diff --git a/LICENSE b/LICENSE index e9c68257..4d43f353 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2018 Technical University of Denmark +Copyright (c) 2023 University of Copenhagen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..ae85cd30 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1 @@ +include vamb/kernel.npz diff --git a/README.md b/README.md index 4af3ac63..d3042c1d 100644 --- a/README.md +++ b/README.md @@ -4,26 +4,33 @@ Created by Jakob Nybo Nissen and Simon Rasmussen, Technical University of Denmar Vamb is a metagenomic binner which feeds sequence composition information from a contig catalogue and co-abundance information from BAM files into a variational autoencoder and clusters the latent representation. It performs excellently with multiple samples, and pretty good on single-sample data. Vamb is implemented purely in Python (with a little bit of Cython) and can be used both from command line and from within a Python interpreter. -:star: Vamb is benchmarked in CAMI2. Read our interpretations of the results [here.](https://github.com/RasmussenLab/vamb/blob/master/doc/CAMI2.md) :star: +:star: Vamb is benchmarked in CAMI2. Read our interpretations of the results [here.](https://github.com/github/RasmussenLab/blob/master/doc/CAMI2.md) :star: -Vamb has changed a lot since the pre-print at [biorxiv](https://www.biorxiv.org/content/early/2018/12/19/490078) so we really recommend reading it at [Nature Biotechnology](https://doi.org/10.1038/s41587-020-00777-4). For instance, the current version of Vamb uses "multi-split binning" which is not mentioned in the pre-print. Furthermore, we in the pre-print mention that Vamb can bin gene catalogues - this did not make it in the Nature Biotech paper, but it can still do that. For instance, on the Integrated Gene Catalogue Vamb generated 675 near complete gene-bins compared to 558 and 429 from MSPminer and Canopy, respectively. +For more information about the implementation, methodological considerations, and advanced usage of Vamb, see the [Vamb paper in Nature Biotechnology](https://doi.org/10.1038/s41587-020-00777-4), a [blog post](https://go.nature.com/2JzYUvI) by Jakob on the development of Vamb, and the [Vamb tutorial](https://github.com/github/RasmussenLab/blob/master/doc/tutorial.md). -For more information about the implementation, methodological considerations, and advanced usage of Vamb, see the tutorial file (`doc/tutorial.html`) +:mega: Interested on maximizing the number of high quality bins from your samples? +Please check out Avamb. +Avamb is a metagenomics binning pipeline composed by Vamb and Aamb, an adversarial autoencoder for metagenomics binning, as well as a bin dereplication and quality filtering step that runs CheckM2. +Similar to Vamb, Avamb leverages sequence composition information from a contig catalogue and co-abundance information from BAM files by projecting those features into latent spaces, where the metagenomics binning actually takes place. +Avamb has shown to reconstruct 23% more high quality bins as well as to increase the bins quality. +For more information, please check the [Avamb snakemake page](https://github.com/RasmussenLab/avamb/tree/avamb_new/workflow_avamb). + +Avamb pre-print can be found here [biorxiv]() -**If you are interested in phages you can use Phamb after running Vamb to identify them! [Paper](https://www.nature.com/articles/s41467-022-28581-5) and [GitHub](https://github.com/RasmussenLab/phamb).** # Installation -Vamb is most easily installed with pip - make sure your pip version is up to date, as it won't work with ancient versions (v. <= 9). ### Installation for casual users: Recommended: Vamb can be installed with pip (thanks to contribution from C. Titus Brown): ``` -pip install https://github.com/RasmussenLab/vamb/archive/v3.0.9.zip +pip install vamb ``` -or using [Bioconda's package](https://anaconda.org/bioconda/vamb) (thanks to contribution from Antônio Pedro Camargo). -(note that the BioConda package does not include GPU support). +:bangbang: An active Conda environment can hijack your system's linker, causing an error during installation. Either deactivate `conda`, or delete the `~/miniconda/compiler_compats` directory before installing with pip. + +Alternatively, it can be installed as a [Bioconda's package](https://anaconda.org/bioconda/vamb) (thanks to contribution from Antônio Pedro Camargo). +Currently, the Conda version lags behind the pip version, so we recommend using pip. The BioConda package does not include GPU support. ``` conda install -c pytorch pytorch torchvision cudatoolkit=10.2 @@ -32,7 +39,7 @@ conda install -c bioconda vamb ### Installation for advanced users: -If you want to install the latest version from GitHub you can clone and install it using: +If you want to install the latest version from GitHub, or you want to change Vamb's source code, you should install it like this: ``` # clone the desired branch from the repository, here master @@ -43,26 +50,22 @@ pip install -e . ### Installing by compiling the Cython yourself -If you can't/don't want to use pip/Conda, you can do it the hard way: Get the most recent versions of the Python packages `cython`, `numpy`, `torch` and `pysam`. Compile `src/_vambtools.pyx`, (see `src/build_vambtools.py`) then move the resulting binary to the inner of the two `vamb` directories. Check if it works by importing `vamb` in a Python session. - -### Windows - -Vamb does currently not compile on Windows because it is dependent on `pysam`, but a Windows-friendly version is hopefully on the way (see branch `v4`). Let us know if you want to run Vamb on Windows. +If you can't/don't want to use pip/Conda, you can do it the hard way: Get the most recent versions of the Python packages `cython`, `numpy`, `torch` and `pycoverm`. Compile `src/_vambtools.pyx` then move the resulting binary to the inner of the two `vamb` directories. Check if it works by importing `vamb` in a Python session. # Running - -For a detailed explanation of the parameters of Vamb, or different inputs, see the tutorial in the `doc` directory. - -**Updated in 3.0.2: for a snakemake pipeline see `workflow` directory.** +For more detailed description of the recommended workflow, see the tutorial in the `doc` directory. For more command-line options, see the command-line help menu: ``` vamb -h ``` -## TL;DR: Here's how to run Vamb +To run, Vamb needs a FASTA file with contigs from one or more samples, and a set of BAM files, one from each sample, with reads mapped to the same FASTA file. +You can either create the input files using your own workflow before calling Vamb, or you can use the Snakemake workflow included in Vamb to automatically create the input files before running Vamb. + +## How to run: Using your own input files -For this example, let us suppose you have a directory of short (e.g. Illumina) reads in a +For this example, let us suppose you have a directory of reads in a directory `/path/to/reads`, and that _you have already quality controlled them_. 1. Run your favorite metagenomic assembler on each sample individually: @@ -72,14 +75,13 @@ spades.py --meta /path/to/reads/sample1.fw.fq.gz /path/to/reads/sample1.rv.fq.gz -k 21,29,39,59,79,99 -t 24 -m 100gb -o /path/to/assemblies/sample1 ``` -2. Use Vamb's `concatenate.py` to make the FASTA catalogue of all your assemblies: +2. Concatenate the input contigs to a single FASTA file discarding very short contigs (e.g. < 2 kbp), e.g. using Vamb's `concatenate.py` script: ``` -concatenate.py /path/to/catalogue.fna.gz /path/to/assemblies/sample1/contigs.fasta -/path/to/assemblies/sample2/contigs.fasta [ ... ] +python concatenate.py /path/to/catalogue.fna.gz /path/to/assemblies/sample1/contigs.fasta /path/to/assemblies/sample2/contigs.fasta [ ... ] ``` -3. Use your favorite short-read aligner to map each your read files back to the resulting FASTA file: +3. Use your favorite aligner to map each your read files back to the FASTA file: ``` minimap2 -d catalogue.mmi /path/to/catalogue.fna.gz; # make index @@ -89,44 +91,21 @@ minimap2 -t 8 -N 5 -ax sr catalogue.mmi --split-prefix mmsplit /path/to/reads/sa 4. Run Vamb: ``` -vamb --outdir path/to/outdir --fasta /path/to/catalogue.fna.gz --bamfiles /path/to/bam/*.bam -o C --minfasta 200000 -``` - -Note that we have found that MetaBAT2's `jgi_summarize_bam_contig_depths` program estimates BAM depths more accurate than Vamb's `parsebam` module (see below). If you want to use this approach instead we provide an easy to use `snakemake` workflow which will do this for you. - -5. Apply the relevant postprocessing filters - -Vamb outputs every input contig, even those not binned with other contigs. Depending on your use case, the large number of small bins may not be relevant. Make sure to filter the contigs either using a binning quality control tool, by size, or by aligning the resulting bins to references. - -## Snakemake workflow - -To make it even easier to run Vamb in the best possible way, we have created a [Snakemake](https://snakemake.readthedocs.io/en/stable/#) workflow that will run steps 2-4 above using MetaBAT2's `jgi_summarize_bam_contig_depths` program for improved counting. Additionally it will run [CheckM](https://ecogenomics.github.io/CheckM/) to estimate completeness and contamination of the resulting bins. It can run both on a local machine, a workstation and a HPC system using `qsub` - it is included in the `workflow` folder. - -## Invoking Vamb - -After installation with pip, Vamb will show up in your PATH variable, and you can simply run: - -``` -vamb +vamb --outdir path/to/outdir --fasta /path/to/catalogue.fna.gz --bamfiles /path/to/bam/*.bam -o C ``` -To run Vamb with another Python executable (say, if you want to run with `python3.7`) than the default, you can run: +5. Apply any desired postprocessing to Vamb's output. -``` -python3.7 -m vamb -``` +## How to run: Using the Vamb Snakemake workflow -You can also run the inner `vamb` directory as a script. This will work even if you did not install with pip: +To make it even easier to run Vamb, we have created a [Snakemake](https://snakemake.readthedocs.io/en/stable/#) workflow. +This workflow runs steps 2-5 above using `minimap2` to align, and [CheckM](https://ecogenomics.github.io/CheckM/) to estimate completeness and contamination of the resulting bins. +The workflow can run both on a local machine, a workstation and a HPC system using `qsub`. It can be found in the `workflow` folder - see the file `workflow/README.md` for details. -``` -python my_scripts/vamb/vamb -``` - -# Inputs and outputs +# Detailed user instructions +See the tutorial in `doc/tutorial.md` for even more detailed instructions. ### Inputs -*Also see the section: Recommended workflow* - Vamb relies on two properties of the DNA sequences to be binned: * The kmer-composition of the sequence (here tetranucleotide frequency, *TNF*) and @@ -135,50 +114,56 @@ Vamb relies on two properties of the DNA sequences to be binned: So before you can run Vamb, you need to have files from which Vamb can calculate these values: * TNF is calculated from a regular FASTA file of DNA sequences. -* Depth is calculated from BAM-files of a set of reads from each sample mapped to that same FASTA file. +* Depth is calculated from _sorted_ BAM-files of a set of reads from each sample mapped to that same FASTA file. -:warning: *Important:* Vamb can use information from multi-mapping reads, but all alignments of a single read __must__ be consecutive in the BAM files. See section 4 of *Recommended workflow*. +Remember that the quality of Vamb's bins are no better than the quality of the input files. If the assembly is poor quality, or if your BAM files are constructed carelessly, for example by allowing reads from distinct species to crossmap indiscriminately, your BAM files will not contain information with which Vamb can separate those species. In general, you want reads to map only to contigs within the same phylogenetic distance that you want Vamb to bin together. -Remember that the quality of Vamb's bins are no better than the quality of the input files. If your BAM files are constructed carelessly, for example by allowing reads from distinct species to crossmap indiscriminately, your BAM files will not contain information with which Vamb can separate those species. In general, you want reads to map only to contigs within the same phylogenetic distance that you want Vamb to bin together. - -Estimation of TNF and RPKM is subject to statistical uncertainty. Therefore, Vamb works less well on short sequences and on data with low depth. Vamb *can* work on shorter sequences such as genes, which are more easily homology reduced. However, we recommend *not* using homology reduction on the input sequences, and instead prevent duplicated strains by using binsplitting (see section: _recommended workflow_.) +Estimation of TNF and RPKM is subject to statistical uncertainty. Therefore, Vamb works less well on short sequences and on data with low depth. Therefore, you get the best results using contigs (or scaffolds) with the longest possible length. Do not attempt to deduplicate or homology reduce your input sequences. ### Outputs Vamb produces the following output files: -- `log.txt` - a text file with information about the Vamb run. Look here (and at stderr) if you experience errors. -- `tnf.npz`, `lengths.npz` `rpkm.npz`, `mask.npz` and `latent.npz` - [Numpy .npz](https://numpy.org/devdocs/reference/generated/numpy.lib.format.html) files with TNFs, contig lengths. RPKM, which sequences were successfully encoded, and the latent encoding of the sequences. -- `model.pt` - containing a PyTorch model object of the trained VAE. You can load the VAE from this file using `vamb.encode.VAE.load` from Python. -- `clusters.tsv` - a two-column text file with one row per sequence: Left column for the cluster (i.e bin) name, right column for the sequence name. You can create the FASTA-file bins themselves using `vamb.vambtools.write_bins`, or using the function `vamb.vambtools.write_bins` (see `doc/tutorial.html` for more details). +- `log.txt` - A text file with information about the Vamb run. Look here (and at stderr) if you experience errors. +- `composition.npz`: A Numpy .npz file that contain all kmer composition information computed by Vamb from the FASTA file. This can be provided to another run of Vamb to skip the composition calculation step. +- `abundance.npz`: Similar to `composition.npz`, but this file contains information calculated from the BAM files. Using this as input instead of BAM files will skip re-parsing the BAM files, which take a significant amount of time. +- `model.pt`: A file containing the trained VAE model. When running Vamb from a Python interpreter, the VAE can be loaded from this file to skip training. +- `latent.npz`: This contains the output of the VAE model. +- `clusters.tsv` - A two-column text file with one row per sequence: Left column for the cluster (i.e bin) name, right column for the sequence name. You can create the FASTA-file bins themselves using the script in `src/create_fasta.py` # Recommended workflow +Garbage in, garbage out. To get good results with Vamb, you must make sure the inputs are of high quality. __1) Preprocess the reads and check their quality__ -We use AdapterRemoval combined with FastQC for this - but you can use whichever tool you think gives the best results. +We use `fastp` for this - but you can use whichever tool you think gives the best results. __2) Assemble each sample individually and get the contigs out__ -We recommend using metaSPAdes on each sample individually. You can also use scaffolds or other nucleotide sequences instead of contigs as input sequences to Vamb. Assemble each sample individually, as single-sample assembly followed by samplewise binsplitting gives the best results. +We recommend using metaSPAdes on each sample individually. MEGAHIT also works well. You can also use scaffolds or other nucleotide sequences instead of contigs as input sequences to Vamb. Assemble each sample individually, as single-sample assembly followed by samplewise binsplitting gives the best results. __3) Concatenate the FASTA files together while making sure all contig headers stay unique, and filter away small contigs__ You can use the function `vamb.vambtools.concatenate_fasta` for this or the script `src/concatenate.py`. -:warning: *Important:* Vamb uses a neural network to encode sequences, and neural networks overfit on small datasets. We have tested that Vamb's neural network does not overfit too badly on all datasets we have worked with, but we have not tested on any dataset with fewer than 50,000 contigs. +:warning: *Important:* Vamb uses a neural network to encode sequences, and neural networks overfit on small datasets. We have designed Vamb to underfit most datasets, but it may still overfit if you have less than e.g. 20,000 contigs. -You should not try to bin very short sequences. When deciding the length cutoff for your input sequences, there's a tradeoff here between choosing a too low cutoff, retaining hard-to-bin contigs which adversely affects the binning of *all* contigs, and choosing a too high one, throwing out good data. We use a length cutoff of 2000 bp as default but haven't actually run tests for the optimal value. +You should not try to bin very short sequences. When deciding the length cutoff for your input sequences, there's a tradeoff here between on one hand choosing a too low cutoff, retaining short contigs that are hard to bin AND which adversely affects the binning of *all* contigs, and on the other hand choosing a too high cutoff, throwing out good data. +We use a length cutoff of 2000 bp as default but haven't actually run tests for the optimal value. The optimal tradeoff will depend on your specific dataset and the biological information you are interested in. -Your contig headers must be unique. Furthermore, if you want to use binsplitting (and you should!), your contig headers must be of the format {Samplename}{Separator}{X}, such that the part of the string before the *first* occurrence of {Separator} gives a name of the sample it originated from. For example, you could call contig number 115 from sample number 9 "S9C115", where "S9" would be {Samplename}, "C" is {Separator} and "115" is {X}. +Your contig headers must be unique. Furthermore, if you want to use binsplitting (and you should!), your contig headers must be of the format {Samplename}{Separator}{X}, such that the part of the string before the *first* occurrence of {Separator} gives a name of the sample it originated from. +For example, you could call contig number 115 from sample number 9 "S9C115", where "S9" would be {Samplename}, "C" is {Separator} and "115" is {X}. +The script `src/concatenate.py` will automatically rename your sequences to this format. -Vamb is faily memory efficient, and we have run Vamb with 1000 samples and 5.9 million contigs using <30 GB of RAM. If you have a dataset too large to fit in RAM and feel the temptation to bin each sample individually, you can instead use a tool like MASH to group similar samples together in smaller batches, bin these batches individually. This way, you can still leverage co-abundance. **NB:** We have a version using memory-mapping that is much more RAM-efficient but 10-20% slower. Here we have processed a dataset of 942 samples with 30M contigs (total of 117Gbp contig sequence) in 40Gb RAM - see branch `mmap`. +Vamb is fairly memory efficient, and we have run Vamb with 1000 samples and 5.9 million contigs using <30 GB of RAM. +If you have a dataset too large to fit in RAM and feel the temptation to bin each sample individually, you can instead use a tool like `sourmash` to group similar samples together in smaller batches, bin these batches individually. +This way, you can still leverage co-abundance, and will get much better results. __4) Map the reads to the FASTA file to obtain BAM files__ -:warning: *Important:* If you allow reads to map to multiple contigs, the abundance estimation will be more accurate. However, all BAM records for a single read *must* be consecutive in the BAM file, or else Vamb will miscount these alignments. This is the default order in the output of almost all aligners, but if you use BAM files sorted by alignment position and have multi-mapping reads, you must sort them by read name first. +:warning: *Important:* Vamb only accepts BAM files sorted by coordinate. You can sort BAM files with `samtools sort`. -Be careful to choose proper parameters for your aligner - in general, if reads from contig A align to contig B, then Vamb will bin A and B together. So your aligner should map reads with the same level of discrimination that you want Vamb to use. Although you can use any aligner that produces a specification-compliant BAM file, we prefer using `minimap2` (though be aware of [this annoying bug in minimap2](https://github.com/lh3/minimap2/issues/15)): +Be careful to choose proper parameters for your aligner - in general, if reads from contig A align to contig B, then Vamb will bin A and B together. So your aligner should map reads with the same level of discrimination that you want Vamb to use. Although you can use any aligner that produces a specification-compliant BAM file, we prefer using `minimap2` (though be aware of [this annoying bug in minimap2](https://github.com/lh3/minimap2/issues/15) - see our Snakemake script for how to work around it): ``` minimap2 -d catalogue.mmi /path/to/catalogue.fna.gz; # make index @@ -187,30 +172,26 @@ minimap2 -t 28 -N 5 -ax sr catalogue.mmi --split-prefix mmsplit sample1.forward. :warning: *Important:* Do *not* filter the aligments for mapping quality as specified by the MAPQ field of the BAM file. This field gives the probability that the mapping position is correct, which is influenced by the number of alternative mapping locations. Filtering low MAPQ alignments away removes alignments to homologous sequences which biases the depth estimation. -If you are using BAM files where you do not trust the validity of every alignment in the file, you can filter the alignments for minimum nucleotide identity using the `-z` flag (uses the `NM` optional field of the alignment, we recommend setting it to `0.95`), and/or filter for minimum alignments score using the `-s` flag (uses the `AS` optional field of the alignment.) - -We have found that MetaBAT2's `jgi_summarize_bam_contig_depths` program estimates BAM depths more accurate than Vamb's `parsebam` module. For the best results, we recommend [downloading MetaBAT2](https://bitbucket.org/berkeleylab/metabat/src/master/), using `jgi_summarize_bam_contig_depths` to estimate depths, and then running Vamb with `--jgi` instead of `--bamfiles`. Also consider using the `snakemake` workflow which will do this for you. +If you are using BAM files where you do not trust the validity of every alignment in the file, you can filter the alignments for minimum nucleotide identity using the `-z` option. __5) Run Vamb__ By default, Vamb does not output any FASTA files of the bins. In the examples below, the option `--minfasta 200000` is set, meaning that all bins with a size of 200 kbp or more will be output as FASTA files. -If you trust the alignments in your BAM files, use: +Run Vamb with: `vamb -o SEP --outdir OUT --fasta FASTA --bamfiles BAM1 BAM2 [...] --minfasta 200000`, where `SEP` in the {Separator} chosen in step 3, e.g. `C` in that example, `OUT` is the name of the output directory to create, `FASTA` the path to the FASTA file and `BAM1` the path to the first BAM file. You can also use shell globbing to input multiple BAM files: `my_bamdir/*bam`. -If you don't trust your alignments, set the `-z` and `-s` flag as appropriate, depending on the properties of your aligner. For example, if I used the aligner BWA MEM, I would use: - -`vamb -o SEP -z 0.95 -s 30 --outdir OUT --fasta FASTA --bamfiles BAM1 BAM2 [...] --minfasta 200000` +Note that if you provide insufficient memory (RAM) to Vamb, it will run very slowly due to [thrashing](https://en.wikipedia.org/wiki/Thrashing_(computer_science)). Make sure you don't run out of RAM! __6) Postprocess the results__ -Vamb will bin every input contig. Contigs that cannot be binned with other contigs will result in single-bin contigs. Therefore, the output of Vamb includes many tiny bins, which may be relevant if you are looking for e.g. plasmids or viruses, but not if you are looking for cellular genomes. These small bins may account for the large majority of bins. When using the output of Vamb, it is therefore critical to filter the output bins based on whatever criteria is relevant for your particular analysis. You could use a binning quality control tool such as CheckM, or align the bins to reference genomes, or simply filter away all bins smaller than, say, 500 kbp. +Vamb will bin every input contig. Contigs that cannot be binned with other contigs will result in single-bin contigs. Therefore, the output of Vamb includes many tiny bins, which may be relevant if you are looking for e.g. plasmids or viruses, but not if you are looking for cellular genomes. These small bins may account for the large majority of bins. When using the output of Vamb, it is therefore CRITICAL to filter the output bins based on whatever criteria is relevant for your particular analysis. You could use a binning quality control tool such as CheckM, or align the bins to reference genomes, or simply filter away all bins smaller than, say, 500 kbp. ## Parameter optimisation (optional) -The default hyperparameters of Vamb will provide good performance on any dataset. However, since running Vamb is fast (especially using GPUs) it is possible to try to run Vamb with different hyperparameters to see if better performance can be achieved (note that here we measure performance as the number of near-complete bins assessed by CheckM). We recommend to try to increase and decrease the size of the neural network and have used Vamb on datasets where increasing the network resulted in more near-complete bins and other datasets where decreasing the network resulted in more near-complete bins. To do this you can run Vamb as (default for multiple samples is `-l 32 -n 512 512`)`: +The default hyperparameters of Vamb will provide good performance on any dataset. However, since running Vamb is fast (especially using GPUs) it is possible to try to run Vamb with different hyperparameters to see if better performance can be achieved (note that here we measure performance as the number of near-complete bins assessed by CheckM). We recommend to try to increase and decrease the size of the neural network and have used Vamb on datasets where increasing the network resulted in more near-complete bins and other datasets where decreasing the network resulted in more near-complete bins. To do this you can run Vamb as (default for multiple samples is `-l 32 -n 512 512`): ``` vamb -l 24 -n 384 384 --outdir path/to/outdir --fasta /path/to/catalogue.fna.gz --bamfiles /path/to/bam/*.bam -o C --minfasta 200000 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..13785649 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,26 @@ +[project] +name = "vamb" +dynamic = ["version"] +dependencies = [ + "numpy ~= 1.20", + "torch ~= 1.13", + "pycoverm ~= 0.6" +] +# Currently pycoverm does not have binaries for Python > 3.11. +# The dependency resolver, will not error on Python 3.11, but attempt +# to build pycoverm from source, but will not get the deps required for that. +requires-python = "<3.12.*,>=3.9.0" +scripts = {vamb = "vamb.__main__:main"} + +[metadata] +author = "Pau Piera and Jakob Nybo Nissen" +author_email = "jakobnybonissen@gmail.com" +url = "https://github.com/RasmussenLab/vamb" +description = "Variational and Adversarial autoencoders for Metagenomic Binning" +license = "MIT" +[tool.setuptools.dynamic] +version = {attr = "vamb.__version__"} + +[build-system] +requires = ["setuptools ~= 63.0", "Cython ~= 0.29"] +build-backend = "setuptools.build_meta" diff --git a/setup.cfg b/setup.cfg index b88034e4..b8e6ce8f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,2 +1,3 @@ -[metadata] -description-file = README.md +[options] +packages = find: +include_package_data = True diff --git a/setup.py b/setup.py index 27eb47cb..f851939f 100644 --- a/setup.py +++ b/setup.py @@ -1,35 +1,3 @@ -import sys -from setuptools import setup, find_packages -from setuptools import Extension -import os +from setuptools import Extension, setup -SETUP_METADATA = \ - { - "name": "vamb", - "description": "Variational autoencoder for metagenomic binning", - "url": "https://github.com/RasmussenLab/vamb", - "author": "Jakob Nybo Nissen and Simon Rasmussen", - "author_email": "jakobnybonissen@gmail.com", - "version": "3.0.9", - "license": "MIT", - "packages": find_packages(), - "package_data": {"vamb": ["kernel.npz"]}, - "entry_points": {'console_scripts': [ - 'vamb = vamb.__main__:main' - ] - }, - "scripts": ['src/concatenate.py'], - "ext_modules": [Extension("vamb._vambtools", - sources=["src/_vambtools.pyx"], - language="c")], - "install_requires": ["numpy>=1.20", "torch>=1.13", "pysam>=0.14"], - "setup_requires": ['Cython>=0.29', "setuptools>=58.0"], - "python_requires": ">=3.5", - "classifiers":[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - ], - } - -setup(**SETUP_METADATA) +setup(ext_modules=[Extension(name="vamb._vambtools", sources=["src/_vambtools.pyx"])]) diff --git a/src/_vambtools.c b/src/_vambtools.c deleted file mode 100644 index 38dcf5ff..00000000 --- a/src/_vambtools.c +++ /dev/null @@ -1,25196 +0,0 @@ -/* Generated by Cython 3.0a6 */ - 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PyThread_delete_key(*key); - *key = Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { - return PyThread_set_key_value(*key, value); -} -static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { - return PyThread_get_key_value(*key); -} -#endif -#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) -#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) -#else -#define __Pyx_PyDict_NewPresized(n) PyDict_New() -#endif -#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS -#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) -static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { - PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); - if (res == NULL) PyErr_Clear(); - return res; -} -#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) -#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError -#define __Pyx_PyDict_GetItemStr PyDict_GetItem -#else -static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { -#if CYTHON_COMPILING_IN_PYPY - return PyDict_GetItem(dict, name); -#else - PyDictEntry *ep; - PyDictObject *mp = (PyDictObject*) dict; - long hash = ((PyStringObject *) name)->ob_shash; - assert(hash != -1); - ep = (mp->ma_lookup)(mp, name, hash); - if (ep == NULL) { - return NULL; - } - return ep->me_value; -#endif -} -#define __Pyx_PyDict_GetItemStr PyDict_GetItem -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) -#else - #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) -#endif -#if CYTHON_USE_TYPE_SLOTS - #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) - #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) -#else - #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) - #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define CYTHON_PEP393_ENABLED 1 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111) - #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) - #define __Pyx_PyUnicode_DATA(u) ((void*)u) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) -#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) - #define CYTHON_PEP393_ENABLED 1 - #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ - 0 : _PyUnicode_Ready((PyObject *)(op))) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) - #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) - #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) - #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) - #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) - #else - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) - #endif -#else - #define CYTHON_PEP393_ENABLED 0 - #define PyUnicode_1BYTE_KIND 1 - #define PyUnicode_2BYTE_KIND 2 - #define PyUnicode_4BYTE_KIND 4 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) - #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) - #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) -#endif -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) -#else - #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ - PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) - #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) - #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) - #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) -#endif -#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) -#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) -#else - #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) -#endif -#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) - #define PyObject_ASCII(o) PyObject_Repr(o) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBaseString_Type PyUnicode_Type - #define PyStringObject PyUnicodeObject - #define PyString_Type PyUnicode_Type - #define PyString_Check PyUnicode_Check - #define PyString_CheckExact PyUnicode_CheckExact -#ifndef PyObject_Unicode - #define PyObject_Unicode PyObject_Str -#endif -#endif -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) - #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) -#else - #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) - #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) -#endif -#if CYTHON_COMPILING_IN_CPYTHON - #define __Pyx_PySequence_ListKeepNew(obj)\ - (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) -#else - #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) -#endif -#ifndef PySet_CheckExact - #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) -#endif -#if PY_VERSION_HEX >= 0x030900A4 - #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) - #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) -#else - #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) - #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) -#endif -#if CYTHON_ASSUME_SAFE_MACROS - #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) -#else - #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyIntObject PyLongObject - #define PyInt_Type PyLong_Type - #define PyInt_Check(op) PyLong_Check(op) - #define PyInt_CheckExact(op) PyLong_CheckExact(op) - #define PyInt_FromString PyLong_FromString - #define PyInt_FromUnicode PyLong_FromUnicode - #define PyInt_FromLong PyLong_FromLong - #define PyInt_FromSize_t PyLong_FromSize_t - #define PyInt_FromSsize_t PyLong_FromSsize_t - #define PyInt_AsLong PyLong_AsLong - #define PyInt_AS_LONG PyLong_AS_LONG - #define PyInt_AsSsize_t PyLong_AsSsize_t - #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask - #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask - #define PyNumber_Int PyNumber_Long -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBoolObject PyLongObject -#endif -#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY - #ifndef PyUnicode_InternFromString - #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) - #endif -#endif -#if PY_VERSION_HEX < 0x030200A4 - typedef long Py_hash_t; - #define __Pyx_PyInt_FromHash_t PyInt_FromLong - #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t -#else - #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t - #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t -#endif -#if CYTHON_USE_ASYNC_SLOTS - #if PY_VERSION_HEX >= 0x030500B1 - #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods - #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) - #else - #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) - #endif -#else - #define __Pyx_PyType_AsAsync(obj) NULL -#endif -#ifndef __Pyx_PyAsyncMethodsStruct - typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; - } __Pyx_PyAsyncMethodsStruct; -#endif - -#if defined(WIN32) || defined(MS_WINDOWS) - #define _USE_MATH_DEFINES -#endif -#include -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif -#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) -#define __Pyx_truncl trunc -#else -#define __Pyx_truncl truncl -#endif - -#define __PYX_MARK_ERR_POS(f_index, lineno) \ - { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } -#define __PYX_ERR(f_index, lineno, Ln_error) \ - { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } - -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#endif - -#define __PYX_HAVE__vamb___vambtools -#define __PYX_HAVE_API__vamb___vambtools -/* Early includes */ -#include "pythread.h" -#include -#include -#include -#include "pystate.h" -#ifdef _OPENMP -#include -#endif /* _OPENMP */ - -#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { - return (size_t) i < (size_t) limit; -} -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) - #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if CYTHON_COMPILING_IN_LIMITED_API -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) -{ - const wchar_t *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#endif -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); -#define __Pyx_PySequence_Tuple(obj)\ - (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); 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- -/* RaiseDoubleKeywords.proto */ -static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); - -/* ParseKeywords.proto */ -static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, - PyObject **argnames[], - PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, - const char* function_name); - -/* ArgTypeTest.proto */ -#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ - ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ - __Pyx__ArgTypeTest(obj, type, name, exact)) -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); - -/* RaiseException.proto */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); - -/* PyFunctionFastCall.proto */ -#if CYTHON_FAST_PYCALL -#if !CYTHON_VECTORCALL -#define __Pyx_PyFunction_FastCall(func, args, nargs)\ - __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); -#endif -#define __Pyx_BUILD_ASSERT_EXPR(cond)\ - (sizeof(char [1 - 2*!(cond)]) - 1) -#ifndef Py_MEMBER_SIZE -#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) -#endif - static size_t __pyx_pyframe_localsplus_offset = 0; - #include "frameobject.h" - #define __Pxy_PyFrame_Initialize_Offsets()\ - ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ - (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) - #define __Pyx_PyFrame_GetLocalsplus(frame)\ - (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) -#endif - -/* PyObjectCall.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); -#else -#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) -#endif - -/* PyObjectCallMethO.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); -#endif - -/* PyObjectFastCall.proto */ -#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, nargs, NULL) -static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); - -/* RaiseUnexpectedTypeError.proto */ -static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); - -/* BuildPyUnicode.proto */ -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char); - -/* CIntToPyUnicode.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); - -/* CIntToPyUnicode.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); - -/* JoinPyUnicode.proto */ -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - Py_UCS4 max_char); - -/* StrEquals.proto */ -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals -#else -#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals -#endif - -/* PyObjectFormatSimple.proto */ -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - PyObject_Format(s, f)) -#elif PY_MAJOR_VERSION < 3 - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ - PyObject_Format(s, f)) -#elif CYTHON_USE_TYPE_SLOTS - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ - likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ - PyObject_Format(s, f)) -#else - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - PyObject_Format(s, f)) -#endif - -static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ -/* GetAttr.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); - -/* GetItemInt.proto */ -#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ - __Pyx_GetItemInt_Generic(o, to_py_func(i)))) -#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck); - -/* PyObjectCallOneArg.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); - -/* ObjectGetItem.proto */ -#if CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); -#else -#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) -#endif - -/* KeywordStringCheck.proto */ -static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); - -/* None.proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); - -/* UnaryNegOverflows.proto */ -#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ - (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) - -/* GetAttr3.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); - -/* PyDictVersioning.proto */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) -#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) -#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ - (version_var) = __PYX_GET_DICT_VERSION(dict);\ - (cache_var) = (value); -#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ - static PY_UINT64_T __pyx_dict_version = 0;\ - static PyObject *__pyx_dict_cached_value = NULL;\ - if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ - (VAR) = __pyx_dict_cached_value;\ - } else {\ - (VAR) = __pyx_dict_cached_value = (LOOKUP);\ - __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ - }\ -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); -#else -#define __PYX_GET_DICT_VERSION(dict) (0) -#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) -#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); -#endif - -/* GetModuleGlobalName.proto */ -#if CYTHON_USE_DICT_VERSIONS -#define __Pyx_GetModuleGlobalName(var, name) {\ - static PY_UINT64_T __pyx_dict_version = 0;\ - static PyObject *__pyx_dict_cached_value = NULL;\ - (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ - (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ - __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} -#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ - PY_UINT64_T __pyx_dict_version;\ - PyObject *__pyx_dict_cached_value;\ - (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); -#else -#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) -#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); -#endif - -/* RaiseTooManyValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); - -/* RaiseNeedMoreValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); - -/* RaiseNoneIterError.proto */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); - -/* ExtTypeTest.proto */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); - -/* GetTopmostException.proto */ -#if CYTHON_USE_EXC_INFO_STACK -static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); -#endif - -/* SaveResetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); -#else -#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) -#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) -#endif - -/* GetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* SwapException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* Import.proto */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); - -/* ImportDottedModule.proto */ -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); - -/* ssize_strlen.proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); - -/* FastTypeChecks.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); -#else -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) -#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) -#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) -#endif -#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_Occurred(), err1, err2) -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) - -static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -/* ListCompAppend.proto */ -#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS -static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { - PyListObject* L = (PyListObject*) list; - Py_ssize_t len = Py_SIZE(list); - if (likely(L->allocated > len)) { - Py_INCREF(x); - PyList_SET_ITEM(list, len, x); - __Pyx_SET_SIZE(list, len + 1); - return 0; - } - return PyList_Append(list, x); -} -#else -#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) -#endif - -/* SetItemInt.proto */ -#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ - __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) -static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, - int is_list, int wraparound, int boundscheck); - -/* None.proto */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); - -/* None.proto */ -static CYTHON_INLINE long __Pyx_div_long(long, long); - -/* ImportFrom.proto */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); - -/* HasAttr.proto */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); - -/* PyObject_GenericGetAttrNoDict.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr -#endif - -/* PyObject_GenericGetAttr.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr -#endif - -/* SetVTable.proto */ -static int __Pyx_SetVtable(PyObject *dict, void *vtable); - -/* SetupReduce.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_setup_reduce(PyObject* type_obj); -#endif - -/* FetchCommonType.proto */ -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); -#if CYTHON_COMPILING_IN_LIMITED_API -static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyType_Spec *spec, PyObject *bases); -#endif - -/* PyMethodNew.proto */ -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, CYTHON_UNUSED PyObject *typ) { - if (!self) - return __Pyx_NewRef(func); - return PyMethod_New(func, self); -} -#else - #define __Pyx_PyMethod_New PyMethod_New -#endif - -/* PyVectorcallFastCallDict.proto */ -#if CYTHON_METH_FASTCALL -static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, Py_ssize_t nargs, PyObject *kw); -#endif - -/* CythonFunctionShared.proto */ -#define __Pyx_CyFunction_USED -#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 -#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 -#define __Pyx_CYFUNCTION_CCLASS 0x04 -#define __Pyx_CyFunction_GetClosure(f)\ - (((__pyx_CyFunctionObject *) (f))->func_closure) -#define __Pyx_CyFunction_GetClassObj(f)\ - (((__pyx_CyFunctionObject *) (f))->func_classobj) -#define __Pyx_CyFunction_Defaults(type, f)\ - ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) -#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ - ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) -typedef struct { - PyCFunctionObject func; -#if CYTHON_BACKPORT_VECTORCALL - __pyx_vectorcallfunc func_vectorcall; -#endif -#if PY_VERSION_HEX < 0x030500A0 - PyObject *func_weakreflist; -#endif - PyObject *func_dict; - PyObject *func_name; - PyObject *func_qualname; - PyObject *func_doc; - PyObject *func_globals; - PyObject *func_code; - PyObject *func_closure; - PyObject *func_classobj; - void *defaults; - int defaults_pyobjects; - size_t defaults_size; // used by FusedFunction for copying defaults - int flags; - PyObject *defaults_tuple; - PyObject *defaults_kwdict; - PyObject *(*defaults_getter)(PyObject *); - PyObject *func_annotations; -} __pyx_CyFunctionObject; -#if !CYTHON_COMPILING_IN_LIMITED_API -static PyTypeObject *__pyx_CyFunctionType = 0; -#endif -#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) -#define __Pyx_IsCyOrPyCFunction(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) -#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) -static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, - int flags, PyObject* qualname, - PyObject *closure, - PyObject *module, PyObject *globals, - PyObject* code); -static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, - size_t size, - int pyobjects); -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, - PyObject *tuple); -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, - PyObject *dict); -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, - PyObject *dict); -static int __pyx_CyFunction_init(void); -#if CYTHON_METH_FASTCALL -static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -#if CYTHON_BACKPORT_VECTORCALL -#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) -#else -#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func.vectorcall) -#endif -#endif - -/* CythonFunction.proto */ -static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, - int flags, PyObject* qualname, - PyObject *closure, - PyObject *module, PyObject *globals, - PyObject* code); - -/* CLineInTraceback.proto */ -#ifdef CYTHON_CLINE_IN_TRACEBACK -#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) -#else -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); -#endif - -/* CodeObjectCache.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -typedef struct { - PyCodeObject* code_object; - int code_line; -} __Pyx_CodeObjectCacheEntry; -struct __Pyx_CodeObjectCache { - int count; - int max_count; - __Pyx_CodeObjectCacheEntry* entries; -}; -static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); -static PyCodeObject *__pyx_find_code_object(int code_line); -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); -#endif - -/* AddTraceback.proto */ -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename); - -#if PY_MAJOR_VERSION < 3 - static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); 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-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type); - -/* TypeInfoCompare.proto */ -static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); - -/* MemviewSliceValidateAndInit.proto */ -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_float(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_unsigned_char(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_char(unsigned char value); - -/* MemviewDtypeToObject.proto */ -static CYTHON_INLINE PyObject *__pyx_memview_get_unsigned_char(const char *itemp); -static CYTHON_INLINE int __pyx_memview_set_unsigned_char(const char *itemp, PyObject *obj); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* MemviewSliceCopyTemplate.proto */ -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object); - -/* MemviewSliceInit.proto */ -#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d -#define __Pyx_MEMVIEW_DIRECT 1 -#define __Pyx_MEMVIEW_PTR 2 -#define __Pyx_MEMVIEW_FULL 4 -#define __Pyx_MEMVIEW_CONTIG 8 -#define __Pyx_MEMVIEW_STRIDED 16 -#define __Pyx_MEMVIEW_FOLLOW 32 -#define __Pyx_IS_C_CONTIG 1 -#define __Pyx_IS_F_CONTIG 2 -static int __Pyx_init_memviewslice( - struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference); -static CYTHON_INLINE int __pyx_add_acquisition_count_locked( - __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); -static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( - __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); -#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) -#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) -#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) -#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) -static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); -static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE unsigned char __Pyx_PyInt_As_unsigned_char(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); - -/* FormatTypeName.proto */ -#if CYTHON_COMPILING_IN_LIMITED_API -typedef PyObject *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%U" -static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); -#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) -#else -typedef const char *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%.200s" -#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) -#define __Pyx_DECREF_TypeName(obj) -#endif - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* InitStrings.proto */ -#if CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str); -#else -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); -#endif - -/* #### Code section: module_declarations ### */ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ -static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ -static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ -static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ - -/* Module declarations from "vamb._vambtools" */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static PyTypeObject *__pyx_array_type = 0; -static PyTypeObject *__pyx_MemviewEnum_type = 0; -static PyTypeObject *__pyx_memoryview_type = 0; -static PyTypeObject *__pyx_memoryviewslice_type = 0; -#endif -static PyObject *generic = 0; -static PyObject *strided = 0; -static PyObject *indirect = 0; -static PyObject *contiguous = 0; -static PyObject *indirect_contiguous = 0; -static int __pyx_memoryview_thread_locks_used; -static PyThread_type_lock __pyx_memoryview_thread_locks[8]; -static int __pyx_f_4vamb_10_vambtools__overwrite_matrix(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ -static void __pyx_f_4vamb_10_vambtools__kmercounts(__Pyx_memviewslice, int, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ -static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ -static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ -static void *__pyx_align_pointer(void *, size_t); /*proto*/ -static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ -static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ -static PyObject *_unellipsify(PyObject *, int); /*proto*/ -static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ -static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ -static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ -static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ -static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ -static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ -static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ -static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ -static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ -static int __pyx_memoryview_err_no_memory(void); /*proto*/ -static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ -static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ -static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ -/* #### Code section: typeinfo ### */ -static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_unsigned_char = { "unsigned char", NULL, sizeof(unsigned char), { 0 }, 0, IS_UNSIGNED(unsigned char) ? 