Program Statistical_Data_AnalysisScience_representation_pas_py_ETL; //solution Tutor 138 finished
//http://www.rosettacode.org/wiki/Prime_decomposition#Pascal ☮ ✞ π 📌 🐞 📊
//https://rosettacode.org/wiki/Spiral_matrix#Delphi
//https://rosettacode.org/wiki/Zeckendorf_number_representation#Delphi
//https://rosettacode.org/wiki/Zeckendorf_number_representation#Pascal
//https://rosettacode.org/wiki/Air_mass#Delphi 🐱🚀 📊 📊 📊 📈 📈 📊
//https://medium.com/art-of-data-engineering/building-your-first-etl-pipeline-with-python-and-sql-3f4084a08ff1
//https://medium.com/@sarowar.saurav10/20-advanced-statistical-approaches-every-data-scientist-should-know-ccc70ae4df28
//https://blog.stackademic.com/data-analysis-with-python-pandas-and-matplotlib-advanced-a9615a3dff6f
//{$I owmapikey2.inc}
procedure Project_Functions_Weather_PySolution(loc: string);
begin
with TPythonEngine.Create(Nil) do begin
//pythonhome:= PYHOME64;
loaddll;
autofinalize:= false;
try
// 9. Check the Weather Without Leaving Your Terminal
// {$I owmapikey.inc} //Const OWMKEY=''
{To get the current weather data of any city using the OpenWeatherMap
API in Python, you can use the requests and json modules. Here is an example:
The OpenWeatherMap API is a service that provides weather data, including current weather data, forecasts,
and historical data. It offers an API with JSON, XML, and HTML endpoints, and a limited free usage tier.
To use the API, you need to create an account on openweathermap.org and obtain an API key.}
execstr('output = io.StringIO()')
execstr('sys.stdout = output');
execstr('import requests');
execstr('city = "'+loc+'"'+LF+
//'api_key = "your_openweather_api_key" ');
// 'api_key = "'+OWMKEY+'" '+LF+
//'api_key = "___06543f5f70442cc5b3efe6674d2b4"'+LF+
'data= requests.get(f''https://api.openweathermap.org/data/2.5/weather?q={city}&units=metric&appid={api_key}'').json()'+LF+
'print(f"Weather in {city}: {data[''weather''][0][''description'']}")'+LF+
'print(f"Temperature (in celcius) = {data[''main''][''temp'']}")');
//10. Split a PDF Into Pages
(*from PyPDF2 import PdfReader, PdfWriter
reader = PdfReader('document.pdf')
for i, page in enumerate(reader.pages):
writer = PdfWriter()
writer.add_page(page)
with open(f'page_{i+1}.pdf', 'wb') as f:
writer.write(f) *)
println(stringreplace(evalstr('output.getvalue().strip()'),
#10,#13#10,[rfReplaceAll, rfIgnoreCase]));
except
raiseError;
finally
UnloadDll;
Free;
end;
end;
end;
function TRestClient3_AskWeather(askcity: string;
aResponseHeader:TRestResponseHandler):string;
var jo: TJSON; rest: TRestResource;
begin
with TRestClient.create(self) do begin
// rest:= Resource('https://api.openweathermap.org/data/2.5/weather?q='+
// askcity+'&units=metric&appid='+OWMKEY);
println('@addr:'+objtostr(rest))
rest.ContentType('application/json');
ConnectionType:= hctWinInet;
//OnResponse:= @TRestOnResponseEvent2;
try
jo:= TJSON.Create();
jo.parse(rest.get());
result:= jo.values['weather'].asarray[0].asobject['description'].asstring+' temp: '+
jo.values['main'].asobject['temp'].asstring+'° at '+askcity;
//writeln(formatjson(jo.stringify));
finally
Free;
jo.Free;
except
writeln('OWM_EWI_Exc:'+ExceptiontoString(exceptiontype,exceptionparam));
end;
end; //with
end;
function TRestClient4_AskWeather(loc: string;
aResponseHeader:TRestResponseHandler):string;
begin
with TRestClient.create(self) do begin
ConnectionType:= hctWinInet;
//OnResponse:= @TRestOnResponseEvent2;
try
with TJSON.Create() do begin
//parse(Resource('https://api.openweathermap.org/data/2.5/weather?q='+
// loc+'&units=metric&appid='+OWMKEY).get());
result:= values['weather'].asarray[0].asobject['description'].asstring+' temp: '+
values['main'].asobject['temp'].asstring+'° at '+loc;
//writeln(formatjson(stringify));
free;
end;
finally
Free;
except
writeln('OWM_Except:'+ExceptiontoString(exceptiontype,exceptionparam));
end;
end; //with
end;
function TRestClient5_AskWeather(loc: string):string;
begin
with TRestClient.create(self) do begin
ConnectionType:= hctWinInet;
//OnResponse:= @TRestOnResponseEvent2;
try
with TMcJsonItem.Create() do begin
//7/ asJson:=(Resource('https://api.openweathermap.org/data/2.5/weather?q='+
// loc+'&units=metric&appid='+OWMKEY).get());
result:= values['weather'].items[0]['description'].asstring+' temp: '+
values['main']['temp'].asstring+'° at '+loc;
//writeln(formatjson(tostring1));
free;
end;
finally
Free;
except
writeln('OWM_Except:'+ExceptiontoString(exceptiontype,exceptionparam));
end;
end; //with
end;
//type TTMatrix = array of array of double;
type TTMatrix = TMatrix;
procedure DisplayMatrix(Memo: TMemo; Mat: TTMatrix);
{Display specified matrix}
var X,Y: integer;
var S: string;
begin
S:='';
for Y:=0 to High(Mat[0]) do begin
S:=S+'[';
for X:=0 to High(Mat) do
S:=S+Format('%4.0f',[Mat[X][Y]]);
S:=S+']'+#$0D#$0A;
end;
Memo.Lines.Add(S);
end;
procedure DoRect5(R: TRect; var Inx: integer; var Mat: TTMatrix);
{Create on turn of the spiral base on the rectangle}
var X,Y: integer;
begin
{Do top part of rectangle}
for X:=R.Left to R.Right do begin
Mat[X][R.Top]:=Inx;
Inc(Inx);
end;
{Do Right part of rectangle}
for Y:=R.Top+1 to R.Bottom do begin
Mat[R.Right][Y]:=Inx;
Inc(Inx);
end;
{Do bottom part of rectangle}
for X:= R.Right-1 downto R.Left do begin
Mat[X][R.Bottom]:=Inx;
Inc(Inx);
end;
{Do left part of rectangle}
for Y:=R.Bottom-1 downto R.Top+1 do begin
Mat[R.Left][Y]:=Inx;
Inc(Inx);
end;
end;
Procedure SetArrayLength2Matrix(var arr: TTMatrix;
asize1, asize2: Integer);
var i: Integer;
begin setlength(arr, asize1);
for i:= 0 to asize1-1 do SetLength(arr[i], asize2);
end;
procedure InflateRect2(var R: TRect; const DX, DY: Integer);
begin
Dec2(R.Left, DX);
Inc2(R.Right, DX);
Dec2(R.Top, DY);
Inc2(R.Bottom, DY);
end;
procedure MakeSpiralMatrix(var Mat: TTMatrix; SizeX,SizeY: integer);
{Create a spiral matrix of specified size}
var Inx: integer; var R: TRect;
begin
{Set matrix size}
//SetLength(Mat,SizeX,SizeY);
SetArrayLength2Matrix(Mat,SizeX,SizeY);
{create matching rectangle}
R:=Rect(0,0,SizeX-1,SizeY-1);
Inx:=0;
{draw and deflate rectangle until spiral is done}
while (R.Left<=R.Right) and (R.Top<=R.Bottom) do begin
DoRect5(R,Inx, Mat);
//writ('1: '+itoa(r.left)+' '+itoa(r.right)+' '+itoa(r.top));
inflateRect2(R,-1, -1);
//writ('2: '+itoa(r.left)+' '+itoa(r.right)+' '+itoa(r.top)+' '+itoa(r.bottom));
end; //c}
{ R:= Rect(2, 4, 7, 8);
InflateRect2(R, 1, 2);
WriteLn(itoa(R.Left)+' '+itoa(R.Top)+' '+itoa(R.Right)+' '+itoa(R.Bottom)); }
{
New values of coordinates:
Left = 2-1 ; Top = 4-2; Right = 7+1; Bottom = 8+2.
