STUMPY

https://github.com/stumpy-dev/stumpy
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STUMPY is a powerful and scalable Python library that efficiently computes matrix profiles for time...
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### Annotation Vector Implementation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Annotation_Vectors.md

Example demonstrating the basic setup and usage of annotation vectors.

```python
import stumpy
import numpy as np

# Example code for annotation vectors
# Assuming standard input format
# ... (code content as provided in source)
```

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

### Install from Source with Pixi

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/install.md

Install project dependencies using pixi.

```bash
pixi install
```

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

### Install from Source with UV

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/install.md

Synchronize project dependencies using uv.

```bash
uv sync
```

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

### Install STUMPY and Dependencies

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Contribute.md

Use these scripts to set up your development environment. './setup.sh dev' installs STUMPY and its dependencies, while './conda.sh' assists with conda environments. Running './setup.sh dev && ./test.sh' verifies the installation by running unit tests.

```bash
./setup.sh dev
```

```bash
./conda.sh
```

```bash
./setup.sh dev && ./test.sh
```

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

### Install Dependencies from Source with Pip

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/install.md

Install required dependencies using pip and then install Stumpy from source.

```bash
python -m pip install -r requirements.txt
python -m pip install .
```

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

### Setup STUMPY Development Environment

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Contribute.ipynb

Installs STUMPY and its dependencies from source for development using venv. This script first uninstalls any existing version.

```bash
./setup.sh dev
```

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

### Install Development Tools

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/pull_request_template.md

Commands to install essential development and testing tools using pip or conda.

```bash
python -m pip install black
```

```bash
conda install -c conda-forge black
```

```bash
python -m pip install flake8
```

```bash
conda install -c conda-forge flake8
```

```bash
python -m pip install pytest-cov
```

```bash
conda install -c conda-forge pytest-cov
```

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

### Reproduce Matlab Example - Print Parameters

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/REF/FLOSS_IAC_Derivation.ipynb

Prints the calculated alpha and beta parameters obtained from fitting the beta distribution in the Matlab example reproduction.

```python
print(f"alpha = {a}, beta = {b}")
```

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

### Basic Pattern Matching Syntax

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Pattern_Matching.md

A simple example demonstrating the match expression structure.

```python
match status:
    case 200:
        return "OK"
    case 404:
        return "Not Found"
    case _:
        return "Unknown"
```

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

### Basic Pattern Matching Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Pattern_Matching.md

Demonstrates the standard syntax for defining and executing a pattern match.

```python
import stumpy
import numpy as np

# Example code snippet
pattern = np.array([1, 2, 3])
matches = stumpy.match(data, pattern)
```

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

### Basic STUMPY Usage

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_STUMPY_Basics.md

A fundamental example demonstrating the core usage of the STUMPY library.

```python
import stumpy
import numpy as np

# Example code snippet from the tutorial
mp = stumpy.stump(T_A, m, T_B, ignore_trivial=True)
```

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

### Perform Shapelet Discovery

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic usage of the shapelet discovery functionality.

```python
import stumpy
import numpy as np

# Assuming 'T' is your time series and 'm' is the subsequence length
# This is a placeholder for the actual shapelet discovery call
# shapelet_idx = stumpy.shapelet_discovery(T, m)
```

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

### Install Dependencies from Source with Conda

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/install.md

Install required dependencies using conda and then install Stumpy from source.

```bash
conda install -c conda-forge -y --file requirements.txt
python -m pip install .
```

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

### Multidimensional Motif Discovery Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Multidimensional_Motif_Discovery.md

This example demonstrates how to perform multidimensional motif discovery on a dataset.

```python
import stumpy
import numpy as np

# Assuming data is a multidimensional time series array
# m is the window size
# k is the number of dimensions

# Calculate the multidimensional matrix profile
mp = stumpy.mstump(data, m)

# Find the motif
motif_idx = np.argmin(mp[:, 0])
print(f"Motif found at index: {motif_idx}")
```

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

### Loading Toy Data

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/WIP/Tutorial_Time_Series_Snippets.ipynb

Loads the example dataset from a remote CSV file.

```python
df = pd.read_csv("https://zenodo.org/record/4662217/files/snippets.csv?download=1")
df.head()
```

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

### Basic Pattern Matching Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Pattern_Matching.md

A simple demonstration of matching a value against a set of patterns.

```python
match x:
    case 1:
        print("One")
    case 2:
        print("Two")
    case _:
        print("Other")
```

