv7oaFMNlO7NYJ7gEfc/T4zuydb/1jjDllY/AowS1pzfcLMDjYExgfc/VCb5RAZbgZhMEEUbyeKthOGo2u/xQzLG0F+OSS86lQXqpTPluvbwrGQaCoinMhqDflAcwFtMe0Gwe3A27LlB4FHaQoC4CeAr7n7HICZfQ24FfijNr9bZLhZSBxNEUU7CKPtBEH7Y/otNMJtIeG25c9KSgml0yV4JdsQQDQZEU6l+wWjAUGkJqVB1m4Q7HH32j+PV4FW47OuBY43rJ/IttX8nplVgT8mbTZqOcDPzO4G7gY4cOBAm8UWGUwWxOnwzmgHYTTRlY7eILey78ATp7pUpXJ+uXc6GAkIp9KaQzASpE1KqjUMjDWDwMy+Duxt8dLHG1fc3c3sSqO0W/mAu580swnSIPhp4Pdb7eju9wP3A8zOzm70e0QGVtreP50d/Ht/ty0LjHA0hIbWp6ScUJmrUDpVSvcxI5wI076GMXVE97s1g8Dd37Haa2Z2ysz2ufsrZrYPaNXRe5Ll5iOA/aRNSLj7yez5opn9IfBWVgkCkWEShmNE2UVd62nv77UgDiCGkLRJqd4RfaKIu4OD5Yx4KibeGRNOhFigGkO/aLdp6CBwJ3Bf9vwnLfZ5GPhVM9uRrb8L+CUzi4Dt7v6amcXAvwS+3mZ5RAZWevCfJoqmCcPBnhenZUd0xamcr1A6XcJiI78vT7QjHZ0kvdVuENwHfNHM7gKOAe8HMLNZ4MPu/iF3nzOzTwGPZ++5N9s2BjychUBIGgK/02Z5RAZKGI4TxelVvYM4ncNGWGSE4yEhIV5xiieLFI4XCMdDcvtyRJOROp17pK0gcPezwNtbbD8EfKhh/QHggaZ9LgE/1M73iwyiMBwnzu0c2Ll8OsEiI5pKDz9JIWHp8BIY5HbniHfFhGOhOpu7SFcWi3RB2uyzcyjO/DeqNsrIE6d8tkzpVIlwJKslbI/UydwFCgKRTRKE24jjaaJo58C3+XeDBUY0kdUSSglLLyxhGNF0RG5PjnBcHcybRUEg0kFBmCeKdmUzefb/aJ9+Vbt2wT290vnS3CUsUgfzZlEQiLTJgpg43kkc7eyLcf5biZkRjoWEY607mOOpWHMhdYCCQORqWEgcT2cH/wl1bHZBqw7mJVtatYNZ9yFYPwWByHqZEYXbieOdRLHm8e8ldTB3loJAZA3pcM9dRNHOjkzsJp2jDubOUBCItBCEeeJ4JrvKd6TXxZF1aNXBHIwEjP/guPoR1qAgEKmxkDjeSS7eRRiN97o0cpUaO5jL58t44gqCNSgIZLgZROEOtfvLUFMQyFBKL/baRRzvIgj0v4EMN/0fIEOjPt4/ntHFXiINFASytRnE0Y704B9Naby/SAsKAtmSwnCsYcin/pmLXIn+D5Etw4KIKNpJLp4hjLb1ujgiA0NBIIOtPupnVzbqR00/IhulIJCBFISjDaN+4l4XR2SgKQhkcNQv+JrRLJ8iHaQgkL4XRVNZ088OXfAlsgkUBNKXanP9pE0/urWjyGZSEEj/sIA4ms7O/id7XRqRodFWPdvMps3sa2Z2OHvescp+XzWz82b2Z03bbzSzvzWzI2b2BTPTqd8AmfsSLD4BhafhyG1w4atX9zlhOMHI6E1MTNzC6LabFAIiXdZug+s9wCPufjPwSLbeyq8BP91i+2eAX3f31wHngLvaLI90ydyX4MTPgRfT9cqr8Oqvrj8MLMiRy1/D2PibGBv/fnK5XZhprn+RXmg3CG4HHsyWHwTe02ond38EuNi4zdIB3z8OfHmt90v/efVT4Esrt3kBzvyPK7zJAuJ4F9u2vYHx8X/MyMh+zfUv0gfa7SPY4+6vZMuvAns28N6dwHl3r2TrJ4BrV9vZzO4G7gY4cODAVRRVOql8svX2yqnLt4XhBHFuhjjeobN+kT60ZhCY2deBvS1e+njjiru7mXmnCtbM3e8H7geYnZ3dtO+R9YmvhfKJy7dH2anA8h2+dhKG+e4WTkQ2ZM0gcPd3rPaamZ0ys33u/oqZ7QNOb+C7zwLbzSzKagX7gVXOM6Xf7P1E1kfQ0DxkI7D3oxOMjV2nO3yJDJB2+wgOAndmy3cCf7LeN7q7A38OvPdq3i+9Nf0+2P8bYNnJfrwv4oZP3cg1P/n9CgGRAdNuENwHvNPMDgPvyNYxs1kz+1xtJzP7S+BLwNvN7ISZ/UT20seAnzezI6R9Br/bZnmkS6Joiv13vp7JH5lk/JZxbnn0LczcNtPrYonIVWirs9jdzwJvb7H9EPChhvUfXeX9R4G3tlMG6Z4wHCOX200c71ke7WMteodFZKDoymK5oiAYIZfbTS63hzDURG8iW5GCQC4TBOmIn1xuN1Gkq3xFtjoFgQAQBDnieDe53AxhOKkbvIgMEQXBEFs+89fBX2SYKQiGTBhuq0/vHEUTvS6OiPQBBcEWZ2aE4SRxvJM43kUY6qbuIrKSgmALMouI42nieCdRNK17+orIFSkItoD0rH+CKNpBHE+rvV9ENkRBMKDCcJQo2lF/BIH+lCJydXT0GBBhOEYUTRFF24miKYJAM3qKSGcoCPqQWUQUTRCGE4ThZHbgVzu/yEa4O2jC+nVREPRYEOQIwzHCcDw78E8QhqO9LpbIwEpKCdXFKjjEO2MsVH/ZWoYuCHK53QRBjmp1kWr1EkmySJIspWcPm8QsIgjyBMEIYbiNINhGGI4RBNvUti/SAZ44yWJCUkoIRgJGrh8h3hET5NqdYHk4DN1RKAjy5HIr76jpnpAkSyRJEfcySVLGvYx7iSQpAwm1OmZzYJhF2SNsWF4+8AdBXrdnFNkkSTEhWUxwnNzuHPFMTDgWatTcBg1dELRiFmTNM5pdU6TfedWpLlTxqhNOhIy8boRoKiKIdPZ/tRQEItL33J1kKSEpJlho5PbliHfGhKOqbXeCgkBE+lZjx2+0I2L0xlHCiRAL1PTTSQoCEekr6vjtPgWBiPQFdfz2joJARHpGHb/9QUEgIl2ljt/+01bsmtm0mX3NzA5nzztW2e+rZnbezP6safv/MrMXzOyp7PHmdsojIv3JK071UpXyuTKV8xWCbQFjbxhj4i0TjOwfUQj0WLs1gnuAR9z9PjO7J1v/WIv9fg3YBvy7Fq/9ort/uc1yiEifcHe85CSFpD7fj+WMaDIiP5UnmozU8dtn2g2C24G3ZcsPAo/SIgjc/REze1vzdhEZfEk5wYtOUk6A7P4YEyH5/XnCsZBgJNCBv8+1GwR73P2VbPlVYM+Vdl7Fp83sk8AjwD3uXmy1k5ndDdwNcODAgaspq4i0yRMnKaYH/trMnsFIQDQdpZ28IwHBSKCRPgNmzSAws68De