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The News92

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Python

A Coding Implementation on Loguru for Designing Sturdy, Structured, Concurrent, and Manufacturing-Prepared Python Logging Pipelines

A Coding Implementation on Loguru for Designing Sturdy, Structured, Concurrent, and Manufacturing-Prepared Python Logging Pipelines

Naveed Ahmad02 mins

banner(“1) logger.configure(): handlers + customized degree + additional + patcher”) mem = MemorySink() logger.configure( handlers=[ {“sink”: sys.stderr, “format”: console_formatter, “level”: “DEBUG”, “colorize”: True, “backtrace”: True, “diagnose”: True}, {“sink”: mem, “level”: “DEBUG”, “format”: “{message}”}, {“sink”: “structured.jsonl”, “serialize”: True, “level”: “DEBUG”, “enqueue”: True}, {“sink”: “errors.log”, “level”: “ERROR”, “enqueue”: True, “backtrace”: True, “diagnose”: False, “format”: “{time:YYYY-MM-DD HH:mm:ss} |…

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How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations

Naveed Ahmad02 mins

print(“\n” + “=”*70 + “\n SECTION 6 — NetworkX analytics\n” + “=”*70) def kg_to_networkx(graph): G = nx.MultiDiGraph() for e in graph.entities: G.add_node(e) for s, p, o in graph.relations: G.add_edge(s, o, label=p) return G G = kg_to_networkx(g_big) print(f”Nodes: {G.number_of_nodes()} Edges: {G.number_of_edges()}”) H = nx.Graph(G) deg_cent = nx.degree_centrality(H) btw_cent = nx.betweenness_centrality(H) pr_cent = nx.pagerank(nx.DiGraph(G)) if G.number_of_edges() else…

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How to Build Technical Analysis and Backtesting Workflow with pandas-ta-classic, Strategy Signals, and Performance Metrics

Naveed Ahmad02 mins

entries = df.index[(df[“pos”].diff() == 1)] exits = df.index[(df[“pos”].diff() == -1)] fig, (ax1, ax2, ax3) = plt.subplots( 3, 1, figsize=(13, 10), sharex=True, gridspec_kw={“height_ratios”: [3, 1, 2]}, ) ax1.plot(df.index, df[“close”], lw=1.1, color=”black”, label=”Close”) ax1.plot(df.index, df[“SMA_20″], lw=0.9, label=”SMA 20”) ax1.plot(df.index, df[“SMA_50″], lw=0.9, label=”SMA 50”) bbu, bbl = “BBU_20_2.0”, “BBL_20_2.0” if bbu in df and bbl in df: ax1.fill_between(df.index,…

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