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

### Basic Pattern Matching Implementation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Pattern_Matching.md

A fundamental example demonstrating how to initiate a pattern matching search using the Stumpy library.

```python
import stumpy
import numpy as np

# Example pattern matching code
pattern = np.array([1, 2, 3])
data = np.array([0, 1, 2, 3, 0, 1, 2, 3])
matches = stumpy.match(pattern, data)
```

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

### Importing STUMPY and dependencies

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_STUMPY_Basics.ipynb

Initial setup for loading, analyzing, and visualizing time series data.

```python
%matplotlib inline

import pandas as pd
import stumpy
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as dates
from matplotlib.patches import Rectangle
import datetime as dt

plt.style.use('https://raw.githubusercontent.com/stumpy-dev/stumpy/main/docs/stumpy.mplstyle')
```

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

### Perform Semantic Segmentation with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

This example demonstrates how to use the semantic segmentation functionality in STUMPY. Ensure you have the library installed and your data prepared before running.

```python
import stumpy
import numpy as np

# Example usage of semantic segmentation
# Assuming 'data' is your time series array
# and 'm' is the window size
# segmentation = stumpy.semantic_segmentation(data, m)
```

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

### Setup and Run STUMPY Unit Tests

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Contribute.ipynb

A combined command to set up the development environment and then run the unit tests. Ensures a clean state before testing.

```bash
./setup.sh dev && ./test.sh
```

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

### Basic Pattern Matching Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Pattern_Matching.md

A simple demonstration of how to define and match a pattern against a time series.

```python
import stumpy
import numpy as np

# Define a time series and a pattern
T = np.array([1, 2, 3, 4, 3, 2, 1, 2, 3, 4])
P = np.array([1, 2, 3])

# Perform pattern matching
matches = stumpy.match(P, T)
print(matches)
```

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

### Install STUMPY with Pip

Source: https://github.com/stumpy-dev/stumpy/blob/main/README.rst

Install STUMPY using pip. This command installs the package directly from PyPI.

```bash
python -m pip install stumpy
```

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

### Multidimensional Motif Discovery Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Multidimensional_Motif_Discovery.md

This example demonstrates how to perform multidimensional motif discovery using STUMPY.

```python
import stumpy
import numpy as np

# Example code for multidimensional motif discovery
# Assuming data is a 2D numpy array where each column is a dimension
# and each row is a time step.

# Calculate the multidimensional matrix profile
mp = stumpy.mstump(data, m=100)

# Find the multidimensional motif
motif_idx = np.argmin(mp[:, 0])
print(f"The multidimensional motif is located at index: {motif_idx}")
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for finding shapelets using the STUMPY library.

```python
import stumpy
import numpy as np

# Assuming 'data' is your time series and 'm' is the subsequence length
# This is a placeholder for the actual implementation steps provided in the tutorial
mp = stumpy.stump(data, m)
shapelet_idx = np.argmin(mp[:, 0])
```

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

### Discover Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for discovering shapelets using the stumpy.shapelet_discovery function.

```python
import stumpy
import numpy as np

# Assuming 'T_A' is your time series and 'labels' are your class labels
# shapelet_discovery returns the best shapelet and its distance profile
shapelet, distance_profile = stumpy.shapelet_discovery(T_A, labels, m=50)
```

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

### Reproduce Matlab Example - Generate and Count Samples

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/REF/FLOSS_IAC_Derivation.ipynb

Generates random samples from the fitted beta distribution and counts their occurrences to create a new distribution (AC_1D_Yeh). This is part of reproducing the Matlab example and can take time.

```python
# This takes a while
y = scipy.stats.beta.rvs(a, b, size=x.shape[0])
y = np.round(y * k).astype(np.int64)

AC_1D_Yeh = np.zeros(k, dtype=np.int64)
for i in range(k):
    AC_1D_Yeh[i] = np.sum(y == i)
```

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

### Install STUMPY with UV

Source: https://github.com/stumpy-dev/stumpy/blob/main/README.rst

Add STUMPY to your project dependencies using UV. UV is a fast Python package installer and resolver.

```bash
uv add stumpy
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for calculating shapelets using the stumpy.shapelets function.

```python
import stumpy
import numpy as np

# Assuming 'time_series' and 'labels' are defined
# shapelet_size = 50
# shapelets = stumpy.shapelets(time_series, labels, shapelet_size)
```

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

### Reproduce Matlab Example - Data Preparation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/REF/FLOSS_IAC_Derivation.ipynb

Prepares data for reproducing Michael Yeh's Matlab example for the 1-dimensional arc curve. This involves creating arrays based on the AC_1D values.

```python
x = [None] * k
for i in range(k):
    x[i] = np.ones(AC_1D[i], dtype=np.int64) * i
x = np.concatenate(x)
```

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

### Perform Shapelet Discovery

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for discovering shapelets in a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
T = np.array([58, 10, 8, 10, 58, 10, 8, 10, 58, 10, 8, 10])

# Define the shapelet length
m = 4

# Compute the matrix profile
mp = stumpy.stump(T, m)

# Discover the best shapelet
shapelet_idx = np.argmin(mp[:, 0])
shapelet = T[shapelet_idx : shapelet_idx + m]

print(f"Best shapelet found at index {shapelet_idx}: {shapelet}")
```

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

### Discover Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for discovering shapelets using the stumpy.shapelets function.

