I want this indicator and the indicator below to appear in the same chart like the main window on the graph ,put them in one script
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © loxx

//@version=5
indicator(“STD-Filtered, N-Pole Gaussian Filter [Loxx]”,
shorttitle=“STDFNPGF [Loxx]”,
overlay = true)

import loxx/loxxexpandedsourcetypes/4

greencolor = #2DD204
redcolor = #D2042D

//factorial calc
fact(int n)=>
float a = 1
for i = 1 to n
a = i
a

//alpha calc
_alpha(int period, int poles)=>
w = 2.0 * math.pi / period
float b = (1.0 - math.cos(w)) / (math.pow(1.414, 2.0 / poles) - 1.0)
float a = - b + math.sqrt(b * b + 2.0 * b)
a

//n-pole calc
_makeCoeffs(simple int period, simple int order)=>
coeffs = matrix.new<float>(order + 1, 3, 0.)
float a = _alpha(period, order)
for r = 0 to order
out = nz(fact(order) / (fact(order - r) * fact®), 1)
matrix.set(coeffs, r, 0, out)
matrix.set(coeffs, r, 1, math.pow(a, r))
matrix.set(coeffs, r, 2, math.pow(1.0 - a, r))
coeffs

//n-pole calc
_npolegf(float src, simple int period, simple int order)=>
var coeffs = _makeCoeffs(period, order)
float filt = src * matrix.get(coeffs, order, 1)
int sign = 1
for r = 1 to order
filt += sign * matrix.get(coeffs, r, 0) * matrix.get(coeffs, r, 2) * nz(filt[r])
sign = -1
filt

//std filter
_filt(float src, int len, float filter)=>
float price = src
float filtdev = filter * ta.stdev(src, len)
price := math.abs(price - nz(price[1])) < filtdev ? nz(price[1]) : price
price

smthtype = input.string(“Kaufman”, “Heiken-Ashi Better Smoothing”, options = [“AMA”, “T3”, “Kaufman”], group= “Source Settings”)
srcoption = input.string(“Close”, “Source”, group= “Source Settings”,
options =
[“Close”, “Open”, “High”, “Low”, “Median”, “Typical”, “Weighted”, “Average”, “Average Median Body”, “Trend Biased”, “Trend Biased (Extreme)”,
“HA Close”, “HA Open”, “HA High”, “HA Low”, “HA Median”, “HA Typical”, “HA Weighted”, “HA Average”, “HA Average Median Body”, “HA Trend Biased”, “HA Trend Biased (Extreme)”,
“HAB Close”, “HAB Open”, “HAB High”, “HAB Low”, “HAB Median”, “HAB Typical”, “HAB Weighted”, “HAB Average”, “HAB Average Median Body”, “HAB Trend Biased”, “HAB Trend Biased (Extreme)”])

period = input.int(25,‘Period’, group = “Basic Settings”)
order = input.int(5,‘Order’, group = “Basic Settings”, minval = 1)

filterop = input.string(“Gaussian Filter”, “Filter Options”, options = [“Price”, “Gaussian Filter”, “Both”, “None”], group= “Filter Settings”)
filter = input.float(1, “Filter Devaitions”, minval = 0, group= “Filter Settings”)
filterperiod = input.int(10, “Filter Period”, minval = 0, group= “Filter Settings”)

colorbars = input.bool(true, “Color bars?”, group = “UI Options”)
showSigs = input.bool(true, “Show signals?”, group= “UI Options”)

kfl=input.float(0.666, title=" Kaufman’s Adaptive MA (KAMA) Only - Fast End", group = “Moving Average Inputs”)
ksl=input.float(0.0645, title=" Kaufman’s Adaptive MA (KAMA) Only - Slow End", group = “Moving Average Inputs”)
amafl = input.int(2, title=“* Adaptive Moving Average (AMA) Only - Fast”, group = “Moving Average Inputs”)
amasl = input.int(30, title=“* Adaptive Moving Average (AMA) Only - Slow”, group = “Moving Average Inputs”)

