Update solar spot data and correct spectrum power

files/SN_y_tot_V2.0.txt

@@ -313,3 +313,5 @@
 2012.5   84.5   6.7   5753  
 2013.5   94.0   6.9   5347  
 2014.5  113.3   8.0   5273  
+2015.5   69.8   6.4   8903  
+2016.5   39.8   3.9   9940  

scipy-tutorial-clean.ipynb

@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true
    },
@@ -154,9 +154,7 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "import scipy as sp"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -337,7 +335,7 @@
     "\n",
     "In the next example we will use the Fast Fourier Transform (FFT) in order to transform time-dependent data into the frequency domain. By doing so, it is possible to analyse if any predominant frequencies exists - i.e. if there is any periodicity in the data. \n",
     "\n",
-    "The example data we will use is the sunspot activity measurements from year 1700 to year 2014 provided by the Sunspot index and Long-term Solar Observation. The data set is the yearly mean total sunspot observations available via HTTP.\n",
+    "The example data we will use is the sunspot activity measurements from year 1700 to year 2016 provided by the Sunspot index and Long-term Solar Observation. The data set is the yearly mean total sunspot observations available via HTTP.\n",
     "\n",
     "The datafile is also included in `files/SN_y_tot_V2.0.txt`. The sunspot data have been used for illustrating the power of FFT with respect to finding a periodicity in sunspot activity in various computer languages, e.g. Matlab and BASIC. "
    ]
@@ -551,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,

scipy-tutorial.ipynb

@@ -366,11 +366,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "#help(integrate)"
+    "help(integrate)"
    ]
   },
   {
@@ -389,7 +389,7 @@
     "\n",
     "In the next example we will use the Fast Fourier Transform (FFT) in order to transform time-dependent data into the frequency domain. By doing so, it is possible to analyse if any predominant frequencies exists - i.e. if there is any periodicity in the data. \n",
     "\n",
-    "The example data we will use is the sunspot activity measurements from year 1700 to year 2014 provided by the Sunspot index and Long-term Solar Observation. The data set is the yearly mean total sunspot observations available via HTTP.\n",
+    "The example data we will use is the sunspot activity measurements from year 1700 to year 2016 provided by the Sunspot index and Long-term Solar Observation. The data set is the yearly mean total sunspot observations available via HTTP.\n",
     "\n",
     "The datafile is also included in `files/SN_y_tot_V2.0.txt`. The sunspot data have been used for illustrating the power of FFT with respect to finding a periodicity in sunspot activity in various computer languages, e.g. Matlab and BASIC. "
    ]
@@ -427,7 +427,6 @@
    },
    "outputs": [],
    "source": [
-    "solarspots\n",
     "years = solarspots[:,0]\n",
     "spots = solarspots[:,1]"
    ]
@@ -518,7 +517,7 @@
     "N = spots_fft.size\n",
     "timestep = 1 # year\n",
     "freq = fftfreq(N, d=timestep)   # 1/year\n",
-    "power = np.abs(spots_fft)   # proportional to this\n",
+    "power = np.abs(spots_fft)**2   # proportional to this\n",
     "\n",
     "freq = freq[1:N/2]\n",
     "power = power[1:N/2]"