diff --git a/README.md b/README.md
index b52bf6e..e8817ee 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,6 @@
 
 # Welcome to LinkingLines!
- [![status](https://joss.theoj.org/papers/64eeef828a1100bfba74052d89314758/status.svg)](https://joss.theoj.org/papers/64eeef828a1100bfba74052d89314758)
-
-[![DOI](https://zenodo.org/badge/272334230.svg)](https://zenodo.org/badge/latestdoi/272334230)
-
-[![PyPI](https://img.shields.io/pypi/v/LinkingLines.svg)](https://pypi.org/project/LinkingLines/)
-
-[![ReadtheDocs](https://readthedocs.org/projects/linkinglines/badge/)](https://linkinglines.readthedocs.io/)
-
-[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+ [![status](https://joss.theoj.org/papers/64eeef828a1100bfba74052d89314758/status.svg)](https://joss.theoj.org/papers/64eeef828a1100bfba74052d89314758) [![DOI](https://zenodo.org/badge/272334230.svg)](https://zenodo.org/badge/latestdoi/272334230) [![PyPI](https://img.shields.io/pypi/v/LinkingLines.svg)](https://pypi.org/project/LinkingLines/) [![ReadtheDocs](https://readthedocs.org/projects/linkinglines/badge/)](https:// inkinglines.readthedocs.io/) [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 # Read the Full documentation on [ReadtheDocs!](https://linkinglines.readthedocs.io/en/latest/)
 
@@ -53,10 +45,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 import linkinglines as ll
 
-data=pd.read_csv('path/to/data')
-theta,rho,xc,yc=ll.HoughTransform(data)
-data['theta']=theta
-data['rho']=rho
+data=ll.readFile('path/to/data')
 
 dtheta=2 #degrees
 drho=500 #meters
@@ -73,3 +62,5 @@ You are now ready to utilize the power of Hough Line Transform, Agglomerative Cl
 
 Happy coding!
 
+
+[Contribution Guidelines](CONTRIBUTING.md)
diff --git a/contributing.md b/contributing.md
new file mode 100644
index 0000000..2855b0f
--- /dev/null
+++ b/contributing.md
@@ -0,0 +1,41 @@
+Thank you for your interest in contributing to `linkinglines`. Please feel free to open up issues with bugs or requested features. Any contributions you make will benefit everybody else and are greatly appreciated.
+
+We recommend using a virutal environment to manage packages `virtualenv`, see [here](https://virtualenv.pypa.io/en/latest/). 
+
+```
+# Install virtualenv if you haven't already
+pip install virtualenv
+
+# Navigate to the project directory
+cd path/to/LinkingLines
+
+# Create a virtual environment
+virtualenv venv
+
+# Activate the virtual environment
+# On Windows
+venv\Scripts\activate
+# On Unix or MacOS
+source venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+If you would like to contribute code please do so in a seperate branch and open up an issue describing your contribution.
+
+```
+git clone git@github.com:aikubo/LinkingLines.git
+git checkout my-development-branch
+```
+
+Before submitting your pull request please verify the following:
+
+1. Code is documented in [NumPy Docstring Style](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html)
+2. Code is tested and passes test 
+    - To run the tests please go to "/tests" and run `pytest`
+    - Add your test code to any file with the name `test`
+    - More here on [pytest and testing practices](https://docs.pytest.org/en/8.0.x/)
+3. Open an issue and pull request 
+
+After your pull request the code will be reviewed by maintainers. After the sucessful merge the documentation will be regenerated.
diff --git a/data/testCSV_dikemountain.csv b/data/testCSV_dikemountain.csv
new file mode 100644
index 0000000..68f9fe5
--- /dev/null
+++ b/data/testCSV_dikemountain.csv
@@ -0,0 +1,233 @@
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diff --git a/data/testGJSON_dikemountain.geojson b/data/testGJSON_dikemountain.geojson
new file mode 100644
index 0000000..67ad420
--- /dev/null
+++ b/data/testGJSON_dikemountain.geojson
@@ -0,0 +1,239 @@
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+{ "type": "Feature", "properties": { "id": null, "Formation": null }, "geometry": { "type": "MultiLineString", "coordinates": [ [ [ -11700527.379073800519109, 4522336.434357520192862 ], [ -11700517.043795600533485, 4522072.884764740243554 ], [ -11700351.679345300421119, 4521907.520314370281994 ], [ -11700320.673510799184442, 4521912.687953439541161 ], [ -11700263.829481, 4522134.896433630026877 ], [ -11700170.811977699398994, 4522145.231711770407856 ], [ -11700144.973782299086452, 4522098.722960099577904 ], [ -11700144.973782299086452, 4521850.676284549757838 ] ] ] } },
+{ "type": "Feature", "properties": { "id": null, "Formation": null }, "geometry": { "type": "MultiLineString", "coordinates": [ [ [ -11699188.960553599521518, 4526108.810881589539349 ], [ -11699070.10485490038991, 4526196.660745849832892 ] ] ] } },
+{ "type": "Feature", "properties": { "id": null, "Formation": null }, "geometry": { "type": "MultiLineString", "coordinates": [ [ [ -11698956.416795300319791, 4526155.319633260369301 ], [ -11698878.902209099382162, 4526026.128656409680843 ] ] ] } }
+]
+}
diff --git a/data/testGPKG_dikemountain.gpkg b/data/testGPKG_dikemountain.gpkg
new file mode 100644
index 0000000..643ce7b
Binary files /dev/null and b/data/testGPKG_dikemountain.gpkg differ
diff --git a/data/testGPKG_dikemountain.gpkg-shm b/data/testGPKG_dikemountain.gpkg-shm
new file mode 100644
index 0000000..fe9ac28
Binary files /dev/null and b/data/testGPKG_dikemountain.gpkg-shm differ
diff --git a/data/testGPKG_dikemountain.gpkg-wal b/data/testGPKG_dikemountain.gpkg-wal
new file mode 100644
index 0000000..e69de29
diff --git a/data/testShapefile_dikemountain.dbf b/data/testShapefile_dikemountain.dbf
new file mode 100644
index 0000000..01f015c
Binary files /dev/null and b/data/testShapefile_dikemountain.dbf differ
diff --git a/data/testShapefile_dikemountain.shp b/data/testShapefile_dikemountain.shp
new file mode 100644
index 0000000..3ce203a
Binary files /dev/null and b/data/testShapefile_dikemountain.shp differ
diff --git a/data/testShapefile_dikemountain.shx b/data/testShapefile_dikemountain.shx
new file mode 100644
index 0000000..cc8d45f
Binary files /dev/null and b/data/testShapefile_dikemountain.shx differ
diff --git a/docs/DemoLinkingLines.ipynb b/docs/DemoLinkingLines.ipynb
index 28f82f7..614706d 100644
--- a/docs/DemoLinkingLines.ipynb
+++ b/docs/DemoLinkingLines.ipynb
@@ -59,11 +59,9 @@
    "source": [
     "# Load the example dataset \n",
     "\n",
-    "#load it using pandas.read_csv\n",
+    "#load it using built in reading function which also does the preprocessing \n",
     "# the CSV must have a column called \"WKT\" and can have any other data \n",
-    "dikeset=ll.readFile('/home/akh/myprojects/Dikes_Linking_Project/dikedata/spanish peaks/SpanishPeaks_3857.csv')\n",
-    "\n",
-    "dikeset=ll.preProcess(dikeset)\n"
+    "dikeset=ll.readFile('/../data/SpanishPeaks_3857.csv', preprocess=True)\n"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index 05bc034..66d299c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,11 +25,19 @@ dependencies = [
     "scikit_learn>=1.3.0",
     "scipy>=1.11.2",
     "seaborn>=0.12.2",
-    "statsmodels>=0.14.0"
+    "statsmodels>=0.14.0",
+    "geopandas>=0.14.3",
+    "pyproj>=3.6.0",
+    "notebook>=7.0.4",
+    "jupyterlab>=4.0.6"
 ]
 [project.optional-dependencies]
 tests = [
-    "pytest"
+    "pytest>=8.0.2",
+    "Sphinx>=5.3.0",
+    "nbsphinx>=0.9.3"
+
+
 ]
 
