I'm not sure what's happening when I print my dictionary.
In Python 3, I have a dictionary of parse_blast objects called transSwiss. Each object's proteinID is the key with the entire object as the value.
I can print transSwiss in it's entirety and I can also print blasto.protein, but not when I combine them to get a dictionary value. I'm not sure what is happening when I use:
print(transSwiss[blasto.protein])
<__main__.parse_blast object at 0x000000373C5666A0>
Here is the code
class parse_blast(object):
def __init__(self, line):
#Strip end-of-line and split on tabs
self.fields = line.strip("\n").split("\t")
self.transcriptId, self.isoform = self.fields[0].split("|")
self.swissStuff = self.fields[1].split("|")
self.swissProtId = self.swissStuff[3]
self.percentId = self.fields[2]
def filterblast(self):
return float(self.percentId) > 95
class parse_matrix(object):
#Consider __init__ as a Constructor
def __init__(self, matrix_lines):
(self.protein,
self.Sp_ds,
self.Sp_hs,
self.Sp_log,
self.Sp_plat) = matrix_lines.strip("\n").split("\t")
def separate_tuples(one_tuple):
return "\t".join(one_tuple)
blastmap = map(parse_blast, blast_output.readlines())
filtered = filter(parse_blast.filterblast, blastmap)
matrixmap = map(parse_matrix, matrix_output.readlines()[1:])
transSwiss = {blasto.transcriptId:blasto for blasto in filtered}
for matrixo in matrixmap:
print(transSwiss[matrixo.protein])
Because your object is defined by you, you also need to tell python how you want it to print. You can do this by defining a function called "__str__" that returns how you want to print your object.
https://en.wikibooks.org/wiki/Python_Programming/Classes#str
So Python isn't my strong suit and I've encountered what I view to be a strange issue. I've narrowed the problem down to a few lines of code, simplifying it to make asking this question easier. I have a list of objects, this object:
class FinalRecord():
ruid = 0
drugs = {}
I create them in the shell like this:
finalRecords = []
fr = FinalRecord()
fr.ruid = 7
finalRecords.append(fr)
fr2 = FinalRecord()
fr2.ruid = 10
finalRecords.append(fr2)
As soon as I want to change the drugs dict on one object, it changes it for the other one too
finalRecords[0].drugs["Avonex"] = "Found"
I print out this:
finalRecords[1].drugs
and it shows:
{'Avonex':'Found'}
When I'm expecting it to actually be empty. I know I'm not completely understand how Python is working with the objects, can anyone help me out here?
The reason for this is because drugs is a class attribute. So if you change it for one object it will in fact change in others.
If you are looking to not have this behaviour, then you are looking for instance attributes. Set drugs in your __init__ like this:
class FinalRecord():
def __init__(self):
self.ruid = 0
self.drugs = {}
Take note of the use of self, which is a reference to your object.
Here is some info on class vs instance attributes
So, full demo illustrating this behaviour:
>>> class FinalRecord():
... def __init__(self):
... self.ruid = 0
... self.drugs = {}
...
>>> obj1 = FinalRecord()
>>> obj2 = FinalRecord()
>>> obj1.drugs['stuff'] = 2
>>> print(obj1.drugs)
{'stuff': 2}
>>> print(obj2.drugs)
{}
You define drugs as a class attribute, not an instance attribute. Because of that, you are always modifying the same object. You should instead define drugs in the __init__ method. I would also suggest using ruid as an argument:
class FinalRecord():
def __init__(self, ruid):
self.ruid = ruid
self.drugs = {}
It could then be used as this:
fr = FinalRecord(7)
finalRecords.append(fr)
fr2 = FinalRecord(10)
finalRecords.append(fr2)
Or more simply:
finalRecords.append(FinalRecord(7))
finalRecords.append(FinalRecord(10))
I've got a bad smell in my code. Perhaps I just need to let it air out for a bit, but right now it's bugging me.
I need to create three different input files to run three Radiative Transfer Modeling (RTM) applications, so that I can compare their outputs. This process will be repeated for thousands of sets of inputs, so I'm automating it with a python script.
I'd like to store the input parameters as a generic python object that I can pass to three other functions, who will each translate that general object into the specific parameters needed to run the RTM software they are responsible. I think this makes sense, but feel free to criticize my approach.
There are many possible input parameters for each piece of RTM software. Many of them over-lap. Most of them are kept at sensible defaults, but should be easily changed.
I started with a simple dict
config = {
day_of_year: 138,
time_of_day: 36000, #seconds
solar_azimuth_angle: 73, #degrees
solar_zenith_angle: 17, #degrees
...
}
There are a lot of parameters, and they can be cleanly categorized into groups, so I thought of using dicts within the dict:
config = {
day_of_year: 138,
time_of_day: 36000, #seconds
solar: {
azimuth_angle: 73, #degrees
zenith_angle: 17, #degrees
...
},
...
}
I like that. But there are a lot of redundant properties. The solar azimuth and zenith angles, for example, can be found if the other is known, so why hard-code both? So I started looking into python's builtin property. That lets me do nifty things with the data if I store it as object attributes:
class Configuration(object):
day_of_year = 138,
time_of_day = 36000, #seconds
solar_azimuth_angle = 73, #degrees
#property
def solar_zenith_angle(self):
return 90 - self.solar_azimuth_angle
...
config = Configuration()
But now I've lost the structure I had from the second dict example.
Note that some of the properties are less trivial than my solar_zenith_angle example, and might require access to other attributes outside of the group of attributes it is a part of. For example I can calculate solar_azimuth_angle if I know the day of year, time of day, latitude, and longitude.
What I'm looking for:
A simple way to store configuration data whose values can all be accessed in a uniform way, are nicely structured, and may exist either as attributes (real values) or properties (calculated from other attributes).
A possibility that is kind of boring:
Store everything in the dict of dicts I outlined earlier, and having other functions run over the object and calculate the calculatable values? This doesn't sound fun. Or clean. To me it sounds messy and frustrating.
An ugly one that works:
After a long time trying different strategies and mostly getting no where, I came up with one possible solution that seems to work:
My classes: (smells a bit func-y, er, funky. def-initely.)
class SubConfig(object):
"""
Store logical groupings of object attributes and properties.
The parent object must be passed to the constructor so that we can still
access the parent object's other attributes and properties. Useful if we
want to use them to compute a property in here.
"""
def __init__(self, parent, *args, **kwargs):
super(SubConfig, self).__init__(*args, **kwargs)
self.parent = parent
class Configuration(object):
"""
Some object which holds many attributes and properties.
Related configurations settings are grouped in SubConfig objects.
"""
def __init__(self, *args, **kwargs):
super(Configuration, self).__init__(*args, **kwargs)
self.root_config = 2
class _AConfigGroup(SubConfig):
sub_config = 3
#property
def sub_property(self):
return self.sub_config * self.parent.root_config
self.group = _AConfigGroup(self) # Stinky?!
How I can use them: (works as I would like)
config = Configuration()
# Inspect the state of the attributes and properties.
print("\nInitial configuration state:")
print("config.rootconfig: %s" % config.root_config)
print("config.group.sub_config: %s" % config.group.sub_config)
print("config.group.sub_property: %s (calculated)" % config.group.sub_property)
# Inspect whether the properties compute the correct value after we alter
# some attributes.
config.root_config = 4
config.group.sub_config = 5
print("\nState after modifications:")
print("config.rootconfig: %s" % config.root_config)
print("config.group.sub_config: %s" % config.group.sub_config)
print("config.group.sub_property: %s (calculated)" % config.group.sub_property)
The behavior: (output of execution of all of the above code, as expected)
Initial configuration state:
config.rootconfig: 2
config.group.sub_config: 3
config.group.sub_property: 6 (calculated)
State after modifications:
config.rootconfig: 4
config.group.sub_config: 5
config.group.sub_property: 20 (calculated)
Why I don't like it:
Storing configuration data in class definitions inside of the main object's __init__() doesn't feel elegant. Especially having to instantiate them immediately after definition like that. Ugh. I can deal with that for the parent class, sure, but doing it in a constructor...
Storing the same classes outside the main Configuration object doesn't feel elegant either, since properties in the inner classes may depend on the attributes of Configuration (or their siblings inside it).
I could deal with defining the functions outside of everything, so inside having things like
#property
def solar_zenith_angle(self):
return calculate_zenith(self.solar_azimuth_angle)
but I can't figure out how to do something like
#property
def solar.zenith_angle(self):
return calculate_zenith(self.solar.azimuth_angle)
(when I try to be clever about it I always run into <property object at 0xXXXXX>)
So what is the right way to go about this? Am I missing something basic or taking a very wrong approach? Does anyone know a clever solution?
Help! My python code isn't beautiful! I must be doing something wrong!
Phil,
Your hesitation about func-y config is very familiar to me :)
I suggest you to store your config not as a python file but as a structured data file. I personally prefer YAML because it looks clean, just as you designed in the very beginning. Of course, you will need to provide formulas for the auto calculated properties, but it is not too bad unless you put too much code. Here is my implementation using PyYAML lib.
