### Basic Web Request (GET)

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to make a simple GET request to a URL using the 'requests' library. Ensure 'requests' is installed.

```python
import requests

url = 'https://api.github.com'
response = requests.get(url)

print(f"Status Code: {response.status_code}")
# print(response.json()) # Uncomment to see the JSON response
```

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

### Creating a Bar Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example demonstrates how to create a simple bar chart using matplotlib. Matplotlib should be installed and imported.

```python
import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 5, 6]

plt.bar(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Simple Bar Chart")
plt.show()
```

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

### Install laptrack and dependencies

Source: https://laptrack.readthedocs.io/en/stable/examples/bright_spots.html

Installs necessary packages for the project. Use this to set up your environment.

```python
%pip install -q --upgrade -r requirements.txt
#%pip install --upgrade -r requirements.txt
#%pip uninstall -y laptrack
#%pip install -e ../../
```

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

### Install Package with Development Requirements

Source: https://laptrack.readthedocs.io/en/stable/contributing.html

Install the project package along with its development dependencies using Poetry.

```bash
$ poetry install

```

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

### Install Dependencies

Source: https://laptrack.readthedocs.io/en/stable/examples/3D_tracking.html

Installs necessary packages from a requirements file. Ensure 'requirements.txt' is present in the current directory.

```python
%pip install -q --upgrade -r requirements.txt

```

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

### Visualize bright spots on a graph

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example demonstrates how to visualize identified bright spots on a graph. Ensure matplotlib is installed for plotting.

```python
import matplotlib.pyplot as plt

def plot_bright_spots(data, bright_spots):
    plt.plot(data, label='Data')
    plt.scatter(bright_spots, [data[i] for i in bright_spots], color='red', label='Bright Spots')
    plt.legend()
    plt.show()
```

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

### Creating a Pie Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Example of creating a pie chart to show proportions. Matplotlib is required.

```python
import matplotlib.pyplot as plt

labels = ['Frogs', 'Hogs', 'Dogs', 'Logs']
sizes = [15, 30, 45, 10]

plt.figure(figsize=(8, 8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Pie Chart Example')
plt.show()
```

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

### Creating a Bar Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Example of creating a bar chart using matplotlib. Requires matplotlib to be installed.

```python
import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [5, 7, 4]

plt.figure(figsize=(8, 6))
plt.bar(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Simple Bar Chart")
plt.show()
```

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

### Adding a Point Layer Interactively

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example shows how to add a point layer to a napari viewer and interactively add points. Ensure napari and numpy are installed.

```python
import napari
import numpy as np

# Create a viewer
viewer = napari.Viewer()

# Add an empty point layer
points_layer = viewer.add_points(name='my_points')

# Now you can click in the viewer to add points.
# To programmatically add points:
# points_layer.data = np.array([[10, 10], [20, 30], [50, 50]])

# To get the current points data:
# current_points = points_layer.data
```

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

### Visualize Bright Spots

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to visualize the calculated bright spots on a time series plot. Ensure matplotlib is installed for plotting.

```python
import pandas as pd
import matplotlib.pyplot as plt
from bright_spots import calculate_bright_spots

data = {
    'timestamp': pd.to_datetime(['2023-01-01 00:00:00', '2023-01-01 01:00:00', '2023-01-01 02:00:00', '2023-01-01 03:00:00', '2023-01-01 04:00:00']),
    'value': [10, 12, 15, 13, 11]
}
df = pd.DataFrame(data)
df = df.set_index('timestamp')

bright_spots_df = calculate_bright_spots(df, 'value')

plt.figure(figsize=(10, 6))
plt.plot(df.index, df['value'], label='Original Data')
plt.scatter(bright_spots_df.index, bright_spots_df['value'], color='red', label='Bright Spots')
plt.xlabel('Timestamp')
plt.ylabel('Value')
plt.title('Bright Spots Visualization')
plt.legend()
plt.grid(True)
plt.show()
```

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

### Install LapTrack via Pip

Source: https://laptrack.readthedocs.io/en/stable/index.html

Install the LapTrack package using pip. This is the standard installation method for most users.

```bash
$ pip install laptrack

```

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

### Pie Chart Example

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This snippet demonstrates creating a pie chart. Ensure matplotlib is imported.

```python
import matplotlib.pyplot as plt

labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Pie Chart Example')
plt.show()
```

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

### Basic Plotting with Seaborn

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example demonstrates creating a simple plot using the Seaborn library, which is built on top of Matplotlib. Seaborn and Matplotlib must be installed.

```python
import seaborn as sns
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 3, 5]

sns.lineplot(x=x, y=y)
plt.title('Seaborn Line Plot')
plt.show()
```

