Flake Changes NO_JIRA

Author: Alex-AMC

api_paper_2024/example_1/ReadMe.md

@@ -1,10 +1,7 @@
-# Extending Substructure Searching using the CSD Python API 
-
+# Extending Substructure Searching using the CSD Python API
 By searching the CSD using a SMARTS query through the CSD Python API and further inspect molecules as required.
 Using SMARTS, it is possible to express a complex Isoflavone query which is recursive in nature.
-
-
-## To Run: 
+## To Run
 ```bash
 python isovflavone_search.py
-```
+```

api_paper_2024/example_1/isovflavone_search.py

@@ -12,6 +12,7 @@
 """
 Credit - this code was written by Jason C. Cole, Natalie Johnson and Alex Moldovan
 """
+import argparse
 
 from ccdc.io import EntryReader
 from ccdc.search import SubstructureSearch, SMARTSSubstructure
@@ -106,12 +107,12 @@ def run(self):
 if __name__ == "__main__":
 
     sub = "$([#1]),$([OH1]),$([OX1H0]),$(O[CH3]),$(Oc1ccccc1)"
-    query_string = f"c(!@[{sub}:1])1c(!@[{sub}:2])c(!@[{sub}:3])c(!@[{sub}:4])c(OC(!@[{sub}:5])=C(c2c(!@[{sub}:6])c(!@[{sub}:7])c(!@[{sub}:8])c(!@[{sub}:9])c(!@[{sub}:10])2)C(=O)c1)" # noqa
+    query_string = (f"c(!@[{sub}:1])1c(!@[{sub}:2])c(!@[{sub}:3])c(!@[{sub}:4])c(OC(!@[{sub}:5])"
+                    f"=C(c2c(!@[{sub}:6])c(!@[{sub}:7])c(!@[{sub}:8])c(!@[{sub}:9])c(!@[{sub}:10])2)C(=O)c1)")  # noqa
 
-
-    import argparse
     parser = argparse.ArgumentParser()
-    parser.add_argument('-d','--database', default='CSD',help='Path to the file to search or "CSD" to use the CSD')
+    parser.add_argument('-d', '--database', default='CSD',
+                        help='Path to the file to search or "CSD" to use the CSD')
     args = parser.parse_args()
 
     database = 'tiny.sdf'

api_paper_2024/example_2/ReadMe.md

@@ -1,21 +1,18 @@
 # Mining third-party resources for structural context
-
 The CSD Python API, combined with other Python and REST APIs, can be very powerful for mining for such relationships.
 Each CCDC refcode has associated publication information, and many have an associated publication document object
-identifier (DOI). ##TODO Add very quick summary of tool. 
+identifier (DOI).
 
 ## Dependencies
-
 - habanero
 
 Optional:
 
 - wordcloud
-- nltk (after install please consult https://www.nltk.org/data.html)
+- nltk (after install please consult <https://www.nltk.org/data.html>)
 
 ```conda install -c conda-forge habanero wordcloud nltk```
 
 ## To Run
-
 Add script folder through CSD Python API drop down menu.
-With a structure opened, click  `about_entry.py` in menu dropdown. 
+With a structure opened, click  `about_entry.py` in menu dropdown.

api_paper_2024/example_2/about_entry.py

@@ -17,7 +17,7 @@
 # through mercury.
 import warnings
 
-warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=DeprecationWarning)  # noqa
 import os
 import time
 

api_paper_2024/example_2/language_processing.py

@@ -28,7 +28,7 @@
 # For now, to prevent deprecation warnings tripping things up
 import warnings
 
-warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=DeprecationWarning)  # noqa
 
 from wordcloud import WordCloud
 
@@ -37,6 +37,7 @@
 from nltk.stem.wordnet import WordNetLemmatizer
 
 import pandas as pd
+from csv import writer
 
 
 class FrequencyCalculator:
@@ -83,7 +84,7 @@ def write_word_frequency_table(self, how_many, filename='single_word_frequencies
         n_elements, rowdata = self.most_common_words(how_many)
 
         with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
-            w = writer(csvfile)  ## Writer is not assigned - missing import?
+            w = writer(csvfile)
             w.writerow(['word', 'count', 'frequency'])
             for row in rowdata:
                 towrite = [row[0], row[1], row[1] * 100.0 / n_elements]

api_paper_2024/example_2/opencitations.py

@@ -174,9 +174,9 @@ def run(self, doi):
 
 def test_dois():
     test_dois_links = ['10.1021/jp103212z', '10.1016/j.jorganchem.2012.03.020',
-            '10.1016/j.poly.2018.06.030', '10.1002/bip.360231115',
-            '10.1007/978-1-4684-1354-0_4', '10.1002/chin.197846077',
-            '10.1038/292055a0']
+                       '10.1016/j.poly.2018.06.030', '10.1002/bip.360231115',
+                       '10.1007/978-1-4684-1354-0_4', '10.1002/chin.197846077',
+                       '10.1038/292055a0']
 
     import ccdc.utilities
 

api_paper_2024/example_2/url_requesting.py

@@ -55,7 +55,7 @@ def run(self, url, retries=3):
 
         try:
             return request.json()
-        except requests.exceptions.JSONDecodeError as exc:
+        except requests.exceptions.JSONDecodeError:
             if self._logger is not None:
                 self._logger.warning(f"Decode of request failed {request} {url}")
             raise URLRequestError(f"Decode of request failed {request} {url}", -9999)

api_paper_2024/example_3/ReadMe.md

@@ -3,15 +3,15 @@
 A key feature used in this script is GOLD in interactive docking mode;
 this allows a user to set up a GOLD daemon object on a socket and send individual molecules to the socket for docking.
 This avoids repeated initialisation when used to receive ligands from external data sources.
-The ChEMBL Python API (Davies et al., 2015) is used to facilitate similarity searching in the ChEMBL database. 
+The ChEMBL Python API (Davies et al., 2015) is used to facilitate similarity searching in the ChEMBL database.
 
 ## Dependencies
 
 Optional:
-- chembl_webresource_client
 
+- chembl_webresource_client
 
-## To Run 
+## To Run
 ```bash
 python similarity_docking.py Example/unconstrained.conf
-```
+```

api_paper_2024/example_3/similarity_docking.py

@@ -15,7 +15,7 @@
 
     This has been adapted from the example program provided in the CSD Python API to allow for
     use of other data sources.
-    
+
     Original program written by Richard Sykes
     Modified for use with Chembl by Jason C. Cole and Alex Moldovan
 """
@@ -75,7 +75,7 @@ def __init__(self, smiles, identifier):
             raise ChemblParseError(smiles, identifier, f"{e}")
 
         if conformers is None or len(conformers) == 0:
-            raise ChemblParseError(smiles, identifier, f"No conformers generated ")
+            raise ChemblParseError(smiles, identifier, "No conformers generated ")
 
         self.molecule = conformers[0].molecule.copy()
         self.molecule.identifier = identifier

api_paper_2024/example_4/readme.md

@@ -41,4 +41,4 @@ maximum radius for interactions
 
 ## Author
 
-Chris Kingsbury (2024)
+Chris Kingsbury (2024)

api_paper_2024/example_5/ReadMe.md

@@ -5,7 +5,6 @@ The structure loading and morphology calculations can be replaced with customise
 The main shape classification is carried out in `shape_classification.py` whilst the visualisation is handled
 in `visualiser.py`
 
-
 ## Dependencies
 - plotly 
 ```conda install -c conda-forge plotly```