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The long title contain also a mini-exaple because I couldn't explain well what I'm trying to do. Nonethless, the similar questions windows led me to various implementation. But since I read multiple times that it's a bad design, I would like to ask if what I'm trying to do is a bad design rather asking how to do it. For this reason I will try to explain my use case with a minial functional code.
Suppose I have a two classes, each of them with their own parameters:
class MyClass1:
def __init__(self,param1=1,param2=2):
self.param1=param1
self.param2=param2
class MyClass2:
def __init__(self,param3=3,param4=4):
self.param3=param3
self.param4=param4
I want to print param1...param4 as a string (i.e. "param1"..."param4") and not its value (i.e.=1...4).
Why? Two reasons in my case:
I have a GUI where the user is asked to select one of of the class
type (Myclass1, Myclass2) and then it's asked to insert the values
for the parameters of that class. The GUI then must show the
parameter names ("param1", "param2" if MyClass1 was chosen) as a
label with the Entry Widget to get the value. Now, suppose the
number of MyClass and parameter is very high, like 10 classes and 20
parameters per class. In order to minimize the written code and to
make it flexible (add or remove parameters from classes without
modifying the GUI code) I would like to cycle all the parameter of
Myclass and for each of them create the relative widget, thus I need
the paramx names under the form od string. The real application I'm
working on is even more complex, like parameter are inside other
objects of classes, but I used the simpliest example. One solution
would be to define every parameter as an object where
param1.name="param1" and param1.value=1. Thus in the GUI I would
print param1.name. But this lead to a specifi problem of my
implementation, that's reason 2:
MyClass1..MyClassN will be at some point printed in a JSON. The JSON
will be a huge file, and also since it's a complex tree (the example
is simple) I want to make it as simple as possibile. To explain why
I don't like to solution above, suppose this situation:
class MyClass1:
def init(self,param1,param2,combinations=[]):
self.param1=param1
self.param2=param2
self.combinations=combinations
Supposse param1 and param2 are now list of variable size, and
combination is a list where each element is composed by all the
combination of param1 and param2, and generate an output from some
sort of calculation. Each element of the list combinations is an
object SingleCombination,for example (metacode):
param1=[1,2] param2=[5,6] SingleCombination.param1=1
SingleCombination.param2=5 SingleCombination.output=1*5
MyInst1.combinations.append(SingleCombination).
In my case I will further incapsulated param1,param2 in a object
called parameters, so every condition will hace a nice tree with
only two object, parameters and output, and expanding parameters
node will show all the parameters with their value.
If I use JSON pickle to generate a JSON from the situation above, it
is nicely displayed since the name of the node will be the name of
the varaible ("param1", "param2" as strings in the JSON). But if I
do the trick at the end of situation (1), creating an object of
paramN as paramN.name and paramN.value, the JSON tree will become
ugly but especially huge, because if I have a big number of
condition, every paramN contains 2 sub-element. I wrote the
situation and displayed with a JSON Viewer, see the attached immage
I could pre processing the data structure before creating the JSON,
the problem is that I use the JSON to recreate the data structure in
another session of the program, so I need all the pieces of the data
structure to be in the JSON.
So, from my requirements, it seems that the workround to avoid print the variable names creates some side effect on the JSON visualization that I don't know how to solve without changing the logic of my program...
If you use dataclasses, getting the field names is pretty straightforward:
from dataclasses import dataclass, fields
#dataclass
class MyClass1:
first:int = 4
>>> fields(MyClass1)
(Field(name='first',type=<class 'int'>,default=4,...),)
This way, you can iterate over the class fields and ask your user to fill them. Note the field has a type, which you could use to eg ask the user for several values, as in your example.
You could add functions to extract programatically the param names (_show_inputs below ) from the class and values from instances (_json below ):
def blossom(cls):
"""decorate a class with `_json` (classmethod) and `_show_inputs` (bound)"""
def _json(self):
return json.dumps(self, cls=DataClassEncoder)
def _show_inputs(cls):
return {
field.name: field.type.__name__
for field in fields(cls)
}
cls._json = _json
cls._show_inputs = classmethod(_show_inputs)
return cls
NOTE 1: There's actually no need to decorate the classes with blossom. You could just use its internal functions programatically.
Using a custom json encoder to dump the dataclass objects, including properties:
import json
class DataClassPropEncoder(json.JSONEncoder): # https://stackoverflow.com/a/51286749/7814595
def default(self, o):
if is_dataclass(o):
cls = type(o)
# inject instance properties
props = {
name: getattr(o, name)
for name, value in cls.__dict__.items() if isinstance(value, property)
}
return {
**props,
**asdict(o)
}
return super().default(o)
Finally, wrap the computations inside properties so they are
serialized as well when using the decorated class. Full code example:
from dataclasses import asdict
from dataclasses import dataclass
from dataclasses import fields
from dataclasses import is_dataclass
import json
from itertools import product
from typing import List
class DataClassPropEncoder(json.JSONEncoder): # https://stackoverflow.com/a/51286749/7814595
def default(self, o):
if is_dataclass(o):
cls = type(o)
props = {
name: getattr(o, name)
for name, value in cls.__dict__.items() if isinstance(value, property)
}
return {
**props,
**asdict(o)
}
return super().default(o)
def blossom(cls):
def _json(self):
return json.dumps(self, cls=DataClassEncoder)
def _show_inputs(cls):
return {
field.name: field.type.__name__
for field in fields(cls)
}
cls._json = _json
cls._show_inputs = classmethod(_show_inputs)
return cls
#blossom
#dataclass
class MyClass1:
param1:int
param2:int
#blossom
#dataclass
class MyClass2:
param3: List[str]
param4: List[int]
def _compute_single(self, values): # TODO: implmement this
return values[0]*values[1]
#property
def combinations(self):
# TODO: cache if used more than once
# TODO: combinations might explode
field_names = []
field_values = []
cls = type(self)
for field in fields(cls):
field_names.append(field.name)
field_values.append(getattr(self, field.name))
results = []
for values in product(*field_values):
result = {
**{
field_names[idx]: value
for idx, value in enumerate(values)
},
"output": self._compute_single(values)
}
results.append(result)
return results
>>> print(f"MyClass1:\n{MyClass1._show_inputs()}")
MyClass1:
{'param1': 'int', 'param2': 'int'}
>>> print(f"MyClass2:\n{MyClass2._show_inputs()}")
MyClass2:
{'param3': 'List', 'param4': 'List'}
>>> obj_1 = MyClass1(3,4)
>>> print(f"obj_1:\n{obj_1._json()}")
obj_1:
{"param1": 3, "param2": 4}
>>> obj_2 = MyClass2(["first", "second"],[4,2])._json()
>>> print(f"obj_2:\n{obj_2._json()}")
obj_2:
{"combinations": [{"param3": "first", "param4": 4, "output": "firstfirstfirstfirst"}, {"param3": "first", "param4": 2, "output": "firstfirst"}, {"param3": "second", "param4": 4, "output": "secondsecondsecondsecond"}, {"param3": "second", "param4": 2, "output": "secondsecond"}], "param3": ["first", "second"], "param4": [4, 2]}
NOTE 2: If you need to perform several computations per class, it might be a good idea to abstract away the pattern in the combinations property to avoid repeating code.
NOTE 3: If you need access to the properties several times and not ust once, you might want to consider caching their values to avoid re-computation.
Once you have an instance of MyClass / MyClass2, you can call vars() or vars().keys() and it will give you the attributes as a str. Unlike dir, it will not show all the builtin attributes/methods starting with __.
class MyClass2:
def __init__(self,param3=3,param4=4):
self.param3=param3
self.param4=param4
instance_of_myclass2 = MyClass2(param3="what", param4="ever")
print(vars(instance_of_myclass2))
{'param3': 'what', 'param4': 'ever'}
print(vars(instance_of_myclass2).keys())
dict_keys(['param3', 'param4'])
dir(instance_of_myclass2)
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'param3', 'param4']
I want to append multiple list items to a JSON file, but it creates a list within a list, and therefore I cannot acces the list from python. Since the code is overwriting existing data in the JSON file, there should not be any list there. I also tried it by having just an text in the file without brackets. It just creates a list within a list so [["x", "y","z"]] instead of ["x", "y","z"]
import json
filename = 'vocabulary.json'
print("Reading %s" % filename)
try:
with open(filename, "rt") as fp:
data = json.load(fp)
print("Data: %s" % data)#check
except IOError:
print("Could not read file, starting from scratch")
data = []
# Add some data
TEMPORARY_LIST = []
new_word = input("give new word: ")
TEMPORARY_LIST.append(new_word.split())
print(TEMPORARY_LIST)#check
data = TEMPORARY_LIST
print("Overwriting %s" % filename)
with open(filename, "wt") as fp:
json.dump(data, fp)
example and output with appending list with split words:
Reading vocabulary.json
Data: [['my', 'dads', 'house', 'is', 'nice']]
give new word: but my house is nicer
[['but', 'my', 'house', 'is', 'nicer']]
Overwriting vocabulary.json
So, if I understand what you are trying to accomplish correctly, it looks like you are trying to overwrite a list in a JSON file with a new list created from user input. For easiest data manipulation, set up your JSON file in dictionary form:
{
"words": [
"my",
"dad's",
"house",
"is",
"nice"
]
}
You should then set up functions to separate your functionality to make it more manageable:
def load_json(filename):
with open(filename, "r") as f:
return json.load(f)
Now, we can use those functions to load the JSON, access the words list, and overwrite it with the new word.
data = load_json("vocabulary.json")
new_word = input("Give new word: ").split()
data["words"] = new_word
write_json("vocabulary.json", data)
If the user inputs "but my house is nicer", the JSON file will look like this:
{
"words": [
"but",
"my",
"house",
"is",
"nicer"
]
}
Edit
Okay, I have a few suggestions to make before I get into solving the issue. Firstly, it's great that you have delegated much of the functionality of the program over to respective functions. However, using global variables is generally discouraged because it makes things extremely difficult to debug as any of the functions that use that variable could have mutated it by accident. To fix this, use method parameters and pass around the data accordingly. With small programs like this, you can think of the main() method as the point in which all data comes to and from. This means that the main() function will pass data to other functions and receive new or edited data back. One final recommendation, you should only be using all capital letters for variable names if they are going to be constant. For example, PI = 3.14159 is a constant, so it is conventional to make "pi" all caps.
Without using global, main() will look much cleaner:
def main():
choice = input("Do you want to start or manage the list? (start/manage)")
if choice == "start":
data = load_json()
words = data["words"]
dictee(words)
elif choice == "manage":
manage_list()
You can use the load_json() function from earlier (notice that I deleted write_json(), more on that later) if the user chooses to start the game. If the user chooses to manage the file, we can write something like this:
def manage_list():
choice = input("Do you want to add or clear the list? (add/clear)")
if choice == "add":
words_to_add = get_new_words()
add_words("vocabulary.json", words_to_add)
elif choice == "clear":
clear_words("vocabulary.json")
We get the user input first and then we can call two other functions, add_words() and clear_words():
def add_words(filename, words):
with open(filename, "r+") as f:
data = json.load(f)
data["words"].extend(words)
f.seek(0)
json.dump(data, f, indent=4)
def clear_words(filename):
with open(filename, "w+") as f:
data = {"words":[]}
json.dump(data, f, indent=4)
I did not utilize the load_json() function in the two functions above. My reasoning for this is because it would call for opening the file more times than needed, which would hurt performance. Furthermore, in these two functions, we already need to open the file, so it is okayt to load the JSON data here because it can be done with only one line: data = json.load(f). You may also notice that in add_words(), the file mode is "r+". This is the basic mode for reading and writing. "w+" is used in clear_words(), because "w+" not only opens the file for reading and writing, it overwrites the file if the file exists (that is also why we don't need to load the JSON data in clear_words()). Because we have these two functions for writing and/or overwriting data, we don't need the write_json() function that I had initially suggested.
We can then add to the list like so:
>>> Do you want to start or manage the list? (start/manage)manage
>>> Do you want to add or clear the list? (add/clear)add
>>> Please enter the words you want to add, separated by spaces: these are new words
And the JSON file becomes:
{
"words": [
"but",
"my",
"house",
"is",
"nicer",
"these",
"are",
"new",
"words"
]
}
We can then clear the list like so:
>>> Do you want to start or manage the list? (start/manage)manage
>>> Do you want to add or clear the list? (add/clear)clear
And the JSON file becomes:
{
"words": []
}
Great! Now, we implemented the ability for the user to manage the list. Let's move on to creating the functionality for the game: dictee()
You mentioned that you want to randomly select an item from a list and remove it from that list so it doesn't get asked twice. There are a multitude of ways you can accomplish this. For example, you could use random.shuffle:
def dictee(words):
correct = 0
incorrect = 0
random.shuffle(words)
for word in words:
# ask word
# evaluate response
# increment correct/incorrect
# ask if you want to play again
pass
random.shuffle randomly shuffles the list around. Then, you can iterate throught the list using for word in words: and start the game. You don't necessarily need to use random.choice here because when using random.shuffle and iterating through it, you are essentially selecting random values.
I hope this helped illustrate how powerful functions and function parameters are. They not only help you separate your code, but also make it easier to manage, understand, and write cleaner code.
I downloaded a dataset for facial key point detection the image and the labels were in a CSV file I extracted it using pandas but I don't know how to convert it into a tensor and load it into a data loader for training.
dataframe = pd.read_csv("training_facial_keypoints.csv")
dataframe['Image'] = dataframe['Image'].apply(lambda i: np.fromstring(i, sep=' '))
dataframe= dataframe.dropna()
images_array = np.vstack(dataframe['Image'].values)/255.0
images_array = images_array.astype(np.float32)
images_array = images_array.reshape(-1, 96, 96, 1)
print(images_array.shape)
labels_array = dataframe[dataframe.columns[:-1]].values
labels_array = (labels_array-48)/48
labels_array = labels_array.astype(np.float32)
I have the images and labels in two arrays. How do I create a training set from this and use transforms.
Then load it using a dataloader.
Create a subclass of torch.utils.data.Dataset, fill it with your data.
You can pass desired torchvision.transforms to it and apply them to your data in __getitem__(self, index).
Than you can pass it to torch.utils.data.DataLoader which allows multi-threaded loading of data.
And PyTorch has an overwhelming documentation you should first refer to.
Table structure like -
db.define_table('parent',
Field('name'),format='%(name)s')
db.define_table('children',
Field('name'),
Field('mother','reference parent'),
Field('father','reference parent'))
db.children.mother.requires = IS_IN_DB(db, db.parent.id,'%(name)s')
db.children.father.requires = IS_IN_DB(db, db.parent.id,'%(name)s')
Controller :
grid = SQLFORM.grid(db.children, orderby=[db.children.id],
csv=True,
fields=[db.children.id, db.children.name, db.children.mother, db.children.father])
return dict(grid=grid)
Here grid shows proper values i.e names of the mother and father from the parent table.
But when I try to export it via csv link - resulted excelsheet shows ids and not the names of mother and father.
Please help!
The CSV download just gives you the raw database values without first applying each field's represent attribute. If you want the "represented" values of each field, you have two options. First, you can choose the TSV (tab-separated-values) download instead of CSV. Second, you can define a custom export class:
import cStringIO
class CSVExporter(object):
file_ext = "csv"
content_type = "text/csv"
def __init__(self, rows):
self.rows = rows
def export(self):
if self.rows:
s = cStringIO.StringIO()
self.rows.export_to_csv_file(s, represent=True)
return s.getvalue()
else:
return ''
grid = SQLFORM.grid(db.mytable, exportclasses=dict(csv=(CSVExporter, 'CSV')))
The exportclasses argument is a dictionary of custom download types that can be used to override existing types or add new ones. Each item is a tuple including the exporter class and the label to be used for the download link in the UI.
We should probably add this as an option.
BACKGROUND:
I am having issues trying to search through some CSV files.
I've gone through the python documentation: http://docs.python.org/2/library/csv.html
about the csv.DictReader(csvfile, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds) object of the csv module.
My understanding is that the csv.DictReader assumes the first line/row of the file are the fieldnames, however, my csv dictionary file simply starts with "key","value" and goes on for atleast 500,000 lines.
My program will ask the user for the title (thus the key) they are looking for, and present the value (which is the 2nd column) to the screen using the print function. My problem is how to use the csv.dictreader to search for a specific key, and print its value.
Sample Data:
Below is an example of the csv file and its contents...
"Mamer","285713:13"
"Champhol","461034:2"
"Station Palais","972811:0"
So if i want to find "Station Palais" (input), my output will be 972811:0. I am able to manipulate the string and create the overall program, I just need help with the csv.dictreader.I appreciate any assistance.
EDITED PART:
import csv
def main():
with open('anchor_summary2.csv', 'rb') as file_data:
list_of_stuff = []
reader = csv.DictReader(file_data, ("title", "value"))
for i in reader:
list_of_stuff.append(i)
print list_of_stuff
main()
The documentation you linked to provides half the answer:
class csv.DictReader(csvfile, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds)
[...] maps the information read into a dict whose keys are given by the optional fieldnames parameter. If the fieldnames parameter is omitted, the values in the first row of the csvfile will be used as the fieldnames.
It would seem that if the fieldnames parameter is passed, the given file will not have its first record interpreted as headers (the parameter will be used instead).
# file_data is the text of the file, not the filename
reader = csv.DictReader(file_data, ("title", "value"))
for i in reader:
list_of_stuff.append(i)
which will (apparently; I've been having trouble with it) produce the following data structure:
[{"title": "Mamer", "value": "285713:13"},
{"title": "Champhol", "value": "461034:2"},
{"title": "Station Palais", "value": "972811:0"}]
which may need to be further massaged into a title-to-value mapping by something like this:
data = {}
for i in list_of_stuff:
data[i["title"]] = i["value"]
Now just use the keys and values of data to complete your task.
And here it is as a dictionary comprehension:
data = {row["title"]: row["value"] for row in csv.DictReader(file_data, ("title", "value"))}
The currently accepted answer is fine, but there's a slightly more direct way of getting at the data. The dict() constructor in Python can take any iterable.
In addition, your code might have issues on Python 3, because Python 3's csv module expects the file to be opened in text mode, not binary mode. You can make your code compatible with 2 and 3 by using io.open instead of open.
import csv
import io
with io.open('anchor_summary2.csv', 'r', newline='', encoding='utf-8') as f:
data = dict(csv.reader(f))
print(data['Champhol'])
As a warning, if your csv file has two rows with the same value in the first column, the later value will overwrite the earlier value. (This is also true of the other posted solution.)
If your program really is only supposed to print the result, there's really no reason to build a keyed dictionary.
import csv
import io
# Python 2/3 compat
try:
input = raw_input
except NameError:
pass
def main():
# Case-insensitive & leading/trailing whitespace insensitive
user_city = input('Enter a city: ').strip().lower()
with io.open('anchor_summary2.csv', 'r', newline='', encoding='utf-8') as f:
for city, value in csv.reader(f):
if user_city == city.lower():
print(value)
break
else:
print("City not found.")
if __name __ == '__main__':
main()
The advantage of this technique is that the csv isn't loaded into memory and the data is only iterated over once. I also added a little code the calls lower on both the keys to make the match case-insensitive. Another advantage is if the city the user requests is near the top of the file, it returns almost immediately and stops looking through the file.
With all that said, if searching performance is your primary consideration, you should consider storing the data in a database.