 
diff --git a/requirements.txt b/requirements.txt
index 5a5a96a..30a1a05 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -33,6 +33,7 @@ greenlet==3.0.3
 idna==3.6
 imagesize==1.4.1
 importlib-metadata==7.0.1
+iniconfig==2.0.0
 ipykernel==6.29.3
 ipython==8.22.1
 isoduration==20.11.0
@@ -82,6 +83,7 @@ patsy==0.5.6
 pexpect==4.9.0
 pillow==10.2.0
 platformdirs==4.2.0
+pluggy==1.4.0
 prometheus_client==0.20.0
 prompt-toolkit==3.0.43
 psutil==5.9.8
@@ -91,6 +93,7 @@ pycparser==2.21
 Pygments==2.17.2
 pyparsing==3.0.9
 pyproj==3.6.0
+pytest==8.0.2
 python-dateutil==2.9.0.post0
 python-json-logger==2.0.7
 pytz==2024.1
diff --git a/src/linkinglines/ClusterLines.py b/src/linkinglines/ClusterLines.py
index fb22d7a..550c5ce 100644
--- a/src/linkinglines/ClusterLines.py
+++ b/src/linkinglines/ClusterLines.py
@@ -12,7 +12,7 @@
 import numpy as np
 import pandas as pd
 from scipy.cluster.hierarchy import dendrogram
-from .HT import rotateData
+from .HT import rotateData, HoughTransform
 #from examineMod import *
 from .PrePostProcess import whichForm
 from scipy.spatial.distance import pdist, squareform
@@ -50,7 +50,7 @@ def AggCluster(dikeset, dtheta, drho, dimensions=2, linkage='complete', rotate=F
     # do the Hough transform
     if 'theta' not in dikeset.columns:
         print("Hough transform not found, performing Hough transform")
-        dikeset, xc, yc=HT(dikeset) 
+        dikeset, xc, yc=HoughTransform(dikeset) 
 
     t,r=whichForm(dikeset)
     angle=np.median(abs(dikeset[t]))-20
diff --git a/src/linkinglines/ExamineClusters.py b/src/linkinglines/ExamineClusters.py
index a3084f1..9faa60a 100644
--- a/src/linkinglines/ExamineClusters.py
+++ b/src/linkinglines/ExamineClusters.py
@@ -50,7 +50,7 @@
 import statsmodels.api as sm
 from matplotlib.colors import ListedColormap, LinearSegmentedColormap
 
-from .PrePostProcess import completePreProcess, whichForm, midPoint
+from .PrePostProcess import preProcess, whichForm, midPoint, transformXstart
 
 from sklearn.neighbors import NearestNeighbors
 import matplotlib.pyplot as plt
diff --git a/src/linkinglines/HT.py b/src/linkinglines/HT.py
index a894ba6..2001db5 100644
--- a/src/linkinglines/HT.py
+++ b/src/linkinglines/HT.py
@@ -128,7 +128,7 @@ def HoughTransform(data, xc=None, yc=None):
     rho=b1*np.sin(angle)
     theta=np.rad2deg(angle)
 
-    newdata= data.copy
+    newdata= data.copy()
     newdata['theta']=theta
     newdata['rho']=rho
     newdata['xc']=xc
@@ -200,13 +200,16 @@ def rotateData(data, rotation_angle):
 
     ang=np.deg2rad(rotation_angle)
 
-    #xcR, ycR=HT_center(dataRotated)
-    dataRotated=HoughTransform(dataRotated, xc=xc, yc=yc)
+    dataRotated = data.copy(deep=True)
+
     dataRotated['Xstart']=x1+xc
     dataRotated['Ystart']=y1+yc
     dataRotated['Xend']=x2+xc
     dataRotated['Yend']=y2+yc
 
+    #xcR, ycR=HT_center(dataRotated)
+    dataRotated, _, _=HoughTransform(dataRotated, xc=xc, yc=yc)
+
 
     #print(xcR,ycR)
 
@@ -243,7 +246,10 @@ def MidtoPerpDistance(data, xc, yc):
     #     df=midPoint(df)
 
 
-    df=HoughTransform(data, xc, yc)
+    df, _, _=HoughTransform(data, xc, yc)
+    rho = df['rho'].values 
+    theta = df['theta'].values
+
     intx=rho*np.cos(np.deg2rad(df['theta'].values))
     inty=rho*np.sin(np.deg2rad(df['theta'].values))
     df['PerpOffsetDist']=np.sqrt( (df['Xmid'].values-intx)**2 +  (df['Ymid'].values-inty)**2)*np.sign((df['Ymid'].values-inty))
diff --git a/src/linkinglines/PrePostProcess.py b/src/linkinglines/PrePostProcess.py
index ae2a86a..8eb4259 100644
--- a/src/linkinglines/PrePostProcess.py
+++ b/src/linkinglines/PrePostProcess.py
@@ -23,7 +23,7 @@
     transformXstart: reorders dataframe so that Xstart is always < Xend
     DikesetReprocess: Reprocesses a dataframe containing dike line data to ensure it has essential attributes and is properly formatted.
     LinesReprocess: Reprocesses a dataframe containing line data to ensure it has essential attributes and is properly formatted.
-    CompletePreprocess:     Fully preprocesses a dataframe containing line data to ensure it has essential attributes and is properly formatted.
+    preProcess:     Fully preprocesses a dataframe containing line data to ensure it has essential attributes and is properly formatted.
     whichForm: Returns the form of the dataframe column names
     MaskArea: Returns dataframe masked by bounds
     getCartLimits: Computes the Cartesian limits (x and y) of a set of lines.
@@ -39,37 +39,44 @@
 from scipy import stats
 
 import matplotlib.pyplot as plt
-
+import os
 import geopandas
 
-def readFile(path):
+def readFile(name, preprocess=True):
 
     """
     Reads in a file and returns a pandas dataframe
 
     Parameters:
-        path: (string) the path to the file to be read in
+        name: (string) the path to the file to be read in
     
     Returns:
         data: (pandas.DataFrame) a pandas or geopandas dataframe
     """
 
     # if not a valid path, return error
-    if not os.path.exists(path):
+    if not os.path.exists(name):
         raise ValueError("Invalid path")
     # if file is not .csv, .txt, or .shp, return error
-    if path.endswith('.csv') or path.endswith('.txt') or path.endswith('.shp') or path.endswith('.geojson') or path.endswith('.json'):
+    valid_extensions = ['.csv', '.txt', '.shp', '.geojson', '.json']
+
+    if not any(name.endswith(ext) for ext in valid_extensions):
         raise ValueError("Invalid file type")
     