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

### Visualize Bright Spots

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example visualizes the calculated bright spots. It assumes 'bright_spots_results' is available from a previous calculation.

```python
from bright_spots.plotting import plot_bright_spots

# Assuming 'bright_spots_results' is the output from the bright_spots function
plot_bright_spots(bright_spots_results)
```

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

### Install and Upgrade Packages

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/api_example.ipynb.txt

Installs or upgrades necessary packages for laptrack, matplotlib, spacy, flask, and pandas. Includes a conditional check for Google Colab environments.

```python
try:
    import google.colab

    %pip install -q --upgrade laptrack matplotlib spacy flask pandas
    # upgrade packages to avoid pip warnings if the notebook is run in colab
except:
    %pip install -q --upgrade laptrack matplotlib pandas
```

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

### Load and Display an Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image from a file path and displays it. Ensure the 'imageio' library is installed.

```python
import imageio.v3 as iio
import matplotlib.pyplot as plt

# Load an image
image = iio.imread("/path/to/your/image.png")

# Display the image
plt.figure(figsize=(8, 8))
plt.imshow(image)
plt.axis("off")
plt.show()
```

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

### Histogram Example

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to create a histogram. Matplotlib's pyplot module is used.

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

data = np.random.randn(1000)

plt.hist(data, bins=30)
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.title("Histogram of Random Data")
plt.show()
```

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

### Creating a Sunburst Chart with Custom Colors

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to use custom color sequences for a sunburst chart to highlight specific hierarchical levels or values. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.sunburst(df, path=['continent', 'country'], values='pop',
                  color='lifeExp',
                  color_continuous_scale='Blues',
                  title='Sunburst Chart of Population by Country and Continent with Blues Scale')
fig.show()
```

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

### Applying a Gaussian Filter Interactively

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example shows how to apply a Gaussian filter to an image layer interactively within Napari. It utilizes the `napari.viewer.interactive` context and the `scipy.ndimage.gaussian_filter` function. Ensure `scipy` is installed.

```python
import napari
import numpy as np
from scipy.ndimage import gaussian_filter

viewer = napari.Viewer()

# Add a noisy image layer
image = np.random.rand(200, 200) * 0.5 + np.random.rand(200, 200) * 0.5
viewer.add_image(image, name='noisy_image')

# Use interactive mode to apply a filter
with viewer.interactive():
    print("Interactive mode for Gaussian filter. Adjust sigma in the code or GUI.")
    # Example: Apply Gaussian filter with a sigma value
    sigma = 2
    filtered_image = gaussian_filter(viewer.layers['noisy_image'].data, sigma=sigma)
    viewer.layers['noisy_image'].data = filtered_image

print("Gaussian filter applied interactively.")

# Note: In a real interactive session, sigma might be controlled by a slider
# or other GUI elements linked to this code.
```

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

### Load and Display Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image from a file path and displays it. Ensure the 'napari' library is installed for visualization.

```python
from pathlib import Path
from skimage.io import imread
import napari

# Define the path to your image file
image_path = Path("../data/images/cell_segmentation/cell_segmentation_00.tif")

# Read the image
image = imread(image_path)

# Create a napari viewer and add the image layer
viewer = napari.Viewer()
viewer.add_image(image, name="Cell Segmentation Image")

# Show the viewer (this will block until the viewer is closed)
napari.run()
```

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

### Install Pre-commit Hooks with Nox

Source: https://laptrack.readthedocs.io/en/stable/contributing.html

Install pre-commit as a Git hook using Nox to enforce linting and code formatting checks before committing changes.

```bash
$ nox --session=pre-commit -- install

```

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

### Using `intervaltree` for Overlap Queries

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

Demonstrates how to use the `intervaltree` library for efficient overlap queries. Install with `pip install intervaltree`.

```python
from intervaltree import Interval, IntervalTree

# Create an interval tree
tree = IntervalTree()

# Add intervals
tree.add(Interval(0, 10, "data1"))
tree.add(Interval(5, 15, "data2"))
tree.add(Interval(12, 20, "data3"))
tree.add(Interval(25, 30, "data4"))

# Query for intervals overlapping with [8, 18]
overlapping_intervals = tree[8:18]

print(f"Intervals overlapping with [8, 18]:")
for interval in sorted(overlapping_intervals):
    print(interval)
```

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

### Visualize Bright Spots

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example visualizes data and highlights the calculated bright spots. It requires matplotlib for plotting.

```python
import matplotlib.pyplot as plt

def calculate_bright_spots(data, threshold):
    return [x for x in data if x > threshold]

def visualize_bright_spots(data, threshold):
    bright_spots = calculate_bright_spots(data, threshold)
    plt.figure(figsize=(10, 6))
    plt.plot(data, label='Data')
    plt.scatter(range(len(data)), data, color='red', label='Bright Spots')
    plt.axhline(y=threshold, color='r', linestyle='--', label='Threshold')
    plt.legend()
    plt.title('Bright Spots Visualization')
    plt.xlabel('Index')
    plt.ylabel('Value')
    plt.grid(True)
    plt.show()

data = [1, 5, 10, 15, 20, 25, 30]
threshold = 12
visualize_bright_spots(data, threshold)
```

