Goal¶

analyze the pairwise features for Nanog

Tasks¶

[x] remove duplicates
[ ] get an overview over the instances
- [ ] global overview
  - # instances per sequence
- [ ] select a few promising seqlets
- [ ] local overview
  - positional distribution within the sequence
  - match distribution (natural, p-scale)
  - importance distribution (natural, p-scale)

from basepair.imports import *
from basepair.plot.profiles import extract_signal
from basepair.math import softmax
from basepair.plot.heatmaps import heatmap_stranded_profile, multiple_heatmap_stranded_profile
from basepair.plot.profiles import  plot_stranded_profile, multiple_plot_stranded_profile
import plotnine
from plotnine import *
import statsmodels.api as sm
import statsmodels.formula.api as smf
from basepair.modisco.results import Seqlet, resize_seqlets
from basepair.modisco.core import dfi2seqlets, annotate_profile
from basepair.cli.modisco import load_profiles

from basepair.modisco.utils import shorten_pattern

import dask.dataframe as dd

task = 'Nanog'

model_dir = Path(f"{ddir}/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/")
modisco_pdir = model_dir / "modisco/by_peak_tasks/weighted/"
modisco_dir = modisco_pdir / task

mr = ModiscoResult(modisco_dir / "modisco.h5")
mr.open()

# Load the data
d = HDF5Reader(model_dir / "grad.all.h5")
d.open()

ddfi = dd.read_parquet(f"{modisco_pdir}/{task}/instances.parq").compute()

dfi = ddfi

#dfi = pd.read_parquet(f"{modisco_pdir}/{task}/instances.parq")

ddfi.tail()

# add some columns
dfi['pattern_start_abs'] = dfi['example_start'] + dfi['pattern_start']
dfi['pattern_end_abs'] = dfi['example_start'] + dfi['pattern_end']
dfi['pattern_short'] = dfi['pattern'].map({k: shorten_pattern(k) for k in dfi.pattern.unique()})

dfi.columns

Index(['pattern', 'example_idx', 'pattern_start', 'pattern_end', 'strand',
       'pattern_len', 'pattern_center', 'match_weighted', 'match_weighted_p',
       'match_weighted_cat', 'match_max', 'match_max_task', 'imp_weighted',
       'imp_weighted_p', 'imp_weighted_cat', 'imp_max', 'imp_max_task',
       'seq_match', 'seq_match_p', 'seq_match_cat', 'match/Klf4',
       'match/Nanog', 'match/Oct4', 'match/Sox2', 'imp/Klf4', 'imp/Nanog',
       'imp/Oct4', 'imp/Sox2', 'example_chrom', 'example_start', 'example_end',
       'example_strand', 'example_interval_from_task', 'pattern_start_abs',
       'pattern_end_abs', 'pattern_short'],
      dtype='object')

dfi.shape

(21405367, 36)

Skip duplicated instances¶

dfi_dedup = dfi.drop_duplicates(['pattern', 'example_chrom', 'pattern_start_abs', 'pattern_end_abs', 'strand'])

# number of removed duplicates
len(dfi) - len(dfi_dedup)

82107

# use de-duplicated instances from now on
dfi = dfi_dedup

General stats on the scores¶

Total number of instances
instances per sequence

n_patterns = dfi.groupby(['pattern_short', 'match_weighted_cat', 'imp_weighted_cat']).size().reset_index()
n_patterns = n_patterns.rename(columns={0: "n_instances"})

import hvplot.pandas

import holoviews as hv

hv.extension('bokeh', 'matplotlib')

#%%output backend='matplotlib'
fig, ax = plt.subplots(figsize=(10,8))
n_patterns[n_patterns>100000].sort_values(ascending=False).plot.barh()

<matplotlib.axes._subplots.AxesSubplot at 0x7fd02259ac18>

dfn = pd.DataFrame({'pattern_short': shorten_pattern(p), 'seqlets': mr.n_seqlets(*p.split('/'))} for p in mr.patterns())

dfn.merge(n_patterns, on='pattern_short').query('match_weighted_cat=="high"').\
    hvplot.scatter(x='seqlets', y='n_instances', by='imp_weighted_cat', alpha=0.5)

dfn.merge(n_patterns, on='pattern_short').query('match_weighted_cat=="high"').query('imp_weighted_cat=="high"').\
    hvplot.scatter(x='seqlets', y='n_instances', by='pattern_short', alpha=0.5, legend=False)

These points on the diagonal are typically patterns 0 from differnet metaclusters

# there seems to be a correlation between the detected number of instances and 
# number of detected seqlets for some points
dfn.merge(n_patterns, on='pattern_short').query('match_weighted_cat=="high"').query('imp_weighted_cat=="high"').\
    plot.scatter(x='seqlets', y='n_instances', alpha=0.5)

<matplotlib.axes._subplots.AxesSubplot at 0x7fd003787d30>

fig, ax = plt.subplots(figsize=(5,5))
dfn.merge(n_patterns.reset_index(), on='pattern_short').rename(columns={0: 'scanned_instances'}).\
    plot.scatter(x='seqlets', y='scanned_instances', ax=ax);

from vdom import a

a('Motif instances', 
  href=f'http://mitra.stanford.edu/kundaje/avsec/chipnexus/oct-sox-nanog-klf/models/n_dil_layers=9/modisco/by_peak_tasks/weighted/{task}/pattern_table.html')

# most frequent seqlets
dfn.sort_values('seqlets', ascending=False).head(9)

# most frequent motif instances
n_patterns.reset_index().rename(columns={0: 'scanned_instances'}).sort_values('scanned_instances', ascending=False).head(9)

Manually select motifs to check¶

motifs = OrderedDict([
    ("Oct4", "m0_p0"),
    ("Sox2", "m0_p1"),
    #("Err2", "m0_p3"),
    ("Nanog", "m1_p2"),
    #("Nanog-2", "m1_p0"),
    ("Klf4", "m3_p0"),
])

# define a set of core motifs

dfn.head()

Number of instances per motif¶

dfi_motifs = dfi[dfi.pattern_short.isin(list(motifs.values()))]
dfi_motifs['tf'] = dfi_motifs.pattern_short.map({v: k for k,v in motifs.items()})

n_patterns_per_example = dfi_motifs.groupby(['tf', 'example_idx', 'match_weighted_cat', 'imp_weighted_cat']).size().reset_index()#.groupby(['pattern_short'])[0].value_counts()

n_patterns_per_example = n_patterns_per_example.rename(columns={0: "n seqlets per example"})

n_patterns_per_example.hvplot.box(y='n seqlets per example', by=['tf', 'match_weighted_cat'], invert=True, height=500, width=800, legend=False)

Distribution of values for the top motifs¶

dfi_motifs.hvplot.kde(y='match_weighted', by='tf')

dfi_motifs.hvplot.kde(y='match_weighted_p', by='tf')

dfi_motifs.hvplot.kde(y='imp_weighted', by='tf')

dfi_motifs.hvplot.kde(y='imp_weighted_p', by='tf')

from basepair.stats import low_medium_high

Re-compute the motif instances¶

p = mr.get_pattern(pattern).trim_seq_ic(0.08)

dfi.head()

Nanog¶

task = "Nanog"
pattern = "metacluster_0/pattern_0"

Dev - compute the profile similarity¶

# load profiles
profiles = load_profiles(modisco_dir, model_dir/'grad.all.h5')

tasks = list(profiles)

dfim = dfi_motifs[dfi_motifs.match_weighted_cat == 'high']

dfim.shape

(42721, 37)

Annotate the major motifs with the profile features

dfi_motifs_anno = annotate_profile(dfim, mr, profiles)

100%|██████████| 5/5 [01:09<00:00, 13.85s/it]

Sanity check¶

assert len(dfi_motifs_anno) == len(dfi_filter_valid(dfim, 70, 1000))

correlate importance vs profile counts

dfi_motifs_anno.columns

Index(['pattern', 'example_idx', 'pattern_start', 'pattern_end', 'strand',
       'pattern_len', 'pattern_center', 'match_weighted', 'match_weighted_p',
       'match_weighted_cat', 'match_max', 'match_max_task', 'imp_weighted',
       'imp_weighted_p', 'imp_weighted_cat', 'imp_max', 'imp_max_task',
       'seq_match', 'seq_match_p', 'seq_match_cat', 'match/Klf4',
       'match/Nanog', 'match/Oct4', 'match/Sox2', 'imp/Klf4', 'imp/Nanog',
       'imp/Oct4', 'imp/Sox2', 'example_chrom', 'example_start', 'example_end',
       'example_strand', 'example_interval_from_task', 'pattern_start_abs',
       'pattern_end_abs', 'pattern_short', 'tf', 'id', 'Klf4/profile_match',
       'Klf4/profile_match_p', 'Klf4/profile_counts', 'Klf4/profile_counts_p',
       'Klf4/profile_max', 'Klf4/profile_max_p', 'Nanog/profile_match',
       'Nanog/profile_match_p', 'Nanog/profile_counts',
       'Nanog/profile_counts_p', 'Nanog/profile_max', 'Nanog/profile_max_p',
       'Oct4/profile_match', 'Oct4/profile_match_p', 'Oct4/profile_counts',
       'Oct4/profile_counts_p', 'Oct4/profile_max', 'Oct4/profile_max_p',
       'Sox2/profile_match', 'Sox2/profile_match_p', 'Sox2/profile_counts',
       'Sox2/profile_counts_p', 'Sox2/profile_max', 'Sox2/profile_max_p'],
      dtype='object')

dfi_motifs_anno.query('tf=="Oct4-Sox2"').hvplot.scatter(x='Oct4/profile_counts_p', y='imp_weighted_p', alpha=0.1)

dfi_motifs_anno.query('tf=="Nanog"').hvplot.scatter(x='Nanog/profile_counts_p', y='imp_weighted_p', alpha=0.05, datashade=False)

Question: How do others contribute counts?¶

Features¶

Count matrix¶

from joblib import Parallel, delayed

def abs_min(x):
    return x[np.abs(x).argmin()]

def pairwise_distances_single(dfi_ex, dfi_motifs_anno_ex):
    out = []
    for i, core_motif in dfi_motifs_anno_ex.iterrows():
        #core_motif = dfi_motifs_anno_ex.iloc[i]
        # remove self 
        select = (dfi_ex.pattern == core_motif.pattern) & (dfi_ex.pattern_center == core_motif.pattern_center)
        assert select.sum() == 1
        dfi_ex = dfi_ex[~select]

        # relative distance to the pattern_center
        dfi_ex['pattern_center_rel'] = dfi_ex.pattern_center - core_motif.pattern_center

        # number of other examples
        df_scores = dfi_ex.groupby(['pattern', 'match_weighted_cat', 'imp_weighted_cat']).pattern_center_rel.agg([np.size, abs_min]).reset_index()
        df_scores.columns = [f'side/{c}' for c in df_scores.columns]
        df_scores = df_scores.assign(**core_motif.to_dict())
        out.append(df_scores)
    return pd.concat(out, axis=0)

def pariwise_distances(dfi, dfi_motifs_anno, n_jobs=8):
    #dfi_motifs_anno_tf = dfi_motifs_anno[dfi_motifs_anno.tf == tf]
    all_example_idx = dfi_motifs_anno.example_idx.unique()
    tf_task = tf
    out = Parallel(n_jobs=n_jobs)(delayed(pairwise_distances_single)(dfi[dfi.example_idx == example_idx], dfi_motifs_anno[dfi_motifs_anno.example_idx == example_idx])
                              for example_idx in tqdm(all_example_idx))
    return pd.concat(out, axis=0)

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df_dist = pariwise_distances(dfi, dfi_motifs_anno[dfi_motifs_anno.tf == task], n_jobs=20)


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  3%|▎         | 397/13872 [00:37<21:05, 10.65it/s]
  3%|▎         | 399/13872 [00:37<21:02, 10.67it/s]
  3%|▎         | 401/13872 [00:37<20:59, 10.69it/s]
  3%|▎         | 403/13872 [00:37<20:57, 10.71it/s]
  3%|▎         | 405/13872 [00:37<20:56, 10.72it/s]
  3%|▎         | 407/13872 [00:37<20:54, 10.73it/s]
  3%|▎         | 410/13872 [00:38<20:49, 10.77it/s]
  3%|▎         | 412/13872 [00:38<20:46, 10.79it/s]
  3%|▎         | 415/13872 [00:38<20:42, 10.83it/s]
  3%|▎         | 418/13872 [00:38<20:37, 10.87it/s]
  3%|▎         | 421/13872 [00:38<20:32, 10.91it/s]
  3%|▎         | 424/13872 [00:38<20:28, 10.94it/s]
  3%|▎         | 427/13872 [00:38<20:25, 10.97it/s]
  3%|▎         | 429/13872 [00:39<20:22, 10.99it/s]
  3%|▎         | 432/13872 [00:39<20:18, 11.03it/s]
  3%|▎         | 434/13872 [00:39<20:15, 11.05it/s]
  3%|▎         | 436/13872 [00:39<20:13, 11.07it/s]
  3%|▎         | 439/13872 [00:39<20:09, 11.11it/s]
  3%|▎         | 442/13872 [00:39<20:04, 11.15it/s]
  3%|▎         | 445/13872 [00:39<20:00, 11.18it/s]
  3%|▎         | 448/13872 [00:39<19:56, 11.22it/s]
  3%|▎         | 451/13872 [00:40<19:52, 11.25it/s]
  3%|▎         | 454/13872 [00:40<19:49, 11.28it/s]
  3%|▎         | 457/13872 [00:40<19:45, 11.32it/s]
  3%|▎         | 460/13872 [00:40<19:41, 11.36it/s]
  3%|▎         | 463/13872 [00:40<19:37, 11.39it/s]
  3%|▎         | 466/13872 [00:40<19:34, 11.42it/s]
  3%|▎         | 469/13872 [00:40<19:30, 11.45it/s]
  3%|▎         | 472/13872 [00:41<19:27, 11.48it/s]
  3%|▎         | 474/13872 [00:41<19:25, 11.50it/s]
  3%|▎         | 477/13872 [00:41<19:21, 11.53it/s]
  3%|▎         | 480/13872 [00:41<19:18, 11.56it/s]
  3%|▎         | 483/13872 [00:41<19:15, 11.59it/s]
  3%|▎         | 485/13872 [00:41<19:13, 11.60it/s]
  4%|▎         | 487/13872 [00:41<19:11, 11.62it/s]
  4%|▎         | 490/13872 [00:42<19:08, 11.66it/s]
  4%|▎         | 493/13872 [00:42<19:04, 11.69it/s]
  4%|▎         | 496/13872 [00:42<19:01, 11.72it/s]
  4%|▎         | 499/13872 [00:42<18:58, 11.74it/s]
  4%|▎         | 501/13872 [00:42<18:56, 11.76it/s]
  4%|▎         | 503/13872 [00:42<18:54, 11.78it/s]
  4%|▎         | 505/13872 [00:42<18:53, 11.79it/s]
  4%|▎         | 507/13872 [00:42<18:51, 11.81it/s]
  4%|▎         | 509/13872 [00:43<18:49, 11.83it/s]
  4%|▎         | 511/13872 [00:43<18:47, 11.85it/s]
  4%|▎         | 514/13872 [00:43<18:45, 11.87it/s]
  4%|▎         | 516/13872 [00:43<18:43, 11.89it/s]
  4%|▎         | 519/13872 [00:43<18:39, 11.92it/s]
  4%|▍         | 522/13872 [00:43<18:36, 11.95it/s]
  4%|▍         | 525/13872 [00:43<18:33, 11.98it/s]
  4%|▍         | 528/13872 [00:43<18:31, 12.01it/s]
  4%|▍         | 531/13872 [00:44<18:28, 12.04it/s]
  4%|▍         | 534/13872 [00:44<18:25, 12.07it/s]
  4%|▍         | 537/13872 [00:44<18:22, 12.09it/s]
  4%|▍         | 540/13872 [00:44<18:19, 12.12it/s]
  4%|▍         | 543/13872 [00:44<18:17, 12.15it/s]
  4%|▍         | 546/13872 [00:44<18:14, 12.17it/s]
  4%|▍         | 549/13872 [00:45<18:12, 12.20it/s]
  4%|▍         | 551/13872 [00:45<18:10, 12.21it/s]
  4%|▍         | 553/13872 [00:45<18:09, 12.23it/s]
  4%|▍         | 556/13872 [00:45<18:06, 12.26it/s]
  4%|▍         | 559/13872 [00:45<18:03, 12.29it/s]
  4%|▍         | 562/13872 [00:45<18:01, 12.31it/s]
  4%|▍         | 565/13872 [00:45<17:58, 12.33it/s]
  4%|▍         | 568/13872 [00:45<17:56, 12.35it/s]
  4%|▍         | 570/13872 [00:46<17:55, 12.37it/s]
  4%|▍         | 572/13872 [00:46<17:53, 12.39it/s]
  4%|▍         | 574/13872 [00:46<17:52, 12.40it/s]
  4%|▍         | 577/13872 [00:46<17:49, 12.43it/s]
  4%|▍         | 580/13872 [00:46<17:47, 12.45it/s]
  4%|▍         | 582/13872 [00:46<17:46, 12.47it/s]
  4%|▍         | 585/13872 [00:46<17:43, 12.49it/s]
  4%|▍         | 587/13872 [00:46<17:42, 12.50it/s]
  4%|▍         | 589/13872 [00:47<17:41, 12.52it/s]
  4%|▍         | 592/13872 [00:47<17:38, 12.55it/s]
  4%|▍         | 595/13872 [00:47<17:35, 12.57it/s]
  4%|▍         | 598/13872 [00:47<17:34, 12.59it/s]
  4%|▍         | 601/13872 [00:47<17:31, 12.62it/s]
  4%|▍         | 603/13872 [00:47<17:30, 12.63it/s]
  4%|▍         | 605/13872 [00:47<17:29, 12.64it/s]
  4%|▍         | 607/13872 [00:47<17:28, 12.65it/s]
  4%|▍         | 609/13872 [00:48<17:28, 12.65it/s]
  4%|▍         | 612/13872 [00:48<17:25, 12.68it/s]
  4%|▍         | 614/13872 [00:48<17:24, 12.70it/s]
  4%|▍         | 616/13872 [00:48<17:23, 12.71it/s]
  4%|▍         | 619/13872 [00:48<17:20, 12.73it/s]
  4%|▍         | 621/13872 [00:48<17:19, 12.75it/s]
  4%|▍         | 623/13872 [00:48<17:18, 12.76it/s]
  5%|▍         | 625/13872 [00:48<17:17, 12.77it/s]
  5%|▍         | 628/13872 [00:49<17:14, 12.80it/s]
  5%|▍         | 631/13872 [00:49<17:12, 12.82it/s]
  5%|▍         | 634/13872 [00:49<17:10, 12.85it/s]
  5%|▍         | 637/13872 [00:49<17:08, 12.87it/s]
  5%|▍         | 640/13872 [00:49<17:06, 12.89it/s]
  5%|▍         | 643/13872 [00:49<17:04, 12.92it/s]
  5%|▍         | 646/13872 [00:49<17:02, 12.94it/s]
  5%|▍         | 649/13872 [00:50<17:00, 12.96it/s]
  5%|▍         | 652/13872 [00:50<16:58, 12.98it/s]
  5%|▍         | 655/13872 [00:50<16:56, 13.00it/s]
  5%|▍         | 658/13872 [00:50<16:54, 13.02it/s]
  5%|▍         | 661/13872 [00:50<16:52, 13.05it/s]
  5%|▍         | 664/13872 [00:50<16:50, 13.07it/s]
  5%|▍         | 667/13872 [00:50<16:48, 13.09it/s]
  5%|▍         | 670/13872 [00:51<16:46, 13.11it/s]
  5%|▍         | 673/13872 [00:51<16:44, 13.14it/s]
  5%|▍         | 676/13872 [00:51<16:43, 13.15it/s]
  5%|▍         | 679/13872 [00:51<16:41, 13.17it/s]
  5%|▍         | 682/13872 [00:51<16:39, 13.19it/s]
  5%|▍         | 685/13872 [00:51<16:38, 13.21it/s]
  5%|▍         | 688/13872 [00:51<16:36, 13.24it/s]
  5%|▍         | 691/13872 [00:52<16:34, 13.26it/s]
  5%|▌         | 694/13872 [00:52<16:32, 13.28it/s]
  5%|▌         | 697/13872 [00:52<16:31, 13.29it/s]
  5%|▌         | 700/13872 [00:52<16:29, 13.31it/s]
  5%|▌         | 702/13872 [00:52<16:28, 13.32it/s]
  5%|▌         | 704/13872 [00:52<16:27, 13.33it/s]
  5%|▌         | 706/13872 [00:52<16:27, 13.34it/s]
  5%|▌         | 709/13872 [00:53<16:25, 13.36it/s]
  5%|▌         | 711/13872 [00:53<16:24, 13.37it/s]
  5%|▌         | 714/13872 [00:53<16:22, 13.39it/s]
  5%|▌         | 717/13872 [00:53<16:20, 13.41it/s]
  5%|▌         | 720/13872 [00:53<16:19, 13.43it/s]
  5%|▌         | 723/13872 [00:53<16:17, 13.45it/s]
  5%|▌         | 726/13872 [00:53<16:15, 13.48it/s]
  5%|▌         | 729/13872 [00:54<16:13, 13.50it/s]
  5%|▌         | 732/13872 [00:54<16:12, 13.51it/s]
  5%|▌         | 735/13872 [00:54<16:10, 13.53it/s]
  5%|▌         | 738/13872 [00:54<16:09, 13.55it/s]
  5%|▌         | 741/13872 [00:54<16:07, 13.57it/s]
  5%|▌         | 744/13872 [00:54<16:06, 13.58it/s]
  5%|▌         | 746/13872 [00:54<16:05, 13.59it/s]
  5%|▌         | 748/13872 [00:54<16:04, 13.60it/s]
  5%|▌         | 750/13872 [00:55<16:04, 13.61it/s]
  5%|▌         | 752/13872 [00:55<16:04, 13.60it/s]
  5%|▌         | 754/13872 [00:55<16:03, 13.61it/s]
  5%|▌         | 756/13872 [00:55<16:02, 13.62it/s]
  5%|▌         | 759/13872 [00:55<16:01, 13.64it/s]
  5%|▌         | 761/13872 [00:55<16:00, 13.65it/s]
  6%|▌         | 763/13872 [00:55<15:59, 13.66it/s]
  6%|▌         | 766/13872 [00:55<15:58, 13.68it/s]
  6%|▌         | 769/13872 [00:56<15:56, 13.70it/s]
  6%|▌         | 772/13872 [00:56<15:54, 13.72it/s]
  6%|▌         | 775/13872 [00:56<15:53, 13.74it/s]
  6%|▌         | 778/13872 [00:56<15:51, 13.76it/s]