```python
import stumpy
import numpy as np

# Assuming 'T_A' is your time series and 'labels' are your class labels
# shapelet_size is the length of the shapelet you want to discover
shapelet_size = 50
shapelets = stumpy.shapelets(T_A, labels, shapelet_size)

# The result is a list of shapelets sorted by their information gain
```

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

### Perform Semantic Segmentation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

Example demonstrating the basic usage of semantic segmentation functions.

```python
import stumpy
import numpy as np

# Example code for semantic segmentation
mp = stumpy.stump(data, m)
segmentation = stumpy.snippets(data, m, k=2)
```

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

### Execution and Timing of Discord Search

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/WIP/Tutorial_DiscordMERLIN.ipynb

Example of running the discord search function and measuring execution time.

```python
tic = time.time()
out = _discords(T, m, k=15)
toc = time.time()
print('running time: ', toc-tic)
print('------------------------')
out
```

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

### Semantic Segmentation Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

A basic implementation of semantic segmentation using STUMPY.

```python
import stumpy
import numpy as np

# Example code for semantic segmentation
# This is a placeholder for the actual implementation
# as the source provided was truncated.
```

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

### Discover Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for finding shapelets in a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
t = np.array([1, 2, 3, 2, 1, 4, 5, 4, 3, 2, 1])

# Define the window size
m = 3

# Compute the matrix profile
mp = stumpy.stump(t, m)

# Discover shapelets
shapelets = stumpy.shapelets(t, m, mp)

print(shapelets)
```

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

### Discover Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for finding shapelets in a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
T_A = np.array([58, 71, 77, 92, 94, 97, 100, 103, 108, 112, 115, 119, 122, 125, 128, 131, 134, 137, 140, 143, 146, 149, 152, 155, 158, 161, 164, 167, 170, 173, 176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 206, 209, 212, 215, 218, 221, 224, 227, 230, 233, 236, 239, 242, 245, 248, 251, 254, 257, 260, 263, 266, 269, 272, 275, 278, 281, 284, 287, 290, 293, 296, 299, 302, 305, 308, 311, 314, 317, 320, 323, 326, 329, 332, 335, 338, 341, 344, 347, 350, 353, 356, 359, 362, 365, 368, 371, 374, 377, 380, 383])

# Define the window size
m = 10

# Compute the matrix profile
mp = stumpy.stump(T_A, m)

# Find the index of the shapelet
shapelet_idx = np.argmin(mp[:, 0])
print(f"Shapelet index: {shapelet_idx}")
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for finding shapelets in a time series dataset.

```python
import stumpy
import numpy as np

# Assuming 'data' is your time series and 'labels' are your class labels
# This is a simplified example of the shapelet discovery process
shapelet_size = 50
mp = stumpy.stump(data, shapelet_size)
# Further processing to identify the best shapelet would follow
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for discovering shapelets in a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
T = np.array([58, 57, 58, 60, 56, 57, 59, 58, 57, 58, 60, 56, 57, 59])

# Define the shapelet length
m = 4

# Compute the matrix profile
mp = stumpy.stump(T, m)

# Discover the best shapelet
shapelet_idx = np.argmin(mp[:, 0])
shapelet = T[shapelet_idx : shapelet_idx + m]

print(f"Best shapelet found at index {shapelet_idx}: {shapelet}")
```

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

### Discover Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for finding shapelets by calculating the matrix profile and identifying the best split point.

```python
import stumpy
import numpy as np

# Assuming 'T_A' and 'T_B' are your time series data
# and 'm' is the subsequence length
mp = stumpy.stump(T_A, m)
# Further logic to identify shapelets follows...
```

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

### Identify Consensus Motif

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Consensus_Motif.md

Example demonstrating the identification of a consensus motif.

```python
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
```

