diff --git a/docs/propkatraj-example.ipynb b/docs/propkatraj-example.ipynb index 552fff2..194c77e 100644 --- a/docs/propkatraj-example.ipynb +++ b/docs/propkatraj-example.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import MDAnalysis as mda\n", @@ -29,9 +27,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "u = mda.Universe(PSF, DCD)" @@ -66,10 +62,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "import propkatraj" @@ -77,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -86,7 +80,7 @@ "text": [ "Help on function get_propka in module propkatraj.propkatraj:\n", "\n", - "get_propka(universe, sel='protein', start=None, stop=None, step=None)\n", + "get_propka(universe, sel='protein', start=None, stop=None, step=None, skip_failure=False)\n", " Get and store pKas for titrateable residues near the binding site.\n", " \n", " Parameters\n", @@ -103,6 +97,10 @@ " step : int\n", " Step by which to iterate through trajectory frames. propka is slow,\n", " so set according to how finely you need resulting timeseries.\n", + " skip_failure : bool\n", + " If set to ``True``, skip frames where PROPKA fails. If ``False``\n", + " raise an exception. The default is ``False``.\n", + " Log file (at level warning) contains information on failed frames.\n", " \n", " Results\n", " -------\n", @@ -120,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -129,7 +127,7 @@ "" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -147,7 +145,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 98/98 t= 97.000 ps [100.0%]\n" + " 1/98 t= 1.000 ps [ 1.0%]/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/mdanalysis/package/MDAnalysis/coordinates/PDB.py:906: UserWarning: Found no information for attr: 'altLocs' Using default value of ' '\n", + " \"\".format(attrname, default))\n", + "/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/mdanalysis/package/MDAnalysis/coordinates/PDB.py:906: UserWarning: Found no information for attr: 'icodes' Using default value of ' '\n", + " \"\".format(attrname, default))\n", + "/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/mdanalysis/package/MDAnalysis/coordinates/PDB.py:906: UserWarning: Found no information for attr: 'occupancies' Using default value of '1.0'\n", + " \"\".format(attrname, default))\n", + "/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/mdanalysis/package/MDAnalysis/coordinates/PDB.py:906: UserWarning: Found no information for attr: 'tempfactors' Using default value of '0.0'\n", + " \"\".format(attrname, default))\n", + " 98/98 t= 98.000 ps [100.0%]\n" ] } ], @@ -164,6 +170,19 @@ "data": { "text/html": [ "
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" \n", + " \n", " \n", " \n", " \n", @@ -553,7 +572,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -561,7 +580,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -577,15 +596,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -601,15 +620,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -625,7 +644,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -649,7 +668,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -673,7 +692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -697,15 +716,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -721,7 +740,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -745,9 +764,9 @@ " \n", " \n", " \n", - 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" \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1009,11 +1028,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1027,13 +1046,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1043,7 +1062,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1057,7 +1076,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1081,7 +1100,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1091,7 +1110,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1105,7 +1124,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1129,11 +1148,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1147,17 +1166,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1171,13 +1190,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1201,11 +1220,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1219,15 +1238,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1249,11 +1268,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1267,13 +1286,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1297,11 +1316,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1315,13 +1334,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1331,7 +1350,7 @@ " \n", " \n", " \n", - 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" 33 36 40 44 ... 192 \\\n", - "time ... \n", - "0.000000 2.364393 14.056423 10.092552 3.505057 ... 11.181185 \n", - "1.000000 2.638269 13.262729 10.088877 3.794538 ... 11.221025 \n", - "2.000000 2.235340 13.454440 10.065400 3.569236 ... 11.217360 \n", - "3.000000 1.891373 13.442788 10.079494 3.280471 ... 11.272248 \n", - "4.000000 2.458694 13.349054 10.050490 3.805812 ... 11.202101 \n", - "5.000000 2.373255 14.912229 9.960275 3.702452 ... 11.276864 \n", - "5.999999 2.545704 13.553502 10.072688 3.628277 ... 11.275164 \n", - "6.999999 2.676321 13.342114 10.056925 3.948229 ... 11.223411 \n", - "7.999999 3.170560 13.115886 10.149841 3.727501 ... 11.219728 \n", - "8.999999 3.299541 13.851676 10.087693 3.731568 ... 11.306882 \n", - "9.999999 3.211439 12.838007 10.101450 3.951770 ... 11.238651 \n", - "10.999999 3.010283 13.683737 10.060025 3.978007 ... 11.233255 \n", - "11.999999 2.308900 14.928786 10.147672 3.845300 ... 11.289601 \n", - "12.999999 2.247972 13.981869 10.149026 3.918088 ... 11.253381 \n", - 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\n", + "std 0.190336 0.159992 0.627494 0.303016 0.355354 0.864402 \n", + "min 7.295912 12.268418 7.608654 2.947118 10.985647 10.264614 \n", + "25% 8.127513 12.703356 9.423932 3.240923 11.152677 10.915393 \n", + "50% 8.201208 12.762834 9.544703 3.395460 11.221198 11.460753 \n", + "75% 8.273955 12.849670 9.710506 3.718057 11.620171 12.428121 \n", + "max 8.458619 13.246053 10.449350 4.278064 12.282208 13.113857 \n", "\n", - " 33 36 40 44 ... 192 \\\n", - "count 98.000000 98.000000 98.000000 98.000000 ... 98.000000 \n", - "mean 2.496965 14.049808 10.348338 3.734203 ... 10.754325 \n", - "std 0.418063 0.506330 0.129717 0.261795 ... 0.589032 \n", - "min 1.412159 12.838007 9.960275 2.799962 ... 9.661010 \n", - "25% 2.234819 13.556806 10.252726 3.625440 ... 10.111005 \n", - "50% 2.430872 14.136217 10.410696 3.835666 ... 11.209223 \n", - "75% 2.682613 14.404762 10.430810 3.918784 ... 11.260263 \n", - "max 3.608263 15.182562 10.460633 4.010099 ... 11.331529 \n", + " 33 36 40 44 ... 192 193 \\\n", + "count 98.000000 98.000000 98.000000 98.000000 ... 98.000000 98.000000 \n", + "mean 2.496964 14.049793 10.348338 3.734205 ... 10.754325 14.010479 \n", + "std 0.418080 0.506324 0.129717 0.261796 ... 0.589032 0.259118 \n", + "min 1.412159 12.838007 9.960275 2.799962 ... 9.661010 13.174104 \n", + "25% 2.234819 13.556806 10.252726 3.625440 ... 10.111005 13.878456 \n", + "50% 2.430872 14.136217 10.410696 3.835666 ... 11.209223 14.035418 \n", + "75% 2.682613 14.404762 10.430810 3.918784 ... 11.260263 14.164137 \n", + "max 3.608263 15.182562 10.460633 4.010099 ... 