Publish Your Software in conda-forge

US RSE-23 Tutorial
Valerio Maggio and Dave Clements, Anaconda
October 10, 2023

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Before we start

In this hands-on tutorial, we will be assuming that:

• you are already familiar with Python program and module structure best practices;
• you have a basic knowledge of Git and GitHub, including how to fork projects, update your copy, and then submit pull requests.
• Have basic familiarity with a command line interface and a text editor program

A word for Windows users

If you have the Linux Subsystem for Windows installed then you can use the mainline instructions in this tutorial. You will only need to follow the "Windows specific" instructions if you don't have LSW installed.

When instructions are different on Windows than they are on Linux, macOS, or LSW there will be a separate section with Windows instructions like this:

Windows (non LSW) 💁

Launch the command prompt.

Start → Windows System → Command Prompt

We'll be using the command prompt and then the anaconda prompt throughout.

Getting started

Before diving into the exercises, let's make sure we have everything we need to get started.

1. A working internet connection! (If you're reading this online, you could already mark ✅ this.. but better be sure! 🙃)
2. Working versions of conda and git installed on your computer.
3. A preferred Code editor of choice with syntax highlighting for the Python language.

If you don't have all of these then please have a look at the next section for detailed instructions on how to install what you need.

Otherwise, please feel free to jump directly to the Create Conda Environment section to proceed.

Set up your computer

1. Install Miniconda

For this tutorial we recommend Miniconda, a free minimal installer for conda. It includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others.

Use the conda install command to install over 3,000 additional conda packages from the Anaconda repository, and over 20,000 packages from conda-forge.

Please download and install the most recent Miniconda installer that matches your computer architecture and Operating System. Accept the default settings when installing.

Anaconda Distribution 🐍

Differently from Miniconda, Anaconda Distribution provides immediate access to lots of
packages that gets installed on your computer along side the Python distribution.

For the sake of this tutorial, installing Miniconda would be more than enough. However, if you want to install Anaconda Distribution, or you already have it installed on your computer, that would work too!

In that case, you would only need to make sure that you are running the latest version of conda. To do so, please run the following command in the Terminal / Anaconda Prompt:

conda update -n base conda

2. Install Git

Installing Git will vary depending on your Operating System. You could follow all the instructions reported here for a detailed step-by-step setup.

3. Code Editor

Choosing the "right" code editor is always a matter of personal taste, laziness (to adjust to new UIs) and religious attachment to certain lines of code. Also, what you can possibly find online isn't that helpful either, with oxymoronic titles like "The 5 Best Code Editors".

For these very reason, we are not requiring you to use any specific code editor. Any editor that you like which has syntax highlighting for the Python language will do.

During the live coding sessions of the tutorial, we will be using Visual Studio Code, with the MS Python Extension.

Create Conda Environment

If you are here, it's because you already have a working version of conda installed on your computer. The first thing to do now is to create a virtual Conda environment that we will be using throughout the tutorial.

Windows (non LSW) 💁

Launch the newly installed Anaconda Prompt:

Start → Anaconda Prompt (miniconda3)

To create our new packaging conda environment just type the following instruction in the terminal/command line/Anaconda Prompt:

conda create -n packaging -y -c conda-forge python=3.10 pytest ipython 

This will create a new conda environment using Python 3.10, and then install IPython and pytest as extra packages.

This is all we need to get started. At this point, we just have to activate our new environment, and then we will be ready to go!

conda activate packaging

The command line now starts with (packaging) instead of (base) reflecting that you now in the packaging environment.

D&D ROLLER

In this tutorial we will be working together on a new and never-been-seen-before application that will be implement a die roller to be used during our campaign of Dungeons & Dragons.

The specs of this applications are pretty simple:

1. We would need to roll a single die, choosing one of d4, d6, d8, d10, d12, d20, d100

2. We would need to make multiple die rolls, of multiple dice (e.g. 2d4 and 3d6).

   • In this case, we want to do all the rolls and generate a tabular report like the one below:
     

Nice, let's get started! Shall we!?

1. Create & Clone the dnd-roller GitHub repository

Let's start by creating the GitHub repository that will host our dnd-roller project.

