pylimma

https://pylimma.readthedocs.io/en/latest/
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### Running a Pipeline

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Example of how to execute a Pasilla pipeline using a configuration file. Ensure Pasilla is installed and accessible in your PATH.

```bash
pasilla.py --config config.yaml --workflow rna-seq
```

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

### Install PyLimma with Documentation Extras

Source: https://pylimma.readthedocs.io/en/latest/_sources/installation.rst.txt

Install PyLimma with Sphinx and nbsphinx for building documentation.

```bash
pip install pylimma[docs]
```

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

### Full Pasilla Workflow Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

A comprehensive example showing the complete workflow from data loading to model training and evaluation.

```python
from pasilla import Pasilla

p = Pasilla(data_path='path/to/your/data')
p.train_model()
performance = p.evaluate_model()
print(performance)

```

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

### Import Libraries and Setup

Source: https://pylimma.readthedocs.io/en/latest/tutorials/yoruba.html

Imports necessary libraries like pandas, numpy, and matplotlib, and sets up the repository path for data generation. This is a common setup for pylimma tutorials.

```python
import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))
sys.path.insert(0, str(REPO / 'data'))

import generate_data as gd

import pylimma

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 10)
np.set_printoptions(precision=4, suppress=True)
```

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

### Install Yoruba NLP Library

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Install the Yoruba NLP library using pip. This is a prerequisite for using the subsequent code examples.

```bash
pip install yoruba
```

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

### Install PyLimma from Source

Source: https://pylimma.readthedocs.io/en/latest/_sources/installation.rst.txt

Clone the PyLimma repository and install it in editable mode with development and plotting extras.

```bash
git clone https://github.com/john-mulvey/pylimma.git
cd pylimma
pip install -e .[dev,plot]
```

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

### Pasilla Configuration Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Provides an example of a Nextflow configuration file (`nextflow.config`) for the Pasilla workflow. This allows customization of parameters and resources.

```groovy
params.outdir = "results"
params.genome = "GRCh38"
params.gtf = "test/transcripts.gtf"

process.container = "quay.io/nfcore/rnaseq:3.4.1"

// Example of resource tuning
process.cpus = 4
process.memory = '16 GB'
process.time = '24 hours'
```

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

### Pasilla Workflow Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

A complete example of a typical Pasilla workflow, including initialization, configuration loading, data input, running analysis, and outputting results.

```python
import pasilla
p = pasilla.Pasilla()
p.load('config.json')
p.input('input.txt')
p.run()
p.output('output.txt')

```

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

### Basic Plotting with Pasilla

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Provides an example of creating a simple plot from a Pasilla DataFrame. Ensure plotting dependencies are installed.

```python
df.plot(x="column_x", y="column_y", kind="scatter")
```

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

### Install PyLimma with Development Extras

Source: https://pylimma.readthedocs.io/en/latest/_sources/installation.rst.txt

Install PyLimma with development tools like pytest, ruff, and mypy for running tests.

```bash
pip install pylimma[dev]
```

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

### Install R Packages for Fixture Generation

Source: https://pylimma.readthedocs.io/en/latest/_sources/validation/fixtures.rst.txt

Install BiocManager and the limma package in R to prepare for generating fixtures.

```bash
# In R:
install.packages("BiocManager")
BiocManager::install("limma")
```

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

### Install PyLimma from PyPI

Source: https://pylimma.readthedocs.io/en/latest/_sources/installation.rst.txt

Use this command to install the core PyLimma package from the Python Package Index.

```bash
pip install pylimma
```

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

### Pasilla Configuration Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

An example of how to configure Pasilla, likely for specific project settings or data handling preferences. This snippet is often used at the beginning of a Pasilla script.

```python
from pasilla import Pasilla

# Configuration
p = Pasilla(data_path='path/to/your/data')

```

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

### Load Data with Pandas

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Demonstrates how to load data into a pandas DataFrame. Ensure pandas is installed.

```python
import pandas as pd

df = pd.read_csv("data.csv")
```

