class CLIProgressBar(object):
widgets = [' ', Bar(marker=RotatingMarker()), ' ', Percentage()]
def __init__(self, size=100, label="", color=None, fd=sys.stdout):
self.progress_bar = PB(widgets=[label] + self.widgets, maxval=size, fd=fd)
self.progress = 0
def start(self, width='50%'):
self.progress_bar.start()
self.progress = 0
def increment(self, i=1):
p = self.progress + i
self.progress_bar.update(p)
def update(self, progress):
self.progress_bar.update(progress)
def reset(self):
self.progress_bar.start()
self.progress = 0
def end(self):
self.progress_bar.finish()
def __call__(self, iterable):
return self.progress_bar(iterable)
def example3():
widgets = [Bar('>'), ' ', ETA(), ' ', ReverseBar('<')]
pbar = ProgressBar(widgets=widgets, maxval=10000000).start()
for i in range(1000000):
# do something
pbar.update(10 * i + 1)
pbar.finish()
class CLIProgressBar(object):
widgets = [' ', Bar(marker=RotatingMarker()), ' ', Percentage()]
def __init__(self, size=100, label="", color=None, fd=sys.stdout):
self.progress_bar = PB(widgets=[label] + self.widgets,
maxval=size,
fd=fd)
self.progress = 0
def start(self, width='50%'):
self.progress_bar.start()
self.progress = 0
def increment(self, i=1):
p = self.progress + i
self.progress_bar.update(p)
def update(self, progress):
self.progress_bar.update(progress)
def reset(self):
self.progress_bar.start()
self.progress = 0
def end(self):
self.progress_bar.finish()
def __call__(self, iterable):
return self.progress_bar(iterable)
def example1():
widgets = ['Test: ', Percentage(), ' ', Bar(marker=RotatingMarker()),
' ', ETA(), ' ', FileTransferSpeed()]
pbar = ProgressBar(widgets=widgets, maxval=10000000).start()
for i in range(1000000):
# do something
pbar.update(10 * i + 1)
pbar.finish()
def example18():
widgets = [Percentage(), ' ', Bar(), ' ', ETA(), ' ', AdaptiveETA()]
pbar = ProgressBar(widgets=widgets, maxval=500)
pbar.start()
for i in range(500):
time.sleep(0.01 + (i < 100) * 0.01 + (i > 400) * 0.9)
pbar.update(i + 1)
pbar.finish()
def example17():
widgets = [FormatLabel('Animated Bouncer: value %(value)d - '),
BouncingBar(marker=RotatingMarker())]
pbar = ProgressBar(widgets=widgets)
for i in pbar((i for i in range(180))):
time.sleep(0.05)
def example4():
widgets = ['Test: ', Percentage(), ' ',
Bar(marker='0', left='[', right=']'),
' ', ETA(), ' ', FileTransferSpeed()]
pbar = ProgressBar(widgets=widgets, maxval=500)
pbar.start()
for i in range(100, 500 + 1, 50):
time.sleep(0.2)
pbar.update(i)
pbar.finish()
def example15():
# You may need python 3.x to see this correctly
try:
widgets = ['Wheels: ', AnimatedMarker(markers='◐◓◑◒')]
pbar = ProgressBar(widgets=widgets)
for i in pbar((i for i in range(24))):
time.sleep(0.3)
except UnicodeError:
sys.stdout.write('Unicode error: skipping example')
def example4():
widgets = ['Test: ', Percentage(), ' ',
Bar(marker='0', left='[', right=']'),
' ', ETA(), ' ', FileTransferSpeed()]
pbar = ProgressBar(widgets=widgets, maxval=500)
pbar.start()
for i in range(100, 500 + 1, 50):
time.sleep(0.2)
pbar.update(i)
pbar.finish()
def example3():
widgets = [Bar('>'), ' ', ETA(), ' ', ReverseBar('<')]
pbar = ProgressBar(widgets=widgets, maxval=10000000).start()
for i in range(1000000):
# do something
pbar.update(10 * i + 1)
pbar.finish()
def example18():
widgets = [Percentage(),
' ', Bar(),
' ', ETA(),
' ', AdaptiveETA()]
pbar = ProgressBar(widgets=widgets, maxval=500)
pbar.start()
for i in range(500):
time.sleep(0.01 + (i < 100) * 0.01 + (i > 400) * 0.9)
pbar.update(i + 1)
pbar.finish()
def example1():
widgets = ['Test: ', Percentage(), ' ', Bar(marker=RotatingMarker()),
' ', ETA(), ' ', FileTransferSpeed()]
pbar = ProgressBar(widgets=widgets, maxval=10000000).start()
for i in range(1000000):
# do something
pbar.update(10 * i + 1)
pbar.finish()
def example2():
class CrazyFileTransferSpeed(FileTransferSpeed):
"""It's bigger between 45 and 80 percent."""
