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Search found 16 results on 1 pages for 'colorbar'.

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• How do I set a matplotlib colorbar extents?

  - by Adam Fraser

  I'd like to display a colorbar representing an image's raw values along side a matplotlib imshow subplot which displays that image, normalized. I've been able to draw the image and a colorbar successfully like this, but the colorbar min and max values represent the normalized (0,1) image instead of the raw (0,99) image. f = plt.figure() # create toy image im = np.ones((100,100)) for x in range(100): im[x] = x # create imshow subplot ax = f.add_subplot(111) result = ax.imshow(im / im.max()) # Create the colorbar axc, kw = matplotlib.colorbar.make_axes(ax) cb = matplotlib.colorbar.Colorbar(axc, result) # Set the colorbar result.colorbar = cb If someone has a better mastery of the colorbar API, I'd love to hear from you. Thanks! Adam

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• AttributeError while adding colorbar in matplotlib

  - by bgbg

  The following code fails to run on Python 2.5.4: from matplotlib import pylab as pl import numpy as np data = np.random.rand(6,6) fig = pl.figure(1) fig.clf() ax = fig.add_subplot(1,1,1) ax.imshow(data, interpolation='nearest', vmin=0.5, vmax=0.99) pl.colorbar() pl.show() The error message is C:\temp>python z.py Traceback (most recent call last): File "z.py", line 10, in <module> pl.colorbar() File "C:\Python25\lib\site-packages\matplotlib\pyplot.py", line 1369, in colorbar ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw) File "C:\Python25\lib\site-packages\matplotlib\figure.py", line 1046, in colorbar cb = cbar.Colorbar(cax, mappable, **kw) File "C:\Python25\lib\site-packages\matplotlib\colorbar.py", line 622, in __init__ mappable.autoscale_None() # Ensure mappable.norm.vmin, vmax AttributeError: 'NoneType' object has no attribute 'autoscale_None' How can I add colorbar to this code? Following is the interpreter information: Python 2.5.4 (r254:67916, Dec 23 2008, 15:10:54) [MSC v.1310 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>

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• creating a color coded time chart using colorbar and colormaps in python

  - by Rusty

  I'm trying to make a time tracking chart based on a daily time tracking file that I used. I wrote code that crawls through my files and generates a few lists. endTimes is a list of times that a particular activity ends in minutes going from 0 at midnight the first day of the month to however many minutes are in a month. labels is a list of labels for the times listed in endTimes. It is one shorter than endtimes since the trackers don't have any data about before 0 minute. Most labels are repeats. categories contains every unique value of labels in order of how well I regard that time. I want to create a colorbar or a stack of colorbars (1 for eachday) that will depict how I spend my time for a month and put a color associated with each label. Each value in categories will have a color associated. More blue for more good. More red for more bad. It is already in order for the jet colormap to be right, but I need to get desecrate color values evenly spaced out for each value in categories. Then I figure the next step would be to convert that to a listed colormap to use for the colorbar based on how the labels associated with the categories. I think this is the right way to do it, but I am not sure. I am not sure how to associate the labels with color values. Here is the last part of my code so far. I found one function to make a discrete colormaps. It does, but it isn't what I am looking for and I am not sure what is happening. Thanks for the help! # now I need to develop the graph import numpy as np from matplotlib import pyplot,mpl import matplotlib from scipy import interpolate from scipy import * def contains(thelist,name): # checks if the current list of categories contains the one just read for val in thelist: if val == name: return True return False def getCategories(lastFile): ''' must determine the colors to use I would like to make a gradient so that the better the task, the closer to blue bad labels will recieve colors closer to blue read the last file given for the information on how I feel the order should be then just keep them in the order of how good they are in the tracker use a color range and develop discrete values for each category by evenly spacing them out any time not found should assume to be sleep sleep should be white ''' tracker = open(lastFile+'.txt') # open the last file # find all the categories categories = [] for line in tracker: pos = line.find(':') # does it have a : or a ? if pos==-1: pos=line.find('?') if pos != -1: # ignore if no : or ? name = line[0:pos].strip() # split at the : or ? if contains(categories,name)==False: # if the category is new categories.append(name) # make a new one return categories # find good values in order of last day newlabels=[] for val in getCategories(lastDay): if contains(labels,val): newlabels.append(val) categories=newlabels # convert discrete colormap to listed colormap python for ii,val in enumerate(labels): if contains(categories,val)==False: labels[ii]='sleep' # create a figure fig = pyplot.figure() axes = [] for x in range(endTimes[-1]%(24*60)): ax = fig.add_axes([0.05, 0.65, 0.9, 0.15]) axes.append(ax) # figure out the colors to use # stole this function to make a discrete colormap # http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations def cmap_discretize(cmap, N): """Return a discrete colormap from the continuous colormap cmap. cmap: colormap instance, eg. cm.jet. N: Number of colors. Example x = resize(arange(100), (5,100)) djet = cmap_discretize(cm.jet, 5) imshow(x, cmap=djet) """ cdict = cmap._segmentdata.copy() # N colors colors_i = np.linspace(0,1.,N) # N+1 indices indices = np.linspace(0,1.,N+1) for key in ('red','green','blue'): # Find the N colors D = np.array(cdict[key]) I = interpolate.interp1d(D[:,0], D[:,1]) colors = I(colors_i) # Place these colors at the correct indices. A = zeros((N+1,3), float) A[:,0] = indices A[1:,1] = colors A[:-1,2] = colors # Create a tuple for the dictionary. L = [] for l in A: L.append(tuple(l)) cdict[key] = tuple(L) # Return colormap object. return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024) # jet colormap goes from blue to red (good to bad) cmap = cmap_discretize(mpl.cm.jet, len(categories)) cmap.set_over('0.25') cmap.set_under('0.75') #norm = mpl.colors.Normalize(endTimes,cmap.N) print endTimes print labels # make a color list by matching labels to a picture #norm = mpl.colors.ListedColormap(colorList) cb1 = mpl.colorbar.ColorbarBase(axes[0],cmap=cmap ,orientation='horizontal' ,boundaries=endTimes ,ticks=endTimes ,spacing='proportional') pyplot.show()

