We Keep Coding

I'm trying to figure out this error " TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'"

I'm running this code any this error keeps popping up "TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'". Most likely this line is causing a problem
----> 2 sols = odeint(VJJmodel,[0,0],ts, args=tuple([params]) )
Please advise.
from scipy.integrate import odeint
import numpy as np
import scipy.constants as const
from matplotlib import pyplot as plt
from scipy.interpolate import interp1d
from scipy.integrate import quad
import collections
Ic = 1e-6
Rsg = 10e3
Rn = Rsg
Cj = 2e-15
Rin = 100e0
Vgap = 4*Ic*Rn/np.pi #Gap voltage linked to IcRn, SIS relation
Vn = 1.1 #0.1*Vgap/Ic/Rin
V0 = Vn*Rin*Ic
phi0 = const.value('mag. flux quantum')
wp = 1/np.sqrt(Cj*phi0/2/np.pi/Ic)
params = {'Vn':Vn, 'Rsg': Rsg, 'Rn':Rn, 'Vgap':Vgap, 'Cj':Cj, 'Rin':Rin, 'wp':wp}
print(Vn)
taustop = 20* wp * Rsg*Cj
params['taustop'] = taustop
print(params)
def Rj(V,params):
Vgap = params['Vgap']
if V>Vgap:
return params['Rn']
elif V<=Vgap:
return params['Rsg']
def Q(V,params):
Cj = params['Cj']
Rin = params['Rin']
wp = params['wp']
return Cj*wp/(1/Rj(V,params)+1/Rin)
def Vs(t,params):
if not hasattr(t,'__len__'):
t = np.array([t])
result = []
for x in t:
if x <params['taustop']/2:
result.append(x/(params['taustop']/2)*params['Vn'])
else:
result.append(params['Vn'])
#return params['Vn']
result = np.ones(len(t))*params['Vn']
return np.array(result)
def VJJmodel(z,t,params):
f = z[0]
g = z[1]
V = phi0/2/np.pi*g
fp = g
gp = Vs(t,params)-(1/Q(V,params)*g+np.sin(f))
return np.array([fp,gp])
ts = np.linspace(0,2*taustop,2000)
sols = odeint(VJJmodel,[0,0],ts, args=tuple([params]) )
solsfunc = interp1d(ts,sols.T)
Vjunc = lambda x: phi0/2/np.pi * wp * solsfunc(x)[1]
taurep = 2*np.pi/np.average(np.diff(sols[:,0])/np.diff(ts))
fig = plt.figure(figsize=(10,10))
plt.clf()
plt.subplot(3, 1, 1)
plt.title('Voltage/Vgap')
plt.plot(ts,Vjunc(ts) / Vgap)
plt.subplot(3, 1, 2)
plt.title('Current')
plt.plot(ts,np.sin(solsfunc(ts)[0]))
plt.plot(ts,(Vs(ts,params)*Ic*Rin-Vjunc(ts))/Rin/Ic)
plt.ylim([-2,2])
plt.subplot(3, 1, 3)
plt.title('Phase')
plt.plot(ts,solsfunc(ts)[0])
plt.show()
plt.close(fig)
I tried to rewrite it but it didn't work.

Python function calling with variable vs raw numbers

I am trying to implement a pso algorithm from Wikipedia https://en.wikipedia.org/wiki/Particle_swarm_optimization.
My problem is that when I am calling the cost function with a variable (Gbest), and then manually calling the cost function (with the Gbest data) I get a different output (cost) like the image bellow:
Code fault
I am new to python so thank you for any suggestions.
Here is the complete code:
import matplotlib.pyplot as plt
import numpy as np
from control.matlab import *
A = np.array([[0,0,1],[0,1,0],[1,2,-2]])
B = np.array( [[0],[1],[0]])
C = np.array([[0, 1,0]])
D = np.zeros([C.shape[0],B.shape[1]])
sys = ss(A,B,C,D)
sys_tf = tf(sys)
s = tf('s')
def cost(kp,ki):
global sys_tf, G, y, t, r
G = kp + ki/s
C = feedback(sys_tf*G, 1)
y, t = step(C, linspace(0,100))
r = np.ones(len(t))
return np.sum(y-r)**2
part = 100
ite = 10000
dim = 2
w = 0.001
wdamp = 0.99
phip = 0.9
phig = 0.1
blo, bup = -10,10
x = np.zeros([dim, part])
v = np.zeros([dim, part])
pbest = np.zeros([dim, part])
gbest = np.array([1000000,1000000])
for i in range(part):
for k in range(dim):
x[k][i] = pbest[k][i] = np.random.uniform(blo, bup)
v[k][i] = np.random.uniform(-np.abs(bup - blo), np.abs(bup - blo))
if cost(pbest[0][i], pbest[1][i]) < cost(gbest[0], gbest[1]):
gbest = np.array([pbest[0][i], pbest[1][i]])
for it in range(ite):
for i in range(part):
for k in range(dim):
rp = np.random.uniform(0,1)
rg = np.random.uniform(0,1)
v[k,:] = w*v[k,:] + phip*rp*(pbest[k,:] - x[k,:]) + phig*rg*(gbest[k] - x[k,:])
x[k,:] = x[k,:] + v[k,:]
w = w*wdamp
if cost(x[0][i], x[1][i]) < cost(pbest[0][i], pbest[1][i]):
pbest[:,i] = x[:,i]
if cost(pbest[0][i], pbest[1][i]) < cost(gbest[0], gbest[1]):
gbest = np.array([pbest[0][i], pbest[1][i]])
plt.plot(t, y, 'ro')
plt.plot(t, r, 'x')
plt.pause(0.005)
plt.title(gbest)
print([gbest, cost(gbest[0], gbest[1])])

