Working code but getting a "confusing" plot with labels - python-2.7

I was trying to put labes on the streamlines around a body whose symmetric profile is generated by a vortex and a uniform flow, so far I must get something like this (with labels)
which I get it with the next code:
import numpy as np
import matplotlib.pyplot as plt
vortex_height = 18.0
h = vortex_height
vortex_intensity = 55.0
cv = vortex_intensity
permanent_speed = 10.0
U1 = permanent_speed
Y, X = np.mgrid[-25:25:100j, -25:25:100j]
U = 5.0 + 37.0857 * (Y - 18.326581) / (X ** 2 + (Y - 18.326581) ** 2) +- 37.0857 * (Y + 18.326581) / (X ** 2 + (Y + 18.326581) ** 2)
V = - 37.0857 * (X) / (X ** 2 + (Y - 18.326581) ** 2) + 37.0857 * (X) / (X ** 2 + (Y + 18.326581) ** 2)
speed = np.sqrt(U*U + V*V)
plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
plt.colorbar()
#CS = plt.contour(U, v, speed)
#plt.clabel(CS, inline=1, fontsize=10)
#f, (ax1, ax2) = plt.subplots(ncols=2)
#ax1.streamplot(X, Y, U, V, density=[0.5, 1])
#lw = 5*speed/speed.max()
#ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
plt.savefig("stream_plot5.png")
plt.show()
So I was changing the next example code (which use pylab):
from numpy import exp,arange
from pylab import meshgrid,cm,imshow,contour,clabel,colorbar,axis,title,show
# the function that I'm going to plot
def z_func(x,y):
return (1-(x**2+y**3))*exp(-(x**2+y**2)/2)
x = arange(-3.0,3.0,0.1)
y = arange(-3.0,3.0,0.1)
X,Y = meshgrid(x, y) # grid of point
Z = z_func(X, Y) # evaluation of the function on the grid
im = imshow(Z,cmap=cm.RdBu) # drawing the function
# adding the Contour lines with labels
cset = contour(Z,arange(-1,1.5,0.2),linewidths=2,cmap=cm.Set2)
clabel(cset,inline=True,fmt='%1.1f',fontsize=10)
colorbar(im) # adding the colobar on the right
# latex fashion title
title('$z=(1-x^2+y^3) e^{-(x^2+y^2)/2}$')
show()
with this plot:
And finally I get it like this:
import numpy as np
from numpy import exp,arange,log
from pylab import meshgrid,cm,imshow,contour,clabel,colorbar,axis,title,show
# PSI = streamline
def streamLine(x, y, U = 5, hv = 18.326581, cv = 37.0857):
x2 = x ** 2
y2plus = (y + hv) ** 2
y2minus = (y - hv) ** 2
PSI_1 = U * y
PSI_2 = 0.5 * cv * log(x2 + y2minus)
PSI_3 = - 0.5 * cv * log(x2 + y2plus)
psi = PSI_1 + PSI_2 + PSI_3
return psi
"""
def streamLine(x, y):
return 0.5 * 37.0857 * log(x ** 2 + (y - 18.326581) ** 2)
# (5.0 * y + 0.5 * 37.0857 * math.log(x ** 2 + (y - 18.326581) ** 2) - 0.5 * 37.0857 * math.log(x ** 2 + (y + 18.326581) ** 2))
"""
x = arange(-20.0,20.0,0.1)
y = arange(-20.0,20.0,0.1)
X,Y = meshgrid(x, y) # grid of point
#Z = z_func(X, Y) # evaluation of the function on the grid
Z= streamLine(X, Y)
im = imshow(Z,cmap=cm.RdBu) # drawing the function
# adding the Contour lines with labels
cset = contour(Z,arange(-5,6.5,0.2),linewidths=2,cmap=cm.Set2)
clabel(cset,inline=True,fmt='%1.1f',fontsize=9)
colorbar(im) # adding the colobar on the right
# latex fashion title
title('$phi= 5.0 y + (1/2)* 37.0857 log(x^2 + (y - 18.326581)^2)-(1/2)* 37.085...$')
show()
#print type(Z)
#print len(Z)
But then I get the next plot:
which is something that keeps me wondering what's wrong because the axis are not where they should be.

contour() draws contour lines of a scalar field, and streamplot() is draw of vector field,
Vector fields can be constructed out of scalar fields using the gradient operator.
Here is an example:
import numpy as np
from numpy import exp,arange,log
from pylab import meshgrid,cm,imshow,contour,clabel,colorbar,axis,title,show,streamplot
# PSI = streamline
def f(x, y, U = 5, hv = 18.326581, cv = 37.0857):
x2 = x ** 2
y2plus = (y + hv) ** 2
y2minus = (y - hv) ** 2
PSI_1 = U * y
PSI_2 = 0.5 * cv * log(x2 + y2minus)
PSI_3 = - 0.5 * cv * log(x2 + y2plus)
psi = PSI_1 + PSI_2 + PSI_3
return psi
x = arange(-20.0,20.0,0.1)
y = arange(-20.0,20.0,0.1)
X,Y = meshgrid(x, y) # grid of point
#Z = z_func(X, Y) # evaluation of the function on the grid
Z= f(X, Y)
dx, dy = 1e-6, 1e-6
U = (f(X+dx, Y) - f(X, Y))/dx
V = (f(X, Y+dy) - f(X, Y))/dy
streamplot(X, Y, U, V, linewidth=1, color=(0, 0, 1, 0.3))
cset = contour(X, Y, Z,arange(-20,20,2.0),linewidths=2,cmap=cm.Set2)
clabel(cset,inline=True,fmt='%1.1f',fontsize=9)
colorbar(im) # adding the colobar on the right
# latex fashion title
title('$phi= 5.0 y + (1/2)* 37.0857 log(x^2 + (y - 18.326581)^2)-(1/2)* 37.085...$')
show()
output:

Related

Sum of increasing alternate terms in python

I need to evaluate the finite sum of numbers which are increasing in absolute value, but are alternate. Problem is: the abolute values grow too fast and it starts accumulating numerical errors. These the functions definitions, one (Gj_full) straight to it and the other (Gj) recursively. fact_quo is a simple factorial function.
def fact_quo(n, m=1):
if (type(n) != int) or (type(m) != int):
raise TypeError("Arguments must be integers.")
if (n < 0) or (m < 0):
raise ValueError("n=" + str(n) + "\t m=" + str(m))
if (n == 0) or (n == 1) or (n == m):
return 1
if (n < m):
raise ValueError("n=" + str(n) + "\t m=" + str(m))
f = n
while n > (m+1):
n -= 1
f *= n
return f
def Gj_full(X, Y, Xl, Yl, j=0, coef=1):
if (X - Y + Xl + Yl) % 2 or X < Y or Y < j:
raise ValueError
u = (X - Y + Xl + Yl) // 2
v = coef * (2 ** (X - Y) * fact_quo(X, Y-j) * fact_quo(u+j, j) *
4 ** j * (-1) ** j)
w = 3 ** (u+j) * fact_quo(X-Y+j)
den2 = fact_quo(X) * fact_quo(Xl) * fact_quo(Yl)
z = (np.sqrt(fact_quo(X)) * np.sqrt(fact_quo(Y))
* np.sqrt(fact_quo(Xl)) * np.sqrt(fact_quo(Yl)))
return (v / (den2 * w)) * z
def Gj(X, Y, Xl, Yl, j=0, coef=1):
if (X - Y + Xl + Yl) % 2 or X < Y or Y < j:
raise ValueError
kX, kY, kXl, kYl, kj = X % 2, Y % 2, Xl % 2, Yl % 2, 0
g = coef * Gj_full(kX, kY, kXl, kYl, kj)
while kX < X:
u = (kX - kY + kXl + kYl) // 2
v = 4 * (u + kj + 1)
w = 3 * (kX + 2 - kY + kj) * (kX + 1 - kY + kj)
g *= (v / w) * np.sqrt(kX + 2) * np.sqrt(kX + 1)
kX += 2
while kXl < Xl:
u = (kX - kY + kXl + kYl) // 2
v = u + kj + 1
w = 3 * (kXl + 2) * (kXl + 1)
g *= (v / w) * np.sqrt(kXl + 2) * np.sqrt(kXl + 1)
kXl += 2
while kYl < Yl:
u = (kX - kY + kXl + kYl) // 2
v = u + kj + 1
w = 3 * (kYl + 2) * (kYl + 1)
g *= (v / w) * np.sqrt(kYl + 2) * np.sqrt(kYl + 1)
kYl += 2
while kY < Y:
u = (kX - kY + kXl + kYl) // 2
v = 3 * (kX - kY + kj) * (kX - kY - 1 + kj)
w = 4 * (kY + 2 - kj) * (kY + 1 - kj) * (u + kj)
g *= (v / w) * np.sqrt(kY + 2) * np.sqrt(kY + 1)
kY += 2
while kj < j:
u = (kX - kY + kXl + kYl) // 2
v = -4 * (kY - kj) * (u + kj + 1)
w = 3 * (kX - kY + kj + 1) * (kj + 1)
g *= (v / w)
kj += 1
return g
The (4/3) ** j and the factorials quicly increase the absolute value of the summing terms. The sum, however, are supposed to be smaller than 1. In fact, for X = Y and Xl = Yl = 0, the sum equals to (-1/3) ** X.
The precision for infinitely large numbers for floats are not available yet without using a lib. Therefore you should look into the decimal lib, you can even set the precision. Eg.
import decimal
decimal.getcontext().prec = 100
def pi():
pi = decimal.Decimal(0)
for k in range(350):
pi += (decimal.Decimal(4)/(decimal.Decimal(8)*decimal.Decimal(k+1))...)
If you manage to force all the numbers to be integers, you don't need to worry about it

Perceptron implementation, decision boundary will not plot

I am attempting to implement a perceptron. I have loaded a 100x2 array of values between 0 and 100. Each item in the array has a label of either -1 or 1.
I believe the perceptron is working, however I cannot plot decision boundary as shown here: plot decision boundary matplotlib
When I run my code I only see a single color background. I would expect to see two colors, one color for each label in my data set (-1 and 1).
My current output, I expect to see 2 colors for the background (-1 or 1)
An example of what I hope to see, from the sklearn documentation
import numpy as np
from matplotlib import pyplot as plt
def generate_data():
#generate a dataset that is linearly seperable
group_1 = np.random.randint(50, 100, size=(50,2))
group_1_labels = np.full((50,1), 1)
group_2 = np.random.randint(0, 49, size =(50,2))
group_2_labels = np.full((50,1), -1)
#add a bias value of -1
bias = np.full((50,1), -1)
#add labels, upper right quadrant are 1, lower left are -1
group_1_with_bias = np.hstack((group_1, bias))
group_2_with_bias = np.hstack((group_2, bias))
group_1_labeled = np.hstack((group_1_with_bias, group_1_labels))
group_2_labeled = np.hstack((group_2_with_bias, group_2_labels))
#merge our labeled data and shuffle!
merged_data = np.vstack((group_1_labeled, group_2_labeled))
np.random.shuffle(merged_data)
return merged_data
data = generate_data()
#load data, strip labels, add a -1 bias value
X = data[:, :3]
#create labels matrix
l = np.ravel(data[:, 3:])
def perceptron_sgd(X, l, c, epochs):
#initialize weights
w = np.zeros(3)
errors = []
for epoch in range(epochs):
total_error = 0
for i, x in enumerate(X):
if (np.dot(x, w) * l[i]) <= 0:
total_error += (np.dot(x, w) * l[i])
w = w + c * (x * l[i])
errors.append(total_error * -1)
print "epoch " + str(epoch) + ": " + str(w)
return w, errors
def classify(X, l, w):
z = np.dot(X, w)
print z
z[z <= 0] = -1
z[z > 0] = 1
#return a matrix of predicted labels
return z
w, errors = perceptron_sgd(X, l, .001, 36)
# X - some data in 2dimensional np.array
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, .2), np.arange(y_min, y_max, .2))
# here "model" is your model's prediction (classification) function
Z = classify(np.c_[xx.ravel(), yy.ravel()], l, w[:-1]) #strip the bias from weights
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
plt.axis('off')
#Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=l, cmap=plt.cm.Paired)
I got it to work.
Standardized your X
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(X[:, :-1])
X_trans = np.column_stack((scaler.transform(X[:, :-1]), X[:, -1]))
Better initialization than zero.
#initialize weights
r = np.sqrt(2)
w = np.random.uniform(-r, r, (3,))
Add learned biases during prediction
z = np.dot(X, w[:-1]) + w[-1]
Standardize during prediction as well (using standardization learned from input)
Z = classify(scaler.transform(np.c_[xx.ravel(), yy.ravel()]),
l, w) #strip the bias from weights
Generally, always a good idea to standardize the inputs.
Entire code:
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
def generate_data():
#generate a dataset that is linearly seperable
group_1 = np.random.randint(50, 100, size=(50,2))
group_1_labels = np.full((50,1), 1)
group_2 = np.random.randint(0, 49, size =(50,2))
group_2_labels = np.full((50,1), -1)
#add a bias value of -1
bias = np.full((50,1), -1)
#add labels, upper right quadrant are 1, lower left are -1
group_1_with_bias = np.hstack((group_1, bias))
group_2_with_bias = np.hstack((group_2, bias))
group_1_labeled = np.hstack((group_1_with_bias, group_1_labels))
group_2_labeled = np.hstack((group_2_with_bias, group_2_labels))
#merge our labeled data and shuffle!
merged_data = np.vstack((group_1_labeled, group_2_labeled))
np.random.shuffle(merged_data)
return merged_data
data = generate_data()
#load data, strip labels, add a -1 bias value
X = data[:, :3]
#create labels matrix
l = np.ravel(data[:, 3:])
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(X[:, :-1])
X_trans = np.column_stack((scaler.transform(X[:, :-1]), X[:, -1]))
def perceptron_sgd(X, l, c, epochs):
#initialize weights
r = np.sqrt(2)
w = np.random.uniform(-r, r, (3,))
errors = []
for epoch in range(epochs):
total_error = 0
for i, x in enumerate(X):
if (np.dot(x, w) * l[i]) <= 0:
total_error += (np.dot(x, w) * l[i])
w = w + c * (x * l[i])
errors.append(total_error * -1)
print("epoch " + str(epoch) + ": " + str(w))
return w, errors
def classify(X, l, w):
z = np.dot(X, w[:-1]) + w[-1]
print(z)
z[z <= 0] = -1
z[z > 0] = 1
#return a matrix of predicted labels
return z
w, errors = perceptron_sgd(X_trans, l, .01, 25)
# X - some data in 2dimensional np.array
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, .1), np.arange(y_min, y_max, .1))
# here "model" is your model's prediction (classification) function
Z = classify(scaler.transform(np.c_[xx.ravel(), yy.ravel()]), l, w) #strip the bias from weights
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.4)
#plt.axis('off')
#Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=l, cmap=plt.cm.Paired)

Normalizing data and applying colormap results in rotated image using matplotlib?

So I wanted to see if I could make fractal flames using matplotlib and figured a good test would be the sierpinski triangle. I modified a working version I had that simply performed the chaos game by normalizing the x range from -2, 2 to 0, 400 and the y range from 0, 2 to 0, 200. I also truncated the x and y coordinates to 2 decimal places and multiplied by 100 so that the coordinates could be put in to a matrix that I could apply a color map to. Here's the code I'm working on right now (please forgive the messiness):
import numpy as np
import matplotlib.pyplot as plt
import math
import random
def f(x, y, n):
N = np.array([[x, y]])
M = np.array([[1/2.0, 0], [0, 1/2.0]])
b = np.array([[.5], [0]])
b2 = np.array([[0], [.5]])
if n == 0:
return np.dot(M, N.T)
elif n == 1:
return np.dot(M, N.T) + 2*b
elif n == 2:
return np.dot(M, N.T) + 2*b2
elif n == 3:
return np.dot(M, N.T) - 2*b
def norm_x(n, minX_1, maxX_1, minX_2, maxX_2):
rng = maxX_1 - minX_1
n = (n - minX_1) / rng
rng_2 = maxX_2 - minX_2
n = (n * rng_2) + minX_2
return n
def norm_y(n, minY_1, maxY_1, minY_2, maxY_2):
rng = maxY_1 - minY_1
n = (n - minY_1) / rng
rng_2 = maxY_2 - minY_2
n = (n * rng_2) + minY_2
return n
# Plot ranges
x_min, x_max = -2.0, 2.0
y_min, y_max = 0, 2.0
# Even intervals for points to compute orbits of
x_range = np.arange(x_min, x_max, (x_max - x_min) / 400.0)
y_range = np.arange(y_min, y_max, (y_max - y_min) / 200.0)
mat = np.zeros((len(x_range) + 1, len(y_range) + 1))
random.seed()
x = 1
y = 1
for i in range(0, 100000):
n = random.randint(0, 3)
V = f(x, y, n)
x = V.item(0)
y = V.item(1)
mat[norm_x(x, -2, 2, 0, 400), norm_y(y, 0, 2, 0, 200)] += 50
plt.xlabel('x0')
plt.ylabel('y')
fig = plt.figure(figsize=(10,10))
plt.imshow(mat, cmap="spectral", extent=[-2,2, 0, 2])
plt.show()
The mathematics seem solid here so I suspect something weird is going on with how I'm handling where things should go into the 'mat' matrix and how the values in there correspond to the colormap.
If I understood your problem correctly, you need to transpose your matrix using the method .T. So just replace
fig = plt.figure(figsize=(10,10))
plt.imshow(mat, cmap="spectral", extent=[-2,2, 0, 2])
plt.show()
by
fig = plt.figure(figsize=(10,10))
ax = gca()
ax.imshow(mat.T, cmap="spectral", extent=[-2,2, 0, 2], origin="bottom")
plt.show()
The argument origin=bottom tells to imshow to have the origin of your matrix at the bottom of the figure.
Hope it helps.

integrating orbital trajectories 2

The original second order ODEs are
x'' - 2 * omega * y' - omega ** 2 * x = - mue * (x + pi2 * r12) / np.sqrt((x + pi2 * r12) ** 2 + y ** 2) ** 3 - mum * (x - pi1 * r12) / np.sqrt((x - pi1 * r12) ** 2 + y ** 2)
y'' + 2 * omega * x' - omega **2 * y = - mue * y / np.sqrt((x + pi2 * r12) ** 2 + y ** 2) ** 3 - mum * y / np.sqrt((x - pi1 * r12) ** 2 + y ** 2)
z'' = 0
So here is the code I used to solve the ODE but first I broke it up into 2 first orders.
I am receiving the error that the module on line 61 is not callable.
Line 61 is u = odeint(deriv, u0, dt)
#!/usr/bin/env python
import numpy as np
import scipy.integrate as odeint
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
me = 5.974 * 10 ** (24) # mass of the earth
mm = 7.348 * 10 ** (22) # mass of the moon
G = 6.67259 * 10 ** (-20) # gravitational parameter
re = 6378.0 # radius of the earth in km
rm = 1737.0 # radius of the moon in km
r12 = 384400.0 # distance between the CoM of the earth and moon
M = me + mm
pi1 = me / M
pi2 = mm / M
mue = 398600.0 # gravitational parameter of earth km^3/sec^2
mum = G * mm # grav param of the moon
mu = mue + mum
omega = np.sqrt(mu / r12 ** 3)
nu = 0.0 # flight path angle
x = 327156.0 # x location where the moon's SOI effects the spacecraft
y = 33050.0 # y location
vbo = 10.85 # velocity at burnout
gamma = -141.868 * np.pi / 180 # angle in radians of true anomaly
vx = vbo * (np.sin(gamma) * np.cos(nu) - np.cos(gamma) * np.sin(nu))
# velocity of the bo in the x direction
vy = vbo * (np.sin(gamma) * np.sin(nu) + np.cos(gamma) * np.cos(nu))
# velocity of the bo in the y direction
xrel = (re + 300.0) * np.cos(gamma)
# spacecraft x location relative to the earth
yrel = (re + 300.0) * np.sin(gamma)
# r0 = [xrel, yrel, 0]
# v0 = [vx, vy, 0]
u0 = [xrel, yrel, 0, vx, vy, 0]
def deriv(u, dt):
n1 = -((mue * (u[0] + pi2 * r12) / np.sqrt((u[0] + pi2 * r12) ** 2
+ u[1] ** 2) ** 3)
- (mum * (u[0] - pi1 * r12) / np.sqrt((u[0] - pi1 * r12) ** 2
+ u[1] ** 2) ** 3))
n2 = -((mue * u[1] / np.sqrt((u[0] + pi2 * r12) ** 2 + u[1] ** 2) ** 3)
- (mum * u[1] / np.sqrt((u[0] - pi1 * r12) ** 2 + u[1] ** 2) ** 3))
return [u[3], # dotu[0] = u[3]
u[4], # dotu[1] = u[4]
u[5], # dotu[2] = u[5]
2 * omega * u[5] + omega ** 2 * u[0] + n1, # dotu[3] = that
omega ** 2 * u[1] - 2 * omega * u[4] + n2, # dotu[4] = that
0] # dotu[5] = 0
dt = np.arange(0.0, 250000.0, .1)
u = odeint(deriv, u0, dt)
x, y, z, x2, y2, z2 = u.T
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(x, y, z)
plt.show()
Assuming you mean this error:
~/coding$ python orbit1.py
Traceback (most recent call last):
File "orbit1.py", line 61, in <module>
u = odeint(deriv, u0, dt)
TypeError: 'module' object is not callable
This is because you want the function named odeint in scipy.integrate. Your line
import scipy.integrate as odeint
imports the entire module and gives it the name odeint. Try
from scipy.integrate import odeint
instead, or
import scipy.integrate
[...]
u = scipy.integrate.odeint(deriv, u0, dt)
which should give you

2d rotation opengl

Here is the code I am using.
#define ANGLETORADIANS 0.017453292519943295769236907684886f // PI / 180
#define RADIANSTOANGLE 57.295779513082320876798154814105f // 180 / PI
rotation = rotation *ANGLETORADIANS;
cosRotation = cos(rotation);
sinRotation = sin(rotation);
for(int i = 0; i < 3; i++)
{
px[i] = (vec[i].x + centerX) * (cosRotation - (vec[i].y + centerY)) * sinRotation;
py[i] = (vec[i].x + centerX) * (sinRotation + (vec[i].y + centerY)) * cosRotation;
printf("num: %i, px: %f, py: %f\n", i, px[i], py[i]);
}
so far it seams my Y value is being fliped.. say I enter the value of X = 1 and Y = 1 with a 45 rotation you should see about x = 0 and y = 1.25 ish but I get x = 0 y = -1.25.
Also my 90 degree rotation always return x = 0 and y = 0.
p.s I know I'm only centering my values and not putting them back where they came from. It's not needed to put them back as all I need to know is the value I'm getting now.
Your bracket placement doesn't look right to me. I would expect:
px[i] = (vec[i].x + centerX) * cosRotation - (vec[i].y + centerY) * sinRotation;
py[i] = (vec[i].x + centerX) * sinRotation + (vec[i].y + centerY) * cosRotation;
Your brackets are wrong. It should be
px[i] = ((vec[i].x + centerX) * cosRotation) - ((vec[i].y + centerY) * sinRotation);
py[i] = ((vec[i].x + centerX) * sinRotation) + ((vec[i].y + centerY) * cosRotation);
instead