#179 update docs

peptdeep/hla/hla_class1.py

@@ -20,16 +20,6 @@
     nonspecific_digest_cat_proteins,
 )
 
-
-_model_zip_name = global_settings['local_hla_model_zip_name']
-_model_url = global_settings['hla_model_url']
-_model_zip = os.path.join(
-    pretrain_dir, _model_zip_name
-)
-
-if not os.path.exists(_model_zip):
-    download_models(url=_model_url, target_path=_model_zip)
-
 class HLA_Class_I_LSTM(torch.nn.Module):
     """
     HLA-I-binding peptide prediction model using LSTM.
@@ -132,6 +122,13 @@ class HLA1_Binding_Classifier(ModelInterface):
     """
     Class to predict HLA-binding probabilities of peptides.
     """
+
+    _model_zip_name = global_settings['local_hla_model_zip_name']
+    _model_url = global_settings['hla_model_url']
+    _model_zip = os.path.join(
+        pretrain_dir, _model_zip_name
+    )
+
     def __init__(self,
         dropout:float=0.1,
         model_class:type=HLA_Class_I_LSTM, # model defined above
@@ -398,11 +395,16 @@ def predict_from_proteins(self,
         )
         return peptide_df
 
+    def _download_pretrained_hla_model(self):
+        download_models(url=self._model_url, target_path=self._model_zip)
+
     def load_pretrained_hla_model(self):
         """
         Load pretrained `HLA1_IEDB.pt` model.
         """
+        if not os.path.exists(self._model_zip):
+            self._download_pretrained_hla_model()
         self.load(
-            model_file=_model_zip,
+            model_file=self._model_zip,
             model_path_in_zip="HLA1_IEDB.pt"
         )

peptdeep/hla/hla_utils.py

@@ -11,14 +11,14 @@
 from alphabase.protein.fasta import load_all_proteins
 
 def load_prot_df(
-    protein_data:Union[str,list,dict],
+    protein_data:Union[str,list,tuple,set,dict],
 )->pd.DataFrame:
     """
     Load protein dataframe from input protein_data.
 
     Parameters
     ----------
-    protein_data : Union[str,list,dict]
+    protein_data : Union[str,list,tuple,set,dict]
         str: fasta file
         list (tuple, or set): a list of fasta files
         dict: protein dict
@@ -27,17 +27,23 @@ def load_prot_df(
     -------
     pd.DataFrame
         protein dataframe
+
+    Raises
+    ------
+    TypeError
+        protein_data type is not one of str, list, tuple, set, or dict.
     """
     if isinstance(protein_data, str):
         protein_dict = load_all_proteins([protein_data])
     elif isinstance(protein_data, (list,tuple,set)):
         protein_dict = load_all_proteins(protein_data)
-    elif isinstance(protein_data, str):
-        protein_dict = load_all_proteins([protein_data])
     elif isinstance(protein_data, dict):
         protein_dict = protein_data
     else:
-        return pd.DataFrame()
+        raise TypeError(
+            "`protein_data` must be str, list, tuple, set or dict, "
+            f"`{type(protein_data)}` is given."
+        )
     prot_df = pd.DataFrame().from_dict(protein_dict, orient='index')
     prot_df['nAA'] = prot_df.sequence.str.len()
     return prot_df

peptdeep/pretrained_models.py

@@ -83,11 +83,11 @@ def download_models(
     ----------
     url : str, optional
         Remote or local path.
-        Defaults to `peptdeep.pretrained_models.model_url`
+        Defaults to :data:`peptdeep.pretrained_models.model_url`
 
     target_path : str, optional
         Target file path after download.
-        Defaults to `peptdeep.pretrained_models.model_zip`
+        Defaults to :data:`peptdeep.pretrained_models.model_zip`
 
     overwrite : bool, optional
         overwirte old model files.

requirements/requirements_hla.txt

@@ -1 +1 @@
-pydivsufsort
+pydivsufsort # used by alphabase.protein.lcp_digest