import numpy as np
motif_name = 'SPI1'
aitacdir = "/mnt/lab_data2/msharmin/oc-atlas/DanSkinData/fold_a_alex/{}".format(motif_name)

ave_filt_infl = np.load(aitacdir+"/ave_filt_infl.npy")

from matlas.matches import DenovoAitac

ob = DenovoAitac(aitacdir, influence=ave_filt_infl)
# ob.fetch_tomtom_matches(
#             meme_db="/mnt/lab_data/kundaje/users/msharmin/annotations/HOCOMOCOv11_core_pwms_HUMAN_mono.renamed.nonredundant.annotated.meme",
#             database_name="HOCOMOCO.nonredundant.annotated",
#             save_report=True, tomtom_dir= "{0}/{1}_tomtomout".format(aitacdir, "HOCOMOCO.nonredundant.annotated"))
ob.load_matched_motifs(database_name="HOCOMOCO.nonredundant.annotated")
ob.get_motif_per_celltype(match_threshold=0.05, database_name="HOCOMOCO.nonredundant.annotated")

pattern_tab, pattern_dict = ob.visualize_pattern_table()
tf_tab, tf_dict = ob.visualize_tf_table("Aitac")

from vdom.helpers import (b, summary, details)
from IPython.display import display

display(details(summary('Click here for ', b('Denovo Patterns'), ' by ', b('{}'.format('Aitac')),
                        ' in ', b(motif_name),
                        ": #{}".format(len(pattern_dict)),
                       ), pattern_tab))

Click here for Denovo Patterns by Aitac in SPI1: #64

Pattern Name	TF Name(s)	Aitac	Influence
filter55			0.0016662171422743533
filter30			0.0011375795752958783
filter2			0.0011362232563674993
filter40			0.0011205492258733888
filter21			0.001085931770628199
filter34			0.0010600107763623406
filter15			0.0010532060734413475
filter49			0.0010258274072870196
filter47			0.001024721999204059
filter61			0.0010230831694936899
filter33			0.0010228030609228714
filter48			0.0010222643303475535
filter54			0.001022251933254513
filter6			0.0010222111208425036
filter46			0.0010221224303011943
filter12			0.0010220570631263122
filter17			0.0010219212641666123
filter13			0.0010219212641666123
filter11			0.0010219212641666123
filter5			0.0010219212641666123
filter27			0.0010219212641666123
filter25			0.0010219212641666123
filter22			0.0010219212641666123
filter23			0.0010219212641666123
filter7			0.0010219212641666123
filter8			0.0010219212641666123
filter20			0.0010219212641666123
filter19			0.0010219212641666123
filter62			0.0010219212641666123
filter16			0.0010219212641666123
filter31			0.0010219212641666123
filter10			0.0010219212641666123
filter32			0.0010219212641666123
filter42			0.0010219212641666123
filter57			0.0010219212641666123
filter53			0.0010219212641666123
filter52			0.0010219212641666123
filter51			0.0010219212641666123
filter3			0.0010219212641666123
filter43			0.0010219212641666123
filter44			0.0010219212641666123
filter4			0.0010219212641666123
filter37			0.0010219212641666123
filter9			0.0010219212641666123
filter36			0.0010219035060997212
filter56			0.001021459603514293
filter35			0.0010213468840259824
filter60			0.00101985602743808
filter26			0.0010196238237310292
filter41			0.0010193772592299389
filter50			0.0010191908251362988
filter39			0.0010187230522024201
filter1			0.0010175314172953503
filter29			0.0010174002517844122
filter63			0.0010162766566788213
filter18			0.0010157884533452657
filter58			0.0010124154006902317
filter14			0.0010118500257437243
filter59			0.0010110718678013621
filter45			0.0010065601077586567
filter24			0.0010039702935219098
filter38			0.0010031855494826033
filter28			0.0010020477912694133
filter0			0.0009960069965125045

display(details(summary('Click here for ', b('Motifs'), ' by ', b('{}'.format('Aitac')),
                        ' in ', b(motif_name),
                        ": #{}".format(len(tf_dict)),
                       ), tf_tab))

Click here for Motifs by Aitac in SPI1: #0

TF Name	Pattern(s)