Mailman 3 January 2002 - SciPy-User

Can't import plt, path issues with win32 ???
by Jay L. de Halas 24 Jan '02

24 Jan '02

This one is driving me nuts... I can use all of scipy's functionality within the Python Shell, but I can not import and use the plt function from within my path. However, I can import most of the other functions within the scipy module. I have tried appending the sys.path, changing the registry, modifing the autoexec file. I am way confused !!! Examples: 1. From within the Shell... Python 2.1.1 (#20, Jul 20 2001, 01:19:29) [MSC 32 bit (Intel)] on win32 Type "copyright", "credits" or "license" for more information. IDLE Fork 0.8 -- press F1 for help >>> sys.path ['C:\\Python21\\Tools\\idle_VPython', 'C:\\Python21\\Programs\\Demos', 'C:\\PYTHON21\\DLLs', 'C:\\PYTHON21\\lib', 'C:\\PYTHON21\\lib\\plat-win', 'C:\\PYTHON21\\lib\\lib-tk', 'C:\\PYTHON21', 'C:\\PYTHON21\\Numeric'] >>> from scipy import plt >>> import gui_thread >>> plt.plot((1,2,3)) <wxPython imported> >>> <C wxFrame instance at _1f24440_wxFrame_p> >>> **** Works Fine **** 2. From my test script..... # import the necessary modules import sys #sys.path.append('C:\Python21\wxPython') sys.path.append('C:\Python21\scipy') #sys.path.append('C:\Python21\scipy\plt') sys.path.append('C:\PYTHON21\scipy\gui_thread') print '\n', sys.path, '\n' import gui_thread from scipy import plt from scipy import * print '\n', dir(scipy), '\n' # create some data to plot x = arange(0,5,.5) y = special.j1(x) plt.plot(x,y) # now add some titles plt.title('First Order Bessel Function') plt.xtitle('x axis') plt.ytitle('y axis') # and x-axis bounds equal to that of the data plt.xaxis('fit') 2a. The output..... ['c:\\python21', 'C:\\Python21\\Programs\\Demos', 'C:\\PYTHON21\\DLLs', 'C:\\PYTHON21\\lib', 'C:\\PYTHON21\\lib\\plat-win', 'C:\\PYTHON21\\lib\\lib-tk', 'C:\\PYTHON21', 'C:\\PYTHON21\\Numeric', 'C:\\Python21\\scipy', 'C:\\PYTHON21\\scipy\\gui_thread'] Traceback (innermost last) File "c:\python21\test02.py", line 14, in ? from scipy import plt SystemError: C:\Code\2.1.1\dist\src\Objects\listobject.c:69: bad argument to internal function Program disconnected. **** Does NOT Work**** Anyhow, I suspect that this is a basic problem, but it has me stumped !!! Thanks !!! and best regards Jay

1 0

Need help installing with Python 2.2 / 2.1 on Windows
by Michael George Lerner 23 Jan '02

23 Jan '02

Hi, I have both Python 2.2 and Python 2.1 installed on my Windows box (in c:\python22 and c:\21). When I try to install SciPy with the binary installer SciPy-0.1.win32-py2.1.exe, which I downloaded from the website on Jan. 23, the second screen says "Select python installation to choose" and only allows me to choose Python 2.1. It doesn't notice my Python 2.2 installation, so it's not in the list at the top of the screen. Also, I can't edit the text in the text entry box at the bottom of the screen to manually point it at c:\python22. Is there some way to point SciPy at Python 2.2, or will I have to install with version 2.1? thanks, -michael

