Generate scRNA-Seq data using scBoolSeq¶

In [1]:

from IPython.display import HTML, display
display(HTML('<style>''.container { width:92.5% !important; }''</style>'))

import ipywidgets as widgets
from ipywidgets import HBox, VBox

import matplotlib.pyplot as plt
%matplotlib inline

In [2]:

from pathlib import Path

import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import plotnine as p9

from sklearn import metrics
from scipy.spatial import distance
import itertools

import bonesis
import scboolseq
from scboolseq import scBoolSeq

from colomoto_jupyter import tabulate

from sklearn.utils import Bunch

In [3]:

SEED = 1234
root = Path(".").resolve()
here = Path(".").resolve()

In [4]:

checkpoint_dir = here / "multilevel/"

Experiment config¶

In [5]:

import mpbn

In [6]:

import booleantraces as btrace

In [7]:

confs = pd.read_csv(checkpoint_dir / "10_configurations.csv", index_col=0)
confs.head()

Out[7]:

	MESENDO	ES	ECTO
x0	0	0	1
x2	0	1	1
x3	0	1	0
x4	1	1	0
x7	0	0	0

In [8]:

confs.columns = ['stable3', 'bifurcation', 'init', 'stable2', 'stable1']

In [9]:

confs

Out[9]:

	stable3	bifurcation	init	stable2	stable1
x0	0	0	0	0	1
x2	0	0	1	0	1
x3	0	0	1	0	0
x4	0	1	1	0	0
x7	0	0	0	0	0
x9	0	0	0	0	1
x10	0	1	1	1	1
x11	0	0	0	1	1
x17	0	1	0	0	1
x20	0	0	1	0	0
x22	0	0	1	0	0
x23	0	1	1	0	0
x24	0	1	1	0	0
x27	0	1	1	0	0
x28	0	0	0	0	0
x29	0	1	1	0	0
x1	1	0	0	0	0
x5	1	1	1	1	1
x6	1	1	1	0	0
x8	1	0	0	0	0
x12	1	1	0	1	0
x13	1	1	1	0	0
x14	1	1	1	1	0
x15	1	0	0	1	0
x16	1	0	0	1	1
x18	1	0	0	1	1
x19	1	0	0	1	1
x21	1	0	0	0	0
x25	1	1	1	1	1
x26	1	1	1	1	0

In [10]:

f = mpbn.load((checkpoint_dir/"10_bn.bnet").as_posix())
_ig = f.influence_graph()
bonesis.InfluenceGraph(_ig)

# computing graph layout...

Out[10]:

In [11]:

wcc = nx.induced_subgraph(_ig, max(nx.weakly_connected_components(_ig)))
isolated = [_x for _x in _ig.nodes() if _x not in wcc]
isolated

Out[11]:

['x16', 'x18', 'x19', 'x22', 'x25', 'x27', 'x28', 'x29', 'x5', 'x7']

In [12]:

ig = bonesis.InfluenceGraph(wcc)

In [13]:

nx.nx_pydot.write_dot(ig, "multilevel_random_network_ig.dot")

In [14]:

[f.pop(i) for i in isolated]

Out[14]:

[TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE]

In [15]:

confs = confs.loc[confs.index.difference(isolated), :]

In [16]:

confs.index.isin(f.keys()).all()

Out[16]:

True

In [17]:

confs['bifurcation'] !=  confs['stable3']

Out[17]:

x0     False
x1      True
x10     True
x11    False
x12    False
x13    False
x14    False
x15     True
x17     True
x2     False
x20    False
x21     True
x23     True
x24     True
x26    False
x3     False
x4      True
x6     False
x8      True
x9     False
dtype: bool

In [18]:

init_transient = btrace.labelled_trajectory(f, confs, start="init", stop="bifurcation", _trans_label="init_to_bifurcation")
init_transient.shape[0] - 2

Grounding...done in 0.0s

Out[18]:

In [19]:

init_stable1 = btrace.labelled_trajectory(f, confs, start="init", stop="stable1", _trans_label="init_to_stable1")
init_stable1.shape[0] -  2

Grounding...done in 0.1s

Out[19]:

In [20]:

transient_stable2 = btrace.labelled_trajectory(f, confs, start="bifurcation", stop="stable2", _trans_label="bifurcation_to_stable2")
transient_stable2.shape[0] - 2

Grounding...done in 0.0s

Out[20]:

In [21]:

transient_stable3 = btrace.labelled_trajectory(f, confs, start="bifurcation", stop="stable3", _trans_label="bifurcation_to_stable3")
transient_stable3.shape[0] - 2

Grounding...done in 0.0s

Out[21]:

In [22]:

trajectory_df = pd.concat([
    init_transient,
    init_stable1,
    transient_stable2,
    transient_stable3,
])
trajectory_df.shape

Out[22]:

(39, 20)

In [23]:

traj_df = trajectory_df.drop_duplicates()
traj_df.drop_duplicates()

Out[23]:

	x0	x1	x10	x11	x12	x13	x14	x15	x17	x2	x20	x21	x23	x24	x26	x3	x4	x6	x8	x9
init	0	0	1	0	0	1	1	0	0	1	1	0	1	1	1	1	1	1	0	0
init_to_bifurcation_1	1	0	1	0	0	1	1	0	0	1	0	0	1	1	1	1	1	1	0	0
init_to_bifurcation_2	1	0	1	0	0	1	1	0	0	0	0	0	1	1	1	0	1	1	0	0
init_to_bifurcation_3	0	0	1	0	0	1	1	0	1	0	0	0	1	1	1	0	1	1	0	0
init_to_bifurcation_4	0	0	1	0	1	1	0	0	1	0	0	0	1	1	1	0	1	1	0	0
bifurcation	0	0	1	0	1	1	1	0	1	0	0	0	1	1	1	0	1	1	0	0
init_to_stable1_1	0	0	1	0	0	1	1	0	0	1	1	0	0	1	1	1	1	1	0	0
init_to_stable1_2	0	0	1	1	0	1	1	0	0	1	1	0	0	1	1	1	1	1	0	0
init_to_stable1_3	0	0	1	1	0	1	1	0	0	1	1	0	0	1	1	1	0	1	0	0
init_to_stable1_4	0	0	1	1	0	1	1	0	0	1	1	0	0	1	1	0	0	1	0	0
init_to_stable1_5	0	0	1	1	0	1	1	0	0	1	1	0	0	1	1	0	0	0	0	0
init_to_stable1_6	1	0	1	1	0	1	1	0	0	1	1	0	0	1	1	0	0	0	0	0
init_to_stable1_7	1	0	1	1	0	1	1	0	0	1	1	0	0	1	1	0	0	0	0	1
init_to_stable1_8	1	0	1	1	0	1	1	0	1	1	1	0	0	1	1	0	0	0	0	1
init_to_stable1_9	1	0	1	1	0	1	1	0	1	1	1	0	0	1	0	0	0	0	0	1
init_to_stable1_10	1	0	1	1	0	0	1	0	1	1	1	0	0	1	0	0	0	0	0	1
init_to_stable1_11	1	0	1	1	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	1
init_to_stable1_12	1	0	1	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	1
stable1	1	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1
bifurcation_to_stable2_1	0	0	1	0	1	1	1	0	1	0	0	0	0	1	1	0	1	1	0	0
bifurcation_to_stable2_2	0	0	1	0	1	0	1	0	1	0	0	0	0	1	1	0	1	1	0	0
bifurcation_to_stable2_3	0	0	1	0	1	0	1	0	1	0	0	0	0	1	1	0	0	1	0	0
bifurcation_to_stable2_4	0	0	1	1	1	0	1	0	1	0	0	0	0	1	1	0	0	1	0	0
bifurcation_to_stable2_5	0	0	1	1	1	0	1	0	1	0	0	0	0	1	1	0	0	0	0	0
bifurcation_to_stable2_6	0	0	1	1	1	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0
bifurcation_to_stable2_7	0	0	1	1	1	0	1	1	1	0	0	0	0	0	1	0	0	0	0	0
stable2	0	0	1	1	1	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0
bifurcation_to_stable3_1	0	1	1	0	1	1	1	0	1	0	0	0	1	1	1	0	1	1	0	0
bifurcation_to_stable3_2	0	1	1	0	1	1	1	0	1	0	0	0	1	1	1	0	0	1	0	0
bifurcation_to_stable3_3	0	1	0	0	1	1	1	0	1	0	0	0	1	1	1	0	0	1	0	0
bifurcation_to_stable3_4	0	1	0	0	1	1	1	0	1	0	0	1	1	1	1	0	0	1	0	0
bifurcation_to_stable3_5	0	1	0	0	1	1	1	0	1	0	0	1	1	1	1	0	0	1	1	0
bifurcation_to_stable3_6	0	1	0	0	1	1	1	0	0	0	0	1	1	1	1	0	0	1	1	0
bifurcation_to_stable3_7	0	1	0	0	1	1	1	1	0	0	0	1	1	1	1	0	0	1	1	0
bifurcation_to_stable3_8	0	1	0	0	1	1	1	1	0	0	0	1	0	1	1	0	0	1	1	0
stable3	0	1	0	0	1	1	1	1	0	0	0	1	0	0	1	0	0	1	1	0

In [24]:

ref_data = pd.read_csv("data_filtered_vargenes/GSE81682_Hematopoiesis.csv", index_col=0)
ref_data.head()

Out[24]:

