238 lines
8.2 KiB
Python
238 lines
8.2 KiB
Python
import io
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import base64
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from fastapi import APIRouter
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel, Field
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from sqlmodel import Session, func, select
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from sqlmodel.sql.expression import SelectOfScalar
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from db import Chemistry, MVPRanking, Player, engine
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import networkx as nx
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import numpy as np
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import matplotlib
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matplotlib.use("agg")
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import matplotlib.pyplot as plt
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analysis_router = APIRouter(prefix="/analysis")
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C = Chemistry
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R = MVPRanking
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P = Player
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def sociogram_json():
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nodes = []
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necessary_nodes = set()
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edges = []
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players = {}
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with Session(engine) as session:
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for p in session.exec(select(P)).fetchall():
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nodes.append({"id": p.display_name, "label": p.display_name})
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players[p.id] = p.display_name
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subquery = (
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select(C.user, func.max(C.time).label("latest")).group_by(C.user).subquery()
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)
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statement2 = select(C).join(
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subquery, (C.user == subquery.c.user) & (C.time == subquery.c.latest)
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)
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for c in session.exec(statement2):
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# G.add_node(c.user)
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necessary_nodes.add(c.user)
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for p in [players[p_id] for p_id in c.love]:
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# G.add_edge(c.user, p)
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# p_id = session.exec(select(P.id).where(P.name == p)).one()
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necessary_nodes.add(p)
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edges.append({"from": players[c.user], "to": p, "relation": "likes"})
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for p in [players[p_id] for p_id in c.hate]:
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edges.append({"from": players[c.user], "to": p, "relation": "dislikes"})
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# nodes = [n for n in nodes if n["name"] in necessary_nodes]
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return JSONResponse({"nodes": nodes, "edges": edges})
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def graph_json():
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nodes = []
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edges = []
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players = {}
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with Session(engine) as session:
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for p in session.exec(select(P)).fetchall():
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players[p.id] = p.display_name
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nodes.append({"id": p.display_name, "label": p.display_name})
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subquery = (
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select(C.user, func.max(C.time).label("latest")).group_by(C.user).subquery()
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)
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statement2 = select(C).join(
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subquery, (C.user == subquery.c.user) & (C.time == subquery.c.latest)
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)
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for c in session.exec(statement2):
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user = players[c.user]
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for i, p_id in enumerate(c.love):
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p = players[p_id]
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edges.append(
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{
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"id": f"{user}->{p}",
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"source": user,
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"target": p,
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"size": max(1.0 - 0.1 * i, 0.3),
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"data": {
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"relation": 2,
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"origSize": max(1.0 - 0.1 * i, 0.3),
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"origFill": "#bed4ff",
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},
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}
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)
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for p_id in c.hate:
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p = players[p_id]
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edges.append(
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{
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"id": f"{user}-x>{p}",
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"source": user,
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"target": p,
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"size": 0.3,
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"data": {"relation": 0, "origSize": 0.3, "origFill": "#ff7c7c"},
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"fill": "#ff7c7c",
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}
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)
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G = nx.DiGraph()
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G.add_weighted_edges_from([(e["source"], e["target"], e["size"]) for e in edges])
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in_degrees = G.in_degree(weight="weight")
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nodes = [
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dict(node, **{"data": {"inDegree": in_degrees[node["id"]]}}) for node in nodes
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]
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return JSONResponse({"nodes": nodes, "edges": edges})
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def sociogram_data(show: int | None = 2):
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G = nx.DiGraph()
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with Session(engine) as session:
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players = {}
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for p in session.exec(select(P)).fetchall():
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G.add_node(p.display_name)
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players[p.id] = p.display_name
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subquery = (
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select(C.user, func.max(C.time).label("latest")).group_by(C.user).subquery()
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)
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statement2 = (
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select(C)
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# .where(C.user.in_(["Kruse", "Franz", "ck"]))
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.join(subquery, (C.user == subquery.c.user) & (C.time == subquery.c.latest))
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)
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for c in session.exec(statement2):
