Started the reorganization of Scripts for github. Some notes changes.

Scripts/API_Tests/add-pgaids.py

@@ -4,8 +4,8 @@ from pathlib import Path
 import Apikeys
 import os
 
-basefile = "/Users/normrasmussen/Downloads/mizuno-monthly-completions.csv"
-api_key = Apikeys.mizuno
+basefile = "/Users/normrasmussen/Downloads/Recast-learners.csv"
+api_key = Apikeys.recast
 uuid_url = "https://api.northpass.com/v2/people?filter[email][eq]="
 prop_url = "https://api.northpass.com/v2/properties/people/"
 headers = {
@@ -30,16 +30,12 @@ def load_file(basefile):
                 response = requests.get(url, headers=headers)
                 if response.status_code == 200:
                     response = response.json()
-                    print(response)
                     uuid = response["data"][0]["id"]
-                    print(uuid)
                     url2 = prop_url + f"{uuid}"
                     response = requests.get(url2, headers=headers)
                     data = response.json()
                     pgaid = data["data"]["attributes"]["properties"]["account_number"]
                     row_dict["pgaid"] = pgaid
-                elif response.status_code == 404:
-                    row_dict["pgaid"] = "User Not Found"
                 dict_list.append(row_dict)
         print("No errors! Passing along the dictionary!")
     except KeyError as e:

Scripts/API_Tests/add_learnerprop.py

@@ -2,8 +2,8 @@ import requests
 import pandas as pd
 import Apikeys
 
-csv = "/Users/normrasmussen/Downloads/Recast-learners.csv"
-apiKey = Apikeys.skuid
+csv = "/Users/normrasmussen/Downloads/client-learners.csv"
+apiKey = Apikeys.client
 baseUrlemail = "https://api.northpass.com/v2/people?filter[email][eq]="
 
 
@@ -37,11 +37,8 @@ def getfromEmail(emails, baseUrlemail, apiKey, data):
         else:
             print("Another Error!")
 
-    # print(email_ids)
     data["uuid"] = data["email"].map(email_ids)
     data.dropna(how='any', inplace=True)
-    # print(data)
-    # print(errors)
     assignProps(data, apiKey)
     errorstoCsv(errors)
 
@@ -49,7 +46,7 @@ def getfromEmail(emails, baseUrlemail, apiKey, data):
 def errorstoCsv(errors):
     errorcsv = pd.DataFrame(errors)
     errorcsv.to_csv(
-            path_or_buf="/Users/normrasmussen/Downloads/Talkspace_Emails_Not_Northpass.csv",
+            path_or_buf="/Users/normrasmussen/Downloads/Emails_Not_Northpass.csv",
             index=False
         )
 

Scripts/API_Tests/add_prop_csv.py

@@ -4,8 +4,8 @@ from pathlib import Path
 import Apikeys
 import os
 
-basefile = "/Users/normrasmussen/Downloads/Recast-learners.csv"
-api_key = Apikeys.recast
+basefile = "/Users/normrasmussen/Downloads/client-learners.csv"
+api_key = Apikeys.client
 prop_url = "https://api.northpass.com/v2/properties/people/"
 headers = {
         "accept": "*/*",
@@ -45,7 +45,7 @@ def load_file(basefile):
             completions.loc[completions.index[row], "Company"] = comp
         print(completions)
         completions.to_csv(
-                "/Users/normrasmussen/Downloads/Recast_learners_w_Company.csv",
+                "/Users/normrasmussen/Downloads/client_learners_w_Company.csv",
                 index=False
                 )
 

Scripts/API_Tests/addalltogroup.py

@@ -1,7 +1,7 @@
 import requests
+import Apikeys
 
-apiKey = "84GO7zb7a990UJrnFJqiYcd0m"
-# Skuid's API
+apiKey = Apikeys.client
 
 
 def getallUuid(apiKey):

