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Add h5unifrac_all method #153

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Apr 24, 2023
Merged

Add h5unifrac_all method #153

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2971b14
Add h5unifrac_all and add multi support for h5unifrac
sfiligoi Apr 24, 2023
fa24dea
Add multi h5 support to h5pcoa
sfiligoi Apr 24, 2023
f6b7fca
Add unifrac h5 interface tests
sfiligoi Apr 24, 2023
5898295
Update method list in README
sfiligoi Apr 24, 2023
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7 changes: 7 additions & 0 deletions .github/workflows/main.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -114,6 +114,13 @@ jobs:
python -c "import h5py,skbio; dm = skbio.DistanceMatrix.read('ci/test.dm'); f_u=h5py.File('ci/test2.dm.h5','r'); dm_u=skbio.stats.distance.DistanceMatrix(f_u['matrix'][:,:],f_u['order'][:]); t=abs(dm_u.data-dm.data).max(); print(t); assert t < 0.1"
python -c "import h5py; f_u=h5py.File('ci/test2.dm.h5','r'); print(f_u.keys()); assert len(f_u['stat_methods'][:]) == 1"
python -c "import h5py; f_u=h5py.File('ci/test3.dm.h5','r'); print(f_u.keys()); assert len(f_u['pcoa_eigvals'][:]) == 2"
# repeat using unifrac's h5 interfaces
python -c "import unifrac; dm_u=unifrac.h5unifrac('ci/test.dm.h5'); dm_l=unifrac.h5unifrac_all('ci/test.dm.h5')"
python -c "import unifrac,skbio; dm = skbio.DistanceMatrix.read('ci/test.dm'); dm_u=unifrac.h5unifrac('ci/test.dm.h5'); t=abs(dm_u.data-dm.data).max(); print(t); assert t < 0.1"
python -c "import unifrac,skbio; dm = skbio.DistanceMatrix.read('ci/test.dm'); dm_u=unifrac.h5unifrac_all('ci/test.dm.h5')[0]; t=abs(dm_u.data-dm.data).max(); print(t); assert t < 0.1"
python -c "import unifrac,skbio; dm = skbio.DistanceMatrix.read('ci/test.dm'); dm_u=unifrac.h5unifrac('ci/test2.dm.h5'); t=abs(dm_u.data-dm.data).max(); print(t); assert t < 0.1"
python -c "import unifrac; st_l=unifrac.h5permanova_dict('ci/test2.dm.h5'); assert len(st_l) == 1"
python -c "import unifrac; pc=unifrac.h5pcoa('ci/test3.dm.h5'); print(pc); assert len(pc.eigvals) == 2"
if [[ "$(uname -s)" == "Linux" ]];
then
MD5=md5sum
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15 changes: 8 additions & 7 deletions README.md
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Expand Up @@ -140,13 +140,14 @@ The library can be accessed directly from within Python. If operating in this mo
>>> import unifrac
>>> dir(unifrac)
['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__',
'__package__', '__path__', '__spec__', '__version__', '_api', '_meta', '_methods', 'set_random_seed', 'faith_pd',
'generalized', 'generalized_fp32', 'generalized_fp32_to_file', 'generalized_fp64', 'generalized_fp64_to_file', 'generalized_to_file',
'h5pcoa', 'h5pcoa_all', 'h5permanova', 'h5permanova_dict', 'h5unifrac', 'meta', 'pkg_resources', 'ssu', 'ssu_fast', 'ssu_inmem', 'ssu_to_file', 'ssu_to_file_v2',
'unweighted', 'unweighted_fp32', 'unweighted_fp32_to_file', 'unweighted_fp64', 'unweighted_fp64_to_file', 'unweighted_to_file',
'weighted_normalized', 'weighted_normalized_fp32', 'weighted_normalized_fp32_to_file', 'weighted_normalized_fp64', 'weighted_normalized_fp64_to_file',
'weighted_normalized_to_file', 'weighted_unnormalized', 'weighted_unnormalized_fp32', 'weighted_unnormalized_fp32_to_file', 'weighted_unnormalized_fp64',
'weighted_unnormalized_fp64_to_file', 'weighted_unnormalized_to_file']
'__package__', '__path__', '__spec__', '__version__', '_api', '_meta', '_methods',
'faith_pd', 'generalized', 'generalized_fp32', 'generalized_fp32_to_file', 'generalized_fp64', 'generalized_fp64_to_file', 'generalized_to_file',
'h5pcoa', 'h5pcoa_all', 'h5permanova', 'h5permanova_dict', 'h5unifrac', 'h5unifrac_all', 'meta', 'pkg_resources',
'set_random_seed', 'ssu', 'ssu_fast', 'ssu_inmem', 'ssu_to_file', 'ssu_to_file_v2', 'unweighted', 'unweighted_fp32',
'unweighted_fp32_to_file', 'unweighted_fp64', 'unweighted_fp64_to_file', 'unweighted_to_file', 'weighted_normalized',
'weighted_normalized_fp32', 'weighted_normalized_fp32_to_file', 'weighted_normalized_fp64', 'weighted_normalized_fp64_to_file',
'weighted_normalized_to_file', 'weighted_unnormalized', 'weighted_unnormalized_fp32', 'weighted_unnormalized_fp32_to_file',
'weighted_unnormalized_fp64', 'weighted_unnormalized_fp64_to_file', 'weighted_unnormalized_to_file']
>>> print(unifrac.unweighted.__doc__)
Compute Unweighted UniFrac

