From 6934a107ac0f4a08ed41efe9092871c67643f9f8 Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Sun, 19 Jul 2020 13:29:08 -0700
Subject: [PATCH 01/11] Revert "rebasing"

This reverts commit 27102382b11241cb11bbb474262007cc5dcc9c11.
---
 .idea/misc.xml                                |   6 -
 .idea/modules.xml                             |   8 -
 .idea/vcs.xml                                 |   6 -
 .idea/workspace.xml                           | 256 ---------
 build/lib/exabiome/__init__.py                |   0
 build/lib/exabiome/nn/__init__.py             |   1 -
 build/lib/exabiome/nn/infer.py                | 190 -------
 build/lib/exabiome/nn/loader.py               | 308 -----------
 build/lib/exabiome/nn/loss.py                 |  17 -
 build/lib/exabiome/nn/metric.py               |  44 --
 build/lib/exabiome/nn/models/__init__.py      |  49 --
 build/lib/exabiome/nn/models/fc.py            |  50 --
 build/lib/exabiome/nn/models/lit.py           | 106 ----
 build/lib/exabiome/nn/models/roznet.py        | 120 ----
 build/lib/exabiome/nn/models/smallroznet.py   |  97 ----
 build/lib/exabiome/nn/models/spp_cnn.py       | 159 ------
 build/lib/exabiome/nn/optim.py                | 169 ------
 build/lib/exabiome/nn/summarize.py            | 110 ----
 build/lib/exabiome/nn/train.py                | 191 -------
 build/lib/exabiome/nn/utils.py                |  79 ---
 build/lib/exabiome/response/__init__.py       |   0
 build/lib/exabiome/response/embedding.py      |  88 ---
 build/lib/exabiome/response/fulldmat.py       |  91 ----
 build/lib/exabiome/response/gtdb.py           |  29 -
 build/lib/exabiome/response/hierarchy.py      | 102 ----
 build/lib/exabiome/response/isomap.py         |  62 ---
 build/lib/exabiome/response/mds.py            |  70 ---
 .../exabiome/response/replace_embeddings.py   |  31 --
 build/lib/exabiome/response/tree.py           | 152 ------
 build/lib/exabiome/response/umap.py           | 121 -----
 build/lib/exabiome/response/umap_sweep.py     | 136 -----
 build/lib/exabiome/sequence/__init__.py       |  10 -
 build/lib/exabiome/sequence/convert.py        | 362 ------------
 build/lib/exabiome/sequence/dna_table.py      | 513 ------------------
 .../sequence/schema/deep_index.namespace.yaml |   7 -
 .../exabiome/sequence/schema/dna_table.yaml   | 127 -----
 build/lib/exabiome/sequence/tetramers.py      | 130 -----
 build/lib/exabiome/tools/__init__.py          |   0
 build/lib/exabiome/tools/make_fof.py          |  66 ---
 build/lib/exabiome/tools/prepare_data.py      | 190 -------
 build/lib/exabiome/tools/summarize_loss.py    |  61 ---
 build/lib/exabiome/utils.py                   | 105 ----
 deep-taxon.iml                                |   9 -
 dist/exabiome-0.0.0-py3.6.egg                 | Bin 121897 -> 0 bytes
 src/exabiome/nn/models/Untitled.ipynb         | 173 ------
 src/exabiome/nn/models/roznet.py              |  30 +-
 src/exabiome/nn/optim.py                      |   1 -
 47 files changed, 4 insertions(+), 4628 deletions(-)
 delete mode 100644 .idea/misc.xml
 delete mode 100644 .idea/modules.xml
 delete mode 100644 .idea/vcs.xml
 delete mode 100644 .idea/workspace.xml
 delete mode 100644 build/lib/exabiome/__init__.py
 delete mode 100644 build/lib/exabiome/nn/__init__.py
 delete mode 100644 build/lib/exabiome/nn/infer.py
 delete mode 100644 build/lib/exabiome/nn/loader.py
 delete mode 100644 build/lib/exabiome/nn/loss.py
 delete mode 100644 build/lib/exabiome/nn/metric.py
 delete mode 100644 build/lib/exabiome/nn/models/__init__.py
 delete mode 100644 build/lib/exabiome/nn/models/fc.py
 delete mode 100644 build/lib/exabiome/nn/models/lit.py
 delete mode 100644 build/lib/exabiome/nn/models/roznet.py
 delete mode 100644 build/lib/exabiome/nn/models/smallroznet.py
 delete mode 100644 build/lib/exabiome/nn/models/spp_cnn.py
 delete mode 100644 build/lib/exabiome/nn/optim.py
 delete mode 100644 build/lib/exabiome/nn/summarize.py
 delete mode 100644 build/lib/exabiome/nn/train.py
 delete mode 100644 build/lib/exabiome/nn/utils.py
 delete mode 100644 build/lib/exabiome/response/__init__.py
 delete mode 100644 build/lib/exabiome/response/embedding.py
 delete mode 100644 build/lib/exabiome/response/fulldmat.py
 delete mode 100644 build/lib/exabiome/response/gtdb.py
 delete mode 100644 build/lib/exabiome/response/hierarchy.py
 delete mode 100644 build/lib/exabiome/response/isomap.py
 delete mode 100644 build/lib/exabiome/response/mds.py
 delete mode 100644 build/lib/exabiome/response/replace_embeddings.py
 delete mode 100644 build/lib/exabiome/response/tree.py
 delete mode 100644 build/lib/exabiome/response/umap.py
 delete mode 100644 build/lib/exabiome/response/umap_sweep.py
 delete mode 100644 build/lib/exabiome/sequence/__init__.py
 delete mode 100644 build/lib/exabiome/sequence/convert.py
 delete mode 100644 build/lib/exabiome/sequence/dna_table.py
 delete mode 100644 build/lib/exabiome/sequence/schema/deep_index.namespace.yaml
 delete mode 100644 build/lib/exabiome/sequence/schema/dna_table.yaml
 delete mode 100644 build/lib/exabiome/sequence/tetramers.py
 delete mode 100644 build/lib/exabiome/tools/__init__.py
 delete mode 100644 build/lib/exabiome/tools/make_fof.py
 delete mode 100644 build/lib/exabiome/tools/prepare_data.py
 delete mode 100644 build/lib/exabiome/tools/summarize_loss.py
 delete mode 100644 build/lib/exabiome/utils.py
 delete mode 100644 deep-taxon.iml
 delete mode 100644 dist/exabiome-0.0.0-py3.6.egg
 delete mode 100644 src/exabiome/nn/models/Untitled.ipynb

diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index 1c0e413..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,6 +0,0 @@
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\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index f85e2c1..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
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\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100644
index 35eb1dd..0000000
--- a/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
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-    <mapping directory="" vcs="Git" />
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\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
deleted file mode 100644
index 8477f94..0000000
--- a/.idea/workspace.xml
+++ /dev/null
@@ -1,256 +0,0 @@
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diff --git a/build/lib/exabiome/__init__.py b/build/lib/exabiome/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/exabiome/nn/__init__.py b/build/lib/exabiome/nn/__init__.py
deleted file mode 100644
index 992e9eb..0000000
--- a/build/lib/exabiome/nn/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .loader import get_loader, train_test_loaders, SeqDataset
diff --git a/build/lib/exabiome/nn/infer.py b/build/lib/exabiome/nn/infer.py
deleted file mode 100644
index baa3391..0000000
--- a/build/lib/exabiome/nn/infer.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import sys
-from ..utils import check_argv, parse_logger
-from .utils import process_gpus, process_model, process_output
-import glob
-import argparse
-import torch
-
-
-def parse_args(*addl_args, argv=None):
-    """
-    Parse arguments for running inference
-    """
-    argv = check_argv(argv)
-
-    epi = """
-    output can be used as a checkpoint
-    """
-    desc = "Run network training"
-    parser = argparse.ArgumentParser(description=desc, epilog=epi)
-    parser.add_argument('model', type=str, choices=list(models._models.keys()),
-                        help='the model type to run inference with')
-    parser.add_argument('input', type=str, help='the HDF5 DeepIndex file used to train the model')
-    parser.add_argument('output', type=str, help='directory to save model outputs to')
-    parser.add_argument('-E', '--experiment', type=str, default='default',
-                        help='the experiment name to get the checkpoint from')
-    parser.add_argument('-c', '--checkpoint', type=str, default=None,
-                        help='read the checkpoint from the given checkpoint file')
-    parser.add_argument('-g', '--gpus', nargs='?', const=True, default=False, help='use GPU')
-    parser.add_argument('-L', '--load', action='store_true', default=False,
-                        help='load data into memory before running inference')
-    parser.add_argument('-U', '--umap', action='store_true', default=False,
-                        help='compute a 2D UMAP embedding for vizualization')
-    parser.add_argument('-d', '--debug', action='store_true', default=False,
-                        help='run in debug mode i.e. only run two batches')
-    parser.add_argument('-l', '--logger', type=parse_logger, default='', help='path to logger [stdout]')
-    parser.add_argument('-b', '--batch_size', type=int, help='batch size', default=None)
-    parser.add_argument('--train', action='append_const', const='train', dest='loaders',
-                        help='do inference on training data')
-    parser.add_argument('--validate', action='append_const', const='validate', dest='loaders',
-                        help='do inference on validation data')
-    parser.add_argument('--test', action='append_const', const='test', dest='loaders', help='do inference on test data')
-
-    for a in addl_args:
-        parser.add_argument(*a[0], **a[1])
-
-    if len(argv) == 0:
-        parser.print_help()
-        sys.exit(1)
-
-    args = parser.parse_args(argv)
-
-    if args.loaders is None:
-        args.loaders = ['train', 'validate', 'test']
-
-    return args
-
-
-def process_args(argv=None):
-    """
-    Process arguments for running inference
-    """
-    if not isinstance(argv, argparse.Namespace):
-        args = parse_args(argv=argv)
-    else:
-        args = argv
-
-    logger = args.logger
-    # set up logger
-    if logger is None:
-        import logging
-        logger = logging.getLogger()
-        logger.setLevel(logging.INFO)
-    if args.debug:
-        logger.setLevel(logging.DEBUG)
-    args.logger = logger
-
-    targs = dict()
-
-    # this does not get used in this command,
-    # but leave it here in case we figure out how to
-    # do infernce with Lightning someday
-    targs['gpus'] = process_gpus(args.gpus)
-    if targs['gpus'] != 1:
-        targs['distributed_backend'] = 'dp'
-    if args.gpus:
-        dev = "cuda:0"
-    else:
-        dev = "cpu"
-    print(f'using {dev} device')
-    args.device = torch.device(dev)
-    del args.gpus
-
-    if args.debug:
-        targs['fast_dev_run'] = True
-
-    # Figure out the checkpoint file to read from
-    # and where to save outputs to
-    outbase, output = process_output(args)
-    if args.checkpoint is None:
-        ckpt = list(glob.glob(f"{outbase}/*.ckpt"))
-        if len(ckpt) > 1:
-            print(f'More than one checkpoint file found in {outbase}. '
-                  'Please specify checkpoint with -c', file=sys.stderr)
-            sys.exit(1)
-        args.checkpoint = ckpt[0]
-    args.output = '%s.outputs.h5' % args.checkpoint[:-5]
-
-    # setting classify to so that we can get labels when
-    # we load data. We do this here because we assume that
-    # network is going to output features, and we want to use the
-    # labels for downstream analysis
-    args.classify = True
-
-    # load the model and override batch size
-    model = process_model(args)
-    model.set_inference(True)
-    if args.batch_size is not None:
-        model.hparams.batch_size = args.batch_size
-
-    # return the model, any arguments, and Lighting Trainer args just in case
-    # we want to use them down the line when we figure out how to use Lightning for
-    # inference
-    ret = [model, args, targs]
-
-    return tuple(ret)
-
-
-def run_inference(argv=None):
-    """
-    Run inference
-
-    Args:
-        argv: a command-line string or argparse.Namespace object to use for running inference
-              If none are given, read from command-line i.e. like running argparse.ArgumentParser.parse_args
-    """
-    model, args, addl_targs = process_args(argv=argv)
-    import h5py
-    import numpy as np
-    model.to(args.device)
-    model.eval()
-    n_outputs = model.classifier[-1].out_features
-    n_samples = len(model.train_dataloader().dataset) +\
-        len(model.val_dataloader().dataset) +\
-        len(model.test_dataloader().dataset)
-
-    f = h5py.File(args.output, 'w')
-
-    emb_dset = f.create_dataset('outputs', shape=(n_samples, n_outputs), dtype=float)
-    label_dset = f.create_dataset('labels', shape=(n_samples,), dtype=int)
-    f.create_dataset('taxon_id', data=model.train_dataloader().dataset.difile.taxa_table['taxon_id'][:])
-
-    for loader_key in args.loaders:
-        mask_dset = f.create_dataset(loader_key, shape=(n_samples,), dtype=bool, fillvalue=False)
-        loader = model.loaders[loader_key]
-        args.logger.info(f'computing outputs for {loader_key}')
-        idx, outputs, labels = get_outputs(model, loader, args.device)
-        order = np.argsort(idx)
-        idx = idx[order]
-        emb_dset[idx] = outputs[order]
-        label_dset[idx] = labels[order]
-        mask_dset[idx] = True
-
-    if args.umap:
-        # compute UMAP arguments for convenience
-        args.logger.info('Running UMAP embedding')
-        from umap import UMAP
-        umap = UMAP(n_components=2)
-        tfm = umap.fit_transform(emb_dset[:])
-        umap_dset = f.create_dataset('viz_emb', shape=(n_samples, 2), dtype=float)
-        umap_dset[:] = tfm
-
-
-def get_outputs(model, loader, device):
-    """
-    Get model outputs for all samples in the given loader
-    """
-    ret = list()
-    indices = list()
-    labels = list()
-    for i, X, y, olen in loader:
-        ret.append(model(X.to(device)).to('cpu'))
-        indices.append(i)
-        labels.append(y)
-    return torch.cat(indices).detach().numpy(), torch.cat(ret).detach().numpy(), torch.cat(labels).detach().numpy()
-
-
-from . import models  # noqa: E402
-
-if __name__ == '__main__':
-    run_inference()
diff --git a/build/lib/exabiome/nn/loader.py b/build/lib/exabiome/nn/loader.py
deleted file mode 100644
index 65ef6a2..0000000
--- a/build/lib/exabiome/nn/loader.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import torch.nn.functional as F
-import torch
-import numpy as np
-from torch.utils.data import DataLoader, Dataset, SubsetRandomSampler
-from sklearn.model_selection import train_test_split
-from sklearn.utils import check_random_state
-from hdmf.common import get_hdf5io
-
-from ..sequence import WindowChunkedDIFile
-
-
-def check_window(window, step):
-    if window is None:
-        return None, None
-    else:
-        if step is None:
-            step = window
-        return window, step
-
-
-def read_dataset(path):
-    hdmfio = get_hdf5io(path, 'r')
-    difile = hdmfio.read()
-    dataset = SeqDataset(difile)
-    return dataset, hdmfio
-
-def process_dataset(args, inference=False):
-    """
-    Process *input* argument and return dataset and HDMFIO object
-
-    Args:
-        args (Namespace):       command-line arguments passed by parser
-        inference (bool):       load data for inference
-    """
-    # First, get the dataset, so we can figure
-    # out how many outputs there are
-    #io = get_hdf5io(args.input, 'r')
-    #difile = io.read()
-    #dataset = SeqDataset(difile)
-    dataset, io = read_dataset(args.input)
-
-
-    if not hasattr(args, 'classify'):
-        raise ValueError('Parser must check for classify/regression/manifold '
-                         'to determine the number of outputs')
-    if args.classify:
-        dataset.set_classify(True)
-        n_outputs = len(dataset.difile.taxa_table)
-    elif args.manifold:
-        dataset.set_classify(True)
-        n_outputs = 32        #TODO make this configurable #breakpoint
-    else:
-        args.regression = True
-        dataset.set_classify(False)
-    args.window, args.step = check_window(args.window, args.step)
-
-    # Process any arguments that impact how we set up the dataset
-    if args.window is not None:
-        dataset.difile = WindowChunkedDIFile(dataset.difile, args.window, args.step)
-    if args.load:
-        dataset.load()
-
-    return dataset, io
-
-
-class DistanceCollater:
-    def __init__(self, dmat):
-        if len(dmat.shape) == 1:
-            from scipy.spatial.distance import squareform
-            dmat = squareform(dmat)
-        self.dmat = torch.as_tensor(dmat, dtype=torch.float).pow(2)
-
-    def __call__(self, samples):
-        """
-        A function to collate samples and return a sub-distance matrix
-        """
-        maxlen = 0
-        l_idx = -1
-        for i, X, y in samples:
-            if maxlen < X.shape[l_idx]:
-                maxlen = X.shape[l_idx]
-        X_ret = list()
-        y_idx = list()
-        idx_ret = list()
-        size_ret = list()
-        for i, X, y in samples:
-            dif = maxlen - X.shape[l_idx]
-            X_ = X
-            if dif > 0:
-                X_ = F.pad(X, (0, dif))
-            X_ret.append(X_)
-            size_ret.append(X.shape[l_idx])
-            idx_ret.append(i)
-            y_idx.append(y)
-        X_ret = torch.stack(X_ret)
-        y_idx = torch.stack(y_idx)
-        # Get distances
-        y_ret = self.dmat[y_idx][:, y_idx]
-        size_ret = torch.tensor(size_ret)
-        idx_ret = torch.tensor(idx_ret)
-        return (idx_ret, X_ret, y_ret, size_ret)
-
-def collate(samples):
-    """
-    A function to collate variable length sequence samples
-    """
-    maxlen = 0
-    l_idx = -1
-    for i, X, y in samples:
-        if maxlen < X.shape[l_idx]:
-            maxlen = X.shape[l_idx]
-    X_ret = list()
-    y_ret = list()
-    idx_ret = list()
-    size_ret = list()
-    for i, X, y in samples:
-        dif = maxlen - X.shape[l_idx]
-        X_ = X
-        if dif > 0:
-            X_ = F.pad(X, (0, dif))
-        X_ret.append(X_)
-        y_ret.append(y)
-        size_ret.append(X.shape[l_idx])
-        idx_ret.append(i)
-    X_ret = torch.stack(X_ret)
-    y_ret = torch.stack(y_ret)
-    size_ret = torch.tensor(size_ret)
-    idx_ret = torch.tensor(idx_ret)
-    return (idx_ret, X_ret, y_ret, size_ret)
-
-
-class SeqDataset(Dataset):
-    """
-    A torch Dataset to handle reading samples read from a DeepIndex file
-    """
-
-    def __init__(self, difile, classify=False):
-        self.difile = difile
-
-        self.one_hot = self.get_one_hot(True)
-
-        self.set_classify(classify)
-        self._target_key = 'class_label' if classify else 'embedding'
-
-
-    def set_classify(self, classify):
-        self._classify = classify
-        if classify:
-            self._label_key = 'id'
-            self._label_dtype = torch.int64
-        else:
-            self._label_key = 'embedding'
-            self._label_dtype = torch.float32
-
-        # THIS HAS BEEN A MAJOR SOURCE OF PAIN. DYNAMIC TABLE NEEDS BETTER SLICING
-        # It should be possible to select individual columns without haveing to modify
-        # the state of the underlying DynamicTable
-        self.difile.set_label_key(self._label_key)
-
-    def __len__(self):
-        return len(self.difile)
-
-    @staticmethod
-    def _to_numpy(data):
-        return data[:]
-
-    @staticmethod
-    def _to_torch(device=None, dtype=None):
-        def func(data):
-            return torch.tensor(data, device=device, dtype=dtype)
-        return func
-
-    def _check_load(self, data, transforms):
-        if not isinstance(data.data, torch.Tensor):
-            if not isinstance(transforms, (tuple, list)):
-                transforms = [transforms]
-            for tfm in transforms:
-                data.transform(tfm)
-
-    def load(self, device=None):
-        tfm = self._to_torch(device)
-        def to_sint(data):
-            return data[:].astype(np.int16)
-        self._check_load(self.difile.seq_table['sequence'].target, [to_sint, tfm])
-        self._check_load(self.difile.taxa_table[self._label_key], tfm)
-
-    def __getitem__(self, i):
-        # get sequence
-        idx, seq, label = self.difile[i]
-        ## one-hot encode sequence
-        seq = F.one_hot(torch.as_tensor(seq, dtype=torch.int64)).float().T
-        label = torch.as_tensor(label, dtype=self._label_dtype)
-        return (idx, seq, label)
-
-    @staticmethod
-    def get_one_hot(torch=True):
-        if torch:
-            return lambda seq: F.one_hot(seq.long()).float()
-        else:
-            return lambda seq: np.eye(np.max(seq)+1)[seq]
-
-
-def get_loader(path, **kwargs):
-    """
-    Return a DataLoader that loads data from the given DeepIndex file
-
-    Args:
-        path (str): the path to the DeepIndex file
-        kwargs    : any additional arguments to pass into torch.DataLoader
-    """
-    hdmfio = get_hdf5io(path, 'r')
-    loader = DataLoader(SeqDataset(hdmfio), collate_fn=collate, **kwargs)
-    return loader
-
-
-def train_test_validate_split(data, stratify=None, random_state=None,
-                              test_size=0.1, train_size=0.8, validation_size=0.1):
-    """
-    Return train test validation split of given data
-
-
-    test_size, train_size, validation_size will all be normalized before subsequent
-    calls to train_test_split
-
-    Args:
-        data (str): the path to the DeepIndex file
-        kwargs    : any additional arguments to pass into torch.DataLoader
-    """
-    indices = np.arange(len(data))
-
-    tot = train_size + test_size + validation_size
-    train_size /= tot
-    test_size /= tot
-    validation_size /= tot
-
-    random_state = check_random_state(random_state)
-
-    train_idx, tmp_idx = train_test_split(indices,
-                                          train_size=train_size,
-                                          stratify=stratify,
-                                          random_state=random_state)
-
-    if stratify is not None:
-        stratify = stratify[tmp_idx]
-
-    tot = test_size + validation_size
-    test_size /= tot
-    validation_size /= tot
-
-    test_idx, val_idx = train_test_split(tmp_idx,
-                                         train_size=test_size,
-                                         stratify=stratify,
-                                         random_state=random_state)
-
-    train_idx = indices[train_idx]
-    test_idx = indices[test_idx]
-    val_idx = indices[val_idx]
-
-    return train_idx, test_idx, val_idx
-
-
-class DatasetSubset(Dataset):
-
-    def __init__(self, dataset, index):
-        self.index = index
-        self.dataset = dataset
-
-    def __getitem__(self, i):
-        return self.dataset[self.index[i]]
-
-    def __len__(self):
-        return len(self.index)
-
-    def __getattr__(self, attr):
-        return getattr(self.dataset, attr)
-
-
-def train_test_loaders(dataset, random_state=None, downsample=None, distances=False,
-                       **kwargs):
-    """
-    Return DataLoaders for training and test datasets.
-
-    Args:
-        path (str):                  the path to the DeepIndex file
-        distances (str):             return distances for the
-        kwargs    : any additional arguments to pass into torch.DataLoader
-    """
-    index = np.arange(len(dataset))
-    stratify = dataset.difile.labels
-    if downsample is not None:
-        index, _, stratify, _ = train_test_split(index, stratify,
-                                                 train_size=downsample,
-                                                 random_state=random_state)
-
-    train_idx, test_idx, validate_idx = train_test_validate_split(index,
-                                                                  stratify=stratify,
-                                                                  random_state=random_state)
-
-    collater = collate
-    if distances:
-        collater = DistanceCollater(dataset.difile.distances.data[:])
-
-    train_dataset = DatasetSubset(dataset, train_idx)
-    test_dataset = DatasetSubset(dataset, test_idx)
-    validate_dataset = DatasetSubset(dataset, validate_idx)
-    return (DataLoader(train_dataset, collate_fn=collater, **kwargs),
-            DataLoader(test_dataset, collate_fn=collater, **kwargs),
-            DataLoader(validate_dataset, collate_fn=collater, **kwargs))
diff --git a/build/lib/exabiome/nn/loss.py b/build/lib/exabiome/nn/loss.py
deleted file mode 100644
index 811b827..0000000
--- a/build/lib/exabiome/nn/loss.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class DistMSELoss(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-
-    def forward(self, output, target):
-        x2 = output.pow(2).sum(axis=1)
-        xy = 2*output.mm(output.T)
-        dist = (((x2 - xy).T + x2))
-        n = output.shape[0]
-        loss = (dist - target).pow(2).sum()/(n*(n-1))
-        return loss
diff --git a/build/lib/exabiome/nn/metric.py b/build/lib/exabiome/nn/metric.py
deleted file mode 100644
index 712abb6..0000000
--- a/build/lib/exabiome/nn/metric.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch
-
-class AbstractMetric(object, metaclass=ABCMeta):
-
-    @abstractmethod
-    def score(self, target, output):
-        pass
-
-
-    def __call__(self, target, output):
-        return self.score(target, output)
-
-
-class NCorrect(AbstractMetric):
-
-    def score(self, target, output):
-        _, pred = torch.max(output.data, 1)
-        n_correct = (pred == target).sum().item()
-        return n_correct
-
-
-class NeighborNCorrect(AbstractMetric):
-
-    def __init__(self, difile):
-        self.knn = difile.get_knn_classifier()
-
-    def score(self, target, output):
-        dat = output.data.numpy()
-        labels = target.numpy()
-        pred = self.knn.predict(dat)
-        n_correct = (pred == labels).sum()
-        return n_correct
-
-class Accuracy(AbstractMetric):
-
-    def __init__(self, ncorrect):
-        self.ncorrect = ncorrect
-
-    def score(self, target, output):
-        n_correct = self.ncorrect(target, output)
-        return n_correct/output.shape[0]
-
diff --git a/build/lib/exabiome/nn/models/__init__.py b/build/lib/exabiome/nn/models/__init__.py
deleted file mode 100644
index 756d2a2..0000000
--- a/build/lib/exabiome/nn/models/__init__.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from .lit import AbstractLit
-
-_models = dict()
-
-def add(k, cls):
-    global _models
-    _models[k] = cls
-
-def model(short_hand):
-    def dec(cls):
-        global _models
-        _models[short_hand] = cls
-        #add(short_hand, cls)
-        cls.short_hand = short_hand
-        return cls
-    return dec
-
-
-#from . import roznet
-import importlib
-import pkgutil
-
-
-def import_submodules(package, recursive=True):
-    """ Import all submodules of a module, recursively, including subpackages
-
-    :param package: package (name or actual module)
-    :type package: str | module
-    :rtype: dict[str, types.ModuleType]
-    """
-    if isinstance(package, str):
-        package = importlib.import_module(package)
-    results = {}
-    for loader, name, is_pkg in pkgutil.walk_packages(package.__path__):
-        full_name = package.__name__ + '.' + name
-        results[full_name] = importlib.import_module(full_name)
-        if recursive and is_pkg:
-            results.update(import_submodules(full_name))
-    return results
-
-import_submodules(__name__)
-
-#import pkgutil
-#
-#__all__ = []
-#for loader, module_name, is_pkg in  pkgutil.walk_packages(__path__):
-#    __all__.append(module_name)
-#    _module = loader.find_module(module_name).load_module(module_name)
-#    globals()[module_name] = _module
diff --git a/build/lib/exabiome/nn/models/fc.py b/build/lib/exabiome/nn/models/fc.py
deleted file mode 100644
index 29d7bda..0000000
--- a/build/lib/exabiome/nn/models/fc.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import math
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-
-from . import model, AbstractLit
-
-@model('fc')
-class FC(AbstractLit):
-    '''
-    A 1D CNN with 5 convolutional layers, followed by 3 fully-connected layers
-
-    Args:
-        input_nc (int):  the input number of channels
-    '''
-
-    def __init__(self, input_nc):
-        super(FC, self).__init__()
-        self.layers = nn.Sequential(
-            nn.Linear(input_nc, input_nc),
-            nn.Linear(input_nc, 2),
-        )
-
-    def forward(self, x, **kwargs):
-        x = torch.flatten(x, 1)
-        x = self.layers(x)
-        return x
-
-
-
-if __name__ == '__main__':
-    from .train import parse_args, run_serial
-    import torch.optim as optim
-
-    args = parse_args("Train CNN with Spatial Pyramidal Pooling")
-                      #[['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])
-
-    input_nc = 4
-    if args['protein']:
-        input_nc = 26
-
-    if args['sanity']:
-        input_nc = 25
-
-    model = FC(input_nc)
-    optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
-
-    args['pad'] = True
-
-    run_serial(model=model, optimizer=optimizer, **args)
diff --git a/build/lib/exabiome/nn/models/lit.py b/build/lib/exabiome/nn/models/lit.py
deleted file mode 100644
index 9df4fda..0000000
--- a/build/lib/exabiome/nn/models/lit.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from .. import train_test_loaders
-from pytorch_lightning import LightningModule
-import torch.optim as optim
-import torch.nn as nn
-import torch
-
-#from .. import SeqDataset
-#from hdmf.common import get_hdf5io
-from ..loader import process_dataset
-
-from ...sequence import WindowChunkedDIFile
-from ..loss import DistMSELoss
-
-class AbstractLit(LightningModule):
-
-    def __init__(self, hparams):
-        super().__init__()
-        self.hparams = hparams
-        if self.hparams.manifold:
-            self._loss = DistMSELoss()
-        elif self.hparams.classify:
-            self._loss = nn.CrossEntropyLoss()
-        else:
-            self._loss =  nn.MSELoss()
-        self.set_inference(False)
-
-    def set_inference(self, inference=True):
-        self._inference = inference
-
-    def set_dataset(self, dataset, load=True, inference=False):
-        kwargs = dict(random_state=self.hparams.seed,
-                      batch_size=self.hparams.batch_size,
-                      distances=self.hparams.manifold,
-                      downsample=self.hparams.downsample)
-
-        if inference:
-            kwargs['distances'] = False
-        tr, te, va = train_test_loaders(dataset, **kwargs)
-        self.loaders = {'train': tr, 'test': te, 'validate': va}
-
-    def _check_loaders(self):
-        """
-        Load dataset if it has not been loaded yet
-        """
-        if not hasattr(self, 'loaders'):
-            dataset, io = process_dataset(self.hparams)
-            if self.hparams.load:
-                dataset.load()
-            kwargs = dict(random_state=self.hparams.seed,
-                          batch_size=self.hparams.batch_size,
-                          distances=self.hparams.manifold,
-                          downsample=self.hparams.downsample)
-            if self._inference:
-                kwargs['distances'] = False
-            tr, te, va = train_test_loaders(dataset, **kwargs)
-            self.loaders = {'train': tr, 'test': te, 'validate': va}
-            self.dataset = dataset
-            self.io = io
-
-    def configure_optimizers(self):
-        optimizer = optim.Adam(self.parameters(), lr=self.hparams.lr)
-        #schedular = optim.lr_scheduler.StepLR(optimizer, step_size=1)
-        #return optimizer, schedular
-        return optimizer
-
-    # TRAIN
-    def train_dataloader(self):
-        self._check_loaders()
-        return self.loaders['train']
-
-    def training_step(self, batch, batch_idx):
-        idx, seqs, target, olen = batch
-        output = self.forward(seqs)
-        loss = self._loss(output, target)
-        return {'loss': loss}
-
-    def training_epoch_end(self, outputs):
-        return {'log': outputs[0]}
-
-    # VALIDATION
-    def val_dataloader(self):
-        self._check_loaders()
-        return self.loaders['validate']
-
-    def validation_step(self, batch, batch_idx):
-        idx, seqs, target, olen = batch
-        output = self(seqs)
-        return {'val_loss': self._loss(output, target)}
-
-    def validation_epoch_end(self, outputs):
-        val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean()
-        return {'log': {'val_loss': val_loss_mean}}
-
-    # TEST
-    def test_dataloader(self):
-        self._check_loaders()
-        return self.loaders['test']
-
-    def test_step(self, batch, batch_idx):
-        idx, seqs, target, olen = batch
-        output = self(seqs)
-        return {'test_loss': self._loss(output, target)}
-
-    def test_epoch_end(self, outputs):
-        test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()
-        return {'test_loss': test_loss_mean}
diff --git a/build/lib/exabiome/nn/models/roznet.py b/build/lib/exabiome/nn/models/roznet.py
deleted file mode 100644
index a07a59d..0000000
--- a/build/lib/exabiome/nn/models/roznet.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import math
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-
-from . import model, AbstractLit
-
-class Self_Attention(nn.Module):
-    """ Self attention Layer """
-    def __init__(self, in_dim, activation):
-        self.in_channel = in_dim
-        self.activation = activation
-        self.query = nn.Conv1d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
-        self.key = nn.Conv1d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
-        self.value = nn.Conv1d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)
-        self.gamma = nn.Parameter(torch.zeros(1))
-
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x):
-        """
-            inputs :
-                x : input feature maps( B X C X W X H)
-            returns :
-                out : self attention value + input feature
-                attention: B X N X N (N is Width*Height)
-        """
-
-@model('roznet')
-class RozNet(AbstractLit):
-    '''
-    A 1D CNN with 5 convolutional layers, followed by 3 fully-connected layers
-
-    Args:
-        input_nc (int):  the input number of channels
-    '''
-
-    def __init__(self, hparams):
-        super().__init__(hparams)
-        input_nc = getattr(hparams, 'input_nc', None)
-        n_outputs = getattr(hparams, 'n_outputs', 2)
-        first_kernel_size = getattr(hparams, 'first_kernel_size', 7)
-        maxpool = getattr(hparams, 'maxpool', True)
-        self.features = nn.Sequential(
-            nn.Conv1d(input_nc, 64, kernel_size=first_kernel_size, stride=1, padding=2),
-            nn.BatchNorm1d(64),
-            nn.ReLU(inplace=True),
-            nn.MaxPool1d(kernel_size=3, stride=1),
-            nn.Conv1d(64, 192, kernel_size=5, padding=2),
-            nn.BatchNorm1d(192),
-            nn.ReLU(inplace=True),
-            nn.MaxPool1d(kernel_size=3, stride=1),
-            nn.Conv1d(192, 384, kernel_size=3, padding=1),
-            nn.BatchNorm1d(384),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(384, 256, kernel_size=3, padding=1),
-            nn.BatchNorm1d(256),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(256, 256, kernel_size=3, padding=1),
-            nn.BatchNorm1d(256),
-            nn.ReLU(inplace=True),
-            nn.MaxPool1d(kernel_size=3, stride=1),
-        )
-        pool_size = 24
-
-        self.pool = nn.AdaptiveMaxPool1d(pool_size)
-
-        self.classifier = nn.Sequential(
-            nn.Dropout(hparams.dropout_rate),
-            nn.Linear(256*pool_size, 1024),
-            nn.BatchNorm1d(1024),
-            nn.ReLU(inplace=True),
-            nn.Dropout(hparams.dropout_rate),
-            nn.Linear(1024, 1024),
-            nn.BatchNorm1d(1024),
-            nn.ReLU(inplace=True),
-            nn.Linear(1024, n_outputs),
-            #nn.BatchNorm1d(n_outputs)
-        )
-
-        self.attention = Self_Attention(24, 'relu')
-
-    def forward(self, x, **kwargs):
-        x = self.features(x)
-        x = self.pool(x)
-        x = self.attention(x)
-        x = torch.flatten(x, 1)
-        x = self.classifier(x)
-        return x
-
-
-
-if __name__ == '__main__':
-    from .train import parse_args, run, check_model, load_dataset
-
-    args = parse_args("Train CNN with Spatial Pyramidal Pooling")
-                      #[['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])
-
-    input_nc = 5
-    if args['protein']:
-        input_nc = 26
-
-    if args['sanity']:
-        input_nc = 5
-
-
-    dataset, io = load_dataset(path=args['input'], **args)
-
-    if args['classify']:
-        n_outputs = len(dataset.difile.taxa_table)
-    else:
-        n_outputs = dataset.difile.n_emb_components
-
-    model = check_model(RozNet(input_nc, n_outputs=n_outputs), **args)
-
-    args['pad'] = True
-
-    run(dataset, model, **args)
-
-    io.close()
diff --git a/build/lib/exabiome/nn/models/smallroznet.py b/build/lib/exabiome/nn/models/smallroznet.py