'U' : 'I', IS_UNSIGNED(unsigned char), 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 }; -/* #### Code section: before_global_var ### */ -#define __Pyx_MODULE_NAME "vamb._vambtools" -extern int __pyx_module_is_main_vamb___vambtools; -int __pyx_module_is_main_vamb___vambtools = 0; - -/* Implementation of "vamb._vambtools" */ -/* #### Code section: global_var ### */ -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_MemoryError; -static PyObject *__pyx_builtin_enumerate; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_Ellipsis; -static PyObject *__pyx_builtin_id; -static PyObject *__pyx_builtin_AssertionError; -static PyObject *__pyx_builtin_IndexError; -/* #### Code section: string_decls ### */ -static const char __pyx_k_[] = ": "; -static const char __pyx_k_O[] = "O"; -static const char __pyx_k_c[] = "c"; -static const char __pyx_k_k[] = "k"; -static const char __pyx_k__2[] = "."; -static const char __pyx_k__3[] = "*"; -static const char __pyx_k__6[] = "'"; -static const char __pyx_k__7[] = ")"; -static const char __pyx_k_id[] = "id"; -static const char __pyx_k__19[] = "?"; -static const char __pyx_k_and[] = " and "; -static const char __pyx_k_got[] = " (got "; -static const char __pyx_k_new[] = "__new__"; -static const char __pyx_k_obj[] = "obj"; -static const char __pyx_k_base[] = "base"; -static const char __pyx_k_dict[] = "__dict__"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_mask[] = "mask"; -static const char __pyx_k_mode[] = "mode"; -static const char __pyx_k_name[] = "name"; -static const char __pyx_k_ndim[] = "ndim"; -static const char __pyx_k_pack[] = "pack"; -static const char __pyx_k_size[] = "size"; -static const char __pyx_k_spec[] = "__spec__"; -static const char __pyx_k_step[] = "step"; -static const char __pyx_k_stop[] = "stop"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_ASCII[] = "ASCII"; -static const char __pyx_k_class[] = "__class__"; -static const char __pyx_k_error[] = "error"; -static const char __pyx_k_flags[] = "flags"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_shape[] = "shape"; -static const char __pyx_k_start[] = "start"; -static const char __pyx_k_counts[] = "counts"; -static const char __pyx_k_encode[] = "encode"; -static const char __pyx_k_format[] = "format"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_matrix[] = "matrix"; -static const char __pyx_k_name_2[] = "__name__"; -static const char __pyx_k_pickle[] = "pickle"; -static const char __pyx_k_reduce[] = "__reduce__"; -static const char __pyx_k_struct[] = "struct"; -static const char __pyx_k_unpack[] = "unpack"; -static const char __pyx_k_update[] = "update"; -static const char __pyx_k_fortran[] = "fortran"; -static const char __pyx_k_memview[] = "memview"; -static const char __pyx_k_Ellipsis[] = "Ellipsis"; -static const char __pyx_k_getstate[] = "__getstate__"; -static const char __pyx_k_itemsize[] = "itemsize"; -static const char __pyx_k_pyx_type[] = "__pyx_type"; -static const char __pyx_k_setstate[] = "__setstate__"; -static const char __pyx_k_TypeError[] = "TypeError"; -static const char __pyx_k_enumerate[] = "enumerate"; -static const char __pyx_k_pyx_state[] = "__pyx_state"; -static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; -static const char __pyx_k_IndexError[] = "IndexError"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_bytesarray[] = "bytesarray"; -static const char __pyx_k_kmercounts[] = "_kmercounts"; -static const char __pyx_k_pyx_result[] = "__pyx_result"; -static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; -static const char __pyx_k_MemoryError[] = "MemoryError"; -static const char __pyx_k_PickleError[] = "PickleError"; -static const char __pyx_k_initializing[] = "_initializing"; -static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; -static const char __pyx_k_stringsource[] = "stringsource"; -static const char __pyx_k_class_getitem[] = "__class_getitem__"; -static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; -static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; -static const char __pyx_k_AssertionError[] = "AssertionError"; -static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; -static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; -static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; -static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; -static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; -static const char __pyx_k_vamb__vambtools[] = "vamb._vambtools"; -static const char __pyx_k_overwrite_matrix[] = "_overwrite_matrix"; -static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; -static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; -static const char __pyx_k_src__vambtools_pyx[] = "src/_vambtools.pyx"; -static const char __pyx_k_strided_and_direct[] = ""; -static const char __pyx_k_strided_and_indirect[] = ""; -static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; -static const char __pyx_k_contiguous_and_direct[] = ""; -static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; -static const char __pyx_k_MemoryView_of_r_object[] = ""; -static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; -static const char __pyx_k_contiguous_and_indirect[] = ""; -static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; -static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; -static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; -static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; -static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; -static const char __pyx_k_strided_and_direct_or_indirect[] = ""; -static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; -static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; -static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; -static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; -static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; -static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; -static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; -static const char __pyx_k_Incompatible_checksums_s_vs_0x6a[] = "Incompatible checksums (%s vs 0x6ae9995 = (name))"; -static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; -static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; -static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; -static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; -static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; -static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; -static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; -#if !CYTHON_COMPILING_IN_LIMITED_API -static PyObject *__pyx_kp_u_; -static PyObject *__pyx_n_s_ASCII; -static PyObject *__pyx_kp_s_All_dimensions_preceding_dimensi; -static PyObject *__pyx_n_s_AssertionError; -static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; -static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; -static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; -static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; -static PyObject *__pyx_kp_u_Cannot_index_with_type; -static PyObject *__pyx_kp_s_Cannot_transpose_memoryview_with; -static PyObject *__pyx_kp_s_Dimension_d_is_not_direct; -static PyObject *__pyx_n_s_Ellipsis; -static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; -static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0x6a; -static PyObject *__pyx_n_s_IndexError; -static PyObject *__pyx_kp_s_Index_out_of_bounds_axis_d; -static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; -static PyObject *__pyx_kp_u_Invalid_mode_expected_c_or_fortr; -static PyObject *__pyx_kp_u_Invalid_shape_in_axis; -static PyObject *__pyx_n_s_MemoryError; -static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; -static PyObject *__pyx_kp_s_MemoryView_of_r_object; -static PyObject *__pyx_n_b_O; -static PyObject *__pyx_kp_u_Out_of_bounds_on_buffer_access_a; -static PyObject *__pyx_n_s_PickleError; -static PyObject *__pyx_kp_s_Step_may_not_be_zero_axis_d; -static PyObject *__pyx_n_s_TypeError; -static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; -static PyObject *__pyx_n_s_ValueError; -static PyObject *__pyx_n_s_View_MemoryView; -static PyObject *__pyx_n_s__19; -static PyObject *__pyx_kp_u__2; -static PyObject *__pyx_n_s__3; -static PyObject *__pyx_kp_u__6; -static PyObject *__pyx_kp_u__7; -static PyObject *__pyx_n_s_allocate_buffer; -static PyObject *__pyx_kp_u_and; -static PyObject *__pyx_n_s_base; -static PyObject *__pyx_n_s_bytesarray; -static PyObject *__pyx_n_s_c; -static PyObject *__pyx_n_u_c; -static PyObject *__pyx_n_s_class; -static PyObject *__pyx_n_s_class_getitem; -static PyObject *__pyx_n_s_cline_in_traceback; -static PyObject *__pyx_kp_s_contiguous_and_direct; -static PyObject *__pyx_kp_s_contiguous_and_indirect; -static PyObject *__pyx_n_s_counts; -static PyObject *__pyx_n_s_dict; -static PyObject *__pyx_n_s_dtype_is_object; -static PyObject *__pyx_n_s_encode; -static PyObject *__pyx_n_s_enumerate; -static PyObject *__pyx_n_s_error; -static PyObject *__pyx_n_s_flags; -static PyObject *__pyx_n_s_format; -static PyObject *__pyx_n_s_fortran; -static PyObject *__pyx_n_u_fortran; -static PyObject *__pyx_n_s_getstate; -static PyObject *__pyx_kp_u_got; -static PyObject *__pyx_kp_u_got_differing_extents_in_dimensi; -static PyObject *__pyx_n_s_id; -static PyObject *__pyx_n_s_import; -static PyObject *__pyx_n_s_initializing; -static PyObject *__pyx_n_s_itemsize; -static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; -static PyObject *__pyx_n_s_k; -static PyObject *__pyx_n_s_kmercounts; -static PyObject *__pyx_n_s_main; -static PyObject *__pyx_n_s_mask; -static PyObject *__pyx_n_s_matrix; -static PyObject *__pyx_n_s_memview; -static PyObject *__pyx_n_s_mode; -static PyObject *__pyx_n_s_name; -static PyObject *__pyx_n_s_name_2; -static PyObject *__pyx_n_s_ndim; -static PyObject *__pyx_n_s_new; -static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; -static PyObject *__pyx_n_s_obj; -static PyObject *__pyx_n_s_overwrite_matrix; -static PyObject *__pyx_n_s_pack; -static PyObject *__pyx_n_s_pickle; -static PyObject *__pyx_n_s_pyx_PickleError; -static PyObject *__pyx_n_s_pyx_checksum; -static PyObject *__pyx_n_s_pyx_getbuffer; -static PyObject *__pyx_n_s_pyx_result; -static PyObject *__pyx_n_s_pyx_state; -static PyObject *__pyx_n_s_pyx_type; -static PyObject *__pyx_n_s_pyx_unpickle_Enum; -static PyObject *__pyx_n_s_pyx_vtable; -static PyObject *__pyx_n_s_range; -static PyObject *__pyx_n_s_reduce; -static PyObject *__pyx_n_s_reduce_cython; -static PyObject *__pyx_n_s_reduce_ex; -static PyObject *__pyx_n_s_setstate; -static PyObject *__pyx_n_s_setstate_cython; -static PyObject *__pyx_n_s_shape; -static PyObject *__pyx_n_s_size; -static PyObject *__pyx_n_s_spec; -static PyObject *__pyx_kp_s_src__vambtools_pyx; -static PyObject *__pyx_n_s_start; -static PyObject *__pyx_n_s_step; -static 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__pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ -static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ -static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ -static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ -static PyObject 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Py_CLEAR(clear_module_state->__pyx_kp_u_got); - Py_CLEAR(clear_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); - Py_CLEAR(clear_module_state->__pyx_n_s_id); - Py_CLEAR(clear_module_state->__pyx_n_s_import); - Py_CLEAR(clear_module_state->__pyx_n_s_initializing); - Py_CLEAR(clear_module_state->__pyx_n_s_itemsize); - Py_CLEAR(clear_module_state->__pyx_kp_s_itemsize_0_for_cython_array); - Py_CLEAR(clear_module_state->__pyx_n_s_k); - Py_CLEAR(clear_module_state->__pyx_n_s_kmercounts); - Py_CLEAR(clear_module_state->__pyx_n_s_main); - Py_CLEAR(clear_module_state->__pyx_n_s_mask); - Py_CLEAR(clear_module_state->__pyx_n_s_matrix); - Py_CLEAR(clear_module_state->__pyx_n_s_memview); - Py_CLEAR(clear_module_state->__pyx_n_s_mode); - Py_CLEAR(clear_module_state->__pyx_n_s_name); - Py_CLEAR(clear_module_state->__pyx_n_s_name_2); - Py_CLEAR(clear_module_state->__pyx_n_s_ndim); - Py_CLEAR(clear_module_state->__pyx_n_s_new); - 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Py_CLEAR(clear_module_state->__pyx_n_s_setstate_cython); - Py_CLEAR(clear_module_state->__pyx_n_s_shape); - Py_CLEAR(clear_module_state->__pyx_n_s_size); - Py_CLEAR(clear_module_state->__pyx_n_s_spec); - Py_CLEAR(clear_module_state->__pyx_kp_s_src__vambtools_pyx); - Py_CLEAR(clear_module_state->__pyx_n_s_start); - Py_CLEAR(clear_module_state->__pyx_n_s_step); - Py_CLEAR(clear_module_state->__pyx_n_s_stop); - Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct); - Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct_or_indirect); - Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_indirect); - Py_CLEAR(clear_module_state->__pyx_kp_s_stringsource); - Py_CLEAR(clear_module_state->__pyx_n_s_struct); - Py_CLEAR(clear_module_state->__pyx_n_s_test); - Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data); - Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); - Py_CLEAR(clear_module_state->__pyx_n_s_unpack); - 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-#define __pyx_tuple__14 __pyx_mstate_global->__pyx_tuple__14 -#define __pyx_tuple__15 __pyx_mstate_global->__pyx_tuple__15 -#define __pyx_tuple__16 __pyx_mstate_global->__pyx_tuple__16 -#define __pyx_tuple__17 __pyx_mstate_global->__pyx_tuple__17 -#define __pyx_codeobj__9 __pyx_mstate_global->__pyx_codeobj__9 -#define __pyx_codeobj__11 __pyx_mstate_global->__pyx_codeobj__11 -#define __pyx_codeobj__18 __pyx_mstate_global->__pyx_codeobj__18 -#endif -/* #### Code section: module_code ### */ - -/* "src/_vambtools.pyx":3 - * #cython: language_level=3, boundscheck=False, wraparound=False, cdivision=True, initializedcheck=False - * - * cpdef int _overwrite_matrix(float[:,::1] matrix, unsigned char[::1] mask): # <<<<<<<<<<<<<< - * """Given a float32 matrix and Uint8 mask, does the same as setting the first - * rows of matrix to matrix[mask], but in-place. - */ - -static PyObject *__pyx_pw_4vamb_10_vambtools_1_overwrite_matrix(PyObject *__pyx_self, -#if CYTHON_METH_FASTCALL -PyObject *const 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shape: - */ - __pyx_t_1 = ((__pyx_v_start < 0) != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":840 - * - * if start < 0: - * start += shape # <<<<<<<<<<<<<< - * if not 0 <= start < shape: - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) - */ - __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - - /* "View.MemoryView":839 - * if not is_slice: - * - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if not 0 <= start < shape: - */ - } - - /* "View.MemoryView":841 - * if start < 0: - * start += shape - * if not 0 <= start < shape: # <<<<<<<<<<<<<< - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) - * else: - */ - __pyx_t_1 = (0 <= __pyx_v_start); - if (__pyx_t_1) { - __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); - } - __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":842 - * start += shape - * if not 0 <= start < shape: - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< - * else: - * - */ - __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 842, __pyx_L1_error) - - /* "View.MemoryView":841 - * if start < 0: - * start += shape - * if not 0 <= start < shape: # <<<<<<<<<<<<<< - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) - * else: - */ - } - - /* "View.MemoryView":837 - * cdef bint negative_step - * - * if not is_slice: # <<<<<<<<<<<<<< - * - * if start < 0: - */ - goto __pyx_L3; - } - - /* "View.MemoryView":845 - * else: - * - * if have_step: # <<<<<<<<<<<<<< - * negative_step = step < 0 - * if step == 0: - */ - /*else*/ { - __pyx_t_2 = (__pyx_v_have_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":846 - * - * if have_step: - * negative_step = step < 0 # <<<<<<<<<<<<<< - * if step == 0: - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - */ - __pyx_v_negative_step = (__pyx_v_step < 0); - - /* "View.MemoryView":847 - * if have_step: - * negative_step = step < 0 - * if step == 0: # <<<<<<<<<<<<<< - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - * else: - */ - __pyx_t_2 = ((__pyx_v_step == 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":848 - * negative_step = step < 0 - * if step == 0: - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< - * else: - * negative_step = False - */ - __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 848, __pyx_L1_error) - - /* "View.MemoryView":847 - * if have_step: - * negative_step = step < 0 - * if step == 0: # <<<<<<<<<<<<<< - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - * else: - */ - } - - /* "View.MemoryView":845 - * else: - * - * if have_step: # <<<<<<<<<<<<<< - * negative_step = step < 0 - * if step == 0: - */ - goto __pyx_L6; - } - - /* "View.MemoryView":850 - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - * else: - * negative_step = False # <<<<<<<<<<<<<< - * step = 1 - * - */ - /*else*/ { - __pyx_v_negative_step = 0; - - /* "View.MemoryView":851 - * else: - * negative_step = False - * step = 1 # <<<<<<<<<<<<<< - * - * - */ - __pyx_v_step = 1; - } - __pyx_L6:; - - /* "View.MemoryView":854 - * - * - * if have_start: # <<<<<<<<<<<<<< - * if start < 0: - * start += shape - */ - __pyx_t_2 = (__pyx_v_have_start != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":855 - * - * if have_start: - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if start < 0: - */ - __pyx_t_2 = ((__pyx_v_start < 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":856 - * if have_start: - * if start < 0: - * start += shape # <<<<<<<<<<<<<< - * if start < 0: - * start = 0 - */ - __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - - /* "View.MemoryView":857 - * if start < 0: - * start += shape - * if start < 0: # <<<<<<<<<<<<<< - * start = 0 - * elif start >= shape: - */ - __pyx_t_2 = ((__pyx_v_start < 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":858 - * start += shape - * if start < 0: - * start = 0 # <<<<<<<<<<<<<< - * elif start >= shape: - * if negative_step: - */ - __pyx_v_start = 0; - - /* "View.MemoryView":857 - * if start < 0: - * start += shape - * if start < 0: # <<<<<<<<<<<<<< - * start = 0 - * elif start >= shape: - */ - } - - /* "View.MemoryView":855 - * - * if have_start: - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if start < 0: - */ - goto __pyx_L9; - } - - /* "View.MemoryView":859 - * if start < 0: - * start = 0 - * elif start >= shape: # <<<<<<<<<<<<<< - * if negative_step: - * start = shape - 1 - */ - __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":860 - * start = 0 - * elif start >= shape: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - __pyx_t_2 = (__pyx_v_negative_step != 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else: - */ - /*else*/ { - __pyx_t_2 = (__pyx_v_negative_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":866 - * else: - * if negative_step: - * start = shape - 1 # <<<<<<<<<<<<<< - * else: - * start = 0 - */ - __pyx_v_start = (__pyx_v_shape - 1); - - /* "View.MemoryView":865 - * start = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - goto __pyx_L12; - } - - /* "View.MemoryView":868 - * start = shape - 1 - * else: - * start = 0 # <<<<<<<<<<<<<< - * - * if have_stop: - */ - /*else*/ { - __pyx_v_start = 0; - } - __pyx_L12:; - } - __pyx_L8:; - - /* "View.MemoryView":870 - * start = 0 - * - * if have_stop: # <<<<<<<<<<<<<< - * if stop < 0: - * stop += shape - */ - __pyx_t_2 = (__pyx_v_have_stop != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":871 - * - * if have_stop: - * if stop < 0: # <<<<<<<<<<<<<< - * stop += shape - * if stop < 0: - */ - __pyx_t_2 = ((__pyx_v_stop < 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":872 - * if 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- /* "View.MemoryView":876 - * stop = 0 - * elif stop > shape: - * stop = shape # <<<<<<<<<<<<<< - * else: - * if negative_step: - */ - __pyx_v_stop = __pyx_v_shape; - - /* "View.MemoryView":875 - * if stop < 0: - * stop = 0 - * elif stop > shape: # <<<<<<<<<<<<<< - * stop = shape - * else: - */ - } - __pyx_L14:; - - /* "View.MemoryView":870 - * start = 0 - * - * if have_stop: # <<<<<<<<<<<<<< - * if stop < 0: - * stop += shape - */ - goto __pyx_L13; - } - - /* "View.MemoryView":878 - * stop = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * stop = -1 - * else: - */ - /*else*/ { - __pyx_t_2 = (__pyx_v_negative_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":879 - * else: - * if negative_step: - * stop = -1 # <<<<<<<<<<<<<< - * else: - * stop = shape - */ - __pyx_v_stop = -1L; - - /* "View.MemoryView":878 - * stop = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * stop = -1 - * else: - */ - goto __pyx_L16; - } - - /* "View.MemoryView":881 - * stop = -1 - * 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return 0; - #endif - p = ((struct __pyx_memoryview_obj *)o); - p->__pyx_vtab = __pyx_vtabptr_memoryview; - p->obj = Py_None; Py_INCREF(Py_None); - p->_size = Py_None; Py_INCREF(Py_None); - p->_array_interface = Py_None; Py_INCREF(Py_None); - p->view.obj = NULL; - if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; - return o; - bad: - Py_DECREF(o); o = 0; - return NULL; -} - -static void __pyx_tp_dealloc_memoryview(PyObject *o) { - struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; - #if CYTHON_USE_TP_FINALIZE - if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { - if (PyObject_CallFinalizerFromDealloc(o)) return; - } - #endif - PyObject_GC_UnTrack(o); - { - PyObject *etype, *eval, *etb; - PyErr_Fetch(&etype, &eval, &etb); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); - __pyx_memoryview___dealloc__(o); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); - PyErr_Restore(etype, eval, etb); - } - Py_CLEAR(p->obj); - Py_CLEAR(p->_size); - Py_CLEAR(p->_array_interface); - (*Py_TYPE(o)->tp_free)(o); -} - -static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { - int e; - struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; - if (p->obj) { - e = (*v)(p->obj, a); if (e) return e; - } - if (p->_size) { - e = (*v)(p->_size, a); if (e) return e; - } - if (p->_array_interface) { - e = (*v)(p->_array_interface, a); if (e) return e; - } - if (p->view.obj) { - e = (*v)(p->view.obj, a); if (e) return e; - } - return 0; -} - -static int __pyx_tp_clear_memoryview(PyObject *o) { - PyObject* tmp; - struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; - tmp = ((PyObject*)p->obj); - p->obj = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - tmp = ((PyObject*)p->_size); - p->_size = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - tmp = ((PyObject*)p->_array_interface); - p->_array_interface = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - Py_CLEAR(p->view.obj); - return 0; -} -static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { - PyObject *r; - PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; - r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); - Py_DECREF(x); - return r; -} - -static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { - __Pyx_TypeName o_type_name; - if (v) { - return __pyx_memoryview___setitem__(o, i, v); - } - else { - o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); - PyErr_Format(PyExc_NotImplementedError, - "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); - __Pyx_DECREF_TypeName(o_type_name); - return -1; - } -} - -static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); -} - -static PyMethodDef __pyx_methods_memoryview[] = { - {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {0, 0, 0, 0} -}; - -static struct PyGetSetDef __pyx_getsets_memoryview[] = { - {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, - {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, - {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, - {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, - {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, - {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, - {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, - {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, - {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, - {0, 0, 0, 0, 0} -}; -#if CYTHON_COMPILING_IN_LIMITED_API -static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { - {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, - {Py_tp_repr, (void *)__pyx_memoryview___repr__}, - {Py_sq_length, (void *)__pyx_memoryview___len__}, - {Py_sq_item, (void *)__pyx_sq_item_memoryview}, - {Py_mp_length, (void *)__pyx_memoryview___len__}, - {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, - {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, - {Py_tp_str, (void *)__pyx_memoryview___str__}, - {Py_tp_getattro, (void *)PyObject_GenericGetAttr}, - {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, - {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, - {Py_tp_methods, (void *)__pyx_methods_memoryview}, - {Py_tp_getset, (void *)__pyx_getsets_memoryview}, - {Py_tp_new, (void *)__pyx_tp_new_memoryview}, - {0, 0}, -}; -static PyType_Spec __pyx_type___pyx_memoryview_spec = { - "vamb._vambtools.memoryview", - sizeof(struct __pyx_memoryview_obj), - 0, - Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, - __pyx_type___pyx_memoryview_slots, -}; -#else - -static PySequenceMethods __pyx_tp_as_sequence_memoryview = { - __pyx_memoryview___len__, /*sq_length*/ - 0, /*sq_concat*/ - 0, /*sq_repeat*/ - __pyx_sq_item_memoryview, /*sq_item*/ - 0, /*sq_slice*/ - 0, /*sq_ass_item*/ - 0, /*sq_ass_slice*/ - 0, /*sq_contains*/ - 0, /*sq_inplace_concat*/ - 0, /*sq_inplace_repeat*/ -}; - -static PyMappingMethods __pyx_tp_as_mapping_memoryview = { - __pyx_memoryview___len__, /*mp_length*/ - __pyx_memoryview___getitem__, /*mp_subscript*/ - __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ -}; - -static PyBufferProcs __pyx_tp_as_buffer_memoryview = { - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getreadbuffer*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getwritebuffer*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getsegcount*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getcharbuffer*/ - #endif - __pyx_memoryview_getbuffer, /*bf_getbuffer*/ - 0, /*bf_releasebuffer*/ -}; - -static PyTypeObject __pyx_type___pyx_memoryview = { - PyVarObject_HEAD_INIT(0, 0) - "vamb._vambtools.""memoryview", /*tp_name*/ - sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ - 0, /*tp_itemsize*/ - __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ - #if PY_VERSION_HEX < 0x030800b4 - 0, /*tp_print*/ - #endif - #if PY_VERSION_HEX >= 0x030800b4 - 0, /*tp_vectorcall_offset*/ - #endif - 0, /*tp_getattr*/ - 0, /*tp_setattr*/ - #if PY_MAJOR_VERSION < 3 - 0, /*tp_compare*/ - #endif - #if PY_MAJOR_VERSION >= 3 - 0, /*tp_as_async*/ - #endif - __pyx_memoryview___repr__, /*tp_repr*/ - 0, /*tp_as_number*/ - &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ - &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ - 0, /*tp_hash*/ - 0, /*tp_call*/ - __pyx_memoryview___str__, /*tp_str*/ - 0, /*tp_getattro*/ - 0, /*tp_setattro*/ - &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ - Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ - 0, /*tp_doc*/ - __pyx_tp_traverse_memoryview, /*tp_traverse*/ - __pyx_tp_clear_memoryview, /*tp_clear*/ - 0, /*tp_richcompare*/ - 0, /*tp_weaklistoffset*/ - 0, /*tp_iter*/ - 0, /*tp_iternext*/ - __pyx_methods_memoryview, /*tp_methods*/ - 0, /*tp_members*/ - __pyx_getsets_memoryview, /*tp_getset*/ - 0, /*tp_base*/ - 0, /*tp_dict*/ - 0, /*tp_descr_get*/ - 0, /*tp_descr_set*/ - 0, /*tp_dictoffset*/ - 0, /*tp_init*/ - 0, /*tp_alloc*/ - __pyx_tp_new_memoryview, /*tp_new*/ - 0, /*tp_free*/ - 0, /*tp_is_gc*/ - 0, /*tp_bases*/ - 0, /*tp_mro*/ - 0, /*tp_cache*/ - 0, /*tp_subclasses*/ - 0, /*tp_weaklist*/ - 0, /*tp_del*/ - 0, /*tp_version_tag*/ - #if PY_VERSION_HEX >= 0x030400a1 - 0, /*tp_finalize*/ - #endif - #if PY_VERSION_HEX >= 0x030800b1 - 0, /*tp_vectorcall*/ - #endif - #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 - 0, /*tp_print*/ - #endif - #if CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM+0 >= 0x06000000 - 0, /*tp_pypy_flags*/ - #endif -}; -#endif -static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; - -static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { - struct __pyx_memoryviewslice_obj *p; - #if CYTHON_COMPILING_IN_LIMITED_API - newfunc new_func = (newfunc)PyType_GetSlot(__pyx_memoryview_type, Py_tp_new); - PyObject *o = new_func(t, a, k); - #else - PyObject *o = __pyx_tp_new_memoryview(t, a, k); - #endif - if (unlikely(!o)) return 0; - p = ((struct __pyx_memoryviewslice_obj *)o); - p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; - p->from_object = Py_None; Py_INCREF(Py_None); - p->from_slice.memview = NULL; - return o; -} - -static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { - struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; - #if CYTHON_USE_TP_FINALIZE - if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { - if (PyObject_CallFinalizerFromDealloc(o)) return; - } - #endif - PyObject_GC_UnTrack(o); - { - PyObject *etype, *eval, *etb; - PyErr_Fetch(&etype, &eval, &etb); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); - __pyx_memoryviewslice___dealloc__(o); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); - PyErr_Restore(etype, eval, etb); - } - Py_CLEAR(p->from_object); - PyObject_GC_Track(o); - __pyx_tp_dealloc_memoryview(o); -} - -static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { - int e; - struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; - e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; - if (p->from_object) { - e = (*v)(p->from_object, a); if (e) return e; - } - return 0; -} - -static int __pyx_tp_clear__memoryviewslice(PyObject *o) { - PyObject* tmp; - struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; - __pyx_tp_clear_memoryview(o); - tmp = ((PyObject*)p->from_object); - p->from_object = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - __PYX_XDEC_MEMVIEW(&p->from_slice, 1); - return 0; -} - -static PyMethodDef __pyx_methods__memoryviewslice[] = { - {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {0, 0, 0, 0} -}; -#if CYTHON_COMPILING_IN_LIMITED_API -static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = { - {Py_tp_dealloc, (void 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- } - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = ( - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key) - ); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - #if PY_MAJOR_VERSION < 3 - PyErr_Format(PyExc_TypeError, - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - PyErr_Format(PyExc_TypeError, - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* ArgTypeTest */ -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) -{ - __Pyx_TypeName type_name; - __Pyx_TypeName obj_type_name; - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - else if (exact) { - #if PY_MAJOR_VERSION == 2 - if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(__Pyx_TypeCheck(obj, type))) return 1; - } - type_name = __Pyx_PyType_GetName(type); - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME - ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); - __Pyx_DECREF_TypeName(type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return 0; -} - -/* RaiseException */ -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* PyFunctionFastCall */ -#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = __Pyx_PyFrame_GetLocalsplus(f); - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif - -/* PyObjectCall */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallMethO */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectFastCall */ -static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { - PyObject *argstuple; - PyObject *result; - Py_ssize_t i; - argstuple = PyTuple_New(nargs); - if (unlikely(!argstuple)) return NULL; - for (i = 0; i < nargs; i++) { - Py_INCREF(args[i]); - PyTuple_SET_ITEM(argstuple, i, args[i]); - } - result = __Pyx_PyObject_Call(func, argstuple, kwargs); - Py_DECREF(argstuple); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t _nargs, PyObject *kwargs) { - Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); -#if CYTHON_COMPILING_IN_CPYTHON - if (nargs == 0 && kwargs == NULL) { -#ifdef __Pyx_CyFunction_USED - if (__Pyx_IsCyOrPyCFunction(func)) -#else - if (PyCFunction_Check(func)) -#endif - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - } - else if (nargs == 1 && kwargs == NULL) { - if (PyCFunction_Check(func)) - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, args[0]); - } - } - } -#endif - #if PY_VERSION_HEX < 0x030800B1 - #if CYTHON_FAST_PYCCALL - if (PyCFunction_Check(func)) { - if (kwargs) { - return _PyCFunction_FastCallDict(func, args, nargs, kwargs); - } else { - return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); - } - } - #if PY_VERSION_HEX >= 0x030700A1 - if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { - return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); - } - #endif - #endif - #if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); - } - #endif - #endif - #if CYTHON_VECTORCALL - vectorcallfunc f = _PyVectorcall_Function(func); - if (f) { - return f(func, args, nargs, kwargs); - } - #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL - if (__Pyx_CyFunction_CheckExact(func)) { - __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); - if (f) return f(func, args, nargs, kwargs); - } - #endif - if (nargs == 0) { - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); - } - return __Pyx_PyObject_FastCall_fallback(func, args, nargs, kwargs); -} - -/* RaiseUnexpectedTypeError */ -static int -__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) -{ - __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, - expected, obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return 0; -} - -/* CIntToDigits */ -static const char DIGIT_PAIRS_10[2*10*10+1] = { - "00010203040506070809" - "10111213141516171819" - "20212223242526272829" - "30313233343536373839" - "40414243444546474849" - "50515253545556575859" - "60616263646566676869" - "70717273747576777879" - "80818283848586878889" - "90919293949596979899" -}; -static const char DIGIT_PAIRS_8[2*8*8+1] = { - "0001020304050607" - "1011121314151617" - "2021222324252627" - "3031323334353637" - "4041424344454647" - "5051525354555657" - "6061626364656667" - "7071727374757677" -}; -static const char DIGITS_HEX[2*16+1] = { - "0123456789abcdef" - "0123456789ABCDEF" -}; - -/* BuildPyUnicode */ -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char) { - PyObject *uval; - Py_ssize_t uoffset = ulength - clength; -#if CYTHON_USE_UNICODE_INTERNALS - Py_ssize_t i; -#if CYTHON_PEP393_ENABLED - void *udata; - uval = PyUnicode_New(ulength, 127); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_DATA(uval); -#else - Py_UNICODE *udata; - uval = PyUnicode_FromUnicode(NULL, ulength); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_AS_UNICODE(uval); -#endif - if (uoffset > 0) { - i = 0; - if (prepend_sign) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); - i++; - } - for (; i < uoffset; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); - } - } - for (i=0; i < clength; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); - } -#else - { - PyObject *sign = NULL, *padding = NULL; - uval = NULL; - if (uoffset > 0) { - prepend_sign = !!prepend_sign; - if (uoffset > prepend_sign) { - padding = PyUnicode_FromOrdinal(padding_char); - if (likely(padding) && uoffset > prepend_sign + 1) { - PyObject *tmp; - PyObject *repeat = PyInt_FromSize_t(uoffset - prepend_sign); - if (unlikely(!repeat)) goto done_or_error; - tmp = PyNumber_Multiply(padding, repeat); - Py_DECREF(repeat); - Py_DECREF(padding); - padding = tmp; - } - if (unlikely(!padding)) goto done_or_error; - } - if (prepend_sign) { - sign = PyUnicode_FromOrdinal('-'); - if (unlikely(!sign)) goto done_or_error; - } - } - uval = PyUnicode_DecodeASCII(chars, clength, NULL); - if (likely(uval) && padding) { - PyObject *tmp = PyNumber_Add(padding, uval); - Py_DECREF(uval); - uval = tmp; - } - if (likely(uval) && sign) { - PyObject *tmp = PyNumber_Add(sign, uval); - Py_DECREF(uval); - uval = tmp; - } -done_or_error: - Py_XDECREF(padding); - Py_XDECREF(sign); - } -#endif - return uval; -} - -/* CIntToPyUnicode */ -#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) -#define GCC_DIAGNOSTIC -#endif -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { - char digits[sizeof(int)*3+2]; - char *dpos, *end = digits + sizeof(int)*3+2; - const char *hex_digits = DIGITS_HEX; - Py_ssize_t length, ulength; - int prepend_sign, last_one_off; - int remaining; -#ifdef GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (format_char == 'X') { - hex_digits += 16; - format_char = 'x'; - } - remaining = value; - last_one_off = 0; - dpos = end; - do { - int digit_pos; - switch (format_char) { - case 'o': - digit_pos = abs((int)(remaining % (8*8))); - remaining = (int) (remaining / (8*8)); - dpos -= 2; - *(uint16_t*)dpos = ((const uint16_t*)DIGIT_PAIRS_8)[digit_pos]; - last_one_off = (digit_pos < 8); - break; - case 'd': - digit_pos = abs((int)(remaining % (10*10))); - remaining = (int) (remaining / (10*10)); - dpos -= 2; - *(uint16_t*)dpos = ((const uint16_t*)DIGIT_PAIRS_10)[digit_pos]; - last_one_off = (digit_pos < 10); - break; - case 'x': - *(--dpos) = hex_digits[abs((int)(remaining % 16))]; - remaining = (int) (remaining / 16); - break; - default: - assert(0); - break; - } - } while (unlikely(remaining != 0)); - assert(!last_one_off || *dpos == '0'); - dpos += last_one_off; - length = end - dpos; - ulength = length; - prepend_sign = 0; - if (!is_unsigned && value <= neg_one) { - if (padding_char == ' ' || width <= length + 1) { - *(--dpos) = '-'; - ++length; - } else { - prepend_sign = 1; - } - ++ulength; - } - if (width > ulength) { - ulength = width; - } - if (ulength == 1) { - return PyUnicode_FromOrdinal(*dpos); - } - return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); -} - -/* CIntToPyUnicode */ -#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) -#define GCC_DIAGNOSTIC -#endif -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { - char digits[sizeof(Py_ssize_t)*3+2]; - char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; - const char *hex_digits = DIGITS_HEX; - Py_ssize_t length, ulength; - int prepend_sign, last_one_off; - Py_ssize_t remaining; -#ifdef GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; -#ifdef GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (format_char == 'X') { - hex_digits += 16; - format_char = 'x'; - } - remaining = value; - last_one_off = 0; - dpos = end; - do { - int digit_pos; - switch (format_char) { - case 'o': - digit_pos = abs((int)(remaining % (8*8))); - remaining = (Py_ssize_t) (remaining / (8*8)); - dpos -= 2; - *(uint16_t*)dpos = ((const uint16_t*)DIGIT_PAIRS_8)[digit_pos]; - last_one_off = (digit_pos < 8); - break; - case 'd': - digit_pos = abs((int)(remaining % (10*10))); - remaining = (Py_ssize_t) (remaining / (10*10)); - dpos -= 2; - *(uint16_t*)dpos = ((const uint16_t*)DIGIT_PAIRS_10)[digit_pos]; - last_one_off = (digit_pos < 10); - break; - case 'x': - *(--dpos) = hex_digits[abs((int)(remaining % 16))]; - remaining = (Py_ssize_t) (remaining / 16); - break; - default: - assert(0); - break; - } - } while (unlikely(remaining != 0)); - assert(!last_one_off || *dpos == '0'); - dpos += last_one_off; - length = end - dpos; - ulength = length; - prepend_sign = 0; - if (!is_unsigned && value <= neg_one) { - if (padding_char == ' ' || width <= length + 1) { - *(--dpos) = '-'; - ++length; - } else { - prepend_sign = 1; - } - ++ulength; - } - if (width > ulength) { - ulength = width; - } - if (ulength == 1) { - return PyUnicode_FromOrdinal(*dpos); - } - return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); -} - -/* JoinPyUnicode */ -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - CYTHON_UNUSED Py_UCS4 max_char) { -#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - PyObject *result_uval; - int result_ukind, kind_shift; - Py_ssize_t i, char_pos; - void *result_udata; -#if CYTHON_PEP393_ENABLED - result_uval = PyUnicode_New(result_ulength, max_char); - if (unlikely(!result_uval)) return NULL; - result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; - kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; - result_udata = PyUnicode_DATA(result_uval); -#else - result_uval = PyUnicode_FromUnicode(NULL, result_ulength); - if (unlikely(!result_uval)) return NULL; - result_ukind = sizeof(Py_UNICODE); - kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; - result_udata = PyUnicode_AS_UNICODE(result_uval); -#endif - assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); - char_pos = 0; - for (i=0; i < value_count; i++) { - int ukind; - Py_ssize_t ulength; - void *udata; - PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); - if (unlikely(__Pyx_PyUnicode_READY(uval))) - goto bad; - ulength = __Pyx_PyUnicode_GET_LENGTH(uval); - if (unlikely(!ulength)) - continue; - if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) - goto overflow; - ukind = __Pyx_PyUnicode_KIND(uval); - udata = __Pyx_PyUnicode_DATA(uval); - if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { - memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); - } else { - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) - _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); - #else - Py_ssize_t j; - for (j=0; j < ulength; j++) { - Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); - __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); - } - #endif - } - char_pos += ulength; - } - return result_uval; -overflow: - PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); -bad: - Py_DECREF(result_uval); - return NULL; -#else - result_ulength++; - value_count++; - return PyUnicode_Join(__pyx_empty_unicode, value_tuple); -#endif -} - -/* GetAttr */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_USE_TYPE_SLOTS -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -/* GetItemInt */ -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (unlikely(!j)) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyList_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; - PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; - if (mm && mm->mp_subscript) { - PyObject *r, *key = PyInt_FromSsize_t(i); - if (unlikely(!key)) return NULL; - r = mm->mp_subscript(o, key); - Py_DECREF(key); - return r; - } - if (likely(sm && sm->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { - Py_ssize_t l = sm->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return sm->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* PyObjectCallOneArg */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *args[2] = {NULL, arg}; - return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); -} - -/* ObjectGetItem */ -#if CYTHON_USE_TYPE_SLOTS -static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { - PyObject *runerr; - Py_ssize_t key_value; - key_value = __Pyx_PyIndex_AsSsize_t(index); - if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { - return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); - } - if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { - __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); - PyErr_Clear(); - PyErr_Format(PyExc_IndexError, - "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); - __Pyx_DECREF_TypeName(index_type_name); - } - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { - __Pyx_TypeName obj_type_name; - if (likely(PyType_Check(obj))) { - PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); - if (meth) { - PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); - Py_DECREF(meth); - return result; - } - } - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { - PyTypeObject *tp = Py_TYPE(obj); - PyMappingMethods *mm = tp->tp_as_mapping; - PySequenceMethods *sm = tp->tp_as_sequence; - if (likely(mm && mm->mp_subscript)) { - return mm->mp_subscript(obj, key); - } - if (likely(sm && sm->sq_item)) { - return __Pyx_PyObject_GetIndex(obj, key); - } - return __Pyx_PyObject_GetItem_Slow(obj, key); -} -#endif - -/* KeywordStringCheck */ -static int __Pyx_CheckKeywordStrings( - PyObject *kw, - const char* function_name, - int kw_allowed) -{ - PyObject* key = 0; - Py_ssize_t pos = 0; -#if CYTHON_COMPILING_IN_PYPY - if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) - goto invalid_keyword; - return 1; -#else - if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { - if (unlikely(PyTuple_GET_SIZE(kw) == 0)) - return 1; - if (!kw_allowed) { - key = PyTuple_GET_ITEM(kw, 0); - goto invalid_keyword; - } -#if PY_VERSION_HEX < 0x03090000 - for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) { - key = PyTuple_GET_ITEM(kw, pos); - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } -#endif - return 1; - } - while (PyDict_Next(kw, &pos, &key, 0)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_Check(key))) - #endif - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } - if (!kw_allowed && unlikely(key)) - goto invalid_keyword; - return 1; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - return 0; -#endif -invalid_keyword: - #if PY_MAJOR_VERSION < 3 - PyErr_Format(PyExc_TypeError, - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - PyErr_Format(PyExc_TypeError, - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif - return 0; -} - -/* None */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* GetAttr3 */ -static PyObject *__Pyx_GetAttr3Default(PyObject *d) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - return NULL; - __Pyx_PyErr_Clear(); - Py_INCREF(d); - return d; -} -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { - PyObject *r; -#if CYTHON_USE_TYPE_SLOTS - if (likely(PyString_Check(n))) { - r = __Pyx_PyObject_GetAttrStrNoError(o, n); - if (unlikely(!r) && likely(!PyErr_Occurred())) { - r = __Pyx_NewRef(d); - } - return r; - } -#endif - r = PyObject_GetAttr(o, n); - return (likely(r)) ? r : __Pyx_GetAttr3Default(d); -} - -/* PyDictVersioning */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { - PyObject **dictptr = NULL; - Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; - if (offset) { -#if CYTHON_COMPILING_IN_CPYTHON - dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); -#else - dictptr = _PyObject_GetDictPtr(obj); -#endif - } - return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; -} -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) - return 0; - return obj_dict_version == __Pyx_get_object_dict_version(obj); -} -#endif - -/* GetModuleGlobalName */ -#if CYTHON_USE_DICT_VERSIONS -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) -#else -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) -#endif -{ - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 - result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } else if (unlikely(PyErr_Occurred())) { - return NULL; - } -#elif CYTHON_COMPILING_IN_LIMITED_API - if (unlikely(!__pyx_m)) { - return NULL; - } - result = PyObject_GetAttr(__pyx_m, name); - if (likely(result)) { - return result; - } -#else - result = PyDict_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } -#endif -#else - result = PyObject_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } - PyErr_Clear(); -#endif - return __Pyx_GetBuiltinName(name); -} - -/* RaiseTooManyValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* ExtTypeTest */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - __Pyx_TypeName obj_type_name; - __Pyx_TypeName type_name; - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - type_name = __Pyx_PyType_GetName(type); - PyErr_Format(PyExc_TypeError, - "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, - obj_type_name, type_name); - __Pyx_DECREF_TypeName(obj_type_name); - __Pyx_DECREF_TypeName(type_name); - return 0; -} - -/* GetTopmostException */ -#if CYTHON_USE_EXC_INFO_STACK -static _PyErr_StackItem * -__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) -{ - _PyErr_StackItem *exc_info = tstate->exc_info; - while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && - exc_info->previous_item != NULL) - { - exc_info = exc_info->previous_item; - } - return exc_info; -} -#endif - -/* SaveResetException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - *type = exc_info->exc_type; - *value = exc_info->exc_value; - *tb = exc_info->exc_traceback; - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - #endif - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = type; - exc_info->exc_value = value; - exc_info->exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* GetException */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) -#endif -{ - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if CYTHON_USE_EXC_INFO_STACK - { - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = local_type; - exc_info->exc_value = local_value; - exc_info->exc_traceback = local_tb; - } - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* SwapException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = *type; - exc_info->exc_value = *value; - exc_info->exc_traceback = *tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - #endif - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); - PyErr_SetExcInfo(*type, *value, *tb); - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#endif - -/* Import */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *module = 0; - PyObject *empty_dict = 0; - PyObject *empty_list = 0; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (unlikely(!py_import)) - goto bad; - if (!from_list) { - empty_list = PyList_New(0); - if (unlikely(!empty_list)) - goto bad; - from_list = empty_list; - } - #endif - empty_dict = PyDict_New(); - if (unlikely(!empty_dict)) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { - #if CYTHON_COMPILING_IN_LIMITED_API - module = PyImport_ImportModuleLevelObject( - name, empty_dict, empty_dict, from_list, 1); - #else - module = PyImport_ImportModuleLevelObject( - name, __pyx_d, empty_dict, from_list, 1); - #endif - if (unlikely(!module)) { - if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (unlikely(!py_level)) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); - Py_DECREF(py_level); - #else - #if CYTHON_COMPILING_IN_LIMITED_API - module = PyImport_ImportModuleLevelObject( - name, empty_dict, empty_dict, from_list, level); - #else - module = PyImport_ImportModuleLevelObject( - name, __pyx_d, empty_dict, from_list, level); - #endif - #endif - } - } -bad: - Py_XDECREF(empty_dict); - Py_XDECREF(empty_list); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - return module; -} - -/* ImportDottedModule */ -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { - PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; - if (unlikely(PyErr_Occurred())) { - PyErr_Clear(); - } - if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { - partial_name = name; - } else { - PyObject *sep; - PyObject *slice = PySequence_GetSlice(parts_tuple, 0, count); - if (unlikely(!slice)) - goto bad; - sep = PyUnicode_FromStringAndSize(".", 1); - if (unlikely(!sep)) - goto bad; - partial_name = PyUnicode_Join(sep, slice); - } - PyErr_Format( -#if PY_MAJOR_VERSION < 3 - PyExc_ImportError, - "No module named '%s'", PyString_AS_STRING(partial_name)); -#else -#if PY_VERSION_HEX >= 0x030600B1 - PyExc_ModuleNotFoundError, -#else - PyExc_ImportError, -#endif - "No module named '%U'", partial_name); -#endif -bad: - Py_XDECREF(sep); - Py_XDECREF(slice); - Py_XDECREF(partial_name); - return NULL; -} -#endif -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { - PyObject *imported_module; -#if PY_VERSION_HEX < 0x030700A1 - PyObject *modules = PyImport_GetModuleDict(); - if (unlikely(!modules)) - return NULL; - imported_module = __Pyx_PyDict_GetItemStr(modules, name); - Py_XINCREF(imported_module); -#else - imported_module = PyImport_GetModule(name); -#endif - return imported_module; -} -#endif -static PyObject *__Pyx__ImportDottedModule(PyObject *name, CYTHON_UNUSED PyObject *parts_tuple) { -#if PY_MAJOR_VERSION < 3 - PyObject *module, *from_list, *star = __pyx_n_s__3; - from_list = PyList_New(1); - if (unlikely(!from_list)) - return NULL; - Py_INCREF(star); - PyList_SET_ITEM(from_list, 0, star); - module = __Pyx_Import(name, from_list, 0); - Py_DECREF(from_list); - return module; -#else - Py_ssize_t i, nparts; - PyObject *imported_module; - PyObject *module = __Pyx_Import(name, NULL, 0); - if (!parts_tuple || unlikely(!module)) - return module; - imported_module = __Pyx__ImportDottedModule_Lookup(name); - if (likely(imported_module)) { - Py_DECREF(module); - return imported_module; - } - PyErr_Clear(); - nparts = PyTuple_GET_SIZE(parts_tuple); - for (i=1; i < nparts && module; i++) { - PyObject *part, *submodule; -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - part = PyTuple_GET_ITEM(parts_tuple, i); -#else - part = PySequence_ITEM(parts_tuple, i); -#endif - submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); -#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) - Py_DECREF(part); -#endif - Py_DECREF(module); - module = submodule; - } - if (likely(module)) - return module; - return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); -#endif -} -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 - PyObject *module = __Pyx__ImportDottedModule_Lookup(name); - if (likely(module)) { - PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); - if (likely(spec)) { - PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); - if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { - Py_DECREF(spec); - spec = NULL; - } - Py_XDECREF(unsafe); - } - if (likely(!spec)) { - PyErr_Clear(); - return module; - } - Py_DECREF(spec); - Py_DECREF(module); - } else if (PyErr_Occurred()) { - PyErr_Clear(); - } -#endif - return __Pyx__ImportDottedModule(name, parts_tuple); -} - -/* ssize_strlen */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { - size_t len = strlen(s); - if (unlikely(len > PY_SSIZE_T_MAX)) { - PyErr_SetString(PyExc_OverflowError, "byte string is too long"); - return -1; - } - return (Py_ssize_t) len; -} - -/* FastTypeChecks */ -#if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = a->tp_base; - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (cls == a || cls == b) return 1; - mro = cls->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - PyObject *base = PyTuple_GET_ITEM(mro, i); - if (base == (PyObject *)a || base == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - if (exc_type1) { - return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); - } else { - return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } -} -#endif -static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - assert(PyExceptionClass_Check(exc_type)); - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; i= 0)) ? i : i + PyList_GET_SIZE(o)); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { - PyObject* old = PyList_GET_ITEM(o, n); - Py_INCREF(v); - PyList_SET_ITEM(o, n, v); - Py_DECREF(old); - return 1; - } - } else { - PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; - PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; - if (mm && mm->mp_ass_subscript) { - int r; - PyObject *key = PyInt_FromSsize_t(i); - if (unlikely(!key)) return -1; - r = mm->mp_ass_subscript(o, key, v); - Py_DECREF(key); - return r; - } - if (likely(sm && sm->sq_ass_item)) { - if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { - Py_ssize_t l = sm->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return -1; - PyErr_Clear(); - } - } - return sm->sq_ass_item(o, i, v); - } - } -#else -#if CYTHON_COMPILING_IN_PYPY - if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) -#else - if (is_list || PySequence_Check(o)) -#endif - { - return PySequence_SetItem(o, i, v); - } -#endif - return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); -} - -/* None */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { - PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); -} - -/* None */ -static CYTHON_INLINE long __Pyx_div_long(long a, long b) { - long q = a / b; - long r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* ImportFrom */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -/* HasAttr */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { - PyObject *r; - if (unlikely(!