1u89PHGFXd3M9vozG2/RBogOeB+0trEva12dPf7s32YnZ3V5LIiXZCU0k5dqtmGAKLJiHBvSLgtJBgNNMJnC1gzCNz9Hau9ZmanzGyfu79iZvuA0xv58obaRNHMfg/4hY28X0Q6x6vp2X5STOrbwrGQ3O4c4UTWxJPX2f5W1G7T0EHgTuC+7PlPNvLmhhAx4D3Ad9osj3TZm7/xZuYfn+91MWSD6h26xQSvphVsi7MO3f15wtH0wK+5/IdDu0FwH/BFM7sLOAa8H8DMZoEPu/uHsvW/BN4AjJvZCeAud38Y+LyZzQAGPAV8uM3yiAjp2b1XHS97fRkDIzuwG4TjIfmdecJxdegOu7aCwN3PAm9vsf0Q8KGG9R9d5f0/3s73iwwjd8crDtV0fL5Xso5bAycbrhlb2ok7FaXt+PkAiy19RKZ2fVlBVxaL9BlPvH6A94rXm24w0oO8GUE+IBgNsBEjHA3TA38cYFF2sNfsnLIBCgIZKLWD5Iqbkjcsr7iDXPPYslXec9l3rPYZV/i8y95zheNwvbO1cb/aGb07FqZn87UO2nA0XD7AR9lDHbbSQQoC6Rv1du3K8nNd7Ww4snozR/2lxrPfxuOjrVxfsV9Ty8hln1FrSjdb+ZnB6u9ZcXBuLget1+vvCVk+o1cHrXSZgkC6YsVBvrLcebm8Q3aQbzwTHgmXz4DV5CGyaRQE0hFJMVk+k68d5GtNH9lcM0G+6SDf2NQR6SAv0isKAmmPQbQ9giS9+Kg+QqX5TF5t2iJ9S0EgbTEzxr5vrNfFEJE2aDCxiMiQUxCIiAw5BYGIyJBTEIiIDDkFgYjIkFMQiIgMOQWBiMiQUxCIiAw5WzFr4oAwszOkN8K5GruA1zpYnEGg3zwc9Ju3vnZ/7/XuPtO8cSCDoB1mdsjdZ3tdjm7Sbx4O+s1b32b9XjUNiYgMOQWBiMiQG8YguL/XBegB/ebhoN+89W3K7x26PgIREVlpGGsEIiLSQEEgIjLktmwQmNmtZvacmR0xs3tavP7zZvasmT1tZo+Y2fW9KGcnreM3f9jMvm1mT5nZX5nZD/SinJ201m9u2O8nzczNbKCHGq7jb/xBMzuT/Y2fMrMP9aKcnbSev7GZvT/7//kZM/vDbpex09bxd/71hr/x98zsfFtf6O5b7gGEwPPATUAO+HvgB5r2+efAtmz53wNf6HW5u/CbJxuWbwO+2utyb/ZvzvabAP4f8Bgw2+tyb/Lf+IPAb/W6rF3+zTcDTwI7svXdvS73Zv/mpv3/I/BAO9+5VWsEbwWOuPtRdy8BDwG3N+7g7n/u7ovZ6mPA/i6XsdPW85vnG1bHSG8vP8jW/M2ZTwGfAQrdLNwmWO/v3UrW85t/Bvisu58DcPfTXS5jp2307/yvgT9q5wu3ahBcCxxvWD+RbVvNXcD/3dQSbb51/WYz+1kzex74b8BHu1S2zbLmbzaztwDXufv/6WbBNsl6/13/ZNbk+WUzu647Rds06/nNrwdeb2Z/bWaPmdmtXSvd5lj38Str0r4R+EY7X7hVg2DdzOzfArPAr/W6LN3g7p91938EfAz45V6XZzOZWQD8d+A/9bosXfSnwA3u/ibga8CDPS5PN0SkzUNvIz07/h0z297LAnXRHcCX3b3azods1SA4CTSeCe3Ptq1gZu8APg7c5u7FLpVts6zrNzd4CHjPZhaoC9b6zRPADwKPmtmLwD8BDg5wh/Gaf2N3P9vwb/lzwA91qWybZT3/rk8AB9297O4vAN8jDYZBtZH/l++gzWYhYMt2FkfAUdIqU62z5Y1N+9xC2iFzc6/L28XffHPD8ruBQ70u92b/5qb9H2WwO4vX8zfe17D8r4DHel3uLvzmW4EHs+VdpM0qO3td9s38zdl+bwBeJLswuJ1HdPUR0r/cvWJmHwEeJu2Bf8DdnzGze0kPfgdJm4LGgS+ZGcBL7n5bzwrdpnX+5o9ktaAycA64s3clbt86f/OWsc7f+1Ezuw2oAHOko4gG1jp/88PAu8zsWaAK/KK7n+1dqduzgX/XdwAPeZYK7dAUEyIiQ26r9hGIiMg6KQhERIacgkBEZMgpCEREhpyCQERkyCkIRESGnIJARGTI/X8Hxvawj9WHXAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -366,18 +374,18 @@ "output_type": "stream", "text": [ "[0.38822829 0.43232054 0.47648507 0.52124882 0.56884832 0.61346745]\n", - "[0.02577676027485377, -0.004634594094046028, 0.009362238644591379, 0.0399565122401013, 0.011028406638186772, -0.02904250832926559]\n", - "[0.04597703199272112, 0.015929327912808416, 0.010705580375442255, 0.010100088957583455, 0.010894348022276694, 0.018294481385701833]\n", - "[0.04744782287371499, 0.024946886819680467, 0.023155649567557408, 0.042729465614225644, 0.023495325883433987, 0.008266943879455188]\n" + "[0.02730289274036019, -0.0014597138392790396, 0.010703685990983447, 0.037597069359448745, 0.009976336673625293, -0.027361490883506714]\n", + "[0.04362703619893575, 0.015173049365928074, 0.010187767370779567, 0.00957325172297474, 0.010315181753137033, 0.01733643206153998]\n", + "[0.05279063685981732, 0.0247137522383055, 0.025494748363327657, 0.046625204780231924, 0.02554105604967316, 0.007495265788480337]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_28464/3192017738.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/3192017738.