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  • In plain English, what are Django generic views?

    - by allyourcode
    The first two paragraphs of this page explain that generic views are supposed to make my life easier, less monotonous, and make me more attractive to women (I made up that last one): http://docs.djangoproject.com/en/dev/topics/generic-views/#topics-generic-views I'm all for improving my life, but what do generic views actually do? It seems like lots of buzzwords are being thrown around, which confuse more than they explain. Are generic views similar to scaffolding in Ruby on Rails? The last bullet point in the intro seems to indicate this. Is that an accurate statement?

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  • django on appengine

    - by aks
    I am impressed with django.Am am currenty a java developer.I want to make some cool websites for myself but i want to host it in some third pary environmet. Now the question is can i host the django application on appengine?If yes , how?? Are there any site built using django which are already hosted on appengine?

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  • Get particular row as series from pandas dataframe

    - by Pratyush
    How do we get a particular filtered row as series? Example dataframe: >>> df = pd.DataFrame({'date': [20130101, 20130101, 20130102], 'location': ['a', 'a', 'c']}) >>> df date location 0 20130101 a 1 20130101 a 2 20130102 c I need to select the row where location is c as a series. I tried: row = df[df["location"] == "c"].head(1) # gives a dataframe row = df.ix[df["location"] == "c"] # also gives a dataframe with single row In either cases I can't the row as series.

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  • How can I handle dynamic calculated attributes in a model in Django?

    - by bullfish
    In Django I calculate the breadcrumb (a list of fathers) for an geographical object. Since it is not going to change very often, I am thinking of pre calculating it once the object is saved or initialized. 1.) What would be better? Which solution would have a better performance? To calculate it at _init_ or to calculate it when the object is saved (the object takes about 500-2000 characters in the DB)? 2.) I tried to overwrite the _init_ or save() methods but I don't know how to use attributes of the just saved object. Accessing *args, **kwargs did not work. How can I access them? Do I have to save, access the father and then save again? 3.) If I decide to save the breadcrumb. Whats the best way to do it? I used http://www.djangosnippets.org/snippets/1694/ and have crumb = PickledObjectField(). Thats the method to calculate the attribute crumb() def _breadcrumb(self): breadcrumb = [ ] x = self while True: x = x.father try: if hasattr(x, 'country'): breadcrumb.append(x.country) elif hasattr(x, 'region'): breadcrumb.append(x.region) elif hasattr(x, 'city'): breadcrumb.append(x.city) else: break except: break breadcrumb.reverse() return breadcrumb Thats my save-Method: def save(self,*args, **kwargs): # how can I access the father ob the object? father = self.father # does obviously not work father = kwargs['father'] # does not work either # the breadcrumb gets calculated here self.crumb = self._breadcrumb(father) super(GeoObject, self).save(*args,**kwargs) Please help me out. I am working on this for days now. Thank you.

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  • Infinite loop when adding a row to a list in a class in python3

    - by Margaret
    I have a script which contains two classes. (I'm obviously deleting a lot of stuff that I don't believe is relevant to the error I'm dealing with.) The eventual task is to create a decision tree, as I mentioned in this question. Unfortunately, I'm getting an infinite loop, and I'm having difficulty identifying why. I've identified the line of code that's going haywire, but I would have thought the iterator and the list I'm adding to would be different objects. Is there some side effect of list's .append functionality that I'm not aware of? Or am I making some other blindingly obvious mistake? class Dataset: individuals = [] #Becomes a list of dictionaries, in which each dictionary is a row from the CSV with the headers as keys def field_set(self): #Returns a list of the fields in individuals[] that can be used to split the data (i.e. have more than one value amongst the individuals def classified(self, predicted_value): #Returns True if all the individuals have the same value for predicted_value def fields_exhausted(self, predicted_value): #Returns True if all the individuals are identical except for predicted_value def lowest_entropy_value(self, predicted_value): #Returns the field that will reduce <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">entropy</a> the most def __init__(self, individuals=[]): and class Node: ds = Dataset() #The data that is associated with this Node links = [] #List of Nodes, the offspring Nodes of this node level = 0 #Tree depth of this Node split_value = '' #Field used to split out this Node from the parent node node_value = '' #Value used to split out this Node from the parent Node def split_dataset(self, split_value): fields = [] #List of options for split_value amongst the individuals datasets = {} #Dictionary of Datasets, each one with a value from fields[] as its key for field in self.ds.field_set()[split_value]: #Populates the keys of fields[] fields.append(field) datasets[field] = Dataset() for i in self.ds.individuals: #Adds individuals to the datasets.dataset that matches their result for split_value datasets[i[split_value]].individuals.append(i) #<---Causes an infinite loop on the second hit for field in fields: #Creates subnodes from each of the datasets.Dataset options self.add_subnode(datasets[field],split_value,field) def add_subnode(self, dataset, split_value='', node_value=''): def __init__(self, level, dataset=Dataset()): My initialisation code is currently: if __name__ == '__main__': filename = (sys.argv[1]) #Takes in a CSV file predicted_value = "# class" #Identifies the field from the CSV file that should be predicted base_dataset = parse_csv(filename) #Turns the CSV file into a list of lists parsed_dataset = individual_list(base_dataset) #Turns the list of lists into a list of dictionaries root = Node(0, Dataset(parsed_dataset)) #Creates a root node, passing it the full dataset root.split_dataset(root.ds.lowest_entropy_value(predicted_value)) #Performs the first split, creating multiple subnodes n = root.links[0] n.split_dataset(n.ds.lowest_entropy_value(predicted_value)) #Attempts to split the first subnode.

