diff --git a/autostreamtree/__init__.py b/autostreamtree/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/autostreamtree/aggregators.py b/autostreamtree/aggregators.py
new file mode 100644
index 0000000..bbcf806
--- /dev/null
+++ b/autostreamtree/aggregators.py
@@ -0,0 +1,54 @@
+import numpy as np
+import scipy
+
+
+def aggregate_dist(method, stuff):
+    """
+    Aggregates the given array according to the specified method.
+
+    Args:
+        method (str): The aggregation method to use (e.g. "HARM", "ARITH",
+                      "GEOM", etc.).
+        stuff (array-like): The input array to be aggregated.
+
+    Returns:
+        float: The aggregated result.
+    """
+    if method == "HARM":
+        stuff = [x for x in stuff if x > 0]
+        return scipy.stats.hmean(stuff) if len(stuff) > 0 else 0.0
+    elif method == "ARITH":
+        return np.mean(stuff)
+    elif method == "GEOM":
+        return scipy.stats.mstats.gmean(stuff)
+    elif method == "MEDIAN":
+        return np.median(stuff)
+    elif method == "MAX":
+        return np.max(stuff)
+    elif method == "MIN":
+        return np.min(stuff)
+    elif method == "ADJHARM":
+        return adjusted_harmonic_mean(stuff)
+    elif method == "SD":
+        return np.std(stuff)
+    elif method == "VAR":
+        return np.var(stuff)
+
+
+def adjusted_harmonic_mean(stuff):
+    """
+    Computes an adjusted harmonic mean that is corrected for non-positive
+    values.
+
+    Args:
+        stuff (array-like): The input array.
+
+    Returns:
+        float: The adjusted harmonic mean.
+    """
+    s = np.array(stuff)
+    vals = s[s > 0.0]
+    bads = s[s <= 0.0]
+    mu = (1.0 / (np.sum([1.0/x for x in vals]) /
+                 (len(vals)-len(bads)))) * ((len(vals)-len(bads))/len(vals))
+    return mu
diff --git a/autostreamtree/cli.py b/autostreamtree/cli.py
new file mode 100755
index 0000000..01381ad
--- /dev/null
+++ b/autostreamtree/cli.py
@@ -0,0 +1,318 @@
+import sys
+import pandas as pd
+import numpy as np
+import geopandas as gpd
+import pyogrio
+import random
+import matplotlib.pyplot as plt
+import time
+
+import autostreamtree.functions as ast
+from autostreamtree.params import parseArgs
+import autostreamtree.cluster_pops as clust
+import autostreamtree.report_refs as ref
+import autostreamtree.aggregators as agg
+
+
+def main():
+
+    params = parseArgs()
+
+    # Set the seed for random number generation
+    if params.seed is not None:
+        np.random.seed(params.seed)
+        random.seed(params.seed)
+    else:
+        # Use clock time to seed RNG if params.seed is None
+        clock_seed = int(time.time() * 1000) % (2**32)
+        np.random.seed(clock_seed)
+        random.seed(clock_seed)
+
+    # Step 1: Reading in datasets
+    print("Running autostream tree", params.run, "workflow\n")
+    G = ast.read_network(params.network, params.shapefile)
+    # prune graph if list of edges passed
+    if params.edge_list:
+        df = pd.read_csv(params.edge_list, sep='\t')
+        edge_list = df[params.reachid_col].tolist()
+        G = ast.prune_graph(G, edge_list, params.reachid_col)
+
+    # read point coordinate data
+    points = pd.read_csv(params.geodb, sep="\t", header=0)
+    (point_coords, pop_coords, popmap) = ast.process_samples(params, points, G)
+
+    # read genetic data from VCF
+    if params.run != "STREAMDIST":
+        seqs = ast.read_vcf(params.vcf, concat=params.concat, popmap=popmap)
+
+    # Step 2: Calculating genetic distance matrix(-ces)
+    # Optional: Calculate separately for each locus
+
+    gen = None
+    pop_gen = None
+    # calculate genetic distance matrix
+    if params.run != "STREAMDIST" and params.run != "RUNLOCI":
+        if not params.genmat:
+            print("\nCalculating genetic distances...")
+            (gen, pop_gen) = ast.get_gendist_mats(params, point_coords, popmap,
+                                                  seqs)
+        else:
+            print("\nReading genetic distances from provided matrix:",
+                  params.genmat)
+            inmat = pd.read_csv(params.genmat, header=0, index_col=0, sep="\t")
+            (gen, pop_gen) = ast.parse_input_genmat(params, inmat,
+                                                    point_coords, popmap, seqs)
+
+        ast.report_genmats(params, gen, pop_gen, point_coords, pop_coords)
+
+        # exit if this was the only step requested
+        if params.run == "GENDIST":
+            sys.exit(0)
+
+    # IMPORTANT NOTE: This function will silently skip any loci for which
+    # calculations aren't possible (e.g., population having no data)
+    if params.run == "RUNLOCI":
+        genlist = list()
+        popgenlist = list()
+        for loc in ast.get_loc_data(seqs):
+            try:
+                (gen, pop_gen) = ast.get_gendist_mats(params, point_coords,
+                                                      popmap, loc)
+                genlist.append(gen)
+                popgenlist.append(pop_gen)
+            except Exception:
+                pass
+        print(
+            "\nCalculating average of locus-wise genetic distance matrices..."
+        )
+        if genlist[0] is not None:
+            gen = np.mean(genlist, axis=0)
+        else:
+            gen = genlist[0]
+        if popgenlist[0] is not None:
+            pop_gen = np.mean(popgenlist, axis=0)
+        else:
+            pop_gen = popgenlist[0]
+
+        ast.report_genmats_list(params, genlist, popgenlist, point_coords,
+                                pop_coords)
+
+    # Step 4: Constructing a minimal subgraph
+
+    # Extract subgraph if needed
+    if params.run != "GENDIST":
+        K = ast.parse_subgraph_from_points(params, point_coords, pop_coords, G)
+
+    # Step 5: Construct stream distance matrix
+    # Optional: Mantel test
+
+    # calculate pairwise observed stream distances and indence matrix
+    # calculate incidence matrix X, which takes the form:
+    # nrows = rows in column vector form of D
+    # ncols = number of collapsed branches in stream network K
+    if params.run in ["STREAMDIST", "DISTANCES", "STREAMTREE", "IBD", "ALL",
+                      "RUNLOCI"]:
+        if params.pop or params.geopop or params.clusterpop:
+            points = pop_coords
+            gen = pop_gen
+        else:
+            points = point_coords
+
+        # calculate stream distances and incidence matrix
+        (sdist, inc) = ast.get_stream_mats(points, K, params.length_col)
+        print("\nStream distances:")
+        print(sdist)
+        sDF = pd.DataFrame(sdist, columns=list(points.keys()),
+                           index=list(points.keys()))
+        sDF.to_csv((str(params.out) + ".streamDistMat.txt"), sep="\t",
+                   index=True)
+        del sDF
+        if params.run == "STREAMDIST":
+            sys.exit(0)
+
+        # if user requested testing isolation-by-distance:
+        if params.run in ['ALL', 'IBD']:
+            # IBD calculations and plots
+            print("\nTesting for isolation-by-distance using Mantel test with",
+                  params.permutations, "permutations")
+            ast.test_ibd(gen, sdist, params.out, params.permutations)
+
+            # plot geographic x genetic DISTANCES
+            ast.plot_gen_by_geo(gen, sdist, params.out)
+
+            # if log request, re-do with logged geographic distances
+            if params.and_log:
+                print("\nTesting for isolation-by-distance with log",
+                      "geographic distances using Mantel test with",
+                      params.permutations, "permutations")
+                out = params.out+".log"
+                ast.test_ibd(gen, sdist, out, params.permutations, log=True)
+                ast.plot_gen_by_geo(gen, sdist, out, log=True)
+
+    #########################################################
+    # Step 6: Least-squares fitting
+    # Optional: Weighted least-squares
+    # Optional: Fit for each locus
+    #########################################################
+
+    if params.run in ["STREAMTREE", "ALL", "RUNLOCI"]:
+        if params.pop or params.geopop or params.clusterpop:
+            gen = pop_gen
+        print("\nIncidence matrix:")
+        print(inc)
+        ofh = params.out+".incidenceMatrix.txt"
+        with np.printoptions(precision=0, suppress=True):
+            np.savetxt(ofh, inc, delimiter="\t")
+        print("Incidence matrix dimensions:")
+        print(inc.shape)
+
+        # fit least-squares branch lengths
+        if params.run != "RUNLOCI":
+            print()
+            R = ast.fit_least_squares_distances(gen, inc.astype(int),
+                                                params.iterative,
+                                                params.out, params.weight)
+            print("\nFitted least-squares distances:")
+            print(R)
+        else:
+            print()
+            if params.pop or params.geopop or params.clusterpop:
+                genlist = popgenlist
+            print("Fitting StreamTree distances on per-locus matrices...")
+            Rlist = list()
+            ast.block_print()
+            for gen in genlist:
+                r = ast.fit_least_squares_distances(gen, inc.astype(int),
+                                                    params.iterative,
+                                                    params.out, params.weight)
+                Rlist.append(r)
+            ast.enable_print()
+
+            # aggregate locus distances (mean)
+            print("\nCalculating average fitted distances across loci using:",
+                  params.loc_agg)
+            averageR = np.array(
+                [agg.aggregate_dist(params.loc_agg, col) for
+                 col in zip(*Rlist)])
+            print(averageR)
+
+            # get standard-deviation locus distances as well
+            print("\nCalculating standard-deviation of fitted distances",
+                  "across loci using:", params.loc_agg)
+            sdR = np.array([agg.aggregate_dist("SD", col) for
+                            col in zip(*Rlist)])
+            print(sdR)
+
+        # check observed versus fitted distances:
+        if params.run == "RUNLOCI":
+            R = averageR
+        pred = ast.get_fitted_d(points, gen, inc, R)
+        print("\nComparing observed versus predicted genetic distances:")
+        print(pred)
+        ast.output_fitted_d(pred, params.out)
+
+        # Step 7: Outputs
+
+        # Now, annotate originate geoDF with dissolved reach IDs
+        # also, need to collect up the stream tree fitted D to each reach
+        # finally, could add residuals of fitting D vs LENGTH_KM?
+        # maybe include logDxlength, DxlogLength, logDxlogLength as well?
+
+        # get list of all REACHIDs to extract from geoDF
+        # edge_data = nx.get_edge_attributes(K,params.reachid_col)
+        reach_to_edge = dict()
+        i = 0
+        edges = list()
+        for e in K.edges():
+            edge_data = K[e[0]][e[1]][params.reachid_col]
+            for r in edge_data:
+                reach_to_edge[r] = str(i)
+            edges.append(i)
+            i += 1
+        del edge_data
+
+        # save reach_to_edge table to file
+        r2eDF = pd.DataFrame(list(reach_to_edge.items()),
+                             columns=[params.reachid_col, 'EDGE_ID'])
+        r2eDF.to_csv((str(params.out)+".reachToEdgeTable.txt"),
+                     sep="\t", index=False)
+
+        # read in original shapefile as geoDF and subset it
+        print("\nExtracting attributes from original dataframe...")
+        # geoDF = gpd.read_file(params.shapefile)
+        geoDF = pyogrio.read_dataframe(params.shapefile)
+        mask = geoDF[params.reachid_col].isin(list(reach_to_edge.keys()))
+        temp = geoDF.loc[mask]
+        del mask
+        del reach_to_edge
+
+        # join EDGE_ID to geoDF
+        geoDF = temp.merge(r2eDF, on=params.reachid_col, how='left')
+        for col in geoDF.columns:
+            if col.endswith('_x'):
+                geoDF.drop(col, axis=1, inplace=True)
+            elif col.endswith('_y'):
+                geoDF.rename(columns={col: col.rstrip('_y')}, inplace=True)
+        print(geoDF)
+        del temp
+        del r2eDF
+
+        # plot by edge ID
+        base = geoDF.plot(column="EDGE_ID", cmap="prism")
+        coords = clust.coords_to_dataframe(points)
+        geo_coords = gpd.GeoDataFrame(coords,
+                                      geometry=gpd.points_from_xy(coords.long,
+                                                                  coords.lat))
+        geo_coords.plot(ax=base, marker='o', color='black', markersize=10,
+                        zorder=10)
+
+        plt.title("Stream network colored by EDGE_ID")
+        plt.savefig((str(params.out)+".streamsByEdgeID.pdf"))
+
+        # add in fitted distances & plot
+        print("\nPlotting StreamTree fitted distances and writing new",
+              "shapefile...")
+
+        if params.run == "RUNLOCI":
+            fittedD = pd.DataFrame({'EDGE_ID': list(edges), 'fittedD': R})
+            sdD = pd.DataFrame({'EDGE_ID': list(edges), 'stdevD': sdR})
+            i = 1
+            for locfit in Rlist:
+                name = "locD_" + str(i)
+                fittedD[name] = locfit
+                i += 1
+        else:
+            fittedD = pd.DataFrame({'EDGE_ID': list(edges), 'fittedD': R})
+        geoDF['EDGE_ID'] = geoDF['EDGE_ID'].astype(int)
+        # geoDF.drop(columns=["EDGE_ID_x", "EDGE_ID_y"], inplace=True)
+        geoDF = geoDF.merge(fittedD, on='EDGE_ID', how="left")
+        for col in geoDF.columns:
+            if col.endswith('_x'):
+                geoDF.drop(col, axis=1, inplace=True)
+            elif col.endswith('_y'):
+                geoDF.rename(columns={col: col.rstrip('_y')}, inplace=True)
+        if params.run == "RUNLOCI":
+            geoDF = geoDF.merge(sdD, on='EDGE_ID')
+            geoDF.plot(column="stdevD", cmap="RdYlGn_r", legend=True)
+            plt.title("Stream network colored by standard deviation of",
+                      "StreamTree fitted distances")
+            plt.savefig((str(params.out)+".streamsBystdevD.pdf"))
+        geoDF.plot(column="fittedD", cmap="RdYlGn_r", legend=True)
+        plt.title("Stream network colored by StreamTree fitted distances")
+        plt.savefig((str(params.out)+".streamsByFittedD.pdf"))
+
+        # output a final annotated stream network layer
+        geoDF.to_csv((str(params.out)+".streamTree.txt"), sep="\t",
+                     index=False)
+        ast.write_geodataframe(geoDF, (str(params.out)+".streamTree"),
+                               params.output_driver)
+
+    refs = ref.fetch_references(params)
+    print(refs)
+
+    print("\nDone!\n")
+
+
+# Call main function
+if __name__ == '__main__':
+    main()
diff --git a/autostreamtree/cluster_pops.py b/autostreamtree/cluster_pops.py
new file mode 100644
index 0000000..868f61a
--- /dev/null
+++ b/autostreamtree/cluster_pops.py
@@ -0,0 +1,255 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.cluster import DBSCAN
+from shapely.geometry import MultiPoint
+from sortedcontainers import SortedDict
+
+from typing import Tuple, Dict, Optional
+
+
+# credit to Geoff Boeing at
+# https://geoffboeing.com/2014/08/clustering-to-reduce-spatial-data-set-size/
+# for the tutorial
+# here coords should be a dictionary with key=ID and value=tuple of lat,long
+# epsilon=max distance (in km) between points in a cluster
+def dbscan_cluster(coords: Dict[str, Tuple[float, float]], epsilon: float,
+                   min_samples: int, out: Optional[str] = None) -> SortedDict:
+    """
+    Cluster geographic coordinates using DBSCAN algorithm.
+
+    Parameters:
+    coords (Dict[str, Tuple[float, float]]): A dictionary of coordinate pairs
+                                             with the key as the location
+                                             identifier and values as tuples of
+                                             (latitude, longitude).
+    epsilon (float): Maximum distance between two samples for them to be
+                     considered as in the same neighborhood.
+    min_samples (int): The number of samples (or total weight) in a
+                       neighborhood for a point to be considered as a core
+                       point.
+    out (Optional[str]): Output file path to save cluster labels.
+
+    Returns:
+    SortedDict: A sorted dictionary of cluster labels with keys as cluster name
+                and values as lists of location identifiers in that cluster.
+
+    Raises:
+    ValueError: If any coordinate value is missing or not a float.
+
+    Examples:
+    >>> coords = {'A': (33.4484, -112.0740), 'B': (37.7749,
+                  -122.4194), 'C': (40.7128, -74.0060)}
+    >>> dbscan_cluster(coords, 300, 2)
+    SortedDict({'DB_-1': ['C'], 'DB_0': ['A'], 'DB_1': ['B']})
+    """
+    # Check if coordinates have correct values
+    for k, v in coords.items():
+        if not isinstance(v, tuple) or len(v) != 2:
+            raise ValueError(
+                f"Coordinate value for '{k}' is missing or",
+                "not a tuple of size 2."
+            )
+        if not all(isinstance(coord, float) for coord in v):
+            raise ValueError(
+                f"Coordinate value for '{k}' has non-float values."
+            )
+
+    # Convert coordinates to a numpy array for clustering
+    points = np.array(list(coords.values()))
+    kms_per_radian = 6371.0088
+    eps = epsilon / kms_per_radian
+
+    # Run DBSCAN clustering
+    db = DBSCAN(
+        eps=eps, min_samples=min_samples, algorithm='ball_tree',
+        metric='haversine').fit(np.radians(points))
+
+    # Save cluster labels
+    cluster_labels = db.labels_
+
+    # Build SortedDict to return
+    popmap = SortedDict()
+    for i, k in enumerate(coords.keys()):
+        pop = "DB_" + str(cluster_labels[i])
+        if pop not in popmap:
+            names = [k]
+            popmap[pop] = names
+        else:
+            popmap[pop].append(k)
+
+    # Save cluster labels to file if output path provided
+    if out:
+        with open(out, 'w') as f:
+            for i, k in enumerate(coords.keys()):
+                f.write(f"{k}\t{cluster_labels[i]}\n")
+
+    return popmap
+
+
+def coords_to_matrix(coords):
+    """
+    Convert a dictionary of geographic coordinates to a NumPy array.
+
+    Args:
+    - coords (dict): A dictionary containing the coordinates in the format
+                     {id:(long, lat)}.
+
+    Returns:
+    - A NumPy array of coordinates in the format [[long, lat], ...].
+
+    """
+    if not coords:
+        return np.array([], dtype=float).reshape(0, 2)
+
+    return pd.DataFrame(
+        [[coords[k][0], coords[k][1]] for k in coords],
+        columns=["long", "lat"]).to_numpy()
+
+
+def coords_to_dataframe(coords):
+    """
+    Convert a dictionary of geographic coordinates to a Pandas DataFrame.
+
+    Args:
+    - coords (dict): A dictionary containing the coordinates in the format
+                     {id:(long, lat)}.
+
+    Returns:
+    - A Pandas DataFrame of coordinates with columns "long" and "lat".
+
+    """
+    df = pd.DataFrame(
+        [[coords[k][0], coords[k][1]] for k in coords],
+        index=list(coords.keys()),
+        columns=["long", "lat"]
+    )
+    if df.empty:
+        df = df.astype({'long': 'float64', 'lat': 'float64'})
+    return df
+
+
+# function to find the centroid of a set of points
+# requires a SortedDict of coordinates and a SortedDict giving population IDs
+# """Coords:
+#     key     value
+#     SampleName    Tuple(Lat, Long)
+#     popmap:
+#     PopulationName    list(SampleName,...)
+# """
+def get_cluster_centroid(coords, popmap, out=None):
+    centroids = SortedDict()
+    ofh = None
+    if out:
+        ofh = out + ".clusterCentroids.txt"
+    log = ""
+    for i, pop in enumerate(list(popmap.keys())):
+        cluster = get_pop_coords_matrix(coords, popmap[pop])
+        if len(cluster) < 1:
+            raise ValueError(f"No coordinates in cluster: {pop}")
+
+        # add cluster to logfile (if provided)
+        log = log + "Population=" + str(i) + "\n"
+        log = log + str(cluster) + "\n"
+
+        # get centroid point
+        centroid = (
+            MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y
+        )
+        log = log + "Centroid=" + str(centroid) + "\n"
+        centroids[pop] = centroid
+
+    # write optional logfile
+    if out:
+        f = open(ofh, "w")
+        f.write(log)
+        f.close()
+    return centroids
+
+
+def get_pop_coords_matrix(d, subset):
+    if not isinstance(d, dict):
+        raise TypeError("Expected 'd' to be a dictionary")
+    if not isinstance(subset, list):
+        raise TypeError("Expected 'subset' to be a list")
+
+    ret = []
+    for k in subset:
+        if k in d:
+            if isinstance(d[k], tuple) and len(d[k]) == 2:
+                ret.append([d[k][0], d[k][1]])
+            else:
+                raise ValueError(f"Invalid coordinate format for key: {k}")
+        else:
+            raise ValueError(f"Key not found in dictionary: {k}")
+
+    return np.array(ret, dtype=float).reshape(-1, 2)
+
+
+# function plots clustered coordinates given a SortedDict of coords and a
+# population map
+def plot_clustered_points(point_coords, popmap, out, centroids=None):
+    # set output file name
+    ofh = out+".clusteredPoints.pdf"
+    sns.set(style="ticks")
+
+    # get 1D popmap
+    pmap = flatten_popmap(popmap)
+
+    df = pd.DataFrame(
+        [[ind, pmap[ind], point_coords[ind][0], point_coords[ind][1]]
+         for ind in point_coords], columns=["sample", "pop", "long", "lat"])
+
+    ax = sns.scatterplot(x="long", y="lat", hue="pop", palette="Set2", data=df)
+
+    # plot centroid positions if available
+    if centroids:
+        cdf = pd.DataFrame(
+            [[p, centroids[p][0], centroids[p][1]] for p in centroids],
+            columns=["pop", "long", "lat"])
+        sns.scatterplot(
+            x="long", y="lat", hue="pop", palette="Set2", data=cdf,
+            legend=False, marker="X", ax=ax
+        )
+
+    plt.savefig(ofh)
+    plt.clf()
+
+
+def plot_histogram(dat, out):
+    """
+    Plots a histogram of snap distances and saves to a PDF file.
+
+    Args:
+        dat (list): List of snap distances.
+        out (str): Output file path without extension.