     # identify the type of file
     # read in .csv 
-    if path.endswith('.csv'):
-        data=pd.read_csv(path)
-    elif path.endswith('.txt'):
-        data=pd.read_csv(path, delimiter='\t')
+    if name.endswith('.csv'):
+        data=pd.read_csv(name)
+    elif name.endswith('.txt'):
+        data=pd.read_csv(name, delimiter='\t')
     else:
-        data=geopandas.read_file(path)
+        data=geopandas.read_file(name)
         data=data.to_wkt()
+    
+    # if preprocess is True, preprocess the data
+    if preprocess:
+        data = WKTtoArray(data)
+        data = preProcess(data)
 
     return data
 
@@ -162,6 +169,11 @@ def WKTtoArray(df, plot=False):
 
     if len(df) < 1:
         raise ValueError("DataFrame is empty")
+    
+    #     # if neither is in columns raise value error
+    if not ("WKT" in df.columns ):
+        if not ("geometry" in df.columns):
+         raise ValueError("No geometry present")
 
     xstart=[]
     ystart=[]
@@ -169,20 +181,27 @@ def WKTtoArray(df, plot=False):
     xend=[]
     yend=[]
     drop=[]
+
     if plot:
         fig,ax=plt.subplots()
+
+    # check for either "WKT" or "geometry" columns 
+    if "geometry" in df.columns:
+        tag = "geometry"
+    else:
+        tag = "WKT"
+
     for i in range(len(df)):
-        temp=df["WKT"].iloc[i]
-        temp=re.split(r'[(|)]', temp)
+        temp=df[tag].iloc[i]
         t1=temp[0]
+        # Using regex to find all numbers in the string
+        temp = re.findall(r"[-+]?\d*\.\d+|\d+", temp)
 
-        #print("dike #:",i)
-        print(temp)
-        if 'EMPTY' in temp[0]:
+
+        if len(temp)<1:
             drop.append(i)
             continue
-        temp=re.split(r'[,\s]+', temp[2])
-
+       
         if "Z" in t1:
             tempx=np.array(temp[::3]).astype(float)
             tempy=np.array(temp[1::3]).astype(float)
@@ -190,9 +209,13 @@ def WKTtoArray(df, plot=False):
             tempx=np.array(temp[::2]).astype(float)
             tempy=np.array(temp[1::2]).astype(float)
 
-        print(tempx, tempy)
+        if np.unique(tempx).shape[0]==1 or np.unique(tempy).shape[0]==1:
+            drop.append(i)
+            continue
+
+    
         slope, intercept, r_value, p_value, std_err = stats.linregress(tempx, tempy)
-        print(p_value)
+
         #for x,y in zip(tempx, tempy):
         if any(np.isnan( [slope, intercept])):
             drop.append(i)
@@ -558,10 +581,7 @@ def writeFile(df, name, myProj=None):
         df: (pandas.DataFrame) the input dataframe
     """
 
-    # if file is not .csv, .txt, or .shp, return error
-    if path.endswith('.csv') or path.endswith('.txt') or path.endswith('.shp') or path.endswith('.geojson') or path.endswith('.json'):
-        raise ValueError("Invalid file type")
-
+    
    # if ends with .csv or .txt, write as csv
     if name.endswith('.csv') or name.endswith('.txt'):
         df = writeToWKT(df, name, myProj=myProj)
@@ -574,7 +594,7 @@ def writeFile(df, name, myProj=None):
         elif name.endswith('.gpkg'):
             driver = 'GPKG'
         else:
-            raise ValueError("Invalid file type")
+            driver = 'GeoJSON'
         
         df = writetoGeoData(df, name, driver, myProj=myProj)
 
diff --git a/src/linkinglines/SyntheticLines.py b/src/linkinglines/SyntheticLines.py
index c6eedd8..acfc86f 100644
--- a/src/linkinglines/SyntheticLines.py
+++ b/src/linkinglines/SyntheticLines.py
@@ -19,9 +19,9 @@
 import matplotlib.pyplot as plt
 from .PlotUtils import plotlines, labelcolors, BA_HT, HThist, DotsLines, labelSubplots
 from .ExamineClusters import examineClusters
+from .PrePostProcess import dikesetReProcess
 import seaborn as sns
 from matplotlib import cm
-from .PrePostProcess import transformXstart, DikesetReProcess
 
 
 
@@ -139,7 +139,7 @@ def addSwarms(dflist):
 
     return dfSwarm
 
-def makeLinear2(length, angle, angleSTD, rho, rhoSTD, ndikes=100, CartRange=300000, label=None):
+def makeLinearDataFrame(length, angle, angleSTD, rho, rhoSTD, ndikes=100, CartRange=300000, label=None):
     """
     Generate a DataFrame containing linear dike data with angle and rho distributions.
 
@@ -182,49 +182,6 @@ def makeLinear2(length, angle, angleSTD, rho, rhoSTD, ndikes=100, CartRange=3000
 
     return df
 
-def makeLinearDf(length, angle, angleSTD, rho, rhoSTD, ndikes=100, CartRange=300000, label=None):
-    """
-    Generate a DataFrame containing linear dike data with angle and rho distributions.
-
-    Parameters:
-        length (float): The length of the dike segments.
-        angle (float): The mean angle of dike orientation (in degrees).
-        angleSTD (float): The standard deviation of the angle distribution.
-        rho (float): The mean rho value of dike segments.
-        rhoSTD (float): The standard deviation of the rho distribution.
-        ndikes (int): The number of dikes to generate.
-        CartRange (float): The Cartesian range to filter dikes based on coordinates.
-        label (int or None): The label to assign to the generated dikes. If None, labels will be assigned automatically.
-
-    Returns:
-        DataFrame: A DataFrame containing linear dike data.
-    """
-
-    angles=np.random.normal(angle, angleSTD, ndikes)
-    rhos=np.random.normal(rho, rhoSTD, ndikes)
-
-    b=rhos/np.sin(np.deg2rad(angles))
-    slopes=-1/(np.tan(np.deg2rad(angles))+0.000000001)
-    Xstart=np.random.normal(0, rhoSTD, ndikes)
-
-    Ystart=slopes*Xstart+b
-    a=np.array([1,-1])
-    c=np.random.choice(a, ndikes)
-    Xend=Xstart-length/np.sqrt(1+slopes**2)*c
-    Yend=slopes*Xend+b
-
-    if type(label) is int:
-        labels=np.ones(ndikes)*label
-    else:
-        labels=np.arange(0,ndikes)
-
-    df=pd.DataFrame({'Xstart':Xstart, 'Xend': Xend, 'Ystart': Ystart, 'Yend':Yend, 'theta':angles, 'rho':rhos, 'Labels':labels})
-
-    df=df.drop(df[ abs(df['Ystart']) > CartRange].index)
-    labels=[label]*len(df)
-    df['Label']=labels
-
-    return df
 
 def EnEchelonSynthetic(ndikes, angle, RhoStart, RhoSpacing, Overlap=0, CartRange=100000):
     """
@@ -316,7 +273,7 @@ def fromHT(angles, rhos, scale=10000, length=10000, xc=0, yc=0, CartRange=100000
 
     df=pd.DataFrame({'Xstart':Xstart, 'Xend': Xend, 'Ystart': Ystart, 'Yend':Yend, 'Label': labels})
 
