At the time of this writing, Ortingo does not have an official API. Fortunately, Simple Data Toolkit provides an unofficial API for reading posts (at the time of this writing, the release of this is pending for complete support, but it is coming soon)
To retrieve all posts for a given user in Python, using Simple Data Toolkit, we can do the following:
from sdtk import com_sdtk_api_OrtingoAPI
def printer(data, reader):
print(reader.toArrayOfNativeMaps(None))
com_sdtk_api_OrtingoAPI.postsAPI().retrieveData({"owner": "60CQ59FN46SVQFXJ"}, printer)
from sdtk import com_sdtk_api_OrtingoAPI
def printer(data, reader):
print(reader.filterColumnsOnly(["title"]).toArrayOfNativeMaps(None))
com_sdtk_api_OrtingoAPI.postsAPI().retrieveData({"owner": "60CQ59FN46SVQFXJ"}, printer)
from sdtk import com_sdtk_api_OrtingoAPI
def printerUrls(data, reader):
print(reader.filterColumnsOnly(["url"]).toArrayOfNativeMaps(None))
com_sdtk_api_OrtingoAPI.suggestionsAPI().retrieveData({"query": "data"}, printerUrls)
from sdtk import com_sdtk_api_OrtingoAPI
def printerComments(data, reader):
print(reader.filterColumnsOnly(["commentDate", "post"]).toArrayOfNativeMaps(None))
com_sdtk_api_OrtingoAPI.commentsAPI().retrieveData({"owner": "60CQ59FN46SVQFXJ", "id": "test"}, printerComments)
from sdtk import com_sdtk_api_GitAPI def printer(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_GitAPI.reposAPI().retrieveData({"owner": "Vis-LLC"}, printer)To retrieve all branches a repo has using Simple Data Toolkit, we can do the following:
from sdtk import com_sdtk_api_GitAPI def printer(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_GitAPI.branchesAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit"}, printer)To retrieve all the files in a branch using Simple Data Toolkit, we can do the following:
from sdtk import com_sdtk_api_GitAPI def printer(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_GitAPI.filesAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit", "branch": "main"}, printer)To retrieve the data in a file using Simple Data Toolkit, we can do the following:
from sdtk import com_sdtk_api_GitAPI def printerData(data, reader): print(data) com_sdtk_api_GitAPI.retrieveAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit-UI", "branch": "main", "path": "index.html"}, printerData)We can also login using a personal access token (https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens)
from sdtk import com_sdtk_api_GitAPI def printerData(data, reader): print(data) com_sdtk_api_GitAPI.instance().setKey("Personal Access Token Here").retrieveAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit-UI", "branch": "main", "path": "index.html"}, printerData)Simple Data Toolkit - Tutorial - IEEE Events API - PythonSimple Data Toolkit provides an unofficial API for reading events from the IEEE API. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all events in Python, using Simple Data Toolkit, we can do the following:
from sdtk import com_sdtk_api_IEEEAPI,com_sdtk_calendar_IEEEEventFormat def printer(data, reader): ieee = com_sdtk_calendar_IEEEEventFormat.instance for event in reader.toArrayOfNativeMaps(None): ci = ieee.read(event) print(ci.summary) com_sdtk_api_IEEEAPI.eventsAPI().retrieveData({"limit": "2"}, printer)We can search using the following parameters: - limit - The limit to the number of events to return - start - The start datetime to search - end - The end datetime to search The columns supported at the time of this writing are: - created-at mapped to created - start-time mapped to start - end-time mapped to end - title mapped to summary - uid mapped to uidSimple Data Toolkit - Python - Loading Data and Text Through ChatGPTSimple Data Toolkit provides an API for passing data and text to ChatGPT and extracting related data or text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) Below is an example which uses an embedded data of users and orders, plus text loaded in from a file.
from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader #Set def callbackData(reader): print(reader.toArrayOfNativeMaps(None)) def callbackText(data): print(data) users = [ {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"}, {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"}, {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"}, {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"}, {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"}, ] orders = [ {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"}, {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"}, {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"}, {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"}, {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"} ] com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData) com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users? As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText) content = "" with open('sample.html', 'r') as content_file: content = content_file.read() com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users? As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.
from sdtk import com_sdtk_api_ChatGPTAPI def callback(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)Simple Data Toolkit - Python - Loading Data and Text Through ChatGPTSimple Data Toolkit provides an API for passing data and text to ChatGPT and extracting related data or text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) Below is an example which uses an embedded data of users and orders, plus text loaded in from a file.
