Simple Data Toolkit - Tutorial - Ethereum via Etherscan - Python

November 07, 2023 at 6:54 PM UTC

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Simple 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.

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Simple Data Toolkit - Tutorial - Git API - PythonSimple Data Toolkit provides an unofficial API for reading files, commits, branches, and repos from the Git API. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all repos a user 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.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 uid Simple Data Toolkit - Tutorial - BitTorrent API - PythonSimple Data Toolkit provides an API for retrieving transactions from BitTorrent. To retrieve all transactions in Python, using Simple Data Toolkit, we can do the following:

from sdtk import com_sdtk_api_BitTorrentAPI

def printer(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_BitTorrentAPI.transactionsAPI().retrieveData({}, printer)

We can also convert the transactions, to SDTK's internal event/calendar format with the BitTorrentFormat class like so:

from sdtk import com_sdtk_api_BitTorrentAPI,com_sdtk_calendar_BitTorrentFormat

def printerEvents(data, reader):
    etherScan = com_sdtk_calendar_BitTorrentFormat.instance()
    for event in reader.toArrayOfNativeMaps(None):
        ci = etherScan.read(event)
        print(ci)

com_sdtk_api_BitTorrentAPI.transactionsAPI().retrieveData({}, printerEvents)

We can also search by address, startTimestamp, and endTimestamp.Simple Data Toolkit - Tutorial - Ortingo API - PythonAt 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)

Let's suppose we want only a list of titles for a given user, we can do this instead:

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)

We can also pull suggested content from Ortingo with the following, where the topics we are searching on are provided with the query parameter (in this case it's value is data):

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)

And finally, we can also pull comments attached to a post in Ortingo with the following, where the user is myself and the post is a test post I created:

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)

The columns supported at the time of this writing are: - id - owner - title - subtitle - post - url For comments the following columns are supported: - id - owner - commentDate - replyTo - post 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 - Playstation Trophies - PythonSimple Data Toolkit provides an API for retrieving trophies from PlayStation. (Release pending) You will need to get an NPSSO token first: Login via https://store.playstation.com And then access https://ca.account.sony.com/api/v1/ssocookie This will need to be set in an environment variable PSN_NPSSO Or you can set it in Python with com_sdtk_api_PSNAPI.setNpsso To retrieve all trophies for an account id in Python, using Simple Data Toolkit, we can do the following:

from sdtk import com_sdtk_api_PSNAPI

def printer(data, reader):
	print(reader.toArrayOfNativeMaps(None))
    
com_sdtk_api_PSNAPI.trophyTitlesAPI().retrieveData({"accountId": None}, printer)

To retrieve all trophies for a given title, you'll need it's communication id and use the following Python code:

from sdtk import com_sdtk_api_PSNAPI

def printer(data, reader):
	print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_PSNAPI.trophiesForTitleAPI().retrieveData({"communicationId": "NPWR20188_00", "trophyGroupId": None}, printer)

To retrieve all trophies earned for a given title, you'll need it's communication id and use the following Python code:

from sdtk import com_sdtk_api_PSNAPI

def printer(data, reader):
	print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_PSNAPI.trophiesEarnedForATitleAPI().retrieveData({"accountId": None, "communicationId": "NPWR20188_00", "trophyGroupId": None}, printer)

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)

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)

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)

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 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 - GUI - Python ScriptLet's suppose you want to create a task that loads a TSV file into a CSV file on a daily basis. This task is meant to be part of a Python script, but you are unfamiliar with Python and are unsure of how to write the code. Good news, you can use the Sample Data Toolkit UI to figure out this code for you as follows: 1) Go to the app page for the Simple Data Toolkit UI, at the time of this writing it is here: https://www.vis-software.com/#sdtk 2) Click Choose Files. 3) Find the file you want to convert. 4) Click Open. 5) Select the output type you want, for this tutorial we will select SQL. 6) Then for script, select Python. 7) Then click Download Script. You will now have a simple Python script that uses SDTK to convert the file you selected to CSV. You can now either run this script or integrate it with another one. If you need to install SDTK for Python, you can do it like so on the terminal/commandline prompt with the following command: pip install sdtk-visllc The script will look something like this: from sdtk import com_sdtk_table_Converter as sdtk sdtk.start().readFile("complex.csv").tsv().textOnly().output().writeFile("complex.csv").csv().execute();

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Simple Data Toolkit - Tutorial - Ethereum via Etherscan - Python

November 07, 2023 at 6:54 PM UTC

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