I'm getting a ZeroDivisionError from the following code:
#stacking the array into a complex array allows np.unique to choose
#truely unique points. We also keep a handle on the unique indices
#to allow us to index `self` in the same order.
unique_points,index = np.unique(xdata[mask]+1j*ydata[mask],
return_index=True)
#Now we break it into the data structure we need.
points = np.column_stack((unique_points.real,unique_points.imag))
xx1,xx2 = self.meta['rcm_xx1'],self.meta['rcm_xx2']
yy1 = self.meta['rcm_yy2']
gx = np.arange(xx1,xx2+dx,dx)
gy = np.arange(-yy1,yy1+dy,dy)
GX,GY = np.meshgrid(gx,gy)
xi = np.column_stack((GX.ravel(),GY.ravel()))
gdata = griddata(points,self[mask][index],xi,method='linear',
fill_value=np.nan)
Here, xdata,ydata and self are all 2D numpy.ndarrays (or subclasses thereof) with the same shape and dtype=np.float32. mask is a 2d ndarray with the same shape and dtype=bool. Here's a link for those wanting to peruse the scipy.interpolate.griddata documentation.
Originally, xdata and ydata are derived from a non-uniform cylindrical grid that has a 4 point stencil -- I thought that the error might be coming from the fact that the same point was defined multiple times, so I made the set of input points unique as suggested in this question. Unfortunately, that hasn't seemed to help. The full traceback is:
Traceback (most recent call last):
File "/xxxxxxx/rcm.py", line 428, in <module>
x[...,1].to_pz0()
File "/xxxxxxx/rcm.py", line 285, in to_pz0
fill_value=fill_value)
File "/usr/local/lib/python2.7/site-packages/scipy/interpolate/ndgriddata.py", line 183, in griddata
ip = LinearNDInterpolator(points, values, fill_value=fill_value)
File "interpnd.pyx", line 192, in scipy.interpolate.interpnd.LinearNDInterpolator.__init__ (scipy/interpolate/interpnd.c:2935)
File "qhull.pyx", line 996, in scipy.spatial.qhull.Delaunay.__init__ (scipy/spatial/qhull.c:6607)
File "qhull.pyx", line 183, in scipy.spatial.qhull._construct_delaunay (scipy/spatial/qhull.c:1919)
ZeroDivisionError: float division
For what it's worth, the code "works" (No exception) if I use the "nearest" method.
Related
I'm trying to assess an interpolation scheme, so I'm looking to compare the input points (red) to the resultant grid (blue). I've built two K-D trees looking to find the nearest blue dot to each red dot (with some radius). My limited understanding suggests that the query_ball_tree would provide the results I'm looking for once my trees are built.
gridTree = spatial.cKDTree(data=np.c_[np.array(xx.flatten()), np.array(yy.flatten())])
obsTree = spatial.cKDTree(data=np.c_[np.array(bathy['x']), np.array(bathy['y'])])
dist, idx = obsTree.query_ball_tree(gridTree, 2) # find points in grid that match obs
which produces the below error.
Traceback (most recent call last):
File "/home/repos/pyObjectiveMapping/operation_FRF_duneLidarDEM_bathy_interp.py", line 211, in <module>
dist, idx = obsTree.query_ball_tree(gridTree, 2)
ValueError: too many values to unpack (expected 2)
if I try to remove the "extra" argument:
dist, idx = obsTree.query_ball_tree(gridTree)
I get the below error (as expected, because it needs both)
Traceback (most recent call last):
File "/home/spike/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-022d5ab7a46f>", line 4, in <module>
dist, idx = obsTree.query_ball_tree(gridTree)
File "ckdtree.pyx", line 1001, in scipy.spatial.ckdtree.cKDTree.query_ball_tree
TypeError: query_ball_tree() takes at least 2 positional arguments (1 given)
so the error is not representing the problem effectively. I've checked and removed all masked data before entering it into the trees. I've tried removing the reds that were outside of the blue domain, that didn't help (didn't think it should matter, but wanted to check anyway). I've tried different radii values and had similar results.
I'm trying to plot a function that gives the arctan of the angle of several scatterplots (it's a physics experiment):
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
filename='rawPhaseDataf2f_17h_15m.dat'
datatype=np.dtype( [('Shotnumber',np.dtype('>f8')),('A1',np.dtype('>f8')), ('A2',np.dtype('>f8')), ('f2f',np.dtype('>f8')), ('intensity',np.dtype('>f8'))])
data=np.fromfile(filename,dtype=datatype)
#time=data['Shotnumber']/9900 # reprate is 9900 Hz -> time in seconds
A1=data['A1']
A2=data['A2']
#np.sort()
i=range(1,209773)
def x(i) :
return arctan((A1.item(i)/A2.item(i))*(i/209772))
def y(i) :
return i*2*pi/209772
plot(x,y)
plt.figure('Scatterplot')
plt.plot(A1,A2,',') #Scatterplot
plt.xlabel('A1')
plt.ylabel('A2')
plt.figure('2D Histogram')
plt.hist2d(A1,A2,100) # 2D Histogram
plt.xlabel('A1')
plt.ylabel('A2')
plt.show()
My error is:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell /sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/nelly/Bureau/ Téléchargements/Kr4 Experiment/read_rawPhaseData.py", line 21, in <module>
plot(x,y)
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 2987, in plot
ret = ax.plot(*args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 4138, in plot
self.add_line(line)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 1497, in add_line
self._update_line_limits(line)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 1508, in _update_line_limits
path = line.get_path()
File "/usr/lib/pymodules/python2.7/matplotlib/lines.py", line 743, in get_path
self.recache()
File "/usr/lib/pymodules/python2.7/matplotlib/lines.py", line 420, in recache
x = np.asarray(xconv, np.float_)
File "/usr/lib/python2.7/dist-packages/numpy/core/numeric.py", line 460, in asarray
return array(a, dtype, copy=False, order=order)
TypeError: float() argument must be a string or a number
I know that the problem is from the plot(x,y). I think that my error comes from the definition of x and y. A1 and A2 are matrix, N the number of points and Ak is the index of the matrix. I want to have arctan(A1k/A2k)*(k/N).
There are lots of problems with your code, and your understanding of python and array operations. I'm just going to handle the first part of the code (and the error you get), and hopefully you can continue to fix it from there.
This should fix the error you're getting and generate a plot:
# size = 209772
size = A1.size # I'm assuming that the size of the array is 209772
z = np.arange(1, size+1)/(size+1) # construct an array from [1/209773, 1.0]
# Calculate the x and y arrays
x = np.arctan((A1/A2)*z)
y = z*2*pi
# Plot x and y
plt.plot(x, y)
Discussion:
There are lots of issues with this chunk of code:
i=range(1,209773)
def x(i) :
return arctan((A1.item(i)/A2.item(i))*(i/209772))
def y(i) :
return i*2*pi/209772
plot(x, y)
You're defining two functions called x and y, and then you are passing those functions to the plotting method. The plotting method accepts numbers (in lists or arrays), not functions. That is the reason for the error that you are getting. So you instead need to construct a list/array of numbers and pass that to the function.
You're defining a variable i which is a list of numbers. But when you define the functions x and y, you are creating new variables named i which have nothing to do with the list you created earlier. This is because of how "scope" works in python.
The functions arctan and plot are not defined "globally", instead they are only defined in the packages numpy and matplotlib. So you need to call them from those packages.
I used fsolve to find the zeros of an example sinus function, and worked great. However, I wanted to do the same with a dataset. Two lists of floats, later converted to arrays with numpy.asarray(), containing the (x,y) values, namely 't' and 'ys'.
Although I found some related questions, I failed to implement the code provided in them, as I try to show here. Our arrays of interest are stored in a 2D list (data[i][j], where 'i' corresponds to a variable (e.g. data[0]==t==time==x values) and 'j' are the values of said variable along the x axis (e.g. data[1]==Force). Keep in mind that each data[i] is an array of floats.
Could you offer an example code that takes two inputs (the two mentioned arrays) and returns its intersecting points with a defined function (e.g. 'y=0').
I include some testing I made regarding the other related question. ( #HYRY 's answer)
I do not think it is relevant, but I'm using Spyder through Anaconda.
Thanks in advance!
"""
Following the answer provided by #HYRY in the 'related questions' (see link above).
At this point of the code, the variable 'data' has already been defined as stated before.
"""
from scipy.optimize import fsolve
def tfun(x):
return data[0][x]
def yfun(x):
return data[14][x]
def findIntersection(fun1, fun2, x0):
return [fsolve(lambda x:fun1(x)-fun2(x, y), x0) for y in range(1, 10)]
print findIntersection(tfun, yfun, 0)
Which returns the next error
File "E:/Data/Anaconda/[...]/00-Latest/fsolvestacktest001.py", line 36, in tfun
return data[0][x]
IndexError: arrays used as indices must be of integer (or boolean) type
The full output is as it follows:
Traceback (most recent call last):
File "<ipython-input-16-105803b235a9>", line 1, in <module>
runfile('E:/Data/Anaconda/[...]/00-Latest/fsolvestacktest001.py', wdir='E:/Data/Anaconda/[...]/00-Latest')
File "C:\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 580, in runfile
execfile(filename, namespace)
File "E:/Data/Anaconda/[...]/00-Latest/fsolvestacktest001.py", line 44, in <module>
print findIntersection(tfun, yfun, 0)
File "E:/Data/Anaconda/[...]/00-Latest/fsolvestacktest001.py", line 42, in findIntersection
return [fsolve(lambda x:fun1(x)-fun2(x, y), x0) for y in range(1, 10)]
File "C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py", line 140, in fsolve
res = _root_hybr(func, x0, args, jac=fprime, **options)
File "C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py", line 209, in _root_hybr
ml, mu, epsfcn, factor, diag)
File "E:/Data/Anaconda/[...]/00-Latest/fsolvestacktest001.py", line 42, in <lambda>
return [fsolve(lambda x:fun1(x)-fun2(x, y), x0) for y in range(1, 10)]
File "E:/Data/Anaconda/[...]/00-Latest/fsolvestacktest001.py", line 36, in tfun
return data[0][x]
IndexError: arrays used as indices must be of integer (or boolean) type
You can 'convert' a datasets (arrays) to continuous functions by means of interpolation. scipy.interpolate.interp1d is a factory that provides you with the resulting function, which you could then use with your root finding algorithm.
--edit-- an example for computing an intersection of sin and cos from 20 samples (I've used cubic spline interpolation, as piecewise linear gives warnings about the smoothness):
>>> import numpy, scipy.optimize, scipy.interpolate
>>> x = numpy.linspace(0,2*numpy.pi, 20)
>>> x
array([ 0. , 0.33069396, 0.66138793, 0.99208189, 1.32277585,
1.65346982, 1.98416378, 2.31485774, 2.64555171, 2.97624567,
3.30693964, 3.6376336 , 3.96832756, 4.29902153, 4.62971549,
4.96040945, 5.29110342, 5.62179738, 5.95249134, 6.28318531])
>>> y1sampled = numpy.sin(x)
>>> y2sampled = numpy.cos(x)
>>> y1int = scipy.interpolate.interp1d(x,y1sampled,kind='cubic')
>>> y2int = scipy.interpolate.interp1d(x,y2sampled,kind='cubic')
>>> scipy.optimize.fsolve(lambda x: y1int(x) - y2int(x), numpy.pi)
array([ 3.9269884])
>>> scipy.optimize.fsolve(lambda x: numpy.sin(x) - numpy.cos(x), numpy.pi)
array([ 3.92699082])
Note that interpolation will give you 'guesses' about what data should be between the sampling points. No way to tell how good these guesses are. (but for my example, you can see it's a pretty good estimation)
The code could compute Fourier transform from a .tiff image on my Ubuntu 11.04. On Windows XP it produces memory error. What to change? Thank you.
def fouriertransform(result): #function for Fourier transform computation
for filename in glob.iglob ('*.tif')
imgfourier = scipy.misc.imread(filename) #read the image
arrayfourier = numpy.array([imgfourier])#make an array
# Take the fourier transform of the image.
F1 = fftpack.fft2(arrayfourier)
# Now shift so that low spatial frequencies are in the center.
F2 = fftpack.fftshift(F1)
# the 2D power spectrum is:
psd2D = np.abs(F2)**2
L = psd2D
np.set_printoptions(threshold=3)
#np.set_printoptions(precision = 3, threshold = None, edgeitems = None, linewidth = 3, suppress = True, nanstr = None, infstr = None, formatter = None)
for subarray in L:
for array in subarray:
for array in subarray:
for elem in array:
print '%3.10f\n' % elem
The error output is:
Traceback (most recent call last):
File "C:\Documents and Settings\HrenMudak\Мои документы\Моя музыка\fourier.py", line 27, in <module>
F1 = fftpack.fft2(arrayfourier)
File "C:\Python27\lib\site-packages\scipy\fftpack\basic.py", line 571, in fft2
return fftn(x,shape,axes,overwrite_x)
File "C:\Python27\lib\site-packages\scipy\fftpack\basic.py", line 521, in fftn
return _raw_fftn_dispatch(x, shape, axes, overwrite_x, 1)
File "C:\Python27\lib\site-packages\scipy\fftpack\basic.py", line 535, in _raw_fftn_dispatch
return _raw_fftnd(tmp,shape,axes,direction,overwrite_x,work_function)
File "C:\Python27\lib\site-packages\scipy\fftpack\basic.py", line 463, in _raw_fftnd
x, copy_made = _fix_shape(x, s[i], waxes[i])
File "C:\Python27\lib\site-packages\scipy\fftpack\basic.py", line 134, in _fix_shape
z = zeros(s,x.dtype.char)
MemoryError
I've tried to run your code, except that I replaced the mahotas.imread with the scipy.misc.imread function, because I don't have that library, and I could not reproduce your error.
Some further remarks:
can you try to use the scipy.misc.imread function instead of the mahotas function? I suppose the issue could be there
what is the actual exception that is thrown? (+other output?)
what are the dimensions of your image? Gray-scale / RGB? Printing all values for a large image could indeed take up quite some memory, so it might be better to visualize the results with e.g. matplotlibs imshow function.
For a subplot (self.intensity), I want to shade the area under the graph.
I tried this, hoping it was the correct syntax:
self.intensity.fill_between(arange(l,r), 0, projection)
Which I intend as to do shading for projection numpy array within (l,r) integer limits.
But it gives me an error. How do I do it correctly?
Heres the traceback:
Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_wx.py", line 1289, in _onLeftButtonDown
FigureCanvasBase.button_press_event(self, x, y, 1, guiEvent=evt)
File "/usr/lib/pymodules/python2.7/matplotlib/backend_bases.py", line 1576, in button_press_event
self.callbacks.process(s, mouseevent)
File "/usr/lib/pymodules/python2.7/matplotlib/cbook.py", line 265, in process
proxy(*args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/cbook.py", line 191, in __call__
return mtd(*args, **kwargs)
File "/root/dev/spectrum/spectrum/plot_handler.py", line 55, in _onclick
self._call_click_callback(event.xdata)
File "/root/dev/spectrum/spectrum/plot_handler.py", line 66, in _call_click_callback
self.__click_callback(data)
File "/root/dev/spectrum/spectrum/plot_handler.py", line 186, in _on_plot_click
band_data = self._band_data)
File "/root/dev/spectrum/spectrum/plot_handler.py", line 95, in draw
self.intensity.fill_between(arange(l,r), 0, projection)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 6457, in fill_between
raise ValueError("Argument dimensions are incompatible")
ValueError: Argument dimensions are incompatible
It seems like you are trying to fill the part of the projection from l to r. fill_between expects the x and y arrays to be of equal lengths, so you can not expect to fill part of the curve only.
To get what you want, you can do either of the following:
1. send only part of the projection that needs to be filled to the command; and draw the rest of the projection separately.
2. send a separate boolean array as argument that defines the sections to fill in. See the documentation!
For the former method, see the example code below:
from pylab import *
a = subplot(111)
t = arange(1, 100)/50.
projection = sin(2*pi*t)
# Draw the original curve
a.plot(t, projection)
# Define areas to fill in
l, r = 10, 50
# Fill the areas
a.fill_between(t[l:r], projection[l:r])
show()