Involving to the end-user

The Knit Editor software aims to be installable and usable by end-users. In the whole summer of code, we focused on development and code documentation from the perspective of a developer. In this blog post we will discuss how the Knit Editor is presented to the end user. So, you reading this blog: Please comment with your thoughts on the sketches.

Currently, a site is in the making that shall present the Knit Editor software as is state of the art. The inspiration came from the talk by Tracy Osborn at PyCon 2016: “Web Design for Non Designers”. The site is currently in the making at fossasia.github.io/kniteditor. If you click on the following images, you get redirected to the implementation of the concept.

The Main Page

First thing that comes into view is the download button. This leads to the download site. Then, wen can see three popular use-cases of the knit editor. At the bottom, new developers can see that they can contribute.

End users are knitters of all ages. As tested with my mom, they expect the language to be at the top-right of the page.Main Page

Both, the download and the start developing button are highlighted in a different color to make certain that they are an action the user is expected to perform.

When you access the site you get automatically redirected to your browsers configured language.

The Download Site

When you click the download button on the main page, you reach the download site. Depending on the operating system information the browser sends, your download starts automatically, below, this is  sketched for Windows.

P1040129

Next to the download page, you may want to find other versions of the software or not. This is to be evaluated. Maybe a slightly less visible button is right for that or it can be left out. Usually, no-one uses the old software.

At the bottom, you can see that there is a predecessor of the software which is called “AYAB-Apparat”. Some people may expect to find this software, too.

The Developer Site

If you clicked “Start Developing” on the main page, you will be confronted with the site for development.

There are two ways main flavors of contributing. Either you translate or you write program code. Therefore, we have two buttons that skip to the corresponding sections.

P1040130

At the bottom, you can see that there are tutorials on how to set up the environment for development. Videos for this can be found under this Youtube playlist.

Summary

At the end of GSoC we should document the code. Since we did documentation-driven development, there was already a focus on the developers from the start. End-user involvement fell short during the development phase. “Documentation is the way of informing people.” – this is something I learned from a talk. Thus, I create the new site for the knit editor as a documentation about the project fit for non-developers.

Knit Editor Package Overview

In this years Google Summer of Code, we created several Python package. They are all available on the Python Package Index (PyPi) and installable via “pip install”. They are listed on knitting.fossasia.org but their interconnections are shown here.

In the following figure, you can see the different packages created during GSoC with a solid line. Other packages that are used by the “kniteditor” application are shown here with a dotted line.

 

Knit Editor Package Architecture
Knit Editor Package Architecture

Overall, five packages we created. The design is driven by responsibility. Thus the responsibilities of each packages should be clearly separated from the other packages. We describe the responsibilities of the packages as follows:

knittingpattern is the library for converting, loading, saving and manipulating knitting patterns. These patterns include in formation abour how to knit. Unlike a picture this includes more than a color: adding meshes, removing meshes, types of holes, the possibility of non-planar knit pieces ad more.

ObservableList is a list whose content can be observed. Whenever elements are added or removed, this list notifies the observers. This is used by the rows of instructions to provide a more convenient interface.

crc8 computes the crc8 hash from bytes. I did not find a Python library implementing ths functionality so I created it myself. The design follows the design of the hash functions in the Python standard library. This package is used by the AYABInterface package. Through creating a new package, this is also usable by other applications.

AYABInterface controls the knitting machines connected to the AYAB hack. Through the serial interface, it can send messages to the controllers and receive answers. It also provides hint for actions which the user should take in order to produce the desired outcome.

kniteditor contains the user interface to edit knitting patterns and control the knitting machines with the AYAB shield.

All these packages also include installation instructions.

The new AYABInterface module

One create knit work with knitting machines and the AYAB shield. Therefore, the computer communicates with the machine. This communication shall be done, in the future, with this new library, the AYABInterface.

Here are some design decisions:

Complete vs. Incomplete

The idea is to have the AYAB seperated from the knittingpattern format. The knittingpattern format is an incomplete format that can be extended for any use case.  In contrast, the AYAB machine has a complete instruction set. The knittingpattern format is a means to transform these formats into different complete instruction sets. They should be convertible but not mixed.

Desciptive vs. Imperative

The idea is to be able to pass the format to the AYABInterface as a description. As much knowledge about the behavior is capsuled in the AYABInterface module. With this striving, we are less prone to intermix concerns across the applications.

