Medaka

Fixed
- `medaka smolecule` was broken by change from `medaka consensus` to `medaka inference`.
Changed
- Improved error message when model is not found.

Switched from tensorflow to pytorch.

Existing models for recent basecallers have been converted to the new format.
Pytorch format models contain a ``_pt`` suffix in the filename.

Changed
- Inference is now performed using PyTorch instead of TensorFlow.
- The `medaka consensus` command has been renamed to `medaka inference` to reflect
its function in running an arbitrary model and avoid confusion with `medaka_consensus`.
- The `medaka stitch` command has been renamed to `medaka sequence` to reflect its
function in creating a consensus sequence.
- The `medaka variant` command has been renamed to `medaka vcf` to reflect its function
in consolidating variants and avoid confusion with `medaka_variant`.
- Order of arguments to `medaka vcf` has been changed to be more consistent
with `medaka sequence`.
- The helper script `medaka_haploid_variant` has been renamed `medaka_variant` to
save typing.
- Make `--ignore_read_groups` option available to more medaka subcommands including `inference`.
Removed
- The `medaka snp` command has been removed. This was long defunct as diploid SNP calling
had been deprecated, and `medaka variant` is used to create VCFs for current models.
- Loading models in hdf format has been deprecated.
- Deleted minimap2 and racon wrappers in `medaka/wrapper.py`.
Added
- Release conda packages for Linux (x86 and aarch64) and macOS (arm64).
- Option `--lr_schedule` allows using cosine learning rate schedule in training.
- Option `--max_valid_samples` to set number of samples in a training validation batch.
Fixed
- Training models with DiploidLabelScheme uses categorical cross-entropy loss
instead of binary cross-entropy.

Changed
- Minor edits to README around model selection and package installation.
Added
- Release conda packages for Linux (x86 and aarch64) and macOS (arm64).

Switched from tensorflow to pytorch.

Existing models for recent basecallers have been converted to the new format.
Pytorch format models contain a ``_pt`` suffix in the filename.
Changed
- Inference is now performed using PyTorch instead of TensorFlow.
- The `medaka consensus` command has been renamed to `medaka inference` to reflect
its function in running an arbitrary model and avoid confusion with `medaka_consensus`.
- The `medaka stitch` command has been renamed to `medaka sequence` to reflect its
function in creating a consensus sequence.
- The `medaka variant` command has been renamed to `medaka vcf` to reflect its function
in consolidating variants and avoid confusion with `medaka_variant`.
- Order of arguments to `medaka vcf` has been changed to be more consistent
with `medaka sequence`.
- The helper script `medaka_haploid_variant` has been renamed `medaka_variant` to
save typing.
Removed
- The `medaka snp` command has been removed. This was long defunct as diploid SNP calling
had been deprecated, and `medaka variant` is used to create VCFs for current models.
- Loading models in hdf format has been deprecated.
- Deleted minimap2 and racon wrappers in `medaka/wrapper.py`.
Added
- Option `--lr_schedule` allows using cosine learning rate schedule in training.
- Option `--max_valid_samples` to set number of samples in a training validation batch.
Fixed
- Training models with DiploidLabelScheme uses categorical cross-entropy loss
instead of binary cross-entropy.

(Probably) final version of medaka using tensorflow. Future versions will use
pytorch instead.
Fixed
- medaka_consensus: only keep bam tags if input file matches joint polishing pipeline.
- Pin numpy to <2.0.0.
Added
- Consensus and variant models lookup for v3.5.1 Dorado models.

Fixed
- tandem: Use haplotag 0 in unphased mode.
- tandem: Don't run consensus if regions set is empty.
Added
- Models for version 5 basecaller models.
- Expose `sym_indels` option for training.
- Expose `--min_mapq` minimum mapping quality alignment fitering option for medaka consensus.
- tandem: Option `--ignore_read_groups` to ignore read groups present in input file.
- Wrapper script `medaka_consensus_joint` and convenience tools (`prepare_tagged_bam`,
`get_model_dtypes`) to facilitate joint polishing with multiple datatypes.