	Bhlhb9	Vps35	Slc18a1	Fam107b	Plekhn1	9030619P08Rik	Prtn3	Lrrn4	Gm25926	Plppr3	Memo1	Cdkn1c	Gm2a	B2m	Tbxas1	Glipr2	Trim58	Gm10384	Sla2	Trp53i13	Gypc	Clic5	Tubb4b	AC149090.1	Gm26580	Clec1a	Ccl9	Gimap3	Spi1	2900041M22Rik	Klk8	Gm37637	Gp9	Idh3a	Akr1c13	2810408A11Rik	Npr2	Pik3ip1	ENSMUSG00000043858	Grina	Txnip	Gm26541	Msn	Pomgnt2	Fcer1g	Cd69	Gm14276	Napa	Cd24a	Nsun2	Marveld2	Ms4a4b	Cdk6	Tada2b	Mki67	Btg2	Reep2	8430419K02Rik	Pdgfd	H2-K1	Sesn1	Bcap29	Gm21814	Eif5a	Stk25	Maged1	Zfp449	9130604C24Rik	6430590A07Rik	Tcirg1	Il4	Slc35a4	Cysltr2	Cmtm7	Arl8b	Gbx2	Cyth4	S1pr4	Gdi1	Vill	Slc38a6	Calu	Gm16565	Smim3	Sar1a	4833407H14Rik	Dut	Ifngr1	Rasa3	Fbxl2	Pde4b	Ptp4a3	Cdt1	Fxyd1	Arl11	Shisa8	Tmem173	Atxn10	Rpn2	Ccl3	Nudt16	Kif3c	Hars	Ccl4	Myo1g	Cpq	Dok2	Fcgrt	Elmo1	Cyc1	Calr	Tbxa2r	Gpr183	Alad	Taz	Echdc3	Cd300a	Ispd	Itm2b	Gata2	Prss16	Lppos	Lhcgr	Fundc1	Lat	Tnfrsf26	Prr36	Vldlr	Mfge8	Enkur	Ndn	Btla	Zyx	Cd33	Dkc1	Rab37	Cdcp1	Hoxa9	Oat	Gas6	Oasl2	Tacc1	Colgalt1	Rab32	Dusp2	Tmem176a	Gnai3	Rnf13	Elane	Cd48	Mthfd1	Igkc	Fnta	Ripply3	Ptger2	P2rx7	Wdfy2	Eif1a	Gstm7	Tmem14c	2900018N21Rik	Mpl	Sash3	Tgm2	Gm19569	Ehd3	Cish	Calml4	Mesdc2	Nfil3	Tcn2	Saraf	Meis1	Serpina3g	Pcx	Gpr171	Cks1b	Coa5	Farsa	Ctsl	Iigp1	P2ry14	Cd82	Slc18a2	Cd302	Parp12	Isyna1	S100a8	B130034C11Rik	Arhgap27	Klf1	Cd84	Bag2	Tmem123	Emilin1	4930568A12Rik	Clec11a	Mat2a	Tespa1	Wfdc17	Tspan13	S100a9	Gcnt2	Ppic	Zfp72	Gm4841	Ckb	C130013H08Rik	Spo11	Ctss	Vwa5a	Serpinb1a	Trib3	Slc28a2	Xbp1	Tsc22d3	Cd74	Rac2	Il15	Ift43	Tnfrsf13c	Plod2	Minpp1	Cs	Trim27	Rrm1	Krt18	Rap1b	Anxa2	D130040H23Rik	Tmsb4x	1810034E14Rik	2810021J22Rik	F630028O10Rik	Cyb5r3	Laptm5	Adam11	Rab17	Pkia	Hist3h2ba	Zfp36	Mcm5	Ssc4d	Gm45837	Fgf3	Car1	Adssl1	Il21r	Slc50a1	Serpinb9	Pttg1ip	Fam83d	Tspan32	Tnfrsf13b	Gdi2	Mapk12	Wfdc18	Thbs1	Selenop	Gm43852	Hk3	Irgm1	Nceh1	Myl10	Tmx1	Acsl5	Tnfaip8	4632427E13Rik	Hspa9	Gstt1	Il12rb1	Esam	Unc93b1	Casp4	F2r	Dynlt3	Adgrg1	Tmem38a	Stap1	Cep170b	Frmd8	Bgn	Ifi206	Fes	Apol7e	Ifi47	Cdca7	Srm	Zfp418	Zfp882	Mmp2	Fam32a	Flna	Lyz2	Cd53	Sptbn1	Mapre3	Ptprc	Gulp1	Cd63	Fam131a	Dntt	Ifi213	Tcp1	Clptm1l	Sigirr	Robo3	Mcfd2	Vpreb1	Casp12	Ppt1	H1f0	Tpst2	Pnrc1	Sdhaf2	Hnrnpdl	Cyp7b1	Ptpre	Slc25a45	Wdr35	Capg	Sf3b3	Gata1	Abce1	Golph3l	P2ry10	Adgrg3	Fkbp4	Gbp2	Plcg2	4930486L24Rik	E230001N04Rik	Itih5	Chrnb1	Gm16712	Top2a	Gm5577	Nt5c3	Ctf1	Il1rl2	Gm5148	Car2	Vim	A630033H20Rik	Cd81	Zfp563	Ctla2a	Mfsd2b	Idh2	Atp6ap2	Ces2g	Gba2	Apoe	Sdha	Dlg3	Dhx58	Ccr9	Rgs2	Ctsc	Parp8	Tspo2	Pear1	Ltb	AA467197	Unc5cl	Mtpn	Mcm6	Atp2a3	Tyms	Il12a	Celf2	Axl	Tpm4	Cyp2j9	Fxyd5	B230216N24Rik	Trbc2	Fam161b	Gm30948	Apbb1	Igfbp4	Pcp4l1	Rassf5	Cyp2r1	Srgn	Pygm	Arhgef6	Fah	BC017643	Ak3	Icam1	Gm10505	C1qbp	Efna1	AW112010	Anxa1	Fgf11	Layn	Tspan3	Gm43201	Arrdc1	Hjurp	4921507P07Rik	Sla	Ms4a6b	Serp1	Arhgef18	Hmox1	Pkm	Arhgap15	Ddx39	Tes	Lysmd2	Pgrmc1	Efna4	Uba7	Xist	Sumo3	Flt3	Mycn	1300017J02Rik	Scn1b	Rab38	Tnfaip2	Hdac2	Mndal	Fut4	Casp1	Slc14a1	Rit1	Ifi203	Calr3	Gm38243	Gm43313	Slc25a5	Bcam	Ighv1-74	Traf3ip3	Lgals9	Gm26982	Clec10a	Klhl8	Crlf3	Ighv1-77	Lcp2	Gm37423	Selplg	Clip3	Ccdc157	Mpo	Eya1	Ctso	Gm26917	Rarb	Cmtm6	Rgs1	Ptpn6	Cd1d1	Prkaa2	Gm4759	Aplp2	Gm8995	Gng11	Gm5111	Ankrd50	Neurl3	Slc44a2	Gm128	Fam132a	M6pr	Ankrd33b	Hexa	Pdcd1lg2	Dnajc9	Lck	Ccnb2	Wls	Gramd1a	Gm37642	St8sia4	Sqstm1	Slc25a35	Tipin	4930555A03Rik	Rn18s-rs5	Samd12	Ncf4	Myc	Irf9	Paics	Gstm1	Padi4	Tor3a	Rgs18	Ppm1g	Zfand5	Smpd5	Gnl3	Rnase6	Lgals3bp	Slc2a10	Rdh14	Dnah1	Fut8	Itpkc	Ap1s2	Hpcal1	Gm43178	Adgrl4	Nek6	Clk1	Eya2	Csf3r	Pirb	Msmo1	Gm15133	Pim2	Ric8a	Spns2	Tspan14	Itm2a	Ddx1	Slc22a18	Lcp1	Casp2	Epsti1	Ifi44	Phf11b	Ctsb	H2afy	Ctsg	Gimap9	Hsd3b7	Smpdl3a	Gm43162	Trim47	Cpa3	Cat	Cers2	Ifit3	Ifit1bl1	Slc9a3r2	Mcm3	Fscn1	Cd93	Sept6	Irf5	Rab19	Ctsz	Wfs1	Mef2c	Blvrb	Gimap1	Hgfac	Gimap6	Gm15201	Gimap5	BC035044	Ly6e	Zfp62	Vpreb3	Hn1l	Hlf	Gmpr	Lancl1	Slc16a1	Plcb2	Sdpr	Rhoh	Dock2	Pdlim2	Clk4	Nubp2	Treml2	Sell	Polm	Pglyrp2	Tsc22d1	9130008F23Rik	Ptprcap	Hck	Adamts10	Ly6a	Anxa5	Parvg	Cd79a	Tmem175	Ms4a4c	Bank1	Gatsl3	BC026585	Wfdc2	Camk1	Treml1	Ebi3	Magee1	Ldhb	Bpifb5	Trim30a	Ifitm1	Ddx58	Ifitm3	Rmnd5b	Tagln2	Tspyl3	Dennd1c	Slc2a1	Large1	Stxbp4	Muc3a	Awat2	Plac8	AI506816	Canx	Prdx6	F11r	Alg2	Lurap1	Inpp5k	Atp6ap1	Dapp1	Tuba4a	Gclm	Ruvbl2	Apobr	Cct6a	Vav1	Rfc2	Nradd	Ypel3	Tor4a	Gm19590	Ivns1abp	C1qtnf6	4933424M12Rik	Mageh1	Gm11696	Cbfb	Zfp422	Mfng	Klf8	Stk17b	AI606181	Ikzf2	Slc34a2	1700066B19Rik	Mcm7	Ecscr	Lyplal1	Ankrd37	Plek	Ppp1r3d	Exoc3l2	Tmem53	Wsb1	Rtkn	Tfr2	Leprot	Arhgap25	Plpp7	Rsrp1	Samsn1	Prdx3	Gm37663	Itgb3	Nrros	Dennd2d	Por	Hsd17b12	Capza2	Spns3	Cd34	4921531C22Rik	Trmt2a	Gm26692	Col16a1	Uqcrc1	Hacd4	Fam129c	Rhof	Sdcbp	Rcsd1	Mmrn1	Tprgl	Gem	Gm38071	Ccng2	Ldha	Ramp1	Ucp2	Sdsl	Ttc4	Afap1l1	Ccnd2	Ube2l6	Clec12a	Gm43643	Aqp1	Creg1	Muc13	Slc25a11	Ncf1	B4galt4	Gm16758	Nucb1	Fam69b	Tmem35b	Mx1	Ppp1r26	Mx2	Ppp1r15a	Pdzk1ip1	Coro1a	Mob3c	Oas2	Kctd12b	Gm12250	Sh2d5	Igtp	Ermap	Smu1	Maged2	Mcm4	Gm42576	Anxa6	Rtp4	Sh3bgrl	Napsa	Kctd21	Chpf	Gm7160	Neurl1b	Cct3	Nkx2-3	Wdr78	Dusp1	Def8	Rbm5	Emb	Elovl1	Myo1f	Lax1	Cyp2j6	Pstpip1	Cd44	Mtch2	Pgm2	1110028F11Rik	Acot7	Irf1	H2-DMa	Zfp263	Klhl6	A130014A01Rik	Ssbp2	Rbp1	Rsl1d1	Asah1	Tmem37	Spcs3	Cenpa	Tmprss3	Pqlc3	Dhrs3	Jak3	Fam213b	Pafah2	Jun	Lamp1	Camsap3	Grtp1	Gm16794	Cacybp	Slfn8	Stom	H2-DMb1	H2-D1	Rab44	4930579K19Rik	Fos	Nedd4	Cyb561	Mapkapk3	Inpp4b	Ccdc69	Slfn2	2610035D17Rik	Snap23	Nprl2	Rasal3	Ighv1-82	Batf	Rrm2	Surf4	Zfp386	Sstr2	Cers5	Ighd	Ighm	Cyp4v3	Limd2	Nlrp1a	H2-Q6	P4hb	Grn	H2-Q7	Hhex	Pcbp4	Tubb5	Itgb7	Gpam	Cbx5	Fam184a	Tmem106a	Cd79b	Nfe2	H2-Ob	Tcp11l2	Shisa5	Samd10	Tcea2	Ctsd	Rgs19	Procr	Tnni2	Acap1	Bin2	Lmbrd2	Laptm4b	Capsl	Angpt1	Spag1	H2-Ab1	Gfi1b	H2-Aa	Ctsw	Hspa5	Tbc1d10c	Rom1	Ctsf	Fgd2	Cct8	Mamdc2	Stip1	Cd27	Prss57	Arpc1b	Fut7	Cd86	Serpinf1	Cd9	Gm37274	Angptl6	Arhgdib	Eogt	Cst7	Pcna	Rnf114	Cacfd1	Jam3	Fkbp1a	Eps8	Traf1	Pdia3	Mgst1	Lmo2	Eng	Skap2	Vamp5	Tmem176b	Mcm2	Slc52a3	Cct7	Ptgs1	Cnn2	Prim1	Matk	Atp5b	Cdk4	Gm15915	Gm10101	Abhd4	Rab36	Metap2	Fzr1	Pa2g4	Smim24	Gm16104	Fig4	Myct1	Lsp1	Gpr4	Tyrobp	Nkg7	Osbpl1a	Gm4951	Zfp438	Rab18
HSPC_025	5.392129	8.852337	0.000000	2.614548	0.000000	6.711045	7.977803	8.117451	0.000000	8.195180	7.977803	0.000000	9.173640	12.912375	8.427644	7.839405	1.189716	0.000000	0.000000	5.935712	8.021134	0.000000	7.309000	10.293674	0.000000	0.000000	2.275971	0.000000	9.014787	0.000000	0.000000	0.000000	1.832751	8.774285	1.189716	1.189716	1.832751	3.118770	7.285499	7.135838	8.744431	0.000000	8.852337	1.832751	1.189716	1.189716	0.000000	7.225016	8.876132	7.940672	6.184685	1.189716	8.691769	1.189716	3.491677	7.161885	1.832751	6.779938	8.490658	11.925211	7.410259	3.317239	0.000000	10.416407	4.242991	2.275971	1.832751	1.189716	0.000000	7.894815	0.000000	9.681046	1.189716	9.723202	4.141925	0.000000	7.574401	7.174734	2.275971	2.614548	0.000000	2.888576	0.000000	7.806760	8.268934	3.491677	8.585122	2.888576	2.614548	0.000000	8.344797	6.461289	7.285499	0.000000	7.593666	6.657127	2.275971	8.655561	9.077708	0.000000	1.832751	1.189716	7.695253	10.437884	1.832751	7.814991	1.189716	5.935712	4.588530	2.275971	9.025466	1.189716	0.000000	3.118771	1.189716	1.189716	7.161885	0.000000	10.103505	2.888576	0.000000	0.00000	0.000000	7.977803	6.675324	0.000000	0.000000	7.054751	0.000000	0.000000	0.000000	0.000000	9.101455	7.040782	9.449536	0.000000	0.000000	8.310278	8.495788	0.000000	2.888576	8.687292	7.940672	1.189716	11.307754	7.781782	7.894815	8.361752	0.000000	2.888576	9.067410	4.509590	8.310278	0.000000	0.000000	0.000000	0.000000	7.343549	0.000000	5.671352	7.564672	10.551846	6.763021	0.000000	1.189716	0.000000	0.000000	1.189716	9.272583	0.000000	1.189716	8.021134	8.150349	2.275971	0.000000	7.747791	8.130700	3.647281	7.593666	1.832751	1.189716	7.790156	7.421084	0.000000	0.000000	6.779938	2.275971	6.079295	0.000000	2.275971	2.888576	9.014787	1.189716	8.182512	7.977803	4.141925	1.189716	9.826553	0.000000	9.822478	8.594715	6.796659	7.273603	0.000000	4.141925	1.189716	6.845691	0.000000	1.189716	6.923879	2.888576	5.776244	0.000000	0.000000	3.118771	8.475161	0.000000	10.807313	7.122636	0.000000	6.711045	4.242991	7.525084	2.275971	4.033241	9.167225	0.000000	3.317239	7.747791	0.000000	9.816341	0.000000	0.000000	0.000000	1.189716	10.088305	7.955639	0.000000	0.00000	0.000000	11.315027	7.631440	0.000000	0.000000	1.189716	6.079295	8.356122	7.554876	9.390178	0.000000	7.887028	1.832751	9.681046	9.032541	10.105184	6.282898	6.502641	3.787723	7.730490	0.000000	7.992392	7.902559	8.274914	7.668250	9.339726	8.063203	9.710022	1.189716	4.337438	7.593666	7.320609	1.832751	7.871328	7.584065	1.189716	1.832751	10.034692	0.000000	1.832751	3.317239	5.347433	0.000000	5.634622	7.554876	6.502641	7.640730	9.278547	8.971261	0.000000	7.012433	0.000000	8.035294	1.832751	9.438924	7.879200	6.159051	0.000000	8.028232	0.000000	8.156838	0.000000	2.888576	1.832751	8.400554	4.426081	1.832751	0.000000	9.345421	0.000000	0.000000	8.400554	3.118771	8.989556	7.174734	7.704143	7.273603	0.000000	8.433001	4.337438	0.000000	8.124090	9.656065	0.000000	7.095865	8.454235	0.000000	6.132952	7.040782	0.000000	5.905197	0.000000	1.189716	7.839405	5.809578	7.297297	3.491677	0.000000	8.490658	0.000000	0.000000	0.000000	11.130318	8.207737	0.000000	8.993188	0.000000	9.521714	2.614548	9.882450	9.506549	2.275971	3.915699	6.923879	9.690023	1.189716	0.000000	0.000000	7.285499	7.631440	3.118771	2.275971	7.161885	8.926381	1.189716	0.000000	9.342576	10.287772	5.742120	2.275971	8.350471	8.744431	7.847452	