[haclose, haopen, hahigh, halow, hamedian, hatypical, haweighted, haaverage] = request.security(ticker.heikinashi(syminfo.tickerid), timeframe.period, [close, open, high, low, hl2, hlc3, hlcc4, ohlc4])

float src = switch srcoption
“Close” => loxxexpandedsourcetypes.rclose()
“Open” => loxxexpandedsourcetypes.ropen()
“High” => loxxexpandedsourcetypes.rhigh()
“Low” => loxxexpandedsourcetypes.rlow()
“Median” => loxxexpandedsourcetypes.rmedian()
“Typical” => loxxexpandedsourcetypes.rtypical()
“Weighted” => loxxexpandedsourcetypes.rweighted()
“Average” => loxxexpandedsourcetypes.raverage()
“Average Median Body” => loxxexpandedsourcetypes.ravemedbody()
“Trend Biased” => loxxexpandedsourcetypes.rtrendb()
“Trend Biased (Extreme)” => loxxexpandedsourcetypes.rtrendbext()
“HA Close” => loxxexpandedsourcetypes.haclose(haclose)
“HA Open” => loxxexpandedsourcetypes.haopen(haopen)
“HA High” => loxxexpandedsourcetypes.hahigh(hahigh)
“HA Low” => loxxexpandedsourcetypes.halow(halow)
“HA Median” => loxxexpandedsourcetypes.hamedian(hamedian)
“HA Typical” => loxxexpandedsourcetypes.hatypical(hatypical)
“HA Weighted” => loxxexpandedsourcetypes.haweighted(haweighted)
“HA Average” => loxxexpandedsourcetypes.haaverage(haaverage)
“HA Average Median Body” => loxxexpandedsourcetypes.haavemedbody(haclose, haopen)
“HA Trend Biased” => loxxexpandedsourcetypes.hatrendb(haclose, haopen, hahigh, halow)
“HA Trend Biased (Extreme)” => loxxexpandedsourcetypes.hatrendbext(haclose, haopen, hahigh, halow)
“HAB Close” => loxxexpandedsourcetypes.habclose(smthtype, amafl, amasl, kfl, ksl)
“HAB Open” => loxxexpandedsourcetypes.habopen(smthtype, amafl, amasl, kfl, ksl)
“HAB High” => loxxexpandedsourcetypes.habhigh(smthtype, amafl, amasl, kfl, ksl)
“HAB Low” => loxxexpandedsourcetypes.hablow(smthtype, amafl, amasl, kfl, ksl)
“HAB Median” => loxxexpandedsourcetypes.habmedian(smthtype, amafl, amasl, kfl, ksl)
“HAB Typical” => loxxexpandedsourcetypes.habtypical(smthtype, amafl, amasl, kfl, ksl)
“HAB Weighted” => loxxexpandedsourcetypes.habweighted(smthtype, amafl, amasl, kfl, ksl)
“HAB Average” => loxxexpandedsourcetypes.habaverage(smthtype, amafl, amasl, kfl, ksl)
“HAB Average Median Body” => loxxexpandedsourcetypes.habavemedbody(smthtype, amafl, amasl, kfl, ksl)
“HAB Trend Biased” => loxxexpandedsourcetypes.habtrendb(smthtype, amafl, amasl, kfl, ksl)
“HAB Trend Biased (Extreme)” => loxxexpandedsourcetypes.habtrendbext(smthtype, amafl, amasl, kfl, ksl)
=> haclose

src := filterop == “Both” or filterop == “Price” and filter > 0 ? _filt(src, filterperiod, filter) : src

out = _npolegf(src, period, order)

out := filterop == “Both” or filterop == “Gaussian Filter” and filter > 0 ? _filt(out, filterperiod, filter) : out

sig = nz(out[1])

state = 0
if (out > sig)
state := 1
if (out < sig)
state := -1

pregoLong = out > sig and (nz(out[1]) < nz(sig[1]) or nz(out[1]) == nz(sig[1]))
pregoShort = out < sig and (nz(out[1]) > nz(sig[1]) or nz(out[1]) == nz(sig[1]))

contsw = 0
contsw := nz(contsw[1])
contsw := pregoLong ? 1 : pregoShort ? -1 : nz(contsw[1])

goLong = pregoLong and nz(contsw[1]) == -1
goShort = pregoShort and nz(contsw[1]) == 1

var color colorout = na
colorout := state == -1 ? redcolor : state == 1 ? greencolor : nz(colorout[1])

plot(out, “N-Pole GF”, color = colorout, linewidth = 3)
barcolor(colorbars ? colorout : na)

plotshape(showSigs and goLong, title = “Long”, color = color.yellow, textcolor = color.yellow, text = “L”, style = shape.triangleup, location = location.belowbar, size = size.tiny)
plotshape(showSigs and goShort, title = “Short”, color = color.fuchsia, textcolor = color.fuchsia, text = “S”, style = shape.triangledown, location = location.abovebar, size = size.tiny)

alertcondition(goLong, title = “Long”, message = “STD-Filtered, N-Pole Gaussian Filter [Loxx]: Long\nSymbol: {{ticker}}\nPrice: {{close}}”)
alertcondition(goShort, title = “Short”, message = “STD-Filtered, N-Pole Gaussian Filter [Loxx]: Short\nSymbol: {{ticker}}\nPrice: {{close}}”)


and this one 


// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © DonovanWall

//██████╗ ██╗    ██╗
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//██║  ██║██║ █╗ ██║
//██║  ██║██║███╗██║
//██████╔╝╚███╔███╔╝
//╚═════╝  ╚══╝╚══╝ 

