You've already forked wakapi-readme-stats
new graph drawing backend
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pseusys
@@ -1,106 +1,68 @@
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import os
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import pandas as pd
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from typing import Dict
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from os.path import join, dirname
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from json import load
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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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import os
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# npm install vega-lite vega-cli canvas
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import matplotlib.patches as mpatches
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import matplotlib.pyplot as plt
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class BarGraph:
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MAX_LANGUAGES = 5
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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(os.path.join(os.path.dirname(__file__), 'colors.json')) as f:
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colors = json.load(f)
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allColorsValues = []
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def build_graph(yearly_data: Dict) -> str:
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"""
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Draws graph of lines of code written by user by quarters of years.
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Picks top `MAX_LANGUAGES` languages from each quarter only.
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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()),
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key=lambda lang: self.yearly_data[year][quarter][lang], reverse=True)[
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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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:param yearly_data: GitHub user yearly data.
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:return: String, path to graph file.
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"""
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with open(join(dirname(__file__), "colors.json")) as f:
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colors = load(f)
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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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languages_all_loc = dict()
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years = len(yearly_data.keys())
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year_indexes = np.arange(years)
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# print(self.yearly_data)
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for i, y in enumerate(sorted(yearly_data.keys())):
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for q in yearly_data[y].keys():
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langs = sorted(yearly_data[y][q].keys(), key=lambda l: yearly_data[y][q][l], reverse=True)[0:MAX_LANGUAGES]
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all_languages = list(top_languages.keys())
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for lang in langs:
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if lang not in languages_all_loc:
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languages_all_loc[lang] = np.array([[0] * years] * 4)
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languages_all_loc[lang][q - 1][i] = yearly_data[y][q][lang]
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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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fig = plt.figure()
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ax = fig.add_axes([0, 0, 1.5, 1])
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languages_all_loc = {}
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language_handles = []
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cumulative = np.array([[0] * years] * 4)
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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, 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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for key, value in languages_all_loc.items():
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color = colors[key]["color"] if colors[key]["color"] is not None else "w"
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language_handles += [mpatches.Patch(color=color, label=key)]
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# print(languages_all_loc)
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for quarter in range(4):
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ax.bar(year_indexes + quarter * 0.21, value[quarter], 0.2, bottom=cumulative[quarter], color=color)
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cumulative[quarter] = np.add(cumulative[quarter], value[quarter])
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language_df = {}
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ax.set_ylabel("LOC added", fontdict=dict(weight="bold"))
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ax.set_xticks(np.array([np.arange(i, i + 0.84, step=0.21) for i in year_indexes]).flatten(), labels=["Q1", "Q2", "Q3", "Q4"] * years)
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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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sax = ax.secondary_xaxis("top")
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sax.set_xticks(year_indexes + 0.42, labels=sorted(yearly_data.keys()))
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sax.spines["top"].set_visible(False)
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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()),
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columns=["Q1", "Q2", "Q3", "Q4"])
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ax.legend(title="Language", handles=language_handles, loc="upper left", bbox_to_anchor=(1, 1), framealpha=0, title_fontproperties=dict(weight="bold"))
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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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sax.tick_params(axis="both", length=0)
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sax.spines["top"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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df = pd.concat(language_df.values())
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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'
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plt.ylim(0, 1.05 * np.amax(cumulative))
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plt.savefig("bar_graph.png", bbox_inches="tight")
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plt.close(fig)
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return "bar_graph.png"
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