I'm writing a Pine Script Indicator and I'm getting 4 errors and one warning after compiling:
Cannot call 'hline' with argument 'price'='upperBound'. An argument of 'series float' type was used but a 'input float' is expected
Cannot call 'hline' with argument 'price'='lowerBound'. An argument of 'series float' type was used but a 'input float' is expected
Cannot call 'fill' with argument 'hline1'='upperBound'. An argument of 'series float' type was used but a 'hline' is expected
Cannot call 'fill' with argument 'hline2'='lowerBound'. An argument of 'series float' type was used but a 'hline' is expected
(1) warning: The transp argument is deprecated. We recommend using color.new() or color.rgb() functions to specify the transparency of the plots instead. Additionally, note that transp has no effect in plots where the color is calculated at runtime
This is my Pine Script Code:
'''
//#version=5
// Define the number of bars to be analyzed for finding clusters
clusterLength = input(title="Cluster Length", defval=100)
// Define the number of standard deviations from the mean to determine the cluster
stdDev = input(title="Number of Standard Deviations", defval=2.0)
// Calculate the mean and standard deviation for the defined number of bars
mean = ta.sma(close, clusterLength)
stddev = ta.stdev(close, clusterLength)
// Plot the upper and lower bounds of the clusters as horizontal lines
upperBound = mean + stddev * stdDev
lowerBound = mean - stddev * stdDev
hline(upperBound, color=color.red, linewidth=2, title="Upper Bound")
hline(lowerBound, color=color.blue, linewidth=2, title="Lower Bound")
// Fill the area between the bounds to visually represent the cluster
fill(upperBound, lowerBound, color=color.gray, transp=70)
'''
I would appreciate if you provide a solution.
Thanks in advance
You cannot use dynamic values in hline().
You can try using plot() or line instead.
And you should be calling the fill() function with plots or hlines. upperBound and lowerBound are just variables.
See the signature below:
fill(hline1, hline2, color, title, editable, fillgaps, display) → void
fill(plot1, plot2, color, title, editable, show_last, fillgaps, display) → void
Related
I am writing a Vpython simulation for projectile motion and keep getting the error ('float' object has no attribute '_x') on this line(ball.vel.y = ball.vel.y + g*dt) . I have tried changing the values of ball.vel.y to an integer and changing g to an integer but the same error occurs. Here is the code
from vpython import *
import math
ball=sphere(radius=0.1, color=color.red, pos=vector(0.1,0.1,0),make_trail=True)
floor=box(pos=vector(0,0,0), length=10, height=0.01, width=0.01)
g= vector(0,-9.8 ,0)
ball.vel=vector(10*cos(43),10*sin(43),0)
dt=0.1
t=0.0
while(ball.pos.y>-0.001):
rate(100)
t=t+dt
ball.pos.x = ball.pos.x + ball.vel.x*dt
ball.vel.y = ball.vel.y + g*dt
ball.pos.y = ball.pos.y + ball.vel.y*dt
g is a vector, as is g*dt, but ball.vel.y is a scalar, and you can't add a vector to a scalar. It's unfortunate that the error message doesn't just say "You can't add a vector to a scalar". I note that if you reverse the two quantities the error message is a bit more understandable: TypeError: unsupported operand type(s) for +: 'vpython.cyvector.vector' and 'float'
I am trying to downsample or reduce the resolution of a 3D array only on the first two axes. For example, if the array size is 40x50x300, downsampling it with a degree of 2 will make it 20x25x300
for this purpose, I have found a function in scikit-image
def create_img(nX, nY, nMZ):
"this function creates a demo array"
img = np.zeros((nX,nY,nMZ))
img_sp = np.arange(nX*nY).reshape(nX, nY) + 1
for r in range(img.shape[0]):
for c in range(img.shape[1]):
img[r,c,:] = img_sp[r,c]
return img
image = create_img(7, 5, 10)
Now every pixel in the image(2D) has corresponding values on the z axis.
from skimage.measure import block_reduce
img_block = block_reduce(image, block_size=(2, 2, 1), cval=0, func=np.min)
now, the block_reduce function will take every minimum value in sliding 2x2 window and downsample the image on the x and y-axis.
If func arg is changed to np.max it will take the maximum value in the 2x2 window. The other supporting func are np.mean, np.median and so...
But I want to take XY values based on location/indices for example 0th element on 2x2 or max indice elements.
How to achieve that?
i'm trying to make the object parallel to the z - axis by using bpy.ops.transform.rotate(value=90.0, axis=(1,0,0)) but all i got is this
enter image description here
import bpy
ungu = bpy.data.materials.new('Ungu')
ungu.diffuse_color=(0.6,0.1,0.3)
for i in range (5) :
x = i*2
y = 0
z = 0
bpy.ops.mesh.primitive_plane_add(location=(x,y,z))
ob=bpy.context.object
ob.name='PLANE'
mymesh=ob.data
ob.scale=((0.5,3,2))
#aplikasikan warna ungu ke objek mesh
mymesh.materials.append(ungu)
bpy.ops.transform.rotate(value=90.0, axis=(1,0,0))
so what number should i put in value parameter?
