1. Clarify which features require additional configuration via the `dac`
constructor argument.
2. Document when DAC settings apply immediatly/are staged.
3. Document how staged DAC settings may be applied
4. Calrify operation of `dac_sync`
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
When Phaser is powered on and `init()` is first called, enabling the
DAC-mixer while leaving the NCO disabled causes malformed output.
This commit implements a workaround by making sure the NCO is enabled,
before being set to the disired state.
This commit also avoids the following procedure, resulting in
malformed output:
1. Operate Phaser with the DAC Mixer and NCO enabled
2. Set the NCO to a non-zero frequency
3. Disable the NCO in the device_db
4. Re-initialise Phaser
After this procedure, with CMIX disabled, incorrect output is produced.
To clear the fault one must re-enable the NCO and write the NCO freqeuncy
to zero before disabling the NCO.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
The CMIX bits are bits 12-15 in register 0x0d. This has been checked
against the datasheet and verified on hardware. Until now, the bit for
CMIX1 was written to CMIX0. The CMIX0 bit was written to a reserved bit.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
in some use cases a larger tunable range than available via the DUC may
be needed. Some use cases may wish to combine the coarse mixer with the
DUC to extend the tunable range.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
Currently, `init()` leaves a single oscillator at full scale. The phase
accumulator of this oscillator is held continuously cleared. Provided no
upconverting mechanism is active (DUC, CMIX, NCO), this produces a full-scale
DC voltage. The DC voltage is blocked by hardware capacitors. This behaviour
is not mentioned by the `init` documentation.
If one attempts to use any other oscillator without reducing the amplitude
of the oscillator enabled by `init`, there is by significant clipping.
In the case that the NCO or CMIX are configured via the device_db
(suggested in the docs), leaving the osillator at full scale results in
full RF output power after calling `init()`. This may plausibly damage loads
driven by phaser.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
The suitable PFD clock depends on the use case and will likely need
to be configured by some users. All things being equal, a higher PFD
clock is desirable as is results in lower local oscillator phase-noise.
Phaser was designed around a maximum PFD clock of 62.5 MHz. In integer mode,
with no local oscillator frequency divisor set, a 62.5 MHz PFD clock results
in a 125 MHz local oscillator step size. Given the +-200 MHz range of the DUC
(more if using the DAC mixer), this step size will be acceptable to many.
This seems like the most appropreate default configuration as it should offer
the best phase-noise performance.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
`sif_sync` must be triggered to apply NCO frequency changes. To achieve per
channel frequency tunability exeeding the range of the DUC, the NCO frequeny must
adjusted. User code will need to trigger `sif_sync` to achieve this.
`sif_sync` can only be triggered if the bit was cleared. To avoid this pitfall,
the clearing of `sif_sync` is automated.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
Currently running `voltage_to_mu()` or `voltage_group_to_mu()` on the host will
convert all machine unit values to int64. This leads to issues when machine units
are returned from RPCs.
Signed-off-by: Marius Weber <marius.weber@physics.ox.ac.uk>
It was possible to crash the dashboard by opening the context menu
before an applet entry had been selected for the first time (e.g.
immediately after startup) and selecting one of the Group CCB
actions, as the enable update slot would not have been run.
This broke after b8cd163978, but
is invalid code to start with; this would have previously
crashed the code generator had the code actually been compiled.
(Allowing implicit conversion to bool would be a separate debate.)
The previous code could have never worked as-is, as the result slot
went unused, and it tried to append the load instruction to the
block just terminated with the invoke.
GitHub: Fixes#1506, #1531.
Since we don't implement any integer-like operations for TBool
(addition, bitwise not, etc.), TBool is currently neither
strictly equivalent to builtin bool nor numpy.bool_, but through
very obvious compiler errors (operation not supported) rather than
silently different runtime behaviour.
Just mapping both to TBool thus is a huge improvement over the
current behaviour (where numpy.False_ is a true-like object). In
the future, we could still implement more operations for TBool,
presumably following numpy.bool_ rather than the builtin type,
just like builtin integers get translated to the numpy-like
TInt{32,64}.
GitHub: Fixes#1275.
Previously, any type would be accepted for the test expression,
leading to internal errors in the code generator if the passed
value wasn't in fact a bool.
array([...]), the constructor for NumPy arrays, currently has the
status of some weird kind of macro in ARTIQ Python, as it needs
to determine the number of dimensions in the resulting array
type, which is a fixed type parameter on which inference cannot
be performed.
This leads to an ambiguity for empty lists, which could contain
elements of arbitrary type, including other lists (which would
add to the number of dimensions).
Previously, I had chosen to make array([]) to be of completely
indeterminate type for this reason. However, this is different
to how the call behaves in host NumPy, where this is a well-formed
call creating an empty 1D array (or 2D for array([[], []]), etc.).
This commit adds special matching for (recursive lists of) empty
ListT AST nodes to treat them as scalar dimensions, with the
element type still unknown.
This also happens to fix type inference for embedding empty 1D
NumPy arrays from host object attributes, although multi-dimensional
arrays will still require work (see GitHub #1633).
GitHub: Fixes#1626.
Strided slicing of one-dimensional arrays (i.e. with non-trivial
steps) might have previously been working, but would have had
different semantics, as all slices were copies rather than a view
into the original data.
Fixing this in the future will require adding support for an index
stride field/tuple to our array representation (and all the
associated indexing logic).
GitHub: Fixes#1627.
This was a long-standing issue affecting both lists and
the new NumPy array implementation, just caused by the
generic inference passes not being run on the slice
subexpressions (and thus e.g. ints not being monomorphized).
GitHub: Fixes#1632.
