| Name | jdgenometracks JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | A Python package for plotting genomic data. |
| upload_time | 2024-09-11 17:43:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
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# **jdgenometracks**
## Overview
**jdgenometracks** is a Python package designed to simplify the visualization of genomic data using **Plotly** and **Matplotlib**. The package supports visualizing different track types such as **BED**, **BEDGRAPH**, and **axis** tracks, making it ideal for managing and plotting large genomic datasets with customizable visual styles.
At the core of the package is the **TrackFactory**, which allows users to easily create and manage genomic tracks, enabling the use of both Plotly and Matplotlib with the same set of track definitions.
## Key Features
- **TrackFactory** for easy creation of tracks (e.g., BED, BEDGRAPH, axis, and spacer tracks).
- Supports **Plotly** and **Matplotlib** visualization.
- Customizable plotting options (e.g., colors, markers, lines).
- Easily handle large genomic datasets and visualize them in multiple columns or rows.
## Installation
```bash
pip install jdgenometracks
```
## Getting Started
### Example: Creating and Plotting Tracks with Plotly
```python
import jdgenometracks as jdg
# Define a set of tracks to visualize
tracks = [
jdg.TrackFactory.create_track(
file_path="example_data/track1.bedgraph",
track_name="Track 1",
track_type="bedgraph",
plot_type="points",
plotly_options={
"marker_color": "blue",
"marker_size": 4
}
),
jdg.TrackFactory.create_track(
file_path="example_data/track2.bed",
track_name="Track 2",
track_type="bed",
plotly_options={
"fillcolor": "red",
"line_color": "black"
}
),
jdg.TrackFactory.create_track(
track_name="Bottom Axis",
track_type="axis",
axis_type="verbose"
)
]
# Now, pass these tracks to a Plotly plotting utility (example):
plotter = jdg.PlotlyPlotter(tracks, total_height=800)
fig = plotter.plot_all_tracks(column_titles=["Sample Data"])
fig.show()
```
### Example: Creating and Plotting Tracks with Matplotlib
```python
import jdgenometracks as jdg
# Define a set of tracks for Matplotlib
tracks = [
jdg.TrackFactory.create_track(
file_path="example_data/track1.bedgraph",
track_name="Track 1",
track_type="bedgraph",
plot_type="lines",
mpl_plot_options={
"color": "blue",
"linewidth": 2
}
),
jdg.TrackFactory.create_track(
file_path="example_data/track2.bed",
track_name="Track 2",
track_type="bed",
mpl_rect_options={
"color": "red",
"linewidth": 2
},
mpl_text_options={
"va": "center"
},
mpl_text_alignment="right"
),
jdg.TrackFactory.create_track(
track_name="Bottom Axis",
track_type="axis",
axis_type="verbose"
)
]
# Use a Matplotlib plotter
plotter = jdg.MPLPlotter(tracks, total_height=8)
fig, axes = plotter.plot_all_tracks(plot_title="Genomic Data")
plt.show()
```
### Example: Displaying Two Columns of Tracks with Plotly
```python
import jdgenometracks as jdg
import numpy as np
# Define two sets of tracks to display in two columns
tracks = [
# First Column (Column 1)
jdg.TrackFactory.create_track(
file_path="example_data/track1_column1.bedgraph",
track_name="Track 1 - Column 1",
track_type="bedgraph",
plot_type="points",
plotly_options={
"marker_color": "blue",
"marker_size": 4
}
),
jdg.TrackFactory.create_track(
file_path="example_data/track2_column1.bed",
track_name="Track 2 - Column 1",
track_type="bed",
plotly_options={
"fillcolor": "red",
"line_color": "black"
}
),
jdg.TrackFactory.create_track(
track_name="Bottom Axis - Column 1",
track_type="axis",
axis_type="verbose"
),
# Second Column (Column 2)
jdg.TrackFactory.create_track(
file_path="example_data/track1_column2.bedgraph",
track_name="Track 1 - Column 2",
track_type="bedgraph",
plot_type="lines",
plotly_options={
"line_color": "green",
"line_width": 2
}
),
jdg.TrackFactory.create_track(
file_path="example_data/track2_column2.bed",
track_name="Track 2 - Column 2",
track_type="bed",
plotly_options={
"fillcolor": "purple",
"line_color": "black"
}
),