deleted file mode 100644
index 7104f82..0000000
--- a/build/lib/exabiome/nn/models/smallroznet.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-
-from . import model, AbstractLit
-
-
-@model('smallroznet')
-class SmallRozNet(AbstractLit):
-    '''
-    A 1D CNN with 5 convolutional layers, followed by 3 fully-connected layers
-
-    Args:
-        input_nc (int):  the input number of channels
-    '''
-
-    def __init__(self, hparams):
-        super().__init__(hparams)
-        input_nc = getattr(hparams, 'input_nc', None)
-        n_outputs = getattr(hparams, 'n_outputs', 2)
-        first_kernel_size = getattr(hparams, 'first_kernel_size', 7)
-        maxpool = getattr(hparams, 'maxpool', True)
-        self.features = nn.Sequential(
-            nn.Conv1d(input_nc, 32, kernel_size=first_kernel_size, stride=1, padding=2),
-            nn.BatchNorm1d(32),
-            nn.ReLU(inplace=True),
-            nn.MaxPool1d(kernel_size=3, stride=1),
-            nn.Conv1d(32, 64, kernel_size=5, padding=2),
-            nn.BatchNorm1d(64),
-            nn.ReLU(inplace=True),
-            nn.MaxPool1d(kernel_size=3, stride=1),
-            nn.Conv1d(64, 96, kernel_size=3, padding=1),
-            nn.BatchNorm1d(96),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(96, 96, kernel_size=3, padding=1),
-            nn.BatchNorm1d(96),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(96, 64, kernel_size=3, padding=1),
-            nn.BatchNorm1d(64),
-            nn.ReLU(inplace=True),
-            nn.MaxPool1d(kernel_size=3, stride=1),
-        )
-        if maxpool:
-            self.pool = nn.AdaptiveMaxPool1d(12)
-        else:
-            self.pool = nn.AdaptiveAvgPool1d(12)
-        self.classifier = nn.Sequential(
-            nn.Dropout(),
-            nn.Linear(64*12, 256),
-            nn.BatchNorm1d(256),
-            nn.ReLU(inplace=True),
-            nn.Dropout(),
-            nn.Linear(256, 256),
-            nn.BatchNorm1d(256),
-            nn.ReLU(inplace=True),
-            nn.Linear(256, n_outputs),
-            #nn.BatchNorm1d(n_outputs)
-        )
-
-    def forward(self, x, **kwargs):
-        x = self.features(x)
-        x = self.pool(x)
-        x = torch.flatten(x, 1)
-        x = self.classifier(x)
-        return x
-
-
-
-if __name__ == '__main__':
-    from .train import parse_args, run, check_model, load_dataset
-
-    args = parse_args("Train CNN with Spatial Pyramidal Pooling")
-                      #[['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])
-
-    input_nc = 5
-    if args['protein']:
-        input_nc = 26
-
-    if args['sanity']:
-        input_nc = 5
-
-
-    dataset, io = load_dataset(path=args['input'], **args)
-
-    if args['classify']:
-        n_outputs = len(dataset.difile.taxa_table)
-    else:
-        n_outputs = dataset.difile.n_emb_components
-
-    model = check_model(SmallRozNet(input_nc, n_outputs=n_outputs), **args)
-
-    args['pad'] = True
-
-    run(dataset, model, **args)
-
-    io.close()
diff --git a/build/lib/exabiome/nn/models/spp_cnn.py b/build/lib/exabiome/nn/models/spp_cnn.py
deleted file mode 100644
index a860311..0000000
--- a/build/lib/exabiome/nn/models/spp_cnn.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import math
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-
-
-class SpatialPyramidPool1d(nn.Module):
-    """
-    A 1D spatial-pyramidal pooling layer
-    """
-
-    def __init__(self, num_levels, shift=0, pool_type='max_pool'):
-        super(SpatialPyramidPool1d, self).__init__()
-        self.num_levels = num_levels
-        # the shift in sample length as
-        # a result of doing convolutions
-        self.shift = shift
-        if pool_type == 'max_pool':
-            self.pool = F.max_pool1d
-        elif pool_type == 'avg_pool':
-            self.pool = F.avg_pool1d
-        else:
-            raise ValueError('unrecognized pool_type: %s' % pool_type)
-
-    def _pool_ragged(self, x, orig_len):
-        ret = torch.zeros([x.shape[0], x.shape[1]*(2**self.num_levels - 1)], device=x.device)
-        for x_i in range(x.shape[0]):
-            sample = x[x_i, :, :orig_len[x_i] - self.shift].unsqueeze(0)
-            f = sample.shape[2]
-            s = 0
-            e = sample.shape[1]
-            for i in range(self.num_levels):
-                ret[x_i, s:s + e] = self.pool(sample,
-                                              kernel_size=math.ceil(f),
-                                              stride=math.floor(f)).view(e)
-                s += e
-                e *= 2
-                f /= 2
-        return ret
-
-    def forward(self, x, orig_len=None):
-        if orig_len is None:
-            orig_len = torch.ones(x.shape[0]) * x.shape[2]
-        return self._pool_ragged(x, orig_len)
-
-
-class SPP_CNN(nn.Module):
-    '''
-    A CNN model which adds SPP layer so that we can input variable length tensors
-
-    Args:
-        input_nc (int):  the input number of channels
-    '''
-    def __init__(self, input_nc, output_nc=None, n_levels=2, n_tasks=2, kernel_size=21, emb_nc=0):
-        super(SPP_CNN, self).__init__()
-        n_lin = 0
-        if output_nc is None:
-            output_nc = input_nc
-
-        self.embedding = None
-        if emb_nc > 0:
-            self.embedding = nn.Embedding(input_nc, emb_nc)
-            input_nc = emb_nc
-
-        self.conv1 = nn.Conv1d(input_nc, output_nc, kernel_size,
-                               stride=1,
-                               padding=0,
-                               dilation=1,
-                               bias=False)
-        self.bn1 = nn.BatchNorm1d(output_nc)
-
-        self.spp1 = SpatialPyramidPool1d(n_levels, kernel_size-1)
-        n_lin += self.conv1.out_channels * (2**n_levels - 1)
-
-        self.conv2 = nn.Conv1d(input_nc, output_nc, kernel_size,
-                               stride=1,
-                               padding=0,
-                               dilation=1,
-                               bias=False)
-        self.bn2 = nn.BatchNorm1d(output_nc)
-        self.spp2 = SpatialPyramidPool1d(n_levels, kernel_size-1, pool_type='avg_pool')
-        n_lin += self.conv2.out_channels * (2**n_levels - 1)
-
-        self.fc1 = nn.Linear(n_lin, n_tasks)
-
-    def forward(self, x, orig_len=None):
-
-        if self.embedding is not None:
-            x = self.embedding(x).permute(0, 2, 1)
-
-        x1 = x
-        x1 = self.conv1(x1)
-        x1 = self.bn1(x1)
-        x1 = F.leaky_relu(x1)
-        x1 = self.spp1(x1, orig_len=orig_len)
-
-        x2 = x
-        x2 = self.conv2(x2)
-        x2 = self.bn2(x2)
-        x2 = F.leaky_relu(x2)
-        x2 = self.spp2(x2, orig_len=orig_len)
-
-        xf = self.fc1(torch.cat([x1, x2], dim=1))
-
-        return xf
-
-
-class GroupPool(nn.Module):
-    """
-    This might be unnecessary
-
-    I think pooling by taxa will discard intra-genome variation
-    """
-    def __init__(self, pool_type='avg_pool'):
-        super(GroupPool, self).__init__()
-        if pool_type == 'avg_pool':
-            self.pool = self.__mean
-        elif pool_type == 'max_pool':
-            self.pool = self.__max
-        else:
-            raise ValueError("Unrecognized pooling option: %s" % pool_type)
-
-    @staticmethod
-    def __max(x):
-        return torch.max(x, axis=1)[0]
-
-    @staticmethod
-    def __mean(x):
-        return torch.mean(x, axis=1)
-
-    def _pool_groups(self, x, group):
-        groups = torch.unique(group)
-        ret = torch.zeros([groups.shape[0], x.shape[1]])
-        for g_i, g in enumerate(groups):
-            mask = group == g
-            ret[g_i] = self.pool(x[mask])
-        return ret
-
-    def forward(self, x, group=None):
-        return self._pool_groups(x, group)
-
-if __name__ == '__main__':
-    from .train import parse_args, run_serial
-    import torch.optim as optim
-
-    args = parse_args("Train CNN with Spatial Pyramidal Pooling",
-                      [['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])
-
-    input_nc = 4
-    if args['protein']:
-        input_nc = 26
-
-    model = SPP_CNN(input_nc, 64, kernel_size=7, emb_nc=args['emb_nc'])
-    optimizer = optim.Adam(model.parameters(), lr=args['lr'])
-
-    if args['emb_nc'] > 0:
-        args['ohe'] = False
-
-    run_serial(model=model, optimizer=optimizer, **args)
diff --git a/build/lib/exabiome/nn/optim.py b/build/lib/exabiome/nn/optim.py
deleted file mode 100644
index 14cf1f3..0000000
--- a/build/lib/exabiome/nn/optim.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from .train import parse_args
-import os
-import sys
-import copy
-
-import pytorch_lightning as pl
-from pytorch_lightning import Callback
-
-import optuna
-from optuna.integration import PyTorchLightningPruningCallback
-
-class MetricsCallback(Callback):
-    """PyTorch Lightning metric callback."""
-
-    def __init__(self):
-        super().__init__()
-        self.metrics = []
-
-    def on_validation_end(self, trainer, pl_module):
-        self.metrics.append(trainer.callback_metrics)
-
-
-class Objective:
-    """
-    A helper class for building the objective function
-    """
-
-    def __init__(self, model_cls, hparams, dataset, monitor_metric='val_loss', targs=None):
-        self.model_cls = model_cls
-        self.hparams = hparams
-        self.dataset = dataset
-        self.monitor_metric = monitor_metric
-        if targs is not None:
-            # do not let user overwrite checkpoint_callback or callbacks
-            if 'checkpoint_callback' in targs:
-                raise ValueError("checkpoint_callback cannot be specified if running with optuna")
-            if 'early_stop_callback' in targs:
-                raise ValueError("early_stop_callback cannot be specified if running with optuna")
-            if 'callbacks' in targs:
-                if isinstance(targs['callbacks'], (tuple, list)):
-                    targs['callbacks'] = list(targs['callbacks']) + [metrics_callback]
-                elif isinstance(targs['callbacks'], Callback):
-                    targs['callbacks'] = [targs['callbacks'], metrics_callback]
-                else:
-                    raise ValueError("callbacks must be a Callback, a tuple, or a list")
-        else:
-            targs = dict()
-        self.targs = targs
-
-    def get_hparams(self, trial):
-        ret = dict()
-        ret['dropout_rate'] = trial.suggest_uniform('dropout_rate', 0.0, 1.0)
-        # ret['num_layers'] = trial.suggest_int('num_layers', 2, 10)
-        return ret
-
-    def __call__(self, trial):
-        # Filenames for each trial must be made unique in order to access each checkpoint.
-        ckpt_path = os.path.join(self.hparams.output,
-                                 trial.study.study_name,
-                                 "trial_{}".format(trial.number),
-                                 "{epoch:03d}")
-        checkpoint_callback = pl.callbacks.ModelCheckpoint(ckpt_path, monitor=self.monitor_metric)
-
-        # set hyperparameters under optimization
-        hparams = copy.copy(self.hparams)
-        for k, v in self.get_hparams(trial).items():
-            setattr(hparams, k, v)
-        model = self.model_cls(hparams)
-        model.set_dataset(self.dataset)
-
-        # The default logger in PyTorch Lightning writes to event files to be consumed by
-        # TensorBoard. We don't use any logger here as it requires us to implement several abstract
-        # methods. Instead we setup a simple callback, that saves metrics from each validation step.
-        metrics_callback = MetricsCallback()
-
-        # set up arguments required for integrating with optuna
-        _targs = dict(
-            logger=False,
-            checkpoint_callback=checkpoint_callback,
-            callbacks=[metrics_callback],
-            early_stop_callback=PyTorchLightningPruningCallback(trial, monitor=self.monitor_metric),
-        )
-        _targs.update(self.targs)
-
-        trainer = pl.Trainer(**_targs)
-        trainer.fit(model)
-
-        return metrics_callback.metrics[-1][self.monitor_metric]
-
-
-def get_objective(cmdline):
-    """
-    Return an Optuna objective for the given command-line arguments
-    """
-
-    model_cls, dataset, hparams, addl_targs = parse_args(argv=cmdline)
-
-    return Objective(model_cls, hparams, dataset, targs=addl_targs)
-
-
-def parse_args(*addl_args, argv=None, return_io=False):
-    """
-    Parse arguments for training executable
-    """
-    import argparse
-    from ..utils import parse_seed
-
-    if argv is None:
-        argv = sys.argv[1:]
-    if isinstance(argv, str):
-        argv = argv.strip().split()
-
-    desc = "run network hyperparameter optimization"
-    parser = argparse.ArgumentParser(description=desc)
-
-    parser.add_argument('storage', help='Optuna storage database')
-    parser.add_argument('study_name', help='the study to run trials for')
-    parser.add_argument('output', type=str, help='file to checkpoints to')
-    parser.add_argument('-t', '--n_trials', type=int, default=1, help='the number of trials to run')
-    parser.add_argument('-s', '--seed', type=parse_seed, default='', help='seed to use for train-test split in this study')
-
-
-    group = parser.add_argument_group(title='Training runtime arguments',
-                                      description='Arguments that configure how training is run')
-    group.add_argument('-g', '--gpus', nargs='?', const=True, default=False, help='use GPU')
-    group.add_argument('-d', '--debug', action='store_true', default=False, help='run in debug mode i.e. only run two batches')
-    parser.add_argument('-L', '--load', action='store_true', default=False, help='load data into memory before running training loop')
-
-    if len(argv) == 0:
-        parser.print_help()
-        sys.exit(1)
-
-    args = parser.parse_args(argv)
-
-    study = optuna.create_study(storage=args.storage,
-                                study_name=args.study_name,
-                                load_if_exists=True)
-
-    # set seed of study if it has not yet been set
-    # otherwise, ignore the provided seed
-    if hasattr(study, 'seed'):
-        args.seed = study.seed
-    else:
-        study.set_user_attr('seed', args.seed)
-
-    n_trials = args.n_trials
-
-    del args.storage
-    del args.study_name
-    del args.n_trials
-
-    return study, n_trials, args
-
-
-def optuna_run(train_cmdline, objective_cls=Objective):
-    from .train import parse_args as parse_train_args, process_args as process_train_args
-
-    study, n_trials, rt_args = parse_args()
-
-    train_args = parse_train_args(argv=train_cmdline)
-
-    for k,v in vars(rt_args).items():
-        setattr(train_args, k, v)
-
-    model_cls, dataset, hparams, addl_targs = process_train_args(train_args)
-
-    objective = Objective(model_cls, hparams, dataset, targs=addl_targs)
-
-    study.optimize(objective, n_trials=n_trials)
diff --git a/build/lib/exabiome/nn/summarize.py b/build/lib/exabiome/nn/summarize.py
deleted file mode 100644
index 5b63256..0000000
--- a/build/lib/exabiome/nn/summarize.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import matplotlib.pyplot as plt
-import h5py
-import seaborn as sns
-import numpy as np
-from matplotlib.patches import Circle
-import pandas as pd
-
-def plot_results(path, tvt=True, pred=True, fig_height=7, rf_kwargs=None):
-    n_plots = 1
-    if pred:
-        n_plots += 1
-    if tvt:
-        n_plots += 1
-    plot_count = 1
-
-    if rf_kwargs is None:
-        rf_kwargs = dict()
-
-    fig_height = 7
-    plt.figure(figsize=(n_plots*fig_height, fig_height))
-
-    # read data
-    dat = None
-    with h5py.File(path, 'r') as f:
-        if 'viz_emb' in f:
-            dat = f['viz_emb'][:]
-        labels = f['labels'][:]
-        train_mask = f['train'][:]
-        test_mask = f['test'][:]
-        outputs = f['outputs'][:]
-        validate_mask = f['validate'][:]
-        taxon_id = f['taxon_id'][:]
-    if dat is None:
-        from umap import UMAP
-        umap = UMAP(n_components=2)
-        dat = umap.fit_transform(outputs[:])
-
-    uniq_labels = np.unique(labels)
-    n_classes = len(uniq_labels)
-    class_pal = sns.color_palette('tab10', n_classes)
-    colors = np.array([class_pal[i] for i in labels])
-
-    # plot embeddings
-    plt.subplot(1, n_plots, plot_count)
-    class_handles = list()
-    for cl, col in zip(uniq_labels, class_pal):
-        mask = labels == cl
-        plt.scatter(dat[mask,0], dat[mask,1], s=0.5, c=[col], label=cl)
-        class_handles.append(Circle(0, 0, color=col))
-    plt.legend(class_handles, taxon_id)
-    plt.title('/'.join(path.split('/')[-2:]))
-    plot_count += 1
-
-    # plot train/validation/testing data
-    if tvt:
-        pal = ['gray', 'red', 'yellow']
-        plt.subplot(1, n_plots, plot_count)
-        dsubs = ['train', 'validation', 'test'] # data subsets
-        dsub_handles = list()
-        for (mask, dsub, col) in zip([train_mask, validate_mask, test_mask], dsubs, pal):
-            plt.scatter(dat[mask, 0], dat[mask, 1], s=0.1, c=[col], label=dsub)
-            dsub_handles.append(Circle(0, 0, color=col))
-        plt.legend(dsub_handles, dsubs)
-        plot_count += 1
-
-    # run some predictions and plot report
-    if pred:
-        from sklearn.ensemble import RandomForestClassifier
-        from sklearn.metrics import classification_report, accuracy_score
-
-        # build random forest classifier
-        rf_kwargs.setdefault('n_estimators', 30)
-        rfc = RandomForestClassifier(**rf_kwargs)
-        X_train = outputs[train_mask]
-        y_train = labels[train_mask]
-        rfc.fit(X_train, y_train)
-        X_test = outputs[test_mask]
-        y_test = labels[test_mask]
-        y_pred = rfc.predict(X_test)
-        report = classification_report(y_test, y_pred, output_dict=True)
-
-        # plot classification report
-        df = pd.DataFrame(report)
-        subdf = df[[str(l) for l in uniq_labels]].iloc[:-1]
-        ax = plt.subplot(1, n_plots, plot_count)
-        subdf.plot.barh(ax=ax, color=class_pal, legend=False)
-        #plt.legend(taxon_id, loc='lower left', ncol=len(uniq_labels))
-        plt.title('accuracy: %.4f' %  report['accuracy'])
-    plt.tight_layout()
-
-def main():
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument('input', type=str, help='the HDF5 file with network outputs')
-    parser.add_argument('output', type=str, nargs='?', help='the file to save the summary figure to')
-
-    args = parser.parse_args()
-    if args.output is None:
-        s = args.input
-        if s.endswith('.h5'):
-            s = s[:-3]
-        args.output = s + '.png'
-    plt.figure(figsize=(21, 7))
-    plot_results(args.input)
-    plt.savefig(args.output, dpi=100)
-
-
-if __name__ == '__main__':
-    main()
-
diff --git a/build/lib/exabiome/nn/train.py b/build/lib/exabiome/nn/train.py
deleted file mode 100644
index ca18e9a..0000000
--- a/build/lib/exabiome/nn/train.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import sys
-import os
-import os.path
-import pickle
-from datetime import datetime
-import numpy as np
-from ..utils import parse_seed, check_argv, parse_logger
-from .utils import process_gpus, process_model, process_output
-from hdmf.utils import docval
-
-import argparse
-import logging
-
-
-def parse_train_size(string):
-    ret = float(string)
-    if ret > 1.0:
-        ret = int(ret)
-    return ret
-
-
-def parse_args(*addl_args, argv=None):
-    """
-    Parse arguments for training executable
-    """
-    argv = check_argv(argv)
-
-    epi = """
-    output can be used as a checkpoint
-    """
-    desc = "Run network training"
-    parser = argparse.ArgumentParser(description=desc, epilog=epi)
-    parser.add_argument('model', type=str, help='the model to run', choices=list(models._models.keys()))
-    parser.add_argument('input', type=str, help='the HDF5 DeepIndex file')
-    parser.add_argument('output', type=str, help='file to save model', default=None)
-    type_group = parser.add_mutually_exclusive_group()
-    type_group.add_argument('-C', '--classify', action='store_true', help='run a classification problem', default=False)
-    type_group.add_argument('-M', '--manifold', action='store_true', help='run a manifold learning problem', default=False)
-    type_group.add_argument('-R', '--regression', action='store_true', help='run a regression problem', default=False)
-    parser.add_argument('-c', '--checkpoint', type=str, help='resume training from file', default=None)
-    parser.add_argument('-T', '--test', action='store_true', help='run test data through model', default=False)
-    parser.add_argument('-A', '--accuracy', action='store_true', help='compute accuracy', default=False)
-    parser.add_argument('-b', '--batch_size', type=int, help='batch size', default=64)
-    parser.add_argument('-e', '--epochs', type=int, help='number of epochs to use', default=1)
-    parser.add_argument('-D', '--dropout_rate', type=float, help='the dropout rate to use', default=0.5)
-    parser.add_argument('-p', '--protein', action='store_true', default=False, help='input contains protein sequences')
-    parser.add_argument('-g', '--gpus', nargs='?', const=True, default=False, help='use GPU')
-    parser.add_argument('-s', '--seed', type=parse_seed, default='', help='seed to use for train-test split')
-    parser.add_argument('-t', '--train_size', type=parse_train_size, default=0.8, help='size of train split')
-    parser.add_argument('-d', '--debug', action='store_true', default=False, help='run in debug mode i.e. only run two batches')
-    parser.add_argument('--downsample', type=float, default=None, help='downsample input before training')
-    parser.add_argument('-E', '--experiment', type=str, default='default', help='the experiment name')
-    parser.add_argument('-l', '--logger', type=parse_logger, default='', help='path to logger [stdout]')
-    parser.add_argument('--prof', type=str, default=None, metavar='PATH', help='profile training loop dump results to PATH')
-    parser.add_argument('--sanity', action='store_true', default=False, help='copy response data into input data')
-    parser.add_argument('-L', '--load', action='store_true', default=False, help='load data into memory before running training loop')
-    parser.add_argument('--lr', type=float, default=0.01, help='the learning rate for Adam')
-    parser.add_argument('-W', '--window', type=int, default=None, help='the window size to use to chunk sequences')
-    parser.add_argument('-S', '--step', type=int, default=None, help='the step between windows. default is to use window size (i.e. non-overlapping chunks)')
-
-    for a in addl_args:
-        parser.add_argument(*a[0], **a[1])
-
-    if len(argv) == 0:
-        parser.print_help()
-        sys.exit(1)
-
-    args = parser.parse_args(argv)
-
-    return args
-
-
-def process_args(args=None, return_io=False):
-    """
-    Process arguments for running training
-    """
-    if not isinstance(args, argparse.Namespace):
-        args = parse_args(args)
-
-    logger = args.logger
-    # set up logger
-    if logger is None:
-        logger = logging.getLogger()
-        logger.setLevel(logging.INFO)
-    if args.debug:
-        logger.setLevel(logging.DEBUG)
-    args.logger = logger
-
-    # determing number of input channels:
-    # 5 for DNA, 26 for protein
-    # 5 for sanity check (this probably doesn't work anymore)
-    input_nc = 5
-    if args.protein:
-        input_nc = 26
-    if args.sanity:
-        input_nc = 5
-    args.input_nc = input_nc
-
-    model = process_model(args)
-
-    targs = dict(
-        max_epochs=args.epochs,
-    )
-
-    targs['gpus'] = process_gpus(args.gpus)
-    if targs['gpus'] != 1:
-        targs['distributed_backend'] = 'ddp'
-    del args.gpus
-
-    if args.debug:
-        targs['fast_dev_run'] = True
-
-    ret = [model, args, targs]
-    if return_io:
-        ret.append(io)
-
-    return tuple(ret)
-
-
-from pytorch_lightning import Trainer, seed_everything
-from pytorch_lightning.loggers import TensorBoardLogger
-from pytorch_lightning.callbacks import ModelCheckpoint
-
-def run_lightening():
-    model, args, addl_targs = process_args(parse_args())
-
-    outbase, output = process_output(args)
-
-    # save arguments
-    with open(output('args.pkl'), 'wb') as f:
-        pickle.dump(args, f)
-
-    seed_everything(args.seed)
-
-    # get dataset so we can set model parameters that are
-    # dependent on the dataset, such as final number of outputs
-
-    targs = dict(
-        checkpoint_callback=ModelCheckpoint(filepath=output('seed=%d-{epoch:02d}-{val_loss:.2f}' % args.seed), save_weights_only=False),
-        logger = TensorBoardLogger(save_dir=os.path.join(args.output, 'tb_logs'), name=args.experiment),
-        row_log_interval=10,
-        log_save_interval=100
-    )
-    targs.update(addl_targs)
-
-    trainer = Trainer(**targs)
-    #if args.test:
-    #    if args.checkpoint is None:
-    #        print('If running with --test, must provide argument to --checkpoint', file=sys.stderr)
-    #        sys.exit(1)
-
-    #    if args.debug:
-    #        print_dataloader(model.test_dataloader())
-    #        print_dataloader(model.train_dataloader())
-    #        print_dataloader(model.val_dataloader())
-    #    if args.accuracy:
-    #        from .metric import NCorrect, NeighborNCorrect
-    #        if model.hparams.classify:
-    #            metric = NCorrect()
-    #        else:
-    #            metric = NeighborNCorrect(dataset.difile)
-    #        model.test_dataloader().dataset.load()
-    #        model.test_dataloader().dataset.set_classify(True)
-    #        total_correct = overall_metric(model, model.test_dataloader(), metric)
-    #        print(total_correct/len(model.test_dataloader().sampler))
-    #    else:
-    #        model.test_dataloader().dataset.load()
-    #        if model.hparams.classify:
-    #            model.test_dataloader().dataset.set_classify(True)
-    #        trainer.test(model)
-    if args.debug:
-        print_dataloader(model.test_dataloader())
-        print_dataloader(model.train_dataloader())
-        print_dataloader(model.val_dataloader())
-    trainer.fit(model)
-
-def print_dataloader(dl):
-    print(dl.dataset.index[0], dl.dataset.index[-1])
-
-def overall_metric(model, loader, metric):
-    val = 0.0
-    for idx, seqs, target, olen in loader:
-        output = model(seqs)
-        val += metric(target, output)
-    return val
-
-
-from . import models
-
-if __name__ == '__main__':
-    run_lightening()
diff --git a/build/lib/exabiome/nn/utils.py b/build/lib/exabiome/nn/utils.py
deleted file mode 100644
index ccd8bdb..0000000
--- a/build/lib/exabiome/nn/utils.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import sys
-import os
-
-from .loader import read_dataset
-from . import models
-
-from ..utils import check_directory
-
-
-def process_gpus(gpus):
-    ret = gpus
-    if isinstance(ret, str):
-        ret = [int(g) for g in ret.split(',')]
-        if len(ret) == 1:
-            # user specified the number of GPUs to use,
-            # else assume they just specified which specific GPUS to use
-            ret = ret[0]
-    elif isinstance(ret, bool):
-        if ret:
-            # use all GPUs
-            ret = -1
-        else:
-            # don't use any GPUs
-            ret = None
-    return ret
-
-
-def process_model(args, inference=False):
-    """
-    Process a model argument
-
-    Args:
-        args (Namespace):       command-line arguments passed by parser
-        inference (bool):       load data for inference
-    """
-    # First, get the dataset, so we can figure
-    # out how many outputs there are
-    dataset, io = read_dataset(args.input)
-
-    # Next, build our model object so we can get
-    # the parameters used if we were given a checkpoint
-    model = models._models[args.model]
-    del args.model
-
-    if args.checkpoint is not None:
-        model = model.load_from_checkpoint(args.checkpoint)
-    else:
-        if not hasattr(args, 'classify'):
-            raise ValueError('Parser must check for classify/regression/manifold '
-                             'to determine the number of outputs')
-        if args.classify:
-            n_outputs = len(dataset.difile.taxa_table)
-        elif args.manifold:
-            n_outputs = 32        #TODO make this configurable #breakpoint
-        else:
-            args.regression = True
-            n_outputs = dataset.difile.n_emb_components
-        args.n_outputs = n_outputs
-
-        model = model(args)
-
-    io.close()
-
-    return model
-
-
-def process_output(args):
-    """
-    Process dataset arguments
-    """
-    outbase = args.output
-    if args.experiment:
-        outbase = os.path.join(outbase, 'training_results', args.experiment)
-    check_directory(outbase)
-
-    def output(fname):
-        return os.path.join(outbase, fname)
-
-    return outbase, output
diff --git a/build/lib/exabiome/response/__init__.py b/build/lib/exabiome/response/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/exabiome/response/embedding.py b/build/lib/exabiome/response/embedding.py
deleted file mode 100644
index 0202b06..0000000
--- a/build/lib/exabiome/response/embedding.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import h5py
-import pandas as pd
-from scipy.spatial.distance import squareform as _squareform
-
-from sklearn.manifold import MDS
-from sklearn.preprocessing import normalize
-
-
-
-def read_distances(dist_h5, squareform=False):
-    """
-    Read distances produced by tree2dmat
-    Returns:
-        tuple of distances and leaf names
-    """
-    with h5py.File(dist_h5, 'r') as f:
-        dist = f['distances'][:]
-        names = f['leaf_names'][:].astype('U')
-    if squareform:
-        dist = _squareform(dist)
-    return dist, names
-
-
-def save_embedding(out_h5, embedding, names, **kwargs):
-    """
-    Save an embedding file
-
-    Args:
-        out_h5:            the output HDF5 file
-        embedding:         the embedding data
-        names:             the name or accession for each sample
-        kwargs:            any metadata to add to the file
-    """
-    with h5py.File(out_h5, 'w') as f:
-        dset = f.create_dataset('embedding', data=embedding)
-        dset = f.create_dataset('leaf_names', shape=names.shape, dtype=h5py.special_dtype(vlen=str))
-        dset[:] = names
-        for k, v in kwargs.items():
-            f.attrs[k] = v
-
-
-def read_embedding(emb_h5, df=False):
-    """
-    Read embeddings
-
-    Args:
-        emb_h5:         path to HDF5 file
-        df:             return as a DataFrame. default is False
-
-    Returns:
-        tuple of embedding and leaf names
-    """
-    with h5py.File(emb_h5, 'r') as f:
-        emb = f['embedding'][:]
-        taxa = f['leaf_names'][:].astype('U').tolist()
-
-    if df:
-        colnames = ["dim%02g" % i for i in range(emb.shape[1])]
-        df = pd.DataFrame(data=emb, columns=colnames, index=taxa)
-        return df
-    else:
-        return emb, taxa
-
-
-def mds(dist, n_components=2, metric=False, logger=None):
-    """
-    Run MDS on distances produced by tree2dmat
-
-    Args:
-        dist (str):             A distance matrix, square or condensed form
-        n_components (int):     number of components to produce
-        metric (bool):          Whether or not to run metric MDS. default is to run non-metric
-        logger (Logger):        Logger to use. default is no logging
-
-    Return:
-        emb (np.array):         the MDS embedding
-    """
-    if len(dist.shape) == 1:
-        if logger is not None:
-            logger.info('computing squareform')
-        dist = _squareform(dist)
-
-    if logger is not None:
-        logger.info('computing %d components with %s MDS' % (n_components, "metric" if metric else "non-metric"))
-
-    mds = MDS(dissimilarity='precomputed', metric=metric, n_components=n_components)
-    emb = mds.fit_transform(dist)
-    return emb
diff --git a/build/lib/exabiome/response/fulldmat.py b/build/lib/exabiome/response/fulldmat.py
deleted file mode 100644
index d6ef8f3..0000000
--- a/build/lib/exabiome/response/fulldmat.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-from exabiome.response.tree import read_tree, get_phylo_stats
-from exabiome.response.embedding import read_distances
-
-from scipy.spatial.distance import pdist, squareform
-from scipy.stats import spearmanr, pearsonr
-
-from umap import UMAP
-from datetime import datetime
-
-from scipy.sparse import lil_matrix, csr_matrix
-
-import numpy as np
-
-
-def _tally_dist(left, right, distances, dmat):
-    for i in left:
-        for j in right:
-            dmat[i, j] = distances[i] + distances[j]
-            dmat[j, i] = dmat[i, j]
-    ret = left + right
-    return ret
-
-
-def accum_dist(node, distances, dmat, names):
-    if len(node.children) == 0:
-        distances[node.id] = node.length
-        ret = [node.id]
-        names[node.id] = node.name
-    else:
-        names[node.id] = node.name or "internode-%d" % node.id
-        left = accum_dist(node.children[0], distances, dmat, names)
-        right = accum_dist(node.children[1], distances, dmat, names)
-        ret = _tally_dist(left, right, distances, dmat)
-        dmat[node.id, ret] = distances[ret]
-        dmat[ret, node.id] = distances[ret]
-    return ret
-
-def _add_dist(node, children, distances, dmat):
-    dmat[node.id, children] = distances[children] + node.length
-    dmat[children, node.id] = dmat[node.id, children]
-
-def get_full_dmat(_tree):
-    n_nodes = _tree.count(True) * 2 -2
-    dmat = np.zeros((n_nodes, n_nodes))
-    distances = np.zeros(n_nodes)
-    names = [""] * n_nodes
-    left_node = _tree.children[0]
-    right_node = _tree.children[1]
-    mid_blen = left_node.length
-
-    left = accum_dist(left_node, distances, dmat, names)
-    right = accum_dist(right_node, distances, dmat, names)
-
-    distances[left] = distances[left] + right_node.length
-    _tally_dist(left, right, distances, dmat)
-    distances[left] = distances[left] - right_node.length
-
-    dmat[left_node.id, right_node.id] = left_node.length
-    dmat[right_node.id, left_node.id] = left_node.length
-    distances[left_node.id] = mid_blen
-    distances[right_node.id] = mid_blen
-
-    _add_dist(right_node, left, distances, dmat)
-    _add_dist(left_node, right, distances, dmat)
-
-    return dmat, np.array(names)
-
-
-if __name__ == '__main__':
-
-
-    from six import StringIO
-    from skbio import TreeNode
-    tree = TreeNode.read(StringIO("((a:1,b:2)c:6,(d:4,e:5)f:6)root;")).unrooted_copy()
-    tree.assign_ids()
-
-
-    result = np.array([[ 0.,  3., 11., 12.,  1.,  7.],
-                       [ 3.,  0., 12., 13.,  2.,  8.],
-                       [11., 12.,  0.,  9., 10.,  4.],
-                       [12., 13.,  9.,  0., 11.,  5.],
-                       [ 1.,  2., 10., 11.,  0.,  6.],
-                       [ 7.,  8.,  4.,  5.,  6.,  0.]])
-
-    dmat, names = get_dmat(tree)
-    np.testing.assert_equal(dmat, result)
-    print(names)
-    print(dmat)
diff --git a/build/lib/exabiome/response/gtdb.py b/build/lib/exabiome/response/gtdb.py
deleted file mode 100644
index f327576..0000000
--- a/build/lib/exabiome/response/gtdb.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import pandas as pd
-
-_src_tags = {'RS_', 'GB_'}
-def check_accession(acc, trim_src_tag=False):
-    if trim_src_tag:
-        if acc[:3] in _src_tags:
-            acc = acc[3:]
-    return acc
-
-
-def check_accessions(accessions, trim_src_tag=False):
-    if isinstance(accessions, str):
-        raise ValueError("'accessions' must be array-like")
-    return [check_accession(acc, trim_src_tag=trim_src_tag) for acc in accessions]
-
-
-def read_taxonomy(path, leaf_names=None, trim_src_tag=False):
-    taxlevels = ['domain', 'phylum', 'class', 'order', 'family', 'genus', 'species']
-    def func(row):
-        dat = dict(zip(taxlevels, row['gtdb_taxonomy'].split(';')))
-        dat['species'] = dat['species'].split(' ')[1]
-        dat['accession'] = check_accession(row['accession'], trim_src_tag=trim_src_tag)
-        return pd.Series(data=dat)
-    taxdf = pd.read_csv(path, header=0, sep='\t')[['accession', 'gtdb_taxonomy']]\
-                        .apply(func, axis=1)\
-                        .set_index('accession')
-    if leaf_names is not None:
-        taxdf = taxdf.filter(items=leaf_names, axis=0)
-    return taxdf
diff --git a/build/lib/exabiome/response/hierarchy.py b/build/lib/exabiome/response/hierarchy.py
deleted file mode 100644
index 790a19d..0000000
--- a/build/lib/exabiome/response/hierarchy.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import numpy as np
-import pandas as pd
-
-
-_ranks = {'phylum', 'class', 'order', 'family', 'genus'}
-def check_rank(rank)
-    if rank not in _ranks:
-        raise ValueError(f'please choose rank from {_ranks}')
-    return rank
-
-def collapse(dist, mdf, rank, linkage='average'):
-    """
-    Args:
-        dist:         the original distance matrix
-        mdf:          the metadata DataFrame
-        rank:         the rank to collapse on
-        linkage:      the linkage method to use when collapsing
-                      Options are 'average', 'complete', 'single'
-                      Default is 'average'
-
-    Return: ndarray (n_target_taxon, n_target_taxon)
-        square distance matrix for the specificed taxonomic rank
-    """
-    rank = check_rank(rank)
-    if linkage == 'average':
-        _link = np.mean
-    elif linkage == 'complete':
-        _link = np.max
-    elif linkage == 'single':
-        _link = np.min
-    else:
-        raise ValueError("Unrecognized linkage - '%s'. Please choose "
-                         "from 'average', 'complete', or 'single'")
-
-    r_types = np.unique(mdf[rank])
-    r_dist = np.zeros((r_types.shape[0], r_types.shape[0]))
-    for i, type_i in enumerate(r_types):
-        mask_i = mdf[rank] == type_i
-        dist_i = dist[mask_i]
-        for j, type_j in enumerate(r_types[i+1:], i+1):
-            mask_j = mdf[rank] == type_j
-            r_dist[i,j] = _link(dist_i[:,mask_j])
-            r_dist[j,i] = r_dist[i,j]
-    return r_dist, r_types
-
-
-def expand_labels(parent_rank, labels, target_rank, mdf):
-    """
-    Args:
-        parent_rank:  the upper taxonomic rank
-        labels:       the labels for each unique parent taxon
-        target_rank:  the lower taxonomic rank
-        mdf:          the metadata DataFrame
-
-    Return: ndarray
-        array with the target taxonomic rank labels
-    """
-    parent_rank = check_rank(parent_rank)
-    target_rank = check_rank(target_rank)
-    df = pd.DataFrame()
-    df[parent_rank] = np.unique(mdf[parent_rank])
-    if len(df[parent_rank]) != labels.shape[0]:
-        raise ValueError("please provide one label for each parent")
-    for d in range(labels.shape[1]):
-        df['col%d' % d] = labels[:,d]
-
-    res = mdf.merge(df, on=parent_rank)
-    # grouping by target rank assumes there are
-    # no parent ranks that contain the same target rank
-    # For example, the same genus name does not exist in different
-    # families
-    if target_rank == 'species':
-        return res[df.columns[1:]].values
-    else:
-        return res.groupby(target_rank)[df.columns[1:]].mean().values
-
-
-def supervised_emb(dist, mdf, parent_rank, parent_labels, target_rank, model, linkage='average'):
-    """
-    Args:
-        dist:               the original distance matrix
-        mdf:                the metadata DataFrame
-        parent_rank:        the upper taxonomic rank
-        parent_labels:      the embedding for each parent
-        target_rank:        the lower taxonomic rank
-        model:              the UMAP object to run supervised embedding with
-        mdf:                the metadata DataFrame
-        linkage:            the linkage method to use when collapsing
-                            distance matrix
-                            Options are 'average', 'complete', 'single'
-                            Default is 'average'
-
-    Returns: (t_emb, t_dist, targets)
-        t_emb:              the embedding of the target taxonomic rank
-        t_dist:             the distance matrix for the target taxonomic rank
-        targets:            the names for taxons in the target taxonomic rank
-    """
-    t_dist, targets = collapse(dist, mdf, target_rank)
-    t_labels = expand_labels(parent_rank, parent_labels, target_rank, mdf)
-    model.set_params(target_metric='l2')
-    t_emb = model.fit_transform(t_dist, y=t_labels)
-    return t_emb, t_dist, targets
diff --git a/build/lib/exabiome/response/isomap.py b/build/lib/exabiome/response/isomap.py
deleted file mode 100644
index 1c73b3c..0000000
--- a/build/lib/exabiome/response/isomap.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import h5py
-from scipy.spatial.distance import squareform as _squareform
-
-from sklearn.manifold import Isomap
-from sklearn.neighbors import NearestNeighbors
-
-from .embedding import read_distances, save_embedding
-
-
-def isomap(dist, n_components=2, logger=None):
-    """
-    Run Isomap on distances produced by tree2dmat
-
-    Args:
-        dist (str):             A distance matrix, square or condensed form
-        n_components (int):     number of components to produce
-        logger (Logger):        Logger to use. default is no logging
-
-    Return:
-        emb (np.array):         the MDS embedding
-    """
-    if len(dist.shape) == 1:
-        if logger is not None:
-            logger.info('computing squareform')
-        dist = _squareform(dist)
-
-    if logger is not None:
-        logger.info(f'computing {n_components} components with IsoMap')
-
-    iso = Isomap(metric='precomputed', n_components=n_components)
-    emb = iso.fit_transform(dist)
-    return emb
-
-
-if __name__ == '__main__':
-
-    import sys
-    import argparse
-    import logging
-    import numpy as np
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dist_h5', type=str, help='the HDF5 file with distance data (output by tree2dmat)')
-    parser.add_argument('out_h5', type=str, help='the HDF5 file to save the embedding to')
-    parser.add_argument('-p', '--n_components', type=int, default=2, help='the number of components to use')
-    parser.add_argument('-s', '--sqrt', action='store_true', default=False, help='square-root the distances before MDS')
-    args = parser.parse_args()
-
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(message)s')
-    logger = logging.getLogger()
-
-    logger.info('reading %s' % args.dist_h5)
-    dist, names = read_distances(args.dist_h5)
-
-    if args.sqrt:
-        logger.info('taking square root of distances')
-        dist = np.sqrt(dist)
-
-    emb = isomap(dist, n_components=args.n_components, logger=logger)
-
-    logger.info('saving embedding to %s' % args.out_h5)
-    save_embedding(args.out_h5, emb, names)
diff --git a/build/lib/exabiome/response/mds.py b/build/lib/exabiome/response/mds.py
deleted file mode 100644
index 35150c0..0000000
--- a/build/lib/exabiome/response/mds.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import h5py
-from scipy.spatial.distance import squareform as _squareform
-
-from sklearn.manifold import MDS
-from sklearn.preprocessing import normalize
-
-from .embedding import read_distances, save_embedding
-
-
-def mds(dist, n_components=2, metric=False, logger=None):
-    """
-    Run MDS on distances produced by tree2dmat
-
-    Args:
-        dist (str):             A distance matrix, square or condensed form
-        n_components (int):     number of components to produce
-        metric (bool):          Whether or not to run metric MDS. default is to run non-metric
-        logger (Logger):        Logger to use. default is no logging
-
-    Return:
-        emb (np.array):         the MDS embedding
-    """
-    if len(dist.shape) == 1:
-        if logger is not None:
-            logger.info('computing squareform')
-        dist = _squareform(dist)
-
-    if logger is not None:
-        logger.info('computing %d components with %s MDS' % (n_components, "metric" if metric else "non-metric"))
-
-    mds = MDS(dissimilarity='precomputed', metric=metric, n_components=n_components)
-    emb = mds.fit_transform(dist)
-    return emb
-
-
-if __name__ == '__main__':
-
-    import sys
-    import argparse
-    import logging
-    import numpy as np
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dist_h5', type=str, help='the HDF5 file with distance data (output by tree2dmat)')
-    parser.add_argument('out_h5', type=str, help='the HDF5 file to save the embedding to')
-    parser.add_argument('-p', '--n_components', type=int, default=2, help='the number of components to use')
-    parser.add_argument('-m', '--metric', action='store_true', default=False, help='perform metric MDS')
-    parser.add_argument('-s', '--sqrt', action='store_true', default=False, help='square-root the distances before MDS')
-    parser.add_argument('-n', '--normalize', action='store_true', default=False,
-                        help='normalize samples after computing distances')
-    args = parser.parse_args()
-
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(message)s')
-    logger = logging.getLogger()
-
-    logger.info('reading %s' % args.dist_h5)
-    dist, names = read_distances(args.dist_h5)
-
-    if args.sqrt:
-        logger.info('taking square root of distances')
-        dist = np.sqrt(dist)
-
-    emb = mds(dist, n_components=args.n_components, metric=args.metric, logger=logger)
-
-    if args.normalize:
-        logger.info("normalizing samples")
-        emb = normalize(emb, norm='l2', axis=1)
-
-    logger.info('saving embedding to %s' % args.out_h5)
-    save_embedding(args.out_h5, emb, names)
diff --git a/build/lib/exabiome/response/replace_embeddings.py b/build/lib/exabiome/response/replace_embeddings.py
deleted file mode 100644
index cf902bd..0000000
--- a/build/lib/exabiome/response/replace_embeddings.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import exabiome.sequence
-from hdmf.common import get_hdf5io
-from exabiome.response.embedding import read_embedding
-
-import pandas as pd
-
-if __name__ == '__main__':
-
-    import argparse
-
-
-    parser = argparse.ArgumentParser(description='substitute new embeddings in a DeepIndex input file')
-    parser.add_argument('new_embeddings', type=str, help='the embeddings to add to the DeepIndex input file')
-    parser.add_argument('deep_index_input', type=str, help='the DeepIndex file with embeddings to overwwrite')
-    args = parser.parse_args()
-
-    emb, leaf_names = read_embedding(args.new_embeddings)
-    leaf_names = [_[3:] for _ in leaf_names]
-
-    emb_df = pd.DataFrame(data={'embedding1': emb[:,0], 'embedding2': emb[:,1]}, index=leaf_names)
-
-    hdmfio = get_hdf5io(args.deep_index_input, mode='a')
-    difile = hdmfio.read()
-    di_taxa = difile.taxa_table['taxon_id'][:]
-    di_emb = difile.taxa_table['embedding'].data   # h5py.Dataset
-
-    emb_df = emb_df.filter(items=di_taxa, axis=0)
-
-    di_emb[:] = emb_df.values
-
-    hdmfio.close()
diff --git a/build/lib/exabiome/response/tree.py b/build/lib/exabiome/response/tree.py
deleted file mode 100644
index fc657ac..0000000
--- a/build/lib/exabiome/response/tree.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import h5py
-import numpy as np
-import sys
-import argparse
-import logging
-
-from scipy.spatial.distance import squareform, pdist
-from scipy.cluster.hierarchy import linkage, to_tree
-from skbio.tree import nj, TreeNode
-from skbio.stats.distance import DistanceMatrix
-
-from sklearn.preprocessing import normalize as _normalize
-
-from .embedding import read_embedding
-from .gtdb import check_accession
-
-
-def get_dmat(embedding, leaf_names, metric='euclidean', logger=None):
-    """
-    Compute distances from embedding and return scikit-bio DistanceMatrix
-
-    Args:
-        embedding:          the embedding for each taxa
-        leaf_names:         the leafe
-    """
-    if logger:
-        logger.info("computing %s distances" % metric)
-    dist = squareform(pdist(embedding, metric=metric))
-    dmat = DistanceMatrix(dist, leaf_names)
-    return dmat
-
-
-def swap_space(tree):
-    for n in tree.tips():
-        n.name = n.name.replace(' ', '_')
-
-
-levels = ('phylum', 'class', 'order', 'family', 'genus')
-def get_phylo_stats(tree, metadata):
-    n_taxa = tree.count(True)
-    ret = dict()
-    for tl in levels:
-        total = 0
-        for p in np.unique(metadata[tl]):
-            p_members = [tree.find(_) for _ in metadata[metadata[tl] == p].index]
-            lca = tree.lowest_common_ancestor(p_members)
-            if lca.count(True) == 0 and lca.count(False):
-                c = 1
-            else:
-                c = lca.count(True)
-            total += c
-        ret[tl] = total/n_taxa
-    return ret
-
-
-def nj_tree(dmat):
-    tree = nj(dmat)
-    swap_space(tree)
-    return tree
-
-
-def _cn2tn(cn, names):
-    if cn.is_leaf():
-        return TreeNode(name=names[cn.id], length=cn.dist)
-    left = _cn2tn(cn.left, names)
-    right = _cn2tn(cn.right, names)
-    return TreeNode(name=cn.id, length=cn.dist, children=[left, right])
-
-
-def upgma_tree(dmat):
-    Z = linkage(squareform(dmat.data), method='average')
-    return _cn2tn(to_tree(Z), dmat.ids)
-
-
-def read_tree(nwk_path, leaf_names=None, trim_src_tag=False):
-    """
-    Read a tree in Newick format
-
-    Returns:
-        TreeNode object for the root of the tree
-    """
-    tree = TreeNode.read(nwk_path, format='newick')
-    swap_space(tree)
-    if leaf_names is not None:
-        tree = tree.shear(leaf_names)
-    tree = tree.unrooted_copy()
-    tree.assign_ids()
-
-    if trim_src_tag:
-        for n in tree.tips():
-            n.name = check_accession(n.name)
-    return tree
-
-
-def compare_tree(tree, target_nwk_path):
-    """
-    Compare tree and tree from target_nwk_path
-
-    Return:
-        Tuple of topology similarity and branch-length similarity
-    """
-    target_tree = read_tree(target_nwk_path)
-    top_sim = target_tree.compare_subsets(tree)
-    blen_sim = target_tree.compare_tip_distances(tree)
-    return top_sim, blen_sim
-
-
-def get_tree(emb_h5, nj=False, normalize=False, metric='euclidean', logger=None):
-    if logger:
-        logger.info("reading data from %s" % args.emb_h5)
-    emb, leaf_names = read_embedding(emb_h5)
-    if normalize:
-        if logger:
-            logger.info("normalizing samples")
-        emb = _normalize(emb, norm='l2', axis=1)
-
-    dmat = get_dmat(emb, leaf_names, metric=metric, logger=logger)
-    if logger:
-        logger.info("computing neighbor-joining tree")
-    if nj:
-        tree = nj_tree(dmat)
-    else:
-        tree = upgma_tree(dmat)
-    if logger:
-        logger.info("done")
-    return tree
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('emb_h5', type=str, help='the HDF5 file with embedding')
-    parser.add_argument('target_tree', type=str, help='the tree file to compare to')
-    parser.add_argument('-n', '--normalize', action='store_true', default=False,
-                        help='normalize samples before computing distances')
-    parser.add_argument('-m', '--metric', choices=['euclidean', 'mahalanobis', 'cosine'], default='euclidean',
-                        help='the metric to use for computing distances from embeddings')
-    parser.add_argument('-o', '--output', type=str, default=None,
-                        help='save the tree to OUTPUT')
-    args = parser.parse_args()
-    logger = logging.getLogger()
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(message)s')
-
-    tree = get_tree(args.emb_h5, normalize=args.normalize, metric=args.metric, logger=logger)
-
-    logger.info("comparing trees")
-    top_sim, blen_sim = compare_tree(tree, args.target_tree)
-    logger.info(f"done. topology similarity: {top_sim} branch length similarity: {blen_sim}")
-    if args.output:
-        logger.info(f"saving tree to {args.output}")
-        with open(args.output, 'w') as f:
-            tree.write(f)
diff --git a/build/lib/exabiome/response/umap.py b/build/lib/exabiome/response/umap.py
deleted file mode 100644
index f041c61..0000000
--- a/build/lib/exabiome/response/umap.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import h5py
-from scipy.spatial.distance import squareform as _squareform
-from sklearn.preprocessing import LabelEncoder
-
-from umap import UMAP
-
-from ..utils import parse_seed
-
-from .embedding import read_distances, save_embedding
-from .gtdb import read_taxonomy
-
-
-def run_umap(dist, logger=None, labels=None, **kwargs):
-    """
-    Run MDS on distances produced by tree2dmat
-
-    Args:
-        dist (str):             A distance matrix, square or condensed form
-        n_components (int):     number of components to produce
-        metric (bool):          Whether or not to run metric MDS. default is to run non-metric
-        logger (Logger):        Logger to use. default is no logging
-
-    Return:
-        emb (np.array):         the MDS embedding
-    """
-    if len(dist.shape) == 1:
-        if logger is not None:
-            logger.info('computing squareform')
-        dist = _squareform(dist)
-
-    kwargs.setdefault('n_neighbors', 100)
-    kwargs.setdefault('n_components', 2)
-
-    if logger is not None:
-        logger.info('computing {n_components} components with UMAP'.format(**kwargs))
-        logger.info('using {n_neighbors} neighbors and {min_dist} min_dist'.format(**kwargs))
-
-    kwargs['verbose'] = True
-    umap = UMAP(**kwargs)
-    emb = umap.fit_transform(dist, y=labels)
-    return emb
-
-
-if __name__ == '__main__':
-
-    import sys
-    import argparse
-    import logging
-    import numpy as np
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dist_h5', type=str, help='the HDF5 file with distance data (output by tree2dmat)')
-    parser.add_argument('out_h5', type=str, help='the HDF5 file to save the embedding to')
-    parser.add_argument('-p', '--n_components', type=int, default=2, help='the number of components to compute')
-    parser.add_argument('-n', '--n_neighbors', type=int, default=100, help='the n_neighbors parameter of UMAP')
-    parser.add_argument('-d', '--min_dist', type=float, default=0.1, help='the min_dist parameter of UMAP')
-    parser.add_argument('-l', '--learning_rate', type=float, default=1.0, help='the learning rate for UMAP SGD')
-    parser.add_argument('-s', '--seed', type=parse_seed, default='', help='the seed to use for UMAP')
-    parser.add_argument('-S', '--supervised', type=str, default=None, help='do supervised embedding')
-    parser.add_argument('-r', '--taxonomic_rank', type=str, default='phylum', help='the taxonomic rank to use for supervised embedding',
-                        choices=['phylum', 'class', 'order', 'family', 'genus'])
-    parser.add_argument('--sqrt', action='store_true', default=False, help='square-root the distances before UMAP')
-    args = parser.parse_args()
-
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(message)s')
-    logger = logging.getLogger()
-
-    logger.info('reading %s' % args.dist_h5)
-    dist, names = read_distances(args.dist_h5, squareform=True)
-
-    labels = None
-    target_metric = 'categorical'
-    if args.supervised is not None:
-        rank = args.taxonomic_rank
-        mdf = read_taxonomy(args.supervised, leaf_names=names)
-        labels = LabelEncoder().fit_transform(mdf[args.taxonomic_rank])
-
-        phyla = np.unique(mdf[rank])
-
-        p_dist = np.zeros((phyla.shape[0], phyla.shape[0]))
-
-        for i, phyla_i in enumerate(phyla):
-            mask_i = mdf[rank] == phyla_i
-            dist_i = dist[mask_i]
-            for j, phyla_j in enumerate(phyla[i+1:], i+1):
-                mask_j = mdf[rank] == phyla_j
-                p_dist[i,j] = dist[mask_i][:,mask_j].mean()
-                p_dist[j,i] = p_dist[i,j]
-
-        p_emb = UMAP(n_components=5, metric='precomputed', min_dist=1, n_neighbors=15, learning_rate=0.001).fit_transform(p_dist)
-        ## for taxonomic rank class supervision
-        # p_emb = UMAP(n_components=10, metric='precomputed', min_dist=1, n_neighbors=15, learning_rate=0.001, verbose=True, n_epochs=1000).fit_transform(p_dist)
-
-        labels = np.zeros((mdf.shape[0], p_emb.shape[1]))
-        for i, phyla_i in enumerate(phyla):
-            mask = mdf[rank] == phyla_i
-            labels[mask] = p_emb[i]
-
-        target_metric = 'l2'
-
-    if args.sqrt:
-        logger.info('taking square root of distances')
-        dist = np.sqrt(dist)
-
-    logger.info(f'using seed {args.seed}')
-
-    emb = run_umap(dist, logger=logger,
-                   labels=labels,
-                   target_metric=target_metric,
-                   random_state=args.seed,
-                   n_components=args.n_components,
-                   learning_rate=args.learning_rate,
-                   min_dist=args.min_dist,
-                   n_neighbors=args.n_neighbors)
-
-    logger.info('saving embedding to %s' % args.out_h5)
-    save_embedding(args.out_h5, emb, names,
-                   random_state=args.seed,
-                   n_components=args.n_components,
-                   min_dist=args.min_dist,
-                   n_neighbors=args.n_neighbors)
diff --git a/build/lib/exabiome/response/umap_sweep.py b/build/lib/exabiome/response/umap_sweep.py
deleted file mode 100644
index 68ac911..0000000
--- a/build/lib/exabiome/response/umap_sweep.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from exabiome.response.tree import upgma_tree
-from scipy.spatial.distance import pdist, squareform
-from skbio.stats.distance import DistanceMatrix
-from datetime import datetime
-from scipy.stats import spearmanr, pearsonr
-import numpy as np
-from .tree import get_phylo_stats
-
-
-
-def plot_species_embedding(embedding, metadata, **kwargs):
-    ax = sns.scatterplot(embedding[:,0], embedding[:,1], **kwargs)
-    leg = ax.legend(loc='lower right', bbox_to_anchor=(1.40, 0.39))
-    return ax, leg
-
-
-def emb_tree(embedder, dist, leaf_names, target_tree, taxa_metadata, metric='euclidean', metric_kwargs=dict(), **fit_kwargs):
-    emb = embedder.fit_transform(dist, **fit_kwargs)
-    _dist = pdist(emb, metric=metric, **metric_kwargs)
-    tree = upgma_tree(DistanceMatrix(squareform(_dist), leaf_names))
-    ret = dict()
-    ret['rfd'] = target_tree.compare_rfd(tree)
-    ret['subsets'] = target_tree.compare_subsets(tree)
-    ret['tip_distances'] = target_tree.compare_tip_distances(tree)
-    ret['pearson'] = spearmanr(_dist, squareform(dist))[0]
-    ret['spearman'] = pearsonr(_dist, squareform(dist))[0]
-    ret.update(get_phylo_stats(tree, taxa_metadata))
-    return ret
-
-
-if __name__ == '__main__':
-
-    from collections import OrderedDict
-    from .tree import read_tree, get_dmat
-    from .embedding import read_distances, read_embedding
-    from .gtdb import read_taxonomy
-    from ..utils import int_list, float_list, parse_seed
-    from sklearn.model_selection import ParameterGrid
-    from sklearn.preprocessing import LabelEncoder
-    from umap import UMAP
-    import pandas as pd
-    import multiprocessing as mp
-    import logging
-
-    from argparse import ArgumentParser
-    import sys
-
-    parser = ArgumentParser()
-    parser.add_argument('distances', type=str)
-    parser.add_argument('target_tree', type=str)
-    parser.add_argument('metadata', type=str)
-    parser.add_argument('output', type=str)
-    parser.add_argument('-s', '--seed', type=parse_seed, default='')
-    parser.add_argument('-p', '--n_components', type=int_list, default=[2])
-    parser.add_argument('-n', '--n_neighbors', type=int_list, default=[50])
-    parser.add_argument('-d', '--min_dist', type=float_list, default=[1.0])
-    parser.add_argument('-w', '--target_weight', type=float_list, default=[0.01])
-    parser.add_argument('-U', '--unsupervised', action='store_true', default=False)
-    parser.add_argument('-r', '--tax_rank', choices=['phylum', 'class', 'order', 'family', 'genus'], default='phylum')
-    parser.add_argument('-i', '--n_iters', default=1, type=int)
-    parser.add_argument('-P', '--n_procs', default=1, type=int)
-
-    args = parser.parse_args()
-
-    logger = None
-#    if args.n_procs > 1:
-#        logger = mp.get_logger()
-#        #hdlr = logging.StreamHandler(sys.stderr)
-#        #hdlr.setLevel(logging.DEBUG)
-#        #hdlr.setFormatter(logging.Formatter(fmt='%(processName)s - %(asctime)s - %(message)s'))
-#        #logger.addHandler(hdlr)
-#    else:
-#        logging.basicConfig(stream=sys.stderr, level=logging.INFO, format='%(asctime)s - %(message)s')
-#        logger = logging.getLogger()
-#
-#    logger.info('HELLO WORLD')
-#    print('FUCK THIS')
-
-
-    dist, leaf_names = read_distances(args.distances, squareform=True)
-    tree = read_tree(args.target_tree, leaf_names=leaf_names)
-    mdf = read_taxonomy(args.metadata, leaf_names=leaf_names)
-
-    params = dict(
-        n_components=args.n_components,
-        n_neighbors=args.n_neighbors,
-        min_dist=args.min_dist
-    )
-
-    emb_kwargs = dict()
-    if not args.unsupervised:
-        params['target_metric'] = ['categorical']
-        params['target_weight'] = args.target_weight
-        emb_kwargs['y'] = LabelEncoder().fit_transform(mdf[args.tax_rank])
-
-    grid = ParameterGrid(params)
-    data = list()
-
-    def func(fargs):
-        p_id, p = fargs
-        row = OrderedDict()
-        row['param_id'] = p_id
-        print(datetime.now().isoformat(), p_id, p)
-        emb = UMAP(**p)
-        row.update(emb.get_params())
-        row.update(emb_tree(emb, dist, leaf_names, tree, mdf, **emb_kwargs))
-        return row
-
-    args_list = list()
-    for p_id, p in enumerate(grid):
-        for n in range(args.n_iters):
-            p_copy = p.copy()
-            p_copy['random_state'] = args.seed + p_id + n
-            args_list.append((p_id, p_copy))
-
-    map_func = map
-    if args.n_procs > 1:
-        pool = mp.Pool(args.n_procs)
-        map_func = pool.imap
-
-    def nstr(s):
-        if s is None:
-            return ''
-        return str(s)
-
-    it = map_func(func, args_list)
-    out = open(args.output, 'w')
-    count = 0
-    try:
-        line = next(it)
-        print(',' + ','.join(line.keys()), file=out)
-        print('0,' + ','.join(map(nstr, line.values())), file=out)
-        for count, line in enumerate(it, 1):
-            print(str(count) + ',' + ','.join(map(nstr, line.values())), file=out)
-    finally:
-        out.close()
diff --git a/build/lib/exabiome/sequence/__init__.py b/build/lib/exabiome/sequence/__init__.py
deleted file mode 100644
index 5973cca..0000000
--- a/build/lib/exabiome/sequence/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from pkg_resources import resource_filename