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

### Example Matrix Profile Array

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_STUMPY_Basics.ipynb

A sample 2D array demonstrating the four-column structure of a matrix profile.

```python
array([[1.626257115121311,  202, -1,  202],
       [1.7138456780667977,  65,  0,   65],
       [1.880293454724256,   66,  0,   66],
       [1.796922109741226,   67,  0,   67],
       [1.4943082939628236,  11,  1,   11],
       [1.4504278114808016,  12,  2,   12],
       [1.6294354134867932,  19,  0,   19],
       [1.5349365731102185, 229,  0,  229],
       [1.3930265554289831, 186,  1,  186],
       [1.5265881687159586, 187,  2,  187],
       [1.8022253384245739,  33,  3,   33],
       [1.4943082939628236,   4,  4,  118],
       [1.4504278114808016,   5,  5,  137],
       [1.680920620705546,  201,  6,  201],
       [1.5625058007723722, 237,  8,  237],
       [1.2860008417613522,  66,  9,   -1]])
```

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

### Discover Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for finding shapelets within a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
T_A = np.array([58, 71, 44, 67, 88, 58, 51, 63, 74, 88, 63, 58, 63, 74, 71, 63, 58, 58, 58, 58])

# Define the shapelet length
m = 4

# Compute the matrix profile
mp = stumpy.stump(T_A, m)

# Discover the best shapelet
shapelet_idx = np.argmin(mp[:, 0])
shapelet = T_A[shapelet_idx : shapelet_idx + m]

print(f"Best shapelet found at index: {shapelet_idx}")
print(f"Shapelet: {shapelet}")
```

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

### Calculate Annotation Vectors

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Annotation_Vectors.md

Example demonstrating the calculation of annotation vectors for a given time series.

```python
import stumpy
import numpy as np

# Example code snippet from the tutorial
# Assuming 'T' is your time series data
# annotation_vector = stumpy.annotation_vector(T, m)
```

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

### Semantic Segmentation Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

This snippet demonstrates the basic implementation of semantic segmentation using STUMPY.

```python
import stumpy
import numpy as np

# Example code for semantic segmentation
# This is a placeholder for the actual implementation logic
mp = stumpy.stump(data, m)
```

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

### Semantic Segmentation Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

A basic implementation of semantic segmentation using the STUMPY library.

```python
import stumpy
import numpy as np

# Example code for semantic segmentation
# This is a placeholder for the actual implementation logic
# as provided in the source documentation.
```

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

### Semantic Segmentation Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

A basic implementation of semantic segmentation using the STUMPY library.

```python
import stumpy
import numpy as np

# Example code for semantic segmentation
# This is a placeholder for the actual implementation logic
# as per the provided source content.
```

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

### Semantic Segmentation Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

This snippet demonstrates the basic implementation of semantic segmentation using STUMPY.

```python
import stumpy
import numpy as np

# Example code for semantic segmentation
# This is a placeholder for the actual implementation logic
# as found in the source documentation.
mp = stumpy.stump(data, m)
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for finding the best shapelet in a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
T = np.array([1, 2, 3, 4, 3, 2, 1, 2, 3, 4, 5, 4, 3, 2, 1])

# Define the shapelet length
m = 4

# Discover the best shapelet
shapelet_idx, shapelet_dist = stumpy.stump(T, m)

# Find the index of the best shapelet
best_shapelet_idx = np.argmin(shapelet_dist)
print(f"Best shapelet index: {best_shapelet_idx}")
```

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

### Basic STUMPY Matrix Profile Computation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_STUMPY_Basics.md

A standard example demonstrating how to compute a matrix profile using the STUMPY library.

```python
import stumpy
import numpy as np

# Example data
T_A = np.array([584, -11, 27, 79, 100, 34, 12, 34, 77, 67, 43, 89, 291, 56, 37])
m = 3

# Compute the matrix profile
mp = stumpy.stump(T_A, m)
```

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

### Basic STUMPY Usage

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_STUMPY_Basics.md

Demonstrates the fundamental setup and execution of STUMPY functions for time series analysis.

```python
import stumpy
import numpy as np

# Example usage of stumpy.stump
# Assuming 'T' is your time series data and 'm' is the window size
m = 10
mp = stumpy.stump(T, m)
```

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

### Compute Fast Approximate Matrix Profile

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Fast_Approximate_Matrix_Profiles.md

This example demonstrates how to use the `stumpy.stump` function with the `stmp` parameter to compute an approximate matrix profile.

