a lot

doc/requirements.txt

@@ -4,3 +4,5 @@ numpydoc==0.7.0
 six
 nbsphinx
 seaborn
+ExpectException
+sphinx_bootstrap_theme

doc/source/conf.py

@@ -105,15 +105,15 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+import sphinx_bootstrap_theme
+html_theme = 'bootstrap'
+html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
+
+html_logo = "_static/logo_medium.png"
 
 html_theme_options = {
-    'fixed_sidebar': True,
-    'logo': 'logo_medium.png',
-    'github_user': 'bitprophet',
-    'github_repo': 'alabaster',
-    'page_width': "92%",
-    'logo_name': False
+    'navbar_pagenav_name': "Content",
+    'bootswatch_theme': "cerulean",
 }
 
 if on_rtd:

doc/source/tutorial.py

@@ -123,19 +123,55 @@ def as_table(features, values):
     rows = zip(features, values)
     return HTML(RESULT_TABLE_TEMPLATE.render(rows=rows))
 
+
 DOC_TEMPLATE = jinja2.Template("""
-    <div class="features-doc">
-      <ol>
-          {% for name, doc in rows %}
-          <li class="extractor {{ name }}">
-              <div>
-                 <h3>Extractor <code>{{ name }}</code>.</h3>
-                 <p>{{ doc }}</p>
-              </div>
-          </li>
-          {% endfor %}
-      </ol>
+<div class="panel-group" id="extractors" role="tablist" aria-multiselectable="true">
+  {% for name, doc, ext, fresume, datas in rows %}
+  <div class="panel panel-default">
+    <div class="panel-heading" role="tab" id="heading-feature-{{ name }}">
+      <h4 class="panel-title">
+        <a role="button" data-toggle="collapse" data-parent="#extractors" href="#collapse-feature-{{ name }}" aria-expanded="true" aria-controls="collapse-feature-{{ name }}">
+          Extractor <span class="text-info">{{ name }}</span>
+        
+         <span class="pull-right">
+             {% for feature in fresume %}
+             <span class="label lb-lg feature-resume label-default">{{ feature }}</span>
+             {% endfor %}
+         </span>
+         </a>
+         
+      </h4>
+    </div>
+    <div id="collapse-feature-{{ name }}" class="panel-collapse collapse" role="tabpanel" aria-labelledby="heading-feature-{{ name }}">
+      <div class="panel-body">
+         <p>{{ doc }}</p>
+        <h5>Required Data</h5>
+         <div>
+             {% for data in datas %}
+                 <span class="label label-info">{{ data }}</span>
+             {% endfor %}
+         </div>
+         
+         <h5>Full list of features</h5>
+         <div>
+             {% for feature in ext.get_features() %}
+             <span class="label label-default">{{ feature }}</span>
+             {% endfor %}
+         </div>
+         
+         <h5>Dependencies</h5>
+         <div>
+         {% for dep in ext.get_dependencies() %}
+            <span class="label label-warning">{{ dep }}</span>
+         {% else %}
+             -
+         {% endfor %}
+         </div>
+      </div>
     </div>
+  </div>
+  {% endfor %}
+</div>
 
 """)
 
@@ -151,6 +187,12 @@ def features_doc():
         name = ext.__name__
         doc = publish_parts(ext.__doc__ or name, 
                             writer_name='html')["html_body"]
-        rows.append((name, doc))
+        features = ext.get_features()
+        data = sorted(ext.get_data(), key=feets.extractors.DATAS.index)
+
+        if len(features) > 4:
+            features = list(features)[:4] + ["..."]
+        rows.append((name, doc, ext, features, data))
+    rows.sort()
     return HTML(DOC_TEMPLATE.render(rows=rows))
 

feets/extractors/ext_amplitude.py

@@ -54,7 +54,14 @@
 # =============================================================================
 
 class Amplitude(Extractor):
-    """Half the difference between the maximum and the minimum magnitude"""
+    """
+    **Amplitude**
+
+    The amplitude is defined as the half of the difference between the median
+    of the maximum 5% and the median of the minimum 5% magnitudes. For a
+    sequence of numbers from 0 to 1000 the amplitude should be equal to 475.5.
+
+    """
 
     data = ['magnitude']
     features = ['Amplitude']

feets/extractors/ext_anderson_darling.py

@@ -54,6 +54,19 @@
 # =============================================================================
 
 class AndersonDarling(Extractor):
+    """
+    **AndersonDarling**
+
+    The Anderson-Darling test is a statistical test of whether a given
+    sample of data is drawn from a given probability distribution. When
+    applied to testing if a normal distribution adequately describes a set of
+    data, it is one of the most powerful statistical tools for detecting most
+    departures from normality.
+
+    For a normal distribution the Anderson-Darling statistic should take values
+    close to 0.25.
+
+    """
 
     data = ['magnitude']
     features = ["AndersonDarling"]

feets/extractors/ext_lomb_scargle.py

@@ -87,6 +87,45 @@ def fap(power, fmax, time, mag, method, normalization, method_kwds=None):
 # =============================================================================
 
 class LombScargle(Extractor):
+    """
+    **PeriodLS**
+
+    The Lomb-Scargle (L-S) algorithm (Scargle, 1982) is a variation of the
+    Discrete Fourier Transform (DFT), in which a time series is decomposed
+    into a linear combination of sinusoidal functions. The basis of sinusoidal
+    functions transforms the data from the time domain to the frequency domain.
+    DFT techniques often assume evenly spaced data points in the time series,
+    but this is rarely the case with astrophysical time-series data. Scargle
+    has derived a formula for transform coefficients that is similiar to the
+    DFT in the limit of evenly spaced observations. In addition, an adjustment
+    of the values used to calculate the transform coefficients makes the
+    transform invariant to time shifts.
+
+    The Lomb-Scargle periodogram is optimized to identify sinusoidal-shaped
+    periodic signals in time-series data. Particular applications include
+    radial velocity data and searches for pulsating variable stars. L-S is not
+    optimal for detecting signals from transiting exoplanets, where the shape
+    of the periodic light-curve is not sinusoidal.
+
+    Next, we perform a test on the synthetic periodic light-curve we created
+    (which period is 20) to confirm the accuracy of the period found by the
+    L-S method
+
+    **Period_fit**
+
+    The false alarm probability of the largest periodogram value. Let's
+    test it for a normal distributed data and for a periodic one.
+
+    **Psi_CS** (:math:`\Psi_{CS}`)
+
+    :math:`R_{CS}` applied to the phase-folded light curve (generated using
+    the period estimated from the Lomb-Scargle method).
+
+    **Psi_eta** (:math:`\Psi_{\eta}`)
+
+    :math:`\eta^e`  index calculated from the folded light curve.
+
+    """
 
     data = ['magnitude', 'time']
     features = ["PeriodLS", "Period_fit", "Psi_CS", "Psi_eta"]