{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "# Start\n", "\n", "This notebook gets you started with using\n", "[Text-Fabric](https://github.com/Nino-cunei/uruk/blob/master/docs/textfabric.md) for coding in cuneiform tablet transcriptions.\n", "\n", "Familiarity with the underlying\n", "[data model](https://annotation.github.io/text-fabric/tf/about/datamodel.html)\n", "is recommended.\n", "\n", "For provenance, see the documentation:\n", "[about](https://github.com/Nino-cunei/uruk/blob/master/docs/about.md)." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Overview\n", "\n", "* we tell you how to get Text-Fabric on your system;\n", "* we tell you how to get the Uruk IV-III corpus on your system." ] }, { "cell_type": "markdown", "metadata": {}, "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": [ "### Get the data\n", "\n", "Text-Fabric will get the data for you and store it on your system.\n", "\n", "If you have cloned the github repo with the data,\n", "[Nino-cunei/uruk](https://github.com/Nino-cunei/uruk),\n", "your data is already in place, and nothing will be downloaded.\n", "\n", "Otherwise, on first run, Text-Fabric will load the data and store it in the folder\n", "`text-fabric-data` in your home directory.\n", "This only happens if the data is not already there.\n", "\n", "Not only transcription data will be downloaded, also linearts and photos.\n", "These images are contained in a zipfile of 550 MB,\n", "so take care that you have a good internet connection when it comes to downloading the images." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start the engines\n", "\n", "Navigate to this directory in a terminal and say\n", "\n", "```\n", "jupyter notebook\n", "```\n", "\n", "(just literally).\n", "\n", "Your browser opens with a directory view, and you'll see `start.ipynb`.\n", "Click on it. A new browser tab opens, and a Python engine has been allocated to this\n", "notebook.\n", "\n", "Now we are ready to compute .\n", "The next cell is a code cell that can be executed if you have downloaded this\n", "notebook and have issued the `jupyter notebook` command.\n", "\n", "You execute a code cell by standing in it and press `Shift Enter`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The code" ] }, { "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": "2018-05-17T09:33:07.029915Z", "start_time": "2018-05-17T09:33:07.006073Z" } }, "outputs": [], "source": [ "import sys, os\n", "from tf.app import use" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "View the next cell as an *incantation*.\n", "You just have to say it to get things underway." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the very last version, use `hot`.\n", "\n", "For the latest release, use `latest`.\n", "\n", "If you have cloned the repos (TF app and data), use `clone`.\n", "\n", "If you do not want/need to upgrade, leave out the checkout specifiers." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:33:10.782586Z", "start_time": "2018-05-17T09:33:09.929360Z" } }, "outputs": [ { "data": { "text/html": [ "TF-app: ~/text-fabric-data/Nino-cunei/uruk/app" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/Nino-cunei/uruk/tf/uruk/1.0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 9.2.2\n", "Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html\n", "\n", "33 features found and 0 ignored\n" ] }, { "data": { "text/html": [ "Text-Fabric: Text-Fabric API 9.2.2, Nino-cunei/uruk/app v3, Search Reference
Data: URUK, Character table, Feature docs
Features:
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Uruk IV/III: Proto-cuneiform tablets \n", "
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\n", " identifier of tablet in catalog (http://www.flutopedia.com/tablets.htm)\n", "
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uruk
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Cuneiform tablets from the Uruk IV-III period
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2018-05-01T14:10:47Z
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dirk.roorda@dans.knaw.nl
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Cuneiform tablets from the Uruk IV-III period
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dataset:
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Cuneiform tablets from the Uruk IV-III period
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Cuneiform tablets from the Uruk IV-III period
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encoders:
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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encoders:
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dataset:
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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email2:
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encoders:
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source:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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email2:
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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CLDI
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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dateWritten:
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email1:
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email2:
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dirk.roorda@dans.knaw.nl
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
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CLDI
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sourceUrl:
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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email2:
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dirk.roorda@dans.knaw.nl
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
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CLDI
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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email2:
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
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CLDI
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sourceUrl:
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Text-Fabric
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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dateWritten:
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email2:
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dirk.roorda@dans.knaw.nl
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
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CLDI
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https://cdli.ucla.edu
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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dateWritten:
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2018-05-01T14:10:47Z
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email1:
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email2:
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
\n", "
CLDI
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sourceUrl:
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https://cdli.ucla.edu
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writtenBy:
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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dateWritten:
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2018-05-01T14:10:47Z
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email2:
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uruk
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Cuneiform tablets from the Uruk IV-III period
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2018-05-01T14:10:48Z
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https://www.universiteitleiden.nl/en/staffmembers/cale-johnson#tab-1
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dirk.roorda@dans.knaw.nl
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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CLDI
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https://cdli.ucla.edu
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str
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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2018-05-01T14:10:48Z
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email1:
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https://www.universiteitleiden.nl/en/staffmembers/cale-johnson#tab-1
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email2:
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dirk.roorda@dans.knaw.nl
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
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CLDI
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https://cdli.ucla.edu
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1.0
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dataset:
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uruk
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datasetName:
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Cuneiform tablets from the Uruk IV-III period
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dateWritten:
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2018-05-01T14:10:49Z
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email1:
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https://www.universiteitleiden.nl/en/staffmembers/cale-johnson#tab-1
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email2:
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dirk.roorda@dans.knaw.nl
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encoders:
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CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
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source:
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CLDI
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sourceUrl:
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https://cdli.ucla.edu
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1.0
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Text-Fabric
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none
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dataset:
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uruk
\n", "
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\n", "
datasetName:
\n", "
Cuneiform tablets from the Uruk IV-III period
\n", "
\n", "\n", "
\n", "
dateWritten:
\n", "
2018-05-01T14:10:49Z
\n", "
\n", "\n", "
\n", "
email1:
\n", "
https://www.universiteitleiden.nl/en/staffmembers/cale-johnson#tab-1
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\n", "
email2:
\n", "
dirk.roorda@dans.knaw.nl
\n", "
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\n", "
CDLI (transcription),Cale Johnson (expertise)and Dirk Roorda (TF)
\n", "
\n", "\n", "
\n", "
source:
\n", "
CLDI
\n", "
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sourceUrl:
\n", "
https://cdli.ucla.edu
\n", "
\n", "\n", "
\n", "
version:
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1.0
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writtenBy:
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Text-Fabric
\n", "
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\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Text-Fabric API: names N F E L T S C TF directly usable
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/Nino-cunei/uruk/sources/cdli/images" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Found 2095 ideograph linearts
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Found 2724 tablet linearts
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Found 5495 tablet photos
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A = use(\"Nino-cunei/uruk\", hoist=globals())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The output\n", "\n", "The output shows some statistics about the images found in the Uruk data.\n", "\n", "Then there are links to the documentation.\n", "\n", "**Tip:** open them, and have a quick look.\n", "\n", "Every notebook that you set up with `Cunei` will have such links.\n", "\n", "**GitHub and NBViewer**\n", "\n", "If you have made your own notebook, and used this incantation,\n", "and pushed the notebook to GitHub, links to the online version\n", "of *your* notebook on GitHub and NBViewer will be generated and displayed.\n", "\n", "By the way, GitHub shows notebooks nicely.\n", "Sometimes NBViewer does it better, although it fetches exactly the same notebook from GitHub.\n", "\n", "NBViewer is handy to navigate all the notebooks of a particular organization.\n", "Try the [Nino-cunei starting point](http://nbviewer.jupyter.org/github/Nino-cunei/).\n", "\n", "These links you can share with colleagues." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test\n", "\n", "We perform a quick test to see that everything works.\n", "\n", "### Count the signs\n", "\n", "We count how many signs there are in the corpus.\n", "In a next notebook we'll explain code like this." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:33:55.756471Z", "start_time": "2018-05-17T09:33:55.746000Z" } }, "outputs": [ { "data": { "text/plain": [ "140094" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(F.otype.s(\"sign\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show photos and lineart\n", "\n", "We show the photo and lineart of a tablet, to whet your appetite." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:34:09.968339Z", "start_time": "2018-05-17T09:34:09.963447Z" } }, "outputs": [], "source": [ "example = T.nodeFromSection((\"P005381\",))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:34:39.740281Z", "start_time": "2018-05-17T09:34:39.732573Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.photo(example)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that you can click on the photo to see a better version on CDLI.\n", "\n", "Here comes the lineart:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:34:52.818587Z", "start_time": "2018-05-17T09:34:52.803184Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.lineart(example)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A pretty representation of the transcription with embedded lineart for quads and signs:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:39:55.059912Z", "start_time": "2018-05-17T09:39:55.044710Z" } }, "outputs": [ { "data": { "text/html": [ "
P005381
tablet:148166 P005381
MSVO 3, 70uruk-iii
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case:167736 1a
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.