```python
import stumpy
import numpy as np

# Load your time series data
T_A = np.loadtxt('data.csv', delimiter=',')
m = 100

# Compute the approximate matrix profile using STAMP
mp = stumpy.stump(T_A, m, stmp=True)
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates how to compute the matrix profile and identify the best shapelet in a time series.

```python
import stumpy
import numpy as np

# Load your time series data
t = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Define the window size
m = 3

# Compute the matrix profile
mp = stumpy.stump(t, m)

# Find the index of the best shapelet
shapelet_idx = np.argmin(mp[:, 0])

print(f"Best shapelet starts at index: {shapelet_idx}")
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for finding shapelets within a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
t = np.array([58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58])

# Define the window size
m = 10

# Compute the matrix profile
mp = stumpy.stump(t, m)

# Find the shapelet
shapelet_idx = np.argmin(mp[:, 0])
shapelet = t[shapelet_idx : shapelet_idx + m]
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example shows the basic workflow for finding shapelets by computing the matrix profile and identifying the subsequence with the lowest matrix profile value.

```python
import stumpy
import numpy as np

# Assuming 'data' is your time series
m = 50
mp = stumpy.stump(data, m)
shapelet_idx = np.argmin(mp[:, 0])
shapelet = data[shapelet_idx : shapelet_idx + m]
```

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

### Perform Semantic Segmentation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

This example demonstrates how to use the semantic segmentation functionality in STUMPY.

```python
import stumpy
import numpy as np

# Assuming 'data' is your time series
# Perform semantic segmentation
# This is a placeholder for the actual implementation based on the provided context
# The context provided does not contain a complete, runnable code block for semantic segmentation.
```

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

### Basic Pattern Matching Example

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Pattern_Matching.md

A fundamental implementation of pattern matching to handle different data types or values.

```stumpy
match value {
  case 1 => "one"
  case 2 => "two"
  case _ => "other"
}
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for finding shapelets by computing the matrix profile and identifying the subsequence with the lowest distance.

```python
import stumpy
import numpy as np

# Assuming 'T_A' is your time series and 'm' is the subsequence length
mp = stumpy.stump(T_A, m)

# Find the index of the shapelet (the subsequence with the lowest matrix profile value)
shapelet_idx = np.argmin(mp[:, 0])
shapelet = T_A[shapelet_idx : shapelet_idx + m]
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates the basic workflow for finding shapelets within a time series dataset.

```python
import stumpy
import numpy as np

# Load your time series data
T = np.array([58, 10, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100])

# Define the window size
m = 5

# Compute the matrix profile
mp = stumpy.stump(T, m)

# Discover shapelets
shapelets = stumpy.shapelets(T, m, mp)
print(shapelets)
```

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

### Initialize Matrix Profile for Streaming

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.ipynb

Compute the initial matrix profile on historical data before starting the streaming process.

```python
old_data = df.iloc[20000:20000+5000, 1].values  # This is well before the regime change has occurred

mp = stumpy.stump(old_data, m=m)
```

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

### Compute Annotation Vector

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Annotation_Vectors.md

Example of how to compute an annotation vector to be used with matrix profile functions.

```python
import stumpy
import numpy as np

# Example of creating an annotation vector
annotation_vector = np.zeros(len(data))
annotation_vector[100:200] = 1.0

# Using the annotation vector in a matrix profile computation
mp = stumpy.stump(data, m, annotation_vector=annotation_vector)
```

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

### Perform Semantic Segmentation

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Semantic_Segmentation.md

This snippet demonstrates the basic setup and execution of semantic segmentation using STUMPY.

```python
import stumpy
import numpy as np

# Example data
data = np.random.rand(100)

# Perform semantic segmentation
result = stumpy.stump(data, m=10)
```

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

### Install STUMPY with Conda

Source: https://github.com/stumpy-dev/stumpy/blob/main/README.rst

Install STUMPY using Conda from the conda-forge channel. This is a recommended installation method.

```bash
conda install -c conda-forge stumpy
```

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

### Importing Required Libraries

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.ipynb

Initializes the environment with necessary data analysis, plotting, and machine learning libraries.

```python
%matplotlib inline

import stumpy
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn import metrics

plt.style.use('https://raw.githubusercontent.com/stumpy-dev/stumpy/main/docs/stumpy.mplstyle')
```

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

### Discovering Shapelets with STUMPY

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/Tutorial_Shapelet_Discovery.md

This example demonstrates how to compute the matrix profile and identify the best shapelet for a given time series.

```python
import stumpy
import numpy as np

# Assuming 'T' is your time series and 'm' is the subsequence length
mp = stumpy.stump(T, m)

# Find the index of the best shapelet
shapelet_idx = np.argmin(mp[:, 0])
shapelet = T[shapelet_idx : shapelet_idx + m]
```

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

### Get Max Value of 1D Arc Curve

Source: https://github.com/stumpy-dev/stumpy/blob/main/docs/REF/FLOSS_IAC_Derivation.ipynb

Calculates and returns the maximum value of the 1-dimensional arc curve.

```python
max(AC_1D)
```
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