Scripts/API_Tests/addgroup_users.py

@@ -0,0 +1,86 @@
+import requests
+import sys
+import pandas as pd
+import Apikeys
+
+# Enter your API Key between the quotation marks.
+apiKey = ""
+groupName = sys.argv[1]
+peopleCsv = "/path/to/file/peopletogroups.csv"
+
+
+def createGroup(groupName, apiKey):
+    # apiKey = input("What is your API Key?  ")
+    # Create a Group endpoint - params: group_uuid
+    url = "https://api.northpass.com/v2/groups"
+    payload = {"data": {"attributes": {"name": f"{groupName}"}}}
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+        "X-Api-Key": apiKey,
+    }
+    groupresponse = requests.post(url, json=payload, headers=headers)
+    if "already have a group with this name" in groupresponse.text:
+        groupName = input("Group already created, what name would you like instead? ")
+        print("Got it, thanks! Attempting to create the group now.")
+        createGroup(groupName, apiKey)
+    else:
+        readCSV(apiKey, groupresponse)
+
+
+def readCSV(apiKey, groupresponse):
+    people = []
+    readExport = pd.read_csv(
+        peopleCsv,
+        usecols=["UUID"],
+        skipinitialspace=True,
+    )
+    people.extend(readExport["UUID"].tolist())
+    getgroupId(people, groupresponse, apiKey)
+
+
+def getgroupId(people, groupresponse, apiKey):
+    groupresponse = groupresponse.json()
+    groupID = groupresponse["data"]["id"]
+    ppltoGroup(people, groupID, apiKey)
+
+
+def ppltoGroup(people, groupID, apiKey):
+    for uuid in people:
+        payload = {
+            "data": [
+                {"type": "people", "id": uuid},
+            ]
+        }
+        # Add People to a Group. Params needed: group_uuid
+        url = f"https://api.northpass.com/v2/groups/{groupID}/relationships/people"
+        headers = {
+            "accept": "*/*",
+            "content-type": "application/json",
+            "X-Api-Key": apiKey,
+        }
+        response = requests.post(url, json=payload, headers=headers)
+        if "404" in response.text:
+            print("Error returned for this user. Collecting names.")
+            errorNames(apiKey, uuid)
+        else:
+            pass
+
+
+def errorNames(apiKey, uuid):
+    url = f"https://api.northpass.com/v2/people/{uuid}"
+    headers = {"accept": "application/json", "X-Api-Key": apiKey}
+    response = requests.get(url, headers=headers)
+    response = response.json()
+    errorList = []
+    names = response["data"]["attributes"]["full_name"]
+    errorList.append(names)
+    fullNames(errorList)
+
+
+def fullNames(errorList):
+    print(errorList)
+
+
+if __name__ == "__main__":
+    createGroup(groupName, apiKey)

Scripts/API_Tests/addtogroup_fromcsv.py

@@ -1,13 +1,12 @@
 import requests
 import pandas as pd
+import Apikeys
 
-baseCsv = "/Users/normrasmussen/Downloads/Incomplete list for NP monthly communcation 12.19.22 - Users.csv"
+baseCsv = "/Users/normrasmussen/Downloads/Incomplete list of Users.csv"
 baseUrl = "https://api.northpass.com/v2/people/"
 baseUrlname = "https://api.northpass.com/v2/people?filter[name][eq]="
 baseUrlemail = "https://api.northpass.com/v2/people?filter[email][eq]="
-# apiKey = "JRDpCGQ7vSRiva6t5OkWDr5eJ"  # G2
-# apiKey = "6hUfJdAartHTHhHc0WIRZYPWe" #Walmart
-apiKey = "84GO7zb7a990UJrnFJqiYcd0m"  # Skuid's API
+apikey = Apikeys.client
 
 
 def getfromEmail(baseUrlemail, baseCsv, apiKey):