2.614548	1.189716	9.812235	3.915699	0.000000	1.189716	1.189716	0.000000	6.796659	7.695253	7.659135	0.000000	9.716627	7.320609	9.428233	0.000000	1.189716	6.983514	1.189716	0.000000	8.536176	0.000000	8.156838	1.832751	0.000000	0.000000	7.535083	0.000000	7.515015	8.274914	0.00000	4.242991	6.711045	7.095865	0.000000	0.000000	9.000423	7.712979	5.045572	7.871328	1.189716	8.774285	0.000000	8.256901	9.091325	9.254540	7.054751	2.888576	1.832751	0.000000	7.399354	0.000000	9.681046	8.570612	0.000000	6.418716	1.832751	1.189716	1.832751	0.000000	0.000000	0.000000	9.903830	1.189716	1.189716	1.189716	8.459496	0.000000	0.000000	1.832751	1.832751	1.189716	5.935712	1.189716	2.275971	1.189716	0.00000	2.275971	0.000000	1.189716	9.707812	0.000000	0.000000	8.786894	8.978607	8.691769	0.000000	9.785260	1.832751	8.967574	0.000000	6.983514	0.0000	6.779938	3.491677	0.000000	2.614548	8.782703	0.000000	9.004027	0.000000	4.802330	4.734524	3.491677	1.189716	7.320609	0.000000	9.299230	9.501458	1.189716	8.555954	0.000000	15.299863	0.000000	1.832751	9.365173	6.106372	9.656065	8.176136	6.258964	2.275971	9.118180	3.491677	9.901899	0.000000	9.362368	1.189716	7.764886	0.000000	1.189716	0.000000	5.392129	2.275971	0.000000	0.000000	0.000000	8.226370	0.000000	9.475731	2.275971	1.832751	1.189716	1.832751	3.491677	1.189716	7.148920	1.832751	9.018355	0.000000	8.280868	0.000000	9.322510	8.536176	0.000000	8.722721	7.756364	9.457444	9.874597	0.000000	1.189716	1.832751	1.189716	6.763021	0.000000	1.189716	3.317239	8.887885	1.189716	1.189716	1.832751	8.922578	0.00000	2.275971	2.888576	0.000000	1.189716	9.454813	6.542841	8.117451	1.832751	8.378509	0.000000	8.627797	1.189716	1.189716	2.275971	11.533395	5.842161	0.000000	4.663373	7.814991	1.189716	1.189716	8.256901	8.333382	1.189716	9.164007	7.721761	6.418716	7.879200	7.535083	6.877476	0.000000	0.000000	6.968835	9.384659	0.000000	9.351091	0.000000	3.118771	9.428233	7.545014	8.872193	0.000000	6.693295	0.000000	1.189716	0.000000	2.275971	0.000000	9.124817	1.832751	6.581951	0.000000	8.464736	1.832751	8.744431	11.084242	1.189716	10.748367	7.525084	9.263590	1.832751	8.832203	7.012433	1.832751	10.351401	0.000000	0.000000	8.021134	2.275971	9.658353	9.000423	1.189716	0.000000	0.000000	3.118771	8.531189	8.753024	7.421084	3.118771	8.367359	5.347433	9.160781	2.614548	8.327641	4.033241	6.693295	7.187470	9.514152	8.213975	6.209872	0.000000	3.317239	0.000000	2.614548	7.122636	9.491221	1.189716	1.189716	0.000000	8.169733	11.876887	0.000000	8.316089	0.000000	0.000000	0.000000	2.888576	0.000000	1.189716	6.184685	1.189716	5.558232	2.614548	7.082290	8.083785	0.000000	8.989556	9.202161	3.317239	0.000000	0.000000	8.832203	1.189716	8.459496	7.695253	9.989955	0.000000	10.686910	0.000000	2.275971	1.189716	0.000000	7.068586	7.739166	1.189716	0.000000	9.892208	7.970453	1.189716	7.484379	9.141275	0.000000	2.275971	8.511066	8.244766	10.309781	0.000000	7.068586	5.518464	7.332125	8.832203	1.189716	5.965594	3.118771	8.384051	2.275971	8.238660	4.588530	1.189716	0.000000	8.411450	6.877476	7.421084	1.832751	6.258964	0.000000	5.842161	0.000000	11.228325	1.189716	5.253659	1.189716	6.923879	0.000000	1.832751	1.832751	3.118771	0.00000	9.157549	0.000000	0.000000	8.433001	7.442493	7.261610	0.000000	7.704143	0.000000	0.000000	9.588054	1.189716	0.000000	11.117064	2.275971	9.563837	8.967574	6.893110	8.575464	1.832751	1.189716	8.673779	9.114851	3.118771	0.000000	0.000000	8.316089	10.128487	6.968835	7.174734	8.056275	1.189716	7.442493	1.189716	8.848332	9.296293	0.000000	7.747791	2.275971	1.189716	0.000000	1.832751	7.730490	0.000000	7.999631	7.535083	9.438924	0.000000	1.189716	0.000000	7.212608	9.084533	1.189716	0.000000	11.455247	1.189716	0.000000	0.000000	6.418716	0.000000	0.000000	0.000000	8.748734	1.189716	1.189716	6.159051	7.494663	8.718340	1.189716	7.649962	2.614548	1.832751	6.306441	0.000000	7.135838	0.000000	5.253659	4.337438	8.395074	0.000000	8.350471	9.714428	8.526184	7.649962	8.718340	0.000000	10.165970	8.137279	0.000000	7.309000	1.189716	0.000000	1.832751	8.641746	7.992392	0.000000	9.331144	7.431828	1.189716	9.334011	9.403884	0.000000	1.189716	9.007623	9.390178	0.000000	8.028232	0.000000	7.721761	0.000000	0.000000	1.189716	1.189716	6.581951	8.475161	7.054751	0.000000	0.000000	6.939021	9.742748	6.829531	7.992392	8.130700	1.189716	9.214658	7.887028	0.000000	7.399354	2.275971	1.189716	6.763021	10.830842	0.000000	1.832751	8.608986	8.316089	1.189716	0.000000	9.173640	5.809578	0.000000	9.414757	7.612677	10.214998	6.374848	7.545014	6.051698	9.921090	8.860312	0.000000	8.815891	9.269591	7.739166	7.545014	0.000000	9.953140	10.011602	2.275971	0.000000	8.110780	0.000000	8.883978	7.273603	9.449536	0.000000	0.000000	6.282898	1.189716	9.245434	0.000000	1.189716	2.614548	0.000000	0.000000	1.189716	9.263590
HSPC_031	0.686872	7.637939	0.000000	6.838205	0.000000	8.951078	8.584765	0.000000	0.000000	6.956259	7.579348	0.000000	4.175851	13.622584	7.800662	0.000000	0.686872	0.686872	0.000000	0.000000	7.018651	1.150286	3.253543	7.387780	0.686872	0.000000	0.000000	0.000000	6.348589	0.686872	0.686872	0.000000	0.000000	2.827390	8.409573	0.686872	1.782055	8.765434	8.059983	8.638735	9.676164	0.686872	1.782055	1.150286	1.150286	0.000000	0.000000	9.235683	10.222019	2.946297	0.000000	0.000000	9.531507	0.000000	1.500480	0.686872	0.000000	0.000000	0.000000	11.665664	9.216587	5.672502	0.000000	9.484386	0.000000	1.150286	0.000000	0.000000	0.000000	0.000000	0.000000	6.991254	0.000000	9.159273	3.158217	7.403453	8.372889	8.587055	1.500480	8.401790	0.686872	2.017546	0.686872	0.686872	1.500480	7.574740	7.527841	3.342958	0.000000	1.150286	8.598449	1.782055	1.782055	0.000000	0.686872	1.150286	2.219938	2.219938	9.404856	0.686872	1.150286	0.686872	2.017546	0.000000	0.686872	0.000000	7.824144	7.148337	2.827390	2.397399	9.564413	6.623022	0.000000	2.397399	7.032157	0.000000	0.000000	0.000000	11.892010	1.782055	0.000000	0.00000	1.150286	0.000000	7.828021	1.150286	5.925034	0.000000	0.686872	0.000000	0.000000	0.686872	8.915083	0.000000	9.006865	8.155637	0.000000	9.640235	2.397399	0.000000	2.017546	9.543345	0.686872	0.000000	9.419062	9.750561	1.150286	0.686872	0.686872	6.549791	8.233698	3.653748	1.500480	0.000000	0.000000	0.000000	0.000000	8.662811	0.000000	3.253543	0.000000	2.219938	9.646834	0.000000	0.000000	0.000000	0.000000	0.000000	9.962022	0.686872	8.785511	9.802712	8.826781	0.686872	0.000000	8.545278	2.697797	9.449579	8.201186	0.000000	0.000000	8.900430	2.397399	1.500480	0.000000	0.686872	3.582099	0.000000	0.000000	3.506705	2.219938	10.152610	1.150286	6.348589	0.000000	0.000000	0.000000	7.780798	1.150286	1.150286	8.951078	0.000000	0.000000	0.000000	0.000000	0.000000	6.293577	8.896743	7.116992	0.686872	8.445345	8.842198	0.000000	0.686872	7.045538	1.782055	0.000000	8.900430	0.686872	1.500480	9.076959	0.000000	2.219938	9.871007	8.938581	2.017546	6.977358	2.697797	1.150286	8.195195	10.778556	0.000000	0.000000	0.000000	2.017546	10.784055	0.000000	0.000000	0.00000	5.004467	5.565109	3.506705	0.000000	0.000000	0.000000	5.706594	0.686872	7.135881	7.615685	0.000000	9.234223	0.000000	9.659944	1.500480	8.627656	0.000000	0.000000	2.017546	9.060576	0.000000	8.904107	1.782055	6.942020	8.069839	8.233698	4.631208	9.284465	6.783355	7.214989	0.000000	0.000000	7.377236	0.686872	0.000000	0.686872	0.686872	10.663397	0.000000	0.000000	0.000000	7.255855	0.686872	1.150286	4.021900	0.000000	2.219938	3.849535	2.827391	0.000000	0.000000	8.467882	9.385257	8.095797	10.225698	7.393023	9.179137	0.000000	1.782055	5.637585	8.561668	5.449074	9.228369	4.223722	2.946297	6.380618	9.852094	0.000000	9.311023	0.000000	0.686872	0.000000	7.273021	1.500480	9.495397	7.209055	3.427154	0.000000	10.735345	6.022986	0.000000	7.565481	2.555402	2.397399	8.570951	8.009667	8.874422	1.150286	9.864415	0.000000	6.984322	0.000000	0.000000	7.694245	0.000000	0.000000	2.397399	0.686872	8.259764	0.000000	0.686872	0.000000	9.967304	8.682216	0.000000	4.520574	5.723342	8.989663	1.782055	2.827391	8.030005	3.342958	0.000000	8.137007	2.017546	0.686872	0.686872	0.000000	8.477787	8.566318	6.502060	1.500480	0.000000	1.500480	0.000000	0.686872	2.397399	3.253543	8.393966	2.017546	8.251127	7.719509	0.000000	0.686872	1.500480	9.514768	0.000000	0.000000	0.686872	8.533455	0.686872	7.664200	8.304972	1.150286	0.000000	10.264835	0.000000	9.858742	9.715412	8.730652	1.500480	6.462704	0.686872	4.595264	0.000000	10.762946	0.686872	0.000000	0.000000	8.989663	0.686872	0.686872	7.892380	0.00000	1.782055	2.697797	2.219938	2.697797	0.686872	8.785511	6.830496	7.317821	7.659856	0.000000	2.219938	0.000000	2.219938	9.460858	3.342958	8.124452	8.716082	0.000000	7.672848	1.500480	2.017546	8.830651	0.686872	0.000000	8.568637	6.101957	0.000000	10.293858	0.686872	8.186162	0.000000	3.427154	0.000000	0.000000	6.076110	4.075063	0.686872	0.686872	0.000000	7.065378	0.000000	5.387358	0.000000	2.017546	0.000000	0.00000	2.697797	7.877493	5.672502	11.297833	0.686872	2.697797	1.500480	7.999391	1.500480	0.686872	5.967830	2.827391	8.502254	0.686872	8.161794	4.0219	8.753254	8.177073	0.686872	3.722005	5.865923	8.880034	2.219938	0.000000	2.397399	0.000000	1.500480	7.005018	9.698571	0.686872	1.150286	9.231299	0.000000	5.449074	6.492322	14.849717	0.686872	0.000000	2.697797	1.500480	3.722005	8.149453	8.417314	0.686872	8.817061	7.209055	8.634314	0.686872	5.925034	0.686872	8.972252	0.000000	7.839589	0.000000	8.354193	0.000000	