{ "metadata": { "name": "player" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": "execfile('/Users/alanrw/work/sourcecode/python/leiden/DataHackLeiden/tavernaPlayerClient/TavernaPlayerClient.py')", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": "execfile('/Users/alanrw/work/sourcecode/python/leiden/DataHackLeiden/tavernaPlayerClient/Workflow.py')", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": "execfile('/Users/alanrw/work/sourcecode/python/leiden/DataHackLeiden/tavernaPlayerClient/RunTemplate.py')", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": "execfile('/Users/alanrw/work/sourcecode/python/leiden/DataHackLeiden/tavernaPlayerClient/Run.py')", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": "client = TavernaPlayerClient('http://dev.at.biovel.eu', 'player', 'player')", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": "workflows = client.getWorkflows()", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": "for w in workflows:\n print str(w.identifier) + ' = ' + w.title", "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "1 = Various output types\n2 = Ecological niche modelling workflow v20\n4 = Select Model For Me with components\n5 = Define with PartitionFinder, infer and validate Phylogeny - short run\n6 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime\n10 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime\n11 = A workflow\n12 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime\n13 = Bioclim workflow with interaction\n15 = A workflow\n16 = A workflow\n19 = BioVeL ESW DIFF - ENM Statistical Workflow with raster difference computation\n20 = BioVeL ESW STACK - ENM Statistical Workflow with raster stack computation\n21 = Data Refinement Workflow v13\n24 = Biome-BGC ESI Regulation, test & demo version 1.0\n25 = Define with PartitionFinder, infer and validate Phylogeny - short run\n31 = Biome-BGC CARBON test & demo version 1.0 annotated\n32 = Select Model For Me with components\n33 = Bioclim workflow with interaction\n36 = Phylogenetic Inference:Select Model For me- no component\n40 = Phylogenetic Inference:Select Model For me- no component no questions\n42 = Ecological niche modelling workflow v21\n43 = Ecological niche modelling workflow\n46 = [BETA] Data Refinement Workflow v14\n48 = Matrix Population Model analysis v10\n49 = WebDAV Component Test\n50 = A workflow\n52 = Multi-interaction\n53 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime\n54 = All File Lister using external tool\n56 = All File Lister using external tool\n57 = Ecological niche modelling workflow\n58 = Data Refinement Workflow v13\n59 = Biome-BGC SA test & demo version 1.0 annotated\n97 = A workflow\n98 = Biome-BGC MCE test & demo version 1.1 annotated\n103 = Data Refinement Workflow v14\n105 = A workflow\n107 = A workflow\n115 = Data Refinement Workflow v14\n117 = A workflow\n118 = Simple ask\n119 = BioVeL ESW STACK - ENM Statistical Workflow with raster stack computation\n120 = Retrieving FASTA format\n130 = Data Refinement Workflow v15\n131 = List output\n" } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": "w = filter(lambda x: x.identifier == 48, workflows)[0]", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": "print w.description", "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "The Matrix Population Models Workflow provides an environment to perform several analyses on a stage-matrix with no density dependence:\n- Eigen analysis;\n- Age specific survival;\n- Generation time (T);\n- Net reproductive rate (Ro);\n- Transient Dynamics;\n- Bootstrap of observed census transitions (Confidence intervals of lambda);\n- Survival curve;\n- Keyfitz delta;\n- Cohen's cumulative distance.\n\nThis workflow requires an instance of Rserve on localhost\n\nThis workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU\u2019s Seventh Framework Program, grant no. 283359. \n\nThis workflow uses R packages \u2018popbio\u2019 (Stubben & Milligan 2007; Stubben, Milligan & Nantel 2011) and 'popdemo' (Stott, Hodgson and Townley 2013).\n\nReferences:\n\nCaswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233.\n\nCaswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts.\n\nde Kroon, H. J., A. Plaiser, J. van Groenendael, and H. Caswell. 1986. Elasticity: The relative contribution of demographic parameters to population growth rate. Ecology 67: 1427-1431. \n\nHorvitz, C., D.W. Schemske, and Hal Caswell. 1997. The relative \"importance\" of life-history stages to population growth: Prospective and retrospective analyses. In S. Tuljapurkar and H. Caswell. Structured population models in terrestrial and freshwater systems. Chapman and Hall, New York.\n\nJongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopaedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423\n\nMesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468.\n\nOostermeijer J.G.B., M.L. Brugman; E.R. de Boer; H.C.M. Den Nijs. 1996. Temporal and Spatial Variation in the Demography of Gentiana pneumonanthe, a Rare Perennial Herb. The Journal of Ecology, Vol. 84(2): 153-166.\n\nStott, I., S. Townley and D.J. Hodgson 2011. A framework for studying transient dynamics of population projection matrix models. Ecology Letters 14: 959\u2013970\n\nStubben, C & B. Milligan. 2007. Estimating and Analysing Demographic Models Using the popbio Package in R. Journal of Statistical Software 22 (11): 1-23\n\nStubben, C., B. Milligan, P. Nantel. 2011. Package \u2018popbio\u2019. Construction and analysis of matrix population models. Version 2.3.1\n\nvan Groenendael, J., H. de Kroon, S. Kalisz, and S. Tuljapurkar. 1994. Loop analysis: Evaluating life history pathways in population projection matrices. Ecology 75: 2410-2415.\n" } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": "rt = w.getRunTemplate()", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": "print rt.inputs", "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "{u'longTermYears': u'50', u'label': u'Gentiana pneumonanthe, Terschelling', u'iterations': u'10000', u'stageMatrixFile': u'0.0000\\t0.0000\\t0.0000\\t7.6660\\t0.0000\\r\\n0.0579\\t0.0100\\t0.0000\\t8.5238\\t0.0000\\r\\n0.4637\\t0.8300\\t0.9009\\t0.2857\\t0.8604\\r\\n0.0000\\t0.0400\\t0.0090\\t0.6190\\t0.1162\\r\\n0.0000\\t0.0300\\t0.0180\\t0.0000\\t0.0232', u'stages': u'[S, J, V, G, D]', u'shortTermYears': u'10'}\n" } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": "outputs = w.run('leiden', {})", "language": "python", "metadata": {}, "outputs": [ { "html": "", "metadata": {}, "output_type": "display_data" } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": "import imghdr", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": "from IPython.display import *", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": "for k in outputs:\n v = outputs[k]\n display_html(HTML('

