%load_ext autoreload
%autoreload 2
%matplotlib inline
import sys
import matplotlib.pyplot as plt
import glob
import itertools
import ipyparallel as ipp
import pandas as pd
import numpy as np
import os
import seaborn as sns
from IPython.display import display
import MESS
from MESS.util import set_params
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)
pd.set_option('display.width', 1000)
Pandas has a kind of fun plotting thing called "Andrews Curves".
from pandas.plotting import andrews_curves
simfile = "/home/iovercast/Continuosity/MESS/analysis/figure5-sims/fig5_sims/SIMOUT.txt"
sim_df = pd.read_csv(simfile, sep="\t", header=0)
stats = MESS.stats.feature_sets()["all"]
plt.figure(figsize=(15, 10))
#tmp_df = sim_df[sim_df["_lambda"] > 0.85]
#tmp_df = sim_df[sim_df["generations"] < 10]
time = "generation"
select = 10
tol = 0.1
tmp_df = sim_df[(sim_df[time] < (select * (1 + tol))) & (sim_df[time] > (select * (1 - tol)))]
andrews_curves(tmp_df[stats + ["community_assembly_model"]][:500],\
'community_assembly_model', color=["blue", "orange", "red"], alpha=0.5)
<matplotlib.axes._subplots.AxesSubplot at 0x2aaaef910590>
lambdas = [0.05, 0.1, 0.25, 0.5, 0.75, 1]
simfile = "/home/isaac/Continuosity/MESS/analysis/figure5-sims/SIMOUT.txt"
sim_df = pd.read_csv(simfile, header=0, sep="\t")
sim_df
S_m | J_m | speciation_rate | death_proportion | trait_rate_meta | ecological_strength | generations | community_assembly_model | speciation_model | mutation_rate | alpha | sequence_length | J | m | speciation_prob | generation | _lambda | migrate_calculated | extrate_calculated | trait_rate_local | filtering_optimum | S | abund_h1 | abund_h2 | abund_h3 | abund_h4 | pi_h1 | pi_h2 | pi_h3 | pi_h4 | mean_pi | std_pi | skewness_pi | kurtosis_pi | median_pi | iqr_pi | mean_dxys | std_dxys | skewness_dxys | kurtosis_dxys | median_dxys | iqr_dxys | trees | trait_h1 | trait_h2 | trait_h3 | trait_h4 | mean_local_traits | std_local_traits | skewness_local_traits | kurtosis_local_traits | median_local_traits | iqr_local_traits | mean_regional_traits | std_regional_traits | skewness_regional_traits | kurtosis_regional_traits | median_regional_traits | iqr_regional_traits | reg_loc_mean_trait_dif | reg_loc_std_trait_dif | reg_loc_skewness_trait_dif | reg_loc_kurtosis_trait_dif | reg_loc_median_trait_dif | reg_loc_iqr_trait_dif | abundance_dxy_cor | abundance_pi_cor | abundance_trait_cor | dxy_pi_cor | dxy_trait_cor | pi_trait_cor | SGD_0 | SGD_1 | SGD_2 | SGD_3 | SGD_4 | SGD_5 | SGD_6 | SGD_7 | SGD_8 | SGD_9 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.02676 | 0.0 | neutral | point_mutation | 0.0 | 3041 | 570.0 | 2942.0 | 0.00155 | 0.00287 | 966.0 | 0.94086 | 0.00160 | 0.00375 | 0.58824 | -0.84042 | 65.0 | 14.24472 | 7.30576 | 5.52449 | 4.77490 | 23.29364 | 17.49563 | 13.67382 | 11.41103 | 0.00060 | 0.00098 | 3.00644 | 12.42661 | 0.00000 | 0.00097 | 0.03804 | 0.02896 | 0.06628 | -1.56031 | 0.03211 | 0.06614 | 0.0 | 21.05220 | 11.54010 | 8.87926 | 7.73223 | -1.68426 | 3.00312 | 1.80240 | 4.20165 | -2.51892 | 2.56642 | -2.65502 | 3.18041 | 2.33030 | 6.04429 | -3.83588 | 1.97522 | -0.97076 | 0.17730 | 0.52790 | 1.84264 | -1.31696 | -0.59121 | 0.02672 | 0.35621 | 0.05547 | -0.30198 | 0.06503 | 0.04088 | 41.0 | 13.0 | 5.0 | 3.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
