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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) |
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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) |
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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] |
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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 | |
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# 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() |
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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): |
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import PIL.Image | |
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]]) | |
for side in ["right","top","bottom","left"]: |
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 36, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd\n", | |
"import numpy as np\n", |
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