Added style #4

04-candy-factory/README.md

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+Style #4
+==============================
+
+Constraints:
+
+- Larger problem decomposed in functional abstractions. Functions, according to Mathematics, are relations from inputs to outputs.
+- Larger problem solved as a pipeline of function applications
+
+Possible names:
+
+- Functional
+- Pipeline

04-candy-factory/tf-04.py

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+import sys, re, operator, string
+
+#
+# The functions
+#
+def read_file(path_to_file):
+    """
+    Takes a path to a file and returns the entire
+    contents of the file as a string
+    """
+    f = open(path_to_file)
+    data = f.read()
+    f.close()
+    return data
+
+def filter_chars(str_data):
+    """
+    Takes a string and returns a copy with all nonalphanumeric 
+    chars replaced by white space
+    """
+    pattern = re.compile('[\W_]+')
+    return pattern.sub(' ', str_data)
+
+def normalize(str_data):
+    """
+    Takes a string and returns a copy with all chars in lower case
+    """
+    return str_data.lower()
+
+def scan(str_data):
+    """
+    Takes a string and scans for words, returning
+    a list of words.
+    """
+    return str_data.split()
+
+def remove_stop_words(word_list):
+    """ 
+    Takes a list of words and returns a copy with all stop 
+    words removed 
+    """
+    f = open('../stop_words.txt')
+    stop_words = f.read().split(',')
+    f.close()
+    # add single-letter words
+    stop_words.extend(list(string.ascii_lowercase))
+    return [w for w in word_list if not w in stop_words]
+
+def frequencies(word_list):
+    """
+    Takes a list of words and returns a dictionary associating
+    words with frequencies of occurrence
+    """
+    word_freqs = {}
+    for w in word_list:
+        if w in word_freqs:
+            word_freqs[w] += 1
+        else:
+            word_freqs[w] = 1
+    return word_freqs
+
+def sort(word_freq):
+    """
+    Takes a dictionary of words and their frequencies
+    and returns a list of pairs where the entries are
+    sorted by frequency 
+    """
+    return sorted(word_freq.iteritems(), key=operator.itemgetter(1), reverse=True)
+
+
+#
+# The main function
+#
+word_freqs = sort(frequencies(remove_stop_words(scan(normalize(filter_chars(read_file(sys.argv[1])))))))
+
+for tf in word_freqs[0:25]:
+    print tf[0], ' - ', tf[1]
+