diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..55b547b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,105 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.idea
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
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+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
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+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
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+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
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+
+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
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+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/Readme.md b/Readme.md
new file mode 100644
index 0000000..e8970a8
--- /dev/null
+++ b/Readme.md
@@ -0,0 +1 @@
+Python tool for analyzing raw eye-tracking data from the Varjo VR and XR headsets.
\ No newline at end of file
diff --git a/arff_helper.py b/arff_helper.py
new file mode 100644
index 0000000..d8d3307
--- /dev/null
+++ b/arff_helper.py
@@ -0,0 +1,152 @@
+from collections import OrderedDict
+import arff
+import warnings
+import numpy as np
+import numpy.lib.recfunctions as rfn
+
+class ArffHelper(object):
+    """
+    The class is based on general arff handler with an extra keyword %@METADATA
+    (they are comment lines in the description, i.e. lines *before* the
+    `relation` keyword).
+
+    Metadata fields contains metadata names and related values (separated by space characters).
+
+    - Lines starting with "%" are comments, except for line starting with %@METADATA
+    - Lines starting with "@" that is followed by a word (without space), are considered
+      keywords. The available keywords are the following:
+        @RELATION: a string with the name of the data set.
+        @ATTRIBUTES: a list of attributes representing names of data columns
+                     followed by the types of data. The available data types
+                     are 'NUMERIC', 'REAL', 'INTEGER' or a list of string.
+        @DESCRIPTION: a string with the description of the data set.
+        @DATA: a list of data instances. The data should follow the order that
+               the attributes were presented.
+    - Metadata ('%@METADATA <KEY> <VALUE>' lines) can have any keys, but is not currently used
+
+    """
+    _METADATA_STRING = '@metadata'
+    _METADATA_COLUMNS_COUNT = 3  # @METADATA KEY VALUE
+    _METADATA_KEY_COLUMN = 1     # First key,
+    _METADATA_VALUE_COLUMN = 2   # then value
+    _ATTRIBUTES_TYPE = {'NUMERIC': np.float32, 'REAL': np.double, 'INTEGER': np.int64}
+
+    def __init__(self):
+        pass
+
+    # Public interface functions (I/O)
+    #
+    # I. Loading functions (from file or string)
+    #
+
+    @staticmethod
+    def add_column(obj, name, dtype, default_value):
+        """
+        Add a new column to @obj['data'] and a new attribute to @obj['attributes']
+        (i.e. the name of the new column and the data type for this column).
+        This operation is performed in-place, so the @obj itself is changed.
+
+        :param obj: arff object before adding new column.
+        :param name: name of the new column.
+        :param dtype: data type of the new column.
+                      Available data types:
+                      'NUMERIC', 'REAL', 'INTEGER' or a list of strings (then it's a categorical column with
+                      the provided values as options).
+        :param default_value: default value of the new column (we need to somehow assign the data in the new column).
+        :return: arff object with an additional column.
+
+        """
+        obj['data'] = ArffHelper.add_column_to_array(obj['data'], name, dtype, default_value)
+        obj['attributes'].append((name, dtype))
+
+        return obj
+
+    @staticmethod
+    def add_column_to_array(arr, name, dtype, def_value):
+        """
+        Add a new column to a structured numpy array.
+
+        :param arr: numpy array before adding column.
+        :param name: name of the new column.
+        :param dtype: data type of the new column.
+                      Available data types:
+                      'NUMERIC', 'REAL', 'INTEGER' or a list of strings (then it's a categorical column with
+                      the provided values as options).
+        :param def_value: default value of the new column.
+        :return: numpy array with new column.
+
+        """
+        # check if def_value is in dtype
+        if type(def_value) == str and def_value not in dtype:
+            warnings.warn("The type of the default value is not the same as type of column data"
+                          " or the default value is not in the list (date type provided is {})".format(name))
+
+        if name in arr.dtype.names:
+            raise ValueError('Array @arr already has a field {}'.format(name))
+
+        if arr.size != 0:
+            arr = rfn.append_fields(base=arr,
+                                    names=name,
+                                    data=[def_value] * len(arr),
+                                    dtypes=ArffHelper._convert_dtype_to_numpy(dtype),
+                                    usemask=False)
+        else:
+            # If @arr is empty, it should have been created with ArffHelper.create_empty() method, or in a similar
+            # fashion. In that case, it has a length (passed as a parameter at creation), but no elements.
+            arr = np.array([def_value] * len(arr), dtype=[(name, ArffHelper._convert_dtype_to_numpy(dtype))])
+        return arr
+
+    @staticmethod
+    def remove_column(obj, name):
+        """
+        Remove a column with respective name from @obj['data'] and its attributes (@obj['attributes']).
+
+        :param obj: arff object before adding new column.
+        :param name: name of the deleted column.
+        :return: arff object without the column @name.
+
+        """
+        deleted_column_index = [column_name for column_name, _ in obj['attributes']].index(name)
+        obj['attributes'].pop(deleted_column_index)
+        # keep just the remaining attributes
+        obj['data'] = rfn.drop_fields(base=obj['data'],
+                                      drop_names=name,
+                                      usemask=False)
+        return obj
+
+    @staticmethod
+    def convert_data_to_structured_array(obj):
+        """
+        Convert data in @obj['data'] into a structured numpy array according to the data type in
+        @obj['attributes'].
+
+        :param obj: arff object before data conversion.
+        :return: arff object after data conversion.
+
+        """
+        d = np.dtype([(str(at[0]), ArffHelper._convert_dtype_to_numpy(at[1])) for at in obj['attributes']])
+        obj['data'] = np.array([tuple(item) for item in obj['data']], dtype=d)
+        return obj
+
+    @staticmethod
+    def _convert_dtype_to_numpy(data_type):
+        """
+        Validate input @data_type as ARFF-supported data type and convert to numpy.dtype.
+
+        :param data_type: input data_type, string.
+                          Available data types:
+                          'NUMERIC', 'REAL', 'INTEGER' or a tuple of string (then it's a categorical attribute).
+        :return: converted numpy.dtype from input data_type.
+
+        """
+        if data_type in ArffHelper._ATTRIBUTES_TYPE.keys():
+            return ArffHelper._ATTRIBUTES_TYPE[data_type]
+        else:
+            if type(data_type) == tuple:
+                max_length = max(map(len, data_type))
+            else:
+                raise ValueError("Wrong data type in attributes. "
+                                 "It should be a list of strings or one of the data types in {}".format(
+                                  ', '.join(ArffHelper._ATTRIBUTES_TYPE.keys())))
+
+            return '<U{}'.format(max_length)
diff --git a/blink_detector.py b/blink_detector.py
new file mode 100644
index 0000000..3fc8fc4
--- /dev/null
+++ b/blink_detector.py
@@ -0,0 +1,87 @@
+import copy
+import numpy as np
+
+"""
+Detecting blinks by extending the 0-confidence intervals into nearby saccades. The maximal distance to saccade is
+an initialization parameter. When the observer performs a blink, the eye tracker usually first detects a saccade
+downwards, then looses the eye image, then detects a saccade upwards when the eye is opened.
+"""
+
+def BlinkDetector(param, gaze_points, inplace=False):
+    """
+    This method labels blinks in the provided gaze_points, which should be an arff object. We extend the
+    0-confidence intervals by adding the nearest saccade (up to one from the left and up to one from the right)
+    if it is no more than @param["MAXIMAL"]_DISTANCE_TO_SACCADE_MICROSEC away from the 0-confidence sample.
+
+
+    :param gaze_points: gaze recording data, an arff object (i.e. a dictionary with 'data', 'metadata'
+                        and etc. keys). If the array under the 'data' key has no 'confidence' column, the method
+                        does nothing.
+    :param inplace: whether to replace the data inside @gaze_points or create a new structure
+    :return: gaze points with added labels BLINK
+    """
+    if not inplace:
+        gaze_points = copy.deepcopy(gaze_points)
+
+    if 'status' not in gaze_points['data'].dtype.names:
+        return gaze_points
+
+    # determine 0 and 1 array from status in data in unity recording
+    is_blink = (gaze_points['data']['status'] == 0).astype(int)
+    # find blink onsets, adding fake not-blink sample before recording
+    blink_diff = np.diff(np.hstack([[0], is_blink]))
+    blink_onsets = np.nonzero(blink_diff == 1)[0]
+    # find blink offsets, adding fake not-blink sample after recording
+    blink_diff = np.diff(np.hstack([is_blink, [0]]))
+    blink_offsets = np.nonzero(blink_diff == -1)[0]
+
+    times = gaze_points['data']['time']
+
+    assert len(blink_onsets) == len(blink_offsets)
+    for onset, offset in zip(blink_onsets, blink_offsets):
+
+        if param["VERBOSE"]:
+            print("Found blink from {} to {}".format(times[onset], times[offset]))
+
+        # go back in time and look for a saccade
+        onset_candidate = onset
+        while onset_candidate >= 0 \
+                and times[onset] - times[onset_candidate] < param["MAXIMAL_DISTANCE_TO_SACCADE_MILLISEC"]:
+            if gaze_points['data'][onset_candidate]['EYE_MOVEMENT_TYPE'] == 'SACCADE':
+                # Found a saccade! The blink will start at the start of this saccade
+                sacc_index = gaze_points['data'][onset_candidate]['SACC_INTERVAL_INDEX']
+                first_saccade_index = np.nonzero(
+                    gaze_points['data']['SACC_INTERVAL_INDEX'] == sacc_index)[0][0]
+                onset = first_saccade_index
+                break
+            # otherwise just continue the search backwards
+            onset_candidate -= 1
+
+        # go forward in time and look for a saccade
+        offset_candidate = offset
+        while offset_candidate < len(times) \
+                and times[offset_candidate] - times[offset] < param["MAXIMAL_DISTANCE_TO_SACCADE_MILLISEC"]:
+            if gaze_points['data'][offset_candidate]['EYE_MOVEMENT_TYPE'] == 'SACCADE':
+                # Found a saccade! The blink will end at the end of this saccade
+                sacc_index = gaze_points['data'][offset_candidate]['SACC_INTERVAL_INDEX']
+                last_saccade_index = np.nonzero(
+                    gaze_points['data']['SACC_INTERVAL_INDEX'] == sacc_index)[0][-1]
+                offset = last_saccade_index
+                break
+            # otherwise just continue the search forwards
+            offset_candidate += 1
+
+        if param["VERBOSE"]:
+            print("Extended it to {} {}".format(times[onset], times[offset]))
+
+        # remove gaps in data that are to short to be blinks.