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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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• Is it possible to add colorbar properties to a maltlab plot?

  - by clowny

  Hello, I'd really like to know if there's a way to add the colorbar properties we usually find in surface or mesh, but this time for a simple plot. Thanx you!

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• Can IDL create a contour plot colorbar like this?

  - by Carthage

  At the bottom of this image, you'll see a nice colorbar that matches the colors of the graph correctly: http://stribog.cc.umanitoba.ca/ceos/20100517_00z_prod/ I couldn't find anything that created a color bar with exactly the colors I wanted, it always seemed to involve a spectrum that included colors I didn't use. I have a vector of colors I use for my data. Is there I way I can use that vector to create a color bar with only those colors?

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• unevenly centered subplots in matplotlib in Python?

  - by user248237

  I am plotting a simple pair of subplots in matplotlib that are for some reason unevenly centered. I plot them as follows: plt.figure() # first subplot s1 = plt.subplot(2, 1, 1) plt.bar([1, 2, 3], [4, 5, 6]) # second subplot s2 = plt.subplot(2, 1, 2) plt.pcolor(rand(5,5)) # add colorbar plt.colorbar() # square axes axes_square(s1) axes_square(s2) where axes_square is simply: def axes_square(plot_handle): plot_handle.axes.set_aspect(1/plot_handle.axes.get_data_ratio()) The plot I get is attached. The top and bottom plots are unevenly centered. I'd like their yaxis to be aligned and their boxes to be aligned. If I remove the plt.colorbar() call, the plots become centered. How can I have the plots centered while the colorbar of pcolor is still shown? I want the axes to be centered and have the colorbar be outside of that alignment, either to the left or to the right of the pcolor matrix. image of plots link thanks.

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• Trouble using latex in Matplotlib / Scipy etc.

  - by ajhall

  I'm having some issues with my first attempts at using matplotlib and scipy to make some scatter plots of my data (too many variables, trying to see many things at once). Here's some code of mine that is working fairly well... import numpy from scipy import * import pylab from matplotlib import * import h5py FileID = h5py.File('3DiPVDplot1.mat','r') # (to view the contents of: list(FileID) ) group = FileID['/'] CurrentsArray = group['Currents'].value IvIIIarray = group['IvIII'].value PFarray = group['PF'].value growthTarray = group['growthT'].value fig = pylab.figure() ax = fig.add_subplot(111) cax = ax.scatter(IvIIIarray, growthTarray, PFarray, CurrentsArray, alpha=0.75) cbar = fig.colorbar(cax) ax.set_xlabel('Cu / III') ax.set_ylabel('Growth T') ax.grid(True) pylab.show() I tried to change the code to include latex fonts and interpreting, none of it seems to work for me, however. Here's an example attempt that didn't work: import numpy from scipy import * import pylab from matplotlib import * import h5py rc('text', usetex=True) rc('font', family='serif') FileID = h5py.File('3DiPVDplot1.mat','r') # (to view the contents of: list(FileID) ) group = FileID['/'] CurrentsArray = group['Currents'].value IvIIIarray = group['IvIII'].value PFarray = group['PF'].value growthTarray = group['growthT'].value fig = pylab.figure() ax = fig.add_subplot(111) cax = ax.scatter(IvIIIarray, growthTarray, PFarray, CurrentsArray, alpha=0.75) cbar = fig.colorbar(cax) ax.set_xlabel(r'Cu / III') ax.set_ylabel(r'Growth T') ax.grid(True) pylab.show() I'm using fink installed python26 with corresponding packages for scipy matplotlib etc. I've been using iPython and manual work instead of scripts in python. Since I'm completely new to python and scipy, I'm sure I'm making some stupid simple mistakes. Please enlighten me! I greatly appreciate the help!