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.pretty(example, withNodes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can suppress the lineart:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2018-05-09T16:53:12.266894Z", "start_time": "2018-05-09T16:53:12.256212Z" } }, "outputs": [ { "data": { "text/html": [ "
P005381
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.pretty(example, showGraphics=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The transliteration:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2018-05-17T09:37:28.755437Z", "start_time": "2018-05-17T09:37:28.742473Z" } }, "outputs": [ { "data": { "text/plain": [ "['&P005381 = MSVO 3, 70',\n", " '#atf: lang qpc ',\n", " '@obverse ',\n", " '@column 1 ',\n", " '1.a. 2(N14) , SZE~a SAL TUR3~a NUN~a ',\n", " '1.b. 3(N19) , |GISZ.TE| ',\n", " '2. 1(N14) , NAR NUN~a SIG7 ',\n", " '3. 2(N04)# , PIRIG~b1 SIG7 URI3~a NUN~a ',\n", " '@column 2 ',\n", " '1. 3(N04) , |GISZ.TE| GAR |SZU2.((HI+1(N57))+(HI+1(N57)))| GI4~a ',\n", " '2. , GU7 AZ SI4~f ',\n", " '@reverse ',\n", " '@column 1 ',\n", " '1. 3(N14) , SZE~a ',\n", " '2. 3(N19) 5(N04) , ',\n", " '3. , GU7 ',\n", " '@column 2 ',\n", " '1. , AZ SI4~f ']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.getSource(example)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the lines ans cases of this tablet in a table:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "table = []\n", "for sub in L.d(example):\n", " if F.otype.v(sub) in {\"line\", \"case\"}:\n", " table.append((sub,))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
npline
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3P005381 obverse:1:11b3(N19) |GISZ.TE|
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5P005381 obverse:1:32(N04) PIRIG~b1 SIG7 URI3~a NUN~a
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.table(table, showGraphics=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can include the lineart in plain displays:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
npline
1P005381 obverse:1:11a2(N14) SZE~a SAL TUR3~a NUN~a 1b3(N19) |GISZ.TE|
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7P005381 obverse:2:2GU7 AZ SI4~f
8P005381 reverse:1:13(N14) SZE~a
9P005381 reverse:1:23(N19) 5(N04)
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11P005381 reverse:2:1AZ SI4~f
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.table(table, showGraphics=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just the beginning.\n", "\n", "In the next chapters we show you how to\n", "* fine-tune tablet displays,\n", "* step and jump around in the corpus,\n", "* search for patterns,\n", "* drill down to quads and signs,\n", "* and study frequency distributions of signs in subcases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Next\n", "\n", "[imagery](imagery.ipynb)\n", "\n", "*Get the big picture ...*\n", "\n", "All chapters:\n", "**start**\n", "[imagery](imagery.ipynb)\n", "[steps](steps.ipynb)\n", "[search](search.ipynb)\n", "[calc](calc.ipynb)\n", "[signs](signs.ipynb)\n", "[quads](quads.ipynb)\n", "[jumps](jumps.ipynb)\n", "[cases](cases.ipynb)\n", "\n", "---\n", "\n", "CC-BY Dirk Roorda" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.2" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "607px", "left": "0px", "right": "983px", "top": "110px", "width": "297px" }, "toc_section_display": "block", "toc_window_display": false }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }