Run black over codebase (#430)

* Run black over codebase

* Added pre commit hook

.pre-commit-config.yaml

@@ -0,0 +1,5 @@
+repos:
+-   repo: https://github.com/ambv/black
+    rev: stable
+    hooks:
+    - id: black

doc/KPyCon2009/code/wedding.py

@@ -8,43 +8,45 @@
 
 max_tables = 5
 max_table_size = 4
-guests = 'A B C D E F G I J K L M N O P Q R'.split()
+guests = "A B C D E F G I J K L M N O P Q R".split()
+
 
 def happiness(table):
     """
     Find the happiness of the table
     - by calculating the maximum distance between the letters
     """
     return abs(ord(table[0]) - ord(table[-1]))
-
-#create list of all possible tables
-possible_tables = [tuple(c) for c in pulp.allcombinations(guests, 
-                                        max_table_size)]
 
-#create a binary variable to state that a table setting is used
-x = pulp.LpVariable.dicts('table', possible_tables, 
-                            lowBound = 0,
-                            upBound = 1,
-                            cat = pulp.LpInteger)
+
+# create list of all possible tables
+possible_tables = [tuple(c) for c in pulp.allcombinations(guests, max_table_size)]
+
+# create a binary variable to state that a table setting is used
+x = pulp.LpVariable.dicts(
+    "table", possible_tables, lowBound=0, upBound=1, cat=pulp.LpInteger
+)
 
 seating_model = pulp.LpProblem("Wedding Seating Model", pulp.LpMinimize)
 
 seating_model += sum([happiness(table) * x[table] for table in possible_tables])
 
-#specify the maximum number of tables
-seating_model += sum([x[table] for table in possible_tables]) <= max_tables, \
-                            "Maximum_number_of_tables"
+# specify the maximum number of tables
+seating_model += (
+    sum([x[table] for table in possible_tables]) <= max_tables,
+    "Maximum_number_of_tables",
+)
 
-#A guest must seated at one and only one table
+# A guest must seated at one and only one table
 for guest in guests:
-    seating_model += sum([x[table] for table in possible_tables
-                                if guest in table]) == 1, "Must_seat_%s"%guest
+    seating_model += (
+        sum([x[table] for table in possible_tables if guest in table]) == 1,
+        "Must_seat_%s" % guest,
+    )
 
 seating_model.solve()
 
-print("The choosen tables are out of a total of %s:"%len(possible_tables))
+print("The choosen tables are out of a total of %s:" % len(possible_tables))
 for table in possible_tables:
     if x[table].value() == 1.0:
         print(table)
-
-

doc/KPyCon2009/code/whiskas.py

@@ -3,36 +3,34 @@
 """
 import pulp
 
-#initialise the model
-whiskas_model = pulp.LpProblem('The Whiskas Problem', pulp.LpMinimize)
+# initialise the model
+whiskas_model = pulp.LpProblem("The Whiskas Problem", pulp.LpMinimize)
 # make a list of ingredients
-ingredients = ['chicken', 'beef', 'mutton', 'rice', 'wheat', 'gel']
+ingredients = ["chicken", "beef", "mutton", "rice", "wheat", "gel"]
 # create a dictionary of pulp variables with keys from ingredients
 # the default lower bound is -inf
-x = pulp.LpVariable.dict('x_%s', ingredients, lowBound =0)
+x = pulp.LpVariable.dict("x_%s", ingredients, lowBound=0)
 
 # cost data
 cost = dict(zip(ingredients, [0.013, 0.008, 0.010, 0.002, 0.005, 0.001]))
 # create the objective
-whiskas_model += sum( [cost[i] * x[i] for i in ingredients])
+whiskas_model += sum([cost[i] * x[i] for i in ingredients])
 
 # ingredient parameters
 protein = dict(zip(ingredients, [0.100, 0.200, 0.150, 0.000, 0.040, 0.000]))
 fat = dict(zip(ingredients, [0.080, 0.100, 0.110, 0.010, 0.010, 0.000]))
 fibre = dict(zip(ingredients, [0.001, 0.005, 0.003, 0.100, 0.150, 0.000]))
 salt = dict(zip(ingredients, [0.002, 0.005, 0.007, 0.002, 0.008, 0.000]))
 
