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@paulstothard
paulstothard committed Jul 10, 2024
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Expand Up @@ -3066,221 +3066,217 @@ Another option is to use [csvjoin](#merge-csv-files-on-a-specified-column-or-col

### Use a local Conda environment as a Python kernel

1. Create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal):
Create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal):

```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=plot-python
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the jupyter and ipykernel packages
mamba install -y -c conda-forge jupyter ipykernel
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda numpy pandas matplotlib seaborn scikit-learn
# Add the Python kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the Conda environment
mamba deactivate
```
```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=plot-python
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the jupyter and ipykernel packages
mamba install -y -c conda-forge jupyter ipykernel
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda numpy pandas matplotlib seaborn scikit-learn
# Add the Python kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the Conda environment
mamba deactivate
```

1. Restart VS Code or Jupyter Notebook.
1. Open a notebook and select the Jupyter kernel that you created.
Restart VS Code or Jupyter Notebook and open a notebook and select the Jupyter kernel that you created.

### Use a remote Conda environment as a Python kernel

1. On the remote system create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal on the remote system):
On the remote system create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal on the remote system):

```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=plot-python
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the jupyter and ipykernel packages
mamba install -y -c conda-forge jupyter ipykernel
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda numpy pandas matplotlib seaborn scikit-learn
# Add the Python kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the Conda environment
mamba deactivate
```
```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=plot-python
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the jupyter and ipykernel packages
mamba install -y -c conda-forge jupyter ipykernel
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda numpy pandas matplotlib seaborn scikit-learn
# Add the Python kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the Conda environment
mamba deactivate
```

1. On the remote system launch `tmux`, activate the environment, and start Jupyter:
On the remote system launch `tmux`, activate the environment, and start Jupyter:

```bash
tmux new -s jupyter
NEW_ENV_NAME=plot-python
mamba activate "$NEW_ENV_NAME"
jupyter notebook --no-browser --port=8888
```
```bash
tmux new -s jupyter
NEW_ENV_NAME=plot-python
mamba activate "$NEW_ENV_NAME"
jupyter notebook --no-browser --port=8888
```

Note the token that is displayed in the output of the `jupyter notebook` command.
Note the token that is displayed in the output of the `jupyter notebook` command.

For example:
For example:

```text
399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```
```text
399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```

Now disconnect from the tmux session by pressing `Ctrl-b` then `d`.
Now disconnect from the tmux session by pressing `Ctrl-b` then `d`.

1. On the local system, create an SSH tunnel to the remote system (`gentec` is an entry in the SSH configuration file, `~/.ssh/config`; change the name as needed, for example to **user@host**):
On the local system, create an SSH tunnel to the remote system (`gentec` is an entry in the SSH configuration file, `~/.ssh/config`; change the name as needed, for example to **user@host**):

```bash
ssh -L 8888:localhost:8888 gentec
```
```bash
ssh -L 8888:localhost:8888 gentec
```

The above will forward port 8888 on the remote machine to port 8888 on the local machine. Leave this terminal open.
The above will forward port 8888 on the remote machine to port 8888 on the local machine. Leave this terminal open.

1. Open a Jupyter Notebook in a VS Code and click **Select Kernel** and then **Existing Jupyter Server**. Enter the following URL (appending the token from the remote system):
Open a Jupyter Notebook in a VS Code and click **Select Kernel** and then **Existing Jupyter Server**. Enter the following URL (appending the token from the remote system):

```text
http://localhost:8888/?token=399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```
```text
http://localhost:8888/?token=399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```

Then choose the kernel that you created, from the available kernels.
Then choose the kernel that you created, from the available kernels.

1. To end the session, close the Jupyter Notebook and the terminal running the SSH tunnel. Connect to the server and stop the Jupyter Notebook server by pressing `Ctrl-c` in the terminal where the server is running (use `tmux a -t jupyter` to attach to the session). Then close the tmux session by running `exit` in the tmux session.
To end the session, close the Jupyter Notebook and the terminal running the SSH tunnel. Connect to the server and stop the Jupyter Notebook server by pressing `Ctrl-c` in the terminal where the server is running (use `tmux a -t jupyter` to attach to the session). Then close the tmux session by running `exit` in the tmux session.