Scripts/API_Tests/api_extracts.py

@@ -0,0 +1,77 @@
+import requests
+import subprocess
+import gzip
+import urllib.request
+import base64
+
+# Variables of the needed elements
+extract_id = "506c7250-2365-456d-be91-b1e146a398fe"
+auth_token = "g5k8QpUlePaL5UFSz9Kob4nbYHIyEH0W2lhtbrDu"
+base_url = "https://analytics.northpass.io/extracts"
+encryption_key = "ba6iy6jPmTxlIs73f+7FcXC8FDXt98mW46WbmKpLPsY="
+current_dir = "./"
+
+# Set the API endpoint, authentication headers, and other parameters
+headers = {
+    "accept": "application/json",
+    "X-Api-Key": "g5k8QpUlePaL5UFSz9Kob4nbYHIyEH0W2lhtbrDu",
+}
+
+
+# This first call provides you with the fileID and IV value to download the encrypted file
+def get_extract_id(base_url, headers, extract_id, encryption_key):
+    url = f"{base_url}/{extract_id}/files/latest/"
+    response = requests.get(url, headers=headers)
+    data = response.json()
+    file_id = data["fileId"]
+    iv = data["initializationVector"]
+    download_file(base_url, headers, extract_id, file_id, encryption_key, iv)
+
+
+# This function downloads the file, changes the IV and EK from base64 to Hex, and then decrypts it.
+# It then opens and prints the results for verification that the data is human readable.
+def download_file(base_url, headers, extract_id, file_id, encryption_key, iv):
+    url = f"{base_url}/{extract_id}/files/{file_id}/download"
+    response = requests.get(url, headers=headers)
+    data = response.json()
+    download_url = data["url"]
+
+    # Downloads the file to the cwd
+    url_resp = urllib.request.urlretrieve(download_url, "UXDesign_API_Extract.csv.gz")
+    file_name = url_resp[0]
+    decrypted_file = "UXDesign_Accessible.csv.gz"
+
+    # Base64 > Hex
+    encryption_key = base64.b64decode(encryption_key).hex()
+    print(encryption_key)
+    iv = base64.b64decode(iv).hex()
+    print(iv)
+
+    # Decryption Command as provided in Northpass Documentation
+    resp = subprocess.run(
+        [
+            "openssl",
+            "enc",
+            "-aes-256-cbc",
+            "-d",
+            "-nosalt",
+            "-in",
+            file_name,
+            "-out",
+            decrypted_file,
+            "-K",
+            encryption_key,
+            "-iv",
+            iv,
+        ]
+    )
+    print(resp)
+
+    # Print data in CSV to ensure it is human readable
+    with gzip.open(decrypted_file, "rb") as uncompressed:
+        file = uncompressed.read()
+        print(file)
+
+
+if __name__ == "__main__":
+    get_extract_id(base_url, headers, extract_id, encryption_key)

Scripts/API_Tests/api_test.py

@@ -3,6 +3,10 @@ import requests
 import Apikeys
 
 
+'''
+This file is mostly used for random testing of unknown endpoints, like our V1 endpoints.
+'''
+
 # url ="https://api.northpass.com/v2/groups/e6ef3e5f-b5a2-4b10-868b-8c165d76d263/learning_paths"
 # url = "https://api.northpass.com/v1/media?limit=1"
 # url = "https://api.northpass.com/v1/learning_paths"

Scripts/API_Tests/assignCoursetoGroups.py

@@ -1,6 +1,7 @@
 import requests
+import Apikeys
 
-apiKey = "Bknf8kidbluRfcKu3m3lKoxS8"
+apiKey = Apikeys.client
 groups = [
     "Armonk",
     "Babylon",

Scripts/API_Tests/create_groups.py

@@ -24,7 +24,7 @@ for group in groups_to_create:
     payload2.append({"name": group})
     payload = {"data": {"attributes": {"groups": payload2}}}
 
-# print(payload)
-response = requests.post(url, json=payload, headers=headers)
-print(response.text)
-print(response)
+print(payload)
+# response = requests.post(url, json=payload, headers=headers)
+# print(response.text)
+# print(response)