-#note these are constraints and not an objective as there is a equality/inequality
-whiskas_model += sum([protein[i]*x[i] for i in ingredients]) >= 8.0
-whiskas_model += sum([fat[i]*x[i] for i in ingredients]) >= 6.0
-whiskas_model += sum([fibre[i]*x[i] for i in ingredients]) <= 2.0
-whiskas_model += sum([salt[i]*x[i] for i in ingredients]) <= 0.4
+# note these are constraints and not an objective as there is a equality/inequality
+whiskas_model += sum([protein[i] * x[i] for i in ingredients]) >= 8.0
+whiskas_model += sum([fat[i] * x[i] for i in ingredients]) >= 6.0
+whiskas_model += sum([fibre[i] * x[i] for i in ingredients]) <= 2.0
+whiskas_model += sum([salt[i] * x[i] for i in ingredients]) <= 0.4
 
-#problem is then solved with the default solver
+# problem is then solved with the default solver
 whiskas_model.solve()
 
-#print the result
+# print the result
 for ingredient in ingredients:
-    print('The mass of %s is %s grams per can'%(ingredient,
-                                                x[ingredient].value()))
-
+    print("The mass of %s is %s grams per can" % (ingredient, x[ingredient].value()))

doc/source/_static/plotter.py

@@ -2,51 +2,55 @@
 # -*- encoding: utf-8 -*-
 
 from matplotlib import rc
-rc('text', usetex=True)
-rc('font', family='serif')
 
-
-
-def plot_interval(a,c,x_left, x_right,i, fbound):
-    lh = c*(1-a[0])
-    rh = c*(1+a[1])
-    x=arange(x_left, x_right+1)
-    y=0*x
-    arrow_r = Arrow(c,0, c*a[1],0,0.2)
-    arrow_l = Arrow(c,0,-c*a[0],0,0.2)
-    plot(x,y)
-    text((x_left+lh)/2.0,0.1,'freebound interval [%s, %s] is penalty-free' % (lh,rh))
-    text((x_left+lh)/2.0, 0.2, 'rhs=%s,    %s' % (c, fbound))
+rc("text", usetex=True)
+rc("font", family="serif")
+
+
+def plot_interval(a, c, x_left, x_right, i, fbound):
+    lh = c * (1 - a[0])
+    rh = c * (1 + a[1])
+    x = arange(x_left, x_right + 1)
+    y = 0 * x
+    arrow_r = Arrow(c, 0, c * a[1], 0, 0.2)
+    arrow_l = Arrow(c, 0, -c * a[0], 0, 0.2)
+    plot(x, y)
+    text(
+        (x_left + lh) / 2.0,
+        0.1,
+        "freebound interval [%s, %s] is penalty-free" % (lh, rh),
+    )
+    text((x_left + lh) / 2.0, 0.2, "rhs=%s,    %s" % (c, fbound))
     cur_ax = gca()
     cur_ax.add_patch(arrow_l)
     cur_ax.add_patch(arrow_r)
-    axis([x_left,x_right,-0.1,0.3])
+    axis([x_left, x_right, -0.1, 0.3])
     yticks([])
-    title('Elasticized constraint\_%s   $C(x)= %s $' % (i, c))
+    title("Elasticized constraint\_%s   $C(x)= %s $" % (i, c))
+
 
 figure()
 subplots_adjust(hspace=0.5)
 
-fbound = 'proportionFreeBound'
-i=1
-subplot(2,1,i)
-a=[0.01,0.01]
+fbound = "proportionFreeBound"
+i = 1
+subplot(2, 1, i)
+a = [0.01, 0.01]
 c = 200
-x_left = 0.97*c
-x_right = 1.03*c
-fb_string = '%s%s = %s' %(fbound,'', a[0])
-plot_interval(a,c,x_left, x_right,i, fb_string)
+x_left = 0.97 * c
+x_right = 1.03 * c
+fb_string = "%s%s = %s" % (fbound, "", a[0])
+plot_interval(a, c, x_left, x_right, i, fb_string)
 
 i += 1
-subplot(2,1,i)
-a=[0.02, 0.05]
+subplot(2, 1, i)
+a = [0.02, 0.05]
 c = 500
-x_left = 0.9*c  #scale of window
-x_right = 1.2*c #scale of window
-fb_string = '%s%s = [%s,%s]' % (fbound,'List', a[0],a[1])
-plot_interval(a,c,x_left, x_right,i, fb_string)
-savefig('freebound.jpg')
-savefig('freebound.pdf')
+x_left = 0.9 * c  # scale of window
+x_right = 1.2 * c  # scale of window
+fb_string = "%s%s = [%s,%s]" % (fbound, "List", a[0], a[1])
+plot_interval(a, c, x_left, x_right, i, fb_string)
+savefig("freebound.jpg")
+savefig("freebound.pdf")
 
 # vim: fenc=utf-8: ft=python:sw=4:et:nu:fdm=indent:fdn=1:syn=python
-