the value argument should be in radians but you are using degrees. I am not sure which version of Blender you are using, but acording to docs for Blender 2.82a https://docs.blender.org/api/current/bpy.ops.transform.html the transform function should be called like this:
bpy.ops.transform.rotate(value=3.14/2, orient_axis='X')
I am unable to understand the source of this error:
line 327, in function_wrapper
return function(*(wrapper_args + args))
TypeError: SSVOptionPriceObjFunc() missing 1 required positional argument: 'marketVolSurface'
The relevant code is below:
x0 = [1.0, 0.0] # (lambda0, rho)
x0 = np.asarray(x0)
args = (spot, 0.01*r, daysInYear, mktPrices, volSurface)
# constraints: lambd0 >0, -1<= rho <=1
boundsHere = ((0, None), (-1, 1))
res = minimize(SSVOptionPriceObjFunc, x0, args, method='L-BFGS-B', jac=None,
bounds=boundsHere,options={'xtol': 1e-8, 'disp': True})
The function to be minimized is below. The first two arguments are the free variables, while the other five are fixed as parameters.
def SSVOptionPriceObjFunc(lambda0, rho, spot, spotInterestRate, daysInYear, marketPrices,
marketVolSurface):
My intention is to find (lambd0, rho) giving a minimum. From the debugger, it seems that my initial guess x0 is interpreted as a single variable, not as a vector, giving the error about a missing positional argument. I have tried passing x0 as a list, tuple, and ndarray; all fail. Can someone spot an error, or suggest a workaround? Thank you in advance.
Update: I have found a solution: use a wrapper function from the functools package to set the parameters.
import functools as ft
SSVOptionPriceObjFuncWrapper = ft.partial(SSVOptionPriceObjFunc, spot=spot,
spotInterestRate=0.01 * r, daysInYear=daysInYear, marketPrices=mktPrices,
marketVolSurface=volSurface)
Then pass SSVOptionPriceObjFuncWrapper to the minimizer with args = None
Thank you for the replies.
Take the documented minimize inputs seriously. It's your job to write the function to fit what minimize does, not the other way around.
scipy.optimize.minimize(fun, x0, args=(),
fun: callable
The objective function to be minimized.
fun(x, *args) -> float
where x is an 1-D array with shape (n,) and args is a tuple of the fixed
parameters needed to completely specify the function.
I have the following function with multiple arguments that I would like to minimize with Optim.jl:
function post(parm,y,x,n)
# Evaluate the log of the marginal posterior for parm at a point
fgamma=zeros(n,1);
for ii = 1:2
fgamma = fgamma + parm[ii+1]*(x[:,ii+1].^parm[4]);
end
fgamma = fgamma.^(1/parm[4]);
fgamma = fgamma + parm[1]*ones(n,1);
lpost = .5*n*log.((y - fgamma)'*(y-fgamma));
end
However, when i try to use optimize, Julia returns an error.
Old error (with parm):
MethodError: no method matching finite_difference!(::##1#2, ::Array{Float64,2}, ::Array{Float64,2}, ::Symbol)
New error(with parm2):
MethodError: Cannot `convert` an object of type Array{Float64,2} to an object of type Float64
The complete script with data and optimize call I am using is this:
using Distributions
using Optim
n = 200;
k = 3;
x = ones(n,k);
fgamma=zeros(n,1);
gam = [1.01; 0.6; 0.8; 1.5];
x[:,2] = rand(Chisq(10),n);
x[:,3] = rand(Chisq(5),n);
epsl = rand(Normal(0,1),n);
y = zeros(n,1);
for i = 1:n
y[i,1] = gam[1] + (gam[2]*x[i,2]^gam[4] + gam[3]*x[i,3]^gam[4])^(1/gam[4]) + epsl[i];
end
# Sim
bols = inv(x'x)x'y;
s2 = (y-x*bols)'*(y-x*bols)/(n-k);
sse=(n-k)*s2;
bolscov = s2.*inv(x'*x);
bolssd=zeros(k,1);
for i = 1:k
bolssd[i,1]=sqrt(bolscov[i,i]);
end
# Calculate posterior mode and Hessian at mode
nparam=k+1;
parm = ones(nparam,1);
parm[1:k,1]=bols;
parm2 = vec(parm);
opt = Optim.Options(f_tol = 1e-8, iterations = 1000);
Optim.after_while!{T}(d, state::Optim.BFGSState{T}, method::BFGS, options) = global invH = state.invH
res = optimize(p -> post(p,y,x,n), parm2, BFGS(), opt)
Does anyone knows what I am doing wrong? I think that the there is a problem with the type of lpost in the function post, since it returns a 1x1 Array{Float64,2}. Unfortunately, i couldn't handle it well.
The error message
MethodError: Cannot `convert` an object of type Array{Float64,2} to an object of type Float64
is caused by an attempt to convert a matrix into a scalar. In general this is not possible, but when the matrix is a 1x1 matrix (as the question pointed out), there is a natural transformation: scalar = matrix[1,1].
optimize wants a scalar value returned because it is a scalar non-linear optimization routine. Optimizing a vector value is even hard to unambiguously define (concepts such as Pareto optima is an attempt to do so).
So, after this prelude, the fix is simple, together with an issue with Complex optimization #fst (the poster) later tackled. Again, a single dimensional scalar is required, so real(...) was used to make a scalar out of a complex value (more precisely an ordered scalar, as complex numbers are scalars too). The resulting post function is:
function post(parm,y,x,n)
# Evaluate the log of the marginal posterior for parm at a point
fgamma=zeros(n,1);
for ii = 1:2
fgamma = fgamma + parm[ii+1]*(x[:,ii+1].^parm[4]);
end
fgamma = fgamma.^Complex(1/parm[4]);
fgamma = fgamma + parm[1]*ones(n,1);
lpost = .5*n*log.((y - fgamma)'*(y-fgamma));
return real(lpost[1,1])
end