Let's go to your GitHub profile page (i.e. https://github.com/<your-gh-username>); click the green 🖥️ New button in the upper right corner.

Name the new repository dnd-roller, with the following description:

Simple python app to roll dice for D&D (Dungeons&Dragons)

Also:

• Mark the repository as Public;
• Add a README file
• Add gitignore, choosing the template for Python
• Add an MIT License

We're now ready to proceed, and to hit the button Create repository.

At this point, all that remains is to clone this repo on your local computer:

cd <to-some-working-space-on-your-laptop>
mkdir CondaPkgTutorial
cd CondaPkgTutorial
git clone https://github.com/<your-gh-username>/dnd-roller

Image from teeturtle.com

2. Let's dig into our dnd-roller

It's finally time to write some Python code for our dnd-roller!

We will start by creating the new dnd_roller Python package, and its corresponding folder for tests.

cd dnd-roller 
mkdir dnd_roller
mkdir tests

Note the underscore in the dnd_roller subdirectory name. The directory of your project will look something like this:

dnd-roller
|_ dnd_roller
|_ tests
|_ .gitignore
|_ LICENSE
|_ README.md

The dice.py Module

Let's now work on our new dice.py module: dnd_roller/dice.py. This module will contain all the functions that implement the main core functionalities of our dnd-roller.

Let's recap what we need to implement in our dice roller:

1. We need a function to roll a single die (i.e. roll)
   • This die must be one of the seven dice used in D&D: d4|6|8|10|12|100
2. We need a function to roll a single die, multiple times (i.e. dice_roll)
3. We need a function that would receive a sequence of dice rolls (e.g. "2d4, 3d6, 1d10, 6d12") and will generate a tabular report of the outcomes.

Here is the code for dice.py. Paste this into a file named dnd_roller/dice.py using your text editor.

# file: dnd_roller/dice.py

from random import randint
from tabulate import tabulate

SUPPORTED_DICE = (4, 6, 8, 10, 12, 20, 100)
HEADERS = ["dice", "rolls", "sum"]


def roll(d: int, verbose: bool = False) -> int:
    """Roll a single die choosing one of d4, d6, d8, d10, d12, d20, d100"""
    try:
        d = int(d)
    except ValueError:
        raise ValueError(f"'{d}' is not an appropriate number of faces.")
    else:
        if d not in SUPPORTED_DICE:
            raise ValueError("Unsupported Game die for D&D")
        r = randint(1, d) if d != 100 else (randint(1, 10) * 10)

        if verbose:
            print("You rolled ", r)
        return r


def dice_roll(throws: int, sides: int) -> list[int]:
    """Rolls a single die with given `sides` a `throws` number of times"""
    return [roll(d=sides) for _ in range(throws)]


def sequence_rolls(sequence: str, verbose: bool = True) -> str:
    """Generating a tabular summary of a sequence of dice rolls passed in input.
    Each dice roll are comma separated, and defined in the form of
    `throwsdface`, e.g. 2d12."""
    rolls_in_sequence = {
        roll: dice_roll(*tuple(map(int, roll.split("d"))))
        for roll in map(str.strip, sequence.split(","))
    }
    
    if verbose:
        table = [(seq, rolls, sum(rolls)) for seq, rolls in rolls_in_sequence.items()]
        print(tabulate(table, headers=HEADERS, tablefmt="fancy_grid"))
    
    return rolls_in_sequence

Here is more information about this module. We will use this to implement tests.

1. The roll function

The requirements for this function are pretty simple indeed:

• roll(d=4) -> return a number in [1,4]
• roll(d=10) -> return a number in [1,10]
• roll(d=5) -> Exception: Unsupported die
• [A little catch] roll(d=100) -> roll(d=10) * 10

💡 A few comments:

In this implementation, we first want to be sure that the parameter d does actually correspond to an integer. We do so adopting an idiomatic approach known as EAFP (Easier to ask for Forgiveness than Permission). Afterwards, we check that the number of sides chosen for the die is indeed valid in D&D, and then we simply rely on the random.randint function to generate the result of our rolling. Simple as that!

2. The dice_roll function

Given that all the heavy-lifting has been done already in the roll function, the implementation of the dice_roll function is pretty straightforward: it just needs to call the roll function multiple times, and return the outcomes as a Python list.