  6%|▌         | 781/13872 [00:56<15:50, 13.77it/s]
  6%|▌         | 784/13872 [00:56<15:49, 13.79it/s]
  6%|▌         | 787/13872 [00:57<15:47, 13.81it/s]
  6%|▌         | 790/13872 [00:57<15:46, 13.83it/s]
  6%|▌         | 793/13872 [00:57<15:44, 13.84it/s]
  6%|▌         | 796/13872 [00:57<15:43, 13.86it/s]
  6%|▌         | 799/13872 [00:57<15:41, 13.88it/s]
  6%|▌         | 802/13872 [00:57<15:40, 13.90it/s]
  6%|▌         | 805/13872 [00:57<15:38, 13.92it/s]
  6%|▌         | 808/13872 [00:58<15:38, 13.93it/s]
  6%|▌         | 811/13872 [00:58<15:36, 13.94it/s]
  6%|▌         | 814/13872 [00:58<15:35, 13.96it/s]
  6%|▌         | 817/13872 [00:58<15:33, 13.98it/s]
  6%|▌         | 820/13872 [00:58<15:33, 13.99it/s]
  6%|▌         | 822/13872 [00:58<15:32, 13.99it/s]
  6%|▌         | 824/13872 [00:58<15:31, 14.00it/s]
  6%|▌         | 826/13872 [00:58<15:31, 14.01it/s]
  6%|▌         | 829/13872 [00:59<15:30, 14.02it/s]
  6%|▌         | 831/13872 [00:59<15:29, 14.03it/s]
  6%|▌         | 833/13872 [00:59<15:28, 14.04it/s]
  6%|▌         | 836/13872 [00:59<15:27, 14.06it/s]
  6%|▌         | 838/13872 [00:59<15:26, 14.07it/s]
  6%|▌         | 841/13872 [00:59<15:25, 14.08it/s]
  6%|▌         | 844/13872 [00:59<15:23, 14.10it/s]
  6%|▌         | 847/13872 [00:59<15:22, 14.13it/s]
  6%|▌         | 850/13872 [01:00<15:20, 14.15it/s]
  6%|▌         | 853/13872 [01:00<15:18, 14.17it/s]
  6%|▌         | 856/13872 [01:00<15:16, 14.20it/s]
  6%|▌         | 859/13872 [01:00<15:15, 14.22it/s]
  6%|▌         | 863/13872 [01:00<15:12, 14.26it/s]
  6%|▋         | 867/13872 [01:00<15:09, 14.29it/s]
  6%|▋         | 871/13872 [01:00<15:07, 14.33it/s]
  6%|▋         | 874/13872 [01:00<15:05, 14.35it/s]
  6%|▋         | 877/13872 [01:00<15:03, 14.38it/s]
  6%|▋         | 881/13872 [01:01<15:01, 14.41it/s]
  6%|▋         | 884/13872 [01:01<14:59, 14.44it/s]
  6%|▋         | 887/13872 [01:01<14:57, 14.46it/s]
  6%|▋         | 890/13872 [01:01<14:56, 14.49it/s]
  6%|▋         | 894/13872 [01:01<14:54, 14.52it/s]
  6%|▋         | 897/13872 [01:01<14:52, 14.54it/s]
  6%|▋         | 900/13872 [01:01<14:51, 14.56it/s]
  7%|▋         | 903/13872 [01:01<14:49, 14.58it/s]
  7%|▋         | 906/13872 [01:02<14:47, 14.60it/s]
  7%|▋         | 909/13872 [01:02<14:46, 14.63it/s]
  7%|▋         | 912/13872 [01:02<14:45, 14.64it/s]
  7%|▋         | 915/13872 [01:02<14:43, 14.66it/s]
  7%|▋         | 918/13872 [01:02<14:42, 14.69it/s]
  7%|▋         | 921/13872 [01:02<14:40, 14.71it/s]
  7%|▋         | 924/13872 [01:02<14:38, 14.73it/s]
  7%|▋         | 928/13872 [01:02<14:36, 14.77it/s]
  7%|▋         | 931/13872 [01:02<14:35, 14.79it/s]
  7%|▋         | 934/13872 [01:03<14:33, 14.81it/s]
  7%|▋         | 937/13872 [01:03<14:31, 14.83it/s]
  7%|▋         | 940/13872 [01:03<14:30, 14.85it/s]
  7%|▋         | 943/13872 [01:03<14:29, 14.87it/s]
  7%|▋         | 946/13872 [01:03<14:28, 14.89it/s]
  7%|▋         | 949/13872 [01:03<14:26, 14.91it/s]
  7%|▋         | 952/13872 [01:03<14:25, 14.93it/s]
  7%|▋         | 955/13872 [01:03<14:23, 14.95it/s]
  7%|▋         | 958/13872 [01:03<14:22, 14.97it/s]
  7%|▋         | 961/13872 [01:04<14:21, 15.00it/s]
  7%|▋         | 964/13872 [01:04<14:19, 15.02it/s]
  7%|▋         | 967/13872 [01:04<14:18, 15.04it/s]
  7%|▋         | 970/13872 [01:04<14:16, 15.06it/s]
  7%|▋         | 973/13872 [01:04<14:15, 15.08it/s]
  7%|▋         | 976/13872 [01:04<14:14, 15.10it/s]
  7%|▋         | 979/13872 [01:04<14:12, 15.12it/s]
  7%|▋         | 982/13872 [01:04<14:11, 15.13it/s]
  7%|▋         | 985/13872 [01:05<14:10, 15.15it/s]
  7%|▋         | 988/13872 [01:05<14:09, 15.17it/s]
  7%|▋         | 991/13872 [01:05<14:08, 15.18it/s]
  7%|▋         | 994/13872 [01:05<14:07, 15.20it/s]
  7%|▋         | 997/13872 [01:05<14:06, 15.20it/s]
  7%|▋         | 1000/13872 [01:05<14:05, 15.22it/s]
  7%|▋         | 1003/13872 [01:05<14:04, 15.24it/s]
  7%|▋         | 1006/13872 [01:05<14:03, 15.26it/s]
  7%|▋         | 1009/13872 [01:06<14:01, 15.28it/s]
  7%|▋         | 1012/13872 [01:06<14:00, 15.30it/s]
  7%|▋         | 1015/13872 [01:06<14:00, 15.30it/s]
  7%|▋         | 1018/13872 [01:06<13:59, 15.32it/s]
  7%|▋         | 1021/13872 [01:06<13:57, 15.34it/s]
  7%|▋         | 1024/13872 [01:06<13:56, 15.35it/s]
  7%|▋         | 1027/13872 [01:06<13:55, 15.38it/s]
  7%|▋         | 1030/13872 [01:06<13:54, 15.40it/s]
  7%|▋         | 1033/13872 [01:07<13:52, 15.42it/s]
  7%|▋         | 1036/13872 [01:07<13:51, 15.44it/s]
  7%|▋         | 1039/13872 [01:07<13:50, 15.46it/s]
  8%|▊         | 1042/13872 [01:07<13:48, 15.48it/s]
  8%|▊         | 1045/13872 [01:07<13:47, 15.49it/s]
  8%|▊         | 1048/13872 [01:07<13:46, 15.51it/s]
  8%|▊         | 1051/13872 [01:07<13:45, 15.53it/s]
  8%|▊         | 1054/13872 [01:07<13:44, 15.55it/s]
  8%|▊         | 1057/13872 [01:07<13:42, 15.57it/s]
  8%|▊         | 1060/13872 [01:07<13:41, 15.59it/s]
  8%|▊         | 1064/13872 [01:08<13:39, 15.62it/s]
  8%|▊         | 1067/13872 [01:08<13:38, 15.64it/s]
  8%|▊         | 1070/13872 [01:08<13:37, 15.66it/s]
  8%|▊         | 1073/13872 [01:08<13:36, 15.67it/s]
  8%|▊         | 1076/13872 [01:08<13:35, 15.69it/s]
  8%|▊         | 1079/13872 [01:08<13:34, 15.70it/s]
  8%|▊         | 1082/13872 [01:08<13:33, 15.72it/s]
  8%|▊         | 1085/13872 [01:08<13:32, 15.74it/s]
  8%|▊         | 1088/13872 [01:09<13:31, 15.75it/s]
  8%|▊         | 1091/13872 [01:09<13:31, 15.76it/s]
  8%|▊         | 1094/13872 [01:09<13:30, 15.77it/s]
  8%|▊         | 1097/13872 [01:09<13:29, 15.78it/s]
  8%|▊         | 1100/13872 [01:09<13:29, 15.79it/s]
  8%|▊         | 1103/13872 [01:09<13:28, 15.80it/s]
  8%|▊         | 1106/13872 [01:09<13:27, 15.81it/s]
  8%|▊         | 1109/13872 [01:10<13:26, 15.82it/s]
  8%|▊         | 1112/13872 [01:10<13:26, 15.83it/s]
  8%|▊         | 1115/13872 [01:10<13:25, 15.83it/s]
  8%|▊         | 1118/13872 [01:10<13:25, 15.84it/s]
  8%|▊         | 1120/13872 [01:10<13:24, 15.85it/s]
  8%|▊         | 1123/13872 [01:10<13:23, 15.86it/s]
  8%|▊         | 1126/13872 [01:10<13:22, 15.88it/s]
  8%|▊         | 1129/13872 [01:11<13:22, 15.88it/s]
  8%|▊         | 1132/13872 [01:11<13:22, 15.88it/s]
  8%|▊         | 1135/13872 [01:11<13:21, 15.90it/s]
  8%|▊         | 1138/13872 [01:11<13:20, 15.91it/s]
  8%|▊         | 1141/13872 [01:11<13:19, 15.92it/s]
  8%|▊         | 1144/13872 [01:11<13:18, 15.93it/s]
  8%|▊         | 1147/13872 [01:11<13:17, 15.95it/s]
  8%|▊         | 1150/13872 [01:12<13:16, 15.97it/s]
  8%|▊         | 1153/13872 [01:12<13:15, 15.98it/s]
  8%|▊         | 1156/13872 [01:12<13:14, 16.00it/s]
  8%|▊         | 1159/13872 [01:12<13:14, 16.01it/s]
  8%|▊         | 1162/13872 [01:12<13:13, 16.02it/s]
  8%|▊         | 1165/13872 [01:12<13:12, 16.04it/s]
  8%|▊         | 1168/13872 [01:12<13:11, 16.05it/s]
  8%|▊         | 1171/13872 [01:12<13:10, 16.07it/s]
  8%|▊         | 1174/13872 [01:12<13:09, 16.08it/s]
  8%|▊         | 1177/13872 [01:13<13:08, 16.10it/s]
  9%|▊         | 1180/13872 [01:13<13:07, 16.11it/s]
  9%|▊         | 1183/13872 [01:13<13:06, 16.13it/s]
  9%|▊         | 1186/13872 [01:13<13:05, 16.15it/s]
  9%|▊         | 1189/13872 [01:13<13:05, 16.15it/s]
  9%|▊         | 1192/13872 [01:13<13:04, 16.16it/s]
  9%|▊         | 1195/13872 [01:13<13:03, 16.18it/s]
  9%|▊         | 1198/13872 [01:13<13:02, 16.19it/s]
  9%|▊         | 1201/13872 [01:14<13:01, 16.21it/s]
  9%|▊         | 1204/13872 [01:14<13:00, 16.23it/s]
  9%|▊         | 1207/13872 [01:14<12:59, 16.24it/s]
  9%|▊         | 1210/13872 [01:14<12:58, 16.26it/s]
  9%|▊         | 1213/13872 [01:14<12:57, 16.27it/s]
  9%|▉         | 1216/13872 [01:14<12:57, 16.29it/s]