Responsibilty Driven Design

I see these separated responsibilities:

  • A communication part focusing on the protocol to talk and the messages sent across the wire. It is an interpreter of the protocol, transforming it from bytes to objects.
  • A configuration that is passed to the interface
  • Different Machines types supported.
  • Actions the user shall perform.

Different Representations

I see these representations:

  • Commands are transferred across the wire. (PySerial)
  • For each movement of a carriage, the needles are used and put into a new position, B or D. (communication)
  • We would like to knit a list of rows with different colors. (interface)
    • Holes can be described by a list of orders in which meshes are moved to other locations, i.e. on needle 1 we can find mesh 1, on needle 2 we find mesh 2 first and then mesh 3, so mesh 2 and mesh 3 are knit together in the following step
  • The knitting pattern format.

Actions and Information for the User

The user should be informed about actions to take. These actions should not be in the form of text but rather in the form of an object that represents the action, i.e. [“move”, “this carriage”, “from right to left”]. This way, they can be adequately represented in the UI and translated somewhere central in the UI.

Summary

The new design separates concerns and allows testing. The bridge between the machine and the knittnigpattern format are primitive, descriptive objects such as lists and integers.

Transcript from the Python Toolbox 101

At the Python User Group Berlin, I lead a talk/discussion about free-of-charge tools for open-source development based on what we use GSoC. The whole content was in an Etherpad and people could add their ideas.

Because there are a lot of tools, I thought, I would share it with you. Maybe it is of use. Here is the talk:


Python Users Berlin 2016/07/14 Talk & Discussion

 

START: 19:15
Agenda 1min END: 19:15
======
– Example library
– What is code
– Version Control
  – Python Package Index
– …, see headings
– discussion: write down, what does not fit into my structure
Example Library (2min)  19:17
======================
What is Code (2min) 19:19
===================
.. note:: This frames our discussion
– Source files .py, .pyw
– tests
– documentation
– quality
– readability
– bugs and problems
– <3
Configurationsfiles plain Text for editing
Version Control (2min) 19:21
======================
.. note:: Sharing and Collaboration
– no Version Control:
  – Dropbox
  – Google drive
  – Telekom cloud
  – ftp, windows share
– Version Control Tools:
  – git
    – gitweb own server
    – 
  – mecurial
  – svn
  – perforce (proprietary)
  
  
  
  
  
