How to use the tqdm._utils._range function in tqdm

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github tqdm / tqdm / tqdm / _tqdm_regress.py View on Github external
def format_meter(self, n, total, elapsed, ncols=None, prefix='',
                     ascii=False, unit='it', unit_scale=False, rate=None,
                     bar_format=None):

        # Add new point and fit
        # But only if new iteration (else can be called by __repr__() and refresh())
        if n > self.model.last_it:
            # Add point (n, elapsed)
            self.model.add_sample([n], elapsed, n)
            # Fit (learn/train)
            # We do in a loop because to avoid the need to manually adjust learning rate
            for _ in _range(self.repeat):
                self.model.fit(**self.fit_params)
            # Plot if required
            if self.plot:
                self.model.plot(n)

            # Compute rate and ending time
            if n > 0 and total:
                # Predict ending time
                predicted_endtime = float(self.model.predict([total]))
                # Predicted endtime must be greater than current time else fitting failed!
                if predicted_endtime > 0 and predicted_endtime >= elapsed:
                    # Pessimistic: predict rate against remaining time, not total time
                    rate = (total - n) / (predicted_endtime - elapsed)

        # Do the rest as usual, with the provided rate computed by polynomial regression
        return super(tqdm_regress, self).format_meter(n, total, elapsed, ncols, prefix,
github tqdm / tqdm / tqdm / _tqdm.py View on Github external
def trange(*args, **kwargs):
    """
    A shortcut for tqdm(xrange(*args), **kwargs).
    On Python3+ range is used instead of xrange.
    """
    return tqdm(_range(*args), **kwargs)
github tqdm / tqdm / tqdm / _tqdm_custom.py View on Github external
def tcrange(*args, **kwargs):
    """
    A shortcut for tqdm_custom(xrange(*args), **kwargs).
    On Python3+ range is used instead of xrange.
    """
    return tqdm_custom(_range(*args), **kwargs)
github tqdm / tqdm / tqdm / _tqdm_regress.py View on Github external
"""Compute polynomials from a single value
        If order = z.5 (z + 1/2) then sqrt, log and exp will be added.
        Note: should polynomize always before feature scaling."""
        if not isinstance(x, list):
            x = [x]
        x_poly = deepcopy(x)  # copy the list
        if order > 1.0:
            # special funcs: sqrt, log and exp
            if order - int(order) > 0.0:
                for xi in x:
                    try:
                        x_poly.extend([xi**0.5, log(xi), exp(xi)])
                    except ValueError:  # log(0.0) is undefined
                        x_poly.extend([0.0, 0.0, 0.0])
            # Higher order polynomials (starting from x^2)
            for o in _range(2, int(order)+1):  # start from x^2
                x_poly.extend([xi**o for xi in x])
        return x_poly
github tqdm / tqdm / tqdm / _tqdm_custommulti.py View on Github external
filler_lines = c_symb[-1][0].splitlines() if isinstance(c_symb[-1], list) else c_symb[-1].splitlines()
                        filler_width = len(filler_lines[0])
                        bar_length, frac_bar_length = divmod(
                            int(frac * int(N_BARS/filler_width) * nb_symb), nb_symb)

                        # If animated, get what animation we will show
                        if isinstance(c_symb[-1], list):
                            # If random, pick frame randomly
                            if bar_format['symbols'].get('random', False):
                                # Need filler symbol
                                if bar_length:
                                    # Randomly select the frame for each filler
                                    filler_rand = [random.choice(c_symb[-1]).splitlines() for _ in _range(bar_length)]
                                    # Generate filler (stack lines horizontally)
                                    filler_lines = []
                                    for i in _range(len(filler_rand[0])):
                                        filler_lines.append(''.join(frame[i] for frame in filler_rand))
                                # Just started, no filler, only frac,
                                # generate empty lines
                                else:
                                    filler_lines = ['' for line in filler_lines]
                                # Generate frac randomly
                                frac_lines = random.choice(c_symb[frac_bar_length]).splitlines()
                            # Else advance one frame per display
                            else:
                                # Get current bar animation frame
                                filler_symb = c_symb[-1][divmod(self.n_anim, len(c_symb[-1]))[1]]
                                frac_symb = c_symb[frac_bar_length][divmod(self.n_anim, len(c_symb[frac_bar_length]))[1]]
                                # Repeat as required
                                filler_lines = [line * bar_length for line in filler_symb.splitlines()]
                                frac_lines = frac_symb.splitlines()
                        # Not animated, don't need to select a frame
github tqdm / tqdm / tqdm / _tqdm_custommulti.py View on Github external
def tcmrange(*args, **kwargs):
    """
    A shortcut for tqdm_custommulti(xrange(*args), **kwargs).
    On Python3+ range is used instead of xrange.
    """
    return tqdm_custommulti(_range(*args), **kwargs)
github tqdm / tqdm / tqdm / _tqdm_custommulti.py View on Github external
# Calculate bar length given progress
                        # last symbol is always the filler
                        nb_symb = len(c_symb)
                        filler_lines = c_symb[-1][0].splitlines() if isinstance(c_symb[-1], list) else c_symb[-1].splitlines()
                        filler_width = len(filler_lines[0])
                        bar_length, frac_bar_length = divmod(
                            int(frac * int(N_BARS/filler_width) * nb_symb), nb_symb)

                        # If animated, get what animation we will show
                        if isinstance(c_symb[-1], list):
                            # If random, pick frame randomly
                            if bar_format['symbols'].get('random', False):
                                # Need filler symbol
                                if bar_length:
                                    # Randomly select the frame for each filler
                                    filler_rand = [random.choice(c_symb[-1]).splitlines() for _ in _range(bar_length)]
                                    # Generate filler (stack lines horizontally)
                                    filler_lines = []
                                    for i in _range(len(filler_rand[0])):
                                        filler_lines.append(''.join(frame[i] for frame in filler_rand))
                                # Just started, no filler, only frac,
                                # generate empty lines
                                else:
                                    filler_lines = ['' for line in filler_lines]
                                # Generate frac randomly
                                frac_lines = random.choice(c_symb[frac_bar_length]).splitlines()
                            # Else advance one frame per display
                            else:
                                # Get current bar animation frame
                                filler_symb = c_symb[-1][divmod(self.n_anim, len(c_symb[-1]))[1]]
                                frac_symb = c_symb[frac_bar_length][divmod(self.n_anim, len(c_symb[frac_bar_length]))[1]]
                                # Repeat as required
github tqdm / tqdm / tqdm / _tqdm_regress.py View on Github external
def tsrange(*args, **kwargs):
    """
    A shortcut for tqdm_regress(xrange(*args), **kwargs).
    On Python3+ range is used instead of xrange.
    """
    return tqdm_regress(_range(*args), **kwargs)