diff --git a/metric/cellcharter_purity/cellcharter_purity.json b/metric/cellcharter_purity/cellcharter_purity.json
new file mode 100644
index 00000000..6dc46f5d
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+++ b/metric/cellcharter_purity/cellcharter_purity.json
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+{
+    "technology": "MERFISH",
+    "sample_file": null
+}
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diff --git a/metric/cellcharter_purity/cellcharter_purity.py b/metric/cellcharter_purity/cellcharter_purity.py
new file mode 100755
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+++ b/metric/cellcharter_purity/cellcharter_purity.py
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+#!/usr/bin/env python
+
+# Author_and_contribution: Niklas Mueller-Boetticher; created template
+# Author_and_contribution: Louis B. Kuemmerle; wrote script
+# Author_and_contribution: Marco Varrone; gave input on setting hyperparameters and heuristics that generalise well and helped with debugging
+
+# At the time of development (12th Dec 2023) cellcharter doesn't have release tags. The code used for development can
+# be accessed via (commit 2b15549968559d9023d1fc92f9798a8c38dffcac):
+# 
+# git clone https://github.com/CSOgroup/cellcharter.git
+# cd cellcharter
+# git checkout -b [new-branch-name] 2b15549968559d9023d1fc92f9798a8c38dffcac
+# pip install -e .
+
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import anndata as ad
+import squidpy as sq
+import cellcharter as cc
+
+# TODO: The arguments don't fully follow the template right now. This needs to be adjusted after final input args decisions + tests.
+parser = argparse.ArgumentParser(description="Calculate cellcharter's purity metric that measures if cells assigned to other connected components are located within the boundaries of given components.")
+parser.add_argument("--coordinates", help="Path to coordinates (as tsv).", required=True)
+parser.add_argument("-l", "--labels", help="Labels from domain clustering.", required=True)
+parser.add_argument(
+    "-c", 
+    "--config",
+    help="Config file (json) used to pass additional parameters.",
+    required=True,
+)
+parser.add_argument("-o", "--out_file", help="Output file.", required=True)
+parser.add_argument("--fig_path", help="File path for plot to check if components look reasonable.", required=False, default=False) #TODO: Delete
+parser.add_argument("--connectivity", help="Config how to calculate connectivity.", required=False, default="neighs")
+
+SUPPORTED_TECHNOLOGIES = ["CODEX", "CosMx", "MERFISH", "IMC", "Visium", "RNA + ATAC"]
+# for each technology we need to specify how to compute the graph connectivity and how to restrict connected components
+
+
+###### helper functions ######
+
+from anndata import AnnData
+from scipy.sparse import csr_matrix
+from squidpy._constants._constants import CoordType, Transform
+from squidpy._constants._pkg_constants import Key
+from squidpy.gr._utils import _assert_connectivity_key
+
+def remove_long_links(
+    adata: AnnData,
+    distance_percentile: float | None = 99.0,
+    mean_distance_factor: float | None = 4.0,
+    connectivity_key: str | None = None,
+    distances_key: str | None = None,
+    neighs_key: str | None = None,
+    copy: bool = False,
+) -> tuple[csr_matrix, csr_matrix] | None:
+    """
+    Remove links between cells at a distance bigger than a certain percentile or mean distance of all positive distances.
+
+    It is designed for data with generic coordinates.
+    
+    Note: This function is copied from cellcharter.graph.remove_long_links and extended to allow for a mean distance 
+    factor.
+
+    Parameters
+    ----------
+    %(adata)s
+
+    distance_percentile
+        Percentile of the distances between cells over which links are trimmed after the network is built.
+    mean_distance_factor
+        Factor to multiply the mean distance between cells by to get the threshold for trimming links. If
+        ``distance_percentile`` is not ``None``, this parameter is ignored.
+    %(conn_key)s
+
+    distances_key
+        Key in :attr:`anndata.AnnData.obsp` where spatial distances are stored.
+        Default is: :attr:`anndata.AnnData.obsp` ``['{{Key.obsp.spatial_dist()}}']``.