-from os.path import join
-from hdmf.common import load_namespaces
-
-
-load_namespaces(join(resource_filename(__name__, 'schema'), 'deep_index.namespace.yaml'))
-
-
-from . import dna_table
-from .dna_table import *
diff --git a/build/lib/exabiome/sequence/convert.py b/build/lib/exabiome/sequence/convert.py
deleted file mode 100644
index 77c7413..0000000
--- a/build/lib/exabiome/sequence/convert.py
+++ /dev/null
@@ -1,362 +0,0 @@
-import skbio.io
-from skbio.sequence import DNA, Protein
-import re
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder
-import numpy as np
-from collections import deque
-from abc import ABCMeta, abstractmethod
-from itertools import chain
-
-
-class AbstractSeqIterator(object, metaclass=ABCMeta):
-
-    @classmethod
-    @abstractmethod
-    def characters(cls):
-        pass
-
-    @abstractmethod
-    def pack(self, seq):
-        pass
-
-    ltag_re = re.compile('>lcl|([A-Za-z0-9_.]+)')
-
-    def __init__(self, paths, logger=None, faa=False, min_seq_len=None):
-        self.logger = logger
-
-        # setup our characters
-        chars = self.characters()
-        chars = list(chars.upper() + chars.lower())
-        self.nchars = len(chars)//2
-
-        self.ohe = OneHotEncoder(sparse=False)
-        self.ohe.fit(np.array(chars).reshape(-1, 1))
-
-        if isinstance(paths, str):
-            paths = [paths]
-
-        self.__paths = paths
-        self.__path_iter = None
-        self.__name_queue = None
-        self.__len_queue = None
-        self.__index_queue = None
-        self.__taxon_queue = None
-        self.__id_queue = None
-        self.__total_len = 0
-        self.__nseqs = 0
-        self.skbio_cls = Protein if faa else DNA
-        self.logger.info('reading %s' % self.skbio_cls.__name__)
-
-        self.__curr_block = np.zeros((0, self.nchars), dtype=np.uint8)
-        self.__curr_block_idx = 0
-
-        if min_seq_len is None:
-            if faa:
-                min_seq_len = 150
-            else:
-                min_seq_len = 50
-        self.min_seq_len = min_seq_len
-
-    @property
-    def names(self):
-        return self.__name_queue
-
-    @property
-    def taxon(self):
-        return self.__taxon_queue
-
-    @property
-    def index(self):
-        return self.__index_queue
-
-    @property
-    def seqlens(self):
-        return self.__len_queue
-
-    @property
-    def ids(self):
-        return self.__id_queue
-
-    @property
-    def index_iter(self):
-        return QueueIterator(self.index, self)
-
-    @property
-    def names_iter(self):
-        return QueueIterator(self.names, self)
-
-    @property
-    def id_iter(self):
-        return QueueIterator(self.ids, self)
-
-    @property
-    def seqlens_iter(self):
-        return QueueIterator(self.seqlens, self)
-
-    @property
-    def taxon_iter(self):
-        return QueueIterator(self.taxon, self)
-
-    def __iter__(self):
-        self.__path_iter = iter(self.__paths)
-        # initialize the sequence iterator
-        self.__curr_iter = self._read_seq(next(self.__path_iter))
-        self.__name_queue = deque()
-        self.__index_queue = deque()
-        self.__len_queue = deque()
-        self.__taxon_queue = deque()
-        self.__id_queue = deque()
-        self.__total_len = 0
-        self.__nseqs = 0
-        self.__curr_block = np.zeros((0, self.nchars), dtype=np.uint8)
-        self.__curr_block_idx = 0
-        self.__curr_file_idx = 0
-        return self
-
-    @property
-    def total_len(self):
-        return self.__total_len
-
-    @property
-    def curr_block_idx(self):
-        return self.__curr_block_idx
-
-    @property
-    def curr_block(self):
-        return self.__curr_block
-
-    def __read_next_seq(self):
-        while True:
-            try:
-                seq, seqname = next(self.__curr_iter)
-                if len(seq) <= self.min_seq_len:
-                    continue
-                self.__name_queue.append(seqname)
-                self.__len_queue.append(len(seq))
-                self.__taxon_queue.append(np.uint16(self.__curr_file_idx))
-                self.__id_queue.append(self.__nseqs)
-                self.__nseqs += 1
-                return seq
-            except StopIteration:
-                try:
-                    self.__curr_iter = self._read_seq(next(self.__path_iter))
-                    self.__curr_file_idx += 1
-                except StopIteration:
-                    self.__name_queue.append(None)
-                    self.__index_queue.append(None)
-                    self.__taxon_queue.append(None)
-                    self.__name_queue.append(None)
-                    raise StopIteration()
-
-    def __next__(self):
-        self._load_buffer()
-        ret = self.__curr_block[self.__curr_block_idx]
-        self.__curr_block_idx += 1
-        return ret
-
-    def _load_buffer(self):
-        if self.__curr_block_idx == self.__curr_block.shape[0]:
-            # this raises the final StopIteration
-            # when nothing is left to read
-            seq = self.__read_next_seq()
-            self.__curr_block = self.pack(seq)
-            self.__total_len += self.__curr_block.shape[0]
-            self.__index_queue.append(self.__total_len)
-            self.__curr_block_idx = 0
-
-    @classmethod
-    def get_seqname(cls, seq):
-        return seq.metadata['id']
-
-    def _read_seq(self, path):
-        self.logger.info('reading %s', path)
-        kwargs = {'format': 'fasta', 'constructor': self.skbio_cls, 'validate': False}
-        for seq_i, seq in enumerate(skbio.io.read(path, **kwargs)):
-            ltag = self.get_seqname(seq)
-            yield seq, ltag
-
-
-class DNASeqIterator(AbstractSeqIterator):
-
-    @classmethod
-    def characters(cls):
-        return 'ATCGN'
-
-    def __init__(self, paths, logger=None, min_seq_len=None):
-        super().__init__(paths, logger=logger, faa=False, min_seq_len=min_seq_len)
-
-        categories = self.ohe.categories_[0][:self.nchars]
-
-        self._col2drop = categories == 'N'
-        self._row_mask = np.zeros(len(categories), dtype=bool)
-        self._row_mask[np.logical_not(self._col2drop)] = True
-
-    def pack(self, seq):
-        tfm = self.ohe.transform(seq.values.astype('U').reshape(-1, 1)).T
-        # the first half will be uppercase and the second half will be lowercase
-        # - combine upper and lower
-        tfm[:self.nchars] += tfm[self.nchars:]
-        tfm = tfm[:self.nchars]
-
-        col_mask = tfm[self._col2drop].squeeze() == 1
-        tfm = (tfm[self._row_mask]).astype(np.uint8)
-        tfm[:, col_mask] = 1
-        tfm = tfm.T
-        if tfm.shape[0] % 2 == 1:
-            tfm = np.append(tfm, [[0, 0, 0, 0]], axis=0)
-        packed = np.packbits(np.concatenate((tfm[0::2], tfm[1::2]), axis=1))
-        return packed
-
-    @classmethod
-    def pack_dna(cls, seq, ohe=None):
-        if ohe is None:
-            ohe = OneHotEncoder(sparse=False)
-            ohe.fit(np.array(list('atcgnATCGN')).reshape(-1, 1))
-            nchars = 5
-        categories = ohe.categories_[0][:nchars]
-        col2drop = categories == 'N'
-        row_mask = np.zeros(len(categories), dtype=bool)
-        row_mask[np.logical_not(col2drop)] = True
-
-        if isinstance(seq, DNA):
-            seq = seq.values.astype('U')
-        elif isinstance(seq, str):
-            seq = np.array(list(seq))
-        seq = seq.reshape(-1, 1)
-
-        tfm = ohe.transform(seq).T
-        # the first half will be uppercase and the second half will be lowercase
-        # - combine upper and lower
-        tfm[:nchars] += tfm[nchars:]
-        tfm = tfm[:nchars]
-
-        col_mask = tfm[col2drop].squeeze() == 1
-        tfm = (tfm[row_mask]).astype(np.uint8)
-        tfm[:, col_mask] = 1
-        tfm = tfm.T
-        if tfm.shape[0] % 2 == 1:
-            tfm = np.append(tfm, [[0, 0, 0, 0]], axis=0)
-        packed = np.packbits(np.concatenate((tfm[0::2], tfm[1::2]), axis=1))
-        return packed
-
-
-class UnrecognizedCharacter(Exception):
-
-    def __init__(self, c):
-        super().__init__('Unrecognized character: %s' % chr(c))
-        self.character = c
-
-
-
-
-class AASeqIterator(AbstractSeqIterator):
-
-    aamap = dict(zip(chain(range(97, 123), range(65, 91)),
-                     chain(np.arange(1, 27, dtype=np.uint8), np.arange(1, 27, dtype=np.uint8))))
-
-    @classmethod
-    def characters(cls):
-        return 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-
-    def __init__(self, paths, logger=None, min_seq_len=None, max_degenerate=None):
-        """
-        Args:
-            paths (list): list of sequence paths
-            logger (logging.Logger): logger object to use
-            max_degenerate (float): maximum fraction of degenerates and
-                                    gaps to allow in sequence
-        """
-        super().__init__(paths, logger=logger, faa=True, min_seq_len=min_seq_len)
-        self.max_degenerate = 0.25 if max_degenerate is None else max_degenerate
-        logger.info('skipping sequences with more than %0.4f %% gaps or degenerates' % (self.max_degenerate*100))
-
-    def pack(self, seq):
-        nbits = len(seq)*5
-        start = (8 - nbits % 8)
-        nbits += start
-        bits = np.zeros(nbits, dtype=np.uint8)
-        s = start
-        for i, aa in enumerate(str(seq)):
-            try:
-                num = self.aamap[ord(aa)]
-            except KeyError as e:
-                if e.args[0] == 42:
-                    continue
-                else:
-                    raise UnrecognizedCharacter(e.args[0]) from e
-            e = s + 5
-            bits[s:e] = np.unpackbits(num)[3:]
-            s = e
-        packed = np.packbits(bits)
-        return packed
-
-    def _read_seq(self, path):
-        gen = super()._read_seq(path)
-        for seq, ltag in gen:
-            y_seq, y_ltag = seq, ltag
-            if y_seq.has_stops():
-                stops = y_seq.stops()
-                nstops = np.sum(stops)
-                if nstops > 1:
-                    # skip sequence if it has more than one stop codon
-                    continue
-                else:
-                    # trim trailing stop codon if it exists
-                    if stops[-1]:
-                        y_seq = y_seq[:-1]
-            if y_seq.has_degenerates():
-                perc_bad = y_seq.degenerates().mean() + y_seq.gaps().mean()
-                if perc_bad > self.max_degenerate:
-                    continue
-            yield y_seq, y_ltag
-
-
-
-class VocabIterator(AbstractSeqIterator):
-
-    def __init__(self, paths, logger=None, min_seq_len=None):
-        super().__init__(paths, logger=logger, min_seq_len=min_seq_len)
-        self.lenc = LabelEncoder()
-        self.lenc.fit(list(self.characters() + self.characters().lower()))
-
-    def pack(self, seq):
-        tfm = self.lenc.transform(seq.values.astype('U')).astype(np.uint8) % len(self.characters())
-        return tfm
-
-    @property
-    def encoded_vocab(self):
-        return self.lenc.classes_[0:len(self.lenc.classes_)//2]
-
-
-class DNAVocabIterator(VocabIterator):
-
-    @classmethod
-    def characters(cls):
-        return 'ATCGN'
-
-
-class AAVocabIterator(VocabIterator):
-
-    @classmethod
-    def characters(cls):
-        return 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-
-
-class QueueIterator(object):
-
-    def __init__(self, queue, seqit):
-        self.__queue = queue
-        self.q = self.__queue
-        self.__seqit = seqit
-        self.sit = self.__seqit
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if len(self.__queue) == 0:
-            self.__seqit._load_buffer()
-        ret = self.__queue.popleft()
-        if ret is None:
-            raise StopIteration()
-        return ret
diff --git a/build/lib/exabiome/sequence/dna_table.py b/build/lib/exabiome/sequence/dna_table.py
deleted file mode 100644
index 8af6bb5..0000000
--- a/build/lib/exabiome/sequence/dna_table.py
+++ /dev/null
@@ -1,513 +0,0 @@
-from abc import ABCMeta, abstractmethod
-import math
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import sklearn.neighbors as skn
-
-from hdmf.common import VectorIndex, VectorData, DynamicTable,\
-                        DynamicTableRegion, register_class, VocabData
-from hdmf.utils import docval, call_docval_func, get_docval, popargs
-from hdmf.data_utils import DataIO
-from hdmf import Container, Data
-
-
-__all__ = ['DeepIndexFile',
-           'AbstractChunkedDIFile',
-           'WindowChunkedDIFile',
-           'SequenceTable',
-           'TaxaTable']
-
-NS = 'deep-index'
-
-
-class BitpackedIndex(VectorIndex, metaclass=ABCMeta):
-
-    def _start_end(self, i):
-        start = 0 if i == 0 else self.data[i-1]
-        end = self.data[i]
-        return start, end
-
-    @abstractmethod
-    def _get_single_item(self, i):
-        pass
-
-    def __getitem__(self, args):
-        """
-        Slice ragged array of *packed* one-hot encoded DNA sequence
-        """
-        if np.issubdtype(type(args), np.integer):
-            return self._get_single_item(args)
-        else:
-            raise ValueError("Can only index bitpacked sequence with integers")
-
-
-@register_class('PackedDNAIndex', NS)
-class PackedDNAIndex(BitpackedIndex):
-
-    def _get_single_item(self, i):
-        start, end = self._start_end(i)
-        shift = start % 2
-        unpacked = np.unpackbits(self.target[start//2:math.ceil(end/2)])
-        unpacked = unpacked.reshape(-1, 4)[shift:shift+end-start].T
-        return unpacked
-
-
-@register_class('PackedAAIndex', NS)
-class PackedAAIndex(BitpackedIndex):
-
-    def _get_single_item(self, i):
-        start, end = self._start_end(i)
-        packed = self.target[start:end]
-        bits = np.unpackbits(packed)
-        bits = bits[bits.shape[0] % 5:]
-        bits = bits.reshape(-1, 5)
-        unpacked = np.pad(bits, ((0, 0), (3, 0)), mode='constant', constant_values=((0, 0), (0, 0)))
-        ohe_pos = np.packbits(unpacked, axis=1).squeeze(axis=1)
-        # trim leading zeros that may be left from padding to
-        # ensure enough bits for pack
-        # trim trailing zeros in case a non-AA character was
-        # used to terminate the original sequence
-        ohe_pos = np.trim_zeros(ohe_pos)
-        ohe_pos = ohe_pos - 1
-        return ohe_pos
-
-
-class TorchableMixin:
-
-
-    def __init__(self, *args, **kwargs):
-        self.use_torch = False
-        self.classify = False
-
-    def get_torch_conversion(self, **kwargs):
-        dtype = kwargs.get('dtype')
-        device = kwargs.get('device')
-        maxlen = kwargs.get('maxlen')
-        if np.issubdtype(type(maxlen), np.integer):
-            def func(x):
-                ret = torch.zeros((maxlen, x.shape[1]))
-                ret[0:x.shape[0]] = x
-                return ret
-        else:
-            def func(x):
-                return torch.as_tensor(x, dtype=dtype, device=device)
-        return func
-
-    def get_numpy_conversion(self, **kwargs):
-        """
-        Args:
-            maxlen (int):        the maximum sequence length to pad to
-        """
-        maxlen = kwargs.get('maxlen')
-        if np.issubdtype(type(maxlen), np.integer):
-            def func(x):
-                ret = np.zeros((maxlen, x.shape[1]))
-                ret[0:x.shape[0]] = x
-                return ret
-        else:
-            def func(x):
-                return x
-        return func
-
-    def set_raw(self):
-        self.convert = lambda x: x
-
-
-class AbstractSequenceTable(DynamicTable, TorchableMixin, metaclass=ABCMeta):
-
-    @abstractmethod
-    def get_sequence_index(self, data, target):
-        pass
-
-    @abstractmethod
-    def get_sequence_data(self, data):
-        pass
-
-    @docval(*get_docval(DynamicTable.__init__),
-            {'name': 'sequence_name', 'type': ('array_data', 'data', VectorData), 'doc': 'sequence names'},
-            {'name': 'sequence', 'type': ('array_data', 'data', VectorData), 'doc': 'bitpacked DNA sequence'},
-            {'name': 'sequence_index', 'type': ('array_data', 'data', VectorIndex), 'doc': 'index for sequence'},
-            {'name': 'length', 'type': ('array_data', 'data', VectorData), 'doc': 'lengths of sequence'},
-            {'name': 'taxon', 'type': ('array_data', 'data', VectorData), 'doc': 'index for sequence'},
-            {'name': 'taxon_table', 'type': DynamicTable, 'doc': "target for 'taxon'", 'default': None},
-            {'name': 'pad', 'type': bool, 'doc': 'pad sequences to the maximum length sequence', 'default': False},
-            {'name': 'bitpacked', 'type': bool, 'doc': 'sequence data are packed', 'default': True},
-            {'name': 'vocab', 'type': 'array_data', 'doc': 'the characters in the sequence vocabulary.', 'default': None})
-    def __init__(self, **kwargs):
-        sequence_name, index, sequence, taxon, taxon_table = popargs('sequence_name',
-                                                             'sequence_index',
-                                                             'sequence',
-                                                             'taxon',
-                                                             'taxon_table',
-                                                             kwargs)
-        self._bitpacked = popargs('bitpacked', kwargs)
-        vocab = popargs('vocab', kwargs)
-
-        self.pad = popargs('pad', kwargs)
-        seqlens = popargs('length', kwargs)
-        columns = list()
-        self.maxlen = None
-        if isinstance(sequence_name, VectorData):      # data is being read -- here we assume that everything is a VectorData
-            columns.append(sequence_name)
-            columns.append(index)
-            columns.append(sequence)
-            columns.append(seqlens)
-            columns.append(taxon)
-            if self.pad:   # if we need to pad, compute the maxlen
-                self.maxlen = np.max(seqlens.data[:])
-        else:
-            columns.append(VectorData('sequence_name', 'sequence names', data=sequence_name))
-            columns.append(self.get_sequence_data(sequence))
-            columns.append(self.get_sequence_index(index, columns[-1]))
-            columns.append(VectorData('length', 'sequence lengths', data=seqlens))
-            columns.append(DynamicTableRegion('taxon', taxon, 'taxa for each sequence', taxon_table))
-        kwargs['columns'] = columns
-        call_docval_func(super().__init__, kwargs)
-        self.convert = self.get_numpy_conversion(maxlen=self.maxlen)
-
-    def __getitem__(self, key):
-        if isinstance(key, str):
-            return super().__getitem__(key)
-        else:
-            ret = list(super().__getitem__(key))
-            # sequence data will come from the third column
-            return tuple(ret)
-
-
-@register_class('SequenceTable', NS)
-class SequenceTable(AbstractSequenceTable):
-
-    def get_sequence_index(self, index, data):
-        return VectorIndex('sequence_index', index, data)
-
-    def get_sequence_data(self, data):
-        if isinstance(data, DataIO):
-            vocab = data.data.data.encoded_vocab
-        else:
-            vocab = self.vocab
-        return VocabData('sequence', 'sequence data from a vocabulary', data=data, vocabulary=vocab)
-
-    dna = ['A', 'C', 'G', 'T', 'N']
-
-    protein = ['A', 'B', 'C', 'D', 'E', 'F', 'G',
-               'H', 'I', 'J', 'K', 'L', 'M', 'N',
-               'O', 'P', 'Q', 'R', 'S', 'T', 'U',
-               'V', 'W', 'X', 'Y', 'Z']
-
-    @docval(*get_docval(DynamicTable.__init__),
-            {'name': 'sequence_name', 'type': ('array_data', 'data', VectorData), 'doc': 'sequence names'},
-            {'name': 'sequence', 'type': ('array_data', 'data', VectorData), 'doc': 'bitpacked DNA sequence'},
-            {'name': 'sequence_index', 'type': ('array_data', 'data', VectorIndex), 'doc': 'index for sequence'},
-            {'name': 'length', 'type': ('array_data', 'data', VectorData), 'doc': 'lengths of sequence'},
-            {'name': 'taxon', 'type': ('array_data', 'data', VectorData), 'doc': 'index for sequence'},
-            {'name': 'taxon_table', 'type': DynamicTable, 'doc': "target for 'taxon'", 'default': None},
-            {'name': 'pad', 'type': bool, 'doc': 'pad sequences to the maximum length sequence', 'default': False},
-            {'name': 'vocab', 'type': ('array_data', str), 'doc': 'the characters in the sequence vocabulary. '\
-                                                                  '*dna* for nucleic acids, *protein* for default amino acids',
-             'default': 'dna'}, )
-    def __init__(self, **kwargs):
-        vocab = popargs('vocab', kwargs)
-        if vocab is not None:
-            if isinstance(vocab, str):
-                if vocab == 'dna':
-                    vocab = self.dna
-                elif vocab == 'protein':
-                    vocab = self.protein
-        self.vocab = vocab
-        super().__init__(**kwargs)
-
-
-@register_class('DNATable', NS)
-class DNATable(SequenceTable):
-
-    def get_sequence_index(self, data, target):
-        return PackedDNAIndex('sequence_index', data, target)
-
-
-@register_class('AATable', NS)
-class AATable(SequenceTable):
-
-    charmap = np.array(['A', 'B',
-                        'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O',
-                        'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '', '', '',
-                        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
-                        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''],
-                       dtype='<U1')
-
-    def get_numpy_conversion(self, **kwargs):
-        """
-        Args:
-            maxlen (int):        the maximum sequence length to pad to
-        """
-        maxlen = kwargs.get('maxlen', None)
-        if np.issubdtype(type(maxlen), np.integer):
-            def func(x):
-                ret = np.zeros([maxlen, 26], dtype=float)
-                ret[np.arange(x.shape[0]), x] = 1.0
-                return ret
-        else:
-            def func(x):
-                ret = np.zeros([x.shape[0], 26], dtype=float)
-                ret[np.arange(x.shape[0]), x] = 1.0
-                return ret
-        return func
-
-    def get_torch_conversion(self, **kwargs):
-        """
-        Args:
-            dtype:               the dtype to return data as
-            device:              the device to send data to
-            maxlen (int):        the maximum sequence length to pad to
-        """
-        dtype = kwargs.get('dtype')
-        device = kwargs.get('device')
-        maxlen = kwargs.get('maxlen')
-        if kwargs.get('ohe', False):
-            if np.issubdtype(type(maxlen), np.integer):
-                def func(x):
-                    ret = torch.zeros([maxlen, 26], dtype=dtype, device=device)
-                    ret[np.arange(x.shape[0]), x.tolist()] = 1.0
-                    ret = ret.T
-                    return ret
-            else:
-                def func(x):
-                    ret = torch.zeros([x.shape[0], 26], dtype=dtype, device=device)
-                    ret[np.arange(x.shape[0]), x.tolist()] = 1.0
-                    ret = ret.T
-                    return ret
-        else:
-            if np.issubdtype(type(maxlen), np.integer):
-                def func(x):
-                    ret = torch.tensor(x, device=device, dtype=torch.int64)
-                    return ret
-            else:
-                def func(x):
-                    ret = torch.zeros(maxlen, device=device, dtype=torch.int64)
-                    ret[0:x.shape[0]] = x
-                    return ret
-        return func
-
-    def get_sequence_index(self, data, target):
-        return PackedAAIndex('sequence_index', data, target)
-
-    @classmethod
-    def to_sequence(self, data):
-        return "".join(self.charmap[i] for i in np.where(data)[1])
-
-    def get(self, idx, sequence=False):
-        ret = list(self[idx])
-        if sequence:
-            ret[2] = self.to_sequence(ret[2])
-        return ret
-
-
-@register_class('TaxaTable', NS)
-class TaxaTable(DynamicTable, TorchableMixin):
-
-    @docval(*get_docval(DynamicTable.__init__),
-            {'name': 'taxon_id', 'type': ('array_data', 'data', VectorData), 'doc': 'the taxon ID'},
-            {'name': 'embedding', 'type': ('array_data', 'data', VectorData), 'doc': 'the embedding for each taxon'},
-            {'name': 'phylum', 'type': ('array_data', 'data', VectorData), 'doc': 'the phylum for each taxon'},
-            {'name': 'class', 'type': ('array_data', 'data', VectorData), 'doc': 'the class for each taxon'},
-            {'name': 'order', 'type': ('array_data', 'data', VectorData), 'doc': 'the order for each taxon'},
-            {'name': 'family', 'type': ('array_data', 'data', VectorData), 'doc': 'the family for each taxon'},
-            {'name': 'genus', 'type': ('array_data', 'data', VectorData), 'doc': 'the genus for each taxon'},
-            {'name': 'species', 'type': ('array_data', 'data', VectorData), 'doc': 'the species for each taxon'})
-    def __init__(self, **kwargs):
-        taxon_id, embedding = popargs('taxon_id', 'embedding', kwargs)
-        taxonomy_labels = ['phylum', 'class', 'order', 'family', 'genus', 'species']
-        taxonomy = popargs(*taxonomy_labels, kwargs)
-
-        columns = list()
-        if isinstance(taxon_id, VectorData):      # data is being read -- here we assume that everything is a VectorData
-            columns.append(taxon_id)
-            columns.append(embedding)
-            columns.extend(taxonomy)
-        else:
-            columns.append(VectorData('taxon_id', 'taxonomy IDs from NCBI', data=taxon_id))
-            columns.append(VectorData('embedding', 'an embedding for each taxon', data=embedding))
-            for l, t in zip(taxonomy_labels, taxonomy):
-                columns.append(VectorData(l, 'the %s for each taxon' % l, data=t))
-        kwargs['columns'] = columns
-        call_docval_func(super().__init__, kwargs)
-        self.convert = self.get_numpy_conversion()
-
-
-    def taxid_torch_conversion(self, num_classes, device=None):
-        def func(x):
-            ret = torch.zeros(num_classes, dtype=torch.long, device=device)
-            ret[x] = 1
-            return ret
-        return func
-
-    def taxid_numpy_conversion(self, num_classes):
-        def func(x):
-            ret = np.zeros(num_classes, dtype=np.uint8)
-            ret[x] = 1
-            return ret
-        return func
-
-    def __getitem__(self, key):
-        if isinstance(key, str):
-            return super().__getitem__(key)
-        else:
-            ret = list(super().__getitem__(key))
-            return tuple(ret)
-
-
-@register_class('CondensedDistanceMatrix', NS)
-class CondensedDistanceMatrix(Data):
-    pass
-
-
-@register_class('NewickString', NS)
-class NewickString(Data):
-    pass
-
-
-@register_class('DeepIndexFile', NS)
-class DeepIndexFile(Container):
-
-    __fields__ = ({'name': 'seq_table', 'child': True},
-                  {'name': 'taxa_table', 'child': True},
-                  {'name': 'distances', 'child': True},
-                  {'name': 'tree', 'child': True})
-
-    @docval({'name': 'seq_table', 'type': (AATable, DNATable, SequenceTable), 'doc': 'the table storing DNA sequences'},
-            {'name': 'taxa_table', 'type': TaxaTable, 'doc': 'the table storing taxa information'},
-            {'name': 'distances', 'type': CondensedDistanceMatrix, 'doc': 'the table storing taxa information'},
-            {'name': 'tree', 'type': NewickString, 'doc': 'the table storing taxa information'})
-    def __init__(self, **kwargs):
-        seq_table, taxa_table, distances, tree = popargs('seq_table', 'taxa_table', 'distances', 'tree', kwargs)
-        call_docval_func(super().__init__, {'name': 'root'})
-        self.seq_table = seq_table
-        self.taxa_table = taxa_table
-        self.distances = distances
-        self.tree = tree
-        self._sanity = False
-        self._sanity_features = 5
-        self.__labels = None
-        self.__n_emb_components = self.taxa_table['embedding'].data.shape[1]
-        self.label_key = 'id'
-
-    def set_label_key(self, val):
-        self.label_key = val
-
-    def set_sanity(self, sanity, n_features=5):
-        self._sanity = sanity
-        self._sanity_features = n_features
-
-    @property
-    def labels(self):
-        if self.__labels is None:
-            self.__labels = self.seq_table['taxon'].data[:]
-        return self.__labels
-
-    @property
-    def n_emb_components(self):
-        return self.__n_emb_components
-
-    def __getitem__(self, i):
-        """
-        Return a tuple containing (taxon_name, sequence_name, sequence, taxon_embedding)
-        """
-        return self.get(i)
-
-    def get(self, arg):
-        idx = self.seq_table.id[arg]
-        seq = self.seq_table['sequence'].get(arg, index=True)   # sequence data
-        label = self.seq_table['taxon'].get(arg, index=True)    # index to taxon table
-        label = self.taxa_table[self.label_key][label]          # get the interesting information from the taxon table i.e. embedding
-        return (idx, seq, label)
-
-    def __len__(self):
-        return len(self.seq_table)
-
-    def set_raw(self):
-        self.seq_table.set_raw()
-
-    def to_sequence(self, data):
-        return self.seq_table.to_sequence(data)
-
-    def get_knn_classifier(self, **kwargs):
-        """
-        Build a KNeighborsClassifier from taxonomic embeddings
-
-        By default, use only a single neighbor
-
-        Args:
-            kwargs  : arguments to sklearn.neighbors.KNeighborsClassifier constructor
-
-
-        """
-        kwargs.setdefault('n_neighbors', 1)
-        ret = skn.KNeighborsClassifier(**kwargs)
-        emb = self.taxa_table['embedding'][:]
-        ret.fit(emb, np.arange(emb.shape[0]))
-        return ret
-
-
-
-class AbstractChunkedDIFile(object):
-    """
-    An abstract class for chunking sequences from a DeepIndexFile
-    """
-
-    def __init__(self, difile, seq_idx, start, end, labels):
-        self.difile = difile
-        self.seq_idx = np.asarray(seq_idx)
-        self.start = np.asarray(start)
-        self.end = np.asarray(end)
-        self.labels = np.asarray(labels)
-
-    def __len__(self):
-        return len(self.seq_idx)
-
-    def __getitem__(self, i):
-        if not isinstance(i, (int, np.integer)):
-            raise ValueError("ChunkedDIFile only supportsd indexing with an integer")
-        seq_i = self.seq_idx[i]
-        item = self.difile[seq_i]
-        s = self.start[i]
-        e = self.end[i]
-        #return seq_i, item[1][s:e], item[2]
-        return i, item[1][s:e], item[2]
-
-    def __getattr__(self, attr):
-        """Delegate retrival of attributes to the data in self.data"""
-        return getattr(self.difile, attr)
-
-
-class WindowChunkedDIFile(AbstractChunkedDIFile):
-    """
-    A class for chunking sequences with a sliding window
-
-    By default windows are not overlapping
-    """
-
-    def __init__(self, difile, wlen, step=None):
-        if not isinstance(difile, DeepIndexFile):
-            raise ValueError(f'difile must be a DeepIndexFile, got {type(difile)}')
-        if step is None:
-            step = wlen
-        self.wlen = wlen
