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Problems using fsolve to find two unknowns in Python

I've been having problems trying to estimate two variables while using fsolve. Below is the code
def f(variables):
#Ideal
n=0 #this is passed as an argument but given in this example for simplicity
P1 = 101325; T1 = 300
q = 'H2:2,O2:1,N2:{}'.format(molno[n])
mech = 'H2O2sandan.cti'
cj_speed,R2,plot_data = CJspeed(P1,T1,q,mech,fullOutput=True)
gas = PostShock_fr(cj_speed, P1, T1, q, mech)
Ta = gas.T; Ps = gas.P;
CVout1 = cvsolve(gas)
taua = CVout1['ind_time']
Tb = Ta*1.01
gas.TPX = Tb,Ps,q
CVout2 = cvsolve(gas)
taub = CVout2['ind_time']
#Constant Volume
k,Ea = variables
T_0_1= Tvn_mg_d[n]
T_vn_1 = Tvn_mg_d[n]
den = rhovn_mg_d[n]
t = np.linspace(0,0.0001,100000000)
qr = Q_mg[n]
perfect_var = T_vn_1,den,Ea,k,qr
sol_t= odeint(ode,T_0_1,t=t,args=(perfect_var,))
index = np.argmax(np.gradient(sol_t[:,0]))
tau_cv_1 = t[index]
T_0_2= Tvn_mg_d[n]*1.01
T_vn_2 = Tvn_mg_d[n]*1.01
den = rhovn_mg_d[n]
t = np.linspace(0,0.0001,100000000)
qr = Q_mg[n]
perfect_var = T_vn_2,den,Ea,k,qr
sol_t= odeint(ode,T_0_2,t=t,args=(perfect_var,))
index = np.argmax(np.gradient(sol_t[:,0]))
tau_cv_2 = t[index]
root1 = taua - t_cv_1
root2 = taub - t_cv_2
return[root1,root2]
import scipy.optimize as opt
k_guess = 95000
Ea_guess = 28*300
solution = opt.fsolve(f,(k_guess,Ea_guess))
print(solution)
I want to find values of k_guess and Ea_guess such that roo1 and roo2 are 0 (i.e. taua = t_cv_1 and taub = t_cv_2). However I don't know if I've used fsolve the right way as the values returned seem to be way off. Am I returning the right thing? I also get the below error:
lsoda-- warning..internal t (=r1) and h (=r2) are
such that in the machine, t + h = t on the next step
(h = step size). solver will continue anyway
What am I doing wrong here?

Using a for loop to plot arrays from dictionaries

I have a dictionary with multiple key defined as (arbitrary inputs):
colors = {}
colors['red'] = {}
colors['blue'] = {}
colors['red'][clustname] = np.array([])
colors['blue'][clustname] = np.array([])
basically I want to plot a red v blue graph for each 'cluster'. I have 13 'clusters' in total with differing color values for each. The names in my code are different from the arbitrary ones above, but I figured it would be easier to understand with basic values then to look at the overall code:
colpath = '/home/jacob/PHOTOMETRY/RESTFRAME_COLOURS/' #This is the path to the restframe colors
goodcolindx = {}
colfiledat = {}
colors = {}
colors['UMINV'] = {}
colors['VMINJ'] = {}
colors['NUVMINV'] = {}
colors['id'] = {}
for iclust in range(len(clustname)):
colors['UMINV'][clustname[iclust]] = np.array([])
colors['VMINJ'][clustname[iclust]] = np.array([])
colors['id'][clustname[iclust]] = np.array([])
colors['NUVMINV'][clustname[iclust]] = np.array([])
filepath = catpath + clustname[iclust] + "_totalall_" + extname[iclust] + ".cat"
photdat[clustname[iclust]] = ascii.read(filepath)
filepath = zpath + "compilation_" + clustname[iclust] + ".dat"
