![](\"images/tf.png\")
\n",
"![](\"images/logo.png\"){kind=logo}
\n",
"![](\"images/etcbc.png\")
\n",
"![](\"images/dans.png\")
\n",
"\n",
"# Tutorial\n",
"\n",
"This notebook gets you started with using\n",
"[Text-Fabric](https://annotation.github.io/text-fabric/) for coding in the Dead-Sea Scrolls.\n",
"\n",
"Familiarity with the underlying\n",
"[data model](https://annotation.github.io/text-fabric/tf/about/datamodel.html)\n",
"is recommended."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cookbook\n",
"\n",
"This tutorial and its sister tutorials are meant to showcase most of things TF can do.\n",
"\n",
"But we also have a [cookbook](cookbook) with a set of focused recipes on tricky things."
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"## Installing Text-Fabric\n",
"\n",
"See [here](https://annotation.github.io/text-fabric/tf/about/install.html)"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## Tip\n",
"If you start computing with this tutorial, first copy its parent directory to somewhere else,\n",
"outside your repository.\n",
"If you pull changes from the repository later, your work will not be overwritten.\n",
"Where you put your tutorial directory is up to you.\n",
"It will work from any directory."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"\n",
"Text-Fabric will fetch the data set for you from GitHub, and check for updates.\n",
"\n",
"The data will be stored in the `text-fabric-data` in your home directory."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Features\n",
"The data of the corpus is organized in features.\n",
"They are *columns* of data.\n",
"Think of the corpus as a gigantic spreadsheet, where row 1 corresponds to the\n",
"first sign, row 2 to the second sign, and so on, for all ~ 1.5 M signs,\n",
"followed by ~ 500 K word nodes and yet another 200 K nodes of other types.\n",
"\n",
"The information which reading each sign has, constitutes a column in that spreadsheet.\n",
"The DSS corpus contains > 50 columns.\n",
"\n",
"Instead of putting that information in one big table, the data is organized in separate columns.\n",
"We call those columns **features**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T09:17:16.202764Z",
"start_time": "2018-05-18T09:17:16.197546Z"
}
},
"outputs": [],
"source": [
"import os\n",
"import collections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incantation\n",
"\n",
"The simplest way to get going is by this *incantation*:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T09:17:17.537171Z",
"start_time": "2018-05-18T09:17:17.517809Z"
}
},
"outputs": [],
"source": [
"from tf.app import use"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"TF-app: ~/text-fabric-data/etcbc/dss/app"
],
"text/plain": [
"