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tjlane/X3P4S1_HEC16-X3P4S1_HEC16_stats.txt

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plot CC1/2 vs resolution based on AMACORD tables
Raw
mmCPD-X3P4S1_stats.txt
Resolution (Å) Reflections Possible reflections Completeness (%) Multiplicity SNR Rsplit CC1/2 CC*
54.054–5.4 2,094 2,093 100.05 7,386.3 22.09 6.42 0.988 0.997
5.4–4.286 1,965 1,965 100 6,032 20.45 6.31 0.989 0.997
4.286–3.745 1,925 1,925 100 5,981.4 21.37 6.36 0.984 0.996
3.745–3.403 1,908 1,908 100 6,251.7 19.21 6.63 0.988 0.997
3.403–3.159 1,923 1,923 100 6,337.9 15.3 8.66 0.981 0.995
3.159–2.973 1,881 1,881 100 6,428 11.9 11.46 0.968 0.992
2.973–2.824 1,867 1,867 100 5,732.8 9.52 14.81 0.942 0.985
2.824–2.701 1,926 1,926 100 5,449.6 7.95 18.29 0.914 0.977
2.701–2.597 1,875 1,875 100 5,991.5 7.18 20.04 0.892 0.971
2.597–2.507 1,874 1,874 100 6,277.9 6.15 23.21 0.846 0.957
2.507–2.429 1,869 1,869 100 6,664.9 5.83 26.33 0.817 0.948
2.429–2.36 1,880 1,880 100 6,807 4.81 31.58 0.718 0.914
2.36–2.297 1,855 1,855 100 7,016.6 4.2 33.83 0.735 0.921
2.297–2.241 1,865 1,865 100 7,138.2 3.74 39.4 0.622 0.876
2.241–2.19 1,890 1,890 100 7,325.3 3.21 45.03 0.566 0.85
2.19–2.144 1,846 1,846 100 7,405.3 2.93 48.89 0.46 0.794
2.144–2.101 1,863 1,863 100 7,606.1 2.76 54.22 0.322 0.698
2.101–2.061 1,837 1,837 100 7,635.6 2.4 59.01 0.296 0.676
2.061–2.024 1,895 1,895 100 7,797.3 1.57 87.07 0.212 0.591
2.024–1.99 1,836 1,836 100 7,953.1 1.45 93.64 0.181 0.554
Raw
mmCPD-X3S1_stats.txt
Resolution (Å) Reflections Possible reflections Completeness (%) Multiplicity SNR Rsplit CC1/2 CC*
54.054–5.807 1,699 1,698 100.06 9,520.6 20.88 11.13 0.966 0.991
5.807–4.61 1,586 1,586 100 7,247.2 18.66 11.42 0.94 0.984
4.61–4.027 1,563 1,563 100 7,173.9 21.14 10.29 0.952 0.988
4.027–3.659 1,553 1,553 100 7,058.5 19.88 9.76 0.972 0.993
3.659–3.397 1,532 1,532 100 7,166.5 18.64 10.88 0.959 0.99
3.397–3.197 1,535 1,535 100 7,098.4 15.45 12.97 0.957 0.989
3.197–3.037 1,516 1,516 100 7,399.4 12.49 16.96 0.923 0.98
3.037–2.904 1,536 1,536 100 6,920.8 11.05 19.76 0.897 0.973
2.904–2.793 1,530 1,530 100 6,050 8.66 24.63 0.865 0.963
2.793–2.696 1,499 1,499 100 6,178.1 8.02 27.17 0.803 0.944
2.696–2.612 1,526 1,526 100 6,596.7 7.29 28.82 0.798 0.942
2.612–2.537 1,492 1,492 100 7,044.4 6.28 35.94 0.724 0.916
2.537–2.47 1,520 1,520 100 7,066.3 5.96 37.29 0.673 0.897
2.47–2.41 1,479 1,479 100 7,552 5.62 40.26 0.644 0.885
2.41–2.355 1,527 1,527 100 7,412.6 4.87 45.99 0.542 0.838
2.355–2.305 1,511 1,511 100 7,782.5 4.27 48.65 0.531 0.833
2.305–2.259 1,481 1,481 100 7,762.5 3.95 57.29 0.468 0.798
2.259–2.216 1,521 1,521 100 7,927.6 3.25 69.61 0.378 0.741
2.216–2.177 1,469 1,469 100 8,164 3.2 67.55 0.331 0.706
2.177–2.14 1,518 1,518 100 8,073.7 2.78 75.21 0.279 0.661
Display the source blob
Display the rendered blob
Raw
