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Create Matplotlib visualizations from the command-line

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Create Matplotlib visualizations from the command-line

I do lots of data analyses in the command-line and I was missing a simple utility to plot the output of a command (without having to script it). I like very much the Matplotlib API but I found no CLI to pipe data into it. So, I wrote MatplotCLI, a simple CLI that reads data from the stdin and allows to easily create interactive plots from the command-line. Have a look at the README for examples and recipes. Let me know what you think, thanks! Some examples: $ plt "hist(x,30)" < sample.json $ cat sample.json | plt --no-show "hist(x,30); savefig('myimage.png')" $ plt --no-input " x = np.linspace(-1,1,2000); y = x*np.sin(1/x); plot(x,y); axis('scaled'); grid(True)" $ echo ' {"a":0, "b":1} {"a":1, "b":0} {"a":3, "b":3}' | plt "plot(a,b)"

Visit productView on Hacker NewsMar 22, 2022
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, 000, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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