diff --git a/nd2reader/reader.py b/nd2reader/reader.py
index 8246125..f99101d 100644
--- a/nd2reader/reader.py
+++ b/nd2reader/reader.py
@@ -1,207 +1,315 @@
-from pims import Frame
-from pims.base_frames import FramesSequenceND
+# -*- coding: utf-8 -*-
+import struct
 
-from nd2reader.exceptions import EmptyFileError, InvalidFileType
-from nd2reader.parser import Parser
+import array
+import six
+import warnings
+from pims.base_frames import Frame
 import numpy as np
 
+from nd2reader.common import get_version, read_chunk
+from nd2reader.exceptions import InvalidVersionError
+from nd2reader.label_map import LabelMap
+from nd2reader.raw_metadata import RawMetadata
 
-class ND2Reader(FramesSequenceND):
-    """PIMS wrapper for the ND2 parser.
-    This is the main class: use this to process your .nd2 files.
-    """
 
-    class_priority = 12
+class Parser(object):
+    """Parses ND2 files and creates a Metadata and driver object.
 
-    def __init__(self, filename):
-        super(ND2Reader, self).__init__()
+    """
+    CHUNK_HEADER = 0xabeceda
+    CHUNK_MAP_START = six.b("ND2 FILEMAP SIGNATURE NAME 0001!")
+    CHUNK_MAP_END = six.b("ND2 CHUNK MAP SIGNATURE 0000001!")
 
-        if not filename.endswith(".nd2"):
-            raise InvalidFileType("The file %s you want to read with nd2reader does not have extension .nd2." % filename)
+    supported_file_versions = {(3, None): True}
 
-        self.filename = filename
+    def __init__(self, fh):
+        self._fh = fh
+        self._label_map = None
+        self._raw_metadata = None
+        self.metadata = None
 
-        # first use the parser to parse the file
-        self._fh = open(filename, "rb")
-        self._parser = Parser(self._fh)
+        # First check the file version
+        self.supported = self._check_version_supported()
 
-        # Setup metadata
-        self.metadata = self._parser.metadata
+        # Parse the metadata
+        self._parse_metadata()
 
-        # Set data type
-        self._dtype = self._parser.get_dtype_from_metadata()
+    def calculate_image_properties(self, index):
+        """Calculate FOV, channels and z_levels
 
-        # Setup the axes
-        self._setup_axes()
+        Args:
+            index(int): the index (which is simply the order in which the image was acquired)
 
-        # Other properties
-        self._timesteps = None
+        Returns:
+            tuple: tuple of the field of view, the channel and the z level
 
-    @classmethod
-    def class_exts(cls):
-        """Let PIMS open function use this reader for opening .nd2 files
+        """
+        field_of_view = self._calculate_field_of_view(index)
+        channel = self._calculate_channel(index)
+        z_level = self._calculate_z_level(index)
+        return field_of_view, channel, z_level
 
+    def get_image(self, index):
         """
-        return {'nd2'} | super(ND2Reader, cls).class_exts()
+        Creates an Image object and adds its metadata, based on the index (which is simply the order in which the image
+        was acquired). May return None if the ND2 contains multiple channels and not all were taken in each cycle (for
+        example, if you take bright field images every minute, and GFP images every five minutes, there will be some
+        indexes that do not contain an image. The reason for this is complicated, but suffice it to say that we hope to
+        eliminate this possibility in future releases. For now, you'll need to check if your image is None if you're
+        doing anything out of the ordinary.
 
-    def close(self):
-        """Correctly close the file handle
+        Args:
+            index(int): the index (which is simply the order in which the image was acquired)
 
-        """
-        if self._fh is not None:
-            self._fh.close()
+        Returns:
+            Frame: the image
 
-    def _get_default(self, coord):
+        """
+        field_of_view, channel, z_level = self.calculate_image_properties(index)
+        channel_offset = index % len(self.metadata["channels"])
+        image_group_number = int(index / len(self.metadata["channels"]))
+        frame_number = self._calculate_frame_number(image_group_number, field_of_view, z_level)
         try:
-            return self.default_coords[coord]
-        except KeyError:
-            return 0
+            timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self.metadata["height"],
+                                                        self.metadata["width"])
+        except (TypeError):
+            return Frame([], frame_no=frame_number, metadata=self._get_frame_metadata())
+        else:
+            return Frame(image, frame_no=frame_number, metadata=self._get_frame_metadata())
+
+    def get_image_by_attributes(self, frame_number, field_of_view, channel, z_level, height, width):
+        """Gets an image based on its attributes alone
 
