alexlenail

import h5py
import numpy as np

def read_h5_to_dict(h5_path):
    
    out_dict = {}

    def add_h5_node_to_dict(name, node, out_dict=out_dict):

        fullname = node.name

alexlenail

import scipy
import scipy.stats
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster import hierarchy

def sort_df_by_hclust_olo(df, how='both', method='ward', metric='euclidean'):
    '''
    how={'index', 'columns', 'both'}
    '''
    df = df.fillna(0)

alexlenail

from scipy.stats import gmean
def geometric_mean(df):
    '''https://www.reddit.com/r/learnpython/comments/mq5ea7/pandas_calculate_geometric_mean_while_ignoring/'''
    return df.replace(0, np.nan).apply(lambda row: gmean(row[~row.isna()]), axis=1).fillna(0)

alexlenail

import matplotlib.pyplot as plt
from matplotlib_venn import venn2, venn3
import upsetplot

import matplotlib_inline.backend_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('svg')
%config InlineBackend.figure_format = 'retina'
%matplotlib inline

plt.rcParams['figure.figsize'] = [6, 3]

alexlenail

import sys
from types import ModuleType, FunctionType
from gc import get_referents

# Custom objects know their class.
# Function objects seem to know way too much, including modules.
# Exclude modules as well.
BLACKLIST = type, ModuleType, FunctionType

alexlenail

# Long running cell
import multiprocessing, time

def network_call():
    for i in range(20):
        print(i)
        time.sleep(1)
    
multiprocessing.Process(target=network_call).start()

alexlenail

import mygene

def namespace_mapping(names, map_from=['symbol', 'alias'], map_to='symbol', species='human'):

    names = pd.Series(names)

    print(f"passed {len(names)} symbols")

    names_stripped = names.str.strip()
    if any(names_stripped != names):

alexlenail

import PIL.Image
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
def show(*Ms): 
    fig, axs = plt.subplots(ncols=len(Ms))
    for M, ax in zip(Ms, (axs if type(axs) == list else [axs])):
        M = np.atleast_2d(M)
        ax.imshow(PIL.Image.fromarray((M - M.min()) * 255 / M.max()))
        ax.set_xticks([0, M.shape[1]])
        ax.set_yticks([0, M.shape[0]])

alexlenail

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",

alexlenail