+
+    Returns:
+        None
+    """
+    of = f"{out}.snapDistances.pdf"
+    sns.set(style="ticks")
+
+    x = pd.Series(dat, name="Snap distance (km)")
+    _ = sns.displot(x, kde=True, rug=True)
+    plt.savefig(of)
+    plt.clf()
+
+
+def flatten_popmap(popmap):
+    """
+    Flattens the popmap dictionary from a form of key=pop; value=list(inds) to
+    key=ind; value=pop.
+
+    Args:
+        popmap (dict): Dictionary in key=pop; value=list(inds) format.
+
+    Returns:
+        dict: Dictionary in key=ind; value=pop format.
+    """
+    new_popmap = {}
+    for k, v in popmap.items():
+        for i in v:
+            new_popmap[i] = k
+    return new_popmap
diff --git a/autostreamtree/data/test.clusterCentroids.txt b/autostreamtree/data/test.clusterCentroids.txt
new file mode 100644
index 0000000..406d193
--- /dev/null
+++ b/autostreamtree/data/test.clusterCentroids.txt
@@ -0,0 +1,272 @@
+Population=0
+[[90.396139 26.925414]
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diff --git a/autostreamtree/data/test.clusteredPoints.pdf b/autostreamtree/data/test.clusteredPoints.pdf
new file mode 100644
index 0000000..665147c
Binary files /dev/null and b/autostreamtree/data/test.clusteredPoints.pdf differ
diff --git a/autostreamtree/data/test.coords b/autostreamtree/data/test.coords
new file mode 100644
index 0000000..43d9d86
--- /dev/null
+++ b/autostreamtree/data/test.coords
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diff --git a/autostreamtree/data/test.cpg b/autostreamtree/data/test.cpg
new file mode 100644
index 0000000..cd89cb9
--- /dev/null
+++ b/autostreamtree/data/test.cpg
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diff --git a/autostreamtree/data/test.dbf b/autostreamtree/data/test.dbf
new file mode 100644
index 0000000..641dc90
Binary files /dev/null and b/autostreamtree/data/test.dbf differ
diff --git a/autostreamtree/data/test.full.network b/autostreamtree/data/test.full.network
new file mode 100644
index 0000000..a2d0e1d
Binary files /dev/null and b/autostreamtree/data/test.full.network differ
diff --git a/autostreamtree/data/test.incidenceMatrix.txt b/autostreamtree/data/test.incidenceMatrix.txt
new file mode 100644
index 0000000..c171569
--- /dev/null
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diff --git a/autostreamtree/data/test.isolationByDistance.pdf b/autostreamtree/data/test.isolationByDistance.pdf
new file mode 100644
index 0000000..eb7647a
Binary files /dev/null and b/autostreamtree/data/test.isolationByDistance.pdf differ
diff --git a/autostreamtree/data/test.isolationByDistance.txt b/autostreamtree/data/test.isolationByDistance.txt
new file mode 100644
index 0000000..8347d70
--- /dev/null
+++ b/autostreamtree/data/test.isolationByDistance.txt
@@ -0,0 +1,3 @@
+test	method	perms	r	p	z
+genXgeo	pearson	1000	0.08143601628060718	0.043	2.0299985780266883
+genXgeo	spearman	1000	0.10136487058394149	0.008	2.541948696138218
diff --git a/autostreamtree/data/test.leastSquaresConstrained.txt b/autostreamtree/data/test.leastSquaresConstrained.txt
new file mode 100644
index 0000000..e01ce33
--- /dev/null
+++ b/autostreamtree/data/test.leastSquaresConstrained.txt
@@ -0,0 +1,48 @@
+LS.original	LS.constrained
+0.007544918220478758	0.007544918220478536
+0.005262966137846992	0.0035256375804652573
+0.007761889887129003	0.007761889887128892
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+-0.003895673116176515	0.0
+0.0037292727971235187	0.0
+0.014776173544580273	0.012281223964009073
+-0.00018317073133941597	0.0
+-0.0021256839156947142	0.0
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+-0.00515777517416266	0.0
+-0.008166164119750118	0.0
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diff --git a/autostreamtree/data/test.network b/autostreamtree/data/test.network
new file mode 100644
index 0000000..6c1544a
Binary files /dev/null and b/autostreamtree/data/test.network differ
diff --git a/autostreamtree/data/test.obsVersusFittedD.pdf b/autostreamtree/data/test.obsVersusFittedD.pdf
new file mode 100644
index 0000000..f8a5520
Binary files /dev/null and b/autostreamtree/data/test.obsVersusFittedD.pdf differ
diff --git a/autostreamtree/data/test.obsVersusFittedD.txt b/autostreamtree/data/test.obsVersusFittedD.txt
new file mode 100644
index 0000000..53d0a17
--- /dev/null
+++ b/autostreamtree/data/test.obsVersusFittedD.txt
@@ -0,0 +1,667 @@
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+sing	thri	0.013325826440844601	0.046532699361190255	0.03320687292034565
+sing	tsri	0.04949448481292452	0.046532699361190255	0.0029617854517342626
+sing	unar	0.04053947746413811	0.046532699361190255	0.005993221897052148
+sing	yong	0.08326641518456691	0.046532699361190255	0.03673371582337666
+sing	yued	0.016940195766761177	0.031024906904842244	0.014084711138081067
+sing	zarp	0.08490320577918027	0.046532699361190255	0.038370506417990016
+thew	thri	0.04896468975200149	0.029231389197493712	0.019733300554507775
+thew	tsri	0.0	0.029231389197493712	0.029231389197493712
+thew	unar	0.0	0.029231389197493712	0.029231389197493712
+thew	yong	0.03752934827465565	0.029231389197493712	0.008297959077161937
+thew	yued	0.05047416310318358	0.013723596741145698	0.036750566362037884
+thew	zarp	0.08857779084251283	0.029231389197493712	0.059346401645019115
+thri	tsri	0.002083324841725514	0.0	0.002083324841725514
+thri	unar	0.0	0.0	0.0
+thri	yong	0.0	0.0	0.0
+thri	yued	0.0	0.015507792456348014	0.015507792456348014
+thri	zarp	0.015513897866838992	0.0	0.015513897866838992
+tsri	unar	0.0	0.0	0.0
+tsri	yong	0.0	0.0	0.0
+tsri	yued	0.0	0.015507792456348014	0.015507792456348014
+tsri	zarp	0.03401495503849286	0.0	0.03401495503849286
+unar	yong	0.00029535429315011237	0.0	0.00029535429315011237
+unar	yued	0.0	0.015507792456348014	0.015507792456348014
+unar	zarp	0.0	0.0	0.0
+yong	yued	2.8430190307858428e-05	0.015507792456348014	0.015479362266040155
+yong	zarp	0.03210956272076062	0.0	0.03210956272076062
+yued	zarp	0.0	0.015507792456348014	0.015507792456348014
diff --git a/autostreamtree/data/test.pointCoords.txt b/autostreamtree/data/test.pointCoords.txt
new file mode 100644
index 0000000..e518739
--- /dev/null
+++ b/autostreamtree/data/test.pointCoords.txt
@@ -0,0 +1,38 @@
+sample	lat	long
+burk	26.927083333332924	90.39374999999947
+cdikc	27.264583333332887	90.03958333333279
+dakp	27.14791666666624	90.68958333333276
+digl	26.893749999999564	91.75208333333276
+dikc	26.881249999999547	90.26874999999947
+dima	26.887499999999584	91.59791666666612
+dolk	26.877083333332898	90.34374999999952
+dort	26.868749999999572	89.37291666666616
+dusa	26.810416666666235	91.49791666666616
+dusb	26.810416666666235	91.49791666666616
+hara	27.218749999999567	90.1520833333328
+kali	26.706249999999606	89.83958333333291
+kame	27.27291666666624	90.03124999999946
+karo	27.27291666666624	90.03124999999946
+kiri	26.968749999999595	90.86041666666617
+labr	26.756249999999547	89.88541666666615
+mapt	26.968749999999595	90.86041666666617
+mart	26.872916666666235	91.56458333333288
+mori	27.260416666666224	91.1937499999996
+nyrb	26.931249999999586	91.67708333333283
+nyrc	26.997916666666235	91.54791666666617
+pach	26.929166666666255	89.38333333333279
+pipp	26.7645833333329	89.73541666666611
+raid	26.931249999999586	91.67708333333283
+rich	26.931249999999586	91.67708333333283
+rind	27.002083333332898	90.83541666666613
+ring	27.002083333332898	90.83541666666613
+rong	26.947916666666252	90.5104166666662
+sher	27.252083333332912	91.41041666666618
+sing	26.839583333332932	89.44791666666626
+thew	26.92291666666626	90.5104166666662
+thri	27.27291666666624	91.14791666666619
+tsri	27.402083333332904	91.55208333333283
+unar	27.214583333332904	91.20624999999941
+yong	27.214583333332904	91.20624999999941
+yued	26.935416666666235	90.8687499999995
+zarp	26.872916666666235	91.07291666666609
diff --git a/autostreamtree/data/test.popGenDistMat.txt b/autostreamtree/data/test.popGenDistMat.txt
new file mode 100644
index 0000000..81779f6
--- /dev/null
+++ b/autostreamtree/data/test.popGenDistMat.txt
@@ -0,0 +1,38 @@
+	burk	cdikc	dakp	digl	dikc	dima	dolk	dort	dusa	dusb	hara	kali	kame	karo	kiri	labr	mapt	mart	mori	nyrb	nyrc	pach	pipp	raid	rich	rind	ring	rong	sher	sing	thew	thri	tsri	unar	yong	yued	zarp
+burk	0.0	0.0014802217183169629	0.0	0.0	0.0	0.026342937812102188	0.045977448051180765	0.029925148455710576	0.0	0.044983910298079605	0.035872583823133644	0.0061536247244463466	0.009082647066089525	0.07893065332587364	0.00345617934324962	0.0	0.0	0.05706993564394839	0.007141826673280652	0.04488402121653114	0.08797314541901434	0.024480187799768252	0.03617018296722939	0.001779309252151267	0.0637212903055235	0.029661292193246796	0.008314759350839366	0.0	0.07245593886390382	0.018061970332396565	0.03833886017057654	0.0	0.0	0.0	0.018402835319592848	0.0	0.15218564490016184
+cdikc	0.0014802217183169629	0.0	0.0	0.0	0.0	0.04026091986126609	0.008744656043528963	0.0	0.0	0.011081309743798102	0.008282309877002134	0.0	0.01493476393562735	0.04469719562208839	0.0	0.005626715394018169	0.0	0.04362307712496379	0.0	0.05010894297428158	0.07858486668441278	0.0	0.0	0.0	0.01049488504010881	0.0	0.0	0.017983564789849082	0.011590102882422034	0.009929074447843914	0.039681109679951844	0.0	0.0	0.0	0.05060640675624757	0.0	0.04577020681690396
+dakp	0.0	0.0	0.0	0.0	0.022615577657951226	0.005734962577761138	0.005202944642275364	0.0	0.0	0.03902488354219362	0.02182185050482492	0.014553013092117534	0.0	0.045145761444010814	0.01424176606130388	0.0003059102001146987	0.0	0.033142700438753996	0.0	0.0	0.02235008738114323	0.026648422438735345	0.03212234663683743	0.0	0.030199952665254305	0.020990969340491043	0.0	0.048654492969031314	0.051726079987341834	0.009442888437803134	0.046912038393522926	0.0	0.01897269377740829	0.000897751625375456	0.028849210201036174	0.0	0.030030332241135
+digl	0.0	0.0	0.0	0.0	0.0	0.06281991031093877	0.015517845455102399	0.0	0.0	0.0	0.0	0.0	0.015581792771481083	0.05401937651510344	0.0	0.0	0.0	0.0	0.0003328846897309698	0.0	0.01836200344804916	0.0	0.03172691628924	0.0	0.0	0.0	0.0	0.0005640747310427935	0.0	0.0	0.010715490483605166	0.0	0.0	0.0	0.02295374655541444	0.0	0.0
+dikc	0.0	0.0	0.022615577657951226	0.0	0.0	0.00553396098036321	0.0	0.004607476970330893	0.024300644418563153	0.0	0.0	0.008565465358971671	0.013296484563410448	0.09104514114976509	0.0	0.0	0.0	0.03584160080164578	0.0	0.07988063119563278	0.029830402238725084	0.0	0.0	0.0	0.0	0.01798078199231747	0.0	0.006008798233742331	0.034467609917931595	0.0024868398424682204	0.0	0.0	0.0	0.0	0.013444693650169934	0.0	0.07379046293073441
+dima	0.026342937812102188	0.04026091986126609	0.005734962577761138	0.06281991031093877	0.00553396098036321	0.0	0.0	0.03674279358335166	0.0	0.04094970763484937	0.11291303108348184	0.07352782949053806	0.029241904347189564	0.06268507188861386	0.04485437933122862	0.04500604542254848	0.024332729233759835	0.04173248905630107	0.03910186112823819	0.02542251491585031	0.08111492245054444	0.056351287316794556	0.07128055711987001	0.03875007212141883	0.04794323649744515	0.07206300322015055	0.03252651046463154	0.07005009387826831	0.05983635607117084	0.07784323810701008	0.03859163501244937	0.011694501384817991	0.030697446930268192	0.053551391131799346	0.058407971948876855	0.011543133646905263	0.10812713779952149
+dolk	0.045977448051180765	0.008744656043528963	0.005202944642275364	0.015517845455102399	0.0	0.0	0.0	0.04166570751289351	0.012443118466457227	0.01646323274243367	0.043142137095863786	0.03113018288367931	0.01720825452652547	0.10219937785691942	0.013802439286005918	0.005200173958534819	0.0	0.056913300457238324	0.010276638069961756	0.09643739441112001	0.07301495745350177	0.0373602165262928	0.0029180017470312493	0.005820563373593012	0.02966772255266933	0.05521159572336468	0.0	0.036782760522808156	0.00729066865651324	0.0531237647336688	0.04469030058207967	0.0	0.02477189944753989	0.009968854886681791	0.03808918874493862	0.0	0.058287144645092034
+dort	0.029925148455710576	0.0	0.0	0.0	0.004607476970330893	0.03674279358335166	0.04166570751289351	0.0	0.0	0.03513310557884055	0.010352103932831484	0.0	0.0	0.033125496721009375	0.0	0.028784843293974516	0.0	0.04824027489974724	0.0010889370719149728	0.01257093597488615	0.03869055217720004	0.0	0.028064219233377673	0.0	0.005962511713865262	0.030816583958023676	0.00924401018681574	0.0351143747069184	0.017595391685039506	0.040847819945812464	0.0026123975793902383	0.032470045261653695	0.0	0.0	0.030679053988620277	0.004136781880673586	0.03627852616249214
+dusa	0.0	0.0	0.0	0.0	0.024300644418563153	0.0	0.012443118466457227	0.0	0.0	0.05016810904425362	0.0	0.0	0.0033907256765481767	0.02236677907437903	0.0	0.016016235230658444	0.0	0.02797009657859068	0.0	0.0	0.060911769790818956	0.0	0.05025732198133216	0.0	0.031141868512110725	0.0312845483160813	0.0	0.0	0.0	0.03112558568037558	0.0	0.0	0.0	0.0	0.0029937958429128225	0.0	0.0
+dusb	0.044983910298079605	0.011081309743798102	0.03902488354219362	0.0	0.0	0.04094970763484937	0.01646323274243367	0.03513310557884055	0.05016810904425362	0.0	0.010271653546870929	0.04751440484503076	0.04062843952889451	0.09117133204198677	0.05446466175468799	0.017754335193878628	0.06455868865186255	0.00014795295156407121	0.03578501967453942	0.08377745313199804	0.028470378585419866	0.04124254944813097	0.023138928744934863	0.07594428954501505	0.0	0.04614950809463835	0.036204886791708434	0.05500921037012064	0.0614713834539213	0.04168443578175307	0.045344478681377	0.0	0.0374577529798246	0.0137857425237448	0.08511381284714957	0.0	0.08262018551652699
+hara	0.035872583823133644	0.008282309877002134	0.02182185050482492	0.0	0.0	0.11291303108348184	0.043142137095863786	0.010352103932831484	0.0	0.010271653546870929	0.0	0.04718899274117195	0.01748079254572739	0.07648168158092612	0.018955537525020626	0.07112386958816261	0.0	0.03464336574926836	0.0	0.028686298798559124	0.009439763209380583	0.04833864817778579	0.0618612872577836	0.014136759762154952	0.0	0.09701258006750534	0.0	0.05502045651828404	0.06514129428064942	0.07340331488414668	0.023382768044157374	0.0	0.018071948084685558	0.010386993600888633	0.024457291385413717	0.018511478697995867	0.0
+kali	0.0061536247244463466	0.0	0.014553013092117534	0.0	0.008565465358971671	0.07352782949053806	0.03113018288367931	0.0	0.0	0.04751440484503076	0.04718899274117195	0.0	0.01181648639796728	0.051696520347627214	0.0	0.0	0.0	0.03512114693626198	0.023995079170531033	0.023382218791730126	0.0787379533969376	0.0	0.011503185480723951	0.0	0.023600914599655912	0.044798681140557274	0.0	0.025625923192751136	0.031742838913079906	0.05201096067908944	0.021904732450183016	0.04449826514764685	0.0	0.0	0.07004971538501059	0.0	0.0011264156698539084
+kame	0.009082647066089525	0.01493476393562735	0.0	0.015581792771481083	0.013296484563410448	0.029241904347189564	0.01720825452652547	0.0	0.0033907256765481767	0.04062843952889451	0.01748079254572739	0.01181648639796728	0.0	0.016738648836649928	0.006303573549272711	0.02807364777022066	0.0	0.055032741952694995	0.0	0.005617393404496901	0.06325057150332146	0.026284464822639773	0.019830556347940533	0.0	0.017413389970678386	0.03946235720911232	0.0	0.03781910628057295	0.04100240688367955	0.06914974163202484	0.017429524975809286	0.0019165072647327544	0.0	0.004649575805227708	0.0	0.0	0.0483122390032245
+karo	0.07893065332587364	0.04469719562208839	0.045145761444010814	0.05401937651510344	0.09104514114976509	0.06268507188861386	0.10219937785691942	0.033125496721009375	0.02236677907437903	0.09117133204198677	0.07648168158092612	0.051696520347627214	0.016738648836649928	0.0	0.050049878872144696	0.056232689442882434	0.06876002913891738	0.10401390744283501	0.04391334662742454	0.05524133033580559	0.1714044266746803	0.036444590263404665	0.06485506792037157	0.0	0.13434546360552987	0.06618822262605176	0.05777784614743975	0.056539933012568266	0.06595027971456738	0.06199164159216033	0.03565608855713195	0.0892582281161358	0.029194531804856354	0.06076855777557194	0.03829253923398555	0.0	0.31478479589735203
+kiri	0.00345617934324962	0.0	0.01424176606130388	0.0	0.0	0.04485437933122862	0.013802439286005918	0.0	0.0	0.05446466175468799	0.018955537525020626	0.0	0.006303573549272711	0.050049878872144696	0.0	0.0	0.0	0.04944601553009911	0.0	0.06674951932779982	0.05989466137081822	0.0	0.005945759501333355	0.0	0.0	0.027488984537875873	0.0	0.0	0.0	0.05020102474054907	0.0	0.0	0.0	0.0	0.018111273880197346	0.0	0.020947318845555757
+labr	0.0	0.005626715394018169	0.0003059102001146987	0.0	0.0	0.04500604542254848	0.005200173958534819	0.028784843293974516	0.016016235230658444	0.017754335193878628	0.07112386958816261	0.0	0.02807364777022066	0.056232689442882434	0.0	0.0	0.0	0.0690087443738658	0.009878183924170563	0.045637968589486246	0.07099540181457907	0.028064014857605644	0.025227603800133714	0.011598317946443509	0.03119703639913345	0.013896707658940825	0.0	0.015978459722145042	0.043422056431075946	0.02534582684634753	0.021845592211107132	0.0	0.02236407811852538	0.0	0.07421208566337813	0.0007487892439854719	0.044538381483251975
+mapt	0.0	0.0	0.0	0.0	0.0	0.024332729233759835	0.0	0.0	0.0	0.06455868865186255	0.0	0.0	0.0	0.06876002913891738	0.0	0.0	0.0	0.05197089581074235	0.0	0.0	0.05881250018351648	0.0	0.0	0.0	0.0	0.03367234692981171	0.0	0.0	0.0	0.010025787197902593	0.0	0.0	0.0	0.0	0.0	0.0	0.0
+mart	0.05706993564394839	0.04362307712496379	0.033142700438753996	0.0	0.03584160080164578	0.04173248905630107	0.056913300457238324	0.04824027489974724	0.02797009657859068	0.00014795295156407121	0.03464336574926836	0.03512114693626198	0.055032741952694995	0.10401390744283501	0.04944601553009911	0.0690087443738658	0.05197089581074235	0.0	0.03544182061068754	0.0190851264828841	0.05456720437114865	0.04141662883050264	0.039135709761136415	0.05574066553190587	0.012825634773184632	0.1284868296549361	0.04584465938051079	0.09880949808839393	0.05138129291798305	0.06533492195746135	0.05054070217873647	0.06659201566351414	0.0426414106458233	0.021814238193357618	0.11351133132857229	0.026791807193395446	0.1469550958865167
+mori	0.007141826673280652	0.0	0.0	0.0003328846897309698	0.0	0.03910186112823819	0.010276638069961756	0.0010889370719149728	0.0	0.03578501967453942	0.0	0.023995079170531033	0.0	0.04391334662742454	0.0	0.009878183924170563	0.0	0.03544182061068754	0.0	0.04112605609192665	0.04661028677079359	0.014151095730710073	0.023504532192587996	0.0	0.043373493489389466	0.04270423440997783	0.0	0.04204536361087176	0.04384010883981767	0.04609849795198699	0.0	0.0	0.004072393636923172	0.0	0.0	0.0	0.02547403633767352
+nyrb	0.04488402121653114	0.05010894297428158	0.0	0.0	0.07988063119563278	0.02542251491585031	0.09643739441112001	0.01257093597488615	0.0	0.08377745313199804	0.028686298798559124	0.023382218791730126	0.005617393404496901	0.05524133033580559	0.06674951932779982	0.045637968589486246	0.0	0.0190851264828841	0.04112605609192665	0.0	0.1300536698157847	0.059590339693979415	0.10625293490540282	0.0	0.1554946765030799	0.14426297481558123	0.09585038331378497	0.05754497661061472	0.055425795527472385	0.04822162690288071	0.02093264981273283	0.16978841888793064	0.017745203794733915	0.0	0.055745288170358204	0.0	0.5009487666034156
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diff --git a/autostreamtree/data/test.popmap b/autostreamtree/data/test.popmap
new file mode 100644
index 0000000..f09fa9f
--- /dev/null
+++ b/autostreamtree/data/test.popmap
@@ -0,0 +1,198 @@
+63burk03	burk
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diff --git a/autostreamtree/data/test.popmap.txt b/autostreamtree/data/test.popmap.txt
new file mode 100644
index 0000000..be71a5f
--- /dev/null
+++ b/autostreamtree/data/test.popmap.txt
@@ -0,0 +1,199 @@
+IND_ID	POP_ID
+63burk03	burk
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+63tsri01	tsri
+63tsri02	tsri
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+63tsri07	tsri