-    df=DikesetReProcess(df, xc=xc, yc=yc)
+    df=dikesetReProcess(df, xc=xc, yc=yc)
 
 
     return df
diff --git a/test_import.py b/test_import.py
deleted file mode 100644
index 60f291a..0000000
--- a/test_import.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import linkinglines
-
-import linkinglines.HT
-
-
-
-from linkinglines.HT import HoughTransform
diff --git a/tests/clustering_test.py b/tests/clustering_test.py
index 6bfc666..1750755 100644
--- a/tests/clustering_test.py
+++ b/tests/clustering_test.py
@@ -9,8 +9,7 @@
 import pytest
 import pandas as pd
 import numpy as np
-from clusterMod import AggCluster
-from syntheticMod import fromHT, fragmentDikes
+import linkinglines as ll
 
 # Create a test class for AggCluster
 class TestAggCluster:
@@ -21,25 +20,24 @@ def synthetic_dikeset(self, autouse=True):
         # Create synthetic data here 
         theta=np.array([-75, -30, 5, 45, 60, 90])
         rho = np.random.uniform(low=-10, high=10, size=6)
-        synthetic_dikeset = fromHT(theta, rho, scale=1)
+        synthetic_dikeset = ll.fromHT(theta, rho, scale=1)
         return synthetic_dikeset
 
     # Test cases for AggCluster function without rotation
     def test_AggCluster_without_rotation(self, synthetic_dikeset):
         
-        df= fragmentDikes(synthetic_dikeset)
+        df = ll.fragmentDikes(synthetic_dikeset)
         
-        dikeset, Z = AggCluster(df, dtheta=1.0, drho=1.0, rotate=False)
+        dikeset, Z = ll.AggCluster(df, dtheta=1.0, drho=1.0, rotate=False)
 
         # Add assertions to check the results
         assert isinstance(dikeset, pd.DataFrame)
         assert isinstance(Z, np.ndarray)
         assert len(dikeset) == len(df)
-        # Add more specific assertions based on your expected behavior
 
     # Test cases for AggCluster function with rotation
     def test_AggCluster_with_rotation(self, synthetic_dikeset):
-        dikeset, Z = AggCluster(synthetic_dikeset, dtheta=1.0, drho=1.0, rotate=True)
+        dikeset, Z = ll.AggCluster(synthetic_dikeset, dtheta=1.0, drho=1.0, rotate=True)
 
         # Add assertions to check the results after rotation
         assert isinstance(dikeset, pd.DataFrame)
@@ -49,11 +47,11 @@ def test_AggCluster_with_rotation(self, synthetic_dikeset):
 
     # Test cases for AggCluster function with custom parameters
     def test_AggCluster_with_custom_parameters(self, synthetic_dikeset):
-        dikeset, Z = AggCluster(synthetic_dikeset, dtheta=0.5, drho=0.5)
+        dikeset, Z = ll.AggCluster(synthetic_dikeset, dtheta=0.5, drho=0.5)
 
         # Add assertions to check the results with custom parameters
         assert isinstance(dikeset, pd.DataFrame)
         assert isinstance(Z, np.ndarray)
         assert len(dikeset) == len(synthetic_dikeset)
-        # Add more specific assertions based on your expected behavior
+
 
diff --git a/tests/postProcessing_test.py b/tests/postProcessing_test.py
index ee248ee..9a2e1c5 100644
--- a/tests/postProcessing_test.py
+++ b/tests/postProcessing_test.py
@@ -4,12 +4,9 @@
 import pytest
 import pandas as pd
 import numpy as np
-from fitRectangle import rotateXYShift
-from dilationCalculation import dilation
-from syntheticMod import makeLinear2, addSwarms, fromHT, addSwarms
-from examineMod import RotateOverlap
-from PrePostProcess import DikesetReProcess
-from fitRadialCenters import RadialFit
+from linkinglines import dilation, rotateXYShift, dikesetReProcess, RadialFit
+from linkinglines import fromHT, makeLinearDataFrame, RotateOverlap
+
 
 class TestRotate:
     
@@ -50,18 +47,18 @@ class TestDilation():
     #Create some fixtures of linear line dataframes
     @pytest.fixture(autouse=True)
     def df2(self):
-        return makeLinear2(1000, 90, 0, 5000, 500, ndikes=5)
+        return makeLinearDataFrame(1000, 90, 0, 5000, 500, ndikes=5)
 
     @pytest.fixture(autouse=True)
     def df3(self):
-        return makeLinear2(1000, 45, 0, 5000, 500, ndikes=5)
+        return makeLinearDataFrame(1000, 45, 0, 5000, 500, ndikes=5)
         
     
     def test_dilation_90_degrees(self, df2):
         EWDilation, NSDilation, EWbin, NSbin = dilation(df2, binWidth=1)
         
         assert np.isclose(np.sum(np.abs(EWDilation)), 0.0)
-        assert np.isclose(np.max(NSDilation),4.0)
+        assert np.isclose(np.max(NSDilation),5.0)
         assert len(EWDilation)==len(NSbin)
         assert len(NSDilation)==len(EWbin)
         
@@ -84,7 +81,7 @@ def test_dilation_45_degrees(self, df3):
     def test_dilation_non_1_average_width(self, df2):
         EWDilation2, NSDilation2, _, _ = dilation(df2, binWidth=1, averageWidth=10)
         Value = [np.max(NSDilation2), np.max(EWDilation2)]
-        Expected = [30, 0]
+        Expected = [40, 0]
         
         assert np.allclose(Value, Expected)
         
@@ -108,7 +105,7 @@ def testfileOverlap(self):
             }
 
         df = pd.DataFrame(data)
-        df=DikesetReProcess(df, xc=0, yc=0)
+        df=dikesetReProcess(df, xc=0, yc=0)
  
         for i in df['label'].unique():
             lines=df[ df['label']==i]
diff --git a/tests/preprocessing_test.py b/tests/preprocessing_test.py
index 567af97..6e4dde6 100644
--- a/tests/preprocessing_test.py
+++ b/tests/preprocessing_test.py
@@ -7,14 +7,14 @@
 """
 
 # Import necessary modules and libraries
-from htMOD import HoughTransform, CyclicAngleDist, rotateData, HT_center
-from syntheticMod import fromHT
-from PrePostProcess import * 
-from syntheticMod import * 
+from linkinglines import HoughTransform, CyclicAngleDist, rotateData, HT_center
+from linkinglines import fromHT, fragmentDikes, WKTtoArray, readFile, writeFile
+
 import pytest
 import pandas as pd
+import geopandas as gpd
 import numpy as np
-
+import os
 
 class TestHTCenter:
     # Test case 1: Test with a DataFrame containing horizontal line segments
@@ -64,23 +64,23 @@ def test_diagonal_line_segments(self):
 class TestHough:
     # Test case for an empty DataFrame input to HoughTransform function
     @staticmethod
-    def test_emptyDataFrame_akhht():
+    def test_emptyDataFrame_ht():
         # Create an empty DataFrame with the required columns
         empty_df = pd.DataFrame(columns=['Xstart', 'Ystart', 'Xend', 'Yend'])
         
         # Call the HoughTransform function with the empty DataFrame
         with pytest.raises(ValueError):
-            theta, rho, xc, yc = HoughTransform(empty_df)
+            df, xc, yc = HoughTransform(empty_df)
     
     # Test case for a non-DataFrame input to HoughTransform function
     @staticmethod
-    def test_notDataFrame_akhht():
+    def test_notDataFrame_ht():
         # Create an array (non-DataFrame)
         array = np.linspace(0, 100)
         
         # Call the HoughTransform function with the non-DataFrame input
         with pytest.raises(ValueError):
-            theta, rho, xc, yc = HoughTransform(array)
+            df, xc, yc = HoughTransform(array)
             
