The Text-Fabric package contains a command to work with your corpus in your browser. It sets up a local web server, which interacts with your web browser. Then you can view and search the corpus without programming and without internet connection.
Below, when you see
app, you have to substitute it by the name
of an existing TF app.
The Text-Fabric browser fetches the apps and corpora (
it needs from GitHub/GitLab automatically.
You can click the Start Menu, and type
in the search box, and then Enter.
On Linux or Macos?
You can open a terminal (command prompt), and just say
The corpus data will be downloaded automatically, and be loaded into text-fabric. Then your browser will open and load the search interface. There you'll find links to further help.
You can let TF use extra features:
text-fabric app --mod=org/repo/path text-fabric app --mod=org/repo/path -c text-fabric app --mod=org/repo/path,org/repo/path
path must be replaced with a github user or organization,
a github repo, and a path within that repo.
Read more about your data life-cycle in the Data guide (
You can create custom sets of nodes, give them a name, and use those names in search templates. The TF browser can import those sets, so that you can use such queries in the browser too.
text-fabric app --sets=filePath
- Start a TF browser for
- Loads custom sets from
filePath must specify a file on your local system
(you may use
~ for your home directory).
That file must have been written by calling
If so,it contains a dictionary of named node sets.
These names can be used in search templates,
and the TF browser will use this dictionary to resolve those names.
Your session (aka job) will be saved in your browser,
under the name app
or another name if you rename, duplicate, import or create new sessions.
You can close the web server by pressing Ctrl-C in the terminal
or command prompt where you have started
Work with exported results
You can export your results to CSV files which you can process with various tools, including your own.
You can use the "Export" tab to tell the story behind your query and then export your view. A new page will open, which you can save as a PDF.
There is also a button to download all your results as data files.
- A json file with all information associated with your current session. You can import this in the Jobs section, and restore the session by which you created these results.
- a Markdown file with your description and some provenance metadata.
- contains your precise search results, decorated with the features you have used in your searchTemplate. Not only the results on the current page, but all results.
- contains your precise search results, as a list of tuples of nodes. Not only the results on the current page, but all results.
- contains the sections you have selected as a list of nodes.
- contains the nodes you have selected as a list of tuples of nodes.
- contains the nodes you have selected as a list of tuples of nodes, decorated with location information and text content.
Now, if you want to share your results for checking and replication, put all this in a research repository or in a GitHub/GitLab repository, which you can then archive to ZENODO to obtain a DOI.
Unicode in Excel CSVs
resultsx.tsv is not in the usual
The reason is that this is the only encoding
in which Excel handles CSV files properly.
So if you work with this file in Python, specify the encoding
with open('resultsx.tsv', encoding='utf_16') as fh: for row in fh: # do something with row
Conversely, if you want to write a CSV with Hebrew in it, to be opened in Excel, take care to:
- give the file name extension
- make the file tab separated (do not use the comma or semicolon!)
- use the encoding
- start the file with a BOM mark.
with open('mydata.tsv', 'w', encoding='utf_16_le') as fh: fh.write('\uFEFF') for row in myData: fh.write('\t'.join(row)) fh.write('\n')
The file has been written with the
and the first character is the unicode
That is needed for machines so that they can see which byte in a 16 bits word is
the least end (
le) and which one is the big end (
Knowing that the first character is FEFF,
all machines can see whether this is in a least-endian (le) encoding
or in a
Hence this character is called the Byte Order Mark (BOM).
See more on wikipedia.
When reading a file with encoding
Python reads the BOM, draws its conclusions, and strips the BOM.
So when you iterate over its lines, you will not see the BOM, which is good.
But when you read a file with encoding
Python passes the BOM through, and you have to skip it yourself.
That is unpleasant.
utf_16 for reading.
Expand source code Browse git
""" .. include:: ../docs/about/browser.md """