  
Python Package Index (3min) 19:24
—————————
.. note:: Shipping to the users
hosts python packages you develop.
Example: “knittingpattern” package
pip
Installation from Pypi:
    $ python3 -m pip install knittingpattern # Linux
    > py -3.4 -m pip install knittingpattern # Windows
Documentation upload included!
Documentation (3min) 19:27
====================
.. note:: Inform users
I came across a talk:
Documentation can be:
– tutorials
– how to
– introduction to the community/development process
– code documentation!!!
– chat
– 
Building the documentation (3min)  19:30
———————————
Formats:
– HTML
– PDF
– reRST
– EPUB
– doc strings in source code
– test?
Tools:
– Sphinx
– doxygen
– doc strings
  – standard how to put in docstrings in Python
    – 
Example: Sphinx  3min 19:33
~~~~~~~~~~~~~~~
– Used for Python
– Used for knittingpattern
Python file:
Documentation file with sphinx.ext.autodoc:
Built documentation:
    See the return type str, Intersphinx can reference across documentations.
    Intersphinx uses objects inventory, hosted with the documentation:
Testing the documentation:
    – TODO: link
      – evertying is included in the docs
      – everything that is public is documented
      
      syntax
      – numpy 
      – google 
      – sphinx
Hosting the Documentation (3min) 19:36
——————————–
Tools:
– pythonhosted
  only latest version
– readthedocs.io
  several branches, versions, languages
– wiki pages
– 
Code Testing 2min 19:38
============
.. note:: Tests show the presence of mistakes, not their absence.
What can be tested:
– features
– style: pep8, pylint, 
– documentation
– complexity
– 
Testing Features with unit tests 4min 19:42
——————————–
code:
    def fib(i): …
Tools with different styles
– unittest
  
    import unittest
    from fibonacci import fib
    class FibonacciTest(unittest.TestCase):
        def testCalculation(self):
            self.assertEqual(fib(0), 0)
            self.assertEqual(fib(1), 1)
            self.assertEqual(fib(5), 5)
            self.assertEqual(fib(10), 55)
            self.assertEqual(fib(20), 6765)
    if __name__ == “__main__”: 
        unittest.main()
 
– doctest
    import doctest
    def fib(n):
        “”” 
        Calculates the n-th Fibonacci number iteratively  
        >>> fib(0)
        0
        >>> fib(1)
        1
        >>> fib(10) 
        55
        >>> fib(15)
        610
        >>> 
        “””
        a, b = 0, 1
        for i in range(n):
            a, b = b, a + b
        return a
    if __name__ == “__main__”: 
        doctest.testmod()
– pytest (works with unittest)
    import pytest
    from fibonacci import fib
    
    @pytest.mark.parametrize(“parameter,value”,[(0, 0), (1, 1), (10, 55), (15, 610)])
    def test_fibonacci(parameter, value):
        assert fib(parameter) == value
– nose tests?
– …
– pyhumber
– assert in code,  PyHamcrest
– Behaviour driven development
  – human test
Automated Test Run & Continuous Integration 2min 19:44
===========================================
.. note:: 
Several branches:
– production branch always works
– feature branches
– automated test before feature is put into production
Tools running tests 6min 19:50
——————-
– Travis CI for Mac, Ubuntu
– Appveyor for Windows
Host yourself:
– buildbot
– Hudson
– Jenkins
– Teamcity
– circle CI
  + selenium for website test
– 
– …?????!!!!!!
Tools for code quality 4min 19:54
———————-
– landscape
  complexity, style, documentation
  – libraries are available separately
    – flake8
    – destinate
    – pep257
– codeclimate
  code duplication, code coverage
  – libraries are available separately
– PyCharm
  – integrated what landscape has 
  – + complexity
Bugs, Issues, Pull Requests, Milestones 4min 19:58
=======================================
.. note:: this is also a way to get people into the project
1. find bug
2. open issue if big bug, discuss
3. create pull request
4. merge
5. deploy
– github
  issue tracker
– waffle.io – scrumboard
  merge several github issues tracker
– Redmine
JIRA
– trac 
– github issues + zenhub integrated in github
– gitlab
– gerrit framework that does alternative checking https://www.gerritcodereview.com/
  1. propose change
  2. test
  3. someone reviews the code
      – X people needed
  QT company uses it
Localization 2min 20:00
============
crowdin.com
    Crowdsourced translation tool:
    
Discussion
– spellchecker is integrated in PyCharm
  – character set
  – new vocabulary
  – not for continuous integration (CI)
– Emacs
  – 
– pylint plugin 
   – not all languages?
– readthedocs
  – add github project, 
  – hosts docs
– sphinx-plugin?
– PyCon testing talk:
    – Hypothesis package
      – tries to break your code
      – throws in a lot of edge cases (huge number, nothing, …)