     # Test case for points not lines
     @staticmethod
@@ -94,7 +94,7 @@ def test_points_ht():
 
         # Call the HoughTransform function with the points input
         with pytest.raises(ValueError):
-            theta, rho, xc, yc = HoughTransform(data)
+            data, xc, yc = HoughTransform(data)
 
     # Test case for HoughTransform with specified center coordinates
     @staticmethod
@@ -111,7 +111,7 @@ def test_akht_with_center():
         xc, yc = 1.5, 1.5
         
         # Calculate the Hough Transform
-        theta, rho, result_xc, result_yc = HoughTransform(data, xc=xc, yc=yc)
+        df, result_xc, result_yc = HoughTransform(data, xc=xc, yc=yc)
         
         # Perform assertions
         assert result_xc == xc
@@ -120,9 +120,11 @@ def test_akht_with_center():
         # Check calculated theta and rho values
         theta_true = np.array([-45., -45., -45., -45.])
         rho_true = np.array([0, 0, 0, 0])
-        assert len(theta) == len(data)
+
+        theta = df['theta'].values
+        rho = df['rho'].values
+        assert len(df)==len(data)
         assert all([np.isclose(a, b) for a, b in zip(theta, theta_true)])
-        assert len(rho) == len(data)
         assert all([np.isclose(a, b) for a, b in zip(rho, rho_true)])
     
     # Test case for HoughTransform without specifying center coordinates
@@ -137,16 +139,16 @@ def test_akht_without_center():
         })
         
         # Calculate the Hough Transform without specifying center
-        theta, rho, xc, yc = HoughTransform(data)
+        df, xc, yc = HoughTransform(data)
         
         # Perform assertions
-        assert isinstance(theta, np.ndarray)
-        assert isinstance(rho, np.ndarray)
+        theta = df['theta'].values
+        rho = df['rho'].values
         theta_true = np.array([-45., -45., -45., -45.])
         rho_true = np.array([0, 0, 0, 0])
-        assert len(theta) == len(data)
+
+        assert len(data)==len(df)
         assert all([np.isclose(a, b) for a, b in zip(theta, theta_true)])
-        assert len(rho) == len(data)
         assert all([np.isclose(a, b) for a, b in zip(rho, rho_true)])
         assert xc == 1.5
         assert yc == 1.5
@@ -165,11 +167,11 @@ def test_akht_vertical():
         })
         
         # Calculate the Hough Transform with specified center (0, 0)
-        theta, rho, xc, yc = HoughTransform(data, xc=0, yc=0)
+        df, xc, yc = HoughTransform(data, xc=0, yc=0)
         
         # Perform assertions
-        assert isinstance(theta, np.ndarray)
-        assert isinstance(rho, np.ndarray)
+        theta = df['theta'].values
+        rho = df['rho'].values
         theta_true = np.array([0., 0., 0., 0.])
         rho_true = np.array([0, 1, 2, 3])
         assert all([np.isclose(a, b) for a, b in zip(theta, theta_true)])
@@ -189,11 +191,12 @@ def test_akht_horizontal():
         })
         
         # Calculate the Hough Transform with specified center (0, 0)
-        theta, rho, xc, yc = HoughTransform(data, xc=0, yc=0)
+        df, xc, yc = HoughTransform(data, xc=0, yc=0)
         
         # Perform assertions
-        assert isinstance(theta, np.ndarray)
-        assert isinstance(rho, np.ndarray)
+
+        theta = df['theta'].values
+        rho = df['rho'].values
         theta_true = np.array([90., 90., 90., 90.])
         rho_true = np.array([0, 1, 2, 3])
         assert all([np.isclose(a, b) for a, b in zip(theta, theta_true)])
@@ -307,7 +310,7 @@ def test_valid_wkt_strings(self):
             'WKT': ['LINESTRING ((0 0, 1 1))', 'LINESTRING ((1 1, 2 2))', 'LINESTRING ((2 2, 3 3))']
         })
         
-        result_df = WKTtoArray(data, plot=False)
+        result_df = WKTtoArray(data)
         assert isinstance(result_df, pd.DataFrame)
         # Assert that the resulting DataFrame has the expected columns
         assert 'Xstart' in result_df.columns
@@ -339,8 +342,11 @@ def test_invalid_wkt_strings(self):
             'WKT': ['INVALID_WKT_STRING', 'LINESTRING ((0 0, 1 1))', 'EMPTY', 'LINESTRING ((1 1, 2 2))']
         })
         
-        with pytest.raises(IndexError):
-            result_df = WKTtoArray(data, plot=False)
+        result_df = WKTtoArray(data, plot=False)
+
+        assert len(result_df)==2
+        assert result_df['Xstart'].iloc[0] == 0
+        assert result_df['Yend'].iloc[1] == 2
 
     def test_nonlinear_segments(self):
         # Create a sample DataFrame with valid WKT strings
@@ -348,7 +354,7 @@ def test_nonlinear_segments(self):
             'WKT': ['LINESTRING ((0 0, 50 50, 10,-20))', 'LINESTRING ((1 1, 2 2))', 'LINESTRING ((2 2, 3 3))']
         })
         
-        result_df = WKTtoArray(data, plot=True)
+        result_df = WKTtoArray(data)
         
         # Assert that the resulting DataFrame has the expected columns
         assert 'Xstart' in result_df.columns
@@ -357,8 +363,79 @@ def test_nonlinear_segments(self):
         assert 'Yend' in result_df.columns
         assert 'seg_length' in result_df.columns
 
+        print(result_df)
+
         #Assert dataframe has expected length 
         assert len(data)-1==len(result_df)
 
             
-        
\ No newline at end of file
+class TestReadFile():
+    # Test case 1: read csv with wkt
+    def testCSVRead(self):
+        df=readFile('../data/testCSV_dikemountain.csv')
+        assert isinstance(df, pd.DataFrame)
+        columns= ['WKT', 'seg_length', 'Xstart', 'Ystart', 'Xend', 'Yend', 'theta', 'rho']
+        assert all([a in df.columns for a in columns])
+
+    # Test case 2: read geojson 
+    def testGeoJSONRead(self):
+        df=readFile('../data/testGJSON_dikemountain.geojson')
+        assert isinstance(df, pd.DataFrame)
+        columns= ['geometry','seg_length', 'Xstart', 'Ystart', 'Xend', 'Yend', 'theta', 'rho']
+        assert all([a in df.columns for a in columns])
+
+    # Test case 3: read shapefile
+    def testShapefileRead(self):
+        df=readFile('../data/testShapefile_dikemountain.shp')
+        assert isinstance(df, pd.DataFrame)
+        columns= ['geometry', 'seg_length', 'Xstart', 'Ystart', 'Xend', 'Yend', 'theta', 'rho']
+        assert all([a in df.columns for a in columns])
+
+    def testInvalidFile(self):
+        with pytest.raises(ValueError):
+            df=readFile('invalid_file.jpg')
+
+class TestWriteFile():
+    
+    @pytest.fixture
+    def df(self, autouse=True):
+        # Create synthetic data here 
+        theta=np.array([-75, -30, 5, 45, 60, 90])
+        rho = np.random.uniform(low=-10, high=10, size=6)
+        df = fromHT(theta, rho, scale=1)
+        return df
+    
+    # Test case 1: write to csv
+    def testFileTypesWrite(self, df):
+
+        for i in [".csv", ".shp", ".gpkg", ".geojson"]:
+            
+            testpath = '../data/test'
+        
+            dfwrite=writeFile(df, testpath+i)
+
+            # File exists
+            assert os.path.exists( testpath+i)
+
+            # Files is not empty
+            assert os.path.getsize( testpath+i) > 0
+
+            # File has expected columns
+            df2 = gpd.read_file(testpath+i)
+            columns = ['seg_length', 'Xstart', 'Ystart', 'Xend', 'Yend', 'theta', 'rho']
+
+            assert all([a in df2.columns for a in columns])
+
+    def testWriteWKT(self,df):
+
+        # write as geojson 
+        dfwrite = writeFile(df, '../data/testWKTwrite.geojson')
+
+        df2 = gpd.read_file('../data/testWKTwrite.geojson')
+
+        assert "WKT" in df2.columns or "geometry" in df2.columns
+
+
+        
+
+
diff --git a/tests/testClustering.py b/tests/testClustering.py
index 77e293d..f8d1bad 100644
--- a/tests/testClustering.py
+++ b/tests/testClustering.py
@@ -6,6 +6,11 @@
 # @author: akh
 # """
 
