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• Converting excel files to python to frequency

  - by Jacob

  Essentially I've got an excel files with voltage in the first column, and time in the second. I want to find the period of the voltages, as it returns a graph of voltage in y axis and time in x axis with a periodicity, looking similar to a sine function. To find the frequency I have uploaded my excel file to python as I think this will make it easier- there may be something I've missed that will simplify this. So far in python I have: import xlrd import numpy as N import numpy.fft as F import matplotlib.pyplot as P wb = xlrd.open_workbook('temp7.xls') #LOADING EXCEL FILE wb.sheet_names() sh = wb.sheet_by_index(0) first_column = sh.col_values(1) #VALUES FROM EXCEL second_column = sh.col_values(2) #VALUES FROM EXCEL Now how do I find the frequency from this? Huge thanks to anyone who can help! Jacob

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• How can I draw a log-normalized imshow plot with a colorbar representing the raw data in matplotlib

  - by Adam Fraser

  I'm using matplotlib to plot log-normalized images but I would like the original raw image data to be represented in the colorbar rather than the [0-1] interval. I get the feeling there's a more matplotlib'y way of doing this by using some sort of normalization object and not transforming the data beforehand... in any case, there could be negative values in the raw image. import matplotlib.pyplot as plt import numpy as np def log_transform(im): '''returns log(image) scaled to the interval [0,1]''' try: (min, max) = (im[im > 0].min(), im.max()) if (max > min) and (max > 0): return (np.log(im.clip(min, max)) - np.log(min)) / (np.log(max) - np.log(min)) except: pass return im a = np.ones((100,100)) for i in range(100): a[i] = i f = plt.figure() ax = f.add_subplot(111) res = ax.imshow(log_transform(a)) # the colorbar drawn shows [0-1], but I want to see [0-99] cb = f.colorbar(res) I've tried using cb.set_array, but that didn't appear to do anything, and cb.set_clim, but that rescales the colors completely. Thanks in advance for any help :)

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• Plotting 3D Polygons in python-matplotlib

  - by Developer

  I was unsuccessful browsing web for a solution for the following simple question: How to draw 3D polygon (say a filled rectangle or triangle) using vertices values? I have tried many ideas but all failed, see: from mpl_toolkits.mplot3d import Axes3D from matplotlib.collections import PolyCollection import matplotlib.pyplot as plt fig = plt.figure() ax = Axes3D(fig) x = [0,1,1,0] y = [0,0,1,1] z = [0,1,0,1] verts = [zip(x, y,z)] ax.add_collection3d(PolyCollection(verts),zs=z) plt.show() I appreciate in advance any idea/comment. Updates based on the accepted answer: import mpl_toolkits.mplot3d as a3 import matplotlib.colors as colors import pylab as pl import scipy as sp ax = a3.Axes3D(pl.figure()) for i in range(10000): vtx = sp.rand(3,3) tri = a3.art3d.Poly3DCollection([vtx]) tri.set_color(colors.rgb2hex(sp.rand(3))) tri.set_edgecolor('k') ax.add_collection3d(tri) pl.show() Here is the result:

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• numpy array C api

  - by wiso

  I have a C++ function returning a std::vector and I want to use it in python, so I'm using the C numpy api: static PyObject * py_integrate(PyObject *self, PyObject *args){ ... std::vector<double> integral; cpp_function(integral); // this change integral npy_intp size = {integral.size()}; PyObject *out = PyArray_SimpleNewFromData(1, &size, NPY_DOUBLE, &(integral[0])); return out; } when I call it from python, if I do import matplotlib.pyplot as plt a = py_integrate(parameters) print a fig = plt.figure() ax = fig.add_subplot(111) ax.plot(a) print a the first print is ok, the values are correct, but when I plot a they are not, and in particular in the second print I see very strange values like 1E-308 1E-308 ... or 0 0 0 ... as an unitialized memory. I don't understand why the first print is ok.

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• Confusion between numpy, scipy, matplotlib and pylab

  - by goFrendiAsgard

  Numpy, scipy, matplotlib, and pylab are common terms among they who use python for scientific computation. I just learn a bit about pylab, and I got a lot of confusion. Whenever I want to import numpy, I can always do: import numpy as np I just consider, that once I do from pylab import * The numpy will be imported as well (with np alias). So basically the second one do more things compared to the first one. There are few things I want to ask. Is it right that pylab is just a wrapper for numpy, scipy and matplotlib? As np is the numpy alias, what is the scipy and matplotlib alias? (as far as I know, plt is alias of matplotlib.pyplot, but I don't know the alias for the matplotlib itself) Thanks in advance.

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• subplot matplotlib wrong syntax

  - by madptr

  I am using matplotlib to subplot in a loop. For instance, i would like to subplot 49 data sets, and from the doc, i implemented it this way; import numpy as np import matplotlib.pyplot as plt X1=list(range(0,10000,1)) X1 = [ x/float(10) for x in X1 ] nb_mix = 2 parameters = [] for i in range(49): param = [] Y = [0] * len(X1) for j in range(nb_mix): mean = 5* (1 + (np.random.rand() * 2 - 1 ) * 0.5 ) var = 10* (1 + np.random.rand() * 2 - 1 ) scale = 5* ( 1 + (np.random.rand() * 2 - 1) * 0.5 ) Y = [ Y[k] + scale * np.exp(-((X1[k] - mean)/float(var))**2) for k in range(len(X1)) ] param = param + [[mean, var, scale]] ax = plt.subplot(7, 7, i + 1) ax.plot(X1, Y) parameters = parameters + [param] ax.show() However, i have an index out of range error from i=0 onwards. Where can i do better to have it works ? Thanks

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• python how to put data on y-axis when plotting histogram

  - by user3041107

  I don't quite understand how to control y - axis when using plt.hist plot in python. I read my .txt data file - it contains 10 columns with various data. If I want to plot distribution of strain on x axis I take column n.5. But what kind of value appears on y axis ??? Don't understand that. here is the code: import numpy import matplotlib.pyplot as plt from pylab import * from scipy.stats import norm import sys strain = [] infile = sys.argv[1] for line in infile: ret = numpy.loadtxt(infile) strain += list(ret[:,5]) fig = plt.figure() plt.hist(strain, bins = 20) plt.show() Thanks for help!

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• matplotlib plot window won't appear

  - by user1518837

  I'm using Python 2.7.3 in 64-bit. I installed pandas as well as matplotlib 1.1.1, both for 64-bit. Right now, none of my plots are showing. After attempting to plot from several different dataframes, I gave up in frustration and tried the following first example from http://pandas.pydata.org/pandas-docs/dev/visualization.html: INPUT: import matplotlib.pyplot as plt ts = Series(randn(1000), index=date_range ('1/1/2000', periods=1000)) ts = ts.cumsum() ts.plot() pylab.show() OUTPUT: Axes(0.125,0.1;0.775x0.8) And no plot window appeared. Other StackOverflow threads I've read suggested I might be missing DLLs. Any suggestions?

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• why am i getting an error SyntaxError : invalid syntax for this code

  - by eragon1189

  This is a code in python which calculates f (x) =? ((-1)*x)/(x*x+n*n) n from 1 to infinite.... correct to 0.0001, for the range 1 < x < 100 in steps of 0.1.But i am getting an syntax error,as i am new to programming in python... from scipy import * from matplotlib.pyplot import * x=arange(0.1,100,0.1) f=zeros(len(x)) s=-1 for n in range (1,10000): t=s*x/(x*x+n*n) f +=t s =-s if max(abs(t))< 1e-4 break for xx in c_[x,f]: print "%f %f" % (xx[0],xx[1])

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• Matplotlib pick event order for overlapping artists

  - by Ajean

  I'm hitting a very strange issue with matplotlib pick events. I have two artists that are both pickable and are non-overlapping to begin with ("holes" and "pegs"). When I pick one of them, during the event handling I move the other one to where I just clicked (moving a "peg" into the "hole"). Then, without doing anything else, a pick event from the moved artist (the peg) is generated even though it wasn't there when the first event was generated. My only explanation for it is that somehow the event manager is still moving through artist layers when the event is processed, and therefore hits the second artist after it is moved under the cursor. So then my question is - how do pick events (or any events for that matter) iterate through overlapping artists on the canvas, and is there a way to control it? I think I would get my desired behavior if it moved from the top down always (rather than bottom up or randomly). I haven't been able to find sufficient enough documentation, and a lengthy search on SO has not revealed this exact issue. Below is a working example that illustrates the problem, with PathCollections from scatter as pegs and holes: import matplotlib.pyplot as plt import sys class peg_tester(): def __init__(self): self.fig = plt.figure(figsize=(3,1)) self.ax = self.fig.add_axes([0,0,1,1]) self.ax.set_xlim([-0.5,2.5]) self.ax.set_ylim([-0.25,0.25]) self.ax.text(-0.4, 0.15, 'One click on the hole, and I get 2 events not 1', fontsize=8) self.holes = self.ax.scatter([1], [0], color='black', picker=0) self.pegs = self.ax.scatter([0], [0], s=100, facecolor='#dd8800', edgecolor='black', picker=0) self.fig.canvas.mpl_connect('pick_event', self.handler) plt.show() def handler(self, event): if event.artist is self.holes: # If I get a hole event, then move a peg (to that hole) ... # but then I get a peg event also with no extra clicks! offs = self.pegs.get_offsets() offs[0,:] = [1,0] # Moves left peg to the middle self.pegs.set_offsets(offs) self.fig.canvas.draw() print 'picked a hole, moving left peg to center' elif event.artist is self.pegs: print 'picked a peg' sys.stdout.flush() # Necessary when in ipython qtconsole if __name__ == "__main__": pt = peg_tester() I have tried setting the zorder to make the pegs always above the holes, but that doesn't change how the pick events are generated, and particularly this funny phantom event.

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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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• Matplotlib canvas drawing

  - by Morgoth

  Let's say I define a few functions to do certain matplotlib actions, such as def dostuff(ax): ax.scatter([0.],[0.]) Now if I launch ipython, I can load these functions and start a new figure: In [1]: import matplotlib.pyplot as mpl In [2]: fig = mpl.figure() In [3]: ax = fig.add_subplot(1,1,1) In [4]: run functions # run the file with the above defined function If I now call dostuff, then the figure does not refresh: In [6]: dostuff(ax) I have to then explicitly run: In [7]: fig.canvas.draw() To get the canvas to draw. Now I can modify dostuff to be def dostuff(ax): ax.scatter([0.],[0.]) ax.get_figure().canvas.draw() This re-draws the canvas automatically. But now, say that I have the following code: def dostuff1(ax): ax.scatter([0.],[0.]) ax.get_figure().canvas.draw() def dostuff2(ax): ax.scatter([1.],[1.]) ax.get_figure().canvas.draw() def doboth(ax): dostuff1(ax) dostuff2(ax) ax.get_figure().canvas.draw() I can call each of these functions, and the canvas will be redrawn, but in the case of doboth(), it will get redrawn multiple times. My question is: how could I code this, such that the canvas.draw() only gets called once? In the above example it won't change much, but in more complex cases with tens of functions that can be called individually or grouped, the repeated drawing is much more obvious, and it would be nice to be able to avoid it. I thought of using decorators, but it doesn't look as though it would be simple. Any ideas?

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• how to handle an asymptote/discontinuity with Matplotlib

  - by Geddes

  Hello all. Firstly - thanks again for all your help. Sorry not to have accepted the responses to my previous questions as I did not know how the system worked (thanks to Mark for pointing that out!). I have since been back and gratefully acknowledged the kind help I have received. My question: when plotting a graph with a discontinuity/asymptote/singularity/whatever, is there any automatic way to prevent Matplotlib from 'joining the dots' across the 'break'? (please see code/image below). I read that Sage has a [detect_poles] facility that looked good, but I really want it to work with Matplotlib. Thanks and best wishes, Geddes import matplotlib.pyplot as plt import numpy as np from sympy import sympify, lambdify from sympy.abc import x fig = plt.figure(1) ax = fig.add_subplot(111) # set up axis ax.spines['left'].set_position('zero') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position('zero') ax.spines['top'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') # setup x and y ranges and precision xx = np.arange(-0.5,5.5,0.01) # draw my curve myfunction=sympify(1/(x-2)) mylambdifiedfunction=lambdify(x,myfunction,'numpy') ax.plot(xx, mylambdifiedfunction(xx),zorder=100,linewidth=3,color='red') #set bounds ax.set_xbound(-1,6) ax.set_ybound(-4,4) plt.show()

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• Unexpected performance curve from CPython merge sort

  - by vkazanov

  I have implemented a naive merge sorting algorithm in Python. Algorithm and test code is below: import time import random import matplotlib.pyplot as plt import math from collections import deque def sort(unsorted): if len(unsorted) <= 1: return unsorted to_merge = deque(deque([elem]) for elem in unsorted) while len(to_merge) > 1: left = to_merge.popleft() right = to_merge.popleft() to_merge.append(merge(left, right)) return to_merge.pop() def merge(left, right): result = deque() while left or right: if left and right: elem = left.popleft() if left[0] > right[0] else right.popleft() elif not left and right: elem = right.popleft() elif not right and left: elem = left.popleft() result.append(elem) return result LOOP_COUNT = 100 START_N = 1 END_N = 1000 def test(fun, test_data): start = time.clock() for _ in xrange(LOOP_COUNT): fun(test_data) return time.clock() - start def run_test(): timings, elem_nums = [], [] test_data = random.sample(xrange(100000), END_N) for i in xrange(START_N, END_N): loop_test_data = test_data[:i] elapsed = test(sort, loop_test_data) timings.append(elapsed) elem_nums.append(len(loop_test_data)) print "%f s --- %d elems" % (elapsed, len(loop_test_data)) plt.plot(elem_nums, timings) plt.show() run_test() As much as I can see everything is OK and I should get a nice N*logN curve as a result. But the picture differs a bit: Things I've tried to investigate the issue: PyPy. The curve is ok. Disabled the GC using the gc module. Wrong guess. Debug output showed that it doesn't even run until the end of the test. Memory profiling using meliae - nothing special or suspicious. ` I had another implementation (a recursive one using the same merge function), it acts the similar way. The more full test cycles I create - the more "jumps" there are in the curve. So how can this behaviour be explained and - hopefully - fixed? UPD: changed lists to collections.deque UPD2: added the full test code UPD3: I use Python 2.7.1 on a Ubuntu 11.04 OS, using a quad-core 2Hz notebook. I tried to turn of most of all other processes: the number of spikes went down but at least one of them was still there.

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• How to map coordinates in AxesImage to coordinates in saved image file?

  - by Vebjorn Ljosa

  I use matplotlib to display a matrix of numbers as an image, attach labels along the axes, and save the plot to a PNG file. For the purpose of creating an HTML image map, I need to know the pixel coordinates in the PNG file for a region in the image being displayed by imshow. I have found an example of how to do this with a regular plot, but when I try to do the same with imshow, the mapping is not correct. Here is my code, which saves an image and attempts to print the pixel coordinates of the center of each square on the diagonal: import numpy as np import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) axim = ax.imshow(np.random.random((27,27)), interpolation='nearest') for x, y in axim.get_transform().transform(zip(range(28), range(28))): print int(x), int(fig.get_figheight() * fig.get_dpi() - y) plt.savefig('foo.png', dpi=fig.get_dpi()) Here is the resulting foo.png, shown as a screenshot in order to include the rulers: The output of the script starts and ends as follows: 73 55 92 69 111 83 130 97 149 112 … 509 382 528 396 547 410 566 424 585 439 As you see, the y-coordinates are correct, but the x-coordinates are stretched: they range from 73 to 585 instead of the expected 135 to 506, and they are spaced 19 pixels o.c. instead of the expected 14. What am I doing wrong?

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• PySide Qt script doesn't launch from Spyder but works from shell

  - by Maxim Zaslavsky

  I have a weird bug in my project that uses PySide for its Qt GUI, and in response I'm trying to test with simpler code that sets up the environment. Here is the code I am testing with: http://stackoverflow.com/a/6906552/130164 When I launch that from my shell (python test.py), it works perfectly. However, when I run that script in Spyder, I get the following error: Traceback (most recent call last): File "/home/test/Desktop/test/test.py", line 31, in <module> app = QtGui.QApplication(sys.argv) RuntimeError: A QApplication instance already exists. If it helps, I also get the following warning: /usr/lib/pymodules/python2.6/matplotlib/__init__.py:835: UserWarning: This call to matplotlib.use() has no effect because the the backend has already been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot, or matplotlib.backends is imported for the first time. Why does that code work when launched from my shell but not from Spyder? Update: Mata answered that the problem happens because Spyder uses Qt, which makes sense. For now, I've set up execution in Spyder using the "Execute in an external system terminal" option, which doesn't cause errors but doesn't allow debugging, either. Does Spyder have any built-in workarounds to this?

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• Have no idea with python-excel read data file

  - by Protoss Reed

  I am a student and haven't a big expirence to do this work. So problem is next. I have a part of code: import matplotlib.pyplot as plt from pylab import * import cmath def sf(prompt): """ """ error_message = "Value must be integer and greater or equal than zero" while True: val = raw_input(prompt) try: val = float(val) except ValueError: print(error_message) continue if val <= 0: print(error_message) continue return val def petrogen_elements(): """Input and calculations the main parameters for pertogen elements""" print "Please enter Petrogen elements: \r" SiO2 = sf("SiO2: ") Al2O3= sf("Al2O3: ") Na2O = sf("Na2O: ") K2O = sf("K2O: ") petro = [SiO2,TiO2,Al2O3,] Sum = sum(petro) Alcal = Na2O + K2O TypeA lcal= Na2O / K2O Ka= (Na2O + K2O)/ Al2O3 print '-'*20, "\r Alcal: %s \r TypeAlcal: %s \ \r Ka: %s \r" % (Alcal, TypeAlcal,Ka,) petrogen_elements() So the problem is next. I have to load and read excel file and read all data in it. After that program have to calculate for example Alcaline, Type of Alcaline etc. Excel file has only this structure 1 2 3 4 5   1 name1 SiO2 Al2O3 Na2O K2O 2 32 12 0.21 0.1 3 name2 SiO2 Al2O3 Na2O K2O 4 45 8 7.54 5 5 name3 SiO2 Al2O3 Na2O K2O 6. … …. …. … … … All excel file has only 5 columns and unlimited rows. User has choice input data or import excel file. First part of work I have done but it stays a big part Finally I need to read all file and calculate the values. I would be so grateful for some advice

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• Increasing figure size in Matplotlib

  - by Anirudh

  I am trying to plot a graph from a distance matrix. The code words fine and gives me a image in 800 * 600 pixels. The image being too small, All the nodes are packed together. I want increase the size of the image. so I added the following line to my code - figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k') After this all I get is a blank 1000 * 1000 image file. My overall code - import networkx as nx import pickle import matplotlib.pyplot as plt print "Reading from pickle." p_file = open('pickles/names') Names = pickle.load(p_file) p_file.close() p_file = open('pickles/distance') Dist = pickle.load(p_file) p_file.close() G = nx.Graph() print "Inserting Nodes." for n in Names: G.add_node(n) print "Inserting Edges." for i in range(601): for j in range(601): G.add_edge(Names[i],Names[j],weight=Dist[i][j]) print "Drawing Graph." nx.draw(G) print "Saving Figure." #plt.figure(num=None, figsize=(10, 10)) plt.savefig('new.png') print "Success!"

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• Unable to plot graph using matplotlib

  - by Aman Deep Gautam

  I have the following code which searches all the directory in the current directory and then takes data from those files to plot the graph. The data is read correctly as verified by printing but there are no points plotted on graph. import argparse import os import matplotlib.pyplot as plt #find the present working directory pwd=os.path.dirname(os.path.abspath(__file__)) #find all the folders in the present working directory. dirs = [f for f in os.listdir('.') if os.path.isdir(f)] plt.figure() plt.xlim(0, 20000) plt.ylim(0, 1) for directory in dirs: os.chdir(os.path.join(pwd, directory)); chd_dir = os.path.dirname(os.path.abspath(__file__)) files = [ fl for fl in os.listdir('.') if os.path.isfile(fl) ] print files for f in files: f_obj = open(os.path.join(chd_dir, f), 'r') list_x = [] list_y = [] for i in xrange(0,4): f_obj.next() for line in f_obj: temp_list = line.split() print temp_list list_y.append(temp_list[0]) list_x.append(temp_list[1]) print 'final_lsit' print list_x print list_y plt.plot(list_x, list_y, 'r.') f_obj.close() os.chdir(pwd) plt.savefig("test.jpg") The input files look like the following: 5 865 14709 15573 14709 1.32667e-06 664 0.815601 14719 1.55333e-06 674 0.813277 14729 1.82667e-06 684 0.810185 14739 1.4e-06 694 0.808459 Can anybody help me with why this is happening? Being new I would like to know some tutorial where I can get help with kind of plotting as the tutorial I was following made me end up here. Any help appreciated.

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• Why do I get rows of zeros in my 2D fft?

  - by Nicholas Pringle

  I am trying to replicate the results from a paper. "Two-dimensional Fourier Transform (2D-FT) in space and time along sections of constant latitude (east-west) and longitude (north-south) were used to characterize the spectrum of the simulated flux variability south of 40degS." - Lenton et al(2006) The figures published show "the log of the variance of the 2D-FT". I have tried to create an array consisting of the seasonal cycle of similar data as well as the noise. I have defined the noise as the original array minus the signal array. Here is the code that I used to plot the 2D-FT of the signal array averaged in latitude: import numpy as np from numpy import ma from matplotlib import pyplot as plt from Scientific.IO.NetCDF import NetCDFFile ### input directory indir = '/home/nicholas/data/' ### get the flux data which is in ### [time(5day ave for 10 years),latitude,longitude] nc = NetCDFFile(indir + 'CFLX_2000_2009.nc','r') cflux_southern_ocean = nc.variables['Cflx'][:,10:50,:] cflux_southern_ocean = ma.masked_values(cflux_southern_ocean,1e+20) # mask land nc.close() cflux = cflux_southern_ocean*1e08 # change units of data from mmol/m^2/s ### create an array that consists of the seasonal signal fro each pixel year_stack = np.split(cflux, 10, axis=0) year_stack = np.array(year_stack) signal_array = np.tile(np.mean(year_stack, axis=0), (10, 1, 1)) signal_array = ma.masked_where(signal_array > 1e20, signal_array) # need to mask ### average the array over latitude(or longitude) signal_time_lon = ma.mean(signal_array, axis=1) ### do a 2D Fourier Transform of the time/space image ft = np.fft.fft2(signal_time_lon) mgft = np.abs(ft) ps = mgft**2 log_ps = np.log(mgft) log_mgft= np.log(mgft) Every second row of the ft consists completely of zeros. Why is this? Would it be acceptable to add a randomly small number to the signal to avoid this. signal_time_lon = signal_time_lon + np.random.randint(0,9,size=(730, 182))*1e-05 EDIT: Adding images and clarify meaning The output of rfft2 still appears to be a complex array. Using fftshift shifts the edges of the image to the centre; I still have a power spectrum regardless. I expect that the reason that I get rows of zeros is that I have re-created the timeseries for each pixel. The ft[0, 0] pixel contains the mean of the signal. So the ft[1, 0] corresponds to a sinusoid with one cycle over the entire signal in the rows of the starting image. Here are is the starting image using following code: plt.pcolormesh(signal_time_lon); plt.colorbar(); plt.axis('tight') Here is result using following code: ft = np.fft.rfft2(signal_time_lon) mgft = np.abs(ft) ps = mgft**2 log_ps = np.log1p(mgft) plt.pcolormesh(log_ps); plt.colorbar(); plt.axis('tight') It may not be clear in the image but it is only every second row that contains completely zeros. Every tenth pixel (log_ps[10, 0]) is a high value. The other pixels (log_ps[2, 0], log_ps[4, 0] etc) have very low values.

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• matplotlib and python multithread file processing

  - by Napseis

  I have a large number of files to process. I have written a script that get, sort and plot the datas I want. So far, so good. I have tested it and it gives the desired result. Then I wanted to do this using multithreading. I have looked into the doc and examples on the internet, and using one thread in my program works fine. But when I use more, at some point I get random matplotlib error, and I suspect some conflict there, even though I use a function with names for the plots, and iI can't see where the problem could be. Here is the whole script should you need more comment, i'll add them. Thank you. #!/usr/bin/python import matplotlib matplotlib.use('GTKAgg') import numpy as np from scipy.interpolate import griddata import matplotlib.pyplot as plt import matplotlib.colors as mcl from matplotlib import rc #for latex import time as tm import sys import threading import Queue #queue in 3.2 and Queue in 2.7 ! import pdb #the debugger rc('text', usetex=True)#for latex map=0 #initialize the map index. It will be use to index the array like this: array[map,[x,y]] time=np.zeros(1) #an array to store the time middle_h=np.zeros((0,3)) #x phi c #for the middle of the box current_file=open("single_void_cyl_periodic_phi_c_middle_h_out",'r') for line in current_file: if line.startswith('# === time'): map+=1 np.append(time,[float(line.strip('# === time '))]) elif line.startswith('#'): pass else: v=np.fromstring(line,dtype=float,sep=' ') middle_h=np.vstack( (middle_h,v[[1,3,4]]) ) current_file.close() middle_h=middle_h.reshape((map,-1,3)) #3d array: map, x, phi,c ##### def load_and_plot(): #will load a map file, and plot it along with the corresponding profile loaded before while not exit_flag: print("fecthing work ...") #try: if not tasks_queue.empty(): map_index=tasks_queue.get() print("----> working on map: %s" %map_index) x,y,zp=np.loadtxt("single_void_cyl_growth_periodic_post_map_"+str(map_index),unpack=True, usecols=[1, 2,3]) for i,el in enumerate(zp): if el<0.: zp[i]=0. xv=np.unique(x) yv=np.unique(y) X,Y= np.meshgrid(xv,yv) Z = griddata((x, y), zp, (X, Y),method='nearest') figure=plt.figure(num=map_index,figsize=(14, 8)) ax1=plt.subplot2grid((2,2),(0,0)) ax1.plot(middle_h[map_index,:,0],middle_h[map_index,:,1],'*b') ax1.grid(True) ax1.axis([-15, 15, 0, 1]) ax1.set_title('Profiles') ax1.set_ylabel(r'$\phi$') ax1.set_xlabel('x') ax2=plt.subplot2grid((2,2),(1,0)) ax2.plot(middle_h[map_index,:,0],middle_h[map_index,:,2],'*r') ax2.grid(True) ax2.axis([-15, 15, 0, 1]) ax2.set_ylabel('c') ax2.set_xlabel('x') ax3=plt.subplot2grid((2,2),(0,1),rowspan=2,aspect='equal') sub_contour=ax3.contourf(X,Y,Z,np.linspace(0,1,11),vmin=0.) figure.colorbar(sub_contour,ax=ax3) figure.savefig('single_void_cyl_'+str(map_index)+'.png') plt.close(map_index) tasks_queue.task_done() else: print("nothing left to do, other threads finishing,sleeping 2 seconds...") tm.sleep(2) # except: # print("failed this time: %s" %map_index+". Sleeping 2 seconds") # tm.sleep(2) ##### exit_flag=0 nb_threads=2 tasks_queue=Queue.Queue() threads_list=[] jobs=list(range(map)) #each job is composed of a map print("inserting jobs in the queue...") for job in jobs: tasks_queue.put(job) print("done") #launch the threads for i in range(nb_threads): working_bee=threading.Thread(target=load_and_plot) working_bee.daemon=True print("starting thread "+str(i)+' ...') threads_list.append(working_bee) working_bee.start() #wait for all tasks to be treated tasks_queue.join() #flip the flag, so the threads know it's time to stop exit_flag=1 for t in threads_list: print("waiting for threads %s to stop..."%t) t.join() print("all threads stopped")

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• Matplotlib plotting non uniform data in 3D surface

  - by Raj Tendulkar

  I have a simple code to plot the points in 3D for Matplotlib as below - from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np from numpy import genfromtxt import csv fig = plt.figure() ax = fig.add_subplot(111, projection='3d') my_data = genfromtxt('points1.csv', delimiter=',') points1X = my_data[:,0] points1Y = my_data[:,1] points1Z = my_data[:,2] ## I remove the header of the CSV File. points1X = np.delete(points1X, 0) points1Y = np.delete(points1Y, 0) points1Z = np.delete(points1Z, 0) # Convert the array to 1D array points1X = np.reshape(points1X,points1X.size) points1Y = np.reshape(points1Y,points1Y.size) points1Z = np.reshape(points1Z,points1Z.size) my_data = genfromtxt('points2.csv', delimiter=',') points2X = my_data[:,0] points2Y = my_data[:,1] points2Z = my_data[:,2] ## I remove the header of the CSV File. points2X = np.delete(points2X, 0) points2Y = np.delete(points2Y, 0) points2Z = np.delete(points2Z, 0) # Convert the array to 1D array points2X = np.reshape(points2X,points2X.size) points2Y = np.reshape(points2Y,points2Y.size) points2Z = np.reshape(points2Z,points2Z.size) ax.plot(points1X, points1Y, points1Z, 'd', markersize=8, markerfacecolor='red', label='points1') ax.plot(points2X, points2Y, points2Z, 'd', markersize=8, markerfacecolor='blue', label='points2') plt.show() My problem is that I tried to make a decent surface plot out of these data points that I have. I already tried to use ax.plot_surface() function to make it look nice. For this I eliminated some points, and recalculated the matrix kind of input needed by this function. However, the graph I generated was far more difficult to interpret and understand. So there might be 2 possibilities: either I am not using the function correctly, or otherwise, the data I am trying to plot is not good for the surface plot. What I was expecting was 3D graph which would have an effect similar to that we have of 3D pie chart. We see that one piece (that which is extracted out) is part of another piece. I was not expecting it to be exactly same like that, but some kind of effect like that. What I would like to ask is: Do you think it will be possible to make such 3D graph? Is there any way better, I could express my data in 3 dimension? Here are the 2 files - points1.csv Dim1,Dim2,Dim3 3,8,1 3,8,2 3,8,3 3,8,4 3,8,5 3,9,1 3,9,2 3,9,3 3,9,4 3,9,5 3,10,1 3,10,2 3,10,3 3,10,4 3,10,5 3,11,1 3,11,2 3,11,3 3,11,4 3,11,5 3,12,1 3,12,2 3,13,1 3,13,2 3,14,1 3,14,2 3,15,1 3,15,2 3,16,1 3,16,2 3,17,1 3,17,2 3,18,1 3,18,2 4,8,1 4,8,2 4,8,3 4,8,4 4,8,5 4,9,1 4,9,2 4,9,3 4,9,4 4,9,5 4,10,1 4,10,2 4,10,3 4,10,4 4,10,5 4,11,1 4,11,2 4,11,3 4,11,4 4,11,5 4,12,1 4,13,1 4,14,1 4,15,1 4,16,1 4,17,1 4,18,1 5,8,1 5,8,2 5,8,3 5,8,4 5,8,5 5,9,1 5,9,2 5,9,3 5,9,4 5,9,5 5,10,1 5,10,2 5,10,3 5,10,4 5,10,5 5,11,1 5,11,2 5,11,3 5,11,4 5,11,5 5,12,1 5,13,1 5,14,1 5,15,1 5,16,1 5,17,1 5,18,1 6,8,1 6,8,2 6,8,3 6,8,4 6,8,5 6,9,1 6,9,2 6,9,3 6,9,4 6,9,5 6,10,1 6,11,1 6,12,1 6,13,1 6,14,1 6,15,1 6,16,1 6,17,1 6,18,1 7,8,1 7,8,2 7,8,3 7,8,4 7,8,5 7,9,1 7,9,2 7,9,3 7,9,4 7,9,5 and points2.csv Dim1,Dim2,Dim3 3,12,3 3,12,4 3,12,5 3,13,3 3,13,4 3,13,5 3,14,3 3,14,4 3,14,5 3,15,3 3,15,4 3,15,5 3,16,3 3,16,4 3,16,5 3,17,3 3,17,4 3,17,5 3,18,3 3,18,4 3,18,5 4,12,2 4,12,3 4,12,4 4,12,5 4,13,2 4,13,3 4,13,4 4,13,5 4,14,2 4,14,3 4,14,4 4,14,5 4,15,2 4,15,3 4,15,4 4,15,5 4,16,2 4,16,3 4,16,4 4,16,5 4,17,2 4,17,3 4,17,4 4,17,5 4,18,2 4,18,3 4,18,4 4,18,5 5,12,2 5,12,3 5,12,4 5,12,5 5,13,2 5,13,3 5,13,4 5,13,5 5,14,2 5,14,3 5,14,4 5,14,5 5,15,2 5,15,3 5,15,4 5,15,5 5,16,2 5,16,3 5,16,4 5,16,5 5,17,2 5,17,3 5,17,4 5,17,5 5,18,2 5,18,3 5,18,4 5,18,5 6,10,2 6,10,3 6,10,4 6,10,5 6,11,2 6,11,3 6,11,4 6,11,5 6,12,2 6,12,3 6,12,4 6,12,5 6,13,2 6,13,3 6,13,4 6,13,5 6,14,2 6,14,3 6,14,4 6,14,5 6,15,2 6,15,3 6,15,4 6,15,5 6,16,2 6,16,3 6,16,4 6,16,5 6,17,2 6,17,3 6,17,4 6,17,5 6,18,2 6,18,3 6,18,4 6,18,5 7,10,1 7,10,2 7,10,3 7,10,4 7,10,5 7,11,1 7,11,2 7,11,3 7,11,4 7,11,5 7,12,1 7,12,2 7,12,3 7,12,4 7,12,5 7,13,1 7,13,2 7,13,3 7,13,4 7,13,5 7,14,1 7,14,2 7,14,3 7,14,4 7,14,5 7,15,1 7,15,2 7,15,3 7,15,4 7,15,5 7,16,1 7,16,2 7,16,3 7,16,4 7,16,5 7,17,1 7,17,2 7,17,3 7,17,4 7,17,5 7,18,1 7,18,2 7,18,3 7,18,4 7,18,5

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• Creating a new plugin for mpld3

  - by sjp14051

  Toward learning how to create a new mpld3 plugin, I took an existing example, LinkedDataPlugin (http://mpld3.github.io/examples/heart_path.html), and modified it slightly by deleting references to lines object. That is, I created the following: class DragPlugin(plugins.PluginBase): JAVASCRIPT = r""" mpld3.register_plugin("drag", DragPlugin); DragPlugin.prototype = Object.create(mpld3.Plugin.prototype); DragPlugin.prototype.constructor = DragPlugin; DragPlugin.prototype.requiredProps = ["idpts", "idpatch"]; DragPlugin.prototype.defaultProps = {} function DragPlugin(fig, props){ mpld3.Plugin.call(this, fig, props); }; DragPlugin.prototype.draw = function(){ var patchobj = mpld3.get_element(this.props.idpatch, this.fig); var ptsobj = mpld3.get_element(this.props.idpts, this.fig); var drag = d3.behavior.drag() .origin(function(d) { return {x:ptsobj.ax.x(d[0]), y:ptsobj.ax.y(d[1])}; }) .on("dragstart", dragstarted) .on("drag", dragged) .on("dragend", dragended); patchobj.path.attr("d", patchobj.datafunc(ptsobj.offsets, patchobj.pathcodes)); patchobj.data = ptsobj.offsets; ptsobj.elements() .data(ptsobj.offsets) .style("cursor", "default") .call(drag); function dragstarted(d) { d3.event.sourceEvent.stopPropagation(); d3.select(this).classed("dragging", true); } function dragged(d, i) { d[0] = ptsobj.ax.x.invert(d3.event.x); d[1] = ptsobj.ax.y.invert(d3.event.y); d3.select(this) .attr("transform", "translate(" + [d3.event.x,d3.event.y] + ")"); patchobj.path.attr("d", patchobj.datafunc(ptsobj.offsets, patchobj.pathcodes)); } function dragended(d, i) { d3.select(this).classed("dragging", false); } } mpld3.register_plugin("drag", DragPlugin); """ def __init__(self, points, patch): print "Points ID : ", utils.get_id(points) self.dict_ = {"type": "drag", "idpts": utils.get_id(points), "idpatch": utils.get_id(patch)} However, when I try to link the plugin to a figure, as in plugins.connect(fig, DragPlugin(points[0], patch)) I get an error, 'module' is not callable, pointing to this line. What does this mean and why doesn't it work? Thanks. I'm adding additional code to show that linking more than one Plugin might be problematic. But this may be entirely due to some silly mistake on my part, or there is a way around it. The following code based on LinkedViewPlugin generates three panels, in which the top and the bottom panel are supposed to be identical. Mouseover in the middle panel was expected to control the display in the top and bottom panels, but updates occur in the bottom panel only. It would be nice to be able to figure out how to reflect the changes in multiple panels. Thanks. import matplotlib import matplotlib.pyplot as plt import numpy as np import mpld3 from mpld3 import plugins, utils class LinkedView(plugins.PluginBase): """A simple plugin showing how multiple axes can be linked""" JAVASCRIPT = """ mpld3.register_plugin("linkedview", LinkedViewPlugin); LinkedViewPlugin.prototype = Object.create(mpld3.Plugin.prototype); LinkedViewPlugin.prototype.constructor = LinkedViewPlugin; LinkedViewPlugin.prototype.requiredProps = ["idpts", "idline", "data"]; LinkedViewPlugin.prototype.defaultProps = {} function LinkedViewPlugin(fig, props){ mpld3.Plugin.call(this, fig, props); }; LinkedViewPlugin.prototype.draw = function(){ var pts = mpld3.get_element(this.props.idpts); var line = mpld3.get_element(this.props.idline); var data = this.props.data; function mouseover(d, i){ line.data = data[i]; line.elements().transition() .attr("d", line.datafunc(line.data)) .style("stroke", this.style.fill); } pts.elements().on("mouseover", mouseover); }; """ def __init__(self, points, line, linedata): if isinstance(points, matplotlib.lines.Line2D): suffix = "pts" else: suffix = None self.dict_ = {"type": "linkedview", "idpts": utils.get_id(points, suffix), "idline": utils.get_id(line), "data": linedata} class LinkedView2(plugins.PluginBase): """A simple plugin showing how multiple axes can be linked""" JAVASCRIPT = """ mpld3.register_plugin("linkedview", LinkedViewPlugin2); LinkedViewPlugin2.prototype = Object.create(mpld3.Plugin.prototype); LinkedViewPlugin2.prototype.constructor = LinkedViewPlugin2; LinkedViewPlugin2.prototype.requiredProps = ["idpts", "idline", "data"]; LinkedViewPlugin2.prototype.defaultProps = {} function LinkedViewPlugin2(fig, props){ mpld3.Plugin.call(this, fig, props); }; LinkedViewPlugin2.prototype.draw = function(){ var pts = mpld3.get_element(this.props.idpts); var line = mpld3.get_element(this.props.idline); var data = this.props.data; function mouseover(d, i){ line.data = data[i]; line.elements().transition() .attr("d", line.datafunc(line.data)) .style("stroke", this.style.fill); } pts.elements().on("mouseover", mouseover); }; """ def __init__(self, points, line, linedata): if isinstance(points, matplotlib.lines.Line2D): suffix = "pts" else: suffix = None self.dict_ = {"type": "linkedview", "idpts": utils.get_id(points, suffix), "idline": utils.get_id(line), "data": linedata} fig, ax = plt.subplots(3) # scatter periods and amplitudes np.random.seed(0) P = 0.2 + np.random.random(size=20) A = np.random.random(size=20) x = np.linspace(0, 10, 100) data = np.array([[x, Ai * np.sin(x / Pi)] for (Ai, Pi) in zip(A, P)]) points = ax[1].scatter(P, A, c=P + A, s=200, alpha=0.5) ax[1].set_xlabel('Period') ax[1].set_ylabel('Amplitude') # create the line object lines = ax[0].plot(x, 0 * x, '-w', lw=3, alpha=0.5) ax[0].set_ylim(-1, 1) ax[0].set_title("Hover over points to see lines") linedata = data.transpose(0, 2, 1).tolist() plugins.connect(fig, LinkedView(points, lines[0], linedata)) # second set of lines exactly the same but in a different panel lines2 = ax[2].plot(x, 0 * x, '-w', lw=3, alpha=0.5) ax[2].set_ylim(-1, 1) ax[2].set_title("Hover over points to see lines #2") plugins.connect(fig, LinkedView2(points, lines2[0], linedata)) mpld3.show()

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