U _TU _`_[]UM • 2 FU]_`M MOTU [ 5[OW ] – 5[OW ] U M [R_bM] _[ YM MS aU]_`M YMOTU – G PU _]UN`_ M UYMS [ bTUOT 24:C U ] U _M P https://www.docker.com/ https://hub.docker.com/r/oacis/oacis_jupyter/
_d U _ [R UY` M_[] • D[ O[ P`O_ UY` M_U[ # b P _[ ] SU _ ] M UY` M_[] [ 24:C – B` _T R[ [bU S O[YYM P _[ ] SU _ ] M MY UY` M_[] ` P U _TU _`_[]UM – G bU M] T[b _[ ] SU _ ] [`] UY` M_[] U _T c_ U[ docker exec -it -u oacis my_oacis bash -l ( in the container) git clone https://github.com/yohm/sim_ns_model.git sim_ns_model/install_on_oacis.sh
_T TM _]M U_U[ N _b _T R] R [b TM M P _T O[ S _U[ TM • C[`]O O[P [R _TU UY` M_[] – T__ 0 SU_T`N O[Y d[TY U YK KY[P – `_ `_ RU [R _TU UY` M_[] • M C RU O[ _MU U S Ma ]MS a [OU_d M P R [b • M M T[_ A 8 RU position time
the created ParameterSet. Values of the parameters are displayed. List of Runs under this ParameterSet. Click • 2 b AM]MY _ ]C _ M P M B` M] O] M_ P – DT _M_` [R _T B` bU OTM S U M R b O[ P
] ` _ The page of Run Contents of “_output.json” file is saved in OACIS DB. List of output files. Click it to access. Figures (bmp,jpg,png…) are displayed inline. A button to download the archive of these results.
file system. Each result has its own URL. Ex. URL for this figure file: http://192.168.99.100:3000/Result_development/56 1cdf093135350450000000/561dfaad356339008d260 000/561dfaad356339008d530000/traffic.png It is useful to summarize the results in your notebook by keeping this URL.
R[]Y _[ O] M_ AM]MY _ ]C _ – RU U _T aM ` [R M]MY _ ] M O[YYM M]M_ P aM ` • a 1 f(#)# # #,g • ]T[ 1 f ,# (# (,# )# ),# # ,# # ,# ,g – C _ fDM]S _ [R B` g _[ L(L – 4 UOW 4] M_ Select ”1” Click fill in values in a CSV form MWU S Y` _U V[N
CLI command to make PS in bulk. Copy and Paste to the terminal to run the command. docker exec -it -u oacis oacis_tutorial bash -l cd oacis [Paste the command shown in the web interface]
SU_T`N O[Y d[TY UYK [b ]KY M K MY U S – D[][W#H `]M #9 9 [ _ M # GTM_ 3US 5M_M _ 0 CMY U S _T [OUM _b[]W Nd O[YY` UOM_U[ OTM g# ATd B a 6 ) (-" docker exec -it -u oacis my_oacis bash -l (in the container) git clone https://github.com/yohm/sim_power_mean_sampling.git sim_power_mean_sampling/install.sh Create ParameterSets with various alpha and beta for "NetworkSamplingTunedF0" simulator, and see how the assortativity of the sampled network depends on these parameters. alpha = [0.6, 0.8, 1.0], beta = [-2.0, -1.0, 0.0, 1.0, 2.0] I[ _U[ M J 2 [_T ] MY
SU_T`N O[Y d[TY UYK [ KY[P – D CTUYMPM 2 ` Ua ] M _]M U_U[ U _T ][N` _ [R a[ aU S [ d _ Y COU B 0 .) ) ( " docker exec -it -u oacis my_oacis bash -l (in the container) git clone https://github.com/yohm/sim_eos_model.git sim_eos_model/install.sh I[ _U[ M J MY ) Run "EOS_model" simulator for m=[3,5,7,9,11,13,15,17,19,21,23], and see that "Divergence Speed" is positive only for 5<= m <= 17. courtesy of T. Shimada To see a clear transition, set "t" to a much bigger value than the default value.
SU_T`N O[Y d[TY Pd MYUOM KS]M TKY[P – H `]M _ M # 2 UY Y[P R[] W b P OU UR _UY PU _]UN`_U[ # b ATd ) ( " docker exec -it -u oacis my_oacis bash -l (in the container) git clone https://github.com/yohm/dynamical_graph_model.git dynamical_graph_model/install.sh I[ _U[ M J MY Run "DynamicalGraphModel" simulator with the default parameters, and see how the lifetime distribution looks like.
• : _M M_U[ [R 5[OW ] M P 24:C • TM P [ [R UY` M_U[ c O`_U[ Nd 24:C – O_U S _T CUY` M_[] – O] M_U S AM]MY _ ]C _ M P B` – MOO U S _T ] ` _ • [`_ `_ RU • [_ i : _T c_ TM P [ b bU U _]`O_ T[b _[ UY Y _ H EB UY` M_[]