### Set up Local Development Environment

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/CONTRIBUTING.rst

Install the project in a virtual environment using `python setup.py develop`.

```bash
mkvirtualenv mdvoxelclustering
cd mdvoxelclustering/
python setup.py develop
```

--------------------------------

### Start a New Branch

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Initiates a new branch for development by checking out the development branch, pulling the latest changes, and creating a new branch.

```bash
git checkout development
git pull
git branch "New-Branch"
```

--------------------------------

### Install MDVoxelSegmentation using pip

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Install the MDVoxelSegmentation package directly from its GitHub repository using pip. This command ensures you get the latest version.

```bash
pip install git+https://github.com/marrink-lab/MDVoxelSegmentation
```

--------------------------------

### Get Lipid Segments from Selection

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.html

Retrieves lipid segments for a given selection in the current frame. The selection must follow basic mda selection syntax.

```python
def get_segments_from_selection(self, selection):
    """Returns an array of lipids segments for the given resname 
    in the current frame. The selection should adhere
    to the basic mda selection syntax."""
    selection = self.u.select_atoms(selection)
    resids = selection.residues.ix
    segments = [container.get_segment_from_resid(resid) 
                    for resid in resids]
    return np.asarray(segments, dtype=int)
```

--------------------------------

### Clone the Repository

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/CONTRIBUTING.rst

Clone your forked repository locally to begin development.

```bash
$ git clone git@github.com:your_name_here/mdvoxelclustering.git
```

--------------------------------

### Run Tests and Checks

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/CONTRIBUTING.rst

Ensure your changes pass flake8 and tests, including cross-version compatibility with tox.

```bash
flake8 mdvoxelclustering tests
python setup.py test
tox
```

--------------------------------

### Loading Molecular Dynamics Universe

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Initializes an MDAnalysis Universe object with the provided topology and trajectory files. Note: Ignore UserWarnings related to mass guessing for coarse-grained beads.

```python
u = mda.Universe(gro, xtc)
```

--------------------------------

### Importing Libraries

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Imports necessary libraries for data manipulation, visualization, and molecular dynamics analysis.

```python
import matplotlib.pyplot as plt
import numpy as np
import MDAnalysis as mda
```

--------------------------------

### Check MDVoxelSegmentation help

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Run this command in your terminal to display the help message for the mdvseg command-line tool. This shows available options and usage.

```bash
mdvseg -h
```

--------------------------------

### Visualize VMD Clusters

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Launches VMD with a specified cluster visualization file. This automatically assigns colors and materials to the first 32 segments for easy selection and visualization.

```bash
vmd -e vmd_clusters_visualization.vmd
```

--------------------------------

### Run a Subset of Tests

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/CONTRIBUTING.rst

Execute a specific test module using the `unittest` module.

```python
python -m unittest tests.test_mdvoxelclustering
```

--------------------------------

### Defining File Paths

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Specifies the file paths for the simulation's topology (gro), trajectory (xtc), and segmentation data.

```python
gro = '../eq_dry.gro'
xtc = '../md_dry.xtc'
segmentation_data = '../segmentation_new/clusters.npy'
```

--------------------------------

### Create a New Branch

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/CONTRIBUTING.rst

Create a new branch for your bugfix or feature development.

```bash
git checkout -b name-of-your-bugfix-or-feature
```

--------------------------------

### Initializing Container and Defining Lipids

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Creates an instance of the Container class and defines the target leaflet IDs and residues of interest (cholesterol).

```python
# Create our container
container = Container(u, segmentation)

# We explicitly state our leaflets and lipids of interest
leaflet1 = 1
leaflet2 = 2
residues = container.u.select_atoms('resname CHOL').residues
```

--------------------------------

### Activate and Check Branch Status

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Switches to the newly created branch and displays its current status.

```bash
git checkout "New-Branch"
git status
```

--------------------------------

### Commit and Push Changes

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/CONTRIBUTING.rst

Commit your local changes and push the branch to your GitHub fork.

```bash
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-bugfix-or-feature
```

--------------------------------

### Initialize and Assign Segments

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Initializes segment masks and assigns segment IDs to frames. This sets up the base data for flip-flop analysis.

```python
leaflet_mask1 = segments_per_frame==leaflet1
leaflet_mask2 = segments_per_frame==leaflet2
segments_per_frame[:] = 0
segments_per_frame[leaflet_mask1] = 1
segments_per_frame[leaflet_mask2] = 2
print(segments_per_frame)
```

--------------------------------

### Perform Basic Segmentation

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Perform default segmentation on GRO and XTC files for coarse-grain Martini systems. The output is saved to clusters.npy. Adjusting force segmentation (-fs) and recursion depth (-rd) impacts performance.

```bash
mdvseg -f path_to_your.gro -x path_to_your.xtc
```

--------------------------------

### Preparing Segment Tracking Array

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Initializes a NumPy array to store the segment assignments for each lipid residue across all trajectory frames.

```python
# Preparing our segment per resid array
segments_per_frame = np.zeros((len(container.u.trajectory), 
                               len(residues)))
segments_per_frame.shape
```

--------------------------------

### Atomistic Segmentation with CHARMM Force Field

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Configure selections for atomistic segmentation of DOPE lipids using the CHARMM force field. This involves defining headgroups, linkers, and tails. Run mdvseg with specified selections and disable hyper-resolution for atomistic data.

```bash
$ vi selections.inp
## Create an empty `selections.inp` and add the following lines, the selection 
##  syntax should always be one line and directly follow its header description.

# It is not bad to include the linkers in the headgroups as well, but this is often 
#  not important.
[charmm_heads]
(name N P C12 C11 O11 O12 O13 O14)

[charmm_linkers]
(name C1 C2 O21 C21 C3 O31 C31)

# Not adding the first carbons of the tail can improve performance, but again, 
#  this is usually not important.
[charmm_tails]
(name C22 C23 C24 C25 C26 C27 C28 C29 C210 C211 C212 C213 C214 C215 C216 C217 C218 C32 C33 C34 C35 C36 C37 C38 C39 C310 C311 C312 C313 C314 C315 C316 C317 C318)


## Run the mdvseg, hyper resolution can be turned off for there are more than
##  enough particles due to the atomistic resolution!
$ mdvseg -f your.gro -x your.xtc -hg charmm_heads -lg charmm_linkers -tg charmm_tails -hres 0
```

--------------------------------

### Plot Flip-Flop Results

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.html

Plots the calculated sums of absolute flips per frame to visualize the flip-flop activity over the simulation trajectory.

```python
import matplotlib.pyplot as plt
plt.plot(sums)
plt.show()
```

--------------------------------

### Add New Branch Online

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Pushes the newly created local branch to the remote repository and sets it as the upstream branch.

```bash
git push --set-upstream origin "New-Branch"
```

--------------------------------

### Citation for MDVoxelSegmentation

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

This is the citation information for the MDVoxelSegmentation article. It should be used when referencing the software in publications.

```bibtex
@article{Bruininks2021,
  doi = {10.1021/acs.jctc.1c00446},
  url = {https://doi.org/10.1021/acs.jctc.1c00446},
  year = {2021},
  month = oct,
  publisher = {American Chemical Society ({ACS})},
  volume = {17},
  number = {12},
  pages = {7873--7885},
  author = {Bart M. H. Bruininks and Albert S. Thie and Paulo C. T. Souza and Tsjerk A. Wassenaar and Shirin Faraji and Siewert J. Marrink},
  title = {Sequential Voxel-Based Leaflet Segmentation of Complex Lipid Morphologies},
  journal = {Journal of Chemical Theory and Computation}
}
```

--------------------------------

### Setting Active Frame

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Sets the current trajectory frame to the first frame (index 0) for initial analysis.

```python
# Set the active frame to frame 0
container.u.trajectory[0]
```

--------------------------------

### Loading Segmentation Data

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Loads the pre-computed segmentation data from a NumPy file.

```python
segmentation = np.load(segmentation_data)
```

--------------------------------

### Plot Up and Down Flips

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Plots the counts of upward and downward flips separately over time, allowing for a detailed analysis of flip-flop dynamics.

```python
plt.plot(ups_and_downs['ups'])
plt.plot(ups_and_downs['downs'])
plt.show()
```

--------------------------------

### Fetch Segment ID from Residue or Selection

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Provides a Python class 'Container' to interact with MDAnalysis universes and segmentation data. It allows fetching segment IDs by residue index or by MDAnalysis selection syntax.

```python
class Container():
    """A simple container class for our universe and segmentation data."""
    def __init__(self, universe, segmentation):
        self.u = universe
        self.segmentation = segmentation

    def get_segment_from_resid(self, resid):
        """Returns the residue segment id in the current frame."""
        residue_index = self.u.residues[resid].atoms[0].ix
        current_frame = self.u.trajectory.frame
        residue_segment = self.segmentation[current_frame, residue_index] 
        return residue_segment

    def get_segments_from_selection(self, selection):
        """Returns an array of lipid segments for the given selection 
        in the current frame. The selection should adhere
        to the basic mda selection syntax."""
        selection = self.u.select_atoms(selection)
        resids = selection.residues.ix
        segments = [container.get_segment_from_resid(resid) 
                        for resid in resids]
        return np.asarray(segments, dtype=int)
        
# File paths
gro = 'your.gro'
xtc = 'your.xtc'
segmentation_data = 'clusters.npy'

# Creating universe and loading segmentation
u = mda.Universe(gro, xtc)
segmentation = np.load(segmentation_data)

# Create our container
container = Container(u, segmentation)

# Segment from resid
resid1_segmentID = container.get_segment_from_resid(1)

# Segment from selection
all_cholesterol_segmentsIDs = container.get_segments_from_selection('resname CHOL')
```

--------------------------------

### Resync and Merge Changes

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Pulls the latest changes from the remote repository to synchronize the local branch. Merging should be handled via GUI or terminal.

```bash
git pull
```

--------------------------------

### Handle Unassigned Segments

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Iterates through simulation frames to fill in unassigned segments (value 0) with the segment ID from the previous frame. This ensures continuity in segment assignments.

```python
# Setting all values which are not relevant to their previous segment.
#  This is for loop and probably we can do this better, but 
#  for now it is at least clear what we are doing.
for idx, frame in enumerate(segments_per_frame):
    if idx == 0:
        continue
    else:
        # Find the indices of all the residues which 
        #  are not asigne to 0.
        bad_resids = np.where(frame == 0)[0]
        if bad_resids.size:
            print('There is a bad one!')
            # Set the value of a non set segment to the previous
            #  value.
            previous_frame = segments_per_frame[idx-1]
            frame[list(bad_resids)] = previous_frame[bad_resids]
```

--------------------------------

### Populating Segment Data Across Frames

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Iterates through each frame of the trajectory, extracts segment assignments for cholesterol lipids using the Container's method, and prints progress.

```python
# Obtain the resid segment for each frame
for idx, frame in enumerate(container.u.trajectory):
    segments_per_frame[idx] = container.get_segments_from_selection('resname CHOL')
    print(f'\rWorking on frame {idx+1}/{segments_per_frame.shape[0]}.             ', 
          end='')
```

--------------------------------

### Verify Segment Assignment

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Prints segment assignments for specific frame and residue indices to verify the correctness of the segment assignment process.

```python
# These values should now be the same.
print(segments_per_frame[ 9, [11, 12]])
print(segments_per_frame[10, [11, 12]])
```

--------------------------------

### Manually Control Plotting Script

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

After running the basic segmentation, a plotting script is generated. You can manually alter this script and re-run it to customize the segmentation graph output.

```bash
python plotting.py # alter this and rerun
```

--------------------------------

### Displaying Segment Data

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Prints the entire NumPy array containing segment assignments for each lipid across all frames.

```python
print(segments_per_frame)
```

--------------------------------

### Commit Changes to Current Branch

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Stages all changes, checks their status, and commits them with a specified message.

```bash
git add .
git status
git commit . -m "write message here"
```

--------------------------------

### Plot Total Flips

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Plots the sum of absolute flips per frame to visualize the overall activity of segment changes over time.

```python
plt.plot(sums)
plt.show()
```

--------------------------------

### Non-Amphipathic Density Segmentation

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Define a basic non-solvent Martini density selection for segmentation. Run mdvseg without an exclusion group and with forced segmentation turned off.

```bash
# Define the density selection
$ vi selections.inp

[density]
(not resname W WF ION) # Basic non-solvent Martini density

[none]
False

# Run mdvseg with the selections.inp without an exclusion group nor forced segmenation. 
mdvseg -f your.gro -x your.xtc -hg density -tg none -eg none -fs 0
```

--------------------------------

### Calculate Frame Differences for Flips

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Calculates the difference between consecutive frames to identify segment flips. A difference of 1 indicates an upward flip, and -1 indicates a downward flip.

```python
# Calculating the flips by differentiating
flips = segments_per_frame[1:] - segments_per_frame[:-1]
```

--------------------------------

### Process Lipid Flip Data

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.html

Calculates the number of 'ups' (positive flips) and 'downs' (negative flips) from a given dataset of flips. This is useful for quantifying directional changes in lipid movement.

```python
ups_and_downs = dict()
ups_and_downs['ups']   = []
ups_and_downs['downs'] = []
for row in flips:
    ups_and_downs['ups'].append(len(np.where(row == 1)[0]))
    ups_and_downs['downs'].append(-len(np.where(row == -1)[0]))
```

--------------------------------

### Addressing Irrelevant Assignments

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Explains the strategy to handle irrelevant segment assignments by replacing target values, ensuring that -1 and 1 specifically denote flip events.

```python
# To solve the problem of irrelevant assignment we replace out targets,
#  by doing so we make sure that a value of -1 and 1 reflect a flip. 
```

--------------------------------

### Container Class for MD Data

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

A helper class to encapsulate the MDAnalysis Universe and segmentation data, providing methods to access segment information for residues and selections.

```python
class Container():
    """A simple container class for our universe and segmentation data."""
    def __init__(self, universe, segmentation):
        self.u = universe
        self.segmentation = segmentation
    
    def get_segment_from_resid(self, resid):
        """Returns the residue segment id in the current frame."""
        residue_index = self.u.residues[resid].atoms[0].ix
        current_frame = self.u.trajectory.frame
        residue_segment = self.segmentation[current_frame, residue_index] 
        return residue_segment
    
    def get_segments_from_selection(self, selection):
        """Returns an array of lipids segments for the given resname 
        in the current frame. The selection should adhere
        to the basic mda selection syntax."""
        selection = self.u.select_atoms(selection)
        resids = selection.residues.ix
        segments = [container.get_segment_from_resid(resid) 
                        for resid in resids]
        return np.asarray(segments, dtype=int)
```

--------------------------------

### Close Local Branch

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Switches back to the development branch and deletes the local feature branch.

```bash
git checkout development
git branch -d "New-Branch"
```

--------------------------------

### Count Up and Down Flips

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Counts the number of upward (value 1) and downward (value -1) flips in each frame. This provides a directional breakdown of segment changes.

```python
# Be careful, up and down is relative and this should be checked!
ups_and_downs = dict()
ups_and_downs['ups']   = []
ups_and_downs['downs'] = []
for row in flips:
    ups_and_downs['ups'].append(len(np.where(row == 1)[0]))
    ups_and_downs['downs'].append(-len(np.where(row == -1)[0]))
```

--------------------------------

### Push Intermediate Changes

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/.gitsyncing.txt

Pushes the committed changes from the local branch to the remote repository.

```bash
git push
```

--------------------------------

### Define Data Container Class

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.html

A helper class to encapsulate the MDAnalysis Universe and segmentation data. It provides methods for accessing residue information within the current trajectory frame.

```python
class Container():
    """A simple container class for our universe and segmentation data."""
    def __init__(self, universe, segmentation):
        self.u = universe
        self.segmentation = segmentation
    
    def get_segment_from_resid(self, resid):
        """Returns the residue segment id in the current frame."""
        residue_index = self.u.residues[resid].atoms[0].ix
        current_frame = self.u.trajectory.frame
```

--------------------------------

### Plot Segment Count Over Time

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/README.rst

Loads segmentation data from 'clusters.npy' and plots the number of unique segments per frame using NumPy and Matplotlib. The plot is saved as 'amount_of_segments_over_time.png'.

```python
## Importing numpy and matplotlib.
import numpy as np
import matplotlib.pyplot as plt

## Loading the segmentation data.
segments_over_time = np.load('clusters.npy')

## Calculating the amount of segments in each frame.
# Make an empty array which has one int32 for every frame.
segments_per_frame = np.zeros(segments_over_time.shape[0], dtype='int32') 
# Fill the array with the amount of non-zero segments in each frame.
segments_per_frame[:] = [len(np.unique(frame)) - 1 for frame in segments_over_time]

## Plotting the results.
# Making an empty plot.
fig, ax = plt.subplots()
# Adding required data to plot.
ax.plot(segments_per_frame)
# Set ticks to a sensible regime.
start, end = [round(limit) for limit in ax.get_ylim()]
ax.yaxis.set_ticks(np.arange(start, end + 1, 1))
# Add labels to axes.
ax.set_xlabel('Frame count')
ax.set_ylabel('Number of segments')
# Save the plot.
fig.savefig('amount_of_segments_over_time.png', dpi=300)
# Usually people don't like it if you pop windows, however
#  if you would like to automatically show the result uncomment
#  the following line.
#fig.show()
```

--------------------------------

### Sum Absolute Flips per Frame

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Calculates the sum of absolute differences for each frame, representing the total number of segment changes in that frame.

```python
sums = [np.sum(np.abs(frame)) for frame in flips]
```

--------------------------------

### Normalize Leaflet Assignments

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.html

Replaces segment assignments to normalize them, ensuring that values 1 and 2 represent the target leaflets. All other values are considered irrelevant for flip-flop detection.

```python
# To solve the problem of irrelevant assignment we replace out targets,
#  by doing so we make sure that a value of -1 and 1 reflect a flip. 
#  All other values are not relevant.
leaflet_mask1 = segments_per_frame==leaflet1
leaflet_mask2 = segments_per_frame==leaflet2
segments_per_frame[:] = 0
segments_per_frame[leaflet_mask1] = 1
segments_per_frame[leaflet_mask2] = 2
print(segments_per_frame)
```

--------------------------------

### Modifying Segment Data for Testing

Source: https://github.com/marrink-lab/mdvoxelsegmentation/blob/development/src/mdvoxelsegmentation/templates/lipid_flip-flop.ipynb

Introduces specific modifications to the segment data array for testing purposes, assigning invalid segment IDs.

```python
# Create two faulty ones just for testing.
segments_per_frame[10, 12] = 0
segments_per_frame[10, 12] = 15
segments_per_frame[10, [12, 13]]
```

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