+    neighs_key
+        Key in :attr:`anndata.AnnData.uns` where the parameters from gr.spatial_neighbors are stored.
+        Default is: :attr:`anndata.AnnData.uns` ``['{{Key.uns.spatial_neighs()}}']``.
+
+    %(copy)s
+
+    Returns
+    -------
+    If ``copy = True``, returns a :class:`tuple` with the new spatial connectivities and distances matrices.
+
+    Otherwise, modifies the ``adata`` with the following keys:
+        - :attr:`anndata.AnnData.obsp` ``['{{connectivity_key}}']`` - the new spatial connectivities.
+        - :attr:`anndata.AnnData.obsp` ``['{{distances_key}}']`` - the new spatial distances.
+        - :attr:`anndata.AnnData.uns`  ``['{{neighs_key}}']`` - :class:`dict` containing parameters.
+    """
+    connectivity_key = Key.obsp.spatial_conn(connectivity_key)
+    distances_key = Key.obsp.spatial_dist(distances_key)
+    neighs_key = Key.uns.spatial_neighs(neighs_key)
+    _assert_connectivity_key(adata, connectivity_key)
+    _assert_connectivity_key(adata, distances_key)
+
+    conns, dists = adata.obsp[connectivity_key], adata.obsp[distances_key]
+
+    if copy:
+        conns, dists = conns.copy(), dists.copy()
+
+    if distance_percentile is not None:
+        threshold = np.percentile(np.array(dists[dists != 0]).squeeze(), distance_percentile)
+    else:
+        threshold = np.mean(np.array(dists[dists != 0]).squeeze()) * mean_distance_factor
+    conns[dists > threshold] = 0
+    dists[dists > threshold] = 0
+
+    conns.eliminate_zeros()
+    dists.eliminate_zeros()
+
+    if copy:
+        return conns, dists
+    else:
+        adata.uns[neighs_key]["params"]["radius"] = threshold
+
+def get_alpha_start(adata, factor = 0.05):
+    """ Compute alpha start for boundary computation.
+    
+    Alpha start is computed based on a heuristic. We take the minimum length of the diagonals of the bounding boxes
+    over connected components and multiply it with a factor.
+    
+    The alpha start shouldn't be too large, otherwise alpha shapes get too close to the convex hull. Alpha start should
+    also not be too small to reduce computation time when searching for the optimal alpha.
+    
+    """
+    distances = []
+    for component in adata.obs["component"].dropna().unique():
+        coords = adata[adata.obs["component"]==component].obsm["spatial"]
+        x1 = np.array([np.min(coords[:,0]), np.min(coords[:,1])])
+        x2 = np.array([np.max(coords[:,0]), np.max(coords[:,1])])
+        distances.append(np.linalg.norm(x1-x2))
+        
+    return np.min(distances) * factor
+
+
+###### functions for debug plots ######
+# It's important that reasonable connected components are identified. This needs to be checked on different 
+# technologies, tissues, and spatial domains. Potentially the adjacency graph parameters need to be adjusted for better
+# generalisation.
+
+def get_n_colors(n):
+    """Get a list of n different colors"""
+    from random import shuffle
+    
+    # Generate a slightly larger list of colors to account for the removal of black and white
+    cmap = plt.cm.get_cmap('nipy_spectral', n + 2)  # Adjusting for potential removal
+
+    # Generate colors and convert to hex
+    colors = cmap(np.linspace(0, 1, n + 2))
+    hex_colors = ['#%02x%02x%02x' % (int(r*255), int(g*255), int(b*255)) for r, g, b, _ in colors]
+
+    # Remove black and white from the list
+    hex_colors = [color for color in hex_colors if color not in ['#000000', '#ffffff']]
+
+    # Ensure the list has exactly n colors by taking the first n colors
+    shuffle(hex_colors)
+    return hex_colors[:n]
+
+import matplotlib.pyplot as plt
+
+def plot_components(adata, save=False):
+    """ Plot connected components and their boundaries colored by spatial domains.