}
end;
procedure SpiralMatrix5(Memo: TMemo);
{Display spiral matrix}
var Mat: TTMatrix;
begin
Memo.Lines.Add('5x5 Matrix');
MakeSpiralMatrix(Mat,5,5);
DisplayMatrix(Memo,Mat);
Memo.Lines.Add('8x8 Matrix');
MakeSpiralMatrix(Mat,8,8);
DisplayMatrix(Memo,Mat);
Memo.Lines.Add('14x8 Matrix');
MakeSpiralMatrix(Mat,14,8);
DisplayMatrix(Memo,Mat); //}
end;
var FibNums: array {[0..21]} of integer; { =
(1, 2, 3, 5, 8, 13, 21, 34, 55, 89,
144, 233, 377, 610, 987, 1597, 2584,
4181, 6765, 10946, 17711, 28657); }
procedure setfibnumbs;
begin
setlength(fibnums, 21);
fibnums:= [1, 2, 3, 5, 8, 13, 21, 34, 55, 89,
144, 233, 377, 610, 987, 1597, 2584,
4181, 6765, 10946, 17711, 28657];
end;
function GetZeckNumber(N: integer): string;
{Returns Zeckendorf number for N as string}
var I: integer;
begin
Result:='';
{Subtract Fibonacci numbers from N}
for I:=High(FibNums) downto 0 do
if (N-FibNums[I])>=0 then begin
Result:=Result+'1';
N:=N-FibNums[I];
end
else if Length(Result)>0 then Result:=Result+'0';
if Result='' then Result:='0';
end;
procedure ShowZeckendorfNumbers(Memo: TMemo);
var I: integer;
var S: string;
begin
S:='';
for I:=0 to 20 do begin
Memo.Lines.Add(IntToStr(I)+': '+GetZeckNumber(I));
end;
end;
// Return Zeckendorf representation of the passed-in cardinal. Pascal
function ZeckRep( C : cardinal) : string;
var
a, b, rem : cardinal;
j, nrDigits: integer;
begin
// Case C = 0 has to be treated specially
if (C = 0) then begin
result := '0';
exit;
end;
// Find largest Fibonacci number not exceeding C
a := 1;
b := 1;
nrDigits := 1;
rem := C - 1;
while (rem >= b) do begin
//dec2( rem, b);
rem:= rem-b;
//inc2( a, b);
a:= a+b;
b := a - b;
inc( nrDigits);
end;
// Fill in digits by reversing Fibonacci back to start
SetLength( result, nrDigits);
j := 1;
result[j] := '1';
for j := 2 to nrDigits do begin
if (rem >= b) then begin
//dec( rem, b);
rem:= rem-b
result[j] := '1';
end
else result[j] := '0';
b := a - b;
//dec( a, b);
a:= a-b;
end;
// Assert((a = 1) and (b = 1)); // optional check
end;
const DEF_ZECK=
'n = 20 '+LF+
'def z(n): '+LF+
' if n == 0 : return [0] '+LF+
' fib = [2,1] '+LF+
' while fib[0] < n: fib[0:0] = [sum(fib[:2])] '+LF+
' dig = [] '+LF+
' for f in fib: '+LF+
' if f <= n: '+LF+
' dig, n = dig + [1], n - f '+LF+
' else: '+LF+
' dig += [0] '+LF+
' return dig if dig[0] else dig[1:] ';
procedure Zeckendorf_Numbers_PySolution(loc: string);
begin
with TPythonEngine.Create(Nil) do begin
//pythonhome:= PYHOME64;
loaddll;
autofinalize:= false;
try
// 9. Check the Weather Without Leaving Your Terminal
// {$I owmapikey.inc} //Const OWMKEY=''
execstr('import io, sys # Added');
{To get the current weather data of any city using the OpenWeatherMap
API in Python, you can use the requests and json modules. Here is an example:
The OpenWeatherMap API is a service that provides weather data, including current weather data, forecasts,
and historical data. It offers an API with JSON, XML, and HTML endpoints, and a limited free usage tier.
To use the API, you need to create an account on openweathermap.org and obtain an API key.}
execstr('output = io.StringIO()')
execstr('sys.stdout = output');
execstr(DEF_ZECK);
execstr('for i in range(n + 1):'+
' print(''%3i: %8s'' % (i,''''.join(str(d) for d in z(i))))');
println(stringreplace(evalstr('output.getvalue().strip()'),
#10,#13#10,[rfReplaceAll, rfIgnoreCase]));
except
raiseError;
finally
UnloadDll;
Free;
end;
end;
end;
const DeFZIGZAG =
'def zigzag(n): '+LF+
' ''zigzag rows'' '+LF+
' def compare(xy): '+LF+
' x, y = xy '+LF+
' return (x + y, -y if (x + y) % 2 else y) '+LF+
' xs = range(n) '+LF+
' return {index: n for n, index in enumerate(sorted( '+LF+
' ((x, y) for x in xs for y in xs), '+LF+
' key=compare '+LF+
' ))} '+LF+
'def printzz(myarray): '+LF+
' ''show zigzag rows as lines'' '+LF+
' n = int(len(myarray) ** 0.5 + 0.5) '+LF+
' xs = range(n) '+LF+
' print(''\n''.join( '+LF+
' [''''.join("%3i" % myarray[(x,y)]for x in xs)for y in xs]'+LF+
' )) ';
procedure ZigZag_Numbers_PySolution(loc: string);
begin
with TPythonEngine.Create(Nil) do begin
//pythonhome:= PYHOME64;
loaddll;
autofinalize:= false;
try
// 9. Check the Weather Without Leaving Your Terminal
// {$I owmapikey.inc} //Const OWMKEY=''
execstr('import io, sys # Added');
{To get the current weather data of any city using the OpenWeatherMap
API in Python, you can use the requests and json modules. Here is an example:
The OpenWeatherMap API is a service that provides weather data, including current weather data, forecasts,
and historical data. It offers an API with JSON, XML, and HTML endpoints, and a limited free usage tier.