+        if times[offset] - times[onset] < param['MINIMAL_BLINK_DURATION']:
+            gaze_points['data'][onset:offset + 1]['EYE_MOVEMENT_TYPE'] = 'NOISE'
+        else:
+            gaze_points['data'][onset:offset + 1]['EYE_MOVEMENT_TYPE'] = 'BLINK'
+            # this is not a saccade anymore
+            gaze_points['data'][onset:offset + 1]['SACC_INTERVAL_INDEX'] = -1
+            # nor is it a normal sequence between saccades
+            gaze_points['data'][onset:offset + 1]['INTERSACC_INTERVAL_INDEX'] = -1
+
+    return gaze_points
diff --git a/calculators.py b/calculators.py
new file mode 100644
index 0000000..5e84cc0
--- /dev/null
+++ b/calculators.py
@@ -0,0 +1,251 @@
+import numpy
+import numpy as np
+
+
+def fixation(x, y, time, events):
+    """Calculate fixation measures
+	
+	arguments
+	time		-	numpy array of EyeTribe timestamps
+	events      -   numpy array of of detected gaze events
+
+	returns
+				Fixations	-	list of lists, each containing [starttime, endtime, duration, endx, endy]
+	"""
+
+    # empty list to contain data
+    Fixations = []
+
+    # check where the missing samples are
+    idx = numpy.array(events == 'FIX', dtype=int)
+
+    # check where the starts and ends are (+1 to counteract shift to left)
+    diff = numpy.diff(idx)
+    starts = numpy.where(diff == 1)[0] + 1
+    ends = numpy.where(diff == -1)[0] + 1
+
+    # delete detected starts and ends from before and after recording
+    if idx[-0] == 1:
+        starts = starts[0:-1]
+    if idx[0] == 1:
+        ends = ends[1::]
+    if len(starts) > len(ends):
+        ends = np.append(ends, len(idx) - 1)
+
+    # compile fixation starts and ends
+    for i in range(len(starts)):
+        # get starting index
+        s = starts[i]
+        # get ending index
+        if i < len(ends):
+            e = ends[i]
+        elif len(ends) > 0:
+            e = ends[-1]
+        else:
+            e = -1
+
+        # add ending time
+        xpos = np.mean([x[s], x[e]])
+        ypos = np.mean([y[s], y[e]])
+        duration = time[e] - time[s]
+        Fixations.append([time[s], time[e], duration, xpos, ypos])
+
+    Fixations = numpy.array(Fixations)
+    if Fixations.any():
+        numFix = len(Fixations)
+        avgFix = 1000 * sum(Fixations[:, 2]) / numFix
+        print('Number of fixations: ' + str(numFix))
+        print('     Average fixation duration: ' + str(avgFix) + 'ms')
+
+    return Fixations
+
+
+def saccade(x, y, v, time, events):
+    """Calculate Saccade measures
+
+	arguments
+
+	x		-	numpy array of x positions
+	y		-	numpy array of y positions
+	v       -   numpy array of velocities
+	time	-	numpy array of tracker timestamps in milliseconds
+    events      -   numpy array of of detected gaze events
+
+	returns
+			Saccades	-	list of lists, each containing [starttime, endtime, duration, startx, starty, endx, endy,  amplitude, meanvel, maxvel])]
+	"""
+
+    # empty list to contain data
+    Saccades = []
+
+    # check where the missing samples are
+    idx = numpy.array(events == 'SACCADE', dtype=int)
+
+    # check where the starts and ends are (+1 to counteract shift to left)
+    diff = numpy.diff(idx)
+    starts = numpy.where(diff == 1)[0] + 1
+    ends = numpy.where(diff == -1)[0] + 1
+
+    # delete detected starts and ends from before and after recording
+    if idx[-0] == 1:
+        starts = starts[0:-1]
+    if idx[0] == 1:
+        ends = ends[1::]
+    if len(starts) > len(ends):
+        ends = np.append(ends, len(idx) - 1)
+
+    # compile saccade starts and ends
+    for i in range(len(starts)):
+        # get starting index
+        s = starts[i]
+        # get ending index
+        if i < len(ends):
+            e = ends[i]
+        elif len(ends) > 0:
+            e = ends[-1]
+        else:
+            e = -1
+
+        # writing data
+        duration = time[e] - time[s]
+        amplitude = ((x[s] - x[e]) ** 2 + (y[s] - y[e]) ** 2) ** 0.5
+        meanvel = sum(v[s:e]) / len(v[s:e])
+        maxvel = max(v[s:e])
+
+        Saccades.append([time[s], time[e], duration, x[s], y[s], x[e], y[e], amplitude, meanvel, maxvel])
+
+    Saccades = numpy.array(Saccades)
+    if Saccades.any():
+        numSac = len(Saccades)
+        avgSacT = 1000 * sum(Saccades[:, 2]) / numSac
+        avgSacA = sum(Saccades[:, 7]) / numSac
+        avgSacV = sum(Saccades[:, 8]) / numSac
+        avgSacMV = sum(Saccades[:, 9]) / numSac
+        print('Number of saccades: ' + str(numSac))
+        print('     Average saccades duration: ' + str(avgSacT) + ' ms')
+        print('     Average saccades Amplitude: ' + str(avgSacA) + ' deg')
+        print('     Average saccades velocity: ' + str(avgSacV) + ' deg/s')
+        print('     Average max saccades velocity: ' + str(avgSacMV) + ' deg/s')
+
+    return Saccades
+
+
+def persuit(x, y, v, time, events):
+    """Calculates Persuit measures
+	arguments
+
+	x		-	numpy array of x positions
+	y		-	numpy array of y positions
+	v       -   numpy array of velocities
+	time	-	numpy array of tracker timestamps in milliseconds
+    events      -   numpy array of of detected gaze events
+
+	returns
+				Persuits	-	list of lists, each containing [starttime, endtime, duration, startx, starty, endx, endy,  amplitude, meanvel, maxvel])]
+	"""
+    # empty list to contain data
+    Persuits = []
+
+    # check where the missing samples are
+    idx = numpy.array(events == 'SP', dtype=int)
+
+    # check where the starts and ends are (+1 to counteract shift to left)
+    diff = numpy.diff(idx)
+    starts = numpy.where(diff == 1)[0] + 1
+    ends = numpy.where(diff == -1)[0] + 1
+
+    # delete detected starts and ends from before and after recording
+    if idx[-0] == 1:
+        starts = starts[0:-1]
+    if idx[0] == 1:
+        ends = ends[1::]
+    if len(starts) > len(ends):
+        ends = np.append(ends, len(idx) - 1)
+
+    # compile persuit starts and ends
+    for i in range(len(starts)):
+        # get starting index
+        s = starts[i]
+        # get ending index
+        if i < len(ends):
+            e = ends[i]
+        elif len(ends) > 0:
+            e = ends[-1]
+        else:
+            e = -1
+
+        # writing data
+        duration = time[e] - time[s]
+        amplitude = ((x[s] - x[e]) ** 2 + (y[s] - y[e]) ** 2) ** 0.5
+        meanvel = sum(v[s:e]) / len(v[s:e])
+        maxvel = max(v[s:e])
+
+        Persuits.append([time[s], time[e], duration, x[s], y[s], x[e], y[e], amplitude, meanvel, maxvel])
+
+    Persuits = numpy.array(Persuits)
+    if Persuits.any():
+        numSP = len(Persuits)
+        avgSPT = 1000 * sum(Persuits[:, 2]) / numSP
+        avgSPA = sum(Persuits[:, 7]) / numSP
+        avgSPV = sum(Persuits[:, 8]) / numSP
+        avgSPMV = sum(Persuits[:, 9]) / numSP
+        print('Number of Smooth Persuits: ' + str(numSP))
+        print('     Average persuit duration: ' + str(avgSPT) + 'ms')
+        print('     Average persuit Amplitude: ' + str(avgSPA) + ' deg')
+        print('     Average persuit velocity: ' + str(avgSPV) + ' deg/s')
+        print('     Average max persuit velocity: ' + str(avgSPMV) + ' deg/s')
+
+    return Persuits
+
+
+def blink(time, events):
+    """Calculates Blink measures
+	arguments
+                time		-	numpy array of EyeTribe timestamps
+
+	returns
+		        Blinks	-	list of lists, each containing [starttime, endtime, duration]
+		        events      -   numpy array of of detected gaze events
+	"""
+    # empty list to contain data
+    Blinks = []
+
+    # check where the missing samples are
+    idx = numpy.array(events == 'BLINK', dtype=int)
+
+    # check where the starts and ends are (+1 to counteract shift to left)
+    diff = numpy.diff(idx)
+    starts = numpy.where(diff == 1)[0] + 1
+    ends = numpy.where(diff == -1)[0] + 1
+
+    # delete detected starts and ends from before and after recording
+    if idx[-0] == 1:
+        starts = starts[0:-1]
+    if idx[0] == 1:
+        ends = ends[1::]
+    if len(starts) > len(ends):
+        ends = np.append(ends, len(idx) - 1)
+
+    # compile blink starts and ends
+    for i in range(len(starts)):
+        # get starting index
+        s = starts[i]
+        # get ending index
+        if i < len(ends):
+            e = ends[i]
+        elif len(ends) > 0:
+            e = ends[-1]
+        else:
+            e = -1
+
+        # add ending time
+        Blinks.append([time[s], time[e], time[e] - time[s]])
+
+    Blinks = numpy.array(Blinks)
+    if Blinks.any():
+        numBlk = len(Blinks)
+        avgBlk = 1000 * sum(Blinks[:, 2]) / numBlk
+        print('Number of blinks: ' + str(numBlk))
+        print('     Average blink duration: ' + str(avgBlk) + 'ms')
+
+    return Blinks
diff --git a/fixation_detector.py b/fixation_detector.py
new file mode 100644
index 0000000..94462d3
--- /dev/null
+++ b/fixation_detector.py
@@ -0,0 +1,169 @@
+import copy
+import math
+import numpy as np
+from arff import xrange
+
+import functions
+from arff_helper import ArffHelper
+
+def FixationDetector(param, gaze_points, inplace=False):
+    """
+    Identify and label fixation intervals as 'FIX' and some others as 'NOISE'.
