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

# <img align="right" src="images/tf.png" width="128"/>
# <img align="right" src="images/huc.png"/>
# <img align="right" src="images/logo.png" width="200"/>
# 
# # Start
# 
# This notebook gets you started with using
# [Text-Fabric](https://github.com/annotation/text-fabric) for coding in the
# letters of René Descartes.
# 
# 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/CLARIAH/descartes-tf/blob/master/docs/about.md).

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

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

# # Running Text-Fabric
# 
# We will run computer code in the cells below, and this code makes use of the
# text-fabric library, shortly called `tf`.
# 
# We import some standard Python modules and then we import the `use` function from text-fabric.

# In[1]:


import sys, os
from tf.app import use


# Now we are going to *use* the `use` function.
# We want to *use* a corpus, and if we specify what corpus, text-fabric will the data for us.
# 
# If you have cloned the `CLARIAH/descartes-tf` repository to your local machine under the directory
# 
# `~/github/CLARIAH/descartes-tf`
# 
# then you already have the data.
# In that case you have to call the use command like this:
# 
# ```
# A = use("CLARIAH/descartes-tf:clone", checkout="clone", hoist=globals())
# ```
# 
# Below we give the command for the case where you have not cloned the repository.
# Text-Fabric will fetch the data from the internet and store it in your directory
# 
# `~/text-fabric-data/github/CLARIAH/descartes-tf`.
# 
# In both cases, the corpus data will be optimised for fast processing, a one time job.

# In[2]:


A = use("CLARIAH/descartes-tf", hoist=globals())


# # The following loads will be much quicker!
# 
# Just to show the results of the optimization step: if we give the same command again,
# the data is loaded much quicker.

# In[3]:


A = use("CLARIAH/descartes-tf", hoist=globals())


# ### The output
# 
# The messages after loading the corpus contain a lot of information about it.
# 
# **Tip:** click the triangles and the links, and have a quick look.
# 
# The **Text-Fabric** line has various links to the API docs.
# 
# Under **Node types** you find statistics about the corpus.
# 
# Under **Descartes = Descartes, all letters** you find the *features* of the corpus
# with short descriptions.
# 
# This corpus has additional material: *illustrations*.
# They have been downloaded automatically in the process, and you see how many there are.

# # Highlights
# 
# This corpus is special in that it has mathematical formulas and illustrations.
# 
# We show some of them to whet your appetite.
# 
# ## Formulas
# 
# There are simple formulas and complex formulas.
# The latter are represented as TeX codes, and will be typeset nicely.
# 
# Let's find the complex ones.

# In[4]:


query = """
formula notation=TeX
"""

results = A.search(query)


# Let's show a few.

# In[5]:


A.table(results, end=3)


# You can see them in context as well:

# In[6]:


A.show(results, end=3)


# ---
# 
# # Next steps
# 
# By now you have an impression how to orient yourself in this corpus.
# The next steps will show you how to get powerful: searching and computing.
# 
# After that it is time for collecting results, use them in new annotations and share them.
# 
# * **start** intro and highlights
# * **[search](search.ipynb)** turbo charge your hand-coding with search templates
# * **[compute](compute.ipynb)** sink down a level and compute it yourself
# * **[exportExcel](exportExcel.ipynb)** make tailor-made spreadsheets out of your results
# 
# Advanced
# 
# * **[similar sentences](similar.ipynb)** find similar sentences
# 
# CC-BY Dirk Roorda