2 1

SciPy for Python 2.2
by Zhu, Yi 22 Jan '02

22 Jan '02

Hi, Is there a SciPy version for Python 2.2? Thanks, Yi

1 0

Re: [SciPy-user] unittests for scipy.stats:
by Louis Luangkesorn 22 Jan '02

22 Jan '02

Eric, I sent an email to Jim Weirich, who is the maintainer of EiffleUnit. EiffleUnit has an assert_approximate function. His reply is below. My feeling regarding the scipy_test.py is that I rewrite the assert_approx_equals to match the behavior of the EiffelUnit assert_approximate, since it does the same thing that I want, and that we name the scipy function assert_approximate_equals, in the interests of consistancy with other XUnit type modules. Oh, I'm going to have an update on assert_approx_equals regardless. There are a couple of rather embarassing cases I don't account for. Louis ----forwarded message---- Hello Louis. The short form of the assert_approximate function is ... assert_approximate (message: STRING; expected, actual, tolerance: DOUBLE) is -- Assert that two values are approximately equal -- (within a `tolerance'). require message_exists: message /= Void end Assert_approximate uses a tolerance value to specify the value for approximate. This allows the user to choose what close-enough means. For example ... assert_approximate("absolute", expected, actual, 0.00001); assert_approximate("relative", expected, actual, 0.001 * expected); I believe that JUnit has a version of assertEquals that takes two double arguments and a double tolerance. assert_approximate mimics that behavior. I hope this helps. -- -- Jim Weirich jweirich(a)one.net http://w3.one.net/~jweirich > From: "eric" <eric(a)scipy.org> > To: <scipy-user(a)scipy.org> > Subject: Re: [SciPy-user] unittests for scipy.stats: > assert_almost_equal question > Date: Sun, 20 Jan 2002 22:38:03 -0500 > Organization: enthought > Reply-To: scipy-user(a)scipy.net > > Hey Louis, > > > Thanks. In my cursory checks, it looks to me like you've counted > digits correctly. I'll add this to scipy_test. Does anyone have > better names for > these. They are quite arbitrary and indistinguishable right now. > How about: > > assert_significant_equal > assert_decimal_equal > > These names give a little more info about what is happening in > the function. > > thoughts? > > eric > > > > > --__--__-- > > _______________________________________________ > SciPy-user mailing list > SciPy-user(a)scipy.net > http://www.scipy.net/mailman/listinfo/scipy-user > > > End of SciPy-user Digest ===== When I consider your heavens, the work of your fingers, the moon and the stars, which you have set in place, what is man that you are mindful of him, the son of man that you care for him? Psalm 8:3-4 Y!M, AOL, jabber.com: lluang __________________________________________________ Do You Yahoo!? Send FREE video emails in Yahoo! Mail! http://promo.yahoo.com/videomail/

1 0

Re: [SciPy-user] stats.anova problem
by Louis Luangkesorn 22 Jan '02

22 Jan '02

Hmmm, I see. Does that mean that noone is using the anova functions in SciPy? As an aside, I pulled the stats.py module from Gary Strangman's site. The anova in his version (0.4) runs (he has a test suite that calls all of his functions, somewhat like what the random number generator test suite does.) Something must have happened when stats.py changed to 0.5. Oh, if I follow up to this, I will go to the scipy-dev list. But first I have a proposal to get out. . . Louis >Message: 4 >Date: Sun, 20 Jan 2002 18:49:07 +0100 >To: scipy-user(a)scipy.org >From: Magnus =?iso-8859-1?Q?Lyck=E5?= <magnus(a)thinkware.se> >Subject: Re: [SciPy-user] stats.anova problem >Reply-To: scipy-user(a)scipy.net > >At 22:59 2002-01-19 -0800, you wrote: >>Hello >> >>I'm trying to test the anova package. But I'm doing something >>wrong here. This is what I try. > >See http://www.scipy.net/pipermail/scipy-dev/2001-November/date.html >and mails with subject "State of stats modules?" > > >-- >Magnus Lyck�, Thinkware AB > ===== When I consider your heavens, the work of your fingers, the moon and the stars, which you have set in place, what is man that you are mindful of him, the son of man that you care for him? Psalm 8:3-4 Y!M, AOL, jabber.com: lluang __________________________________________________ Do You Yahoo!? Send FREE video emails in Yahoo! Mail! http://promo.yahoo.com/videomail/

1 0

Re: [SciPy-user] unittests for scipy.stats: assert_almost_equal question
by Louis Luangkesorn 21 Jan '02