__Pyx_PyBaseString_Check(n))) { - PyErr_SetString(PyExc_TypeError, - "hasattr(): attribute name must be string"); - return -1; - } - r = __Pyx_GetAttr(o, n); - if (!r) { - PyErr_Clear(); - return 0; - } else { - Py_DECREF(r); - return 1; - } -} - -/* PyObject_GenericGetAttrNoDict */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { - __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", - type_name, attr_name); -#else - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", - type_name, PyString_AS_STRING(attr_name)); -#endif - __Pyx_DECREF_TypeName(type_name); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { - PyObject *descr; - PyTypeObject *tp = Py_TYPE(obj); - if (unlikely(!PyString_Check(attr_name))) { - return PyObject_GenericGetAttr(obj, attr_name); - } - assert(!tp->tp_dictoffset); - descr = _PyType_Lookup(tp, attr_name); - if (unlikely(!descr)) { - return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); - } - Py_INCREF(descr); - #if PY_MAJOR_VERSION < 3 - if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) - #endif - { - descrgetfunc f = Py_TYPE(descr)->tp_descr_get; - if (unlikely(f)) { - PyObject *res = f(descr, obj, (PyObject *)tp); - Py_DECREF(descr); - return res; - } - } - return descr; -} -#endif - -/* PyObject_GenericGetAttr */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { - if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { - return PyObject_GenericGetAttr(obj, attr_name); - } - return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); -} -#endif - -/* SetVTable */ -#if CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_SetVtable(PyObject *type, void *vtable) { -#else -static int __Pyx_SetVtable(PyObject *dict, void *vtable) { -#endif - PyObject *ob = PyCapsule_New(vtable, 0, 0); - if (!ob) - goto bad; -#if CYTHON_COMPILING_IN_LIMITED_API - if (PyObject_SetAttr(type, __pyx_n_s_pyx_vtable, ob) < 0) -#else - if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) -#endif - goto bad; - Py_DECREF(ob); - return 0; -bad: - Py_XDECREF(ob); - return -1; -} - -/* SetupReduce */ - #if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; -#else - if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; -#endif -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); - if (likely(reduce_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (reduce == object_reduce || PyErr_Occurred()) { - goto __PYX_BAD; - } - setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); - if (likely(setstate_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (!setstate || PyErr_Occurred()) { - goto __PYX_BAD; - } - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto __PYX_GOOD; -__PYX_BAD: - if (!PyErr_Occurred()) { - __Pyx_TypeName type_obj_name = - __Pyx_PyType_GetName((PyTypeObject*)type_obj); - PyErr_Format(PyExc_RuntimeError, - "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); - __Pyx_DECREF_TypeName(type_obj_name); - } - ret = -1; -__PYX_GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} -#endif - -/* FetchCommonType */ - static PyObject *__Pyx_FetchSharedCythonABIModule(void) { - PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME); - if (!abi_module) return NULL; - Py_INCREF(abi_module); - return abi_module; -} -static int __Pyx_VerifyCachedType(PyObject *cached_type, - const char *name, - Py_ssize_t basicsize, - Py_ssize_t expected_basicsize) { - if (!PyType_Check(cached_type)) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s is not a type object", name); - return -1; - } - if (basicsize != expected_basicsize) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s has the wrong size, try recompiling", - name); - return -1; - } - return 0; -} -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { - PyObject* abi_module; - PyTypeObject *cached_type = NULL; - abi_module = __Pyx_FetchSharedCythonABIModule(); - if (!abi_module) return NULL; - cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, type->tp_name); - if (cached_type) { - if (__Pyx_VerifyCachedType( - (PyObject *)cached_type, - type->tp_name, - cached_type->tp_basicsize, - type->tp_basicsize) < 0) { - goto bad; - } - goto done; - } - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - if (PyType_Ready(type) < 0) goto bad; - if (PyObject_SetAttrString(abi_module, type->tp_name, (PyObject *)type) < 0) - goto bad; - Py_INCREF(type); - cached_type = type; -done: - Py_DECREF(abi_module); - return cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} -#if CYTHON_COMPILING_IN_LIMITED_API -static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyType_Spec *spec, PyObject *bases) { - PyObject *abi_module, *py_basicsize, *cached_type = NULL; - Py_ssize_t basicsize; - abi_module = __Pyx_FetchSharedCythonABIModule(); - if (!abi_module) return NULL; - cached_type = PyObject_GetAttrString(abi_module, spec->name); - if (cached_type) { - py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); - if (!py_basicsize) goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; - if (__Pyx_VerifyCachedType( - cached_type, - spec->name, - basicsize, - spec->basicsize) < 0) { - goto bad; - } - goto done; - } - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - cached_type = PyType_FromSpecWithBases(spec, bases); - if (unlikely(!cached_type)) goto bad; - if (PyObject_SetAttrString(abi_module, spec->name, cached_type) < 0) goto bad; -done: - Py_DECREF(abi_module); - assert(cached_type == NULL || PyType_Check(cached_type)); - return (PyTypeObject *) cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} -#endif - -/* PyVectorcallFastCallDict */ - #if CYTHON_METH_FASTCALL -static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, Py_ssize_t nargs, PyObject *kw) -{ - PyObject *res = NULL; - PyObject *kwnames; - PyObject **newargs; - PyObject **kwvalues; - Py_ssize_t i, pos; - PyObject *key, *value; - unsigned long keys_are_strings; - Py_ssize_t nkw = PyDict_GET_SIZE(kw); - newargs = (PyObject **)PyMem_Malloc((nargs + nkw) * sizeof(args[0])); - if (unlikely(newargs == NULL)) { - PyErr_NoMemory(); - return NULL; - } - for (i = 0; i < nargs; i++) newargs[i] = args[i]; - kwnames = PyTuple_New(nkw); - if (unlikely(kwnames == NULL)) { - PyMem_Free(newargs); - return NULL; - } - kwvalues = newargs + nargs; - pos = i = 0; - keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; - while (PyDict_Next(kw, &pos, &key, &value)) { - keys_are_strings &= Py_TYPE(key)->tp_flags; - Py_INCREF(key); - Py_INCREF(value); - PyTuple_SET_ITEM(kwnames, i, key); - kwvalues[i] = value; - i++; - } - if (unlikely(!keys_are_strings)) { - PyErr_SetString(PyExc_TypeError, "keywords must be strings"); - goto cleanup; - } - res = vc(func, newargs, nargs, kwnames); -cleanup: - Py_DECREF(kwnames); - for (i = 0; i < nkw; i++) - Py_DECREF(kwvalues[i]); - PyMem_Free(newargs); - return res; -} -static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, Py_ssize_t nargs, PyObject *kw) -{ - if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { - return vc(func, args, nargs, NULL); - } - return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); -} -#endif - -/* CythonFunctionShared */ - #include -static PyObject * -__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) -{ - if (unlikely(op->func_doc == NULL)) { - if (op->func.m_ml->ml_doc) { -#if PY_MAJOR_VERSION >= 3 - op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); -#else - op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); -#endif - if (unlikely(op->func_doc == NULL)) - return NULL; - } else { - Py_INCREF(Py_None); - return Py_None; - } - } - Py_INCREF(op->func_doc); - return op->func_doc; -} -static int -__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ - if (value == NULL) { - value = Py_None; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_doc, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - if (unlikely(op->func_name == NULL)) { -#if PY_MAJOR_VERSION >= 3 - op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); -#else - op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); -#endif - if (unlikely(op->func_name == NULL)) - return NULL; - } - Py_INCREF(op->func_name); - return op->func_name; -} -static int -__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__name__ must be set to a string object"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_name, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - Py_INCREF(op->func_qualname); - return op->func_qualname; -} -static int -__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__qualname__ must be set to a string object"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_qualname, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) -{ - PyObject *self; - self = m->func_closure; - if (self == NULL) - self = Py_None; - Py_INCREF(self); - return self; -} -static PyObject * -__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - if (unlikely(op->func_dict == NULL)) { - op->func_dict = PyDict_New(); - if (unlikely(op->func_dict == NULL)) - return NULL; - } - Py_INCREF(op->func_dict); - return op->func_dict; -} -static int -__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ - if (unlikely(value == NULL)) { - PyErr_SetString(PyExc_TypeError, - "function's dictionary may not be deleted"); - return -1; - } - if (unlikely(!PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "setting function's dictionary to a non-dict"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_dict, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - Py_INCREF(op->func_globals); - return op->func_globals; -} -static PyObject * -__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - Py_INCREF(Py_None); - return Py_None; -} -static PyObject * -__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - PyObject* result = (op->func_code) ? op->func_code : Py_None; 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-static PyMemberDef __pyx_CyFunction_members[] = { - {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0}, -#if CYTHON_COMPILING_IN_LIMITED_API - {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0}, -#if PY_VERSION_HEX < 0x030500A0 - {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0}, -#else - {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0}, -#endif -#endif - {0, 0, 0, 0, 0} -}; -static PyObject * -__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) -{ -#if PY_MAJOR_VERSION >= 3 - Py_INCREF(m->func_qualname); - return m->func_qualname; -#else - return PyString_FromString(m->func.m_ml->ml_name); -#endif -} -static PyMethodDef __pyx_CyFunction_methods[] = { - {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, - {0, 0, 0, 0} -}; -#if PY_VERSION_HEX < 0x030500A0 -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) -#else -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) -#endif -static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, - PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { - if (unlikely(op == NULL)) - return NULL; 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- m->defaults = NULL; - } - return 0; -} -static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - if (__Pyx_CyFunction_weakreflist(m) != NULL) - PyObject_ClearWeakRefs((PyObject *) m); - __Pyx_CyFunction_clear(m); - PyObject_GC_Del(m); -} -static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - PyObject_GC_UnTrack(m); - __Pyx__CyFunction_dealloc(m); -} -static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) -{ - Py_VISIT(m->func_closure); - Py_VISIT(m->func.m_module); - Py_VISIT(m->func_dict); - Py_VISIT(m->func_name); - Py_VISIT(m->func_qualname); - Py_VISIT(m->func_doc); - Py_VISIT(m->func_globals); - Py_VISIT(m->func_code); - Py_VISIT(m->func_classobj); - Py_VISIT(m->defaults_tuple); - Py_VISIT(m->defaults_kwdict); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_VISIT(pydefaults[i]); - } - return 0; -} -static PyObject* -__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromFormat("", - op->func_qualname, (void *)op); -#else - return PyString_FromFormat("", - PyString_AsString(op->func_qualname), (void *)op); -#endif -} -static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { - PyCFunctionObject* f = (PyCFunctionObject*)func; - PyCFunction meth = f->m_ml->ml_meth; - Py_ssize_t size; - switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { - case METH_VARARGS: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) - return (*meth)(self, arg); - break; - case METH_VARARGS | METH_KEYWORDS: - return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); - case METH_NOARGS: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) { - size = PyTuple_GET_SIZE(arg); - if (likely(size == 0)) - return (*meth)(self, NULL); - PyErr_Format(PyExc_TypeError, - "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", - f->m_ml->ml_name, size); - return NULL; - } - break; - case METH_O: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) { - size = PyTuple_GET_SIZE(arg); - if (likely(size == 1)) { - PyObject *result, *arg0; - #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - arg0 = PyTuple_GET_ITEM(arg, 0); - #else - arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; - #endif - result = (*meth)(self, arg0); - #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) - Py_DECREF(arg0); - #endif - return result; - } - PyErr_Format(PyExc_TypeError, - "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", - f->m_ml->ml_name, size); - return NULL; - } - break; - default: - PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); - return NULL; - } - PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", - f->m_ml->ml_name); - return NULL; -} -static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { - return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); -} -static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { - PyObject *result; - __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; -#if CYTHON_METH_FASTCALL - __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc); - if (vc) { -#if CYTHON_ASSUME_SAFE_MACROS - return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), PyTuple_GET_SIZE(args), kw); -#else - (void) &__Pyx_PyVectorcall_FastCallDict; - return PyVectorcall_Call(func, args, kw); -#endif - } -#endif - if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { - Py_ssize_t argc; - PyObject *new_args; - PyObject *self; - argc = PyTuple_GET_SIZE(args); - new_args = PyTuple_GetSlice(args, 1, argc); - if (unlikely(!new_args)) - return NULL; - self = PyTuple_GetItem(args, 0); - if (unlikely(!self)) { - Py_DECREF(new_args); - return NULL; - } - result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); - Py_DECREF(new_args); - } else { - result = __Pyx_CyFunction_Call(func, args, kw); - } - return result; -} -#if CYTHON_METH_FASTCALL -static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames) -{ - int ret = 0; - if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { - if (unlikely(nargs < 1)) { - PyErr_Format(PyExc_TypeError, "%.200s() needs an argument", - cyfunc->func.m_ml->ml_name); - return -1; - } - ret = 1; - } - if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) { - PyErr_Format(PyExc_TypeError, - "%.200s() takes no keyword arguments", cyfunc->func.m_ml->ml_name); - return -1; - } - return ret; -} -static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) -{ - __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; - PyMethodDef* def = cyfunc->func.m_ml; -#if CYTHON_BACKPORT_VECTORCALL - Py_ssize_t nargs = (Py_ssize_t)nargsf; -#else - Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); -#endif - PyObject *self; - switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { - case 1: - self = args[0]; - args += 1; - nargs -= 1; - break; - case 0: - self = cyfunc->func.m_self; 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"'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number, ndim; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ndim = ctx->head->field->type->ndim; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - CYTHON_FALLTHROUGH; - case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && - (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - CYTHON_FALLTHROUGH; - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} - -/* TypeInfoCompare */ - static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -/* MemviewSliceValidateAndInit */ - static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (unlikely(buf->strides[dim] != sizeof(void *))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (unlikely(buf->strides[dim] != buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (unlikely(stride < buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - } else { - if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (unlikely(buf->suboffsets)) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) -{ - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessible " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; - } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (unlikely(buf->ndim != ndim)) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; - } - if (unlikely((unsigned) buf->itemsize != dtype->size)) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->len > 0) { - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) - goto fail; - if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) - goto fail; - } - if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) - goto fail; - } - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_float(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, - &__Pyx_TypeInfo_float, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_unsigned_char(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, - &__Pyx_TypeInfo_unsigned_char, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_char(unsigned char value) { - const unsigned char neg_one = (unsigned char) ((unsigned char) 0 - (unsigned char) 1), const_zero = (unsigned char) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(unsigned char) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(unsigned char) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned char) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(unsigned char) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned char) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(unsigned char), - little, !is_unsigned); - } -} - -/* MemviewDtypeToObject */ - static CYTHON_INLINE PyObject *__pyx_memview_get_unsigned_char(const char *itemp) { - return (PyObject *) __Pyx_PyInt_From_unsigned_char(*(unsigned char *) itemp); -} -static CYTHON_INLINE int __pyx_memview_set_unsigned_char(const char *itemp, PyObject *obj) { - unsigned char value = __Pyx_PyInt_As_unsigned_char(obj); - if (unlikely((value == (unsigned char)-1) && PyErr_Occurred())) - return 0; - *(unsigned char *) itemp = value; - return 1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* MemviewSliceCopyTemplate */ - static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object) -{ - __Pyx_RefNannyDeclarations - int i; - __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; - struct __pyx_memoryview_obj *from_memview = from_mvs->memview; - Py_buffer *buf = &from_memview->view; - PyObject *shape_tuple = NULL; - PyObject *temp_int = NULL; - struct __pyx_array_obj *array_obj = NULL; - struct __pyx_memoryview_obj *memview_obj = NULL; - __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); - for (i = 0; i < ndim; i++) { - if (unlikely(from_mvs->suboffsets[i] >= 0)) { - PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " - "indirect dimensions (axis %d)", i); - goto fail; - } - } - shape_tuple = PyTuple_New(ndim); - if (unlikely(!shape_tuple)) { - goto fail; - } - __Pyx_GOTREF(shape_tuple); - for(i = 0; i < ndim; i++) { - temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); - if(unlikely(!temp_int)) { - goto fail; - } else { - PyTuple_SET_ITEM(shape_tuple, i, temp_int); - temp_int = NULL; - } - } - array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); - if (unlikely(!array_obj)) { - goto fail; - } - __Pyx_GOTREF(array_obj); - memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - (PyObject *) array_obj, contig_flag, - dtype_is_object, - from_mvs->memview->typeinfo); - if (unlikely(!memview_obj)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) - goto fail; - if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, - dtype_is_object) < 0)) - goto fail; - goto no_fail; -fail: - __Pyx_XDECREF(new_mvs.memview); - new_mvs.memview = NULL; - new_mvs.data = NULL; -no_fail: - __Pyx_XDECREF(shape_tuple); - __Pyx_XDECREF(temp_int); - __Pyx_XDECREF(array_obj); - __Pyx_RefNannyFinishContext(); - return new_mvs; -} - -/* MemviewSliceInit */ - static int -__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference) -{ - __Pyx_RefNannyDeclarations - int i, retval=-1; - Py_buffer *buf = &memview->view; - __Pyx_RefNannySetupContext("init_memviewslice", 0); - if (unlikely(memviewslice->memview || memviewslice->data)) { - PyErr_SetString(PyExc_ValueError, - "memviewslice is already initialized!"); - goto fail; - } - if (buf->strides) { - for (i = 0; i < ndim; i++) { - memviewslice->strides[i] = buf->strides[i]; - } - } else { - Py_ssize_t stride = buf->itemsize; - for (i = ndim - 1; i >= 0; i--) { - memviewslice->strides[i] = stride; - stride *= buf->shape[i]; - } - } - for (i = 0; i < ndim; i++) { - memviewslice->shape[i] = buf->shape[i]; - if (buf->suboffsets) { - memviewslice->suboffsets[i] = buf->suboffsets[i]; - } else { - memviewslice->suboffsets[i] = -1; - } - } - memviewslice->memview = memview; - memviewslice->data = (char *)buf->buf; - if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { - Py_INCREF(memview); - } - retval = 0; - goto no_fail; -fail: - memviewslice->memview = 0; - memviewslice->data = 0; - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} -#ifndef Py_NO_RETURN -#define Py_NO_RETURN -#endif -static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { - va_list vargs; - char msg[200]; -#ifdef HAVE_STDARG_PROTOTYPES - va_start(vargs, fmt); -#else - va_start(vargs); -#endif - vsnprintf(msg, 200, fmt, vargs); - va_end(vargs); - Py_FatalError(msg); -} -static CYTHON_INLINE int -__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)++; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE int -__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)--; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE void -__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) -{ - __pyx_atomic_int_type old_acquisition_count; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (unlikely(!memview || (PyObject *) memview == Py_None)) { - return; - } - old_acquisition_count = __pyx_add_acquisition_count(memview); - if (unlikely(old_acquisition_count <= 0)) { - if (likely(old_acquisition_count == 0)) { - if (have_gil) { - Py_INCREF((PyObject *) memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_INCREF((PyObject *) memview); - PyGILState_Release(_gilstate); - } - } else { - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - } - } -} -static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, - int have_gil, int lineno) { - __pyx_atomic_int_type old_acquisition_count; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (unlikely(!memview || (PyObject *) memview == Py_None)) { - memslice->memview = NULL; - return; - } - old_acquisition_count = __pyx_sub_acquisition_count(memview); - memslice->data = NULL; - if (likely(old_acquisition_count > 1)) { - memslice->memview = NULL; - } else if (likely(old_acquisition_count == 1)) { - if (have_gil) { - Py_CLEAR(memslice->memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_CLEAR(memslice->memview); - PyGILState_Release(_gilstate); - } - } else { - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if (CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE unsigned char __Pyx_PyInt_As_unsigned_char(PyObject *x) { - const unsigned char neg_one = (unsigned char) ((unsigned char) 0 - (unsigned char) 1), const_zero = (unsigned char) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(unsigned char) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(unsigned char, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (unsigned char) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (unsigned char) 0; - case 1: __PYX_VERIFY_RETURN_INT(unsigned char, digit, digits[0]) - case 2: - if (8 * sizeof(unsigned char) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) >= 2 * PyLong_SHIFT) { - return (unsigned char) (((((unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(unsigned char) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) >= 3 * PyLong_SHIFT) { - return (unsigned char) (((((((unsigned char)digits[2]) << PyLong_SHIFT) | (unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(unsigned char) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) >= 4 * PyLong_SHIFT) { - return (unsigned char) (((((((((unsigned char)digits[3]) << PyLong_SHIFT) | (unsigned char)digits[2]) << PyLong_SHIFT) | (unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (unsigned char) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(unsigned char) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned char, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned char) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (unsigned char) 0; - case -1: __PYX_VERIFY_RETURN_INT(unsigned char, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(unsigned char, digit, +digits[0]) - case -2: - if (8 * sizeof(unsigned char) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) - 1 > 2 * PyLong_SHIFT) { - return (unsigned char) (((unsigned char)-1)*(((((unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(unsigned char) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) - 1 > 2 * PyLong_SHIFT) { - return (unsigned char) ((((((unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(unsigned char) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) - 1 > 3 * PyLong_SHIFT) { - return (unsigned char) (((unsigned char)-1)*(((((((unsigned char)digits[2]) << PyLong_SHIFT) | (unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(unsigned char) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) - 1 > 3 * PyLong_SHIFT) { - return (unsigned char) ((((((((unsigned char)digits[2]) << PyLong_SHIFT) | (unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(unsigned char) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) - 1 > 4 * PyLong_SHIFT) { - return (unsigned char) (((unsigned char)-1)*(((((((((unsigned char)digits[3]) << PyLong_SHIFT) | (unsigned char)digits[2]) << PyLong_SHIFT) | (unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(unsigned char) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned char) - 1 > 4 * PyLong_SHIFT) { - return (unsigned char) ((((((((((unsigned char)digits[3]) << PyLong_SHIFT) | (unsigned char)digits[2]) << PyLong_SHIFT) | (unsigned char)digits[1]) << PyLong_SHIFT) | (unsigned char)digits[0]))); - } - } - break; - } -#endif - if (sizeof(unsigned char) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned char, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned char) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned char, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if (CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available, cannot convert large numbers"); -#else - unsigned char val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (unsigned char) -1; - } - } else { - unsigned char val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (unsigned char) -1; - val = __Pyx_PyInt_As_unsigned_char(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to unsigned char"); - return (unsigned char) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned char"); - return (unsigned char) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if (CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { - const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(char) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (char) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (char) 0; - case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) - case 2: - if (8 * sizeof(char) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { - return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(char) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { - return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(char) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { - return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (char) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(char) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (char) 0; - case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) - case -2: - if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(char) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { - return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(char) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(char) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { - return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - } -#endif - if (sizeof(char) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if (CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available, cannot convert large numbers"); -#else - char val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (char) -1; - } - } else { - char val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (char) -1; - val = __Pyx_PyInt_As_char(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to char"); - return (char) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; -} - -/* FormatTypeName */ - #if CYTHON_COMPILING_IN_LIMITED_API -static __Pyx_TypeName -__Pyx_PyType_GetName(PyTypeObject* tp) -{ - PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, - __pyx_n_s_name_2); - if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { - PyErr_Clear(); - Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__19)); - } - return name; -} -#endif - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* InitStrings */ - #if PY_MAJOR_VERSION >= 3 -static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { - if (t.is_unicode | t.is_str) { - if (t.intern) { - *str = PyUnicode_InternFromString(t.s); - } else if (t.encoding) { - *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); - } else { - *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); - } - } else { - *str = PyBytes_FromStringAndSize(t.s, t.n - 1); - } - if (!*str) - return -1; - if (PyObject_Hash(*str) == -1) - return -1; - return 0; -} -#endif -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION >= 3 - __Pyx_InitString(*t, t->p); - #else - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - if (!*t->p) - return -1; - if (PyObject_Hash(*t->p) == -1) - return -1; - #endif - ++t; - } - return 0; -} -#endif - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -#if !CYTHON_PEP393_ENABLED -static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -} -#else -static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (likely(PyUnicode_IS_ASCII(o))) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -} -#endif -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { - return __Pyx_PyUnicode_AsStringAndSize(o, length); - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { - int retval; - if (unlikely(!x)) return -1; - retval = __Pyx_PyObject_IsTrue(x); - Py_DECREF(x); - return retval; -} -static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { - __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result)); -#if PY_MAJOR_VERSION >= 3 - if (PyLong_Check(result)) { - if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, - "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). " - "The ability to return an instance of a strict subclass of int is deprecated, " - "and may be removed in a future version of Python.", - result_type_name)) { - __Pyx_DECREF_TypeName(result_type_name); - Py_DECREF(result); - return NULL; - } - __Pyx_DECREF_TypeName(result_type_name); - return result; - } -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")", - type_name, type_name, result_type_name); - __Pyx_DECREF_TypeName(result_type_name); - Py_DECREF(result); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x) || PyLong_Check(x))) -#else - if (likely(PyLong_Check(x))) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = m->nb_int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = m->nb_long(x); - } - #else - if (likely(m && m->nb_int)) { - name = "int"; - res = m->nb_int(x); - } - #endif -#else - if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { - res = PyNumber_Int(x); - } -#endif - if (likely(res)) { -#if PY_MAJOR_VERSION < 3 - if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { -#else - if (unlikely(!PyLong_CheckExact(res))) { -#endif - return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(b); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { - if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { - return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); -#if PY_MAJOR_VERSION < 3 - } else if (likely(PyInt_CheckExact(o))) { - return PyInt_AS_LONG(o); -#endif - } else { - Py_ssize_t ival; - PyObject *x; - x = PyNumber_Index(o); - if (!x) return -1; - ival = PyInt_AsLong(x); - Py_DECREF(x); - return ival; - } -} -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { - return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -/* #### Code section: end ### */ -#endif /* Py_PYTHON_H */ diff --git a/src/_vambtools.pyx b/src/_vambtools.pyx index a5daa7a8..2ada3926 100644 --- a/src/_vambtools.pyx +++ b/src/_vambtools.pyx @@ -45,22 +45,24 @@ cpdef void _kmercounts(unsigned char[::1] bytesarray, int k, int[::1] counts): cdef int countdown = k-1 cdef int contiglength = len(bytesarray) cdef int mask = (1 << (2 * k)) - 1 - cdef unsigned int* lut = [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 0, 4, 1, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, - 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4] + cdef unsigned char* lut = [ + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 0, 4, 1, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 0, 4, 1, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + ] for i in range(contiglength): character = bytesarray[i] diff --git a/src/abundance.py b/src/abundance.py index fa2fa594..e428503e 100644 --- a/src/abundance.py +++ b/src/abundance.py @@ -8,11 +8,12 @@ Print the median RPKM abundance for each bin in each sample to STDOUT. Will read the RPKM file into memory - beware.""", formatter_class=argparse.RawDescriptionHelpFormatter, - add_help=False) + add_help=False, +) -parser.add_argument('rpkmpath', help='Path to RPKM file') -parser.add_argument('clusterspath', help='Path to clusters.tsv') -parser.add_argument('headerpath', help='Path to list of headers') +parser.add_argument("rpkmpath", help="Path to RPKM file") +parser.add_argument("clusterspath", help="Path to clusters.tsv") +parser.add_argument("headerpath", help="Path to list of headers") if len(sys.argv) == 1: parser.print_help() @@ -26,12 +27,12 @@ raise FileNotFoundError(infile) # Load Vamb -sys.path.append('../vamb') +sys.path.append("../vamb") import vamb # Load in files with open(args.headerpath) as file: - indexof = {line.strip():i for i,line in enumerate(file)} + indexof = {line.strip(): i for i, line in enumerate(file)} with open(args.clusterspath) as file: clusters = vamb.vambtools.read_clusters(file) @@ -59,5 +60,5 @@ median_depths = np.median(depths, axis=0) - print(clustername, end='\t') - print('\t'.join([str(i) for i in median_depths])) + print(clustername, end="\t") + print("\t".join([str(i) for i in median_depths])) diff --git a/src/cmd_benchmark.py b/src/cmd_benchmark.py index c69cf540..daa81add 100644 --- a/src/cmd_benchmark.py +++ b/src/cmd_benchmark.py @@ -1,21 +1,28 @@ #!/usr/bin/python import sys import argparse +import vamb +import os parser = argparse.ArgumentParser( description="""Command-line benchmark utility.""", formatter_class=argparse.RawDescriptionHelpFormatter, - add_help=False) - -parser.add_argument('clusterspath', help='Path to clusters.tsv') -parser.add_argument('refpath', help='Path to reference file') -parser.add_argument('--tax', dest='taxpath', help='Path to taxonomic maps') -parser.add_argument('-m', dest='min_bin_size', metavar='', type=int, - default=200000, help='Minimum size of bins [200000]') -parser.add_argument('-c', dest='min_contigs', metavar='', type=int, - default=None, help='Minimum number of contigs per bin [None=1]') -parser.add_argument('-s', dest='separator', help='Binsplit separator', default=None) -parser.add_argument('--disjoint', action='store_true', help='Enforce disjoint clusters') + add_help=False, +) + +parser.add_argument("clusterspath", help="Path to clusters.tsv") +parser.add_argument("refpath", help="Path to reference file") +parser.add_argument("--tax", dest="taxpath", help="Path to taxonomic maps") +parser.add_argument( + "-m", + dest="min_bin_size", + metavar="", + type=int, + default=200000, + help="Minimum size of bins [200000]", +) +parser.add_argument("-s", dest="separator", help="Binsplit separator", default=None) +parser.add_argument("--disjoint", action="store_true", help="Enforce disjoint clusters") if len(sys.argv) == 1: parser.print_help() @@ -23,9 +30,6 @@ args = parser.parse_args() -import vamb -import os - # Check that files exist for path in args.clusterspath, args.refpath, args.taxpath: if path is not None and not os.path.isfile(path): @@ -41,8 +45,13 @@ with open(args.taxpath) as file: reference.load_tax_file(file) -binning = vamb.benchmark.Binning(clusters, reference, minsize=args.min_bin_size, disjoint=args.disjoint, - binsplit_separator=args.separator, mincontigs=args.min_contigs) +binning = vamb.benchmark.Binning( + clusters, + reference, + minsize=args.min_bin_size, + disjoint=args.disjoint, + binsplit_separator=args.separator, +) for rank in range(len(binning.counters)): binning.print_matrix(rank) diff --git a/src/concatenate.py b/src/concatenate.py index e23b82fb..4de145ae 100644 --- a/src/concatenate.py +++ b/src/concatenate.py @@ -4,30 +4,37 @@ import os import argparse import gzip +import vamb parser = argparse.ArgumentParser( description="""Creates the input FASTA file for Vamb. Input should be one or more FASTA files, each from a sample-specific assembly. If keepnames is False, resulting FASTA can be binsplit with separator 'C'.""", formatter_class=argparse.RawDescriptionHelpFormatter, - add_help=False) - -parser.add_argument('outpath', help='Path to output FASTA file') -parser.add_argument('inpaths', help='Paths to input FASTA file(s)', nargs='+') -parser.add_argument('-m', dest='minlength', metavar='', type=int, - default=2000, help='Discard sequences below this length [2000]') -parser.add_argument('--keepnames', action='store_true', help='Do not rename sequences [False]') -parser.add_argument('--nozip', action='store_true', help='Do not gzip output [False]') - -if len(sys.argv) == 1 or sys.argv[1] in ("-h", "--help"): + add_help=False, +) + +parser.add_argument("outpath", help="Path to output FASTA file") +parser.add_argument("inpaths", help="Paths to input FASTA file(s)", nargs="+") +parser.add_argument( + "-m", + dest="minlength", + metavar="", + type=int, + default=2000, + help="Discard sequences below this length [2000]", +) +parser.add_argument( + "--keepnames", action="store_true", help="Do not rename sequences [False]" +) +parser.add_argument("--nozip", action="store_true", help="Do not gzip output [False]") + +if len(sys.argv) == 1: parser.print_help() sys.exit() args = parser.parse_args() -sys.path.append('../vamb') -import vamb - # Check inputs for path in args.inpaths: if not os.path.isfile(path): @@ -36,9 +43,20 @@ if os.path.exists(args.outpath): raise FileExistsError(args.outpath) +parent = os.path.dirname(args.outpath) +if not os.path.isdir(parent): + raise NotADirectoryError( + f'Output file cannot be created: Parent directory "{parent}" is not an existing directory' + ) + # Run the code. Compressing DNA is easy, this is about 8% bigger than compresslevel 9, # but ~15x faster. -filehandle = open(args.outpath, "w") if args.nozip else gzip.open(args.outpath, "wt", compresslevel=3) -vamb.vambtools.concatenate_fasta(filehandle, args.inpaths, minlength=args.minlength, - rename=(not args.keepnames)) +filehandle = ( + open(args.outpath, "w") + if args.nozip + else gzip.open(args.outpath, "wt", compresslevel=3) +) +vamb.vambtools.concatenate_fasta( + filehandle, args.inpaths, minlength=args.minlength, rename=(not args.keepnames) +) filehandle.close() diff --git a/src/create_fasta.py b/src/create_fasta.py index 6b5ec141..4cc5a86b 100644 --- a/src/create_fasta.py +++ b/src/create_fasta.py @@ -1,17 +1,18 @@ import sys -import os import argparse +import vamb parser = argparse.ArgumentParser( description="""Command-line bin creator. Will read the entire content of the FASTA file into memory - beware.""", formatter_class=argparse.RawDescriptionHelpFormatter, - add_help=False) + add_help=False, +) -parser.add_argument('fastapath', help='Path to FASTA file') -parser.add_argument('clusterspath', help='Path to clusters.tsv') -parser.add_argument('minsize', help='Minimum size of bin', type=int, default=0) -parser.add_argument('outdir', help='Directory to create') +parser.add_argument("fastapath", help="Path to FASTA file") +parser.add_argument("clusterspath", help="Path to clusters.tsv") +parser.add_argument("minsize", help="Minimum size of bin", type=int, default=0) +parser.add_argument("outdir", help="Directory to create") if len(sys.argv) == 1: parser.print_help() @@ -19,13 +20,12 @@ args = parser.parse_args() -sys.path.append('../vamb') -import vamb - with open(args.clusterspath) as file: clusters = vamb.vambtools.read_clusters(file) -with vamb.vambtools.Reader(args.fastapath, 'rb') as file: +with vamb.vambtools.Reader(args.fastapath) as file: fastadict = vamb.vambtools.loadfasta(file) -vamb.vambtools.write_bins(args.outdir, clusters, fastadict, maxbins=None, minsize=args.minsize) +vamb.vambtools.write_bins( + args.outdir, clusters, fastadict, maxbins=None, minsize=args.minsize +) diff --git a/src/create_kernel.py b/src/create_kernel.py index ae393d4a..1238bb0a 100644 --- a/src/create_kernel.py +++ b/src/create_kernel.py @@ -34,20 +34,23 @@ import itertools from scipy.linalg import null_space + def reverse_complement(nuc): table = str.maketrans("ACGT", "TGCA") return nuc[::-1].translate(table) + def all_kmers(k): for i in itertools.product("ACGT", repeat=k): - yield(''.join(i)) + yield ("".join(i)) + def create_projection_kernel(): - indexof = {kmer:i for i,kmer in enumerate(all_kmers(4))} + indexof = {kmer: i for i, kmer in enumerate(all_kmers(4))} linear_equations = list() # Constraint one: Frequencies sum to one (or in this scaled case, zero) - linear_equations.append([1]*256) + linear_equations.append([1] * 256) # Constaint two: Frequencies are same as that of reverse complement for kmer in all_kmers(4): @@ -57,14 +60,14 @@ def create_projection_kernel(): if kmer >= revcomp: continue - line = [0]*256 + line = [0] * 256 line[indexof[kmer]] = 1 line[indexof[revcomp]] = -1 linear_equations.append(line) # Constraint three: sum(ABCx) = sum(xABC) for trimer in all_kmers(3): - line = [0]*256 + line = [0] * 256 for suffix in "ACGT": line[indexof[trimer + suffix]] += 1 for prefix in "ACGT": @@ -76,8 +79,9 @@ def create_projection_kernel(): assert kernel.shape == (256, 103) return kernel + def create_rc_kernel(): - indexof = {kmer:i for i,kmer in enumerate(all_kmers(4))} + indexof = {kmer: i for i, kmer in enumerate(all_kmers(4))} rc_matrix = np.zeros((256, 256), dtype=np.float32) for col, kmer in enumerate(all_kmers(4)): revcomp = reverse_complement(kmer) @@ -86,9 +90,11 @@ def create_rc_kernel(): return rc_matrix + def create_dual_kernel(): return np.dot(create_rc_kernel(), create_projection_kernel()) + dual_kernel = create_dual_kernel() # Prevent overwriting kernel when running tests diff --git a/src/mergeclusters.py b/src/mergeclusters.py index 59e090cf..0c28bda8 100644 --- a/src/mergeclusters.py +++ b/src/mergeclusters.py @@ -1,4 +1,3 @@ - __doc__ = """Merge clusters according to overlap in contigs. This module is obsolete in the current version of Vamb, as VAMB now creates @@ -29,7 +28,6 @@ """ - import sys as _sys import os as _os import itertools as _itertools @@ -41,7 +39,6 @@ # import igraph as _igraph - def _iter_overlapping_pairs(contigsof, threshold=0.5): """This creates a generator that yields (clst1, clst2), overlap for all pairs of clusters with nonzero overlap.""" @@ -69,7 +66,6 @@ def _iter_overlapping_pairs(contigsof, threshold=0.5): yield (cluster1, cluster2), overlap - # def comminuty_merge(contigsof, threshold=0.5, steps=4): # """Merges all communities of clusters using the Walktrap algorithm(1) # (1) http://arxiv.org/abs/physics/0512106 @@ -113,6 +109,7 @@ def _iter_overlapping_pairs(contigsof, threshold=0.5): # return merged + def _bron_kerbosch(r, p, x, cliques, edges): """Finds all maximal cliques in a graph""" @@ -127,6 +124,7 @@ def _bron_kerbosch(r, p, x, cliques, edges): else: cliques.append(r) + def clique_merge(contigsof, threshold=0.5): """Merges all maximal cliques of clusters. @@ -157,7 +155,7 @@ def clique_merge(contigsof, threshold=0.5): mergedclusters = dict() for i, clique in enumerate(cliques): - mergedname = 'cluster_' + str(i + 1) + mergedname = "cluster_" + str(i + 1) contigs = set() for cluster in clique: @@ -168,7 +166,6 @@ def clique_merge(contigsof, threshold=0.5): return mergedclusters - def subgraph_merge(contigsof, threshold=0.5): """Merges all connected graphs of clusters together. @@ -195,7 +192,7 @@ def subgraph_merge(contigsof, threshold=0.5): mergedclusters = dict() for i, subgraph in enumerate(deduplicated_subgraphs): - mergedname = 'cluster_' + str(i + 1) + mergedname = "cluster_" + str(i + 1) contigs = set() for cluster in subgraph: @@ -205,6 +202,7 @@ def subgraph_merge(contigsof, threshold=0.5): return mergedclusters + def hierarchical_merge(contigsof, threshold=0.5): """Merges pairs together from most to least connected. @@ -248,10 +246,10 @@ def hierarchical_merge(contigsof, threshold=0.5): clustersof[contig].remove(set1) for contig in set2: - clustersof[contig].discard(set2) # no error for set(set1 & set2) + clustersof[contig].discard(set2) # no error for set(set1 & set2) existing.remove(set1) - existing.discard(set2) # don't raise error if set1 == set2 + existing.discard(set2) # don't raise error if set1 == set2 # Use clustersof to get list of all sets which may have some overlap with newset # We only do this to avoid checking for overlap with *every* existing set @@ -276,7 +274,7 @@ def hierarchical_merge(contigsof, threshold=0.5): mergedclusters = dict() for number, cluster in enumerate(existing): - mergedname = 'cluster_' + str(number + 1) + mergedname = "cluster_" + str(number + 1) mergedclusters[mergedname] = set(cluster) return mergedclusters diff --git a/vamb/__init__.py b/vamb/__init__.py index 629d6c82..6d7a5bb3 100644 --- a/vamb/__init__.py +++ b/vamb/__init__.py @@ -11,21 +11,22 @@ General workflow: 1) Filter contigs by size using vamb.vambtools.filtercontigs -2) Map reads to contigs to obtain BAM file -3) Calculate TNF of contigs using vamb.parsecontigs -4) Create RPKM table from BAM files using vamb.parsebam +2) Map reads to contigs to obtain BAM files +3) Calculate a Composition of contigs using vamb.parsecontigs +4) Create Abundance object from BAM files using vamb.parsebam 5) Train autoencoder using vamb.encode 6) Cluster latent representation using vamb.cluster 7) Split bins using vamb.vambtools """ -__authors__ = 'Jakob Nybo Nissen', 'Simon Rasmussen' -__licence__ = 'MIT' -__version__ = (3, 0, 9) +__authors__ = "Jakob Nybo Nissen", "Simon Rasmussen" +__licence__ = "MIT" +__version__ = (4, 0, 0, "DEV") import sys as _sys -if _sys.version_info[:2] < (3, 5): - raise ImportError('Python version must be >= 3.5') + +if _sys.version_info[:2] < (3, 9): + raise ImportError("Python version must be >= 3.9") from . import vambtools from . import parsebam diff --git a/vamb/__main__.py b/vamb/__main__.py index 7f640bce..3bbf8d84 100755 --- a/vamb/__main__.py +++ b/vamb/__main__.py @@ -2,207 +2,349 @@ # More imports below, but the user's choice of processors must be parsed before # numpy can be imported. +import vamb +import numpy as np import sys import os import argparse import torch import datetime import time -import shutil +from math import isfinite +from typing import Optional, IO -DEFAULT_PROCESSES = min(os.cpu_count(), 8) +_ncpu = os.cpu_count() +DEFAULT_THREADS = 8 if _ncpu is None else min(_ncpu, 8) # These MUST be set before importing numpy # I know this is a shitty hack, see https://github.com/numpy/numpy/issues/11826 -os.environ["MKL_NUM_THREADS"] = str(DEFAULT_PROCESSES) -os.environ["NUMEXPR_NUM_THREADS"] = str(DEFAULT_PROCESSES) -os.environ["OMP_NUM_THREADS"] = str(DEFAULT_PROCESSES) +os.environ["MKL_NUM_THREADS"] = str(DEFAULT_THREADS) +os.environ["NUMEXPR_NUM_THREADS"] = str(DEFAULT_THREADS) +os.environ["OMP_NUM_THREADS"] = str(DEFAULT_THREADS) # Append vamb to sys.path to allow vamb import even if vamb was not installed # using pip parentdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(parentdir) -import numpy as np -import vamb -################################# DEFINE FUNCTIONS ########################## -def log(string, logfile, indent=0): - print(('\t' * indent) + string, file=logfile) - logfile.flush() +from vamb import aamb_encode -def calc_tnf(outdir, fastapath, tnfpath, namespath, lengthspath, mincontiglength, logfile): - begintime = time.time() - log('\nLoading TNF', logfile, 0) - log('Minimum sequence length: {}'.format(mincontiglength), logfile, 1) - # If no path to FASTA is given, we load TNF from .npz files - if fastapath is None: - log('Loading TNF from npz array {}'.format(tnfpath), logfile, 1) - tnfs = vamb.vambtools.read_npz(tnfpath) - log('Loading contignames from npz array {}'.format(namespath), logfile, 1) - contignames = vamb.vambtools.read_npz(namespath) - log('Loading contiglengths from npz array {}'.format(lengthspath), logfile, 1) - contiglengths = vamb.vambtools.read_npz(lengthspath) - - if not tnfs.dtype == np.float32: - raise ValueError('TNFs .npz array must be of float32 dtype') - - if not np.issubdtype(contiglengths.dtype, np.integer): - raise ValueError('contig lengths .npz array must be of an integer dtype') - - if not (len(tnfs) == len(contignames) == len(contiglengths)): - raise ValueError('Not all of TNFs, names and lengths are same length') - - # Discard any sequence with a length below mincontiglength - mask = contiglengths >= mincontiglength - tnfs = tnfs[mask] - contignames = list(contignames[mask]) - contiglengths = contiglengths[mask] - - # Else parse FASTA files - else: - log('Loading data from FASTA file {}'.format(fastapath), logfile, 1) - with vamb.vambtools.Reader(fastapath, 'rb') as tnffile: - ret = vamb.parsecontigs.read_contigs(tnffile, minlength=mincontiglength) +################################# DEFINE FUNCTIONS ########################## - tnfs, contignames, contiglengths = ret - vamb.vambtools.write_npz(os.path.join(outdir, 'tnf.npz'), tnfs) - vamb.vambtools.write_npz(os.path.join(outdir, 'lengths.npz'), contiglengths) - elapsed = round(time.time() - begintime, 2) - ncontigs = len(contiglengths) - nbases = contiglengths.sum() +def log(string: str, logfile: IO[str], indent: int = 0): + print(("\t" * indent) + string, file=logfile) + logfile.flush() - print('', file=logfile) - log('Kept {} bases in {} sequences'.format(nbases, ncontigs), logfile, 1) - log('Processed TNF in {} seconds'.format(elapsed), logfile, 