Bar graph added.

make_bar_graph.py

@@ -0,0 +1,107 @@
+import pandas as pd
+import numpy as np
+import altair as alt
+import json
+# npm install vega-lite vega-cli canvas
+
+
+class BarGraph:
+
+	def __init__(self, yearly_data):
+		self.yearly_data=yearly_data
+
+	def build_graph(self):
+
+		with open('colors.json') as f:
+		  colors = json.load(f)
+		allColorsValues=[]
+
+		#filter data
+		max_languages=5
+		top_languages={}
+		for year in self.yearly_data.keys():
+			for quarter in self.yearly_data[year].keys():
+				for language in sorted(list(self.yearly_data[year][quarter].keys()), key=lambda lang:self.yearly_data[year][quarter][lang], reverse=True)[0:max_languages]:
+					if 'top' not in self.yearly_data[year][quarter]:
+						self.yearly_data[year][quarter]['top']={}
+					if self.yearly_data[year][quarter][language]!=0:
+						self.yearly_data[year][quarter]['top'][language]=self.yearly_data[year][quarter][language]
+							
+						if language not in top_languages:
+							top_languages[language] =1
+						top_languages[language]+=1
+
+		# print(self.yearly_data)
+
+		all_languages=list(top_languages.keys())
+
+		for language in all_languages:
+			if colors[language]['color'] is not None:
+				allColorsValues.append(colors[language]['color'])
+
+		languages_all_loc={}
+
+		for language in all_languages:
+			language_year=[]
+			for year in self.yearly_data.keys():
+				language_quarter=[0,0,0]
+				for quarter in self.yearly_data[year].keys():
+					if language in self.yearly_data[year][quarter]['top']:
+						language_quarter[quarter-1]=self.yearly_data[year][quarter]['top'][language] 
+					else:
+						language_quarter[quarter-1]=0
+				language_year.append(language_quarter)
+			languages_all_loc[language]=language_year
+
+		print(languages_all_loc)
+
+		language_df={}
+
+		def prep_df(df, name):
+		    df = df.stack().reset_index()
+		    df.columns = ['c1', 'c2', 'values']
+		    df['Language'] = name
+		    return df
+
+		for language in languages_all_loc.keys():
+			language_df[language]=pd.DataFrame(languages_all_loc[language],index=list(self.yearly_data.keys()),columns=["Q1","Q2","Q3"])
+
+		for language in language_df.keys():
+			language_df[language]=prep_df(language_df[language], language)
+
+
+
+		df=pd.concat(list(language_df.values()))
+
+
+		# print(df)
+
+
+		chart=alt.Chart(df).mark_bar().encode(
+
+		    # tell Altair which field to group columns on
+		    x=alt.X('c2:N', title=None),
+
+		    # tell Altair which field to use as Y values and how to calculate
+		    y=alt.Y('sum(values):Q',
+		        axis=alt.Axis(
+		            grid=False,
+		            title='LOC added')),
+
+		    # tell Altair which field to use to use as the set of columns to be  represented in each group
+		    column=alt.Column('c1:N', title=None),
+
+		    # tell Altair which field to use for color segmentation 
+		    color=alt.Color('Language:N',
+		            scale=alt.Scale(
+		            	domain=all_languages,
+		                # make it look pretty with an enjoyable color pallet
+		                range=allColorsValues,
+		            ),
+		        ))\
+		    .configure_view(
+		        # remove grid lines around column clusters
+		        strokeOpacity=0    
+		    )
+		chart.save('bar_graph.png')
+		return 'bar_graph.png'