-    def get_frame_2D(self, c=0, t=0, z=0, x=0, y=0, v=0):
-        """Gets a given frame using the parser
         Args:
-            x: The x-index (pims expects this)
-            y: The y-index (pims expects this)
-            c: The color channel number
-            t: The frame number
-            z: The z stack number
-            v: The field of view index
+            frame_number: the frame number
+            field_of_view: the field of view
+            channel_name: the color channel name
+            z_level: the z level
+            height: the height of the image
+            width: the width of the image
+
         Returns:
-            pims.Frame: The requested frame
+            Frame: the requested image
+
         """
-        # This needs to be set to width/height to return an image
-        x = self.metadata["width"]
-        y = self.metadata["height"]
+        frame_number = 0 if frame_number is None else frame_number
+        field_of_view = 0 if field_of_view is None else field_of_view
+        channel = 0 if channel is None else channel
+        z_level = 0 if z_level is None else z_level
 
-        return self._parser.get_image_by_attributes(t, v, c, z, y, x)
+        image_group_number = self._calculate_image_group_number(frame_number, field_of_view, z_level)
+        try:
+            timestamp, raw_image_data = self._get_raw_image_data(image_group_number, channel,
+                                                                 height, width)
+        except (TypeError):
+            return Frame([], frame_no=frame_number, metadata=self._get_frame_metadata())
+        else:
+            return Frame(raw_image_data, frame_no=frame_number, metadata=self._get_frame_metadata())
+
+    @staticmethod
+    def get_dtype_from_metadata():
+        """Determine the data type from the metadata.
+
+        For now, always use float64 to prevent unexpected overflow errors when manipulating the data (calculating sums/
+        means/etc.)
 
-    @property
-    def parser(self):
-        """
-        Returns the parser object.
-        Returns:
-            Parser: the parser object
         """
-        return self._parser
+        return np.float64
 
-    @property
-    def pixel_type(self):
-        """Return the pixel data type
+    def _check_version_supported(self):
+        """Checks if the ND2 file version is supported by this reader.
 
         Returns:
-            dtype: the pixel data type
-
+            bool: True on supported
         """
-        return self._dtype
+        major_version, minor_version = get_version(self._fh)
+        supported = self.supported_file_versions.get(
+            (major_version, minor_version)) or self.supported_file_versions.get((major_version, None))
 
-    @property
-    def timesteps(self):
-        """Get the timesteps of the experiment
+        if not supported:
+            print("Warning: No parser is available for your current ND2 version (%d.%d). " % (
+                    major_version, minor_version) + "This might lead to unexpected behaviour.")
 
-        Returns:
-            np.ndarray: an array of times in milliseconds.
+        return supported
+
+    def _parse_metadata(self):
+        """Reads all metadata and instantiates the Metadata object.
 
         """
-        if self._timesteps is None:
-            return self.get_timesteps()
-        return self._timesteps
+        # Retrieve raw metadata from the label mapping
+        self._label_map = self._build_label_map()
+        self._raw_metadata = RawMetadata(self._fh, self._label_map)
+        self.metadata = self._raw_metadata.__dict__
+        self.acquisition_times = self._raw_metadata.acquisition_times
 
-    @property
-    def events(self):
-        """Get the events of the experiment
+    def _build_label_map(self):
+        """
+        Every label ends with an exclamation point, however, we can't directly search for those to find all the labels
+        as some of the bytes contain the value 33, which is the ASCII code for "!". So we iteratively find each label,
+        grab the subsequent data (always 16 bytes long), advance to the next label and repeat.
 