0.000000	3.158217	0.686872	9.163881	5.084431	9.607904	0.686872	8.894896	0.686872	0.000000	0.000000	0.686872	1.500480	0.000000	6.861088	0.000000	9.492957	8.775508	9.517171	0.000000	7.928939	2.397399	0.000000	3.056145	11.210691	0.686872	9.372041	6.511734	9.587467	0.000000	9.053970	1.500480	9.685810	5.184588	0.000000	0.000000	0.000000	9.000010	0.00000	4.700515	8.140128	0.000000	1.500480	8.654102	0.000000	9.431856	3.909308	3.506705	5.655149	8.634314	1.500480	6.861088	9.823251	10.901846	0.000000	0.000000	1.782055	9.126594	0.000000	8.728580	2.219938	4.595264	0.000000	9.771850	8.789493	0.000000	1.782055	9.018787	3.787179	8.822902	0.000000	5.322884	2.397399	1.150286	10.055096	0.686872	0.000000	8.612002	3.056145	8.751214	0.000000	9.337102	0.000000	0.000000	0.000000	6.807118	0.000000	8.318813	0.000000	1.150286	1.150286	0.686872	1.150286	7.925325	11.145000	0.686872	10.561133	8.130743	9.722719	2.946297	8.765435	0.000000	0.000000	1.150286	7.267321	0.000000	9.810572	2.219938	9.793820	9.333015	4.700515	5.184588	0.000000	1.500480	1.782055	7.788776	3.342958	3.253543	6.807118	0.000000	7.537344	8.820957	8.288184	0.000000	7.129612	0.686872	6.640763	6.472644	0.000000	0.000000	0.000000	0.000000	3.582099	2.017546	8.043406	0.000000	0.000000	1.150286	7.261599	11.510276	0.686872	2.555402	0.000000	0.000000	0.000000	0.686872	0.000000	7.655500	0.000000	4.075063	0.000000	9.710171	1.782055	7.877493	0.000000	9.001726	9.143804	6.009394	0.000000	1.150286	10.457334	0.686872	8.482714	7.220899	5.850759	0.000000	9.467087	7.350532	1.782055	0.000000	3.158217	8.994845	8.934992	0.000000	0.686872	6.511734	2.219938	0.686872	0.686872	0.686872	0.000000	0.000000	10.249019	9.670777	9.728952	2.555402	1.500480	9.080214	6.807118	8.846027	0.000000	0.000000	2.397399	5.278242	2.397399	7.429204	7.740229	0.686872	0.000000	2.219938	9.839666	8.596177	0.000000	0.000000	0.000000	0.000000	0.000000	9.762764	1.150286	0.686872	0.000000	0.686872	0.000000	0.000000	1.782055	9.107582	0.00000	2.697797	0.686872	10.531721	1.782055	1.150286	5.255393	0.686872	0.686872	0.000000	0.000000	3.506705	4.919811	0.000000	0.686872	5.565109	9.814485	9.297106	5.689649	8.781518	0.000000	9.254531	7.154525	2.555402	7.873747	1.150286	1.150286	8.878166	1.150286	9.615775	1.500480	1.782055	0.000000	8.959939	6.853501	9.008575	4.358484	0.686872	1.782055	2.827391	0.000000	6.623022	0.000000	0.000000	0.000000	1.150286	5.672502	2.017546	0.000000	0.686872	0.000000	6.248005	4.595264	1.782055	6.876145	10.860805	1.150286	1.150286	0.686872	8.236617	0.000000	8.697130	0.000000	0.686872	0.000000	8.470365	0.686872	9.470811	6.152300	0.000000	0.000000	2.697797	5.058266	8.477787	8.164863	1.150286	0.000000	7.513468	0.000000	5.739898	5.655149	9.317932	6.701193	0.000000	8.401790	1.150286	0.686872	8.460409	0.686872	0.000000	2.946297	0.000000	0.686872	0.686872	9.285875	1.782055	5.208578	9.719592	0.000000	9.479464	6.359344	8.380830	0.686872	0.000000	9.978684	9.464599	0.686872	0.686872	0.000000	2.397399	0.000000	1.150286	2.017546	0.000000	7.574740	9.898919	0.686872	0.000000	0.686872	0.686872	9.306862	0.000000	1.782055	10.211670	0.000000	9.347942	2.017546	0.000000	1.782055	7.985573	0.000000	0.000000	8.713988	0.000000	0.686872	3.582099	2.219938	0.686872	0.000000	8.504679	0.000000	0.000000	7.961069	7.943307	11.641084	6.783355	8.111786	8.133878	9.748516	1.150286	0.000000	3.787179	0.000000	8.809238	1.782055	7.454503	10.444765	8.362235	2.017546	0.000000	0.686872	0.000000	5.508257	0.686872	7.615685	0.686872	0.000000	0.000000	0.000000	7.439376	2.827391	3.158217	2.219938	2.017546	0.000000	0.000000	0.686872
HSPC_037	1.869808	7.938080	0.000000	9.405107	0.000000	7.199831	9.495433	0.000000	0.000000	7.394504	3.164226	0.000000	8.086368	13.104234	9.083711	8.014129	0.000000	0.000000	8.418471	0.000000	2.657566	0.000000	8.337902	10.692584	0.000000	1.218731	8.500077	0.000000	8.308001	0.000000	7.275944	0.000000	0.000000	8.862937	0.000000	0.000000	1.218731	8.954567	2.316800	2.316800	11.106246	0.000000	8.510617	1.869808	5.397461	2.316800	0.000000	8.006701	9.250198	6.744074	1.869808	0.000000	10.085909	0.000000	1.218731	6.928313	0.000000	0.000000	1.218731	12.131326	5.568630	8.093396	1.218731	10.313640	2.657566	1.869808	1.218731	0.000000	7.682451	1.869808	1.218731	2.933024	0.000000	9.998750	7.772788	0.000000	1.218731	1.218731	8.148427	1.218731	0.000000	2.657566	1.218731	8.621744	7.815920	0.000000	4.637700	8.546913	2.316800	0.000000	3.538458	8.592276	1.218731	0.000000	0.000000	1.218731	7.615671	1.218731	7.514567	8.833854	2.316800	1.218731	2.657566	4.475001	6.943951	3.694521	1.218731	0.000000	8.664848	6.490844	7.482801	5.647156	1.218731	2.316800	1.218731	0.000000	0.000000	0.000000	10.392692	7.160213	0.000000	0.00000	0.000000	2.316800	1.218731	8.355550	0.000000	2.933024	0.000000	1.218731	0.000000	0.000000	8.669560	9.162687	9.873687	7.755167	1.218731	7.615671	1.218731	0.000000	8.626598	9.247050	2.316800	7.371559	6.779369	7.133183	1.869807	8.246273	0.000000	6.183499	7.348242	3.963623	8.808450	1.218731	1.218731	1.218731	1.218731	7.914462	4.190334	7.133183	0.000000	1.869807	6.813823	0.000000	0.000000	0.000000	1.218731	0.000000	9.162687	0.000000	6.689457	9.741228	7.605873	2.316800	1.218731	8.760682	3.363442	2.933024	8.636256	7.160213	1.869807	10.783395	8.057904	0.000000	0.000000	6.235258	3.164226	0.000000	0.000000	8.429623	3.694520	1.869807	0.000000	7.710151	1.218731	0.000000	0.000000	9.498083	0.000000	10.651504	10.660998	0.000000	7.482801	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.729440	1.218731	8.854687	1.218731	0.000000	5.303604	9.218403	1.869807	11.052258	1.218731	0.000000	0.000000	0.000000	3.164226	6.156907	7.755167	2.933024	0.000000	2.933024	3.164226	0.000000	9.700377	0.000000	0.000000	8.977543	8.577312	9.332705	1.869807	0.000000	0.00000	0.000000	3.835337	2.933024	1.218731	1.218731	1.218731	6.016053	7.348242	7.999236	8.367196	6.285223	8.531469	0.000000	10.283226	2.316800	7.930251	0.000000	9.087235	0.000000	9.492777	0.000000	1.869807	6.864006	2.657566	1.869807	5.303604	7.336441	7.450320	1.869807	4.190334	0.000000	0.000000	1.218731	9.111674	4.386210	1.869807	8.467986	7.849521	0.000000	9.859326	0.000000	7.953614	1.218731	0.000000	1.218731	0.000000	6.813823	7.428252	3.164226	0.000000	0.000000	2.316800	8.907487	3.164226	4.386210	10.124163	9.413556	3.164226	9.759018	0.000000	1.218731	0.000000	3.164226	0.000000	7.275944	7.238389	2.933024	0.000000	3.164226	0.000000	1.869807	4.978682	9.915931	7.173540	2.933024	0.000000	9.615036	0.000000	8.678935	7.874218	3.164226	7.383077	2.316800	1.869807	2.316800	3.363442	0.000000	9.080177	9.040723	0.000000	8.283626	0.000000	0.000000	1.218731	0.000000	0.000000	1.869807	0.000000	8.314031	0.000000	4.190334	0.000000	10.867803	1.218731	1.218731	8.014129	0.000000	9.506006	3.164226	7.472055	7.119476	3.164226	1.869807	2.316800	7.428252	0.000000	4.558643	1.218731	9.457801	2.316800	2.316800	2.316800	1.218731	2.933024	0.000000	0.000000	9.855197	7.968982	1.869807	2.933024	9.090753	8.577312	1.218731	8.148427	8.320036	9.018738	0.000000	0.000000	7.535363	0.000000	0.000000	7.849521	2.657566	5.150271	5.303604	11.012021	0.000000	2.933024	0.000000	0.000000	9.275141	1.869807	0.000000	7.644670	0.000000	8.289759	7.146761	0.000000	3.164226	8.331971	0.000000	10.386982	10.113830	6.55335	2.657566	1.218731	7.077557	4.916631	0.000000	10.516827	8.050700	8.395907	6.707893	1.218731	8.107351	7.700976	8.515859	10.286297	8.546913	10.304583	2.657566	0.000000	0.000000	2.933024	2.933024	2.316800	8.760682	1.218731	7.906503	3.164226	1.869807	8.833854	0.000000	1.869807	1.218731	8.484121	0.000000	0.000000	9.076634	8.372984	0.000000	6.285223	0.000000	8.484121	0.000000	1.218731	0.000000	3.363442	0.000000	0.00000	2.657566	0.000000	8.765091	10.404042	0.000000	7.566005	3.694520	8.923351	7.545649	0.000000	5.986154	8.467986	4.190334	0.000000	6.553350	0.0000	8.301946	7.635068	7.394504	7.348242	9.388059	0.000000	8.361384	1.218731	7.682451	8.641061	1.869807	8.636256	9.756806	6.309572	9.612592	9.883858	1.218731	7.615671	2.657566	15.812094	0.000000	6.074055	10.234780	7.359948	8.271282	7.312546	1.869807	0.000000	9.998750	7.857800	9.885883	0.000000	8.808450	2.316800	7.898499	0.000000	0.000000	0.000000	1.869807	1.218731	2.657566	0.000000	0.000000	10.679799	0.000000	9.476740	2.316800	4.291594	0.000000	1.218731	0.000000	1.218731	9.889926	1.218731	8.233604	0.000000	8.384491	0.000000	10.199386	7.824394	1.869807	5.038175	0.000000	6.896517	11.337700	0.000000	1.218731	2.933024	1.218731	0.000000	1.218731	0.000000	8.867044	9.933662	8.367196	5.527704	0.000000	7.359948	0.00000	8.246273	9.231205	1.218731	0.000000	8.557117	0.000000	7.048918	3.963623	6.129815	0.000000	9.427529	0.000000	7.857800	8.655380	10.146300	0.000000	0.000000	6.235258	10.552968	1.218731	0.000000	1.218731	8.014129	0.000000	4.783895	6.943952	6.726097	2.933024	4.291594	0.000000	3.164226	0.000000	8.582317	9.529518	0.000000	9.828064	1.218731	8.214389	3.363442	2.316800	9.215184	0.000000	1.218731	0.000000	0.000000	0.000000	1.218731	0.000000	1.869807	0.000000	6.912502	0.000000	2.316800	1.869807	3.363442	9.865499	5.859945	10.011792	8.181786	9.476740	7.225650	7.186745	1.218731	1.218731	0.000000	0.000000	1.869807	8.462568	3.538458	7.300448	9.935619	0.000000	1.869807	5.859945	3.538458	8.494778	10.042988	4.475001	3.363442	8.355550	0.000000	3.538458	8.557117	4.978682	0.000000	8.121174	1.218731	8.587306	9.111674	6.553350	0.000000	0.000000	