Merge pull request #1 from SynBioHub/library-updates

Plugins Updated

Author: zane-perry

README.md

@@ -1,6 +1,6 @@
 # INSTALL 
 ## Docker
-Run `docker run --publish 8080:5000 --detach --name component-use-plug synbiohub/plugin-visual-component-use` Check it is up using localhost:8080/sankey/status
+Run `docker run --publish 8095:5000 --detach --name component-use-plug synbiohub/plugin-visual-component-use` Check it is up using localhost:8095/sankey/status
 
 ## Python
 Using python run `pip install -r requirements.txt` to install the requirements. 

app.py

@@ -12,6 +12,7 @@
 from toggle_bars import toggle_bars
 
 import tempfile, os, shutil
+import traceback
 
 app = Flask(__name__)
 
@@ -27,7 +28,7 @@ def Sankey_Evaluate():
 
     ########## REPLACE THIS SECTION WITH OWN RUN CODE #################
     #uses rdf types
-    accepted_types = {'Component'}
+    accepted_types = {'Component', 'ComponentDefinition'}
 
     acceptable = rdf_type in accepted_types
 
@@ -51,6 +52,7 @@ def Sankey_Run():
     size = data['size']
     rdf_type = data['type']
     shallow_sbol = data['shallow_sbol']
+    token = data['token']
 
     url = complete_sbol.replace('/sbol','')
 
@@ -61,15 +63,13 @@ def Sankey_Run():
         #top_level_url = 'https://dev.synbiohub.org/public/igem/BBa_B0012/1'
 
         #retrieve information about the poi
-        self_df, display_id, title, role, count = input_data(top_level_url, instance_url)
+        self_df, display_id, title, role, count = input_data(top_level_url, instance_url, token)
 
-        #print("Find role name")
         #Find the role name in the ontology of the part of interest
         role_link = find_role_name(role, plural = False)
 
         #create data for the sankey diagram and format it correctly
-        df_sankey = sankey(url, top_level_url, title, instance_url)
-
+        df_sankey = sankey(url, top_level_url, title, instance_url, token)
         sankey_title = "Parts Co-Located with "+ title + " (a "+role_link+")"
 
         #create a temporary directory
@@ -90,7 +90,8 @@ def Sankey_Run():
 
         return result 
     except Exception as e:
-        print(e)
+        print(e, flush=True)
+        print(traceback.format_exc(), flush=True)
         abort(400)
 
 #flask run --host=0.0.0.0
@@ -106,7 +107,7 @@ def Bar_Evaluate():
 
     ########## REPLACE THIS SECTION WITH OWN RUN CODE #################
     #uses rdf types
-    accepted_types = {'Component'}
+    accepted_types = {'Component', 'ComponentDefinition'}
 
     acceptable = rdf_type in accepted_types
 
@@ -130,17 +131,18 @@ def Bar_Run():
     size = data['size']
     rdf_type = data['type']
     shallow_sbol = data['shallow_sbol']
+    token = data['token']
 
     url = complete_sbol.replace('/sbol','')
 
     try:
 
         #create input data
-        self_df, display_id, title, role, count = input_data(top_level_url, instance_url)
+        self_df, display_id, title, role, count = input_data(top_level_url, instance_url, token)
 
         #create and format data for the most_used barchart
         bar_df = most_used_bar(top_level_url, instance_url, display_id, title, role, 
-                        count)
+                        count, token)
 
         #graph title for most used barchart
         graph_title = f'Top Ten Parts by Number of Uses Compared to <a href="{url}" target="_blank">{title}</a>'
@@ -161,7 +163,7 @@ def Bar_Run():
         role_link = find_role_name(role, plural = False)
 
         bar_df = most_used_by_type_bar(top_level_url,instance_url, display_id, title, 
-                      role, count)
+                      role, count, token)
 
         #graph title for most used barchart
         graph_title = f'Top Ten {role_link} by Number of Uses Compared to <a href="{url}" target="_blank">{title}</a>'
@@ -180,5 +182,6 @@ def Bar_Run():
 
         return toggle_display
     except Exception as e:
-        print(e)
+        print(e, flush=True)
+        print(traceback.format_exc(), flush=True)
         abort(400)

input_data.py

@@ -1,16 +1,16 @@
 import json
 import requests
-from pandas.io.json import json_normalize
+from pandas import json_normalize
 
-def input_data(uri, instance):
+def input_data(uri, instance, token = None):
     """
     Finds information about an SBOL part based on its uri
     
     Requirements
     -------
     import json
     import requests
-    from pandas.io.json import json_normalize
+    from pandas import json_normalize
     Input_Query.txt
     
