I would like to plot a 1D profile of a 2D image along an arbitrary line. The code below loads the image data hosted on github and plots it:
import urllib
import numpy as np
import matplotlib.pyplot as plt
url = "https://gist.github.com/andreiberceanu/7141843/raw/0b9d50d3d417b1cbe651560470c098700df5a1fc/image.dat"
f = urllib.urlopen(url)
data = np.loadtxt(f)
plt.imshow(data)
The red line in the plot above was drawn by hand, as an example. I suppose one can parametrize it in the form a*x + b. I am also guessing some sort of interpolation is necessary, because the line passes though points which may not be part of the original 2D array of data.
You want to use scipy.ndimage.map_coordinates. You need to build up a 2xn array that is the coordinates at which to sample and then do map_coordinates(im, samples).
I think this is it:
def sliceImage(I, a, b, *arg, **kws):
from scipy import linspace, asarray
from scipy.ndimage import map_coordinates
from scipy.linalg import norm
dst = norm(asarray(b) - a) + 1
return map_coordinates(I, [linspace(strt, end, dst)
for strt, end in zip(a, b)],
*arg, **kws)
Edit:
On further consideration, I think this is more elegant:
def sliceImage(I, a, b, *arg, **kws):
from scipy import linspace, asarray
from scipy.ndimage import map_coordinates
from scipy.linalg import norm
a = asarray(a)
b = asarray(b)
dst = norm(b - a) + 1
return map_coordinates(I, (a[:,newaxis] * linspace(1, 0, dst) +
b[:,newaxis] * linspace(0, 1, dst)),
*arg, **kws)
Edit: Thanks tcaswell: Added 1 to dst.
Related
I am trying to plot this data as a decaying exponential, all of the data has the same x values just the y values differ. y= a*[(-1)*exp(-x/t)].
I am not getting the correct chart when it goes through. csv file In the image is the type of curve I am looking for. I need to plot all of the data in csv (preferably on the same plot) in pycharm. I am relatively new to pycharm so I am starting from scratch! (excel just wouldn't behave for this data) Willing to start fresh as well if there is a simpler way of writing the code, I sparsed this together with some help from the internet.
import scipy.signal as scp
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy.core.function_base
def decaying_exponential(x,a,t,c):
return a *(-1)* np.exp(-1 * (x) / t) + c
import os
for f in os.listdir("/Users/flyar/My Python Stuff/"):
print(f)
df = numpy.transpose(pd.read_csv("D:/Grad Lab/NMR/Data/T1 Data/mineral oil/F0009CH1.CSV", names= ['a','b','c','d']).to_numpy())
temp = scp.find_peaks(df[2], height = 0)
df_subset = [(df[1][n], df[2][n]) for n in temp[0]]
print(df_subset)
plt.scatter([df[2][n] for n in temp[0]], [df[1][n] for n in temp[0]])
y = np.linspace(min(df[2]), max(df[2]), 1000)
params, covs = curve_fit(decaying_exponential, [df[1][n] for n in temp[0][2::]],
[df[2][n] for n in temp[0][2::]], maxfev=10000)
print(params)
plt.plot(y, [decaying_exponential(l, 5, params[1], params[2]) for l in y])
plt.show()
I have the following np.array which describes a rectangular wave. I would like to transform it into a callable function with a continuous argument. The np.array is:
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import seed
from numpy.random import rand
def piecewise_control( off_times,amp_inj, period_inj ):
def select(T):
return lambda t: (-T/2 <= t) & (t < T/2)
def pulse_train(t, at, shape):
return np.sum(shape(t - at[:,np.newaxis]), axis=0)
for i in range(1,len(off_times)):
off_times[i] += off_times[i-1] + period_inj
return amp_inj*pulse_train(t,off_times,shape=select(period_inj))
t=np.linspace(0,100,10000)
off_times = 10*rand(10)
period_inj = 1
amp_inj = 1
control = piecewise_control( off_times,amp_inj, period_inj )
plt.plot(t,control)
plt.show()
This answer inspired me.
The plot is the following:
The question is: can we transform the array control into a function with a continuous argument?
Of course if we did:
def ccontrol(t, control):
return control[t]
unfortunately we would get a function which only depends on integers.
You can subclass numpy.ndarray and implement the __call__ method:
import numpy as np
class MyArray(np.ndarray):
def __call__(self, idx):
return self[idx]
control = np.random.rand(100)
control_view = control.view(MyArray)
print(control_view(5), control[5])
For interpolation you can use scipy.interpolate. In fact, interpolation routines can return functions that you can call with any input, not necessarily integers.
I'm getting an import error for "norm". What am I not doing correct??
I'm open to constructive feedback on improving the code, however I have to keep the parameters as they are!
Thanks!!!
Code is below:
import numpy as np
from numpy import norm, inalg, array, zeros, diag, diagflat, dot, linalg
"""Test Case Data"""
A = np.matrix([[4,-1,-1],[-2,6,1],[-1,1,7]])
b = np.matrix([[3],[9],[-6]])
x = np.matrix([[0],[0],[0]])
"""Main Function"""
def jacobi(A, b, x, Tolerance, Iterations):
V = np.diag(A)
D = np.diag(V)
R = D-A
D_I = D.I
D = np.asmatrix(D)
Counter_1 = 1
tol_gauge = 100
while Counter_1 <= Iterations:
# I considered using the "dot" function in NUMPY but I was wary of mixed results
iterative_approach_form = D_I * ((R*x)+b)
tol_gauge = np.linalg.norm(iterative_approach_form-x)
x = iterative_approach_form
if initial_tol <= Tolerance:
return("The Solution x = {},y={}, z={} ".format(x[0], x[1], x[2]))
return("The Solution was found in %s interation(s)" %(Counter_1))
else:
pass
Counter_1 +=1
return("The Solution was not found in {} iteration(s)".format(Iterations))
You need to specify which numpy module you are importing from. The following works if you want to use a function only by its name:
from numpy import linalg
from numpy.linalg import norm
from numpy import zeros, array, diag, diagflat, dot
Looking at you code however, you don't need the second import line, because in the rest of the code the numpy functions are specified according to the accepted norm. For example, norm is already present in your code as np.linalg.norm.
There are three more issues with your code: 1) initial_tol is not assigned a value; 2) tol_gauge is assigned but not used in the code; 3) the last return statement is not indented properly (perhaps only here) and the same is very likely for the block in your while loop.
I want to plot the two solutions of quadratic equation as a function of a parameter ( function coeff(t) ). I am using function numpy.roots (I am sure that all roots are real in this case), and I am trying to invoke it from within pyplot.plot as below:
import numpy as np
import matplotlib.pyplot as plt
r = 3.74
def coeff(pp):
return np.array([pp-1,r+1-0.5*pp,-r])
def sroot(t):
return np.roots(coeff(t))
a = np.linspace(0,0.9,100)
fig = plt.figure()
plt.plot(a,sroot(a)[0,:])
plt.plot(a,sroot(a)[1,:])
plt.show()
I get error message:
File "quest.py", line 18, in <module>
plt.plot(a,sroot(a)[0,:])
File "quest.py", line 10, in sroot
return np.roots(coeff(t))
File "/usr/lib64/python2.7/site-packages/numpy/lib/polynomial.py", line 218, in roots
p = p.astype(float)
I understand that the numpy.roots takes only list of parameters and is unable to recognize a row in array 3xlen(a). Is there a way to do it in one line, preferably inside the pyplot.plot? I would like to avoid using loop.
This is because you transform all of your coefficient at once and try to call the numpy roots solver on all of them. np.roots only accept 1-d array and solves a single polynomial. Here is a script that does what you want:
import numpy as np
import matplotlib.pyplot as plt
# Parameters
r = 3.74
T = np.linspace(0.0,0.9,100)
# Coefficients
C = np.zeros((len(T),3))
C[:,0] = T-1
C[:,1] = r + 1 - 0.5*T
C[:,2] = r
# Roots
R = np.zeros((len(T),2))
for i in range(len(T)):
R[i] = np.roots(C[i])
# Plot
fig = plt.figure()
plt.plot(T,R[:,0])
plt.plot(T,R[:,1])
plt.show()
Is it possible to pass data arrays (numpy arrays) to the interact function ?
My data are loaded from a csv file bad.csv and stored into numpy arrays: u, v, w and uNorm for example.
I've tried to use the following code snippet without luck:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
import numpy as np
from IPython.html.widgets import interact, fixed
fileName = './bad.csv'
p,u,v,w,x,y,z = np.loadtxt(fileName,delimiter=',',skiprows=1,usecols=(0,1,2,3,4,5,6),unpack=True)
uNorm = np.sqrt(u**2 + v**2 + w**2)
r = np.sqrt(y**2 + x**2)
tang = (y / x)
aux_tri = mtri.Triangulation(r/np.max(r), tang/np.max(tang))
triang = mtri.Triangulation(x, y, aux_tri.triangles)
triang.set_mask(mtri.TriAnalyzer(aux_tri).get_flat_tri_mask())
def plotContour(data=w, legendName='Legend', gridOn=True, edgeColors='black'):
plt.gca().set_aspect('equal')
plt.tripcolor(triang,data,NbLevels,cmap=cm.hot_r,edgecolors=edgeColors)
plt.grid(gridOn)
cbar = plt.colorbar()
cbar.set_label(legendName,labelpad=10)
interact(plotContour, data={'u':fixed(u),'v':fixed(v),'w':fixed(w),'Magnitude':fixed(uNorm)}, edgeColors=('Black','None'));
The first time this snippet is run everything works correctly as the default value for data is w.
But when I try to use the dropdown menus I got the following error:
/usr/local/lib/python2.7/dist-packages/matplotlib/tri/tripcolor.pyc in tripcolor(ax, *args, **kwargs)
74 # length of C whether it refers to points or faces.
75 # Do not do this for gouraud shading.
---> 76 if (facecolors is None and len(C) == len(tri.triangles) and
77 len(C) != len(tri.x) and shading != 'gouraud'):
78 facecolors = C
TypeError: len() of unsized object
So how to pass numpy arrays in a dropdown menus ?