locpix 0.1.0.3
Package for analysing SMLM data
Requires Python: >=3.7

locpix
======

**locpix** is a Python library for analysing point cloud data from SMLM.

   This project is under active development

This is a short ReadMe just containing a QuickStart guide.
For more comprehensive documentation please see https://oubino.github.io/locpix/

**locpix** includes the following functionality in order they are used in a normal workflow:

#. `Preprocess`_ : Initialises project and converts .csv files representing SMLM data (point cloud) into .parquet files, **necessary for this software**
#. `Annotate`_ : Generating histograms from the SMLM data and manually annotating these histograms to extract relevant localisations & labelling with seeds for watershed algorithm
#. Segmentation:

   #. `Classic segmentation`_ : Use classic method to segment histograms to extract relevant localisations
   #. `Cellpose segmentation`_ : Use Cellpose method to segment histograms to extract relevant localisations
   #. `Ilastik segmentation`_ : Use Ilastik method to segment histograms to extract relevant localisations
   #. `UNET segmentation`_ : Use UNET to segment histograms to extract relevant localisations

#. `Membrane performance`_ : Performance metrics calculation based on the localisations (not the histograms!)

Project Structure
-----------------

We assume your input SMLM data are .csv files.

This input data must first be preprocessed into a user chosen project directory, using the  `Preprocess`_ script.
We strongly suggest this project directory is located outside the locpix folder.

The input and output of all further scripts will remain located inside the project directory, the input data folder
will not be accessed again!

Usage configuration
-------------------

Each script needs a configuration file (.yaml file), which should be
specified using the -c flag.

Each configuration used will be saved in the project directory.

The templates for the configuration files can be found in the `templates folder <https://github.com/oubino/locpix/tree/master/src/locpix/templates>`_.

For instructions for the parameters in the configuration files see https://oubino.github.io/locpix/user_guide/templates/index.html

Quickstart
==========

Installation
------------

Prerequisites
^^^^^^^^^^^^^

You will need anaconda or miniconda or mamba.
We recommend `mamba <https://mamba.readthedocs.io/en/latest/>`_


Install
^^^^^^^

Create an environment and install via pypi

.. code-block:: console

   conda create -n locpix-env python==3.10
   conda activate locpix-env
   pip install locpix


Preprocessing
-------------

Preprocess
^^^^^^^^^^

This script preprocesses the input .csv data for later use AND **must be run first**.

This script will take in .csv files, and convert them to .parquet files,
while also wrangling the data into our data format.

To run the script -i -c and -o flags should be specified

.. code-block:: console

   preprocess -i path/to/input/data -c path/to/config/file -o path/to/project/directory

Optionally we can add:

* -s flag to check the correct files are selected before preprocessing them
* -p flag if our files are .parquet files not .csv files.

Annotate
^^^^^^^^

This script allows for manual segmentation of the localisations and adding markers to the images as seeds for the watershed algorithm.

To run the script -i and -c flags should be specified

.. code-block:: console

   annotate -i path/to/project/directory -c path/to/config/file

Optionally we can add:

* -m flag to check the metadata of the project before running
* -r flag to relabel histograms, will assume some labels are already present

Image segmentation
------------------

Classic segmentation
^^^^^^^^^^^^^^^^^^^^

Perform classic segmentation on our localisation dataset.

To run the script -i and -c flags should be specified

.. code-block:: console

   classic -i path/to/project/directory -c path/to/config/file

Optionally we can add:

* -m flag to check the metadata of the project before running

Cellpose segmentation
^^^^^^^^^^^^^^^^^^^^^

Need to activate extra requirements - these are big and not included in initial install.

Note that if you have a GPU this will speed this up.

Note we modified Cellpose to fit in with our analysis, therefore you need to install our forked repository - note below will clone the Cellpose repository to wherever you are located

If you have a GPU

.. code-block:: console

   pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu117
   git clone https://github.com/oubino/cellpose
   cd cellpose
   pip install .

If you don't have a GPU

.. code-block:: console

   pip install pytorch
   git clone https://github.com/oubino/cellpose
   cd cellpose
   pip install .