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if show >= 1:
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for i, p_id in enumerate(c.love):
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p = players[p_id]
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G.add_edge(c.user, p, group="love", rank=i, popularity=1 - 0.08 * i)
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if show <= 1:
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for i, p_id in enumerate(c.hate):
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p = players[p_id]
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G.add_edge(c.user, p, group="hate", rank=8, popularity=-0.16)
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return G
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class Params(BaseModel):
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node_size: int | None = Field(default=2400, alias="nodeSize")
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font_size: int | None = Field(default=10, alias="fontSize")
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arrow_size: int | None = Field(default=20, alias="arrowSize")
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edge_width: float | None = Field(default=1, alias="edgeWidth")
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distance: float | None = 0.2
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weighting: bool | None = True
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popularity: bool | None = True
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show: int | None = 2
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ARROWSTYLE = {"love": "-|>", "hate": "-|>"}
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EDGESTYLE = {"love": "-", "hate": ":"}
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EDGECOLOR = {"love": "#404040", "hate": "#cc0000"}
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async def render_sociogram(params: Params):
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plt.figure(figsize=(16, 10), facecolor="none")
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ax = plt.gca()
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ax.set_facecolor("none") # Set the axis face color to none (transparent)
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ax.axis("off") # Turn off axis ticks and frames
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G = sociogram_data(show=params.show)
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pos = nx.spring_layout(G, scale=2, k=params.distance, iterations=50, seed=None)
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nodes = nx.draw_networkx_nodes(
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G,
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pos,
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node_color=[
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v for k, v in G.in_degree(weight="popularity" if params.weighting else None)
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]
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if params.popularity
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else "#99ccff",
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edgecolors="#404040",
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linewidths=0,
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# node_shape="8",
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node_size=params.node_size,
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cmap="coolwarm",
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alpha=0.86,
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)
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if params.popularity:
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cbar = plt.colorbar(nodes)
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cbar.ax.set_xlabel("popularity")
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nx.draw_networkx_labels(G, pos, font_size=params.font_size)
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nx.draw_networkx_edges(
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G,
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pos,
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arrows=True,
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edge_color=[EDGECOLOR[G.edges()[*edge]["group"]] for edge in G.edges()],
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arrowsize=params.arrow_size,
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node_size=params.node_size,
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width=params.edge_width,
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style=[EDGESTYLE[G.edges()[*edge]["group"]] for edge in G.edges()],
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arrowstyle=[ARROWSTYLE[G.edges()[*edge]["group"]] for edge in G.edges()],
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connectionstyle="arc3,rad=0.12",
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alpha=[1 - 0.08 * G.edges()[*edge]["rank"] for edge in G.edges()]
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if params.weighting
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else 1,
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)
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buf = io.BytesIO()
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plt.savefig(buf, format="png", bbox_inches="tight", dpi=300, transparent=True)
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buf.seek(0)
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encoded_image = base64.b64encode(buf.read()).decode("UTF-8")
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plt.close()
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return {"image": encoded_image}
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def mvp():
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ranks = dict()
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with Session(engine) as session:
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players = {p.id: p.display_name for p in session.exec(select(P)).fetchall()}
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subquery = (
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select(R.user, func.max(R.time).label("latest")).group_by(R.user).subquery()
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)
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statement2 = select(R).join(
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subquery, (R.user == subquery.c.user) & (R.time == subquery.c.latest)
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)
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for r in session.exec(statement2):
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for i, p_id in enumerate(r.mvps):
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p = players[p_id]
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ranks[p] = ranks.get(p, []) + [i + 1]
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return [
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{
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"name": p,
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"rank": f"{np.mean(v):.02f}",
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"std": f"{np.std(v):.02f}",
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"n": len(v),
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}
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for p, v in ranks.items()
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]
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analysis_router.add_api_route("/json", endpoint=sociogram_json, methods=["GET"])
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analysis_router.add_api_route("/graph_json", endpoint=graph_json, methods=["GET"])
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analysis_router.add_api_route("/image", endpoint=render_sociogram, methods=["POST"])
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analysis_router.add_api_route("/mvp", endpoint=mvp, methods=["GET"])
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if __name__ == "__main__":
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with Session(engine) as session:
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statement: SelectOfScalar[P] = select(func.count(P.id))
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print("players in DB: ", session.exec(statement).first())
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G = sociogram_data()
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pos = nx.spring_layout(G, scale=1, k=2, iterations=50, seed=42)
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