6.813823	7.914462	8.175175	7.930251	0.000000	1.869807	0.000000	8.812716	12.473957	0.000000	5.757471	0.000000	1.218731	0.000000	9.293568	0.000000	5.351295	0.000000	6.796699	0.000000	2.933024	8.451670	1.869807	1.218731	11.369171	10.099937	7.186745	1.218731	0.000000	8.283626	0.000000	9.237565	2.316800	4.081427	0.000000	10.726889	7.336441	7.728326	0.000000	0.000000	3.835337	6.880354	0.000000	0.000000	7.781518	3.164226	1.218731	4.475001	8.473385	2.933024	7.737329	8.289759	9.379458	9.663070	1.869807	8.424058	7.719267	8.981337	9.076634	1.218731	6.333516	2.657566	3.694520	4.190334	9.094260	8.141662	0.000000	0.000000	8.155161	8.114280	7.790196	4.712649	0.000000	0.000000	1.218731	1.218731	9.991242	1.869807	2.933024	8.378749	9.149260	6.129815	1.218731	3.363442	8.181786	0.00000	5.150271	0.000000	1.869807	8.577312	8.331971	7.439328	0.000000	0.000000	7.394504	0.000000	9.379458	8.510617	1.218731	2.933024	2.316800	10.080613	9.895967	3.694520	1.218731	7.091666	0.000000	8.692885	6.490843	8.277468	0.000000	0.000000	8.706702	1.218731	8.833854	3.538458	8.923351	0.000000	8.271282	1.218731	8.747376	1.218731	7.324543	2.657566	3.363442	1.869807	0.000000	1.218731	1.869807	1.218731	1.869807	2.933024	8.825436	0.000000	0.000000	0.000000	8.724920	0.000000	2.316800	1.869807	11.461703	0.000000	0.000000	2.316800	6.403053	0.000000	1.869807	6.016053	1.218731	0.000000	1.218731	7.586077	8.036182	9.734499	0.000000	0.000000	1.218731	4.783895	1.869807	0.000000	7.405841	0.000000	9.115131	0.000000	8.911469	0.000000	7.874218	8.911469	2.657566	7.348242	6.045346	1.869807	10.078843	1.218731	1.218731	2.933024	0.000000	1.218731	0.000000	9.000159	7.417090	9.073083	7.383077	1.218731	2.316800	4.190334	9.083711	2.657566	1.218731	3.164226	10.033882	0.000000	2.657566	0.000000	8.418471	0.000000	1.869807	1.869807	7.832819	1.218731	9.101251	10.376936	0.000000	8.966101	5.924429	8.489459	0.000000	3.694520	10.911210	1.218731	9.804700	7.439328	1.218731	2.657566	1.218731	0.000000	1.218731	11.608145	0.000000	0.000000	6.309572	2.657566	0.000000	0.000000	4.081427	0.000000	0.000000	8.816968	6.309572	10.319646	5.485582	7.019698	8.155161	9.396608	2.657566	0.000000	9.122022	0.000000	1.218731	2.933024	0.000000	10.647927	8.931218	2.657566	0.000000	9.287452	0.000000	7.866032	3.363442	8.384491	8.114280	0.000000	8.320036	8.148427	7.615671	0.000000	7.336441	8.407233	1.218731	0.000000	1.218731	8.641061
LT-HSC_001	7.965715	5.885018	0.000000	8.962827	0.000000	2.364517	8.080749	0.000000	4.137438	3.749470	2.364517	0.000000	7.347331	13.944537	8.516418	3.749470	0.000000	0.000000	0.000000	0.000000	7.552600	0.000000	3.749470	9.893764	6.820748	2.364517	0.000000	0.000000	6.872760	0.000000	7.520353	0.000000	0.000000	2.364517	0.000000	0.000000	3.217169	7.232592	7.107933	8.564652	11.130502	0.000000	9.931038	0.000000	7.584142	8.397178	5.095857	8.414827	9.184807	3.217169	0.000000	2.364517	4.442877	0.000000	2.364517	0.000000	0.000000	0.000000	0.000000	13.092126	4.137438	3.217169	0.000000	9.639814	3.217169	8.187283	0.000000	0.000000	0.000000	3.749470	6.074609	2.364517	2.364517	8.742914	8.888979	0.000000	0.000000	8.227787	3.217169	2.364517	0.000000	8.685906	0.000000	7.271861	9.312700	8.914017	7.487369	4.909226	0.000000	0.000000	7.271861	2.364517	3.217169	5.982925	0.000000	8.548753	4.137438	2.364517	3.749470	0.000000	2.364517	0.000000	3.749470	7.645229	2.364517	3.749470	3.217169	7.917005	7.989468	7.674830	8.397178	2.364517	0.000000	8.207678	7.866592	0.000000	0.000000	0.000000	10.297362	7.584142	0.000000	5.77998	0.000000	8.267186	4.137438	2.364517	0.000000	2.364517	2.364517	0.000000	0.000000	0.000000	7.645229	3.217169	8.058464	7.107933	0.000000	7.232592	7.760152	8.267186	4.137438	9.967373	0.000000	8.187283	6.971475	8.926375	9.143059	7.063866	0.000000	3.749470	3.217169	12.461628	5.543737	0.000000	0.000000	0.000000	0.000000	9.110936	0.000000	3.217169	0.000000	10.136473	8.483344	0.000000	0.000000	2.364517	9.033078	0.000000	8.397178	0.000000	8.305537	8.466517	7.760152	9.522611	0.000000	7.760152	2.364517	2.364517	4.137438	8.058464	4.909226	3.749470	3.749470	0.000000	2.364517	8.700371	0.000000	0.000000	2.364517	8.080749	0.000000	8.532677	8.580378	7.674830	8.305537	0.000000	0.000000	4.137438	0.000000	7.645229	8.685906	0.000000	8.901552	2.364517	0.000000	2.364517	2.364517	0.000000	8.227787	5.885018	0.000000	3.217169	5.982925	7.645229	5.543737	9.705823	10.273376	9.438827	0.000000	0.000000	8.863498	0.000000	8.166596	4.694814	3.217169	3.217169	8.516418	9.464477	2.364517	3.217169	10.330287	2.364517	2.364517	0.000000	2.364517	10.442098	0.000000	7.107933	3.74947	5.885018	8.449492	2.364517	0.000000	0.000000	0.000000	4.442877	0.000000	7.192224	8.286489	3.217169	3.749470	0.000000	9.312700	7.150694	8.595934	0.000000	2.364517	2.364517	8.671295	0.000000	3.217169	8.058464	3.217169	2.364517	3.749470	8.166596	7.018409	7.150694	6.160812	0.000000	0.000000	2.364517	4.442877	2.364517	0.000000	2.364517	11.081615	4.137438	3.217169	3.749470	9.594069	2.364517	0.000000	2.364517	0.000000	2.364517	3.217169	0.000000	0.000000	0.000000	0.000000	8.532677	6.820749	9.979284	8.124310	6.461820	0.000000	5.261087	0.000000	8.305537	0.000000	4.694814	0.000000	9.522611	8.124310	7.703835	0.000000	0.000000	3.217169	3.217169	9.849024	7.674830	8.770595	6.652419	3.749470	2.364517	0.000000	4.442877	3.217169	0.000000	4.442877	9.455977	2.364517	3.749470	9.055756	0.000000	2.364517	9.264843	9.174482	9.530727	7.917005	0.000000	8.080749	3.217169	0.000000	8.286489	0.000000	3.217169	0.000000	0.000000	5.779980	8.227787	3.217169	0.000000	9.055756	0.000000	8.414827	0.000000	7.453613	3.749470	2.364517	0.000000	7.965715	6.971475	0.000000	0.000000	2.364517	9.078081	7.271861	2.364517	0.000000	8.837559	9.021605	0.000000	0.000000	7.941566	8.548753	8.286489	0.000000	0.000000	9.078081	0.000000	4.137438	7.347332	9.078081	0.000000	2.364517	0.000000	2.364517	0.000000	3.217169	3.217169	4.442877	2.364517	10.437787	8.483344	4.909226	0.000000	3.749470	8.449492	0.000000	0.000000	8.124310	2.364517	4.694814	3.217169	6.766791	7.941566	5.666688	0.000000	8.286489	8.863498	0.00000	3.217169	2.364517	8.305537	3.749470	0.000000	10.229170	4.137438	3.749470	8.187283	0.000000	7.674830	0.000000	9.055756	9.377142	7.787507	3.217169	3.217169	0.000000	2.364517	10.104202	0.000000	9.078081	9.153609	0.000000	3.749470	0.000000	0.000000	9.705823	0.000000	0.000000	0.000000	9.489678	0.000000	0.000000	3.217169	9.066961	0.000000	0.000000	0.000000	2.364517	0.000000	8.466517	0.000000	9.164084	0.000000	0.00000	3.217169	0.000000	4.694814	8.756821	0.000000	0.000000	3.749470	9.121723	3.749470	0.000000	9.184806	8.595934	6.591640	7.271861	0.000000	0.0000	6.591640	8.449492	0.000000	3.217169	4.137438	0.000000	8.548753	0.000000	3.217169	0.000000	0.000000	2.364517	9.789383	0.000000	4.137438	6.591640	8.641622	4.137438	0.000000	15.593200	0.000000	7.063866	9.421470	10.020216	5.261087	7.674830	0.000000	0.000000	7.760152	3.217169	7.584142	0.000000	5.885018	0.000000	8.548753	2.364517	2.364517	7.107933	7.018409	0.000000	0.000000	4.137438	0.000000	8.035830	3.749470	4.694814	6.710738	0.000000	8.499976	9.447428	5.666688	0.000000	9.594069	8.483344	9.340672	2.364517	0.000000	3.217169	10.219159	0.000000	3.217169	9.078081	0.000000	9.887456	8.342895	2.364517	8.876294	2.364517	9.368110	0.000000	0.000000	0.000000	5.666688	5.982925	2.364517	5.095857	0.000000	5.261087	0.00000	4.442877	3.217169	0.000000	0.000000	3.217169	0.000000	3.217169	3.217169	10.082279	0.000000	7.760152	0.000000	7.232592	3.749470	11.172608	0.000000	3.217169	6.591640	7.419049	8.267186	2.364517	0.000000	8.166596	0.000000	3.217169	10.115040	7.732268	4.442877	4.694814	8.124310	4.137438	0.000000	7.584142	8.714692	0.000000	9.912520	0.000000	5.095857	10.283019	10.093283	4.442877	4.909226	0.000000	2.364517	5.095857	2.364517	4.694814	0.000000	3.217169	0.000000	8.466517	0.000000	8.207678	0.000000	4.442877	11.771131	8.012835	10.802043	9.089116	11.133169	2.364517	6.461820	3.217169	0.000000	9.554808	2.364517	0.000000	4.442877	3.217169	5.543737	5.666688	6.591640	2.364517	0.000000	8.656534	3.217169	9.554808	8.532677	2.364517	6.710738	0.000000	2.364517	7.840709	3.217169	0.000000	8.611324	5.409323	6.710738	10.509359	4.442877	3.217169	0.000000	0.000000	5.261087	0.000000	8.714692	0.000000	0.000000	0.000000	8.342895	12.910433	0.000000	4.442877	7.107933	0.000000	2.364517	2.364517	2.364517	0.000000	0.000000	2.364517	0.000000	0.000000	3.217169	8.811145	0.000000	10.120428	6.710738	7.732268	0.000000	0.000000	4.694814	0.000000	10.788568	8.742914	6.971475	2.364517	4.909226	0.000000	9.768942	0.000000	0.000000	6.820749	0.000000	0.000000	6.319152	9.340672	8.626553	4.137438	8.124310	8.247621	0.000000	0.000000	10.002816	8.926375	5.982925	0.000000	3.749470	3.749470	9.669524	5.982925	0.000000	0.000000	0.000000	8.397178	4.137438	7.383635	0.000000	5.409323	0.000000	9.293747	8.626553	6.160812	0.000000	0.000000	4.694814	10.805393	6.528191	8.247621	9.205236	0.000000	3.217169	8.756821	0.000000	9.143059	2.364517	7.347332	0.00000	6.319152	0.000000	5.409323	9.768942	3.217169	3.217169	7.645229	2.364517	2.364517	0.000000	7.383635	6.319152	2.364517	6.392249	3.217169	7.150694	8.671295	6.766791	5.885018	0.000000	2.364517	7.107933	8.685906	8.012835	2.364517	0.000000	4.137438	2.364517	3.217169	7.453613	8.671295	0.000000	7.760152	6.160812	3.217169	8.499976	0.000000	7.520354	0.000000	0.000000	3.749470	7.814354	8.499976	0.000000	6.766791	9.205236	8.102695	2.364517	0.000000	0.000000	5.261087	9.089116	0.000000	3.217169	12.063208	0.000000	0.000000	8.938628	9.421470	0.000000	3.217169	0.000000	5.666688	9.918720	7.552600	3.217169	3.749470	7.674830	0.000000	2.364517	4.442877	4.442877	8.499976	0.000000	2.364517	0.000000	12.165795	0.000000	9.215343	0.000000	10.471911	5.982925	0.000000	9.322084	7.674830	0.000000	10.389508	2.364517	0.000000	9.601794	0.000000	0.000000	4.442877	10.104202	0.000000	8.361217	9.164084	0.000000	7.866592	8.700371	8.548753	9.143059	0.000000	0.000000	7.965715	0.000000	9.033078	0.000000	10.577548	0.000000	8.564652	2.364517	10.987443	0.000000	9.464477	8.824412	0.000000	0.000000	0.000000	9.802852	0.000000	4.137438	9.943251	0.000000	9.937158	2.364517	0.000000	8.324337	2.364517	0.000000	0.000000	10.420417	0.000000	2.364517	8.080749	2.364517	0.000000	8.145608	6.319152	0.000000	8.811145	9.132430	0.000000	10.173233	9.676858	7.989468	8.267186	10.812068	2.364517	2.364517	9.264843	7.552600	8.580378	2.364517	8.166596	10.933363	7.732268	0.000000	0.000000	2.364517	0.000000	8.166596	8.770595	2.364517	0.000000	2.364517	0.000000	9.121723	9.481327	0.000000	0.000000	3.749470	6.820749	7.107933	2.364517	6.242153