zdat[clustname[iclust]] = ascii.read(filepath)
colfilepath = colpath + 'RESTFRAME_MASTER_' + clustname[iclust] + '_indivredshifts.cat'
colfiledat[clustname[iclust]] = ascii.read(colfilepath)
goodcolindx[clustname[iclust]] = np.where((colfiledat[clustname[iclust]]['REDSHIFTUSED'] > 0.9) & \
(colfiledat[clustname[iclust]]['REDSHIFTUSED'] < 1.5) & \
(photdat[clustname[iclust]]['totmask'] == 0) & \
(photdat[clustname[iclust]]['K_flag'] == 0) & \
((zdat[clustname[iclust]]['quality'] == 3) | (zdat[clustname[iclust]]['quality'] == 4)))
goodcolindx[clustname[iclust]] = goodcolindx[clustname[iclust]][0]
for igood in range(len(goodcolindx[clustname[iclust]])):
idstring = str(photdat[clustname[iclust]]['id'][goodcolindx[clustname[iclust]][igood]])
colors['NUVMINV'][clustname[iclust]] = np.append(colors['NUVMINV'][clustname[iclust]], -2.5 *
np.log10(colfiledat[clustname[iclust]]['NUV'][goodcolindx[clustname[iclust]][igood]]
/ colfiledat[clustname[iclust]]['V'][goodcolindx[clustname[iclust]][igood]]))'SpARCS-0035'
colors['UMINV'][clustname[iclust]] = np.append(colors['UMINV'][clustname[iclust]], colfiledat[clustname[iclust]]['UMINV'][goodcolindx[clustname[iclust]][igood]])
colors['id'][clustname[iclust]] = np.append(colors['id'][clustname[iclust]], photdat[clustname[iclust]]['id'][goodcolindx[clustname[iclust]][igood]])
colors['VMINJ'][clustname[iclust]] = np.append(colors['VMINJ'][clustname[iclust]], colfiledat[clustname[iclust]]['VMINJ'][goodcolindx[clustname[iclust]][igood]])
for iclustc in colors:
plt.plot(colors['VMINJ'][clustname[iclustc]], colors['UMINV'][clustname[iclustc]], 'ko')
plt.show()
So in this case, my 'red' is the VMINJ and my 'blue' is the UMINV. I am trying to use a for loop to cycle through all the cluster names that I have, but I keep getting the error back 'String indices must be integers'. I understand the basics of that, but don't know how to fix my code to make plots for each 'red' v 'blue' for each cluster. Any help would be awesome, let me know if you have questions

I figured it out. I changed the for loop to:
for iclust in range(len(clustname)):
plt.plot(colors['UMINV'][clustname[iclust]]....
and that worked

How to get correct output shape in Unet?

I am building a u net for binary image image segmentation. I am using Tensorflow's tf.nn api. My input image has dimensions (256,256,3) and output binary image has dimensions(256,256,1). The output of the U net model must be (1,256,256,1) but output shape results to be (7,256,256,3).For the convolutional kernels I am using Tensorflow's truncated normal initializer with each datatype as float32. Am I creating multiple output layers somewhere in the code
def get_filter(shape,na):
w =tf.get_variable(name=na,shape=shape,dtype='float32',initializer=tf.truncated_normal_initializer(dtype='float32'))
return w
def unet(inp):
#f1 = get_filter(shape=[3,3,3,16])
lay_16_1 = tf.nn.conv2d(inp,filter=get_filter(shape=[3,3,3,16],na='w_1'),strides=[1,1,1,1],padding='SAME',name='conv_16_1')
lay_16_2 = tf.nn.relu(lay_16_1,name='re_16_1')