jdg.TrackFactory.create_track(
track_name="Bottom Axis - Column 2",
track_type="axis",
axis_type="verbose"
)
]
# Reshape the tracks to 2 columns (first 3 tracks in column 1, next 3 in column 2)
tracks = np.array(tracks).reshape(-1, 2)
# Plot using PlotlyPlotter
plotter = jdg.PlotlyPlotter(tracks, total_height=800)
fig = plotter.plot_all_tracks(
column_titles=["Column 1", "Column 2"], # Titles for each column
height_props=[1, 1, 0.1], # Proportions for each row (tracks and axis)
width_props=[1, 1] # Equal width for both columns
)
# Display the figure
fig.show()
```
## TrackFactory Usage
The **TrackFactory** allows you to create different types of tracks. Here are some examples of how to use the factory to create tracks:
### BEDGRAPH Track
```python
jdg.TrackFactory.create_track(
file_path="path/to/file.bedgraph",
track_name="Example BEDGRAPH",
track_type="bedgraph",
plot_type="lines",
plotly_options={
"line_color": "green",
"line_width": 2
}
)
```
### BED Track
```python
jdg.TrackFactory.create_track(
file_path="path/to/file.bed",
track_name="Example BED",
track_type="bed",
plotly_options={
"fillcolor": "purple",
"line_width": 2
}
)
```
### Axis Track
```python
jdg.TrackFactory.create_track(
track_name="X-Axis",
track_type="axis",
axis_type="verbose"
)
```
## Customizing Plots
Each track can be customized using **Plotly** keyword arguments or **Matplotlib** keyword arguments. Example options include:
- **Colors**: Set line and marker colors.
- **Line Styles**: Adjust line widths and types.
- **Markers**: Control marker styles (e.g., size, symbol).
- **Axes**: Customize axis labels and ticks.
## Advanced Usage
For more advanced use cases, you can extend the package to support new track types or customize existing ones by modifying the `TrackFactory` to handle additional parameters or features.
Raw data
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"description": "# **jdgenometracks**\n\n## Overview\n\n**jdgenometracks** is a Python package designed to simplify the visualization of genomic data using **Plotly** and **Matplotlib**. The package supports visualizing different track types such as **BED**, **BEDGRAPH**, and **axis** tracks, making it ideal for managing and plotting large genomic datasets with customizable visual styles.\n\nAt the core of the package is the **TrackFactory**, which allows users to easily create and manage genomic tracks, enabling the use of both Plotly and Matplotlib with the same set of track definitions.\n\n## Key Features\n\n- **TrackFactory** for easy creation of tracks (e.g., BED, BEDGRAPH, axis, and spacer tracks).\n- Supports **Plotly** and **Matplotlib** visualization.\n- Customizable plotting options (e.g., colors, markers, lines).\n- Easily handle large genomic datasets and visualize them in multiple columns or rows.\n\n## Installation\n\n```bash\npip install jdgenometracks\n```\n\n## Getting Started\n\n### Example: Creating and Plotting Tracks with Plotly\n\n```python\nimport jdgenometracks as jdg\n\n# Define a set of tracks to visualize\ntracks = [\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track1.bedgraph\",\n track_name=\"Track 1\",\n track_type=\"bedgraph\",\n plot_type=\"points\",\n plotly_options={\n \"marker_color\": \"blue\",\n \"marker_size\": 4\n }\n ),\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track2.bed\",\n track_name=\"Track 2\",\n track_type=\"bed\",\n plotly_options={\n \"fillcolor\": \"red\",\n \"line_color\": \"black\"\n }\n ),\n jdg.TrackFactory.create_track(\n track_name=\"Bottom Axis\",\n track_type=\"axis\",\n axis_type=\"verbose\"\n )\n]\n\n# Now, pass these tracks to a Plotly plotting utility (example):\nplotter = jdg.PlotlyPlotter(tracks, total_height=800)\nfig = plotter.plot_all_tracks(column_titles=[\"Sample Data\"])\nfig.show()\n```\n\n### Example: Creating and Plotting Tracks with Matplotlib\n\n```python\nimport jdgenometracks as jdg\n\n# Define a set of tracks for Matplotlib\ntracks = [\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track1.bedgraph\",\n track_name=\"Track 