gokceneraslan

gokceneraslan

gokceneraslan

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)

gokceneraslan

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

gokceneraslan

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
  

gokceneraslan

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:

gokceneraslan

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

gokceneraslan

# 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', ...) {

gokceneraslan

#!/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 = {}

gokceneraslan

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):