Say I have choices defined as follows:
choices = (('1','a'),
('2','b'),
('3','c'))
And a form that renders and inputs these values in a MultipleChoiceField,
class Form1(forms.Form):
field = forms.MultipleChoiceField(choices=choices)
What is the right way to store field in a model.
I can of course loop through the forms.cleaned_data['field'] and obtain a value that fits in models.CommaSeperatedIntegerField.
Again, each time I retrieve these values, I will have to loop and convert into options.
I think there is a better way to do so, as in this way, I am in a way re-implementing the function that CommaSeperateIntegerField is supposed to do.
The first thing I would consider is better normalization of your database schema; if a single instance of your model can have multiple values for this field, the field perhaps should be a linked model with a ForeignKey instead.
If you're using Postgres, you could also use an ARRAY field; Django now has built-in support.
If you can't do either of those, then you do basically need to reimplement a (better) version of CommaSeparatedIntegerField. The reason is that CommaSeparatedIntegerField is nothing but a plain CharField whose default formfield representation is a regex-validated text input. In other words, it doesn't do anything that's useful to you.
What you need to write is a custom ListField or MultipleValuesField that expects a Python list and returns a Python list, but internally converts that list to/from a comma-separated string for insertion in the database. Read the documentation on custom model fields; I think in your case you'll want a subclass of CharField with two methods overridden: to_python (convert CSV string to Python list) and get_db_prep_value (convert Python list to CSV string).
I just had this same problem and the solution (to me) because as Carl Meyer put it. I don't want a normalized version of this "list of strings" is to just have a CharField in the model. This way your model will store the normalized list of items. In my case this is countries.
So the model declaration is just
countries = CharField(max_lenght=XXX)
where XXX is a precalculated value of 2x my country list. Because it's simpler for us to apply a check to see if the current country is in this list rather than do it as a M2M to a Country table.
Related
I'm using sqlalchemy currently but I can't store multiple values in a column. The only values I can put in a db are strings, int, etc. but not lists. I was thinking what if I wanted a list of integers and I just made it a string in this format: "1|10|91" and then split it afterwards. Would that work or would I run out of memory or something?
It should work, in case you take care of the length of the column string in the database. So, for this case, it's better to use Text type column which has extended size in most of the database servers.
class model_name(db.Model):
id = db.Column(db.Integer, primary_key=True)
integer_values = db.Column(db.Text)
In python, you can convert a dictionary easy to a JSON file. A dictionary consists of a bunch of variables. You can then convert the JSON file easy to SQL.
JSON files are often used to convert variables from one programming language to another.
I am trying to iterate over a model's attributes, getting both the verbose name and value for each attribute. I have no problem for character/numerical values, but I am struggling with DateTime values. Of course I can do this manually, but some of my models have dozens of attributes (only a few of which are DateTimes), so it would be nice if I could figure out how to do this programatically.
My code currently (with the 'History' Model class):
def get_fields(self):
return [(field.verbose_name, field.value_to_string(self)) for field in History._meta.fields]
In the case of a DateTime, of course this outputs a string, such as: 2011-06-16T04:00:00+00:00
Suggestions on pulling out DateTime values? Apologies if this is a very basic question -- I have been using Django for about a year, but am more recently trying to tackle area's I am unfamiliar with. Thank you!
You can use strftime() on datetime objects to specify how the string representation should be formatted, but you probably have to override the value_to_string() method in the datetimefield if you want to iterate over all fields in a uniform way.
See python doc of datetime.strftime() here:
https://docs.python.org/2/library/datetime.html
somewhere in my view, I'm creating querysets this way :
scan_events = ScanEvent.objects.order_by('scan_date')
userewards_events = UseRewardEvent.objects.order_by('use_date')
Now I'd like to send to my template one list containing both objects sorted by scan_date /use_date (which of course are both Django DateTimeField).
If I simply "append" my 2 querysets to a new list, they won't be sorted by Date.
I guess there is a simple way to do that, but can't find it.
What I'm trying to display in my page is ONE table with The type of Event, and the date.
This means I have to be able to know the type of objects in my list, when displaying them in the template.
Thanks.