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " CSVs = df[df['Q2']==5.5][df['xbj_corr']==ix]['CSV']\n", - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_28464/3192017738.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_96920/3192017738.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " CSVs_errs = df[df['Q2']==5.5][df['xbj_corr']==ix]['CSV_err']\n" ] } @@ -418,7 +426,7 @@ { "data": { "text/plain": [ - "<matplotlib.collections.PolyCollection at 0x11eac34c0>" + "<matplotlib.collections.PolyCollection at 0x11e329ac0>" ] }, "execution_count": 8, @@ -427,7 +435,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -457,7 +465,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] diff --git a/Parametrization.ipynb b/Parametrization.ipynb index cf3d9809fced9d955f3b4f1f3a0f64a7a121e85a..3b2da86d14cb6841d2e85d4863cac26c1e1c282b 100644 --- a/Parametrization.ipynb +++ b/Parametrization.ipynb @@ -47,7 +47,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x12069dc10>" + "<matplotlib.legend.Legend at 0x1278f8ac0>" ] }, "execution_count": 2, @@ -114,7 +114,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x117d94fa0>" + "<matplotlib.legend.Legend at 0x116ebd580>" ] }, "execution_count": 4, @@ -249,10 +249,10 @@ "output_type": "stream", "text": [ "LHAPDF 6.3.0 loading /Users/shuojia/CSV/lhapdf/build/share/LHAPDF/cteq6l1/cteq6l1_0000.dat\n", + "Q2 is 4\n", "cteq6l1 PDF set, member #0, version 4; LHAPDF ID = 10042\n", - "Q2 is LHAPDF 6.3.0 loading /Users/shuojia/CSV/lhapdf/build/share/LHAPDF/cteq6l1/cteq6l1_0000.dat\n", + "LHAPDF 6.3.0 loading /Users/shuojia/CSV/lhapdf/build/share/LHAPDF/cteq6l1/cteq6l1_0000.dat\n", "cteq6l1 PDF set, member #0, version 4; LHAPDF ID = 10042\n", - " 4\n", "Q2 is 4.75\n", "Q2 is 5.5\n" ] @@ -394,7 +394,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x120d35730>" + "<matplotlib.legend.Legend at 0x12ea0d0a0>" ] }, "execution_count": 10, @@ -462,17 +462,7 @@ }, { "data": { - "text/plain": [ - "<matplotlib.legend.Legend at 0x121030f40>" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -504,7 +494,9 @@ "\n", "plt.xlabel(\"x\")\n", "plt.ylabel(r'$xq_{CSV}(x)$')\n", - "plt.legend()" + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('notebook_results/MRST_XqCSV.pdf')" ] }, { @@ -675,6 +667,47 @@ { "cell_type": "code", "execution_count": 17, + "id": "d6d8d894", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "zs = np.arange(0.05,0.8,0.01)\n", + "Duplus = []\n", + "Duminus = []\n", + "Ddplus = []\n", + "Ddminus = []\n", + "for zi in zs:\n", + " Duplus.append(fDSS_.fdss(1,1,1,zi,4.0)[0])\n", + " Duminus.append(fDSS_.fdss(1,-1,1,zi,4.0)[0])\n", + " Ddplus.append(fDSS_.fdss(1,1,1,zi,4.0)[2])\n", + " Ddminus.append(fDSS_.fdss(1,-1,1,zi,4.0)[2])\n", + "plt.plot(zs,Duplus,label = r'$D_u^+$') \n", + "plt.plot(zs,Duminus,label = r'$D_u^-$')\n", + "plt.plot(zs,Ddplus,label = r'$D_d^+$')\n", + "plt.plot(zs,Ddminus,label = r'$D_d^-$')\n", + "plt.legend()\n", + "plt.title('DSS fragmentation functions')\n", + "plt.xlabel('z')\n", + "plt.ylabel('FF')\n", + "plt.savefig('notebook_results/DSS_FFs_NLO.