--- a
+++ b/tma_utils/tma_utils.py
@@ -0,0 +1,143 @@
+import numpy as np
+import openslide
+import pandas as pd
+import pyvips
+import re
+from typing import List, Tuple
+
+
+def extract_core(slide: openslide.OpenSlide, 
+                 df: pd.DataFrame, 
+                 level: int=0,
+                 core_id: int=None, 
+                 core_label: str=None) -> np.ndarray:
+    """Extracts core as numpy array from TMA slide.
+
+    Args:
+        slide (openslide.OpenSlide): TMA slide.
+        df (pd.DataFrame): DataFrame with core annotations.
+        level (int, optional): Level to sample. Defaults to 0.
+        core_id (int, optional): Core identifier (e.g. 123). Defaults to None.
+        core_label (str, optional): Core label (e.g. P123_1). Defaults to None.
+
+    Raises:
+        ValueError: If core_id and core_label do not match when both are provided.
+        ValueError: If neither core_id nor core_label are provided.
+
+    Returns:
+        np.ndarray: Core as numpy array with RGBA format.
+    """
+
+    if core_label is not None:
+        XY = df[['core_id', 'core_label', 'x', 'y']].loc[df['core_label'] == core_label]
+    elif core_id is not None:
+        XY = df[['core_id', 'core_label', 'x', 'y']].loc[df['core_id'] == core_id]
+    elif core_label is not None and core_id is not None:
+        XY = df[['core_id', 'core_label', 'x', 'y']].loc[df['core_id'] == core_id]
+        if XY.core_label.unique()[0] != core_label:
+            raise ValueError('core_id and core_label do not match') 
+    else:
+        raise ValueError('Expected either core_id or core_label.')
+
+    if XY.shape[0] == 0:
+        print(f'No annotations for core_id {core_id} available.')
+        
+    else:    
+        x_min, y_min = int(min(XY.x)), int(min(XY.y))
+        width, height = int(np.ptp(XY.x)), int(np.ptp(XY.y))
+        
+        downf = slide.level_downsamples[level]
+        location = x_min, y_min
+        size = int(width/downf), int(height/downf)   
+        core = slide.read_region(location=location, size=size, level=level)
+
+        return np.asarray(core) 
+
+
+
+def core_2_vips(core: np.ndarray) -> pyvips.Image:
+    """Converts numpy array to pyvips image.
+
+    Args:
+        core (np.ndarray): Numpy array.
+
+    Returns:
+        pyvips.Image: Pyvips image. 
+    """
+    height, width, bands = core.shape
+    vi = pyvips.Image.new_from_memory(core, width, height, bands, 'uchar')
+    return vi 
+
+
+
+def check_patientID_range(slide_id: int, low: int, high: int, df: pd.DataFrame) -> None:
+    """Checks whether all patient ID's on a TMA are in the correct range. Verifys that no other labels are on the TMA.
+
+    Args:
+        slide_id (int): Slide identifier.
+        low (int): Lowest patient ID.
+        high (int): Highest patient ID.
+        df (pd.DataFrame): Dataframe with core annotations.
+
+    Returns:
+        bool: True if all ID's are in the correct range.
+    """
+    persons_str = df.core_label.loc[df.slide_id == slide_id]
+    persons_set = set([int(re.split('P|_', i)[1]) for i in persons_str])
+    correct = (min(persons_set)>=low) and (max(persons_set)<=high)
+    print(f'slide_id {slide_id}: {correct}')
+
+
+
+def check_coreID_range(slide_id: int, df: pd.DataFrame, min: int = 1500, max: int = 6000, verbose: bool=False) -> Tuple[List[np.ndarray]]:
+    """Checks the size of the core annotations. Cores that are too large indicate annotation mistakes (e.g. 2 cores apart with same label).
+
+    Args:
+        slide_id (int): Slide identifier
+        df (pd.DataFrame): Dataframe with core annotations.
+        min (int, optional): Minimum core size. Defaults to 1500.
+        max (int, optional): Maximum core size. Defaults to 6000.
+    """
+    x_ranges, y_ranges = [], []
+    too_big, too_small = [], []
+    subdf = df[df.slide_id == slide_id]
+    for core_id in subdf.core_id.unique():
+        XY = subdf[['x', 'y']].loc[subdf['core_id'] == core_id]
+        if XY.shape[0] > 0:
+            x_range, y_range = np.ptp(XY.x), np.ptp(XY.y)
+            x_ranges.append(x_range)
+            y_ranges.append(y_range)
+            if x_range > max or y_range > max:
+                too_big.append((core_id, x_range, y_range)) 
+            if x_range < min or y_range < min:
+                too_small.append((core_id, x_range, y_range)) 
+    
+    if verbose:
+        if len(too_big) > 0:
+            print(f'\nSlide {slide_id} with {len(too_big)} suspicously large core annotations.')
+            for core_id, x_range, y_range in too_big:
+                print(f'Core {core_id}, x_range {x_range}, y_range {y_range}')
+        else:
+            print(f'\nSlide {slide_id} without suspicously large core annotations.')
+        
+        if len(too_small) > 0:
+            print(f'\nSlide {slide_id} with {len(too_small)} suspicously small core annotations.')
+            for core_id, x_range, y_range in too_small:
+                print(f'Core {core_id}, x_range {x_range}, y_range {y_range}')
+        else:
+            print(f'\nSlide {slide_id} without suspicously small core annotations.')
+
+    return x_ranges, y_ranges, too_big, too_small
+
+
+    
+
+    
+    
+
+
+    
+
+    
+
+