3. The sequence_rolls function

The last function in dnd-roller is sequence_rolls.

Generating the dice rolls is pretty straightforward. All that is really left to do is to parse the input sequence so that it becomes input parameters for the dice_roll function. However that is pretty simple to do as well, so we do it in a quite convoluted way using a combination of functional programming and dictionary comprehension to make it fun.

Dependencies and tabulate

As for the tabulation part, we will leverage the tabulate package, that is directly available in the default conda channel:

conda install tabulate

Alternatively, you could install the tabulate package directly from PyPI:

pip install tabulate

__init__.py

We'll need an __init__.py file to make Python treat our directory as a package. The file can be empty, but it must exist. Create it in the main dnd_roller directory. __init__.py is run once when the module is first imported and it's an important part of defining the public interface to our module.

Our module has three functions, roll, dice_roll, and sequence_rolls. The roll function supports the dice_roll function, which in turn supports the sequence_rolls function. So, it is possible that we want only sequence_rolls in the package's public interface. But, these 3 functions all serve useful functions so, let's put all 3 in the public interface. Create __init_py__ in our top level directory:

# file: dnd_roller/__init__.py

from .dice import roll, dice_roll, sequence_rolls, SUPPORTED_DICE

which enables this

from dnd_roller import dice_roll

rolls = dice_roll(throws=3, sides=6)

instead of this

from dnd_roller.dice import dice_roll

rolls = dice_roll(throws=3, sides=6)

Users of your package don't need to know the details of where you implemented your API.

💡 This is a good opportunity to remind you that it is indeed possible to install non-conda packages into a Conda environment 💫

Let's try it out

🎉 We are finally done with our fancy dnd-roller. All we need to do is to try to generate some tabular report. In a [I]Python interpreter:

We could try the new implementation interactively in the Python interpreter.
Note: ipython (or even default python interpreter) would equally do:

Testing

Published packages (and all software for that matter) should include test cases.

To verify that this implementation does everything we expect it to, the best possible way is to write a few tests.

We will be using PyTest as our testing framework, so let's first create a pytest.ini configuration file in the main root folder. This file will instruct pytest to run the test by managing the namespace resolution properly:

# file: pytest.ini
[pytest]
# Add pytest options here:
# https://docs.pytest.org/en/7.1.x/reference/customize.html

roll tests

Now let's create a new test module test_roll.py under the tests folder with a bunch of pretty simple test functions that test roll:

# file: tests/test_roll.py

import pytest
from dnd_roller import roll, SUPPORTED_DICE


@pytest.mark.parametrize("dface", SUPPORTED_DICE[:-1])
def test_roll_die_return_a_valid_number(dface):
    assert roll(dface) in range(1, dface+1)


def test_roll_d100():
    assert roll(100) in range(1, 101)
    assert roll(100) % 10 == 0


def test_roll_with_nan_string_will_raise_exception():
    with pytest.raises(ValueError):
        roll("not a die")


@pytest.mark.parametrize("dface", SUPPORTED_DICE[:-1])
def test_roll_with_no_string_will_still_work_as_expected(dface):
    assert roll(str(dface)) in range(1, dface+1)


def test_roll_with_unsupported_dice_will_raise_exception():
    with pytest.raises(ValueError):
        roll(45)


@pytest.mark.parametrize("dface", [-4, 2.3, 11.8])
def test_roll_with_negative_or_float_no_will_raise_exception(dface):
    with pytest.raises(ValueError):
        roll(dface)

Using pytest, and its parametrize feature, we're checking a few corner cases (e.g. NaN inputs, negative or float numbers), as well as correct expected behaviours for our roll function (including the only "problematic" case of the d100).

dice_roll tests

Testing should verify that the actual implementation of the dice_roll is indeed calling the roll function (multiple times), without duplicating code! (which is a very bad practice, ed.). To do so, we will be creating a Mock (more here).

