Merge pull request #352 from picrust/v2.5.3

V2.5.3

.circleci/config.yml

@@ -19,5 +19,5 @@ jobs:
             conda init bash
             source ~/.bashrc
             conda activate picrust2
-            pip install --editable .
+            pip install --use-pep517 --config-settings editable_mode=compat -e .
             pytest -v

README.md

@@ -1,7 +1,8 @@
 # PICRUSt2
 
-Pleae see the [PICRUSt2 wiki](https://github.com/picrust/picrust2/wiki) for the documentation and tutorials.
+Please see the [PICRUSt2 wiki](https://github.com/picrust/picrust2/wiki) for the documentation and tutorials.
 
 Note that this repository contains the source code for MinPath, which is distributed under the GNU General Public License.
 
-**If you have a question about PICRUSt2, please ask this on the [PICRUSt users Google group](https://groups.google.com/g/picrust-users) rather than creating an issue on Github.**
+**If you have a question about PICRUSt2, please ask it on the [PICRUSt users Google group](https://groups.google.com/g/picrust-users) rather than creating an issue on GitHub.**
+

picrust2-env.yaml

@@ -11,7 +11,8 @@ dependencies:
 - biom-format >=2.1.10
 - cython
 - epa-ng=0.3.8
-- gappa=0.8.0
+- dendropy=4.5.2
+- gappa >=0.8.0,<=0.8.5
 - glpk >=4.65
 - h5py >=2.10.0
 - hmmer >=3.1b2,<=3.2.1
@@ -21,8 +22,8 @@ dependencies:
 - pandas >=1.1.5
 - pytest >=4.4.1
 - pytest-cov >=2.6.1
-- python >=3.5,<3.9
+- python >=3.5
 - r-base >=3.5.1
 - r-castor >=1.7.2
 - scipy >=1.2.1
-- sepp=4.3.10
+- sepp=4.4.0

picrust2/metagenome_pipeline.py

@@ -153,7 +153,7 @@ def strat_funcs_by_samples(func_abun, sample_abun, rare_seqs=[],
 
     # Return dataframe and also unstratified dataframe if specified.
     if return_unstrat:
-        return(strat_func, strat_func.groupby(level='function', axis=0).sum())
+        return(strat_func, strat_func.groupby(level='function').sum())
     else:
         return(strat_func)
 
@@ -392,7 +392,7 @@ def contrib_to_unstrat(contrib_table, sample_order=None):
     contrib_table = pd.pivot_table(data=contrib_table, columns='sample',
                                    index='function',
                                    values='taxon_function_abun',
-                                   aggfunc=np.sum, fill_value=0)
+                                   aggfunc='sum', fill_value=0)
 
     contrib_table.index.name = None
     contrib_table.columns.name = None

picrust2/pathway_pipeline.py

@@ -605,7 +605,7 @@ def path_abun_weighted_by_seq(reaction_abun, func_ids, total_sum, path_abun,
 
     # Get dataframe with sum of all genes per sequence.
     seq_path_abun = pd.pivot_table(data=reaction_abun, index="sequence",
-                                   aggfunc=np.sum)
+                                   aggfunc='sum')
 
     # Get weighted pathway abundance.
     strat_path_abun = (seq_path_abun / total_sum) * path_abun
@@ -640,7 +640,7 @@ def contributional_path_abun(reaction_abun, func_ids, total_sum, path_abun,
                                          'taxon',
                                          'taxon_abun',
                                          'taxon_rel_abun'],
-                                  aggfunc=np.sum)
+                                  aggfunc='sum')
 
     contrib_path.reset_index(inplace=True)
 
@@ -859,7 +859,7 @@ def contrib_format_pathway_levels(sample_id, contrib_input, minpath_map, out_dir
     unstrat_input = contrib_to_unstrat(contrib_input)
 
     # Define dictionary for keeping track of reaction abundances.
-    reaction_abun = unstrat_input[sample_id].to_dict(defaultdict(int))
+    reaction_abun = unstrat_input[sample_id].to_dict(into=defaultdict(int))
 
     if run_minpath:
         pathways_present = minpath_wrapper(sample_id, unstrat_input,
@@ -961,7 +961,7 @@ def basic_strat_pathway_levels(sample_id, strat_input, minpath_map, out_dir,
     unstrat_input = strat_to_unstrat_counts(strat_input)
 
     # Define dictionary for keeping track of reaction abundances.
-    reaction_abun = unstrat_input[sample_id].to_dict(defaultdict(int))
+    reaction_abun = unstrat_input[sample_id].to_dict(into=defaultdict(int))
 
     if run_minpath:
 
@@ -1043,7 +1043,7 @@ def unstrat_pathway_levels(sample_id, unstrat_input, minpath_map, out_dir,
     unstrat_input.set_index("function", drop=True, inplace=True)
 
     # Define dictionary for keeping track of reaction abundances.
-    reaction_abun = unstrat_input[sample_id].to_dict(defaultdict(int))
+    reaction_abun = unstrat_input[sample_id].to_dict(into=defaultdict(int))
 
     if run_minpath:
 
@@ -1193,16 +1193,16 @@ def regroup_func_ids(in_df, in_format, mapfile, proc):
                                          index=['sample', 'function', 'taxon',
                                                 'taxon_abun',
                                                 'taxon_rel_abun'],
-                                         aggfunc=np.sum)
+                                         aggfunc='sum')
 
     elif in_format == "strat":
         regrouped_table = pd.pivot_table(raw_new_ids_combined,
                                          index=["function", "sequence"],
-                                         aggfunc=np.sum)
+                                         aggfunc='sum')
     elif in_format == "unstrat":
         regrouped_table = pd.pivot_table(raw_new_ids_combined,
                                          index="function",
-                                         aggfunc=np.sum)
+                                         aggfunc='sum')
 
     regrouped_table.reset_index(inplace=True)
     regrouped_table.columns.name = None
@@ -1427,4 +1427,4 @@ def strat_to_unstrat_counts(strat_df, func_col="function"):
 
     strat_df = strat_df.drop(["sequence"], axis=1)
 
-    return(pd.pivot_table(data=strat_df, index=func_col, aggfunc=np.sum))
+    return(pd.pivot_table(data=strat_df, index=func_col, aggfunc='sum'))

pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=64", "wheel"]
+build-backend = "setuptools.build_meta"

setup.py

@@ -4,8 +4,8 @@
 from glob import glob
 
 __license__ = "GPL"
-__version__ = "2.5.2"
-__maintainer__ = "Gavin Douglas"
+__version__ = "2.5.3"
+__maintainer__ = "Langille Lab"
 
 long_description = ("Douglas et al. 2020. PICRUSt2 for prediction of "
                     "metagenome functions. Nature Biotechnology. doi: "