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  • Union on ValuesQuerySet in django

    - by Wuxab
    I've been searching for a way to take the union of querysets in django. From what I read you can use query1 | query2 to take the union... This doesn't seem to work when using values() though. I'd skip using values until after taking the union but I need to use annotate to take the sum of a field and filter on it and since there's no way to do "group by" I have to use values(). The other suggestions I read were to use Q objects but I can't think of a way that would work. Do I pretty much need to just use straight SQL or is there a django way of doing this? What I want is: q1 = mymodel.objects.filter(date__lt = '2010-06-11').values('field1','field2').annotate(volsum=Sum('volume')).exclude(volsum=0) q2 = mymodel.objects.values('field1','field2').annotate(volsum=Sum('volume')).exclude(volsum=0) query = q1|q2 But this doesn't work and as far as I know I need the "values" part because there's no other way for Sum to know how to act since it's a 15 column table.

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  • Reverse mapping from a table to a model in SQLAlchemy

    - by Jace
    To provide an activity log in my SQLAlchemy-based app, I have a model like this: class ActivityLog(Base): __tablename__ = 'activitylog' id = Column(Integer, primary_key=True) activity_by_id = Column(Integer, ForeignKey('users.id'), nullable=False) activity_by = relation(User, primaryjoin=activity_by_id == User.id) activity_at = Column(DateTime, default=datetime.utcnow, nullable=False) activity_type = Column(SmallInteger, nullable=False) target_table = Column(Unicode(20), nullable=False) target_id = Column(Integer, nullable=False) target_title = Column(Unicode(255), nullable=False) The log contains entries for multiple tables, so I can't use ForeignKey relations. Log entries are made like this: doc = Document(name=u'mydoc', title=u'My Test Document', created_by=user, edited_by=user) session.add(doc) session.flush() # See note below log = ActivityLog(activity_by=user, activity_type=ACTIVITY_ADD, target_table=Document.__table__.name, target_id=doc.id, target_title=doc.title) session.add(log) This leaves me with three problems: I have to flush the session before my doc object gets an id. If I had used a ForeignKey column and a relation mapper, I could have simply called ActivityLog(target=doc) and let SQLAlchemy do the work. Is there any way to work around needing to flush by hand? The target_table parameter is too verbose. I suppose I could solve this with a target property setter in ActivityLog that automatically retrieves the table name and id from a given instance. Biggest of all, I'm not sure how to retrieve a model instance from the database. Given an ActivityLog instance log, calling self.session.query(log.target_table).get(log.target_id) does not work, as query() expects a model as parameter. One workaround appears to be to use polymorphism and derive all my models from a base model which ActivityLog recognises. Something like this: class Entity(Base): __tablename__ = 'entities' id = Column(Integer, primary_key=True) title = Column(Unicode(255), nullable=False) edited_at = Column(DateTime, onupdate=datetime.utcnow, nullable=False) entity_type = Column(Unicode(20), nullable=False) __mapper_args__ = {'polymorphic_on': entity_type} class Document(Entity): __tablename__ = 'documents' __mapper_args__ = {'polymorphic_identity': 'document'} body = Column(UnicodeText, nullable=False) class ActivityLog(Base): __tablename__ = 'activitylog' id = Column(Integer, primary_key=True) ... target_id = Column(Integer, ForeignKey('entities.id'), nullable=False) target = relation(Entity) If I do this, ActivityLog(...).target will give me a Document instance when it refers to a Document, but I'm not sure it's worth the overhead of having two tables for everything. Should I go ahead and do it this way?