//@version=4
study(title="Gaussian Channel [DW]", shorttitle="GC [DW]", overlay=true)

// This study is an experiment utilizing the Ehlers Gaussian Filter technique combined with lag reduction techniques and true range to analyze trend activity.
// Gaussian filters, as Ehlers explains it, are simply exponential moving averages applied multiple times.
// First, beta and alpha are calculated based on the sampling period and number of poles specified. The maximum number of poles available in this script is 9.
// Next, the data being analyzed is given a truncation option for reduced lag, which can be enabled with "Reduced Lag Mode".
// Then the alpha and source values are used to calculate the filter and filtered true range of the dataset.
// Filtered true range with a specified multiplier is then added to and subtracted from the filter, generating a channel.
// Lastly, a one pole filter with a N pole alpha is averaged with the filter to generate a faster filter, which can be enabled with "Fast Response Mode". 

//Custom bar colors are included.

//Note: Both the sampling period and number of poles directly affect how much lag the indicator has, and how smooth the output is.
//      Larger inputs will result in smoother outputs with increased lag, and smaller inputs will have noisier outputs with reduced lag.
//      For the best results, I recommend not setting the sampling period any lower than the number of poles + 1. Going lower truncates the equation.

//-----------------------------------------------------------------------------------------------------------------------------------------------------------------
//Updates:
// Huge shoutout to @e2e4mfck for taking the time to improve the calculation method!
// -> migrated to v4
// -> pi is now calculated using trig identities rather than being explicitly defined.
// -> The filter calculations are now organized into functions rather than being individually defined.
// -> Revamped color scheme.

//-----------------------------------------------------------------------------------------------------------------------------------------------------------------
//Functions - courtesy of @e2e4mfck
//-----------------------------------------------------------------------------------------------------------------------------------------------------------------
 
//Filter function 
f_filt9x (_a, _s, _i) => 
    int _m2 = 0, int _m3 = 0, int _m4 = 0, int _m5 = 0, int _m6 = 0, 
    int _m7 = 0, int _m8 = 0, int _m9 = 0, float _f = .0, _x = (1 - _a)
    // Weights. 
    // Initial weight _m1 is a pole number and equal to _i
    _m2 := _i == 9 ? 36  : _i == 8 ? 28 : _i == 7 ? 21 : _i == 6 ? 15 : _i == 5 ? 10 : _i == 4 ? 6 : _i == 3 ? 3 : _i == 2 ? 1 : 0
    _m3 := _i == 9 ? 84  : _i == 8 ? 56 : _i == 7 ? 35 : _i == 6 ? 20 : _i == 5 ? 10 : _i == 4 ? 4 : _i == 3 ? 1 : 0
    _m4 := _i == 9 ? 126 : _i == 8 ? 70 : _i == 7 ? 35 : _i == 6 ? 15 : _i == 5 ? 5  : _i == 4 ? 1 : 0
    _m5 := _i == 9 ? 126 : _i == 8 ? 56 : _i == 7 ? 21 : _i == 6 ? 6  : _i == 5 ? 1  : 0 
    _m6 := _i == 9 ? 84  : _i == 8 ? 28 : _i == 7 ? 7  : _i == 6 ? 1  : 0 
    _m7 := _i == 9 ? 36  : _i == 8 ? 8  : _i == 7 ? 1  : 0 
    _m8 := _i == 9 ? 9   : _i == 8 ? 1  : 0 
    _m9 := _i == 9 ? 1   : 0
    // filter
    _f :=   pow(_a, _i) * nz(_s) + 
      _i  *     _x      * nz(_f[1])      - (_i >= 2 ? 
      _m2 * pow(_x, 2)  * nz(_f[2]) : 0) + (_i >= 3 ? 
      _m3 * pow(_x, 3)  * nz(_f[3]) : 0) - (_i >= 4 ? 
      _m4 * pow(_x, 4)  * nz(_f[4]) : 0) + (_i >= 5 ? 
      _m5 * pow(_x, 5)  * nz(_f[5]) : 0) - (_i >= 6 ? 
      _m6 * pow(_x, 6)  * nz(_f[6]) : 0) + (_i >= 7 ? 
      _m7 * pow(_x, 7)  * nz(_f[7]) : 0) - (_i >= 8 ? 
      _m8 * pow(_x, 8)  * nz(_f[8]) : 0) + (_i == 9 ? 
      _m9 * pow(_x, 9)  * nz(_f[9]) : 0)