+
+    Fixation identification includes the following steps:
+    - First, all inter-saccadic intervals with a dispersion of less than
+      a certain spread threshold (@param["PREFILTERING_INTERVAL_SPREAD_THRESHOLD_DEGREES"] are marked as fixations.
+    - Then, a temporal window (@param["SLIDING_WINDOW_WIDTH_MILLISEC"]ms) is shifted across the
+      remaining data and a non-fixation onset (offset) is marked every
+      time speed rises above (fell below) threshold (@param["SPEED_THRESHOLD_DEGREES_PER_SEC"].
+    - There are two ways for speed calculation: spread and speed.
+        -'speed': speed from start point to end point is larger than
+                  threshold.
+        -'spread': maximum moving speed of either x or y is larger than
+                   threshold.
+      Data with speed below threshold are labeled as 'FIX'.
+    - Finally, non-fixation episodes longer than @param["MINIMAL_SP_DURATION_MILLISEC"]are kept as 'UNKNOWN',
+      the shorter ones are labeled as 'NOISE' (these are fairly dynamic episodes that however should not be SP).
+
+    :param gaze_points: arff object with saccades detected (and intersaccadic intervals labelled)
+    :param inplace: whether to replace the data inside @gaze_points or create a new structure
+    :return: arff object with data labeled as 'FIX' and 'NOISE'. Some 'UNKNOWN' labels are kept for the next stage.
+
+    """
+    if not inplace:
+        gaze_points = copy.deepcopy(gaze_points)
+    # add a global index column (to keep track of where we are even if working within an intersaccadic interval)
+    gaze_points = ArffHelper.add_column(gaze_points, name='global_index', dtype='INTEGER', default_value=-1)
+    gaze_points['data']['global_index'] = np.arange(gaze_points['data'].shape[0])
+
+    # I. First step of fixation removal: rough prefiltering
+    speed_thd = param["SPEED_THRESHOLD_DEGREES_PER_SEC"]
+    prefiltering_spread_thd = param["PREFILTERING_INTERVAL_SPREAD_THRESHOLD_DEGREES"]
+
+    # record intersaccadic interval indices of those intervals that are not labelled as FIX by the prefiltering
+    unknown_interval_index = []
+    unknown_interval_masks = []
+    for i in xrange(max(gaze_points['data']['INTERSACC_INTERVAL_INDEX']) + 1):
+        mask = gaze_points['data']['INTERSACC_INTERVAL_INDEX'] == i
+        intersacc_interval = gaze_points['data'][mask]
+        if len(intersacc_interval) == 0:
+            continue
+
+        dispersion = [max(intersacc_interval['x']) - min(intersacc_interval['x']),
+                      max(intersacc_interval['y']) - min(intersacc_interval['y'])]
+
+        if any(thd >= prefiltering_spread_thd for thd in dispersion):
+            unknown_interval_index.append(i)  # keep unknown
+            unknown_interval_masks.append(mask.copy())  # cache the indexing
+        else:
+            gaze_points['data']['EYE_MOVEMENT_TYPE'][mask] = 'FIX'
+
+    # II. Second step of fixation removal: finer prefiltering
+    #
+
+    for i, interval_mask in zip(unknown_interval_index, unknown_interval_masks):
+        # We record the borders of the non-FIX episodes to validate their duration. If the non-FIX episode is very
+        # short, we mark it as NOISE (not enough duration for a candidate for smooth pursuit)
+        onset_timestamp = None
+        onset_index = None
+
+        intersacc_interval = gaze_points['data'][interval_mask]
+        intersacc_interval = functions.get_xy_moving_average(intersacc_interval,
+                        param["NORMALIZATION_SLIDING_WINDOW_SIZE_SAMPLES"],
+                        inplace=False)
+
+        # for intervals shorter than @param["INTERSACCADIC_INTERVAL_DURATION_THRESHOLD_MILLISEC"]
+        # cannot do further filtering. The label remains 'UNKNOWN'
+        if intersacc_interval['time'][-1] - intersacc_interval['time'][0] < \
+                param["INTERSACCADIC_INTERVAL_DURATION_THRESHOLD_MILLISEC"]:
+            continue
+
+        # for intervals that longer than param["SLIDING_WINDOW_WIDTH_MILLISEC"] do further pre-filtering.
+        # Label data as 'FIX' or 'NOISE', or keep 'UNKNOWN'
+        else:
+            # window is shifted by 1 sample every time
+            for index, item in enumerate(intersacc_interval):
+                x_start = item['x']
+                y_start = item['y']
+                shift_window_interval = intersacc_interval[
+                    (intersacc_interval['time'] >= item['time']) *
+                    (intersacc_interval['time'] <= item['time'] + param["SLIDING_WINDOW_WIDTH_MILLISEC"])
+                ]
+
+                # if distance between current data and the end of interval is shorter than
+                # param["SLIDING_WINDOW_WIDTH_MILLISEC"](i.e. if the end of the window matches the end of the
+                # intersaccadic interval), we keep the previous label if it was FIX, otherwise keep UNKNOWN
+                if shift_window_interval['time'][-1] == intersacc_interval['time'][-1]:
+                    if intersacc_interval['EYE_MOVEMENT_TYPE'][index - 1] == 'FIX':
+                        gaze_points['data']['EYE_MOVEMENT_TYPE'][
+                            (gaze_points['data']['time'] == item['time'])] = 'FIX'
+
+                        # we do not keep track of the non-fixation interval anymore since it will be all fixation
+                        # until the end of the intersaccadic interval
+                        onset_timestamp = None
+                        onset_index = None
+                    else:
+                        # new non-fixation interval is starting
+                        onset_timestamp = item['time']
+                        onset_index = item['global_index']
+
+                # if distance between current data and the end of interval is larger than window size, continue
+                # with the process
+                else:
+                    # get window duration in seconds
+                    period = (shift_window_interval['time'][-1] - shift_window_interval['time'][0]) * 1e-6
+
+                    # is the fixation criterion satisfied?
+                    fixation_flag = True
+                    if param["SLIDING_WINDOW_CRITERION"] == 'speed':
+                        # if the current speed is larger than speed threshold --
+                        # mark as onset(UNKNOWN, NOISE). else -- mark as offset(FIX)
+                        x_end = shift_window_interval['x'][-1]
+                        y_end = shift_window_interval['y'][-1]
+
+                        if math.sqrt((x_start - x_end) ** 2 + (y_start - y_end) ** 2) >= speed_thd * period:
+                            # will not be a fixation
+                            fixation_flag = False
+                    else:  # spread
+                        # if either x_max - x_min or y_max - y_min is larger than speed threshold * time --
+                        # mark as onset. else -- mark as offset
+                        x_max = max(shift_window_interval['x'])
+                        x_min = min(shift_window_interval['x'])
+                        y_max = max(shift_window_interval['y'])
+                        y_min = min(shift_window_interval['y'])
+
+                        if max(x_max - x_min, y_max - y_min) >= speed_thd * period:
+                            # will not be a fixation
+                            fixation_flag = False
+
+                    if fixation_flag:
+                        gaze_points['data']['EYE_MOVEMENT_TYPE'][item['global_index']] = 'FIX'
+
+                    # either a fixation start or the whole interval end
+                    if fixation_flag or index == len(intersacc_interval) - 1:
+                        # if we had a non-fixation interval going on before, check it's duration
+                        if onset_index is not None:
+                            # onset episode larger than 50ms: UNKNOWN. else: NOISE
+                            if item['time'] - onset_timestamp < param["MIN_SP_DURATION_MILLISEC"]:
+                                offset_timestamp = item['time'] - 1
+                                offset_index = item['global_index'] - 1
+                                # if this is not the beginning of fixation,
+                                # the last item also should be labelled as NOISE
+                                if not fixation_flag:
+                                    offset_timestamp += 1
+                                    offset_index += 1
+
+                                gaze_points['data'][onset_index:(offset_index + 1)]['EYE_MOVEMENT_TYPE'] = 'NOISE'
+
+                            # episode is finished
+                            onset_timestamp = None
+                            onset_index = None
+                    else:
+                        # if new non-fixation interval started
+                        if onset_timestamp is None:
+                            onset_timestamp = item['time']
+                            onset_index = item['global_index']
+                        # otherwise it just continues, don't have to do anything
+    # can now remove the global_index column
+    gaze_points = ArffHelper.remove_column(gaze_points, 'global_index')
+    return gaze_points
diff --git a/functions.py b/functions.py
new file mode 100644
index 0000000..371f5eb
--- /dev/null
+++ b/functions.py
@@ -0,0 +1,150 @@
+import numpy as np
+from arff_helper import ArffHelper
+from collections import OrderedDict
+
+def load_CSV_as_arff_object(x, y, t, s, fname):
+    """
+    Load data from the given input .csv file and return an arff object.