def update(self, pbar):
if 45 < pbar.percentage() < 80:
return 'Bigger Now ' + FileTransferSpeed.update(self, pbar)
else:
return FileTransferSpeed.update(self, pbar)
widgets = [CrazyFileTransferSpeed(), ' <<<', Bar(), '>>> ',
Percentage(), ' ', ETA()]
pbar = ProgressBar(widgets=widgets, maxval=10000000)
# maybe do something
pbar.start()
for i in range(2000000):
# do something
pbar.update(5 * i + 1)
pbar.finish()
def optimize_strains(self, pathways, view, aerobic=True):
"""
Optimize targets for the identified pathways. The optimization will only run if the pathway can be optimized.
Arguments
---------
pathways: list
A list of dictionaries to optimize ([Host, Model] -> PredictedPathways).
view: object
A view for multi, single os distributed processing.
aerobic: bool
If True, it will set `model.reactions.EX_o2_e.lower_bound` to 0.
Returns
-------
pandas.DataFrame
A data frame with strain designs processed and ranked.
"""
opt_gene_runner = _OptGeneRunner(self.debug)
differential_fva_runner = _DifferentialFVARunner(self.debug)
strategies = [
(host, model, pathway, aerobic) for (host, model) in pathways
for pathway in pathways[host, model]
if pathway.needs_optimization(model, objective=model.biomass)
]
print("Optimizing %i pathways" % len(strategies))
results = DataFrame(columns=[
"host", "model", "manipulations", "heterologous_pathway",
"fitness", "yield", "product", "biomass", "method"
])
mapped_designs1 = view.map(opt_gene_runner, strategies)
mapped_designs2 = view.map(differential_fva_runner, strategies)
progress = ProgressBar(
maxval=len(mapped_designs1) + len(mapped_designs2),
widgets=["Processing solutions: ",
Bar(), ETA()])
progress.start()
results = self.build_results_data(mapped_designs1, strategies, results,
progress)
results = self.build_results_data(mapped_designs2,
strategies,
results,
progress,
offset=len(mapped_designs1))
progress.finish()
return results
def example2():
class CrazyFileTransferSpeed(FileTransferSpeed):
"""It's bigger between 45 and 80 percent."""
def update(self, pbar):
if 45 < pbar.percentage() < 80:
return 'Bigger Now ' + FileTransferSpeed.update(self, pbar)
else:
return FileTransferSpeed.update(self, pbar)
widgets = [CrazyFileTransferSpeed(), ' <<<', Bar(), '>>> ',
Percentage(), ' ', ETA()]
pbar = ProgressBar(widgets=widgets, maxval=10000000)
# maybe do something
pbar.start()
for i in range(2000000):
# do something
pbar.update(5 * i + 1)
pbar.finish()
def example10():
widgets = ['Processed: ', Counter(), ' lines (', Timer(), ')']
pbar = ProgressBar(widgets=widgets)
for i in pbar((i for i in range(150))):
time.sleep(0.1)
def example9():
pbar = ProgressBar(widgets=['Working: ', AnimatedMarker()])
for i in pbar((i for i in range(50))):
time.sleep(.08)
def example8():
pbar = ProgressBar(maxval=80) # Progressbar can't guess maxval.
for i in pbar((i for i in range(80))):
time.sleep(0.01)
def run(self,
surface_only=True,
improvements_only=True,
progress=True,
view=None,
fraction_of_optimum=1.0):
"""Run the differential flux variability analysis.
Parameters
----------
surface_only : bool, optional
If only the surface of the n-dimensional production envelope should be scanned (defaults to True).
improvements_only : bool, optional
If only grid points should should be scanned that constitute and improvement in production
over the reference state (defaults to True).
progress : bool, optional
If a progress bar should be shown.
view : SequentialView or MultiprocessingView or ipython.cluster.DirectView, optional
A parallelization view (defaults to SequentialView).
fraction_of_optimum : float, optional
A value between zero and one that determines the width of the
flux ranges of the reference solution. The lower the value,
the larger the ranges.
