### Install klarfkit using pip

Source: https://github.com/michaelhotaling/klarfkit/blob/main/README.md

Install the klarfkit library directly from its GitHub repository using pip. This command ensures you get the latest version.

```bash
pip install git+https://github.com/MichaelHotaling/klarfkit.git
```

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

### Display Wafer Metadata with WaferMap.describe

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Use the WaferMap.describe() method to print a formatted summary of all wafer metadata and configuration parameters. This is useful for quickly inspecting the contents of a loaded KLARF file.

```python
from klarfkit import WaferMap

wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# Display all wafer metadata
wafer.describe()
```

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

### WaferMap.describe

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Displays a formatted summary of all wafer metadata and configuration parameters.

```APIDOC
## WaferMap.describe

### Description
Print a formatted summary of all wafer metadata and configuration parameters. Useful for quickly inspecting the contents of a loaded KLARF file.
```

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

### WaferMap Constructor

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Creates a new WaferMap instance programmatically using custom defect data and configuration parameters.

```APIDOC
## WaferMap Constructor

### Description
Create a WaferMap instance programmatically with custom defect data and wafer configuration. This is useful for generating synthetic data or importing defects from other sources.

### Parameters
#### Request Body
- **defect_record** (DataFrame) - Required - Pandas DataFrame containing columns: DEFECTID, XINDEX, YINDEX, XREL, YREL, XSIZE, YSIZE, CLASSNUMBER.
- **sample_size** (float) - Required - Size of the wafer in micrometers.
- **sample_center_location** (tuple) - Required - (x, y) coordinates of the wafer center.
- **die_pitch** (tuple) - Required - (x, y) dimensions of the dies.
- **die_origin** (tuple) - Required - (x, y) origin coordinates.
- **lot_id** (string) - Optional - Identifier for the lot.
- **wafer_id** (string) - Optional - Identifier for the wafer.
- **slot** (int) - Optional - Slot number.
- **orientation_marker** (string) - Optional - 'NOTCH' or 'FLAT'.
- **orientation** (string) - Optional - 'UP', 'DOWN', 'LEFT', or 'RIGHT'.
- **class_lookup** (dict) - Optional - Mapping of class numbers to descriptions.
```

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

### Load KLARF File with WaferMap.read_klarf

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Use WaferMap.read_klarf to load a KLARF file into a WaferMap object. This object contains all defect records, metadata, and wafer configuration. Requires the file path as an argument.

```python
from klarfkit import WaferMap

# Load a KLARF file from disk
wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# The wafer object now contains all parsed data
print(f"Wafer ID: {wafer.wafer_id}")
print(f"Lot ID: {wafer.lot_id}")
print(f"Number of defects: {len(wafer.defect_list)}")
print(f"Sample size: {wafer.sample_size} um")
print(f"Die pitch: {wafer.die_pitch}")
```

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

### Create Custom WaferMap with DataFrame

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Instantiate a WaferMap object programmatically using a pandas DataFrame for defect data. This method is useful for generating synthetic data or importing defects from other sources. Ensure the DataFrame includes required columns like XINDEX, YINDEX, XREL, YREL, XSIZE, YSIZE, and CLASSNUMBER.

```python
import pandas as pd
from klarfkit import WaferMap

# Create defect data as a DataFrame
# Required columns: XINDEX, YINDEX, XREL, YREL (die index and relative position)
defects = pd.DataFrame({
    'DEFECTID': [1, 2, 3, 4, 5],
    'XINDEX': [0, 1, -1, 2, -2],
    'YINDEX': [0, 1, -1, 0, 0],
    'XREL': [5000, 3000, 7000, 2000, 8000],
    'YREL': [5000, 4000, 6000, 5000, 5000],
    'XSIZE': [2.5, 3.0, 1.5, 4.0, 2.0],
    'YSIZE': [2.5, 3.0, 1.5, 4.0, 2.0],
    'CLASSNUMBER': [82, 81, 84, 82, 81]  # Defect class codes
})

# Create a custom WaferMap for a 300mm wafer
wafer = WaferMap(
    defect_record=defects,
    sample_size=300_000,              # 300mm wafer in micrometers
    sample_center_location=(150_000, 150_000),  # Center of wafer
    die_pitch=(10_000, 10_000),       # 10mm x 10mm dies
    die_origin=(0, 0),
    lot_id='LOT001',
    wafer_id='W01',
    slot=1,
    orientation_marker='NOTCH',       # Options: 'NOTCH' or 'FLAT'
    orientation='DOWN',               # Notch position: UP, DOWN, LEFT, RIGHT
    class_lookup={
        81: 'Big_Particle',
        82: 'Small_Particle',
        84: 'Scratch'
    }
)

print(f"Created wafer with {len(wafer.defect_list)} defects")
```

