moving to 80 char lines

Author: jsstylos

examples/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2015 IBM All Rights Reserved.
+# Copyright 2016 IBM All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

examples/alchemy_data_news_v1.py

@@ -3,7 +3,8 @@
 
 alchemy_data_news = AlchemyDataNewsV1(api_key='YOUR API KEY')
 
-results = alchemy_data_news.get_news_documents(start='now-7d', end='now', time_slice='12h')
+results = alchemy_data_news.get_news_documents(start='now-7d', end='now',
+                                               time_slice='12h')
 print(json.dumps(results, indent=2))
 
 results = alchemy_data_news.get_news_documents(
@@ -13,5 +14,7 @@
                    'enriched.url.url',
                    'enriched.url.author',
                    'enriched.url.publicationDate'],
-    query_fields={'q.enriched.url.enrichedTitle.entities.entity': '|text=IBM,type=company|'})
+    query_fields={
+        'q.enriched.url.enrichedTitle.entities.entity':
+            '|text=IBM,type=company|'})
 print(json.dumps(results, indent=2))

examples/alchemy_language_v1.py

@@ -4,16 +4,22 @@
 
 alchemy_language = AlchemyLanguageV1(api_key='YOUR API KEY')
 
-url = 'https://developer.ibm.com/watson/blog/2015/11/03/price-reduction-for-watson-personality-insights/'
+url = 'https://developer.ibm.com/watson/blog/2015/11/03/price-reduction-for' \
+      '-watson-personality-insights/'
 
-print(json.dumps(alchemy_language.targeted_sentiment(text='I love cats! Dogs are smelly.',
-                                                     targets=['cats', 'dogs'], language='english'), indent=2))
-# print(json.dumps(alchemy_language.targeted_emotion(text='I love apples. I hate bananas',
-#                                                    targets=['apples', 'bananas'], language='english'), indent=2))
+print(json.dumps(
+    alchemy_language.targeted_sentiment(text='I love cats! Dogs are smelly.',
+                                        targets=['cats', 'dogs'],
+                                        language='english'), indent=2))
+# print(json.dumps(alchemy_language.targeted_emotion(text='I love apples. I
+# hate bananas',
+#                                                    targets=['apples',
+# 'bananas'], language='english'), indent=2))
 
 # print(json.dumps(alchemy_language.author(url=url), indent=2))
 # print(json.dumps(alchemy_language.concepts(max_items=2, url=url), indent=2))
-# print(json.dumps(alchemy_language.dates(url=url, anchor_date='2016-03-22 00:00:00'), indent=2))
+# print(json.dumps(alchemy_language.dates(url=url, anchor_date='2016-03-22
+# 00:00:00'), indent=2))
 # print(json.dumps(alchemy_language.emotion(url=url), indent=2))
 # print(json.dumps(alchemy_language.entities(url=url), indent=2))
 # print(json.dumps(alchemy_language.keywords(max_items=5, url=url), indent=2))
@@ -25,12 +31,19 @@
 # print(json.dumps(alchemy_language.raw_text(url=url), indent=2))
 # print(json.dumps(alchemy_language.title(url=url), indent=2))
 # print(json.dumps(alchemy_language.feeds(url=url), indent=2))
-# print(json.dumps(alchemy_language.microformats(url='http://microformats.org/wiki/hcard-examples'), indent=2))
+# print(json.dumps(alchemy_language.microformats(
+# url='http://microformats.org/wiki/hcard-examples'), indent=2))
 # print(json.dumps(alchemy_language.publication_date(url=url), indent=2))
 # print(json.dumps(alchemy_language.taxonomy(url=url), indent=2))
-combined_operations = ['page-image', 'entity', 'keyword', 'title', 'author', 'taxonomy', 'concept', 'doc-emotion']
-print(json.dumps(alchemy_language.combined(url=url, extract=combined_operations), indent=2))
+combined_operations = ['page-image', 'entity', 'keyword', 'title', 'author',
+                       'taxonomy', 'concept', 'doc-emotion']
+print(
+json.dumps(alchemy_language.combined(url=url, extract=combined_operations),
+           indent=2))
 