Returns
-------
pandas.Panel
A pandas Panel containing a results DataFrame for every grid point scanned.
"""
# Calculate the reference state.
self.reference_flux_dist = pfba(
self.reference_model, fraction_of_optimum=fraction_of_optimum)
self.reference_flux_ranges = flux_variability_analysis(
self.reference_model,
reactions=self.included_reactions,
view=view,
remove_cycles=False,
fraction_of_optimum=fraction_of_optimum).data_frame
self.reference_flux_ranges[
self.reference_flux_ranges.abs() < non_zero_flux_threshold] = 0.0
reference_intervals = self.reference_flux_ranges.loc[
self.included_reactions, ['lower_bound', 'upper_bound']].values
if self.normalize_ranges_by is not None:
logger.debug(
self.reference_flux_ranges.loc[self.normalize_ranges_by, ])
# The most obvious flux to normalize by is the biomass reaction
# flux. This is probably always greater than zero. Just in case
# the model is defined differently or some other normalizing
# reaction is chosen, we use the absolute value.
norm = abs(self.reference_flux_ranges.at[self.normalize_ranges_by,
"lower_bound"])
if norm > non_zero_flux_threshold:
normalized_reference_intervals = reference_intervals / norm
else:
raise ValueError(
"The reaction that you have chosen for normalization '{}' "
"has zero flux in the reference state. Please choose another "
"one.".format(self.normalize_ranges_by))
with TimeMachine() as tm:
# Make sure that the design_space_model is initialized to its original state later
for variable in self.variables:
reaction = self.design_space_model.reactions.get_by_id(
variable)
tm(do=int,
undo=partial(setattr, reaction, 'lower_bound',
reaction.lower_bound))
tm(do=int,
undo=partial(setattr, reaction, 'upper_bound',
reaction.upper_bound))
target_reaction = self.design_space_model.reactions.get_by_id(
self.objective)
tm(do=int,
undo=partial(setattr, target_reaction, 'lower_bound',
target_reaction.lower_bound))
tm(do=int,
undo=partial(setattr, target_reaction, 'upper_bound',
target_reaction.upper_bound))
if view is None:
view = config.default_view
else:
view = view
self._init_search_grid(surface_only=surface_only,
improvements_only=improvements_only)
func_obj = _DifferentialFvaEvaluator(self.design_space_model,
self.variables,
self.objective,
self.included_reactions)
if progress:
progress = ProgressBar(len(self.grid))
results = list(
progress(view.imap(func_obj, self.grid.iterrows())))
else:
results = list(view.map(func_obj, self.grid.iterrows()))
solutions = dict((tuple(point.iteritems()), fva_result)
for (point, fva_result) in results)
for sol in solutions.values():
sol[sol.abs() < non_zero_flux_threshold] = 0.0
intervals = sol.loc[self.included_reactions,
['lower_bound', 'upper_bound']].values
gaps = [
self._interval_gap(interval1, interval2)
for interval1, interval2 in zip(reference_intervals, intervals)
]
sol['gaps'] = gaps
if self.normalize_ranges_by is not None:
# See comment above regarding normalization.
normalizer = abs(sol.lower_bound[self.normalize_ranges_by])
if normalizer > non_zero_flux_threshold:
normalized_intervals = sol.loc[
self.included_reactions,
['lower_bound', 'upper_bound']].values / normalizer
sol['normalized_gaps'] = [
self._interval_gap(interval1, interval2)
for interval1, interval2 in zip(
normalized_reference_intervals,
normalized_intervals)
]
else:
sol['normalized_gaps'] = numpy.nan
else:
sol['normalized_gaps'] = gaps
# Determine where the reference flux range overlaps with zero.
zero_overlap_mask = numpy.asarray([
self._interval_overlap(interval1, (0, 0)) > 0
for interval1 in reference_intervals
],
dtype=bool)
collection = list()
for key, df in solutions.items():
df['biomass'] = key[0][1]
df['production'] = key[1][1]
df['KO'] = False
df['flux_reversal'] = False
df['suddenly_essential'] = False
df['free_flux'] = False
df.loc[(df.lower_bound == 0) & (df.upper_bound == 0) &
(~zero_overlap_mask), 'KO'] = True
df.loc[((self.reference_flux_ranges.upper_bound < 0) &
(df.lower_bound > 0) |
((self.reference_flux_ranges.lower_bound > 0) &
(df.upper_bound < 0))), 'flux_reversal'] = True
df.loc[(zero_overlap_mask & (df.lower_bound > 0)) |
(zero_overlap_mask & (df.upper_bound < 0)),
'suddenly_essential'] = True
is_reversible = numpy.asarray([
self.design_space_model.reactions.get_by_id(i).reversibility
for i in df.index
],
dtype=bool)
not_reversible = ~is_reversible
df.loc[((df.lower_bound == -1000) &
(df.upper_bound == 1000) & is_reversible) |
((df.lower_bound == 0) &
(df.upper_bound == 1000) & not_reversible) |
((df.lower_bound == -1000) &
(df.upper_bound == 0) & not_reversible),
'free_flux'] = True
df['reaction'] = df.index
df['excluded'] = df['reaction'].isin(self.exclude)
collection.append(df)
# multi_index = [(key[0][1], key[1][1]) for key in solutions]
# solutions_multi_index = pandas.concat(list(solutions.values()),
# axis=0, keys=multi_index)#
# solutions_multi_index.index.set_names(['biomass', 'production',
# 'reaction'], inplace=True)
total = pandas.concat(collection, ignore_index=True, copy=False)
total.sort_values(['biomass', 'production', 'reaction'], inplace=True)
total.index = total['reaction']
return DifferentialFVAResult(total, self.envelope,
self.reference_flux_ranges)
def example0():
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=300).start()
for i in range(300):
time.sleep(0.01)
pbar.update(i + 1)
pbar.finish()
def example19():
pbar = ProgressBar()
for i in pbar([]):
pass
pbar.finish()
def run(self, surface_only=True, improvements_only=True, view=None):
"""Run the differential flux variability analysis.
Parameters
----------
surface_only : bool, optional
If only the surface of the n-dimensional production envelope should be scanned (defaults to True).
improvements_only : bool, optional
If only grid points should should be scanned that constitute and improvement in production
over the reference state (defaults to True).
view : SequentialView or MultiprocessingView or ipython.cluster.DirectView, optional
A parallelization view (defaults to SequentialView).
Returns
-------
pandas.Panel
A pandas Panel containing a results DataFrame for every grid point scanned.
"""
with TimeMachine() as tm:
# Make sure that the design_space_model is initialized to its original state later
for variable in self.variables:
reaction = self.design_space_model.reactions.get_by_id(
variable)
tm(do=int,
undo=partial(setattr, reaction, 'lower_bound',
reaction.lower_bound))
tm(do=int,
undo=partial(setattr, reaction, 'upper_bound',
reaction.upper_bound))
target_reaction = self.design_space_model.reactions.get_by_id(
self.objective)
tm(do=int,
undo=partial(setattr, target_reaction, 'lower_bound',
target_reaction.lower_bound))
tm(do=int,
undo=partial(setattr, target_reaction, 'upper_bound',
target_reaction.upper_bound))
if view is None:
view = config.default_view
else:
view = view
included_reactions = [
reaction.id for reaction in self.reference_model.reactions
if reaction.id not in self.exclude
]
self.reference_flux_ranges = flux_variability_analysis(
self.reference_model,
reactions=included_reactions,
view=view,
remove_cycles=False).data_frame
self._init_search_grid(surface_only=surface_only,
improvements_only=improvements_only)
progress = ProgressBar(len(self.grid))
func_obj = _DifferentialFvaEvaluator(self.design_space_model,
self.variables,
self.objective,
included_reactions)
results = list(progress(view.imap(func_obj, self.grid.iterrows())))
solutions = dict((tuple(point.iteritems()), fva_result.data_frame)
for (point, fva_result) in results)
reference_intervals = self.reference_flux_ranges[[
'lower_bound', 'upper_bound'
