Entrypoint setup

README.md

@@ -1,8 +1,8 @@
-# GraphMB
+# GraphMB: Assembly Graph Metagenomic Binner
 
 # Introduction
 
-GraphMB is a metagenomics binner developed for long-read assemblies, that takes advantage of graph machine learning 
+GraphMB is a Metagenomic Binner developed for long-read assemblies, that takes advantage of graph machine learning 
 algorithms and the assembly graph generated during assembly. It has been tested on (meta)flye assemblies.
 
 ## Dependencies
@@ -76,6 +76,8 @@ You can also run on CPU and limit the number of threads to use:
 python src/graphmb/main.py  --assembly data/strong100/ --numcores 4
 ```
 
+If installed with pip, you can also use `graphmb` instead of `python src/graphmb/main.py`.
+
 ## Typical workflow
 Our workflows are available [here](https://github.com/AndreLamurias/binning_workflows).
 On this section we present an overview of how to get your data ready for GraphMB.

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = graphmb-andrelamurias
-version = 0.1.1
+version = 0.1.2
 author = Andre Lamurias
 author_email = [email protected]
 description = Binner based on deep learning and assembly graph embeddings
@@ -21,7 +21,7 @@ package_dir =
 packages = find:
 python_requires = >=3.7
 install_requires = 
-    vamb @ git+ssh://git@github.com/AndreLamurias/vamb.git
+    vamb @ git+git://github.com/AndreLamurias/vamb.git
     networkx==2.6.2
     #torch==1.7.1
     scikit-learn==0.24.2
@@ -33,3 +33,8 @@ include_package_data = True
 [options.packages.find]
 where = src
 
+[options.entry_points]
+console_scripts =
+    graphmb = graphmb:main.main
+
+

setup.py

@@ -6,19 +6,22 @@
 
 setup(
     name="graphmb",
-    version="0.1.1",
+    version="0.1.2",
     packages=["graphmb"],
     package_dir={"": "src"},
     setup_requires=["setuptools~=58.0", "wheel"],
     install_requires=[
         "wheel",
         "requests",
         "setuptools>57.5.0" 'importlib; python_version == "3.7"',
-        "vamb @ git+https://github.com/AndreLamurias/vamb",
+        "vamb @ git+git://github.com/AndreLamurias/vamb",
         "networkx==2.6.2",
         # torch==1.7.1
         "scikit-learn==0.24.2",
         "dgl==0.6.1",
         "tqdm==4.61.2",
     ],
+    entry_points={
+        "console_scripts": ["graphmb=graphmb.main:main"],
+    },
 )

src/graphmb/__init__.py

@@ -1 +1,4 @@
-
+from . import contigsdataset
+from . import graph_functions
+from . import evaluate
+from . import graphsage_unsupervised

src/graphmb/contigsdataset.py

@@ -8,7 +8,7 @@
 import networkx as nx
 from dgl.data import DGLDataset
 from dgl.data.utils import makedirs, save_info, load_info
-from graph_functions import read_reads_mapping_sam, count_kmers, get_kmer_to_id
+from graphmb.graph_functions import read_reads_mapping_sam, count_kmers, get_kmer_to_id
 
 
 class ContigsDataset(DGLDataset):

src/graphmb/graph_functions.py

@@ -12,7 +12,7 @@
 import dgl
 import random
 
-from evaluate import read_contig_genes, read_marker_gene_sets, evaluate_contig_sets
+from graphmb.evaluate import read_contig_genes, read_marker_gene_sets, evaluate_contig_sets
 import torch
 
 SEED = 0

src/graphmb/graphsage_unsupervised.py

@@ -14,7 +14,7 @@
 import copy
 import sklearn
 
-from graph_functions import cluster_eval, set_seed
+from graphmb.graph_functions import cluster_eval, set_seed
 
 # Based on this implemention: https://github.com/dmlc/dgl/blob/master/examples/pytorch/graphsage/train_sampling_unsupervised.py
 

src/graphmb/main.py

@@ -18,12 +18,12 @@
 
 
 import os
-from contigsdataset import ContigsDataset
+from graphmb.contigsdataset import ContigsDataset
 from pathlib import Path
 import scipy.stats as stats
-from evaluate import read_marker_gene_sets, read_contig_genes, evaluate_contig_sets, get_markers_to_contigs
-from graphsage_unsupervised import train_graphsage, SAGE
-from graph_functions import (
+from graphmb.evaluate import read_marker_gene_sets, read_contig_genes, evaluate_contig_sets, get_markers_to_contigs
+from graphmb.graphsage_unsupervised import train_graphsage, SAGE
+from graphmb.graph_functions import (
     plot_embs,
     cluster_embs,
     evaluate_binning,