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  • Creating a dataframe in pandas by multiplying two series together

    - by Aoife
    Say I have two series in pandas, series A and series B. How do I create a dataframe in which all of those values are multiplied together, i.e. with series A down the left hand side and series B along the top. Basically the same concept as this, where series A would be the yellow on the left and series B the yellow along the top, and all the values in between would be filled in by multiplication: http://www.google.co.uk/imgres?imgurl=http://www.vaughns-1-pagers.com/computer/multiplication-tables/times-table-12x12.gif&imgrefurl=http://www.vaughns-1-pagers.com/computer/multiplication-tables.htm&h=533&w=720&sz=58&tbnid=9B8R_kpUloA4NM:&tbnh=90&tbnw=122&zoom=1&usg=__meqZT9kIAMJ5b8BenRzF0l-CUqY=&docid=j9BT8tUCNtg--M&sa=X&ei=bkBpUpOWOI2p0AWYnIHwBQ&ved=0CE0Q9QEwBg Thanks!

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  • Math on Django Templates

    - by Leandro Abilio
    Here's another question about Django. I have this code: views.py cursor = connections['cdr'].cursor() calls = cursor.execute("SELECT * FROM cdr where calldate > '%s'" %(start_date)) result = [SQLRow(cursor, r) for r in cursor.fetchall()] return render_to_response("cdr_user.html", {'calls':result }, context_instance=RequestContext(request)) I use a MySQL query like that because the database is not part of a django project. My cdr table has a field called duration, I need to divide that by 60 and multiply the result by a float number like 0.16. Is there a way to multiply this values using the template tags? If not, is there a good way to do it in my views? My template is like this: {% for call in calls %} <tr class="{% cycle 'odd' 'even' %}"><h3> <td valign="middle" align="center"><h3>{{ call.calldate }}</h3></td> <td valign="middle" align="center"><h3>{{ call.disposition }}</h3></td> <td valign="middle" align="center"><h3>{{ call.dst }}</h3></td> <td valign="middle" align="center"><h3>{{ call.billsec }}</h3></td> <td valign="middle" align="center">{{ (call.billsec/60)*0.16 }}</td></h3> </tr> {% endfor %} The last is where I need to show the value, I know the "(call.billsec/60)*0.16" is impossible to be done there. I wrote it just to represent what I need to show.

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  • Pretty-printing of numpy.array

    - by camillio
    Hello, I'm curious, whether there is any way to print formated numpy.arrays, e.g., in the way similar to this: x = 1.23456 print '%.3f' % x If I want to print the numpy.array of floats, it prints several decimals, often in 'scientific' format, which is rather hard to read even for low-dimensional arrays. However, numpy.array apparently has to be printed as a string, i.e., with %s. Is there any solution ready for this purpose? Many thanks in advance :-)

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  • Django: name of many to many items in the admin interface

    - by Adam
    I have a many to many field, which I'm displaying in the django admin panel. When I add multiple items, they all come up as "ASGGroup object" in the display selector. Instead, I want them to come up as whatever the ASGGroup.name field is set to. How do I do this? My models looks like: class Thing(Model): read_groups = ManyToManyField('ASGGroup', related_name="thing_read", blank=True) class ASGGroup(Model): name = CharField(max_length=63, null=True) But what I'm seeing the m2m widget display is:

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  • openerp client customization

    - by iamgopal
    openerp client seems to be nice and working , i would like to hack it and use it as a front end to my open erp solution. but the documentation regarding client side design or customization is poor on openerp site , is there any good reference or documentation available for further digging in to openerp client side coding ? or more : if any similar client solution available that can be plug in to any back end system. ( i.e. rich internet client )

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  • How To Create Per-Request Singleton in Pylons?

    - by dave mankoff
    In our Pylons based web-app, we're creating a class that essentially provides some logging functionality. We need a new instance of this class for each http request that comes in, but only one per request. What is the proper way to go about this? Should we just create the object in middleware and store in in request.environ? Is there a more appropriate way to go about this?