HSPC_001	8.395500	0.000000	0.377367	0.676211	0.676211	0.000000	8.923709	0.377367	0.000000	8.851780	9.012407	7.123321	7.640356	12.746870	9.232729	9.914646	0.000000	5.069857	0.000000	0.000000	1.628905	0.000000	9.412471	11.118833	0.000000	0.000000	0.676211	0.377367	6.802432	0.000000	4.414804	0.377367	7.107768	8.010188	5.867946	0.000000	9.303379	8.824449	1.134796	1.482235	8.987981	0.000000	1.318950	0.377367	0.000000	0.377367	0.000000	1.482235	7.239191	8.617097	0.000000	0.000000	2.921687	0.000000	1.762030	0.000000	0.000000	0.000000	0.000000	12.396286	0.000000	0.676211	0.000000	11.169149	0.377367	9.699115	0.000000	0.000000	0.377367	0.000000	0.000000	8.285068	0.000000	11.201561	0.923649	0.377367	0.676211	5.216270	9.912410	8.639983	0.000000	2.374501	7.993360	0.000000	7.224846	0.676211	7.224846	9.392715	0.000000	0.000000	9.060862	1.628905	0.923649	0.000000	0.000000	4.434885	8.771148	1.628905	10.008081	8.286449	8.276747	8.061140	1.762030	0.000000	8.663575	4.586200	8.923709	0.000000	7.925790	1.482235	9.740544	8.240127	0.000000	1.883902	8.577000	0.000000	0.000000	7.520972	9.818564	0.000000	0.377367	0.00000	0.000000	0.377367	9.422563	0.377367	0.000000	8.668884	0.377367	0.000000	5.918739	0.000000	4.265819	0.000000	9.042166	0.923649	0.000000	3.519027	8.720921	0.000000	8.950983	8.410789	0.676211	0.000000	0.676211	8.584884	6.136699	7.233470	1.628905	0.377367	1.996276	2.288830	9.442536	0.000000	0.000000	0.000000	0.000000	6.111968	0.000000	2.604657	0.000000	10.683373	8.965724	3.853357	0.000000	0.377367	0.000000	0.000000	1.482235	0.000000	0.000000	9.960822	7.587515	1.318950	0.000000	9.564895	7.530335	1.628905	8.609387	8.121239	0.676211	8.514683	1.762030	0.377367	0.000000	0.000000	1.628905	0.377367	0.000000	0.000000	2.288830	1.628905	1.134796	1.134796	0.000000	0.000000	0.377367	10.135483	0.676211	1.318950	8.785881	0.000000	7.854899	0.377367	0.000000	7.663942	0.000000	0.377367	0.000000	5.670382	0.000000	7.499681	0.000000	0.000000	9.520952	9.448104	0.000000	8.703439	1.318950	6.563421	0.377367	0.000000	8.443370	1.318950	9.606986	6.723001	0.377367	8.671001	0.377367	0.000000	10.844519	0.000000	0.000000	0.676211	1.883902	10.464271	3.882894	0.000000	0.00000	0.000000	8.356553	1.482235	0.000000	7.281391	0.000000	4.173225	8.508774	0.923649	7.330454	0.000000	9.775414	0.377367	8.996450	0.377367	9.137149	0.000000	0.000000	0.000000	9.482276	7.088883	0.377367	1.318950	8.542711	7.809477	1.482235	9.920888	3.968042	1.318950	9.261827	0.000000	0.000000	9.772458	6.786892	0.676211	0.676211	5.030808	11.404691	0.000000	7.758600	7.079347	7.278616	6.625896	0.000000	0.000000	0.000000	9.288282	8.392936	9.193486	0.377367	0.000000	0.000000	0.923649	9.289661	10.363761	7.273049	4.099659	1.134796	2.977514	0.000000	9.935939	0.000000	1.996276	0.000000	4.493504	1.134796	9.102259	3.592379	0.923649	0.000000	7.607558	0.377367	0.377367	0.000000	3.995344	7.902545	9.643577	0.377367	0.923649	8.056288	0.000000	1.883902	1.628905	1.628905	8.057907	9.902980	0.676211	1.482235	8.315172	0.000000	8.858300	0.000000	0.000000	3.083081	0.000000	0.000000	2.921687	0.000000	9.107741	0.377367	0.000000	6.608321	10.655834	1.628905	0.000000	9.932853	0.000000	8.069193	0.676211	8.487300	8.833932	1.628905	5.509264	2.604657	1.883902	8.511140	7.281391	0.000000	0.377367	9.534391	0.676211	1.318950	0.676211	7.709989	0.000000	6.074058	0.923649	6.970060	0.676211	1.628905	1.134796	3.519027	0.000000	0.676211	8.524086	8.845230	0.377367	0.000000	0.000000	0.000000	0.000000	4.394439	0.000000	9.883934	0.000000	10.435905	0.000000	9.766528	0.377367	9.098330	6.086805	0.000000	0.000000	8.816817	0.000000	7.525660	8.147335	0.377367	3.401495	6.237319	0.000000	1.134796	1.482235	0.00000	8.566798	0.000000	1.134796	9.515068	0.000000	9.761568	8.943118	1.628905	9.188320	7.544266	0.676211	5.974679	8.515861	9.857676	9.503825	3.882894	8.969171	0.000000	0.000000	1.134796	0.676211	0.676211	5.082642	7.000696	0.000000	0.923649	0.676211	1.482235	0.000000	0.377367	0.000000	9.410571	0.000000	0.000000	8.086751	2.531943	0.000000	0.000000	0.000000	8.571341	0.000000	0.377367	0.000000	0.676211	0.000000	6.38397	3.031264	5.004176	6.759286	10.834868	0.000000	7.183903	10.077985	8.673117	8.596073	0.377367	0.676211	1.628905	1.628905	8.208397	0.000000	0.0000	0.923649	0.676211	0.000000	4.787026	8.127421	0.377367	9.011572	7.007417	1.482235	0.676211	0.377367	0.000000	0.377367	0.377367	0.676211	1.482235	0.377367	8.144289	6.763263	14.466537	0.000000	0.377367	6.021909	0.676211	8.618195	9.168968	8.661447	0.000000	8.059525	1.996276	1.996276	0.000000	8.620389	0.000000	9.508570	0.000000	1.482235	0.000000	1.134796	0.000000	0.000000	7.676647	0.000000	8.396780	0.000000	2.100526	9.437569	0.676211	0.000000	0.000000	0.000000	8.639983	9.758583	5.600985	9.989108	0.000000	1.318950	0.000000	9.627852	0.676211	7.553480	0.000000	0.000000	7.478070	9.471989	1.628905	0.377367	8.708603	0.676211	0.000000	0.000000	0.377367	8.396780	8.817774	0.377367	0.000000	0.000000	1.883902	4.83315	0.000000	8.618195	0.000000	0.000000	1.762030	0.000000	2.977514	3.031264	8.331331	0.000000	10.336666	0.000000	0.377367	1.134796	9.692875	5.069857	0.000000	1.482235	8.778042	0.000000	0.923649	3.853357	8.359182	0.000000	8.839592	0.923649	0.000000	4.690105	8.598301	0.000000	1.318950	0.377367	7.408690	8.307024	0.000000	11.226616	0.000000	0.377367	8.840533	8.171473	8.984579	0.000000	0.000000	0.000000	5.953955	0.000000	0.000000	7.377903	0.923649	0.000000	7.458585	0.000000	8.116584	8.717852	9.644115	11.752090	0.000000	10.211298	9.051950	9.945156	9.002350	9.150871	2.531943	0.377367	0.377367	0.377367	0.000000	2.921687	7.820968	9.959956	9.118646	0.377367	8.493298	0.000000	0.923649	1.628905	2.197748	9.216145	3.519027	1.134796	0.000000	6.167028	10.550200	7.941667	0.000000	5.537397	0.000000	0.676211	8.250077	0.377367	0.000000	0.000000	0.000000	1.482235	0.377367	9.303379	0.000000	0.377367	0.000000	4.604046	10.497768	0.000000	2.531943	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.867946	0.000000	0.923649	1.762030	4.099659	0.000000	10.043252	1.134796	1.762030	0.000000	0.000000	0.377367	0.000000	7.636025	1.134796	9.846475	0.676211	9.338449	0.000000	0.377367	0.377367	0.000000	9.600889	8.113473	0.000000	0.377367	10.449541	2.673882	9.999258	0.676211	0.000000	0.377367	0.000000	10.079579	9.070521	10.222171	8.300199	0.676211	8.450785	10.698247	7.622956	0.923649	0.000000	0.676211	8.228669	8.283684	9.610853	0.000000	0.000000	2.604657	9.476233	6.599451	0.923649	0.000000	0.000000	0.000000	0.000000	8.156434	9.612506	0.377367	0.000000	0.000000	7.873411	0.000000	0.000000	1.628905	6.414630	5.52808	8.885003	0.676211	0.676211	7.242044	1.318950	2.921687	0.000000	0.377367	0.000000	0.377367	10.542702	8.477653	0.000000	0.923649	8.533429	2.288830	9.603663	9.580752	0.000000	0.000000	5.370192	5.712118	1.482235	1.628905	0.000000	0.676211	1.134796	0.923649	7.362259	9.059246	0.377367	0.000000	8.791733	5.661887	8.810106	0.000000	0.000000	1.482235	0.377367	0.000000	0.000000	0.676211	0.676211	0.000000	8.979461	7.441317	0.923649	0.000000	0.000000	0.377367	8.317878	0.676211	1.318950	8.222906	12.126537	0.000000	0.000000	0.676211	0.676211	0.377367	0.676211	0.000000	0.377367	0.000000	8.644301	8.425918	0.377367	0.000000	0.000000	0.000000	2.739936	8.959672	0.377367	0.000000	0.000000	0.377367	1.883902	0.000000	10.312110	0.000000	4.124600	8.799498	1.134796	3.360089	7.558065	2.374501	10.136633	1.482235	0.000000	7.551182	0.000000	7.451210	0.676211	10.394527	8.633481	0.377367	9.589737	0.000000	0.000000	7.974620	9.727375	9.550012	0.000000	9.245576	9.297222	0.000000	8.703439	0.377367	8.125878	0.377367	0.000000	1.318950	0.676211	0.676211	6.237319	6.833019	0.377367	7.043829	0.000000	8.814903	0.000000	8.390367	8.732122	8.673117	9.525641	0.923649	0.000000	10.337334	3.853357	0.000000	0.676211	11.414046	0.377367	0.923649	1.628905	0.676211	0.000000	0.000000	8.230106	0.000000	0.000000	9.630032	0.000000	10.754757	8.291965	7.555774	9.070521	9.177187	1.762030	0.000000	8.746254	0.377367	7.629506	1.134796	0.000000	10.477341	10.039568	1.318950	0.377367	6.563421	0.377367	9.440054	1.134796	1.134796	7.707927	0.000000	0.377367	5.359726	9.581316	0.000000	0.377367	1.134796	1.134796	1.883902	0.000000	1.318950