### Use a Python virtual environment as a Python kernel

1. Create a Python virtual environment and add it as a kernel to Jupyter (run the following commands in the terminal, in the project root directory):

```bash
# Create a new Python virtual environment, changing the name as needed
NEW_ENV_NAME=plot-venv
PYTHON_VERSION=python3.12
echo "${NEW_ENV_NAME}/" >> .gitignore
$PYTHON_VERSION -m venv "$NEW_ENV_NAME"
source "${NEW_ENV_NAME}/bin/activate"
# Install the desired packages, e.g.
pip install numpy pandas matplotlib seaborn scikit-learn
# Install the jupyter and ipykernel packages
pip install jupyter ipykernel
# Add the virtual environment kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the virtual environment
deactivate
```
Create a Python virtual environment and add it as a kernel to Jupyter (run the following commands in the terminal, in the project root directory):

```bash
# Create a new Python virtual environment, changing the name as needed
NEW_ENV_NAME=plot-venv
PYTHON_VERSION=python3.12
echo "${NEW_ENV_NAME}/" >> .gitignore
$PYTHON_VERSION -m venv "$NEW_ENV_NAME"
source "${NEW_ENV_NAME}/bin/activate"
# Install the desired packages, e.g.
pip install numpy pandas matplotlib seaborn scikit-learn
# Install the jupyter and ipykernel packages
pip install jupyter ipykernel
# Add the virtual environment kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the virtual environment
deactivate
```

1. Restart VS Code or Jupyter Notebook.
1. Open a notebook and select the Jupyter kernel that you created.
Restart VS Code or Jupyter Notebook and open a notebook and select the Jupyter kernel that you created.

### Use a Python virtual environment created using venv_wrapper

The `venv_wrapper` script simplifies the creation of Python virtual environments. It is in my dotfiles and is based [on this script](https://gist.github.com/dbtek/fb2ddccb18f0cf63a654ea2cc94c8f19).

1. Create a Python virtual environment and add it as a kernel to Jupyter (run the following commands in the terminal, in the project root directory):

```bash
# Create a new Python virtual environment, changing the name and Python version as needed
NEW_ENV_NAME=plot-venv
PYTHON_VERSION=3.12
echo "$HOME/.venv/${NEW_ENV_NAME}/" >> .gitignore
# Create and activate the new virtual environment using the wrapper commands
mkvenv "$NEW_ENV_NAME" "$PYTHON_VERSION"
# Install the desired packages, e.g.
pip install numpy pandas matplotlib seaborn scikit-learn
# Install the jupyter and ipykernel packages
pip install jupyter ipykernel
# Add the virtual environment kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the virtual environment
deactivate
```
Create a Python virtual environment and add it as a kernel to Jupyter (run the following commands in the terminal, in the project root directory):

```bash
# Create a new Python virtual environment, changing the name and Python version as needed
NEW_ENV_NAME=plot-venv
PYTHON_VERSION=3.12
echo "$HOME/.venv/${NEW_ENV_NAME}/" >> .gitignore
# Create and activate the new virtual environment using the wrapper commands
mkvenv "$NEW_ENV_NAME" "$PYTHON_VERSION"
# Install the desired packages, e.g.
pip install numpy pandas matplotlib seaborn scikit-learn
# Install the jupyter and ipykernel packages
pip install jupyter ipykernel
# Add the virtual environment kernel to Jupyter
python -m ipykernel install --user --name "$NEW_ENV_NAME" --display-name="Python ($NEW_ENV_NAME)"
# Deactivate the virtual environment
deactivate
```

1. Restart VS Code or Jupyter Notebook.
1. Open a notebook and select the Jupyter kernel that you created.
Restart VS Code or Jupyter Notebook and open a notebook and select the Jupyter kernel that you created.

### Use a local Conda environment as a Bash kernel

1. Create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal):
Create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal):

```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=vep-bash
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda ensembl-vep
# Install the jupyter and bash_kernel packages
mamba install -y -c conda-forge jupyter bash_kernel
# Install the Bash kernel within the environment
python -m bash_kernel.install
# Deactivate the Conda environment
mamba deactivate
```
```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=vep-bash
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda ensembl-vep
# Install the jupyter and bash_kernel packages
mamba install -y -c conda-forge jupyter bash_kernel
# Install the Bash kernel within the environment
python -m bash_kernel.install
# Deactivate the Conda environment
mamba deactivate
```

1. Restart VS Code or Jupyter Notebook.
1. Open a notebook and select the Jupyter kernel that you created.
Restart VS Code or Jupyter Notebook and open a notebook and select the Jupyter kernel that you created.