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confidence_interval_95pc_of_lambda

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long_term_proportional_stage_vector_plot

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OvHXr1po1ayRJVq0m4IdP8JvUKYiIymO4B3327Nndu3dbWVkNHjw4JCSkT58+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\nIiKZYkETEcnU/wPk+w1Zk3mQ0wAAAABJRU5ErkJggg==\n" }, { "html": "

net_reproductive_rate

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": "5.568521857873462\n" }, { "html": "

sensitivity_matrix

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": " S J V G D\nS 0.00000000 0.00000000 0.0000000 0.005956842 0.00000000\nJ 0.14133539 0.16509663 0.0000000 0.022817127 0.00000000\nV 0.08083208 0.09442153 0.3753343 0.013049498 0.00789583\nG 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854\nD 0.00000000 0.33985571 1.3509578 0.000000000 0.02841982\n\n" }, { "html": "

projection_matrix

", "metadata": {}, "output_type": "display_data" }, { "metadata": {}, "output_type": "display_data", "png": 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histogram_ci_95pc_of_lambda

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}, { "html": "

fundamental_matrix

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": "$N\n S J V G D\nS 1.00000000 0.0000000 0.0000000 0.0000000 0.0000000\nJ 0.05848485 1.0101010 0.0000000 0.0000000 0.0000000\nV 6.88603853 11.9918695 13.3528343 10.0128734 12.9527790\nG 0.20805087 0.4661775 0.3904662 2.9174703 0.6909984\nD 0.12868882 0.2520032 0.2460596 0.1845124 1.2624386\n\n$var\n S J V G D\nS 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000\nJ 0.05624588 0.01020304 0.0000000 0.0000000 0.0000000\nV 129.59269836 164.45409019 164.9453506 157.1299726 165.1853617\nG 0.96262845 2.03661916 1.7354172 5.5941629 2.8634574\nD 0.17967383 0.32076827 0.3146653 0.2473139 0.3313126\n\n$cv\n S J V G D\nS 0.000000 NaN NaN NaN NaN\nJ 4.055104 0.100000 NaN NaN NaN\nV 1.653183 1.069388 0.9618261 1.2519033 0.9922539\nG 4.715848 3.061284 3.3737933 0.8107017 2.4488847\nD 3.293833 2.247448 2.2797338 2.6952479 0.4559411\n\n$meaneta\n S J V G D \n 8.281263 13.720151 13.989360 13.114856 14.906216 \n\n$vareta\n S J V G D \n143.2630 181.0125 181.4811 177.0582 181.0617 \n\n\n" }, { "html": "

stable_stage_distribution

", "metadata": {}, "output_type": "display_data" }, { "metadata": {}, "output_type": "display_data", "png": 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j5+cnhPD396+urj5/qcbGxvMbd+zYkZmZeZnqBIA/at26dcePH79MK9eiB22Vh4dHaWlp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\nbGx89tlnO7JmALi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eigenanalysis_sensitivity_matrix_1

