[{"content":"Groceror is an inventory and operations platform designed specifically for independent grocery stores and small supermarket operators. It helps store owners gain better visibility into their inventory, track stock levels in real time, monitor product movement, and make data-driven decisions to reduce waste and prevent stockouts.\nBeyond inventory management, Groceror aims to provide actionable business insights through analytics, sales trends, and operational reporting, helping neighborhood grocers operate with the efficiency and intelligence of larger retail chains. By combining inventory tracking, order management, and business analytics in a single platform, Groceror empowers grocery store owners to spend less time managing spreadsheets and more time growing their business.\nRepositories groceror — core backend groceror-fe — web frontend groceror-mobile — mobile app groceror-users — user management service groceror-orders — orders service groceror-email — email service for transactional events (e.g. user registration) ","permalink":"https://lordlabakdas.github.io/projects/groceror/","summary":"\u003cp\u003eGroceror is an inventory and operations platform designed specifically for independent grocery stores and small supermarket operators. It helps store owners gain better visibility into their inventory, track stock levels in real time, monitor product movement, and make data-driven decisions to reduce waste and prevent stockouts.\u003c/p\u003e\n\u003cp\u003eBeyond inventory management, Groceror aims to provide actionable business insights through analytics, sales trends, and operational reporting, helping neighborhood grocers operate with the efficiency and intelligence of larger retail chains. By combining inventory tracking, order management, and business analytics in a single platform, Groceror empowers grocery store owners to spend less time managing spreadsheets and more time growing their business.\u003c/p\u003e","title":"Groceror"},{"content":"pcapprocessor is a Python toolkit that automates the full pipeline from ns-3 network simulation to publication-ready figures. Available on PyPI.\npip install pcapprocessor What it does Given a config file describing simulation parameters, pcapprocessor:\nRuns multiple simulation replications via the ns-3 WAF build system Parses each pcap output with pyshark to extract per-flow TCP metrics Aggregates metrics across runs (mean, std dev, 95% confidence intervals) Writes results to tab-separated CSV files — one per protocol/flow Generates matplotlib figures with confidence interval error bars Metrics Extracts 12 TCP-level metrics per flow including throughput, one-way delay, goodput, link utilization, retransmitted packets, queue occupancy, and flow completion time.\nPipeline Config (.ini) → BfsRunner → ns-3 simulation → pcap traces ↓ TraceProcessor (pyshark) ↓ MetricStats + CI aggregation ↓ MetricsWriter → CSV files ↓ Reporter → PNG/SVG plots Usage from pcapprocessor import BfsRunner runner = BfsRunner(config_file=\u0026#34;sim.ini\u0026#34;, scenario=\u0026#34;bottleneckDelay\u0026#34;) runner.run() # Writes results/metrics_tcp0.csv, results/metrics_udp0.csv, ... Generate figures from existing CSVs:\nfrom pcapprocessor import Reporter Reporter( csvs=[\u0026#34;results/metrics_tcp0.csv\u0026#34;, \u0026#34;results/metrics_udp0.csv\u0026#34;], metrics=[\u0026#34;throughput\u0026#34;, \u0026#34;delay\u0026#34;, \u0026#34;utilization\u0026#34;], output_dir=\u0026#34;plots\u0026#34;, ).plot() Links GitHub PyPI ","permalink":"https://lordlabakdas.github.io/projects/pcapprocessor/","summary":"\u003cp\u003e\u003ca href=\"https://github.com/lordlabakdas/pcapprocessor\"\u003epcapprocessor\u003c/a\u003e is a Python toolkit that automates the full pipeline from ns-3 network simulation to publication-ready figures. Available on \u003ca href=\"https://pypi.org/project/pcapprocessor/\"\u003ePyPI\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight\"\u003e\u003cpre tabindex=\"0\" style=\"color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;\"\u003e\u003ccode class=\"language-bash\" data-lang=\"bash\"\u003e\u003cspan style=\"display:flex;\"\u003e\u003cspan\u003epip install pcapprocessor\n\u003c/span\u003e\u003c/span\u003e\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\u003ch2 id=\"what-it-does\"\u003eWhat it does\u003c/h2\u003e\n\u003cp\u003eGiven a config file describing simulation parameters, pcapprocessor:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRuns multiple simulation replications via the ns-3 WAF build system\u003c/li\u003e\n\u003cli\u003eParses each pcap output with pyshark to extract per-flow TCP metrics\u003c/li\u003e\n\u003cli\u003eAggregates metrics across runs (mean, std dev, 95% confidence intervals)\u003c/li\u003e\n\u003cli\u003eWrites results to tab-separated CSV files — one per protocol/flow\u003c/li\u003e\n\u003cli\u003eGenerates matplotlib figures with confidence interval error bars\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"metrics\"\u003eMetrics\u003c/h2\u003e\n\u003cp\u003eExtracts 12 TCP-level metrics per flow including throughput, one-way delay, goodput, link utilization, retransmitted packets, queue occupancy, and flow completion time.\u003c/p\u003e","title":"pcapprocessor"},{"content":"Hi there!\nAbout blog Documenting things that interest me.\nTwitter My Twitter Bio\nPhoto Timestamp: August 24, 2013 Taken at: Grinter Farms, Lawrence, Kansas\n","permalink":"https://lordlabakdas.github.io/posts/2020-05-11-introduction/","summary":"\u003cp\u003eHi there!\u003c/p\u003e\n\u003ch2 id=\"about-blog\"\u003eAbout blog\u003c/h2\u003e\n\u003cp\u003eDocumenting things that interest me.\u003c/p\u003e\n\u003ch2 id=\"twitter\"\u003eTwitter\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://twitter.com/lordulabakudas/\"\u003eMy Twitter Bio\u003c/a\u003e\u003c/p\u003e\n\u003ch2 id=\"photo\"\u003ePhoto\u003c/h2\u003e\n\u003cp\u003eTimestamp: August 24, 2013\nTaken at: \u003ca href=\"http://www.kansastravel.org/lawrence/grinterssunflowerfarm.htm\"\u003eGrinter Farms, Lawrence, Kansas\u003c/a\u003e\u003c/p\u003e","title":"Introduction"},{"content":"","permalink":"https://lordlabakdas.github.io/photography/fireworks_1/","summary":"","title":"Fireworks"},{"content":"","permalink":"https://lordlabakdas.github.io/photography/winterfell/","summary":"","title":"Winterfell"},{"content":"","permalink":"https://lordlabakdas.github.io/photography/grinter-farms/","summary":"","title":"Grinter Farms, Kansas"},{"content":"Hi, I\u0026rsquo;m Siddharth Gangadhar.\n[Add your bio here.]\n","permalink":"https://lordlabakdas.github.io/about/","summary":"\u003cp\u003eHi, I\u0026rsquo;m Siddharth Gangadhar.\u003c/p\u003e\n\u003cp\u003e[Add your bio here.]\u003c/p\u003e","title":"About"},{"content":" Twitter/X: @lordulabakudas Google Scholar: Profile GitHub: lordlabakdas ","permalink":"https://lordlabakdas.github.io/contact/","summary":"\u003cul\u003e\n\u003cli\u003eTwitter/X: \u003ca href=\"https://twitter.com/lordulabakudas\"\u003e@lordulabakudas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eGoogle Scholar: \u003ca href=\"https://scholar.google.com/citations?user=IB6FpfAAAAAJ\u0026amp;hl=en\"\u003eProfile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eGitHub: \u003ca href=\"https://github.com/lordlabakdas\"\u003elordlabakdas\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e","title":"Contact"}]