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  • find the colour name from a hexadecimal colour code

    - by sree01
    Hi , i want to find the name of a colour from the hexadecimal colour code. When i get a hex colour code i want to find the most matching colour name. for example for the code #c06040 , how to find out if it is a shade of brown, blue or yellow ?. so that i can find the colour of an object in the image without human intervention. Is there any relation between the hexadecimal code of the shades of a colour? please give some sample code if there is any.

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  • HttpError 502 with Google Wave Active Robot API fetch_wavelet()

    - by Drew LeSueur
    I am trying to use the Google Wave Active Robot API fetch_wavelet() and I get an HTTP 502 error example: from waveapi import robot import passwords robot = robot.Robot('gae-run', 'http://images.com/fake-image.jpg') robot.setup_oauth(passwords.CONSUMER_KEY, passwords.CONSUMER_SECRET, server_rpc_base='http://www-opensocial.googleusercontent.com/api/rpc') wavelet = robot.fetch_wavelet('googlewave.com!w+dtuZi6t3C','googlewave.com!conv+root') robot.submit(wavelet) self.response.out.write(wavelet.creator) But the error I get is this: Traceback (most recent call last): File "/base/python_runtime/python_lib/versions/1/google/appengine/ext/webapp/__init__.py", line 511, in __call__ handler.get(*groups) File "/base/data/home/apps/clstff/gae-run.342467577023864664/main.py", line 23, in get robot.submit(wavelet) File "/base/data/home/apps/clstff/gae-run.342467577023864664/waveapi/robot.py", line 486, in submit res = self.make_rpc(pending) File "/base/data/home/apps/clstff/gae-run.342467577023864664/waveapi/robot.py", line 251, in make_rpc raise IOError('HttpError ' + str(code)) IOError: HttpError 502 Any ideas? Edit: When [email protected] is not a member of the wave I get the correct error message Error: RPC Error500: internalError: [email protected] is not a participant of wave id: [WaveId:googlewave.com!w+Pq1HgvssD] wavelet id: [WaveletId:googlewave.com!conv+root]. Unable to apply operation: {'method':'robot.fetchWave','id':'655720','waveId':'googlewave.com!w+Pq1HgvssD','waveletId':'googlewave.com!conv+root','blipId':'null','parameters':{}} But when [email protected] is a member of the wave I get the http 502 error. IOError: HttpError 502

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  • Sqlite / SQLAlchemy: how to enforce Foreign Keys?

    - by Nick Perkins
    The new version of SQLite has the ability to enforce Foreign Key constraints, but for the sake of backwards-compatibility, you have to turn it on for each database connection separately! sqlite> PRAGMA foreign_keys = ON; I am using SQLAlchemy -- how can I make sure this always gets turned on? What I have tried is this: engine = sqlalchemy.create_engine('sqlite:///:memory:', echo=True) engine.execute('pragma foreign_keys=on') ...but it is not working!...What am I missing?

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  • Which Django 1.2.x multilingual application to use?

    - by mawimawi
    There are a couple of different applications for internationalized content in Django. As of now I only have used http://code.google.com/p/django-multilingual/ in my production environments, but I wonder if there are "better" solutions for my wishes. What my staff users need is the following: An object is being created by a staff user in any language (e.g. "de") This object should be displayed in the german version of the website. When a staff user translates the object into a different language (e.g. "fr"), then the page must be visible in the french version as well. If an object is not translated in the visitor's currently selected language (e.g. "en"), then calling the objects url shall raise a 404 Error (or even better a notice that the object is only available in the languages "de" and "fr", and the visitor might be able to select one of the languages) My staff users are working in the admin interface, so the multilingual application must support this as well. I don't really care whether the multilingual app uses a single table with many fields (like title_en, title_de, title_fr) or a foreign key to a related table (as it is implemented in django-multlingual). I only want it to have a good admin interface and no "default" language, because some content might be available just in "de", and some other just in "fr" and "en". And the most important issue of course is compatibility with Django 1.2.x. What are your experiences and preferred apps, and why?