      -> find obscure edge cases
      
Did someone create a Pylint plugin
– question:
    – cyclomatic code complexity
    – which metrics tools do you know?
    –
Virtual Environment:
    nobody should install everything in the system
    -> switch between different python versions
    – python3-venv
      – slightly different than virtual-env(more mature)
Beginners:
    Windows:
        install Anaconda

Deploying a Kivy Application with PyInstaller for Mac OSX with Travis CI to Github

In this sprint for the kniteditor library we focused on automatic deployment for Windows and Mac. The idea: whenever a tag is pushed to Github, a new travis build is triggered. The new built app is uploaded to Github as an “.dmg” file.

Travis

Travis is configured with the “.travis.yml” file which you can see here:

language: python

# see https://docs.travis-ci.com/user/multi-os/
matrix:
  include:
    - os: linux
      python: 3.4
    - os: osx
      language: generic
  allow_failures:
    - os: osx

install:
  - if [ "$TRAVIS_OS_NAME" == "osx"   ] ; then   mac-build/install.sh ; fi

script:
  - if [ "$TRAVIS_OS_NAME" == "osx"   ] ; then   mac-build/test.sh ; fi

before_deploy:
  - if [ "$TRAVIS_OS_NAME" == "osx"   ] ; then cp mac-build/dist/KnitEditor.dmg /Users/travis/KnitEditor.dmg ; fi

deploy:
  # see https://docs.travis-ci.com/user/deployment/releases/
  - provider: releases
    api_key:
      secure: v18ZcrXkIMgyb7mIrKWJYCXMpBmIGYWXhKul4/PL/TVpxtg2f/zfg08qHju7mWnAZYApjTV/EjOwWCtqn/hm2CfPFo=
    file: /Users/travis/KnitEditor.dmg
    on:
      tags: true
      condition:  "\"$TRAVIS_OS_NAME\" == \"osx\""
      repo: AllYarnsAreBeautiful/kniteditor

Note that it builds both Linux and OSX. Thus, for each step one must distinguish. Here, only the OSX parts are shown. These steps are executed:

  1. Installation. The app and dmg files are built.
  2. Testing. The tests are shipped with the app in our case. This allows us to execute them at many more locations – where the user is.
  3. Before Deploy. Somehow Travis did not manage to upload from the original location. Maybe it was a bug. Thus, a absolute path was created for the use in (4).
  4. Deployment to github. In this case we use an API key. One could also use a password.

Installation:

#!/bin/bash
#
# execute with --user to pip install in the user home
#
set -e

HERE="`dirname \"$0\"`"
USER="$1"
cd "$HERE"

brew update

echo "# install python3"
brew install python3
echo -n "Python version: "
python3 --version
python3 -m pip install --upgrade pip

echo "# install pygame"
python3 -m pip uninstall -y pygame || true
# locally compiled pygame version
# see https://bitbucket.org/pygame/pygame/issues/82/homebrew-on-leopard-fails-to-install#comment-636765
brew install sdl sdl_image sdl_mixer sdl_ttf portmidi
brew install mercurial || true
python3 -m pip install $USER hg+http://bitbucket.org/pygame/pygame

echo "# install kivy dependencies"
brew install sdl2 sdl2_image sdl2_ttf sdl2_mixer gstreamer

echo "# install requirements"
python3 -m pip install $USER -I Cython==0.23 \
                       --install-option="--no-cython-compile"
USE_OSX_FRAMEWORKS=0 python3 -m pip install $USER kivy
python3 -m pip uninstall -y Cython==0.23
python3 -m pip install $USER -r ../requirements.txt
python3 -m pip install $USER -r ../test-requirements.txt
python3 -m pip install $USER PyInstaller

./build.sh $USER

The first step is to update brew. It cost me 4 hours to find this bug, 2 hours to work around it before. If brew is not updated, Python 3.4 is installed instead of Python 3.5.

Then, Python, Pygame as the window provider for Kivy is installed, and the other requirements. It goes on with the build step. While installation is executed once on a personal Mac, the build step is executed several times, when the source code is changed.

#!/bin/bash
#
# execute with --user to make pip install in the user home
#
set -e

HERE="`dirname \"$0\"`"
USER="$1"
cd "$HERE"

(
  cd ..

  echo "# removing old installation of kniteditor"
  python3 -m pip uninstall -y kniteditor || true

  echo "# build the distribution"
  python3 -m pip install $USER wheel
  python3 setup.py sdist --formats=zip
  python3 setup.py bdist_wheel
  python3 -m pip uninstall -y wheel

  echo "# install the current version from the build"
  python3 -m pip install $USER --upgrade dist/kniteditor-`linux-build/package_version`.zip

  echo "# install test requirements"
  python3 -m pip install $USER --upgrade -r test-requirements.txt
)

echo "# build the app"
# see https://pythonhosted.org/PyInstaller/usage.html
python3 -m PyInstaller -d -y KnitEditor.spec

echo "# create the .dmg file"
# see http://stackoverflow.com/a/367826/1320237
KNITEDITOR_DMG="`pwd`/dist/KnitEditor.dmg"
rm -f "$KNITEDITOR_DMG"