1 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.00607 | 0.0 | neutral | point_mutation | 0.0 | 4511 | 570.0 | 9466.0 | 0.00517 | 0.00183 | 189.0 | 0.47201 | 0.00509 | 0.00246 | 0.58824 | -1.43628 | 117.0 | 10.65045 | 3.05727 | 2.34517 | 2.13412 | 49.41923 | 39.17596 | 33.06670 | 29.35757 | 0.00082 | 0.00115 | 1.98579 | 4.13073 | 0.00035 | 0.00117 | 0.03068 | 0.01570 | -0.57234 | -0.72594 | 0.03526 | 0.02018 | 0.0 | 24.68657 | 11.67343 | 9.35103 | 8.39183 | -1.33886 | 1.82385 | 1.52073 | 2.45704 | -1.91155 | 1.97892 | -1.77905 | 1.52450 | 2.04701 | 6.37694 | -2.08433 | 1.18946 | -0.44019 | -0.29935 | 0.52629 | 3.91989 | -0.17279 | -0.78946 | -0.02970 | 0.31862 | 0.08426 | -0.07154 | -0.15468 | 0.05512 | 62.0 | 26.0 | 12.0 | 7.0 | 2.0 | 1.0 | 5.0 | 1.0 | 0.0 | 1.0 |
2 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.11716 | 0.0 | filtering | point_mutation | 0.0 | 3074 | 570.0 | 8277.0 | 0.00304 | 0.00378 | 34.0 | 1.00000 | 0.00324 | 0.00490 | 0.58824 | 3.38778 | 32.0 | 1.81253 | 1.31026 | 1.23121 | 1.20335 | 7.95104 | 7.18892 | 6.65164 | 6.26448 | 0.00022 | 0.00041 | 1.83888 | 2.40821 | 0.00000 | 0.00035 | 0.01118 | 0.00456 | 0.03447 | 0.14316 | 0.01193 | 0.00447 | 0.0 | 7.93390 | 5.47806 | 4.91552 | 4.65652 | 3.34666 | 0.18048 | -3.06385 | 12.77644 | 3.36011 | 0.15316 | 1.50781 | 2.38364 | -1.56209 | 1.92037 | 2.20311 | 1.52484 | -1.83885 | 2.20316 | 1.50176 | -10.85607 | -1.15700 | 1.37169 | -0.08136 | 0.20245 | -0.01467 | 0.17929 | -0.33780 | 0.14061 | 23.0 | 0.0 | 3.0 | 0.0 | 1.0 | 2.0 | 1.0 | 1.0 | 0.0 | 1.0 |
3 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.05655 | 0.0 | competition | point_mutation | 0.0 | 6122 | 570.0 | 2039.0 | 0.00317 | 0.00069 | 19.0 | 0.41785 | 0.00270 | 0.00162 | 0.58824 | 3.62888 | 20.0 | 4.37626 | 2.69134 | 2.22982 | 2.05104 | 6.56331 | 6.22229 | 5.97218 | 5.79230 | 0.00020 | 0.00030 | 1.08414 | -0.43175 | 0.00000 | 0.00035 | 0.00283 | 0.00295 | 0.69462 | -1.23331 | 0.00114 | 0.00588 | 0.0 | 6.59762 | 5.05298 | 4.62467 | 4.44658 | 1.96682 | 5.62680 | -0.57094 | -1.55461 | 5.50992 | 11.02466 | -0.21923 | 4.14664 | 0.35043 | -1.02853 | -1.07166 | 7.38971 | -2.18605 | -1.48016 | 0.92137 | 0.52608 | -6.58159 | -3.63495 | 0.11455 | 0.70389 | 0.48862 | 0.38446 | -0.21386 | 0.13710 | 13.0 | 0.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 |