In [25]:

scbool = scBoolSeq().fit(ref_data)
scbool

Out[25]:

scBoolSeqBinarizer()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

In [26]:

np.where(traj_df.index.map(lambda x: "_to_" not in x))[0]

Out[26]:

array([ 0,  5, 18, 26, 35])

In [27]:

traj_df.iloc[np.where(traj_df.index.map(lambda x: "_to_" not in x))[0], :]

Out[27]:

	x0	x1	x10	x11	x12	x13	x14	x15	x17	x2	x20	x21	x23	x24	x26	x3	x4	x6	x8	x9
init	0	0	1	0	0	1	1	0	0	1	1	0	1	1	1	1	1	1	0	0
bifurcation	0	0	1	0	1	1	1	0	1	0	0	0	1	1	1	0	1	1	0	0
stable1	1	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1
stable2	0	0	1	1	1	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0
stable3	0	1	0	0	1	1	1	1	0	0	0	1	0	0	1	0	0	1	1	0

In [28]:

traj_df.iloc[np.where(traj_df.index.map(lambda x: "_to_" not in x))[0], :].sum(axis=1)

Out[28]:

init           11
bifurcation    10
stable1         6
stable2         6
stable3         9
dtype: int64

In [29]:

traj_df.columns[(traj_df.var() == 0)]

Out[29]:

Index([], dtype='object')

In [30]:

n_samples = np.random.default_rng(SEED).integers(70, 150, size=len(traj_df.index))
_non_transitory = np.where(traj_df.index.map(lambda x: "_to_" not in x))[0]
n_samples[_non_transitory] = np.random.default_rng(SEED).integers(500, 600, size=len(_non_transitory))

n_samples

Out[30]:

array([597, 148, 149, 100,  83, 597,  78,  90,  81,  95, 112,  79, 133,
        89, 132,  95, 133, 147, 598,  91, 114, 105,  90, 118, 141, 139,
       538, 139, 111, 123, 146, 122,  73, 128, 129, 517])

In [31]:

d = traj_df.copy(deep=True).values.repeat(n_samples, axis=0)
bindata = pd.DataFrame(d, columns=traj_df.columns)
bindata.shape

Out[31]:

(6360, 20)

In [32]:

counts = scbool.sample_counts(bindata, n_samples_per_state=1, random_state=SEED)
counts.head()

Out[32]:

	x0	x1	x10	x11	x12	x13	x14	x17	x2	x20	x21	x23	x24	x26	x3	x4	x6	x8	x9
0	6.356687	4.144478	11.073216	0.000000	4.458495	9.192170	16.163407	4.591142	5.931198	9.789617	0.000000	8.545147	8.337190	11.048758	1.202813	8.120571	11.889274	0.000000	0.199554
1	0.835645	2.010283	12.316921	1.462056	3.928711	9.494834	16.425950	3.341043	8.640534	11.174345	1.422358	8.078405	9.230566	11.760354	0.000000	13.295775	5.718735	0.000000	0.436888
2	3.852430	3.139584	9.896885	2.567859	1.168861	10.984037	15.591004	2.768003	8.290386	11.755922	0.000000	7.578716	9.167828	10.098188	0.000000	12.921173	6.217599	0.000000	1.077996
3	0.469563	1.511944	11.849830	1.692185	4.213604	11.058825	16.287910	3.773210	9.472351	11.699550	3.937692	10.299978	8.135352	9.011899	2.208726	8.904294	7.919568	0.000000	1.819151
4	1.670469	0.000000	9.900362	0.484062	3.776691	9.951019	15.854611	1.754616	9.250049	11.667626	0.000000	8.795610	9.340702	8.964883	0.000000	12.023884	9.864496	4.878799	0.000000