21 Jan '02

Hmmm, I think that I would like to be testing in terms of significant digits as opposed to decimal places, especially when working with floating point. Since you are asking for such a test, here it is. The attached file has a function meant to go into the scipy_test.py module. I wrote assert_approx_equal following the same form as assert_almost_equal assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=1): compares 'actual' and 'desired' and determines the first 'significant' significant digits and checks for accuracy. Let's see, I think I counted significant digits correctly. Can anyone tell? Louis > From: "eric" <eric(a)scipy.org> > To: <scipy-user(a)scipy.org> > Subject: Re: [SciPy-user] unittests for scipy.stats: > assert_almost_equal question > Date: Sun, 20 Jan 2002 05:36:31 -0500 > Organization: enthought > Reply-To: scipy-user(a)scipy.net > > Hey Louis, > <previous message snipped throughout> > A thousand blessing upon you. I immediately commited it to the > CVS! > <snip> > > > > The assert_almost_equal function is being called using the > default > > setting of decimal=7 Since here it seems I have 10 digits of > > accuracy, why did this fail? > > 7 decimals is not 7 significant digits but actually 7 decimal > places to the > left of 0. The way almost_eqaul works is by using the following > comparison: > round(abs(desired - actual),decimal) == 0 > In this case, abs(desired-actual) is 470. rounding it to the 7 > digits past the decimal leaves it as 470 which fails the test of > being equal to 0. > Maybe a test function needs to be added that tests to a specified > number of significant digits for cases like these. > > Thanks tons for working on this, > > eric > ===== When I consider your heavens, the work of your fingers, the moon and the stars, which you have set in place, what is man that you are mindful of him, the son of man that you care for him? Psalm 8:3-4 Y!M, AOL, jabber.com: lluang __________________________________________________ Do You Yahoo!? Send FREE video emails in Yahoo! Mail! http://promo.yahoo.com/videomail/ def assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=1): """ Raise an assertion if two items are not equal. I think this should be part of unittest.py Approximately equal is defined as the number of significant digits correct """ msg = '\nItems are not equal:\n' + err_msg desired = desired/pow(10,math.floor(math.log10(desired))) actual = actual/pow(10,math.floor(math.log10(actual))) try: if ( verbose and len(str(desired)) < 100 and len(str(actual)) ): msg = msg \ + 'DESIRED: ' + str(desired) \ + '\nACTUAL: ' + str(actual) except: msg = msg \ + 'DESIRED: ' + str(desired) \ + '\nACTUAL: ' + str(actual) assert math.fabs(desired - actual) < pow(10.,-1*significant), msg

2 1

stats.anova problem
by Louis Luangkesorn 20 Jan '02

20 Jan '02

Hello I'm trying to test the anova package. But I'm doing something wrong here. This is what I try. import scipy anovadata = ([[1,'A1','B1',2], [2,'A1','B1',1], [3,'A1','B1',3], [4,'A1','B1',2], [5,'A1','B2',3], [6,'A1','B2',4], [7,'A1','B2',5], [8,'A2','B1',4], [9,'A2','B1',6], [10,'A2','B1',5], [11,'A1','B2',2], [12,'A1','B2',4], [13,'A1','B2',5]]) y=scipy.stats.anova(anovadata) Then, I get: Traceback (most recent call last): File "C:\apps\Python\Pythonwin\pywin\framework\scriptutils.py", line 301, in RunScript exec codeObject in __main__.__dict__ File "C:\Documents and Settings\kll560\My Documents\testdata\anovatest.py", line 16, in ? y=scipy.stats.anova(anovadata) File "C:\apps\Python\scipy\stats\stats.py", line 234, in __call__ return apply(self._dispatch[type(arg1)], (arg1,) + args, kw) File "C:\apps\Python\scipy\stats\stats.py", line 3933, in aanova M = pstat.collapse(data,Bscols,-1,None,None,mean) TypeError: collapse() takes at most 5 arguments (6 given) By the way, in pstats.py, the call for collapse() is in line 183: def collapse (listoflists,keepcols,collapsecols,sterr=None,ns=None): Any ideas? Louis ===== When I consider your heavens, the work of your fingers, the moon and the stars, which you have set in place, what is man that you are mindful of him, the son of man that you care for him? Psalm 8:3-4 Y!M, AOL, jabber.com: lluang __________________________________________________ Do You Yahoo!? Send FREE video emails in Yahoo! Mail! http://promo.yahoo.com/videomail/

