ddth-id | ID generator library | Generator Utils library

by DDTH Java Version: Current License: MIT

kandi X-RAY | ddth-id Summary

ddth-id is a Java library typically used in Generator, Generator Utils, Unity applications. ddth-id has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.

DDTH’s ID Generator Library. ddth-id requires Java 8+ since v0.5.0.

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ddth-id has a low active ecosystem.
It has 13 star(s) with 10 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
ddth-id has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of ddth-id is current.

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ddth-id has 0 bugs and 0 code smells.

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security Security

ddth-id has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
ddth-id code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

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license License

ddth-id is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

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Top functions reviewed by kandi - BETA

kandi has reviewed ddth-id and discovered the below as its top functions. This is intended to give you an instant insight into ddth-id implemented functionality, and help decide if they suit your requirements.

Retrieves the next unique ID

Set new value
Gets next unique id

Sets the value of the given namespace

Performs a single insert
Update set

Returns the current row identifier

Executes a SELECT statement

Initializes the session

Creates a session manager

Destroys the data source

Set the session manager

Initializes HikariDataSource

Gets the current id for the given namespace

Destroy the curator

Waits until the clock moves to the next second

Sets the id s value of the specified namespace

Generates next unique id

Destroy the pool

Destroy method

Returns an instance of the zookeeper id generator

Sets a value in the specified namespace

Returns the current session id

Returns the current state of the node

Updates the value associated with a namespace

Returns the next unique id

Get all kandi verified functions for this library.

ddth-id Key Features

ID generator library

ddth-id Examples and Code Snippets

Community Discussions

Trending Discussions on Generator

How can I make an object with an interface like a random number generator, but that actually generates a specified sequence?
Translating async generator into sync one
Return generator instead of list from df.to_dict()
python call generator function from other function
Mixing yield and return. `yield [cand]; return` vs `return [[cand]]`. Why do they lead to different output?
Python Ruler Sequence Generator
Are generators with context managers an anti-pattern?
Python: Generate a unique batch from given dataset
Can I get the current value of generator in JavaScript?
Continue to other generators once a generator has been exhausted in a list of generators?

Trending Discussions on Generator

QUESTION

How can I make an object with an interface like a random number generator, but that actually generates a specified sequence?

Asked 2022-Mar-31 at 13:47

I'd like to construct an object that works like a random number generator, but generates numbers in a specified sequence.

# a random number generator
rng = lambda : np.random.randint(2,20)//2

# a non-random number generator
def nrng():
    numbers = np.arange(1,10.5,0.5)
    for i in range(len(numbers)):
        yield numbers[i]

for j in range(10):
    print('random number', rng())
    print('non-random number', nrng())

The issue with the code above that I cannot call nrng in the last line because it is a generator. I know that the most straightforward way to rewrite the code above is to simply loop over the non-random numbers instead of defining the generator. I would prefer getting the example above to work because I am working with a large chunk of code that include a function that accepts a random number generator as an argument, and I would like to add the functionality to pass non-random number sequences without rewriting the entire code.

EDIT: I see some confusion in the comments. I am aware that python's random number generators generate pseudo-random numbers. This post is about replacing a pseudo-random-number generator by a number generator that generates numbers from a non-random, user-specified sequence (e.g., a generator that generates the number sequence 1,1,2,2,1,0,1 if I want it to).

ANSWER

Answered 2022-Mar-29 at 00:47

You can call next() with a generator or iterator as an argument to withdraw exactly one element from it. Saving the generator to a variable beforehand allows you to do this multiple times.

# make it a generator
def _rng():
    while True:
        yield np.random.randint(2,20)//2

# a non-random number generator
def _nrng():
    numbers = np.arange(1,10.5,0.5)
    for i in range(len(numbers)):
        yield numbers[i]

rng = _rng()
nrng = _nrng()
for j in range(10):
    print('random number', next(rng))
    print('non-random number', next(nrng))

Source https://stackoverflow.com/questions/71655179

QUESTION

Translating async generator into sync one

Asked 2022-Mar-23 at 02:39

Imagine we have an original API that returns a generator (it really is a mechanisms that brings pages/chunks of results from a server while the providing a simple generator to the user, and lets him iterate over these results one by one. For simplicity:

# Original sync generator
def get_results():
     # fetch from server
     yield 1
     yield 2
     # fetch next page
     yield 3
     yield 4
     # ....