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KIBLi1AWM6AehAMPlOHUBAN7PRexdfLcC5JdANQBCARQ9YLjYUry7L+IjIRAg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\nCgAAAABJRU5ErkJggg==\n" 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short_term_stage_vector_plot

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9b926tWnTpuXLl4uSjIjIxhkvcQwfPjwmJqZmzZpXrlzx8PD49ddfeSo7omK9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\ns2Zt3rxZ2avfqB7yAQxzYbuEjkvouATrSVz/K3/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eigenanalysis_sensitivity_matrix_2

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survival_curve_plot

", "metadata": {}, "output_type": "display_data" }, { "metadata": {}, "output_type": "display_data", "png": 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OJSX46ivMny86TSPwNDsiMn23buH55xESAk9PJCUZRzuDBU1EJu/ECQwciA0b8NZbOHIE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\nEJEZU+ICE6mpqbt27bK1tT1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generation_time

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keyfitz_delta

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cohen_cumulative_distance

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long_term_logarithmic_stage_vector_plot

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WIgloUSA8JqEe03fwW9qzOYmML5FfHGldkZPWa07z+usUK8bp0zRqpFoDhj2M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\nAElFTkSuQmCC\n" 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sensitivity_plot

", "metadata": {}, "output_type": "display_data" }, { "metadata": {}, "output_type": "display_data", "png": 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bttu3b1+wYIG3t3dCQsLOnTv53DpphG9OYPdl1iFqIhAgKQeHo1jnIESd+O7J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\nBR4/jgnPL2klrzKP20pTx81Pi8q9iHxJi9rnKS1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}, { "html": "

eigenanalysis

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": "$lambda1\n[1] 1.237596\n\n$stable.stage\n S J V G D \n0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 \n\n$sensitivities\n S J V G D\nS 0.00000000 0.00000000 0.0000000 0.005956842 0.00000000\nJ 0.14133539 0.16509663 0.0000000 0.022817127 0.00000000\nV 0.08083208 0.09442153 0.3753343 0.013049498 0.00789583\nG 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854\nD 0.00000000 0.33985571 1.3509578 0.000000000 0.02841982\n\n$elasticities\n S J V G D\nS 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000\nJ 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000\nV 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301\nG 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311\nD 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586\n\n$repro.value\n S J V G D \n 1.000000 3.830406 2.190674 66.184553 7.884991 \n\n$damping.ratio\n[1] 2.092025\n\n\n" }, { "html": "

elasticity_plot

", "metadata": {}, "output_type": "display_data" }, { "metadata": {}, "output_type": "display_data", "png": 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dRTlGbsSYn4UOgpgWg+YkCwsLu379usZGdS9znz59VqxYoeMuRPyuISkYHnLY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\n2UxmKJmhZF9AGADs6uHUHq9xCC2GVPJm6s44in0D1NVxpY8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}, { "html": "

stage_matrix

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": " S J V G D\nS 0.0000 0.00 0.0000 7.6660 0.0000\nJ 0.0579 0.01 0.0000 8.5238 0.0000\nV 0.4637 0.83 0.9009 0.2857 0.8604\nG 0.0000 0.04 0.0090 0.6190 0.1162\nD 0.0000 0.03 0.0180 0.0000 0.0232\n\n" }, { "html": "

damping_ratio

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": "2.0920247472869264\n" }, { "html": "

elasticity_matrix

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": " S J V G D\nS 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000\nJ 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000\nV 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301\nG 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311\nD 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586\n\n" }, { "html": "