]].values
for sol in six.itervalues(solutions):
intervals = sol[['lower_bound', 'upper_bound']].values
gaps = [
self._interval_gap(interval1, interval2) for interval1,
interval2 in my_zip(reference_intervals, intervals)
]
sol['gaps'] = gaps
if self.normalize_ranges_by is not None:
for sol in six.itervalues(solutions):
normalized_intervals = sol[[
'lower_bound', 'upper_bound'
]].values / sol.lower_bound[self.normalize_ranges_by]
normalized_gaps = [
self._interval_gap(interval1, interval2)
for interval1, interval2 in my_zip(reference_intervals,
normalized_intervals)
]
sol['normalized_gaps'] = normalized_gaps
for df in six.itervalues(solutions):
ko_selection = df[(df.lower_bound == 0) & (df.upper_bound == 0)
& (self.reference_flux_ranges.lower_bound != 0)
& self.reference_flux_ranges.upper_bound != 0]
df['KO'] = False
df.loc[ko_selection.index]['KO'] = True
for df in six.itervalues(solutions):
flux_reversal_selection = df[(
(self.reference_flux_ranges.upper_bound < 0) &
(df.lower_bound > 0) |
((self.reference_flux_ranges.lower_bound > 0) &
(df.upper_bound < 0)))]
df['flux_reversal'] = False
df.loc[flux_reversal_selection.index]['flux_reversal'] = True
for df in six.itervalues(solutions):
flux_reversal_selection = df[(
(self.reference_flux_ranges.lower_bound <= 0) &
(df.lower_bound > 0)) | (
(self.reference_flux_ranges.upper_bound >= 0) &
(df.upper_bound <= 0))]
df['suddenly_essential'] = False
df.loc[flux_reversal_selection.index]['suddenly_essential'] = True
# solutions['reference_flux_ranges'] = self.reference_flux_ranges
return DifferentialFVAResult(pandas.Panel(solutions), self.envelope,
self.variables, self.objective)
def example0():
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=300).start()
for i in range(300):
time.sleep(0.01)
pbar.update(i + 1)
pbar.finish()
def run(self,
surface_only=True,
improvements_only=True,
progress=True,
view=None):
"""Run the differential flux variability analysis.
Parameters
----------
surface_only : bool, optional
If only the surface of the n-dimensional production envelope should be scanned (defaults to True).
improvements_only : bool, optional
If only grid points should should be scanned that constitute and improvement in production
over the reference state (defaults to True).
progress : bool, optional
If a progress bar should be shown.
view : SequentialView or MultiprocessingView or ipython.cluster.DirectView, optional
A parallelization view (defaults to SequentialView).
Returns
-------
pandas.Panel
A pandas Panel containing a results DataFrame for every grid point scanned.
"""
with TimeMachine() as tm:
# Make sure that the design_space_model is initialized to its original state later
for variable in self.variables:
reaction = self.design_space_model.reactions.get_by_id(
variable)
tm(do=int,
undo=partial(setattr, reaction, 'lower_bound',
reaction.lower_bound))
tm(do=int,
undo=partial(setattr, reaction, 'upper_bound',
reaction.upper_bound))
target_reaction = self.design_space_model.reactions.get_by_id(
self.objective)
tm(do=int,
undo=partial(setattr, target_reaction, 'lower_bound',
target_reaction.lower_bound))
tm(do=int,
undo=partial(setattr, target_reaction, 'upper_bound',
target_reaction.upper_bound))
if view is None:
view = config.default_view
else:
view = view
included_reactions = [
reaction.id for reaction in self.reference_model.reactions
if reaction.id not in self.exclude
] + self.variables + [self.objective]
self.reference_flux_dist = pfba(self.reference_model,
fraction_of_optimum=0.99)
self.reference_flux_ranges = flux_variability_analysis(
self.reference_model,
reactions=included_reactions,
view=view,
remove_cycles=False,
fraction_of_optimum=0.75).data_frame
self._init_search_grid(surface_only=surface_only,
improvements_only=improvements_only)
func_obj = _DifferentialFvaEvaluator(self.design_space_model,
self.variables,
self.objective,
included_reactions)
if progress:
progress = ProgressBar(len(self.grid))
results = list(
progress(view.imap(func_obj, self.grid.iterrows())))
else:
results = list(view.map(func_obj, self.grid.iterrows()))
solutions = dict((tuple(point.iteritems()), fva_result)
for (point, fva_result) in results)
reference_intervals = self.reference_flux_ranges[[