  9%|▉         | 1219/13872 [01:14<12:56, 16.30it/s]
  9%|▉         | 1222/13872 [01:14<12:55, 16.31it/s]
  9%|▉         | 1225/13872 [01:15<12:54, 16.33it/s]
  9%|▉         | 1228/13872 [01:15<12:53, 16.34it/s]
  9%|▉         | 1231/13872 [01:15<12:52, 16.35it/s]
  9%|▉         | 1234/13872 [01:15<12:51, 16.37it/s]
  9%|▉         | 1237/13872 [01:15<12:51, 16.39it/s]
  9%|▉         | 1240/13872 [01:15<12:50, 16.40it/s]
  9%|▉         | 1243/13872 [01:15<12:49, 16.41it/s]
  9%|▉         | 1246/13872 [01:15<12:48, 16.43it/s]
  9%|▉         | 1249/13872 [01:15<12:47, 16.44it/s]
  9%|▉         | 1252/13872 [01:16<12:46, 16.46it/s]
  9%|▉         | 1255/13872 [01:16<12:45, 16.47it/s]
  9%|▉         | 1258/13872 [01:16<12:45, 16.49it/s]
  9%|▉         | 1261/13872 [01:16<12:47, 16.44it/s]
  9%|▉         | 1264/13872 [01:16<12:46, 16.45it/s]
  9%|▉         | 1267/13872 [01:16<12:45, 16.46it/s]
  9%|▉         | 1270/13872 [01:17<12:44, 16.47it/s]
  9%|▉         | 1273/13872 [01:17<12:44, 16.49it/s]
  9%|▉         | 1276/13872 [01:17<12:43, 16.50it/s]
  9%|▉         | 1279/13872 [01:17<12:42, 16.52it/s]
  9%|▉         | 1282/13872 [01:17<12:41, 16.53it/s]
  9%|▉         | 1285/13872 [01:17<12:40, 16.55it/s]
  9%|▉         | 1288/13872 [01:17<12:40, 16.56it/s]
  9%|▉         | 1291/13872 [01:17<12:39, 16.57it/s]
  9%|▉         | 1294/13872 [01:18<12:38, 16.59it/s]
  9%|▉         | 1297/13872 [01:18<12:37, 16.60it/s]
  9%|▉         | 1300/13872 [01:18<12:36, 16.61it/s]
  9%|▉         | 1303/13872 [01:18<12:35, 16.63it/s]
  9%|▉         | 1306/13872 [01:18<12:35, 16.64it/s]
  9%|▉         | 1309/13872 [01:18<12:34, 16.65it/s]
  9%|▉         | 1312/13872 [01:18<12:33, 16.67it/s]
  9%|▉         | 1315/13872 [01:18<12:32, 16.68it/s]
 10%|▉         | 1318/13872 [01:18<12:32, 16.69it/s]
 10%|▉         | 1321/13872 [01:19<12:31, 16.71it/s]
 10%|▉         | 1324/13872 [01:19<12:30, 16.71it/s]
 10%|▉         | 1327/13872 [01:19<12:30, 16.72it/s]
 10%|▉         | 1331/13872 [01:19<12:28, 16.75it/s]
 10%|▉         | 1334/13872 [01:19<12:28, 16.76it/s]
 10%|▉         | 1337/13872 [01:19<12:27, 16.77it/s]
 10%|▉         | 1341/13872 [01:19<12:26, 16.79it/s]
 10%|▉         | 1344/13872 [01:19<12:25, 16.81it/s]
 10%|▉         | 1347/13872 [01:20<12:24, 16.82it/s]
 10%|▉         | 1350/13872 [01:20<12:23, 16.84it/s]
 10%|▉         | 1353/13872 [01:20<12:23, 16.85it/s]
 10%|▉         | 1356/13872 [01:20<12:22, 16.87it/s]
 10%|▉         | 1359/13872 [01:20<12:21, 16.88it/s]
 10%|▉         | 1362/13872 [01:20<12:20, 16.89it/s]
 10%|▉         | 1365/13872 [01:20<12:19, 16.90it/s]
 10%|▉         | 1368/13872 [01:20<12:19, 16.92it/s]
 10%|▉         | 1371/13872 [01:20<12:18, 16.93it/s]
 10%|▉         | 1374/13872 [01:21<12:17, 16.95it/s]
 10%|▉         | 1377/13872 [01:21<12:16, 16.96it/s]
 10%|▉         | 1380/13872 [01:21<12:16, 16.97it/s]
 10%|▉         | 1383/13872 [01:21<12:15, 16.98it/s]
 10%|▉         | 1386/13872 [01:21<12:14, 16.99it/s]
 10%|█         | 1389/13872 [01:21<12:14, 17.01it/s]
 10%|█         | 1392/13872 [01:21<12:13, 17.02it/s]
 10%|█         | 1395/13872 [01:21<12:12, 17.03it/s]
 10%|█         | 1398/13872 [01:21<12:11, 17.05it/s]
 10%|█         | 1401/13872 [01:22<12:10, 17.06it/s]
 10%|█         | 1405/13872 [01:22<12:09, 17.08it/s]
 10%|█         | 1408/13872 [01:22<12:09, 17.09it/s]
 10%|█         | 1411/13872 [01:22<12:08, 17.10it/s]
 10%|█         | 1414/13872 [01:22<12:07, 17.11it/s]
 10%|█         | 1417/13872 [01:22<12:07, 17.13it/s]
 10%|█         | 1420/13872 [01:22<12:06, 17.14it/s]
 10%|█         | 1423/13872 [01:22<12:05, 17.15it/s]
 10%|█         | 1426/13872 [01:23<12:05, 17.16it/s]
 10%|█         | 1429/13872 [01:23<12:04, 17.18it/s]
 10%|█         | 1432/13872 [01:23<12:03, 17.19it/s]
 10%|█         | 1435/13872 [01:23<12:03, 17.20it/s]
 10%|█         | 1438/13872 [01:23<12:02, 17.21it/s]
 10%|█         | 1441/13872 [01:23<12:02, 17.21it/s]
 10%|█         | 1444/13872 [01:23<12:01, 17.23it/s]
 10%|█         | 1447/13872 [01:23<12:00, 17.24it/s]
 10%|█         | 1450/13872 [01:24<11:59, 17.26it/s]
 10%|█         | 1453/13872 [01:24<11:59, 17.27it/s]
 10%|█         | 1456/13872 [01:24<11:58, 17.28it/s]
 11%|█         | 1459/13872 [01:24<11:57, 17.30it/s]
 11%|█         | 1462/13872 [01:24<11:56, 17.31it/s]
 11%|█         | 1465/13872 [01:24<11:56, 17.32it/s]
 11%|█         | 1468/13872 [01:24<11:55, 17.34it/s]
 11%|█         | 1471/13872 [01:24<11:55, 17.34it/s]
 11%|█         | 1474/13872 [01:24<11:54, 17.35it/s]
 11%|█         | 1477/13872 [01:25<11:53, 17.36it/s]
 11%|█         | 1480/13872 [01:25<11:53, 17.38it/s]
 11%|█         | 1483/13872 [01:25<11:52, 17.39it/s]
 11%|█         | 1486/13872 [01:25<11:51, 17.40it/s]
 11%|█         | 1489/13872 [01:25<11:50, 17.42it/s]
 11%|█         | 1492/13872 [01:25<11:50, 17.43it/s]
 11%|█         | 1495/13872 [01:25<11:49, 17.45it/s]
 11%|█         | 1498/13872 [01:25<11:48, 17.46it/s]
 11%|█         | 1501/13872 [01:25<11:47, 17.47it/s]
 11%|█         | 1505/13872 [01:26<11:47, 17.49it/s]
 11%|█         | 1508/13872 [01:26<11:46, 17.50it/s]
 11%|█         | 1511/13872 [01:26<11:46, 17.50it/s]
 11%|█         | 1514/13872 [01:26<11:45, 17.51it/s]
 11%|█         | 1517/13872 [01:26<11:45, 17.51it/s]
 11%|█         | 1520/13872 [01:26<11:45, 17.52it/s]
 11%|█         | 1523/13872 [01:26<11:44, 17.52it/s]
 11%|█         | 1526/13872 [01:27<11:44, 17.53it/s]
 11%|█         | 1529/13872 [01:27<11:44, 17.53it/s]
 11%|█         | 1532/13872 [01:27<11:43, 17.54it/s]
 11%|█         | 1535/13872 [01:27<11:43, 17.54it/s]
 11%|█         | 1538/13872 [01:27<11:42, 17.55it/s]
 11%|█         | 1541/13872 [01:27<11:42, 17.56it/s]
 11%|█         | 1544/13872 [01:27<11:41, 17.57it/s]
 11%|█         | 1547/13872 [01:28<11:41, 17.57it/s]
 11%|█         | 1550/13872 [01:28<11:40, 17.58it/s]
 11%|█         | 1553/13872 [01:28<11:40, 17.59it/s]
 11%|█         | 1556/13872 [01:28<11:39, 17.60it/s]
 11%|█         | 1559/13872 [01:28<11:39, 17.61it/s]
 11%|█▏        | 1562/13872 [01:28<11:38, 17.62it/s]
 11%|█▏        | 1565/13872 [01:28<11:38, 17.63it/s]
 11%|█▏        | 1568/13872 [01:28<11:37, 17.63it/s]
 11%|█▏        | 1571/13872 [01:29<11:37, 17.64it/s]
 11%|█▏        | 1574/13872 [01:29<11:36, 17.65it/s]
 11%|█▏        | 1577/13872 [01:29<11:36, 17.65it/s]
 11%|█▏        | 1580/13872 [01:29<11:36, 17.66it/s]
 11%|█▏        | 1583/13872 [01:29<11:35, 17.67it/s]
 11%|█▏        | 1586/13872 [01:29<11:35, 17.67it/s]
 11%|█▏        | 1589/13872 [01:29<11:35, 17.67it/s]
 11%|█▏        | 1592/13872 [01:30<11:34, 17.68it/s]
 11%|█▏        | 1595/13872 [01:30<11:34, 17.68it/s]
 12%|█▏        | 1598/13872 [01:30<11:33, 17.69it/s]
 12%|█▏        | 1601/13872 [01:30<11:33, 17.70it/s]
 12%|█▏        | 1604/13872 [01:30<11:32, 17.71it/s]
 12%|█▏        | 1607/13872 [01:30<11:32, 17.72it/s]
 12%|█▏        | 1610/13872 [01:30<11:31, 17.73it/s]
 12%|█▏        | 1613/13872 [01:30<11:30, 17.74it/s]
 12%|█▏        | 1616/13872 [01:31<11:30, 17.75it/s]
 12%|█▏        | 1619/13872 [01:31<11:29, 17.76it/s]
 12%|█▏        | 1622/13872 [01:31<11:29, 17.77it/s]
 12%|█▏        | 1625/13872 [01:31<11:28, 17.79it/s]
 12%|█▏        | 1629/13872 [01:31<11:27, 17.80it/s]
 12%|█▏        | 1632/13872 [01:31<11:27, 17.81it/s]
 12%|█▏        | 1635/13872 [01:31<11:26, 17.82it/s]
 12%|█▏        | 1638/13872 [01:31<11:25, 17.84it/s]
 12%|█▏        | 1642/13872 [01:31<11:24, 17.85it/s]
 12%|█▏        | 1645/13872 [01:32<11:24, 17.86it/s]
 12%|█▏        | 1648/13872 [01:32<11:23, 17.88it/s]
 12%|█▏        | 1651/13872 [01:32<11:23, 17.89it/s]
 12%|█▏        | 1654/13872 [01:32<11:22, 17.90it/s]
 12%|█▏        | 1657/13872 [01:32<11:21, 17.91it/s]