Is there a way to fix value-error problem

Hello guys i am trying to implement an algortihm to remove water from underwater images and make image more noticable , but i got an errror ValueError: max() arg is an empty sequence , at the function homomorpic on this line r = max(np.ravel(result[:,:i])) , the error is caused because the result array is empty but i filled it above .Here the code below.
import numpy as np
import cv2
def homomorpic(img):
img = np.float32(img)
#img = img/255
rows , cols , dim = img.shape
(rh,rl,cutoff) = 1.3, 0.8, 32
b,g,r = cv2.split(img)
y_log_b = np.log(b + 0.01)
y_log_g = np.log(g + 0.01)
y_log_r = np.log(r + 0.01)
y_fft_b= np.fft.fft2(y_log_b)
y_fft_g= np.fft.fft2(y_log_g)
y_fft_r= np.fft.fft2(y_log_r)
y_fft_b_shift = np.fft.fftshift(y_log_b)
y_fft_g_shift = np.fft.fftshift(y_log_g)
y_fft_r_shift = np.fft.fftshift(y_log_r)
D0=cols/cutoff
H= np.ones((rows,cols))
B= np.ones((rows,cols))
for i in range(rows):
for j in range(cols):
H[i][j] = ((rh-rl)* (1-np.exp(-((i-rows/2)**2+(j-cols/2)**2)/(2*D0**2))))+rl
result_filter_b = H* y_fft_b_shift
result_filter_g = H* y_fft_g_shift
result_filter_r = H* y_fft_r_shift
result_b_intern = np.real(np.fft.ifft2(np.fft.ifftshift(result_filter_b)))
result_g_intern = np.real(np.fft.ifft2(np.fft.ifftshift(result_filter_g)))
result_r_intern = np.real(np.fft.ifft2(np.fft.ifftshift(result_filter_r)))
result_b = np.exp(result_b_intern)
result_g = np.exp(result_g_intern)
result_r = np.exp(result_r_intern)
result = np.zeros((rows,cols,dim))
result[:,:,0] = result_b
result[:,:,1] = result_g
result[:,:,2] = result_r
ma = -1
mi = 500
for i in range(3):
r = max(np.ravel(result[:,:i]))
x = min(np.ravel(result[:,:i]))
if r > ma :
ma = r
if x < mi :
mi = x
return(result)
image = cv2.imread("eg.png")
image2 = homomorpic(image)
Thanks for any help or suggestion.

In this loop for i in range(3): the first value of i would be 0.
This will later on lead to this r = max(np.ravel(result[:,:0])) where the result from the slicing would be empty.
You would want to shift yourrange forward like this:
for i in range(1, 3+1):

