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 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 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 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 example0():
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=300).start()
for i in range(300):
time.sleep(0.01)
pbar.update(i + 1)
pbar.finish()