#!/usr/bin/env python
# coding: utf-8

# <img align="right" src="images/ninologo.png" width="150"/>
# <img align="right" src="images/tf-small.png" width="125"/>
# <img align="right" src="images/dans.png" width="150"/>
# 
# # Start
# 
# This notebook gets you started with using
# [Text-Fabric](https://github.com/Nino-cunei/uruk/blob/master/docs/textfabric.md) for coding in cuneiform tablet transcriptions.
# 
# Familiarity with the underlying
# [data model](https://annotation.github.io/text-fabric/tf/about/datamodel.html)
# is recommended.
# 
# For provenance, see the documentation:
# [about](https://github.com/Nino-cunei/uruk/blob/master/docs/about.md).

# ## Overview
# 
# * we tell you how to get Text-Fabric on your system;
# * we tell you how to get the Uruk IV-III corpus on your system.

# ## Installing Text-Fabric
# 
# See [here](https://annotation.github.io/text-fabric/tf/about/install.html)

# ## Tip
# If you start computing with this tutorial, first copy its parent directory to somewhere else,
# outside your repository.
# If you pull changes from the repository later, your work will not be overwritten.
# Where you put your tutorial directory is up to you.
# It will work from any directory.

# ### Get the data
# 
# Text-Fabric will get the data for you and store it on your system.
# 
# If you have cloned the github repo with the data,
# [Nino-cunei/uruk](https://github.com/Nino-cunei/uruk),
# your data is already in place, and nothing will be downloaded.
# 
# Otherwise, on first run, Text-Fabric will load the data and store it in the folder
# `text-fabric-data` in your home directory.
# This only happens if the data is not already there.
# 
# Not only transcription data will be downloaded, also linearts and photos.
# These images are contained in a zipfile of 550 MB,
# so take care that you have a good internet connection when it comes to downloading the images.

# ## Start the engines
# 
# Navigate to this directory in a terminal and say
# 
# ```
# jupyter notebook
# ```
# 
# (just literally).
# 
# Your browser opens with a directory view, and you'll see `start.ipynb`.
# Click on it. A new browser tab opens, and a Python engine has been allocated to this
# notebook.
# 
# Now we are ready to compute .
# The next cell is a code cell that can be executed if you have downloaded this
# notebook and have issued the `jupyter notebook` command.
# 
# You execute a code cell by standing in it and press `Shift Enter`.

# ### The code

# In[1]:


get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


# In[2]:


import sys, os
from tf.app import use


# View the next cell as an *incantation*.
# You just have to say it to get things underway.

# For the very last version, use `hot`.
# 
# For the latest release, use `latest`.
# 
# If you have cloned the repos (TF app and data), use `clone`.
# 
# If you do not want/need to upgrade, leave out the checkout specifiers.

# In[4]:


A = use("Nino-cunei/uruk", hoist=globals())


# ### The output
# 
# The output shows some statistics about the images found in the Uruk data.
# 
# Then there are links to the documentation.
# 
# **Tip:** open them, and have a quick look.
# 
# Every notebook that you set up with `Cunei` will have such links.
# 
# **GitHub and NBViewer**
# 
# If you have made your own notebook, and used this incantation,
# and pushed the notebook to GitHub, links to the online version
# of *your* notebook on GitHub and NBViewer will be generated and displayed.
# 
# By the way, GitHub shows notebooks nicely.
# Sometimes NBViewer does it better, although it fetches exactly the same notebook from GitHub.
# 
# NBViewer is handy to navigate all the notebooks of a particular organization.
# Try the [Nino-cunei starting point](http://nbviewer.jupyter.org/github/Nino-cunei/).
# 
# These links you can share with colleagues.

# ## Test
# 
# We perform a quick test to see that everything works.
# 
# ### Count the signs
# 
# We count how many signs there are in the corpus.
# In a next notebook we'll explain code like this.

# In[5]:


len(F.otype.s("sign"))


# ### Show photos and lineart
# 
# We show the photo and lineart of a tablet, to whet your appetite.

# In[6]:


example = T.nodeFromSection(("P005381",))


# In[7]:


A.photo(example)


# Note that you can click on the photo to see a better version on CDLI.
# 
# Here comes the lineart:

# In[8]:


A.lineart(example)


# A pretty representation of the transcription with embedded lineart for quads and signs:

# In[9]:


A.pretty(example, withNodes=True)


# We can suppress the lineart:

# In[10]:


A.pretty(example, showGraphics=False)


# The transliteration:

# In[11]:


A.getSource(example)


# Now the lines ans cases of this tablet in a table:

# In[12]:


table = []
for sub in L.d(example):
    if F.otype.v(sub) in {"line", "case"}:
        table.append((sub,))


# In[13]:


A.table(table, showGraphics=False)


# We can include the lineart in plain displays:

# In[14]:


A.table(table, showGraphics=True)


# This is just the beginning.
# 
# In the next chapters we show you how to
# * fine-tune tablet displays,
# * step and jump around in the corpus,
# * search for patterns,
# * drill down to quads and signs,
# * and study frequency distributions of signs in subcases.

# # Next
# 
# [imagery](imagery.ipynb)
# 
# *Get the big picture ...*
# 
# All chapters:
# **start**
# [imagery](imagery.ipynb)
# [steps](steps.ipynb)
# [search](search.ipynb)
# [calc](calc.ipynb)
# [signs](signs.ipynb)
# [quads](quads.ipynb)
# [jumps](jumps.ipynb)
# [cases](cases.ipynb)
# 
# ---
# 
# CC-BY Dirk Roorda