plot_ccs.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "823ab92f",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9b335229",
"metadata": {},
"outputs": [],
"source": [
"def load_stats_table(filename):\n",
"\n",
" df = pd.read_csv(\n",
" filename,\n",
" sep='\\t',\n",
" decimal='.',\n",
" thousands=','\n",
" )\n",
"\n",
" # Split \"Resolution (Å)\" into two float columns\n",
" df[['high resolution limit', 'low resolution limit']] = (\n",
" df['Resolution (Å)']\n",
" .str.split('–', expand=True)\n",
" .astype(float)\n",
" )\n",
"\n",
"\n",
" # Reorder columns to put the new ones first (optional)\n",
" cols = df.columns.tolist()\n",
" cols = cols[-2:] + cols[:-2]\n",
" df = df[cols]\n",
"\n",
" df['mean resolution'] = (df['high resolution limit'] + df['low resolution limit']) / 2\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "7131b369",
"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>high resolution limit</th>\n",
" <th>low resolution limit</th>\n",
" <th>Resolution (Å)</th>\n",
" <th>Reflections</th>\n",
" <th>Possible reflections</th>\n",
" <th>Completeness (%)</th>\n",
" <th>Multiplicity</th>\n",
" <th>SNR</th>\n",
" <th>Rsplit</th>\n",
" <th>CC1/2</th>\n",
" <th>CC*</th>\n",
" <th>mean resolution</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>54.348</td>\n",
" <td>6.349</td>\n",
" <td>54.348–6.349</td>\n",
" <td>1337</td>\n",
" <td>1336</td>\n",
" <td>100.07</td>\n",
" <td>2324.8</td>\n",
" <td>13.74</td>\n",
" <td>7.15</td>\n",
" <td>0.988</td>\n",
" <td>0.997</td>\n",
" <td>30.3485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6.349</td>\n",
" <td>5.040</td>\n",
" <td>6.349–5.04</td>\n",
" <td>1238</td>\n",
" <td>1238</td>\n",
" <td>100.00</td>\n",
" <td>1903.5</td>\n",
" <td>10.73</td>\n",
" <td>9.40</td>\n",
" <td>0.984</td>\n",
" <td>0.996</td>\n",
" <td>5.6945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.040</td>\n",
" <td>4.403</td>\n",
" <td>5.04–4.403</td>\n",
" <td>1229</td>\n",
" <td>1229</td>\n",
" <td>100.00</td>\n",
" <td>1868.8</td>\n",
" <td>11.56</td>\n",
" <td>8.94</td>\n",
" <td>0.983</td>\n",
" <td>0.996</td>\n",
" <td>4.7215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.403</td>\n",
" <td>4.002</td>\n",
" <td>4.403–4.002</td>\n",
" <td>1204</td>\n",
" <td>1204</td>\n",
" <td>100.00</td>\n",
" <td>1893.3</td>\n",
" <td>11.02</td>\n",
" <td>9.38</td>\n",
" <td>0.981</td>\n",
" <td>0.995</td>\n",
" <td>4.2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.002</td>\n",
" <td>3.715</td>\n",
" <td>4.002–3.715</td>\n",
" <td>1203</td>\n",
" <td>1203</td>\n",
" <td>100.00</td>\n",
" <td>1927.2</td>\n",
" <td>9.58</td>\n",
" <td>10.71</td>\n",
" <td>0.980</td>\n",
" <td>0.995</td>\n",
" <td>3.8585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.715</td>\n",
" <td>3.495</td>\n",
" <td>3.715–3.495</td>\n",
" <td>1180</td>\n",
" <td>1180</td>\n",
" <td>100.00</td>\n",
" <td>1959.0</td>\n",
" <td>7.66</td>\n",
" <td>13.31</td>\n",
" <td>0.972</td>\n",
" <td>0.993</td>\n",
" <td>3.6050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.495</td>\n",
" <td>3.320</td>\n",
" <td>3.495–3.32</td>\n",
" <td>1212</td>\n",
" <td>1212</td>\n",
" <td>100.00</td>\n",
" <td>2009.9</td>\n",
" <td>6.36</td>\n",
" <td>16.52</td>\n",
" <td>0.955</td>\n",
" <td>0.988</td>\n",
" <td>3.4075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.320</td>\n",
" <td>3.176</td>\n",
" <td>3.32–3.176</td>\n",
" <td>1188</td>\n",
" <td>1188</td>\n",
" <td>100.00</td>\n",
" <td>2056.6</td>\n",
" <td>4.89</td>\n",
" <td>22.08</td>\n",
" <td>0.923</td>\n",
" <td>0.980</td>\n",
" <td>3.2480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.176</td>\n",
" <td>3.053</td>\n",
" <td>3.176–3.053</td>\n",
" <td>1162</td>\n",
" <td>1162</td>\n",
" <td>100.00</td>\n",
" <td>2103.5</td>\n",
" <td>3.74</td>\n",
" <td>28.77</td>\n",
" <td>0.872</td>\n",
" <td>0.965</td>\n",
" <td>3.1145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.053</td>\n",
" <td>2.948</td>\n",
" <td>3.053–2.948</td>\n",
" <td>1202</td>\n",
" <td>1202</td>\n",
" <td>100.00</td>\n",
" <td>2039.3</td>\n",
" <td>3.26</td>\n",
" <td>33.63</td>\n",
" <td>0.838</td>\n",
" <td>0.955</td>\n",
" <td>3.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2.948</td>\n",
" <td>2.856</td>\n",
" <td>2.948–2.856</td>\n",
" <td>1165</td>\n",
" <td>1165</td>\n",
" <td>100.00</td>\n",
" <td>1852.6</td>\n",
" <td>2.69</td>\n",
" <td>42.16</td>\n",
" <td>0.717</td>\n",
" <td>0.914</td>\n",
" <td>2.9020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2.856</td>\n",
" <td>2.775</td>\n",
" <td>2.856–2.775</td>\n",
" <td>1197</td>\n",
" <td>1197</td>\n",
" <td>100.00</td>\n",
" <td>1735.4</td>\n",
" <td>2.13</td>\n",
" <td>55.33</td>\n",
" <td>0.642</td>\n",
" <td>0.884</td>\n",
" <td>2.8155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2.775</td>\n",
" <td>2.701</td>\n",
" <td>2.775–2.701</td>\n",
" <td>1153</td>\n",
" <td>1153</td>\n",
" <td>100.00</td>\n",
" <td>1802.3</td>\n",
" <td>2.00</td>\n",
" <td>58.98</td>\n",
" <td>0.519</td>\n",
" <td>0.826</td>\n",
" <td>2.7380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2.701</td>\n",
" <td>2.635</td>\n",
" <td>2.701–2.635</td>\n",
" <td>1196</td>\n",
" <td>1196</td>\n",
" <td>100.00</td>\n",
" <td>1929.8</td>\n",
" <td>1.88</td>\n",
" <td>60.54</td>\n",
" <td>0.506</td>\n",
" <td>0.820</td>\n",
" <td>2.6680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2.635</td>\n",
" <td>2.575</td>\n",
" <td>2.635–2.575</td>\n",
" <td>1162</td>\n",
" <td>1162</td>\n",
" <td>100.00</td>\n",
" <td>2040.5</td>\n",
" <td>1.58</td>\n",
" <td>72.80</td>\n",
" <td>0.368</td>\n",
" <td>0.734</td>\n",
" <td>2.6050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2.575</td>\n",
" <td>2.521</td>\n",
" <td>2.575–2.521</td>\n",
" <td>1170</td>\n",
" <td>1170</td>\n",
" <td>100.00</td>\n",
" <td>2089.8</td>\n",
" <td>1.43</td>\n",
" <td>80.57</td>\n",
" <td>0.325</td>\n",
" <td>0.700</td>\n",
" <td>2.5480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2.521</td>\n",
" <td>2.470</td>\n",
" <td>2.521–2.47</td>\n",
" <td>1168</td>\n",
" <td>1168</td>\n",
" <td>100.00</td>\n",
" <td>2152.2</td>\n",
" <td>1.34</td>\n",
" <td>84.59</td>\n",
" <td>0.349</td>\n",
" <td>0.719</td>\n",
" <td>2.4955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2.470</td>\n",
" <td>2.424</td>\n",
" <td>2.47–2.424</td>\n",
" <td>1159</td>\n",
" <td>1159</td>\n",
" <td>100.00</td>\n",
" <td>2209.3</td>\n",
" <td>1.25</td>\n",
" <td>92.66</td>\n",
" <td>0.277</td>\n",
" <td>0.659</td>\n",
" <td>2.4470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2.424</td>\n",
" <td>2.380</td>\n",
" <td>2.424–2.38</td>\n",
" <td>1180</td>\n",
" <td>1180</td>\n",
" <td>100.00</td>\n",
" <td>2224.0</td>\n",
" <td>1.03</td>\n",
" <td>107.14</td>\n",
" <td>0.270</td>\n",
" <td>0.652</td>\n",
" <td>2.4020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2.380</td>\n",
" <td>2.340</td>\n",
" <td>2.38–2.34</td>\n",
" <td>1167</td>\n",
" <td>1167</td>\n",
" <td>100.00</td>\n",
" <td>2272.7</td>\n",
" <td>0.97</td>\n",
" <td>118.46</td>\n",
" <td>0.187</td>\n",
" <td>0.561</td>\n",
" <td>2.3600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" high resolution limit low resolution limit Resolution (Å) Reflections \\\n",
"0 54.348 6.349 54.348–6.349 1337 \n",
"1 6.349 5.040 6.349–5.04 1238 \n",
"2 5.040 4.403 5.04–4.403 1229 \n",
"3 4.403 4.002 4.403–4.002 1204 \n",
"4 4.002 3.715 4.002–3.715 1203 \n",
"5 3.715 3.495 3.715–3.495 1180 \n",
"6 3.495 3.320 3.495–3.32 1212 \n",
"7 3.320 3.176 3.32–3.176 1188 \n",
"8 3.176 3.053 3.176–3.053 1162 \n",
"9 3.053 2.948 3.053–2.948 1202 \n",
"10 2.948 2.856 2.948–2.856 1165 \n",
"11 2.856 2.775 2.856–2.775 1197 \n",
"12 2.775 2.701 2.775–2.701 1153 \n",
"13 2.701 2.635 2.701–2.635 1196 \n",
"14 2.635 2.575 2.635–2.575 1162 \n",
"15 2.575 2.521 2.575–2.521 1170 \n",
"16 2.521 2.470 2.521–2.47 1168 \n",
"17 2.470 2.424 2.47–2.424 1159 \n",
"18 2.424 2.380 2.424–2.38 1180 \n",
"19 2.380 2.340 2.38–2.34 1167 \n",
"\n",
" Possible reflections Completeness (%) Multiplicity SNR Rsplit \\\n",
"0 1336 100.07 2324.8 13.74 7.15 \n",
"1 1238 100.00 1903.5 10.73 9.40 \n",
"2 1229 100.00 1868.8 11.56 8.94 \n",
"3 1204 100.00 1893.3 11.02 9.38 \n",
"4 1203 100.00 1927.2 9.58 10.71 \n",
"5 1180 100.00 1959.0 7.66 13.31 \n",
"6 1212 100.00 2009.9 6.36 16.52 \n",
"7 1188 100.00 2056.6 4.89 22.08 \n",
"8 1162 100.00 2103.5 3.74 28.77 \n",
"9 1202 100.00 2039.3 3.26 33.63 \n",
"10 1165 100.00 1852.6 2.69 42.16 \n",
"11 1197 100.00 1735.4 2.13 55.33 \n",
"12 1153 100.00 1802.3 2.00 58.98 \n",
"13 1196 100.00 1929.8 1.88 60.54 \n",
"14 1162 100.00 2040.5 1.58 72.80 \n",
"15 1170 100.00 2089.8 1.43 80.57 \n",
"16 1168 100.00 2152.2 1.34 84.59 \n",