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "id": "3f434d20", "metadata": { "scrolled": false @@ -683,10 +716,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x12143d3d0>" + "<matplotlib.legend.Legend at 0x127bb7760>" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, @@ -730,17 +763,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "36aded1c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x120dbeb50>" + "<matplotlib.legend.Legend at 0x12ea98a30>" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -767,17 +800,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "7a1102de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x120cc5340>" + "<matplotlib.legend.Legend at 0x127beee50>" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -806,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "54dd1695", "metadata": {}, "outputs": [ @@ -827,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "7f3c1df2", "metadata": { "scrolled": true @@ -847,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "97481d45", "metadata": { "scrolled": true @@ -856,10 +889,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x120b722b0>" + "<matplotlib.legend.Legend at 0x127fb6cd0>" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, @@ -917,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "1b722edd", "metadata": { "scrolled": false @@ -937,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "1a381399", "metadata": {}, "outputs": [], @@ -947,7 +980,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "38f81eaf", "metadata": {}, "outputs": [ @@ -985,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "id": "a07ec65c", "metadata": {}, "outputs": [ @@ -1041,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "id": "53cd14e9", "metadata": { "scrolled": false @@ -1051,7 +1084,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "LHAPDF 6.3.0 loading /Users/shuojia/CSV/lhapdf/build/share/LHAPDF/JAM20-SIDIS_FF_pion_nlo/JAM20-SIDIS_FF_pion_nlo_0000.dat\n", + "LHAPDF 6.3.0 loading /Users/shuojia/CSV/lhapdf/build/share/LHAPDF/JAM20-SIDIS_FF_pion_nlo/JAM20-SIDIS_FF_pion_nlo_0000.dat\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x127def2b0>" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "JAM20-SIDIS_FF_pion_nlo PDF set, member #0, version 2\n", "LHAPDF 6.3.0 loading /Users/shuojia/CSV/lhapdf/build/share/LHAPDF/NNFF10_PIm_lo/NNFF10_PIm_lo_0000.dat\n", "NNFF10_PIm_lo PDF set, member #0, version 1; LHAPDF ID = 2000000\n", @@ -1063,16 +1112,6 @@ "MAPFF10NLOPIp PDF set, member #0, version 2\n" ] }, - { - "data": { - "text/plain": [ - "<matplotlib.legend.Legend at 0x121573ee0>" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -1142,17 +1181,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "id": "d13ce7bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x127951be0>" + "<matplotlib.legend.Legend at 0x127d06610>" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, @@ -1220,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "53adb745", "metadata": { "scrolled": true @@ -1232,7 +1271,7 @@ "Text(0.5, 1.0, 'JAM20 fragmentation functions')" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, @@ -1281,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "4c1b92ca", "metadata": {}, "outputs": [], @@ -1291,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "d4fb0300", "metadata": { "scrolled": true @@ -1318,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "id": "4de8fa63", "metadata": { "scrolled": true @@ -1327,10 +1366,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x1279423a0>" + "<matplotlib.legend.Legend at 0x12febe9a0>" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, @@ -1369,17 +1408,17 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "id": "11009b45", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x127de58e0>" + "<matplotlib.legend.Legend at 0x12fefe6a0>" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, @@ -1405,8 +1444,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "4b605f83", + "execution_count": 35, + "id": "c3a1b735", "metadata": { "scrolled": true }, @@ -1414,10 +1453,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x127e85640>" + "<matplotlib.legend.Legend at 0x1420a3f40>" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, @@ -1456,8 +1495,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "c8509c34", + "execution_count": 36, + "id": "ec7bd8ad", "metadata": { "scrolled": true }, @@ -1465,10 +1504,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x127f0b430>" + "<matplotlib.legend.Legend at 0x142077940>" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, @@ -1494,8 +1533,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "8ac54ef7", + "execution_count": 37, + "id": "2d9647f3", "metadata": { "scrolled": false }, @@ -1503,10 +1542,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x127f872e0>" + "<matplotlib.legend.Legend at 0x1420841f0>" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, @@ -1545,17 +1584,17 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "8b5d3969", + "execution_count": 38, + "id": "cd4c6330", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x14301a730>" + "<matplotlib.legend.Legend at 0x142167a90>" ] }, - "execution_count": 37, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, @@ -1581,7 +1620,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "id": "6105361b", "metadata": { "scrolled": true @@ -1618,7 +1657,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "id": "16f7b154", "metadata": { "scrolled": true @@ -1627,10 +1666,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x12143d760>" + "<matplotlib.legend.Legend at 0x14231df40>" ] }, - "execution_count": 39, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, @@ -1663,7 +1702,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "id": "e6b3e1cd", "metadata": { "scrolled": true @@ -1672,10 +1711,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x1278d6dc0>" + "<matplotlib.legend.Legend at 0x14236e160>" ] }, - "execution_count": 40, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, @@ -1722,7 +1761,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "id": "02b21d33", "metadata": { "scrolled": true @@ -1739,10 +1778,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x14323ecd0>" + "<matplotlib.legend.Legend at 0x14246e7f0>" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, @@ -1779,7 +1818,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "id": "5d8743d0", "metadata": {}, "outputs": [ @@ -1789,7 +1828,7 @@ "Text(0.5, 1.0, '2')" ] }, - "execution_count": 42, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, @@ -1821,7 +1860,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "id": "2bf8d571", "metadata": {}, "outputs": [ @@ -1836,10 +1875,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x14333d610>" + "<matplotlib.legend.Legend at 0x14227d880>" ] }, - "execution_count": 43, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, @@ -1877,7 +1916,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "id": "7a70b6fd", "metadata": {}, "outputs": [ @@ -1887,7 +1926,7 @@ "Text(0.5, 1.0, '2')" ] }, - "execution_count": 44, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, @@ -1925,7 +1964,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "id": "cca9c9e6", "metadata": { "scrolled": true @@ -1937,7 +1976,7 @@ "Text(0.5, 1.0, '2')" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -1975,7 +2014,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "id": "dd69f5bf", "metadata": {}, "outputs": [ @@ -2016,7 +2055,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "id": "15721d61", "metadata": {}, "outputs": [ @@ -2057,7 +2096,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "id": "074813dd", "metadata": { "scrolled": true @@ -2067,9 +2106,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_27364/948078783.py:8: RuntimeWarning: divide by zero encountered in true_divide\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_21535/948078783.py:8: RuntimeWarning: divide by zero encountered in true_divide\n", " plt.plot(x_axis,np.asarray(cteq6l1_u)/np.asarray(cteq6l1_d),label = \"cteq6 u/d\")\n", - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_27364/948078783.py:9: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_21535/948078783.py:9: RuntimeWarning: invalid value encountered in true_divide\n", " plt.plot(x_axis,np.asarray(cteq6l1_ub)/np.asarray(cteq6l1_db),label = r\"cteq6 $\\overline{u}/\\overline{d}$\")\n" ] }, @@ -2109,7 +2148,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "id": "0a99511a", "metadata": {}, "outputs": [ @@ -2119,7 +2158,7 @@ "Text(0.5, 1.0, '2')" ] }, - "execution_count": 49, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, @@ -2157,7 +2196,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "id": "bac49983", "metadata": { "scrolled": true @@ -2195,7 +2234,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "id": "7f00069f", "metadata": {}, "outputs": [ @@ -2220,7 +2259,7 @@ " 'cteq6l1']" ] }, - "execution_count": 51, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2231,7 +2270,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "id": "12610599", "metadata": { "scrolled": false @@ -2423,7 +2462,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "id": "f42e3f76", "metadata": {}, "outputs": [ @@ -2438,10 +2477,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x143581c10>" + "<matplotlib.legend.Legend at 0x1427ab130>" ] }, - "execution_count": 53, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, @@ -2502,22 +2541,24 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "id": "683f4074", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LHAPDF 6.3.0 loading all 196 PDFs in set JAM20-SIDIS_FF_pion_nlo\n", - "JAM20-SIDIS_FF_pion_nlo, version 2; 196 PDF members\n", - "196\n" + "196\n", + "JAM20-SIDIS_FF_pion_nlo, version 2; 196 PDF members\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -2565,8 +2606,8 @@ " xs_JAM20PDF_All = []\n", " xsb_JAM20PDF_All = []\n", " for i_PDFset in