I have started using web2py for a web application and try to use SQLFORM.grid(...) to display a paginated listing of one of my db-table's data like in the following minimal example.
grid=SQLFORM.grid(query,
links=links,
fields=[db.example.date,db.example.foo, db.example.bar])
The db.example.date field contains a Python datetime.datetime object in UTC. At the moment it is displayed just plainly like that. However, I want to have more control about the actual output in a way that I can set the local timezone and modify the output string to have something like "2 hours ago".
As seen in another question[0] I can use the links to insert new columns. Unfortunately I can't seem to sort the rows by a field I have inserted in such way. Also, they are inserted on the right instead of actually replacing my first column. So that does not seem to be a solution.
To sum it up: How do I gain control about the way db.example.date is printed out in the end?
[0] Calculated Fields in web2py sqlgrid
You can achieve your goal when you define the table in your model. The represent parameter in the Field constructor that you used in define_table will be recognized by the SQLFORM.grid. For example, if you wanted to just print the date with the month name you could put the following in your model.
Field('a_date', type='date', represent=lambda x, row: x.strftime("%B %d, %Y")),
your function could also convert to local time.
You need to use prettydate to change the datetime arid format in a humanized string, and call it in the represent parameter of your Field() descriptor. For example :
from gluon.tools import prettydate
db.example.date.represent = lambda v,r: prettydate(r.date)
That way, any display of the db.example.date would be displayed humanized, including through SQLFORM.grid
If you don't want to have the date always represented in this way as per David Nehme's answer. Just before your grid creation, you can set the db.table.field.represent in the controller.
db.example.date.represent = lambda value, row: value.strftime("%B %d, %Y")
followed by.
grid = SQLFORM.grid(query,....
I use this often when I join tables. If there is a row.field in the represent from the model file it breaks because it then must be more specific, row.table.field.
I have a scientific model which I am running in Python which produces a lookup table as output. That is, it produces a many-dimensional 'table' where each dimension is a parameter in the model and the value in each cell is the output of the model.
My question is how best to store this lookup table in Python. I am running the model in a loop over every possible parameter combination (using the fantastic itertools.product function), but I can't work out how best to store the outputs.
It would seem sensible to simply store the output as a ndarray, but I'd really like to be able to access the outputs based on the parameter values not just indices. For example, rather than accessing the values as table[16][5][17][14] I'd prefer to access them somehow using variable names/values, for example:
table[solar_z=45, solar_a=170, type=17, reflectance=0.37]
or something similar to that. It'd be brilliant if I were able to iterate over the values and get their parameter values back - that is, being able to find out that table[16]... corresponds to the outputs for solar_z = 45.
Is there a sensible way to do this in Python?
Why don't you use a database? I have found MongoDB (and the official Python driver, Pymongo) to be a wonderful tool for scientific computing. Here are some advantages:
Easy to install - simply download the executables for your platform (2 minutes tops, seriously).
Schema-less data model
Blazing fast
Provides map/reduce functionality
Very good querying functionalities
So, you could store each entry as a MongoDB entry, for example:
{"_id":"run_unique_identifier",
"param1":"val1",
"param2":"val2" # etcetera
}
Then you could query the entries as you will:
import pymongo
data = pymongo.Connection("localhost", 27017)["mydb"]["mycollection"]
for entry in data.find(): # this will yield all results
yield entry["param1"] # do something with param1
Whether or not MongoDB/pymongo are the answer to your specific question, I don't know. However, you could really benefit from checking them out if you are into data-intensive scientific computing.
If you want to access the results by name, then you could use a python nested dictionary instead of ndarray, and serialize it in a .JSON text file using json module.
One option is to use a numpy ndarray for the data (as you do now), and write a parser function to convert the query values into row/column indices.
For example:
solar_z_dict = {...}
solar_a_dict = {...}
...
def lookup(dataArray, solar_z, solar_a, type, reflectance):
return dataArray[solar_z_dict[solar_z] ], solar_a_dict[solar_a], ...]
You could also convert to string and eval, if you want to have some of the fields to be given as "None" and be translated to ":" (to give the full table for that variable).
For example, rather than accessing the values as table[16][5][17][14]
I'd prefer to access them somehow using variable names/values
That's what numpy's dtypes are for:
dt = [('L','float64'),('T','float64'),('NMSF','float64'),('err','float64')]
data = plb.loadtxt(argv[1],dtype=dt)
Now you can access the data elements using date['T']['L']['NMSF']
More info on dtypes:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.html