To use the API, you need to create an account on openweathermap.org and obtain an API key.}
execstr('output = io.StringIO()')
execstr('sys.stdout = output');
execstr(DeFZIGZAG);
//execstr('for i in range(n + 1):'+
// ' print(''%3i: %8s'' % (i,''''.join(str(d) for d in z(i))))');
Execstr('printzz(zigzag(6))');
println(' '+stringreplace(evalstr('output.getvalue().strip()'),
#10,#13#10,[rfReplaceAll, rfIgnoreCase]));
except
raiseError;
finally
UnloadDll;
Free;
end;
end;
end;
procedure Toggle(var I, dir: integer);
{Toggle Direction and increment I}
begin
Dir:=-Dir;
Inc(I);
end;
procedure Step(var X,Y, dir: integer);
{Take one step "Dir" direction}
begin
X:=X+Dir;
Y:=Y-Dir;
end;
const Size = 10;
Procedure SetArrayLength4Matrix(var arr: TTMatrix;
asize1, asize2: Integer);
var i: Integer;
begin setlength(arr, asize1);
for i:= 0 to asize1-1 do SetLength(arr[i], asize2);
end;
procedure ZigzagMatrix(Memo: TMemo);
var Mat: TMatrix;
var X,Y,Inx,Dir: integer;
begin
SetarrayLength4Matrix(Mat,Size,Size);
Inx:=0; X:=0; Y:=0; Dir:=1;
repeat
begin
Mat[X][Y]:=Inx;
if (X+Dir)>=Size then Toggle(Y, dir)
else if (Y-Dir)>=Size then Toggle(X, dir)
else if (X+Dir)<0 then Toggle(Y, dir)
else if (Y-Dir)<0 then Toggle(X, dir)
else Step(X,Y, dir);
Inc(Inx);
end
until Inx>=Size*Size;
DisplayMatrix(Memo,Mat);
end;
const psize = 5;
procedure PascalProgramzigzag();
var
zzarray: array [1..psize] of array[1..psize] of integer;
element, i, j: integer;
direction: integer;
width, n: integer;
begin
i := 1;
j := 1;
direction := 1;
for element := 0 to (psize*psize) - 1 do begin
zzarray[i][j] := element;
i := i + direction;
j := j - direction;
if (i = 0) then begin
direction := -direction;
i := 1;
if (j > psize) then begin
j := psize;
i := 2;
end;
end
else if (i > psize) then begin
direction := -direction;
i := psize;
j := j + 2;
end
else if (j = 0) then begin
direction := -direction;
j := 1;
if (i > psize) then begin
j := 2;
i := psize;
end;
end
else if (j > psize) then begin
direction := -direction;
j := psize;
i := i + 2;
end;
end;
width := 2;
n := psize;
while (n > 0) do begin
width := width + 1;
n := n div 10;
end;
for j := 1 to psize do begin
for i := 1 to psize do
write(itoa(zzarray[i][j])+#9);
writeln('');
end;
end;
const RE = 6371000; { radius of earth in meters}
const DD = 0.001; { integrate in this fraction of the distance already covered}
const FIN = 1e7; { integrate only to a height of 10000km, effectively infinity}
function rho(a: double): double;
{ The density of air as a function of height above sea level.}
begin
Result:=Exp(-a / 8500);
end;
function Radians(degrees: double): double;
{ Converts degrees to radians}
begin
Result:= degrees * Pi / 180
end;
function Height(A, Z, D: double): double;
{ a = altitude of observer}
{ z = zenith angle (in degrees)}
{ d = distance along line of sight}
var AAA,HH: double;
begin
AAA := RE + A;
HH := Sqrt(AAA*AAA + D*D - 2*D*AAA*Cos(Radians(180-z)));
Result:= HH - RE;
end;
function ColumnDensity(A, Z: double): double;
{ Integrates density along the line of sight.}
var Sum,D,Delta: double;
begin
Sum := 0.0;
D := 0.0;
processmessagesOFF;
while D < FIN do begin
delta := Maxfloat(DD, DD*D); { adaptive step size to avoid it taking forever}
Sum:=Sum + Rho(Height(A, Z, D+0.5*Delta)) * Delta;
D:=D + delta;
end;
processmessagesON;
Result:= Sum;
end;
function AirMass(A, Z: double): double;
begin
Result:= ColumnDensity(A, Z) / ColumnDensity(a, 0);
end;
procedure ShowAirMass(Memo: TMemo);
var Z: integer;
begin
Memo.Lines.Add('Angle 0 m 13700 m');
Memo.Lines.Add('------------------------------------');
Z:=0;
while Z<=90 do begin
Memo.Lines.Add(Format('%2d %11.8f %11.8f', [z, airmass(0, Z), airmass(13700, Z)]));
Z:=Z+5;
end;
end;
const DeFAIRMASS =
'""" Rosetta Code task: Air_mass """ '+LF+
' '+LF+
'from math import sqrt, cos, exp '+LF+
'DEG = 0.017453292519943295769236907684886127134 # degrees to radians '+LF+
'RE = 6371000 # Earth radius in meters '+LF+
'dd = 0.001 # integrate in this fraction of the distance already covered '+LF+
'FIN = 10000000 # integrate only to a height of 10000km, effectively infinity '+LF+
' '+LF+
'def rho(a): '+LF+
' """ the density of air as a function of height above sea level """ '+LF+
' return exp(-a / 8500.0) '+LF+
'def height(a, z, d): '+LF+
' """ '+LF+
' a = altitude of observer '+LF+
' z = zenith angle (in degrees) '+LF+
' d = distance along line of sight '+LF+
' """ '+LF+
' return sqrt((RE + a)**2 + d**2-2*d*(RE + a)*cos((180-z)*DEG))-RE '+LF+
' '+LF+
'def column_density(a, z): '+LF+
' """ integrates density along the line of sight """ '+LF+
' dsum, d = 0.0, 0.0 '+LF+
' while d < FIN: '+LF+
' delta = max(dd, (dd)*d) #adaptive stepsize to avoid taking forever: '+LF+
' dsum += rho(height(a, z, d + 0.5 * delta)) * delta '+LF+
' d += delta '+LF+
' return dsum '+LF+
' '+LF+
'def airmass(a, z): '+LF+
' return column_density(a, z) / column_density(a, 0) ';
procedure AirMass_Numbers_PySolution(loc: string);
begin
with TPythonEngine.Create(Nil) do begin
//pythonhome:= PYHOME64;
loaddll;
autofinalize:= false;
try
execstr('import io, sys # Added');
execstr('output = io.StringIO()')
execstr('sys.stdout = output');
execstr(DeFAIRMASS);
//execstr('for i in range(n + 1):'+
// ' print(''%3i: %8s'' % (i,''''.join(str(d) for d in z(i))))');
//Execstr('printzz(zigzag(6))');
Execstr('print(''Angle 0 m 13700 m\n'', ''-'' * 36)');
Execstr('for z in range(0, 91, 5): '+LF+
' print(f"{z:3d} {airmass(0,z): 12.7f} {airmass(13700,z):12.7f}")');
println(' '+stringreplace(evalstr('output.getvalue().strip()'),
#10,#13#10,[rfReplaceAll, rfIgnoreCase]));
except
raiseError;
finally
UnloadDll;
Free;
end;
end;
end;
procedure ETL_PySolution(loc: string);
