Merge pull request #28 from LSSTDESC/eac/docs

Eac/docs

src/rail/estimation/algos/cc_yaw.py

@@ -111,11 +111,12 @@ class YawCacheCreate(
     The cache can be constructed from input files or tabular data in memory.
     Column names for sky coordinates are required, redshifts and per-object
     weights are optional. One out of three patch create methods must be
-    specified:
-    1. Splitting the data into predefined patches (from ASCII file or an
+    specified:    
+
+    #. Splitting the data into predefined patches (from ASCII file or an
        existing cache instance, linked as optional stage input).
-    2. Splitting the data based on a column with patch indices.
-    3. Generating approximately equal size patches using k-means clustering of
+    #. Splitting the data based on a column with patch indices.
+    #. Generating approximately equal size patches using k-means clustering of
        objects positions (preferably randoms if provided).
 
     **Note:** The cache directory must be deleted manually when it is no longer

src/rail/pipelines/estimation/plot_output.py

@@ -8,9 +8,12 @@
 import pickle
 import os
 
-with open(os.path.join("data", "output_summarize.pkl"), "rb") as f:
-    ncc = pickle.load(f)
-    ncc.to_files(os.path.join("data", "output"))
-ax = ncc.plot(indicate_zero=True)
-ax.set_xlabel("Redshift")
-ax.figure.savefig(os.path.join("data", "checkplot.png"))
+
+if __name__ == '__main__':
+
+    with open(os.path.join("data", "output_summarize.pkl"), "rb") as f:
+        ncc = pickle.load(f)
+        ncc.to_files(os.path.join("data", "output"))
+    ax = ncc.plot(indicate_zero=True)
+    ax.set_xlabel("Redshift")
+    ax.figure.savefig(os.path.join("data", "checkplot.png"))