+63unar01	unar
+63unar02	unar
+63unar03	unar
+63unar04	unar
+63unar05	unar
+63unar06	unar
+63yong01	yong
+63yong02	yong
+63yong03	yong
+63yong04	yong
+63yued01	yued
+63yued02	yued
+63yued03	yued
+63zarp01	zarp
diff --git a/autostreamtree/data/test.prj b/autostreamtree/data/test.prj
new file mode 100644
index 0000000..f45cbad
--- /dev/null
+++ b/autostreamtree/data/test.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/autostreamtree/data/test.reachToEdgeTable.txt b/autostreamtree/data/test.reachToEdgeTable.txt
new file mode 100644
index 0000000..49b987e
--- /dev/null
+++ b/autostreamtree/data/test.reachToEdgeTable.txt
@@ -0,0 +1,632 @@
+HYRIV_ID	EDGE_ID
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diff --git a/autostreamtree/data/test.shp b/autostreamtree/data/test.shp
new file mode 100644
index 0000000..48351dd
Binary files /dev/null and b/autostreamtree/data/test.shp differ
diff --git a/autostreamtree/data/test.shx b/autostreamtree/data/test.shx
new file mode 100644
index 0000000..f4f4a3e
Binary files /dev/null and b/autostreamtree/data/test.shx differ
diff --git a/autostreamtree/data/test.snapDistances.pdf b/autostreamtree/data/test.snapDistances.pdf
new file mode 100644
index 0000000..d8df4af
Binary files /dev/null and b/autostreamtree/data/test.snapDistances.pdf differ
diff --git a/autostreamtree/data/test.snapDistances.txt b/autostreamtree/data/test.snapDistances.txt
new file mode 100644
index 0000000..9326e48
--- /dev/null
+++ b/autostreamtree/data/test.snapDistances.txt
@@ -0,0 +1,38 @@
+name	km
+burk	0.47807860806873154
+cdikc	0.8768546619503365
+dakp	0.2838387916138005
+digl	0.6558551864677619
+dikc	0.3145351414850561
+dima	2.695435604507553
+dolk	1.5021105927830318
+dort	0.18138289931237495
+dusa	0.4378075463677163
+dusb	1.7342602138463286
+hara	0.37742561407145647
+kali	1.7564171469798984
+kame	0.6048551274470593
+karo	0.6032690832698347
+kiri	0.4393796975145123
+labr	0.5374837836095249
+mapt	0.3324529222125474
+mart	0.28393465337172397
+mori	0.49662456501328
+nyrb	0.4183600822496242
+nyrc	0.49262345091772225
+pach	2.8018695692295306
+pipp	0.6713696879260264
+raid	0.4003997007102557
+rich	0.4003997007102557
+rind	0.33765815079399963
+ring	0.33765815079399963
+rong	0.5813483181498812
+sher	1.0718680161337657
+sing	2.687428532002008
+thew	1.7662693286685778
+thri	0.4440247298911057
+tsri	0.2700802110164703
+unar	0.5611719901203581
+yong	0.49356477957085126
+yued	0.29618702325963436
+zarp	1.2310914649836253
diff --git a/autostreamtree/data/test.streamDistMat.txt b/autostreamtree/data/test.streamDistMat.txt
new file mode 100644
index 0000000..83d4e9e
--- /dev/null
+++ b/autostreamtree/data/test.streamDistMat.txt
@@ -0,0 +1,38 @@
+	burk	cdikc	dakp	digl	dikc	dima	dolk	dort	dusa	dusb	hara	kali	kame	karo	kiri	labr	mapt	mart	mori	nyrb	nyrc	pach	pipp	raid	rich	rind	ring	rong	sher	sing	thew	thri	tsri	unar	yong	yued	zarp
+burk	0.0	439.38999999999993	299.43	337.46	319.79999999999995	306.71999999999997	233.94	413.85999999999996	304.26000000000005	304.26000000000005	438.7699999999999	355.8499999999999	440.88999999999993	440.88999999999993	268.18	363.5399999999999	268.18	312.6600000000001	324.56000000000006	339.71	357.28	432.81999999999994	374.95	339.71	339.71	272.83	272.83	58.87	328.87000000000006	396.15999999999997	55.769999999999996	330.13000000000005	355.36000000000007	318.37000000000006	318.37000000000006	263.84000000000003	262.76000000000005
+cdikc	439.38999999999993	0.0	492.7399999999999	530.7699999999999	381.90999999999985	500.02999999999986	390.6899999999999	369.1699999999999	497.56999999999994	497.56999999999994	19.2	89.75999999999999	1.5	1.5	461.4899999999999	78.75	461.4899999999999	505.96999999999997	517.87	533.0199999999999	550.5899999999999	388.1299999999999	330.25999999999993	533.0199999999999	533.0199999999999	466.1399999999999	466.1399999999999	439.29999999999995	522.18	351.4699999999999	436.19999999999993	523.44	548.67	511.67999999999995	511.67999999999995	457.1499999999999	456.06999999999994
+dakp	299.43	492.7399999999999	0.0	346.77	373.15	316.03	287.29	467.21	265.23	265.23	492.11999999999995	409.19999999999993	494.23999999999995	494.23999999999995	31.25	416.88999999999993	31.25	273.63000000000005	128.35000000000002	349.02	366.59	486.16999999999996	428.3	349.02	349.02	26.6	26.6	299.34000000000003	132.66	449.51	296.24	133.92000000000002	159.15	122.16000000000001	122.16000000000001	35.59	66.55000000000001
+digl	337.46	530.7699999999999	346.77	0.0	411.17999999999995	212.77999999999997	325.32	505.23999999999995	351.6	351.6	530.1499999999999	447.2299999999999	532.2699999999999	532.2699999999999	315.52	454.9199999999999	315.52	360.00000000000006	371.90000000000003	15.59	33.16	524.1999999999999	466.33	15.59	15.59	320.16999999999996	320.16999999999996	337.37	376.21000000000004	487.53999999999996	334.27	377.47	402.70000000000005	365.71000000000004	365.71000000000004	311.18	310.1
+dikc	319.79999999999995	381.90999999999985	373.15	411.17999999999995	0.0	380.43999999999994	271.09999999999997	356.37999999999994	377.98	377.98	381.2899999999999	298.3699999999999	383.4099999999999	383.4099999999999	341.9	306.0599999999999	341.9	386.38000000000005	398.28000000000003	413.42999999999995	430.99999999999994	375.3399999999999	317.46999999999997	413.42999999999995	413.42999999999995	346.54999999999995	346.54999999999995	319.71	402.59000000000003	338.67999999999995	316.60999999999996	403.85	429.08000000000004	392.09000000000003	392.09000000000003	337.56	336.48
+dima	306.71999999999997	500.02999999999986	316.03	212.77999999999997	380.43999999999994	0.0	294.58	474.5	320.86	320.86	499.40999999999997	416.48999999999995	501.53	501.53	284.78	424.17999999999995	284.78	329.26000000000005	341.16	215.02999999999994	232.59999999999994	493.46	435.59000000000003	215.02999999999994	215.02999999999994	289.42999999999995	289.42999999999995	306.63000000000005	345.47	456.8	303.53000000000003	346.73	371.96000000000004	334.97	334.97	280.44	279.36
+dolk	233.94	390.6899999999999	287.29	325.32	271.09999999999997	294.58	0.0	365.15999999999997	292.12	292.12	390.06999999999994	307.1499999999999	392.18999999999994	392.18999999999994	256.04	314.8399999999999	256.04	300.52000000000004	312.42	327.57	345.14	384.11999999999995	326.25	327.57	327.57	260.69	260.69	233.84999999999997	316.73	347.46	230.74999999999997	317.99	343.22	306.23	306.23	251.70000000000002	250.62
+dort	413.85999999999996	369.1699999999999	467.21	505.23999999999995	356.37999999999994	474.5	365.15999999999997	0.0	472.03999999999996	472.03999999999996	368.54999999999995	285.62999999999994	370.66999999999996	370.66999999999996	435.9599999999999	293.31999999999994	435.9599999999999	480.44	492.34	507.4899999999999	525.06	18.96	197.75	507.4899999999999	507.4899999999999	440.6099999999999	440.6099999999999	413.77	496.65	177.29999999999998	410.66999999999996	497.90999999999997	523.14	486.15	486.15	431.61999999999995	430.53999999999996
+dusa	304.26000000000005	497.56999999999994	265.23	351.6	377.98	320.86	292.12	472.03999999999996	0.0	0.0	496.95	414.03	499.07	499.07	233.98000000000008	421.71999999999997	233.98000000000008	38.44	290.36000000000007	353.85	371.42	491.0	433.13000000000005	353.85	353.85	238.63000000000008	238.63000000000008	304.1700000000001	294.6700000000001	454.34000000000003	301.07000000000005	295.93000000000006	321.1600000000001	284.1700000000001	284.1700000000001	229.64000000000007	228.56000000000006
+dusb	304.26000000000005	497.56999999999994	265.23	351.6	377.98	320.86	292.12	472.03999999999996	0.0	0.0	496.95	414.03	499.07	499.07	233.98000000000008	421.71999999999997	233.98000000000008	38.44	290.36000000000007	353.85	371.42	491.0	433.13000000000005	353.85	353.85	238.63000000000008	238.63000000000008	304.1700000000001	294.6700000000001	454.34000000000003	301.07000000000005	295.93000000000006	321.1600000000001	284.1700000000001	284.1700000000001	229.64000000000007	228.56000000000006
+hara	438.7699999999999	19.2	492.11999999999995	530.1499999999999	381.2899999999999	499.40999999999997	390.06999999999994	368.54999999999995	496.95	496.95	0.0	89.14	20.7	20.7	460.8699999999999	78.13000000000001	460.8699999999999	505.34999999999997	517.25	532.3999999999997	549.9699999999998	387.50999999999993	329.64	532.3999999999997	532.3999999999997	465.51999999999987	465.51999999999987	438.67999999999995	521.56	350.84999999999997	435.5799999999999	522.82	548.05	511.05999999999995	511.05999999999995	456.5299999999999	455.44999999999993
+kali	355.8499999999999	89.75999999999999	409.19999999999993	447.2299999999999	298.3699999999999	416.48999999999995	307.1499999999999	285.62999999999994	414.03	414.03	89.14	0.0	91.25999999999999	91.25999999999999	377.94999999999993	13.909999999999998	377.94999999999993	422.43	434.33	449.4799999999999	467.0499999999999	304.5899999999999	246.71999999999997	449.4799999999999	449.4799999999999	382.5999999999999	382.5999999999999	355.75999999999993	438.64	267.92999999999995	352.6599999999999	439.9	465.13	428.14	428.14	373.60999999999996	372.53
+kame	440.88999999999993	1.5	494.23999999999995	532.2699999999999	383.4099999999999	501.53	392.18999999999994	370.66999999999996	499.07	499.07	20.7	91.25999999999999	0.0	0.0	462.9899999999999	80.25	462.9899999999999	507.46999999999997	519.37	534.5199999999999	552.0899999999999	389.6299999999999	331.75999999999993	534.5199999999999	534.5199999999999	467.6399999999999	467.6399999999999	440.79999999999995	523.68	352.9699999999999	437.69999999999993	524.94	550.17	513.18	513.18	458.6499999999999	457.56999999999994
+karo	440.88999999999993	1.5	494.23999999999995	532.2699999999999	383.4099999999999	501.53	392.18999999999994	370.66999999999996	499.07	499.07	20.7	91.25999999999999	0.0	0.0	462.9899999999999	80.25	462.9899999999999	507.46999999999997	519.37	534.5199999999999	552.0899999999999	389.6299999999999	331.75999999999993	534.5199999999999	534.5199999999999	467.6399999999999	467.6399999999999	440.79999999999995	523.68	352.9699999999999	437.69999999999993	524.94	550.17	513.18	513.18	458.6499999999999	457.56999999999994
+kiri	268.18	461.4899999999999	31.25	315.52	341.9	284.78	256.04	435.9599999999999	233.98000000000008	233.98000000000008	460.8699999999999	377.94999999999993	462.9899999999999	462.9899999999999	0.0	385.63999999999993	0.0	242.38000000000002	97.10000000000001	317.77000000000004	335.34000000000003	454.91999999999996	397.05	317.77000000000004	317.77000000000004	4.65	4.65	268.09000000000003	101.41	418.26	264.99	102.67000000000002	127.9	90.91000000000001	90.91000000000001	4.34	35.300000000000004
+labr	363.5399999999999	78.75	416.88999999999993	454.9199999999999	306.0599999999999	424.17999999999995	314.8399999999999	293.31999999999994	421.71999999999997	421.71999999999997	78.13000000000001	13.909999999999998	80.25	80.25	385.63999999999993	0.0	385.63999999999993	430.12	442.02	457.1699999999999	474.7399999999999	312.2799999999999	254.40999999999997	457.1699999999999	457.1699999999999	390.2899999999999	390.2899999999999	363.44999999999993	446.33	275.61999999999995	360.3499999999999	447.59	472.82	435.83	435.83	381.29999999999995	380.21999999999997
+mapt	268.18	461.4899999999999	31.25	315.52	341.9	284.78	256.04	435.9599999999999	233.98000000000008	233.98000000000008	460.8699999999999	377.94999999999993	462.9899999999999	462.9899999999999	0.0	385.63999999999993	0.0	242.38000000000002	97.10000000000001	317.77000000000004	335.34000000000003	454.91999999999996	397.05	317.77000000000004	317.77000000000004	4.65	4.65	268.09000000000003	101.41	418.26	264.99	102.67000000000002	127.9	90.91000000000001	90.91000000000001	4.34	35.300000000000004
+mart	312.6600000000001	505.96999999999997	273.63000000000005	360.00000000000006	386.38000000000005	329.26000000000005	300.52000000000004	480.44	38.44	38.44	505.34999999999997	422.43	507.46999999999997	507.46999999999997	242.38000000000002	430.12	242.38000000000002	0.0	298.76000000000005	362.25	379.82	499.4	441.53000000000003	362.25	362.25	247.03000000000006	247.03000000000006	312.57000000000005	303.07000000000005	462.74	309.47	304.33000000000004	329.56000000000006	292.57000000000005	292.57000000000005	238.04000000000005	236.96000000000004
+mori	324.56000000000006	517.87	128.35000000000002	371.90000000000003	398.28000000000003	341.16	312.42	492.34	290.36000000000007	290.36000000000007	517.25	434.33	519.37	519.37	97.10000000000001	442.02	97.10000000000001	298.76000000000005	0.0	374.15	391.71999999999997	511.29999999999995	453.43	374.15	374.15	101.75	101.75	324.47	63.03	474.64	321.37	5.57	89.52000000000001	6.19	6.19	92.75999999999999	61.8
+nyrb	339.71	533.0199999999999	349.02	15.59	413.42999999999995	215.02999999999994	327.57	507.4899999999999	353.85	353.85	532.3999999999997	449.4799999999999	534.5199999999999	534.5199999999999	317.77000000000004	457.1699999999999	317.77000000000004	362.25	374.15	0.0	24.57	526.4499999999999	468.58	0.0	0.0	322.41999999999996	322.41999999999996	339.62	378.46000000000004	489.78999999999996	336.52	379.72	404.95000000000005	367.96000000000004	367.96000000000004	313.43	312.35
+nyrc	357.28	550.5899999999999	366.59	33.16	430.99999999999994	232.59999999999994	345.14	525.06	371.42	371.42	549.9699999999998	467.0499999999999	552.0899999999999	552.0899999999999	335.34000000000003	474.7399999999999	335.34000000000003	379.82	391.71999999999997	24.57	0.0	544.02	486.15	24.57	24.57	339.98999999999995	339.98999999999995	357.19	396.03000000000003	507.35999999999996	354.09	397.29	422.52000000000004	385.53000000000003	385.53000000000003	331.0	329.92
+pach	432.81999999999994	388.1299999999999	486.16999999999996	524.1999999999999	375.3399999999999	493.46	384.11999999999995	18.96	491.0	491.0	387.50999999999993	304.5899999999999	389.6299999999999	389.6299999999999	454.91999999999996	312.2799999999999	454.91999999999996	499.4	511.29999999999995	526.4499999999999	544.02	0.0	216.71000000000004	526.4499999999999	526.4499999999999	459.56999999999994	459.56999999999994	432.73	515.61	196.26	429.63	516.87	542.1	505.11	505.11	450.58	449.5
+pipp	374.95	330.25999999999993	428.3	466.33	317.46999999999997	435.59000000000003	326.25	197.75	433.13000000000005	433.13000000000005	329.64	246.71999999999997	331.75999999999993	331.75999999999993	397.05	254.40999999999997	397.05	441.53000000000003	453.43	468.58	486.15	216.71000000000004	0.0	468.58	468.58	401.7	401.7	374.86	457.74000000000007	180.05	371.76	459.00000000000006	484.2300000000001	447.24000000000007	447.24000000000007	392.71000000000004	391.63000000000005
+raid	339.71	533.0199999999999	349.02	15.59	413.42999999999995	215.02999999999994	327.57	507.4899999999999	353.85	353.85	532.3999999999997	449.4799999999999	534.5199999999999	534.5199999999999	317.77000000000004	457.1699999999999	317.77000000000004	362.25	374.15	0.0	24.57	526.4499999999999	468.58	0.0	0.0	322.41999999999996	322.41999999999996	339.62	378.46000000000004	489.78999999999996	336.52	379.72	404.95000000000005	367.96000000000004	367.96000000000004	313.43	312.35
+rich	339.71	533.0199999999999	349.02	15.59	413.42999999999995	215.02999999999994	327.57	507.4899999999999	353.85	353.85	532.3999999999997	449.4799999999999	534.5199999999999	534.5199999999999	317.77000000000004	457.1699999999999	317.77000000000004	362.25	374.15	0.0	24.57	526.4499999999999	468.58	0.0	0.0	322.41999999999996	322.41999999999996	339.62	378.46000000000004	489.78999999999996	336.52	379.72	404.95000000000005	367.96000000000004	367.96000000000004	313.43	312.35
+rind	272.83	466.1399999999999	26.6	320.16999999999996	346.54999999999995	289.42999999999995	260.69	440.6099999999999	238.63000000000008	238.63000000000008	465.51999999999987	382.5999999999999	467.6399999999999	467.6399999999999	4.65	390.2899999999999	4.65	247.03000000000006	101.75	322.41999999999996	339.98999999999995	459.56999999999994	401.7	322.41999999999996	322.41999999999996	0.0	0.0	272.74000000000007	106.06	422.91	269.64000000000004	107.32	132.55	95.56	95.56	8.99	39.95
+ring	272.83	466.1399999999999	26.6	320.16999999999996	346.54999999999995	289.42999999999995	260.69	440.6099999999999	238.63000000000008	238.63000000000008	465.51999999999987	382.5999999999999	467.6399999999999	467.6399999999999	4.65	390.2899999999999	4.65	247.03000000000006	101.75	322.41999999999996	339.98999999999995	459.56999999999994	401.7	322.41999999999996	322.41999999999996	0.0	0.0	272.74000000000007	106.06	422.91	269.64000000000004	107.32	132.55	95.56	95.56	8.99	39.95
+rong	58.87	439.29999999999995	299.34000000000003	337.37	319.71	306.63000000000005	233.84999999999997	413.77	304.1700000000001	304.1700000000001	438.67999999999995	355.75999999999993	440.79999999999995	440.79999999999995	268.09000000000003	363.44999999999993	268.09000000000003	312.57000000000005	324.47	339.62	357.19	432.73	374.86	339.62	339.62	272.74000000000007	272.74000000000007	0.0	328.78000000000003	396.07	3.1	330.04	355.27000000000004	318.28000000000003	318.28000000000003	263.75	262.67
+sher	328.87000000000006	522.18	132.66	376.21000000000004	402.59000000000003	345.47	316.73	496.65	294.6700000000001	294.6700000000001	521.56	438.64	523.68	523.68	101.41	446.33	101.41	303.07000000000005	63.03	378.46000000000004	396.03000000000003	515.61	457.74000000000007	378.46000000000004	378.46000000000004	106.06	106.06	328.78000000000003	0.0	478.95	325.68	68.6	26.490000000000002	56.839999999999996	56.839999999999996	97.07	66.10999999999999
+sing	396.15999999999997	351.4699999999999	449.51	487.53999999999996	338.67999999999995	456.8	347.46	177.29999999999998	454.34000000000003	454.34000000000003	350.84999999999997	267.92999999999995	352.9699999999999	352.9699999999999	418.26	275.61999999999995	418.26	462.74	474.64	489.78999999999996	507.35999999999996	196.26	180.05	489.78999999999996	489.78999999999996	422.91	422.91	396.07	478.95	0.0	392.97	480.21000000000004	505.44000000000005	468.45000000000005	468.45000000000005	413.92	412.84000000000003
+thew	55.769999999999996	436.19999999999993	296.24	334.27	316.60999999999996	303.53000000000003	230.74999999999997	410.66999999999996	301.07000000000005	301.07000000000005	435.5799999999999	352.6599999999999	437.69999999999993	437.69999999999993	264.99	360.3499999999999	264.99	309.47	321.37	336.52	354.09	429.63	371.76	336.52	336.52	269.64000000000004	269.64000000000004	3.1	325.68	392.97	0.0	326.94000000000005	352.1700000000001	315.18000000000006	315.18000000000006	260.65000000000003	259.57000000000005
+thri	330.13000000000005	523.44	133.92000000000002	377.47	403.85	346.73	317.99	497.90999999999997	295.93000000000006	295.93000000000006	522.82	439.9	524.94	524.94	102.67000000000002	447.59	102.67000000000002	304.33000000000004	5.57	379.72	397.29	516.87	459.00000000000006	379.72	379.72	107.32	107.32	330.04	68.6	480.21000000000004	326.94000000000005	0.0	95.09	11.760000000000002	11.760000000000002	98.33000000000001	67.37
+tsri	355.36000000000007	548.67	159.15	402.70000000000005	429.08000000000004	371.96000000000004	343.22	523.14	321.1600000000001	321.1600000000001	548.05	465.13	550.17	550.17	127.9	472.82	127.9	329.56000000000006	89.52000000000001	404.95000000000005	422.52000000000004	542.1	484.2300000000001	404.95000000000005	404.95000000000005	132.55	132.55	355.27000000000004	26.490000000000002	505.44000000000005	352.1700000000001	95.09	0.0	83.33	83.33	123.56	92.6
+unar	318.37000000000006	511.67999999999995	122.16000000000001	365.71000000000004	392.09000000000003	334.97	306.23	486.15	284.1700000000001	284.1700000000001	511.05999999999995	428.14	513.18	513.18	90.91000000000001	435.83	90.91000000000001	292.57000000000005	6.19	367.96000000000004	385.53000000000003	505.11	447.24000000000007	367.96000000000004	367.96000000000004	95.56	95.56	318.28000000000003	56.839999999999996	468.45000000000005	315.18000000000006	11.760000000000002	83.33	0.0	0.0	86.57	55.61