# file: tests/test_dice_roll.py

import pytest
from unittest.mock import patch
from dnd_roller.dice import dice_roll


@pytest.mark.parametrize("throws, sides", [(2, 4), (1, 6), (4, 8), (1, 10)])
def test_dice_rolls(throws, sides):
    rolls = dice_roll(throws=throws, sides=sides)
    assert len(rolls) == throws
    assert all([r in range(1, sides + 1) for r in rolls])


@patch("dnd_roller.dice.roll")
def test_dice_roll_calls_roll(roll_mock):
    dice_roll(throws=2, sides=4)
    roll_mock.assert_called()
    roll_mock.assert_called_with(d=4)

The more interesting part here is about the tests:

• first we could re-use the same strategy we used with roll by using pytest.mark.parametrize to generate a few test inputs (a.k.a. fixtures).
  In this case we will be generating a few combinations of throws and sides values, and we will be checking that

  1. The number of returned rolls are exactly equal to throws
  2. That all the rolls are within the expected range, [1, sides]

sequence_rolls tests

Our last function needs some tests too. We will be adding the tests for the sequence_rolls function into a new tests/test_sequence_rolls.py test module.

# file: tests/test_sequence_rolls.py

import pytest
from random import seed, randint
from unittest.mock import patch
from dnd_roller.dice import sequence_rolls, roll


@patch("dnd_roller.dice.dice_roll")
def test_dice_roll_is_called_in_sequence_rolls(dice_roll_mock):
    sequence_rolls(sequence="2d4")
    dice_roll_mock.assert_called()
    dice_roll_mock.assert_called_with(2, 4)


def unfair_dice(sequence, rnd_seed: int) -> dict[int, list[int]]:
    """generates a fixed list of t rolls for each die in sequence."""

    seed(rnd_seed)  # this does the trick!
    return {d: [randint(1, d) for _ in range(t)] for t, d in sequence}


def test_roll_is_repeatable():
    # FIX the random seed
    rnd_seed = 123456
    unfair_rolls = unfair_dice(((2, 4),), rnd_seed=rnd_seed)
    seed(rnd_seed)
    assert roll(4) == unfair_rolls[4][0]
    assert roll(4) == unfair_rolls[4][1]


def test_sequence_rolls_with():
    sequence = "2d4, 6d6, 4d8"
    rnd_seed = 4567

    unfair_rolls = unfair_dice(((2, 4), (6, 6), (4, 8)), rnd_seed=rnd_seed)
    seed(rnd_seed)

    rolls = sequence_rolls(sequence=sequence)
    assert "2d4" in rolls
    assert len(rolls["2d4"]) == 2
    assert all(r == unfr for r, unfr in zip(rolls["2d4"], unfair_rolls[4]))

    assert "4d8" in rolls
    assert len(rolls["4d8"]) == 4
    assert all(r == unfr for r, unfr in zip(rolls["4d8"], unfair_rolls[8]))

    assert "6d6" in rolls
    assert len(rolls["6d6"]) == 6
    assert all(r == unfr for r, unfr in zip(rolls["6d6"], unfair_rolls[6]))

The first test is pretty similar to the last previously discussed: we're just checking that sequence_rolls is not reinventing the wheel, and that the dice_roll function is called instead, with the right parameters. Again, we are leveraging on unittest.mock.patch to do so.

The other tests are more generally testing the output generated by sequence_rolls, so that each sequence has (a) the correct number of rolls, and (b) exactly the very same rolls we are expecting. To do so, we use a trick that sets the random seed. We repeat the calls to random in the same way/order. In this way, we are absolutely sure to always generate the same sequence of numbers.
FYI, this is the foundation on which Reproducibility in Data Science could be obtained (e.g. see Reproducibility in Deep learning).

Test!

To run our tests, let's move back to our terminal:

python -m pytest -v

You should get an output similar to the one reported below:

3. Time to pack 📦

It's finally time to pack! Our dnd-roller is ready to become a re-usable Python package for everybody to use. So, let's create the skeleton for our future package-to-be, following the instructions reported in the official Python documentation.