hdiutil create -srcfolder dist/KnitEditor.app "$KNITEDITOR_DMG"

echo "The installer can be found in \"$KNITEDITOR_DMG\"."

In the first steps we install the kniteditor from the built “sdist”  zip file. This way we can uninstall it with pip. Also, the dependencies are installed. Then, PyInstaller is invoked with a spec and then the .dmg file is created.

The spec looks like this:

# -*- mode: python -*-

import sys
site_packages = [path for path in sys.path if path.rstrip("/\\").endswith('site-packages')]
print("site_packages:", site_packages)

from kivy.tools.packaging.pyinstaller_hooks import get_deps_all, \
    hookspath, runtime_hooks

block_cipher = None

added_files = [(site_packages_, ".") for site_packages_ in site_packages]

kwargs = get_deps_all()
kwargs["datas"] = added_files
kwargs["hiddenimports"] += ['queue', 'unittest', 'unittest.mock']


a = Analysis(['_KnitEditor.py'],
             pathex=[],
             binaries=None,
             win_no_prefer_redirects=False,
             win_private_assemblies=False,
             cipher=block_cipher,
             hookspath=hookspath(),
             runtime_hooks=runtime_hooks(),
             **kwargs)
pyz = PYZ(a.pure, a.zipped_data,
             cipher=block_cipher)
exe = EXE(pyz,
          a.scripts,
          exclude_binaries=True,
          name='KnitEditorX',
          debug=False,
          strip=False,
          upx=True,
          console=True )
coll = COLLECT(exe,
               a.binaries,
               a.zipfiles,
               a.datas,
               strip=False,
               upx=True,
               name='KnitEditor')
app = BUNDLE(coll,
             name='KnitEditor.app',
             icon=None,
             bundle_identifier="com.ayab-knitting.KnitEditor")

Note, that all files in all site-packages are included. This way, we do not need to cope with missing modules. Also, there are three different names for

  • the entry script “_KnitEditor.py”
  • the executable “KnitEditorX”
  • the library “kniteditor”

While on the command line, OSX is case sensitive, it is not sensitive on the file system. Thus, if one of the names is the same, we can get errors durig the PyInstaller build.

Lessons learned

Do “brew update” on travis.

Use absolute paths for deployment on Mac OSX travis.

Never use the same names in PyInstaller for the main script, a library and the executable. Otherwise you get a “not a directory” or “not a file” error.

Travis OSX build max out from time to time. It is much faster to have a Mac computer there, to create the scripts.

How to create a Windows Installer from tagged commits

I working on an open-source Python project, an editor for knit work called the “KnitEditor”. Development takes place at Github. Here, I would like to give some insight in how we automated deployment of the application to a Windows installer.

The process works like this:

  1. Create a tag with git and push it to Github.
  2. AppVeyor build the application.
  3. AppVeyor pushes the application to the Github release.

(1) Create a tag and push it

Tags should reflect the version of the software. Version “0.0.1” is in tag “v0.0.1”. We automated the tagging with the “setup.py” in the repository. Now, you can run

py -3.4 setup.py tag_and_deploy

Which checks that there is no such tag already. Several commits have the same version, so, we like to make sure that we do not have two versions with the same name. Also, this can only be executed on the master branch. This way, the software has gone through all the automated quality assurance. Here is the code from the setup.py:

from distutils.core import Command
# ...
class TagAndDeployCommand(Command):

    description = "Create a git tag for this version and push it to origin."\
                  "To trigger a travis-ci build and and deploy."
    user_options = []
    name = "tag_and_deploy"
    remote = "origin"
    branch = "master"

    def initialize_options(self):
        pass

    def finalize_options(self):
        pass

    def run(self):
        if subprocess.call(["git", "--version"]) != 0:
            print("ERROR:\n\tPlease install git.")
            exit(1)
        status_lines = subprocess.check_output(
            ["git", "status"]).splitlines()
        current_branch = status_lines[0].strip().split()[-1].decode()
        print("On branch {}.".format(current_branch))
        if current_branch != self.branch:
            print("ERROR:\n\tNew tags can only be made from branch"
                  " \"{}\".".format(self.branch))
            print("\tYou can use \"git checkout {}\" to switch"
                  " the branch.".format(self.branch))
            exit(1)
        tags_output = subprocess.check_output(["git", "tag"])
        tags = [tag.strip().decode() for tag in tags_output.splitlines()]