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  • Add data to Django form class using modelformset_factory

    - by dean
    I have a problem where I need to display a lot of forms for detail data for a hierarchical data set. I want to display some relational fields as labels for the forms and I'm struggling with a way to do this in a more robust way. Here is the code... class Category(models.Model): name = models.CharField(max_length=160) class Item(models.Model): category = models.ForeignKey('Category') name = models.CharField(max_length=160) weight = models.IntegerField(default=0) class Meta: ordering = ('category','weight','name') class BudgetValue(models.Model): value = models.IntegerField() plan = models.ForeignKey('Plan') item = models.ForeignKey('Item') I use the modelformset_factory to create a formset of budgetvalue forms for a particular plan. What I'd like is item name and category name for each BudgetValue. When I iterate through the forms each one will be labeled properly. class BudgetValueForm(forms.ModelForm): item = forms.ModelChoiceField(queryset=Item.objects.all(),widget=forms.HiddenInput()) plan = forms.ModelChoiceField(queryset=Plan.objects.all(),widget=forms.HiddenInput()) category = "" < assign dynamically on form creation > item = "" < assign dynamically on form creation > class Meta: model = BudgetValue fields = ('item','plan','value') What I started out with is just creating a dictionary of budgetvalue.item.category.name, budgetvalue.item.name, and the form for each budget value. This gets passed to the template and I render it as I intended. I'm assuming that the ordering of the forms in the formset and the querset used to genererate the formset keep the budgetvalues in the same order and the dictionary is created correctly. That is the budgetvalue.item.name is associated with the correct form. This scares me and I'm thinking there has to be a better way. Any help would be greatly appreciated.

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  • [numpy] storing record arrays in object arrays

    - by Peter Prettenhofer
    I'd like to convert a list of record arrays -- dtype is (uint32, float32) -- into a numpy array of dtype np.object: X = np.array(instances, dtype = np.object) where instances is a list of arrays with data type np.dtype([('f0', '<u4'), ('f1', '<f4')]). However, the above statement results in an array whose elements are also of type np.object: X[0] array([(67111L, 1.0), (104242L, 1.0)], dtype=object) Does anybody know why? The following statement should be equivalent to the above but gives the desired result: X = np.empty((len(instances),), dtype = np.object) X[:] = instances X[0] array([(67111L, 1.0), (104242L, 1.0), dtype=[('f0', '<u4'), ('f1', '<f4')]) thanks & best regards, peter

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  • Implementing __concat__

    - by Casebash
    I tried to implement __concat__, but it didn't work >>> class lHolder(): ... def __init__(self,l): ... self.l=l ... def __concat__(self, l2): ... return self.l+l2 ... def __iter__(self): ... return self.l.__iter__() ... >>> lHolder([1])+[2] Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: unsupported operand type(s) for +: 'lHolder' and 'list' How can I fix this?

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  • Should we have a database independent SQL like query language in Django? [closed]

    - by Yugal Jindle
    Note : I know we have Django ORM already that keeps things database independent and converts to the database specific SQL queries. Once things starts getting complicated it is preferred to write raw SQL queries for better efficiency. When you write raw sql queries your code gets trapped with the database you are using. I also understand its important to use the full power of your database that can-not be achieved with the django orm alone. My Question : Until I use any database specific feature, why should one be trapped with the database. For instance : We have a query with multiple joins and we decided to write a raw sql query. Now, that makes my website postgres specific. Even when I have not used any postgres specific feature. I feel there should be some fake sql language which can translate to any database's sql query. Even Django's ORM can be built over it. So, that if you go out of ORM but not database specific - you can still remain database independent. I asked the same question to Jacob Kaplan Moss (In person) : He advised me to stay with the database that I like and endure its whole power, to which I agree. But my point was not that we should be database independent. My point is we should be database independent until we use a database specific feature. Please explain, why should be there a fake sql layer over the actual sql ?

