diff --git a/day3/Interp_ds107_sub012_t1r2.nii b/day3/Interp_ds107_sub012_t1r2.nii
new file mode 100644
index 0000000..0ebb8be
Binary files /dev/null and b/day3/Interp_ds107_sub012_t1r2.nii differ
diff --git a/day3/slice_time_image.py b/day3/slice_time_image.py
index ff73e3d..2423310 100644
--- a/day3/slice_time_image.py
+++ b/day3/slice_time_image.py
@@ -21,10 +21,12 @@
 """
 
 # Add any extra imports here
+import os
 import sys
-
+import numpy as np
+import matplotlib.pyplot as plt
 import nibabel as nib
-
+from scipy.interpolate import InterpolatedUnivariateSpline as IUS
 
 def slice_time_image(img, slice_times, TR):
     """Run slice-timing correction on nibabel image 'img'.
@@ -47,8 +49,32 @@ def slice_time_image(img, slice_times, TR):
         A new copy of the input image with slice-time interpolation applied
     """
     # Get the image data as an array;
+    data = img.get_data()
     # Make a new empty array "interp_data" to hold the interpolated data;
+    interp_data = np.zeros(data.shape)
+    
     # Do the interpolation;
+    time_offset = slice_times/slice_times.shape * TR
+    print "Time Offsets: %s" % time_offset
+    slice_0_times = TR * np.arange(data.shape[-1])
+    
+    # For each z coordinate (slice index):
+    for z in range(data.shape[2]):
+        print "Starting Slice %s of %s" % (z+1, data.shape[2])
+        slice_z_times = slice_0_times + time_offset[z]
+        # For each x coordinate:
+        for x in range(data.shape[0]):
+            # For each y coordinate:
+            for y in range(data.shape[1]):
+                # extract the time series at this x, y, z coordinate;
+                current_timecourse = data[x,y,z,:]
+                # make a linear interpolator object with the `slice_z_times` and the extracted time series;
+                current_interp = IUS(slice_z_times, current_timecourse, k=1)
+                # resample this interpolator at the slice 0 times;
+                current_timecourse_estimate = current_interp(slice_0_times)
+                # put this new resampled time series into the new data at the same position
+                interp_data[x,y,z,:] = current_timecourse_estimate
+
     # This is how to make a new image with the interpolated data
     new_img = nib.Nifti1Image(interp_data, img.affine, img.header)
     return new_img
@@ -71,7 +97,13 @@ def slice_time_file(fname, slice_times, TR):
     TR: double
         Repetition time in seconds.
     """
-    # Hint: use os.path.split and os.path.join to make the new filename
+    img = nib.load(fname)
+    interp_img = slice_time_image(img, slice_times, TR)
+    
+    path, tail = os.path.split(fname)
+    new_tail = 'Interp_' + tail  # I decided to use a different new filename
+    # I thought it would provide more information than just adding 'a'
+    new_fname = os.path.join(path,new_tail)
     nib.save(interp_img, new_fname)
 
 
@@ -82,12 +114,32 @@ def main():
     fname = sys.argv[1]
     # Assume the TR
     TR = 2.0
+    
     # Assume the slices were acquired even slices first, inferior to
     # superior, then odd slices, inferior to superior, where the most inferior
     # slice is index 0 and 0 is an even number.  What are the slice acquisition
     # times in seconds, where the first value is the acquisition time of slice
     # 0, the second is acquisition time of slice 1, etc?
-    slice_times = ?
+    
+    # have to load in image to know the number of slices there are
+    img = nib.load(fname)
+    num_slices = img.shape[2]
+    print "Number of slices: %s" % num_slices
+    
+    # use ceil in the next two lines in case of an odd number of slices
+    first_half = np.arange(0, np.ceil(num_slices/2.))
