{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pancreatic endocrinogenesis\n",
"\n",
"This tutorial uses raw data from [scvelo](https://github.com/theislab/scvelo) package. Special thanks go to the scvelo team! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get the latest version from pypi \n",
"# for other installations approaches, see https://dynamo-release.readthedocs.io/en/latest/ten_minutes_to_dynamo.html#how-to-install\n",
"!pip install dynamo-release --upgrade --quiet"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# from IPython.core.display import display, HTML\n",
"# display(HTML(\"\"))\n",
"# %matplotlib inline\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import dynamo as dyn "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"this is like R's sessionInfo()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | package | \n",
" dynamo-release | \n",
" pre-commit | \n",
" cvxopt | \n",
" trimap | \n",
" numdifftools | \n",
" colorcet | \n",
" python-igraph | \n",
" pynndescent | \n",
" hdbscan | \n",
" loompy | \n",
" matplotlib | \n",
" networkx | \n",
" numba | \n",
" numpy | \n",
" pandas | \n",
" scikit-learn | \n",
" scipy | \n",
" seaborn | \n",
" setuptools | \n",
" statsmodels | \n",
" tqdm | \n",
" umap-learn | \n",
"
\n",
" \n",
" \n",
" \n",
" | version | \n",
" 1.0.0 | \n",
" 2.11.1 | \n",
" 1.2.6 | \n",
" 1.4.3.dev1 | \n",
" 0.9.39 | \n",
" 2.0.6 | \n",
" 0.9.0 | \n",
" 0.5.2 | \n",
" 0.8.27 | \n",
" 3.0.6 | \n",
" 3.4.1 | \n",
" 2.5.1 | \n",
" 0.53.1 | \n",
" 1.20.3 | \n",
" 1.2.3 | \n",
" 0.23.2 | \n",
" 1.6.1 | \n",
" 0.11.1 | \n",
" 54.2.0 | \n",
" 0.12.2 | \n",
" 4.58.0 | \n",
" 0.5.1 | \n",
"
\n",
" \n",
"
\n",
"