Sourcery refactored main branch by sourcery-ai[bot] · Pull Request #6 · fairdataihub/AQUA

sourcery-ai · 2022-05-25T17:53:11Z

Branch main refactored by Sourcery.

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sourcery-ai · 2022-05-25T17:53:12Z


 def plot_most_freq_search_term(n=10):
-    title='<b> Top '+str(n)+' Most Frequent Search Terms </b>'
+    title = f'<b> Top {str(n)} Most Frequent Search Terms </b>'


Function plot_most_freq_search_term refactored with the following changes:

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2022-05-25T17:53:13Z

-    else:
-        df_search=pd.DataFrame.from_dict(rsp['hits']['hits'])
-        df_search=pd.concat([df_search['_source'].apply(pd.Series)['pennsieve'].apply(pd.Series)[['identifier']],
-        df_search[['_id']],#df_search[['_id','_score']],
-        df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)[['name','description']],
-        df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)['published'].apply(pd.Series)['status'],
-        ], axis=1)
-        df_search['link']= 'https://sparc.science/datasets/'+df_search['identifier']
-        df_search=df_search[['_id','name','description','status','link']]
-        return df_search
+    df_search=pd.DataFrame.from_dict(rsp['hits']['hits'])
+    df_search=pd.concat([df_search['_source'].apply(pd.Series)['pennsieve'].apply(pd.Series)[['identifier']],
+    df_search[['_id']],#df_search[['_id','_score']],
+    df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)[['name','description']],
+    df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)['published'].apply(pd.Series)['status'],
+    ], axis=1)
+    df_search['link']= 'https://sparc.science/datasets/'+df_search['identifier']
+    df_search=df_search[['_id','name','description','status','link']]
+    return df_search


Function retrieve_search_to_df refactored with the following changes:

Remove unnecessary else after guard condition (remove-unnecessary-else)

sourcery-ai · 2022-05-25T17:53:13Z

-        f = open(fileName, "r")
-        htmlText = f.read()
-        f.close()
+        with open(fileName, "r") as f:
+            htmlText = f.read()
    except:
-        return html("We are sorry,there was an error ..., %s is not found" % fileName)
+        return html(f"We are sorry,there was an error ..., {fileName} is not found")


Function loadMain refactored with the following changes:

Use with when opening file to ensure closure (ensure-file-closed)

Replace interpolated string formatting with f-string (replace-interpolation-with-fstring)

sourcery-ai · 2022-05-25T17:53:13Z

    summary = {}
-    url = 'https://scicrunch.org/api/1/elastic/SPARC_PortalDatasets_pr/_search?api_key='+api_key
+    url = f'https://scicrunch.org/api/1/elastic/SPARC_PortalDatasets_pr/_search?api_key={api_key}'
+


Function getSearch refactored with the following changes:

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2022-05-25T17:53:13Z

-    url = 'https://scicrunch.org/api/1/scigraph/vocabulary/suggestions/'+query
+    url = f'https://scicrunch.org/api/1/scigraph/vocabulary/suggestions/{query}'


Function __getSuggestions refactored with the following changes:

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2022-05-25T17:53:13Z

-    url = 'https://scicrunch.org/api/1/scigraph/vocabulary/autocomplete/'+query
+    url = f'https://scicrunch.org/api/1/scigraph/vocabulary/autocomplete/{query}'


Function __getAutoComplete_sc refactored with the following changes:

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2022-05-25T17:53:13Z

-    else:
-        df_search=pd.DataFrame.from_dict(rsp['hits']['hits'])
-        df_search=pd.concat([
-            df_search['_source'].apply(pd.Series)['pennsieve'].apply(pd.Series)[['identifier']],
-            df_search[['_id','_score']],
-            df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)[['name','description']],
-            df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)['published'].apply(pd.Series)['status'],
-            ], axis=1)
-        df_search['link']= 'https://sparc.science/datasets/'+df_search['identifier']
-
-        return df_search
+    df_search=pd.DataFrame.from_dict(rsp['hits']['hits'])
+    df_search=pd.concat([
+        df_search['_source'].apply(pd.Series)['pennsieve'].apply(pd.Series)[['identifier']],
+        df_search[['_id','_score']],
+        df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)[['name','description']],
+        df_search['_source'].apply(pd.Series)['item'].apply(pd.Series)['published'].apply(pd.Series)['status'],
+        ], axis=1)
+    df_search['link']= 'https://sparc.science/datasets/'+df_search['identifier']
+
+    return df_search


Function retrieve_search_to_df refactored with the following changes:

Remove unnecessary else after guard condition (remove-unnecessary-else)

sourcery-ai · 2022-05-25T17:53:26Z

Sourcery Code Quality Report

✅ Merging this PR will increase code quality in the affected files by 0.31%.

Quality metrics	Before	After	Change
Complexity	6.43 ⭐	6.40 ⭐	-0.03 👍
Method Length	77.10 🙂	77.19 🙂	0.09 👎
Working memory	8.27 🙂	8.12 🙂	-0.15 👍
Quality	67.53% 🙂	67.84% 🙂	0.31% 👍

Other metrics	Before	After	Change
Lines	721	725	4

Changed files	Quality Before	Quality After	Quality Change
NotifyMe/NM_analytics_utils.py	63.56% 🙂	63.56% 🙂	0.00%
NotifyMe/Notifyme_utils.py	73.97% 🙂	74.57% 🙂	0.60% 👍
aqua_docker/app/utils/helper.py	55.47% 🙂	55.21% 🙂	-0.26% 👎
aqua_docker/app/utils/notifyme_utils.py	74.70% 🙂	75.30% ⭐	0.60% 👍

Here are some functions in these files that still need a tune-up:

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
aqua_docker/app/utils/helper.py	getSearch	32 😞	602 ⛔	14 😞	18.49% ⛔	Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
NotifyMe/Notifyme_utils.py	send_mail_capture_error	17 🙂	87 🙂	11 😞	53.27% 🙂	Extract out complex expressions
aqua_docker/app/utils/notifyme_utils.py	send_mail_capture_error	17 🙂	87 🙂	11 😞	53.27% 🙂	Extract out complex expressions
NotifyMe/Notifyme_utils.py	scan_waiting_list	3 ⭐	147 😞	8 🙂	64.52% 🙂	Try splitting into smaller methods
aqua_docker/app/utils/notifyme_utils.py	scan_waiting_list	3 ⭐	140 😞	8 🙂	65.29% 🙂	Try splitting into smaller methods

Legend and Explanation

The emojis denote the absolute quality of the code:

⭐ excellent
🙂 good
😞 poor
⛔ very poor

The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request.

Please see our documentation here for details on how these metrics are calculated.

We are actively working on this report - lots more documentation and extra metrics to come!

Help us improve this quality report!

'Refactored by Sourcery'

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sourcery-ai Bot requested a review from bvhpatel May 25, 2022 17:53

sourcery-ai Bot commented May 25, 2022

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Sourcery refactored main branch#6

Sourcery refactored main branch#6
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		url = 'https://scicrunch.org/api/1/scigraph/vocabulary/suggestions/'+query
		url = f'https://scicrunch.org/api/1/scigraph/vocabulary/suggestions/{query}'

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sourcery-ai Bot commented May 25, 2022

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