+yong	318.37000000000006	511.67999999999995	122.16000000000001	365.71000000000004	392.09000000000003	334.97	306.23	486.15	284.1700000000001	284.1700000000001	511.05999999999995	428.14	513.18	513.18	90.91000000000001	435.83	90.91000000000001	292.57000000000005	6.19	367.96000000000004	385.53000000000003	505.11	447.24000000000007	367.96000000000004	367.96000000000004	95.56	95.56	318.28000000000003	56.839999999999996	468.45000000000005	315.18000000000006	11.760000000000002	83.33	0.0	0.0	86.57	55.61
+yued	263.84000000000003	457.1499999999999	35.59	311.18	337.56	280.44	251.70000000000002	431.61999999999995	229.64000000000007	229.64000000000007	456.5299999999999	373.60999999999996	458.6499999999999	458.6499999999999	4.34	381.29999999999995	4.34	238.04000000000005	92.75999999999999	313.43	331.0	450.58	392.71000000000004	313.43	313.43	8.99	8.99	263.75	97.07	413.92	260.65000000000003	98.33000000000001	123.56	86.57	86.57	0.0	30.960000000000004
+zarp	262.76000000000005	456.06999999999994	66.55000000000001	310.1	336.48	279.36	250.62	430.53999999999996	228.56000000000006	228.56000000000006	455.44999999999993	372.53	457.56999999999994	457.56999999999994	35.300000000000004	380.21999999999997	35.300000000000004	236.96000000000004	61.8	312.35	329.92	449.5	391.63000000000005	312.35	312.35	39.95	39.95	262.67	66.10999999999999	412.84000000000003	259.57000000000005	67.37	92.6	55.61	55.61	30.960000000000004	0.0
diff --git a/autostreamtree/data/test.streamTree.cpg b/autostreamtree/data/test.streamTree.cpg
new file mode 100644
index 0000000..cd89cb9
--- /dev/null
+++ b/autostreamtree/data/test.streamTree.cpg
@@ -0,0 +1 @@
+ISO-8859-1
\ No newline at end of file
diff --git a/autostreamtree/data/test.streamTree.dbf b/autostreamtree/data/test.streamTree.dbf
new file mode 100644
index 0000000..6d7224d
Binary files /dev/null and b/autostreamtree/data/test.streamTree.dbf differ
diff --git a/autostreamtree/data/test.streamTree.gpkg b/autostreamtree/data/test.streamTree.gpkg
new file mode 100644
index 0000000..b7f459f
Binary files /dev/null and b/autostreamtree/data/test.streamTree.gpkg differ
diff --git a/autostreamtree/data/test.streamTree.prj b/autostreamtree/data/test.streamTree.prj
new file mode 100644
index 0000000..f45cbad
--- /dev/null
+++ b/autostreamtree/data/test.streamTree.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/autostreamtree/data/test.streamTree.shp b/autostreamtree/data/test.streamTree.shp
new file mode 100644
index 0000000..48351dd
Binary files /dev/null and b/autostreamtree/data/test.streamTree.shp differ
diff --git a/autostreamtree/data/test.streamTree.shx b/autostreamtree/data/test.streamTree.shx
new file mode 100644
index 0000000..f4f4a3e
Binary files /dev/null and b/autostreamtree/data/test.streamTree.shx differ
diff --git a/autostreamtree/data/test.streamTree.txt b/autostreamtree/data/test.streamTree.txt
new file mode 100644
index 0000000..909a264
--- /dev/null
+++ b/autostreamtree/data/test.streamTree.txt
@@ -0,0 +1,632 @@
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+40926634	40929032	41067217	2.32	377.4	358.1	3.09	23673.3	0	595.728	6	2	4	4120900670	595.728	52.037	1674.025	1031	100	9	100	24	100	27	0	1	136	0	0	39.37	10676.758	1720.279	166809.859	2	21	2481	20	25	3	193	0	13	106	248	115	178	285	178	201	241	271	274	277	282	285	280	265	225	190	2459	1827	10	9	38	107	347	566	499	385	314	167	15	1	1403	1040	88	101	150	158	152	131	125	114	106	106	91	82	1047	757	50	46	55	62	114	131	125	114	106	106	80	58	175	156	43	14	-89	-91	-75	-32	56	77	75	70	66	37	-83	-99	0	9	0	0	0	0	0	0	0	0	0	0	0	0	0	16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100	0	0	0	0	0	0	21	1	0	23	0	0	0	0	1	0	12	1	10	0	0	21	5	0	0	0	5	0	1	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	18	46	1	2	0	9	0	0	0	9	0	3	10	0	2	3	100	15	50	15	0	0	50	50	0	0	0	0	0	0	0	0	15	0	0	0	0	0	0	45	93	17	0	9	7	6	0	2	0	5	0	32	1	238	10	709	-999	18	-999	33	-999	48	-999	67	76	69	58	46	37	38	68	97	100	100	100	100	90	72	1	0	7	302	10294	1.681	3850.222	1299.992	165.185	0	2	0	200	17	152	123	81	133	95	19	2385	0	19428917248	579	LINESTRING (89.76041666666615 25.793749999999598, 89.76041666666615 25.772916666666255)	7	0.0
+40926635	40929032	41067217	5.83	377.5	2976.4	15.46	469146.5	0	20366.459	8	1	2	4120900680	20366.459	3249.436	47179.066	1033	85	6	100	15	100	19	0	4	38748	320	0	634.857	318927.875	86745.914	14485489.0	11	24	3307	20	28	3	176	2	13	106	247	77	179	285	179	200	242	271	274	276	281	285	280	264	226	190	2463	1086	10	9	39	109	349	568	499	385	312	168	15	1	1395	959	87	101	149	157	151	130	123	114	105	105	90	82	1044	614	50	46	55	63	115	130	123	114	105	105	80	58	177	97	43	-17	-89	-91	-74	-31	57	77	75	70	66	37	-83	-99	0	11	0	0	0	0	0	0	0	0	0	0	0	0	0	16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100	0	0	0	0	0	0	10	1	0	8	0	0	0	0	2	0	7	3	39	4	0	12	4	0	2	0	6	0	1	58	0	0	0	0	0	0	0	0	0	0	0	0	42	0	15	18	0	1	0	0	0	2	0	11	0	3	48	0	2	3	100	11	26	10	0	0	74	26	0	0	0	0	0	0	0	1	9	0	0	0	0	0	0	19	66	9	0	32	7	1	0	4	0	16	0	11	1	238	10	709	23	18	31	30	45	51	18	64	76	60	58	46	38	39	69	97	100	100	100	100	90	72	1	0	19	1457	8651	15.506	33644.059	1268.859	73.2	0	1	0	100	17	116	118	59	128	69	19	2385	8511559	138110337024	579	LINESTRING (89.8062499999995 25.797916666666246, 89.80208333333283 25.793749999999598, 89.7937499999995 25.793749999999598, 89.78541666666618 25.785416666666272, 89.77291666666613 25.785416666666272, 89.76041666666615 25.772916666666255)	7	0.0
diff --git a/autostreamtree/data/test.streamsByEdgeID.pdf b/autostreamtree/data/test.streamsByEdgeID.pdf
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diff --git a/autostreamtree/data/test.streamsByFittedD.pdf b/autostreamtree/data/test.streamsByFittedD.pdf
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index 0000000..e06d9c6
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diff --git a/autostreamtree/data/test.subGraph.pdf b/autostreamtree/data/test.subGraph.pdf
new file mode 100644
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diff --git a/autostreamtree/data/test_pop.coords.tsv b/autostreamtree/data/test_pop.coords.tsv
new file mode 100644
index 0000000..e518739
--- /dev/null
+++ b/autostreamtree/data/test_pop.coords.tsv
@@ -0,0 +1,38 @@
+sample	lat	long
+burk	26.927083333332924	90.39374999999947
+cdikc	27.264583333332887	90.03958333333279
+dakp	27.14791666666624	90.68958333333276
+digl	26.893749999999564	91.75208333333276
+dikc	26.881249999999547	90.26874999999947
+dima	26.887499999999584	91.59791666666612
+dolk	26.877083333332898	90.34374999999952
+dort	26.868749999999572	89.37291666666616
+dusa	26.810416666666235	91.49791666666616
+dusb	26.810416666666235	91.49791666666616
+hara	27.218749999999567	90.1520833333328
+kali	26.706249999999606	89.83958333333291
+kame	27.27291666666624	90.03124999999946
+karo	27.27291666666624	90.03124999999946
+kiri	26.968749999999595	90.86041666666617
+labr	26.756249999999547	89.88541666666615
+mapt	26.968749999999595	90.86041666666617
+mart	26.872916666666235	91.56458333333288
+mori	27.260416666666224	91.1937499999996
+nyrb	26.931249999999586	91.67708333333283
+nyrc	26.997916666666235	91.54791666666617
+pach	26.929166666666255	89.38333333333279
+pipp	26.7645833333329	89.73541666666611
+raid	26.931249999999586	91.67708333333283
+rich	26.931249999999586	91.67708333333283
+rind	27.002083333332898	90.83541666666613
+ring	27.002083333332898	90.83541666666613
+rong	26.947916666666252	90.5104166666662
+sher	27.252083333332912	91.41041666666618
+sing	26.839583333332932	89.44791666666626
+thew	26.92291666666626	90.5104166666662
+thri	27.27291666666624	91.14791666666619
+tsri	27.402083333332904	91.55208333333283
+unar	27.214583333332904	91.20624999999941
+yong	27.214583333332904	91.20624999999941
+yued	26.935416666666235	90.8687499999995
+zarp	26.872916666666235	91.07291666666609
diff --git a/autostreamtree/data/test_sub100.vcf.gz b/autostreamtree/data/test_sub100.vcf.gz
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diff --git a/autostreamtree/data/test_sub100.vcf.gz.tbi b/autostreamtree/data/test_sub100.vcf.gz.tbi
new file mode 100644
index 0000000..17e8f8c
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diff --git a/autostreamtree/functions.py b/autostreamtree/functions.py
new file mode 100644
index 0000000..d4b0317
--- /dev/null
+++ b/autostreamtree/functions.py
@@ -0,0 +1,1260 @@
+import sys
+import os
+import itertools
+import traceback
+import math
+import momepy
+import warnings
+import pyogrio
+import pandas as pd
+import numpy as np
+import networkx as nx
+import seaborn as sns
+from pysam import VariantFile
+from scipy import stats
+from sortedcontainers import SortedDict
+from networkx import NodeNotFound
+import matplotlib.pyplot as plt
+import pickle
+import mantel
+import geopandas as gpd
+from math import radians, sin, cos, acos
+
+import autostreamtree.cluster_pops as clust
+import autostreamtree.sequence as seq
+import autostreamtree.genetic_distances as gendist
+
+
+# suppress irrelevant warning from momepy
+def custom_warn_handler(message, category, filename, lineno, file=None,
+                        line=None):
+    if "momepy/utils.py" in filename and issubclass(category, UserWarning):
+        return
+    return original_showwarning(message, category, filename, lineno, file,
+                                line)
+
+
+original_showwarning = warnings.showwarning
+warnings.showwarning = custom_warn_handler
+
+
+def read_vcf(vcf, concat="none", popmap=None):
+    """
+    Reads a VCF file and returns a dictionary of sample genotypes.
+
+    ARgs:
+        vcf: Path to the input VCF file.
+        concat: Specifies the concatenation mode for genotypes. Options are
+                "all", "loc", and "none".
+                   "all": Concatenate genotypes of all loci for each sample.
+                   "loc": Concatenate genotypes within the same chromosome for
+                          each sample.
+                   "none": Do not concatenate genotypes.
+        popmap: Optional dictionary that maps populations to a list of samples.
+                If provided, only samples in the popmap will be retained in the
+                output dictionary.
+    Returns:
+        A dictionary with sample names as keys and lists of genotypes as values
+    """
+
+    bcf_in = VariantFile(vcf)
+
+    # get all samples in the VCF
+    vcf_samples = list(bcf_in.header.samples)
+
+    # set up data dict
+    dat = dict()
+    samples = list((bcf_in.header.samples))
+    for s in samples:
+        if concat == "all":
+            dat[s] = list()
+            dat[s].append(["", ""])
+        else:
+            dat[s] = list()
+
+    # if popmap, make list of samples to drop that aren't in a pop
+    if popmap:
+        keep = list()
+        for pop in popmap:
+            keep.extend(popmap[pop])
+        keep = [s for s in keep if s in vcf_samples]
+        bcf_in.subset_samples(keep)
+
+    chrom = "FIRST"
+    for record in bcf_in.fetch():
+        for i, sample in enumerate(record.samples):
+            if concat == "all":
+                loc = seq.decode(record.samples[i]['GT'], record.ref,
+                                 record.alts, as_list=True)
+                dat[sample][-1][0] = dat[sample][-1][0]+loc[0]
+                dat[sample][-1][1] = dat[sample][-1][1]+loc[1]
+            elif concat == "loc":
+                if record.chrom != chrom:
+                    dat[sample].append(["", ""])
+                loc = seq.decode(record.samples[i]['GT'], record.ref,
+                                 record.alts, as_list=True)
+                dat[sample][-1][0] = dat[sample][-1][0]+loc[0]
+                dat[sample][-1][1] = dat[sample][-1][1]+loc[1]
+            else:
+                loc = seq.decode(record.samples[i]['GT'], record.ref,
+                                 record.alts)
+                dat[sample].append(loc)
+        chrom = record.chrom
+    if concat != "none":
+        for sample in dat:
+            dat[sample] = ["/".join(x) for x in dat[sample]]
+    for sample in list(dat.keys()):
+        if len(dat[sample]) < 1:
+            del dat[sample]
+        elif len(dat[sample]) == 1 and dat[sample][0][0] == "":
+            del dat[sample]
+    return dat
+
+
+def prune_graph(G, edge_list, reachid_col):
+    """
+    Prunes a graph to only retain edges whose 'reachid_col' matches values in
+    'edge_list'.
+
+    Args:
+        G: The input NetworkX Graph.
+        edge_list: A list of values to filter edges.
+        reachid_col: The edge attribute to be checked against 'edge_list'.
+
+    Returns:
+        A pruned NetworkX Graph.
+    """
+    for u, v in G.edges():
+        _ = G.get_edge_data(u, v)
+        # print(f"Edge ({u}, {v}): {data}")
+    # Get edges to be retained using get_edge_data method
+    edges_to_keep = [(u, v) for u, v in G.edges() if
+                     G.get_edge_data(u, v).get(reachid_col) in edge_list]
+
+    # Check if there are no edges to keep
+    if not edges_to_keep:
+        raise ValueError(
+            "There are no edges to retain based on the given edge",
+            "list and attribute."
+        )
+
+    # Create a new graph with only the edges to be retained
+    pruned_G = G.edge_subgraph(edges_to_keep).copy()
+
+    # Remove isolated nodes
+    isolated_nodes = list(nx.isolates(pruned_G))
+    pruned_G.remove_nodes_from(isolated_nodes)
+
+    return pruned_G
+
+
+def read_network(network, shapefile):
+    """
+    Reads a network from a saved file or builds a network from a shapefile.
+
+    Args:
+        network: Path to the saved network file (pickle format). If provided,
+                  the function will read the network from this file.
+        shapefile: Path to the shapefile to build the network from. This is
+                   used if the `network` parameter is not provided.
+    Returns:
+        A NetworkX Graph object representing the network.
+    """
+
+    # Check if a saved network file is provided
+    if network:
+        print("Reading network from saved file: ", network)
+        # Read the network from the saved file and convert it to an undirected
+        # graph
+        with open(network, 'rb') as f:
+            G = nx.Graph(pickle.load(f)).to_undirected()
+    else:
+        # If no saved network file is provided, build the network from the
+        # shapefile
+        print("Building network from shapefile:", shapefile)
+        print("WARNING: This can take a while with very large files!")
+        # Read the shapefile
+        # rivers = gpd.read_file(shapefile)
+        rivers = pyogrio.read_dataframe(shapefile)
+        # print(rivers.head())
+        # Convert the GeoDataFrame to a NetworkX Graph object
+        G = momepy.gdf_to_nx(rivers, approach="primal", directed=False,
+                             multigraph=False)
+
+    return G
+
+
+def parse_subgraph_from_points(params, point_coords, pop_coords, G):
+    """
+    Extracts a subgraph from a given graph based on input points.
+
+    Args:
+        params: A custom object containing various input parameters.
+        point_coords: A list of point coordinates to use for extracting the
+                      subgraph.
+        pop_coords: A list of population coordinates to use for extracting the
+                    subgraph.
+        G: A NetworkX Graph object representing the input graph.
+
+    Returns:
+        A NetworkX Graph object representing the extracted subgraph.
+    """
+
+    # Choose the appropriate set of points based on input parameters
+    if params.pop or params.geopop or params.clusterpop:
+        points = pop_coords
+    else:
+        points = point_coords
+
+    # Output points to a table
+    p = get_point_table(points)
+    p.to_csv((str(params.out) + ".pointCoords.txt"), sep="\t", index=False)
+    del p
+
+    # First pass extracts a subgraph from the master shapefile graph
+    print("\nExtracting full subgraph...")
+    ktemp = path_subgraph(G, points, extract_full_subgraph, params.reachid_col,
+                          params.length_col)
+    del G
+
+    # Second pass to simplify subgraph and collapse redundant nodes
+    print("\nMerging redundant paths...\n")
+    K = path_subgraph(ktemp, points, extract_minimal_subgraph,
+                      params.reachid_col, params.length_col)
+
+    # Grab real coordinates as node positions for plotting
+    pos = {n: n for n in K.nodes}
+
+    # Make a color map to color sample points and junctions differently
+    color_map = ["blue" if node in points.values() else "black" for node in K]
+
+    # Draw networkx
+    nx.draw_networkx(K, pos, with_labels=False, node_color=color_map,
+                     node_size=50)
+
+    # Get LENGTH_KM attributes for labelling edges
+    edge_labels = nx.get_edge_attributes(K, params.length_col)
+    for e in edge_labels:
+        edge_labels[e] = "{:.2f}".format(edge_labels[e])
+
+    nx.draw_networkx_edge_labels(K, pos, edge_labels=edge_labels, font_size=6)
+
+    # Save minimized network to file (unless we already read from one)
+    if not params.network or params.overwrite:
+        net_out = str(params.out) + ".network"
+        with open(net_out, 'wb') as f:
+            pickle.dump(K, f, pickle.HIGHEST_PROTOCOL)
+        net_full_out = str(params.out) + ".full.network"
+        with open(net_full_out, 'wb') as f:
+            pickle.dump(ktemp, f, pickle.HIGHEST_PROTOCOL)
+    else:
+        print(
+            "NOTE: Not over-writing existing network. To change this, use",
+            "--overwrite"
+            )
+
+    network_plot = str(params.out) + ".subGraph.pdf"
+    plt.savefig(network_plot)
+
+    del ktemp
+    return K
+
+
+def report_genmats(params, gen, pop_gen, point_coords, pop_coords):
+    """
+    Prints genetic distance matrices and writes them to files.
+
+    ARgs:
+        params: A custom object containing various input parameters.
+        gen: A NumPy array representing the individual genetic distance matrix.
+        pop_gen: A NumPy array representing the population genetic distance
+                 matrix.
+        point_coords: A dictionary containing individual point coordinates.
+        pop_coords: A dictionary containing population point coordinates.
+    """
+
+    # If the individual genetic distance matrix is not None, print and write
+    # it to file
+    if gen is not None:
+        print("Genetic distances:")
+        np.set_printoptions(precision=3)
+        print(gen, "\n")
+
+        # Write individual genetic distances to file
+        ind_genDF = pd.DataFrame(gen, columns=list(point_coords.keys()),
+                                 index=list(point_coords.keys()))
+        ind_genDF.to_csv((str(params.out) + ".indGenDistMat.txt"), sep="\t",
+                         index=True)
+
+    # If the population genetic distance matrix is not None, print and write
+    # it to file
+    if pop_gen is not None:
+        print("Population genetic distances:")
+        np.set_printoptions(precision=3)
+        print(pop_gen, "\n")
+
+        # Write population genetic distances to file
+        pop_genDF = pd.DataFrame(pop_gen, columns=list(pop_coords.keys()),
+                                 index=list(pop_coords.keys()))
+        pop_genDF.to_csv((str(params.out) + ".popGenDistMat.txt"), sep="\t",
+                         index=True)
+        del pop_genDF
+
+
+def get_loc_data(seqs):
+    """
+    Generator function that yields a dictionary of individual loci data for
+    each locus in the input sequences.
+
+    Args:
+        seqs: A dictionary containing sequences as values and individual
+        identifiers as keys.
+
+    Returns:
+        A generator that yields a dictionary with individual identifiers as
+        keys and a list containing the
+             corresponding locus as the value.