### Use a remote Conda environment as a Bash kernel

1. On the remote system create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal on the remote system):
On the remote system create the Conda environment and add it as a kernel to Jupyter (run the following commands in the terminal on the remote system):

```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=vep-bash
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda ensembl-vep
# Install the jupyter and bash_kernel packages
mamba install -y -c conda-forge jupyter bash_kernel
# Install the Bash kernel within the environment
python -m bash_kernel.install
# Deactivate the Conda environment
mamba deactivate
```
```bash
# Create a new Conda environment, changing the name as needed
NEW_ENV_NAME=vep-bash
PYTHON_VERSION=3.12
mamba create -y --name "$NEW_ENV_NAME" python=$PYTHON_VERSION
mamba activate "$NEW_ENV_NAME"
# Use the next command if using Apple silicon and osx-arm64 packages aren't available
conda config --env --set subdir osx-64
# Install the desired packages, e.g.
mamba install -y -c conda-forge -c bioconda ensembl-vep
# Install the jupyter and bash_kernel packages
mamba install -y -c conda-forge jupyter bash_kernel
# Install the Bash kernel within the environment
python -m bash_kernel.install
# Deactivate the Conda environment
mamba deactivate
```

1. On the remote system launch `tmux`, activate the environment, and start Jupyter:
On the remote system launch `tmux`, activate the environment, and start Jupyter:

```bash
tmux new -s jupyter
NEW_ENV_NAME=vep-bash
mamba activate "$NEW_ENV_NAME"
jupyter notebook --no-browser --port=8888
```
```bash
tmux new -s jupyter
NEW_ENV_NAME=vep-bash
mamba activate "$NEW_ENV_NAME"
jupyter notebook --no-browser --port=8888
```

Note the token that is displayed in the output of the `jupyter notebook` command.
Note the token that is displayed in the output of the `jupyter notebook` command.

For example:
For example:

```text
399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```
```text
399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```

Now disconnect from the tmux session by pressing `Ctrl-b` then `d`.
Now disconnect from the tmux session by pressing `Ctrl-b` then `d`.

1. On the local system, create an SSH tunnel to the remote system (`gentec` is an entry in the SSH configuration file, `~/.ssh/config`; change the name as needed, for example to **user@host**):
On the local system, create an SSH tunnel to the remote system (`gentec` is an entry in the SSH configuration file, `~/.ssh/config`; change the name as needed, for example to **user@host**):

```bash
ssh -L 8888:localhost:8888 gentec
```
```bash
ssh -L 8888:localhost:8888 gentec
```

The above will forward port 8888 on the remote machine to port 8888 on the local machine. Leave this terminal open.
The above will forward port 8888 on the remote machine to port 8888 on the local machine. Leave this terminal open.

1. Open a Jupyter Notebook in a VS Code and click **Select Kernel** and then **Existing Jupyter Server**. Enter the following URL (appending the token from the remote system):
Open a Jupyter Notebook in a VS Code and click **Select Kernel** and then **Existing Jupyter Server**. Enter the following URL (appending the token from the remote system):

```text
http://localhost:8888/?token=399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```
```text
http://localhost:8888/?token=399401bd7d1259c4f7d51bd005602f30a0c28e184bf3313e
```

Then choose the kernel that you created, from the available kernels.
Then choose the kernel that you created, from the available kernels.

1. To end the session, close the Jupyter Notebook and the terminal running the SSH tunnel. Connect to the server and stop the Jupyter Notebook server by pressing `Ctrl-c` in the terminal where the server is running (use `tmux a -t jupyter` to attach to the session). Then close the tmux session by running `exit` in the tmux session.
To end the session, close the Jupyter Notebook and the terminal running the SSH tunnel. Connect to the server and stop the Jupyter Notebook server by pressing `Ctrl-c` in the terminal where the server is running (use `tmux a -t jupyter` to attach to the session). Then close the tmux session by running `exit` in the tmux session.

## Mamba

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