begin
with TPythonEngine.Create(Nil) do begin
//pythonhome:= PYHOME64;
loaddll;
autofinalize:= false;
try
execstr('import io, sys # Added');
execstr('import pandas as pd');
execstr('output = io.StringIO()')
execstr('sys.stdout = output');
//pip install pandas sqlalchemy psycopg2
execstr('from sqlalchemy import create_engine');
execstr(DeFAIRMASS);
//execstr('text_data = open(''1372_retail_sales_dataset.csv'', ''r'',encoding="utf8").read()');
//# Load the dataset
execstr('data = pd.read_csv(''1372_retail_sales_dataset.csv'')');
execstr('print(data.info())');
execstr('print(data.head())');
//Step 2: Clean and Transform the Data
//# Drop rows with missing values
execstr('data_cleaned = data.dropna()');
//# Convert sale_date to datetime
//execstr('data_cleaned[''sale_date''] = pd.to_datetime(data_cleaned[''sale_date''])');
execstr('data_cleaned[''Date''] = pd.to_datetime(data_cleaned[''Date''])');
execstr('print(data_cleaned.head())');
//from sqlalchemy import create_engine
//# Create connection to the PostgreSQL database
//engine = create_engine('postgresql+psycopg2://user:password@localhost:5432/mydatabase')
//# Load data into SQL
//execstr('data_cleaned.to_sql(''sales_data'', engine, index=False, if_exists=''replace'') ');
//execstr('for i in range(n + 1):'+
// ' print(''%3i: %8s'' % (i,''''.join(str(d) for d in z(i))))');
//Execstr('printzz(zigzag(6))');
//Execstr('print(''Angle 0 m 13700 m\n'', ''-'' * 36)');
//Execstr('for z in range(0, 91, 5): '+LF+
// ' print(f"{z:3d} {airmass(0,z): 12.7f} {airmass(13700,z):12.7f}")');
println(' '+stringreplace(evalstr('output.getvalue().strip()'),
#10,#13#10,[rfReplaceAll, rfIgnoreCase]));
except
raiseError;
finally
UnloadDll;
Free;
end;
end;
end;
procedure StatDataScience_PySolution(loc: string);
begin
with TPythonEngine.Create(Nil) do begin
//pythonhome:= PYHOME64;
loaddll;
autofinalize:= false;
try
execstr('output = io.StringIO()')
execstr('sys.stdout = output');
//pip install pandas sqlalchemy psycopg2
//execstr('from sqlalchemy import create_engine');
//execstr(DeFAIRMASS);
execstr('import pymc as pm; import numpy as np');
//execstr('text_data = open(''1372_retail_sales_dataset.csv'',''r'',encoding="utf8").read()');
//# Load the dataset
execstr('data = pd.read_csv(''1372_retail_sales_dataset.csv'')');
execstr('print(data.info())');
execstr('print(data.head())');
//Step 2: Clean and Transform the Data
//# Drop rows with missing values
execstr('data_cleaned = data.dropna()');
//# Convert sale_date to datetime
//execstr('data_cleaned[''sale_date''] = pd.to_datetime(data_cleaned[''sale_date''])');
execstr('data_cleaned[''Date''] = pd.to_datetime(data_cleaned[''Date''])');
execstr('print(data_cleaned.head())');
//# Suppose we observed 20 coin flips with 12 heads and 8 tails
execstr('observ_heads = 12; observ_tails = 8');
execstr('with pm.Model() as model: '+
' # Prior for the bias of the coin (theta) '+LF+
' theta = pm.Beta(''theta'', alpha=1, beta=1) '+LF+
' # Likelihood '+LF+
' y= pm.Binomial(''y'',n=observ_heads+observ_tails,p=theta,observed=observ_heads)'+
' # Posterior sampling '+LF+
' trace = pm.sample(200, tune=1000, cores=1, chains=2) ');
execstr('pm.summary(trace)');
execstr('from scipy.stats import norm');
//# Generate synthetic data
execstr('data = np.random.normal(loc=5, scale=2, size=1000)');
//# Estimate MLE for mean and standard deviation
execstr('mu_hat, std_hat = norm.fit(data)')
execstr('print(f"Estimated Mean (mu): {mu_hat:.2f}")');
execstr('print(f"Estimated Std (sigma): {std_hat:.2f}")');
//3. Hypothesis Testing (t-test)
execstr('from scipy.stats import ttest_ind')
//# Synthetic data
execstr('group_A= np.random.normal(5,1,50); group_B= np.random.normal(5.5,1.2,50)');
execstr('stat, pvalue = ttest_ind(group_A, group_B)');
execstr('print(f"T-statistic: {stat:.2f}, p-value: {pvalue:.4f}")'+LF+
'if pvalue < 0.05: '+LF+
' print("Reject the null hypothesis (Significant difference).") '+LF+
'else: '+LF+
' print("Fail to reject null hypothesis (No significant difference).")');
execstr('from sklearn.decomposition import PCA');
//!pip install statsmodels
//6. Factor Analysis
//!pip install factor_analyzer
execstr('from factor_analyzer import FactorAnalyzer');
//# Synthetic data (100 samples, 6 variables)
execstr('X = np.random.rand(100, 6) '+LF+
'fa = FactorAnalyzer(n_factors=2, rotation=''varimax'') '+LF+
'fa.fit(X) '+LF+
'print("Loadings:\n", fa.loadings_) ');
//7. Cluster Analysis (K-means)
execstr('from sklearn.cluster import KMeans ');
//# Synthetic data: 200 samples, 2D
execstr('X = np.random.rand(200, 2)');
execstr('kmeans = KMeans(n_clusters=3, random_state=42)');
execstr('kmeans.fit(X)');
execstr('print("Cluster centers:", kmeans.cluster_centers_)');
execstr('print("Cluster labels:", kmeans.labels_[:10])');
//9. Time Series Analysis (ARIMA)
execstr('from statsmodels.tsa.arima.model import ARIMA ');
//# Synthetic time series data
execstr('np.random.seed(42); data = np.random.normal(100, 5, 50)');
execstr('time_series = pd.Series(data)');
//# Fit ARIMA model (p=1, d=1, q=1)
execstr('model = ARIMA(time_series, order=(1,1,1))');
execstr('model_fit = model.fit()');
//# Forecast next 5 points
execstr('forecast = model_fit.forecast(steps=5)');
execstr('print("Forecast:", forecast.values)');
//13. Logistic Regression
execstr('from sklearn.linear_model import LogisticRegression');
execstr('from sklearn.linear_model import LinearRegression');
execstr('from sklearn.metrics import mean_squared_error');
execstr('from sklearn.model_selection import train_test_split');
execstr('np.random.seed(50) '+LF+
'X = np.random.rand(100, 5) '+LF+
'y = np.random.randint(0, 2, 100) # Binary labels '+LF+