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

### Basic Plotting with Matplotlib

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

A simple example of plotting data using matplotlib. Requires matplotlib to be installed and imported.

```python
import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 5, 6]

plt.plot(x, y)
plt.show()
```

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

### Creating a Simple Plot

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Provides an example of generating a basic plot from DataFrame columns. Requires matplotlib to be installed.

```python
import matplotlib.pyplot as plt

plt.plot(df['x_axis'], df['y_axis'])
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('Simple Plot')
plt.show()
```

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

### Getting Overlap Results

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

This example shows how to retrieve the computed overlap results after tracking. The format of the results can vary.

```python
results = tracker.get_results()
print(results)

```

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

### File Reading

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to read the content of a text file in Python. Ensure the file 'example.txt' exists in the same directory.

```python
with open('example.txt', 'r') as f:
    content = f.read()
    print(content)
```

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

### Initialize Napari Viewer and Load Camera State

Source: https://laptrack.readthedocs.io/en/stable/examples/napari_interactive_fix.html

Creates a napari viewer instance, adds the loaded images and labels, and applies a predefined camera state from a JSON file for consistent viewing.

```python
viewer = napari.Viewer()
viewer.add_image(images, name="images")
viewer.add_labels(labels, name="labels")
original_camera_state = viewer.camera.dict()
with open("napari_interactive_fix_data/camera_state.json", "r") as f:
    camera_state = json.load(f)
```

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

### Visualize 3D Tracks

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Visualizes the generated 3D tracks. This example assumes you have a plotting library like matplotlib installed.

```python
import matplotlib.pyplot as plt

# Assuming 'tracks' is the output from LapTrack
for track_id, track_data in tracks.items():
    x_coords = [p[0] for p in track_data]
    y_coords = [p[1] for p in track_data]
    plt.plot(x_coords, y_coords, label=f'Track {track_id}')

plt.xlabel('X Coordinate')
plt.ylabel('Y Coordinate')
plt.title('3D Tracks Visualization')
plt.legend()
plt.show()

```

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

### Analyze Bright Spots Over Time

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to analyze the evolution of bright spots over time. It requires a time-series dataset.

```python
from bright_spots.analysis import analyze_time_series_bright_spots

# Assuming 'data' is a time-series DataFrame and 'time_column' is the name of the time column
time_series_analysis = analyze_time_series_bright_spots(data, time_column='timestamp')
```

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

### Using the `napari.util.colormaps` Module

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

Demonstrates how to access and use colormaps provided by Napari. Colormaps are essential for visualizing image data.

```python
from napari.util import colormaps

# Get a list of available colormaps
print(colormaps.available)

# Get a specific colormap
viridis_cmap = colormaps.get_colormap('viridis')

# You can then use this colormap when adding or updating layers
```

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

### Displaying a Sankey Diagram

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to create a Sankey diagram, which visualizes the flow of energy or materials between different nodes. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.sankey(df, values='pop', title='Sankey Diagram of Population by Continent')
fig.show()
```

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

### Load Data for Bright Spots Analysis

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Demonstrates how to load a dataset for bright spots analysis. Ensure the file path is correct.

```python
import pandas as pd

df = pd.read_csv("/websites/laptrack_readthedocs_io_en_stable/content/examples/bright_spots.ipynb.csv")
df.head()
```

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

### Generating a Line Plot

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example illustrates how to generate a line plot, suitable for showing trends over time or continuous data. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.gapminder().query("country=='Canada'")
fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada')
fig.show()
```

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

### Creating a Pie Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Demonstrates how to create a pie chart to show proportions of a whole. Best suited for a small number of categories.

```python
import matplotlib.pyplot as plt

labels = ['Frogs', 'Hogs', 'Dogs', 'Logs']
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

plt.figure(figsize=(8, 6))
plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
plt.title('Pie Chart Example')
plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
```

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

### Perform Watershed Segmentation

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

This example demonstrates the watershed algorithm for segmenting cells. It requires a preprocessed binary image.

```python
import cv2
import numpy as np

def watershed_segmentation(thresh_image):
    # Noise removal
    kernel = np.ones((3,3),np.uint8)
    opening = cv2.morphologyEx(thresh_image, cv2.MORPH_OPEN, kernel, iterations = 2)
    
    # Sure background area
    sure_bg = cv2.dilate(opening, kernel, iterations=3)
    
    # Finding sure foreground area
    dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
    ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0)
    
    # Finding unknown region
    sure_fg = np.uint8(sure_fg)
    unknown = cv2.subtract(sure_bg, sure_fg)
    
    # Marker labelling
    ret, markers = cv2.connectedComponents(sure_fg)
    
    # Add one to all labels so that sure background is not 0, but 1
    markers = markers+1
    
    # Now, mark the region of unknown with zero
    markers[unknown==255] = 0
    
    # Apply watershed algorithm
    markers = cv2.watershed(cv2.cvtColor(thresh_image, cv2.COLOR_GRAY2BGR), markers)
    
    # The boundary of the segmented regions will be marked with -1
    # Return the segmented image where boundaries are -1
    return markers
```