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4 changes: 2 additions & 2 deletions unifrac/__init__.py
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Expand Up @@ -33,7 +33,7 @@
weighted_unnormalized_fp32_to_file,
generalized_fp32_to_file,
meta,
h5unifrac,
h5unifrac, h5unifrac_all,
h5pcoa, h5pcoa_all,
h5permanova, h5permanova_dict)
from unifrac._api import ssu, ssu_fast, faith_pd, set_random_seed
Expand All @@ -57,7 +57,7 @@
'weighted_normalized_fp32_to_file',
'weighted_unnormalized_fp32_to_file',
'generalized_fp32_to_file',
'h5unifrac', 'h5pcoa', 'h5pcoa_all',
'h5unifrac', 'h5unifrac_all', 'h5pcoa', 'h5pcoa_all',
'h5permanova', 'h5permanova_dict',
'ssu', 'ssu_fast', 'faith_pd',
'ssu_to_file', 'ssu_to_file_v2',
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72 changes: 67 additions & 5 deletions unifrac/_methods.py
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Expand Up @@ -2508,7 +2508,7 @@ def generalized_fp32_to_file(table: str,


def h5unifrac(h5file: str) -> skbio.DistanceMatrix:
"""Read UniFrac from a hdf5 file
"""Read UniFrac distance matrix from a hdf5 file

Parameters
----------
Expand Down Expand Up @@ -2538,13 +2538,68 @@ def h5unifrac(h5file: str) -> skbio.DistanceMatrix:
"""

with h5py.File(h5file, "r") as f_u:
dm = skbio.DistanceMatrix(
if 'matrix:0' in f_u.keys():
# multi format
dm = skbio.DistanceMatrix(
f_u['matrix:0'][:, :],
[c.decode('ascii') for c in f_u['order'][:]])
else:
# single format
dm = skbio.DistanceMatrix(
f_u['matrix'][:, :],
[c.decode('ascii') for c in f_u['order'][:]])

return dm


def h5unifrac_all(h5file: str) -> skbio.DistanceMatrix:
"""Read all UniFrac distance matrices from a hdf5 file

Parameters
----------
h5file : str
A filepath to a hdf5 file.

Returns
-------
tuple(skbio.DistanceMatrix)
The distance matrices.

Raises
------
OSError
If the hdf5 file is not found
KeyError
If the hdf5 does not have the necessary fields

References
----------
.. [1] Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for
comparing microbial communities. Appl. Environ. Microbiol. 71, 8228-8235
(2005).
.. [2] Chang, Q., Luan, Y. & Sun, F. Variance adjusted weighted UniFrac: a
powerful beta diversity measure for comparing communities based on
phylogeny. BMC Bioinformatics 12:118 (2011).
"""

with h5py.File(h5file, "r") as f_u:
order = [c.decode('ascii') for c in f_u['order'][:]]
if 'matrix' in f_u.keys():
# single format
dms = [skbio.DistanceMatrix(
f_u['matrix'][:, :], order)]
else:
# multi format
dms = []
i = 0
while 'matrix:%i' % i in f_u.keys():
dms.append(skbio.DistanceMatrix(
f_u['matrix:%i' % i][:, :], order))
i = i + 1

return dms


def _build_pcoa(f_u, long_method_name, order_index,
eigval_key, samples_key, prop_key):
axis_labels = ["PC%d" % i for i in
Expand Down Expand Up @@ -2597,9 +2652,16 @@ def h5pcoa(h5file: str) -> skbio.OrdinationResults:
order_index = [c.decode('ascii')
for c in f_u['order'][:]]

pc = _build_pcoa(f_u, long_method_name, order_index,
'pcoa_eigvals', 'pcoa_samples',
'pcoa_proportion_explained')
if 'pcoa_eigvals:0' in f_u.keys():
# multi interface
pc = _build_pcoa(f_u, long_method_name, order_index,
'pcoa_eigvals:0', 'pcoa_samples:0',
'pcoa_proportion_explained:0')
else:
# single interface
pc = _build_pcoa(f_u, long_method_name, order_index,
'pcoa_eigvals', 'pcoa_samples',
'pcoa_proportion_explained')

return pc

Expand Down