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