"17 1159 100.00 2209.3 1.25 92.66 \n",
"18 1180 100.00 2224.0 1.03 107.14 \n",
"19 1167 100.00 2272.7 0.97 118.46 \n",
"\n",
" CC1/2 CC* mean resolution \n",
"0 0.988 0.997 30.3485 \n",
"1 0.984 0.996 5.6945 \n",
"2 0.983 0.996 4.7215 \n",
"3 0.981 0.995 4.2025 \n",
"4 0.980 0.995 3.8585 \n",
"5 0.972 0.993 3.6050 \n",
"6 0.955 0.988 3.4075 \n",
"7 0.923 0.980 3.2480 \n",
"8 0.872 0.965 3.1145 \n",
"9 0.838 0.955 3.0005 \n",
"10 0.717 0.914 2.9020 \n",
"11 0.642 0.884 2.8155 \n",
"12 0.519 0.826 2.7380 \n",
"13 0.506 0.820 2.6680 \n",
"14 0.368 0.734 2.6050 \n",
"15 0.325 0.700 2.5480 \n",
"16 0.349 0.719 2.4955 \n",
"17 0.277 0.659 2.4470 \n",
"18 0.270 0.652 2.4020 \n",
"19 0.187 0.561 2.3600 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"load_stats_table(\"X3P4S1_HEC16-X3P4S1_HEC16_stats.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "23229257",
"metadata": {},
"outputs": [],
"source": [
"stat_to_plot = \"CC1/2\"\n",
"files_to_load = [\n",
" \"X3P4S1_HEC16-X3P4S1_HEC16_stats.txt\",\n",
" \"mmCPD-X3S1_stats.txt\",\n",
" \"mmCPD-X3P4S1_stats.txt\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b5b737c5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot()\n",
"\n",
"for filename in files_to_load:\n",
" df = load_stats_table(filename)\n",
" plt.plot(df[\"mean resolution\"], df[stat_to_plot])\n",
"\n",
"plt.xlim([5, 1.5])\n",
"plt.legend(files_to_load)\n",
"plt.grid()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5225ccb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Raw
X3P4S1_HEC16-X3P4S1_HEC16_stats.txt
Resolution (Å) Reflections Possible reflections Completeness (%) Multiplicity SNR Rsplit CC1/2 CC*
54.348–6.349 1,337 1,336 100.07 2,324.8 13.74 7.15 0.988 0.997
6.349–5.04 1,238 1,238 100 1,903.5 10.73 9.4 0.984 0.996
5.04–4.403 1,229 1,229 100 1,868.8 11.56 8.94 0.983 0.996
4.403–4.002 1,204 1,204 100 1,893.3 11.02 9.38 0.981 0.995
4.002–3.715 1,203 1,203 100 1,927.2 9.58 10.71 0.98 0.995
3.715–3.495 1,180 1,180 100 1,959 7.66 13.31 0.972 0.993
3.495–3.32 1,212 1,212 100 2,009.9 6.36 16.52 0.955 0.988
3.32–3.176 1,188 1,188 100 2,056.6 4.89 22.08 0.923 0.98
3.176–3.053 1,162 1,162 100 2,103.5 3.74 28.77 0.872 0.965
3.053–2.948 1,202 1,202 100 2,039.3 3.26 33.63 0.838 0.955
2.948–2.856 1,165 1,165 100 1,852.6 2.69 42.16 0.717 0.914
2.856–2.775 1,197 1,197 100 1,735.4 2.13 55.33 0.642 0.884
2.775–2.701 1,153 1,153 100 1,802.3 2 58.98 0.519 0.826
2.701–2.635 1,196 1,196 100 1,929.8 1.88 60.54 0.506 0.82
2.635–2.575 1,162 1,162 100 2,040.5 1.58 72.8 0.368 0.734
2.575–2.521 1,170 1,170 100 2,089.8 1.43 80.57 0.325 0.7
2.521–2.47 1,168 1,168 100 2,152.2 1.34 84.59 0.349 0.719
2.47–2.424 1,159 1,159 100 2,209.3 1.25 92.66 0.277 0.659
2.424–2.38 1,180 1,180 100 2,224 1.03 107.14 0.27 0.652
2.38–2.34 1,167 1,167 100 2,272.7 0.97 118.46 0.187 0.561
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