4 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.00387 | 0.0 | neutral | point_mutation | 0.0 | 5873 | 570.0 | 7087.0 | 0.00175 | 0.00259 | 335.0 | 0.47114 | 0.00175 | 0.00320 | 0.58824 | 0.06183 | 109.0 | 11.50857 | 3.39932 | 2.55229 | 2.30124 | 44.02504 | 32.28898 | 25.14570 | 21.17907 | 0.00069 | 0.00106 | 2.91179 | 10.89280 | 0.00035 | 0.00097 | 0.03234 | 0.01361 | -1.31267 | 0.37033 | 0.03772 | 0.01105 | 0.0 | 29.14738 | 14.66232 | 11.75516 | 10.42880 | 5.28557 | 2.75129 | -1.63275 | 1.71233 | 6.26043 | 1.81204 | 5.97726 | 2.08296 | -2.50759 | 6.43554 | 6.54266 | 1.10190 | 0.69169 | -0.66833 | -0.87484 | 4.72322 | 0.28223 | -0.71013 | 0.06694 | 0.23478 | 0.08465 | -0.22481 | -0.22867 | 0.17051 | 63.0 | 23.0 | 13.0 | 5.0 | 2.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
24434 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.00125 | 0.0 | neutral | point_mutation | 0.0 | 9502 | 570.0 | 968.0 | 0.00458 | 0.00307 | 99.0 | 0.34711 | 0.00438 | 0.00528 | 0.58824 | 2.53708 | 23.0 | 3.91263 | 2.13814 | 1.81386 | 1.70041 | 10.26328 | 8.96659 | 8.11042 | 7.55728 | 0.00037 | 0.00047 | 1.19862 | 0.43406 | 0.00035 | 0.00062 | 0.00354 | 0.00391 | 0.91679 | -0.68599 | 0.00158 | 0.00798 | 0.0 | 9.39822 | 6.23117 | 5.51996 | 5.21021 | 1.40314 | 1.67976 | -0.59187 | 0.31084 | 1.65162 | 0.96642 | 1.55528 | 2.70836 | -0.80855 | 1.55334 | 1.88197 | 1.98710 | 0.15213 | 1.02860 | -0.21668 | 1.24250 | 0.23034 | 1.02067 | 0.38164 | 0.37750 | -0.22815 | -0.10131 | -0.57499 | -0.05479 | 11.0 | 0.0 | 5.0 | 0.0 | 3.0 | 0.0 | 2.0 | 0.0 | 0.0 | 2.0 |
24435 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.00179 | 0.0 | competition | point_mutation | 0.0 | 2570 | 570.0 | 842.0 | 0.00920 | 0.00330 | 277.0 | 0.98931 | 0.00924 | 0.00725 | 0.58824 | -1.37638 | 25.0 | 4.83638 | 2.41966 | 1.99323 | 1.84886 | 7.59267 | 6.50622 | 5.75715 | 5.25602 | 0.00026 | 0.00044 | 1.94671 | 3.46526 | 0.00000 | 0.00035 | 0.00194 | 0.00242 | 2.79660 | 8.29755 | 0.00158 | 0.00140 | 0.0 | 8.36043 | 6.13359 | 5.55459 | 5.29055 | -0.47354 | 3.86218 | -0.03087 | -0.85690 | -0.19450 | 6.95416 | -0.10621 | 3.36917 | -0.08839 | -0.21641 | -0.01987 | 3.64651 | 0.36733 | -0.49301 | -0.05753 | 0.64049 | 0.17463 | -3.30766 | 0.45722 | 0.68472 | 0.17992 | 0.34035 | 0.33791 | 0.12475 | 16.0 | 4.0 | 0.0 | 1.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 |
24436 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.00899 | 0.0 | filtering | point_mutation | 0.0 | 4172 | 570.0 | 903.0 | 0.00645 | 0.00124 | 13.0 | 0.03544 | 0.00713 | 0.00491 | 0.58824 | -5.47759 | 10.0 | 1.22344 | 1.06722 | 1.05018 | 1.04448 | 2.49006 | 2.17391 | 1.99630 | 1.89463 | 0.00027 | 0.00052 | 1.94608 | 2.49887 | 0.00000 | 0.00026 | 0.00256 | 0.00189 | 0.39948 | -1.05749 | 0.00228 | 0.00237 | 0.0 | 13.96239 | 12.23238 | 11.32283 | 10.76737 | -0.87932 | 2.93609 | -0.07994 | -1.39031 | -0.25078 | 4.43927 | -1.49827 | 2.82644 | 0.50240 | 0.03903 | -2.07975 | 3.88124 | -0.61894 | -0.10965 | 0.58234 | 1.42934 | -1.82897 | -0.55803 | 0.08696 | 0.44817 | 0.16052 | 0.12754 | 0.18293 | -0.17150 | 7.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
