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• MemoryError when running Numpy Meshgrid

  - by joaoc

  I have 8823 data points with x,y coordinates. I'm trying to follow the answer on how to get a scatter dataset to be represented as a heatmap but when I go through the X, Y = np.meshgrid(x, y) instruction with my data arrays I get MemoryError. I am new to numpy and matplotlib and am essentially trying to run this by adapting the examples I can find. Here's how I built my arrays from a file that has them stored: XY_File = open ('XY_Output.txt', 'r') XY = XY_File.readlines() XY_File.close() Xf=[] Yf=[] for line in XY: Xf.append(float(line.split('\t')[0])) Yf.append(float(line.split('\t')[1])) x=array(Xf) y=array(Yf) Is there a problem with my arrays? This same code worked when put into this example but I'm not too sure. Why am I getting this MemoryError and how can I fix this?

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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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• Make a simulation using python

  - by user3727759

  I am new to programming and using python. What I am trying to do is create a simulation of a thermostat system by using python. Is there a way to create a program that I can input data, for example temperature and humidity values and then have python constantly plotting the data as I enter the values. This is to simulate a device gathering data and sending it to this program and having it being plotted. I have found ways to plot data by using matplotlib but I have not been able to find a way that I can input the data and have the plot upgrade constantly. Thanks any advise is appreciated.

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• Pip installs on Archlinux fails to build egg

  - by stmfunk

  I was trying to install nltk on my Archlinux server but it repeatedly fails with the following error output /usr/lib/python3.3/distutils/dist.py:257: UserWarning: Unknown distribution option: 'entry_points' warnings.warn(msg) /usr/lib/python3.3/distutils/dist.py:257: UserWarning: Unknown distribution option: 'zip_safe' warnings.warn(msg) /usr/lib/python3.3/distutils/dist.py:257: UserWarning: Unknown distribution option: 'test_suite' warnings.warn(msg) usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...] or: setup.py --help [cmd1 cmd2 ...] or: setup.py --help-commands or: setup.py cmd --help error: invalid command 'bdist_egg' /tmp/pip_build_root/nltk/distribute-0.6.21-py3.3.egg Traceback (most recent call last): File "./distribute_setup.py", line 143, in use_setuptools raise ImportError ImportError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "", line 16, in File "/tmp/pip_build_root/nltk/setup.py", line 23, in distribute_setup.use_setuptools() File "./distribute_setup.py", line 145, in use_setuptools return _do_download(version, download_base, to_dir, download_delay) File "./distribute_setup.py", line 125, in _do_download _build_egg(egg, tarball, to_dir) File "./distribute_setup.py", line 116, in _build_egg raise IOError('Could not build the egg.') OSError: Could not build the egg. ---------------------------------------- Cleaning up... Command python setup.py egg_info failed with error code 1 in /tmp/pip_build_root/nltk Storing complete log in /root/.pip/pip.log This error is also occurring for matplotlib buts thats the only other library I found it to fail on so far. pyyaml installs fine. The install works perfectly under virtualenv on my mac which is using python 2.7 but the server is using python 3.3. Any help is appreciated.

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• How do I pass a lot of parameters to views in Django?

  - by Mark

  I'm very new to Django and I'm trying to build an application to present my data in tables and charts. Till now my learning process went very smooth, but now I'm a bit stuck. My pageview retrieves large amounts of data from a database and puts it in the context. The template then generates different html-tables. So far so good. Now I want to add different charts to the template. I manage to do this by defining <img src=".../> tags. The Matplotlib chart is generate in my chartview an returned via: response=HttpResponse(content_type='image/png') canvas.print_png(response) return response Now I have different questions: the data is retrieved twice from the database. Once in the pageview to render the tables, and again in the chartview for making the charts. What is the best way to pass the data, already in the context of the page to the chartview? I need a lot of charts, each with different datasets. I could make a chartview for each chart, but probably there is a better way. How do I pass the different dataset names to the chartview? Some charts have 20 datasets, so I don't think that passing these dataset parameters via the url (like: <imgm src="chart/dataset1/dataset2/.../dataset20/chart.png />) is the right way. Any advice?

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• Replicating SQL's 'Join' in Python

  - by Daniel Mathews

  I'm in the process of trying to switch from R to Python (mainly issues around general flexibility). With Numpy, matplotlib and ipython, I've am able to cover all my use cases save for merging 'datasets'. I would like to simulate SQL's join by clause (inner, outer, full) purely in python. R handles this with the 'merge' function. I've tried the numpy.lib.recfunctions join_by, but it critical issues with duplicates along the 'key': join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False) Join arrays r1 and r2 on key key. The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if the key field cannot be found in the two input arrays. Neither r1 nor r2 should have any duplicates along key: the presence of duplicates will make the output quite unreliable. Note that duplicates are not looked for by the algorithm. source: http://presbrey.mit.edu:1234/numpy.lib.recfunctions.html Any pointers or help will be most appreciated!

