Xparx

import scanpy as sc
import pandas as pd

data = pd.read_csv('path_to_file.csv')
feature_annotations = pd.read_csv('path_to_annotations.csv')

# Make sure samples are rows and features are columns. if not data = data.T
adata = sc.AnnData(data, obs=sample_names, var=feature_annotations)

sc.pp.pca(adata, n_comps=10)

Xparx

import os
import numpy as np
import scipy as sp
import pandas as pd
import scanpy as sc
import scprocessing.pipeline as scpipe
import scprocessing.preprocessing as scpp
import scprocessing.preprocessing.Svensson2019 as Svensson2019
import scprocessing.plotting.anndata as scpl
import dewakss.decomposition as dede

Xparx

import scanpy.api as _sc
import scanpy.plotting as _scp
import seaborn as _sns
import numpy as _np
import matplotlib.pyplot as plt


def clustered_heatmap(data, var_names=None, groupby=None, figsize=(12, 6), cluster=True, use_raw=None, log=False, num_categories=7, cbar=False, vmin=None, vmax=None, **kwds):

    from anndata import AnnData

Xparx

library(tm)
library(wordcloud)
library(memoise)

text_file <- 'SBW2015_book.txt'

# Using "memoise" to automatically cache the results
text <- readLines(sprintf("./%s", text_file), encoding="UTF-8")

text <- stemDocument(text) # doesn't seem to do anything

Xparx

(defun change-theme-on-time ()
  (destructuring-bind (sec min h m year junk junk junk junk) (decode-time (current-time)))
  (if (or (> (string-to-number (format-time-string "%H" (current-time))) 18)
          (< (string-to-number (format-time-string "%H" (current-time))) 9))
      (unless custom-enabled-themes (load-theme 'my-theme t)) (disable-theme custom-enabled-themes)))

(add-hook 'before-make-frame-hook 'change-theme-on-time)