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

### Basic Data Loading and Display

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Demonstrates how to load a dataset and display its first few rows. This is a common starting point for data exploration.

```python
import pandas as pd

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

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

### Get Active Tracks

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

This example shows how to retrieve the currently active tracks from the tracker. Each track is represented by a tuple (track_id, x, y, w, h).

```python
active_tracks = track.get_active_tracks()
print(active_tracks)
```

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

### Clearing All Tracked Objects

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

This example demonstrates how to clear all objects currently being tracked by the OverlapTracker. Use this when starting a new tracking session or resetting the state.

```python
tracker.clear_objects()

```

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

### Load and Display an Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image from a file path and displays it. Ensure the 'imageio' and 'matplotlib' libraries are installed.

```python
import imageio
import matplotlib.pyplot as plt

# Load an image
image = imageio.imread('path/to/your/image.png')

# Display the image
plt.imshow(image)
plt.title('Loaded Image')
plt.axis('off')
plt.show()
```

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

### Load and Display Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image from a file path and displays it. Ensure the 'skimage.io' and 'matplotlib.pyplot' libraries are installed.

```python
from skimage import io
import matplotlib.pyplot as plt

# Load an image
image = io.imread('path/to/your/image.tif')

# Display the image
plt.figure(figsize=(8, 8))
plt.imshow(image, cmap='gray')
plt.title('Loaded Image')
plt.axis('off')
plt.show()
```

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

### Creating a Pie Chart with Custom Legend Titles

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to customize the titles of the legend entries in a pie chart for better clarity. Ensure 'plotly' is installed.

```python
import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='size', names='day', title='Proportion of Bill Size by Day',
             labels={'day': 'Day of Week', 'size': 'Number of People'})
fig.show()
```

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

### Create a Simple Image Layer

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

Demonstrates creating a basic image layer with specified data and metadata. This is useful for generating synthetic data or visualizing arrays.

```python
import numpy as np

# Create some random data
data = np.random.random((512, 512))

# Add the image layer to the viewer
viewer.add_image(data, name='random_image')
```

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

### Creating a Histogram with Custom Bins

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to specify custom bin sizes and ranges for a histogram, allowing for more control over the data aggregation.

```python
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill", nbins=20, title='Histogram of Total Bill with 20 Bins')
fig.show()
```

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

### Creating a Scatter Plot with Custom Hover Data

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to customize the information displayed when hovering over data points in a scatter plot. Ensure 'plotly' is installed.

```python
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
                 hover_data=['petal_width', 'petal_length'])
fig.show()
```

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

### Basic Napari Viewer Initialization

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

Initializes a basic Napari viewer. This is a fundamental step for any Napari application.

```python
import napari
viewer = napari.Viewer()
```

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

### Creating a Treemap with Custom Hover Data

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to add custom information to the hover data for treemap nodes, providing more context about each segment. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.treemap(df, path=['continent', 'country'], values='pop',
                 color='lifeExp', hover_data=['iso_alpha', 'gdpPercap'])
fig.show()
```

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

### Creating a Choropleth Map

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to create a choropleth map, which uses color intensity to represent data values across geographical regions. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha", color="lifeExp",
                    hover_name="country", color_continuous_scale=px.colors.sequential.Plasma)
fig.show()
```

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

### Displaying a Density Heatmap

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to create a density heatmap to visualize the relationship between two continuous variables. It's useful for identifying areas of high concentration.

```python
import plotly.express as px
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip")
fig.show()
```

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

### Add and Control a Dataset Layer in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example demonstrates adding a dataset layer, which can contain multiple images or other layer types. Requires napari.

```python
import napari
from skimage import data

# Create a napari viewer
viewer = napari.Viewer()

# Load multiple images
image1 = data.astronaut()
image2 = data.camera()

# Create a dataset (dictionary mapping names to data)
dataset = {
    'astronaut': image1,
    'camera': image2
}

# Add the dataset as layers
dataset_layer = viewer.add_image(dataset, name='my_dataset')

# The 'my_dataset' layer in the layer list will contain 'astronaut' and 'camera' sub-layers.
```

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

### Creating a Bar Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Demonstrates how to create a bar chart using matplotlib. Requires matplotlib and pandas to be installed and imported.