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  • Rebuilding website from Django 0.96 to Django 1.2

    - by Neytiri
    I've got a website done in Django 0.96 (done in 2007), and now we are thinking about rebuilding it (not just migrating) for Django 1.2 . Can anyone point me to the new (and worth the while) widgets, plugins and other stuff for Django 1.2 (released in april 2010). I've heard of "South" and of a widget for debugging (can't remember the name), but I'm a little lost here.

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  • How to chroot Django

    - by Brian M. Hunt
    Can one run Django in a chroot? Notably, what's necessary in order to set up (for example) /var/www as a chroot'd directory and then have Django run in that chroot'd directory? Thank you - I'm grateful for any input.

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  • Socket Lose Connection

    - by Dave Dixon
    I know Twisted can do this well but what about just plain socket? How'd you tell if you randomly lost your connection in socket? Like, If my internet was to go out of a second and come back on.

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  • PyML 0.7.2 - How to prevent accuracy from dropping after storing/loading a classifier?

    - by Michael Aaron Safyan
    This is a followup from "Save PyML.classifiers.multi.OneAgainstRest(SVM()) object?". The solution to that question was close, but not quite right, (the SparseDataSet is broken, so attempting to save/load with that dataset container type will fail, no matter what. Also, PyML is inconsistent in terms of whether labels should be numbers or strings... it turns out that the oneAgainstRest function is actually not good enough, because the labels need to be strings and simultaneously convertible to floats, because there are places where it is assumed to be a string and elsewhere converted to float) and so after a great deal of hacking and such I was finally able to figure out a way to save and load my multi-class classifier without it blowing up with an error.... however, although it is no longer giving me an error message, it is still not quite right as the accuracy of the classifier drops significantly when it is saved and then reloaded (so I'm still missing a piece of the puzzle). I am currently using the following custom mutli-class classifier for training, saving, and loading: class SVM(object): def __init__(self,features_or_filename,labels=None,kernel=None): if isinstance(features_or_filename,str): filename=features_or_filename; if labels!=None: raise ValueError,"Labels must be None if loading from a file."; with open(os.path.join(filename,"uniquelabels.list"),"rb") as uniquelabelsfile: self.uniquelabels=sorted(list(set(pickle.load(uniquelabelsfile)))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; self.classifiers=[]; for classidx, classname in enumerate(self.uniquelabels): self.classifiers.append(PyML.classifiers.svm.loadSVM(os.path.join(filename,str(classname)+".pyml.svm"),datasetClass = PyML.VectorDataSet)); else: features=features_or_filename; if labels==None: raise ValueError,"Labels must not be None when training."; self.uniquelabels=sorted(list(set(labels))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; points = [[float(xij) for xij in xi] for xi in features]; self.classifiers=[PyML.SVM(kernel) for label in self.uniquelabels]; for i in xrange(len(self.uniquelabels)): currentlabel=self.uniquelabels[i]; currentlabels=['+1' if k==currentlabel else '-1' for k in labels]; currentdataset=PyML.VectorDataSet(points,L=currentlabels,positiveClass='+1'); self.classifiers[i].train(currentdataset,saveSpace=False); def accuracy(self,pts,labels): logger=logging.getLogger("ml"); correct=0; total=0; classindexes=[self.labeltoindex[label] for label in labels]; h=self.hypotheses(pts); for idx in xrange(len(pts)): if h[idx]==classindexes[idx]: logger.info("RIGHT: Actual \"%s\" == Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])); correct+=1; else: logger.info("WRONG: Actual \"%s\" != Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])) total+=1; return float(correct)/float(total); def prediction(self,pt): h=self.hypothesis(pt); if h!=None: return self.uniquelabels[h]; return h; def predictions(self,pts): h=self.hypotheses(self,pts); return [self.uniquelabels[x] if x!=None else None for x in h]; def hypothesis(self,pt): bestvalue=None; bestclass=None; dataset=PyML.VectorDataSet([pt]); for classidx, classifier in enumerate(self.classifiers): val=classifier.decisionFunc(dataset,0); if (bestvalue==None) or (val>bestvalue): bestvalue=val; bestclass=classidx; return bestclass; def hypotheses(self,pts): bestvalues=[None for pt in pts]; bestclasses=[None for pt in pts]; dataset=PyML.VectorDataSet(pts); for classidx, classifier in enumerate(self.classifiers): for ptidx in xrange(len(pts)): val=classifier.decisionFunc(dataset,ptidx); if (bestvalues[ptidx]==None) or (val>bestvalues[ptidx]): bestvalues[ptidx]=val; bestclasses[ptidx]=classidx; return bestclasses; def save(self,filename): if not os.path.exists(filename): os.makedirs(filename); with open(os.path.join(filename,"uniquelabels.list"),"wb") as uniquelabelsfile: pickle.dump(self.uniquelabels,uniquelabelsfile,pickle.HIGHEST_PROTOCOL); for classidx, classname in enumerate(self.uniquelabels): self.classifiers[classidx].save(os.path.join(filename,str(classname)+".pyml.svm")); I am using the latest version of PyML (0.7.2, although PyML.__version__ is 0.7.0). When I construct the classifier with a training dataset, the reported accuracy is ~0.87. When I then save it and reload it, the accuracy is less than 0.001. So, there is something here that I am clearly not persisting correctly, although what that may be is completely non-obvious to me. Would you happen to know what that is?