1) - return tnfs, contignames, contiglengths +def calc_tnf( + outdir: str, + fastapath: Optional[str], + npzpath: Optional[str], + mincontiglength: int, + logfile: IO[str], +) -> vamb.parsecontigs.Composition: + begintime = time.time()/60 + log("\nLoading TNF", logfile, 0) + log(f"Minimum sequence length: {mincontiglength}", logfile, 1) + + if npzpath is not None: + log(f"Loading composition from npz {npzpath}", logfile, 1) + composition = vamb.parsecontigs.Composition.load(npzpath) + composition.filter_min_length(mincontiglength) + else: + assert fastapath is not None + log(f"Loading data from FASTA file {fastapath}", logfile, 1) + with vamb.vambtools.Reader(fastapath) as file: + composition = vamb.parsecontigs.Composition.from_file( + file, minlength=mincontiglength + ) + composition.save(os.path.join(outdir, "composition.npz")) + + elapsed = round(time.time()/60 - begintime, 2) + print("", file=logfile) + log( + f"Kept {composition.count_bases()} bases in {composition.nseqs} sequences", + logfile, + 1, + ) + log(f"Processed TNF in {elapsed} minutes", logfile, 1) + + return composition + + +def calc_rpkm( + outdir: str, + bampaths: Optional[list[str]], + npzpath: Optional[str], + comp_metadata: vamb.parsecontigs.CompositionMetaData, + verify_refhash: bool, + minid: float, + nthreads: int, + logfile: IO[str], +) -> vamb.parsebam.Abundance: + + begintime = time.time()/60 + log("\nLoading depths", logfile) + log( + f'Reference hash: {comp_metadata.refhash.hex() if verify_refhash else "None"}', + logfile, + 1, + ) -def calc_rpkm(outdir, bampaths, rpkmpath, jgipath, mincontiglength, refhash, ncontigs, - minalignscore, minid, subprocesses, logfile): - begintime = time.time() - log('\nLoading RPKM', logfile) # If rpkm is given, we load directly from .npz file - if rpkmpath is not None: - log('Loading RPKM from npz array {}'.format(rpkmpath), logfile, 1) - rpkms = vamb.vambtools.read_npz(rpkmpath) + if npzpath is not None: + log(f"Loading depths from npz array {npzpath}", logfile, 1) + abundance = vamb.parsebam.Abundance.load( + npzpath, comp_metadata.refhash if verify_refhash else None + ) + # I don't want this check in any constructors of abundance, since the constructors + # should be able to skip this check in case comp and abundance are independent. + # But when running the main Vamb workflow, we need to assert this. + if abundance.nseqs != comp_metadata.nseqs: + assert not verify_refhash + raise ValueError( + f"Loaded abundance has {abundance.nseqs} sequences, " + f"but composition has {comp_metadata.nseqs}." + ) - if not rpkms.dtype == np.float32: - raise ValueError('RPKMs .npz array must be of float32 dtype') - else: - log('Reference hash: {}'.format(refhash if refhash is None else refhash.hex()), logfile, 1) - - # Else if JGI is given, we load from that - if jgipath is not None: - log('Loading RPKM from JGI file {}'.format(jgipath), logfile, 1) - with open(jgipath) as file: - rpkms = vamb.vambtools._load_jgi(file, mincontiglength, refhash) - - elif bampaths is not None: - log('Parsing {} BAM files with {} subprocesses'.format(len(bampaths), subprocesses), - logfile, 1) - log('Min alignment score: {}'.format(minalignscore), logfile, 1) - log('Min identity: {}'.format(minid), logfile, 1) - log('Min contig length: {}'.format(mincontiglength), logfile, 1) - log('\nOrder of columns is:', logfile, 1) - log('\n\t'.join(bampaths), logfile, 1) - print('', file=logfile) - - dumpdirectory = os.path.join(outdir, 'tmp') - rpkms = vamb.parsebam.read_bamfiles(bampaths, dumpdirectory=dumpdirectory, - refhash=refhash, minscore=minalignscore, - minlength=mincontiglength, minid=minid, - subprocesses=subprocesses, logfile=logfile) - print('', file=logfile) - vamb.vambtools.write_npz(os.path.join(outdir, 'rpkm.npz'), rpkms) - shutil.rmtree(dumpdirectory) - - if len(rpkms) != ncontigs: - raise ValueError("Length of TNFs and length of RPKM does not match. Verify the inputs") - - elapsed = round(time.time() - begintime, 2) - log('Processed RPKM in {} seconds'.format(elapsed), logfile, 1) - - return rpkms - -def trainvae(outdir, rpkms, tnfs, nhiddens, nlatent, alpha, beta, dropout, cuda, - batchsize, nepochs, lrate, batchsteps, logfile): - - begintime = time.time() - log('\nCreating and training VAE', logfile) + assert bampaths is not None + log(f"Parsing {len(bampaths)} BAM files with {nthreads} threads", logfile, 1) + + abundance = vamb.parsebam.Abundance.from_files( + bampaths, comp_metadata, verify_refhash, minid, nthreads + ) + abundance.save(os.path.join(outdir, "abundance.npz")) + + log(f"Min identity: {abundance.minid}\n", logfile, 1) + log("Order of columns is:", logfile, 1) + log("\n\t".join(abundance.samplenames), logfile, 1) + + elapsed = round(time.time()/60 - begintime, 2) + print("", file=logfile) + log(f"Processed RPKM in {elapsed} minutes", logfile, 1) + + return abundance + + +def trainvae( + outdir: str, + rpkms: np.ndarray, + tnfs: np.ndarray, + lengths: np.ndarray, + nhiddens: Optional[list[int]], # set automatically if None + nlatent: int, + alpha: Optional[float], # set automatically if None + beta: float, + dropout: Optional[float], # set automatically if None + cuda: bool, + batchsize: int, + nepochs: int, + lrate: float, + batchsteps: list[int], + logfile: IO[str], +) -> tuple[np.ndarray, np.ndarray]: + + begintime = time.time()/60 + log("\nCreating and training VAE", logfile) + + assert len(rpkms) == len(tnfs) nsamples = rpkms.shape[1] - vae = vamb.encode.VAE(nsamples, nhiddens=nhiddens, nlatent=nlatent, - alpha=alpha, beta=beta, dropout=dropout, cuda=cuda) - - log('Created VAE', logfile, 1) - dataloader, mask = vamb.encode.make_dataloader(rpkms, tnfs, batchsize, - destroy=True, cuda=cuda) - log('Created dataloader and mask', logfile, 1) - vamb.vambtools.write_npz(os.path.join(outdir, 'mask.npz'), mask) - n_discarded = len(mask) - mask.sum() - log('Number of sequences unsuitable for encoding: {}'.format(n_discarded), logfile, 1) - log('Number of sequences remaining: {}'.format(len(mask) - n_discarded), logfile, 1) - print('', file=logfile) + vae = vamb.encode.VAE( + nsamples, + nhiddens=nhiddens, + nlatent=nlatent, + alpha=alpha, + beta=beta, + dropout=dropout, + cuda=cuda, + ) - modelpath = os.path.join(outdir, 'model.pt') - vae.trainmodel(dataloader, nepochs=nepochs, lrate=lrate, batchsteps=batchsteps, - logfile=logfile, modelfile=modelpath) + log("Created VAE", logfile, 1) + dataloader, mask = vamb.encode.make_dataloader( + rpkms, tnfs, lengths, batchsize, destroy=True, cuda=cuda + ) + log("Created dataloader and mask", logfile, 1) + vamb.vambtools.write_npz(os.path.join(outdir, "mask.npz"), mask) + n_discarded = len(mask) - mask.sum() + log(f"Number of sequences unsuitable for encoding: {n_discarded}", logfile, 1) + log(f"Number of sequences remaining: {len(mask) - n_discarded}", logfile, 1) + print("", file=logfile) + + modelpath = os.path.join(outdir, "model.pt") + vae.trainmodel( + dataloader, + nepochs=nepochs, + lrate=lrate, + batchsteps=batchsteps, + logfile=logfile, + modelfile=modelpath, + ) - print('', file=logfile) - log('Encoding to latent representation', logfile, 1) + print("", file=logfile) + log("Encoding to latent representation", logfile, 1) latent = vae.encode(dataloader) - vamb.vambtools.write_npz(os.path.join(outdir, 'latent.npz'), latent) - del vae # Needed to free "latent" array's memory references? + vamb.vambtools.write_npz(os.path.join(outdir, "latent.npz"), latent) + del vae # Needed to free "latent" array's memory references? - elapsed = round(time.time() - begintime, 2) - log('Trained VAE and encoded in {} seconds'.format(elapsed), logfile, 1) + elapsed = round(time.time()/60 - begintime, 2) + log(f"Trained VAE and encoded in {elapsed} minutes", logfile, 1) return mask, latent -def cluster(clusterspath, latent, contignames, windowsize, minsuccesses, maxclusters, - minclustersize, separator, cuda, logfile): - begintime = time.time() +def trainaae( + outdir: str, + rpkms: np.ndarray, + tnfs: np.ndarray, + lengths: np.ndarray, + nhiddens: Optional[list[int]], # set automatically if None + nlatent_z: int, + nlatent_y: int, + alpha: Optional[float], # set automatically if None + sl: float, + slr: float, + temp: Optional[float] , + cuda: bool, + batchsize: int, + nepochs: int, + lrate: float, + batchsteps: list[int], + logfile: IO[str], + contignames: np.ndarray +) -> tuple[np.ndarray, np.ndarray,dict()]: + + begintime = time.time()/60 + log("\nCreating and training AAE", logfile) + nsamples = rpkms.shape[1] - log('\nClustering', logfile) - log('Windowsize: {}'.format(windowsize), logfile, 1) - log('Min successful thresholds detected: {}'.format(minsuccesses), logfile, 1) - log('Max clusters: {}'.format(maxclusters), logfile, 1) - log('Min cluster size: {}'.format(minclustersize), logfile, 1) - log('Use CUDA for clustering: {}'.format(cuda), logfile, 1) - log('Separator: {}'.format(None if separator is None else ('"'+separator+'"')), - logfile, 1) + assert len(rpkms) == len(tnfs) - it = vamb.cluster.cluster(latent, contignames, destroy=True, windowsize=windowsize, - normalized=False, minsuccesses=minsuccesses, cuda=cuda) - renamed = ((str(i+1), c) for (i, (n,c)) in enumerate(it)) + aae = aamb_encode.AAE(nsamples, nhiddens, nlatent_z, nlatent_y, sl, slr , alpha ,cuda) - # Binsplit if given a separator - if separator is not None: - renamed = vamb.vambtools.binsplit(renamed, separator) - - with open(clusterspath, 'w') as clustersfile: - _ = vamb.vambtools.write_clusters(clustersfile, renamed, max_clusters=maxclusters, - min_size=minclustersize, rename=False) - clusternumber, ncontigs = _ - - print('', file=logfile) - log('Clustered {} contigs in {} bins'.format(ncontigs, clusternumber), logfile, 1) + log("Created AAE", logfile,1) + dataloader, mask = vamb.encode.make_dataloader( + rpkms, tnfs, lengths, batchsize, destroy=True, cuda=cuda + ) + log("Created dataloader and mask", logfile, 1) + #vamb.vambtools.write_npz(os.path.join(outdir, "mask.npz"), mask) + n_discarded = len(mask) - mask.sum() + log(f"Number of sequences unsuitable for encoding: {n_discarded}", logfile, 1) + log(f"Number of sequences remaining: {len(mask) - n_discarded}", logfile, 1) + print("", file=logfile) + + modelpath = os.path.join(outdir, "aae_model.pt") + aae.trainmodel(dataloader, + nepochs, + batchsteps, + temp, + lrate, + logfile, + modelpath) + + + + print("", file=logfile) + log("Encoding to latent representation", logfile, 1) + clusters_y_dict,latent = aae.get_latents(contignames, dataloader) + vamb.vambtools.write_npz(os.path.join(outdir, "aae_z_latent.npz"), latent) + #vamb.vambtools.write_npz(os.path.join(outdir, "aae_y_latent.npz"), latenty_) + + del aae # Needed to free "latent" array's memory references? + + elapsed = round(time.time()/60 - begintime, 2) + log(f"Trained AAE and encoded in {elapsed} minutes", logfile, 1) + + return mask, latent, clusters_y_dict + + +def cluster( + clusterspath: str, + latent: np.ndarray, + contignames: np.ndarray, # of dtype object + windowsize: int, + minsuccesses: int, + maxclusters: Optional[int], + minclustersize: int, + separator: Optional[str], + cuda: bool, + logfile: IO[str], + cluster_prefix : str +) -> None: + begintime = time.time()/60 + + log("\nClustering", logfile) + log(f"Windowsize: {windowsize}", logfile, 1) + log(f"Min successful thresholds detected: {minsuccesses}", logfile, 1) + log(f"Max clusters: {maxclusters}", logfile, 1) + log(f"Min cluster size: {minclustersize}", logfile, 1) + log(f"Use CUDA for clustering: {cuda}", logfile, 1) + log( + "Separator: {}".format(None if separator is None else ('"' + separator + '"')), + logfile, + 1, + ) - elapsed = round(time.time() - begintime, 2) - log('Clustered contigs in {} seconds'.format(elapsed), logfile, 1) + cluster_generator = vamb.cluster.ClusterGenerator( + latent, + windowsize=windowsize, + minsuccesses=minsuccesses, + destroy=True, + normalized=False, + cuda=cuda, + ) -def write_fasta(outdir, clusterspath, fastapath, contignames, contiglengths, minfasta, logfile): - begintime = time.time() + renamed = ( + (str(cluster_index + 1), {contignames[i] for i in members}) + for (cluster_index, (_, members)) in enumerate( + map(lambda x: x.as_tuple(), cluster_generator) + ) + ) - log('\nWriting FASTA files', logfile) - log('Minimum FASTA size: {}'.format(minfasta), logfile, 1) + # Binsplit if given a separator + if separator is not None: + maybe_split = vamb.vambtools.binsplit(renamed, separator) + else: + maybe_split = renamed + + with open(clusterspath, "w") as clustersfile: + clusternumber, ncontigs = vamb.vambtools.write_clusters( + clustersfile, + maybe_split, + max_clusters=maxclusters, + min_size=minclustersize, + rename=False, + cluster_prefix=cluster_prefix + ) + + print("", file=logfile) + log(f"Clustered {ncontigs} contigs in {clusternumber} bins", logfile, 1) + + elapsed = round(time.time()/60 - begintime, 2) + log(f"Clustered contigs in {elapsed} minutes", logfile, 1) + + +def write_fasta( + outdir: str, + clusterspath: str, + fastapath: str, + contignames: np.ndarray, # of object + contiglengths: np.ndarray, + minfasta: int, + logfile: IO[str], + separator: str, +) -> None: + begintime = time.time()/60 + + log("\nWriting FASTA files", logfile) + log("Minimum FASTA size: "+str(minfasta), logfile, 1) + assert len(contignames) == len(contiglengths) lengthof = dict(zip(contignames, contiglengths)) - filtered_clusters = dict() + filtered_clusters: dict[str, set[str]] = dict() with open(clusterspath) as file: clusters = vamb.vambtools.read_clusters(file) @@ -213,157 +355,522 @@ def write_fasta(outdir, clusterspath, fastapath, contignames, contiglengths, min filtered_clusters[cluster] = clusters[cluster] del lengthof, clusters - keep = set() + keep: set[str] = set() for contigs in filtered_clusters.values(): keep.update(set(contigs)) - - with vamb.vambtools.Reader(fastapath, 'rb') as file: - fastadict = vamb.vambtools.loadfasta(file, keep=keep) - - vamb.vambtools.write_bins(os.path.join(outdir, "bins"), filtered_clusters, fastadict, maxbins=None) + + with vamb.vambtools.Reader(fastapath) as file: + vamb.vambtools.write_bins( + os.path.join(outdir, "bins"), filtered_clusters, file, maxbins=None, separator=separator + ) ncontigs = sum(map(len, filtered_clusters.values())) nfiles = len(filtered_clusters) - print('', file=logfile) - log('Wrote {} contigs to {} FASTA files'.format(ncontigs, nfiles), logfile, 1) - - elapsed = round(time.time() - begintime, 2) - log('Wrote FASTA in {} seconds'.format(elapsed), logfile, 1) - -def run(outdir, fastapath, tnfpath, namespath, lengthspath, bampaths, rpkmpath, jgipath, - mincontiglength, norefcheck, minalignscore, minid, subprocesses, nhiddens, nlatent, - nepochs, batchsize, cuda, alpha, beta, dropout, lrate, batchsteps, windowsize, - minsuccesses, minclustersize, separator, maxclusters, minfasta, logfile): - - log('Starting Vamb version ' + '.'.join(map(str, vamb.__version__)), logfile) - log('Date and time is ' + str(datetime.datetime.now()), logfile, 1) - begintime = time.time() - + print("", file=logfile) + log(f"Wrote {ncontigs} contigs to {nfiles} FASTA files", logfile, 1) + + elapsed = round(time.time()/60 - begintime, 2) + log(f"Wrote FASTA in {elapsed} minutes", logfile, 1) + + +def run( + outdir: str, + fastapath: Optional[str], + compositionpath: Optional[str], ## ??? + bampaths: Optional[list[str]], # + rpkmpath: Optional[str], + mincontiglength: int, + norefcheck: bool, + noencode: bool, + minid: float, + nthreads: int, + nhiddens: Optional[list[int]], + nhiddens_aae: Optional[list[int]], + nlatent: int, + nlatent_aae_z: int, + nlatent_aae_y: int, + nepochs: int, + nepochs_aae: int, + batchsize: int, + batchsize_aae: int, + cuda: bool, + alpha: Optional[float], + beta: float, + dropout: Optional[float], + sl: float, + slr: float, + temp: float, + lrate: float, + batchsteps: list[int], + batchsteps_aae: list[int], + windowsize: int, + minsuccesses: int, + minclustersize: int, + separator: Optional[str], + maxclusters: Optional[int], + minfasta: Optional[int], + model_selection: str, + logfile: IO[str] +): + + log("Starting Vamb version " + ".".join(map(str, vamb.__version__)), logfile) + log("Date and time is " + str(datetime.datetime.now()), logfile, 1) + begintime = time.time()/60 + # Get TNFs, save as npz - tnfs, contignames, contiglengths = calc_tnf(outdir, fastapath, tnfpath, namespath, - lengthspath, mincontiglength, logfile) - + composition = calc_tnf(outdir, fastapath, compositionpath, mincontiglength, logfile) + + # Parse BAMs, save as npz - refhash = None if norefcheck else vamb.vambtools._hash_refnames( - (name.split(maxsplit=1)[0] for name in contignames) + abundance = calc_rpkm( + outdir, + bampaths, + rpkmpath, + composition.metadata, + not norefcheck, + minid, + nthreads, + logfile, ) - rpkms = calc_rpkm(outdir, bampaths, rpkmpath, jgipath, mincontiglength, refhash, - len(tnfs), minalignscore, minid, subprocesses, logfile) - - # Train, save model - mask, latent = trainvae(outdir, rpkms, tnfs, nhiddens, nlatent, alpha, beta, - dropout, cuda, batchsize, nepochs, lrate, batchsteps, logfile) - - del tnfs, rpkms - contignames = [c for c, m in zip(contignames, mask) if m] - - # Cluster, save tsv file - clusterspath = os.path.join(outdir, 'clusters.tsv') - cluster(clusterspath, latent, contignames, windowsize, minsuccesses, maxclusters, - minclustersize, separator, cuda, logfile) - - del latent - - if minfasta is not None: - write_fasta(outdir, clusterspath, fastapath, contignames, contiglengths, minfasta, - logfile) - - elapsed = round(time.time() - begintime, 2) - log('\nCompleted Vamb in {} seconds'.format(elapsed), logfile) - + timepoint_gernerate_input=time.time()/60 + time_generating_input= round(timepoint_gernerate_input-begintime,2) + + if noencode: + elapsed = round(time.time()/60 - begintime, 2) + log( + f"\nNoencode set, skipping encoding and clustering.\n\nCompleted Avamb in {elapsed} minutes", + logfile, + ) + return None + log(f"\nTNF and coabundances generated in {time_generating_input}", logfile, 1) + + if 'vae' in model_selection: + begin_train_vae=time.time()/60 + # Train, save model + mask, latent = trainvae( + outdir, + abundance.matrix, + composition.matrix, + composition.metadata.lengths, + nhiddens, + nlatent, + alpha, + beta, + dropout, + cuda, + batchsize, + nepochs, + lrate, + batchsteps, + logfile, + ) + fin_train_vae=time.time()/60 + time_training_vae=round(fin_train_vae-begin_train_vae,2) + log(f"\nVAE trained in {time_training_vae}", logfile, 1) + + if 'aae' in model_selection: + begin_train_aae = time.time()/60 + # Train, save model + mask, latent_z, clusters_y_dict = trainaae( + outdir, + abundance.matrix, + composition.matrix, + composition.metadata.lengths, + nhiddens_aae, + nlatent_aae_z, + nlatent_aae_y, + alpha, + sl, + slr, + temp, + cuda, + batchsize_aae, + nepochs_aae, + lrate, + batchsteps_aae, + logfile, + composition.metadata.identifiers, + ) + fin_train_aae=time.time()/60 + time_training_aae=round(fin_train_aae-begin_train_aae,2) + log(f"\nAAE trained in {time_training_aae}", logfile, 1) + + # Free up memory + comp_metadata = composition.metadata + del composition, abundance + + comp_metadata.filter_mask(mask) # type: ignore + # Write contignames and contiglengths needed for dereplication purposes + np.savetxt(os.path.join(outdir,'contignames'),comp_metadata.identifiers, fmt='%s') + np.savez(os.path.join(outdir,'lengths.npz'),comp_metadata.lengths) + + if 'vae' in model_selection: + assert comp_metadata.nseqs == len(latent) + + begin_cluster_latent=time.time()/60 + # Cluster, save tsv file + clusterspath = os.path.join(outdir, "vae_clusters.tsv") + cluster( + clusterspath, + latent, + comp_metadata.identifiers, + windowsize, + minsuccesses, + maxclusters, + minclustersize, + separator, + cuda, + logfile, + 'vae_', + ) + fin_cluster_latent=time.time()/60 + time_clustering_latent=round(fin_cluster_latent-begin_cluster_latent,2) + log(f"\nVAE latent clustered in {time_clustering_latent}", logfile, 1) + + del latent + + if minfasta is not None and fastapath is not None: + # We have already checked fastapath is not None if minfasta is not None. + write_fasta( + outdir, + clusterspath, + fastapath, + comp_metadata.identifiers, + comp_metadata.lengths, + minfasta, + logfile, + separator + ) + + writing_bins_time = round(time.time()/60 - fin_cluster_latent, 2) + log(f"\nVAE bins written in {writing_bins_time} minutes", logfile) + + #log(f"\nCompleted Vamb in {elapsed} minutes", logfile) + if 'aae' in model_selection: + assert comp_metadata.nseqs == len(latent_z) + + begin_cluster_latent_z=time.time()/60 + # Cluster, save tsv file + clusterspath = os.path.join(outdir, "aae_z_clusters.tsv") + cluster( + clusterspath, + latent_z, + comp_metadata.identifiers, + windowsize, + minsuccesses, + maxclusters, + minclustersize, + separator, + cuda, + logfile, + 'aae_z_', + ) + fin_cluster_latent_z=time.time()/60 + time_clustering_latent_z=round(fin_cluster_latent_z-begin_cluster_latent_z,2) + log(f"\nAAE z latent clustered in {time_clustering_latent_z}", logfile, 1) + + del latent_z + + if minfasta is not None and fastapath is not None: + # We have already checked fastapath is not None if minfasta is not None. + write_fasta( + outdir, + clusterspath, + fastapath, + comp_metadata.identifiers, + comp_metadata.lengths, + minfasta, + logfile, + separator + ) + time_writing_bins_z=time.time()/60 + writing_bins_time_z = round(time_writing_bins_z - fin_cluster_latent_z, 2) + log(f"\nAAE z bins written in {writing_bins_time_z} minutes", logfile) + + clusterspath= os.path.join(outdir, "aae_y_clusters.tsv") + # Binsplit if given a separator + if separator is not None: + maybe_split = vamb.vambtools.binsplit(clusters_y_dict, separator) + else: + maybe_split = clusters_y_dict + with open(clusterspath, "w") as clustersfile: + clusternumber, ncontigs = vamb.vambtools.write_clusters( + clustersfile, + maybe_split, + max_clusters=maxclusters, + min_size=minclustersize, + rename=False, + cluster_prefix='aae_y_' + ) + + print("", file=logfile) + log(f"Clustered {ncontigs} contigs in {clusternumber} bins", logfile, 1) + time_start_writin_z_bins=time.time()/60 + if minfasta is not None and fastapath is not None: + # We have already checked fastapath is not None if minfasta is not None. + write_fasta( + outdir, + clusterspath, + fastapath, + comp_metadata.identifiers, + comp_metadata.lengths, + minfasta, + logfile, + separator + ) + time_writing_bins_y=time.time()/60 + writing_bins_time_y = round(time_writing_bins_y - time_start_writin_z_bins , 2) + log(f"\nAAE y bins written in {writing_bins_time_y} minutes", logfile) + def main(): - doc = f"""Vamb: Variational autoencoders for metagenomic binning. + doc = f"""Avamb: Adversarial and Variational autoencoders for metagenomic binning. Version: {'.'.join([str(i) for i in vamb.__version__])} - + Default use, good for most datasets: - vamb --outdir out --fasta my_contigs.fna --bamfiles *.bam + vamb --outdir out --fasta my_contigs.fna --bamfiles *.bam -o C - For advanced use and extensions of Vamb, check documentation of the package - at https://github.com/RasmussenLab/vamb.""" + For advanced use and extensions of Avamb, check documentation of the package + at https://github.com/RasmussenLab/vamb.""" # must be updated with the new github parser = argparse.ArgumentParser( prog="vamb", description=doc, formatter_class=argparse.RawDescriptionHelpFormatter, usage="%(prog)s outdir tnf_input rpkm_input [options]", - add_help=False) + add_help=False, + ) # Help - helpos = parser.add_argument_group(title='Help', description=None) - helpos.add_argument('-h', '--help', help='print help and exit', action='help') + helpos = parser.add_argument_group(title="Help and version", description=None) + helpos.add_argument("-h", "--help", help="print help and exit", action="help") + helpos.add_argument( + "--version", + action="version", + version=f'Vamb {".".join(map(str, vamb.__version__))}', + ) # Positional arguments - reqos = parser.add_argument_group(title='Output (required)', description=None) - reqos.add_argument('--outdir', metavar='', required=True, help='output directory to create') + reqos = parser.add_argument_group(title="Output (required)", description=None) + reqos.add_argument( + "--outdir", metavar="", required=True, help="output directory to create" + ) # TNF arguments - tnfos = parser.add_argument_group(title='TNF input (either fasta or all .npz files required)') - tnfos.add_argument('--fasta', metavar='', help='path to fasta file') - tnfos.add_argument('--tnfs', metavar='', help='path to .npz of TNF') - tnfos.add_argument('--names', metavar='', help='path to .npz of names of sequences') - tnfos.add_argument('--lengths', metavar='', help='path to .npz of seq lengths') + tnfos = parser.add_argument_group( + title="TNF input (either fasta or all .npz files required)" + ) + tnfos.add_argument("--fasta", metavar="", help="path to fasta file") + tnfos.add_argument("--composition", metavar="", help="path to .npz of composition") # RPKM arguments - rpkmos = parser.add_argument_group(title='RPKM input (either BAMs, JGI or .npz required)') - rpkmos.add_argument('--bamfiles', metavar='', help='paths to (multiple) BAM files', nargs='+') - rpkmos.add_argument('--rpkm', metavar='', help='path to .npz of RPKM') - rpkmos.add_argument('--jgi', metavar='', help='path to output of jgi_summarize_bam_contig_depths') + rpkmos = parser.add_argument_group( + title="RPKM input (either BAMs or .npz required)" + ) + rpkmos.add_argument( + "--bamfiles", metavar="", help="paths to (multiple) BAM files", nargs="+" + ) + rpkmos.add_argument("--rpkm", metavar="", help="path to .npz of RPKM (abundances)") # Optional arguments - inputos = parser.add_argument_group(title='IO options', description=None) - - inputos.add_argument('-m', dest='minlength', metavar='', type=int, default=100, - help='ignore contigs shorter than this [100]') - inputos.add_argument('-s', dest='minascore', metavar='', type=int, default=None, - help='ignore reads with alignment score below this [None]') - inputos.add_argument('-z', dest='minid', metavar='', type=float, default=None, - help='ignore reads with nucleotide identity below this [None]') - inputos.add_argument('-p', dest='subprocesses', metavar='', type=int, default=DEFAULT_PROCESSES, - help=('number of subprocesses to spawn ' - '[min(' + str(DEFAULT_PROCESSES) + ', nbamfiles)]')) - inputos.add_argument('--norefcheck', help='skip reference name hashing check [False]', - action='store_true') - inputos.add_argument('--minfasta', dest='minfasta', metavar='', type=int, default=None, - help='minimum bin size to output as fasta [None = no files]') + inputos = parser.add_argument_group(title="IO options", description=None) + + inputos.add_argument( + "-m", + dest="minlength", + metavar="", + type=int, + default=250, + help="ignore contigs shorter than this [250]", + ) + inputos.add_argument( + "-z", + dest="minid", + metavar="", + type=float, + default=0.0, + help="ignore reads with nucleotide identity below this [0.0]", + ) + inputos.add_argument( + "-p", + dest="nthreads", + metavar="", + type=int, + default=DEFAULT_THREADS, + help=( + "number of threads to use " "[min(" + str(DEFAULT_THREADS) + ", nbamfiles)]" + ), + ) + inputos.add_argument( + "--norefcheck", + help="skip reference name hashing check [False]", + action="store_true", + ) + inputos.add_argument( + "--minfasta", + dest="minfasta", + metavar="", + type=int, + default=None, + help="minimum bin size to output as fasta [None = no files]", + ) + inputos.add_argument( + "--noencode", + help="Output tnfs and abundances only, do not encode or cluster [False]", + action="store_true", + ) + # Model selection argument + model_selection = parser.add_argument_group(title='Model selection', description=None) + + model_selection.add_argument('--model', dest='model', metavar='', type=str, default='vae&aae', + help='Choose which model to run; only vae (vae), only aae (aae), the combination of vae and aae (vae&aae), [vae&aae]') # VAE arguments - vaeos = parser.add_argument_group(title='VAE options', description=None) - - vaeos.add_argument('-n', dest='nhiddens', metavar='', type=int, nargs='+', - default=None, help='hidden neurons [Auto]') - vaeos.add_argument('-l', dest='nlatent', metavar='', type=int, - default=32, help='latent neurons [32]') - vaeos.add_argument('-a', dest='alpha', metavar='',type=float, - default=None, help='alpha, weight of TNF versus depth loss [Auto]') - vaeos.add_argument('-b', dest='beta', metavar='',type=float, - default=200.0, help='beta, capacity to learn [200.0]') - vaeos.add_argument('-d', dest='dropout', metavar='',type=float, - default=None, help='dropout [Auto]') - vaeos.add_argument('--cuda', help='use GPU to train & cluster [False]', action='store_true') - - trainos = parser.add_argument_group(title='Training options', description=None) - - trainos.add_argument('-e', dest='nepochs', metavar='', type=int, - default=500, help='epochs [500]') - trainos.add_argument('-t', dest='batchsize', metavar='', type=int, - default=256, help='starting batch size [256]') - trainos.add_argument('-q', dest='batchsteps', metavar='', type=int, nargs='*', - default=[25, 75, 150, 300], help='double batch size at epochs [25 75 150 300]') - trainos.add_argument('-r', dest='lrate', metavar='',type=float, - default=1e-3, help='learning rate [0.001]') + vaeos = parser.add_argument_group(title="VAE options", description=None) + + vaeos.add_argument( + "-n", + dest="nhiddens", + metavar="", + type=int, + nargs="+", + default=None, + help="hidden neurons [Auto]", + ) + vaeos.add_argument( + "-l", + dest="nlatent", + metavar="", + type=int, + default=32, + help="latent neurons [32]", + ) + vaeos.add_argument( + "-a", + dest="alpha", + metavar="", + type=float, + default=None, + help="alpha, weight of TNF versus depth loss [Auto]", + ) + vaeos.add_argument( + "-b", + dest="beta", + metavar="", + type=float, + default=200.0, + help="beta, capacity to learn [200.0]", + ) + vaeos.add_argument( + "-d", + dest="dropout", + metavar="", + type=float, + default=None, + help="dropout [Auto]", + ) + vaeos.add_argument( + "--cuda", help="use GPU to train & cluster [False]", action="store_true" + ) + + trainos = parser.add_argument_group(title="Training options", description=None) + + trainos.add_argument( + "-e", dest="nepochs", metavar="", type=int, default=300, help="epochs [300]" + ) + trainos.add_argument( + "-t", + dest="batchsize", + metavar="", + type=int, + default=256, + help="starting batch size [256]", + ) + trainos.add_argument( + "-q", + dest="batchsteps", + metavar="", + type=int, + nargs="*", + default=[25, 75, 150, 225], + help="double batch size at epochs [25 75 150 225]", + ) + trainos.add_argument( + "-r", + dest="lrate", + metavar="", + type=float, + default=1e-3, + help="learning rate [0.001]", + ) + # AAE arguments + aaeos = parser.add_argument_group(title='AAE options', description=None) + + aaeos.add_argument('--n_aae', dest='nhiddens_aae', metavar='', type=int, nargs='+', + default=547, help='hidden neurons AAE [547]') + aaeos.add_argument('--z_aae', dest='nlatent_aae_z', metavar='', type=int, + default=283, help='latent neurons AAE continuous latent space [283]') + aaeos.add_argument('--y_aae', dest='nlatent_aae_y', metavar='', type=int, + default=700, help='latent neurons AAE categorical latent space [700]') + aaeos.add_argument('--sl_aae', dest='sl', metavar='', type=float, + default=0.00964, help='loss scale between reconstruction loss and adversarial losses [0.00964] ') + aaeos.add_argument('--slr_aae', dest='slr', metavar='', type=float, + default=0.5, help='loss scale between reconstruction adversarial losses [0.5] ') + aaeos.add_argument('--aae_temp', dest='temp', metavar='', type=float, + default=0.1596, help=' Temperature of the softcategorical prior [0.1596]') + + aaetrainos = parser.add_argument_group(title='Training options AAE', description=None) + + aaetrainos.add_argument('--e_aae', dest='nepochs_aae', metavar='', type=int, + default=70, help='epochs AAE [70]') + aaetrainos.add_argument('--t_aae', dest='batchsize_aae', metavar='', type=int, + default=256, help='starting batch size AAE [256]') + aaetrainos.add_argument('--q_aae', dest='batchsteps_aae', metavar='', type=int, nargs='*', + default=[25,50], help='double batch size at epochs AAE [25,50]') + aaetrainos.add_argument('--r_aae', dest='lrate_aae', metavar='',type=float, + default=1e-3, help='learning rate AAE [0.001]') # Clustering arguments - clusto = parser.add_argument_group(title='Clustering options', description=None) - clusto.add_argument('-w', dest='windowsize', metavar='', type=int, - default=200, help='size of window to count successes [200]') - clusto.add_argument('-u', dest='minsuccesses', metavar='', type=int, - default=20, help='minimum success in window [20]') - clusto.add_argument('-i', dest='minsize', metavar='', type=int, - default=1, help='minimum cluster size [1]') - clusto.add_argument('-c', dest='maxclusters', metavar='', type=int, - default=None, help='stop after c clusters [None = infinite]') - clusto.add_argument('-o', dest='separator', metavar='', type=str, - default=None, help='binsplit separator [None = no split]') + clusto = parser.add_argument_group(title="Clustering options", description=None) + clusto.add_argument( + "-w", + dest="windowsize", + metavar="", + type=int, + default=200, + help="size of window to count successes [200]", + ) + clusto.add_argument( + "-u", + dest="minsuccesses", + metavar="", + type=int, + default=20, + help="minimum success in window [20]", + ) + clusto.add_argument( + "-i", + dest="minsize", + metavar="", + type=int, + default=1, + help="minimum cluster size [1]", + ) + clusto.add_argument( + "-c", + dest="maxclusters", + metavar="", + type=int, + default=None, + help="stop after c clusters [None = infinite]", + ) + clusto.add_argument( + "-o", + dest="separator", + metavar="", + type=str, + default=None, + help="binsplit separator [None = no split]", + ) ######################### PRINT HELP IF NO ARGUMENTS ################### if len(sys.argv) == 1: @@ -371,167 +878,251 @@ def main(): sys.exit() args = parser.parse_args() + + outdir: str = os.path.abspath(args.outdir) + fasta: Optional[str] = args.fasta + composition: Optional[str] = args.composition + bamfiles: Optional[list[str]] = args.bamfiles + rpkm: Optional[str] = args.rpkm + minlength: int = args.minlength + + if args.minid != 0.0 and bamfiles is None: + raise argparse.ArgumentTypeError( + "If minid is set, RPKM must be passed as bam files" + ) + + minid: float = args.minid + nthreads: int = args.nthreads + norefcheck: bool = args.norefcheck + minfasta: Optional[int] = args.minfasta + noencode: bool = args.noencode + nhiddens: Optional[list[int]] = args.nhiddens + nlatent: int = args.nlatent + + nhiddens_aae: Optional[list[int]] = args.nhiddens_aae + nlatent_aae_z: int = args.nlatent_aae_z + nlatent_aae_y: int = args.nlatent_aae_y + + + alpha: Optional[float] = args.alpha + beta: float = args.beta + dropout: Optional[float] = args.dropout + cuda: bool = args.cuda + nepochs: int = args.nepochs + + nepochs_aae: int = args.nepochs_aae + + batchsize: int = args.batchsize + batchsteps: list[int] = args.batchsteps + + batchsize_aae: int = args.batchsize_aae + batchsteps_aae: list[int] = args.batchsteps_aae + + lrate: float = args.lrate + + lrate_aae: float = args.lrate_aae + + windowsize: int = args.windowsize + minsuccesses: int = args.minsuccesses + minsize: int = args.minsize + maxclusters: Optional[int] = args.maxclusters + separator: Optional[str] = args.separator + + ######################### CHECK INPUT/OUTPUT FILES ##################### # Outdir does not exist - args.outdir = os.path.abspath(args.outdir) - if os.path.exists(args.outdir): - raise FileExistsError(args.outdir) + #if os.path.exists(outdir): + # raise FileExistsError(outdir) # Outdir is in an existing parent dir - parentdir = os.path.dirname(args.outdir) + parentdir = os.path.dirname(outdir) if parentdir and not os.path.isdir(parentdir): raise NotADirectoryError(parentdir) # Make sure only one TNF input is there - if args.fasta is None: - for path in args.tnfs, args.names, args.lengths: - if path is None: - raise argparse.ArgumentTypeError('Must specify either FASTA or the three .npz inputs') - if not os.path.isfile(path): - raise FileNotFoundError(path) - else: - for path in args.tnfs, args.names, args.lengths: - if path is not None: - raise argparse.ArgumentTypeError('Must specify either FASTA or the three .npz inputs') - if not os.path.isfile(args.fasta): - raise FileNotFoundError('Not an existing non-directory file: ' + args.fasta) + if not (composition is None) ^ (fasta is None): + raise argparse.ArgumentTypeError( + "Must specify either FASTA or composition path" + ) + + for path in (fasta, composition): + if path is not None and not os.path.isfile(path): + raise FileNotFoundError(path) # Make sure only one RPKM input is there - if sum(i is not None for i in (args.bamfiles, args.rpkm, args.jgi)) != 1: - raise argparse.ArgumentTypeError('Must specify exactly one of BAM files, JGI file or RPKM input') + if not ((bamfiles is None) ^ (rpkm is None)): + raise argparse.ArgumentTypeError( + "Must specify exactly one of BAM files or RPKM input" + ) - for path in args.rpkm, args.jgi: - if path is not None and not os.path.isfile(path): - raise FileNotFoundError('Not an existing non-directory file: ' + args.rpkm) + if rpkm is not None and not os.path.isfile(rpkm): + raise FileNotFoundError("Not an existing non-directory file: " + rpkm) - if args.bamfiles is not None: - for bampath in args.bamfiles: + if bamfiles is not None: + for bampath in bamfiles: if not os.path.isfile(bampath): - raise FileNotFoundError('Not an existing non-directory file: ' + bampath) + raise FileNotFoundError( + "Not an existing non-directory file: " + bampath + ) + + # Check this early, since I expect users will forget about this + if not vamb.parsebam.pycoverm.is_bam_sorted(bampath): + raise ValueError(f"BAM file {bampath} is not sorted by reference.") # Check minfasta settings - if args.minfasta is not None and args.fasta is None: - raise argparse.ArgumentTypeError('If minfasta is not None, ' - 'input fasta file must be given explicitly') + if minfasta is not None and fasta is None: + raise argparse.ArgumentTypeError( + "If minfasta is not None, " "input fasta file must be given explicitly" + ) - if args.minfasta is not None and args.minfasta < 0: - raise argparse.ArgumentTypeError('Minimum FASTA output size must be nonnegative') + if minfasta is not None and minfasta < 0: + raise argparse.ArgumentTypeError( + "Minimum FASTA output size must be nonnegative" + ) ####################### CHECK ARGUMENTS FOR TNF AND BAMFILES ########### - if args.minlength < 100: - raise argparse.ArgumentTypeError('Minimum contig length must be at least 100') - - if args.minid is not None and (args.minid < 0 or args.minid >= 1.0): - raise argparse.ArgumentTypeError('Minimum nucleotide ID must be in [0,1)') + if minlength < 250: + raise argparse.ArgumentTypeError("Minimum contig length must be at least 250") - if args.minid is not None and args.bamfiles is None: - raise argparse.ArgumentTypeError('If minid is set, RPKM must be passed as bam files') + if not isfinite(minid) or minid < 0.0 or minid > 1.0: + raise argparse.ArgumentTypeError("Minimum nucleotide ID must be in [0,1]") - if args.minascore is not None and args.bamfiles is None: - raise argparse.ArgumentTypeError('If minascore is set, RPKM must be passed as bam files') + if nthreads < 1: + raise argparse.ArgumentTypeError("Zero or negative subprocesses requested") - if args.subprocesses < 1: - raise argparse.ArgumentTypeError('Zero or negative subprocesses requested') + ####################### CHECK VAE/AAE OPTIONS ################################ + if nhiddens is not None and any(i < 1 for i in nhiddens): + raise argparse.ArgumentTypeError( + f"Minimum 1 neuron per layer, not {min(nhiddens)}" + ) - ####################### CHECK VAE OPTIONS ################################ - if args.nhiddens is not None and any(i < 1 for i in args.nhiddens): - raise argparse.ArgumentTypeError('Minimum 1 neuron per layer, not {}'.format(min(args.hidden))) + if nlatent < 1: + raise argparse.ArgumentTypeError(f"Minimum 1 latent neuron, not {nlatent}") - if args.nlatent < 1: - raise argparse.ArgumentTypeError('Minimum 1 latent neuron, not {}'.format(args.latent)) + if alpha is not None and (alpha <= 0 or alpha >= 1): + raise argparse.ArgumentTypeError("alpha must be above 0 and below 1") - if args.alpha is not None and (args.alpha <= 0 or args.alpha >= 1): - raise argparse.ArgumentTypeError('alpha must be above 0 and below 1') + if beta <= 0: + raise argparse.ArgumentTypeError("beta cannot be negative or zero") - if args.beta <= 0: - raise argparse.ArgumentTypeError('beta cannot be negative or zero') + if dropout is not None and (dropout < 0 or dropout >= 1): + raise argparse.ArgumentTypeError("dropout must be in 0 <= d < 1") - if args.dropout is not None and (args.dropout < 0 or args.dropout >= 1): - raise argparse.ArgumentTypeError('dropout must be in 0 <= d < 1') - - if args.cuda and not torch.cuda.is_available(): - raise ModuleNotFoundError('Cuda is not available on your PyTorch installation') + if cuda and not torch.cuda.is_available(): + raise ModuleNotFoundError("Cuda is not available on your PyTorch installation") ###################### CHECK TRAINING OPTIONS #################### - if args.nepochs < 1: - raise argparse.ArgumentTypeError('Minimum 1 epoch, not {}'.format(args.nepochs)) + if nepochs < 1: + raise argparse.ArgumentTypeError(f"Minimum 1 epoch, not {nepochs}") - if args.batchsize < 1: - raise argparse.ArgumentTypeError('Minimum batchsize of 1, not {}'.format(args.batchsize)) + if batchsize < 1: + raise argparse.ArgumentTypeError(f"Minimum batchsize of 1, not {batchsize}") - args.batchsteps = sorted(set(args.batchsteps)) - if max(args.batchsteps, default=0) >= args.nepochs: - raise argparse.ArgumentTypeError('All batchsteps must be less than nepochs') + batchsteps = sorted(set(batchsteps)) + if max(batchsteps, default=0) >= nepochs: + raise argparse.ArgumentTypeError("All batchsteps must be less than nepochs") - if min(args.batchsteps, default=1) < 1: - raise argparse.ArgumentTypeError('All batchsteps must be 1 or higher') + if min(batchsteps, default=1) < 1: + raise argparse.ArgumentTypeError("All batchsteps must be 1 or higher") - if args.lrate <= 0: - raise argparse.ArgumentTypeError('Learning rate must be positive') + if lrate <= 0: + raise argparse.ArgumentTypeError("Learning rate must be positive") ###################### CHECK CLUSTERING OPTIONS #################### - if args.minsize < 1: - raise argparse.ArgumentTypeError('Minimum cluster size must be at least 0') + if minsize < 1: + raise argparse.ArgumentTypeError("Minimum cluster size must be at least 0") - if args.windowsize < 1: - raise argparse.ArgumentTypeError('Window size must be at least 1') + if windowsize < 1: + raise argparse.ArgumentTypeError("Window size must be at least 1") - if args.minsuccesses < 1 or args.minsuccesses > args.windowsize: - raise argparse.ArgumentTypeError('Minimum cluster size must be in 1:windowsize') + if minsuccesses < 1 or minsuccesses > windowsize: + raise argparse.ArgumentTypeError("Minimum cluster size must be in 1:windowsize") - if args.separator is not None and len(args.separator) == 0: - raise argparse.ArgumentTypeError('Binsplit separator cannot be an empty string') + if separator is not None and len(separator) == 0: + raise argparse.ArgumentTypeError("Binsplit separator cannot be an empty string") ###################### SET UP LAST PARAMS ############################ # This doesn't actually work, but maybe the PyTorch folks will fix it sometime. - subprocesses = args.subprocesses - torch.set_num_threads(args.subprocesses) - if args.bamfiles is not None: - subprocesses = min(subprocesses, len(args.bamfiles)) + torch.set_num_threads(nthreads) ################### RUN PROGRAM ######################### try: - os.mkdir(args.outdir) + os.mkdir(outdir) except FileExistsError: pass except: raise - - logpath = os.path.join(args.outdir, 'log.txt') - - with open(logpath, 'w') as logfile: - run(args.outdir, - args.fasta, - args.tnfs, - args.names, - args.lengths, - args.bamfiles, - args.rpkm, - args.jgi, - mincontiglength=args.minlength, - norefcheck=args.norefcheck, - minalignscore=args.minascore, - minid=args.minid, - subprocesses=subprocesses, - nhiddens=args.nhiddens, - nlatent=args.nlatent, - nepochs=args.nepochs, - batchsize=args.batchsize, - cuda=args.cuda, - alpha=args.alpha, - beta=args.beta, - dropout=args.dropout, - lrate=args.lrate, - batchsteps=args.batchsteps, - windowsize=args.windowsize, - minsuccesses=args.minsuccesses, - minclustersize=args.minsize, - separator=args.separator, - maxclusters=args.maxclusters, - minfasta=args.minfasta, - logfile=logfile) - -if __name__ == '__main__': + if "aae" in args.model: + try: + os.mkdir(os.path.join(outdir,'tmp')) # needed for dereplication logs and files + except FileExistsError: + pass + except: + raise + try: + os.mkdir(os.path.join(outdir,'tmp','ripped_bins')) # needed for dereplication logs and files + except FileExistsError: + pass + except: + raise + try: + os.mkdir(os.path.join(outdir,'tmp','checkm2_all')) # needed for dereplication logs and files + except FileExistsError: + pass + except: + raise + try: + os.mkdir(os.path.join(outdir,'NC_bins')) # needed for dereplication logs and files + except FileExistsError: + pass + except: + raise + + logpath = os.path.join(outdir, "log.txt") + + with open(logpath, "w") as logfile: + run( + outdir, + fasta, + composition, + bamfiles, + rpkm, + mincontiglength=minlength, + norefcheck=norefcheck, + noencode=noencode, + minid=minid, + nthreads=nthreads, + nhiddens=nhiddens, + nhiddens_aae=nhiddens_aae, + nlatent=nlatent, + nlatent_aae_z=nlatent_aae_z, + nlatent_aae_y=nlatent_aae_y, + nepochs=nepochs, + nepochs_aae=nepochs_aae, + batchsize=batchsize, + batchsize_aae=batchsize_aae, + cuda=cuda, + alpha=alpha, + beta=beta, + dropout=dropout, + sl=args.sl, + slr=args.slr, + temp=args.temp, + lrate=lrate, + batchsteps=batchsteps, + batchsteps_aae=batchsteps_aae, + windowsize=windowsize, + minsuccesses=minsuccesses, + minclustersize=minsize, + separator=separator, + maxclusters=maxclusters, + minfasta=minfasta, + model_selection=args.model, + logfile=logfile, + ) + + +if __name__ == "__main__": main() diff --git a/vamb/aamb_encode.py b/vamb/aamb_encode.py new file mode 100644 index 00000000..30453094 --- /dev/null +++ b/vamb/aamb_encode.py @@ -0,0 +1,529 @@ +"""Adversarial autoencoders (AAE) for metagenomics binning, this files contains the implementation of the AAE""" + + +import numpy as np +from math import log +import time +from torch.utils.data.dataset import TensorDataset as TensorDataset +from torch.autograd import Variable +from torch.distributions.relaxed_categorical import RelaxedOneHotCategorical +import torch.nn as nn +import torch.nn.functional as F +import torch + +from torch.utils.data import DataLoader as _DataLoader + +from typing import Optional + +random_seed = 42 +torch.manual_seed(random_seed) +torch.cuda.manual_seed(random_seed) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +np.random.seed(random_seed) + +############################################################################# MODEL ########################################################### +class AAE(nn.Module): + def __init__( + self, + nsamples: int, + nhiddens: int, + nlatent_l: int, + nlatent_y, + sl: float, + slr: float, + alpha: Optional[float], + _cuda: bool, + ): + if nsamples is None: + raise ValueError( + f"Number of samples should be provided to define the encoder input layer as well as the categorical latent dimension, not {nsamples}" + ) + + super(AAE, self).