lay_16_3 = tf.layers.batch_normalization(lay_16_2,axis=-1,name='bn_16')
lay_16_4 = tf.nn.conv2d(lay_16_3,filter=get_filter([3,3,16,16],na='w_2'),strides=[1,1,1,1],padding='SAME',name='conv_16_2')
lay_16_5 = tf.nn.relu(lay_16_4,name='re_16_2')
lay_p1 = tf.nn.max_pool(lay_16_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_1')
lay_32_1 = tf.nn.conv2d(lay_p1,filter=get_filter([3,3,16,32],na='w_3'),strides=[1,1,1,1],padding='SAME',name='conv_32_1')
lay_32_2 = tf.nn.relu(lay_32_1,name='re_32_1')
lay_32_3 = tf.layers.batch_normalization(lay_32_2,axis=-1,name='bn_32')
lay_32_4 = tf.nn.conv2d(lay_32_3,filter=get_filter([3,3,32,32],na='w_4'),strides=[1,1,1,1],padding='SAME',name='conv_32_2')
lay_32_5 = tf.nn.relu(lay_32_4,name='re_32_2')
lay_p2 = tf.nn.max_pool(lay_32_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_2')
lay_64_1 = tf.nn.conv2d(lay_p2,filter=get_filter([3,3,32,64],na='w_5'),strides=[1,1,1,1],padding='SAME',name='conv_64_1')
lay_64_2 = tf.nn.relu(lay_64_1,name='re_64_1')
lay_64_3 = tf.layers.batch_normalization(lay_64_2,axis=-1,name='bn_64')
lay_64_4 = tf.nn.conv2d(lay_64_3,filter=get_filter([3,3,64,64],na='w_6'),strides=[1,1,1,1],padding='SAME',name='conv_64_2')
lay_64_5 = tf.nn.relu(lay_64_4,name='re_64_2')
lay_p3 = tf.nn.max_pool(lay_64_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_3')
lay_128_1 = tf.nn.conv2d(lay_p3,filter=get_filter([3,3,64,128],na='w_7'),strides=[1,1,1,1],padding='SAME',name='conv_128_1')
lay_128_2 = tf.nn.relu(lay_128_1,name='re_128_1')
lay_128_3 = tf.layers.batch_normalization(lay_128_2,axis=-1,name='bn_128')
lay_128_4 = tf.nn.conv2d(lay_128_3,filter=get_filter([3,3,128,128],na='w_8'),strides=[1,1,1,1],padding='SAME',name='conv_128_2')
lay_128_5 = tf.nn.relu(lay_128_4,name='re_128_2')
lay_p4 = tf.nn.max_pool(lay_128_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_4')
lay_256_1 = tf.nn.conv2d(lay_p4,filter=get_filter([3,3,128,256],na='w_9'),strides=[1,1,1,1],padding='SAME',name='conv_256_1')
lay_256_2 = tf.nn.relu(lay_256_1,name='re_256_1')
lay_256_3 = tf.layers.batch_normalization(lay_256_2,axis=-1,name='bn_256')
lay_256_4 = tf.nn.conv2d(lay_256_3,filter=get_filter([3,3,256,256],na='w_10'),strides=[1,1,1,1],padding='SAME',name='conv_256_2')
lay_256_5 = tf.nn.relu(lay_256_4,name='re_256_2')
lay_p5 = tf.nn.max_pool(lay_256_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_5')
lay_512_1 = tf.nn.conv2d(lay_p5,filter=get_filter([3,3,256,512],na='w_11'),strides=[1,1,1,1],padding='SAME',name='conv_512_1')
lay_512_2 = tf.nn.relu(lay_512_1,name='re_512_1')
lay_512_3 = tf.layers.batch_normalization(lay_512_2,axis=-1,name='bn_512')
lay_512_4 = tf.nn.conv2d(lay_512_3,filter=get_filter([3,3,512,512],na='w_12'),strides=[1,1,1,1],padding='SAME',name='conv_512_2')
lay_512_5 = tf.nn.relu(lay_512_4,name='re_512_2')
lay_p6 = tf.nn.max_pool(lay_512_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_6')
lay_1024_1 = tf.nn.conv2d(lay_p6,filter=get_filter([3,3,512,1024],na='w_13'),strides=[1,1,1,1],padding='SAME',name='conv_1024_1')
