from tavernaPlayerClient import *
c = Client('http://dev.at.biovel.eu', 'player', 'player')
workflows = c.workflows
for w in workflows:
print str(w.identifier) + ' = ' + w.title
1 = Various output types 2 = Ecological niche modelling workflow v20 4 = Select Model For Me with components 5 = Define with PartitionFinder, infer and validate Phylogeny - short run 6 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime 10 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime 11 = A workflow 12 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime 13 = Bioclim workflow with interaction 15 = A workflow 16 = A workflow 19 = BioVeL ESW DIFF - ENM Statistical Workflow with raster difference computation 20 = BioVeL ESW STACK - ENM Statistical Workflow with raster stack computation 21 = Data Refinement Workflow v13 24 = Biome-BGC ESI Regulation, test & demo version 1.0 25 = Define with PartitionFinder, infer and validate Phylogeny - short run 31 = Biome-BGC CARBON test & demo version 1.0 annotated 32 = Select Model For Me with components 33 = Bioclim workflow with interaction 36 = Phylogenetic Inference:Select Model For me- no component 40 = Phylogenetic Inference:Select Model For me- no component no questions 42 = Ecological niche modelling workflow v21 43 = Ecological niche modelling workflow 46 = [BETA] Data Refinement Workflow v14 48 = Matrix Population Model analysis v10 49 = WebDAV Component Test 50 = A workflow 52 = Multi-interaction 53 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime 54 = All File Lister using external tool 56 = All File Lister using external tool 57 = Ecological niche modelling workflow 58 = Data Refinement Workflow v13 59 = Biome-BGC SA test & demo version 1.0 annotated 97 = A workflow 98 = Biome-BGC MCE test & demo version 1.1 annotated 103 = Data Refinement Workflow v14 105 = A workflow 107 = A workflow 115 = Data Refinement Workflow v14 117 = A workflow 118 = Simple ask 119 = BioVeL ESW STACK - ENM Statistical Workflow with raster stack computation 120 = Retrieving FASTA format 130 = Data Refinement Workflow v15 131 = List output 136 = Biome-BGC ESI ALL version 1.2.1
w = c.get_workflow(48)
print w.title
Matrix Population Model analysis v10
print w.description
The Matrix Population Models Workflow provides an environment to perform several analyses on a stage-matrix with no density dependence: - Eigen analysis; - Age specific survival; - Generation time (T); - Net reproductive rate (Ro); - Transient Dynamics; - Bootstrap of observed census transitions (Confidence intervals of lambda); - Survival curve; - Keyfitz delta; - Cohen's cumulative distance. This workflow requires an instance of Rserve on localhost This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359. This workflow uses R packages ‘popbio’ (Stubben & Milligan 2007; Stubben, Milligan & Nantel 2011) and 'popdemo' (Stott, Hodgson and Townley 2013). References: Caswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233. Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. de 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. Horvitz, 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. Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopaedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423 Mesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468. Oostermeijer 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. Stott, I., S. Townley and D.J. Hodgson 2011. A framework for studying transient dynamics of population projection matrix models. Ecology Letters 14: 959–970 Stubben, C & B. Milligan. 2007. Estimating and Analysing Demographic Models Using the popbio Package in R. Journal of Statistical Software 22 (11): 1-23 Stubben, C., B. Milligan, P. Nantel. 2011. Package ‘popbio’. Construction and analysis of matrix population models. Version 2.3.1 van 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.