-        self.step = step
-
-        seq_idx = list()
-        chunk_start = list()
-        chunk_end = list()
-        labels = list()
-
-        lengths = difile.seq_table['length'][:]
-        seqlabels = difile.labels
-        for i in range(len(difile)):
-            label = seqlabels[i]
-            for start in range(0, lengths[i], step):
-                labels.append(label)
-                seq_idx.append(i)
-                chunk_start.append(start)
-                chunk_end.append(start+step)
-
-        super().__init__(difile, seq_idx, chunk_start, chunk_end, labels)
diff --git a/build/lib/exabiome/sequence/schema/deep_index.namespace.yaml b/build/lib/exabiome/sequence/schema/deep_index.namespace.yaml
deleted file mode 100644
index 816413f..0000000
--- a/build/lib/exabiome/sequence/schema/deep_index.namespace.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-namespaces:
-- doc: Model for storing data to be read in for deep-index training
-  name: deep-index
-  version: 0.1.0
-  schema:
-  - namespace: hdmf-common
-  - source: dna_table.yaml
diff --git a/build/lib/exabiome/sequence/schema/dna_table.yaml b/build/lib/exabiome/sequence/schema/dna_table.yaml
deleted file mode 100644
index 142067c..0000000
--- a/build/lib/exabiome/sequence/schema/dna_table.yaml
+++ /dev/null
@@ -1,127 +0,0 @@
-datasets:
-- data_type_def: PackedDNAIndex
-  data_type_inc: VectorIndex
-  dtype: int
-  doc: A VectorIndex that indexes bitpacked DNA
-
-- data_type_def: PackedAAIndex
-  data_type_inc: VectorIndex
-  dtype: int
-  doc: A VectorIndex that indexes bitpacked amino acids
-
-- data_type_def: NewickString
-  data_type_inc: Data
-  dtype: text
-  shape: null
-  doc: A tree in Newick format
-
-- data_type_def: CondensedDistanceMatrix
-  data_type_inc: Data
-  dtype: float32
-  shape:
-    - null
-  doc: a compressed distance matrix
-
-groups:
-- data_type_def: AbstractSeqTable
-  data_type_inc: DynamicTable
-  doc: A table for storing sequence data
-  datasets:
-  - name: sequence_name
-    data_type_inc: VectorData
-    dtype: text
-    doc: Names for sequences
-  - name: length
-    data_type_inc: VectorData
-    dtype: uint32
-    doc: Lengths of sequences
-  - name: sequence
-    data_type_inc: VectorData
-    dtype: uint8
-    doc: Packed sequence
-  - name: taxon
-    data_type_inc: DynamicTableRegion
-    doc: integer ID for the taxa that each sequence belongs to
-
-- data_type_def: DNATable
-  data_type_inc: AbstractSeqTable
-  doc: A table for storing sequence data
-  datasets:
-  - name: sequence_index
-    data_type_inc: PackedDNAIndex
-    doc: Index for 'sequence'
-
-- data_type_def: AATable
-  data_type_inc: AbstractSeqTable
-  doc: A table for storing sequence data
-  datasets:
-  - name: sequence_index
-    data_type_inc: PackedAAIndex
-    doc: Index for 'sequence'
-
-- data_type_def: SequenceTable
-  data_type_inc: AbstractSeqTable
-  doc: A table for storing sequence data
-  datasets:
-  - name: sequence_index
-    data_type_inc: VectorIndex
-    doc: Index for 'sequence'
-  - name: sequence
-    data_type_inc: VocabData
-    doc: DNA or protein sequence data
-
-- data_type_def: TaxaTable
-  data_type_inc: DynamicTable
-  doc: A table for storing taxa information
-  datasets:
-  - name: taxon_id
-    data_type_inc: VectorData
-    dtype: text
-    doc: Taxon ID from GTDB
-  - name: embedding
-    data_type_inc: VectorData
-    dtype: float
-    doc: embedding of each taxa on n-dimensional space
-  - name: phylum
-    data_type_inc: VectorData
-    dtype: text
-    doc: The phylum in the taxonomy
-  - name: class
-    data_type_inc: VectorData
-    dtype: text
-    doc: The class in the taxonomy
-  - name: order
-    data_type_inc: VectorData
-    dtype: text
-    doc: The order in the taxonomy
-  - name: family
-    data_type_inc: VectorData
-    dtype: text
-    doc: The family in the taxonomy
-  - name: genus
-    data_type_inc: VectorData
-    dtype: text
-    doc: The genus in the taxonomy
-  - name: species
-    data_type_inc: VectorData
-    dtype: text
-    doc: The species in the taxonomy
-
-- data_type_def: DeepIndexFile
-  data_type_inc: Container
-  doc: A file that has taxonomy and sequence information for running deep index
-  datasets:
-  - name: tree
-    data_type_inc: NewickString
-    doc: the tree for the taxa in this file
-  - name: distances
-    data_type_inc: CondensedDistanceMatrix
-    doc: distances between all pairs of taxa in this file
-  groups:
-  - name: seq_table
-    data_type_inc: AbstractSeqTable
-    doc: the AATable or DNATable storing sequence data for each taxa in this file
-    quantity: '?'
-  - name: taxa_table
-    data_type_inc: TaxaTable
-    doc: Taxa table
diff --git a/build/lib/exabiome/sequence/tetramers.py b/build/lib/exabiome/sequence/tetramers.py
deleted file mode 100644
index 8a9791f..0000000
--- a/build/lib/exabiome/sequence/tetramers.py
+++ /dev/null
@@ -1,130 +0,0 @@
-from collections import Counter
-import pandas as pd
-import numpy as np
-import skbio.io
-import h5py
-
-
-rcmap = ['' for i in range(128)]
-rcmap[ord('A')] = 'T'
-rcmap[ord('T')] = 'A'
-rcmap[ord('C')] = 'G'
-rcmap[ord('G')] = 'C'
-rcmap[ord('N')] = 'N'
-def ckmer(s):
-    rc = ''.join(rcmap[ord(c)] for c in s[::-1])
-    if s < rc:
-        return s
-    else:
-        return rc
-
-
-def get_windows(path, wlen, step, **kwargs):
-    for seqobj in skbio.io.read(path, 'fasta', **kwargs):
-        for start in range(0, len(seqobj), step):
-            subseq = seqobj[start:start+step]
-            subseq.metadata['id'] += "|%d-%d" % (start, start+step)
-            yield subseq
-
-def get_tetramers(seq_it):
-    tnf = list()
-    seqnames = list()
-    for seq_i, seqobj in enumerate(seq_it):
-        seq = str(seqobj)
-        seqnames.append(seqobj.metadata['id'])
-        tnf.append(Counter(str(seq[i:i+4]) for i in range(len(seq)-3)))
-    df = pd.DataFrame(data=tnf, index=seqnames).fillna(0.0)
-
-    to_drop = list()
-    for kmer in df.columns:
-        if 'N' in kmer:
-            to_drop.append(kmer)
-            continue
-        ck = ckmer(kmer)
-        if ck != kmer:
-            to_drop.append(kmer)
-            df[ck] = df[ck] + df[kmer]
-
-    df = df.drop(to_drop, axis=1)
-    df = df.div(df.sum(axis=1), axis=0)
-    return df
-
-def write_tnf_dataset(group, tnf_table, label_df):
-    species = tnf_table['species']
-    tnf_table = tnf_table.drop('species', axis=1)
-    group.create_dataset('tnf', data=tnf_table.values, dtype=float)
-    strtype = h5py.special_dtype(vlen=str)
-    group.create_dataset('tetramers', data=tnf_table.columns.values, dtype=strtype)
-    group.create_dataset('sequence_ids', data=tnf_table.index.values, dtype=strtype)
-    group.create_dataset('embedding_idx', data=species, dtype=int)
-    group.create_dataset('embedding', data=label_df.values, dtype=float)
-    group.create_dataset('leaf_names', data=label_df.index.values, dtype=strtype)
-
-
-def read_tnf_dataset(path, group=None):
-    with h5py.File(path, 'r') as f:
-        g = f
-        if group is not None:
-            g = f[group]
-        X = g['tnf'][:]
-        emb = g['embedding'][:]
-        labels = g['embedding_idx'][:]
-        y = emb[labels]
-
-    return X, y, labels
-
-if __name__ == '__main__':
-    import sys
-    import argparse
-    from ..utils import get_accession
-    from ..response.embedding import read_embedding
-    from ..response.gtdb import check_accessions
-
-    from functools import partial
-    import logging
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('fof', type=str, help='file of files containing sequences to count tetramers for')
-    parser.add_argument('embedding_h5', type=str, help='file that contains embeddings')
-    parser.add_argument('output_h5', type=str, help='file to write kmer counts to')
-    parser.add_argument('-C', '--chunk', action='store_true', default=False, help='chunk sequences by sliding window')
-    parser.add_argument('-w', '--winlen', type=int, default=1000, help='window length to chunk sequences into')
-    parser.add_argument('-s', '--step', type=int, default=100, help='the step size to take windows')
-
-    args = parser.parse_args()
-
-    accessions = list()
-    tnf_tables = list()
-    species = list()
-
-    logging.basicConfig(format="%(asctime)s - %(message)s", level=logging.DEBUG, stream=sys.stderr)
-    logger = logging.getLogger()
-
-
-    if args.chunk:
-        load_func = partial(get_windows, wlen=args.winlen, step=args.step)
-    else:
-        load_func = partial(skbio.io.read, format='fasta')
-
-    fof = open(args.fof, 'r')
-    for i, path in enumerate(fof):
-        path = path[:-1]
-        logger.info(f'reading {path}')
-        accessions.append(get_accession(path))
-        tnf = get_tetramers(load_func(path))
-        species.extend([i]*tnf.shape[0])
-        tnf_tables.append(tnf)
-
-    tnf_df = pd.concat(tnf_tables, sort=False)
-    tnf_df.fillna(0.0, inplace=True)
-    tnf_df['species'] = species
-
-
-    emb, taxa = read_embedding(args.embedding_h5)
-    taxa = check_accessions(taxa, trim_src_tag=True)
-    colnames = ["dim%02g" % i for i in range(emb.shape[1])]
-    emb_df = pd.DataFrame(data=emb, columns=colnames, index=taxa)
-    emb_df = emb_df.filter(items=accessions, axis=0)
-
-    with h5py.File(args.output_h5, 'w') as f:
-        write_tnf_dataset(f, tnf_df, emb_df)
diff --git a/build/lib/exabiome/tools/__init__.py b/build/lib/exabiome/tools/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/exabiome/tools/make_fof.py b/build/lib/exabiome/tools/make_fof.py
deleted file mode 100644
index fd06fc2..0000000
--- a/build/lib/exabiome/tools/make_fof.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import os
-
-def get_taxa_id(path):
-    c, n = os.path.basename(path).split('_')[0:2]
-    return c + '_' + n
-
-if __name__ == '__main__':
-    import argparse
-    import logging
-    import sys
-    import glob
-
-    from skbio import TreeNode
-
-    from ..utils import get_faa_path, get_fna_path
-
-
-    epi = "fadir should be organized with the same structure as the NCBI FTP site"
-    parser = argparse.ArgumentParser(epilog=epi)
-    parser.add_argument('fadir', type=str, help='directory with NCBI sequence files')
-    parser.add_argument('accessions', type=str, help='A file containing accessions')
-    parser.add_argument('-t', '--tree', action='store_true', default=False, help='accessions are from a tree in Newick format')
-    parser.add_argument('-p', '--protein', action='store_true', default=False, help='get paths for protein files')
-    parser.add_argument('-v', '--verbose', action='store_true', default=False, help='log to stderr')
-
-
-    args = parser.parse_args()
-
-    loglvl = logging.DEBUG if args.verbose else logging.WARNING
-    logging.basicConfig(stream=sys.stderr, level=loglvl, format='%(asctime)s - %(message)s')
-    logger = logging.getLogger()
-
-    if args.tree:
-        #############################
-        # read tree
-        #############################
-        logger.info('reading tree from %s' % args.accessions)
-        tree = TreeNode.read(args.accessions, format='newick')
-
-        logger.info('getting taxa IDs from tree')
-        tid_set = set()
-        for tip in tree.tips():
-            name = tip.name
-            if name[0:2] in ('RS', 'GB'):
-                name = name[3:]
-            name = name.replace(' ', '_')
-            tid_set.add(name)
-
-
-        logger.info('getting Fasta paths from %s' % args.fadir)
-        suffix = 'protein' if args.protein else 'cds_from_genomic'
-        pattern = "%s/all/G*/[0-9][0-9][0-9]/[0-9][0-9][0-9]/[0-9][0-9][0-9]/*/*%s*.gz" % (args.fadir, suffix)
-        for path in glob.iglob(pattern):
-            taxa_id = get_taxa_id(path)
-            if taxa_id in tid_set:
-                print(path, file=sys.stdout)
-                tid_set.remove(taxa_id)
-
-        for tid in sorted(tid_set):
-            print(tid, file=sys.stderr)
-
-    else:
-        func = get_faa_path if args.protein else get_fna_path
-        with open(args.accessions, 'r') as f:
-            for line in f.readlines():
-                print(func(line.strip(), args.fadir), file=sys.stdout)
diff --git a/build/lib/exabiome/tools/prepare_data.py b/build/lib/exabiome/tools/prepare_data.py
deleted file mode 100644
index 5ecec3c..0000000
--- a/build/lib/exabiome/tools/prepare_data.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import math
-import numpy as np
-import os
-
-
-def select_distances(ids_to_select, taxa_ids, distances):
-    id_map = {t[3:]: i for i, t in enumerate(taxa_ids)}
-    indices = np.array([id_map[tid] for tid in ids_to_select])
-    idx = np.zeros(len(indices)*(len(indices)-1)//2, dtype=int)
-    k = 0
-    n = int(1/2 + math.sqrt(1/4 + 2 * len(distances)))
-    for s_i, _i in enumerate(indices):
-        for s_j, _j in enumerate(indices[s_i+1:]):
-            if _i > _j:
-                i, j = _j, _i
-            else:
-                i, j = _i, _j
-            idx[k] = i*n - ((i+1)*(i+2)//2) + j
-            k += 1
-    return distances[idx]
-
-
-def select_embeddings(ids_to_select, taxa_ids, embeddings):
-    id_map = {t[3:]: i for i, t in enumerate(taxa_ids)}
-    indices = [id_map[tid] for tid in ids_to_select]
-    return embeddings[indices]
-
-
-def get_taxa_id(path):
-    c, n = os.path.basename(path).split('_')[0:2]
-    return c + '_' + n
-
-if __name__ == '__main__':
-
-    import argparse
-    import io
-    import sys
-    import logging
-    import h5py
-    import pandas as pd
-
-    from skbio import TreeNode
-
-    from hdmf.common import get_hdf5io
-    from hdmf.data_utils import DataChunkIterator
-
-    from exabiome.sequence.convert import AASeqIterator, DNASeqIterator, DNAVocabIterator
-    from exabiome.sequence.dna_table import AATable, DNATable, SequenceTable, TaxaTable, DeepIndexFile, NewickString, CondensedDistanceMatrix
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('fof', type=str, help='file of Fasta files')
-    parser.add_argument('emb_h5', type=str, help='embedding file')
-    parser.add_argument('dist_h5', type=str, help='the distances file')
-    parser.add_argument('tree', type=str, help='the distances file')
-    parser.add_argument('metadata', type=str, help='metadata file from GTDB')
-    parser.add_argument('out', type=str, help='output HDF5')
-    parser.add_argument('-p', '--protein', action='store_true', default=False, help='input is amino acids')
-    parser.add_argument('-d', '--max_deg', type=float, default=None, help='max number of degenerate characters in protein sequences')
-    parser.add_argument('-l', '--min_len', type=float, default=None, help='min length of sequences')
-    parser.add_argument('-V', '--vocab', action='store_true', default=False, help='store sequences as vocabulary data')
-
-    if len(sys.argv) == 1:
-        parser.print_help()
-        sys.exit(1)
-
-    args = parser.parse_args()
-
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(message)s')
-    logger = logging.getLogger()
-
-    logger.info('reading Fasta paths from %s' % args.tree)
-    with open(args.fof, 'r') as f:
-        fapaths = [l[:-1] for l in f.readlines()]
-
-    taxa_ids = list(map(get_taxa_id, fapaths))
-
-    #############################
-    # read and filter distances
-    #############################
-    logger.info('reading distances from %s' % args.dist_h5)
-    with h5py.File(args.dist_h5, 'r') as f:
-        dist = f['distances'][:]
-        dist_taxa = f['leaf_names'][:].astype('U')
-    logger.info('selecting distances for taxa found in %s' % args.fof)
-    dist = select_distances(taxa_ids, dist_taxa, dist)
-    dist = CondensedDistanceMatrix('distances', data=dist)
-
-
-    #############################
-    # read and filter taxonomies
-    #############################
-    logger.info('reading taxonomies from %s' % args.metadata)
-    taxlevels = ['domain', 'phylum', 'class', 'order', 'family', 'genus', 'species']
-    def func(row):
-        dat = dict(zip(taxlevels, row['gtdb_taxonomy'].split(';')))
-        dat['species'] = dat['species'].split(' ')[1]
-        dat['accession'] = row['accession'][3:]
-        return pd.Series(data=dat)
-
-    logger.info('selecting GTDB taxonomy for taxa found in %s' % args.fof)
-    taxdf = pd.read_csv(args.metadata, header=0, sep='\t')[['accession', 'gtdb_taxonomy']]\
-                        .apply(func, axis=1)\
-                        .set_index('accession')\
-                        .filter(items=taxa_ids, axis=0)
-
-
-    #############################
-    # read and filter embeddings
-    #############################
-    logger.info('reading embeddings from %s' % args.emb_h5)
-    with h5py.File(args.emb_h5, 'r') as f:
-        emb = f['embedding'][:]
-        emb_taxa = f['leaf_names'][:]
-    logger.info('selecting embeddings for taxa found in %s' % args.fof)
-    emb = select_embeddings(taxa_ids, emb_taxa, emb)
-
-    #############################
-    # read and trim tree
-    #############################
-    logger.info('reading tree from %s' % args.tree)
-    tree = TreeNode.read(args.tree, format='newick')
-
-    logger.info('transforming leaf names for shearing')
-    for tip in tree.tips():
-        tip.name = tip.name[3:].replace(' ', '_')
-
-    logger.info('shearing taxa not found in %s' % args.fof)
-    tree = tree.shear(taxa_ids)
-
-    logger.info('converting tree to Newick string')
-    bytes_io = io.BytesIO()
-    tree.write(bytes_io, format='newick')
-    tree_str = bytes_io.getvalue()
-    tree = NewickString('tree', data=tree_str)
-
-
-    h5path = args.out
-
-    logger.info("reading %d Fasta files" % len(fapaths))
-    logger.info("Total size: %d", sum(os.path.getsize(f) for f in fapaths))
-
-    if args.vocab:
-        if args.protein:
-            seqit = AAVocabIterator(fapaths, logger=logger, min_seq_len=args.min_len)
-        else:
-            seqit = DNAVocabIterator(fapaths, logger=logger, min_seq_len=args.min_len)
-        SeqTable = SequenceTable
-    else:
-        if args.protein:
-            logger.info("reading and writing protein sequences")
-            seqit = AASeqIterator(fapaths, logger=logger, max_degenerate=args.max_deg, min_seq_len=args.min_len)
-            SeqTable = AATable
-        else:
-            logger.info("reading and writing DNA sequences")
-            seqit = DNASeqIterator(fapaths, logger=logger, min_seq_len=args.min_len)
-            SeqTable = DNATable
-
-    seqit_bsize = 2**15 if args.protein else 2**18
-    packed = DataChunkIterator.from_iterable(iter(seqit), maxshape=(None,), buffer_size=seqit_bsize, dtype=np.dtype('uint8'))
-    seqindex = DataChunkIterator.from_iterable(seqit.index_iter, maxshape=(None,), buffer_size=2**0, dtype=np.dtype('int'))
-    names = DataChunkIterator.from_iterable(seqit.names_iter, maxshape=(None,), buffer_size=2**0, dtype=np.dtype('U'))
-    ids = DataChunkIterator.from_iterable(seqit.id_iter, maxshape=(None,), buffer_size=2**0, dtype=np.dtype('int'))
-    taxa = DataChunkIterator.from_iterable(seqit.taxon_iter, maxshape=(None,), buffer_size=2**0, dtype=np.dtype('uint16'))
-    seqlens = DataChunkIterator.from_iterable(seqit.seqlens_iter, maxshape=(None,), buffer_size=2**0, dtype=np.dtype('uint32'))
-
-    io = get_hdf5io(h5path, 'w')
-
-    tt_args = ['taxa_table', 'a table for storing taxa data', taxa_ids, emb]
-    for t in taxlevels[1:]:
-        tt_args.append(taxdf[t].values)
-
-    taxa_table = TaxaTable(*tt_args)
-
-    seq_table = SeqTable('seq_table', 'a table storing sequences for computing sequence embedding',
-                         io.set_dataio(names,    compression='gzip', chunks=(2**15,)),
-                         io.set_dataio(packed,   compression='gzip', maxshape=(None,), chunks=(2**15,)),
-                         io.set_dataio(seqindex, compression='gzip', maxshape=(None,), chunks=(2**15,)),
-                         io.set_dataio(seqlens, compression='gzip', maxshape=(None,), chunks=(2**15,)),
-                         io.set_dataio(taxa, compression='gzip', maxshape=(None,), chunks=(2**15,)),
-                         taxon_table=taxa_table,
-                         id=io.set_dataio(ids, compression='gzip', maxshape=(None,), chunks=(2**15,)))
-
-    difile = DeepIndexFile(seq_table, taxa_table, dist, tree)
-
-    io.write(difile, exhaust_dci=False)
-    io.close()
-
-    logger.info("reading %s" % (h5path))
-    h5size = os.path.getsize(h5path)
-    logger.info("HDF5 size: %d", h5size)
diff --git a/build/lib/exabiome/tools/summarize_loss.py b/build/lib/exabiome/tools/summarize_loss.py
deleted file mode 100644
index 1dc4543..0000000
--- a/build/lib/exabiome/tools/summarize_loss.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import sys
-import numpy as np
-import torch
-import pickle
-import argparse
-import os.path
-import matplotlib.pyplot as plt
-
-
-def plot(lossd, log=False):
-    plt.plot(lossd['train_loss'], label='train')
-    plt.plot(lossd['test_loss'], label='test')
-    if log:
-        plt.xscale('log')
-        plt.yscale('log')
-    plt.legend()
-
-def get_loss(checkpoint_path):
-    print('reading %s' % checkpoint_path, file=sys.stderr)
-    try:
-        checkpoint = torch.load(checkpoint_path)
-    except Exception as e:
-        print(f'unable to read {checkpoint_path} - {type(e)}: {e}')
-        return
-    ret = dict(
-        train_loss = checkpoint['train_loss'],
-        test_loss = checkpoint['test_loss']
-    )
-    return ret
-
-def main(args=None):
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('files', nargs='+')
-    parser.add_argument('-o', '--output', type=str, default=None)
-    parser.add_argument('-f', '--figure', type=str, default=None)
-
-    args = parser.parse_args(args=args)
-
-    dat = {f : L for f in args.files if (L := get_loss(f)) is not None}
-
-    out = open(args.output, 'wb') if args.output is not None else sys.stdout
-
-    pickle.dump(dat, out)
-
-    if args.figure:
-        s = len(os.path.commonprefix(list(dat.keys())))
-        ncols = int(np.ceil(np.sqrt(len(dat))))
-        nrows = int(np.ceil(len(dat)/ncols))
-        plt.figure(figsize=(ncols*5.5, nrows*7.5/2))
-        for i, k in enumerate(dat):
-            plt.subplot(nrows, ncols, i+1)
-            plot(dat[k], log=False)
-            plt.title(k[s:-4])
-        plt.savefig(args.figure)
-
-
-if __name__ == '__main__':
-    main()
-
-
diff --git a/build/lib/exabiome/utils.py b/build/lib/exabiome/utils.py
deleted file mode 100644
index 1705555..0000000
--- a/build/lib/exabiome/utils.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from datetime import datetime
-import argparse
-import glob
-import os
-import sys
-import logging
-
-
-def check_argv(argv=None):
-    if argv is None:
-        argv = sys.argv[1:]
-    if isinstance(argv, str):
-        argv = argv.strip().split()
-    return argv
-
-
-def parse_logger(string):
-    if not string:
-        ret = logging.getLogger('stdout')
-        hdlr = logging.StreamHandler(sys.stdout)
-    else:
-        ret = logging.getLogger(string)
-        hdlr = logging.FileHandler(string)
-    ret.setLevel(logging.INFO)
-    ret.addHandler(hdlr)
-    hdlr.setFormatter(logging.Formatter('%(asctime)s - %(message)s'))
-    return ret
-
-
-def check_directory(path):
-    if os.path.exists(path):
-        if not os.path.isdir(path):
-            raise ValueError(f'{path} already exists as a file')
-    else:
-        os.makedirs(path)
-
-def get_seed():
-    return int(datetime.now().timestamp()*1000000) % (2**32 -1)
-
-
-def parse_seed(string):
-    if string:
-        try:
-            i = int(string)
-            if i > 2**32 - 1:
-                raise ValueError(string)
-            return i
-        except :
-            raise argparse.ArgumentTypeError(f'{string} is not a valid seed')
-    else:
-        return get_seed()
-
-
-def _get_path_helper(acc, directory, sfx):
-    if acc[:3] in ('RS_', 'GB_'):
-        acc = acc[3:]
-    l = [directory, 'all', acc[:3], acc[4:7], acc[7:10], acc[10:13], "%s*"%acc, "%s*%s" % (acc, sfx)]
-    glob_str = os.path.join(*l)
-    result = glob.glob(glob_str)
-    if len(result) > 1:
-        raise ValueError(f'more than one file matching {glob_str}')
-    if len(result) == 0:
-        raise ValueError(f'no file matching {glob_str}')
-    return result[0]
-
-
-def get_faa_path(acc, directory):
-    return _get_path_helper(acc, directory, '_protein.faa.gz')
-
-
-def get_fna_path(acc, directory):
-    return _get_path_helper(acc, directory, '_cds_from_genomic.fna.gz')
-
-
-def get_accession(path):
-    """
-    Return the genome accession from a given path
-
-    Example:
-    given GCA_000309865.1_MMad_1.0/GCA_000309865.1_MMad_1.0_cds_from_genomic.fna.gz,
-    return  GCA_000309865.1
-    """
-    basename = os.path.basename(path)
-    return basename[:15]
-
-
-def _num_list(string, t):
-    if isinstance(string, (list, tuple)):
-        return string
-    elif isinstance(string, str):
-        if ':' in string:
-            ar = [t(a) for a in string.split(":")]
-            return list(range(ar[0], ar[1]+1))
-        else:
-            return list(map(t, string.split(",")))
-    else:
-        raise argparse.ArgumentTypeError(f'cannot parse {string} as list of {t}')
-
-
-def int_list(string):
-    return _num_list(string, int)
-
-
-def float_list(string):
-    return _num_list(string, float)
diff --git a/deep-taxon.iml b/deep-taxon.iml
deleted file mode 100644
index ad3c0a3..0000000
--- a/deep-taxon.iml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager" inherit-compiler-output="true">
-    <exclude-output />
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/dist/exabiome-0.0.0-py3.6.egg b/dist/exabiome-0.0.0-py3.6.egg
deleted file mode 100644
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diff --git a/src/exabiome/nn/models/Untitled.ipynb b/src/exabiome/nn/models/Untitled.ipynb
deleted file mode 100644
index 92679fc..0000000
--- a/src/exabiome/nn/models/Untitled.ipynb
+++ /dev/null
@@ -1,173 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "attempted relative import with no known parent package",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-0a8a2a244a7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAbstractLit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'roznet'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: attempted relative import with no known parent package"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "import torch.nn as nn\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "\n",
-    "from . import model, AbstractLit\n",
-    "\n",
-    "@model('roznet')\n",
-    "class Self_Attention(AbstractLit):\n",
-    "    \"\"\" Self attention Layer \"\"\"\n",
-    "    def __init__(self, in_dim, activation):\n",
-    "        self.in_channel = in_dim\n",
-    "        self.activation = activation\n",
-    "        self.query = nn.Conv1d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)\n",
-    "        self.key = nn.Conv1d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)\n",
-    "        self.value = nn.Conv1d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)\n",
-    "        self.gamma = nn.Parameter(torch.zeros(1))\n",
-    "\n",
-    "        self.softmax = nn.Softmax(dim=-1)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        \"\"\"\n",
-    "            inputs :\n",
-    "                x : input feature maps( B X C X W X H)\n",
-    "            returns :\n",
-    "                out : self attention value + input feature\n",
-    "                attention: B X N X N (N is Width*Height)\n",
-    "        \"\"\"\n",
-    "\n",
-    "@model('roznet')\n",
-    "class RozNet(AbstractLit):\n",
-    "    '''\n",
-    "    A 1D CNN with 5 convolutional layers, followed by 3 fully-connected layers\n",
-    "\n",
-    "    Args:\n",
-    "        input_nc (int):  the input number of channels\n",
-    "    '''\n",
-    "\n",
-    "    def __init__(self, hparams):\n",
-    "        super().__init__(hparams)\n",
-    "        input_nc = getattr(hparams, 'input_nc', None)\n",
-    "        n_outputs = getattr(hparams, 'n_outputs', 2)\n",
-    "        first_kernel_size = getattr(hparams, 'first_kernel_size', 7)\n",
-    "        maxpool = getattr(hparams, 'maxpool', True)\n",
-    "        self.features = nn.Sequential(\n",
-    "            nn.Conv1d(input_nc, 64, kernel_size=first_kernel_size, stride=1, padding=2),\n",
-    "            nn.BatchNorm1d(64),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.MaxPool1d(kernel_size=3, stride=1),\n",
-    "            nn.Conv1d(64, 192, kernel_size=5, padding=2),\n",
-    "            nn.BatchNorm1d(192),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.MaxPool1d(kernel_size=3, stride=1),\n",
-    "            nn.Conv1d(192, 384, kernel_size=3, padding=1),\n",
-    "            nn.BatchNorm1d(384),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.Conv1d(384, 256, kernel_size=3, padding=1),\n",
-    "            nn.BatchNorm1d(256),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.Conv1d(256, 256, kernel_size=3, padding=1),\n",
-    "            nn.BatchNorm1d(256),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.MaxPool1d(kernel_size=3, stride=1),\n",
-    "        )\n",
-    "        pool_size = 24\n",
-    "\n",
-    "        self.pool = nn.AdaptiveMaxPool1d(pool_size)\n",
-    "\n",
-    "        self.classifier = nn.Sequential(\n",
-    "            nn.Dropout(hparams.dropout_rate),\n",
-    "            nn.Linear(256*pool_size, 1024),\n",
-    "            nn.BatchNorm1d(1024),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.Dropout(hparams.dropout_rate),\n",
-    "            nn.Linear(1024, 1024),\n",
-    "            nn.BatchNorm1d(1024),\n",
-    "            nn.ReLU(inplace=True),\n",
-    "            nn.Linear(1024, n_outputs),\n",
-    "            #nn.BatchNorm1d(n_outputs)\n",
-    "        )\n",
-    "\n",
-    "        self.attention = Self_Attention(24, 'relu')\n",
-    "\n",
-    "    def forward(self, x, **kwargs):\n",
-    "        x = self.features(x)\n",
-    "        x = self.pool(x)\n",
-    "        x = self.attention(x)\n",
-    "        x = torch.flatten(x, 1)\n",
-    "        x = self.classifier(x)\n",
-    "        return x\n",
-    "\n",
-    "\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    from .train import parse_args, run, check_model, load_dataset\n",
-    "\n",
-    "    args = parse_args(\"Train CNN with Spatial Pyramidal Pooling\")\n",
-    "                      #[['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])\n",
-    "\n",
-    "    input_nc = 5\n",
-    "    if args['protein']:\n",
-    "        input_nc = 26\n",
-    "\n",
-    "    if args['sanity']:\n",
-    "        input_nc = 5\n",
-    "\n",
-    "\n",
-    "    dataset, io = load_dataset(path=args['input'], **args)\n",
-    "\n",
-    "    if args['classify']:\n",
-    "        n_outputs = len(dataset.difile.taxa_table)\n",
-    "    else:\n",
-    "        n_outputs = dataset.difile.n_emb_components\n",
-    "\n",
-    "    model = check_model(RozNet(input_nc, n_outputs=n_outputs), **args)\n",
-    "\n",
-    "    args['pad'] = True\n",
-    "\n",
-    "    run(dataset, model, **args)\n",
-    "\n",
-    "    io.close()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/src/exabiome/nn/models/roznet.py b/src/exabiome/nn/models/roznet.py
index f0f83b9..f3dc941 100644
--- a/src/exabiome/nn/models/roznet.py
+++ b/src/exabiome/nn/models/roznet.py
@@ -5,26 +5,6 @@
 