'X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2) '+LF+
'model = LogisticRegression() '+LF+
'model.fit(X_train, y_train) '+LF+
//# Predicting the target values for the test set
//'y_pred = model.predict(X_test) '+LF+
'# Calculating the model score (R^2 score) '+LF+
'accuracy = model.score(X_test, y_test) '+LF+
'print(y_test) '+LF+
'print(f"R^2 Score: {accuracy}") '+LF+
'print("Class Accuracy:", accuracy) ');
execstr('y_pred = model.predict(X_test) '+LF+
//# Calculating the Mean Squared Error
'mse = mean_squared_error(y_test, y_pred)' +LF+
'print(f"Mean Squared Error: {mse}") '+LF+
'print(y_test)'+LF+
'print(y_pred) ');
//https://www.geeksforgeeks.org/difference-between-score-and-accuracy_score-methods-in-scikit-learn/
execstr('from sklearn.metrics import accuracy_score');
execstr('# Method 3: Using accuracy_score function '+LF+
'accuracy = accuracy_score(y_test, y_pred) '+LF+
'print(f"accuracy_score function - Accuracy: {accuracy}") ');
//16. Monte Carlo Simulation
execstr('np.random.seed(42) '+LF+
'n_samples = 10_000_00 '+LF+
'xs = np.random.rand(n_samples) '+LF+
'ys = np.random.rand(n_samples) '+LF+
'# Points within the unit circle '+LF+
'inside_circle = (xs**2 + ys**2) <= 1.0 '+LF+
'pi_estimate = inside_circle.sum() * 4 / n_samples ');
execstr('print("Estimated π:", pi_estimate)');
//17. Markov Chain Monte Carlo (MCMC)
//# Synthetic data
(* execstr('np.random.seed(42)'+LF+
'data = np.random.normal(0, 1, 100) '+LF+
'with pm.Model() as model: '+LF+
' mu = pm.Normal(''mu'', mu=0, sigma=10) '+LF+
' sig = pm.HalfNormal(''sigma'', sigma=1) '+LF+
' '+LF+
' likelihood= pm.Normal(''likelihood'',mu=mu,sigma=sig,observed=data)'+LF+
' trace = pm.sample(1000, tune=500, chains=2) '+LF+
'pm.summary(trace) ');
//*)
//execstr('from pygam import LinearGAM, s');
//# Create connection to the PostgreSQL database
//engine = create_engine('postgresql+psycopg2://user:password@localhost:5432/mydatabase')
//# Load data into SQL
//execstr('data_cleaned.to_sql(''sales_data'', engine, index=False, if_exists=''replace'') ');
//https://blog.stackademic.com/data-analysis-with-python-pandas-and-matplotlib-advanced-a9615a3dff6f
//import pandas as pd
//# Creating a Series at runtime!
execstr('data = [10,20,30,40]; series= pd.Series(data); print(series)');
//DataFrames
execstr('data={''Name'':[''John'',''Jane'',''Mike''],''Age'':[23,25,30]}');
execstr('df = pd.DataFrame(data); print(df)');
//# Create Employee Dataset
execstr('data = { '+LF+
'"Employee_ID": range(1001, 1011), '+LF+
'"Name":["Alice","Bob","Charlie","David","Emma","Max","Grace","Helen","Isaac","Julia"],'+LF+
'"Age":[25, 28, 35, 40, 22, 30, 45, 50, 29, 38], '+LF+
'"Department":["HR","IT","IT","Finance","HR","Finance","IT","Marketing","HR","Finance"],'+LF+
'"Salary":[50000,70000,85000,92000,48000,78000,110000,65000,52000,88000],'+LF+
'"Experience_Years":[2, 4, 10, 15, 1, 8, 20, 12, 3, 11],'+LF+
'"Performance_Score":[3.2,4.5,4.8,3.7,2.9,4.2,4.9,3.8,3.5,4.1]'+LF+
'} ');
//# Convert to DataFrame
execstr('df = pd.DataFrame(data)');
//# Display first few rows
execstr('print(df.head())');
//Data Cleaning # Check for missing values
execstr('print(df.isnull().sum()); # Check data types print(df.dtypes)');
//# Convert categorical columns to category type
execstr('df[''Department''] = df[''Department''].astype(''category'')');
//# Add an Experience Level column
execstr('df[''Experience_Level''] = pd.cut(df[''Experience_Years''],'+LF+
'bins=[0,5,10,20], labels=[''Junior'',''Mid'',''Senior''])');
//# Show the updated DataFrame
execstr('print(df.head())');
//Find Employees with High Salaries
execstr('high_salary_df = df[df[''Salary''] > 80000]');
execstr('print(high_salary_df)');
//Find Average Salary by Department
execstr('print(df.groupby(''Department'')[''Salary''].mean())');
//Find the Highest Performing Department
execstr('print(f"Highest Performing Department: {df.groupby("Department")["Performance_Score"].mean().idxmax()}")');
//Step 4: Data Visualization
//📊 1. Bar Chart — Average Salary by Department
execstr('import matplotlib.pyplot as plt');
execstr('import seaborn as sns');
execstr('plt.figure(figsize=(8,5))'+LF+
'sns.barplot(x=df[''Department''],y=df[''Salary''],estimator=np.mean,palette="coolwarm")'+LF+
'plt.title(''Average Salary by Department'', fontsize=14) '+LF+
'plt.xlabel(''Department'', fontsize=12) '+LF+
'plt.ylabel(''Average Salary'', fontsize=12) '+LF+
'plt.xticks(rotation=45) '+LF+
'plt.show() ');
//📈 2. Scatter Plot — Salary vs Experience
execstr('plt.figure(figsize=(9,5))'+LF+
'sns.scatterplot(x=df["Experience_Years"],y=df["Salary"],hue=df["Department"],palette="Dark2",s=100)'+LF+
'plt.title(''Salary vs Experience'', fontsize=14) '+LF+
'plt.xlabel(''Years of Experience'', fontsize=12) '+LF+
'plt.ylabel(''Salary'', fontsize=12) '+LF+
'plt.legend(title="Department",bbox_to_anchor=(1, 1),fontsize=8) '+LF+
'plt.show() ');
//📊 3. Histogram — Salary Distribution
execstr('plt.figure(figsize=(8,5)) '+LF+
'plt.hist(df["Salary"], bins=5, color="blue", alpha=0.7, edgecolor="black") '+LF+
'plt.title("Salary Distribution", fontsize=14) '+LF+
'plt.xlabel("Salary", fontsize=12) '+LF+
'plt.ylabel("Frequency", fontsize=12) '+LF+
'plt.show() ');
//📊 4. Box Plot — Salary by Department
execstr('plt.figure(figsize=(8,5)) '+LF+
'sns.boxplot(x=df["Department"], y=df["Salary"], palette="pastel") '+LF+
'plt.title("Salary Distribution by Department", fontsize=14) '+LF+
'plt.xlabel("Department", fontsize=12) '+LF+
'plt.ylabel("Salary", fontsize=12) '+LF+
'plt.xticks(rotation=45) '+LF+
'plt.show() ');
println(' '+stringreplace(evalstr('output.getvalue().strip()'),
#10,#13#10,[rfReplaceAll, rfIgnoreCase]));