+    This is a "model" function for writing new data adapters. To create a similarly-functioning method,
+    one would need to parse the file under @fname to extract an an arff object (dictionary with special keys)
+    ofr the following structure:
+
+    arff_obj = {
+        'relation': 'gaze_recording',
+        'description': '',
+        'data': [],
+        'metadata': {},
+        'attributes': [('time', 'NUMERIC'),
+                       ('x', 'NUMERIC'),
+                       ('y', 'NUMERIC'),
+                       ('v', 'NUMERIC'),
+                       ('statis', 'INTEGER'),
+                       ('EventID', 'INTEGER']},
+    and fill in its fields.
+
+    'data' should first contain a numpy list of lists (the latter lists should be of the same length as 'attributes'.
+    'description' is just a string that gets put into the beginning of the file.
+    'metadata' is a dictionary, where the following keys are needed later on:
+        - "width_px", "height_px" - pixel dimensions of the video
+        - "width_mm", "height_mm" - physical dimensions of the video (in millimeters)
+        - "distance_mm" - distance between the observer's eyes and the monitor (in millimeters)
+    'attributes' (if additional ones are required) is a list of tuples, each tuple consisting of 2 elements:
+        - attribute name
+        - attribute type, can be INTEGER (=int64), NUMERIC (=float32), REAL (=double), or a list of strings, which
+          means it is a categorical attribute and only these values are accepted.
+
+    After 'data' is filled with appropriate lists of values, call
+    >> arff_obj = ArffHelper.convert_data_to_structured_array(arff_obj)
+    to (unsurprisingly) convert the data in @arff_obj['data'] into a structured numpy array for easier data access.
+
+
+    :param fname: name of .coord file.
+    :return: an arff object with keywords:
+             "@RELATION, @DESCRIPTION, @DATA, @METADATA, @ATTRIBUTES".
+    """
+
+    COMMENT_PREFIX = '#'
+    # the 'gaze ... ...' line has this many "fields" (defines the video resolution)
+    GAZE_FORMAT_FIELD_COUNT = 0
+    # Samples are in lines that look like <timestamp> <x> <y> <confidence>.
+    # In case of binocular tracking, these are the mean coordinates of the two eyes anyway.
+    GAZE_SAMPLE_FIELDS = 3
+
+    arff_obj = {
+        'relation': 'gaze_recording',
+        'description': [],
+        'data': [],
+        'metadata': OrderedDict(),
+        'attributes': [('time', 'NUMERIC'),
+                       ('x', 'NUMERIC'),
+                       ('y', 'NUMERIC'),
+                       ('status', 'INTEGER')]
+    }
+
+    description = ""
+    data = np.array([t, x, y, s])
+    arff_obj['data'] = np.array([tuple(item) for item in np.transpose(data)])
+    arff_obj['metadata']['filename'] = fname
+    arff_obj['description'] = '\n'.join(description)
+
+    arff_obj = ArffHelper.convert_data_to_structured_array(arff_obj)
+
+    # add velocity type attribute
+    arff_obj = ArffHelper.add_column(arff_obj, 'v', 'NUMERIC', 0.0)
+
+    # add eye movement type attribute
+    EVENTS = ('UNKNOWN', 'FIX', 'SACCADE', 'SP', 'NOISE', 'BLINK', 'NOISE_CLUSTER', 'PSO')
+    arff_obj = ArffHelper.add_column(arff_obj, 'EYE_MOVEMENT_TYPE', EVENTS, EVENTS[0])
+
+    return arff_obj
+
+def get_xy_moving_average(data, window_size, inplace=False):
+    """
+    Get moving average of 'x', 'y' columns of input data (the moving window is centered around the data point).
+
+    Some data at the beginning and in the end will be left unchanged (where the window does not fit fully).
+    Thus the length of offset is equal to (window_size - 1)/2.
+    The rest of data will be replaced with central moving average method.
+
+    :param data: structured numpy array that contains columns 'x' and 'y'.
+    :param window_size: width of moving average calculation.
+    :param inplace: whether to replace input data with processed data (False by default)
+    :return: data set with moving average applied to 'x' and 'y' columns.
+
+    """
+    assert window_size % 2 == 1, "The @normalization_sliding_window_size_samples parameter is set to {}, but it " \
+                                 "has to be odd, so that we can centre the moving window around the current sample.".\
+        format(window_size)
+    if not inplace:
+        data = data.copy()
+    offset = int((window_size - 1) / 2)
+    for column in ['x', 'y']:
+        res = np.cumsum(data[column], dtype=float)
+        res[window_size:] = res[window_size:] - res[:-window_size]
+        res = res[window_size - 1:] / window_size
+        if offset > 0:
+            data[column][offset:-offset] = res
+        else:
+            data[column][:] = res
+    return data
+
+def fill_blink_gaps(Tx, Ty, t, s,):
+    # find blinks for Varjo base recording by gaps in time array
+    dt = np.diff(t)
+    blink_onsets = np.nonzero(dt > 30)[0]
+    blink_offsets = np.array([blink + 1 for blink in blink_onsets])
+
+    #interpolate for each gap the x, y ,t and s data
+    if min(s) != 0:
+        shift = 0
+        for onset, offset in zip(blink_onsets, blink_offsets):
+            onset  += shift
+            offset += shift
+
+            gaptime = t[offset] - t[onset]
+            npoints = int(gaptime/dt.mean())
+
+            # create patches
+            timepatch  = np.linspace(t[onset], t[offset], npoints+2)
+            timepatch = timepatch[1:-1]
+            datapatch  = np.zeros(npoints)
+
+            # append the patches in the data arrays
+            t  = np.insert(t,  onset+1, timepatch)
+            Tx = np.insert(Tx, onset+1, datapatch)
+            Ty = np.insert(Ty, onset+1, datapatch)
+            s  = np.insert(s,  onset+1, datapatch)
+
+            # shift indexes with patch length
+            shift += npoints
+
+    return Tx, Ty, t, s
+
+def save_csv(data, fname, datapath):
+    allnames = ["t_start", 't_end', 'duration', 'x_start', 'y_start', 'x_end', 'y_end', 'amplitude', 'mean_vel', 'max_vel']
+    names = allnames[0:len(data[1, :])]
+    delimiter = ','
+    header = delimiter.join(names)
+
+    np.savetxt(datapath+fname, data, delimiter=delimiter, header=header, comments='')
+
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..056f5bd
--- /dev/null
+++ b/main.py
@@ -0,0 +1,74 @@
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import readers
+import calculators
+import plotters
+import functions
+import run_detection
+
+debugging   = False
+savedata    = False
+showfig     = True
+savefig     = False
+
+# Import csv files --------------------------------------------------------------------------------------------------
+datapath = os.getcwd() + "/testdata/"  # put the path to your data here
+participants = 2
+trails = 2  # per participant
+filename = 'varjo_gaze_output'  # + date (automatically added)
+
+for participant in range(1, participants + 1):
+    print()
+    print()
+    print('Analyisis results for participant {}'.format(participant))
+    #start plot
+    fig, axs = plt.subplots(trails, figsize=[25.60, 14.40])
+    fig.suptitle('Detection per trail for: Participant {}'.format(participant))
+
+    for trail in range(1, trails + 1):
+        trailpath = datapath + '{}/{}/'.format(participant,trail)
+        print()
+        print('Trail ' + str(trail))
+        gazedata = readers.Gaze(datapath, participant, trail, filename)
+        pupildata = readers.Pupil(datapath, participant, trail, filename)
+        focusdata = readers.Focus(datapath, participant, trail, filename)
+
+# classify gaze events ----------------------------------------------------------------------------------------------
+        classifiedgazedata = run_detection.DetectGazeEvents(gazedata, debugging)
+
+        t = classifiedgazedata['data']['time'] / 1000  # [s]
+        x = classifiedgazedata['data']['x']  # [deg]
+        y = classifiedgazedata['data']['y']  # [deg]
+        v = classifiedgazedata['data']['v']  # [deg/s]
+        e = classifiedgazedata['data']['EYE_MOVEMENT_TYPE']  # ('UNKNOWN', 'FIX', 'SACCADE', 'SP', 'NOISE', 'BLINK', 'NOISE_CLUSTER', 'PSO')
+
+        dt = 1000 / np.mean(np.diff(classifiedgazedata['data']['time']))
+        print("Gaze data recorded at: {} Hz".format(dt))
+
+# Analyzing and saving ----------------------------------------------------------------------------------------------
+        Fixations = calculators.fixation(x, y, t, e)
+        Saccades = calculators.saccade(x, y, v, t, e)
+        Persuits = calculators.persuit(x, y, v, t, e)
+        Blinks = calculators.blink(t, e)
+
+        if savedata:
+            functions.save_csv(Fixations, 'fixations.csv', trailpath)
+            functions.save_csv(Saccades, 'saccades.csv', trailpath)
+            functions.save_csv(Persuits, 'persuits.csv', trailpath)
+            functions.save_csv(Blinks, 'blinks.csv', trailpath)
+
+# Plotting and saving------------------------------------------------------------------------------------------------
+        plotters.detection(x, y, t, v, Fixations, Saccades, Persuits, Blinks, trail, axs)
+
+        plotters.calculation(Fixations, Saccades, Persuits, Blinks, trail, participant)
+        outputpath = trailpath + "calculation-p{}-t{}.png".format(participant,trail,participant,trail)
+        if savefig: plt.savefig(outputpath, bbox_inches='tight')
+        plt.close(plt.figure(trail+1))
+
+    plt.figure(1)
+    outputpath = datapath + '{}/detection-p{}.png'.format(participant, participant)
+    if savefig: plt.savefig(outputpath, bbox_inches='tight')
+    if showfig: plt.show()
+    plt.close(plt.figure(1))
+
diff --git a/plotters.py b/plotters.py
new file mode 100644
index 0000000..f0480b8
--- /dev/null
+++ b/plotters.py