 12%|█▏        | 1660/13872 [01:32<11:21, 17.92it/s]
 12%|█▏        | 1663/13872 [01:32<11:20, 17.93it/s]
 12%|█▏        | 1666/13872 [01:32<11:20, 17.94it/s]
 12%|█▏        | 1669/13872 [01:32<11:19, 17.95it/s]
 12%|█▏        | 1673/13872 [01:33<11:18, 17.97it/s]
 12%|█▏        | 1676/13872 [01:33<11:18, 17.98it/s]
 12%|█▏        | 1679/13872 [01:33<11:17, 17.99it/s]
 12%|█▏        | 1683/13872 [01:33<11:16, 18.01it/s]
 12%|█▏        | 1686/13872 [01:33<11:16, 18.01it/s]
 12%|█▏        | 1689/13872 [01:33<11:16, 18.02it/s]
 12%|█▏        | 1693/13872 [01:33<11:15, 18.04it/s]
 12%|█▏        | 1696/13872 [01:33<11:14, 18.05it/s]
 12%|█▏        | 1699/13872 [01:34<11:14, 18.06it/s]
 12%|█▏        | 1702/13872 [01:34<11:13, 18.07it/s]
 12%|█▏        | 1705/13872 [01:34<11:13, 18.07it/s]
 12%|█▏        | 1708/13872 [01:34<11:12, 18.09it/s]
 12%|█▏        | 1711/13872 [01:34<11:12, 18.10it/s]
 12%|█▏        | 1714/13872 [01:34<11:11, 18.10it/s]
 12%|█▏        | 1717/13872 [01:34<11:11, 18.11it/s]
 12%|█▏        | 1720/13872 [01:34<11:10, 18.12it/s]
 12%|█▏        | 1723/13872 [01:35<11:10, 18.13it/s]
 12%|█▏        | 1726/13872 [01:35<11:09, 18.14it/s]
 12%|█▏        | 1729/13872 [01:35<11:09, 18.15it/s]
 12%|█▏        | 1732/13872 [01:35<11:08, 18.16it/s]
 13%|█▎        | 1735/13872 [01:35<11:07, 18.17it/s]
 13%|█▎        | 1738/13872 [01:35<11:07, 18.18it/s]
 13%|█▎        | 1741/13872 [01:35<11:06, 18.19it/s]
 13%|█▎        | 1744/13872 [01:35<11:06, 18.20it/s]
 13%|█▎        | 1747/13872 [01:35<11:05, 18.21it/s]
 13%|█▎        | 1750/13872 [01:36<11:05, 18.21it/s]
 13%|█▎        | 1753/13872 [01:36<11:04, 18.23it/s]
 13%|█▎        | 1756/13872 [01:36<11:04, 18.23it/s]
 13%|█▎        | 1759/13872 [01:36<11:04, 18.24it/s]
 13%|█▎        | 1762/13872 [01:36<11:03, 18.25it/s]
 13%|█▎        | 1765/13872 [01:36<11:03, 18.26it/s]
 13%|█▎        | 1768/13872 [01:36<11:02, 18.26it/s]
 13%|█▎        | 1771/13872 [01:36<11:02, 18.27it/s]
 13%|█▎        | 1774/13872 [01:37<11:01, 18.28it/s]
 13%|█▎        | 1777/13872 [01:37<11:01, 18.28it/s]
 13%|█▎        | 1780/13872 [01:37<11:00, 18.29it/s]
 13%|█▎        | 1783/13872 [01:37<11:00, 18.30it/s]
 13%|█▎        | 1786/13872 [01:37<11:00, 18.31it/s]
 13%|█▎        | 1789/13872 [01:37<10:59, 18.32it/s]
 13%|█▎        | 1792/13872 [01:37<10:59, 18.32it/s]
 13%|█▎        | 1795/13872 [01:37<10:58, 18.33it/s]
 13%|█▎        | 1798/13872 [01:38<10:58, 18.34it/s]
 13%|█▎        | 1801/13872 [01:38<10:57, 18.35it/s]
 13%|█▎        | 1804/13872 [01:38<10:57, 18.36it/s]
 13%|█▎        | 1807/13872 [01:38<10:56, 18.37it/s]
 13%|█▎        | 1810/13872 [01:38<10:56, 18.38it/s]
 13%|█▎        | 1813/13872 [01:38<10:55, 18.39it/s]
 13%|█▎        | 1816/13872 [01:38<10:55, 18.40it/s]
 13%|█▎        | 1819/13872 [01:38<10:54, 18.41it/s]
 13%|█▎        | 1822/13872 [01:38<10:54, 18.42it/s]
 13%|█▎        | 1825/13872 [01:38<10:53, 18.44it/s]
 13%|█▎        | 1828/13872 [01:39<10:52, 18.45it/s]
 13%|█▎        | 1831/13872 [01:39<10:52, 18.45it/s]
 13%|█▎        | 1834/13872 [01:39<10:52, 18.46it/s]
 13%|█▎        | 1837/13872 [01:39<10:51, 18.47it/s]
 13%|█▎        | 1840/13872 [01:39<10:51, 18.48it/s]
 13%|█▎        | 1843/13872 [01:39<10:50, 18.48it/s]
 13%|█▎        | 1846/13872 [01:39<10:50, 18.49it/s]
 13%|█▎        | 1849/13872 [01:39<10:50, 18.50it/s]
 13%|█▎        | 1852/13872 [01:40<10:49, 18.50it/s]
 13%|█▎        | 1855/13872 [01:40<10:49, 18.51it/s]
 13%|█▎        | 1858/13872 [01:40<10:48, 18.52it/s]
 13%|█▎        | 1861/13872 [01:40<10:48, 18.53it/s]
 13%|█▎        | 1864/13872 [01:40<10:47, 18.54it/s]
 13%|█▎        | 1867/13872 [01:40<10:47, 18.55it/s]
 13%|█▎        | 1870/13872 [01:40<10:46, 18.56it/s]
 14%|█▎        | 1873/13872 [01:40<10:46, 18.57it/s]
 14%|█▎        | 1876/13872 [01:40<10:45, 18.58it/s]
 14%|█▎        | 1880/13872 [01:41<10:44, 18.60it/s]
 14%|█▎        | 1883/13872 [01:41<10:44, 18.61it/s]
 14%|█▎        | 1887/13872 [01:41<10:43, 18.62it/s]
 14%|█▎        | 1890/13872 [01:41<10:43, 18.63it/s]
 14%|█▎        | 1893/13872 [01:41<10:42, 18.64it/s]
 14%|█▎        | 1896/13872 [01:41<10:42, 18.65it/s]
 14%|█▎        | 1900/13872 [01:41<10:41, 18.66it/s]
 14%|█▎        | 1903/13872 [01:41<10:40, 18.67it/s]
 14%|█▎        | 1906/13872 [01:42<10:40, 18.68it/s]
 14%|█▍        | 1909/13872 [01:42<10:40, 18.69it/s]
 14%|█▍        | 1912/13872 [01:42<10:39, 18.70it/s]
 14%|█▍        | 1915/13872 [01:42<10:39, 18.71it/s]
 14%|█▍        | 1918/13872 [01:42<10:38, 18.72it/s]
 14%|█▍        | 1921/13872 [01:42<10:38, 18.72it/s]
 14%|█▍        | 1924/13872 [01:42<10:38, 18.72it/s]
 14%|█▍        | 1927/13872 [01:42<10:37, 18.72it/s]
 14%|█▍        | 1930/13872 [01:43<10:37, 18.73it/s]
 14%|█▍        | 1933/13872 [01:43<10:37, 18.74it/s]
 14%|█▍        | 1936/13872 [01:43<10:36, 18.75it/s]
 14%|█▍        | 1939/13872 [01:43<10:36, 18.75it/s]
 14%|█▍        | 1942/13872 [01:43<10:36, 18.75it/s]
 14%|█▍        | 1945/13872 [01:43<10:36, 18.74it/s]
 14%|█▍        | 1948/13872 [01:43<10:35, 18.75it/s]
 14%|█▍        | 1951/13872 [01:43<10:35, 18.76it/s]
 14%|█▍        | 1954/13872 [01:44<10:34, 18.77it/s]
 14%|█▍        | 1957/13872 [01:44<10:34, 18.77it/s]
 14%|█▍        | 1960/13872 [01:44<10:34, 18.78it/s]
 14%|█▍        | 1963/13872 [01:44<10:33, 18.79it/s]
 14%|█▍        | 1966/13872 [01:44<10:33, 18.80it/s]
 14%|█▍        | 1969/13872 [01:44<10:32, 18.81it/s]
 14%|█▍        | 1972/13872 [01:44<10:32, 18.82it/s]
 14%|█▍        | 1975/13872 [01:44<10:32, 18.82it/s]
 14%|█▍        | 1978/13872 [01:45<10:31, 18.82it/s]
 14%|█▍        | 1981/13872 [01:45<10:31, 18.83it/s]
 14%|█▍        | 1984/13872 [01:45<10:31, 18.83it/s]
 14%|█▍        | 1987/13872 [01:45<10:31, 18.83it/s]
 14%|█▍        | 1990/13872 [01:45<10:30, 18.84it/s]
 14%|█▍        | 1993/13872 [01:45<10:30, 18.84it/s]
 14%|█▍        | 1996/13872 [01:45<10:30, 18.84it/s]
 14%|█▍        | 1999/13872 [01:46<10:30, 18.84it/s]
 14%|█▍        | 2002/13872 [01:46<10:29, 18.84it/s]
 14%|█▍        | 2005/13872 [01:46<10:29, 18.84it/s]
 14%|█▍        | 2007/13872 [01:46<10:29, 18.84it/s]
 14%|█▍        | 2009/13872 [01:46<10:29, 18.84it/s]
 14%|█▍        | 2011/13872 [01:46<10:29, 18.84it/s]
 15%|█▍        | 2013/13872 [01:46<10:29, 18.84it/s]
 15%|█▍        | 2016/13872 [01:46<10:29, 18.85it/s]
 15%|█▍        | 2019/13872 [01:47<10:28, 18.85it/s]
 15%|█▍        | 2022/13872 [01:47<10:28, 18.86it/s]
 15%|█▍        | 2025/13872 [01:47<10:28, 18.86it/s]
 15%|█▍        | 2028/13872 [01:47<10:27, 18.87it/s]
 15%|█▍        | 2031/13872 [01:47<10:27, 18.87it/s]
 15%|█▍        | 2034/13872 [01:47<10:27, 18.86it/s]
 15%|█▍        | 2037/13872 [01:47<10:27, 18.87it/s]
 15%|█▍        | 2040/13872 [01:48<10:27, 18.87it/s]
 15%|█▍        | 2042/13872 [01:48<10:27, 18.87it/s]
 15%|█▍        | 2044/13872 [01:48<10:27, 18.86it/s]
 15%|█▍        | 2047/13872 [01:48<10:26, 18.87it/s]
 15%|█▍        | 2050/13872 [01:48<10:26, 18.87it/s]
 15%|█▍        | 2053/13872 [01:48<10:26, 18.88it/s]
 15%|█▍        | 2056/13872 [01:48<10:25, 18.88it/s]
 15%|█▍        | 2059/13872 [01:49<10:25, 18.89it/s]
 15%|█▍        | 2062/13872 [01:49<10:25, 18.89it/s]
 15%|█▍        | 2065/13872 [01:49<10:24, 18.90it/s]
 15%|█▍        | 2068/13872 [01:49<10:24, 18.91it/s]
 15%|█▍        | 2071/13872 [01:49<10:23, 18.92it/s]
 15%|█▍        | 2074/13872 [01:49<10:23, 18.92it/s]
 15%|█▍        | 2077/13872 [01:49<10:23, 18.93it/s]
 15%|█▍        | 2080/13872 [01:49<10:22, 18.93it/s]
 15%|█▌        | 2083/13872 [01:49<10:22, 18.94it/s]
 15%|█▌        | 2086/13872 [01:50<10:22, 18.95it/s]
 15%|█▌        | 2089/13872 [01:50<10:21, 18.95it/s]
