Merge pull request #9 from quantopian/python3-update

Updated PyMC chapters to use Python 3.

Author: mmargenot

Chapter1_Introduction/Chapter1.ipynb

@@ -0,0 +1,53 @@
+import sys
+
+import numpy as np
+from IPython.core.display import Image
+
+import praw
+
+
+reddit = praw.Reddit("BayesianMethodsForHackers")
+subreddit  = reddit.get_subreddit( "crazyideas" )
+
+top_submissions = subreddit.get_top(limit=100)
+
+n_sub = int( sys.argv[1] ) if sys.argv[1] else 1
+
+i = 0
+while i < n_sub:
+    top_submission = next(top_submissions)
+    #while "i.imgur.com" not in top_submission.url:
+    #    #make sure it is linking to an image, not a webpage.
+    #    top_submission = next(top_submissions)
+    i+=1
+
+#print("Post contents: \n", top_submission.title)
+top_post = top_submission.title
+#top_submission.replace_more_comments(limit=5, threshold=0)
+#print(top_post_url)
+
+upvotes = []
+downvotes = []
+contents = []
+"""_all_comments = top_submission.comments
+all_comments=[]
+for comment in _all_comments:
+            try:
+                #ups = int(round((ratio*comment.score)/(2*ratio - 1)) if ratio != 0.5 else round(comment.score/2))
+                #upvotes.append(ups)
+                #downvotes.append(ups - comment.score)
+                scores.append( comment.score )
+                contents.append( comment.body )
+            except Exception as e:
+                continue
+"""
+for sub in top_submissions:
+    try:
+        ratio = reddit.get_submission(sub.permalink).upvote_ratio
+        ups = int(round((ratio*sub.score)/(2*ratio - 1)) if ratio != 0.5 else round(sub.score/2))
+        upvotes.append(ups)
+        downvotes.append(ups - sub.score)
+        contents.append(sub.title)
+    except Exception as e:
+        continue
+votes = np.array( [ upvotes, downvotes] ).T

Chapter2_MorePyMC/Chapter2.ipynb

@@ -58,7 +58,7 @@ def calc_delta_r(x_predicted,y_predicted,x_true,y_true):
     for  perm in it.permutations(a[num_halos-2],num_halos):
         which_true_halos=[]
         which_predicted_halos=[]
-        for j in xrange(num_halos): #loop through all the true halos with the
+        for j in range(num_halos): #loop through all the true halos with the
 
             distances_perm[count,j]=np.sqrt((x_true[j]-x_predicted[int(perm[j])])**2\
                                       +(y_true[j]-y_predicted[int(perm[j])])**2)
@@ -141,7 +141,7 @@ def convert_to_360(angle, x_in, y_in):
         theta: the angle in the range 0:2pi
     """
     n = len(x_in)
-    for i in xrange(n):
+    for i in range(n):
         if x_in[i] < 0 and y_in[i] > 0:
             angle[i] = angle[i]+mt.pi
         elif x_in[i] < 0 and y_in[i] < 0:
@@ -204,7 +204,7 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre
 
         x_predicted=np.array([],dtype=float)
         y_predicted=np.array([],dtype=float)
-        for i in xrange(nhalo):
+        for i in range(nhalo):
             x_predicted=np.append(x_predicted,float(sky[0])) #get the predicted values
             y_predicted=np.append(y_predicted,float(sky[1]))
             #The solution file for the test data provides masses 
@@ -271,9 +271,9 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre
     W1=1./1000. #Weight the av_r such that < 1 is a good score > 1 is not so good.
     W2=1.
     metric = W1*av_r + W2*angle_vec #Weighted metric, weights TBD
-    print 'Your average distance in pixels you are away from the true halo is', av_r
-    print 'Your average angular vector is', angle_vec
-    print 'Your score for the training data is', metric
+    print('Your average distance in pixels you are away from the true halo is', av_r)
+    print('Your average angular vector is', angle_vec)
+    print('Your score for the training data is', metric)
     return metric
 
 
@@ -316,10 +316,9 @@ def main(user_fname, fname):
                                                          #first input would be
                                                          #a float, if succeed it
                                                          #is not a header
-        print 'THE INPUT FILE DOES NOT APPEAR TO HAVE A HEADER'
+        print('THE INPUT FILE DOES NOT APPEAR TO HAVE A HEADER')
     except :
-        print 'THE INPUT FILE APPEARS TO HAVE A HEADER, SKIPPING THE FIRST LINE'
-
+        print('THE INPUT FILE APPEARS TO HAVE A HEADER, SKIPPING THE FIRST LINE')
         skip_header = sky_prediction.next()
 
 
@@ -331,7 +330,7 @@ def main(user_fname, fname):
         if does_it_exist > 0: #If it does then find the matching solutions to the sky_id
                             selectskyinsolutions=true_sky_id.index(sky_id)-1
         else: #Otherwise exit
-            print 'Sky_id does not exist, formatting problem: ',sky_id
+            print('Sky_id does not exist, formatting problem: ',sky_id)
             sys.exit(2)
 
 
@@ -342,7 +341,7 @@ def main(user_fname, fname):
 
         x_predicted=np.array([],dtype=float)
         y_predicted=np.array([],dtype=float)
-        for i in xrange(nhalo):
+        for i in range(nhalo):
             x_predicted=np.append(x_predicted,float(sky[2*i+1])) #get the predicted values
             y_predicted=np.append(y_predicted,float(sky[2*i+2]))
             #The solution file for the test data provides masses 
@@ -409,9 +408,9 @@ def main(user_fname, fname):
     W1=1./1000. #Weight the av_r such that < 1 is a good score > 1 is not so good.
     W2=1.
     metric = W1*av_r + W2*angle_vec #Weighted metric, weights TBD
-    print 'Your average distance in pixels you are away from the true halo is', av_r
-    print 'Your average angular vector is', angle_vec
-    print 'Your score for the training data is', metric
+    print('Your average distance in pixels you are away from the true halo is', av_r)
+    print('Your average angular vector is', angle_vec)
+    print('Your score for the training data is', metric)
 
 
 if __name__ == "__main__":

Chapter3_MCMC/Chapter3.ipynb

@@ -2,14 +2,14 @@
 from matplotlib.patches import Ellipse
 import numpy as np
 
-def draw_sky( galaxies ):
+def draw_sky(galaxies):
     """adapted from Vishal Goklani"""
     size_multiplier = 45
     fig = plt.figure(figsize=(10,10))
     #fig.patch.set_facecolor("blue")
     ax = fig.add_subplot(111, aspect='equal')
     n = galaxies.shape[0]
-    for i in xrange(n):
+    for i in range(n):
         _g = galaxies[i,:]
         x,y = _g[0], _g[1]
         d = np.sqrt( _g[2]**2 + _g[3]**2 )