1\",\n track_type=\"bedgraph\",\n plot_type=\"lines\",\n mpl_plot_options={\n \"color\": \"blue\",\n \"linewidth\": 2\n }\n ),\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track2.bed\",\n track_name=\"Track 2\",\n track_type=\"bed\",\n mpl_rect_options={\n \"color\": \"red\",\n \"linewidth\": 2\n },\n mpl_text_options={\n \"va\": \"center\"\n },\n mpl_text_alignment=\"right\"\n ),\n jdg.TrackFactory.create_track(\n track_name=\"Bottom Axis\",\n track_type=\"axis\",\n axis_type=\"verbose\"\n )\n]\n\n# Use a Matplotlib plotter\nplotter = jdg.MPLPlotter(tracks, total_height=8)\nfig, axes = plotter.plot_all_tracks(plot_title=\"Genomic Data\")\nplt.show()\n```\n\n### Example: Displaying Two Columns of Tracks with Plotly\n\n```python\nimport jdgenometracks as jdg\nimport numpy as np\n\n# Define two sets of tracks to display in two columns\ntracks = [\n # First Column (Column 1)\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track1_column1.bedgraph\",\n track_name=\"Track 1 - Column 1\",\n track_type=\"bedgraph\",\n plot_type=\"points\",\n plotly_options={\n \"marker_color\": \"blue\",\n \"marker_size\": 4\n }\n ),\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track2_column1.bed\",\n track_name=\"Track 2 - Column 1\",\n track_type=\"bed\",\n plotly_options={\n \"fillcolor\": \"red\",\n \"line_color\": \"black\"\n }\n ),\n jdg.TrackFactory.create_track(\n track_name=\"Bottom Axis - Column 1\",\n track_type=\"axis\",\n axis_type=\"verbose\"\n ),\n\n # Second Column (Column 2)\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track1_column2.bedgraph\",\n track_name=\"Track 1 - Column 2\",\n track_type=\"bedgraph\",\n plot_type=\"lines\",\n plotly_options={\n \"line_color\": \"green\",\n \"line_width\": 2\n }\n ),\n jdg.TrackFactory.create_track(\n file_path=\"example_data/track2_column2.bed\",\n track_name=\"Track 2 - Column 2\",\n track_type=\"bed\",\n plotly_options={\n \"fillcolor\": \"purple\",\n \"line_color\": \"black\"\n }\n ),\n jdg.TrackFactory.create_track(\n track_name=\"Bottom Axis - Column 2\",\n track_type=\"axis\",\n axis_type=\"verbose\"\n )\n]\n\n# Reshape the tracks to 2 columns (first 3 tracks in column 1, next 3 in column 2)\ntracks = np.array(tracks).reshape(-1, 2)\n\n# Plot using PlotlyPlotter\nplotter = jdg.PlotlyPlotter(tracks, total_height=800)\nfig = plotter.plot_all_tracks(\n column_titles=[\"Column 1\", \"Column 2\"], # Titles for each column\n height_props=[1, 1, 0.1], # Proportions for each row (tracks and axis)\n width_props=[1, 1] # Equal width for both columns\n)\n\n# Display the figure\nfig.show()\n```\n\n## TrackFactory Usage\n\nThe **TrackFactory** allows you to create different types of tracks. Here are some examples of how to use the factory to create tracks:\n\n### BEDGRAPH Track\n\n```python\njdg.TrackFactory.create_track(\n file_path=\"path/to/file.bedgraph\",\n track_name=\"Example BEDGRAPH\",\n track_type=\"bedgraph\",\n plot_type=\"lines\",\n plotly_options={\n \"line_color\": \"green\",\n \"line_width\": 2\n }\n)\n```\n\n### BED Track\n\n```python\njdg.TrackFactory.create_track(\n file_path=\"path/to/file.bed\",\n track_name=\"Example BED\",\n track_type=\"bed\",\n plotly_options={\n \"fillcolor\": \"purple\",\n \"line_width\": 2\n }\n)\n```\n\n### Axis Track\n\n```python\njdg.TrackFactory.create_track(\n track_name=\"X-Axis\",\n track_type=\"axis\",\n axis_type=\"verbose\"\n)\n```\n\n## Customizing Plots\n\nEach track can be customized using **Plotly** keyword arguments or **Matplotlib** keyword arguments. Example options include:\n\n- **Colors**: Set line and marker colors.\n- **Line Styles**: Adjust line widths and types.\n- **Markers**: Control marker styles (e.g., size, symbol).\n- **Axes**: Customize axis labels and ticks.\n\n## Advanced Usage\n\nFor more advanced use cases, you can extend the package to support new track types or customize existing ones by modifying the `TrackFactory` to handle additional parameters or features.\n\n",
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