__init__() + if alpha is None: + alpha = 0.15 if nsamples > 1 else 0.50 + + self.nsamples = nsamples + input_len = 103 + self.nsamples + self.h_n = nhiddens + self.ld = nlatent_l + self.y_len = nlatent_y + self.input_len = int(input_len) + self.sl = sl + self.slr = slr + self.alpha = alpha + self.usecuda = _cuda + + # encoder + self.encoder = nn.Sequential( + nn.Linear(self.input_len, self.h_n), + nn.BatchNorm1d(self.h_n), + nn.LeakyReLU(), + nn.Linear(self.h_n, self.h_n), + nn.BatchNorm1d(self.h_n), + nn.LeakyReLU(), + ) + # latent layers + self.mu = nn.Linear(self.h_n, self.ld) + self.logvar = nn.Linear(self.h_n, self.ld) + self.y_vector = nn.Linear(self.h_n, self.y_len) + + # decoder + self.decoder = nn.Sequential( + nn.Linear(int(self.ld + self.y_len), self.h_n), + nn.BatchNorm1d(self.h_n), + nn.LeakyReLU(), + nn.Linear(self.h_n, self.h_n), + nn.BatchNorm1d(self.h_n), + nn.LeakyReLU(), + nn.Linear(self.h_n, self.input_len), + ) + + # discriminator z + self.discriminator_z = nn.Sequential( + nn.Linear(self.ld, self.h_n), + nn.LeakyReLU(), + nn.Linear(self.h_n, int(self.h_n / 2)), + nn.LeakyReLU(), + nn.Linear(int(self.h_n / 2), 1), + nn.Sigmoid(), + ) + + # discriminator Y + self.discriminator_y = nn.Sequential( + nn.Linear(self.y_len, self.h_n), + nn.LeakyReLU(), + nn.Linear(self.h_n, int(self.h_n / 2)), + nn.LeakyReLU(), + nn.Linear(int(self.h_n / 2), 1), + nn.Sigmoid(), + ) + + if _cuda: + self.cuda() + + ## Reparametrisation trick + def _reparameterization(self, mu, logvar): + + Tensor = torch.cuda.FloatTensor if self.usecuda else torch.FloatTensor + + std = torch.exp(logvar / 2) + sampled_z = Variable(Tensor(np.random.normal(0, 1, (mu.size(0), self.ld)))) + + if self.usecuda: + sampled_z = sampled_z.cuda() + z = sampled_z * std + mu + + return z + + ## Encoder + def _encode(self, depths, tnfs): + _input = torch.cat((depths, tnfs), 1) + x = self.encoder(_input) + mu = self.mu(x) + logvar = self.logvar(x) + _y = self.y_vector(x) + y = F.softmax(_y, dim=1) + + return mu, logvar, y + + def y_length(self): + return self.y_len + + def z_length(self): + return self.ld + + def samples_num(self): + return self.nsamples + + ## Decoder + def _decode(self, z, y): + z_y = torch.cat((z, y), 1) + + reconstruction = self.decoder(z_y) + + _depths_out, tnf_out = ( + reconstruction[:, : self.nsamples], + reconstruction[:, self.nsamples :], + ) + + depths_out = F.softmax(_depths_out, dim=1) + + return depths_out, tnf_out + + ## Discriminator Z space (continuous latent space defined by mu and sigma layers) + def _discriminator_z(self, z): + return self.discriminator_z(z) + + ## Discriminator Y space (categorical latent space defined by Y layer) + + def _discriminator_y(self, y): + return self.discriminator_y(y) + + def calc_loss(self, depths_in, depths_out, tnf_in, tnf_out): + # If multiple samples, use cross entropy, else use SSE for abundance + if self.nsamples > 1: + # Add 1e-9 to depths_out to avoid numerical instability. + ce = -((depths_out + 1e-9).log() * depths_in).sum(dim=1).mean() + ce_weight = (1 - self.alpha) / log(self.nsamples) + else: + ce = (depths_out - depths_in).pow(2).sum(dim=1).mean() + ce_weight = 1 - self.alpha + sse = (tnf_out - tnf_in).pow(2).sum(dim=1).mean() + sse_weight = self.alpha / (tnf_in.shape[1] * 2) + loss = ce * ce_weight + sse * sse_weight + return loss, ce, sse + + def forward(self, depths_in, tnfs_in, z_prior, y_prior): + mu, logvar, y_latent = self._encode(depths_in, tnfs_in) + z_latent = self._reparameterization(mu, logvar) + depths_out, tnfs_out = self._decode(z_latent, y_latent) + d_z_latent = self._discriminator_z(z_latent) + d_y_latent = self._discriminator_y(y_latent) + + return mu, depths_out, tnfs_out, z_latent, y_latent, d_z_latent, d_y_latent + + # ---------- + # Training + # ---------- + + def trainmodel( + self, data_loader, nepochs, batchsteps, T, lr, logfile=None, modelfile=None + ): + + Tensor = torch.cuda.FloatTensor if self.usecuda else torch.FloatTensor + batchsteps_set = set(batchsteps) + ncontigs, _ = data_loader.dataset.tensors[0].shape + + # Initialize generator and discriminator + + if logfile is not None: + print("\tNetwork properties:", file=logfile) + print("\tCUDA:", self.usecuda, file=logfile) + print("\tAlpha:", self.alpha, file=logfile) + print("\tY length:", self.y_len, file=logfile) + print("\tZ length:", self.ld, file=logfile) + print("\n\tTraining properties:", file=logfile) + print("\tN epochs:", nepochs, file=logfile) + print("\tStarting batch size:", data_loader.batch_size, file=logfile) + batchsteps_string = ( + ", ".join(map(str, sorted(batchsteps))) if batchsteps_set else "None" + ) + print("\tBatchsteps:", batchsteps_string, file=logfile) + print("\tN sequences:", ncontigs, file=logfile) + print("\tN samples:", self.nsamples, file=logfile, end="\n\n") + + # we need to separate the paramters due to the adversarial training + + disc_z_params = [] + disc_y_params = [] + enc_params = [] + dec_params = [] + for name, param in self.named_parameters(): + if "discriminator_z" in name: + disc_z_params.append(param) + elif "discriminator_y" in name: + disc_y_params.append(param) + elif "encoder" in name: + enc_params.append(param) + else: + dec_params.append(param) + + # Define adversarial loss for the discriminators + adversarial_loss = torch.nn.BCELoss() + if self.usecuda: + adversarial_loss.cuda() + + #### Optimizers + optimizer_E = torch.optim.Adam(enc_params, lr=lr) + optimizer_D = torch.optim.Adam(dec_params, lr=lr) + + optimizer_D_z = torch.optim.Adam(disc_z_params, lr=lr) + optimizer_D_y = torch.optim.Adam(disc_y_params, lr=lr) + + for epoch_i in range(nepochs): + if epoch_i in batchsteps: + data_loader = _DataLoader( + dataset=data_loader.dataset, + batch_size=data_loader.batch_size * 2, + shuffle=True, + drop_last=True, + num_workers=data_loader.num_workers, + pin_memory=data_loader.pin_memory, + ) + + ( + ED_loss_e, + D_z_loss_e, + D_y_loss_e, + V_loss_e, + CE_e, + SSE_e, + ) = (0, 0, 0, 0, 0, 0) + + total_batches_inthis_epoch = len(data_loader) + time_epoch_0 = time.time() + + for depths_in, tnfs_in, _ in data_loader: # weights currently unused here + nrows, _ = depths_in.shape + + # Adversarial ground truths + + labels_prior = Variable( + Tensor(nrows, 1).fill_(1.0), requires_grad=False + ) + labels_latent = Variable( + Tensor(nrows, 1).fill_(0.0), requires_grad=False + ) + + # Sample noise as discriminator Z,Y ground truth + + if self.usecuda: + z_prior = torch.cuda.FloatTensor(nrows, self.ld).normal_() + z_prior.cuda() + ohc = RelaxedOneHotCategorical( + torch.tensor([T], device="cuda"), + torch.ones([nrows, self.y_len], device="cuda"), + ) + y_prior = ohc.sample() + y_prior = y_prior.cuda() + + else: + z_prior = Variable( + Tensor(np.random.normal(0, 1, (nrows, self.ld))) + ) + ohc = RelaxedOneHotCategorical( + T, torch.ones([nrows, self.y_len]) + ) + y_prior = ohc.sample() + + del ohc + + if self.usecuda: + depths_in = depths_in.cuda() + tnfs_in = tnfs_in.cuda() + + # ----------------- + # Train Generator + # ----------------- + optimizer_E.zero_grad() + optimizer_D.zero_grad() + + # Forward pass + ( + _, + depths_out, + tnfs_out, + z_latent, + y_latent, + d_z_latent, + d_y_latent, + ) = self(depths_in, tnfs_in, z_prior, y_prior) + + vae_loss, ce, sse = self.calc_loss( + depths_in, depths_out, tnfs_in, tnfs_out + ) + g_loss_adv_z = adversarial_loss( + self._discriminator_z(z_latent), labels_prior + ) + g_loss_adv_y = adversarial_loss( + self._discriminator_y(y_latent), labels_prior + ) + + ed_loss = ( + (1 - self.sl) * vae_loss + + (self.sl * self.slr) * g_loss_adv_z + + (self.sl * (1 - self.slr)) * g_loss_adv_y + ) + + ed_loss.backward() + optimizer_E.step() + optimizer_D.step() + + # ---------------------- + # Train Discriminator z + # ---------------------- + + optimizer_D_z.zero_grad() + mu, logvar = self._encode(depths_in, tnfs_in)[:2] + z_latent = self._reparameterization(mu, logvar) + + d_z_loss_prior = adversarial_loss( + self._discriminator_z(z_prior), labels_prior + ) + d_z_loss_latent = adversarial_loss( + self._discriminator_z(z_latent), labels_latent + ) + d_z_loss = 0.5 * (d_z_loss_prior + d_z_loss_latent) + + d_z_loss.backward() + optimizer_D_z.step() + + # ---------------------- + # Train Discriminator y + # ---------------------- + + optimizer_D_y.zero_grad() + y_latent = self._encode(depths_in, tnfs_in)[2] + d_y_loss_prior = adversarial_loss( + self._discriminator_y(y_prior), labels_prior + ) + d_y_loss_latent = adversarial_loss( + self._discriminator_y(y_latent), labels_latent + ) + d_y_loss = 0.5 * (d_y_loss_prior + d_y_loss_latent) + + d_y_loss.backward() + optimizer_D_y.step() + + ED_loss_e += float(ed_loss.item()) + V_loss_e += float(vae_loss.item()) + D_z_loss_e += float(d_z_loss.item()) + D_y_loss_e += float(d_y_loss.item()) + CE_e += float(ce.item()) + SSE_e += float(sse.item()) + + time_epoch_1 = time.time() + time_e = np.round((time_epoch_1 - time_epoch_0) / 60, 3) + + if logfile is not None: + print( + "\tEpoch: {}\t Loss Enc/Dec: {:.6f}\t Rec. loss: {:.4f}\t CE: {:.4f}\tSSE: {:.4f}\t Dz loss: {:.7f}\t Dy loss: {:.6f}\t Batchsize: {}\t Epoch time(min): {: .4}".format( + epoch_i + 1, + ED_loss_e / total_batches_inthis_epoch, + V_loss_e / total_batches_inthis_epoch, + CE_e / total_batches_inthis_epoch, + SSE_e / total_batches_inthis_epoch, + D_z_loss_e / total_batches_inthis_epoch, + D_y_loss_e / total_batches_inthis_epoch, + data_loader.batch_size, + time_e, + ), + file=logfile, + ) + logfile.flush() + + # save model + if modelfile is not None: + try: + checkpoint = { + "state": self.state_dict(), + "optimizer_ED": optimizer_ED.state_dict(), + "optimizer_D_z": optimizer_D_z.state_dict(), + "optimizer_D_y": optimizer_D_y.state_dict(), + "nsamples": self.num_samples, + "alpha": self.alpha, + "nhiddens": self.h_n, + "nlatent_l": self.ld, + "nlatent_y": self.y_len, + "sl": self.sl, + "slr": self.slr, + "temp": self.T, + } + torch.save(checkpoint, modelfile) + + except: + pass + + return None + + ########### funciton that retrieves the clusters from Y latents + def get_latents(self, contignames, data_loader, last_epoch=True): + """Retrieve the categorical latent representation (y) and the contiouous latents (l) of the inputs + + Inputs: + dataloader + contignames + last_epoch + + + Output: + y_clusters_dict ({clust_id : [contigs]}) + l_latents array""" + self.eval() + + new_data_loader = _DataLoader( + dataset=data_loader.dataset, + batch_size=data_loader.batch_size, + shuffle=False, + drop_last=False, + num_workers=1, + pin_memory=data_loader.pin_memory, + ) + + depths_array, _, _ = data_loader.dataset.tensors + + length = len(depths_array) + latent = np.empty((length, self.ld), dtype=np.float32) + index_contigname = 0 + row = 0 + clust_y_dict = dict() + Tensor = torch.cuda.FloatTensor if self.usecuda else torch.FloatTensor + with torch.no_grad(): + + for depths_in, tnfs_in, _ in new_data_loader: + nrows, _ = depths_in.shape + I = torch.cat((depths_in, tnfs_in), dim=1) + + if self.usecuda: + z_prior = torch.cuda.FloatTensor(nrows, self.ld).normal_() + z_prior.cuda() + ohc = RelaxedOneHotCategorical( + torch.tensor([0.15], device="cuda"), + torch.ones([nrows, self.y_len], device="cuda"), + ) + y_prior = ohc.sample() + y_prior = y_prior.cuda() + + else: + z_prior = Variable( + Tensor(np.random.normal(0, 1, (nrows, self.ld))) + ) + ohc = RelaxedOneHotCategorical( + 0.15, torch.ones([nrows, self.y_len]) + ) + y_prior = ohc.sample() + + if self.usecuda: + depths_in = depths_in.cuda() + tnfs_in = tnfs_in.cuda() + + if last_epoch: + mu, _, _, _, y_sample = self(depths_in, tnfs_in, z_prior, y_prior)[ + 0:5 + ] + else: + y_sample = self(depths_in, tnfs_in, z_prior, y_prior)[4] + + if self.usecuda: + Ys = y_sample.cpu().detach().numpy() + if last_epoch: + mu = mu.cpu().detach().numpy() + latent[row : row + len(mu)] = mu + row += len(mu) + else: + Ys = y_sample.detach().numpy() + if last_epoch: + mu = mu.detach().numpy() + latent[row : row + len(mu)] = mu + row += len(mu) + del y_sample + + for _y in Ys: + contig_name = contignames[index_contigname] + contig_cluster = np.argmax(_y) + 1 + contig_cluster_str = str(contig_cluster) + + if contig_cluster_str not in clust_y_dict: + clust_y_dict[contig_cluster_str] = set() + + clust_y_dict[contig_cluster_str].add(contig_name) + + index_contigname += 1 + del Ys + + if last_epoch: + return clust_y_dict, latent + else: + return clust_y_dict diff --git a/vamb/benchmark.py b/vamb/benchmark.py index 654da98c..47e23609 100644 --- a/vamb/benchmark.py +++ b/vamb/benchmark.py @@ -1,83 +1,125 @@ # The approach to benchmarking is this: -# -# Most fundamentally, we have a Contig. A Contig is associated with a subject, where -# it comes from and aligns to. It thus has a start and end position in that subject. -# A set of contigs has a breadth. This is the number of basepairs covered by the -# contigs in their respective subjects, where basepairs covered by multiple Contigs are -# only counted once. -# -# A Genome is simply a collection of contigs. A set of Genomes constitute a Reference. -# -# A Binning is a collection of sets of contigs, each set representing a bin. It is -# required that all contigs in a Binning comes from Genomes in the same Reference. -# The breadth of a Binning is the sum of breadths of each bin. If the same subject are -# covered by multiple different bins, these are counted multiple times. This implies that -# the breadth of a Binning can exceed the total length of all the underlying Genomes. -# -# When creating a Binning, for each genome/bin pair, the following base statistics -# are calculated: -# * True positives (TP) is the breadth of the intersection of the bin and the genome contigs -# * False positives (FP) is the bin breadth minus TP -# * False negatives (FN) is the Genome breadth minus TP -# * True negatives (TN) is the Reference breadth minus TP+FP+FN -# -# From these values, the recall, precision, Matthew's correlation coefficient (MCC) -# and F1 statistics are calculated. -# -# Since each genome has a set of statistics for each bin, the MCC or F1 of a genome -# is the maximal MCC or F1 among all bins. The mean MCC or F1 is then the mean of -# these maximal MCCs or F1. + +# A "subject" is a source sequence, e.g. a sequence directly from an underlying genome. +# A subject has only a name and length. Subject names are unique. + +# A set of subjects is a Genome. The subjects are assumed to not be overlapping. Hence, +# the "breadth" of a genome is the sum of subject lengths in that genome. Genome names +# are assumed to be unique. + +# A Contig is composed of a name, a source subject, and a start and end position within +# that subject, where the contig maps to. Contig names are assumed to be unique. +# Contigs always have a source. If the source is unknown, a dummy source can be used, +# i.e. a source not used by any other contig. + +# A named set of Contigs is a Bin. Bin names are unique. The "breadth" of a Bin is the total +# number of basepairs (positions) covered by any contig in the bin. Contigs can overlap the +# same subject, but a position covered multiple times only count once towards breadth. + +# A Bin/Genome pair has an "intersection". This is the number of basepairs (positions) in +# the Bin which come from a subject in the given Genome. In other words, it is the breadth +# of the subset of contigs in the Bin that originates from the Genome. + +# Bin/Genome pairs also have other statistics: +# * True positives (TP) is the intersection +# * False positives (FP) is Bin breadth minus TP +# * False negatives (FN) is Genome breadth minus TP +# * Recall, precision and F-score is calculated from TP, FP and FN as usual. + +# A binning result is described by two objects, the Reference and the Binning. + +# A Reference object represents the information in the test dataset, and is independent of +# any concrete binning tool or binning result. Hence, a test dataset has one Reference object. +# It is composed of: +# * The total set of Contigs input to the binning +# * The total set of Genomes that the set of Contigs represent. This implies that all Contigs +# in the Reference must derive from a subject which is also in a Genome in the Reference, i.e. +# there must be no contigs where the source is not from a Genome (via a subject) in the Reference. +# * A list of "taxmaps". A taxmap describe taxonomic information about the Genomes of the +# Reference. A taxmap is a dictionary that maps the name of a clade to its parent clade. +# For example, the pair "Ecoli":"Escherichia" could be present. +# The first taxmap has all Genome names as keys. +# The Nth taxmap has as keys the values of the N-1th taxmap (except None). +# A clade for which no higher level clade is known or defined maps to None. + +# The Binning object contains the matching Reference and a collection of Bins. +# When instantiated, it can do a number of filters or transformations of the bins, e.g. +# binsplitting or filtering of bins by number of contigs. +# The only responsibility of the Binning object is to store the input bins, do this filtering +# and transformation, and do the benchmarking itself. In practice, this means computing a +# series of "counters", one for each Reference taxmap: + +# The first counter is a map from minimum (recall, precision) to the number of genomes +# for which at least one bin in the Binning has a recall, precision level at or above the +# minimum recall/precision level. + +# All the other counters are computed iteratively from the previous one based on the corresponding +# Reference taxmap. For a given bin B and a clade C where C is not a Genome (i.e. not the lowest +# level of the taxmaps), the recall of the B/C pair is defined as the max of the recalls B/Cchild for +# all Cchild taxonomic children of C according to the taxmap. +# The precision is calculated as the SUM of all B/Cchild. + __doc__ = """Benchmark script This benchmarks bins using number of covered sites. -First, a Reference is needed. This can be generated from a tab-separated file: -contigname genomename subjectname start end - -Where "contigname" is the name of the binned contig, which belongs to genome -"genomename", and aligns to the reference contig "subjectname" at positions -"start"-"end", both inclusive. +First, a Reference is needed. This can be loaded from a JSON file, see +the Reference class source code for details. You also need a file with the binning. This is simply the clusters.tsv file as produced by Vamb, i.e. first column is the name of the bin of a contig, second column the name of the contig. -Recall of a genome/bin pair is defined as the number of bases in that genome -covered by contigs in the bin divided by the number of bases covered in that -genome from all bins total. Precision is the number of bases in that genome -covered by contigs in the bin divided by the number of bases covered in any -genome from that bin. +Recall of a genome/bin pair is defined as the number of genome basepairs +covered by contigs in the bin divided by total number of basepairs of that +genome. +Precision is the number of bases in that genome covered by contigs in the +bin divided by the number of bases covered in any genome from that bin. Usage: >>> ref = Reference.from_file(open_reference_file_hande) ->>> ref.load_tax_file(open_tax_file_handle) # optional >>> bins = Binning.from_file(open_clusters_file_handle, ref) >>> bins.print_matrix(rank=1) """ -import collections as _collections -from operator import add as _add -from itertools import product as _product -import sys as _sys -from math import sqrt as _sqrt -import vamb.vambtools as _vambtools +from collections import defaultdict +from itertools import product +import sys +import json +from math import isfinite +from vamb import vambtools +from collections.abc import Iterable, Sequence +from typing import Optional, TypeVar, IO, Any + +C = TypeVar("C", bound="Contig") +G = TypeVar("G", bound="Genome") +Bn = TypeVar("Bn", bound="Bin") +R = TypeVar("R", bound="Reference") +Bs = TypeVar("Bs", bound="Binning") + class Contig: """An object representing a contig mapping to a subject at position start:end. Mapping positions use the half-open interval, like Python ranges and slices. Instantiate either with name, subject and mapping start/end: - Contig('contig_41', 'subject_1', 11, 510) + Contig('contig_41', 'subject_1', 600, 11, 510) Or with only name and length Contig.subjectless('contig_41', 499) A subjectless Contig uses itself as a subject (implying it only maps to itself). """ - __slots__ = ['name', 'subject', 'start', 'end'] - def __init__(self, name, subject, start, end): - if end <= start: - raise ValueError('Contig end must be higher than start') + __slots__ = ["name", "subject", "start", "end"] + + def __init__(self, name: str, subject: str, start: int, end: int): + if start < 0: + raise ValueError(f'Contig "{name}" has negative start index {start}') + elif end <= start: + raise ValueError( + "Contig end must be higher than start, but " + f'contig "{name}" spans {start}-{end}.' + ) self.name = name self.subject = subject @@ -85,536 +127,505 @@ def __init__(self, name, subject, start, end): self.end = end @classmethod - def subjectless(cls, name, length): + def subjectless(cls: type[C], name: str, length: int) -> C: "Instantiate with only name and length" return cls(name, name, 0, length) - def __repr__(self): - return 'Contig({}, subject={}, {}:{})'.format(self.name, self.subject, self.start, self.end) + def __repr__(self) -> str: + return f'Contig("{self.name}", "{self.subject}", {self.start}, {self.end})' + + def __eq__(self, other: Any) -> bool: + return isinstance(other, Contig) and self.name == other.name - def __len__(self): + def __hash__(self) -> int: + return hash(self.name) ^ 3458437981 + + def __len__(self) -> int: return self.end - self.start + class Genome: - """A set of contigs known to come from the same organism. - The breadth must be updated after adding/removing contigs with self.update_breadth(), - before it is accurate. - >>> genome = Genome('E. coli') - >>> genome.add(contig) - >>> genome.update_breadth() + """An object representing a set of subjects (or sources), i.e. genomes or source contigs + that the binning contigs are drawn from. + >>> g = Genome("Ecoli") + >>> g.add("chrom", 5_300_000) """ - __slots__ = ['name', 'breadth', 'contigs'] - def __init__(self, name): + __slots__ = ["name", "sources", "breadth"] + + def __init__(self, name: str): self.name = name - self.contigs = set() + self.sources: dict[str, int] = dict() self.breadth = 0 - def add(self, contig): - self.contigs.add(contig) - - def remove(self, contig): - self.contigs.remove(contig) - - def discard(self, contig): - self.contigs.discard(contig) - - @property - def ncontigs(self): - return len(self.contigs) - - @staticmethod - def getbreadth(contigs): - "This calculates the total number of bases covered at least 1x in ANY Genome." - bysubject = _collections.defaultdict(list) - for contig in contigs: - bysubject[contig.subject].append(contig) - - breadth = 0 - for contiglist in bysubject.values(): - contiglist.sort(key=lambda contig: contig.start) - rightmost_end = float('-inf') + def __eq__(self, other: Any) -> bool: + return isinstance(other, Genome) and self.name == other.name - for contig in contiglist: - breadth += max(contig.end, rightmost_end) - max(contig.start, rightmost_end) - rightmost_end = max(contig.end, rightmost_end) + def __hash__(self) -> int: + return hash(self.name) ^ 4932511201 - return breadth + def add(self, source: str, len: int) -> None: + if len <= 0: + raise ValueError( + "Source sequence must have nonzero positive length, " + f"but {source} has length {len}" + ) + if source in self.sources: + raise ValueError(f"Genome {self.name} already has source {source}.") - def update_breadth(self): - "Updates the breadth of the genome" - self.breadth = self.getbreadth(self.contigs) + self.sources[source] = len + self.breadth += len def __repr__(self): - return 'Genome({}, ncontigs={}, breadth={})'.format(self.name, self.ncontigs, self.breadth) - -class Reference: - """A set of Genomes known to represent the ground truth for binning. - Instantiate with any iterable of Genomes. + return f'Genome("{self.name}")' - >>> print(my_genomes) - [Genome('E. coli'), ncontigs=95, breadth=5012521), - Genome('Y. pestis'), ncontigs=5, breadth=46588721)] - >>> Reference(my_genomes) - Reference(ngenomes=2, ncontigs=100) - Properties: - self.genomes: {genome_name: genome} dict - self.contigs: {contig_name: contig} dict - self.genomeof: {contig: genome} dict - self.breadth: Total length of all genomes - self.ngenomes - self.ncontigs +class Bin: + """An object representing a set of Contigs. + Should be instantiated with Bin.from_contigs. See that method for how to safely + instantiate the object. """ - # Instantiate with any iterable of Genomes - def __init__(self, genomes, taxmaps=list()): - self.genomes = dict() # genome_name : genome dict - self.contigs = dict() # contig_name : contig dict - self.genomeof = dict() # contig : genome dict + __slots__ = ["name", "contigs", "intersections", "breadth"] - # This is a list of dicts: The first one maps genomename to name of next taxonomic level - # The second maps name of second level to name of third level etc. - self.taxmaps = taxmaps - - # Load genomes into list in case it's a one-time iterator - genomes_backup = list(genomes) if iter(genomes) is genomes else genomes - - # Check that there are no genomes with same name - if len({genome.name for genome in genomes_backup}) != len(genomes_backup): - raise ValueError('Multiple genomes with same name not allowed in Reference.') - - for genome in genomes_backup: - self.add(genome) - - self.breadth = sum(genome.breadth for genome in genomes_backup) - - def load_tax_file(self, line_iterator, comment='#'): - """Load in a file with N+1 columns, the first being genomename, the next being - the equivalent taxonomic annotation at different ranks - Replaces the Reference's taxmaps list.""" - taxmaps = list() - isempty = True + def __init__(self, name: str) -> None: + self.name = name + self.contigs: set[Contig] = set() + self.intersections: dict[Genome, int] = dict() + self.breadth: int = 0 - for line in line_iterator: - if line.startswith(comment): - continue + @property + def ncontigs(self) -> int: + return len(self.contigs) - genomename, *clades = line[:-1].split('\t') + @classmethod + def from_contigs( + cls: type[Bn], + name: str, + contigs: Iterable[Contig], + genomeof: dict[Contig, Genome], + ) -> Bn: + instance = cls(name) + instance.contigs.update(contigs) + instance._finalize(genomeof) # remember to do this + return instance + + def __repr__(self) -> str: + return f'Bin("{self.name}")' - if isempty: - if not clades: - raise ValueError('Must have at least two columns') + @staticmethod + def _intersection(contigs: list[Contig]) -> int: + # Note: All contigs MUST come from same source, else the code is invalid + contigs.sort(key=lambda x: x.start) + result = 0 + rightmost_end = -1 - for i in clades: - taxmaps.append(dict()) - isempty = False + for contig in contigs: + result += max(contig.end, rightmost_end) - max(contig.start, rightmost_end) + rightmost_end = max(contig.end, rightmost_end) + + return result + + def _finalize(self, genomeof: dict[Contig, Genome]) -> None: + self.intersections.clear() + by_source: defaultdict[tuple[Genome, str], list[Contig]] = defaultdict(list) + for contig in self.contigs: + by_source[(genomeof[contig], contig.subject)].append(contig) + for ((genome, _), contigs) in by_source.items(): + self.intersections[genome] = self.intersections.get( + genome, 0 + ) + self._intersection(contigs) + self.breadth = sum(self.intersections.values()) + + def confusion_matrix(self, genome: Genome) -> tuple[int, int, int]: + "Given a genome and a binname, returns TP, FP, FN" + # This is None if it's not updated, we want it to throw type errors then + d: dict[Genome, int] = self.intersections + breadth: int = self.breadth + tp = d.get(genome, 0) + fp = breadth - tp + fn = genome.breadth - tp + return (tp, fp, fn) + + def recall_precision(self, genome: Genome) -> tuple[float, float]: + (tp, fp, fn) = self.confusion_matrix(genome) + recall = tp / (tp + fn) + precision = tp / (tp + fp) + return (recall, precision) + + def fscore(self, b: float, genome: Genome) -> float: + recall, precision = self.recall_precision(genome) + + # Some people say the Fscore is undefined in this case. + # We define it to be 0.0 + if (recall + precision) == 0.0: + return 0.0 + + return (1 + b * b) * (recall * precision) / ((b * b * precision) + recall) + + def f1(self, genome: Genome) -> float: + return self.fscore(1.0, genome) - if genomename in taxmaps[0]: - raise KeyError("Genome name {} present more than once in taxfile".format(genomename)) - previousrank = genomename - for nextrank, rankdict in zip(clades, taxmaps): - existing = rankdict.get(previousrank, nextrank) - if existing != nextrank: - raise KeyError("Rank {} mapped to both {} and {}".format(previousrank, existing, nextrank)) +class Reference: + """An object that represent a set of Genome and Contigs, where the Contigs are sampled + from the genomes. Also contain the phylogenetic tree for the contained genomes. + Either instantiate directly and use self.add_contig and self.add_taxonomy, else + use Reference.from_file + """ - rankdict[previousrank] = nextrank - previousrank = nextrank + __slots__ = ["genomes", "genomeof", "contig_by_name", "taxmaps"] - self.taxmaps = taxmaps + def __init__(self): + self.genomes: set[Genome] = set() # genome_name : genome dict + self.genomeof: dict[Contig, Genome] = dict() # contig : genome dict + self.contig_by_name: dict[str, Contig] = dict() + # This is a list of dicts: The first one maps genomename to name of next taxonomic level + # The second maps name of second level to name of third level etc. None means it's the top level + self.taxmaps: list[dict[str, Optional[str]]] = [dict()] + + def _add_genome(self, genome: Genome) -> None: + if genome in self.genomes: + raise ValueError(f'Genome "{genome.name}" already in reference.') + self.genomes.add(genome) + self.taxmaps[0][genome.name] = None + + def _add_contig(self, contig: Contig, genome: Genome) -> None: + if contig in self.genomeof: + raise ValueError(f'Reference already has Contig "{contig.name}"') + if contig.subject not in genome.sources: + raise ValueError( + f'Attempted to add contig "{contig.name}" with source "{contig.subject}" ' + f'to genome "{genome.name}", but genome has no such source' + ) + if genome not in self.genomes: + raise ValueError(f'Genome "{genome.name}" is not in reference.') + if genome.sources[contig.subject] < contig.end: + raise IndexError( + f'Attempted to add contig "{contig.name}" with mapping end {contig.end} ' + f'to subject "{contig.subject}", ' + f"but the subject only has length {genome.sources[contig.subject]}" + ) + + self.genomeof[contig] = genome + self.contig_by_name[contig.name] = contig + + def _add_taxonomy(self, level: int, child: str, parent: str) -> None: + existing = self.taxmaps[level][child] + if existing is None: + self.taxmaps[level][child] = parent + # Add parent of parent, if not already present + if level + 1 == self.nranks: + self.taxmaps.append(dict()) + + grandparent = self.taxmaps[level + 1].get(parent, 0) + if grandparent == 0: + self.taxmaps[level + 1][parent] = None + + elif existing != parent: + raise ValueError( + f'Clade "{child}" maps to both parent clade "{parent}" and "{existing}"' + ) @property - def ngenomes(self): + def ngenomes(self) -> int: return len(self.genomes) @property - def ncontigs(self): - return len(self.contigs) + def ncontigs(self) -> int: + return len(self.genomeof) - def __repr__(self): - ranks = len(self.taxmaps) + 1 - return 'Reference(ngenomes={}, ncontigs={}, ranks={})'.format(self.ngenomes, self.ncontigs, ranks) + @property + def nranks(self) -> int: + return len(self.taxmaps) - @staticmethod - def _parse_subject_line(line): - "Returns contig, genome_name from a reference file line with subjects" - contig_name, genome_name, subject, start, end = line[:-1].split('\t') - start = int(start) - end = int(end) + 1 # semi-open interval used in internals, like range() - contig = Contig(contig_name, subject, start, end) - return contig, genome_name + def __repr__(self) -> str: + return f"" - @staticmethod - def _parse_subjectless_line(line): - "Returns contig, genome_name from a reference file line without subjects" - contig_name, genome_name, length = line[:-1].split('\t') - length = int(length) - contig = Contig.subjectless(contig_name, length) - return contig, genome_name + def parse_bins( + self, io: Iterable[str], binsplit_sep: Optional[str] = None + ) -> list[Bin]: + clusters = vambtools.read_clusters(io).items() + if binsplit_sep is not None: + clusters = vambtools.binsplit(clusters, binsplit_sep) + return self.load_bins(clusters) + + def load_bins(self, bins: Iterable[tuple[str, Iterable[str]]]) -> list[Bin]: + """Convert a set of bin names to a list of Bins""" + result: list[Bin] = list() + for (binname, contignames) in bins: + contigs = (self.contig_by_name[name] for name in contignames) + result.append(Bin.from_contigs(binname, contigs, self.genomeof)) + + return result @classmethod - def _parse_file(cls, filehandle, subjectless=False): - "Returns a list of genomes from a reference file" - function = cls._parse_subjectless_line if subjectless else cls._parse_subject_line - - genomes = dict() - for line in filehandle: - # Skip comments - if line.startswith('#'): - continue - - contig, genome_name = function(line) - genome = genomes.get(genome_name) - if genome is None: - genome = Genome(genome_name) - genomes[genome_name] = genome - genome.add(contig) - - # Update all genomes - genomes = list(genomes.values()) - for genome in genomes: - genome.update_breadth() - - return genomes + def from_file(cls: type[R], io: IO[str]) -> R: + json_dict = json.load(io) + return cls.from_dict(json_dict) @classmethod - def from_file(cls, filehandle, subjectless=False): - """Instantiate a Reference from an open filehandle. - "subjectless" refers to the style of reference file: If true, assumes columns are - [contig_name, genome_name, contig_length]. If false, assume - [contig_name, genome_name, subject_name, mapping_start, mapping_end] - - >>> with open('my_reference.tsv') as filehandle: - Reference.from_file(filehandle) - Reference(ngenomes=2, ncontigs=100) - """ - - genomes = cls._parse_file(filehandle, subjectless=subjectless) - return cls(genomes) - - def add(self, genome): - "Adds a genome to this Reference. If already present, do nothing." - if genome.name not in self.genomes: - self.genomes[genome.name] = genome - for contig in genome.contigs: - if contig.name in self.contigs: - raise KeyError("Contig name '{}' multiple times in Reference.".format(contig.name)) - - self.contigs[contig.name] = contig - self.genomeof[contig] = genome - - def remove(self, genome): - "Removes a genome from this Reference, raising an error if it is not present." - del self.genomes[genome.name] - - for contig in genome.contigs: - del self.contigs[contig.name] - del self.genomeof[contig] - - def discard(self, genome): - "Remove a genome if it is present, else do nothing." - if genome.name in self.genomes: - self.remove(genome) + def from_dict(cls: type[R], json_dict: dict[str, Any]) -> R: + instance = cls() + for (genomename, sourcesdict) in json_dict["genomes"].items(): + genome = Genome(genomename) + instance._add_genome(genome) + for (sourcename, (sourcelen, contigdict)) in sourcesdict.items(): + genome.add(sourcename, sourcelen) + for (contigname, (start, end)) in contigdict.items(): + # JSON format is 1-indexed and includes endpoints, whereas + # Contig struct is not, so compensate. + contig = Contig(contigname, sourcename, start - 1, end) + instance._add_contig(contig, genome) + + for _ in range(len(json_dict["taxmaps"]) - 1): + instance.taxmaps.append(dict()) + for (level, taxmap) in enumerate(json_dict["taxmaps"]): + for (child, parent) in taxmap.items(): + instance._add_taxonomy(level, child, parent) + + return instance + + def save(self, io: IO[str]) -> None: + json_dict: dict[str, Any] = {"genomes": dict(), "taxmaps": []} + + # "genomes": {genomename: [subject_len, {contigname: [start, stop]}]} + genome_dict = json_dict["genomes"] + for genome in self.genomes: + source_dict: dict[str, list[Any]] = dict() + genome_dict[genome.name] = source_dict + for (sourcename, length) in genome.sources.items(): + source_dict[sourcename] = [length, dict()] + + for (contig, genome) in self.genomeof.items(): + # JSON format is 1-indexes and includes endpoints, whereas + # Contig struct is not, so compensate. + genome_dict[genome.name][contig.subject][1][contig.name] = [ + contig.start + 1, + contig.end, + ] + + for taxmap in self.taxmaps: + d: dict[str, str] = dict() + for (child, parent) in taxmap.items(): + if parent is not None: + d[child] = parent + if len(d) > 0: + json_dict["taxmaps"].append(d) + + json.dump(json_dict, io) + class Binning: - """The result of a set of clusters applied to a Reference. - >>> ref - (Reference(ngenomes=2, ncontigs=5) - >>> b = Binning({'bin1': {contig1, contig2}, 'bin2': {contig3, contig4}}, ref) - Binning(4/5 contigs, ReferenceID=0x7fe908180be0) - >>> b[0.5, 0.9] # num. genomes 0.5 recall, 0.9 precision - 1 - - Init arguments: - ----------- Required --------- - contigsof: Dict of clusters, each sequence present in the Reference - reference: Associated Reference object - ----------- Optional --------- - recalls: Iterable of minimum recall thresholds - precisions: Iterable of minimum precision thresholds - checkpresence: Whether to raise an error if a sequence if not present in Reference - disjoint: Whether to raise an error if a sequence is in multiple bins - binsplit_separator: Split bins according to prefix before this separator in seq name - minsize: Minimum sum of sequence lengths in a bin to not be ignored - mincontigs: Minimum number of sequence in a bin to not be ignored + """The result of a set of Bins applied to a Reference. + See Binning.from_file for more usage. + >>> with open("clusters.tsv") as file: + ... binning = Binning.from_file(file, reference) Properties: - self.reference: Reference object of this benchmark - self.recalls: Sorted tuple of recall thresholds - self.precisions: Sorted tuple of precision thresholds - self.nbins: Number of bins - self.ncontigs: Number of binned contigs - self.contigsof: {bin_name: {contig set}} - self.binof: {contig: bin_name(s)}, val is str or set - self.breadthof: {bin_name: breadth} - self.intersectionsof: {genome: {bin:_name: intersection}} - self.breadth: Total breadth of all bins - self.counters: List of (rec, prec) Counters of genomes for each taxonomic rank + * reference: Reference + * bins: list[Bin] + + Properties after self.benchmark() + * counters: list[dict[tuple[float, float], int]]: Genomes at recall/prec thresholds + * recalls: Recall thresholds used to compute counters + * precisions: Precision thresholds used to compute counters + + Extra arguments to Binning.from_file: + * disjoint: If True, do not allow same contig in multiple bins + * binsplit_separator: If str and not None, split bins by separator + * minsize: Filter away all bins with breadth less than this + * mincontigs: Filter away bins with fewer contigs than this """ - _DEFAULTRECALLS = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99] - _DEFAULTPRECISIONS = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99] - - @property - def nbins(self): - return len(self.contigsof) - @property - def ncontigs(self): - return len(self.binof) - - def _iter_intersections(self, genome): - """Given a genome, return a generator of (bin_name, intersection) for - all binning bins with a nonzero recall and precision. - """ - # Get set of all binning bin names with contigs from that genome - bin_names = set() - for contig in genome.contigs: - bin_name = self.binof.get(contig) - if bin_name is None: - continue - elif isinstance(bin_name, set): - bin_names.update(bin_name) - else: - bin_names.add(bin_name) - - for bin_name in bin_names: - intersecting_contigs = genome.contigs.intersection(self.contigsof[bin_name]) - intersection = Genome.getbreadth(intersecting_contigs) - yield bin_name, intersection - - def confusion_matrix(self, genome, bin_name): - "Given a genome and a binname, returns TP, TN, FP, FN" - - true_positives = self.intersectionsof[genome].get(bin_name, 0) - false_positives = self.breadthof[bin_name] - true_positives - false_negatives = genome.breadth - true_positives - true_negatives = self.reference.breadth - false_negatives - false_positives + true_positives - - return true_positives, true_negatives, false_positives, false_negatives - - def mcc(self, genome, bin_name): - "Calculate Matthew's correlation coefficient between a genome and a bin." - - tp, tn, fp, fn = self.confusion_matrix(genome, bin_name) - mcc_num = tp * tn - fp * fn - mcc_den = (tp + fp) * (tp + fn) - mcc_den *= (tn + fp) * (tn + fn) - return 0 if mcc_den == 0 else mcc_num / _sqrt(mcc_den) - - def f1(self, genome, bin_name): - "Calculate F1 score between genome and a bin" - - tp, tn, fp, fn = self.confusion_matrix(genome, bin_name) - return 2*tp / (2*tp + fp + fn) - - def _getseen(self, recprecof): - """Make a {genome: isseen} dict, where isseen is a boolean vector - (implemented as an integer), 1 if a genome is seen at that recall, prec level, - 0 otherwise - """ - isseen = dict() - for genome, _dict in recprecof.items(): - seen = 0 - for binname, (recall, precision) in _dict.items(): - for i, (min_recall, min_precision) in enumerate(_product(self.recalls, self.precisions)): - if recall < min_recall: - break - - if precision >= min_precision: - seen |= 1 << i - isseen[genome] = seen - return isseen - - def _accumulate(self, seen, counts): - "Given a 'seen' dict, make a dict of counts at each threshold level" - nsums = (len(self.recalls) * len(self.precisions)) - sums = [0] * nsums - for v in seen.values(): - for i in range(nsums): - sums[i] += (v >> i) & 1 == 1 - - for i, (recall, precision) in enumerate(_product(self.recalls, self.precisions)): - counts[(recall, precision)] = sums[i] - - def _get_prec_rec_dict(self): - recprecof = _collections.defaultdict(dict) - for genome, intersectiondict in self.intersectionsof.items(): - for binname in intersectiondict: - tp, tn, fp, fn = self.confusion_matrix(genome, binname) - recall = tp / (tp + fn) - precision = tp / (tp + fp) - recprecof[genome.name][binname] = (recall, precision) - - return recprecof - - def _getcounts(self): - # One count per rank (+1 for inclusive "genome" rank) - counts = [_collections.Counter() for i in range(len(self.reference.taxmaps) + 1)] - recprecof = self._get_prec_rec_dict() - seen = self._getseen(recprecof) - # Calculate counts for each taxonomic level - for counter, taxmap in zip(counts, self.reference.taxmaps): - self._accumulate(seen, counter) - newseen = dict() - for clade, v in seen.items(): - newclade = taxmap[clade] - newseen[newclade] = newseen.get(newclade, 0) | v - seen = newseen - - self._accumulate(seen, counts[-1]) - - return counts - - def __init__(self, contigsof, reference, recalls=_DEFAULTRECALLS, - precisions=_DEFAULTPRECISIONS, checkpresence=True, disjoint=True, - binsplit_separator=None, minsize=None, mincontigs=None): - # See class docstring for explanation of arguments - - # Checkpresence enforces that each contig in a bin is also in the reference, - # disjoint enforces that each contig is only present in one bin. - if not isinstance(reference, Reference): - raise ValueError('reference must be a Reference') - - self.precisions = tuple(sorted(precisions)) - self.recalls = tuple(sorted(recalls)) + __slots__ = ["reference", "bins", "counters", "recalls", "precisions"] + _DEFAULTRECALLS = (0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99) + _DEFAULTPRECISIONS = (0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99) + + def __init__( + self, + bins: Sequence[Bin], + reference: Reference, + recalls: Sequence[float] = _DEFAULTRECALLS, + precisions: Sequence[float] = _DEFAULTPRECISIONS, + disjoint: bool = True, + ) -> None: + self.recalls: tuple[float] = self._validate_rec_prec(recalls) + self.precisions: tuple[float] = self._validate_rec_prec(precisions) self.reference = reference + self.bins: list[Bin] = list(bins) + # This is initialized by self._benchmark + self.counters: list[dict[tuple[float, float], int]] = list() + + if disjoint: + seen_contigs: set[Contig] = set() + for bin in self.bins: + for contig in bin.contigs: + if contig in seen_contigs: + raise ValueError( + f'Contig "{contig.name}" seen twice in disjoint binning.' + ) + seen_contigs.add(contig) + self._benchmark() - self.contigsof = dict() # bin_name: {contigs} dict - self.binof = dict() # contig: bin_name or {bin_names} dict - self.breadthof = dict() # bin_name: int dict - self._parse_bins(contigsof, checkpresence, disjoint, binsplit_separator, minsize, mincontigs) - self.breadth = sum(self.breadthof.values()) - - # intersectionsof[genome] = {genome: {binname: tp, binname: tp ... }} - # for all bins with nonzero true positives - intersectionsof = dict() - for genome in reference.genomes.values(): - intersectionsof[genome] = dict() - for bin_name, intersection in self._iter_intersections(genome): - intersectionsof[genome][bin_name] = intersection - self.intersectionsof = intersectionsof - - # Set counts - self.counters = self._getcounts() - - def _parse_bins(self, contigsof, checkpresence, disjoint, binsplit_separator, minsize, mincontigs): - "Fills self.binof, self.contigsof and self.breadthof during instantiation" - - if binsplit_separator is not None: - contigsof = _vambtools.binsplit(contigsof, binsplit_separator) - - if minsize is not None or mincontigs is not None: - minsize = 1 if minsize is None else minsize - mincontigs = 1 if mincontigs is None else mincontigs - contigsof = filter_clusters(contigsof, self.reference, minsize, mincontigs, checkpresence=checkpresence) - - for bin_name, contig_names in contigsof.items(): - contigset = set() - # This stores each contig by their true genome name. - contigsof_genome = _collections.defaultdict(list) - - for contig_name in contig_names: - contig = self.reference.contigs.get(contig_name) - - # Check that the contig is in the reference - if contig is None: - if checkpresence: - raise KeyError('Contig {} not in reference.'.format(contig_name)) - else: - continue - - # Check that contig is only present one time in input - existing = self.binof.get(contig) - if existing is None: - self.binof[contig] = bin_name - else: - if disjoint: - raise KeyError('Contig {} found in multiple bins'.format(contig_name)) - elif isinstance(existing, str): - self.binof[contig] = {existing, bin_name} - else: - self.binof[contig].add(bin_name) - - contigset.add(contig) - genome = self.reference.genomeof[self.reference.contigs[contig_name]] - contigsof_genome[genome.name].append(contig) - - self.contigsof[bin_name] = contigset - - # Now calculate breadth of bin. This is the sum of the number of covered - # base pairs for all the genomes present in the bin. - breadth = 0 - for contigs in contigsof_genome.values(): - breadth += Genome.getbreadth(contigs) - self.breadthof[bin_name] = breadth + @classmethod + def from_file( + cls: type[Bs], + filehandle: Iterable[str], + reference: Reference, + recalls: Sequence[float] = _DEFAULTRECALLS, + precisions: Sequence[float] = _DEFAULTPRECISIONS, + disjoint: bool = True, + binsplit_separator: Optional[str] = None, + minsize: int = 1, + mincontigs: int = 1, + ) -> Bs: + bins = reference.parse_bins(filehandle, binsplit_separator) + bins = cls.filter_bins(bins, minsize, mincontigs) + instance = cls(bins, reference, recalls, precisions, disjoint) + return instance @classmethod - def from_file(cls, filehandle, reference, recalls=_DEFAULTRECALLS, - precisions=_DEFAULTPRECISIONS, checkpresence=True, disjoint=True, - binsplit_separator=None, minsize=None, mincontigs=None): - contigsof = dict() - for line in filehandle: - if line.startswith('#'): - continue - - line = line.rstrip() - bin_name, tab, contig_name = line.partition('\t') - - if bin_name not in contigsof: - contigsof[bin_name] = [contig_name] - else: - contigsof[bin_name].append(contig_name) - - return cls(contigsof, reference, recalls, precisions, checkpresence, disjoint, - binsplit_separator, minsize, mincontigs) - - def print_matrix(self, rank, file=_sys.stdout): + def gold_standard( + cls: type[Bs], + reference: Reference, + recalls: Sequence[float] = _DEFAULTRECALLS, + precisions: Sequence[float] = _DEFAULTPRECISIONS, + ) -> Bs: + "Return a Binning from a given Reference where each Genome is precisely one Bin" + contigsof: defaultdict[Genome, list[Contig]] = defaultdict(list) + for contig in reference.contig_by_name.values(): + contigsof[reference.genomeof[contig]].append(contig) + bins = [ + Bin.from_contigs(genome.name, contigs, reference.genomeof) + for (genome, contigs) in contigsof.items() + ] + instance = cls(bins, reference, recalls, precisions, disjoint=False) + return instance + + def print_matrix(self, rank: int, file: IO[str] = sys.stdout) -> None: """Prints the recall/precision number of bins to STDOUT.""" + recalls: tuple[float] = self.recalls + precisions: tuple[float] = self.precisions + assert self.counters is not None - if rank >= len(self.counters): + if rank >= self.reference.nranks: raise IndexError("Taxonomic rank out of range") - print('\tRecall', file=file) - print('Prec.', '\t'.join([str(r) for r in self.recalls]), sep='\t', file=file) + print("\tRecall", file=file) + print("Prec.", "\t".join([str(r) for r in recalls]), sep="\t", file=file) - for min_precision in self.precisions: - row = [self.counters[rank][(min_recall, min_precision)] for min_recall in self.recalls] - print(min_precision, '\t'.join([str(i) for i in row]), sep='\t', file=file) + for min_precision in precisions: + row = [ + self.counters[rank][(min_recall, min_precision)] + for min_recall in recalls + ] + print(min_precision, "\t".join([str(i) for i in row]), sep="\t", file=file) - def __repr__(self): - fields = (self.ncontigs, self.reference.ncontigs, hex(id(self.reference))) - return 'Binning({}/{} contigs, ReferenceID={})'.format(*fields) - - def summary(self, precision=0.9, recalls=None): - if recalls is None: - recalls = self.recalls - return [[counter[(recall, precision)] for recall in recalls] for counter in self.counters] - -def filter_clusters(clusters, reference, minsize, mincontigs, checkpresence=True): - """Creates a shallow copy of clusters, but without any clusters with a total size - smaller than minsize, or fewer contigs than mincontigs. - If checkpresence is True, raise error if a contig is not present in reference, else - ignores it when counting cluster size. - """ + def __repr__(self) -> str: + return ( + f"" + ) - filtered = dict() - for binname, contignames in clusters.items(): - if len(contignames) < mincontigs: - continue - - size = 0 - for contigname in contignames: - contig = reference.contigs.get(contigname) + @property + def nbins(self) -> int: + return len(self.bins) - if contig is not None: - size += len(contig) - elif checkpresence: - raise KeyError('Contigname {} not in reference'.format(contigname)) - else: - pass + @staticmethod + def filter_bins(bins: Iterable[Bin], minsize: int, mincontigs: int) -> list[Bin]: + def is_ok(bin: Bin) -> bool: + breadth: int = bin.breadth + return breadth >= minsize and bin.ncontigs >= mincontigs + + return list(filter(is_ok, bins)) + + def _benchmark(self) -> None: + counters: list[dict[tuple[float, float], int]] = list() + # key here is name of genome (and later, other taxonomic ranks) + rp_by_name: dict[str, dict[Bin, tuple[float, float]]] = { + g.name: dict() for g in self.reference.genomes + } + for bin in self.bins: + assert bin.intersections is not None + for genome in bin.intersections: + rp_by_name[genome.name][bin] = bin.recall_precision(genome) + bitvectors = self._get_seen_bitvectors(rp_by_name) + counters.append(self._counter_from_bitvectors(bitvectors)) + + for rank in range(self.reference.nranks - 1): + rp_by_name = self._uprank_rp_by_name(rank, rp_by_name) + bitvectors = self._get_seen_bitvectors(rp_by_name) + counters.append(self._counter_from_bitvectors(bitvectors)) + + self.counters = counters - if size >= minsize: - filtered[binname] = contignames.copy() + @staticmethod + def _validate_rec_prec(x: Iterable[float]) -> tuple[float]: + s: set[float] = set() + for i in x: + if i in s: + raise ValueError(f"Recall/precision value {i} present multiple times.") + if not isfinite(i) or i <= 0.0 or i > 1: + raise ValueError( + f"Recall/precision value {i} is not a finite value in (0;1]" + ) + s.add(i) + if len(s) == 0: + raise ValueError("Must provide at least 1 recall/precision value") + return tuple(sorted(s)) + + def _get_seen_bitvectors( + self, rp_by_name: dict[str, dict[Bin, tuple[float, float]]] + ) -> dict[str, int]: + recalls: tuple[float] = self.recalls + precisions: tuple[float] = self.precisions + bitvectors: dict[str, int] = dict() + + for (cladename, d) in rp_by_name.items(): + bitvector = 0 + for (recall, precision) in d.values(): + for (i, (min_recall, min_precision)) in enumerate( + product(recalls, precisions) + ): + if recall >= min_recall and precision >= min_precision: + bitvector |= 1 << i + # Shortcut: Once we pass min recall, rest of values can be skipped + elif recall < min_recall: + break - return filtered + bitvectors[cladename] = bitvector + + return bitvectors + + def _counter_from_bitvectors( + self, bitvectors: dict[str, int] + ) -> dict[tuple[float, float], int]: + recalls: tuple[float] = self.recalls + precisions: tuple[float] = self.precisions + result: dict[tuple[float, float], int] = { + (r, p): 0 for (r, p) in product(recalls, precisions) + } + for (_, bitvector) in bitvectors.items(): + for (i, rp) in enumerate(product(recalls, precisions)): + result[rp] += (bitvector >> i) & 1 + + return result + + def _uprank_rp_by_name( + self, fromrank: int, rp_by_name: dict[str, dict[Bin, tuple[float, float]]] + ) -> dict[str, dict[Bin, tuple[float, float]]]: + result: dict[str, dict[Bin, tuple[float, float]]] = dict() + for (child, parent) in self.reference.taxmaps[fromrank].items(): + # If no parent clade, we just name the "parent" same as child + if parent is None: + parent = child + + if parent not in result: + result[parent] = dict() + + parent_dict = result[parent] + for (bin, (old_recall, old_prec)) in rp_by_name[child].items(): + (new_recall, new_prec) = parent_dict.get(bin, (0.0, 0.0)) + parent_dict[bin] = (max(old_recall, new_recall), old_prec + new_prec) + + return result diff --git a/vamb/cluster.py b/vamb/cluster.py index 9ea8e560..acdc4275 100644 --- a/vamb/cluster.py +++ b/vamb/cluster.py @@ -10,9 +10,12 @@ import random as _random import numpy as _np import torch as _torch -from collections import defaultdict as _defaultdict, deque as _deque +from collections import deque as _deque from math import ceil as _ceil +from torch.functional import Tensor as _Tensor import vamb.vambtools as _vambtools +from typing import Optional +from collections.abc import Sequence, Iterable _DEFAULT_RADIUS = 0.06 # Distance within which to search for medoid point @@ -24,20 +27,66 @@ # This is the PDF of normal with µ=0, s=0.01 from -0.075 to 0.075 with intervals # of DELTA_X, for a total of 31 values. We multiply by _DELTA_X so the density # of one point sums to approximately one -_NORMALPDF = _DELTA_X * _torch.Tensor( - [2.43432053e-11, 9.13472041e-10, 2.66955661e-08, 6.07588285e-07, - 1.07697600e-05, 1.48671951e-04, 1.59837411e-03, 1.33830226e-02, - 8.72682695e-02, 4.43184841e-01, 1.75283005e+00, 5.39909665e+00, - 1.29517596e+01, 2.41970725e+01, 3.52065327e+01, 3.98942280e+01, - 3.52065327e+01, 2.41970725e+01, 1.29517596e+01, 5.39909665e+00, - 1.75283005e+00, 4.43184841e-01, 8.72682695e-02, 1.33830226e-02, - 1.59837411e-03, 1.48671951e-04, 1.07697600e-05, 6.07588285e-07, - 2.66955661e-08, 9.13472041e-10, 2.43432053e-11]) +_NORMALPDF = _DELTA_X * _Tensor( + [ + 2.43432053e-11, + 9.13472041e-10, + 2.66955661e-08, + 6.07588285e-07, + 1.07697600e-05, + 1.48671951e-04, + 1.59837411e-03, + 1.33830226e-02, + 8.72682695e-02, + 4.43184841e-01, + 1.75283005e00, + 5.39909665e00, + 1.29517596e01, + 2.41970725e01, + 3.52065327e01, + 3.98942280e01, + 3.52065327e01, + 2.41970725e01, + 1.29517596e01, + 5.39909665e00, + 1.75283005e00, + 4.43184841e-01, + 8.72682695e-02, + 1.33830226e-02, + 1.59837411e-03, + 1.48671951e-04, + 1.07697600e-05, + 6.07588285e-07, + 2.66955661e-08, + 9.13472041e-10, + 2.43432053e-11, + ] +) -class Cluster: - __slots__ = ['medoid', 'seed', 'members', 'pvr', 'radius', 'isdefault', 'successes', 'attempts'] - def __init__(self, medoid, seed, members, pvr, radius, isdefault, successes, attempts): +class Cluster: + __slots__ = [ + "medoid", + "seed", + "members", + "pvr", + "radius", + "isdefault", + "successes", + "attempts", + ] + + def __init__( + self, + medoid: int, + seed: int, + members: _np.ndarray, + pvr: float, + radius: float, + isdefault: bool, + successes: int, + attempts: int, + ): self.medoid = medoid self.seed = seed self.members = members @@ -47,31 +96,31 @@ def __init__(self, medoid, seed, members, pvr, radius, isdefault, successes, att self.successes = successes self.attempts = attempts - def __repr__(self): - return ''.format(self.medoid, len(self.members)) + def __repr__(self) -> str: + return f"" - def as_tuple(self, labels=None): - if labels is None: - return (self.medoid, {i for i in self.members}) - else: - return (labels[self.medoid], {labels[i] for i in self.members}) + def as_tuple(self) -> tuple[int, set[int]]: + return (self.medoid, {i for i in self.members}) - def dump(self): - return '{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(self.medoid, self.seed, self.pvr, self.radius, - self.isdefault, self.successes, self.attempts, ','.join([str(i) for i in self.members])) + def dump(self) -> str: + return ( + "{self.medoid}\t{self.seed}\t{self.pvt}\t{self.radius}\t{self.isdefault}" + "\t{self.successes}\t{self.attempts}\t" + ) + ",".join([str(i) for i in self.members]) - def __str__(self): - radius = "{:.3f}".format(self.radius) + def __str__(self) -> str: + radius = f"{self.radius:.3f}" if self.isdefault: radius += " (fallback)" - return """Cluster of medoid {} - N members: {} - seed: {} - radius: {} - successes: {} / {} - pvr: {:.1f} - """.format(self.medoid, len(self.members), self.seed, radius, self.successes, self.attempts, self.pvr) + return f"""Cluster of medoid {self.medoid} + N members: {len(self.members)} + seed: {self.seed} + radius: {radius} + successes: {self.successes} / {self.attempts} + pvr: {self.pvr:.1f} + """ + class ClusterGenerator: """Iterative medoid cluster generator. Iterate this object to get clusters. @@ -86,64 +135,78 @@ class ClusterGenerator: cuda: Accelerate clustering with GPU [False] """ - __slots__ = ['MAXSTEPS', 'MINSUCCESSES', 'CUDA', 'RNG', 'matrix', 'indices', - 'seed', 'nclusters', 'peak_valley_ratio', 'attempts', 'successes', - 'histogram', 'kept_mask'] - - def __repr__(self): - return "ClusterGenerator({} points, {} clusters)".format(len(self.matrix), self.nclusters) - - def __str__(self): - return """ClusterGenerator({} points, {} clusters) - CUDA: {} - MAXSTEPS: {} - MINSUCCESSES: {} - pvr: {} - successes: {}/{} -""".format(len(self.matrix), self.nclusters, self.CUDA, self.MAXSTEPS, self.MINSUCCESSES, - self.peak_valley_ratio, self.successes, len(self.attempts)) + __slots__ = [ + "maxsteps", + "minsuccesses", + "cuda", + "rng", + "matrix", + "indices", + "seed", + "nclusters", + "peak_valley_ratio", + "attempts", + "successes", + "histogram", + "kept_mask", + ] + + def __repr__(self) -> str: + return f"ClusterGenerator({len(self.matrix)} points, {self.nclusters} clusters)" + + def __str__(self) -> str: + return f"""ClusterGenerator({len(self.matrix)} points, {self.nclusters} clusters) + CUDA: {self.cuda} + maxsteps: {self.maxsteps} + minsuccesses: {self.minsuccesses} + pvr: {self.peak_valley_ratio} + successes: {self.successes}/{len(self.attempts)} +""" - def _check_params(self, matrix, maxsteps, windowsize, minsuccesses): + def _check_params( + self, matrix: _np.ndarray, maxsteps: int, windowsize: int, minsuccesses: int + ) -> None: """Checks matrix, and maxsteps.""" if matrix.dtype != _np.float32: - raise(ValueError("Matrix must be of dtype float32")) + raise (ValueError("Matrix must be of dtype float32")) if maxsteps < 1: - raise ValueError('maxsteps must be a positive integer, not {}'.format(maxsteps)) + raise ValueError(f"maxsteps must be a positive integer, not {maxsteps}") if windowsize < 1: - raise ValueError('windowsize must be at least 1, not {}'.format(windowsize)) + raise ValueError(f"windowsize must be at least 1, not {windowsize}") if minsuccesses < 1 or minsuccesses > windowsize: - raise ValueError('minsuccesses must be between 1 and windowsize, not {}'.format(minsuccesses)) + raise ValueError( + f"minsuccesses must be between 1 and windowsize, not {minsuccesses}" + ) if len(matrix) < 1: - raise ValueError('Matrix must have at least 1 observation.') + raise ValueError("Matrix must have at least 1 observation.") - def _init_histogram_kept_mask(self, N): + def _init_histogram_kept_mask(self, N: int) -> tuple[_Tensor, _Tensor]: "N is number of contigs" - if _torch.