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• how to set a fixed color bar for pcolor in python matplotlib?

  - by user248237

  I am using pcolor with a custom color map to plot a matrix of values. I set my color map so that low values are white and high values are red, as shown below. All of my matrices have values between 0 and 20 (inclusive) and I'd like 20 to always be pure red and 0 to always be pure white, even if the matrix has values that don't span the entire range. For example, if my matrix only has values between 2 and 7, I don't want it to plot 2 as white and 7 as red, but rather color it as if the range is still 0 to 20. How can I do this? I tried using the "ticks=" option of colorbar but it did not work. Here is my current code (assume "my_matrix" contains the values to be plotted): cdict = {'red': ((0.0, 1.0, 1.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 1.0, 1.0), (0.5, 1.0, 1.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 1.0, 1.0), (0.5, 1.0, 1.0), (1.0, 0.0, 0.0))} my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256) colored_matrix = plt.pcolor(my_matrix, cmap=my_cmap) plt.colorbar(colored_matrix, ticks=[0, 5, 10, 15, 20]) any idea how I can fix this to get the right result? thanks very much.

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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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• Confusion Matrix with number of classified/misclassified instances on it (Python/Matplotlib)

  - by Pinkie

  I am plotting a confusion matrix with matplotlib with the following code: from numpy import * import matplotlib.pyplot as plt from pylab import * conf_arr = [[33,2,0,0,0,0,0,0,0,1,3], [3,31,0,0,0,0,0,0,0,0,0], [0,4,41,0,0,0,0,0,0,0,1], [0,1,0,30,0,6,0,0,0,0,1], [0,0,0,0,38,10,0,0,0,0,0], [0,0,0,3,1,39,0,0,0,0,4], [0,2,2,0,4,1,31,0,0,0,2], [0,1,0,0,0,0,0,36,0,2,0], [0,0,0,0,0,0,1,5,37,5,1], [3,0,0,0,0,0,0,0,0,39,0], [0,0,0,0,0,0,0,0,0,0,38] ] norm_conf = [] for i in conf_arr: a = 0 tmp_arr = [] a = sum(i,0) for j in i: tmp_arr.append(float(j)/float(a)) norm_conf.append(tmp_arr) plt.clf() fig = plt.figure() ax = fig.add_subplot(111) res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest') cb = fig.colorbar(res) savefig("confmat.png", format="png") But I want to the confusion matrix to show the numbers on it like this graphic (the right one): http://i48.tinypic.com/2e30kup.jpg How can I plot the conf_arr on the graphic?

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• Python. Draw rectangle in basemap

  - by user2928318

  I need to add several rectangles in my basemap. I nee four rectangles with lat and log ranges as below. 1) llcrnrlon=-10, urcrnrlon=10, llcrnrlat=35,urcrnrlat=60 2) llcrnrlon=10.5, urcrnrlon=35, llcrnrlat=35,urcrnrlat=60 3) llcrnrlon=35.5, urcrnrlon=52, llcrnrlat=30,urcrnrlat=55 4) llcrnrlon=-20, urcrnrlon=35, llcrnrlat=20,urcrnrlat=34.5 My script is below. I found "polygon" packages to add lines but I do not exactly know how to do. Please help me!! Thanks a lot for your help in advance! from mpl_toolkits.basemap import Basemap m=basemaputpart.Basemap(llcrnrlon=-60, llcrnrlat=20, urcrnrlon=60, urcrnrlat=70, resolution='i', projection='cyl', lon_0=0, lat_0=45) lon1=np.array([[-180.+j*0.5 for j in range(721)] for i in range(181)]) lat1=np.array([[i*0.5 for j in range(721)] for i in range(181) ]) Nx1,Ny1=m(lon1,lat1,inverse=False) toplot=data[:,:] toplot[data==0]=np.nan toplot=np.ma.masked_invalid(toplot) plt.pcolor(Nx1,Ny1,np.log(toplot),vmin=0, vmax=5) cbar=plt.colorbar() m.drawcoastlines(zorder=2) m.drawcountries(zorder=2) plt.show()

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• How to add legend to imshow() in matplotlib

  - by rankthefirst

  I am using matplotlib In plot() or bar(), we can easily put legend, if we add labels to them. but what if it is a contourf() or imshow() I know there is a colorbar() which can present the color range, but it is not satisfied. I want such a legend which have names(labels). For what I can think of is that, add labels to each element in the matrix, then ,try legend(), to see if it works, but how to add label to the element, like a value?? in my case, the raw data is like: 1,2,3,3,4 2,3,4,4,5 1,1,1,2,2 for example, 1 represents 'grass', 2 represents 'sand', 3 represents 'hill'... and so on. imshow() works perfectly with my case, but without the legend. my question is: Is there a function that can automatically add legend, for example, in my case, I just have to do like this: someFunction('grass','sand',...) If there isn't, how do I add labels to each value in the matrix. For example, label all the 1 in the matrix 'grass', labell all the 2 in the matrix 'sand'...and so on. Thank you!

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• Fixing color in scatter plots in matplotlib

  - by ajhall

  Hi guys, I'm going to have to come back and add some examples if you need them, which you might. But, here's the skinny- I'm plotting scatter plots of lab data for my research. I need to be able to visually compare the scatter plots from one plot to the next, so I want to fix the color range on the scatter plots and add in a colorbar to each plot (which will be the same in each figure). Essentially, I'm fixing all aspects of the axes and colorspace etc. so that the plots are directly comparable by eye. For the life of me, I can't seem to get my scatter() command to properly set the color limits in the colorspace (default)... i.e., I figure out my total data's min and total data's max, then apply them to vmin, vmax, for the subset of data, and the color still does not come out properly in both plots. This must come up here and there, I can't be the only one that wants to compare various subsets of data amongst plots... so, how do you fix the colors so that each data keeps it's color between plots and doesn't get remapped to a different color due to the change in max/min of the subset -v- the whole set? I greatly appreciate all your thoughts!!! A mountain-dew and fiery-hot cheetos to all! -Allen

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• image processing algorithm in MATLAB

  - by user261002

  I am trying to reconstruct an algorithm belong to this paper: Decomposition of biospeckle images in temporary spectral bands Here is an explanation of the algorithm: We recorded a sequence of N successive speckle images with a sampling frequency fs. In this way it was possible to observe how a pixel evolves through the N images. That evolution can be treated as a time series and can be processed in the following way: Each signal corresponding to the evolution of every pixel was used as input to a bank of filters. The intensity values were previously divided by their temporal mean value to minimize local differences in reflectivity or illumination of the object. The maximum frequency that can be adequately analyzed is determined by the sampling theorem and s half of sampling frequency fs. The latter is set by the CCD camera, the size of the image, and the frame grabber. The bank of filters is outlined in Fig. 1. In our case, ten 5° order Butterworth11 filters were used, but this number can be varied according to the required discrimination. The bank was implemented in a computer using MATLAB software. We chose the Butter-worth filter because, in addition to its simplicity, it is maximally flat. Other filters, an infinite impulse response, or a finite impulse response could be used. By means of this bank of filters, ten corresponding signals of each filter of each temporary pixel evolution were obtained as output. Average energy Eb in each signal was then calculated: where pb(n) is the intensity of the filtered pixel in the nth image for filter b divided by its mean value and N is the total number of images. In this way, en values of energy for each pixel were obtained, each of hem belonging to one of the frequency bands in Fig. 1. With these values it is possible to build ten images of the active object, each one of which shows how much energy of time-varying speckle there is in a certain frequency band. False color assignment to the gray levels in the results would help in discrimination. and here is my MATLAB code base on that : clear all for i=0:39 str = num2str(i); str1 = strcat(str,'.mat'); load(str1); D{i+1}=A; end new_max = max(max(A)); new_min = min(min(A)); for i=20:180 for j=20:140 ts = []; for k=1:40 ts = [ts D{k}(i,j)]; %%% kth image pixel i,j --- ts is time series end ts = double(ts); temp = mean(ts); ts = ts-temp; ts = ts/temp; N = 5; % filter order W = [0.00001 0.05;0.05 0.1;0.1 0.15;0.15 0.20;0.20 0.25;0.25 0.30;0.30 0.35;0.35 0.40;0.40 0.45;0.45 0.50]; N1 = 5; for ind = 1:10 Wn = W(ind,:); [B,A] = butter(N1,Wn); ts_f(ind,:) = filter(B,A,ts); end for ind=1:10 imag_test1{ind}(i,j) =sum((ts_f(ind,:)./mean(ts_f(ind,:))).^2); end end end for i=1:10 temp_imag = imag_test1{i}(:,:); x=isnan(temp_imag); temp_imag(x)=0; temp_imag=medfilt2(temp_imag); t_max = max(max(temp_imag)); t_min = min(min(temp_imag)); temp_imag = (temp_imag-t_min).*(double(new_max-new_min)/double(t_max-t_min))+double(new_min); imag_test2{i}(:,:) = temp_imag; end for i=1:10 A=imag_test2{i}(:,:); B=A/max(max(A)); B=histeq(B); figure,imshow(B) colorbar end but I am not getting the same result as paper. has anybody has aby idea why? or where I have gone wrong? Refrence Link to the paper

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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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