From e0e7787f712322111c561b45b32a577c430616ef Mon Sep 17 00:00:00 2001
From: KevinMidboe <kevin.midboe@gmail.com>
Date: Sat, 29 Jul 2017 18:28:17 +0200
Subject: [PATCH] Can now handle all the outputs of yr weather forcast with
 additions that there can be multiple conditions and finds the correct emoji
 for the tokens.

---
 emojiParser.py | 131 ++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 113 insertions(+), 18 deletions(-)

diff --git a/emojiParser.py b/emojiParser.py
index a7d5ed2..9453bd5 100755
--- a/emojiParser.py
+++ b/emojiParser.py
@@ -3,40 +3,135 @@
 # @Author: KevinMidboe
 # @Date:   2017-07-29 11:56:24
 # @Last Modified by:   KevinMidboe
-# @Last Modified time: 2017-07-29 12:34:58
+# @Last Modified time: 2017-07-29 18:26:06
 
 from fuzzywuzzy import process
 
-weather_nouns = ['cleary sky', 'fair', 'cloudy', 'rain showers', 'rain', 'sleet',
-	'sleet showers', 'snow showers', 'thunder', 'sleet', 'snow']
-
 # Find the first word, if it is a noun or a adjective. 
 # Remove the adjective and split if there is a AND
 # Then match the first noun to list and add that emoji
 # and then match the second to list and add that emoji 
 # REGEX this bitch up
 
-# Splits and lowers the condition text for easier parsing
-def splitCondition(condition):
-	condition = condition.lower()
-	return condition.split()
+symbol_table = {
+	'clear sky': '☀️',
+	'fair': '🌤',
+	'partly cloudy': '⛅️',
+	'cloudy': '☁️',
+	'thunder': '⚡️',
+	
+	'rain showers': '🌦',
+	'rain': '🌧',
+	'sleet showers': '🌦 💦',
+	'sleet': '🌨 💦',
+	'snow showers': '⛅ ❄️',
+	'snow': '🌨',
 
-# Trying to analyze the semantics of the condition text
-def findConditionContext(condition_text):
-	condition_expression = splitCondition(condition_text)
+	'rain': '🌧',
+	'sleet': '🌧',
+	'snow': '🌨',
 
-	# Iterate over each word and find what matches 100%
-	for expression_value in condition_expression:
-		noun_matches = process.extract(expression_value, weather_nouns)
-		print(expression_value + ': ' + str(noun_matches))
+	'showers': '🌤'
+	}
+
+severity = {
+		'rain': ['💧', ' ☂️', ' ☔️'],
+		'sleet': [' 💦 ', ' 💧 ', ' 💧 💦 '],
+		'snow': [' ❄️ ', ' ❄️ ❄️ ', ' ❄️ ❄️ ❄️ ']
+		}
+
+class EmojiParser(object):
+	def __init__(self, condition_text):
+		self.condition_expression = condition_text
+		self.severity = None
+		self.nouns = []
+
+		self.weather_nouns = ['cleary sky', 'fair', 'cloudy', 'rain', 'rain showers', 'sleet',
+			'sleet showers', 'snow showers', 'thunder', 'snow']
+		self.weather_adjectives = {'light': 0, 'normal': 1, 'heavy': 2}
+
+	def __str__(self):
+	 	return str([self.condition_expression, self.severity, self.nouns])
+
+	# Splits and lowers the condition text for easier parsing
+	def splitCondition(self, condition):
+		condition = condition.lower()
+		return condition.split()
+
+	def findAdjective(self, sentence=None):
+		if sentence is None:
+			sentence = self.condition_expression
+
+		expression = self.splitCondition(sentence)
+		for word in expression:
+			if word in self.weather_adjectives:
+				return word
+
+		return None
+
+	def severityValue(self):
+		adjective = self.findAdjective()
+
+		if adjective:
+			self.severity = self.weather_adjectives[adjective]
+		else:
+			self.severity = self.weather_adjectives['normal']
 
 
-def emojiParser(condition_text):
-	findConditionContext(condition_text)
+	def removeAdjective(self):
+		adjective = self.findAdjective()
+		if adjective:
+			expression = self.splitCondition(self.condition_expression)
+			expression.remove(adjective)
+			return ' '.join(expression)
+		else:
+			return self.condition_expression
+
+	def findWeatherTokens(self):
+		sentence = self.removeAdjective()
+		
+		if 'and' in sentence:
+			self.nouns = sentence.split(' and ')
+		else:
+			self.nouns = [sentence]
+
+
+	def emojify(self, noun):
+		return symbol_table[noun]
+
+	def emojifyList(self, noun_list):
+		returnList = []
+		
+		for noun in noun_list:
+			returnList.append(self.emojify(noun))
+
+		return '  '.join(returnList)
+
+
+
+	# Trying to analyze the semantics of the condition text
+	def emojifyWeatherForecast(self):
+		self.findWeatherTokens()
+		
+		noun_list = self.nouns
+		print(noun_list)
+
+		primary_forcast = noun_list.pop(0)
+		primary_severity = severity[primary_forcast][self.severity]
+		secondary_forcast = self.emojifyList(noun_list)
+		
+		print('%s %s  %s' % (self.emojify(primary_forcast), primary_severity, secondary_forcast))
+
+
+	def convertSematicsToEmoji(self):
+		self.severityValue()
+		self.emojifyWeatherForecast()
 
 
 def main():
-	emojiParser('Rain showers')
+	emojiParser = EmojiParser('Light rain')
+	emojiParser.convertSematicsToEmoji()
+	print(emojiParser)
 
 
 if __name__ == '__main__':