To evaluate Cellpose model on the localisation dataset without any retraining on your dataset run the script with -i and -c flags specified

   .. code-block:: console

      cellpose_eval -i path/to/project/directory -c path/to/config/file

Optionally we can add:

* -m flag to check the metadata of the project before running
* -o flag to specify folder in project dir to save output (defaults to cellpose_no_train)
* -u flag to specify a user model to load in

To retrain first then evaluate we instead

   Prepare data for training

   .. code-block:: console

      train_prep -i path/to/project/directory -c path/to/config/file

   Optionally we can add:

   * -m flag to check the metadata of the project before running

   Train cellpose

   .. code-block:: console

      cellpose_train -i path/to/project/directory -ct path/to/config/train_file -ce path/to/config/eval_file

   Optionally we can add:

   * -m flag to check the metadata of the project before running

UNET segmentation
^^^^^^^^^^^^^^^^^

Need to activate extra requirements - these are big and not included in initial install.

Note that if you have a GPU this will speed this up.

Note this is only needed if haven't done for cellpose above

If you have a GPU

.. code-block:: console

   pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu117

If you don't have a GPU

.. code-block:: console

   pip install pytorch

To train UNET

   .. code-block:: console

      unet -i path/to/project/directory -c path/to/config/file

Optionally we can add:

* -m flag to check the metadata of the project before running

Ilastik segmentation
^^^^^^^^^^^^^^^^^^^^

Need to prepare the data for Ilastik segmentation

.. code-block:: console

   ilastik_prep -i path/to/project/directory -c path/to/config/file

Optionally we can add:

* -m flag to check the metadata of the project before running

Then run the data through the Ilastik GUI, which needs to be installed from
`Ilastik <https://www.ilastik.org/download.html>`_  and to run it
please see https://oubino.github.io/locpix/user_guide/usage.html#id7

Then convert the output of the Ilastik GUI back into our format

.. code-block:: console

   ilastik_output -i path/to/project/directory

Optionally we can add:

* -m flag to check the metadata of the project before running

Membrane performance
^^^^^^^^^^^^^^^^^^^^

To evaluate membrane performance for a particular method, run below, where method name needs to match where the segmentation files are

.. code-block:: console

   membrane_performance_method -i path/to/project/directory -c path/to/config/file -o method_name

Optionally we can add:

* -m flag to check the metadata of the project before running

To evaluate performance of  membrane segmentation from classic, cellpose and ilastik

.. code-block:: console

   membrane_performance -i path/to/project/directory -c path/to/config/file

Optionally we can add:

* -m flag to check the metadata of the project before running

To aggregate the performance over the folds for methods classic, cellpose without training, cellpose with training and ilastik

.. code-block:: console

   agg_metrics -i path/to/project/directory

Licenses
--------

+----------------------------------------+----------------------------------------------------------------------+
|                Package                 |                               License                                |
+========================================+======================================================================+
|            alabaster 0.7.12            |                               UNKNOWN                                |
+----------------------------------------+----------------------------------------------------------------------+
|            app-model 0.1.1             |                         BSD 3-Clause License                         |
+----------------------------------------+----------------------------------------------------------------------+
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|              build 0.9.0               |                                 MIT                                  |
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|              cachey 0.2.1              |                                 BSD                                  |
+----------------------------------------+----------------------------------------------------------------------+
|     cellpose 2.1.2.dev26+g731fe4e      |                                 BSD                                  |
+----------------------------------------+----------------------------------------------------------------------+
|           certifi 2022.9.24            |                               MPL-2.0                                |
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|               cfgv 3.3.1               |                                 MIT                                  |
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|           cloudpickle 2.2.0            |                         BSD 3-Clause License                         |
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|             colorama 0.4.6             |                               UNKNOWN                                |
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|             debugpy 1.6.4              |                                 MIT                                  |
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|             distlib 0.3.6              |                            Python license                            |
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| locpix 0.0.12.dev70+ga7833b4.d20230120 |                               UNKNOWN                                |
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|           setuptools 67.7.2            |                               UNKNOWN                                |
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|               six 1.16.0               |                                 MIT                                  |
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|     sphinxcontrib-applehelp 1.0.2      |                                 BSD                                  |
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|      sphinxcontrib-devhelp 1.0.2       |                                 BSD                                  |
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|      sphinxcontrib-htmlhelp 2.0.0      |                                 BSD                                  |
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