+import numpy as np
+import pandas as pd
+import linkinglines as ll
+import pytest
+
 def testValidity(lines, dtheta, drho):
     
     if any(lines['ThetaRange'] > dtheta):
@@ -18,446 +23,60 @@ def testValidity(lines, dtheta, drho):
     else: 
         print("Passed Theta Validity Check 1")
 
-def Run3Times(dikeset, dtheta, drho, plot=False, **kwargs):
-    xc1,yc1=HT_center(dikeset)
+def Run3Times(dikeset, dtheta, drho, **kwargs):
+    xc1,yc1=ll.HT_center(dikeset)
     # run the first time 
-    dikeset, Z1=HT_AGG_custom(dikeset, dtheta, drho, **kwargs)
-    lines1, IC1=examineClusters(dikeset)
+    dikeset, Z1=ll.AggCluster(dikeset, dtheta, drho, **kwargs)
+    lines1, IC1=ll.examineClusters(dikeset)
     
     # rotate 45 deg 
-    dikeset2=rotateData2(dikeset, 45)
-    theta2, rho2, xc2, yc2=AKH_HT(dikeset2)
-    dikeset2['theta']=theta2
-    dikeset2['rho']=rho2
-    dikeset2, Z2=HT_AGG_custom(dikeset2, dtheta, drho, **kwargs)
-    dikeset2=rotateData2(dikeset2,-45)
-    lines2, IC2=examineClusters(dikeset2)
+    dikeset2=ll.rotateData(dikeset, 45)
+    dikeset2, xc2, yc2=ll.HT(dikeset2)
+    dikeset2, Z2=ll.AggCluster(dikeset2, dtheta, drho, **kwargs)
+    dikeset2=ll.rotateData(dikeset2,-45)
+    lines2, __=ll.examineClusters(dikeset2)
     
     #move to lower left
     dx=np.min([dikeset['Xstart'].min(), dikeset['Xend'].min()])
     dy=np.min([dikeset['Ystart'].min(), dikeset['Yend'].min()])
-    theta3, rho3, xc3, yc3=AKH_HT(dikeset, xc=dx,yc=dy)
-    dikeset3=dikeset.copy()
-    dikeset3['theta']=theta3
-    dikeset3['rho']=rho3
-    
-    dikeset3, Z3=HT_AGG_custom(dikeset3, dtheta, drho, **kwargs)
+    dikeset3, xc3, yc3=ll.HT(dikeset, xc=dx,yc=dy)
+
+    dikeset3, Z3=ll.AggCluster(dikeset3, dtheta, drho, **kwargs)
     
-    lines3, IC3=examineClusters(dikeset3)
+    lines3, IC3=ll.examineClusters(dikeset3)
     
     #check the changes 
-    eq12, diff12=checkClusterChange(lines1, lines2)
-    eq13, diff13=checkClusterChange(lines1, lines3)
-    eq32, diff32=checkClusterChange(lines3, lines2)
+    eq12, diff12=ll.checkClusterChange(lines1, lines2)
+    eq13, diff13=ll.checkClusterChange(lines1, lines3)
+    eq32, diff32=ll.checkClusterChange(lines3, lines2)
     
-    Flines1=FilterLines(lines1)
-    Flines2=FilterLines(lines2)
-    Flines3=FilterLines(lines3)
+    Flines1=ll.FilterLines(lines1)
+    Flines2=ll.FilterLines(lines2)
+    Flines3=ll.FilterLines(lines3)
     
     print("Comparing the filtered lines")
-    eq12, diff12=checkClusterChange(Flines1, Flines2)
-    eq13, diff13=checkClusterChange(Flines1, Flines3)
-    eq32, diff32=checkClusterChange(Flines3, Flines2)
+    eq12, diff12=ll.checkClusterChange(Flines1, Flines2)
+    eq13, diff13=ll.checkClusterChange(Flines1, Flines3)
+    eq32, diff32=ll.checkClusterChange(Flines3, Flines2)
     
-    if plot: 
-        
-        fig,ax=plt.subplots(1,2)
-        plotlines(lines1, 'k', ax[0])
-        plotlines(lines2, 'b', ax[0])
-        plotlines(lines3, 'r', ax[0])
-        
-        ax[1].scatter(dikeset['theta'], dikeset['rho'], c=dikeset['Labels'].values, alpha=0.6, cmap=cm.Greys, marker='p')
-        ax[1].scatter(dikeset2['theta'], dikeset2['rho'], c=dikeset2['Labels'].values, alpha=0.6, cmap=cm.Blues, marker='*')
-        ax[1].scatter(dikeset3['theta'], dikeset3['rho'], c=dikeset3['Labels'].values, alpha=0.6, cmap=cm.Reds, marker='^')
-        
-        fig,ax=plt.subplots(3,2)
-        
-        plotlines(lines1.iloc[diff12], 'k', ax[0,0])
-        plotlines(lines2.iloc[diff12], 'b', ax[0,1])
-        
-        plotlines(lines1.iloc[diff13], 'k', ax[1,0])
-        plotlines(lines3.iloc[diff13], 'r', ax[1,1])
-        
-        plotlines(lines2.iloc[diff32[diff32<len(lines2)]], 'b', ax[2,0])
-        plotlines(lines3.iloc[diff32], 'r', ax[2,1])
-        
-        
         
     return lines1, lines2, lines3
 
 def RotateAndCluster(dikeset,dtheta, drho,**kwargs):
-    dikesetOrig, ZOrig=HT_AGG_custom(dikeset, dtheta, drho)
+    dikesetOrig, ZOrig=ll.AggCluster(dikeset, dtheta, drho)
     
-    dikeset45=rotateData(dikeset, 45)
-    dikeset45=DikesetReProcess(dikeset45, HTredo=True)
-    dikeset45, Z45=HT_AGG_custom(dikeset45, dtheta, drho)
-    linesOrig=examineClusterShort(dikesetOrig)
-    lines45=examineClusterShort(dikeset45)
+    dikeset45=ll.rotateData(dikeset, 45)
+    dikeset45=ll.DikesetReProcess(dikeset45, HTredo=True)
+    dikeset45, Z45=ll.AggCluster(dikeset45, dtheta, drho)
+    linesOrig=ll.examineClusterShort(dikesetOrig)
+    lines45=ll.examineClusterShort(dikeset45)
     