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  • What algorithms are suitable for this simple machine learning problem?

    - by user213060
    I have a what I think is a simple machine learning question. Here is the basic problem: I am repeatedly given a new object and a list of descriptions about the object. For example: new_object: 'bob' new_object_descriptions: ['tall','old','funny']. I then have to use some kind of machine learning to find previously handled objects that had similar descriptions, for example, past_similar_objects: ['frank','steve','joe']. Next, I have an algorithm that can directly measure whether these objects are indeed similar to bob, for example, correct_objects: ['steve','joe']. The classifier is then given this feedback training of successful matches. Then this loop repeats with a new object. a Here's the pseudo-code: Classifier=new_classifier() while True: new_object,new_object_descriptions = get_new_object_and_descriptions() past_similar_objects = Classifier.classify(new_object,new_object_descriptions) correct_objects = calc_successful_matches(new_object,past_similar_objects) Classifier.train_successful_matches(object,correct_objects) But, there are some stipulations that may limit what classifier can be used: There will be millions of objects put into this classifier so classification and training needs to scale well to millions of object types and still be fast. I believe this disqualifies something like a spam classifier that is optimal for just two types: spam or not spam. (Update: I could probably narrow this to thousands of objects instead of millions, if that is a problem.) Again, I prefer speed when millions of objects are being classified, over accuracy. What are decent, fast machine learning algorithms for this purpose?

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  • Multi choice form field in Django

    - by Dingo
    Hi! I'am developing application on app-engine-path. I would like to make form with multichoice (acceptably languages for user). Code look like this: Language settings: settings.LANGUAGES = ((u"cs", u"Ceština"), (u"en", u"English")) Form model: class UserForm(forms.ModelForm): first_name = forms.CharField(max_length=100) last_name = forms.CharField(max_length=100) languages = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple, choices=settings.LANGUAGES) The form is rendered o.k. (all languages have checkbox. IDs, NAMEs is ok.) But if I save some languages for user, those languages don't check checkboxes. User model look like this class User(User): #... languages = db.StringListProperty() #... and view: def edit_profile(request): user = request.user if request.method == 'POST': form = UserForm(request.POST) if form.is_valid(): # ... else: form = UserForm(instance=user) data = {"user":user, "form": form} return render_to_response(request, 'user_profile/user_profile.html', data)

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