lay_1024_2 = tf.nn.relu(lay_1024_1,name='re_1024_1')
lay_1024_3 = tf.layers.batch_normalization(lay_1024_2,axis=-1,name='bn_1024')
lay_1024_4 = tf.nn.conv2d(lay_1024_3,filter=get_filter([3,3,1024,1024],na='w_14'),strides=[1,1,1,1],padding='SAME',name='conv_1024_2')
lay_1024_5 = tf.nn.relu(lay_1024_4,name='re_1024_2')
#lay_p7 = tf.nn.max_pool(lay_1024,ksize=[1,2,2,1],strides=[1,1,1,1],padding='SAME',name='pool_7')
up_512 = tf.image.resize_images(images=lay_1024_5,size=[8,8],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_512_1 = tf.nn.conv2d(up_512,filter=get_filter([3,3,1024,512],na='w_15'),strides=[1,1,1,1],padding='SAME',name='mer_512_1')
con_512_2 = tf.nn.relu(con_512_1,name='rel_512_1')
mer_512 = tf.concat([lay_512_5,con_512_2],axis=0,name='mer_512_2')
con_512_3 = tf.nn.conv2d(mer_512,filter=get_filter([3,3,512,512],na='w_16'),strides=[1,1,1,1],padding='SAME',name='mer_512_3')
con_512_4 = tf.nn.relu(con_512_3,name='rel_512_2')
con_512_5 = tf.layers.batch_normalization(con_512_4,axis=-1,name='mer_bn_512')
con_512_6 = tf.nn.conv2d(con_512_5,filter=get_filter([3,3,512,512],na='w_17'),strides=[1,1,1,1],padding='SAME',name='mer_512_4')
con_512_7 = tf.nn.relu(con_512_6,name='rel_512_3')
up_256 = tf.image.resize_images(images=con_512_7,size=[16,16],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_256_1 = tf.nn.conv2d(up_256,filter=get_filter([3,3,512,256],na='w_18'),strides=[1,1,1,1],padding='SAME',name='mer_256_1')
con_256_2 = tf.nn.relu(con_256_1,name='rel_256_1')
mer_256 = tf.concat([lay_256_5,con_256_2],axis=0,name='mer_256_2')
con_256_3 = tf.nn.conv2d(mer_256,filter=get_filter([3,3,256,256],na='w_19'),strides=[1,1,1,1],padding='SAME',name='mer_256_3')
con_256_4 = tf.nn.relu(con_256_3,name='rel_256_2')
con_256_5 = tf.layers.batch_normalization(con_256_4,axis=-1,name='mer_bn_256')
con_256_6 = tf.nn.conv2d(con_256_5,filter=get_filter([3,3,256,256],na='w_20'),strides=[1,1,1,1],padding='SAME',name='mer_256_4')
con_256_7 = tf.nn.relu(con_256_6,name='rel_256_3')
up_128 = tf.image.resize_images(images=con_256_7,size=[32,32],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_128_1 = tf.nn.conv2d(up_128,filter=get_filter([3,3,256,128],na='w_21'),strides=[1,1,1,1],padding='SAME',name='mer_128_1')
con_128_2 = tf.nn.relu(con_128_1,name='rel_128_1')
mer_128 = tf.concat([lay_128_5,con_128_2],axis=0,name='mer_128_2')
con_128_3 = tf.nn.conv2d(mer_128,filter=get_filter([3,3,128,128],na='w_22'),strides=[1,1,1,1],padding='SAME',name='mer_128_3')
con_128_4 = tf.nn.relu(con_128_3,name='rel_128_2')
con_128_5 = tf.layers.batch_normalization(con_128_4,axis=-1,name='mer_bn_128')
con_128_6 = tf.nn.conv2d(con_128_5,filter=get_filter([3,3,128,128],na='w_23'),strides=[1,1,1,1],padding='SAME',name='mer_128_4')
con_128_7 = tf.nn.relu(con_128_6,name='rel_128_3')
up_64 = tf.image.resize_images(images=con_128_7,size=[64,64],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_64_1 = tf.nn.conv2d(up_64,filter=get_filter([3,3,128,64],na='w_24'),strides=[1,1,1,1],padding='SAME',name='mer_64_1')
