Metadata-Version: 2.1
Name: ms-collect
Version: 0.0.3.1
Summary: A series of modules that provide easy storage/accesss to Mass Spec related Raw Data/Groupings of Data.
Home-page: https://github.com/jakuzo/ms-collect
Author: jakuzo
Author-email: jackcampanella@protonmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/jakuzo/ms-collect/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# ms-collect
A series of modules that provide easy storage/accesss to Mass Spec related Raw Data / Groupings of Data.

> Note this package is currently in BETA and is under active development. Moreover, the docs are a work in progress.

## Installation
```sh
pip install ms-collect
```

## Background
In regions of m/z and Retention Time, there exists various signals, features, and collections of data.
This package aims to provide an interface for intuitively representing that data and providing actions/visualizations on said collections.

MS-1 3D Plot             |  MS-1 3D Spectrum
:-------------------------:|:-------------------------:
![3D collection](threeD_collection.png "3D plot of MS-1 Data")  |  ![3D Spectrum](threeD_spectrum.png "3D spectrum plot of MS-1 Data")

![3D collection as spectrum](spectrum.png "Standard Spectrum representation of an MS-1 Collection")
### **ms-collect**, at its core, provides a base _Collection_ interface to which entities extend from.

For instance, this package has named entities that represent MS Features and Isotopic Traces. But fundamentally they are just a collection of points where each point has 3 primary attributes: mass to charge (m/z), Retention Time, and Intensity/Abundance. 

It is often quite useful to visualize what is going on in these regions. The figures above were generated using this package.

## Usage
> This section includes very basic usage, refer to the examples section for more details/advanced usage.
Coming soon: Docs via sphynx.

```python
# Import the package and modules of interest
from ms_collect.scope import Scope
from ms_collect.envelope import Envelope
from ms_collection.point import Point
from ms_collection.collection import Collection


# Define a scope from an m/z and Retention Time region
min_mz = 437.844
max_mz = 438.94
min_rt = 946.004
max_rt = 981.107

my_scope = Scope([min_mz, max_mz, min_rt, max_rt])

# Define an MS-1 Feature/Envelope From said region with a list of Points.
my_points = [...]
my_envelope = Envelope(scope=my_scope, points=my_points)

# Define a point with some m/z, Retention Time, and Intensity values
point = Point(mz=542.3, rt=800.0, intensity=3447661568.0

# Add this point to the envelope and perform some basic envelope collection operations
my_envelope.add_points([point])
my_envelope.avg_mz()
# -> 436.32
my_envelope.cumulative_intensity()
# -> 933484802048.0

# Determine the convex hull of this collection
convexhull = my_envelope.convex_hull()
ch.hull()
# -> [(437.8870316623645, 958.66042827198), (437.88703282730677, 956.1165142560001), (437.8870410510394, 952.112642559), (437.8870495438886, 948.82877457498), (437.887053517079, 948.0991699829999), (437.88705863613876, 947.368067583),(438.22419874976924, 957.5778559199999), (438.2229531181884, 960.11862664002), (437.88945990101047, 962.3162991049801), (437.8882413409473, 961.2199352969999)]

# For some visual tools you have access to things like:
# (methods below render figures via a matplotlib backbone)

some_pts = [..]
collection = Collection(points=some_pts)

collection.three_d() # -> plots the points in 3d space.
collection.spectrum() # -> displays these points in a 'spectrum' fashion
collection.three_d_spectrum() # -> displays a spectrum in a 3d orientation.

```

### Upcoming features
- Visual utilities for the various types of collections.
- Optimizations on resource intensive class methods.
- Better import/export and IO related functionality.
- Converting a collection to a dataframe.
- Better Api doc experience.
- If you would like to see something here we are accepting feature requests! Please submit an issue or ping us!

## Contribute
Please refer to CONTRIBUTE.md for instructions on on contributing.