@@ -0,0 +1,117 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import rayleigh
+from scipy.stats import norm
+
+def detection(x, y, t, v, Fixations, Saccades, Persuits, Blinks, trail, axs):
+
+    axs1 = axs[trail - 1]
+    axs1.set_title('Trail ' + str(trail))
+    axs1.set_xlabel('Time [s]')
+
+    axs1.plot(t, v, 'silver')
+    axs1.set_ylabel('Velocity [deg/s]')
+    axs1.set_ylim([-1000, 1000])
+    axs1.legend(["Velocity"])
+
+    axs2 = axs1.twinx()
+    axs2.plot(t, x, 'tab:orange')
+    axs2.plot(t, y, 'tab:green')
+    axs2.set_ylabel('Angel [deg]')
+    axs2.set_ylim([-22.5, 22.5])
+    axs2.legend(["Horizontal", "Vertical"])
+
+    for i in range(len(Fixations)):
+        axs1.axvspan(Fixations[i, 0], Fixations[i, 1], color='g', alpha=.1)
+    for i in range(len(Saccades)):
+        axs1.axvspan(Saccades[i, 0], Saccades[i, 1], color='r', alpha=.1)
+    for i in range(len(Persuits)):
+        axs1.axvspan(Persuits[i, 0], Persuits[i, 1], color='y', alpha=.1)
+    for i in range(len(Blinks)):
+        axs1.axvspan(Blinks[i, 0], Blinks[i, 1], color='b', alpha=.1)
+
+
+
+def calculation(Fixations, Saccades, Persuits, Blinks, trail, participant):
+    plt.figure(trail+1, figsize=[25.60, 14.40])
+    plt.subplot(4, 4, 1)
+    histogramreighley(Fixations[:, 2])
+    plt.title("Fixation duration")
+    plt.xlabel('Time [s]')
+
+    plt.subplot(4, 4, 5)
+    histogramreighley(Saccades[:, 2])
+    plt.title("Saccade duration")
+    plt.xlabel('Time [s]')
+    plt.subplot(4, 4, 6)
+    histogramreighley(Saccades[:, 7])
+    plt.title("Saccade amplitude")
+    plt.xlabel('Amplitude [deg]')
+    plt.subplot(4, 4, 7)
+    histogramreighley(Saccades[:, 8])
+    plt.title("Saccade mean velocity")
+    plt.xlabel('Velocity [deg/s]')
+    plt.subplot(4, 4, 8)
+    histogramreighley(Saccades[:, 9])
+    plt.title("Saccade max velocity")
+    plt.xlabel('Velocity [deg/s]')
+
+    plt.subplot(4, 4, 9)
+    histogramreighley(Persuits[:, 2])
+    plt.title("Persuit duration")
+    plt.xlabel('Time [s]')
+    plt.subplot(4, 4, 10)
+    histogramreighley(Persuits[:, 7])
+    plt.title("Persuit amplitude")
+    plt.xlabel('Amplitude [deg]')
+    plt.subplot(4, 4, 11)
+    plt.xlabel('Amplitude [deg]')
+    histogramreighley(Persuits[:, 8])
+    plt.title("Persuit mean velocity")
+    plt.xlabel('Velocity [deg/s]')
+    plt.subplot(4, 4, 12)
+    histogramreighley(Persuits[:, 9])
+    plt.title("Persuit max velocity")
+    plt.xlabel('Velocity [deg/s]')
+
+    plt.subplot(4, 4, 13)
+    histogramreighley(Blinks[:, 2])
+    plt.title("Blink duration")
+    plt.xlabel('Time [s]')
+
+    plt.tight_layout()
+
+def histogramreighley(data):
+    N = len(data)
+    scale = data.mean() / np.sqrt(np.pi / 2)
+    V_norm_hist = scale * np.sqrt(-2 * np.log(np.random.uniform(0, 1, N)))
+
+    num_bins = 30
+    _binvalues, bins, _patches = plt.hist(V_norm_hist, bins=num_bins, density=False, rwidth=1, ec='white',
+                                          label='Histogram data')
+    x = np.linspace(bins[0], bins[-1], 100)
+    binwidth = (bins[-1] - bins[0]) / num_bins
+
+    scale = V_norm_hist.mean() / np.sqrt(np.pi / 2)
+
+    plt.plot(x, rayleigh(loc=0, scale=scale).pdf(x) * len(V_norm_hist) * binwidth, lw=5, alpha=0.6,
+             label=f'Rayleigh pdf (s={scale:.3f})')
+    plt.ylabel('samples [n]')
+    plt.grid(True)
+    plt.legend()
+
+def histogramfit(data):
+    # best fit of data
+    (mu, sigma) = norm.fit(data)
+
+    # the histogram of the data
+    n, bins, patches = plt.hist(data, len(data), alpha=0.75)
+
+    # add a 'best fit' line
+    y = norm.pdf(bins, mu, sigma)
+    l = plt.plot(bins, y, 'r--', linewidth=2)
+
+    # plot
+    plt.ylabel('samples [n]')
+    plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=%.3f,\ \sigma=%.3f$' % (mu, sigma))
+    plt.grid(True)
diff --git a/readers.py b/readers.py
new file mode 100644
index 0000000..5e14e60
--- /dev/null
+++ b/readers.py
@@ -0,0 +1,59 @@
+import os
+
+import pandas
+import math
+import functions
+import numpy as np
+
+def file_reader(path, participant, trail, filename):
+    # read data file
+    path = path + str(participant) + "/" + str(trail) + "/"
+    file = [i for i in os.listdir(path) if os.path.isfile(os.path.join(path, i)) and \
+            filename in i]
+
+    csvdata = pandas.read_csv(path + file[0], delimiter=',')
+
+    return csvdata
+
+def Gaze(path, participant, trail, filename):
+
+    gazeData = file_reader(path, participant, trail, filename)
+    fname = "P" + str(participant) + "_T" + str(trail)
+
+    # Raw Gaze data
+    s = np.array(gazeData['status'])
+    x = np.array(gazeData['gaze_forward_x'])
+    y = np.array(gazeData['gaze_forward_y'])
+    z = np.array(gazeData['gaze_forward_z'])
+
+    # get time stamps
+    t = gazeData['raw_timestamp'] / 10 ** 6
+    t = np.array(t - t[0])
+
+    # convert to angles in deg
+    Tx = (180 / math.pi) * np.arcsin(x)
+    Ty = (180 / math.pi) * np.arcsin(y)
+
+    # interpolate missing gaps in the data that represent Blinks (for Varjo Base recordings)
+    [Tx, Ty, t, s,] = functions.fill_blink_gaps(Tx, Ty, t, s)
+
+    #convert data tor arff object for processing
+    gaze_points = functions.load_CSV_as_arff_object(Tx, Ty, t, s, fname)
+
+    return gaze_points
+
+def Pupil(path, participant, trail, filename):
+
+    gazeData = file_reader(path, participant, trail, filename)
+
+    left = np.array(gazeData['left_pupil_size'])
+    right = np.array(gazeData['right_pupil_size'])
+
+    return left, right
+
+def Focus(path, participant, trail, filename):
+
+    gazeData = file_reader(path, participant, trail, filename)
+    focus = np.array(gazeData['focus_distance'])
+
+    return focus
diff --git a/run_detection.py b/run_detection.py
new file mode 100644
index 0000000..d304f82
--- /dev/null
+++ b/run_detection.py
@@ -0,0 +1,82 @@
+from saccade_detector import SaccadeDetector
+from blink_detector import BlinkDetector
+from fixation_detector import FixationDetector
+from sp_detector import SmoothPursuitDetector
+
+# DEFAULT PARAMETERS:
+# {
+#     "SaccadeDetector": {
+#         "tolerance": 0.0,
+#         "threshold_onset_fast_degree_per_sec": 137.5,
+#         "threshold_onset_slow_degree_per_sec": 17.1875,
+#         "threshold_offset_degree_per_sec": 17.1875,
+#         "max_speed_degree_per_sec": 1031.25,
+#         "min_duration_millisec": 15,
+#         "max_duration_millisec": 160,
+#         "velocity_integral_interval_millisec": 40
+#     },
+#     "BlinkDetector": {
+#         "max_distance_to_saccade_millisec": 25
+#     },
+#     "FixationDetector": {
+#         "prefiltering_interval_spread_threshold_degrees": 1.4142135623730951,
+#         "min_sp_duration_millisec": 141.35623730952,
+#         "sliding_window_width_millisec": 100,
+#         "normalization_sliding_window_size_samples": 5,
+#         "speed_threshold_degrees_per_sec": 2.0,
+#         "sliding_window_criterion": "speed",
+#         "intersaccadic_interval_duration_threshold_millisec": 75
+#     },
+#     "SmoothPursuitDetector": {
+#         "min_pts": 160,
+#         "min_observers": null,
+#         "eps_deg": 4.0,
+#         "time_slice_millisec": 80
+#     }
+# }
+
+def DetectGazeEvents(gazedata, verbose):
+    # Saccade Detection --------------------------------------------------------------------------------------------------
+    sacparam = dict()
+    sacparam["THRESHOLD_ONSET_FAST_DEGREE_PER_SEC"] = 137.5  # deg/s
+    sacparam["THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC"] = 17.1875  # deg/s
+    sacparam["THRESHOLD_OFFSET_DEGREE_PER_SEC"] = 17.1875  # deg/s
+    sacparam["MAX_SPEED_DEGREE_PER_SEC"] = 1031.25  # deg/s
+    sacparam["MIN_DURATION_MILLISEC"] = 15  # milliseconds
+    sacparam["MAX_DURATION_MILLISEC"] = 160  # milliseconds
+    sacparam["VELOCITY_INTEGRAL_INTERVAL_MILLISEC"] = 4  # milliseconds
+    sacparam["VERBOSE"] = verbose  # debug mode
+    gazedata = SaccadeDetector(sacparam, gazedata)
+
+    # Blink detection----------------------------------------------------------------------------------------------------
+    blkparam = dict()
+    blkparam['MINIMAL_BLINK_DURATION'] = 10 # milliseconds
+    blkparam["MAXIMAL_DISTANCE_TO_SACCADE_MILLISEC"] = 20  # milliseconds
+    blkparam["VERBOSE"] = verbose
+    gazedata = BlinkDetector(blkparam, gazedata)
+
+    # Fixation detection-------------------------------------------------------------------------------------------------
+    fixparam = dict()
+    fixparam["PREFILTERING_INTERVAL_SPREAD_THRESHOLD_DEGREES"] = 1.4142135623730951  # deg
+    fixparam["MIN_SP_DURATION_MILLISEC"] = 141.42135623730952  # milliseconds
+    fixparam["SLIDING_WINDOW_WIDTH_MILLISEC"] = 100  # milliseconds
+    fixparam["NORMALIZATION_SLIDING_WINDOW_SIZE_SAMPLES"] = 5  # n samples
+    fixparam["SPEED_THRESHOLD_DEGREES_PER_SEC"] = 2  # deg/s
+    fixparam["SLIDING_WINDOW_CRITERION"] = 'speed'  # 'speed' or 'spread'
+    fixparam["INTERSACCADIC_INTERVAL_DURATION_THRESHOLD_MILLISEC"] = 75  # milliseconds
+    fixparam["VERBOSE"] = verbose  # debug mode
+    gazedata = FixationDetector(fixparam, gazedata)
+
+    # Smooth Persuit detection-------------------------------------------------------------------------------------------
+    SPparam = dict()
+    SPparam["MIN_PTS"] = 1 # int(160 * (1 / 46.9) * (90 / 250))  # minimum points for a neighborhood (default value, e.g. 160) * (N_observers / 46.9) * (F_hz / 250)
+    SPparam["EPS_DEG"] = 4  # deg
+    SPparam["TIME_SLICE_MILLISEC"] = 80  # milliseconds
+    SPparam["VERBOSE"] = verbose  # debug mode
+
+    sp_detector = SmoothPursuitDetector(param=SPparam)
+    classifiedgazedata = sp_detector.detect(gaze_points_list=gazedata)
+
+    return classifiedgazedata
+
+