from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader #Set def callbackData(reader): print(reader.toArrayOfNativeMaps(None)) def callbackText(data): print(data) users = [ {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"}, {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"}, {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"}, {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"}, {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"}, ] orders = [ {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"}, {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"}, {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"}, {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"}, {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"} ] com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData) com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users? As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText) content = "" with open('sample.html', 'r') as content_file: content = content_file.read() com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users? As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.
from sdtk import com_sdtk_api_ChatGPTAPI def callback(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)Simple Data Toolkit - Python - Loading Data and Text Through ChatGPTSimple Data Toolkit provides an API for passing data and text to ChatGPT and extracting related data or text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) Below is an example which uses an embedded data of users and orders, plus text loaded in from a file.
from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader #Set def callbackData(reader): print(reader.toArrayOfNativeMaps(None)) def callbackText(data): print(data) users = [ {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"}, {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"}, {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"}, {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"}, {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"}, ] orders = [ {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"}, {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"}, {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"}, {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"}, {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"} ] com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData) com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users? As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText) content = "" with open('sample.html', 'r') as content_file: content = content_file.read() com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users? As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.
from sdtk import com_sdtk_api_ChatGPTAPI def callback(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)Simple Data Toolkit - Tutorial - Ethereum via Etherscan - PythonSimple Data Toolkit provides an unofficial API for reading Ethereum transactions from the Etherscan API. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all Ethereum transactions for a particular address we can do the following:
from sdtk import com_sdtk_api_EtherscanAPI def printer(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_EtherscanAPI.transactionsAPI().retrieveData({"address": "0xAddress"}, printer)Replace 0xAddress with the address you wish to search on. Additionally, we can convert the transactions to our standard internal event interface like so:
from sdtk import com_sdtk_api_EtherscanAPI,com_sdtk_calendar_EtherscanFormat def printerEvents(data, reader): etherScan = com_sdtk_calendar_EtherscanFormat.instance() for event in reader.toArrayOfNativeMaps(None): ci = etherScan.read(event) print(ci.start.toString()) com_sdtk_api_EtherscanAPI.transactionsAPI().retrieveData({"address": "0xAddress"}, printerEvents)Replace 0xAddress with the address you wish to search on.Simple Data Toolkit - Tutorial - ACM Events - PythonSimple Data Toolkit provides an API for retrieving events from ACM (Association of Computing Machinery). (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all events in Python, using Simple Data Toolkit, we can do the following:
from sdtk import com_sdtk_api_ACMAPI def printer(data, reader): print(reader.toArrayOfNativeMaps(None)) com_sdtk_api_ACMAPI.eventsAPI().retrieveData({}, printer)We can also convert the transactions, to SDTK's internal event/calendar format with the ACMEventFormat class like so:
from sdtk import com_sdtk_api_ACMAPI,com_sdtk_calendar_ACMEventFormat def printerEvents(data, reader): acm = com_sdtk_calendar_ACMEventFormat.instance() for event in reader.toArrayOfNativeMaps(None): ci = acm.read(event) print(ci) com_sdtk_api_ACMAPI.eventsAPI().retrieveData({}, printerEvents)Simple Data Toolkit - Python - Loading Images Through ChatGPTSimple Data Toolkit provides an API for passing images to ChatGPT and extracting related text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To generate a LaTeX document based on a series of images (effectively doing OCR) we can do the following:
from sdtk import com_sdtk_api_ChatGPTAPI import os import fnmatch import re texData = "" texBegin = "\n\\begin{document}\n" texEnd = "\n\\end{document}" header = "" footer = "" foundBody = False # Get a list of all .png files in the current directory png_files = [file for file in os.listdir('.') if fnmatch.fnmatch(file, '*.png')] def result(text): global texData global header global footer global foundBody text = text.replace("```latex", "").replace("```", "") body_match = re.search(r'\\begin{document}(.*?)\\end{document}', text, re.DOTALL) if body_match: body_content = body_match.group(1).strip() if len(texData) > 0: texData = texData + "\n" + "\\newpage" + "\n" else: header_match = re.search(r'^(.*?)\\begin{document}', text, re.DOTALL) header = header_match.group(1).strip() footer_match = re.search(r'\\end{document}(.*)$', text, re.DOTALL) footer = footer_match.group(1).strip() texData = texData + body_content foundBody = True else: foundBody = False print(text) # Loop over each .png file for file_name in png_files: foundBody = False while foundBody == False: com_sdtk_api_ChatGPTAPI.instance().query(result, "Can you generate a LaTeX document to represent the text in this image and format it correctly and return only the LaTeX code?", file_name) with open("output.tex", "w+") as file: # Write the string to the file file.write(header + texBegin + texData + texEnd + footer)Simple Data Toolkit - Tutorial - Windows - Scheduled TaskLet's suppose you want to create a task that loads a Pipe Delimited File into a database on a daily basis. The task is to run on Windows and you are using Snowflake for the database. You can do it with SDTK as follows: 1) Download SDTK. For this tutorial, we're going to use the latest version of the Windows Shell Script version as of this writing (0.1.2) which is here: https://sourceforge.net/projects/simple-data-toolkit/files/0.1.3/stc-wsh.js/download 2) Create a new text file called ConvertPSV.cmd 3) Open the file with a text editor (like Notepad or Visual Studio Code) Enter the following text: cscript stc-wsh.js clients.psv clients.sql createorreplace clients (For more info on STC and SDTK see the following URL: https://www.vis-software.com/#sdtk) (For more info on cscript see the following URL: https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/cscript) SET SNOWSQL_PWD=password snowsql -a myorganization-myaccount -u jsmith -f clients.sql -d database -s public -o quiet=true -o friendly=false (For more info on snowsql see the following URL: https://docs.snowflake.com/en/user-guide/snowsql-use.html) 4) Go to the Start Menu. 5) Search for Task Scheduler. (For more info on Task Scheduler see the following URL: https://docs.microsoft.com/en-us/windows/win32/taskschd/about-the-task-scheduler) 6) Open Task Scheduler. 7) Click Create Task. 8) For name enter: Convert PSV to SQL 9) Click Triggers Tab 10) Click New button 11) Select Daily 12) Start start time to 8:00 AM. 13) Click the OK button. 14) Click Actions tab. 15) Click New button 16) For Program/script click Browse. 17) Find the ConvertPSV.cmd script. 18) Click Open. 19) Set Start in to the directory that the ConvertPSV.cmd script is in. 20) Click the OK button. 21) Click the OK button. 22) To test, right click Convert PSV to SQL. 23) Select Run.Hello WorldToday I want to take a moment to share how we all own our conversations. Let's start with we all are as offended as we think we are. For some of us the bar is very low. Others are not easily offended at all! Because of this, we also have a difficult time understanding when an offence is genuine and when it is manipulation. Controlling others is not the answer! When you are faced with something that triggeres righteous indignation, try humor instead.Simple Data Toolkit - Tutorial - Mac OS - Scheduled TaskLet's suppose you want to create a task that loads a Pipe Delimited File into a database on a daily basis. The task is to run on Mac OS and you are using Snowflake for the database. You can do it with SDTK as follows: 1) Download SDTK. For this tutorial, we're going to use the latest version of the Python version as of this writing (0.1.2) which is here: https://sourceforge.net/projects/simple-data-toolkit/files/0.1.3/stc.py/download 2) Create a new text file called ConvertPSV.sh 3) Open the file with a text editor (like Text Edit) Enter the following text: #!/bin/sh python stc.py clients.psv clients.sql createorreplace clients (For more info on STC and SDTK see the following URL: https://www.vis-software.com/#sdtk) export SNOWSQL_PWD=password snowsql -a myorganization-myaccount -u jsmith -f clients.sql -d database -s public -o quiet=true -o friendly=false (For more info on snowsql see the following URL: https://docs.snowflake.com/en/user-guide/snowsql-use.html) 4) Go to the Terminal. 5) Edit the list of cron jobs with the following command: nano crontab -e 6) Add the following to a new line: 0 8 * * * cd ~ && ConvertPSV.sh 7) Press CTRL+O and CTRL+X to save and exit nano.Simple Data Toolkit - Tutorial - Linux - Scheduled TaskLet's suppose you want to create a task that loads a Pipe Delimited File into a database on a daily basis. The task is to run on Linux and you are using Snowflake for the database. You can do it with SDTK as follows: 1) Download SDTK. For this tutorial, we're going to use the latest version of the Python version as of this writing (0.1.2) which is here: https://sourceforge.net/projects/simple-data-toolkit/files/0.1.3/stc.py/download 2) Create a new text file called ConvertPSV.sh 3) Open the file with a text editor (like Text Edit) Enter the following text: #!/bin/sh python stc.py clients.psv clients.sql createorreplace clients (For more info on STC and SDTK see the following URL: https://www.vis-software.com/#sdtk) export SNOWSQL_PWD=password snowsql -a myorganization-myaccount -u jsmith -f clients.sql -d database -s public -o quiet=true -o friendly=false (For more info on snowsql see the following URL: https://docs.snowflake.com/en/user-guide/snowsql-use.html) 4) Go to the Terminal. 5) Edit the list of cron jobs with the following command: nano crontab -e 6) Add the following to a new line: 0 8 * * * cd ~ && ConvertPSV.sh 7) Press CTRL+O and CTRL+X to save and exit nano.
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