diff --git a/CDTools/tools/data.py b/CDTools/tools/data.py
new file mode 100644
index 0000000..4ef1796
--- /dev/null
+++ b/CDTools/tools/data.py
@@ -0,0 +1,344 @@
+from __future__ import division, print_function, absolute_import
+
+import h5py
+import numpy as np
+
+__all__ = ['get_entry_info',
+           'get_sample_info',
+           'get_wavelength',
+           'get_detector_geometry',
+           'get_mask',
+           'get_data',
+           'get_ptycho_translations']
+
+#
+#
+# I will put here some thoughts about how to load data into this program.
+#
+# 
+# The reconstructions should have the ability to generate datasets.
+# So you could write a reconstruction engine and then it would be
+# able to simulate data directly in the engine for you to use as a
+# reconstruction
+#
+# I don't even think there needs to be a loading tool for loading cxi files
+# because there isn't really a better method beyond just loading the
+# file into an h5py object. This file could host the simple cxi file
+# browser, perhaps. But I think the reality is that we need individual
+# loaders for each kind of experiment. Perhaps we could put some basic
+# reuseable tools for inspecting cxi-type h5 files in this file. 
+#
+#
+# Then, there can be some more sophisticated tools that load data for
+# specific use cases that are common - loading data for a 2D CDI experiment,
+# loading data for a 2D Ptycho experiment, loading data for Bragg Ptycho in
+# 3D, loading data for a 3D CDI experiment, etc.
+#
+#
+# Perhaps one good way to package this is for the kind of data associated
+# with any particular experiment to have it's own kind of dataset or view.
+# So there would be a "2D Ptychography" data viewer, which would contain
+# all the measured data that comes from a 2D ptychography experiment.
+# The specialized functions would plop out these data viewers, and the
+# reconstruction classes could be designed around a particular kind of
+# viewer with the most general kind just requiring a generic data viewer.
+#
+# Data viewers could have simple tools like the ability to send themselves
+# to the GPU, CPU, change the datatype, etc. I think the most generic thing
+# is as a subclass of the torch Data objects, where they would for each slice
+# return the index, a set of defining parameters (translation, angle, energy,
+# whatever), and a diffraction pattern. They would also have a "setup"
+# attribute, or "metadata", or whatever you'd want to call it, that contain
+# the various fixed experimental parameters (energy, distance, etc.)
+# 
+# And I think the cxi visualizer should really go into it's own script,
+# because it's not a reuseable component.
+#
+
+
+#
+# Functions to inspect the basic attributes of a cxi file represented as an
+# h5 file object
+#
+
+def get_entry_info(cxi_file):
+    """Returns a dictionary with the basic metadata from the cxi file's entry_1 attribute
+    
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+
+    Returns:
+        dict : A dictionary with basic metadata defined in the cxi file
+
+    """
+    e1 = cxi_file['entry_1']
+    metadata_attrs = ['title',
+                      'experiment_identifier',
+                      'experiment_description',
+                      'program_name',
+                      'start_time',
+                      'end_time']
+    metadata = {attr: str(e1[attr][()].decode()) for attr in metadata_attrs
+                if attr in e1}
+    return metadata
+
+
+def get_sample_info(cxi_file):
+    """Returns a dictionary with the basic metadata from the cxi file's entry_1/sample_1 attribute
+    
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+
+    Returns:
+        dict : A dictionary with basic metadata from the sample defined in the cxi file
+
+    """
+    if 'entry_1/sample_1' not in cxi_file:
+        return None
+    
+    s1 = cxi_file['entry_1/sample_1']
+    metadata_attrs = ['name','description','unit_cell_group']
+    metadata = {attr: str(s1[attr][()].decode()) for attr in metadata_attrs
+                if attr in s1}
+    
+    float_attrs = ['concentration',
+                   'mass',
+                   'temperature',
+                   'thickness',
+                   'unit_cell_volume']
+    for attr in float_attrs:
+        if attr in s1:
+            metadata[attr] = np.float32(s1[attr][()])
+
+    if 'unit_cell' in s1:
+        metadata['unit_cell'] = np.array(s1['unit_cell']).astype(np.float32)
+
+    # TODO: Add my nonstandard "surface normal" attribute here
+    
+    # TODO: I should add the sample geometry as a valid metadata that can
+    # be copied over
+    
+    return metadata
+
+
+def get_wavelength(cxi_file):
+    """Returns the wavelength of the source defined in the cxi file object, in m
+    
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+    
+    Returns:
+        np.float32 : The wavelength of the source defined in the cxi file
+    """
+    i1 = cxi_file['entry_1/instrument_1']
+    if 'source_1/wavelength' in i1:
+        wavelength = np.float32(i1['source_1/wavelength'])
+    elif 'source_1/energy' in i1:
+        energy = np.float32(i1['source_1/energy'])
+        wavelength = 1.9864459e-25 / energy
+    else:
+        raise KeyError('Neither Wavelength or Energy Defined in provided .cxi File')
+    
+    return wavelength
+
+
+def get_detector_geometry(cxi_file):
+    """Returns a standardized description of the detector geometry defined in the cxi file object
+
+    It makes intelligent assumptions based on the definitions in the cxi
+    file definition. The standardized description of the geometry that it
+    outputs includes the sample to detector distance, the corner location
+    of the detector, and the basis vectors defining the detector. It can
+    only handle detectors defined as rectangular grids of pixels.
+    
+    The distance and corner_location values are technically overdetermining
+    the detector location, but for many experiments (particularly
+    transmission experiments), the distance is needed and the exact 
+    corner location is not. If the corner location is not reported in
+    the cxi file, no attempt will be made to calculate it.
+
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+    
+    Returns:
+        distance (np.float32) : The sample to detector distance, in m
+        basis_vectors (np.array) : The basis vectors for the detector
+        corner_location (np.array) : The location of the (0,0) pixel in the detector
+
+    """
+    i1 = cxi_file['entry_1/instrument_1']
+    d1 = i1['detector_1']
+
+    if 'detector_1/basis_vectors' in i1:
+        basis_vectors = np.array(d1['basis_vectors'])
+    else:
+        # This whole thing just to account for all the ways people can
+        # implicitly define the x or y pixel size for a detector. I've
+        # seen too many of these in the wild, unfortunately...
+        try:
+            x_pixel_size = np.float32(d1['x_pixel_size'])
+        except:
+            x_pixel_size = None
+        try:
+            y_pixel_size = np.float32(d1['y_pixel_size'])
+        except:
+            y_pixel_size = None
+
+        if x_pixel_size is None and y_pixel_size is not None:
+            x_pixel_size = y_pixel_size
+        elif x_pixel_size is not None and y_pixel_size is None:
+            y_pixel_size = x_pixel_size
+        if x_pixel_size is None and y_pixel_size is None:
+            raise KeyError('Detector pixel size not defined in file.')
+        basis_vectors = np.array([[0,-y_pixel_size,0],
+                                  [-x_pixel_size,0,0]]).transpose()
+        
+    try:
+        distance = np.float32(d1['distance'])
+    except:
+        distance = None
+    try:
+        corner_position = np.array(d1['corner_position'])
+    except:
+        corner_position = None
+    
+    # Don't pretend to calculate corner position from distance if it's
+    # if it's not defined, but do calculate distance from corner position
+    # if distance is not defined. If neither is defined, then raise
+    # an error.
+    if distance is None and corner_position is not None:
+        detector_normal = np.cross(basis_vectors[:,0],
+                                   basis_vectors[:,1])
+        detector_normal /= np.linalg.norm(detector_normal)
+        distance = np.linalg.norm(np.dot(corner_position, detector_normal))
+        
+    if distance is None and corner_position is not None:
+        raise KeyError('Neither sample to detector distance or corner position is defined in file.')