11.331529 14.472733 \n", "\n", - " 193 195 197 200 204 206 \\\n", + " 195 197 200 204 206 208 \\\n", "count 98.000000 98.000000 98.000000 98.000000 98.000000 98.000000 \n", - "mean 14.010479 12.066464 2.110729 12.593243 2.899029 14.802091 \n", - "std 0.259118 0.142012 0.325367 0.046812 0.147478 0.420380 \n", - "min 13.174104 11.733061 1.249881 12.442245 2.767964 13.814350 \n", - "25% 13.878456 11.970498 1.980458 12.566704 2.839813 14.535488 \n", - "50% 14.035418 12.066676 2.177655 12.587065 2.861878 14.795910 \n", - "75% 14.164137 12.191065 2.293660 12.621373 2.895128 15.024900 \n", - "max 14.472733 12.392177 3.000020 12.692147 3.817193 15.909253 \n", + "mean 12.066464 2.110729 12.593243 2.899029 14.802081 1.901808 \n", + "std 0.142012 0.325367 0.046812 0.147478 0.420351 0.121053 \n", + "min 11.733061 1.249881 12.442245 2.767964 13.814350 1.528530 \n", + "25% 11.970498 1.980458 12.566704 2.839813 14.535355 1.836325 \n", + "50% 12.066676 2.177655 12.587065 2.861878 14.795689 1.888335 \n", + "75% 12.191065 2.293660 12.621373 2.895128 15.024791 1.968771 \n", + "max 12.392177 3.000020 12.692147 3.817193 15.909253 2.200353 \n", "\n", - " 208 210 211 \n", - "count 98.000000 98.000000 98.000000 \n", - "mean 1.901808 2.643808 12.076064 \n", - "std 0.121053 0.331949 0.120737 \n", - "min 1.528530 1.861152 11.334325 \n", - "25% 1.836325 2.591431 12.055367 \n", - "50% 1.888335 2.754077 12.089534 \n", - "75% 1.968771 2.852710 12.133085 \n", - "max 2.200353 3.381706 12.207916 \n", + " 210 211 \n", + "count 98.000000 98.000000 \n", + "mean 2.643797 12.076064 \n", + "std 0.331960 0.120737 \n", + "min 1.861115 11.334325 \n", + "25% 2.591431 12.055367 \n", + "50% 2.754017 12.089534 \n", + "75% 2.852710 12.133085 \n", + "max 3.381706 12.207916 \n", "\n", "[8 rows x 75 columns]" ] @@ -2250,34 +2282,35 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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HcvDg8h4o293nyAx72DrvHwBgJ0mqAADbtlkioJkkQCIRkCz/W12YOp/sH2580PDyEpQX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\nfPMPbOl8//fPbj0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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "fig = sns.plt.figure(figsize=(28, 8))\n", + "fig = plt.figure(figsize=(28, 8))\n", "ax = fig.add_subplot(1,1,1)\n", "sns.boxplot(data=pkas, ax=ax)\n", "ax.set_xlabel(\"residue number\")\n", @@ -2293,10 +2326,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "resids = u.select_atoms('resname LYS').residues.resids" @@ -2304,23 +2335,25 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "sns.boxplot(data=pkas[resids])\n", - "ax = sns.plt.gca()\n", + "ax = plt.gca()\n", "ax.set_xlabel(\"residue number\")\n", "ax.set_ylabel(r\"p$K_a$\");" ] @@ -2335,21 +2368,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" + "pygments_lexer": "ipython3", + "version": "3.6.7" } }, "nbformat": 4,
mean8.18072312.76062912.7606359.4022813.4883733.48839411.41813511.6426972.49696514.0498082.49696414.04979310.3483383.7342033.734205...10.75432514.0104792.11072912.5932432.89902914.80209114.8020811.9018082.6438082.64379712.076064
std0.1903360.1599730.1599920.6274940.3030280.3030160.3553540.8644020.4180630.5063300.4180800.5063240.1297170.2617950.261796...0.5890320.2591180.3253670.0468120.1474780.4203800.4203510.1210530.3319490.3319600.120737
7.29591212.2684187.6086542.9469002.94711810.98564710.2646141.4121592.76796413.8143501.5285301.8611521.86111511.334325
8.12751312.7033569.4239323.2407143.24092311.15267710.9153932.2348191.98045812.5667042.83981314.53548814.5353551.8363252.59143112.0553678.20120812.7628349.5447033.3956403.39546011.22119811.4607532.4308722.17765512.5870652.86187814.79591014.7956891.8883352.7540772.75401712.089534
75%8.27395512.84990512.8496709.7105063.71805711.6201712.29366012.6213732.89512815.02490015.0247911.9687712.85271012.1330858.45861913.24605310.4493504.2789614.27806412.28220813.1138573.608263