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  • Using adaptive step sizes with scipy.integrate.ode

    - by Mike
    The (brief) documentation for scipy.integrate.ode says that two methods (dopri5 and dop853) have stepsize control and dense output. Looking at the examples and the code itself, I can only see a very simple way to get output from an integrator. Namely, it looks like you just step the integrator forward by some fixed dt, get the function value(s) at that time, and repeat. My problem has pretty variable timescales, so I'd like to just get the values at whatever time steps it needs to evaluate to achieve the required tolerances. That is, early on, things are changing slowly, so the output time steps can be big. But as things get interesting, the output time steps have to be smaller. I don't actually want dense output at equal intervals, I just want the time steps the adaptive function uses.

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  • vectorizing a for loop in numpy/scipy?

    - by user248237
    I'm trying to vectorize a for loop that I have inside of a class method. The for loop has the following form: it iterates through a bunch of points and depending on whether a certain variable (called "self.condition_met" below) is true, calls a pair of functions on the point, and adds the result to a list. Each point here is an element in a vector of lists, i.e. a data structure that looks like array([[1,2,3], [4,5,6], ...]). Here is the problematic function: def myClass: def my_inefficient_method(self): final_vector = [] # Assume 'my_vector' and 'my_other_vector' are defined numpy arrays for point in all_points: if not self.condition_met: a = self.my_func1(point, my_vector) b = self.my_func2(point, my_other_vector) else: a = self.my_func3(point, my_vector) b = self.my_func4(point, my_other_vector) c = a + b final_vector.append(c) # Choose random element from resulting vector 'final_vector' self.condition_met is set before my_inefficient_method is called, so it seems unnecessary to check it each time, but I am not sure how to better write this. Since there are no destructive operations here it is seems like I could rewrite this entire thing as a vectorized operation -- is that possible? any ideas how to do this?

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  • error when plotting log'd array in matplotlib/scipy/numpy

    - by user248237
    I have two arrays and I take their logs. When I do that and try to plot their scatter plot, I get this error: File "/Library/Python/2.6/site-packages/matplotlib-1.0.svn_r7892-py2.6-macosx-10.6-universal.egg/matplotlib/pyplot.py", line 2192, in scatter ret = ax.scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, faceted, verts, **kwargs) File "/Library/Python/2.6/site-packages/matplotlib-1.0.svn_r7892-py2.6-macosx-10.6-universal.egg/matplotlib/axes.py", line 5384, in scatter self.add_collection(collection) File "/Library/Python/2.6/site-packages/matplotlib-1.0.svn_r7892-py2.6-macosx-10.6-universal.egg/matplotlib/axes.py", line 1391, in add_collection self.update_datalim(collection.get_datalim(self.transData)) File "/Library/Python/2.6/site-packages/matplotlib-1.0.svn_r7892-py2.6-macosx-10.6-universal.egg/matplotlib/collections.py", line 153, in get_datalim offsets = transOffset.transform_non_affine(offsets) File "/Library/Python/2.6/site-packages/matplotlib-1.0.svn_r7892-py2.6-macosx-10.6-universal.egg/matplotlib/transforms.py", line 1924, in transform_non_affine self._a.transform(points)) File "/Library/Python/2.6/site-packages/matplotlib-1.0.svn_r7892-py2.6-macosx-10.6-universal.egg/matplotlib/transforms.py", line 1420, in transform return affine_transform(points, mtx) ValueError: Invalid vertices array. the code is simply: myarray_x = log(my_array[:, 0]) myarray_y = log(my_array[:, 1]) plt.scatter(myarray_x, myarray_y) any idea what could be causing this? thanks.

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  • reading csv files in scipy/numpy in Python