con_64_2 = tf.nn.relu(con_64_1,name='rel_64_1')
mer_64 = tf.concat([lay_64_5,con_64_2],axis=0,name='mer_64_2')
con_64_3 = tf.nn.conv2d(mer_64,filter=get_filter([3,3,64,64],na='w_25'),strides=[1,1,1,1],padding='SAME',name='mer_64_3')
con_64_4 = tf.nn.relu(con_64_3,name='rel_64_2')
con_64_5 = tf.layers.batch_normalization(con_64_4,axis=-1,name='mer_bn_64')
con_64_6 = tf.nn.conv2d(con_64_5,filter=get_filter([3,3,64,64],na='w_26'),strides=[1,1,1,1],padding='SAME',name='mer_64_4')
con_64_7 = tf.nn.relu(con_64_6,name='rel_64_3')
up_32 = tf.image.resize_images(images=con_64_7,size=[128,128],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_32_1 = tf.nn.conv2d(up_32,filter=get_filter([3,3,64,32],na='w_27'),strides=[1,1,1,1],padding='SAME',name='mer_32_1')
con_32_2 = tf.nn.relu(con_32_1,name='rel_32_1')
mer_32 = tf.concat([lay_32_5,con_32_2],axis=0,name='mer_32_2')
con_32_3 = tf.nn.conv2d(mer_32,filter=get_filter([3,3,32,32],na='w_28'),strides=[1,1,1,1],padding='SAME',name='mer_32_3')
con_32_4 = tf.nn.relu(con_32_3,name='rel_32_2')
con_32_5 = tf.layers.batch_normalization(con_32_4,axis=-1,name='mer_bn_32')
con_32_6 = tf.nn.conv2d(con_32_5,filter=get_filter([3,3,32,32],na='w_29'),strides=[1,1,1,1],padding='SAME',name='mer_32_4')
con_32_7 = tf.nn.relu(con_32_6,name='rel_32_3')
up_16 = tf.image.resize_images(images=con_32_7,size=[256,256],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_16_1 = tf.nn.conv2d(up_16,filter=get_filter([3,3,32,16],na='w_30'),strides=[1,1,1,1],padding='SAME',name='mer_16_1')
con_16_2 = tf.nn.relu(con_16_1,name='rel_16_1')
mer_16 = tf.concat([lay_16_5,con_16_2],axis=0,name='mer_16_2')
con_16_3 = tf.nn.conv2d(mer_16,filter=get_filter([3,3,16,16],na='w_31'),strides=[1,1,1,1],padding='SAME',name='mer_16_3')
con_16_4 = tf.nn.relu(con_16_3,name='rel_16_2')
con_16_5 = tf.layers.batch_normalization(con_16_4,axis=-1,name='mer_bn_16')
con_16_6 = tf.nn.conv2d(con_16_5,filter=get_filter([3,3,16,16],na='w_32'),strides=[1,1,1,1],padding='SAME',name='mer_16_4')
con_16_7 = tf.nn.relu(con_16_6,name='rel_16_3')
fin_img = tf.nn.conv2d(con_16_7,filter=get_filter([1,1,16,1],na='w_33'),strides=[1,1,1,1],padding='SAME',name='final_image')
#fin_img = tf.nn.sigmoid(fin_img)
return fin_img

Using keyboard to move a ball, X and Z axis, in VPython

Doing a project for physics class. vPython documentation is not very good I think and it's a bit confusing. I need to move a ball, which currently is being moved by mouse, but I need to make this work with 2 players, so I need to use keyboard events.
Currently this is the code:
#GlowScript 2.7 VPython
scene.range = 30
scene.forward = vec(0,-0.25,-1)
#red side of the field
redField = box()
redField.size = vec(40,1,60)
redField.pos= vec(-20,0,0)
redField.color = color.red
#blue side of the field
redField = box()
redField.size = vec(40,1,60)
redField.pos= vec(20,0,0)
redField.color = color.blue
cueBall = sphere()
cueBall.radius = 1.5
cueBall.pos = vec(0,2,0)
cueBall.vel = vec(3,0,0) # velocity in m/s
cueBall.mass = 2.0
blueBall = sphere()
blueBall.radius = 1.5
blueBall.pos = vec(4,2,0)
blueBall.color = color.blue
blueBall.mass = 1.5
blueBall.vel = vec(0,0,0)
blueBall.force = arrow(axis = vec(0,0,0), color = color.blue)
rail1 = box()
rail1.texture = {'file': "https://i.imgur.com/ss5OcnL.jpg"}
rail1.size = vec(3,3,55)
rail1.pos = vec(-39,1,0)
rail2 = box()
rail2.texture = {'file': "https://i.imgur.com/ss5OcnL.jpg"}
rail2.size = vec(3,3,55)
rail2.pos = vec(39,1,0)
rail3 = box()
rail3.texture = {'file': "https://i.imgur.com/ss5OcnL.jpg"}
rail3.size = vec(80,3,3)
rail3.pos = vec(0,1,29)
rail4 = box()
rail4.texture = {'file': "https://i.imgur.com/ss5OcnL.jpg"}
rail4.size = vec(80,3,3)
rail4.pos = vec(0,1,-29)
redBall = sphere()
redBall.radius = 1.5
redBall.pos = vec(-3,2,0)
redBall.color = color.red
redBall.force = arrow(axis = vec(0,0,0), color = color.red)
redBall.vel = vec(0,0,0)
redBall.mass = 1.5
yBall = sphere()
yBall.radius = 1.5
yBall.pos = vec(-9,2,0)
yBall.color = color.yellow
yBall.vel = vec(0,0,0)
yBall.mass = 1.5
yBall.force = arrow(axis = vec(0,0,0), color = color.yellow)
rail = []
rail.append(rail1, rail2, rail3, rail4)
ball = []
ball.append(cueBall, blueBall, redBall, yBall)
ball[0].force = arrow(axis = vec(0,0,0), color = color.white)
drag = False
chosenObject = None
scene.bind("mousedown", down)
scene.bind("mousemove", move)
scene.bind("mouseup", up)
def down():
nonlocal drag, chosenObject
chosenObject = scene.mouse.pick()
drag = True
def move():
nonlocal drag, chosenObject
if (drag == True):
chosenObject.force.axis = scene.mouse.pos - chosenObject.pos
chosenObject.force.pos = chosenObject.pos
chosenObject.force.axis.y = 0
def up():
nonlocal drag, chosenObject
chosenObject.force.axis = vec(0,0,0)
chosenObject = None
drag = False
elasticConst = 500
dt = 0.01
time = 0
while (True):
rate(1/dt)
for i in range(len(ball)):
ball[i].vel = ball[i].vel + ( ball[i].force.axis * dt / ball[i].mass )
ball[i].force.pos = ball[i].pos
for i in range(len(ball)):
for j in range(len(ball)):
if (i == j): continue
separation = ball[i].pos - ball[j].pos
contactSeparation = separation.norm() * (ball[i].radius + ball[j].radius)
if (separation.mag < contactSeparation.mag):
elasticForce = - elasticConst * (separation - contactSeparation)
ball[i].vel = ball[i].vel + (elasticForce / ball[i].mass) * dt
for i in range(len(ball)):
if ( ball[i].pos.x < rail1.pos.x + 2):
ball[i].pos.x = rail1.pos.x + 2
ball[i].vel.x = - ball[i].vel.x
if ( ball[i].pos.x > rail2.pos.x - 2):
ball[i].pos.x = rail2.pos.x - 2
ball[i].vel.x = - ball[i].vel.x
if ( ball[i].pos.z > rail3.pos.z - 2):
ball[i].pos.z = rail3.pos.z - 2
ball[i].vel.z = - ball[i].vel.z
if ( ball[i].pos.z < rail4.pos.z + 2):
ball[i].pos.z = rail4.pos.z + 2
ball[i].vel.z = - ball[i].vel.z
## for i in range(len(ball)):
# for j in range(len(hole)):
# if ((ball[i].pos - hole[j].pos).mag < ball[i].radius + hole[j].radius):
# ball[i].visible = False
for i in range(len(ball)):
ball[i].pos = ball[i].pos + ball[i].vel * dt
If you could help me understand how I could increment the position variables on keyboard.