Scripts/API_Tests/find_completed.py

@@ -0,0 +1,76 @@
+from collections import Counter
+import pandas as pd
+
+basecsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/Skuid_MCA125.csv"
+lpcsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/skuidlps.csv"
+
+"""
+Example multivalue dictionary
+
+dict = {key1: [value1, value2, value3, value4],
+        key2: [value5, value6, value 7],
+        }
+
+So this could be used for each learning path. In other words:
+
+learning_paths = {'01:Skuid Ethos' : ["Congratulations", "Create", "Skuid Resources"]} etc etc
+
+Ideally, we will add Alexa's "levels" in this dictionary as well. Could we do:
+
+learning_paths = {'Level_1': [{'01:Skuid Ethos' : ["Congratulations", "Create", "Skuid Resources"]},
+                              {'02:Composer' : ["Overview", "Get Started with Composer", "Manage Pages"}]
+                              {'03:Design System Studio' : ["Get Started with Design Systems", etc etc]},
+                  'Level_2': [{'10 - Data' : ["Tips to Optimize", "Smarter Conditions"]},
+                              {'11-Components': ["Battle", "Engage"]},
+                  ]
+                }
+
+How to create this by automation?
+"""
+
+
+def lpLevels(basecsv, lpcsv):
+    levels = pd.read_csv(
+        lpcsv,
+        index_col=1,
+    )
+    newDf = levels.groupby("Learning Path")
+    newDf2 = newDf.apply(lambda x: x["Course Name"].unique())
+    learningpaths = newDf2.apply(pd.Series)
+    learningpaths.rename_axis(index=0)
+    mainFunc(basecsv, learningpaths)
+    # print(levels.Level.unique())  # Print only unique values from the Level column
+
+
+def mainFunc(basecsv, learningpaths):
+    # Part 1
+    readData = pd.read_csv(
+        basecsv,
+    )
+    group = readData.groupby("Learner Full Name")
+    df2 = group.apply(lambda x: x["Course Name"].unique())
+    df2 = df2.apply(pd.Series, dtype="string")
+    # print(df2)
+    # This prints a dataframe with the learner's name as the index column and the courses as adjacent columns
+
+    # Part 2
+    courses = learningpaths.set_index(0)
+    # print(courses)
+    # lp_dict = learningpaths.to_dict("index")
+    # courses = lp_dict.values()
+    # Part 3
+    print(df2.isin(df2))
+    # This produces a bunch of T/F in the dataframe. Is the solution to do:
+    # for courses in lp_dict, for row(person) in readData
+    # if number of True == length/# of values in courses
+    # Add to "Finished List"
+
+    # df3 = df2.columns
+    # print(df3)
+    # for name in df3.items():
+    #    print(f"name: {name}")
+
+
+if __name__ == "__main__":
+    # mainFunc(basecsv)
+    lpLevels(basecsv, lpcsv)

Scripts/API_Tests/fix_unenroll.py

@@ -0,0 +1,28 @@
+from collections import Counter
+import pandas as pd 
+
+basecsv = "/Users/normrasmussen/Documents/Northpass/Scripts/G2_Unenroll/skuid_05lp.csv"
+
+def mainFunc(basecsv):
+    readData = pd.read_csv(
+            basecsv,
+            index_col=False,
+            )
+    parsedData = readData.drop_duplicates(subset='Course_Name', keep="first")
+    if parsedData.loc[readData['Course Name'].isin([
+                    'Get Started with Models - Level 1',
+                    'Configure Model Fields - Level 1',
+                    'Configure Model Conditions - Level 1',
+                    'Configure Model Actions - Level 1',
+                    'Manage Models - Level 1',
+                    'Intro to UI Only Fields - Level 1'])]:
+        extractedList = readData.loc[readData['Email'].tolist()
+        fourOccs = Counter(extractedList)
+        finalNames = []
+        for name, occurences in fourOccs.items():
+            if occurences == 6:
+                finalNames.append(name)
+
+
+if __name__ == "__main__":
+    mainFunc(basecsv)