The config file (config.yml):
day_of_year: 138
time_of_day: 36000 # seconds
solar:
azimuth_angle: 73 # degrees
zenith_angle: !property 90 - self.azimuth_angle
The code:
import yaml
yaml.add_constructor("tag:yaml.org,2002:map", lambda loader, node:
type("Config", (object,), loader.construct_mapping(node))())
yaml.add_constructor("!property", lambda loader, node:
property(eval("lambda self: " + loader.construct_scalar(node))))
config = yaml.load(open("config.yml"))
print "LOADED config.yml"
print "config.day_of_year:", config.day_of_year
print "config.time_of_day:", config.time_of_day
print "config.solar.azimuth_angle:", config.solar.azimuth_angle
print "config.solar.zenith_angle:", config.solar.zenith_angle, "(calculated)"
print
config.solar.azimuth_angle = 65
print "CHANGED config.solar.azimuth_angle = 65"
print "config.solar.zenith_angle:", config.solar.zenith_angle, "(calculated)"
The output:
LOADED config.yml
config.day_of_year: 138
config.time_of_day: 36000
config.solar.azimuth_angle: 73
config.solar.zenith_angle: 17 (calculated)
CHANGED config.solar.azimuth_angle = 65
config.solar.zenith_angle: 25 (calculated)
The config can be of any depth and properties can use any subgroup values. Try this for example:
a: 1
b:
c: 3
d: some text
e: true
f:
g: 7.01
x: !property self.a + self.b.c + self.b.f.g
Assuming you already loaded this config:
>>> config
<__main__.Config object at 0xbd0d50>
>>> config.a
1
>>> config.b
<__main__.Config object at 0xbd3bd0>
>>> config.b.c
3
>>> config.b.d
'some text'
>>> config.b.e
True
>>> config.b.f
<__main__.Config object at 0xbd3c90>
>>> config.b.f.g
7.01
>>> config.x
11.01
>>> config.b.f.g = 1000
>>> config.x
1004
UPDATE
Let us have a property config.b.x which uses both self, parent and subgroup attributes in its formula:
a: 1
b:
x: !property self.parent.a + self.c + self.d.e
c: 3
d:
e: 5
Then we just need to add a reference to parent in subgroups:
import yaml
def construct_config(loader, node):
attrs = loader.construct_mapping(node)
config = type("Config", (object,), attrs)()
for k, v in attrs.iteritems():
if v.__class__.__name__ == "Config":
setattr(v, "parent", config)
return config
yaml.add_constructor("tag:yaml.org,2002:map", construct_config)
yaml.add_constructor("!property", lambda loader, node:
property(eval("lambda self: " + loader.construct_scalar(node))))
config = yaml.load(open("config.yml"))
And let's see how it works:
>>> config.a
1
>>> config.b.c
3
>>> config.b.d.e
5
>>> config.b.parent == config
True
>>> config.b.d.parent == config.b
True
>>> config.b.x
9
>>> config.a = 1000
>>> config.b.x
1008
Well, here's an ugly way to at least make sure your properties get called:
class ConfigGroup(object):
def __init__(self, config):
self.config = config
def __getattribute__(self, name):
v = object.__getattribute__(self, name)
if hasattr(v, '__get__'):
return v.__get__(self, ConfigGroup)
return v
class Config(object):
def __init__(self):
self.a = 10
self.group = ConfigGroup(self)
self.group.a = property(lambda group: group.config.a*2)
Of course, at this point you might as well forego property entirely and just check if the attribute is callable in __getattribute__.
Or you could go all out and have fun with metaclasses:
def config_meta(classname, parents, attrs):
defaults = {}
groups = {}
newattrs = {'defaults':defaults, 'groups':groups}
for name, value in attrs.items():
if name.startswith('__'):
newattrs[name] = value
elif isinstance(value, type):
groups[name] = value
else:
defaults[name] = value
def init(self):
for name, value in defaults.items():
self.__dict__[name] = value
for name, value in groups.items():
group = value()
group.config = self
self.__dict__[name] = group
newattrs['__init__'] = init
return type(classname, parents, newattrs)
class Config2(object):
__metaclass__ = config_meta
a = 10
b = 2
class group(object):
c = 5
#property
def d(self):
return self.c * self.config.a
Use it like this:
>>> c2.a
10
>>> c2.group.d
50
>>> c2.a = 6
>>> c2.group.d
30
Final edit (?): if you don't want to have to "backtrack" using self.config in subgroup property definitions, you can use the following instead:
class group_property(property):
def __get__(self, obj, objtype=None):
return super(group_property, self).__get__(obj.config, objtype)
def __set__(self, obj, value):
super(group_property, self).__set__(obj.config, value)
def __delete__(self, obj):
return super(group_property, self).__del__(obj.config)
class Config2(object):
...
class group(object):
...
