remove flat argument from plot, solves https://github.com/CamDavidson…

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 #### 0.9.3 (in dev)
  - adding `plot_loglogs` to `KaplanMeierFitter`
+ - removing `flat` argument in `plot` methods. It was causing confusion. To replicate it, one can set `ci_force_lines=True` and `show_censors=True`.
 
 #### 0.9.2
  - deprecates Pandas versions before 0.18.

lifelines/plotting.py

@@ -299,8 +299,6 @@ def plot_estimate(cls, estimate):
         Matplotlib plot arguments can be passed in inside the kwargs, plus
 
         Parameters:
-          flat: an opiniated design style with stepped lines and no shading.
-                Similar to R's plotting. Default: False
           show_censors: place markers at censorship events. Default: False
           censor_styles: If show_censors, this dictionary will be passed into
                          the plot call.
@@ -326,7 +324,7 @@ def plot_estimate(cls, estimate):
           ax: a pyplot axis object
         """ % estimate
 
-    def plot(ix=None, iloc=None, flat=False, show_censors=False,
+    def plot(ix=None, iloc=None, show_censors=False,
              censor_styles=None, ci_legend=False, ci_force_lines=False,
              ci_alpha=0.25, ci_show=True, at_risk_counts=False,
              bandwidth=None, **kwargs):
@@ -341,11 +339,6 @@ def plot(ix=None, iloc=None, flat=False, show_censors=False,
         set_kwargs_color(kwargs)
         set_kwargs_drawstyle(kwargs)
 
-        # R-style graphics
-        if flat:
-            ci_force_lines = True
-            show_censors = True
-
         if estimate == "hazard_":
             if bandwidth is None:
                 raise ValueError('Must specify a bandwidth parameter in the call to plot_hazard.')

tests/test_plotting.py

@@ -181,23 +181,11 @@ def test_show_censor_with_index_0(self, block, kmf):
         self.plt.show(block=block)
         return
 
-    def test_flat_style_and_marker(self, block, kmf):
-        data1 = np.random.exponential(10, size=200)
-        data2 = np.random.exponential(2, size=200)
-        C1 = np.random.binomial(1, 0.9, size=200)
-        C2 = np.random.binomial(1, 0.95, size=200)
-        kmf.fit(data1, C1, label='test label 1')
-        ax = kmf.plot(flat=True, censor_styles={'marker': '+', 'mew': 2, 'ms': 7})
-        kmf.fit(data2, C2, label='test label 2')
-        kmf.plot(ax=ax, censor_styles={'marker': 'o', 'ms': 7}, flat=True)
-        self.plt.title("testing kmf flat styling + marker")
-        self.plt.show(block=block)
-        return
-
-    def test_flat_style_no_censor(self, block, kmf):
+    def test_flat_style_with_customer_censor_styles(self, block, kmf):
         data1 = np.random.exponential(10, size=200)
         kmf.fit(data1, label='test label 1')
-        kmf.plot(flat=True, censor_styles={'marker': '+', 'mew': 2, 'ms': 7})
+        kmf.plot(ci_force_lines=True, show_censors=True,
+                 censor_styles={'marker': '+', 'mew': 2, 'ms': 7})
         self.plt.title('test_flat_style_no_censor')
         self.plt.show(block=block)
         return