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diff --git a/prody/dynamics/plotting.py b/prody/dynamics/plotting.py
index 994f5f2..c5d54cf 100644
--- a/prody/dynamics/plotting.py
+++ b/prody/dynamics/plotting.py
@@ -511,7 +511,7 @@ def showCrossProjection(ensemble, mode_x, mode_y, scale=None, *args, **kwargs):
     return show
 
 
-def showOverlapTable(rows, cols, *args, **kwargs):
+def showOverlapTable(rows, cols, **kwargs):

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#!/usr/bin/env python

import urllib2 as ul
from lxml import etree

def parse_votes(url):
    doc = ul.urlopen(url).read()
    tree = etree.HTML(doc)
    res = {}

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# labels is a NxL data.frame where N=number of observations being clustered and
# L = number of clustering results (number of vertical lines)

plot.label.distribution <- function(labels,
                                    groupColumn,
                                    alphaLines = 1/10,
                                    useSplines = T,
                                    showPoints = T,
                                    scale='uniminmax', ...) {

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bin.signal <- function(data, bin.width) {
  if (is.matrix(data)) data <- list(data)

  lapply(data, function(d) {
    window.size <- ncol(d)
    res <- t(apply(d, 1, function(row) {
      sapply(seq(1, window.size, bin.width), function(s) sum(row[s:(s+bin.width-1)]))
    }))
    colnames(res) <- paste0('bin', seq_len(ncol(res)))
    res

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Keybase proof

I hereby claim:

• I am gokceneraslan on github.
• I am gokcen (https://keybase.io/gokcen) on keybase.
• I have a public key whose fingerprint is 8755 72CC F7D6 DF05 0C63 8DCF 47D6 92B1 11C3 8A5F

To claim this, I am signing this object:

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library(FNN)
library(parallel)

# cnt: cell x gene matrix (can be raw count or library size normalized, but not log transformed)
impute <- function(cnt, k=30, cores=6, pca.dim=100) {
  print(paste0('Starting imputation with ', nrow(cnt), ' cells and ', ncol(cnt), ' genes...'))
  
  print('Performing PCA...')
  pca <- prcomp(log(cnt+1), center = T, rank.=pca.dim)$x
  

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import os

import numpy as np
import pandas as pd
import plotnine as p9
from tqdm import tqdm

import torch
from torch.autograd import Variable
from torch.utils.data import TensorDataset, DataLoader

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import numpy as np
import pandas as pd
import plotnine as p9

import torch
from torch.autograd import Variable
from torch.utils.data import TensorDataset, DataLoader, Dataset

np.random.seed(555)
torch.manual_seed(555)

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