You've already forked wakapi-readme-stats
Merge pull request #363 from anmol098/feat/new_graph_drawing_backend
New graph drawing backend
This commit is contained in:
Alexander Sergeev
@@ -1,4 +1,4 @@
|
||||
FROM nikolaik/python-nodejs:python3.9-nodejs16
|
||||
FROM python:3.9-alpine
|
||||
|
||||
ENV PYTHONUNBUFFERED 1
|
||||
ENV PYTHONDONTWRITEBYTECODE 1
|
||||
@@ -6,9 +6,7 @@ ENV PYTHONDONTWRITEBYTECODE 1
|
||||
WORKDIR /waka-readme-stats
|
||||
|
||||
ADD requirements.txt ./requirements.txt
|
||||
RUN pip install --upgrade pip && pip install -r requirements.txt
|
||||
RUN npm i npm@next-8 && npm i vega vega-lite vega-cli canvas
|
||||
RUN apk add --no-cache g++ jpeg-dev zlib-dev libjpeg make && pip3 install -r requirements.txt
|
||||
|
||||
ADD sources/* ./
|
||||
|
||||
ENTRYPOINT python3 /waka-readme-stats/main.py
|
||||
|
||||
@@ -1,10 +1,7 @@
|
||||
PyGithub==1.54.1
|
||||
matplotlib==3.4.1
|
||||
matplotlib==3.6.3
|
||||
numpy==1.24.2
|
||||
python-dotenv==0.17.0
|
||||
numpy==1.24.1
|
||||
pandas==1.2.3
|
||||
altair==4.1.0
|
||||
altair-saver==0.5.0
|
||||
pytz==2021.1
|
||||
humanize==3.3.0
|
||||
httpx==0.23.3
|
||||
|
||||
@@ -5,7 +5,7 @@ from github import Github, InputGitAuthor, AuthenticatedUser
|
||||
import datetime
|
||||
|
||||
from download_manager import DownloadManager
|
||||
from make_bar_graph import BarGraph
|
||||
from make_bar_graph import build_graph
|
||||
|
||||
|
||||
class LinesOfCode:
|
||||
@@ -28,8 +28,7 @@ class LinesOfCode:
|
||||
return yearly_data
|
||||
|
||||
async def plotLoc(self, yearly_data):
|
||||
graph = BarGraph(yearly_data)
|
||||
await graph.build_graph()
|
||||
await build_graph(yearly_data)
|
||||
self.pushChart()
|
||||
|
||||
def getQuarter(self, timeStamp):
|
||||
|
||||
@@ -1,105 +1,69 @@
|
||||
import pandas as pd
|
||||
import altair as alt
|
||||
from typing import Dict
|
||||
from os.path import join, dirname
|
||||
from json import load
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.patches as mpatches
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from download_manager import DownloadManager
|
||||
|
||||
|
||||
# npm install vega-lite vega-cli canvas
|
||||
MAX_LANGUAGES = 5
|
||||
|
||||
|
||||
class BarGraph:
|
||||
async def build_graph(yearly_data: Dict) -> str:
|
||||
"""
|
||||
Draws graph of lines of code written by user by quarters of years.
|
||||
Picks top `MAX_LANGUAGES` languages from each quarter only.
|
||||
|
||||
def __init__(self, yearly_data):
|
||||
self.yearly_data = yearly_data
|
||||
:param yearly_data: GitHub user yearly data.
|
||||
:return: String, path to graph file.
|
||||
"""
|
||||
colors = await DownloadManager.get_remote_yaml("linguist")
|
||||
|
||||
async def build_graph(self):
|
||||
languages_all_loc = dict()
|
||||
years = len(yearly_data.keys())
|
||||
year_indexes = np.arange(years)
|
||||
|
||||
colors = await DownloadManager.get_remote_yaml("linguist")
|
||||
allColorsValues = []
|
||||
for i, y in enumerate(sorted(yearly_data.keys())):
|
||||
for q in yearly_data[y].keys():
|
||||
langs = sorted(yearly_data[y][q].keys(), key=lambda l: yearly_data[y][q][l], reverse=True)[0:MAX_LANGUAGES]
|
||||
|
||||
# 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]
|
||||
for lang in langs:
|
||||
if lang not in languages_all_loc:
|
||||
languages_all_loc[lang] = np.array([[0] * years] * 4)
|
||||
languages_all_loc[lang][q - 1][i] = yearly_data[y][q][lang]
|
||||
|
||||
if language not in top_languages:
|
||||
top_languages[language] = 1
|
||||
top_languages[language] += 1
|
||||
fig = plt.figure()
|
||||
ax = fig.add_axes([0, 0, 1.5, 1])
|
||||
|
||||
# print(self.yearly_data)
|
||||
language_handles = []
|
||||
cumulative = np.array([[0] * years] * 4)
|
||||
|
||||
all_languages = list(top_languages.keys())
|
||||
for key, value in languages_all_loc.items():
|
||||
color = colors[key]["color"] if colors[key]["color"] is not None else "w"
|
||||
language_handles += [mpatches.Patch(color=color, label=key)]
|
||||
|
||||
for language in all_languages:
|
||||
if colors[language]['color'] is not None:
|
||||
allColorsValues.append(colors[language]['color'])
|
||||
for quarter in range(4):
|
||||
ax.bar(year_indexes + quarter * 0.21, value[quarter], 0.2, bottom=cumulative[quarter], color=color)
|
||||
cumulative[quarter] = np.add(cumulative[quarter], value[quarter])
|
||||
|
||||
languages_all_loc = {}
|
||||
ax.set_ylabel("LOC added", fontdict=dict(weight="bold"))
|
||||
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)
|
||||
|
||||
for language in all_languages:
|
||||
language_year = []
|
||||
for year in self.yearly_data.keys():
|
||||
language_quarter = [0, 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
|
||||
sax = ax.secondary_xaxis("top")
|
||||
sax.set_xticks(year_indexes + 0.42, labels=sorted(yearly_data.keys()))
|
||||
sax.spines["top"].set_visible(False)
|
||||
|
||||
# print(languages_all_loc)
|
||||
ax.legend(title="Language", handles=language_handles, loc="upper left", bbox_to_anchor=(1, 1), framealpha=0, title_fontproperties=dict(weight="bold"))
|
||||
|
||||
language_df = {}
|
||||
sax.tick_params(axis="both", length=0)
|
||||
sax.spines["top"].set_visible(False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.spines["right"].set_visible(False)
|
||||
|
||||
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", "Q4"])
|
||||
|
||||
for language in language_df.keys():
|
||||
language_df[language] = prep_df(language_df[language], language)
|
||||
|
||||
df = pd.concat(language_df.values())
|
||||
|
||||
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'
|
||||
plt.ylim(0, 1.05 * np.amax(cumulative))
|
||||
plt.savefig("bar_graph.png", bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
return "bar_graph.png"
|
||||
|
||||
Reference in New Issue
Block a user