rt = w.run_template
print rt.inputs
{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'}
r = w.run('Example notebook run', {})
import imghdr
from IPython.display import *
for k in sorted(r.outputs):
v = r.outputs[k]
display_html(HTML('<h2>' + k + '</h2>'))
guess = imghdr.what('', v)
if guess == 'png':
display_png(Image(v))
else:
print v
2.7622718909505335
2.5% 97.5% 0.9577327 1.4814221
2.0920247472869264
$lambda1 [1] 1.237596 $stable.stage S J V G D 0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 $sensitivities S J V G D S 0.00000000 0.00000000 0.0000000 0.005956842 0.00000000 J 0.14133539 0.16509663 0.0000000 0.022817127 0.00000000 V 0.08083208 0.09442153 0.3753343 0.013049498 0.00789583 G 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854 D 0.00000000 0.33985571 1.3509578 0.000000000 0.02841982 $elasticities S J V G D S 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000 J 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000 V 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301 G 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311 D 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586 $repro.value S J V G D 1.000000 3.830406 2.190674 66.184553 7.884991 $damping.ratio [1] 2.092025
S J V G D S 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000 J 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000 V 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301 G 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311 D 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586
$N S J V G D S 1.00000000 0.0000000 0.0000000 0.0000000 0.0000000 J 0.05848485 1.0101010 0.0000000 0.0000000 0.0000000 V 6.88603853 11.9918695 13.3528343 10.0128734 12.9527790 G 0.20805087 0.4661775 0.3904662 2.9174703 0.6909984 D 0.12868882 0.2520032 0.2460596 0.1845124 1.2624386 $var S J V G D S 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 J 0.05624588 0.01020304 0.0000000 0.0000000 0.0000000 V 129.59269836 164.45409019 164.9453506 157.1299726 165.1853617 G 0.96262845 2.03661916 1.7354172 5.5941629 2.8634574 D 0.17967383 0.32076827 0.3146653 0.2473139 0.3313126 $cv S J V G D S 0.000000 NaN NaN NaN NaN J 4.055104 0.100000 NaN NaN NaN V 1.653183 1.069388 0.9618261 1.2519033 0.9922539 G 4.715848 3.061284 3.3737933 0.8107017 2.4488847 D 3.293833 2.247448 2.2797338 2.6952479 0.4559411 $meaneta S J V G D 8.281263 13.720151 13.989360 13.114856 14.906216 $vareta S J V G D 143.2630 181.0125 181.4811 177.0582 181.0617
8.055186065644556
0.36601706946206447
1.2375958894297714
5.568521857873462
$lambda [1] 1.237596 $stable.stage S J V G D 0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 $stage.vectors 0 1 2 3 4 5 6 7 S 69 160.9860 178.89071 190.39177 223.94351 268.87303 328.10282 403.26362 J 111 184.1049 210.07013 224.15446 262.26727 314.54803 383.52967 471.21993 V 100 257.2122 471.11791 697.69349 924.15509 1181.00728 1483.42272 1848.82326 G 21 23.3356 24.83587 29.21256 35.07344 42.79974 52.60418 64.88797 D 43 6.1276 10.29513 15.02107 19.63161 24.95826 31.27360 38.93305 8 9 10 11 12 13 14 S 497.43117 614.64363 760.10376 940.36011 1163.5840 1439.9273 1781.9775 J 581.15324 718.03301 887.92505 1098.47255 1359.2167 1682.0141 2081.5668 V 2295.74724 2845.73491 3524.54805 4363.55062 5401.2496 6685.1188 8273.8036 G 80.17788 99.15259 122.66633 151.78502 187.8329 232.4521 287.6763 D 48.31866 59.87904 74.15341 91.79998 113.6278 140.6352 174.0553 15 16 17 18 19 20 21 S 