MPC with python and Error ValueError: `f0` passed has more than 1 dimension

I wrote a MPC with Python and it worked before. After a long time I want to use it again but I got this Error
f0 passed has more than 1 dimension.
But I didn't change anything on my code. It is some kind of strange.
Here is my code:
import numpy as np
import numpy.linalg as npl
import matplotlib.pyplot as plt
from scipy.optimize import minimize
def mpcAugment(Am, Bm, Cm ):
"Function for Augmented Model"
nx, nu = Bm.shape
ny = Cm.shape[0]
A = np.zeros((nx+ny,nx+ny))
A[0:nx,0:nx] = Am
A[nx:nx+ny,0:nx] = Cm#Am
A[nx:nx+ny,nx:nx+ny] = np.eye(ny)
B = np.zeros((nx+ny,nu))
B[0:nx,:nu] = Bm
B[nx:nx+ny,:nu] = Cm#Bm
C = np.zeros((ny,nx+ny))
C[:ny,nx:nx+ny] = np.eye(ny)
return A, B, C
'Define Parameters'
k = 0.4
AICB = 153.8
mcp = 8.8e4
vamb1 = 30
vamb2 = 45
a = -k*AICB/mcp
b = -1/mcp
Ts = 20
VICBref = -5.0
Am = np.array([[1+Ts*a]])
Bm = np.array([[Ts*b]])
Gm = np.array([[-Ts*a]])
Cm = np.array([[1]])
A, B, C = mpcAugment(Am,Bm,Cm)
A, G, C = mpcAugment(Am,Gm,Cm)
nx, nu = B.shape
ny = C.shape[0]
nd = G.shape[1]
Np = 20
Nu = 5
F = np.zeros((Np*ny,nx))
PHI = np.zeros((Np*ny,Nu*nu))
PHIw = np.zeros((Np*ny,Np*nd))
for i in range(0,Np):
Ai = npl.matrix_power(A, i+1)
F[i*ny:(i+1)*ny,:] = C#Ai
for j in range(0, Nu):
if j <= i:
Aij = np.linalg.matrix_power(A, i-j)
PHI[i*ny:(i+1)*ny, j*nu:(j+1)*nu] = C#Aij#B
for j in range(0, Np):
if j <= i:
Aij = np.linalg.matrix_power(A, i-j)
PHIw[i*ny:(i+1)*ny, j*nd:(j+1)*nd] = C#Aij#G
umax = 3100
umin = 0
Q = np.eye(Np*ny)
R = 1e-2*np.eye(Nu*nu)
Rs = VICBref*np.ones((Np*ny,1))
Ainq = np.zeros((2*Nu*nu,Nu*nu))
binq = np.zeros((2*Nu*nu,1))
cinq = np.zeros((2*Nu*nu,1))
for i in range(0,Nu):
binq[i*nu:(i+1)*nu] = umax
binq[(i+Nu)*nu:(Nu+i+1)*nu] = 1
cinq[i*nu:(i+1)*nu] = 1
cinq[(i+Nu)*nu:(Nu+i+1)*nu] = -1
for j in range(0,i+1):
Ainq[i*nu:(i+1)*nu,j*nu:(j+1)*nu] = np.eye(nu)
Ainq[(i+Nu)*nu:(Nu+i+1)*nu,j*nu:(j+1)*nu] = np.eye(nu)
u0 = 0
def objective(du):
dU = np.array(du).reshape((len(du),1))
Y = F#x + PHI#dU + PHIw#w
return np.transpose((Rs-Y))#(Rs-Y)+np.transpose(dU)#R#(dU)
def constraint1(du):
dU = np.array(du).reshape((len(du),1))
return (binq - Ainq#dU - cinq*u0)[0]
#print(objective([1,1,1]))
ulim = (umin, umax)
bnds = np.kron(np.ones((Nu,1)),ulim)
#print(bnds)
Um = np.ones((nu*Nu,1))
Tsim = 5e4
time = np.arange(0,Tsim,Ts)
Nt = len(time)
xm = np.zeros((Nt,1))
um = np.zeros((Nt,nu))
ym = np.zeros((Nt,ny))
xm[0] = 0
ym[0] = Cm.dot(xm[0])
w = np.zeros((Np*nd,1))
print('Am = ',Am)
print('Bm = ',Bm)
print('Cm = ',Cm)
x = np.zeros((nx,1))
x[1] = xm[0]
vamb = vamb1
Vamb = np.zeros((Nt,1))
Ns = int(np.floor(Nt/2))
Vamb[0:Ns] = vamb1*np.ones((Ns,1))
Vamb[Ns:Nt] = vamb2*np.ones((Nt-Ns,1))
Vref = VICBref*np.ones((Nt,1))
con = {'type':'ineq','fun':constraint1}
for i in range(0,Nt-1):
sol = minimize(objective, Um, method = 'SLSQP',constraints = con)
if sol.success == False:
print('Error Cant solve problem')
exit()
Um = sol.x
um[i+1] = um[i] + Um[0]
u0 = um[i+1]
xm[i+1] = Am.dot(xm[i])+Bm.dot(um[i+1])+Gm.dot(Vamb[i])
ym[i+1] = Cm.dot(xm[i+1])
for j in range(0,Np):
if i+j < Nt:
Rs[j] = Vref[i+j]
w[j] = Vamb[i+j]-Vamb[i+j-1]
else:
Rs[j] = Vref[Nt-1]
w[j] = 0
x[0] = xm[i+1] - xm[i]
x[1] = xm[i+1]
print('Q = ',um[i+1],' , VICB = ',xm[i+1], ' vamb = ', Vamb[i])
hour = 60*60
plt.figure()
plt.subplot(2,1,1)
plt.plot(time/hour,ym)
plt.plot(time/hour,Vref,'--')
plt.xlabel('time(hours)')
plt.xlim([0, Tsim/hour])
plt.subplot(2,1,2)
plt.plot(time/hour,um)
plt.xlim([0, Tsim/hour])
plt.show()
It about a controller, which control the temperature of a cool box.
Is that possible that anything changed in main simply code?
I think the problem is now in minimizations part.