__version__ >= '1.2': - # https://github.com/pytorch/pytorch/issues/20208 fixed in PyTorch 1.2 - cuda_hist_dtype = _torch.float - # Masking using uint8 tensors deprecated in PyTorch 1.2 - kept_mask_dtype = _torch.bool - else: - cuda_hist_dtype = _torch.long - kept_mask_dtype = _torch.uint8 - kept_mask = _torch.ones(N, dtype=kept_mask_dtype) - - if self.CUDA: - histogram = _torch.empty(_ceil(_XMAX/_DELTA_X), dtype=cuda_hist_dtype).cuda() + kept_mask = _torch.ones(N, dtype=_torch.bool) + if self.cuda: + histogram = _torch.empty(_ceil(_XMAX / _DELTA_X), dtype=_torch.float).cuda() kept_mask = kept_mask.cuda() else: - histogram = _torch.empty(_ceil(_XMAX/_DELTA_X)) + histogram = _torch.empty(_ceil(_XMAX / _DELTA_X)) return histogram, kept_mask - def __init__(self, matrix, maxsteps=25, windowsize=200, minsuccesses=20, destroy=False, - normalized=False, cuda=False): + def __init__( + self, + matrix: _np.ndarray, + maxsteps: int = 25, + windowsize: int = 200, + minsuccesses: int = 20, + destroy: bool = False, + normalized: bool = False, + cuda: bool = False, + ): self._check_params(matrix, maxsteps, windowsize, minsuccesses) if not destroy: matrix = matrix.copy() @@ -153,28 +216,28 @@ def __init__(self, matrix, maxsteps=25, windowsize=200, minsuccesses=20, destroy _np.random.RandomState(0).shuffle(matrix) indices = _np.random.RandomState(0).permutation(len(matrix)) indices = _torch.from_numpy(indices) - matrix = _torch.from_numpy(matrix) + torch_matrix = _torch.from_numpy(matrix) if not normalized: - _normalize(matrix, inplace=True) + _normalize(torch_matrix, inplace=True) # Move to GPU if cuda: - matrix = matrix.cuda() + torch_matrix = torch_matrix.cuda() - self.MAXSTEPS = maxsteps - self.MINSUCCESSES = minsuccesses - self.CUDA = cuda - self.RNG = _random.Random(0) + self.maxsteps: int = maxsteps + self.minsuccesses: int = minsuccesses + self.cuda: bool = cuda + self.rng: _random.Random = _random.Random(0) - self.matrix = matrix + self.matrix = torch_matrix # This refers to the indices of the original matrix. As we remove points, these # indices do not correspond to merely range(len(matrix)) anymore. self.indices = indices self.seed = -1 self.nclusters = 0 self.peak_valley_ratio = 0.1 - self.attempts = _deque(maxlen=windowsize) + self.attempts: _deque[bool] = _deque(maxlen=windowsize) self.successes = 0 histogram, kept_mask = self._init_histogram_kept_mask(len(indices)) @@ -185,16 +248,16 @@ def __init__(self, matrix, maxsteps=25, windowsize=200, minsuccesses=20, destroy def __iter__(self): return self - def __next__(self): + def __next__(self) -> Cluster: # Stop criterion. For CUDA, inplace masking the array is too slow, so the matrix is # unchanged. On CPU, we continually modify the matrix by removing rows. - if self.CUDA: + if self.cuda: if not _torch.any(self.kept_mask).item(): raise StopIteration elif len(self.matrix) == 0: raise StopIteration - cluster, medoid, points = self._findcluster() + cluster, _, points = self._findcluster() self.nclusters += 1 for point in points: @@ -202,40 +265,59 @@ def __next__(self): # Remove all points that's been clustered away. Is slow it itself, but speeds up # distance calculation by having fewer points. Worth it on CPU, not on GPU - if not self.CUDA: + if not self.cuda: _vambtools.torch_inplace_maskarray(self.matrix, self.kept_mask) - self.indices = self.indices[self.kept_mask] # no need to inplace mask small array + # no need to inplace mask small array + self.indices = self.indices[self.kept_mask] self.kept_mask.resize_(len(self.matrix)) self.kept_mask[:] = 1 return cluster - def _findcluster(self): + def _findcluster(self) -> tuple[Cluster, int, _Tensor]: """Finds a cluster to output.""" - threshold = None + threshold, success, medoid = None, None, -1 # Keep looping until we find a cluster + distances = None while threshold is None: # If on GPU, we need to take next seed which has not already been clusted out. # if not, clustered points have been removed, so we can just take next seed - if self.CUDA: + if self.cuda: self.seed = (self.seed + 1) % len(self.matrix) while self.kept_mask[self.seed] == False: self.seed = (self.seed + 1) % len(self.matrix) else: self.seed = (self.seed + 1) % len(self.matrix) - medoid, distances = _wander_medoid(self.matrix, self.kept_mask, self.seed, self.MAXSTEPS, self.RNG, self.CUDA) + medoid, distances = _wander_medoid( + self.matrix, + self.kept_mask, + self.seed, + self.maxsteps, + self.rng, + self.cuda, + ) # We need to make a histogram of only the unclustered distances - when run on GPU # these have not been removed and we must use the kept_mask - if self.CUDA: - _torch.histc(distances[self.kept_mask], len(self.histogram), 0, _XMAX, out=self.histogram) + if self.cuda: + _torch.histc( + distances[self.kept_mask], + len(self.histogram), + 0, + _XMAX, + out=self.histogram, + ) else: - _torch.histc(distances, len(self.histogram), 0, _XMAX, out=self.histogram) - self.histogram[0] -= 1 # Remove distance to self + _torch.histc( + distances, len(self.histogram), 0, _XMAX, out=self.histogram + ) + self.histogram[0] -= 1 # Remove distance to self - threshold, success = _find_threshold(self.histogram, self.peak_valley_ratio, self.CUDA) + threshold, success = _find_threshold( + self.histogram, self.peak_valley_ratio, self.cuda + ) # If success is not None, either threshold detection failed or succeded. if success is not None: @@ -249,21 +331,39 @@ def _findcluster(self): # If less than minsuccesses of the last maxlen attempts were successful, # we relax the clustering criteria and reset counting successes. - if len(self.attempts) == self.attempts.maxlen and self.successes < self.MINSUCCESSES: + if ( + len(self.attempts) == self.attempts.maxlen + and self.successes < self.minsuccesses + ): self.peak_valley_ratio += 0.1 self.attempts.clear() self.successes = 0 # These are the points of the final cluster AFTER establishing the threshold used - points = _smaller_indices(distances, self.kept_mask, threshold, self.CUDA) - isdefault = success is None and threshold == _DEFAULT_RADIUS and self.peak_valley_ratio > 0.55 - - cluster = Cluster(self.indices[medoid].item(), self.seed, self.indices[points].numpy(), - self.peak_valley_ratio, - threshold, isdefault, self.successes, len(self.attempts)) + assert isinstance(distances, _Tensor) + points = _smaller_indices(distances, self.kept_mask, threshold, self.cuda) + isdefault = ( + success is None + and threshold == _DEFAULT_RADIUS + and self.peak_valley_ratio > 0.55 + ) + + cluster = Cluster( + int(self.indices[medoid].item()), # type: ignore + self.seed, + self.indices[points].numpy(), + self.peak_valley_ratio, + threshold, + isdefault, + self.successes, + len(self.attempts), + ) return cluster, medoid, points -def _calc_densities(histogram, cuda, pdf=_NORMALPDF): + +def _calc_densities( + histogram: _Tensor, cuda: bool, pdf: _Tensor = _NORMALPDF +) -> _Tensor: """Given an array of histogram, smoothes the histogram.""" pdf_len = len(pdf) @@ -272,13 +372,16 @@ def _calc_densities(histogram, cuda, pdf=_NORMALPDF): densities = _torch.zeros(len(histogram) + pdf_len - 1) for i in range(len(densities) - pdf_len + 1): - densities[i:i+pdf_len] += pdf * histogram[i] + densities[i : i + pdf_len] += pdf * histogram[i] densities = densities[15:-15] return densities -def _find_threshold(histogram, peak_valley_ratio, cuda): + +def _find_threshold( + histogram: _Tensor, peak_valley_ratio: float, cuda: bool +) -> tuple[Optional[float], Optional[bool]]: """Find a threshold distance, where where is a dip in point density that separates an initial peak in densities from the larger bulk around 0.5. Returns (threshold, success), where succes is False if no threshold could @@ -302,6 +405,7 @@ def _find_threshold(histogram, peak_valley_ratio, cuda): # Else we analyze the point densities to find the valley x = 0 + density_at_minimum = 0.0 for density in densities: # Define the first "peak" in point density. That's simply the max until # the peak is defined as being over. @@ -343,20 +447,24 @@ def _find_threshold(histogram, peak_valley_ratio, cuda): return threshold, success -def _smaller_indices(tensor, kept_mask, threshold, cuda): + +def _smaller_indices( + tensor: _Tensor, kept_mask: _Tensor, threshold: float, cuda: bool +) -> _Tensor: """Get all indices where the tensor is smaller than the threshold. Uses Numpy because Torch is slow - See https://github.com/pytorch/pytorch/pull/15190""" # If it's on GPU, we remove the already clustered points at this step. if cuda: - return _torch.nonzero((tensor <= threshold) & kept_mask).flatten().cpu() + return _torch.nonzero((tensor <= threshold) & kept_mask).flatten() else: arr = tensor.numpy() indices = (arr <= threshold).nonzero()[0] torch_indices = _torch.from_numpy(indices) return torch_indices -def _normalize(matrix, inplace=False): + +def _normalize(matrix: _Tensor, inplace: bool = False) -> _Tensor: """Preprocess the matrix to make distance calculations faster. The distance functions in this module assumes input has been normalized and will not work otherwise. @@ -370,18 +478,21 @@ def _normalize(matrix, inplace=False): # If any rows are kept all zeros, the distance function will return 0.5 to all points # inclusive itself, which can break the code in this module zeromask = matrix.sum(dim=1) == 0 - matrix[zeromask] = 1/matrix.shape[1] - matrix /= (matrix.norm(dim=1).reshape(-1, 1) * (2 ** 0.5)) + matrix[zeromask] = 1 / matrix.shape[1] + matrix /= matrix.norm(dim=1).reshape(-1, 1) * (2**0.5) return matrix -def _calc_distances(matrix, index): +def _calc_distances(matrix: _Tensor, index: int) -> _Tensor: "Return vector of cosine distances from rows of normalized matrix to given row." dists = 0.5 - matrix.matmul(matrix[index]) - dists[index] = 0.0 # avoid float rounding errors + dists[index] = 0.0 # avoid float rounding errors return dists -def _sample_medoid(matrix, kept_mask, medoid, threshold, cuda): + +def _sample_medoid( + matrix: _Tensor, kept_mask: _Tensor, medoid: int, threshold: float, cuda: bool +) -> tuple[_Tensor, _Tensor, float]: """Returns: - A vector of indices to points within threshold - A vector of distances to all points @@ -399,25 +510,36 @@ def _sample_medoid(matrix, kept_mask, medoid, threshold, cuda): return cluster, distances, average_distance -def _wander_medoid(matrix, kept_mask, medoid, max_attempts, rng, cuda): +def _wander_medoid( + matrix: _Tensor, + kept_mask: _Tensor, + medoid: int, + max_attempts: int, + rng: _random.Random, + cuda: bool, +) -> tuple[int, _Tensor]: """Keeps sampling new points within the cluster until it has sampled max_attempts without getting a new set of cluster with lower average distance""" futile_attempts = 0 - tried = {medoid} # keep track of already-tried medoids - cluster, distances, average_distance = _sample_medoid(matrix, kept_mask, medoid, _MEDOID_RADIUS, cuda) + tried = {medoid} # keep track of already-tried medoids + cluster, distances, average_distance = _sample_medoid( + matrix, kept_mask, medoid, _MEDOID_RADIUS, cuda + ) while len(cluster) - len(tried) > 0 and futile_attempts < max_attempts: - sampled_medoid = rng.choice(cluster).item() + sampled_medoid = int(rng.choice(cluster).item()) - # Prevent sampling same medoid multiple times. + # Prevent sampling same medoid multiple times. while sampled_medoid in tried: - sampled_medoid = rng.choice(cluster).item() + sampled_medoid = int(rng.choice(cluster).item()) tried.add(sampled_medoid) - sampling = _sample_medoid(matrix, kept_mask, sampled_medoid, _MEDOID_RADIUS, cuda) + sampling = _sample_medoid( + matrix, kept_mask, sampled_medoid, _MEDOID_RADIUS, cuda + ) sample_cluster, sample_distances, sample_avg = sampling # If the mean distance of inner points of the sample is lower, @@ -435,30 +557,10 @@ def _wander_medoid(matrix, kept_mask, medoid, max_attempts, rng, cuda): return medoid, distances -def cluster(matrix, labels=None, maxsteps=25, windowsize=200, minsuccesses=20, destroy=False, - normalized=False, cuda=False): - """Create iterable of (medoid, {point1, point2 ...}) tuples for each cluster. - Inputs: - matrix: A (obs x features) Numpy matrix of data type numpy.float32 - labels: None or list of labels of points [None = range(len(matrix))] - maxsteps: Stop searching for optimal medoid after N futile attempts [25] - windowsize: Length of window to count successes [200] - minsuccesses: Minimum acceptable number of successes [15] - destroy: Save memory by destroying matrix while clustering [False] - normalized: Matrix is already preprocessed [False] - cuda: Accelerate clustering with GPU [False] - - Output: Generator of (medoid, {point1, point2 ...}) tuples for each cluster. - """ - if labels is not None and len(matrix) != len(labels): - raise ValueError("Got {} labels for {} points".format(len(labels), len(matrix))) - - it = ClusterGenerator(matrix, maxsteps, windowsize, minsuccesses, destroy, normalized, cuda) - for cluster in it: - yield cluster.as_tuple(labels) - -def pairs(clustergenerator, labels): +def pairs( + clustergenerator: ClusterGenerator, labels: Sequence[str] +) -> Iterable[tuple[str, set[str]]]: """Create an iterable of (N, {label1, label2 ...}) for each cluster in a ClusterGenerator, where N is "1", "2", "3", etc. Useful to pass to e.g. vambtools.writer_clusters. @@ -470,8 +572,13 @@ def pairs(clustergenerator, labels): Generator yielding ("1", {label1, label2 ... }) for each cluster """ maxindex = clustergenerator.indices.max() - if len(labels) < maxindex: - raise ValueError("Cluster generator contains point no {}, " - "but was given only {} labels".format(maxindex, len(labels))) - - return ((str(i+1), c.as_tuple(labels)[1]) for (i, c) in enumerate(clustergenerator)) + if len(labels) <= maxindex: + raise ValueError( + f"Cluster generator contains point with index {maxindex}, " + f"but was given only {len(labels)} labels" + ) + + return ( + (str(i + 1), {labels[j] for j in c.as_tuple()[1]}) + for (i, c) in enumerate(clustergenerator) + ) diff --git a/vamb/encode.py b/vamb/encode.py index b248ce9c..790725cd 100644 --- a/vamb/encode.py +++ b/vamb/encode.py @@ -1,3 +1,13 @@ +from typing import Optional, IO, Union +import vamb.vambtools as _vambtools +from torch.utils.data.dataset import TensorDataset as _TensorDataset +from torch.utils.data import DataLoader as _DataLoader +from torch.nn.functional import softmax as _softmax +from torch.optim import Adam as _Adam +from torch import Tensor +from torch import nn as _nn +from math import log as _log + __doc__ = """Encode a depths matrix and a tnf matrix to latent representation. Creates a variational autoencoder in PyTorch and tries to represent the depths @@ -5,7 +15,7 @@ Usage: >>> vae = VAE(nsamples=6) ->>> dataloader, mask = make_dataloader(depths, tnf) +>>> dataloader, mask = make_dataloader(depths, tnf, lengths) >>> vae.trainmodel(dataloader) >>> latent = vae.encode(dataloader) # Encode to latent representation >>> latent.shape @@ -16,21 +26,18 @@ import numpy as _np import torch as _torch -_torch.manual_seed(0) - -from math import log as _log -from torch import nn as _nn -from torch.optim import Adam as _Adam -from torch.nn.functional import softmax as _softmax -from torch.utils.data import DataLoader as _DataLoader -from torch.utils.data.dataset import TensorDataset as _TensorDataset -import vamb.vambtools as _vambtools +_torch.manual_seed(0) -if _torch.__version__ < '0.4': - raise ImportError('PyTorch version must be 0.4 or newer') -def make_dataloader(rpkm, tnf, batchsize=256, destroy=False, cuda=False): +def make_dataloader( + rpkm: _np.ndarray, + tnf: _np.ndarray, + lengths: _np.ndarray, + batchsize: int = 256, + destroy: bool = False, + cuda: bool = False, +) -> tuple[_DataLoader[tuple[Tensor, Tensor, Tensor]], _np.ndarray]: """Create a DataLoader and a contig mask from RPKM and TNF. The dataloader is an object feeding minibatches of contigs to the VAE. @@ -51,64 +58,92 @@ def make_dataloader(rpkm, tnf, batchsize=256, destroy=False, cuda=False): """ if not isinstance(rpkm, _np.ndarray) or not isinstance(tnf, _np.ndarray): - raise ValueError('TNF and RPKM must be Numpy arrays') + raise ValueError("TNF and RPKM must be Numpy arrays") if batchsize < 1: - raise ValueError('Minimum batchsize of 1, not {}'.format(batchsize)) + raise ValueError(f"Batch size must be minimum 1, not {batchsize}") - if len(rpkm) != len(tnf): - raise ValueError('Lengths of RPKM and TNF must be the same') + if len(rpkm) != len(tnf) or len(tnf) != len(lengths): + raise ValueError("Lengths of RPKM, TNF and lengths arrays must be the same") if not (rpkm.dtype == tnf.dtype == _np.float32): - raise ValueError('TNF and RPKM must be Numpy arrays of dtype float32') + raise ValueError("TNF and RPKM must be Numpy arrays of dtype float32") - mask = tnf.sum(axis=1) != 0 + ### Copy arrays and mask them ### + # Copy if not destroy - this way we can have all following operations in-place + # for simplicity + if not destroy: + rpkm = rpkm.copy() + tnf = tnf.copy() + + # Normalize samples to have same depth + sample_depths_sum = rpkm.sum(axis=0) + if _np.any(sample_depths_sum == 0): + raise ValueError("One or more samples have zero depth in all sequences, so cannot be depth normalized") + rpkm *= 1_000_000 / sample_depths_sum # If multiple samples, also include nonzero depth as requirement for accept # of sequences + mask = tnf.sum(axis=1) != 0 + depthssum = None if rpkm.shape[1] > 1: depthssum = rpkm.sum(axis=1) mask &= depthssum != 0 depthssum = depthssum[mask] + assert isinstance(depthssum, _np.ndarray) if mask.sum() < batchsize: - raise ValueError('Fewer sequences left after filtering than the batch size.') + raise ValueError( + "Fewer sequences left after filtering than the batch size. " + + "This probably means you try to run on a too small dataset (below ~10k sequences), " + + "or that nearly all sequences were filtered away. Check the log file, " + + "and verify BAM file content is sensible." + ) - if destroy: - rpkm = _vambtools.numpy_inplace_maskarray(rpkm, mask) - tnf = _vambtools.numpy_inplace_maskarray(tnf, mask) - else: - # The astype operation does not copy due to "copy=False", but the masking - # operation does. - rpkm = rpkm[mask].astype(_np.float32, copy=False) - tnf = tnf[mask].astype(_np.float32, copy=False) + _vambtools.numpy_inplace_maskarray(rpkm, mask) + _vambtools.numpy_inplace_maskarray(tnf, mask) # If multiple samples, normalize to sum to 1, else zscore normalize if rpkm.shape[1] > 1: + assert depthssum is not None # we set it so just above rpkm /= depthssum.reshape((-1, 1)) else: _vambtools.zscore(rpkm, axis=0, inplace=True) - # Normalize arrays and create the Tensors (the tensors share the underlying memory) - # of the Numpy arrays + # Normalize TNF _vambtools.zscore(tnf, axis=0, inplace=True) - depthstensor = _torch.from_numpy(rpkm) - tnftensor = _torch.from_numpy(tnf) - # Create dataloader + # Create weights + lengths = (lengths[mask]).astype(_np.float32) + weights = _np.log(lengths).astype(_np.float32) - 5.0 + weights[weights < 2.0] = 2.0 + weights *= len(weights) / weights.sum() + weights.shape = (len(weights), 1) + + ### Create final tensors and dataloader ### + depthstensor = _torch.from_numpy(rpkm) # this is a no-copy operation + tnftensor = _torch.from_numpy(tnf) + weightstensor = _torch.from_numpy(weights) n_workers = 4 if cuda else 1 - dataset = _TensorDataset(depthstensor, tnftensor) - dataloader = _DataLoader(dataset=dataset, batch_size=batchsize, drop_last=True, - shuffle=True, num_workers=n_workers, pin_memory=cuda) + dataset = _TensorDataset(depthstensor, tnftensor, weightstensor) + dataloader = _DataLoader( + dataset=dataset, + batch_size=batchsize, + drop_last=True, + shuffle=True, + num_workers=n_workers, + pin_memory=cuda, + ) return dataloader, mask + class VAE(_nn.Module): """Variational autoencoder, subclass of torch.nn.Module. Instantiate with: nsamples: Number of samples in abundance matrix - nhiddens: List of n_neurons in the hidden layers [None=Auto] + nhiddens: list of n_neurons in the hidden layers [None=Auto] nlatent: Number of neurons in the latent layer [32] alpha: Approximate starting TNF/(CE+TNF) ratio in loss. [None = Auto] beta: Multiply KLD by the inverse of this value [200] @@ -125,13 +160,21 @@ class VAE(_nn.Module): 0.99 and 0.0, respectively """ - def __init__(self, nsamples, nhiddens=None, nlatent=32, alpha=None, - beta=200, dropout=0.2, cuda=False): + def __init__( + self, + nsamples: int, + nhiddens: Optional[list[int]] = None, + nlatent: int = 32, + alpha: Optional[float] = None, + beta: float = 200.0, + dropout: Optional[float] = 0.2, + cuda: bool = False, + ): if nlatent < 1: - raise ValueError('Minimum 1 latent neuron, not {}'.format(latent)) + raise ValueError(f"Minimum 1 latent neuron, not {nlatent}") if nsamples < 1: - raise ValueError('nsamples must be > 0, not {}'.format(nsamples)) + raise ValueError(f"nsamples must be > 0, not {nsamples}") # If only 1 sample, we weigh alpha and nhiddens differently if alpha is None: @@ -144,16 +187,16 @@ def __init__(self, nsamples, nhiddens=None, nlatent=32, alpha=None, dropout = 0.2 if nsamples > 1 else 0.0 if any(i < 1 for i in nhiddens): - raise ValueError('Minimum 1 neuron per layer, not {}'.format(min(nhiddens))) + raise ValueError(f"Minimum 1 neuron per layer, not {min(nhiddens)}") if beta <= 0: - raise ValueError('beta must be > 0, not {}'.format(beta)) + raise ValueError(f"beta must be > 0, not {beta}") if not (0 < alpha < 1): - raise ValueError('alpha must be 0 < alpha < 1, not {}'.format(alpha)) + raise ValueError(f"alpha must be 0 < alpha < 1, not {alpha}") if not (0 <= dropout < 1): - raise ValueError('dropout must be 0 <= dropout < 1, not {}'.format(dropout)) + raise ValueError(f"dropout must be 0 <= dropout < 1, not {dropout}") super(VAE, self).__init__() @@ -174,7 +217,9 @@ def __init__(self, nsamples, nhiddens=None, nlatent=32, alpha=None, self.decodernorms = _nn.ModuleList() # Add all other hidden layers - for nin, nout in zip([self.nsamples + self.ntnf] + self.nhiddens, self.nhiddens): + for nin, nout in zip( + [self.nsamples + self.ntnf] + self.nhiddens, self.nhiddens + ): self.encoderlayers.append(_nn.Linear(nin, nout)) self.encodernorms.append(_nn.BatchNorm1d(nout)) @@ -198,7 +243,7 @@ def __init__(self, nsamples, nhiddens=None, nlatent=32, alpha=None, if cuda: self.cuda() - def _encode(self, tensor): + def _encode(self, tensor: Tensor) -> tuple[Tensor, Tensor]: tensors = list() # Hidden layers @@ -223,7 +268,7 @@ def _encode(self, tensor): return mu, logsigma # sample with gaussian noise - def reparameterize(self, mu, logsigma): + def reparameterize(self, mu: Tensor, logsigma: Tensor) -> Tensor: epsilon = _torch.randn(mu.size(0), mu.size(1)) if self.usecuda: @@ -232,11 +277,11 @@ def reparameterize(self, mu, logsigma): epsilon.requires_grad = True # See comment above regarding softplus - latent = mu + epsilon * _torch.exp(logsigma/2) + latent = mu + epsilon * _torch.exp(logsigma / 2) return latent - def _decode(self, tensor): + def _decode(self, tensor: Tensor) -> tuple[Tensor, Tensor]: tensors = list() for decoderlayer, decodernorm in zip(self.decoderlayers, self.decodernorms): @@ -255,7 +300,9 @@ def _decode(self, tensor): return depths_out, tnf_out - def forward(self, depths, tnf): + def forward( + self, depths: Tensor, tnf: Tensor + ) -> tuple[Tensor, Tensor, Tensor, Tensor]: tensor = _torch.cat((depths, tnf), 1) mu, logsigma = self._encode(tensor) latent = self.reparameterize(mu, logsigma) @@ -263,54 +310,76 @@ def forward(self, depths, tnf): return depths_out, tnf_out, mu, logsigma - def calc_loss(self, depths_in, depths_out, tnf_in, tnf_out, mu, logsigma): + def calc_loss( + self, + depths_in: Tensor, + depths_out: Tensor, + tnf_in: Tensor, + tnf_out: Tensor, + mu: Tensor, + logsigma: Tensor, + weights: Tensor, + ) -> tuple[Tensor, Tensor, Tensor, Tensor]: # If multiple samples, use cross entropy, else use SSE for abundance if self.nsamples > 1: # Add 1e-9 to depths_out to avoid numerical instability. - ce = - ((depths_out + 1e-9).log() * depths_in).sum(dim=1).mean() + ce = -((depths_out + 1e-9).log() * depths_in).sum(dim=1) ce_weight = (1 - self.alpha) / _log(self.nsamples) else: - ce = (depths_out - depths_in).pow(2).sum(dim=1).mean() + ce = (depths_out - depths_in).pow(2).sum(dim=1) ce_weight = 1 - self.alpha - sse = (tnf_out - tnf_in).pow(2).sum(dim=1).mean() - kld = -0.5 * (1 + logsigma - mu.pow(2) - logsigma.exp()).sum(dim=1).mean() + sse = (tnf_out - tnf_in).pow(2).sum(dim=1) + kld = -0.5 * (1 + logsigma - mu.pow(2) - logsigma.exp()).sum(dim=1) sse_weight = self.alpha / self.ntnf kld_weight = 1 / (self.nlatent * self.beta) - loss = ce * ce_weight + sse * sse_weight + kld * kld_weight - - return loss, ce, sse, kld - - def trainepoch(self, data_loader, epoch, optimizer, batchsteps, logfile): + reconstruction_loss = ce * ce_weight + sse * sse_weight + kld_loss = kld * kld_weight + loss = (reconstruction_loss + kld_loss) * weights + + return loss.mean(), ce.mean(), sse.mean(), kld.mean() + + def trainepoch( + self, + data_loader: _DataLoader, + epoch: int, + optimizer, + batchsteps: list[int], + logfile, + ) -> _DataLoader[tuple[Tensor, Tensor, Tensor]]: self.train() - epoch_loss = 0 - epoch_kldloss = 0 - epoch_sseloss = 0 - epoch_celoss = 0 + epoch_loss = 0.0 + epoch_kldloss = 0.0 + epoch_sseloss = 0.0 + epoch_celoss = 0.0 if epoch in batchsteps: - data_loader = _DataLoader(dataset=data_loader.dataset, - batch_size=data_loader.batch_size * 2, - shuffle=True, - drop_last=True, - num_workers=data_loader.num_workers, - pin_memory=data_loader.pin_memory) - - for depths_in, tnf_in in data_loader: + data_loader = _DataLoader( + dataset=data_loader.dataset, + batch_size=data_loader.batch_size * 2, # type: ignore + shuffle=True, + drop_last=True, + num_workers=data_loader.num_workers, + pin_memory=data_loader.pin_memory, + ) + + for depths_in, tnf_in, weights in data_loader: depths_in.requires_grad = True tnf_in.requires_grad = True if self.usecuda: depths_in = depths_in.cuda() tnf_in = tnf_in.cuda() + weights = weights.cuda() optimizer.zero_grad() depths_out, tnf_out, mu, logsigma = self(depths_in, tnf_in) - loss, ce, sse, kld = self.calc_loss(depths_in, depths_out, tnf_in, - tnf_out, mu, logsigma) + loss, ce, sse, kld = self.calc_loss( + depths_in, depths_out, tnf_in, tnf_out, mu, logsigma, weights + ) loss.backward() optimizer.step() @@ -321,20 +390,24 @@ def trainepoch(self, data_loader, epoch, optimizer, batchsteps, logfile): epoch_celoss += ce.data.item() if logfile is not None: - print('\tEpoch: {}\tLoss: {:.6f}\tCE: {:.7f}\tSSE: {:.6f}\tKLD: {:.4f}\tBatchsize: {}'.format( - epoch + 1, - epoch_loss / len(data_loader), - epoch_celoss / len(data_loader), - epoch_sseloss / len(data_loader), - epoch_kldloss / len(data_loader), - data_loader.batch_size, - ), file=logfile) + print( + "\tEpoch: {}\tLoss: {:.6f}\tCE: {:.7f}\tSSE: {:.6f}\tKLD: {:.4f}\tBatchsize: {}".format( + epoch + 1, + epoch_loss / len(data_loader), + epoch_celoss / len(data_loader), + epoch_sseloss / len(data_loader), + epoch_kldloss / len(data_loader), + data_loader.batch_size, + ), + file=logfile, + ) logfile.flush() + self.eval() return data_loader - def encode(self, data_loader): + def encode(self, data_loader) -> _np.ndarray: """Encode a data loader to a latent representation with VAE Input: data_loader: As generated by train_vae @@ -344,14 +417,16 @@ def encode(self, data_loader): self.eval() - new_data_loader = _DataLoader(dataset=data_loader.dataset, - batch_size=data_loader.batch_size, - shuffle=False, - drop_last=False, - num_workers=1, - pin_memory=data_loader.pin_memory) + new_data_loader = _DataLoader( + dataset=data_loader.dataset, + batch_size=data_loader.batch_size, + shuffle=False, + drop_last=False, + num_workers=1, + pin_memory=data_loader.pin_memory, + ) - depths_array, tnf_array = data_loader.dataset.tensors + depths_array, _, _ = data_loader.dataset.tensors length = len(depths_array) # We make a Numpy array instead of a Torch array because, if we create @@ -361,19 +436,19 @@ def encode(self, data_loader): row = 0 with _torch.no_grad(): - for depths, tnf in new_data_loader: + for depths, tnf, weights in new_data_loader: # Move input to GPU if requested if self.usecuda: depths = depths.cuda() tnf = tnf.cuda() # Evaluate - out_depths, out_tnf, mu, logsigma = self(depths, tnf) + _, _, mu, _ = self(depths, tnf) if self.usecuda: mu = mu.cpu() - latent[row: row + len(mu)] = mu + latent[row : row + len(mu)] = mu row += len(mu) assert row == length @@ -385,19 +460,22 @@ def save(self, filehandle): Input: Path or binary opened filehandle Output: None """ - state = {'nsamples': self.nsamples, - 'alpha': self.alpha, - 'beta': self.beta, - 'dropout': self.dropout, - 'nhiddens': self.nhiddens, - 'nlatent': self.nlatent, - 'state': self.state_dict(), - } + state = { + "nsamples": self.nsamples, + "alpha": self.alpha, + "beta": self.beta, + "dropout": self.dropout, + "nhiddens": self.nhiddens, + "nlatent": self.nlatent, + "state": self.state_dict(), + } _torch.save(state, filehandle) @classmethod - def load(cls, path, cuda=False, evaluate=True): + def load( + cls, path: Union[IO[bytes], str], cuda: bool = False, evaluate: bool = True + ): """Instantiates a VAE from a model file. Inputs: @@ -412,13 +490,13 @@ def load(cls, path, cuda=False, evaluate=True): # Forcably load to CPU even if model was saves as GPU model dictionary = _torch.load(path, map_location=lambda storage, loc: storage) - nsamples = dictionary['nsamples'] - alpha = dictionary['alpha'] - beta = dictionary['beta'] - dropout = dictionary['dropout'] - nhiddens = dictionary['nhiddens'] - nlatent = dictionary['nlatent'] - state = dictionary['state'] + nsamples = dictionary["nsamples"] + alpha = dictionary["alpha"] + beta = dictionary["beta"] + dropout = dictionary["dropout"] + nhiddens = dictionary["nhiddens"] + nlatent = dictionary["nlatent"] + state = dictionary["state"] vae = cls(nsamples, nhiddens, nlatent, alpha, beta, dropout, cuda) vae.load_state_dict(state) @@ -431,8 +509,15 @@ def load(cls, path, cuda=False, evaluate=True): return vae - def trainmodel(self, dataloader, nepochs=500, lrate=1e-3, - batchsteps=[25, 75, 150, 300], logfile=None, modelfile=None): + def trainmodel( + self, + dataloader: _DataLoader[tuple[Tensor, Tensor, Tensor]], + nepochs: int = 500, + lrate: float = 1e-3, + batchsteps: Optional[list[int]] = [25, 75, 150, 300], + logfile: Optional[IO[str]] = None, + modelfile: Union[None, str, IO[bytes]] = None, + ): """Train the autoencoder from depths array and tnf array. Inputs: @@ -447,50 +532,65 @@ def trainmodel(self, dataloader, nepochs=500, lrate=1e-3, """ if lrate < 0: - raise ValueError('Learning rate must be positive, not {}'.format(lrate)) + raise ValueError(f"Learning rate must be positive, not {lrate}") if nepochs < 1: - raise ValueError('Minimum 1 epoch, not {}'.format(nepochs)) + raise ValueError("Minimum 1 epoch, not {nepochs}") if batchsteps is None: - batchsteps_set = set() + batchsteps_set: set[int] = set() else: # First collect to list in order to allow all element types, then check that # they are integers batchsteps = list(batchsteps) if not all(isinstance(i, int) for i in batchsteps): - raise ValueError('All elements of batchsteps must be integers') + raise ValueError("All elements of batchsteps must be integers") if max(batchsteps, default=0) >= nepochs: - raise ValueError('Max batchsteps must not equal or exceed nepochs') - last_batchsize = dataloader.batch_size * 2**len(batchsteps) - if len(dataloader.dataset) < last_batchsize: - raise ValueError('Last batch size exceeds dataset length') + raise ValueError("Max batchsteps must not equal or exceed nepochs") + last_batchsize = dataloader.batch_size * 2 ** len(batchsteps) + if len(dataloader.dataset) < last_batchsize: # type: ignore + raise ValueError( + f"Last batch size of {last_batchsize} exceeds dataset length " + f"of {len(dataloader.dataset)}. " # type: ignore + "This means you have too few contigs left after filtering to train. " + "It is not adviced to run Vamb with fewer than 10,000 sequences " + "after filtering. " + "Please check the Vamb log file to see where the sequences were " + "filtered away, and verify BAM files has sensible content." + ) batchsteps_set = set(batchsteps) # Get number of features - ncontigs, nsamples = dataloader.dataset.tensors[0].shape + # Following line is un-inferrable due to typing problems with DataLoader + ncontigs, nsamples = dataloader.dataset.tensors[0].shape # type: ignore optimizer = _Adam(self.parameters(), lr=lrate) if logfile is not None: - print('\tNetwork properties:', file=logfile) - print('\tCUDA:', self.usecuda, file=logfile) - print('\tAlpha:', self.alpha, file=logfile) - print('\tBeta:', self.beta, file=logfile) - print('\tDropout:', self.dropout, file=logfile) - print('\tN hidden:', ', '.join(map(str, self.nhiddens)), file=logfile) - print('\tN latent:', self.nlatent, file=logfile) - print('\n\tTraining properties:', file=logfile) - print('\tN epochs:', nepochs, file=logfile) - print('\tStarting batch size:', dataloader.batch_size, file=logfile) - batchsteps_string = ', '.join(map(str, sorted(batchsteps))) if batchsteps_set else "None" - print('\tBatchsteps:', batchsteps_string, file=logfile) - print('\tLearning rate:', lrate, file=logfile) - print('\tN sequences:', ncontigs, file=logfile) - print('\tN samples:', nsamples, file=logfile, end='\n\n') + print("\tNetwork properties:", file=logfile) + print("\tCUDA:", self.usecuda, file=logfile) + print("\tAlpha:", self.alpha, file=logfile) + print("\tBeta:", self.beta, file=logfile) + print("\tDropout:", self.dropout, file=logfile) + print("\tN hidden:", ", ".join(map(str, self.nhiddens)), file=logfile) + print("\tN latent:", self.nlatent, file=logfile) + print("\n\tTraining properties:", file=logfile) + print("\tN epochs:", nepochs, file=logfile) + print("\tStarting batch size:", dataloader.batch_size, file=logfile) + batchsteps_string = ( + ", ".join(map(str, sorted(batchsteps_set))) + if batchsteps_set + else "None" + ) + print("\tBatchsteps:", batchsteps_string, file=logfile) + print("\tLearning rate:", lrate, file=logfile) + print("\tN sequences:", ncontigs, file=logfile) + print("\tN samples:", nsamples, file=logfile, end="\n\n") # Train for epoch in range(nepochs): - dataloader = self.trainepoch(dataloader, epoch, optimizer, batchsteps_set, logfile) + dataloader = self.trainepoch( + dataloader, epoch, optimizer, sorted(batchsteps_set), logfile + ) # Save weights - Lord forgive me, for I have sinned when catching all exceptions if modelfile is not None: diff --git a/vamb/parsebam.py b/vamb/parsebam.py index c5bbe52e..e90532f1 100644 --- a/vamb/parsebam.py +++ b/vamb/parsebam.py @@ -1,375 +1,142 @@ -# This script calculates RPKM, when paired end reads are mapped to a contig -# catalogue with BWA MEM. It will not be accurate with single end reads or -# any other mapper than BWA MEM. - -# Theory: -# We want a simple way to estimate abundance of redundant contig catalogues. -# Earlier we used to run analysis on deduplicated gene catalogues, but since -# both depth and kmer composition are only stable for longer contigs, we have -# moved to contig catalogues. We have not found a way of deduplicating contigs. - -# For this we have until now used two methods: -# 1) Only counting the primary hits. In this case the read will never be -# assigned to any contig which differ by just 1 basepair. Even for -# identical contigs, reads are assigned randomly which causes noise. - -# 2) Using MetaBAT's jgi_summarize_bam_contig_depths, a script which is not -# documented and we cannot figure out how works. When testing with small -# toy data, it produces absurd results. - -# This script is an attempt to take an approach as simple as possible while -# still being sound technically. We simply count the number of reads in a -# contig normalized by contig length and total number of reads. - -# We look at all hits, including secondary hits. We do not discount partial -# alignments. Also, if a read maps to N contigs, we count each hit as 1/N reads. -# The reason for all these decisions is that if the aligner believes it's a hit, -# we believe the contig is present. - -# We do not take varying insert sizes into account. It is unlikely that -# any contig with enough reads to provide a reliable estimate of depth would, -# by chance, only recruit read pairs with short or long insert size. So this -# will average out over all contigs. - -# We count each read independently, because BWA MEM often assigns mating reads -# to different contigs.. - -__doc__ = """Estimate RPKM (depths) from BAM files of reads mapped to contigs. +__doc__ = """Estimate depths from BAM files of reads mapped to contigs. Usage: >>> bampaths = ['/path/to/bam1.bam', '/path/to/bam2.bam', '/path/to/bam3.bam'] ->>> rpkms = read_bamfiles(bampaths) +>>> rpkms = Abundance.from_file(bampaths, metadata, True, 0.1, 3) """ -import pysam as _pysam - -import sys as _sys +import pycoverm import os as _os -import multiprocessing as _multiprocessing import numpy as _np -import time as _time +from math import isfinite +from vamb.parsecontigs import CompositionMetaData import vamb.vambtools as _vambtools - -DEFAULT_SUBPROCESSES = min(8, _os.cpu_count()) - -def mergecolumns(pathlist): - """Merges multiple npz files with columns to a matrix. - - All paths must be npz arrays with the array saved as name 'arr_0', - and with the same length. - - Input: pathlist: List of paths to find .npz files to merge - Output: Matrix with one column per npz file - """ - - if len(pathlist) == 0: - return _np.array([], dtype=_np.float32) - - for path in pathlist: - if not _os.path.exists(path): - raise FileNotFoundError(path) - - first = _np.load(pathlist[0])['arr_0'] - length = len(first) - ncolumns = len(pathlist) - - result = _np.zeros((length, ncolumns), dtype=_np.float32) - result[:,0] = first - - for columnno, path in enumerate(pathlist[1:]): - column = _np.load(path)['arr_0'] - if len(column) != length: - raise ValueError("Length of data at {} is not equal to that of " - "{}".format(path, pathlist[0])) - result[:,columnno + 1] = column - - return result - -def _identity(segment): - "Return the nucleotide identity of the given aligned segment." - mismatches, matches = 0, 0 - for kind, number in segment.cigartuples: - # 0, 7, 8, is match/mismatch, match, mismatch, respectively - if kind in (0, 7, 8): - matches += number - # 1, 2 is insersion, deletion - elif kind in (1, 2): - mismatches += number - matches -= segment.get_tag('NM') - return matches / (matches+mismatches) - -def _filter_segments(segmentiterator, minscore, minid): - """Returns an iterator of AlignedSegment filtered for reads with low - alignment score. - """ - - for alignedsegment in segmentiterator: - # Skip if unaligned or suppl. aligment - if alignedsegment.flag & 0x804 != 0: - continue - - if minscore is not None and alignedsegment.get_tag('AS') < minscore: - continue - - if minid is not None and _identity(alignedsegment) < minid: - continue - - yield alignedsegment - -def count_reads(bamfile, minscore=None, minid=None): - """Count number of reads mapping to each reference in a bamfile, - optionally filtering for score and minimum id. - Multi-mapping reads MUST be consecutive in file, and their counts are - split among the references. - - Inputs: - bamfile: Open pysam.AlignmentFile - minscore: Minimum alignment score (AS field) to consider [None] - minid: Discard any reads with ID lower than this [None] - - Output: Float32 Numpy array of read counts for each reference in file. - """ - # Use 64-bit floats for better precision when counting - readcounts = _np.zeros(len(bamfile.lengths)) - - # Initialize with first aligned read - return immediately if the file - # is empty - filtered_segments = _filter_segments(bamfile, minscore, minid) - try: - segment = next(filtered_segments) - read_name = segment.query_name - multimap = 1.0 - reference_ids = [segment.reference_id] - except StopIteration: - return readcounts.astype(_np.float32) - - # Now count up each read in the BAM file - for segment in filtered_segments: - # If we reach a new read_name, we tally up the previous read - # towards all its references, split evenly. - if segment.query_name != read_name: - read_name = segment.query_name - to_add = 1.0 / multimap - for reference_id in reference_ids: - readcounts[reference_id] += to_add - reference_ids.clear() - multimap = 0.0 - - multimap += 1.0 - reference_ids.append(segment.reference_id) - - # Add final read - to_add = 1.0 / multimap - for reference_id in reference_ids: - readcounts[reference_id] += to_add - - return readcounts.astype(_np.float32) - -def calc_rpkm(counts, lengths, minlength=None): - """Calculate RPKM based on read counts and sequence lengths. - - Inputs: - counts: Numpy vector of read counts from count_reads - lengths: Iterable of contig lengths in same order as counts - minlength [None]: Discard any references shorter than N bases - - Output: Float32 Numpy vector of RPKM for all seqs with length >= minlength - """ - lengtharray = _np.array(lengths) - if len(counts) != len(lengtharray): - raise ValueError("counts length and lengths length must be same") - - millionmappedreads = counts.sum() / 1e6 - - # Prevent division by zero - if millionmappedreads == 0: - rpkm = _np.zeros(len(lengtharray), dtype=_np.float32) - else: - kilobases = lengtharray / 1000 - rpkm = (counts / (kilobases * millionmappedreads)).astype(_np.float32) - - # Now filter away small contigs - if minlength is not None: - lengthmask = lengtharray >= minlength - rpkm = rpkm[lengthmask] - - return rpkm - -def _check_bamfile(path, bamfile, refhash, minlength): - "Checks bam file for correctness (refhash and sort order). To be used before parsing." - # If refhash is set, check ref hash matches what is found. - if refhash is not None: - if minlength is None: - refnames = bamfile.references - else: - pairs = zip(bamfile.references, bamfile.lengths) - refnames = (ref for (ref, len) in pairs if len >= minlength) - - hash = _vambtools._hash_refnames(refnames) - if hash != refhash: - errormsg = ('BAM file {} has reference hash {}, expected {}. ' - 'Verify that all BAM headers and FASTA headers are ' - 'identical and in the same order.') - raise ValueError(errormsg.format(path, hash.hex(), refhash.hex())) - - # Check that file is unsorted or sorted by read name. - hd_header = bamfile.header.get("HD", dict()) - sort_order = hd_header.get("SO") - if sort_order in ("coordinate", "unknown"): - errormsg = ("BAM file {} is marked with sort order '{}', must be " - "unsorted or sorted by readname.") - raise ValueError(errormsg.format(path, sort_order)) - - -def _get_contig_rpkms(inpath, outpath, refhash, minscore, minlength, minid): - """Returns RPKM (reads per kilobase per million mapped reads) - for all contigs present in BAM header. - - Inputs: - inpath: Path to BAM file - outpath: Path to dump depths array to or None - refhash: Expected reference hash (None = no check) - minscore: Minimum alignment score (AS field) to consider - minlength: Discard any references shorter than N bases - minid: Discard any reads with ID lower than this - - Outputs: - path: Same as input path - rpkms: - If outpath is not None: None - Else: A float32-array with RPKM for each contig in BAM header - length: Length of rpkms array - """ - - bamfile = _pysam.AlignmentFile(inpath, "rb") - _check_bamfile(inpath, bamfile, refhash, minlength) - counts = count_reads(bamfile, minscore, minid) - rpkms = calc_rpkm(counts, bamfile.lengths, minlength) - bamfile.close() - - # If dump to disk, array returned is None instead of rpkm array - if outpath is not None: - arrayresult = None - _np.savez_compressed(outpath, rpkms) - else: - arrayresult = rpkms - - return inpath, arrayresult, len(rpkms) - -def read_bamfiles(paths, dumpdirectory=None, refhash=None, minscore=None, minlength=None, - minid=None, subprocesses=DEFAULT_SUBPROCESSES, logfile=None): - "Placeholder docstring - replaced after this func definition" - - # Define callback function depending on whether a logfile exists or not - if logfile is not None: - def _callback(result): - path, _rpkms, _length = result - print('\tProcessed', path, file=logfile) - logfile.flush() - - else: - def _callback(result): - pass - - # Bam files must be unique. - if len(paths) != len(set(paths)): - raise ValueError('All paths to BAM files must be unique.') - - # Bam files must exist - for path in paths: - if not _os.path.isfile(path): - raise FileNotFoundError(path) - - if dumpdirectory is not None: - # Dumpdirectory cannot exist, but its parent must exist - dumpdirectory = _os.path.abspath(dumpdirectory) - if _os.path.exists(dumpdirectory): - raise FileExistsError(dumpdirectory) - - parentdir = _os.path.dirname(_os.path.abspath(dumpdirectory)) - if not _os.path.isdir(parentdir): - raise FileNotFoundError("Parent dir of " + dumpdirectory) - - # Create directory to dump in - _os.mkdir(dumpdirectory) - - # Spawn independent processes to calculate RPKM for each of the BAM files - processresults = list() - - # Queue all the processes - with _multiprocessing.Pool(processes=subprocesses) as pool: - for pathnumber, path in enumerate(paths): - if dumpdirectory is None: - outpath = None - else: - outpath = _os.path.join(dumpdirectory, str(pathnumber) + '.npz') - - arguments = (path, outpath, refhash, minscore, minlength, minid) - processresults.append(pool.apply_async(_get_contig_rpkms, arguments, - callback=_callback)) - - all_done, any_fail = False, False - while not (all_done or any_fail): - _time.sleep(5) - all_done = all(process.ready() and process.successful() for process in processresults) - any_fail = any(process.ready() and not process.successful() for process in processresults) - - if all_done: - pool.close() # exit gently - if any_fail: - pool.terminate() # exit less gently - - # Wait for all processes to be cleaned up - pool.join() - - # Raise the error if one of them failed. - for path, process in zip(paths, processresults): - if process.ready() and not process.successful(): - print('\tERROR WHEN PROCESSING:', path, file=logfile) - print('Vamb aborted due to error in subprocess. See stacktrace for source of exception.') - if logfile is not None: - logfile.flush() - process.get() - - ncontigs = None - for processresult in processresults: - path, rpkm, length = processresult.get() - - # Verify length of contigs are same for all BAM files - if ncontigs is None: - ncontigs = length - elif length != ncontigs: - raise ValueError('First BAM file has {} headers, {} has {}.'.format( - ncontigs, path, length)) - - # If we did not dump to disk, load directly from process results to - # one big matrix... - if dumpdirectory is None: - columnof = {p:i for i, p in enumerate(paths)} - rpkms = _np.zeros((ncontigs, len(paths)), dtype=_np.float32) - - for processresult in processresults: - path, rpkm, length = processresult.get() - rpkms[:, columnof[path]] = rpkm - - # If we did, instead merge them from the disk - else: - dumppaths = [_os.path.join(dumpdirectory, str(i) + '.npz') for i in range(len(paths))] - rpkms = mergecolumns(dumppaths) - - return rpkms - -read_bamfiles.