few more doc changes

docs/Survival analysis with lifelines.rst

@@ -34,7 +34,7 @@ as the censorship event. This is also an example where the current time
 is not the only cause of censorship -- there are alternative
 events (eg: death in office) that can censor.)
 
-To estimate the survival function, we use the `Kaplan-Meier
+To estimate the survival function, we first will use the `Kaplan-Meier
 Estimate <http://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator>`__,
 defined:
 
@@ -44,6 +44,7 @@ where :math:`d_i` are the number of death events at time :math:`t` and
 :math:`n_i` is the number of subjects at risk of death just prior to time
 :math:`t`.
 
+
 Let's bring in our dataset. 
 
 .. code:: python
@@ -60,7 +61,6 @@ Let's bring in our dataset.
     #was observed or not.
 
 
-
 .. raw:: html
 
     <div style="max-height:1000px;max-width:1500px;overflow:auto;">
@@ -188,6 +188,8 @@ From the ``lifelines`` library, we'll need the
     from lifelines import KaplanMeierFitter
     kmf = KaplanMeierFitter()
 
+..  note:: Other ways to estimate the survival function in lifelines are ``BreslowFlemingHarringtonFitter``, ``WeibullFitter``, ``ExponentialFitter``
+
 For this estimation, we need the duration each leader was/has been in
 office, and whether or not they were observed to have left office
 (leaders who died in office or were in office in 2008, the latest date

tests/test_statistics.py

@@ -7,15 +7,18 @@
 from lifelines import statistics as stats
 from lifelines.datasets import load_waltons, load_g3
 
+
 def test_sample_size_necessary_under_cph():
     assert stats.sample_size_necessary_under_cph(0.8, 1, 0.8, 0.2, 0.139) == (14, 14)
     assert stats.sample_size_necessary_under_cph(0.8, 1, 0.5, 0.5, 1.2) == (950, 950)
     assert stats.sample_size_necessary_under_cph(0.8, 1.5, 0.5, 0.5, 1.2) == (1231, 821)
     assert stats.sample_size_necessary_under_cph(0.8, 1.5, 0.5, 0.5, 1.2, alpha=0.01) == (1832, 1221)
 
+
 def test_power_under_cph():
-    assert abs(stats.power_under_cph(12,12, 0.8, 0.2, 0.139) - 0.744937) < 10e-6
-    assert abs(stats.power_under_cph(12,20, 0.8, 0.2, 1.2) - 0.05178317) < 10e-6
+    assert abs(stats.power_under_cph(12, 12, 0.8, 0.2, 0.139) - 0.744937) < 10e-6
+    assert abs(stats.power_under_cph(12, 20, 0.8, 0.2, 1.2) - 0.05178317) < 10e-6
+
 
 def test_unequal_intensity_with_random_data():
     data1 = np.random.exponential(5, size=(2000, 1))