 15%|█▌        | 2092/13872 [01:50<10:21, 18.96it/s]
 15%|█▌        | 2095/13872 [01:50<10:21, 18.96it/s]
 15%|█▌        | 2098/13872 [01:50<10:20, 18.97it/s]
 15%|█▌        | 2101/13872 [01:50<10:20, 18.98it/s]
 15%|█▌        | 2104/13872 [01:50<10:19, 18.98it/s]
 15%|█▌        | 2107/13872 [01:50<10:19, 18.99it/s]
 15%|█▌        | 2110/13872 [01:51<10:19, 18.99it/s]
 15%|█▌        | 2113/13872 [01:51<10:19, 18.99it/s]
 15%|█▌        | 2116/13872 [01:51<10:19, 18.99it/s]
 15%|█▌        | 2119/13872 [01:51<10:18, 19.00it/s]
 15%|█▌        | 2122/13872 [01:51<10:18, 19.00it/s]
 15%|█▌        | 2125/13872 [01:51<10:17, 19.01it/s]
 15%|█▌        | 2128/13872 [01:51<10:17, 19.01it/s]
 15%|█▌        | 2131/13872 [01:52<10:17, 19.02it/s]
 15%|█▌        | 2134/13872 [01:52<10:16, 19.03it/s]
 15%|█▌        | 2137/13872 [01:52<10:16, 19.03it/s]
 15%|█▌        | 2140/13872 [01:52<10:16, 19.04it/s]
 15%|█▌        | 2143/13872 [01:52<10:15, 19.04it/s]
 15%|█▌        | 2146/13872 [01:52<10:15, 19.05it/s]
 15%|█▌        | 2149/13872 [01:52<10:15, 19.05it/s]
 16%|█▌        | 2152/13872 [01:52<10:15, 19.06it/s]
 16%|█▌        | 2155/13872 [01:53<10:14, 19.06it/s]
 16%|█▌        | 2158/13872 [01:53<10:14, 19.07it/s]
 16%|█▌        | 2161/13872 [01:53<10:13, 19.08it/s]
 16%|█▌        | 2164/13872 [01:53<10:13, 19.08it/s]
 16%|█▌        | 2167/13872 [01:53<10:13, 19.09it/s]
 16%|█▌        | 2170/13872 [01:53<10:12, 19.10it/s]
 16%|█▌        | 2173/13872 [01:53<10:12, 19.10it/s]
 16%|█▌        | 2176/13872 [01:53<10:11, 19.11it/s]
 16%|█▌        | 2179/13872 [01:53<10:11, 19.12it/s]
 16%|█▌        | 2182/13872 [01:54<10:11, 19.12it/s]
 16%|█▌        | 2185/13872 [01:54<10:10, 19.13it/s]
 16%|█▌        | 2188/13872 [01:54<10:10, 19.13it/s]
 16%|█▌        | 2191/13872 [01:54<10:10, 19.12it/s]
 16%|█▌        | 2194/13872 [01:54<10:10, 19.12it/s]
 16%|█▌        | 2197/13872 [01:54<10:10, 19.12it/s]
 16%|█▌        | 2200/13872 [01:55<10:10, 19.12it/s]
 16%|█▌        | 2202/13872 [01:55<10:10, 19.12it/s]
 16%|█▌        | 2204/13872 [01:55<10:10, 19.12it/s]
 16%|█▌        | 2206/13872 [01:55<10:10, 19.12it/s]
 16%|█▌        | 2209/13872 [01:55<10:09, 19.12it/s]
 16%|█▌        | 2212/13872 [01:55<10:09, 19.13it/s]
 16%|█▌        | 2215/13872 [01:55<10:09, 19.13it/s]
 16%|█▌        | 2218/13872 [01:55<10:08, 19.14it/s]
 16%|█▌        | 2221/13872 [01:56<10:08, 19.14it/s]
 16%|█▌        | 2224/13872 [01:56<10:08, 19.15it/s]
 16%|█▌        | 2227/13872 [01:56<10:08, 19.15it/s]
 16%|█▌        | 2230/13872 [01:56<10:07, 19.16it/s]
 16%|█▌        | 2233/13872 [01:56<10:07, 19.17it/s]
 16%|█▌        | 2236/13872 [01:56<10:06, 19.18it/s]
 16%|█▌        | 2239/13872 [01:56<10:06, 19.18it/s]
 16%|█▌        | 2242/13872 [01:56<10:06, 19.18it/s]
 16%|█▌        | 2245/13872 [01:57<10:06, 19.18it/s]
 16%|█▌        | 2248/13872 [01:57<10:05, 19.19it/s]
 16%|█▌        | 2251/13872 [01:57<10:05, 19.19it/s]
 16%|█▌        | 2254/13872 [01:57<10:05, 19.20it/s]
 16%|█▋        | 2257/13872 [01:57<10:05, 19.20it/s]
 16%|█▋        | 2260/13872 [01:57<10:04, 19.20it/s]
 16%|█▋        | 2263/13872 [01:57<10:04, 19.20it/s]
 16%|█▋        | 2266/13872 [01:58<10:04, 19.20it/s]
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 16%|█▋        | 2272/13872 [01:58<10:03, 19.21it/s]
 16%|█▋        | 2275/13872 [01:58<10:03, 19.21it/s]
 16%|█▋        | 2278/13872 [01:58<10:03, 19.21it/s]
 16%|█▋        | 2281/13872 [01:58<10:03, 19.21it/s]
 16%|█▋        | 2284/13872 [01:58<10:03, 19.21it/s]
 16%|█▋        | 2287/13872 [01:59<10:02, 19.22it/s]
 17%|█▋        | 2290/13872 [01:59<10:02, 19.22it/s]
 17%|█▋        | 2293/13872 [01:59<10:02, 19.21it/s]
 17%|█▋        | 2295/13872 [01:59<10:02, 19.21it/s]
 17%|█▋        | 2297/13872 [01:59<10:02, 19.21it/s]
 17%|█▋        | 2299/13872 [01:59<10:02, 19.21it/s]
 17%|█▋        | 2302/13872 [01:59<10:02, 19.21it/s]
 17%|█▋        | 2304/13872 [01:59<10:02, 19.21it/s]
 17%|█▋        | 2307/13872 [02:00<10:01, 19.21it/s]
 17%|█▋        | 2310/13872 [02:00<10:01, 19.22it/s]
 17%|█▋        | 2313/13872 [02:00<10:01, 19.23it/s]
 17%|█▋        | 2316/13872 [02:00<10:01, 19.23it/s]
 17%|█▋        | 2319/13872 [02:00<10:00, 19.23it/s]
 17%|█▋        | 2322/13872 [02:00<10:00, 19.24it/s]
 17%|█▋        | 2325/13872 [02:00<10:00, 19.24it/s]
 17%|█▋        | 2328/13872 [02:00<09:59, 19.25it/s]
 17%|█▋        | 2331/13872 [02:01<09:59, 19.26it/s]
 17%|█▋        | 2334/13872 [02:01<09:58, 19.27it/s]
 17%|█▋        | 2337/13872 [02:01<09:58, 19.27it/s]
 17%|█▋        | 2340/13872 [02:01<09:58, 19.27it/s]
 17%|█▋        | 2343/13872 [02:01<09:57, 19.28it/s]
 17%|█▋        | 2346/13872 [02:01<09:57, 19.29it/s]
 17%|█▋        | 2349/13872 [02:01<09:57, 19.29it/s]
 17%|█▋        | 2352/13872 [02:01<09:57, 19.30it/s]
 17%|█▋        | 2355/13872 [02:02<09:56, 19.30it/s]
 17%|█▋        | 2358/13872 [02:02<09:56, 19.30it/s]
 17%|█▋        | 2361/13872 [02:02<09:56, 19.30it/s]
 17%|█▋        | 2364/13872 [02:02<09:56, 19.31it/s]
 17%|█▋        | 2367/13872 [02:02<09:55, 19.31it/s]
 17%|█▋        | 2370/13872 [02:02<09:55, 19.31it/s]
 17%|█▋        | 2373/13872 [02:02<09:55, 19.31it/s]
 17%|█▋        | 2376/13872 [02:02<09:55, 19.32it/s]
 17%|█▋        | 2379/13872 [02:03<09:54, 19.32it/s]
 17%|█▋        | 2382/13872 [02:03<09:54, 19.33it/s]
 17%|█▋        | 2385/13872 [02:03<09:54, 19.34it/s]
 17%|█▋        | 2388/13872 [02:03<09:53, 19.34it/s]
 17%|█▋        | 2391/13872 [02:03<09:53, 19.35it/s]
 17%|█▋        | 2394/13872 [02:03<09:53, 19.36it/s]
 17%|█▋        | 2397/13872 [02:03<09:52, 19.36it/s]
 17%|█▋        | 2401/13872 [02:03<09:52, 19.37it/s]
 17%|█▋        | 2404/13872 [02:04<09:52, 19.37it/s]
 17%|█▋        | 2407/13872 [02:04<09:52, 19.36it/s]
 17%|█▋        | 2410/13872 [02:04<09:52, 19.36it/s]
 17%|█▋        | 2412/13872 [02:04<09:52, 19.36it/s]
 17%|█▋        | 2414/13872 [02:04<09:52, 19.35it/s]
 17%|█▋        | 2416/13872 [02:04<09:52, 19.35it/s]
 17%|█▋        | 2419/13872 [02:05<09:51, 19.35it/s]
 17%|█▋        | 2422/13872 [02:05<09:51, 19.35it/s]
 17%|█▋        | 2424/13872 [02:05<09:51, 19.35it/s]
 17%|█▋        | 2426/13872 [02:05<09:51, 19.35it/s]
 18%|█▊        | 2428/13872 [02:05<09:51, 19.35it/s]
 18%|█▊        | 2430/13872 [02:05<09:51, 19.35it/s]
 18%|█▊        | 2432/13872 [02:05<09:51, 19.35it/s]
 18%|█▊        | 2434/13872 [02:05<09:51, 19.34it/s]
 18%|█▊        | 2436/13872 [02:05<09:51, 19.35it/s]
 18%|█▊        | 2439/13872 [02:06<09:50, 19.35it/s]
 18%|█▊        | 2442/13872 [02:06<09:50, 19.35it/s]
 18%|█▊        | 2445/13872 [02:06<09:50, 19.35it/s]
 18%|█▊        | 2448/13872 [02:06<09:50, 19.35it/s]
 18%|█▊        | 2451/13872 [02:06<09:49, 19.36it/s]
 18%|█▊        | 2454/13872 [02:06<09:49, 19.37it/s]
 18%|█▊        | 2457/13872 [02:06<09:49, 19.37it/s]
 18%|█▊        | 2460/13872 [02:06<09:48, 19.38it/s]
 18%|█▊        | 2463/13872 [02:07<09:48, 19.39it/s]
 18%|█▊        | 2466/13872 [02:07<09:48, 19.39it/s]
 18%|█▊        | 2469/13872 [02:07<09:47, 19.40it/s]
 18%|█▊        | 2472/13872 [02:07<09:47, 19.41it/s]
 18%|█▊        | 2476/13872 [02:07<09:46, 19.42it/s]
 18%|█▊        | 2479/13872 [02:07<09:46, 19.43it/s]
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 18%|█▊        | 2491/13872 [02:08<09:47, 19.38it/s]
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 18%|█▊        | 2497/13872 [02:08<09:46, 19.39it/s]
 18%|█▊        | 2500/13872 [02:08<09:46, 19.39it/s]
 18%|█▊        | 2503/13872 [02:09<09:46, 19.39it/s]
 18%|█▊        | 2506/13872 [02:09<09:46, 19.39it/s]
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 18%|█▊        | 2515/13872 [02:09<09:45, 19.40it/s]
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TODO¶