__doc__ = """Spawns processes to parse BAM files and get contig rpkms. - -Input: - path: List or tuple of paths to BAM files - dumpdirectory: [None] Dir to create and dump per-sample depths NPZ files to - refhash: [None]: Check all BAM references md5-hash to this (None = no check) - minscore [None]: Minimum alignment score (AS field) to consider - minlength [None]: Ignore any references shorter than N bases - minid [None]: Discard any reads with nucleotide identity less than this - subprocesses [{}]: Number of subprocesses to spawn - logfile: [None] File to print progress to - -Output: A (n_contigs x n_samples) Numpy array with RPKM -""".format(DEFAULT_SUBPROCESSES) +from typing import Optional, TypeVar, Union, IO, Sequence + +_ncpu = _os.cpu_count() +DEFAULT_THREADS = 8 if _ncpu is None else _ncpu + +A = TypeVar("A", bound="Abundance") + + +class Abundance: + "Object representing contig abundance. Contains a matrix and refhash." + + __slots__ = ["matrix", "samplenames", "minid", "refhash"] + + def __init__( + self, + matrix: _np.ndarray, + samplenames: Sequence[str], + minid: float, + refhash: bytes, + ): + assert matrix.dtype == _np.float32 + assert matrix.ndim == 2 + assert matrix.shape[1] == len(samplenames) + assert isfinite(minid) and 0.0 <= minid and minid <= 1.0 + + self.matrix = matrix + self.samplenames = _np.array(samplenames, dtype=object) + self.minid = minid + self.refhash = refhash + + @property + def nseqs(self) -> int: + return len(self.matrix) + + @property + def nsamples(self) -> int: + return len(self.samplenames) + + def verify_refhash(self, refhash: bytes) -> None: + if self.refhash != refhash: + raise ValueError( + f"BAM files reference name hash to {self.refhash.hex()}, " + f"expected {refhash.hex()}. " + "Make sure all BAM and FASTA headers are identical " + "and in the same order." + ) + + def save(self, io: Union[str, IO[bytes]]): + _np.savez_compressed( + io, + matrix=self.matrix, + samplenames=self.samplenames, + minid=self.minid, + refhash=self.refhash, + ) + + @classmethod + def load(cls: type[A], io: Union[str, IO[bytes]], refhash: Optional[bytes]) -> A: + arrs = _np.load(io, allow_pickle=True) + abundance = cls( + _vambtools.validate_input_array(arrs["matrix"]), + arrs["samplenames"], + arrs["minid"].item(), + arrs["refhash"].item(), + ) + if refhash is not None: + abundance.verify_refhash(refhash) + + return abundance + + @classmethod + def from_files( + cls: type[A], + paths: list[str], + comp_metadata: CompositionMetaData, + verify_refhash: bool, + minid: float, + nthreads: int, + ) -> A: + """Input: + paths: List of paths to BAM files + comp_metadata: CompositionMetaData of sequence catalogue used to make BAM files + verify_refhash: Whether to verify composition and BAM references are the same + minid: Discard any reads with nucleotide identity less than this + nthreads: Use this number of threads for coverage estimation + """ + if minid < 0 or minid > 1: + raise ValueError(f"minid must be between 0 and 1, not {minid}") + + for path in paths: + if not _os.path.isfile(path): + raise FileNotFoundError(path) + + if not pycoverm.is_bam_sorted(path): + raise ValueError(f"Path {path} is not sorted by reference.") + + # Workaround: Currently pycoverm has a bug where it filters contigs when mindid == 0 + # (issue #7). Can be solved by setting it to a low value + _minid = minid if minid > 0.001 else 0.001 + headers, coverage = pycoverm.get_coverages_from_bam( + paths, + threads=nthreads, + min_identity=_minid, + # Note: pycoverm's trim_upper=0.1 is same as CoverM trim-upper 90. + trim_upper=0.1, + trim_lower=0.1, + ) + + assert len(headers) == len(coverage) + assert coverage.shape[1] == len(paths) + + # Filter length, using comp_metadata's mask, which has been set by minlength + if len(comp_metadata.mask) != len(headers): + raise ValueError( + f"CompositionMetaData was created with {len(comp_metadata.mask)} sequences, " + f"but number of refs in BAM files are {len(headers)}." + ) + + headers = [h for (h, m) in zip(headers, comp_metadata.mask) if m] + _vambtools.numpy_inplace_maskarray(coverage, comp_metadata.mask) + + refhash = _vambtools.hash_refnames(headers) + abundance = cls(coverage, paths, minid, refhash) + + # Check refhash + if verify_refhash: + abundance.verify_refhash(comp_metadata.refhash) + + return abundance diff --git a/vamb/parsecontigs.py b/vamb/parsecontigs.py index abe96338..43baafb8 100644 --- a/vamb/parsecontigs.py +++ b/vamb/parsecontigs.py @@ -1,75 +1,208 @@ __doc__ = """Calculate tetranucleotide frequency from a FASTA file. Usage: ->>> with open('/path/to/contigs.fna', 'rb') as filehandle +>>> with open('/path/to/contigs.fna') as filehandle ... tnfs, contignames, lengths = read_contigs(filehandle) """ -import sys as _sys import os as _os import numpy as _np import vamb.vambtools as _vambtools +from collections.abc import Iterable, Sequence +from typing import IO, Union, TypeVar # This kernel is created in src/create_kernel.py. See that file for explanation -_KERNEL = _vambtools.read_npz(_os.path.join(_os.path.dirname(_os.path.abspath(__file__)), - "kernel.npz")) - -def _project(fourmers, kernel=_KERNEL): - "Project fourmers down in dimensionality" - s = fourmers.sum(axis=1).reshape(-1, 1) - s[s == 0] = 1.0 - fourmers *= 1/s - fourmers += -(1/256) - return _np.dot(fourmers, kernel) - -def _convert(raw, projected): - "Move data from raw PushArray to projected PushArray, converting it." - raw_mat = raw.take().reshape(-1, 256) - projected_mat = _project(raw_mat) - projected.extend(projected_mat.ravel()) - raw.clear() - -def read_contigs(filehandle, minlength=100): - """Parses a FASTA file open in binary reading mode. - - Input: - filehandle: Filehandle open in binary mode of a FASTA file - minlength: Ignore any references shorter than N bases [100] - - Outputs: - tnfs: An (n_FASTA_entries x 103) matrix of tetranucleotide freq. - contignames: A list of contig headers - lengths: A Numpy array of contig lengths +_KERNEL: _np.ndarray = _vambtools.read_npz( + _os.path.join(_os.path.dirname(_os.path.abspath(__file__)), "kernel.npz") +) + + +class CompositionMetaData: + """A class containing metadata of sequence composition. + Current fields are: + * identifiers: A Numpy array of objects, str identifiers of kept sequences + * lengths: A Numpy vector of 32-bit uint lengths of kept sequences + * mask: A boolean Numpy vector of which sequences were kept in original file + * refhash: A bytes object representing the hash of the identifiers + * minlength: The minimum contig length used for filtering """ - if minlength < 4: - raise ValueError('Minlength must be at least 4, not {}'.format(minlength)) - - raw = _vambtools.PushArray(_np.float32) - projected = _vambtools.PushArray(_np.float32) - lengths = _vambtools.PushArray(_np.int) - contignames = list() - - entries = _vambtools.byte_iterfasta(filehandle) - - for entry in entries: - if len(entry) < minlength: - continue - - raw.extend(entry.kmercounts(4)) - - if len(raw) > 256000: - _convert(raw, projected) - - lengths.append(len(entry)) - contignames.append(entry.header) - - # Convert rest of contigs - _convert(raw, projected) - tnfs_arr = projected.take() - - # Don't use reshape since it creates a new array object with shared memory - tnfs_arr.shape = (len(tnfs_arr)//103, 103) - lengths_arr = lengths.take() + __slots__ = ["identifiers", "lengths", "mask", "refhash", "minlength"] + + def __init__( + self, + identifiers: _np.ndarray, + lengths: _np.ndarray, + mask: _np.ndarray, + minlength: int, + ): + assert len(identifiers) == len(lengths) + assert identifiers.dtype == _np.dtype("O") + assert _np.issubdtype(lengths.dtype, _np.integer) + assert mask.dtype == bool + assert mask.sum() == len(lengths) + assert lengths.min(initial=minlength) >= minlength + + if len(set(identifiers)) < len(identifiers): + raise ValueError( + "Sequence names must be unique, but are not. " + "Vamb only uses the identifier (e.g. header before whitespace) as " + "sequence identifiers. Verify identifier uniqueness." + ) + + self.identifiers = identifiers + self.lengths = lengths + self.mask = mask + self.minlength = minlength + self.refhash = _vambtools.hash_refnames(identifiers) + + @property + def nseqs(self) -> int: + "Number of sequences after filtering" + return len(self.identifiers) + + def filter_mask(self, mask: Sequence[bool]): + "Filter contigs given a mask whose length should be nseqs" + assert len(mask) == self.nseqs + ind = 0 + for i in range(len(self.mask)): + if self.mask[i]: + self.mask[i] &= mask[ind] + ind += 1 + + self.identifiers = self.identifiers[mask] + self.lengths = self.lengths[mask] + self.refhash = _vambtools.hash_refnames(self.identifiers) + + def filter_min_length(self, length: int): + "Set or reset minlength of this object" + if length <= self.minlength: + return None + + self.filter_mask(self.lengths >= length) + self.minlength = length + + +C = TypeVar("C", bound="Composition") + + +class Composition: + """A class containing a CompositionMetaData and its TNF matrix. + Current fields are: + * metadata: A CompositionMetaData object + * matrix: The composition matrix itself + """ - return tnfs_arr, contignames, lengths_arr + __slots__ = ["metadata", "matrix"] + + def __init__(self, metadata: CompositionMetaData, matrix: _np.ndarray): + assert matrix.dtype == _np.float32 + assert matrix.shape == (metadata.nseqs, 103) + + self.metadata = metadata + self.matrix = matrix + + def count_bases(self) -> int: + return self.metadata.lengths.sum() + + @property + def nseqs(self) -> int: + return self.metadata.nseqs + + def save(self, io: Union[str, IO[bytes]]): + _np.savez_compressed( + io, + matrix=self.matrix, + identifiers=self.metadata.identifiers, + lengths=self.metadata.lengths, + mask=self.metadata.mask, + minlength=self.metadata.minlength, + ) + + @classmethod + def load(cls, io: Union[str, IO[bytes]]): + arrs = _np.load(io, allow_pickle=True) + metadata = CompositionMetaData( + _vambtools.validate_input_array(arrs["identifiers"]), + _vambtools.validate_input_array(arrs["lengths"]), + _vambtools.validate_input_array(arrs["mask"]), + arrs["minlength"].item(), + ) + return cls(metadata, _vambtools.validate_input_array(arrs["matrix"])) + + def filter_min_length(self, length: int): + if length <= self.metadata.minlength: + return None + + mask = self.metadata.lengths >= length + self.metadata.filter_mask(mask) + self.metadata.minlength = length + _vambtools.numpy_inplace_maskarray(self.matrix, mask) + + @staticmethod + def _project(fourmers: _np.ndarray, kernel: _np.ndarray = _KERNEL) -> _np.ndarray: + "Project fourmers down in dimensionality" + s = fourmers.sum(axis=1).reshape(-1, 1) + s[s == 0] = 1.0 + fourmers *= 1 / s + fourmers += -(1 / 256) + return _np.dot(fourmers, kernel) + + @staticmethod + def _convert(raw: _vambtools.PushArray, projected: _vambtools.PushArray): + "Move data from raw PushArray to projected PushArray, converting it." + raw_mat = raw.take().reshape(-1, 256) + projected_mat = Composition._project(raw_mat) + projected.extend(projected_mat.ravel()) + raw.clear() + + @classmethod + def from_file(cls: type[C], filehandle: Iterable[bytes], minlength: int = 100) -> C: + """Parses a FASTA file open in binary reading mode, returning Composition. + + Input: + filehandle: Filehandle open in binary mode of a FASTA file + minlength: Ignore any references shorter than N bases [100] + """ + + if minlength < 4: + raise ValueError(f"Minlength must be at least 4, not {minlength}") + + raw = _vambtools.PushArray(_np.float32) + projected = _vambtools.PushArray(_np.float32) + lengths = _vambtools.PushArray(_np.int32) + mask = bytearray() # we convert to Numpy at end + contignames: list[str] = list() + + entries = _vambtools.byte_iterfasta(filehandle) + + for entry in entries: + skip = len(entry) < minlength + mask.append(not skip) + + if skip: + continue + + raw.extend(entry.kmercounts(4)) + + if len(raw) > 256000: + Composition._convert(raw, projected) + + lengths.append(len(entry)) + contignames.append(entry.header) + + # Convert rest of contigs + Composition._convert(raw, projected) + tnfs_arr = projected.take() + + # Don't use reshape since it creates a new array object with shared memory + tnfs_arr.shape = (len(tnfs_arr) // 103, 103) + lengths_arr = lengths.take() + + metadata = CompositionMetaData( + _np.array(contignames, dtype=object), + lengths_arr, + _np.array(mask, dtype=bool), + minlength, + ) + return cls(metadata, tnfs_arr) diff --git a/vamb/vambtools.py b/vamb/vambtools.py index 08a57a00..54fc62f2 100644 --- a/vamb/vambtools.py +++ b/vamb/vambtools.py @@ -1,13 +1,19 @@ __doc__ = "Various classes and functions Vamb uses internally." +import sys import os as _os import gzip as _gzip import bz2 as _bz2 import lzma as _lzma import numpy as _np +import re as _re from vamb._vambtools import _kmercounts, _overwrite_matrix import collections as _collections from hashlib import md5 as _md5 +from collections.abc import Iterable, Iterator, Generator +from typing import Optional, IO, Union +from pathlib import PurePath as _PurePath + class PushArray: """Data structure that allows efficient appending and extending a 1D Numpy array. @@ -22,54 +28,55 @@ class PushArray: array([5. , 4. , 3.5, 3. ]) """ - __slots__ = ['data', 'capacity', 'length'] + __slots__ = ["data", "capacity", "length"] - def __init__(self, dtype, start_capacity=1<<16): - self.capacity = start_capacity - self.data = _np.empty(self.capacity, dtype=dtype) + def __init__(self, dtype, start_capacity: int = 1 << 16): + self.capacity: int = start_capacity + self.data: _np.ndarray = _np.empty(self.capacity, dtype=dtype) self.length = 0 - def __len__(self): + def __len__(self) -> int: return self.length - def _setcapacity(self, n): + def _setcapacity(self, n: int) -> None: self.data.resize(n, refcheck=False) self.capacity = n - def _grow(self, mingrowth): + def _grow(self, mingrowth: int) -> None: """Grow capacity by power of two between 1/8 and 1/4 of current capacity, though at least mingrowth""" growth = max(int(self.capacity * 0.125), mingrowth) nextpow2 = 1 << (growth - 1).bit_length() self._setcapacity(self.capacity + nextpow2) - def append(self, value): + def append(self, value) -> None: if self.length == self.capacity: self._grow(64) self.data[self.length] = value self.length += 1 - def extend(self, values): + def extend(self, values) -> None: lenv = len(values) if self.length + lenv > self.capacity: self._grow(lenv) - self.data[self.length:self.length+lenv] = values + self.data[self.length : self.length + lenv] = values self.length += lenv - def take(self): + def take(self) -> _np.ndarray: "Return the underlying array" self._setcapacity(self.length) return self.data - def clear(self, force=False): - "Empties the PushArray. If force is true, also truncates the underlying memory." + def clear(self) -> None: + "Empties the PushArray. Does not clear the underlying memory" self.length = 0 - if force: - self._setcapacity(0) -def zscore(array, axis=None, inplace=False): + +def zscore( + array: _np.ndarray, axis: Optional[int] = None, inplace: bool = False +) -> _np.ndarray: """Calculates zscore for an array. A cheap copy of scipy.stats.zscore. Inputs: @@ -78,50 +85,53 @@ def zscore(array, axis=None, inplace=False): inplace: Do not create new array, change input array [False] Output: - If inplace is True: None - else: New normalized Numpy-array""" + If inplace is True: Input numpy array + else: New normalized Numpy-array + """ - if axis is not None and axis >= array.ndim: - raise _np.AxisError('array only has {} axes'.format(array.ndim)) + if axis is not None and (axis >= array.ndim or axis < 0): + raise _np.AxisError(str(axis)) if inplace and not _np.issubdtype(array.dtype, _np.floating): - raise TypeError('Cannot convert a non-float array to zscores') + raise TypeError("Cannot convert a non-float array to zscores") mean = array.mean(axis=axis) std = array.std(axis=axis) if axis is None: if std == 0: - std = 1 # prevent divide by zero + std = 1 # prevent divide by zero else: - std[std == 0.0] = 1 # prevent divide by zero + std[std == 0.0] = 1 # prevent divide by zero shape = tuple(dim if ax != axis else 1 for ax, dim in enumerate(array.shape)) mean.shape, std.shape = shape, shape if inplace: array -= mean array /= std - return None + return array else: return (array - mean) / std -def numpy_inplace_maskarray(array, mask): + +def numpy_inplace_maskarray(array: _np.ndarray, mask: _np.ndarray) -> _np.ndarray: """In-place masking of a Numpy array, i.e. if `mask` is a boolean mask of same length as `array`, then array[mask] == numpy_inplace_maskarray(array, mask), but does not allocate a new array. """ if len(mask) != len(array): - raise ValueError('Lengths of array and mask must match') + raise ValueError("Lengths of array and mask must match") elif len(array.shape) != 2: - raise ValueError('Can only take a 2 dimensional-array.') + raise ValueError("Can only take a 2 dimensional-array.") uints = _np.frombuffer(mask, dtype=_np.uint8) index = _overwrite_matrix(array, uints) array.resize((index, array.shape[1]), refcheck=False) return array + def torch_inplace_maskarray(array, mask): """In-place masking of a Tensor, i.e. if `mask` is a boolean mask of same length as `array`, then array[mask] == torch_inplace_maskarray(array, mask), @@ -129,9 +139,9 @@ def torch_inplace_maskarray(array, mask): """ if len(mask) != len(array): - raise ValueError('Lengths of array and mask must match') + raise ValueError("Lengths of array and mask must match") elif array.dim() != 2: - raise ValueError('Can only take a 2 dimensional-array.') + raise ValueError("Can only take a 2 dimensional-array.") np_array = array.numpy() np_mask = _np.frombuffer(mask.numpy(), dtype=_np.uint8) @@ -139,6 +149,7 @@ def torch_inplace_maskarray(array, mask): array.resize_((index, array.shape[1])) return array + class Reader: """Use this instead of `open` to open files which are either plain text, gzipped, bzip2'd or zipped with LZMA. @@ -149,34 +160,27 @@ class Reader: TEST LINE """ - def __init__(self, filename, readmode='r'): - if readmode not in ('r', 'rt', 'rb'): - raise ValueError("the Reader cannot write, set mode to 'r' or 'rb'") - if readmode == 'r': - self.readmode = 'rt' - else: - self.readmode = readmode - + def __init__(self, filename: str): self.filename = filename - with open(self.filename, 'rb') as f: + with open(self.filename, "rb") as f: signature = f.peek(8)[:8] # Gzipped files begin with the two bytes 0x1F8B if tuple(signature[:2]) == (0x1F, 0x8B): - self.filehandle = _gzip.open(self.filename, self.readmode) + self.filehandle = _gzip.open(self.filename, "rb") # bzip2 files begin with the signature BZ - elif signature[:2] == b'BZ': - self.filehandle = _bz2.open(self.filename, self.readmode) + elif signature[:2] == b"BZ": + self.filehandle = _bz2.open(self.filename, "rb") # .XZ files begins with 0xFD377A585A0000 elif tuple(signature[:7]) == (0xFD, 0x37, 0x7A, 0x58, 0x5A, 0x00, 0x00): - self.filehandle = _lzma.open(self.filename, self.readmode) + self.filehandle = _lzma.open(self.filename, "rb") # Else we assume it's a text file. else: - self.filehandle = open(self.filename, self.readmode) + self.filehandle = open(self.filename, "rb") def close(self): self.filehandle.close() @@ -184,66 +188,93 @@ def close(self): def __enter__(self): return self - def __exit__(self, type, value, traceback): + def __exit__(self, _type, _value, _traceback): self.close() def __iter__(self): return self.filehandle + class FastaEntry: - """One single FASTA entry. Instantiate with string header and bytearray + """One single FASTA entry. Instantiate with byte header and bytearray sequence.""" - basemask = bytearray.maketrans(b'acgtuUswkmyrbdhvnSWKMYRBDHV', - b'ACGTTTNNNNNNNNNNNNNNNNNNNNN') - __slots__ = ['header', 'sequence'] + # IUPAC ambiguous DNA letters + u + allowed = b"acgtuswkmyrbdhvn" + allowed += allowed.upper() + # Allow only the same identifier chars that can be in the BAM file, otherwise + # users will be frustrated with FASTA and BAM headers do not match. + # BAM only includes identifier, e.g. stuff before whitespace. So we accept anything + # after whitespace, but do not use it as header. + # This brutal regex is derived from the SAM specs, valid identifiers, + # but disallow leading # sign, and allow trailing whitespace + ignored description + regex = _re.compile( + b"([0-9A-Za-z!$%&+./:;?@^_|~-][0-9A-Za-z!#$%&*+./:;=?@^_|~-]*)([^\\S\r\n][^\r\n]*)?$" + ) + __slots__ = ["identifier", "description", "sequence"] + + def _verify_header(self, header: bytes) -> tuple[str, str]: + m = self.regex.match(header) + if m is None: + raise ValueError( + f'Invalid header in FASTA: "{header.decode()}". ' + '\nMust conform to identifier regex pattern of SAM specification: "' + '>([0-9A-Za-z!$%&+./:;?@^_|~-][0-9A-Za-z!#$%&*+./:;=?@^_|~-]*)([^\\S\\r\\n][^\\r\\n]*)?$"' + ) + identifier, description = m.groups() + description = "" if description is None else description.decode() + return (identifier.decode(), description) + + def __init__(self, header: bytes, sequence: bytearray): + identifier, description = self._verify_header(header) + self.identifier: str = identifier + self.description: str = description + masked = sequence.translate(None, b" \t\n\r") + stripped = masked.translate(None, self.allowed) + if len(stripped) > 0: + codeunit = stripped[0] + bad_character = chr(codeunit) + raise ValueError( + f"Non-IUPAC DNA/RNA byte in sequence {identifier}: '{bad_character}', byte value {codeunit}" + ) - def __init__(self, header, sequence): - if len(header) > 0 and (header[0] in ('>', '#') or header[0].isspace()): - raise ValueError('Header cannot begin with #, > or whitespace') - if '\t' in header: - raise ValueError('Header cannot contain a tab') + self.sequence: bytearray = masked - masked = sequence.translate(self.basemask, b' \t\n\r') - stripped = masked.translate(None, b'ACGTN') - if len(stripped) > 0: - bad_character = chr(stripped[0]) - msg = "Non-IUPAC DNA byte in sequence {}: '{}'" - raise ValueError(msg.format(header, bad_character)) + @property + def header(self) -> str: + return self.identifier + self.description - self.header = header - self.sequence = masked + def rename(self, header: bytes) -> None: + identifier, description = self._verify_header(header) + self.identifier = identifier + self.description = description - def __len__(self): + def __len__(self) -> int: return len(self.sequence) - def __str__(self): - return '>{}\n{}'.format(self.header, self.sequence.decode()) - - def format(self, width=60): + def format(self, width: int = 60) -> str: sixtymers = range(0, len(self.sequence), width) - spacedseq = '\n'.join([self.sequence[i: i+width].decode() for i in sixtymers]) - return '>{}\n{}'.format(self.header, spacedseq) - - def __getitem__(self, index): - return self.sequence[index] + spacedseq = "\n".join( + [self.sequence[i : i + width].decode() for i in sixtymers] + ) + return f">{self.header}\n{spacedseq}" - def __repr__(self): - return ''.format(self.header) - - def kmercounts(self, k): + def kmercounts(self, k: int) -> _np.ndarray: if k < 1 or k > 10: - raise ValueError('k must be between 1 and 10 inclusive') + raise ValueError("k must be between 1 and 10 inclusive") - counts = _np.zeros(1 << (2*k), dtype=_np.int32) + counts = _np.zeros(1 << (2 * k), dtype=_np.int32) _kmercounts(self.sequence, k, counts) return counts -def byte_iterfasta(filehandle, comment=b'#'): + +def byte_iterfasta( + filehandle: Iterable[bytes], comment: bytes = b"#" +) -> Iterator[FastaEntry]: """Yields FastaEntries from a binary opened fasta file. Usage: - >>> with Reader('/dir/fasta.fna', 'rb') as filehandle: + >>> with Reader('/dir/fasta.fna') as filehandle: ... entries = byte_iterfasta(filehandle) # a generator Inputs: @@ -262,39 +293,50 @@ def byte_iterfasta(filehandle, comment=b'#'): if stripped.startswith(comment): pass - elif probeline[0:1] == b'>': + elif probeline[0:1] == b">": break else: - raise ValueError('First non-comment line is not a Fasta header') + raise ValueError("First non-comment line is not a Fasta header") - else: # no break - raise ValueError('Empty or outcommented file') + else: # no break + return None + # raise ValueError('Empty or outcommented file') except TypeError: - errormsg = 'First line does not contain bytes. Are you reading file in binary mode?' + errormsg = ( + "First line does not contain bytes. Are you reading file in binary mode?" + ) raise TypeError(errormsg) from None - header = probeline[1:-1].decode() - buffer = list() + header = probeline[1:] + buffer: list[bytes] = list() # Iterate over lines for line in line_iterator: if line.startswith(comment): pass - elif line.startswith(b'>'): + elif line.startswith(b">"): yield FastaEntry(header, bytearray().join(buffer)) buffer.clear() - header = line[1:-1].decode() + header = line[1:] else: buffer.append(line) yield FastaEntry(header, bytearray().join(buffer)) -def write_clusters(filehandle, clusters, max_clusters=None, min_size=1, - header=None, rename=True): + +def write_clusters( + filehandle: IO[str], + clusters: Iterable[tuple[str, set[str]]], + max_clusters: Optional[int] = None, + min_size: int = 1, + header: Optional[str] = None, + rename: bool = True, + cluster_prefix= str, +) -> tuple[int, int]: """Writes clusters to an open filehandle. Inputs: filehandle: An open filehandle that can be written to @@ -303,29 +345,25 @@ def write_clusters(filehandle, clusters, max_clusters=None, min_size=1, min_size: Don't output clusters smaller than N contigs header: Commented one-line header to add rename: Rename clusters to "cluster_1", "cluster_2" etc. + cluster_prefix: prepend a tag to identify which model produced the clusters (vae,aae_l, aae_y) Outputs: clusternumber: Number of clusters written ncontigs: Number of contigs written """ - if not hasattr(filehandle, 'writable') or not filehandle.writable(): - raise ValueError('Filehandle must be a writable file') - - # Special case to allows dicts even though they are not iterators of - # clustername, {cluster} - if isinstance(clusters, dict): - clusters = clusters.items() + if not hasattr(filehandle, "writable") or not filehandle.writable(): + raise ValueError("Filehandle must be a writable file") if max_clusters is not None and max_clusters < 1: - raise ValueError('max_clusters must None or at least 1, not {}'.format(max_clusters)) + raise ValueError("max_clusters must None or at least 1, not {max_clusters}") if header is not None and len(header) > 0: - if '\n' in header: - raise ValueError('Header cannot contain newline') + if "\n" in header: + raise ValueError("Header cannot contain newline") - if header[0] != '#': - header = '# ' + header + if header[0] != "#": + header = "# " + header print(header, file=filehandle) @@ -337,10 +375,12 @@ def write_clusters(filehandle, clusters, max_clusters=None, min_size=1, continue if rename: - clustername = 'cluster_' + str(clusternumber + 1) + clustername = cluster_prefix + "cluster_" + str(clusternumber + 1) + else: + clustername = cluster_prefix + str(clusternumber + 1) for contig in contigs: - print(clustername, contig, sep='\t', file=filehandle) + print(clustername, contig, sep="\t", file=filehandle) filehandle.flush() clusternumber += 1 @@ -351,7 +391,8 @@ def write_clusters(filehandle, clusters, max_clusters=None, min_size=1, return clusternumber, ncontigs -def read_clusters(filehandle, min_size=1): + +def read_clusters(filehandle: Iterable[str], min_size: int =1) -> dict[str, set[str]]: """Read clusters from a file as created by function `writeclusters`. Inputs: @@ -360,61 +401,40 @@ def read_clusters(filehandle, min_size=1): Output: A {clustername: set(contigs)} dict""" - contigsof = _collections.defaultdict(set) + contigsof: _collections.defaultdict[str, set[str]] = _collections.defaultdict(set) for line in filehandle: stripped = line.strip() - if not stripped or stripped[0] == '#': + if not stripped or stripped[0] == "#": continue - clustername, contigname = stripped.split('\t') + clustername, contigname = stripped.split("\t") contigsof[clustername].add(contigname) - contigsof = {cl: co for cl, co in contigsof.items() if len(co) >= min_size} - - return contigsof - - -def loadfasta(byte_iterator, keep=None, comment=b'#', compress=False): - """Loads a FASTA file into a dictionary. - - Usage: - >>> with Reader('/dir/fasta.fna', 'rb') as filehandle: - ... fastadict = loadfasta(filehandle) - - Input: - byte_iterator: Iterator of binary lines of FASTA file - keep: Keep entries with headers in `keep`. If None, keep all entries - comment: Ignore lines beginning with any whitespace + comment - compress: Keep sequences compressed [False] - - Output: {header: FastaEntry} dict - """ - - entries = dict() - - for entry in byte_iterfasta(byte_iterator, comment=comment): - if keep is None or entry.header in keep: - if compress: - entry.sequence = bytearray(_gzip.compress(entry.sequence, compresslevel=2)) + contigsof_dict = {cl: co for cl, co in contigsof.items() if len(co) >= min_size} - entries[entry.header] = entry + return contigsof_dict - return entries -def write_bins(directory, bins, fastadict, compressed=False, maxbins=250, minsize=0): +def write_bins( + directory: Union[str, _PurePath], + bins: dict[str, set[str]], + fastaio: Iterable[bytes], + maxbins: Optional[int] = 250, + minsize: int = 0, + separator: str = None, +): """Writes bins as FASTA files in a directory, one file per bin. Inputs: directory: Directory to create or put files in - bins: {'name': {set of contignames}} dictionary (can be loaded from + bins: dict[str: set[str]] (can be loaded from clusters.tsv using vamb.cluster.read_clusters) - fastadict: {contigname: FastaEntry} dict as made by `loadfasta` - compressed: Sequences in dict are compressed [False] + fastaio: bytes iterator containing FASTA file with all sequences maxbins: None or else raise an error if trying to make more bins than this [250] minsize: Minimum number of nucleotides in cluster to be output [0] - + separator: string that separates the contig/cluster name from the sample ; i.e. sample_id_separator_contig_name/cluster_name Output: None """ @@ -422,32 +442,28 @@ def write_bins(directory, bins, fastadict, compressed=False, maxbins=250, minsiz # If you do this on a compute cluster it can grind the entire cluster to # a halt and piss people off like you wouldn't believe. if maxbins is not None and len(bins) > maxbins: - raise ValueError('{} bins exceed maxbins of {}'.format(len(bins), maxbins)) + raise ValueError(f"{len(bins)} bins exceed maxbins of {maxbins}") # Check that the directory is not a non-directory file, # and that its parent directory indeed exists abspath = _os.path.abspath(directory) parentdir = _os.path.dirname(abspath) - if parentdir != '' and not _os.path.isdir(parentdir): + if parentdir != "" and not _os.path.isdir(parentdir): raise NotADirectoryError(parentdir) if _os.path.isfile(abspath): - raise NotADirectoryError(abspath) + raise FileExistsError(abspath) if minsize < 0: raise ValueError("Minsize must be nonnegative") - # Check that all contigs in all bins are in the fastadict - allcontigs = set() - - for contigs in bins.values(): - allcontigs.update(set(contigs)) - - allcontigs -= fastadict.keys() - if allcontigs: - nmissing = len(allcontigs) - raise IndexError('{} contigs in bins missing from fastadict'.format(nmissing)) + byteslen_by_id: dict[str, tuple[bytes, int]] = dict() + for entry in byte_iterfasta(fastaio): + byteslen_by_id[entry.identifier] = ( + _gzip.compress(entry.format().encode(), compresslevel=1), + len(entry), + ) # Make the directory if it does not exist - if it does, do nothing try: @@ -459,36 +475,53 @@ def write_bins(directory, bins, fastadict, compressed=False, maxbins=250, minsiz # Now actually print all the contigs to files for binname, contigs in bins.items(): - # Load bin into a list, decompress that bin if necessary - bin = [] + size = 0 + if separator is not None: + binsample=next(iter(contigs)).split(separator)[0] for contig in contigs: - entry = fastadict[contig] - if compressed: - uncompressed = bytearray(_gzip.decompress(entry.sequence)) - entry = FastaEntry(entry.header, uncompressed) - bin.append(entry) - - # Skip bin if it's too small - if minsize > 0 and sum(len(entry) for entry in bin) < minsize: + byteslen = byteslen_by_id.get(contig) + if byteslen is None: + raise IndexError( + f'Contig "{contig}" in bin missing from input FASTA file' + ) + size += byteslen[1] + + if size < minsize: continue - + # added by pau to split bin files into sample dirs + if separator is not None: + + bin_dir=_os.path.join(directory,binsample) + try: + _os.mkdir(bin_dir) + except FileExistsError: + pass + except: + raise + else: + bin_dir=directory # Print bin to file - filename = _os.path.join(directory, binname + '.fna') - with open(filename, 'w') as file: - for entry in bin: - print(entry.format(), file=file) + filename = _os.path.join(bin_dir, binname + ".fna") + + #filename = _os.path.join(directory, binname + ".fna") + with open(filename, "wb") as file: + for contig in contigs: + file.write(_gzip.decompress(byteslen_by_id[contig][0])) + file.write(b"\n") -def validate_input_array(array): + +def validate_input_array(array: _np.ndarray) -> _np.ndarray: "Returns array similar to input array but C-contiguous and with own data." - if not array.flags['C_CONTIGUOUS']: + if not array.flags["C_CONTIGUOUS"]: array = _np.ascontiguousarray(array) - if not array.flags['OWNDATA']: + if not array.flags["OWNDATA"]: array = array.copy() - assert (array.flags['C_CONTIGUOUS'] and array.flags['OWNDATA']) + assert array.flags["C_CONTIGUOUS"] and array.flags["OWNDATA"] return array -def read_npz(file): + +def read_npz(file) -> _np.ndarray: """Loads array in .npz-format Input: Open file or path to file with npz-formatted array @@ -497,12 +530,13 @@ def read_npz(file): """ npz = _np.load(file) - array = validate_input_array(npz['arr_0']) + array = validate_input_array(npz["arr_0"]) npz.close() return array -def write_npz(file, array): + +def write_npz(file, array: _np.ndarray): """Writes a Numpy array to an open file or path in .npz format Inputs: @@ -513,24 +547,10 @@ def write_npz(file, array): """ _np.savez_compressed(file, array) -def filtercontigs(infile, outfile, minlength=2000): - """Creates new FASTA file with filtered contigs - Inputs: - infile: Binary opened input FASTA file - outfile: Write-opened output FASTA file - minlength: Minimum contig length to keep [2000] - - Output: None - """ - - fasta_entries = byte_iterfasta(infile) - - for entry in fasta_entries: - if len(entry) > minlength: - print(entry.format(), file=outfile) - -def concatenate_fasta(outfile, inpaths, minlength=2000, rename=True): +def concatenate_fasta( + outfile: IO[str], inpaths: Iterable[str], minlength: int = 2000, rename: bool = True +): """Creates a new FASTA file from input paths, and optionally rename contig headers to the pattern "S{sample number}C{contig identifier}". @@ -543,36 +563,35 @@ def concatenate_fasta(outfile, inpaths, minlength=2000, rename=True): Output: None """ - headers = set() + identifiers: set[str] = set() for (inpathno, inpath) in enumerate(inpaths): - with Reader(inpath, "rb") as infile: - - # If we rename, seq headers only have to be unique for each sample - if rename: - headers.clear() - entries = byte_iterfasta(infile) + try: + with Reader(inpath) as infile: - for entry in entries: - if len(entry) < minlength: - continue + # If we rename, seq identifiers only have to be unique for each sample + if rename: + identifiers.clear() - header = entry.header - identifier = header.split()[0] + for entry in byte_iterfasta(infile): + if len(entry) < minlength: + continue - if rename: - newheader = "S{}C{}".format(inpathno + 1, identifier) - else: - newheader = identifier + if rename: + entry.rename(f"S{inpathno + 1}C{entry.identifier}".encode()) - if newheader in headers: - raise ValueError("Multiple sequences would be given " - "header {}.".format(newheader)) - headers.add(newheader) + if entry.identifier in identifiers: + raise ValueError( + "Multiple sequences would be given " + f'identifier "{entry.identifier}".' + ) + identifiers.add(entry.identifier) + print(entry.format(), file=outfile) + except Exception as e: + print(f"Exception occured when parsing file {inpath}", file=sys.stderr) + raise e from None - entry.header = newheader - print(entry.format(), file=outfile) -def _hash_refnames(refnames): +def hash_refnames(refnames: Iterable[str]) -> bytes: "Hashes an iterable of strings of reference names using MD5." hasher = _md5() for refname in refnames: @@ -580,81 +599,37 @@ def _hash_refnames(refnames): return hasher.digest() -def _load_jgi(filehandle, minlength, refhash): - "This function can be merged with load_jgi below in the next breaking release (post 3.0)" - header = next(filehandle) - fields = header.strip().split('\t') - if not fields[:3] == ["contigName", "contigLen", "totalAvgDepth"]: - raise ValueError('Input file format error: First columns should be "contigName,"' - '"contigLen" and "totalAvgDepth"') - - columns = tuple([i for i in range(3, len(fields)) if not fields[i].endswith("-var")]) - array = PushArray(_np.float32) - identifiers = list() - - for row in filehandle: - fields = row.split('\t') - # We use float because very large numbers will be printed in scientific notation - if float(fields[1]) < minlength: - continue - - for col in columns: - array.append(float(fields[col])) - - identifiers.append(fields[0]) - - if refhash is not None: - hash = _hash_refnames(identifiers) - if hash != refhash: - errormsg = ('JGI file has reference hash {}, expected {}. ' - 'Verify that all BAM headers and FASTA headers are ' - 'identical and in the same order.') - raise ValueError(errormsg.format(hash.hex(), refhash.hex())) - - result = array.take() - result.shape = (len(result) // len(columns), len(columns)) - return validate_input_array(result) - -def load_jgi(filehandle): - """Load depths from the --outputDepth of jgi_summarize_bam_contig_depths. - See https://bitbucket.org/berkeleylab/metabat for more info on that program. - - Usage: - with open('/path/to/jgi_depths.tsv') as file: - depths = load_jgi(file) - Input: - File handle of open output depth file - Output: - N_contigs x N_samples Numpy matrix of dtype float32 - """ - return _load_jgi(filehandle, 0, None) -def _split_bin(binname, headers, separator, bysample=_collections.defaultdict(set)): +def _split_bin( + binname: str, + headers: Iterable[str], + separator: str, + bysample: _collections.defaultdict[str, set[str]] = _collections.defaultdict(set), +) -> Generator[tuple[str, set[str]], None, None]: "Split a single bin by the prefix of the headers" bysample.clear() for header in headers: - if not isinstance(header, str): - raise TypeError('Can only split named sequences, not of type {}'.format(type(header))) + if not isinstance(header, str): # type: ignore + raise TypeError( + f"Can only split named sequences, not of type {type(header)}" + ) - sample, _sep, identifier = header.partition(separator) + sample, _, identifier = header.partition(separator) if not identifier: - raise KeyError("Separator '{}' not in sequence label: '{}'".format(separator, header)) + raise KeyError(f"Separator '{separator}' not in sequence label: '{header}'") bysample[sample].add(header) for sample, splitheaders in bysample.items(): - newbinname = "{}{}{}".format(sample, separator, binname) + newbinname = f"{sample}{separator}{binname}" yield newbinname, splitheaders -def _binsplit_generator(cluster_iterator, separator): - "Return a generator over split bins with the function above." - for binname, headers in cluster_iterator: - for newbinname, splitheaders in _split_bin(binname, headers, separator): - yield newbinname, splitheaders -def binsplit(clusters, separator): +def binsplit( + clusters: Iterable[tuple[str, Iterable[str]]], separator: str +) -> Generator[tuple[str, set[str]], None, None]: """Splits a set of clusters by the prefix of their names. The separator is a string which separated prefix from postfix of contignames. The resulting split clusters have the prefix and separator prepended to them. @@ -667,11 +642,9 @@ def binsplit(clusters, separator): >>> binsplit(clusters, "-") {'s2-bin1': {'s1-c1', 's1-c3'}, 's1-bin1': {'s1-c1', 's1-c5'}, 's5-bin1': {'s1-c8'}} """ - if iter(clusters) is clusters: # clusters is an iterator - return _binsplit_generator(clusters, separator) - - elif isinstance(clusters, dict): - return dict(_binsplit_generator(clusters.items(), separator)) - - else: - raise TypeError("clusters must be iterator of pairs or dict") + if isinstance(clusters, dict): # added by Pau since y clusters are in a dictionary object + clusters = clusters.items() + + for binname, headers in clusters: + for newbinname, splitheaders in _split_bin(binname, headers, separator): + yield newbinname, splitheaders diff --git a/workflow_avamb/README.md b/workflow_avamb/README.md new file mode 100644 index 00000000..55848fac --- /dev/null +++ b/workflow_avamb/README.md @@ -0,0 +1,130 @@ +# AVAMB snakemake workflow + +This is a snakemake workflow that performs all the necessary steps to run Avamb, as input it takes quality control filtered paired end reads and individual sample _de novo_ assemblies, process them through best practice pipeline (multi-split), runs Vamb and Aamb, runs CheckM2 to assess the quality of the bins as well as to dereplicate the bins per sample and binner. + +In short it will: + +``` +1. Filter contigs for 2000bp and rename them to conform with the multi-split workflow +2. Index resulting contig-file with minimap2 and create a sequence dictionary +3. Map reads with minimap2 to the combined contig set +4. Sort bam-files +5. Run Vamb and Aamb to bin the contigs, generating 3 sets of bins: vae_bins, aae_z_bins and aae_y_bins +6. Determine completeness and contamination of the bins using CheckM2 +7. Dereplicate near complete bins, assuring contigs are present in one bin only. +``` + +The nice thing about using snakemake for this is that it will keep track of which jobs have finished and it allows the workflow to be run on different hardware such as a laptop, a linux workstation and a HPC facility (currently with qsub). + +## Installation +To run the workflow first install a Python3 version of [Miniconda](https://docs.conda.io/en/latest/miniconda.html) and [mamba](https://mamba.readthedocs.io/en/latest/installation.html#fresh-install). Avamb uses CheckM2 to score the bins, unfortunately, due to some dependencies conflicts, CheckM2 can not be installed in the same environment than vamb, therefore a specific environment should be created for CheckM2 and for Avamb: + +``` + # Install CheckM2 in checkm2 environment + git clone https://github.com/chklovski/CheckM2.git + mamba create -n checkm2 + mamba env update -n checkm2 --file CheckM2/checkm2.yml + conda activate checkm2 + cd CheckM2 && python setup.py install && cd .. + checkm2 database --download + conda deactivate + # Install Avamb in avamb environment + conda config --add channels conda-forge + conda config --add channels bioconda + mamba create -n avamb python=3.9 + mamba install -n avamb snakemake pip biopython netowrkx + conda activate avamb + pip install ordered-set + #pip install vamb # uncomment once it's merged with avamb + git clone https://github.com/RasmussenLab/avamb.git -b avamb_new # remove once it's merged with vamb + cd avamb && pip install -e . && cd .. # remove once it's merged with avamb +``` +However, despite avamb and CheckM2 being in different environments, snakemake will be taking care of which is the right environment for each task. So now we should be ready to move forward and configure the input data to run our workflow. + +## Set up configuration with your data + +To run the snakemake workflow you need to set up three files: the configuration file (`config.json`), a file with paths to your contig-files (`contigs.txt`) and a file with paths to your reads (`samples2data.txt`). Example files are included and described here for an example dataset of four samples: + +`contigs.txt` contains paths to each of the per-sample assemblies: +``` +assemblies/contigs.sample_1.fna.gz +assemblies/contigs.sample_2.fna.gz +assemblies/contigs.sample_3.fna.gz +assemblies/contigs.sample_4.fna.gz +``` + +`samples2data.txt` contains sample name, path to read-pair1 and path to read-pair2 (tab-separated): +``` +sample_1 reads/sample_1.r1.fq.gz reads/sample_1.r2.fq.gz +sample_2 reads/sample_2.r1.fq.gz reads/sample_2.r2.fq.gz +sample_3 reads/sample_3.r1.fq.gz reads/sample_3.r2.fq.gz +sample_4 reads/sample_4.r1.fq.gz reads/sample_4.r2.fq.gz + +``` + +Then the configuration file (`config.json`). The first two lines points to the files containing contigs and read information; `index_size` is size of the minimap2 index(es); `mem` and `ppn` shows the amount of memory and cores set aside for minimap2, AVAMB, CheckM2 and CheckM2 dereplicate, `avamb_params` gives the parameters used by AVAMB, finally `outdir` points to the directory that wiil be created and where all output files will be stored. Here we tell it to use the multi-split approach (`-o C`), to skip contigs smaller than 2kb (`-m 2000`) and to write all bins larger than 500kb as fasta (`--minfasta 500000`). With regard to `index_size` this is the amount of Gbases that minimap will map to at the same time. You can increase the size of this if you have more memory available on your machine (a value of `12G` can be run using `"minimap_mem": "12gb"`). + +``` +{ + "contigs": "contigs.txt", + "sample_data": "samples2data.txt", + "index_size": "3G", + "min_contig_size": "2000", + "minimap_mem": "15gb", + "minimap_ppn": "10", + "avamb_mem": "10gb", + "avamb_ppn": "10", + "checkm2_mem": "30gb", + "checkm2_ppn": "30", + "checkm2_mem_r": "15gb", + "checkm2_ppn_r": "15", + "avamb_params": "-o C -m 2000 --minfasta 500000", + "avamb_preload": "", + "outdir":"avamb_outdir" +} + +``` +## Example run + +When running the workflow use snakemake, give it the maximum number of cores you want to use and the path to the configfile as well as the snakemake file. You can then run snakemake on a laptop or workstation as below - remember to activate the conda environment first before running snakemake. + +``` +conda activate avamb +snakemake --cores 20 --configfile /path/to/vamb/workflow_avamb/config.json --snakefile /path/to/vamb/workflow_avamb/avamb.snake.conda.smk --use-conda +``` + +If you want to use snakemake on a compute cluster using `qsub` we add the following `--cluster` option below. +``` + +conda activate avamb +snakemake --jobs 20 --configfile /path/to/vamb/workflow_avamb/config.json --snakefile /path/to/vamb/workflow_avamb/avamb.snake.conda.smk --latency-wait 60 --use-conda --cluster "qsub -l walltime={params.walltime} -l nodes=1:ppn={params.ppn} -l mem={params.mem}" +``` + +Note 1: If you want to re-run with different parameters of AVAMB you can change `avamb_params` in the config-file, but remember to rename the `outdir` configuration file entry, otherwise it will overwrite it. + + +## Using a GPU to speed up Avamb + +Using a GPU can speed up Avamb considerably - especially when you are binning millions of contigs. In order to enable it you need to make a couple of changes to the configuration file. Basically we need to add `--cuda` to the `avamb_params` to tell Avamb to use the GPU. Then if you are using the `--cluster` option, you also need to update `avamb_ppn` accordingly - e.g. on our system (qsub) we exchange `"avamb_ppn": "10"` to `"avamb_ppn": "10:gpus=1"`. Therefore the `config.json` file looks like this if I want to use GPU acceleration: + +``` +{ + "contigs": "contigs.txt", + "sample_data": "samples2data.txt", + "index_size": "3G", + "minimap_mem": "15gb", + "minimap_ppn": "10", + "avamb_mem": "10gb", + "avamb_ppn": "10:gpus=1", + "checkm_mem": "25gb", + "checkm_ppn": "10", + "avamb_params": "-o C -m 2000 --minfasta 500000 --outdir avamb --cuda", + "avamb_preload": "module load cuda/toolkit/10.2.89;", + "outdir":"avamb_outdir" +} +``` + +Note that I could not get `avamb` to work with `cuda` on our cluster when installing from bioconda. Therefore I added a line to preload cuda toolkit to the configuration file that will load this module when running `avamb`. + +Please let us know if you have any issues and we can try to help out. + diff --git a/workflow_avamb/avamb.snake.conda.smk b/workflow_avamb/avamb.snake.conda.smk new file mode 100644 index 00000000..488e967a --- /dev/null +++ b/workflow_avamb/avamb.snake.conda.smk @@ -0,0 +1,565 @@ +import re +import os +import sys +from vamb.vambtools import concatenate_fasta +SNAKEDIR = os.path.dirname(workflow.snakefile) + +sys.path.append(os.path.join(SNAKEDIR, 'src')) + + +def get_config(name, default, regex): + res = config.get(name, default).strip() + m = re.match(regex, res) + if m is None: + raise ValueError( + f"Config option \"{name}\" is \"{res}\", but must conform to regex \"{regex}\"") + return res + + +# set configurations +CONTIGS = get_config("contigs", "contigs.txt", r".*") # each line is a contigs path from a given sample +SAMPLE_DATA = get_config("sample_data", "samples2data.txt", r".*") # each line is composed by 3 elements: sample id, forward_reads_path , backward_reads_path +INDEX_SIZE = get_config("index_size", "12G", r"[1-9]\d*[GM]$") +MIN_CONTIG_SIZE = get_config("min_contig_size", "2000", r"[1-9]\d*$") + +MM_MEM = get_config("minimap_mem", "35gb", r"[1-9]\d*gb$") +MM_PPN = get_config("minimap_ppn", "10", r"[1-9]\d*$") +AVAMB_MEM = get_config("avamb_mem", "20gb", r"[1-9]\d*gb$") +AVAMB_PPN = get_config("avamb_ppn", "10", r"[1-9]\d*(:gpus=[1-9]\d*)?$") + +CHECKM_MEM = get_config("checkm2_mem", "10gb", r"[1-9]\d*gb$") +CHECKM_PPN = get_config("checkm2_ppn", "10", r"[1-9]\d*$") +CHECKM_MEM_r = get_config("checkm2_mem_r", "30gb", r"[1-9]\d*gb$") +CHECKM_PPN_r = get_config("checkm2_ppn_r", "30", r"[1-9]\d*$") + + +AVAMB_PARAMS = get_config("avamb_params"," -o C --minfasta 200000 -m 2000 ", r".*") +AVAMB_PRELOAD = get_config("avamb_preload", "", r".*") + +OUTDIR= get_config("outdir", "outdir_avamb", r".*") + +try: + os.makedirs(os.path.join(OUTDIR,"log"), exist_ok=True) +except FileExistsError: + pass +except: + raise + + +# parse if GPUs is needed # +avamb_threads, sep, avamb_gpus = AVAMB_PPN.partition(":gpus=") +AVAMB_PPN = avamb_threads +CUDA = len(avamb_gpus) > 0 + +## read in sample information ## + +# read in sample2path +IDS = [] +sample2path = {} +fh_in = open(SAMPLE_DATA, 'r') +for line in fh_in: + line = line.rstrip() + fields = line.split('\t') + IDS.append(fields[0]) + sample2path[fields[0]] = [fields[1], fields[2]] + +# read in list of per-sample assemblies +contigs_list = [] +fh_in = open(CONTIGS, 'r') +for line in fh_in: + line = line.rstrip() + contigs_list.append(line) + +# target +rule all: + input: + os.path.join(OUTDIR,'avamb/tmp/workflow_finished_avamb.log') +rule cat_contigs: + input: + contigs_list + output: + os.path.join(OUTDIR,"contigs.flt.fna.gz") + params: + path=os.path.join(os.path.dirname(SNAKEDIR), "src", "concatenate.py"), + walltime="864000", + nodes="1", + ppn="1", + mem="5gb" + threads: + 1 + log: + o = os.path.join(OUTDIR,"log/contigs/catcontigs.o"), + e = os.path.join(OUTDIR,"log/contigs/catcontigs.e") + + conda: + "avamb" + shell: "python {params.path} {output} {input} -m {MIN_CONTIG_SIZE}" + +rule index: + input: + contigs = os.path.join(OUTDIR,"contigs.flt.fna.gz") + output: + mmi = os.path.join(OUTDIR,"contigs.flt.mmi") + params: + walltime="864000", + nodes="1", + ppn="1", + mem="90gb" + threads: + 1 + log: + out_ind = os.path.join(OUTDIR,"log/contigs/index.log"), + o = os.path.join(OUTDIR,"log/contigs/index.o"), + e = os.path.join(OUTDIR,"log/contigs/index.e") + + + conda: + "envs/minimap2.yaml" + shell: + "minimap2 -I {INDEX_SIZE} -d {output} {input} 2> {log.out_ind}" + +# This rule creates a SAM header from a FASTA file. +# We need it because minimap2 for truly unknowable reasons will write +# SAM headers INTERSPERSED in the output SAM file, making it unparseable. +# To work around this mind-boggling bug, we remove all header lines from +# minimap2's SAM output by grepping, then re-add the header created in this +# rule. +rule dict: + input: + contigs = os.path.join(OUTDIR,"contigs.flt.fna.gz") + output: + dict = os.path.join(OUTDIR,"contigs.flt.dict") + params: + walltime="864000", + nodes="1", + ppn="1", + mem="10gb" + threads: + 1 + log: + out_dict= os.path.join(OUTDIR,"log/contigs/dict.log"), + o = os.path.join(OUTDIR,"log/contigs/index.o"), + e = os.path.join(OUTDIR,"log/contigs/index.e") + + conda: + "envs/samtools.yaml" + shell: + "samtools dict {input} | cut -f1-3 > {output} 2> {log.out_dict}" + +rule minimap: + input: + fq = lambda wildcards: sample2path[wildcards.sample], + mmi = os.path.join(OUTDIR,"contigs.flt.mmi"), + dict = os.path.join(OUTDIR,"contigs.flt.dict") + output: + bam = temp(os.path.join(OUTDIR,"mapped/{sample}.bam")) + params: + walltime="864000", + nodes="1", + ppn=MM_PPN, + mem=MM_MEM + threads: + int(MM_PPN) + log: + out_minimap = os.path.join(OUTDIR,"log/map/{sample}.minimap.log"), + o = os.path.join(OUTDIR,"log/map/{sample}.minimap.o"), + e = os.path.join(OUTDIR,"log/map/{sample}.minimap.e") + + conda: + "envs/minimap2.yaml" + shell: + # See comment over rule "dict" to understand what happens here + "minimap2 -t {threads} -ax sr {input.mmi} {input.fq} -N 5" + " | grep -v '^@'" + " | cat {input.dict} - " + " | samtools view -F 3584 -b - " # supplementary, duplicate read, fail QC check + " > {output.bam} 2> {log.out_minimap}" + +rule sort: + input: + os.path.join(OUTDIR,"mapped/{sample}.bam") + output: + os.path.join(OUTDIR,"mapped/{sample}.sort.bam") + params: + walltime="864000", + nodes="1", + ppn="2", + mem="15gb", + prefix=os.path.join(OUTDIR,"mapped/tmp.{sample}") + threads: + 2 + log: + out_sort = os.path.join(OUTDIR,"log/map/{sample}.sort.log"), + o = os.path.join(OUTDIR,"log/map/{sample}.sort.o"), + e = os.path.join(OUTDIR,"log/map/{sample}.sort.e") + + conda: + "envs/samtools.yaml" + shell: + "samtools sort {input} -T {params.prefix} --threads 1 -m 3G -o {output} 2>{log.out_sort}" + + + +rule run_avamb: + input: + contigs=os.path.join(OUTDIR,"contigs.flt.fna.gz"), + bam_files=expand(os.path.join(OUTDIR,"mapped/{sample}.sort.bam"), sample=IDS) + + output: + outdir_avamb=directory(os.path.join(OUTDIR,"avamb")), + clusters_aae_z=os.path.join(OUTDIR,"avamb/aae_z_clusters.tsv"), + clusters_aae_y=os.path.join(OUTDIR,"avamb/aae_y_clusters.tsv"), + clusters_vamb=os.path.join(OUTDIR,"avamb/vae_clusters.tsv"), + contignames=os.path.join(OUTDIR,"avamb/contignames"), + contiglenghts=os.path.join(OUTDIR,"avamb/lengths.npz") + params: + walltime="86400", + nodes="1", + ppn=AVAMB_PPN, + mem=AVAMB_MEM, + cuda="--cuda" if CUDA else "" + threads: + int(avamb_threads) + conda: + "avamb" + + log: + vamb_out=os.path.join(OUTDIR,"avamb/tmp/avamb_finished.log"), + o=os.path.join(OUTDIR,'log','run_avamb.out'), + e=os.path.join(OUTDIR,'log','run_avamb.err') + + shell: + """ + rm -rf {output.outdir_avamb} + {AVAMB_PRELOAD} + vamb --outdir {output.outdir_avamb} --fasta {input.contigs} -p {threads} --bamfiles {input.bam_files} {params.cuda} {AVAMB_PARAMS} + touch {log.vamb_out} + """ + +checkpoint samples_with_bins: + input: + os.path.join(OUTDIR,"avamb/tmp/avamb_finished.log") + output: + os.path.join(OUTDIR,"avamb/tmp/samples_with_bins.txt") + params: + walltime="300", + nodes="1", + ppn="1", + mem="1gb" + log: + o=os.path.join(OUTDIR,'log','samples_with_bins.out'), + e=os.path.join(OUTDIR,'log','samples_with_bins.err') + + threads: + 1 + shell: + "find {OUTDIR}/avamb/bins/*/ -type d ! -empty |sed 's=.