+    second_half = np.arange(np.ceil(num_slices/2.), num_slices)
+    slice_times = np.array([])
+    
+    for i in first_half:
+        slice_times = np.append(slice_times,first_half[i])
+        if i < len(second_half):
+            # if there are an odd number of slices
+            # len(second_half)<len(first_half)
+            # so we have to make sure that there are more slices to append
+            slice_times = np.append(slice_times,second_half[i])
+
+    print "Slice Ordering: %s" % slice_times
     slice_time_file(fname, slice_times, TR)
 
 
diff --git a/day3/slice_timing.ipynb b/day3/slice_timing.ipynb
index 80ff6f2..bb173bf 100644
--- a/day3/slice_timing.ipynb
+++ b/day3/slice_timing.ipynb
@@ -1,397 +1,571 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:3461f19d4aa3477de91d2d540be5749504689528e34562609f3a6e20cc8b6505"
- },
- "name": "slice_timing",
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# - compatibility with Python 3\n",
-      "from __future__ import print_function  # print('me') instead of print 'me'\n",
-      "from __future__ import division  # 1/2 == 0.5, not 0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# - show figures inside the notebook\n",
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# - import common modules\n",
-      "import numpy as np  # the Python array package\n",
-      "import matplotlib.pyplot as plt  # the Python plotting package"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# - set defaults for plotting\n",
-      "plt.rcParams['image.cmap'] = 'gray'\n",
-      "plt.rcParams['image.interpolation'] = 'nearest'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Load the image `ds114_sub009_t2r1.nii` with nibabel.  Get the data:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Load the image 'ds114_sub009_t2r1.nii' with nibabel\n",
-      "# Get the data array from the image"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As you remember, the first volume in this dataset is a lot different from the rest, and this will mess up our interpolation in time.\n",
-      "\n",
-      "So, we need to remove the first volume from the data first, using slicing:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Remove the first volume by slicing"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We start off with example time-courses from the first and second slice.\n",
-      "\n",
-      "Use slicing to get a z slice 0 time series for an example voxel at voxel coordinates (23, 19, 0).\n",
-      "\n",
-      "Do the same for a z slice 1 time series from (23, 19, 1).\n",
-      "\n",
-      "Plot these time series against volume number (TR number) on the same graph:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Slice out time series for voxel (23, 19, 0)\n",
-      "# Slice out time series for voxel (23, 19, 1)\n",
-      "# Plot both these time series against volume number, on the same graph"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The scanner collected slices for these data in an \"ascending interleaved\" order.  That is, the scanner first collected z slice 0, then z slice 2, up to z slice 28.  It then went back to collect z slice 1, 3, 5 up to z slice 29.\n",
-      "\n",
-      "That means the scanner started collecting slice 0 in each volume, at the beginning of the TR.\n",
-      "\n",
-      "The TR is 2.5 seconds."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# - the time between scans\n",
-      "TR = 2.5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Make a time vector, length 172, that corresponds to the start time in seconds of each volume.  This also gives the slice 0 start times."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make time vector containing start times in second of each volume,\n",
-      "# relative to start of first volume.\n",
-      "# Call this `slice_0_times`"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The scanner starts to collect z slice 1 exactly half way through the TR.  Make a new vector that is the start time of acquisition of slice 1."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make time vector containing start times in seconds of z slice 1,\n",
-      "# relative to start of first volume.\n",
-      "# Call this `slice_1_times`"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now plot the first 10 values for the slice 0 times, against the first 10 values of the slice 0 time series.\n",
-      "\n",
-      "To the same plot for the first 10 values of the slice 1 times, against the first 10 values of the slice 1 time series.\n",
-      "\n",
-      "Use the `:+` line marker for the plots to get the actual position of the points, and dotted lines betweeen them."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Plot first 10 values of slice 0 times against first 10 of slice 0 time series\n",
-      "# Plot first 10 values of slice 1 times against first 10 of slice 1 time series\n",
-      "# Use ':+' marker"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Import `InterpolatedUnivariateSpline` from `scipy.interpolate`.  Make a new linear (`k=1`) interpolation object for slice 1, with the slice 1 times and values."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Import `InterpolatedUnivariateSpline` from `scipy.interpolate`\n",