 # Get sentiment and emotion information results for detected entities/keywords:
-# print(json.dumps(alchemy_language.entities(url=url, sentiment=True, emotion=True), indent=2))
-# print(json.dumps(alchemy_language.keywords(max_items=5, url=url, sentiment=True, emotion=True), indent=2))
+# print(json.dumps(alchemy_language.entities(url=url, sentiment=True,
+# emotion=True), indent=2))
+# print(json.dumps(alchemy_language.keywords(max_items=5, url=url,
+#                                            sentiment=True, emotion=True),
+#                  indent=2))

examples/authorization_v1.py

@@ -6,4 +6,5 @@
     username='YOUR SERVICE USERNAME',
     password='YOUR SERVICE PASSWORD')
 
-print(json.dumps(authorization.get_token(url=SpeechToTextV1.default_url), indent=2))
+print(json.dumps(authorization.get_token(url=SpeechToTextV1.default_url),
+                 indent=2))

examples/conversation_tone_analyzer_integration/tone_conversation_integration.v1.py

@@ -8,7 +8,8 @@
 # import tone detection
 import tone_detection
 
-# load the .env file containing your environment variables for the required services (conversation and tone)
+# load the .env file containing your environment variables for the required
+# services (conversation and tone)
 load_dotenv(find_dotenv())
 
 # replace with your own conversation credentials or put them in a .env file
@@ -33,29 +34,37 @@
 # Payload for the Watson Conversation Service
 # user input text required - replace "I am happy" with user input text.
 payload = {
-    'workspace_id':workspace_id,
+    'workspace_id': workspace_id,
     'input': {
-      'text': "I am happy"
+        'text': "I am happy"
     }
 }
 
-def invokeToneConversation (payload, maintainToneHistoryInContext):
-    '''
-     invokeToneConversation calls the the Tone Analyzer service to get the tone information for the user's
-     input text (input['text'] in the payload json object), adds/updates the user's tone in the payload's context,
-     and sends the payload to the conversation service to get a response which is printed to screen.
-     :param payload: a json object containing the basic information needed to converse with the Conversation Service's message endpoint.
+
+def invokeToneConversation(payload, maintainToneHistoryInContext):
+    """
+     invokeToneConversation calls the the Tone Analyzer service to get the
+     tone information for the user's input text (input['text'] in the payload
+     json object), adds/updates the user's tone in the payload's context,
+     and sends the payload to the
+     conversation service to get a response which is printed to screen.
+     :param payload: a json object containing the basic information needed to
+     converse with the Conversation Service's message endpoint.
      :param maintainHistoryInContext:
 
 
-     Note: as indicated below, the console.log statements can be replaced with application-specific code to process the err or data object returned by the Conversation Service.
-    '''
+     Note: as indicated below, the console.log statements can be replaced
+     with application-specific code to process the err or data object
+     returned by the Conversation Service.
+    """
     tone = tone_analyzer.tone(text=payload['input']['text'])
-    conversation_payload =  tone_detection.updateUserTone(payload, tone, maintainToneHistoryInContext)
-    response = conversation.message(workspace_id=workspace_id, message_input=conversation_payload['input'], context=conversation_payload['context'])
+    conversation_payload = tone_detection.\
+        updateUserTone(payload, tone, maintainToneHistoryInContext)
+    response = conversation.message(workspace_id=workspace_id,
+                                    message_input=conversation_payload['input'],
+                                    context=conversation_payload['context'])
     print(json.dumps(response, indent=2))
 
-# synchronous call to conversation with tone included in the context
-invokeToneConversation(payload,maintainToneHistoryInContext)
-
 
+# synchronous call to conversation with tone included in the context
+invokeToneConversation(payload, maintainToneHistoryInContext)