24437 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.03888 | 0.0 | neutral | point_mutation | 0.0 | 4358 | 570.0 | 573.0 | 0.00956 | 0.00133 | 166.0 | 0.39791 | 0.00886 | 0.00647 | 0.58824 | 7.87516 | 21.0 | 3.89708 | 2.26273 | 1.92450 | 1.79699 | 4.75684 | 4.04196 | 3.65821 | 3.43333 | 0.00025 | 0.00052 | 2.25365 | 4.11211 | 0.00000 | 0.00035 | 0.00476 | 0.00556 | 0.85855 | -0.99038 | 0.00211 | 0.00982 | 0.0 | 9.48694 | 5.73637 | 4.92422 | 4.59457 | -1.17316 | 2.04465 | 1.26671 | 1.32891 | -1.89885 | 2.11643 | 0.14948 | 2.73906 | 0.78294 | 0.42169 | -0.41747 | 3.79387 | 1.32264 | 0.69441 | -0.48378 | -0.90722 | 1.48138 | 1.67744 | 0.02774 | 0.49606 | -0.22160 | 0.05275 | -0.11090 | 0.20229 | 15.0 | 3.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 |
24438 | 250 | 500000 | 2.0 | 0.7 | 2.0 | 0.00187 | 0.0 | competition | point_mutation | 0.0 | 5726 | 570.0 | 740.0 | 0.00113 | 0.00418 | 311.0 | 0.96081 | 0.00110 | 0.00500 | 0.58824 | 3.08621 | 25.0 | 11.18027 | 7.58428 | 6.15679 | 5.43543 | 10.26751 | 8.20624 | 7.18854 | 6.63873 | 0.00057 | 0.00082 | 1.69267 | 1.84781 | 0.00035 | 0.00062 | 0.01218 | 0.00635 | -1.04204 | -0.57551 | 0.01439 | 0.00175 | 0.0 | 14.66320 | 9.49212 | 7.72950 | 6.93577 | 1.58811 | 1.76251 | 0.08372 | 1.56785 | 1.88087 | 1.38086 | 1.17843 | 1.86201 | -1.13157 | 2.12334 | 1.48763 | 1.37600 | -0.40968 | 0.09950 | -1.21529 | 0.55549 | -0.39324 | -0.00486 | 0.20617 | 0.15238 | -0.31782 | 0.25588 | -0.37742 | -0.21113 | 11.0 | 6.0 | 2.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 |
24439 rows × 81 columns
data = MESS.Region("wat", quiet=False, log_files=True)
data.set_param("project_dir", "/home/isaac/Continuosity/MESS/MESS/default_MESS")
ipyclient = ipp.Client(cluster_id="MESS")
print(len(ipyclient))
for l in lambdas:
print(l)
data.set_param("generations", l)
data.run(sims=100, ipyclient=ipyclient, quiet=False)
40 0.05 Generating 100 simulation(s). [####################] 100% Performing Simulations | 0:00:12 | [####################] 100% Finished 100 simulations 0.1 Generating 100 simulation(s). [####################] 100% Performing Simulations | 0:00:15 | [####################] 100% Finished 100 simulations 0.25 Generating 100 simulation(s). [####################] 100% Performing Simulations | 0:00:24 | [####################] 100% Finished 100 simulations 0.5 Generating 100 simulation(s). [####################] 100% Performing Simulations | 0:00:40 | [####################] 100% Finished 100 simulations 0.75 Generating 100 simulation(s). [####################] 100% Performing Simulations | 0:01:13 | [####################] 100% Finished 100 simulations 1 Generating 100 simulation(s). [####################] 100% Performing Simulations | 0:02:53 | [####################] 100% Finished 100 simulations