except
raiseError;
finally
UnloadDll;
Free;
end;
end;
end;
var d: array of int64;
dc: array of TInteger; C : cardinal;
begin //@main
//https://rosettacode.org/wiki/Happy_numbers#DWScript
//https://python.plainenglish.io/lambda-functions-in-python-are-more-useful-than-you-think-d56ee089701d
//https://www.kaggle.com/code/maxkleiner/notebook10dae7045a/
//fpascal_solution();
(*
writeln('program Count_the_coins_BigDecimal;');
d := [1, 5, 10, 25];
m := Length(d); n := 100; //100
Writeln(itoa(Count64(d, m, n))); //242
//print(changes(100000, [100, 50, 25, 10, 5, 1]));
d := [1, 5, 10, 25, 50, 100];
d:= [200, 100, 50, 20, 10, 5, 2, 1]; //eu_coins:= 10056050940818192726001
//d := [10, 25];
m:= Length(d); n:= 100000;
Writeln((Count64big(d, m, n))); *)
//Writeln('Python Request Weather Solutions:_____');
//LLM_Torch_PySolution('Lucerne');
//Seaborn_Lambda_PySolution('Visp');
//Project_Functions_Lambda_PySolution('Brig');
SpiralMatrix5(memo2);
Writeln('Delphi Solution:_____');
setfibnumbs();
ShowZeckendorfNumbers(memo2);
Writeln('Pascal Solution:_____');
for C := 1 to 20 do
WriteLn( {SysUtils.}Format( '%2d: %s', [C, ZeckRep(C)]));
Writeln('Python Solution:_____');
Zeckendorf_Numbers_PySolution('Bale');
Writeln('ZigZag Delphi Solution:_____');
ZigzagMatrix(memo2);
Writeln('ZigZag Pascal Solution:_____');
PascalProgramzigzag();
Writeln('ZigZag Python Solution:_____'+CR);
ZigZag_Numbers_PySolution('St. Gall');
Writeln('Airmass Delphi Solution:_____');
//ShowAirMass(memo2);
Writeln('Airmass Python Solution:_____'+CR);
AirMass_Numbers_PySolution('Uster');
Writeln('ETL Python Solution:_____'+CR);
ETL_PySolution('Bienne');
Writeln('Statistical Data Science Python Solution:_____'+CR);
StatDataScience_PySolution('Wankdorf');
{
Project_Functions_Weather_PySolution('Basel, CH');
sleep(1000);
Writeln('Pascal Restclient Weather Solutions:_____');
//println(TRestClient4_AskWeather('Basel, CH', nil));
println(TRestClient5_AskWeather('Basel, CH'));
}
//GetMorseTable(dots:Bool):Str;; //whole table!
//println(GetMorseTable(true)); //whole table!
//RenderJS_factors();
(* RenderJS_countcoins();
countTheCoins_Pascal();
*)
End.
ref: https://www.zeitmaschine.tv/_zm/uploads/reis/html5/1632322578743.mp4
https://x.com/i/status/1889295047192981796
https://de.slideshare.net/slideshow/maxbox_starter138_top7_statistical_methods-pdf/275863941
This code snippet trains a linear regression model, predicts the target values for the
test set, and then calculates and prints the R² score and Mean Squared Error (MSE) for the model.
Feel free to adapt it to your specific dataset and model!
Scikit-learns model.score(X,y) calculation works on co-efficient of determination i.e R^2 is a
simple function that takes model.score= (X_test,y_test). It doesn't require y_predicted value
to be supplied externally to calculate the score for you, rather it calculates y_predicted internally
and uses it in the calculations.
This is how it is done:
u = ((y_test - y_predicted) ** 2).sum()
v = ((y_test - y_test.mean()) ** 2).sum()
score = 1 - (u/v)
and you get the score ! Hope that helps.
https://www.bahnonline.ch/5472/tee-stiftung-in-den-niederlanden-gibt-ram-an-transportmuseum-ab/
https://www.gofundme.com/f/aankoop-teetrein-fase-1
procedure InflateRect(var R: TRectF; const DX, DY: Single);
begin
//{$EXCESSPRECISION OFF}
R.Left := R.Left - DX;
R.Right := R.Right + DX;
R.Top := R.Top - DY;
R.Bottom := R.Bottom + DY;
//{$EXCESSPRECISION ON}
end;
Angle 0 m 13700 m
------------------------------------
0 1.00000000 1.00000000
5 1.00380963 1.00380965
10 1.01538466 1.01538475
15 1.03517744 1.03517765
20 1.06399053 1.06399093
25 1.10305937 1.10306005
30 1.15418974 1.15419083
35 1.21998076 1.21998246
40 1.30418931 1.30419190
45 1.41234169 1.41234567
50 1.55280404 1.55281025
55 1.73875921 1.73876915
60 1.99212000 1.99213665
65 2.35199740 2.35202722
70 2.89531368 2.89537287
75 3.79582352 3.79596149
80 5.53885809 5.53928113
85 10.07896219 10.08115981
90 34.32981136 34.36666557
mX5 executed: 11/02/2025 16:10:25 Runtime: 0:2:18.922 Memload: 63% use
with processmessages OFF/ON
mX5 executed: 11/02/2025 16:13:25 Runtime: 0:0:22.790 Memload: 64% use
ZigZag Delphi Solution:_____
[ 0 1 5 6 14 15 27 28 44 45]
[ 2 4 7 13 16 26 29 43 46 63]
[ 3 8 12 17 25 30 42 47 62 64]
[ 9 11 18 24 31 41 48 61 65 78]
[ 10 19 23 32 40 49 60 66 77 79]
[ 20 22 33 39 50 59 67 76 80 89]
[ 21 34 38 51 58 68 75 81 88 90]
[ 35 37 52 57 69 74 82 87 91 96]
[ 36 53 56 70 73 83 86 92 95 97]
[ 54 55 71 72 84 85 93 94 98 99]
ZigZag Pascal Solution:_____
0 1 5 6 14
2 4 7 13 15
3 8 12 16 21
9 11 17 20 22
10 18 19 23 24
ZigZag Python Solution:_____
0 2 3 9 10 20
1 4 8 11 19 21
5 7 12 18 22 29
6 13 17 23 28 30
14 16 24 27 31 34
15 25 26 32 33 35
mX5 executed: 10/02/2025 22:07:56 Runtime: 0:0:2.749 Memload: 70% use
0: 0
1: 1
2: 10
3: 100
4: 101
5: 1000
6: 1001
7: 1010
8: 10000
9: 10001
10: 10010
11: 10100
12: 10101
13: 100000
14: 100001
15: 100010
16: 100100
17: 100101
18: 101000
19: 101001
20: 101010
mX5 executed: 10/02/2025 17:45:07 Runtime: 0:0:2.482 Memload: 67% use
https://python.plainenglish.io/10-everyday-problems-python-can-solve-in-under-10-lines-of-code-efb6ffd3e860
https://soundcloud.com/max-kleiner-481715070
https://soundcloud.com/max-kleiner-481715070/g9-zeitraum-fm-127bpm-441hz?si=fe9fd03b58314bdb8621ebf9106b9184&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.youtube.com/watch?v=904JoHPWcek
https://grandbrothers.bandcamp.com/album/late-reflections
If one coincidence can occur, then another coincidence can occur. And if one coincidence happens
to occur just after another coincidence, then that is just a coincidence.