*bins/==g' |sed 's=/==g' > {output}" + + +def samples_with_bins_f(wildcards): + # decision based on content of output file + with checkpoints.samples_with_bins.get().output[0].open() as f: + samples_with_bins = [sample.strip() for sample in f.readlines()] + samples_with_bins_paths=expand(os.path.join(OUTDIR,"avamb/tmp/checkm2_all_{sample}_bins_finished.log"),sample=samples_with_bins) + return samples_with_bins_paths + + +rule run_checkm2_per_sample_all_bins: + input: + bins_dir_sample=os.path.join(OUTDIR,"avamb/bins/{sample}") + #out_dir_checkm2=os.path.join(OUTDIR,"avamb/tmp/checkm2_all") + output: + out_log_file=os.path.join(OUTDIR,"avamb/tmp/checkm2_all_{sample}_bins_finished.log") + params: + walltime="86400", + nodes="1", + ppn=CHECKM_PPN, + mem=CHECKM_MEM + threads: + int(CHECKM_PPN) + log: + o=os.path.join(OUTDIR,'log','checkm2_{sample}.out'), + e=os.path.join(OUTDIR,'log','checkm2_{sample}.err') + + conda: + "checkm2" + shell: + "checkm2 predict --threads {threads} --input {input.bins_dir_sample}/*.fna --output-directory {OUTDIR}/avamb/tmp/checkm2_all/{wildcards.sample} 2> {output.out_log_file}" + +rule cat_checkm2_all: + input: + samples_with_bins_f + output: + os.path.join(OUTDIR,"avamb/tmp/checkm2_finished.txt") + params: + walltime="86400", + nodes="1", + ppn="1", + mem="1gb" + threads: + 1 + log: + o=os.path.join(OUTDIR,'log','cat_checkm2.out'), + e=os.path.join(OUTDIR,'log','cat_checkm2.err') + + shell: + "touch {output}" + +rule create_cluster_scores_bin_path_dictionaries: + input: + checkm2_finished_log_file = os.path.join(OUTDIR,"avamb/tmp/checkm2_finished.txt") + output: + cluster_score_dict_path_avamb = os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb.json"), + bin_path_dict_path_avamb = os.path.join(OUTDIR,"avamb/tmp/bp_d_avamb.json"), + params: + path = os.path.join(SNAKEDIR, "src", "create_cluster_scores_bin_path_dict.py"), + walltime = "86400", + nodes = "1", + ppn = "4", + mem = "1gb" + threads: + 5 + conda: + "avamb" + log: + o=os.path.join(OUTDIR,'log','cs_bp_dicts.out'), + e=os.path.join(OUTDIR,'log','cs_bp_dicts.err') + + shell: + "python {params.path} --s {OUTDIR}/avamb/tmp/checkm2_all --b {OUTDIR}/avamb/bins --cs_d {output.cluster_score_dict_path_avamb} --bp_d {output.bin_path_dict_path_avamb} " + +rule run_drep_manual_vamb_z_y: + input: + cluster_score_dict_path_avamb=os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb.json"), + contignames=os.path.join(OUTDIR,"avamb/contignames"), + contiglengths=os.path.join(OUTDIR,"avamb/lengths.npz"), + clusters_aae_z=os.path.join(OUTDIR,"avamb/aae_z_clusters.tsv"), + clusters_aae_y=os.path.join(OUTDIR,"avamb/aae_y_clusters.tsv"), + clusters_vamb=os.path.join(OUTDIR,"avamb/vae_clusters.tsv") + + output: + clusters_avamb_manual_drep=os.path.join(OUTDIR,"avamb/tmp/avamb_manual_drep_clusters.tsv") + params: + path=os.path.join(SNAKEDIR, "src", "manual_drep_JN.py"), + walltime="86400", + nodes="1", + ppn="5", + mem="5gb" + threads: + 5 + conda: + "avamb" + log: + o=os.path.join(OUTDIR,'log','dereplication.out'), + e=os.path.join(OUTDIR,'log','dereplication.err') + + shell: + """ + python {params.path} --cs_d {input.cluster_score_dict_path_avamb} --names {input.contignames}\ + --lengths {input.contiglengths} --output {output.clusters_avamb_manual_drep}\ + --clusters {input.clusters_aae_z} {input.clusters_aae_y} {input.clusters_vamb} + """ + + +checkpoint create_ripped_bins_avamb: + input: + path_avamb_manually_drep_clusters = os.path.join(OUTDIR,"avamb/tmp/avamb_manual_drep_clusters.tsv"), + bin_path_dict_path = os.path.join(OUTDIR,"avamb/tmp/bp_d_avamb.json") + + output: + path_avamb_manually_drep_clusters_ripped = os.path.join(OUTDIR,"avamb/tmp/avamb_manual_drep_not_ripped_clusters.tsv"), + name_bins_ripped_file = os.path.join(OUTDIR,"avamb/tmp/bins_ripped_avamb.log") + params: + path = os.path.join(SNAKEDIR, "src", "rip_bins.py"), + walltime = "86400", + nodes = "1", + ppn = "5", + mem = "10gb" + threads: + 5 + conda: + "avamb" + log: + o=os.path.join(OUTDIR,'log','ripping.out'), + e=os.path.join(OUTDIR,'log','ripping.err') + + shell: + """ + python {params.path} -r {OUTDIR}/avamb/ --ci {input.path_avamb_manually_drep_clusters}\ + --co {output.path_avamb_manually_drep_clusters_ripped} -l {OUTDIR}/avamb/lengths.npz\ + -n {OUTDIR}/avamb/contignames --bp_d {input.bin_path_dict_path} --br {OUTDIR}/avamb/tmp/ripped_bins\ + --bin_separator C --log_nc_ripped_bins {output.name_bins_ripped_file} + """ + +rule nc_clusters_and_bins_from_mdrep_clusters_avamb: + input: + clusters_avamb_manual_drep = os.path.join(OUTDIR,"avamb/tmp/avamb_manual_drep_clusters.tsv"), + cluster_score_dict_path_avamb = os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb.json"), + nc_bins_path = os.path.join(OUTDIR,"avamb/NC_bins") + output: + clusters_avamb_after_drep_disjoint = os.path.join(OUTDIR,"avamb/avamb_manual_drep_disjoint_clusters.tsv") + log: + log_fin=os.path.join(OUTDIR,"avamb/tmp/avamb_nc_clusters_and_bins_from_mdrep_clusters.log"), + o=os.path.join(OUTDIR,'log','dereplicated_nc_bins.out'), + e=os.path.join(OUTDIR,'log','dereplicated_nc_bins.err') + + params: + path = os.path.join(SNAKEDIR, "src", "mv_bins_from_mdrep_clusters.py"), + walltime = "86400", + nodes = "1", + ppn = "4", + mem = "1gb" + threads: + 5 + conda: + "avamb" + shell: + """ + python {params.path} --c {input.clusters_avamb_manual_drep} \ + --cf {output.clusters_avamb_after_drep_disjoint} --b {OUTDIR}/avamb/bins \ + --cs_d {input.cluster_score_dict_path_avamb} --d {input.nc_bins_path} \ + --bin_separator C 2> {log.log_fin} + """ + + + +def ripped_bins_avamb_check_output_f(wildcards): + # decision based on content of output file + with checkpoints.create_ripped_bins_avamb.get().output[1].open() as f: + n_ripped_bins=int(f.readline()) + if n_ripped_bins == 0 : + return os.path.join(OUTDIR,"avamb/tmp/avamb_nc_clusters_and_bins_from_mdrep_clusters.log") + else: + return os.path.join(OUTDIR,"avamb/tmp/final_avamb_clusters_written.log") + + +rule run_checkm2_ripped_bins_avamb: + input: + os.path.join(OUTDIR,"avamb/tmp/bins_ripped_avamb.log") + output: + os.path.join(OUTDIR,"avamb/tmp/ripped_bins/checkm2_out/quality_report.tsv") + log: + log_fin=os.path.join(OUTDIR,"avamb/tmp/checkm2_ripped_avamb_run_finished.log"), + o=os.path.join(OUTDIR,'log','checkm2_ripped.out'), + e=os.path.join(OUTDIR,'log','checkm2_ripped.err') + + params: + walltime="86400", + nodes="1", + ppn=CHECKM_PPN_r, + mem=CHECKM_MEM_r + threads: + int(CHECKM_PPN_r) + conda: + "checkm2" + shell: + """ + checkm2 predict --threads {CHECKM_PPN_r} --input {OUTDIR}/avamb/tmp/ripped_bins \ + --output-directory {OUTDIR}/avamb//tmp/ripped_bins/checkm2_out 2> {log.log_fin} + """ + +rule update_cs_d_avamb: + input: + scores_bins_ripped = os.path.join(OUTDIR,"avamb/tmp/ripped_bins/checkm2_out/quality_report.tsv"), + cluster_score_dict_path_avamb = os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb.json") + output: + os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb_updted.log") + params: + path = os.path.join(SNAKEDIR, "src", "update_cluster_scores_dict_after_ripping.py"), + walltime = "86400", + nodes = "1", + ppn = "4", + mem = "1gb" + threads: + 5 + conda: + "avamb" + log: + o=os.path.join(OUTDIR,'log','update_cs_bp_dicts.out'), + e=os.path.join(OUTDIR,'log','update_cs_bp_dicst.err') + + shell: + "python {params.path} --s {input.scores_bins_ripped} --cs_d {input.cluster_score_dict_path_avamb} 2> {output}" + + +rule aggregate_nc_bins_avamb: + input: + cs_updated_log = os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb_updted.log"), + drep_clusters = os.path.join(OUTDIR,"avamb/tmp/avamb_manual_drep_clusters.tsv"), + drep_clusters_not_ripped = os.path.join(OUTDIR,"avamb/tmp/avamb_manual_drep_not_ripped_clusters.tsv"), + scores_bins_ripped = os.path.join(OUTDIR,"avamb/tmp/ripped_bins/checkm2_out/quality_report.tsv"), + cluster_scores_dict_path_avamb = os.path.join(OUTDIR,"avamb/tmp/cs_d_avamb.json"), + bin_path_dict_path_avamb = os.path.join(OUTDIR,"avamb/tmp/bp_d_avamb.json"), + path_bins_ripped = os.path.join(OUTDIR,"avamb/tmp/ripped_bins"), + checkm_finished_file = os.path.join(OUTDIR,"avamb/tmp/checkm2_ripped_avamb_run_finished.log") + output: + os.path.join(OUTDIR,"avamb/tmp/contigs_transfer_finished_avamb.log") + params: + path = os.path.join(SNAKEDIR, "src", "transfer_contigs_and_aggregate_all_nc_bins.py"), + walltime = "86400", + nodes = "1", + ppn = "5", + mem = "10gb" + threads: + 5 + conda: + "avamb" + + log: + o=os.path.join(OUTDIR,'log','aggregate_final_ncs.out'), + e=os.path.join(OUTDIR,'log','aggregate_final_ncs.err') + + shell: + """ + python {params.path} -r {OUTDIR}/avamb/ --c {input.drep_clusters} \ + --cnr {input.drep_clusters_not_ripped} --sbr {input.scores_bins_ripped} \ + --cs_d {input.cluster_scores_dict_path_avamb} --bp_d {input.bin_path_dict_path_avamb} \ + --br {input.path_bins_ripped} -d{OUTDIR}/avamb/NC_bins --bin_separator C \ + 2> {output} + """ + + +rule write_clusters_from_nc_folders: + input: + contigs_transfered_log = os.path.join(OUTDIR,"avamb/tmp/contigs_transfer_finished_avamb.log"), + nc_bins = os.path.join(OUTDIR,"avamb/NC_bins") + + output: + os.path.join(OUTDIR,"avamb/avamb_manual_drep_disjoint_clusters.tsv") + log: + log_fin=os.path.join(OUTDIR,"avamb/tmp/final_avamb_clusters_written.log"), + o=os.path.join(OUTDIR,'log','create_final_clusters.out'), + e=os.path.join(OUTDIR,'log','create_final_clusters.err') + + params: + path = os.path.join(SNAKEDIR, "src", "write_clusters_from_dereplicated_and_ripped_bins.sh"), + walltime = "86400", + nodes = "1", + ppn = "4", + mem = "1gb" + threads: + 5 + conda: + "avamb" + + shell: + "sh {params.path} -d {input.nc_bins} -o {output} 2> {log.log_fin} " + +rule workflow_finished: + input: + ripped_bins_avamb_check_output_f + output: + os.path.join(OUTDIR,"avamb/tmp/workflow_finished_avamb.log") + params: + walltime = "86400", + nodes = "1", + ppn = "1", + mem = "1gb" + threads: + 1 + log: + o=os.path.join(OUTDIR,'log','workflow_finished.out'), + e=os.path.join(OUTDIR,'log','workflow_finished.err') + shell: + "touch {output}" + diff --git a/workflow_avamb/config.json b/workflow_avamb/config.json new file mode 100644 index 00000000..d841364e --- /dev/null +++ b/workflow_avamb/config.json @@ -0,0 +1,17 @@ +{ + "contigs": "contigs.txt", + "sample_data": "samples2data.txt", + "index_size": "3G", + "min_contig_size": "2000", + "minimap_mem": "15gb", + "minimap_ppn": "15", + "avamb_mem": "15gb", + "avamb_ppn": "30", + "checkm2_mem": "15gb", + "checkm2_ppn": "15", + "checkm2_mem_r": "30gb", + "checkm2_ppn_r": "30", + "avamb_params": "-o C --minfasta 200000 ", + "avamb_preload": "", + "outdir": "avamb_outdir" +} diff --git a/workflow_avamb/envs/minimap2.yaml b/workflow_avamb/envs/minimap2.yaml new file mode 100644 index 00000000..bbe81442 --- /dev/null +++ b/workflow_avamb/envs/minimap2.yaml @@ -0,0 +1,6 @@ +name: minimap2 +channels: + - bioconda +dependencies: + - minimap2 + - samtools diff --git a/workflow_avamb/envs/samtools.yaml b/workflow_avamb/envs/samtools.yaml new file mode 100644 index 00000000..61dfbc38 --- /dev/null +++ b/workflow_avamb/envs/samtools.yaml @@ -0,0 +1,5 @@ +name: samtools +channels: + - bioconda +dependencies: + - samtools diff --git a/workflow_avamb/src/create_cluster_scores_bin_path_dict.py b/workflow_avamb/src/create_cluster_scores_bin_path_dict.py new file mode 100644 index 00000000..c4d9023b --- /dev/null +++ b/workflow_avamb/src/create_cluster_scores_bin_path_dict.py @@ -0,0 +1,65 @@ +import numpy as np +import os +import json +import argparse + +from typing import cast + + +def get_cluster_score_bin_path( + path_checkm_all: str, path_bins: str, bins: set[str] +) -> tuple[dict[str, tuple[float, float]], dict[str, str]]: + """Given CheckM has been run for all samples, create 2 dictionaries: + - {bin:path_bin} + - {bin:[completeness, contamination]}""" + cluster_score: dict[str, tuple[float, float]] = dict() + bin_path: dict[str, str] = dict() + for sample in os.listdir(path_checkm_all): + path_quality_s = os.path.join(path_checkm_all, sample, "quality_report.tsv") + c_com_con = np.loadtxt( + path_quality_s, + delimiter="\t", + skiprows=1, + usecols=(0, 1, 2), + dtype=str, + ndmin=2, + ) + + for row in c_com_con: + cluster, com, con = row + cluster = cast(str, cluster) + com, con = float(com), float(con) + bin_name = cluster + ".fna" + if bin_name in bins: + cluster_score[cluster] = (com, con) + bin_path[cluster + ".fna"] = os.path.join( + path_bins, sample, cluster + ".fna" + ) + return cluster_score, bin_path + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--s", type=str, help="path checkm2 that contains all samples") + parser.add_argument("--b", type=str, help="path all bins ") + parser.add_argument( + "--cs_d", type=str, help="cluster_score dictionary will be stored here" + ) + parser.add_argument( + "--bp_d", type=str, help="bin_path dictionary will be stored here " + ) + + opt = parser.parse_args() + + bins_set = set() + for sample in os.listdir(opt.b): + for bin_ in os.listdir(os.path.join(opt.b, sample)): + if ".fna" in bin_: + bins_set.add(bin_) + + cluster_score, bin_path = get_cluster_score_bin_path(opt.s, opt.b, bins_set) + with open(opt.cs_d, "w") as f: + json.dump(cluster_score, f) + + with open(opt.bp_d, "w") as f: + json.dump(bin_path, f) diff --git a/workflow_avamb/src/manual_drep_JN.py b/workflow_avamb/src/manual_drep_JN.py new file mode 100644 index 00000000..fb955953 --- /dev/null +++ b/workflow_avamb/src/manual_drep_JN.py @@ -0,0 +1,311 @@ +import vamb +import numpy as np +import os +import itertools + +from typing import NewType, Union, Optional +from collections.abc import Sequence, Mapping, Iterable +from pathlib import Path + +import argparse +import json + +ContigId = NewType("ContigId", int) +BinId = NewType("BinId", int) + + +def main( + # Path to output clusters file. Will error if it already exists + outpath: Path, + # Path to names file of contig names. CHANGED + names: Path, + # Path to length.npz of contig length. Output by Vamb + lengths_npz: Path, + # Path to CheckM2 quality_report.tsv file + quality_report: dict[str, list], + # List of paths to clusters.tsv files as output by Vamb. + # Names of clusters must match those in CheckM2 quality_report, + # and those of contigs match those in names_npz + binnings: Sequence[Path], + # min coverage to consdier 2 bins the same with 100% above this coverage + min_cov: float, + # min completentes for bin to be included into the dereplication process + min_comp: float, + # max contamination for bin to be included into the dereplication process + max_cont: float, + bins_extension: str, +) -> None: + # Load contig names + contig_names: list[str] = list(np.loadtxt(names, dtype=object)) + + assert isinstance(contig_names, list) + assert isinstance(contig_names[0], str) + + # Load lengths + lengths: np.ndarray = vamb.vambtools.read_npz(lengths_npz) + assert len(lengths) == len(contig_names) + + # Load CheckM2 + (bin_names, qualities, bin_by_name) = load_checkm2( + quality_report, min_comp, max_cont, bins_extension + ) + # Load bins + (bin_lengths, union_bins) = load_binnings( + binnings, contig_names, lengths, bin_by_name + ) + del bin_by_name + + dereplicated = dereplicate(union_bins, qualities, lengths, bin_lengths, min_cov) + del bin_lengths + + if os.path.exists(outpath): + raise FileExistsError(outpath) + + with open(outpath, "w") as file: + for bin in dereplicated: + bin_name = bin_names[bin] + bin_name = bin_name.replace(".fna", "") + for contig in union_bins[bin]: + print(bin_name, contig_names[contig], sep="\t", file=file) + + +def load_checkm2( + quality_report: dict[str, list], + min_completeness: float, + max_contamination: float, + bins_extension: str, +) -> tuple[ + list[str], # Bin names + list[tuple[float, float]], # Bin qualities + dict[str, Optional[BinId]], # Mapping to binid, if not skipped +]: + """Extract all bin names and assign them either a BinId, or else None, + if their completeness/contamination is so bad the bin should be discarded + """ + # This is None if the bin is to be discarded + bin_by_name: dict[str, Optional[BinId]] = dict() + bin_names: list[str] = [] + qualities: list[tuple[float, float]] = [] + + # The file looks like this: + # Name Completeness Contamination Completeness_Model_Used Translation_Table_Used Additional_Notes + # AAE_UC_Y_1980340Ccluster_501--AAE_UC_v3_1980340Ccluster_2599 5.18 0.0 Neural Network (Specific Model) 11 None + + for cluster, scores in quality_report.items(): + name = cluster + bins_extension + comp, cont = scores + completeness = float(comp) / 100 + contamination = float(cont) / 100 + assert 0.0 <= completeness <= 1.0 + assert 0.0 <= contamination # can be unbounded + + if completeness >= min_completeness and contamination <= max_contamination: + bin = BinId(len(bin_names)) + bin_names.append(name) + qualities.append((completeness, contamination)) + bin_by_name[name] = bin + else: + bin_by_name[name] = None + + assert sum(1 for i in bin_by_name.values() if isinstance(i, int)) == len(bin_names) + return (bin_names, qualities, bin_by_name) + + +def load_binnings( + binnings: Sequence[Path], + contig_names: Sequence[str], + lengths: np.ndarray, + bin_by_name: Mapping[str, Optional[BinId]], +) -> tuple[list[int], list[set[ContigId]]]: + """ + Load clusters.tsv files from each binning, and filter away those assigned to be discarded based on CheckM2 data. + Return bin length and bins, each represented as a set of ContigId + """ + id_len_of_contig_name: dict[str, tuple[ContigId, int]] = dict() + for (index, (name, length)) in enumerate(zip(contig_names, lengths)): + id_len_of_contig_name[name] = (ContigId(index), length) + + # Load binnings + n_union_bins = sum(1 for i in bin_by_name.values() if i is not None) + + lengthof = dict(zip(contig_names, lengths)) + + union_bins: list[Optional[set[ContigId]]] = [None] * n_union_bins + for binning_path in binnings: + with open(binning_path) as file: + clusters = vamb.vambtools.read_clusters(file) + clusters_filtered = filterclusters(clusters, lengthof) + # filter by clusters larger than 200kbs + for (bin_name, contigs) in clusters_filtered.items(): + + bin_name += ".fna" + # None is a valid value, so we use -1 as sentinel for missing + bin = bin_by_name.get(bin_name, -1) + if bin == -1: + raise ValueError( + f"Bin {bin_name} found in binning {binning_path}, but is not scored by CheckM2" + ) + # Means: Below threshold, so skip it + elif bin is None: + continue + else: + ids: set[ContigId] = set() + for contig in contigs: + existing = id_len_of_contig_name.get(contig) + if existing is None: + raise KeyError( + f"Cluster file {binning_path} contain contig {contig}, " + "but that name is not present in provided names npz file" + ) + ids.add(existing[0]) + union_bins[bin] = ids + + bin_lengths: list[int] = [] + + for i in union_bins: + assert isinstance(i, set) + union_bins_asserted: list[set[ContigId]] = union_bins # type: ignore + + for contigs in union_bins_asserted: + bin_lengths.append(sum(lengths[contig] for contig in contigs)) + + return (bin_lengths, union_bins_asserted) + + +def filterclusters( + clusters: Mapping[str, set], lengthof: Mapping[str, int] +) -> Mapping[str, set]: + filtered_bins = dict() + for medoid, contigs in clusters.items(): + binsize = sum(lengthof[contig] for contig in contigs) + + if binsize >= 200000: + filtered_bins[medoid] = contigs + + return filtered_bins + + +def dereplicate( + union_bins: Sequence[set[ContigId]], + qualities: Sequence[tuple[float, float]], + contig_lengths: np.ndarray, + bin_lengths: Sequence[int], + threshold: float, +) -> list[BinId]: + "Removes bins if they are too similar to another bin. Return list of kept bins" + assert len(union_bins) == len(qualities) == len(bin_lengths) + + overlapping_pairs = get_overlapping_bin_pairs(get_binsof(union_bins), qualities) + to_remove = compute_to_remove( + union_bins, overlapping_pairs, contig_lengths, bin_lengths, threshold + ) + return [BinId(i) for i in range(len(bin_lengths)) if BinId(i) not in to_remove] + + +def get_binsof(union_bins: Iterable[Iterable[ContigId]]) -> dict[ContigId, list[BinId]]: + "Makes a dict from contig -> list of bins the contig is present in, if in multiple bins" + binsof: dict[ContigId, Union[BinId, list[BinId]]] = dict() + for (bin_int, contigs) in enumerate(union_bins): + bin = BinId(bin_int) + for contig in contigs: + existing = binsof.get(contig) + if existing is None: + binsof[contig] = bin + elif isinstance(existing, int): + binsof[contig] = [existing, bin] + else: + assert isinstance(existing, list) + existing.append(bin) + return {k: v for (k, v) in binsof.items() if isinstance(v, list)} + + +def bin_score(completeness: float, contamination: float) -> float: + return completeness - 5 * contamination + +def get_overlapping_bin_pairs( + binsof: Mapping[ContigId, list[BinId]], qualities: Sequence[tuple[float, float]] +) -> Sequence[tuple[BinId, BinId]]: + "Get a list of pairs of bins that share at least one contig" + pairs: set[tuple[BinId, BinId]] = set() + for overlapping_bins in binsof.values(): + for (a, b) in itertools.combinations(overlapping_bins, r=2): + # Order them so we don't have (a, b) and (b, a) as distinct pairs + if a > b: + (a, b) = (b, a) + pairs.add((a, b)) + + # Now be sure to order them as (worst, best) depending on score + # If they tie, then use lexographic order (a, b) we added them + # in above + result: list[tuple[BinId, BinId]] = [] + for (a, b) in pairs: + score_a = bin_score(*qualities[a]) + score_b = bin_score(*qualities[b]) + if score_a > score_b: + result.append((b, a)) + else: + result.append((a, b)) + + return result + + +def compute_to_remove( + union_bins: Sequence[set[ContigId]], + overlapping_pairs: Iterable[tuple[BinId, BinId]], + lengths: np.ndarray, + bin_lengths: Sequence[int], + threshold: float, +) -> set[BinId]: + "Create a list of bins to remove because they overlap with another bin" + result: set[BinId] = set() + for (bin_a, bin_b) in overlapping_pairs: + if bin_a in result or bin_b in result: + continue + + intersection = union_bins[bin_a] & union_bins[bin_b] + int_len = sum(lengths[i] for i in intersection) + if int_len / min(bin_lengths[bin_a], bin_lengths[bin_b]) >= threshold: + # We remove an arbitrary one + result.add(bin_a) + return result + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--cs_d", type=str, help="path bins_scores dictionary") + parser.add_argument("--names", type=str, help="Contig names txt file path ") + parser.add_argument("--lengths", type=str, help="Contig lengths npz file path ") + parser.add_argument( + "--output", + type=str, + help="Path output clusters generated by dereplicating bins", + ) + parser.add_argument( + "--clusters", + type=str, + nargs="*", + help="Path input clusters generated by aamb and vamb", + ) + parser.add_argument("--cov", type=float, default=0.75, help="Min coverage ") + parser.add_argument("--comp", type=float, default=0.9, help="Min completeness ") + parser.add_argument("--cont", type=float, default=0.05, help="Max contamination ") + parser.add_argument( + "--bins_extension", type=str, default=".fna", help="Extension of the bins " + ) + + opt = parser.parse_args() + args = vars(parser.parse_args()) + with open(opt.cs_d) as f: + cluster_scores = json.load(f) + + main( + outpath=opt.output, + names=opt.names, + lengths_npz=opt.lengths, + quality_report=cluster_scores, + binnings=opt.clusters, + min_cov=opt.cov, + min_comp=opt.comp, + max_cont=opt.cont, + bins_extension=opt.bins_extension, + ) diff --git a/workflow_avamb/src/mv_bins_from_mdrep_clusters.py b/workflow_avamb/src/mv_bins_from_mdrep_clusters.py new file mode 100644 index 00000000..b94e5f39 --- /dev/null +++ b/workflow_avamb/src/mv_bins_from_mdrep_clusters.py @@ -0,0 +1,150 @@ +import vamb +import argparse +import shutil +import os +import json + +from typing import Optional + + +def main( + cluster_scores: dict[str, tuple[float, float]], + cluster_contigs: dict[str, set[str]], + bin_separator: Optional[str], + path_nc_bins_folder: str, + path_bins_folder: str, + path_nc_clusters: str, + min_comp: float = 0.9, + max_cont: float = 0.05, +): + + cluster_sample = get_cluster_sample(cluster_contigs, bin_separator) + nc_cluster_scores = get_nc_cluster_scores( + cluster_scores, cluster_sample, min_comp, max_cont + ) + create_nc_sample_folders(nc_cluster_scores, cluster_sample, path_nc_bins_folder) + write_nc_bins_from_mdrep_clusters( + nc_cluster_scores, cluster_sample, path_nc_bins_folder, path_bins_folder + ) + write_quality_report(nc_cluster_scores, path_nc_bins_folder) + write_final_nc_clusters(nc_cluster_scores, cluster_contigs, path_nc_clusters) + + +def get_nc_cluster_scores( + cluster_scores: dict[str, tuple[float, float]], + cluster_sample: dict[str, str], + min_comp: float, + max_cont: float, +) -> dict[str, tuple[float, float]]: + nc_cluster_scores: dict[str, tuple[float, float]] = dict() + for cluster, scores in cluster_scores.items(): + comp, cont = scores + comp, cont = float(comp), float(cont) + comp, cont = comp / 100, cont / 100 + if cluster not in cluster_sample.keys(): + continue + if comp >= min_comp and cont <= max_cont: + nc_cluster_scores[cluster] = (comp, cont) + + return nc_cluster_scores + + +def get_cluster_sample( + cluster_contigs: dict[str, set[str]], bin_separator: Optional[str] +) -> dict[str, str]: + cluster_sample: dict[str, str] = dict() + for cluster_ in cluster_contigs.keys(): + contigs = cluster_contigs[cluster_] + contig_i = next(iter(contigs)) + sample = contig_i.split(bin_separator)[0] + cluster_sample[cluster_] = sample + + return cluster_sample + + +def create_nc_sample_folders( + cluster_scores: dict[str, tuple[float, float]], + cluster_sample: dict[str, str], + path_nc_bins_folder: str, +): + nc_samples: set[str] = set() + for cluster in cluster_scores.keys(): + + sample = cluster_sample[cluster] + nc_samples.add(sample) + + for sample in nc_samples: + try: + os.mkdir(os.path.join(path_nc_bins_folder, sample)) + except FileExistsError: + pass + + +def write_nc_bins_from_mdrep_clusters( + cluster_scores: dict[str, tuple[float, float]], + cluster_sample: dict[str, str], + path_nc_bins_folder: str, + path_bins_folder: str, +): + + for cluster in cluster_scores.keys(): + sample = cluster_sample[cluster] + src_bin = os.path.join(path_bins_folder, sample, cluster + ".fna") + trg_bin = os.path.join(path_nc_bins_folder, sample, cluster + ".fna") + shutil.move(src_bin, trg_bin) + + +def write_quality_report( + cluster_scores: dict[str, tuple[float, float]], path_nc_bins_folder: str +): + with open(os.path.join(path_nc_bins_folder, "quality_report.tsv"), "w") as file: + print("Name completeness contamination", sep="\t", file=file) + file.flush() + for nc_cluster, (completeness, contaminaton) in cluster_scores.items(): + print(nc_cluster, completeness, contaminaton, sep="\t", file=file) + file.flush() + + +def write_final_nc_clusters( + cluster_scores: dict[str, tuple[float, float]], + cluster_contigs: dict[str, set[str]], + path_nc_clusters: str, +): + with open(path_nc_clusters, "w") as file: + for nc_cluster in cluster_scores.keys(): + nc_contigs = cluster_contigs[nc_cluster] + for nc_contig in nc_contigs: + print(nc_cluster, nc_contig, sep="\t", file=file) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--c", type=str, help="path clusters file from tmp folder") + parser.add_argument("--cf", type=str, help="path clusters file final") + parser.add_argument("--cs_d", type=str, help="cluster_scores dictionary path ") + parser.add_argument("--b", type=str, help="path all bins ") + parser.add_argument( + "--d", type=str, help="path to folder that will contain all nc bins" + ) + parser.add_argument("--bin_separator", type=str, help="separator ") + parser.add_argument("--comp", type=float, default=0.9, help="Min completeness ") + parser.add_argument("--cont", type=float, default=0.05, help="Max contamination ") + + opt = parser.parse_args() + + with open(opt.c) as clusters_file: + cluster_contigs = vamb.vambtools.read_clusters(clusters_file) + + with open(opt.cs_d) as f: + cluster_scores = json.load(f) + + main( + cluster_scores, + cluster_contigs, + opt.bin_separator, + opt.d, + opt.b, + opt.cf, + opt.comp, + opt.cont, + ) diff --git a/workflow_avamb/src/rip_bins.py b/workflow_avamb/src/rip_bins.py new file mode 100644 index 00000000..8d88a6b0 --- /dev/null +++ b/workflow_avamb/src/rip_bins.py @@ -0,0 +1,477 @@ +from Bio import SeqIO +import os +import numpy as np +from collections import OrderedDict +import argparse +import networkx as nx +import vamb +import json + +from collections.abc import Sequence +from typing import cast + + +def main( + contig_names: Sequence[str], + contig_lengths: Sequence[int], + cluster_contigs: dict[str, set[str]], + bin_path: dict[str, str], + path_ripped: str, + path_clusters_not_ripped: str, +) -> dict[str, str]: + + cluster_contigs_original = cluster_contigs.copy() + + contig_length = get_contig_length(contig_names, contig_lengths) + cluster_length = get_cluster_length(cluster_contigs, contig_length) + graph_clusters = get_graph_clusters(cluster_contigs, contig_length, cluster_length) + ( + cluster_contigs, + graph_clusters, + cluster_length, + clusters_changed_but_not_intersecting_contigs, + ) = remove_meaningless_edges_from_pairs( + graph_clusters, cluster_length, cluster_contigs, contig_length + ) + + ( + cluster_contigs, + graph_clusters, + cluster_length, + clusters_changed_but_not_intersecting_contigs_2, + ) = make_all_components_pair( + graph_clusters, cluster_contigs, cluster_length, contig_length + ) + + ripped_clusters_set = create_ripped_clusters_and_write_ripped_bins( + graph_clusters, cluster_contigs, bin_path, path_ripped + ) + + + clusters_changed_but_not_intersecting_contigs_total = clusters_changed_but_not_intersecting_contigs.copy() + clusters_changed_but_not_intersecting_contigs_total.update(clusters_changed_but_not_intersecting_contigs_2) + + ( + bin_path, + cluster_contigs_not_ripped, + ) = find_remaining_clusters_ripped_and_write_ripped_bins( + cluster_contigs_original, + clusters_changed_but_not_intersecting_contigs_total, + bin_path, + path_ripped, + ) + + # Remove the clusters that have been ripped because of removing the intersections + for cluster in ripped_clusters_set: + if cluster in cluster_contigs_not_ripped.keys(): + print("%s cluster removed from the set since it was ripped" % (cluster)) + del cluster_contigs_not_ripped[cluster] + + with open(path_clusters_not_ripped, "w") as file: + for cluster, contigs_nr in cluster_contigs_not_ripped.items(): + contigs_o = cluster_contigs_original[cluster] + if contigs_o != contigs_nr: + raise ValueError( + "Clusters that have been ripped remain in the non ripped set of clusters" + ) + for contig in contigs_nr: + print(cluster, contig, sep="\t", file=file) + file.flush() + return bin_path + + +def get_contig_length(names: Sequence[str], lengths: Sequence[int]) -> dict[str, int]: + return dict(zip(names, lengths)) + + +def get_cluster_length( + cluster_contigs: dict[str, set[str]], contig_length: dict[str, int] +) -> OrderedDict[str, int]: + cluster_length: OrderedDict[str, int] = OrderedDict() + for (cluster_name, contigs) in cluster_contigs.items(): + cluster_length[cluster_name] = 0 + for contig in contigs: + cluster_length[cluster_name] += contig_length[contig] + return cluster_length + + +def get_graph_clusters( + cluster_contigs: dict[str, set[str]], + contig_length: dict[str, int], + cluster_length: OrderedDict[str, int], +): + """Generate a graph where each cluster is a node and each edge connecting 2 nodes is + created if clusters share some contigs, edges are weighted by the intersection length""" + graph_clusters = nx.Graph() + for i, cluster_i in enumerate(cluster_contigs.keys()): + for j, cluster_j in enumerate(cluster_contigs.keys()): + if j <= i: + j += 1 + continue + + cluster_i_contigs, cluster_j_contigs = ( + cluster_contigs[cluster_i], + cluster_contigs[cluster_j], + ) + cluster_i_j_contigs_intersection = cluster_i_contigs.intersection( + cluster_j_contigs + ) + + if len(cluster_i_j_contigs_intersection) > 0: + cluster_i_length, cluster_j_length = ( + cluster_length[cluster_i], + cluster_length[cluster_j], + ) + cluster_i_j_contigs_intersection_length = sum( + ( + contig_length[contig_k] + for contig_k in cluster_i_j_contigs_intersection + ) + ) + intersection_length_ratio = ( + cluster_i_j_contigs_intersection_length + / min(cluster_i_length, cluster_j_length) + ) + graph_clusters.add_node(cluster_i) + graph_clusters.add_node(cluster_j) + graph_clusters.add_edge( + cluster_i, cluster_j, weight=intersection_length_ratio + ) + return graph_clusters + + +def remove_meaningless_edges_from_pairs( + graph_clusters: nx.Graph, + cluster_length: OrderedDict[str, int], + cluster_contigs: dict[str, set[str]], + contig_length: dict[str, int], + weight_thr: float = 0.001, +): + """Iterate over all the components of the graph and remove components composed by 2 node/clusters + according the following criteria: + - For edges with weight smaller than threshold, just remove edge and remove intersecting contigs from + larger cluster + """ + components: list[set[str]] = list() + + for component in nx.connected_components(graph_clusters): + components.append(component) + + clusters_changed_but_not_intersecting_contigs: dict[str, set[str]] = dict() + for component in components: + if len(component) == 2: + cl_i = component.pop() + cl_j = component - {cl_i} + assert len(cl_j) == 1 + cl_j = cl_j.pop() + cl_i_cl_j_edge_weight: float = graph_clusters.get_edge_data(cl_i, cl_j)[ + "weight" + ] + if cl_i_cl_j_edge_weight > weight_thr: + pass + else: + cluster_updated = move_intersection_to_smaller_cluster( + graph_clusters, + cl_i, + cl_j, + cluster_contigs, + cluster_length, + contig_length, + ) + print("Cluster ripped because of a meaningless edge ", cluster_updated) + clusters_changed_but_not_intersecting_contigs[ + cluster_updated + ] = cluster_contigs[cluster_updated] + + components: list[set[str]] = list() + for component in nx.connected_components(graph_clusters): + components.append(component) + + for component in components: + if len(component) == 1: + cl_i = component.pop() + graph_clusters.remove_node(cl_i) + + print("Meaningless edges removed from pairs") + + return ( + cluster_contigs, + graph_clusters, + cluster_length, + clusters_changed_but_not_intersecting_contigs, + ) + + +def move_intersection_to_smaller_cluster( + graph_bins: nx.Graph, + cl_i: str, + cl_j: str, + cluster_contigs: dict[str, set[str]], + cluster_length: OrderedDict[str, int], + contig_length: dict[str, int], +) -> str: + """Move the intersecting contigs to the shortest cluster""" + cl_i_contigs, cl_j_contigs = cluster_contigs[cl_i], cluster_contigs[cl_j] + cl_i_cl_j_intersection_contigs = cl_i_contigs.intersection(cl_j_contigs) + cl_i_length, cl_j_length = cluster_length[cl_i], cluster_length[cl_j] + + if cl_i_length < cl_j_length: + cluster_contigs[cl_j] = cl_j_contigs - cl_i_cl_j_intersection_contigs + cluster_length[cl_j] = sum( + [contig_length[contig_i] for contig_i in cluster_contigs[cl_j]] + ) + cluster_updated = cl_j + else: + cluster_contigs[cl_i] = cl_i_contigs - cl_i_cl_j_intersection_contigs + cluster_length[cl_i] = sum( + [contig_length[contig_i] for contig_i in cluster_contigs[cl_i]] + ) + cluster_updated = cl_i + graph_bins.remove_edge(cl_i, cl_j) + + return cluster_updated + + +def make_all_components_pair( + graph_clusters: nx.Graph, + cluster_contigs: dict[str, set[str]], + cluster_length: OrderedDict[str, int], + contig_length: dict[str, int], +): + """Iterate over all graph components that contain more than 2 nodes/clusters and break them down to + 2 nodes/clusters components according the following criteria: + - Remove the weaker edge by removing the intersecting contigs from the larger cluster""" + + components: list[set[str]] = list() + for component in nx.connected_components(graph_clusters): + components.append(component) + + clusters_changed_but_not_intersecting_contigs: dict[str, set[str]] = dict() + for component in components: + if len(component) > 2: + weight_edges: dict[float, set[str]] = dict() + for cl_i in component: + for cl_j in component - {cl_i}: + if graph_clusters.has_edge(cl_i, cl_j): + cl_i_cl_j_edge_weight: float = graph_clusters.get_edge_data( + cl_i, cl_j + )["weight"] + if ( + cl_i_cl_j_edge_weight in weight_edges.keys() + and weight_edges[cl_i_cl_j_edge_weight] != {cl_i, cl_j} + ): + cl_i_cl_j_edge_weight += 1 * 10**-30 + weight_edges[cl_i_cl_j_edge_weight] = {cl_i, cl_j} + + dsc_sorted_weights: list[float] = [min(weight_edges.keys())] + i = 0 + component_len = len(component) + while component_len > 2: + weight_i = dsc_sorted_weights[i] + cl_i, cl_j = weight_edges[weight_i] + + cluster_updated = move_intersection_to_smaller_cluster( + graph_clusters, + cl_i, + cl_j, + cluster_contigs, + cluster_length, + contig_length, + ) + print("Cluster ripped because of a pairing component ", cluster_updated) + clusters_changed_but_not_intersecting_contigs[ + cluster_updated + ] = cluster_contigs[cluster_updated] + component_len = max( + [ + len(nx.node_connected_component(graph_clusters, node_i)) + for node_i in component + ] + ) + + i += 1 + + components: list[set[str]] = list() + for component in nx.connected_components(graph_clusters): + components.append(component) + + for component in components: + assert len(component) < 3 + + if len(component) == 1: + cl_i = component.pop() + graph_clusters.remove_node(cl_i) + print("All components are reduced to 2 nodes per component") + return ( + cluster_contigs, + graph_clusters, + cluster_length, + clusters_changed_but_not_intersecting_contigs, + ) + + +def create_ripped_clusters_and_write_ripped_bins( + graph_clusters: nx.Graph, + cluster_contigs: dict[str, set[str]], + bin_path: dict[str, str], + path_ripped: str, +) -> set[str]: + """Given the components of the graph, write ripped bins where each bin misses the intersection with the other bin""" + ripped_clusters_set: set[str] = set() + try: + os.mkdir(path_ripped) + except FileExistsError: + pass + + for component in nx.connected_components(graph_clusters): + cl_i = next(iter(component)) + cl_j = component - {cl_i} + assert len(cl_j) == 1 + cl_j = next(iter(cl_j)) + cl_i_contigs, cl_j_contigs = cluster_contigs[cl_i], cluster_contigs[cl_j] + cluster_i_j_contigs_intersection = cl_i_contigs.intersection(cl_j_contigs) + print( + "Cluster %s and cluster %s share %i contigs " + % (cl_i, cl_j, len(cluster_i_j_contigs_intersection)) + ) + + bin_i_path = bin_path[cl_i + ".fna"] + bin_j_path = bin_path[cl_j + ".fna"] + + bin_i_ripped_path = os.path.join(path_ripped, cl_i + "--" + cl_j + ".fna") + bin_j_ripped_path = os.path.join(path_ripped, cl_j + "--" + cl_i + ".fna") + print(bin_i_path, bin_i_ripped_path) + write_ripped_bin( + cl_i_contigs - cluster_i_j_contigs_intersection, + bin_i_path, + bin_i_ripped_path, + ) + + print(bin_j_path, bin_j_ripped_path) + write_ripped_bin( + cl_j_contigs - cluster_i_j_contigs_intersection, + bin_j_path, + bin_j_ripped_path, + ) + + ripped_clusters_set.add(cl_i) + ripped_clusters_set.add(cl_j) + + if len(os.listdir(path_ripped)) == 0: + os.rmdir(path_ripped) + print("Ripped bins created from components") + return ripped_clusters_set + + +def write_ripped_bin( + contigs_cluster_ripped, bin_i_path: str, bin_i_ripped_path: str +) -> None: + """Write ripped bin""" + contig_sequences = [] + for contig in SeqIO.parse(bin_i_path, "fasta"): + if contig.id in contigs_cluster_ripped: + contig_sequences.append(contig) + with open(bin_i_ripped_path, "w") as output_handle: + SeqIO.write(contig_sequences, output_handle, "fasta") + + +def find_remaining_clusters_ripped_and_write_ripped_bins( + cluster_contigs_original: dict[str, set[str]], + clusters_changed_but_not_intersecting_contigs_total: dict[str, set[str]], + bin_path: dict[str, str], + path_ripped: str, +): + """Compare cluster_contigs_ripped_only_by_meaningless_edges_or_not_ripped_at_all with cluster_contigs + to find the clusters that remain different, so we can re evaluate them with checkM2, those clusters + are the product of the meaningless ripping or the parition of n>2 components to generate + all components n<=2""" + cluster_contigs_original_copy = cluster_contigs_original.copy() + + try: + os.mkdir(path_ripped) + except FileExistsError: + pass + + for ( + cluster, + contigs_r, + ) in clusters_changed_but_not_intersecting_contigs_total.items(): + contigs_o = cluster_contigs_original[cluster] + + assert contigs_r != contigs_o + assert len(contigs_r) < len(contigs_o) + bin_o_path = bin_path[cluster + ".fna"] + bin_r_path = os.path.join(path_ripped, cluster + ".fna") + write_ripped_bin(contigs_r, bin_o_path, bin_r_path) + bin_path[cluster + ".fna"] = bin_r_path + del cluster_contigs_original_copy[cluster] + + return bin_path, cluster_contigs_original_copy + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-r", type=str, help="path run generated by AVAMB") + parser.add_argument("--ci", type=str, help="path dereplicated clusters file") + parser.add_argument( + "--co", type=str, help="path dereplicated clusters without ripped clusters" + ) + parser.add_argument("-l", type=str, help="path to contig lengths") + parser.add_argument("-n", type=str, help="path to contig names") + parser.add_argument("--bp_d", type=str, help="bin_path dictionary path") + parser.add_argument("--br", type=str, help="path ripped bins") + parser.add_argument("--bin_separator", type=str, help="path ripped bins") + parser.add_argument( + "--log_nc_ripped_bins", + type=str, + help="path to og file that contains the # of ripped bins", + ) + + opt = parser.parse_args() + + path_run = ( + opt.r + ) # '/Users/mbv396/aamb/vamb_aamb_on_almeida_hmp2/runs/vamb_aamb_on_almeida/' + + clusters_path = ( + opt.ci + ) # os.path.join(path_run,'aae_l_y_vamb_manual_drep_85_10_clusters.tsv') + clusters_not_ripped_path = ( + opt.co + ) # os.path.join(path_run,'aae_l_y_vamb_manual_drep_85_10_not_ripped_clusters.tsv') + + with open(clusters_path) as file: + cluster_contigs = vamb.vambtools.read_clusters(file) + + contig_lengths_file = opt.l + contig_lengths = cast(Sequence[int], np.load(contig_lengths_file)["arr_0"]) + + contignames_file = opt.n + contig_names = cast(Sequence[str], np.loadtxt(contignames_file, dtype=object)) + + path_ripped = cast(str, opt.br) + + with open(opt.bp_d) as f: + bin_path = json.load(f) + + bin_separator = opt.bin_separator + + bin_path_ = main( + contig_names, + contig_lengths, + cluster_contigs, + bin_path, + path_ripped, + clusters_not_ripped_path, + ) + + with open(opt.bp_d, "w") as f: + json.dump(bin_path_, f) + + n_bins_ripped_in_folder = 0 + for _file in os.listdir(path_ripped): + if ".fna" in _file: + n_bins_ripped_in_folder += 1 + + with open(opt.log_nc_ripped_bins, "w") as file: + file.write(str(n_bins_ripped_in_folder)) diff --git a/workflow_avamb/src/transfer_contigs_and_aggregate_all_nc_bins.py b/workflow_avamb/src/transfer_contigs_and_aggregate_all_nc_bins.py new file mode 100644 index 00000000..183a6e46 --- /dev/null +++ b/workflow_avamb/src/transfer_contigs_and_aggregate_all_nc_bins.py @@ -0,0 +1,389 @@ +from Bio import SeqIO +import os +import numpy as np +from collections import OrderedDict +import argparse +from time import time +import vamb +import shutil +from ordered_set import OrderedSet +import json + + +def main( + path_run, + cluster_contigs, + # clusters_scores_ar, + cluster_scores, + cluster_not_r_contigs, + ripped_bins_scores_ar, + drep_folder, + # path_bins, + bin_path, + path_bins_ripped, + bin_separator, + min_comp, + max_cont, +): + + # {cluster:sample} dictionary for the manually drep clusters + cluster_sample = get_cluster_sample(cluster_contigs, bin_separator) + for sample in set(cluster_sample.values()): + try: + os.makedirs(os.path.join(path_run, drep_folder, sample)) + except: + pass + + # Given all the clusters that have no connection (no intersection) with any other cluster + # mv them to the final bins folder that contains the final set of NC bins if they are NC + nc_clusters_unchanged = mv_nc_not_r_nc_bins( + cluster_not_r_contigs, cluster_sample, cluster_scores, drep_folder, bin_path + ) + + # Given the checkM2 scores for all ripped bins, create a dictionary + # {ripped_bin:[comp,cont]} and also the ripped_bins pairs that share the interseciton + cluster_r_scores, cluster_r_pairs = get_cluster_r_scores(ripped_bins_scores_ar) + + # Now, given the ripped_bins scores and the pairs, for each pair select one ripped and one original + # per pair, and move them to the final NC folder + nc_clusters_unchanged_2, nc_clusters_ripped = choose_best_ripped_bin_and_mv_if_nc( + cluster_r_scores, + cluster_scores, + cluster_r_pairs, + cluster_sample, + bin_path, + drep_folder, + path_run, + path_bins_ripped, + ) + + # for the bins that have been ripped because of meaningless edges and after the ripping present + # no intersection with any other bin, also move them to the final set of NC bins if + # they are NC + nc_clusters_ripped_single = mv_single_ripped_nc_bins( + ripped_bins_scores_ar, + drep_folder, + cluster_r_pairs, + bin_path, + cluster_scores, + path_run, + cluster_sample, + min_comp, + max_cont, + ) + + nc_clusters_unchanged = nc_clusters_unchanged.union(nc_clusters_unchanged_2) + nc_clusters_ripped = nc_clusters_ripped.union(nc_clusters_ripped_single) + quality_file = os.path.join(path_run, drep_folder, "quality_report.tsv") + write_nc_bin_scores( + cluster_scores, + cluster_r_scores, + nc_clusters_unchanged, + nc_clusters_ripped, + quality_file, + ) + + +def get_cluster_sample(cluster_contigs, bin_separator): + """{cluster:sample} for all clusters""" + cluster_sample = {} + + for cluster, contigs in cluster_contigs.items(): + contig_i = next(iter(contigs)) + # sample=contig_i.split('C')[0] + sample = contig_i.split(bin_separator)[0] + + cluster_sample[cluster] = sample + return cluster_sample + + +def mv_nc_not_r_nc_bins( + cluster_not_r_contigs, cluster_sample, cluster_scores, drep_folder, bin_path +): + + """mv not ripped NC bins from bins folder to drep folder""" + nc_clusters_unchanged = set() + for cluster in cluster_not_r_contigs.keys(): + comp, cont = cluster_scores[cluster] + if comp > 90 and cont < 5: + + # src_bin=os.path.join(path_bins,cluster_sample[cluster],cluster+'.fna') + src_bin = bin_path[cluster + ".fna"] + trg_bin = os.path.join( + path_run, drep_folder, cluster_sample[cluster], cluster + ".fna" + ) + print( + "Bin %s has no intersection with any other bin so it is directly moved from %s to %s" + % (cluster, src_bin, trg_bin) + ) + shutil.move( + src_bin, trg_bin + ) # this should be changed to mv once we assure it works properly + nc_clusters_unchanged.add(cluster) + return nc_clusters_unchanged + + +# For ripped bins + + +def mv_single_ripped_nc_bins( + ripped_bins_scores_ar, + drep_folder, + cluster_r_pairs, + # path_bins_ripped, + bin_path, + cluster_score, + path_run, + cluster_sample, +): + """Mv nc bins that were ripped only because they had meaningless edges""" + nc_clusters_ripped_single = set() + clusters_alredy_moved = set( + [element for pair in cluster_r_pairs for element in pair] + ) + for row in ripped_bins_scores_ar: + cluster, comp, cont = row[:3] + if "--" in cluster or cluster in clusters_alredy_moved: + continue + + comp, cont = float(comp), float(cont) + comp_, cont_ = cluster_score[cluster] + assert comp == comp_ + assert cont == cont_ + + if comp > min_comp and cont < max_cont: + # cluster = bin_name.replace('.fna','') + # src_bin=os.path.join(path_bins,cluster_sample[cluster],bin_name+'.fna') + src_bin = bin_path[cluster + ".fna"] + + if os.path.isfile(src_bin): + trg_bin = os.path.join( + path_run, drep_folder, cluster_sample[cluster], cluster + ".fna" + ) + print( + "Bin %s was ripped because of meaningless edges or pairing and afterwards no intersection was shared with any other bin so it is moved from %s to %s" + % (cluster, src_bin, trg_bin) + ) + shutil.move(src_bin, trg_bin) + nc_clusters_ripped_single.add(cluster) + return nc_clusters_ripped_single + + +def get_cluster_r_scores(ripped_bins_scores_ar): + """{cluster:[comp,cont]} for ripped clusters""" + + cluster_r_pairs = [] + cluster_ripped_scores_dict = dict() + + for row in ripped_bins_scores_ar: + # print(row) + bin_A_bin_B_name, comp, cont = row[:3] + if "--" in bin_A_bin_B_name: + bin_A_name, bin_B_name = bin_A_bin_B_name.split("--") + cluster_A_name, cluster_B_name = bin_A_name, bin_B_name + + cluster_ripped_scores_dict[cluster_A_name] = [float(comp), float(cont)] + + cluster_r_pairs.append(OrderedSet([cluster_A_name, cluster_B_name])) + else: + cluster_A_name = bin_A_bin_B_name.replace(".fna", "") + cluster_ripped_scores_dict[cluster_A_name] = [float(comp), float(cont)] + + return cluster_ripped_scores_dict, cluster_r_pairs + + +def choose_best_ripped_bin_and_mv_if_nc( + cluster_r_scores, + cluster_scores, + cluster_r_pairs, + cluster_sample, + bin_path, + drep_folder, + path_run, + path_bins_ripped, +): + + nc_clusters_unchanged, nc_clusters_ripped = set(), set() + clusters_already_processed = set() + """So given 2 ripped bins that miss the interseciton, we have to choose who keeps the contigs""" + for cluster_A_r, cluster_B_r in cluster_r_pairs: + + if ( + cluster_A_r in clusters_already_processed + or cluster_B_r in clusters_already_processed + ): + continue + print("Bin %s and bin %s share some contigs " % (cluster_A_r, cluster_B_r)) + + cluster_A_complet, cluster_A_cont = cluster_scores[cluster_A_r] + cluster_A_r_complet, cluster_A_r_cont = cluster_r_scores[cluster_A_r] + cluster_A_score = cluster_A_complet - 5 * cluster_A_cont + cluster_A_r_score = cluster_A_r_complet - 5 * cluster_A_r_cont + + cluster_B_complet, cluster_B_cont = cluster_scores[cluster_B_r] + cluster_B_r_complet, cluster_B_r_cont = cluster_r_scores[cluster_B_r] + cluster_B_score = cluster_B_complet - 5 * cluster_B_cont + cluster_B_r_score = cluster_B_r_complet - 5 * cluster_B_r_cont + + cluster_B_dif = cluster_B_score - cluster_B_r_score + cluster_A_dif = cluster_A_score - cluster_A_r_score + + keeper = np.argmax([cluster_A_dif, cluster_B_dif]) + + if keeper == 0: + print( + "%s keeps the intersecting contigs whereas %s does not " + % (cluster_A_r, cluster_B_r) + ) + + # bin_A keeps the contigs + if cluster_A_complet > 90 and cluster_A_cont < 5: + bin_A_name = cluster_A_r + ".fna" + src_bin = bin_path[bin_A_name] + trg_bin = os.path.join( + path_run, drep_folder, cluster_sample[cluster_A_r], bin_A_name + ) + shutil.move(src_bin, trg_bin) + nc_clusters_unchanged.add(cluster_A_r) + print("%s keeps the contigs so src_path is %s " % (bin_A_name, src_bin)) + # and bin B not + if cluster_B_r_complet > 90 and cluster_B_r_cont < 5: + bin_B_ripped_name = cluster_B_r + "--" + cluster_A_r + ".fna" + bin_B_name = cluster_B_r + ".fna" + src_bin = os.path.join(path_bins_ripped, bin_B_ripped_name) + trg_bin = os.path.join( + path_run, drep_folder, cluster_sample[cluster_B_r], bin_B_name + ) + shutil.move(src_bin, trg_bin) + nc_clusters_ripped.add(cluster_B_r) + print( + "%s looses the contigs so src_path is %s " % (bin_B_name, src_bin) + ) + + if keeper == 1: + # print('%s keeps the intersecting contigs whereas %s does not '%(bin_B_name,bin_A_name)) + print( + "%s keeps the intersecting contigs whereas %s does not " + % (cluster_B_r, cluster_A_r) + ) + + # bin_B keeps the contigs + if cluster_B_complet > 90 and cluster_B_cont < 5: + bin_B_name = cluster_B_r + ".fna" + src_bin = bin_path[bin_B_name] + trg_bin = os.path.join( + path_run, drep_folder, cluster_sample[cluster_B_r], bin_B_name + ) + shutil.move(src_bin, trg_bin) + nc_clusters_unchanged.add(cluster_B_r) + print("%s keeps the contigs so src_path is %s " % (bin_B_name, src_bin)) + # and bin A not + if cluster_A_r_complet > 90 and cluster_A_r_cont < 5: + bin_A_ripped_name = cluster_A_r + "--" + cluster_B_r + ".fna" + bin_A_name = cluster_A_r + ".fna" + src_bin = os.path.join(path_bins_ripped, bin_A_ripped_name) + trg_bin = os.path.join( + path_run, drep_folder, cluster_sample[cluster_A_r], bin_A_name + ) + shutil.move(src_bin, trg_bin) + nc_clusters_ripped.add(cluster_A_r) + print( + "%s looses the contigs so src_path is %s " % (bin_A_name, src_bin) + ) + + clusters_already_processed.add(cluster_A_r) + clusters_already_processed.add(cluster_B_r) + + return nc_clusters_unchanged, nc_clusters_ripped + + +def write_nc_bin_scores( + cluster_scores, + cluster_r_scores, + nc_clusters_unchanged, + nc_clusters_ripped, + quality_file, +): + """Given that we know which bins were ripped and which not, write a final + quality_score.tsv file that collect comp and cont for all NC final bins""" + with open(quality_file, "a") as file_: + print("Name", "completeness", "contamination", sep="\t", file=file_) + file_.flush() + for nc_cluster in nc_clusters_unchanged: + comp, cont = cluster_scores[nc_cluster] + print(nc_cluster, comp, cont, sep="\t", file=file_) + file_.flush() + for nc_cluster_r in nc_clusters_ripped: + comp, cont = cluster_r_scores[nc_cluster_r] + print(nc_cluster_r, comp, cont, sep="\t", file=file_) + file_.flush() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-r", type=str, help="path run generated by AVAMB") + parser.add_argument("--c", type=str, help="path clusters file") + parser.add_argument("--cnr", type=str, help="path dereplicated clusters non ripped") + parser.add_argument("--sbr", type=str, help="path scores ripped bins ") + parser.add_argument("--cs_d", type=str, help="cluster_scores dictionary path ") + parser.add_argument("--bp_d", type=str, help="bin_path dictionary path") + parser.add_argument("--br", type=str, help="path bins ripped ") + parser.add_argument( + "-d", type=str, help="folder name that will contain all nc bins" + ) + parser.add_argument("--bin_separator", type=str, help="path ripped bins") + parser.add_argument("--comp", type=float, default=0.9, help="Min completeness ") + parser.add_argument("--cont", type=float, default=0.05, help="Max contamination ") + + opt = parser.parse_args() + + path_run = ( + opt.r + ) #'/Users/mbv396/aamb/vamb_aamb_on_cami2/oral/manual_drep/vamb_aamb_cpu_jgi_oral_310122_run_2_manual_drep_disjoint2_copy/' + + path_clusters = ( + opt.c + ) # os.path.join(path_run,'aae_l_y_vamb_manual_drep_clusters.tsv') + with open(path_clusters) as file: + cluster_contigs = vamb.vambtools.read_clusters(file) + + with open(opt.cs_d) as f: + cluster_scores = json.load(f) + + path_clusters_not_r = ( + opt.cnr + ) # os.path.join(path_run,'aae_l_y_vamb_manual_drep_not_ripped_clusters.tsv') + + with open(path_clusters_not_r) as file: + cluster_not_r_contigs = vamb.vambtools.read_clusters(file) + + path_bins_ripped_checkm_quality_report = ( + opt.sbr + ) # os.path.join(path_bins_ripped,'checkm2_out','quality_report.tsv') + + ripped_bins_scores_ar = np.loadtxt( + path_bins_ripped_checkm_quality_report, dtype=str, skiprows=1, ndmin=2 + ) + + drep_folder = opt.d #'drep_trial' + + with open(opt.bp_d) as f: + bin_path = json.load(f) + + path_bins_ripped = opt.br # os.path.join(path_run,'ripped_bins') + + bin_separator = opt.bin_separator + + main( + path_run, + cluster_contigs, + cluster_scores, + cluster_not_r_contigs, + ripped_bins_scores_ar, + drep_folder, + bin_path, + path_bins_ripped, + bin_separator, + opt.comp, + opt.cont, + ) diff --git a/workflow_avamb/src/update_cluster_scores_dict_after_ripping.py b/workflow_avamb/src/update_cluster_scores_dict_after_ripping.py new file mode 100644 index 00000000..aa842371 --- /dev/null +++ b/workflow_avamb/src/update_cluster_scores_dict_after_ripping.py @@ -0,0 +1,50 @@ +import numpy as np +import json +import argparse + + +def update_cluster_score_bin_path( + path_checkm_ripped: str, cluster_score: dict[str, tuple[float, float]] +): + c_com_con = np.loadtxt( + path_checkm_ripped, + delimiter="\t", + skiprows=1, + usecols=(0, 1, 2), + dtype=str, + ndmin=2, + ) + for row in c_com_con: + cluster, com, con = row + if "--" in cluster: + continue + com, con = float(com), float(con) + print(cluster, "scores were", cluster_score[cluster]) + + cluster_score[cluster] = (com, con) + print("and now are", cluster_score[cluster]) + return cluster_score + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--s", + type=str, + help="path checkm2 that contains quality_report.tsv file for ripped bins", + ) + parser.add_argument( + "--cs_d", + type=str, + help="cluster_score dictionary path, which will be updated for clusters that where ripped either becuase of meaningless edges or when making the component lenght <= 2 ", + ) + + opt = parser.parse_args() + + with open(opt.cs_d) as f: + cluster_score = json.load(f) + + cluster_score = update_cluster_score_bin_path(opt.s, cluster_score) + + with open(opt.cs_d, "w") as f: + json.dump(cluster_score, f) diff --git a/workflow_avamb/src/workflow_tools.py b/workflow_avamb/src/workflow_tools.py new file mode 100644 index 00000000..46795abc --- /dev/null +++ b/workflow_avamb/src/workflow_tools.py @@ -0,0 +1,61 @@ +import numpy as np +import os +import json +from typing import cast, Optional +import vamb +import shutil + + +def get_cluster_score_bin_path( + path_checkm_all: str, path_bins: str, bins: set[str] +) -> tuple[dict[str, tuple[float, float]], dict[str, str]]: + """Given CheckM has been run for all samples, create 2 dictionaries: + - {bin:path_bin} + - {bin:[completeness, contamination]}""" + cluster_score: dict[str, tuple[float, float]] = dict() + bin_path: dict[str, str] = dict() + for sample in os.listdir(path_checkm_all): + path_quality_s = os.path.join(path_checkm_all, sample, "quality_report.tsv") + c_com_con = np.loadtxt( + path_quality_s, + delimiter="\t", + skiprows=1, + usecols=(0, 1, 2), + dtype=str, + ndmin=2, + ) + + for row in c_com_con: + cluster, com, con = row + cluster = cast(str, cluster) + com, con = float(com), float(con) + bin_name = cluster + ".fna" + if bin_name in bins: + cluster_score[cluster] = (com, con) + bin_path[cluster + ".fna"] = os.path.join( + path_bins, sample, cluster + ".fna" + ) + return cluster_score, bin_path + + +def update_cluster_score_bin_path( + path_checkm_ripped: str, cluster_score: dict[str, tuple[float, float]] +) -> dict[str, tuple[float,float]] : + c_com_con = np.loadtxt( + path_checkm_ripped, + delimiter="\t", + skiprows=1, + usecols=(0, 1, 2), + dtype=str, + ndmin=2, + ) + for row in c_com_con: + cluster, com, con = row + if "--" in cluster: + continue + com, con = float(com), float(con) + print(cluster, "scores were", cluster_score[cluster]) + + cluster_score[cluster] = (com, con) + print("and now are", cluster_score[cluster]) + return cluster_score diff --git a/workflow_avamb/src/write_clusters_from_dereplicated_and_ripped_bins.sh b/workflow_avamb/src/write_clusters_from_dereplicated_and_ripped_bins.sh new file mode 100644 index 00000000..4d2a605d --- /dev/null +++ b/workflow_avamb/src/write_clusters_from_dereplicated_and_ripped_bins.sh @@ -0,0 +1,54 @@ +#!/usr/bin/bash + + +while getopts "d:o:" opt; do + case $opt in + d) drep_dir=$OPTARG ;; + o) clusters_file=$OPTARG ;; + *) echo 'error' >&2 + exit 1 + esac +done +echo 'creating z y v clusters from the final set of bins' +for s in $(ls $drep_dir) +do +s="$drep_dir"/"$s"/ +if [ -d "$s" ] +then +cd $s +#for bin in $(ls dereplicated_genomes 2> /dev/null) +for bin in $(ls . 2> /dev/null) + +do +if [[ $bin == **".fna" ]] +then +#echo $bin + +cluster_name=$(echo $bin | sed 's=.fna==g' | sed 's=.fa==g') +#echo $cluster_name +#bin="$s"dereplicated_genomes/"$bin" +#bin="$s""$bin" +#echo $bin +#grep '>' $bin | sed 's=>==g' +for contig in $(grep '>' $bin | sed 's=>==g') +do +echo -e "$cluster_name""\t""$contig" >> $clusters_file +done + + +fi +done +#for bin in $(ls ripped_bins_selected 2> /dev/null) +#do +#if [[ $bin == **".fna" ]] +#then +#bin="$s"ripped_bins_selected/"$bin" +# +#for contig in $(grep '>' $bin | sed 's=>==g') +#do +#echo -e "$cluster_name""$\t""$contig" >> $clusters_file +#done +#fi +#done +fi +done