+
+    return distance, basis_vectors, corner_position
+
+
+def get_mask(cxi_file):
+    """Returns the detector mask defined in the cxi file object
+
+    This function converts from the format specified in the cxi file
+    definition to a simple on/off mask, where a value of 1 defines a
+    good pixel (on) and a value of 0 defines a bad pixel (off).
+
+    If any bit is set in the mask at all, it will be defined as a bad
+    pixel, with the exception of pixels marked exactly as 0x00001000,
+    which is defined to mean that the pixel has signal above the
+    background. These pixels are treated as on pixels
+    
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+    
+    Returns:
+        np.array : An array storing the mask from the cxi file
+    """
+    
+    i1 = cxi_file['entry_1/instrument_1']
+    if 'detector_1/mask' in i1:
+        mask = np.array(i1['detector_1/mask']).astype(np.uint32)
+        mask_on = np.equal(mask,np.uint32(0))
+        mask_has_signal = np.equal(mask,np.uint32(0x00001000))
+        return np.logical_or(mask_on,mask_has_signal).astype(np.uint8)
+    else:
+        return None
+
+
+def get_data(cxi_file):
+    """Returns an array with the full stack of detector data defined in the cxi file object
+
+    This function will make sure to check all the various places that it's
+    okay to store the data in, to ensure that it can find the data regardless
+    of whether the creator of the .cxi file has remembered to link the data
+    to all the required locations.
+
+    It will return the data array in whatever shape it's defined in.
+    
+    It will also read out the axes attribute of the data into a list
+    of strings
+    
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+    
+    Returns:
+        np.array : An array storing the data defined in the cxi file
+        list : A list of the axes defined in the axes attribute, if any
+    """
+    # Possible locations for the data
+    # 
+    # entry_1/detector_1/data
+    if 'entry_1/data_1/data' in cxi_file:
+        pull_from = 'entry_1/data_1/data'
+    elif 'entry_1/instrument_1/detector_1/data' in cxi_file:
+        pull_from = 'entry_1/instrument_1/detector_1/data'
+    else:
+        raise KeyError('Data is not defined within cxi file')
+    data = np.array(cxi_file[pull_from]).astype(np.float32)
+    if 'axes' in cxi_file[pull_from].attrs:
+        axes = str(cxi_file[pull_from].attrs['axes'].decode()).split(':')
+        axes = [axis.strip().lower() for axis in axes]
+    else:
+        axes = None
+
+    return data, axes
+
+
+
+def get_ptycho_translations(cxi_file):
+    """Gets an array of x,y,z translations, if such an array has been defined in the file
+
+    It applies two operations to the translations. First, it negates them,
+    because the CXI file format is designed to specify translations of the
+    samples and the CDTools code specifies translations of the optics.
+    Second, it transposes the array so that the first axis is translation
+    ID and the second axis is the (x,y,z) components of the translation
+
+    Args:
+        cxi_file (h5py.File) : a file object to be read
+    
+    Returns:
+        np.array : An array storing the translations defined in the cxi file
+        list : A list of the axes defined in the axes attribute, if any
+    """
+    
+    if 'entry_1/data_1/translation' in cxi_file:
+        pull_from = 'entry_1/data_1/translation'
+    elif 'entry_1/sample_1/geometry_1/translation' in cxi_file:
+        pull_from = 'entry_1/sample_1/geometry_1/translation'
+    elif 'entry_1/instrument_1/detector_1/translation' in cxi_file:
+        pull_from = 'entry_1/instrument_1/detector_1/translation'
+    else:
+        raise KeyError('Translations are not defined within cxi file')
+
+    translations = -np.array(cxi_file[pull_from]).astype(np.float32).transpose()
+    return translations
+    
+
+#
+# It might be useful to make some helper functions to help write cxi files
+#
+
+#
+# A function to place the skeleton of a cxi file down
+# 
+
+#
+# A function to define the source attributes
+#
+
+#
+# A function to define the detector geometry
+#
+
+#
+# A function to save out a mask, converting it to the correct format
+#
+
+#
+# Perhaps a function to store the data and link it correctly? But this might
+# have to change too much situation to situation
+#
+
diff --git a/tests/tools/.#test_losses.py b/tests/tools/.#test_losses.py
deleted file mode 120000
index 5f0d87f..0000000
--- a/tests/tools/.#test_losses.py
+++ /dev/null
@@ -1 +0,0 @@
-abe@paulsimon.2556:1553086360
\ No newline at end of file
diff --git a/tests/tools/test_data.py b/tests/tools/test_data.py
new file mode 100644
index 0000000..c8b54ee
--- /dev/null
+++ b/tests/tools/test_data.py
@@ -0,0 +1,395 @@
+from __future__ import division, print_function, absolute_import
+
+from CDTools.tools import data
+import numpy as np
+import torch as t
+import h5py
+import pytest
+import os
+import datetime
+from pathlib import Path
+
+
+#
+# First, we write a few fixtures to generate specific data files we
+# want with deliberate pathologies.