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  • Estimating the boundary of arbitrarily distributed data

    - by Dave
    I have two dimensional discrete spatial data. I would like to make an approximation of the spatial boundaries of this data so that I can produce a plot with another dataset on top of it. Ideally, this would be an ordered set of (x,y) points that matplotlib can plot with the plt.Polygon() patch. My initial attempt is very inelegant: I place a fine grid over the data, and where data is found in a cell, a square matplotlib patch is created of that cell. The resolution of the boundary thus depends on the sampling frequency of the grid. Here is an example, where the grey region are the cells containing data, black where no data exists. OK, problem solved - why am I still here? Well.... I'd like a more "elegant" solution, or at least one that is faster (ie. I don't want to get on with "real" work, I'd like to have some fun with this!). The best way I can think of is a ray-tracing approach - eg: from xmin to xmax, at y=ymin, check if data boundary crossed in intervals dx y=ymin+dy, do 1 do 1-2, but now sample in y An alternative is defining a centre, and sampling in r-theta space - ie radial spokes in dtheta increments. Both would produce a set of (x,y) points, but then how do I order/link neighbouring points them to create the boundary? A nearest neighbour approach is not appropriate as, for example (to borrow from Geography), an isthmus (think of Panama connecting N&S America) could then close off and isolate regions. This also might not deal very well with the holes seen in the data, which I would like to represent as a different plt.Polygon. The solution perhaps comes from solving an area maximisation problem. For a set of points defining the data limits, what is the maximum contiguous area contained within those points To form the enclosed area, what are the neighbouring points for the nth point? How will the holes be treated in this scheme - is this erring into topology now? Apologies, much of this is me thinking out loud. I'd be grateful for some hints, suggestions or solutions. I suspect this is an oft-studied problem with many solution techniques, but I'm looking for something simple to code and quick to run... I guess everyone is, really! Cheers, David

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  • Decorator for determining HTTP response from a view

    - by polera
    I want to create a decorator that will allow me to return a raw or "string" representation of a view if a GET parameter "raw" equals "1". The concept works, but I'm stuck on how to pass context to my renderer. Here's what I have so far: from django.shortcuts import render_to_response from django.http import HttpResponse from django.template.loader import render_to_string def raw_response(template): def wrap(view): def response(request,*args,**kwargs): if request.method == "GET": try: if request.GET['raw'] == "1": render = HttpResponse(render_to_string(template,{}),content_type="text/plain") return render except Exception: render = render_to_response(template,{}) return render return response return wrap Currently, the {} is there just as a place holder. Ultimately, I'd like to be able to pass a dict like this: @raw_response('my_template_name.html') def view_name(request): render({"x":42}) Any assistance is appreciated.

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  • getting global name not defined error

    - by nashr rafeeg
    i have the following class class notify(): def __init__(self,server="localhost", port=23053): self.host = server self.port = port register = gntp.GNTPRegister() register.add_header('Application-Name',"SVN Monitor") register.add_notification("svnupdate",True) growl(register) def svn_update(self, author="Unknown", files=0): notice = gntp.GNTPNotice() notice.add_header('Application-Name',"SVN Monitor") notice.add_header('Notification-Name', "svnupdate") notice.add_header('Notification-Title',"SVN Commit") # notice.add_header('Notification-Icon',"") notice.add_header('Notification-Text',Msg) growl(notice) def growl(data): s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((self.host,self.port)) s.send(data) response = gntp.parse_gntp(s.recv(1024)) print response s.close() but when ever i try to use this class via the follwoing code i get 'NameError: global name 'growl' is not defined' from growlnotify import * n = notify() n.svn_update() any one has an idea what is going on here ? cheers nash

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  • Matplotlib autodatelocator custom date formatting?