 from . import model, AbstractLit
 
-class Self_Attention(nn.Module):
-    """ Self attention Layer """
-    def __init__(self, in_dim, activation):
-        self.in_channel = in_dim
-        self.activation = activation
-        self.query = nn.Conv1d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
-        self.key = nn.Conv1d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
-        self.value = nn.Conv1d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)
-        self.gamma = nn.Parameter(torch.zeros(1))
-
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x):
-        """
-            inputs :
-                x : input feature maps( B X C X W X H)
-            returns :
-                out : self attention value + input feature
-                attention: B X N X N (N is Width*Height)
-        """
 
 @model('roznet')
 class RozNet(AbstractLit):
@@ -63,9 +43,10 @@ def __init__(self, hparams):
             nn.MaxPool1d(kernel_size=3, stride=1),
         )
         pool_size = 24
-
-        self.pool = nn.AdaptiveMaxPool1d(pool_size)
-
+        if maxpool:
+            self.pool = nn.AdaptiveMaxPool1d(pool_size)
+        else:
+            self.pool = nn.AdaptiveAvgPool1d(pool_size)
         self.classifier = nn.Sequential(
             nn.Dropout(hparams.dropout_rate),
             nn.Linear(256*pool_size, 1024),
@@ -79,12 +60,9 @@ def __init__(self, hparams):
             #nn.BatchNorm1d(n_outputs)
         )
 
-        self.attention = Self_Attention(24, 'relu')
-
     def forward(self, x, **kwargs):
         x = self.features(x)
         x = self.pool(x)
-        x = self.attention(x)
         x = torch.flatten(x, 1)
         x = self.classifier(x)
         return x
diff --git a/src/exabiome/nn/optim.py b/src/exabiome/nn/optim.py
index 59e4212..aea4c04 100644
--- a/src/exabiome/nn/optim.py
+++ b/src/exabiome/nn/optim.py
@@ -50,7 +50,6 @@ def __init__(self, model_cls, hparams, dataset, monitor_metric='val_loss', targs
     def get_hparams(self, trial):
         ret = dict()
         ret['dropout_rate'] = trial.suggest_uniform('dropout_rate', 0.0, 1.0)
-        # ret['num_layers'] = trial.suggest_int('num_layers', 2, 10)
         return ret
 
     def __call__(self, trial):

From a917e5a28813871906dbc2def7626b4a7d2838ae Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Mon, 17 Aug 2020 17:17:20 -0700
Subject: [PATCH 02/11] merging with upstream

---
 src/exabiome/nn/loader.py          |  75 ++++++-----
 src/exabiome/nn/models/roznet.py   |  12 +-
 src/exabiome/sequence/dna_table.py | 200 ++++++++++++++++-------------
 3 files changed, 165 insertions(+), 122 deletions(-)

diff --git a/src/exabiome/nn/loader.py b/src/exabiome/nn/loader.py
index 71f2888..3a0fde9 100644
--- a/src/exabiome/nn/loader.py
+++ b/src/exabiome/nn/loader.py
@@ -6,7 +6,7 @@
 from sklearn.utils import check_random_state
 from hdmf.common import get_hdf5io
 
-from ..sequence import WindowChunkedDIFile
+from ..sequence import WindowChunkedDIFile, RevCompFilter
 
 
 def check_window(window, step):
@@ -24,21 +24,15 @@ def read_dataset(path):
     dataset = SeqDataset(difile)
     return dataset, hdmfio
 
-def process_dataset(args, inference=False):
+
+def process_dataset(args, path=None, inference=False):
     """
     Process *input* argument and return dataset and HDMFIO object
-
     Args:
         args (Namespace):       command-line arguments passed by parser
         inference (bool):       load data for inference
     """
-    # First, get the dataset, so we can figure
-    # out how many outputs there are
-    #io = get_hdf5io(args.input, 'r')
-    #difile = io.read()
-    #dataset = SeqDataset(difile)
-    dataset, io = read_dataset(args.input)
-
+    dataset, io = read_dataset(path or args.input)
 
     if not hasattr(args, 'classify'):
         raise ValueError('Parser must check for classify/regression/manifold '
@@ -57,8 +51,12 @@ def process_dataset(args, inference=False):
     args.window, args.step = check_window(args.window, args.step)
 
     # Process any arguments that impact how we set up the dataset
+    dataset.set_ohe(False)
     if args.window is not None:
-        dataset.difile = WindowChunkedDIFile(dataset.difile, args.window, args.step)
+        dataset.set_chunks(args.window, args.step)
+        #dataset.difile = WindowChunkedDIFile(dataset.difile, args.window, args.step)
+    if getattr(args, 'revcomp', False):
+        dataset.set_revcomp()
     if args.load:
         dataset.load()
 
@@ -124,6 +122,7 @@ def __init__(self, difile, classify=False):
         self.set_classify(classify)
         self._target_key = 'class_label' if classify else 'embedding'
         self.vocab_len = len(self.difile.seq_table['sequence'].target.vocabulary)
+        self.__ohe = True
 
     def set_classify(self, classify):
         self._classify = classify
@@ -139,6 +138,15 @@ def set_classify(self, classify):
         # the state of the underlying DynamicTable
         self.difile.set_label_key(self._label_key)
 
+    def set_chunks(self, window, step=None):
+        self.difile = WindowChunkedDIFile(self.difile, window, step)
+
+    def set_revcomp(self, revcomp=True):
+        self.difile = RevCompFilter(self.difile)
+
+    def set_ohe(self, ohe=True):
+        self.__ohe = ohe
+
     def __len__(self):
         return len(self.difile)
 
@@ -169,39 +177,25 @@ def to_sint(data):
     def __getitem__(self, i):
         # get sequence
         item = self.difile[i]
-        idx, seq, label, seq_id = None, None, None, -1
-        if len(item) == 4:
-            idx, seq, label, seq_id = item
-        else:
-            idx, seq, label = item
+        idx = item['id']
+        seq = item['seq']
+        label = item['label']
+        seq_id = item.get('seq_idx', -1)
         ## one-hot encode sequence
-        seq = F.one_hot(torch.as_tensor(seq, dtype=torch.int64), num_classes=self.vocab_len).float().T
+        seq = torch.as_tensor(seq, dtype=torch.int64)
+        if self.__ohe:
+            seq = F.one_hot(seq, num_classes=self.vocab_len).float()
+        seq = seq.T
         label = torch.as_tensor(label, dtype=self._label_dtype)
         return (idx, seq, label, seq_id)
 
 
-def get_loader(path, **kwargs):
-    """
-    Return a DataLoader that loads data from the given DeepIndex file
-
-    Args:
-        path (str): the path to the DeepIndex file
-        kwargs    : any additional arguments to pass into torch.DataLoader
-    """
-    hdmfio = get_hdf5io(path, 'r')
-    loader = DataLoader(SeqDataset(hdmfio), collate_fn=collate, **kwargs)
-    return loader
-
-
 def train_test_validate_split(data, stratify=None, random_state=None,
                               test_size=0.1, train_size=0.8, validation_size=0.1):
     """
     Return train test validation split of given data
-
-
     test_size, train_size, validation_size will all be normalized before subsequent
     calls to train_test_split
-
     Args:
         data (str): the path to the DeepIndex file
         kwargs    : any additional arguments to pass into torch.DataLoader
@@ -259,7 +253,6 @@ def train_test_loaders(dataset, random_state=None, downsample=None, distances=Fa
                        **kwargs):
     """
     Return DataLoaders for training and test datasets.
-
     Args:
         path (str):                  the path to the DeepIndex file
         distances (str):             return distances for the
@@ -286,3 +279,17 @@ def train_test_loaders(dataset, random_state=None, downsample=None, distances=Fa
     return (DataLoader(train_dataset, collate_fn=collater, **kwargs),
             DataLoader(test_dataset, collate_fn=collater, **kwargs),
             DataLoader(validate_dataset, collate_fn=collater, **kwargs))
+
+def get_loader(dataset, distances=False, **kwargs):
+    """
+    Return a DataLoader that loads data from the given Dataset
+    Args:
+        dataset (Dataset): the dataset to return a DataLoader for
+        distances  (bool): whether or not to return distances for a batch
+    """
+    collater = collate
+    if distances:
+        if dataset.difile.distances is None:
+            raise ValueError('DeepIndexFile {dataset.difile} does not contain distances')
+        collater = DistanceCollater(dataset.difile.distances.data[:])
+    return DataLoader(dataset, collate_fn=collater, **kwargs)
\ No newline at end of file
diff --git a/src/exabiome/nn/models/roznet.py b/src/exabiome/nn/models/roznet.py
index f3dc941..f951ba0 100644
--- a/src/exabiome/nn/models/roznet.py
+++ b/src/exabiome/nn/models/roznet.py
@@ -22,8 +22,10 @@ def __init__(self, hparams):
         n_outputs = getattr(hparams, 'n_outputs', 2)
         first_kernel_size = getattr(hparams, 'first_kernel_size', 7)
         maxpool = getattr(hparams, 'maxpool', True)
+        emb_dim = 8
+        self.embedding = nn.Embedding(input_nc, emb_dim)
         self.features = nn.Sequential(
-            nn.Conv1d(input_nc, 64, kernel_size=first_kernel_size, stride=1, padding=2),
+            nn.Conv1d(emb_dim, 64, kernel_size=first_kernel_size, stride=1, padding=2),
             nn.BatchNorm1d(64),
             nn.ReLU(inplace=True),
             nn.MaxPool1d(kernel_size=3, stride=1),
@@ -61,10 +63,16 @@ def __init__(self, hparams):
         )
 
     def forward(self, x, **kwargs):
+        print("===1", x.shape)
+        x = self.embedding(x).permute(0, 2, 1)
+        print("===2", x.shape)
         x = self.features(x)
+        print("===3", x.shape)
         x = self.pool(x)
         x = torch.flatten(x, 1)
+        print("===4", x.shape)
         x = self.classifier(x)
+        print("====5", x.shape)
         return x
 
 
@@ -73,7 +81,7 @@ def forward(self, x, **kwargs):
     from .train import parse_args, run, check_model, load_dataset
 
     args = parse_args("Train CNN with Spatial Pyramidal Pooling")
-                      #[['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])
+    #[['-E', '--emb_nc'], dict(type=int, default=0, help='the number of embedding channels. default is no embedding')])
 
     input_nc = 5
     if args['protein']:
diff --git a/src/exabiome/sequence/dna_table.py b/src/exabiome/sequence/dna_table.py
index 52aafcd..e2a953c 100644
--- a/src/exabiome/sequence/dna_table.py
+++ b/src/exabiome/sequence/dna_table.py
@@ -16,64 +16,13 @@
 __all__ = ['DeepIndexFile',
            'AbstractChunkedDIFile',
            'WindowChunkedDIFile',
+           'RevCompFilter',
            'SequenceTable',
            'TaxaTable']
 
 NS = 'deep-index'
 
 
-class BitpackedIndex(VectorIndex, metaclass=ABCMeta):
-
-    def _start_end(self, i):
-        start = 0 if i == 0 else self.data[i-1]
-        end = self.data[i]
-        return start, end
-
-    @abstractmethod
-    def _get_single_item(self, i):
-        pass
-
-    def __getitem__(self, args):
-        """
-        Slice ragged array of *packed* one-hot encoded DNA sequence
-        """
-        if np.issubdtype(type(args), np.integer):
-            return self._get_single_item(args)
-        else:
-            raise ValueError("Can only index bitpacked sequence with integers")
-
-
-@register_class('PackedDNAIndex', NS)
-class PackedDNAIndex(BitpackedIndex):
-
-    def _get_single_item(self, i):
-        start, end = self._start_end(i)
-        shift = start % 2
-        unpacked = np.unpackbits(self.target[start//2:math.ceil(end/2)])
-        unpacked = unpacked.reshape(-1, 4)[shift:shift+end-start].T
-        return unpacked
-
-
-@register_class('PackedAAIndex', NS)
-class PackedAAIndex(BitpackedIndex):
-
-    def _get_single_item(self, i):
-        start, end = self._start_end(i)
-        packed = self.target[start:end]
-        bits = np.unpackbits(packed)
-        bits = bits[bits.shape[0] % 5:]
-        bits = bits.reshape(-1, 5)
-        unpacked = np.pad(bits, ((0, 0), (3, 0)), mode='constant', constant_values=((0, 0), (0, 0)))
-        ohe_pos = np.packbits(unpacked, axis=1).squeeze(axis=1)
-        # trim leading zeros that may be left from padding to
-        # ensure enough bits for pack
-        # trim trailing zeros in case a non-AA character was
-        # used to terminate the original sequence
-        ohe_pos = np.trim_zeros(ohe_pos)
-        ohe_pos = ohe_pos - 1
-        return ohe_pos
-
-
 class TorchableMixin:
 
 
@@ -198,7 +147,7 @@ def get_sequence_data(self, data):
 
     @docval(*get_docval(DynamicTable.__init__),
             {'name': 'sequence_name', 'type': ('array_data', 'data', VectorData), 'doc': 'sequence names'},
-            {'name': 'sequence', 'type': ('array_data', 'data', VectorData), 'doc': 'bitpacked DNA sequence'},
+            {'name': 'sequence', 'type': ('array_data', 'data', VocabData), 'doc': 'bitpacked DNA sequence'},
             {'name': 'sequence_index', 'type': ('array_data', 'data', VectorIndex), 'doc': 'index for sequence'},
             {'name': 'length', 'type': ('array_data', 'data', VectorData), 'doc': 'lengths of sequence'},
             {'name': 'taxon', 'type': ('array_data', 'data', VectorData), 'doc': 'index for sequence'},
@@ -209,6 +158,7 @@ def get_sequence_data(self, data):
              'default': 'dna'}, )
     def __init__(self, **kwargs):
         vocab = popargs('vocab', kwargs)
+        self.vocab_type = vocab
         if vocab is not None:
             if isinstance(vocab, str):
                 if vocab == 'dna':
@@ -222,11 +172,9 @@ def __init__(self, **kwargs):
 @register_class('DNAData', NS)
 class DNAData(VocabData):
 
-    def get(self, key, rev=False, **kwargs):
+    def get(self, key, **kwargs):
         idx = self.data[key]
-        if rev:
-            idx = (idx[::-1] + 9) % 18
-        return self._get_helper(idx, **kwargs)
+        return super()._get_helper(idx, **kwargs)
 
 
 @register_class('DNATable', NS)
@@ -240,7 +188,7 @@ def get_sequence_data(self, data):
         return DNAData('sequence', 'sequence data from a vocabulary', data=data, vocabulary=vocab)
 
     def get_sequence_index(self, data, target):
-        return PackedDNAIndex('sequence_index', data, target)
+        return VectorIndex('sequence_index', data, target)
 
 
 @register_class('AATable', NS)
@@ -307,7 +255,7 @@ def func(x):
         return func
 
     def get_sequence_index(self, data, target):
-        return PackedAAIndex('sequence_index', data, target)
+        return VectorIndex('sequence_index', data, target)
 
     @classmethod
     def to_sequence(self, data):
@@ -331,7 +279,8 @@ class TaxaTable(DynamicTable, TorchableMixin):
         {'name': 'family', 'description': 'the family for each taxon'},
         {'name': 'genus', 'description': 'the genus for each taxon'},
         {'name': 'species', 'description': 'the species for each taxon'},
-        {'name': 'embedding', 'description': 'the embedding for each taxon'}
+        {'name': 'embedding', 'description': 'the embedding for each taxon'},
+        {'name': 'rep_taxon_id', 'description': 'the taxon ID for the this species representative'}
     )
 
     @docval(*get_docval(DynamicTable.__init__),
@@ -342,9 +291,10 @@ class TaxaTable(DynamicTable, TorchableMixin):
             {'name': 'family', 'type': ('array_data', 'data', VectorData), 'doc': 'the family for each taxon'},
             {'name': 'genus', 'type': ('array_data', 'data', VectorData), 'doc': 'the genus for each taxon'},
             {'name': 'species', 'type': ('array_data', 'data', VectorData), 'doc': 'the species for each taxon'},
-            {'name': 'embedding', 'type': ('array_data', 'data', VectorData), 'doc': 'the embedding for each taxon', 'default': None})
+            {'name': 'embedding', 'type': ('array_data', 'data', VectorData), 'doc': 'the embedding for each taxon', 'default': None},
+            {'name': 'rep_taxon_id', 'type': ('array_data', 'data', VectorData), 'doc': 'the taxon ID for the species representative', 'default': None})
     def __init__(self, **kwargs):
-        taxon_id, embedding = popargs('taxon_id', 'embedding', kwargs)
+        taxon_id, embedding, rep_taxon_id = popargs('taxon_id', 'embedding', 'rep_taxon_id', kwargs)
         taxonomy_labels = ['phylum', 'class', 'order', 'family', 'genus', 'species']
         taxonomy = popargs(*taxonomy_labels, kwargs)
 
@@ -353,9 +303,11 @@ def __init__(self, **kwargs):
             columns.append(taxon_id)
             columns.extend(taxonomy)
             if embedding is not None: columns.append(embedding)
+            if rep_taxon_id is not None: columns.append(rep_taxon_id)
         else:
             columns.append(VectorData('taxon_id', 'taxonomy IDs from NCBI', data=taxon_id))
             if embedding is not None: columns.append(VectorData('embedding', 'an embedding for each taxon', data=embedding))
+            if rep_taxon_id is not None: columns.append(VectorData('rep_taxon_id', 'the taxon ID for the this species representative', data=rep_taxon_id))
             for l, t in zip(taxonomy_labels, taxonomy):
                 columns.append(VectorData(l, 'the %s for each taxon' % l, data=t))
         kwargs['columns'] = columns
@@ -405,8 +357,8 @@ class DeepIndexFile(Container):
 
     @docval({'name': 'seq_table', 'type': (AATable, DNATable, SequenceTable), 'doc': 'the table storing DNA sequences'},
             {'name': 'taxa_table', 'type': TaxaTable, 'doc': 'the table storing taxa information'},
-            {'name': 'distances', 'type': CondensedDistanceMatrix, 'doc': 'the table storing taxa information'},
-            {'name': 'tree', 'type': NewickString, 'doc': 'the table storing taxa information'})
+            {'name': 'tree', 'type': NewickString, 'doc': 'the table storing taxa information'},
+            {'name': 'distances', 'type': CondensedDistanceMatrix, 'doc': 'the table storing taxa information', 'default': None})
     def __init__(self, **kwargs):
         seq_table, taxa_table, distances, tree = popargs('seq_table', 'taxa_table', 'distances', 'tree', kwargs)
         call_docval_func(super().__init__, {'name': 'root'})
@@ -419,6 +371,7 @@ def __init__(self, **kwargs):
         self.__labels = None
         self.__n_emb_components = self.taxa_table['embedding'].data.shape[1] if 'embedding' in self.taxa_table else 0
         self.label_key = 'id'
+        self.__rev = False
 
     def set_label_key(self, val):
         self.label_key = val
@@ -427,6 +380,11 @@ def set_sanity(self, sanity, n_features=5):
         self._sanity = sanity
         self._sanity_features = n_features
 
+    def set_revcomp(self, revcomp=True):
+        if revcomp and self.seq_table.vocab_type != 'dna':
+                raise ValueError("Can only set reverse complement on DNA sequence data")
+        self.__rev = revcomp
+
     @property
     def labels(self):
         if self.__labels is None:
@@ -447,11 +405,12 @@ def get(self, arg):
         idx = self.seq_table.id[arg]
         seq = self.seq_table['sequence'].get(arg, index=True)   # sequence data
         label = self.seq_table['taxon'].get(arg, index=True)    # index to taxon table
+        length = self.seq_table['length'].get(arg)
         label = self.taxa_table[self.label_key][label]          # get the interesting information from the taxon table i.e. embedding
-        return (idx, seq, label)
+        return {'id': idx, 'seq': seq, 'label': label, 'length': length}
 
     def __len__(self):
-        return len(self.seq_table)
+        return len(self.seq_table) * (2 if self.__rev else 1)
 
     def set_raw(self):
         self.seq_table.set_raw()
@@ -476,15 +435,22 @@ def get_knn_classifier(self, **kwargs):
         ret.fit(emb, np.arange(emb.shape[0]))
         return ret
 
+class DIFileFilter(object):
 
+    def __init__(self, difile):
+        self.difile = difile
+
+    def __getattr__(self, attr):
+        """Delegate retrival of attributes to the data in self.data"""
+        return getattr(self.difile, attr)
 
-class AbstractChunkedDIFile(object):
+class AbstractChunkedDIFile(DIFileFilter):
     """
     An abstract class for chunking sequences from a DeepIndexFile
     """
 
     def __init__(self, difile, seq_idx, start, end, labels):
-        self.difile = difile
+        super().__init__(difile)
         self.seq_idx = np.asarray(seq_idx)
         self.start = np.asarray(start)
         self.end = np.asarray(end)
@@ -500,12 +466,12 @@ def __getitem__(self, i):
         item = self.difile[seq_i]
         s = self.start[i]
         e = self.end[i]
-        #return seq_i, item[1][s:e], item[2]
-        return i, item[1][s:e], item[2], seq_i
-
-    def __getattr__(self, attr):
-        """Delegate retrival of attributes to the data in self.data"""
-        return getattr(self.difile, attr)
+        item['seq'] = item['seq'][s:e]
+        item['seq_idx'] = seq_i
+        item['id'] = i
+        # item['seq_name'] += f'|{s}-{e}'
+        item['length'] = e - s
+        return item
 
 
 class WindowChunkedDIFile(AbstractChunkedDIFile):
@@ -515,27 +481,89 @@ class WindowChunkedDIFile(AbstractChunkedDIFile):
     By default windows are not overlapping
     """
 
-    def __init__(self, difile, wlen, step=None):
-        if not isinstance(difile, DeepIndexFile):
-            raise ValueError(f'difile must be a DeepIndexFile, got {type(difile)}')
+    def __init__(self, difile, wlen, step=None, min_seq_len=100):
+        if not isinstance(difile, (DeepIndexFile, DIFileFilter)):
+            raise ValueError(f'difile must be a DeepIndexFile or a DIFileFilter, got {type(difile)}')
         if step is None:
             step = wlen
         self.wlen = wlen
         self.step = step
+        self.min_seq_len = min_seq_len
 
         seq_idx = list()
         chunk_start = list()
         chunk_end = list()
         labels = list()
 
-        lengths = difile.seq_table['length'][:]
-        seqlabels = difile.labels
+        # lengths = difile.seq_table['length'][:]
+        # seqlabels = difile.labels
         for i in range(len(difile)):
-            label = seqlabels[i]
-            for start in range(0, lengths[i], step):
-                labels.append(label)
-                seq_idx.append(i)
-                chunk_start.append(start)
-                chunk_end.append(start+step)
-
+            row = difile[i]
+            label = row['label'] # seqlabels[i]
+            #for start in range(0, lengths[i], step):
+            seqlen = row['length']
+            for start in range(0, seqlen, step):
+                end = min(seqlen, start+step)
+                if (end - start) >= self.min_seq_len:
+                    labels.append(label)
+                    seq_idx.append(i)
+                    chunk_start.append(start)
+                    chunk_end.append(end)
         super().__init__(difile, seq_idx, chunk_start, chunk_end, labels)
+
+
+class RevCompFilter(DIFileFilter):
+
+    rcmap = torch.tensor([ 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                           0,  1,  2,  3,  4,  5,  6,  7,  8])
+
+    chars = {
+        'A': 'T',
+        'G': 'C',
+        'C': 'G',
+        'T': 'A',
+        'Y': 'R',
+        'R': 'Y',
+        'W': 'W',
+        'S': 'S',
+        'K': 'M',
+        'M': 'K',
+        'D': 'H',
+        'V': 'B',
+        'H': 'D',
+        'B': 'V',
+        'X': 'X',
+        'N': 'N',
+    }
+
+    @classmethod
+    def get_revcomp_map(cls, vocab):
+        d = {c: i for i, c in enumerate(vocab)}
+        rcmap = np.zeros((len(vocab),))
+        for i, base in enumerate(vocab):
+            rc_base = chars[c]
+            base_i = d[base]
+            rc_base_i = d[rc_base]
+            rcmap[base_i] = rc_base_i
+        return rcmap
+
+    def __init__(self, difile):
+        super().__init__(difile)
+        self.labels = np.concatenate([self.labels, self.labels])
+        vocab = difile.sequence_table.sequences.vocabulary
+        self.rcmap = torch.as_tensor(self.get_revcomp_map(vocab), dtype=torch.long)
+
+    def __len__(self):
+        return 2*len(self.difile)
+
+    def __getitem__(self, arg):
+        oarg = arg
+        arg, rev = divmod(arg, 2)
+        item = self.difile[arg]
+        if rev:
+            item['seq'] = self.rcmap[item['seq'].long()]
+            # item['seq_name'] += '|rev'
+        # else:
+            # item['seq_name'] += '|fwd'
+        item['id'] = oarg
+        return item
\ No newline at end of file

From c69d369fcfa68e0258e7a597d98c719cf4eb67ea Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Mon, 24 Aug 2020 18:08:57 -0700
Subject: [PATCH 03/11] syncing upstream

---
 src/exabiome/gtdb/sample.py                 | 51 +++++-------
 src/exabiome/nn/models/roznet.py            |  6 --
 src/exabiome/nn/models/roznet_attn.py       | 88 +++++++++++++++++----
 src/exabiome/nn/models/roznet_multi_attn.py |  2 -
 4 files changed, 95 insertions(+), 52 deletions(-)

diff --git a/src/exabiome/gtdb/sample.py b/src/exabiome/gtdb/sample.py
index cb731bd..bd53016 100644
--- a/src/exabiome/gtdb/sample.py
+++ b/src/exabiome/gtdb/sample.py
@@ -1,12 +1,12 @@
 import numpy as np
 
-def sample_taxa(taxa_df, n_classes=1, n_genera=1, n_species=1):
+def sample_taxa(taxa_df, n_classes=1, n_genera=1, n_species=1, n_total=None):
     """
     Sample taxa from the given taxonomy data frame.
 