+    """
+
+    # Iterate through the loci in the sequences
+    for loc in range(0, len(seqs[list(seqs.keys())[0]])):
+        d = dict()
+
+        # Iterate through the individuals in the sequences
+        for ind in seqs.keys():
+            # Add the locus data for the individual to the dictionary
+            d[ind] = [seqs[ind][loc]]
+
+        # Yield the dictionary containing the locus data for the current locus
+        yield d
+
+
+def report_genmats_list(params, genlist, popgenlist, point_coords, pop_coords):
+    """
+    Writes individual and population genetic distance matrices to files for
+    each locus in genlist and popgenlist.
+
+    Args:
+        params: A namespace object containing parameters, including the output
+                directory.
+        genlist: A list of individual genetic distance matrices for each locus.
+        popgenlist: A list of population genetic distance matrices for each
+                    locus.
+        point_coords: A dictionary containing individual point coordinates.
+        pop_coords: A dictionary containing population point coordinates.
+    """
+
+    # Create an output directory for the genetic distance matrices
+    dir = str(params.out) + "_locmats"
+    os.makedirs(dir, exist_ok=True)
+
+    i = 0
+    # Iterate through the individual genetic distance matrices
+    for gen in genlist:
+        if gen is not None:
+            # Write individual genetic distances to a file
+            ind_genDF = pd.DataFrame(gen, columns=list(point_coords.keys()),
+                                     index=list(point_coords.keys()))
+            ind_genDF.to_csv((str(dir) + "/loc_" + str(i) +
+                              ".indGenDistMat.txt"), sep="\t", index=True)
+            del ind_genDF
+            i += 1
+
+    j = 0
+    # Iterate through the population genetic distance matrices
+    for pop_gen in popgenlist:
+        if pop_gen is not None:
+            # Write population genetic distances to a file
+            pop_genDF = pd.DataFrame(pop_gen, columns=list(pop_coords.keys()),
+                                     index=list(pop_coords.keys()))
+            pop_genDF.to_csv((str(dir) + "/loc_" + str(j) +
+                              ".popGenDistMat.txt"), sep="\t", index=True)
+            del pop_genDF
+            j += 1
+
+
+def block_print():
+    """
+    Disables standard output by redirecting it to a null device, effectively
+    blocking any print statements.
+    """
+    sys.stdout = open(os.devnull, 'w')
+
+
+def enable_print():
+    """
+    Restores standard output to its original state, allowing print statements
+    to be displayed again.
+    """
+    sys.stdout = sys.__stdout__
+
+
+def parse_input_genmat(params, inmat, point_coords, popmap, seqs=None):
+    """
+    Parses an input genetic distance matrix and verifies if it matches the
+    user input parameters. Aggregates individual distances if required by the
+    user input.
+
+    Args:
+        params: Input parameters provided by the user.
+        inmat (pd.DataFrame): The input genetic distance matrix.
+        point_coords (dict): Dictionary containing point coordinates.
+        popmap (dict): Dictionary containing the population map.
+
+    Returns:
+        tuple: A tuple containing the genetic distance matrix (gen) and
+        population genetic distance matrix (pop_gen).
+    """
+    gen = None
+    pop_gen = None
+    if params.coercemat:
+        inmat[inmat < 0.0] = 0.0
+    if set(list(inmat.columns.values)) != set(list(inmat.index.values)):
+        print(inmat.columns.values)
+        print(inmat.index.values)
+        print("Input matrix columns and/ or rows don't appear to be",
+              "labelled. Please provide an input matrix with column and row",
+              "names!")
+        sys.exit(1)
+    else:
+        agg = False
+        # first check if it fits whatever the user input was (i.e. --pop)
+        if params.pop:
+            if len(inmat.columns) != len(popmap.keys()):
+                print("Found", str(len(inmat.columns)),
+                      "columns in provided matrix. This doesn't match number",
+                      "of populations from popmap.")
+                if (len(inmat.columns)) != len(point_coords):
+                    print("Doesn't match number of individuals either! Please",
+                          "check your matrix format.")
+                    sys.exit(1)
+                else:
+                    print("Assuming input matrix has individual distances...",
+                          "Aggregating using the following (--pop_agg):",
+                          str(params.pop_agg))
+                    agg = True
+            else:
+                # re-order using pop orders
+                inmat = inmat.reindex(list(popmap.keys()))
+                inmat = inmat[list(popmap.keys())]
+                pop_gen = inmat.to_numpy()
+                del inmat
+        elif params.geopop or params.clusterpop:
+            if (len(inmat.columns)) != len(point_coords):
+                print("Found", str(len(inmat.columns)),
+                      "columns in provided matrix. This doesn't match number",
+                      "of individuals.")
+                print("When using --geopop or --clusterpop, the provided",
+                      "matrix must represent individual-level distances.")
+                sys.exit(1)
+            else:
+                # re-order using pop orders
+                inmat = inmat.reindex(list(point_coords.keys()))
+                inmat = inmat[list(point_coords.keys())]
+                gen = inmat.to_numpy()
+                agg = True
+                del inmat
+        else:
+            if (len(inmat.columns)) != len(point_coords):
+                print("Found", str(len(inmat.columns)),
+                      "columns in provided matrix. This doesn't match number",
+                      "of individuals.")
+                sys.exit(1)
+            else:
+                # re-order using pop orders
+                inmat = inmat.reindex(list(point_coords.keys()))
+                inmat = inmat[list(point_coords.keys())]
+                gen = inmat.to_numpy()
+                del inmat
+        # if --geopop or --clusterpop, it should be an ind matrix
+        # if so, need to aggregate according to --pop_agg
+        # print(pop_gen)
+        if agg:
+            print("Aggregating user-provided individual-level distance matrix",
+                  "using:", params.pop_agg)
+            pop_gen = gendist.get_pop_genmat("PDIST", gen, popmap,
+                                             point_coords, seqs,
+                                             pop_agg=params.pop_agg,
+                                             loc_agg=params.loc_agg,
+                                             ploidy=params.ploidy,
+                                             global_het=params.global_het)
+
+    return (gen, pop_gen)
+
+
+def read_popmap(popmap):
+    """
+    Reads a population map file and returns a dictionary with individuals as
+    keys and populations as values.
+
+    Args:
+        popmap (str): Path to the population map file.
+
+    Returns:
+        dict: A dictionary with individuals as keys and populations as values.
+    """
+    popdict = dict()
+    with open(popmap, "r") as fin:
+        for line in fin:
+            line = line.strip()
+            if not line:
+                continue
+            cols = line.split()
+            ind = cols[0]
+            pop = cols[1]
+            popdict[ind] = pop
+    return popdict
+
+
+def process_samples(params, points, G):
+    """
+    Processes input sample data by snapping points to a graph, calculating
+    coordinates, and processing populations if required.
+
+    Args:
+        params: Input parameters provided by the user.
+        points (pd.DataFrame): DataFrame containing sample points.
+        G (networkx.Graph): Graph object representing the road network.
+
+    Returns:
+        tuple: A tuple containing point coordinates, population coordinates,
+        and the population map.
+    """
+    popmap = SortedDict()
+    point_coords = SortedDict()
+    pop_coords = SortedDict()
+    snapDists = dict()
+
+    if params.pop:
+        popmap_temp = read_popmap(params.pop)
+        mask = points[points.columns[0]].isin(popmap_temp)
+        points = points[mask]
+
+    for _, row in points.iterrows():
+        name = None
+        data = None
+        row["lat"] = float(row["lat"])
+        row["long"] = float(row["long"])
+        if params.run == "GENDIST":
+            name = row["sample"]
+            data = tuple([row["long"], row["lat"]])
+        else:
+            if not params.pop and not params.clusterpop:
+                # --geopop and individual-level snap coordinates to nodes here
+                node = snap_to_node(G, tuple([row["long"], row["lat"]]))
+                snapDists[row["sample"]] = great_circle(node[0], node[1],
+                                                        row["long"],
+                                                        row["lat"])
+            else:
+                # if pop or clusterpop, extract centroid later
+                node = tuple([row["long"], row["lat"]])
+            data = node
+            name = row["sample"]
+        point_coords[name] = data
+
+        # Process population-level analyses
+        if params.geopop:
+            if point_coords[name] not in popmap:
+                names = [name]
+                popmap[point_coords[name]] = names
+            else:
+                popmap[point_coords[name]].append(row["sample"])
+        elif params.pop:
+            if popmap_temp[row.iloc[0]] not in popmap:
+                names = [name]
+                popmap[popmap_temp[row.iloc[0]]] = names
+            else:
+                popmap[popmap_temp[row.iloc[0]]].append(name)
+
+    print("Read", str(len(point_coords.keys())), "individuals.")
+    print()
+
+    if params.pop or params.geopop:
+        print("Read", str(len(popmap.keys())), "populations.")
+        print()
+
+    # For population-level analyses, generate population maps and centroids
+    # here according to user-input options: --pop, --geopop, --clusterpop
+    # get population centroid
+    if params.pop or params.geopop or params.clusterpop:
+        if params.clusterpop:
+            # create population clusters using DBSCAN
+            print("Running DBSCAN clustering with min_samples=",
+                  params.min_samples, "and epsilon=", params.epsilon)
+            popmap = clust.dbscan_cluster(point_coords, params.epsilon,
+                                          params.min_samples)
+            num_clusters = len(popmap.keys())
+            print("Found", str(num_clusters), "clusters!")
+
+            # calculate centroids for clusters
+            pop_temp = clust.get_cluster_centroid(point_coords, popmap,
+                                                  params.out)
+
+        elif params.pop or params.geopop:
+            # popmap generated earlier when parsing input file!
+            # still need to calculate centroids:
+            print("Calculating population centroids...")
+            pop_temp = clust.get_cluster_centroid(point_coords, popmap,
+                                                  params.out)
+            # note in the case of --geopop the centroid is the joint
+            # snapped-to location
+
+        # now, snap pop_coords to nodes
+        pop_coords = SortedDict()
+        for p in pop_temp:
+            node = snap_to_node(G, pop_temp[p])
+            snapDists[p] = great_circle(node[0], node[1], pop_temp[p][0],
+                                        pop_temp[p][1])
+            pop_coords[p] = node
+        # write popmap to file
+        flat = clust.flatten_popmap(popmap)
+        temp = pd.DataFrame(list(flat.items()), columns=['IND_ID', 'POP_ID'])
+        temp.to_csv((str(params.out) + ".popmap.txt"), sep="\t", index=False)
+        del flat
+        del temp
+
+        # plot grouped samples
+        clust.plot_clustered_points(point_coords, popmap, params.out,
+                                    pop_coords)
+
+    # Plot histogram of snap distances and write snap distances to a file
+    clust.plot_histogram(list(snapDists.values()), params.out)
+    dtemp = pd.DataFrame(list(snapDists.items()), columns=['name', 'km'])
+    dtout = str(params.out) + ".snapDistances.txt"
+    dtemp.to_csv(dtout, sep="\t", index=False)
+    del dtemp
+    del dtout
+    del snapDists
+
+    # return everything
+    return point_coords, pop_coords, popmap
+
+
+def get_gendist_mats(params, point_coords, popmap, seqs):
+    """Returns population genetic distance matrices.
+
+    Args:
+        params (object): An object that contains parameters for the analysis.
+        point_coords (list): A list of coordinates for the sampled points.
+        popmap (dict): A dictionary that maps each sample to its corresponding
+                       population.
+        seqs (list): A list of DNA sequences for the sampled points.
+
+    Returns:
+        tuple: A tuple containing two matrices. The first is a pairwise
+               distance matrix for all
+        samples. The second is a matrix of pairwise genetic distances between
+                 populations.
+
+    Raises:
+        SystemExit: If distance metric is not possible without population data.
+
+    """
+    gen = None  # Initialize variable to hold pairwise dist matrix
+    pop_gen = None  # Initialize variable to hold population dist matrix
+
+    if params.dist in ["PDIST", "TN84", "TN93", "K2P", "JC69"]:
+        # Calculate pairwise distance matrix using selected method
+        gen = gendist.get_genmat(params.dist, point_coords, seqs,
+                                 ploidy=params.ploidy, het=params.het,
+                                 loc_agg=params.loc_agg)
+
+        if params.pop or params.geopop or params.clusterpop:
+            print("Aggregating pairwise population genetic distances from",
+                  "individual distances using:", params.pop_agg)
+
+    else:
+        # If distance metric requires population data, but none is provided
+        if not params.pop and not params.geopop:
+            print("ERROR: Distance metric", params.dist,
+                  "not possible without population data.")
+            sys.exit(1)
+
+    # Calculate population genetic distance matrix
+    if params.pop or params.geopop or params.clusterpop:
+        pop_gen = gendist.get_pop_genmat(params.dist, gen, popmap,
+                                         point_coords, seqs,
+                                         pop_agg=params.pop_agg,
+                                         loc_agg=params.loc_agg,
+                                         ploidy=params.ploidy,
+                                         global_het=params.global_het)
+
+    # Return pairwise distance matrix and population genetic distance matrix
+    return gen, pop_gen
+
+
+def get_point_table(points):
+    """Returns a pandas DataFrame from a dictionary of points.
+
+    Args:
+        points (dict): A dictionary of points with their latitude and longitude
+                       values.
+
+    Returns:
+        pandas.DataFrame: A dataframe with columns for 'sample', 'lat', and
+                          'long'.
+
+    """
+    temp = []  # Initialize list to hold temporary values
+    for p in points:
+        # Append values for each point to temporary list
+        temp.append([p, points[p][1], points[p][0]])
+    p = pd.DataFrame(temp, columns=['sample', 'lat', 'long'])
+    return p
+
+
+def r2(x, y):
+    """Returns the Pearson correlation coefficient squared between two arrays.
+
+    Args:
+        x (array): An array of values.
+        y (array): An array of values.
+
+    Returns:
+        float: The squared Pearson correlation coefficient between the two
+               arrays.
+
+    """
+    return (stats.pearsonr(x, y)[0] ** 2)
+
+
+def get_fitted_d(points, genmat, inc, r):
+    """Calculates predicted genetic distances based on fitted streamtree
+    distances.
+
+    Args:
+        points (dict): A dictionary of points with their latitude and
+                       longitude values.
+        genmat (ndarray): A pairwise genetic distance matrix.
+        inc (ndarray): An incidence matrix representing the presence or absence
+                       of streams for each point.
+        r (ndarray): A fitted streamtree distance matrix.
+
+    Returns:
+        pandas.DataFrame: A dataframe with columns for 'from', 'to',
+                          'observed_D', 'predicted_D', and 'abs_diff'.
+
+    """
+    rows = []  # Initialize list to hold temporary values
+    names = list(points.keys())  # Get names of points
+
+    # Iterate over all pairwise combinations of points
+    inc_row = 0  # tracks what ROW (pair) we are in the incidence matrix
+    for ia, ib in itertools.combinations(range(0, len(points)), 2):
+        obs = genmat[ia, ib]
+        inc_streams = inc[inc_row,]
+        pred_dist = np.sum(r[inc_streams == 1])
+        inc_row += 1
+        rows.append([names[ia], names[ib], obs, pred_dist,
+                     np.abs(obs-pred_dist)])
+    # Create dataframe from temporary list and return it
+    D = pd.DataFrame(rows, columns=['from', 'to', 'observed_D',
+                                    'predicted_D', 'abs_diff'])
+    return D
+
+
+def plot_gen_by_geo(gen, sdist, out, log=False):
+    """
+    Plots genetic distance against geographic distance to visualize isolation
+    by distance.
+
+    Args:
+        gen (numpy.ndarray): Genetic distance matrix.
+        sdist (numpy.ndarray): Spatial distance matrix.
+        out (str): Output file prefix for the generated plot.
+        log (bool, optional): If True, the geographic distance axis will be
+                              log-transformed. Defaults to False.
+    """
+
+    genetic_distance = get_lower_tri(gen)
+    geographic_distance = get_lower_tri(sdist)
+
+    data = pd.DataFrame({'Geographic Distance': geographic_distance,
+                         'Genetic Distance': genetic_distance})
+
+    if not log:
+        sns.jointplot(data=data, x='Geographic Distance', y='Genetic Distance',
+                      kind="reg")
+        plt.savefig(str(out) + ".isolationByDistance.pdf")
+    else:
+        geographic_distance = replace_zeroes(geographic_distance)
+        log_geo = np.log(geographic_distance)
+        data['Log Geographic Distance'] = log_geo
+
+        sns.jointplot(data=data, x='Log Geographic Distance',
+                      y='Genetic Distance', kind="reg")
+        plt.savefig(str(out) + ".isolationByDistance.pdf")
+
+    del geographic_distance
+    del genetic_distance
+
+
+def great_circle(lon1, lat1, lon2, lat2, thresh=0.0000001):
+    """
+    Calculates the great circle distance between two points on a sphere, using
+    their longitudes and latitudes.
+
+    Args:
+        lon1 (float): Longitude of the first point.
+        lat1 (float): Latitude of the first point.
+        lon2 (float): Longitude of the second point.
+        lat2 (float): Latitude of the second point.
+        thresh (float, optional): Threshold for determining if the points are
+                                  the same. Defaults to 0.0000001.
+
+    Returns:
+        float: Great circle distance in kilometers.
+    """
+    if (abs(lon1 - lon2)) < thresh and (abs(lat1 - lat2)) < thresh:
+        return 0.0
+    else:
+        lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
+        return 6371 * (
+            acos(sin(lat1) * sin(lat2) + cos(lat1) * cos(lat2) *
+                 cos(lon1 - lon2))
+        )
+
+
+def get_lower_tri(mat):
+    """
+    Extracts the lower triangular elements from a square matrix.
+
+    Args:
+        mat (numpy.ndarray): Input square matrix.
+
+    Returns:
+        numpy.ndarray: 1D array containing the lower triangular elements of
+                       the input matrix.
+    """
+    n = mat.shape[0]
+    i = np.tril_indices(n, -1)
+    return mat[i]
+
+
+def replace_zeroes(data):
+    """
+    Replaces zeroes in the input array with the smallest non-zero value.
+
+    Args:
+        data (numpy.ndarray): Input array.
+
+    Returns:
+        numpy.ndarray: Array with zeroes replaced by the smallest non-zero
+                       value.
+    """
+    min_nonzero = np.min(data[np.nonzero(data)])
+    data[data == 0] = min_nonzero
+    return data
+
+
+# function computes Mantel test using various transformations
+def test_ibd(gen, geo, out, perms, log=False):
+    # get flattened lower triangle of each matrix
+    gen = get_lower_tri(gen)
+    geo = get_lower_tri(geo)
+
+    if log is True:
+        geo = replace_zeroes(geo)
+        geo = np.log(geo)
+
+    # non-log pearson
+    res = mantel.test(geo, gen, perms=int(perms), method='pearson')
+    rows = list()
+    rows.append(['genXgeo', 'pearson', str(perms), res.r, res.p, res.z])
+
+    # non-log spearman
+    res = mantel.test(geo, gen, perms=int(perms), method='spearman')
+    rows.append(['genXgeo', 'spearman', str(perms), res.r, res.p, res.z])
+
+    ibd = pd.DataFrame(rows,  columns=['test', 'method', 'perms', 'r', 'p',
+                                       'z'])
+    print("Mantel test results:")
+    print(ibd)
+    ibd.to_csv((str(out) + ".isolationByDistance.txt"), sep="\t", index=False)
+    print()
+
+
+def output_fitted_d(pred, out):
+    pred.to_csv((str(out)+".obsVersusFittedD.txt"), sep="\t", index=False)
+    sns.jointplot(x="observed_D", y="predicted_D", data=pred, kind="reg")
+    plt.savefig((str(out)+".obsVersusFittedD.pdf"))
+    del pred
+
+
+def fit_least_squares_distances(D, X, iterative, out, weight="CSE67"):
+    """
+    Computes least-squares branch lengths from a vector of genetic distances D
+    and incidence matrix X. When iterative=True, negative distances are
+    constrained to 0 and then recomputed.
+
+    Args:
+        D (numpy.ndarray): Vector of genetic distances.
+        X (numpy.ndarray): Incidence matrix.
+        iterative (bool): Whether to use an iterative approach to constrain
+                          negative distances.
+        out (str): Output file prefix.
+        weight: Weight type. Defaults to CSE67.
+
+    Returns:
+        numpy.ndarray: Least-squared optimized distances.
+    """
+    num_segments = (np.size(X, 1))
+    ls = np.zeros(num_segments)
+    d = vectorize_mat(D)
+
+    # calculate weights matrix and write to file
+    W = generate_weights_matrix(d, weight)
+    print("Weights matrix:")
+    print(W)
+    # ofh=out+".weightsMatrix.txt"
+    # np.savetxt(ofh, W, delimiter="\t")
+
+    # weighted least-squares optimization
+    ls = np.matmul(np.linalg.inv(
+        np.matmul(np.matmul(X.transpose(), W), X)),
+        np.matmul(np.matmul(X.transpose(), W), d))
+
+    if iterative:
+        ls_old = ls
+        if (np.count_nonzero(ls < 0.0) > 0):
+            print("\nLS-optimized distances contain negative values: Using",
+                  "iterative approach to re-calculate...")
+        constrains = list()  # save indices of all constrained values
+
+        # if negative distances, use iterative procedure to re-calculate
+        while (np.count_nonzero(ls < 0.0) > 0):
+            bad_ind = np.argmin(ls)
+            constrains.append(bad_ind)
+            # constrain to 0 by removing from incidence matrix
+            X = np.delete(X, bad_ind, 1)
+            # re-compute values
+            ls = np.matmul(np.linalg.inv(
+                np.matmul(np.matmul(X.transpose(), W), X)),
+                np.matmul(np.matmul(X.transpose(), W), d))
+        for i in reversed(constrains):
+            ls = np.insert(ls, i, 0.0)
+        # print(ls)
+
+        # write original and constrained results to log file
+        ofh = out+".leastSquaresConstrained.txt"
+        df = pd.DataFrame({'LS.original': ls_old, 'LS.constrained': ls})
+        df.to_csv(ofh, sep="\t", index=False)
+
+        return ls
+    else:
+        return ls
+
+
+def generate_weights_matrix(d, weight):
+    """
+    Generates a weights matrix for the least-squares method, where weights are
+    on the diagonals.
+
+    Args:
+        d (numpy.ndarray): Vector of genetic distances.
+        weight (str): Weighting method to use, options: 'CSE67', 'BEYER74',
+                      'FM67'.
+
+    Returns:
+        numpy.ndarray: Weights matrix.
+    """
+    W = np.zeros((len(d), len(d)), dtype=float)
+    row, col = np.diag_indices(W.shape[0])
+
+    if weight.upper() == "CSE67":
+        W[row, col] = np.ones(len(d))
+    elif weight.upper() == "BEYER74":
+        if np.count_nonzero(d == 0) > 0:
+            print(
+                "WARNING: Divide-by-zero in weighted least-squares."