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

### Plot Wafer Map with Custom Styling

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Customize the appearance of wafer map plots by adjusting marker size, style, and line transparency. Save the styled map to a file.

```python
plt.figure(figsize=(10, 10))
wafer.plot_wafer_map(
    color='blue',
    die_line_alpha=0.3,        # Transparency of die grid lines
    die_line_color='gray',     # Color of die grid lines
    s=100,                     # Marker size
    marker='x'                 # Marker style
)
plt.title(f'Lot: {wafer.lot_id} | Wafer: {wafer.wafer_id}')
plt.savefig('wafer_styled_map.png', dpi=150)
plt.show()
```

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

### Access and Analyze Defect Data as DataFrame

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Read a KLARF file and access the raw defect data as a pandas DataFrame. Perform filtering, grouping, and export the data to CSV or Excel.

```python
import pandas as pd
from klarfkit import WaferMap

wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# Access defect data as DataFrame
defects = wafer.defect_list

# View available columns
print("Defect columns:", defects.columns.tolist())
# Output: ['DEFECTID', 'XREL', 'YREL', 'XINDEX', 'YINDEX', 'XSIZE', 'YSIZE',
#          'DEFECTAREA', 'DSIZE', 'CLASSNUMBER', 'TEST', 'ROUGHBINNUMBER',
#          'IMAGECOUNT', 'IMAGELIST', '_XACTUAL', '_YACTUAL']

# Filter defects by size
large_defects = defects[defects['DSIZE'] > 10]
print(f"Found {len(large_defects)} large defects (DSIZE > 10)")

# Group defects by class
class_counts = defects.groupby('CLASSNUMBER').size()
print("\nDefects per class:")
print(class_counts)

# Find defects in a specific die
die_defects = defects[(defects['XINDEX'] == 0) & (defects['YINDEX'] == 0)]
print(f"\nDefects in die (0,0): {len(die_defects)}")

# Export to CSV for external analysis
defects.to_csv('defect_export.csv', index=False)

# Export to Excel
defects.to_excel('defect_export.xlsx', index=False)

# Statistical summary
print("\nDefect size statistics:")
print(defects['DSIZE'].describe())
```

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

### WaferMap.read_klarf

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Parses a standard KLARF file and initializes a WaferMap object containing defect records and metadata.

```APIDOC
## WaferMap.read_klarf

### Description
Parses the standard KLARF format (typically .001 extension) and creates a WaferMap object containing all defect records, metadata, and wafer configuration.

### Parameters
#### Path Parameters
- **filepath** (string) - Required - The path to the KLARF file on disk.

### Response
- **WaferMap** (object) - An instance containing parsed data including wafer_id, lot_id, defect_list, sample_size, and die_pitch.
```