#group_property
def e(config):
return config.group.c * config.a
group_property receives the base config object instead of the group object, so paths always start from the root. Therefore, e is equivalent to the previously defined d.
BTW, supporting nested groups is left as an exercise for the reader.
Wow, I just read an article about descriptors on r/python today, but I don't think hacking descriptors is going to give you what you want.
The only thing I know that handles sub-configurations like that is flatland. Here's how it would work in Flatland anyhow.
But you could do:
class Configuration(Form):
day_of_year = Integer
time_of_day = Integer
class solar(Form):
azimuth_angle = Integer
solar_angle = Integer
Then load the dictionary in
config = Configuration({
day_of_year: 138,
time_of_day: 36000, #seconds
solar: {
azimuth_angle: 73, #degrees
zenith_angle: 17, #degrees
...
},
...
})
I love flatland, but I'm not sure you gain much by using it.
You could add a metaclass or decorator to your class definition.
something like
def instantiate(klass):
return klass()
class Configuration(object):
#instantiate
class solar(object):
#property
def azimuth_angle(self):
return self.azimuth_angle
That might be better. Then create a nice __init__ on Configuration that can load all the data from a dictionary. I dunno maybe someone else has a better idea.
Here's something a little more complete (without as much magic as LaC's answer, but slightly less generic).
def instantiate(clazz): return clazz()
#dummy functions for testing
calc_zenith_angle = calc_azimuth_angle = lambda(x): 3
class Solar(object):
def __init__(self):
if getattr(self,'azimuth_angle',None) is None and getattr(self,'zenith_angle',None) is None:
return AttributeError("must have either azimuth_angle or zenith_angle provided")
if getattr(self,'zenith_angle',None) is None:
self.zenith_angle = calc_zenith_angle(self.azimuth_angle)
elif getattr(self,'azimuth_angle',None) is None:
self.azimuth_angle = calc_azimuth_angle(self.zenith_angle)
class Configuration(object):
day_of_year = 138
time_of_day = 3600
#instantiate
class solar(Solar):
azimuth_angle = 73
#zenith_angle = 17 #not defined
#if you don't want auto-calculation to be done automagically
class ConfigurationNoAuto(object):
day_of_year = 138
time_of_day = 3600
#instantiate
class solar(Solar):
azimuth_angle = 73
#property
def zenith_angle(self):
return calc_zenith_angle(self.azimuth_angle)
config = Configuration()
config_no_auto = ConfigurationNoAuto()
>>> config.day_of_year
138
>>> config_no_auto.day_of_year
138
>>> config_no_auto.solar.azimuth_angle
73
>>> config_no_auto.solar.zenith_angle
3
>>> config.solar.zenith_angle
3
>>> config.solar.azimuth_angle
7
I think I would rather subclass dict so that it fell back to a default if no data was available. Something like this:
class fallbackdict(dict):
...
defaults = { 'pi': 3.14 }
x_config = fallbackdict(defaults)
x_config.update({
'planck': 6.62606957e-34
})
The other aspect can be addressed with callables. Wether this is elegant or ugly depends on wether datatype declarations are useful:
pi: (float, 3.14)
calc = lambda v: v[0](v[1])
x_config.update({
'planck': (double, 6.62606957e-34),
'calculated': (lambda x: 1.0 - calc(x_config['planck']), None)
})
Depending on the circumstances, the lambda might be broken out if it is used many times.
Don't know if it is better, but it mostly preserves the dictionary style.
I am writing a program to add to and update an address book. Here is my code:
EDITED
import sys
import os
list = []
class bookEntry(dict):
total = 0
def __init__(self):
bookEntry.total += 1
self.d = {}
def __del__(self):
bookEntry.total -= 1
list.remove(self)
class Person(bookEntry):
def __init__(self, n):
self.n = n
print '%s has been created' % (self.n)
def __del__(self):
print '%s has been deleted' % (self.n)
def addnewperson(self, n, e = '', ph = '', note = ''):
self.d['name'] = n
self.d['email'] = e
self.d['phone'] = ph
self.d['note'] = note
list.append()
def updateperson(self):
key = raw_input('What else would you like to add to this person?')
val = raw_input('Please add a value for %s' % (key))
self.d[key] = val
def startup():
aor = raw_input('Hello! Would you like to add an entry or retrieve one?')
if aor == 'add':
info = raw_input('Would you like to add a person or a company?')
if info == 'person':
n = raw_input('Please enter this persons name:')
e = raw_input('Please enter this persons email address:')
ph = raw_input('Please enter this persons phone number:')
note = raw_input('Please add any notes if applicable:')
X = Person(n)
X.addnewperson(n, e, ph, note)
startup()
When I run this code I get the following error:
in addnewperson
self.d['name'] = n
AttributeError: 'Person' object has no attribute 'd'
I have two questions:
UPDATED QUESTIONS
1. why isnt the d object being inherited from bookentry()?