-      "# Make a new linear (`k=1`) interpolation object for slice 1, with slice 1 times and values."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Call the object you got with the slice 0 times, to get the estimated time series values for slice 1, if slice 1 had been collected at the same time as slice 0:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Call interpolator with `slice_0_times` to get estimated values"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Repeat the plot of the first 10 values of the time series.  This time, on the same plot, add the estimated values for slice 1, if they had been collected at the same time as slice 0.  Use a black `x` for the estimated points (marker `kx'`):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Plot first 10 values of slice 0 times against first 10 of slice 0 time series\n",
-      "# Plot first 10 values of slice 1 times against first 10 of slice 1 time series\n",
-      "# Plot first 10 values of slice 0 times against first 10 of interpolated slice 1 time series"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Make a new data matrix to contain the slice time corrected values for all voxels.  We want to keep the values for z slice 0 unchanged, so make the new data matrix by copying the old one (and therefore getting the slice 0 values):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Copy old data to a new array"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Loop over every x voxel coordinate, and then loop over every y voxel coordinate.\n",
-      "\n",
-      "For each x, y voxel coordinate:\n",
-      "\n",
-      "* extract the time series at this x, y coordinate for slice 1;\n",
-      "* make a linear interpolator object with the slice 1 times and the extracted time series;\n",
-      "* resample this interpolator at the slice 0 times;\n",
-      "* put this new resampled time series into the new data at the same position"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# loop over all x coordinate values\n",
-      "# loop over all y coordinate values\n",
-      "# extract the time series at this x, y coordinate for slice 1;\n",
-      "# make a linear interpolator object with the slice 1 times and the extracted time series;\n",
-      "# resample this interpolator at the slice 0 times;\n",
-      "# put this new resampled time series into the new data at the same position"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we need to do the same thing for all the z slices."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To do this, we want to construct an offset vector (call it `time_offset`) of length (number of z slices) such that adding the `time_offset[z]` to the acquisition time of the the first slice will give us the time of acquisition of slice `z`.  The next few steps are to get to that `time_offset` vector."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, make a new vector `acquisition_order` that is length 30, where `acquisition_order[i]` is the order of acquisition of slice index `i`.  For example, the first 4 elements of `acqusition_order` should be 0, 15, 1, 16."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make acquisition_order vector, length 30, with values:\n",
-      "# 0, 15, 1, 16 ... 14, 29"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Divide the acquisition order vector by number of slices, and multiply by the TR, to get the time offset for each z slice, relative to the start of the scan:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Divide acquisition_order by number of slices, multiply by TR"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we can do our whole slice time correction, for every slice.\n",
-      "\n",
-      "* For each z coordinate (slice index):\n",
-      "    * Make a time vector by adding the slice time offset for this slice, to the slice_0 times.  Call this the `slice_z_times` vector;\n",
-      "    * For each x coordinate:\n",
-      "        * For each y coordinate:\n",
-      "            * extract the time series at this x, y, z coordinate;\n",
-      "            * make a linear interpolator object with the `slice_z_times` and the extracted time series;\n",
-      "            * resample this interpolator at the slice 0 times;\n",
-      "            * put this new resampled time series into the new data at the same position"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# For each z coordinate (slice index):\n",
-      "## Make `slice_z_times` vector for this slice\n",
-      "### For each x coordinate:\n",
-      "#### For each y coordinate:\n",
-      "##### extract the time series at this x, y, z coordinate;\n",
-      "##### make a linear interpolator object with the `slice_z_times` and the extracted time series;\n",
-      "##### resample this interpolator at the slice 0 times;\n",
-      "##### put this new resampled time series into the new data at the same position"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Congratulations - you have just done slice timing correction on this 4D image."