        tag = "v" + __version__
        if tag in tags:
            print("Warning: \n\tTag {} already exists.".format(tag))
            print("\tEdit the version information in {}".format(
                    os.path.join(HERE, PACKAGE_NAME, "__init__.py")
                ))
        else:
            print("Creating tag \"{}\".".format(tag))
            subprocess.check_call(["git", "tag", tag])
        print("Pushing tag \"{}\" to remote \"{}\"."
              "".format(tag, self.remote))
        subprocess.check_call(["git", "push", self.remote, tag])
# ...
SETUPTOOLS_METADATA = dict(
# ...
    cmdclass={
# ...
        TagAndDeployCommand.name: TagAndDeployCommad
    },
)
# ...
if __name__ == "__main__":
    import setuptools
    METADATA.update(SETUPTOOLS_METADATA)
    setuptools.setup(**METADATA) # METADATA can be found in several other 

Above, you can see a “distutils” command that executed git through the command line interface.

(2) AppVeyor builds the application

As mentioned above, you can configure AppVeyor to build your application. Here are some parts of the “appveyor.yml” file, that I comment on inline:

# see https://packaging.python.org/appveyor/#adding-appveyor-support-to-your-project
environment:
  PYPI_USERNAME: niccokunzmann3
  PYPI_PASSWORD:
    secure: Gxrd9WI60wyczr9mHtiQHvJ45Oq0UyQZNrvUtKs2D5w=

  # For Python versions available on Appveyor, see
  # http://www.appveyor.com/docs/installed-software#python
  # The list here is complete (excluding Python 2.6, which
  # isn't covered by this document) at the time of writing.

  # we only need Python 3.4 for kivy
  PYTHON: "C:\\Python34"


install:
  - "%PYTHON%\\python.exe -m pip install docutils pygments pypiwin32 kivy.deps.sdl2 kivy.deps.glew"
  - "%PYTHON%\\python.exe -m pip install -r requirements.txt"
  - "%PYTHON%\\python.exe -m pip install -r test-requirements.txt"
  - "%PYTHON%\\python.exe setup.py install"
  
build_script:
- cmd: cmd /c windows-build\build.bat

test_script:
  # Put your test command here.
  # If you don't need to build C extensions on 64-bit Python 3.3 or 3.4,
  # you can remove "build.cmd" from the front of the command, as it's
  # only needed to support those cases.
  # Note that you must use the environment variable %PYTHON% to refer to
  # the interpreter you're using - Appveyor does not do anything special
  # to put the Python version you want to use on PATH.
  - windows-build\dist\KnitEditor\KnitEditor.exe /test
  - "%PYTHON%\\python.exe -m pytest --pep8 kniteditor"

artifacts:
  # bdist_wheel puts your built wheel in the dist directory
- path: dist/*
  name: distribution
- path: windows-build/dist/Installer/KnitEditorInstaller.exe
  name: installer
- path: windows-build/dist/KnitEditor
  name: standalone

deploy:
- provider: GitHub
  # http://www.appveyor.com/docs/deployment/github
  tag: $(APPVEYOR_REPO_TAG_NAME)
  description: "Release $(APPVEYOR_REPO_TAG_NAME)"
  auth_token:
    secure: j1EbCI55pgsetM/QyptIM/QDZC3SR1i4Xno6jjJt9MNQRHsBrFiod0dsuS9lpcC7
  artifact: installer
  force_update: true
  draft: false
  prerelease: false
  on:
    branch: master                 # release from master branch only
    appveyor_repo_tag: true        # deploy on tag push only

Note that the line

  - windows-build\dist\KnitEditor\KnitEditor.exe /test

executes the tests in the built application.

These commands are executed to build the application and are executed by this step:

build_script:
- cmd: cmd /c windows-build\build.bat
"%PYTHON%\python.exe" -m pip install pyinstaller

The line above installs pyinstaller

"%PYTHON%\python.exe" -m PyInstaller KnitEditor.spec

The line above uses pyinstaller to create an executable from the specification.

"Inno Setup 5\ISCC.exe" KnitEditor.iss

The line above uses Inno Setup to create the Installer for the built application.

(3) Deploy to Github

As you can see in the “appveyor.yml” file, the resulting executable is listed as an artifact. Artifacts can be downloaded directly from appveyor or used to deploy. In this case, we use the github deploy, which can be customized via the UI of appveyor.

- path: windows-build/dist/Installer/KnitEditorInstaller.exe
  name: installer
deploy:
- provider: GitHub
  # http://www.appveyor.com/docs/deployment/github
  tag: $(APPVEYOR_REPO_TAG_NAME)
  description: "Release $(APPVEYOR_REPO_TAG_NAME)"
  auth_token:
    secure: j1EbCI55pgsetM/QyptIM/QDZC3SR1i4Xno6jjJt9MNQRHsBrFiod0dsuS9lpcC7
  artifact: installer
  force_update: true
  draft: false
  prerelease: false
  on:
    branch: master                 # release from master branch only
    appveyor_repo_tag: true        # deploy on tag push only

Summary

Now, every time we push a tag to Github, AppVeyor build a new installer for our application.