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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 do I pass a lot of parameters to views in Dango?

  - by Mark

  I'm very new to Django and I'm trying to build an application to present my data in tables and charts. Till now my learning process went very smooth, but now I'm a bit stuck. My pageview retrieves large amounts of data from a database and puts it in the context. The template then generates different html-tables. So far so good. Now I want to add different charts to the template. I manage to do this by defining <img src=".../> tags. The Matplotlib chart is generate in my chartview an returned via: response=HttpResponse(content_type='image/png') canvas.print_png(response) return response Now I have different questions: the data is retrieved twice from the database. Once in the pageview to render the tables, and again in the chartview for making the charts. What is the best way to pass the data, already in the context of the page to the chartview? I need a lot of charts, each with different datasets. I could make a chartview for each chart, but probably there is a better way. How do I pass the different dataset names to the chartview? Some charts have 20 datasets, so I don't think that passing these dataset parameters via the url (like: <imgm src="chart/dataset1/dataset2/.../dataset20/chart.png />) is the right way. Any advice?

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• How to manage multiple python versions ?

  - by Gyom

  short version: how can I get rid of the multiple-versions-of-python nightmare ? long version: over the years, I've used several versions of python, and what is worse, several extensions to python (e.g. pygame, pylab, wxPython...). Each time it was on a different setup, with different OSes, sometimes different architectures (like my old PowerPC mac). Nowadays I'm using a mac (OSX 10.6 on x86-64) and it's a dependency nightmare each time I want to revive script older than a few months. Python itself already comes in three different flavours in /usr/bin (2.5, 2.6, 3.1), but I had to install 2.4 from macports for pygame, something else (cannot remember what) forced me to install all three others from macports as well, so at the end of the day I'm the happy owner of seven (!) instances of python on my system. But that's not the problem, the problem is, none of them has the right (i.e. same set of) libraries installed, some of them are 32bits, some 64bits, and now I'm pretty much lost. For example right now I'm trying to run a three-year-old script (not written by me) which used to use matplotlib/numpy to draw a real-time plot within a rectangle of a wxwidgets window. But I'm failing miserably: py26-wxpython from macports won't install, stock python has wxwidgets included but also has some conflict between 32 bits and 64 bits, and it doesn't have numpy... what a mess ! Obviously, I'm doing things the wrong way. How do you usally cope with all that chaos ?e

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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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• Python on Mac: Fink? MacPorts? Builtin? Homebrew? Binary installer?

  - by BastiBechtold

  For the last few days, I have been trying to use Python for some audio development. The thing is, Mac OSX does not handle uninstalling stuff well. Actually, there is no way to uninstall anything. Once it is on your system, you better pray that it didn't do any funny stuff. Hence, I don't really want to rely on installer packages for Python. So I turn to Homebrew and install Python using Homebrew. Works fabulously. Using pip, Numpy, SciPy, Matplotlib were no (big) problem, either. Now I want to play audio. There is a host of different packages out there, but pip does not seem willing to install any. But, there is a binary distribution for PyGame, which I guess should work with the built-in Python. Hence my question: What would you do? Would you just install the binary distributions and hope that they interoperate well and never need uninstalling? Would you hack your way through whichever package control management system you prefer and deal with its problems? Something else?

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• How to maintain long-lived python projects w.r.t. dependencies and python versions ?

  - by Gyom

  short version: how can I get rid of the multiple-versions-of-python nightmare ? long version: over the years, I've used several versions of python, and what is worse, several extensions to python (e.g. pygame, pylab, wxPython...). Each time it was on a different setup, with different OSes, sometimes different architectures (like my old PowerPC mac). Nowadays I'm using a mac (OSX 10.6 on x86-64) and it's a dependency nightmare each time I want to revive script older than a few months. Python itself already comes in three different flavours in /usr/bin (2.5, 2.6, 3.1), but I had to install 2.4 from macports for pygame, something else (cannot remember what) forced me to install all three others from macports as well, so at the end of the day I'm the happy owner of seven (!) instances of python on my system. But that's not the problem, the problem is, none of them has the right (i.e. same set of) libraries installed, some of them are 32bits, some 64bits, and now I'm pretty much lost. For example right now I'm trying to run a three-year-old script (not written by me) which used to use matplotlib/numpy to draw a real-time plot within a rectangle of a wxwidgets window. But I'm failing miserably: py26-wxpython from macports won't install, stock python has wxwidgets included but also has some conflict between 32 bits and 64 bits, and it doesn't have numpy... what a mess ! Obviously, I'm doing things the wrong way. How do you usally cope with all that chaos ?

