black formated

Author: parikshit14

setup.cfg

@@ -34,6 +34,7 @@ classifiers =
 packages = find:
 install_requires =
    pyyaml
+   pymongo
    numpy
    tabulate
    attrs

src/modelspec/base_types.py

@@ -183,9 +183,7 @@ def to_bson_file(self, filename: str, include_metadata: bool = True) -> str:
             filename = f"{self.id}.bson"
 
         with open(filename, "wb") as outfile:
-            bson_data = bson.encode(
-                bson_converter.unstructure(self.to_dict()),
-            )
+            bson_data = bson.encode(bson_converter.unstructure(self.to_dict()),)
             outfile.write(bson_data)
 
         return filename
@@ -280,7 +278,7 @@ def from_bson_file(cls, filename: str) -> "Base":
         Returns:
             An modelspec :class:`.Base` for this BSON
         """
-        with open("document.bson",'rb') as infile:
+        with open("document.bson", "rb") as infile:
             data_encoded = infile.read()
             d = bson.decode(data_encoded)
             return cls.from_dict(d)
@@ -668,13 +666,11 @@ def insert_links(text, format=MARKDOWN_FORMAT):
                 doc_dict[name]["allowed_parameters"][f]["description"] = description
 
             elif format == MARKDOWN_FORMAT:
-                doc_string += (
-                    "\n  <tr>\n    <td><b>{}</b></td>\n    <td>{}</td>".format(
-                        f,
-                        f'<a href="#{type_str.lower()}">{type_str}</a>'
-                        if referencable
-                        else type_str,
-                    )
+                doc_string += "\n  <tr>\n    <td><b>{}</b></td>\n    <td>{}</td>".format(
+                    f,
+                    f'<a href="#{type_str.lower()}">{type_str}</a>'
+                    if referencable
+                    else type_str,
                 )
                 doc_string += "\n    <td><i>%s</i></td>\n </tr>\n\n" % (
                     insert_links(description)
@@ -729,13 +725,11 @@ def insert_links(text, format=MARKDOWN_FORMAT):
                 ][0]
 
             elif format == MARKDOWN_FORMAT:
-                doc_string += (
-                    "\n  <tr>\n    <td><b>{}</b></td>\n    <td>{}</td>".format(
-                        c,
-                        f'<a href="#{type_str.lower()}">{type_str}</a>'
-                        if referencable
-                        else type_str,
-                    )
+                doc_string += "\n  <tr>\n    <td><b>{}</b></td>\n    <td>{}</td>".format(
+                    c,
+                    f'<a href="#{type_str.lower()}">{type_str}</a>'
+                    if referencable
+                    else type_str,
                 )
                 doc_string += "\n    <td><i>%s</i></td>\n  </tr>\n\n" % (
                     insert_links(description)
@@ -921,8 +915,7 @@ def _is_list_base(cl):
 
 converter.register_unstructure_hook_factory(_is_list_base, _unstructure_list_base)
 converter.register_unstructure_hook_factory(
-    lambda cl: issubclass(cl, Base),
-    _base_unstruct_hook_factory,
+    lambda cl: issubclass(cl, Base), _base_unstruct_hook_factory,
 )
 
 converter.register_structure_hook_factory(_is_list_base, _structure_list_base)

src/modelspec/utils.py

@@ -37,7 +37,7 @@ def load_bson(filename):
     """
     Load a generic BSON file
     """
-    with open(filename,'rb') as infile:
+    with open(filename, "rb") as infile:
         data_encoded = infile.read()
         data = bson.decode(data_encoded)
 

tests/test_base.py

@@ -313,6 +313,7 @@ class Node(Base):
     model = Node(id="a", metadata={"b": np.array([0])})
     model.to_json()
 
+
 def test_ndarray_bson_metadata():
     import numpy as np
 
@@ -323,9 +324,12 @@ class Node(Base):
             kw_only=True, default=None, validator=optional(instance_of(dict))
         )
 
-    model = Node(id="a", metadata={"b": np.random.randint(10,10000,1000)}) #creates an array of 1000 integers ranging from 1 to 10000
+    model = Node(
+        id="a", metadata={"b": np.random.randint(10, 10000, 1000)}
+    )  # creates an array of 1000 integers ranging from 1 to 10000
     model.to_bson_file("sample.bson")
-    model.to_json_file("sample.json") #to compare the size
+    model.to_json_file("sample.json")  # to compare the size
     model.from_bson_file("sample.bson")
 
+
 test_ndarray_bson_metadata()