    - by jawonlee
    I'm using Matplotlib to dynamically generate .png charts from a database. The user may set as the x-axis any given range of datetimes, and I need to account for all of it. While Matplotlib has the dates.AutoDateLocator(), I want the datetime format printed on the chart to be context-specific - e.g. if the user is charting from 3 p.m. to 5 p.m., the year/month/day information doesn't need to be displayed. Right now, I'm manually creating Locator and Formatter objects thusly: def get_ticks(start, end): from datetime import timedelta as td delta = end - start if delta <= td(minutes=10): loc = mdates.MinuteLocator() fmt = mdates.DateFormatter('%I:%M %p') elif delta <= td(minutes=30): loc = mdates.MinuteLocator(byminute=range(0,60,5)) fmt = mdates.DateFormatter('%I:%M %p') elif delta <= td(hours=1): loc = mdates.MinuteLocator(byminute=range(0,60,15)) fmt = mdates.DateFormatter('%I:%M %p') elif delta <= td(hours=6): loc = mdates.HourLocator() fmt = mdates.DateFormatter('%I:%M %p') elif delta <= td(days=1): loc = mdates.HourLocator(byhour=range(0,24,3)) fmt = mdates.DateFormatter('%I:%M %p') elif delta <= td(days=3): loc = mdates.HourLocator(byhour=range(0,24,6)) fmt = mdates.DateFormatter('%I:%M %p') elif delta <= td(weeks=2): loc = mdates.DayLocator() fmt = mdates.DateFormatter('%b %d') elif delta <= td(weeks=12): loc = mdates.WeekdayLocator() fmt = mdates.DateFormatter('%b %d') elif delta <= td(weeks=52): loc = mdates.MonthLocator() fmt = mdates.DateFormatter('%b') else: loc = mdates.MonthLocator(interval=3) fmt = mdates.DateFormatter('%b %Y') return loc,fmt Is there a better way of doing this?

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  • any faster alternative??

    - by kaushik
    cost=0 for i in range(12): cost=cost+math.pow(float(float(q[i])-float(w[i])),2) cost=(math.sqrt(cost)) Any faster alternative to this? i am need to improve my entire code so trying to improve each statements performance. thanking u

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  • any faster alternative??

    - by kaushik
    I have to read a file from a particular line number and i know the line number say "n": i have been thinking of two choice: 1)for i in range(n) fname.readline() k=readline() print k 2)i=0 for line in fname: dictionary[i]=line i=i+1 but i want to know faster alternative as i might have to perform this on different files 20000 times. is there is any other better alternatives?? thanking u

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  • What could cause Django to start failing its own tests after an OS and Django reinstall?

    - by Macha
    I had to reinstall my OS, and so, I reinstalled django 1.1. Since reinstalling, when I run tests in my app, I get several failures from django.contrib.auth. Logs: http://dpaste.com/178153/ I asked on #django, and no one is too sure what the cause of the errors are. Some of my own code fails its tests, because it's not fully written yet, but that shouldn't cause django to fail it's core tests... I have included django.contrib.admin, which was mentioned as a possible cause.

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  • Deterministic key serialization

    - by Mike Boers
    I'm writing a mapping class which uses SQLite as the storage backend. I am currently allowing only basestring keys but it would be nice if I could use a couple more types hopefully up to anything that is hashable (ie. same requirements as the builtin dict). To that end I would like to derive a deterministic serialization scheme. Ideally, I would like to know if any implementation/protocol combination of pickle is deterministic for hashable objects (e.g. can only use cPickle with protocol 0). I noticed that pickle and cPickle do not match: >>> import pickle >>> import cPickle >>> def dumps(x): ... print repr(pickle.dumps(x)) ... print repr(cPickle.dumps(x)) ... >>> dumps(1) 'I1\n.' 'I1\n.' >>> dumps('hello') "S'hello'\np0\n." "S'hello'\np1\n." >>> dumps((1, 2, 'hello')) "(I1\nI2\nS'hello'\np0\ntp1\n." "(I1\nI2\nS'hello'\np1\ntp2\n." Another option is to use repr to dump and ast.literal_eval to load. This would only be valid for builtin hashable types. I have written a function to determine if a given key would survive this process (it is rather conservative on the types it allows): def is_reprable_key(key): return type(key) in (int, str, unicode) or (type(key) == tuple and all( is_reprable_key(x) for x in key)) The question for this method is if repr itself is deterministic for the types that I have allowed here. I believe this would not survive the 2/3 version barrier due to the change in str/unicode literals. This also would not work for integers where 2**32 - 1 < x < 2**64 jumping between 32 and 64 bit platforms. Are there any other conditions (ie. do strings serialize differently under different conditions)? (If this all fails miserably then I can store the hash of the key along with the pickle of both the key and value, then iterate across rows that have a matching hash looking for one that unpickles to the expected key, but that really does complicate a few other things and I would rather not do it.) Any insights?

