2f:T63fc,omega_params = { let params = {} params["sim_tree_sp_5FGomega3_2.5"] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params["sim_tree_sp_5FGomega3_2"] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params["sim_tree_sp_5FGomega3_1.5"] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params["sim_tree_sp_5FGomega3_1.25"] = { FG: [0.1,0.5,0.5,0.3,1.250000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params["sim_tree_sp_5FGomega3_1.15"] = { FG: [0.1,0.5,0.5,0.3,1.150000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params["sim_tree_sp_5FGomega3_1.1"] = { FG: [0.1,0.5,0.5,0.3,1.100000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] } params['sim_tree_sp_5FGomega3_4.5'] = { FG: [0.1,0.5,0.5,0.3,4.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGomega3_4'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGomega3_3.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGomega3_3'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGomega3_2.75'] = { FG: [0.1,0.5,0.5,0.3,2.750000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGomega3_2.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.4'] = { FG: [0.1,0.5,0.5,0.1,4.5,0.4], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.35'] = { FG: [0.1,0.5,0.5,0.15,4.5,0.35], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.3'] = { FG: [0.1,0.5,0.5,0.2,4.5,0.3], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.25'] = { FG: [0.1,0.5,0.5,0.25,4.5,0.25], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.2'] = { FG: [0.1,0.5,0.5,0.3,4.5,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.15'] = { FG: [0.1,0.5,0.5,0.35,4.5,0.15], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5FGpart3_0.1'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.1], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_5BGpart3_0.4'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.1,4.5,0.4] }, params['sim_tree_sp_5BGpart3_0.35'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.15,4.5,0.35] }, params['sim_tree_sp_5BGpart3_0.3'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.2,4.5,0.3] }, params['sim_tree_sp_5BGpart3_0.25'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.25,4.5,0.25] }, params['sim_tree_sp_5BGpart3_0.2'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,4.5,0.2] }, params['sim_tree_sp_5BGpart3_0.15'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.35,4.5,0.15] }, params['sim_tree_sp_5BGpart3_0.1'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.4,4.5,0.1] }, params['sim_tree_sp_5BGomega3_1'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,1.000000,0.2] }, params['sim_tree_sp_5BGomega3_1.5'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] }, params['sim_tree_sp_5BGomega3_2'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] }, params['sim_tree_sp_5BGomega3_2.5'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] }, params['sim_tree_sp_5BGomega3_3'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] }, params['sim_tree_sp_5BGomega3_3.5'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] }, params['sim_tree_sp_5BGomega3_4'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] }, params['sim_tree_sp_5BGomega3_4.5'] = { FG: [0.1,0.5,0.5,0.4,4.5,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] }, params['sim_tree_sp_11FGomega3_4.5'] = { FG: [0.1,0.5,0.5,0.3,4.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_4'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_3.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_3'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_2.75'] = { FG: [0.1,0.5,0.5,0.3,2.750000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_2.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_2'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_11FGomega3_1.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_4.5'] = { FG: [0.1,0.5,0.5,0.3,4.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_4'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_3.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_3'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_2.75'] = { FG: [0.1,0.5,0.5,0.3,2.750000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_2.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_2'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_14FGomega3_1.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_4.5'] = { FG: [0.1,0.5,0.5,0.3,4.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_4'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_3.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_3'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_2.75'] = { FG: [0.1,0.5,0.5,0.3,2.750000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_2.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_2'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_17FGomega3_1.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_4.5'] = { FG: [0.1,0.5,0.5,0.3,4.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_4'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_3.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_3'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_2.75'] = { FG: [0.1,0.5,0.5,0.3,2.750000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_2.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_2'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] }, params['sim_tree_sp_8FGomega3_1.