population_projection

", "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": "$lambda\n[1] 1.237596\n\n$stable.stage\n S J V G D \n0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 \n\n$stage.vectors\n 0 1 2 3 4 5 6 7\nS 69 160.9860 178.89071 190.39177 223.94351 268.87303 328.10282 403.26362\nJ 111 184.1049 210.07013 224.15446 262.26727 314.54803 383.52967 471.21993\nV 100 257.2122 471.11791 697.69349 924.15509 1181.00728 1483.42272 1848.82326\nG 21 23.3356 24.83587 29.21256 35.07344 42.79974 52.60418 64.88797\nD 43 6.1276 10.29513 15.02107 19.63161 24.95826 31.27360 38.93305\n 8 9 10 11 12 13 14\nS 497.43117 614.64363 760.10376 940.36011 1163.5840 1439.9273 1781.9775\nJ 581.15324 718.03301 887.92505 1098.47255 1359.2167 1682.0141 2081.5668\nV 2295.74724 2845.73491 3524.54805 4363.55062 5401.2496 6685.1188 8273.8036\nG 80.17788 99.15259 122.66633 151.78502 187.8329 232.4521 287.6763\nD 48.31866 59.87904 74.15341 91.79998 113.6278 140.6352 174.0553\n 15 16 17 18 19 20 21\nS 2205.3263 2729.2780 3377.7286 4180.2544 5173.4605 6402.6505 7923.8921\nJ 2576.0871 3188.1244 3945.5917 4883.0374 6043.2207 7479.0614 9256.0535\nV 10239.8194 12672.8732 15683.9637 19410.4492 24022.3160 29729.9336 36793.6519\nG 356.0237 440.6116 545.2980 674.8579 835.2009 1033.6410 1279.2297\nD 215.4136 266.5970 329.9405 408.3337 505.3526 625.4225 774.0204\n 22 23 24 25 26 27 28\nS 9806.5752 12136.577 15020.177 18588.909 23005.557 28471.583 35236.314\nJ 11455.2524 14176.973 17545.362 21714.068 26873.241 33258.213 41160.228\nV 45535.6772 56354.770 69744.433 86315.425 106823.616 132204.468 163615.706\nG 1583.1694 1959.324 2424.851 3000.986 3714.008 4596.441 5688.536\nD 957.9246 1185.524 1467.199 1815.800 2247.226 2781.158 3441.950\n 29 30 31 32 33 34 35\nS 43608.317 53969.47 66792.399 82662.00 102302.150 126608.72 156690.43\nJ 50939.728 63042.80 78021.508 96559.10 119501.143 147894.12 183033.16\nV 202490.126 250600.95 310142.703 383831.33 475028.081 587892.80 727573.71\nG 7040.109 8712.81 10782.938 13344.92 16515.617 20439.66 25296.04\nD 4259.743 5271.84 6524.408 8074.58 9993.067 12367.38 15305.82\n 36 37 38 39 40 41 42\nS 193919.43 239993.90 297015.46 367585.11 454921.82 563009.38 696778.1\nJ 226521.08 280341.56 346949.57 429383.36 531403.08 657662.26 813920.1\nV 900442.24 1114383.61 1379156.58 1706838.51 2112376.32 2614268.25 3235407.6\nG 31306.27 38744.52 47950.05 59342.79 73442.39 90892.00 112487.6\nD 18942.42 23443.06 29013.03 35906.41 44437.62 54995.82 68062.6\n 43 44 45 46 47 48 49\nS 862329.70 1067215.7 1320781.8 1634594.1 2022966.9 2503615.5 3098464.3\nJ 1007304.19 1246635.5 1542831.0 1909401.3 2363067.2 2924522.3 3619376.7\nV 4004127.20 4955491.4 6132895.7 7590046.6 9393410.4 11625246.1 14387356.8\nG 139214.15 172290.9 213226.5 263888.2 326586.9 404182.7 500214.8\nD 84233.99 104247.6 129016.5 159670.2 197607.2 244557.9 302663.8\n\n$pop.sizes\n [1] 3.440000e+02 6.317663e+02 8.952097e+02 1.156473e+03 1.465071e+03\n [6] 1.832186e+03 2.278933e+03 2.827128e+03 3.502828e+03 4.337443e+03\n[11] 5.369397e+03 6.645968e+03 8.225511e+03 1.018015e+04 1.259908e+04\n[16] 1.559267e+04 1.929748e+04 2.388252e+04 2.955693e+04 3.657955e+04\n[21] 4.527071e+04 5.602685e+04 6.933860e+04 8.581317e+04 1.062020e+05\n[26] 1.314352e+05 1.626636e+05 2.013119e+05 2.491427e+05 3.083380e+05\n[31] 3.815979e+05 4.722640e+05 5.844719e+05 7.233401e+05 8.952027e+05\n[36] 1.107899e+06 1.371131e+06 1.696907e+06 2.100085e+06 2.599056e+06\n[41] 3.216581e+06 3.980828e+06 4.926656e+06 6.097209e+06 7.545881e+06\n[46] 9.338751e+06 1.155760e+07 1.430364e+07 1.770212e+07 2.190808e+07\n\n$pop.changes\n [1] 1.836530 1.416995 1.291846 1.266844 1.250579 1.243833 1.240549 1.239006\n [9] 1.238269 1.237917 1.237750 1.237669 1.237631 1.237613 1.237604 1.237600\n[17] 1.237598 1.237597 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596\n[25] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596\n[33] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596\n[41] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596\n[49] 1.237596\n\n\n" }, { "html": "

eigenanalysis_elasticity_matrix

", "metadata": {}, "output_type": "display_data" }, { "metadata": {}, "output_type": "display_data", "png": 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