[ ] the effect of multiple instances on the final value
- stratify into different classes
[ ] effect of distance onto the effect

# compute the pairwise distances only for a single motif

df_dist.head()

df_dist.shape

(3819299, 67)

from plotnine import *

df_dist['side_pattern_short'] = df_dist['side/pattern'].map({k: shorten_pattern(k) for k in df_dist['side/pattern'].unique()})

df_dist['side_imp_weighted_cat'] = df_dist['side/imp_weighted_cat']

df_dist['n_motifs'] = pd.Categorical(np.minimum(df_dist['side/size'], 3))

df_dist

plotnine.options.figure_size = (20,10)
ggplot(aes(x='side/imp_weighted_cat', y='Nanog/profile_counts_p', color='n_motifs'), df_dist[df_dist['side/match_weighted_cat'] == 'high']) +  \
  facet_wrap("~side_pattern_short", ncol = 10, scales='free_x') + geom_boxplot() + \
  theme_bw()

<ggplot: (-9223363254613168440)>

list(motifs.values())

['m0_p0', 'm0_p1', 'm1_p2', 'm3_p0']

motifs

OrderedDict([('Oct4', 'm0_p0'),
             ('Sox2', 'm0_p1'),
             ('Nanog', 'm1_p2'),
             ('Klf4', 'm3_p0')])

df_dist_filt = df_dist[(df_dist['side/match_weighted_cat'] == 'high') & (df_dist['side_pattern_short'].isin(motifs.values()))]

df_dist_filt['side_tf'] = df_dist_filt.side_pattern_short.map({v:k for k,v in motifs.items()})

plotnine.options.figure_size = (20,3)
ggplot(aes(x='side/imp_weighted_cat', y='imp_weighted', color='n_motifs'), df_dist_filt) +  \
  facet_wrap("~side_tf", ncol = 10, scales='free_x') + geom_boxplot() + \
  theme_bw()

<ggplot: (-9223363254652952660)>

plotnine.options.figure_size = (20,3)
ggplot(aes(x='side/imp_weighted_cat', y='Nanog/profile_max_p', color='n_motifs'), df_dist[(df_dist['side/match_weighted_cat'] == 'high') & (df_dist['side_pattern_short'].isin(motifs.values()))]) +  \
  facet_wrap("~side_tf", ncol = 10, scales='free_x') + geom_boxplot() + \
  theme_bw()

---------------------------------------------------------------------------
PlotnineError                             Traceback (most recent call last)
~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
    700                 type_pprinters=self.type_printers,
    701                 deferred_pprinters=self.deferred_printers)
--> 702             printer.pretty(obj)
    703             printer.flush()
    704             return stream.getvalue()

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    398                         if cls is not object \
    399                                 and callable(cls.__dict__.get('__repr__')):
--> 400                             return _repr_pprint(obj, self, cycle)
    401 
    402             return _default_pprint(obj, self, cycle)

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
    693     """A pprint that just redirects to the normal repr function."""
    694     # Find newlines and replace them with p.break_()
--> 695     output = repr(obj)
    696     for idx,output_line in enumerate(output.splitlines()):
    697         if idx:

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/ggplot.py in __repr__(self)
     84         Print/show the plot
     85         """
---> 86         self.draw()
     87         plt.show()
     88         return '<ggplot: (%d)>' % self.__hash__()

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/ggplot.py in draw(self, return_ggplot)
    177         # assign a default theme
    178         self = deepcopy(self)
--> 179         self._build()
    180 
    181         # If no theme we use the default

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/ggplot.py in _build(self)
    270         # Initialise panels, add extra data for margins & missing
    271         # facetting variables, and add on a PANEL variable to data
--> 272         layout.setup(layers, self)
    273 
    274         # Compute aesthetics to produce data with generalised

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/facets/layout.py in setup(self, layers, plot)
     57         # Generate panel layout
     58         data = self.facet.setup_data(data)
---> 59         self.layout = self.facet.compute_layout(data)
     60         self.layout = self.coord.setup_layout(self.layout)
     61         self.check_layout()

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/facets/facet_wrap.py in compute_layout(self, data)
     71 
     72         base = combine_vars(data, self.plot.environment,
---> 73                             self.vars, drop=self.drop)
     74         n = len(base)
     75         dims = wrap_dims(n, self.nrow, self.ncol)

~/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/facets/facet.py in combine_vars(data, environment, vars, drop)
    552         raise PlotnineError(
    553             "At least one layer must contain all variables " +
--> 554             "used for facetting")
    555     base = pd.concat([x for i, x in enumerate(values) if has_all[i]],
    556                      axis=0)

PlotnineError: 'At least one layer must contain all variables used for facetting'

df_dist_filt['rel_dist'] = df_dist_filt['side/abs_min']

df_dist_filt['side_imp_weighted_cat'] = pd.Categorical(df_dist_filt['side_imp_weighted_cat'], categories=['high', 'medium', 'low'])

plotnine.options.figure_size = (20,5)
ggplot(aes(x='rel_dist', y='Nanog/profile_max'), df_dist_filt) +  \
  xlim([-150, 150]) + \
  scale_y_log10() + \
  facet_grid("side_imp_weighted_cat~side_tf") + geom_point(alpha=0.05) + \
  theme_classic()

<ggplot: (8782193727727)>

plotnine.options.figure_size = (20,5)
ggplot(aes(x='rel_dist', y='Nanog/profile_counts'), df_dist_filt) +  \
  xlim([-150, 150]) + \
  scale_y_log10() + \
  facet_grid("side_imp_weighted_cat~side_tf") + geom_point(alpha=0.1) + \
  theme_classic()

<ggplot: (8782203583401)>

plotnine.options.figure_size = (20,5)
ggplot(aes(x='rel_dist', y='imp_weighted'), df_dist_filt) +  \
  xlim([-150, 150]) + \
  facet_grid("side_imp_weighted_cat~side_tf") + geom_point(alpha=0.1) + \
  theme_classic()

<ggplot: (8782204920710)>

np.arange(0, 150, 10)

array([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120,
       130, 140])

def averaged_distance(df_dist_filt, side_tf, variable='imp_weighted', strand='-', window=15, log=False):
    rp = df_dist_filt[(df_dist_filt.side_tf == side_tf) & (df_dist_filt.side_imp_weighted_cat == 'high')]
    rp.rel_dist = rp.rel_dist.abs()
    rp = rp.sort_values("rel_dist")
    
    rp = rp[(rp.rel_dist < 150) & (rp.strand == strand)]
    if log:
        p = np.log10(rp.set_index('rel_dist')[variable]).rolling(window).mean().reset_index()
    else:
        p = rp.set_index('rel_dist')[variable].rolling(window).mean().reset_index()
    return p

tasks = list(motifs)

variable = 'imp_weighted'
fig, axes = plt.subplots(2, len(tasks), figsize=(30,5), sharex=True);
for i, strand in enumerate(['+', '-']):
    for j, side_tf in enumerate(list(motifs)):
        p = averaged_distance(df_dist_filt, side_tf, variable=variable, strand=strand, window=15)
        p.plot(x='rel_dist', y=variable, ax=axes[i,j], legend=False)
        if i ==0:
            axes[i,j].set_title(side_tf)
        if j == 0:
            axes[i,j].set_ylabel(f"{variable}, {strand}")

variable = 'Nanog/profile_counts'
fig, axes = plt.subplots(2, len(tasks), figsize=(30,5), sharex=True);
for i, strand in enumerate(['+', '-']):
    for j, side_tf in enumerate(list(motifs)):
        p = averaged_distance(df_dist_filt, side_tf, variable=variable, strand=strand, window=15, log=True)
        p.plot(x='rel_dist', y=variable, ax=axes[i,j], legend=False)
        if i ==0:
            axes[i,j].set_title(side_tf)
        if j == 0:
            axes[i,j].set_ylabel(f"{strand}")

variable = 'Nanog/profile_max'
fig, axes = plt.subplots(2, len(tasks), figsize=(30,5), sharex=True);
for i, strand in enumerate(['+', '-']):
    for j, side_tf in enumerate(list(motifs)):
        p = averaged_distance(df_dist_filt, side_tf, variable=variable, strand=strand, window=15, log=True)
        p.plot(x='rel_dist', y=variable, ax=axes[i,j], legend=False)
        if i ==0:
            axes[i,j].set_title(side_tf)
        if j == 0:
            axes[i,j].set_ylabel(f"{strand}")

rp = df_dist_filt[(df_dist_filt.side_tf == 'Nanog') & (df_dist_filt.side_imp_weighted_cat == 'high')].sort_values("rel_dist")
rp = rp[(rp.rel_dist < 150) & (rp.strand == '-') & (rp.rel_dist >= 0)]

p = rp.set_index('rel_dist').imp_weighted.rolling(15).mean().reset_index()

fig, ax = plt.subplots(1, 1, figsize=(10,3));
p.plot(x='rel_dist', y='imp_weighted', ax=ax)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcc7b885a90>

plotnine.options.figure_size = (6,8)
ggplot(aes(x='rel_dist', y='imp_weighted'), df_dist_filt[(df_dist_filt.side_tf == 'Nanog') & (df_dist_filt.side_imp_weighted_cat == 'high')]) +  \
  xlim([0, 150]) + \
  geom_point(alpha=0.1) + \
  facet_grid("strand~.", labeller='label_both') + geom_point(alpha=0.05) + \
  scale_x_continuous(limits = [0, 150], breaks=np.arange(0, 150, 10)) + \
  theme_classic()

<ggplot: (-9223363254615301190)>

plotnine.options.figure_size = (6,8)
ggplot(aes(x='rel_dist', y='Nanog/profile_counts'), df_dist_filt[(df_dist_filt.side_tf == 'Nanog') & (df_dist_filt.side_imp_weighted_cat == 'high')]) +  \
  geom_point(alpha=0.1) + \
  scale_y_log10() + \
  facet_grid("strand~.", labeller='label_both') + geom_point(alpha=0.05) + \
  scale_x_continuous(limits = [0, 150], breaks=np.arange(0, 150, 10)) + \
  theme_classic()

<ggplot: (-9223363254666614654)>

plotnine.options.figure_size = (6,8)
ggplot(aes(x='rel_dist', y='Nanog/profile_max'), df_dist_filt[(df_dist_filt.side_tf == 'Nanog') & (df_dist_filt.side_imp_weighted_cat == 'high')]) +  \
  xlim([0, 150]) + \
  geom_point(alpha=0.1) + \
  scale_y_log10() + \
  facet_grid("strand~.", labeller='label_both') + geom_point(alpha=0.05) + \
  scale_x_continuous(limits = [0, 150], breaks=np.arange(0, 150, 10)) + \
  theme_classic()