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,1,0.2] } params['sim_zoo_0'] = { FG: [0.07454862252286218,0.9259182011606176,0.2800785525326374,0.07382108916576517,4000.154300933014,0.00030223415751460647], BG: [0.000561921147619216,0.7156736002926507,0.008620354935471246,0.1681797237521157,0.6061153939240205,0.11730120876656294] } params['sim_zoo_1'] = { FG: [0.09369076661977983,0.9674118425911573,0.18125747569040915,0.031877072557464525,961.7619761683685,0.00035871118207256487], BG: [0.01005338789483077,0.6972953672264934,0.03140270161152522,0.14238670585613772,0.7518966122902734,0.12097105418628096] } params['sim_zoo_2'] = { FG: [0.07865771324165183,0.05583914416466765,0.541742822926635,0.9438822444357556,537.6624760382396,0.00039205173120079623], BG: [0.033745622558217864,0.6568716060701973,0.04881524060642383,0.20226481204512353,0.8118891346521411,0.09171583884190533] } params['sim_zoo_3'] = { FG: [0.014228656211531557,0.4036084830269349,0.10816970812698096,0.5952567261568458,323.73101113583414,0.0007010281767036075], BG: [0.005272025588997903,0.6759161262760247,0.01701286941465821,0.2656246021355726,1.002785362063654,0.05845927158840269] } params['sim_zoo_4'] = { FG: [0.09335179282246811,0.09347480854200926,0.2128134401800426,0.9060437726230397,134.54690111905248,0.00048141883495108045], BG: [0.0011685779109708095,0.20757725949444067,0.03163441839248794,0.6865521377236538,1.1474245349721786,0.09263452759583389] } params['sim_zoo_5'] = { FG: [0.06272925750057584,0.41559254477674923,0.18108618186013142,0.5816394303118568,48.98927364819881,0.001886562270332727], BG: [0.000024714291000395832,0.3036432722605645,0.03498923152985882,0.5984351540160372,0.9756794496028421,0.11156514710428844] } params['sim_zoo_6'] = { FG: [0.06098727675029408,0.14921731224943763,0.24901335751586945,0.8472535783486337,25.565119473898122,0.005295866625490637], BG: [0.03426017319929267,0.2473056396659178,0.0742395274822846,0.720829199126885,1.2271309908960206,0.0549935398663303] } params['sim_zoo_7'] = { FG: [0.05063038839595005,0.6751857006770633,0.1168932294822432,0.30591598301355766,12.551806985812771,0.011547573979257923], BG: [0,0.27873818385708515,0.007265183803853216,0.5902292506532352,0.7800725308535759,0.11112982804379795] } params['sim_zoo_8'] = { FG: [0.03143368965320109,0.9338023548698452,0.5250941389131876,0.037645579367828134,7.069820837617552,0.03186926410667974], BG: [0.0026538265667977222,0.44856611789618095,0.11622680187642787,0.528406219530092,0.9592546590121618,0.07520009649266281] } params['sim_zoo_9'] = { FG: [0,0.65126275064465,0.0234000262238034,0.2330995704925205,3.9173053527651214,0.053467732387196876], BG: [0.002272099133937779,0.6982712492037746,0.01709931019010688,0.20459356126041783,0.772530906856953,0.09376337566863731] } params["sim_tree_sp_5simulation0"] = { FG: [0.1,0.5,0.5,0.3,1.0,0.2], BG: [0.1,0.5,0.5,0.3,1.0,0.2] } params["sim_tree_sp_5simulation1"] = { FG: [0.0,0.5,0.1,0.3,0.5,0.2], BG: [0.0,0.5,0.1,0.3,0.5,0.2] } params["sim_tree_sp_5simulation2"] = { FG: [0.0,0.5,0.5,0.3,5,0.2], BG: [0.0,0.5,0.5,0.3,5,0.2] } params["sim_tree_sp_5simulation3"] = { FG: [0.0,0.5,0.5,0.3,1,0.2], BG: [0.0,0.5,0.5,0.3,5,0.2] } params["sim_tree_sp_5simulation4"] = { FG: [0.0,0.5,0.5,0.3,5,0.2], BG: [0.0,0.5,0.5,0.3,1,0.2] } params["sim_tree_sp_5simulation5"] = { FG: [0.1,0.5,0.5,0.3,5,0.2], BG: [0.1,0.5,0.5,0.3,3,0.2] } params["sim_tree_sp_5simulation6"] = { FG: [0.1,0.5,0.5,0.3,3,0.2], BG: [0.1,0.5,0.5,0.3,5,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG1om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,1.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG1.5om_3_BG5'] = { FG: [0.1,0.5,0.5,0.3,1.500000,0.2], BG: [0.1,0.5,0.5,0.3,5.000000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG2om_3_BG5'] = { FG: [0.1,0.5,0.5,0.3,2.000000,0.2], BG: [0.1,0.5,0.5,0.3,5.000000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG2.5om_3_BG5'] = { FG: [0.1,0.5,0.5,0.3,2.500000,0.2], BG: [0.1,0.5,0.5,0.3,5.000000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG3om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,3.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG3.5om_3_BG5'] = { FG: [0.1,0.5,0.5,0.3,3.500000,0.2], BG: [0.1,0.5,0.5,0.3,5.000000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5om3_FG4om_3_BG5'] = { FG: [0.1,0.5,0.5,0.3,4.000000,0.2], BG: [0.1,0.5,0.5,0.3,5.000000,0.2] } params['sim_tree_sp_5om3_FG4.5om_3_BG5'] = { FG: [0.1,0.5,0.5,0.3,4.500000,0.2], BG: [0.1,0.5,0.5,0.3,5.000000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG1.5'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,1.500000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG2'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.000000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG2.5'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,2.500000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG3'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.000000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG3.5'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,3.500000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG4'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.000000,0.2] } params['sim_tree_sp_5om3_FG5om_3_BG4.5'] = { FG: [0.1,0.5,0.5,0.3,5.000000,0.2], BG: [0.1,0.5,0.5,0.3,4.500000,0.2] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.1'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.400000,4.5,0.100000] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.15'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.350000,4.5,0.150000] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.2'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.300000,4.5,0.200000] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.25'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.250000,4.5,0.250000] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.3'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.200000,4.5,0.300000] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.35'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.150000,4.5,0.350000] } params['sim_tree_sp_5FG_part3_0.1BG_part3_0.4'] = { FG: [0.1,0.5,0.5,0.400000,4.5,0.100000], BG: [0.1,0.5,0.5,0.100000,4.5,0.400000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.1'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.400000,4.5,0.100000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.15'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.350000,4.5,0.150000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.2'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.300000,4.5,0.200000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.25'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.250000,4.5,0.250000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.3'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.200000,4.5,0.300000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.35'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.150000,4.5,0.350000] } params['sim_tree_sp_5FG_part3_0.15BG_part3_0.4'] = { FG: [0.1,0.5,0.5,0.350000,4.5,0.150000], BG: [0.1,0.5,0.5,0.100000,4.5,0.400000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.1'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.400000,4.5,0.100000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.15'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.350000,4.5,0.150000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.2'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.300000,4.5,0.200000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.25'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.250000,4.5,0.250000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.3'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.200000,4.5,0.300000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.35'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.150000,4.5,0.350000] } params['sim_tree_sp_5FG_part3_0.25BG_part3_0.4'] = { FG: [0.1,0.5,0.5,0.250000,4.5,0.250000], BG: [0.1,0.5,0.5,0.100000,4.5,0.400000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.1'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.400000,4.5,0.100000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.15'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.350000,4.5,0.150000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.2'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.300000,4.5,0.200000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.25'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.250000,4.5,0.250000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.3'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.200000,4.5,0.300000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.35'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.150000,4.5,0.350000] } params['sim_tree_sp_5FG_part3_0.3BG_part3_0.4'] = { FG: [0.1,0.5,0.5,0.200000,4.5,0.300000], BG: [0.1,0.5,0.5,0.100000,4.5,0.400000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.1'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.400000,4.5,0.100000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.15'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.350000,4.5,0.150000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.2'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.300000,4.5,0.200000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.25'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.250000,4.5,0.250000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.3'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.200000,4.5,0.300000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.35'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.150000,4.5,0.350000] } params['sim_tree_sp_5FG_part3_0.35BG_part3_0.4'] = { FG: [0.1,0.5,0.5,0.150000,4.5,0.350000], BG: [0.1,0.5,0.5,0.100000,4.5,0.400000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.1'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.400000,4.5,0.100000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.15'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.350000,4.5,0.150000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.2'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.300000,4.5,0.200000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.25'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.250000,4.5,0.250000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.3'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.200000,4.5,0.300000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.35'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.150000,4.5,0.350000] } params['sim_tree_sp_5FG_part3_0.4BG_part3_0.4'] = { FG: [0.1,0.5,0.5,0.100000,4.5,0.400000], BG: [0.1,0.5,0.5,0.100000,4.5,0.400000] } return params } // let foreground= {[0.1,0.5,0.5,0.3,1.0,0.2], [0.0,0.5,0.1,0.3,0.5,0.2], [0.0,0.5,0.5,0.3,5,0.2], [0.0,0.5,0.5,0.3,1,0.2], [0.0,0.5,0.5,0.3,5,0.2], [0.1,0.5,0.5,0.3,5,0.2], [0.1,0.5,0.5,0.3,3,0.2]} // let background={[0.1,0.5,0.5,0.3,1.0,0.2], [0.0,0.5,0.1,0.3,0.5,0.2], [0.0,0.5,0.5,0.3,5,0.2], [0.0,0.5,0.5,0.3,5,0.2], [0.0,0.5,0.5,0.3,1,0.2], [0.1,0.5,0.5,0.3,3,0.2] [0.1,0.5,0.5,0.3,5,0.2]}30:T9d3,decision_tree = { if (pv_table[2]["Pos rate (test)"] < .5) { return ("**there is no evidence of selection associated with the chosen phenotype.** \n\nOnly " + String(Math.round(pv_table[2]["Pos rate (test)"]*10000)/100) + "% of simulations show evidence of positive selection in the foreground at p< 0.05") } else { if (pv_table[2]["Pos rate (background)"] < .5) { if (pv_table[2]["Pos rate (different)"] < .5) { return "there is evidence of selection associated with the chosen phenotype, but no statistically significant difference between the test and background selective regimes." } else { return ("**there is strong evidence of selection is associated with the chosen phenotype.