diff --git a/saccade_detector.py b/saccade_detector.py
new file mode 100644
index 0000000..a0d8f52
--- /dev/null
+++ b/saccade_detector.py
@@ -0,0 +1,226 @@
+import copy
+import numpy as np
+from arff_helper import ArffHelper
+
+def SaccadeDetector(param, gaze_points, inplace=False):
+    """
+    This method labels saccades (also noise) in the provided gaze_points, which should be an arff object
+    :param gaze_points: gaze recording data, an arff object (i.e. a dictionary with 'data', 'metadata'
+                        and etc. keys)
+    :param inplace: whether to replace the data inside @gaze_points or create a new structure
+    :return: gaze points with added labels SACCADE, NOISE
+    """
+    if not inplace:
+        gaze_points = copy.deepcopy(gaze_points)
+
+    # also keep track of saccadic and intersaccadic intervals
+    detected_saccades_count = 0
+    if 'SACC_INTERVAL_INDEX' not in gaze_points['data'].dtype.names:
+        ArffHelper.add_column(gaze_points, 'SACC_INTERVAL_INDEX', 'INTEGER', -1)
+
+    # a virtual saccade that finished before the recording for uniform processing
+    last_saccade_end = -1
+    intersaccadic_intervals_count = 0
+    if 'INTERSACC_INTERVAL_INDEX' not in gaze_points['data'].dtype.names:
+        ArffHelper.add_column(gaze_points, 'INTERSACC_INTERVAL_INDEX', 'INTEGER', -1)
+
+    # verify timestamps
+    times = gaze_points['data']['time']
+    # -1 so that the exact value ends up on the right of the searched timestamp
+    searchable_timestamps = times - param["VELOCITY_INTEGRAL_INTERVAL_MILLISEC"] - 1
+    # find the indices of the first
+    prev_indices = np.searchsorted(times, searchable_timestamps, side='right')
+    cur_indices = np.arange(len(prev_indices))
+    # if the index after search points towards this very data point, take the previous one
+    prev_indices[prev_indices == cur_indices] -= 1
+    # except for the very first sample
+    prev_indices[0] = 0
+
+    # computing velocities ----------------------------------------------------------------------------------------
+    dx = gaze_points['data']['x'][cur_indices] - gaze_points['data']['x'][prev_indices]
+    dy = gaze_points['data']['y'][cur_indices] - gaze_points['data']['y'][prev_indices]
+    dTh = np.linalg.norm(np.vstack([dx, dy]), axis=0)
+    dt = gaze_points['data']['time'][cur_indices] - gaze_points['data']['time'][prev_indices]
+    # keep it above 0, the dTh are 0 there anyway
+    dt[dt == 0] += 1
+    velocities = dTh / dt  # deg per millisecond
+    velocities *= 1e3  # degree per second
+    gaze_points['data']['v'] = velocities
+
+    # How many samples back is it reasonable to go?
+    time_step = np.diff(times).mean()
+    # a big margin of error, 10 times as many samples as would normally need
+    extra_samples_count = int(np.round((param["MAX_DURATION_MILLISEC"] * 10) / time_step))
+
+    # Glitch detection: glitches are definedb by velocities that exceed \a maxSpeed --------------------------------------------
+    # (default currently set to ~1000 degrees/s) are regarded as glitches and labelled as noise
+    is_glitch = np.zeros(gaze_points['data'].shape[0], dtype=np.bool)
+    is_glitch[velocities > param["MAX_SPEED_DEGREE_PER_SEC"]] = True
+    gaze_points['data']['EYE_MOVEMENT_TYPE'][velocities > param["MAX_SPEED_DEGREE_PER_SEC"]] = 'NOISE'
+
+    # Remember first sample after glitch:
+    # to prevent saccade detection at the first non-glitch sample
+    # that follows, saccade detection is inhibited for that first sample.
+    post_glitch = np.diff(is_glitch.astype(int)) == -1
+    post_glitch = np.hstack(([False], post_glitch))
+    # Remember last sample before glitch:
+    # since we normally would suspend the other criteria (incl. speed) if we are inside glitch, we try to avoid
+    # border effects in both next-after and last-before glitch samples
+    pre_glitch = np.diff(is_glitch.astype(int)) == 1
+    pre_glitch = np.hstack((pre_glitch, [False]))
+    all_glitch = is_glitch + post_glitch + pre_glitch
+    # we will assign glitch samples' labels to NOISE after the saccades have been detected
+
+    # recompute speeds for post-glitch samples
+    pre_glitch_indices = np.nonzero(pre_glitch)[0]
+    for i in np.nonzero(post_glitch)[0]:
+        # find the corresponding start of the glitch
+        corresponding_pre_glitch = np.searchsorted(pre_glitch_indices, i) - 1
+        if corresponding_pre_glitch < 0:
+            # no correspondence found, it's the glitch from the beginning of recording ==> set velocity to 0
+            velocities[i] = 0
+        else:
+            # found a completed glitch
+            velocities[i] = np.linalg.norm([
+                gaze_points['data']['x'][i] - gaze_points['data']['x'][corresponding_pre_glitch],
+                gaze_points['data']['y'][i] - gaze_points['data']['y'][corresponding_pre_glitch]
+            ]) / (times[i] - times[corresponding_pre_glitch])
+            velocities[i] *= 1e3  # degrees per second
+
+    gaze_points['data']['v'] = velocities
+
+    # Looking for saccade seed points-------------------------------------------------------------------------------
+    # saccade seed point should
+    # (1) exceed the fast threshold
+    # (2) be biologically plausible
+    # (3) not be inside a glitch
+    saccade_seeds = (velocities > param["THRESHOLD_ONSET_FAST_DEGREE_PER_SEC"]) * \
+                    (velocities < param["MAX_SPEED_DEGREE_PER_SEC"]) * \
+                    (1 - all_glitch)
+    saccade_seed_indices = np.nonzero(saccade_seeds)[0]
+    for potential_seed_index in saccade_seed_indices:
+        if gaze_points['data']['EYE_MOVEMENT_TYPE'][potential_seed_index] != 'UNKNOWN':
+            # already labelled this before, ex. as a saccade that started from another seed point
+            continue
+        if param["VERBOSE"] == True:
+            print('potential seed index', potential_seed_index)
+        # Looking for onset:
+        # (1) should be above slow threshold speed
+        # (2) should not be a glitch
+        # (3) does not yet have a label
+        onset_candidates_check = (velocities[max(0, potential_seed_index - extra_samples_count):potential_seed_index] >=
+                                  param["THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC"]) * \
+                                 (1 - is_glitch[
+                                      max(0, potential_seed_index - extra_samples_count):potential_seed_index]) * \
+                                 (gaze_points['data']['EYE_MOVEMENT_TYPE'][
+                                  max(0, potential_seed_index - extra_samples_count):potential_seed_index
+                                  ] == 'UNKNOWN')
+
+        # find the last zero (the next sample after it is the beginning of the last uninterrupted 1-sequence,
+        # i.e. the saccade onset
+        try:
+            last_zero_index = np.nonzero(1 - onset_candidates_check)[0][-1]
+        except IndexError:
+            # not found
+            continue
+        saccade_onset_index = last_zero_index + 1 + max(0,
+                                                        potential_seed_index - extra_samples_count)  # shift accordingly
+        # also this should not be the glitch or post/pre-glitch sample
+        while all_glitch[saccade_onset_index]:
+            saccade_onset_index += 1
+
+        # looking for offset
+        # (1) should be above offset speed threshold
+        # (2) should not exceed biologically plausible duration threshold
+        # (3) should not yet have a label (i.e. not NOISE labelled above)
+        offset_candidates_check = (velocities[potential_seed_index:potential_seed_index + extra_samples_count] >=
+                                   param["THRESHOLD_OFFSET_DEGREE_PER_SEC"]) * \
+                                  (times[potential_seed_index:potential_seed_index + extra_samples_count] -
+                                   times[saccade_onset_index] <= param["MAX_DURATION_MILLISEC"])
+        # we ignore the criterion around the glitch
+        offset_candidates_check += is_glitch[potential_seed_index:potential_seed_index + extra_samples_count]
+        offset_candidates_check += post_glitch[potential_seed_index:potential_seed_index + extra_samples_count]
+
+        # but there should not yet be a label present, i.e. it's not the NOISE labelled above
+        offset_candidates_check *= (gaze_points['data']['EYE_MOVEMENT_TYPE'][
+                                    potential_seed_index:potential_seed_index + extra_samples_count
+                                    ] == 'UNKNOWN')
+
+        # find the first zero (this is the first sample with speed below the threshold, i.e. the saccade offset
+        try:
+            saccade_offset_index = np.nonzero(1 - offset_candidates_check)[0][0]
+        except IndexError:
+            # no offset found
+            continue
+        # the index was starting at potential_seed_index
+        saccade_offset_index += potential_seed_index
+
+        # if we are finished inside the glitch, we have reached a biological limit of some sorts ==> discard
+        if is_glitch[saccade_offset_index]:
+            continue
+
+        if param["VERBOSE"] == True:
+            print('Found onset/offset indices', saccade_onset_index, saccade_offset_index)
+
+        # now validate the saccade parameters
+        # (1) it spans at least the minimal necessary interval
+        saccade_time = times[saccade_offset_index] - times[saccade_onset_index]
+        if saccade_time < param["MIN_DURATION_MILLISEC"]:
+            # If the resulting saccade is shorter than
+            # a minDuration, we assume that we have only encountered
+            # some noise impulse and discard this saccade.