+            )
+        W[row, col] = np.divide(1.0, d, out=np.zeros_like(d, dtype=float),
+                                where=d != 0)
+
+    elif weight.upper() == "FM67":
+        if np.count_nonzero(d == 0) > 0:
+            print(
+                "WARNING: Divide-by-zero in weighted least-squares."
+            )
+            W[row, col] = np.divide(1.0, np.square(d), out=np.zeros_like(d,
+                                    dtype=float), where=d != 0)
+
+    else:
+        print(f"ERROR: Weight option {weight} not recognized. Using ordinary",
+              "least-squares instead.")
+        W[row, col] = np.ones(len(d))
+
+    return W
+
+
+def vectorize_mat(mat):
+    """
+    Converts a pairwise matrix to a 1D vector.
+
+    Args:
+        mat (numpy.ndarray): Pairwise matrix.
+
+    Returns:
+        numpy.ndarray: 1D vector of matrix elements.
+    """
+    size = nCr(np.size(mat, 0), 2)
+    vec = np.zeros(size)
+    index = 0
+
+    for ia, ib in itertools.combinations(range(0, np.size(mat, 0)), 2):
+        vec[index] = mat[ia, ib]
+        index += 1
+
+    return vec
+
+
+def get_stream_mats(points, graph, len_col):
+    """
+    Computes pairwise stream distances and 0/1 incidence matrix for StreamTree
+    calculations.
+
+    Args:
+        points (dict): Dictionary of point indices and their corresponding node
+                       IDs in the graph.
+        graph (networkx.Graph): NetworkX graph object representing the stream
+                                network.
+        len_col (str): Attribute name for the length of the edges in the graph.
+
+    Returns:
+        tuple: Pair of numpy.ndarray representing the pairwise stream distance
+               matrix and incidence matrix.
+    """
+    dist = np.zeros((len(points), len(points)))
+    inc = np.zeros((nCr(len(points), 2), len(graph.edges())), dtype=int)
+
+    # Establish as NaN
+    dist[:] = np.nan
+
+    def dijkstra_weight(left, right, attributes):
+        # Calculates weights for Dijkstra's shortest path algorithm by
+        # inverting the edge length with a small constant to avoid division by
+        # zero.
+        epsilon = 1e-9
+        return 1 / (attributes[len_col] + epsilon)
+
+    index = 0
+    for ia, ib in itertools.combinations(range(0, len(points)), 2):
+        path = nx.bidirectional_dijkstra(graph, points.values()[ia],
+                                         points.values()[ib],
+                                         weight=dijkstra_weight)
+        if path:
+            dist[ia, ib] = float(sum(path_edge_attributes(graph, path[1],
+                                                          len_col)))
+            dist[ib, ia] = dist[ia, ib]
+
+        # Incidence matrix: assign 1 if edge is in the path, 0 otherwise
+        for ie, edge in enumerate(graph.edges()):
+            if find_pair(path[1], edge[0], edge[1]):
+                inc[index, ie] = 1
+            else:
+                inc[index, ie] = 0
+        index += 1
+
+    np.fill_diagonal(dist, 0.0)
+    return dist, inc
+
+
+def find_pair(lst, x, y):
+    """
+    Check if two elements are consecutive in a list, irrespective of their
+    order.
+
+    Args:
+        lst (list): The list to search for the pair.
+        x (Any): The first element of the pair.
+        y (Any): The second element of the pair.
+
+    Returns:
+        bool: True if the elements are consecutive in the list, False otherwise
+    """
+    if x not in lst or y not in lst:
+        return False
+    elif abs(lst.index(x) - lst.index(y)) == 1:
+        return True
+    else:
+        return False
+
+
+def nCr(n, k):
+    """
+    Calculate the number of combinations, n choose k.
+
+    Args:
+        n (int): The number of elements.
+        k (int): The number of elements to choose.
+
+    Returns:
+        int: The number of possible combinations.
+    """
+    f = math.factorial
+    return f(n) // f(k) // f(n - k)
+
+
+def path_edge_attributes(graph, path, attribute):
+    """
+    Get the attribute values for edges in a given path.
+
+    Args:
+        graph (NetworkX Graph): The graph containing the edges.
+        path (list): The list of nodes forming the path.
+        attribute (str): The edge attribute to get the values for.
+
+    Returns:
+        list: A list of attribute values for the edges in the path.
+    """
+    return [graph[u][v][attribute] for (u, v) in zip(path, path[1:])]
+
+
+def path_subgraph(graph, nodes, method, id_col, len_col):
+    """
+    Find and extract paths between points from a graph.
+
+    Args:
+        graph (NetworkX Graph): The input graph.
+        nodes (dict): A dictionary of nodes to extract paths between.
+        method (callable): The method to build the subgraph.
+        id_col (str): The column name for node ID.
+        len_col (str): The column name for edge length.
+
+    Returns:
+        NetworkX Graph: A subgraph containing the extracted paths.
+    """
+    k = nx.Graph()
+
+    def dijkstra_weight(left, right, attributes):
+        # Calculates weights for Dijkstra's shortest path algorithm by
+        # inverting the edge length with a small constant to avoid division by
+        # zero.
+        epsilon = 1e-9
+        return 1 / (attributes[len_col] + epsilon)
+
+    p1 = list(nodes.values())[0]
+    for p2 in list(nodes.values())[1:]:
+        try:
+            # Find the shortest path between the two points
+            path = nx.bidirectional_dijkstra(graph, p1, p2,
+                                             weight=dijkstra_weight)
+
+            # Traverse the nodes in the path to build a minimal set of edges
+            method(k, graph, nodes.values(), id_col, len_col, path[1])
+
+            if p1 not in k:
+                k.add_node(p1)
+            if p2 not in k:
+                k.add_node(p2)
+
+        except NodeNotFound as e:
+            print("Node not found:", e)
+        except Exception as e:
+            traceback.print_exc()
+            print("Something unexpected happened:", e)
+            sys.exit(1)
+    return k
+
+
+def extract_full_subgraph(subgraph, graph, nodelist, id_col, len_col, path):
+    """
+    Extracts the full subgraph from the given nodes.
+
+    Args:
+        subgraph (NetworkX Graph): The subgraph to be modified.
+        graph (NetworkX Graph): The input graph.
+        nodelist (list): The list of nodes.
+        id_col (str): The column name for node ID.
+        len_col (str): The column name for edge length.
+        path (list): The path between nodes.
+    """
+    # Iterate through the nodes in the path
+    for first, second in zip(path, path[1:]):
+        # Add nodes to the subgraph if they are not already present
+        if first not in subgraph:
+            subgraph.add_node(first)
+        if second not in subgraph:
+            subgraph.add_node(second)
+
+        # Add the edge between the nodes with the corresponding edge data
+        dat = graph.get_edge_data(first, second)
+        subgraph.add_edge(first, second, **dat)
+
+
+def extract_minimal_subgraph(subgraph, graph, nodelist, id_col, len_col, path):
+    """
+    Extracts a simplified subgraph from paths, keeping only terminal and
+    junction nodes.
+
+    Args:
+        subgraph (NetworkX Graph): The subgraph to be modified.
+        graph (NetworkX Graph): The input graph.
+        nodelist (list): The list of nodes.
+        id_col (str): The column name for edge ID.
+        len_col (str): The column name for edge length.
+        path (list): The path between nodes.
+    """
+    curr_edge = {id_col: list(), len_col: 0.0}
+    curr_start = None
+
+    # Iterate through each pair of nodes in the path
+    for first, second in zip(path, path[1:]):
+        if not curr_start:
+            curr_start = first
+            if first in nodelist or len(graph[first]) > 2:
+                subgraph.add_node(first)
+
+        # Add path attributes to current edge
+        dat = graph.get_edge_data(first, second)
+        curr_edge[id_col].extend([dat[id_col]] if not
+                                 isinstance(dat[id_col], list) else
+                                 dat[id_col])
+        curr_edge[len_col] = float(curr_edge[len_col]) + float(dat[len_col])
+
+        # If the second node is a STOP node (in nodelist or is a junction)
+        if second in nodelist or len(graph[second]) > 2:
+            # Add node to subgraph
+            subgraph.add_node(second)
+            # Link current attribute data
+            subgraph.add_edge(curr_start, second, **curr_edge)
+            # Empty edge attributes and set current second to curr_start
+            curr_edge = {id_col: list(), len_col: 0}
+            curr_start = second
+        else:
+            # Otherwise, continue building the current edge
+            continue
+
+
+def snap_to_node(graph, pos):
+    """
+    Finds the closest node to the given [x, y] coordinates in the graph.
+
+    Args:
+        graph (NetworkX Graph): The input graph.
+        pos (tuple): A tuple of [x, y] coordinates.
+
+    Returns:
+        tuple: The closest node to the input coordinates.
+    """
+    nodes = np.array(graph.nodes())
+    node_pos = np.argmin(np.sum((nodes - pos) ** 2, axis=1))
+    return tuple(nodes[node_pos])
+
+
+def write_geodataframe(gdf, output_prefix, output_driver):
+    gpd.options.io_engine = "pyogrio"
+    extension = {
+        "SHP": ".shp",
+        "GPKG": ".gpkg",
+        "GDB": ".gdb"
+    }.get(output_driver.upper(), ".gpkg")  # Default to .gpkg
+    if output_driver.upper() == "SHP":
+        output_driver = "ESRI Shapefile"
+
+    output_path = f"{output_prefix}{extension}"
+
+    if output_driver == 'GDB' and not os.path.exists(output_path):
+        os.makedirs(output_path)
+
+    gdf.to_file(output_path, driver=output_driver.upper())
diff --git a/autostreamtree/genetic_distances.py b/autostreamtree/genetic_distances.py
new file mode 100644
index 0000000..5f726f3
--- /dev/null
+++ b/autostreamtree/genetic_distances.py
@@ -0,0 +1,785 @@
+import sys
+import itertools
+import numpy as np
+
+import autostreamtree.aggregators as agg
+import autostreamtree.sequence as seq
+
+
+"""
+Note several distance calculations not currently in use, I plan to expand this
+further in the future, but for now only the following are supported:
+- p distances
+- Weir and Cockerham's Fst (linearised or non-linearised)
+- Jost's D
+- Chord distance
+"""
+
+
+def get_pop_genmat(dist, indmat, popmap, dat, seqs, pop_agg="ARITH",
+                   loc_agg="ARITH", ploidy=2, global_het=False):
+    # make matrix
+    genmat = np.zeros((len(popmap), len(popmap)))
+    # establish as nan
+    genmat[:] = np.nan
+    # for each combination, either average ind distances or calc freq dist
+    for ia, ib in itertools.combinations(range(0, len(popmap)), 2):
+        if dist in ["PDIST"]:
+            inds1 = [dat.index(x) for x in popmap.values()[ia]]
+            inds2 = [dat.index(x) for x in popmap.values()[ib]]
+            genmat[ia, ib] = (agg.aggregate_dist(
+                pop_agg, ([indmat[i, j] for i in inds1 for j in inds2]))
+            )
+            genmat[ib, ia] = genmat[ia, ib]
+        elif dist == "JOST" or dist == "HARMD":
+            # Arrays to store Hs, Ht, and D for each locus
+            hs_vals, ht_vals, d_vals = [], [], []
+            gn = len(popmap)
+
+            for loc in range(0, len(seqs[popmap.values()[ia][-1]])):
+                seqs1 = get_alleles(
+                    [seqs[x][loc] for x in popmap.values()[ia]])
+                seqs2 = get_alleles(
+                    [seqs[x][loc] for x in popmap.values()[ib]])
+                if (not clean_list(set(seqs1), ["n", "N", "-", "?"]) or
+                        not clean_list(set(seqs2), ["n", "N", "-", "?"])):
+                    continue
+                hs, ht, d = two_pop_jost_d(seqs1, seqs2, global_het)
+                hs_vals.append(hs)
+                ht_vals.append(ht)
+                d_vals.append(d)
+
+            if loc_agg not in ("ARITH", "HARM", "ADJHARM"):
+                raise ValueError("Invalid aggregation method for loci.")
+
+            # Aggregate the results for Hs and Ht
+            global_hs = agg.aggregate_dist(loc_agg, hs_vals)
+            global_ht = agg.aggregate_dist(loc_agg, ht_vals)
+            hs_vals.append(global_hs)
+
+            # Calculate global D
+            if dist == "JOST":
+                if global_ht == 0:
+                    global_d = 0.0
+                else:
+                    global_d = (global_ht - global_hs) / (1 - global_hs) * \
+                        (gn / (gn - 1))
+            else:
+                if loc_agg in ("HARM", "ADJHARM"):
+                    global_d = agg.aggregate_dist(loc_agg, d_vals)
+                else:
+                    global_d = agg.aggregate_dist("HARM", d_vals)
+            genmat[ia, ib] = genmat[ib, ia] = global_d
+
+        # elif dist == "GST" or dist == "GSTPRIME":
+        #     HT = list()
+        #     HS = list()
+        #     for loc in range(0, len(seqs[popmap.values()[ia][-1]])):
+        #         seqs1 = get_alleles(
+        #             [seqs[x][loc] for x in popmap.values()[ia]]
+        #         )
+        #         seqs2 = get_alleles(
+        #             [seqs[x][loc] for x in popmap.values()[ib]]
+        #         )
+        #         if (not clean_list(set(seqs1), ["n", "N", "-", "?"]) or
+        #                 not clean_list(set(seqs2), ["n", "N", "-", "?"])):
+        #             continue
+        #         if dist == "GST" or "GSTPRIME":
+        #             (ht, hs) = two_pop_ht_hs(seqs1, seqs2, ploidy,global_het)
+        #             HT.append(ht)
+        #             HS.append(hs)
+        #     Ht_global = np.mean(HT)
+        #     Hs_global = np.mean(HS)
+        #     if dist == "GST":
+        #         if Ht_global <= 0.0:
+        #             genmat[ia, ib] = genmat[ib, ia] = 0.0
+        #         Gst = ((Ht_global - Hs_global) / Ht_global)
+        #         GprimeST = ((Gst * (1.0 + Hs_global)) / (1.0 - Hs_global))
+        #         genmat[ia, ib] = genmat[ib, ia] = GprimeST
+        #     elif dist == "GSTPRIME":
+        #         Ghedrick = ((2.0*(Ht_global - Hs_global)) /
+        #                     (((2.0*Ht_global) - Hs_global) *
+        #                      (1.0 - Hs_global)))
+        #         genmat[ia, ib] = genmat[ib, ia] = Ghedrick
+        elif dist == "FST" or dist == "LINFST":
+            num = list()  # numerator; a
+            denom = list()  # denominator; a*b*c
+            for loc in range(0, len(seqs[popmap.values()[ia][-1]])):
+                seqs1 = clean_inds([seqs[x][loc] for x in popmap.values()[ia]])
+                seqs2 = clean_inds([seqs[x][loc] for x in popmap.values()[ib]])
+                if (not all("/" in x for x in seqs1) or
+                        not all("/" in x for x in seqs2)):
+                    print("ERROR: FST estimates require phased data.")
+                    sys.exit(1)
+                if len(seqs1) == 0 or len(seqs2) == 0:
+                    continue
+                (n, d) = two_pop_weir_cockerham_fst(seqs1, seqs2)
+                num.append(n)
+                denom.append(d)
+
+            # if either population lacking data, set value to nan
+            if len(num) <= 0 or len(denom) <= 0:
+                np.nan
+            # if denominator is 0, set Fst to 0
+            elif np.sum(denom) == 0.0:
+                theta = 0.0
+            # otherwise, calculate as normal
+            else:
+                theta = np.sum(num) / np.sum(denom)
+
+            if dist == "FST":
+                genmat[ia, ib] = genmat[ib, ia] = theta
+            elif dist == "LINFST":
+                if theta != 1.0:
+                    genmat[ia, ib] = genmat[ib, ia] = (theta / (1-theta))
+                else:
+                    genmat[ia, ib] = genmat[ib, ia] = theta
+        # elif dist == "NEI83":
+        #     results = list()
+        #     loci = 0.0
+        #     for loc in range(0, len(seqs[popmap.values()[ia][-1]])):
+        #         seqs1 = get_alleles(
+        #             [seqs[x][loc] for x in popmap.values()[ia]]
+        #         )
+        #         seqs2 = get_alleles(
+        #             [seqs[x][loc] for x in popmap.values()[ib]]
+        #         )
+        #         if (not clean_list(set(seqs1), ["n", "N", "-", "?"]) or
+        #                 not clean_list(set(seqs2), ["n", "N", "-", "?"])):
+        #             continue
+        #         loci += 1.0
+        #         results.append(two_pop_nei_da(seqs1, seqs2))
+        #     Da = (1.0 - (np.sum(results) / loci))
+        #     genmat[ia, ib] = genmat[ib, ia] = Da
+        elif dist == "CHORD":
+            sum_squares = 0.0
+            for loc in range(0, len(seqs[popmap.values()[ia][-1]])):
+                seqs1 = get_alleles(
+                    [seqs[x][loc] for x in popmap.values()[ia]])
+                seqs2 = get_alleles(
+                    [seqs[x][loc] for x in popmap.values()[ib]])
+
+                if (not clean_list(set(seqs1), ["n", "N", "-", "?"]) or
+                        not clean_list(set(seqs2), ["n", "N", "-", "?"])):
+                    continue
+
+                sq_diff, _ = two_pop_chord_dist(seqs1, seqs2)
+                sum_squares += sq_diff
+            Dch = np.sqrt(sum_squares)
+            genmat[ia, ib] = genmat[ib, ia] = Dch
+        else:
+            raise ValueError(f"Unsupported distance option {dist}")
+    np.fill_diagonal(genmat, 0.0)
+    if 0.0 in genmat:
+        print("WARNING: Coercing negative distances to 0.0")
+        genmat[genmat < 0.0] = 0.0
+    return genmat
+
+
+# function computes pairwise p-distance or JC69-corrected distances
+def get_genmat(dist, points, seqs, ploidy, het, loc_agg):
+    # make matrix
+    genmat = np.zeros((len(points), len(points)))
+    # establish as nan
+    genmat[:] = np.nan
+
+    # NOT USED CURRENTLY
+    # for models which relax equal nuc frequencies, get global frequencies
+    # for each locus
+    # freqs will be a list of loci, with each locus as a dist of freqs
+    # if dist in ["TN84", "TN93"]:
+    #     freqs = seq.get_nuc_freqs(seqs, ploidy)
+    #     index = 1
+    #     for f in freqs:
+    #         print("Empirical base frequencies for locus",index, end=": [ ")
+    #         for n in f:
+    #             print(f'{n}={f[n]:.3f} ', end="")
+    #         print("]")
+    #         index = index + 1
+
+    # for each combination, calc jukes-cantor corrected distance
+    for ia, ib in itertools.combinations(range(0, len(points)), 2):
+        results = list()
+        for loc in range(0, len(seqs[points.keys()[ia]])):
+            seq1 = seqs[points.keys()[ia]][loc]
+            seq2 = seqs[points.keys()[ib]][loc]
+            if "/" in seq1:
+                seq1 = seq.dna_consensus(seq1)
+            if "/" in seq2:
+                seq2 = seq.dna_consensus(seq2)
+            if dist == "PDIST":
+                if het:
+                    results.append(p_distance(seq1, seq2))
+                else:
+                    results.append(hamming_distance(seq1, seq2))
+            else:
+                raise ValueError(f"{dist} not implemented")
+        # aggregate results across loci
+        genmat[ia, ib] = agg.aggregate_dist(loc_agg, results)
+        genmat[ib, ia] = genmat[ia, ib]
+    # fill diagonals
+    np.fill_diagonal(genmat, 0.0)
+    return genmat
+
+
+# NOT IN USE
+# def jukes_cantor_distance(seq1: str, seq2: str, het: bool = False) -> float:
+#     """
+#     Description: This function calculates the JC69-corrected p-distance
+#                  between two DNA sequences.
+
+#     Args:
+#     - seq1 (str): The first DNA sequence.
+#     - seq2 (str): The second DNA sequence.
+#     - het (bool): Whether to use the Jukes-Cantor correction for heterozygous
+#                   sites (True) or for all sites (False). Default is False.
+
+#     Returns:
+#     - dist (float): The JC69-corrected p-distance between the two sequences.
+#     """
+#     obs = 0.0
+#     # calculate the observed distance
+#     if het:
+#         obs = p_distance(seq1, seq2)
+#     else:
+#         obs = hamming_distance(seq1, seq2)
+
+#     # apply the JC69 correction
+#     if obs >= 0.74999:
+#         obs = 0.749
+#     dist = -0.75 * np.log(1.0 - ((4.0/3.0) * obs))
+
+#     # ensure distance is not negative and return
+#     if not dist > 0.0:
+#         return 0.0
+#     return dist
+
+
+# NOT IN USE
+# # function to return Kimura 2-parameter distances
+# def k2p_distance(seq1, seq2, het=False):
+#     """
+#     Computes the Kimura 2-parameter distance between two DNA sequences.
+
+#     Args:
+#     - seq1 (str): The first DNA sequence to compare.
+#     - seq2 (str): The second DNA sequence to compare.
+#     - het (bool): Whether to use the p-distance or Jukes-Cantor distance.
+#                   Defaults to False (Jukes-Cantor distance).
+
+#     Returns:
+#     - dist (float): The Kimura 2-parameter distance between the two sequences
+#     """
+#     P = 0.0
+#     Q = 0.0
+#     if het:
+#         (P, Q) = p_distance(seq1, seq2, trans=True)
+#     else:
+#         (P,Q)=hamming_distance(seq1, seq2, trans=True)
+
+#     dist=-0.5*(np.log((1.0-(2.0*P)-Q) * math.sqrt(1.0-(2.0*Q))))
+#     #print(dist)
+#     if dist <= 0.0:
+#         return(0.0)
+#     return(dist)
+
+
+# NOT IN USE
+# def tn84_distance(seq1: str, seq2: str, freqs: Dict[str, float], het:
+#                   bool = False) -> float:
+#     """
+#     Compute the TN84 distance between two DNA sequences.
+
+#     Args:
+#     seq1: str
+#         A DNA sequence.
+#     seq2: str
+#         Another DNA sequence.
+#     freqs: Dict[str, float]
+#         A dictionary containing the frequency of each nucleotide.