+    
+    """    
+    n = len(adata.obs["spatial_cluster"].dropna().unique())
+    colors = get_n_colors(n)
+    component_to_spatial_cluster = dict(zip(adata.obs["component"], adata.obs["spatial_cluster"]))
+    
+    # color_code strings 
+    df = adata.obs
+    color_code = df.loc[~df['spatial_cluster'].isnull(),'spatial_cluster'].unique()
+    label_to_color = dict(zip(color_code,colors))
+    df['color'] = 'lightgray'
+    df.loc[~df['spatial_cluster'].isnull(),'color'] = df.loc[~df['spatial_cluster'].isnull(),'spatial_cluster'].map(label_to_color)
+    
+    fig, axs = plt.subplots(1,2,figsize=(20,10))
+    plt.sca(axs[0])
+    for i, polygon1 in adata.uns[f"shape_component"]["boundary"].items():
+        plt.plot(*polygon1.exterior.xy, color=label_to_color[component_to_spatial_cluster[i]])
+    plt.scatter(adata.obsm["spatial"][:,0],adata.obsm["spatial"][:,1],c=adata.obs['color'], s=1)
+
+    plt.sca(axs[1])
+    for i, polygon1 in adata.uns[f"shape_component"]["boundary"].items():
+        plt.plot(*polygon1.exterior.xy, color=label_to_color[component_to_spatial_cluster[i]])
+    plt.title(f"n components = {len(adata.uns[f'shape_component']['boundary'])}")
+
+    if save:
+        fig.savefig(save)
+
+
+###### main function ######
+
+def script():
+    
+    args = parser.parse_args()
+    
+    # Set paths and variables
+    coord_file = args.coordinates
+    label_file = args.labels
+    fig_path = args.fig_path #TODO: Delete?
+    connectivity = args.connectivity #TODO: Delete
+    
+    if args.config is not None:
+        config_file = args.config
+        with open(config_file) as f:
+            config = json.load(f)
+        sample_file = config["sample_file"] if "sample_file" in config else None #TODO: Is this the best place to define the path?
+        technology = config["technology"] if "technology" in config else None
+        if technology not in SUPPORTED_TECHNOLOGIES:
+            raise ValueError(f"Technology {technology} not supported. Supported technologies are {SUPPORTED_TECHNOLOGIES}.")
+    
+    # Init adata
+    obs = pd.DataFrame(
+        data={
+            "spatial_cluster":pd.read_table(label_file,index_col=0).iloc[:,0].values,
+            "sample": "sample1" if sample_file is None else pd.read_table(sample_file,index_col=0).iloc[:,0].values,
+            }
+    )
+    obs.index = obs.index.astype(str)
+    obsm = {"spatial": pd.read_table(coord_file,index_col=0).values[:,:2]}
+    adata = ad.AnnData(X=None, obs=obs, obsm=obsm) 
+    
+    # Compute graph connectivity
+    """
+    From cellcharter paper:
+    Spatial network construction
+    Depending on the technology used, we employed different approaches implemented in the 
+    Squidpy library (v.1.3.0) to construct the network. For the Visium and RNA + ATAC data, 
+    which exhibit a regular structure, we assigned the six and four closest surrounding spots, 
+    respectively, as neighbors for each spot. For the CODEX, CosMx, MERFISH and IMC data, we 
+    constructed the network using Delaunay triangulation. However, Delaunay triangulation 
+    can result in long edges between cells, especially at the slide borders. Therefore, we 
+    eliminated edges between nodes if the distance between them exceeded the 99th percentile 
+    of all distances between connected nodes.