     Parameters
@@ -55,7 +55,10 @@ def input_data(uri, instance):
 
     status = 200
 
-    req = requests.get(instance)
+    headers = {}
+    if token is not None:
+        headers['X-authorization'] = token
+    req = requests.get(instance, headers=headers)
     if req.status_code != 200: #if synbiohub is offline return an error
         status = 424
     else:
@@ -66,7 +69,7 @@ def input_data(uri, instance):
         sparqlquery = sparqlquery.replace('https://synbiohub.org/public/igem/BBa_B0012/1',uri)
 
         #accept repsonses
-        r = requests.post(instance+"sparql", data = {"query":sparqlquery}, headers = {"Accept":"application/json"})
+        r = requests.post(instance+"sparql", data = {"query":sparqlquery}, headers = {"Accept":"application/json", "X-authorization": token} if token is not None else {"Accept":"application/json"})
 
         #format responses
         d = json.loads(r.text)

most_used_bar.py

@@ -1,10 +1,10 @@
 import pandas as pd
 import requests
 import json
-from pandas.io.json import json_normalize
+from pandas import json_normalize
 from uri_to_url import uri_to_url
 
-def most_used_bar(uri, instance, display_id, title, role, count):
+def most_used_bar(uri, instance, display_id, title, role, count, token):
     """
     Uses a sparql query to obtain information about the most used parts and format the data in such a way
     that a graph can be made comparing the poi (part of interest) to the most used parts
@@ -76,9 +76,10 @@ def most_used_bar(uri, instance, display_id, title, role, count):
     fl = open("Most_Used_Query.txt", "r")
     sparqlquery = fl.read()
 
+
     #send the query
     r = requests.post(instance+"sparql", data = {"query":sparqlquery}, 
-                      headers = {"Accept":"application/json"})
+                      headers = {"Accept":"application/json", "X-authorization": token} if token is not None else {"Accept":"application/json"})
 
     #format query results
     d = json.loads(r.text)

most_used_by_type_bar.py

@@ -1,10 +1,10 @@
 import pandas as pd
 import requests
 import json
-from pandas.io.json import json_normalize
+from pandas import json_normalize
 from uri_to_url import uri_to_url
 
-def most_used_by_type_bar(uri, instance, display_id, title, role, count):
+def most_used_by_type_bar(uri, instance, display_id, title, role, count, token):
     """
     Uses a sparql query to obtain information about the most used parts (of the same type as the poi e.g. all terminators)
     and format the data in such a way that a graph can be made comparing the poi (part of interest) to the most used parts
@@ -81,7 +81,7 @@ def most_used_by_type_bar(uri, instance, display_id, title, role, count):
     sparql_query = sparql_query.replace("0000167", role)
 
     #perform the query
-    r = requests.post(instance+"sparql", data = {"query":sparql_query}, headers = {"Accept":"application/json"})
+    r = requests.post(instance+"sparql", data = {"query":sparql_query}, headers = {"Accept":"application/json", "X-authorization": token} if token is not None else {"Accept":"application/json"})
 
     #format the data
     d = json.loads(r.text)

requirements.txt

@@ -1,8 +1,9 @@
 bs4==0.0.1
-Flask==2.0.1
+Flask==2.2.5
 jsonschema==3.0.1
-pandas==0.24.2
+pandas==2.2.3
 plotly==3.9.0
 requests==2.21.0
-
-
+Werkzeug==2.2.3
+waitress
+lxml

sankey.py

@@ -1,10 +1,10 @@
 import requests
 import json
 import pandas as pd
-from pandas.io.json import json_normalize
+from pandas import json_normalize
 from uri_to_url import uri_to_url
 
-def sankey(url, uri, title, instance):
+def sankey(url, uri, title, instance, token):
     """
     This function creates the table needed to make the sankey diagram
     to create the sankey diagram two linked tables are needed
@@ -16,7 +16,7 @@ def sankey(url, uri, title, instance):
     import requests
     import json
     import pandas as pd
-    from pandas.io.json import json_normalize
+    from pandas import json_normalize
     Preceding_Percent_Query.txt
     
     
@@ -58,7 +58,7 @@ def sankey(url, uri, title, instance):
 
     #substitute in the name of the particular part
     sparqlquery = sparqlquery.replace('https://synbiohub.org/public/igem/BBa_E0040/1',uri)
-    r = requests.post(instance+"sparql", data = {"query":sparqlquery}, headers = {"Accept":"application/json"})
+    r = requests.post(instance+"sparql", data = {"query":sparqlquery}, headers = {"Accept":"application/json", "X-authorization": token} if token is not None else {"Accept":"application/json"})
 
     #reformat query results
     d = json.loads(r.text)