+#         For example, {'A': 0.3, 'C': 0.2, 'G': 0.2, 'T': 0.3}.
+#     het: bool, optional (default=False)
+#         Indicates whether heterozygosity is used.
+
+#     Returns:
+#     dist: float
+#         TN84 distance between seq1 and seq2.
+
+#     """
+#     D=0.0
+#     if het:
+#         D=p_distance(seq1, seq2, trans=False)
+#     else:
+#         D=hamming_distance(seq1, seq2, trans=False)
+
+#     ss=0.0
+#     for n in freqs:
+#         ss = ss + np.square(freqs[n])
+#     b=float(1.0-ss)
+#     dist=-b*np.log(1.0-((1.0/b)*D))
+#     #print(dist)
+#     if dist <= 0.0:
+#         return(0.0)
+#     return(dist)
+
+
+# NOT IN USE
+# def tn93_distance(seq1: str, seq2: str, freqs: Dict[str, float],
+#                   het: bool = False) -> float:
+#     """
+#     Compute TN93 distances between two sequences.
+
+#     Args:
+#         seq1: A DNA sequence.
+#         seq2: A DNA sequence.
+#         freqs: A dictionary of nucleotide frequencies, where the keys are
+#                nucleotides (A, T, C, G)
+#                and the values are the frequency of the corresponding nucleoti
+#                in the alignment.
+#         het: Whether the nucleotide differences are considered heterozygous.
+#              Default is False.
+
+#     Returns:
+#         The TN93 distance between the two input sequences.
+
+#     """
+
+#     P1 = 0.0
+#     P2 = 0.0
+#     Q = 0.0
+#     if het:
+#         P1, P2, Q = p_distance(seq1, seq2, trans=False, transSplit=True)
+#     else:
+#         P1, P2, Q = hamming_distance(seq1, seq2, trans=False,transSplit=True)
+
+#     # Calculate nucleotide frequencies
+#     gR = freqs['g'] + freqs['a']
+#     gY = freqs['c'] + freqs['t']
+
+#     # Calculate k values
+#     k1 = (2.0 * (freqs['a'] * freqs['g'])) / gR
+#     k2 = (2.0 * (freqs['t'] * freqs['c'])) / gY
+#     k3 = 2.0 * ((gR * gY) - ((freqs['a'] * freqs['g'] * gY) / gR) -
+#                 ((freqs['t'] * freqs['c'] * gR) / gY))
+
+#     # Calculate weight values
+#     w1 = 1.0 - (P1 / k1) - (Q / (2.0 * gR))
+#     w2 = 1.0 - (P2 / k3) - (Q / (2.0 * gY))
+#     w3 = 1.0 - (Q / (2.0 * gR * gY))
+
+#     # Calculate the distance
+#     dist = -(k1 * np.log(w1)) - (k2 * np.log(w2)) - (k2 * np.log(w3))
+
+#     # Check for negative or NaN distances
+#     if dist <= 0.0 or np.isnan(dist):
+#         return 0.0
+
+#     return dist
+
+
+# p distance = D / L (differences / length)
+# L is the UNGAPPED distance
+# ambigs are expanded
+# when trans = true, returns two values:
+# P (transitions/L) and Q (transversions/L)
+def p_distance(seq1, seq2):
+    """
+    Calculate p-distance, accounting for IUPAC ambiguity codes and partial
+    matches.
+
+    Args:
+        seq1 (str): First sequence.
+        seq2 (str): Second sequence.
+
+    Returns:
+        float: p-distance.
+    """
+    L = min(len(seq1), len(seq2))
+    D = 0.0
+
+    for n1, n2 in zip(seq1.lower(), seq2.lower()):
+        if n1 in ["?", "-", "n"] or n2 in ["?", "-", "n"]:
+            L -= 1
+            continue
+        else:
+            ex1 = set(seq.get_iupac_caseless(n1))
+            ex2 = set(seq.get_iupac_caseless(n2))
+
+            if ex1 == ex2:
+                # Exact match
+                continue
+            elif ex1.isdisjoint(ex2):
+                # No match
+                D += 1.0
+            else:
+                # Partial match
+                D += 0.5
+
+    if L <= 0:
+        return 0.0
+    return D / L
+
+
+# p distance = D / L (differences / length)
+# gaps ignored
+# ambigs are treated as alleles
+def hamming_distance(seq1, seq2):
+    """
+    Calculate the Hamming distance between two sequences.
+
+    Args:
+        seq1 (str): First sequence.
+        seq2 (str): Second sequence.
+
+    Returns:
+        float: Hamming distance.
+    """
+    # Ensure sequences are of equal length
+    L = min(len(seq1), len(seq2))
+    D = 0.0
+
+    for n1, n2 in zip(seq1.lower(), seq2.lower()):
+        if n1 in ["?", "-", "n"] or n2 in ["?", "-", "n"]:
+            L -= 1  # Ignore positions with gaps or ambiguous nucleotides
+        elif n1 != n2:
+            D += 1.0  # Count mismatches
+
+    if L <= 0:
+        return 0.0  # Avoid division by zero
+    return D / L
+
+
+# NOT IN USE
+# def two_pop_nei_da(s1: List[str], s2: List[str]) -> float:
+#     """
+#     Computes Nei's 1983 Da estimator for two populations.
+
+#     Args:
+#         s1 (List[str]): A list of phased genotypes from population 1,
+# e.g. ['A/A', 'A/B', 'B/B', ...].
+#         s2 (List[str]): A list of phased genotypes from population 2,
+# e.g. ['A/A', 'A/C', 'C/C', ...].
+#     Returns:
+#         float: The Nei's 1983 Da estimator.
+#     """
+#     # Clean the input lists by removing individuals with unknown or gap
+# alleles.
+#     s1 = clean_list(s1, ["n", "?", "-", "N"])
+#     s2 = clean_list(s2, ["n", "?", "-", "N"])
+#     # Get the list of unique alleles from both populations.
+#     uniques = uniq_alleles(s1+s2)
+#     # Compute the sum of squared roots of frequencies for each allele.
+#     sumSqRt = 0.0
+#     for allele in uniques:
+#         if allele in ["-", "?", "n", "N"]:
+#             continue
+#         else:
+#             Xu = float(s1.count(allele) / len(s1))
+#             Yu = float(s2.count(allele) / len(s2))
+#             sumSqRt += np.sqrt(Xu*Yu)
+#     return sumSqRt
+
+
+# NOT IN USE
+# def two_pop_euclid_dist(s1: List[str], s2: List[str]) -> float:
+#     """
+#     Computes Euclidean distance between two populations for a single locus.
+
+#     Args:
+#     - s1 (List[str]): List of allele calls for population 1 at a given locus
+#     - s2 (List[str]): List of allele calls for population 2 at a given locus
+
+#     Returns:
+#     - Euclidean distance between the two populations for the given locus
+
+#     """
+#     # Get unique alleles in the two populations
+#     uniques = seq.uniq_alleles(s1+s2)
+#     # Clean the sequences by removing any unknown or gap alleles
+#     s1 = clean_list(s1, ["n", "?", "-", "N"])
+#     s2 = clean_list(s2, ["n", "?", "-", "N"])
+#     # Calculate Euclidean distance
+#     sumSq = 0.0
+#     for allele in uniques:
+#         if allele in ["-", "?", "n", "N"]:
+#             continue
+#         else:
+#             Xu = float(s1.count(allele) / len(s1))
+#             Yu = float(s2.count(allele) / len(s2))
+#             sumSq += np.square(Xu - Yu)
+#     return sumSq
+
+
+# Cavalli-Sforza and Edwards 1967 chord distance
+# non-nucleotide alleles are deleted
+def two_pop_chord_dist(s1, s2):
+    s1 = clean_list(s1, ["n", "?", "-", "N"])
+    s2 = clean_list(s2, ["n", "?", "-", "N"])
+    uniques = uniq_alleles(s1 + s2)
+
+    sum_squares = 0.0
+    for allele in uniques:
+        if allele in ["-", "?", "n", "N"]:
+            continue
+        Xu = float(s1.count(allele) / len(s1))
+        Yu = float(s2.count(allele) / len(s2))
+        sum_squares += np.square(Xu - Yu)
+
+    return sum_squares, len(uniques)
+
+
+def two_pop_weir_cockerham_fst(s1, s2):
+    """
+    Computes Weir and Cockerham's THETAst Fst approximation for two populations
+
+    Args:
+        s1 (list): A list of phased genotypes for population 1.
+        s2 (list): A list of phased genotypes for population 2.
+
+    Returns:
+        tuple: A tuple containing two floats. The first float is the numerator
+        of the THETAst estimator for the locus. The second float is the
+        denominator of the THETAst estimator for the locus.
+
+    Raises:
+        ValueError: If the inputs are not valid.
+
+    """
+    # Check inputs
+    if not isinstance(s1, list) or not isinstance(s2, list):
+        raise ValueError("Inputs must be lists.")
+    if not s1 or not s2:
+        raise ValueError("Inputs must not be empty.")
+    if (not all(isinstance(x, str) for x in s1) or
+            not all(isinstance(x, str) for x in s2)):
+        raise ValueError("Inputs must only contain strings.")
+    if not all("/" in x for x in s1) or not all("/" in x for x in s2):
+        raise ValueError("Inputs must be phased genotypes.")
+
+    # Initialize variables
+    num = 0.0
+    denom = 0.0
+
+    # mean sample size
+    alleles1 = get_alleles(s1)  # split alleles s1
+    alleles2 = get_alleles(s2)  # split alleles s2
+    uniques = uniq_alleles(s1+s2)  # list of unique alleles only
+    r = 2.0  # number of pops
+    n1 = float(len(s1))  # pop size of pop 1
+    n2 = float(len(s2))  # pop size of pop 2
+    csd = np.std([n1, n2])
+    cm = np.mean([n1, n2])
+    nbar = cm
+    csquare = (csd*csd) / (cm*cm)
+    nC = nbar * (1.0 - (csquare/r))  # coeff of pop size variance
+    for allele in uniques:
+        ac1 = float(alleles1.count(allele))
+        ac2 = float(alleles2.count(allele))
+        p1 = ac1 / float(len(alleles1))
+        p2 = ac2 / float(len(alleles2))
+        h1 = get_het_from_phased(allele, s1, count=True)
+        h2 = get_het_from_phased(allele, s2, count=True)
+        pbar = (ac1+ac2) / (float(len(alleles1)) + float(len(alleles2)))
+        ssquare = ((np.sum([(n1 * (np.square(p1 - pbar))),
+                            (n2 * (np.square(p2 - pbar)))])) / ((r-1.0)*nbar))
+        hbar = ((h1 + h2) / (r * nbar))
+        if nbar != 1.0:
+            a = ((nbar/nC) * (ssquare -
+                              ((1.0 / (nbar-1.0)) *
+                               ((pbar * (1.0 - pbar)) -
+                                ((r - 1.0) * ssquare / r) -
+                                (hbar / 4.0)))))
+            b = ((nbar / (nbar-1.0)) *
+                 ((pbar * (1.0 - pbar)) -
+                  ((r - 1.0) * ssquare / r) -
+                  (((2.0 * nbar) - 1.0) * hbar / (4.0 * nbar))))
+            c = hbar/2.0
+            d = a+b+c
+            num += a
+            denom += d
+    return num, denom
+
+
+def clean_inds(inds):
+    """
+    Removes individuals with unknown or gap alleles.
+
+    Args:
+        inds (list): A list of individuals.
+
+    Returns:
+        list: A list of individuals without unknown or gap alleles.
+    """
+    ret = []
+    for ind in inds:
+        if ("-" not in ind and "?" not in ind and "n" not in ind and
+                "N" not in ind):
+            ret.append(ind)
+    return ret
+
+
+def two_pop_jost_d(seqs1, seqs2, global_het=False):
+    if global_het:
+        Ht = get_global_het(seqs1 + seqs2)
+    else:
+        Ht = get_average_het(seqs1, seqs2)
+    Hs = np.mean([get_global_het(seqs1), get_global_het(seqs2)])
+    n = len(seqs1 + seqs2)
+    D = (Ht - Hs) / (1 - Hs) * (n / (n - 1))
+    return Hs, Ht, D
+
+
+# NOT IN USE
+# def two_pop_ht_hs(seqs1, seqs2, ploidy, global_het=False):
+#     """
+#     Computes Nei's Fst estimator (Gst) using Nei and Chessers Hs and Ht
+# estimators
+#     also applies Hedrick's (2005) sample size correction, thus returning
+# G'st.
+
+#     Args:
+#     - seqs1, seqs2 (list): lists of sequences for populations 1 and 2.
+#     - ploidy (int): ploidy of the sequences.
+#     - global_het (bool, optional): If True, computes global heterozygosity.
+# Defaults to False.
+
+#     Returns:
+#     - A tuple containing Ht_est and Hs_est.
+
+#     """
+#     if global_het:
+#         Ht = get_global_het(seqs1 + seqs2)
+#     else:
+#         Ht = get_average_het(seqs1, seqs2)
+
+#     Hs = np.mean([get_global_het(seqs1), get_global_het(seqs2)])
+#     harmN = scipy.stats.hmean([(len(seqs1) / ploidy), (len(seqs1) / ploidy)])
+
+#     # Hs correction based on Hedrick (2005)
+#     Hs_est = Hs * ((2.0 * harmN) / ((2.0 * harmN) - 1.0))
+
+#     # Ht correction based on Hedrick (2005)
+#     Ht_est = Ht + (Hs / (harmN * 2.0 * 2.0))
+
+#     # Gst = ((Ht_est - Hs_est) / Ht_est )
+
+#     # GprimeST = ((Gst * (1.0 + Hs_est)) / (1.0 - Hs_est))
+#     return (Ht_est, Hs_est)
+
+
+def get_het_from_phased(allele, phasedList, count=False):
+    """
+    Returns observed heterozygosity of an allele given a list of phased
+    genotypes (e.g. allele1/allele2 for each individual). Assumes diploid.
+
+    Args:
+    allele (str): The allele for which to compute heterozygosity.
+    phasedList (list): A list of phased genotypes, where each element is a
+                       string of the form 'allele1/allele2'.
+    count (bool): Whether to return the number of heterozygotes instead of the
+                  proportion.
+
+    Returns:
+    float: The proportion of heterozygotes for the given allele, unless `count`
+           is True, in which case the count of heterozygotes is returned.
+    """
+    hets = 0.0
+    twoN = (len(phasedList)) * 2.0
+    for genotype in phasedList:
+        if "/" not in genotype:
+            print(
+                "ERROR (get_het_from_phased): Phased genotypes are required."
+            )
+        gens = genotype.split("/")
+        if gens[0] == allele and gens[1] != allele:
+            hets += 1.0
+            continue
+        elif gens[1] == allele and gens[0] != allele:
+            hets += 1.0
+            continue
+        else:
+            continue
+    if count:
+        return hets
+    else:
+        return hets / twoN
+
+
+def get_global_het(seqs):
+    """
+    Computes global expected heterozygosity (Ht) from a set of sequences.
+
+    Args:
+    seqs (list): A list of sequences.
+
+    Returns:
+    float: The Ht estimate for the given sequences.
+    """
+    hom = 0.0
+    uniq_alleles = clean_list(set(seqs), ["n", "?", "-", "N"])
+    freqs = [np.square(float(seqs.count(x) / len(seqs))) for x in uniq_alleles]
+    hom = np.sum(freqs)
+    return 1.0 - hom
+
+
+def get_average_het(s1, s2):
+    """Computes the mean expected heterozygosity (Ht) from two populations.
+
+    Args:
+        s1 (list): A list of alleles from population 1.
+        s2 (list): A list of alleles from population 2.
+
+    Returns:
+        float: The Ht estimate per locus.
+    """
+    hom = 0.0
+    # Get unique alleles
+    uniq_alleles = clean_list(set(s1+s2), ["n", "?", "-", "N"])
+    # Compute frequency for each allele
+    freqs = [np.square(
+        np.mean([float(s1.count(x)/len(s1)),
+                 float(s2.count(x)/len(s2))])) for x in uniq_alleles]
+    hom = np.sum(freqs)
+    return 1.0 - hom
+
+
+def clean_list(to_clean, bads):
+    """Removes bad items from a list.
+
+    Args:
+        l (list): A list to clean.
+        bads (list): A list of items to remove.
+
+    Returns:
+        list: A cleaned list.
+    """
+    if not any(item not in bads for item in set(to_clean)):
+        return False
+    for b in bads:
+        if b in to_clean:
+            to_clean.remove(b)
+    return to_clean
+
+
+def get_alleles(s):
+    """Splits a string of alleles separated by "/" into a list.
+
+    Args:
+        s (str): A string of alleles separated by "/".
+
+    Returns:
+        list: A list of alleles.
+    """
+    return sum([x.split("/") for x in s], [])
+
+
+def uniq_alleles(s):
+    """Returns the unique alleles in a list of alleles.
+
+    Args:
+        s (list): A list of alleles.
+
+    Returns:
+        set: A set of unique alleles.
+    """
+    return set(sum([x.split("/") for x in s], []))
diff --git a/autostreamtree/params.py b/autostreamtree/params.py
new file mode 100644
index 0000000..8447ff4
--- /dev/null
+++ b/autostreamtree/params.py
@@ -0,0 +1,335 @@
+import sys
+import getopt
+
+
+# Object to parse command-line arguments
+class parseArgs():
+    def __init__(self):
+        # Define options
+        try:
+            options, _ = getopt.getopt(
+                sys.argv[1:], 'hs:i:r:p:d:a:lw:o:gP:L:Scn:G:v:C:O:',
+                ["shp=", "help", "input=", "run=", "pop=", "popmap=", "dist=",
+                 "agg_method=", "het", "genmat=", "snp", "snps", "msat",
+                 "msats", "log", "and_log", "iterative", "weight=", "out=",
+                 "method=", "plots", "plot", "perm=", "phased", "median",
+                 "diploid", "geopop", "geopops", "global_het", "haploid",
+                 "loc_agg=", "pop_agg=", "sdist_agg=", "clusterpop",
+                 "epsilon=", "min_samples=", "sclusterpop", "network=",
+                 "overwrite", "reachid_col=", "length_col=", "coercemat",
+                 "locmatdir=", "vcf=", "concat=", "edge_list=", "gdf_out=",
+                 "seed="]
+            )
+        except getopt.GetoptError as err:
+            print(err)
+            self.display_help(
+                "\nExiting because getopt returned non-zero exit status.")
+        # Default values for params
+        # Input params
+        self.shapefile = None
+        self.locmatdir = None
+        self.geodb = None
+        self.vcf = None
+        self.concat = "none"
+        self.run = "ALL"
+        self.network = None
+        self.pop = False
+        self.overwrite = False
+        self.geopop = False
+        self.clusterpop = False
+        self.sclusterpop = False
+        self.output_driver = "GPKG"
+        self.dist = "LINFST"
+        self.het = False
+        self.genmat = None
+        self.log = False
+        self.and_log = False
+        self.iterative = True
+        self.weight = "CSE67"
+        self.permutations = 1000
+        self.coercemat = False
+        self.method = "PEARSON"
+        self.edge_list = None
+        self.plots = False
+        self.out = "out"
+        self.median = False
+        self.ploidy = 2
+        self.global_het = False
+        self.loc_agg = "ARITH"
+        self.pop_agg = "ARITH"
+        self.sdist_agg = "ARITH"
+        self.seed = None
+
+        self.reachid_col = "HYRIV_ID"
+        self.length_col = "LENGTH_KM"
+
+        # dbscan Options
+        self.min_samples = 1
+        self.epsilon = 20
+
+        # First pass to see if help menu was called
+        for o, a in options:
+            if o in ("-h", "-help", "--help"):
+                self.display_help("Exiting because help menu was called.")
+
+        # Second pass to set all args.
+        for opt, arg in options:
+            arg = arg.strip()
+            opt = opt.replace("-", "")
+
+            if opt in ('s', 'shp'):
+                self.shapefile = arg
+            elif opt in ('h', 'help'):
+                pass
+            elif opt in ('i', 'coords'):
+                self.geodb = arg
+            elif opt in ("C", "concat"):
+                arg_lower = str(arg).lower()
+                if arg_lower in ("none", "all", "loc"):
+                    self.concat = arg_lower
+                else:
+                    self.display_help(f"Invalid option {arg_lower} \
+                                      for option <--concat>")
+            elif opt in ('v', 'vcf'):
+                self.vcf = arg
+            elif opt == "seed":
+                self.seed = int(arg)
+            elif opt in ('r', 'run'):
+                self.run = arg.upper()
+                valid_runs = ["ALL", "GENDIST", "IBD", "STREAMDIST",
+                              "STREAMTREE", "DISTANCES", "RUNLOCI"]
+                if self.run not in valid_runs:
+                    self.display_help(f"Invalid option {self.run} \
+                                      for option <-r/--run>")
+            elif opt in ('p', 'pop', "popmap"):
+                self.pop = arg
+            elif opt in ("g", "geopop", "geopops"):
+                self.geopop = True
+            elif opt in ('d', 'dist'):
+                self.dist = arg.upper()
+                valid_dists = ["PDIST", "FST",
+                               "LINFST", "JOST", "NEI83",
+                               "CHORD", "GST", "HARMD"]
+                if self.dist not in valid_dists:
+                    self.display_help(f"Invalid option {self.dist} for \
+                                      option <-d/--dist>")
+            elif opt == "het":
+                self.het = True
+            elif opt == "locmatdir":
+                self.locmatdir = arg
+            elif opt == "reachid_col":
+                self.reachid_col = arg
+            elif opt == "length_col":
+                self.length_col = arg
+            elif opt in ("genmat", "G"):
+                self.genmat = arg
+            elif opt in ('l', 'log'):
+                self.log = True
+            elif opt == "and_log":
+                self.and_log = True
+            elif opt == "edge_list":
+                self.edge_list = arg
+            elif opt in ("clusterpop", "c"):
+                self.clusterpop = True
+            elif opt == "epsilon":
+                self.epsilon = float(arg)
+            elif opt == "min_samples":
+                self.min_samples = int(arg)
+            elif opt in ('w', 'weight'):
+                self.weight = arg.upper()
+                weight_mappings = {
+                    "FM": "FM67", "1/D": "FM67",
+                    "BEYER": "BEYER74", "1/D^2": "BEYER74",
+                    "1": "CSE67", "CSE": "CSE67"
+                }
+                self.weight = weight_mappings.get(self.weight, self.weight)
+                valid_weights = ["CSE67", "FM67", "BEYER74"]
+                if self.weight not in valid_weights:
+                    self.display_help(f"Invalid option {self.weight} \
+                                      for option <-w/--weight>")
+            elif opt in ('o', 'out'):
+                self.out = arg
+            elif opt == "stream_fit":
+                self.stream_fit = True
+            elif opt == "overwrite":
+                self.overwrite = True
+            elif opt == "perm":
+                self.permutations = int(arg)
+            elif opt == "method":
+                print("Sorry: Option --method is not yet implemented.")
