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- Simple Data Toolkit - Tutorial - Ortingo API - Python
Simple Data Toolkit - Tutorial - Ortingo API - Python
November 08, 2023 at 10:30 PM UTC
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Franklin Powers
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)
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
Check out these similar posts and Ortingos?
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 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 - 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 - 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 - 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.Atotium News - Business Aggregate - 2024-11-22Title: Nvidia beats earnings expectations as investors eye demand for Blackwell AI chips
URL: https://apnews.com/article/nvidia-ai-earnings-report-adc942aa0e0c5d1a550b7bad486b942a
Summary: Nvidia has reported a significant increase in its third-quarter profit and sales, with revenue reaching $35.08 billion, driven by sustained demand for its AI-specific computer chips. Earnings were $19.31 billion, surpassing Wall Street expectations. However, Nvidia's stock dipped slightly despite a strong quarterly performance. The company anticipates fourth-quarter revenue to reach approximately $37.5 billion, reflecting continued growth expectations. Analysts are focused on Nvidia's Blackwell graphics processor, a next-generation AI chip expected to experience demand exceeding supply for several quarters. Nvidia is widely recognized as a leader in AI technology, with substantial market value and plans to meet surging Blackwell demand. The company's stock has seen significant appreciation, supporting its status as a prominent figure in the AI revolution.
Title: 'I have no money': Thousands of Americans see their savings vanish in Synapse fintech crisis
URL: https://www.cnbc.com/amp/2024/11/22/synapse-bankruptcy-thousands-of-americans-see-their-savings-vanish.html
Summary: The article outlines a financial crisis involving Synapse, a company acting as an intermediary between fintech startups and small banks like Evolve Bank. Synapse's bankruptcy led to the disappearance of up to $96 million in customer funds, affecting thousands of Americans who trusted fintech solutions for their banking needs. Customers believed their funds were FDIC-insured through Synapse's bank partners but are now finding discrepancies in their accounts, with some unable to recover their savings. Efforts to locate the missing funds are stalled due to lack of coordination among the banks involved and insufficient resources to conduct a full reconciliation. Affected individuals, including members of a group called Fight For Our Funds, are advocating for regulatory action. However, financial regulators like the FDIC and Federal Reserve have yet to offer assistance, and the recovery process is mired in legal and organizational challenges
Title: Your Junk Is Needed for the New Electric Era
URL: https://www.wsj.com/finance/commodities-futures/your-junk-is-needed-for-the-new-electric-era-504a7e8f
Summary: The demand for copper is soaring as the world shifts towards renewable energy and digital technologies. To meet this demand, mining companies like Glencore are increasingly turning to recycled sources, mining old electronics, vehicles, and other scrap. With copper being infinitely recyclable, scrap offers a crucial resource to balance supply and demand in the market. Glencore and other companies are investing heavily to expand North American recycling facilities, aiming to nearly double the proportion of recycled copper by 2050. The process involves collecting and processing scrap, ensuring it's fit for recycling into fresh copper slabs to fuel the electric and data age.
Title: Why Robinhood is spending $300 million to buy a wealth management platform
URL: https://finance.yahoo.com/news/why-robinhood-spending-300-million-154859907.html
Summary: Robinhood, known for catering to millennial and Gen Z investors, is acquiring TradePMR, a wealth management platform for registered investment advisors, for $300 million. This move aims to address the evolving investment needs of its aging user base by providing fiduciary financial advice through a referral network with TradePMR's advisors. The acquisition is strategic in light of an expected $84 trillion wealth transfer over the coming decades, positioning Robinhood to compete with firms like Charles Schwab by offering comprehensive financial services. This deal is anticipated to close in the first half of 2025, pending regulatory approval.
Title: Deficit Threat Drives Bond Yields Higher
URL: https://www.wsj.com/finance/investing/deficit-threat-drives-bond-yields-higher-6a043d44
Summary: The expectation of a rising federal budget deficit is pushing bond yields higher as investors prepare for a worsening fiscal situation post-election. A recent $69 billion auction of 2-year Treasury notes saw weak demand, contributing to a broader bond market selloff. The situation is exacerbated by the prospect of increased deficits, particularly if Republicans control the White House and Congress, which could lead to tax cuts. Analysts predict that the fiscal year 2025 deficit could hit $2 trillion. The Treasury is expected to maintain large debt sales, with potential increases in 2025, amid ongoing debates about borrowing strategies and their market impact
Title: Xcel Energy unveils $45B capital plan as data center pipeline nears 9 GW
URL: https://finance.yahoo.com/news/xcel-energy-unveils-45b-capital-090000617.html
Summary: Xcel Energy announced a $45 billion capital investment plan over the next five years, focusing on clean energy, customer electrification, load growth, and reliability. This investment includes significant upgrades to transmission and distribution systems, scheduled to claim 63% of the total funding. While data centers contribute to half of Xcel's expected sales growth, other growth areas include the electrification of oil and gas production and electric vehicle adoption. Xcel anticipates completing only 25% of its data center projects soon, reflecting a broader industry challenge to expand transmission and generation infrastructure rapidly. This plan does not cover an additional $10 billion in potential investments or the recently announced large-scale projects in the Upper Midwest and Colorado. Xcel aims to lower electricity costs for customers by efficiently integrating large energy consumers like data centers and EVs into its system.
Title: Amazon reports boost in quarterly profits, exceeds revenue estimates as it invests in AI
URL: https://financialpost.com/pmn/amazon-reports-boost-in-quarterly-profits-exceeds-revenue-estimates-as-it-invests-in-ai
Summary: Amazon announced a significant increase in quarterly profits, surpassing revenue expectations with $158.9 billion in revenue, compared to the anticipated $157.28 billion. The company's net income for the quarter ending September 30 was $15.3 billion, above the $12.21 billion predicted by analysts. Boosted by robust sales from its Prime Day event, Amazonu2019s core online retail business contributed $61.41 billion in revenue. Additionally, Amazon Web Services grew by 19%, aligning with Amazonu2019s increased investment in AI and technological infrastructure. These outcomes follow Amazon's previous quarter where it missed revenue estimates. The tech giant is investing extensively in AI, nuclear energy, and partnerships, despite facing regulatory scrutiny. For the upcoming fourth quarter, Amazon forecasts revenues between $181.5 billion and $188.5 billion, preparing optimistically for the holiday shopping season.
Title: Techstars revives Boulder accelerator, with a twist
URL: https://www.bizjournals.com/denver/news/2024/10/29/techstars-boulder-accelerator-program-founders.html
Summary: Techstars has announced the revival of its Boulder accelerator program under a new initiative called Techstars Colorado, eight months after moving its headquarters to New York City. CEO David Cohen, along with Techstars veterans Nicole Glaros and Natty Zola, aims to create a community-centric accelerator that combines local ownership with access to global resources. The program, expected to launch as early as this summer, will focus on strengthening Colorado's startup ecosystem, with a managing partner soon to be appointed and a search underway for a physical location to host the hub. This marks a return to Techstars' roots, which began in Boulder in 2007Simple 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 - 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();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)
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