+            gaze_points['data']['EYE_MOVEMENT_TYPE'][saccade_onset_index:saccade_offset_index + 1] = 'NOISE'
+
+            if param["VERBOSE"] == True:
+                print('Discarding due to low duration: needed {}, had {}'. \
+                    format(param["MIN_DURATION_MILLISEC"], saccade_time))
+            continue
+
+        # (2) mean velocity is not below the slow onset threshold
+        saccade_displacement = np.linalg.norm([
+            gaze_points['data']['x'][saccade_offset_index] - gaze_points['data']['x'][saccade_onset_index],
+            gaze_points['data']['y'][saccade_offset_index] - gaze_points['data']['y'][saccade_onset_index],
+        ])
+        mean_speed = saccade_displacement / saccade_time # degrees per millisecond
+        mean_speed *= 1e3  # degrees per second
+
+        if mean_speed < param["THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC"]:
+            # Saccades where the average velocity drops below the offset threshold
+            # are also discarded (those are often due to some high-velocity samples
+            # going in one direction, then jumping back - which is unbiological).
+            if param["VERBOSE"] == True:
+                print('Discarding due to low average speed: needed {}, had {}'.format(
+                    param["THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC"], mean_speed))
+            continue
+
+        # If all is okay, we detected a whole saccade
+        gaze_points['data']['EYE_MOVEMENT_TYPE'][saccade_onset_index:saccade_offset_index + 1] = 'SACCADE'
+        # write the saccade index into the appropriate field and update the global count
+        gaze_points['data']['SACC_INTERVAL_INDEX'][saccade_onset_index:saccade_offset_index + 1] = \
+            detected_saccades_count
+        detected_saccades_count += 1
+        # from the end of last saccade till the beginning of this one, put appropriate intersaccadic interval index
+        # also update the global count of intersaccadic intervals
+        gaze_points['data']['INTERSACC_INTERVAL_INDEX'][last_saccade_end + 1:saccade_onset_index] = \
+            intersaccadic_intervals_count
+        intersaccadic_intervals_count += 1
+        last_saccade_end = saccade_offset_index
+
+        if param["VERBOSE"]:
+            print('{0} {1:0.1f} {2:0.1f} {3} {4:0.1f} {5:0.1f}'.format(
+                gaze_points['data'][saccade_onset_index]['time'],
+                gaze_points['data'][saccade_onset_index]['x'],
+                gaze_points['data'][saccade_onset_index]['y'],
+                gaze_points['data'][saccade_offset_index]['time'],
+                gaze_points['data'][saccade_offset_index]['x'],
+                gaze_points['data'][saccade_offset_index]['y'],
+            ))
+    # final intersaccadic interval, if there is one
+    gaze_points['data']['INTERSACC_INTERVAL_INDEX'][last_saccade_end + 1:] = \
+        intersaccadic_intervals_count
+    intersaccadic_intervals_count += 1
+
+    # Override erroneous samples' labels
+    gaze_points['data']['EYE_MOVEMENT_TYPE'][is_glitch] = 'NOISE'
+
+    return gaze_points
diff --git a/sp_detector.py b/sp_detector.py
new file mode 100644
index 0000000..5465068
--- /dev/null
+++ b/sp_detector.py
@@ -0,0 +1,271 @@
+import numpy as np
+import abc
+import copy
+
+from arff import xrange
+from arff_helper import ArffHelper
+
+
+class SmoothPursuitDetector(object):
+    """
+    DBSCAN-based smooth pursuit detector. All the logic is in a DBSCANWithMinPts class, this is just a wrapper
+    that based on the arguments to __init__ method initiates DBSCANWithMinPts
+    """
+    def __init__(self, param):
+        """
+        Initialize the SmoothPursuitDetector object
+
+        :param eps_deg: Spatial Euclidean distance threshold that defines the neighbourhood in the XY-plane.
+                        Given in degrees of visual field.
+        :param time_slice_millisec: Width of the time slice that defines the size of the neighbourhood on the time axis.
+                                    Value is given in milliseconds. The neighbourhood essentially has cylindrical shape.
+        :param min_pts: of points required to  form a "valid" neighbourhood
+                            (that integer indicating the minimum number of a core points).
+        """
+        min_pts = param['MIN_PTS']
+        eps_deg = param['EPS_DEG']
+        time_slice_millisec = param['TIME_SLICE_MILLISEC']
+
+        self.clustering = DBSCANWithMinPts(eps_deg=eps_deg, time_slice_millisec=time_slice_millisec,
+                                               min_pts=min_pts)
+
+    def detect(self, gaze_points_list, inplace=False):
+        return self.clustering.cluster(gaze_points_list=gaze_points_list,
+                                       inplace=inplace)
+
+
+class DBSCANWithTimeSlice(object):
+    """
+    The class is based on DBSCAN algorithm used for density-based data clustering
+    (we run this to detect SP, after pre-filtering has removed saccades and fixations).
+
+    Rather than only using spatial locations, the algorithm uses spatio-temporal
+    information, i.e. we cluster gaze points data in three-dimensional (t, x, y) space.
+
+    Since there is no a priori optimal scaling factor between time and space,
+    we modify the classical DBSCAN notion of the neighbourhood (i.e. a sphere of radius @eps).
+    Instead of it, we consider the cylinder with its axis aligned with the time axis.
+    This way we have a XY-neighbourhood defined by Euclidean distance and its threshold of @eps,
+    and on the temporal axis we take a time slice of @time_slice_millisec width (hence the class name)
+
+    Neighbourhood validation is implemented by two classes that implement the DBSCANWithTimeSlice interface.
+    It is done in two different ways, namely "minPts" (validating that the number of other gaze points in the
+    neighbourhood is at least @min_pts, closer to original DBSCAN) and "minObservers" (we validate that samples
+    of at least @min_observers different observers are present in the neighbourhood).
+
+    """
+    __metaclass__ = abc.ABCMeta
+
+    def __init__(self, eps_deg=2.0, time_slice_millisec=40):
+        """
+        :param eps_deg: Spatial Euclidean distance threshold that defines the neighbourhood in the XY-plane.
+                        Given in degrees of visual field.
+        :param time_slice_millisec: Width of the time slice that defines the size of the neighbourhood on the time axis.
+                                    Value is given in microseconds.
+
+        """
+        self.time_slice = time_slice_millisec
+        self.eps_deg = eps_deg
+
+        # initialize empty data
+        self._data_set = None
+        # store timestamps separately for efficiency
+        self._timestamps = None
+
+    def cluster(self, gaze_points_list, inplace=False):
+        """
+        Find clusters of input gaze data and label clustered points as smooth pursuit.
+        Labels (sets the 'EYE_MOVEMENT_TYPE' field) the clusters of data points as 'SP',
+        other samples as 'NOISE_CLUSTER'.
+
+        New column 'CLUSTER_ID' is added into the @DATA section of each arff object in @gaze_points_list,
+        indicating cluster group ID.
+
+        :param gaze_points_list: a list of arff objects (dictionary with fields such as 'data' and 'metadata')
+        :param inplace: whether to modify the original input gaze data with gaze data after clustering or use a copy
+        :return: gaze data after clustering in the same form as the input data.
