what is imax-analysis fr?

omarirfa/imax-analysis — explained in plain English

Analysis updated 2026-07-24

★ 0TypeScriptAudience · generalComplexity · 2/5Setup · moderate

vibe map

mindmap
  root((repo))
    What it does
      Tracks all IMAX films since 1994
      Interactive fifteen-section dashboard
      Custom dataset of 1406 films
    Key findings
      Four distinct IMAX formats exist
      Marvel drives IMAX volume
      Real 70mm IMAX is rare
    Tech stack
      Python data pipeline
      Next.js static site
      Chart.js for visuals
    Use cases
      Explore IMAX format differences
      Analyze box office patterns
      Study cinema format history
    Audience
      Data enthusiasts
      Cinema fans
      Data storytellers

mindmap root((repo)) What it does Tracks all IMAX films since 1994 Interactive fifteen-section dashboard Custom dataset of 1406 films Key findings Four distinct IMAX formats exist Marvel drives IMAX volume Real 70mm IMAX is rare Tech stack Python data pipeline Next.js static site Chart.js for visuals Use cases Explore IMAX format differences Analyze box office patterns Study cinema format history Audience Data enthusiasts Cinema fans Data storytellers

what do people make with this?

VIBE 1

Explore the interactive dashboard to learn how IMAX formats differ and which venues can project real 70mm IMAX.

VIBE 2

Analyze box office patterns across 1,406 IMAX films to see what drives revenue in premium cinema.

VIBE 3

Use the Python pipeline as a reference for scraping and assembling a custom film dataset from web sources.

what's the stack?

TypeScriptNext.jsChart.jsPythonNetlify

how it stacks up fr

	omarirfa/imax-analysis	0xkinno/astraea	0xkinno/halcyon
Stars	0	0	0
Language	TypeScript	TypeScript	TypeScript
Setup difficulty	moderate	hard	hard
Complexity	2/5	4/5	4/5
Audience	general	developer	developer

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 30min

Requires running a Python scraping pipeline to generate data files, then installing Node.js dependencies to serve the Next.js dashboard locally.

No license information is provided in the project, so default copyright restrictions apply.

in plain english

This project is a data study of every film released in IMAX from 1994 to 2026. The author built it to understand how IMAX has grown in popularity and how it compares against other premium cinema formats. The result pairs a custom dataset of 1,406 IMAX films with an interactive dashboard that presents the findings as a fifteen-section visual essay. The dataset was assembled by hand because this information was not readily available from existing sources. The project has two main parts. A Python pipeline scrapes and processes data from various websites into CSV files. A static website built with Next.js then turns those files into an interactive dashboard using Chart.js. The site is hosted on Netlify and works offline. You can visit the live dashboard at imax-analysis.netlify.app. The central finding is that "IMAX" is not one thing but a ladder of at least four different formats. The tallest frame, called 1.43:1, is the real IMAX shot on 70mm film. It is rare: only 84 of 492 catalogued IMAX venues can project it, and most of those are in the US and Canada. The more common digital IMAX uses a shorter 1.90:1 frame. About 71% of IMAX films are ordinary movies upscaled through a process called DMR. Only about 20 films in history were shot on real IMAX film cameras, and even those use the tall frame for only part of their runtime. The data reveals some surprising patterns. How much IMAX footage a film contains does not predict its box office performance. What does predict reach is franchise membership, opening weekend numbers, and how many premium formats a film stacks together. Marvel is the volume engine, with 41 IMAX films totaling $35.5 billion. The business is hit-driven, with the top 10% of films earning 40% of the total $233 billion gross. The dashboard reads as a four-act argument covering what counts as IMAX, what happened to formats and geography over time, and what the findings mean. The full README is longer than what was shown.

prompts (copy fr)

prompt 1

Clone the IMAX-Analysis repo and run the Python pipeline to regenerate the CSV data files from the source websites. Then start the Next.js dev server and open the dashboard locally.

prompt 2

Using the IMAX-Analysis dataset, build a new chart section that compares the total box office gross of Marvel IMAX films versus non-Marvel IMAX films over time.

prompt 3

Extend the IMAX-Analysis dashboard by adding a map visualization showing the geographic distribution of the 84 venues capable of projecting 1.43:1 70mm IMAX.

prompt 4

Explore the IMAX-Analysis repo and summarize the Python scraping pipeline: what websites it pulls from, how it processes the data, and what CSV files it outputs for the dashboard.

Frequently asked questions