```python
import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame({
    "col1": [1, 2],
    "col2": [3, 4]
})

df.plot.bar(x='col1', y='col2')
```

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

### Basic Web Request (POST)

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This snippet demonstrates how to make a POST request with data using the 'requests' library. Ensure 'requests' is installed.

```python
import requests

url = 'https://httpbin.org/post'
data = {'key': 'value'}

response = requests.post(url, json=data)

print(f"Status Code: {response.status_code}")
print(response.json())
```

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

### Generating a Violin Plot

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to generate a violin plot, which combines aspects of box plots and kernel density plots to show data distribution. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.tips()
fig = px.violin(df, y="total_bill", box=True, points="all",
                 hover_data=False)
fig.show()
```

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

### Creating a Bar Chart with Custom Axis Labels and Order

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to customize axis labels and control the order of categories in a bar chart for better presentation. Ensure 'plotly' is installed.

```python
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="day", y="total_bill", color="sex",
             category_orders={"day": ["Thur", "Fri", "Sat", "Sun"]},
             labels={"total_bill": "Total Bill Amount"})
fig.show()
```

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

### Importing a Module

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to import and use a function from a Python module. Standard library modules are often used.

```python
import math

radius = 5
area = math.pi * radius**2
print(f"The area of the circle is: {area}")
```

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

### Creating a Scatter Plot on a Map with Custom Hover Data

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to add custom information to the hover data for scatter plots on a map, providing more details about each location. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.carshare()
fig = px.scatter_mapbox(df, lat="centroid_lat", lon="centroid_lon", color="peak_hour", size="car_hours",
                        hover_name="all_cars", zoom=11,
                        hover_data={'car_hours':True, 'peak_hour':False})
fig.update_layout(mapbox_style="open-street-map")
fig.show()
```

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

### Initialize 3D Tracker

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Initializes the 3D tracker with specified parameters. Ensure the 'tracker_config' is correctly set up.

```python
from laptrack import LapTrack
tracker = LapTrack(tracker_config)

```

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

### Creating a Scatter Matrix with Custom Hover Data

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to add custom information to the hover data for scatter matrix plots, providing more details about each data point. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.iris()
fig = px.scatter_matrix(df, dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
                        color="species",
                        hover_data=['petalLength'])
fig.show()
```

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

### Interactive Setting of Dimension Contrast Limits in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example shows how to set contrast limits for dimensions, which can be useful for adjusting the display range of corrected data.

```python
viewer.dims.contrast_limits = [0.2, 0.8]
```

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

### Overlay bright spots on an image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example demonstrates how to overlay markers or highlights on an image to indicate the locations of detected bright spots. This is useful for visual inspection and reporting.

```python
import cv2
import numpy as np

def overlay_bright_spots_on_image(image, bright_spot_coords, color=(0, 0, 255), radius=5):
    # Assumes image is a NumPy array (e.g., loaded with OpenCV)
    # Assumes bright_spot_coords is a list of (x, y) tuples
    output_image = image.copy()
    for x, y in bright_spot_coords:
        cv2.circle(output_image, (int(x), int(y)), radius, color, 2) # Draw a circle
    return output_image
```

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

### Creating a Heatmap with Custom Color Scale and Text Labels

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to apply a custom color scale and display text labels on a heatmap for better data interpretation. Requires 'plotly' to be installed.

```python
import plotly.express as px
import numpy as np

z = np.random.rand(10, 10)
fig = px.imshow(z, text_auto='.2f', aspect="auto",
                color_continuous_scale='Plasma')
fig.show()
```

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

### Creating a Parallel Coordinates Plot with Custom Color Scale

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to apply a custom color scale to a parallel coordinates plot to highlight specific data ranges or patterns. Ensure 'plotly' is installed.

```python
import plotly.express as px
df = px.data.iris()
fig = px.parallel_coordinates(df, color='petalLength', 
                              dimensions=['sepalWidth', 'sepalLength', 'petalWidth', 'petalLength'],
                              color_continuous_scale=px.colors.sequential.Viridis)
fig.show()
```

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

### Creating a Table

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Shows how to display a DataFrame as a table. Requires pandas.

```python
import pandas as pd

df = pd.DataFrame({
    "col1": [1, 2, 3],
    "col2": [4, 5, 6],
})
df.to_html()
```

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

### Creating a 3D Scatter Plot with Custom Hover Data

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to add custom information to the hover data for 3D scatter plots, providing more details about each point in 3D space. Requires 'plotly' to be installed.

```python
import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_width', y='sepal_length', z='petal_width',
                    color='species',
                    hover_data=['petalLength'])
fig.show()
```

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

### Load and Display Image in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

Loads a sample image and displays it in the Napari viewer. This is a foundational step for most interactive operations.

```python
from napari_workflows.gui import NapariWorkflow
from napari_workflows.utils import get_sample_data

nw = NapariWorkflow()
nw.viewer.open(get_sample_data('cells'))
```

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

### Creating a 3D Surface Plot with Custom Color Scale

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to apply a custom color scale to a 3D surface plot for better visualization of the surface's elevation or other values. Requires 'plotly' to be installed.

```python
import plotly.express as px
import numpy as np
x, y = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
z = np.sin(np.sqrt(x**2 + y**2))
fig = px.surface(x=x, y=y, z=z, color_continuous_scale='Viridis')
fig.show()
```

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

### Respond to Viewer Events in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example illustrates how to connect a callback function to viewer events, such as mouse clicks or layer changes. This allows for dynamic interaction with the napari viewer.

```python
from napari.utils import set_choice

# Assuming 'viewer' is an existing napari viewer instance

def on_mouse_click(event):
    print(f"Mouse clicked at: {event.pos}")

# Connect the callback to the viewer's mouse press event
viewer.window.qt_viewer.mouse_press.connect(on_mouse_click)
```

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

### Load and Preprocess Images for Segmentation

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Demonstrates loading image data and performing basic preprocessing steps. Ensure you have the necessary libraries installed.

```python
import os
import cv2
import numpy as np

def load_and_preprocess_images(image_dir):
    images = []
    for filename in os.listdir(image_dir):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.tif', '.tiff')):
            img_path = os.path.join(image_dir, filename)
            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
            if img is not None:
                # Apply Gaussian blur for noise reduction
                img_blurred = cv2.GaussianBlur(img, (5, 5), 0)
                # Normalize image to range [0, 1]
                img_normalized = img_blurred.astype(np.float32) / 255.0
                images.append(img_normalized)
    return images

# Example usage:
# image_directory = 'path/to/your/images'
# preprocessed_images = load_and_preprocess_images(image_directory)
```

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

### Adjust Contrast Limits Interactively

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example demonstrates how to interactively adjust the contrast limits of an image layer in Napari. This is useful for enhancing image visibility and exploring different intensity ranges.

```python
import napari
import numpy as np

viewer = napari.Viewer()

# Create a dummy image
image = np.random.rand(512, 512)
viewer.add_image(image, name='random_image')

# Set contrast limits interactively
viewer.layers['random_image'].contrast_limits = (0.1, 0.9)
viewer.layers['random_image'].interactive_contrast_limits = True
```

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

### Interactive Fix Example

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This snippet shows an example of an interactive fix. It requires the napari library.

```python
import napari
from napari.utils.interactions import ( 
    mouse_event_to_console_event, 
    update_viewer_from_console_event, 
) 

viewer = napari.Viewer()

def on_mouse_move(event):
    console_event = mouse_event_to_console_event(event, viewer.dims.current_view)
    update_viewer_from_console_event(viewer, console_event)

viewer.bind_key('m', on_mouse_move)

napari.run()
```

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

### Creating an Interval Tree

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

This example shows how to construct an IntervalTree from a list of tuples. The tree is then used for efficient querying of overlapping intervals.

```python
from intervaltree import Interval, IntervalTree

# Example intervals: (start, end)
interval_data = [(10, 20), (15, 25), (30, 40), (35, 45)]

# Create an IntervalTree
tree = IntervalTree.from_tuples([(start, end, f'Interval {i+1}') for i, (start, end) in enumerate(interval_data)])

# Print the tree to verify
print(tree)
```

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

### Interactive Dimension Navigation in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example demonstrates navigating through dimensions using `viewer.dims.set_point`. It's essential for exploring multi-dimensional datasets and pinpointing specific slices for correction.

```python
viewer.dims.set_point(0, 50)
```

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

### Creating an Area Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to create an area chart, which is similar to a line chart but fills the area below the line with color. Useful for showing cumulative totals.

```python
import plotly.express as px
df = px.data.gapminder().query("country=='Japan'")
fig = px.area(df, x='year', y='pop', title='Population of Japan Over Time')
fig.show()
```

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

### Load and Display an Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image using OpenCV and displays it. Ensure you have OpenCV installed (`pip install opencv-python`).

```python
import cv2
import matplotlib.pyplot as plt

# Load an image
img = cv2.imread('path/to/your/image.png')

# Convert BGR to RGB for Matplotlib display
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Display the image
plt.figure(figsize=(8, 8))
plt.imshow(img_rgb)
plt.title('Loaded Image')
plt.axis('off')
plt.show()
```

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

### Interactive Setting of Multiple Dimension Points in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example demonstrates setting multiple dimension points simultaneously using `viewer.dims.set_point`. This is efficient for navigating complex multi-dimensional datasets.

```python
viewer.dims.set_point((0, 100, 50, 25))
```

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

### Load and Display an Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image using OpenCV and displays it. Ensure you have OpenCV installed (`pip install opencv-python`).

```python
import cv2
import matplotlib.pyplot as plt

# Load an image
img = cv2.imread('path/to/your/image.png')

# Convert BGR to RGB for matplotlib
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Display the image
plt.imshow(img_rgb)
plt.title('Loaded Image')
plt.axis('off')
plt.show()
```

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

### Load and Display Bright Spots

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This snippet shows how to load bright spot data and display it. Ensure you have the necessary libraries installed.

```python
import pandas as pd
import plotly.express as px

df = pd.read_csv("../data/bright_spots.csv")

fig = px.scatter(df, x="x", y="y", color="bright_spot",
                 title="Bright Spots Visualization")
fig.show()
```

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

### Load and Display Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image using OpenCV and displays it. Ensure you have OpenCV installed (`pip install opencv-python`).

```python
import cv2
import matplotlib.pyplot as plt

# Load an image
img = cv2.imread('path/to/your/image.png')

# Convert BGR to RGB for matplotlib display
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Display the image
plt.figure(figsize=(8, 8))
plt.imshow(img_rgb)
plt.title('Loaded Image')
plt.axis('off')
plt.show()
```

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

### Load and Display an Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image using OpenCV and displays it. Ensure you have OpenCV installed (`pip install opencv-python`).

```python
import cv2
import matplotlib.pyplot as plt

# Load an image
img = cv2.imread('path/to/your/image.png')

# Convert to RGB for matplotlib display
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Display the image
plt.imshow(img_rgb)
plt.title('Loaded Image')
plt.axis('off')
plt.show()
```

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

### Load and Display an Image

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/cell_segmentation.ipynb.txt

Loads an image using OpenCV and displays it. Ensure you have OpenCV installed (`pip install opencv-python`).

```python
import cv2
import matplotlib.pyplot as plt

# Load an image
img = cv2.imread('path/to/your/image.png')

# Convert the image from BGR to RGB (OpenCV loads in BGR)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Display the image
plt.figure(figsize=(8, 8))
plt.imshow(img_rgb)
plt.title('Loaded Image')
plt.axis('off')
plt.show()

```

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

### Set up Matplotlib Figure for Screenshot

Source: https://laptrack.readthedocs.io/en/stable/examples/napari_interactive_fix.html

Initializes a matplotlib figure with a specified size, preparing it for taking a napari viewer screenshot. This is often a precursor to capturing the viewer's state.

```python
plt.figure(figsize=(6, 3))
viewer.camera.update(camera_state)
```

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

### Basic SQL Query (SELECT)

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows a basic SQL SELECT statement. This is a fundamental query for retrieving data from a database.

```sql
SELECT column1, column2
FROM your_table_name
WHERE condition;
```

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

### Install LapTrack and Dependencies in Google Colab

Source: https://laptrack.readthedocs.io/en/stable/index.html

Install LapTrack and other necessary packages in a Google Colab environment. This command upgrades pre-installed packages.

```bash
$ pip install --upgrade laptrack spacy flask matplotlib

```

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

### Creating a Simple HTML Table

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This example shows how to generate a simple HTML table string using Python. This is useful for generating reports or web content.

```python
html_table = """
<table>
  <tr>
    <th>Header 1</th>
    <th>Header 2</th>
  </tr>
  <tr>
    <td>Data 1</td>
    <td>Data 2</td>
  </tr>
</table>
"""
print(html_table)
```

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

### Get Track History

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

This snippet demonstrates how to get the historical data for a specific track ID. The history is a list of bounding boxes for the track.

```python
track_id_to_query = 1
history = track.get_track_history(track_id_to_query)
print(history)
```

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

### Initialize Napari Viewer

Source: https://laptrack.readthedocs.io/en/stable/examples/overlap_tracking.html

Creates a Napari viewer and adds the loaded segmentation labels to it. This is useful for visualizing the input data.

```python
viewer = napari.Viewer()
viewer.add_labels(labels)
```

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

### Basic Overlap Detection with `shapely`

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

Demonstrates finding overlaps between two geometric objects using the `shapely` library. Ensure `shapely` is installed (`pip install shapely`).

```python
from shapely.geometry import Point

point1 = Point(0, 0)
point2 = Point(1, 1)

# Check if point1 intersects with a buffer around point2
intersects = point1.intersects(point2.buffer(1.5))
print(f"Do the geometries intersect? {intersects}")
```

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

### Interactive Setting of Dimension Cropping in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example shows how to set cropping for dimensions, which can be useful for focusing on specific regions of corrected data.

```python
viewer.dims.crop = ((10, 90), (10, 90))
```

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

### Basic Overlap Detection with `intervaltree`

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

Demonstrates a simple way to find overlapping intervals using the `intervaltree` library. Ensure the library is installed (`pip install intervaltree`).

```python
from intervaltree import Interval, IntervalTree

tree = IntervalTree()
tree.add(Interval(0, 10, "data1"))
tree.add(Interval(5, 15, "data2"))
tree.add(Interval(12, 20, "data3"))

overlaps = tree.overlap(8, 13)
print(overlaps)
```

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

### Creating a Pie Chart

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Shows how to create a pie chart to represent proportions of a whole. This is effective for displaying the composition of a dataset.

```python
import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='size', names='day', title='Proportion of Bill Size by Day')
fig.show()
```

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

### Using Shapely for Geometric Overlap Detection

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

This snippet utilizes the Shapely library to detect overlaps between geometric shapes (e.g., polygons). Ensure Shapely is installed (`pip install shapely`).

```python
from shapely.geometry import Polygon

def detect_polygon_overlap(poly1_coords, poly2_coords):
    """Detect if two polygons overlap using Shapely.

    Args:
        poly1_coords: A list of (x, y) tuples for the first polygon.
        poly2_coords: A list of (x, y) tuples for the second polygon.

    Returns:
        True if the polygons overlap, False otherwise.
    """
    polygon1 = Polygon(poly1_coords)
    polygon2 = Polygon(poly2_coords)
    return polygon1.intersects(polygon2)
```

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

### Creating an `IntervalTree` from a List

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/overlap_tracking.ipynb.txt

Shows how to efficiently build an `IntervalTree` from a list of `Interval` objects. This is useful for initializing the tree with existing data.

```python
from intervaltree import Interval, IntervalTree

intervals = [
    Interval(0, 10, "data1"),
    Interval(5, 15, "data2"),
    Interval(12, 20, "data3")
]
tree = IntervalTree(intervals)
print(tree)
```

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

### Find the start and end indices of bright spot events

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This snippet identifies the precise start and end indices for each continuous bright spot event. This is more granular than just listing the indices within the event.

```python
def get_bright_spot_event_boundaries(data):
    boundaries = []
    start_index = None
    for i in range(len(data)):
        if data[i] > 0.5:  # Example condition
            if start_index is None:
                start_index = i
        elif start_index is not None:
            boundaries.append((start_index, i - 1))
            start_index = None
    if start_index is not None:
        boundaries.append((start_index, len(data) - 1))
    return boundaries
```

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

### Box Plot Example

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This code generates a box plot. Matplotlib is required.

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

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

plt.boxplot(data, patch_artist=True)
plt.xlabel("Groups")
plt.ylabel("Values")
plt.title("Box Plot Example")
plt.show()
```

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

### Initialize 3D Tracker

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Initializes the 3D tracker with specified parameters. Ensure the tracker is properly configured before use.

```python
from laptrack import LapTrack
tracker = LapTrack(max_time=10, max_dist=100, cost_function='iou', track_thresh=0.5, frame_rate=30)

```

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

### Visualize Tracks

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Generates a visualization of the tracked objects. This function requires matplotlib to be installed.

```python
import matplotlib.pyplot as plt

track.plot_tracks(plt)
```

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

### Initialize 3D Tracker

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Initializes the 3D tracker with specified parameters. Ensure the tracker is properly configured before use.

```python
from laptrack import LapTrack
tracker = LapTrack(max_time_diff=10, max_distance=100, adaptive_threshold=True, adaptive_n_points=10)

```

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

### Initialize 3D Tracker

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Initializes the 3D tracker with specified parameters. Ensure the tracker is properly configured before use.

```python
from laptrack import LapTrack
tracker = LapTrack(max_time_gap=10, adaptive_threshold=0.7, frame_rate=30)

```

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

### Line Plot Example

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

This code generates a line plot. Matplotlib is required for this functionality.

```python
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

plt.plot(x, y)
plt.xlabel("Input")
plt.ylabel("Output")
plt.title("Line Plot")
plt.show()
```

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

### Visualize Tracks

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/3D_tracking.ipynb.txt

Visualizes the tracked objects. This example assumes a plotting environment is available.

```python
from laptrack.utils import plot_tracks
import matplotlib.pyplot as plt

fig, ax = plt.subplots(1)
plot_tracks(tracker.tracks, ax=ax)
plt.show()

```

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

### Interactive Setting of Dimension Selection in Napari

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/napari_interactive_fix.ipynb.txt

This example shows how to set the selection for dimensions, which can be useful for focusing on specific corrected planes or channels.

```python
viewer.dims.selection = (slice(0, 100), slice(0, 100))
```

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

### Load Data with Pandas

Source: https://laptrack.readthedocs.io/en/stable/_sources/examples/bright_spots.ipynb.txt

Loads a dataset into a pandas DataFrame for further analysis. Ensure pandas is installed.

```python
import pandas as pd

df = pd.read_csv("/mnt/data/bright_spots.csv")
print(df.head())

```