I know this question/code is lengthy but I do not know where to go from here. Any help would be greatly appreciated.
The addnewperson shoud have 'self' as first argument; actually, the name doesn't matter ('self' is just a convention), but the first argument represent the object itself. In your case, it's interpreting n as the "self" and the other 3 as regular arguments.
____del____ must not take arguments besides 'self'.
Edit: BTW I spotted a few other problems in your example, that maybe you're not aware of:
1) d in bookentry is a class member, not an instance member. It's shared by all bookentry's instances. To create an instance member, use:
class bookentry(dict):
def __init__(self,n):
self.d = {}
# rest of your constructor
2) you're trying to access d directly (as you would do in Java, C++ etc), but Python doesn't support that. You must have a 'self' parameter in your methods, and access instance variables through it:
class person(bookentry):
def foo(self,bar):
self.d[bar] = ...
person().foo(bar)
Update: for the last problem, the solution is to call the super constructor (which must be done explicitly in Python):
class Person(bookEntry):
def __init__(self, n):
super(Person, self).__init__()
self.n = n
print '%s has been created' % (self.n)
A brief explanation: for people with background in OO languages without multiple inheritance, it feels natural to expect the super type constructor to be called implicitly, automatically choosing the most suitable one if no one is mentioned explicitly. However, things get messy when a class can inherit from two or more at the same time, for this reason Python requires the programmer to make the choices himself: which superclass constructor to call first? Or at all?
The behavior of constructors (and destructors) can vary wildly from language to language. If you have further questions about the life cycle of Python objects, a good place to start would be here, here and here.
why isnt the d object being inherited from bookentry()?
That's because __init__ of the bookEntry is not called in the __init__ of the Person:
super(Person, self).__init__()
BTW, why inherit from dict if its functionality is not used? It's better to remove it and inherit from object instead (also class names are usually CamelCased):
class BookEntry(object):
I've got a piece of code which contains a for loop to draw things from an XML file;
for evoNode in node.getElementsByTagName('evolution'):
evoName = getText(evoNode.getElementsByTagName( "type")[0].childNodes)
evoId = getText(evoNode.getElementsByTagName( "typeid")[0].childNodes)
evoLevel = getText(evoNode.getElementsByTagName( "level")[0].childNodes)
evoCost = getText(evoNode.getElementsByTagName("costperlevel")[0].childNodes)
evolutions.append("%s x %s" % (evoLevel, evoName))
Currently it outputs into a list called evolutions as it says in the last line of that code, for this and several other for functions with very similar functionality I need it to output into a class instead.
class evolutions:
def __init__(self, evoName, evoId, evoLevel, evoCost)
self.evoName = evoName
self.evoId = evoId
self.evoLevel = evoLevel
self.evoCost = evoCost
How to create a series of instances of this class, each of which is a response from that for function? Or what is a core practical solution? This one doesn't really need the class but one of the others really does.
A list comprehension might be a little cleaner. I'd also move the parsing logic to the constructor to clean up the implemenation:
class Evolution:
def __init__(self, node):
self.node = node
self.type = property("type")
self.typeid = property("typeid")
self.level = property("level")
self.costperlevel = property("costperlevel")
def property(self, prop):
return getText(self.node.getElementsByTagName(prop)[0].childNodes)
evolutionList = [Evolution(evoNode) for evoNode in node.getElementsByTagName('evolution')]
Alternatively, you could use map:
evolutionList = map(Evolution, node.getElementsByTagName('evolution'))
for evoNode in node.getElementsByTagName('evolution'):
evoName = getText(evoNode.getElementsByTagName("type")[0].childNodes)
evoId = getText(evoNode.getElementsByTagName("typeid")[0].childNodes)
evoLevel = getText(evoNode.getElementsByTagName("level")[0].childNodes)
evoCost = getText(evoNode.getElementsByTagName("costperlevel")[0].childNodes)
temporaryEvo = Evolutions(evoName, evoId, evoLevel, evoCost)
evolutionList.append(temporaryEvo)
# Or you can go with the 1 liner
evolutionList.append(Evolutions(evoName, evoId, evoLevel, evoCost))
I renamed your list because it shared the same name as your class and was confusing.