Now there is a need to implement an asyncio version of the API, however we need to keep the old API operational as well. This is where things get complicated, we kind of want to translate an async generator into sync one, but I can't find an elegant way to do that. The best I could make work so far is "fetch all result into a list first, then provide a fake sync generator on that list". Which kind of defeats the purpose:

# Async generator
async def get_results_async():
     # await fetch from server
     yield 1
     yield 2
     # await fetch next page
     yield 3
     yield 4
     # ....


# Backward compatible sync generator
def get_results():

    async def gather_all_results():
        res = []
        async for i in get_results_async():
            res.append(i)
        return res

    res = asyncio.run(gather_all_results())
    for i in res:
        yield i

Is there a better, more elegant way to do that without fetching all the results before returning them?

Thanks

ANSWER

Answered 2022-Mar-23 at 02:39

For the reason that asyncio is contagious, it's hard to write elegant code to integrate asyncio code into the old codes. For the scenario above, the flowing code is a little better, but I don't think it's elegant enough.

async def get_results_async():
    # await fetch from server
    yield 1
    yield 2
    # await fetch next page
    yield 3
    yield 4
    # ....


# Backward compatible sync generator
def get_results():
    gen = get_results_async()
    while True:
        try:
            yield asyncio.run(gen.__anext__())
        except StopAsyncIteration:
            break

And you can re-use your event loop and not to create a new one.

async def get_results_async():
    # await fetch from server
    yield 1
    yield 2
    # await fetch next page
    yield 3
    yield 4
    # ....

# loop that you save in somewhere.
loop = asyncio.get_event_loop()

# Backward compatible sync generator
def get_results():
    gen = get_results_async()
    while True:
        try:
            yield loop.run_until_complete(gen.__anext__())
        except StopAsyncIteration:
            break

Source https://stackoverflow.com/questions/71580727

QUESTION

Return generator instead of list from df.to_dict()

Asked 2022-Feb-25 at 22:32

I am working on a large Pandas DataFrame which needs to be converted into dictionaries before being processed by another API.

The required dictionaries can be generated by calling the .to_dict(orient='records') method. As stated in the docs, the returned value depends on the orient option:

Returns: dict, list or collections.abc.Mapping

Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

For my case, passing orient='records', a list of dictionaries is returned. When dealing with lists, the complete memory required to store the list items, is reserved/allocated. As my dataframe can get rather large, this might lead to memory issues especially as the code might be executed on lower spec target systems.

I could certainly circumvent this issue by processing the dataframe chunk-wise and generate the list of dictionaries for each chunk which is then passed to the API. Furthermore, calling iter(df.to_dict(orient='records')) would return the desired generator, but would not reduce the required memory footprint as the list is created intermediately.

Is there a way to directly return a generator expression from df.to_dict(orient='records') instead of a list in order to reduce the memory footprint?

ANSWER

Answered 2022-Feb-25 at 22:32

There is not a way to get a generator directly from to_dict(orient='records'). However, it is possible to modify the to_dict source code to be a generator instead of returning a list comprehension:

from pandas.core.common import standardize_mapping
from pandas.core.dtypes.cast import maybe_box_native


def dataframe_records_gen(df_):
    columns = df_.columns.tolist()
    into_c = standardize_mapping(dict)

    for row in df_.itertuples(index=False, name=None):
        yield into_c(
            (k, maybe_box_native(v)) for k, v in dict(zip(columns, row)).items()
        )

Sample Code:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4]
})

# Using Generator
for row in dataframe_records_gen(df):
    print(row)

# For Comparison with to_dict function
print("to_dict", df.to_dict(orient='records'))

Output:

{'A': 1, 'B': 3}
{'A': 2, 'B': 4}
to_dict [{'A': 1, 'B': 3}, {'A': 2, 'B': 4}]

For more natural syntax, it's also possible to register a custom accessor:

import pandas as pd
from pandas.core.common import standardize_mapping
from pandas.core.dtypes.cast import maybe_box_native


@pd.api.extensions.register_dataframe_accessor("gen")
class GenAccessor:
    def __init__(self, pandas_obj):
        self._obj = pandas_obj

    def records(self):
        columns = self._obj.columns.tolist()
        into_c = standardize_mapping(dict)

        for row in self._obj.itertuples(index=False, name=None):
            yield into_c(
                (k, maybe_box_native(v))
                for k, v in dict(zip(columns, row)).items()
            )

Which makes this generator accessible via the gen accessor in this case:

df = pd.DataFrame({
        'A': [1, 2],
        'B': [3, 4]
    })

# Using Generator through registered custom accessor
for row in df.gen.records():
    print(row)

# For Comparison with to_dict function
print("to_dict", df.to_dict(orient='records'))

Output:

{'A': 1, 'B': 3}
{'A': 2, 'B': 4}
to_dict [{'A': 1, 'B': 3}, {'A': 2, 'B': 4}]

Source https://stackoverflow.com/questions/71272151

QUESTION

Mixing yield and return. `yield [cand]; return` vs `return [[cand]]`. Why do they lead to different output?

Asked 2022-Feb-17 at 20:53

Why does

yield [cand]
return

lead to different output/behavior than

return [[cand]]

Minimal viable example

uses recursion
the output of the version using yield [1]; return is different than the output of the version using return [[1]]

def foo(i):
    if i != 1:
        yield [1]
        return
    yield from foo(i-1)    

def bar(i):
    if i != 1:  
        return [[1]]
    yield from bar(i-1)

print(list(foo(1))) # [[1]]
print(list(bar(1))) # []

Min viable counter example

does not use recurion
the output of the version using yield [1]; return is the same as the output of the version using return [[1]]

def foo():
    yield [1]
    return

def foofoo():
    yield from foo()

def bar():
    return [[1]]

def barbar():
    yield from bar()

print(list(foofoo())) # [[1]]
print(list(barbar())) # [[1]]

Full context

I'm solving Leetcode #39: Combination Sum and was wondering why one solution works, but not the other:

Working solution

from functools import cache # requires Python 3.9+

class Solution:
    def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:
        @cache
        def helper(targ, i=0):
            if i == N or targ < (cand := candidates[i]):
                return
            if targ == cand:
                yield [cand]
                return
            for comb in helper(targ - cand, i):
                yield comb + [cand]
            yield from helper(targ, i+1)
        
        N = len(candidates)
        candidates.sort()
        yield from helper(target)

Non-working solution

from functools import cache # requires Python 3.9+

class Solution:
    def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:
        @cache
        def helper(targ, i=0):
            if i == N or targ < (cand := candidates[i]):
                return
            if targ == cand:
                return [[cand]]
            for comb in helper(targ - cand, i):
                yield comb + [cand]
            yield from helper(targ, i+1)
        
        N = len(candidates)
        candidates.sort()
        yield from helper(target)

Output

On the following input

candidates = [2,3,6,7]
target = 7
print(Solution().combinationSum(candidates, target))

the working solution correctly prints

[[3,2,2],[7]]

while the non-working solution prints

[]

I'm wondering why yield [cand]; return works, but return [[cand]] doesn't.

ANSWER

Answered 2022-Feb-17 at 20:53

In a generator function, return just defines the value associated with the StopIteration exception implicitly raised to indicate an iterator is exhausted. It's not produced during iteration, and most iterating constructs (e.g. for loops) intentionally ignore the StopIteration exception (it means the loop is over, you don't care if someone attached random garbage to a message that just means "we're done").

For example, try:

>>> def foo():
...     yield 'onlyvalue'  # Existence of yield keyword makes this a generator
...     return 'returnvalue'
...

>>> f = foo()  # Makes a generator object, stores it in f

>>> next(f)  # Pull one value from generator
'onlyvalue'

>>> next(f)  # There is no other yielded value, so this hits the return; iteration over
--------------------------------------------------------------------------
StopIteration                            Traceback (most recent call last)
...
StopIteration: 'returnvalue'

As you can see, your return value does get "returned" in a sense (it's not completely discarded), but it's never seen by anything iterating normally, so it's largely useless. Outside of rare cases involving using generators as coroutines (where you're using .send() and .throw() on instances of the generator and manually advancing it with next(genobj)), the return value of a generator won't be seen.

In short, you have to pick one:

Use yield anywhere in a function, and it's a generator (whether or not the code path of a particular call ever reaches a yield) and return just ends generation (while maybe hiding some data in the StopIteration exception). No matter what you do, calling the generator function "returns" a new generator object (which you can loop over until exhausted), it can never return a raw value computed inside the generator function (which doesn't even begin running until you loop over it at least once).
Don't use yield, and return works as expected (because it's not a generator function).

As an example to explain what happens to the return value in normal looping constructs, this is what for x in gen(): effectively expands to a C optimized version of:

__unnamed_iterator = iter(gen())
while True:
    try:
        x = next(__unnamed_iterator)
    except StopIteration:  # StopIteration caught here without inspecting it
        break              # Loop ends, StopIteration exception cleaned even from sys.exc_info() to avoid possible reference cycles

    # body of loop goes here

# Outside of loop, there is no StopIteration object left

As you can see, the expanded form of the for loop has to look for a StopIteration to indicate the loop is over, but it doesn't use it. And for anything that's not a generator, the StopIteration never has any associated values; the for loop has no way to report them even if it did (it has to end the loop when it's told iteration is over, and the arguments to StopIteration are explicitly not part of the values iterated anyway). Anything else that consumes the generator (e.g. calling list on it) is doing roughly the same thing as the for loop, ignoring the StopIteration in the same way; nothing except code that specifically expects generators (as opposed to more generalized iterables and iterators) will ever bother to inspect the StopIteration object (at the C layer, there are optimizations that StopIteration objects aren't even produced by most iterators; they return NULL and leave the set exception empty, which all iterator protocol using things know is equivalent to returning NULL and setting a StopIteration object, so for anything but a generator, there isn't even an exception to inspect much of the time).

Source https://stackoverflow.com/questions/71164795

QUESTION

Are generators with context managers an anti-pattern?

Asked 2022-Jan-17 at 17:17

I'm wondering about code like this:

def all_lines(filename):
    with open(filename) as infile:
        yield from infile

The point of a context manager is to have explicit control over the lifetime of some form of state, e.g. a file handle. A generator, on the other hand, keeps its state until it is exhausted or deleted.

I do know that both cases work in practice. But I'm worried about whether it is a good idea. Consider for example this:

def all_first_lines(filenames):
    return [next(all_lines(filename), None) for filename in filenames]

I never exhaust the generators. Instead, their state is destroyed when the generator object is deleted. This works fine in reference-counted implementations like CPython, but what about garbage-collected implementations? I'm practically relying on the reference counter for managing state, something that context managers were explicitly designed to avoid!

And even in CPython it shouldn't be too hard to construct cases were a generator is part of a reference cycle and needs the garbage collector to be destroyed.

To summarize: Would you consider it prudent to avoid context managers in generators, for example by refactoring the above code into something like this?

def all_lines(filename):
    with open(filename) as infile:
        return infile.readlines()

def first_line(filename):
    with open(filename) as infile:
        return next(infile, None)

def all_first_lines(filenames):
    return [first_line(filename) for filename in filenames]

ANSWER

Answered 2022-Jan-17 at 14:48

There are two answers to your question :

the absolutist : indeed, the context managers will not serve their role, the GC will have to clean the mess that should not have happened
the pragmatic : true, but is it actually a problem ? Your file handle will get released a few milliseconds later, what's the bother ? Does it have a measurable impact on production, or is it just bikeshedding ?

I'm not an expert to Python alt implementations' differences (see this page for PyPy's example), but I posit that this lifetime problem will not occur in 99% of cases. If you happen to hit in prod, then yes, you should address it (either with your proposal, or a mix of generator with context manager) otherwise, why bother ? I mean it in a kind way : your point is strictly valid, but irrelevant to most cases.

Source https://stackoverflow.com/questions/70729329

QUESTION

Python: Generate a unique batch from given dataset

Asked 2021-Nov-27 at 06:30

I'm applying a CNN to classify a given dataset.

My function:

def batch_generator(dataset, input_shape = (256, 256), batch_size = 32):
    dataset_images = []
    dataset_labels = []
    for i in range(0, len(dataset)):
        dataset_images.append(cv2.resize(cv2.imread(dataset[i], cv2.IMREAD_COLOR), 
                     input_shape, interpolation = cv2.INTER_AREA))
        dataset_labels.append(labels[dataset[i].split('/')[-2]])
    return dataset_images, dataset_labels

This function is supposed to be called for every epoch and it should return a unique batch of size 'batch_size' containing dataset_images (each image is 256x256) and corresponding dataset_label from the labels dictionary.

input 'dataset' contains path to all the images, so I'm opening them and resizing them to 256x256. Can someone help me in adding to this code so that is returns the desired batches?

ANSWER

Answered 2021-Nov-25 at 17:50

As @jodag suggests, using DataLoaders is a good idea.