+#
+# * Energy defined but no wavelength
+# * Wavelength defined but no energy
+# * Distance and pixel pitch defined but no corner position or basis
+# * Corner position and basis defined but no distance or pixel pitch
+# * No mask defined
+# * Mask defined
+#
+# Each file will get a fixture that loads the cxi file but also loads
+# a dictionary with the relevant information for the cxi file
+#
+#
+
+
+# This just grabs the directory whose name matches the file we're running
+@pytest.fixture(scope='module')
+def datadir(request):
+    filename = request.module.__file__
+    test_dir, _ = os.path.splitext(filename)
+    return Path(test_dir)
+
+
+@pytest.fixture(scope='module')
+def ptycho_cxi_1():
+    """Creates an example file for CXI ptychography. This file is defined
+    to have everything done as correctly as possible with lots of attributes
+    defined. It will return both a dictionary describing what is expected
+    to be loaded and a file with the data stored in it.
+    """
+    
+    expected = {}
+    f = h5py.File('ptycho_cxi_1',driver='core',backing_store=False)
+
+    # Start by defining the basic structure
+    f.create_dataset('cxi_version', data=150)  
+    f.create_dataset('number_of_entries',data=1)
+
+    # Then define a bunch of metadata for entry_1
+    e1f = f.create_group('entry_1')
+    expected['entry metadata'] = {}
+    e1e = expected['entry metadata']
+    e1e['start_time'] = datetime.datetime.now().isoformat()
+    e1f['start_time'] = np.string_(e1e['start_time'])
+    e1e['end_time'] = datetime.datetime.now().isoformat()
+    e1f['end_time'] = np.string_(e1e['end_time'])
+    e1e['experiment_identifier'] = 'Fake Experiment 1'
+    e1f['experiment_identifier'] = np.string_(e1e['experiment_identifier'])
+    e1e['experiment_description'] = 'A fully defined ptychography experiment to test the data loading'
+    e1f['experiment_description'] = np.string_(e1e['experiment_description'])
+    e1e['program_name'] = 'CDTools'
+    e1f['program_name'] = np.string_(e1e['program_name'])
+    e1e['title'] = 'The one experiment we did'
+    e1f['title'] = np.string_(e1e['title'])
+
+    # Set up the sample info
+    s1f = e1f.create_group('sample_1')
+    expected['sample info'] = {}
+    s1e = expected['sample info']
+    s1e['name'] = 'Fake Sample'
+    s1f['name'] = np.string_(s1e['name'])
+    s1e['description'] = 'A sample that isn\'t real'
+    s1f['description'] = np.string_(s1e['description'])
+    s1e['unit_cell_group'] = 'P1'
+    s1f['unit_cell_group'] = np.string_(s1e['unit_cell_group'])
+    s1e['concentration'] = np.float32(np.random.rand())
+    s1f['concentration'] = s1e['concentration']
+    s1e['mass'] = np.float32(np.random.rand())
+    s1f['mass'] = s1e['mass']
+    s1e['temperature'] = np.float32(np.random.rand()*100)
+    s1f['temperature'] = s1e['temperature']
+    s1e['thickness'] = np.float32(np.random.rand()*1e-7)
+    s1f['thickness'] = s1e['thickness']
+    s1e['unit_cell_volume'] = np.float32(np.random.rand() * 1e-27)
+    s1f['unit_cell_volume'] = s1e['unit_cell_volume']
+    s1e['unit_cell'] = np.array([1,1,1,90,90,90]).astype(np.float32)
+    s1f.create_dataset('unit_cell',data = s1e['unit_cell'])
+
+    i1f = e1f.create_group('instrument_1')
+    source1f = i1f.create_group('source_1')
+
+    energy = np.float32(1.3618e-16) #Joules, = 850 eV
+    source1f['energy'] = energy
+    expected['wavelength'] = np.float32(1.9864459e-25) / energy
+    source1f['wavelength'] = expected['wavelength'] 
+
+    d1f = i1f.create_group('detector_1')
+    expected['detector'] = {}