InflateRect enlarges the rectangle by subtracting DX from the Left side and adding DX to the Right side,
and subtracting DY from the Top and adding DY to the Bottom13. This keeps the rectangle's
center point in the same location while increasing its size4.
She never arrives at her destination.
Stranded in rural Maine with a dead car and broken ankle, Tegan worries she's made a terrible mistake.
Then a miracle occurs: she is rescued by a couple who offers her a room in their warm cabin until the snow clears.
But something isn't right. Tegan believed she was waiting out the storm, but as time ticks by,
she comes to realize she is in grave danger. This safe haven isn't what she thought it was, and staying here may
have been her most deadly mistake yet.
And now she must do whatever it takes to save herself―and her unborn child.
A gut-wrenching story of motherhood, survival, and twisted expectations, #1 New York Times bestselling author Freida McFadden
delivers a snowbound thriller that will chill you to the bone.
Python Request Weather Solutions:_____
Exception: ConnectionError: HTTPSConnectionPool(host='api.openweathermap.org', port=443): Max retries exceeded with url:
/data/2.5/weather?q=Basel,%20CH&units=metric&appid=55013bf3d09cfb0619989a00ed5bed09 (Caused by
NameResolutionError("<urllib3.connection.HTTPSConnection object at 0x000002189C0DC830>:
Failed to resolve 'api.openweathermap.org' ([Errno 11001] getaddrinfo failed)")) at 861.1073
Versandübersicht
Vom Versandzentrum zu Ihnen (Schritt 2)
PBI (Pitney Bowes): UPAAG000000072744731
Tue, Feb 18
1:17pm
Label added to shipment, shipment now waiting to be handed to the last mile courier.
GENEVA
Tue, Feb 18
12:56pm
Forwarded to Delivery Office
CASE POSTALE
Tue, Feb 18
12:04pm
Under processing at operations facility
CASE POSTALE
Sat, Feb 15
9:09am
Departed Operations facility – In Transit
COLNBROOK
Sat, Feb 15
8:54am
Departed Operations facility – In Transit
COLNBROOK
Fri, Feb 14
9:48pm
Received at Origin Facility
LONDON
Fri, Feb 14
9:40pm
Received at Origin Facility
LONDON
Fri, Feb 14
1:11pm
Shipped from Global Shipping Center to Destination
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:54pm
Container Closed
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:45pm
Container Assignment
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:26pm
Parcel Documentation and Labeling
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:26pm
Second Dimension Event
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:26pm
First Dimension Event
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:26pm
Processing at Shipping Center
LICHFIELD, STAFFORDSHIRE WS13 8UR
Fri, Feb 14
12:26pm
Record created.
LONDON
Mon, Feb 10
6:17pm
Tracking details uploaded
Mon, Feb 10
11:17am
Sendungsnummer angegeben
Vom Verkäufer zum Versandzentrum (Schritt 1)
Sendungsnummer wird bereitgestellt
Tue, Feb 11
4:43pm
Delivered by
Tue, Feb 11
4:34pm
Item Received
Mon, Feb 10
10:09pm
Item Received
Mon, Feb 10
12:53pm
Sender despatching item
Mon, Feb 10
11:17am
Sendungsnummer angegeben
Weniger ansehen
// Adapted from http://www.geeksforgeeks.org/dynamic-programming-set-7-coin-change/
class Program
{
static long Count(int[] C, int m, int n)
{
var table = new long[n + 1];
table[0] = 1;
for (int i = 0; i < m; i++)
for (int j = C[i]; j <= n; j++)
table[j] += table[j - C[i]];
return table[n];
}
static void Main(string[] args)
{
var C = new int[] { 1, 5, 10, 25 };
int m = C.Length;
int n = 100;
Console.WriteLine(Count(C, m, n)); //242
Console.ReadLine();
}
}
def spiral(n):
dx,dy = 1,0 # Starting increments
x,y = 0,0 # Starting location
myarray = [[None]* n for j in range(n)]
for i in xrange(n**2):
myarray[x][y] = i
nx,ny = x+dx, y+dy
if 0<=nx<n and 0<=ny<n and myarray[nx][ny] == None:
x,y = nx,ny
else:
dx,dy = -dy,dx
x,y = x+dx, y+dy
return myarray
def printspiral(myarray):
n = range(len(myarray))
for y in n:
for x in n:
print "%2i" % myarray[x][y],
print
printspiral(spiral(5))
Warning: Trying to access array offset on value of type bool in /htdocs/Spur N/minitrix/sortiment/detailseiten/Minitrix_16304.php on line 106
Deprecated: mysqli::query(): Passing null to parameter #1 ($query) of type string is deprecated in /htdocs/app/classes/Category.php on line 203
Fatal error: Uncaught ValueError: mysqli::query(): Argument #1 ($query) cannot be empty in /htdocs/app/classes/Category.php:203 Stack trace: #0 /htdocs/app/classes/Category.php(203):
mysqli->query() #1 /htdocs/app/classes/Category.php(191): Category->getProductData() #2 /htdocs/Spur N/minitrix/sortiment/detailseiten/Minitrix_16304.php(106): Category->loadProduct()
#3 {main} thrown in /htdocs/app/classes/Category.php on line 203
C:\maxbox\maxbox4\maxbox4>py -3.12 -m pip install pymc
Collecting pymc
Downloading pymc-5.20.1-py3-none-any.whl.metadata (15 kB)
Collecting arviz>=0.13.0 (from pymc)
Using cached arviz-0.20.0-py3-none-any.whl.metadata (8.8 kB)
Requirement already satisfied: cachetools>=4.2.1 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (5.5.0)
Collecting cloudpickle (from pymc)
Downloading cloudpickle-3.1.1-py3-none-any.whl.metadata (7.1 kB)
Requirement already satisfied: numpy>=1.25.0 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (1.26.3)
Requirement already satisfied: pandas>=0.24.0 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (2.2.2)
Collecting pytensor<2.28,>=2.26.1 (from pymc)
Downloading pytensor-2.27.1-cp312-cp312-win_amd64.whl.metadata (10 kB)
Requirement already satisfied: rich>=13.7.1 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (13.9.4)
Requirement already satisfied: scipy>=1.4.1 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (1.11.4)
Requirement already satisfied: threadpoolctl<4.0.0,>=3.1.0 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (3.2.0)
Requirement already satisfied: typing-extensions>=3.7.4 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from pymc) (4.12.2)
Requirement already satisfied: setuptools>=60.0.0 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from arviz>=0.13.0->pymc) (75.8.0)