An strange python "if" syntax error

I get this error: Invaild syntax in my "if" statement and rly can't figur why, can anyone of you guys help me? I'm using python 3.2
here is the part of my code whit the error my code:
L = list()
LT = list()
tn = 0
players = 0
newplayer = 0
newplayerip = ""
gt = "start"
demsg = "start"
time = 1
status = 0
day = 1
conclient = 1
print("DONE! The UDP Server is now started and Waiting for client's on port 5000")
while 1:
try:
data, address = server_socket.recvfrom(1024)
if not data: break
################### reciving data! ###################
UPData = pickle.loads(data)
status = UPData[0][[0][0]
if status > 998: ##### it is here the error are given####
try:
e = len(L)
ori11 = UPData[0][1][0]
ori12 = UPData[0][1][1]
ori13 = UPData[0][1][2]
ori14 = UPData[0][1][3]
ori21 = UPData[0][1][4]
ori22 = UPData[0][1][5]
ori23 = UPData[0][1][6]
ori24 = UPData[0][1][7]
ori31 = UPData[0][2][0]
ori32 = UPData[0][2][1]
ori33 = UPData[0][2][2]
ori34 = UPData[0][2][3]
ori41 = UPData[0][2][4]
ori42 = UPData[0][2][5]
ori43 = UPData[0][2][6]
ori44 = UPData[0][2][7]
ori51 = UPData[0][3][0]
ori52 = UPData[0][3][1]
ori53 = UPData[0][3][2]
ori54 = UPData[0][3][3]
ori61 = UPData[0][3][4]
ori62 = UPData[0][3][5]
ori63 = UPData[0][3][6]
ori64 = UPData[0][3][7]
ori71 = UPData[0][4][0]
ori72 = UPData[0][4][1]
ori73 = UPData[0][4][2]
ori74 = UPData[0][4][3]
ori81 = UPData[0][4][4]
ori82 = UPData[0][4][5]
ori83 = UPData[0][4][6]
ori84 = UPData[0][4][7]
ori91 = UPData[0][5][0]
ori92 = UPData[0][5][1]
ori93 = UPData[0][5][2]
ori94 = UPData[0][5][3]
ori101 = UPData[0][5][4]
ori102 = UPData[0][5][5]
ori103 = UPData[0][5][6]
ori104 = UPData[0][5][7]
npcp11 = UPData[0][6][0]
npcp12 = UPData[0][6][1]
npcp13 = UPData[0][6][2]
npcp21 = UPData[0][6][3]
npcp22 = UPData[0][6][4]
npcp23 = UPData[0][6][5]
npcp31 = UPData[0][6][6]
npcp32 = UPData[0][6][7]
npcp33 = UPData[0][7][0]
npcp41 = UPData[0][7][1]
npcp42 = UPData[0][7][2]
npcp43 = UPData[0][7][3]
npcp51 = UPData[0][7][4]
npcp52 = UPData[0][7][5]
npcp53 = UPData[0][7][6]
npcp61 = UPData[0][7][7]
npcp62 = UPData[0][8][0]
npcp63 = UPData[0][8][1]
npcp71 = UPData[0][8][2]
npcp72 = UPData[0][8][3]
npcp73 = UPData[0][8][4]
npcp81 = UPData[0][8][5]
npcp82 = UPData[0][8][6]
npcp83 = UPData[0][8][7]
npcp91 = UPData[1][0][0]
npcp92 = UPData[1][0][1]
npcp93 = UPData[1][0][2]
npcp101 = UPData[1][0][3]
npcp102 = UPData[1][0][4]
npcp103 = UPData[1][0][5]
d0 = (status, )
d1 = (ori11,ori12,ori13,ori14,ori21,ori22,ori23,ori24)
d2 = (ori31,ori32,ori33,ori34,ori41,ori42,ori43,ori44)
d3 = (ori51,ori52,ori53,ori54,ori61,ori62,ori63,ori64)
d4 = (ori71,ori72,ori73,ori74,ori81,ori82,ori83,ori84)
d5 = (ori91,ori92,ori93,ori94,ori101,ori102,ori103,ori104)
d6 = (npcp11,npcp21,npcp31,npcp21,npcp22,npcp23,npcp31,npcp32)
d7 = (npcp33,npcp41,npcp42,npcp43,npcp51,npcp52,npcp53,npcp61)
d8 = (npcp62,npcp63,npcp71,npcp72,npcp72,npcp81,npcp82,npcp83)
d9 = (npcp91,npcp92,npcp93,npcp101,npcp102,npcp103)
pack1 = (d0,d1,d2,d3,d4,d5,d6,d7,d8)
pack2 = (d9, )
dat = pickle.dumps((pack1,pack2))
while tn < e:
server_socket.sendto(dat, (L[tn],3560))
tn = tn + 1
except:
pass
print("could not send data to some one or could not run the server at all")
else:
the part where the console tells me my error is is here:
if status > 998:

The problem is here:
status = UPData[0][[0][0]
The second opened bracket [ is not closed. The Python compiler keeps looking for the closing bracket, finds if on the next line and gets confused because if is not supposed to be inside brackets.
You may want to remove this bracket, or close it, according to your specific needs (the structure of UPData)