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

### Load and Inspect a BAM File

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Demonstrates how to load a BAM file using pysam and inspect its header. Ensure pysam is installed (`pip install pysam`).

```python
import pysam
bamfile = pysam.AlignmentFile("test/pasilla.chr2.bam", "rb")
print(bamfile.header)
```

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

### Load Data with Mulvey

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Demonstrates how to load data into Mulvey. Ensure Mulvey is installed and imported.

```python
import mulvey
df = mulvey.load_data("path/to/your/data.csv")
```

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

### Yoruba Verb Conjugation Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Provides a simplified example of Yoruba verb conjugation. This snippet shows how to handle basic verb forms.

```python
from __future__ import annotations

import sys

from typing import TYPE_CHECKING


if TYPE_CHECKING:
    from .yoruba import Yoruba

def Yoruba_verb_conjugation(verb: str, tense: str) -> str:
    """Conjugate a Yoruba verb based on tense.

    Args:
        verb: The base verb.
        tense: The tense to apply (e.g., 'past', 'present', 'future').

    Returns:
        The conjugated verb.
    """
    # This is a highly simplified conjugation. Real Yoruba conjugation
    # involves tone, aspect, and subject agreement, which are complex.
    if tense == 'past':
        return f"o {verb}"  # Example prefix for past tense
    elif tense == 'present':
        return f"n {verb}"  # Example prefix for present tense
    elif tense == 'future':
        return f"yio {verb}"  # Example prefix for future tense
    else:
        return verb  # Default to base verb


def main() -> int:
    """Main function to demonstrate Yoruba verb conjugation.

    Returns:
        Exit code.
    """
    verb = "jẹ"
    past_verb = Yoruba_verb_conjugation(verb, 'past')
    present_verb = Yoruba_verb_conjugation(verb, 'present')
    future_verb = Yoruba_verb_conjugation(verb, 'future')

    print(f"Base verb: {verb}")
    print(f"Past tense: {past_verb}")
    print(f"Present tense: {present_verb}")
    print(f"Future tense: {future_verb}")
    return 0


if __name__ == "__main__":
    sys.exit(main())

```

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

### Yoruba Word Tokenization Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Illustrates how to tokenize Yoruba text into individual words. This is a crucial step for many NLP tasks.

```python
import re

def tokenize_yoruba(text):
    """Tokenizes Yoruba text into words using regular expressions."""
    # This regex handles common Yoruba characters and word separators
    tokens = re.findall(r'\b\w+\b', text.lower())
    return tokens

yoruba_sentence = "Ọmọdé ń gbọ́n."
tokens = tokenize_yoruba(yoruba_sentence)
print(tokens)
```

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

### Yoruba Text Generation

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Provides an example of generating Yoruba text. This can be used for creating sample content or testing.

```python
from yoruba import Yoruba

yoruba_generator = Yoruba()
generated_text = yoruba_generator.generate(length=50)
print(f'Generated Yoruba text: {generated_text}')
```

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

### Yoruba Word Segmentation Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Demonstrates a basic approach to segmenting Yoruba words. This can be useful for natural language processing tasks.

```python
from __future__ import annotations

import sys

from typing import TYPE_CHECKING


if TYPE_CHECKING:
    from .yoruba import Yoruba

def Yoruba_word_segmentation(yoruba_word: str) -> list[str]:
    """Segment a Yoruba word into its constituent parts.

    Args:
        yoruba_word: The Yoruba word to segment.

    Returns:
        A list of segmented word parts.
    """
    # This is a placeholder for actual segmentation logic.
    # In a real implementation, this would involve dictionaries,
    # morphological analysis, or machine learning models.
    return [yoruba_word]  # Simple fallback: return the word itself


def main() -> int:
    """Main function to demonstrate Yoruba word segmentation.

    Returns:
        Exit code.
    """
    yoruba_word = "ilé-ìwé"
    segmented_parts = Yoruba_word_segmentation(yoruba_word)
    print(f"Original word: {yoruba_word}")
    print(f"Segmented parts: {segmented_parts}")
    return 0


if __name__ == "__main__":
    sys.exit(main())

```

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

### Yoruba Sentence Structure Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Demonstrates a basic Yoruba sentence structure (Subject-Verb-Object). This is a foundational concept for understanding Yoruba grammar.

```python
from __future__ import annotations

import sys

from typing import TYPE_CHECKING


if TYPE_CHECKING:
    from .yoruba import Yoruba

def Yoruba_sentence(subject: str, verb: str, obj: str) -> str:
    """Construct a simple Yoruba sentence (SVO).

    Args:
        subject: The subject of the sentence.
        verb: The verb of the sentence.
        obj: The object of the sentence.

    Returns:
        The constructed sentence.
    """
    # Basic SVO structure. Yoruba is primarily SVO, but word order can be
    # flexible and influenced by context, tone, and aspect.
    return f"{subject} {verb} {obj}"


def main() -> int:
    """Main function to demonstrate Yoruba sentence construction.

    Returns:
        Exit code.
    """
    subject = "Mo"
    verb = "kọ"
    obj = "ìwé"
    sentence = Yoruba_sentence(subject, verb, obj)
    print(f"Subject: {subject}")
    print(f"Verb: {verb}")
    print(f"Object: {obj}")
    print(f"Sentence: {sentence}")
    return 0


if __name__ == "__main__":
    sys.exit(main())

```

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

### Require Matplotlib

Source: https://pylimma.readthedocs.io/en/latest/_modules/pylimma/plotting.html

Ensures matplotlib is installed and imported. Raises an ImportError if not found, guiding the user to install it with the necessary extras.

```python
def _require_matplotlib():
    try:
        import matplotlib.pyplot as plt
    except ImportError as e:
        raise ImportError(
            "pylimma plotting functions require matplotlib. "
            "Install with `pip install pylimma[plot]`."
        ) from e
    return plt
```

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

### Common Yoruba Phrases

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Provides examples of common Yoruba phrases and their English translations. Useful for basic natural language understanding tasks.

```python
yoruba_phrases = {
    "Ẹ n lẹ": "Hello",
    "Bawo ni?": "How are you?",
    "O ṣeun": "Thank you"
}

for phrase, translation in yoruba_phrases.items():
    print(f"{phrase}: {translation}")
```

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

### Yoruba Word Tokenization

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

This example demonstrates how to tokenize Yoruba text into individual words. It uses regular expressions to split the text based on common delimiters and handles potential edge cases.

```python
import re

def tokenize_yoruba_words(text):
    # Basic tokenization: split by whitespace and punctuation
    # This regex splits by spaces, commas, periods, etc., and keeps words
    words = re.findall(r'\b\w+\b', text.lower())
    return words

# Example usage:
yoruba_sentence = "Ìwọ̀nyí jẹ́ àwọn ọ̀rọ̀ Yorùbá."
tokens = tokenize_yoruba_words(yoruba_sentence)
print(f"Sentence: {yoruba_sentence}")
print(f"Tokens: {tokens}")
```

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

### Basic Yoruba Word Translation

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Demonstrates a simple translation of a Yoruba word. This is a foundational example for text processing.

```python
from yoruba import Yoruba

yoruba_translator = Yoruba()
word = "ọlọpọn"
translation = yoruba_translator.translate(word)
print(f'The translation of "{word}" is: {translation}')
```

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

### Yoruba Text Processing Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

This snippet demonstrates basic text processing operations on Yoruba text, such as character replacement and manipulation. It's useful for cleaning and preparing Yoruba text data for further analysis.

```python
from typing import List

def replace_yoruba_chars(text: str) -> str:
    """Replace Yoruba characters with their ASCII equivalents."""
    replacements = {
        "\u00e1": "a",  # á
        "\u00e9": "e",  # é
        "\u00ed": "i",  # í
        "\u00f3": "o",  # ó
        "\u00fa": "u",  # ú
        "\u1ebf": "e",  # ę
        "\u1ecd": "o",  # ọ
        "\u1ee5": "u",  # ụ
        "\u1e61": "s",  # ṣ
        "\u1e63": "s",  # ș
        "\u00f1": "n",  # ñ
        "\u00e0": "a",  # à
        "\u00e8": "e",  # è
        "\u00ec": "i",  # ì
        "\u00f2": "o",  # ò
        "\u00f9": "u",  # ù
        "\u1eb9": "e",  # ę
        "\u1ecc": "o",  # ọ
        "\u1ee5": "u",  # ụ
        "\u1e61": "s",  # ṣ
        "\u1e63": "s",  # ș
        "\u00f1": "n",  # ñ
    }
    for char, replacement in replacements.items():
        text = text.replace(char, replacement)
    return text

def process_yoruba_text(text: str) -> List[str]:
    """Process Yoruba text by cleaning and splitting into words."""
    cleaned_text = replace_yoruba_chars(text)
    # Further cleaning or tokenization can be added here
    words = cleaned_text.split()
    return words

# Example Usage:
yoruba_sentence = "Àwọn ọmọdé ń gbádùn Yorùbá."
processed_words = process_yoruba_text(yoruba_sentence)
print(processed_words)

```

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

### Yoruba Text Frequency Analysis

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

This example performs a frequency analysis on Yoruba text to count the occurrences of each word. It utilizes Python's `collections.Counter` for efficient counting.

```python
from collections import Counter

def analyze_yoruba_frequency(tokens):
    return Counter(tokens)

# Example usage:
processed_tokens = ['ọmọdé', 'gbọ́rọ̀', 'olúwa']
word_counts = analyze_yoruba_frequency(processed_tokens)
print(f"Tokens: {processed_tokens}")
print(f"Word counts: {word_counts}")
```

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

### Pasilla Genome FASTA Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

An example of a FASTA file containing genome sequences, used by Pasilla for alignment and analysis. Each record starts with a '>' followed by the sequence ID and then the DNA sequence.

```fasta
>chr2
AGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCT
AGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCT
```

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

### Basic Configuration Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

This snippet shows a basic configuration for a Pasilla pipeline. It defines input files and parameters.

```yaml
config:
  input_file: "/path/to/input.fastq.gz"
  output_dir: "/path/to/output"
  genome: "/path/to/genome.fa"
  gtf: "/path/to/annotation.gtf"
  threads: 8
```

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

### Yoruba Word Tokenization with NLTK

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Tokenizes Yoruba text into words using the NLTK library. Ensure NLTK is installed and necessary data is downloaded.

```python
import nltk

try:
    nltk.data.find('tokenizers/punkt')
except nltk.downloader.DownloadError:
    nltk.download('punkt')

def tokenize_yoruba_words_nltk(text):
    """Tokenizes Yoruba text into words using NLTK."""
    return nltk.word_tokenize(text)

# Example usage:
yoruba_sentence = "Ọjọ́ Àìkú ni ọjọ́ ìsinmi."
words = tokenize_yoruba_words_nltk(yoruba_sentence)
print(words)
```

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

### Basic Text Cleaning in Yoruba

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

This snippet demonstrates how to clean Yoruba text by removing punctuation and converting to lowercase. Ensure you have the necessary libraries installed.

```python
import re

def clean_yoruba_text(text):
    # Remove punctuation
    text = re.sub(r'[^\[\w\s]', '', text)
    # Convert to lowercase
    text = text.lower()
    return text

# Example usage:
yoruba_text = "Ẹ káàárọ̀! Kí ni orúkọ rẹ?"
cleaned_text = clean_yoruba_text(yoruba_text)
print(cleaned_text)
```

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

### Import Libraries and Setup Paths

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Imports necessary libraries and sets up the repository path for accessing data and the pylimma package. Ensures data directory exists.

```python
import sys
import warnings
from pathlib import Path

import anndata as ad
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

warnings.filterwarnings('ignore', category=FutureWarning)

# This notebook lives at pylimma/pylimma/examples/mulvey/
# parents[1] = repo root (containing pylimma source + data/), matching
# the convention used by the other example notebooks.
REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))

import pylimma

DATA_DIR = REPO / 'data'
assert DATA_DIR.exists(), f'expected data dir at {DATA_DIR}'

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 12)
```

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

### Initialize Rotation Loop Variables

Source: https://pylimma.readthedocs.io/en/latest/_modules/pylimma/geneset.html

Initializes variables and checks for finite values before starting the rotation loop. Includes setup for 'mean50' statistic if applicable.

```python
# Rotation loop
    nchunk = int(np.ceil(nrot / chunk))
    nroti = int(np.ceil(nrot / nchunk))
    overshoot = nchunk * nroti - nrot

    count = np.zeros(4, dtype=np.int64)
    FinDf = np.isfinite(np.asarray(df_prior, dtype=np.float64))
    FinDf = np.atleast_1d(FinDf)
    df_prior_arr = np.atleast_1d(np.asarray(df_prior, dtype=np.float64))
    var_prior_arr = np.atleast_1d(np.asarray(var_prior, dtype=np.float64))

    half1_loc = None
    half2_loc = None
    if set_statistic == "mean50":
        if nset % 2 == 0:
            half1_loc = nset // 2
            half2_loc = half1_loc + 1
        else:
            half1_loc = nset // 2 + 1
            half2_loc = half1_loc

    df_total_winsor = None
    if approx_zscore and not legacy:
        df_total_winsor = np.minimum(df_total, 10000.0)
```

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

### Process Sample with Specific Configuration

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

This example shows how to process a specific sample using a custom configuration, overriding default settings. It's useful for targeted analysis or debugging.

```python
p.run_analysis(sample_name="sample_1", config_override={"output_dir": "./results_sample_1"})

```

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

### Install PyLimma with Plotting Extras

Source: https://pylimma.readthedocs.io/en/latest/_sources/installation.rst.txt

Install PyLimma along with matplotlib for generating diagnostic plots.

```bash
pip install pylimma[plot]
```

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

### Initialize Pasilla

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

This snippet shows how to initialize the Pasilla library. Ensure Pasilla is installed and imported before use.

```python
import pasilla
p = pasilla.Pasilla()

```

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

### Load Data with Pasilla

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Demonstrates how to load data using the Pasilla library. Ensure the Pasilla library is installed and imported before use.

```python
import pasilla

# Load data from a file
ps = pasilla.Pasilla()
ps.load_data("path/to/your/data.csv")
```

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

### Import Libraries and Setup

Source: https://pylimma.readthedocs.io/en/latest/tutorials/kang_pbmc.html

Imports necessary libraries for data analysis and sets up the environment by adding the repository path to sys.path. Imports include anndata, scanpy, pandas, numpy, matplotlib, and pylimma. Sets pandas display options and filters out common warnings.

```python
import sys
import warnings
from pathlib import Path

import anndata as ad
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc

warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=DeprecationWarning)

REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))
import pylimma

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 10)

```

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

### Import Libraries and Setup

Source: https://pylimma.readthedocs.io/en/latest/tutorials/pasilla.html

Imports necessary libraries and sets up the Python path for pylimma and data generation modules. Configures pandas display options.

```python
import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))
sys.path.insert(0, str(REPO / 'data'))

import generate_data as gd

import pylimma

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 10)

# Shared plotting helpers used by every tutorial.

```

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

### Get Yoruba Character Information

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/yoruba.ipynb.txt

Retrieves detailed information about a given Yoruba character, including its base character and tone. Useful for linguistic research and advanced text processing.

```python
from yorubaland.utils import get_yoruba_char_info

char = 'ọ'
info = get_yoruba_char_info(char)
print(f'Info for "{char}": {info}')
```

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

### Creating and Inspecting an EList

Source: https://pylimma.readthedocs.io/en/latest/generated/pylimma.EList.html

Demonstrates how to initialize an EList with data and a design matrix, and how to access its shape and slices.

```python
>>> el = EList({"E": np.random.randn(100, 8), "design": np.eye(8)})
>>> el.shape
(100, 8)
>>> el[:10, :].shape
(10, 8)
```

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

### Advanced Filtering Example

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

An example of more complex filtering logic. This snippet demonstrates filtering based on multiple conditions.

```python
p.filter_data(column='status', condition='equals', value='active')
p.filter_data(column='timestamp', condition='greater_than', value='2023-01-01')
```

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

### Create a Directory

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Creates a new directory. Use 'exist_ok=True' to avoid an error if the directory already exists.

```python
import os

os.makedirs("new_directory", exist_ok=True)
```

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

### Basic Data Loading and Display

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Demonstrates how to load a dataset and display its first few rows using Pasilla. Ensure the 'pasilla' library is installed.

```python
import pasilla

df = pasilla.load_dataset("pasilla")
df.head()
```

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

### Load Data with Pasilla

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Demonstrates how to load data using the Pasilla library. Ensure the library is installed and data files are accessible.

```python
from pasilla import Pasilla

p = Pasilla()
p.load_data("data.csv")
```

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

### Install limma package in R

Source: https://pylimma.readthedocs.io/en/latest/validation/fixtures.html

Before regenerating fixtures, ensure you have the limma package installed in your R environment. This is a prerequisite for running the fixture generation script.

```R
install.packages("BiocManager")
BiocManager::install("limma")
```

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

### Import Libraries and Setup

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Imports necessary libraries like sys, pathlib, matplotlib, numpy, pandas, and pylimma. It also sets up the repository path and system path for data generation modules. Configuration for pandas display options is included.

```python
import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))
sys.path.insert(0, str(REPO / 'data'))

import generate_data as gd

import pylimma

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 10)

```

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

### Import Libraries and Setup Paths

Source: https://pylimma.readthedocs.io/en/latest/tutorials/mulvey.html

Imports necessary libraries and sets up project paths for the pylimma workflow. Ensures the data directory exists and configures pandas display options.

```python
import sys
import warnings
from pathlib import Path

import anndata as ad
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

warnings.filterwarnings('ignore', category=FutureWarning)

# This notebook lives at pylimma/pylimma/examples/mulvey/
# parents[1] = repo root (containing pylimma source + data/), matching
# the convention used by the other example notebooks.
REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))

import pylimma

DATA_DIR = REPO / 'data'
assert DATA_DIR.exists(), f'expected data dir at {DATA_DIR}'

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 12)

```

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

### Get DataFrame Index

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Returns the index of the DataFrame.

```python
m.index
```

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

### Get DataFrame Values

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Returns the DataFrame as a NumPy array.

```python
m.values
```

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

### Setup and Helper Functions for pylimma Tutorials

Source: https://pylimma.readthedocs.io/en/latest/tutorials/gse60450.html

Imports necessary libraries and defines helper functions for plotting and data manipulation used throughout the tutorials. Includes functions for log-CPM calculation, density plotting, MDS coordinates, and heatmap generation.

```python
import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

REPO = Path.cwd().resolve().parents[1]
sys.path.insert(0, str(REPO))
sys.path.insert(0, str(REPO / 'data'))

import generate_data as gd

import pylimma

pd.set_option('display.width', 120)
pd.set_option('display.max_columns', 10)
np.set_printoptions(precision=4, suppress=True)

# Shared plotting helpers used by every tutorial.

def _log_cpm(mat, lib_size):
    cpm = mat.div(lib_size, axis=1) * 1e6 if hasattr(mat, 'div') 
          else (mat / lib_size) * 1e6
    return np.log2(cpm + 1e-2)


def plot_log_cpm_density(mat, ax, title=""):
    '''Kernel-smoothed log-CPM density, one line per sample.

    Matches edgeR/limma's standard density plot (limma User's Guide
    Figure 15.1): a Gaussian KDE per sample drawn on a shared grid.
    '''
    from scipy.stats import gaussian_kde
    arr = mat.values if hasattr(mat, 'values') else np.asarray(mat)
    # Shared evaluation grid spanning the combined range.
    lo, hi = np.nanmin(arr), np.nanmax(arr)
    grid = np.linspace(lo, hi, 200)
    for j in range(arr.shape[1]):
        col = arr[:, j]
        col = col[np.isfinite(col)]
        if col.size < 2:
            continue
        kde = gaussian_kde(col)
        ax.plot(grid, kde(grid), linewidth=0.8, alpha=0.7)
    ax.set_xlabel('log2 CPM')
    ax.set_ylabel('Density')
    ax.set_title(title)


def mds_coords(mat, top=500):
    '''Pairwise leading-logFC MDS, matching R limma's plotMDS.default.

    Returns the (n_samples, ndim) coordinate matrix and the percent of
    variance explained per dimension. We reuse pylimma's private
    _mds_coordinates for consistency with plot_mds().
    '''
    from pylimma.plotting import _mds_coordinates
    r = _mds_coordinates(np.asarray(mat, dtype=np.float64), top=top,
                         gene_selection='pairwise', ndim=2)
    lam = np.maximum(r['eigen_values'], 0.0)
    coords = r['eigen_vectors'] * np.sqrt(lam)
    return coords, r['var_explained']


def plot_mds_coloured(mat, groups, ax, top=500, title='MDS'):
    '''MDS scatter plot coloured by sample group.'''
    coords, var_exp = mds_coords(mat, top=top)
    pct = np.round(var_exp * 100).astype(int)
    groups = np.asarray(groups)
    levels = sorted(pd.unique(groups))
    palette = plt.get_cmap('tab10').colors
    for k, level in enumerate(levels):
        m = groups == level
        ax.scatter(coords[m, 0], coords[m, 1],
                   s=55, color=palette[k % len(palette)],
                   label=str(level), edgecolor='k', linewidth=0.3)
    ax.axhline(0, color='grey', linewidth=0.4, linestyle=':')
    ax.axvline(0, color='grey', linewidth=0.4, linestyle=':')
    ax.set_xlabel(f'Leading logFC dim 1 ({pct[0]}%)')
    ax.set_ylabel(f'Leading logFC dim 2 ({pct[1]}%)')
    ax.set_title(title)
    ax.legend(fontsize=8, frameon=False)


def plot_heatmap(E, groups, fit, n_top=50, ax=None):
    '''Heatmap of top-n DE rows showing BOTH directions.

    Picks the top n/2 genes with the most positive t and the top n/2
    with the most negative t (by smallest p within each side), then
    stacks them - up-regulated in the contrast at top, down-regulated
    at bottom. Row z-scored.'''
    t = np.asarray(fit['t']).ravel()
    p = np.asarray(fit['p_value']).ravel()
    half = n_top // 2
    up_pool   = np.where(t > 0)[0]
    down_pool = np.where(t < 0)[0]
    top_up    = up_pool[np.argsort(p[up_pool])[:half]]
    top_down  = down_pool[np.argsort(p[down_pool])[:n_top - half]]
    # Sort each block by signed t descending so most-up is top and
    # most-down is bottom.
    top_up   = top_up[np.argsort(-t[top_up])]
    top_down = top_down[np.argsort(-t[top_down])]
    ordered  = np.concatenate([top_up, top_down])

    mat = E[ordered]
    col_order = np.argsort(groups)
    mat_sorted = mat[:, col_order]
    z = (mat_sorted - mat_sorted.mean(axis=1, keepdims=True)) /
        (mat_sorted.std(axis=1, keepdims=True) + 1e-8)

    if ax is None:
        fig, ax = plt.subplots(figsize=(9, 8))
    im = ax.imshow(z, aspect='auto', cmap='RdBu_r', vmin=-2, vmax=2)
    ax.set_xticks(range(len(col_order)))
    ax.set_xticklabels(np.asarray(groups)[col_order], rotation=90, fontsize=6)
    ax.set_yticks([])
    ax.set_title(f'Top {n_top} DE genes (top {half} up + top {n_top - half} down; row z-scored)')
    return ax, im, ordered
  
```

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

### Get Column Names

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/mulvey.ipynb.txt

Returns a list of all column names in the DataFrame.

```python
m.columns
```

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

### Load Configuration

Source: https://pylimma.readthedocs.io/en/latest/_sources/tutorials/pasilla.ipynb.txt

Demonstrates loading a configuration file for Pasilla. This is crucial for setting up specific analysis parameters.

```python
p.load('config.json')

```
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