### Install BitBIRCH Package

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Installs the bitbirch package from GitHub using git clone and pip install -e. Requires a terminal environment.

```shell
git clone https://github.com/mqcomplab/bitbirch.git
cd bitbirch
pip install -e .
```

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

### Install BitBirch

Source: https://github.com/mqcomplab/bitbirch/blob/main/README.md

Clones the BitBirch repository from GitHub and installs it using pip. This is the standard procedure for setting up the project on your local machine.

```bash
git clone https://github.com/mqcomplab/bitbirch.git
cd bitbirch
pip install -e .
```

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

### Generate Molecular Fingerprints with RDKit

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Converts SMILES strings to RDKit molecules and then generates binary fingerprints. Requires RDKit to be installed.

```python
mols = [Chem.MolFromSmiles(S) for S in smiles]
fps = np.array([Chem.RDKFingerprint(mol) for mol in mols])
```

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

### Example Usage of population_plot Function in Python

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Demonstrates how to call the `population_plot` function with pre-sorted `clustered_ids` and `smiles` data, specifying the number of top clusters to visualize. This snippet shows a practical application of the plotting function.

```python
population_plot(clustered_ids, smiles, top_clusters=20)
```

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

### Get Number of Clusters and Clusters with More Than 10 Molecules in Python

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Calculates and prints the total number of identified clusters and counts how many of these clusters contain more than 10 molecules. This provides a quick statistical overview of the clustering results.

```python
print(f'Number of clusters: {len(clustered_ids)}')
print(f'number of clusters with more than 10 molecules: {len([cluster for cluster in clustered_ids if len(cluster) > 10])}')
```

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

### Import Necessary Libraries

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Imports core libraries for data manipulation, molecular processing, and BitBIRCH functionality. Requires numpy, rdkit, and bitbirch.

```python
import numpy as np
from rdkit import Chem
import bitbirch.bitbirch as bb
import bitbirch.plotting_utils as plotting_utils
import pandas as pd
```

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

### Initialize BitBirch Clustering

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Sets the merging strategy to 'diameter' and initializes a BitBirch object with specified threshold and branching factor, then fits it to the fingerprints.

```python
branching_factor = 50
threshold = 0.65

bb.set_merge('diameter')
brc = bb.BitBirch(threshold=threshold, branching_factor=branching_factor)
brc.fit(fps)
```

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

### Refine Clustering with Tolerance Merging

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Prepares data for refinement by splitting into well-defined and large clusters, then refits using 'tolerance' merging with a higher threshold. Uses fit_BFs for incremental fitting.

```python
# Prepare the data from the first tree to refit
rest, big = brc.prepare_data_BFs(fps)

# Delete the initial tree to save memory
#del brc

# Create a new tree, this time with the data from the first tree and using tolerance merging and a higher threshold
bb.set_merge('tolerance', 0.00)
threshold = 0.70
brc_refined = bb.BitBirch(threshold=threshold, branching_factor=branching_factor)

# Fit the new tree, notice that for this we are using fit_BFs, since we took the BitFeatures from the first tree
brc_refined.fit_BFs(rest)
brc_refined.fit_BFs(big)
```

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

### Save Metrics and Initialize Plotting

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Saves clustering metrics to a CSV file and initializes plotting for the 'diameter' method. Requires the bitbirch.plotting_utils module.

```python
plotting_utils.save_metrics_to_csv(brc, fps, smiles, method='diameter')
plotting_utils.init_plot('diameter')
```

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

### Save Refined Metrics and Initialize Plotting

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Saves clustering metrics for the refined results to a CSV file and initializes plotting for the 'refined' method. Requires the bitbirch.plotting_utils module.

```python
plotting_utils.save_metrics_to_csv(brc_refined, fps, smiles, method='refined')
plotting_utils.init_plot('refined')
```

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

### Analyze Cluster Sizes

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Retrieves molecule IDs for each cluster and counts the total number of clusters and the number of clusters containing more than 10 molecules.

```python
mol_ids = brc_refined.get_cluster_mol_ids()
print("Number of clusters: ", len(mol_ids))

# Get the clusters with more than 10 molecules
clusters = [cluster for cluster in mol_ids if len(cluster) > 10]
print("Number of clusters with more than 10 molecules: ", len(clusters))
```

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

### Load Pre-computed Fingerprints

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Loads pre-computed molecular fingerprints from a .npy file using memory-mapping. Assumes the file path is correct.

```python
fps = np.load('/home/kenneth/Documents/birch/bitbirch_sandbox/data/chembl_33_np.npy', mmap_mode='r')
```

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

### Load Clustered IDs

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Loads the clustered IDs from a pickle file named 'clustered_ids_parallel.pkl'. Requires the 'pickle' library.

```python
import pickle

with open('clustered_ids_parallel.pkl', 'rb') as f:
    clustered_ids = pickle.load(f)
```

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

### Split and Save Fingerprints

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Splits the generated fingerprints and their corresponding indices into a specified number of batches. Saves each batch as a .npy file in the 'fps' directory and records the index ranges in 'fps_splits.txt'. Requires 'numpy' and 'os'.

```python
import numpy as np
import os

n_splits = 5
fps_splits = np.array_split(fps, n_splits)
index_splits = np.array_split(np.arange(len(fps)), n_splits)

os.makedirs('fps', exist_ok=True)

with open('fps_splits.txt', 'w') as f:
    for i, (index_split, fps_split) in enumerate(zip(index_splits, fps_splits)):
        initial_index = index_split[0]
        final_index = index_split[-1]
        f.write(f'{initial_index}_{final_index}\n')
        np.save(f'fps/fps_{initial_index}_{final_index}.npy', fps_split)
```

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

### Visualize Molecules in a Specific Cluster

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Generates images to visually inspect molecules within a specified cluster. The function splits molecules into multiple images for better readability. Requires the bitbirch.plotting_utils module.

```python
plotting_utils.mol_visualization(smiles, brc_refined, cluster=1243)
```

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

### Load Fingerprint Batches

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Reads all .npy files from the 'fps/' directory and concatenates them into a single NumPy array. Requires 'glob' and 'numpy'.

```python
import glob
import numpy as np

fps = []
for file in glob.glob('fps/*.npy'):
    fps.extend(np.load(file))

fps = np.array(fps)
```

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

### Generate and Save Final Assignments

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Generates cluster assignments for all molecules and saves them along with their SMILES strings to a CSV file named 'smiles_assignments.csv'.

```python
assignments = brc_refined.get_assignments(n_mols=n_mols)

# Save assignments to our smiles
df = pd.DataFrame({'smiles': smiles, 'assignments': assignments})
df.to_csv('smiles_assignments.csv', index=False)
```

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

### Execute Parallel Clustering Script

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Executes an external shell script ('sub_parallel.sh') to perform parallel clustering on the generated fingerprint batches. This is typically run from the command line.

```shell
os.system('./sub_parallel.sh')
```

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

### Read SMILES Strings from File

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Reads SMILES strings from a .smi file into a NumPy array. Assumes the file path is correct and contains one SMILES per line.

```python
with open('../../data/chembl_33_np.smi', 'r') as f:
    smiles = np.array([line.strip() for line in f])
print("Number of smiles: ", smiles.shape[0])
```

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

### Generate t-SNE Plot for Spatial Visualization

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Creates a t-SNE plot to visualize the spatial distribution of the clustered data. Requires the bitbirch.plotting_utils module.

```python
plotting_utils.tsne_plot(brc_refined, fps, "refined", title="refined")
```

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

### Generate Molecule Images from a Specific Cluster in Python

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Generates and saves images of molecules belonging to a specified cluster. It uses RDKit's `Chem.MolFromSmiles` to create molecule objects and `Draw.MolsToGridImage` to arrange them into a grid. The code iterates through molecules in a cluster, saving images in batches.

```python
label = 0
mols = [Chem.MolFromSmiles(smiles[i]) for i in clustered_ids[label]]
print('Number of molecules:', len(mols))

for i in range(0, len(mols), 30):

    img_1 = Draw.MolsToGridImage(mols[i:i+30], molsPerRow=5)

    # Save the images from the .data attribute
    with open(f'cluster_{label}_{i}.png', 'wb') as f:
        f.write(img_1.data)
```

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

### Check Fingerprint Quality

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/simple_birch/birch_tutorial.ipynb

Verifies that the molecular fingerprints are binary (contain only 0s and 1s) and have the required int64 or float64 data type.

```python
print(f'Bits are binary: {np.all(np.isin(fps, [0, 1]))}')
print(f'Data type is int64: {fps.dtype == np.int64 or fps.dtype == np.float64}')
```

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

### Generate RDKit Fingerprints

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Converts SMILES strings to RDKit molecule objects and then generates RDKit fingerprints. Requires the 'rdkit' and 'numpy' libraries. Handles potential errors if SMILES are invalid.

```python
from rdkit import Chem
import numpy as np

mols = [Chem.MolFromSmiles(smi) for smi in smiles]
fps = np.array([Chem.RDKFingerprint(mol) for mol in mols])
```

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

### Execute Final Clustering Script

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Executes an external shell script ('sub_final_clustering.sh') to perform the final clustering step on the results of the parallel jobs. This script is intended to be run from the command line, potentially with arguments like '-t 0.675'.

```shell
os.system('./sub_final_clustering.sh')
```

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

### Define and Plot Cluster Population and Unique Scaffolds in Python

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Defines a Python function `population_plot` that sorts molecular clusters by size, plots the population of the top clusters, and visualizes the number of unique scaffolds within each cluster. It utilizes `matplotlib` for plotting and `rdkit` for scaffold analysis. The function takes sorted cluster IDs, SMILES strings, and the number of top clusters to display as input.

```python
def population_plot(clustered_ids, smiles, top_clusters=20):
    # Sort BFs by size
    clustered_ids = sorted(clustered_ids, key=lambda x: len(x), reverse=True)

    # Get the BFs of interest
    mol_indices = clustered_ids[:top_clusters]

    # Plot the population
    plt.figure(figsize=(10, 10))
    plt.bar(range(top_clusters), [len(x) for x in mol_indices], color = 'blue', label='Population')

    # Anotate the number of molecules in each cluster above the bars
    for i, mol in enumerate(mol_indices):
        plt.text(i, len(mol), len(mol), ha='center', va='bottom')

    # Get the number of unique scaffolds in each cluster
    unique_scaffolds = []
    for cluster_of_interest in mol_indices:
        # Get the smiles in the cluster of interest
        smiles_interest = [smiles[i] for i in cluster_of_interest]

        # Get the Scaffols of the molecules in the cluster of interest
        scaffolds = [MurckoScaffold.MurckoScaffoldSmilesFromSmiles(smile) for smile in smiles_interest]

        # Get the unique scaffolds
        unique_scaffolds.append(len(list(set(scaffolds))))
    
    # Plot the number of unique scaffolds in each cluster
    plt.bar(range(top_clusters), unique_scaffolds, color = 'orange', label='Unique Scaffolds')

    # Anotate the number of unique scaffolds in each cluster above the bars
    for i, mol in enumerate(unique_scaffolds):
        plt.text(i, mol, mol, ha='center', va='bottom', color='white')

        plt.xticks(range(top_clusters), rotation=45)

    plt.show()
```

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

### Read SMILES Input File

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Reads SMILES strings from a specified file into a NumPy array. Assumes each line in the file contains a valid SMILES string. Requires the 'numpy' library.

```python
import numpy as np

with open('chembl_33_np.smi', 'r') as f:
    smiles = np.array(f.readlines())
```

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

### Check Fingerprint Data Type and Format

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Verifies if the generated fingerprints are binary (0 or 1) and checks their data type (e.g., int64 or float64). Uses 'numpy' for array operations and checks.

```python
import numpy as np

print(f'Bits are binary: {np.all(np.isin(fps, [0, 1]))}')
print(f'Data type is int64: {fps.dtype == np.int64 or fps.dtype == np.float64}')
```

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

### Check Fingerprint Data Size

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Reports the number of fingerprints generated, the number of bits per fingerprint, and confirms consistency with the number of input SMILES strings. Uses 'numpy' for shape attributes.

```python
import numpy as np

print(f'Number of fingerprints: {fps.shape[0]}')
print(f'Number of bits: {fps.shape[1]}')
print(f'Number of fingerprints mathces the number of smiles: {fps.shape[0] == len(smiles)}')

```

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

### Delete Molecules to Save Memory

Source: https://github.com/mqcomplab/bitbirch/blob/main/examples/parallel_birch/parallel_example.ipynb

Frees up memory by deleting the list of RDKit molecule objects after fingerprint generation. This is a memory management step.

```python
# Delete the mols to save memory
del mols
```

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