+
+        """
+        if not inplace:
+            gaze_points_list = copy.deepcopy(gaze_points_list)
+
+        # add global indexing to be able to reference the particular sample even after clustering all in one structure
+        ArffHelper.add_column(gaze_points_list, name='global_index', dtype='INTEGER', default_value=-1)
+        gaze_points_list['data']['global_index'] = np.arange(gaze_points_list['data'].shape[0])
+
+        self._data_set = self._aggregate_data(gaze_points_list)
+        # has to be a copy, so that is is placed continuously in memory
+        self._timestamps = self._data_set['time'].copy()
+
+        current_cluster_id = 0
+
+        for i in xrange(len(self._data_set)):
+            if self._data_set[i]['visited_flag'] == 1:
+                continue
+            else:
+                self._data_set[i]['visited_flag'] = 1
+                neighbourhood = self._get_neighbourhood(i)
+                if self._validate_neighbourhood(neighbourhood):
+                    # if not: mark current point as NOISE
+                    self._expand_cluster(i, neighbourhood, current_cluster_id)
+                    current_cluster_id += 1
+
+        # create a new column in gaze_points_list for CLUSTER_ID
+        ArffHelper.add_column(gaze_points_list, 'CLUSTER_ID', 'NUMERIC', -1)
+
+        # label data in gaze_points_list as SP according to CLUSTER_ID
+        for i in xrange(len(self._data_set)):
+            global_index = self._data_set[i]['global_index']
+
+            if self._data_set[i]['CLUSTER_ID'] != -1:
+                gaze_points_list['data']['EYE_MOVEMENT_TYPE'][global_index] = 'SP'
+                gaze_points_list['data']['CLUSTER_ID'][global_index] = self._data_set[i]['CLUSTER_ID']
+            else:
+                gaze_points_list['data']['EYE_MOVEMENT_TYPE'][global_index] = 'NOISE_CLUSTER'
+
+        # can now remove the global_index column
+        ArffHelper.remove_column(gaze_points_list, name='global_index')
+
+        return gaze_points_list
+
+    def _expand_cluster(self, current_point, neighbourhood, current_cluster_id):
+        """
+        Check all points within neighbourhood of current core point in order
+        to expand neighbourhood. Processes points in the @self._data_set
+        (a 6-column numpy array as data set to be clustered)
+
+        :param current_point: index of the current core point.
+        :param neighbourhood: index list as neighbourhood of current core point.
+        :param current_cluster_id: index of current cluster.
+        :return: index list of expanded neighbourhood points.
+
+        """
+
+        self._data_set[current_point]['CLUSTER_ID'] = current_cluster_id
+        for neighbour in neighbourhood:
+            if self._data_set[neighbour]['visited_flag'] == 0:
+                self._data_set[neighbour]['visited_flag'] = 1
+                new_neighbourhood = self._get_neighbourhood(neighbour)  # eps as input parameter
+                if self._validate_neighbourhood(new_neighbourhood):
+                    new_neighbourhood_set = set(new_neighbourhood)
+                    new_neighbours = list(new_neighbourhood_set.difference(neighbourhood))
+                    neighbourhood.extend(new_neighbours)    # something wrong if use neighbourhood_set.update
+
+            if self._data_set[neighbour]['CLUSTER_ID'] == -1:
+                self._data_set[neighbour]['CLUSTER_ID'] = current_cluster_id
+
+        return neighbourhood
+
+    def _aggregate_data(self, gaze_points_list):
+        """
+        Aggregate data from @DATA of all arff objects in the input list into a
+        new data set in form of a numpy array.
+
+        :param gaze_points_list: gaze data to be clustered in form of list of arff objects.
+        :return: data set to be clustered in form of a 6-column numpy array,
+                 i.e. ['time','x','y','observer_id','CLUSTER_ID','visited_flag'],
+                 ordered by 'time' column value.
+
+        """
+        data_set = []
+        gaze_points_data = gaze_points_list['data'][
+            (gaze_points_list['data']['EYE_MOVEMENT_TYPE'] == 'UNKNOWN')][['time', 'x', 'y', 'global_index']]
+        gaze_points_data = ArffHelper.add_column_to_array(gaze_points_data, 'CLUSTER_ID', 'NUMERIC', -1)
+        gaze_points_data = ArffHelper.add_column_to_array(gaze_points_data, 'visited_flag', 'NUMERIC', 0)
+        if len(gaze_points_data) > 0:
+            data_set.append(gaze_points_data)
+        data_set = np.concatenate(data_set)
+        data_set = np.sort(data_set, order='time')
+
+        return data_set
+
+    def _get_neighbourhood(self, current_point):
+        """
+        Get neighbourhood of current point in self._data_set (a 6-column numpy array as data set to be clustered)
+
+        :param current_point: index of the current core point candidate.
+        :return: index list of the neighbourhood of current point.
+
+        """
+        # cast to the appropriate type just in case
+        start_index = np.searchsorted(self._timestamps,
+                                      self._timestamps[current_point] - self._timestamps.dtype.type(self.time_slice),
+                                      side='left')
+        end_index = np.searchsorted(self._timestamps,
+                                    self._timestamps[current_point] + self._timestamps.dtype.type(self.time_slice),
+                                    side='right')
+
+        distance = np.linalg.norm([self._data_set[start_index:end_index]['x'] - self._data_set[current_point]['x'],
+                                   self._data_set[start_index:end_index]['y'] - self._data_set[current_point]['y']],
+                                  axis=0)
+        neighbourhood = (np.where(distance <= self.eps_deg)[0] + start_index).tolist()
+
+        return neighbourhood
+
+    @abc.abstractmethod
+    def _validate_neighbourhood(self, *args, **kwargs):
+        """
+        Should return a boolean value after neighbourhood validation. Returns True if the point with such neighbourhood
+        is a core point (see DBSCAN method explanation for details).
+
+        Abstract method - implemented in subclasses.
+
+        """
+        raise NotImplementedError("Implemented in subclass methods.")
+
+
+class DBSCANWithMinPts(DBSCANWithTimeSlice):
+    """
+    DBSCAN with time slice that uses MinPts as neighbourhood validation method
+    (validating that the number of other gaze points in the neighbourhood is at least @min_pts before declaring
+    this a core point).
+
+    This method is dependent on the frame rate of the gaze position recording, since the number of points in a
+    fixed temporal slice will grow proportionally to gaze recording fps. If more independence from the fps is desired,
+    use DBSCANWithMinObservers. Tha default value here was used on a dataset with 250 Hz tracker used.
+
+    """
+    def __init__(self, eps_deg=2.0, time_slice_millisec=40, min_pts=1):
+        """
+        Initialize DBSCANWithMinPts object.
+        :param eps_deg: Spatial Euclidean distance threshold that defines the neighbourhood in the XY-plane.
+                        Given in degrees of visual field, a pixel value is assigned when the recordings' data
+                        is provided.
+        :param time_slice_millisec: Width of the time slice that defines the size of the neighbourhood on the time axis.
+                                    Value is given in microseconds.
+        :param min_pts: integer indicating the minimum number of points required to
+                        form a "valid" neighbourhood (that of a core point).
+                        Could also be a 'num_observers' string (default), in which case
+                        the actual value is determined during the self._setup_internal_parameters() call
+        """
+        super(DBSCANWithMinPts, self).__init__(eps_deg=eps_deg, time_slice_millisec=time_slice_millisec)
+
+        self.min_pts = min_pts
+        if type(self.min_pts) == int:
+            self.min_pts_abs_value = self.min_pts
+
+    def _setup_internal_parameters(self, gaze_points_list):
+        """
+        If min_pts was 'num_observers', set it accordingly here
+        :param gaze_points_list: a list of arff objects (dictionary with fields such as 'data' and 'metadata')
+
+        """
+        if self.min_pts == 'num_observers':
+            self.min_pts_abs_value = len(gaze_points_list)
+
+    def _validate_neighbourhood(self, neighbourhood):
+        """
+        Compare the size of @neighbourhood with @self.min_pts and return boolean value
+        as result of validation. True if this is the neighbourhood of a core point, false otherwise.
+        @self._data_set (a 6-column numpy array as data set to be clustered) is used to interpret
+        the @neighbourhood list.
+
+        :param neighbourhood: index list as neighbourhood to be validated.
+        :return: boolean value.
+                 True if @neighbourhood contains more than @self.min_pts points, False if not.
+
+        """
+        if len(neighbourhood) >= self.min_pts_abs_value:
+            return True
+        else:
+            return False
\ No newline at end of file
diff --git a/testdata/1/1/varjo_gaze_output_2021-06-21_17-14-36-126-copy.csv b/testdata/1/1/varjo_gaze_output_2021-06-21_17-14-36-126-copy.csv
new file mode 100644
index 0000000..04e64aa
--- /dev/null
+++ b/testdata/1/1/varjo_gaze_output_2021-06-21_17-14-36-126-copy.csv
@@ -0,0 +1,4410 @@
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diff --git a/testdata/1/2/varjo_gaze_output_2021-06-21_17-14-49-439.csv b/testdata/1/2/varjo_gaze_output_2021-06-21_17-14-49-439.csv
new file mode 100644
index 0000000..9131c2b
--- /dev/null
+++ b/testdata/1/2/varjo_gaze_output_2021-06-21_17-14-49-439.csv
@@ -0,0 +1,4300 @@
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diff --git a/testdata/2/1/varjo_gaze_output_2021-06-21_15-47-28-439.csv b/testdata/2/1/varjo_gaze_output_2021-06-21_15-47-28-439.csv
new file mode 100644
index 0000000..f4bbbda
--- /dev/null
+++ b/testdata/2/1/varjo_gaze_output_2021-06-21_15-47-28-439.csv
@@ -0,0 +1,10540 @@
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diff --git a/testdata/2/2/varjo_gaze_output_2021-06-21_15-47-07-972.csv b/testdata/2/2/varjo_gaze_output_2021-06-21_15-47-07-972.csv
new file mode 100644
index 0000000..fb39353
--- /dev/null
+++ b/testdata/2/2/varjo_gaze_output_2021-06-21_15-47-07-972.csv
@@ -0,0 +1,13143 @@
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