2 1

unittests for scipy.stats: assert_almost_equal question
by Louis Luangkesorn 20 Jan '02

20 Jan '02

Hi I'm writting unittests for the scipy.stats framework (partly to teach myself unit testing, partly to teach myself the stats functions). I'm writing them using Wilkinson's Statistic Quiz (ref. in code attached) for data and comparison numbers. Right now, I get 2 failures and 2 errors (using Numeric arrays). I think the complete framework needs another set of tests for the same data in lists form (if I understand the scipy.stats module correctly) Of the failures/errors, some of them may be in the code, but one of them maybe something I'm missing in the unit testing framework. For one of the tests, I get the following error: ====================================================================== FAIL: check_stdHUGE (__main__.test_basicstats) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Documents and Settings\kll560\My Documents\testdata\test_stats.py", line 150, in check_stdHUGE assert_almost_equal(y,2.738612788e+12) File "C:\apps\Python\scipy\scipy_test.py", line 223, in assert_almost_equal assert desired == actual, msg AssertionError: Items are not equal: DESIRED: 2.738612788e+012 ACTUAL: 2.73861278753e+012 ---------------------------------------------------------------------- The assert_almost_equal function is being called using the default setting of decimal=7 Since here it seems I have 10 digits of accuracy, why did this fail? Louis PS I'm attaching my code. I followed the pattern from test_misc.py ===== When I consider your heavens, the work of your fingers, the moon and the stars, which you have set in place, what is man that you are mindful of him, the son of man that you care for him? Psalm 8:3-4 Y!M, AOL, jabber.com: lluang __________________________________________________ Do You Yahoo!? Send FREE video emails in Yahoo! Mail! http://promo.yahoo.com/videomail/ """ Test functions for stats module THIS IS BEING REWRITTEN BY LOUIS LUANGKESORN <lluang(a)yahoo.com> FOR THE STATS MODULE BASED ON WILKINSON'S STATISTICS QUIZ """ import unittest import scipy from scipy.scipy_test import assert_array_equal, assert_equal from scipy.scipy_test import assert_almost_equal, assert_array_almost_equal #from scipy import * from scipy import stats import math import Numeric N = Numeric ## Numbers in comments refer to the section numbers and headings ## found in the STATISTICS QUIZ of Leland Wilkinson. These are ## considered to be essential functionality. True testing and ## evaluation of a statistics package requires use of the ## NIST Statistical test data. See McCoullough(1999) Assessing The Reliability ## of Statistical Software for a test methodology and its ## implementation in testing SAS, SPSS, and S-Plus ################################################## ## Datasets ## These data sets are from the nasty.dat sets used by Wilkinson ## for MISS, need to be able to represent missing values ## For completeness, I should write the relavant tests and count them as failures ## Somewhat acceptable, since this is still beta software. It would count as a ## good target for 1.0 status X = N.array([1,2,3,4,5,6,7,8,9],N.Float) ZERO= N.array([0,0,0,0,0,0,0,0,0], N.Float) #MISS=N.array([.,.,.,.,.,.,.,.,.], N.Float) BIG=N.array([99999991,99999992,99999993,99999994,99999995,99999996,99999997,99999998,99999999],N.Float) LITTLE=N.array([0.99999991,0.99999992,0.99999993,0.99999994,0.99999995,0.99999996,0.99999997,0.99999998,0.99999999],N.Float) HUGE=N.array([1e+12,2e+12,3e+12,4e+12,5e+12,6e+12,7e+12,8e+12,9e+12],N.Float) TINY=N.array([1e-12,2e-12,3e-12,4e-12,5e-12,6e-12,7e-12,8e-12,9e-12],N.Float) ROUND=N.array([0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5],N.Float) ##X = [1,2,3,4,5,6,7,8,9] ##ZERO= [0,0,0,0,0,0,0,0,0] ###MISS=[.,.,.,.,.,.,.,.,.] ##BIG=[99999991,99999992,99999993,99999994,99999995,99999996,99999997,99999998,99999999] ##LITTLE=[0.99999991,0.99999992,0.99999993,0.99999994,0.99999995,0.99999996,0.99999997,0.99999998,0.99999999] ##HUGE=[1e+12,2e+12,3e+12,4e+12,5e+12,6e+12,7e+12,8e+12,9e+12] ##TINY=[1e-12,2e-12,3e-12,4e-12,5e-12,6e-12,7e-12,8e-12,9e-12] ##ROUND=[0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5] ## II.A Round ## A. Print ROUND with only