'lower_bound', 'upper_bound'
]].values
for sol in six.itervalues(solutions):
intervals = sol[['lower_bound', 'upper_bound']].values
gaps = [
self._interval_gap(interval1, interval2) for interval1,
interval2 in my_zip(reference_intervals, intervals)
]
sol['gaps'] = gaps
if self.normalize_ranges_by is not None:
normalizer = sol.lower_bound[self.normalize_ranges_by]
if normalizer > non_zero_flux_threshold:
normalized_intervals = sol[['lower_bound', 'upper_bound'
]].values / normalizer
sol['normalized_gaps'] = [
self._interval_gap(interval1, interval2)
for interval1, interval2 in my_zip(
reference_intervals, normalized_intervals)
]
else:
sol['normalized_gaps'] = [numpy.nan] * len(sol.lower_bound)
else:
sol['normalized_gaps'] = gaps
ref_upper_bound = self.reference_flux_ranges.upper_bound.apply(
lambda v: 0 if abs(v) < non_zero_flux_threshold else v)
ref_lower_bound = self.reference_flux_ranges.lower_bound.apply(
lambda v: 0 if abs(v) < non_zero_flux_threshold else v)
collection = list()
for key, df in six.iteritems(solutions):
df['biomass'] = key[0][1]
df['production'] = key[1][1]
df['KO'] = False
df['flux_reversal'] = False
df['suddenly_essential'] = False
df['free_flux'] = False
df.loc[(df.lower_bound == 0) & (df.upper_bound == 0) &
(ref_upper_bound != 0) & (ref_lower_bound != 0),
'KO'] = True
df.loc[((ref_upper_bound < 0) & (df.lower_bound > 0) |
((ref_lower_bound > 0) & (df.upper_bound < 0))),
'flux_reversal'] = True
df.loc[((df.lower_bound <= 0) & (df.lower_bound > 0)) |
((ref_lower_bound >= 0) & (df.upper_bound <= 0)),
'suddenly_essential'] = True
is_reversible = numpy.asarray([
self.design_space_model.reactions.get_by_id(i).reversibility
for i in df.index
],
dtype=bool)
not_reversible = numpy.logical_not(is_reversible)
df.loc[((df.lower_bound == -1000) &
(df.upper_bound == 1000) & is_reversible) |
((df.lower_bound == 0) &
(df.upper_bound == 1000) & not_reversible) |
((df.lower_bound == -1000) &
(df.upper_bound == 0) & not_reversible),
'free_flux'] = True
df['reaction'] = df.index
df['excluded'] = df['reaction'].isin(self.exclude)
collection.append(df)
# multi_index = [(key[0][1], key[1][1]) for key in solutions]
# solutions_multi_index = pandas.concat(list(solutions.values()),
# axis=0, keys=multi_index)#
# solutions_multi_index.index.set_names(['biomass', 'production',
# 'reaction'], inplace=True)
total = pandas.concat(collection, ignore_index=True, copy=False)
total.sort_values(['biomass', 'production', 'reaction'], inplace=True)
total.index = total['reaction']
return DifferentialFVAResult(total, self.envelope,
self.reference_flux_ranges,
self.reference_flux_dist)
def example11():
widgets = [FormatLabel('Processed: %(value)d lines (in: %(elapsed)s)')]
pbar = ProgressBar(widgets=widgets)
for i in pbar((i for i in range(150))):
time.sleep(0.1)
def example12():
widgets = ['Balloon: ', AnimatedMarker(markers='.oO@* ')]
pbar = ProgressBar(widgets=widgets)
for i in pbar((i for i in range(24))):
time.sleep(0.3)
def __init__(self, size=100, label="", color=None, fd=sys.stdout):
self.progress_bar = PB(widgets=[label] + self.widgets,
maxval=size,
fd=fd)
self.progress = 0
def example16():
widgets = [FormatLabel('Bouncer: value %(value)d - '), BouncingBar()]
pbar = ProgressBar(widgets=widgets)
for i in pbar((i for i in range(180))):
time.sleep(0.05)
def example5():
pbar = ProgressBar(widgets=[SimpleProgress()], maxval=17).start()
for i in range(17):
time.sleep(0.2)
pbar.update(i + 1)
pbar.finish()
def example19():
pbar = ProgressBar()
for i in pbar([]):
pass
pbar.finish()
def example5():
pbar = ProgressBar(widgets=[SimpleProgress()], maxval=17).start()
for i in range(17):
time.sleep(0.2)
pbar.update(i + 1)
pbar.finish()
def example6():
pbar = ProgressBar().start()
for i in range(100):
time.sleep(0.01)
pbar.update(i + 1)
pbar.finish()
def example7():
pbar = ProgressBar() # Progressbar can guess maxval automatically.
for i in pbar(range(80)):
time.sleep(0.01)
def __init__(self, size=100, label="", color=None, fd=sys.stdout):
self.progress_bar = PB(widgets=[label] + self.widgets, maxval=size, fd=fd)
self.progress = 0
def example6():
pbar = ProgressBar().start()
for i in range(100):
time.sleep(0.01)
pbar.update(i + 1)
pbar.finish()