Creating setup.py and package metadata

First thing we do is to create a new setup.py file that uses Python setuptools.setup to specify initial package metadata:

# file: setup.py

import os
from pathlib import Path
from setuptools import find_packages, setup

PKG_FOLDER = Path(os.path.abspath(os.path.dirname(__file__)))

with open(PKG_FOLDER / "README.md") as f:
    long_description = f.read()


setup(
    name="dnd-roller",
    version="0.0.1",
    author="YOUR NAME",
    author_email="YOUR EMAIL ADDRESS",
    description="Python package to roll D&D dice in the terminal.",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="http://github.com/YOUR-GITHUB_ID/dnd-roller",
    include_package_data=True,
    packages=find_packages(exclude=[]),
    install_requires=["tabulate>=0.8.10"],
)

💡 Please note that we specified our external dependencies in the install_requires=[...] parameter of the setup function, that is tabulate.

❗️ At this point, please feel free to add any additional metadata to the README.md file as this will be used for the long_description of the dnd-roller package. For example:

The `dnd_roller` provides three main functions: `roll`, `dice_roll`, and `sequence_rolls` to generate a single die roll, multiple rolls of the same die, or multiple rolls of multiple dice.

The former (i.e. `roll()`) could generate a simple output in the terminal, whilst the latter (i.e. `sequence_rolls()`) generate a tabular report for the outcome of each roll in the sequence.

Please have a look at the examples below for additional details.

### Examples

Rolling a single game die:

```python
>>> from dnd_roller import roll
>>> roll(d=4)
4
>>> roll(d=20, verbose=true)
You rolled  17
17
```
Rolling multiple times the same game die:

```python 

>>> from dnd_roller import dice_roll
>>> dice_roll(throws=3, sides=4)
[3, 2, 4]
```

Rolling a sequence of dice rolls:

```python
>>> from dnd_roller import sequence_rolls
>>> sequence_rolls(sequence="12d20, 4d4, 2d10, 1d100", verbose=True)

╒════════╤════════════════════════════════════════════════╤═══════╕
│ dice   │ rolls                                          │   sum │
╞════════╪════════════════════════════════════════════════╪═══════╡
│ 12d20  │ [15, 9, 13, 2, 14, 13, 18, 15, 13, 10, 17, 18] │   157 │
├────────┼────────────────────────────────────────────────┼───────┤
│ 4d4    │ [2, 3, 2, 1]                                   │     8 │
├────────┼────────────────────────────────────────────────┼───────┤
│ 2d10   │ [8, 5]                                         │    13 │
├────────┼────────────────────────────────────────────────┼───────┤
│ 1d100  │ [50]                                           │    50 │
╘════════╧════════════════════════════════════════════════╧═══════╛
{'12d20': [15, 9, 13, 2, 14, 13, 18, 15, 13, 10, 17, 18],
 '4d4': [2, 3, 2, 1],
 '2d10': [8, 5],
 '1d100': [50]}
```

Finally we will add some additional metadata in the setup.cfg and pyproject.toml configuration files, such as the license, and the package classifiers.

💡 Note: We will soon see how these metadata can be consumed by automatic build tools for packaging.

# file: setup.cfg

[metadata]
version = 0.0.1
license_file = LICENSE
classifiers =
	License :: OSI Approved :: MIT License
    Environment :: Console
	Intended Audience :: Developers
	Intended Audience :: Science/Research
	Programming Language :: Python :: 3

[options]
python_requires = >=3.10

Similarly, in pyproject.toml:

# file: pyproject.toml

version = "0.0.1"
name = "dnd-roller"
authors = [
    {name = "Valerio Maggio", email = "[e-mail]"},
    {name = "Dave Clements", email="[e-mail]"}
]
description = "Python package to roll D&D dice in the terminal."
requires-python = ">=3.10"
classifiers = [
    "License :: OSI Approved :: MIT License",
    "Environment :: Console",
	  "Intended Audience :: Developers",
	  "Intended Audience :: Science/Research",
	  "Programming Language :: Python :: 3"
]
dependencies = [
    "tabulate>=0.8.10"
]


[build-system]
requires = ["setuptools", "setuptools-scm"]
build-backend = "setuptools.build_meta"

🎉 Whoot whoot! Now everything is ready for our dnd-roller package!

Publish on GitHub

Time to publish everything on GitHub:

git add .
git commit -m "dnd-roller 0.0.1"
git push

If we now open the browser, and visit the GH repository url (e.g., https://github.com/leriomaggio/dnd-roller), you should see something similar to the image below.