+    """
+    if technology in ["CODEX", "CosMx", "MERFISH", "IMC"]:
+        
+        #TODO: this should be reduced to one version (i.e. removing the `connectivity` param), needs to be tested on multiple datasets
+        #NOTE: One difference to the initial cellcharter implementation: we only consider cells that are assigned to a spatial domain
+        adata_domains = adata[adata.obs["spatial_cluster"].notna()].copy()
+        if connectivity == "delaunay_perc":
+            # Initial version from cellcharter
+            sq.gr.spatial_neighbors(adata_domains, delaunay=True, coord_type='generic')
+            cc.gr.remove_long_links(adata_domains, distance_percentile = 99.0)
+        elif connectivity == "delaunay_dist":
+            # Alternative cellcharter like version that should be more robust than percentiles
+            sq.gr.spatial_neighbors(adata_domains, delaunay=True, coord_type='generic')
+            remove_long_links(adata_domains, distance_percentile=None, mean_distance_factor=2.0)
+        elif connectivity == "neighs":
+            # Simple n neighbors version (danger: long range connections are not removed)
+            sq.gr.spatial_neighbors(adata_domains, delaunay=False, n_neighs=20, coord_type='generic')
+
+    elif technology in ["Visium"]:
+        sq.gr.spatial_neighbors(adata, n_neighs=6, coord_type="grid")
+    elif technology in ["RNA + ATAC"]: 
+        sq.gr.spatial_neighbors(adata, n_neighs=4, coord_type="grid")
+    else:
+        raise NotImplementedError(f"Graph connectivity calculation for the given technology {technology} is not implemented.")
+    
+    # Compute connected components
+    if technology in ["CODEX", "CosMx", "MERFISH", "IMC"]:
+        min_cells = 100
+        cc.gr.connected_components(adata_domains, cluster_key='spatial_cluster',min_cells = min_cells)
+        adata.obs["component"] = adata_domains.obs["component"]
+    elif technology in ["Visium", "RNA + ATAC"]:
+        min_cells = 3
+        cc.gr.connected_components(adata, cluster_key='spatial_cluster',min_cells = min_cells)
+    else:
+        raise NotImplementedError(f"Min Nr of cells for connected component identification for the given technology {technology} is not implemented.")
+        
+    # Compute boundaries of domain clusters (saved in adata.uns[f"shape_component"]["boundary"]}
+    """
+    From cellcharter paper:
+    Shape characterization
+    To determine the boundaries of a cluster component, we developed a new technique based on alpha shapes86. For each 
+    component, we computed the alpha shape with a starting value of alpha that depends on the resolution of the data. 
+    If the alpha value was too small, the alpha shape of the component would yield multiple separated polygons. In such 
+    cases, we doubled the alpha value and recomputed the alpha shape. This procedure is iterated until an alpha shape 
+    composed of a single polygon is obtained. 
+
+    In this script we automated the setting of the minimum alpha: `get_alpha_start`
+    """
+    a_start = get_alpha_start(adata)    
+    cc.tl.boundaries(adata, cluster_key = "component", min_hole_area_ratio = 0.1, alpha_start = a_start, copy = False)
+    
+    # Compute purities for each connected component
+    purities = cc.tl.purity(adata, cluster_key="component", library_key="sample", exterior=False, copy=True)
+    
+    # Compute purity metric (mean over purities of all connected components)
+    metric: float = np.mean([p for _,p in purities.items()]) 
+        
+    # Write files for debug purposes
+    if fig_path:
+        plot_components(adata, save=fig_path)
+    # If adata is saved for debug purposes writing fails when the polygons are still in adata
+    # del adata.uns[f"shape_component"]
+    # adata.write(args.out_file.replace(".tsv",".h5ad")) #TODO: DELETE
+        
+    ## Write output
+    from pathlib import Path
+    
+    Path(args.out_file).parent.mkdir(parents=True, exist_ok=True)
+    
+    with open(args.out_file, "w") as file:
+        file.write(f"{metric:.5e}\n")
+    
+# NOTE: __name__ == "__main__" is required, otherwise the multiprocessing might fail
+if __name__ == "__main__":
+    script()
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diff --git a/metric/cellcharter_purity/cellcharter_purity.yml b/metric/cellcharter_purity/cellcharter_purity.yml
new file mode 100644
index 00000000..e64b3a3f
--- /dev/null
+++ b/metric/cellcharter_purity/cellcharter_purity.yml
@@ -0,0 +1,8 @@
+channels:
+  - conda-forge
+dependencies:
+  - python=3.10.13
+  - pip
+  - pip:
+    - cellcharter
+    # cellcharter doesn't have release tags yet
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