     Sample at a minimum one species from each class. If *n_species*, *n_genera*,
     or *n_classes* are specified, more species/genera will be sampled within
-    the specificed number of classes.
+    the specified number of classes.
 
     For example, if n_classes=1, n_genera=1, and n_species=2, one class will have
     2 species sampled within a single genus.
@@ -20,47 +20,39 @@ def sample_taxa(taxa_df, n_classes=1, n_genera=1, n_species=1):
     """
 
     def get_it(col):
-        """Get taxa sorted by the number of times they occurr"""
+        """Get taxa sorted by the number of times they occur"""
         return sorted(zip(*np.unique(col, return_counts=True)), key=lambda x: -1*x[1])
 
     taxa = list()
-    n_sampled_genera = 0
     n_sampled_classes = 0
-
-    for c, count in get_it(taxdf['class']):
-        #n_sampled_classes += 1
-        ocol = taxdf['genus'][taxdf['class'] == c]
+    for c, count in get_it(taxa_df['class']):
+        if n_sampled_classes == n_classes:
+            break
+        ocol = taxa_df['genus'][taxa_df['class'] == c]
+        n_sampled_genera = 0
 
         # get genera
         for g, g_count in get_it(ocol):
-            scol = taxdf['species'][taxdf['genus'] == g]
+            if n_sampled_genera == n_genera:
+                break
+            scol = taxa_df['species'][taxa_df['genus'] == g]
             n_sampled_species = 0
 
             # get species
             for s, s_count in get_it(scol):
-                taxa.append((c, g, s))
-                n_sampled_species += 1
-                if n_sampled_genera >= n_genera:
-                    break
                 if n_sampled_species == n_species:
                     break
-
-            if n_sampled_species == n_species:   # we found a deep genera
-                n_sampled_genera += 1
-
-            if n_sampled_genera >= n_genera:
-                n_sampled_classes += 1
-                break
-
-        if n_sampled_classes < n_classes:
-            n_sampled_genera = 0
+                taxa.append((c, g, s))
+                n_sampled_species += 1
+            n_sampled_genera += 1
+        n_sampled_classes += 1
 
     # get accessions from (class, genus, species) tuples
     accessions = list()
+    taxa = taxa[:n_total]
     for c, g, s in taxa:
-        accessions.append(taxdf[(taxdf['class'] == c) & (taxdf['genus'] == g) & (taxdf['species'] == s)].index[0])
-
-    return taxdf.filter(accessions, axis=0)
+        accessions.append(taxa_df[(taxa_df['class'] == c) & (taxa_df['genus'] == g) & (taxa_df['species'] == s)].index[0])
+    return taxa_df.filter(accessions, axis=0)
 
 
 from .. import command
@@ -83,6 +75,8 @@ def sample_tree(argv=None):
     parser.add_argument('-c', '--n_classes', type=int, help='the number of classes to sample in depth', default=1)
     parser.add_argument('-g', '--n_genera', type=int, help='the number of genera to sample in depth', default=1)
     parser.add_argument('-s', '--n_species', type=int, help='the number of species to sample in depth', default=1)
+    parser.add_argument('-t', '--n_total', type=int, help='the total number of species to sample')
+
     grp = parser.add_mutually_exclusive_group()
     grp.add_argument('-P', '--protein', action='store_true', default=False, help='get paths for protein files')
     grp.add_argument('-C', '--cds', action='store_true', default=False, help='get paths for CDS files')
@@ -91,8 +85,7 @@ def sample_tree(argv=None):
     args = parser.parse_args(args=argv)
 
     taxdf, tree = read_metadata(args.metadata, args.tree)
-
-    df = sample_taxa(taxdf, args.n_classes, args.n_genera, args.n_species)
+    df = sample_taxa(taxdf, args.n_classes, args.n_genera, args.n_species, args.n_total)
 
     if args.fadir is not None:
         func = get_genomic_path
@@ -105,7 +98,5 @@ def sample_tree(argv=None):
     else:
         for acc in df.index:
             print(acc, file=sys.stdout)
-
-
 if __name__ == '__main__':
     sample_tree()
diff --git a/src/exabiome/nn/models/roznet.py b/src/exabiome/nn/models/roznet.py
index 11918bc..48f064f 100644
--- a/src/exabiome/nn/models/roznet.py
+++ b/src/exabiome/nn/models/roznet.py
@@ -63,14 +63,8 @@ def __init__(self, hparams):
         )
 
     def forward(self, x, **kwargs):
-<<<<<<< HEAD
-        print("===1", x.shape)
-        x = self.embedding(x).permute(0, 2, 1)
-        print("===2", x.shape)
-=======
         x = self.embedding(x)
         x = x.permute(0, 2, 1)
->>>>>>> upstream/master
         x = self.features(x)
         print("===3", x.shape)
         x = self.pool(x)
diff --git a/src/exabiome/nn/models/roznet_attn.py b/src/exabiome/nn/models/roznet_attn.py
index 7fac09b..fc90969 100644
--- a/src/exabiome/nn/models/roznet_attn.py
+++ b/src/exabiome/nn/models/roznet_attn.py
@@ -5,16 +5,15 @@
 
 from . import model, AbstractLit
 
-class Self_Attention(nn.Module):
+class SelfAttention(nn.Module):
     """ Self attention Layer """
-    def __init__(self, in_dim, activation):
+    def __init__(self, in_dim):
         super().__init__()
         self.in_channel = in_dim
-        self.activation = activation
 
-        self.query = nn.Conv1d(in_channels = in_dim, out_channels = in_dim//8, kernel_size= 1)
-        self.key = nn.Conv1d(in_channels = in_dim, out_channels = in_dim//8, kernel_size= 1)
-        self.value = nn.Conv1d(in_channels = in_dim, out_channels = in_dim, kernel_size= 1)
+        self.query = nn.Conv1d(in_channels=in_dim, out_channels=in_dim//8, kernel_size= 1)
+        self.key = nn.Conv1d(in_channels=in_dim, out_channels=in_dim//8, kernel_size= 1)
+        self.value = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size= 1)
         self.gamma = nn.Parameter(torch.zeros(1))
 
         self.softmax = nn.Softmax(dim=-1)
@@ -31,13 +30,11 @@ def forward(self,x):
             2. matmul transposed query (B x W x C/8) by key (B x C/8 x W) = scores (B x W x W)
             3. normalize to sum to 1 with softmax
             4. matmul value (B x C x W) by scores (B x W x W) = out (B x C x W)
-            
-
         """
         batch_size, conv, width = x.size()
-        proj_query  = self.query(x).view(batch_size, -1, width).permute(0, 2, 1) 
-        proj_key =  self.key(x).view(batch_size, -1, width) 
-        scores =  torch.bmm(proj_query, proj_key) 
+        proj_query = self.query(x).view(batch_size, -1, width).permute(0, 2, 1)
+        proj_key = self.key(x).view(batch_size, -1, width)
+        scores = torch.bmm(proj_query, proj_key)
         attention = self.softmax(scores) 
         proj_value = self.value(x).view(batch_size, -1, width)
         out = torch.bmm(proj_value, attention)
@@ -45,7 +42,67 @@ def forward(self,x):
 
         out = self.gamma*out + x
         return out, attention
-    
+
+class MultiHeadAttention(AbstractLit):
+    """ multi-headed attention Layer """
+    def __init__(self, in_dim, activation, num_heads, hparams):
+        super().__init__(hparams)
+        self.in_channel = in_dim
+        self.activation = activation
+        self.num_attention_heads = num_heads
+        assert in_dim % self.num_attention_heads == 0, "The input size is not a multiple of the number of attention heads"
+        self.attention_head_size = int(in_dim / self.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
+        self.key = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
+        self.value = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
+        self.gamma = nn.Parameter(torch.zeros(1))
+
+        self.softmax = nn.Softmax(dim=-1)
+
+        self.conv_out = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
+
+    def forward(self, x):
+        """
+            inputs :
+                x : input feature maps(B X C X W)
+            returns :
+                out : self attention value + input feature (B x C x W)
+                attention: softmax scores (B X W X W)
+            steps:
+
+        """
+        batch_size, conv, width = x.size()
+
+        proj_query = self.query(x).permute(0, 2, 1)
+        proj_key = self.key(x).permute(0, 2, 1)
+        proj_value = self.value(x).permute(0, 2, 1)
+
+        query_layer_size = proj_query.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        proj_query = proj_query.view(query_layer_size)
+        query_layer = proj_query.permute(0, 2, 1, 3)
+        key_layer_size = proj_key.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        proj_key = proj_key.view(key_layer_size)
+        key_layer = proj_key.permute(0, 2, 1, 3)
+        value_layer_size = proj_value.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        proj_value = proj_value.view(value_layer_size)
+        value_layer = proj_value.permute(0, 2, 1, 3)
+
+        scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        scores = scores / math.sqrt(self.attention_head_size)
+        attention_probs = nn.Softmax(dim=-1)(scores)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        context_layer = context_layer.view(batch_size, conv, width)
+        out = self.conv_out(context_layer)
+        return out
+
 
 @model('roznet_attn')
 class RozNetAttn(AbstractLit):
@@ -63,8 +120,10 @@ def __init__(self, hparams):
         n_outputs = getattr(hparams, 'n_outputs', 2)
         first_kernel_size = getattr(hparams, 'first_kernel_size', 7)
         maxpool = getattr(hparams, 'maxpool', True)
+        emb_dim = 8
+        self.embedding = nn.Embedding(input_nc, emb_dim)
         self.features = nn.Sequential(
-            nn.Conv1d(input_nc, 64, kernel_size=first_kernel_size, stride=1, padding=2),
+            nn.Conv1d(emb_dim, 64, kernel_size=first_kernel_size, stride=1, padding=2),
             nn.BatchNorm1d(64),
             nn.ReLU(inplace=True),
             nn.MaxPool1d(kernel_size=3, stride=1),
@@ -100,9 +159,10 @@ def __init__(self, hparams):
             #nn.BatchNorm1d(n_outputs)
         )
 
-        self.attention = Self_Attention(256, 'relu')
+        self.attention = SelfAttention(256)
 
     def forward(self, x, **kwargs):
+        x = self.embedding(x).permute(0, 2, 1)
         x = self.features(x)
         x = self.attention(x)[0]
         x = self.pool(x)
diff --git a/src/exabiome/nn/models/roznet_multi_attn.py b/src/exabiome/nn/models/roznet_multi_attn.py
index 948a05e..3ada780 100644
--- a/src/exabiome/nn/models/roznet_multi_attn.py
+++ b/src/exabiome/nn/models/roznet_multi_attn.py
@@ -10,8 +10,6 @@ def __init__(self, in_dim, activation, num_heads, hparams):
         super().__init__(hparams)
         self.in_channel = in_dim
         self.activation = activation
-        print("====", num_heads)
-        # add to config later
         self.num_attention_heads = num_heads
         assert in_dim % self.num_attention_heads == 0, "The input size is not a multiple of the number of attention heads"
         self.attention_head_size = int(in_dim / self.num_attention_heads)

From bdf7f132e5ce7872ee599801f52cfbcf42a8d184 Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Fri, 4 Sep 2020 23:57:47 -0700
Subject: [PATCH 04/11] return locus tag as id if --cds is true

---
 src/exabiome/gtdb/prepare_data.py |  8 ++++++--
 src/exabiome/sequence/convert.py  | 12 ++++++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/src/exabiome/gtdb/prepare_data.py b/src/exabiome/gtdb/prepare_data.py
index 85be781..74a881e 100644
--- a/src/exabiome/gtdb/prepare_data.py
+++ b/src/exabiome/gtdb/prepare_data.py
@@ -85,7 +85,7 @@ def prepare_data(argv=None):
     from hdmf.data_utils import DataChunkIterator
 
     from ..utils import get_faa_path, get_fna_path, get_genomic_path
-    from exabiome.sequence.convert import AASeqIterator, DNASeqIterator, DNAVocabIterator
+    from exabiome.sequence.convert import AASeqIterator, DNASeqIterator, DNAVocabIterator, DNAVocabGeneIterator
     from exabiome.sequence.dna_table import AATable, DNATable, SequenceTable, TaxaTable, DeepIndexFile, NewickString, CondensedDistanceMatrix
 
     parser = argparse.ArgumentParser()
@@ -215,7 +215,11 @@ def func(row):
             seqit = AAVocabIterator(fapaths, logger=logger, min_seq_len=args.min_len)
         else:
             SeqTable = DNATable
-            seqit = DNAVocabIterator(fapaths, logger=logger, min_seq_len=args.min_len)
+            if args.cds:
+                logger.info("reading and writing CDS sequences")
+                seqit = DNAVocabGeneIterator(fapaths, logger=logger, min_seq_len=args.min_len)
+            else:
+                seqit = DNAVocabIterator(fapaths, logger=logger, min_seq_len=args.min_len)
     else:
         if args.protein:
             logger.info("reading and writing protein sequences")
diff --git a/src/exabiome/sequence/convert.py b/src/exabiome/sequence/convert.py
index d6e155c..099e832 100644
--- a/src/exabiome/sequence/convert.py
+++ b/src/exabiome/sequence/convert.py
@@ -240,6 +240,7 @@ def pack_dna(cls, seq, ohe=None):
         return packed
 
 
+
 class UnrecognizedCharacter(Exception):
 
     def __init__(self, c):
@@ -360,6 +361,17 @@ class DNAVocabIterator(VocabIterator):
 
     chars, basemap = _get_DNA_map()
 
+class DNAVocabGeneIterator(VocabIterator):
+
+    chars, basemap = _get_DNA_map()
+
+    def _read_seq(self, path):
+        self.logger.info('reading %s', path)
+        kwargs = {'format': 'fasta', 'constructor': self.skbio_cls, 'validate': False}
+        for seq in skbio.io.read(path, **kwargs):
+            description = re.findall("\[([^[\]]*)\]", seq.metadata['description'])
+            ltag = [s.lstrip("locus_tag=") for s in description if "locus_tag=" in s]
+            yield seq, ltag
 
 def _get_AA_map():
     chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

From 3db87114da37214b31554ba6f2ccc30179dfe046 Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Sat, 5 Sep 2020 00:05:46 -0700
Subject: [PATCH 05/11] change sample-taxa to do what comment says

---
 src/exabiome/gtdb/sample.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/exabiome/gtdb/sample.py b/src/exabiome/gtdb/sample.py
index d57ee02..fe9d174 100644
--- a/src/exabiome/gtdb/sample.py
+++ b/src/exabiome/gtdb/sample.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 
-def sample_taxa(taxdf, n_classes=1, n_genera=1, n_species=1):
+def sample_taxa(taxdf, n_classes=1, n_genera=1, n_species=1, n_total=None):
     """
     Sample taxa from the given taxonomy data frame.
 
@@ -26,17 +26,17 @@ def get_it(col):
 
     taxa = list()
     n_sampled_classes = 0
-    for c, count in get_it(taxa_df['class']):
+    for c, count in get_it(taxdf['class']):
         if n_sampled_classes == n_classes:
             break
-        ocol = taxa_df['genus'][taxa_df['class'] == c]
+        ocol = taxdf['genus'][taxdf['class'] == c]
         n_sampled_genera = 0
 
         # get genera
         for g, g_count in get_it(ocol):
             if n_sampled_genera == n_genera:
                 break
-            scol = taxa_df['species'][taxa_df['genus'] == g]
+            scol = taxdf['species'][taxdf['genus'] == g]
             n_sampled_species = 0
 
             # get species
@@ -52,8 +52,8 @@ def get_it(col):
     accessions = list()
     taxa = taxa[:n_total]
     for c, g, s in taxa:
-        accessions.append(taxa_df[(taxa_df['class'] == c) & (taxa_df['genus'] == g) & (taxa_df['species'] == s)].index[0])
-    return taxa_df.filter(accessions, axis=0)
+        accessions.append(taxdf[(taxdf['class'] == c) & (taxdf['genus'] == g) & (taxdf['species'] == s)].index[0])
+    return taxdf.filter(accessions, axis=0)
 
 
 from .. import command

From 553bc9409d07d8c15555866f172f1b55d05ac77a Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Sat, 5 Sep 2020 00:10:14 -0700
Subject: [PATCH 06/11] remove multi

---
 src/exabiome/nn/models/roznet_attn.py | 60 ---------------------------
 1 file changed, 60 deletions(-)

diff --git a/src/exabiome/nn/models/roznet_attn.py b/src/exabiome/nn/models/roznet_attn.py
index fc90969..c556b3b 100644
--- a/src/exabiome/nn/models/roznet_attn.py
+++ b/src/exabiome/nn/models/roznet_attn.py
@@ -43,66 +43,6 @@ def forward(self,x):
         out = self.gamma*out + x
         return out, attention
 
-class MultiHeadAttention(AbstractLit):
-    """ multi-headed attention Layer """
-    def __init__(self, in_dim, activation, num_heads, hparams):
-        super().__init__(hparams)
-        self.in_channel = in_dim
-        self.activation = activation
-        self.num_attention_heads = num_heads
-        assert in_dim % self.num_attention_heads == 0, "The input size is not a multiple of the number of attention heads"
-        self.attention_head_size = int(in_dim / self.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
-        self.key = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
-        self.value = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
-        self.gamma = nn.Parameter(torch.zeros(1))
-
-        self.softmax = nn.Softmax(dim=-1)
-
-        self.conv_out = nn.Conv1d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
-
-    def forward(self, x):
-        """
-            inputs :
-                x : input feature maps(B X C X W)
-            returns :
-                out : self attention value + input feature (B x C x W)
-                attention: softmax scores (B X W X W)
-            steps:
-
-        """
-        batch_size, conv, width = x.size()
-
-        proj_query = self.query(x).permute(0, 2, 1)
-        proj_key = self.key(x).permute(0, 2, 1)
-        proj_value = self.value(x).permute(0, 2, 1)
-
-        query_layer_size = proj_query.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        proj_query = proj_query.view(query_layer_size)
-        query_layer = proj_query.permute(0, 2, 1, 3)
-        key_layer_size = proj_key.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        proj_key = proj_key.view(key_layer_size)
-        key_layer = proj_key.permute(0, 2, 1, 3)
-        value_layer_size = proj_value.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        proj_value = proj_value.view(value_layer_size)
-        value_layer = proj_value.permute(0, 2, 1, 3)
-
-        scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        scores = scores / math.sqrt(self.attention_head_size)
-        attention_probs = nn.Softmax(dim=-1)(scores)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(new_context_layer_shape)
-
-        context_layer = context_layer.view(batch_size, conv, width)
-        out = self.conv_out(context_layer)
-        return out
-
 
 @model('roznet_attn')
 class RozNetAttn(AbstractLit):

From af91d6a2dac38e389a8ad667e0ca876fa6ecb5fc Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Sat, 5 Sep 2020 00:10:42 -0700
Subject: [PATCH 07/11] add seaborn

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index fe9c3c1..fc2267c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,3 +6,4 @@ hdmf
 umap
 torch
 pytorch_lightning
+seaborn
\ No newline at end of file

From 98ed566192356fa37e7dedb8166c1910e8961e9b Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Tue, 8 Sep 2020 22:19:00 -0700
Subject: [PATCH 08/11] add locus tag to findall  grouping

---
 src/exabiome/sequence/convert.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/exabiome/sequence/convert.py b/src/exabiome/sequence/convert.py
index 099e832..e16eaba 100644
--- a/src/exabiome/sequence/convert.py
+++ b/src/exabiome/sequence/convert.py
@@ -369,8 +369,7 @@ def _read_seq(self, path):
         self.logger.info('reading %s', path)
         kwargs = {'format': 'fasta', 'constructor': self.skbio_cls, 'validate': False}
         for seq in skbio.io.read(path, **kwargs):
-            description = re.findall("\[([^[\]]*)\]", seq.metadata['description'])
-            ltag = [s.lstrip("locus_tag=") for s in description if "locus_tag=" in s]
+            ltag = re.findall("\[locus_tag=([^[\]]*)\]", seq.metadata['description'])
             yield seq, ltag
 
 def _get_AA_map():

From f8ca4fa7c079bfa7b87a6f23cf967ffb37bde3a8 Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Thu, 10 Sep 2020 15:06:08 -0700
Subject: [PATCH 09/11] get string id

---
 src/exabiome/sequence/convert.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/exabiome/sequence/convert.py b/src/exabiome/sequence/convert.py
index e16eaba..56463df 100644
--- a/src/exabiome/sequence/convert.py
+++ b/src/exabiome/sequence/convert.py
@@ -369,7 +369,7 @@ def _read_seq(self, path):
         self.logger.info('reading %s', path)
         kwargs = {'format': 'fasta', 'constructor': self.skbio_cls, 'validate': False}
         for seq in skbio.io.read(path, **kwargs):
-            ltag = re.findall("\[locus_tag=([^[\]]*)\]", seq.metadata['description'])
+            ltag = re.findall("\[locus_tag=([^[\]]*)\]", seq.metadata['description'])[0]
             yield seq, ltag
 
 def _get_AA_map():

From 7bca3adba54bf5e0c1bdbba404032c4835712074 Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Fri, 11 Sep 2020 12:41:10 -0700
Subject: [PATCH 10/11] filter by locus tag input file

---
 src/exabiome/gtdb/prepare_data.py | 9 ++++++++-
 src/exabiome/sequence/convert.py  | 7 +++++--
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/src/exabiome/gtdb/prepare_data.py b/src/exabiome/gtdb/prepare_data.py
index d3db8b2..8271375 100644
--- a/src/exabiome/gtdb/prepare_data.py
+++ b/src/exabiome/gtdb/prepare_data.py
@@ -87,11 +87,13 @@ def prepare_data(argv=None):
 
     parser = argparse.ArgumentParser()
     parser.add_argument('accessions', type=str, help='file of the NCBI accessions of the genomes to convert')
+
     parser.add_argument('fadir', type=str, help='directory with NCBI sequence files')
     parser.add_argument('metadata', type=str, help='metadata file from GTDB')
     parser.add_argument('tree', type=str, help='the distances file')
     parser.add_argument('out', type=str, help='output HDF5')
     grp = parser.add_mutually_exclusive_group()
+    parser.add_argument('--locus_tags', type=str, help='file of the NCBI locus tags of the genes to convert', default=None)
     parser.add_argument('-e', '--emb', type=str, help='embedding file', default=None)
     grp.add_argument('-P', '--protein', action='store_true', default=False, help='get paths for protein files')
     grp.add_argument('-C', '--cds', action='store_true', default=False, help='get paths for CDS files')
@@ -118,6 +120,11 @@ def prepare_data(argv=None):
     with open(args.accessions, 'r') as f:
         taxa_ids = [l[:-1] for l in f.readlines()]
 
+    # read locus tags
+    logger.info('reading locus tags %s' % args.locus_tags)
+    with open(args.locus_tags, 'r') as f:
+        locus_ids = [l[:-1] for l in f.readlines()]
+
     # get paths to Fasta Files
     fa_path_func = get_genomic_path
     if args.cds:
@@ -214,7 +221,7 @@ def func(row):
             SeqTable = DNATable
             if args.cds:
                 logger.info("reading and writing CDS sequences")
-                seqit = DNAVocabGeneIterator(fapaths, logger=logger, min_seq_len=args.min_len)
+                seqit = DNAVocabGeneIterator(fapaths, locus_ids=locus_ids, logger=logger, min_seq_len=args.min_len)
             else:
                 seqit = DNAVocabIterator(fapaths, logger=logger, min_seq_len=args.min_len)
     else:
diff --git a/src/exabiome/sequence/convert.py b/src/exabiome/sequence/convert.py
index 56463df..09c936d 100644
--- a/src/exabiome/sequence/convert.py
+++ b/src/exabiome/sequence/convert.py
@@ -362,7 +362,9 @@ class DNAVocabIterator(VocabIterator):
     chars, basemap = _get_DNA_map()
 
 class DNAVocabGeneIterator(VocabIterator):
-
+    def __init__(self, paths, locus_ids=None, logger=None, min_seq_len=None):
+        super().__init__(paths, logger=logger, min_seq_len=min_seq_len)
+        self.locus_ids = locus_ids
     chars, basemap = _get_DNA_map()
 
     def _read_seq(self, path):
@@ -370,7 +372,8 @@ def _read_seq(self, path):
         kwargs = {'format': 'fasta', 'constructor': self.skbio_cls, 'validate': False}
         for seq in skbio.io.read(path, **kwargs):
             ltag = re.findall("\[locus_tag=([^[\]]*)\]", seq.metadata['description'])[0]
-            yield seq, ltag
+            if ltag in self.locus_ids:
+                yield seq, ltag
 
 def _get_AA_map():
     chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

From e93e43e5a70ad6316ca6c4e389497aefac8018f8 Mon Sep 17 00:00:00 2001
From: Kelly Huang <kellyhuang@berkeley.edu>
Date: Mon, 14 Sep 2020 16:23:59 -0700
Subject: [PATCH 11/11] change list to set

---
 src/exabiome/gtdb/prepare_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/exabiome/gtdb/prepare_data.py b/src/exabiome/gtdb/prepare_data.py
index 8271375..bc993b8 100644
--- a/src/exabiome/gtdb/prepare_data.py
+++ b/src/exabiome/gtdb/prepare_data.py
@@ -123,7 +123,7 @@ def prepare_data(argv=None):
     # read locus tags
     logger.info('reading locus tags %s' % args.locus_tags)
     with open(args.locus_tags, 'r') as f:
-        locus_ids = [l[:-1] for l in f.readlines()]
+        locus_ids = set([l[:-1] for l in f.readlines()])
 
     # get paths to Fasta Files
     fa_path_func = get_genomic_path