+    d1e = expected['detector']
+    d1e['distance'] = np.float32(0.3)
+    d1f['distance'] = d1e['distance']
+    d1e['basis'] = np.array([[0,-30e-6,0],
+                             [-20e-6,0,0]]).astype(np.float32).transpose()
+    d1f.create_dataset('basis_vectors',data=d1e['basis'])
+    d1f['x_pixel_size'] = np.float32(20e-6)
+    d1f['y_pixel_size'] = np.float32(30e-6)
+    d1e['corner'] = np.array((2550e-6,3825e-6,0.3)).astype(np.float32)
+    d1f.create_dataset('corner_position', data=d1e['corner'])
+
+    # Remember the format for the CXI file differs from the format used
+    # internally
+    mask = np.zeros((100,256,256)).astype(np.uint32)
+    expected['mask'] = np.ones((100,256,256)).astype(np.uint8)
+    d1f.create_dataset('mask',data=mask)
+
+    data1f = e1f.create_group('data_1')
+    
+    data = np.random.rand(100,256,256).astype(np.float32)
+    expected['data'] = data
+    d1f.create_dataset('data',data=data)
+    data1f['data'] = h5py.SoftLink('/entry_1/instrument_1/detector_1/data')
+
+    d1f['data'].attrs['axes'] = np.string_('translation:y:x')
+    expected['axes'] = ['translation','y','x']
+
+    g1f = s1f.create_group('geometry_1')
+    translations = np.arange(300).reshape((100,3)).astype(np.float32)
+    g1f.create_dataset('translation',data=translations)
+    data1f['translation'] = h5py.SoftLink('/entry_1/sample_1/geometry_1/translation')
+    d1f['translation'] = h5py.SoftLink('/entry_1/sample_1/geometry_1/translation')
+    expected['translations'] = -translations.transpose()
+    
+    return f, expected
+
+
+@pytest.fixture(scope='module')
+def ptycho_cxi_2():
+    """Creates an example file for CXI ptychography. This file is defined
+    to have a subset of things missing. In particular, it:
+
+    * Defines the wavelength but not the energy
+    * Defines the corner position but not the sample-detector distance
+    * Defines pixel sizes but no basis vectors
+    * Doesn't define a mask
+    * Only defines data in the relevant places, not under data_1
+    * Doesn't explicitly define axes for the data arrays
+    * Is missing many allowed metadata attributes
+
+    """
+    
+    expected = {}
+    f = h5py.File('ptycho_cxi_2',driver='core',backing_store=False)
+
+    # Start by defining the basic structure
+    f.create_dataset('cxi_version', data=150)  
+    f.create_dataset('number_of_entries',data=1)
+
+    # Then define a bunch of metadata for entry_1
+    e1f = f.create_group('entry_1')
+    expected['entry metadata'] = {}
+    e1e = expected['entry metadata']
+    e1e['title'] = 'The one experiment we did'
+    e1f['title'] = np.string_(e1e['title'])
+
+    # Set up the sample info
+    s1f = e1f.create_group('sample_1')
+    expected['sample info'] = {}
+    s1e = expected['sample info']
+    s1e['temperature'] = np.float32(np.random.rand()*100)
+    s1f['temperature'] = s1e['temperature']
+
+    i1f = e1f.create_group('instrument_1')
+    source1f = i1f.create_group('source_1')
+
+    energy = np.float32(1.3618e-16) #Joules, = 850 eV
+    expected['wavelength'] = np.float32(1.9864459e-25) / energy
+    source1f['wavelength'] = expected['wavelength'] 
+
+    d1f = i1f.create_group('detector_1')
+    expected['detector'] = {}
+    d1e = expected['detector']
+    d1e['distance'] = np.float32(0.3)
+    d1e['basis'] = np.array([[0,-30e-6,0],
+                             [-20e-6,0,0]]).astype(np.float32).transpose()
+    d1f['x_pixel_size'] = np.float32(20e-6)
+    d1f['y_pixel_size'] = np.float32(30e-6)
+    d1e['corner'] = np.array((2550e-6,3825e-6,0.3)).astype(np.float32)