one digit. You should get the ## numbers 1 to 9. Many language compilers, such as Turbo Pascal and ## Lattice C, fail this test (they round numbers inconsistently). ## Needless to say, statical packages written in these languages ## may fail the test as well. You can also check the following ## expressions: ## Y = INT(2.6*7 -0.2) (Y should be 18) ## Y = 2-INT(EXP(LOG(SQR(2)*SQR(2)))) (Y should be 0) ## Y = INT(3-EXP(LOG(SQR(2)*SQR(2)))) (Y should be 1) ## ## INT is the integer function. It converts decimal numbers to ## integers by throwing away numbers after the decimal point. EXP ## is exponential, LOG is logarithm, and SQR is suqare root. You may ## have to substitute similar names for these functions for different ## packages. Since the square of a square root should return the same ## number, and the exponential of a log should return the same number, ## we should get back a 2 from this function of functions. By taking ## the integer result and subtracting from 2, we are exposing the ## roundoff errors. These simple functions are at the heart of ## statistical calculations. class test_round(unittest.TestCase): def check_rounding0(self): for i in range(0,9): y = scipy.round(ROUND[i]) assert_almost_equal(y,i+1) def check_rounding1(self): y = math.floor(2.6*7 -0.2) assert_almost_equal(y, 18.0) def check_rounding2(self): y=2-math.floor(math.exp(math.log(math.sqrt(2)*math.sqrt(2)))) assert_almost_equal(y,0.0) def check_rounding3(self): y=(math.floor(3-math.exp(math.log(math.sqrt(2.0)*math.sqrt(2.0))))) assert_almost_equal(y,1.00000000) ## II.C. Compute basic statistic on all the variables. The means should ## be the fifth value of all the variables (case FIVE). The standard ## deviations should be "undefined" or missing for MISS, 0 for ZERO, ## and 2.738612788 (times 10 to a power) for all the other variables. ## II. C. Basic Statistics ## ## variable mean standard deviation ## X 5.000000000 2.738612788 ## ZERO 0.000000000 0.000000000 ## MISS . . ## BIG 99999995.00 2.738612788 ## LITTLE 0.999999950 2.73861E-08 ## HUGE 5.00000E+12 0.22739E+13 ## TINY 0.000000000 0.000000000 ## ROUND 4.500000000 2.738612788 class test_basicstats(unittest.TestCase): def check_meanX(self): y = scipy.stats.mean(X) assert_almost_equal(y,5.0) def check_stdX(self): y = scipy.stats.stdev(X) assert_almost_equal(y,2.738612788) def check_meanZERO(self): y = scipy.stats.mean(ZERO) assert_almost_equal(y,0.0) def check_stdZERO(self): y = scipy.stats.stdev(ZERO) assert_almost_equal(y,0.0) ## Really need to write these tests to handle missing values properly ## def check_meanMISS(self): ## y = scipy.stats.mean(MISS) ## assert_almost_equal(y,0.0) ## ## def check_stdMISS(self): ## y = scipy.stats.stdev(MISS) ## assert_almost_equal(y,0.0) def check_meanBIG(self): y = scipy.stats.mean(BIG) assert_almost_equal(y,99999995.00) def check_stdBIG(self): y = scipy.stats.stdev(BIG) assert_almost_equal(y,2.738612788) def check_meanLITTLE(self): y = scipy.stats.mean(LITTLE) assert_almost_equal(y,0.999999950) def check_stdLITTLE(self): y = scipy.stats.stdev(LITTLE) assert_almost_equal(y,2.738612788e-08) def check_meanHUGE(self): y = scipy.stats.mean(HUGE) assert_almost_equal(y,5.00000e+12) def check_stdHUGE(self): y = scipy.stats.stdev(HUGE) assert_almost_equal(y,2.738612788e+12) def check_meanTINY(self): y = scipy.stats.mean(TINY) assert_almost_equal(y,0.0) def check_stdTINY(self): y = scipy.stats.stdev(TINY) assert_almost_equal(y,0.0) def check_meanROUND(self): y = scipy.stats.mean(ROUND) assert_almost_equal(y,4.500000000) def check_stdROUND(self): y = scipy.stats.stdev(ROUND) assert_almost_equal(y,2.738612788) ## II.D. Compute a correlation matrix on all the variables. All the ## correlations, except for ZERO and MISS, shoud be exactly 1. ZERO ## and MISS should have undefined or missing correlations with the ## other variables. The same should go for SPEARMAN corelations, if ## your program has them. testcorr = N.array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1.]]) class test_corr(unittest.TestCase): ## check_ZERO should end up with UNDEFINED ## def check_ZERO(self): ## Z = N.array((X,ZERO)) ## y = scipy.stats.acorrelation(Z) ## assert_array_almost_equal(y,testcorr): ## check_MISS should result with