4. By the power of Grayskull… I have the Conda recipe

Image Credits: https://he-man.fandom.com/

All the hipster geeks in the audience shouldn't require further references and explanation.
And Yes: you are still in the right room! We are still talking about Conda and Python packaging.

From the official documentation:

Grayskull is an automatic conda recipe generator.

The main goal of this project is to generate concise recipes for conda-forge. The Grayskull project was created with the intention to eventually replace conda skeleton.

Presently Grayskull can generate recipes for Python packages available on PyPI and also those not published on PyPI but available as GitHub repositories. Grayskull can also generate recipes for R packages published on CRAN. Future versions of Grayskull will support recipe generation for packages of other repositories such as Conan and CPAN etc..

🎉 Looks like a fantastic treat! We will be using grayskull to automatically generate a recipe for our dnd-roller project, so that we can build a conda project for it!

Installing Grayskull

We start by installing grayskull using conda, from the conda-forge channel:

conda install -c conda-forge grayskull -y

Creating a Release on GitHub

The next thing we want to do, is to create a release of our project on GitHub. Once we have done that, we will be able to use grayskull to generate our recipe. In fact, grayskull will fetch all the necessary information (and package) from GitHub to prepare our conda-recipe.

To do a release, we can use the GitHub interface directly. The only thing to bear in mind is to specify a proper version tag for our release: v0.0.1. The version tag is what grayskull will be using to gather the version of our package, as well as the name of the archive generated by GitHub. Click Create a new release on the right hand side of your GitHub's repo page, and then create the release

Once we have a release, we are able to proceed to generate our conda-recipe with Grayskull.

Generating conda recipe for dnd-roller

One of the main advantages of using grayskull is not only that we don't need to worry about (manually) creating the conda-recipe to build our package, but it can also get everything that is required directly from GitHub.

Generate the recipe for dnd-roller with grayskull is just two-steps away:

1. if you are located in the dnd-roller folder, please move away (say, in the parent directory), and create a new folder named grayskull (or as you prefer, the name does not matter):

cd ..
mkdir -p grayskull

2. Now let's move into this new grayskull folder, and generate the recipe:

cd grayskull
grayskull pypi https://github.com/<you-gh-username>/dnd-roller

🎉 When it's completed, you should now see a dnd-roller folder, containing a meta.yml.
This is indeed your conda recipe we where hoping for! 👨‍🍳

5. It's time for conda build

Now that we have our recipe, all that's need to do is to use it to build our dnd-roller conda package.

First install conda-build which will enable the conda build command.

conda install -c conda-forge conda-build

Next, still within the grayskull folder, let's type:

conda build dnd-roller

If everything goes well, a dnd-roller.tar.gz archive should have been created here

$CONDA_PREFIX_1/conda-bld/noarch/dnd-roller-0.0.1-py_0.tar.bz2 

Windows 💁

%CONDA_PREFIX_1%\conda-bld\noarch\dnd-roller-0.0.1-py_0.tar.bz2 

6. Submitting to conda-forge

Today, we are not going to submit multiple (or even single) copies of our dnd-roller package to conda-forge. We don't want to test the patience of the conda-forge gods.

But, we will get you to the point just before submission, and time allowing, we will also show you how to create your own channel on anaconda.org and publish packages there.

conda-forge's step-by-step instructions

The instructions here are heavily based on the conda-forge instructions for package submission.

From conda-forge

1. Ensure your source code can be downloaded as a single file. Source code should be downloadable as an archive (.tar.gz, .zip, .tar.bz2, .tar.xz) or tagged on GitHub, to ensure that it can be verified. (For further detail, see Build from tarballs, not repos).

We got this!

2. Fork and clone the staged-recipes repository from GitHub.

We will fork the repo using the GitHub web interface, and then clone that fork on our laptop

• Go to the staged-recipes repo in GitHub.
• Click on Fork in the upper right, and create a copy of the repo under your user.

Now, clone the new repo locally.

cd ..     # up out of grayskull directory
git clone https://github.com/<your-github-username>/staged-recipes.git

That clone may take a bit - the repo is around 100mb.

3. Checkout a new branch from the staged-recipes main branch.

cd staged-recipes
git checkout -b dnd-roller-submission

4. Through the CLI, cd inside the ‘staged-recipes/recipes’ directory.

cd recipes

5. Within your forked copy, create a new folder in the recipes folder for your package (i.e, ...staged-recipes/recipes/<name-of-package>)

mkdir dnd-roller

6. Copy meta.yaml from the example directory. All the changes in the following steps will happen in the COPIED meta.yaml (i.e., ...staged-recipes/recipes/<name-of-package>/meta.yaml). Please leave the example directory unchanged!

We could do this, but grayskull has already generated a perfectly good meta.yaml that we can use, so let's use that instead.

cd dnd-roller
cp ../../../grayskull/dnd-roller/meta.yaml meta.yaml

Windows (non WSL) 💁

cd dnd-roller
copy ..\..\..\grayskull\dnd-roller\meta.yaml meta.yaml

7. Modify the copied recipe (meta.yml) as needed. To see how to modify meta.yaml, take a look at the recipe meta.yaml.

Some things to note:

1. We still need to modify the meta.yaml file generated by grayskull. See below.
2. The staged-recipes/recipes/example/meta.yaml file is full of useful guidance, as is the conda-forge meta.yml documentation. Spend some time getting to understand the contents of this file.

Now, lets tidy up the meta.yaml file we just copied in. The end of that file says:

extra:
  recipe-maintainers:
    - AddYourGitHubIdHere

You need to replace AddYourGitHubIdHere with your GitHub ID.

8. Generate the SHA256 key for your source code archive, as described in the example recipe using the openssl tool. As an alternative, you can also go to the package description on PyPI from which you can directly copy the SHA256.

Thanks to the power of Grayskull we already have a SHA256.

9. Be sure to fill in the test section. The simplest test will simply test that the module can be imported, as described in the example.

Thanks to the power of Valerio we have already created our tests.

10. Remove all irrelevant comments in the meta.yaml file.

The file generated by Grayskull contains no comments.

conda-forge's checklist

The conda-forge documentation follows the above instructions with this checklist:

• Ensure that the license and license family descriptors (optional) have the right case and that the license is correct. Note that case sensitive inputs are required (e.g. Apache-2.0 rather than APACHE 2.0). Using SPDX identifiers for license field is recommended. (see SPDX Identifiers and Expressions)

Our meta.yaml says MIT and MIT is on the example list of approved strings, so we are good.

• Ensure that you have included a license file if your license requires one – most do. (see here)

Some of the packages that are merged into conda-forge have a LICENSE.txt file alongside the meta.yaml file, and some don't.

Does the MIT license require this? We have no idea, but many merged PRs use an MIT license and do not include a top level LICENSE.txt file. If they don't need one, we don't either!

• In case your project has tests included, you need to decide if these tests should be executed while building the conda-forge feedstock.

• Make sure that all tests pass successfully at least on your development machine.

Already done.

• Recommended: run the test locally on your source code to ensure the recipe works locally (see Running tests locally for staged recipes).

Already done.

• Make sure that your changes do not interfere with other recipes that are in the recipes folder (e.g. the example recipe).

Our folder is dnd-roller and that does not collide in anyway with example which is the only other folder in the recipes directory.

Commit, push, request

Let's get our staged recipe into our GitHub repo, and then submit a pull request to conda-forge.

cd ..      # cd at least up to recipes
git add .
git commit -m "DND Roller rolls dice of all sizes."
git push   # oops, but error message tells us what to do next:
git push --set-upstream origin dnd-roller-submission

The status message in the push tells us where to go next:

https://github.com/<your-github-id>/staged-recipes/pull/new/dnd-roller-submission

Go there to create (almost) a conda-forge PR submissions. conda-forge PRs use this PR template:

Thank you very much for putting in this recipe PR!