    - by user248237
    I am having trouble reading a csv file, delimited by tabs, in python. I use the following function: def csv2array(filename, skiprows=0, delimiter='\t', raw_header=False, missing=None, with_header=True): """ Parse a file name into an array. Return the array and additional header lines. By default, parse the header lines into dictionaries, assuming the parameters are numeric, using 'parse_header'. """ f = open(filename, 'r') skipped_rows = [] for n in range(skiprows): header_line = f.readline().strip() if raw_header: skipped_rows.append(header_line) else: skipped_rows.append(parse_header(header_line)) f.close() if missing: data = genfromtxt(filename, dtype=None, names=with_header, deletechars='', skiprows=skiprows, missing=missing) else: if delimiter != '\t': data = genfromtxt(filename, dtype=None, names=with_header, delimiter=delimiter, deletechars='', skiprows=skiprows) else: data = genfromtxt(filename, dtype=None, names=with_header, deletechars='', skiprows=skiprows) if data.ndim == 0: data = array([data.item()]) return (data, skipped_rows) the problem is that genfromtxt complains about my files, e.g. with the error: Line #27100 (got 12 columns instead of 16) I am not sure where these errors come from. Any ideas? Here's an example file that causes the problem: #Gene 120-1 120-3 120-4 30-1 30-3 30-4 C-1 C-2 C-5 genesymbol genedesc ENSMUSG00000000001 7.32 9.5 7.76 7.24 11.35 8.83 6.67 11.35 7.12 Gnai3 guanine nucleotide binding protein alpha ENSMUSG00000000003 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Pbsn probasin Is there a better way to write a generic csv2array function? thanks.

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  • vectorized approach to binning with numpy/scipy in Python

    - by user248237
    I am binning a 2d array (x by y) in Python into the bins of its x value (given in "bins"), using np.digitize: elements_to_bins = digitize(vals, bins) where "vals" is a 2d array, i.e.: vals = array([[1, v1], [2, v2], ...]). elements_to_bins just says what bin each element falls into. What I then want to do is get a list whose length is the number of bins in "bins", and each element returns the y-dimension of "vals" that falls into that bin. I do it this way right now: points_by_bins = [] for curr_bin in range(min(elements_to_bins), max(elements_to_bins) + 1): curr_indx = where(elements_to_bins == curr_bin)[0] curr_bin_vals = vals[:, curr_indx] points_by_bins.append(curr_bin_vals) is there a more elegant/simpler way to do this? All I need is a list of of lists of the y-values that fall into each bin. thanks.

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  • making binned boxplot in matplotlib with numpy and scipy in Python

    - by user248237
    I have a 2-d array containing pairs of values and I'd like to make a boxplot of the y-values by different bins of the x-values. I.e. if the array is: my_array = array([[1, 40.5], [4.5, 60], ...]]) then I'd like to bin my_array[:, 0] and then for each of the bins, produce a boxplot of the corresponding my_array[:, 1] values that fall into each box. So in the end I want the plot to contain number of bins-many box plots. I tried the following: min_x = min(my_array[:, 0]) max_x = max(my_array[:, 1]) num_bins = 3 bins = linspace(min_x, max_x, num_bins) elts_to_bins = digitize(my_array[:, 0], bins) However, this gives me values in elts_to_bins that range from 1 to 3. I thought I should get 0-based indices for the bins, and I only wanted 3 bins. I'm assuming this is due to some trickyness with how bins are represented in linspace vs. digitize. What is the easiest way to achieve this? I want num_bins-many equally spaced bins, with the first bin containing the lower half of the data and the upper bin containing the upper half... i.e., I want each data point to fall into some bin, so that I can make a boxplot. thanks.

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  • building a pairwise matrix in scipy/numpy in Python from dictionaries

    - by user248237
    I have a dictionary whose keys are strings and values are numpy arrays, e.g.: data = {'a': array([1,2,3]), 'b': array([4,5,6]), 'c': array([7,8,9])} I want to compute a statistic between all pairs of values in 'data' and build an n by x matrix that stores the result. Assume that I know the order of the keys, i.e. I have a list of "labels": labels = ['a', 'b', 'c'] What's the most efficient way to compute this matrix? I can compute the statistic for all pairs like this: result = [] for elt1, elt2 in itertools.product(labels, labels): result.append(compute_statistic(data[elt1], data[elt2])) But I want result to be a n by n matrix, corresponding to "labels" by "labels". How can I record the results as this matrix? thanks.

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  • slicing arrays in numpy/scipy

    - by user248237
    I have an array like: a = array([[1,2,3],[3,4,5],[4,5,6]]) what's the most efficient way to slice out a 1x2 array out of this that has only the first two columns of "a"? I.e., array([[2,3],[4,5],[5,6]]) in this case. thanks.

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  • How to make scipy.interpolate give a an extrapolated result beyond the input range?

    - by Salim Fadhley
    I'm trying to port a program which uses a hand-rolled interpolator (developed by a mathematitian colleage) over to use the interpolators provided by scipy. I'd like to use or wrap the scipy interpolator so that it has as close as possible behavior to the old interpolator. A key difference between the two functions is that in our original interpolator - if the input value is above or below the input range, our original interpolator will extrapolate the result. If you try this with the scipy interpolator it raises a ValueError. Consider this program as an example: import numpy as np from scipy import interpolate x = np.arange(0,10) y = np.exp(-x/3.0) f = interpolate.interp1d(x, y) print f(9) print f(11) # Causes ValueError, because it's greater than max(x) Is there a sensible way to make it so that instead of crashing, the final line will simply do a linear extrapolate, continuing the gradients defined by the first and last two pouints to infinity. Note, that in the real software I'm not actually using the exp function - that's here for illustration only!

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  • Scipy sparse... arrays?

    - by spitzanator
    Hey, folks. So, I'm doing some Kmeans classification using numpy arrays that are quite sparse-- lots and lots of zeroes. I figured that I'd use scipy's 'sparse' package to reduce the storage overhead, but I'm a little confused about how to create arrays, not matrices. I've gone through this tutorial on how to create sparse matrices: http://www.scipy.org/SciPy_Tutorial#head-c60163f2fd2bab79edd94be43682414f18b90df7 To mimic an array, I just create a 1xN matrix, but as you may guess, Asp.dot(Bsp) doesn't quite work because you can't multiply two 1xN matrices. I'd have to transpose each array to Nx1, and that's pretty lame, since I'd be doing it for every dot-product calculation. Next up, I tried to create an NxN matrix where column 1 == row 1 (such that you can multiply two matrices and just take the top-left corner as the dot product), but that turned out to be really inefficient. I'd love to use scipy's sparse package as a magic replacement for numpy's array(), but as yet, I'm not really sure what to do. Any advice? Thank you very much!

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  • What is most efficient way of setting row to zeros for a sparce scipy matrix?

    - by Alex Reinking
    I'm trying to convert the following MATLAB code to Python and am having trouble finding a solution that works in any reasonable amount of time. M = diag(sum(a)) - a; where = vertcat(in, out); M(where,:) = 0; M(where,where) = 1; Here, a is a sparse matrix and where is a vector (as are in/out). The solution I have using Python is: M = scipy.sparse.diags([degs], [0]) - A where = numpy.hstack((inVs, outVs)).astype(int) M = scipy.sparse.lil_matrix(M) M[where, :] = 0 # This is the slowest line M[where, where] = 1 M = scipy.sparse.csc_matrix(M) But since A is 334863x334863, this takes like three minutes. If anyone has any suggestions on how to make this faster, please contribute them! For comparison, MATLAB does this same step imperceptibly fast. Thanks!

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  • how to install numpy and scipy on OS X?

    - by amateur
    Hey guys I'm new to Mac so please bear with me. I'm using snow leopard 10.6.4 at the moment. I want to install numpy and scipy, so I downloaded the python2.6,numpy and scipy dmg files from their official site. However, I'm having problem import numpy: Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/numpy/core/multiarray.so: no matching architecture in universal wrapper Can anyone shed some light to this problem?

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  • how to solve a weired swig python c++ interfacing type error

    - by user2981648
    I want to use swig to switch a simple cpp function to python and use "scipy.integrate.quadrature" function to calculate the integration. But python 2.7 reports a type error. Do you guys know what is going on here? Thanks a lot. Furthermore, "scipy.integrate.quad" runs smoothly. So is there something special for "scipy.integrate.quadrature" function? The code is in the following: File "testfunctions.h": #ifndef TESTFUNCTIONS_H #define TESTFUNCTIONS_H double test_square(double x); #endif File "testfunctions.cpp": #include "testfunctions.h" double test_square(double x) { return x * x; } File "swig_test.i" : /* File : swig_test.i */ %module swig_test %{ #include "testfunctions.h" %} /* Let's just grab the original header file here */ %include "testfunctions.h" File "test.py": import scipy.integrate import _swig_test print scipy.integrate.quadrature(_swig_test.test_square, 0., 1.) error info: UMD has deleted: _swig_test Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Python27\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 523, in runfile execfile(filename, namespace) File "D:\data\haitaliu\Desktop\Projects\swig_test\Release\test.py", line 4, in <module> print scipy.integrate.quadrature(_swig_test.test_square, 0., 1.) File "C:\Python27\lib\site-packages\scipy\integrate\quadrature.py", line 161, in quadrature newval = fixed_quad(vfunc, a, b, (), n)[0] File "C:\Python27\lib\site-packages\scipy\integrate\quadrature.py", line 61, in fixed_quad return (b-a)/2.0*sum(w*func(y,*args),0), None File "C:\Python27\lib\site-packages\scipy\integrate\quadrature.py", line 90, in vfunc return func(x, *args) TypeError: in method 'test_square', argument 1 of type 'double'

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  • What is the easiest way to read wav-files using Python [summary]?

    - by Roman
    I want to use Python to access a wav-file and write its content in a form which allows me to analyze it (let's say arrays). I heard that "audiolab" is a suitable tool for that (it transforms numpy arrays into wav and vica versa). I have installed the "audiolab" but I had a problem with the version of numpy (I could not "from numpy.testing import Tester"). I had 1.1.1. version of numpy. I have installed a newer version on numpy (1.4.0). But then I got a new set of errors: Traceback (most recent call last): File "test.py", line 7, in import scikits.audiolab File "/usr/lib/python2.5/site-packages/scikits/audiolab/init.py", line 25, in from pysndfile import formatinfo, sndfile File "/usr/lib/python2.5/site-packages/scikits/audiolab/pysndfile/init.py", line 1, in from _sndfile import Sndfile, Format, available_file_formats, available_encodings File "numpy.pxd", line 30, in scikits.audiolab.pysndfile._sndfile (scikits/audiolab/pysndfile/_sndfile.c:9632) ValueError: numpy.dtype does not appear to be the correct type object I gave up to use audiolab and thought that I can use "wave" package to read in a wav-file. I asked a question about that but people recommended to use scipy instead. OK, I decided to focus on scipy (I have 0.6.0. version). But when I tried to do the following: from scipy.io import wavfile x = wavfile.read('/usr/share/sounds/purple/receive.wav') I get the following: Traceback (most recent call last): File "test3.py", line 4, in <module> from scipy.io import wavfile File "/usr/lib/python2.5/site-packages/scipy/io/__init__.py", line 23, in <module> from numpy.testing import NumpyTest ImportError: cannot import name NumpyTest So, I gave up to use scipy. Can I use just wave package? I do not need much. I just need to have content of wav-file in human readable format and than I will figure out what to do with that.

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  • Scipy Negative Distance? What?

    - by disappearedng
    I have a input file which are all floating point numbers to 4 decimal place. i.e. 13359 0.0000 0.0000 0.0001 0.0001 0.0002` 0.0003 0.0007 ... (the first is the id). My class uses the loadVectorsFromFile method which multiplies it by 10000 and then int() these numbers. On top of that, I also loop through each vector to ensure that there are no negative values inside. However, when I perform _hclustering, I am continually seeing the error, "Linkage Z contains negative values". I seriously think this is a bug because: I checked my values, the values are no where small enough or big enough to approach the limits of the floating point numbers and the formula that I used to derive the values in the file uses absolute value (my input is DEFINITELY right). Can someone enligten me as to why I am seeing this weird error? What is going on that is causing this negative distance error? ===== def loadVectorsFromFile(self, limit, loc, assertAllPositive=True, inflate=True): """Inflate to prevent "negative" distance, we use 4 decimal points, so *10000 """ vectors = {} self.winfo("Each vector is set to have %d limit in length" % limit) with open( loc ) as inf: for line in filter(None, inf.read().split('\n')): l = line.split('\t') if limit: scores = map(float, l[1:limit+1]) else: scores = map(float, l[1:]) if inflate: vectors[ l[0]] = map( lambda x: int(x*10000), scores) #int might save space else: vectors[ l[0]] = scores if assertAllPositive: #Assert that it has no negative value for dirID, l in vectors.iteritems(): if reduce(operator.or_, map( lambda x: x < 0, l)): self.werror( "Vector %s has negative values!" % dirID) return vectors def main( self, inputDir, outputDir, limit=0, inFname="data.vectors.all", mappingFname='all.id.features.group.intermediate'): """ Loads vector from a file and start clustering INPUT vectors is { featureID: tfidfVector (list), } """ IDFeatureDic = loadIdFeatureGroupDicFromIntermediate( pjoin(self.configDir, mappingFname)) if not os.path.exists(outputDir): os.makedirs(outputDir) vectors = self.loadVectorsFromFile( limit, pjoin( inputDir, inFname)) for threshold in map( lambda x:float(x)/30, range(20,30)): clusters = self._hclustering(threshold, vectors) if clusters: outputLoc = pjoin(outputDir, "threshold.%s.result" % str(threshold)) with open(outputLoc, 'w') as outf: for clusterNo, cluster in clusters.iteritems(): outf.write('%s\n' % str(clusterNo)) for featureID in cluster: feature, group = IDFeatureDic[featureID] outline = "%s\t%s\n" % (feature, group) outf.write(outline.encode('utf-8')) outf.write("\n") else: continue def _hclustering(self, threshold, vectors): """function which you should call to vary the threshold vectors: { featureID: [ tfidf scores, tfidf score, .. ] """ clusters = defaultdict(list) if len(vectors) > 1: try: results = hierarchy.fclusterdata( vectors.values(), threshold, metric='cosine') except ValueError, e: self.werror("_hclustering: %s" % str(e)) return False for i, featureID in enumerate( vectors.keys()):

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  • efficiently finding the interval with non-zeros in scipy/numpy in Python?

    - by user248237
    suppose I have a python list or a python 1-d array (represented in numpy). assume that there is a contiguous stretch of elements how can I find the start and end coordinates (i.e. indices) of the stretch of non-zeros in this list or array? for example, a = [0, 0, 0, 0, 1, 2, 3, 4] nonzero_coords(a) should return [4, 7]. for: b = [1, 2, 3, 4, 0, 0] nonzero_coords(b) should return [0, 2]. thanks.

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  • Building up an array in numpy/scipy by iteration in Python?

    - by user248237
    Often, I am building an array by iterating through some data, e.g.: my_array = [] for n in range(1000): # do operation, get value my_array.append(value) # cast to array my_array = array(my_array) I find that I have to first build a list and then cast it (using "array") to an array. Is there a way around these? all these casting calls clutter the code... how can I iteratively build up "my_array", with it being an array from the start? thanks.

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  • install python2.7.3 + numpy + scipy + matplotlib + scikits.statsmodels + pandas0.7.3 correctly

    - by boldnik
    ...using Linux (xubuntu). How to install python2.7.3 + numpy + scipy + matplotlib + scikits.statsmodels + pandas0.7.3 correctly ? My final aim is to have them working. The problem: ~$ python --version Python 2.7.3 so i already have a system-default 2.7.3, which is good! ~$ dpkg -s python-numpy Package: python-numpy Status: install ok installed and i already have numpy installed! great! But... ~$ python Python 2.7.3 (default, Oct 23 2012, 01:07:38) [GCC 4.6.1] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as nmp Traceback (most recent call last): File "<stdin>", line 1, in <module> ImportError: No module named numpy this module couldn't be find by python. The same with scipy, matplotlib. Why? ~$ sudo apt-get install python-numpy [...] Reading package lists... Done Building dependency tree Reading state information... Done python-numpy is already the newest version. [...] why it does not see numpy and others ? update: >>> import sys >>> print sys.path ['', '/usr/local/lib/python27.zip', '/usr/local/lib/python2.7', '/usr/local/lib/python2.7/plat-linux2', '/usr/local/lib/python2.7/lib-tk', '/usr/local/lib/python2.7/lib-old', '/usr/local/lib/python2.7/lib-dynload', '/usr/local/lib/python2.7/site-packages'] >>> so i do have /usr/local/lib/python2.7 ~$ pip freeze Warning: cannot find svn location for distribute==0.6.16dev-r0 BzrTools==2.4.0 CDApplet==1.0 [...] matplotlib==1.0.1 mutagen==1.19 numpy==1.5.1 [...] pandas==0.7.3 papyon==0.5.5 [...] pytz==2012g pyxdg==0.19 reportlab==2.5 scikits.statsmodels==0.3.1 scipy==0.11.0 [...] zope.interface==3.6.1 as you can see, those modules are already installed! But! ls -la /usr/local/lib/ gives ONLY python2.7 dir. And still ~$ python -V Python 2.7.3 and import sys sys.version '2.7.3 (default, Oct 23 2012, 01:07:38) \n[GCC 4.6.1]' updated: Probably I've missed another instance... One at /usr/Python-2.7.3/ and second (seems to be installed "by hands" far far ago) at /usr/python2.7.3/Python-2.7.3/ But how two identical versions can work at the same time??? Probably, one of them is "disabled" (not used by any program, but I don't know how to check if any program uses it). ~$ ls -la /usr/bin/python* lrwxrwxrwx 1 root root 9 2011-11-01 11:11 /usr/bin/python -> python2.7 -rwxr-xr-x 1 root root 2476800 2012-09-28 19:48 /usr/bin/python2.6 -rwxr-xr-x 1 root root 1452 2012-09-28 19:45 /usr/bin/python2.6-config -rwxr-xr-x 1 root root 2586060 2012-07-21 01:42 /usr/bin/python2.7 -rwxr-xr-x 1 root root 1652 2012-07-21 01:40 /usr/bin/python2.7-config lrwxrwxrwx 1 root root 9 2011-10-05 23:53 /usr/bin/python3 -> python3.2 lrwxrwxrwx 1 root root 11 2011-09-06 02:04 /usr/bin/python3.2 -> python3.2mu -rwxr-xr-x 1 root root 2852896 2011-09-06 02:04 /usr/bin/python3.2mu lrwxrwxrwx 1 root root 16 2011-10-08 19:50 /usr/bin/python-config -> python2.7-config there is a symlink python-python2.7, maybe I can ln -f -s this link to exact /usr/Python-2.7.3/python destination without harm ?? And how correctly to remove the 'copy' of 2.7.3?

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  • Using fft2 with reshaping for an RGB filter

    - by Mahmoud Aladdin
    I want to apply a filter on an image, for example, blurring filter [[1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0]]. Also, I'd like to use the approach that convolution in Spatial domain is equivalent to multiplication in Frequency domain. So, my algorithm will be like. Load Image. Create Filter. convert both Filter & Image to Frequency domains. multiply both. reconvert the output to Spatial Domain and that should be the required output. The following is the basic code I use, the image is loaded and displayed as cv.cvmat object. Image is a class of my creation, it has a member image which is an object of scipy.matrix and toFrequencyDomain(size = None) uses spf.fftshift(spf.fft2(self.image, size)) where spf is scipy.fftpack and dotMultiply(img) uses scipy.multiply(self.image, image) f = Image.fromMatrix([[1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0]]) lena = Image.fromFile("Test/images/lena.jpg") print lena.image.shape lenaf = lena.toFrequencyDomain(lena.image.shape) ff = f.toFrequencyDomain(lena.image.shape) lenafm = lenaf.dotMultiplyImage(ff) lenaff = lenafm.toTimeDomain() lena.display() lenaff.display() So, the previous code works pretty well, if I told OpenCV to load the image via GRAY_SCALE. However, if I let the image to be loaded in color ... lena.image.shape will be (512, 512, 3) .. so, it gives me an error when using scipy.fttpack.ftt2 saying "When given, Shape and Axes should be of same length". What I tried next was converted my filter to 3-D .. as [[[1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0]], [[1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0]], [[1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0], [1/9.0, 1/9.0, 1/9.0]]] And, not knowing what the axes argument do, I added it with random numbers as (-2, -1, -1), (-1, -1, -2), .. etc. until it gave me the correct filter output shape for the dotMultiply to work. But, of course it wasn't the correct value. Things were totally worse. My final trial, was using fft2 function on each of the components 2-D matrices, and then re-making the 3-D one, using the following code. # Spiltting the 3-D matrix to three 2-D matrices. for i, row in enumerate(self.image): r.append(list()) g.append(list()) b.append(list()) for pixel in row: r[i].append(pixel[0]) g[i].append(pixel[1]) b[i].append(pixel[2]) rfft = spf.fftshift(spf.fft2(r, size)) gfft = spf.fftshift(spf.fft2(g, size)) bfft = spf.fftshift(spf.fft2(b, size)) newImage.image = sp.asarray([[[rfft[i][j], gfft[i][j], bfft[i][j]] for j in xrange(len(rfft[i]))] for i in xrange(len(rfft))] ) return newImage Any help on what I made wrong, or how can I achieve that for both GreyScale and Coloured pictures.

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  • How to calculate cointegrations of two lists?

    - by Damiano
    Hello everybody! Thank you in advance for your help! I have two lists with some stocks prices, example: a = [10.23, 11.65, 12.36, 12.96] b = [5.23, 6.10, 8.3, 4.98] I can calculate the correlation of these two lists, with: import scipy.stats scipy.stats.pearsonr(a, b)[0] But, I didn't found a method to calculate the co-integration of two lists. Could you give me some advices? Thank you very much!

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  • Converting from samplerate/cutoff frequency to pi-radians/sample in a discrete time sampled IIR filter system.

    - by Fake Name
    I am working on doing some digital filter work using Python and Numpy/Scipy. I'm using scipy.signal.iirdesign to generate my filter coefficents, but it requires the filter passband coefficents in a format I am not familiar with wp, ws : float Passband and stopband edge frequencies, normalized from 0 to 1 (1 corresponds to pi radians / sample). For example: Lowpass: wp = 0.2, ws = 0.3 Highpass: wp = 0.3, ws = 0.2 (from here) I'm not familiar with digital filters (I'm coming from a hardware design background). In an analog context, I would determine the desired slope and the 3db down point, and calculate component values from that. In this context, how do I take a known sample rate, a desired corner frequency, and a desired rolloff, and calculate the wp, ws values from that? (This might be more appropriate for math.stackexchange. I'm not sure)

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  • Python Least-Squares Natural Splines

    - by Eldila
    I am trying to find a numerical package which will fit a natural which minimizes weighted least squares. There is a package in scipy which does what I want for unnatural splines. import numpy as np import matplotlib.pyplot as plt from scipy import interpolate import random x = np.arange(0,5,1.0/2) xs = np.arange(0,5,1.0/500) y = np.sin(x+1) for i in range(len(y)): y[i] += .2*random.random() - .1 knots = np.array([1,2,3,4]) tck = interpolate.splrep(x,y,s=1,k=3,t=knots,task=-1) ynew = interpolate.splev(xs,tck,der=0) plt.figure() plt.plot(xs,ynew,x,y,'x')

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  • plotting results of hierarchical clustering ontop of a matrix of data in python

    - by user248237
    How can I plot a dendrogram right on top of a matrix of values, reordered appropriately to reflect the clustering, in Python? An example is in the bottom of the following figure: http://www.coriell.org/images/microarray.gif I use scipy.cluster.dendrogram to make my dendrogram and perform hierarchical clustering on a matrix of data. How can I then plot the data as a matrix where the rows have been reordered to reflect a clustering induced by the cutting the dendrogram at a particular threshold, and have the dendrogram plotted alongside the matrix? I know how to plot the dendrogram in scipy, but not how to plot the intensity matrix of data with the right scale bar next to it. Any help on this would be greatly appreciated.

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  • Polynomial fitting with log log plot

    - by viral parekh
    I have a simple problem to fit a straight line on log-log scale. My code is, data=loadtxt(filename) xdata=data[:,0] ydata=data[:,1] polycoeffs = scipy.polyfit(xdata, ydata, 1) yfit = scipy.polyval(polycoeffs, xdata) pylab.plot(xdata, ydata, 'k.') pylab.plot(xdata, yfit, 'r-') Now I need to plot fit line on log scale so I just change x and y axis, ax.set_yscale('log') ax.set_xscale('log') then its not plotting correct fit line. So how can I change fit function (in log scale) so that it can plot fit line on log-log scale? Thanks -Viral

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  • Iterate with binary structure over numpy array to get cell sums

    - by Curlew
    In the package scipy there is the function to define a binary structure (such as a taxicab (2,1) or a chessboard (2,2)). import numpy from scipy import ndimage a = numpy.zeros((6,6), dtype=numpy.int) a[1:5, 1:5] = 1;a[3,3] = 0 ; a[2,2] = 2 s = ndimage.generate_binary_structure(2,2) # Binary structure #.... Calculate Sum of result_array = numpy.zeros_like(a) What i want is to iterate over all cells of this array with the given structure s. Then i want to append a function to the current cell value indexed in a empty array (example function sum), which uses the values of all cells in the binary structure. For example: array([[0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 0], [0, 1, 2, 1, 1, 0], [0, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0]]) # The array a. The value in cell 1,2 is currently one. Given the structure s and an example function such as sum the value in the resulting array (result_array) becomes 7 (or 6 if the current cell value is excluded). Someone got an idea?

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