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

### Automatic Wafer Map Visualization

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

The WaferMap class automatically generates a visual representation when displayed in interactive environments like Jupyter notebooks. This includes the wafer boundary, die grid, and defect markers.

```python
from klarfkit import WaferMap

wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# In Jupyter notebook, simply evaluating the wafer displays a plot
wafer  # Displays wafer map visualization automatically

# The __repr__ method creates a 3x3 inch figure showing:
# - Circular wafer boundary
# - Die grid lines
# - Red markers for each defect location
```

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

### Visualize Defect Distribution with plot_wafer_map

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Generate a visual plot of the wafer map showing die grid and defect locations using plot_wafer_map. Supports customization of colors and can color-code defects by any column in the defect DataFrame. Requires matplotlib for plotting.

```python
import matplotlib.pyplot as plt
from klarfkit import WaferMap

wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# Basic wafer map plot with default red defect markers
plt.figure(figsize=(8, 8))
wafer.plot_wafer_map(color='red', s=50)  # s=marker size
plt.title(f'Wafer {wafer.wafer_id} - Defect Map')
plt.savefig('wafer_defect_map.png', dpi=150)
plt.show()
```

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

### Color Wafer Map Defects by Class

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Visualize defects on a wafer map, coloring them based on a specific data column like 'CLASSNUMBER' and applying a colormap. Includes saving the plot.

```python
plt.figure(figsize=(10, 10))
wafer.plot_wafer_map(
    color='CLASSNUMBER',       # Color by this DataFrame column
    cmap='viridis',            # Matplotlib colormap
    s=80
)
plt.colorbar(label='Defect Class')
plt.title('Defects Colored by Classification')
plt.savefig('wafer_class_map.png', dpi=150)
plt.show()
```

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

### Access Wafer Class Lookup Table

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Retrieve the class lookup dictionary from a WaferMap object to map defect class numbers to human-readable descriptions. This can be used to enrich defect data.

```python
from klarfkit import WaferMap

wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# Access the class lookup dictionary
class_lookup = wafer.class_lookup

# Print some class definitions
print("Sample class definitions:")
for class_num in [0, 81, 82, 84, 174]:
    if class_num in class_lookup:
        print(f"  Class {class_num}: {class_lookup[class_num]}")

# Output:
#   Class 0: Undefined
#   Class 81: Big_Particle
#   Class 82: Small_Particle
#   Class 84: Scratch
#   Class 174: MicroScrtch

# Add class descriptions to defect data
defects = wafer.defect_list.copy()
defects['CLASS_NAME'] = defects['CLASSNUMBER'].map(class_lookup)
print("\nDefects with class names:")
print(defects[['DEFECTID', 'CLASSNUMBER', 'CLASS_NAME', 'DSIZE']].head())
```

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

### WaferMap.plot_wafer_map

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Generates a visual plot of the wafer map showing the die grid and defect locations.

```APIDOC
## WaferMap.plot_wafer_map

### Description
Generate a visual plot of the wafer map showing the die grid and defect locations. Supports customization of colors and can color-code defects by any column in the defect DataFrame.

### Parameters
#### Query Parameters
- **color** (string) - Optional - Color of the defect markers.
- **s** (int) - Optional - Size of the defect markers.
```

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

### Modify Die Pitch and Center Location

Source: https://context7.com/michaelhotaling/klarfkit/llms.txt

Update the die pitch and center location of a WaferMap object. These changes automatically recalculate relative defect coordinates.

```python
from klarfkit import WaferMap

wafer = WaferMap.read_klarf('sample_files/CPS3TwithoutReview.001')

# View original configuration
print(f"Original die pitch: {wafer.die_pitch}")
print(f"Original center: {wafer.center_location}")

# Update die pitch - relative coordinates recalculated automatically
wafer.die_pitch = (3000, 2500)
print(f"New die pitch: {wafer.die_pitch}")

# Update center location
wafer.center_location = (150_000, 150_000)
print(f"New center: {wafer.center_location}")

# Access other configuration properties
print(f"Sample size: {wafer.sample_size}")
print(f"Die origin: {wafer.die_origin}")
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.