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authorShivesh Mandalia <shivesh.mandalia@outlook.com>2020-03-01 23:23:21 +0000
committerShivesh Mandalia <shivesh.mandalia@outlook.com>2020-03-01 23:23:21 +0000
commit059f6ec9ead0c34004c98af0ef58565b2d410e93 (patch)
treea1faa98c431f8513bae63f5c1a951bac37e1286e /examples/Untitled.ipynb
parentbd23e7cdec7e8c70ac4feeb77b21cfcf2c775f3c (diff)
downloadGolemFlavor-059f6ec9ead0c34004c98af0ef58565b2d410e93.tar.gz
GolemFlavor-059f6ec9ead0c34004c98af0ef58565b2d410e93.zip
Add example notebooks to show how to use GolemFlavor
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-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "%load_ext autoreload\n",
- "%autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from __future__ import absolute_import, division, print_function"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# GolemFlavor Basic Example"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "In this example, we will generate a fake measured flavour composition using a multivariate Gaussian distribution"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}