range(len(p_JAM20PDF_all)):\n", - " xu_JAM20PDF_All.append(p_JAM20PDF_all[i_PDFset].xfxQ(2,xi,Q)-p_JAM20PDF_all[i_set].xfxQ(-2,xi,Q))\n", - " xd_JAM20PDF_All.append(p_JAM20PDF_all[i_PDFset].xfxQ(1,xi,Q)-p_JAM20PDF_all[i_set].xfxQ(-1,xi,Q))\n", + " xu_JAM20PDF_All.append(p_JAM20PDF_all[i_PDFset].xfxQ(2,xi,Q)-p_JAM20PDF_all[i_PDFset].xfxQ(-2,xi,Q))\n", + " xd_JAM20PDF_All.append(p_JAM20PDF_all[i_PDFset].xfxQ(1,xi,Q)-p_JAM20PDF_all[i_PDFset].xfxQ(-1,xi,Q))\n", " xs_JAM20PDF_All.append(p_JAM20PDF_all[i_PDFset].xfxQ(3,xi,Q))\n", " xsb_JAM20PDF_All.append(p_JAM20PDF_all[i_PDFset].xfxQ(-3,xi,Q))\n", " \n", @@ -2592,7 +2633,99 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, + "id": "3436e5ab", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "196LHAPDF 6.3.0 loading all 196 PDFs in set JAM20-SIDIS_FF_pion_nlo\n", + "\n", + "JAM20-SIDIS_FF_pion_nlo, version 2; 196 PDF members\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Q = math.sqrt(4.6)\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "plt.plot(x_axis,np.asarray(R_sea_NS_n),label=r\"$R_{NS}$\")\n", + "plt.fill_between(x_axis,np.asarray(R_sea_NS_n)+np.asarray(R_sea_NS_s),np.asarray(R_sea_NS_n)-np.asarray(R_sea_NS_s),alpha = 0.15)\n", + "\n", + "\n", + "set_JAM20_FF = lhapdf.getPDFSet(\"JAM20-SIDIS_FF_pion_nlo\")\n", + "JAM20_FF = set_JAM20_FF.mkPDFs()\n", + "print(len(JAM20_FF))\n", + "zs = [0.4,0.5,0.6,0.7]\n", + "for zi in zs:\n", + " duplus = []\n", + " duminus = []\n", + " dsplus = []\n", + " dsminus = []\n", + " for i_FFset in range(len(JAM20_FF)):\n", + " duplus.append(JAM20_FF[i_FFset].xfxQ(2,zi,Q))\n", + " duminus.append(JAM20_FF[i_FFset].xfxQ(-2,zi,Q))\n", + " dsplus.append(JAM20_FF[i_FFset].xfxQ(3,zi,Q))\n", + " dsminus.append(JAM20_FF[i_FFset].xfxQ(-3,zi,Q))\n", + " Dup = ufloat(np.asarray(duplus).mean(),np.asarray(duplus).std())\n", + " Dum = ufloat(np.asarray(duminus).mean(),np.asarray(duminus).std()) \n", + " Dsp = ufloat(np.asarray(dsplus).mean(),np.asarray(dsplus).std())\n", + " Dsm = ufloat(np.asarray(dsminus).mean(),np.asarray(dsminus).std()) \n", + " rsea_s_d = (Dsp+Dsm)/Dup/(1+Dum/Dup)\n", + " #r_sea_s_d_err = ((Dsp+Dsm)/Dup/(1+Dum/Dup)).s\n", + " #rsea_s_d = (dsplus+dsminus)/duplus/(1+duminus/duplus)\n", + " R_sea_strange_n = []\n", + " R_sea_strange_err = []\n", + " for xi in x_axis:\n", + " xu_JAM20PDF_All = []\n", + " xd_JAM20PDF_All = []\n", + " xs_JAM20PDF_All = []\n", + " xsb_JAM20PDF_All = []\n", + " for i_PDFset in range(len(p_cteq66_all)):\n", + " xu_JAM20PDF_All.append(p_cteq66_all[i_PDFset].xfxQ(2,xi,Q)-p_cteq66_all[i_PDFset].xfxQ(-2,xi,Q))\n", + " xd_JAM20PDF_All.append(p_cteq66_all[i_PDFset].xfxQ(1,xi,Q)-p_cteq66_all[i_PDFset].xfxQ(-1,xi,Q))\n", + " xs_JAM20PDF_All.append(p_cteq66_all[i_PDFset].xfxQ(3,xi,Q))\n", + " xsb_JAM20PDF_All.append(p_cteq66_all[i_PDFset].xfxQ(-3,xi,Q))\n", + " \n", + " u = ufloat(np.asarray(xu_JAM20PDF_All).mean(),np.asarray(xu_JAM20PDF_All).std())\n", + " d = ufloat(np.asarray(xd_JAM20PDF_All).mean(),np.asarray(xd_JAM20PDF_All).std())\n", + " s = ufloat(np.asarray(xs_JAM20PDF_All).mean(),np.asarray(xs_JAM20PDF_All).std())\n", + " sb = ufloat(np.asarray(xsb_JAM20PDF_All).mean(),np.asarray(xsb_JAM20PDF_All).std())\n", + " rsea_s = rsea_s_d*(s+sb)/(u+d)\n", + " #rsea_s = rsea_s_d*(y_s+y_sbar)/(y_d+y_u)\n", + " R_sea_strange_n.append(rsea_s.n)\n", + " R_sea_strange_err.append(rsea_s.s)\n", + " plt.plot(x_axis,np.asarray(R_sea_strange_n),label=f\"$R_{{S}}(z = {zi})$\")\n", + " plt.fill_between(x_axis,np.asarray(R_sea_strange_n)-np.asarray(R_sea_strange_err),np.asarray(R_sea_strange_n)+np.asarray(R_sea_strange_err),alpha = 0.15)\n", + "ax.set_yscale(\"log\")\n", + "plt.xlim(0,0.7)\n", + "plt.ylim(5e-4,1e1)\n", + "plt.xlabel(r\"$x_{bj}$\")\n", + "plt.ylabel(r\"$R(x) for Q^2 = 4.6 GeV^2$\")\n", + "plt.legend()\n", + "#plt.xlim(0.3,0.8)\n", + "plt.savefig('/Users/shuojia/CSV/notebook/notebook_results/Bterm_cteq66PDF_JAM20FF.