semvec/pypi-packages huggingface dataset:
weighted-tqdm 0.9
weighted_tqdm allows for weighted iterations in tqdm progress bars.
Requires Python: >=3.6

# weighted_tqdm

Install via 
`pip install weighted-tqdm`

Import via
`from weighted_tqdm import *`

## Description
**weighted_tqdm** works equivalently to tqdm, accepting all the same arguments, but also accepts a `weights` argument. This argument specifies the weights of each item in the iterable and can be given as a function of the iterable or be any iterable (list, array, tuple...). The progress bar will then take into account the weights of each item in it's prediciton of the time, and it's progress bar will be weighted accordingly. To the left of the progress bar an iteration counter is shown.
```
how_many_qubits = [1,2,3,4,5]
qubits weights = lambda x: (2**x)**3
for i in weighted_tqdm(how_many_qubits, weights=weights):
    # do something
```
New: Now supports `p_tqdm` with an additionally passed function handle for parallel execution. This functionality extends to the generators. 

**qudit_tqdm** is a special variant of tqdm, that predicts the remaining time for calculations in quantum mechanics, with the added arguments `dit` specifying whether its a calculation of qubits (default `dit=2` or `dit=3` for qutrits), and the argument `exp` specifying the scaling of computational time with the dimension of a hilbert space. 
```
for i in qudit_tqdm(how_many_qubits, dim=2, exp=3):
    time.sleep((2**i)**3)
```

**progress** is a generator of progress bars which allows each iteration within a nested set of loops to update a single progress bar. It allows adding classic `tqdm`, but also `weighted_tqdm` or `qudit_tqdm` to different nested loops.
```
p = progress()
for i in p.weighted_tqdm(range(5), weights=lambda i: (i+1), name='outer'):
    for j in p.tqdm((1,2,3,4,5), name='inner'):
        time.sleep(0.1*(i+1))
```

**weighted_kronbinations_tqdm** is the final progress bar, which allows the use of weighted progress bars for `kronbination` loops. Unlike the other functions, tehe update has to manually be added to every iteration.
```
list_of_iterators = [range(3), [1,2,3]]    # list of the iterators
list_of_weights = [ones(3), lambda x: (2**x)**3]    # weights for each iterator
indexes = np.array([[0,0], [0,1], [0,2], [1,0], [1,1], [1,2]]])    # specify the indexes which are being executed 
p = weighted_kronbinations_tqdm(list_of_iterators, list_of_weights)    # initialize the generator
p.init(indexes)    # prepare a progress bar 
for i in indexes:
    p.increment()  # update the progress bar
    time.sleep((2**list_of_iterators[1][i[1]])**3)    # do something
```

## Authors: 
By Michael Schilling