In [33]:

ids = [f"{x}_{y}" for i,x in enumerate(traj_df.index) for y in range(n_samples[i])]
counts.index = ids
counts.index.name = "cellID"
#counts.index.map(lambda x: x.split("_")[0]).unique()
counts

Out[33]:

	x0	x1	x10	x11	x12	x13	x14	x15	x17	x2	x20	x21	x23	x24	x26	x3	x4	x6	x8	x9
cellID
init_0	6.356687	4.144478	11.073216	0.000000	4.458495	9.192170	16.163407	0.000000	4.591142	5.931198	9.789617	0.000000	8.545147	8.337190	11.048758	1.202813	8.120571	11.889274	0.000000	0.199554
init_1	0.835645	2.010283	12.316921	1.462056	3.928711	9.494834	16.425950	0.000000	3.341043	8.640534	11.174345	1.422358	8.078405	9.230566	11.760354	0.000000	13.295775	5.718735	0.000000	0.436888
init_2	3.852430	3.139584	9.896885	2.567859	1.168861	10.984037	15.591004	0.000000	2.768003	8.290386	11.755922	0.000000	7.578716	9.167828	10.098188	0.000000	12.921173	6.217599	0.000000	1.077996
init_3	0.469563	1.511944	11.849830	1.692185	4.213604	11.058825	16.287910	0.000000	3.773210	9.472351	11.699550	3.937692	10.299978	8.135352	9.011899	2.208726	8.904294	7.919568	0.000000	1.819151
init_4	1.670469	0.000000	9.900362	0.484062	3.776691	9.951019	15.854611	0.000000	1.754616	9.250049	11.667626	0.000000	8.795610	9.340702	8.964883	0.000000	12.023884	9.864496	4.878799	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
stable3_512	1.300855	8.825579	7.985000	0.810334	10.846489	9.787957	15.642349	7.944253	5.088337	0.274981	4.116469	8.465650	0.424795	2.757575	9.170532	0.347563	3.955298	8.645170	6.032245	1.778593
stable3_513	4.199834	7.993578	8.270309	0.000000	11.307799	9.681840	15.369320	6.869272	3.920587	1.151862	0.000000	6.296890	3.405255	1.457118	8.582127	0.000000	4.562706	7.944660	5.977704	1.293997
stable3_514	3.964094	8.388342	9.156939	5.837674	9.780715	9.857774	15.715133	0.000000	3.564653	3.870135	3.941707	7.034641	4.114664	1.730180	9.014032	0.582223	4.082082	8.046503	6.155325	0.000000
stable3_515	3.208976	7.845625	8.703443	2.044151	6.673158	10.069703	15.674563	8.202008	5.028622	2.230930	4.098484	8.628150	2.762687	3.654045	10.527947	0.000000	4.209269	11.592137	5.953017	0.000000
stable3_516	4.803261	5.711744	6.740230	0.036132	10.755981	10.121012	16.008064	7.343627	3.703978	1.591004	4.117606	7.208061	0.844144	3.306094	8.971130	0.000000	4.227802	7.920768	7.117525	0.000000

6360 rows × 20 columns

In [34]:

init = counts.index[ np.where(counts.index.map(lambda x: "fp2" in x))[0] ]
init

Out[34]:

Index([], dtype='object', name='cellID')

In [35]:

if init.has_duplicates:
    print("Index has duplicates")
    print(f"{len(init)} elements, only {len(init.drop_duplicates())} are unique")

In [36]:

%time synth_scbool = scBoolSeq().fit(counts)
synth_scbool

CPU times: user 46.8 s, sys: 51.9 s, total: 1min 38s
Wall time: 7.12 s

Out[36]:

scBoolSeqBinarizer()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

In [37]:

synth_scbool.criteria_.plot.scatter(x="Mean", y="Variance", c="DropOutRate")

Out[37]:

<Axes: xlabel='Mean', ylabel='Variance'>

In [38]:

def label_of_index(idx):
    return "_".join(idx.split("_")[:-1]) if "_to_" in idx else idx

labels = [label_of_index(idx) for idx in traj_df.index]

color_map_1 = {
    "ES": "#00f7ff", #"#32a899",
    "ES_to_ECTO": "#3277a8",
    "ECTO": "#3252a8",
    "ES_to_MESENDO": "#32a836",
    "MESENDO_to_DEFENDO": "#99a832",
    "MESENDO": "#50822f",
    "DEFENDO": "#dbdb23",
    "MESENDO_to_MESO": "#db7323",
    "MESO": "#c24438",
}

color_map = {
    "init": "#00f7ff", #"#32a899",
    "init_to_stable1": "#3277a8",
    "stable1": "#3252a8",
    "init_to_bifurcation": "#32a836",
    "bifurcation_to_stable3": "#99a832",
    "bifurcation": "#50822f",
    "stable3": "#dbdb23",
    "bifurcation_to_stable2": "#db7323",
    "stable2": "#c24438",
}


assert len(color_map.values()) == len(set(color_map.values())), "Colours are not unique"

In [39]:

sorted(set(labels))

Out[39]:

['bifurcation',
 'bifurcation_to_stable2',
 'bifurcation_to_stable3',
 'init',
 'init_to_bifurcation',
 'init_to_stable1',
 'stable1',
 'stable2',
 'stable3']

In [40]:

metadata = [[l, color_map[l]] for i,l in enumerate(labels) for _ in range(n_samples[i])]
metadata = pd.DataFrame(metadata, columns=["label", "label_color"])
metadata.index = counts.index
metadata

Out[40]:

	label	label_color
cellID
init_0	init	#00f7ff
init_1	init	#00f7ff
init_2	init	#00f7ff
init_3	init	#00f7ff
init_4	init	#00f7ff
...	...	...
stable3_512	stable3	#dbdb23
stable3_513	stable3	#dbdb23
stable3_514	stable3	#dbdb23
stable3_515	stable3	#dbdb23
stable3_516	stable3	#dbdb23

6360 rows × 2 columns

In [41]:

EXPORT = False

if EXPORT:
    counts.T.to_csv(checkpoint_dir / "stream_expr_data.tsv", sep="\t")
    metadata.to_csv(checkpoint_dir / "stream_expr_metadata.tsv", sep="\t")

In [42]:

from sklearn.decomposition import PCA
from sklearn.manifold import LocallyLinearEmbedding, TSNE, SpectralEmbedding
from sklearn.pipeline import Pipeline, FunctionTransformer

import matplotlib.pyplot as plt

In [74]:

def rename_columns(df):
    """ """
    df = pd.DataFrame(df, index=metadata.index)
    df.columns = [f"dim{i}" for i in range(df.shape[1])]
    return df

N_COMPONENTS = 3

vis_pipeline = Pipeline([
    ('pca', PCA()),
    ('subset_pca', FunctionTransformer(lambda x: x.iloc[:, :N_COMPONENTS] if isinstance(x, pd.DataFrame) else x[:, ][:, :N_COMPONENTS])),
    ('rename_cols', FunctionTransformer(rename_columns))
])

In [75]:

%time projected = vis_pipeline.fit_transform(counts)

CPU times: user 0 ns, sys: 29.8 ms, total: 29.8 ms
Wall time: 6.08 ms

In [76]:

%time raw_vis_frame = TSNE(perplexity=100, random_state=SEED).fit_transform(projected)

CPU times: user 53.8 s, sys: 198 ms, total: 54 s
Wall time: 54.1 s

In [77]:

vis_frame = rename_columns(raw_vis_frame).join(metadata)

In [78]:

ax = None
for label, frame in vis_frame.groupby('label'):
    scatter_kwargs = dict(x='dim0', y='dim1', label=label, c=frame['label_color'], alpha=.5)
    if ax is not None:
        scatter_kwargs.update({'ax': ax})
    ax = frame.plot.scatter(**scatter_kwargs)

In [79]:

fig = ax.get_figure()

In [80]:

fig.tight_layout()
fig.subplots_adjust(right=0.96)

In [81]:

fig

Out[81]:

for _f in ['svg', 'pdf', 'png']: fig.savefig(f"multilevel_random_network_tsne.{_f}")

	stable3	bifurcation	init	stable2	stable1
x0	0	0	0	0	1
x2	0	0	1	0	1
x3	0	0	1	0	0
x4	0	1	1	0	0
x7	0	0	0	0	0
x9	0	0	0	0	1
x10	0	1	1	1	1
x11	0	0	0	1	1
x17	0	1	0	0	1
x20	0	0	1	0	0
x22	0	0	1	0	0
x23	0	1	1	0	0
x24	0	1	1	0	0
x27	0	1	1	0	0
x28	0	0	0	0	0
x29	0	1	1	0	0
x1	1	0	0	0	0
x5	1	1	1	1	1
x6	1	1	1	0	0
x8	1	0	0	0	0
x12	1	1	0	1	0
x13	1	1	1	0	0
x14	1	1	1	1	0
x15	1	0	0	1	0
x16	1	0	0	1	1
x18	1	0	0	1	1
x19	1	0	0	1	1
x21	1	0	0	0	0
x25	1	1	1	1	1
x26	1	1	1	1	0

	stable3	bifurcation	init	stable2	stable1
x0	0	0	0	0	1
x2	0	0	1	0	1
x3	0	0	1	0	0
x4	0	1	1	0	0
x7	0	0	0	0	0
x9	0	0	0	0	1
x10	0	1	1	1	1
x11	0	0	0	1	1
x17	0	1	0	0	1
x20	0	0	1	0	0
x22	0	0	1	0	0
x23	0	1	1	0	0
x24	0	1	1	0	0
x27	0	1	1	0	0
x28	0	0	0	0	0
x29	0	1	1	0	0
x1	1	0	0	0	0
x5	1	1	1	1	1
x6	1	1	1	0	0
x8	1	0	0	0	0
x12	1	1	0	1	0
x13	1	1	1	0	0
x14	1	1	1	1	0
x15	1	0	0	1	0
x16	1	0	0	1	1
x18	1	0	0	1	1
x19	1	0	0	1	1
x21	1	0	0	0	0
x25	1	1	1	1	1
x26	1	1	1	1	0

	stable3	bifurcation	init	stable2	stable1
x0	0	0	0	0	1
x2	0	0	1	0	1
x3	0	0	1	0	0
x4	0	1	1	0	0
x7	0	0	0	0	0
x9	0	0	0	0	1
x10	0	1	1	1	1
x11	0	0	0	1	1
x17	0	1	0	0	1
x20	0	0	1	0	0
x22	0	0	1	0	0
x23	0	1	1	0	0
x24	0	1	1	0	0
x27	0	1	1	0	0
x28	0	0	0	0	0
x29	0	1	1	0	0
x1	1	0	0	0	0
x5	1	1	1	1	1
x6	1	1	1	0	0
x8	1	0	0	0	0
x12	1	1	0	1	0
x13	1	1	1	0	0
x14	1	1	1	1	0
x15	1	0	0	1	0
x16	1	0	0	1	1
x18	1	0	0	1	1
x19	1	0	0	1	1
x21	1	0	0	0	0
x25	1	1	1	1	1
x26	1	1	1	1	0