         Returns:
-            iterator of events as dict
+            LabelMap: the computed label map
+
         """
+        # go 8 bytes back from file end
+        self._fh.seek(-8, 2)
+        chunk_map_start_location = struct.unpack("Q", self._fh.read(8))[0]
+        self._fh.seek(chunk_map_start_location)
+        raw_text = self._fh.read(-1)
+        return LabelMap(raw_text)
 
-        return self._get_metadata_property("events")
+    def _calculate_field_of_view(self, index):
+        """Determines what field of view was being imaged for a given image.
+
+        Args:
+            index(int): the index (which is simply the order in which the image was acquired)
 
-    @property
-    def frame_rate(self):
-        """The (average) frame rate
-        
         Returns:
-            float: the (average) frame rate in frames per second
+            int: the field of view
         """
-        total_duration = 0.0
+        images_per_cycle = len(self.metadata["z_levels"]) * len(self.metadata["channels"])
+        return int((index - (index % images_per_cycle)) / images_per_cycle) % len(self.metadata["fields_of_view"])
 
-        for loop in self.metadata['experiment']['loops']:
-            total_duration += loop['duration']
+    def _calculate_channel(self, index):
+        """Determines what channel a particular image is.
 
-        if total_duration == 0:
-            total_duration = self.timesteps[-1]
+        Args:
+            index(int): the index (which is simply the order in which the image was acquired)
 
-            if total_duration == 0:
-                raise ValueError('Total measurement duration could not be determined from loops')
+        Returns:
+            string: the name of the color channel
 
-        return self.metadata['num_frames'] / (total_duration/1000.0)
+        """
+        return self.metadata["channels"][index % len(self.metadata["channels"])]
 
-    def _get_metadata_property(self, key, default=None):
-        if self.metadata is None:
-            return default
+    def _calculate_z_level(self, index):
+        """Determines the plane in the z-axis a given image was taken in.
 
-        if key not in self.metadata:
-            return default
+        In the future, this will be replaced with the actual offset in micrometers.
 
-        if self.metadata[key] is None:
-            return default
+        Args:
+            index(int): the index (which is simply the order in which the image was acquired)
 
-        return self.metadata[key]
+        Returns:
+            The z level
 
-    def _setup_axes(self):
-        """Setup the xyctz axes, iterate over t axis by default
+        """
+        return self.metadata["z_levels"][int(
+            ((index - (index % len(self.metadata["channels"]))) / len(self.metadata["channels"])) % len(
+                self.metadata["z_levels"]))]
 
+    def _calculate_image_group_number(self, frame_number, fov, z_level):
         """
-        self._init_axis_if_exists('x', self._get_metadata_property("width", default=0))
-        self._init_axis_if_exists('y', self._get_metadata_property("height", default=0))
-        self._init_axis_if_exists('c', len(self._get_metadata_property("channels", default=[])), min_size=2)
-        self._init_axis_if_exists('t', len(self._get_metadata_property("frames", default=[])))
-        self._init_axis_if_exists('z', len(self._get_metadata_property("z_levels", default=[])), min_size=2)
-        self._init_axis_if_exists('v', len(self._get_metadata_property("fields_of_view", default=[])), min_size=2)
+        Images are grouped together if they share the same time index, field of view, and z-level.
 
-        if len(self.sizes) == 0:
-            raise EmptyFileError("No axes were found for this .nd2 file.")
+        Args:
+            frame_number: the time index
+            fov: the field of view number
+            z_level: the z level number
 
-        # provide the default
-        self.iter_axes = self._guess_default_iter_axis()
+        Returns:
+            int: the image group number
 
-        self._register_get_frame(self.get_frame_2D, 'yx')
+        """
+        z_length = len(self.metadata['z_levels'])
+        z_length = z_length if z_length > 0 else 1
+        fields_of_view = len(self.metadata["fields_of_view"])
+        fields_of_view = fields_of_view if fields_of_view > 0 else 1
 
-    def _init_axis_if_exists(self, axis, size, min_size=1):
-        if size >= min_size:
-            self._init_axis(axis, size)
+        return frame_number * fields_of_view * z_length + (fov * z_length + z_level)
 
