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Common Lisp

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Common Lisp is a general-purpose programming language in the Lisp language family. Its syntax is defined on top of s-expressions, however it can be extended through the use of reader macros. It supports compile-time meta-programming through the use of macros. It supports the OOP paradigm through the Common Lisp Object System. The API upon which CLOS is implemented is exposed to the programmer so they can extent the object system. This API is refered as the Meta-Object Protocol. There are multiple implementations available: SBCL, which generates fast code, CCL, which compiles code fast, ABCL, which runs on the JVM, JSCL which runs on Node, and the browser, etc.

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mirkov
mirkov commented Nov 4, 2020

The example in the section on walking/traversing directories lists the arguments of the uiop:collect-sub*directories

It would be good to reorder the list in the order of this function's arguments like so:

  • a directory
  • a collectp function
  • a recursep function
  • a collector function

That will make the example more readable

dkochmanski
dkochmanski commented Sep 30, 2021

Currently the option pane is activated only after pressing it - then it is possible to select the appropriate option in the popup.
This ticket requires writing a handle-event method specialized on the class generic-option-pane and pointer-scroll-event that:

  • on scroll up selects the option above (if any, for topmost it is a no-op)
  • on scroll down selects the option below (if any, for bo
enhancement good first issue
stylewarning
stylewarning commented Nov 19, 2021

right now, absolute jumps are conflated in the label object here. I'd prefer we instead introduce a new object called absolute-program-location or something like that. (I don't want to use "address" only because it feels too dishonest.) If we do this, then we'd change patch-labels to not overwrite these label objects, and we'

enhancement good first issue
stylewarning
stylewarning commented Nov 23, 2021

Write a test which does a Rabi experiment and a sine-curve fit on successive RX angles. Something like:

data = []
angles = linspace([0, 2pi], 25)
num_shots = 100
for angle in angles:
    p = RX(angle) 0
    histogram = qvm(p, shots=num_shots)
    data += histogram[1]/num_shots
fit, error = fit_sinusoid(angles, data)
assert error < some_threshold

Intention is to emulate a so

enhancement good first issue

Created by X3J13

Released 1984

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