undefined ## def check_MISS(self): ## Z = N.array((X,MISS)) ## y = scipy.stats.acorrelation(Z) ## assert_array_almost_equal(y,testcorr): def check_BIG(self): Z = N.array((X,BIG)) y = scipy.stats.acorrelation(Z) assert_array_almost_equal(y,testcorr) def check_LITTLE(self): Z = N.array((X,LITTLE)) y = scipy.stats.acorrelation(Z) assert_array_almost_equal(y,testcorr) def check_HUGE(self): Z = N.array((X,HUGE)) y = scipy.stats.acorrelation(Z) assert_array_almost_equal(y,testcorr) def check_TINY(self): Z = N.array((X,TINY)) y = scipy.stats.acorrelation(Z) assert_array_almost_equal(y,testcorr) def check_ROUND(self): Z = N.array((X,ROUND)) y = scipy.stats.acorrelation(Z) assert_array_almost_equal(y,testcorr) ## II.E. Tabulate X against X, using BIG as a case weight. The values ## should appear on the diagonal and the total should be 899999955. ## If the table cannot hold these values, forget about working with ## census data. You can also tabulate HUGE against TINY. There is no ## reason a tabulation program should not be able to digtinguish ## different values regardless of their magnitude. ### I need to figure out how to do this one. X1 = X X2 = N.matrixmultiply(X1,X) X3 = N.matrixmultiply(X2,X) X4 = N.matrixmultiply(X3,X) X5 = N.matrixmultiply(X4,X) X6 = N.matrixmultiply(X5,X) X7 = N.matrixmultiply(X6,X) X8 = N.matrixmultiply(X7,X) X9 = N.matrixmultiply(X8,X) ## II.F. Regress BIG on X. The constant should be 99999990 and the ## regression coefficient should be 1. class test_regression(unittest.TestCase): def check_linregressBIGX(self): y = scipy.stats.linregress(BIG,X) intercept = y[1] r=y[2] assert_almost_equal(intercept,99999990) assert_almost_equal(r,1.0) ## IV.A. Take the NASTY dataset above. Use the variable X as a ## basis for computing polynomials. Namely, compute X1=X, X2=X*X, ## X3=X*X*X, and so on up to 9 products. Use the algebraic ## transformation language within the statistical package itself. You ## will end up with 9 variables. Now regress X1 on X2-X9 (a perfect ## fit). If the package balks (singular or roundoff error messages), ## try X1 on X2-X8, and so on. Most packages cannot handle more than ## a few polynomials. ## B. Regress X on X. The constant should be exactly 0 and the ## regression coefficient should be 1. This is a perfectly valid ## regression. The program should not complain. def check_regressXX(self): y = scipy.stats.linregress(X,X) intercept = y[1] r=y[2] assert_almost_equal(intercept,0.0) assert_almost_equal(r,1.0) ## C. Regress X on BIG and LITTLE (two predictors). The program ## should tell you that this model is "singular" because BIG and ## LITTLE are linear combinations of each other. Cryptic error ## messages are unacceptable here. Singularity is the most ## fundamental regression error. ### Need to figure out how to handle multiple linear regression. Not obvious ## D. Regress ZERO on X. The program should inform you that ZERO has ## no variance or it should go ahead and compute the regression ## and report a correlation and total sum of squares of exactly 0. def check_regressZEROX(self): y = scipy.stats.linregress(ZERO,X) intercept = y[1] r=y[2] assert_almost_equal(intercept,0.0) assert_almost_equal(r,0.0) ##anovadata = ([[1,'A1','B1',2], ## [2,'A1','B1',1], ## [3,'A1','B1',3], ## [4,'A1','B1',2], ## [5,'A1','B2',3], ## [6,'A1','B2',4], ## [7,'A1','B2',5], ## [8,'A2','B1',4], ## [9,'A2','B1',6], ## [10,'A2','B1',5], ## [11,'A1','B2',2], ## [12,'A1','B2',4], ## [13,'A1','B2',5]]) ## ##class test_anova(unittest.TestCase): ## y=scipy.stats.anova(anovadata) # Utility def compare_results(res,desired): for i in range(len(desired)): assert_array_equal(res[i],desired[i]) ################################################## def test_suite(): suites = [] suites.append( unittest.makeSuite(test_round, 'check_') ) suites.append( unittest.makeSuite(test_basicstats, 'check_') ) suites.append( unittest.makeSuite(test_corr, 'check_') ) suites.append( unittest.makeSuite(test_regression, 'check_') ) ## suites.append( unittest.makeSuite(test_anova, 'check_') ) total_suite = unittest.TestSuite(suites) return total_suite def test(): all_tests = test_suite() runner = unittest.TextTestRunner() runner.run(all_tests) return runner if __name__ == "__main__": test()