-    def _guess_default_iter_axis(self):
+    def _calculate_frame_number(self, image_group_number, field_of_view, z_level):
         """
-        Guesses the default axis to iterate over based on axis sizes.
+        Images are in the same frame if they share the same group number and field of view and are taken sequentially.
+
+        Args:
+            image_group_number: the image group number (see _calculate_image_group_number)
+            field_of_view: the field of view number
+            z_level: the z level number
+
         Returns:
-            the axis to iterate over
+
         """
-        priority = ['t', 'z', 'c', 'v']
-        found_axes = []
-        for axis in priority:
-            try:
-                current_size = self.sizes[axis]
-            except KeyError:
-                continue
+        return (image_group_number - (field_of_view * len(self.metadata["z_levels"]) + z_level)) / (
+                len(self.metadata["fields_of_view"]) * len(self.metadata["z_levels"]))
 
-            if current_size > 1:
-                return axis
+    @property
+    def _channel_offset(self):
+        """
+        Image data is interleaved for each image set. That is, if there are four images in a set, the first image
+        will consist of pixels 1, 5, 9, etc, the second will be pixels 2, 6, 10, and so forth.
 
-            found_axes.append(axis)
+        Returns:
+            dict: the channel offset for each channel
 
-        return found_axes[0]
+        """
+        return {channel: n for n, channel in enumerate(self.metadata["channels"])}
+
+    def _remove_unwanted_bytes(self, image_group_data, image_data_start, height, width):
+        # Remove unwanted 0-bytes that can appear in stitched images
+        number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
+        unwanted_bytes_len = (len(image_group_data[image_data_start:]))%(height*width)
+        if unwanted_bytes_len:
+            warnings.warn('Identified unwanted bytes in the ND2 file, possibly stitched.')
+            byte_ids = range(image_data_start+height*number_of_true_channels, len(image_group_data)-unwanted_bytes_len+1, height*number_of_true_channels)
+            if all([0 == image_group_data[byte_ids[i]+i] for i in range(len(byte_ids))]):
+                warnings.warn('All unwanted bytes are zero-bytes, correctly removed.')
+                for i in range(len(byte_ids)):
+                    del image_group_data[byte_ids[i]]
+
+    def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
+        """Reads the raw bytes and the timestamp of an image.
 
-    def get_timesteps(self):
-        """Get the timesteps of the experiment
+        Args:
+            image_group_number: the image group number (see _calculate_image_group_number)
+            channel_offset: the number of the color channel
+            height: the height of the image
+            width: the width of the image
 
         Returns:
-            np.ndarray: an array of times in milliseconds.
 
         """
-        if self._timesteps is not None and len(self._timesteps) > 0:
-            return self._timesteps
+        chunk = self._label_map.get_image_data_location(image_group_number)
+        data = read_chunk(self._fh, chunk)
+
+        # All images in the same image group share the same timestamp! So if you have complicated image data,
+        # your timestamps may not be entirely accurate. Practically speaking though, they'll only be off by a few
+        # seconds unless you're doing something super weird.
+        timestamp = struct.unpack("d", data[:8])[0]
+        image_group_data = array.array("H", data)
+        image_data_start = 4 + channel_offset
+
+        # The images for the various channels are interleaved within the same array. For example, the second image
+        # of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design
+        # a data structure that way, please send the author of this library a message.
+        number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
+        self._remove_unwanted_bytes(image_group_data, image_data_start, height, width)
+        try:
+            image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, width))
+        except ValueError:
+            image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, int(round(len(image_group_data[image_data_start::number_of_true_channels])/height))))
+
+        # Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you
+        # don't have the same number of images each cycle. We discovered this because we only took GFP images every
+        # other cycle to reduce phototoxicity, but NIS Elements still allocated memory as if we were going to take
+        # them every cycle.
+        if np.any(image_data):
+            return timestamp, image_data
+
+        # If a blank "gap" image is encountered, generate an array of corresponding height and width to avoid 
+        # errors with ND2-files with missing frames. Array is filled with nan to reflect that data is missing. 
+        else:
+            empty_frame = np.full((height, width), np.nan)
+            warnings.warn('ND2 file contains gap frames which are represented by np.nan-filled arrays; to convert to zeros use e.g. np.nan_to_num(array)')
+            return timestamp, image_data
+
+    def _get_frame_metadata(self):
+        """Get the metadata for one frame
 
-        self._timesteps = np.array(list(self._parser._raw_metadata.acquisition_times), dtype=np.float) * 1000.0
+        Returns:
+            dict: a dictionary containing the parsed metadata
 
-        return self._timesteps
+        """
+        return self.metadata