From 8cae4702c579dc5a46849ff05e992187cdf418e5 Mon Sep 17 00:00:00 2001
From: rhoadesScholar <rhoadesScholar@users.noreply.github.com>
Date: Tue, 19 Mar 2024 18:09:42 +0000
Subject: [PATCH] :art: Format Python code with psf/black

---
 .../examples/distance_task/cosem_example.py   | 58 +++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/dacapo/examples/distance_task/cosem_example.py b/dacapo/examples/distance_task/cosem_example.py
index 30dc262eb..da07f091c 100644
--- a/dacapo/examples/distance_task/cosem_example.py
+++ b/dacapo/examples/distance_task/cosem_example.py
@@ -1,3 +1,61 @@
+# %% [markdown]
+# # Dacapo
+#
+# DaCapo is a framework that allows for easy configuration and execution of established machine learning techniques on arbitrarily large volumes of multi-dimensional images.
+#
+# DaCapo has 4 major configurable components:
+# 1. **dacapo.datasplits.DataSplit**
+#
+# 2. **dacapo.tasks.Task**
+#
+# 3. **dacapo.architectures.Architecture**
+#
+# 4. **dacapo.trainers.Trainer**
+#
+# These are then combined in a single **dacapo.experiments.Run** that includes your starting point (whether you want to start training from scratch or continue off of a previously trained model) and stopping criterion (the number of iterations you want to train).
+
+# %% [markdown]
+# ## Environment setup
+# If you have not already done so, you will need to install DaCapo. You can do this by first creating a new environment and then installing DaCapo using pip.
+#
+# ```bash
+# conda create -n dacapo python=3.10
+# conda activate dacapo
+# ```
+#
+# Then, you can install DaCapo using pip, via GitHub:
+#
+# ```bash
+# pip install git+https://github.com/janelia-cellmap/dacapo.git
+# ```
+#
+# Or you can clone the repository and install it locally:
+#
+# ```bash
+# git clone https://github.com/janelia-cellmap/dacapo.git
+# cd dacapo
+# pip install -e .
+# ```
+#
+# Be sure to select this environment in your Jupyter notebook or JupyterLab.
+
+# %% [markdown]
+"""
+## Config Store
+To define where the data goes, create a dacapo.yaml configuration file either in `~/.config/dacapo/dacapo.yaml` or in `./dacapo.yaml`. Here is a template:
+
+```yaml
+type: files
+runs_base_dir: /path/to/my/data/storage
+```
+The `runs_base_dir` defines where your on-disk data will be stored. The `type` setting determines the database backend. The default is `files`, which stores the data in a file tree on disk. Alternatively, you can use `mongodb` to store the data in a MongoDB database. To use MongoDB, you will need to provide a `mongodbhost` and `mongodbname` in the configuration file:
+
+```yaml
+...
+mongodbhost: mongodb://dbuser:dbpass@dburl:dbport/
+mongodbname: dacapo
+"""
+
 # %%
 # First we need to create a config store to store our configurations
 from dacapo.store.create_store import create_config_store