editing files for binder compatability

Event Related Potential Tutorial.ipynb

@@ -4,10 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#Cognitive and Neural Dynamics Lab Tutorials\n",
-    "##Event Related Potentials (ERP)\n",
+    "# Cognitive and Neural Dynamics Lab Tutorials\n",
+    "## Event Related Potentials (ERP)\n",
     "\n",
-    "#####Torben Noto 2015\n",
+    "##### Torben Noto 2015\n",
     "\n"
    ]
   },
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -83,7 +83,7 @@
    "source": [
     "#loading ECoG data\n",
     "filename = 'emodat.mat'\n",
-    "filename = os.path.join('/Users/Torben/Desktop/Voytek_Things/', filename)\n",
+    "filename = os.path.join('./', filename)\n",
     "data = sp.io.loadmat(filename)\n",
     "srate = data['srate'] # sampling rate\n",
     "srate = srate[0][0];\n",
@@ -97,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "####Step 1:\n",
+    "#### Step 1:\n",
     "Let's start simple and look at the data surround 1 event"
    ]
   },
@@ -322,7 +322,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,

Filtering and PAC.ipynb

@@ -102,7 +102,7 @@
     "    '''\n",
     "    Same as PAC function but only with long variable names of interest\n",
     "    '''\n",
-    "    data=np.load('C:/gh/bv/tutorials/exampledata.npy')\n",
+    "    data=np.load('./exampledata.npy')\n",
     "    pac(data,beta_center_freq,beta_bandwidth,gamma_center_freq,gamma_bandwidth,\n",
     "        beta_cycles,beta_transition,gamma_cycles,gamma_transition)"
    ]

Introduction to iPython Notebook.ipynb

@@ -172,7 +172,7 @@
    "source": [
     "filename = 'emodat.mat'; #just a string for the name of the file\n",
     "\n",
-    "filename = os.path.join('/Users/Torben/Desktop/Voytek_Things/', filename) #this is how we tell python where the file is\n",
+    "filename = os.path.join('./', filename) #this is how we tell python where the file is\n",
     "\n",
     "datafile = sp.io.loadmat(filename) #this is how we load the file.\n",
     "#there are lots of variables in this .mat file. Let's just look at the voltages at each time point. \n",

Phase Amplitude Coupling Tutorial.ipynb

@@ -62,7 +62,7 @@
    "source": [
     "#loading ECoG data from local save\n",
     "filename = 'emodat.mat'\n",
-    "filename = os.path.join('/Users/Torben/Desktop/Voytek_things/', filename)\n",
+    "filename = os.path.join('./', filename)\n",
     "data = sp.io.loadmat(filename)\n",
     "srate = data['srate'];\n",
     "data = data['data']; # time series\n",

PowerLawPSD.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##In this iPython Notebook Tutorial, you can play with the Poisson spiking neuron simulation to see how increased firing influences the slope of the ECoG PSD. \n",
+    "## In this iPython Notebook Tutorial, you can play with the Poisson spiking neuron simulation to see how increased firing influences the slope of the ECoG PSD. \n",
     "\n",
     "Step through the tutorial for a full explanation of what is happening, or go straight to the last block (after running the import block) to play with the simulation."
    ]
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#####Define a population of spiking neurons...\n",
+    "##### Define a population of spiking neurons...\n",
     "by thresholding N (N being the number of neurons) stream of randomly generated, uniformly distributed numbers. This actually gives you a Bernoulli process, but in the limit of infinitesmal time bins, we approach a Poisson process.\n",
     "\n",
     "We will see what the summed signal (LFP) looks like in time domain and in frequency domain (its PSD)."
@@ -133,7 +133,7 @@
    "source": [
     "Note that the PSD of the two traces are both relatively flat, with the main difference being a constant offset.\n",
     "\n",
-    "######Now, we add these two signals to the same 1/f spectrum, and robustly fit a line to the log-log representation to find the slope and intersection frequency."
+    "###### Now, we add these two signals to the same 1/f spectrum, and robustly fit a line to the log-log representation to find the slope and intersection frequency."
    ]
   },
   {
@@ -198,7 +198,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###Now we can vary the Poisson firing rate with a slider to see in (almost) real-time, how increasing firing rate changes the PSD slope and intersection frequency.\n",
+    "### Now we can vary the Poisson firing rate with a slider to see in (almost) real-time, how increasing firing rate changes the PSD slope and intersection frequency.\n",
     "\n",
     "Be gentle with the slider, the simulation has to run in real time to output the spiking neuron LFP so depending on how fast your computer is, you might have to wait a second or two."
    ]

Spike Field Coherence Tutorial.ipynb

@@ -71,8 +71,8 @@
    "source": [
     "# Importing Buzsáki's data from a local source\n",
     "# This data can be downloaded from www.github.com/voytekresearch/Tutorials\n",
-    "fpath = ('/Users/Torben/Desktop/Voytek_Things/datasets/')\n",
-    "filename = 'lfp_spiketimes_ec013.527.mat'\n",
+    "fpath = ('./')\n",
+    "filename = 'lfp_example.mat'\n",
     "filename = os.path.join(fpath+filename)\n",
     "\n",
     "data = sp.io.loadmat(filename)\n",