I reinstalled all of my libraries and it worked

Adaptive DBSCAN achievement

I am doing the DBSCAN clustering in python. I want to achieve an adaptive way to return the number of clusters by self calculating its eps and Minpts parameters. Below is my code.
import math
import copy
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN
def loadDataSet(fileName, splitChar='\t'):
dataSet = []
with open(fileName) as fr:
for line in fr.readlines():
curline = line.strip().split(splitChar)
fltline = list(map(float, curline))
dataSet.append(fltline)
return dataSet
def dist(a,b):
return math.sqrt(math.pow(a[0]-b[0],2) + math.pow(a[1]-b[1],2))
def returnDk(matrix,k):
Dk = []
for i in range(len(matrix)):
Dk.append(matrix[i][k])
return Dk
def returnDkAverage(Dk):
sum = 0
for i in range(len(Dk)):
sum = sum + Dk[i]
return sum/len(Dk)
def CalculateDistMatrix(dataset):
DistMatrix = [[0 for j in range(len(dataset))] for i in range(len(dataset))]
for i in range(len(dataset)):
for j in range(len(dataset)):
DistMatrix[i][j] = dist(dataset[i], dataset[j])
return DistMatrix
def returnEpsCandidate(dataSet):
DistMatrix = CalculateDistMatrix(dataSet)
tmp_matrix = copy.deepcopy(DistMatrix)
for i in range(len(tmp_matrix)):
tmp_matrix[i].sort()
EpsCandidate = []
for k in range(1,len(dataSet)):
Dk = returnDk(tmp_matrix,k)
DkAverage = returnDkAverage(Dk)
EpsCandidate.append(DkAverage)
return EpsCandidate
def returnMinptsCandidate(DistMatrix,EpsCandidate):
MinptsCandidate = []
for k in range(len(EpsCandidate)):
tmp_eps = EpsCandidate[k]
tmp_count = 0
for i in range(len(DistMatrix)):
for j in range(len(DistMatrix[i])):
if DistMatrix[i][j] <= tmp_eps:
tmp_count = tmp_count + 1
MinptsCandidate.append(tmp_count/len(dataSet))
return MinptsCandidate
def returnClusterNumberList(dataset,EpsCandidate,MinptsCandidate):
np_dataset = np.array(dataset)
ClusterNumberList = []
for i in range(len(EpsCandidate)):
clustering = DBSCAN(eps= EpsCandidate[i],min_samples= MinptsCandidate[i]).fit(np_dataset)
num_clustering = max(clustering.labels_)
ClusterNumberList.append(num_clustering)
return ClusterNumberList
if __name__ == '__main__':
data = pd.read_csv('/Users/Desktop/Mic/recorder_test1/New folder/MFCCresultsforclustering/MFCCresultsforclustering.csv')
dataSet = data.iloc[:,0:13].values
EpsCandidate = returnEpsCandidate(dataSet)
DistMatrix = CalculateDistMatrix(dataSet)
MinptsCandidate = returnMinptsCandidate(DistMatrix,EpsCandidate)
ClusterNumberList = returnClusterNumberList(dataSet,EpsCandidate,MinptsCandidate)
print(EpsCandidate)
print(MinptsCandidate)
print('cluster number list is')
print(ClusterNumberList)
However, the output with the loading data set is all [-1]s. I am wondering where is the mistake. Am I right for this general direction? If not, how can I achieve the adaptive DBSCAN clustering?