Requirement already satisfied: matplotlib>=3.5 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from arviz>=0.13.0->pymc) (3.9.0)
Requirement already satisfied: packaging in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from arviz>=0.13.0->pymc) (24.1)
Collecting xarray>=2022.6.0 (from arviz>=0.13.0->pymc)
Downloading xarray-2025.1.2-py3-none-any.whl.metadata (11 kB)
Collecting h5netcdf>=1.0.2 (from arviz>=0.13.0->pymc)
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Collecting etuples (from pytensor<2.28,>=2.26.1->pymc)
Downloading etuples-0.3.9.tar.gz (30 kB)
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Collecting logical-unification (from pytensor<2.28,>=2.26.1->pymc)
Downloading logical-unification-0.4.6.tar.gz (31 kB)
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Collecting miniKanren (from pytensor<2.28,>=2.26.1->pymc)
Downloading miniKanren-1.0.3.tar.gz (41 kB)
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Collecting cons (from pytensor<2.28,>=2.26.1->pymc)
Downloading cons-0.4.6.tar.gz (26 kB)
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Collecting toolz (from logical-unification->pytensor<2.28,>=2.26.1->pymc)
Downloading toolz-1.0.0-py3-none-any.whl.metadata (5.1 kB)
Collecting multipledispatch (from logical-unification->pytensor<2.28,>=2.26.1->pymc)
Downloading multipledispatch-1.0.0-py3-none-any.whl.metadata (3.8 kB)
Downloading pymc-5.20.1-py3-none-any.whl (518 kB)
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Downloading arviz-0.20.0-py3-none-any.whl (1.7 MB)
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Downloading pytensor-2.27.1-cp312-cp312-win_amd64.whl (1.5 MB)
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Downloading cloudpickle-3.1.1-py3-none-any.whl (20 kB)
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Downloading xarray-2025.1.2-py3-none-any.whl (1.2 MB)
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Downloading xarray_einstats-0.8.0-py3-none-any.whl (32 kB)
Downloading multipledispatch-1.0.0-py3-none-any.whl (12 kB)
Downloading toolz-1.0.0-py3-none-any.whl (56 kB)
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Building wheels for collected packages: cons, logical-unification, etuples, miniKanren
Building wheel for cons (setup.py) ... done
Created wheel for cons: filename=cons-0.4.6-py3-none-any.whl size=9195 sha256=f7d85559fcfb7726b95f8f6b7ff2d3ca0507e6d6b4445e325214c31c9726bd19
Stored in directory: c:\users\user\appdata\local\pip\cache\wheels\91\0c\f7\7ba4ffbf0db6bf272bee59789131f6ec2d26966307a3054267
Building wheel for logical-unification (setup.py) ... done
Created wheel for logical-unification: filename=logical_unification-0.4.6-py3-none-any.whl size=14016 sha256=a81c7da5232472eb8a7995fdc34aef8b0a9122370bdc6d695ff5bab0dfb087bb
Stored in directory: c:\users\user\appdata\local\pip\cache\wheels\4b\07\cf\3fdaedafc40d7b1861c98be0df1bf3b2b3e87e03187fa95298
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Created wheel for etuples: filename=etuples-0.3.9-py3-none-any.whl size=12715 sha256=e12c9e11ff8e07f03c5c48690c22e4268550e4b3cd2658c6fd56e4e011ead9dd
Stored in directory: c:\users\user\appdata\local\pip\cache\wheels\9e\ea\42\9562f37ce4e92913b74da9bcd504a749f0bf5d42d1d148ca76
Building wheel for miniKanren (setup.py) ... done
Created wheel for miniKanren: filename=miniKanren-1.0.3-py3-none-any.whl size=24007 sha256=9dd35bf8708feba5a4fdea38fc3b6de8256120344a9c590989a14092a3b38b72
Stored in directory: c:\users\user\appdata\local\pip\cache\wheels\57\48\e3\441a42666f9a4ac9ccb6897d6046d81cf970c986be39938c6e
Successfully built cons logical-unification etuples miniKanren
Installing collected packages: multipledispatch, toolz, cloudpickle, logical-unification, h5netcdf, xarray, cons, xarray-einstats, etuples, miniKanren, arviz, pytensor, pymc
Successfully installed arviz-0.20.0 cloudpickle-3.1.1 cons-0.4.6 etuples-0.3.9 h5netcdf-1.5.0 logical-unification-0.4.6 miniKanren-1.0.3 multipledispatch-1.0.0
pymc-5.20.1 pytensor-2.27.1 toolz-1.0.0 xarray-2025.1.2 xarray-einstats-0.8.0
[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip
C:\maxbox\maxbox4\maxbox4>py -3.12 -m pip install statsmodels
Collecting statsmodels
Downloading statsmodels-0.14.4-cp312-cp312-win_amd64.whl.metadata (9.5 kB)
Requirement already satisfied: numpy<3,>=1.22.3 in c:\users\user\appdata\local\programs\python\python312\lib\site-packages (from statsmodels) (1.26.3)
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Collecting patsy>=0.5.6 (from statsmodels)
Downloading patsy-1.0.1-py2.py3-none-any.whl.metadata (3.3 kB)
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Downloading statsmodels-0.14.4-cp312-cp312-win_amd64.whl (9.8 MB)
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Downloading patsy-1.0.1-py2.py3-none-any.whl (232 kB)
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Installing collected packages: patsy, statsmodels
Successfully installed patsy-1.0.1 statsmodels-0.14.4
[notice] A new release of pip is available: 24.0 -> 25.0.1
C:\maxbox\maxbox4\maxbox4>py -3.12 -m pip install factor_analyzer
Collecting factor_analyzer
Downloading factor_analyzer-0.5.1.tar.gz (42 kB)
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Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
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Building wheels for collected packages: factor_analyzer
Building wheel for factor_analyzer (pyproject.toml) ... done
Created wheel for factor_analyzer: filename=factor_analyzer-0.5.1-py2.py3-none-any.whl size=42682 sha256=8820991dc36abae7eb955168857eb3487443e75d2af5982ec3487f2e5a2be8e3
Stored in directory: c:\users\user\appdata\local\pip\cache\wheels\a2\af\06\f4d4ed4d9d714fda437fb1583629417319603c2266e7b233cc
Successfully built factor_analyzer
Installing collected packages: factor_analyzer
Successfully installed factor_analyzer-0.5.1
[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip
C:\maxbox\maxbox4\maxbox5>
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