-     ]
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# - compatibility with Python 3\n",
+    "from __future__ import print_function  # print('me') instead of print 'me'\n",
+    "from __future__ import division  # 1/2 == 0.5, not 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# - show figures inside the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# - import common modules\n",
+    "import numpy as np  # the Python array package\n",
+    "import matplotlib.pyplot as plt  # the Python plotting package\n",
+    "import nibabel as nib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# - set defaults for plotting\n",
+    "plt.rcParams['image.cmap'] = 'gray'\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the image `ds114_sub009_t2r1.nii` with nibabel.  Get the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(64, 64, 30, 173)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the image 'ds114_sub009_t2r1.nii' with nibabel\n",
+    "img = nib.load('ds114_sub009_t2r1.nii')\n",
+    "# Get the data array from the image\n",
+    "data = img.get_data()\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you remember, the first volume in this dataset is a lot different from the rest, and this will mess up our interpolation in time.\n",
+    "\n",
+    "So, we need to remove the first volume from the data first, using slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Remove the first volume by slicing\n",
+    "good_data = data[..., 1:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start off with example time-courses from the first and second slice.\n",
+    "\n",
+    "Use slicing to get a z slice 0 time series for an example voxel at voxel coordinates (23, 19, 0).\n",
+    "\n",
+    "Do the same for a z slice 1 time series from (23, 19, 1).\n",
+    "\n",
+    "Plot these time series against volume number (TR number) on the same graph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10d921550>]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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16ovqk8pBL3/+MrY2bkV+Xj70eXr4wj5xodFsbpxeVy8Ck4G0BvVR/yiqTdWo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\nMOCLkpA/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d921c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Slice out time series for voxel (23, 19, 0)\n",
+    "voxel_0_timecourse = good_data[23,19,0,:]\n",
+    "# Slice out time series for voxel (23, 19, 1)\n",
+    "voxel_1_timecourse = good_data[23,19,1,:]\n",
+    "# Plot both these time series against volume number, on the same graph\n",
+    "plt.plot(voxel_0_timecourse,'b-')\n",
+    "plt.plot(voxel_1_timecourse,'g-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scanner collected slices for these data in an \"ascending interleaved\" order.  That is, the scanner first collected z slice 0, then z slice 2, up to z slice 28.  It then went back to collect z slice 1, 3, 5 up to z slice 29.\n",
+    "\n",
+    "That means the scanner started collecting slice 0 in each volume, at the beginning of the TR.\n",
+    "\n",
+    "The TR is 2.5 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# - the time between scans\n",
+    "TR = 2.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a time vector, length 172, that corresponds to the start time in seconds of each volume.  This also gives the slice 0 start times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make time vector containing start times in second of each volume,\n",
+    "# relative to start of first volume.\n",
+    "# Call this `slice_0_times`\n",
+    "slice_0_times = TR*np.arange(172)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scanner starts to collect z slice 1 exactly half way through the TR.  Make a new vector that is the start time of acquisition of slice 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make time vector containing start times in seconds of z slice 1,\n",
+    "# relative to start of first volume.\n",
+    "# Call this `slice_1_times`\n",
+    "slice_1_times = TR*np.arange(172) + TR/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now plot the first 10 values for the slice 0 times, against the first 10 values of the slice 0 time series.\n",
+    "\n",
+    "To the same plot for the first 10 values of the slice 1 times, against the first 10 values of the slice 1 time series.\n",
+    "\n",
+    "Use the `:+` line marker for the plots to get the actual position of the points, and dotted lines betweeen them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10d954190>]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JjlikbaIoG0pm/eEPwV3Yhx0WdSRtl/Zpmc3sbKDC3ecntrv7c0AlsAIoB37r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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d9542d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot first 10 values of slice 0 times against first 10 of slice 0 time series\n",
+    "# Plot first 10 values of slice 1 times against first 10 of slice 1 time series\n",
+    "# Use ':+' marker\n",
+    "plt.plot(slice_0_times[0:10],voxel_0_timecourse[0:10],'b:+')\n",
+    "plt.plot(slice_1_times[0:10],voxel_1_timecourse[0:10],'g:+')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import `InterpolatedUnivariateSpline` from `scipy.interpolate`.  Make a new linear (`k=1`) interpolation object for slice 1, with the slice 1 times and values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Import `InterpolatedUnivariateSpline` from `scipy.interpolate`\n",
+    "from scipy.interpolate import InterpolatedUnivariateSpline as IUS\n",
+    "# Make a new linear (`k=1`) interpolation object for slice 1, with slice 1 times and values.\n",
+    "interp = IUS(slice_1_times, voxel_1_timecourse, k=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call the object you got with the slice 0 times, to get the estimated time series values for slice 1, if slice 1 had been collected at the same time as slice 0:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Call interpolator with `slice_0_times` to get estimated values\n",
+    "voxel_1_timecourse_est = interp(slice_0_times)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat the plot of the first 10 values of the time series.  This time, on the same plot, add the estimated values for slice 1, if they had been collected at the same time as slice 0.  Use a black `x` for the estimated points (marker `kx'`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x113b68610>]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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siBFte/7119f28zfBEnmP56237K++aHTrVrsCWLK77jrndznvvOTOBSmf+D/7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\nQ0eXHgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113a62b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Plot first 10 values of slice 0 times against first 10 of slice 0 time series\n",
+    "# Plot first 10 values of slice 1 times against first 10 of slice 1 time series\n",
+    "# Plot first 10 values of slice 0 times against first 10 of interpolated slice 1 time series\n",
+    "plt.plot(slice_0_times[0:10],voxel_0_timecourse[0:10],'b:+')\n",
+    "plt.plot(slice_1_times[0:10],voxel_1_timecourse[0:10],'g:+')\n",
+    "plt.plot(slice_0_times[0:10],voxel_1_timecourse_est[0:10],'kx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a new data matrix to contain the slice time corrected values for all voxels.  We want to keep the values for z slice 0 unchanged, so make the new data matrix by copying the old one (and therefore getting the slice 0 values):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Copy old data to a new array\n",
+    "new_data = good_data.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loop over every x voxel coordinate, and then loop over every y voxel coordinate.\n",
+    "\n",
+    "For each x, y voxel coordinate:\n",
+    "\n",
+    "* extract the time series at this x, y coordinate for slice 1;\n",
+    "* make a linear interpolator object with the slice 1 times and the extracted time series;\n",
+    "* resample this interpolator at the slice 0 times;\n",
+    "* put this new resampled time series into the new data at the same position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# loop over all x coordinate values\n",
+    "for x in range(good_data.shape[0]):\n",
+    "    # loop over all y coordinate values\n",
+    "    for y in range(good_data.shape[1]):\n",
+    "        # extract the time series at this x, y coordinate for slice 1;\n",
+    "        current_timecourse = good_data[x,y,1,:]\n",
+    "        # make a linear interpolator object with the slice 1 times and the extracted time series;\n",
+    "        current_interp = IUS(slice_1_times, current_timecourse, k=1)\n",
+    "        # resample this interpolator at the slice 0 times;\n",
+    "        current_timecourse_estimate = current_interp(slice_0_times)\n",
+    "        # put this new resampled time series into the new data at the same position\n",
+    "        new_data[x,y,1,:] = current_timecourse_estimate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to do the same thing for all the z slices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To do this, we want to construct an offset vector (call it `time_offset`) of length (number of z slices) such that adding the `time_offset[z]` to the acquisition time of the the first slice will give us the time of acquisition of slice `z`.  The next few steps are to get to that `time_offset` vector."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, make a new vector `acquisition_order` that is length 30, where `acquisition_order[i]` is the order of acquisition of slice index `i`.  For example, the first 4 elements of `acqusition_order` should be 0, 15, 1, 16."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.,  15.,   1.,  16.,   2.,  17.,   3.,  18.,   4.,  19.,   5.,\n",
+       "        20.,   6.,  21.,   7.,  22.,   8.,  23.,   9.,  24.,  10.,  25.,\n",
+       "        11.,  26.,  12.,  27.,  13.,  28.,  14.,  29.])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Make acquisition_order vector, length 30, with values:\n",
+    "# 0, 15, 1, 16 ... 14, 29\n",
+    "first_half = np.arange(0,15)\n",
+    "second_half = np.arange(15,30)\n",
+    "acquisition_order = np.array([])\n",
+    "for i in first_half:\n",
+    "    acquisition_order = np.append(acquisition_order,first_half[i])\n",
+    "    acquisition_order = np.append(acquisition_order,second_half[i])\n",
+    "acquisition_order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Divide the acquisition order vector by number of slices, and multiply by the TR, to get the time offset for each z slice, relative to the start of the scan:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        ,  1.25      ,  0.08333333,  1.33333333,  0.16666667,\n",
+       "        1.41666667,  0.25      ,  1.5       ,  0.33333333,  1.58333333,\n",
+       "        0.41666667,  1.66666667,  0.5       ,  1.75      ,  0.58333333,\n",
+       "        1.83333333,  0.66666667,  1.91666667,  0.75      ,  2.        ,\n",
+       "        0.83333333,  2.08333333,  0.91666667,  2.16666667,  1.        ,\n",
+       "        2.25      ,  1.08333333,  2.33333333,  1.16666667,  2.41666667])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Divide acquisition_order by number of slices, multiply by TR\n",
+    "time_offset = acquisition_order/acquisition_order.shape * TR\n",
+    "time_offset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can do our whole slice time correction, for every slice.\n",
+    "\n",
+    "* For each z coordinate (slice index):\n",
+    "    * Make a time vector by adding the slice time offset for this slice, to the slice_0 times.  Call this the `slice_z_times` vector;\n",
+    "    * For each x coordinate:\n",
+    "        * For each y coordinate:\n",
+    "            * extract the time series at this x, y, z coordinate;\n",
+    "            * make a linear interpolator object with the `slice_z_times` and the extracted time series;\n",
+    "            * resample this interpolator at the slice 0 times;\n",
+    "            * put this new resampled time series into the new data at the same position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# For each z coordinate (slice index):\n",
+    "for z in range(good_data.shape[2]):\n",
+    "    # Make `slice_z_times` vector for this slice\n",
+    "    slice_z_times = slice_0_times + time_offset[z]\n",
+    "    # For each x coordinate:\n",
+    "    for x in range(good_data.shape[0]):\n",
+    "        # For each y coordinate:\n",
+    "        for y in range(good_data.shape[1]):\n",
+    "            # extract the time series at this x, y, z coordinate;\n",
+    "            current_timecourse = good_data[x,y,z,:]\n",
+    "            # make a linear interpolator object with the `slice_z_times` and the extracted time series;\n",
+    "            current_interp = IUS(slice_z_times, current_timecourse, k=1)\n",
+    "            # resample this interpolator at the slice 0 times;\n",
+    "            current_timecourse_estimate = current_interp(slice_0_times)\n",
+    "            # put this new resampled time series into the new data at the same position\n",
+    "            new_data[x,y,z,:] = current_timecourse_estimate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congratulations - you have just done slice timing correction on this 4D image."
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/day3/slice_timing_solutions.ipynb b/day3/slice_timing_solutions.ipynb
index 0becd97..bbda0a1 100644
--- a/day3/slice_timing_solutions.ipynb
+++ b/day3/slice_timing_solutions.ipynb
@@ -646,7 +646,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,