//9 var declaration fun
f_pole (_a, _s, _i) =>
    _f1 =            f_filt9x(_a, _s, 1),      _f2 = (_i >= 2 ? f_filt9x(_a, _s, 2) : 0), _f3 = (_i >= 3 ? f_filt9x(_a, _s, 3) : 0)
    _f4 = (_i >= 4 ? f_filt9x(_a, _s, 4) : 0), _f5 = (_i >= 5 ? f_filt9x(_a, _s, 5) : 0), _f6 = (_i >= 6 ? f_filt9x(_a, _s, 6) : 0)
    _f7 = (_i >= 2 ? f_filt9x(_a, _s, 7) : 0), _f8 = (_i >= 8 ? f_filt9x(_a, _s, 8) : 0), _f9 = (_i == 9 ? f_filt9x(_a, _s, 9) : 0)
    _fn = _i == 1 ? _f1 : _i == 2 ? _f2 : _i == 3 ? _f3 :
      _i == 4     ? _f4 : _i == 5 ? _f5 : _i == 6 ? _f6 :
      _i == 7     ? _f7 : _i == 8 ? _f8 : _i == 9 ? _f9 : na
    [_fn, _f1]

//-----------------------------------------------------------------------------------------------------------------------------------------------------------------
//Inputs
//-----------------------------------------------------------------------------------------------------------------------------------------------------------------

//Source
src = input(defval=hlc3, title="Source")

//Poles
int N = input(defval=4, title="Poles", minval=1, maxval=9)

//Period
int per = input(defval=144, title="Sampling Period", minval=2)

//True Range Multiplier
float mult = input(defval=1.414, title="Filtered True Range Multiplier", minval=0)

//Lag Reduction
bool modeLag  = input(defval=false, title="Reduced Lag Mode")
bool modeFast = input(defval=false, title="Fast Response Mode")

//-----------------------------------------------------------------------------------------------------------------------------------------------------------------
//Definitions
//-----------------------------------------------------------------------------------------------------------------------------------------------------------------

//Beta and Alpha Components
beta  = (1 - cos(4*asin(1)/per)) / (pow(1.414, 2/N) - 1)
alpha = - beta + sqrt(pow(beta, 2) + 2*beta)

//Lag
lag = (per - 1)/(2*N)

//Data
srcdata = modeLag ? src + (src - src[lag]) : src
trdata  = modeLag ? tr(true) + (tr(true) - tr(true)[lag]) : tr(true)

//Filtered Values
[filtn, filt1]     = f_pole(alpha, srcdata, N)
[filtntr, filt1tr] = f_pole(alpha, trdata,  N)

//Lag Reduction
filt   = modeFast ? (filtn + filt1)/2 : filtn
filttr = modeFast ? (filtntr + filt1tr)/2 : filtntr

//Bands
hband = filt + filttr*mult
lband = filt - filttr*mult

// Colors
color1   = #0aff68
color2   = #00752d
color3   = #ff0a5a
color4   = #990032
fcolor   = filt > filt[1] ? #0aff68 : filt < filt[1] ? #ff0a5a : #cccccc
barcolor = (src > src[1]) and (src > filt) and (src < hband) ? #0aff68 : (src > src[1]) and (src >= hband) ? #0aff1b : (src <= src[1]) and (src > filt) ? #00752d : 
           (src < src[1]) and (src < filt) and (src > lband) ? #ff0a5a : (src < src[1]) and (src <= lband) ? #ff0a11 : (src >= src[1]) and (src < filt) ? #990032 : #cccccc

//-----------------------------------------------------------------------------------------------------------------------------------------------------------------
//Outputs
//-----------------------------------------------------------------------------------------------------------------------------------------------------------------

//Filter Plot
filtplot = plot(filt, title="Filter", color=fcolor, linewidth=3)

//Band Plots
hbandplot = plot(hband, title="Filtered True Range High Band", color=fcolor)
lbandplot = plot(lband, title="Filtered True Range Low Band", color=fcolor)

//Channel Fill
fill(hbandplot, lbandplot, title="Channel Fill", color=fcolor, transp=80)

//Bar Color
barcolor(barcolor)