+                sys.exit(0)
+            elif opt in ("plot", "plots"):
+                self.plots = True
+            elif opt == "phased":
+                self.phased = True
+                print("WARNING: Option <--snp> not yet implemented")
+            elif opt == "median":
+                self.median = True
+            elif opt == "diploid":
+                self.ploidy = 2
+            elif opt == "haploid":
+                self.ploidy = 1
+            elif opt in ("O", "gdf_out"):
+                arg_upper = str(arg).upper()
+                if arg_upper not in ["GPKG", "SHP", "GDB"]:
+                    self.display_help(f"Invalid option {arg_upper} for \
+                                      option <--gdf_out>")
+                self.output_driver = arg_upper
+            elif opt == "global_het":
+                self.global_het = True
+            elif opt == "coercemat":
+                self.coercemat = True
+            elif opt in ("network", "n"):
+                self.network = arg
+            elif opt in ("pop_agg", "P"):
+                self.pop_agg = arg.upper()
+                valid_agg_methods = ["HARM", "ADJHARM", "ARITH",
+                                     "GEOM", "MEDIAN", "MAX", "MIN"]
+                if self.pop_agg not in valid_agg_methods:
+                    self.display_help(f"Invalid option {self.pop_agg} for \
+                                      option <--pop_agg>")
+            elif opt in ("loc_agg", "L"):
+                self.loc_agg = arg.upper()
+                valid_agg_methods = ["HARM", "ADJHARM", "ARITH", "GEOM",
+                                     "MEDIAN", "MAX", "MIN"]
+                if self.loc_agg not in valid_agg_methods:
+                    self.display_help(f"Invalid option {self.loc_agg} for \
+                                      option <--loc_agg>")
+            else:
+                assert False, f"Unhandled option {opt}"
+
+        if not self.geodb:
+            self.display_help("No input provided <-i/--coords>")
+        if not self.shapefile and self.run != "GENDIST":
+            self.display_help("No shapefile provided <-s/--shp>")
+        if self.ploidy > 2 or self.ploidy < 1:
+            self.display_help(
+                f"Ploidy of {self.ploidy} not currently \
+                    allowable. Please choose 1 (haploid) or 2 (diploid)")
+
+        # sanity checks
+        if self.dist not in ["PDIST"]:
+            if not self.pop and not self.geopop:
+                self.display_help(
+                    f"ERROR: Distance metric {self.dist} not possible \
+                        without --pop or --geopop data."
+                )
+
+    def display_help(self, message=None):
+        if message is not None:
+            print("\n", message)
+
+        print("\nautostreamtree\n")
+        print("Author: Tyler K Chafin, Biomathematics and Statistics Scotland")
+        print("Description: Methods for analysing genetic distances in \
+networks.")
+        print(
+            "\nMandatory arguments:\n"
+            "    -s, --shp       : Path to shapefile containing cleaned, \
+contiguous stream reaches\n"
+            "                       (can also support geodatabase or GPKG \
+files)\n"
+            "    -i, --input     : Input .tsv file containing sample \
+coordinates\n"
+            "    -v, --vcf       : Input VCF file containing genotypes\n\n"
+
+            "General options:\n"
+            "    -o, --out       : Output prefix [default=\"out\"]\n"
+            "    -O, --gdf_out   : Output driver for annotated geodataframe \
+(options \"SHP\", \"GPKG\", \"GDB\")\n"
+            "    -C, --concat    : Concatenate all SNPs (\"all\"), by locus \
+(\"loc\"), or not at all (\"none\")\n"
+            "    -n, --network   : Provide an already optimized network \
+output from a previous run\n"
+            "    --overwrite     : Overwrite an input network (Only relevant \
+with --network)\n"
+            "    -h, --help      : Displays help menu\n"
+            "    -r, --run       : Run which steps? Options:\n"
+            "        ALL         : Run all steps\n"
+            "        GENDIST     : Only calculate genetic distance matrix\n"
+            "        STREAMDIST  : Only compute pairwise stream distances\n"
+            "        DISTANCES   : Only compute GENDIST + STREAMDIST\n"
+            "        IBD         : GENDIST + STREAMDIST + Mantel test\n"
+            "        STREAMTREE  : GENDIST + STREAMDIST + fit StreamTree \
+model\n"
+            "        RUNLOCI     : Run STREAMTREE fitting on each locus\n"
+            "    -p, --pop       : Pool individuals based on an input \
+population map tsv file\n"
+            "        NOTE: The location will be taken as the centroid among \
+individual samples\n"
+            "    -g, --geopop    : Pool individuals having identical \
+coordinates\n"
+            "    -c, --clusterpop: Use DBSCAN algorithm to automatically \
+cluster populations\n"
+            "    --reachid_col   : Attribute name representing primary key in \
+shapefile [default=\"HYRIV_ID\"]\n"
+            "    --length_col    : Attribute name giving length in kilometers \
+[default=\"LENGTH_KM\"]\n\n"
+            "    --seed          : Seed for RNG\n\n"
+
+            "Genetic distance options:\n"
+            "    -d, --dist      : Use which metric of distance? Options:\n"
+            "        Individual-based:\n"
+            "          PDIST     : Uncorrected p-distances [# Differences / \
+Length]\n"
+            "        Frequency models (when using --pop):\n"
+            "          FST       : Weir and Cockerham's Fst formulation \
+(=THETAst)\n"
+            "          LINFST    : [default] Rousset's (1997) Fst \
+[=Fst/(1-Fst)]\n"
+            "          JOST      : Global estimate Jost's (2008) D\n"
+            "          HARMD     : Harmonic mean of Jost's D per-locus\n"
+            "          CHORD     : Cavalli-Sforza and Edwards (1967) chord \
+distance\n"
+            "          --NOTE: Individual-based metrics can also be computed \
+for\n"
+            "                    populations. You can set how these are \
+aggregated w/ --pop_agg\n"
+            "          --NOTE: Multiple loci for PDIST\n"
+            "                  will be reported using the method defined in \
+--loc_agg\n"
+            "    -G, --genmat    : Skip calculation and use the provided \
+labeled .tsv matrix\n"
+            "    --coercemat     : [Boolean] Coerce negative values in input \
+matrix to zero\n"
+            "    --het           : [Boolean] Count partial differences [e.g. \
+ind1=T, ind2=W]\n"
+            "    --global_het    : Estimate Ht using global frequencies \
+(default is averaged over pops)\n\n"
+
+            "DBSCAN options (only when --clusterpop):\n"
+            "    --min_samples   : Minimum samples per cluster [default=1]\n"
+            "    --epsilon       : Maximum distance (in km) within a cluster \
+[default=20]\n\n"
+
+            "Aggregation options:\n"
+            "    -P, --pop_agg   : Define aggregator function for certain \
+genetic distances in pop samples\n"
+            "    -L, --loc_agg   : Define aggregator function for aggregating \
+locus-wise distances\n"
+            "        All of these can take the following options:\n"
+            "          ARITH     : [default] Use arithmetic mean\n"
+            "          MEDIAN    : Use median distance\n"
+            "          HARM      : Use harmonic mean\n"
+            "          ADJHARM   : Adjusted harmonic mean (see docs)\n"
+            "          GEOM      : Use geometric mean\n"
+            "          MIN       : Use minimum distance\n"
+            "          MAX       : Use maximum distance\n\n"
+
+            "IBD options:\n"
+            "    --perm          : Number of permutations for mantel test \
+[def=1000]\n"
+            "    --and_log       : Also perform IBD steps with log geographic \
+distances\n\n"
+
+            "StreamTree options (see Kalinowski et al. 2008) :\n"
+            "    -w, --weight    : Desired weighting for least-squares \
+fitting:\n"
+            "        Options:\n"
+            "          FM67      : Fitch and Margoliash (1967) [w = 1/D^2]\n"
+            "          BEYER74   : Beyer et al. (1974) weights [w = 1/D]\n"
+            "          CSE67     : [default] Cavalli-Sforza & Edwards (1967) \
+[w = 1]\n\n"
+        )
+        print()
+        sys.exit()
diff --git a/autostreamtree/report_refs.py b/autostreamtree/report_refs.py
new file mode 100644
index 0000000..a721d56
--- /dev/null
+++ b/autostreamtree/report_refs.py
@@ -0,0 +1,100 @@
+def fetch_references(params):
+    """
+    Build an output reference string for all methods a user chose in a run.
+
+    Parameters:
+    -----------
+    params : object
+        An object containing all user input parameters.
+
+    Returns:
+    --------
+    refs : str
+        A string containing all relevant references to be cited.
+    """
+    refs = "\n\nIf you found this software useful for your research please \
+cite the following (a full manuscript will come at a later date):"
+    refs = refs + "\nChafin TK, Mussmann SM, Douglas MR, Douglas ME. 2023. \
+autoStreamTree: Automated workflows for examining patterns of genetic \
+    differentiation in stream networks. github.com/tkchafin/autoStreamTree"
+
+    if params.run == "STREAMTREE" or params.run == "ALL":
+        refs = refs + "\n\nPlease cite the StreamTree method as: \nKalinowski \
+ST, MH Meeuwig, SR Narum, ML Taper (2008) Stream trees: a statistical method \
+for mapping genetic differences between populations of freshwater organisms to\
+the sections of streams that connect them. Canadian Journal of Fisheries and \
+Aquatic Sciences (65:2752-2760)"
+        if params.weight == "FM67":
+            refs = refs + "\n\nFor the LS optimization, you chose the FM67 \
+weighting. Please also cite: \nFitch WM, Margloiash E. 1967. \
+Construction of phylogenetic trees. Science.155: 279-84"
+        elif params.weight == "CSE67":
+            refs = refs + "\n\nFor the LS optimization, you chose the CSE67 \
+weighting. Please also cite: \nCavalli-Sforza LL, Edwards AWF. 1967. \
+Phylogenetic analysis: model and estimation procedures. American Journal of \
+Human Genetics. 19: 233-257"
+        elif params.weight == "BEYER74":
+            refs = refs + "\n\nFor the LS optimization, you chose the BEYER74 \
+weighting. Please also cite: \nBeyer WM, Stein M, Smith T, Ulam S. 1974. A \
+molecular sequence metric and evolutionary trees. Mathematical Biosciences. \
+19: 9-25"
+    if params.run != "STREAMDIST" and not params.genmat:
+        if params.dist != "PDIST":
+            refs = refs + "\n\nPlease cite the following for your genetic \
+distance calculations:"
+            if params.dist == "JC69":
+                refs = refs + "\nJukes TH, Cantor CR. 1969. Evolution of \
+protein molecules. New York: Academic Press."
+            elif params.dist == "FST":
+                refs = refs + "\nWeir BS, Cockerham CC. 1984. Estimating \
+F-statistics for the analysis of population structure. Evolution. 38: \
+1358-1370."
+                refs = refs + "\nNei M. 1987. Molecular Evolutionary \
+Genetics. Columbia University Press, New York"
+            elif params.dist == "LINFST":
+                refs = refs + "\nRousset F. 1997. Genetic differentiation and \
+estimation of gene flow from F-statistics under isolation by distance. \
+Genetics. 145: 1219-28."
+                refs = refs + "\nWeir BS, Cockerham CC. 1984. Estimating \
+F-statistics for the analysis of population structure. Evolution. 38: \
+1358-1370."
+            elif params.dist == "JOST":
+                refs = refs + "\nJost L. 2008. Gst and its relatives do not \
+measure differentiation. Molecular Ecology. 17: 4015-4026."
+            elif params.dist == "NEI83":
+                refs = refs + "\nNei M, Chesser RK. 1983. Estimation of \
+fixation indices and gene diversities. Annals of Human Genetics 47(3): 253-259"
+            elif params.dist == "CHORD":
+                refs = refs + "\nCavalli-Sforza LL, Edwards AWF. 1967. \
+Phylogenetic analysis: model and estimation procedures. American Journal of \
+Human Genetics. 19: 233-257."
+
+    if params.run == "IBD" or params.run == "ALL":
+        refs = refs + "\n\nFor the Mantel test, please cite:\nMantel N. 1967. \
+The detection of disease clustering and a generalized regression approach. \
+Cancer Research 27(2): 209-220."
+
+    if params.clusterpop:
+        refs = refs + "\n\nFor the DBSCAN approach, please cite:"
+        refs = refs + "\nEster M, Kriegel HP, Sander J, Xu X. 1996. A \
+density-based algorithm for discovering  clusters in large spatial databases \
+with noise. IN: Simoudis E, Han J, Fayyad UM. (eds.). Proceedings of the \
+Second International Conference on Knowledge Discovery and Data Mining \
+(KDD-96). AAAI Press. pp. 226–231."
+        refs = refs + "\nPedregosa F, Varoquaux G, Gramfort A, Michel V, \
+Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, \
+Vanderplas J. 2011. Scikit-learn: Machine learning in Python. The Journal of \
+machine Learning research. 1(12):2825-30"
+
+    if params.pop_agg == "ADJHARM" or params.loc_agg == "ADJHARM":
+        refs = refs + "\n\nFor the adjusted harmonic mean, please cite:\
+ \nRossmann LA. DFLOW User's Manual. U.S. Environmental Protection Agency."
+
+    if params.run != "GENDIST":
+        refs = refs + "\n\nFor the network methods, please cite: \nHagberg A, \
+Swart P, S Chult D. 2008. Exploring network structure, dynamics, and function \
+using NetworkX. Los Alamos National Lab.(LANL), Los Alamos, NM"
+
+    refs = refs + "\n"
+
+    return refs
diff --git a/autostreamtree/sequence.py b/autostreamtree/sequence.py
new file mode 100644
index 0000000..1a7d746
--- /dev/null
+++ b/autostreamtree/sequence.py
@@ -0,0 +1,271 @@
+from typing import List
+
+
+def decode(gt: tuple, ref: str, alts: list, as_iupac: bool = False,
+           as_tuple: bool = False, as_list: bool = False) -> str:
+    """
+    Decode a genotype from a VCF file and return the corresponding DNA sequence
+
+    Args:
+        gt (tuple): The genotype as a tuple of integers representing the
+                    indices of the reference and alternate alleles.
+        ref (str): The reference allele.
+        alts (list): A list of alternate alleles.
+        as_iupac (bool): Whether to return the consensus IUPAC symbol if the
+                         genotype is heterozygous. Default is False.
+        as_tuple (bool): Whether to return the result as a tuple of two strings
+                         Default is False.
+        as_list (bool): Whether to return the result as a list of two strings.
+                        Default is False.
+
+    Returns:
+        ret (str): The decoded genotype as a string. By default, this is a
+                   string in the format "ref/alt", but the format can be
+                   customized using the optional arguments.
+    """
+    # initialize return value
+    ret = [None, None]
+
+    # if genotype is missing, set to "N"
+    if gt[0] is None or gt[1] is None:
+        ret = ["N", "N"]
+    else:
+        # decode each allele
+        if gt[0] == 0:
+            ret[0] = ref
+        else:
+            ret[0] = alts[gt[0] - 1]
+        if gt[1] == 0:
+            ret[1] = ref
+        else:
+            ret[1] = alts[gt[1] - 1]
+
+    # apply optional formatting
+    if as_iupac:
+        return dna_consensus("/".join(ret))
+    elif as_tuple:
+        return tuple(ret)
+    elif as_list:
+        return ret
+    else:
+        return "/".join(ret)
+
+
+def get_nuc_freqs(seqs: dict, ploidy: int) -> list:
+    """
+    Compute the nucleotide frequencies of a set of DNA sequences.
+
+    Args:
+        seqs (dict): A dictionary of DNA sequences. The keys are sample names
+                     and the values are strings of nucleotides.
+        ploidy (int): The ploidy of the sequences. If ploidy=1, ambiguities
+                      will be skipped. If ploidy=2, ambiguities will be
+                      resolved.
+
+    Returns:
+        freqs (list): A list of dictionaries, where each dictionary contains
+                      the nucleotide frequencies for a single position in the
+                      sequences.
+
+    Raises:
+        ValueError: If any sequence is empty or contains only invalid
+                    characters, or if sequences are not all the same length.
+    """
+    if not all(seqs):
+        raise ValueError("One or more sequences are empty.")
+
+    sequence_length = len(seqs[list(seqs.keys())[0]])
+    if not all(len(seq) == sequence_length for seq in seqs.values()):
+        raise ValueError("Sequences are not all the same length.")
+
+    freqs = []
+    for loc in range(sequence_length):
+        allnucs = ""
+        for samp in seqs.keys():
+            allnucs += dna_consensus(seqs[samp][loc]).lower()
+
+        badchars = ["?", "-", "n"]
+        if ploidy == 1:
+            badchars += ["r", "y", "s", "w", "k", "m", "b", "d", "h", "v"]
+        allnucs = ''.join([nuc for nuc in allnucs if nuc not in badchars])
+
+        if ploidy == 2:
+            iupacs = ["r", "y", "s", "w", "k", "m", "b", "d", "h", "v"]
+            for ambig in iupacs:
+                allnucs = allnucs.replace(
+                    ambig, "".join(get_iupac_caseless(ambig))
+                )
+            for nuc in ["a", "c", "t", "g"]:
+                allnucs = allnucs.replace(nuc, nuc + nuc)
+
+        total = len(allnucs)
+        counts = {"a": 0, "g": 0, "c": 0, "t": 0}
+        for c in allnucs:
+            if c in counts:
+                counts[c] += 1
+        if total <= 0:
+            freqs.append({"a": 0, "g": 0, "c": 0, "t": 0})
+        else:
+            for nuc in counts.keys():
+                counts[nuc] = float(counts[nuc] / total)
+            freqs.append(counts)
+    return freqs
+
+
+def dna_consensus(seq: str) -> str:
+    """
+    Make a consensus DNA sequence from alleles separated by a "/" character.
+
+    Args:
+        seq (str): A string of DNA sequences separated by a "/" character.
+
+    Returns:
+        consens (str): A consensus DNA sequence derived from the input
+                       sequences.
+    """
+    if not seq:
+        return None
+    if len(seq) <= 0:
+        return None
+    alleles = seq.split("/")
+    consens = ""
+    if len(alleles) < 1:
+        return None
+    elif not all(len(x) == len(alleles[0]) for x in alleles):
+        raise ValueError(
+            "Not all alleles are the same length: " + str(alleles)
+        )
+    elif len(alleles) == 1:
+        return alleles[0]
+    else:
+        for i in range(len(alleles[0])):
+            nucs = ""
+            for a in alleles:
+                nucs += a[i]
+            temp = list_to_sort_unique_string(nucs.upper())
+            consens += reverse_iupac_case(temp)
+    return consens
+
+
+def reverse_iupac_case(char: str) -> str:
+    """
+    Translate a string of DNA bases to an IUPAC ambiguity code, retaining case.
+
+    Args:
+        char (str): A string of DNA bases.
+
+    Returns:
+        iupac (str): An IUPAC ambiguity code.
+    """
+    iupac = {
+        'A': 'A',
+        'N': 'N',
+        '-': '-',
+        'C': 'C',
+        'G': 'G',
+        'T': 'T',
+        'AG': 'R',
+        'CT': 'Y',
+        'AC': 'M',
+        'GT': 'K',
+        'AT': 'W',
+        'CG': 'S',
+        'CGT': 'B',
+        'AGT': 'D',
+        'ACT': 'H',
+        'ACG': 'V',
+        'ACGT': 'N',
+        'a': 'a',
+        'n': 'n',
+        'c': 'c',
+        'g': 'g',
+        't': 't',
+        'ag': 'r',
+        'ct': 'y',
+        'ac': 'm',
+        'gt': 'k',
+        'at': 'w',
+        'cg': 's',
+        'cgt': 'b',
+        'agt': 'd',
+        'act': 'h',
+        'acg': 'v',
+        'acgt': 'n'
+    }
+    return iupac[char]
+
+
+def list_to_sort_unique_string(input: list) -> str:
+    """
+    Convert a list of characters to a sorted, unique string.
+
+    Args:
+        l (list): A list of characters.
+
+    Returns:
+        s (str): A string containing the characters from the input list, sorted
+                 and with duplicates removed.
+    """
+    s = ''.join(sorted(set(input)))
+    return s
+
+
+def get_iupac_caseless(char: str) -> List[str]:
+    """
+    Split a character to IUPAC codes, assuming diploidy.
+
+    Args:
+        char (str): A character to be split.
+
+    Returns:
+        codes (list): A list of IUPAC codes corresponding to the input
+                      character.
+    """
+    lower = False
+    if char.islower():
+        lower = True
+        char = char.upper()
+    iupac = {
+        "A": ["A"],
+        "G": ["G"],
+        "C": ["C"],
+        "T": ["T"],
+        "N": ["A", "C", "G", "T"],
+        "?": ["A", "C", "G", "T"],
+        "-": ["A", "C", "G", "T", "-"],
+        "R": ["A", "G"],
+        "Y": ["C", "T"],
+        "S": ["G", "C"],
+        "W": ["A", "T"],
+        "K": ["G", "T"],
+        "M": ["A", "C"],
+        "B": ["C", "G", "T"],
+        "D": ["A", "G", "T"],
+        "H": ["A", "C", "T"],
+        "V": ["A", "C", "G"]
+    }
+    codes = iupac[char]
+    if lower:
+        codes = [c.lower() for c in codes]
+    return codes
+
+
+def phase_snp(snp: str) -> str:
+    """
+    Phase a single nucleotide polymorphism (SNP) by splitting its IUPAC code
+    into two alleles.
+
+    Args:
+        snp (str): The IUPAC code for the SNP.
+
+    Returns:
+        phase (str): The SNP alleles separated by a "/" character.
+    """
+    nucs = get_iupac_caseless(snp)
+    if len(nucs) > 2 or len(nucs) < 1:
+        return "n/n"
+    elif len(nucs) == 1:
+        phase = nucs[0] + '/' + nucs[0]
+        return phase
+    else:
+        return "/".join(nucs)