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, "id": "1e3bef0b", "metadata": {}, "outputs": [ @@ -2746,7 +2879,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 58, "id": "07d97524", "metadata": {}, "outputs": [ @@ -2754,17 +2887,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_27364/3014397017.py:2: RuntimeWarning: divide by zero encountered in power\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_21535/3014397017.py:2: RuntimeWarning: divide by zero encountered in power\n", " return (1-x)**params[0]*x**params[1]*(x-params[2])\n" ] }, { "data": { "text/plain": [ - "[<matplotlib.lines.Line2D at 0x143505b20>]" + "[<matplotlib.lines.Line2D at 0x142657460>]" ] }, - "execution_count": 56, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, @@ -2798,7 +2931,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 59, "id": "1de1de6a", "metadata": {}, "outputs": [ @@ -2814,9 +2947,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_27364/1994076592.py:15: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_21535/1994076592.py:15: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", " if abs(integrate.quad(delta_udv,0,1,(a,b,k,d))[0])>p_inter:\n", - "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_27364/1994076592.py:19: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", + "/var/folders/ll/8_2_l77j4yl2zjb6stl8d9b00000gn/T/ipykernel_21535/1994076592.py:19: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", " p_inter = abs(integrate.quad(delta_udv,0,1,(a,b,k,d))[0])\n" ] } @@ -2848,7 +2981,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 60, "id": "fccb7ca8", "metadata": {}, "outputs": [ @@ -2882,7 +3015,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 61, "id": "8e3b7d39", "metadata": {}, "outputs": [ @@ -2900,17 +3033,17 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 62, "id": "76efa219", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[<matplotlib.lines.Line2D at 0x1546ac700>]" + "[<matplotlib.lines.Line2D at 0x16d1f3700>]" ] }, - "execution_count": 60, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, diff --git a/csv_systematic.csv b/csv_systematic.csv new file mode 100644 index 0000000000000000000000000000000000000000..8ec084d0dcba1dd35e69109b9adc0b018840d18c --- /dev/null +++ b/csv_systematic.csv @@ -0,0 +1 @@ +4,4.356491142043409,5,0.5140773118369732,0.475,0.4627899406554628,fDSSNLO,-1 \ No newline at end of file diff --git a/csv_systematic.txt b/csv_systematic.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1eaf84ef0165437c3c3658ca5b1fee100dab024 --- /dev/null +++ b/csv_systematic.txt @@ -0,0 +1 @@ +Q2,Q2_corr,xbj,xbj_corr,z,z_corr,FF_model,rho_coe,CSV \ No newline at end of file diff --git a/results_W2Wp2_4_2p6_withHGC/csv_datasub.csv b/results_W2Wp2_4_2p6_withHGC/csv_datasub.csv new file mode 100644 index 0000000000000000000000000000000000000000..f19f513b300d9cb534b0864884c785b0c4e3425d --- /dev/null +++ b/results_W2Wp2_4_2p6_withHGC/csv_datasub.csv @@ -0,0 +1,670 @@ 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