This repository is very active, so if you need help with a PR, please let the
right people know. There are language-specific teams for reviewing recipes.

| Language        | Name of review team           |
| --------------- | ----------------------------- |
| python          | `@conda-forge/help-python`    |
| python/c hybrid | `@conda-forge/help-python-c`  |
| r               | `@conda-forge/help-r`         |
| java            | `@conda-forge/help-java`      |
| nodejs          | `@conda-forge/help-nodejs`    |
| c/c++           | `@conda-forge/help-c-cpp`     |
| perl            | `@conda-forge/help-perl`      |
| Julia           | `@conda-forge/help-julia`     |
| ruby            | `@conda-forge/help-ruby`      |
| other           | `@conda-forge/staged-recipes` |

Once the PR is ready for review, please mention one of the teams above in a
new comment. i.e. `@conda-forge/help-some-language, ready for review!`
Then, a bot will label the PR as 'review-requested'.

Due to GitHub limitations, first time contributors to conda-forge are unable
to ping conda-forge teams directly, but you can [ask a bot to ping the team][1]
using a special command in a comment on the PR to get the attention of the
`staged-recipes` team. You can also consider asking on our [Gitter channel][2]
if your recipe isn't reviewed promptly.

[1]: https://conda-forge.org/docs/maintainer/infrastructure.html#conda-forge-admin-please-ping-team
[2]: https://gitter.im/conda-forge/conda-forge.github.io
-->

We have a decidedly Pythonic submission.

Next there is a checklist. Read it and add x's as you confirm each item.

Checklist
- [ ] Title of this PR is meaningful: e.g. 
      "Adding my_nifty_package", not 
      "updated meta.yaml".
- [ ] License file is packaged (see 
      [here](https://github.com/conda-forge/staged-recipes/blob/5eddbd7fc9d1502169089da06c3688d9759be978/recipes/example/meta.yaml#L64-L73)
      for an example).
- [ ] Source is from official source.
- [ ] Package does not vendor other packages. 
      (If a package uses the source of another 
      package, they should be separate packages 
      or the licenses of all packages need to 
      be packaged).
- [ ] If static libraries are linked in, the 
      license of the static library is packaged.
- [ ] Package does not ship static libraries.
      If static libraries are needed, 
      [follow CFEP-18](https://github.com/conda-forge/cfep/blob/main/cfep-18.md).
- [ ] Build number is 0.
- [ ] A tarball (`url`) rather than a repo 
      (e.g. `git_url`) is used in your recipe 
      (see [here](https://conda-forge.org/docs/maintainer/adding_pkgs.html#build-from-tarballs-not-repos) 
      for more details).
- [ ] GitHub users listed in the maintainer 
      section have posted a comment confirming 
      they are willing to be listed there.
- [ ] When in trouble, please check our 
      [knowledge base documentation](https://conda-forge.org/docs/maintainer/knowledge_base.html) 
      before pinging a team.

When it's ready, click Create pull request to submit it. EXCEPT DON'T DO THAT TODAY.

After submitting the PR

There is a whole post-staging process for what happens to your PR after submission.

conda-forge is an all volunteer organization, and depending on how much else is going on, it may take a while for the conda-forge team to engage with your PR. After the initial ping to the appropriate team (see above), how long should you wait before pinging conda-forge again? If you can, try to wait at least a week, and always (always) be polite in your communications.

Publishing in conda-forge

From the conda-forge doc:

Once the PR containing the recipe for a package is merged in the staged-recipes repository, a new repository is created automatically called <package-name>-feedstock. A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configuration files for automatic builds using freely available continuous integration (CI) services.

Each feedstock contains various files that are generated automatically using our automated provisioning tool conda-smithy.

7. Publishing in your own channel (time allowing)

You can also publish your package in your own channel on Anaconda.org.

These instructions are based on the conda documentation.

1. Create an account on Anaconda.org

   • Once you log in, it will tell you to verify your email. Please do so. Sometimes, you have to ask for the email to be resent.

2. Install anaconda-client:

   conda install anaconda-client

3. Login

   anaconda login

4. Upload your package to Anaconda.org:

   ​​​​anaconda upload $CONDA_PREFIX_1/conda-bld/noarch/dnd-roller-0.0.1-py_0.tar.bz2 
   

   Windows 💁

   ​​​​anaconda upload %CONDA_PREFIX_1%\conda-bld\noarch\dnd-roller-0.0.1-py_0.tar.bz2 
   

Now, go to your profile page on anaconda.com, which is at https://anaconda.org/YOUR_USERNAME.