2205.3263 2729.2780 3377.7286 4180.2544 5173.4605 6402.6505 7923.8921 J 2576.0871 3188.1244 3945.5917 4883.0374 6043.2207 7479.0614 9256.0535 V 10239.8194 12672.8732 15683.9637 19410.4492 24022.3160 29729.9336 36793.6519 G 356.0237 440.6116 545.2980 674.8579 835.2009 1033.6410 1279.2297 D 215.4136 266.5970 329.9405 408.3337 505.3526 625.4225 774.0204 22 23 24 25 26 27 28 S 9806.5752 12136.577 15020.177 18588.909 23005.557 28471.583 35236.314 J 11455.2524 14176.973 17545.362 21714.068 26873.241 33258.213 41160.228 V 45535.6772 56354.770 69744.433 86315.425 106823.616 132204.468 163615.706 G 1583.1694 1959.324 2424.851 3000.986 3714.008 4596.441 5688.536 D 957.9246 1185.524 1467.199 1815.800 2247.226 2781.158 3441.950 29 30 31 32 33 34 35 S 43608.317 53969.47 66792.399 82662.00 102302.150 126608.72 156690.43 J 50939.728 63042.80 78021.508 96559.10 119501.143 147894.12 183033.16 V 202490.126 250600.95 310142.703 383831.33 475028.081 587892.80 727573.71 G 7040.109 8712.81 10782.938 13344.92 16515.617 20439.66 25296.04 D 4259.743 5271.84 6524.408 8074.58 9993.067 12367.38 15305.82 36 37 38 39 40 41 42 S 193919.43 239993.90 297015.46 367585.11 454921.82 563009.38 696778.1 J 226521.08 280341.56 346949.57 429383.36 531403.08 657662.26 813920.1 V 900442.24 1114383.61 1379156.58 1706838.51 2112376.32 2614268.25 3235407.6 G 31306.27 38744.52 47950.05 59342.79 73442.39 90892.00 112487.6 D 18942.42 23443.06 29013.03 35906.41 44437.62 54995.82 68062.6 43 44 45 46 47 48 49 S 862329.70 1067215.7 1320781.8 1634594.1 2022966.9 2503615.5 3098464.3 J 1007304.19 1246635.5 1542831.0 1909401.3 2363067.2 2924522.3 3619376.7 V 4004127.20 4955491.4 6132895.7 7590046.6 9393410.4 11625246.1 14387356.8 G 139214.15 172290.9 213226.5 263888.2 326586.9 404182.7 500214.8 D 84233.99 104247.6 129016.5 159670.2 197607.2 244557.9 302663.8 $pop.sizes [1] 3.440000e+02 6.317663e+02 8.952097e+02 1.156473e+03 1.465071e+03 [6] 1.832186e+03 2.278933e+03 2.827128e+03 3.502828e+03 4.337443e+03 [11] 5.369397e+03 6.645968e+03 8.225511e+03 1.018015e+04 1.259908e+04 [16] 1.559267e+04 1.929748e+04 2.388252e+04 2.955693e+04 3.657955e+04 [21] 4.527071e+04 5.602685e+04 6.933860e+04 8.581317e+04 1.062020e+05 [26] 1.314352e+05 1.626636e+05 2.013119e+05 2.491427e+05 3.083380e+05 [31] 3.815979e+05 4.722640e+05 5.844719e+05 7.233401e+05 8.952027e+05 [36] 1.107899e+06 1.371131e+06 1.696907e+06 2.100085e+06 2.599056e+06 [41] 3.216581e+06 3.980828e+06 4.926656e+06 6.097209e+06 7.545881e+06 [46] 9.338751e+06 1.155760e+07 1.430364e+07 1.770212e+07 2.190808e+07 $pop.changes [1] 1.836530 1.416995 1.291846 1.266844 1.250579 1.243833 1.240549 1.239006 [9] 1.238269 1.237917 1.237750 1.237669 1.237631 1.237613 1.237604 1.237600 [17] 1.237598 1.237597 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [25] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [33] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [41] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [49] 1.237596
S J V G D S 0.00000000 0.00000000 0.0000000 0.005956842 0.00000000 J 0.14133539 0.16509663 0.0000000 0.022817127 0.00000000 V 0.08083208 0.09442153 0.3753343 0.013049498 0.00789583 G 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854 D 0.00000000 0.33985571 1.3509578 0.000000000 0.02841982
S J V G D S 0.0000 0.00 0.0000 7.6660 0.0000 J 0.0579 0.01 0.0000 8.5238 0.0000 V 0.4637 0.83 0.9009 0.2857 0.8604 G 0.0000 0.04 0.0090 0.6190 0.1162 D 0.0000 0.03 0.0180 0.0000 0.0232