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  • Counting amount of items in Pythons 'for'

    - by Markum
    Kind of hard to explain, but when I run something like this: fruits = ['apple', 'orange', 'banana', 'strawberry', 'kiwi'] for fruit in fruits: print fruit.capitalize() It gives me this, as expected: Apple Orange Banana Strawberry Kiwi How would I edit that code so that it would "count" the amount of times it's performing the for, and print this? 1 Apple 2 Orange 3 Banana 4 Strawberry 5 Kiwi

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  • Django and mod_python intermittent error?

    - by Peter
    I have a Django site at http://sm.rutgers.edu/relive/af_api/index/. It is supposed to display "Home of the relive APIs". If you refresh this page many times, you can see different renderings. 1) The expected page. 2) Django "It worked!" page. 3) "ImportError at /index/" page. If you scroll down enough to ROOT_URLCONF part, you will see it says 'relive.urls'. But apparently, it should be 'af_api.urls', which is in my settings.py file. Since these results happen randomly, is it possible that either Django or mod_python is working unstably?

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  • Clean Method for a ModelForm in a ModelFormSet made by modelformset_factory

    - by Salyangoz
    I was wondering if my approach is right or not. Assuming the Restaurant model has only a name. forms.py class BaseRestaurantOpinionForm(forms.ModelForm): opinion = forms.ChoiceField(choices=(('yes', 'yes'), ('no', 'no'), ('meh', 'meh')), required=False, )) class Meta: model = Restaurant fields = ['opinion'] views.py class RestaurantVoteListView(ListView): queryset = Restaurant.objects.all() template_name = "restaurants/list.html" def dispatch(self, request, *args, **kwargs): if request.POST: queryset = self.request.POST.dict() #clean here return HttpResponse(json.dumps(queryset), content_type="application/json") def get_context_data(self, **kwargs): context = super(EligibleRestaurantsListView, self).get_context_data(**kwargs) RestaurantFormSet = modelformset_factory( Restaurant,form=BaseRestaurantOpinionForm ) extra_context = { 'eligible_restaurants' : self.get_eligible_restaurants(), 'forms' : RestaurantFormSet(), } context.update(extra_context) return context Basically I'll be getting 3 voting buttons for each restaurant and then I want to read the votes. I was wondering from where/which clean function do I need to call to get something like: { ('3' : 'yes'), ('2' : 'no') } #{ 'restaurant_id' : 'vote' } This is my second/third question so tell me if I'm being unclear. Thanks.

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  • Scrape zipcode table for different urls based on county

    - by Dr.Venkman
    I used lxml and ran into a wall as my new computer wont install lxml and the code doesnt work. I know this is simple - maybe some one can help with a beautiful soup script. this is my code: import codecs import lxml as lh from selenium import webdriver import time import re results = [] city = [ 'amador'] state = [ 'CA'] for state in states: for city in citys: browser = webdriver.Firefox() link2 = 'http://www.getzips.com/cgi-bin/ziplook.exe?What=3&County='+ city +'&State=' + state + '&Submit=Look+It+Up' browser.get(link2) bcontent = browser.page_source zipcode = bcontent[bcontent.find('<td width="15%"'):bcontent.find('<p>')+0] if len(zipcode) > 0: print zipcode else: print 'none' browser.quit() Thanks for the help

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  • Ternary operator

    - by Antoine Leclair
    In PHP, I often use the ternary operator to add an attribute to an html element if it applies to the element in question. For example: <select name="blah"> <option value="1"<?= $blah == 1 ? ' selected="selected"' : '' ?>> One </option> <option value="2"<?= $blah == 2 ? ' selected="selected"' : '' ?>> Two </option> </select> I'm starting a project with Pylons using Mako for the templating. How can I achieve something similar? Right now, I see two possibilities that are not ideal. Solution 1: <select name="blah"> % if blah == 1: <option value="1" selected="selected">One</option> % else: <option value="1">One</option> % endif % if blah == 2: <option value="2" selected="selected">Two</option> % else: <option value="2">Two</option> % endif </select> Solution 2: <select name="blah"> <option value="1" % if blah == 1: selected="selected" % endif >One</option> <option value="2" % if blah == 2: selected="selected" % endif >Two</option> </select> In this particular case, the value is equal to the variable tested (value="1" = blah == 1), but I use the same pattern in other situations, like <?= isset($variable) ? ' value="$variable" : '' ?>. I am looking for a clean way to achieve this using Mako.

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