better handling of default class1 models configuration

mhcflurry/class1_affinity_predictor.py

@@ -18,7 +18,7 @@
 
 from .class1_neural_network import Class1NeuralNetwork
 from .common import random_peptides
-from .downloads import get_path
+from .downloads import get_default_class1_models_dir
 from .encodable_sequences import EncodableSequences
 from .percent_rank_transform import PercentRankTransform
 from .regression_target import to_ic50
@@ -283,7 +283,7 @@ def load(models_dir=None, max_models=None):
         `Class1AffinityPredictor` instance
         """
         if models_dir is None:
-            models_dir = get_path("models_class1", "models")
+            models_dir = get_default_class1_models_dir()
 
         manifest_path = join(models_dir, "manifest.csv")
         manifest_df = pandas.read_csv(manifest_path, nrows=max_models)

mhcflurry/downloads.py

@@ -9,7 +9,7 @@
 )
 import logging
 import yaml
-from os.path import join, exists
+from os.path import join, exists, relpath
 from pipes import quote
 from os import environ
 from collections import OrderedDict
@@ -20,11 +20,14 @@
     "MHCFLURRY_DATA_DIR",
     "MHCFLURRY_DOWNLOADS_CURRENT_RELEASE",
     "MHCFLURRY_DOWNLOADS_DIR",
+    "MHCFLURRY_DEFAULT_CLASS1_MODELS"
 ]
 
 _DOWNLOADS_DIR = None
 _CURRENT_RELEASE = None
 _METADATA = None
+_MHCFLURRY_DEFAULT_CLASS1_MODELS_DIR = environ.get(
+    "MHCFLURRY_DEFAULT_CLASS1_MODELS_DIR")
 
 
 def get_downloads_dir():
@@ -51,6 +54,37 @@ def get_downloads_metadata():
     return _METADATA
 
 
+def get_default_class1_models_dir(test_exists=True):
+    """
+    Return the absolute path to the default class1 models dir.
+
+    If environment variable MHCFLURRY_DEFAULT_CLASS1_MODELS_DIR is set to an
+    absolute path, return that path. If it's set to a relative path (i.e. does
+    not start with /) then return that path taken to be relative to the mhcflurry
+    downloads dir.
+
+    If environment variable MHCFLURRY_DEFAULT_CLASS1_MODELS_DIR is NOT set,
+    then return the path to downloaded models in the "models_class1" download.
+
+    Parameters
+    ----------
+
+    test_exists : boolean, optional
+        Whether to raise an exception of the path does not exist
+
+    Returns
+    -------
+    string : absolute path
+    """
+    if _MHCFLURRY_DEFAULT_CLASS1_MODELS_DIR:
+        result = join(get_downloads_dir(), _MHCFLURRY_DEFAULT_CLASS1_MODELS_DIR)
+        if test_exists and not exists(result):
+            raise IOError("No such directory: %s" % result)
+        return result
+    else:
+        return get_path("models_class1", "models", test_exists=test_exists)
+
+
 def get_current_release_downloads():
     """
     Return a dict of all available downloads in the current release.

mhcflurry/downloads_command.py

@@ -229,15 +229,13 @@ def exists_string(path):
 
     downloads = get_current_release_downloads()
 
-    format_string = "%-40s  %-12s   %-12s  %-20s "
-    print(format_string % (
-        "DOWNLOAD NAME", "DOWNLOADED?", "DEFAULT?", "URL"))
+    format_string = "%-40s  %-12s  %-20s "
+    print(format_string % ("DOWNLOAD NAME", "DOWNLOADED?", "URL"))
 
     for (item, info) in downloads.items():
         print(format_string % (
             item,
             yes_no(info['downloaded']),
-            yes_no(info['metadata']['default']),
             info['metadata']["url"]))
 
 

mhcflurry/predict_command.py

@@ -33,7 +33,7 @@
 
 import pandas
 
-from .downloads import get_path
+from .downloads import get_default_class1_models_dir
 from .class1_affinity_predictor import Class1AffinityPredictor
 
 
@@ -122,7 +122,7 @@
     metavar="DIR",
     default=None,
     help="Directory containing models. "
-    "Default: %s" % get_path("models_class1", "models", test_exists=False))
+    "Default: %s" % get_default_class1_models_dir(test_exists=False))
 model_args.add_argument(
     "--include-individual-model-predictions",
     action="store_true",
@@ -143,7 +143,7 @@ def run(argv=sys.argv[1:]):
         # The reason we set the default here instead of in the argument parser is that
         # we want to test_exists at this point, so the user gets a message instructing
         # them to download the models if needed.
-        models_dir = get_path("models_class1", "models")
+        models_dir = get_default_class1_models_dir(test_exists=True)
     predictor = Class1AffinityPredictor.load(models_dir)
 
     # The following two are informative commands that can come