I have a snippet of that I use for some of my CNN in Pytorch

from torch.utils.data import Dataset, DataLoader
import torch
class Data(Dataset):
    """
    Constructs a Dataset to be parsed into a DataLoader
    """
    def __init__(self,X,y):
        X = torch.from_numpy(X).float()

        #Transpose to fit dimensions of my network
        X = torch.transpose(X,1,2)

        y = torch.from_numpy(y).float()
        self.X,self.y = X,y

    def __getitem__(self, i):
        return self.X[i],self.y[i]

    def __len__(self):
        return self.X.shape[0]

def create_data_loader(X,y,batch_size,**kwargs):
    """
    Creates a data-loader for the data X and y

    params:
    -------

    X: np.array
        - numpy array of size "n" x k where n is samples an "k" is number of features

    y: np.array
        - numpy array of sie "n"

    batch_size: int
        - Take a wild guess, dumbass

    kwargs:
        - Additional keyword-arguments for "DataLoader"

    return
    ------

    dl: torch.utils.data.DataLoader object
    """

    data = Data(X, y)

    dl = DataLoader(data, batch_size=batch_size,num_workers=0,**kwargs)
    return dl

which is used like this;

from create_data_loader import create_data_loader

train_data_loader= create_data_loader(X_train,y_train,batch_size=32) #Note, it has "shuffle=True" as default!
val_data_loader= create_data_loader(X_val,y_val,batch_size=32,shuffle=False) #If you want to keep index'es in the same order for e.g cross-validate


for x_train, y_train in train_data_loader:
   logit = net(x_train,y_train)
   .
   .
   net.eval()
   for x_val,y_val in val_data_loader:
       logit  = net(x_val,y_val)
       classes_pred = logit.argmax(axis=1)
       print(f"Val accuracy: {(y_val==classes_pred).mean()}")

Source https://stackoverflow.com/questions/70114892

QUESTION

Can I get the current value of generator in JavaScript?

Asked 2021-Nov-06 at 22:06

Let's say I want to rotate class names for my button on click. Clicked once becomes button-green, twice - button-yellow, thrice - button-red. And then it repeats, so fourth click makes it button-green again.

I know other techniques how to do it, I'm not asking for implementation advice. I made up this example to understand something about generators in JavaScript.

Here's my code with generator:

function* rotator(items) {
    while (true) {
        for (const item of items) {
            yield item;
        }
    }
}

const classRotator = rotator([
    'button-green',
    'button-yellow',
    'button-red',
]);

document.getElementById('my-button').addEventListener('click', event => {
    event.currentTarget.classList.add(classRotator.next().value);
});

It works fine, except it never gets rid of the previous class. The most convenient way would be to read the current state before getting the next one:

// .current is not a thing:
event.currentTarget.classList.remove(classRotator.current);

Of course I can keep this value on my own and use it. Likewise, I can clear all classes I use in the rotator() myself. I can even make my generator function yield both previous and current value:

function* rotator(items) {
    let previous;

    while (true) {
        for (const item of items) {
            yield {
                item,
                previous
            };

            previous = item;
        }
    }
}

And then use it like this:

document.getElementById('my-button').addEventListener('click', event => {
    const {item: className, previous: previousClassName} = classRotator.next().value;

    event.currentTarget.classList.remove(previousClassName);
    event.currentTarget.classList.add(className);
});

But that's not the point - for educational purpose I'm asking this question:

Can I read the current value of generator function in JavaScript? If not, is it to avoid using memory when it's not needed (this value can potentially be very big)?

ANSWER

Answered 2021-Nov-06 at 19:59

JavaScript "native" APIs generally are willing to create new objects with wild abandon. Conserving memory is generally not, by any appearances, a fundamental goal of the language committee.

It would be quite simple to create a general facility to wrap the result of invoking a generator in an object that delegates the .next() method to the actual result object, but also saves each returned value as a .current() value (or whatever works for your application). Having a .current() is useful, for such purposes as a lexical analyzer for a programming language. The basic generator API, however, does not make provisions for that.

Source https://stackoverflow.com/questions/69867197

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ddth-id

Latest release version: 0.5.0. See [RELEASE-NOTES.md](RELEASE-NOTES.md). Maven dependency: if only a sub-set of ddth-id is used, choose the corresponding dependency artifact(s) to reduce the number of unused jar files. ddth-id-core: in-memory id-generator and Snowflake id-generator; Cassandra, Jdbc, Redis and Zookeeper dependencies are optional. ddth-id-cassandra: include all ddth-id-core and Cassandra dependencies. ddth-id-jdbc: include all ddth-id-core and Jdbc dependencies. ddth-id-redis: include all ddth-id-core and Redis dependencies. ddth-id-zookeeper: include all ddth-id-core and ZooKeeper dependencies.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: