### Example Usage: FASTQ with Legacy Plotting

Source: https://github.com/wdecoster/nanoplot/blob/master/README.md

An example demonstrating FASTQ processing with specific thread count, plot types, and legacy plotting enabled for hex plots.

```bash
NanoPlot -t 2 --fastq reads1.fastq.gz reads2.fastq.gz --maxlength 40000 --plots dot --legacy hex
```

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

### Run NanoPlot with various input formats

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Command-line examples for processing different sequencing data file types.

```bash
# Standard FASTQ files (supports gzip, bzip2, bgzip compression)
NanoPlot --fastq sample.fastq.gz -o output

# FASTA files
NanoPlot --fasta assembly.fasta -o output

# Rich FASTQ with channel/time info (albacore, MinKNOW, guppy)
NanoPlot --fastq_rich basecalled.fastq -o output

# Minimal FASTQ extraction (faster parsing)
NanoPlot --fastq_minimal basecalled.fastq -o output

# Sequencing summary files (gzip, bz2, zip, xz compression supported)
NanoPlot --summary sequencing_summary.txt -o output

# Sorted BAM files
NanoPlot --bam aligned_sorted.bam -o output

# Unmapped BAM files
NanoPlot --ubam unmapped.bam -o output

# CRAM files
NanoPlot --cram aligned.cram -o output

# Previously stored pickle data
NanoPlot --pickle NanoPlot-data.pickle -o output

# Arrow/Feather files
NanoPlot --feather data.feather -o output

# Multiple files of the same type
NanoPlot --fastq sample1.fastq.gz sample2.fastq.gz sample3.fastq.gz -o output
```

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

### Example Usage: BAM with Downsampling and Output

Source: https://github.com/wdecoster/nanoplot/blob/master/README.md

Processes BAM files with multiple threads, custom color, downsampling, and specifies an output directory for plots.

```bash
NanoPlot -t 12 --color yellow --bam alignment1.bam alignment2.bam alignment3.bam --downsample 10000 -o bamplots_downsampled
```

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

### NanoPlot Command Line Interface

Source: https://context7.com/wdecoster/nanoplot/llms.txt

The main entry point for NanoPlot, providing comprehensive options for input data sources, filtering, and visualization customization.

```APIDOC
## NanoPlot Command Line Interface

### Description
The main entry point for NanoPlot, providing comprehensive options for input data sources, filtering, and visualization customization.

### Usage Examples

**Basic usage with FASTQ files:**
```bash
NanoPlot --fastq reads.fastq.gz -o output_directory
```

**Process sequencing summary file with log-transformed lengths:**
```bash
NanoPlot --summary sequencing_summary.txt --loglength -o summary-plots-log-transformed
```

**Multi-threaded processing of multiple FASTQ files with length filtering:**
```bash
NanoPlot -t 2 --fastq reads1.fastq.gz reads2.fastq.gz --maxlength 40000 --plots kde dot
```

**BAM file analysis with downsampling and custom color:**
```bash
NanoPlot --color yellow --bam alignment1.bam alignment2.bam alignment3.bam --downsample 10000 -o bamplots
```

**Full analysis with filtering options:**
```bash
NanoPlot --summary sequencing_summary.txt \
  --minlength 500 \
  --maxlength 50000 \
  --minqual 7 \
  --drop_outliers \
  --loglength \
  --N50 \
  --title "My Sequencing Run" \
  -o output_dir
```

**Process barcoded samples:**
```bash
NanoPlot --summary sequencing_summary.txt --barcoded -o barcoded_output
```

**Use aligned read lengths from BAM files:**
```bash
NanoPlot --bam aligned.bam --alength -o aligned_output
```

**Generate legacy plots with hex binning:**
```bash
NanoPlot --fastq reads.fastq.gz --plots kde dot --legacy hex -o legacy_output
```

**Custom output formats and DPI settings:**
```bash
NanoPlot --fastq reads.fastq.gz -f png svg pdf --dpi 300 -o high_quality_output
```

**Store extracted data for future use:**
```bash
NanoPlot --fastq reads.fastq.gz --store --raw -o stored_data
```
```

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

### Initialize Plotly Visualization

Source: https://github.com/wdecoster/nanoplot/blob/master/scripts/agm_tests/WeightedLogTransformed_HistogramReadlength.html

Configures the Plotly environment and initializes a bar chart with specific data and layout templates.

```javascript
window.PlotlyConfig = {MathJaxConfig: 'local'};

window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("93f45ef8-a392-4147-90de-050d15d2e02f")) { Plotly.newPlot( "93f45ef8-a392-4147-90de-050d15d2e02f", [{"hoverinfo":"y+text","hovertext":["268","287","308","330","354","380","407","437","468","502","538","577","619","664","712","763","818","877","940","1008","1081","1159","1243","1333","1429","1532","1643","1761","1889","2025","2171","2328","2496","2676","2870","3077","3299","3537","3793","4067","4360","4675","5013","5375","5763","6179","6625","7104","7617","8167","8757","9389","10067","10794","11574","12410","13306","14267","15297","16402","17586","18856","20218","21678","23243","24922","26722","28652","30721","32939","35318"],"marker":{"color":"#4CB391"},"x":{"dtype":"f8","bdata":"bp+I6f9sA0BcHTmnBKsDQEqb6WQJ6QNAOBmaIg4nBEAml0rgEmUEQBQV+50XowRAApOrWxzhBEDwEFwZIR8FQN6ODNclXQVAzAy9lCqbBUC5im1SL9kFQKcIHhA0FwZAlYbOzThVBkCDBH+LPZMGQHGCL0lC0QZAXwDgBkcPB0BNfpDES00HQDv8QIJQiwdAKXrxP1XJB0AX+KH9WQcIQAV2UrteRQhA8\u002fMCeWODCEDhcbM2aMEIQM\u002fvY\u002fRs\u002fwhAvW0UsnE9CUCq68RvdnsJQJhpdS17uQlAhucl63\u002f3CUB0ZdaohDUKQGLjhmaJcwpAUGE3JI6xCkA+3+fhku8KQCxdmJ+XLQtAGttIXZxrC0AIWfkaoakLQPbWqdil5wtA5FRalqolDEDS0gpUr2MMQMBQuxG0oQxArs5rz7jfDECcTByNvR0NQIrKzErCWw1AeEh9CMeZDUBmxi3Gy9cNQFRE3oPQFQ5AQsKOQdVTDkAwQD\u002f\u002f2ZEOQB6+77zezw5ADDygeuMND0D6uVA46EsPQOc3AfbsiQ9A1bWxs\u002fHHD0DiGbE4+wIQQNhYiZf9IRBA0Jdh9v9AEEDG1jlVAmAQQL4VErQEfxBAtFTqEgeeEECsk8JxCb0QQKLSmtAL3BBAmRFzLw77EECQUEuOEBoRQIePI+0SORFAfs77SxVYEUB1DdSqF3cRQGxMrAkalhFAY4uEaBy1EUBaylzHHtQRQFEJNSYh8xFASEgNhSMSEkA\u002fh+XjJTESQA=="},"y":{"dtype":"f8","bdata":"poFHbyalpEDJdLhPCcKkQD6BaltolqVAj6wUbWBspUDHqjEoGt+mQAUqbhsjCaZAfqDZs4Prp0ANu9mTgGumQMc7ktJ+RqdAPy0CzwhQp0CNfL6c1oKnQDgnCQgnV6lALbUONoMvqEAphn9RklOpQDiigFkVM6lA4lf8QNbYqEAw030YPP2oQB8MuSE7z6pA6VDJW7AeqkCFMEnuLrGrQNA+Rl5qAatAMdEQC+twqUB6MhtjpkSqQG2X7bW7FatAV23qFn9hq0CgzkcXBzypQDRuVy1RQKlAOTtBtr6Iq0B659sBpOCqQBcKuf\u002fTm6lADUZvvZSFqkABZFnciTypQDouWuW0SKlABU81RePUp0ARzGR0uy2oQF902ZdD36ZA58LwZUonpUBNoprWkl+kQDwCNfMbRaNA5y5VoI3boUAuC1HagE6gQGNmgyixzZ1AWYT9QhCrnECICbg8fnSXQPKjpPRLspdAd5QAkEuGkkDSy+lPxP6KQOO\u002f0rhkI4dACktn06HfgUDfr8CiNXZ9QB3jWmJ4bXFAY4bzXkfAZUBAtkqDgO1YQN62En9RH1pABvYgxw0yUUAsbmknxWVVQPwqC7YBkERAyhgF2PjYSECW7A6KmNA0QDdUhuQuLylA2EBJjPZfOUDzSkmGswoxQG7RlX73IiFAnJG\u002fdxNVIUAHUolsK1kRQAAAAAAAAAAAhK5cyeKdIUAAAAAAAAAAAAAAAAAAAAAAb1iWm2kFEkA\u002fh+XjJTESQA=="},"type":"bar"}], {"template":{"data":{"histogram2dcontour":[{"type":"histogram2dcontour","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"choropleth":[{"type":"choropleth","colorbar":{"outlinewidth":0,"ticks":""}}],"histogram2d":[{"type":"histogram2d","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"heatmap":[{"type":"heatmap","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"contourcarpet":[{"type":"contourcarpet","colorbar":{"outlinewidth":0,"ticks":""}}],"contour":[{"type":"contour","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"surface":[{"type":"surface","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"mesh3d":[{"type":"mesh3d","colorbar":{"outlinewidth":0,"ticks":""}}],"scatter":[{"fillpattern":{"fillmode":"overlay","size":10,"solidity":0.2},"type":"scatter"}],"parcoords":[{"type":"parcoords","line":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scatterpolargl":[{"type":"scatterpolargl","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"bar":[{"error_
```

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

### Write Statistical Summaries with nanomath

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Export statistical data to files using the write_stats utility function.

```python
import pandas as pd
import numpy as np
from nanomath import write_stats
```

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

### NanoPlot Command Line Interface Usage

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Execute various quality control and visualization tasks using the NanoPlot CLI. Options include input file formats, filtering, multi-threading, and output customization.

```bash
# Basic usage with FASTQ files
NanoPlot --fastq reads.fastq.gz -o output_directory

# Process sequencing summary file with log-transformed lengths
NanoPlot --summary sequencing_summary.txt --loglength -o summary-plots-log-transformed

# Multi-threaded processing of multiple FASTQ files with length filtering
NanoPlot -t 2 --fastq reads1.fastq.gz reads2.fastq.gz --maxlength 40000 --plots kde dot

# BAM file analysis with downsampling and custom color
NanoPlot --color yellow --bam alignment1.bam alignment2.bam alignment3.bam --downsample 10000 -o bamplots

# Full analysis with filtering options
NanoPlot --summary sequencing_summary.txt \
  --minlength 500 \
  --maxlength 50000 \
  --minqual 7 \
  --drop_outliers \
  --loglength \
  --N50 \
  --title "My Sequencing Run" \
  -o output_dir

# Process barcoded samples
NanoPlot --summary sequencing_summary.txt --barcoded -o barcoded_output

# Use aligned read lengths from BAM files
NanoPlot --bam aligned.bam --alength -o aligned_output

# Generate legacy plots with hex binning
NanoPlot --fastq reads.fastq.gz --plots kde dot --legacy hex -o legacy_output

# Custom output formats and DPI settings
NanoPlot --fastq reads.fastq.gz -f png svg pdf --dpi 300 -o high_quality_output

# Store extracted data for future use
NanoPlot --fastq reads.fastq.gz --store --raw -o stored_data
```

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

### FASTQ Data Plotting with Options

Source: https://github.com/wdecoster/nanoplot/blob/master/README.md

Processes FASTQ files with specified threads, maximum length, and plot types. Supports 'dot' and 'hex' plots.

```bash
NanoPlot -t 2 --fastq reads1.fastq.gz reads2.fastq.gz --maxlength 40000 --plots hex dot
```

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

### Basic Summary Plotting

Source: https://github.com/wdecoster/nanoplot/blob/master/README.md

Generates summary plots from a sequencing summary file. Use --loglength for log-transformed read length histograms.

```bash
NanoPlot --summary sequencing_summary.txt --loglength -o summary-plots-log-transformed
```

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

### Generate Read Length Distribution Plots

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Create various read length distribution plots, including weighted and non-weighted histograms, and their log-transformed versions. Requires `nanomath.get_N50`.

```python
import numpy as np
import nanoplotter
from nanomath import get_N50

# Sample read length data
lengths = np.random.lognormal(8, 1.2, 10000).astype('uint64')
n50 = get_N50(np.sort(lengths))

settings = {
    'format': ['png'],
    'no_static': False,
    'dpi': 300
}

# Generate length distribution plots
plots = nanoplotter.length_plots(
    array=lengths,
    name='Read length',
    path='output/',
    n50=n50,
    color='#4CB391',
    title='Sequencing Run Length Distribution',
    settings=settings
)
```

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

### Customize NanoPlot colors and colormaps

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Command-line options for setting plot colors and listing available colormaps.

```bash
# Use named color
NanoPlot --fastq reads.fastq.gz --color teal -o output

# Use hex color code
NanoPlot --fastq reads.fastq.gz --color "#FF6B6B" -o output

# Custom colormap for heatmaps
NanoPlot --summary summary.txt --colormap Viridis -o output

# List available colors
NanoPlot --listcolors

# List available colormaps (Greens, Blues, Viridis, Cividis, etc.)
NanoPlot --listcolormaps
```

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

### Initialize Plotly Bar Chart

Source: https://github.com/wdecoster/nanoplot/blob/master/scripts/agm_tests/Non_weightedHistogramReadlength.html

Configures the Plotly environment and renders a bar chart into a specified HTML element. Ensure the target element ID exists in the DOM before execution.

```javascript
window.PlotlyConfig = {MathJaxConfig: 'local'};

window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("9bc95e55-35fa-4b75-bf1d-bd21cc5a0fd2")) { Plotly.newPlot( "9bc95e55-35fa-4b75-bf1d-bd21cc5a0fd2", [{"marker":{"color":"#4CB391"},"x":{"dtype":"f8","bdata":"6sn47VI\u002fh0DqyfjtUleTQOAu9WT8DptA6sn47VJjoUBl\u002fHapJz+lQOAu9WT8GqlAWmFzINH2rEDqyfjtUmmwQCjjt0s9V7JAZfx2qSdFtECiFTYHEjO2QOAu9WT8ILhAHUi0wuYOukBaYXMg0fy7QJh6Mn676r1A1ZPx26XYv0CJVtgcSOPAQCjjt0s92sFAxm+XejLRwkBl\u002fHapJ8jDQASJVtgcv8RAohU2BxK2xUBBohU2B63GQOAu9WT8o8dAfrvUk\u002fGayEAdSLTC5pHJQLzUk\u002fHbiMpAWmFzINF\u002fy0D57VJPxnbMQJh6Mn67bc1ANgcSrbBkzkDVk\u002fHbpVvPQDqQaIVNKdBAiVbYHMik0EDYHEi0QiDRQCjjt0u9m9FAd6kn4zcX0kDGb5d6spLSQBY2BxItDtNAZfx2qaeJ00C0wuZAIgXUQASJVticgNRAU0\u002fGbxf81ECiFTYHknfVQPLbpZ4M89VAQaIVNodu1kCQaIXNAerWQOAu9WR8ZddAL\u002fVk\u002fPbg10B+u9STcVzYQM6BRCvs19hAHUi0wmZT2UBsDiRa4c7ZQLzUk\u002fFbStpAC5sDidbF2kBaYXMgUUHbQKon47fLvNtA+e1ST0Y43EBItMLmwLPcQJh6Mn47L91A50CiFbaq3UA2BxKtMCbeQIbNgUSrod5A1ZPx2yUd30AkWmFzoJjfQDqQaIUNCuBAYXMg0cpH4ECJVtgciIXgQLE5kGhFw+BA2BxItAIB4UAAAAAAwD7hQA=="},"y":{"dtype":"i2","bdata":"b0RfIA4Ujw5UCtYHzQWVBDsDmgIoAnYB+ADIAIIAcAA6AC8AEwARABQAEQAHAA8ABQAGAAcABQAHAAIAAQABAAIAAgADAAEAAgABAAIAAAAAAAAAAQACAAAAAAAAAAAAAAAAAAEAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAABAA=="},"type":"bar"}], {"template":{"data":{"histogram2dcontour":[{"type":"histogram2dcontour","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"choropleth":[{"type":"choropleth","colorbar":{"outlinewidth":0,"ticks":""}}],"histogram2d":[{"type":"histogram2d","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"heatmap":[{"type":"heatmap","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"contourcarpet":[{"type":"contourcarpet","colorbar":{"outlinewidth":0,"ticks":""}}],"contour":[{"type":"contour","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"surface":[{"type":"surface","colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0.0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1.0,"#f0f921"]]}],"mesh3d":[{"type":"mesh3d","colorbar":{"outlinewidth":0,"ticks":""}}],"scatter":[{"fillpattern":{"fillmode":"overlay","size":10,"solidity":0.2},"type":"scatter"}],"parcoords":[{"type":"parcoords","line":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scatterpolargl":[{"type":"scatterpolargl","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"bar":[{"error_x":{"color":"#2a3f5f"},"error_y":{"color":"#2a3f5f"},"marker":{"line":{"color":"#E5ECF6","width":0.5},"pattern":{"fillmode":"overlay","size":10,"solidity":0.2}},"type":"bar"}],"scattergeo":[{"type":"scattergeo","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scatterpolar":[{"type":"scatterpolar","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"histogram":[{"marker":{"pattern":{"fillmode":"overlay","size":10,"solidity":0.2}},"type":"histogram"}],"scattergl":[{"type":"scattergl","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scatter3d":[{"type":"scatter3d","line":{"colorbar":{"outlinewidth":0,"ticks":""}},"marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scattermap":[{"type":"scattermap","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scattermapbox":[{"type":"scattermapbox","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scatterternary":[{"type":"scatterternary","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"scattercarpet":[{"type":"scattercarpet","marker":{"colorbar":{"outlinewidth":0,"ticks":""}}}],"carpet":[{"aaxis":{"endlinecol
```

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

### Compute Sequencing Statistics with nanomath.Stats

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Calculate comprehensive sequencing metrics from a pandas DataFrame containing read lengths, quality scores, and channel IDs.

```python
import pandas as pd
import numpy as np
from nanomath import Stats

# Create sample DataFrame with sequencing data
df = pd.DataFrame({
    'lengths': np.random.lognormal(8, 1, 1000).astype(int),
    'quals': np.random.normal(15, 3, 1000),
    'channelIDs': np.random.randint(1, 512, 1000)
})

# Calculate statistics
stats = Stats(df)
print(f"Number of reads: {stats.number_of_reads}")
print(f"Total bases: {stats.number_of_bases}")
print(f"Mean read length: {stats.mean_read_length:.1f}")
print(f"Median read length: {stats.median_read_length:.1f}")
print(f"Read length N50: {stats.n50}")
print(f"Mean quality: {stats.mean_qual:.2f}")
print(f"Active channels: {stats.active_channels}")

# Export as dictionary for TSV output
stats_dict = stats.to_dict()
```

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

### Create Flowcell Activity Heatmap

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Visualize flowcell activity by generating a heatmap of reads per channel. Automatically detects flowcell type (Flongle, MinION, PromethION).

```python
import numpy as np
from nanoplotter.spatial_heatmap import spatial_heatmap

# Sample channel data (MinION has channels 1-512)
channel_ids = np.random.choice(range(1, 513), size=50000, replace=True)

settings = {
    'format': ['png'],
    'no_static': False,
    'dpi': 300
}

# Generate flowcell activity heatmap
plots = spatial_heatmap(
    array=channel_ids,
    path='output/ActivityMap_ReadsPerChannel',
    colormap='Greens',
    settings=settings,
    title='Flowcell Channel Activity'
)
```

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

### Create Scatter Plots with Marginal Histograms

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Generate bivariate scatter plots with optional KDE and dot plot styles. Supports log transformation for one axis. Ensure output directory exists.

```python
import pandas as pd
import numpy as np
import nanoplotter

# Sample data
df = pd.DataFrame({
    'lengths': np.random.lognormal(8, 1, 5000).astype(int),
    'quals': np.random.normal(15, 3, 5000)
})

settings = {
    'format': ['png'],
    'no_static': False,
    'dpi': 300
}

# Create scatter plots (KDE and dot)
plots = nanoplotter.scatter(
    x=df['lengths'],
    y=df['quals'],
    legacy={},
    names=['Read lengths', 'Average read quality'],
    path='output/LengthvsQualityScatterPlot',
    plots={'kde': 1, 'dot': 1, 'hex': 0, 'pauvre': 0},
    color='#4CB391',
    colormap='Greens',
    settings=settings,
    title='Read Length vs Quality'
)

# Create log-transformed scatter plot
log_plots = nanoplotter.scatter(
    x=np.log10(df['lengths']),
    y=df['quals'],
    legacy={},
    names=['Read lengths', 'Average read quality'],
    path='output/LogLengthvsQualityScatterPlot',
    plots={'kde': 1, 'dot': 1, 'hex': 0, 'pauvre': 0},
    color='#4CB391',
    colormap='Greens',
    settings=settings,
    log=True,
    title='Log Read Length vs Quality'
)
```

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

### Calculate Average Quality with nanomath

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Compute the average Phred quality score for a list of reads, with an optional parameter to round the result to the nearest integer.

```python
from nanomath import ave_qual

# Calculate average quality from integer Phred scores
quality_scores = [20, 25, 30, 28, 22, 35, 18, 27, 31, 24]
avg_quality = ave_qual(quality_scores)
print(f"Average quality: {avg_quality:.2f}")
# Output: Average quality: 24.87

# Round to nearest integer
avg_quality_rounded = ave_qual(quality_scores, qround=True)
print(f"Rounded average quality: {avg_quality_rounded}")
# Output: Rounded average quality: 25
```

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

### Generate Time-Based Analysis Plots

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Create plots for time-series sequencing data, including cumulative yield, reads over time, and violin plots of length/quality over time. Requires both `df` and `subsampled_df`.

```python
import pandas as pd
import numpy as np
from datetime import timedelta
from nanoplotter.timeplots import time_plots

# Create sample time-series data
n_reads = 5000
df = pd.DataFrame({
    'lengths': np.random.lognormal(8, 1, n_reads).astype(int),
    'quals': np.random.normal(15, 3, n_reads),
    'channelIDs': np.random.randint(1, 512, n_reads),
    'start_time': [timedelta(hours=np.random.uniform(0, 48)) for _ in range(n_reads)]
})

# Subsample for violin plots
subsampled_df = df.sample(min(10000, len(df)))

settings = {
    'format': ['png'],
    'no_static': False,
    'dpi': 300
}

# Generate all time-based plots
plots = time_plots(
    df=df,
    subsampled_df=subsampled_df,
    path='output/',
    settings=settings,
    title='48-Hour Sequencing Run',
    color='#4CB391',
    log_length=False
)
```