Scripts/API_Tests/manual_lps.py

@@ -0,0 +1,59 @@
+from collections import Counter
+import pandas as pd
+import glob
+import re
+
+basecsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/Skuid_MCA125.csv"
+directory = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/LPCSVs/*.csv"
+lpcsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/skuidlps.csv"
+
+# Unfortunately, what this needs is a CSV per learning path/MCA export. That will be painful to run.
+
+
+def manualFunc(lpcsv, directory):
+    lps = pd.read_csv(lpcsv)
+    lpcourses = lps.groupby(["Learning Path"])["Course Name"]
+    coursenums = lpcourses.nunique()
+    coursenums = coursenums.to_dict()
+    lpnames = coursenums.keys()
+    # print(lpnames)
+    # num = re.match(r"(\d{2})")
+    # for row in coursenums:
+    #     print(row)
+    # print(coursenums.keys())
+    # keys() and values() will show the respective columns. Adding the full LP string will show the values.
+    # What if... I do something like
+    #      The next step is to compare each of the items in this list with the results of each fname. I wonder if I can do it by regex?
+    lp_dict = {}
+    lp_list = []
+    for fname in glob.glob(directory):
+        readData = pd.read_csv(
+            fname,
+        )
+        emailGroups = readData.groupby(["Email", "Learner Full Name"])[
+            "Course Name"
+        ].nunique()
+        peoples = emailGroups.to_dict()
+        num = re.findall(r"(\d{2})", fname)
+        for lp, nums in coursenums.items():
+            file_num = str(num[0])
+            file_num = f"{file_num:02}"
+            lp_num = re.findall(r"(\d{2})", lp)
+            lp_num = str(lp_num[0])
+            if file_num == lp_num:
+                lp_list = []
+                # print(file_num+"+"+lp_num)
+                for person, completions in peoples.items():
+                    if completions == nums:
+                        lp_list.append(person)
+        ppl_todict = {fname: lp_list}
+        lp_dict.update(ppl_todict)
+
+    final = pd.DataFrame({key: pd.Series(value) for key, value in lp_dict.items()})
+    final.to_csv(
+        "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/finaltest3.csv"
+    )
+
+
+if __name__ == "__main__":
+    manualFunc(lpcsv, directory)

Scripts/API_Tests/usercomparison.py

@@ -0,0 +1,54 @@
+import csv
+from Levenshtein import distance as lev
+import pandas as pd
+import itertools
+import sys
+
+peopleCsv = "/Users/normrasmussen/Downloads/TalkspaceAllLearners.csv"
+
+
+def readCsv(peopleCsv):
+    people = []
+    readExport = pd.read_csv(
+        peopleCsv,
+        usecols=["Learner Full Name", "Email"],
+        skipinitialspace=True,
+        # index_col=True,
+    )
+    people.extend(readExport["Email"].tolist())
+    startCompare(peopleCsv, people, readExport)
+
+
+# itertools combinations
+def startCompare(peopleCsv, people, readExport):
+    email1 = []
+    email2 = []
+    for (
+        name1,
+        name2,
+    ) in itertools.combinations(people, 2):
+        # print(name1, name2) - prints all pairs, working so far.
+        distance = lev(name1, name2)
+        # print(distance) - successfully returns numbers
+        if distance > 0 and distance < 2:
+            email1.append(name1)
+            email2.append(name2)
+    writenewColumn(email1, email2, peopleCsv, readExport)
+
+
+def writenewColumn(email1, email2, peopleCsv, readExport):
+    df = pd.DataFrame(readExport)
+    print(df)
+    df["Email1"] = pd.Series(email1)
+    df["Email2"] = pd.Series(email2)
+    df.drop_duplicates("Email1", inplace=True)
+    df.drop_duplicates("Email2", inplace=True)
+    df.drop_duplicates(subset=["Email1", "Email2"])
+    # keep = 'last').reset_index(drop=True)
+    writeLst = df.to_csv(
+        "/Users/normrasmussen/Downloads/TalkspaceDupes_singlechange.csv",
+    )
+
+
+if __name__ == "__main__":
+    readCsv(peopleCsv)