+    d1f.create_dataset('corner_position', data=d1e['corner'])
+
+    # Remember the format for the CXI file differs from the format used
+    # internally
+    expected['mask'] = None
+
+    data1f = e1f.create_group('data_1')
+    
+    data = np.random.rand(100,256,256).astype(np.float32)
+    expected['data'] = data
+    d1f.create_dataset('data',data=data)
+
+    expected['axes'] = None
+
+    g1f = s1f.create_group('geometry_1')
+    translations = np.arange(300).reshape((100,3)).astype(np.float32)
+    g1f.create_dataset('translation',data=translations)
+    expected['translations'] = -translations.transpose()
+    
+    return f, expected
+
+
+@pytest.fixture(scope='module')
+def ptycho_cxi_3():
+    """Creates an example file for CXI ptychography. This file is defined
+    to have a different subset of information missing. In particular, it:
+
+    * Has no sample_1 group
+    * Defines the energy but not the wavelength of light
+    * Has the data only defined under the data_1 group, not in the relevant places
+    * Defines the detector basis but no pixel sizes
+    * Defines a mask as all pixels flagged as "above the background"
+    * Defines the sample to detector distance but no corner location
+    * Is missing some of the allowed metadata
+    """
+    
+    expected = {}
+    f = h5py.File('ptycho_cxi_3',driver='core',backing_store=False)
+
+    # Start by defining the basic structure
+    f.create_dataset('cxi_version', data=150)  
+    f.create_dataset('number_of_entries',data=1)
+
+    # Then define a bunch of metadata for entry_1
+    e1f = f.create_group('entry_1')
+    expected['entry metadata'] = {}
+    e1e = expected['entry metadata']
+    e1e['start_time'] = datetime.datetime.now().isoformat()
+    e1f['start_time'] = np.string_(e1e['start_time'])
+    e1e['end_time'] = datetime.datetime.now().isoformat()
+    e1f['end_time'] = np.string_(e1e['end_time'])
+
+    # Set up the sample info
+    expected['sample info'] = None
+
+    i1f = e1f.create_group('instrument_1')
+    source1f = i1f.create_group('source_1')
+
+    energy = np.float32(1.3618e-16) #Joules, = 850 eV
+    source1f['energy'] = energy
+    expected['wavelength'] = np.float32(1.9864459e-25) / energy
+
+    d1f = i1f.create_group('detector_1')
+    expected['detector'] = {}
+    d1e = expected['detector']
+    d1e['distance'] = np.float32(0.3)
+    d1f['distance'] = d1e['distance']
+    d1e['basis'] = np.array([[0,-30e-6,0],
+                             [-20e-6,0,0]]).astype(np.float32).transpose()
+    d1f.create_dataset('basis_vectors',data=d1e['basis'])
+    d1e['corner'] = None
+
+    # Remember the format for the CXI file differs from the format used
+    # internally
+    mask = np.ones((100,256,256)).astype(np.uint32) * 0x00001000
+    expected['mask'] = np.ones((100,256,256)).astype(np.uint8)
+    d1f.create_dataset('mask',data=mask)
+
+    data1f = e1f.create_group('data_1')
+    
+    data = np.random.rand(100,256,256).astype(np.float32)
+    expected['data'] = data
+    data1f.create_dataset('data',data=data)
+
+    data1f['data'].attrs['axes'] = np.string_('translation:y:x')
+    expected['axes'] = ['translation','y','x']
+
+    translations = np.arange(300).reshape((100,3)).astype(np.float32)
+    data1f.create_dataset('translation',data=translations)
+    expected['translations'] = -translations.transpose()
+    
+    return f, expected
+
+
+@pytest.fixture(scope='module')
+def real_ptycho_CSX(datadir):
+    """Loads a real ptychography file from CSX @ NSLS-II along with a
+    dictionary describing what is expected to be loaded from it.