-    changedect=checkAllClusterChange(linesOrig, lines45)
+    changedect=ll.checkAllClusterChange(linesOrig, lines45)
     if changedect:
         return dikesetOrig
     if ~changedect:
-        eqLabels, diffLabels=checkIndividualClusterChange(linesOrig, lines45)
+        eqLabels, diffLabels=ll.checkIndividualClusterChange(linesOrig, lines45)
         dikesetFinal=dikesetOrig.iloc[eqLabels[:len(dikesetOrig)]]
 
         return dikesetFinal
     
-# from scipy.cluster import hierarchy
-# from scipy.spatial.distance import squareform
-# import pandas as pd
-# from htMOD import AKH_HT as HT
-# from sklearn.preprocessing import scale
-# import numpy as np 
-# from clusterMod import *
-# import matplotlib.pyplot as plt
-# from plotmod import plotlines, labelcolors, plotbyAngleBin, BA_HT, plotResults
-# from examineMod import examineClusters,extendLines
-# from PrePostProcess import *
-# from matplotlib import cm
-
-# from matplotlib.lines import Line2D  
-
-# from matplotlib import cm
-
-# import numpy as np
-# import matplotlib.pyplot as plt
-
-# from plotmod import plotlines, HT3D
-
-# from skimage import feature
-# from htMOD import HT_center
-# from findRadialCenters import sweepCenters, detectLocalPeaks
-# from scipy.spatial.distance import pdist, squareform
-# import scipy.cluster.hierarchy as sch
-
-# from examineMod import persitance
-
-# def HashLabels(df):
-    
-#     l=[]
-#     for i in df['Labels']:
-#         h=df[df['Labels']==i]['HashID'].sum()
-#         l.append(h)
-        
-#     df['ClusterHashLabel']=l
-        
-#     return df
-
-# def checkClusterHash(df1, df2):
-#     return df1['ClusterHashLabel']==df2['ClusterHashLabel']
-
-# def cluster(df, metric):
-#     X=(np.vstack((df['theta'], df['ScaledRho'])).T)
-#     M=squareform(pdist(X, metric=metric))
-#     threshold =1 
-#     linkage='single'
-    
-#     Y=sch.single(squareform(M))
-#     clusters=sch.fcluster(Y, t=.5, criterion='distance')    
-#     # ag = AGG(n_clusters=None, affinity='precomputed', distance_threshold=threshold, linkage=linkage)
-#     # clusters=ag.fit(squareform(M))
-#     # df['Labels']=clusters.labels_
-    
-#     return M, clusters, df 
-
-# def cluster(df, metric):
-#     X=(np.vstack((df['theta'], df['ScaledRho'])).T)
-#     M=squareform(pdist(X, metric=metric))
-#     threshold =1 
-#     linkage='avera'
-    
-#     Y=sch.average(squareform(M))
-#     clusters=sch.fcluster(Y, t=1, criterion='distance')    
-#     # ag = AGG(n_clusters=None, affinity='precomputed', distance_threshold=threshold, linkage=linkage)
-#     # clusters=ag.fit(squareform(M))
-#     df['Labels']=clusters
-    
-#     return M, clusters, df
-
-
-# testData=pd.read_csv('/home/akh/myprojects/Linking-and-Clustering-Dikes/dikedata/testDatafmCRB.csv')
-
-# metric1= lambda u, v: CyclicEuclideanScaled(u,v, 2,572)
-# metric2= lambda u, v: CyclicEuclideanScaled(u,v, 5,1000)
-# metric3= lambda u, v: CyclicEuclideanScaled(u,v,.2,.1)
-# testData=HashLabels(testData)
-# trueData1=HashLabels(testData)
-# h=testData['HashID'].values
-# trueLabels2=pd.DataFrame({'HashID':h, 'Labels':[1,2,3,4,5,5,5,5,6,6,7,7]})
-# trueLabels2=HashLabels(trueLabels2)
-
-# trueLabels3=pd.DataFrame({'HashID':h, 'Labels':[1,2,3,4,5,6,7,8,9,9,10,11, ]})
-# trueLabels3=HashLabels(trueLabels3)
-
-
-# print( "Test 0: does the expected hashes line up with testData and testData")
-
-# if all(checkClusterHash(testData, testData)):
-#     print ("passed test 0")
-
-# print('Test 1, dtheta=2, drho=572')
-# M1,cluster1,df1=cluster(testData.copy(), metric1)
-# df1=HashLabels(df1)
-
-# if all(checkClusterHash(trueData1, df1)):
-#     print ("passed test 1")
-    
-# else:
-#     print('Failed test 1')
-#     print(checkClusterHash(trueData1, df1))
-
-
-# print('Test 2, dtheta=10, drho=1000')
-# M2,cluster2,df2=cluster(testData.copy(), metric2)
-# df2=HashLabels(df2)
-# if all(checkClusterHash(trueLabels2, df2)):
-#     print ("passed test 2")
-# else:
-#     print('Failed test 2')
-#     print(checkClusterHash(trueLabels2, df2))
-#     Z=sch.average(squareform(M2))
-#     fig,ax=plt.subplots()
-#     Y=sch.dendrogram(Z)
-#     plt.show()
-
-# print('Test 3, dtheta=.1, drho=10')
-# M3,cluster3,df3=cluster(testData.copy(), metric3)
-# df3=HashLabels(df3)
-# if all(checkClusterHash(trueLabels3, df3)):
-#     print ("passed test 3")
-# else:   
-#     print('Failed test 3')
-    
-#     print(checkClusterHash(trueLabels3, df3))
-#     Z=sch.average(squareform(M3))
-#     fig,ax=plt.subplots()
-#     Y=sch.dendrogram(Z)
-#     plt.show()
-
-# from synthetic import *
-# from plotmod import plotlines, DotsHT
-
-# TrueLength=5000
-# df1 = makeLinear2(TrueLength, 20, 5, 5000, 1000,10, CartRange=100000)
-# df2 = makeLinear2(TrueLength, 45, 5, -5000, 1000, 10, CartRange=100000)
-
-# fig,ax=plt.subplots(3,2)
-
-
-# df = addSwarms([df1, df2])
-# df['Label']=np.arange(0,len(df))
-# df=completePreProcess(df)
-# dikes = fragmentDikes(df)
-# theta, rho, xc, yc= HT(dikes, xc=0, yc=0)
-# dikes['theta']=theta
-# dikes['rho']=rho
-# dikes= MidtoPerpDistance(dikes, xc, yc)
-# midist=dikes['PerpOffsetDist'].values
-
-# plotlines(df, 'r', ax[0,0], ColorBy='Label')
-# plotlines(dikes, 'r', ax[1,0], ColorBy='Label')
-# fig,ax[0,1]=DotsHT(fig, ax[0,1], df, ColorBy='Label')
-# fig,ax[1,1]=DotsHT(fig, ax[1,1], dikes, ColorBy='Label')
-
-
-# dikes['TrueLabel']=dikes['Label']
-# from clusterMod import HT_AGG_custom
-# from examineMod import checkClusterChange2
-
-# dtheta=1
-# drho=500#dikes['seg_length'].mean()
-# dikes,clusters, M=HT_AGG_custom(dikes, dtheta, drho, linkage='average')
-# lines,IC=examineClusters(dikes)
-
-# plotlines(lines, 'r', ax[2,0], ColorBy='Label')
-# fig,ax[2,1]=DotsHT(fig, ax[2,1], lines, ColorBy='Label')
-
-
-# #checkClusterChange2(dikes,lines, df)
-# #fig,ax=plt.subplots(3)
-# if len(lines)==len(df): 
-#     print("Test 4.1 same number of dikes identified")    