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• Statistical analysis on large data set to be published on the web

  - by dassouki

  I have a non-computer related data logger, that collects data from the field. This data is stored as text files, and I manually lump the files together and organize them. The current format is through a csv file per year per logger. Each file is around 4,000,000 lines x 7 loggers x 5 years = a lot of data. some of the data is organized as bins item_type, item_class, item_dimension_class, and other data is more unique, such as item_weight, item_color, date_collected, and so on ... Currently, I do statistical analysis on the data using a python/numpy/matplotlib program I wrote. It works fine, but the problem is, I'm the only one who can use it, since it and the data live on my computer. I'd like to publish the data on the web using a postgres db; however, I need to find or implement a statistical tool that'll take a large postgres table, and return statistical results within an adequate time frame. I'm not familiar with python for the web; however, I'm proficient with PHP on the web side, and python on the offline side. users should be allowed to create their own histograms, data analysis. For example, a user can search for all items that are blue shipped between week x and week y, while another user can search for sort the weight distribution of all items by hour for all year long. I was thinking of creating and indexing my own statistical tools, or automate the process somehow to emulate most queries. This seemed inefficient. I'm looking forward to hearing your ideas Thanks

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• wxPython: MainLoop() seems to hang

  - by BandGap

  Hi all, my problem stems from the use of wxApp as far as I can tell. Inside a litte subroutine I call a wx.MessageDialog to ask for a yes or no. I retrieve the answer an process some stuff acordingly. My example code below actually works (though it might be ugly) except that the Dialog box stays open after hitting one of the buttons... import wx from os import path def HasFile(filename): if path.isfile(filename ): app = wx.App() dial = wx.MessageDialog(None, 'Die Datei "'+filename+'" wurde gefunden.\nSoll sie eingelesen werden?', 'Bitte antworten', wx.YES_NO | wx.YES_DEFAULT | wx.ICON_QUESTION) result = dial.ShowModal() dial.EndModal(retCode=0) dial.Destroy() app.MainLoop() return result == wx.ID_YES else: return False So while the rest of the program does whatever is expected, the box just sits there. It also seems like my calls to matplotlib later on are producing errors (a Tkinter error to be precise), maybe because of the wx stuff? Edit: I tried to end the app with a call to app.Destroy(). This doesn't change the fact that the box is still there. When I issue a app.IsActive() afterwards the whole program exits (almost like a sys.exit())! How come? Edit 2: Adding a wxApp like this isn't a good idea since the other scripts get affected as well. Subsequent plotting commands don't get displayed and I don't know how to fix this. Thus I decided to remove the DialogBox alltogether.

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• Passing C++ object to C++ code through Python?

  - by cornail

  Hi all, I have written some physics simulation code in C++ and parsing the input text files is a bottleneck of it. As one of the input parameters, the user has to specify a math function which will be evaluated many times at run-time. The C++ code has some pre-defined function classes for this (they are actually quite complex on the math side) and some limited parsing capability but I am not satisfied with this construction at all. What I need is that both the algorithm and the function evaluation remain speedy, so it is advantageous to keep them both as compiled code (and preferrably, the math functions as C++ function objects). However I thought of glueing the whole simulation together with Python: the user could specify the input parameters in a Python script, while also implementing storage, visualization of the results (matplotlib) and GUI, too, in Python. I know that most of the time, exposing C++ classes can be done, e.g. with SWIG but I still have a question concerning the parsing of the user defined math function in Python: Is it possible to somehow to construct a C++ function object in Python and pass it to the C++ algorithm? E.g. when I call f = WrappedCPPGaussianFunctionClass(sigma=0.5) WrappedCPPAlgorithm(f) in Python, it would return a pointer to a C++ object which would then be passed to a C++ routine requiring such a pointer, or something similar... (don't ask me about memory management in this case, though :S) The point is that no callback should be made to Python code in the algorithm. Later I would like to extend this example to also do some simple expression parsing on the Python side, such as sum or product of functions, and return some compound, parse-tree like C++ object but let's stay at the basics for now. Sorry for the long post and thx for the suggestions in advance.

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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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