MATLAB and Julia

ArrayView uses the Python environment selected by the host application.

MATLAB

Select a Python environment with ArrayView installed, then import and call it:

pyenv(Version="/path/to/python")
py.importlib.import_module("arrayview");

h = py.arrayview.view(A);

Local MATLAB follows the normal display selection and prefers a native window when available. MATLAB started through SSH or VS Code follows that environment's browser or VS Code route.

The array is converted to NumPy before registration. Keep h when explicit cleanup is needed:

h.close()

Julia

PythonCall:

using PythonCall

arrayview = pyimport("arrayview")
h = arrayview.view(A)

PyCall:

using PyCall

arrayview = pyimport("arrayview")
h = arrayview.view(A)

Julia currently supports one array per view() call. Call view() again for another array.

ArrayView copies the array to a temporary NumPy file and starts or reuses a separate Python server process. This avoids blocking on Julia's Python lock and lets the viewer survive after the call returns. The returned value is a Python ViewHandle wrapper with url, sid, port, update(), and close().

h.close()

IJulia

Use the same PythonCall or PyCall code in an IJulia notebook. ArrayView displays an inline iframe by default. Inline display is a side effect; the call may return nothing instead of a handle.

Display routing

Host	Default display
Local desktop	Native window when available, otherwise browser
VS Code terminal	VS Code tab
VS Code remote or tunnel	VS Code tab through a forwarded port
Jupyter or IJulia	Inline iframe
Plain SSH	Browser URL through SSH port forwarding

For plain SSH, follow Remote. VS Code tunnel forwarding is handled by the extension.

Cleanup

In Python, close a handle directly or use a context manager:

handle = arrayview.view(a)
handle.close()

with arrayview.view(a) as handle:
    print(handle.url)

close() releases that viewer session. Calling it again is a no-op. If cleanup fails, it raises an error and can be retried.

Advanced server setup

Start with view(A). Use a persistent server only for shared or multi-hop setups:

arrayview --serve
arrayview data.npy

Use --relay PORT only when a reverse SSH tunnel exposes ArrayView on a different remote port. See Remote.