bjulius

let
    Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
    ToRows = Table.FromList( Table.ToRows( Source ), Splitter.SplitByNothing(), {"Trans"}),
    BuySell = Table.AddColumn(ToRows, "BuySell", each [ 
        a = [Trans],
        b = List.Transform(a, each List.PositionOf(a, _)),
        c = List.Transform(a, each 1),
        d = Table.FromColumns( {a, b, c}, {"Buy", "BuyPos", "B1"}),
        e = Table.FromColumns( {a, b, c}, {"Sell", "SellPos", "S1"}), 
        f = Table.SelectRows( Table.Join( d, "B1", e, "S1" ), each [BuyPos] < [SellPos]),

bjulius

REM Uses pbi-tools to extract the data model info and create the BIM file associated with each PBIX file in the directory
REM Batch file code written by Brian Julius, October 8, 2024

REM @echo off
SETLOCAL ENABLEDELAYEDEXPANSION
REM ============================================================
REM Batch Script to Extract BIM Files from PBIX Files
REM All PBIX files are in C:\test
REM All BIM files will be written to C:\test\bim_files
REM ============================================================

bjulius

//C# script for Tabular Editor/TE3 to extract table, column, relationship, and measure info in json format from Power BI PBIX files
//Written by Brian Julius, 3 Oct 2024

// Specify the output file path
string outputFile = @"C:\Temp\PowerBI_Model_Info.txt"; // Change this to your desired path

using (var writer = new System.IO.StreamWriter(outputFile))
{
    // TMSCHEMA_TABLES
    writer.WriteLine("TMSCHEMA_TABLES");

bjulius

// C# TE3 script to add measure name, measure description/purpose, and DAX code to the measure tooltip for each measure in a selected table
// developed by Brian Julius, Sep 27, 2024

string apiKey = System.Environment.GetEnvironmentVariable("OPEN_AI_TE3_API_KEY");
if (string.IsNullOrEmpty(apiKey))
{
    Error("API key not found in environment variable 'OPEN_AI_TE3_API_KEY'.");
    return;
}

bjulius

This GPT directly collects no data regarding your queries provided, and does not give Open AI permission to train its models 
on the conversations with the GPT

It does use the Power Query formatter API, documented at https://www.durchblick-durch-daten.de/power-query/m-formatter/ 

This formatter does not collect any metadata, with the exception of Error tracking. 
It does track Errors that occur during formatting in order to fix bugs and improve their service. 
In these cases metadata about the error is sent to the azure app insights remote service which contains a stacktrace. 
This stacktrace might indirectly contain some information about the query that caused the error

bjulius

"gpt-4-turbo" meta [IsParameterQuery=true, List={"gpt-4o-mini", "gpt-4o", "gpt-4", "gpt-4-turbo"}, DefaultValue="gpt-4o-mini", Type="Text", IsParameterQueryRequired=true]

//Created by Brian Julius
//Contact me at: https://www.linkedin.com/posts/brianjuliusdc
//This query needs to accompany the M Code Help function, to allow selection of the appropriate GPT-4 series model.

bjulius

let
    AnalyzeDataWithChatGPT = (HelpRequest as text ) as text =>
    let  
        apikey = Text.FromBinary(File.Contents("C:\temp\PQ_API_KEY.txt")),
        GPTPrompt = Json.FromValue(
            [
                model = ModelToUse,
                messages = {
                    [ role = "system", content = "You are an expert in Power Query and M code. Provide a clear, detailed answer to the question below related to M code.  Include specific examples to help explain concepts" ],
                    [ role = "user", content  = HelpRequest ] 

bjulius

import zipfile
import os

def extract_bim_from_pbix(pbix_path, output_dir):
    # Ensure the output directory exists
    os.makedirs(output_dir, exist_ok=True)
    
    # Extract the pbix file (which is a zip file)
    with zipfile.ZipFile(pbix_path, 'r') as pbix_zip:
        # Extract all files to the output directory

bjulius

let
  Source = Table.AddColumn(
    Table.FromList({1, 2, 5, 10}, Splitter.SplitByNothing(), {"Denoms"}, null, null),
    "Target",
    each 11
  ),
  Rscript = R.Execute(
    "df <- dataset#(lf)coin_combinations <- function(target, coins) {#(lf)  dp <- vector(""list"", target + 1)#(lf)  dp[[1]] <- list(numeric(0))    #(lf)   for (coin in coins) {#(lf)    for (amount in coin:target) {#(lf)      if (!is.null(dp[[amount - coin + 1]])) {#(lf)        for (combination in dp[[amount - coin + 1]]) {#(lf)          dp[[amount + 1]] <- append(dp[[amount + 1]], list(c(combination, coin)))#(lf)        }#(lf)      }#(lf)    }#(lf)  }#(lf)  #(lf)  return(dp[[target + 1]])#(lf)}#(lf)#(lf)target_sum <- max(df$Target)#(lf)coin_denominations <- as.vector(df$Denoms)#(lf)combinations <- coin_combinations(target_sum, coin_denominations)#(lf)max_length <- max(sapply(combinations, length))#(lf)padded_combinations <- lapply(combinations, function(x) {#(lf)  length(x) <- max_length#(lf)  return(x)#(lf)})#(lf)#(lf)combs_df <- do.call(

bjulius

let
    Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
    AddIndex = Table.AddIndexColumn(Source, "Index", 0, 1, Int64.Type),
    AddIndex2 = Table.AddIndexColumn(AddIndex, "Index.1", 0, 1, Int64.Type),
    IntDiv = Table.TransformColumns(AddIndex2, {{"Index", each Number.IntegerDivide(_, 2), Int64.Type}}),
    Modulo = Table.TransformColumns(IntDiv, {{"Index.1", each Number.Mod(_, 2), type number}}),
    Piv = Table.Pivot(Table.TransformColumnTypes(Modulo, {{"Index.1", type text}}, "en-US"), List.Distinct(Table.TransformColumnTypes(Modulo, {{"Index.1", type text}}, "en-US")[Index.1]), "Index.1", "Date & Orders"),
    RemCol = Table.RemoveColumns(Piv,{"Index"}),
    RenCol = Table.RenameColumns(RemCol,{{"0", "Date"}, {"1", "Order"}}),
    ReType = Table.TransformColumnTypes(RenCol,{{"Date", type date}}),