2 1

Re: IO problems on windows
by Herb Schilling 18 Jan '02

18 Jan '02

>Herb, > >I'm almost sure that opening the file in 'r' mode only is the problem you are >having. I just learned that if you open a binary file with the 'r' >permission instead of 'rb', then the byte '\32' is interpreted as EOF. > >It seems that something like that is happening in your case. > >I hope that fixes your problem. I suppose I could issue a warning if the >permission flag does not have 'b' in it because fopen is primarily used for >binary reading and writing. > Hi Travis, That did it!! Everything works perfectly now. Thank you so much. You saved the day. Maybe the documentation page at http://www.scipy.org/site_content/tutorials/show_ref?input_file=scipy/io/mi… could mentioned this ? PS. That page also doesn't mention the fopen's tell method that you told me about. Maybe that could be included too? -- --------------------------------- Herb Schilling NASA Glenn Research Center Cleveland, OH 44135 Herbert.W.Schilling(a)grc.nasa.gov It is not enough to run, one must start in time. - teabag

1 0

very sorry for the sending large file to the list
by Herb Schilling 17 Jan '02

17 Jan '02

Hi, I just noticed that when I replied to Travis that my message also went to the mailing list ! It would be that bad but I attached a large file. Very sorry about that. I will be more careful in the future. -- --------------------------------- Herb Schilling NASA Glenn Research Center Cleveland, OH 44135 Herbert.W.Schilling(a)grc.nasa.gov It is not enough to run, one must start in time. - teabag

1 0