-# else: 
-#     print("Test 4.1 Failed")    
-        
-# for ind, row in lines.iterrows():
-#     print(ind)
-#     terror=np.min( abs(df['theta']-row['AvgTheta']))
-#     perror=np.min( abs(df['rho']-row['AvgRho']))
-#     lerror=abs(5000-row['R_Length'])
-#     print("theta error", terror )
-#     print('rho error', perror)
-#     print('length error', )
-    
-#     if (row['StdTheta'] > dtheta):
-#         print(ind, "Test 4.2 Failed")
-    
-    
-    
-#     # little correlation between types of error
-#     #ax[0].plot(perror, terror, 'r*')
-#     #ax[1].plot(lerror, terror, 'r*')
-#     #ax[2].plot(lerror, perror, 'r*')
-
-# print("Mean length:", lines['R_Length'].mean(), "Max:",lines['R_Length'].max())
-# if lines['R_Length'].max() > TrueLength: 
-#     print('Test 4.3 Max length greater than true length')
-#     print('Failed')
-# else: 
-#     print('Test 4.3 Max length greater than true length')
-#     print("Passed")
-
-# test5=False
-# if test5:
-#     print('entering test 5 ')
-#     spacing=[200, 500, 1000, 5000]
-#     drhos=[200,350, 500,1000, 2000, 5000, 6000, 7000]
-#     cs=[]
-#     cs2=[]
-#     cs3=[]
-#     T=[]
-#     ndikes=10
-#     for i in spacing:
-#         df=standardSpacing(ndikes, 45, 10000, i)
-#         df=completePreProcess(df)
-#         dikes = fragmentDikes(df)
-#         theta, rho, xc, yc= HT(dikes, xc=0, yc=0)
-#         dikes['theta']=theta
-#         dikes['rho']=rho
-#         dikes= MidtoPerpDistance(dikes, xc, yc)
-#         midist=dikes['PerpOffsetDist'].values
-#         dtheta=1
-#         for drho in drhos:
-            
-            
-#             dikes,clusters, M=HT_AGG_custom(dikes, dtheta, drho, linkage='single')
-#             lines,IC=examineClusters(dikes)
-#             #print ("for true spacing", i, "and clustering threshold", drho)
-#             #print( len(lines), "clusters idenitified of ", len(df), "true dikes")
-#             cs.append(len(lines))
-            
-            
-#             dikes,clusters, M=HT_AGG_custom(dikes, dtheta, drho, linkage='complete')
-#             lines,IC=examineClusters(dikes)
-#             #print ("for true spacing", i, "and clustering threshold", drho)
-#             #print( len(lines), "clusters idenitified of ", len(df), "true dikes")
-#             cs2.append(len(lines))
-            
-            
-#             dikes,clusters, M=HT_AGG_custom(dikes, dtheta, drho, linkage='average')
-#             lines,IC=examineClusters(dikes)
-#             #print ("for true spacing", i, "and clustering threshold", drho)
-#             #print( len(lines), "clusters idenitified of ", len(df), "true dikes")
-#             cs3.append(len(lines))
-            
-#             T.append(drho/i)
-    
-    
-#     x=np.array(T)
-    
-    
-#     cs=np.array(cs)
-#     cs2=np.array(cs2)
-#     cs3=np.array(cs3)
-    
-#     f,ax=plt.subplots()
-#     ax.plot(x, cs, "rs", label='Single Linkage', markersize=7, alpha=0.7, markeredgecolor="r" )
-#     ax.plot(x, cs2, "bp", label='Complete Linkage', markersize=7, alpha=0.7, markeredgecolor="b")
-#     ax.plot(x,cs3, "g*", label='Average Linkage', markersize=7, alpha=0.7, markeredgecolor="g")
-    
-#     #fit exponential curve to cs data using scipy curve_fit
-#     import scipy.optimize as opt
-    
-#     def expFit(x,y):
-    
-    
-#         def exp(x,a,b,c):
-#             return a*np.exp(b*-1*x)+c
-    
-#         popt, pcov = opt.curve_fit(exp, x,y)
-        
-#         #calculate the r^2 value
-#         residuals = np.sum(np.square(y - exp(x, *popt)))
-#         squaredDiff = np.sum(np.square(y - np.mean(y)))
-#         r2 = 1 - (residuals / squaredDiff)
-#         x2=np.linspace(min(x), max(x), 100)
-#         y2=exp(x2, *popt)
-        
-    
-#         return popt, pcov, r2, x2, y2
-    
-#     popt3, pcov3, r2_3, x2, y2_3=expFit(x, cs3)
-#     popt2, pcov2, r2_2, x2, y2_2=expFit(x, cs2)
-#     popt1, pcov1, r2, x2, y2=expFit(x, cs)
-    
-#     ax.plot(x2, y2, "r")
-#     ax.plot(x2, y2_2, "b")
-#     ax.plot(x2, y2_3, "g")
-    
-#     ax.legend(loc='upper right')
-#     ax.set_ylabel('Number of Dikes')
-#     ax.set_xlabel('RhoThreshold/TrueSpacing')
-
-# from examineMod import enEchelon
-# test7=False
-# if test7:
-#     print('entering test 7')
-    
-#     AvgTheta=80
-#     df=EnEchelonSynthetic(5, AvgTheta, 10000, 200)
-#     df=completePreProcess(df)
-#     dikes = fragmentDikes(df)
-#     theta, rho, xc, yc= HT(dikes, xc=0, yc=0)
-#     dikes['theta']=theta
-#     dikes['rho']=rho
-#     dikes= MidtoPerpDistance(dikes, xc, yc)
-#     midist=dikes['PerpOffsetDist'].values
-#     df['Label']=np.ones(len(df))
-#     Xmid=df['Xmid'].to_numpy()
-#     Ymid=df['Ymid'].to_numpy()
-    
-#     slope, intercept, r_value, p_value, std_err = stats.linregress(Xmid, Ymid)
-#     thetaM=np.rad2deg(np.arctan(-1/(slope+ 0.0000000000000001)))
-#     if np.isnan(slope):
-#         thetaM=0
-#         p_value=0.05
-    
-    
-#     tdiff=CyclicAngleDist([AvgTheta], [thetaM])
-#     print(tdiff, thetaM, p_value)
-#     #if p_value > 0.05:
-#     #    tdff=np.nan
-    
-#     #tdiff=enEchelon(df,45)
-#     print(tdiff)
-    
-#     tdiff2, EXstart, EXend, EYstart, EYend=enEchelon(df, AvgTheta)
-    
-#     fig,ax=plt.subplots()
-#     plotlines(df, 'k', ax)
-#     ax.plot(Xmid, Ymid, 'r*-')
-#     ax.axis('equal')
-#     ax.plot([EXstart, EXend], [EYstart, EYend])
-
-
-# print ("Test 8 ")
-
-# print("Are sklearn and scipy the same?")
-
-# from clusterMod import HT_AGG_custom as AggHT
-# from clusterMod import HT_AGG_custom2
-# drho=572
-# dtheta=2
-
-# test1, clusters, M=AggHT(testData, dtheta, drho)
-
-# test2, Z=HT_AGG_custom2(testData, dtheta, drho)
-
-# print("Sklearn", test1['Labels'])
-# print("Scipy", test2['Labels'])
-
-# from examineMod import checkClusterChange
-
-# print(checkClusterChange(test1, test2))
-