## Use different lambda values if you please
def populate_lambda_dists(megalogs, lambdas=lambdas, abund=False):
## Accumulate data
lambda_dict = {x:[] for x in lambdas}
tolerance = 0.05
for f in megalogs:
## get lambda
lamb = float(f.split("/")[-1].split("-")[1])
for l in lambdas:
if abs(l - lamb) < tolerance:
lambda_dict[l].append(f)
for l in lambdas:
list_of_df = [pd.read_csv(x).dropna() for x in lambda_dict[l]]
for i, x in enumerate(list_of_df):
x.drop(x["Ne_local"].idxmax(), inplace=True)
list_of_df[i] = x
if abund:
lambda_dict[l] = list_of_df
else:
lambda_dict[l] = pd.concat(list_of_df)
print([len(x) for x in lambda_dict.values()])
return lambda_dict
def plot_pi_dxys(megalogs):
lambda_dict = populate_lambda_dists(megalogs)
from matplotlib.colors import LogNorm
f, axarr = plt.subplots(2, 3, figsize=(8,4), dpi=300, sharex=True, sharey=True)
axarr = [a for b in axarr for a in b]
cmap="jet"
rang=[[0.001, 0.05], [0, 0.06]]
for i, k, ax in zip(range(0,6), lambdas, axarr):
ax.set_title(u"Λ = {}".format(k), fontsize=10)
_, _, _, im = ax.hist2d(lambda_dict[k]["pi_local"], lambda_dict[k]["dxy"], bins=30, cmap=cmap, norm=LogNorm(), normed=True, range=rang)
f.text(0.5, 0.01, u"Nucleotide diversity (π)", ha='center', fontsize=13)
f.text(0.04, 0.5, r"Absolute divergence ($D_{xy}$)", va='center', rotation='vertical', fontsize=13)
plt.suptitle("Joint distribution of genetic diversity/divergence through time", y=.9999, fontsize=15)
plt.subplots_adjust(hspace=.25)
cb_ax = f.add_axes([0.93, 0.12, 0.015, 0.76])
cbar = f.colorbar(im, cax=cb_ax)
megalogs = glob.glob("/home/isaac/Continuosity/MESS/MESS/default_MESS/*/*megalog.txt")
#print(megalogs[:10])
plot_pi_dxys(megalogs)
[929, 1873, 1375, 1441, 1513, 1519]
def plot_racs(megalogs, nsims=20):
lambda_dict = populate_lambda_dists(megalogs, abund=True)
f, axarr = plt.subplots(2, 3, figsize=(8,4), dpi=300, sharex=True, sharey=True)
axarr = [a for b in axarr for a in b]
for i, k, ax in zip(range(0,6), lambdas, axarr):
ax.set_title(u"Λ = {}".format(k), fontsize=10)
for l in lambda_dict[k][:20]:
abunds = sorted(l["pi_local"], reverse=True)
xs = range(len(abunds))
ax.plot(xs, abunds, c='black', alpha=0.5)
f.text(0.5, 0.01, u"Rank", ha='center', fontsize=13)
f.text(0.04, 0.5, u"Genetic Diversity", va='center', rotation='vertical', fontsize=13)
plt.suptitle("Rank Genetic Diversity Through Time", y=.9999, fontsize=15)
plt.subplots_adjust(hspace=.25)
plt.savefig("RACs.png")
plot_racs(megalogs)
[100, 180, 100, 100, 100, 100]
lambda_dict = populate_lambda_dists(megalogs, abund=True)
print(megalogs[:10])
#abunds = sorted(lambda_dict[0.75]["abundance"], reverse=True)
#xs = range(len(abunds))
#ax.plot(xs, abunds)
abunds
lambda_dict[0.05][0]
[100, 180, 100, 100, 100, 100] ['/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-808992944/Loc1-0.762-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-705430764/Loc1-0.255-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-698271850/Loc1-0.062-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-833942236/Loc1-0.114-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-332254438/Loc1-0.052-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-732853716/Loc1-0.752-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-82938535/Loc1-0.101-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-442770715/Loc1-0.057-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-136487789/Loc1-0.263-megalog.txt', '/home/isaac/Continuosity/MESS/MESS/default_MESS/wat-788251258/Loc1-0.259-megalog.txt']
name | trait | abundance | Ne_local | Ne_meta | tdiv | tree | growth_rate | migration_rate | segsites_tot | pi_tot | segsites_local | segsites_meta | pi_local | pi_meta | dxy | da | TajimaD | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | t77 | -4.259784 | 3 | 2000.000000 | 27506 | 2298 | (t77:5.53101); | 0 | 4.351610e-07 | 8 | 0.003832 | 0 | 7 | 0.000000 | 0.004094 | 0.005439 | 0.003392 | 0.00000 |
1 | t44 | -1.841315 | 8 | 3428.571429 | 98307 | 3504 | (t44:5.53101); | 0 | 4.161910e-07 | 10 | 0.003721 | 0 | 10 | 0.000000 | 0.005965 | 0.004386 | 0.001404 | 0.00000 |
2 | t25 | 3.973998 | 1 | 2000.000000 | 608 | 1016 | (t25:5.53101); | 0 | 0.000000e+00 | 0 | 0.000000 | 0 | 0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
3 | t38 | 3.084453 | 12 | 3333.333333 | 72406 | 6468 | (t38:5.53101); | 0 | 3.710575e-07 | 8 | 0.003019 | 0 | 8 | 0.000000 | 0.005341 | 0.003333 | 0.000663 | 0.00000 |
4 | t16 | 0.907703 | 2 | 2000.000000 | 56880 | 167 | (t16:5.53101); | 0 | 0.000000e+00 | 16 | 0.011616 | 0 | 16 | 0.000000 | 0.013177 | 0.016140 | 0.009552 | 0.00000 |
5 | t14 | -6.545027 | 4 | 2666.666667 | 4380 | 5356 | (t14:5.53101); | 0 | 0.000000e+00 | 5 | 0.002308 | 2 | 3 | 0.001287 | 0.002222 | 0.002807 | 0.001053 | 0.23045 |
6 | t93 | 10.767900 | 2 | 3000.000000 | 13466 | 2421 | (t93:5.53101); | 0 | 1.376842e-07 | 0 | 0.000000 | 0 | 0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
8 | t72 | 3.172395 | 1 | 2000.000000 | 10373 | 900 | (t72:5.53101); | 0 | 0.000000e+00 | 1 | 0.000332 | 0 | 1 | 0.000000 | 0.000624 | 0.000351 | 0.000039 | 0.00000 |
9 | t53 | -3.459182 | 1 | 2000.000000 | 20376 | 7 | (t53:5.53101); | 0 | 0.000000e+00 | 2 | 0.001062 | 0 | 2 | 0.000000 | 0.000975 | 0.001579 | 0.001092 | 0.00000 |
10 | t24 | -5.731964 | 16 | 3500.000000 | 17680 | 4782 | (t24:5.53101); | 0 | 1.194957e-07 | 2 | 0.000351 | 0 | 2 | 0.000000 | 0.000702 | 0.000351 | 0.000000 | 0.00000 |
11 | t86 | -7.777359 | 1 | 2000.000000 | 2160 | 385 | (t86:5.53101); | 0 | 0.000000e+00 | 0 | 0.000000 | 0 | 0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
12 | t75 | -1.075780 | 1 | 2000.000000 | 7115 | 68 | (t75:5.53101); | 0 | 0.000000e+00 | 2 | 0.001219 | 0 | 2 | 0.000000 | 0.001248 | 0.001754 | 0.001131 | 0.00000 |