<ggplot: (8782264351580)>

df_dist_filt[(df_dist_filt.side_tf == 'Nanog') & (df_dist_filt.side_imp_weighted_cat == 'high') & (df_dist_filt.strand == '-') & (df_dist_filt.rel_dist < 150)& (df_dist_filt.rel_dist >= 0)].rel_dist.hvplot.hist(bins=145)

plotnine.options.figure_size = (20,5)
ggplot(aes(x='rel_dist'), df_dist_filt[(df_dist_filt.side_tf == 'Nanog') & (df_dist_filt.side_imp_weighted_cat == 'high')]) +  \
  xlim([0, 150]) + \
  geom_histogram(bins=150) + \
  facet_grid(".~strand", labeller='label_both') + \
  theme_classic()

<ggplot: (8782262851581)>

dfs = df_dist_filt.groupby(['rel_dist', 'side_imp_weighted_cat', 'side_pattern_short']).size().reset_index()
dfs = dfs.rename(columns={0: 'n'})
#dfs = dfs[dfs['side_imp_weighted_cat'] == 'high']

dfs['tf'] = dfs.side_pattern_short.map({v:k for k,v in motifs.items()})

plotnine.options.figure_size = (20,5)
ggplot(aes(x='rel_dist', y='n'), dfs) +  \
  xlim([-150, 150]) + \
  facet_grid("tf~side_imp_weighted_cat", scales='free_y') + geom_line() + \
  theme_classic()

<ggplot: (8782194705160)>

plotnine.options.figure_size = (20,5)
ggplot(aes(x='rel_dist'), df_dist_filt) +  \
  xlim([-150, 150]) + \
  facet_grid("side_imp_weighted_cat~side_pattern_short") + geom_histogram(bins=300) + \
  theme_bw()

<ggplot: (-9223363254650985654)>

df_dist_filt.head()

df_dist.

from basepair.modisco.utils import longer_pattern

count_features[pattern].value_counts().plot.bar();
plt.xlabel("Number of occurences in the sequence")
plt.ylabel("Frequency");

count_features.iloc[:,count_features.columns!=pattern].sum().plot.barh(figsize=(10,15));
plt.ylabel("Sum");

Boxplot for each factor the change in importance¶

plotnine.options.figure_size = (20,10)
ggplot(aes(x='value', y='entropy'), dfmf) + geom_boxplot() + facet_wrap("~pattern", ncol = 10, scales='free_x') + theme_bw()

<ggplot: (8768024965668)>

Boxplot for each factor the change in total counts¶

Get top box-plots¶

Fit a model to determine the effects¶

def ols_formula(df, dependent_var, *excluded_cols):
    '''
    Generates the R style formula for statsmodels (patsy) given
    the dataframe, dependent variable and optional excluded columns
    as strings
    '''
    df_columns = list(df.columns.values)
    df_columns.remove(dependent_var)
    for col in excluded_cols:
        df_columns.remove(col)
    return dependent_var + ' ~ ' + ' + '.join(df_columns)

dm = count_features.assign(counts=np.log10(1+total_counts.sum(axis=-1)))

ols_formula(dm, "counts")

'counts ~ m0_p0 + m0_p1 + m0_p10 + m0_p11 + m0_p12 + m0_p13 + m0_p14 + m0_p15 + m0_p2 + m0_p3 + m0_p4 + m0_p5 + m0_p6 + m0_p7 + m0_p8 + m0_p9 + m1_p0 + m1_p1 + m1_p2 + m1_p3 + m1_p4 + m1_p5 + m1_p6 + m1_p7 + m1_p8 + m10_p0 + m10_p1 + m2_p0 + m2_p1 + m2_p2 + m2_p3 + m2_p4 + m3_p0 + m3_p1 + m3_p2 + m3_p3 + m3_p4 + m3_p5 + m3_p6 + m3_p7 + m3_p8 + m4_p0 + m4_p1 + m4_p2 + m4_p3 + m4_p4 + m4_p5 + m4_p6 + m6_p0 + m6_p1 + m6_p2 + m6_p3 + m7_p0 + m7_p1 + m7_p2 + m7_p3 + m7_p4 + m7_p5 + m8_p0 + m8_p1'

results = smf.ols(ols_formula(dm, "counts"), data=dm).fit()

def tidy_ols(ols_reults):
    smry = results.summary()
    coef = smry.tables[1]
    return pd.DataFrame(coef.data[1:], columns=coef.data[0])

df_fit = tidy_ols(results)

# significant coefficients
df_fit_signif = df_fit[df_fit['P>|t|'].astype(float) < 0.05]
df_fit_signif = df_fit_signif[df_fit_signif[""] != "Intercept"]  # don't show the intercept
df_fit_signif.iloc[df_fit_signif['coef'].astype(float).abs().argsort()].iloc[::-1]  # sort by the effect size

Conclusions¶

Significant negative effect¶

m1_p0 - another Nanog motif

Significant positive effect¶

m3_p4 - Klf4 - homodimer - long motif (only 3 instances...)
m0_p8 - long motif (Oct4?)

pattern_short = rename_pattern(pattern)
single_motif_idx = count_features.index[count_features[pattern_short] == 1]
df_center = df[df.seqname.isin(single_motif_idx)].query(f"pattern == '{pattern}'")[['seqname', 'center']]
df_counts = pd.DataFrame({"seqname": seqlet_idx,
                          "log_counts": np.log10(1+total_counts.mean(axis=-1))})
dfd = pd.merge(df[df.pattern != pattern], df_center, on='seqname', suffixes=("_other", "_core"))
dfd['rel'] = dfd.center_other - dfd.center_core
dfd = dfd.merge(df_counts, on="seqname")

plotnine.options.figure_size = (20,20)
ggplot(aes(x="rel", y='log_counts'), dfd) + geom_point(alpha=0.5) + facet_wrap("~pattern", ncol = 5) + theme_bw() #+ xlim([-400, 400])

<ggplot: (-9223363268808843695)>

plotnine.options.figure_size = (20,20)
ggplot(aes(x="rel", y='log_counts'), dfd) + geom_point(alpha=0.5) + facet_wrap("~pattern", ncol = 5) + theme_bw() + xlim([-70, 70])

/users/avsec/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/plotnine/layer.py:450: UserWarning: geom_point : Removed 611 rows containing missing values.
  self.data = self.geom.handle_na(self.data)

<ggplot: (-9223363268845628777)>

Conclusions¶

not much going on

	pattern	example_idx	pattern_start	pattern_end	strand	pattern_len	pattern_center	match_weighted	match_weighted_p	match_weighted_cat	...	match/Sox2	imp/Klf4	imp/Nanog	imp/Oct4	imp/Sox2	example_chrom	example_start	example_end	example_strand	example_interval_from_task
21405362	metacluster_10/pattern_1	18016	133	152	-	19	142	0.210874	0.104651	low	...	0.270983	0.095580	0.116566	0.026328	0.075849	chr3	10381329	10382329	*	Nanog
21405363	metacluster_10/pattern_1	18016	417	436	-	19	426	0.150076	0.023256	low	...	0.241105	0.308060	0.700803	0.164149	0.321878	chr3	10381329	10382329	*	Nanog
21405364	metacluster_10/pattern_1	18016	431	450	+	19	440	0.222968	0.127907	low	...	0.270327	0.530708	0.747862	0.278486	0.538408	chr3	10381329	10382329	*	Nanog
21405365	metacluster_10/pattern_1	18016	548	567	+	19	557	0.147832	0.023256	low	...	0.166082	0.119512	0.413033	0.061598	0.104597	chr3	10381329	10382329	*	Nanog
21405366	metacluster_10/pattern_1	18016	696	715	-	19	705	0.148159	0.023256	low	...	0.149569	0.087400	0.071005	0.132447	0.097947	chr3	10381329	10382329	*	Nanog

		coef	std err	t	P>\|t\|	[0.025	0.975]
37	m3_p4	0.6035	0.237	2.542	0.011	0.138	1.069
15	m0_p8	0.3219	0.115	2.811	0.005	0.097	0.547
17	m1_p0	-0.1178	0.047	-2.528	0.012	-0.209	-0.026

	pattern_short	seqlets
0	m0_p0	4265
16	m1_p0	1759
1	m0_p1	1678
30	m3_p0	1582
25	m2_p0	1103
2	m0_p2	1018
3	m0_p3	1012
31	m3_p1	748
39	m4_p0	632

	pattern_short	scanned_instances
10	m0_p4	3750942
13	m0_p7	2869063
18	m1_p0	2521586
41	m4_p0	1530469
20	m1_p2	839123
42	m4_p1	821913
24	m1_p6	664611
43	m4_p2	655764
5	m0_p13	622287

	pattern	pattern_start	pattern_end	strand	pattern_len	pattern_center	match_weighted	match_weighted_p	match_weighted_cat	...	match/Sox2	imp/Klf4	imp/Nanog	imp/Oct4	imp/Sox2	example_chrom	example_start	example_end	example_strand	example_interval_from_task
0	metacluster_0/pattern_0	75	91	+	16	83	0.175556	0.193969	low	...	0.206333	0.050587	0.063537	0.090030	0.036130	chrX	143482572	143483572	*	Nanog
1	metacluster_0/pattern_0	152	168	-	16	160	0.141040	0.193969	low	...	0.223384	0.032036	0.020173	0.023113	0.029687	chrX	143482572	143483572	*	Nanog
2	metacluster_0/pattern_0	235	251	-	16	243	0.170543	0.193969	low	...	0.206882	0.018007	0.063500	0.021244	0.022853	chrX	143482572	143483572	*	Nanog
3	metacluster_0/pattern_0	237	253	+	16	245	0.151329	0.193969	low	...	0.186484	0.017459	0.059497	0.021635	0.023506	chrX	143482572	143483572	*	Nanog
4	metacluster_0/pattern_0	245	261	-	16	253	0.205689	0.215127	low	...	0.221023	0.020000	0.051363	0.035843	0.030362	chrX	143482572	143483572	*	Nanog

	side/pattern	side/match_weighted_cat	side/imp_weighted_cat	side/size	side/abs_min	pattern	pattern_start	pattern_end	strand	...	Oct4/profile_counts	Oct4/profile_counts_p	Oct4/profile_max	Oct4/profile_max_p	Sox2/profile_match	Sox2/profile_match_p	Sox2/profile_counts	Sox2/profile_counts_p	Sox2/profile_max	Sox2/profile_max_p
0	metacluster_0/pattern_0	low	high	1	69	metacluster_1/pattern_2	418	443	+	...	7122.0	1.0	997.0	1.0	inf	None	6370.0	1.0	791.0	1.0
1	metacluster_0/pattern_0	low	low	3	-6	metacluster_1/pattern_2	418	443	+	...	7122.0	1.0	997.0	1.0	inf	None	6370.0	1.0	791.0	1.0
2	metacluster_0/pattern_0	low	medium	1	15	metacluster_1/pattern_2	418	443	+	...	7122.0	1.0	997.0	1.0	inf	None	6370.0	1.0	791.0	1.0
3	metacluster_0/pattern_1	low	high	2	11	metacluster_1/pattern_2	418	443	+	...	7122.0	1.0	997.0	1.0	inf	None	6370.0	1.0	791.0	1.0
4	metacluster_0/pattern_1	low	low	2	-3	metacluster_1/pattern_2	418	443	+	...	7122.0	1.0	997.0	1.0	inf	None	6370.0	1.0	791.0	1.0