** \n\n" + String(Math.round(pv_table[2]["Pos rate (test)"]*10000)/100) + "% of simulations show evidence of positive selection in the foreground, while only " + String(Math.round(pv_table[2]["Pos rate (background)"]*10000)/100) + "% of simulations show evidence for positive selection in the background. " + String(Math.round(pv_table[2]["Pos rate (different)"]*10000)/100) + "% of simulations have statiscically different foreground and background selective regimes at p< 0.05") } } else { if (pv_table[2]["Pos rate (different)"] < .5) { return ("**selection is occurring with or without the chosen phenotype, but with similar selective regimes:** \n\n" + String(Math.round(pv_table[2]["Pos rate (test)"]*10000)/100) + "% of simulations show evidence for positive selection in the foreground, and " + String(Math.round(pv_table[2]["Pos rate (background)"]*10000)/100) + "% of simulations show evidence for positive selection in the background. However, only " + String(Math.round(pv_table[2]["Pos rate (different)"]*10000)/100) + "% of simulations have statistically different foreground and background selective regimes. ") } else { return ("selection is occurring with or without the chosen phenotype, but with different selective regimes. \n\n" + String(Math.round(pv_table[2]["Pos rate (test)"]*10000)/100) + "% of simulations show evidence for positive selection in the foreground, and only " + String(Math.round(pv_table[2]["Pos rate (background)"]*10000)/100) + "% of simulations show evidence for positive selection in the background. " + String(Math.round(pv_table[2]["Pos rate (different)"]*10000)/100) + "% of simulations have statistically different foreground and background selective regimes. ") } } } }31:T8ed,displayed_tree = { let tree = tree_object[0]; var t = tree.render("#tree_container", { //height:1024, width:800, 'show-scale' : true, 'left-right-spacing': 'fit-to-size', //'top-bottom-spacing': 'fit-to-size', //'internal-names' : true, 'is-radial' : false, 'node_circle_size' : (n)=>0 }); const bs = (n) => { if (tree.is_leafnode (n)) { return 3; } return 1; }; let nodeStates = null; t.node_bubble_size = bs; t.phylotree.node_bubble_size = bs; t.options["draw-size-bubbles"] = true; t.font_size = 10; t.spacing_x (10); t.phylotree.branch_length_accessor = n=> { //console.log (summary_json[segment_id]); let bl = tree_object[2][n.data.name]; /*if (bl > 0.25) { n.data.censored_bl = true; return 0.25; } else { n.data.censored_bl = false; }*/ return bl; } t.node_label ((n)=>{ return n.data.name.split ("_").slice (0,3).join (" "); //return ""; }); function sort_nodes (asc) { tree.traverse_and_compute (function (n) { var d = 1; if (n.children && n.children.length) { d += d3.max (n.children, function (d) { return d["count_depth"];}); } n["count_depth"] = d; }); tree.resort_children (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_nodes ((e,n) => { e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); if (n.data.name in tree_object[1]) { let binfo = tree_object[1][n.data.name]; e.selectAll ("circle").style ('fill', species_colors (binfo)); e.selectAll ("text").style ('fill', species_colors (binfo)); } //} }); t.style_edges ((e,d) => { if (d.target.data.name in tree_object[1]) { let binfo = tree_object[1][d.target.data.name]; console.log (e); e.style ('stroke', species_colors (binfo)); e.style ('stroke-width', 2); } }); t.placenodes(); t.update(); return t; }32:T58e,fig_data = { let all_sims = _.uniq(_.map(sims, d=> d.Simulation)) const grouped_data = _.groupBy(sims, 'Simulation') //return grouped_data let fig_table = _.map(grouped_data, (items, simId)=> { return { 'Sim ID' : simId, 'Pos rate (test)' : _.filter(sims, (e) => e.Simulation === simId && e['BUSTEDS+PH_pvalue_FG'] <= 0.05).length / _.filter(sims, (e) => e.Simulation === simId).length, 'Pos rate (background)' : _.filter(sims, (e) => e.Simulation === simId && e['BUSTEDS+PH_pvalue_BG'] <= 0.05).length / _.filter(sims, (e) => e.Simulation === simId).length, 'Pos rate (different)' : _.filter(sims, (e) => e.Simulation === simId && e['BUSTEDS+PH_pvalue_shared'] <= 0.05).length / _.filter(sims, (e) => e.Simulation === simId).length, 'FG omega 1' : omega_params[simId]['FG'][0], 'FG prop 1' : omega_params[simId]['FG'][1], 'FG omega 2' : omega_params[simId]['FG'][2], 'FG prop 2' : omega_params[simId]['FG'][3], 'FG omega 3' : omega_params[simId]['FG'][4], 'FG prop 3' : omega_params[simId]['FG'][5], 'BG omega 1' : omega_params[simId]['BG'][0], 'BG prop 1' : omega_params[simId]['BG'][1], 'BG omega 2' : omega_params[simId]['BG'][2], 'BG prop 2' : omega_params[simId]['BG'][3], 'BG omega 3' : omega_params[simId]['BG'][4], 'BG prop 3' : omega_params[simId]['BG'][5], 'Num FG species' : items[0]["BUSTEDS+PH_NumFG_tips"] } }); return fig_table }33:T577,/* canvas = { const h = filtered_sims.length; if (h) { const w = 300; const R = 2; const context = DOM.context2d(w*R*2, 2*h*R, 1); for (let i = 0; i < h; i++) { let from = 0.; let to = filtered_sims[i].p1_t; context.fillStyle = omega_color (filtered_sims[i].w1_t); context.fillRect( from*w*R, i*2*R, R*(to-from)*w, R ); from = to; to += filtered_sims[i].p2_t; context.fillStyle = omega_color (filtered_sims[i].w2_t); context.fillRect( from*w*R, i*2*R, R*(to-from)*w, R ); from = to; to += filtered_sims[i].p3_t; context.fillStyle = omega_color (filtered_sims[i].w3_t); context.fillRect( from*w*R, i*2*R, R*(to-from)*w, R ); from = 0.; to = filtered_sims[i].p1_b; context.fillStyle = omega_color (filtered_sims[i].w1_b); context.fillRect( from*w*R, i*2*R + R, R*(to-from)*w, R ); from = to; to += filtered_sims[i].p2_b; context.fillStyle = omega_color (filtered_sims[i].w2_b); context.fillRect( from*w*R, i*2*R + R , R*(to-from)*w, R ); from = to; to += filtered_sims[i].p3_b; context.fillStyle = omega_color (filtered_sims[i].w3_b); context.fillRect( from*w*R, i*2*R + R , R*(to-from)*w, R ); } return context.canvas; } return DOM.context2d(1,1,1).canvas; } */ 34:T9e7,table_data = { let res = _.map (_.toPairs (result_json), (d)=> { let res = { "ID" : d[0] }; let pfg = (d)=>d.tests.FG["p-value"]; let pbg = (d)=>d.tests.BG["p-value"]; let pdiff = (d)=>d.tests.DIFF["p-value"]; let prelax = (d)=>_.get (d, ["RELAX","test results", "p-value"]); let haz = (d)=>pfg(d)<=0.05 && pbg (d)>0.05 && pdiff (d)<=0.05; let sites = (d)=>d.fg_selected; let sites_bg = (d)=> d.bg_selected; let l = (d)=>_.mapValues(_.groupBy (_.toPairs(d.lengths), (i)=>d.tested[0][i[0]]), (ll)=>d3.sum (ll, (i)=>i[1]))["test"]; let l_bg = (d)=>_.mapValues(_.groupBy (_.toPairs(d.lengths), (i)=>d.tested[0][i[0]]), (ll)=>d3.sum (ll, (i)=>i[1]))["background"]; let inject = [ ["Sequences", ["sequences"]], ["Sites", ["sites"]], ["FG q-value", pfg], ["BG q-value", pbg], ["DIFF q-value", pdiff], ["S.Sites (FG)", sites], ["S.Sites (BG)", sites_bg], ["L (FG)", l], ["L (BG)", l_bg], ["RELAX q-value", prelax], ["RELAX K", ["RELAX","test results","relaxation or intensification parameter"]], ["ω1", (d)=>_.get (d, ["distributions","unconstrained", "Rate Distributions","Test","0","omega"])], ["p1", (d)=>_.get (d, ["distributions","unconstrained", "Rate Distributions","Test","0","proportion"])], ["ω2", (d)=>_.get (d, ["distributions","unconstrained", "Rate Distributions","Test","1","omega"])], ["p2", (d)=>_.get (d, ["distributions","unconstrained", "Rate Distributions","Test","1","proportion"])], ["ω3", (d)=>_.get (d, ["distributions","unconstrained", "Rate Distributions","Test","2","omega"])], ["p3", (d)=>_.get (d, ["distributions","unconstrained", "Rate Distributions","Test","2","proportion"])], ]; _.each (inject, (i)=> { res[i[0]] = _.isFunction (i[1]) ? i[1](d[1], d[0]) :_.get (d[1], i[1]); }); return res; }) /* _.each (["FG q-value", "BG q-value", "DIFF q-value","RELAX q-value"], (field)=> { let fdr = computeFDR (_.map (res, (d)=>d[field])); _.each (fdr, (d,i)=> { d[field] = fdr[i][2]; }); }); */ _.each (res, (rec)=> { rec["PH"] = (rec["FG q-value"] <= critical_value && rec["BG q-value"] > critical_value && rec["DIFF q-value"] <= critical_value); rec["TYPE"] = "C" + (rec["FG q-value"] <= critical_value ? 1 : 0) + (rec["BG q-value"] > critical_value ? 0 : 1) + (rec["DIFF q-value"] <= critical_value ? 1: 0); }); return res; 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2d:{"title":"BUSTED-PH sims","cells":[{"id":0,"value":"# BUSTED-PH sims","pinned":false,"hidden":false,"mode":"md"},{"id":682,"value":" This notebook is for visualizing simulation results used to test BUSTED-PH. ","pinned":false,"hidden":false,"mode":"md"},{"id":138,"value":"## Simulation parameters used, from selected values (see below to select a different simulation):","pinned":false,"hidden":false,"mode":"md"},{"id":250,"value":"Inputs.table(filtered_omega)","pinned":false,"hidden":false,"mode":"ojs"},{"id":715,"value":"##### Rates indicate percentage of simulations that had a significant result for the specific test (test, background, and different, respectively)","pinned":false,"hidden":false,"mode":"md"},{"id":2008,"value":"## BUSTED-PH results from simulations with selected parameters ","pinned":false,"hidden":false,"mode":"md"},{"id":284,"value":"#### P-value analysis:","pinned":false,"hidden":false,"mode":"md"},{"id":70,"value":"Inputs.table(pv_table)","pinned":false,"hidden":false,"mode":"ojs"},{"id":353,"value":"_Interpretation:_\n\n**For the selected sim (with a largest omega value of ${chosen_omega_FG} for ${chosen_prop_FG} of sites) ;** ${decision_tree}","pinned":false,"hidden":false,"mode":"md"},{"id":29,"value":"vegalite ({\n \"data\" : {\"values\" : filtered_sims},\n \"columns\" : 3,\n \"hconcat\" : _.map ([[\"BUSTEDS+PH_pvalue_FG\", \"p (test for positive selection in T)\"],[\"BUSTEDS+PH_pvalue_BG\", \"p (test for positive selection in B)\"],[\"BUSTEDS+PH_pvalue_shared\", \"p (test for difference between T and B)\"]], (d)=> {return {\n \"transform\": [{\n \"sort\": [{\"field\": d[0]}],\n \"window\": [{\"op\": \"count\", \"field\": \"count\", \"as\": \"Cumulative Count\"}],\n \"frame\": [null, 0]\n }],\n \"mark\": {\"type\" : \"area\", \"line\" : true, \"opacity\" : 0.25},\n \"encoding\": {\n \"x\": {\n \"field\": d[0],\n \"type\": \"quantitative\",\n \"title\" : d[1]\n },\n \"y\": {\n \"field\": \"Cumulative Count\",\n \"type\": \"quantitative\"\n }\n }\n}})});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1291,"value":"## Select the following parameters to view a new simulation. \n\nParameters include: \n\nA. Number of species in FG/BG, \n\nB. Strength of positive selection in FG (omega values and proportion), \n\nC. Strength of positive selection in BG (omega values and proportion).","pinned":false,"hidden":false,"mode":"md"},{"id":2001,"value":"### A. Select number of species in FG/BG","pinned":false,"hidden":false,"mode":"md"},{"id":1097,"value":"viewof Tree_part = Inputs.select (new Map([['Zoonomia representative values', 'sim_zoo'], [\"15% FG species, 85% BG species\", 'sim_tree_sp_5'], [\"24% FG species, 76% BG species\", 'sim_tree_sp_8'], [\"32% FG species, 68% BG species\", 'sim_tree_sp_11'], [\"41% FG species, 59% BG species\", 'sim_tree_sp_14'], [\"50% FG species, 50% BG species\", 'sim_tree_sp_17']]), {label: 'FG/BG partition'})\n\n//viewof size = Inputs.select(new Map([[\"Short\", 8], [\"Tall\", 12], [\"Grande\", 16], [\"Venti\", 20]]), {value: 12, label: \"Size\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1141,"value":"trendLegend = swatches({\n color: species_colors\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1138,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1575,"value":"Figure 1. Tree for the selected Foreground (test) / Background partitions.","pinned":false,"hidden":false,"mode":"md"},{"id":2003,"value":"### B. Select strength of positive selection in FG","pinned":false,"hidden":false,"mode":"md"},{"id":737,"value":"View simulation with the following **largest omega value in the FG** (often the omega value corresponding to positive selection)-","pinned":false,"hidden":false,"mode":"md"},{"id":734,"value":"viewof chosen_omega_FG = Inputs.radio(uniqueFG_omega, {value: Math.max(...uniqueFG_omega)})","pinned":false,"hidden":false,"mode":"ojs"},{"id":783,"value":"View the simulation with the following **proportion of sites binned to omega value of ${chosen_omega_FG} in the FG**:","pinned":false,"hidden":false,"mode":"md"},{"id":792,"value":"viewof chosen_prop_FG = Inputs.radio(uniqueFG_prop, {value: BG_default})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2005,"value":"### C. Select strength of positive selection in BG","pinned":false,"hidden":false,"mode":"md"},{"id":954,"value":"View the simulation with the following **largest omega value in the BG**:","pinned":false,"hidden":false,"mode":"md"},{"id":974,"value":"viewof chosen_omega_BG = Inputs.radio(uniqueBG_omega, {value: Math.max(...uniqueBG_omega)})","pinned":false,"hidden":false,"mode":"ojs"},{"id":989,"value":"View the simulation with the following **proportion of sites binned to omega value of ${chosen_omega_BG} in the BG**:","pinned":false,"hidden":false,"mode":"md"},{"id":1000,"value":"viewof chosen_prop_BG = Inputs.radio(uniqueBG_prop, {value: Math.min(...uniqueBG_prop)})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1945,"value":"---------------------------------------------------------------------","pinned":false,"hidden":false,"mode":"md"},{"id":1919,"value":"# Figures for BUSTED-PH paper","pinned":false,"hidden":false,"mode":"md"},{"id":1894,"value":"fig_1.legend(\"color\", {label: \"Pos rate (test)\", ticks: 10, width: 350})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1459,"value":"fig_1 = {\n let filt_2 = _.filter(fig_data, obj => obj['FG prop 3'] === 0.2 && obj['BG omega 3'] === 1 && obj['BG prop 3'] === 0.2);\n //return filt_2\n \nreturn Plot.plot({\n marks: [\n Plot.dot(filt_2, {x: \"FG omega 3\", y: \"Num FG species\", stroke: \"Pos rate (test)\", fill: \"Pos rate (test)\", strokeWidth: 5})\n ],\n color: {\n\n type: \"linear\", \n scheme: \"turbo\", \n //legend: true, \n domain: [0,1]\n\n},\n\n x: {\n //tickRotate: -45,\n ticks: 5,\n tickSize: 5,\n line: true,\n tickPadding: 4,\n nice: true\n //labelOffset: 15\n },\n y: {\n //tickRotate: -45,\n ticks: 8,\n tickSize: 5,\n line: true,\n tickPadding: 8,\n nice: true,\n label: \"Number of extant species in FG\",\n //labelOffset: 15\n },\n height: 300,\n width: 450,\n insetTop: 8\n })\n\n}\n\n\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1569,"value":"Figure 1. Comparing the results of running simulations through BUSTED-PH with a focus on the size of the foreground set of species. The Y axis indicates the number of species in the designated foreground set (see figure xx for trees). The X axis represents the largest omega value simulated in the foreground (values greater than 1 indicate positive selection). Colors indicate the percentage of simulations that detected positive selection in the foreground. All background values were simulated the same way, with background omega values of: 0.1, 0.5, and 1 with proportion of sites at 0.5, 0.3, and 0.2 respectively. ","pinned":false,"hidden":false,"mode":"md"},{"id":1901,"value":"fig_2.legend(\"color\", {label: \"Pos rate (different)\", ticks: 10, width: 350})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1643,"value":"fig_2 = {\nlet filt_2 = _.filter(fig_data, obj => obj['FG prop 3'] === 0.2 && obj['BG prop 3'] === 0.2 && obj['Num FG species'] === 5);\n //return filt_2\n return Plot.plot({\n marks: [\n Plot.dot(filt_2, {x: 'FG omega 3', y: 'BG omega 3', stroke: \"Pos rate (different)\", fill: \"Pos rate (different)\", strokeWidth: 5})\n ],\n color: {\n\n type: \"linear\", \n scheme: \"turbo\", \n //legend: true, \n domain: [0,1]\n\n},\n x: {\n //tickRotate: -45,\n ticks: 5,\n tickSize: 5,\n line: true,\n tickPadding: 4,\n nice: true,\n //labelOffset: 15\n },\n y: {\n //tickRotate: -45,\n ticks: 8,\n tickSize: 5,\n line: true,\n tickPadding: 8,\n nice: true,\n //label: \"Pos rate (difference between FG and BG)\"\n //labelOffset: -15\n },\n \n height: 300,\n width: 450,\n })\n\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1684,"value":"Figure 2. Comparing the results of running simulations through BUSTED-PH with a focus on how omega values affect BUSTED-PH's ability to differentiate between the foreground and background set of species. The Y axis represents the largest omega value simulated in the background (values greater than 1 indicate positive selection). The X axis represents the largest omega value simulated in the foreground. Colors indicate the percentage of simulations that detected a difference between the foreground and background selective regimes. All other values were held constant (number of species in foreground, proportion of sites in each omega class, etc). ","pinned":false,"hidden":false,"mode":"md"},{"id":1905,"value":"fig_3.legend(\"color\", {label: \"Pos rate (test)\", ticks: 10, width: 350})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1711,"value":"fig_3 = {\nlet filt_2 = _.filter(fig_data, obj => obj['FG prop 3'] === 0.2 && obj['BG prop 3'] === 0.2 && obj['Num FG species'] === 5);\n //return filt_2\n return Plot.plot({\n marks: [\n Plot.dot(filt_2, {x: 'FG omega 3', y: 'BG omega 3', stroke: \"Pos rate (test)\", fill: \"Pos rate (test)\", strokeWidth: 5})\n ],\n color: {\n\n type: \"linear\", \n scheme: \"turbo\", \n //legend: true, \n domain: [0,1]\n\n},\n\n x: {\n //tickRotate: -45,\n ticks: 5,\n tickSize: 5,\n line: true,\n tickPadding: 4,\n nice: true,\n //labelOffset: 15\n },\n y: {\n //tickRotate: -45,\n ticks: 8,\n tickSize: 5,\n line: true,\n tickPadding: 8,\n nice: true,\n //label: \"Pos rate (difference between FG and BG)\"\n //labelOffset: -15\n },\n \n height: 300,\n width: 450,\n })\n\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1734,"value":"Figure 3. Comparing the results of running simulations through BUSTED-PH with a focus on how omega values affect BUSTED-PH's ability to detect positive selection in the foreground. The Y axis represents the largest omega value simulated in the background (values greater than 1 indicate positive selection). The X axis represents the largest omega value simulated in the foreground. Colors indicate the percentage of simulations that detected positive selection in the foreground. All other values were held constant (number of species in foreground, proportion of sites in each omega class, etc). ","pinned":false,"hidden":false,"mode":"md"},{"id":1909,"value":"fig_4.legend(\"color\", {label: \"Pos rate (different)\", ticks: 10, width: 350})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1780,"value":"fig_4 = {\nlet filt_2 = _.filter(fig_data, obj => obj['FG omega 3'] === 4.5 && obj['BG omega 3'] === 4.5 && obj['Num FG species'] === 5);\n //return filt_2\n return Plot.plot({\n marks: [\n Plot.dot(filt_2, {x: 'FG prop 3', y: 'BG prop 3', stroke: \"Pos rate (different)\", fill: \"Pos rate (different)\", strokeWidth: 5})\n ],\n color: {\n\n type: \"linear\", \n scheme: \"turbo\", \n //legend: true, \n domain: [0,1]\n\n},\n x: {\n //tickRotate: -45,\n ticks: 5,\n tickSize: 5,\n line: true,\n tickPadding: 4,\n nice: true,\n //labelOffset: 15\n },\n y: {\n //tickRotate: -45,\n ticks: 8,\n tickSize: 5,\n line: true,\n tickPadding: 8,\n nice: true,\n //label: \"Pos rate (difference between FG and BG)\"\n //labelOffset: -15\n },\n \n height: 300,\n width: 450,\n })\n\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1931,"value":"Figure 4","pinned":false,"hidden":false,"mode":"md"},{"id":1914,"value":"fig_5.legend(\"color\", {label: \"Pos rate (test)\", ticks: 10, width: 350})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1881,"value":"fig_5 = {\nlet filt_2 = _.filter(fig_data, obj => obj['FG omega 3'] === 4.5 && obj['BG omega 3'] === 4.5 && obj['Num FG species'] === 5);\n //return filt_2\n return Plot.plot({\n marks: [\n Plot.dot(filt_2, {x: 'FG prop 3', y: 'BG prop 3', stroke: \"Pos rate (test)\", fill: \"Pos rate (test)\", strokeWidth: 5})\n ],\n color: {\n\n type: \"linear\", \n scheme: \"turbo\", \n //legend: true, \n domain: [0,1]\n\n},\n x: {\n //tickRotate: -45,\n ticks: 5,\n tickSize: 5,\n line: true,\n tickPadding: 4,\n nice: true,\n //labelOffset: 15\n },\n y: {\n //tickRotate: -45,\n ticks: 8,\n tickSize: 5,\n line: true,\n tickPadding: 8,\n nice: true,\n //label: \"Pos rate (difference between FG and BG)\"\n //labelOffset: -15\n },\n \n height: 300,\n width: 450,\n })\n\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1935,"value":"Figure 5","pinned":false,"hidden":false,"mode":"md"},{"id":1964,"value":"## Data parsing","pinned":false,"hidden":false,"mode":"md"},{"id":173,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1270,"value":"BG_default = {\n if (Tree_part != 'sim_zoo') {\n return 0.2}\n else {\n return Math.max(...uniqueFG_prop)}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"uniqueFG_omega = {\n let val = []\n for (var key in omega_params) {\n //return(omega_params[key])\n if (key.startsWith(Tree_part)) {\n val.push(omega_params[key].FG[omega_params[key].FG.length-2])\n }\n }\n /*\n let FG_values = Object.keys(omega_params).map(function(sim) {\n return omega_params[sim].FG[omega_params[sim].FG.length - 2];\n})\n let unique_array = [...new Set(FG_values)]\n return unique_array.sort()\n */\n return _.uniq(val).sort((a, b) => a - b)\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":778,"value":"uniqueFG_prop = {\n let val = []\n for (var key in omega_params) {\n if (key.startsWith(Tree_part) && omega_params[key].FG[omega_params[key].FG.length-2] === chosen_omega_FG) {\n val.push(omega_params[key].FG[omega_params[key].FG.length-1]);\n }\n }\nreturn _.uniq(val).sort((a, b) => a - b)\n}\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":968,"value":"uniqueBG_omega = {\n let val = []\n for (var key in omega_params) {\n if (key.startsWith(Tree_part) && omega_params[key].FG[omega_params[key].FG.length-2] === chosen_omega_FG && omega_params[key].FG[omega_params[key].FG.length-1] === chosen_prop_FG) {\n val.push(omega_params[key].BG[omega_params[key].BG.length-2]);\n }\n }\n return _.uniq(val).sort((a, b) => a - b)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":994,"value":"uniqueBG_prop = {\n let val = []\n for (var key in omega_params) {\n if (omega_params[key].FG[omega_params[key].FG.length-2] === chosen_omega_FG && omega_params[key].FG[omega_params[key].FG.length-1] === chosen_prop_FG && omega_params[key].BG[omega_params[key].BG.length-2] === chosen_omega_BG) {\n val.push(omega_params[key].BG[omega_params[key].BG.length-1]);\n }\n }\n return _.uniq(val).sort()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"which_sim = {\n for (var key in omega_params) {\n if (key.startsWith(Tree_part) && omega_params[key].FG[omega_params[key].FG.length-2] === chosen_omega_FG && omega_params[key].FG[omega_params[key].FG.length-1] === chosen_prop_FG && omega_params[key].BG[omega_params[key].BG.length-2] === chosen_omega_BG && omega_params[key].BG[omega_params[key].BG.length-1] === chosen_prop_BG) {\n return key;\n }\n }\n return undefined;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"pv_table = {\n return _.map (pcut, (pc)=> {\n return {\n 'p' : pc,\n 'Pos rate (test)' : _.filter (filtered_sims, (d)=>d['BUSTEDS+PH_pvalue_FG'] <= pc).length / filtered_sims.length ,\n 'Pos rate (background)' : _.filter (filtered_sims, (d)=>d['BUSTEDS+PH_pvalue_BG'] <= pc).length / filtered_sims.length,\n 'Pos rate (different)' : _.filter (filtered_sims, (d)=>d['BUSTEDS+PH_pvalue_shared'] <= pc).length / filtered_sims.length \n };\n });\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":50,"value":"pcut = [0.001,0.01,0.05, 0.1]","pinned":false,"hidden":false,"mode":"ojs"},{"id":130,"value":"sims = {\n let arr = await d3.csv (\"https://data.hyphy.org/web/busted-ph/bph_sims/summary_all_scenarios.csv\",d3.autoType)\n for (let i = 0; i < arr.length; i++) {\n if (typeof arr[i].BUSTEDPH_FG_BG_NODES === 'string') {\n arr[i]['BUSTEDPH_FG_BG_NODES'] = JSON.parse(arr[i].BUSTEDPH_FG_BG_NODES.replace(/'/g, \"\\\"\"));\n }\n if (typeof arr[i].BUSTEDPH_BRANCH_L === 'string') {\n arr[i]['BUSTEDPH_BRANCH_L'] = JSON.parse(arr[i].BUSTEDPH_BRANCH_L.replace(/'/g, \"\\\"\"));\n }\n }\nreturn arr\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":703,"value":"filtered_sims = sims.filter(d => d.Simulation === which_sim)","pinned":false,"hidden":false,"mode":"ojs"},{"id":236,"value":"filtered_omega = {\n let FG = omega_params[which_sim][\"FG\"]\n let BG = omega_params[which_sim][\"BG\"]\n let table = []\n let headers = [\"omega_1\", \"rate_1\", \"omega_2\", \"rate_2\", \"omega_3\", \"rate_3\"]\n table[0] = {Branch_subset: \"FG\", Num_tips: filtered_sims[0]['BUSTEDS+PH_NumFG_tips']}\n table[1] = {Branch_subset: \"BG\", Num_tips: _.filter(filtered_sims[0]['BUSTEDPH_FG_BG_NODES'], (value, key) => value === \"background\" && !key.startsWith(\"NODE\")).length }\n for (let index = 0; index < headers.length; ++index) {\n table[0][headers[index]] = FG[index]\n table[1][headers[index]] = BG[index]\n }\n return table \n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":348,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1213,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1206,"value":"tree_object = [new phylotree.phylotree(filtered_sims[0].TREE || \"()\"), filtered_sims[0]['BUSTEDPH_FG_BG_NODES'] || [], filtered_sims[0]['BUSTEDPH_BRANCH_L']]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1188,"value":"species = _.uniq(_.values(filtered_sims[0]['BUSTEDPH_FG_BG_NODES'])).sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1422,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":84,"value":"/* Legend(omega_color, {\n title: \"dN/dS\",\n color: {\n type: \"sqrt\",\n reverse: true\n },\n tickFormat: \".2\"\n}) */","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1956,"value":"-----------------------------------------------------------------------------------------------------\n\n## Imports","pinned":false,"hidden":false,"mode":"md"},{"id":1144,"value":"species_colors = d3.scaleOrdinal(d3.schemeCategory10).domain ([...species]);\n/*\nspecies_colors = d3.scaleOrdinal()\n .domain(species)\n .range(['#1f77b4', '#ff7f0e']); */","pinned":false,"hidden":false,"mode":"ojs"},{"id":1738,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"omega_color = d3.scaleDiverging([10, 1, 0], d3.interpolatePiYG)","pinned":false,"hidden":false,"mode":"ojs"},{"id":411,"value":"/* validateValue = (t)=> {\n console.log (t.value);\n let tt = parseFloat (t.value);\n if (tt >= 0 && tt <= 1) return true;\n return false;\n} */","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"vegalite = require(\"@observablehq/vega-lite@0.0\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":80,"value":"import {Legend, Swatches, swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1101,"value":"import {select,checkbox, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1203,"value":"phylotree=require (\"https://dl.dropboxusercontent.com/s/i528j2lqnyzknit/phylotree.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1818,"value":"duplicates = {\n\nconst grouped = _.groupBy(_.toPairs(omega_params), ([, {FG, BG}]) => FG.toString() + BG.toString());\n\n// Filter out properties that don't have duplicates\nconst duplicates = _.filter(grouped, group => group.length > 1);\n\n// Map the duplicates to an array of ID pairs\nconst dups = _.map(duplicates, group => group.map(item => item[0]));\n\n\nreturn dups;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1793,"value":"import { TableObject } from \"@washpostgraphics/table-of-an-object\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1796,"value":"import { SummaryTable } from \"@observablehq/summary-table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1147,"value":"-----------------------------------------------------------------------------------------------------\n\n## FOR CALCULATING 'REALISTIC' VALUES FROM MARINE DATA","pinned":false,"hidden":false,"mode":"md"},{"id":893,"value":"result_json = await d3.json (\"https://data.hyphy.org/web/busted-ph/marine-zoonomia.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":901,"value":"critical_value = 0.1","pinned":false,"hidden":false,"mode":"ojs"},{"id":899,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":896,"value":"pos_sel_id = _.map(_.filter(table_data, f => f.TYPE === \"C101\" && f['L (FG)'] <100 && f['L (BG)'] <100), d => d.ID)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1047,"value":"omega_vals = _.map(pos_sel_id, id => result_json[id]['fits']['Unconstrained model']['Rate Distributions']).sort(function (a, b) {\n return b.Test[2].omega - a.Test[2].omega;\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1064,"value":"marine_fg_omega = _.map(omega_vals, d => ({w1: d.Test[0].omega, p1: d.Test[0].proportion, w2: d.Test[1].omega, p2: d.Test[1].proportion, w3: d.Test[2].omega, p3: d.Test[2].proportion,}));","pinned":false,"hidden":false,"mode":"ojs"},{"id":1071,"value":"marine_bg_omega = _.map(omega_vals, d => ({w1: d.Background[0].omega, p1: d.Background[0].proportion, w2: d.Background[1].omega, p2: d.Background[1].proportion, w3: d.Background[2].omega, p3: d.Background[2].proportion,}));","pinned":false,"hidden":false,"mode":"ojs"},{"id":917,"value":"omega_fg_divided =_.chunk(marine_fg_omega, 6);","pinned":false,"hidden":false,"mode":"ojs"},{"id":1080,"value":"omega_bg_divided =_.chunk(marine_bg_omega, 6);","pinned":false,"hidden":false,"mode":"ojs"},{"id":944,"value":"omega_fg_rep = _.map(omega_fg_divided, subArray => {\n return {\n w1: median(subArray.map(item => item.w1)),\n p1: median(subArray.map(item => item.p1)),\n w2: median(subArray.map(item => item.w2)),\n p2: median(subArray.map(item => item.p2)),\n w3: median(subArray.map(item => item.w3)),\n p3: median(subArray.map(item => item.p3))\n };\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1084,"value":"omega_bg_rep = _.map(omega_bg_divided, subArray => {\n return {\n w1: median(subArray.map(item => item.w1)),\n p1: median(subArray.map(item => item.p1)),\n w2: median(subArray.map(item => item.w2)),\n p2: median(subArray.map(item => item.p2)),\n w3: median(subArray.map(item => item.w3)),\n p3: median(subArray.map(item => item.p3))\n };\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":927,"value":"function median(arr) {\n arr.sort(function(a, b) { return a - b; });\n\n if (arr.length % 2 !== 0) {\n return arr[Math.floor(arr.length / 2)];\n } else {\n let midIndex1 = arr.length / 2 - 1;\n let midIndex2 = arr.length / 2;\n return (arr[midIndex1] + arr[midIndex2]) / 2;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}