+    """
+    #file_obj = h5py.File(datadir/'example_CSX_Bragg.cxi','r')
+    #expected = {}
+    #return file_obj, expected
+    pass
+
+
+@pytest.fixture(scope='module')
+def real_ptycho_HXN(datadir):
+    """Loads a real ptychography file from HXN @ NSLS-II along with a
+    dictionary describing what is expected to be loaded from it.
+    """
+    pass
+
+@pytest.fixture(scope='module')
+def real_ptycho_COSMIC(datadir):
+    """Loads a real ptychography file from COSMIC @ ALS along with a
+    dictionary describing what is expected to be loaded from it.
+    """
+    pass
+
+
+@pytest.fixture(scope='module')
+def test_ptycho_cxis(ptycho_cxi_1, ptycho_cxi_2, ptycho_cxi_3):
+    """Loads a list of tuples of ptychography CXI files and dictionaries,
+    describing the expected output from various functions on being called
+    on the cxi files.
+    """
+    return [ptycho_cxi_1, ptycho_cxi_2, ptycho_cxi_3]
+    
+
+
+#
+# Now we have a bunch of tests of the data loading capabilities
+#
+
+
+
+def test_get_entry_info(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        entry_info = data.get_entry_info(cxi)
+        for key in expected['entry metadata']:
+            assert entry_info[key] == expected['entry metadata'][key]
+
+        
+def test_get_sample_info(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        sample_info = data.get_sample_info(cxi)
+        if sample_info is None and  \
+           ('sample info' not in expected or
+            expected['sample info'] is None):
+            # Valid if no sample info is defined at all
+            continue
+        for key in expected['sample info']:
+            if isinstance(expected['sample info'][key],np.ndarray):
+                assert np.allclose(sample_info[key],
+                                   expected['sample info'][key])
+            else:
+                assert sample_info[key] == expected['sample info'][key]
+                
+    
+def test_get_wavelength(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        assert np.isclose(expected['wavelength'],data.get_wavelength(cxi))
+
+
+def test_get_detector_geometry(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        distance, basis, corner = data.get_detector_geometry(cxi)
+        assert np.isclose(distance,expected['detector']['distance'])
+        assert np.allclose(basis,expected['detector']['basis'])
+        if isinstance(expected['detector']['corner'], np.ndarray):
+            assert np.allclose(corner, expected['detector']['corner'])
+        else:
+            assert corner == expected['detector']['corner']
+
+            
+def test_get_mask(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        mask = data.get_mask(cxi)
+        if expected['mask'] is None and mask is None:
+            continue
+        assert np.all(data.get_mask(cxi) == expected['mask'])
+
+    
+def test_get_data(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        patterns, axes = data.get_data(cxi)
+        assert np.allclose(patterns, expected['data'])
+        assert axes == expected['axes']
+
+
+def test_get_ptycho_translations(test_ptycho_cxis):
+    for cxi, expected in test_ptycho_cxis:
+        assert np.allclose(data.get_ptycho_translations(cxi),
+                           expected['translations'])
+
+
+
+#
+# Then, write a test for the data saving. It should create a .cxi file
+# using the data seving tools, and then check that when read with the
+# .cxi reading tools that it gets the same things that were written.
+#
+
+