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

### Calculate N50 with nanomath

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Compute the N50 metric from a sorted array of read lengths using the get_N50 function.

```python
import numpy as np
from nanomath import get_N50

# Calculate N50 from read lengths
read_lengths = np.array([1000, 2000, 3000, 5000, 8000, 10000, 15000, 20000])
sorted_lengths = np.sort(read_lengths)
n50_value = get_N50(sorted_lengths)
print(f"N50: {n50_value} bp")
# Output: N50: 10000 bp
```

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

### nanomath.write_stats

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Write statistical summaries to a file, supporting both TSV format and legacy human-readable format.

```APIDOC
## nanomath.write_stats

### Description
Write statistical summaries to a file, supporting both TSV format and legacy human-readable format.

### Method
```python
write_stats(stats_dict: dict, output_file: str, output_format: str = 'tsv')
```

### Parameters
#### Path Parameters
- **stats_dict** (dict) - Required - A dictionary containing the statistics to write.
- **output_file** (str) - Required - The path to the output file.
- **output_format** (str) - Optional - The format of the output file ('tsv' or 'human'). Defaults to 'tsv'.

### Request Example
```python
import pandas as pd
import numpy as np
from nanomath import Stats, write_stats

# Create sample DataFrame with sequencing data
df = pd.DataFrame({
    'lengths': np.random.lognormal(8, 1, 1000).astype(int),
    'quals': np.random.normal(15, 3, 1000),
    'channelIDs': np.random.randint(1, 512, 1000)
})

# Calculate statistics
stats = Stats(df)
stats_dict = stats.to_dict()

# Write statistics to a TSV file
write_stats(stats_dict, 'stats.tsv', output_format='tsv')

# Write statistics to a human-readable file
write_stats(stats_dict, 'stats.txt', output_format='human')
```
```

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

### Create dynamic histogram with nanoplotter

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Generates a dynamic histogram from a pandas Series of data.

```python
plot = nanoplotter.dynamic_histogram(
    array=pd.Series(percent_identity),
    name='percent identity',
    path='output/PercentIdentityHistogram',
    settings=settings,
    title='Read Percent Identity Distribution',
    color='#4CB391'
)
```

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

### Manage plots with Plot class

Source: https://context7.com/wdecoster/nanoplot/llms.txt

Handles plot object creation, figure attachment, and export to HTML or static images.

```python
from nanoplotter.plot import Plot
import plotly.express as px
import numpy as np

# Create a plot object
my_plot = Plot(
    path='output/custom_plot.html',
    title='Custom Scatter Plot'
)

# Generate plotly figure
x_data = np.random.lognormal(8, 1, 1000)
y_data = np.random.normal(15, 3, 1000)

fig = px.scatter(x=x_data, y=y_data)
fig.update_layout(
    xaxis_title='Read Length',
    yaxis_title='Quality Score',
    title=my_plot.title
)

# Attach figure and generate HTML
my_plot.fig = fig
my_plot.html = fig.to_html(full_html=False, include_plotlyjs='cdn')

# Save with settings
settings = {
    'format': ['png', 'svg'],
    'no_static': False,
    'dpi': 300
}
my_plot.save(settings)

# Encode for HTML report embedding
html_snippet = my_plot.encode()
```

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

### BAM Data Plotting with Downsampling

Source: https://github.com/wdecoster/nanoplot/blob/master/README.md

Analyzes BAM files with specified color and downsampling. Downsampling is applied after data collection and may not significantly speed up processing for smaller datasets.

```bash
NanoPlot --color yellow --bam alignment1.bam alignment2.bam alignment3.bam --downsample 10000
```